scikit-image-0.13.1/000077500000000000000000000000001316256236300141515ustar00rootroot00000000000000scikit-image-0.13.1/.coveragerc000066400000000000000000000003251316256236300162720ustar00rootroot00000000000000# Configuration for coverage.py [run] branch = True source = skimage include = */skimage/* omit = */setup.py */skimage/external/* [report] exclude_lines = def __repr__ if __name__ == .__main__.: scikit-image-0.13.1/.github/000077500000000000000000000000001316256236300155115ustar00rootroot00000000000000scikit-image-0.13.1/.github/CONTRIBUTING.txt000066400000000000000000000310041316256236300201570ustar00rootroot00000000000000Development process ------------------- Here's the long and short of it: 1. If you are a first-time contributor: * Go to `https://github.com/scikit-image/scikit-image `_ and click the "fork" button to create your own copy of the project. * Clone the project to your local computer:: git clone https://github.com/your-username/scikit-image.git * Change the directory:: cd scikit-image * Add the upstream repository:: git remote add upstream https://github.com/scikit-image/scikit-image.git * Now, you have remote repositories named: - ``upstream``, which refers to the ``scikit-image`` repository - ``origin``, which refers to your personal fork 2. Develop your contribution: * Pull the latest changes from upstream:: git checkout master git pull upstream master * Create a branch for the feature you want to work on. Since the branch name will appear in the merge message, use a sensible name such as 'transform-speedups':: git checkout -b transform-speedups * Commit locally as you progress (``git add`` and ``git commit``) 3. To submit your contribution: * Push your changes back to your fork on GitHub:: git push origin transform-speedups * Enter your GitHub username and password (repeat contributors or advanced users can remove this step by connecting to GitHub with SSH. See detailed instructions below if desired). * Go to GitHub. The new branch will show up with a green Pull Request button - click it. * If you want, post on the `mailing list `_ to explain your changes or to ask for review. For a more detailed discussion, read these :doc:`detailed documents ` on how to use Git with ``scikit-image`` (``_). 4. Review process: * Reviewers (the other developers and interested community members) will write inline and/or general comments on your Pull Request (PR) to help you improve its implementation, documentation and style. Every single developer working on the project has their code reviewed, and we've come to see it as friendly conversation from which we all learn and the overall code quality benefits. Therefore, please don't let the review discourage you from contributing: its only aim is to improve the quality of project, not to criticize (we are, after all, very grateful for the time you're donating!). * To update your pull request, make your changes on your local repository and commit. As soon as those changes are pushed up (to the same branch as before) the pull request will update automatically. * `Travis-CI `__, a continuous integration service, is triggered after each Pull Request update to build the code, run unit tests, measure code coverage and check coding style (PEP8) of your branch. The Travis tests must pass before your PR can be merged. If Travis fails, you can find out why by clicking on the "failed" icon (red cross) and inspecting the build and test log. * A pull request must be approved by two core team members before merging. 5. Document changes If your change introduces any API modifications, please update ``doc/source/api_changes.txt``. If your change introduces a deprecation, add a reminder to ``TODO.txt`` for the team to remove the deprecated functionality in the future. .. note:: To reviewers: if it is not obvious from the PR description, add a short explanation of what a branch did to the merge message and, if closing a bug, also add "Closes #123" where 123 is the issue number. Divergence between ``upstream master`` and your feature branch -------------------------------------------------------------- Do *not* ever merge the main branch into yours. If GitHub indicates that the branch of your Pull Request can no longer be merged automatically, rebase onto master:: git checkout master git pull upstream master git checkout transform-speedups git rebase master If any conflicts occur, fix the according files and continue:: git add conflict-file1 conflict-file2 git rebase --continue However, you should only rebase your own branches and must generally not rebase any branch which you collaborate on with someone else. Finally, you must push your rebased branch:: git push --force origin transform-speedups (If you are curious, here's a further discussion on the `dangers of rebasing `__. Also see this `LWN article `__.) Guidelines ---------- * All code should have tests (see `test coverage`_ below for more details). * All code should be documented, to the same `standard `_ as NumPy and SciPy. * For new functionality, always add an example to the gallery. * No changes are ever committed without review and approval by two core team members. Ask on the `mailing list `_ if you get no response to your pull request. **Never merge your own pull request.** * Examples in the gallery should have a maximum figure width of 8 inches. Stylistic Guidelines -------------------- * Set up your editor to remove trailing whitespace. Follow `PEP08 `__. Check code with pyflakes / flake8. * Use numpy data types instead of strings (``np.uint8`` instead of ``"uint8"``). * Use the following import conventions:: import numpy as np import matplotlib.pyplot as plt from scipy import ndimage as ndi cimport numpy as cnp # in Cython code * When documenting array parameters, use ``image : (M, N) ndarray`` and then refer to ``M`` and ``N`` in the docstring, if necessary. * Refer to array dimensions as (plane), row, column, not as x, y, z. See :ref:`Coordinate conventions ` in the user guide for more information. * Functions should support all input image dtypes. Use utility functions such as ``img_as_float`` to help convert to an appropriate type. The output format can be whatever is most efficient. This allows us to string together several functions into a pipeline, e.g.:: hough(canny(my_image)) * Use ``Py_ssize_t`` as data type for all indexing, shape and size variables in C/C++ and Cython code. * Use relative module imports, i.e. ``from .._shared import xyz`` rather than ``from skimage._shared import xyz``. * Wrap Cython code in a pure Python function, which defines the API. This improves compatibility with code introspection tools, which are often not aware of Cython code. * For Cython functions, release the GIL whenever possible, using ``with nogil:``. Test coverage ------------- Tests for a module should ideally cover all code in that module, i.e., statement coverage should be at 100%. To measure the test coverage, install `coverage.py `__ (using ``easy_install coverage``) and then run:: $ make coverage This will print a report with one line for each file in `skimage`, detailing the test coverage:: Name Stmts Exec Cover Missing ------------------------------------------------------------------------------ skimage/color/colorconv 77 77 100% skimage/filter/__init__ 1 1 100% ... Activate Travis-CI for your fork (optional) ------------------------------------------- Travis-CI checks all unittests in the project to prevent breakage. Before sending a pull request, you may want to check that Travis-CI successfully passes all tests. To do so, * Go to `Travis-CI `__ and follow the Sign In link at the top * Go to your `profile page `__ and switch on your scikit-image fork It corresponds to steps one and two in `Travis-CI documentation `__ (Step three is already done in scikit-image). Thus, as soon as you push your code to your fork, it will trigger Travis-CI, and you will receive an email notification when the process is done. Every time Travis is triggered, it also calls on `Coveralls `_ to inspect the current test overage. Building docs ------------- To build docs, run ``make`` from the ``doc`` directory. ``make help`` lists all targets. Requirements ~~~~~~~~~~~~ Sphinx (>= 1.3) and Latex is needed to build doc. **Sphinx:** .. code:: sh pip install sphinx **Latex Ubuntu:** .. code:: sh sudo apt-get install -qq texlive texlive-latex-extra dvipng **Latex Mac:** Install the full `MacTex `__ installation or install the smaller `BasicTex `__ and add *ucs* and *dvipng* packages: .. code:: sh sudo tlmgr install ucs dvipng Fixing Warnings ~~~~~~~~~~~~~~~ - "citation not found: R###" There is probably an underscore after a reference in the first line of a docstring (e.g. [1]\_). Use this method to find the source file: $ cd doc/build; grep -rin R#### - "Duplicate citation R###, other instance in..."" There is probably a [2] without a [1] in one of the docstrings - Make sure to use pre-sphinxification paths to images (not the \_images directory) Auto-generating dev docs ~~~~~~~~~~~~~~~~~~~~~~~~ This set of instructions was used to create scikit-image/tools/deploy-docs.sh - Go to Github account settings -> personal access tokens - Create a new token with access rights ``public_repo`` and ``user:email only`` - Install the travis command line tool: ``gem install travis``. On OSX, you can get gem via ``brew install ruby``. - Take then token generated by Github and run ``travis encrypt GH_TOKEN=`` from inside a scikit-image repo - Paste the output into the secure: field of ``.travis.yml``. - The decrypted GH\_TOKEN env var will be available for travis scripts https://help.github.com/articles/creating-an-access-token-for-command-line-use/ http://docs.travis-ci.com/user/encryption-keys/ Deprecation cycle ----------------- If the behavior of the library has to be changed, a deprecation cycle must be followed to warn users. - a deprecation cycle is *not* necessary when * adding a new function, or * adding a new keyword argument to the *end* of a function signature, or * fixing what was buggy behaviour - a deprecation cycle is necessary for *any breaking API change*, meaning a change where the function, invoked with the same arguments, would return a different result after the change. This includes: * changing the order of arguments or keyword arguments, or * adding arguments or keyword arguments to a function, or * changing a function's name or submodule, or * changing the default value of a function's arguments. Usually, our policy is to put in place a deprecation cycle over two releases. For the sake of illustration, we consider the modification of a default value in a function signature. In version N (therefore, next release will be N+1), we have .. code-block:: python def a_function(image, rescale=True): out = do_something(image, rescale=rescale) return out that has to be changed to .. code-block:: python def a_function(image, rescale=None): if rescale is None: warn('The default value of rescale will change to `False` in version N+3') rescale = True out = do_something(image, rescale=rescale) return out and in version N+3 .. code-block:: python def a_function(image, rescale=False): out = do_something(image, rescale=rescale) return out Here is the process for a 2-release deprecation cycle: - In the signature, set default to `None`, and modify the docstring to specify that it's `True`. - In the function, _if_ rescale is set to `None`, set to `True` and warn that the default will change to `False` in version N+3. - In ``TODO.txt``, create an item in the section related to version N+3 and write "change rescale default to False in a_function". Note that the 2-release deprecation cycle is not a strict rule and in some cases, the developers can agree on a different procedure upon justification (like when we can't detect the change, or it involves moving or deleting an entire function for example). Bugs ---- Please `report bugs on GitHub `_. scikit-image-0.13.1/.github/ISSUE_TEMPLATE.md000066400000000000000000000007671316256236300202300ustar00rootroot00000000000000## Description [Please provide a general introduction to the issue/proposal.] [If reporting a bug, attach the entire traceback from Python.] [If proposing an enhancement/new feature, provide links to related articles, reference examples, etc.] ## Way to reproduce [If reporting a bug, please include the following important information:] - [ ] Code example - [ ] Relevant images (if any) - [ ] Operating system and version - [ ] Python version - [ ] scikit-image version (run `skimage.__version__`) scikit-image-0.13.1/.github/PULL_REQUEST_TEMPLATE.md000066400000000000000000000017141316256236300213150ustar00rootroot00000000000000## Description [Tell us about your new feature, improvement, or fix! If relevant, please supplement the PR with images.] ## Checklist [It's fine to submit PRs which are a work in progress! But before they are merged, all PRs should provide:] - [ ] Clean style in [the spirit of PEP8](https://www.python.org/dev/peps/pep-0008/) - [ ] [Docstrings for all functions](https://github.com/numpy/numpy/blob/master/doc/example.py) - [ ] Gallery example in `./doc/examples` (new features only) - [ ] Unit tests [For detailed information on these and other aspects see [scikit-image contribution guidelines](http://scikit-image.org/docs/dev/contribute.html)] ## References [If this is a bug-fix or enhancement, it closes issue # ] [If this is a new feature, it implements the following paper: ] ## For reviewers - [ ] Check that the PR title is short, concise, and will make sense 1 year later. - [ ] Check that new features are mentioned in `doc/release/release_dev.rst`. scikit-image-0.13.1/.gitignore000066400000000000000000000005011316256236300161350ustar00rootroot00000000000000*.pyc *~ *# .#* *egg-info *.so *.pyd *.bak *.c *.new *.md5 *.old doc/source/api doc/build source/api build dist skimage/version.py *.swp .coverage doc/source/auto_examples/**/* doc/source/_static/random.js .idea/ *.log doc/release/_release_notes_for_docs.txt doc/gh-pages wheels skimage/morphology/_skeletonize_3d_cy.pyx scikit-image-0.13.1/.gitmodules000066400000000000000000000001541316256236300163260ustar00rootroot00000000000000[submodule "doc/tools/gitwash"] path = doc/tools/gitwash url = git://github.com/matthew-brett/gitwash.git scikit-image-0.13.1/.mailmap000066400000000000000000000026531316256236300156000ustar00rootroot00000000000000K.-Michael Aye Nelson Brown Luis Pedro Coelho Marianne Corvellec Riaan van den Dool Emmanuelle Gouillart Thouis (Ray) Jones Gregory R. Lee Andreas Mueller Juan Nunez-Iglesias Nicolas Pinto Johannes Schönberger Tim Sheerman-Chase Matthew Trentacoste James Turner Stefan van der Walt John Wiggins Tony S Yu François Boulogne scikit-image-0.13.1/.travis.yml000066400000000000000000000041051316256236300162620ustar00rootroot00000000000000# vim ft=yaml # After changing this file, check it on: # http://yaml-online-parser.appspot.com/ # See tools/travis_notes.txt for some guidelines language: python sudo: false cache: # See http://docs.travis-ci.com/user/caching/#pip-cache directories: - $HOME/.cache/pip - $HOME/.cache/sphinx - $HOME/.ccache addons: apt: packages: - ccache - libfreeimage3 - texlive - texlive-latex-extra - dvipng - python-qt4 env: global: - GH_REF: github.com/scikit-image/docs.git - secure: "Z8uxAGrFb6yNw/savI7YvA5/BfkVKVGD3HUkDSXamotJAvUKMjIlWnzLc7d9S7PY99FRENgRYaNw4fzZqwJ91hDITcl1/wk6ux1j778O8kUQZayzdqcJG8KpfBsy1vJ4fl4uDGlSmUP413rR3R3dvvgJzIpCLy2qiQR/xujrGvs=" notifications: webhooks: urls: - https://webhooks.gitter.im/e/1fea29525e8b929dd7c7 on_success: change # options: [always|never|change] default: always on_failure: always # options: [always|never|change] default: always on_start: false # default: false matrix: include: - os: linux python: 2.7 env: PIP_FLAGS="--pre" - os: linux python: 2.7 env: WITH_PYAMG=1 MINIMUM_REQUIREMENTS=1 WITH_QT=1 - os: linux python: 3.4 env: OPTIONAL_DEPS=1 WITH_PYSIDE=1 - os: linux python: 3.5 - os: linux python: 3.5 env: PIP_FLAGS="--pre" - os: linux python: 3.6 - os: osx osx_image: xcode7.3 language: objective-c env: TRAVIS_PYTHON_VERSION=3.5 before_install: - if [[ "$TRAVIS_OS_NAME" == "osx" ]]; then source tools/travis_osx_install.sh ; fi - ccache -s - export PATH=/usr/lib/ccache:${PATH} # Attempt to fix bug making virtualenvs for Python 3.6 - python -m pip install -U pip - pip install -U virtualenv - source tools/travis_before_install.sh - which python; python --version - tools/check_bento_build.py - tools/build_versions.py - tools/check_sdist.py install: - section build - python setup.py develop - section_end build script: tools/travis_script.sh after_success: - codecov - bash tools/deploy_docs.sh scikit-image-0.13.1/CONTRIBUTORS.txt000066400000000000000000000135571316256236300166620ustar00rootroot00000000000000- Stefan van der Walt Project coordination - Nicolas Pinto Colour spaces and filters, and image resizing. Shape views: ``util.shape.view_as_windows`` and ``util.shape.view_as_blocks`` Montage helpers: ``util.montage``. - Damian Eads Morphological operators - Mahipal Raythattha Documentation infrastructure - S. Chris Colbert OpenCV wrappers, Scivi, Qt and Gtk gui bits, fast Hough transform, and much more. - Holger Rapp OpenCV functions and better OSX library loader - Ralf Gommers Image IO, color spaces, plots in documentation, cleaner API docs - Helge Reikeras Logic around API docs generation - Tony Yu Reading of paletted images; build, bug and doc fixes. Code to generate skimage logo. Otsu thresholding, histogram equalisation, template matching, and more. - Zachary Pincus Tracing of low cost paths, FreeImage I/O plugin, iso-contours, and more. - Almar Klein Binary heap class and other improvements for graph algorithms Lewiner variant of marching cubes algorithm - Lee Kamentsky and Thouis Jones of the CellProfiler team, Broad Institute, MIT Constant time per pixel median filter, edge detectors, and more. - Dan Farmer Incorporating CellProfiler's Canny edge detector, ctypes loader with Windows support. - Pieter Holtzhausen Incorporating CellProfiler's Sobel edge detector, build and bug fixes. Radon transform, template matching. - Emmanuelle Gouillart Total variation noise filtering, integration of CellProfiler's mathematical morphology tools, random walker segmentation, tutorials, and more. - Maël Primet Total variation noise filtering - Martin Bergholdt Fix missing math.h functions in Windows 7 + MSVCC. - Neil Muller Numerous fixes, including those for Python 3 compatibility, QT image reading. - The IPython team From whom we borrowed the github+web tools / style. - Kyle Mandli CSV to ReST code for feature comparison table. - The Scikit Learn team From whom we borrowed the example generation tools. - Andreas Mueller Example data set loader. Nosetest compatibility functions. Quickshift image segmentation, Felzenszwalbs fast graph based segmentation. - Yaroslav Halchenko For sharing his expert advice on Debian packaging. - Brian Holt Histograms of Oriented Gradients - David-Warde Farley, Sturla Molden Bresenheim line drawing, from snippets on numpy-discussion. - Christoph Gohlke Windows packaging and Python 3 compatibility. - Neil Yager Skeletonization and grey level co-occurrence matrices. - Nelle Varoquaux Renaming of the package to ``skimage``. Harris corner detector - W. Randolph Franklin Point in polygon test. - Gaël Varoquaux Harris corner detector - Nicolas Poilvert Shape views: ``util.shape.view_as_windows`` and ``util.shape.view_as_blocks`` Image resizing. - Johannes Schönberger Drawing functions, adaptive thresholding, regionprops, geometric transformations, LBPs, polygon approximations, web layout, and more. - Pavel Campr Fixes and tests for Histograms of Oriented Gradients. - Joshua Warner Multichannel random walker segmentation, unified peak finder backend, n-dimensional array padding, marching cubes, bug and doc fixes. - Petter Strandmark Perimeter calculation in regionprops. - Olivier Debeir Rank filters (8- and 16-bits) using sliding window. - Luis Pedro Coelho imread plugin - Steven Silvester, Karel Zuiderveld Adaptive Histogram Equalization - Anders Boesen Lindbo Larsen Dense DAISY feature description, circle perimeter drawing. - François Boulogne Drawing: Andres Method for circle perimeter, ellipse perimeter, Bezier curve, anti-aliasing. Circular and elliptical Hough Transforms Thresholding Various fixes - Thouis Jones Vectorized operators for arrays of 16-bit ints. - Xavier Moles Lopez Color separation (color deconvolution) for several stainings. - Jostein Bø Fløystad Tomography: radon/iradon improvements and SART implementation Phase unwrapping integration - Matt Terry Color difference functions - Eugene Dvoretsky Yen, Ridler-Calvard (ISODATA) threshold implementations. - Riaan van den Dool skimage.io plugin: GDAL - Fedor Morozov Drawing: Wu's anti-aliased circle - Michael Hansen novice submodule - Munther Gdeisat Phase unwrapping implementation - Miguel Arevallilo Herraez Phase unwrapping implementation - Hussein Abdul-Rahman Phase unwrapping implementation - Gregor Thalhammer Phase unwrapping integration - François Orieux Image deconvolution http://research.orieux.fr - Vighnesh Birodkar Blob Detection - Axel Donath Blob Detection - Adam Feuer PIL Image import and export improvements - Rebecca Murphy astronaut in examples - Geoffrey French skimage.filters.rank.windowed_histogram and plot_windowed_histogram example. - Alexey Umnov skimage.draw.ellipse bug fix and tests. - Ivana Kajic Updated description and examples in documentation for gabor filters - Matěj Týč Extended the image labelling implementation so it also works on 3D images. - Salvatore Scaramuzzino RectTool example - Kevin Keraudren Fix and test for feature.peak_local_max - Jeremy Metz Adaptation of ImageJ Autothresholder.Li, fixed Qhull error QH6228 - Mike Sarahan Sub-pixel shift registration - Jim Fienup, Alexander Iacchetta In-depth review of sub-pixel shift registration - Damian Eads Structuring elements in morphology module. - Egor Panfilov Inpainting with biharmonic equation - Evgeni Burovski Adaptation of ImageJ 3D skeletonization algorithm. - Alex Izvorski Color spaces for YUV and related spaces - Thomas Lewiner Design and original implementation of the Lewiner marching cubes algorithm - Jeff Hemmelgarn Minimum threshold - Kirill Malev Frangi and Hessian filters implementation - Abdeali Kothari Alpha blending to convert from rgba to rgb - Jeyson Molina Niblack and Sauvola Local thresholding - Scott Sievert Wavelet denoising scikit-image-0.13.1/DEPENDS.txt000066400000000000000000000047101316256236300157760ustar00rootroot00000000000000Build Requirements ------------------ * `Python >= 2.7 `__ * `Numpy >= 1.11 `__ * `Cython >= 0.23 `__ * `Six >=1.7.3 `__ * `SciPy >=0.17.0 `__ * `numpydoc >=0.6 `__ Runtime requirements -------------------- * `Python >= 2.7 `__ * `Numpy >= 1.11 `__ * `SciPy >= 0.17.0 `__ * `Matplotlib >= 1.3.1 `__ * `NetworkX >= 1.8 `__ * `Six >=1.7.3 `__ * `Pillow >= 2.1.0 `__ (or `PIL `__) * `PyWavelets>=0.4.0 `__ You can use pip to automatically install the runtime dependencies as follows:: $ pip install -r requirements.txt Optional Requirements --------------------- You can use this scikit with the basic requirements listed above, but some functionality is only available with the following installed: * `dask[array] >= 0.5.0 `__. For parallel computation using `skimage.util.apply_parallel`. * `PyQt4 `__ The ``qt`` plugin that provides ``imshow(x, fancy=True)`` and `skivi`. * `FreeImage `__ The ``freeimage`` plugin provides support for reading various types of image file formats, including multi-page TIFFs. * `PyAMG `__ The ``pyamg`` module is used for the fast `cg_mg` mode of random walker segmentation. * `Astropy `__ provides FITS io capability. * `SimpleITK `__ Optional io plugin providing a wide variety of `formats `__. including specialized formats using in medical imaging. * `imread `__ Optional io plugin providing most standard `formats `__. Testing requirements -------------------- * `Nose `__ A Python Unit Testing Framework * `Coverage.py `__ A tool that generates a unit test code coverage report Documentation requirements -------------------------- `sphinx >= 1.3 `_ is required to build the documentation. scikit-image-0.13.1/LICENSE.txt000066400000000000000000000030041316256236300157710ustar00rootroot00000000000000Unless otherwise specified by LICENSE.txt files in individual directories, all code is Copyright (C) 2011, the scikit-image team All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of skimage nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. scikit-image-0.13.1/MANIFEST.in000066400000000000000000000007451316256236300157150ustar00rootroot00000000000000include setup*.py include MANIFEST.in include *.txt include Makefile include bento.info include skimage/scripts/skivi recursive-include skimage *.pyx *.pxd *.pxi *.py *.c *.h *.ini *.md5 *.npy *.txt *.in *.cpp *.md recursive-include skimage/data * include doc/Makefile recursive-include doc/examples *.txt recursive-include doc/source *.rst recursive-include doc/tools *.txt recursive-include doc/source/_templates *.html recursive-include doc *.py prune doc/build prune doc/gh-pages scikit-image-0.13.1/Makefile000066400000000000000000000011241316256236300156070ustar00rootroot00000000000000.PHONY: all clean test PYTHON=python NOSETESTS=nosetests all: $(PYTHON) setup.py build_ext --inplace clean: find . -name "*.so" -o -name "*.pyc" -o -name "*.md5" -o -name "*.pyd" | xargs rm -f find . -name "*.pyx" -exec ./tools/rm_pyx_c_file.sh {} \; test: $(PYTHON) -c "import skimage, sys, io; sys.exit(skimage.test_verbose())" doctest: $(PYTHON) -c "import skimage, sys, io; sys.exit(skimage.doctest_verbose())" coverage: $(NOSETESTS) skimage --with-coverage --cover-package=skimage html: pip install -q sphinx pytest-runner sphinx-gallery export SPHINXOPTS=-W; make -C doc html scikit-image-0.13.1/README.md000066400000000000000000000056051316256236300154360ustar00rootroot00000000000000# scikit-image: Image processing in Python [![Gitter](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/scikit-image/scikit-image?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge) [![codecov.io](https://codecov.io/github/scikit-image/scikit-image/coverage.svg?branch=master)](https://codecov.io/github/scikit-image/scikit-image?branch=master) - **Website (including documentation):** [http://scikit-image.org/](http://scikit-image.org) - **Mailing list:** [https://mail.python.org/mailman/listinfo/scikit-image](https://mail.python.org/mailman/listinfo/scikit-image) - **Source:** [https://github.com/scikit-image/scikit-image](https://github.com/scikit-image/scikit-image) ## Installation from binaries - **Debian/Ubuntu:** ``sudo apt-get install python-skimage`` - **OSX:** ``pip install scikit-image`` - **Anaconda:** ``conda install scikit-image`` - **Windows:** Download [Windows binaries](http://www.lfd.uci.edu/~gohlke/pythonlibs/#scikits.image) Also see [http://scikit-image.org/docs/dev/install.html](http://scikit-image.org/docs/dev/install.html) ## Installation from source Install [dependencies](DEPENDS.txt) using: ``` pip install -r requirements.txt ``` Then, install scikit-image using: ``` $ pip install . ``` If you plan to develop the package, you may run it directly from source: ``` $ python setup.py develop # Do this once to add pkg to Python path $ python setup.py build_ext -i # Build binary extensions ``` ## License (Modified BSD) Copyright (C) 2011, the scikit-image team All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of skimage nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. scikit-image-0.13.1/RELEASE.txt000066400000000000000000000146211316256236300157760ustar00rootroot00000000000000How to make a new release of ``skimage`` ======================================== While following this guide, note down all the times that you need to consult a previous release manager, or that you find an instruction unclear. You will make a PR to update these notes after you are done with the release. ;-) - Check ``TODO.txt`` for any outstanding tasks. - Branch v..x from master. This is the "release branch", where you will make your changes gearing up for release, and cherry-pick them as appropriate to master. - In the master branch, update the version number in ``skimage/__init__.py`` and ``bento.info`` to the next ``-dev`` version, commit, and push. - Back on the release branch, update the release notes: 1. Review and cleanup ``doc/release/release_dev.txt``. 2. Make a list of merges and contributors by running ``doc/release/contribs.py ``. 3. Paste this list at the end of the ``release_dev.txt``. Scan the PR titles for highlights, deprecations, and API changes, and mention these in the relevant sections of the notes. 4. Rename to ``doc/release/release_..txt`` 5. Copy ``doc/release/release_template.txt`` to ``doc/release/release_dev.txt`` for the next release. - Update the version number in ``skimage/__init__.py`` and ``bento.info`` and commit. - Update the docs: - Edit ``doc/source/_static/docversions.js`` and commit - Build a clean version of the docs. Run ``python setup.py install`` in the root dir, then ``rm -rf build; make html`` in the docs. - Build using ``make gh-pages``. - Update the symlink to ``stable``. - Push upstream: ``git push origin gh-pages`` in ``doc/gh-pages``. - Add the version number as a tag in git:: git tag -s [-u ] v..0 (If you do not have a gpg key, use -m instead; it is important for Debian packaging that the tags are annotated) - Push the new meta-data to github:: git push --tags upstream master (where ``upstream`` is the name of the ``github.com:scikit-image/scikit-image`` repository.) - Build the package wheels (pre-compiled binaries) for various platforms: - Clone https://github.com/scikit-image/scikit-image-wheels. - Update its ``.travis.yml`` file so that ``BUILD_COMMIT`` points to this release tag (e.g. ``v0.13.0``). - Commit and push. - Wait until the corresponding wheels appear at http://wheels.scikit-image.org (about 15 minutes). - Download the wheels and upload them to PyPI (you may need to ask Stéfan or Juan to give you push access to the scikit-image package on PyPI). - Make sure ``twine`` is available. You can install it with ``pip install twine``. - Download ``wheel-uploader`` [1]_ and place it on your PATH. - Run ``tools/upload_wheels.sh``. .. [1] https://github.com/MacPython/terryfy/master/wheel-uploader - Publish the source distribution on PyPi:: python setup.py sdist twine upload dist/scikit-image-..0.tar.gz - Update the web frontpage: The webpage is kept in a separate repo: scikit-image-web - Sync your branch with the remote repo: ``git pull``. If you try to ``make gh-pages`` when your branch is out of sync, it creates headaches. - Add release date to ``index.rst`` under "Announcements". - Add previous stable version documentation path to disallowed paths in `robots.txt` - Build using ``make gh-pages``. - Push upstream: ``git push origin master`` in ``gh-pages``. - Update the development docs for the new version ``0.Xdev`` just like above - Post release notes on mailing lists, blog, Twitter, etc. - scikit-image@python.org - scipy-user@python.org - scikit-learn@python.org - Update the version and the release date on wikipedia https://en.wikipedia.org/wiki/Scikit-image Conda-forge ----------- A scikit-image build recipe resides at http://github.com/conda-forge/scikit-image-feedstock. You should update it to point to the most recent release. You can do this by following these steps: - Fork the repository at http://github.com/conda-forge/scikit-image-feedstock, and clone it to your machine. - Sprout a new branch, e.g. ``v.``. - Find out the SHA256 hash of the source distribution. You can find this at https://pypi.org/project/scikit-image/, or use the following commands: - ``sha256sum path/to/scikit-image-*.tar.gz`` (Linux) - ``shasum -a 256 dist/scikit-image-*.tar.gz`` (macOS) - ``CertUtil -hashfile dist\scikit-image-*.tar.gz SHA256`` (Windows) - Edit the file ``recipe/meta.yaml``: - Update the version number on the first line. - Update the SHA256 value on line 10. - If necessary, reset the build number to 0. (line 13) - Update any requirements in the appropriate sections (build or run). Note: don't remove ``numpy x.x``. This tells conda-smithy, conda-forge's build system, that the library must be linked against NumPy at build time. - Commit the changes, push to your fork, and submit a pull request to the upstream repo. Debian ------ The below instructions remain here for completeness. However, the Debian scientific team has kindly taken over package maintenance. Simply follow the procedure described at https://www.debian.org/Bugs/Reporting to report a "bug" that there is a new version of scikit-image out (specifying the version number), with severity set to "Wishlist". If you want to take matters into your own hands for some reason, follow the instructions detailed below to cut a Debian release yourself. - Tag the release as per instructions above. - git checkout debian - git merge v0.x.x - uscan <- not sure if this step is necessary - Update changelog (emacs has a good mode, requires package dpkg-dev-el) - C-C C-v add new version, C-c C-c timestamp / save - git commit -m 'Changelog entry for 0.x.x' - git-buildpackage -uc -us -rfakeroot - Sign the changes: debsign skimage_0.x.x-x_amd64.changes - cd ../build-area && dput mentors skimage_0.x.x-x_amd64.changes - The package should now be available at: http://mentors.debian.net/package/skimage For the last lines above to work, you need ``~/.gbp.conf``:: [DEFAULT] upstream-tag = %(version)s [git-buildpackage] sign-tags = True export-dir = ../build-area/ tarball-dir = ../tarballs/ As well as ``~/dput.cf``:: [mentors] fqdn = mentors.debian.net incoming = /upload method = http allow_unsigned_uploads = 0 progress_indicator = 2 # Allow uploads for UNRELEASED packages allowed_distributions = .* scikit-image-0.13.1/TASKS.txt000066400000000000000000000016641316256236300156060ustar00rootroot00000000000000 .. role:: strike .. _howto_contribute: How to contribute to ``skimage`` ====================================== .. toctree:: :hidden: gitwash/index gsoc2011 cell_profiler Developing Open Source is great fun! Join us on the `scikit-image mailing list `_ and tell us which of the following challenges you'd like to solve. * Mentoring is available for those new to scientific programming in Python. * If you're looking for something to implement, you can find a list of `requested features on GitHub `__. In addition, you can browse the `open issues on GitHub `__. * The technical detail of the `development process`_ is summed up below. Refer to the :doc:`gitwash ` for a step-by-step tutorial. .. contents:: :local: scikit-image-0.13.1/TODO.txt000066400000000000000000000042311316256236300154570ustar00rootroot00000000000000Remember to list any API changes below in `doc/source/api_changes.txt`. Version 0.14 ------------ * Remove deprecated ``ntiles_*` kwargs in ``equalize_adapthist``. * Remove deprecated ``skimage.restoration.nl_means_denoising``. * Remove deprecated ``skimage.filters.gaussian_filter``. * Remove deprecated ``skimage.filters.gabor_filter``. * Remove deprecated ``skimage.measure.LineModel`` and add an alias LineModel = LineModelND. While the deprecated LineModel has for parameters `(dist, theta)`, LineModelND has the more general parameters `(origin, direction)`. * Remove deprecated old syntax support for ``skimage.transform.integrate``. * Remove deprecated ``skimage.measure.structural_similarity`` alias and deprecation warning test for this alias. * Remove deprecated ``sigma_range`` kwargs in ``skimage.restoration.denoise_bilateral`` and corresponding tests. * Remove deprecation error on the usage of ``ntiles_x``, ``ntiles_y`` in ``skimage.exposure.equalize_adapthist`` and the corresponding test. * Remove the freeimage plugin shim to imageio. * Remove deprecated `normalise` and corresponding test for ``skimage.feature.hog``. * Remove deprecated ``marching_cubes`` from ``skimage.measure``. Version 0.15 ------------ * In ``skimage.util.dtype_limits``, set default behavior of `clip_negative` to `False`. * In ``skimage.transform.radon``, set default behavior of `circle` to `True`. * In ``skimage.transform.iradon``, set default behavior of `circle` to `True`. * In ``skimage.transform.swirl``, set default behavior of `mode` to `reflect`. * In ``skimage.restoration.denoise_bilateral``, set default behavior of `multichannel` to False * In ``skimage.restoration.denoise_nl_means``, set default behavior of `multichannel` to False * Change the default value of `block_norm` in ``skimage.feature.hog` to L2-Hys, update the reference .npy in ``skimage.data`` and `skimage/feature/tests/test_hog.py`. * Remove deprecated function ``threshold_adaptive`` in ``skimage/filters/thresholding.py``. * In ``skimage.transform.resize``, set default value of ``mode`` to ``'reflect'``. * In ``skimage.transform.rescale``, set default value of ``mode`` to ``'reflect'``. scikit-image-0.13.1/appveyor.yml000066400000000000000000000051661316256236300165510ustar00rootroot00000000000000# AppVeyor.com is a Continuous Integration service to build and run tests under # Windows environment: global: # SDK v7.0 MSVC Express 2008's SetEnv.cmd script will fail if the # /E:ON and /V:ON options are not enabled in the batch script intepreter # See: http://stackoverflow.com/a/13751649/163740 CMD_IN_ENV: "cmd /E:ON /V:ON /C .\\tools\\appveyor\\run_with_env.cmd" matrix: - PYTHON: "C:\\Python27-conda32" PYTHON_VERSION: "2.7" PYTHON_ARCH: "32" # disable other builds until they can be run in parallel #- PYTHON: "C:\\Python27-conda64" # PYTHON_VERSION: "2.7" # PYTHON_ARCH: "64" #- PYTHON: "C:\\Python35-conda32" # PYTHON_VERSION: "3.5" # PYTHON_ARCH: "32" #- PYTHON: "C:\\Python35-conda64" # PYTHON_VERSION: "3.5" # PYTHON_ARCH: "64" install: - ECHO "Filesystem root:" - ps: "ls \"C:/\"" # Install Python and all the required packages. - "powershell ./tools/appveyor/install.ps1" # Prepend newly installed Python to the PATH of this build # (this cannot be done from inside the powershell script as it would require # to restart the parent CMD process). - "SET PATH=%PYTHON%;%PYTHON%\\Scripts;%PATH%" # Check that we have the expected version and architecture for Python - "python --version" - "python -c \"import struct; print(struct.calcsize('P') * 8)\"" # Get stdint headers needed by tifffile.c. - "curl https://raw.githubusercontent.com/chemeris/msinttypes/master/inttypes.h -o skimage/external/tifffile/inttypes.h" - "curl https://raw.githubusercontent.com/chemeris/msinttypes/master/stdint.h -o skimage/external/tifffile/stdint.h" # Install the build and runtime dependencies of the project. - "%CMD_IN_ENV% pip install -v -r requirements.txt" - "%CMD_IN_ENV% python setup.py bdist_wheel bdist_wininst" - ps: "ls dist" # Install the generated wheel package to test it - "pip install --pre --no-index --find-links dist/ scikit-image" # Not a .NET project, we build scikit-image in the install step instead build: false test_script: # Build the docs - pip install sphinx pytest-runner sphinx-gallery - SET PYTHON=%PYTHON%\\python.exe && cd doc && make html # Change to a non-source folder to make sure we run the tests on the # installed library. - "cd C:\\" # Use the Agg backend in Matplotlib - echo backend:Agg > matplotlibrc # Run unit tests with nose - "python -c \"import nose; nose.main()\" -v -s skimage" artifacts: # Archive the generated wheel package in the ci.appveyor.com build report. - path: dist\* #on_success: # - TODO: upload the content of dist/*.whl to a public wheelhouse scikit-image-0.13.1/bento.info000066400000000000000000000146051316256236300161430ustar00rootroot00000000000000Name: scikit-image Version: 0.13.1 Summary: Image processing routines for SciPy Url: http://scikit-image.org DownloadUrl: http://github.com/scikit-image/scikit-image Description: Image Processing SciKit Image processing algorithms for SciPy, including IO, morphology, filtering, warping, color manipulation, object detection, etc. Please refer to the online documentation at http://scikit-image.org/ Maintainer: Stefan van der Walt MaintainerEmail: stefan@sun.ac.za License: Modified BSD Classifiers: Development Status :: 4 - Beta, Environment :: Console, Intended Audience :: Developers, Intended Audience :: Science/Research, License :: OSI Approved :: BSD License, Programming Language :: C, Programming Language :: Python, Programming Language :: Python :: 3, Topic :: Scientific/Engineering, Operating System :: Microsoft :: Windows, Operating System :: POSIX, Operating System :: Unix, Operating System :: MacOS UseBackends: Waf Library: Packages: skimage, skimage.color, skimage.data, skimage.draw, skimage.exposure, skimage.feature, skimage.filters, skimage.future, skimage.future.graph, skimage.graph, skimage.io, skimage.io._plugins, skimage.measure, skimage.morphology, skimage.scripts, skimage.restoration, skimage.segmentation, skimage.transform, skimage.util Extension: skimage.io._plugins._colormixer Sources: skimage/io/_plugins/_colormixer.pyx Extension: skimage.measure._pnpoly Sources: skimage/measure/_pnpoly.pyx Extension: skimage.measure._find_contours_cy Sources: skimage/measure/_find_contours_cy.pyx Extension: skimage.measure._moments_cy Sources: skimage/measure/_moments_cy.pyx Extension: skimage.measure._marching_cubes_classic_cy Sources: skimage/measure/_marching_cubes_classic_cy.pyx Extension: skimage.measure._marching_cubes_lewiner_cy Sources: skimage/measure/_marching_cubes_lewiner_cy.pyx Extension: skimage.graph._mcp Sources: skimage/graph/_mcp.pyx Extension: skimage.io._plugins._histograms Sources: skimage/io/_plugins/_histograms.pyx Extension: skimage.transform._hough_transform Sources: skimage/transform/_hough_transform.pyx Extension: skimage.filters._ctmf Sources: skimage/filters/_ctmf.pyx Extension: skimage.measure._ccomp Sources: skimage/measure/_ccomp.pyx Extension: skimage.morphology._watershed Sources: skimage/morphology/_watershed.pyx Extension: skimage.morphology._convex_hull Sources: skimage/morphology/_convex_hull.pyx Extension: skimage.draw._draw Sources: skimage/draw/_draw.pyx Extension: skimage.graph._spath Sources: skimage/graph/_spath.pyx Extension: skimage.graph.heap Sources: skimage/graph/heap.pyx Extension: skimage.morphology._greyreconstruct Sources: skimage/morphology/_greyreconstruct.pyx Extension: skimage.feature.censure_cy Sources: skimage/feature/censure_cy.pyx Extension: skimage.feature.orb_cy Sources: skimage/feature/orb_cy.pyx Extension: skimage.feature.brief_cy Sources: skimage/feature/brief_cy.pyx Extension: skimage.feature.corner_cy Sources: skimage/feature/corner_cy.pyx Extension: skimage.feature._hoghistogram Sources: skimage/feature/_hoghistogram.pyx Extension: skimage.feature._texture Sources: skimage/feature/_texture.pyx Extension: skimage._shared.transform Sources: skimage/_shared/transform.pyx Extension: skimage._shared.interpolation Sources: skimage/_shared/interpolation.pyx Extension: skimage.segmentation._slic Sources: skimage/segmentation/_slic.pyx Extension: skimage.segmentation._quickshift_cy Sources: skimage/segmentation/_quickshift_cy.pyx Extension: skimage.morphology._skeletonize_cy Sources: skimage/morphology/_skeletonize_cy.pyx Extension: skimage.morphology._skeletonize_3d_cy Sources: skimage/morphology/_skeletonize_3d_cy.pyx Extension: skimage.transform._radon_transform Sources: skimage/transform/_radon_transform.pyx Extension: skimage.transform._warps_cy Sources: skimage/transform/_warps_cy.pyx Extension: skimage.segmentation._felzenszwalb_cy Sources: skimage/segmentation/_felzenszwalb_cy.pyx Extension: skimage._shared.geometry Sources: skimage/_shared/geometry.pyx Extension: skimage.filters.rank.generic_cy Sources: skimage/filters/rank/generic_cy.pyx Extension: skimage.filters.rank.percentile_cy Sources: skimage/filter/rank/percentile_cy.pyx Extension: skimage.filters.rank.core_cy Sources: skimage/filter/rank/core_cy.pyx Extension: skimage.filters.rank.bilateral_cy Sources: skimage/filters/rank/bilateral_cy.pyx Extension: skimage.restoration._unwrap_1d Sources: skimage/restoration/_unwrap_1d.pyx Extension: skimage.restoration._unwrap_2d Sources: skimage/restoration/_unwrap_2d.pyx skimage/exposure/unwrap_2d_ljmu.c Extension: skimage.restoration._unwrap_3d Sources: skimage/restoration/_unwrap_3d.pyx skimage/exposure/unwrap_3d_ljmu.c Extension: skimage.restoration._denoise_cy Sources: skimage/restoration/_denoise_cy.pyx Extension: skimage.restoration._nl_means_denoising Sources: skimage/restoration/_nl_means_denoising.pyx Extension: skimage.feature._hessian_det_appx Sources: skimage/exposure/_hessian_det_appx.pyx Extension: skimage.future.graph._ncut_cy Sources: skimage/future/graph/_ncut_cy.pyx Extension: skimage.external.tifffile._tifffile Sources: skimage/external/tifffile/_tifffile.c Extension: skimage.transform._seam_carving Sources: skimage/transform/_seam_carving.pyx Executable: skivi Module: skimage.scripts.skivi Function: main scikit-image-0.13.1/doc/000077500000000000000000000000001316256236300147165ustar00rootroot00000000000000scikit-image-0.13.1/doc/Makefile000066400000000000000000000107711316256236300163640ustar00rootroot00000000000000# Makefile for Sphinx documentation # # You can set these variables from the command line. PYTHON ?= python SPHINXOPTS ?= -j 1 SPHINXBUILD ?= python $(shell which sphinx-build) SPHINXCACHE ?= build/doctrees PAPER ?= # Internal variables. PAPEROPT_a4 = -D latex_paper_size=a4 PAPEROPT_letter = -D latex_paper_size=letter ALLSPHINXOPTS = -d $(SPHINXCACHE) $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source DEST = build .PHONY: all help clean html dirhtml pickle json htmlhelp qthelp latex changes linkcheck doctest gitwash gh-pages release_notes all: html help: @echo "Please use \`make ' where is one of" @echo " html to make standalone HTML files" @echo " dirhtml to make HTML files named index.html in directories" @echo " pickle to make pickle files" @echo " json to make JSON files" @echo " htmlhelp to make HTML files and a HTML help project" @echo " qthelp to make HTML files and a qthelp project" @echo " devhelp to make HTML files and a Devhelp project" @echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter" @echo " latexpdf to make LaTeX files and run them through pdflatex" @echo " changes to make an overview of all changed/added/deprecated items" @echo " linkcheck to check all external links for integrity" @echo " doctest to run all doctests embedded in the documentation (if enabled)" @echo " gitwash to update the gitwash documentation" clean: -rm -rf $(DEST) -rm -rf api -rm -rf source/api -find ./source/auto_examples/* -type f | grep -v blank | xargs rm -f api: @mkdir -p source/api $(PYTHON) tools/build_modref_templates.py @echo "Build API docs...done." release_notes: @echo "Copying release notes" @tail -n +4 `ls release/*.rst | sort -k 2 -t . -n | tail -n 1` > release/_release_notes_for_docs.rst html: api release_notes $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(DEST)/html cp -r source/plots $(DEST)/html @echo @echo "Build finished. The HTML pages are in build/html." $(PYTHON) source/random_gallery.py dirhtml: $(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(DEST)/dirhtml @echo @echo "Build finished. The HTML pages are in build/dirhtml." pickle: $(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(DEST)/pickle @echo @echo "Build finished; now you can process the pickle files." json: $(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(DEST)/json @echo @echo "Build finished; now you can process the JSON files." htmlhelp: $(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(DEST)/htmlhelp @echo @echo "Build finished; now you can run HTML Help Workshop with the" \ ".hhp project file in build/htmlhelp." qthelp: $(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(DEST)/qthelp @echo @echo "Build finished; now you can run "qcollectiongenerator" with the" \ ".qhcp project file in build/qthelp, like this:" @echo "# qcollectiongenerator build/qthelp/scikitimage.qhcp" @echo "To view the help file:" @echo "# assistant -collectionFile build/qthelp/scikitimage.qhc" devhelp: $(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(DEST)/devhelp @echo @echo "Build finished." @echo "To view the help file:" @echo "# mkdir -p $$HOME/.local/share/devhelp/scikitimage" @echo "# ln -s build/devhelp $$HOME/.local/share/devhelp/scikitimage" @echo "# devhelp" latex: api $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(DEST)/latex @echo @echo "Build finished; the LaTeX files are in $(DEST)/latex." @echo "Run \`make all-pdf' or \`make all-ps' in that directory to" \ "run these through (pdf)latex." latexpdf: latex $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(DEST)/latex @echo "Running LaTeX files through pdflatex..." make -C build/latex all-pdf @echo "pdflatex finished; the PDF files are in build/latex." changes: $(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(DEST)/changes @echo @echo "The overview file is in build/changes." linkcheck: $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(DEST)/linkcheck @echo @echo "Link check complete; look for any errors in the above output " \ "or in build/linkcheck/output.txt." doctest: $(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(DEST)/doctest @echo "Testing of doctests in the sources finished, look at the " \ "results in build/doctest/output.txt." gh-pages: $(PYTHON) gh-pages.py gitwash: $(PYTHON) tools/gitwash/gitwash_dumper.py source scikit-image \ --project-url=http://scikit-image.org \ --project-ml-url=https://mail.python.org/mailman/listinfo/scikit-image \ --repo-name=scikit-image \ --github-user=scikit-image \ --source-suffix=.rst scikit-image-0.13.1/doc/__init__.py000066400000000000000000000000001316256236300170150ustar00rootroot00000000000000scikit-image-0.13.1/doc/examples/000077500000000000000000000000001316256236300165345ustar00rootroot00000000000000scikit-image-0.13.1/doc/examples/README.txt000066400000000000000000000003451316256236300202340ustar00rootroot00000000000000.. _examples_gallery: General examples ------------------- General-purpose and introductory examples for scikit-image. The `narrative documentation <../user_guide.html>`_ introduces conventions and basic image manipulations. scikit-image-0.13.1/doc/examples/color_exposure/000077500000000000000000000000001316256236300216045ustar00rootroot00000000000000scikit-image-0.13.1/doc/examples/color_exposure/README.txt000066400000000000000000000001221316256236300232750ustar00rootroot00000000000000Manipulating exposure and color channels ---------------------------------------- scikit-image-0.13.1/doc/examples/color_exposure/plot_adapt_rgb.py000066400000000000000000000106671316256236300251510ustar00rootroot00000000000000""" ========================================= Adapting gray-scale filters to RGB images ========================================= There are many filters that are designed to work with gray-scale images but not with color images. To simplify the process of creating functions that can adapt to RGB images, scikit-image provides the ``adapt_rgb`` decorator. To actually use the ``adapt_rgb`` decorator, you have to decide how you want to adapt the RGB image for use with the gray-scale filter. There are two pre-defined handlers: ``each_channel`` Pass each of the RGB channels to the filter one-by-one, and stitch the results back into an RGB image. ``hsv_value`` Convert the RGB image to HSV and pass the value channel to the filter. The filtered result is inserted back into the HSV image and converted back to RGB. Below, we demonstrate the use of ``adapt_rgb`` on a couple of gray-scale filters: """ from skimage.color.adapt_rgb import adapt_rgb, each_channel, hsv_value from skimage import filters @adapt_rgb(each_channel) def sobel_each(image): return filters.sobel(image) @adapt_rgb(hsv_value) def sobel_hsv(image): return filters.sobel(image) ###################################################################### # We can use these functions as we would normally use them, but now they work # with both gray-scale and color images. Let's plot the results with a color # image: from skimage import data from skimage.exposure import rescale_intensity import matplotlib.pyplot as plt image = data.astronaut() fig = plt.figure(figsize=(14, 7)) ax_each = fig.add_subplot(121, adjustable='box-forced') ax_hsv = fig.add_subplot(122, sharex=ax_each, sharey=ax_each, adjustable='box-forced') # We use 1 - sobel_each(image) # but this will not work if image is not normalized ax_each.imshow(rescale_intensity(1 - sobel_each(image))) ax_each.set_xticks([]), ax_each.set_yticks([]) ax_each.set_title("Sobel filter computed\n on individual RGB channels") # We use 1 - sobel_hsv(image) but this will not work if image is not normalized ax_hsv.imshow(rescale_intensity(1 - sobel_hsv(image))) ax_hsv.set_xticks([]), ax_hsv.set_yticks([]) ax_hsv.set_title("Sobel filter computed\n on (V)alue converted image (HSV)") ###################################################################### # Notice that the result for the value-filtered image preserves the color of # the original image, but channel filtered image combines in a more # surprising way. In other common cases, smoothing for example, the channel # filtered image will produce a better result than the value-filtered image. # # You can also create your own handler functions for ``adapt_rgb``. To do so, # just create a function with the following signature:: # # def handler(image_filter, image, *args, **kwargs): # # Manipulate RGB image here... # image = image_filter(image, *args, **kwargs) # # Manipulate filtered image here... # return image # # Note that ``adapt_rgb`` handlers are written for filters where the image is # the first argument. # # As a very simple example, we can just convert any RGB image to grayscale # and then return the filtered result: from skimage.color import rgb2gray def as_gray(image_filter, image, *args, **kwargs): gray_image = rgb2gray(image) return image_filter(gray_image, *args, **kwargs) ###################################################################### # It's important to create a signature that uses ``*args`` and ``**kwargs`` # to pass arguments along to the filter so that the decorated function is # allowed to have any number of positional and keyword arguments. # # Finally, we can use this handler with ``adapt_rgb`` just as before: @adapt_rgb(as_gray) def sobel_gray(image): return filters.sobel(image) fig = plt.figure(figsize=(7, 7)) ax = fig.add_subplot(111, sharex=ax_each, sharey=ax_each, adjustable='box-forced') # We use 1 - sobel_gray(image) # but this will not work if image is not normalized ax.imshow(rescale_intensity(1 - sobel_gray(image)), cmap=plt.cm.gray) ax.set_xticks([]), ax.set_yticks([]) ax.set_title("Sobel filter computed\n on the converted grayscale image") plt.show() ###################################################################### # # .. note:: # # A very simple check of the array shape is used for detecting RGB # images, so ``adapt_rgb`` is not recommended for functions that support # 3D volumes or color images in non-RGB spaces. scikit-image-0.13.1/doc/examples/color_exposure/plot_equalize.py000066400000000000000000000057321316256236300250420ustar00rootroot00000000000000""" ====================== Histogram Equalization ====================== This examples enhances an image with low contrast, using a method called *histogram equalization*, which "spreads out the most frequent intensity values" in an image [1]_. The equalized image has a roughly linear cumulative distribution function. While histogram equalization has the advantage that it requires no parameters, it sometimes yields unnatural looking images. An alternative method is *contrast stretching*, where the image is rescaled to include all intensities that fall within the 2nd and 98th percentiles [2]_. .. [1] http://en.wikipedia.org/wiki/Histogram_equalization .. [2] http://homepages.inf.ed.ac.uk/rbf/HIPR2/stretch.htm """ import matplotlib import matplotlib.pyplot as plt import numpy as np from skimage import data, img_as_float from skimage import exposure matplotlib.rcParams['font.size'] = 8 def plot_img_and_hist(img, axes, bins=256): """Plot an image along with its histogram and cumulative histogram. """ img = img_as_float(img) ax_img, ax_hist = axes ax_cdf = ax_hist.twinx() # Display image ax_img.imshow(img, cmap=plt.cm.gray) ax_img.set_axis_off() ax_img.set_adjustable('box-forced') # Display histogram ax_hist.hist(img.ravel(), bins=bins, histtype='step', color='black') ax_hist.ticklabel_format(axis='y', style='scientific', scilimits=(0, 0)) ax_hist.set_xlabel('Pixel intensity') ax_hist.set_xlim(0, 1) ax_hist.set_yticks([]) # Display cumulative distribution img_cdf, bins = exposure.cumulative_distribution(img, bins) ax_cdf.plot(bins, img_cdf, 'r') ax_cdf.set_yticks([]) return ax_img, ax_hist, ax_cdf # Load an example image img = data.moon() # Contrast stretching p2, p98 = np.percentile(img, (2, 98)) img_rescale = exposure.rescale_intensity(img, in_range=(p2, p98)) # Equalization img_eq = exposure.equalize_hist(img) # Adaptive Equalization img_adapteq = exposure.equalize_adapthist(img, clip_limit=0.03) # Display results fig = plt.figure(figsize=(8, 5)) axes = np.zeros((2, 4), dtype=np.object) axes[0, 0] = fig.add_subplot(2, 4, 1) for i in range(1, 4): axes[0, i] = fig.add_subplot(2, 4, 1+i, sharex=axes[0,0], sharey=axes[0,0]) for i in range(0, 4): axes[1, i] = fig.add_subplot(2, 4, 5+i) ax_img, ax_hist, ax_cdf = plot_img_and_hist(img, axes[:, 0]) ax_img.set_title('Low contrast image') y_min, y_max = ax_hist.get_ylim() ax_hist.set_ylabel('Number of pixels') ax_hist.set_yticks(np.linspace(0, y_max, 5)) ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_rescale, axes[:, 1]) ax_img.set_title('Contrast stretching') ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_eq, axes[:, 2]) ax_img.set_title('Histogram equalization') ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_adapteq, axes[:, 3]) ax_img.set_title('Adaptive equalization') ax_cdf.set_ylabel('Fraction of total intensity') ax_cdf.set_yticks(np.linspace(0, 1, 5)) # prevent overlap of y-axis labels fig.tight_layout() plt.show() scikit-image-0.13.1/doc/examples/color_exposure/plot_ihc_color_separation.py000066400000000000000000000050201316256236300273770ustar00rootroot00000000000000""" ============================================== Immunohistochemical staining colors separation ============================================== Color deconvolution consists of the separation of features by their colors. In this example we separate the immunohistochemical (IHC) staining from the hematoxylin counterstaining. The separation is achieved with the method described in [1]_, known as "color deconvolution". The IHC staining expression of the FHL2 protein is here revealed with Diaminobenzidine (DAB) which gives a brown color. .. [1] A. C. Ruifrok and D. A. Johnston, "Quantification of histochemical staining by color deconvolution.," Analytical and quantitative cytology and histology / the International Academy of Cytology [and] American Society of Cytology, vol. 23, no. 4, pp. 291-9, Aug. 2001. """ import matplotlib.pyplot as plt from skimage import data from skimage.color import rgb2hed from matplotlib.colors import LinearSegmentedColormap # Create an artificial color close to the orginal one cmap_hema = LinearSegmentedColormap.from_list('mycmap', ['white', 'navy']) cmap_dab = LinearSegmentedColormap.from_list('mycmap', ['white', 'saddlebrown']) cmap_eosin = LinearSegmentedColormap.from_list('mycmap', ['darkviolet', 'white']) ihc_rgb = data.immunohistochemistry() ihc_hed = rgb2hed(ihc_rgb) fig, axes = plt.subplots(2, 2, figsize=(7, 6), sharex=True, sharey=True, subplot_kw={'adjustable': 'box-forced'}) ax = axes.ravel() ax[0].imshow(ihc_rgb) ax[0].set_title("Original image") ax[1].imshow(ihc_hed[:, :, 0], cmap=cmap_hema) ax[1].set_title("Hematoxylin") ax[2].imshow(ihc_hed[:, :, 1], cmap=cmap_eosin) ax[2].set_title("Eosin") ax[3].imshow(ihc_hed[:, :, 2], cmap=cmap_dab) ax[3].set_title("DAB") for a in ax.ravel(): a.axis('off') fig.tight_layout() ###################################################################### # Now we can easily manipulate the hematoxylin and DAB "channels": import numpy as np from skimage.exposure import rescale_intensity # Rescale hematoxylin and DAB signals and give them a fluorescence look h = rescale_intensity(ihc_hed[:, :, 0], out_range=(0, 1)) d = rescale_intensity(ihc_hed[:, :, 2], out_range=(0, 1)) zdh = np.dstack((np.zeros_like(h), d, h)) fig = plt.figure() axis = plt.subplot(1, 1, 1, sharex=ax[0], sharey=ax[0], adjustable='box-forced') axis.imshow(zdh) axis.set_title("Stain separated image (rescaled)") axis.axis('off') plt.show() scikit-image-0.13.1/doc/examples/color_exposure/plot_local_equalize.py000066400000000000000000000053631316256236300262140ustar00rootroot00000000000000""" ============================ Local Histogram Equalization ============================ This examples enhances an image with low contrast, using a method called *local histogram equalization*, which spreads out the most frequent intensity values in an image. The equalized image [1]_ has a roughly linear cumulative distribution function for each pixel neighborhood. The local version [2]_ of the histogram equalization emphasized every local graylevel variations. References ---------- .. [1] http://en.wikipedia.org/wiki/Histogram_equalization .. [2] http://en.wikipedia.org/wiki/Adaptive_histogram_equalization """ import numpy as np import matplotlib import matplotlib.pyplot as plt from skimage import data from skimage.util.dtype import dtype_range from skimage.util import img_as_ubyte from skimage import exposure from skimage.morphology import disk from skimage.filters import rank matplotlib.rcParams['font.size'] = 9 def plot_img_and_hist(img, axes, bins=256): """Plot an image along with its histogram and cumulative histogram. """ ax_img, ax_hist = axes ax_cdf = ax_hist.twinx() # Display image ax_img.imshow(img, cmap=plt.cm.gray) ax_img.set_axis_off() # Display histogram ax_hist.hist(img.ravel(), bins=bins) ax_hist.ticklabel_format(axis='y', style='scientific', scilimits=(0, 0)) ax_hist.set_xlabel('Pixel intensity') xmin, xmax = dtype_range[img.dtype.type] ax_hist.set_xlim(xmin, xmax) # Display cumulative distribution img_cdf, bins = exposure.cumulative_distribution(img, bins) ax_cdf.plot(bins, img_cdf, 'r') return ax_img, ax_hist, ax_cdf # Load an example image img = img_as_ubyte(data.moon()) # Global equalize img_rescale = exposure.equalize_hist(img) # Equalization selem = disk(30) img_eq = rank.equalize(img, selem=selem) # Display results fig = plt.figure(figsize=(8, 5)) axes = np.zeros((2, 3), dtype=np.object) axes[0, 0] = plt.subplot(2, 3, 1, adjustable='box-forced') axes[0, 1] = plt.subplot(2, 3, 2, sharex=axes[0, 0], sharey=axes[0, 0], adjustable='box-forced') axes[0, 2] = plt.subplot(2, 3, 3, sharex=axes[0, 0], sharey=axes[0, 0], adjustable='box-forced') axes[1, 0] = plt.subplot(2, 3, 4) axes[1, 1] = plt.subplot(2, 3, 5) axes[1, 2] = plt.subplot(2, 3, 6) ax_img, ax_hist, ax_cdf = plot_img_and_hist(img, axes[:, 0]) ax_img.set_title('Low contrast image') ax_hist.set_ylabel('Number of pixels') ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_rescale, axes[:, 1]) ax_img.set_title('Global equalise') ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_eq, axes[:, 2]) ax_img.set_title('Local equalize') ax_cdf.set_ylabel('Fraction of total intensity') # prevent overlap of y-axis labels fig.tight_layout() plt.show() scikit-image-0.13.1/doc/examples/color_exposure/plot_log_gamma.py000066400000000000000000000046121316256236300251420ustar00rootroot00000000000000""" ================================= Gamma and log contrast adjustment ================================= This example adjusts image contrast by performing a Gamma and a Logarithmic correction on the input image. """ import matplotlib import matplotlib.pyplot as plt import numpy as np from skimage import data, img_as_float from skimage import exposure matplotlib.rcParams['font.size'] = 8 def plot_img_and_hist(img, axes, bins=256): """Plot an image along with its histogram and cumulative histogram. """ img = img_as_float(img) ax_img, ax_hist = axes ax_cdf = ax_hist.twinx() # Display image ax_img.imshow(img, cmap=plt.cm.gray) ax_img.set_axis_off() # Display histogram ax_hist.hist(img.ravel(), bins=bins, histtype='step', color='black') ax_hist.ticklabel_format(axis='y', style='scientific', scilimits=(0, 0)) ax_hist.set_xlabel('Pixel intensity') ax_hist.set_xlim(0, 1) ax_hist.set_yticks([]) # Display cumulative distribution img_cdf, bins = exposure.cumulative_distribution(img, bins) ax_cdf.plot(bins, img_cdf, 'r') ax_cdf.set_yticks([]) return ax_img, ax_hist, ax_cdf # Load an example image img = data.moon() # Gamma gamma_corrected = exposure.adjust_gamma(img, 2) # Logarithmic logarithmic_corrected = exposure.adjust_log(img, 1) # Display results fig = plt.figure(figsize=(8, 5)) axes = np.zeros((2, 3), dtype=np.object) axes[0, 0] = plt.subplot(2, 3, 1, adjustable='box-forced') axes[0, 1] = plt.subplot(2, 3, 2, sharex=axes[0, 0], sharey=axes[0, 0], adjustable='box-forced') axes[0, 2] = plt.subplot(2, 3, 3, sharex=axes[0, 0], sharey=axes[0, 0], adjustable='box-forced') axes[1, 0] = plt.subplot(2, 3, 4) axes[1, 1] = plt.subplot(2, 3, 5) axes[1, 2] = plt.subplot(2, 3, 6) ax_img, ax_hist, ax_cdf = plot_img_and_hist(img, axes[:, 0]) ax_img.set_title('Low contrast image') y_min, y_max = ax_hist.get_ylim() ax_hist.set_ylabel('Number of pixels') ax_hist.set_yticks(np.linspace(0, y_max, 5)) ax_img, ax_hist, ax_cdf = plot_img_and_hist(gamma_corrected, axes[:, 1]) ax_img.set_title('Gamma correction') ax_img, ax_hist, ax_cdf = plot_img_and_hist(logarithmic_corrected, axes[:, 2]) ax_img.set_title('Logarithmic correction') ax_cdf.set_ylabel('Fraction of total intensity') ax_cdf.set_yticks(np.linspace(0, 1, 5)) # prevent overlap of y-axis labels fig.tight_layout() plt.show() scikit-image-0.13.1/doc/examples/color_exposure/plot_regional_maxima.py000066400000000000000000000074551316256236300263630ustar00rootroot00000000000000""" ========================= Filtering regional maxima ========================= Here, we use morphological reconstruction to create a background image, which we can subtract from the original image to isolate bright features (regional maxima). First we try reconstruction by dilation starting at the edges of the image. We initialize a seed image to the minimum intensity of the image, and set its border to be the pixel values in the original image. These maximal pixels will get dilated in order to reconstruct the background image. """ import numpy as np import matplotlib.pyplot as plt from scipy.ndimage import gaussian_filter from skimage import data from skimage import img_as_float from skimage.morphology import reconstruction # Convert to float: Important for subtraction later which won't work with uint8 image = img_as_float(data.coins()) image = gaussian_filter(image, 1) seed = np.copy(image) seed[1:-1, 1:-1] = image.min() mask = image dilated = reconstruction(seed, mask, method='dilation') ###################################################################### # Subtracting the dilated image leaves an image with just the coins and a # flat, black background, as shown below. fig, (ax0, ax1, ax2) = plt.subplots(nrows=1, ncols=3, figsize=(8, 2.5), sharex=True, sharey=True) ax0.imshow(image, cmap='gray') ax0.set_title('original image') ax0.axis('off') ax0.set_adjustable('box-forced') ax1.imshow(dilated, vmin=image.min(), vmax=image.max(), cmap='gray') ax1.set_title('dilated') ax1.axis('off') ax1.set_adjustable('box-forced') ax2.imshow(image - dilated, cmap='gray') ax2.set_title('image - dilated') ax2.axis('off') ax2.set_adjustable('box-forced') fig.tight_layout() ###################################################################### # Although the features (i.e. the coins) are clearly isolated, the coins # surrounded by a bright background in the original image are dimmer in the # subtracted image. We can attempt to correct this using a different seed # image. # # Instead of creating a seed image with maxima along the image border, we can # use the features of the image itself to seed the reconstruction process. # Here, the seed image is the original image minus a fixed value, ``h``. h = 0.4 seed = image - h dilated = reconstruction(seed, mask, method='dilation') hdome = image - dilated ###################################################################### # To get a feel for the reconstruction process, we plot the intensity of the # mask, seed, and dilated images along a slice of the image (indicated by red # line). fig, (ax0, ax1, ax2) = plt.subplots(nrows=1, ncols=3, figsize=(8, 2.5)) yslice = 197 ax0.plot(mask[yslice], '0.5', label='mask') ax0.plot(seed[yslice], 'k', label='seed') ax0.plot(dilated[yslice], 'r', label='dilated') ax0.set_ylim(-0.2, 2) ax0.set_title('image slice') ax0.set_xticks([]) ax0.legend() ax1.imshow(dilated, vmin=image.min(), vmax=image.max(), cmap='gray') ax1.axhline(yslice, color='r', alpha=0.4) ax1.set_title('dilated') ax1.axis('off') ax2.imshow(hdome, cmap='gray') ax2.axhline(yslice, color='r', alpha=0.4) ax2.set_title('image - dilated') ax2.axis('off') fig.tight_layout() plt.show() ###################################################################### # As you can see in the image slice, each coin is given a different baseline # intensity in the reconstructed image; this is because we used the local # intensity (shifted by ``h``) as a seed value. As a result, the coins in the # subtracted image have similar pixel intensities. The final result is known # as the h-dome of an image since this tends to isolate regional maxima of # height ``h``. This operation is particularly useful when your images are # unevenly illuminated. scikit-image-0.13.1/doc/examples/color_exposure/plot_tinting_grayscale_images.py000066400000000000000000000133151316256236300302520ustar00rootroot00000000000000""" ========================= Tinting gray-scale images ========================= It can be useful to artificially tint an image with some color, either to highlight particular regions of an image or maybe just to liven up a grayscale image. This example demonstrates image-tinting by scaling RGB values and by adjusting colors in the HSV color-space. In 2D, color images are often represented in RGB---3 layers of 2D arrays, where the 3 layers represent (R)ed, (G)reen and (B)lue channels of the image. The simplest way of getting a tinted image is to set each RGB channel to the grayscale image scaled by a different multiplier for each channel. For example, multiplying the green and blue channels by 0 leaves only the red channel and produces a bright red image. Similarly, zeroing-out the blue channel leaves only the red and green channels, which combine to form yellow. """ import matplotlib.pyplot as plt from skimage import data from skimage import color from skimage import img_as_float grayscale_image = img_as_float(data.camera()[::2, ::2]) image = color.gray2rgb(grayscale_image) red_multiplier = [1, 0, 0] yellow_multiplier = [1, 1, 0] fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(8, 4), sharex=True, sharey=True) ax1.imshow(red_multiplier * image) ax2.imshow(yellow_multiplier * image) ax1.set_adjustable('box-forced') ax2.set_adjustable('box-forced') ###################################################################### # In many cases, dealing with RGB values may not be ideal. Because of that, # there are many other `color spaces`_ in which you can represent a color # image. One popular color space is called HSV, which represents hue (~the # color), saturation (~colorfulness), and value (~brightness). For example, a # color (hue) might be green, but its saturation is how intense that green is # ---where olive is on the low end and neon on the high end. # # In some implementations, the hue in HSV goes from 0 to 360, since hues wrap # around in a circle. In scikit-image, however, hues are float values from 0 # to 1, so that hue, saturation, and value all share the same scale. # # .. _color spaces: # http://en.wikipedia.org/wiki/List_of_color_spaces_and_their_uses # # Below, we plot a linear gradient in the hue, with the saturation and value # turned all the way up: import numpy as np hue_gradient = np.linspace(0, 1) hsv = np.ones(shape=(1, len(hue_gradient), 3), dtype=float) hsv[:, :, 0] = hue_gradient all_hues = color.hsv2rgb(hsv) fig, ax = plt.subplots(figsize=(5, 2)) # Set image extent so hues go from 0 to 1 and the image is a nice aspect ratio. ax.imshow(all_hues, extent=(0, 1, 0, 0.2)) ax.set_axis_off() ###################################################################### # Notice how the colors at the far left and far right are the same. That # reflects the fact that the hues wrap around like the color wheel (see HSV_ # for more info). # # .. _HSV: http://en.wikipedia.org/wiki/HSL_and_HSV # # Now, let's create a little utility function to take an RGB image and: # # 1. Transform the RGB image to HSV 2. Set the hue and saturation 3. # Transform the HSV image back to RGB def colorize(image, hue, saturation=1): """ Add color of the given hue to an RGB image. By default, set the saturation to 1 so that the colors pop! """ hsv = color.rgb2hsv(image) hsv[:, :, 1] = saturation hsv[:, :, 0] = hue return color.hsv2rgb(hsv) ###################################################################### # Notice that we need to bump up the saturation; images with zero saturation # are grayscale, so we need to a non-zero value to actually see the color # we've set. # # Using the function above, we plot six images with a linear gradient in the # hue and a non-zero saturation: hue_rotations = np.linspace(0, 1, 6) fig, axes = plt.subplots(nrows=2, ncols=3, sharex=True, sharey=True) for ax, hue in zip(axes.flat, hue_rotations): # Turn down the saturation to give it that vintage look. tinted_image = colorize(image, hue, saturation=0.3) ax.imshow(tinted_image, vmin=0, vmax=1) ax.set_axis_off() ax.set_adjustable('box-forced') fig.tight_layout() ###################################################################### # You can combine this tinting effect with numpy slicing and fancy-indexing # to selectively tint your images. In the example below, we set the hue of # some rectangles using slicing and scale the RGB values of some pixels found # by thresholding. In practice, you might want to define a region for tinting # based on segmentation results or blob detection methods. from skimage.filters import rank # Square regions defined as slices over the first two dimensions. top_left = (slice(100),) * 2 bottom_right = (slice(-100, None),) * 2 sliced_image = image.copy() sliced_image[top_left] = colorize(image[top_left], 0.82, saturation=0.5) sliced_image[bottom_right] = colorize(image[bottom_right], 0.5, saturation=0.5) # Create a mask selecting regions with interesting texture. noisy = rank.entropy(grayscale_image, np.ones((9, 9))) textured_regions = noisy > 4 # Note that using `colorize` here is a bit more difficult, since `rgb2hsv` # expects an RGB image (height x width x channel), but fancy-indexing returns # a set of RGB pixels (# pixels x channel). masked_image = image.copy() masked_image[textured_regions, :] *= red_multiplier fig, (ax1, ax2) = plt.subplots(ncols=2, nrows=1, figsize=(8, 4), sharex=True, sharey=True) ax1.imshow(sliced_image) ax2.imshow(masked_image) ax1.set_adjustable('box-forced') ax2.set_adjustable('box-forced') plt.show() ###################################################################### # For coloring multiple regions, you may also be interested in # `skimage.color.label2rgb `_. scikit-image-0.13.1/doc/examples/edges/000077500000000000000000000000001316256236300176235ustar00rootroot00000000000000scikit-image-0.13.1/doc/examples/edges/README.txt000066400000000000000000000000401316256236300213130ustar00rootroot00000000000000Edges and lines --------------- scikit-image-0.13.1/doc/examples/edges/plot_active_contours.py000066400000000000000000000065321316256236300244500ustar00rootroot00000000000000""" ==================== Active Contour Model ==================== The active contour model is a method to fit open or closed splines to lines or edges in an image [1]_. It works by minimising an energy that is in part defined by the image and part by the spline's shape: length and smoothness. The minimization is done implicitly in the shape energy and explicitly in the image energy. In the following two examples the active contour model is used (1) to segment the face of a person from the rest of an image by fitting a closed curve to the edges of the face and (2) to find the darkest curve between two fixed points while obeying smoothness considerations. Typically it is a good idea to smooth images a bit before analyzing, as done in the following examples. We initialize a circle around the astronaut's face and use the default boundary condition ``bc='periodic'`` to fit a closed curve. The default parameters ``w_line=0, w_edge=1`` will make the curve search towards edges, such as the boundaries of the face. .. [1] *Snakes: Active contour models*. Kass, M.; Witkin, A.; Terzopoulos, D. International Journal of Computer Vision 1 (4): 321 (1988). """ import numpy as np import matplotlib.pyplot as plt from skimage.color import rgb2gray from skimage import data from skimage.filters import gaussian from skimage.segmentation import active_contour # Test scipy version, since active contour is only possible # with recent scipy version import scipy split_version = scipy.__version__.split('.') if not(split_version[-1].isdigit()): # Remove dev string if present split_version.pop() scipy_version = list(map(int, split_version)) new_scipy = scipy_version[0] > 0 or \ (scipy_version[0] == 0 and scipy_version[1] >= 14) img = data.astronaut() img = rgb2gray(img) s = np.linspace(0, 2*np.pi, 400) x = 220 + 100*np.cos(s) y = 100 + 100*np.sin(s) init = np.array([x, y]).T if not new_scipy: print('You are using an old version of scipy. ' 'Active contours is implemented for scipy versions ' '0.14.0 and above.') if new_scipy: snake = active_contour(gaussian(img, 3), init, alpha=0.015, beta=10, gamma=0.001) fig = plt.figure(figsize=(7, 7)) ax = fig.add_subplot(111) plt.gray() ax.imshow(img) ax.plot(init[:, 0], init[:, 1], '--r', lw=3) ax.plot(snake[:, 0], snake[:, 1], '-b', lw=3) ax.set_xticks([]), ax.set_yticks([]) ax.axis([0, img.shape[1], img.shape[0], 0]) ###################################################################### # Here we initialize a straight line between two points, `(5, 136)` and # `(424, 50)`, and require that the spline has its end points there by giving # the boundary condition `bc='fixed'`. We furthermore make the algorithm # search for dark lines by giving a negative `w_line` value. img = data.text() x = np.linspace(5, 424, 100) y = np.linspace(136, 50, 100) init = np.array([x, y]).T if new_scipy: snake = active_contour(gaussian(img, 1), init, bc='fixed', alpha=0.1, beta=1.0, w_line=-5, w_edge=0, gamma=0.1) fig = plt.figure(figsize=(9, 5)) ax = fig.add_subplot(111) plt.gray() ax.imshow(img) ax.plot(init[:, 0], init[:, 1], '--r', lw=3) ax.plot(snake[:, 0], snake[:, 1], '-b', lw=3) ax.set_xticks([]), ax.set_yticks([]) ax.axis([0, img.shape[1], img.shape[0], 0]) plt.show() scikit-image-0.13.1/doc/examples/edges/plot_canny.py000066400000000000000000000031031316256236300223400ustar00rootroot00000000000000""" =================== Canny edge detector =================== The Canny filter is a multi-stage edge detector. It uses a filter based on the derivative of a Gaussian in order to compute the intensity of the gradients.The Gaussian reduces the effect of noise present in the image. Then, potential edges are thinned down to 1-pixel curves by removing non-maximum pixels of the gradient magnitude. Finally, edge pixels are kept or removed using hysteresis thresholding on the gradient magnitude. The Canny has three adjustable parameters: the width of the Gaussian (the noisier the image, the greater the width), and the low and high threshold for the hysteresis thresholding. """ import numpy as np import matplotlib.pyplot as plt from scipy import ndimage as ndi from skimage import feature # Generate noisy image of a square im = np.zeros((128, 128)) im[32:-32, 32:-32] = 1 im = ndi.rotate(im, 15, mode='constant') im = ndi.gaussian_filter(im, 4) im += 0.2 * np.random.random(im.shape) # Compute the Canny filter for two values of sigma edges1 = feature.canny(im) edges2 = feature.canny(im, sigma=3) # display results fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3, figsize=(8, 3), sharex=True, sharey=True) ax1.imshow(im, cmap=plt.cm.gray) ax1.axis('off') ax1.set_title('noisy image', fontsize=20) ax2.imshow(edges1, cmap=plt.cm.gray) ax2.axis('off') ax2.set_title('Canny filter, $\sigma=1$', fontsize=20) ax3.imshow(edges2, cmap=plt.cm.gray) ax3.axis('off') ax3.set_title('Canny filter, $\sigma=3$', fontsize=20) fig.tight_layout() plt.show() scikit-image-0.13.1/doc/examples/edges/plot_circular_elliptical_hough_transform.py000066400000000000000000000112271316256236300305310ustar00rootroot00000000000000""" ======================================== Circular and Elliptical Hough Transforms ======================================== The Hough transform in its simplest form is a `method to detect straight lines `__ but it can also be used to detect circles or ellipses. The algorithm assumes that the edge is detected and it is robust against noise or missing points. Circle detection ================ In the following example, the Hough transform is used to detect coin positions and match their edges. We provide a range of plausible radii. For each radius, two circles are extracted and we finally keep the five most prominent candidates. The result shows that coin positions are well-detected. Algorithm overview ------------------ Given a black circle on a white background, we first guess its radius (or a range of radii) to construct a new circle. This circle is applied on each black pixel of the original picture and the coordinates of this circle are voting in an accumulator. From this geometrical construction, the original circle center position receives the highest score. Note that the accumulator size is built to be larger than the original picture in order to detect centers outside the frame. Its size is extended by two times the larger radius. """ import numpy as np import matplotlib.pyplot as plt from skimage import data, color from skimage.transform import hough_circle, hough_circle_peaks from skimage.feature import canny from skimage.draw import circle_perimeter from skimage.util import img_as_ubyte # Load picture and detect edges image = img_as_ubyte(data.coins()[160:230, 70:270]) edges = canny(image, sigma=3, low_threshold=10, high_threshold=50) # Detect two radii hough_radii = np.arange(20, 35, 2) hough_res = hough_circle(edges, hough_radii) # Select the most prominent 5 circles accums, cx, cy, radii = hough_circle_peaks(hough_res, hough_radii, total_num_peaks=3) # Draw them fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(10, 4)) image = color.gray2rgb(image) for center_y, center_x, radius in zip(cy, cx, radii): circy, circx = circle_perimeter(center_y, center_x, radius) image[circy, circx] = (220, 20, 20) ax.imshow(image, cmap=plt.cm.gray) plt.show() ###################################################################### # Ellipse detection # ================= # # In this second example, the aim is to detect the edge of a coffee cup. # Basically, this is a projection of a circle, i.e. an ellipse. The problem # to solve is much more difficult because five parameters have to be # determined, instead of three for circles. # # Algorithm overview # ------------------- # # The algorithm takes two different points belonging to the ellipse. It # assumes that it is the main axis. A loop on all the other points determines # how much an ellipse passes to them. A good match corresponds to high # accumulator values. # # A full description of the algorithm can be found in reference [1]_. # # References # ---------- # .. [1] Xie, Yonghong, and Qiang Ji. "A new efficient # ellipse detection method." Pattern Recognition, 2002. Proceedings. # 16th International Conference on. Vol. 2. IEEE, 2002 import matplotlib.pyplot as plt from skimage import data, color, img_as_ubyte from skimage.feature import canny from skimage.transform import hough_ellipse from skimage.draw import ellipse_perimeter # Load picture, convert to grayscale and detect edges image_rgb = data.coffee()[0:220, 160:420] image_gray = color.rgb2gray(image_rgb) edges = canny(image_gray, sigma=2.0, low_threshold=0.55, high_threshold=0.8) # Perform a Hough Transform # The accuracy corresponds to the bin size of a major axis. # The value is chosen in order to get a single high accumulator. # The threshold eliminates low accumulators result = hough_ellipse(edges, accuracy=20, threshold=250, min_size=100, max_size=120) result.sort(order='accumulator') # Estimated parameters for the ellipse best = list(result[-1]) yc, xc, a, b = [int(round(x)) for x in best[1:5]] orientation = best[5] # Draw the ellipse on the original image cy, cx = ellipse_perimeter(yc, xc, a, b, orientation) image_rgb[cy, cx] = (0, 0, 255) # Draw the edge (white) and the resulting ellipse (red) edges = color.gray2rgb(img_as_ubyte(edges)) edges[cy, cx] = (250, 0, 0) fig2, (ax1, ax2) = plt.subplots(ncols=2, nrows=1, figsize=(8, 4), sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'}) ax1.set_title('Original picture') ax1.imshow(image_rgb) ax2.set_title('Edge (white) and result (red)') ax2.imshow(edges) plt.show() scikit-image-0.13.1/doc/examples/edges/plot_contours.py000066400000000000000000000023431316256236300231110ustar00rootroot00000000000000""" =============== Contour finding =============== We use a marching squares method to find constant valued contours in an image. In ``skimage.measure.find_contours``, array values are linearly interpolated to provide better precision of the output contours. Contours which intersect the image edge are open; all others are closed. The `marching squares algorithm `__ is a special case of the marching cubes algorithm (Lorensen, William and Harvey E. Cline. Marching Cubes: A High Resolution 3D Surface Construction Algorithm. Computer Graphics (SIGGRAPH 87 Proceedings) 21(4) July 1987, p. 163-170). """ import numpy as np import matplotlib.pyplot as plt from skimage import measure # Construct some test data x, y = np.ogrid[-np.pi:np.pi:100j, -np.pi:np.pi:100j] r = np.sin(np.exp((np.sin(x)**3 + np.cos(y)**2))) # Find contours at a constant value of 0.8 contours = measure.find_contours(r, 0.8) # Display the image and plot all contours found fig, ax = plt.subplots() ax.imshow(r, interpolation='nearest', cmap=plt.cm.gray) for n, contour in enumerate(contours): ax.plot(contour[:, 1], contour[:, 0], linewidth=2) ax.axis('image') ax.set_xticks([]) ax.set_yticks([]) plt.show() scikit-image-0.13.1/doc/examples/edges/plot_convex_hull.py000066400000000000000000000024031316256236300235600ustar00rootroot00000000000000""" =========== Convex Hull =========== The convex hull of a binary image is the set of pixels included in the smallest convex polygon that surround all white pixels in the input. A good overview of the algorithm is given on `Steve Eddin's blog `__. """ import matplotlib.pyplot as plt from skimage.morphology import convex_hull_image from skimage import data, img_as_float from skimage.util import invert # The original image is inverted as the object must be white. image = invert(data.horse()) chull = convex_hull_image(image) fig, axes = plt.subplots(1, 2, figsize=(8, 4)) ax = axes.ravel() ax[0].set_title('Original picture') ax[0].imshow(image, cmap=plt.cm.gray, interpolation='nearest') ax[0].set_axis_off() ax[1].set_title('Transformed picture') ax[1].imshow(chull, cmap=plt.cm.gray, interpolation='nearest') ax[1].set_axis_off() plt.tight_layout() plt.show() ###################################################################### # We prepare a second plot to show the difference. # chull_diff = img_as_float(chull.copy()) chull_diff[image] = 2 fig, ax = plt.subplots() ax.imshow(chull_diff, cmap=plt.cm.gray, interpolation='nearest') ax.set_title('Difference') plt.show() scikit-image-0.13.1/doc/examples/edges/plot_edge_filter.py000066400000000000000000000055571316256236300235200ustar00rootroot00000000000000""" ============== Edge operators ============== Edge operators are used in image processing within edge detection algorithms. They are discrete differentiation operators, computing an approximation of the gradient of the image intensity function. """ import numpy as np import matplotlib.pyplot as plt from skimage.data import camera from skimage.filters import roberts, sobel, scharr, prewitt image = camera() edge_roberts = roberts(image) edge_sobel = sobel(image) fig, ax = plt.subplots(ncols=2, sharex=True, sharey=True, figsize=(8, 4)) ax[0].imshow(edge_roberts, cmap=plt.cm.gray) ax[0].set_title('Roberts Edge Detection') ax[1].imshow(edge_sobel, cmap=plt.cm.gray) ax[1].set_title('Sobel Edge Detection') for a in ax: a.axis('off') plt.tight_layout() plt.show() ###################################################################### # Different operators compute different finite-difference approximations of # the gradient. For example, the Scharr filter results in a less rotational # variance than the Sobel filter that is in turn better than the Prewitt # filter [1]_ [2]_ [3]_. The difference between the Prewitt and Sobel filters # and the Scharr filter is illustrated below with an image that is the # discretization of a rotation- invariant continuous function. The # discrepancy between the Prewitt and Sobel filters, and the Scharr filter is # stronger for regions of the image where the direction of the gradient is # close to diagonal, and for regions with high spatial frequencies. For the # example image the differences between the filter results are very small and # the filter results are visually almost indistinguishable. # # .. [1] https://en.wikipedia.org/wiki/Sobel_operator#Alternative_operators # # .. [2] B. Jaehne, H. Scharr, and S. Koerkel. Principles of filter design. # In Handbook of Computer Vision and Applications. Academic Press, # 1999. # # .. [3] https://en.wikipedia.org/wiki/Prewitt_operator x, y = np.ogrid[:100, :100] # Rotation-invariant image with different spatial frequencies img = np.exp(1j * np.hypot(x, y)**1.3 / 20.).real edge_sobel = sobel(img) edge_scharr = scharr(img) edge_prewitt = prewitt(img) diff_scharr_prewitt = edge_scharr - edge_prewitt diff_scharr_sobel = edge_scharr - edge_sobel max_diff = np.max(np.maximum(diff_scharr_prewitt, diff_scharr_sobel)) fig, axes = plt.subplots(nrows=2, ncols=2, sharex=True, sharey=True, figsize=(8, 8)) ax = axes.ravel() ax[0].imshow(img, cmap=plt.cm.gray) ax[0].set_title('Original image') ax[1].imshow(edge_scharr, cmap=plt.cm.gray) ax[1].set_title('Scharr Edge Detection') ax[2].imshow(diff_scharr_prewitt, cmap=plt.cm.gray, vmax=max_diff) ax[2].set_title('Scharr - Prewitt') ax[3].imshow(diff_scharr_sobel, cmap=plt.cm.gray, vmax=max_diff) ax[3].set_title('Scharr - Sobel') for a in ax: a.axis('off') plt.tight_layout() plt.show() scikit-image-0.13.1/doc/examples/edges/plot_line_hough_transform.py000066400000000000000000000113531316256236300254520ustar00rootroot00000000000000""" ============================= Straight line Hough transform ============================= The Hough transform in its simplest form is a method to detect straight lines [1]_. In the following example, we construct an image with a line intersection. We then use the `Hough transform `__. to explore a parameter space for straight lines that may run through the image. Algorithm overview ------------------ Usually, lines are parameterised as :math:`y = mx + c`, with a gradient :math:`m` and y-intercept `c`. However, this would mean that :math:`m` goes to infinity for vertical lines. Instead, we therefore construct a segment perpendicular to the line, leading to the origin. The line is represented by the length of that segment, :math:`r`, and the angle it makes with the x-axis, :math:`\\theta`. The Hough transform constructs a histogram array representing the parameter space (i.e., an :math:`M \\times N` matrix, for :math:`M` different values of the radius and :math:`N` different values of :math:`\\theta`). For each parameter combination, :math:`r` and :math:`\\theta`, we then find the number of non-zero pixels in the input image that would fall close to the corresponding line, and increment the array at position :math:`(r, \\theta)` appropriately. We can think of each non-zero pixel "voting" for potential line candidates. The local maxima in the resulting histogram indicates the parameters of the most probably lines. In our example, the maxima occur at 45 and 135 degrees, corresponding to the normal vector angles of each line. Another approach is the Progressive Probabilistic Hough Transform [2]_. It is based on the assumption that using a random subset of voting points give a good approximation to the actual result, and that lines can be extracted during the voting process by walking along connected components. This returns the beginning and end of each line segment, which is useful. The function `probabilistic_hough` has three parameters: a general threshold that is applied to the Hough accumulator, a minimum line length and the line gap that influences line merging. In the example below, we find lines longer than 10 with a gap less than 3 pixels. References ---------- .. [1] Duda, R. O. and P. E. Hart, "Use of the Hough Transformation to Detect Lines and Curves in Pictures," Comm. ACM, Vol. 15, pp. 11-15 (January, 1972) .. [2] C. Galamhos, J. Matas and J. Kittler,"Progressive probabilistic Hough transform for line detection", in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1999. """ import numpy as np from skimage.transform import (hough_line, hough_line_peaks, probabilistic_hough_line) from skimage.feature import canny from skimage import data import matplotlib.pyplot as plt from matplotlib import cm # Constructing test image image = np.zeros((100, 100)) idx = np.arange(25, 75) image[idx[::-1], idx] = 255 image[idx, idx] = 255 # Classic straight-line Hough transform h, theta, d = hough_line(image) # Generating figure 1 fig, axes = plt.subplots(1, 3, figsize=(15, 6), subplot_kw={'adjustable': 'box-forced'}) ax = axes.ravel() ax[0].imshow(image, cmap=cm.gray) ax[0].set_title('Input image') ax[0].set_axis_off() ax[1].imshow(np.log(1 + h), extent=[np.rad2deg(theta[-1]), np.rad2deg(theta[0]), d[-1], d[0]], cmap=cm.gray, aspect=1/1.5) ax[1].set_title('Hough transform') ax[1].set_xlabel('Angles (degrees)') ax[1].set_ylabel('Distance (pixels)') ax[1].axis('image') ax[2].imshow(image, cmap=cm.gray) for _, angle, dist in zip(*hough_line_peaks(h, theta, d)): y0 = (dist - 0 * np.cos(angle)) / np.sin(angle) y1 = (dist - image.shape[1] * np.cos(angle)) / np.sin(angle) ax[2].plot((0, image.shape[1]), (y0, y1), '-r') ax[2].set_xlim((0, image.shape[1])) ax[2].set_ylim((image.shape[0], 0)) ax[2].set_axis_off() ax[2].set_title('Detected lines') plt.tight_layout() plt.show() # Line finding using the Probabilistic Hough Transform image = data.camera() edges = canny(image, 2, 1, 25) lines = probabilistic_hough_line(edges, threshold=10, line_length=5, line_gap=3) # Generating figure 2 fig, axes = plt.subplots(1, 3, figsize=(15, 5), sharex=True, sharey=True) ax = axes.ravel() ax[0].imshow(image, cmap=cm.gray) ax[0].set_title('Input image') ax[1].imshow(edges, cmap=cm.gray) ax[1].set_title('Canny edges') ax[2].imshow(edges * 0) for line in lines: p0, p1 = line ax[2].plot((p0[0], p1[0]), (p0[1], p1[1])) ax[2].set_xlim((0, image.shape[1])) ax[2].set_ylim((image.shape[0], 0)) ax[2].set_title('Probabilistic Hough') for a in ax: a.set_axis_off() a.set_adjustable('box-forced') plt.tight_layout() plt.show() scikit-image-0.13.1/doc/examples/edges/plot_marching_cubes.py000066400000000000000000000041001316256236300241770ustar00rootroot00000000000000""" ============== Marching Cubes ============== Marching cubes is an algorithm to extract a 2D surface mesh from a 3D volume. This can be conceptualized as a 3D generalization of isolines on topographical or weather maps. It works by iterating across the volume, looking for regions which cross the level of interest. If such regions are found, triangulations are generated and added to an output mesh. The final result is a set of vertices and a set of triangular faces. The algorithm requires a data volume and an isosurface value. For example, in CT imaging Hounsfield units of +700 to +3000 represent bone. So, one potential input would be a reconstructed CT set of data and the value +700, to extract a mesh for regions of bone or bone-like density. This implementation also works correctly on anisotropic datasets, where the voxel spacing is not equal for every spatial dimension, through use of the `spacing` kwarg. """ import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d.art3d import Poly3DCollection from skimage import measure from skimage.draw import ellipsoid # Generate a level set about zero of two identical ellipsoids in 3D ellip_base = ellipsoid(6, 10, 16, levelset=True) ellip_double = np.concatenate((ellip_base[:-1, ...], ellip_base[2:, ...]), axis=0) # Use marching cubes to obtain the surface mesh of these ellipsoids verts, faces, normals, values = measure.marching_cubes(ellip_double, 0) # Display resulting triangular mesh using Matplotlib. This can also be done # with mayavi (see skimage.measure.marching_cubes docstring). fig = plt.figure(figsize=(10, 10)) ax = fig.add_subplot(111, projection='3d') # Fancy indexing: `verts[faces]` to generate a collection of triangles mesh = Poly3DCollection(verts[faces]) mesh.set_edgecolor('k') ax.add_collection3d(mesh) ax.set_xlabel("x-axis: a = 6 per ellipsoid") ax.set_ylabel("y-axis: b = 10") ax.set_zlabel("z-axis: c = 16") ax.set_xlim(0, 24) # a = 6 (times two for 2nd ellipsoid) ax.set_ylim(0, 20) # b = 10 ax.set_zlim(0, 32) # c = 16 plt.tight_layout() plt.show() scikit-image-0.13.1/doc/examples/edges/plot_polygon.py000066400000000000000000000050041316256236300227210ustar00rootroot00000000000000""" ================================== Approximate and subdivide polygons ================================== This example shows how to approximate (Douglas-Peucker algorithm) and subdivide (B-Splines) polygonal chains. """ from __future__ import print_function import numpy as np import matplotlib.pyplot as plt from skimage.draw import ellipse from skimage.measure import find_contours, approximate_polygon, \ subdivide_polygon hand = np.array([[1.64516129, 1.16145833], [1.64516129, 1.59375], [1.35080645, 1.921875], [1.375, 2.18229167], [1.68548387, 1.9375], [1.60887097, 2.55208333], [1.68548387, 2.69791667], [1.76209677, 2.56770833], [1.83064516, 1.97395833], [1.89516129, 2.75], [1.9516129, 2.84895833], [2.01209677, 2.76041667], [1.99193548, 1.99479167], [2.11290323, 2.63020833], [2.2016129, 2.734375], [2.25403226, 2.60416667], [2.14919355, 1.953125], [2.30645161, 2.36979167], [2.39112903, 2.36979167], [2.41532258, 2.1875], [2.1733871, 1.703125], [2.07782258, 1.16666667]]) # subdivide polygon using 2nd degree B-Splines new_hand = hand.copy() for _ in range(5): new_hand = subdivide_polygon(new_hand, degree=2, preserve_ends=True) # approximate subdivided polygon with Douglas-Peucker algorithm appr_hand = approximate_polygon(new_hand, tolerance=0.02) print("Number of coordinates:", len(hand), len(new_hand), len(appr_hand)) fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(9, 4)) ax1.plot(hand[:, 0], hand[:, 1]) ax1.plot(new_hand[:, 0], new_hand[:, 1]) ax1.plot(appr_hand[:, 0], appr_hand[:, 1]) # create two ellipses in image img = np.zeros((800, 800), 'int32') rr, cc = ellipse(250, 250, 180, 230, img.shape) img[rr, cc] = 1 rr, cc = ellipse(600, 600, 150, 90, img.shape) img[rr, cc] = 1 plt.gray() ax2.imshow(img) # approximate / simplify coordinates of the two ellipses for contour in find_contours(img, 0): coords = approximate_polygon(contour, tolerance=2.5) ax2.plot(coords[:, 1], coords[:, 0], '-r', linewidth=2) coords2 = approximate_polygon(contour, tolerance=39.5) ax2.plot(coords2[:, 1], coords2[:, 0], '-g', linewidth=2) print("Number of coordinates:", len(contour), len(coords), len(coords2)) ax2.axis((0, 800, 0, 800)) plt.show() scikit-image-0.13.1/doc/examples/edges/plot_shapes.py000066400000000000000000000035621316256236300225240ustar00rootroot00000000000000""" ====== Shapes ====== This example shows how to draw several different shapes: - line - Bezier curve - polygon - circle - ellipse Anti-aliased drawing for: - line - circle """ import math import numpy as np import matplotlib.pyplot as plt from skimage.draw import (line, polygon, circle, circle_perimeter, ellipse, ellipse_perimeter, bezier_curve) fig, (ax1, ax2) = plt.subplots(ncols=2, nrows=1, figsize=(10, 6)) img = np.zeros((500, 500, 3), dtype=np.double) # draw line rr, cc = line(120, 123, 20, 400) img[rr, cc, 0] = 255 # fill polygon poly = np.array(( (300, 300), (480, 320), (380, 430), (220, 590), (300, 300), )) rr, cc = polygon(poly[:, 0], poly[:, 1], img.shape) img[rr, cc, 1] = 1 # fill circle rr, cc = circle(200, 200, 100, img.shape) img[rr, cc, :] = (1, 1, 0) # fill ellipse rr, cc = ellipse(300, 300, 100, 200, img.shape) img[rr, cc, 2] = 1 # circle rr, cc = circle_perimeter(120, 400, 15) img[rr, cc, :] = (1, 0, 0) # Bezier curve rr, cc = bezier_curve(70, 100, 10, 10, 150, 100, 1) img[rr, cc, :] = (1, 0, 0) # ellipses rr, cc = ellipse_perimeter(120, 400, 60, 20, orientation=math.pi / 4.) img[rr, cc, :] = (1, 0, 1) rr, cc = ellipse_perimeter(120, 400, 60, 20, orientation=-math.pi / 4.) img[rr, cc, :] = (0, 0, 1) rr, cc = ellipse_perimeter(120, 400, 60, 20, orientation=math.pi / 2.) img[rr, cc, :] = (1, 1, 1) ax1.imshow(img) ax1.set_title('No anti-aliasing') ax1.axis('off') from skimage.draw import line_aa, circle_perimeter_aa img = np.zeros((100, 100), dtype=np.double) # anti-aliased line rr, cc, val = line_aa(12, 12, 20, 50) img[rr, cc] = val # anti-aliased circle rr, cc, val = circle_perimeter_aa(60, 40, 30) img[rr, cc] = val ax2.imshow(img, cmap=plt.cm.gray, interpolation='nearest') ax2.set_title('Anti-aliasing') ax2.axis('off') plt.show() scikit-image-0.13.1/doc/examples/edges/plot_skeleton.py000066400000000000000000000155621316256236300230700ustar00rootroot00000000000000""" =========== Skeletonize =========== Skeletonization reduces binary objects to 1 pixel wide representations. This can be useful for feature extraction, and/or representing an object's topology. ``skeletonize`` works by making successive passes of the image. On each pass, border pixels are identified and removed on the condition that they do not break the connectivity of the corresponding object. """ from skimage.morphology import skeletonize from skimage import data import matplotlib.pyplot as plt from skimage.util import invert # Invert the horse image image = invert(data.horse()) # perform skeletonization skeleton = skeletonize(image) # display results fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 4), sharex=True, sharey=True, subplot_kw={'adjustable': 'box-forced'}) ax = axes.ravel() ax[0].imshow(image, cmap=plt.cm.gray) ax[0].axis('off') ax[0].set_title('original', fontsize=20) ax[1].imshow(skeleton, cmap=plt.cm.gray) ax[1].axis('off') ax[1].set_title('skeleton', fontsize=20) fig.tight_layout() plt.show() ###################################################################### # **skeletonize vs skeletonize 3d** # # ``skeletonize`` [Zha84]_ works by making successive passes of # the image, removing pixels on object borders. This continues until no # more pixels can be removed. The image is correlated with a # mask that assigns each pixel a number in the range [0...255] # corresponding to each possible pattern of its 8 neighbouring # pixels. A look up table is then used to assign the pixels a # value of 0, 1, 2 or 3, which are selectively removed during # the iterations. # # ``skeletonize_3d`` [Lee94]_ uses an octree data structure to examine a 3x3x3 # neighborhood of a pixel. The algorithm proceeds by iteratively sweeping # over the image, and removing pixels at each iteration until the image # stops changing. Each iteration consists of two steps: first, a list of # candidates for removal is assembled; then pixels from this list are # rechecked sequentially, to better preserve connectivity of the image. # # Note that ``skeletonize_3d`` is designed to be used mostly on 3-D images. # However, for illustrative purposes, we apply this algorithm on a 2-D image. # # .. [Zha84] A fast parallel algorithm for thinning digital patterns, # T. Y. Zhang and C. Y. Suen, Communications of the ACM, # March 1984, Volume 27, Number 3. # # .. [Lee94] T.-C. Lee, R.L. Kashyap and C.-N. Chu, Building skeleton models # via 3-D medial surface/axis thinning algorithms. # Computer Vision, Graphics, and Image Processing, 56(6):462-478, # 1994. # import matplotlib.pyplot as plt from skimage.morphology import skeletonize, skeletonize_3d from skimage.data import binary_blobs data = binary_blobs(200, blob_size_fraction=.2, volume_fraction=.35, seed=1) skeleton = skeletonize(data) skeleton3d = skeletonize_3d(data) fig, axes = plt.subplots(1, 3, figsize=(8, 4), sharex=True, sharey=True, subplot_kw={'adjustable': 'box-forced'}) ax = axes.ravel() ax[0].imshow(data, cmap=plt.cm.gray, interpolation='nearest') ax[0].set_title('original') ax[0].axis('off') ax[1].imshow(skeleton, cmap=plt.cm.gray, interpolation='nearest') ax[1].set_title('skeletonize') ax[1].axis('off') ax[2].imshow(skeleton3d, cmap=plt.cm.gray, interpolation='nearest') ax[2].set_title('skeletonize_3d') ax[2].axis('off') fig.tight_layout() plt.show() ###################################################################### # **Medial axis skeletonization** # # The medial axis of an object is the set of all points having more than one # closest point on the object's boundary. It is often called the *topological # skeleton*, because it is a 1-pixel wide skeleton of the object, with the same # connectivity as the original object. # # Here, we use the medial axis transform to compute the width of the foreground # objects. As the function ``medial_axis`` returns the distance transform in # addition to the medial axis (with the keyword argument ``return_distance=True``), # it is possible to compute the distance to the background for all points of # the medial axis with this function. This gives an estimate of the local width # of the objects. # # For a skeleton with fewer branches, ``skeletonize`` or ``skeletonize_3d`` # should be preferred. from skimage.morphology import medial_axis, skeletonize, skeletonize_3d # Generate the data data = binary_blobs(200, blob_size_fraction=.2, volume_fraction=.35, seed=1) # Compute the medial axis (skeleton) and the distance transform skel, distance = medial_axis(data, return_distance=True) # Compare with other skeletonization algorithms skeleton = skeletonize(data) skeleton3d = skeletonize_3d(data) # Distance to the background for pixels of the skeleton dist_on_skel = distance * skel from skimage.util.colormap import magma fig, axes = plt.subplots(2, 2, figsize=(8, 8), sharex=True, sharey=True, subplot_kw={'adjustable': 'box-forced'}) ax = axes.ravel() ax[0].imshow(data, cmap=plt.cm.gray, interpolation='nearest') ax[0].set_title('original') ax[0].axis('off') ax[1].imshow(dist_on_skel, cmap=magma, interpolation='nearest') ax[1].contour(data, [0.5], colors='w') ax[1].set_title('medial_axis') ax[1].axis('off') ax[2].imshow(skeleton, cmap=plt.cm.gray, interpolation='nearest') ax[2].set_title('skeletonize') ax[2].axis('off') ax[3].imshow(skeleton3d, cmap=plt.cm.gray, interpolation='nearest') ax[3].set_title('skeletonize_3d') ax[3].axis('off') fig.tight_layout() plt.show() ###################################################################### # **Morphological thinning** # # Morphological thinning, implemented in the `thin` function, works on the # same principle as `skeletonize`: remove pixels from the borders at each # iteration until none can be removed without altering the connectivity. The # different rules of removal can speed up skeletonization and result in # different final skeletons. # # The `thin` function also takes an optional `max_iter` keyword argument to # limit the number of thinning iterations, and thus produce a relatively # thicker skeleton. from skimage.morphology import skeletonize, thin skeleton = skeletonize(image) thinned = thin(image) thinned_partial = thin(image, max_iter=25) fig, axes = plt.subplots(2, 2, figsize=(8, 8), sharex=True, sharey=True, subplot_kw={'adjustable': 'box-forced'}) ax = axes.ravel() ax[0].imshow(image, cmap=plt.cm.gray, interpolation='nearest') ax[0].set_title('original') ax[0].axis('off') ax[1].imshow(skeleton, cmap=plt.cm.gray, interpolation='nearest') ax[1].set_title('skeleton') ax[1].axis('off') ax[2].imshow(thinned, cmap=plt.cm.gray, interpolation='nearest') ax[2].set_title('thinned') ax[2].axis('off') ax[3].imshow(thinned_partial, cmap=plt.cm.gray, interpolation='nearest') ax[3].set_title('partially thinned') ax[3].axis('off') fig.tight_layout() plt.show() scikit-image-0.13.1/doc/examples/features_detection/000077500000000000000000000000001316256236300224105ustar00rootroot00000000000000scikit-image-0.13.1/doc/examples/features_detection/README.txt000066400000000000000000000001041316256236300241010ustar00rootroot00000000000000Detection of features and objects --------------------------------- scikit-image-0.13.1/doc/examples/features_detection/plot_blob.py000066400000000000000000000056651316256236300247520ustar00rootroot00000000000000""" ============== Blob Detection ============== Blobs are bright on dark or dark on bright regions in an image. In this example, blobs are detected using 3 algorithms. The image used in this case is the Hubble eXtreme Deep Field. Each bright dot in the image is a star or a galaxy. Laplacian of Gaussian (LoG) ----------------------------- This is the most accurate and slowest approach. It computes the Laplacian of Gaussian images with successively increasing standard deviation and stacks them up in a cube. Blobs are local maximas in this cube. Detecting larger blobs is especially slower because of larger kernel sizes during convolution. Only bright blobs on dark backgrounds are detected. See :py:meth:`skimage.feature.blob_log` for usage. Difference of Gaussian (DoG) ---------------------------- This is a faster approximation of LoG approach. In this case the image is blurred with increasing standard deviations and the difference between two successively blurred images are stacked up in a cube. This method suffers from the same disadvantage as LoG approach for detecting larger blobs. Blobs are again assumed to be bright on dark. See :py:meth:`skimage.feature.blob_dog` for usage. Determinant of Hessian (DoH) ---------------------------- This is the fastest approach. It detects blobs by finding maximas in the matrix of the Determinant of Hessian of the image. The detection speed is independent of the size of blobs as internally the implementation uses box filters instead of convolutions. Bright on dark as well as dark on bright blobs are detected. The downside is that small blobs (<3px) are not detected accurately. See :py:meth:`skimage.feature.blob_doh` for usage. """ from math import sqrt from skimage import data from skimage.feature import blob_dog, blob_log, blob_doh from skimage.color import rgb2gray import matplotlib.pyplot as plt image = data.hubble_deep_field()[0:500, 0:500] image_gray = rgb2gray(image) blobs_log = blob_log(image_gray, max_sigma=30, num_sigma=10, threshold=.1) # Compute radii in the 3rd column. blobs_log[:, 2] = blobs_log[:, 2] * sqrt(2) blobs_dog = blob_dog(image_gray, max_sigma=30, threshold=.1) blobs_dog[:, 2] = blobs_dog[:, 2] * sqrt(2) blobs_doh = blob_doh(image_gray, max_sigma=30, threshold=.01) blobs_list = [blobs_log, blobs_dog, blobs_doh] colors = ['yellow', 'lime', 'red'] titles = ['Laplacian of Gaussian', 'Difference of Gaussian', 'Determinant of Hessian'] sequence = zip(blobs_list, colors, titles) fig, axes = plt.subplots(1, 3, figsize=(9, 3), sharex=True, sharey=True, subplot_kw={'adjustable': 'box-forced'}) ax = axes.ravel() for idx, (blobs, color, title) in enumerate(sequence): ax[idx].set_title(title) ax[idx].imshow(image, interpolation='nearest') for blob in blobs: y, x, r = blob c = plt.Circle((x, y), r, color=color, linewidth=2, fill=False) ax[idx].add_patch(c) ax[idx].set_axis_off() plt.tight_layout() plt.show() scikit-image-0.13.1/doc/examples/features_detection/plot_brief.py000066400000000000000000000037061316256236300251150ustar00rootroot00000000000000""" ======================= BRIEF binary descriptor ======================= This example demonstrates the BRIEF binary description algorithm. The descriptor consists of relatively few bits and can be computed using a set of intensity difference tests. The short binary descriptor results in low memory footprint and very efficient matching based on the Hamming distance metric. BRIEF does not provide rotation-invariance. Scale-invariance can be achieved by detecting and extracting features at different scales. """ from skimage import data from skimage import transform as tf from skimage.feature import (match_descriptors, corner_peaks, corner_harris, plot_matches, BRIEF) from skimage.color import rgb2gray import matplotlib.pyplot as plt img1 = rgb2gray(data.astronaut()) tform = tf.AffineTransform(scale=(1.2, 1.2), translation=(0, -100)) img2 = tf.warp(img1, tform) img3 = tf.rotate(img1, 25) keypoints1 = corner_peaks(corner_harris(img1), min_distance=5) keypoints2 = corner_peaks(corner_harris(img2), min_distance=5) keypoints3 = corner_peaks(corner_harris(img3), min_distance=5) extractor = BRIEF() extractor.extract(img1, keypoints1) keypoints1 = keypoints1[extractor.mask] descriptors1 = extractor.descriptors extractor.extract(img2, keypoints2) keypoints2 = keypoints2[extractor.mask] descriptors2 = extractor.descriptors extractor.extract(img3, keypoints3) keypoints3 = keypoints3[extractor.mask] descriptors3 = extractor.descriptors matches12 = match_descriptors(descriptors1, descriptors2, cross_check=True) matches13 = match_descriptors(descriptors1, descriptors3, cross_check=True) fig, ax = plt.subplots(nrows=2, ncols=1) plt.gray() plot_matches(ax[0], img1, img2, keypoints1, keypoints2, matches12) ax[0].axis('off') ax[0].set_title("Original Image vs. Transformed Image") plot_matches(ax[1], img1, img3, keypoints1, keypoints3, matches13) ax[1].axis('off') ax[1].set_title("Original Image vs. Transformed Image") plt.show() scikit-image-0.13.1/doc/examples/features_detection/plot_censure.py000066400000000000000000000023271316256236300254700ustar00rootroot00000000000000""" ======================== CENSURE feature detector ======================== The CENSURE feature detector is a scale-invariant center-surround detector (CENSURE) that claims to outperform other detectors and is capable of real-time implementation. """ from skimage import data from skimage import transform as tf from skimage.feature import CENSURE from skimage.color import rgb2gray import matplotlib.pyplot as plt img_orig = rgb2gray(data.astronaut()) tform = tf.AffineTransform(scale=(1.5, 1.5), rotation=0.5, translation=(150, -200)) img_warp = tf.warp(img_orig, tform) detector = CENSURE() fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(12, 6)) detector.detect(img_orig) ax[0].imshow(img_orig, cmap=plt.cm.gray) ax[0].scatter(detector.keypoints[:, 1], detector.keypoints[:, 0], 2 ** detector.scales, facecolors='none', edgecolors='r') ax[0].set_title("Original Image") detector.detect(img_warp) ax[1].imshow(img_warp, cmap=plt.cm.gray) ax[1].scatter(detector.keypoints[:, 1], detector.keypoints[:, 0], 2 ** detector.scales, facecolors='none', edgecolors='r') ax[1].set_title('Transformed Image') for a in ax: a.axis('off') plt.tight_layout() plt.show() scikit-image-0.13.1/doc/examples/features_detection/plot_corner.py000066400000000000000000000022311316256236300253060ustar00rootroot00000000000000""" ================ Corner detection ================ Detect corner points using the Harris corner detector and determine the subpixel position of corners ([1]_, [2]_). .. [1] http://en.wikipedia.org/wiki/Corner_detection .. [2] http://en.wikipedia.org/wiki/Interest_point_detection """ from matplotlib import pyplot as plt from skimage import data from skimage.feature import corner_harris, corner_subpix, corner_peaks from skimage.transform import warp, AffineTransform from skimage.draw import ellipse tform = AffineTransform(scale=(1.3, 1.1), rotation=1, shear=0.7, translation=(210, 50)) image = warp(data.checkerboard(), tform.inverse, output_shape=(350, 350)) rr, cc = ellipse(310, 175, 10, 100) image[rr, cc] = 1 image[180:230, 10:60] = 1 image[230:280, 60:110] = 1 coords = corner_peaks(corner_harris(image), min_distance=5) coords_subpix = corner_subpix(image, coords, window_size=13) fig, ax = plt.subplots() ax.imshow(image, interpolation='nearest', cmap=plt.cm.gray) ax.plot(coords[:, 1], coords[:, 0], '.b', markersize=3) ax.plot(coords_subpix[:, 1], coords_subpix[:, 0], '+r', markersize=15) ax.axis((0, 350, 350, 0)) plt.show() scikit-image-0.13.1/doc/examples/features_detection/plot_daisy.py000066400000000000000000000015541316256236300251360ustar00rootroot00000000000000""" =============================== Dense DAISY feature description =============================== The DAISY local image descriptor is based on gradient orientation histograms similar to the SIFT descriptor. It is formulated in a way that allows for fast dense extraction which is useful for e.g. bag-of-features image representations. In this example a limited number of DAISY descriptors are extracted at a large scale for illustrative purposes. """ from skimage.feature import daisy from skimage import data import matplotlib.pyplot as plt img = data.camera() descs, descs_img = daisy(img, step=180, radius=58, rings=2, histograms=6, orientations=8, visualize=True) fig, ax = plt.subplots() ax.axis('off') ax.imshow(descs_img) descs_num = descs.shape[0] * descs.shape[1] ax.set_title('%i DAISY descriptors extracted:' % descs_num) plt.show() scikit-image-0.13.1/doc/examples/features_detection/plot_gabor.py000066400000000000000000000105421316256236300251140ustar00rootroot00000000000000""" ============================================= Gabor filter banks for texture classification ============================================= In this example, we will see how to classify textures based on Gabor filter banks. Frequency and orientation representations of the Gabor filter are similar to those of the human visual system. The images are filtered using the real parts of various different Gabor filter kernels. The mean and variance of the filtered images are then used as features for classification, which is based on the least squared error for simplicity. """ from __future__ import print_function import matplotlib.pyplot as plt import numpy as np from scipy import ndimage as ndi from skimage import data from skimage.util import img_as_float from skimage.filters import gabor_kernel def compute_feats(image, kernels): feats = np.zeros((len(kernels), 2), dtype=np.double) for k, kernel in enumerate(kernels): filtered = ndi.convolve(image, kernel, mode='wrap') feats[k, 0] = filtered.mean() feats[k, 1] = filtered.var() return feats def match(feats, ref_feats): min_error = np.inf min_i = None for i in range(ref_feats.shape[0]): error = np.sum((feats - ref_feats[i, :])**2) if error < min_error: min_error = error min_i = i return min_i # prepare filter bank kernels kernels = [] for theta in range(4): theta = theta / 4. * np.pi for sigma in (1, 3): for frequency in (0.05, 0.25): kernel = np.real(gabor_kernel(frequency, theta=theta, sigma_x=sigma, sigma_y=sigma)) kernels.append(kernel) shrink = (slice(0, None, 3), slice(0, None, 3)) brick = img_as_float(data.load('brick.png'))[shrink] grass = img_as_float(data.load('grass.png'))[shrink] wall = img_as_float(data.load('rough-wall.png'))[shrink] image_names = ('brick', 'grass', 'wall') images = (brick, grass, wall) # prepare reference features ref_feats = np.zeros((3, len(kernels), 2), dtype=np.double) ref_feats[0, :, :] = compute_feats(brick, kernels) ref_feats[1, :, :] = compute_feats(grass, kernels) ref_feats[2, :, :] = compute_feats(wall, kernels) print('Rotated images matched against references using Gabor filter banks:') print('original: brick, rotated: 30deg, match result: ', end='') feats = compute_feats(ndi.rotate(brick, angle=190, reshape=False), kernels) print(image_names[match(feats, ref_feats)]) print('original: brick, rotated: 70deg, match result: ', end='') feats = compute_feats(ndi.rotate(brick, angle=70, reshape=False), kernels) print(image_names[match(feats, ref_feats)]) print('original: grass, rotated: 145deg, match result: ', end='') feats = compute_feats(ndi.rotate(grass, angle=145, reshape=False), kernels) print(image_names[match(feats, ref_feats)]) def power(image, kernel): # Normalize images for better comparison. image = (image - image.mean()) / image.std() return np.sqrt(ndi.convolve(image, np.real(kernel), mode='wrap')**2 + ndi.convolve(image, np.imag(kernel), mode='wrap')**2) # Plot a selection of the filter bank kernels and their responses. results = [] kernel_params = [] for theta in (0, 1): theta = theta / 4. * np.pi for frequency in (0.1, 0.4): kernel = gabor_kernel(frequency, theta=theta) params = 'theta=%d,\nfrequency=%.2f' % (theta * 180 / np.pi, frequency) kernel_params.append(params) # Save kernel and the power image for each image results.append((kernel, [power(img, kernel) for img in images])) fig, axes = plt.subplots(nrows=5, ncols=4, figsize=(5, 6)) plt.gray() fig.suptitle('Image responses for Gabor filter kernels', fontsize=12) axes[0][0].axis('off') # Plot original images for label, img, ax in zip(image_names, images, axes[0][1:]): ax.imshow(img) ax.set_title(label, fontsize=9) ax.axis('off') for label, (kernel, powers), ax_row in zip(kernel_params, results, axes[1:]): # Plot Gabor kernel ax = ax_row[0] ax.imshow(np.real(kernel), interpolation='nearest') ax.set_ylabel(label, fontsize=7) ax.set_xticks([]) ax.set_yticks([]) # Plot Gabor responses with the contrast normalized for each filter vmin = np.min(powers) vmax = np.max(powers) for patch, ax in zip(powers, ax_row[1:]): ax.imshow(patch, vmin=vmin, vmax=vmax) ax.axis('off') plt.show() scikit-image-0.13.1/doc/examples/features_detection/plot_gabors_from_astronaut.py000066400000000000000000000065151316256236300304270ustar00rootroot00000000000000""" ============================================================ Gabors / Primary Visual Cortex "Simple Cells" from an Image ============================================================ How to build a (bio-plausible) *sparse* dictionary (or 'codebook', or 'filterbank') for e.g. image classification without any fancy math and with just standard python scientific libraries? Please find below a short answer ;-) This simple example shows how to get Gabor-like filters [1]_ using just a simple image. In our example, we use a photograph of the astronaut Eileen Collins. Gabor filters are good approximations of the "Simple Cells" [2]_ receptive fields [3]_ found in the mammalian primary visual cortex (V1) (for details, see e.g. the Nobel-prize winning work of Hubel & Wiesel done in the 60s [4]_ [5]_). Here we use McQueen's 'kmeans' algorithm [6]_, as a simple biologically plausible hebbian-like learning rule and we apply it (a) to patches of the original image (retinal projection), and (b) to patches of an LGN-like [7]_ image using a simple difference of gaussians (DoG) approximation. Enjoy ;-) And keep in mind that getting Gabors on natural image patches is not rocket science. .. [1] http://en.wikipedia.org/wiki/Gabor_filter .. [2] http://en.wikipedia.org/wiki/Simple_cell .. [3] http://en.wikipedia.org/wiki/Receptive_field .. [4] http://en.wikipedia.org/wiki/K-means_clustering .. [5] http://en.wikipedia.org/wiki/Lateral_geniculate_nucleus .. [6] D. H. Hubel and T. N., Wiesel Receptive Fields of Single Neurones in the Cat's Striate Cortex, J. Physiol. pp. 574-591 (148) 1959 .. [7] D. H. Hubel and T. N., Wiesel Receptive Fields, Binocular Interaction, and Functional Architecture in the Cat's Visual Cortex, J. Physiol. 160 pp. 106-154 1962 """ import numpy as np from scipy.cluster.vq import kmeans2 from scipy import ndimage as ndi import matplotlib.pyplot as plt from skimage import data from skimage import color from skimage.util.shape import view_as_windows from skimage.util.montage import montage2d np.random.seed(42) patch_shape = 8, 8 n_filters = 49 astro = color.rgb2gray(data.astronaut()) # -- filterbank1 on original image patches1 = view_as_windows(astro, patch_shape) patches1 = patches1.reshape(-1, patch_shape[0] * patch_shape[1])[::8] fb1, _ = kmeans2(patches1, n_filters, minit='points') fb1 = fb1.reshape((-1,) + patch_shape) fb1_montage = montage2d(fb1, rescale_intensity=True) # -- filterbank2 LGN-like image astro_dog = ndi.gaussian_filter(astro, .5) - ndi.gaussian_filter(astro, 1) patches2 = view_as_windows(astro_dog, patch_shape) patches2 = patches2.reshape(-1, patch_shape[0] * patch_shape[1])[::8] fb2, _ = kmeans2(patches2, n_filters, minit='points') fb2 = fb2.reshape((-1,) + patch_shape) fb2_montage = montage2d(fb2, rescale_intensity=True) # -- fig, axes = plt.subplots(2, 2, figsize=(7, 6)) ax = axes.ravel() ax[0].imshow(astro, cmap=plt.cm.gray) ax[0].set_title("Image (original)") ax[1].imshow(fb1_montage, cmap=plt.cm.gray, interpolation='nearest') ax[1].set_title("K-means filterbank (codebook)\non original image") ax[2].imshow(astro_dog, cmap=plt.cm.gray) ax[2].set_title("Image (LGN-like DoG)") ax[3].imshow(fb2_montage, cmap=plt.cm.gray, interpolation='nearest') ax[3].set_title("K-means filterbank (codebook)\non LGN-like DoG image") for a in ax.ravel(): a.axis('off') fig.tight_layout() plt.show() scikit-image-0.13.1/doc/examples/features_detection/plot_glcm.py000066400000000000000000000063161316256236300247500ustar00rootroot00000000000000""" ===================== GLCM Texture Features ===================== This example illustrates texture classification using grey level co-occurrence matrices (GLCMs). A GLCM is a histogram of co-occurring greyscale values at a given offset over an image. In this example, samples of two different textures are extracted from an image: grassy areas and sky areas. For each patch, a GLCM with a horizontal offset of 5 is computed. Next, two features of the GLCM matrices are computed: dissimilarity and correlation. These are plotted to illustrate that the classes form clusters in feature space. In a typical classification problem, the final step (not included in this example) would be to train a classifier, such as logistic regression, to label image patches from new images. """ import matplotlib.pyplot as plt from skimage.feature import greycomatrix, greycoprops from skimage import data PATCH_SIZE = 21 # open the camera image image = data.camera() # select some patches from grassy areas of the image grass_locations = [(474, 291), (440, 433), (466, 18), (462, 236)] grass_patches = [] for loc in grass_locations: grass_patches.append(image[loc[0]:loc[0] + PATCH_SIZE, loc[1]:loc[1] + PATCH_SIZE]) # select some patches from sky areas of the image sky_locations = [(54, 48), (21, 233), (90, 380), (195, 330)] sky_patches = [] for loc in sky_locations: sky_patches.append(image[loc[0]:loc[0] + PATCH_SIZE, loc[1]:loc[1] + PATCH_SIZE]) # compute some GLCM properties each patch xs = [] ys = [] for patch in (grass_patches + sky_patches): glcm = greycomatrix(patch, [5], [0], 256, symmetric=True, normed=True) xs.append(greycoprops(glcm, 'dissimilarity')[0, 0]) ys.append(greycoprops(glcm, 'correlation')[0, 0]) # create the figure fig = plt.figure(figsize=(8, 8)) # display original image with locations of patches ax = fig.add_subplot(3, 2, 1) ax.imshow(image, cmap=plt.cm.gray, interpolation='nearest', vmin=0, vmax=255) for (y, x) in grass_locations: ax.plot(x + PATCH_SIZE / 2, y + PATCH_SIZE / 2, 'gs') for (y, x) in sky_locations: ax.plot(x + PATCH_SIZE / 2, y + PATCH_SIZE / 2, 'bs') ax.set_xlabel('Original Image') ax.set_xticks([]) ax.set_yticks([]) ax.axis('image') # for each patch, plot (dissimilarity, correlation) ax = fig.add_subplot(3, 2, 2) ax.plot(xs[:len(grass_patches)], ys[:len(grass_patches)], 'go', label='Grass') ax.plot(xs[len(grass_patches):], ys[len(grass_patches):], 'bo', label='Sky') ax.set_xlabel('GLCM Dissimilarity') ax.set_ylabel('GLCM Correlation') ax.legend() # display the image patches for i, patch in enumerate(grass_patches): ax = fig.add_subplot(3, len(grass_patches), len(grass_patches)*1 + i + 1) ax.imshow(patch, cmap=plt.cm.gray, interpolation='nearest', vmin=0, vmax=255) ax.set_xlabel('Grass %d' % (i + 1)) for i, patch in enumerate(sky_patches): ax = fig.add_subplot(3, len(sky_patches), len(sky_patches)*2 + i + 1) ax.imshow(patch, cmap=plt.cm.gray, interpolation='nearest', vmin=0, vmax=255) ax.set_xlabel('Sky %d' % (i + 1)) # display the patches and plot fig.suptitle('Grey level co-occurrence matrix features', fontsize=14) plt.show() scikit-image-0.13.1/doc/examples/features_detection/plot_hog.py000066400000000000000000000105161316256236300246000ustar00rootroot00000000000000""" =============================== Histogram of Oriented Gradients =============================== The Histogram of Oriented Gradient (HOG) feature descriptor is popular for object detection [1]_. In the following example, we compute the `HOG descriptor `__ and display a visualisation. Algorithm overview ------------------ Compute a Histogram of Oriented Gradients (HOG) by 1. (optional) global image normalisation 2. computing the gradient image in x and y 3. computing gradient histograms 4. normalising across blocks 5. flattening into a feature vector The first stage applies an optional global image normalisation equalisation that is designed to reduce the influence of illumination effects. In practice we use gamma (power law) compression, either computing the square root or the log of each colour channel. Image texture strength is typically proportional to the local surface illumination so this compression helps to reduce the effects of local shadowing and illumination variations. The second stage computes first order image gradients. These capture contour, silhouette and some texture information, while providing further resistance to illumination variations. The locally dominant colour channel is used, which provides colour invariance to a large extent. Variant methods may also include second order image derivatives, which act as primitive bar detectors - a useful feature for capturing, e.g. bar like structures in bicycles and limbs in humans. The third stage aims to produce an encoding that is sensitive to local image content while remaining resistant to small changes in pose or appearance. The adopted method pools gradient orientation information locally in the same way as the SIFT [2]_ feature. The image window is divided into small spatial regions, called "cells". For each cell we accumulate a local 1-D histogram of gradient or edge orientations over all the pixels in the cell. This combined cell-level 1-D histogram forms the basic "orientation histogram" representation. Each orientation histogram divides the gradient angle range into a fixed number of predetermined bins. The gradient magnitudes of the pixels in the cell are used to vote into the orientation histogram. The fourth stage computes normalisation, which takes local groups of cells and contrast normalises their overall responses before passing to next stage. Normalisation introduces better invariance to illumination, shadowing, and edge contrast. It is performed by accumulating a measure of local histogram "energy" over local groups of cells that we call "blocks". The result is used to normalise each cell in the block. Typically each individual cell is shared between several blocks, but its normalisations are block dependent and thus different. The cell thus appears several times in the final output vector with different normalisations. This may seem redundant but it improves the performance. We refer to the normalised block descriptors as Histogram of Oriented Gradient (HOG) descriptors. The final step collects the HOG descriptors from all blocks of a dense overlapping grid of blocks covering the detection window into a combined feature vector for use in the window classifier. References ---------- .. [1] Dalal, N. and Triggs, B., "Histograms of Oriented Gradients for Human Detection," IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, San Diego, CA, USA. .. [2] David G. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, 60, 2 (2004), pp. 91-110. """ import matplotlib.pyplot as plt from skimage.feature import hog from skimage import data, color, exposure image = color.rgb2gray(data.astronaut()) fd, hog_image = hog(image, orientations=8, pixels_per_cell=(16, 16), cells_per_block=(1, 1), visualise=True) fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4), sharex=True, sharey=True) ax1.axis('off') ax1.imshow(image, cmap=plt.cm.gray) ax1.set_title('Input image') ax1.set_adjustable('box-forced') # Rescale histogram for better display hog_image_rescaled = exposure.rescale_intensity(hog_image, in_range=(0, 0.02)) ax2.axis('off') ax2.imshow(hog_image_rescaled, cmap=plt.cm.gray) ax2.set_title('Histogram of Oriented Gradients') ax1.set_adjustable('box-forced') plt.show() scikit-image-0.13.1/doc/examples/features_detection/plot_holes_and_peaks.py000066400000000000000000000052311316256236300271400ustar00rootroot00000000000000""" =============================== Filling holes and finding peaks =============================== We fill holes (i.e. isolated, dark spots) in an image using morphological reconstruction by erosion. Erosion expands the minimal values of the seed image until it encounters a mask image. Thus, the seed image and mask image represent the maximum and minimum possible values of the reconstructed image. We start with an image containing both peaks and holes: """ import matplotlib.pyplot as plt from skimage import data from skimage.exposure import rescale_intensity image = data.moon() # Rescale image intensity so that we can see dim features. image = rescale_intensity(image, in_range=(50, 200)) ###################################################################### # Now we need to create the seed image, where the minima represent the # starting points for erosion. To fill holes, we initialize the seed image # to the maximum value of the original image. Along the borders, however, we # use the original values of the image. These border pixels will be the # starting points for the erosion process. We then limit the erosion by # setting the mask to the values of the original image. import numpy as np from skimage.morphology import reconstruction seed = np.copy(image) seed[1:-1, 1:-1] = image.max() mask = image filled = reconstruction(seed, mask, method='erosion') ###################################################################### # As shown above, eroding inward from the edges removes holes, since (by # definition) holes are surrounded by pixels of brighter value. Finally, we # can isolate the dark regions by subtracting the reconstructed image from # the original image. # # Alternatively, we can find bright spots in an image using morphological # reconstruction by dilation. Dilation is the inverse of erosion and expands # the *maximal* values of the seed image until it encounters a mask image. # Since this is an inverse operation, we initialize the seed image to the # minimum image intensity instead of the maximum. The remainder of the # process is the same. seed = np.copy(image) seed[1:-1, 1:-1] = image.min() rec = reconstruction(seed, mask, method='dilation') fig, ax = plt.subplots(2, 2, figsize=(5, 4), sharex=True, sharey=True, subplot_kw={'adjustable': 'box-forced'}) ax = ax.ravel() ax[0].imshow(image, cmap='gray') ax[0].set_title('Original image') ax[0].axis('off') ax[1].imshow(filled, cmap='gray') ax[1].set_title('after filling holes') ax[1].axis('off') ax[2].imshow(image-filled, cmap='gray') ax[2].set_title('holes') ax[2].axis('off') ax[3].imshow(image-rec, cmap='gray') ax[3].set_title('peaks') ax[3].axis('off') plt.show() scikit-image-0.13.1/doc/examples/features_detection/plot_local_binary_pattern.py000066400000000000000000000152541316256236300302220ustar00rootroot00000000000000""" =============================================== Local Binary Pattern for texture classification =============================================== In this example, we will see how to classify textures based on LBP (Local Binary Pattern). LBP looks at points surrounding a central point and tests whether the surrounding points are greater than or less than the central point (i.e. gives a binary result). Before trying out LBP on an image, it helps to look at a schematic of LBPs. The below code is just used to plot the schematic. """ from __future__ import print_function import numpy as np import matplotlib.pyplot as plt METHOD = 'uniform' plt.rcParams['font.size'] = 9 def plot_circle(ax, center, radius, color): circle = plt.Circle(center, radius, facecolor=color, edgecolor='0.5') ax.add_patch(circle) def plot_lbp_model(ax, binary_values): """Draw the schematic for a local binary pattern.""" # Geometry spec theta = np.deg2rad(45) R = 1 r = 0.15 w = 1.5 gray = '0.5' # Draw the central pixel. plot_circle(ax, (0, 0), radius=r, color=gray) # Draw the surrounding pixels. for i, facecolor in enumerate(binary_values): x = R * np.cos(i * theta) y = R * np.sin(i * theta) plot_circle(ax, (x, y), radius=r, color=str(facecolor)) # Draw the pixel grid. for x in np.linspace(-w, w, 4): ax.axvline(x, color=gray) ax.axhline(x, color=gray) # Tweak the layout. ax.axis('image') ax.axis('off') size = w + 0.2 ax.set_xlim(-size, size) ax.set_ylim(-size, size) fig, axes = plt.subplots(ncols=5, figsize=(7, 2)) titles = ['flat', 'flat', 'edge', 'corner', 'non-uniform'] binary_patterns = [np.zeros(8), np.ones(8), np.hstack([np.ones(4), np.zeros(4)]), np.hstack([np.zeros(3), np.ones(5)]), [1, 0, 0, 1, 1, 1, 0, 0]] for ax, values, name in zip(axes, binary_patterns, titles): plot_lbp_model(ax, values) ax.set_title(name) ###################################################################### # The figure above shows example results with black (or white) representing # pixels that are less (or more) intense than the central pixel. When # surrounding pixels are all black or all white, then that image region is # flat (i.e. featureless). Groups of continuous black or white pixels are # considered "uniform" patterns that can be interpreted as corners or edges. # If pixels switch back-and-forth between black and white pixels, the pattern # is considered "non-uniform". # # When using LBP to detect texture, you measure a collection of LBPs over an # image patch and look at the distribution of these LBPs. Lets apply LBP to a # brick texture. from skimage.transform import rotate from skimage.feature import local_binary_pattern from skimage import data from skimage.color import label2rgb # settings for LBP radius = 3 n_points = 8 * radius def overlay_labels(image, lbp, labels): mask = np.logical_or.reduce([lbp == each for each in labels]) return label2rgb(mask, image=image, bg_label=0, alpha=0.5) def highlight_bars(bars, indexes): for i in indexes: bars[i].set_facecolor('r') image = data.load('brick.png') lbp = local_binary_pattern(image, n_points, radius, METHOD) def hist(ax, lbp): n_bins = int(lbp.max() + 1) return ax.hist(lbp.ravel(), normed=True, bins=n_bins, range=(0, n_bins), facecolor='0.5') # plot histograms of LBP of textures fig, (ax_img, ax_hist) = plt.subplots(nrows=2, ncols=3, figsize=(9, 6)) plt.gray() titles = ('edge', 'flat', 'corner') w = width = radius - 1 edge_labels = range(n_points // 2 - w, n_points // 2 + w + 1) flat_labels = list(range(0, w + 1)) + list(range(n_points - w, n_points + 2)) i_14 = n_points // 4 # 1/4th of the histogram i_34 = 3 * (n_points // 4) # 3/4th of the histogram corner_labels = (list(range(i_14 - w, i_14 + w + 1)) + list(range(i_34 - w, i_34 + w + 1))) label_sets = (edge_labels, flat_labels, corner_labels) for ax, labels in zip(ax_img, label_sets): ax.imshow(overlay_labels(image, lbp, labels)) for ax, labels, name in zip(ax_hist, label_sets, titles): counts, _, bars = hist(ax, lbp) highlight_bars(bars, labels) ax.set_ylim(ymax=np.max(counts[:-1])) ax.set_xlim(xmax=n_points + 2) ax.set_title(name) ax_hist[0].set_ylabel('Percentage') for ax in ax_img: ax.axis('off') ###################################################################### # The above plot highlights flat, edge-like, and corner-like regions of the # image. # # The histogram of the LBP result is a good measure to classify textures. # Here, we test the histogram distributions against each other using the # Kullback-Leibler-Divergence. # settings for LBP radius = 2 n_points = 8 * radius def kullback_leibler_divergence(p, q): p = np.asarray(p) q = np.asarray(q) filt = np.logical_and(p != 0, q != 0) return np.sum(p[filt] * np.log2(p[filt] / q[filt])) def match(refs, img): best_score = 10 best_name = None lbp = local_binary_pattern(img, n_points, radius, METHOD) n_bins = int(lbp.max() + 1) hist, _ = np.histogram(lbp, normed=True, bins=n_bins, range=(0, n_bins)) for name, ref in refs.items(): ref_hist, _ = np.histogram(ref, normed=True, bins=n_bins, range=(0, n_bins)) score = kullback_leibler_divergence(hist, ref_hist) if score < best_score: best_score = score best_name = name return best_name brick = data.load('brick.png') grass = data.load('grass.png') wall = data.load('rough-wall.png') refs = { 'brick': local_binary_pattern(brick, n_points, radius, METHOD), 'grass': local_binary_pattern(grass, n_points, radius, METHOD), 'wall': local_binary_pattern(wall, n_points, radius, METHOD) } # classify rotated textures print('Rotated images matched against references using LBP:') print('original: brick, rotated: 30deg, match result: ', match(refs, rotate(brick, angle=30, resize=False))) print('original: brick, rotated: 70deg, match result: ', match(refs, rotate(brick, angle=70, resize=False))) print('original: grass, rotated: 145deg, match result: ', match(refs, rotate(grass, angle=145, resize=False))) # plot histograms of LBP of textures fig, ((ax1, ax2, ax3), (ax4, ax5, ax6)) = plt.subplots(nrows=2, ncols=3, figsize=(9, 6)) plt.gray() ax1.imshow(brick) ax1.axis('off') hist(ax4, refs['brick']) ax4.set_ylabel('Percentage') ax2.imshow(grass) ax2.axis('off') hist(ax5, refs['grass']) ax5.set_xlabel('Uniform LBP values') ax3.imshow(wall) ax3.axis('off') hist(ax6, refs['wall']) plt.show() scikit-image-0.13.1/doc/examples/features_detection/plot_multiblock_local_binary_pattern.py000066400000000000000000000050531316256236300324430ustar00rootroot00000000000000""" =========================================================== Multi-Block Local Binary Pattern for texture classification =========================================================== This example shows how to compute multi-block local binary pattern (MB-LBP) features as well as how to visualize them. The features are calculated similarly to local binary patterns (LBPs), except that summed blocks are used instead of individual pixel values. MB-LBP is an extension of LBP that can be computed on multiple scales in constant time using the integral image. 9 equally-sized rectangles are used to compute a feature. For each rectangle, the sum of the pixel intensities is computed. Comparisons of these sums to that of the central rectangle determine the feature, similarly to LBP (See `LBP `_). First, we generate an image to illustrate the functioning of MB-LBP: consider a (9, 9) rectangle and divide it into (3, 3) block, upon which we then apply MB-LBP. """ from __future__ import print_function from skimage.feature import multiblock_lbp import numpy as np from numpy.testing import assert_equal from skimage.transform import integral_image # Create test matrix where first and fifth rectangles starting # from top left clockwise have greater value than the central one. test_img = np.zeros((9, 9), dtype='uint8') test_img[3:6, 3:6] = 1 test_img[:3, :3] = 50 test_img[6:, 6:] = 50 # First and fifth bits should be filled. This correct value will # be compared to the computed one. correct_answer = 0b10001000 int_img = integral_image(test_img) lbp_code = multiblock_lbp(int_img, 0, 0, 3, 3) assert_equal(correct_answer, lbp_code) ###################################################################### # Now let's apply the operator to a real image and see how the visualization # works. from skimage import data from matplotlib import pyplot as plt from skimage.feature import draw_multiblock_lbp test_img = data.coins() int_img = integral_image(test_img) lbp_code = multiblock_lbp(int_img, 0, 0, 90, 90) img = draw_multiblock_lbp(test_img, 0, 0, 90, 90, lbp_code=lbp_code, alpha=0.5) plt.imshow(img, interpolation='nearest') plt.show() ###################################################################### # On the above plot we see the result of computing a MB-LBP and visualization # of the computed feature. The rectangles that have less intensities' sum # than the central rectangle are marked in cyan. The ones that have higher # intensity values are marked in white. The central rectangle is left # untouched. scikit-image-0.13.1/doc/examples/features_detection/plot_orb.py000066400000000000000000000035771316256236300246160ustar00rootroot00000000000000""" ========================================== ORB feature detector and binary descriptor ========================================== This example demonstrates the ORB feature detection and binary description algorithm. It uses an oriented FAST detection method and the rotated BRIEF descriptors. Unlike BRIEF, ORB is comparatively scale and rotation invariant while still employing the very efficient Hamming distance metric for matching. As such, it is preferred for real-time applications. """ from skimage import data from skimage import transform as tf from skimage.feature import (match_descriptors, corner_harris, corner_peaks, ORB, plot_matches) from skimage.color import rgb2gray import matplotlib.pyplot as plt img1 = rgb2gray(data.astronaut()) img2 = tf.rotate(img1, 180) tform = tf.AffineTransform(scale=(1.3, 1.1), rotation=0.5, translation=(0, -200)) img3 = tf.warp(img1, tform) descriptor_extractor = ORB(n_keypoints=200) descriptor_extractor.detect_and_extract(img1) keypoints1 = descriptor_extractor.keypoints descriptors1 = descriptor_extractor.descriptors descriptor_extractor.detect_and_extract(img2) keypoints2 = descriptor_extractor.keypoints descriptors2 = descriptor_extractor.descriptors descriptor_extractor.detect_and_extract(img3) keypoints3 = descriptor_extractor.keypoints descriptors3 = descriptor_extractor.descriptors matches12 = match_descriptors(descriptors1, descriptors2, cross_check=True) matches13 = match_descriptors(descriptors1, descriptors3, cross_check=True) fig, ax = plt.subplots(nrows=2, ncols=1) plt.gray() plot_matches(ax[0], img1, img2, keypoints1, keypoints2, matches12) ax[0].axis('off') ax[0].set_title("Original Image vs. Transformed Image") plot_matches(ax[1], img1, img3, keypoints1, keypoints3, matches13) ax[1].axis('off') ax[1].set_title("Original Image vs. Transformed Image") plt.show() scikit-image-0.13.1/doc/examples/features_detection/plot_shape_index.py000066400000000000000000000104361316256236300263130ustar00rootroot00000000000000""" =========== Shape Index =========== The shape index is a single valued measure of local curvature, derived from the eigen values of the Hessian, defined by Koenderink & van Doorn [1]_. It can be used to find structures based on their apparent local shape. The shape index maps to values from -1 to 1, representing different kind of shapes (see the documentation for details). In this example, a random image with spots is generated, which should be detected. A shape index of 1 represents 'spherical caps', the shape of the spots we want to detect. The leftmost plot shows the generated image, the center shows a 3D render of the image, taking intensity values as height of a 3D surface, and the right one shows the shape index (s). As visible, the shape index readily amplifies the local shape of noise as well, but is insusceptible to global phenomena (e.g. uneven illumination). The blue and green marks are points which deviate no more than 0.05 from the desired shape. To attenuate noise in the signal, the green marks are taken from the shape index (s) after another Gaussian blur pass (yielding s'). Note how spots interjoined too closely are *not* detected, as they do not posses the desired shape. .. [1] Koenderink, J. J. & van Doorn, A. J., "Surface shape and curvature scales", Image and Vision Computing, 1992, 10, 557-564. DOI:10.1016/0262-8856(92)90076-F """ import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from scipy import ndimage as ndi from skimage.feature import shape_index from skimage.draw import circle def create_test_image( image_size=256, spot_count=30, spot_radius=5, cloud_noise_size=4): """ Generate a test image with random noise, uneven illumination and spots. """ state = np.random.get_state() np.random.seed(314159265) # some digits of pi image = np.random.normal( loc=0.25, scale=0.25, size=(image_size, image_size) ) for _ in range(spot_count): rr, cc = circle( np.random.randint(image.shape[0]), np.random.randint(image.shape[1]), spot_radius, shape=image.shape ) image[rr, cc] = 1 image *= np.random.normal(loc=1.0, scale=0.1, size=image.shape) image *= ndi.zoom( np.random.normal( loc=1.0, scale=0.5, size=(cloud_noise_size, cloud_noise_size) ), image_size / cloud_noise_size ) np.random.set_state(state) return ndi.gaussian_filter(image, sigma=2.0) # First create the test image and its shape index image = create_test_image() s = shape_index(image) # In this example we want to detect 'spherical caps', # so we threshold the shape index map to # find points which are 'spherical caps' (~1) target = 1 delta = 0.05 point_y, point_x = np.where(np.abs(s - target) < delta) point_z = image[point_y, point_x] # The shape index map relentlessly produces the shape, even that of noise. # In order to reduce the impact of noise, we apply a Gaussian filter to it, # and show the results once in s_smooth = ndi.gaussian_filter(s, sigma=0.5) point_y_s, point_x_s = np.where(np.abs(s_smooth - target) < delta) point_z_s = image[point_y_s, point_x_s] fig = plt.figure(figsize=(24, 8)) ax1 = fig.add_subplot(1, 3, 1) ax1.imshow(image, cmap=plt.cm.gray) ax1.axis('off') ax1.set_title('Input image', fontsize=18) scatter_settings = dict(alpha=0.75, s=10, linewidths=0) ax1.scatter(point_x, point_y, color='blue', **scatter_settings) ax1.scatter(point_x_s, point_y_s, color='green', **scatter_settings) ax2 = fig.add_subplot(1, 3, 2, projection='3d', sharex=ax1, sharey=ax1) x, y = np.meshgrid( np.arange(0, image.shape[0], 1), np.arange(0, image.shape[1], 1) ) ax2.plot_surface(x, y, image, linewidth=0, alpha=0.5) ax2.scatter( point_x, point_y, point_z, color='blue', label='$|s - 1|<0.05$', **scatter_settings ) ax2.scatter( point_x_s, point_y_s, point_z_s, color='green', label='$|s\' - 1|<0.05$', **scatter_settings ) ax2.legend() ax2.axis('off') ax2.set_title('3D visualization') ax3 = fig.add_subplot(1, 3, 3, sharex=ax1, sharey=ax1) ax3.imshow(s, cmap=plt.cm.gray) ax3.axis('off') ax3.set_title('Shape index, $\sigma=1$', fontsize=18) fig.tight_layout() plt.show() scikit-image-0.13.1/doc/examples/features_detection/plot_template.py000066400000000000000000000034211316256236300256330ustar00rootroot00000000000000""" ================= Template Matching ================= We use template matching to identify the occurrence of an image patch (in this case, a sub-image centered on a single coin). Here, we return a single match (the exact same coin), so the maximum value in the ``match_template`` result corresponds to the coin location. The other coins look similar, and thus have local maxima; if you expect multiple matches, you should use a proper peak-finding function. The ``match_template`` function uses fast, normalized cross-correlation [1]_ to find instances of the template in the image. Note that the peaks in the output of ``match_template`` correspond to the origin (i.e. top-left corner) of the template. .. [1] J. P. Lewis, "Fast Normalized Cross-Correlation", Industrial Light and Magic. """ import numpy as np import matplotlib.pyplot as plt from skimage import data from skimage.feature import match_template image = data.coins() coin = image[170:220, 75:130] result = match_template(image, coin) ij = np.unravel_index(np.argmax(result), result.shape) x, y = ij[::-1] fig = plt.figure(figsize=(8, 3)) ax1 = plt.subplot(1, 3, 1) ax2 = plt.subplot(1, 3, 2, adjustable='box-forced') ax3 = plt.subplot(1, 3, 3, sharex=ax2, sharey=ax2, adjustable='box-forced') ax1.imshow(coin, cmap=plt.cm.gray) ax1.set_axis_off() ax1.set_title('template') ax2.imshow(image, cmap=plt.cm.gray) ax2.set_axis_off() ax2.set_title('image') # highlight matched region hcoin, wcoin = coin.shape rect = plt.Rectangle((x, y), wcoin, hcoin, edgecolor='r', facecolor='none') ax2.add_patch(rect) ax3.imshow(result) ax3.set_axis_off() ax3.set_title('`match_template`\nresult') # highlight matched region ax3.autoscale(False) ax3.plot(x, y, 'o', markeredgecolor='r', markerfacecolor='none', markersize=10) plt.show() scikit-image-0.13.1/doc/examples/features_detection/plot_windowed_histogram.py000066400000000000000000000120361316256236300277170ustar00rootroot00000000000000""" ======================== Sliding window histogram ======================== Histogram matching can be used for object detection in images [1]_. This example extracts a single coin from the ``skimage.data.coins`` image and uses histogram matching to attempt to locate it within the original image. First, a box-shaped region of the image containing the target coin is extracted and a histogram of its greyscale values is computed. Next, for each pixel in the test image, a histogram of the greyscale values in a region of the image surrounding the pixel is computed. ``skimage.filters.rank.windowed_histogram`` is used for this task, as it employs an efficient sliding window based algorithm that is able to compute these histograms quickly [2]_. The local histogram for the region surrounding each pixel in the image is compared to that of the single coin, with a similarity measure being computed and displayed. The histogram of the single coin is computed using ``numpy.histogram`` on a box shaped region surrounding the coin, while the sliding window histograms are computed using a disc shaped structural element of a slightly different size. This is done in aid of demonstrating that the technique still finds similarity in spite of these differences. To demonstrate the rotational invariance of the technique, the same test is performed on a version of the coins image rotated by 45 degrees. References ---------- .. [1] Porikli, F. "Integral Histogram: A Fast Way to Extract Histograms in Cartesian Spaces" CVPR, 2005. Vol. 1. IEEE, 2005 .. [2] S.Perreault and P.Hebert. Median filtering in constant time. Trans. Image Processing, 16(9):2389-2394, 2007. """ from __future__ import division import numpy as np import matplotlib import matplotlib.pyplot as plt from skimage import data, transform from skimage.util import img_as_ubyte from skimage.morphology import disk from skimage.filters import rank matplotlib.rcParams['font.size'] = 9 def windowed_histogram_similarity(image, selem, reference_hist, n_bins): # Compute normalized windowed histogram feature vector for each pixel px_histograms = rank.windowed_histogram(image, selem, n_bins=n_bins) # Reshape coin histogram to (1,1,N) for broadcast when we want to use it in # arithmetic operations with the windowed histograms from the image reference_hist = reference_hist.reshape((1, 1) + reference_hist.shape) # Compute Chi squared distance metric: sum((X-Y)^2 / (X+Y)); # a measure of distance between histograms X = px_histograms Y = reference_hist num = (X - Y) ** 2 denom = X + Y denom[denom == 0] = np.infty frac = num / denom chi_sqr = 0.5 * np.sum(frac, axis=2) # Generate a similarity measure. It needs to be low when distance is high # and high when distance is low; taking the reciprocal will do this. # Chi squared will always be >= 0, add small value to prevent divide by 0. similarity = 1 / (chi_sqr + 1.0e-4) return similarity # Load the `skimage.data.coins` image img = img_as_ubyte(data.coins()) # Quantize to 16 levels of greyscale; this way the output image will have a # 16-dimensional feature vector per pixel quantized_img = img // 16 # Select the coin from the 4th column, second row. # Co-ordinate ordering: [x1,y1,x2,y2] coin_coords = [184, 100, 228, 148] # 44 x 44 region coin = quantized_img[coin_coords[1]:coin_coords[3], coin_coords[0]:coin_coords[2]] # Compute coin histogram and normalize coin_hist, _ = np.histogram(coin.flatten(), bins=16, range=(0, 16)) coin_hist = coin_hist.astype(float) / np.sum(coin_hist) # Compute a disk shaped mask that will define the shape of our sliding window # Example coin is ~44px across, so make a disk 61px wide (2 * rad + 1) to be # big enough for other coins too. selem = disk(30) # Compute the similarity across the complete image similarity = windowed_histogram_similarity(quantized_img, selem, coin_hist, coin_hist.shape[0]) # Now try a rotated image rotated_img = img_as_ubyte(transform.rotate(img, 45.0, resize=True)) # Quantize to 16 levels as before quantized_rotated_image = rotated_img // 16 # Similarity on rotated image rotated_similarity = windowed_histogram_similarity(quantized_rotated_image, selem, coin_hist, coin_hist.shape[0]) fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(10, 10)) axes[0, 0].imshow(quantized_img, cmap='gray') axes[0, 0].set_title('Quantized image') axes[0, 0].axis('off') axes[0, 1].imshow(coin, cmap='gray') axes[0, 1].set_title('Coin from 2nd row, 4th column') axes[0, 1].axis('off') axes[1, 0].imshow(img, cmap='gray') axes[1, 0].imshow(similarity, cmap='hot', alpha=0.5) axes[1, 0].set_title('Original image with overlaid similarity') axes[1, 0].axis('off') axes[1, 1].imshow(rotated_img, cmap='gray') axes[1, 1].imshow(rotated_similarity, cmap='hot', alpha=0.5) axes[1, 1].set_title('Rotated image with overlaid similarity') axes[1, 1].axis('off') plt.tight_layout() plt.show() scikit-image-0.13.1/doc/examples/filters/000077500000000000000000000000001316256236300202045ustar00rootroot00000000000000scikit-image-0.13.1/doc/examples/filters/README.txt000066400000000000000000000000641316256236300217020ustar00rootroot00000000000000Filtering and restoration ------------------------- scikit-image-0.13.1/doc/examples/filters/plot_deconvolution.py000066400000000000000000000031231316256236300245030ustar00rootroot00000000000000""" ===================== Image Deconvolution ===================== In this example, we deconvolve an image using Richardson-Lucy deconvolution algorithm ([1]_, [2]_). The algorithm is based on a PSF (Point Spread Function), where PSF is described as the impulse response of the optical system. The blurred image is sharpened through a number of iterations, which needs to be hand-tuned. .. [1] William Hadley Richardson, "Bayesian-Based Iterative Method of Image Restoration", J. Opt. Soc. Am. A 27, 1593-1607 (1972), DOI:10.1364/JOSA.62.000055 .. [2] http://en.wikipedia.org/wiki/Richardson%E2%80%93Lucy_deconvolution """ import numpy as np import matplotlib.pyplot as plt from scipy.signal import convolve2d as conv2 from skimage import color, data, restoration astro = color.rgb2gray(data.astronaut()) psf = np.ones((5, 5)) / 25 astro = conv2(astro, psf, 'same') # Add Noise to Image astro_noisy = astro.copy() astro_noisy += (np.random.poisson(lam=25, size=astro.shape) - 10) / 255. # Restore Image using Richardson-Lucy algorithm deconvolved_RL = restoration.richardson_lucy(astro_noisy, psf, iterations=30) fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(8, 5)) plt.gray() for a in (ax[0], ax[1], ax[2]): a.axis('off') ax[0].imshow(astro) ax[0].set_title('Original Data') ax[1].imshow(astro_noisy) ax[1].set_title('Noisy data') ax[2].imshow(deconvolved_RL, vmin=astro_noisy.min(), vmax=astro_noisy.max()) ax[2].set_title('Restoration using\nRichardson-Lucy') fig.subplots_adjust(wspace=0.02, hspace=0.2, top=0.9, bottom=0.05, left=0, right=1) plt.show() scikit-image-0.13.1/doc/examples/filters/plot_denoise.py000066400000000000000000000062631316256236300232510ustar00rootroot00000000000000""" ==================== Denoising a picture ==================== In this example, we denoise a noisy version of a picture using the total variation, bilateral, and wavelet denoising filters. Total variation and bilateral algorithms typically produce "posterized" images with flat domains separated by sharp edges. It is possible to change the degree of posterization by controlling the tradeoff between denoising and faithfulness to the original image. Total variation filter ---------------------- The result of this filter is an image that has a minimal total variation norm, while being as close to the initial image as possible. The total variation is the L1 norm of the gradient of the image. Bilateral filter ---------------- A bilateral filter is an edge-preserving and noise reducing filter. It averages pixels based on their spatial closeness and radiometric similarity. Wavelet denoising filter ------------------------ A wavelet denoising filter relies on the wavelet representation of the image. The noise is represented by small values in the wavelet domain which are set to 0. In color images, wavelet denoising is typically done in the `YCbCr color space`_ as denoising in separate color channels may lead to more apparent noise. .. _`YCbCr color space`: https://en.wikipedia.org/wiki/YCbCr """ import numpy as np import matplotlib.pyplot as plt from skimage.restoration import (denoise_tv_chambolle, denoise_bilateral, denoise_wavelet, estimate_sigma) from skimage import data, img_as_float, color from skimage.util import random_noise original = img_as_float(data.chelsea()[100:250, 50:300]) sigma = 0.155 noisy = random_noise(original, var=sigma**2) fig, ax = plt.subplots(nrows=2, ncols=4, figsize=(8, 5), sharex=True, sharey=True, subplot_kw={'adjustable': 'box-forced'}) plt.gray() # Estimate the average noise standard deviation across color channels. sigma_est = estimate_sigma(noisy, multichannel=True, average_sigmas=True) # Due to clipping in random_noise, the estimate will be a bit smaller than the # specified sigma. print("Estimated Gaussian noise standard deviation = {}".format(sigma_est)) ax[0, 0].imshow(noisy) ax[0, 0].axis('off') ax[0, 0].set_title('Noisy') ax[0, 1].imshow(denoise_tv_chambolle(noisy, weight=0.1, multichannel=True)) ax[0, 1].axis('off') ax[0, 1].set_title('TV') ax[0, 2].imshow(denoise_bilateral(noisy, sigma_color=0.05, sigma_spatial=15, multichannel=True)) ax[0, 2].axis('off') ax[0, 2].set_title('Bilateral') ax[0, 3].imshow(denoise_wavelet(noisy, multichannel=True)) ax[0, 3].axis('off') ax[0, 3].set_title('Wavelet denoising') ax[1, 1].imshow(denoise_tv_chambolle(noisy, weight=0.2, multichannel=True)) ax[1, 1].axis('off') ax[1, 1].set_title('(more) TV') ax[1, 2].imshow(denoise_bilateral(noisy, sigma_color=0.1, sigma_spatial=15, multichannel=True)) ax[1, 2].axis('off') ax[1, 2].set_title('(more) Bilateral') ax[1, 3].imshow(denoise_wavelet(noisy, multichannel=True, convert2ycbcr=True)) ax[1, 3].axis('off') ax[1, 3].set_title('Wavelet denoising\nin YCbCr colorspace') ax[1, 0].imshow(original) ax[1, 0].axis('off') ax[1, 0].set_title('Original') fig.tight_layout() plt.show() scikit-image-0.13.1/doc/examples/filters/plot_entropy.py000066400000000000000000000044061316256236300233200ustar00rootroot00000000000000""" ======= Entropy ======= In information theory, information entropy is the log-base-2 of the number of possible outcomes for a message. For an image, local entropy is related to the complexity contained in a given neighborhood, typically defined by a structuring element. The entropy filter can detect subtle variations in the local gray level distribution. In the first example, the image is composed of two surfaces with two slightly different distributions. The image has a uniform random distribution in the range [-14, +14] in the middle of the image and a uniform random distribution in the range [-15, 15] at the image borders, both centered at a gray value of 128. To detect the central square, we compute the local entropy measure using a circular structuring element of a radius big enough to capture the local gray level distribution. The second example shows how to detect texture in the camera image using a smaller structuring element. """ import matplotlib.pyplot as plt import numpy as np from skimage import data from skimage.util import img_as_ubyte from skimage.filters.rank import entropy from skimage.morphology import disk # First example: object detection. noise_mask = 28 * np.ones((128, 128), dtype=np.uint8) noise_mask[32:-32, 32:-32] = 30 noise = (noise_mask * np.random.random(noise_mask.shape) - 0.5 * noise_mask).astype(np.uint8) img = noise + 128 entr_img = entropy(img, disk(10)) fig, (ax0, ax1, ax2) = plt.subplots(nrows=1, ncols=3, figsize=(10, 4)) ax0.imshow(noise_mask, cmap='gray') ax0.set_xlabel("Noise mask") ax1.imshow(img, cmap='gray') ax1.set_xlabel("Noisy image") ax2.imshow(entr_img, cmap='viridis') ax2.set_xlabel("Local entropy") fig.tight_layout() # Second example: texture detection. image = img_as_ubyte(data.camera()) fig, (ax0, ax1) = plt.subplots(ncols=2, figsize=(12, 4), sharex=True, sharey=True, subplot_kw={"adjustable": "box-forced"}) img0 = ax0.imshow(image, cmap=plt.cm.gray) ax0.set_title("Image") ax0.axis("off") fig.colorbar(img0, ax=ax0) img1 = ax1.imshow(entropy(image, disk(5)), cmap='gray') ax1.set_title("Entropy") ax1.axis("off") fig.colorbar(img1, ax=ax1) fig.tight_layout() plt.show() scikit-image-0.13.1/doc/examples/filters/plot_frangi.py000066400000000000000000000012461316256236300230650ustar00rootroot00000000000000""" ============= Frangi filter ============= The Frangi and hybrid Hessian filters can be used to detect continuous edges, such as vessels, wrinkles, and rivers. """ from skimage.data import camera from skimage.filters import frangi, hessian import matplotlib.pyplot as plt image = camera() fig, ax = plt.subplots(ncols=3, subplot_kw={'adjustable': 'box-forced'}) ax[0].imshow(image, cmap=plt.cm.gray) ax[0].set_title('Original image') ax[1].imshow(frangi(image), cmap=plt.cm.gray) ax[1].set_title('Frangi filter result') ax[2].imshow(hessian(image), cmap=plt.cm.gray) ax[2].set_title('Hybrid Hessian filter result') for a in ax: a.axis('off') plt.tight_layout() scikit-image-0.13.1/doc/examples/filters/plot_inpaint.py000066400000000000000000000033131316256236300232560ustar00rootroot00000000000000""" =========== Inpainting =========== Inpainting [1]_ is the process of reconstructing lost or deteriorated parts of images and videos. The reconstruction is supposed to be performed in fully automatic way by exploiting the information presented in non-damaged regions. In this example, we show how the masked pixels get inpainted by inpainting algorithm based on 'biharmonic equation'-assumption [2]_ [3]_. .. [1] Wikipedia. Inpainting https://en.wikipedia.org/wiki/Inpainting .. [2] Wikipedia. Biharmonic equation https://en.wikipedia.org/wiki/Biharmonic_equation .. [3] N.S.Hoang, S.B.Damelin, "On surface completion and image inpainting by biharmonic functions: numerical aspects" """ import numpy as np import matplotlib.pyplot as plt from skimage import data from skimage.restoration import inpaint image_orig = data.astronaut()[0:200, 0:200] # Create mask with three defect regions: left, middle, right respectively mask = np.zeros(image_orig.shape[:-1]) mask[20:60, 0:20] = 1 mask[160:180, 70:155] = 1 mask[30:60, 170:195] = 1 # Defect image over the same region in each color channel image_defect = image_orig.copy() for layer in range(image_defect.shape[-1]): image_defect[np.where(mask)] = 0 image_result = inpaint.inpaint_biharmonic(image_defect, mask, multichannel=True) fig, axes = plt.subplots(ncols=2, nrows=2) ax = axes.ravel() ax[0].set_title('Original image') ax[0].imshow(image_orig) ax[1].set_title('Mask') ax[1].imshow(mask, cmap=plt.cm.gray) ax[2].set_title('Defected image') ax[2].imshow(image_defect) ax[3].set_title('Inpainted image') ax[3].imshow(image_result) for a in ax: a.axis('off') fig.tight_layout() plt.show() scikit-image-0.13.1/doc/examples/filters/plot_nonlocal_means.py000066400000000000000000000024711316256236300246100ustar00rootroot00000000000000""" ================================================= Non-local means denoising for preserving textures ================================================= In this example, we denoise a detail of the astronaut image using the non-local means filter. The non-local means algorithm replaces the value of a pixel by an average of a selection of other pixels values: small patches centered on the other pixels are compared to the patch centered on the pixel of interest, and the average is performed only for pixels that have patches close to the current patch. As a result, this algorithm can restore well textures, that would be blurred by other denoising algoritm. """ import numpy as np import matplotlib.pyplot as plt from skimage import data, img_as_float from skimage.restoration import denoise_nl_means astro = img_as_float(data.astronaut()) astro = astro[30:180, 150:300] noisy = astro + 0.3 * np.random.random(astro.shape) noisy = np.clip(noisy, 0, 1) denoise = denoise_nl_means(noisy, 7, 9, 0.08, multichannel=True) fig, ax = plt.subplots(ncols=2, figsize=(8, 4), sharex=True, sharey=True, subplot_kw={'adjustable': 'box-forced'}) ax[0].imshow(noisy) ax[0].axis('off') ax[0].set_title('noisy') ax[1].imshow(denoise) ax[1].axis('off') ax[1].set_title('non-local means') fig.tight_layout() plt.show() scikit-image-0.13.1/doc/examples/filters/plot_phase_unwrap.py000066400000000000000000000102771316256236300243170ustar00rootroot00000000000000""" ================ Phase Unwrapping ================ Some signals can only be observed modulo 2*pi, and this can also apply to two- and three dimensional images. In these cases phase unwrapping is needed to recover the underlying, unwrapped signal. In this example we will demonstrate an algorithm [1]_ implemented in ``skimage`` at work for such a problem. One-, two- and three dimensional images can all be unwrapped using skimage. Here we will demonstrate phase unwrapping in the two dimensional case. """ import numpy as np from matplotlib import pyplot as plt from skimage import data, img_as_float, color, exposure from skimage.restoration import unwrap_phase # Load an image as a floating-point grayscale image = color.rgb2gray(img_as_float(data.chelsea())) # Scale the image to [0, 4*pi] image = exposure.rescale_intensity(image, out_range=(0, 4 * np.pi)) # Create a phase-wrapped image in the interval [-pi, pi) image_wrapped = np.angle(np.exp(1j * image)) # Perform phase unwrapping image_unwrapped = unwrap_phase(image_wrapped) fig, ax = plt.subplots(2, 2, sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'}) ax1, ax2, ax3, ax4 = ax.ravel() fig.colorbar(ax1.imshow(image, cmap='gray', vmin=0, vmax=4 * np.pi), ax=ax1) ax1.set_title('Original') fig.colorbar(ax2.imshow(image_wrapped, cmap='gray', vmin=-np.pi, vmax=np.pi), ax=ax2) ax2.set_title('Wrapped phase') fig.colorbar(ax3.imshow(image_unwrapped, cmap='gray'), ax=ax3) ax3.set_title('After phase unwrapping') fig.colorbar(ax4.imshow(image_unwrapped - image, cmap='gray'), ax=ax4) ax4.set_title('Unwrapped minus original') ########################################################################### # The unwrapping procedure accepts masked arrays, and can also optionally # assume cyclic boundaries to connect edges of an image. In the example below, # we study a simple phase ramp which has been split in two by masking # a row of the image. # Create a simple ramp image = np.ones((100, 100)) * np.linspace(0, 8 * np.pi, 100).reshape((-1, 1)) # Mask the image to split it in two horizontally mask = np.zeros_like(image, dtype=np.bool) mask[image.shape[0] // 2, :] = True image_wrapped = np.ma.array(np.angle(np.exp(1j * image)), mask=mask) # Unwrap image without wrap around image_unwrapped_no_wrap_around = unwrap_phase(image_wrapped, wrap_around=(False, False)) # Unwrap with wrap around enabled for the 0th dimension image_unwrapped_wrap_around = unwrap_phase(image_wrapped, wrap_around=(True, False)) fig, ax = plt.subplots(2, 2) ax1, ax2, ax3, ax4 = ax.ravel() fig.colorbar(ax1.imshow(np.ma.array(image, mask=mask), cmap='rainbow'), ax=ax1) ax1.set_title('Original') fig.colorbar(ax2.imshow(image_wrapped, cmap='rainbow', vmin=-np.pi, vmax=np.pi), ax=ax2) ax2.set_title('Wrapped phase') fig.colorbar(ax3.imshow(image_unwrapped_no_wrap_around, cmap='rainbow'), ax=ax3) ax3.set_title('Unwrapped without wrap_around') fig.colorbar(ax4.imshow(image_unwrapped_wrap_around, cmap='rainbow'), ax=ax4) ax4.set_title('Unwrapped with wrap_around') plt.show() ########################################################################### # In the figures above, the masked row can be seen as a white line across # the image. The difference between the two unwrapped images in the bottom row # is clear: Without unwrapping (lower left), the regions above and below the # masked boundary do not interact at all, resulting in an offset between the # two regions of an arbitrary integer times two pi. We could just as well have # unwrapped the regions as two separate images. With wrap around enabled for the # vertical direction (lower right), the situation changes: Unwrapping paths are # now allowed to pass from the bottom to the top of the image and vice versa, in # effect providing a way to determine the offset between the two regions. # # References # ---------- # # .. [1] Miguel Arevallilo Herraez, David R. Burton, Michael J. Lalor, # and Munther A. Gdeisat, "Fast two-dimensional phase-unwrapping # algorithm based on sorting by reliability following a noncontinuous # path", Journal Applied Optics, Vol. 41, No. 35, pp. 7437, 2002 scikit-image-0.13.1/doc/examples/filters/plot_rank_mean.py000066400000000000000000000030071316256236300235470ustar00rootroot00000000000000""" ============ Mean filters ============ This example compares the following mean filters of the rank filter package: * **local mean**: all pixels belonging to the structuring element to compute average gray level. * **percentile mean**: only use values between percentiles p0 and p1 (here 10% and 90%). * **bilateral mean**: only use pixels of the structuring element having a gray level situated inside g-s0 and g+s1 (here g-500 and g+500) Percentile and usual mean give here similar results, these filters smooth the complete image (background and details). Bilateral mean exhibits a high filtering rate for continuous area (i.e. background) while higher image frequencies remain untouched. """ import matplotlib.pyplot as plt from skimage import data from skimage.morphology import disk from skimage.filters import rank image = data.coins() selem = disk(20) percentile_result = rank.mean_percentile(image, selem=selem, p0=.1, p1=.9) bilateral_result = rank.mean_bilateral(image, selem=selem, s0=500, s1=500) normal_result = rank.mean(image, selem=selem) fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(10, 10), sharex=True, sharey=True) ax = axes.ravel() titles = ['Original', 'Percentile mean', 'Bilateral mean', 'Local mean'] imgs = [image, percentile_result, bilateral_result, normal_result] for n in range(0, len(imgs)): ax[n].imshow(imgs[n], cmap=plt.cm.gray) ax[n].set_title(titles[n]) ax[n].set_adjustable('box-forced') ax[n].axis('off') plt.tight_layout() plt.show() scikit-image-0.13.1/doc/examples/filters/plot_restoration.py000066400000000000000000000037101316256236300241660ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ ===================== Image Deconvolution ===================== In this example, we deconvolve a noisy version of an image using Wiener and unsupervised Wiener algorithms. This algorithms are based on linear models that can't restore sharp edge as much as non-linear methods (like TV restoration) but are much faster. Wiener filter ------------- The inverse filter based on the PSF (Point Spread Function), the prior regularisation (penalisation of high frequency) and the tradeoff between the data and prior adequacy. The regularization parameter must be hand tuned. Unsupervised Wiener ------------------- This algorithm has a self-tuned regularisation parameters based on data learning. This is not common and based on the following publication [1]_. The algorithm is based on a iterative Gibbs sampler that draw alternatively samples of posterior conditional law of the image, the noise power and the image frequency power. .. [1] François Orieux, Jean-François Giovannelli, and Thomas Rodet, "Bayesian estimation of regularization and point spread function parameters for Wiener-Hunt deconvolution", J. Opt. Soc. Am. A 27, 1593-1607 (2010) """ import numpy as np import matplotlib.pyplot as plt from skimage import color, data, restoration astro = color.rgb2gray(data.astronaut()) from scipy.signal import convolve2d as conv2 psf = np.ones((5, 5)) / 25 astro = conv2(astro, psf, 'same') astro += 0.1 * astro.std() * np.random.standard_normal(astro.shape) deconvolved, _ = restoration.unsupervised_wiener(astro, psf) fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(8, 5), sharex=True, sharey=True, subplot_kw={'adjustable': 'box-forced'}) plt.gray() ax[0].imshow(astro, vmin=deconvolved.min(), vmax=deconvolved.max()) ax[0].axis('off') ax[0].set_title('Data') ax[1].imshow(deconvolved) ax[1].axis('off') ax[1].set_title('Self tuned restoration') fig.tight_layout() plt.show() scikit-image-0.13.1/doc/examples/numpy_operations/000077500000000000000000000000001316256236300221475ustar00rootroot00000000000000scikit-image-0.13.1/doc/examples/numpy_operations/README.txt000066400000000000000000000000661316256236300236470ustar00rootroot00000000000000Operations on NumPy arrays -------------------------- scikit-image-0.13.1/doc/examples/numpy_operations/plot_camera_numpy.py000066400000000000000000000014421316256236300262400ustar00rootroot00000000000000""" Using simple NumPy operations for manipulating images ===================================================== This script illustrates how to use basic NumPy operations, such as slicing, masking and fancy indexing, in order to modify the pixel values of an image. """ import numpy as np from skimage import data import matplotlib.pyplot as plt camera = data.camera() camera[:10] = 0 mask = camera < 87 camera[mask] = 255 inds_x = np.arange(len(camera)) inds_y = (4 * inds_x) % len(camera) camera[inds_x, inds_y] = 0 l_x, l_y = camera.shape[0], camera.shape[1] X, Y = np.ogrid[:l_x, :l_y] outer_disk_mask = (X - l_x / 2)**2 + (Y - l_y / 2)**2 > (l_x / 2)**2 camera[outer_disk_mask] = 0 plt.figure(figsize=(4, 4)) plt.imshow(camera, cmap='gray', interpolation='nearest') plt.axis('off') plt.show() scikit-image-0.13.1/doc/examples/numpy_operations/plot_view_as_blocks.py000066400000000000000000000041411316256236300265510ustar00rootroot00000000000000""" ============================ Block views on images/arrays ============================ This example illustrates the use of ``view_as_blocks`` from :py:func:`skimage.util`. Block views can be incredibly useful when one wants to perform local operations on non-overlapping image patches. We use ``astronaut`` from ``skimage.data`` and virtually 'slice' it into square blocks. Then, on each block, we either pool the mean, the max or the median value of that block. The results are displayed altogether, along with a spline interpolation of order 3 rescaling of the original `astronaut` image. """ import numpy as np from scipy import ndimage as ndi from matplotlib import pyplot as plt import matplotlib.cm as cm from skimage import data from skimage import color from skimage.util import view_as_blocks # get astronaut from skimage.data in grayscale l = color.rgb2gray(data.astronaut()) # size of blocks block_shape = (4, 4) # see astronaut as a matrix of blocks (of shape block_shape) view = view_as_blocks(l, block_shape) # collapse the last two dimensions in one flatten_view = view.reshape(view.shape[0], view.shape[1], -1) # resampling the image by taking either the `mean`, # the `max` or the `median` value of each blocks. mean_view = np.mean(flatten_view, axis=2) max_view = np.max(flatten_view, axis=2) median_view = np.median(flatten_view, axis=2) # display resampled images fig, axes = plt.subplots(2, 2, figsize=(8, 8), sharex=True, sharey=True) ax = axes.ravel() l_resized = ndi.zoom(l, 2, order=3) ax[0].set_title("Original rescaled with\n spline interpolation (order=3)") ax[0].imshow(l_resized, extent=(0, 128, 128, 0), interpolation='nearest', cmap=cm.Greys_r) ax[1].set_title("Block view with\n local mean pooling") ax[1].imshow(mean_view, interpolation='nearest', cmap=cm.Greys_r) ax[2].set_title("Block view with\n local max pooling") ax[2].imshow(max_view, interpolation='nearest', cmap=cm.Greys_r) ax[3].set_title("Block view with\n local median pooling") ax[3].imshow(median_view, interpolation='nearest', cmap=cm.Greys_r) for a in ax: a.set_axis_off() fig.tight_layout() plt.show() scikit-image-0.13.1/doc/examples/segmentation/000077500000000000000000000000001316256236300212315ustar00rootroot00000000000000scikit-image-0.13.1/doc/examples/segmentation/README.txt000066400000000000000000000000601316256236300227230ustar00rootroot00000000000000Segmentation of objects ----------------------- scikit-image-0.13.1/doc/examples/segmentation/plot_boundary_merge.py000066400000000000000000000052271316256236300256510ustar00rootroot00000000000000""" ============================================ Hierarchical Merging of Region Boundary RAGs ============================================ This example demonstrates how to perform hierarchical merging on region boundary Region Adjacency Graphs (RAGs). Region boundary RAGs can be constructed with the :py:func:`skimage.future.graph.rag_boundary` function. The regions with the lowest edge weights are successively merged until there is no edge with weight less than ``thresh``. The hierarchical merging is done through the :py:func:`skimage.future.graph.merge_hierarchical` function. For an example of how to construct region boundary based RAGs, see :any:`plot_rag_boundary`. """ from skimage import data, segmentation, filters, color from skimage.future import graph from matplotlib import pyplot as plt def weight_boundary(graph, src, dst, n): """ Handle merging of nodes of a region boundary region adjacency graph. This function computes the `"weight"` and the count `"count"` attributes of the edge between `n` and the node formed after merging `src` and `dst`. Parameters ---------- graph : RAG The graph under consideration. src, dst : int The vertices in `graph` to be merged. n : int A neighbor of `src` or `dst` or both. Returns ------- data : dict A dictionary with the "weight" and "count" attributes to be assigned for the merged node. """ default = {'weight': 0.0, 'count': 0} count_src = graph[src].get(n, default)['count'] count_dst = graph[dst].get(n, default)['count'] weight_src = graph[src].get(n, default)['weight'] weight_dst = graph[dst].get(n, default)['weight'] count = count_src + count_dst return { 'count': count, 'weight': (count_src * weight_src + count_dst * weight_dst)/count } def merge_boundary(graph, src, dst): """Call back called before merging 2 nodes. In this case we don't need to do any computation here. """ pass img = data.coffee() edges = filters.sobel(color.rgb2gray(img)) labels = segmentation.slic(img, compactness=30, n_segments=400) g = graph.rag_boundary(labels, edges) graph.show_rag(labels, g, img) plt.title('Initial RAG') labels2 = graph.merge_hierarchical(labels, g, thresh=0.08, rag_copy=False, in_place_merge=True, merge_func=merge_boundary, weight_func=weight_boundary) graph.show_rag(labels, g, img) plt.title('RAG after hierarchical merging') plt.figure() out = color.label2rgb(labels2, img, kind='avg') plt.imshow(out) plt.title('Final segmentation') plt.show() scikit-image-0.13.1/doc/examples/segmentation/plot_compact_watershed.py000066400000000000000000000024351316256236300263410ustar00rootroot00000000000000""" ============================================= Find Regular Segments Using Compact Watershed ============================================= The watershed transform is commonly used as a starting point for many segmentation algorithms. However, without a judicious choice of seeds, it can produce very uneven fragment sizes, which can be difficult to deal with in downstream analyses. The *compact* watershed transform remedies this by favoring seeds that are close to the pixel being considered. Both algorithms are implemented in the :py:func:`skimage.morphology.watershed` function. To use the compact form, simply pass a ``compactness`` value greater than 0. """ import numpy as np from skimage import data, util, filters, color from skimage.morphology import watershed import matplotlib.pyplot as plt coins = data.coins() edges = filters.sobel(coins) grid = util.regular_grid(coins.shape, n_points=468) seeds = np.zeros(coins.shape, dtype=int) seeds[grid] = np.arange(seeds[grid].size).reshape(seeds[grid].shape) + 1 w0 = watershed(edges, seeds) w1 = watershed(edges, seeds, compactness=0.01) fig, (ax0, ax1) = plt.subplots(1, 2) ax0.imshow(color.label2rgb(w0, coins)) ax0.set_title('Classical watershed') ax1.imshow(color.label2rgb(w1, coins)) ax1.set_title('Compact watershed') plt.show() scikit-image-0.13.1/doc/examples/segmentation/plot_extrema.py000066400000000000000000000052151316256236300243110ustar00rootroot00000000000000""" =============================== Extrema =============================== We detect local maxima in a galaxy image. The image is corrupted by noise, generating many local maxima. To keep only those maxima with sufficient local contrast, we use h-maxima. """ import numpy as np import matplotlib.pyplot as plt from skimage.measure import label from skimage import data from skimage import color from skimage.morphology import extrema from skimage import exposure color_image = data.hubble_deep_field() img = color.rgb2grey(color_image) # the rescaling is done only for visualization purpose. # the algorithms would work identically in an unscaled version of the # image. However, the parameter h needs to be adapted to the scale. img = exposure.rescale_intensity(img) # for visualization, we work on a crop of the image. x_0 = 70 y_0 = 354 width = 100 height = 100 ############################################################## # MAXIMA DETECTION # Maxima in the galaxy image are detected by mathematical morphology. # There is no a priori constraint on the density. # We find all local maxima local_maxima = extrema.local_maxima(img) label_maxima = label(local_maxima) overlay = color.label2rgb(label_maxima, img, alpha=0.7, bg_label=0, bg_color=None, colors=[(1, 0, 0)]) # We observed in the previous image, that there are many local maxima # that are caused by the noise in the image. # For this, we find all local maxima with a height of h. # This height is the grey level value by which we need to descent # in order to reach a higher maximum and it can be seen as a local # contrast measurement. # The value of h scales with the dynamic range of the image, i.e. # if we multiply the image with a constant, we need to multiply # the value of h with the same constant in order to achieve the same result. h = 0.05 h_maxima = extrema.h_maxima(img, h) label_h_maxima = label(h_maxima) overlay_h = color.label2rgb(label_h_maxima, img, alpha=0.7, bg_label=0, bg_color=None, colors=[(1, 0, 0)]) ############################################################## # GRAPHICAL OUTPUT # a new figure with 3 subplots fig, ax = plt.subplots(1, 3, figsize=(15, 5)) ax[0].imshow(img[y_0:(y_0 + height), x_0:(x_0 + width)], cmap='gray', interpolation='none') ax[0].set_title('Original image') ax[0].axis('off') ax[1].imshow(overlay[y_0:(y_0 + height), x_0:(x_0 + width)], interpolation='none') ax[1].set_title('Local Maxima') ax[1].axis('off') ax[2].imshow(overlay_h[y_0:(y_0 + height), x_0:(x_0 + width)], interpolation='none') ax[2].set_title('h maxima for h = %.2f' % h) ax[2].axis('off') plt.show() scikit-image-0.13.1/doc/examples/segmentation/plot_join_segmentations.py000066400000000000000000000037061316256236300265460ustar00rootroot00000000000000""" ========================================== Find the intersection of two segmentations ========================================== When segmenting an image, you may want to combine multiple alternative segmentations. The :py:func:`skimage.segmentation.join_segmentations` function computes the join of two segmentations, in which a pixel is placed in the same segment if and only if it is in the same segment in *both* segmentations. """ import numpy as np import matplotlib.pyplot as plt from skimage.filters import sobel from skimage.measure import label from skimage.segmentation import slic, join_segmentations from skimage.morphology import watershed from skimage.color import label2rgb from skimage import data coins = data.coins() # Make segmentation using edge-detection and watershed. edges = sobel(coins) # Identify some background and foreground pixels from the intensity values. # These pixels are used as seeds for watershed. markers = np.zeros_like(coins) foreground, background = 1, 2 markers[coins < 30.0] = background markers[coins > 150.0] = foreground ws = watershed(edges, markers) seg1 = label(ws == foreground) # Make segmentation using SLIC superpixels. seg2 = slic(coins, n_segments=117, max_iter=160, sigma=1, compactness=0.75, multichannel=False) # Combine the two. segj = join_segmentations(seg1, seg2) # Show the segmentations. fig, axes = plt.subplots(ncols=2, nrows=2, figsize=(9, 5), sharex=True, sharey=True, subplot_kw={'adjustable': 'box-forced'}) ax = axes.ravel() ax[0].imshow(coins, cmap='gray') ax[0].set_title('Image') color1 = label2rgb(seg1, image=coins, bg_label=0) ax[1].imshow(color1) ax[1].set_title('Sobel+Watershed') color2 = label2rgb(seg2, image=coins, image_alpha=0.5) ax[2].imshow(color2) ax[2].set_title('SLIC superpixels') color3 = label2rgb(segj, image=coins, image_alpha=0.5) ax[3].imshow(color3) ax[3].set_title('Join') for a in ax: a.axis('off') fig.tight_layout() plt.show() scikit-image-0.13.1/doc/examples/segmentation/plot_label.py000066400000000000000000000027011316256236300237200ustar00rootroot00000000000000""" =================== Label image regions =================== This example shows how to segment an image with image labelling. The following steps are applied: 1. Thresholding with automatic Otsu method 2. Close small holes with binary closing 3. Remove artifacts touching image border 4. Measure image regions to filter small objects """ import matplotlib.pyplot as plt import matplotlib.patches as mpatches from skimage import data from skimage.filters import threshold_otsu from skimage.segmentation import clear_border from skimage.measure import label, regionprops from skimage.morphology import closing, square from skimage.color import label2rgb image = data.coins()[50:-50, 50:-50] # apply threshold thresh = threshold_otsu(image) bw = closing(image > thresh, square(3)) # remove artifacts connected to image border cleared = clear_border(bw) # label image regions label_image = label(cleared) image_label_overlay = label2rgb(label_image, image=image) fig, ax = plt.subplots(figsize=(10, 6)) ax.imshow(image_label_overlay) for region in regionprops(label_image): # take regions with large enough areas if region.area >= 100: # draw rectangle around segmented coins minr, minc, maxr, maxc = region.bbox rect = mpatches.Rectangle((minc, minr), maxc - minc, maxr - minr, fill=False, edgecolor='red', linewidth=2) ax.add_patch(rect) ax.set_axis_off() plt.tight_layout() plt.show() scikit-image-0.13.1/doc/examples/segmentation/plot_marked_watershed.py000066400000000000000000000037041316256236300261560ustar00rootroot00000000000000""" =============================== Markers for watershed transform =============================== The watershed is a classical algorithm used for **segmentation**, that is, for separating different objects in an image. Here a marker image is built from the region of low gradient inside the image. In a gradient image, the areas of high values provide barriers that help to segment the image. Using markers on the lower values will ensure that the segmented objects are found. See Wikipedia_ for more details on the algorithm. .. _Wikipedia: http://en.wikipedia.org/wiki/Watershed_(image_processing) """ from scipy import ndimage as ndi import matplotlib.pyplot as plt from skimage.morphology import watershed, disk from skimage import data from skimage.filters import rank from skimage.util import img_as_ubyte image = img_as_ubyte(data.camera()) # denoise image denoised = rank.median(image, disk(2)) # find continuous region (low gradient - # where less than 10 for this image) --> markers # disk(5) is used here to get a more smooth image markers = rank.gradient(denoised, disk(5)) < 10 markers = ndi.label(markers)[0] # local gradient (disk(2) is used to keep edges thin) gradient = rank.gradient(denoised, disk(2)) # process the watershed labels = watershed(gradient, markers) # display results fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(8, 8), sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'}) ax = axes.ravel() ax[0].imshow(image, cmap=plt.cm.gray, interpolation='nearest') ax[0].set_title("Original") ax[1].imshow(gradient, cmap=plt.cm.spectral, interpolation='nearest') ax[1].set_title("Local Gradient") ax[2].imshow(markers, cmap=plt.cm.spectral, interpolation='nearest') ax[2].set_title("Markers") ax[3].imshow(image, cmap=plt.cm.gray, interpolation='nearest') ax[3].imshow(labels, cmap=plt.cm.spectral, interpolation='nearest', alpha=.7) ax[3].set_title("Segmented") for a in ax: a.axis('off') fig.tight_layout() plt.show() scikit-image-0.13.1/doc/examples/segmentation/plot_ncut.py000066400000000000000000000016631316256236300236200ustar00rootroot00000000000000""" ============== Normalized Cut ============== This example constructs a Region Adjacency Graph (RAG) and recursively performs a Normalized Cut on it [1]_. References ---------- .. [1] Shi, J.; Malik, J., "Normalized cuts and image segmentation", Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 22, no. 8, pp. 888-905, August 2000. """ from skimage import data, segmentation, color from skimage.future import graph from matplotlib import pyplot as plt img = data.coffee() labels1 = segmentation.slic(img, compactness=30, n_segments=400) out1 = color.label2rgb(labels1, img, kind='avg') g = graph.rag_mean_color(img, labels1, mode='similarity') labels2 = graph.cut_normalized(labels1, g) out2 = color.label2rgb(labels2, img, kind='avg') fig, ax = plt.subplots(nrows=2, sharex=True, sharey=True, figsize=(6, 8)) ax[0].imshow(out1) ax[1].imshow(out2) for a in ax: a.axis('off') plt.tight_layout() scikit-image-0.13.1/doc/examples/segmentation/plot_niblack_sauvola.py000066400000000000000000000037731316256236300260100ustar00rootroot00000000000000""" ================================ Niblack and Sauvola Thresholding ================================ Niblack and Sauvola thresholds are local thresholding techniques that are useful for images where the background is not uniform, especially for text recognition [1]_, [2]_. Instead of calculating a single global threshold for the entire image, several thresholds are calculated for every pixel by using specific formulae that take into account the mean and standard deviation of the local neighborhood (defined by a window centered around the pixel). Here, we binarize an image using these algorithms compare it to a common global thresholding technique. Parameter `window_size` determines the size of the window that contains the surrounding pixels. .. [1] Niblack, W (1986), An introduction to Digital Image Processing, Prentice-Hall. .. [2] J. Sauvola and M. Pietikainen, "Adaptive document image binarization," Pattern Recognition 33(2), pp. 225-236, 2000. DOI:10.1016/S0031-3203(99)00055-2 """ import matplotlib import matplotlib.pyplot as plt from skimage.data import page from skimage.filters import (threshold_otsu, threshold_niblack, threshold_sauvola) matplotlib.rcParams['font.size'] = 9 image = page() binary_global = image > threshold_otsu(image) window_size = 25 thresh_niblack = threshold_niblack(image, window_size=window_size, k=0.8) thresh_sauvola = threshold_sauvola(image, window_size=window_size) binary_niblack = image > thresh_niblack binary_sauvola = image > thresh_sauvola plt.figure(figsize=(8, 7)) plt.subplot(2, 2, 1) plt.imshow(image, cmap=plt.cm.gray) plt.title('Original') plt.axis('off') plt.subplot(2, 2, 2) plt.title('Global Threshold') plt.imshow(binary_global, cmap=plt.cm.gray) plt.axis('off') plt.subplot(2, 2, 3) plt.imshow(binary_niblack, cmap=plt.cm.gray) plt.title('Niblack Threshold') plt.axis('off') plt.subplot(2, 2, 4) plt.imshow(binary_sauvola, cmap=plt.cm.gray) plt.title('Sauvola Threshold') plt.axis('off') plt.show() scikit-image-0.13.1/doc/examples/segmentation/plot_peak_local_max.py000066400000000000000000000026641316256236300256100ustar00rootroot00000000000000""" ==================== Finding local maxima ==================== The ``peak_local_max`` function returns the coordinates of local peaks (maxima) in an image. A maximum filter is used for finding local maxima. This operation dilates the original image and merges neighboring local maxima closer than the size of the dilation. Locations where the original image is equal to the dilated image are returned as local maxima. """ from scipy import ndimage as ndi import matplotlib.pyplot as plt from skimage.feature import peak_local_max from skimage import data, img_as_float im = img_as_float(data.coins()) # image_max is the dilation of im with a 20*20 structuring element # It is used within peak_local_max function image_max = ndi.maximum_filter(im, size=20, mode='constant') # Comparison between image_max and im to find the coordinates of local maxima coordinates = peak_local_max(im, min_distance=20) # display results fig, axes = plt.subplots(1, 3, figsize=(8, 3), sharex=True, sharey=True, subplot_kw={'adjustable': 'box-forced'}) ax = axes.ravel() ax[0].imshow(im, cmap=plt.cm.gray) ax[0].axis('off') ax[0].set_title('Original') ax[1].imshow(image_max, cmap=plt.cm.gray) ax[1].axis('off') ax[1].set_title('Maximum filter') ax[2].imshow(im, cmap=plt.cm.gray) ax[2].autoscale(False) ax[2].plot(coordinates[:, 1], coordinates[:, 0], 'r.') ax[2].axis('off') ax[2].set_title('Peak local max') fig.tight_layout() plt.show() scikit-image-0.13.1/doc/examples/segmentation/plot_rag.py000066400000000000000000000042721316256236300234170ustar00rootroot00000000000000""" ======================= Region Adjacency Graphs ======================= This example demonstrates the use of the `merge_nodes` function of a Region Adjacency Graph (RAG). The `RAG` class represents a undirected weighted graph which inherits from `networkx.graph` class. When a new node is formed by merging two nodes, the edge weight of all the edges incident on the resulting node can be updated by a user defined function `weight_func`. The default behaviour is to use the smaller edge weight in case of a conflict. The example below also shows how to use a custom function to select the larger weight instead. """ from skimage.future.graph import rag import networkx as nx from matplotlib import pyplot as plt import numpy as np def max_edge(g, src, dst, n): """Callback to handle merging nodes by choosing maximum weight. Returns a dictionary with `"weight"` set as either the weight between (`src`, `n`) or (`dst`, `n`) in `g` or the maximum of the two when both exist. Parameters ---------- g : RAG The graph under consideration. src, dst : int The vertices in `g` to be merged. n : int A neighbor of `src` or `dst` or both. Returns ------- data : dict A dict with the "weight" attribute set the weight between (`src`, `n`) or (`dst`, `n`) in `g` or the maximum of the two when both exist. """ w1 = g[n].get(src, {'weight': -np.inf})['weight'] w2 = g[n].get(dst, {'weight': -np.inf})['weight'] return {'weight': max(w1, w2)} def display(g, title): """Displays a graph with the given title.""" pos = nx.circular_layout(g) plt.figure() plt.title(title) nx.draw(g, pos) nx.draw_networkx_edge_labels(g, pos, font_size=20) g = rag.RAG() g.add_edge(1, 2, weight=10) g.add_edge(2, 3, weight=20) g.add_edge(3, 4, weight=30) g.add_edge(4, 1, weight=40) g.add_edge(1, 3, weight=50) # Assigning dummy labels. for n in g.nodes(): g.node[n]['labels'] = [n] gc = g.copy() display(g, "Original Graph") g.merge_nodes(1, 3) display(g, "Merged with default (min)") gc.merge_nodes(1, 3, weight_func=max_edge, in_place=False) display(gc, "Merged with max without in_place") plt.show() scikit-image-0.13.1/doc/examples/segmentation/plot_rag_boundary.py000066400000000000000000000017421316256236300253210ustar00rootroot00000000000000""" ========================== Region Boundary based RAGs ========================== Construct a region boundary RAG with the ``rag_boundary`` function. The function :py:func:`skimage.future.graph.rag_boundary` takes an ``edge_map`` argument, which gives the significance of a feature (such as edges) being present at each pixel. In a region boundary RAG, the edge weight between two regions is the average value of the corresponding pixels in ``edge_map`` along their shared boundary. """ from skimage.future import graph from skimage import data, segmentation, color, filters, io from matplotlib import pyplot as plt img = data.coffee() gimg = color.rgb2gray(img) labels = segmentation.slic(img, compactness=30, n_segments=400) edges = filters.sobel(gimg) edges_rgb = color.gray2rgb(edges) g = graph.rag_boundary(labels, edges) lc = graph.show_rag(labels, g, edges_rgb, img_cmap=None, edge_cmap='viridis', edge_width=1.2) plt.colorbar(lc, fraction=0.03) io.show() scikit-image-0.13.1/doc/examples/segmentation/plot_rag_draw.py000066400000000000000000000020071316256236300244260ustar00rootroot00000000000000""" ====================================== Drawing Region Adjacency Graphs (RAGs) ====================================== This example constructs a Region Adjacency Graph (RAG) and draws it with the `rag_draw` method. """ from skimage import data, segmentation from skimage.future import graph from matplotlib import pyplot as plt img = data.coffee() labels = segmentation.slic(img, compactness=30, n_segments=400) g = graph.rag_mean_color(img, labels) fig, ax = plt.subplots(nrows=2, sharex=True, sharey=True, figsize=(6, 8)) ax[0].set_title('RAG drawn with default settings') lc = graph.show_rag(labels, g, img, ax=ax[0]) # specify the fraction of the plot area that will be used to draw the colorbar fig.colorbar(lc, fraction=0.03, ax=ax[0]) ax[1].set_title('RAG drawn with grayscale image and viridis colormap') lc = graph.show_rag(labels, g, img, img_cmap='gray', edge_cmap='viridis', ax=ax[1]) fig.colorbar(lc, fraction=0.03, ax=ax[1]) for a in ax: a.axis('off') plt.tight_layout() plt.show() scikit-image-0.13.1/doc/examples/segmentation/plot_rag_mean_color.py000066400000000000000000000015331316256236300256120ustar00rootroot00000000000000""" ================ RAG Thresholding ================ This example constructs a Region Adjacency Graph (RAG) and merges regions which are similar in color. We construct a RAG and define edges as the difference in mean color. We then join regions with similar mean color. """ from skimage import data, io, segmentation, color from skimage.future import graph from matplotlib import pyplot as plt img = data.coffee() labels1 = segmentation.slic(img, compactness=30, n_segments=400) out1 = color.label2rgb(labels1, img, kind='avg') g = graph.rag_mean_color(img, labels1) labels2 = graph.cut_threshold(labels1, g, 29) out2 = color.label2rgb(labels2, img, kind='avg') fig, ax = plt.subplots(nrows=2, sharex=True, sharey=True, figsize=(6, 8)) ax[0].imshow(out1) ax[1].imshow(out2) for a in ax: a.axis('off') plt.tight_layout() scikit-image-0.13.1/doc/examples/segmentation/plot_rag_merge.py000066400000000000000000000044011316256236300245700ustar00rootroot00000000000000""" =========== RAG Merging =========== This example constructs a Region Adjacency Graph (RAG) and progressively merges regions that are similar in color. Merging two adjacent regions produces a new region with all the pixels from the merged regions. Regions are merged until no highly similar region pairs remain. """ from skimage import data, io, segmentation, color from skimage.future import graph import numpy as np def _weight_mean_color(graph, src, dst, n): """Callback to handle merging nodes by recomputing mean color. The method expects that the mean color of `dst` is already computed. Parameters ---------- graph : RAG The graph under consideration. src, dst : int The vertices in `graph` to be merged. n : int A neighbor of `src` or `dst` or both. Returns ------- data : dict A dictionary with the `"weight"` attribute set as the absolute difference of the mean color between node `dst` and `n`. """ diff = graph.node[dst]['mean color'] - graph.node[n]['mean color'] diff = np.linalg.norm(diff) return {'weight': diff} def merge_mean_color(graph, src, dst): """Callback called before merging two nodes of a mean color distance graph. This method computes the mean color of `dst`. Parameters ---------- graph : RAG The graph under consideration. src, dst : int The vertices in `graph` to be merged. """ graph.node[dst]['total color'] += graph.node[src]['total color'] graph.node[dst]['pixel count'] += graph.node[src]['pixel count'] graph.node[dst]['mean color'] = (graph.node[dst]['total color'] / graph.node[dst]['pixel count']) img = data.coffee() labels = segmentation.slic(img, compactness=30, n_segments=400) g = graph.rag_mean_color(img, labels) labels2 = graph.merge_hierarchical(labels, g, thresh=35, rag_copy=False, in_place_merge=True, merge_func=merge_mean_color, weight_func=_weight_mean_color) g2 = graph.rag_mean_color(img, labels2) out = color.label2rgb(labels2, img, kind='avg') out = segmentation.mark_boundaries(out, labels2, (0, 0, 0)) io.imshow(out) io.show() scikit-image-0.13.1/doc/examples/segmentation/plot_random_walker_segmentation.py000066400000000000000000000040241316256236300302430ustar00rootroot00000000000000""" ========================== Random walker segmentation ========================== The random walker algorithm [1]_ determines the segmentation of an image from a set of markers labeling several phases (2 or more). An anisotropic diffusion equation is solved with tracers initiated at the markers' position. The local diffusivity coefficient is greater if neighboring pixels have similar values, so that diffusion is difficult across high gradients. The label of each unknown pixel is attributed to the label of the known marker that has the highest probability to be reached first during this diffusion process. In this example, two phases are clearly visible, but the data are too noisy to perform the segmentation from the histogram only. We determine markers of the two phases from the extreme tails of the histogram of gray values, and use the random walker for the segmentation. .. [1] *Random walks for image segmentation*, Leo Grady, IEEE Trans. Pattern Anal. Mach. Intell. 2006 Nov; 28(11):1768-83 """ import numpy as np import matplotlib.pyplot as plt from skimage.segmentation import random_walker from skimage.data import binary_blobs import skimage # Generate noisy synthetic data data = skimage.img_as_float(binary_blobs(length=128, seed=1)) data += 0.35 * np.random.randn(*data.shape) markers = np.zeros(data.shape, dtype=np.uint) markers[data < -0.3] = 1 markers[data > 1.3] = 2 # Run random walker algorithm labels = random_walker(data, markers, beta=10, mode='bf') # Plot results fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(8, 3.2), sharex=True, sharey=True) ax1.imshow(data, cmap='gray', interpolation='nearest') ax1.axis('off') ax1.set_adjustable('box-forced') ax1.set_title('Noisy data') ax2.imshow(markers, cmap='hot', interpolation='nearest') ax2.axis('off') ax2.set_adjustable('box-forced') ax2.set_title('Markers') ax3.imshow(labels, cmap='gray', interpolation='nearest') ax3.axis('off') ax3.set_adjustable('box-forced') ax3.set_title('Segmentation') fig.tight_layout() plt.show() scikit-image-0.13.1/doc/examples/segmentation/plot_regionprops.py000066400000000000000000000024211316256236300252070ustar00rootroot00000000000000""" ========================= Measure region properties ========================= This example shows how to measure properties of labelled image regions. """ import math import matplotlib.pyplot as plt import numpy as np from skimage.draw import ellipse from skimage.measure import label, regionprops from skimage.transform import rotate image = np.zeros((600, 600)) rr, cc = ellipse(300, 350, 100, 220) image[rr, cc] = 1 image = rotate(image, angle=15, order=0) label_img = label(image) regions = regionprops(label_img) fig, ax = plt.subplots() ax.imshow(image, cmap=plt.cm.gray) for props in regions: y0, x0 = props.centroid orientation = props.orientation x1 = x0 + math.cos(orientation) * 0.5 * props.major_axis_length y1 = y0 - math.sin(orientation) * 0.5 * props.major_axis_length x2 = x0 - math.sin(orientation) * 0.5 * props.minor_axis_length y2 = y0 - math.cos(orientation) * 0.5 * props.minor_axis_length ax.plot((x0, x1), (y0, y1), '-r', linewidth=2.5) ax.plot((x0, x2), (y0, y2), '-r', linewidth=2.5) ax.plot(x0, y0, '.g', markersize=15) minr, minc, maxr, maxc = props.bbox bx = (minc, maxc, maxc, minc, minc) by = (minr, minr, maxr, maxr, minr) ax.plot(bx, by, '-b', linewidth=2.5) ax.axis((0, 600, 600, 0)) plt.show() scikit-image-0.13.1/doc/examples/segmentation/plot_segmentations.py000066400000000000000000000123671316256236300255320ustar00rootroot00000000000000""" ==================================================== Comparison of segmentation and superpixel algorithms ==================================================== This example compares four popular low-level image segmentation methods. As it is difficult to obtain good segmentations, and the definition of "good" often depends on the application, these methods are usually used for obtaining an oversegmentation, also known as superpixels. These superpixels then serve as a basis for more sophisticated algorithms such as conditional random fields (CRF). Felzenszwalb's efficient graph based segmentation ------------------------------------------------- This fast 2D image segmentation algorithm, proposed in [1]_ is popular in the computer vision community. The algorithm has a single ``scale`` parameter that influences the segment size. The actual size and number of segments can vary greatly, depending on local contrast. .. [1] Efficient graph-based image segmentation, Felzenszwalb, P.F. and Huttenlocher, D.P. International Journal of Computer Vision, 2004 Quickshift image segmentation ----------------------------- Quickshift is a relatively recent 2D image segmentation algorithm, based on an approximation of kernelized mean-shift. Therefore it belongs to the family of local mode-seeking algorithms and is applied to the 5D space consisting of color information and image location [2]_. One of the benefits of quickshift is that it actually computes a hierarchical segmentation on multiple scales simultaneously. Quickshift has two main parameters: ``sigma`` controls the scale of the local density approximation, ``max_dist`` selects a level in the hierarchical segmentation that is produced. There is also a trade-off between distance in color-space and distance in image-space, given by ``ratio``. .. [2] Quick shift and kernel methods for mode seeking, Vedaldi, A. and Soatto, S. European Conference on Computer Vision, 2008 SLIC - K-Means based image segmentation --------------------------------------- This algorithm simply performs K-means in the 5d space of color information and image location and is therefore closely related to quickshift. As the clustering method is simpler, it is very efficient. It is essential for this algorithm to work in Lab color space to obtain good results. The algorithm quickly gained momentum and is now widely used. See [3]_ for details. The ``compactness`` parameter trades off color-similarity and proximity, as in the case of Quickshift, while ``n_segments`` chooses the number of centers for kmeans. .. [3] Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Suesstrunk, SLIC Superpixels Compared to State-of-the-art Superpixel Methods, TPAMI, May 2012. Compact watershed segmentation of gradient images ------------------------------------------------- Instead of taking a color image as input, watershed requires a grayscale *gradient* image, where bright pixels denote a boundary between regions. The algorithm views the image as a landscape, with bright pixels forming high peaks. This landscape is then flooded from the given *markers*, until separate flood basins meet at the peaks. Each distinct basin then forms a different image segment. [4]_ As with SLIC, there is an additional *compactness* argument that makes it harder for markers to flood faraway pixels. This makes the watershed regions more regularly shaped. [5]_ .. [4] http://en.wikipedia.org/wiki/Watershed_%28image_processing%29 .. [5] Peer Neubert & Peter Protzel (2014). Compact Watershed and Preemptive SLIC: On Improving Trade-offs of Superpixel Segmentation Algorithms. ICPR 2014, pp 996-1001. DOI:10.1109/ICPR.2014.181 https://www.tu-chemnitz.de/etit/proaut/forschung/rsrc/cws_pSLIC_ICPR.pdf """ from __future__ import print_function import matplotlib.pyplot as plt import numpy as np from skimage.data import astronaut from skimage.color import rgb2gray from skimage.filters import sobel from skimage.segmentation import felzenszwalb, slic, quickshift, watershed from skimage.segmentation import mark_boundaries from skimage.util import img_as_float img = img_as_float(astronaut()[::2, ::2]) segments_fz = felzenszwalb(img, scale=100, sigma=0.5, min_size=50) segments_slic = slic(img, n_segments=250, compactness=10, sigma=1) segments_quick = quickshift(img, kernel_size=3, max_dist=6, ratio=0.5) gradient = sobel(rgb2gray(img)) segments_watershed = watershed(gradient, markers=250, compactness=0.001) print("Felzenszwalb number of segments: {}".format(len(np.unique(segments_fz)))) print('SLIC number of segments: {}'.format(len(np.unique(segments_slic)))) print('Quickshift number of segments: {}'.format(len(np.unique(segments_quick)))) fig, ax = plt.subplots(2, 2, figsize=(10, 10), sharex=True, sharey=True, subplot_kw={'adjustable': 'box-forced'}) ax[0, 0].imshow(mark_boundaries(img, segments_fz)) ax[0, 0].set_title("Felzenszwalbs's method") ax[0, 1].imshow(mark_boundaries(img, segments_slic)) ax[0, 1].set_title('SLIC') ax[1, 0].imshow(mark_boundaries(img, segments_quick)) ax[1, 0].set_title('Quickshift') ax[1, 1].imshow(mark_boundaries(img, segments_watershed)) ax[1, 1].set_title('Compact watershed') for a in ax.ravel(): a.set_axis_off() plt.tight_layout() plt.show() scikit-image-0.13.1/doc/examples/segmentation/plot_thresholding.py000066400000000000000000000042701316256236300253360ustar00rootroot00000000000000""" ============ Thresholding ============ Thresholding is used to create a binary image from a grayscale image [1]_. .. [1] https://en.wikipedia.org/wiki/Thresholding_%28image_processing%29 .. seealso:: A more comprehensive presentation on :ref:`sphx_glr_auto_examples_xx_applications_plot_thresholding.py` """ ###################################################################### # We illustrate how to apply one of these thresholding algorithms. # Otsu's method [2]_ calculates an "optimal" threshold (marked by a red line in the # histogram below) by maximizing the variance between two classes of pixels, # which are separated by the threshold. Equivalently, this threshold minimizes # the intra-class variance. # # .. [2] http://en.wikipedia.org/wiki/Otsu's_method # import matplotlib.pyplot as plt from skimage import data from skimage.filters import threshold_otsu image = data.camera() thresh = threshold_otsu(image) binary = image > thresh fig, axes = plt.subplots(ncols=3, figsize=(8, 2.5)) ax = axes.ravel() ax[0] = plt.subplot(1, 3, 1, adjustable='box-forced') ax[1] = plt.subplot(1, 3, 2) ax[2] = plt.subplot(1, 3, 3, sharex=ax[0], sharey=ax[0], adjustable='box-forced') ax[0].imshow(image, cmap=plt.cm.gray) ax[0].set_title('Original') ax[0].axis('off') ax[1].hist(image.ravel(), bins=256) ax[1].set_title('Histogram') ax[1].axvline(thresh, color='r') ax[2].imshow(binary, cmap=plt.cm.gray) ax[2].set_title('Thresholded') ax[2].axis('off') plt.show() ###################################################################### # If you are not familiar with the details of the different algorithms and the # underlying assumptions, it is often difficult to know which algorithm will give # the best results. Therefore, Scikit-image includes a function to evaluate # thresholding algorithms provided by the library. At a glance, you can select # the best algorithm for you data without a deep understanding of their # mechanisms. # from skimage.filters import try_all_threshold img = data.page() # Here, we specify a radius for local thresholding algorithms. # If it is not specified, only global algorithms are called. fig, ax = try_all_threshold(img, figsize=(10, 8), verbose=False) plt.show() scikit-image-0.13.1/doc/examples/segmentation/plot_watershed.py000066400000000000000000000044601316256236300246330ustar00rootroot00000000000000""" ====================== Watershed segmentation ====================== The watershed is a classical algorithm used for **segmentation**, that is, for separating different objects in an image. Starting from user-defined markers, the watershed algorithm treats pixels values as a local topography (elevation). The algorithm floods basins from the markers, until basins attributed to different markers meet on watershed lines. In many cases, markers are chosen as local minima of the image, from which basins are flooded. In the example below, two overlapping circles are to be separated. To do so, one computes an image that is the distance to the background. The maxima of this distance (i.e., the minima of the opposite of the distance) are chosen as markers, and the flooding of basins from such markers separates the two circles along a watershed line. See Wikipedia_ for more details on the algorithm. .. _Wikipedia: http://en.wikipedia.org/wiki/Watershed_(image_processing) """ import numpy as np import matplotlib.pyplot as plt from scipy import ndimage as ndi from skimage.morphology import watershed from skimage.feature import peak_local_max # Generate an initial image with two overlapping circles x, y = np.indices((80, 80)) x1, y1, x2, y2 = 28, 28, 44, 52 r1, r2 = 16, 20 mask_circle1 = (x - x1)**2 + (y - y1)**2 < r1**2 mask_circle2 = (x - x2)**2 + (y - y2)**2 < r2**2 image = np.logical_or(mask_circle1, mask_circle2) # Now we want to separate the two objects in image # Generate the markers as local maxima of the distance to the background distance = ndi.distance_transform_edt(image) local_maxi = peak_local_max(distance, indices=False, footprint=np.ones((3, 3)), labels=image) markers = ndi.label(local_maxi)[0] labels = watershed(-distance, markers, mask=image) fig, axes = plt.subplots(ncols=3, figsize=(9, 3), sharex=True, sharey=True, subplot_kw={'adjustable': 'box-forced'}) ax = axes.ravel() ax[0].imshow(image, cmap=plt.cm.gray, interpolation='nearest') ax[0].set_title('Overlapping objects') ax[1].imshow(-distance, cmap=plt.cm.gray, interpolation='nearest') ax[1].set_title('Distances') ax[2].imshow(labels, cmap=plt.cm.spectral, interpolation='nearest') ax[2].set_title('Separated objects') for a in ax: a.set_axis_off() fig.tight_layout() plt.show() scikit-image-0.13.1/doc/examples/transform/000077500000000000000000000000001316256236300205475ustar00rootroot00000000000000scikit-image-0.13.1/doc/examples/transform/README.txt000066400000000000000000000001321316256236300222410ustar00rootroot00000000000000Geometrical transformations and registration -------------------------------------------- scikit-image-0.13.1/doc/examples/transform/plot_edge_modes.py000066400000000000000000000017121316256236300242530ustar00rootroot00000000000000""" ========================= Interpolation: Edge Modes ========================= This example illustrates the different edge modes available during interpolation in routines such as :py:func:`skimage.transform.rescale` and :py:func:`skimage.transform.resize`. """ import numpy as np import matplotlib.pyplot as plt from skimage.util import pad img = np.zeros((16, 16)) img[:8, :8] += 1 img[:4, :4] += 1 img[:2, :2] += 1 img[:1, :1] += 2 img[8, 8] = 4 modes = ['constant', 'edge', 'wrap', 'reflect', 'symmetric'] fig, axes = plt.subplots(2, 3) ax = axes.flatten() for n, mode in enumerate(modes): img_padded = pad(img, pad_width=img.shape[0], mode=mode) ax[n].imshow(img_padded, cmap=plt.cm.gray, interpolation='nearest') ax[n].plot([15.5, 15.5, 31.5, 31.5, 15.5], [15.5, 31.5, 31.5, 15.5, 15.5], 'y--', linewidth=0.5) ax[n].set_title(mode) for a in ax: a.set_axis_off() a.set_aspect('equal') plt.tight_layout() plt.show() scikit-image-0.13.1/doc/examples/transform/plot_fundamental_matrix.py000066400000000000000000000056071316256236300260510ustar00rootroot00000000000000""" ============================= Fundamental matrix estimation ============================= This example demonstrates how to robustly estimate epipolar geometry between two views using sparse ORB feature correspondences. The fundamental matrix relates corresponding points between a pair of uncalibrated images. The matrix transforms homogeneous image points in one image to epipolar lines in the other image. Uncalibrated means that the intrinsic calibration (focal lengths, pixel skew, principal point) of the two cameras is not known. The fundamental matrix thus enables projective 3D reconstruction of the captured scene. If the calibration is known, estimating the essential matrix enables metric 3D reconstruction of the captured scene. """ import numpy as np from skimage import data from skimage.color import rgb2gray from skimage.feature import match_descriptors, ORB, plot_matches from skimage.measure import ransac from skimage.transform import FundamentalMatrixTransform import matplotlib.pyplot as plt np.random.seed(0) img_left, img_right, groundtruth_disp = data.stereo_motorcycle() img_left, img_right = map(rgb2gray, (img_left, img_right)) # Find sparse feature correspondences between left and right image. descriptor_extractor = ORB() descriptor_extractor.detect_and_extract(img_left) keypoints_left = descriptor_extractor.keypoints descriptors_left = descriptor_extractor.descriptors descriptor_extractor.detect_and_extract(img_right) keypoints_right = descriptor_extractor.keypoints descriptors_right = descriptor_extractor.descriptors matches = match_descriptors(descriptors_left, descriptors_right, cross_check=True) # Estimate the epipolar geometry between the left and right image. model, inliers = ransac((keypoints_left[matches[:, 0]], keypoints_right[matches[:, 1]]), FundamentalMatrixTransform, min_samples=8, residual_threshold=1, max_trials=5000) inlier_keypoints_left = keypoints_left[matches[inliers, 0]] inlier_keypoints_right = keypoints_right[matches[inliers, 1]] print("Number of matches:", matches.shape[0]) print("Number of inliers:", inliers.sum()) # Compare estimated sparse disparities to the dense ground-truth disparities. disp = inlier_keypoints_left[:, 1] - inlier_keypoints_right[:, 1] disp_coords = np.round(inlier_keypoints_left).astype(np.int64) disp_idxs = np.ravel_multi_index(disp_coords.T, groundtruth_disp.shape) disp_error = np.abs(groundtruth_disp.ravel()[disp_idxs] - disp) disp_error = disp_error[np.isfinite(disp_error)] # Visualize the results. fig, ax = plt.subplots(nrows=2, ncols=1) plt.gray() plot_matches(ax[0], img_left, img_right, keypoints_left, keypoints_right, matches[inliers], only_matches=True) ax[0].axis("off") ax[0].set_title("Inlier correspondences") ax[1].hist(disp_error) ax[1].set_title("Histogram of disparity errors") plt.show() scikit-image-0.13.1/doc/examples/transform/plot_matching.py000066400000000000000000000120141316256236300237470ustar00rootroot00000000000000""" ============================ Robust matching using RANSAC ============================ In this simplified example we first generate two synthetic images as if they were taken from different view points. In the next step we find interest points in both images and find correspondences based on a weighted sum of squared differences of a small neighborhood around them. Note, that this measure is only robust towards linear radiometric and not geometric distortions and is thus only usable with slight view point changes. After finding the correspondences we end up having a set of source and destination coordinates which can be used to estimate the geometric transformation between both images. However, many of the correspondences are faulty and simply estimating the parameter set with all coordinates is not sufficient. Therefore, the RANSAC algorithm is used on top of the normal model to robustly estimate the parameter set by detecting outliers. """ from __future__ import print_function import numpy as np from matplotlib import pyplot as plt from skimage import data from skimage.util import img_as_float from skimage.feature import (corner_harris, corner_subpix, corner_peaks, plot_matches) from skimage.transform import warp, AffineTransform from skimage.exposure import rescale_intensity from skimage.color import rgb2gray from skimage.measure import ransac # generate synthetic checkerboard image and add gradient for the later matching checkerboard = img_as_float(data.checkerboard()) img_orig = np.zeros(list(checkerboard.shape) + [3]) img_orig[..., 0] = checkerboard gradient_r, gradient_c = (np.mgrid[0:img_orig.shape[0], 0:img_orig.shape[1]] / float(img_orig.shape[0])) img_orig[..., 1] = gradient_r img_orig[..., 2] = gradient_c img_orig = rescale_intensity(img_orig) img_orig_gray = rgb2gray(img_orig) # warp synthetic image tform = AffineTransform(scale=(0.9, 0.9), rotation=0.2, translation=(20, -10)) img_warped = warp(img_orig, tform.inverse, output_shape=(200, 200)) img_warped_gray = rgb2gray(img_warped) # extract corners using Harris' corner measure coords_orig = corner_peaks(corner_harris(img_orig_gray), threshold_rel=0.001, min_distance=5) coords_warped = corner_peaks(corner_harris(img_warped_gray), threshold_rel=0.001, min_distance=5) # determine sub-pixel corner position coords_orig_subpix = corner_subpix(img_orig_gray, coords_orig, window_size=9) coords_warped_subpix = corner_subpix(img_warped_gray, coords_warped, window_size=9) def gaussian_weights(window_ext, sigma=1): y, x = np.mgrid[-window_ext:window_ext+1, -window_ext:window_ext+1] g = np.zeros(y.shape, dtype=np.double) g[:] = np.exp(-0.5 * (x**2 / sigma**2 + y**2 / sigma**2)) g /= 2 * np.pi * sigma * sigma return g def match_corner(coord, window_ext=5): r, c = np.round(coord).astype(np.intp) window_orig = img_orig[r-window_ext:r+window_ext+1, c-window_ext:c+window_ext+1, :] # weight pixels depending on distance to center pixel weights = gaussian_weights(window_ext, 3) weights = np.dstack((weights, weights, weights)) # compute sum of squared differences to all corners in warped image SSDs = [] for cr, cc in coords_warped: window_warped = img_warped[cr-window_ext:cr+window_ext+1, cc-window_ext:cc+window_ext+1, :] SSD = np.sum(weights * (window_orig - window_warped)**2) SSDs.append(SSD) # use corner with minimum SSD as correspondence min_idx = np.argmin(SSDs) return coords_warped_subpix[min_idx] # find correspondences using simple weighted sum of squared differences src = [] dst = [] for coord in coords_orig_subpix: src.append(coord) dst.append(match_corner(coord)) src = np.array(src) dst = np.array(dst) # estimate affine transform model using all coordinates model = AffineTransform() model.estimate(src, dst) # robustly estimate affine transform model with RANSAC model_robust, inliers = ransac((src, dst), AffineTransform, min_samples=3, residual_threshold=2, max_trials=100) outliers = inliers == False # compare "true" and estimated transform parameters print(tform.scale, tform.translation, tform.rotation) print(model.scale, model.translation, model.rotation) print(model_robust.scale, model_robust.translation, model_robust.rotation) # visualize correspondence fig, ax = plt.subplots(nrows=2, ncols=1) plt.gray() inlier_idxs = np.nonzero(inliers)[0] plot_matches(ax[0], img_orig_gray, img_warped_gray, src, dst, np.column_stack((inlier_idxs, inlier_idxs)), matches_color='b') ax[0].axis('off') ax[0].set_title('Correct correspondences') outlier_idxs = np.nonzero(outliers)[0] plot_matches(ax[1], img_orig_gray, img_warped_gray, src, dst, np.column_stack((outlier_idxs, outlier_idxs)), matches_color='r') ax[1].axis('off') ax[1].set_title('Faulty correspondences') plt.show() scikit-image-0.13.1/doc/examples/transform/plot_piecewise_affine.py000066400000000000000000000021271316256236300254460ustar00rootroot00000000000000""" =============================== Piecewise Affine Transformation =============================== This example shows how to use the Piecewise Affine Transformation. """ import numpy as np import matplotlib.pyplot as plt from skimage.transform import PiecewiseAffineTransform, warp from skimage import data image = data.astronaut() rows, cols = image.shape[0], image.shape[1] src_cols = np.linspace(0, cols, 20) src_rows = np.linspace(0, rows, 10) src_rows, src_cols = np.meshgrid(src_rows, src_cols) src = np.dstack([src_cols.flat, src_rows.flat])[0] # add sinusoidal oscillation to row coordinates dst_rows = src[:, 1] - np.sin(np.linspace(0, 3 * np.pi, src.shape[0])) * 50 dst_cols = src[:, 0] dst_rows *= 1.5 dst_rows -= 1.5 * 50 dst = np.vstack([dst_cols, dst_rows]).T tform = PiecewiseAffineTransform() tform.estimate(src, dst) out_rows = image.shape[0] - 1.5 * 50 out_cols = cols out = warp(image, tform, output_shape=(out_rows, out_cols)) fig, ax = plt.subplots() ax.imshow(out) ax.plot(tform.inverse(src)[:, 0], tform.inverse(src)[:, 1], '.b') ax.axis((0, out_cols, out_rows, 0)) plt.show() scikit-image-0.13.1/doc/examples/transform/plot_pyramid.py000066400000000000000000000015721316256236300236310ustar00rootroot00000000000000""" ==================== Build image pyramids ==================== The ``pyramid_gaussian`` function takes an image and yields successive images shrunk by a constant scale factor. Image pyramids are often used, e.g., to implement algorithms for denoising, texture discrimination, and scale- invariant detection. """ import numpy as np import matplotlib.pyplot as plt from skimage import data from skimage.transform import pyramid_gaussian image = data.astronaut() rows, cols, dim = image.shape pyramid = tuple(pyramid_gaussian(image, downscale=2)) composite_image = np.zeros((rows, cols + cols // 2, 3), dtype=np.double) composite_image[:rows, :cols, :] = pyramid[0] i_row = 0 for p in pyramid[1:]: n_rows, n_cols = p.shape[:2] composite_image[i_row:i_row + n_rows, cols:cols + n_cols] = p i_row += n_rows fig, ax = plt.subplots() ax.imshow(composite_image) plt.show() scikit-image-0.13.1/doc/examples/transform/plot_radon_transform.py000066400000000000000000000205661316256236300253660ustar00rootroot00000000000000""" =============== Radon transform =============== In computed tomography, the tomography reconstruction problem is to obtain a tomographic slice image from a set of projections [1]_. A projection is formed by drawing a set of parallel rays through the 2D object of interest, assigning the integral of the object's contrast along each ray to a single pixel in the projection. A single projection of a 2D object is one dimensional. To enable computed tomography reconstruction of the object, several projections must be acquired, each of them corresponding to a different angle between the rays with respect to the object. A collection of projections at several angles is called a sinogram, which is a linear transform of the original image. The inverse Radon transform is used in computed tomography to reconstruct a 2D image from the measured projections (the sinogram). A practical, exact implementation of the inverse Radon transform does not exist, but there are several good approximate algorithms available. As the inverse Radon transform reconstructs the object from a set of projections, the (forward) Radon transform can be used to simulate a tomography experiment. This script performs the Radon transform to simulate a tomography experiment and reconstructs the input image based on the resulting sinogram formed by the simulation. Two methods for performing the inverse Radon transform and reconstructing the original image are compared: The Filtered Back Projection (FBP) and the Simultaneous Algebraic Reconstruction Technique (SART). For further information on tomographic reconstruction, see .. [1] AC Kak, M Slaney, "Principles of Computerized Tomographic Imaging", IEEE Press 1988. http://www.slaney.org/pct/pct-toc.html .. [2] Wikipedia, Radon transform, http://en.wikipedia.org/wiki/Radon_transform#Relationship_with_the_Fourier_transform .. [3] S Kaczmarz, "Angenaeherte Aufloesung von Systemen linearer Gleichungen", Bulletin International de l'Academie Polonaise des Sciences et des Lettres, 35 pp 355--357 (1937) .. [4] AH Andersen, AC Kak, "Simultaneous algebraic reconstruction technique (SART): a superior implementation of the ART algorithm", Ultrasonic Imaging 6 pp 81--94 (1984) The forward transform ===================== As our original image, we will use the Shepp-Logan phantom. When calculating the Radon transform, we need to decide how many projection angles we wish to use. As a rule of thumb, the number of projections should be about the same as the number of pixels there are across the object (to see why this is so, consider how many unknown pixel values must be determined in the reconstruction process and compare this to the number of measurements provided by the projections), and we follow that rule here. Below is the original image and its Radon transform, often known as its *sinogram*: """ from __future__ import print_function, division import numpy as np import matplotlib.pyplot as plt from skimage.io import imread from skimage import data_dir from skimage.transform import radon, rescale image = imread(data_dir + "/phantom.png", as_grey=True) image = rescale(image, scale=0.4, mode='reflect') fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4.5)) ax1.set_title("Original") ax1.imshow(image, cmap=plt.cm.Greys_r) theta = np.linspace(0., 180., max(image.shape), endpoint=False) sinogram = radon(image, theta=theta, circle=True) ax2.set_title("Radon transform\n(Sinogram)") ax2.set_xlabel("Projection angle (deg)") ax2.set_ylabel("Projection position (pixels)") ax2.imshow(sinogram, cmap=plt.cm.Greys_r, extent=(0, 180, 0, sinogram.shape[0]), aspect='auto') fig.tight_layout() plt.show() ###################################################################### # # Reconstruction with the Filtered Back Projection (FBP) # ====================================================== # # The mathematical foundation of the filtered back projection is the Fourier # slice theorem [2]_. It uses Fourier transform of the projection and # interpolation in Fourier space to obtain the 2D Fourier transform of the # image, which is then inverted to form the reconstructed image. The filtered # back projection is among the fastest methods of performing the inverse # Radon transform. The only tunable parameter for the FBP is the filter, # which is applied to the Fourier transformed projections. It may be used to # suppress high frequency noise in the reconstruction. ``skimage`` provides a # few different options for the filter. from skimage.transform import iradon reconstruction_fbp = iradon(sinogram, theta=theta, circle=True) error = reconstruction_fbp - image print('FBP rms reconstruction error: %.3g' % np.sqrt(np.mean(error**2))) imkwargs = dict(vmin=-0.2, vmax=0.2) fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4.5), sharex=True, sharey=True, subplot_kw={'adjustable': 'box-forced'}) ax1.set_title("Reconstruction\nFiltered back projection") ax1.imshow(reconstruction_fbp, cmap=plt.cm.Greys_r) ax2.set_title("Reconstruction error\nFiltered back projection") ax2.imshow(reconstruction_fbp - image, cmap=plt.cm.Greys_r, **imkwargs) plt.show() ###################################################################### # # Reconstruction with the Simultaneous Algebraic Reconstruction Technique # ======================================================================= # # Algebraic reconstruction techniques for tomography are based on a # straightforward idea: for a pixelated image the value of a single ray in a # particular projection is simply a sum of all the pixels the ray passes # through on its way through the object. This is a way of expressing the # forward Radon transform. The inverse Radon transform can then be formulated # as a (large) set of linear equations. As each ray passes through a small # fraction of the pixels in the image, this set of equations is sparse, # allowing iterative solvers for sparse linear systems to tackle the system # of equations. One iterative method has been particularly popular, namely # Kaczmarz' method [3]_, which has the property that the solution will # approach a least-squares solution of the equation set. # # The combination of the formulation of the reconstruction problem as a set # of linear equations and an iterative solver makes algebraic techniques # relatively flexible, hence some forms of prior knowledge can be # incorporated with relative ease. # # ``skimage`` provides one of the more popular variations of the algebraic # reconstruction techniques: the Simultaneous Algebraic Reconstruction # Technique (SART) [1]_ [4]_. It uses Kaczmarz' method [3]_ as the iterative # solver. A good reconstruction is normally obtained in a single iteration, # making the method computationally effective. Running one or more extra # iterations will normally improve the reconstruction of sharp, high # frequency features and reduce the mean squared error at the expense of # increased high frequency noise (the user will need to decide on what number # of iterations is best suited to the problem at hand. The implementation in # ``skimage`` allows prior information of the form of a lower and upper # threshold on the reconstructed values to be supplied to the reconstruction. from skimage.transform import iradon_sart reconstruction_sart = iradon_sart(sinogram, theta=theta) error = reconstruction_sart - image print('SART (1 iteration) rms reconstruction error: %.3g' % np.sqrt(np.mean(error**2))) fig, axes = plt.subplots(2, 2, figsize=(8, 8.5), sharex=True, sharey=True, subplot_kw={'adjustable': 'box-forced'}) ax = axes.ravel() ax[0].set_title("Reconstruction\nSART") ax[0].imshow(reconstruction_sart, cmap=plt.cm.Greys_r) ax[1].set_title("Reconstruction error\nSART") ax[1].imshow(reconstruction_sart - image, cmap=plt.cm.Greys_r, **imkwargs) # Run a second iteration of SART by supplying the reconstruction # from the first iteration as an initial estimate reconstruction_sart2 = iradon_sart(sinogram, theta=theta, image=reconstruction_sart) error = reconstruction_sart2 - image print('SART (2 iterations) rms reconstruction error: %.3g' % np.sqrt(np.mean(error**2))) ax[2].set_title("Reconstruction\nSART, 2 iterations") ax[2].imshow(reconstruction_sart2, cmap=plt.cm.Greys_r) ax[3].set_title("Reconstruction error\nSART, 2 iterations") ax[3].imshow(reconstruction_sart2 - image, cmap=plt.cm.Greys_r, **imkwargs) plt.show() scikit-image-0.13.1/doc/examples/transform/plot_ransac.py000066400000000000000000000030731316256236300234310ustar00rootroot00000000000000""" ========================================= Robust line model estimation using RANSAC ========================================= In this example we see how to robustly fit a line model to faulty data using the RANSAC algorithm. """ import numpy as np from matplotlib import pyplot as plt from skimage.measure import LineModelND, ransac np.random.seed(seed=1) # generate coordinates of line x = np.arange(-200, 200) y = 0.2 * x + 20 data = np.column_stack([x, y]) # add faulty data faulty = np.array(30 * [(180., -100)]) faulty += 5 * np.random.normal(size=faulty.shape) data[:faulty.shape[0]] = faulty # add gaussian noise to coordinates noise = np.random.normal(size=data.shape) data += 0.5 * noise data[::2] += 5 * noise[::2] data[::4] += 20 * noise[::4] # fit line using all data model = LineModelND() model.estimate(data) # robustly fit line only using inlier data with RANSAC algorithm model_robust, inliers = ransac(data, LineModelND, min_samples=2, residual_threshold=1, max_trials=1000) outliers = inliers == False # generate coordinates of estimated models line_x = np.arange(-250, 250) line_y = model.predict_y(line_x) line_y_robust = model_robust.predict_y(line_x) fig, ax = plt.subplots() ax.plot(data[inliers, 0], data[inliers, 1], '.b', alpha=0.6, label='Inlier data') ax.plot(data[outliers, 0], data[outliers, 1], '.r', alpha=0.6, label='Outlier data') ax.plot(line_x, line_y, '-k', label='Line model from all data') ax.plot(line_x, line_y_robust, '-b', label='Robust line model') ax.legend(loc='lower left') plt.show() scikit-image-0.13.1/doc/examples/transform/plot_ransac3D.py000066400000000000000000000025071316256236300236210ustar00rootroot00000000000000""" ============================================ Robust 3D line model estimation using RANSAC ============================================ In this example we see how to robustly fit a 3D line model to faulty data using the RANSAC algorithm. """ import numpy as np from matplotlib import pyplot as plt from mpl_toolkits.mplot3d import Axes3D from skimage.measure import LineModelND, ransac np.random.seed(seed=1) # generate coordinates of line point = np.array([0, 0, 0], dtype='float') direction = np.array([1, 1, 1], dtype='float') / np.sqrt(3) xyz = point + 10 * np.arange(-100, 100)[..., np.newaxis] * direction # add gaussian noise to coordinates noise = np.random.normal(size=xyz.shape) xyz += 0.5 * noise xyz[::2] += 20 * noise[::2] xyz[::4] += 100 * noise[::4] # robustly fit line only using inlier data with RANSAC algorithm model_robust, inliers = ransac(xyz, LineModelND, min_samples=2, residual_threshold=1, max_trials=1000) outliers = inliers == False fig = plt.figure() ax = fig.add_subplot(111, projection='3d') ax.scatter(xyz[inliers][:, 0], xyz[inliers][:, 1], xyz[inliers][:, 2], c='b', marker='o', label='Inlier data') ax.scatter(xyz[outliers][:, 0], xyz[outliers][:, 1], xyz[outliers][:, 2], c='r', marker='o', label='Outlier data') ax.legend(loc='lower left') plt.show() scikit-image-0.13.1/doc/examples/transform/plot_register_translation.py000066400000000000000000000054041316256236300264240ustar00rootroot00000000000000""" ===================================== Cross-Correlation (Phase Correlation) ===================================== In this example, we use phase correlation to identify the relative shift between two similar-sized images. The ``register_translation`` function uses cross-correlation in Fourier space, optionally employing an upsampled matrix-multiplication DFT to achieve arbitrary subpixel precision [1]_. .. [1] Manuel Guizar-Sicairos, Samuel T. Thurman, and James R. Fienup, "Efficient subpixel image registration algorithms," Optics Letters 33, 156-158 (2008). DOI:10.1364/OL.33.000156 """ import numpy as np import matplotlib.pyplot as plt from skimage import data from skimage.feature import register_translation from skimage.feature.register_translation import _upsampled_dft from scipy.ndimage import fourier_shift image = data.camera() shift = (-22.4, 13.32) # The shift corresponds to the pixel offset relative to the reference image offset_image = fourier_shift(np.fft.fftn(image), shift) offset_image = np.fft.ifftn(offset_image) print("Known offset (y, x): {}".format(shift)) # pixel precision first shift, error, diffphase = register_translation(image, offset_image) fig = plt.figure(figsize=(8, 3)) ax1 = plt.subplot(1, 3, 1, adjustable='box-forced') ax2 = plt.subplot(1, 3, 2, sharex=ax1, sharey=ax1, adjustable='box-forced') ax3 = plt.subplot(1, 3, 3) ax1.imshow(image, cmap='gray') ax1.set_axis_off() ax1.set_title('Reference image') ax2.imshow(offset_image.real, cmap='gray') ax2.set_axis_off() ax2.set_title('Offset image') # Show the output of a cross-correlation to show what the algorithm is # doing behind the scenes image_product = np.fft.fft2(image) * np.fft.fft2(offset_image).conj() cc_image = np.fft.fftshift(np.fft.ifft2(image_product)) ax3.imshow(cc_image.real) ax3.set_axis_off() ax3.set_title("Cross-correlation") plt.show() print("Detected pixel offset (y, x): {}".format(shift)) # subpixel precision shift, error, diffphase = register_translation(image, offset_image, 100) fig = plt.figure(figsize=(8, 3)) ax1 = plt.subplot(1, 3, 1, adjustable='box-forced') ax2 = plt.subplot(1, 3, 2, sharex=ax1, sharey=ax1, adjustable='box-forced') ax3 = plt.subplot(1, 3, 3) ax1.imshow(image, cmap='gray') ax1.set_axis_off() ax1.set_title('Reference image') ax2.imshow(offset_image.real, cmap='gray') ax2.set_axis_off() ax2.set_title('Offset image') # Calculate the upsampled DFT, again to show what the algorithm is doing # behind the scenes. Constants correspond to calculated values in routine. # See source code for details. cc_image = _upsampled_dft(image_product, 150, 100, (shift*100)+75).conj() ax3.imshow(cc_image.real) ax3.set_axis_off() ax3.set_title("Supersampled XC sub-area") plt.show() print("Detected subpixel offset (y, x): {}".format(shift)) scikit-image-0.13.1/doc/examples/transform/plot_seam_carving.py000066400000000000000000000050141316256236300246150ustar00rootroot00000000000000""" ============ Seam Carving ============ This example demonstrates how images can be resized using seam carving [1]_. Resizing to a new aspect ratio distorts image contents. Seam carving attempts to resize *without* distortion, by removing regions of an image which are less important. In this example we are using the Sobel filter to signify the importance of each pixel. .. [1] Shai Avidan and Ariel Shamir "Seam Carving for Content-Aware Image Resizing" http://www.cs.jhu.edu/~misha/ReadingSeminar/Papers/Avidan07.pdf """ from skimage import data, draw from skimage import transform, util import numpy as np from skimage import filters, color from matplotlib import pyplot as plt hl_color = np.array([0, 1, 0]) img = data.rocket() img = util.img_as_float(img) eimg = filters.sobel(color.rgb2gray(img)) plt.title('Original Image') plt.imshow(img) ###################################################################### resized = transform.resize(img, (img.shape[0], img.shape[1] - 200), mode='reflect') plt.figure() plt.title('Resized Image') plt.imshow(resized) ###################################################################### out = transform.seam_carve(img, eimg, 'vertical', 200) plt.figure() plt.title('Resized using Seam Carving') plt.imshow(out) ###################################################################### # Resizing distorts the rocket and surrounding objects, whereas seam carving # removes empty spaces and preserves object proportions. # # Object Removal # -------------- # # Seam carving can also be used to remove artifacts from images. This # requires weighting the artifact with low values. Recall lower weights are # preferentially removed in seam carving. The following code masks the # rocket's region with low weights, indicating it should be removed. masked_img = img.copy() poly = [(404, 281), (404, 360), (359, 364), (338, 337), (145, 337), (120, 322), (145, 304), (340, 306), (362, 284)] pr = np.array([p[0] for p in poly]) pc = np.array([p[1] for p in poly]) rr, cc = draw.polygon(pr, pc) masked_img[rr, cc, :] = masked_img[rr, cc, :]*0.5 + hl_color*.5 plt.figure() plt.title('Object Marked') plt.imshow(masked_img) ###################################################################### eimg[rr, cc] -= 1000 plt.figure() plt.title('Object Removed') out = transform.seam_carve(img, eimg, 'vertical', 90) resized = transform.resize(img, out.shape, mode='reflect') plt.imshow(out) plt.show() ###################################################################### scikit-image-0.13.1/doc/examples/transform/plot_ssim.py000066400000000000000000000045101316256236300231320ustar00rootroot00000000000000""" =========================== Structural similarity index =========================== When comparing images, the mean squared error (MSE)--while simple to implement--is not highly indicative of perceived similarity. Structural similarity aims to address this shortcoming by taking texture into account [1]_, [2]_. The example shows two modifications of the input image, each with the same MSE, but with very different mean structural similarity indices. .. [1] Zhou Wang; Bovik, A.C.; ,"Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures," Signal Processing Magazine, IEEE, vol. 26, no. 1, pp. 98-117, Jan. 2009. .. [2] Z. Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, "Image quality assessment: From error visibility to structural similarity," IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600-612, Apr. 2004. """ import numpy as np import matplotlib.pyplot as plt from skimage import data, img_as_float from skimage.measure import compare_ssim as ssim img = img_as_float(data.camera()) rows, cols = img.shape noise = np.ones_like(img) * 0.2 * (img.max() - img.min()) noise[np.random.random(size=noise.shape) > 0.5] *= -1 def mse(x, y): return np.linalg.norm(x - y) img_noise = img + noise img_const = img + abs(noise) fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(10, 4), sharex=True, sharey=True, subplot_kw={'adjustable': 'box-forced'}) ax = axes.ravel() mse_none = mse(img, img) ssim_none = ssim(img, img, data_range=img.max() - img.min()) mse_noise = mse(img, img_noise) ssim_noise = ssim(img, img_noise, data_range=img_noise.max() - img_noise.min()) mse_const = mse(img, img_const) ssim_const = ssim(img, img_const, data_range=img_const.max() - img_const.min()) label = 'MSE: {:.2f}, SSIM: {:.2f}' ax[0].imshow(img, cmap=plt.cm.gray, vmin=0, vmax=1) ax[0].set_xlabel(label.format(mse_none, ssim_none)) ax[0].set_title('Original image') ax[1].imshow(img_noise, cmap=plt.cm.gray, vmin=0, vmax=1) ax[1].set_xlabel(label.format(mse_noise, ssim_noise)) ax[1].set_title('Image with noise') ax[2].imshow(img_const, cmap=plt.cm.gray, vmin=0, vmax=1) ax[2].set_xlabel(label.format(mse_const, ssim_const)) ax[2].set_title('Image plus constant') plt.tight_layout() plt.show() scikit-image-0.13.1/doc/examples/transform/plot_swirl.py000066400000000000000000000052351316256236300233240ustar00rootroot00000000000000""" ===== Swirl ===== Image swirling is a non-linear image deformation that creates a whirlpool effect. This example describes the implementation of this transform in ``skimage``, as well as the underlying warp mechanism. Image warping ------------- When applying a geometric transformation on an image, we typically make use of a reverse mapping, i.e., for each pixel in the output image, we compute its corresponding position in the input. The reason is that, if we were to do it the other way around (map each input pixel to its new output position), some pixels in the output may be left empty. On the other hand, each output coordinate has exactly one corresponding location in (or outside) the input image, and even if that position is non-integer, we may use interpolation to compute the corresponding image value. Performing a reverse mapping ---------------------------- To perform a geometric warp in ``skimage``, you simply need to provide the reverse mapping to the :py:func:`skimage.transform.warp` function. E.g., consider the case where we would like to shift an image 50 pixels to the left. The reverse mapping for such a shift would be:: def shift_left(xy): xy[:, 0] += 50 return xy The corresponding call to warp is:: from skimage.transform import warp warp(image, shift_left) The swirl transformation ------------------------ Consider the coordinate :math:`(x, y)` in the output image. The reverse mapping for the swirl transformation first computes, relative to a center :math:`(x_0, y_0)`, its polar coordinates, .. math:: \\theta = \\arctan(y/x) \\rho = \sqrt{(x - x_0)^2 + (y - y_0)^2}, and then transforms them according to .. math:: r = \ln(2) \, \mathtt{radius} / 5 \phi = \mathtt{rotation} s = \mathtt{strength} \\theta' = \phi + s \, e^{-\\rho / r + \\theta} where ``strength`` is a parameter for the amount of swirl, ``radius`` indicates the swirl extent in pixels, and ``rotation`` adds a rotation angle. The transformation of ``radius`` into :math:`r` is to ensure that the transformation decays to :math:`\\approx 1/1000^{\mathsf{th}}` within the specified radius. """ import matplotlib.pyplot as plt from skimage import data from skimage.transform import swirl image = data.checkerboard() swirled = swirl(image, rotation=0, strength=10, radius=120) fig, (ax0, ax1) = plt.subplots(nrows=1, ncols=2, figsize=(8, 3), sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'}) ax0.imshow(image, cmap=plt.cm.gray, interpolation='none') ax0.axis('off') ax1.imshow(swirled, cmap=plt.cm.gray, interpolation='none') ax1.axis('off') plt.show() scikit-image-0.13.1/doc/examples/xx_applications/000077500000000000000000000000001316256236300217415ustar00rootroot00000000000000scikit-image-0.13.1/doc/examples/xx_applications/README.txt000066400000000000000000000001061316256236300234340ustar00rootroot00000000000000Longer examples and demonstrations ---------------------------------- scikit-image-0.13.1/doc/examples/xx_applications/plot_coins_segmentation.py000066400000000000000000000117341316256236300272470ustar00rootroot00000000000000""" ================================================== Comparing edge-based and region-based segmentation ================================================== In this example, we will see how to segment objects from a background. We use the ``coins`` image from ``skimage.data``, which shows several coins outlined against a darker background. """ import numpy as np import matplotlib.pyplot as plt from skimage import data coins = data.coins() hist = np.histogram(coins, bins=np.arange(0, 256)) fig, axes = plt.subplots(1, 2, figsize=(8, 3)) axes[0].imshow(coins, cmap=plt.cm.gray, interpolation='nearest') axes[0].axis('off') axes[1].plot(hist[1][:-1], hist[0], lw=2) axes[1].set_title('histogram of grey values') ###################################################################### # # Thresholding # ============ # # A simple way to segment the coins is to choose a threshold based on the # histogram of grey values. Unfortunately, thresholding this image gives a # binary image that either misses significant parts of the coins or merges # parts of the background with the coins: fig, axes = plt.subplots(1, 2, figsize=(8, 3), sharey=True) axes[0].imshow(coins > 100, cmap=plt.cm.gray, interpolation='nearest') axes[0].set_title('coins > 100') axes[1].imshow(coins > 150, cmap=plt.cm.gray, interpolation='nearest') axes[1].set_title('coins > 150') for a in axes: a.axis('off') a.set_adjustable('box-forced') plt.tight_layout() ###################################################################### # Edge-based segmentation # ======================= # # Next, we try to delineate the contours of the coins using edge-based # segmentation. To do this, we first get the edges of features using the # Canny edge-detector. from skimage.feature import canny edges = canny(coins) fig, ax = plt.subplots(figsize=(4, 3)) ax.imshow(edges, cmap=plt.cm.gray, interpolation='nearest') ax.set_title('Canny detector') ax.axis('off') ax.set_adjustable('box-forced') ###################################################################### # These contours are then filled using mathematical morphology. from scipy import ndimage as ndi fill_coins = ndi.binary_fill_holes(edges) fig, ax = plt.subplots(figsize=(4, 3)) ax.imshow(fill_coins, cmap=plt.cm.gray, interpolation='nearest') ax.set_title('filling the holes') ax.axis('off') ###################################################################### # Small spurious objects are easily removed by setting a minimum size for # valid objects. from skimage import morphology coins_cleaned = morphology.remove_small_objects(fill_coins, 21) fig, ax = plt.subplots(figsize=(4, 3)) ax.imshow(coins_cleaned, cmap=plt.cm.gray, interpolation='nearest') ax.set_title('removing small objects') ax.axis('off') ax.set_adjustable('box-forced') ###################################################################### # However, this method is not very robust, since contours that are not # perfectly closed are not filled correctly, as is the case for one unfilled # coin above. # # Region-based segmentation # ========================= # # We therefore try a region-based method using the watershed transform. # First, we find an elevation map using the Sobel gradient of the image. from skimage.filters import sobel elevation_map = sobel(coins) fig, ax = plt.subplots(figsize=(4, 3)) ax.imshow(elevation_map, cmap=plt.cm.gray, interpolation='nearest') ax.set_title('elevation map') ax.axis('off') ax.set_adjustable('box-forced') ###################################################################### # Next we find markers of the background and the coins based on the extreme # parts of the histogram of grey values. markers = np.zeros_like(coins) markers[coins < 30] = 1 markers[coins > 150] = 2 fig, ax = plt.subplots(figsize=(4, 3)) ax.imshow(markers, cmap=plt.cm.spectral, interpolation='nearest') ax.set_title('markers') ax.axis('off') ax.set_adjustable('box-forced') ###################################################################### # Finally, we use the watershed transform to fill regions of the elevation # map starting from the markers determined above: segmentation = morphology.watershed(elevation_map, markers) fig, ax = plt.subplots(figsize=(4, 3)) ax.imshow(segmentation, cmap=plt.cm.gray, interpolation='nearest') ax.set_title('segmentation') ax.axis('off') ax.set_adjustable('box-forced') ###################################################################### # This last method works even better, and the coins can be segmented and # labeled individually. from skimage.color import label2rgb segmentation = ndi.binary_fill_holes(segmentation - 1) labeled_coins, _ = ndi.label(segmentation) image_label_overlay = label2rgb(labeled_coins, image=coins) fig, axes = plt.subplots(1, 2, figsize=(8, 3), sharey=True) axes[0].imshow(coins, cmap=plt.cm.gray, interpolation='nearest') axes[0].contour(segmentation, [0.5], linewidths=1.2, colors='y') axes[1].imshow(image_label_overlay, interpolation='nearest') for a in axes: a.axis('off') a.set_adjustable('box-forced') plt.tight_layout() scikit-image-0.13.1/doc/examples/xx_applications/plot_geometric.py000066400000000000000000000065511316256236300253360ustar00rootroot00000000000000""" =============================== Using geometric transformations =============================== In this example, we will see how to use geometric transformations in the context of image processing. """ from __future__ import print_function import math import numpy as np import matplotlib.pyplot as plt from skimage import data from skimage import transform as tf ###################################################################### # Basics # ====== # # Several different geometric transformation types are supported: similarity, # affine, projective and polynomial. # # Geometric transformations can either be created using the explicit # parameters (e.g. scale, shear, rotation and translation) or the # transformation matrix: # # First we create a transformation using explicit parameters: tform = tf.SimilarityTransform(scale=1, rotation=math.pi/2, translation=(0, 1)) print(tform.params) ###################################################################### # Alternatively you can define a transformation by the transformation matrix # itself: matrix = tform.params.copy() matrix[1, 2] = 2 tform2 = tf.SimilarityTransform(matrix) ###################################################################### # These transformation objects can then be used to apply forward and inverse # coordinate transformations between the source and destination coordinate # systems: coord = [1, 0] print(tform2(coord)) print(tform2.inverse(tform(coord))) ###################################################################### # Image warping # ============= # # Geometric transformations can also be used to warp images: text = data.text() tform = tf.SimilarityTransform(scale=1, rotation=math.pi/4, translation=(text.shape[0]/2, -100)) rotated = tf.warp(text, tform) back_rotated = tf.warp(rotated, tform.inverse) fig, ax = plt.subplots(nrows=3) ax[0].imshow(text, cmap=plt.cm.gray) ax[1].imshow(rotated, cmap=plt.cm.gray) ax[2].imshow(back_rotated, cmap=plt.cm.gray) for a in ax: a.axis('off') plt.tight_layout() ###################################################################### # Parameter estimation # ==================== # # In addition to the basic functionality mentioned above you can also # estimate the parameters of a geometric transformation using the least- # squares method. # # This can amongst other things be used for image registration or # rectification, where you have a set of control points or # homologous/corresponding points in two images. # # Let's assume we want to recognize letters on a photograph which was not # taken from the front but at a certain angle. In the simplest case of a # plane paper surface the letters are projectively distorted. Simple matching # algorithms would not be able to match such symbols. One solution to this # problem would be to warp the image so that the distortion is removed and # then apply a matching algorithm: text = data.text() src = np.array([[0, 0], [0, 50], [300, 50], [300, 0]]) dst = np.array([[155, 15], [65, 40], [260, 130], [360, 95]]) tform3 = tf.ProjectiveTransform() tform3.estimate(src, dst) warped = tf.warp(text, tform3, output_shape=(50, 300)) fig, ax = plt.subplots(nrows=2, figsize=(8, 3)) ax[0].imshow(text, cmap=plt.cm.gray) ax[0].plot(dst[:, 0], dst[:, 1], '.r') ax[1].imshow(warped, cmap=plt.cm.gray) for a in ax: a.axis('off') plt.tight_layout() scikit-image-0.13.1/doc/examples/xx_applications/plot_morphology.py000066400000000000000000000212431316256236300255520ustar00rootroot00000000000000""" ======================= Morphological Filtering ======================= Morphological image processing is a collection of non-linear operations related to the shape or morphology of features in an image, such as boundaries, skeletons, etc. In any given technique, we probe an image with a small shape or template called a structuring element, which defines the region of interest or neighborhood around a pixel. In this document we outline the following basic morphological operations: 1. Erosion 2. Dilation 3. Opening 4. Closing 5. White Tophat 6. Black Tophat 7. Skeletonize 8. Convex Hull To get started, let's load an image using ``io.imread``. Note that morphology functions only work on gray-scale or binary images, so we set ``as_grey=True``. """ import os import matplotlib.pyplot as plt from skimage.data import data_dir from skimage.util import img_as_ubyte from skimage import io orig_phantom = img_as_ubyte(io.imread(os.path.join(data_dir, "phantom.png"), as_grey=True)) fig, ax = plt.subplots() ax.imshow(orig_phantom, cmap=plt.cm.gray) ###################################################################### # Let's also define a convenience function for plotting comparisons: def plot_comparison(original, filtered, filter_name): fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(8, 4), sharex=True, sharey=True) ax1.imshow(original, cmap=plt.cm.gray) ax1.set_title('original') ax1.axis('off') ax1.set_adjustable('box-forced') ax2.imshow(filtered, cmap=plt.cm.gray) ax2.set_title(filter_name) ax2.axis('off') ax2.set_adjustable('box-forced') ###################################################################### # Erosion # ======= # # Morphological ``erosion`` sets a pixel at (i, j) to the *minimum over all # pixels in the neighborhood centered at (i, j)*. The structuring element, # ``selem``, passed to ``erosion`` is a boolean array that describes this # neighborhood. Below, we use ``disk`` to create a circular structuring # element, which we use for most of the following examples. from skimage.morphology import erosion, dilation, opening, closing, white_tophat from skimage.morphology import black_tophat, skeletonize, convex_hull_image from skimage.morphology import disk selem = disk(6) eroded = erosion(orig_phantom, selem) plot_comparison(orig_phantom, eroded, 'erosion') ###################################################################### # Notice how the white boundary of the image disappears or gets eroded as we # increase the size of the disk. Also notice the increase in size of the two # black ellipses in the center and the disappearance of the 3 light grey # patches in the lower part of the image. # #Dilation #======== # #Morphological ``dilation`` sets a pixel at (i, j) to the *maximum over all #pixels in the neighborhood centered at (i, j)*. Dilation enlarges bright #regions and shrinks dark regions. dilated = dilation(orig_phantom, selem) plot_comparison(orig_phantom, dilated, 'dilation') ###################################################################### # Notice how the white boundary of the image thickens, or gets dilated, as we #increase the size of the disk. Also notice the decrease in size of the two #black ellipses in the centre, and the thickening of the light grey circle #in the center and the 3 patches in the lower part of the image. # #Opening #======= # #Morphological ``opening`` on an image is defined as an *erosion followed by #a dilation*. Opening can remove small bright spots (i.e. "salt") and #connect small dark cracks. opened = opening(orig_phantom, selem) plot_comparison(orig_phantom, opened, 'opening') ###################################################################### #Since ``opening`` an image starts with an erosion operation, light regions #that are *smaller* than the structuring element are removed. The dilation #operation that follows ensures that light regions that are *larger* than #the structuring element retain their original size. Notice how the light #and dark shapes in the center their original thickness but the 3 lighter #patches in the bottom get completely eroded. The size dependence is #highlighted by the outer white ring: The parts of the ring thinner than the #structuring element were completely erased, while the thicker region at the #top retains its original thickness. # #Closing #======= # #Morphological ``closing`` on an image is defined as a *dilation followed by #an erosion*. Closing can remove small dark spots (i.e. "pepper") and #connect small bright cracks. # #To illustrate this more clearly, let's add a small crack to the white #border: phantom = orig_phantom.copy() phantom[10:30, 200:210] = 0 closed = closing(phantom, selem) plot_comparison(phantom, closed, 'closing') ###################################################################### # Since ``closing`` an image starts with an dilation operation, dark regions # that are *smaller* than the structuring element are removed. The dilation # operation that follows ensures that dark regions that are *larger* than the # structuring element retain their original size. Notice how the white # ellipses at the bottom get connected because of dilation, but other dark # region retain their original sizes. Also notice how the crack we added is # mostly removed. # # White tophat # ============ # # The ``white_tophat`` of an image is defined as the *image minus its # morphological opening*. This operation returns the bright spots of the # image that are smaller than the structuring element. # # To make things interesting, we'll add bright and dark spots to the image: phantom = orig_phantom.copy() phantom[340:350, 200:210] = 255 phantom[100:110, 200:210] = 0 w_tophat = white_tophat(phantom, selem) plot_comparison(phantom, w_tophat, 'white tophat') ###################################################################### # As you can see, the 10-pixel wide white square is highlighted since it is # smaller than the structuring element. Also, the thin, white edges around # most of the ellipse are retained because they're smaller than the # structuring element, but the thicker region at the top disappears. # # Black tophat # ============ # # The ``black_tophat`` of an image is defined as its morphological **closing # minus the original image**. This operation returns the *dark spots of the # image that are smaller than the structuring element*. b_tophat = black_tophat(phantom, selem) plot_comparison(phantom, b_tophat, 'black tophat') ###################################################################### #As you can see, the 10-pixel wide black square is highlighted since #it is smaller than the structuring element. # #**Duality** # #As you should have noticed, many of these operations are simply the reverse #of another operation. This duality can be summarized as follows: # # 1. Erosion <-> Dilation # # 2. Opening <-> Closing # # 3. White tophat <-> Black tophat # #Skeletonize #=========== # #Thinning is used to reduce each connected component in a binary image to a #*single-pixel wide skeleton*. It is important to note that this is #performed on binary images only. horse = io.imread(os.path.join(data_dir, "horse.png"), as_grey=True) sk = skeletonize(horse == 0) plot_comparison(horse, sk, 'skeletonize') ###################################################################### # # As the name suggests, this technique is used to thin the image to 1-pixel # wide skeleton by applying thinning successively. # # Convex hull # =========== # # The ``convex_hull_image`` is the *set of pixels included in the smallest # convex polygon that surround all white pixels in the input image*. Again # note that this is also performed on binary images. hull1 = convex_hull_image(horse == 0) plot_comparison(horse, hull1, 'convex hull') ###################################################################### # As the figure illustrates, ``convex_hull_image`` gives the smallest polygon # which covers the white or True completely in the image. # # If we add a small grain to the image, we can see how the convex hull adapts # to enclose that grain: import numpy as np horse_mask = horse == 0 horse_mask[45:50, 75:80] = 1 hull2 = convex_hull_image(horse_mask) plot_comparison(horse_mask, hull2, 'convex hull') ###################################################################### # # Additional Resources # ==================== # # 1. `MathWorks tutorial on morphological processing # `_ # # 2. `Auckland university's tutorial on Morphological Image # Processing `_ # # 3. http://en.wikipedia.org/wiki/Mathematical_morphology scikit-image-0.13.1/doc/examples/xx_applications/plot_rank_filters.py000066400000000000000000000472711316256236300260470ustar00rootroot00000000000000""" ============ Rank filters ============ Rank filters are non-linear filters using the local gray-level ordering to compute the filtered value [1]_. This ensemble of filters share a common base: the local gray-level histogram is computed on the neighborhood of a pixel (defined by a 2-D structuring element). If the filtered value is taken as the middle value of the histogram, we get the classical median filter. Rank filters can be used for several purposes such as: * image quality enhancement e.g. image smoothing, sharpening * image pre-processing e.g. noise reduction, contrast enhancement * feature extraction e.g. border detection, isolated point detection * post-processing e.g. small object removal, object grouping, contour smoothing Some well known filters are specific cases of rank filters [1]_ e.g. morphological dilation, morphological erosion, median filters. In this example, we will see how to filter a gray-level image using some of the linear and non-linear filters available in skimage. We use the ``camera`` image from ``skimage.data`` for all comparisons. .. [1] Pierre Soille, On morphological operators based on rank filters, Pattern Recognition 35 (2002) 527-535, DOI:10.1016/S0031-3203(01)00047-4 """ import numpy as np import matplotlib.pyplot as plt from skimage import img_as_ubyte from skimage import data noisy_image = img_as_ubyte(data.camera()) hist = np.histogram(noisy_image, bins=np.arange(0, 256)) fig, ax = plt.subplots(ncols=2, figsize=(10, 5)) ax[0].imshow(noisy_image, interpolation='nearest', cmap=plt.cm.gray) ax[0].axis('off') ax[1].plot(hist[1][:-1], hist[0], lw=2) ax[1].set_title('Histogram of grey values') plt.tight_layout() ###################################################################### # # Noise removal # ============= # # Some noise is added to the image, 1% of pixels are randomly set to 255, 1% # are randomly set to 0. The **median** filter is applied to remove the # noise. from skimage.filters.rank import median from skimage.morphology import disk noise = np.random.random(noisy_image.shape) noisy_image = img_as_ubyte(data.camera()) noisy_image[noise > 0.99] = 255 noisy_image[noise < 0.01] = 0 fig, axes = plt.subplots(2, 2, figsize=(10, 10), sharex=True, sharey=True) ax = axes.ravel() ax[0].imshow(noisy_image, vmin=0, vmax=255, cmap=plt.cm.gray) ax[0].set_title('Noisy image') ax[1].imshow(median(noisy_image, disk(1)), vmin=0, vmax=255, cmap=plt.cm.gray) ax[1].set_title('Median $r=1$') ax[2].imshow(median(noisy_image, disk(5)), vmin=0, vmax=255, cmap=plt.cm.gray) ax[2].set_title('Median $r=5$') ax[3].imshow(median(noisy_image, disk(20)), vmin=0, vmax=255, cmap=plt.cm.gray) ax[3].set_title('Median $r=20$') for a in ax: a.axis('off') a.set_adjustable('box-forced') plt.tight_layout() ###################################################################### # # The added noise is efficiently removed, as the image defaults are small (1 # pixel wide), a small filter radius is sufficient. As the radius is # increasing, objects with bigger sizes are filtered as well, such as the # camera tripod. The median filter is often used for noise removal because # borders are preserved and e.g. salt and pepper noise typically does not # distort the gray-level. # # Image smoothing # =============== # # The example hereunder shows how a local **mean** filter smooths the camera # man image. from skimage.filters.rank import mean loc_mean = mean(noisy_image, disk(10)) fig, ax = plt.subplots(ncols=2, figsize=(10, 5), sharex=True, sharey=True) ax[0].imshow(noisy_image, vmin=0, vmax=255, cmap=plt.cm.gray) ax[0].set_title('Original') ax[1].imshow(loc_mean, vmin=0, vmax=255, cmap=plt.cm.gray) ax[1].set_title('Local mean $r=10$') for a in ax: a.axis('off') a.set_adjustable('box-forced') plt.tight_layout() ###################################################################### # # One may be interested in smoothing an image while preserving important # borders (median filters already achieved this), here we use the # **bilateral** filter that restricts the local neighborhood to pixel having # a gray-level similar to the central one. # # .. note:: # # A different implementation is available for color images in # :py:func:`skimage.filters.denoise_bilateral`. from skimage.filters.rank import mean_bilateral noisy_image = img_as_ubyte(data.camera()) bilat = mean_bilateral(noisy_image.astype(np.uint16), disk(20), s0=10, s1=10) fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(10, 10), sharex='row', sharey='row') ax = axes.ravel() ax[0].imshow(noisy_image, cmap=plt.cm.gray) ax[0].set_title('Original') ax[1].imshow(bilat, cmap=plt.cm.gray) ax[1].set_title('Bilateral mean') ax[2].imshow(noisy_image[200:350, 350:450], cmap=plt.cm.gray) ax[3].imshow(bilat[200:350, 350:450], cmap=plt.cm.gray) for a in ax: a.axis('off') a.set_adjustable('box-forced') plt.tight_layout() ###################################################################### # One can see that the large continuous part of the image (e.g. sky) is # smoothed whereas other details are preserved. # # Contrast enhancement # ==================== # # We compare here how the global histogram equalization is applied locally. # # The equalized image [2]_ has a roughly linear cumulative distribution # function for each pixel neighborhood. The local version [3]_ of the # histogram equalization emphasizes every local gray-level variations. # # .. [2] http://en.wikipedia.org/wiki/Histogram_equalization # .. [3] http://en.wikipedia.org/wiki/Adaptive_histogram_equalization from skimage import exposure from skimage.filters import rank noisy_image = img_as_ubyte(data.camera()) # equalize globally and locally glob = exposure.equalize_hist(noisy_image) * 255 loc = rank.equalize(noisy_image, disk(20)) # extract histogram for each image hist = np.histogram(noisy_image, bins=np.arange(0, 256)) glob_hist = np.histogram(glob, bins=np.arange(0, 256)) loc_hist = np.histogram(loc, bins=np.arange(0, 256)) fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(12, 12)) ax = axes.ravel() ax[0].imshow(noisy_image, interpolation='nearest', cmap=plt.cm.gray) ax[0].axis('off') ax[1].plot(hist[1][:-1], hist[0], lw=2) ax[1].set_title('Histogram of gray values') ax[2].imshow(glob, interpolation='nearest', cmap=plt.cm.gray) ax[2].axis('off') ax[3].plot(glob_hist[1][:-1], glob_hist[0], lw=2) ax[3].set_title('Histogram of gray values') ax[4].imshow(loc, interpolation='nearest', cmap=plt.cm.gray) ax[4].axis('off') ax[5].plot(loc_hist[1][:-1], loc_hist[0], lw=2) ax[5].set_title('Histogram of gray values') plt.tight_layout() ###################################################################### # Another way to maximize the number of gray-levels used for an image is to # apply a local auto-leveling, i.e. the gray-value of a pixel is # proportionally remapped between local minimum and local maximum. # # The following example shows how local auto-level enhances the camara man # picture. from skimage.filters.rank import autolevel noisy_image = img_as_ubyte(data.camera()) auto = autolevel(noisy_image.astype(np.uint16), disk(20)) fig, ax = plt.subplots(ncols=2, figsize=(10, 5), sharex=True, sharey=True) ax[0].imshow(noisy_image, cmap=plt.cm.gray) ax[0].set_title('Original') ax[1].imshow(auto, cmap=plt.cm.gray) ax[1].set_title('Local autolevel') for a in ax: a.axis('off') a.set_adjustable('box-forced') plt.tight_layout() ###################################################################### # This filter is very sensitive to local outliers, see the little white spot # in the left part of the sky. This is due to a local maximum which is very # high comparing to the rest of the neighborhood. One can moderate this using # the percentile version of the auto-level filter which uses given # percentiles (one inferior, one superior) in place of local minimum and # maximum. The example below illustrates how the percentile parameters # influence the local auto-level result. from skimage.filters.rank import autolevel_percentile image = data.camera() selem = disk(20) loc_autolevel = autolevel(image, selem=selem) loc_perc_autolevel0 = autolevel_percentile(image, selem=selem, p0=.00, p1=1.0) loc_perc_autolevel1 = autolevel_percentile(image, selem=selem, p0=.01, p1=.99) loc_perc_autolevel2 = autolevel_percentile(image, selem=selem, p0=.05, p1=.95) loc_perc_autolevel3 = autolevel_percentile(image, selem=selem, p0=.1, p1=.9) fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(10, 10), sharex=True, sharey=True) ax = axes.ravel() title_list = ['Original', 'auto_level', 'auto-level 0%', 'auto-level 1%', 'auto-level 5%', 'auto-level 10%'] image_list = [image, loc_autolevel, loc_perc_autolevel0, loc_perc_autolevel1, loc_perc_autolevel2, loc_perc_autolevel3] for i in range(0, len(image_list)): ax[i].imshow(image_list[i], cmap=plt.cm.gray, vmin=0, vmax=255) ax[i].set_title(title_list[i]) ax[i].axis('off') ax[i].set_adjustable('box-forced') plt.tight_layout() ###################################################################### # The morphological contrast enhancement filter replaces the central pixel by # the local maximum if the original pixel value is closest to local maximum, # otherwise by the minimum local. from skimage.filters.rank import enhance_contrast noisy_image = img_as_ubyte(data.camera()) enh = enhance_contrast(noisy_image, disk(5)) fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(10, 10), sharex='row', sharey='row') ax = axes.ravel() ax[0].imshow(noisy_image, cmap=plt.cm.gray) ax[0].set_title('Original') ax[1].imshow(enh, cmap=plt.cm.gray) ax[1].set_title('Local morphological contrast enhancement') ax[2].imshow(noisy_image[200:350, 350:450], cmap=plt.cm.gray) ax[3].imshow(enh[200:350, 350:450], cmap=plt.cm.gray) for a in ax: a.axis('off') a.set_adjustable('box-forced') plt.tight_layout() ###################################################################### # The percentile version of the local morphological contrast enhancement uses # percentile *p0* and *p1* instead of the local minimum and maximum. from skimage.filters.rank import enhance_contrast_percentile noisy_image = img_as_ubyte(data.camera()) penh = enhance_contrast_percentile(noisy_image, disk(5), p0=.1, p1=.9) fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(10, 10), sharex='row', sharey='row') ax = axes.ravel() ax[0].imshow(noisy_image, cmap=plt.cm.gray) ax[0].set_title('Original') ax[1].imshow(penh, cmap=plt.cm.gray) ax[1].set_title('Local percentile morphological\n contrast enhancement') ax[2].imshow(noisy_image[200:350, 350:450], cmap=plt.cm.gray) ax[3].imshow(penh[200:350, 350:450], cmap=plt.cm.gray) for a in ax: a.axis('off') a.set_adjustable('box-forced') plt.tight_layout() ###################################################################### # # Image threshold # =============== # # The Otsu threshold [4]_ method can be applied locally using the local gray- # level distribution. In the example below, for each pixel, an "optimal" # threshold is determined by maximizing the variance between two classes of # pixels of the local neighborhood defined by a structuring element. # # The example compares the local threshold with the global threshold # :py:func:`skimage.filters.threshold_otsu`. # # .. note:: # # Local is much slower than global thresholding. A function for global # Otsu thresholding can be found in : # :py:func:`skimage.filters.threshold_otsu`. # # .. [4] http://en.wikipedia.org/wiki/Otsu's_method from skimage.filters.rank import otsu from skimage.filters import threshold_otsu p8 = data.page() radius = 10 selem = disk(radius) # t_loc_otsu is an image t_loc_otsu = otsu(p8, selem) loc_otsu = p8 >= t_loc_otsu # t_glob_otsu is a scalar t_glob_otsu = threshold_otsu(p8) glob_otsu = p8 >= t_glob_otsu fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(12, 12), sharex=True, sharey=True) ax = axes.ravel() fig.colorbar(ax[0].imshow(p8, cmap=plt.cm.gray), ax=ax[0]) ax[0].set_title('Original') fig.colorbar(ax[1].imshow(t_loc_otsu, cmap=plt.cm.gray), ax=ax[1]) ax[1].set_title('Local Otsu ($r=%d$)' % radius) ax[2].imshow(p8 >= t_loc_otsu, cmap=plt.cm.gray) ax[2].set_title('Original >= local Otsu' % t_glob_otsu) ax[3].imshow(glob_otsu, cmap=plt.cm.gray) ax[3].set_title('Global Otsu ($t=%d$)' % t_glob_otsu) for a in ax: a.axis('off') a.set_adjustable('box-forced') plt.tight_layout() ###################################################################### # The following example shows how local Otsu thresholding handles a global # level shift applied to a synthetic image. n = 100 theta = np.linspace(0, 10 * np.pi, n) x = np.sin(theta) m = (np.tile(x, (n, 1)) * np.linspace(0.1, 1, n) * 128 + 128).astype(np.uint8) radius = 10 t = rank.otsu(m, disk(radius)) fig, ax = plt.subplots(ncols=2, figsize=(10, 5), sharex=True, sharey=True) ax[0].imshow(m, cmap=plt.cm.gray) ax[0].set_title('Original') ax[1].imshow(m >= t, interpolation='nearest', cmap=plt.cm.gray) ax[1].set_title('Local Otsu ($r=%d$)' % radius) for a in ax: a.axis('off') a.set_adjustable('box-forced') plt.tight_layout() ###################################################################### # Image morphology # ================ # # Local maximum and local minimum are the base operators for gray-level # morphology. # # Here is an example of the classical morphological gray-level filters: # opening, closing and morphological gradient. from skimage.filters.rank import maximum, minimum, gradient noisy_image = img_as_ubyte(data.camera()) closing = maximum(minimum(noisy_image, disk(5)), disk(5)) opening = minimum(maximum(noisy_image, disk(5)), disk(5)) grad = gradient(noisy_image, disk(5)) # display results fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(10, 10), sharex=True, sharey=True) ax = axes.ravel() ax[0].imshow(noisy_image, cmap=plt.cm.gray) ax[0].set_title('Original') ax[1].imshow(closing, cmap=plt.cm.gray) ax[1].set_title('Gray-level closing') ax[2].imshow(opening, cmap=plt.cm.gray) ax[2].set_title('Gray-level opening') ax[3].imshow(grad, cmap=plt.cm.gray) ax[3].set_title('Morphological gradient') for a in ax: a.axis('off') a.set_adjustable('box-forced') plt.tight_layout() ###################################################################### # # Feature extraction # =================== # # Local histograms can be exploited to compute local entropy, which is # related to the local image complexity. Entropy is computed using base 2 # logarithm i.e. the filter returns the minimum number of bits needed to # encode local gray-level distribution. # # :py:func:`skimage.rank.entropy` returns the local entropy on a given # structuring element. The following example shows applies this filter # on 8- and 16-bit images. # # .. note:: # # to better use the available image bit, the function returns 10x entropy # for 8-bit images and 1000x entropy for 16-bit images. from skimage import data from skimage.filters.rank import entropy from skimage.morphology import disk import numpy as np import matplotlib.pyplot as plt image = data.camera() fig, ax = plt.subplots(ncols=2, figsize=(12, 6), sharex=True, sharey=True) fig.colorbar(ax[0].imshow(image, cmap=plt.cm.gray), ax=ax[0]) ax[0].set_title('Image') fig.colorbar(ax[1].imshow(entropy(image, disk(5)), cmap=plt.cm.gray), ax=ax[1]) ax[1].set_title('Entropy') for a in ax: a.axis('off') a.set_adjustable('box-forced') plt.tight_layout() ###################################################################### # # Implementation # ============== # # The central part of the ``skimage.rank`` filters is build on a sliding window # that updates the local gray-level histogram. This approach limits the # algorithm complexity to O(n) where n is the number of image pixels. The # complexity is also limited with respect to the structuring element size. # # In the following we compare the performance of different implementations # available in ``skimage``. from time import time from scipy.ndimage import percentile_filter from skimage.morphology import dilation from skimage.filters.rank import median, maximum def exec_and_timeit(func): """Decorator that returns both function results and execution time.""" def wrapper(*arg): t1 = time() res = func(*arg) t2 = time() ms = (t2 - t1) * 1000.0 return (res, ms) return wrapper @exec_and_timeit def cr_med(image, selem): return median(image=image, selem=selem) @exec_and_timeit def cr_max(image, selem): return maximum(image=image, selem=selem) @exec_and_timeit def cm_dil(image, selem): return dilation(image=image, selem=selem) @exec_and_timeit def ndi_med(image, n): return percentile_filter(image, 50, size=n * 2 - 1) ###################################################################### # Comparison between # # * ``filters.rank.maximum`` # * ``morphology.dilate`` # # on increasing structuring element size: a = data.camera() rec = [] e_range = range(1, 20, 2) for r in e_range: elem = disk(r + 1) rc, ms_rc = cr_max(a, elem) rcm, ms_rcm = cm_dil(a, elem) rec.append((ms_rc, ms_rcm)) rec = np.asarray(rec) fig, ax = plt.subplots(figsize=(10, 10), sharey=True) ax.set_title('Performance with respect to element size') ax.set_ylabel('Time (ms)') ax.set_xlabel('Element radius') ax.plot(e_range, rec) ax.legend(['filters.rank.maximum', 'morphology.dilate']) plt.tight_layout() ###################################################################### # and increasing image size: r = 9 elem = disk(r + 1) rec = [] s_range = range(100, 1000, 100) for s in s_range: a = (np.random.random((s, s)) * 256).astype(np.uint8) (rc, ms_rc) = cr_max(a, elem) (rcm, ms_rcm) = cm_dil(a, elem) rec.append((ms_rc, ms_rcm)) rec = np.asarray(rec) fig, ax = plt.subplots() ax.set_title('Performance with respect to image size') ax.set_ylabel('Time (ms)') ax.set_xlabel('Image size') ax.plot(s_range, rec) ax.legend(['filters.rank.maximum', 'morphology.dilate']) plt.tight_layout() ###################################################################### # Comparison between: # # * ``filters.rank.median`` # * ``scipy.ndimage.percentile`` # # on increasing structuring element size: a = data.camera() rec = [] e_range = range(2, 30, 4) for r in e_range: elem = disk(r + 1) rc, ms_rc = cr_med(a, elem) rndi, ms_ndi = ndi_med(a, r) rec.append((ms_rc, ms_ndi)) rec = np.asarray(rec) fig, ax = plt.subplots() ax.set_title('Performance with respect to element size') ax.plot(e_range, rec) ax.legend(['filters.rank.median', 'scipy.ndimage.percentile']) ax.set_ylabel('Time (ms)') ax.set_xlabel('Element radius') ###################################################################### # Comparison of outcome of the three methods: fig, ax = plt.subplots(ncols=2, figsize=(10, 5), sharex=True, sharey=True) ax[0].set_title('filters.rank.median') ax[0].imshow(rc, cmap=plt.cm.gray) ax[1].set_title('scipy.ndimage.percentile') ax[1].imshow(rndi, cmap=plt.cm.gray) for a in ax: a.axis('off') a.set_adjustable('box-forced') plt.tight_layout() ###################################################################### # and increasing image size: r = 9 elem = disk(r + 1) rec = [] s_range = [100, 200, 500, 1000] for s in s_range: a = (np.random.random((s, s)) * 256).astype(np.uint8) (rc, ms_rc) = cr_med(a, elem) rndi, ms_ndi = ndi_med(a, r) rec.append((ms_rc, ms_ndi)) rec = np.asarray(rec) fig, ax = plt.subplots() ax.set_title('Performance with respect to image size') ax.plot(s_range, rec) ax.legend(['filters.rank.median', 'scipy.ndimage.percentile']) ax.set_ylabel('Time (ms)') ax.set_xlabel('Image size') plt.tight_layout() scikit-image-0.13.1/doc/examples/xx_applications/plot_thresholding.py000066400000000000000000000152451316256236300260520ustar00rootroot00000000000000""" ============ Thresholding ============ Thresholding is used to create a binary image from a grayscale image [1]_. It is the simplest way to segment objects from a background. Thresholding algorithms implemented in scikit-image can be separated in two categories: - Histogram-based. The histogram of the pixels' intensity is used and certain assumptions are made on the properties of this histogram (e.g. bimodal). - Local. To process a pixel, only the neighboring pixels are used. These algorithms often require more computation time. If you are not familiar with the details of the different algorithms and the underlying assumptions, it is often difficult to know which algorithm will give the best results. Therefore, Scikit-image includes a function to evaluate thresholding algorithms provided by the library. At a glance, you can select the best algorithm for you data without a deep understanding of their mechanisms. .. [1] https://en.wikipedia.org/wiki/Thresholding_%28image_processing%29 .. seealso:: Presentation on :ref:`sphx_glr_auto_examples_xx_applications_plot_rank_filters.py`. """ import matplotlib import matplotlib.pyplot as plt from skimage import data from skimage.filters import try_all_threshold img = data.page() fig, ax = try_all_threshold(img, figsize=(10, 8), verbose=False) plt.show() ###################################################################### # How to apply a threshold? # ========================= # # Now, we illustrate how to apply one of these thresholding algorithms. # This example uses the mean value of pixel intensities. It is a simple # and naive threshold value, which is sometimes used as a guess value. # from skimage.filters import threshold_mean image = data.camera() thresh = threshold_mean(image) binary = image > thresh fig, axes = plt.subplots(ncols=2, figsize=(8, 3)) ax = axes.ravel() ax[0].imshow(image, cmap=plt.cm.gray) ax[0].set_title('Original image') ax[1].imshow(binary, cmap=plt.cm.gray) ax[1].set_title('Result') for a in ax: a.axis('off') plt.show() ###################################################################### # Bimodal histogram # ================= # # For pictures with a bimodal histogram, more specific algorithms can be used. # For instance, the minimum algorithm takes a histogram of the image and smooths it # repeatedly until there are only two peaks in the histogram. from skimage.filters import threshold_minimum image = data.camera() thresh_min = threshold_minimum(image) binary_min = image > thresh_min fig, ax = plt.subplots(2, 2, figsize=(10, 10)) ax[0, 0].imshow(image, cmap=plt.cm.gray) ax[0, 0].set_title('Original') ax[0, 1].hist(image.ravel(), bins=256) ax[0, 1].set_title('Histogram') ax[1, 0].imshow(binary_min, cmap=plt.cm.gray) ax[1, 0].set_title('Thresholded (min)') ax[1, 1].hist(image.ravel(), bins=256) ax[1, 1].axvline(thresh_min, color='r') for a in ax[:, 0]: a.axis('off') plt.show() ###################################################################### # Otsu's method [2]_ calculates an "optimal" threshold (marked by a red line in the # histogram below) by maximizing the variance between two classes of pixels, # which are separated by the threshold. Equivalently, this threshold minimizes # the intra-class variance. # # .. [2] http://en.wikipedia.org/wiki/Otsu's_method # from skimage.filters import threshold_otsu image = data.camera() thresh = threshold_otsu(image) binary = image > thresh fig, axes = plt.subplots(ncols=3, figsize=(8, 2.5)) ax = axes.ravel() ax[0] = plt.subplot(1, 3, 1, adjustable='box-forced') ax[1] = plt.subplot(1, 3, 2) ax[2] = plt.subplot(1, 3, 3, sharex=ax[0], sharey=ax[0], adjustable='box-forced') ax[0].imshow(image, cmap=plt.cm.gray) ax[0].set_title('Original') ax[0].axis('off') ax[1].hist(image.ravel(), bins=256) ax[1].set_title('Histogram') ax[1].axvline(thresh, color='r') ax[2].imshow(binary, cmap=plt.cm.gray) ax[2].set_title('Thresholded') ax[2].axis('off') plt.show() ###################################################################### # Local thresholding # ================== # # If the image background is relatively uniform, then you can use a global # threshold value as presented above. However, if there is large variation in the # background intensity, adaptive thresholding (a.k.a. local or dynamic # thresholding) may produce better results. Note that local is much slower than # global thresholding. # # Here, we binarize an image using the `threshold_local` function, which # calculates thresholds in regions with a characteristic size `block_size` surrounding # each pixel (i.e. local neighborhoods). Each threshold value is the weighted mean # of the local neighborhood minus an offset value. # from skimage.filters import threshold_otsu, threshold_local image = data.page() global_thresh = threshold_otsu(image) binary_global = image > global_thresh block_size = 35 adaptive_thresh = threshold_local(image, block_size, offset=10) binary_adaptive = image > adaptive_thresh fig, axes = plt.subplots(nrows=3, figsize=(7, 8)) ax = axes.ravel() plt.gray() ax[0].imshow(image) ax[0].set_title('Original') ax[1].imshow(binary_global) ax[1].set_title('Global thresholding') ax[2].imshow(binary_adaptive) ax[2].set_title('Adaptive thresholding') for a in ax: a.axis('off') plt.show() ###################################################################### # Now, we show how Otsu's threshold [2]_ method can be applied locally. For # each pixel, an "optimal" threshold is determined by maximizing the variance # between two classes of pixels of the local neighborhood defined by a # structuring element. # # The example compares the local threshold with the global threshold. # from skimage.morphology import disk from skimage.filters import threshold_otsu, rank from skimage.util import img_as_ubyte img = img_as_ubyte(data.page()) radius = 15 selem = disk(radius) local_otsu = rank.otsu(img, selem) threshold_global_otsu = threshold_otsu(img) global_otsu = img >= threshold_global_otsu fig, axes = plt.subplots(2, 2, figsize=(8, 5), sharex=True, sharey=True, subplot_kw={'adjustable': 'box-forced'}) ax = axes.ravel() plt.tight_layout() fig.colorbar(ax[0].imshow(img, cmap=plt.cm.gray), ax=ax[0], orientation='horizontal') ax[0].set_title('Original') ax[0].axis('off') fig.colorbar(ax[1].imshow(local_otsu, cmap=plt.cm.gray), ax=ax[1], orientation='horizontal') ax[1].set_title('Local Otsu (radius=%d)' % radius) ax[1].axis('off') ax[2].imshow(img >= local_otsu, cmap=plt.cm.gray) ax[2].set_title('Original >= Local Otsu' % threshold_global_otsu) ax[2].axis('off') ax[3].imshow(global_otsu, cmap=plt.cm.gray) ax[3].set_title('Global Otsu (threshold = %d)' % threshold_global_otsu) ax[3].axis('off') plt.show() scikit-image-0.13.1/doc/ext/000077500000000000000000000000001316256236300155165ustar00rootroot00000000000000scikit-image-0.13.1/doc/ext/LICENSE.txt000066400000000000000000000105711316256236300173450ustar00rootroot00000000000000The files - docscrape.py - docscrape_sphinx.py - numpydoc.py are Copyright (C) 2008 Stefan van der Walt , Pauli Virtanen and are distributed under the modified BSD license. 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Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. b. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. c. Neither the name of the Scikit-learn Developers nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. scikit-image-0.13.1/doc/ext/notebook_doc.py000066400000000000000000000057421316256236300205450ustar00rootroot00000000000000__all__ = ['python_to_notebook', 'Notebook'] import json import copy import warnings # Skeleton notebook in JSON format skeleton_nb = """{ "metadata": { "name":"" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }""" class Notebook(object): """ Notebook object for building an IPython notebook cell-by-cell. """ def __init__(self): # cell type code self.cell_code = { 'cell_type': 'code', 'collapsed': False, 'input': [ '# Code Goes Here' ], 'language': 'python', 'metadata': {}, 'outputs': [] } # cell type markdown self.cell_md = { 'cell_type': 'markdown', 'metadata': {}, 'source': [ 'Markdown Goes Here' ] } self.template = json.loads(skeleton_nb) self.cell_type = {'input': self.cell_code, 'source': self.cell_md} self.valuetype_to_celltype = {'code': 'input', 'markdown': 'source'} def add_cell(self, value, cell_type='code'): """Add a notebook cell. Parameters ---------- value : str Cell content. cell_type : {'code', 'markdown'} Type of content (default is 'code'). """ if cell_type in ['markdown', 'code']: key = self.valuetype_to_celltype[cell_type] cells = self.template['worksheets'][0]['cells'] cells.append(copy.deepcopy(self.cell_type[key])) # assign value to the last cell cells[-1][key] = value else: warnings.warn('Ignoring unsupported cell type (%s)' % cell_type) def json(self): """Return a JSON representation of the notebook. Returns ------- str JSON notebook. """ return json.dumps(self.template, indent=2) def test_notebook_basic(): nb = Notebook() assert(json.loads(nb.json()) == json.loads(skeleton_nb)) def test_notebook_add(): nb = Notebook() str1 = 'hello world' str2 = 'f = lambda x: x * x' nb.add_cell(str1, cell_type='markdown') nb.add_cell(str2, cell_type='code') d = json.loads(nb.json()) cells = d['worksheets'][0]['cells'] values = [c['input'] if c['cell_type'] == 'code' else c['source'] for c in cells] assert values[1] == str1 assert values[2] == str2 assert cells[1]['cell_type'] == 'markdown' assert cells[2]['cell_type'] == 'code' if __name__ == "__main__": import numpy.testing as npt npt.run_module_suite() scikit-image-0.13.1/doc/ext/plot2rst.py000066400000000000000000000500331316256236300176620ustar00rootroot00000000000000""" Example generation from python files. Generate the rst files for the examples by iterating over the python example files. Files that generate images should start with 'plot'. To generate your own examples, add this extension to the list of ``extensions``in your Sphinx configuration file. In addition, make sure the example directory(ies) in `plot2rst_paths` (see below) points to a directory with examples named `plot_*.py` and include an `index.rst` file. This code was adapted from scikit-image, which took it from scikit-learn. Options ------- The ``plot2rst`` extension accepts the following options: plot2rst_paths : length-2 tuple, or list of tuples Tuple or list of tuples of paths to (python plot, generated rst) files, i.e. (source, destination). Note that both paths are relative to Sphinx 'source' directory. Defaults to ('../examples', 'auto_examples') plot2rst_rcparams : dict Matplotlib configuration parameters. See http://matplotlib.sourceforge.net/users/customizing.html for details. plot2rst_default_thumb : str Path (relative to doc root) of default thumbnail image. plot2rst_thumb_shape : float Shape of thumbnail in pixels. The image is resized to fit within this shape and the excess is filled with white pixels. This fixed size ensures that that gallery images are displayed in a grid. plot2rst_plot_tag : str When this tag is found in the example file, the current plot is saved and tag is replaced with plot path. Defaults to 'PLOT2RST.current_figure'. Suggested CSS definitions ------------------------- div.body h2 { border-bottom: 1px solid #BBB; clear: left; } /*---- example gallery ----*/ .gallery.figure { float: left; margin: 1em; } .gallery.figure img{ display: block; margin-left: auto; margin-right: auto; width: 200px; } .gallery.figure .caption { width: 200px; text-align: center !important; } """ import os import re import shutil import token import tokenize import traceback import itertools import numpy as np import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt from skimage import io from skimage import transform from skimage.util.dtype import dtype_range from notebook_doc import Notebook from docutils.core import publish_parts from sphinx.domains.python import PythonDomain LITERALINCLUDE = """ .. literalinclude:: {src_name} :lines: {code_start}- """ CODE_LINK = """ **Python source code:** :download:`download <{0}>` (generated using ``skimage`` |version|) """ NOTEBOOK_LINK = """ **IPython Notebook:** :download:`download <{0}>` (generated using ``skimage`` |version|) """ TOCTREE_TEMPLATE = """ .. toctree:: :hidden: %s """ IMAGE_TEMPLATE = """ .. image:: images/%s :align: center """ GALLERY_IMAGE_TEMPLATE = """ .. figure:: %(thumb)s :figclass: gallery :target: ./%(source)s.html :ref:`example_%(link_name)s` """ class Path(str): """Path object for manipulating directory and file paths.""" def __new__(self, path): return str.__new__(self, path) @property def isdir(self): return os.path.isdir(self) @property def exists(self): """Return True if path exists""" return os.path.exists(self) def pjoin(self, *args): """Join paths. `p` prefix prevents confusion with string method.""" return self.__class__(os.path.join(self, *args)) def psplit(self): """Split paths. `p` prefix prevents confusion with string method.""" return [self.__class__(p) for p in os.path.split(self)] def makedirs(self): if not self.exists: os.makedirs(self) def listdir(self): return os.listdir(self) def format(self, *args, **kwargs): return self.__class__(super(Path, self).format(*args, **kwargs)) def __add__(self, other): return self.__class__(super(Path, self).__add__(other)) def __iadd__(self, other): return self.__add__(other) def setup(app): app.connect('builder-inited', generate_example_galleries) app.add_config_value('plot2rst_paths', ('../examples', 'auto_examples'), True) app.add_config_value('plot2rst_rcparams', {}, True) app.add_config_value('plot2rst_default_thumb', None, True) app.add_config_value('plot2rst_thumb_shape', (250, 300), True) app.add_config_value('plot2rst_plot_tag', 'PLOT2RST.current_figure', True) app.add_config_value('plot2rst_index_name', 'index', True) def generate_example_galleries(app): cfg = app.builder.config if isinstance(cfg.source_suffix, list): cfg.source_suffix_str = cfg.source_suffix[0] else: cfg.source_suffix_str = cfg.source_suffix doc_src = Path(os.path.abspath(app.builder.srcdir)) # path/to/doc/source if isinstance(cfg.plot2rst_paths, tuple): cfg.plot2rst_paths = [cfg.plot2rst_paths] for src_dest in cfg.plot2rst_paths: plot_path, rst_path = [Path(p) for p in src_dest] example_dir = doc_src.pjoin(plot_path) rst_dir = doc_src.pjoin(rst_path) generate_examples_and_gallery(example_dir, rst_dir, cfg) def generate_examples_and_gallery(example_dir, rst_dir, cfg): """Generate rst from examples and create gallery to showcase examples.""" if not example_dir.exists: print("No example directory found at", example_dir) return rst_dir.makedirs() # we create an index.rst with all examples with open(rst_dir.pjoin('index'+cfg.source_suffix_str), 'w') as gallery_index: # Here we don't use an os.walk, but we recurse only twice: flat is # better than nested. write_gallery(gallery_index, example_dir, rst_dir, cfg) for d in sorted(example_dir.listdir()): example_sub = example_dir.pjoin(d) if example_sub.isdir: rst_sub = rst_dir.pjoin(d) rst_sub.makedirs() write_gallery(gallery_index, example_sub, rst_sub, cfg, depth=1) gallery_index.flush() def write_gallery(gallery_index, src_dir, rst_dir, cfg, depth=0): """Generate the rst files for an example directory, i.e. gallery. Write rst files from python examples and add example links to gallery. Parameters ---------- gallery_index : file Index file for plot gallery. src_dir : 'str' Source directory for python examples. rst_dir : 'str' Destination directory for rst files generated from python examples. cfg : config object Sphinx config object created by Sphinx. """ index_name = cfg.plot2rst_index_name + cfg.source_suffix_str gallery_template = src_dir.pjoin(index_name) if not os.path.exists(gallery_template): print(src_dir) print(80*'_') print('Example directory %s does not have a %s file' % (src_dir, index_name)) print('Skipping this directory') print(80*'_') return with open(gallery_template) as f: gallery_description = f.read() gallery_index.write('\n\n%s\n\n' % gallery_description) rst_dir.makedirs() examples = [fname for fname in sorted(src_dir.listdir(), key=_plots_first) if fname.endswith('py')] ex_names = [ex[:-3] for ex in examples] # strip '.py' extension if depth == 0: sub_dir = Path('') else: sub_dir_list = src_dir.psplit()[-depth:] sub_dir = Path('/'.join(sub_dir_list) + '/') joiner = '\n %s' % sub_dir gallery_index.write(TOCTREE_TEMPLATE % (sub_dir + joiner.join(ex_names))) for src_name in examples: try: write_example(src_name, src_dir, rst_dir, cfg) except Exception: print("Exception raised while running:") print("%s in %s" % (src_name, src_dir)) print('~' * 60) traceback.print_exc() print('~' * 60) continue link_name = sub_dir.pjoin(src_name) link_name = link_name.replace(os.path.sep, '_') if link_name.startswith('._'): link_name = link_name[2:] info = {} info['thumb'] = sub_dir.pjoin('images/thumb', src_name[:-3] + '.png') info['source'] = sub_dir + src_name[:-3] info['link_name'] = link_name gallery_index.write(GALLERY_IMAGE_TEMPLATE % info) def _plots_first(fname): """Decorate filename so that examples with plots are displayed first.""" if not (fname.startswith('plot') and fname.endswith('.py')): return 'zz' + fname return fname def write_example(src_name, src_dir, rst_dir, cfg): """Write rst file from a given python example. Parameters ---------- src_name : str Name of example file. src_dir : 'str' Source directory for python examples. rst_dir : 'str' Destination directory for rst files generated from python examples. cfg : config object Sphinx config object created by Sphinx. """ last_dir = src_dir.psplit()[-1] # to avoid leading . in file names, and wrong names in links if last_dir == '.' or last_dir == 'examples': last_dir = Path('') else: last_dir += '_' src_path = src_dir.pjoin(src_name) example_file = rst_dir.pjoin(src_name) shutil.copyfile(src_path, example_file) image_dir = rst_dir.pjoin('images') thumb_dir = image_dir.pjoin('thumb') notebook_dir = rst_dir.pjoin('notebook') image_dir.makedirs() thumb_dir.makedirs() notebook_dir.makedirs() base_image_name = os.path.splitext(src_name)[0] image_path = image_dir.pjoin(base_image_name + '_{0}.png') basename, py_ext = os.path.splitext(src_name) rst_path = rst_dir.pjoin(basename + cfg.source_suffix_str) notebook_path = notebook_dir.pjoin(basename + '.ipynb') if _plots_are_current(src_path, image_path) and rst_path.exists and \ notebook_path.exists: return print('plot2rst: %s' % basename) blocks = split_code_and_text_blocks(example_file) if blocks[0][2].startswith('#!'): blocks.pop(0) # don't add shebang line to rst file. rst_link = '.. _example_%s:\n\n' % (last_dir + src_name) figure_list, rst = process_blocks(blocks, src_path, image_path, cfg) has_inline_plots = any(cfg.plot2rst_plot_tag in b[2] for b in blocks) if has_inline_plots: example_rst = ''.join([rst_link, rst]) else: # print first block of text, display all plots, then display code. first_text_block = [b for b in blocks if b[0] == 'text'][0] label, (start, end), content = first_text_block figure_list = save_all_figures(image_path) rst_blocks = [IMAGE_TEMPLATE % f.lstrip('/') for f in figure_list] example_rst = rst_link example_rst += eval(content) example_rst += ''.join(rst_blocks) code_info = dict(src_name=src_name, code_start=end) example_rst += LITERALINCLUDE.format(**code_info) example_rst += CODE_LINK.format(src_name) ipnotebook_name = src_name.replace('.py', '.ipynb') ipnotebook_name = './notebook/' + ipnotebook_name example_rst += NOTEBOOK_LINK.format(ipnotebook_name) with open(rst_path, 'w') as f: f.write(example_rst) thumb_path = thumb_dir.pjoin(src_name[:-3] + '.png') first_image_file = image_dir.pjoin(figure_list[0].lstrip('/')) if first_image_file.exists: first_image = io.imread(first_image_file) save_thumbnail(first_image, thumb_path, cfg.plot2rst_thumb_shape) if not thumb_path.exists: if cfg.plot2rst_default_thumb is None: print("WARNING: No plots found and default thumbnail not defined.") print("Specify 'plot2rst_default_thumb' in Sphinx config file.") else: shutil.copy(cfg.plot2rst_default_thumb, thumb_path) # Export example to IPython notebook nb = Notebook() # Add sphinx roles to the examples, otherwise docutils # cannot compile the ReST for the notebook sphinx_roles = PythonDomain.roles.keys() preamble = '\n'.join('.. role:: py:{0}(literal)\n'.format(role) for role in sphinx_roles) # Grab all references to inject them in cells where needed ref_regexp = re.compile('\n(\.\. \[(\d+)\].*(?:\n[ ]{7,8}.*)+)') math_role_regexp = re.compile(':math:`(.*?)`') text = '\n'.join((content for (cell_type, _, content) in blocks if cell_type != 'code')) references = re.findall(ref_regexp, text) for (cell_type, _, content) in blocks: if cell_type == 'code': nb.add_cell(content, cell_type='code') else: if content.startswith('r'): content = content.replace('r"""', '') escaped = False else: content = content.replace('"""', '') escaped = True if not escaped: content = content.replace("\\", "\\\\") content = content.replace('.. seealso::', '**See also:**') content = re.sub(math_role_regexp, r'$\1$', content) # Remove math directive when rendering notebooks # until we implement a smarter way of capturing and replacing # its content content = content.replace('.. math::', '') if not content.strip(): continue content = (preamble + content).rstrip('\n') content = '\n'.join([line for line in content.split('\n') if not line.startswith('.. image')]) # Remove reference links until we can figure out a better way to # preserve them for (reference, ref_id) in references: ref_tag = '[{0}]_'.format(ref_id) if ref_tag in content: content = content.replace(ref_tag, ref_tag[:-1]) html = publish_parts(content, writer_name='html')['html_body'] nb.add_cell(html, cell_type='markdown') with open(notebook_path, 'w') as f: f.write(nb.json()) def save_thumbnail(image, thumb_path, shape): """Save image as a thumbnail with the specified shape. The image is first resized to fit within the specified shape and then centered in an array of the specified shape before saving. """ rescale = min(float(w_1) / w_2 for w_1, w_2 in zip(shape, image.shape)) small_shape = (rescale * np.asarray(image.shape[:2])).astype(int) small_image = transform.resize(image, small_shape) if len(image.shape) == 3: shape = shape + (image.shape[2],) background_value = dtype_range[small_image.dtype.type][1] thumb = background_value * np.ones(shape, dtype=small_image.dtype) i = (shape[0] - small_shape[0]) // 2 j = (shape[1] - small_shape[1]) // 2 thumb[i:i+small_shape[0], j:j+small_shape[1]] = small_image io.imsave(thumb_path, thumb) def _plots_are_current(src_path, image_path): first_image_file = Path(image_path.format(1)) needs_replot = (not first_image_file.exists or _mod_time(first_image_file) <= _mod_time(src_path)) return not needs_replot def _mod_time(file_path): return os.stat(file_path).st_mtime def split_code_and_text_blocks(source_file): """Return list with source file separated into code and text blocks. Returns ------- blocks : list of (label, (start, end+1), content) List where each element is a tuple with the label ('text' or 'code'), the (start, end+1) line numbers, and content string of block. """ block_edges, idx_first_text_block = get_block_edges(source_file) with open(source_file) as f: source_lines = f.readlines() # Every other block should be a text block idx_text_block = np.arange(idx_first_text_block, len(block_edges), 2) blocks = [] slice_ranges = zip(block_edges[:-1], block_edges[1:]) for i, (start, end) in enumerate(slice_ranges): block_label = 'text' if i in idx_text_block else 'code' # subtract 1 from indices b/c line numbers start at 1, not 0 content = ''.join(source_lines[start-1:end-1]) blocks.append((block_label, (start, end), content)) return blocks def get_block_edges(source_file): """Return starting line numbers of code and text blocks Returns ------- block_edges : list of int Line number for the start of each block. Note the idx_first_text_block : {0 | 1} 0 if first block is text then, else 1 (second block better be text). """ block_edges = [] with open(source_file) as f: token_iter = tokenize.generate_tokens(f.readline) for token_tuple in token_iter: t_id, t_str, (srow, scol), (erow, ecol), src_line = token_tuple if (token.tok_name[t_id] == 'STRING' and scol == 0): # Add one point to line after text (for later slicing) block_edges.extend((srow, erow+1)) idx_first_text_block = 0 # when example doesn't start with text block. if not block_edges[0] == 1: block_edges.insert(0, 1) idx_first_text_block = 1 # when example doesn't end with text block. if not block_edges[-1] == erow: # iffy: I'm using end state of loop block_edges.append(erow) return block_edges, idx_first_text_block def process_blocks(blocks, src_path, image_path, cfg): """Run source, save plots as images, and convert blocks to rst. Parameters ---------- blocks : list of block tuples Code and text blocks from example. See `split_code_and_text_blocks`. src_path : str Path to example file. image_path : str Path where plots are saved (format string which accepts figure number). cfg : config object Sphinx config object created by Sphinx. Returns ------- figure_list : list List of figure names saved by the example. rst_text : str Text with code wrapped code-block directives. """ src_dir, src_name = src_path.psplit() if not src_name.startswith('plot'): return [], '' # index of blocks which have inline plots inline_tag = cfg.plot2rst_plot_tag idx_inline_plot = [i for i, b in enumerate(blocks) if inline_tag in b[2]] image_dir, image_fmt_str = image_path.psplit() figure_list = [] plt.rcdefaults() plt.rcParams.update(cfg.plot2rst_rcparams) plt.close('all') example_globals = {} rst_blocks = [] fig_num = 1 for i, (blabel, brange, bcontent) in enumerate(blocks): if blabel == 'code': exec(bcontent, example_globals) rst_blocks.append(codestr2rst(bcontent)) else: if i in idx_inline_plot: plt.savefig(image_path.format(fig_num)) figure_name = image_fmt_str.format(fig_num) fig_num += 1 figure_list.append(figure_name) figure_link = os.path.join('images', figure_name) bcontent = bcontent.replace(inline_tag, figure_link) rst_blocks.append(docstr2rst(bcontent)) return figure_list, '\n'.join(rst_blocks) def codestr2rst(codestr): """Return reStructuredText code block from code string""" code_directive = ".. code-block:: python\n\n" indented_block = '\t' + codestr.replace('\n', '\n\t') return code_directive + indented_block def docstr2rst(docstr): """Return reStructuredText from docstring""" idx_whitespace = len(docstr.rstrip()) - len(docstr) whitespace = docstr[idx_whitespace:] return eval(docstr) + whitespace def save_all_figures(image_path): """Save all matplotlib figures. Parameters ---------- image_path : str Path where plots are saved (format string which accepts figure number). """ figure_list = [] image_dir, image_fmt_str = image_path.psplit() fig_mngr = matplotlib._pylab_helpers.Gcf.get_all_fig_managers() for fig_num in (m.num for m in fig_mngr): # Set the fig_num figure as the current figure as we can't # save a figure that's not the current figure. plt.figure(fig_num) plt.savefig(image_path.format(fig_num)) figure_list.append(image_fmt_str.format(fig_num)) return figure_list scikit-image-0.13.1/doc/gh-pages.py000077500000000000000000000106461316256236300167750ustar00rootroot00000000000000#!/usr/bin/env python """Script to commit the doc build outputs into the github-pages repo. Use: gh-pages.py [tag] If no tag is given, the current output of 'git describe' is used. If given, that is how the resulting directory will be named. In practice, you should use either actual clean tags from a current build or something like 'current' as a stable URL for the mest current version of the """ #----------------------------------------------------------------------------- # Imports #----------------------------------------------------------------------------- import os import re import shutil import sys from os import chdir as cd from subprocess import Popen, PIPE, CalledProcessError, check_call #----------------------------------------------------------------------------- # Globals #----------------------------------------------------------------------------- pages_dir = 'gh-pages' html_dir = 'build/html' pdf_dir = 'build/latex' pages_repo = 'https://github.com/scikit-image/docs.git' #----------------------------------------------------------------------------- # Functions #----------------------------------------------------------------------------- def sh(cmd): """Execute command in a subshell, return status code.""" return check_call(cmd, shell=True) def sh2(cmd): """Execute command in a subshell, return stdout. Stderr is unbuffered from the subshell.x""" p = Popen(cmd, stdout=PIPE, shell=True) out = p.communicate()[0] retcode = p.returncode if retcode: print(out.rstrip()) raise CalledProcessError(retcode, cmd) else: return out.rstrip() def sh3(cmd): """Execute command in a subshell, return stdout, stderr If anything appears in stderr, print it out to sys.stderr""" p = Popen(cmd, stdout=PIPE, stderr=PIPE, shell=True) out, err = p.communicate() retcode = p.returncode if retcode: raise CalledProcessError(retcode, cmd) else: return out.rstrip(), err.rstrip() def init_repo(path): """clone the gh-pages repo if we haven't already.""" sh("git clone %s %s"%(pages_repo, path)) here = os.getcwd() cd(path) sh('git checkout gh-pages') cd(here) #----------------------------------------------------------------------------- # Script starts #----------------------------------------------------------------------------- if __name__ == '__main__': # find the version number from skimage/__init__.py setup_lines = open('../skimage/__init__.py').readlines() tag = 'vUndefined' for l in setup_lines: if l.startswith('__version__'): tag = l.split("'")[1] if "dev" in tag: tag = "dev" else: # Rename e.g. 0.9.0 to 0.9.x tag = '.'.join(tag.split('.')[:-1] + ['x']) break startdir = os.getcwd() if not os.path.exists(pages_dir): # init the repo init_repo(pages_dir) else: # ensure up-to-date before operating cd(pages_dir) sh('git checkout gh-pages') sh('git pull') cd(startdir) dest = os.path.join(pages_dir, tag) # This is pretty unforgiving: we unconditionally nuke the destination # directory, and then copy the html tree in there shutil.rmtree(dest, ignore_errors=True) shutil.copytree(html_dir, dest) # copy pdf file into tree #shutil.copy(pjoin(pdf_dir, 'scikits.image.pdf'), pjoin(dest, 'scikits.image.pdf')) try: cd(pages_dir) status = sh2('git status | head -1') branch = re.match(b'On branch (.*)$', status).group(1) if branch != b'gh-pages': e = 'On %r, git branch is %r, MUST be "gh-pages"' % (pages_dir, branch) raise RuntimeError(e) sh("touch .nojekyll") sh('git add .nojekyll') sh('git add index.html') sh('git add --all %s' % tag) status = sh2('git status | tail -1') if not re.match(b'nothing to commit', status): sh2('git commit -m"Updated doc release: %s"' % tag) else: print('\n! Note: no changes to commit\n') print('Most recent commit:') sys.stdout.flush() sh('git --no-pager log --oneline HEAD~1..') finally: cd(startdir) print('') print('Now verify the build in: %r' % dest) print("If everything looks good, run 'git push' inside doc/gh-pages.") scikit-image-0.13.1/doc/make.bat000066400000000000000000000103441316256236300163250ustar00rootroot00000000000000@ECHO OFF REM Command file for Sphinx documentation if "%SPHINXBUILD%" == "" ( set SPHINXBUILD=sphinx-build ) if "%PYTHON%" == "" ( set PYTHON=python ) set BUILDDIR=_build set ALLSPHINXOPTS=-d %BUILDDIR%/doctrees %SPHINXOPTS% source if NOT "%PAPER%" == "" ( set ALLSPHINXOPTS=-D latex_paper_size=%PAPER% %ALLSPHINXOPTS% ) if "%1" == "" goto help if "%1" == "help" ( :help echo.Please use `make ^` where ^ is one of echo. html to make standalone HTML files echo. dirhtml to make HTML files named index.html in directories echo. singlehtml to make a single large HTML file echo. pickle to make pickle files echo. json to make JSON files echo. htmlhelp to make HTML files and a HTML help project echo. qthelp to make HTML files and a qthelp project echo. devhelp to make HTML files and a Devhelp project echo. epub to make an epub echo. latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter echo. text to make text files echo. man to make manual pages echo. changes to make an overview over all changed/added/deprecated items echo. linkcheck to check all external links for integrity echo. doctest to run all doctests embedded in the documentation if enabled goto end ) if "%1" == "clean" ( for /d %%i in (%BUILDDIR%\*) do rmdir /q /s %%i del /q /s %BUILDDIR%\* goto end ) if "%1" == "html" ( mkdir source/api %PYTHON% tools/build_modref_templates.py cd source && %PYTHON% random_gallery.py cd .. %SPHINXBUILD% -b html %ALLSPHINXOPTS% %BUILDDIR%/html echo. echo.Build finished. The HTML pages are in %BUILDDIR%/html. goto end ) if "%1" == "dirhtml" ( %SPHINXBUILD% -b dirhtml %ALLSPHINXOPTS% %BUILDDIR%/dirhtml echo. echo.Build finished. The HTML pages are in %BUILDDIR%/dirhtml. goto end ) if "%1" == "singlehtml" ( %SPHINXBUILD% -b singlehtml %ALLSPHINXOPTS% %BUILDDIR%/singlehtml echo. echo.Build finished. The HTML pages are in %BUILDDIR%/singlehtml. goto end ) if "%1" == "pickle" ( %SPHINXBUILD% -b pickle %ALLSPHINXOPTS% %BUILDDIR%/pickle echo. echo.Build finished; now you can process the pickle files. goto end ) if "%1" == "json" ( %SPHINXBUILD% -b json %ALLSPHINXOPTS% %BUILDDIR%/json echo. echo.Build finished; now you can process the JSON files. goto end ) if "%1" == "htmlhelp" ( %SPHINXBUILD% -b htmlhelp %ALLSPHINXOPTS% %BUILDDIR%/htmlhelp echo. echo.Build finished; now you can run HTML Help Workshop with the ^ .hhp project file in %BUILDDIR%/htmlhelp. goto end ) if "%1" == "qthelp" ( %SPHINXBUILD% -b qthelp %ALLSPHINXOPTS% %BUILDDIR%/qthelp echo. echo.Build finished; now you can run "qcollectiongenerator" with the ^ .qhcp project file in %BUILDDIR%/qthelp, like this: echo.^> qcollectiongenerator %BUILDDIR%\qthelp\Mapnik.qhcp echo.To view the help file: echo.^> assistant -collectionFile %BUILDDIR%\qthelp\Mapnik.ghc goto end ) if "%1" == "devhelp" ( %SPHINXBUILD% -b devhelp %ALLSPHINXOPTS% %BUILDDIR%/devhelp echo. echo.Build finished. goto end ) if "%1" == "epub" ( %SPHINXBUILD% -b epub %ALLSPHINXOPTS% %BUILDDIR%/epub echo. echo.Build finished. The epub file is in %BUILDDIR%/epub. goto end ) if "%1" == "latex" ( mkdir source/api %PYTHON% tools/build_modref_templates.py %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex echo. echo.Build finished; the LaTeX files are in %BUILDDIR%/latex. goto end ) if "%1" == "text" ( %SPHINXBUILD% -b text %ALLSPHINXOPTS% %BUILDDIR%/text echo. echo.Build finished. The text files are in %BUILDDIR%/text. goto end ) if "%1" == "man" ( %SPHINXBUILD% -b man %ALLSPHINXOPTS% %BUILDDIR%/man echo. echo.Build finished. The manual pages are in %BUILDDIR%/man. goto end ) if "%1" == "changes" ( %SPHINXBUILD% -b changes %ALLSPHINXOPTS% %BUILDDIR%/changes echo. echo.The overview file is in %BUILDDIR%/changes. goto end ) if "%1" == "linkcheck" ( %SPHINXBUILD% -b linkcheck %ALLSPHINXOPTS% %BUILDDIR%/linkcheck echo. echo.Link check complete; look for any errors in the above output ^ or in %BUILDDIR%/linkcheck/output.txt. goto end ) if "%1" == "doctest" ( %SPHINXBUILD% -b doctest %ALLSPHINXOPTS% %BUILDDIR%/doctest echo. echo.Testing of doctests in the sources finished, look at the ^ results in %BUILDDIR%/doctest/output.txt. goto end ) :end scikit-image-0.13.1/doc/release/000077500000000000000000000000001316256236300163365ustar00rootroot00000000000000scikit-image-0.13.1/doc/release/contribs.py000077500000000000000000000027061316256236300205430ustar00rootroot00000000000000#!/usr/bin/env python import subprocess import sys import string import shlex if len(sys.argv) != 2: print("Usage: ./contributors.py tag-of-previous-release") sys.exit(-1) tag = sys.argv[1] def call(cmd): return subprocess.check_output(shlex.split(cmd), universal_newlines=True).split('\n') tag_date = call("git log -n1 --format='%%ci' %s" % tag)[0] print("Release %s was on %s\n" % (tag, tag_date)) merges = call("git log --since='%s' --merges --format='>>>%%B' --reverse" % tag_date) merges = [m for m in merges if m.strip()] merges = '\n'.join(merges).split('>>>') merges = [m.split('\n')[:2] for m in merges] merges = [m for m in merges if len(m) == 2 and m[1].strip()] num_commits = call("git rev-list %s..HEAD --count" % tag)[0] print("A total of %s changes have been committed.\n" % num_commits) print("It contained the following %d merges:\n" % len(merges)) for (merge, message) in merges: if merge.startswith('Merge pull request #'): PR = ' (%s)' % merge.split()[3] else: PR = '' print('- ' + message + PR) print("\nMade by the following committers [alphabetical by last name]:\n") authors = call("git log --since='%s' --format=%%aN" % tag_date) authors = [a.strip() for a in authors if a.strip()] def key(author): author = [v for v in author.split() if v[0] in string.ascii_letters] if len(author) > 0: return author[-1] authors = sorted(set(authors), key=key) for a in authors: print('- ' + a) scikit-image-0.13.1/doc/release/release_0.10.rst000066400000000000000000000063641316256236300211570ustar00rootroot00000000000000Announcement: scikit-image 0.10.0 ================================= We're happy to announce the release of scikit-image v0.10.0! scikit-image is an image processing toolbox for SciPy that includes algorithms for segmentation, geometric transformations, color space manipulation, analysis, filtering, morphology, feature detection, and more. For more information, examples, and documentation, please visit our website: http://scikit-image.org New Features ------------ In addition to many bug fixes, (speed) improvements and new examples, the 118 pull requests (1112 commits) merged for this release include the following new or improved features (PR number in brackets): - BRIEF, ORB and CENSURE features (#834) - Blob Detection (#903) - Phase unwrapping (#644) - Wiener deconvolution (#800) - IPython notebooks in examples gallery (#1000) - Luv colorspace conversion (#798) - Viewer overlays (#810) - ISODATA thresholding (#859) - A new rank filter for summation (#844) - Faster MCP with anisotropy support (#854) - N-d peak finding (`peak_local_max`) (#906) - `imread_collection` support for all plugins (#862) - Enforce SLIC superpixels connectivity and add SLIC-zero (#857, #864) - Correct mesh orientation (for use in external visualizers) in marching cubes algorithm (#882) - Loading from URL and alpha support in novice module (#916, #946) - Equality for regionprops (#956) API changes ----------- The following backward-incompatible API changes were made between 0.9 and 0.10: - Removed deprecated functions in `skimage.filter.rank.*` - Removed deprecated parameter `epsilon` of `skimage.viewer.LineProfile` - Removed backwards-compatability of `skimage.measure.regionprops` - Removed {`ratio`, `sigma`} deprecation warnings of `skimage.segmentation.slic` and also remove explicit `sigma` parameter from doc-string example - Changed default mode of random_walker segmentation to 'cg_mg' > 'cg' > 'bf', depending on which optional dependencies are available. - Removed deprecated `out` parameter of `skimage.morphology.binary_*` - Removed deprecated parameter `depth` in `skimage.segmentation.random_walker` - Removed deprecated logger function in `skimage/__init__.py` - Removed deprecated function `filter.median_filter` - Removed deprecated `skimage.color.is_gray` and `skimage.color.is_rgb` functions - Removed deprecated `skimage.segmentation.visualize_boundaries` - Removed deprecated `skimage.morphology.greyscale_*` - Removed deprecated `skimage.exposure.equalize` Contributors to this release ---------------------------- This release was made possible by the collaborative efforts of many contributors, both new and old. They are listed in alphabetical order by surname: - Raphael Ackermann - Ankit Agrawal - Maximilian Albert - Pietro Berkes - Vighnesh Birodkar - François Boulogne - Olivier Debeir - Christoph Deil - Jaidev Deshpande - Jaime Frio - Jostein Bø Fløystad - Neeraj Gangwar - Christoph Gohlke - Michael Hansen - Almar Klein - Jeremy Metz - Juan Nunez-Iglesias - François Orieux - Guillem Palou - Rishabh Raj - Thomas Robitaille - Michal Romaniuk - Johannes L. Schönberger - Steven Silvester - Julian Taylor - Gregor Thalhammer - Matthew Trentacoste - Siva Prasad Varma - Guillem Palou Visa - Stefan van der Walt - Joshua Warner - Tony S Yu - radioxoma scikit-image-0.13.1/doc/release/release_0.11.rst000066400000000000000000000050751316256236300211560ustar00rootroot00000000000000Announcement: scikit-image 0.11.0 ================================= We're happy to announce the release of scikit-image v0.11.0! scikit-image is an image processing toolbox for SciPy that includes algorithms for segmentation, geometric transformations, color space manipulation, analysis, filtering, morphology, feature detection, and more. For more information, examples, and documentation, please visit our website: http://scikit-image.org Highlights ---------- For this release, we merged over 200 pull requests with bug fixes, cleanups, improved documentation and new features. Highlights include: - Region Adjacency Graphs - Color distance RAGs (#1031) - Threshold Cut on RAGs (#1031) - Similarity RAGs (#1080) - Normalized Cut on RAGs (#1080) - RAG drawing (#1087) - Hierarchical merging (#1100) - Sub-pixel shift registration (#1066) - Non-local means denoising (#874) - Sliding window histogram (#1127) - More illuminants in color conversion (#1130) - Handling of CMYK images (#1360) - `stop_probability` for RANSAC (#1176) - Li thresholding (#1376) - Signed edge operators (#1240) - Full ndarray support for `peak_local_max` (#1355) - Improve conditioning of geometric transformations (#1319) - Standardize handling of multi-image files (#1200) - Ellipse structuring element (#1298) - Multi-line drawing tool (#1065), line handle style (#1179) - Point in polygon testing (#1123) - Rotation around a specified center (#1168) - Add `shape` option to drawing functions (#1222) - Faster regionprops (#1351) - `skimage.future` package (#1365) - More robust I/O module (#1189) API Changes ----------- - The ``skimage.filter`` subpackage has been renamed to ``skimage.filters``. - Some edge detectors returned values greater than 1--their results are now appropriately scaled with a factor of ``sqrt(2)``. Contributors to this release ---------------------------- (Listed alphabetically by last name) - Fedor Baart - Vighnesh Birodkar - François Boulogne - Nelson Brown - Alexey Buzmakov - Julien Coste - Phil Elson - Adam Feuer - Jim Fienup - Geoffrey French - Emmanuelle Gouillart - Charles Harris - Jonathan Helmus - Alexander Iacchetta - Ivana Kajić - Kevin Keraudren - Almar Klein - Gregory R. Lee - Jeremy Metz - Stuart Mumford - Damian Nadales - Pablo Márquez Neila - Juan Nunez-Iglesias - Rebecca Roisin - Jasper St. Pierre - Jacopo Sabbatini - Michael Sarahan - Salvatore Scaramuzzino - Phil Schaf - Johannes Schönberger - Tim Seifert - Arve Seljebu - Steven Silvester - Julian Taylor - Matěj Týč - Alexey Umnov - Pratap Vardhan - Stefan van der Walt - Joshua Warner - Tony S Yu scikit-image-0.13.1/doc/release/release_0.12.rst000066400000000000000000000114771316256236300211620ustar00rootroot00000000000000Announcement: scikit-image 0.12 =============================== We're happy to announce the release of scikit-image v0.12! scikit-image is an image processing toolbox for SciPy that includes algorithms for segmentation, geometric transformations, color space manipulation, analysis, filtering, morphology, feature detection, and more. For more information, examples, and documentation, please visit our website: http://scikit-image.org and our gallery of examples http://scikit-image.org/docs/dev/auto_examples/ Highlights and new features --------------------------- For this release, we merged over 200 pull requests with bug fixes, cleanups, improved documentation and new features. Highlights include: - 3D skeletonization (#1923) - Parallelization framework using ``dask``:``skimage.util.apply_parallel`` (#1493) - Laplacian operator ``filters.laplace`` (#1763) - Synthetic 2-D and 3-D binary data with rounded blobs (#1485) - Plugin for ``imageio`` library (#1575) - Inpainting algorithm (#1804) - New handling of background pixels for ``measure.label``: 0-valued pixels are considered as background by default, and the label of background pixels is 0. - Partial support of 3-D images for ``skimage.measure.regionprops`` (#1505) - Multi-block local binary patterns (MB-LBP) for texture classification (#1536) - Seam Carving (resizing without distortion) (#1459) - Simple image comparison metrics (PSNR, NRMSE) (#1897) - Region boundary based region adjacency graphs (RAG) (#1495) - Construction of RAG from label images (#1826) - ``morphology.remove_small_holes`` now complements ``morphology.remove_small_objects`` (#1689) - Faster cython implementation of ``morphology.skeletonize`` - More appropriate default weights in ``restoration.denoise_tv_chambolle`` and ``feature.peak_local_max`` - Correction of bug in the computation of the Euler characteristic in ``measure.regionprops``. - Replace jet by viridis as default colormap - Alpha layer support for ``color.gray2rgb`` - The measure of structural similarity (``measure.compare_ssim``) is now n-dimensional and supports color channels as well. - We fixed an issue related to incorrect propagation in plateaus in ``segmentation.watershed`` Documentation: - New organization of gallery of examples in sections - More frequent (automated) updates of online documentation API Changes ----------- - ``equalize_adapthist`` now takes a ``kernel_size`` keyword argument, replacing the ``ntiles_*`` arguments. - The functions ``blob_dog``, ``blob_log`` and ``blob_doh`` now return float arrays instead of integer arrays. - ``transform.integrate`` now takes lists of tuples instead of integers to define the window over which to integrate. - `reverse_map` parameter in `skimage.transform.warp` has been removed. - `enforce_connectivity` in `skimage.segmentation.slic` defaults to ``True``. - `skimage.measure.fit.BaseModel._params`, `skimage.transform.ProjectiveTransform._matrix`, `skimage.transform.PolynomialTransform._params`, `skimage.transform.PiecewiseAffineTransform.affines_*` attributes have been removed. - `skimage.filters.denoise_*` have moved to `skimage.restoration.denoise_*`. Deprecations ------------ - ``filters.gaussian_filter`` has been renamed ``filters.gaussian`` - ``filters.gabor_filter`` has been renamed ``filters.gabor`` - ``restoration.nl_means_denoising`` has been renamed ``restoration.denoise_nl_means`` - ``measure.LineModel`` was deprecated in favor of ``measure.LineModelND`` - ``measure.structural_similarity`` has been renamed ``measure.compare_ssim`` - ``data.lena`` has been deprecated, and gallery examples use instead the ``data.astronaut()`` picture. Contributors to this release ---------------------------- (Listed alphabetically by last name) - K.-Michael Aye - Connelly Barnes - Sumit Binnani - Vighnesh Birodkar - François Boulogne - Matthew Brett - Steven Brown - Arnaud De Bruecker - Olivier Debeir - Charles Deledalle - endolith - Eric Lubeck - Victor Escorcia - Ivo Flipse - Joel Frederico - Cédric Gilon - Christoph Gohlke - Korijn van Golen - Emmanuelle Gouillart - J. Goutin - Blake Griffith - M. Hawker - Jonathan Helmus - Dhruv Jawali - Lee Kamentsky - Kevin Keraudren - Julius Bier Kirkegaard - David Koeller - Gustav Larsson - Gregory R. Lee - Guillaume Lemaitre - Benny Lichtner - Himanshu Mishra - Juan Nunez-Iglesias - Ömer Özak - Leena P. - Michael Pacer - Daniil Pakhomov - David Perez-Suarez - Egor Panfilov - David PS - Sergio Pascual - Ariel Rokem - Nicolas Rougier - Christian Sachs - Kshitij Saraogi - Martin Savc - Johannes Schönberger - Arve Seljebu - Tim Sheerman-Chase - Scott Sievert - Steven Silvester - Alexandre Fioravante de Siqueira - Daichi Suzuo - Noah Trebesch - Pratap Vardhan - Gael Varoquaux - Stefan van der Walt - Joshua Warner - Josh Warner - Warren Weckesser - Daniel Wennberg - John Wiggins - Robin Wilson - Olivia Wilson scikit-image-0.13.1/doc/release/release_0.13.rst000066400000000000000000000462621316256236300211630ustar00rootroot00000000000000Announcement: scikit-image 0.13.1 ================================= scikit-image 0.13.1 is a bug-fix and compatibility update. See below for the many new features in 0.13.0. The main contribution in 0.13.1 is Jarrod Millman's valiant work to ensure scikit-image works with both NetworkX 1.11 and 2.0 (#2766). Additional updates include: - Bug fix in similarity transform estimation, by GitHub user @zhongzyd (#2690) - Bug fixes in ``skimage.util.plot_matches`` and ``denoise_wavelet``, by Gregory Lee (#2650, #2640) - Documentation updates by Egor Panfilov (#2716) and Jirka Borovec (#2524) - Documentation build fixes by Gregory Lee (#2666, #2731), Nelle Varoquaux (#2722), and Stéfan van der Walt (#2723, #2810) Announcement: scikit-image 0.13.0 ================================= We're happy to (finally) announce the release of scikit-image v0.13.0! scikit-image is an image processing toolbox for SciPy that includes algorithms for segmentation, geometric transformations, color space manipulation, analysis, filtering, morphology, feature detection, and more. For more information, examples, and documentation, please visit our website: http://scikit-image.org and our gallery of examples http://scikit-image.org/docs/dev/auto_examples/ Highlights ---------- This release is the result of a year of work, with over 200 pull requests by 82 contributors. Highlights include: - Improved n-dimensional image support. This release adds nD support to: * ``regionprops`` computation for centroids (#2083) * ``segmentation.clear_border`` (#2087) * Hessian matrix (#2194) - In addition, the following new functions support nD images: * new wavelet denoising function, ``restoration.denoise_wavelet`` (#1833, #2190, #2238, #2240, #2241, #2242, #2462) * new thresholding functions, ``filters.threshold_sauvola`` and ``filters.threshold_niblack`` (#2266, #2441) * new local maximum, local minimum, hmaxima, hminima functions (#2449) - Grey level co-occurrence matrix (GLCM) now works with uint16 images - ``filters.try_all_threshold`` to rapidly see output of various thresholding methods - Frangi and Hessian filters (2D only) (#2153) - New *compact watershed* algorithm in ``segmentation.watershed`` (#2211) - New *shape index* algorithm in ``feature.shape_index`` (#2312) New functions and features -------------------------- - Add threshold minimum algorithm (#2104) - Implement mean and triangle thresholding (#2126) - Add Frangi and Hessian filters (#2153) - add bbox_area to region properties (#2187) - colorconv: Add rgba2rgb() (#2181) - Lewiner marching cubes algorithm (#2052) - image inversion (#2199) - wavelet denoising (from #1833) (#2190) - routine to estimate the noise standard deviation from an image (#1837) - Add compact watershed and clean up existing watershed (#2211) - Added the missing 'grey2rgb' function. (#2316) - Shape index (#2312) - Fundamental and essential matrix 8-point algorithm (#1357) - Add YUV, YIQ, YPbPr, YCbCr colorspaces - Detection of local extrema from morphology (#2449) - shannon entropy (#2416) Documentation improvements -------------------------- - add details about github SSH keys in contributing page (#2073) - Add example for felzenszwalb image segmentation (#2096) - Sphinx gallery for example gallery (#2078) - Improved region boundary RAG docs (#2106) - Add gallery Lucy-Richardson deconvolution algorithm (#2376) - Gallery: Use Horse to illustrate Convex Hull (#2431) - Add working with OpenCV in user guide (#2519) Code improvements ----------------- - Remove lena image from test suite (#1985) - Remove duplicate mean calculation in skimage.feature.match_template (#1980) - Add nD support to clear_border (#2087) - Add uint16 images support for co-occurrence matrix (#2095) - Add default parameters for Gaussian and median filters (#2151) - try_all to choose the best threshold algorithm (#2110) - Add support for multichannel in Felzenszwalb segmentation (#2134) - Improved SimilarityTransform, new EuclideanTransform class (#2044) - ENH: Speed up Hessian matrix computation (#2194) - add n-dimensional support to denoise_wavelet (#2242) - Speedup ``inpaint_biharmonic`` (#2234) - Update hessian matrix code to include order kwarg (#2327) - Handle cases for label2rgb where input labels are negative and/or nonconsecutive (#2370) - Added watershed_line parameter (#2393) API Changes ----------- - Remove deprecated ``filter`` module. Use ``filters`` instead. (#2023) - Remove ``skimage.filters.canny`` links. Use ``feature.canny`` instead. (#2024) - Removed Python 2.6 support and related checks (#2033) - Remove deprecated {h/v}sobel, {h/v}prewitt, {h/v}scharr, roberts_{positive/negative} filters (#2159) - Remove deprecated ``_mode_deprecations`` (#2156) - Remove deprecated None defaults in ``rescale_intensity`` (#2161) - Parameters ``ntiles_x`` and ``ntiles_y`` have been removed from ``exposure.equalize_adapthist`` - The minimum NumPy version is now 1.11, and the minimum SciPy version is now 0.17 Deprecations ------------ - clip_negative will be set to false by default in version 0.15 (func: dtype_limits) (#2228) - Deprecate "dynamic_range" in favor of "data_range" (#2384) - The default value of the ``circle`` argument to ``radon`` and ``iradon`` transforms will be ``True`` in 0.15 (#2235) - The default value of ``multichannel`` for ``denoise_bilateral`` and ``denoise_nl_means`` will be ``False`` in 0.15 - The default value of ``block_norm`` in ``feature.hog`` will be L2-Hysteresis in 0.15. - The ``threshold_adaptive`` function is deprecated. Use ``threshold_local`` instead. - The default value of ``mode`` in ``transform.swirl``, ``resize``, and ``rescale`` will be "reflect" in 0.15. Contributors to this release ---------------------------- - AbdealiJK - Rodrigo Benenson - Vighnesh Birodkar - Jirka Borovec - François Boulogne - Matthew Brett - Sarwat Fatima - Rachel Finck - Joe Futrelle - Jeroen Van Goey - Christoph Gohlke - Roman Golovanov - Emmanuelle Gouillart - Anshita Gupta - David Haberthür - Jeff Hemmelgarn - Hiyorimi - Daniel Hyams - Alex Izvorski - Kyle Jackson - Jirka - JohnnyTeutonic - Kevin Keraudren - Almar Klein - Yu Kobayashi - Moriyoshi Koizumi - Lachlan - LachlanD - George Laurent - Gregory R. Lee - Evan Limanto - Ben Longo - Victor MARTIN - Oliver Mader - Ken'ichi Matsui - Jeremy Metz - Jeyson Molina - Michael Mueller - Juan Nunez-Iglesias - Egor Panfilov - Paul - PengchengAi - Francisco de la Peña - Pavlin Poličar - Orion Poplawski - Zoe Richards - Todd V. Rovito - Christian Sachs - Sanya - Johannes Schönberger - Pavel Shevchuk - Scott Sievert - Steven Silvester - Shaun Singh - Sourav Singh - Alexandre Fioravante de Siqueira - Samuel St-Jean - Noah Stier - Ole Streicher - Martin Thoma - Matěj Týč - Viraj - Stefan van der Walt - Josh Warner - Olivia Wilson - Robin Wilson - Martin Zackrisson - Yue Zheng - Nick Zoghb - alexandrejaguar - almar - cespenel - danielballan - dmesejo - eli - jwittenbach - lgeorge - mljli - rjeli - skrish13 - tseclaudia - walter Pull requests merged in this release ------------------------------------ - Warn if user tries to build with older Cython version (#1986) - Remove lena image from test suite (#1985) - Add inpaint to module init (#1987) - Pre-calculate tempate mean (#1980) - rgb2grey -> grey2rgb (#1989) - Also expose rgb2gray as rgb2grey (#1990) - Remove all .md5 files on clean (#1992) - avoid deprecation warnings when calling compute_ssim with multichannel=True (#1994) - DOC: Suggest multichannel=True in compute_ssim error (#1999) - [DOC] add link to guide (#2001) - Fix docs-->doc in CONTRIBUTING (#2009) - Turn ``dask`` into an optional dependency (#2013) - Correct regexp for catching mpl warnings (#2014) - BUILD: Use --pre flag for Travis pip installs. (#1938) - Github templates (#1954) - added doc to PaintTool (#1934) - skimage.segmentation.quickshift signature is missing from API docs (#2017) - MAINT: Upgrade tifffile (#2016) - Modified .gitignore to properly ignore auto_example files (#1966) - MAINT: Switch from coveralls -> codecov in CI build (#2015) - skimage.segmentation.quickshift signature is missing from API docs, third attempt (#2021) - MAINT: Remove deprecated ``filter`` module (#2023) - Remove ``skimage.filters.canny`` links (#2024) - Document regionprops bbox property. (#2030) - Fix URL to texturematch paper (#2031) - Improved skimage.segmentation.active_contour input arguments' dtype support (#2032) - Fix local test function (#2034) - Removed Python 2.6 support and related checks (#2033) - Test on OSX (#2038) - Change coverage badge to codecov (#2055) - TST: Speed up bilateral filter tests (#2061) - Speed up colorconv._convert (#2064) - FIX: Fix import of 'warn' in qt_plugin (#2070) - Add YUV, YIQ, YPbPr, YCbCr colorspaces - adding details about github SSH keys in contributing page (#2073) - ENH: Pass np.random.RandomState to RANSAC (#2072) - Handle IO objects with tifffile (#2046) - Updated centroid to use coords - works in 3d (#2083) - [WIP] Hierarchical Merging of Region Boundary RAGs (#2058) - Add nD support to clear_border (#2087) - DOC: update for new API (minor) (#2090) - Add example for felzenszwalb image segmentation (#2096) - DOC: add space before column on variable def (minor...) (#2102) - DOC: Guide new contributors to HTTPS, not SSH (#2082) - Add François Boulogne to the mailmap (#2117) - Move skimage.filters.rank description and todos from README into docstring. (#2115) - Fixing Error and documentation on Otsu Threshold (#2118) - Add scuinto's second email address to mailmap (#2122) - MAINT: around label and regionprops functions. (#2100) - Add threshold minimum algorithm (#2104) - Sphinx gallery for example gallery (#2078) - DOC: make a title shorter in gallery (#2128) - DOC: refactor axes with lists (#2129) - DOC ENH + API fix on houghline transform (#2089) - Fix indentation for example script (#2136) - Implement mean and triangle thresholding (#2126) - Move ``skimage.measure.label`` references to the docstring (#2143) - Fix outdated GraphicsGems link (#2149) - Docstring (#2145) - Add uint16 images support for co-occurrence matrix (#2095) - Remove deprecared {h/v}sobel, {h/v}prewitt, {h/v}scharr, roberts_{positive/negative} filters (#2159) - Remove deprecated ``_mode_deprecations`` (#2156) - Default parameters (#2151) - ENH: try_all to choose the best threshold algorithm and DOC refactoring (#2110) - BUGFIX: inverse_map should not be None (#2160) - Switched felzenszwalb gray to multichannel version (#2134) - Writing, style, and PEP8 fixes for greycomatrix (#2157) - Add Frangi and Hessian filters (#2153) - Improved SimilarityTransform, new EuclideanTransform class (#2044) - color.colorconv: Fix documentation of rgb2gray() (#2169) - fix region merging in ``segmentation.felzenszwalb`` (#2164) - Remove deprecated None defaults in ``rescale_intensity`` (#2161) - DOC: add a note to template_match (#2176) - Added chapter title formatting for numpy_images.rst (#2177) - Fix threshold_triangle to work with non-integer images. (#2171) - Improved region boundary RAG docs (#2106) - ENH add bbox_area to region properties (#2187) - colorconv: Add rgba2rgb() (#2181) - DOC: add DOI to references (#2188) - remove local threshold in try_all_threshold (#2180) - DOC: add a note on warning treatment (#2198) - ENH: Speed up Hessian matrix computation (#2194) - Add missing unittests for data and convert horse to binary (#2196) - Fix ssim example (#2208) - [MRG] MAINT: Replaced gaussian_filter with filters.gaussian (#2210) - [MRG] DOC: corrected mssim docstring to return float (#2218) - FEAT: Lewiner marching cubes algorithm (#2052) - Fix bug in salt and pepper noise (#2223) - TST: Updated AppVeyor to use Conda, added msvc_runtime (#2217) - Improve docstrings for captions (#2185) - Add task update version on wikipedia (#2230) - NEW + DOC: image inversion (#2199) - ENH: Implements wavelet denoising (from #1833) (#2190) - TEST: define seed in setup() / Fix random test failure (#2227) - add n-dimensional support to denoise_wavelet (#2242) - API: clip_negative will be set to false by default in version 0.15 (func: dtype_limits) (#2228) - Speedup ``inpaint_biharmonic`` (#2234) - MAINT dtype.py (PEP8) (#2231) - Removed unused extend_image (#2251) - ENH: routine to estimate the noise standard deviation from an image (#1837) - Restrict sphinx builds to a single process. Remove vendored numpydoc. (#2257) - Added more specific check for image shape in threshold_otsu warning (#2259) - Allow running ``setup.py egg_info`` without numpy installed. (#2260) - Add compact watershed and clean up existing watershed (#2211) - Use numpy.pad directly, removing most shipped code in util.pad (#2265) - DOC: fix references (#2262) - DOC: tiny fixes in gallery (#2226) - DOC: fix typo (#2274) - Update Manifest.in (#2255) - Bugfix unbounded correlation -- Dhyams fix for match template (#2263) - DOC: Refactor example skeletonize in the gallery (#2141) - [MRG+1] Insert metadata in docstrings of images in skimage.data.* (#2236) - MAINT: Radon (docstring, API, PEP8) (#2235) - [MRG+2] MAINT: Fix numpy deprecation (#2283) - Reduce whitespace around plots (#2144) - [MRG+1] By default, clear_border is not inplace (#2285) - Remove unused imports in ``transform.{pyx/pxd}`` (#2288) - [MRG+1] Add community guidelines to doc navigation (#2287) - Adding colors to the IHC (#2279) - FIX: select num_peaks if labels is specified (#2098) - [MRG+1] Add felzenszwalb shape validation (#2286) - [MRG+1] more closesly match the BayesShrink paper in _wavelet_threshold (#2241) - Remove usages of ``subplots_adjust`` (#2289) - [MRG+1] Change documentation page favicon (#2291) - [MRG+1] TST: prefer ``assert_`` from numpy.testing over assert (#2298) - TSTFIX: Bug fix for development version of scipy (#2302) - Enhance ``compare_ssim`` docstring (#2314) - Added the missing 'grey2rgb' function. (#2316) - PEP8 (#2304) - Made Python wrappers for public Cython functions (#2303) - Update mailing list location (#2328) - Shape Index (#2312) - Add pywavelets to runtime requirements in DEPENDS.txt (#2238) - Refactor variable names in ``skimage.draw`` (#2321) - Fix display problem when printing error messages (#2326) - Added catch for zero image in threshold_li (#2338) - FIX: Modified peak_local_max to use relabel_sequential (#2341) - Update favicon in _static (#2355) - Remove incorrect input type assumption in doctrings for rgb2hsv and h… (#2354) - Update the default boundary mode in transform.swirl (#2331) - Update imread() document (#2358) - Check for valid mode in random_walker(). (#2362) - Fix 1 broken test in _shared not executed by nose/travis (#2229) - Update hessian matrix code to include order kwarg (#2327) - Clarify purpose of beta1 and beta2 parameters in documentations of sk… (#2382) - Handle cases for label2rgb where input labels are negative and/or nonconsecutive (#2370) - Update ``exposure.equalize_adapthist`` args and docstring (#2220) - Fix (x, y) origin description in user guide (#2385) - Update docstring for show_rag method (#2375) - Fix display problem when printing error messages (#2372) - Added a check for empty array in _shared.utils.py (#2364) - Fix no peaks blob log (#2349) - ENH: Extend draw.ellipse with orientation kwarg (#2366) - Fundamental and essential matrix 8-point algorithm (#1357) - Fix reference to travis notes (#2403) - Fix deprecated option in sphinx that causes warning treated as error in travis (#2395) - Update Travis Script (#2374) - Remove the freeimage plugin (#1933) - Fix shape type for histogram (#2417) - Add illuminant and observer parameters to the rgb2lab and lab2rgb functions. (#2306) - PEP8 (#2413) - MAINT: merge lists of dtypes (#2420) - Made (partially) ``pep8``-compliant (#2392) - Added titles and text to make plot_brief.py example more clear (#2193) - DOC: Add reference to standard illuminant (#2418) - Added titles and text to the subplots to make it easier to new comers for plot_censure.py example (#2191) - Deprecate "dynamic_range" in favor of "data_range" (#2384) - Make PR 2266 n-D compatible (#4) - Add new "thin" method based on Guo and Hall 1989 (#2294) - local threshold niblack sauvola (from Jeysonmc PR) (#2266) - stable ellipse fitting (#2394) - Add gallery Lucy-Richardson deconvolution algorithm (#2376) - Improve SIFT loader docstring according to comments and StackOverflow (#2404) - Change to Javascript loading of search index (patch by Julian Taylor) (#2438) - Fix segfault in connected components (patch by Yaroslav Halchenko) (#2437) - Refactor ``util/dtype.py`` (#2425) - ENH: Gallery, various little stylish corrections (DFT example). (#2430) - Make peak_local_max return indices sorted, always (#2435) - Correct comment of probabilistic_hough_line(). (#2448) - Added watershed_line parameter (#2393) - Solved Gaussian value range #2383 (#2388) - Gallery: Use Horse to illustrate Convex Hull (#2431) - MRG: update build matrix for Python 3.6 (#2451) - Wavelet denoising in YCbCr color space (#2240) - Gallery: Use gray cmap for coins (#2459) - Bug fix for Sauvola and Niblack thresholding (#2441) - MAINT: removes _wavelet_threshold docstring (#2460) - BUG: fix denoise_wavelet for odd-length input (#2462) - MAINT: warns for new multichannel default in denoise_{bilateral, nl_means} (#2467) - Various enhancements in gallery for denoising (#2461) - Tool for checking completeness of sdist (#2085) - Add different ``skimage.hog`` blocks normalization methods (#2040) - DOC: fix typos and add references (#2478) - update sphinx gallery to 0.1.8 (#2474) - DOC: Fix typo in gaussian filter docstring (#2487) - Add threshold_local, deprecate old threshold_adaptive API (#2490) - Default edge mode change for resize and rescale (#2484) - Add ``dask[array]`` to optional requirements (#2494) - DOC: Adds an instruction to CONTRIBUTING.txt & Updates the git install link for Windows (#2495) - ENH: generalize hough_peak functions (#2109) - Fix gallery examples (#2504) - Bump min scipy version (#2254) - DOC: img_as_float add note about range if input dtype is float (#2499) - Update tifffile for 2017.01.12 changes (#2497) - Replace local_sum by block_reduce in docstrings. (#2498) - MAINT: pass scipys truncate parameter to gaussian filter API (#2508) - DOC: gallery: join segmentation: enhancement (#2507) - Tidy up the deployment of dev docs (#2516) - Do not require cython for normal builds (#2509) - Fix broken ``test_ncut_stable_subgraph`` for Python 3.6, enable Python 3.6 in Travis (#2511) - Improved background labeling (#2381) - For imread's load_func, make the img_num argument optional (#2054) - Make compatible with current networkx master (#2455) - Miscellaneous tidying in HOG code (#2526) - BUG: Fix NumPy error when no descriptors are returned by ORB (#2537) - BUG: ValueError in restoration.denoise_bilateral for zeros image (#2533) - Fix link to Python XY (#2542) - TST: fix ValueError with scipy-0.19.0rc2 (#2544) - DOC: Update URL for data.coins() (#2548) - Replace GRIN URL with Flickr URL (#2547) - Have ``threshold_minimum`` return identical results on i686 and x86_64 (#2549) - Minor Fix (Issue #2554) (#2556) - Remove ``offset`` parameter from ``filters.threshold_sauvola`` docstring (#2566) - Practical guide to reading video files (#1012) - Remove dask from ``requirements.txt`` (#2572) - Fix ``morphology.watershed`` error message (#2570) - DOC: Added working with OpenCV in user guide (#2519) - NEW: add shannon entropy (#2416) - Fix typo in ylabel of GLCM demo (#2576) - Detection of local extrema from morphology (#2449) - Add extrema functions to ``__init__`` (#2588) scikit-image-0.13.1/doc/release/release_0.3.rst000066400000000000000000000025711316256236300210750ustar00rootroot00000000000000Announcement: scikits.image 0.3 =============================== After a brief (!) absence, we're back with a new and shiny version of scikits.image, the image processing toolbox for SciPy. This release runs under all major operating systems where Python (>=2.6 or 3.x), NumPy and SciPy can be installed. For more information, visit our website http://scikits-image.org or the examples gallery at http://scikits-image.org/docs/0.3/auto_examples/ New Features ------------ - Shortest paths - Total variation denoising - Hough and probabilistic Hough transforms - Radon transform with reconstruction - Histogram of gradients - Morphology, including watershed, connected components - Faster homography transformations (rotations, zoom, etc.) - Image dtype conversion routines - Line drawing - Better image collection handling - Constant time median filter - Edge detection (canny, sobel, etc.) - IO: Freeimage, FITS, Qt and other image loaders; video support. - SIFT feature loader - Example data-sets ... as well as many bug fixes and minor updates. Contributors for this release ----------------------------- Martin Bergtholdt Luis Pedro Coelho Chris Colbert Damian Eads Dan Farmer Emmanuelle Gouillart Brian Holt Pieter Holtzhausen Thouis (Ray) Jones Lee Kamentsky Almar Klein Kyle Mandli Andreas Mueller Neil Muller Zachary Pincus James Turner Stefan van der Walt Gael Varoquaux Tony Yu scikit-image-0.13.1/doc/release/release_0.4.rst000066400000000000000000000024441316256236300210750ustar00rootroot00000000000000Announcement: scikits-image 0.4 =============================== We're happy to announce the 0.4 release of scikits-image, an image processing toolbox for SciPy. Please visit our examples gallery to see what we've been up to: http://scikits-image.org/docs/0.4/auto_examples/ Note that, in this release, we renamed the module from ``scikits.image`` to ``skimage``, to work around name space conflicts with other scikits (similarly, the machine learning scikit is now imported as ``sklearn``). A big shout-out also to everyone currently at SciPy India; have fun, and remember to join the scikits-image sprint! This release runs under all major operating systems where Python (>=2.6 or 3.x), NumPy and SciPy can be installed. For more information, visit our website http://scikits-image.org New Features ------------ - Module rename from ``scikits.image`` to ``skimage`` - Contour finding - Grey-level co-occurrence matrices - Skeletonization and medial axis transform - Convex hull images - New test data sets - GDAL I/O plugin ... as well as some bug fixes. Contributors to this release ---------------------------- * Andreas Mueller * Christopher Gohlke * Emmanuelle Gouillart * Neil Yager * Nelle Varoquaux * Riaan van den Dool * Stefan van der Walt * Thouis (Ray) Jones * Tony S Yu * Zachary Pincus scikit-image-0.13.1/doc/release/release_0.5.rst000066400000000000000000000017221316256236300210740ustar00rootroot00000000000000Announcement: scikits-image 0.5 =============================== We're happy to announce the 0.5 release of scikits-image, our image processing toolbox for SciPy. For more information, please visit our website http://scikits-image.org New Features ------------ - Consistent intensity rescaling and improved range conversion. - Random walker segmentation. - Harris corner detection. - Otsu thresholding. - Block views, window views and montage. - Plugin for Christoph Gohlke's "tifffile". - Peak detection. - Improved FreeImage wrappers and meta-data reading. - 8-neighbor and background labelling. ... along with updates to the documentation and website, and a number of bug fixes. Contributors to this release ---------------------------- * Andreas Mueller * Brian Holt * Christoph Gohlke * Emmanuelle Gouillart * Michael Aye * Nelle Varoquaux * Nicolas Pinto * Nicolas Poilvert * Pieter Holtzhausen * Stefan van der Walt * Tony S Yu * Warren Weckesser * Zachary Pincus scikit-image-0.13.1/doc/release/release_0.6.rst000066400000000000000000000020211316256236300210660ustar00rootroot00000000000000Announcement: scikits-image 0.6 =============================== We're happy to announce the 6th version of scikits-image! Scikits-image is an image processing toolbox for SciPy that includes algorithms for segmentation, geometric transformations, color space manipulation, analysis, filtering, morphology, feature detection, and more. For more information, examples, and documentation, please visit our website http://skimage.org New Features ------------ - Packaged in Debian as ``python-skimage`` - Template matching - Fast user-defined image warping - Adaptive thresholding - Structural similarity index - Polygon, circle and ellipse drawing - Peak detection - Region properties - TiffFile I/O plugin ... along with some bug fixes and performance tweaks. Contributors to this release ---------------------------- - Vincent Albufera - David Cournapeau - Christoph Gohlke - Emmanuelle Gouillart - Pieter Holtzhausen - Zachary Pincus - Johannes Schönberger - Tom (tangofoxtrotmike) - James Turner - Stefan van der Walt - Tony S Yu scikit-image-0.13.1/doc/release/release_0.7.rst000066400000000000000000000035401316256236300210760ustar00rootroot00000000000000Announcement: scikits-image 0.7.0 ================================= We're happy to announce the 7th version of scikits-image! Scikits-image is an image processing toolbox for SciPy that includes algorithms for segmentation, geometric transformations, color space manipulation, analysis, filtering, morphology, feature detection, and more. For more information, examples, and documentation, please visit our website http://skimage.org New Features ------------ It's been only 3 months since scikits-image 0.6 was released, but in that short time, we've managed to add plenty of new features and enhancements, including - Geometric image transforms - 3 new image segmentation routines (Felsenzwalb, Quickshift, SLIC) - Local binary patterns for texture characterization - Morphological reconstruction - Polygon approximation - CIE Lab color space conversion - Image pyramids - Multispectral support in random walker segmentation - Slicing, concatenation, and natural sorting of image collections - Perimeter and coordinates measurements in regionprops - An extensible image viewer based on Qt and Matplotlib, with plugins for edge detection, line-profiling, and viewing image collections Plus, this release adds a number of bug fixes, new examples, and performance enhancements. Contributors to this release ---------------------------- This release was only possible due to the efforts of many contributors, both new and old. - Andreas Mueller - Andreas Wuerl - Andy Wilson - Brian Holt - Christoph Gohlke - Dharhas Pothina - Emmanuelle Gouillart - Guillaume Gay - Josh Warner - James Bergstra - Johannes Schonberger - Jonathan J. Helmus - Juan Nunez-Iglesias - Leon Tietz - Marianne Corvellec - Matt McCormick - Neil Yager - Nicolas Pinto - Nicolas Poilvert - Pavel Campr - Petter Strandmark - Stefan van der Walt - Tim Sheerman-Chase - Tomas Kazmar - Tony S Yu - Wei Li scikit-image-0.13.1/doc/release/release_0.8.rst000066400000000000000000000044421316256236300211010ustar00rootroot00000000000000Announcement: scikits-image 0.8.0 ================================= We're happy to announce the 8th version of scikit-image! scikit-image is an image processing toolbox for SciPy that includes algorithms for segmentation, geometric transformations, color space manipulation, analysis, filtering, morphology, feature detection, and more. For more information, examples, and documentation, please visit our website: http://scikit-image.org New Features ------------ - New rank filter package with many new functions and a very fast underlying local histogram algorithm, especially for large structuring elements `skimage.filter.rank.*` - New function for small object removal `skimage.morphology.remove_small_objects` - New circular hough transformation `skimage.transform.hough_circle` - New function to draw circle perimeter `skimage.draw.circle_perimeter` and ellipse perimeter `skimage.draw.ellipse_perimeter` - New dense DAISY feature descriptor `skimage.feature.daisy` - New bilateral filter `skimage.filter.denoise_bilateral` - New faster TV denoising filter based on split-Bregman algorithm `skimage.filter.denoise_tv_bregman` - New linear hough peak detection `skimage.transform.hough_peaks` - New Scharr edge detection `skimage.filter.scharr` - New geometric image scaling as convenience function `skimage.transform.rescale` - New theme for documentation and website - Faster median filter through vectorization `skimage.filter.median_filter` - Grayscale images supported for SLIC segmentation - Unified peak detection with more options `skimage.feature.peak_local_max` - `imread` can read images via URL and knows more formats `skimage.io.imread` Additionally, this release adds lots of bug fixes, new examples, and performance enhancements. Contributors to this release ---------------------------- This release was only possible due to the efforts of many contributors, both new and old. - Adam Ginsburg - Anders Boesen Lindbo Larsen - Andreas Mueller - Christoph Gohlke - Christos Psaltis - Colin Lea - François Boulogne - Jan Margeta - Johannes Schönberger - Josh Warner (Mac) - Juan Nunez-Iglesias - Luis Pedro Coelho - Marianne Corvellec - Matt McCormick - Nicolas Pinto - Olivier Debeir - Paul Ivanov - Sergey Karayev - Stefan van der Walt - Steven Silvester - Thouis (Ray) Jones - Tony S Yu scikit-image-0.13.1/doc/release/release_0.9.rst000066400000000000000000000074301316256236300211020ustar00rootroot00000000000000Announcement: scikit-image 0.9.0 ================================ We're happy to announce the release of scikit-image v0.9.0! scikit-image is an image processing toolbox for SciPy that includes algorithms for segmentation, geometric transformations, color space manipulation, analysis, filtering, morphology, feature detection, and more. For more information, examples, and documentation, please visit our website: http://scikit-image.org New Features ------------ `scikit-image` now runs without translation under both Python 2 and 3. In addition to several bug fixes, speed improvements and examples, the 204 pull requests merged for this release include the following new features (PR number in brackets): Segmentation: - 3D support in SLIC segmentation (#546) - SLIC voxel spacing (#719) - Generalized anisotropic spacing support for random_walker (#775) - Yen threshold method (#686) Transforms and filters: - SART algorithm for tomography reconstruction (#584) - Gabor filters (#371) - Hough transform for ellipses (#597) - Fast resampling of nD arrays (#511) - Rotation axis center for Radon transforms with inverses. (#654) - Reconstruction circle in inverse Radon transform (#567) - Pixelwise image adjustment curves and methods (#505) Feature detection: - [experimental API] BRIEF feature descriptor (#591) - [experimental API] Censure (STAR) Feature Detector (#668) - Octagon structural element (#669) - Add non rotation invariant uniform LBPs (#704) Color and noise: - Add deltaE color comparison and lab2lch conversion (#665) - Isotropic denoising (#653) - Generator to add various types of random noise to images (#625) - Color deconvolution for immunohistochemical images (#441) - Color label visualization (#485) Drawing and visualization: - Wu's anti-aliased circle, line, bezier curve (#709) - Linked image viewers and docked plugins (#575) - Rotated ellipse + bezier curve drawing (#510) - PySide & PyQt4 compatibility in skimage-viewer (#551) Other: - Python 3 support without 2to3. (#620) - 3D Marching Cubes (#469) - Line, Circle, Ellipse total least squares fitting and RANSAC algorithm (#440) - N-dimensional array padding (#577) - Add a wrapper around `scipy.ndimage.gaussian_filter` with useful default behaviors. (#712) - Predefined structuring elements for 3D morphology (#484) API changes ----------- The following backward-incompatible API changes were made between 0.8 and 0.9: - No longer wrap ``imread`` output in an ``Image`` class - Change default value of `sigma` parameter in ``skimage.segmentation.slic`` to 0 - ``hough_circle`` now returns a stack of arrays that are the same size as the input image. Set the ``full_output`` flag to True for the old behavior. - The following functions were deprecated over two releases: `skimage.filter.denoise_tv_chambolle`, `skimage.morphology.is_local_maximum`, `skimage.transform.hough`, `skimage.transform.probabilistic_hough`,`skimage.transform.hough_peaks`. Their functionality still exists, but under different names. Contributors to this release ---------------------------- This release was made possible by the collaborative efforts of many contributors, both new and old. They are listed in alphabetical order by surname: - Ankit Agrawal - K.-Michael Aye - Chris Beaumont - François Boulogne - Luis Pedro Coelho - Marianne Corvellec - Olivier Debeir - Ferdinand Deger - Kemal Eren - Jostein Bø Fløystad - Christoph Gohlke - Emmanuelle Gouillart - Christian Horea - Thouis (Ray) Jones - Almar Klein - Xavier Moles Lopez - Alexis Mignon - Juan Nunez-Iglesias - Zachary Pincus - Nicolas Pinto - Davin Potts - Malcolm Reynolds - Umesh Sharma - Johannes Schönberger - Chintak Sheth - Kirill Shklovsky - Steven Silvester - Matt Terry - Riaan van den Dool - Stéfan van der Walt - Josh Warner - Adam Wisniewski - Yang Zetian - Tony S Yu scikit-image-0.13.1/doc/release/release_dev.rst000066400000000000000000000011531316256236300213460ustar00rootroot00000000000000Announcement: scikit-image 0.X.0 ================================ We're happy to announce the release of scikit-image v0.X.0! scikit-image is an image processing toolbox for SciPy that includes algorithms for segmentation, geometric transformations, color space manipulation, analysis, filtering, morphology, feature detection, and more. For more information, examples, and documentation, please visit our website: http://scikit-image.org New Features ------------ Improvements ------------ API Changes ----------- Deprecations ------------ Contributors to this release ---------------------------- scikit-image-0.13.1/doc/release/release_template.rst000066400000000000000000000012731316256236300224060ustar00rootroot00000000000000Announcement: scikit-image 0.X.0 ================================ We're happy to announce the release of scikit-image v0.X.0! scikit-image is an image processing toolbox for SciPy that includes algorithms for segmentation, geometric transformations, color space manipulation, analysis, filtering, morphology, feature detection, and more. For more information, examples, and documentation, please visit our website: http://scikit-image.org New Features ------------ Improvements ------------ API Changes ----------- Deprecations ------------ - Python 2.6 support has been dropped. Minimal required Python version is 2.7. 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    scikit-image-0.13.1/doc/source/api_changes.rst000066400000000000000000000063251316256236300212170ustar00rootroot00000000000000Version 0.13 ------------ - `skimage.filter` has been removed. Use `skimage.filters` instead. - `skimage.filters.canny` has been removed. `canny` is available only from `skimage.feature` now. - Deprecated filters `hsobel`, `vsobel`, `hscharr`, `vscharr`, `hprewitt`, `vprewitt`, `roberts_positive_diagonal`, `roberts_negative_diagonal` have been removed from `skimage.filters.edges`. - The `sigma` parameter of `skimage.filters.gaussian` and the `selem` parameter of `skimage.filters.median` have been made optional, with default values. - The `clip_negative` parameter of `skimage.util.dtype_limits` is now set to `None` by default, equivalent to `True`, the former value. In version 0.15, will be set to `False`. - The `circle` parameter of `skimage.transform.radon` and `skimage.transform.iradon` are now set to `None` by default, equivalent to `False`, the former value. In version 0.15, will be set to `True`. - Parameters ``ntiles_x``, ``ntiles_y`` have been removed from ``skimage.exposure.equalize_adapthist``. - The ``freeimage`` io plugin is no longer supported, and will use ``imageio`` instead. We will completely remove the ``freeimage`` plugin in Version 0.14. Version 0.12 ------------ - ``equalize_adapthist`` now takes a ``kernel_size`` keyword argument, replacing the ``ntiles_*`` arguments. - The functions ``blob_dog``, ``blob_log`` and ``blob_doh`` now return float arrays instead of integer arrays. - ``transform.integrate`` now takes lists of tuples instead of integers to define the window over which to integrate. - `reverse_map` parameter in `skimage.transform.warp` has been removed. - `enforce_connectivity` in `skimage.segmentation.slic` defaults to ``True``. - `skimage.measure.fit.BaseModel._params`, `skimage.transform.ProjectiveTransform._matrix`, `skimage.transform.PolynomialTransform._params`, `skimage.transform.PiecewiseAffineTransform.affines_*` attributes have been removed. - `skimage.filters.denoise_*` have moved to `skimage.restoration.denoise_*`. - `skimage.data.lena` has been removed. Version 0.11 ------------ - The ``skimage.filter`` subpackage has been renamed to ``skimage.filters``. - Some edge detectors returned values greater than 1--their results are now appropriately scaled with a factor of ``sqrt(2)``. Version 0.10 ------------ - Removed ``skimage.io.video`` functionality due to broken gstreamer bindings Version 0.9 ----------- - No longer wrap ``imread`` output in an ``Image`` class - Change default value of `sigma` parameter in ``skimage.segmentation.slic`` to 0 - ``hough_circle`` now returns a stack of arrays that are the same size as the input image. Set the ``full_output`` flag to True for the old behavior. - The following functions were deprecated over two releases: `skimage.filter.denoise_tv_chambolle`, `skimage.morphology.is_local_maximum`, `skimage.transform.hough`, `skimage.transform.probabilistic_hough`,`skimage.transform.hough_peaks`. Their functionality still exists, but under different names. Version 0.4 ----------- - Switch mask and radius arguments for ``median_filter`` Version 0.3 ----------- - Remove ``as_grey``, ``dtype`` keyword from ImageCollection - Remove ``dtype`` from imread - Generalise ImageCollection to accept a load_func scikit-image-0.13.1/doc/source/cell_profiler.rst000066400000000000000000000033021316256236300215670ustar00rootroot00000000000000CellProfiler BSD license announcement ------------------------------------- :: From: Vebjorn Ljosa To: scikit-image@googlegroups.com Date: June 3, 2010 We have changed the license of some parts of CellProfiler from GNU GPL to BSD. It has previously been proposed [1] that some of the image processing code in CellProfiler be merged into SciPy, and the license change makes this possible. The CellProfiler SVN repository is at https://svn.broadinstitute.org/CellProfiler/trunk/CellProfiler/. The file LICENSE [2] contains a list of BSD-licensed subdirectories as well as other license details. The rest of CellProfiler continues to be licensed under the GNU GPL. The BSD-licensed subdirectories are: * CellProfiler/cpmath [3]: image processing algorithms * CellProfiler/utilities [4]: contains a Java bridge, making it possible to call Java functions from Python * bioformats [5]: wrapper that uses the Java bridge to have Bioformats [6] read or write an image file Good luck with the upcoming scikits.image sprint. I don't think anyone from the CellProfiler team will be able to take part in the sprint this time, but don't hesitate to ask on the cellprofiler-dev@broadinstitute.org mailing list. Thanks, Vebjorn [1] http://stefanv.github.com/scikits.image/contribute.html [2] https://svn.broadinstitute.org/CellProfiler/trunk/CellProfiler/LICENSE [3] https://svn.broadinstitute.org/CellProfiler/trunk/CellProfiler/cellprofiler/cpmath/ [4] https://svn.broadinstitute.org/CellProfiler/trunk/CellProfiler/cellprofiler/utilities/ [5] https://svn.broadinstitute.org/CellProfiler/trunk/CellProfiler/bioformats/ [6] http://www.loci.wisc.edu/software/bio-formats scikit-image-0.13.1/doc/source/conf.py000066400000000000000000000301411316256236300175140ustar00rootroot00000000000000# -*- coding: utf-8 -*- # # skimage documentation build configuration file, created by # sphinx-quickstart on Sat Aug 22 13:00:30 2009. # # This file is execfile()d with the current directory set to its containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. import sys import os import skimage # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. #sys.path.append(os.path.abspath('.')) curpath = os.path.dirname(__file__) sys.path.append(os.path.join(curpath, '..', 'ext')) # -- General configuration ----------------------------------------------------- # Add any Sphinx extension module names here, as strings. They can be extensions # coming with Sphinx (named 'sphinx.ext.*') or your custom ones. extensions = ['sphinx.ext.autodoc', 'sphinx.ext.imgmath', 'numpydoc', 'sphinx.ext.autosummary', 'sphinx.ext.intersphinx', 'sphinx.ext.linkcode', 'sphinx_gallery.gen_gallery' ] autosummary_generate = True #------------------------------------------------------------------------ # Sphinx-gallery configuration #------------------------------------------------------------------------ sphinx_gallery_conf = { 'doc_module' : 'skimage', # path to your examples scripts 'examples_dirs' : '../examples', # path where to save gallery generated examples 'gallery_dirs' : 'auto_examples', 'backreferences_dir': 'api', 'reference_url' : { 'skimage': None, 'matplotlib': 'http://matplotlib.org', 'numpy': 'http://docs.scipy.org/doc/numpy-1.6.0', 'scipy': 'http://docs.scipy.org/doc/scipy-0.11.0/reference',} } # Determine if the matplotlib has a recent enough version of the # plot_directive, otherwise use the local fork. try: from matplotlib.sphinxext import plot_directive except ImportError: use_matplotlib_plot_directive = False else: try: use_matplotlib_plot_directive = (plot_directive.__version__ >= 2) except AttributeError: use_matplotlib_plot_directive = False if use_matplotlib_plot_directive: extensions.append('matplotlib.sphinxext.plot_directive') else: extensions.append('plot_directive') # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix of source filenames. source_suffix = '.rst' # The encoding of source files. #source_encoding = 'utf-8-sig' # The master toctree document. master_doc = 'index' # General information about the project. project = 'skimage' copyright = '2013, the scikit-image team' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. with open('../../skimage/__init__.py') as f: setup_lines = f.readlines() version = 'vUndefined' for l in setup_lines: if l.startswith('__version__'): version = l.split("'")[1] break # The full version, including alpha/beta/rc tags. release = version # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. #language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: #today = '' # Else, today_fmt is used as the format for a strftime call. #today_fmt = '%B %d, %Y' # List of documents that shouldn't be included in the build. #unused_docs = [] # List of directories, relative to source directory, that shouldn't be searched # for source files. exclude_trees = [] # The reST default role (used for this markup: `text`) to use for all documents. #default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. #add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). #add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. #show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # A list of ignored prefixes for module index sorting. #modindex_common_prefix = [] # -- Options for HTML output --------------------------------------------------- # The theme to use for HTML and HTML Help pages. Major themes that come with # Sphinx are currently 'default' and 'sphinxdoc'. html_theme = 'scikit-image' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. #html_theme_options = {} # Add any paths that contain custom themes here, relative to this directory. html_theme_path = ['themes'] # The name for this set of Sphinx documents. If None, it defaults to # " v documentation". html_title = 'skimage v%s docs' % version # A shorter title for the navigation bar. Default is the same as html_title. #html_short_title = None # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. html_favicon = '_static/favicon.ico' # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. #html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. #html_use_smartypants = True # Custom sidebar templates, maps document names to template names. html_sidebars = { '**': ['searchbox.html', 'navigation.html', 'localtoc.html', 'versions.html'], } # Additional templates that should be rendered to pages, maps page names to # template names. # html_additional_pages = {} # If false, no module index is generated. #html_use_modindex = True # If false, no index is generated. #html_use_index = True # If true, the index is split into individual pages for each letter. #html_split_index = False # If true, links to the reST sources are added to the pages. #html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. #html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. #html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. #html_use_opensearch = '' # If nonempty, this is the file name suffix for HTML files (e.g. ".xhtml"). #html_file_suffix = '' # Output file base name for HTML help builder. htmlhelp_basename = 'scikitimagedoc' # -- Options for LaTeX output -------------------------------------------------- # The paper size ('letter' or 'a4'). #latex_paper_size = 'letter' # The font size ('10pt', '11pt' or '12pt'). latex_font_size = '10pt' # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, author, documentclass [howto/manual]). latex_documents = [ ('contents', 'scikit-image.tex', u'The scikit-image Documentation', u'scikit-image development team', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. #latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. #latex_use_parts = False # Additional stuff for the LaTeX preamble. latex_elements = {} latex_elements['preamble'] = r''' \usepackage{enumitem} \setlistdepth{100} \usepackage{amsmath} \DeclareUnicodeCharacter{00A0}{\nobreakspace} % In the parameters section, place a newline after the Parameters header \usepackage{expdlist} \let\latexdescription=\description \def\description{\latexdescription{}{} \breaklabel} % Make Examples/etc section headers smaller and more compact \makeatletter \titleformat{\paragraph}{\normalsize\py@HeaderFamily}% {\py@TitleColor}{0em}{\py@TitleColor}{\py@NormalColor} \titlespacing*{\paragraph}{0pt}{1ex}{0pt} \makeatother ''' # Documents to append as an appendix to all manuals. #latex_appendices = [] # If false, no module index is generated. latex_domain_indices = False # ----------------------------------------------------------------------------- # Numpy extensions # ----------------------------------------------------------------------------- numpydoc_show_class_members = False numpydoc_class_members_toctree = False # ----------------------------------------------------------------------------- # Plots # ----------------------------------------------------------------------------- plot_basedir = os.path.join(curpath, "plots") plot_pre_code = """ import numpy as np import matplotlib.pyplot as plt np.random.seed(0) import matplotlib matplotlib.rcParams.update({ 'font.size': 14, 'axes.titlesize': 12, 'axes.labelsize': 10, 'xtick.labelsize': 8, 'ytick.labelsize': 8, 'legend.fontsize': 10, 'figure.subplot.bottom': 0.2, 'figure.subplot.left': 0.2, 'figure.subplot.right': 0.9, 'figure.subplot.top': 0.85, 'figure.subplot.wspace': 0.4, 'text.usetex': False, }) """ plot_include_source = True plot_formats = [('png', 100), ('pdf', 100)] plot2rst_index_name = 'README' plot2rst_rcparams = {'image.cmap' : 'gray', 'image.interpolation' : 'none'} # ----------------------------------------------------------------------------- # intersphinx # ----------------------------------------------------------------------------- _python_version_str = '{0.major}.{0.minor}'.format(sys.version_info) _python_doc_base = 'http://docs.python.org/' + _python_version_str intersphinx_mapping = { 'python': (_python_doc_base, None), 'numpy': ('http://docs.scipy.org/doc/numpy', (None, './_intersphinx/numpy-objects.inv')), 'scipy': ('http://docs.scipy.org/doc/scipy/reference', (None, './_intersphinx/scipy-objects.inv')), 'sklearn': ('http://scikit-learn.org/stable', (None, './_intersphinx/sklearn-objects.inv')), 'matplotlib': ('http://matplotlib.org/', (None, 'http://matplotlib.org/objects.inv')) } # ---------------------------------------------------------------------------- # Source code links # ---------------------------------------------------------------------------- import inspect from os.path import relpath, dirname # Function courtesy of NumPy to return URLs containing line numbers def linkcode_resolve(domain, info): """ Determine the URL corresponding to Python object """ if domain != 'py': return None modname = info['module'] fullname = info['fullname'] submod = sys.modules.get(modname) if submod is None: return None obj = submod for part in fullname.split('.'): try: obj = getattr(obj, part) except: return None try: fn = inspect.getsourcefile(obj) except: fn = None if not fn: return None try: source, lineno = inspect.findsource(obj) except: lineno = None if lineno: linespec = "#L%d" % (lineno + 1) else: linespec = "" fn = relpath(fn, start=dirname(skimage.__file__)) if 'dev' in skimage.__version__: return ("http://github.com/scikit-image/scikit-image/blob/" "master/skimage/%s%s" % (fn, linespec)) else: return ("http://github.com/scikit-image/scikit-image/blob/" "v%s/skimage/%s%s" % (skimage.__version__, fn, linespec)) scikit-image-0.13.1/doc/source/contribute.rst000066400000000000000000000001111316256236300211170ustar00rootroot00000000000000.. include:: ../../TASKS.txt .. include:: ../../.github/CONTRIBUTING.txt scikit-image-0.13.1/doc/source/gitwash/000077500000000000000000000000001316256236300176645ustar00rootroot00000000000000scikit-image-0.13.1/doc/source/gitwash/branch_dropdown.png000066400000000000000000000376671316256236300235660ustar00rootroot00000000000000PNG  IHDR7'piCCPICC ProfilexTkA6n"Zkx"IYhE6bk Ed3In6&*Ezd/JZE(ޫ(b-nL~7}ov r4 Ril|Bj A4%UN$As{z[V{wwҶ@G*q Y<ߡ)t9Nyx+=Y"|@5-MS%@H8qR>׋infObN~N>! ?F?aĆ=5`5_M'Tq. 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N4Z"(* tm"(F@IE@@Td`kE@H4L(@"$;](@PehD= 1.7 you can ensure that the link is correctly set by using the ``--set-upstream`` option:: git push --set-upstream origin my-new-feature From now on git will know that ``my-new-feature`` is related to the ``my-new-feature`` branch in the github repo. .. _edit-flow: The editing workflow ==================== Overview -------- :: # hack hack git add my_new_file git commit -am 'NF - some message' git push In more detail -------------- #. Make some changes #. See which files have changed with ``git status`` (see `git status`_). You'll see a listing like this one:: # On branch ny-new-feature # Changed but not updated: # (use "git add ..." to update what will be committed) # (use "git checkout -- ..." to discard changes in working directory) # # modified: README # # Untracked files: # (use "git add ..." to include in what will be committed) # # INSTALL no changes added to commit (use "git add" and/or "git commit -a") #. Check what the actual changes are with ``git diff`` (`git diff`_). #. Add any new files to version control ``git add new_file_name`` (see `git add`_). #. To commit all modified files into the local copy of your repo,, do ``git commit -am 'A commit message'``. Note the ``-am`` options to ``commit``. The ``m`` flag just signals that you're going to type a message on the command line. The ``a`` flag |emdash| you can just take on faith |emdash| or see `why the -a flag?`_ |emdash| and the helpful use-case description in the `tangled working copy problem`_. The `git commit`_ manual page might also be useful. #. To push the changes up to your forked repo on github, do a ``git push`` (see `git push`_). Ask for your changes to be reviewed or merged ============================================= When you are ready to ask for someone to review your code and consider a merge: #. Go to the URL of your forked repo, say ``http://github.com/your-user-name/scikit-image``. #. Use the 'Switch Branches' dropdown menu near the top left of the page to select the branch with your changes: .. image:: branch_dropdown.png #. Click on the 'Pull request' button: .. image:: pull_button.png Enter a title for the set of changes, and some explanation of what you've done. Say if there is anything you'd like particular attention for - like a complicated change or some code you are not happy with. If you don't think your request is ready to be merged, just say so in your pull request message. This is still a good way of getting some preliminary code review. Some other things you might want to do ====================================== Delete a branch on github ------------------------- :: git checkout master # delete branch locally git branch -D my-unwanted-branch # delete branch on github git push origin :my-unwanted-branch (Note the colon ``:`` before ``test-branch``. See also: http://github.com/guides/remove-a-remote-branch Several people sharing a single repository ------------------------------------------ If you want to work on some stuff with other people, where you are all committing into the same repository, or even the same branch, then just share it via github. First fork scikit-image into your account, as from :ref:`forking`. Then, go to your forked repository github page, say ``http://github.com/your-user-name/scikit-image`` Click on the 'Admin' button, and add anyone else to the repo as a collaborator: .. image:: pull_button.png Now all those people can do:: git clone git@githhub.com:your-user-name/scikit-image.git Remember that links starting with ``git@`` use the ssh protocol and are read-write; links starting with ``git://`` are read-only. Your collaborators can then commit directly into that repo with the usual:: git commit -am 'ENH - much better code' git push origin master # pushes directly into your repo Explore your repository ----------------------- To see a graphical representation of the repository branches and commits:: gitk --all To see a linear list of commits for this branch:: git log You can also look at the `network graph visualizer`_ for your github repo. Finally the :ref:`fancy-log` ``lg`` alias will give you a reasonable text-based graph of the repository. .. _rebase-on-trunk: Rebasing on trunk ----------------- Let's say you thought of some work you'd like to do. You :ref:`update-mirror-trunk` and :ref:`make-feature-branch` called ``cool-feature``. At this stage trunk is at some commit, let's call it E. Now you make some new commits on your ``cool-feature`` branch, let's call them A, B, C. Maybe your changes take a while, or you come back to them after a while. In the meantime, trunk has progressed from commit E to commit (say) G:: A---B---C cool-feature / D---E---F---G trunk At this stage you consider merging trunk into your feature branch, and you remember that this here page sternly advises you not to do that, because the history will get messy. Most of the time you can just ask for a review, and not worry that trunk has got a little ahead. But sometimes, the changes in trunk might affect your changes, and you need to harmonize them. In this situation you may prefer to do a rebase. rebase takes your changes (A, B, C) and replays them as if they had been made to the current state of ``trunk``. In other words, in this case, it takes the changes represented by A, B, C and replays them on top of G. After the rebase, your history will look like this:: A'--B'--C' cool-feature / D---E---F---G trunk See `rebase without tears`_ for more detail. To do a rebase on trunk:: # Update the mirror of trunk git fetch upstream # go to the feature branch git checkout cool-feature # make a backup in case you mess up git branch tmp cool-feature # rebase cool-feature onto trunk git rebase --onto upstream/master upstream/master cool-feature In this situation, where you are already on branch ``cool-feature``, the last command can be written more succinctly as:: git rebase upstream/master When all looks good you can delete your backup branch:: git branch -D tmp If it doesn't look good you may need to have a look at :ref:`recovering-from-mess-up`. If you have made changes to files that have also changed in trunk, this may generate merge conflicts that you need to resolve - see the `git rebase`_ man page for some instructions at the end of the "Description" section. There is some related help on merging in the git user manual - see `resolving a merge`_. .. _recovering-from-mess-up: Recovering from mess-ups ------------------------ Sometimes, you mess up merges or rebases. Luckily, in git it is relatively straightforward to recover from such mistakes. If you mess up during a rebase:: git rebase --abort If you notice you messed up after the rebase:: # reset branch back to the saved point git reset --hard tmp If you forgot to make a backup branch:: # look at the reflog of the branch git reflog show cool-feature 8630830 cool-feature@{0}: commit: BUG: io: close file handles immediately 278dd2a cool-feature@{1}: rebase finished: refs/heads/my-feature-branch onto 11ee694744f2552d 26aa21a cool-feature@{2}: commit: BUG: lib: make seek_gzip_factory not leak gzip obj ... # reset the branch to where it was before the botched rebase git reset --hard cool-feature@{2} .. _rewriting-commit-history: Rewriting commit history ------------------------ .. note:: Do this only for your own feature branches. There's an embarassing typo in a commit you made? Or perhaps the you made several false starts you would like the posterity not to see. This can be done via *interactive rebasing*. Suppose that the commit history looks like this:: git log --oneline eadc391 Fix some remaining bugs a815645 Modify it so that it works 2dec1ac Fix a few bugs + disable 13d7934 First implementation 6ad92e5 * masked is now an instance of a new object, MaskedConstant 29001ed Add pre-nep for a copule of structured_array_extensions. ... and ``6ad92e5`` is the last commit in the ``cool-feature`` branch. Suppose we want to make the following changes: * Rewrite the commit message for ``13d7934`` to something more sensible. * Combine the commits ``2dec1ac``, ``a815645``, ``eadc391`` into a single one. We do as follows:: # make a backup of the current state git branch tmp HEAD # interactive rebase git rebase -i 6ad92e5 This will open an editor with the following text in it:: pick 13d7934 First implementation pick 2dec1ac Fix a few bugs + disable pick a815645 Modify it so that it works pick eadc391 Fix some remaining bugs # Rebase 6ad92e5..eadc391 onto 6ad92e5 # # Commands: # p, pick = use commit # r, reword = use commit, but edit the commit message # e, edit = use commit, but stop for amending # s, squash = use commit, but meld into previous commit # f, fixup = like "squash", but discard this commit's log message # # If you remove a line here THAT COMMIT WILL BE LOST. # However, if you remove everything, the rebase will be aborted. # To achieve what we want, we will make the following changes to it:: r 13d7934 First implementation pick 2dec1ac Fix a few bugs + disable f a815645 Modify it so that it works f eadc391 Fix some remaining bugs This means that (i) we want to edit the commit message for ``13d7934``, and (ii) collapse the last three commits into one. Now we save and quit the editor. Git will then immediately bring up an editor for editing the commit message. After revising it, we get the output:: [detached HEAD 721fc64] FOO: First implementation 2 files changed, 199 insertions(+), 66 deletions(-) [detached HEAD 0f22701] Fix a few bugs + disable 1 files changed, 79 insertions(+), 61 deletions(-) Successfully rebased and updated refs/heads/my-feature-branch. and the history looks now like this:: 0f22701 Fix a few bugs + disable 721fc64 ENH: Sophisticated feature 6ad92e5 * masked is now an instance of a new object, MaskedConstant If it went wrong, recovery is again possible as explained :ref:`above `. .. include:: links.inc scikit-image-0.13.1/doc/source/gitwash/following_latest.rst000066400000000000000000000015561316256236300240010ustar00rootroot00000000000000.. _following-latest: ============================= Following the latest source ============================= These are the instructions if you just want to follow the latest *scikit-image* source, but you don't need to do any development for now. The steps are: * :ref:`install-git` * get local copy of the `scikit-image github`_ git repository * update local copy from time to time Get the local copy of the code ============================== From the command line:: git clone git://github.com/scikit-image/scikit-image.git You now have a copy of the code tree in the new ``scikit-image`` directory. Updating the code ================= From time to time you may want to pull down the latest code. 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[Г޴"2QJ((/DYYEI?vX0&)<|}lVAlK ZJד_ |-f'DbYt`&wV+|UtKĂ'YwZ:H4* J %ʂnTN@aW;%x1paLͿUSg My::WY:Ҕ*_wY(Ț%pYFrZ<b7ɇ~?MehdE{ix*# hSId3}n÷coK[95w?]sN W9ёӎOQ١܂2V2rWᏠk3t@ -݂ɢX;l :+Jݙ\ڛ\s"Ǵ&&}/o/[^@[f%~5[,A\GFm˲feK'\Vd"kTirMn .Mƿ;8su*$TVY䋑poo kJ U-ro~Z p/gRUkiKJf$h.kj)$\]'q0tJ 7NGA^Ąv} &O Lj_DUXʪӒ(c3|WںhkpYIݮ`e )V2d 3YB^5 )qXE%W[{ {ѿDWҲ4} zTYx|# UC5[:TI/_wrȭpM~XL[,E1w<$!!sJM p8@8G#P>U2V~}<#pA[Ó8@u#-FpA[Ó8@u#Iq8rrIGd-N$IENDB`scikit-image-0.13.1/doc/source/gitwash/forking_hell.rst000066400000000000000000000022571316256236300230670ustar00rootroot00000000000000.. _forking: ====================================================== Making your own copy (fork) of scikit-image ====================================================== You need to do this only once. The instructions here are very similar to the instructions at http://help.github.com/forking/ |emdash| please see that page for more detail. We're repeating some of it here just to give the specifics for the `scikit-image`_ project, and to suggest some default names. Set up and configure a github account ===================================== If you don't have a github account, go to the github page, and make one. You then need to configure your account to allow write access |emdash| see the ``Generating SSH keys`` help on `github help`_. Create your own forked copy of `scikit-image`_ ====================================================== #. Log into your github account. #. Go to the `scikit-image`_ github home at `scikit-image github`_. #. Click on the *fork* button: .. image:: forking_button.png Now, after a short pause and some 'Hardcore forking action', you should find yourself at the home page for your own forked copy of `scikit-image`_. .. include:: links.inc scikit-image-0.13.1/doc/source/gitwash/git_development.rst000066400000000000000000000003401316256236300236000ustar00rootroot00000000000000.. _git-development: ===================== Git for development ===================== Contents: .. toctree:: :maxdepth: 2 forking_hell set_up_fork configure_git development_workflow maintainer_workflow scikit-image-0.13.1/doc/source/gitwash/git_install.rst000066400000000000000000000011021316256236300227210ustar00rootroot00000000000000.. _install-git: ============= Install git ============= Overview ======== ================ ============= Debian / Ubuntu ``sudo apt-get install git`` Fedora ``sudo yum install git-core`` Windows Download and install msysGit_ OS X Use the git-osx-installer_ ================ ============= In detail ========= See the git page for the most recent information. Have a look at the github install help pages available from `github help`_ There are good instructions here: http://book.git-scm.com/2_installing_git.html .. include:: links.inc scikit-image-0.13.1/doc/source/gitwash/git_intro.rst000066400000000000000000000010511316256236300224110ustar00rootroot00000000000000============== Introduction ============== These pages describe a git_ and github_ workflow for the `scikit-image`_ project. There are several different workflows here, for different ways of working with *scikit-image*. This is not a comprehensive git reference, it's just a workflow for our own project. It's tailored to the github hosting service. You may well find better or quicker ways of getting stuff done with git, but these should get you started. For general resources for learning git, see :ref:`git-resources`. .. include:: links.inc scikit-image-0.13.1/doc/source/gitwash/git_links.inc000066400000000000000000000063471316256236300223540ustar00rootroot00000000000000.. This (-*- rst -*-) format file contains commonly used link targets and name substitutions. It may be included in many files, therefore it should only contain link targets and name substitutions. Try grepping for "^\.\. _" to find plausible candidates for this list. .. NOTE: reST targets are __not_case_sensitive__, so only one target definition is needed for nipy, NIPY, Nipy, etc... .. git stuff .. _git: http://git-scm.com/ .. _github: http://github.com .. _github help: http://help.github.com .. _msysgit: https://git-for-windows.github.io/ .. _git-osx-installer: http://code.google.com/p/git-osx-installer/downloads/list .. _subversion: http://subversion.tigris.org/ .. _git cheat sheet: http://github.com/guides/git-cheat-sheet .. _pro git book: http://progit.org/ .. _git svn crash course: http://git-scm.com/course/svn.html .. _learn.github: http://learn.github.com/ .. _network graph visualizer: http://github.com/blog/39-say-hello-to-the-network-graph-visualizer .. _git user manual: http://schacon.github.com/git/user-manual.html .. _git tutorial: http://schacon.github.com/git/gittutorial.html .. _git community book: http://book.git-scm.com/ .. _git ready: http://www.gitready.com/ .. _git casts: http://www.gitcasts.com/ .. _Fernando's git page: http://www.fperez.org/py4science/git.html .. _git magic: http://www-cs-students.stanford.edu/~blynn/gitmagic/index.html .. _git concepts: http://www.eecs.harvard.edu/~cduan/technical/git/ .. _git clone: http://schacon.github.com/git/git-clone.html .. _git checkout: http://schacon.github.com/git/git-checkout.html .. _git commit: http://schacon.github.com/git/git-commit.html .. _git push: http://schacon.github.com/git/git-push.html .. _git pull: http://schacon.github.com/git/git-pull.html .. _git add: http://schacon.github.com/git/git-add.html .. _git status: http://schacon.github.com/git/git-status.html .. _git diff: http://schacon.github.com/git/git-diff.html .. _git log: http://schacon.github.com/git/git-log.html .. _git branch: http://schacon.github.com/git/git-branch.html .. _git remote: http://schacon.github.com/git/git-remote.html .. _git rebase: http://schacon.github.com/git/git-rebase.html .. _git config: http://schacon.github.com/git/git-config.html .. _why the -a flag?: http://www.gitready.com/beginner/2009/01/18/the-staging-area.html .. _git staging area: http://www.gitready.com/beginner/2009/01/18/the-staging-area.html .. _tangled working copy problem: http://tomayko.com/writings/the-thing-about-git .. _git management: http://kerneltrap.org/Linux/Git_Management .. _linux git workflow: http://www.mail-archive.com/dri-devel@lists.sourceforge.net/msg39091.html .. _git parable: http://tom.preston-werner.com/2009/05/19/the-git-parable.html .. _git foundation: http://matthew-brett.github.com/pydagogue/foundation.html .. _deleting master on github: http://matthew-brett.github.com/pydagogue/gh_delete_master.html .. _rebase without tears: http://matthew-brett.github.com/pydagogue/rebase_without_tears.html .. _resolving a merge: http://schacon.github.com/git/user-manual.html#resolving-a-merge .. _ipython git workflow: http://mail.scipy.org/pipermail/ipython-dev/2010-October/006746.html .. other stuff .. _python: http://www.python.org .. |emdash| unicode:: U+02014 .. vim: ft=rst scikit-image-0.13.1/doc/source/gitwash/git_resources.rst000066400000000000000000000034221316256236300232740ustar00rootroot00000000000000.. _git-resources: ============= git resources ============= Tutorials and summaries ======================= * `github help`_ has an excellent series of how-to guides. * `learn.github`_ has an excellent series of tutorials * The `pro git book`_ is a good in-depth book on git. * A `git cheat sheet`_ is a page giving summaries of common commands. * The `git user manual`_ * The `git tutorial`_ * The `git community book`_ * `git ready`_ |emdash| a nice series of tutorials * `git casts`_ |emdash| video snippets giving git how-tos. * `git magic`_ |emdash| extended introduction with intermediate detail * The `git parable`_ is an easy read explaining the concepts behind git. * `git foundation`_ expands on the `git parable`_. * Fernando Perez' git page |emdash| `Fernando's git page`_ |emdash| many links and tips * A good but technical page on `git concepts`_ * `git svn crash course`_: git for those of us used to subversion_ Advanced git workflow ===================== There are many ways of working with git; here are some posts on the rules of thumb that other projects have come up with: * Linus Torvalds on `git management`_ * Linus Torvalds on `linux git workflow`_ . Summary; use the git tools to make the history of your edits as clean as possible; merge from upstream edits as little as possible in branches where you are doing active development. Manual pages online =================== You can get these on your own machine with (e.g) ``git help push`` or (same thing) ``git push --help``, but, for convenience, here are the online manual pages for some common commands: * `git add`_ * `git branch`_ * `git checkout`_ * `git clone`_ * `git commit`_ * `git config`_ * `git diff`_ * `git log`_ * `git pull`_ * `git push`_ * `git remote`_ * `git status`_ .. include:: links.inc scikit-image-0.13.1/doc/source/gitwash/index.rst000066400000000000000000000003651316256236300215310ustar00rootroot00000000000000.. _using-git: Working with *scikit-image* source code ================================================ Contents: .. toctree:: :maxdepth: 2 git_intro git_install following_latest patching git_development git_resources scikit-image-0.13.1/doc/source/gitwash/known_projects.inc000066400000000000000000000026741316256236300234350ustar00rootroot00000000000000.. Known projects .. PROJECTNAME placeholders .. _PROJECTNAME: http://neuroimaging.scipy.org .. _`PROJECTNAME github`: http://github.com/nipy .. _`PROJECTNAME mailing list`: http://projects.scipy.org/mailman/listinfo/nipy-devel .. numpy .. _numpy: http://numpy.org .. _`numpy github`: http://github.com/numpy/numpy .. _`numpy mailing list`: http://mail.scipy.org/mailman/listinfo/numpy-discussion .. scipy .. _scipy: http://www.scipy.org .. _`scipy github`: http://github.com/scipy/scipy .. _`scipy mailing list`: http://mail.scipy.org/mailman/listinfo/scipy-dev .. nipy .. _nipy: http://nipy.org/nipy .. _`nipy github`: http://github.com/nipy/nipy .. _`nipy mailing list`: http://mail.scipy.org/mailman/listinfo/nipy-devel .. ipython .. _ipython: http://ipython.scipy.org .. _`ipython github`: http://github.com/ipython/ipython .. _`ipython mailing list`: http://mail.scipy.org/mailman/listinfo/IPython-dev .. dipy .. _dipy: http://nipy.org/dipy .. _`dipy github`: http://github.com/Garyfallidis/dipy .. _`dipy mailing list`: http://mail.scipy.org/mailman/listinfo/nipy-devel .. nibabel .. _nibabel: http://nipy.org/nibabel .. _`nibabel github`: http://github.com/nipy/nibabel .. _`nibabel mailing list`: http://mail.scipy.org/mailman/listinfo/nipy-devel .. marsbar .. _marsbar: http://marsbar.sourceforge.net .. _`marsbar github`: http://github.com/matthew-brett/marsbar .. _`MarsBaR mailing list`: https://lists.sourceforge.net/lists/listinfo/marsbar-users scikit-image-0.13.1/doc/source/gitwash/links.inc000066400000000000000000000001611316256236300214750ustar00rootroot00000000000000.. compiling links file .. include:: known_projects.inc .. include:: this_project.inc .. include:: git_links.inc scikit-image-0.13.1/doc/source/gitwash/maintainer_workflow.rst000066400000000000000000000060301316256236300244760ustar00rootroot00000000000000.. _maintainer-workflow: ################### Maintainer workflow ################### This page is for maintainers |emdash| those of us who merge our own or other peoples' changes into the upstream repository. Being as how you're a maintainer, you are completely on top of the basic stuff in :ref:`development-workflow`. The instructions in :ref:`linking-to-upstream` add a remote that has read-only access to the upstream repo. Being a maintainer, you've got read-write access. It's good to have your upstream remote have a scary name, to remind you that it's a read-write remote:: git remote add upstream-rw git@github.com:scikit-image/scikit-image.git git fetch upstream-rw ******************* Integrating changes ******************* Let's say you have some changes that need to go into trunk (``upstream-rw/master``). The changes are in some branch that you are currently on. For example, you are looking at someone's changes like this:: git remote add someone git://github.com/someone/scikit-image.git git fetch someone git branch cool-feature --track someone/cool-feature git checkout cool-feature So now you are on the branch with the changes to be incorporated upstream. The rest of this section assumes you are on this branch. A few commits ============= If there are only a few commits, consider rebasing to upstream:: # Fetch upstream changes git fetch upstream-rw # rebase git rebase upstream-rw/master Remember that, if you do a rebase, and push that, you'll have to close any github pull requests manually, because github will not be able to detect the changes have already been merged. A long series of commits ======================== If there are a longer series of related commits, consider a merge instead:: git fetch upstream-rw git merge --no-ff upstream-rw/master The merge will be detected by github, and should close any related pull requests automatically. Note the ``--no-ff`` above. This forces git to make a merge commit, rather than doing a fast-forward, so that these set of commits branch off trunk then rejoin the main history with a merge, rather than appearing to have been made directly on top of trunk. Check the history ================= Now, in either case, you should check that the history is sensible and you have the right commits:: git log --oneline --graph git log -p upstream-rw/master.. The first line above just shows the history in a compact way, with a text representation of the history graph. The second line shows the log of commits excluding those that can be reached from trunk (``upstream-rw/master``), and including those that can be reached from current HEAD (implied with the ``..`` at the end). So, it shows the commits unique to this branch compared to trunk. The ``-p`` option shows the diff for these commits in patch form. Push to trunk ============= :: git push upstream-rw my-new-feature:master This pushes the ``my-new-feature`` branch in this repository to the ``master`` branch in the ``upstream-rw`` repository. .. include:: links.inc scikit-image-0.13.1/doc/source/gitwash/patching.rst000066400000000000000000000100051316256236300222070ustar00rootroot00000000000000================ Making a patch ================ You've discovered a bug or something else you want to change in `scikit-image`_ .. |emdash| excellent! You've worked out a way to fix it |emdash| even better! You want to tell us about it |emdash| best of all! The easiest way is to make a *patch* or set of patches. Here we explain how. Making a patch is the simplest and quickest, but if you're going to be doing anything more than simple quick things, please consider following the :ref:`git-development` model instead. .. _making-patches: Making patches ============== Overview -------- :: # tell git who you are git config --global user.email you@yourdomain.example.com git config --global user.name "Your Name Comes Here" # get the repository if you don't have it git clone git://github.com/scikit-image/scikit-image.git # make a branch for your patching cd scikit-image git branch the-fix-im-thinking-of git checkout the-fix-im-thinking-of # hack, hack, hack # Tell git about any new files you've made git add somewhere/tests/test_my_bug.py # commit work in progress as you go git commit -am 'BF - added tests for Funny bug' # hack hack, hack git commit -am 'BF - added fix for Funny bug' # make the patch files git format-patch -M -C master Then, send the generated patch files to the `scikit-image mailing list`_ |emdash| where we will thank you warmly. In detail --------- #. Tell git who you are so it can label the commits you've made:: git config --global user.email you@yourdomain.example.com git config --global user.name "Your Name Comes Here" #. If you don't already have one, clone a copy of the `scikit-image`_ repository:: git clone git://github.com/scikit-image/scikit-image.git cd scikit-image #. Make a 'feature branch'. This will be where you work on your bug fix. It's nice and safe and leaves you with access to an unmodified copy of the code in the main branch:: git branch the-fix-im-thinking-of git checkout the-fix-im-thinking-of #. Do some edits, and commit them as you go:: # hack, hack, hack # Tell git about any new files you've made git add somewhere/tests/test_my_bug.py # commit work in progress as you go git commit -am 'BF - added tests for Funny bug' # hack hack, hack git commit -am 'BF - added fix for Funny bug' Note the ``-am`` options to ``commit``. The ``m`` flag just signals that you're going to type a message on the command line. The ``a`` flag |emdash| you can just take on faith |emdash| or see `why the -a flag?`_. #. When you have finished, check you have committed all your changes:: git status #. Finally, make your commits into patches. You want all the commits since you branched from the ``master`` branch:: git format-patch -M -C master You will now have several files named for the commits:: 0001-BF-added-tests-for-Funny-bug.patch 0002-BF-added-fix-for-Funny-bug.patch Send these files to the `scikit-image mailing list`_. When you are done, to switch back to the main copy of the code, just return to the ``master`` branch:: git checkout master Moving from patching to development =================================== If you find you have done some patches, and you have one or more feature branches, you will probably want to switch to development mode. You can do this with the repository you have. Fork the `scikit-image`_ repository on github |emdash| :ref:`forking`. Then:: # checkout and refresh master branch from main repo git checkout master git pull origin master # rename pointer to main repository to 'upstream' git remote rename origin upstream # point your repo to default read / write to your fork on github git remote add origin git@github.com:your-user-name/scikit-image.git # push up any branches you've made and want to keep git push origin the-fix-im-thinking-of Then you can, if you want, follow the :ref:`development-workflow`. .. include:: links.inc scikit-image-0.13.1/doc/source/gitwash/pull_button.png000066400000000000000000000311351316256236300227440ustar00rootroot00000000000000PNG  IHDR~\iu pHYs   IDATx]|ToߔMHH PKT,"S, O}%"]zHR@dw޽fIBB$a&{3s̙3s72p8 G# ?G#p!}p]G#?G#p!}p]G#?G#p!HKKCVVVuNPF C./y>xbpˏUVe͛7׭=Obyyy()6c\,4lYѬ)4,= {$H& 0,n8^CtqѸ˱gEsZzl6~?vu;Oͽyv+gkx G#}كSԩSª8xy$;w}Z*.6.O[B$ɴ r)ɝĻB? 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Overview ======== :: git clone git@github.com:your-user-name/scikit-image.git cd scikit-image git remote add upstream git://github.com/scikit-image/scikit-image.git In detail ========= Clone your fork --------------- #. Clone your fork to the local computer with ``git clone git@github.com:your-user-name/scikit-image.git`` #. Investigate. Change directory to your new repo: ``cd scikit-image``. Then ``git branch -a`` to show you all branches. You'll get something like:: * master remotes/origin/master This tells you that you are currently on the ``master`` branch, and that you also have a ``remote`` connection to ``origin/master``. What remote repository is ``remote/origin``? Try ``git remote -v`` to see the URLs for the remote. They will point to your github fork. Now you want to connect to the upstream `scikit-image github`_ repository, so you can merge in changes from trunk. .. _linking-to-upstream: Linking your repository to the upstream repo -------------------------------------------- :: cd scikit-image git remote add upstream git://github.com/scikit-image/scikit-image.git ``upstream`` here is just the arbitrary name we're using to refer to the main `scikit-image`_ repository at `scikit-image github`_. Note that we've used ``git://`` for the URL rather than ``git@``. The ``git://`` URL is read only. This means we that we can't accidentally (or deliberately) write to the upstream repo, and we are only going to use it to merge into our own code. Just for your own satisfaction, show yourself that you now have a new 'remote', with ``git remote -v show``, giving you something like:: upstream git://github.com/scikit-image/scikit-image.git (fetch) upstream git://github.com/scikit-image/scikit-image.git (push) origin git@github.com:your-user-name/scikit-image.git (fetch) origin git@github.com:your-user-name/scikit-image.git (push) .. include:: links.inc scikit-image-0.13.1/doc/source/gitwash/this_project.inc000066400000000000000000000003331316256236300230530ustar00rootroot00000000000000.. scikit-image .. _`scikit-image`: http://scikit-image.org .. _`scikit-image github`: http://github.com/scikit-image/scikit-image .. _`scikit-image mailing list`: https://mail.python.org/mailman/listinfo/scikit-image scikit-image-0.13.1/doc/source/gsoc2011.rst000066400000000000000000000011461316256236300202110ustar00rootroot00000000000000Project ideas for Google Summer of Code 2011 ============================================ - Vastly increase the number of algorithms covered by the SciKit, by implementing several prominent algorithms (probabilistic hough, radon transform, etc.) from papers. Also incorporate code from the Broad Institute's CellProfiler team. - Implement a new backend infrastructure so that the same results may be obtained using either NumPy, OpenCL, Theano, etc. - Implement optimised core routines, such as fast 2D convolution. This should be as fast as CvFilter2D (which is faster than ndimage, for example). scikit-image-0.13.1/doc/source/index.rst000066400000000000000000000023721316256236300200630ustar00rootroot00000000000000**************************** skimage |version| docs **************************** Scikit-image is an image processing toolbox for SciPy_. View the latest `release notes here `_. .. _SciPy: http://www.scipy.org Sections ======== .. toctree:: :hidden: overview api/api api_changes install user_guide contribute license auto_examples/index release_notes_and_installation .. list-table:: :class: contentstable * - `Overview `_ Introduction to skimage. - `API Reference `_ (`changes `_) Documentation for the functions included in skimage. * - `Installation Steps `_ How to install skimage. - `User Guide `_ Usage guidelines. * - `Contribute `_ Take part in development. - `License Info `_ Conditions on the use and redistribution of this package. * - `Examples `_ Introductory examples - Indices ======= .. list-table:: :class: contentstable * - :ref:`search` Search this documentation. - :ref:`genindex` All functions, classes, terms. scikit-image-0.13.1/doc/source/install.rst000066400000000000000000000126671316256236300204320ustar00rootroot00000000000000Installing scikit-image ======================= We are assuming that you have default Python environment already configured on your computer and you intend to install ``scikit-image`` inside of it. If you want to create and work with Python virtual environments, please follow instructions on `venv`_ and `virtual environments`_. There are two ways you can install ``scikit-image`` on your preferred Python environment. 1. Standard Installation 2. Development Installation 1. Standard Installation: ------------------------- ``scikit-image`` comes pre-installed with several Python distributions, including Anaconda_, `Enthought Canopy`_, `Python(x,y)`_ and `WinPython`_. However, you can install or upgrade existing ``scikit-image`` via shell/command prompt. a. Windows `````````` On Windows, you can install ``scikit-image`` using:: pip install scikit-image For Conda-based distributions (Anaconda, Miniconda), execute:: conda install scikit-image If you are using pure Python i.e. the distribution from python.org_, you'll need to manually download packages (such as numpy, scipy and scikit-image) using Python wheels available from `Christoph Gohlke's`_ website. You can install Python wheels using:: pip install SomePackage-1.0-py2.py3-none-any.whl .. _Anaconda: https://store.continuum.io/cshop/anaconda/ .. _Enthought Canopy: https://www.enthought.com/products/canopy/ .. _Python(x,y): http://python-xy.github.io/ .. _WinPython: https://winpython.github.io/ b. Debian and Ubuntu ```````````````````` On Debian and Ubuntu, install ``scikit-image`` with:: sudo apt-get install python-skimage 2. Development Installation: ---------------------------- You can install ``scikit-image`` development version if either your distribution ships an outdated version or you want to develop and work on new features before the package is released officially. a. Windows `````````` Before installing the development version, uninstall the standard version of ``scikit-image`` using pip as:: pip uninstall scikit-image or using conda (for Anaconda users) as:: conda uninstall scikit-image Now clone scikit-image on your local computer:: git clone https://github.com/scikit-image/scikit-image.git Change the directory and build from source code:: cd scikit-image python setup.py develop If you experience the error ``Error:unable to find vcvarsall.bat`` it means that your computer does not have recommended compilers for python. You can either download and install Windows compilers from `here`_ or use `MinGW compilers`_ . If using `MinGW`, make sure to correctly configure distutils by modifying (or create, if not existing) the configuration file ``distutils.cfg`` (located for example at ``C:\Python26\Lib\distutils\distutils.cfg``) to contain:: [build] compiler=mingw32 Once the build process is complete, run:: pip install -U -e . Make sure to give space after ``-e`` and add dot at the end. This will install scikit-image development version and upgrade (or install) all the required dependencies. Otherwise, you can run the following command to skip installation of dependencies:: pip install -U[--no-deps] -e . You can install or upgrade dependencies required for scikit-image anytime after installation using:: pip install -r requirements.txt --upgrade For more details on compiling in Windows, there is a lot of knowledge iterated into the `setup of appveyor`_ (a continuous integration service). .. _miniconda: http://conda.pydata.org/miniconda.html .. _python.org: http://python.org/ .. _Christoph Gohlke's: http://www.lfd.uci.edu/~gohlke/pythonlibs/ .. _setup of appveyor: https://github.com/scikit-image/scikit-image/blob/master/appveyor.yml .. _here: https://wiki.python.org/moin/WindowsCompilers#Microsoft_Visual_C.2B-.2B-_14.0_standalone:_Visual_C.2B-.2B-_Build_Tools_2015_.28x86.2C_x64.2C_ARM.29 .. _venv: https://docs.python.org/3/library/venv.html .. _virtual environments: http://docs.python-guide.org/en/latest/dev/virtualenvs/ .. _MinGW compilers: http://www.mingw.org/wiki/howto_install_the_mingw_gcc_compiler_suite b. Debian and Ubuntu ```````````````````` Install all the required dependencies:: sudo apt-get install python-matplotlib python-numpy python-pil python-scipy Get suitable compilers for successful installation:: sudo apt-get install build-essential cython Obtain the source from the git repository at ``http://github.com/scikit-image/scikit-image`` by running:: git clone https://github.com/scikit-image/scikit-image.git After unpacking, change into the source directory and execute:: pip install -e . To update:: git pull # Grab latest source python setup.py build_ext -i # Compile any modified extensions c. Building with bento `````````````````````` Alternatively, ``scikit-image`` can also be built using `bento `__. Bento depends on `WAF `__ for compilation. Follow the `Bento installation instructions `__ and `download the WAF source `__. Tell Bento where to find WAF by setting the ``WAFDIR`` environment variable:: export WAFDIR= From the ``scikit-image`` source directory:: bentomaker configure bentomaker build -j # (add -i for in-place build) bentomaker install # (when not building in-place) Depending on file permissions, the install commands may need to be run as sudo. .. include:: ../../DEPENDS.txt scikit-image-0.13.1/doc/source/license.rst000066400000000000000000000000671316256236300203750ustar00rootroot00000000000000License ======= .. literalinclude:: ../../LICENSE.txt scikit-image-0.13.1/doc/source/overview.rst000066400000000000000000000013661316256236300206240ustar00rootroot00000000000000Image Processing SciKit ======================= The `scikit-image `__ SciKit (toolkit for `SciPy `__) extends ``scipy.ndimage`` to provide a versatile set of image processing routines. It is written in the `Python `_ language. This `SciKit `_ is developed by the SciPy community. All contributions are most welcome! Please join us on the mailing list (address provided below). Homepage -------- http://scikit-image.org Source, bugs and patches ------------------------ http://github.com/scikit-image/scikit-image Mailing List ------------ https://mail.python.org/mailman/listinfo/scikit-image Contact ------- Stefan van der Walt scikit-image-0.13.1/doc/source/plots/000077500000000000000000000000001316256236300173575ustar00rootroot00000000000000scikit-image-0.13.1/doc/source/plots/hough_tf.py000066400000000000000000000013171316256236300215360ustar00rootroot00000000000000import numpy as np import matplotlib.pyplot as plt from skimage.transform import hough_line from skimage.draw import line img = np.zeros((100, 150), dtype=bool) img[30, :] = 1 img[:, 65] = 1 img[35:45, 35:50] = 1 rr, cc = line(60, 130, 80, 10) img[rr, cc] = 1 img += np.random.random(img.shape) > 0.95 out, angles, d = hough_line(img) fix, axes = plt.subplots(1, 2, figsize=(7, 4)) axes[0].imshow(img, cmap=plt.cm.gray) axes[0].set_title('Input image') axes[1].imshow( out, cmap=plt.cm.bone, extent=(np.rad2deg(angles[-1]), np.rad2deg(angles[0]), d[-1], d[0])) axes[1].set_title('Hough transform') axes[1].set_xlabel('Angle (degree)') axes[1].set_ylabel('Distance (pixel)') plt.tight_layout() plt.show() scikit-image-0.13.1/doc/source/plots/show_collection.py000066400000000000000000000011711316256236300231240ustar00rootroot00000000000000import os import matplotlib.pyplot as plt from mpl_toolkits.axes_grid import AxesGrid from skimage.io import MultiImage from skimage import data_dir # Load the multi-layer image fname = os.path.join(data_dir, 'multipage.tif') img = MultiImage(fname) # Create an image grid fig = plt.figure() grid = AxesGrid(fig, rect=(1, 1, 1), nrows_ncols=(1, 2), axes_pad=0.1) # Plot the layers on the image grid for i, frame in enumerate(img): grid[i].imshow(frame, cmap=plt.cm.gray) grid[i].set_xlabel('Frame %s' % i) grid[i].set_xticks([]) grid[i].set_yticks([]) plt.show() scikit-image-0.13.1/doc/source/random_gallery.py000066400000000000000000000024601316256236300215710ustar00rootroot00000000000000# Generate a javascript snippet that links to a random gallery example import os import glob base = os.path.abspath(os.path.dirname(__file__)) example_dir = os.path.join(base, 'auto_examples') js_fn = os.path.join(base, '_static/random.js') javascript = '''\ function insert_gallery() { var images = {{IMAGES}}; var links = {{LINKS}}; ix = Math.floor(Math.random() * images.length); document.write( '{{GALLERY_DIV}}'.replace('IMG', images[ix]).replace('URL', links[ix]) ); console.log('{{GALLERY_DIV}}'.replace('IMG', images[ix]).replace('URL', links[ix])); }; ''' gallery_div = '''\

    \ ''' examples = glob.glob(os.path.join(example_dir, 'plot_*.py')) images, links = [], [] image_url = 'http://scikit-image.org/docs/dev/_images/%s.png' link_url = 'http://scikit-image.org/docs/dev/auto_examples/%s.html' for e in examples: e = os.path.basename(e) e = e[:-len('.py')] images.append(image_url % e) links.append(link_url % e) javascript = javascript.replace('{{IMAGES}}', str(images)) javascript = javascript.replace('{{LINKS}}', str(links)) javascript = javascript.replace('{{GALLERY_DIV}}', ''.join(gallery_div.split('\n'))) f = open(js_fn, 'w') f.write(javascript) f.close() scikit-image-0.13.1/doc/source/release_notes_and_installation.rst000066400000000000000000000003001316256236300251740ustar00rootroot00000000000000.. |nbsp| unicode:: 0xA0 :trim: .. contents:: |nbsp| Release notes ============= .. include:: ../release/_release_notes_for_docs.rst Installation ============ .. include:: install.rst scikit-image-0.13.1/doc/source/themes/000077500000000000000000000000001316256236300175035ustar00rootroot00000000000000scikit-image-0.13.1/doc/source/themes/scikit-image/000077500000000000000000000000001316256236300220515ustar00rootroot00000000000000scikit-image-0.13.1/doc/source/themes/scikit-image/layout.html000066400000000000000000000107771316256236300242700ustar00rootroot00000000000000{# scikit-image/layout.html ~~~~~~~~~~~~~~~~~ Sphinx layout template for the scikit-image theme, written by Johannes Schönberger. #} {%- set url_root = pathto('', 1) %} {# XXX necessary? #} {%- if url_root == '#' %}{% set url_root = '' %}{% endif %} {%- if not embedded and docstitle %} {%- set titlesuffix = " — "|safe + docstitle|e %} {%- else %} {%- set titlesuffix = "" %} {%- endif %} {%- macro script() %} {%- for scriptfile in script_files %} {%- endfor %} {%- endmacro %} {%- macro css() %} {%- for cssfile in css_files %} {%- endfor %} {%- endmacro %} {%- block htmltitle %} {{ title|striptags|e }}{{ titlesuffix }} {%- endblock %} {{ metatags }} {{ css() }} {{ script() }} {%- if hasdoc('about') %} {%- endif %} {%- if hasdoc('genindex') %} {%- endif %} {%- if hasdoc('search') %} {%- endif %} {%- if hasdoc('copyright') %} {%- endif %} {%- if parents %} {%- endif %} {%- if next %} {%- endif %} {%- if prev %} {%- endif %} {%- block extrahead %}{% endblock %}
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[ 0. 10. 20. 30. 40.] >>> print(img_as_float(image)) [ 0. 0.03921569 0.07843137 0.11764706 0.15686275] Input types =========== Although we aim to preserve the data range and type of input images, functions may support only a subset of these data-types. In such a case, the input will be converted to the required type (if possible), and a warning message printed to the log if a memory copy is needed. Type requirements should be noted in the docstrings. The following utility functions in the main package are available to developers and users: ============= ================================= Function name Description ============= ================================= img_as_float Convert to 64-bit floating point. img_as_ubyte Convert to 8-bit uint. img_as_uint Convert to 16-bit uint. img_as_int Convert to 16-bit int. ============= ================================= These functions convert images to the desired dtype and *properly rescale their values*. If conversion reduces the precision of the image, then a warning is issued:: >>> from skimage import img_as_ubyte >>> image = np.array([0, 0.5, 1], dtype=float) >>> img_as_ubyte(image) WARNING:dtype_converter:Possible precision loss when converting from float64 to uint8 array([ 0, 128, 255], dtype=uint8) Warnings can be locally ignored with a context manager:: >>> import warnings >>> image = np.array([0, 0.5, 1], dtype=float) >>> with warnings.catch_warnings(): ... warnings.simplefilter("ignore") ... img_as_ubyte(image) array([ 0, 128, 255], dtype=uint8) Additionally, some functions take a ``preserve_range`` argument where a range conversion is convenient but not necessary. For example, interpolation in ``transform.warp`` requires an image of type float, which should have a range in [0, 1]. So, by default, input images will be rescaled to this range. However, in some cases, the image values represent physical measurements, such as temperature or rainfall values, that the user does not want rescaled. With ``preserve_range=True``, the original range of the data will be preserved, even though the output is a float image. Users must then ensure this non-standard image is properly processed by downstream functions, which may expect an image in [0, 1]. >>> from skimage import data >>> from skimage.transform import rescale >>> image = data.coins() >>> image.dtype, image.min(), image.max(), image.shape (dtype('uint8'), 1, 252, (303, 384)) >>> rescaled = rescale(image, 0.5) >>> (rescaled.dtype, np.round(rescaled.min(), 4), ... np.round(rescaled.max(), 4), rescaled.shape) (dtype('float64'), 0.0147, 0.9456, (152, 192)) >>> rescaled = rescale(image, 0.5, preserve_range=True) >>> (rescaled.dtype, np.round(rescaled.min()), ... np.round(rescaled.max()), rescaled.shape (dtype('float64'), 4.0, 241.0, (152, 192)) Output types ============ The output type of a function is determined by the function author and is documented for the benefit of the user. While this requires the user to explicitly convert the output to whichever format is needed, it ensures that no unnecessary data copies take place. A user that requires a specific type of output (e.g., for display purposes), may write:: >>> from skimage import img_as_uint >>> out = img_as_uint(sobel(image)) >>> plt.imshow(out) Working with OpenCV =================== It is possible that you may need to use an image created using ``skimage`` with OpenCV_ or vice versa. OpenCV image data can be accessed (without copying) in NumPy (and, thus, in scikit-image). OpenCV uses BGR (instead of scikit-image's RGB) for color images, and its dtype is uint8 by default (See `Image data types and what they mean`_). BGR stands for Blue Green Red. Converting BGR to RGB or vice versa ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The color images in ``skimage`` and OpenCV have 3 dimensions: width, height and color. RGB and BGR use the same color space, except the order of colors is reversed. Note that in ``scikit-image`` we usually refer to ``rows`` and ``columns`` instead of width and height (see :ref:`numpy-images-coordinate-conventions`). The following instruction effectively reverses the order of the colors, leaving the rows and columns unaffected. >>> image = image[:, :, ::-1] Using an image from OpenCV with ``skimage`` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ If cv_image is an array of unsigned bytes, ``skimage`` will understand it by default. If you prefer working with floating point images, :func:`img_as_float` can be used to convert the image:: >>> from skimage import img_as_float >>> image = img_as_float(any_opencv_image) Using an image from ``skimage`` with OpenCV ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The reverse can be achieved with :func:`img_as_ubyte`:: >>> from skimage import img_as_ubyte >>> cv_image = img_as_ubyte(any_skimage_image) Image processing pipeline ========================= This dtype behavior allows you to string together any ``skimage`` function without worrying about the image dtype. On the other hand, if you want to use a custom function that requires a particular dtype, you should call one of the dtype conversion functions (here, ``func1`` and ``func2`` are ``skimage`` functions):: >>> from skimage import img_as_float >>> image = img_as_float(func1(func2(image))) >>> processed_image = custom_func(image) Better yet, you can convert the image internally and use a simplified processing pipeline:: >>> def custom_func(image): ... image = img_as_float(image) ... # do something ... >>> processed_image = custom_func(func1(func2(image))) Rescaling intensity values ========================== When possible, functions should avoid blindly stretching image intensities (e.g. rescaling a float image so that the min and max intensities are 0 and 1), since this can heavily distort an image. For example, if you're looking for bright markers in dark images, there may be an image where no markers are present; stretching its input intensity to span the full range would make background noise look like markers. Sometimes, however, you have images that should span the entire intensity range but do not. For example, some cameras store images with 10-, 12-, or 14-bit depth per pixel. If these images are stored in an array with dtype uint16, then the image won't extend over the full intensity range, and thus, would appear dimmer than it should. To correct for this, you can use the ``rescale_intensity`` function to rescale the image so that it uses the full dtype range:: >>> from skimage import exposure >>> image = exposure.rescale_intensity(img10bit, in_range=(0, 2**10 - 1)) Here, the ``in_range`` argument is set to the maximum range for a 10-bit image. By default, ``rescale_intensity`` stretches the values of ``in_range`` to match the range of the dtype. ``rescale_intensity`` also accepts strings as inputs to ``in_range`` and ``out_range``, so the example above could also be written as:: >>> image = exposure.rescale_intensity(img10bit, in_range='uint10') Note about negative values ========================== People very often represent images in signed dtypes, even though they only manipulate the positive values of the image (e.g., using only 0-127 in an int8 image). For this reason, conversion functions *only spread the positive values* of a signed dtype over the entire range of an unsigned dtype. In other words, negative values are clipped to 0 when converting from signed to unsigned dtypes. (Negative values are preserved when converting between signed dtypes.) To prevent this clipping behavior, you should rescale your image beforehand:: >>> image = exposure.rescale_intensity(img_int32, out_range=(0, 2**31 - 1)) >>> img_uint8 = img_as_ubyte(image) This behavior is symmetric: The values in an unsigned dtype are spread over just the positive range of a signed dtype. References ========== .. _numpy: http://docs.scipy.org/doc/numpy/user/ .. [1] http://docs.scipy.org/doc/numpy/user/basics.types.html .. _OpenCV: http://opencv.org/ scikit-image-0.13.1/doc/source/user_guide/getting_help.rst000066400000000000000000000037771316256236300235720ustar00rootroot00000000000000================================= Getting help on using ``skimage`` ================================= Besides the user guide, there exist other opportunities to get help on using ``skimage``. Examples gallery ---------------- The :ref:`examples_gallery` gallery provides graphical examples of typical image processing tasks. By a quick glance at the different thumbnails, the user may find an example close to a typical use case of interest. Each graphical example page displays an introductory paragraph, a figure, and the source code that generated the figure. Downloading the Python source code enables one to modify quickly the example into a case closer to one's image processing applications. Users are warmly encouraged to report on their use of ``skimage`` on the :ref:`mailing_list`, in order to propose more examples in the future. Contributing examples to the gallery can be done on github (see :ref:`howto_contribute`). Search field ------------ The ``quick search`` field located in the navigation bar of the html documentation can be used to search for specific keywords (segmentation, rescaling, denoising, etc.). Docstrings ---------- Docstrings of ``skimage`` functions are formatted using `Numpy's documentation standard `_, starting with a ``Parameters`` section for the arguments and a ``Returns`` section for the objects returned by the function. Also, most functions include one or more examples. .. _mailing_list: Mailing-list ------------ The scikit-image mailing-list is scikit-image@python.org (users should `join `_ before posting). This mailing-list is shared by users and developers, and it is the right place to ask any question about ``skimage``, or in general, image processing using Python. Posting snippets of code with minimal examples ensures to get more relevant and focused answers. We would love to hear from how you use ``skimage`` for your work on the mailing-list! scikit-image-0.13.1/doc/source/user_guide/getting_started.rst000066400000000000000000000024131316256236300242720ustar00rootroot00000000000000Getting started --------------- ``scikit-image`` is an image processing Python package that works with :mod:`numpy` arrays. The package is imported as ``skimage``: :: >>> import skimage Most functions of ``skimage`` are found within submodules: :: >>> from skimage import data >>> camera = data.camera() A list of submodules and functions is found on the `API reference `_ webpage. Within scikit-image, images are represented as NumPy arrays, for example 2-D arrays for grayscale 2-D images :: >>> type(camera) >>> # An image with 512 rows and 512 columns >>> camera.shape (512, 512) The :mod:`skimage.data` submodule provides a set of functions returning example images, that can be used to get started quickly on using scikit-image's functions: :: >>> coins = data.coins() >>> from skimage import filters >>> threshold_value = filters.threshold_otsu(coins) >>> threshold_value 107 Of course, it is also possible to load your own images as NumPy arrays from image files, using :func:`skimage.io.imread`: :: >>> import os >>> filename = os.path.join(skimage.data_dir, 'moon.png') >>> from skimage import io >>> moon = io.imread(filename) scikit-image-0.13.1/doc/source/user_guide/numpy_images.rst000066400000000000000000000217061316256236300236060ustar00rootroot00000000000000================================== A crash course on NumPy for images ================================== Images manipulated by ``scikit-image`` are simply NumPy arrays. Hence, a large fraction of operations on images will just consist in using NumPy:: >>> from skimage import data >>> camera = data.camera() >>> type(camera) Retrieving the geometry of the image and the number of pixels: :: >>> camera.shape (512, 512) >>> camera.size 262144 Retrieving statistical information about gray values: :: >>> camera.min(), camera.max() (0, 255) >>> camera.mean() 118.31400299072266 NumPy arrays representing images can be of different integer of float numerical types. See :ref:`data_types` for more information about these types and how scikit-image treats them. NumPy indexing -------------- NumPy indexing can be used both for looking at pixel values, and to modify pixel values: :: >>> # Get the value of the pixel on the 10th row and 20th column >>> camera[10, 20] 153 >>> # Set to black the pixel on the 3rd row and 10th column >>> camera[3, 10] = 0 Be careful: in NumPy indexing, the first dimension (``camera.shape[0]``) corresponds to rows, while the second (``camera.shape[1]``) corresponds to columns, with the origin (``camera[0, 0]``) on the top-left corner. This matches matrix/linear algebra notation, but is in contrast to Cartesian (x, y) coordinates. See `Coordinate conventions`_ below for more details. Beyond individual pixels, it is possible to access / modify values of whole sets of pixels, using the different indexing possibilities of NumPy. Slicing:: >>> # Set to black the ten first lines >>> camera[:10] = 0 Masking (indexing with masks of booleans):: >>> mask = camera < 87 >>> # Set to "white" (255) pixels where mask is True >>> camera[mask] = 255 Fancy indexing (indexing with sets of indices) :: >>> inds_r = np.arange(len(camera)) >>> inds_c = 4 * inds_r % len(camera) >>> camera[inds_r, inds_c] = 0 Using masks, especially, is very useful to select a set of pixels on which to perform further manipulations. The mask can be any boolean array of same shape as the image (or a shape broadcastable to the image shape). This can be useful to define a region of interest, such as a disk: :: >>> nrows, ncols = camera.shape >>> row, col = np.ogrid[:nrows, :ncols] >>> cnt_row, cnt_col = nrows / 2, ncols / 2 >>> outer_disk_mask = ((row - cnt_row)**2 + (col - cnt_col)**2 > ... (nrows / 2)**2) >>> camera[outer_disk_mask] = 0 .. image:: ../auto_examples/numpy_operations/images/sphx_glr_plot_camera_numpy_001.png :width: 45% :target: ../auto_examples/numpy_operations/plot_camera_numpy.html Boolean arithmetic can be used to define more complex masks: :: >>> lower_half = row > cnt_row >>> lower_half_disk = np.logical_and(lower_half, outer_disk_mask) >>> camera = data.camera() >>> camera[lower_half_disk] = 0 Color images ------------ All of the above is true of color images, too: a color image is a NumPy array, with an additional trailing dimension for the channels: >>> cat = data.chelsea() >>> type(cat) >>> cat.shape (300, 451, 3) This shows that ``cat`` is a 300-by-451 pixel image with three channels (red, green, and blue). As before, we can get and set pixel values: >>> cat[10, 20] array([151, 129, 115], dtype=uint8) >>> # set the pixel at row 50, column 60 to black >>> cat[50, 60] = 0 >>> # set the pixel at row 50, column 61 to green >>> cat[50, 61] = [0, 255, 0] # [red, green, blue] We can also use 2D boolean masks for a 2D color image, as we did with the grayscale image above: .. plot:: Using a 2D mask on a 2D color image >>> from skimage import data >>> cat = data.chelsea() >>> reddish = cat[:, :, 0] > 160 >>> cat[reddish] = [0, 255, 0] >>> plt.imshow(cat) .. _numpy-images-coordinate-conventions: Coordinate conventions ---------------------- Because we represent images with numpy arrays, our coordinates must match accordingly. Two-dimensional (2D) grayscale images (such as `camera` above) are indexed by row and columns (abbreviated to either ``(row, col)`` or ``(r, c)``), with the lowest element ``(0, 0)`` at the top-left corner. In various parts of the library, you will also see ``rr`` and ``cc`` refer to lists of row and column coordinates. We distinguish this from ``(x, y)``, which commonly denote standard Cartesian coordinates, where ``x`` is the horizontal coordinate, ``y`` the vertical, and the origin is on the bottom left. (Matplotlib, for example, uses this convention.) In the case of color (or multichannel) images, the last dimension contains the color information and is denoted ``channel`` or ``ch``. Finally, for 3D images, such as videos, magnetic resonance imaging (MRI) scans, or confocal microscopy, we refer to the leading dimension as ``plane``, abbreviated as ``pln`` or ``p``. These conventions are summarized below: .. table:: Dimension name and order conventions in scikit-image ========================= ======================================== Image type coordinates ========================= ======================================== 2D grayscale (row, col) 2D multichannel (eg. RGB) (row, col, ch) 3D grayscale (pln, row, col) 3D multichannel (pln, row, col, ch) ========================= ======================================== Many functions in scikit-image operate on 3D images directly: >>> im3d = np.random.rand(100, 1000, 1000) >>> from skimage import morphology >>> from scipy import ndimage as ndi >>> seeds = ndi.label(im3d < 0.1)[0] >>> ws = morphology.watershed(im3d, seeds) In many cases, the third imaging dimension has lower resolution than the other two. Some scikit-image functions provide a ``spacing`` keyword argument to process these images: >>> from skimage import segmentation >>> slics = segmentation.slic(im3d, spacing=[5, 1, 1], multichannel=False) Other times, processing must be done plane-wise. When planes are the leading dimension, we can use the following syntax: >>> from skimage import filters >>> edges = np.zeros_like(im3d) >>> for pln, image in enumerate(im3d): ... # iterate over the leading dimension (planes) ... edges[pln] = filters.sobel(image) Notes on array order -------------------- Although the labeling of the axes seems arbitrary, it can have a significant effect on speed of operations. This is because modern processors never retrieve just one item from memory, but rather a whole chunk of adjacent items. (This is called prefetching.) Therefore, processing elements that are next to each other in memory is faster than processing them in a different order, even if the number of operations is the same: >>> def in_order_multiply(arr, scalar): ... for plane in list(range(arr.shape[0])): ... arr[plane, :, :] *= scalar ... >>> def out_of_order_multiply(arr, scalar): ... for plane in list(range(arr.shape[2])): ... arr[:, :, plane] *= scalar ... >>> import time >>> im3d = np.random.rand(100, 1024, 1024) >>> t0 = time.time(); x = in_order_multiply(im3d, 5); t1 = time.time() >>> print("%.2f seconds" % (t1 - t0)) # doctest: +SKIP 0.14 seconds >>> im3d_t = np.transpose(im3d).copy() # place "planes" dimension at end >>> im3d_t.shape (1024, 1024, 100) >>> s0 = time.time(); x = out_of_order_multiply(im3d, 5); s1 = time.time() >>> print("%.2f seconds" % (s1 - s0)) # doctest: +SKIP 1.18 seconds >>> print("Speedup: %.1fx" % ((s1 - s0) / (t1 - t0))) # doctest: +SKIP Speedup: 8.6x When the dimension you are iterating over is even larger, the speedup is even more dramatic. It is worth thinking about this *data locality* when writing algorithms. In particular, know that scikit-image uses C-contiguous arrays unless otherwise specified, so one should iterate along the last/rightmost dimension in the innermost loop of the computation. A note on time -------------- Although scikit-image does not currently (0.11) provide functions to work specifically with time-varying 3D data, our compatibility with numpy arrays allows us to work quite naturally with a 5D array of the shape (t, pln, row, col, ch): >>> for timepoint in image5d: # doctest: +SKIP ... # each timepoint is a 3D multichannel image ... do_something_with(timepoint) We can then supplement the above table as follows: .. table:: Addendum to dimension names and orders in scikit-image ======================== ======================================== Image type coordinates ======================== ======================================== 2D color video (t, row, col, ch) 3D multichannel video (t, pln, row, col, ch) ======================== ======================================== scikit-image-0.13.1/doc/source/user_guide/plugins.rst000066400000000000000000000045661316256236300225770ustar00rootroot00000000000000I/O Plugin Infrastructure ------------------------- A plugin consists of two files, the source and the descriptor ``.ini``. Let's say we'd like to provide a plugin for ``imshow`` using ``matplotlib``. We'll call our plugin ``mpl``:: skimage/io/_plugins/mpl.py skimage/io/_plugins/mpl.ini The name of the ``.py`` and ``.ini`` files must correspond. Inside the ``.ini`` file, we give the plugin meta-data:: [mpl] <-- name of the plugin, may be anything description = Matplotlib image I/O plugin provides = imshow <-- a comma-separated list, one or more of imshow, imsave, imread, _app_show The "provides"-line lists all the functions provided by the plugin. Since our plugin provides ``imshow``, we have to define it inside ``mpl.py``:: # This is mpl.py import matplotlib.pyplot as plt def imshow(img): plt.imshow(img) Note that, by default, ``imshow`` is non-blocking, so a special function ``_app_show`` must be provided to block the GUI. We can modify our plugin to provide it as follows:: [mpl] provides = imshow, _app_show :: # This is mpl.py import matplotlib.pyplot as plt def imshow(img): plt.imshow(img) def _app_show(): plt.show() Any plugin in the ``_plugins`` directory is automatically examined by ``skimage.io`` upon import. You may list all the plugins on your system:: >>> import skimage.io as io >>> io.find_available_plugins() {'gtk': ['imshow'], 'matplotlib': ['imshow', 'imread', 'imread_collection'], 'pil': ['imread', 'imsave', 'imread_collection'], 'qt': ['imshow', 'imsave', 'imread', 'imread_collection'], 'test': ['imsave', 'imshow', 'imread', 'imread_collection'],} or only those already loaded:: >>> io.find_available_plugins(loaded=True) {'matplotlib': ['imshow', 'imread', 'imread_collection'], 'pil': ['imread', 'imsave', 'imread_collection']} A plugin is loaded using the ``use_plugin`` command:: >>> import skimage.io as io >>> io.use_plugin('pil') # Use all capabilities provided by PIL or :: >>> io.use_plugin('pil', 'imread') # Use only the imread capability of PIL Note that, if more than one plugin provides certain functionality, the last plugin loaded is used. To query a plugin's capabilities, use ``plugin_info``:: >>> io.plugin_info('pil') >>> {'description': 'Image reading via the Python Imaging Library', 'provides': 'imread, imsave'} scikit-image-0.13.1/doc/source/user_guide/transforming_image_data.rst000066400000000000000000000163561316256236300257620ustar00rootroot00000000000000============================================ Image adjustment: transforming image content ============================================ Color manipulation ------------------ .. currentmodule:: skimage.color Most functions for manipulating color channels are found in the submodule :mod:`skimage.color`. Conversion between color models ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Color images can be represented using different `color spaces `_. One of the most common color spaces is the `RGB space `_, where an image has red, green and blue channels. However, other color models are widely used, such as the `HSV color model `_, where hue, saturation and value are independent channels, or the `CMYK model `_ used for printing. :mod:`skimage.color` provides utility functions to convert images to and from different color spaces. Integer-type arrays can be transformed to floating-point type by the conversion operation:: >>> # bright saturated red >>> red_pixel_rgb = np.array([[[255, 0, 0]]], dtype=np.uint8) >>> color.rgb2hsv(red_pixel_rgb) array([[[ 0., 1., 1.]]]) >>> # darker saturated blue >>> dark_blue_pixel_rgb = np.array([[[0, 0, 100]]], dtype=np.uint8) >>> color.rgb2hsv(dark_blue_pixel_rgb) array([[[ 0.66666667, 1. , 0.39215686]]]) >>> # less saturated pink >>> pink_pixel_rgb = np.array([[[255, 100, 255]]], dtype=np.uint8) >>> color.rgb2hsv(pink_pixel_rgb) array([[[ 0.83333333, 0.60784314, 1. ]]]) Conversion from RGBA to RGB - Removing alpha channel through alpha blending ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Converting an RGBA image to an RGB image by alpha blending it with a background is realized with :func:`rgba2rgb` :: >>> from skimage.color import rgba2rgb >>> from skimage import data >>> img_rgba = data.logo() >>> img_rgb = rgba2rgb(img_rgba) Conversion between color and gray values ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Converting an RGB image to a grayscale image is realized with :func:`rgb2gray` :: >>> from skimage.color import rgb2gray >>> from skimage import data >>> img = data.astronaut() >>> img_gray = rgb2gray(img) :func:`rgb2gray` uses a non-uniform weighting of color channels, because of the different sensitivity of the human eye to different colors. Therefore, such a weighting ensures `luminance preservation `_ from RGB to grayscale:: >>> red_pixel = np.array([[[255, 0, 0]]], dtype=np.uint8) >>> color.rgb2gray(red_pixel) array([[ 0.2125]]) >>> green_pixel = np.array([[[0, 255, 0]]], dtype=np.uint8) >>> color.rgb2gray(green_pixel) array([[ 0.7154]]) Converting a grayscale image to RGB with :func:`gray2rgb` simply duplicates the gray values over the three color channels. Image inversion ~~~~~~~~~~~~~~~ An inverted image is also called complementary image. For binary images, True values become False and conversely. For grayscale images, pixel values are replaced by the difference of the maximum value of the data type and the actual value. For RGB images, the same operation is done for each channel. This operation can be achieved with :py:func:`skimage.util.invert`:: >>> from skimage import util >>> img = data.camera() >>> inverted_img = util.invert(img) Painting images with labels ~~~~~~~~~~~~~~~~~~~~~~~~~~~ :func:`label2rgb` can be used to superimpose colors on a grayscale image using an array of labels to encode the regions to be represented with the same color. .. image:: ../auto_examples/segmentation/images/sphx_glr_plot_join_segmentations_001.png :target: ../auto_examples/segmentation/plot_join_segmentations.html :align: center :width: 80% .. topic:: Examples: * :ref:`sphx_glr_auto_examples_color_exposure_plot_tinting_grayscale_images.py` * :ref:`sphx_glr_auto_examples_segmentation_plot_join_segmentations.py` * :ref:`sphx_glr_auto_examples_segmentation_plot_rag_mean_color.py` Contrast and exposure --------------------- .. currentmodule:: skimage.exposure Image pixels can take values determined by the ``dtype`` of the image (see :ref:`data_types`), such as 0 to 255 for ``uint8`` images or ``[0, 1]`` for floating-point images. However, most images either have a narrower range of values (because of poor contrast), or have most pixel values concentrated in a subrange of the accessible values. :mod:`skimage.exposure` provides functions that spread the intensity values over a larger range. A first class of methods compute a nonlinear function of the intensity, that is independent of the pixel values of a specific image. Such methods are often used for correcting a known non-linearity of sensors, or receptors such as the human eye. A well-known example is `Gamma correction `_, implemented in :func:`adjust_gamma`. Other methods re-distribute pixel values according to the *histogram* of the image. The histogram of pixel values is computed with :func:`skimage.exposure.histogram`:: >>> image = np.array([[1, 3], [1, 1]]) >>> exposure.histogram(image) (array([3, 0, 1]), array([1, 2, 3])) :func:`histogram` returns the number of pixels for each value bin, and the centers of the bins. The behavior of :func:`histogram` is therefore slightly different from the one of :func:`numpy.histogram`, which returns the boundaries of the bins. The simplest contrast enhancement :func:`rescale_intensity` consists in stretching pixel values to the whole allowed range, using a linear transformation:: >>> from skimage import exposure >>> text = data.text() >>> text.min(), text.max() (10, 197) >>> better_contrast = exposure.rescale_intensity(text) >>> better_contrast.min(), better_contrast.max() (0, 255) Even if an image uses the whole value range, sometimes there is very little weight at the ends of the value range. In such a case, clipping pixel values using percentiles of the image improves the contrast (at the expense of some loss of information, because some pixels are saturated by this operation):: >>> moon = data.moon() >>> v_min, v_max = np.percentile(moon, (0.2, 99.8)) >>> v_min, v_max (10.0, 186.0) >>> better_contrast = exposure.rescale_intensity( ... moon, in_range=(v_min, v_max)) The function :func:`equalize_hist` maps the cumulative distribution function (cdf) of pixel values onto a linear cdf, ensuring that all parts of the value range are equally represented in the image. As a result, details are enhanced in large regions with poor contrast. As a further refinement, histogram equalization can be performed in subregions of the image with :func:`equalize_adapthist`, in order to correct for exposure gradients across the image. See the example :ref:`sphx_glr_auto_examples_color_exposure_plot_equalize.py`. .. image:: ../auto_examples/color_exposure/images/sphx_glr_plot_equalize_001.png :target: ../auto_examples/color_exposure/plot_equalize.html :align: center :width: 90% .. topic:: Examples: * :ref:`sphx_glr_auto_examples_color_exposure_plot_equalize.py` scikit-image-0.13.1/doc/source/user_guide/tutorial_parallelization.rst000066400000000000000000000036131316256236300262230ustar00rootroot00000000000000======================== How to parallelize loops ======================== In image processing, we frequently apply the same algorithm on a large batch of images. In this paragraph, we propose to use `joblib `_ to parallelize loops. Here is an example of such repetitive tasks: .. code-block:: python from skimage import data, color, util from skimage.restoration import denoise_tv_chambolle from skimage.feature import hog def task(image): """ Apply some functions and return an image. """ image = denoise_tv_chambolle(image[0][0], weight=0.1, multichannel=True) fd, hog_image = hog(color.rgb2gray(image), orientations=8, pixels_per_cell=(16, 16), cells_per_block=(1, 1), visualise=True) return hog_image # Prepare images hubble = data.hubble_deep_field() width = 10 pics = util.view_as_windows(hubble, (width, hubble.shape[1], hubble.shape[2]), step=width) To call the function ``task`` on each element of the list ``pics``, it is usual to write a for loop. To measure the execution time of this loop, you can use ipython and measure the execution time with ``%timeit``. .. code-block:: python def classic_loop(): for image in pics: task(image) %timeit classic_loop() Another equivalent way to code this loop is to use a comprehension list which has the same efficiency. .. code-block:: python def comprehension_loop(): [task(image) for image in pics] %timeit comprehension_loop() ``joblib`` is a library providing an easy way to parallelize for loops once we have a comprehension list. The number of jobs can be specified. .. code-block:: python from joblib import Parallel, delayed def joblib_loop(): Parallel(n_jobs=4)(delayed(task)(i) for i in pics) %timeit joblib_loop() scikit-image-0.13.1/doc/source/user_guide/tutorial_segmentation.rst000066400000000000000000000146071316256236300255330ustar00rootroot00000000000000Image Segmentation ------------------ Image segmentation is the task of labeling the pixels of objects of interest in an image. In this tutorial, we will see how to segment objects from a background. We use the ``coins`` image from ``skimage.data``. This image shows several coins outlined against a darker background. The segmentation of the coins cannot be done directly from the histogram of grey values, because the background shares enough grey levels with the coins that a thresholding segmentation is not sufficient. .. image:: ../auto_examples/xx_applications/images/sphx_glr_plot_coins_segmentation_001.png :target: ../auto_examples/xx_applications/plot_coins_segmentation.html :align: center :: >>> import numpy as np >>> from skimage import data >>> coins = data.coins() >>> histo = np.histogram(coins, bins=np.arange(0, 256)) Simply thresholding the image leads either to missing significant parts of the coins, or to merging parts of the background with the coins. This is due to the inhomogeneous lighting of the image. .. image:: ../auto_examples/xx_applications/images/sphx_glr_plot_coins_segmentation_002.png :target: ../auto_examples/xx_applications/plot_coins_segmentation.html :align: center A first idea is to take advantage of the local contrast, that is, to use the gradients rather than the grey values. Edge-based segmentation ~~~~~~~~~~~~~~~~~~~~~~~ Let us first try to detect edges that enclose the coins. For edge detection, we use the `Canny detector `_ of ``skimage.feature.canny`` :: >>> from skimage.feature import canny >>> edges = canny(coins/255.) As the background is very smooth, almost all edges are found at the boundary of the coins, or inside the coins. :: >>> from scipy import ndimage as ndi >>> fill_coins = ndi.binary_fill_holes(edges) .. image:: ../auto_examples/xx_applications/images/sphx_glr_plot_coins_segmentation_003.png :target: ../auto_examples/xx_applications/plot_coins_segmentation.html :align: center Now that we have contours that delineate the outer boundary of the coins, we fill the inner part of the coins using the ``ndi.binary_fill_holes`` function, which uses mathematical morphology to fill the holes. .. image:: ../auto_examples/xx_applications/images/sphx_glr_plot_coins_segmentation_004.png :target: ../auto_examples/xx_applications/plot_coins_segmentation.html :align: center Most coins are well segmented out of the background. Small objects from the background can be easily removed using the ``ndi.label`` function to remove objects smaller than a small threshold. :: >>> label_objects, nb_labels = ndi.label(fill_coins) >>> sizes = np.bincount(label_objects.ravel()) >>> mask_sizes = sizes > 20 >>> mask_sizes[0] = 0 >>> coins_cleaned = mask_sizes[label_objects] However, the segmentation is not very satisfying, since one of the coins has not been segmented correctly at all. The reason is that the contour that we got from the Canny detector was not completely closed, therefore the filling function did not fill the inner part of the coin. .. image:: ../auto_examples/xx_applications/images/sphx_glr_plot_coins_segmentation_005.png :target: ../auto_examples/xx_applications/plot_coins_segmentation.html :align: center Therefore, this segmentation method is not very robust: if we miss a single pixel of the contour of the object, we will not be able to fill it. Of course, we could try to dilate the contours in order to close them. However, it is preferable to try a more robust method. Region-based segmentation ~~~~~~~~~~~~~~~~~~~~~~~~~ Let us first determine markers of the coins and the background. These markers are pixels that we can label unambiguously as either object or background. Here, the markers are found at the two extreme parts of the histogram of grey values: :: >>> markers = np.zeros_like(coins) >>> markers[coins < 30] = 1 >>> markers[coins > 150] = 2 We will use these markers in a watershed segmentation. The name watershed comes from an analogy with hydrology. The `watershed transform `_ floods an image of elevation starting from markers, in order to determine the catchment basins of these markers. Watershed lines separate these catchment basins, and correspond to the desired segmentation. The choice of the elevation map is critical for good segmentation. Here, the amplitude of the gradient provides a good elevation map. We use the Sobel operator for computing the amplitude of the gradient:: >>> from skimage.filters import sobel >>> elevation_map = sobel(coins) From the 3-D surface plot shown below, we see that high barriers effectively separate the coins from the background. .. image:: data/elevation_map.jpg :align: center and here is the corresponding 2-D plot: .. image:: ../auto_examples/xx_applications/images/sphx_glr_plot_coins_segmentation_006.png :target: ../auto_examples/xx_applications/plot_coins_segmentation.html :align: center The next step is to find markers of the background and the coins based on the extreme parts of the histogram of grey values:: >>> markers = np.zeros_like(coins) >>> markers[coins < 30] = 1 >>> markers[coins > 150] = 2 .. image:: ../auto_examples/xx_applications/images/sphx_glr_plot_coins_segmentation_007.png :target: ../auto_examples/xx_applications/plot_coins_segmentation.html :align: center Let us now compute the watershed transform:: >>> from skimage.morphology import watershed >>> segmentation = watershed(elevation_map, markers) .. image:: ../auto_examples/xx_applications/images/sphx_glr_plot_coins_segmentation_008.png :target: ../auto_examples/xx_applications/plot_coins_segmentation.html :align: center With this method, the result is satisfying for all coins. Even if the markers for the background were not well distributed, the barriers in the elevation map were high enough for these markers to flood the entire background. We remove a few small holes with mathematical morphology:: >>> segmentation = ndi.binary_fill_holes(segmentation - 1) We can now label all the coins one by one using ``ndi.label``:: >>> labeled_coins, _ = ndi.label(segmentation) .. image:: ../auto_examples/xx_applications/images/sphx_glr_plot_coins_segmentation_009.png :target: ../auto_examples/xx_applications/plot_coins_segmentation.html :align: center scikit-image-0.13.1/doc/source/user_guide/tutorials.rst000066400000000000000000000001501316256236300231250ustar00rootroot00000000000000Tutorials ========= .. toctree:: :maxdepth: 1 tutorial_segmentation tutorial_parallelization scikit-image-0.13.1/doc/source/user_guide/video.rst000066400000000000000000000142431316256236300222150ustar00rootroot00000000000000Handling Video Files -------------------- Sometimes it is necessary to read a sequence of images from a standard video file, such as .avi and .mov files. In a scientific context, it is usually better to avoid these formats in favor of a simple directory of images or a multi-dimensional TIF. Video formats are more difficult to read piecemeal, typically do not support random frame access or research-minded meta data, and use lossy compression if not carefully configured. But video files are in widespread use, and they are easy to share, so it is convenient to be equipped to read and write them when necessary. Tools for reading video files vary in their ease of installation and use, their disk and memory usage, and their cross-platform compatibility. This is a practical guide. A Workaround: Convert the Video to an Image Sequence ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ For a one-off solution, the simplest, surest route is to convert the video to a collection of sequentially-numbered image files, often called an image sequence. Then the images files can be read into an `ImageCollection` by `skimage.io.imread_collection`. Converting the video to frames can be done easily in [ImageJ](http://imagej.nih.gov/ij/), a cross-platform, GUI-based program from the bio-imaging community, or [FFmpeg](https://www.ffmpeg.org/), a powerful command-line utility for manipulating video files. In FFmpeg, the following command generates an image file from each frame in a video. The files are numbered with five digits, padded on the left with zeros. .. code-block:: bash ffmpeg -i "video.mov" -f image2 "video-frame%05d.png" More information is available in an `FFmpeg tutorial on image sequences `__. Generating an image sequence has disadvantages: they can be large and unwieldy, and generating them can take some time. It is generally preferrable to work directly with the original video file. For a more direct solution, we need to execute FFmpeg or LibAV from Python to read frames from the video. FFmpeg and LibAV are two large open-source projects that decode video from the sprawling variety of formats used in the wild. There are several ways to use them from Python. Each, unfortunately, has some disadvantages. PyAV ^^^^ `PyAV `__ uses FFmpeg's (or LibAV's) libraries to read image data directly from the video file. It invokes them using Cython bindings, so it is very fast. .. code-block:: python import av v = av.open('path/to/video.mov') PyAV's API reflects the way frames are stored in a video file. .. code-block:: python for packet in container.demux(): for frame in packet.decode(): if frame.type == 'video': img = frame.to_image() # PIL/Pillow image arr = np.asarray(img) # numpy array # Do something! Recently, `conda packages `__ (with `public recipes `__ for PyAV and its dependencies became available for OSX and Linux. .. code-block:: bash conda install -c danielballan pyav Wheels are also avaiable for OSX.:: .. code-block:: bash pip install -f http://wheels.scipy.org av PyAV has also been `shown to build successfully on Windows `__, but convenient binary packages are not publicly distributed as of this writing. PyAV is pip-installable (`pip install av`) on Linux and OSX, but getting the linking to FFmpeg's libraries set up correctly is subtle. The binary installers are recommended. Adding Random Access to PyAV ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ The `Video` class in `PIMS `__ invokes PyAV and adds additional functionality to solve a common problem in scientific applications, accessing a video by frame number. Video file formats are designed to be searched in an approximate way, by time, and they do not support an efficient means of seeking a specific frame number. PIMS adds this missing functionality by decoding (but not reading) the entire video at and producing an internal table of contents that supports indexing by frame. .. code-block:: python import pims v = pims.Video('path/to/video.mov') v[-1] # a 2D numpy array representing the last frame PIMS is installable via conda on the ``soft-matter`` channel .. code-block:: bash conda install -c soft-matter pims and pip .. code-block:: bash pip install pims MoviePy ^^^^^^^ `Moviepy `__ invokes FFmpeg through a subprocess, pipes the decoded video from FFmpeg into RAM, and reads it out. This approach is straightforward, but it can be brittle, and it's not workable for large videos that exceed available RAM. It works on all platforms if FFmpeg is installed. Since it does not link to FFmpeg's underlying libraries, it is easier to install but about `half as fast `__. .. code-block:: python from moviepy.editor import VideoFileClip myclip = VideoFileClip("some_video.avi") MoviePy can be installed with pip. .. code-block:: bash pip install moviepy Imageio ^^^^^^^^ `Imageio `_ takes the same approach as MoviePy. It supports a wide range of other image file formats as well. .. code-block:: python import imageio filename = '/tmp/file.mp4' vid = imageio.get_reader(filename, 'ffmpeg') for num, image in vid.iter_data(): print(image.mean()) metadata = vid.get_meta_data() Imageio can be installed with pip. .. code-block:: bash pip install imageio OpenCV ^^^^^^ Finally, another solution is the `VideoReader `__ class in OpenCV, which has bindings to FFmpeg. If you need OpenCV for other reasons, then this may be the best approach. However, OpenCV can be difficult to install, especially because it must be linked to FFmpeg to support reading video from files. Also, it is known to get the meta data wrong for some video codecs. (See `this issue `__.) scikit-image-0.13.1/doc/source/user_guide/viewer.rst000066400000000000000000000066751316256236300224220ustar00rootroot00000000000000Image Viewer ============ Quick Start ----------- ``skimage.viewer`` provides a matplotlib_-based canvas for displaying images and a Qt-based GUI-toolkit, with the goal of making it easy to create interactive image editors. You can simply use it to display an image: .. code-block:: python from skimage import data from skimage.viewer import ImageViewer image = data.coins() viewer = ImageViewer(image) viewer.show() Of course, you could just as easily use ``imshow`` from matplotlib_ (or alternatively, ``skimage.io.imshow`` which adds support for multiple io-plugins) to display images. The advantage of ``ImageViewer`` is that you can easily add plugins for manipulating images. Currently, only a few plugins are implemented, but it is easy to write your own. Before going into the details, let's see an example of how a pre-defined plugin is added to the viewer: .. code-block:: python from skimage.viewer.plugins.lineprofile import LineProfile viewer = ImageViewer(image) viewer += LineProfile(viewer) overlay, data = viewer.show()[0] The viewer's ``show()`` method returns a list of tuples, one for each attached plugin. Each tuple contains two elements: an overlay of the same shape as the input image, and a data field (which may be ``None``). A plugin class documents its return value in its ``output`` method. In this example, only one plugin is attached, so the list returned by ``show`` will have length 1. We extract the single tuple and bind its ``overlay`` and ``data`` elements to individual variables. Here, ``overlay`` contains an image of the line drawn on the viewer, and ``data`` contains the 1-dimensional intensity profile along that line. At the moment, there are not many plugins pre-defined, but there is a really simple interface for creating your own plugin. First, let us create a plugin to call the total-variation denoising function, ``denoise_tv_bregman``: .. code-block:: python from skimage.filters import denoise_tv_bregman from skimage.viewer.plugins.base import Plugin denoise_plugin = Plugin(image_filter=denoise_tv_bregman) .. note:: The ``Plugin`` assumes the first argument given to the image filter is the image from the image viewer. In the future, this should be changed so you can pass the image to a different argument of the filter function. To actually interact with the filter, you have to add widgets that adjust the parameters of the function. Typically, that means adding a slider widget and connecting it to the filter parameter and the minimum and maximum values of the slider: .. code-block:: python from skimage.viewer.widgets import Slider from skimage.viewer.widgets.history import SaveButtons denoise_plugin += Slider('weight', 0.01, 0.5, update_on='release') denoise_plugin += SaveButtons() Here, we connect a slider widget to the filter's 'weight' argument. We also added some buttons for saving the image to file or to the ``scikit-image`` image stack (see ``skimage.io.push`` and ``skimage.io.pop``). All that's left is to create an image viewer and add the plugin to that viewer. .. code-block:: python viewer = ImageViewer(image) viewer += denoise_plugin denoised = viewer.show()[0][0] Here, we access only the overlay returned by the plugin, which contains the filtered image for the last used setting of ``weight``. .. image:: data/denoise_viewer_window.png .. image:: data/denoise_plugin_window.png .. _matplotlib: http://matplotlib.sourceforge.net/ scikit-image-0.13.1/doc/tools/000077500000000000000000000000001316256236300160565ustar00rootroot00000000000000scikit-image-0.13.1/doc/tools/LICENSE.txt000066400000000000000000000003111316256236300176740ustar00rootroot00000000000000These files were obtained from https://www.mail-archive.com/sphinx-dev@googlegroups.com/msg02472.html and were released under a BSD/MIT license by Fernando Perez, Matthew Brett and the PyMVPA guys. scikit-image-0.13.1/doc/tools/apigen.py000066400000000000000000000424271316256236300177040ustar00rootroot00000000000000""" Attempt to generate templates for module reference with Sphinx To include extension modules, first identify them as valid in the ``_uri2path`` method, then handle them in the ``_parse_module_with_import`` script. Notes ----- This parsing is based on import and introspection of modules. Previously functions and classes were found by parsing the text of .py files. Extension modules should be discovered and included as well. This is a modified version of a script originally shipped with the PyMVPA project, then adapted for use first in NIPY and then in skimage. PyMVPA is an MIT-licensed project. """ # Stdlib imports import os import re from types import BuiltinFunctionType, FunctionType, ModuleType # suppress print statements (warnings for empty files) DEBUG = True class ApiDocWriter(object): ''' Class for automatic detection and parsing of API docs to Sphinx-parsable reST format''' # only separating first two levels rst_section_levels = ['*', '=', '-', '~', '^'] def __init__(self, package_name, rst_extension='.rst', package_skip_patterns=None, module_skip_patterns=None, ): ''' Initialize package for parsing Parameters ---------- package_name : string Name of the top-level package. *package_name* must be the name of an importable package rst_extension : string, optional Extension for reST files, default '.rst' package_skip_patterns : None or sequence of {strings, regexps} Sequence of strings giving URIs of packages to be excluded Operates on the package path, starting at (including) the first dot in the package path, after *package_name* - so, if *package_name* is ``sphinx``, then ``sphinx.util`` will result in ``.util`` being passed for earching by these regexps. If is None, gives default. Default is: ['\.tests$'] module_skip_patterns : None or sequence Sequence of strings giving URIs of modules to be excluded Operates on the module name including preceding URI path, back to the first dot after *package_name*. For example ``sphinx.util.console`` results in the string to search of ``.util.console`` If is None, gives default. Default is: ['\.setup$', '\._'] ''' if package_skip_patterns is None: package_skip_patterns = ['\\.tests$'] if module_skip_patterns is None: module_skip_patterns = ['\\.setup$', '\\._'] self.package_name = package_name self.rst_extension = rst_extension self.package_skip_patterns = package_skip_patterns self.module_skip_patterns = module_skip_patterns def get_package_name(self): return self._package_name def set_package_name(self, package_name): ''' Set package_name >>> docwriter = ApiDocWriter('sphinx') >>> import sphinx >>> docwriter.root_path == sphinx.__path__[0] True >>> docwriter.package_name = 'docutils' >>> import docutils >>> docwriter.root_path == docutils.__path__[0] True ''' # It's also possible to imagine caching the module parsing here self._package_name = package_name root_module = self._import(package_name) self.root_path = root_module.__path__[-1] self.written_modules = None package_name = property(get_package_name, set_package_name, None, 'get/set package_name') def _import(self, name): ''' Import namespace package ''' mod = __import__(name) components = name.split('.') for comp in components[1:]: mod = getattr(mod, comp) return mod def _get_object_name(self, line): ''' Get second token in line >>> docwriter = ApiDocWriter('sphinx') >>> docwriter._get_object_name(" def func(): ") 'func' >>> docwriter._get_object_name(" class Klass(object): ") 'Klass' >>> docwriter._get_object_name(" class Klass: ") 'Klass' ''' name = line.split()[1].split('(')[0].strip() # in case we have classes which are not derived from object # ie. old style classes return name.rstrip(':') def _uri2path(self, uri): ''' Convert uri to absolute filepath Parameters ---------- uri : string URI of python module to return path for Returns ------- path : None or string Returns None if there is no valid path for this URI Otherwise returns absolute file system path for URI Examples -------- >>> docwriter = ApiDocWriter('sphinx') >>> import sphinx >>> modpath = sphinx.__path__[0] >>> res = docwriter._uri2path('sphinx.builder') >>> res == os.path.join(modpath, 'builder.py') True >>> res = docwriter._uri2path('sphinx') >>> res == os.path.join(modpath, '__init__.py') True >>> docwriter._uri2path('sphinx.does_not_exist') ''' if uri == self.package_name: return os.path.join(self.root_path, '__init__.py') path = uri.replace(self.package_name + '.', '') path = path.replace('.', os.path.sep) path = os.path.join(self.root_path, path) # XXX maybe check for extensions as well? if os.path.exists(path + '.py'): # file path += '.py' elif os.path.exists(os.path.join(path, '__init__.py')): path = os.path.join(path, '__init__.py') else: return None return path def _path2uri(self, dirpath): ''' Convert directory path to uri ''' package_dir = self.package_name.replace('.', os.path.sep) relpath = dirpath.replace(self.root_path, package_dir) if relpath.startswith(os.path.sep): relpath = relpath[1:] return relpath.replace(os.path.sep, '.') def _parse_module(self, uri): ''' Parse module defined in *uri* ''' filename = self._uri2path(uri) if filename is None: print(filename, 'erk') # nothing that we could handle here. return ([],[]) f = open(filename, 'rt') functions, classes = self._parse_lines(f) f.close() return functions, classes def _parse_module_with_import(self, uri): """Look for functions and classes in an importable module. Parameters ---------- uri : str The name of the module to be parsed. This module needs to be importable. Returns ------- functions : list of str A list of (public) function names in the module. classes : list of str A list of (public) class names in the module. submodules : list of str A list of (public) submodule names in the module. """ mod = __import__(uri, fromlist=[uri.split('.')[-1]]) # find all public objects in the module. obj_strs = [obj for obj in dir(mod) if not obj.startswith('_')] functions = [] classes = [] submodules = [] for obj_str in obj_strs: # find the actual object from its string representation if obj_str not in mod.__dict__: continue obj = mod.__dict__[obj_str] # figure out if obj is a function or class if isinstance(obj, (FunctionType, BuiltinFunctionType)): functions.append(obj_str) elif isinstance(obj, ModuleType): submodules.append(obj_str) else: try: issubclass(obj, object) classes.append(obj_str) except TypeError: # not a function or class pass return functions, classes, submodules def _parse_lines(self, linesource): ''' Parse lines of text for functions and classes ''' functions = [] classes = [] for line in linesource: if line.startswith('def ') and line.count('('): # exclude private stuff name = self._get_object_name(line) if not name.startswith('_'): functions.append(name) elif line.startswith('class '): # exclude private stuff name = self._get_object_name(line) if not name.startswith('_'): classes.append(name) else: pass functions.sort() classes.sort() return functions, classes def generate_api_doc(self, uri): '''Make autodoc documentation template string for a module Parameters ---------- uri : string python location of module - e.g 'sphinx.builder' Returns ------- S : string Contents of API doc ''' # get the names of all classes and functions functions, classes, submodules = self._parse_module_with_import(uri) if not (len(functions) or len(classes) or len(submodules)) and DEBUG: print('WARNING: Empty -', uri) return '' # Make a shorter version of the uri that omits the package name for # titles uri_short = re.sub(r'^%s\.' % self.package_name,'',uri) ad = '.. AUTO-GENERATED FILE -- DO NOT EDIT!\n\n' # Set the chapter title to read 'module' for all modules except for the # main packages if '.' in uri: title = 'Module: :mod:`' + uri_short + '`' else: title = ':mod:`' + uri_short + '`' ad += title + '\n' + self.rst_section_levels[1] * len(title) ad += '\n.. automodule:: ' + uri + '\n' ad += '\n.. currentmodule:: ' + uri + '\n' ad += '.. autosummary::\n\n' for f in functions: ad += ' ' + uri + '.' + f + '\n' ad += '\n' for c in classes: ad += ' ' + uri + '.' + c + '\n' ad += '\n' for m in submodules: ad += ' ' + uri + '.' + m + '\n' ad += '\n' for f in functions: # must NOT exclude from index to keep cross-refs working full_f = uri + '.' + f ad += f + '\n' ad += self.rst_section_levels[2] * len(f) + '\n' ad += '\n.. autofunction:: ' + full_f + '\n\n' for c in classes: ad += '\n:class:`' + c + '`\n' \ + self.rst_section_levels[2] * \ (len(c)+9) + '\n\n' ad += '\n.. autoclass:: ' + c + '\n' # must NOT exclude from index to keep cross-refs working ad += ' :members:\n' \ ' :undoc-members:\n' \ ' :show-inheritance:\n' \ '\n' \ ' .. automethod:: __init__\n' return ad def _survives_exclude(self, matchstr, match_type): ''' Returns True if *matchstr* does not match patterns ``self.package_name`` removed from front of string if present Examples -------- >>> dw = ApiDocWriter('sphinx') >>> dw._survives_exclude('sphinx.okpkg', 'package') True >>> dw.package_skip_patterns.append('^\\.badpkg$') >>> dw._survives_exclude('sphinx.badpkg', 'package') False >>> dw._survives_exclude('sphinx.badpkg', 'module') True >>> dw._survives_exclude('sphinx.badmod', 'module') True >>> dw.module_skip_patterns.append('^\\.badmod$') >>> dw._survives_exclude('sphinx.badmod', 'module') False ''' if match_type == 'module': patterns = self.module_skip_patterns elif match_type == 'package': patterns = self.package_skip_patterns else: raise ValueError('Cannot interpret match type "%s"' % match_type) # Match to URI without package name L = len(self.package_name) if matchstr[:L] == self.package_name: matchstr = matchstr[L:] for pat in patterns: try: pat.search except AttributeError: pat = re.compile(pat) if pat.search(matchstr): return False return True def discover_modules(self): ''' Return module sequence discovered from ``self.package_name`` Parameters ---------- None Returns ------- mods : sequence Sequence of module names within ``self.package_name`` Examples -------- >>> dw = ApiDocWriter('sphinx') >>> mods = dw.discover_modules() >>> 'sphinx.util' in mods True >>> dw.package_skip_patterns.append('\.util$') >>> 'sphinx.util' in dw.discover_modules() False >>> ''' modules = [self.package_name] # raw directory parsing for dirpath, dirnames, filenames in os.walk(self.root_path): # Check directory names for packages root_uri = self._path2uri(os.path.join(self.root_path, dirpath)) for dirname in dirnames[:]: # copy list - we modify inplace package_uri = '.'.join((root_uri, dirname)) if (self._uri2path(package_uri) and self._survives_exclude(package_uri, 'package')): modules.append(package_uri) else: dirnames.remove(dirname) return sorted(modules) def write_modules_api(self, modules, outdir): # write the list written_modules = [] public_modules = [m for m in modules if not m.split('.')[-1].startswith('_')] for m in public_modules: api_str = self.generate_api_doc(m) if not api_str: continue # write out to file outfile = os.path.join(outdir, m + self.rst_extension) fileobj = open(outfile, 'wt') fileobj.write(api_str) fileobj.close() written_modules.append(m) self.written_modules = written_modules def write_api_docs(self, outdir): """Generate API reST files. Parameters ---------- outdir : string Directory name in which to store files We create automatic filenames for each module Returns ------- None Notes ----- Sets self.written_modules to list of written modules """ if not os.path.exists(outdir): os.mkdir(outdir) # compose list of modules modules = self.discover_modules() self.write_modules_api(modules, outdir) def write_index(self, outdir, froot='gen', relative_to=None): """Make a reST API index file from written files Parameters ---------- path : string Filename to write index to outdir : string Directory to which to write generated index file froot : string, optional root (filename without extension) of filename to write to Defaults to 'gen'. We add ``self.rst_extension``. relative_to : string path to which written filenames are relative. This component of the written file path will be removed from outdir, in the generated index. Default is None, meaning, leave path as it is. """ if self.written_modules is None: raise ValueError('No modules written') # Get full filename path path = os.path.join(outdir, froot+self.rst_extension) # Path written into index is relative to rootpath if relative_to is not None: relpath = (outdir + os.path.sep).replace(relative_to + os.path.sep, '') else: relpath = outdir print("outdir: ", relpath) idx = open(path,'wt') w = idx.write w('.. AUTO-GENERATED FILE -- DO NOT EDIT!\n\n') # We look at the module name. If it is `skimage`, display, if `skimage.submodule`, only show `submodule`, # if it is `skimage.submodule.subsubmodule`, ignore. title = "API Reference for skimage |version|" w(title + "\n") w("=" * len(title) + "\n\n") subtitle = "Submodules" w(subtitle + "\n") w("-" * len(subtitle) + "\n\n") for f in self.written_modules: module_name = f.split('.') if len(module_name) > 2: continue elif len(module_name) == 1: module_name = module_name[0] prefix = "-" elif len(module_name) == 2: module_name = module_name[1] prefix = "\n -" w('{0} `{1} <{2}.html>`__\n'.format(prefix, module_name, os.path.join(f))) w('\n') subtitle = "Submodule Contents" w(subtitle + "\n") w("-" * len(subtitle) + "\n\n") w('.. toctree::\n\n') for f in self.written_modules: w(' %s\n' % os.path.join(relpath,f)) idx.close() scikit-image-0.13.1/doc/tools/build_modref_templates.py000066400000000000000000000035111316256236300231410ustar00rootroot00000000000000#!/usr/bin/env python """Script to auto-generate our API docs. """ # stdlib imports import os, sys # local imports from apigen import ApiDocWriter # version comparison from distutils.version import LooseVersion as V #***************************************************************************** def abort(error): print('*WARNING* API documentation not generated: %s' % error) exit() if __name__ == '__main__': package = 'skimage' # Check that the 'image' package is available. If not, the API # documentation is not (re)generated and existing API documentation # sources will be used. try: __import__(package) except ImportError as e: abort("Can not import skimage") module = sys.modules[package] # Check that the source version is equal to the installed # version. If the versions mismatch the API documentation sources # are not (re)generated. This avoids automatic generation of documentation # for older or newer versions if such versions are installed on the system. installed_version = V(module.__version__) source_lines = open('../skimage/__init__.py').readlines() version = 'vUndefined' for l in source_lines: if l.startswith('__version__'): source_version = V(l.split("'")[1]) break if source_version != installed_version: abort("Installed version does not match source version") outdir = 'source/api' docwriter = ApiDocWriter(package) docwriter.package_skip_patterns += [r'\.fixes$', r'\.externals$', r'filter$', ] docwriter.write_api_docs(outdir) docwriter.write_index(outdir, 'api', relative_to='source/api') print('%d files written' % len(docwriter.written_modules)) scikit-image-0.13.1/doc/tools/gitwash/000077500000000000000000000000001316256236300175245ustar00rootroot00000000000000scikit-image-0.13.1/doc/tools/plot_pr.py000066400000000000000000000100061316256236300201040ustar00rootroot00000000000000import json import urllib import dateutil.parser from collections import OrderedDict from datetime import datetime, timedelta from dateutil.relativedelta import relativedelta import numpy as np import matplotlib.pyplot as plt from matplotlib.ticker import FuncFormatter from matplotlib.transforms import blended_transform_factory cache = '_pr_cache.txt' # Obtain release dates using, e.g., # # git log v0.4 -n 1 --format='%ai' # # The first two releases are commented out. # This was in the era before PRs. # releases = OrderedDict([ #('0.1', u'2009-10-07 13:52:19 +0200'), #('0.2', u'2009-11-12 14:48:45 +0200'), #('0.3', u'2011-10-10 03:28:47 -0700'), ('0.4', u'2011-12-03 14:31:32 -0800'), ('0.5', u'2012-02-26 21:00:51 -0800'), ('0.6', u'2012-06-24 21:37:05 -0700'), ('0.7', u'2012-09-29 18:08:49 -0700'), ('0.8', u'2013-03-04 20:46:09 +0100')]) month_duration = 24 def fetch_PRs(user='scikit-image', repo='scikit-image', state='open'): params = {'state': state, 'per_page': 100, 'page': 1} data = [] page_data = True while page_data: config = {'user': user, 'repo': repo, 'params': urllib.urlencode(params)} fetch_status = ('Fetching page %(page)d (state=%(state)s)' % params + ' from %(user)s/%(repo)s...' % config) print(fetch_status) f = urllib.urlopen( 'https://api.github.com/repos/%(user)s/%(repo)s/pulls?%(params)s' % config ) params['page'] += 1 page_data = json.loads(f.read()) if 'message' in page_data and page_data['message'] == "Not Found": page_data = [] print('Warning: Repo not found (%(user)s/%(repo)s)' % config) else: data.extend(page_data) return data def seconds_from_epoch(dates): seconds = [(dt - epoch).total_seconds() for dt in dates] return seconds def get_month_bins(dates): now = datetime.now(tz=dates[0].tzinfo) this_month = datetime(year=now.year, month=now.month, day=1, tzinfo=dates[0].tzinfo) bins = [this_month - relativedelta(months=i) for i in reversed(range(-1, month_duration))] return seconds_from_epoch(bins) def date_formatter(value, _): dt = epoch + timedelta(seconds=value) return dt.strftime('%Y/%m') for r in releases: releases[r] = dateutil.parser.parse(releases[r]) try: PRs = json.loads(open(cache, 'r').read()) print('Loaded PRs from cache...') except IOError: PRs = fetch_PRs(user='stefanv', repo='scikits.image', state='closed') PRs.extend(fetch_PRs(state='open')) PRs.extend(fetch_PRs(state='closed')) cf = open(cache, 'w') cf.write(json.dumps(PRs)) cf.flush() nrs = [pr['number'] for pr in PRs] print('Processing %d pull requests...' % len(nrs)) dates = [dateutil.parser.parse(pr['created_at']) for pr in PRs] epoch = datetime(2009, 1, 1, tzinfo=dates[0].tzinfo) dates_f = seconds_from_epoch(dates) bins = get_month_bins(dates) fig, ax = plt.subplots(figsize=(7, 5)) n, bins, _ = ax.hist(dates_f, bins=bins, color='blue', alpha=0.6) ax.xaxis.set_major_formatter(FuncFormatter(date_formatter)) ax.set_xticks(bins[2:-1:3]) # Date label every 3 months. labels = ax.get_xticklabels() for l in labels: l.set_rotation(40) l.set_size(10) mixed_transform = blended_transform_factory(ax.transData, ax.transAxes) for version, date in releases.items(): date = seconds_from_epoch([date])[0] ax.axvline(date, color='black', linestyle=':', label=version) ax.text(date, 1, version, color='r', va='bottom', ha='center', transform=mixed_transform) ax.set_title('Pull request activity').set_y(1.05) ax.set_xlabel('Date') ax.set_ylabel('PRs per month', color='blue') cumulative = np.cumsum(n) cumulative += len(dates) - cumulative[-1] ax2 = ax.twinx() ax2.plot(bins[1:], cumulative, color='black', linewidth=2) ax2.set_ylabel('Total PRs', color='black') plt.tight_layout() fig.savefig('PRs.png') plt.show() scikit-image-0.13.1/optional_requirements.txt000066400000000000000000000002531316256236300213420ustar00rootroot00000000000000PySide; python_version != '2.7' imread; python_version != '2.7' SimpleITK; python_version != '3.4' and python_version != '3.5' astropy tifffile imageio dask[array]>=0.5.0 scikit-image-0.13.1/requirements.txt000066400000000000000000000001451316256236300174350ustar00rootroot00000000000000matplotlib>=1.3.1 numpy>=1.11 scipy>=0.17.0 six>=1.7.3 networkx>=1.8 pillow>=2.1.0 PyWavelets>=0.4.0 scikit-image-0.13.1/setup.py000066400000000000000000000121141316256236300156620ustar00rootroot00000000000000#! /usr/bin/env python descr = """Image Processing SciKit Image processing algorithms for SciPy, including IO, morphology, filtering, warping, color manipulation, object detection, etc. Please refer to the online documentation at http://scikit-image.org/ """ DISTNAME = 'scikit-image' DESCRIPTION = 'Image processing routines for SciPy' LONG_DESCRIPTION = descr MAINTAINER = 'Stefan van der Walt' MAINTAINER_EMAIL = 'stefan@sun.ac.za' URL = 'http://scikit-image.org' LICENSE = 'Modified BSD' DOWNLOAD_URL = 'http://github.com/scikit-image/scikit-image' import os import sys import setuptools from distutils.command.build_py import build_py from distutils.command.sdist import sdist if sys.version_info[0] < 3: import __builtin__ as builtins else: import builtins # This is a bit (!) hackish: we are setting a global variable so that the main # skimage __init__ can detect if it is being loaded by the setup routine, to # avoid attempting to load components that aren't built yet: # the numpy distutils extensions that are used by scikit-image to recursively # build the compiled extensions in sub-packages is based on the Python import # machinery. builtins.__SKIMAGE_SETUP__ = True with open('skimage/__init__.py') as fid: for line in fid: if line.startswith('__version__'): VERSION = line.strip().split()[-1][1:-1] break with open('requirements.txt') as fid: INSTALL_REQUIRES = [l.strip() for l in fid.readlines() if l] # requirements for those browsing PyPI REQUIRES = [r.replace('>=', ' (>= ') + ')' for r in INSTALL_REQUIRES] REQUIRES = [r.replace('==', ' (== ') for r in REQUIRES] REQUIRES = [r.replace('[array]', '') for r in REQUIRES] def configuration(parent_package='', top_path=None): if os.path.exists('MANIFEST'): os.remove('MANIFEST') from numpy.distutils.misc_util import Configuration config = Configuration(None, parent_package, top_path) config.set_options( ignore_setup_xxx_py=True, assume_default_configuration=True, delegate_options_to_subpackages=True, quiet=True) config.add_subpackage('skimage') config.add_data_dir('skimage/data') return config if __name__ == "__main__": try: from numpy.distutils.core import setup extra = {'configuration': configuration} # Do not try and upgrade larger dependencies for lib in ['scipy', 'numpy', 'matplotlib', 'pillow']: try: __import__(lib) INSTALL_REQUIRES = [i for i in INSTALL_REQUIRES if lib not in i] except ImportError: pass except ImportError: if len(sys.argv) >= 2 and ('--help' in sys.argv[1:] or sys.argv[1] in ('--help-commands', '--version', 'clean', 'egg_info', 'install_egg_info', 'rotate')): # For these actions, NumPy is not required. # # They are required to succeed without Numpy for example when # pip is used to install scikit-image when Numpy is not yet # present in the system. from setuptools import setup extra = {} else: print('To install scikit-image from source, you will need numpy.\n' + 'Install numpy with pip:\n' + 'pip install numpy\n' 'Or use your operating system package manager. For more\n' + 'details, see http://scikit-image.org/docs/stable/install.html') sys.exit(1) setup( name=DISTNAME, description=DESCRIPTION, long_description=LONG_DESCRIPTION, maintainer=MAINTAINER, maintainer_email=MAINTAINER_EMAIL, url=URL, license=LICENSE, download_url=DOWNLOAD_URL, version=VERSION, classifiers=[ 'Development Status :: 4 - Beta', 'Environment :: Console', 'Intended Audience :: Developers', 'Intended Audience :: Science/Research', 'License :: OSI Approved :: BSD License', 'Programming Language :: C', 'Programming Language :: Python', 'Programming Language :: Python :: 3', 'Topic :: Scientific/Engineering', 'Operating System :: Microsoft :: Windows', 'Operating System :: POSIX', 'Operating System :: Unix', 'Operating System :: MacOS', ], install_requires=INSTALL_REQUIRES, requires=REQUIRES, packages=setuptools.find_packages(exclude=['doc']), include_package_data=True, zip_safe=False, # the package can run out of an .egg file entry_points={ 'console_scripts': ['skivi = skimage.scripts.skivi:main'], }, cmdclass={'build_py': build_py, 'sdist': sdist}, **extra ) scikit-image-0.13.1/skimage/000077500000000000000000000000001316256236300155715ustar00rootroot00000000000000scikit-image-0.13.1/skimage/__init__.py000066400000000000000000000120251316256236300177020ustar00rootroot00000000000000"""Image Processing SciKit (Toolbox for SciPy) ``scikit-image`` (a.k.a. ``skimage``) is a collection of algorithms for image processing and computer vision. The main package of ``skimage`` only provides a few utilities for converting between image data types; for most features, you need to import one of the following subpackages: Subpackages ----------- color Color space conversion. data Test images and example data. draw Drawing primitives (lines, text, etc.) that operate on NumPy arrays. exposure Image intensity adjustment, e.g., histogram equalization, etc. feature Feature detection and extraction, e.g., texture analysis corners, etc. filters Sharpening, edge finding, rank filters, thresholding, etc. graph Graph-theoretic operations, e.g., shortest paths. io Reading, saving, and displaying images and video. measure Measurement of image properties, e.g., similarity and contours. morphology Morphological operations, e.g., opening or skeletonization. novice Simplified interface for teaching purposes. restoration Restoration algorithms, e.g., deconvolution algorithms, denoising, etc. segmentation Partitioning an image into multiple regions. transform Geometric and other transforms, e.g., rotation or the Radon transform. util Generic utilities. viewer A simple graphical user interface for visualizing results and exploring parameters. Utility Functions ----------------- img_as_float Convert an image to floating point format, with values in [0, 1]. img_as_uint Convert an image to unsigned integer format, with values in [0, 65535]. img_as_int Convert an image to signed integer format, with values in [-32768, 32767]. img_as_ubyte Convert an image to unsigned byte format, with values in [0, 255]. """ import os.path as osp import imp import functools import warnings import sys pkg_dir = osp.abspath(osp.dirname(__file__)) data_dir = osp.join(pkg_dir, 'data') __version__ = '0.13.1' try: imp.find_module('nose') except ImportError: def _test(doctest=False, verbose=False): """This would run all unit tests, but nose couldn't be imported so the test suite can not run. """ raise ImportError("Could not load nose. Unit tests not available.") else: def _test(doctest=False, verbose=False): """Run all unit tests.""" import nose import warnings args = ['', pkg_dir, '--exe', '--ignore-files=^_test'] if verbose: args.extend(['-v', '-s']) if doctest: args.extend(['--with-doctest', '--ignore-files=^\.', '--ignore-files=^setup\.py$$', '--ignore-files=test']) # Make sure warnings do not break the doc tests with warnings.catch_warnings(): warnings.simplefilter("ignore") success = nose.run('skimage', argv=args) else: success = nose.run('skimage', argv=args) # Return sys.exit code if success: return 0 else: return 1 # do not use `test` as function name as this leads to a recursion problem with # the nose test suite test = _test test_verbose = functools.partial(test, verbose=True) test_verbose.__doc__ = test.__doc__ doctest = functools.partial(test, doctest=True) doctest.__doc__ = doctest.__doc__ doctest_verbose = functools.partial(test, doctest=True, verbose=True) doctest_verbose.__doc__ = doctest.__doc__ # Logic for checking for improper install and importing while in the source # tree when package has not been installed inplace. # Code adapted from scikit-learn's __check_build module. _INPLACE_MSG = """ It appears that you are importing a local scikit-image source tree. For this, you need to have an inplace install. Maybe you are in the source directory and you need to try from another location.""" _STANDARD_MSG = """ Your install of scikit-image appears to be broken. Try re-installing the package following the instructions at: http://scikit-image.org/docs/stable/install.html """ def _raise_build_error(e): # Raise a comprehensible error local_dir = osp.split(__file__)[0] msg = _STANDARD_MSG if local_dir == "skimage": # Picking up the local install: this will work only if the # install is an 'inplace build' msg = _INPLACE_MSG raise ImportError("""%s It seems that scikit-image has not been built correctly. %s""" % (e, msg)) try: # This variable is injected in the __builtins__ by the build # process. It used to enable importing subpackages of skimage when # the binaries are not built __SKIMAGE_SETUP__ except NameError: __SKIMAGE_SETUP__ = False if __SKIMAGE_SETUP__: sys.stderr.write('Partial import of skimage during the build process.\n') # We are not importing the rest of the scikit during the build # process, as it may not be compiled yet else: try: from ._shared import geometry del geometry except ImportError as e: _raise_build_error(e) from .util.dtype import * del warnings, functools, osp, imp, sys scikit-image-0.13.1/skimage/_build.py000066400000000000000000000062121316256236300174020ustar00rootroot00000000000000import sys import os import hashlib from distutils.version import LooseVersion CYTHON_VERSION = '0.23' # WindowsError is not defined on unix systems try: WindowsError except NameError: class WindowsError(Exception): pass def cython(pyx_files, working_path=''): """Use Cython to convert the given files to C. Parameters ---------- pyx_files : list of str The input .pyx files. """ # Do not build cython files if target is clean if len(sys.argv) >= 2 and sys.argv[1] == 'clean': return try: from Cython import __version__ if LooseVersion(__version__) < CYTHON_VERSION: raise RuntimeError('Cython >= %s needed to build scikit-image' % CYTHON_VERSION) from Cython.Build import cythonize except ImportError: # If cython is not found, the build will make use of # the distributed .c files if present c_files = [f.replace('.pyx.in', '.c').replace('.pyx', '.c') for f in pyx_files] for cfile in [os.path.join(working_path, f) for f in c_files]: if not os.path.isfile(cfile): raise RuntimeError('Cython >= %s is required to build scikit-image from git checkout' \ % CYTHON_VERSION) print("Cython >= %s not found; falling back to pre-built %s" \ % (CYTHON_VERSION, " ".join(c_files))) else: for pyxfile in [os.path.join(working_path, f) for f in pyx_files]: # if the .pyx file stayed the same, we don't need to recompile if not _changed(pyxfile): continue if pyxfile.endswith('.pyx.in'): process_tempita_pyx(pyxfile) pyxfile = pyxfile.replace('.pyx.in', '.pyx') cythonize(pyxfile) def _md5sum(f): m = hashlib.new('md5') while True: # Hash one 8096 byte block at a time d = f.read(8096) if not d: break m.update(d) return m.hexdigest() def _changed(filename): """Compare the hash of a Cython file to the cached hash value on disk. """ filename_cache = filename + '.md5' try: md5_cached = open(filename_cache, 'rb').read() except IOError: md5_cached = '0' with open(filename, 'rb') as f: md5_new = _md5sum(f) with open(filename_cache, 'wb') as cf: cf.write(md5_new.encode('utf-8')) return md5_cached != md5_new.encode('utf-8') def process_tempita_pyx(fromfile): try: try: from Cython import Tempita as tempita except ImportError: import tempita except ImportError: raise Exception('Building requires Tempita: ' 'pip install --user Tempita') template = tempita.Template.from_filename(fromfile, encoding=sys.getdefaultencoding()) pyxcontent = template.substitute() if not fromfile.endswith('.pyx.in'): raise ValueError("Unexpected extension of %s." % fromfile) pyxfile = os.path.splitext(fromfile)[0] # split off the .in ending with open(pyxfile, "w") as f: f.write(pyxcontent) scikit-image-0.13.1/skimage/_shared/000077500000000000000000000000001316256236300171765ustar00rootroot00000000000000scikit-image-0.13.1/skimage/_shared/LICENSE.txt000066400000000000000000000004341316256236300210220ustar00rootroot00000000000000 version_requirements._check_version: Copyright (C) 2013 The IPython Development Team Distributed under the terms of the BSD License. version_requirements.is_installed: Original Copyright (C) 2009-2011 Pierre Raybaut Licensed under the terms of the MIT License. scikit-image-0.13.1/skimage/_shared/__init__.py000066400000000000000000000000001316256236300212750ustar00rootroot00000000000000scikit-image-0.13.1/skimage/_shared/_geometry.py000066400000000000000000000025741316256236300215520ustar00rootroot00000000000000__all__ = ['polygon_clip', 'polygon_area'] import numpy as np from matplotlib import path, transforms def polygon_clip(rp, cp, r0, c0, r1, c1): """Clip a polygon to the given bounding box. Parameters ---------- rp, cp : (N,) ndarray of double Row and column coordinates of the polygon. (r0, c0), (r1, c1) : double Top-left and bottom-right coordinates of the bounding box. Returns ------- r_clipped, c_clipped : (M,) ndarray of double Coordinates of clipped polygon. Notes ----- This makes use of Sutherland-Hodgman clipping as implemented in AGG 2.4 and exposed in Matplotlib. """ poly = path.Path(np.vstack((rp, cp)).T, closed=True) clip_rect = transforms.Bbox([[r0, c0], [r1, c1]]) poly_clipped = poly.clip_to_bbox(clip_rect).to_polygons()[0] # This should be fixed in matplotlib >1.5 if np.all(poly_clipped[-1] == poly_clipped[-2]): poly_clipped = poly_clipped[:-1] return poly_clipped[:, 0], poly_clipped[:, 1] def polygon_area(pr, pc): """Compute the area of a polygon. Parameters ---------- pr, pc : (N,) array of float Polygon row and column coordinates. Returns ------- a : float Area of the polygon. """ pr = np.asarray(pr) pc = np.asarray(pc) return 0.5 * np.abs(np.sum((pc[:-1] * pr[1:]) - (pc[1:] * pr[:-1]))) scikit-image-0.13.1/skimage/_shared/_tempfile.py000066400000000000000000000013671316256236300215230ustar00rootroot00000000000000from tempfile import NamedTemporaryFile from contextlib import contextmanager import os @contextmanager def temporary_file(suffix=''): """Yield a writeable temporary filename that is deleted on context exit. Parameters ---------- suffix : string, optional The suffix for the file. Examples -------- >>> import numpy as np >>> from skimage import io >>> with temporary_file('.tif') as tempfile: ... im = np.zeros((5, 5), np.uint8) ... io.imsave(tempfile, im) ... assert np.all(io.imread(tempfile) == im) """ tempfile_stream = NamedTemporaryFile(suffix=suffix, delete=False) tempfile = tempfile_stream.name tempfile_stream.close() yield tempfile os.remove(tempfile) scikit-image-0.13.1/skimage/_shared/_warnings.py000066400000000000000000000100601316256236300215340ustar00rootroot00000000000000from contextlib import contextmanager import sys import warnings import inspect import re __all__ = ['all_warnings', 'expected_warnings', 'warn'] def warn(message, category=None, stacklevel=2): """A version of `warnings.warn` with a default stacklevel of 2. """ if category is not None: warnings.warn(message, category=category, stacklevel=stacklevel) else: warnings.warn(message, stacklevel=stacklevel) @contextmanager def all_warnings(): """ Context for use in testing to ensure that all warnings are raised. Examples -------- >>> import warnings >>> def foo(): ... warnings.warn(RuntimeWarning("bar")) We raise the warning once, while the warning filter is set to "once". Hereafter, the warning is invisible, even with custom filters: >>> with warnings.catch_warnings(): ... warnings.simplefilter('once') ... foo() We can now run ``foo()`` without a warning being raised: >>> from numpy.testing import assert_warns >>> foo() To catch the warning, we call in the help of ``all_warnings``: >>> with all_warnings(): ... assert_warns(RuntimeWarning, foo) """ # Whenever a warning is triggered, Python adds a __warningregistry__ # member to the *calling* module. The exercize here is to find # and eradicate all those breadcrumbs that were left lying around. # # We proceed by first searching all parent calling frames and explicitly # clearing their warning registries (necessary for the doctests above to # pass). Then, we search for all submodules of skimage and clear theirs # as well (necessary for the skimage test suite to pass). frame = inspect.currentframe() if frame: for f in inspect.getouterframes(frame): f[0].f_locals['__warningregistry__'] = {} del frame for mod_name, mod in list(sys.modules.items()): if 'six.moves' in mod_name: continue try: mod.__warningregistry__.clear() except AttributeError: pass with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") yield w @contextmanager def expected_warnings(matching): """Context for use in testing to catch known warnings matching regexes Parameters ---------- matching : list of strings or compiled regexes Regexes for the desired warning to catch Examples -------- >>> from skimage import data, img_as_ubyte, img_as_float >>> with expected_warnings(['precision loss']): ... d = img_as_ubyte(img_as_float(data.coins())) Notes ----- Uses `all_warnings` to ensure all warnings are raised. Upon exiting, it checks the recorded warnings for the desired matching pattern(s). Raises a ValueError if any match was not found or an unexpected warning was raised. Allows for three types of behaviors: `and`, `or`, and `optional` matches. This is done to accomodate different build enviroments or loop conditions that may produce different warnings. The behaviors can be combined. If you pass multiple patterns, you get an orderless `and`, where all of the warnings must be raised. If you use the `|` operator in a pattern, you can catch one of several warnings. Finally, you can use `|\A\Z` in a pattern to signify it as optional. """ with all_warnings() as w: # enter context yield w # exited user context, check the recorded warnings remaining = [m for m in matching if '\A\Z' not in m.split('|')] for warn in w: found = False for match in matching: if re.search(match, str(warn.message)) is not None: found = True if match in remaining: remaining.remove(match) if not found: raise ValueError('Unexpected warning: %s' % str(warn.message)) if len(remaining) > 0: msg = 'No warning raised matching:\n%s' % '\n'.join(remaining) raise ValueError(msg) scikit-image-0.13.1/skimage/_shared/geometry.pxd000066400000000000000000000005321316256236300215460ustar00rootroot00000000000000cdef unsigned char point_in_polygon(Py_ssize_t nr_verts, double *xp, double *yp, double x, double y) nogil cdef void points_in_polygon(Py_ssize_t nr_verts, double *xp, double *yp, Py_ssize_t nr_points, double *x, double *y, unsigned char *result) nogil scikit-image-0.13.1/skimage/_shared/geometry.pyx000066400000000000000000000032031316256236300215710ustar00rootroot00000000000000#cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False #cython: wraparound=False cdef inline unsigned char point_in_polygon(Py_ssize_t nr_verts, double *xp, double *yp, double x, double y) nogil: """Test whether point lies inside a polygon. Parameters ---------- nr_verts : int Number of vertices of polygon. xp, yp : double array Coordinates of polygon with length nr_verts. x, y : double Coordinates of point. """ cdef Py_ssize_t i cdef unsigned char c = 0 cdef Py_ssize_t j = nr_verts - 1 for i in range(nr_verts): if ( (((yp[i] <= y) and (y < yp[j])) or ((yp[j] <= y) and (y < yp[i]))) and (x < (xp[j] - xp[i]) * (y - yp[i]) / (yp[j] - yp[i]) + xp[i]) ): c = not c j = i return c cdef void points_in_polygon(Py_ssize_t nr_verts, double *xp, double *yp, Py_ssize_t nr_points, double *x, double *y, unsigned char *result) nogil: """Test whether points lie inside a polygon. Parameters ---------- nr_verts : int Number of vertices of polygon. xp, yp : double array Coordinates of polygon with length nr_verts. nr_points : int Number of points to test. x, y : double array Coordinates of points. result : unsigned char array Test results for each point. """ cdef Py_ssize_t n for n in range(nr_points): result[n] = point_in_polygon(nr_verts, xp, yp, x[n], y[n]) scikit-image-0.13.1/skimage/_shared/interpolation.pxd000066400000000000000000000250621316256236300226070ustar00rootroot00000000000000#cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False #cython: wraparound=False """ Note: All edge modes implemented here follow the corresponding numpy.pad conventions. The table below illustrates the behavior for the array [1, 2, 3, 4], if padded by 4 values on each side: pad original pad constant (with c=0) : 0 0 0 0 | 1 2 3 4 | 0 0 0 0 wrap : 1 2 3 4 | 1 2 3 4 | 1 2 3 4 symmetric : 4 3 2 1 | 1 2 3 4 | 4 3 2 1 edge : 1 1 1 1 | 1 2 3 4 | 4 4 4 4 reflect : 3 4 3 2 | 1 2 3 4 | 3 2 1 2 """ from libc.math cimport ceil, floor cdef inline Py_ssize_t round(double r) nogil: return ((r + 0.5) if (r > 0.0) else (r - 0.5)) cdef inline double nearest_neighbour_interpolation(double* image, Py_ssize_t rows, Py_ssize_t cols, double r, double c, char mode, double cval) nogil: """Nearest neighbour interpolation at a given position in the image. Parameters ---------- image : double array Input image. rows, cols : int Shape of image. r, c : double Position at which to interpolate. mode : {'C', 'W', 'S', 'E', 'R'} Wrapping mode. Constant, Wrap, Symmetric, Edge or Reflect. cval : double Constant value to use for constant mode. Returns ------- value : double Interpolated value. """ return get_pixel2d(image, rows, cols, round(r), round(c), mode, cval) cdef inline double bilinear_interpolation(double* image, Py_ssize_t rows, Py_ssize_t cols, double r, double c, char mode, double cval) nogil: """Bilinear interpolation at a given position in the image. Parameters ---------- image : double array Input image. rows, cols : int Shape of image. r, c : double Position at which to interpolate. mode : {'C', 'W', 'S', 'E', 'R'} Wrapping mode. Constant, Wrap, Symmetric, Edge or Reflect. cval : double Constant value to use for constant mode. Returns ------- value : double Interpolated value. """ cdef double dr, dc cdef long minr, minc, maxr, maxc minr = floor(r) minc = floor(c) maxr = ceil(r) maxc = ceil(c) dr = r - minr dc = c - minc top = (1 - dc) * get_pixel2d(image, rows, cols, minr, minc, mode, cval) \ + dc * get_pixel2d(image, rows, cols, minr, maxc, mode, cval) bottom = (1 - dc) * get_pixel2d(image, rows, cols, maxr, minc, mode, cval) \ + dc * get_pixel2d(image, rows, cols, maxr, maxc, mode, cval) return (1 - dr) * top + dr * bottom cdef inline double quadratic_interpolation(double x, double[3] f) nogil: """WARNING: Do not use, not implemented correctly. Quadratic interpolation. Parameters ---------- x : double Position in the interval [0, 2]. f : double[3] Function values at positions [0, 2]. Returns ------- value : double Interpolated value. """ return (x * f[2] * (x - 1)) / 2 - \ x * f[1] * (x - 2) + \ (f[0] * (x - 1) * (x - 2)) / 2 cdef inline double biquadratic_interpolation(double* image, Py_ssize_t rows, Py_ssize_t cols, double r, double c, char mode, double cval) nogil: """WARNING: Do not use, not implemented correctly. Biquadratic interpolation at a given position in the image. Parameters ---------- image : double array Input image. rows, cols : int Shape of image. r, c : double Position at which to interpolate. mode : {'C', 'W', 'S', 'E', 'R'} Wrapping mode. Constant, Wrap, Symmetric, Edge or Reflect. cval : double Constant value to use for constant mode. Returns ------- value : double Interpolated value. """ cdef long r0 = round(r) - 1 cdef long c0 = round(c) - 1 cdef double xr = r - r0 cdef double xc = c - c0 cdef double fc[3] cdef double fr[3] cdef long pr, pc # row-wise cubic interpolation for pr in range(3): for pc in range(3): fc[pc] = get_pixel2d(image, rows, cols, r0 + pr, c0 + pc, mode, cval) fr[pr] = quadratic_interpolation(xc, fc) # cubic interpolation for interpolated values of each row return quadratic_interpolation(xr, fr) cdef inline double cubic_interpolation(double x, double[4] f) nogil: """Cubic interpolation. Parameters ---------- x : double Position in the interval [0, 1]. f : double[4] Function values at positions [-1, 0, 1, 2]. Returns ------- value : double Interpolated value. """ return \ f[1] + 0.5 * x * \ (f[2] - f[0] + x * \ (2.0 * f[0] - 5.0 * f[1] + 4.0 * f[2] - f[3] + x * \ (3.0 * (f[1] - f[2]) + f[3] - f[0]))) cdef inline double bicubic_interpolation(double* image, Py_ssize_t rows, Py_ssize_t cols, double r, double c, char mode, double cval) nogil: """Bicubic interpolation at a given position in the image. Interpolation using Catmull-Rom splines, based on the bicubic convolution algorithm described in [1]_. Parameters ---------- image : double array Input image. rows, cols : int Shape of image. r, c : double Position at which to interpolate. mode : {'C', 'W', 'S', 'E', 'R'} Wrapping mode. Constant, Wrap, Symmetric, Edge or Reflect. cval : double Constant value to use for constant mode. Returns ------- value : double Interpolated value. References ---------- .. [1] R. Keys, (1981). "Cubic convolution interpolation for digital image processing". IEEE Transactions on Signal Processing, Acoustics, Speech, and Signal Processing 29 (6): 1153–1160. """ cdef long r0 = floor(r) cdef long c0 = floor(c) # scale position to range [0, 1] cdef double xr = r - r0 cdef double xc = c - c0 r0 -= 1 c0 -= 1 cdef double fc[4] cdef double fr[4] cdef long pr, pc # row-wise cubic interpolation for pr in range(4): for pc in range(4): fc[pc] = get_pixel2d(image, rows, cols, pr + r0, pc + c0, mode, cval) fr[pr] = cubic_interpolation(xc, fc) # cubic interpolation for interpolated values of each row return cubic_interpolation(xr, fr) cdef inline double get_pixel2d(double* image, Py_ssize_t rows, Py_ssize_t cols, long r, long c, char mode, double cval) nogil: """Get a pixel from the image, taking wrapping mode into consideration. Parameters ---------- image : double array Input image. rows, cols : int Shape of image. r, c : int Position at which to get the pixel. mode : {'C', 'W', 'S', 'E', 'R'} Wrapping mode. Constant, Wrap, Symmetric, Edge or Reflect. cval : double Constant value to use for constant mode. Returns ------- value : double Pixel value at given position. """ if mode == 'C': if (r < 0) or (r >= rows) or (c < 0) or (c >= cols): return cval else: return image[r * cols + c] else: return image[coord_map(rows, r, mode) * cols + coord_map(cols, c, mode)] cdef inline double get_pixel3d(double* image, Py_ssize_t rows, Py_ssize_t cols, Py_ssize_t dims, long r, long c, long d, char mode, double cval) nogil: """Get a pixel from the image, taking wrapping mode into consideration. Parameters ---------- image : double array Input image. rows, cols, dims : int Shape of image. r, c, d : int Position at which to get the pixel. mode : {'C', 'W', 'S', 'E', 'R'} Wrapping mode. Constant, Wrap, Symmetric, Edge or Reflect. cval : double Constant value to use for constant mode. Returns ------- value : double Pixel value at given position. """ if mode == 'C': if (r < 0) or (r >= rows) or (c < 0) or (c >= cols): return cval else: return image[r * cols * dims + c * dims + d] else: return image[coord_map(rows, r, mode) * cols * dims + coord_map(cols, c, mode) * dims + coord_map(dims, d, mode)] cdef inline Py_ssize_t coord_map(Py_ssize_t dim, long coord, char mode) nogil: """Wrap a coordinate, according to a given mode. Parameters ---------- dim : int Maximum coordinate. coord : int Coord provided by user. May be < 0 or > dim. mode : {'W', 'S', 'R', 'E'} Whether to wrap, symmetric reflect, reflect or use the nearest coordinate if `coord` falls outside [0, dim). """ cdef Py_ssize_t cmax cmax = dim - 1 if mode == 'S': # symmetric if coord < 0: coord = -coord - 1 if coord > cmax: if (coord / dim) % 2 != 0: return (cmax - (coord % dim)) else: return (coord % dim) elif mode == 'W': # wrap if coord < 0: return (cmax - ((-coord - 1) % dim)) elif coord > cmax: return (coord % dim) elif mode == 'E': # edge if coord < 0: return 0 elif coord > cmax: return cmax elif mode == 'R': # reflect (mirror) if coord < 0: # How many times times does the coordinate wrap? if (-coord / cmax) % 2 != 0: return cmax - (-coord % cmax) else: return (-coord % cmax) elif coord > cmax: if (coord / cmax) % 2 != 0: return (cmax - (coord % cmax)) else: return (coord % cmax) return coord scikit-image-0.13.1/skimage/_shared/interpolation.pyx000066400000000000000000000004351316256236300226310ustar00rootroot00000000000000from interpolation cimport coord_map, get_pixel2d import numpy as np cimport numpy as cnp def coord_map_py(Py_ssize_t dim, long coord, mode): """Python wrapper for `interpolation.coord_map`.""" cdef char mode_c = ord(mode[0].upper()) return coord_map(dim, coord, mode_c) scikit-image-0.13.1/skimage/_shared/setup.py000066400000000000000000000023141316256236300207100ustar00rootroot00000000000000#!/usr/bin/env python import os from skimage._build import cython base_path = os.path.abspath(os.path.dirname(__file__)) def configuration(parent_package='', top_path=None): from numpy.distutils.misc_util import Configuration, get_numpy_include_dirs config = Configuration('_shared', parent_package, top_path) config.add_data_dir('tests') cython(['geometry.pyx'], working_path=base_path) cython(['transform.pyx'], working_path=base_path) cython(['interpolation.pyx'], working_path=base_path) config.add_extension('geometry', sources=['geometry.c']) config.add_extension('transform', sources=['transform.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension('interpolation', sources=['interpolation.c']) return config if __name__ == '__main__': from numpy.distutils.core import setup setup(maintainer='scikit-image Developers', author='scikit-image Developers', maintainer_email='scikit-image@python.org', description='Transforms', url='https://github.com/scikit-image/scikit-image', license='SciPy License (BSD Style)', **(configuration(top_path='').todict()) ) scikit-image-0.13.1/skimage/_shared/testing.py000066400000000000000000000153001316256236300212240ustar00rootroot00000000000000"""Testing utilities.""" import os import re import threading import functools from tempfile import NamedTemporaryFile from numpy import testing import numpy as np from ._warnings import expected_warnings import warnings from .. import data, io, img_as_uint, img_as_float, img_as_int, img_as_ubyte SKIP_RE = re.compile("(\s*>>>.*?)(\s*)#\s*skip\s+if\s+(.*)$") def _assert_less(a, b, msg=None): message = "%r is not lower than %r" % (a, b) if msg is not None: message += ": " + msg assert a < b, message def _assert_greater(a, b, msg=None): message = "%r is not greater than %r" % (a, b) if msg is not None: message += ": " + msg assert a > b, message try: from nose.tools import assert_less except ImportError: assert_less = _assert_less try: from nose.tools import assert_greater except ImportError: assert_greater = _assert_greater def doctest_skip_parser(func): """ Decorator replaces custom skip test markup in doctests Say a function has a docstring:: >>> something # skip if not HAVE_AMODULE >>> something + else >>> something # skip if HAVE_BMODULE This decorator will evaluate the expression after ``skip if``. If this evaluates to True, then the comment is replaced by ``# doctest: +SKIP``. If False, then the comment is just removed. The expression is evaluated in the ``globals`` scope of `func`. For example, if the module global ``HAVE_AMODULE`` is False, and module global ``HAVE_BMODULE`` is False, the returned function will have docstring:: >>> something # doctest: +SKIP >>> something + else >>> something """ lines = func.__doc__.split('\n') new_lines = [] for line in lines: match = SKIP_RE.match(line) if match is None: new_lines.append(line) continue code, space, expr = match.groups() try: # Works as a function decorator if eval(expr, func.__globals__): code = code + space + "# doctest: +SKIP" except AttributeError: # Works as a class decorator if eval(expr, func.__init__.__globals__): code = code + space + "# doctest: +SKIP" new_lines.append(code) func.__doc__ = "\n".join(new_lines) return func def roundtrip(img, plugin, suffix): """Save and read an image using a specified plugin""" if '.' not in suffix: suffix = '.' + suffix temp_file = NamedTemporaryFile(suffix=suffix, delete=False) fname = temp_file.name temp_file.close() io.imsave(fname, img, plugin=plugin) new = io.imread(fname, plugin=plugin) try: os.remove(fname) except Exception: pass return new def color_check(plugin, fmt='png'): """Check roundtrip behavior for color images. All major input types should be handled as ubytes and read back correctly. """ img = img_as_ubyte(data.chelsea()) r1 = roundtrip(img, plugin, fmt) testing.assert_allclose(img, r1) img2 = img > 128 r2 = roundtrip(img2, plugin, fmt) testing.assert_allclose(img2.astype(np.uint8), r2) img3 = img_as_float(img) with expected_warnings(['precision loss']): r3 = roundtrip(img3, plugin, fmt) testing.assert_allclose(r3, img) with expected_warnings(['precision loss']): img4 = img_as_int(img) if fmt.lower() in (('tif', 'tiff')): img4 -= 100 with expected_warnings(['sign loss']): r4 = roundtrip(img4, plugin, fmt) testing.assert_allclose(r4, img4) else: with expected_warnings(['sign loss|precision loss']): r4 = roundtrip(img4, plugin, fmt) testing.assert_allclose(r4, img_as_ubyte(img4)) img5 = img_as_uint(img) with expected_warnings(['precision loss']): r5 = roundtrip(img5, plugin, fmt) testing.assert_allclose(r5, img) def mono_check(plugin, fmt='png'): """Check the roundtrip behavior for images that support most types. All major input types should be handled. """ img = img_as_ubyte(data.moon()) r1 = roundtrip(img, plugin, fmt) testing.assert_allclose(img, r1) img2 = img > 128 r2 = roundtrip(img2, plugin, fmt) testing.assert_allclose(img2.astype(np.uint8), r2) img3 = img_as_float(img) with expected_warnings(['precision|\A\Z']): r3 = roundtrip(img3, plugin, fmt) if r3.dtype.kind == 'f': testing.assert_allclose(img3, r3) else: testing.assert_allclose(r3, img_as_uint(img)) with expected_warnings(['precision loss']): img4 = img_as_int(img) if fmt.lower() in (('tif', 'tiff')): img4 -= 100 with expected_warnings(['sign loss|\A\Z']): r4 = roundtrip(img4, plugin, fmt) testing.assert_allclose(r4, img4) else: with expected_warnings(['precision loss|sign loss']): r4 = roundtrip(img4, plugin, fmt) testing.assert_allclose(r4, img_as_uint(img4)) img5 = img_as_uint(img) r5 = roundtrip(img5, plugin, fmt) testing.assert_allclose(r5, img5) def setup_test(): """Default package level setup routine for skimage tests. Import packages known to raise warnings, and then force warnings to raise errors. Also set the random seed to zero. """ warnings.simplefilter('default') from scipy import signal, ndimage, special, optimize, linalg from scipy.io import loadmat from skimage import viewer np.random.seed(0) warnings.simplefilter('error') def teardown_test(): """Default package level teardown routine for skimage tests. Restore warnings to default behavior """ warnings.simplefilter('default') def test_parallel(num_threads=2): """Decorator to run the same function multiple times in parallel. This decorator is useful to ensure that separate threads execute concurrently and correctly while releasing the GIL. Parameters ---------- num_threads : int, optional The number of times the function is run in parallel. """ assert num_threads > 0 def wrapper(func): @functools.wraps(func) def inner(*args, **kwargs): threads = [] for i in range(num_threads - 1): thread = threading.Thread(target=func, args=args, kwargs=kwargs) threads.append(thread) for thread in threads: thread.start() result = func(*args, **kwargs) for thread in threads: thread.join() return result return inner return wrapper if __name__ == '__main__': color_check('pil') mono_check('pil') mono_check('pil', 'bmp') mono_check('pil', 'tiff') scikit-image-0.13.1/skimage/_shared/tests/000077500000000000000000000000001316256236300203405ustar00rootroot00000000000000scikit-image-0.13.1/skimage/_shared/tests/__init__.py000066400000000000000000000001661316256236300224540ustar00rootroot00000000000000from ..testing import setup_test, teardown_test def setup(): setup_test() def teardown(): teardown_test() scikit-image-0.13.1/skimage/_shared/tests/test_geometry.py000066400000000000000000000040531316256236300236060ustar00rootroot00000000000000from skimage._shared._geometry import polygon_clip, polygon_area import numpy as np from numpy.testing import assert_equal, assert_almost_equal hand = np.array( [[ 1.64516129, 1.16145833 ], [ 1.64516129, 1.59375 ], [ 1.35080645, 1.921875 ], [ 1.375 , 2.18229167 ], [ 1.68548387, 1.9375 ], [ 1.60887097, 2.55208333 ], [ 1.68548387, 2.69791667 ], [ 1.76209677, 2.56770833 ], [ 1.83064516, 1.97395833 ], [ 1.89516129, 2.75 ], [ 1.9516129 , 2.84895833 ], [ 2.01209677, 2.76041667 ], [ 1.99193548, 1.99479167 ], [ 2.11290323, 2.63020833 ], [ 2.2016129 , 2.734375 ], [ 2.25403226, 2.60416667 ], [ 2.14919355, 1.953125 ], [ 2.30645161, 2.36979167 ], [ 2.39112903, 2.36979167 ], [ 2.41532258, 2.1875 ], [ 2.1733871 , 1.703125 ], [ 2.07782258, 1.16666667 ]]) def test_polygon_area(): x = [0, 0, 1, 1] y = [0, 1, 1, 0] assert_almost_equal(polygon_area(y, x), 1) x = [0, 0, 1] y = [0, 1, 1] assert_almost_equal(polygon_area(y, x), 0.5) x = [0, 0, 0.5, 1, 1, 0.5] y = [0, 1, 0.5, 1, 0, 0.5] assert_almost_equal(polygon_area(y, x), 0.5) def test_poly_clip(): x = [0, 1, 2, 1] y = [0, -1, 0, 1] yc, xc = polygon_clip(y, x, 0, 0, 1, 1) assert_equal(polygon_area(yc, xc), 0.5) x = [-1, 1.5, 1.5, -1] y = [.5, 0.5, 1.5, 1.5] yc, xc = polygon_clip(y, x, 0, 0, 1, 1) assert_equal(polygon_area(yc, xc), 0.5) def test_hand_clip(): (r0, c0, r1, c1) = (1.0, 1.5, 2.1, 2.5) clip_r, clip_c = polygon_clip(hand[:, 1], hand[:, 0], r0, c0, r1, c1) assert_equal(clip_r.size, 19) assert_equal(clip_r[0], clip_r[-1]) assert_equal(clip_c[0], clip_c[-1]) (r0, c0, r1, c1) = (1.0, 1.5, 1.7, 2.5) clip_r, clip_c = polygon_clip(hand[:, 1], hand[:, 0], r0, c0, r1, c1) assert_equal(clip_r.size, 6) (r0, c0, r1, c1) = (1.0, 1.5, 1.5, 2.5) clip_r, clip_c = polygon_clip(hand[:, 1], hand[:, 0], r0, c0, r1, c1) assert_equal(clip_r.size, 5) scikit-image-0.13.1/skimage/_shared/tests/test_interpolation.py000066400000000000000000000021071316256236300246400ustar00rootroot00000000000000from skimage._shared.interpolation import coord_map_py from numpy.testing import assert_array_equal def test_coord_map(): symmetric = [coord_map_py(4, n, 'S') for n in range(-6, 6)] expected_symmetric = [2, 3, 3, 2, 1, 0, 0, 1, 2, 3, 3, 2] assert_array_equal(symmetric, expected_symmetric) wrap = [coord_map_py(4, n, 'W') for n in range(-6, 6)] expected_wrap = [2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1] assert_array_equal(wrap, expected_wrap) edge = [coord_map_py(4, n, 'E') for n in range(-6, 6)] expected_edge = [0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 3, 3] assert_array_equal(edge, expected_edge) reflect = [coord_map_py(4, n, 'R') for n in range(-6, 6)] expected_reflect = [0, 1, 2, 3, 2, 1, 0, 1, 2, 3, 2, 1] assert_array_equal(reflect, expected_reflect) constant = [coord_map_py(4, n, 'C') for n in range(-6, 6)] expected_constant = [0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 0, 0] assert_array_equal(constant, expected_constant) other = [coord_map_py(4, n, 'undefined') for n in range(-6, 6)] assert_array_equal(other, list(range(-6, 6))) scikit-image-0.13.1/skimage/_shared/tests/test_safe_as_int.py000066400000000000000000000033531316256236300242300ustar00rootroot00000000000000import numpy as np from skimage._shared.utils import safe_as_int def test_int_cast_not_possible(): np.testing.assert_raises(ValueError, safe_as_int, 7.1) np.testing.assert_raises(ValueError, safe_as_int, [7.1, 0.9]) np.testing.assert_raises(ValueError, safe_as_int, np.r_[7.1, 0.9]) np.testing.assert_raises(ValueError, safe_as_int, (7.1, 0.9)) np.testing.assert_raises(ValueError, safe_as_int, ((3, 4, 1), (2, 7.6, 289))) np.testing.assert_raises(ValueError, safe_as_int, 7.1, 0.09) np.testing.assert_raises(ValueError, safe_as_int, [7.1, 0.9], 0.09) np.testing.assert_raises(ValueError, safe_as_int, np.r_[7.1, 0.9], 0.09) np.testing.assert_raises(ValueError, safe_as_int, (7.1, 0.9), 0.09) np.testing.assert_raises(ValueError, safe_as_int, ((3, 4, 1), (2, 7.6, 289)), 0.25) def test_int_cast_possible(): np.testing.assert_equal(safe_as_int(7.1, atol=0.11), 7) np.testing.assert_equal(safe_as_int(-7.1, atol=0.11), -7) np.testing.assert_equal(safe_as_int(41.9, atol=0.11), 42) np.testing.assert_array_equal(safe_as_int([2, 42, 5789234.0, 87, 4]), np.r_[2, 42, 5789234, 87, 4]) np.testing.assert_array_equal(safe_as_int(np.r_[[[3, 4, 1.000000001], [7, 2, -8.999999999], [6, 9, -4234918347.]]]), np.r_[[[3, 4, 1], [7, 2, -9], [6, 9, -4234918347]]]) if __name__ == '__main__': np.testing.run_module_suite() scikit-image-0.13.1/skimage/_shared/tests/test_testing.py000066400000000000000000000042461316256236300234340ustar00rootroot00000000000000""" Testing decorators module """ import numpy as np from nose.tools import (assert_true, assert_raises, assert_equal) from skimage._shared.testing import doctest_skip_parser, test_parallel def test_skipper(): def f(): pass class c(): def __init__(self): self.me = "I think, therefore..." docstring = \ """ Header >>> something # skip if not HAVE_AMODULE >>> something + else >>> a = 1 # skip if not HAVE_BMODULE >>> something2 # skip if HAVE_AMODULE """ f.__doc__ = docstring c.__doc__ = docstring global HAVE_AMODULE, HAVE_BMODULE HAVE_AMODULE = False HAVE_BMODULE = True f2 = doctest_skip_parser(f) c2 = doctest_skip_parser(c) assert_true(f is f2) assert_true(c is c2) expected = \ """ Header >>> something # doctest: +SKIP >>> something + else >>> a = 1 >>> something2 """ assert_equal(f2.__doc__, expected) assert_equal(c2.__doc__, expected) HAVE_AMODULE = True HAVE_BMODULE = False f.__doc__ = docstring c.__doc__ = docstring f2 = doctest_skip_parser(f) c2 = doctest_skip_parser(c) assert_true(f is f2) expected = \ """ Header >>> something >>> something + else >>> a = 1 # doctest: +SKIP >>> something2 # doctest: +SKIP """ assert_equal(f2.__doc__, expected) assert_equal(c2.__doc__, expected) del HAVE_AMODULE f.__doc__ = docstring c.__doc__ = docstring assert_raises(NameError, doctest_skip_parser, f) assert_raises(NameError, doctest_skip_parser, c) def test_test_parallel(): state = [] @test_parallel() def change_state1(): state.append(None) change_state1() assert len(state) == 2 @test_parallel(num_threads=1) def change_state2(): state.append(None) change_state2() assert len(state) == 3 @test_parallel(num_threads=3) def change_state3(): state.append(None) change_state3() assert len(state) == 6 if __name__ == '__main__': np.testing.run_module_suite() scikit-image-0.13.1/skimage/_shared/tests/test_utils.py000066400000000000000000000011441316256236300231110ustar00rootroot00000000000000from skimage._shared.utils import (copy_func, assert_nD) import numpy.testing as npt from numpy.testing import assert_raises def test_assert_nD(): z = np.random.random(200**2).reshape((200, 200)) x = z[10:30, 30:10] assert_raises(ValueError, assert_nD, x, 2) def test_copyfunc(): def foo(a): return a bar = copy_func(foo, name='bar') other = copy_func(foo) npt.assert_equal(bar.__name__, 'bar') npt.assert_equal(other.__name__, 'foo') other.__name__ = 'other' npt.assert_equal(foo.__name__, 'foo') if __name__ == "__main__": npt.run_module_suite() scikit-image-0.13.1/skimage/_shared/tests/test_version_requirements.py000066400000000000000000000021551316256236300262440ustar00rootroot00000000000000"""Tests for the version requirement functions. """ import numpy as np from numpy.testing import assert_raises, assert_equal import nose from skimage._shared import version_requirements as version_req def test_get_module_version(): assert version_req.get_module_version('numpy') assert version_req.get_module_version('scipy') assert_raises(ImportError, lambda: version_req.get_module_version('fakenumpy')) def test_is_installed(): assert version_req.is_installed('python', '>=2.7') assert not version_req.is_installed('numpy', '<1.0') def test_require(): # A function that only runs on Python >2.7 and numpy > 1.5 (should pass) @version_req.require('python', '>2.7') @version_req.require('numpy', '>1.5') def foo(): return 1 assert_equal(foo(), 1) # function that requires scipy < 0.1 (should fail) @version_req.require('scipy', '<0.1') def bar(): return 0 assert_raises(ImportError, lambda: bar()) def test_get_module(): assert_equal(version_req.get_module('numpy'), np) assert_equal(version_req.get_module('nose'), nose) scikit-image-0.13.1/skimage/_shared/transform.pxd000066400000000000000000000001771316256236300217330ustar00rootroot00000000000000cdef float integrate(float[:, ::1] sat, Py_ssize_t r0, Py_ssize_t c0, Py_ssize_t r1, Py_ssize_t c1) nogil scikit-image-0.13.1/skimage/_shared/transform.pyx000066400000000000000000000020171316256236300217530ustar00rootroot00000000000000#cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False #cython: wraparound=False cdef float integrate(float[:, ::1] sat, Py_ssize_t r0, Py_ssize_t c0, Py_ssize_t r1, Py_ssize_t c1) nogil: """ Using a summed area table / integral image, calculate the sum over a given window. This function is the same as the `integrate` function in `skimage.transform.integrate`, but this Cython version significantly speeds up the code. Parameters ---------- sat : ndarray of float Summed area table / integral image. r0, c0 : int Top-left corner of block to be summed. r1, c1 : int Bottom-right corner of block to be summed. Returns ------- S : int Sum over the given window. """ cdef float S = 0 S += sat[r1, c1] if (r0 - 1 >= 0) and (c0 - 1 >= 0): S += sat[r0 - 1, c0 - 1] if (r0 - 1 >= 0): S -= sat[r0 - 1, c1] if (c0 - 1 >= 0): S -= sat[r1, c0 - 1] return S scikit-image-0.13.1/skimage/_shared/utils.py000066400000000000000000000166761316256236300207300ustar00rootroot00000000000000import warnings import functools import sys import numpy as np import types import numbers import six from ..util import img_as_float from ._warnings import all_warnings, warn __all__ = ['deprecated', 'get_bound_method_class', 'all_warnings', 'safe_as_int', 'assert_nD', 'warn'] class skimage_deprecation(Warning): """Create our own deprecation class, since Python >= 2.7 silences deprecations by default. """ pass class deprecated(object): """Decorator to mark deprecated functions with warning. Adapted from . Parameters ---------- alt_func : str If given, tell user what function to use instead. behavior : {'warn', 'raise'} Behavior during call to deprecated function: 'warn' = warn user that function is deprecated; 'raise' = raise error. removed_version : str The package version in which the deprecated function will be removed. """ def __init__(self, alt_func=None, behavior='warn', removed_version=None): self.alt_func = alt_func self.behavior = behavior self.removed_version = removed_version def __call__(self, func): alt_msg = '' if self.alt_func is not None: alt_msg = ' Use ``%s`` instead.' % self.alt_func rmv_msg = '' if self.removed_version is not None: rmv_msg = (' and will be removed in version %s' % self.removed_version) msg = ('Function ``%s`` is deprecated' % func.__name__ + rmv_msg + '.' + alt_msg) @functools.wraps(func) def wrapped(*args, **kwargs): if self.behavior == 'warn': func_code = six.get_function_code(func) warnings.simplefilter('always', skimage_deprecation) warnings.warn_explicit(msg, category=skimage_deprecation, filename=func_code.co_filename, lineno=func_code.co_firstlineno + 1) elif self.behavior == 'raise': raise skimage_deprecation(msg) return func(*args, **kwargs) # modify doc string to display deprecation warning doc = '**Deprecated function**.' + alt_msg if wrapped.__doc__ is None: wrapped.__doc__ = doc else: wrapped.__doc__ = doc + '\n\n ' + wrapped.__doc__ return wrapped def get_bound_method_class(m): """Return the class for a bound method. """ return m.im_class if sys.version < '3' else m.__self__.__class__ def safe_as_int(val, atol=1e-3): """ Attempt to safely cast values to integer format. Parameters ---------- val : scalar or iterable of scalars Number or container of numbers which are intended to be interpreted as integers, e.g., for indexing purposes, but which may not carry integer type. atol : float Absolute tolerance away from nearest integer to consider values in ``val`` functionally integers. Returns ------- val_int : NumPy scalar or ndarray of dtype `np.int64` Returns the input value(s) coerced to dtype `np.int64` assuming all were within ``atol`` of the nearest integer. Notes ----- This operation calculates ``val`` modulo 1, which returns the mantissa of all values. Then all mantissas greater than 0.5 are subtracted from one. Finally, the absolute tolerance from zero is calculated. If it is less than ``atol`` for all value(s) in ``val``, they are rounded and returned in an integer array. Or, if ``val`` was a scalar, a NumPy scalar type is returned. If any value(s) are outside the specified tolerance, an informative error is raised. Examples -------- >>> _safe_as_int(7.0) 7 >>> _safe_as_int([9, 4, 2.9999999999]) array([9, 4, 3], dtype=int32) >>> _safe_as_int(53.01) Traceback (most recent call last): ... ValueError: Integer argument required but received 53.1, check inputs. >>> _safe_as_int(53.01, atol=0.01) 53 """ mod = np.asarray(val) % 1 # Extract mantissa # Check for and subtract any mod values > 0.5 from 1 if mod.ndim == 0: # Scalar input, cannot be indexed if mod > 0.5: mod = 1 - mod else: # Iterable input, now ndarray mod[mod > 0.5] = 1 - mod[mod > 0.5] # Test on each side of nearest int try: np.testing.assert_allclose(mod, 0, atol=atol) except AssertionError: raise ValueError("Integer argument required but received " "{0}, check inputs.".format(val)) return np.round(val).astype(np.int64) def assert_nD(array, ndim, arg_name='image'): """ Verify an array meets the desired ndims and array isn't empty. Parameters ---------- array : array-like Input array to be validated ndim : int or iterable of ints Allowable ndim or ndims for the array. arg_name : str, optional The name of the array in the original function. """ array = np.asanyarray(array) msg_incorrect_dim = "The parameter `%s` must be a %s-dimensional array" msg_empty_array = "The parameter `%s` cannot be an empty array" if isinstance(ndim, int): ndim = [ndim] if array.size == 0: raise ValueError(msg_empty_array % (arg_name)) if not array.ndim in ndim: raise ValueError(msg_incorrect_dim % (arg_name, '-or-'.join([str(n) for n in ndim]))) def copy_func(f, name=None): """Create a copy of a function. Parameters ---------- f : function Function to copy. name : str, optional Name of new function. """ return types.FunctionType(six.get_function_code(f), six.get_function_globals(f), name or f.__name__, six.get_function_defaults(f), six.get_function_closure(f)) def check_random_state(seed): """Turn seed into a `np.random.RandomState` instance. Parameters ---------- seed : None, int or np.random.RandomState If `seed` is None, return the RandomState singleton used by `np.random`. If `seed` is an int, return a new RandomState instance seeded with `seed`. If `seed` is already a RandomState instance, return it. Raises ------ ValueError If `seed` is of the wrong type. """ # Function originally from scikit-learn's module sklearn.utils.validation if seed is None or seed is np.random: return np.random.mtrand._rand if isinstance(seed, (numbers.Integral, np.integer)): return np.random.RandomState(seed) if isinstance(seed, np.random.RandomState): return seed raise ValueError('%r cannot be used to seed a numpy.random.RandomState' ' instance' % seed) def convert_to_float(image, preserve_range): """Convert input image to double image with the appropriate range. Parameters ---------- image : ndarray Input image. preserve_range : bool Determines if the range of the image should be kept or transformed using img_as_float. Returns ------- image : ndarray Transformed version of the input. """ if preserve_range: image = image.astype(np.double) else: image = img_as_float(image) return image scikit-image-0.13.1/skimage/_shared/vectorized_ops.h000066400000000000000000000045471316256236300224200ustar00rootroot00000000000000/* Intrinsic declarations */ #if defined(__SSE2__) #include #elif defined(__MMX__) #include #elif defined(__ALTIVEC__) #include #endif /* Compiler peculiarities */ #if defined(__GNUC__) #include #elif defined(_MSC_VER) #define inline __inline typedef unsigned __int16 uint16_t; #endif /** * Add 16 unsigned 16-bit integers using SSE2, MMX or Altivec, if * available. */ #if defined(__SSE2__) static inline void add16(uint16_t *dest, uint16_t *src) { __m128i *d, *s; d = (__m128i *) dest; s = (__m128i *) src; *d = _mm_add_epi16(*d, *s); d++; s++; *d = _mm_add_epi16(*d, *s); } #elif defined(__MMX__) static inline void add16(uint16_t *dest, uint16_t *src) { __m64 *d, *s; d = (__m64 *) dest; s = (__m64 *) src; *d = _mm_add_pi16(*d, *s); d++; s++; *d = _mm_add_pi16(*d, *s); d++; s++; *d = _mm_add_pi16(*d, *s); d++; s++; *d = _mm_add_pi16(*d, *s); } #elif defined(__ALTIVEC__) static inline void add16(uint16_t *dest, uint16_t *src) { vector unsigned short *d, *s; d = (vector unsigned short *) dest; s = (vector unsigned short *) src; *d = vec_add(*d, *s); d++; s++; *d = vec_add(*d, *s); } #else static inline void add16(uint16_t *dest, uint16_t *src) { int i; for (i = 0; i < 16; i++) dest[i] += src[i]; } #endif /** * Subtract 16 unsigned 16-bit integers using SSE2, MMX or Altivec, if * available. */ #if defined(__SSE2__) static inline void sub16(uint16_t *dest, uint16_t *src) { __m128i *d, *s; d = (__m128i *) dest; s = (__m128i *) src; *d = _mm_sub_epi16(*d, *s); d++; s++; *d = _mm_sub_epi16(*d, *s); } #elif defined(__MMX__) static inline void sub16(uint16_t *dest, uint16_t *src) { __m64 *d, *s; d = (__m64 *) dest; s = (__m64 *) src; *d = _mm_sub_pi16(*d, *s); d++; s++; *d = _mm_sub_pi16(*d, *s); d++; s++; *d = _mm_sub_pi16(*d, *s); d++; s++; *d = _mm_sub_pi16(*d, *s); } #elif defined(__ALTIVEC__) static inline void sub16(uint16_t *dest, uint16_t *src) { vector unsigned short *d, *s; d = (vector unsigned short *) dest; s = (vector unsigned short *) src; *d = vec_sub(*d, *s); d++; s++; *d = vec_sub(*d, *s); } #else static inline void sub16(uint16_t *dest, uint16_t *src) { int i; for (i = 0; i < 16; i++) dest[i] -= src[i]; } #endif scikit-image-0.13.1/skimage/_shared/version_requirements.py000066400000000000000000000105331316256236300240420ustar00rootroot00000000000000from distutils.version import LooseVersion import functools import re import sys def _check_version(actver, version, cmp_op): """ Check version string of an active module against a required version. If dev/prerelease tags result in TypeError for string-number comparison, it is assumed that the dependency is satisfied. Users on dev branches are responsible for keeping their own packages up to date. Copyright (C) 2013 The IPython Development Team Distributed under the terms of the BSD License. """ try: if cmp_op == '>': return LooseVersion(actver) > LooseVersion(version) elif cmp_op == '>=': return LooseVersion(actver) >= LooseVersion(version) elif cmp_op == '=': return LooseVersion(actver) == LooseVersion(version) elif cmp_op == '<': return LooseVersion(actver) < LooseVersion(version) else: return False except TypeError: return True def get_module_version(module_name): """Return module version or None if version can't be retrieved.""" mod = __import__(module_name, fromlist=[module_name.rpartition('.')[-1]]) return getattr(mod, '__version__', getattr(mod, 'VERSION', None)) def is_installed(name, version=None): """Test if *name* is installed. Parameters ---------- name : str Name of module or "python" version : str, optional Version string to test against. If version is not None, checking version (must have an attribute named '__version__' or 'VERSION') Version may start with =, >=, > or < to specify the exact requirement Returns ------- out : bool True if `name` is installed matching the optional version. Notes ----- Original Copyright (C) 2009-2011 Pierre Raybaut Licensed under the terms of the MIT License. """ if name.lower() == 'python': actver = sys.version[:6] else: try: actver = get_module_version(name) except ImportError: return False if version is None: return True else: match = re.search('[0-9]', version) assert match is not None, "Invalid version number" symb = version[:match.start()] if not symb: symb = '=' assert symb in ('>=', '>', '=', '<'),\ "Invalid version condition '%s'" % symb version = version[match.start():] return _check_version(actver, version, symb) def require(name, version=None): """Return decorator that forces a requirement for a function or class. Parameters ---------- name : str Name of module or "python". version : str, optional Version string to test against. If version is not None, checking version (must have an attribute named '__version__' or 'VERSION') Version may start with =, >=, > or < to specify the exact requirement Returns ------- func : function A decorator that raises an ImportError if a function is run in the absence of the input dependency. """ def decorator(obj): @functools.wraps(obj) def func_wrapped(*args, **kwargs): if is_installed(name, version): return obj(*args, **kwargs) else: msg = '"%s" in "%s" requires "%s' msg = msg % (obj, obj.__module__, name) if not version is None: msg += " %s" % version raise ImportError(msg + '"') return func_wrapped return decorator def get_module(module_name, version=None): """Return a module object of name *module_name* if installed. Parameters ---------- module_name : str Name of module. version : str, optional Version string to test against. If version is not None, checking version (must have an attribute named '__version__' or 'VERSION') Version may start with =, >=, > or < to specify the exact requirement Returns ------- mod : module or None Module if *module_name* is installed matching the optional version or None otherwise. """ if not is_installed(module_name, version): return None return __import__(module_name, fromlist=[module_name.rpartition('.')[-1]]) scikit-image-0.13.1/skimage/color/000077500000000000000000000000001316256236300167075ustar00rootroot00000000000000scikit-image-0.13.1/skimage/color/__init__.py000066400000000000000000000072641316256236300210310ustar00rootroot00000000000000from .colorconv import (convert_colorspace, guess_spatial_dimensions, rgba2rgb, rgb2hsv, hsv2rgb, rgb2xyz, xyz2rgb, rgb2rgbcie, rgbcie2rgb, rgb2grey, rgb2gray, gray2rgb, grey2rgb, xyz2lab, lab2xyz, lab2rgb, rgb2lab, xyz2luv, luv2xyz, luv2rgb, rgb2luv, rgb2hed, hed2rgb, lab2lch, lch2lab, rgb2yuv, yuv2rgb, rgb2yiq, yiq2rgb, rgb2ypbpr, ypbpr2rgb, rgb2ycbcr, ycbcr2rgb, separate_stains, combine_stains, rgb_from_hed, hed_from_rgb, rgb_from_hdx, hdx_from_rgb, rgb_from_fgx, fgx_from_rgb, rgb_from_bex, bex_from_rgb, rgb_from_rbd, rbd_from_rgb, rgb_from_gdx, gdx_from_rgb, rgb_from_hax, hax_from_rgb, rgb_from_bro, bro_from_rgb, rgb_from_bpx, bpx_from_rgb, rgb_from_ahx, ahx_from_rgb, rgb_from_hpx, hpx_from_rgb) from .colorlabel import color_dict, label2rgb from .delta_e import (deltaE_cie76, deltaE_ciede94, deltaE_ciede2000, deltaE_cmc, ) __all__ = ['convert_colorspace', 'guess_spatial_dimensions', 'rgb2hsv', 'hsv2rgb', 'rgb2xyz', 'xyz2rgb', 'rgb2rgbcie', 'rgbcie2rgb', 'rgb2grey', 'rgb2gray', 'gray2rgb', 'grey2rgb', 'xyz2lab', 'lab2xyz', 'lab2rgb', 'rgb2lab', 'rgb2hed', 'hed2rgb', 'lab2lch', 'lch2lab', 'rgb2yuv', 'yuv2rgb', 'rgb2yiq', 'yiq2rgb', 'rgb2ypbpr', 'ypbpr2rgb', 'rgb2ycbcr', 'ycbcr2rgb', 'separate_stains', 'combine_stains', 'rgb_from_hed', 'hed_from_rgb', 'rgb_from_hdx', 'hdx_from_rgb', 'rgb_from_fgx', 'fgx_from_rgb', 'rgb_from_bex', 'bex_from_rgb', 'rgb_from_rbd', 'rbd_from_rgb', 'rgb_from_gdx', 'gdx_from_rgb', 'rgb_from_hax', 'hax_from_rgb', 'rgb_from_bro', 'bro_from_rgb', 'rgb_from_bpx', 'bpx_from_rgb', 'rgb_from_ahx', 'ahx_from_rgb', 'rgb_from_hpx', 'hpx_from_rgb', 'color_dict', 'label2rgb', 'deltaE_cie76', 'deltaE_ciede94', 'deltaE_ciede2000', 'deltaE_cmc', ] scikit-image-0.13.1/skimage/color/adapt_rgb.py000066400000000000000000000045771316256236300212210ustar00rootroot00000000000000import functools import numpy as np from .. import color from ..util.dtype import convert __all__ = ['adapt_rgb', 'hsv_value', 'each_channel'] def is_rgb_like(image): """Return True if the image *looks* like it's RGB. This function should not be public because it is only intended to be used for functions that don't accept volumes as input, since checking an image's shape is fragile. """ return (image.ndim == 3) and (image.shape[2] in (3, 4)) def adapt_rgb(apply_to_rgb): """Return decorator that adapts to RGB images to a gray-scale filter. This function is only intended to be used for functions that don't accept volumes as input, since checking an image's shape is fragile. Parameters ---------- apply_to_rgb : function Function that returns a filtered image from an image-filter and RGB image. This will only be called if the image is RGB-like. """ def decorator(image_filter): @functools.wraps(image_filter) def image_filter_adapted(image, *args, **kwargs): if is_rgb_like(image): return apply_to_rgb(image_filter, image, *args, **kwargs) else: return image_filter(image, *args, **kwargs) return image_filter_adapted return decorator def hsv_value(image_filter, image, *args, **kwargs): """Return color image by applying `image_filter` on HSV-value of `image`. Note that this function is intended for use with `adapt_rgb`. Parameters ---------- image_filter : function Function that filters a gray-scale image. image : array Input image. Note that RGBA images are treated as RGB. """ # Slice the first three channels so that we remove any alpha channels. hsv = color.rgb2hsv(image[:, :, :3]) value = hsv[:, :, 2].copy() value = image_filter(value, *args, **kwargs) hsv[:, :, 2] = convert(value, hsv.dtype) return color.hsv2rgb(hsv) def each_channel(image_filter, image, *args, **kwargs): """Return color image by applying `image_filter` on channels of `image`. Note that this function is intended for use with `adapt_rgb`. Parameters ---------- image_filter : function Function that filters a gray-scale image. image : array Input image. """ c_new = [image_filter(c, *args, **kwargs) for c in image.T] return np.array(c_new).T scikit-image-0.13.1/skimage/color/colorconv.py000066400000000000000000001442671316256236300213030ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- """Functions for converting between color spaces. The "central" color space in this module is RGB, more specifically the linear sRGB color space using D65 as a white-point [1]_. This represents a standard monitor (w/o gamma correction). For a good FAQ on color spaces see [2]_. The API consists of functions to convert to and from RGB as defined above, as well as a generic function to convert to and from any supported color space (which is done through RGB in most cases). Supported color spaces ---------------------- * RGB : Red Green Blue. Here the sRGB standard [1]_. * HSV : Hue, Saturation, Value. Uniquely defined when related to sRGB [3]_. * RGB CIE : Red Green Blue. The original RGB CIE standard from 1931 [4]_. Primary colors are 700 nm (red), 546.1 nm (blue) and 435.8 nm (green). * XYZ CIE : XYZ Derived from the RGB CIE color space. Chosen such that ``x == y == z == 1/3`` at the whitepoint, and all color matching functions are greater than zero everywhere. * LAB CIE : Lightness, a, b Colorspace derived from XYZ CIE that is intended to be more perceptually uniform * LUV CIE : Lightness, u, v Colorspace derived from XYZ CIE that is intended to be more perceptually uniform * LCH CIE : Lightness, Chroma, Hue Defined in terms of LAB CIE. C and H are the polar representation of a and b. The polar angle C is defined to be on ``(0, 2*pi)`` :author: Nicolas Pinto (rgb2hsv) :author: Ralf Gommers (hsv2rgb) :author: Travis Oliphant (XYZ and RGB CIE functions) :author: Matt Terry (lab2lch) :author: Alex Izvorski (yuv2rgb, rgb2yuv and related) :license: modified BSD References ---------- .. [1] Official specification of sRGB, IEC 61966-2-1:1999. .. [2] http://www.poynton.com/ColorFAQ.html .. [3] http://en.wikipedia.org/wiki/HSL_and_HSV .. [4] http://en.wikipedia.org/wiki/CIE_1931_color_space """ from __future__ import division from warnings import warn import numpy as np from scipy import linalg from ..util import dtype, dtype_limits def guess_spatial_dimensions(image): """Make an educated guess about whether an image has a channels dimension. Parameters ---------- image : ndarray The input image. Returns ------- spatial_dims : int or None The number of spatial dimensions of `image`. If ambiguous, the value is ``None``. Raises ------ ValueError If the image array has less than two or more than four dimensions. """ if image.ndim == 2: return 2 if image.ndim == 3 and image.shape[-1] != 3: return 3 if image.ndim == 3 and image.shape[-1] == 3: return None if image.ndim == 4 and image.shape[-1] == 3: return 3 else: raise ValueError("Expected 2D, 3D, or 4D array, got %iD." % image.ndim) def convert_colorspace(arr, fromspace, tospace): """Convert an image array to a new color space. Parameters ---------- arr : array_like The image to convert. fromspace : str The color space to convert from. Valid color space strings are ``['RGB', 'HSV', 'RGB CIE', 'XYZ', 'YUV', 'YIQ', 'YPbPr', 'YCbCr']``. Value may also be specified as lower case. tospace : str The color space to convert to. Valid color space strings are ``['RGB', 'HSV', 'RGB CIE', 'XYZ', 'YUV', 'YIQ', 'YPbPr', 'YCbCr']``. Value may also be specified as lower case. Returns ------- newarr : ndarray The converted image. Notes ----- Conversion occurs through the "central" RGB color space, i.e. conversion from XYZ to HSV is implemented as ``XYZ -> RGB -> HSV`` instead of directly. Examples -------- >>> from skimage import data >>> img = data.astronaut() >>> img_hsv = convert_colorspace(img, 'RGB', 'HSV') """ fromdict = {'RGB': lambda im: im, 'HSV': hsv2rgb, 'RGB CIE': rgbcie2rgb, 'XYZ': xyz2rgb, 'YUV': yuv2rgb, 'YIQ': yiq2rgb, 'YPbPr': ypbpr2rgb, 'YCbCr': ycbcr2rgb} todict = {'RGB': lambda im: im, 'HSV': rgb2hsv, 'RGB CIE': rgb2rgbcie, 'XYZ': rgb2xyz, 'YUV': rgb2yuv, 'YIQ': rgb2yiq, 'YPbPr': rgb2ypbpr, 'YCbCr': rgb2ycbcr} fromspace = fromspace.upper() tospace = tospace.upper() if fromspace not in fromdict.keys(): raise ValueError('fromspace needs to be one of %s' % fromdict.keys()) if tospace not in todict.keys(): raise ValueError('tospace needs to be one of %s' % todict.keys()) return todict[tospace](fromdict[fromspace](arr)) def _prepare_colorarray(arr): """Check the shape of the array and convert it to floating point representation. """ arr = np.asanyarray(arr) if arr.ndim not in [3, 4] or arr.shape[-1] != 3: msg = ("the input array must be have a shape == (.., ..,[ ..,] 3)), " + "got (" + (", ".join(map(str, arr.shape))) + ")") raise ValueError(msg) return dtype.img_as_float(arr) def _prepare_rgba_array(arr): """Check the shape of the array to be RGBA and convert it to floating point representation. """ arr = np.asanyarray(arr) if arr.ndim not in [3, 4] or arr.shape[-1] != 4: msg = ("the input array must have a shape == (.., ..,[ ..,] 4)), " "got {0}".format(arr.shape)) raise ValueError(msg) return dtype.img_as_float(arr) def rgba2rgb(rgba, background=(1, 1, 1)): """RGBA to RGB conversion. Parameters ---------- rgba : array_like The image in RGBA format, in a 3-D array of shape ``(.., .., 4)``. background : array_like The color of the background to blend the image with. A tuple containing 3 floats between 0 to 1 - the RGB value of the background. Returns ------- out : ndarray The image in RGB format, in a 3-D array of shape ``(.., .., 3)``. Raises ------ ValueError If `rgba` is not a 3-D array of shape ``(.., .., 4)``. References ---------- .. [1] https://en.wikipedia.org/wiki/Alpha_compositing#Alpha_blending Examples -------- >>> from skimage import color >>> from skimage import data >>> img_rgba = data.logo() >>> img_rgb = color.rgba2rgb(img_rgba) """ arr = _prepare_rgba_array(rgba) if isinstance(background, tuple) and len(background) != 3: raise ValueError('the background must be a tuple with 3 items - the ' 'RGB color of the background. Got {0} items.' .format(len(background))) alpha = arr[..., -1] channels = arr[..., :-1] out = np.empty_like(channels) for ichan in range(channels.shape[-1]): out[..., ichan] = np.clip( (1 - alpha) * background[ichan] + alpha * channels[..., ichan], a_min=0, a_max=1) return out def rgb2hsv(rgb): """RGB to HSV color space conversion. Parameters ---------- rgb : array_like The image in RGB format, in a 3-D array of shape ``(.., .., 3)``. Returns ------- out : ndarray The image in HSV format, in a 3-D array of shape ``(.., .., 3)``. Raises ------ ValueError If `rgb` is not a 3-D array of shape ``(.., .., 3)``. Notes ----- Conversion between RGB and HSV color spaces results in some loss of precision, due to integer arithmetic and rounding [1]_. References ---------- .. [1] http://en.wikipedia.org/wiki/HSL_and_HSV Examples -------- >>> from skimage import color >>> from skimage import data >>> img = data.astronaut() >>> img_hsv = color.rgb2hsv(img) """ arr = _prepare_colorarray(rgb) out = np.empty_like(arr) # -- V channel out_v = arr.max(-1) # -- S channel delta = arr.ptp(-1) # Ignore warning for zero divided by zero old_settings = np.seterr(invalid='ignore') out_s = delta / out_v out_s[delta == 0.] = 0. # -- H channel # red is max idx = (arr[:, :, 0] == out_v) out[idx, 0] = (arr[idx, 1] - arr[idx, 2]) / delta[idx] # green is max idx = (arr[:, :, 1] == out_v) out[idx, 0] = 2. + (arr[idx, 2] - arr[idx, 0]) / delta[idx] # blue is max idx = (arr[:, :, 2] == out_v) out[idx, 0] = 4. + (arr[idx, 0] - arr[idx, 1]) / delta[idx] out_h = (out[:, :, 0] / 6.) % 1. out_h[delta == 0.] = 0. np.seterr(**old_settings) # -- output out[:, :, 0] = out_h out[:, :, 1] = out_s out[:, :, 2] = out_v # remove NaN out[np.isnan(out)] = 0 return out def hsv2rgb(hsv): """HSV to RGB color space conversion. Parameters ---------- hsv : array_like The image in HSV format, in a 3-D array of shape ``(.., .., 3)``. Returns ------- out : ndarray The image in RGB format, in a 3-D array of shape ``(.., .., 3)``. Raises ------ ValueError If `hsv` is not a 3-D array of shape ``(.., .., 3)``. Notes ----- Conversion between RGB and HSV color spaces results in some loss of precision, due to integer arithmetic and rounding [1]_. References ---------- .. [1] http://en.wikipedia.org/wiki/HSL_and_HSV Examples -------- >>> from skimage import data >>> img = data.astronaut() >>> img_hsv = rgb2hsv(img) >>> img_rgb = hsv2rgb(img_hsv) """ arr = _prepare_colorarray(hsv) hi = np.floor(arr[:, :, 0] * 6) f = arr[:, :, 0] * 6 - hi p = arr[:, :, 2] * (1 - arr[:, :, 1]) q = arr[:, :, 2] * (1 - f * arr[:, :, 1]) t = arr[:, :, 2] * (1 - (1 - f) * arr[:, :, 1]) v = arr[:, :, 2] hi = np.dstack([hi, hi, hi]).astype(np.uint8) % 6 out = np.choose(hi, [np.dstack((v, t, p)), np.dstack((q, v, p)), np.dstack((p, v, t)), np.dstack((p, q, v)), np.dstack((t, p, v)), np.dstack((v, p, q))]) return out # --------------------------------------------------------------- # Primaries for the coordinate systems # --------------------------------------------------------------- cie_primaries = np.array([700, 546.1, 435.8]) sb_primaries = np.array([1. / 155, 1. / 190, 1. / 225]) * 1e5 # --------------------------------------------------------------- # Matrices that define conversion between different color spaces # --------------------------------------------------------------- # From sRGB specification xyz_from_rgb = np.array([[0.412453, 0.357580, 0.180423], [0.212671, 0.715160, 0.072169], [0.019334, 0.119193, 0.950227]]) rgb_from_xyz = linalg.inv(xyz_from_rgb) # From http://en.wikipedia.org/wiki/CIE_1931_color_space # Note: Travis's code did not have the divide by 0.17697 xyz_from_rgbcie = np.array([[0.49, 0.31, 0.20], [0.17697, 0.81240, 0.01063], [0.00, 0.01, 0.99]]) / 0.17697 rgbcie_from_xyz = linalg.inv(xyz_from_rgbcie) # construct matrices to and from rgb: rgbcie_from_rgb = np.dot(rgbcie_from_xyz, xyz_from_rgb) rgb_from_rgbcie = np.dot(rgb_from_xyz, xyz_from_rgbcie) gray_from_rgb = np.array([[0.2125, 0.7154, 0.0721], [0, 0, 0], [0, 0, 0]]) yuv_from_rgb = np.array([[ 0.299 , 0.587 , 0.114 ], [-0.14714119, -0.28886916, 0.43601035 ], [ 0.61497538, -0.51496512, -0.10001026 ]]) rgb_from_yuv = linalg.inv(yuv_from_rgb) yiq_from_rgb = np.array([[0.299 , 0.587 , 0.114 ], [0.59590059, -0.27455667, -0.32134392], [0.21153661, -0.52273617, 0.31119955]]) rgb_from_yiq = linalg.inv(yiq_from_rgb) ypbpr_from_rgb = np.array([[ 0.299 , 0.587 , 0.114 ], [-0.168736,-0.331264, 0.5 ], [ 0.5 ,-0.418688,-0.081312]]) rgb_from_ypbpr = linalg.inv(ypbpr_from_rgb) ycbcr_from_rgb = np.array([[ 65.481, 128.553, 24.966], [ -37.797, -74.203, 112.0 ], [ 112.0 , -93.786, -18.214]]) rgb_from_ycbcr = linalg.inv(ycbcr_from_rgb) # CIE LAB constants for Observer=2A, Illuminant=D65 # NOTE: this is actually the XYZ values for the illuminant above. lab_ref_white = np.array([0.95047, 1., 1.08883]) # XYZ coordinates of the illuminants, scaled to [0, 1]. For each illuminant I # we have: # # illuminant[I][0] corresponds to the XYZ coordinates for the 2 degree # field of view. # # illuminant[I][1] corresponds to the XYZ coordinates for the 10 degree # field of view. # # The XYZ coordinates are calculated from [1], using the formula: # # X = x * ( Y / y ) # Y = Y # Z = ( 1 - x - y ) * ( Y / y ) # # where Y = 1. The only exception is the illuminant "D65" with aperture angle # 2, whose coordinates are copied from 'lab_ref_white' for # backward-compatibility reasons. # # References # ---------- # .. [1] http://en.wikipedia.org/wiki/Standard_illuminant illuminants = \ {"A": {'2': (1.098466069456375, 1, 0.3558228003436005), '10': (1.111420406956693, 1, 0.3519978321919493)}, "D50": {'2': (0.9642119944211994, 1, 0.8251882845188288), '10': (0.9672062750333777, 1, 0.8142801513128616)}, "D55": {'2': (0.956797052643698, 1, 0.9214805860173273), '10': (0.9579665682254781, 1, 0.9092525159847462)}, "D65": {'2': (0.95047, 1., 1.08883), # This was: `lab_ref_white` '10': (0.94809667673716, 1, 1.0730513595166162)}, "D75": {'2': (0.9497220898840717, 1, 1.226393520724154), '10': (0.9441713925645873, 1, 1.2064272211720228)}, "E": {'2': (1.0, 1.0, 1.0), '10': (1.0, 1.0, 1.0)}} def get_xyz_coords(illuminant, observer): """Get the XYZ coordinates of the given illuminant and observer [1]_. Parameters ---------- illuminant : {"A", "D50", "D55", "D65", "D75", "E"}, optional The name of the illuminant (the function is NOT case sensitive). observer : {"2", "10"}, optional The aperture angle of the observer. Returns ------- (x, y, z) : tuple A tuple with 3 elements containing the XYZ coordinates of the given illuminant. Raises ------ ValueError If either the illuminant or the observer angle are not supported or unknown. References ---------- .. [1] http://en.wikipedia.org/wiki/Standard_illuminant """ illuminant = illuminant.upper() try: return illuminants[illuminant][observer] except KeyError: raise ValueError("Unknown illuminant/observer combination\ (\'{0}\', \'{1}\')".format(illuminant, observer)) # Haematoxylin-Eosin-DAB colorspace # From original Ruifrok's paper: A. C. Ruifrok and D. A. Johnston, # "Quantification of histochemical staining by color deconvolution.," # Analytical and quantitative cytology and histology / the International # Academy of Cytology [and] American Society of Cytology, vol. 23, no. 4, # pp. 291-9, Aug. 2001. rgb_from_hed = np.array([[0.65, 0.70, 0.29], [0.07, 0.99, 0.11], [0.27, 0.57, 0.78]]) hed_from_rgb = linalg.inv(rgb_from_hed) # Following matrices are adapted form the Java code written by G.Landini. # The original code is available at: # http://www.dentistry.bham.ac.uk/landinig/software/cdeconv/cdeconv.html # Hematoxylin + DAB rgb_from_hdx = np.array([[0.650, 0.704, 0.286], [0.268, 0.570, 0.776], [0.0, 0.0, 0.0]]) rgb_from_hdx[2, :] = np.cross(rgb_from_hdx[0, :], rgb_from_hdx[1, :]) hdx_from_rgb = linalg.inv(rgb_from_hdx) # Feulgen + Light Green rgb_from_fgx = np.array([[0.46420921, 0.83008335, 0.30827187], [0.94705542, 0.25373821, 0.19650764], [0.0, 0.0, 0.0]]) rgb_from_fgx[2, :] = np.cross(rgb_from_fgx[0, :], rgb_from_fgx[1, :]) fgx_from_rgb = linalg.inv(rgb_from_fgx) # Giemsa: Methyl Blue + Eosin rgb_from_bex = np.array([[0.834750233, 0.513556283, 0.196330403], [0.092789, 0.954111, 0.283111], [0.0, 0.0, 0.0]]) rgb_from_bex[2, :] = np.cross(rgb_from_bex[0, :], rgb_from_bex[1, :]) bex_from_rgb = linalg.inv(rgb_from_bex) # FastRed + FastBlue + DAB rgb_from_rbd = np.array([[0.21393921, 0.85112669, 0.47794022], [0.74890292, 0.60624161, 0.26731082], [0.268, 0.570, 0.776]]) rbd_from_rgb = linalg.inv(rgb_from_rbd) # Methyl Green + DAB rgb_from_gdx = np.array([[0.98003, 0.144316, 0.133146], [0.268, 0.570, 0.776], [0.0, 0.0, 0.0]]) rgb_from_gdx[2, :] = np.cross(rgb_from_gdx[0, :], rgb_from_gdx[1, :]) gdx_from_rgb = linalg.inv(rgb_from_gdx) # Hematoxylin + AEC rgb_from_hax = np.array([[0.650, 0.704, 0.286], [0.2743, 0.6796, 0.6803], [0.0, 0.0, 0.0]]) rgb_from_hax[2, :] = np.cross(rgb_from_hax[0, :], rgb_from_hax[1, :]) hax_from_rgb = linalg.inv(rgb_from_hax) # Blue matrix Anilline Blue + Red matrix Azocarmine + Orange matrix Orange-G rgb_from_bro = np.array([[0.853033, 0.508733, 0.112656], [0.09289875, 0.8662008, 0.49098468], [0.10732849, 0.36765403, 0.9237484]]) bro_from_rgb = linalg.inv(rgb_from_bro) # Methyl Blue + Ponceau Fuchsin rgb_from_bpx = np.array([[0.7995107, 0.5913521, 0.10528667], [0.09997159, 0.73738605, 0.6680326], [0.0, 0.0, 0.0]]) rgb_from_bpx[2, :] = np.cross(rgb_from_bpx[0, :], rgb_from_bpx[1, :]) bpx_from_rgb = linalg.inv(rgb_from_bpx) # Alcian Blue + Hematoxylin rgb_from_ahx = np.array([[0.874622, 0.457711, 0.158256], [0.552556, 0.7544, 0.353744], [0.0, 0.0, 0.0]]) rgb_from_ahx[2, :] = np.cross(rgb_from_ahx[0, :], rgb_from_ahx[1, :]) ahx_from_rgb = linalg.inv(rgb_from_ahx) # Hematoxylin + PAS rgb_from_hpx = np.array([[0.644211, 0.716556, 0.266844], [0.175411, 0.972178, 0.154589], [0.0, 0.0, 0.0]]) rgb_from_hpx[2, :] = np.cross(rgb_from_hpx[0, :], rgb_from_hpx[1, :]) hpx_from_rgb = linalg.inv(rgb_from_hpx) # ------------------------------------------------------------- # The conversion functions that make use of the matrices above # ------------------------------------------------------------- def _convert(matrix, arr): """Do the color space conversion. Parameters ---------- matrix : array_like The 3x3 matrix to use. arr : array_like The input array. Returns ------- out : ndarray, dtype=float The converted array. """ arr = _prepare_colorarray(arr) return np.dot(arr, matrix.T.copy()) def xyz2rgb(xyz): """XYZ to RGB color space conversion. Parameters ---------- xyz : array_like The image in XYZ format, in a 3-D array of shape ``(.., .., 3)``. Returns ------- out : ndarray The image in RGB format, in a 3-D array of shape ``(.., .., 3)``. Raises ------ ValueError If `xyz` is not a 3-D array of shape ``(.., .., 3)``. Notes ----- The CIE XYZ color space is derived from the CIE RGB color space. Note however that this function converts to sRGB. References ---------- .. [1] http://en.wikipedia.org/wiki/CIE_1931_color_space Examples -------- >>> from skimage import data >>> from skimage.color import rgb2xyz, xyz2rgb >>> img = data.astronaut() >>> img_xyz = rgb2xyz(img) >>> img_rgb = xyz2rgb(img_xyz) """ # Follow the algorithm from http://www.easyrgb.com/index.php # except we don't multiply/divide by 100 in the conversion arr = _convert(rgb_from_xyz, xyz) mask = arr > 0.0031308 arr[mask] = 1.055 * np.power(arr[mask], 1 / 2.4) - 0.055 arr[~mask] *= 12.92 arr[arr < 0] = 0 arr[arr > 1] = 1 return arr def rgb2xyz(rgb): """RGB to XYZ color space conversion. Parameters ---------- rgb : array_like The image in RGB format, in a 3- or 4-D array of shape ``(.., ..,[ ..,] 3)``. Returns ------- out : ndarray The image in XYZ format, in a 3- or 4-D array of shape ``(.., ..,[ ..,] 3)``. Raises ------ ValueError If `rgb` is not a 3- or 4-D array of shape ``(.., ..,[ ..,] 3)``. Notes ----- The CIE XYZ color space is derived from the CIE RGB color space. Note however that this function converts from sRGB. References ---------- .. [1] http://en.wikipedia.org/wiki/CIE_1931_color_space Examples -------- >>> from skimage import data >>> img = data.astronaut() >>> img_xyz = rgb2xyz(img) """ # Follow the algorithm from http://www.easyrgb.com/index.php # except we don't multiply/divide by 100 in the conversion arr = _prepare_colorarray(rgb).copy() mask = arr > 0.04045 arr[mask] = np.power((arr[mask] + 0.055) / 1.055, 2.4) arr[~mask] /= 12.92 return _convert(xyz_from_rgb, arr) def rgb2rgbcie(rgb): """RGB to RGB CIE color space conversion. Parameters ---------- rgb : array_like The image in RGB format, in a 3-D array of shape ``(.., .., 3)``. Returns ------- out : ndarray The image in RGB CIE format, in a 3-D array of shape ``(.., .., 3)``. Raises ------ ValueError If `rgb` is not a 3-D array of shape ``(.., .., 3)``. References ---------- .. [1] http://en.wikipedia.org/wiki/CIE_1931_color_space Examples -------- >>> from skimage import data >>> from skimage.color import rgb2rgbcie >>> img = data.astronaut() >>> img_rgbcie = rgb2rgbcie(img) """ return _convert(rgbcie_from_rgb, rgb) def rgbcie2rgb(rgbcie): """RGB CIE to RGB color space conversion. Parameters ---------- rgbcie : array_like The image in RGB CIE format, in a 3-D array of shape ``(.., .., 3)``. Returns ------- out : ndarray The image in RGB format, in a 3-D array of shape ``(.., .., 3)``. Raises ------ ValueError If `rgbcie` is not a 3-D array of shape ``(.., .., 3)``. References ---------- .. [1] http://en.wikipedia.org/wiki/CIE_1931_color_space Examples -------- >>> from skimage import data >>> from skimage.color import rgb2rgbcie, rgbcie2rgb >>> img = data.astronaut() >>> img_rgbcie = rgb2rgbcie(img) >>> img_rgb = rgbcie2rgb(img_rgbcie) """ return _convert(rgb_from_rgbcie, rgbcie) def rgb2gray(rgb): """Compute luminance of an RGB image. Parameters ---------- rgb : array_like The image in RGB format, in a 3-D or 4-D array of shape ``(.., ..,[ ..,] 3)``, or in RGBA format with shape ``(.., ..,[ ..,] 4)``. Returns ------- out : ndarray The luminance image - an array which is the same size as the input array, but with the channel dimension removed. Raises ------ ValueError If `rgb2gray` is not a 3-D or 4-D arrays of shape ``(.., ..,[ ..,] 3)`` or ``(.., ..,[ ..,] 4)``. References ---------- .. [1] http://www.poynton.com/PDFs/ColorFAQ.pdf Notes ----- The weights used in this conversion are calibrated for contemporary CRT phosphors:: Y = 0.2125 R + 0.7154 G + 0.0721 B If there is an alpha channel present, it is ignored. Examples -------- >>> from skimage.color import rgb2gray >>> from skimage import data >>> img = data.astronaut() >>> img_gray = rgb2gray(img) """ if rgb.ndim == 2: return np.ascontiguousarray(rgb) rgb = _prepare_colorarray(rgb[..., :3]) gray = 0.2125 * rgb[..., 0] gray[:] += 0.7154 * rgb[..., 1] gray[:] += 0.0721 * rgb[..., 2] return gray rgb2grey = rgb2gray def gray2rgb(image, alpha=None): """Create an RGB representation of a gray-level image. Parameters ---------- image : array_like Input image of shape ``(M, N [, P])``. alpha : bool, optional Ensure that the output image has an alpha layer. If None, alpha layers are passed through but not created. Returns ------- rgb : ndarray RGB image of shape ``(M, N, [, P], 3)``. Raises ------ ValueError If the input is not a 2- or 3-dimensional image. """ is_rgb = False is_alpha = False dims = np.squeeze(image).ndim if dims == 3: if image.shape[2] == 3: is_rgb = True elif image.shape[2] == 4: is_alpha = True is_rgb = True if is_rgb: if alpha is False: image = image[..., :3] elif alpha is True and is_alpha is False: alpha_layer = (np.ones_like(image[..., 0, np.newaxis]) * dtype_limits(image, clip_negative=False)[1]) image = np.concatenate((image, alpha_layer), axis=2) return image elif image.ndim != 1 and dims in (1, 2, 3): image = image[..., np.newaxis] if alpha: alpha_layer = (np.ones_like(image) * dtype_limits(image, clip_negative=False)[1]) return np.concatenate(3 * (image,) + (alpha_layer,), axis=-1) else: return np.concatenate(3 * (image,), axis=-1) else: raise ValueError("Input image expected to be RGB, RGBA or gray.") grey2rgb = gray2rgb def xyz2lab(xyz, illuminant="D65", observer="2"): """XYZ to CIE-LAB color space conversion. Parameters ---------- xyz : array_like The image in XYZ format, in a 3- or 4-D array of shape ``(.., ..,[ ..,] 3)``. illuminant : {"A", "D50", "D55", "D65", "D75", "E"}, optional The name of the illuminant (the function is NOT case sensitive). observer : {"2", "10"}, optional The aperture angle of the observer. Returns ------- out : ndarray The image in CIE-LAB format, in a 3- or 4-D array of shape ``(.., ..,[ ..,] 3)``. Raises ------ ValueError If `xyz` is not a 3-D array of shape ``(.., ..,[ ..,] 3)``. ValueError If either the illuminant or the observer angle is unsupported or unknown. Notes ----- By default Observer= 2A, Illuminant= D65. CIE XYZ tristimulus values x_ref=95.047, y_ref=100., z_ref=108.883. See function `get_xyz_coords` for a list of supported illuminants. References ---------- .. [1] http://www.easyrgb.com/index.php?X=MATH&H=07#text7 .. [2] http://en.wikipedia.org/wiki/Lab_color_space Examples -------- >>> from skimage import data >>> from skimage.color import rgb2xyz, xyz2lab >>> img = data.astronaut() >>> img_xyz = rgb2xyz(img) >>> img_lab = xyz2lab(img_xyz) """ arr = _prepare_colorarray(xyz) xyz_ref_white = get_xyz_coords(illuminant, observer) # scale by CIE XYZ tristimulus values of the reference white point arr = arr / xyz_ref_white # Nonlinear distortion and linear transformation mask = arr > 0.008856 arr[mask] = np.power(arr[mask], 1. / 3.) arr[~mask] = 7.787 * arr[~mask] + 16. / 116. x, y, z = arr[..., 0], arr[..., 1], arr[..., 2] # Vector scaling L = (116. * y) - 16. a = 500.0 * (x - y) b = 200.0 * (y - z) return np.concatenate([x[..., np.newaxis] for x in [L, a, b]], axis=-1) def lab2xyz(lab, illuminant="D65", observer="2"): """CIE-LAB to XYZcolor space conversion. Parameters ---------- lab : array_like The image in lab format, in a 3-D array of shape ``(.., .., 3)``. illuminant : {"A", "D50", "D55", "D65", "D75", "E"}, optional The name of the illuminant (the function is NOT case sensitive). observer : {"2", "10"}, optional The aperture angle of the observer. Returns ------- out : ndarray The image in XYZ format, in a 3-D array of shape ``(.., .., 3)``. Raises ------ ValueError If `lab` is not a 3-D array of shape ``(.., .., 3)``. ValueError If either the illuminant or the observer angle are not supported or unknown. UserWarning If any of the pixels are invalid (Z < 0). Notes ----- By default Observer= 2A, Illuminant= D65. CIE XYZ tristimulus values x_ref = 95.047, y_ref = 100., z_ref = 108.883. See function 'get_xyz_coords' for a list of supported illuminants. References ---------- .. [1] http://www.easyrgb.com/index.php?X=MATH&H=07#text7 .. [2] http://en.wikipedia.org/wiki/Lab_color_space """ arr = _prepare_colorarray(lab).copy() L, a, b = arr[:, :, 0], arr[:, :, 1], arr[:, :, 2] y = (L + 16.) / 116. x = (a / 500.) + y z = y - (b / 200.) if np.any(z < 0): invalid = np.nonzero(z < 0) warn('Color data out of range: Z < 0 in %s pixels' % invalid[0].size) z[invalid] = 0 out = np.dstack([x, y, z]) mask = out > 0.2068966 out[mask] = np.power(out[mask], 3.) out[~mask] = (out[~mask] - 16.0 / 116.) / 7.787 # rescale to the reference white (illuminant) xyz_ref_white = get_xyz_coords(illuminant, observer) out *= xyz_ref_white return out def rgb2lab(rgb, illuminant="D65", observer="2"): """RGB to lab color space conversion. Parameters ---------- rgb : array_like The image in RGB format, in a 3- or 4-D array of shape ``(.., ..,[ ..,] 3)``. illuminant : {"A", "D50", "D55", "D65", "D75", "E"}, optional The name of the illuminant (the function is NOT case sensitive). observer : {"2", "10"}, optional The aperture angle of the observer. Returns ------- out : ndarray The image in Lab format, in a 3- or 4-D array of shape ``(.., ..,[ ..,] 3)``. Raises ------ ValueError If `rgb` is not a 3- or 4-D array of shape ``(.., ..,[ ..,] 3)``. References ---------- .. [1] https://en.wikipedia.org/wiki/Standard_illuminant Notes ----- This function uses rgb2xyz and xyz2lab. By default Observer= 2A, Illuminant= D65. CIE XYZ tristimulus values x_ref=95.047, y_ref=100., z_ref=108.883. See function `get_xyz_coords` for a list of supported illuminants. """ return xyz2lab(rgb2xyz(rgb), illuminant, observer) def lab2rgb(lab, illuminant="D65", observer="2"): """Lab to RGB color space conversion. Parameters ---------- lab : array_like The image in Lab format, in a 3-D array of shape ``(.., .., 3)``. illuminant : {"A", "D50", "D55", "D65", "D75", "E"}, optional The name of the illuminant (the function is NOT case sensitive). observer : {"2", "10"}, optional The aperture angle of the observer. Returns ------- out : ndarray The image in RGB format, in a 3-D array of shape ``(.., .., 3)``. Raises ------ ValueError If `lab` is not a 3-D array of shape ``(.., .., 3)``. References ---------- .. [1] https://en.wikipedia.org/wiki/Standard_illuminant Notes ----- This function uses lab2xyz and xyz2rgb. By default Observer= 2A, Illuminant= D65. CIE XYZ tristimulus values x_ref=95.047, y_ref=100., z_ref=108.883. See function `get_xyz_coords` for a list of supported illuminants. """ return xyz2rgb(lab2xyz(lab, illuminant, observer)) def xyz2luv(xyz, illuminant="D65", observer="2"): """XYZ to CIE-Luv color space conversion. Parameters ---------- xyz : (M, N, [P,] 3) array_like The 3 or 4 dimensional image in XYZ format. Final dimension denotes channels. illuminant : {"A", "D50", "D55", "D65", "D75", "E"}, optional The name of the illuminant (the function is NOT case sensitive). observer : {"2", "10"}, optional The aperture angle of the observer. Returns ------- out : (M, N, [P,] 3) ndarray The image in CIE-Luv format. Same dimensions as input. Raises ------ ValueError If `xyz` is not a 3-D or 4-D array of shape ``(M, N, [P,] 3)``. ValueError If either the illuminant or the observer angle are not supported or unknown. Notes ----- By default XYZ conversion weights use observer=2A. Reference whitepoint for D65 Illuminant, with XYZ tristimulus values of ``(95.047, 100., 108.883)``. See function 'get_xyz_coords' for a list of supported illuminants. References ---------- .. [1] http://www.easyrgb.com/index.php?X=MATH&H=16#text16 .. [2] http://en.wikipedia.org/wiki/CIELUV Examples -------- >>> from skimage import data >>> from skimage.color import rgb2xyz, xyz2luv >>> img = data.astronaut() >>> img_xyz = rgb2xyz(img) >>> img_luv = xyz2luv(img_xyz) """ arr = _prepare_colorarray(xyz) # extract channels x, y, z = arr[..., 0], arr[..., 1], arr[..., 2] eps = np.finfo(np.float).eps # compute y_r and L xyz_ref_white = get_xyz_coords(illuminant, observer) L = y / xyz_ref_white[1] mask = L > 0.008856 L[mask] = 116. * np.power(L[mask], 1. / 3.) - 16. L[~mask] = 903.3 * L[~mask] u0 = 4 * xyz_ref_white[0] / np.dot([1, 15, 3], xyz_ref_white) v0 = 9 * xyz_ref_white[1] / np.dot([1, 15, 3], xyz_ref_white) # u' and v' helper functions def fu(X, Y, Z): return (4. * X) / (X + 15. * Y + 3. * Z + eps) def fv(X, Y, Z): return (9. * Y) / (X + 15. * Y + 3. * Z + eps) # compute u and v using helper functions u = 13. * L * (fu(x, y, z) - u0) v = 13. * L * (fv(x, y, z) - v0) return np.concatenate([q[..., np.newaxis] for q in [L, u, v]], axis=-1) def luv2xyz(luv, illuminant="D65", observer="2"): """CIE-Luv to XYZ color space conversion. Parameters ---------- luv : (M, N, [P,] 3) array_like The 3 or 4 dimensional image in CIE-Luv format. Final dimension denotes channels. illuminant : {"A", "D50", "D55", "D65", "D75", "E"}, optional The name of the illuminant (the function is NOT case sensitive). observer : {"2", "10"}, optional The aperture angle of the observer. Returns ------- out : (M, N, [P,] 3) ndarray The image in XYZ format. Same dimensions as input. Raises ------ ValueError If `luv` is not a 3-D or 4-D array of shape ``(M, N, [P,] 3)``. ValueError If either the illuminant or the observer angle are not supported or unknown. Notes ----- XYZ conversion weights use observer=2A. Reference whitepoint for D65 Illuminant, with XYZ tristimulus values of ``(95.047, 100., 108.883)``. See function 'get_xyz_coords' for a list of supported illuminants. References ---------- .. [1] http://www.easyrgb.com/index.php?X=MATH&H=16#text16 .. [2] http://en.wikipedia.org/wiki/CIELUV """ arr = _prepare_colorarray(luv).copy() L, u, v = arr[:, :, 0], arr[:, :, 1], arr[:, :, 2] eps = np.finfo(np.float).eps # compute y y = L.copy() mask = y > 7.999625 y[mask] = np.power((y[mask] + 16.) / 116., 3.) y[~mask] = y[~mask] / 903.3 xyz_ref_white = get_xyz_coords(illuminant, observer) y *= xyz_ref_white[1] # reference white x,z uv_weights = [1, 15, 3] u0 = 4 * xyz_ref_white[0] / np.dot(uv_weights, xyz_ref_white) v0 = 9 * xyz_ref_white[1] / np.dot(uv_weights, xyz_ref_white) # compute intermediate values a = u0 + u / (13. * L + eps) b = v0 + v / (13. * L + eps) c = 3 * y * (5 * b - 3) # compute x and z z = ((a - 4) * c - 15 * a * b * y) / (12 * b) x = -(c / b + 3. * z) return np.concatenate([q[..., np.newaxis] for q in [x, y, z]], axis=-1) def rgb2luv(rgb): """RGB to CIE-Luv color space conversion. Parameters ---------- rgb : (M, N, [P,] 3) array_like The 3 or 4 dimensional image in RGB format. Final dimension denotes channels. Returns ------- out : (M, N, [P,] 3) ndarray The image in CIE Luv format. Same dimensions as input. Raises ------ ValueError If `rgb` is not a 3-D or 4-D array of shape ``(M, N, [P,] 3)``. Notes ----- This function uses rgb2xyz and xyz2luv. References ---------- .. [1] http://www.easyrgb.com/index.php?X=MATH&H=16#text16 .. [2] http://www.easyrgb.com/index.php?X=MATH&H=02#text2 .. [3] http://en.wikipedia.org/wiki/CIELUV """ return xyz2luv(rgb2xyz(rgb)) def luv2rgb(luv): """Luv to RGB color space conversion. Parameters ---------- luv : (M, N, [P,] 3) array_like The 3 or 4 dimensional image in CIE Luv format. Final dimension denotes channels. Returns ------- out : (M, N, [P,] 3) ndarray The image in RGB format. Same dimensions as input. Raises ------ ValueError If `luv` is not a 3-D or 4-D array of shape ``(M, N, [P,] 3)``. Notes ----- This function uses luv2xyz and xyz2rgb. """ return xyz2rgb(luv2xyz(luv)) def rgb2hed(rgb): """RGB to Haematoxylin-Eosin-DAB (HED) color space conversion. Parameters ---------- rgb : array_like The image in RGB format, in a 3-D array of shape ``(.., .., 3)``. Returns ------- out : ndarray The image in HED format, in a 3-D array of shape ``(.., .., 3)``. Raises ------ ValueError If `rgb` is not a 3-D array of shape ``(.., .., 3)``. References ---------- .. [1] A. C. Ruifrok and D. A. Johnston, "Quantification of histochemical staining by color deconvolution.," Analytical and quantitative cytology and histology / the International Academy of Cytology [and] American Society of Cytology, vol. 23, no. 4, pp. 291-9, Aug. 2001. Examples -------- >>> from skimage import data >>> from skimage.color import rgb2hed >>> ihc = data.immunohistochemistry() >>> ihc_hed = rgb2hed(ihc) """ return separate_stains(rgb, hed_from_rgb) def hed2rgb(hed): """Haematoxylin-Eosin-DAB (HED) to RGB color space conversion. Parameters ---------- hed : array_like The image in the HED color space, in a 3-D array of shape ``(.., .., 3)``. Returns ------- out : ndarray The image in RGB, in a 3-D array of shape ``(.., .., 3)``. Raises ------ ValueError If `hed` is not a 3-D array of shape ``(.., .., 3)``. References ---------- .. [1] A. C. Ruifrok and D. A. Johnston, "Quantification of histochemical staining by color deconvolution.," Analytical and quantitative cytology and histology / the International Academy of Cytology [and] American Society of Cytology, vol. 23, no. 4, pp. 291-9, Aug. 2001. Examples -------- >>> from skimage import data >>> from skimage.color import rgb2hed, hed2rgb >>> ihc = data.immunohistochemistry() >>> ihc_hed = rgb2hed(ihc) >>> ihc_rgb = hed2rgb(ihc_hed) """ return combine_stains(hed, rgb_from_hed) def separate_stains(rgb, conv_matrix): """RGB to stain color space conversion. Parameters ---------- rgb : array_like The image in RGB format, in a 3-D array of shape ``(.., .., 3)``. conv_matrix: ndarray The stain separation matrix as described by G. Landini [1]_. Returns ------- out : ndarray The image in stain color space, in a 3-D array of shape ``(.., .., 3)``. Raises ------ ValueError If `rgb` is not a 3-D array of shape ``(.., .., 3)``. Notes ----- Stain separation matrices available in the ``color`` module and their respective colorspace: * ``hed_from_rgb``: Hematoxylin + Eosin + DAB * ``hdx_from_rgb``: Hematoxylin + DAB * ``fgx_from_rgb``: Feulgen + Light Green * ``bex_from_rgb``: Giemsa stain : Methyl Blue + Eosin * ``rbd_from_rgb``: FastRed + FastBlue + DAB * ``gdx_from_rgb``: Methyl Green + DAB * ``hax_from_rgb``: Hematoxylin + AEC * ``bro_from_rgb``: Blue matrix Anilline Blue + Red matrix Azocarmine\ + Orange matrix Orange-G * ``bpx_from_rgb``: Methyl Blue + Ponceau Fuchsin * ``ahx_from_rgb``: Alcian Blue + Hematoxylin * ``hpx_from_rgb``: Hematoxylin + PAS References ---------- .. [1] http://www.dentistry.bham.ac.uk/landinig/software/cdeconv/cdeconv.html Examples -------- >>> from skimage import data >>> from skimage.color import separate_stains, hdx_from_rgb >>> ihc = data.immunohistochemistry() >>> ihc_hdx = separate_stains(ihc, hdx_from_rgb) """ rgb = dtype.img_as_float(rgb, force_copy=True) rgb += 2 stains = np.dot(np.reshape(-np.log(rgb), (-1, 3)), conv_matrix) return np.reshape(stains, rgb.shape) def combine_stains(stains, conv_matrix): """Stain to RGB color space conversion. Parameters ---------- stains : array_like The image in stain color space, in a 3-D array of shape ``(.., .., 3)``. conv_matrix: ndarray The stain separation matrix as described by G. Landini [1]_. Returns ------- out : ndarray The image in RGB format, in a 3-D array of shape ``(.., .., 3)``. Raises ------ ValueError If `stains` is not a 3-D array of shape ``(.., .., 3)``. Notes ----- Stain combination matrices available in the ``color`` module and their respective colorspace: * ``rgb_from_hed``: Hematoxylin + Eosin + DAB * ``rgb_from_hdx``: Hematoxylin + DAB * ``rgb_from_fgx``: Feulgen + Light Green * ``rgb_from_bex``: Giemsa stain : Methyl Blue + Eosin * ``rgb_from_rbd``: FastRed + FastBlue + DAB * ``rgb_from_gdx``: Methyl Green + DAB * ``rgb_from_hax``: Hematoxylin + AEC * ``rgb_from_bro``: Blue matrix Anilline Blue + Red matrix Azocarmine\ + Orange matrix Orange-G * ``rgb_from_bpx``: Methyl Blue + Ponceau Fuchsin * ``rgb_from_ahx``: Alcian Blue + Hematoxylin * ``rgb_from_hpx``: Hematoxylin + PAS References ---------- .. [1] http://www.dentistry.bham.ac.uk/landinig/software/cdeconv/cdeconv.html Examples -------- >>> from skimage import data >>> from skimage.color import (separate_stains, combine_stains, ... hdx_from_rgb, rgb_from_hdx) >>> ihc = data.immunohistochemistry() >>> ihc_hdx = separate_stains(ihc, hdx_from_rgb) >>> ihc_rgb = combine_stains(ihc_hdx, rgb_from_hdx) """ from ..exposure import rescale_intensity stains = dtype.img_as_float(stains) logrgb2 = np.dot(-np.reshape(stains, (-1, 3)), conv_matrix) rgb2 = np.exp(logrgb2) return rescale_intensity(np.reshape(rgb2 - 2, stains.shape), in_range=(-1, 1)) def lab2lch(lab): """CIE-LAB to CIE-LCH color space conversion. LCH is the cylindrical representation of the LAB (Cartesian) colorspace Parameters ---------- lab : array_like The N-D image in CIE-LAB format. The last (``N+1``-th) dimension must have at least 3 elements, corresponding to the ``L``, ``a``, and ``b`` color channels. Subsequent elements are copied. Returns ------- out : ndarray The image in LCH format, in a N-D array with same shape as input `lab`. Raises ------ ValueError If `lch` does not have at least 3 color channels (i.e. l, a, b). Notes ----- The Hue is expressed as an angle between ``(0, 2*pi)`` Examples -------- >>> from skimage import data >>> from skimage.color import rgb2lab, lab2lch >>> img = data.astronaut() >>> img_lab = rgb2lab(img) >>> img_lch = lab2lch(img_lab) """ lch = _prepare_lab_array(lab) a, b = lch[..., 1], lch[..., 2] lch[..., 1], lch[..., 2] = _cart2polar_2pi(a, b) return lch def _cart2polar_2pi(x, y): """convert cartesian coordinates to polar (uses non-standard theta range!) NON-STANDARD RANGE! Maps to ``(0, 2*pi)`` rather than usual ``(-pi, +pi)`` """ r, t = np.hypot(x, y), np.arctan2(y, x) t += np.where(t < 0., 2 * np.pi, 0) return r, t def lch2lab(lch): """CIE-LCH to CIE-LAB color space conversion. LCH is the cylindrical representation of the LAB (Cartesian) colorspace Parameters ---------- lch : array_like The N-D image in CIE-LCH format. The last (``N+1``-th) dimension must have at least 3 elements, corresponding to the ``L``, ``a``, and ``b`` color channels. Subsequent elements are copied. Returns ------- out : ndarray The image in LAB format, with same shape as input `lch`. Raises ------ ValueError If `lch` does not have at least 3 color channels (i.e. l, c, h). Examples -------- >>> from skimage import data >>> from skimage.color import rgb2lab, lch2lab >>> img = data.astronaut() >>> img_lab = rgb2lab(img) >>> img_lch = lab2lch(img_lab) >>> img_lab2 = lch2lab(img_lch) """ lch = _prepare_lab_array(lch) c, h = lch[..., 1], lch[..., 2] lch[..., 1], lch[..., 2] = c * np.cos(h), c * np.sin(h) return lch def _prepare_lab_array(arr): """Ensure input for lab2lch, lch2lab are well-posed. Arrays must be in floating point and have at least 3 elements in last dimension. Return a new array. """ arr = np.asarray(arr) shape = arr.shape if shape[-1] < 3: raise ValueError('Input array has less than 3 color channels') return dtype.img_as_float(arr, force_copy=True) def rgb2yuv(rgb): """RGB to YUV color space conversion. Parameters ---------- rgb : array_like The image in RGB format, in a 3- or 4-D array of shape ``(M, N, [P,] 3)``. Returns ------- out : ndarray The image in YUV format, in a 3- or 4-D array of shape ``(M, N, [P,] 3)``. Raises ------ ValueError If `rgb` is not a 3- or 4-D array of shape ``(M, N, [P,] 3)``. Notes ----- Y is between 0 and 1. Use YCbCr instead of YUV for the color space which is commonly used by video codecs (where Y ranges from 16 to 235) References ---------- .. [1] https://en.wikipedia.org/wiki/YUV """ return _convert(yuv_from_rgb, rgb) def rgb2yiq(rgb): """RGB to YIQ color space conversion. Parameters ---------- rgb : array_like The image in RGB format, in a 3- or 4-D array of shape ``(M, N, [P,] 3)``. Returns ------- out : ndarray The image in YIQ format, in a 3- or 4-D array of shape ``(M, N, [P,] 3)``. Raises ------ ValueError If `rgb` is not a 3- or 4-D array of shape ``(M, N, [P,] 3)``. """ return _convert(yiq_from_rgb, rgb) def rgb2ypbpr(rgb): """RGB to YPbPr color space conversion. Parameters ---------- rgb : array_like The image in RGB format, in a 3- or 4-D array of shape ``(M, N, [P,] 3)``. Returns ------- out : ndarray The image in YPbPr format, in a 3- or 4-D array of shape ``(M, N, [P,] 3)``. Raises ------ ValueError If `rgb` is not a 3- or 4-D array of shape ``(M, N, [P,] 3)``. References ---------- .. [1] https://en.wikipedia.org/wiki/YPbPr """ return _convert(ypbpr_from_rgb, rgb) def rgb2ycbcr(rgb): """RGB to YCbCr color space conversion. Parameters ---------- rgb : array_like The image in RGB format, in a 3- or 4-D array of shape ``(M, N, [P,] 3)``. Returns ------- out : ndarray The image in YCbCr format, in a 3- or 4-D array of shape ``(M, N, [P,] 3)``. Raises ------ ValueError If `rgb` is not a 3- or 4-D array of shape ``(M, N, [P,] 3)``. Notes ----- Y is between 16 and 235. This is the color space which is commonly used by video codecs, it is sometimes incorrectly called "YUV" References ---------- .. [1] https://en.wikipedia.org/wiki/YCbCr """ arr = _convert(ycbcr_from_rgb, rgb) arr[..., 0] += 16 arr[..., 1] += 128 arr[..., 2] += 128 return arr def yuv2rgb(yuv): """YUV to RGB color space conversion. Parameters ---------- yuv : array_like The image in YUV format, in a 3- or 4-D array of shape ``(M, N, [P,] 3)``. Returns ------- out : ndarray The image in RGB format, in a 3- or 4-D array of shape ``(M, N, [P,] 3)``. Raises ------ ValueError If `yuv` is not a 3- or 4-D array of shape ``(M, N, [P,] 3)``. References ---------- .. [1] https://en.wikipedia.org/wiki/YUV """ return _convert(rgb_from_yuv, yuv) def yiq2rgb(yiq): """YIQ to RGB color space conversion. Parameters ---------- yiq : array_like The image in YIQ format, in a 3- or 4-D array of shape ``(M, N, [P,] 3)``. Returns ------- out : ndarray The image in RGB format, in a 3- or 4-D array of shape ``(M, N, [P,] 3)``. Raises ------ ValueError If `yiq` is not a 3- or 4-D array of shape ``(M, N, [P,] 3)``. """ return _convert(rgb_from_yiq, yiq) def ypbpr2rgb(ypbpr): """YPbPr to RGB color space conversion. Parameters ---------- ypbpr : array_like The image in YPbPr format, in a 3- or 4-D array of shape ``(M, N, [P,] 3)``. Returns ------- out : ndarray The image in RGB format, in a 3- or 4-D array of shape ``(M, N, [P,] 3)``. Raises ------ ValueError If `ypbpr` is not a 3- or 4-D array of shape ``(M, N, [P,] 3)``. References ---------- .. [1] https://en.wikipedia.org/wiki/YPbPr """ return _convert(rgb_from_ypbpr, ypbpr) def ycbcr2rgb(ycbcr): """YCbCr to RGB color space conversion. Parameters ---------- ycbcr : array_like The image in YCbCr format, in a 3- or 4-D array of shape ``(M, N, [P,] 3)``. Returns ------- out : ndarray The image in RGB format, in a 3- or 4-D array of shape ``(M, N, [P,] 3)``. Raises ------ ValueError If `ycbcr` is not a 3- or 4-D array of shape ``(M, N, [P,] 3)``. Notes ----- Y is between 16 and 235. This is the color space which is commonly used by video codecs, it is sometimes incorrectly called "YUV" References ---------- .. [1] https://en.wikipedia.org/wiki/YCbCr """ arr = ycbcr.copy() arr[..., 0] -= 16 arr[..., 1] -= 128 arr[..., 2] -= 128 return _convert(rgb_from_ycbcr, arr) scikit-image-0.13.1/skimage/color/colorlabel.py000066400000000000000000000177771316256236300214220ustar00rootroot00000000000000import itertools import numpy as np from .._shared.utils import warn from .. import img_as_float from . import rgb_colors from .colorconv import rgb2gray, gray2rgb import six from six.moves import zip __all__ = ['color_dict', 'label2rgb', 'DEFAULT_COLORS'] DEFAULT_COLORS = ('red', 'blue', 'yellow', 'magenta', 'green', 'indigo', 'darkorange', 'cyan', 'pink', 'yellowgreen') color_dict = dict((k, v) for k, v in six.iteritems(rgb_colors.__dict__) if isinstance(v, tuple)) def _rgb_vector(color): """Return RGB color as (1, 3) array. This RGB array gets multiplied by masked regions of an RGB image, which are partially flattened by masking (i.e. dimensions 2D + RGB -> 1D + RGB). Parameters ---------- color : str or array Color name in `color_dict` or RGB float values between [0, 1]. """ if isinstance(color, six.string_types): color = color_dict[color] # Slice to handle RGBA colors. return np.array(color[:3]) def _match_label_with_color(label, colors, bg_label, bg_color): """Return `unique_labels` and `color_cycle` for label array and color list. Colors are cycled for normal labels, but the background color should only be used for the background. """ # Temporarily set background color; it will be removed later. if bg_color is None: bg_color = (0, 0, 0) bg_color = _rgb_vector([bg_color]) # map labels to their ranks among all labels from small to large unique_labels, mapped_labels = np.unique(label, return_inverse=True) # get rank of bg_label bg_label_rank_list = mapped_labels[label.flat == bg_label] # The rank of each label is the index of the color it is matched to in # color cycle. bg_label should always be mapped to the first color, so # its rank must be 0. Other labels should be ranked from small to large # from 1. if len(bg_label_rank_list) > 0: bg_label_rank = bg_label_rank_list[0] mapped_labels[mapped_labels < bg_label_rank] += 1 mapped_labels[label.flat == bg_label] = 0 else: mapped_labels += 1 # Modify labels and color cycle so background color is used only once. color_cycle = itertools.cycle(colors) color_cycle = itertools.chain(bg_color, color_cycle) return mapped_labels, color_cycle def label2rgb(label, image=None, colors=None, alpha=0.3, bg_label=-1, bg_color=(0, 0, 0), image_alpha=1, kind='overlay'): """Return an RGB image where color-coded labels are painted over the image. Parameters ---------- label : array, shape (M, N) Integer array of labels with the same shape as `image`. image : array, shape (M, N, 3), optional Image used as underlay for labels. If the input is an RGB image, it's converted to grayscale before coloring. colors : list, optional List of colors. If the number of labels exceeds the number of colors, then the colors are cycled. alpha : float [0, 1], optional Opacity of colorized labels. Ignored if image is `None`. bg_label : int, optional Label that's treated as the background. bg_color : str or array, optional Background color. Must be a name in `color_dict` or RGB float values between [0, 1]. image_alpha : float [0, 1], optional Opacity of the image. kind : string, one of {'overlay', 'avg'} The kind of color image desired. 'overlay' cycles over defined colors and overlays the colored labels over the original image. 'avg' replaces each labeled segment with its average color, for a stained-class or pastel painting appearance. Returns ------- result : array of float, shape (M, N, 3) The result of blending a cycling colormap (`colors`) for each distinct value in `label` with the image, at a certain alpha value. """ if kind == 'overlay': return _label2rgb_overlay(label, image, colors, alpha, bg_label, bg_color, image_alpha) else: return _label2rgb_avg(label, image, bg_label, bg_color) def _label2rgb_overlay(label, image=None, colors=None, alpha=0.3, bg_label=-1, bg_color=None, image_alpha=1): """Return an RGB image where color-coded labels are painted over the image. Parameters ---------- label : array, shape (M, N) Integer array of labels with the same shape as `image`. image : array, shape (M, N, 3), optional Image used as underlay for labels. If the input is an RGB image, it's converted to grayscale before coloring. colors : list, optional List of colors. If the number of labels exceeds the number of colors, then the colors are cycled. alpha : float [0, 1], optional Opacity of colorized labels. Ignored if image is `None`. bg_label : int, optional Label that's treated as the background. bg_color : str or array, optional Background color. Must be a name in `color_dict` or RGB float values between [0, 1]. image_alpha : float [0, 1], optional Opacity of the image. Returns ------- result : array of float, shape (M, N, 3) The result of blending a cycling colormap (`colors`) for each distinct value in `label` with the image, at a certain alpha value. """ if colors is None: colors = DEFAULT_COLORS colors = [_rgb_vector(c) for c in colors] if image is None: image = np.zeros(label.shape + (3,), dtype=np.float64) # Opacity doesn't make sense if no image exists. alpha = 1 else: if not image.shape[:2] == label.shape: raise ValueError("`image` and `label` must be the same shape") if image.min() < 0: warn("Negative intensities in `image` are not supported") image = img_as_float(rgb2gray(image)) image = gray2rgb(image) * image_alpha + (1 - image_alpha) # Ensure that all labels are non-negative so we can index into # `label_to_color` correctly. offset = min(label.min(), bg_label) if offset != 0: label = label - offset # Make sure you don't modify the input array. bg_label -= offset new_type = np.min_scalar_type(int(label.max())) if new_type == np.bool: new_type = np.uint8 label = label.astype(new_type) mapped_labels_flat, color_cycle = _match_label_with_color(label, colors, bg_label, bg_color) if len(mapped_labels_flat) == 0: return image dense_labels = range(max(mapped_labels_flat) + 1) label_to_color = np.array([c for i, c in zip(dense_labels, color_cycle)]) mapped_labels = label mapped_labels.flat = mapped_labels_flat result = label_to_color[mapped_labels] * alpha + image * (1 - alpha) # Remove background label if its color was not specified. remove_background = 0 in mapped_labels_flat and bg_color is None if remove_background: result[label == bg_label] = image[label == bg_label] return result def _label2rgb_avg(label_field, image, bg_label=0, bg_color=(0, 0, 0)): """Visualise each segment in `label_field` with its mean color in `image`. Parameters ---------- label_field : array of int A segmentation of an image. image : array, shape ``label_field.shape + (3,)`` A color image of the same spatial shape as `label_field`. bg_label : int, optional A value in `label_field` to be treated as background. bg_color : 3-tuple of int, optional The color for the background label Returns ------- out : array, same shape and type as `image` The output visualization. """ out = np.zeros_like(image) labels = np.unique(label_field) bg = (labels == bg_label) if bg.any(): labels = labels[labels != bg_label] out[bg] = bg_color for label in labels: mask = (label_field == label).nonzero() color = image[mask].mean(axis=0) out[mask] = color return out scikit-image-0.13.1/skimage/color/delta_e.py000066400000000000000000000243401316256236300206610ustar00rootroot00000000000000""" Functions for calculating the "distance" between colors. Implicit in these definitions of "distance" is the notion of "Just Noticeable Distance" (JND). This represents the distance between colors where a human can perceive different colors. Humans are more sensitive to certain colors than others, which different deltaE metrics correct for with varying degrees of sophistication. The literature often mentions 1 as the minimum distance for visual differentiation, but more recent studies (Mahy 1994) peg JND at 2.3 The delta-E notation comes from the German word for "Sensation" (Empfindung). Reference --------- http://en.wikipedia.org/wiki/Color_difference """ from __future__ import division import numpy as np from ..color.colorconv import lab2lch, _cart2polar_2pi def deltaE_cie76(lab1, lab2): """Euclidean distance between two points in Lab color space Parameters ---------- lab1 : array_like reference color (Lab colorspace) lab2 : array_like comparison color (Lab colorspace) Returns ------- dE : array_like distance between colors `lab1` and `lab2` References ---------- .. [1] http://en.wikipedia.org/wiki/Color_difference .. [2] A. R. Robertson, "The CIE 1976 color-difference formulae," Color Res. Appl. 2, 7-11 (1977). """ lab1 = np.asarray(lab1) lab2 = np.asarray(lab2) L1, a1, b1 = np.rollaxis(lab1, -1)[:3] L2, a2, b2 = np.rollaxis(lab2, -1)[:3] return np.sqrt((L2 - L1) ** 2 + (a2 - a1) ** 2 + (b2 - b1) ** 2) def deltaE_ciede94(lab1, lab2, kH=1, kC=1, kL=1, k1=0.045, k2=0.015): """Color difference according to CIEDE 94 standard Accommodates perceptual non-uniformities through the use of application specific scale factors (`kH`, `kC`, `kL`, `k1`, and `k2`). Parameters ---------- lab1 : array_like reference color (Lab colorspace) lab2 : array_like comparison color (Lab colorspace) kH : float, optional Hue scale kC : float, optional Chroma scale kL : float, optional Lightness scale k1 : float, optional first scale parameter k2 : float, optional second scale parameter Returns ------- dE : array_like color difference between `lab1` and `lab2` Notes ----- deltaE_ciede94 is not symmetric with respect to lab1 and lab2. CIEDE94 defines the scales for the lightness, hue, and chroma in terms of the first color. Consequently, the first color should be regarded as the "reference" color. `kL`, `k1`, `k2` depend on the application and default to the values suggested for graphic arts ========== ============== ========== Parameter Graphic Arts Textiles ========== ============== ========== `kL` 1.000 2.000 `k1` 0.045 0.048 `k2` 0.015 0.014 ========== ============== ========== References ---------- .. [1] http://en.wikipedia.org/wiki/Color_difference .. [2] http://www.brucelindbloom.com/index.html?Eqn_DeltaE_CIE94.html """ L1, C1 = np.rollaxis(lab2lch(lab1), -1)[:2] L2, C2 = np.rollaxis(lab2lch(lab2), -1)[:2] dL = L1 - L2 dC = C1 - C2 dH2 = get_dH2(lab1, lab2) SL = 1 SC = 1 + k1 * C1 SH = 1 + k2 * C1 dE2 = (dL / (kL * SL)) ** 2 dE2 += (dC / (kC * SC)) ** 2 dE2 += dH2 / (kH * SH) ** 2 return np.sqrt(dE2) def deltaE_ciede2000(lab1, lab2, kL=1, kC=1, kH=1): """Color difference as given by the CIEDE 2000 standard. CIEDE 2000 is a major revision of CIDE94. The perceptual calibration is largely based on experience with automotive paint on smooth surfaces. Parameters ---------- lab1 : array_like reference color (Lab colorspace) lab2 : array_like comparison color (Lab colorspace) kL : float (range), optional lightness scale factor, 1 for "acceptably close"; 2 for "imperceptible" see deltaE_cmc kC : float (range), optional chroma scale factor, usually 1 kH : float (range), optional hue scale factor, usually 1 Returns ------- deltaE : array_like The distance between `lab1` and `lab2` Notes ----- CIEDE 2000 assumes parametric weighting factors for the lightness, chroma, and hue (`kL`, `kC`, `kH` respectively). These default to 1. References ---------- .. [1] http://en.wikipedia.org/wiki/Color_difference .. [2] http://www.ece.rochester.edu/~gsharma/ciede2000/ciede2000noteCRNA.pdf (doi:10.1364/AO.33.008069) .. [3] M. Melgosa, J. Quesada, and E. Hita, "Uniformity of some recent color metrics tested with an accurate color-difference tolerance dataset," Appl. Opt. 33, 8069-8077 (1994). """ lab1 = np.asarray(lab1) lab2 = np.asarray(lab2) unroll = False if lab1.ndim == 1 and lab2.ndim == 1: unroll = True if lab1.ndim == 1: lab1 = lab1[None, :] if lab2.ndim == 1: lab2 = lab2[None, :] L1, a1, b1 = np.rollaxis(lab1, -1)[:3] L2, a2, b2 = np.rollaxis(lab2, -1)[:3] # distort `a` based on average chroma # then convert to lch coordines from distorted `a` # all subsequence calculations are in the new coordiantes # (often denoted "prime" in the literature) Cbar = 0.5 * (np.hypot(a1, b1) + np.hypot(a2, b2)) c7 = Cbar ** 7 G = 0.5 * (1 - np.sqrt(c7 / (c7 + 25 ** 7))) scale = 1 + G C1, h1 = _cart2polar_2pi(a1 * scale, b1) C2, h2 = _cart2polar_2pi(a2 * scale, b2) # recall that c, h are polar coordiantes. c==r, h==theta # cide2000 has four terms to delta_e: # 1) Luminance term # 2) Hue term # 3) Chroma term # 4) hue Rotation term # lightness term Lbar = 0.5 * (L1 + L2) tmp = (Lbar - 50) ** 2 SL = 1 + 0.015 * tmp / np.sqrt(20 + tmp) L_term = (L2 - L1) / (kL * SL) # chroma term Cbar = 0.5 * (C1 + C2) # new coordiantes SC = 1 + 0.045 * Cbar C_term = (C2 - C1) / (kC * SC) # hue term h_diff = h2 - h1 h_sum = h1 + h2 CC = C1 * C2 dH = h_diff.copy() dH[h_diff > np.pi] -= 2 * np.pi dH[h_diff < -np.pi] += 2 * np.pi dH[CC == 0.] = 0. # if r == 0, dtheta == 0 dH_term = 2 * np.sqrt(CC) * np.sin(dH / 2) Hbar = h_sum.copy() mask = np.logical_and(CC != 0., np.abs(h_diff) > np.pi) Hbar[mask * (h_sum < 2 * np.pi)] += 2 * np.pi Hbar[mask * (h_sum >= 2 * np.pi)] -= 2 * np.pi Hbar[CC == 0.] *= 2 Hbar *= 0.5 T = (1 - 0.17 * np.cos(Hbar - np.deg2rad(30)) + 0.24 * np.cos(2 * Hbar) + 0.32 * np.cos(3 * Hbar + np.deg2rad(6)) - 0.20 * np.cos(4 * Hbar - np.deg2rad(63)) ) SH = 1 + 0.015 * Cbar * T H_term = dH_term / (kH * SH) # hue rotation c7 = Cbar ** 7 Rc = 2 * np.sqrt(c7 / (c7 + 25 ** 7)) dtheta = np.deg2rad(30) * np.exp(-((np.rad2deg(Hbar) - 275) / 25) ** 2) R_term = -np.sin(2 * dtheta) * Rc * C_term * H_term # put it all together dE2 = L_term ** 2 dE2 += C_term ** 2 dE2 += H_term ** 2 dE2 += R_term ans = np.sqrt(dE2) if unroll: ans = ans[0] return ans def deltaE_cmc(lab1, lab2, kL=1, kC=1): """Color difference from the CMC l:c standard. This color difference was developed by the Colour Measurement Committee (CMC) of the Society of Dyers and Colourists (United Kingdom). It is intended for use in the textile industry. The scale factors `kL`, `kC` set the weight given to differences in lightness and chroma relative to differences in hue. The usual values are ``kL=2``, ``kC=1`` for "acceptability" and ``kL=1``, ``kC=1`` for "imperceptibility". Colors with ``dE > 1`` are "different" for the given scale factors. Parameters ---------- lab1 : array_like reference color (Lab colorspace) lab2 : array_like comparison color (Lab colorspace) Returns ------- dE : array_like distance between colors `lab1` and `lab2` Notes ----- deltaE_cmc the defines the scales for the lightness, hue, and chroma in terms of the first color. Consequently ``deltaE_cmc(lab1, lab2) != deltaE_cmc(lab2, lab1)`` References ---------- .. [1] http://en.wikipedia.org/wiki/Color_difference .. [2] http://www.brucelindbloom.com/index.html?Eqn_DeltaE_CIE94.html .. [3] F. J. J. Clarke, R. McDonald, and B. Rigg, "Modification to the JPC79 colour-difference formula," J. Soc. Dyers Colour. 100, 128-132 (1984). """ L1, C1, h1 = np.rollaxis(lab2lch(lab1), -1)[:3] L2, C2, h2 = np.rollaxis(lab2lch(lab2), -1)[:3] dC = C1 - C2 dL = L1 - L2 dH2 = get_dH2(lab1, lab2) T = np.where(np.logical_and(np.rad2deg(h1) >= 164, np.rad2deg(h1) <= 345), 0.56 + 0.2 * np.abs(np.cos(h1 + np.deg2rad(168))), 0.36 + 0.4 * np.abs(np.cos(h1 + np.deg2rad(35))) ) c1_4 = C1 ** 4 F = np.sqrt(c1_4 / (c1_4 + 1900)) SL = np.where(L1 < 16, 0.511, 0.040975 * L1 / (1. + 0.01765 * L1)) SC = 0.638 + 0.0638 * C1 / (1. + 0.0131 * C1) SH = SC * (F * T + 1 - F) dE2 = (dL / (kL * SL)) ** 2 dE2 += (dC / (kC * SC)) ** 2 dE2 += dH2 / (SH ** 2) return np.sqrt(dE2) def get_dH2(lab1, lab2): """squared hue difference term occurring in deltaE_cmc and deltaE_ciede94 Despite its name, "dH" is not a simple difference of hue values. We avoid working directly with the hue value, since differencing angles is troublesome. The hue term is usually written as: c1 = sqrt(a1**2 + b1**2) c2 = sqrt(a2**2 + b2**2) term = (a1-a2)**2 + (b1-b2)**2 - (c1-c2)**2 dH = sqrt(term) However, this has poor roundoff properties when a or b is dominant. Instead, ab is a vector with elements a and b. The same dH term can be re-written as: |ab1-ab2|**2 - (|ab1| - |ab2|)**2 and then simplified to: 2*|ab1|*|ab2| - 2*dot(ab1, ab2) """ lab1 = np.asarray(lab1) lab2 = np.asarray(lab2) a1, b1 = np.rollaxis(lab1, -1)[1:3] a2, b2 = np.rollaxis(lab2, -1)[1:3] # magnitude of (a, b) is the chroma C1 = np.hypot(a1, b1) C2 = np.hypot(a2, b2) term = (C1 * C2) - (a1 * a2 + b1 * b2) return 2 * term scikit-image-0.13.1/skimage/color/rgb_colors.py000066400000000000000000000106151316256236300214170ustar00rootroot00000000000000aliceblue = (0.941, 0.973, 1) antiquewhite = (0.98, 0.922, 0.843) aqua = (0, 1, 1) aquamarine = (0.498, 1, 0.831) azure = (0.941, 1, 1) beige = (0.961, 0.961, 0.863) bisque = (1, 0.894, 0.769) black = (0, 0, 0) blanchedalmond = (1, 0.922, 0.804) blue = (0, 0, 1) blueviolet = (0.541, 0.169, 0.886) brown = (0.647, 0.165, 0.165) burlywood = (0.871, 0.722, 0.529) cadetblue = (0.373, 0.62, 0.627) chartreuse = (0.498, 1, 0) chocolate = (0.824, 0.412, 0.118) coral = (1, 0.498, 0.314) cornflowerblue = (0.392, 0.584, 0.929) cornsilk = (1, 0.973, 0.863) crimson = (0.863, 0.0784, 0.235) cyan = (0, 1, 1) darkblue = (0, 0, 0.545) darkcyan = (0, 0.545, 0.545) darkgoldenrod = (0.722, 0.525, 0.0431) darkgray = (0.663, 0.663, 0.663) darkgreen = (0, 0.392, 0) darkgrey = (0.663, 0.663, 0.663) darkkhaki = (0.741, 0.718, 0.42) darkmagenta = (0.545, 0, 0.545) darkolivegreen = (0.333, 0.42, 0.184) darkorange = (1, 0.549, 0) darkorchid = (0.6, 0.196, 0.8) darkred = (0.545, 0, 0) darksalmon = (0.914, 0.588, 0.478) darkseagreen = (0.561, 0.737, 0.561) darkslateblue = (0.282, 0.239, 0.545) darkslategray = (0.184, 0.31, 0.31) darkslategrey = (0.184, 0.31, 0.31) darkturquoise = (0, 0.808, 0.82) darkviolet = (0.58, 0, 0.827) deeppink = (1, 0.0784, 0.576) deepskyblue = (0, 0.749, 1) dimgray = (0.412, 0.412, 0.412) dimgrey = (0.412, 0.412, 0.412) dodgerblue = (0.118, 0.565, 1) firebrick = (0.698, 0.133, 0.133) floralwhite = (1, 0.98, 0.941) forestgreen = (0.133, 0.545, 0.133) fuchsia = (1, 0, 1) gainsboro = (0.863, 0.863, 0.863) ghostwhite = (0.973, 0.973, 1) gold = (1, 0.843, 0) goldenrod = (0.855, 0.647, 0.125) gray = (0.502, 0.502, 0.502) green = (0, 0.502, 0) greenyellow = (0.678, 1, 0.184) grey = (0.502, 0.502, 0.502) honeydew = (0.941, 1, 0.941) hotpink = (1, 0.412, 0.706) indianred = (0.804, 0.361, 0.361) indigo = (0.294, 0, 0.51) ivory = (1, 1, 0.941) khaki = (0.941, 0.902, 0.549) lavender = (0.902, 0.902, 0.98) lavenderblush = (1, 0.941, 0.961) lawngreen = (0.486, 0.988, 0) lemonchiffon = (1, 0.98, 0.804) lightblue = (0.678, 0.847, 0.902) lightcoral = (0.941, 0.502, 0.502) lightcyan = (0.878, 1, 1) lightgoldenrodyellow = (0.98, 0.98, 0.824) lightgray = (0.827, 0.827, 0.827) lightgreen = (0.565, 0.933, 0.565) lightgrey = (0.827, 0.827, 0.827) lightpink = (1, 0.714, 0.757) lightsalmon = (1, 0.627, 0.478) lightseagreen = (0.125, 0.698, 0.667) lightskyblue = (0.529, 0.808, 0.98) lightslategray = (0.467, 0.533, 0.6) lightslategrey = (0.467, 0.533, 0.6) lightsteelblue = (0.69, 0.769, 0.871) lightyellow = (1, 1, 0.878) lime = (0, 1, 0) limegreen = (0.196, 0.804, 0.196) linen = (0.98, 0.941, 0.902) magenta = (1, 0, 1) maroon = (0.502, 0, 0) mediumaquamarine = (0.4, 0.804, 0.667) mediumblue = (0, 0, 0.804) mediumorchid = (0.729, 0.333, 0.827) mediumpurple = (0.576, 0.439, 0.859) mediumseagreen = (0.235, 0.702, 0.443) mediumslateblue = (0.482, 0.408, 0.933) mediumspringgreen = (0, 0.98, 0.604) mediumturquoise = (0.282, 0.82, 0.8) mediumvioletred = (0.78, 0.0824, 0.522) midnightblue = (0.098, 0.098, 0.439) mintcream = (0.961, 1, 0.98) mistyrose = (1, 0.894, 0.882) moccasin = (1, 0.894, 0.71) navajowhite = (1, 0.871, 0.678) navy = (0, 0, 0.502) oldlace = (0.992, 0.961, 0.902) olive = (0.502, 0.502, 0) olivedrab = (0.42, 0.557, 0.137) orange = (1, 0.647, 0) orangered = (1, 0.271, 0) orchid = (0.855, 0.439, 0.839) palegoldenrod = (0.933, 0.91, 0.667) palegreen = (0.596, 0.984, 0.596) palevioletred = (0.686, 0.933, 0.933) papayawhip = (1, 0.937, 0.835) peachpuff = (1, 0.855, 0.725) peru = (0.804, 0.522, 0.247) pink = (1, 0.753, 0.796) plum = (0.867, 0.627, 0.867) powderblue = (0.69, 0.878, 0.902) purple = (0.502, 0, 0.502) red = (1, 0, 0) rosybrown = (0.737, 0.561, 0.561) royalblue = (0.255, 0.412, 0.882) saddlebrown = (0.545, 0.271, 0.0745) salmon = (0.98, 0.502, 0.447) sandybrown = (0.98, 0.643, 0.376) seagreen = (0.18, 0.545, 0.341) seashell = (1, 0.961, 0.933) sienna = (0.627, 0.322, 0.176) silver = (0.753, 0.753, 0.753) skyblue = (0.529, 0.808, 0.922) slateblue = (0.416, 0.353, 0.804) slategray = (0.439, 0.502, 0.565) slategrey = (0.439, 0.502, 0.565) snow = (1, 0.98, 0.98) springgreen = (0, 1, 0.498) steelblue = (0.275, 0.51, 0.706) tan = (0.824, 0.706, 0.549) teal = (0, 0.502, 0.502) thistle = (0.847, 0.749, 0.847) tomato = (1, 0.388, 0.278) turquoise = (0.251, 0.878, 0.816) violet = (0.933, 0.51, 0.933) wheat = (0.961, 0.871, 0.702) white = (1, 1, 1) whitesmoke = (0.961, 0.961, 0.961) yellow = (1, 1, 0) yellowgreen = (0.604, 0.804, 0.196) scikit-image-0.13.1/skimage/color/tests/000077500000000000000000000000001316256236300200515ustar00rootroot00000000000000scikit-image-0.13.1/skimage/color/tests/__init__.py000066400000000000000000000001771316256236300221670ustar00rootroot00000000000000from ..._shared.testing import setup_test, teardown_test def setup(): setup_test() def teardown(): teardown_test() scikit-image-0.13.1/skimage/color/tests/ciede2000_test_data.txt000066400000000000000000000131151316256236300242160ustar00rootroot00000000000000# input, intermediate, and output values for CIEDE2000 dE function # data taken from "The CIEDE2000 Color-Difference Formula: Implementation Notes, ..." http://www.ece.rochester.edu/~gsharma/ciede2000/ciede2000noteCRNA.pdf # tab delimited data # pair 1 L1 a1 b1 ap1 cp1 hp1 hbar1 G T SL SC SH RT dE 2 L2 a2 b2 ap2 cp2 hp2 1 1 50.0000 2.6772 -79.7751 2.6774 79.8200 271.9222 270.9611 0.0001 0.6907 1.0000 4.6578 1.8421 -1.7042 2.0425 2 50.0000 0.0000 -82.7485 0.0000 82.7485 270.0000 2 1 50.0000 3.1571 -77.2803 3.1573 77.3448 272.3395 271.1698 0.0001 0.6843 1.0000 4.6021 1.8216 -1.7070 2.8615 2 50.0000 0.0000 -82.7485 0.0000 82.7485 270.0000 3 1 50.0000 2.8361 -74.0200 2.8363 74.0743 272.1944 271.0972 0.0001 0.6865 1.0000 4.5285 1.8074 -1.7060 3.4412 2 50.0000 0.0000 -82.7485 0.0000 82.7485 270.0000 4 1 50.0000 -1.3802 -84.2814 -1.3803 84.2927 269.0618 269.5309 0.0001 0.7357 1.0000 4.7584 1.9217 -1.6809 1.0000 2 50.0000 0.0000 -82.7485 0.0000 82.7485 270.0000 5 1 50.0000 -1.1848 -84.8006 -1.1849 84.8089 269.1995 269.5997 0.0001 0.7335 1.0000 4.7700 1.9218 -1.6822 1.0000 2 50.0000 0.0000 -82.7485 0.0000 82.7485 270.0000 6 1 50.0000 -0.9009 -85.5211 -0.9009 85.5258 269.3964 269.6982 0.0001 0.7303 1.0000 4.7862 1.9217 -1.6840 1.0000 2 50.0000 0.0000 -82.7485 0.0000 82.7485 270.0000 7 1 50.0000 0.0000 0.0000 0.0000 0.0000 0.0000 126.8697 0.5000 1.2200 1.0000 1.0562 1.0229 0.0000 2.3669 2 50.0000 -1.0000 2.0000 -1.5000 2.5000 126.8697 8 1 50.0000 -1.0000 2.0000 -1.5000 2.5000 126.8697 126.8697 0.5000 1.2200 1.0000 1.0562 1.0229 0.0000 2.3669 2 50.0000 0.0000 0.0000 0.0000 0.0000 0.0000 9 1 50.0000 2.4900 -0.0010 3.7346 3.7346 359.9847 269.9854 0.4998 0.7212 1.0000 1.1681 1.0404 -0.0022 7.1792 2 50.0000 -2.4900 0.0009 -3.7346 3.7346 179.9862 10 1 50.0000 2.4900 -0.0010 3.7346 3.7346 359.9847 269.9847 0.4998 0.7212 1.0000 1.1681 1.0404 -0.0022 7.1792 2 50.0000 -2.4900 0.0010 -3.7346 3.7346 179.9847 11 1 50.0000 2.4900 -0.0010 3.7346 3.7346 359.9847 89.9839 0.4998 0.6175 1.0000 1.1681 1.0346 0.0000 7.2195 2 50.0000 -2.4900 0.0011 -3.7346 3.7346 179.9831 12 1 50.0000 2.4900 -0.0010 3.7346 3.7346 359.9847 89.9831 0.4998 0.6175 1.0000 1.1681 1.0346 0.0000 7.2195 2 50.0000 -2.4900 0.0012 -3.7346 3.7346 179.9816 13 1 50.0000 -0.0010 2.4900 -0.0015 2.4900 90.0345 180.0328 0.4998 0.9779 1.0000 1.1121 1.0365 0.0000 4.8045 2 50.0000 0.0009 -2.4900 0.0013 2.4900 270.0311 14 1 50.0000 -0.0010 2.4900 -0.0015 2.4900 90.0345 180.0345 0.4998 0.9779 1.0000 1.1121 1.0365 0.0000 4.8045 2 50.0000 0.0010 -2.4900 0.0015 2.4900 270.0345 15 1 50.0000 -0.0010 2.4900 -0.0015 2.4900 90.0345 0.0362 0.4998 1.3197 1.0000 1.1121 1.0493 0.0000 4.7461 2 50.0000 0.0011 -2.4900 0.0016 2.4900 270.0380 16 1 50.0000 2.5000 0.0000 3.7496 3.7496 0.0000 315.0000 0.4998 0.8454 1.0000 1.1406 1.0396 -0.0001 4.3065 2 50.0000 0.0000 -2.5000 0.0000 2.5000 270.0000 17 1 50.0000 2.5000 0.0000 3.4569 3.4569 0.0000 346.2470 0.3827 1.4453 1.1608 1.9547 1.4599 -0.0003 27.1492 2 73.0000 25.0000 -18.0000 34.5687 38.9743 332.4939 18 1 50.0000 2.5000 0.0000 3.4954 3.4954 0.0000 51.7766 0.3981 0.6447 1.0640 1.7498 1.1612 0.0000 22.8977 2 61.0000 -5.0000 29.0000 -6.9907 29.8307 103.5532 19 1 50.0000 2.5000 0.0000 3.5514 3.5514 0.0000 272.2362 0.4206 0.6521 1.0251 1.9455 1.2055 -0.8219 31.9030 2 56.0000 -27.0000 -3.0000 -38.3556 38.4728 184.4723 20 1 50.0000 2.5000 0.0000 3.5244 3.5244 0.0000 11.9548 0.4098 1.1031 1.0400 1.9120 1.3353 0.0000 19.4535 2 58.0000 24.0000 15.0000 33.8342 37.0102 23.9095 21 1 50.0000 2.5000 0.0000 3.7494 3.7494 0.0000 3.5056 0.4997 1.2616 1.0000 1.1923 1.0808 0.0000 1.0000 2 50.0000 3.1736 0.5854 4.7596 4.7954 7.0113 22 1 50.0000 2.5000 0.0000 3.7493 3.7493 0.0000 0.0000 0.4997 1.3202 1.0000 1.1956 1.0861 0.0000 1.0000 2 50.0000 3.2972 0.0000 4.9450 4.9450 0.0000 23 1 50.0000 2.5000 0.0000 3.7497 3.7497 0.0000 5.8190 0.4999 1.2197 1.0000 1.1486 1.0604 0.0000 1.0000 2 50.0000 1.8634 0.5757 2.7949 2.8536 11.6380 24 1 50.0000 2.5000 0.0000 3.7493 3.7493 0.0000 1.9603 0.4997 1.2883 1.0000 1.1946 1.0836 0.0000 1.0000 2 50.0000 3.2592 0.3350 4.8879 4.8994 3.9206 25 1 60.2574 -34.0099 36.2677 -34.0678 49.7590 133.2085 132.0835 0.0017 1.3010 1.1427 3.2946 1.9951 0.0000 1.2644 2 60.4626 -34.1751 39.4387 -34.2333 52.2238 130.9584 26 1 63.0109 -31.0961 -5.8663 -32.6194 33.1427 190.1951 188.8221 0.0490 0.9402 1.1831 2.4549 1.4560 0.0000 1.2630 2 62.8187 -29.7946 -4.0864 -31.2542 31.5202 187.4490 27 1 61.2901 3.7196 -5.3901 5.5668 7.7487 315.9240 310.0313 0.4966 0.6952 1.1586 1.3092 1.0717 -0.0032 1.8731 2 61.4292 2.2480 -4.9620 3.3644 5.9950 304.1385 28 1 35.0831 -44.1164 3.7933 -44.3939 44.5557 175.1161 176.4290 0.0063 1.0168 1.2148 2.9105 1.6476 0.0000 1.8645 2 35.0232 -40.0716 1.5901 -40.3237 40.3550 177.7418 29 1 22.7233 20.0904 -46.6940 20.1424 50.8532 293.3339 291.3809 0.0026 0.3636 1.4014 3.1597 1.2617 -1.2537 2.0373 2 23.0331 14.9730 -42.5619 15.0118 45.1317 289.4279 30 1 36.4612 47.8580 18.3852 47.9197 51.3256 20.9901 21.8781 0.0013 0.9239 1.1943 3.3888 1.7357 0.0000 1.4146 2 36.2715 50.5065 21.2231 50.5716 54.8444 22.7660 31 1 90.8027 -2.0831 1.4410 -3.1245 3.4408 155.2410 167.1011 0.4999 1.1546 1.6110 1.1329 1.0511 0.0000 1.4441 2 91.1528 -1.6435 0.0447 -2.4651 2.4655 178.9612 32 1 90.9257 -0.5406 -0.9208 -0.8109 1.2270 228.6315 218.4363 0.5000 1.3916 1.5930 1.0620 1.0288 0.0000 1.5381 2 88.6381 -0.8985 -0.7239 -1.3477 1.5298 208.2412 33 1 6.7747 -0.2908 -2.4247 -0.4362 2.4636 259.8025 263.0049 0.4999 0.9556 1.6517 1.1057 1.0337 -0.0004 0.6377 2 5.8714 -0.0985 -2.2286 -0.1477 2.2335 266.2073 34 1 2.0776 0.0795 -1.1350 0.1192 1.1412 275.9978 268.0910 0.5000 0.7826 1.7246 1.0383 1.0100 0.0000 0.9082 2 0.9033 -0.0636 -0.5514 -0.0954 0.5596 260.18421 scikit-image-0.13.1/skimage/color/tests/data/000077500000000000000000000000001316256236300207625ustar00rootroot00000000000000scikit-image-0.13.1/skimage/color/tests/data/lab_array_a_2.npy000066400000000000000000000003101316256236300241610ustar00rootroot00000000000000NUMPYF{'descr': 'ۋ7<>Vf(&@@< 4jP@{Ҙ{Fhx:G@DMN^$7@scikit-image-0.13.1/skimage/color/tests/data/lab_array_d50_10.npy000066400000000000000000000003101316256236300244100ustar00rootroot00000000000000NUMPYF{'descr': '_x:G@Q@Y@8?=I@f(&@@xS@$䬣8\Yx:G@5IiĈmI@scikit-image-0.13.1/skimage/color/tests/data/lab_array_d75_2.npy000066400000000000000000000003101316256236300243400ustar00rootroot00000000000000NUMPYF{'descr': 'N,Tax:G@#\>II{y"1]F@scikit-image-0.13.1/skimage/color/tests/data/luv_array_d55_10.npy000066400000000000000000000003101316256236300244650ustar00rootroot00000000000000NUMPYF{'descr': ' HSV roundtrip is about 1e-6 assert_almost_equal(rgb, hsv2rgb(hsv), decimal=4) def test_hsv2rgb_error_grayscale(self): self.assertRaises(ValueError, hsv2rgb, self.img_grayscale) def test_hsv2rgb_error_one_element(self): self.assertRaises(ValueError, hsv2rgb, self.img_rgb[0, 0]) # RGB to XYZ def test_rgb2xyz_conversion(self): gt = np.array([[[0.950456, 1. , 1.088754], [0.538003, 0.787329, 1.06942 ], [0.592876, 0.28484 , 0.969561], [0.180423, 0.072169, 0.950227]], [[0.770033, 0.927831, 0.138527], [0.35758 , 0.71516 , 0.119193], [0.412453, 0.212671, 0.019334], [0. , 0. , 0. ]]]) assert_almost_equal(rgb2xyz(self.colbars_array), gt) # stop repeating the "raises" checks for all other functions that are # implemented with color._convert() def test_rgb2xyz_error_grayscale(self): self.assertRaises(ValueError, rgb2xyz, self.img_grayscale) def test_rgb2xyz_error_one_element(self): self.assertRaises(ValueError, rgb2xyz, self.img_rgb[0, 0]) # XYZ to RGB def test_xyz2rgb_conversion(self): assert_almost_equal(xyz2rgb(rgb2xyz(self.colbars_array)), self.colbars_array) # RGB<->XYZ roundtrip on another image def test_xyz_rgb_roundtrip(self): img_rgb = img_as_float(self.img_rgb) assert_array_almost_equal(xyz2rgb(rgb2xyz(img_rgb)), img_rgb) # RGB<->HED roundtrip with ubyte image def test_hed_rgb_roundtrip(self): img_rgb = img_as_ubyte(self.img_rgb) with expected_warnings(['precision loss']): new = img_as_ubyte(hed2rgb(rgb2hed(img_rgb))) assert_equal(new, img_rgb) # RGB<->HED roundtrip with float image def test_hed_rgb_float_roundtrip(self): img_rgb = img_as_float(self.img_rgb) assert_array_almost_equal(hed2rgb(rgb2hed(img_rgb)), img_rgb) # RGB<->HDX roundtrip with ubyte image def test_hdx_rgb_roundtrip(self): from skimage.color.colorconv import hdx_from_rgb, rgb_from_hdx img_rgb = self.img_rgb conv = combine_stains(separate_stains(img_rgb, hdx_from_rgb), rgb_from_hdx) assert_equal(img_as_ubyte(conv), img_rgb) # RGB<->HDX roundtrip with ubyte image def test_hdx_rgb_roundtrip(self): from skimage.color.colorconv import hdx_from_rgb, rgb_from_hdx img_rgb = img_as_float(self.img_rgb) conv = combine_stains(separate_stains(img_rgb, hdx_from_rgb), rgb_from_hdx) assert_array_almost_equal(conv, img_rgb) # RGB to RGB CIE def test_rgb2rgbcie_conversion(self): gt = np.array([[[ 0.1488856 , 0.18288098, 0.19277574], [ 0.01163224, 0.16649536, 0.18948516], [ 0.12259182, 0.03308008, 0.17298223], [-0.01466154, 0.01669446, 0.16969164]], [[ 0.16354714, 0.16618652, 0.0230841 ], [ 0.02629378, 0.1498009 , 0.01979351], [ 0.13725336, 0.01638562, 0.00329059], [ 0. , 0. , 0. ]]]) assert_almost_equal(rgb2rgbcie(self.colbars_array), gt) # RGB CIE to RGB def test_rgbcie2rgb_conversion(self): # only roundtrip test, we checked rgb2rgbcie above already assert_almost_equal(rgbcie2rgb(rgb2rgbcie(self.colbars_array)), self.colbars_array) def test_convert_colorspace(self): colspaces = ['HSV', 'RGB CIE', 'XYZ'] colfuncs_from = [hsv2rgb, rgbcie2rgb, xyz2rgb] colfuncs_to = [rgb2hsv, rgb2rgbcie, rgb2xyz] assert_almost_equal(convert_colorspace(self.colbars_array, 'RGB', 'RGB'), self.colbars_array) for i, space in enumerate(colspaces): gt = colfuncs_from[i](self.colbars_array) assert_almost_equal(convert_colorspace(self.colbars_array, space, 'RGB'), gt) gt = colfuncs_to[i](self.colbars_array) assert_almost_equal(convert_colorspace(self.colbars_array, 'RGB', space), gt) self.assertRaises(ValueError, convert_colorspace, self.colbars_array, 'nokey', 'XYZ') self.assertRaises(ValueError, convert_colorspace, self.colbars_array, 'RGB', 'nokey') def test_rgb2grey(self): x = np.array([1, 1, 1]).reshape((1, 1, 3)).astype(np.float) g = rgb2grey(x) assert_array_almost_equal(g, 1) assert_equal(g.shape, (1, 1)) def test_rgb2grey_contiguous(self): x = np.random.rand(10, 10, 3) assert rgb2grey(x).flags["C_CONTIGUOUS"] assert rgb2grey(x[:5, :5]).flags["C_CONTIGUOUS"] def test_rgb2grey_alpha(self): x = np.random.rand(10, 10, 4) assert rgb2grey(x).ndim == 2 def test_rgb2grey_on_grey(self): rgb2grey(np.random.rand(5, 5)) # test matrices for xyz2lab and lab2xyz generated using # http://www.easyrgb.com/index.php?X=CALC # Note: easyrgb website displays xyz*100 def test_xyz2lab(self): assert_array_almost_equal(xyz2lab(self.xyz_array), self.lab_array, decimal=3) # Test the conversion with the rest of the illuminants. for I in ["d50", "d55", "d65", "d75"]: for obs in ["2", "10"]: fname = "lab_array_{0}_{1}.npy".format(I, obs) lab_array_I_obs = np.load( os.path.join(os.path.dirname(__file__), 'data', fname)) assert_array_almost_equal(lab_array_I_obs, xyz2lab(self.xyz_array, I, obs), decimal=2) for I in ["a", "e"]: fname = "lab_array_{0}_2.npy".format(I) lab_array_I_obs = np.load( os.path.join(os.path.dirname(__file__), 'data', fname)) assert_array_almost_equal(lab_array_I_obs, xyz2lab(self.xyz_array, I, "2"), decimal=2) def test_lab2xyz(self): assert_array_almost_equal(lab2xyz(self.lab_array), self.xyz_array, decimal=3) # Test the conversion with the rest of the illuminants. for I in ["d50", "d55", "d65", "d75"]: for obs in ["2", "10"]: fname = "lab_array_{0}_{1}.npy".format(I, obs) lab_array_I_obs = np.load( os.path.join(os.path.dirname(__file__), 'data', fname)) assert_array_almost_equal(lab2xyz(lab_array_I_obs, I, obs), self.xyz_array, decimal=3) for I in ["a", "e"]: fname = "lab_array_{0}_2.npy".format(I, obs) lab_array_I_obs = np.load( os.path.join(os.path.dirname(__file__), 'data', fname)) assert_array_almost_equal(lab2xyz(lab_array_I_obs, I, "2"), self.xyz_array, decimal=3) # And we include a call to test the exception handling in the code. try: xs = lab2xyz(lab_array_I_obs, "NaI", "2") # Not an illuminant except ValueError: pass try: xs = lab2xyz(lab_array_I_obs, "d50", "42") # Not a degree except ValueError: pass def test_rgb2lab_brucelindbloom(self): """ Test the RGB->Lab conversion by comparing to the calculator on the authoritative Bruce Lindbloom [website](http://brucelindbloom.com/index.html?ColorCalculator.html). """ # Obtained with D65 white point, sRGB model and gamma gt_for_colbars = np.array([ [100,0,0], [97.1393, -21.5537, 94.4780], [91.1132, -48.0875, -14.1312], [87.7347, -86.1827, 83.1793], [60.3242, 98.2343, -60.8249], [53.2408, 80.0925, 67.2032], [32.2970, 79.1875, -107.8602], [0,0,0]]).T gt_array = np.swapaxes(gt_for_colbars.reshape(3, 4, 2), 0, 2) assert_array_almost_equal(rgb2lab(self.colbars_array), gt_array, decimal=2) def test_lab_rgb_roundtrip(self): img_rgb = img_as_float(self.img_rgb) assert_array_almost_equal(lab2rgb(rgb2lab(img_rgb)), img_rgb) # test matrices for xyz2luv and luv2xyz generated using # http://www.easyrgb.com/index.php?X=CALC # Note: easyrgb website displays xyz*100 def test_xyz2luv(self): assert_array_almost_equal(xyz2luv(self.xyz_array), self.luv_array, decimal=3) # Test the conversion with the rest of the illuminants. for I in ["d50", "d55", "d65", "d75"]: for obs in ["2", "10"]: fname = "luv_array_{0}_{1}.npy".format(I, obs) luv_array_I_obs = np.load( os.path.join(os.path.dirname(__file__), 'data', fname)) assert_array_almost_equal(luv_array_I_obs, xyz2luv(self.xyz_array, I, obs), decimal=2) for I in ["a", "e"]: fname = "luv_array_{0}_2.npy".format(I) luv_array_I_obs = np.load( os.path.join(os.path.dirname(__file__), 'data', fname)) assert_array_almost_equal(luv_array_I_obs, xyz2luv(self.xyz_array, I, "2"), decimal=2) def test_luv2xyz(self): assert_array_almost_equal(luv2xyz(self.luv_array), self.xyz_array, decimal=3) # Test the conversion with the rest of the illuminants. for I in ["d50", "d55", "d65", "d75"]: for obs in ["2", "10"]: fname = "luv_array_{0}_{1}.npy".format(I, obs) luv_array_I_obs = np.load( os.path.join(os.path.dirname(__file__), 'data', fname)) assert_array_almost_equal(luv2xyz(luv_array_I_obs, I, obs), self.xyz_array, decimal=3) for I in ["a", "e"]: fname = "luv_array_{0}_2.npy".format(I, obs) luv_array_I_obs = np.load( os.path.join(os.path.dirname(__file__), 'data', fname)) assert_array_almost_equal(luv2xyz(luv_array_I_obs, I, "2"), self.xyz_array, decimal=3) def test_rgb2luv_brucelindbloom(self): """ Test the RGB->Lab conversion by comparing to the calculator on the authoritative Bruce Lindbloom [website](http://brucelindbloom.com/index.html?ColorCalculator.html). """ # Obtained with D65 white point, sRGB model and gamma gt_for_colbars = np.array([ [100, 0, 0], [97.1393, 7.7056, 106.7866], [91.1132, -70.4773, -15.2042], [87.7347, -83.0776, 107.3985], [60.3242, 84.0714, -108.6834], [53.2408, 175.0151, 37.7564], [32.2970, -9.4054, -130.3423], [0, 0, 0]]).T gt_array = np.swapaxes(gt_for_colbars.reshape(3, 4, 2), 0, 2) assert_array_almost_equal(rgb2luv(self.colbars_array), gt_array, decimal=2) def test_luv_rgb_roundtrip(self): img_rgb = img_as_float(self.img_rgb) assert_array_almost_equal(luv2rgb(rgb2luv(img_rgb)), img_rgb) def test_lab_rgb_outlier(self): lab_array = np.ones((3, 1, 3)) lab_array[0] = [50, -12, 85] lab_array[1] = [50, 12, -85] lab_array[2] = [90, -4, -47] rgb_array = np.array([[[0.501, 0.481, 0]], [[0, 0.482, 1.]], [[0.578, 0.914, 1.]], ]) assert_almost_equal(lab2rgb(lab_array), rgb_array, decimal=3) def test_lab_full_gamut(self): a, b = np.meshgrid(np.arange(-100, 100), np.arange(-100, 100)) L = np.ones(a.shape) lab = np.dstack((L, a, b)) for value in [0, 10, 20]: lab[:, :, 0] = value with expected_warnings(['Color data out of range']): lab2xyz(lab) def test_lab_lch_roundtrip(self): rgb = img_as_float(self.img_rgb) lab = rgb2lab(rgb) lab2 = lch2lab(lab2lch(lab)) assert_array_almost_equal(lab2, lab) def test_rgb_lch_roundtrip(self): rgb = img_as_float(self.img_rgb) lab = rgb2lab(rgb) lch = lab2lch(lab) lab2 = lch2lab(lch) rgb2 = lab2rgb(lab2) assert_array_almost_equal(rgb, rgb2) def test_lab_lch_0d(self): lab0 = self._get_lab0() lch0 = lab2lch(lab0) lch2 = lab2lch(lab0[None, None, :]) assert_array_almost_equal(lch0, lch2[0, 0, :]) def test_lab_lch_1d(self): lab0 = self._get_lab0() lch0 = lab2lch(lab0) lch1 = lab2lch(lab0[None, :]) assert_array_almost_equal(lch0, lch1[0, :]) def test_lab_lch_3d(self): lab0 = self._get_lab0() lch0 = lab2lch(lab0) lch3 = lab2lch(lab0[None, None, None, :]) assert_array_almost_equal(lch0, lch3[0, 0, 0, :]) def _get_lab0(self): rgb = img_as_float(self.img_rgb[:1, :1, :]) return rgb2lab(rgb)[0, 0, :] def test_yuv(self): rgb = np.array([[[1.0, 1.0, 1.0]]]) assert_array_almost_equal(rgb2yuv(rgb), np.array([[[1, 0, 0]]])) assert_array_almost_equal(rgb2yiq(rgb), np.array([[[1, 0, 0]]])) assert_array_almost_equal(rgb2ypbpr(rgb), np.array([[[1, 0, 0]]])) assert_array_almost_equal(rgb2ycbcr(rgb), np.array([[[235, 128, 128]]])) rgb = np.array([[[0.0, 1.0, 0.0]]]) assert_array_almost_equal(rgb2yuv(rgb), np.array([[[0.587, -0.28886916, -0.51496512]]])) assert_array_almost_equal(rgb2yiq(rgb), np.array([[[0.587, -0.27455667, -0.52273617]]])) assert_array_almost_equal(rgb2ypbpr(rgb), np.array([[[0.587, -0.331264, -0.418688]]])) assert_array_almost_equal(rgb2ycbcr(rgb), np.array([[[144.553, 53.797, 34.214]]])) def test_yuv_roundtrip(self): img_rgb = img_as_float(self.img_rgb)[::16, ::16] assert_array_almost_equal(yuv2rgb(rgb2yuv(img_rgb)), img_rgb) assert_array_almost_equal(yiq2rgb(rgb2yiq(img_rgb)), img_rgb) assert_array_almost_equal(ypbpr2rgb(rgb2ypbpr(img_rgb)), img_rgb) assert_array_almost_equal(ycbcr2rgb(rgb2ycbcr(img_rgb)), img_rgb) def test_rgb2yiq_conversion(self): rgb = img_as_float(self.img_rgb)[::16, ::16] yiq = rgb2yiq(rgb).reshape(-1, 3) gt = np.array([colorsys.rgb_to_yiq(pt[0], pt[1], pt[2]) for pt in rgb.reshape(-1, 3)] ) assert_almost_equal(yiq, gt, decimal=2) def test_gray2rgb(): x = np.array([0, 0.5, 1]) assert_raises(ValueError, gray2rgb, x) x = x.reshape((3, 1)) y = gray2rgb(x) assert_equal(y.shape, (3, 1, 3)) assert_equal(y.dtype, x.dtype) assert_equal(y[..., 0], x) assert_equal(y[0, 0, :], [0, 0, 0]) x = np.array([[0, 128, 255]], dtype=np.uint8) z = gray2rgb(x) assert_equal(z.shape, (1, 3, 3)) assert_equal(z[..., 0], x) assert_equal(z[0, 1, :], [128, 128, 128]) def test_gray2rgb_rgb(): x = np.random.rand(5, 5, 4) y = gray2rgb(x) assert_equal(x, y) def test_gray2rgb_alpha(): x = np.random.random((5, 5, 4)) assert_equal(gray2rgb(x, alpha=None).shape, (5, 5, 4)) assert_equal(gray2rgb(x, alpha=False).shape, (5, 5, 3)) assert_equal(gray2rgb(x, alpha=True).shape, (5, 5, 4)) x = np.random.random((5, 5, 3)) assert_equal(gray2rgb(x, alpha=None).shape, (5, 5, 3)) assert_equal(gray2rgb(x, alpha=False).shape, (5, 5, 3)) assert_equal(gray2rgb(x, alpha=True).shape, (5, 5, 4)) assert_equal(gray2rgb(np.array([[1, 2], [3, 4.]]), alpha=True)[0, 0, 3], 1) assert_equal(gray2rgb(np.array([[1, 2], [3, 4]], dtype=np.uint8), alpha=True)[0, 0, 3], 255) if __name__ == "__main__": from numpy.testing import run_module_suite run_module_suite() scikit-image-0.13.1/skimage/color/tests/test_colorlabel.py000066400000000000000000000123561316256236300236070ustar00rootroot00000000000000import itertools import numpy as np from numpy import testing from skimage.color.colorlabel import label2rgb from numpy.testing import (assert_array_almost_equal as assert_close, assert_array_equal, assert_warns) def test_shape_mismatch(): image = np.ones((3, 3)) label = np.ones((2, 2)) testing.assert_raises(ValueError, label2rgb, image, label) def test_rgb(): image = np.ones((1, 3)) label = np.arange(3).reshape(1, -1) colors = [(1, 0, 0), (0, 1, 0), (0, 0, 1)] # Set alphas just in case the defaults change rgb = label2rgb(label, image=image, colors=colors, alpha=1, image_alpha=1) assert_close(rgb, [colors]) def test_alpha(): image = np.random.uniform(size=(3, 3)) label = np.random.randint(0, 9, size=(3, 3)) # If we set `alpha = 0`, then rgb should match image exactly. rgb = label2rgb(label, image=image, alpha=0, image_alpha=1) assert_close(rgb[..., 0], image) assert_close(rgb[..., 1], image) assert_close(rgb[..., 2], image) def test_no_input_image(): label = np.arange(3).reshape(1, -1) colors = [(1, 0, 0), (0, 1, 0), (0, 0, 1)] rgb = label2rgb(label, colors=colors) assert_close(rgb, [colors]) def test_image_alpha(): image = np.random.uniform(size=(1, 3)) label = np.arange(3).reshape(1, -1) colors = [(1, 0, 0), (0, 1, 0), (0, 0, 1)] # If we set `image_alpha = 0`, then rgb should match label colors exactly. rgb = label2rgb(label, image=image, colors=colors, alpha=1, image_alpha=0) assert_close(rgb, [colors]) def test_color_names(): image = np.ones((1, 3)) label = np.arange(3).reshape(1, -1) cnames = ['red', 'lime', 'blue'] colors = [(1, 0, 0), (0, 1, 0), (0, 0, 1)] # Set alphas just in case the defaults change rgb = label2rgb(label, image=image, colors=cnames, alpha=1, image_alpha=1) assert_close(rgb, [colors]) def test_bg_and_color_cycle(): image = np.zeros((1, 10)) # dummy image label = np.arange(10).reshape(1, -1) colors = [(1, 0, 0), (0, 0, 1)] bg_color = (0, 0, 0) rgb = label2rgb(label, image=image, bg_label=0, bg_color=bg_color, colors=colors, alpha=1) assert_close(rgb[0, 0], bg_color) for pixel, color in zip(rgb[0, 1:], itertools.cycle(colors)): assert_close(pixel, color) def test_negative_labels(): labels = np.array([0, -1, -2, 0]) rout = np.array([(0., 0., 0.), (0., 0., 1.), (1., 0., 0.), (0., 0., 0.)]) assert_close(rout, label2rgb(labels, bg_label=0, alpha=1, image_alpha=1)) def test_nonconsecutive(): labels = np.array([0, 2, 4, 0]) colors=[(1, 0, 0), (0, 0, 1)] rout = np.array([(1., 0., 0.), (0., 0., 1.), (1., 0., 0.), (1., 0., 0.)]) assert_close(rout, label2rgb(labels, colors=colors, alpha=1, image_alpha=1)) def test_label_consistency(): """Assert that the same labels map to the same colors.""" label_1 = np.arange(5).reshape(1, -1) label_2 = np.array([0, 1]) colors = [(1, 0, 0), (0, 1, 0), (0, 0, 1), (1, 1, 0), (1, 0, 1)] # Set alphas just in case the defaults change rgb_1 = label2rgb(label_1, colors=colors) rgb_2 = label2rgb(label_2, colors=colors) for label_id in label_2.flat: assert_close(rgb_1[label_1 == label_id], rgb_2[label_2 == label_id]) def test_leave_labels_alone(): labels = np.array([-1, 0, 1]) labels_saved = labels.copy() label2rgb(labels) label2rgb(labels, bg_label=1) assert_array_equal(labels, labels_saved) def test_avg(): # label image label_field = np.array([[1, 1, 1, 2], [1, 2, 2, 2], [3, 3, 3, 3]], dtype=np.uint8) # color image r = np.array([[1., 1., 0., 0.], [0., 0., 1., 1.], [0., 0., 0., 0.]]) g = np.array([[0., 0., 0., 1.], [1., 1., 1., 0.], [0., 0., 0., 0.]]) b = np.array([[0., 0., 0., 1.], [0., 1., 1., 1.], [0., 0., 1., 1.]]) image = np.dstack((r, g, b)) # reference label-colored image rout = np.array([[0.5, 0.5, 0.5, 0.5], [0.5, 0.5, 0.5, 0.5], [0. , 0. , 0. , 0. ]]) gout = np.array([[0.25, 0.25, 0.25, 0.75], [0.25, 0.75, 0.75, 0.75], [0. , 0. , 0. , 0. ]]) bout = np.array([[0. , 0. , 0. , 1. ], [0. , 1. , 1. , 1. ], [0.5, 0.5, 0.5, 0.5]]) expected_out = np.dstack((rout, gout, bout)) # test standard averaging out = label2rgb(label_field, image, kind='avg') assert_array_equal(out, expected_out) # test averaging with custom background value out_bg = label2rgb(label_field, image, bg_label=2, bg_color=(0, 0, 0), kind='avg') expected_out_bg = expected_out.copy() expected_out_bg[label_field == 2] = 0 assert_array_equal(out_bg, expected_out_bg) # test default background color out_bg = label2rgb(label_field, image, bg_label=2, kind='avg') assert_array_equal(out_bg, expected_out_bg) def test_negative_intensity(): labels = np.arange(100).reshape(10, 10) image = -1 * np.ones((10, 10)) assert_warns(UserWarning, label2rgb, labels, image) if __name__ == '__main__': testing.run_module_suite() scikit-image-0.13.1/skimage/color/tests/test_delta_e.py000066400000000000000000000113261316256236300230620ustar00rootroot00000000000000"""Test for correctness of color distance functions""" from os.path import abspath, dirname, join as pjoin import numpy as np from numpy.testing import assert_allclose from skimage.color import (deltaE_cie76, deltaE_ciede94, deltaE_ciede2000, deltaE_cmc) def test_ciede2000_dE(): data = load_ciede2000_data() N = len(data) lab1 = np.zeros((N, 3)) lab1[:, 0] = data['L1'] lab1[:, 1] = data['a1'] lab1[:, 2] = data['b1'] lab2 = np.zeros((N, 3)) lab2[:, 0] = data['L2'] lab2[:, 1] = data['a2'] lab2[:, 2] = data['b2'] dE2 = deltaE_ciede2000(lab1, lab2) assert_allclose(dE2, data['dE'], rtol=1.e-4) def load_ciede2000_data(): dtype = [('pair', int), ('1', int), ('L1', float), ('a1', float), ('b1', float), ('a1_prime', float), ('C1_prime', float), ('h1_prime', float), ('hbar_prime', float), ('G', float), ('T', float), ('SL', float), ('SC', float), ('SH', float), ('RT', float), ('dE', float), ('2', int), ('L2', float), ('a2', float), ('b2', float), ('a2_prime', float), ('C2_prime', float), ('h2_prime', float), ] # note: ciede_test_data.txt contains several intermediate quantities path = pjoin(dirname(abspath(__file__)), 'ciede2000_test_data.txt') return np.loadtxt(path, dtype=dtype) def test_cie76(): data = load_ciede2000_data() N = len(data) lab1 = np.zeros((N, 3)) lab1[:, 0] = data['L1'] lab1[:, 1] = data['a1'] lab1[:, 2] = data['b1'] lab2 = np.zeros((N, 3)) lab2[:, 0] = data['L2'] lab2[:, 1] = data['a2'] lab2[:, 2] = data['b2'] dE2 = deltaE_cie76(lab1, lab2) oracle = np.array([ 4.00106328, 6.31415011, 9.1776999, 2.06270077, 2.36957073, 2.91529271, 2.23606798, 2.23606798, 4.98000036, 4.9800004, 4.98000044, 4.98000049, 4.98000036, 4.9800004, 4.98000044, 3.53553391, 36.86800781, 31.91002977, 30.25309901, 27.40894015, 0.89242934, 0.7972, 0.8583065, 0.82982507, 3.1819238, 2.21334297, 1.53890382, 4.60630929, 6.58467989, 3.88641412, 1.50514845, 2.3237848, 0.94413208, 1.31910843 ]) assert_allclose(dE2, oracle, rtol=1.e-8) def test_ciede94(): data = load_ciede2000_data() N = len(data) lab1 = np.zeros((N, 3)) lab1[:, 0] = data['L1'] lab1[:, 1] = data['a1'] lab1[:, 2] = data['b1'] lab2 = np.zeros((N, 3)) lab2[:, 0] = data['L2'] lab2[:, 1] = data['a2'] lab2[:, 2] = data['b2'] dE2 = deltaE_ciede94(lab1, lab2) oracle = np.array([ 1.39503887, 1.93410055, 2.45433566, 0.68449187, 0.6695627, 0.69194527, 2.23606798, 2.03163832, 4.80069441, 4.80069445, 4.80069449, 4.80069453, 4.80069441, 4.80069445, 4.80069449, 3.40774352, 34.6891632, 29.44137328, 27.91408781, 24.93766082, 0.82213163, 0.71658427, 0.8048753, 0.75284394, 1.39099471, 1.24808929, 1.29795787, 1.82045088, 2.55613309, 1.42491303, 1.41945261, 2.3225685, 0.93853308, 1.30654464 ]) assert_allclose(dE2, oracle, rtol=1.e-8) def test_cmc(): data = load_ciede2000_data() N = len(data) lab1 = np.zeros((N, 3)) lab1[:, 0] = data['L1'] lab1[:, 1] = data['a1'] lab1[:, 2] = data['b1'] lab2 = np.zeros((N, 3)) lab2[:, 0] = data['L2'] lab2[:, 1] = data['a2'] lab2[:, 2] = data['b2'] dE2 = deltaE_cmc(lab1, lab2) oracle = np.array([ 1.73873611, 2.49660844, 3.30494501, 0.85735576, 0.88332927, 0.97822692, 3.50480874, 2.87930032, 6.5783807, 6.57838075, 6.5783808, 6.57838086, 6.67492321, 6.67492326, 6.67492331, 4.66852997, 42.10875485, 39.45889064, 38.36005919, 33.93663807, 1.14400168, 1.00600419, 1.11302547, 1.05335328, 1.42822951, 1.2548143, 1.76838061, 2.02583367, 3.08695508, 1.74893533, 1.90095165, 1.70258148, 1.80317207, 2.44934417 ]) assert_allclose(dE2, oracle, rtol=1.e-8) def test_single_color_cie76(): lab1 = (0.5, 0.5, 0.5) lab2 = (0.4, 0.4, 0.4) deltaE_cie76(lab1, lab2) def test_single_color_ciede94(): lab1 = (0.5, 0.5, 0.5) lab2 = (0.4, 0.4, 0.4) deltaE_ciede94(lab1, lab2) def test_single_color_ciede2000(): lab1 = (0.5, 0.5, 0.5) lab2 = (0.4, 0.4, 0.4) deltaE_ciede2000(lab1, lab2) def test_single_color_cmc(): lab1 = (0.5, 0.5, 0.5) lab2 = (0.4, 0.4, 0.4) deltaE_cmc(lab1, lab2) if __name__ == "__main__": from numpy.testing import run_module_suite run_module_suite() scikit-image-0.13.1/skimage/data/000077500000000000000000000000001316256236300165025ustar00rootroot00000000000000scikit-image-0.13.1/skimage/data/__init__.py000066400000000000000000000244051316256236300206200ustar00rootroot00000000000000# coding: utf-8 """Standard test images. For more images, see - http://sipi.usc.edu/database/database.php """ import os as _os import numpy as np from .. import data_dir from ..io import imread, use_plugin from .._shared._warnings import expected_warnings from ._binary_blobs import binary_blobs from .. import img_as_bool __all__ = ['load', 'astronaut', 'camera', 'checkerboard', 'chelsea', 'clock', 'coffee', 'coins', 'horse', 'hubble_deep_field', 'immunohistochemistry', 'logo', 'moon', 'page', 'text', 'rocket', 'stereo_motorcycle'] def load(f, as_grey=False): """Load an image file located in the data directory. Parameters ---------- f : string File name. as_grey : bool, optional Convert to greyscale. Returns ------- img : ndarray Image loaded from ``skimage.data_dir``. """ use_plugin('pil') return imread(_os.path.join(data_dir, f), as_grey=as_grey) def camera(): """Gray-level "camera" image. Often used for segmentation and denoising examples. Returns ------- camera : (512, 512) uint8 ndarray Camera image. """ return load("camera.png") def astronaut(): """Colour image of the astronaut Eileen Collins. Photograph of Eileen Collins, an American astronaut. She was selected as an astronaut in 1992 and first piloted the space shuttle STS-63 in 1995. She retired in 2006 after spending a total of 38 days, 8 hours and 10 minutes in outer space. This image was downloaded from the NASA Great Images database `__. No known copyright restrictions, released into the public domain. Returns ------- astronaut : (512, 512, 3) uint8 ndarray Astronaut image. """ return load("astronaut.png") def text(): """Gray-level "text" image used for corner detection. Notes ----- This image was downloaded from Wikipedia `__. No known copyright restrictions, released into the public domain. Returns ------- text : (172, 448) uint8 ndarray Text image. """ return load("text.png") def checkerboard(): """Checkerboard image. Checkerboards are often used in image calibration, since the corner-points are easy to locate. Because of the many parallel edges, they also visualise distortions particularly well. Returns ------- checkerboard : (200, 200) uint8 ndarray Checkerboard image. """ return load("chessboard_GRAY.png") def coins(): """Greek coins from Pompeii. This image shows several coins outlined against a gray background. It is especially useful in, e.g. segmentation tests, where individual objects need to be identified against a background. The background shares enough grey levels with the coins that a simple segmentation is not sufficient. Notes ----- This image was downloaded from the `Brooklyn Museum Collection `__. No known copyright restrictions. Returns ------- coins : (303, 384) uint8 ndarray Coins image. """ return load("coins.png") def logo(): """Scikit-image logo, a RGBA image. Returns ------- logo : (500, 500, 4) uint8 ndarray Logo image. """ return load("logo.png") def moon(): """Surface of the moon. This low-contrast image of the surface of the moon is useful for illustrating histogram equalization and contrast stretching. Returns ------- moon : (512, 512) uint8 ndarray Moon image. """ return load("moon.png") def page(): """Scanned page. This image of printed text is useful for demonstrations requiring uneven background illumination. Returns ------- page : (191, 384) uint8 ndarray Page image. """ return load("page.png") def horse(): """Black and white silhouette of a horse. This image was downloaded from `openclipart ` Released into public domain and drawn and uploaded by Andreas Preuss (marauder). Returns ------- horse : (328, 400) bool ndarray Horse image. """ with expected_warnings(['Possible precision loss', 'Possible sign loss']): return img_as_bool(load("horse.png", as_grey=True)) def clock(): """Motion blurred clock. This photograph of a wall clock was taken while moving the camera in an aproximately horizontal direction. It may be used to illustrate inverse filters and deconvolution. Released into the public domain by the photographer (Stefan van der Walt). Returns ------- clock : (300, 400) uint8 ndarray Clock image. """ return load("clock_motion.png") def immunohistochemistry(): """Immunohistochemical (IHC) staining with hematoxylin counterstaining. This picture shows colonic glands where the IHC expression of FHL2 protein is revealed with DAB. Hematoxylin counterstaining is applied to enhance the negative parts of the tissue. This image was acquired at the Center for Microscopy And Molecular Imaging (CMMI). No known copyright restrictions. Returns ------- immunohistochemistry : (512, 512, 3) uint8 ndarray Immunohistochemistry image. """ return load("ihc.png") def chelsea(): """Chelsea the cat. An example with texture, prominent edges in horizontal and diagonal directions, as well as features of differing scales. Notes ----- No copyright restrictions. CC0 by the photographer (Stefan van der Walt). Returns ------- chelsea : (300, 451, 3) uint8 ndarray Chelsea image. """ return load("chelsea.png") def coffee(): """Coffee cup. This photograph is courtesy of Pikolo Espresso Bar. It contains several elliptical shapes as well as varying texture (smooth porcelain to course wood grain). Notes ----- No copyright restrictions. CC0 by the photographer (Rachel Michetti). Returns ------- coffee : (400, 600, 3) uint8 ndarray Coffee image. """ return load("coffee.png") def hubble_deep_field(): """Hubble eXtreme Deep Field. This photograph contains the Hubble Telescope's farthest ever view of the universe. It can be useful as an example for multi-scale detection. Notes ----- This image was downloaded from `HubbleSite `__. The image was captured by NASA and `may be freely used in the public domain `_. Returns ------- hubble_deep_field : (872, 1000, 3) uint8 ndarray Hubble deep field image. """ return load("hubble_deep_field.jpg") def rocket(): """Launch photo of DSCOVR on Falcon 9 by SpaceX. This is the launch photo of Falcon 9 carrying DSCOVR lifted off from SpaceX's Launch Complex 40 at Cape Canaveral Air Force Station, FL. Notes ----- This image was downloaded from `SpaceX Photos `__. The image was captured by SpaceX and `released in the public domain `_. Returns ------- rocket : (427, 640, 3) uint8 ndarray Rocket image. """ return load("rocket.jpg") def stereo_motorcycle(): """Rectified stereo image pair with ground-truth disparities. The two images are rectified such that every pixel in the left image has its corresponding pixel on the same scanline in the right image. That means that both images are warped such that they have the same orientation but a horizontal spatial offset (baseline). The ground-truth pixel offset in column direction is specified by the included disparity map. The two images are part of the Middlebury 2014 stereo benchmark. The dataset was created by Nera Nesic, Porter Westling, Xi Wang, York Kitajima, Greg Krathwohl, and Daniel Scharstein at Middlebury College. A detailed description of the acquisition process can be found in [1]_. The images included here are down-sampled versions of the default exposure images in the benchmark. The images are down-sampled by a factor of 4 using the function `skimage.transform.downscale_local_mean`. The calibration data in the following and the included ground-truth disparity map are valid for the down-sampled images:: Focal length: 994.978px Principal point x: 311.193px Principal point y: 254.877px Principal point dx: 31.086px Baseline: 193.001mm Returns ------- img_left : (500, 741, 3) uint8 ndarray Left stereo image. img_right : (500, 741, 3) uint8 ndarray Right stereo image. disp : (500, 741, 3) float ndarray Ground-truth disparity map, where each value describes the offset in column direction between corresponding pixels in the left and the right stereo images. E.g. the corresponding pixel of ``img_left[10, 10 + disp[10, 10]]`` is ``img_right[10, 10]``. NaNs denote pixels in the left image that do not have ground-truth. Notes ----- The original resolution images, images with different exposure and lighting, and ground-truth depth maps can be found at the Middlebury website [2]_. References ---------- .. [1] D. Scharstein, H. Hirschmueller, Y. Kitajima, G. Krathwohl, N. Nesic, X. Wang, and P. Westling. High-resolution stereo datasets with subpixel-accurate ground truth. In German Conference on Pattern Recognition (GCPR 2014), Muenster, Germany, September 2014. .. [2] http://vision.middlebury.edu/stereo/data/scenes2014/ """ return (load("motorcycle_left.png"), load("motorcycle_right.png"), np.load(_os.path.join(data_dir, "motorcycle_disp.npz"))["arr_0"]) scikit-image-0.13.1/skimage/data/_binary_blobs.py000066400000000000000000000040171316256236300216620ustar00rootroot00000000000000import numpy as np from ..filters import gaussian def binary_blobs(length=512, blob_size_fraction=0.1, n_dim=2, volume_fraction=0.5, seed=None): """ Generate synthetic binary image with several rounded blob-like objects. Parameters ---------- length : int, optional Linear size of output image. blob_size_fraction : float, optional Typical linear size of blob, as a fraction of ``length``, should be smaller than 1. n_dim : int, optional Number of dimensions of output image. volume_fraction : float, default 0.5 Fraction of image pixels covered by the blobs (where the output is 1). Should be in [0, 1]. seed : int, optional Seed to initialize the random number generator. If `None`, a random seed from the operating system is used. Returns ------- blobs : ndarray of bools Output binary image Examples -------- >>> from skimage import data >>> data.binary_blobs(length=5, blob_size_fraction=0.2, seed=1) array([[ True, False, True, True, True], [ True, True, True, False, True], [False, True, False, True, True], [ True, False, False, True, True], [ True, False, False, False, True]], dtype=bool) >>> blobs = data.binary_blobs(length=256, blob_size_fraction=0.1) >>> # Finer structures >>> blobs = data.binary_blobs(length=256, blob_size_fraction=0.05) >>> # Blobs cover a smaller volume fraction of the image >>> blobs = data.binary_blobs(length=256, volume_fraction=0.3) """ rs = np.random.RandomState(seed) shape = tuple([length] * n_dim) mask = np.zeros(shape) n_pts = max(int(1. / blob_size_fraction) ** n_dim, 1) points = (length * rs.rand(n_dim, n_pts)).astype(np.int) mask[[indices for indices in points]] = 1 mask = gaussian(mask, sigma=0.25 * length * blob_size_fraction) threshold = np.percentile(mask, 100 * (1 - volume_fraction)) return np.logical_not(mask < threshold) scikit-image-0.13.1/skimage/data/_blobs_3d_fiji_skeleton.tif000066400000000000000000001047351316256236300237530ustar00rootroot00000000000000MM*   - ƖƗPG{ImageJ=1.50e images=30 slices=30 loop=false IJIJlablim3_0.pngim3_1.pngim3_2.pngim3_3.pngim3_4.pngim3_5.pngim3_6.pngim3_7.pngim3_8.pngim3_9.pngim3_10.pngim3_11.pngim3_12.pngim3_13.pngim3_14.pngim3_15.pngim3_16.pngim3_17.pngim3_18.pngim3_19.pngim3_20.pngim3_21.pngim3_22.pngim3_23.pngim3_24.pngim3_25.pngim3_26.pngim3_27.pngim3_28.pngim3_29.png   - |   -  |   - }   - }   - ~   - ~   -    -#    -'    -+    -/    -3    -7    -; {   -?    -C w   -G    -K s   -O    -S o   -W    -[ k   -_    -c g   -g    -k c   -o    -s _   -w scikit-image-0.13.1/skimage/data/astronaut.png000066400000000000000000030120031316256236300212270ustar00rootroot00000000000000PNG  IHDR{C `iCCPICC profilexH HLinomntrRGB XYZ  1acspMSFTIEC sRGB-HP cprtP3desclwtptbkptrXYZgXYZ,bXYZ@dmndTpdmddvuedLview$lumimeas $tech0 rTRC< gTRC< bTRC< textCopyright (c) 1998 Hewlett-Packard CompanydescsRGB IEC61966-2.1sRGB IEC61966-2.1XYZ QXYZ XYZ o8XYZ bXYZ $descIEC http://www.iec.chIEC http://www.iec.chdesc.IEC 61966-2.1 Default RGB colour space - sRGB.IEC 61966-2.1 Default RGB colour space - sRGBdesc,Reference Viewing Condition in IEC61966-2.1,Reference Viewing Condition in IEC61966-2.1view_. \XYZ L VPWmeassig CRT curv #(-27;@EJOTY^chmrw| %+28>ELRY`gnu| &/8AKT]gqz !-8COZfr~ -;HUcq~ +:IXgw'7HYj{+=Oat 2FZn  % : O d y  ' = T j " 9 Q i  * C \ u & @ Z t .Id %A^z &Ca~1Om&Ed#Cc'Ij4Vx&IlAe@e Ek*Qw;c*R{Gp@j>i  A l !!H!u!!!"'"U"""# #8#f###$$M$|$$% %8%h%%%&'&W&&&''I'z''( (?(q(())8)k))**5*h**++6+i++,,9,n,,- -A-v--..L.../$/Z///050l0011J1112*2c223 3F3334+4e4455M555676r667$7`7788P8899B999:6:t::;-;k;;<' >`>>?!?a??@#@d@@A)AjAAB0BrBBC:C}CDDGDDEEUEEF"FgFFG5G{GHHKHHIIcIIJ7J}JK KSKKL*LrLMMJMMN%NnNOOIOOP'PqPQQPQQR1R|RSS_SSTBTTU(UuUVV\VVWDWWX/X}XYYiYZZVZZ[E[[\5\\]']x]^^l^__a_``W``aOaabIbbcCccd@dde=eef=ffg=ggh?hhiCiijHjjkOkklWlmm`mnnknooxop+ppq:qqrKrss]sttptu(uuv>vvwVwxxnxy*yyzFz{{c{|!||}A}~~b~#G k͂0WGrׇ;iΉ3dʋ0cʍ1fΏ6n֑?zM _ɖ4 uL$h՛BdҞ@iءG&vVǥ8nRĩ7u\ЭD-u`ֲK³8%yhYѹJº;.! zpg_XQKFAǿ=ȼ:ɹ8ʷ6˶5̵5͵6ζ7ϸ9к<Ѿ?DINU\dlvۀ܊ݖޢ)߯6DScs 2F[p(@Xr4Pm8Ww)Km]< pHYs.#.#x?vtIME  ,tEXtCommentFile written by Adobe Photoshop? 5.0PB> IDATx{    *$ !"     $/                                     zuv                                b IDAT      usp                                        spl            m IDAT    plh    #             sqm   $"          wst     #        "                               B,V IDAT                                        #    "                           $                     ""$ .          $              *).9       /w IDAT           #0'      {                                         (*+             %                               *+"            ,   )                           kV IDAT  '                        .    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scikit-image-0.13.1/skimage/data/multi.fits000066400000000000000000010206001316256236300205220ustar00rootroot00000000000000SIMPLE = T / Fits standard BITPIX = 16 / Bits per pixel NAXIS = 0 / Number of axes EXTEND = T / File may contain extensions ORIGIN = 'NOAO-IRAF FITS Image Kernel July 2003' / FITS file originator DATE = '2001-09-10' / Date FITS file was generated IRAF-TLM= '2010-07-02T19:33:03' / Time of last modification COMMENT FITS (Flexible Image Transport System) format defined in Astronomy andCOMMENT Astrophysics Supplement Series v44/p363, v44/p371, v73/p359, v73/p365.COMMENT Contact the NASA Science Office of Standards and Technology for the COMMENT FITS Definition document #100 and other FITS information. INSTRUME= 'GMOS-N ' / Instrument used to acquire data OBJECT = 'NGC1068 ' / Object Name OBSTYPE = 'SCI ' / Observation type GEMPRGID= 'GN-ENG20010910' / Gemini programme ID OBSID = 'GN-ENG20010910-15' / Observation ID / Data label DATALAB = 'GN-ENG20010910-15-001' / DHS data label OBSERVER= 'Jorgensen' / Observer OBSERVAT= 'Gemini-North' / Name of telescope (Gemini-North|Gemini-South) TELESCOP= 'Gemini-North' / Gemini-North PARALLAX= 0. / Parallax of Target RADVEL = 0. / Heliocentric Radial Velocity EPOCH = 2000. / Epoch for Target coordinates EQUINOX = 2000. / Equinox of coordinate system TRKEQUIN= 2000. / Tracking equinox SSA = 'Walther ' / SSA RA = 40.66966667 / Right Ascension DEC = -0.01316667 / Declination of Target ELEVATIO= 0. / Current Elevation AZIMUTH = -135.173831944444 / Current Azimuth CRPA = 0. / Current Cass Rotator Position Angle HA = '+01:14:25.87' / Telescope hour angle LT = '05:00:13.7' / Local time at start of observation TRKFRAME= 'FK5 ' / Tracking co-ordinate DECTRACK= 0. / Differential tracking rate Dec TRKEPOCH= 0. / Differential tracking reference epoch RATRACK = 0. / Differential tracking rate RA FRAME = 'FK5 ' / Target coordinate system PMDEC = 0. / Proper Motion in Declination PMRA = 0. / Proper Motion in RA WAVELENG= 4750. / Effective Target Wavelength RAWIQ = '80-percentile' / Raw Image Quality RAWCC = '50-percentile' / Raw Cloud Cover RAWWV = 'Any ' / Raw Water Vapour/Transparency RAWBG = '80-percentile' / Raw Background RAWPIREQ= 'NO ' / PI Requirements Met RAWGEMQA= 'USABLE ' / Gemini Quality Assessment CGUIDMOD= 'Basic ' / Carousel Mode UT = '15:00:14.2' / UT at observation start CONID = 'GMOS v8.0' / Detector controller ID DETECTOR= 'GMOS + Red1' / Detector name DEWAR = 'Cryostat A' / Dewar name DEW-TEMP= -121.220657276995 / Dewar temperature (Celsius) DETSIZE = '[1:6144,1:4644]' / Detector size NCCDS = 3 / Number of CCD chips NAMPS = 1 / Number of amplifiers SHUTTER = 'OPEN ' / Shutter state during observation AMPINTEG= 5000 / Amplifier integration time TIMESYS = 'UT1 ' / Time system used DATE-OBS= '2001-09-10' / UT Date of observation (YYYY-MM-DD) TIME-OBS= '15:01:11.5' / Time of observation GMOSTHT = 'GOOD ' / Shutter health NSUBEXP = 1 / Number of sub exposures UTSTART = '15:01:11.5' / UT at observation start UTEND = '15:02:11.5' / UT at observation end EXPTIME = 60.0029079914093 / Exposure time in seconds ELAPSED = 60.0029079914093 / Elapsed observation time in seconds DARKTIME= 96.9185329675674 / Dark current integration in seconds INPORT = 1 / Number of ISS port where GMOS was located GMOSCC = 'GMOS-N (V2-8)' / GMOS components controller s/w MASKID = -99 / Mask/IFU barcode MASKNAME= 'IFU ' / Mask name MASKTYP = 0 / Mask/IFU type (0=none/-1=IFU/1=mask) MASKLOC = 1 / Mask/IFU location (-1=unknown/0=FP/1=cassette) FILTER1 = 'open1-6 ' / Filter 1 name FILTID1 = 20000018 / Filter 1 barcode FILTER2 = 'g_G0301 ' / Filter 2 name FILTID2 = 20000018 / Filter 2 barcode GRATING = 'MIRROR ' / Grating name GRATID = 30000008 / Grating barcode GRWLEN = 0. / Grating central wavelength (nm) GRORDER = 0 / Grating order GRTILT = 65. / Grating tilt angle (degrees) DTAX = 103.2 / Detector translation X position (microns) DTAY = 56.64 / Detector translation Y position (microns) DTAZ = 2842. / Detector translation Z position (microns) DTAZST = 0. / Focus at observation start (microns) DTAZEN = 0. / Focus at observation end (microns) DTAZME = 0. / Mean focus during observation (microns) DTMODE = 'FOLLOW-XY' / Detector translation stage mode ADCMODE = ' ' / ADC mode GMOSDC = 'GMOS DC ' / GMOS detector controller s/w DETTYPE = 'SDSU II CCD' / Detector array type DETID = 'EEV9273-16-03EEV9273-20-04EEV9273-20-03' / Chip IDs EXPOSURE= 60. / Requested exposure time in seconds ADCUSED = 1 / ADC used? (1=yes/0=no) DETNROI = 1 / No. regions of interest ADCENPST= 0. / Start entrance prism angle of ADC ADCENPEN= 0. / End entrance prism angle of ADC ADCENPME= 0. / Mean entrance prism angle of ADC ADCEXPST= 0. / Start exit prism angle of ADC ADCEXPEN= 0. / End exit prism angle of ADC ADCEXPME= 0. / Mean exit prism angle of ADC ADCWLEN1= 0. / Lower wavelength for ADC calculation ADCWLEN2= 0. / Upper wavelength for ADC calculation DETRO1X = 1 / ROI 1 X start DETRO1XS= 3072 / ROI 1 X size DETRO1Y = 1 / ROI 1 Y start DETRO1YS= 2304 / ROI 1 Y size AIRMASS = 0. / Airmass averaged throughout the exposure AMSTART = 0. / Airmass at start of exposure AMEND = 0. / Airmass at end of exposure NSCIEXT = 3 / Number of science extensions PIXSCALE= 0.1454 / Pixel scale in Y in arcsec/pixel NEXTEND = 3 / Number of extensions OBSMODE = 'IMAGE ' / Observing mode (IMAGE|IFU|MOS|LONGSLIT) GEM-TLM = '2010-07-02T20:32:41' / UT Last modification with GEMINI GPREPARE= '2010-07-02T20:32:41' / UT Time stamp for GPREPARE END XTENSION= 'IMAGE ' / Image extension BITPIX = 16 / Bits per pixel NAXIS = 2 / Number of axes NAXIS1 = 132 / Axis length NAXIS2 = 288 / Axis length PCOUNT = 0 / No 'random' parameters GCOUNT = 1 / Only one group BSCALE = 1.000000E0 / REAL = TAPE*BSCALE + BZERO BZERO = 3.276800E4 / EXTNAME = 'SCI ' / Extension name EXTVER = 1 / Extension version INHERIT = F / Inherits global header ORIGIN = 'NOAO-IRAF FITS Image Kernel July 2003' / FITS file originator DATE = '2010-07-02T19:55:22' / Date FITS file was generated IRAF-TLM= '2010-07-02T19:59:26' / Time of last modification CTYPE1 = 'RA---TAN' / R.A. in tangent plane projection CRPIX1 = 196.375233233945 / Ref pix of axis 1 CRVAL1 = 306.871725219222 / RA at Ref pix in decimal degrees CTYPE2 = 'DEC--TAN' / DEC. in tangent plane projection CRPIX2 = 143.509119025456 / Ref pix of axis 2 CRVAL2 = 37.3733845984976 / DEC at Ref pix in decimal degrees CD1_1 = -2.9266657155837E-4 / WCS matrix element 1 1 CD1_2 = 1.36727521936194E-4 / WCS matrix element 1 2 CD2_1 = 1.36706238220991E-4 / WCS matrix element 2 1 CD2_2 = 2.93036096725254E-4 / WCS matrix element 2 2 RADECSYS= 'FK5 ' / R.A./DEC. coordinate system reference EQUINOX = 2000. / Equinox of coordinate system MJD-OBS = 52162.6261539214 / MJD of start of obseration CCDNAME = 'EEV 9273-16-03' / CCD chip name(s) AMPNAME = 'EEV 9273-16-03, right' / Amplifier name(s) GAIN = 2.1 / Amplifier gain RDNOISE = 3.5 / Readout noise CCDSIZE = '[1:2048,1:4644]' / CCD chip size CCDSUM = '2 2 ' / CCD sum CCDSEC = '[1:2048,1:4608]' / CCD section(s) DETSEC = '[1:2048,1:4608]' / Detector section(s) DATASEC = '[33:1056,1:2304]' / Data section(s) BIASSEC = '[1:32,1:2304]' / Bias section(s) FRMNAME = 'dhsFrame' / Name assigned to a frame of data. 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/ Axis length NAXIS2 = 288 / Axis length PCOUNT = 0 / No 'random' parameters GCOUNT = 1 / Only one group BSCALE = 1.000000E0 / REAL = TAPE*BSCALE + BZERO BZERO = 3.276800E4 / EXTNAME = 'SCI ' / Extension name EXTVER = 2 / Extension version INHERIT = F / Inherits global header ORIGIN = 'NOAO-IRAF FITS Image Kernel July 2003' / FITS file originator DATE = '2010-07-02T19:55:22' / Date FITS file was generated IRAF-TLM= '2010-07-02T19:59:26' / Time of last modification CTYPE1 = 'RA---TAN' / R.A. in tangent plane projection CRPIX1 = 65.7568549170406 / Ref pix of axis 1 CRVAL1 = 306.871725219222 / RA at Ref pix in decimal degrees CTYPE2 = 'DEC--TAN' / DEC. in tangent plane projection CRPIX2 = 143.564898774826 / Ref pix of axis 2 CRVAL2 = 37.3733845984976 / DEC at Ref pix in decimal degrees CD1_1 = -2.9278046959101E-4 / WCS matrix element 1 1 CD1_2 = 1.37000324401069E-4 / WCS matrix element 1 2 CD2_1 = 1.36969765153489E-4 / WCS matrix element 2 1 CD2_2 = 2.93069759666565E-4 / WCS matrix element 2 2 RADECSYS= 'FK5 ' / R.A./DEC. coordinate system reference EQUINOX = 2000. / Equinox of coordinate system MJD-OBS = 52162.6261539214 / MJD of start of obseration CCDNAME = 'EEV 9273-20-04' / CCD chip name(s) AMPNAME = 'EEV 9273-20-04, right' / Amplifier name(s) GAIN = 2.337 / Amplifier gain RDNOISE = 3.3 / Readout noise CCDSIZE = '[1:2048,1:4644]' / CCD chip size CCDSUM = '2 2 ' / CCD sum CCDSEC = '[1:2048,1:4608]' / CCD section(s) DETSEC = '[2049:4096,1:4608]' / Detector section(s) DATASEC = '[33:1056,1:2304]' / Data section(s) BIASSEC = '[1:32,1:2304]' / Bias section(s) FRMNAME = 'dhsFrame' / Name assigned to a frame of data. 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  z| "#$%')**,.//036:977898898653457;?ABCDDEFGFEEFFGGFFGFCBBDDCCEEEECCDEDA=:730.-,+)'%# ܅υƅ…ą…ƅʅĂz|˅    %+/246:>AA?>=<;:98777642110010/.,*(" ۅօх̅ȅƅ…~oc\XSLFCBCIRWVTTUX[Xz|w؅υ҅҅҅҅҅Ӆԅօ؅ۅ݅ޅ$+1442.*&" ߅Ѕytuvqib\VPLGA;6/$݄؄Ԅ΄Ƅ˄Єфτ΄Є҄Մтz|iy………ÅąŅŅƅDžʅ̅ͅЅׅ ׅυȅwfRB81) ބք΄Ƅ~m\MEB>6.)&%'.7;;98:?D@z{YGmnmllnopqsuwz|}}ɅЅօڅۅօυƅtg\PE<4.*&! ل„}naWRRSQG<3,%  σz{Kn>@?>>@BABDFHLNNNPSUVXYYY[\\]_^\[YWUTSSTW\dlu}|rj`SC0 Մʄ{lX?.&؃σ˃˃˃ƃyqeVG8,&$  !)'XTENSION= 'IMAGE ' / Image extension BITPIX = 16 / Bits per pixel NAXIS = 2 / Number of axes NAXIS1 = 132 / Axis length NAXIS2 = 288 / Axis length PCOUNT = 0 / No 'random' parameters GCOUNT = 1 / Only one group BSCALE = 1.000000E0 / REAL = TAPE*BSCALE + BZERO BZERO = 3.276800E4 / EXTNAME = 'SCI ' / Extension name EXTVER = 3 / Extension version INHERIT = F / Inherits global header ORIGIN = 'NOAO-IRAF FITS Image Kernel July 2003' / FITS file originator DATE = '2010-07-02T19:55:23' / Date FITS file was generated IRAF-TLM= '2010-07-02T19:59:26' / Time of last modification CTYPE1 = 'RA---TAN' / R.A. in tangent plane projection CRPIX1 = -66.6459346493144 / Ref pix of axis 1 CRVAL1 = 306.871725219222 / RA at Ref pix in decimal degrees CTYPE2 = 'DEC--TAN' / DEC. in tangent plane projection CRPIX2 = 143.15396209091 / Ref pix of axis 2 CRVAL2 = 37.3733845984976 / DEC at Ref pix in decimal degrees CD1_1 = -2.9402995676385E-4 / WCS matrix element 1 1 CD1_2 = 1.36706049992071E-4 / WCS matrix element 1 2 CD2_1 = 1.37399803289694E-4 / WCS matrix element 2 1 CD2_2 = 2.93132453649777E-4 / WCS matrix element 2 2 RADECSYS= 'FK5 ' / R.A./DEC. coordinate system reference EQUINOX = 2000. / Equinox of coordinate system MJD-OBS = 52162.6261539214 / MJD of start of obseration CCDNAME = 'EEV 9273-20-03' / CCD chip name(s) AMPNAME = 'EEV 9273-20-03, left' / Amplifier name(s) GAIN = 2.3 / Amplifier gain RDNOISE = 3.0 / Readout noise CCDSIZE = '[1:2048,1:4644]' / CCD chip size CCDSUM = '2 2 ' / CCD sum CCDSEC = '[1:2048,1:4608]' / CCD section(s) DETSEC = '[4097:6144,1:4608]' / Detector section(s) DATASEC = '[1:1024,1:2304]' / Data section(s) BIASSEC = '[1025:1056,1:2304]' / Bias section(s) FRMNAME = 'dhsFrame' / Name assigned to a frame of data. 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LKC`⪻9i:q֤+/sZ~b:w!':ۧ*m4}ܹ4JW˷x[/堠W e? u%:`Z<2QM_[ AzY-3hl!3 N?+4sW4$H$šh'I7x_}{ IENDB`scikit-image-0.13.1/skimage/data/simple.fits000066400000000000000000004160001316256236300206630ustar00rootroot00000000000000SIMPLE = T / Fits standard BITPIX = 16 / Bits per pixel NAXIS = 2 / Number of axes NAXIS1 = 256 / Axis length NAXIS2 = 256 / Axis length EXTEND = F / File may contain extensions ORIGIN = 'NOAO-IRAF FITS Image Kernel July 2003' / FITS file originator DATE = '2010-07-02T15:10:48' / Date FITS file was generated IRAF-TLM= '2010-07-02T15:10:48' / Time of last modification OBJECT = 'm51 B 600s' / Name of the object observed IRAF-MAX= 1.993600E4 / DATA MAX IRAF-MIN= -1.000000E0 / DATA MIN CCDPICNO= 53 / ORIGINAL CCD PICTURE NUMBER ITIME = 600 / REQUESTED INTEGRATION TIME (SECS) TTIME = 600 / TOTAL ELAPSED TIME (SECS) OTIME = 600 / ACTUAL INTEGRATION TIME (SECS) DATA-TYP= 'OBJECT (0)' / OBJECT,DARK,BIAS,ETC. DATE-OBS= '05/04/87' / DATE DD/MM/YY RA = '13:29:24.00' / RIGHT ASCENSION DEC = '47:15:34.00' / DECLINATION EPOCH = 0.00 / EPOCH OF RA AND DEC ZD = '22:14:00.00' / ZENITH DISTANCE UT = ' 9:27:27.00' / UNIVERSAL TIME ST = '14:53:42.00' / SIDEREAL TIME CAM-ID = 1 / CAMERA HEAD ID CAM-TEMP= -106.22 / CAMERA TEMPERATURE, DEG C DEW-TEMP= -180.95 / DEWAR TEMPRATURE, DEG C F1POS = 2 / FILTER BOLT I POSITION F2POS = 0 / FILTER BOLT II POSITION TVFILT = 0 / TV FILTER CMP-LAMP= 0 / COMPARISON LAMP TILT-POS= 0 / TILT POSITION BIAS-PIX= 0 / BI-FLAG = 0 / BIAS SUBTRACT FLAG BP-FLAG = 0 / BAD PIXEL FLAG CR-FLAG = 0 / BAD PIXEL FLAG DK-FLAG = 0 / DARK SUBTRACT FLAG FR-FLAG = 0 / FRINGE FLAG FR-SCALE= 0.00 / FRINGE SCALING PARAMETER TRIM = 'Apr 22 14:11 Trim image section is [3:510,3:510]' BT-FLAG = 'Apr 22 14:11 Overscan correction strip is [515:544,3:510]' FF-FLAG = 'Apr 22 14:11 Flat field image is Flat1.imh with scale=183.9447' CCDPROC = 'Apr 22 14:11 CCD processing done' AIRMASS = 1.08015632629395 / AIRMASS HISTORY 'KPNO-IRAF' HISTORY '24-04-87' HISTORY 'KPNO-IRAF' / HISTORY '08-04-92' / WCSDIM = 2 LTV1 = 0.25 LTV2 = 0.25 LTM1_1 = 0.5 LTM2_2 = 0.5 WAT0_001= 'system=physical' WAT1_001= 'wtype=linear' WAT2_001= 'wtype=linear' END 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..._shared.testing import setup_test, teardown_test def setup(): setup_test() def teardown(): teardown_test() scikit-image-0.13.1/skimage/data/tests/test_data.py000066400000000000000000000046501316256236300221730ustar00rootroot00000000000000import numpy as np import skimage.data as data from numpy.testing import assert_equal, assert_almost_equal def test_astronaut(): """ Test that "astronaut" image can be loaded. """ astronaut = data.astronaut() assert_equal(astronaut.shape, (512, 512, 3)) def test_camera(): """ Test that "camera" image can be loaded. """ cameraman = data.camera() assert_equal(cameraman.ndim, 2) def test_checkerboard(): """ Test that "checkerboard" image can be loaded. """ data.checkerboard() def test_chelsea(): """ Test that "chelsea" image can be loaded. """ data.chelsea() def test_clock(): """ Test that "clock" image can be loaded. """ data.clock() def test_coffee(): """ Test that "coffee" image can be loaded. """ data.coffee() def test_horse(): """ Test that "horse" image can be loaded. """ horse = data.horse() assert_equal(horse.ndim, 2) assert_equal(horse.dtype, np.dtype('bool')) def test_hubble(): """ Test that "Hubble" image can be loaded. """ data.hubble_deep_field() def test_immunohistochemistry(): """ Test that "immunohistochemistry" image can be loaded. """ data.immunohistochemistry() def test_logo(): """ Test that "logo" image can be loaded. """ logo = data.logo() assert_equal(logo.ndim, 3) assert_equal(logo.shape[2], 4) def test_moon(): """ Test that "moon" image can be loaded. """ data.moon() def test_page(): """ Test that "page" image can be loaded. """ data.page() def test_rocket(): """ Test that "rocket" image can be loaded. """ data.rocket() def test_text(): """ Test that "text" image can be loaded. """ data.text() def test_stereo_motorcycle(): """ Test that "stereo_motorcycle" image can be loaded. """ data.stereo_motorcycle() def test_binary_blobs(): blobs = data.binary_blobs(length=128) assert_almost_equal(blobs.mean(), 0.5, decimal=1) blobs = data.binary_blobs(length=128, volume_fraction=0.25) assert_almost_equal(blobs.mean(), 0.25, decimal=1) blobs = data.binary_blobs(length=32, volume_fraction=0.25, n_dim=3) assert_almost_equal(blobs.mean(), 0.25, decimal=1) other_realization = data.binary_blobs(length=32, volume_fraction=0.25, n_dim=3) assert not np.all(blobs == other_realization) if __name__ == "__main__": from numpy.testing import run_module_suite run_module_suite() scikit-image-0.13.1/skimage/data/text.png000066400000000000000000001233201316256236300201750ustar00rootroot00000000000000PNG  IHDR#:IDATxuߒ =Q1\[}ſuvf:O>hѠB  sHHH4cۙveUoY1"ƻRX\sy璄8s@`+y0"3pKy@$ewaŮbt2wv0RӘ J bȵaXNn:uvCwظnuaRGeҏmy& (қQAbvuѩ!n%eb?ٸjS.O)܍qAlP"SpĮw݂!B{"")~li9|fE8Qu24Gn,ypB!!e¸I2 (=gk\K@&0e@% 8"N!awev3#Xq8XQM~zO/7v@څH;g3$DrpUdAֳ 9z-Vs)rO&D J +Z[Jȃb EHa` *aN5zb]92_S#+ÜbE.J't"jN<s@%';8l%oZBݭt$y &@޺aw^~9[5*a*y Ub=?hsJ2~A0S[g::n=}PzDD0dG\e$DUVXSHL0:wwgI*fd0&Vw@N{2818V''$"w):\ d'&k >(Yѕ53=pA[vk0oV t K nM.kBҞ NR5GADh!a^ֱxb0/H܈0~&uLnrFGo +x'3g7LI"l:=Kٚo4'mu1KhM>`0SI=V9H"GG 9<+̹s8X=:ؕj>pC<`Uxr *8 3RTBXxU)dK!Fagh6qe + ڬ:°iD\@nDL! 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Parameters ---------- rr, cc : (N,) ndarray of int Indices of pixels. shape : tuple Image shape which is used to determine the maximum extent of output pixel coordinates. val : (N, D) ndarray of float, optional Values of pixels at coordinates ``[rr, cc]``. Returns ------- rr, cc : (M,) array of int Row and column indices of valid pixels (i.e. those inside `shape`). val : (M, D) array of float, optional Values at `rr, cc`. Returned only if `val` is given as input. """ mask = (rr >= 0) & (rr < shape[0]) & (cc >= 0) & (cc < shape[1]) if val is None: return rr[mask], cc[mask] else: return rr[mask], cc[mask], val[mask] def _line(Py_ssize_t r0, Py_ssize_t c0, Py_ssize_t r1, Py_ssize_t c1): """Generate line pixel coordinates. Parameters ---------- r0, c0 : int Starting position (row, column). r1, c1 : int End position (row, column). Returns ------- rr, cc : (N,) ndarray of int Indices of pixels that belong to the line. May be used to directly index into an array, e.g. ``img[rr, cc] = 1``. See Also -------- line_aa : Anti-aliased line generator """ cdef char steep = 0 cdef Py_ssize_t r = r0 cdef Py_ssize_t c = c0 cdef Py_ssize_t dr = abs(r1 - r0) cdef Py_ssize_t dc = abs(c1 - c0) cdef Py_ssize_t sr, sc, d, i with nogil: if (c1 - c) > 0: sc = 1 else: sc = -1 if (r1 - r) > 0: sr = 1 else: sr = -1 if dr > dc: steep = 1 c, r = r, c dc, dr = dr, dc sc, sr = sr, sc d = (2 * dr) - dc cdef Py_ssize_t[::1] rr = np.zeros(int(dc) + 1, dtype=np.intp) cdef Py_ssize_t[::1] cc = np.zeros(int(dc) + 1, dtype=np.intp) with nogil: for i in range(dc): if steep: rr[i] = c cc[i] = r else: rr[i] = r cc[i] = c while d >= 0: r = r + sr d = d - (2 * dc) c = c + sc d = d + (2 * dr) rr[dc] = r1 cc[dc] = c1 return np.asarray(rr), np.asarray(cc) def _line_aa(Py_ssize_t r0, Py_ssize_t c0, Py_ssize_t r1, Py_ssize_t c1): """Generate anti-aliased line pixel coordinates. Parameters ---------- r0, c0 : int Starting position (row, column). r1, c1 : int End position (row, column). Returns ------- rr, cc, val : (N,) ndarray (int, int, float) Indices of pixels (`rr`, `cc`) and intensity values (`val`). ``img[rr, cc] = val``. References ---------- .. [1] A Rasterizing Algorithm for Drawing Curves, A. Zingl, 2012 http://members.chello.at/easyfilter/Bresenham.pdf """ cdef list rr = list() cdef list cc = list() cdef list val = list() cdef int dc = abs(c0 - c1) cdef int dc_prime cdef int dr = abs(r0 - r1) cdef float err = dc - dr cdef float err_prime cdef int c, r, sign_c, sign_r cdef float ed if c0 < c1: sign_c = 1 else: sign_c = -1 if r0 < r1: sign_r = 1 else: sign_r = -1 if dc + dr == 0: ed = 1 else: ed = sqrt(dc*dc + dr*dr) c, r = c0, r0 while True: cc.append(c) rr.append(r) val.append(fabs(err - dc + dr) / ed) err_prime = err c_prime = c if (2 * err_prime) >= -dc: if c == c1: break if (err_prime + dr) < ed: cc.append(c) rr.append(r + sign_r) val.append(fabs(err_prime + dr) / ed) err -= dr c += sign_c if 2 * err_prime <= dr: if r == r1: break if (dc - err_prime) < ed: cc.append(c_prime + sign_c) rr.append(r) val.append(fabs(dc - err_prime) / ed) err += dc r += sign_r return (np.array(rr, dtype=np.intp), np.array(cc, dtype=np.intp), 1. - np.array(val, dtype=np.float)) def _polygon(r, c, shape): """Generate coordinates of pixels within polygon. Parameters ---------- r : (N,) ndarray Row coordinates of vertices of polygon. c : (N,) ndarray Column coordinates of vertices of polygon. shape : tuple Image shape which is used to determine the maximum extent of output pixel coordinates. This is useful for polygons that exceed the image size. If None, the full extent of the polygon is used. Returns ------- rr, cc : ndarray of int Pixel coordinates of polygon. May be used to directly index into an array, e.g. ``img[rr, cc] = 1``. """ r = np.asanyarray(r) c = np.asanyarray(c) cdef Py_ssize_t nr_verts = c.shape[0] cdef Py_ssize_t minr = int(max(0, r.min())) cdef Py_ssize_t maxr = int(ceil(r.max())) cdef Py_ssize_t minc = int(max(0, c.min())) cdef Py_ssize_t maxc = int(ceil(c.max())) # make sure output coordinates do not exceed image size if shape is not None: maxr = min(shape[0] - 1, maxr) maxc = min(shape[1] - 1, maxc) cdef Py_ssize_t r_i, c_i # make contigous arrays for r, c coordinates cdef cnp.ndarray contiguous_rdata, contiguous_cdata contiguous_rdata = np.ascontiguousarray(r, dtype=np.double) contiguous_cdata = np.ascontiguousarray(c, dtype=np.double) cdef cnp.double_t* rptr = contiguous_rdata.data cdef cnp.double_t* cptr = contiguous_cdata.data # output coordinate arrays cdef list rr = list() cdef list cc = list() for r_i in range(minr, maxr+1): for c_i in range(minc, maxc+1): if point_in_polygon(nr_verts, cptr, rptr, c_i, r_i): rr.append(r_i) cc.append(c_i) return np.array(rr, dtype=np.intp), np.array(cc, dtype=np.intp) def _circle_perimeter(Py_ssize_t r_o, Py_ssize_t c_o, Py_ssize_t radius, method, shape): """Generate circle perimeter coordinates. Parameters ---------- r_o, c_o : int Centre coordinate of circle. radius : int Radius of circle. method : {'bresenham', 'andres'} bresenham : Bresenham method (default) andres : Andres method shape : tuple Image shape which is used to determine the maximum extent of output pixel coordinates. This is useful for circles that exceed the image size. If None, the full extent of the circle is used. Returns ------- rr, cc : (N,) ndarray of int Bresenham and Andres' method: Indices of pixels that belong to the circle perimeter. May be used to directly index into an array, e.g. ``img[rr, cc] = 1``. Notes ----- Andres method presents the advantage that concentric circles create a disc whereas Bresenham can make holes. There is also less distortions when Andres circles are rotated. Bresenham method is also known as midpoint circle algorithm. Anti-aliased circle generator is available with `circle_perimeter_aa`. References ---------- .. [1] J.E. Bresenham, "Algorithm for computer control of a digital plotter", IBM Systems journal, 4 (1965) 25-30. .. [2] E. Andres, "Discrete circles, rings and spheres", Computers & Graphics, 18 (1994) 695-706. """ cdef list rr = list() cdef list cc = list() cdef Py_ssize_t c = 0 cdef Py_ssize_t r = radius cdef Py_ssize_t d = 0 cdef double dceil = 0 cdef double dceil_prev = 0 cdef char cmethod if method == 'bresenham': d = 3 - 2 * radius cmethod = 'b' elif method == 'andres': d = radius - 1 cmethod = 'a' else: raise ValueError('Wrong method') while r >= c: rr.extend([r, -r, r, -r, c, -c, c, -c]) cc.extend([c, c, -c, -c, r, r, -r, -r]) if cmethod == 'b': if d < 0: d += 4 * c + 6 else: d += 4 * (c - r) + 10 r -= 1 c += 1 elif cmethod == 'a': if d >= 2 * (c - 1): d = d - 2 * c c = c + 1 elif d <= 2 * (radius - r): d = d + 2 * r - 1 r = r - 1 else: d = d + 2 * (r - c - 1) r = r - 1 c = c + 1 if shape is not None: return _coords_inside_image(np.array(rr, dtype=np.intp) + r_o, np.array(cc, dtype=np.intp) + c_o, shape) return (np.array(rr, dtype=np.intp) + r_o, np.array(cc, dtype=np.intp) + c_o) def _circle_perimeter_aa(Py_ssize_t r_o, Py_ssize_t c_o, Py_ssize_t radius, shape): """Generate anti-aliased circle perimeter coordinates. Parameters ---------- r_o, c_o : int Centre coordinate of circle. radius : int Radius of circle. shape : tuple Image shape which is used to determine the maximum extent of output pixel coordinates. This is useful for circles that exceed the image size. If None, the full extent of the circle is used. Returns ------- rr, cc, val : (N,) ndarray (int, int, float) Indices of pixels (`rr`, `cc`) and intensity values (`val`). ``img[rr, cc] = val``. Notes ----- Wu's method draws anti-aliased circle. This implementation doesn't use lookup table optimization. References ---------- .. [1] X. Wu, "An efficient antialiasing technique", In ACM SIGGRAPH Computer Graphics, 25 (1991) 143-152. """ cdef Py_ssize_t c = 0 cdef Py_ssize_t r = radius cdef Py_ssize_t d = 0 cdef double dceil = 0 cdef double dceil_prev = 0 cdef list rr = [r, c, r, c, -r, -c, -r, -c] cdef list cc = [c, r, -c, -r, c, r, -c, -r] cdef list val = [1] * 8 while r > c + 1: c += 1 dceil = sqrt(radius**2 - c**2) dceil = ceil(dceil) - dceil if dceil < dceil_prev: r -= 1 rr.extend([r, r - 1, c, c, r, r - 1, c, c]) cc.extend([c, c, r, r - 1, -c, -c, -r, 1 - r]) rr.extend([-r, 1 - r, -c, -c, -r, 1 - r, -c, -c]) cc.extend([c, c, r, r - 1, -c, -c, -r, 1 - r]) val.extend([1 - dceil, dceil] * 8) dceil_prev = dceil if shape is not None: return _coords_inside_image(np.array(rr, dtype=np.intp) + r_o, np.array(cc, dtype=np.intp) + c_o, shape, val=np.array(val, dtype=np.float)) return (np.array(rr, dtype=np.intp) + r_o, np.array(cc, dtype=np.intp) + c_o, np.array(val, dtype=np.float)) def _ellipse_perimeter(Py_ssize_t r_o, Py_ssize_t c_o, Py_ssize_t r_radius, Py_ssize_t c_radius, double orientation, shape): """Generate ellipse perimeter coordinates. Parameters ---------- r_o, c_o : int Centre coordinate of ellipse. r_radius, c_radius : int Minor and major semi-axes. ``(r/r_radius)**2 + (c/c_radius)**2 = 1``. orientation : double Major axis orientation in clockwise direction as radians. shape : tuple Image shape which is used to determine the maximum extent of output pixel coordinates. This is useful for ellipses that exceed the image size. If None, the full extent of the ellipse is used. Returns ------- rr, cc : (N,) ndarray of int Indices of pixels that belong to the ellipse perimeter. May be used to directly index into an array, e.g. ``img[rr, cc] = 1``. References ---------- .. [1] A Rasterizing Algorithm for Drawing Curves, A. Zingl, 2012 http://members.chello.at/easyfilter/Bresenham.pdf """ # If both radii == 0, return the center to avoid infinite loop in 2nd set if r_radius == 0 and c_radius == 0: return np.array(r_o), np.array(c_o) # Pixels cdef list rr = list() cdef list cc = list() # Compute useful values cdef Py_ssize_t rd = r_radius**2 cdef Py_ssize_t cd = c_radius**2 cdef Py_ssize_t r, c, e2, err cdef int ir0, ir1, ic0, ic1, ird, icd cdef double sin_angle, ra, ca, za, a, b if orientation == 0: c = -c_radius r = 0 e2 = rd err = c * (2 * e2 + c) + e2 while c <= 0: # Quadrant 1 rr.append(r_o + r) cc.append(c_o - c) # Quadrant 2 rr.append(r_o + r) cc.append(c_o + c) # Quadrant 3 rr.append(r_o - r) cc.append(c_o + c) # Quadrant 4 rr.append(r_o - r) cc.append(c_o - c) # Adjust `r` and `c` e2 = 2 * err if e2 >= (2 * c + 1) * rd: c += 1 err += (2 * c + 1) * rd if e2 <= (2 * r + 1) * cd: r += 1 err += (2 * r + 1) * cd while r < r_radius: r += 1 rr.append(r_o + r) cc.append(c_o) rr.append(r_o - r) cc.append(c_o) else: sin_angle = sin(orientation) za = (cd - rd) * sin_angle ca = sqrt(cd - za * sin_angle) ra = sqrt(rd + za * sin_angle) a = ca + 0.5 b = ra + 0.5 za = za * a * b / (ca * ra) ir0 = int(r_o - b) ic0 = int(c_o - a) ir1 = int(r_o + b) ic1 = int(c_o + a) ca = ic1 - ic0 ra = ir1 - ir0 za = 4 * za * cos(orientation) w = ca * ra if w != 0: w = (w - za) / (w + w) icd = int(floor(ca * w + 0.5)) ird = int(floor(ra * w + 0.5)) # Draw the 4 quadrants rr_t, cc_t = _bezier_segment(ir0 + ird, ic0, ir0, ic0, ir0, ic0 + icd, 1-w) rr.extend(rr_t) cc.extend(cc_t) rr_t, cc_t = _bezier_segment(ir0 + ird, ic0, ir1, ic0, ir1, ic1 - icd, w) rr.extend(rr_t) cc.extend(cc_t) rr_t, cc_t = _bezier_segment(ir1 - ird, ic1, ir1, ic1, ir1, ic1 - icd, 1-w) rr.extend(rr_t) cc.extend(cc_t) rr_t, cc_t = _bezier_segment(ir1 - ird, ic1, ir0, ic1, ir0, ic0 + icd, w) rr.extend(rr_t) cc.extend(cc_t) if shape is not None: return _coords_inside_image(np.array(rr, dtype=np.intp), np.array(cc, dtype=np.intp), shape) return np.array(rr, dtype=np.intp), np.array(cc, dtype=np.intp) def _bezier_segment(Py_ssize_t r0, Py_ssize_t c0, Py_ssize_t r1, Py_ssize_t c1, Py_ssize_t r2, Py_ssize_t c2, double weight): """Generate Bezier segment coordinates. Parameters ---------- r0, c0 : int Coordinates of the first control point. r1, c1 : int Coordinates of the middle control point. r2, c2 : int Coordinates of the last control point. weight : double Middle control point weight, it describes the line tension. Returns ------- rr, cc : (N,) ndarray of int Indices of pixels that belong to the Bezier curve. May be used to directly index into an array, e.g. ``img[rr, cc] = 1``. Notes ----- The algorithm is the rational quadratic algorithm presented in reference [1]_. References ---------- .. [1] A Rasterizing Algorithm for Drawing Curves, A. Zingl, 2012 http://members.chello.at/easyfilter/Bresenham.pdf """ # Pixels cdef list cc = list() cdef list rr = list() # Steps cdef double sc = c2 - c1 cdef double sr = r2 - r1 cdef double d2c = c0 - c2 cdef double d2r = r0 - r2 cdef double d1c = c0 - c1 cdef double d1r = r0 - r1 cdef double rc = d1c * sr + d1r * sc cdef double cur = d1c * sr - d1r * sc cdef double err cdef bint test1, test2 # If not a straight line if cur != 0 and weight > 0: if (sc * sc + sr * sr > d1c * d1c + d1r * d1r): # Swap point 0 and point 2 # to start from the longer part c2 = c0 c0 -= (d2c) r2 = r0 r0 -= (d2r) cur = -cur d1c = 2 * (4 * weight * sc * d1c + d2c * d2c) d1r = 2 * (4 * weight * sr * d1r + d2r * d2r) # Set steps if c0 < c2: sc = 1 else: sc = -1 if r0 < r2: sr = 1 else: sr = -1 rc = -2 * sc * sr * (2 * weight * rc + d2c * d2r) if cur * sc * sr < 0: d1c = -d1c d1r = -d1r rc = -rc cur = -cur d2c = 4 * weight * (c1 - c0) * sr * cur + d1c / 2 + rc d2r = 4 * weight * (r0 - r1) * sc * cur + d1r / 2 + rc # Flat ellipse, algo fails if weight < 0.5 and (d2r > rc or d2c < rc): cur = (weight + 1) / 2 weight = sqrt(weight) rc = 1. / (weight + 1) # Subdivide curve in half sc = floor((c0 + 2 * weight * c1 + c2) * rc * 0.5 + 0.5) sr = floor((r0 + 2 * weight * r1 + r2) * rc * 0.5 + 0.5) d2c = floor((weight * c1 + c0) * rc + 0.5) d2r = floor((r1 * weight + r0) * rc + 0.5) return _bezier_segment(r0, c0, (d2r), (d2c), (sr), (sc), cur) err = d2c + d2r - rc while d2r <= rc and d2c >= rc: cc.append(c0) rr.append(r0) if c0 == c2 and r0 == r2: # The job is done! return np.array(rr, dtype=np.intp), np.array(cc, dtype=np.intp) # Save boolean values test1 = 2 * err > d2r test2 = 2 * (err + d1r) < -d2r # Move (c0, r0) to the next position if 2 * err < d2c or test2: r0 += (sr) d2r += rc d2c += d1c err += d2c if 2 * err > d2c or test1: c0 += (sc) d2c += rc d2r += d1r err += d2r # Plot line cc_t, rr_t = _line(c0, r0, c2, r2) cc.extend(cc_t) rr.extend(rr_t) return np.array(rr, dtype=np.intp), np.array(cc, dtype=np.intp) def _bezier_curve(Py_ssize_t r0, Py_ssize_t c0, Py_ssize_t r1, Py_ssize_t c1, Py_ssize_t r2, Py_ssize_t c2, double weight, shape): """Generate Bezier curve coordinates. Parameters ---------- r0, c0 : int Coordinates of the first control point. r1, c1 : int Coordinates of the middle control point. r2, c2 : int Coordinates of the last control point. weight : double Middle control point weight, it describes the line tension. shape : tuple Image shape which is used to determine the maximum extent of output pixel coordinates. This is useful for curves that exceed the image size. If None, the full extent of the curve is used. Returns ------- rr, cc : (N,) ndarray of int Indices of pixels that belong to the Bezier curve. May be used to directly index into an array, e.g. ``img[rr, cc] = 1``. Notes ----- The algorithm is the rational quadratic algorithm presented in reference [1]_. References ---------- .. [1] A Rasterizing Algorithm for Drawing Curves, A. Zingl, 2012 http://members.chello.at/easyfilter/Bresenham.pdf """ # Pixels cdef list cc = list() cdef list rr = list() cdef int vc, vr cdef double dc, dr, ww, t, q vc = c0 - 2 * c1 + c2 vr = r0 - 2 * r1 + r2 dc = c0 - c1 dr = r0 - r1 if dc * (c2 - c1) > 0: if dr * (r2 - r1): if abs(dc * vr) > abs(dr * vc): c0 = c2 c2 = (dc + c1) r0 = r2 r2 = (dr + r1) if (c0 == c2) or (weight == 1.): t = (c0 - c1) / vc else: q = sqrt(4. * weight * weight * (c0 - c1) * (c2 - c1) + (c2 - c0) * floor(c2 - c0)) if (c1 < c0): q = -q t = (2. * weight * (c0 - c1) - c0 + c2 + q) / (2. * (1. - weight) * (c2 - c0)) q = 1. / (2. * t * (1. - t) * (weight - 1.) + 1.0) dc = (t * t * (c0 - 2. * weight * c1 + c2) + 2. * t * (weight * c1 - c0) + c0) * q dr = (t * t * (r0 - 2. * weight * r1 + r2) + 2. * t * (weight * r1 - r0) + r0) * q ww = t * (weight - 1.) + 1. ww *= ww * q weight = ((1. - t) * (weight - 1.) + 1.) * sqrt(q) vc = (dc + 0.5) vr = (dr + 0.5) dr = (dc - c0) * (r1 - r0) / (c1 - c0) + r0 rr_t, cc_t = _bezier_segment(r0, c0, (dr + 0.5), vc, vr, vc, ww) cc.extend(cc_t) rr.extend(rr_t) dr = (dc - c2) * (r1 - r2) / (c1 - c2) + r2 r1 = (dr + 0.5) c0 = c1 = vc r0 = vr if (r0 - r1) * floor(r2 - r1) > 0: if (r0 == r2) or (weight == 1): t = (r0 - r1) / (r0 - 2. * r1 + r2) else: q = sqrt(4. * weight * weight * (r0 - r1) * (r2 - r1) + (r2 - r0) * floor(r2 - r0)) if r1 < r0: q = -q t = (2. * weight * (r0 - r1) - r0 + r2 + q) / (2. * (1. - weight) * (r2 - r0)) q = 1. / (2. * t * (1. - t) * (weight - 1.) + 1.) dc = (t * t * (c0 - 2. * weight * c1 + c2) + 2. * t * (weight * c1 - c0) + c0) * q dr = (t * t * (r0 - 2. * weight * r1 + r2) + 2. * t * (weight * r1 - r0) + r0) * q ww = t * (weight - 1.) + 1. ww *= ww * q weight = ((1. - t) * (weight - 1.) + 1.) * sqrt(q) vc = (dc + 0.5) vr = (dr + 0.5) dc = (c1 - c0) * (dr - r0) / (r1 - r0) + c0 rr_t, cc_t = _bezier_segment(r0, c0, vr, (dc + 0.5), vr, vc, ww) cc.extend(cc_t) rr.extend(rr_t) dc = (c1 - c2) * (dr - r2) / (r1 - r2) + c2 c1 = (dc + 0.5) c0 = vc r0 = r1 = vr rr_t, cc_t = _bezier_segment(r0, c0, r1, c1, r2, c2, weight * weight) cc.extend(cc_t) rr.extend(rr_t) if shape is not None: return _coords_inside_image(np.array(rr, dtype=np.intp), np.array(cc, dtype=np.intp), shape) return np.array(rr, dtype=np.intp), np.array(cc, dtype=np.intp) scikit-image-0.13.1/skimage/draw/draw.py000066400000000000000000000565341316256236300200520ustar00rootroot00000000000000# coding: utf-8 import numpy as np from .._shared._geometry import polygon_clip from ._draw import (_coords_inside_image, _line, _line_aa, _polygon, _ellipse_perimeter, _circle_perimeter, _circle_perimeter_aa, _bezier_curve) def _ellipse_in_shape(shape, center, radii, rotation=0.): """Generate coordinates of points within ellipse bounded by shape. Parameters ---------- shape : iterable of ints Shape of the input image. Must be length 2. center : iterable of floats (row, column) position of center inside the given shape. radii : iterable of floats Size of two half axes (for row and column) rotation : float, optional Rotation of the ellipse defined by the above, in radians in range (-PI, PI), in contra clockwise direction, with respect to the column-axis. Returns ------- rows : iterable of ints Row coordinates representing values within the ellipse. cols : iterable of ints Corresponding column coordinates representing values within the ellipse. """ r_lim, c_lim = np.ogrid[0:float(shape[0]), 0:float(shape[1])] r_org, c_org = center r_rad, c_rad = radii rotation %= np.pi sin_alpha, cos_alpha = np.sin(rotation), np.cos(rotation) r, c = (r_lim - r_org), (c_lim - c_org) distances = ((r * cos_alpha + c * sin_alpha) / r_rad) ** 2 \ + ((r * sin_alpha - c * cos_alpha) / c_rad) ** 2 return np.nonzero(distances < 1) def ellipse(r, c, r_radius, c_radius, shape=None, rotation=0.): """Generate coordinates of pixels within ellipse. Parameters ---------- r, c : double Centre coordinate of ellipse. r_radius, c_radius : double Minor and major semi-axes. ``(r/r_radius)**2 + (c/c_radius)**2 = 1``. shape : tuple, optional Image shape which is used to determine the maximum extent of output pixel coordinates. This is useful for ellipses which exceed the image size. By default the full extent of the ellipse are used. rotation : float, optional (default 0.) Set the ellipse rotation (rotation) in range (-PI, PI) in contra clock wise direction, so PI/2 degree means swap ellipse axis Returns ------- rr, cc : ndarray of int Pixel coordinates of ellipse. May be used to directly index into an array, e.g. ``img[rr, cc] = 1``. Examples -------- >>> from skimage.draw import ellipse >>> img = np.zeros((10, 12), dtype=np.uint8) >>> rr, cc = ellipse(5, 6, 3, 5, rotation=np.deg2rad(30)) >>> img[rr, cc] = 1 >>> img array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0], [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8) Notes ----- The ellipse equation:: ((x * cos(alpha) + y * sin(alpha)) / x_radius) ** 2 + ((x * sin(alpha) - y * cos(alpha)) / y_radius) ** 2 = 1 Note that the positions of `ellipse` without specified `shape` can have also, negative values, as this is correct on the plane. On the other hand using these ellipse positions for an image afterwards may lead to appearing on the other side of image, because ``image[-1, -1] = image[end-1, end-1]`` >>> rr, cc = ellipse(1, 2, 3, 6) >>> img = np.zeros((6, 12), dtype=np.uint8) >>> img[rr, cc] = 1 >>> img array([[1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1]], dtype=uint8) """ center = np.array([r, c]) radii = np.array([r_radius, c_radius]) # allow just rotation with in range +/- 180 degree rotation %= np.pi # compute rotated radii by given rotation r_radius_rot = abs(r_radius * np.cos(rotation)) \ + c_radius * np.sin(rotation) c_radius_rot = r_radius * np.sin(rotation) \ + abs(c_radius * np.cos(rotation)) # The upper_left and lower_right corners of the smallest rectangle # containing the ellipse. radii_rot = np.array([r_radius_rot, c_radius_rot]) upper_left = np.ceil(center - radii_rot).astype(int) lower_right = np.floor(center + radii_rot).astype(int) if shape is not None: # Constrain upper_left and lower_right by shape boundary. upper_left = np.maximum(upper_left, np.array([0, 0])) lower_right = np.minimum(lower_right, np.array(shape[:2]) - 1) shifted_center = center - upper_left bounding_shape = lower_right - upper_left + 1 rr, cc = _ellipse_in_shape(bounding_shape, shifted_center, radii, rotation) rr.flags.writeable = True cc.flags.writeable = True rr += upper_left[0] cc += upper_left[1] return rr, cc def circle(r, c, radius, shape=None): """Generate coordinates of pixels within circle. Parameters ---------- r, c : double Centre coordinate of circle. radius : double Radius of circle. shape : tuple, optional Image shape which is used to determine the maximum extent of output pixel coordinates. This is useful for circles that exceed the image size. If None, the full extent of the circle is used. Returns ------- rr, cc : ndarray of int Pixel coordinates of circle. May be used to directly index into an array, e.g. ``img[rr, cc] = 1``. Examples -------- >>> from skimage.draw import circle >>> img = np.zeros((10, 10), dtype=np.uint8) >>> rr, cc = circle(4, 4, 5) >>> img[rr, cc] = 1 >>> img array([[0, 0, 1, 1, 1, 1, 1, 0, 0, 0], [0, 1, 1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8) """ return ellipse(r, c, radius, radius, shape) def polygon_perimeter(r, c, shape=None, clip=False): """Generate polygon perimeter coordinates. Parameters ---------- r : (N,) ndarray Row coordinates of vertices of polygon. c : (N,) ndarray Column coordinates of vertices of polygon. shape : tuple, optional Image shape which is used to determine maximum extents of output pixel coordinates. This is useful for polygons that exceed the image size. If None, the full extents of the polygon is used. clip : bool, optional Whether to clip the polygon to the provided shape. If this is set to True, the drawn figure will always be a closed polygon with all edges visible. Returns ------- rr, cc : ndarray of int Pixel coordinates of polygon. May be used to directly index into an array, e.g. ``img[rr, cc] = 1``. Examples -------- >>> from skimage.draw import polygon_perimeter >>> img = np.zeros((10, 10), dtype=np.uint8) >>> rr, cc = polygon_perimeter([5, -1, 5, 10], ... [-1, 5, 11, 5], ... shape=img.shape, clip=True) >>> img[rr, cc] = 1 >>> img array([[0, 0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 1, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 1, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 1], [1, 0, 0, 0, 0, 0, 0, 0, 0, 1], [1, 0, 0, 0, 0, 0, 0, 0, 0, 1], [1, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 1, 1, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 1, 0, 0, 0, 1, 1, 0], [0, 0, 0, 0, 1, 1, 1, 0, 0, 0]], dtype=uint8) """ if clip: if shape is None: raise ValueError("Must specify clipping shape") clip_box = np.array([0, 0, shape[0] - 1, shape[1] - 1]) else: clip_box = np.array([np.min(r), np.min(c), np.max(r), np.max(c)]) # Do the clipping irrespective of whether clip is set. This # ensures that the returned polygon is closed and is an array. r, c = polygon_clip(r, c, *clip_box) r = np.round(r).astype(int) c = np.round(c).astype(int) # Construct line segments rr, cc = [], [] for i in range(len(r) - 1): line_r, line_c = line(r[i], c[i], r[i + 1], c[i + 1]) rr.extend(line_r) cc.extend(line_c) rr = np.asarray(rr) cc = np.asarray(cc) if shape is None: return rr, cc else: return _coords_inside_image(rr, cc, shape) def set_color(img, coords, color, alpha=1): """Set pixel color in the image at the given coordinates. Coordinates that exceed the shape of the image will be ignored. Parameters ---------- img : (M, N, D) ndarray Image coords : tuple of ((P,) ndarray, (P,) ndarray) Row and column coordinates of pixels to be colored. color : (D,) ndarray Color to be assigned to coordinates in the image. alpha : scalar or (N,) ndarray Alpha values used to blend color with image. 0 is transparent, 1 is opaque. Returns ------- img : (M, N, D) ndarray The updated image. Examples -------- >>> from skimage.draw import line, set_color >>> img = np.zeros((10, 10), dtype=np.uint8) >>> rr, cc = line(1, 1, 20, 20) >>> set_color(img, (rr, cc), 1) >>> img array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1]], dtype=uint8) """ rr, cc = coords if img.ndim == 2: img = img[..., np.newaxis] color = np.array(color, ndmin=1, copy=False) if img.shape[-1] != color.shape[-1]: raise ValueError('Color shape ({}) must match last ' 'image dimension ({}).'.format(color.shape[0], img.shape[-1])) if np.isscalar(alpha): # Can be replaced by ``full_like`` when numpy 1.8 becomes # minimum dependency alpha = np.ones_like(rr) * alpha rr, cc, alpha = _coords_inside_image(rr, cc, img.shape, val=alpha) alpha = alpha[..., np.newaxis] color = color * alpha vals = img[rr, cc] * (1 - alpha) img[rr, cc] = vals + color def line(r0, c0, r1, c1): """Generate line pixel coordinates. Parameters ---------- r0, c0 : int Starting position (row, column). r1, c1 : int End position (row, column). Returns ------- rr, cc : (N,) ndarray of int Indices of pixels that belong to the line. May be used to directly index into an array, e.g. ``img[rr, cc] = 1``. Notes ----- Anti-aliased line generator is available with `line_aa`. Examples -------- >>> from skimage.draw import line >>> img = np.zeros((10, 10), dtype=np.uint8) >>> rr, cc = line(1, 1, 8, 8) >>> img[rr, cc] = 1 >>> img array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8) """ return _line(r0, c0, r1, c1) def line_aa(r0, c0, r1, c1): """Generate anti-aliased line pixel coordinates. Parameters ---------- r0, c0 : int Starting position (row, column). r1, c1 : int End position (row, column). Returns ------- rr, cc, val : (N,) ndarray (int, int, float) Indices of pixels (`rr`, `cc`) and intensity values (`val`). ``img[rr, cc] = val``. References ---------- .. [1] A Rasterizing Algorithm for Drawing Curves, A. Zingl, 2012 http://members.chello.at/easyfilter/Bresenham.pdf Examples -------- >>> from skimage.draw import line_aa >>> img = np.zeros((10, 10), dtype=np.uint8) >>> rr, cc, val = line_aa(1, 1, 8, 8) >>> img[rr, cc] = val * 255 >>> img array([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 255, 74, 0, 0, 0, 0, 0, 0, 0], [ 0, 74, 255, 74, 0, 0, 0, 0, 0, 0], [ 0, 0, 74, 255, 74, 0, 0, 0, 0, 0], [ 0, 0, 0, 74, 255, 74, 0, 0, 0, 0], [ 0, 0, 0, 0, 74, 255, 74, 0, 0, 0], [ 0, 0, 0, 0, 0, 74, 255, 74, 0, 0], [ 0, 0, 0, 0, 0, 0, 74, 255, 74, 0], [ 0, 0, 0, 0, 0, 0, 0, 74, 255, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8) """ return _line_aa(r0, c0, r1, c1) def polygon(r, c, shape=None): """Generate coordinates of pixels within polygon. Parameters ---------- r : (N,) ndarray Row coordinates of vertices of polygon. c : (N,) ndarray Column coordinates of vertices of polygon. shape : tuple, optional Image shape which is used to determine the maximum extent of output pixel coordinates. This is useful for polygons that exceed the image size. If None, the full extent of the polygon is used. Returns ------- rr, cc : ndarray of int Pixel coordinates of polygon. May be used to directly index into an array, e.g. ``img[rr, cc] = 1``. Examples -------- >>> from skimage.draw import polygon >>> img = np.zeros((10, 10), dtype=np.uint8) >>> r = np.array([1, 2, 8, 1]) >>> c = np.array([1, 7, 4, 1]) >>> rr, cc = polygon(r, c) >>> img[rr, cc] = 1 >>> img array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8) """ return _polygon(r, c, shape) def circle_perimeter(r, c, radius, method='bresenham', shape=None): """Generate circle perimeter coordinates. Parameters ---------- r, c : int Centre coordinate of circle. radius: int Radius of circle. method : {'bresenham', 'andres'}, optional bresenham : Bresenham method (default) andres : Andres method shape : tuple, optional Image shape which is used to determine the maximum extent of output pixel coordinates. This is useful for circles that exceed the image size. If None, the full extent of the circle is used. Returns ------- rr, cc : (N,) ndarray of int Bresenham and Andres' method: Indices of pixels that belong to the circle perimeter. May be used to directly index into an array, e.g. ``img[rr, cc] = 1``. Notes ----- Andres method presents the advantage that concentric circles create a disc whereas Bresenham can make holes. There is also less distortions when Andres circles are rotated. Bresenham method is also known as midpoint circle algorithm. Anti-aliased circle generator is available with `circle_perimeter_aa`. References ---------- .. [1] J.E. Bresenham, "Algorithm for computer control of a digital plotter", IBM Systems journal, 4 (1965) 25-30. .. [2] E. Andres, "Discrete circles, rings and spheres", Computers & Graphics, 18 (1994) 695-706. Examples -------- >>> from skimage.draw import circle_perimeter >>> img = np.zeros((10, 10), dtype=np.uint8) >>> rr, cc = circle_perimeter(4, 4, 3) >>> img[rr, cc] = 1 >>> img array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 1, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 1, 0, 0], [0, 1, 0, 0, 0, 0, 0, 1, 0, 0], [0, 1, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 1, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8) """ return _circle_perimeter(r, c, radius, method, shape) def circle_perimeter_aa(r, c, radius, shape=None): """Generate anti-aliased circle perimeter coordinates. Parameters ---------- r, c : int Centre coordinate of circle. radius: int Radius of circle. shape : tuple, optional Image shape which is used to determine the maximum extent of output pixel coordinates. This is useful for circles that exceed the image size. If None, the full extent of the circle is used. Returns ------- rr, cc, val : (N,) ndarray (int, int, float) Indices of pixels (`rr`, `cc`) and intensity values (`val`). ``img[rr, cc] = val``. Notes ----- Wu's method draws anti-aliased circle. This implementation doesn't use lookup table optimization. References ---------- .. [1] X. Wu, "An efficient antialiasing technique", In ACM SIGGRAPH Computer Graphics, 25 (1991) 143-152. Examples -------- >>> from skimage.draw import circle_perimeter_aa >>> img = np.zeros((10, 10), dtype=np.uint8) >>> rr, cc, val = circle_perimeter_aa(4, 4, 3) >>> img[rr, cc] = val * 255 >>> img array([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 60, 211, 255, 211, 60, 0, 0, 0], [ 0, 60, 194, 43, 0, 43, 194, 60, 0, 0], [ 0, 211, 43, 0, 0, 0, 43, 211, 0, 0], [ 0, 255, 0, 0, 0, 0, 0, 255, 0, 0], [ 0, 211, 43, 0, 0, 0, 43, 211, 0, 0], [ 0, 60, 194, 43, 0, 43, 194, 60, 0, 0], [ 0, 0, 60, 211, 255, 211, 60, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8) """ return _circle_perimeter_aa(r, c, radius, shape) def ellipse_perimeter(r, c, r_radius, c_radius, orientation=0, shape=None): """Generate ellipse perimeter coordinates. Parameters ---------- r, c : int Centre coordinate of ellipse. r_radius, c_radius : int Minor and major semi-axes. ``(r/r_radius)**2 + (c/c_radius)**2 = 1``. orientation : double, optional Major axis orientation in clockwise direction as radians. shape : tuple, optional Image shape which is used to determine the maximum extent of output pixel coordinates. This is useful for ellipses that exceed the image size. If None, the full extent of the ellipse is used. Returns ------- rr, cc : (N,) ndarray of int Indices of pixels that belong to the ellipse perimeter. May be used to directly index into an array, e.g. ``img[rr, cc] = 1``. References ---------- .. [1] A Rasterizing Algorithm for Drawing Curves, A. Zingl, 2012 http://members.chello.at/easyfilter/Bresenham.pdf Examples -------- >>> from skimage.draw import ellipse_perimeter >>> img = np.zeros((10, 10), dtype=np.uint8) >>> rr, cc = ellipse_perimeter(5, 5, 3, 4) >>> img[rr, cc] = 1 >>> img array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 1, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 1], [0, 1, 0, 0, 0, 0, 0, 0, 0, 1], [0, 1, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 1, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8) Note that the positions of `ellipse` without specified `shape` can have also, negative values, as this is correct on the plane. On the other hand using these ellipse positions for an image afterwards may lead to appearing on the other side of image, because ``image[-1, -1] = image[end-1, end-1]`` >>> rr, cc = ellipse_perimeter(2, 3, 4, 5) >>> img = np.zeros((9, 12), dtype=np.uint8) >>> img[rr, cc] = 1 >>> img array([[0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1], [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]], dtype=uint8) """ return _ellipse_perimeter(r, c, r_radius, c_radius, orientation, shape) def bezier_curve(r0, c0, r1, c1, r2, c2, weight, shape=None): """Generate Bezier curve coordinates. Parameters ---------- r0, c0 : int Coordinates of the first control point. r1, c1 : int Coordinates of the middle control point. r2, c2 : int Coordinates of the last control point. weight : double Middle control point weight, it describes the line tension. shape : tuple, optional Image shape which is used to determine the maximum extent of output pixel coordinates. This is useful for curves that exceed the image size. If None, the full extent of the curve is used. Returns ------- rr, cc : (N,) ndarray of int Indices of pixels that belong to the Bezier curve. May be used to directly index into an array, e.g. ``img[rr, cc] = 1``. Notes ----- The algorithm is the rational quadratic algorithm presented in reference [1]_. References ---------- .. [1] A Rasterizing Algorithm for Drawing Curves, A. Zingl, 2012 http://members.chello.at/easyfilter/Bresenham.pdf Examples -------- >>> import numpy as np >>> from skimage.draw import bezier_curve >>> img = np.zeros((10, 10), dtype=np.uint8) >>> rr, cc = bezier_curve(1, 5, 5, -2, 8, 8, 2) >>> img[rr, cc] = 1 >>> img array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8) """ return _bezier_curve(r0, c0, r1, c1, r2, c2, weight, shape) scikit-image-0.13.1/skimage/draw/draw3d.py000066400000000000000000000066071316256236300202750ustar00rootroot00000000000000# coding: utf-8 import numpy as np from scipy.special import (ellipkinc as ellip_F, ellipeinc as ellip_E) def ellipsoid(a, b, c, spacing=(1., 1., 1.), levelset=False): """ Generates ellipsoid with semimajor axes aligned with grid dimensions on grid with specified `spacing`. Parameters ---------- a : float Length of semimajor axis aligned with x-axis. b : float Length of semimajor axis aligned with y-axis. c : float Length of semimajor axis aligned with z-axis. spacing : tuple of floats, length 3 Spacing in (x, y, z) spatial dimensions. levelset : bool If True, returns the level set for this ellipsoid (signed level set about zero, with positive denoting interior) as np.float64. False returns a binarized version of said level set. Returns ------- ellip : (N, M, P) array Ellipsoid centered in a correctly sized array for given `spacing`. Boolean dtype unless `levelset=True`, in which case a float array is returned with the level set above 0.0 representing the ellipsoid. """ if (a <= 0) or (b <= 0) or (c <= 0): raise ValueError('Parameters a, b, and c must all be > 0') offset = np.r_[1, 1, 1] * np.r_[spacing] # Calculate limits, and ensure output volume is odd & symmetric low = np.ceil((- np.r_[a, b, c] - offset)) high = np.floor((np.r_[a, b, c] + offset + 1)) for dim in range(3): if (high[dim] - low[dim]) % 2 == 0: low[dim] -= 1 num = np.arange(low[dim], high[dim], spacing[dim]) if 0 not in num: low[dim] -= np.max(num[num < 0]) # Generate (anisotropic) spatial grid x, y, z = np.mgrid[low[0]:high[0]:spacing[0], low[1]:high[1]:spacing[1], low[2]:high[2]:spacing[2]] if not levelset: arr = ((x / float(a)) ** 2 + (y / float(b)) ** 2 + (z / float(c)) ** 2) <= 1 else: arr = ((x / float(a)) ** 2 + (y / float(b)) ** 2 + (z / float(c)) ** 2) - 1 return arr def ellipsoid_stats(a, b, c): """ Calculates analytical surface area and volume for ellipsoid with semimajor axes aligned with grid dimensions of specified `spacing`. Parameters ---------- a : float Length of semimajor axis aligned with x-axis. b : float Length of semimajor axis aligned with y-axis. c : float Length of semimajor axis aligned with z-axis. Returns ------- vol : float Calculated volume of ellipsoid. surf : float Calculated surface area of ellipsoid. """ if (a <= 0) or (b <= 0) or (c <= 0): raise ValueError('Parameters a, b, and c must all be > 0') # Calculate volume & surface area # Surface calculation requires a >= b >= c and a != c. abc = [a, b, c] abc.sort(reverse=True) a = abc[0] b = abc[1] c = abc[2] # Volume vol = 4 / 3. * np.pi * a * b * c # Analytical ellipsoid surface area phi = np.arcsin((1. - (c ** 2 / (a ** 2.))) ** 0.5) d = float((a ** 2 - c ** 2) ** 0.5) m = (a ** 2 * (b ** 2 - c ** 2) / float(b ** 2 * (a ** 2 - c ** 2))) F = ellip_F(phi, m) E = ellip_E(phi, m) surf = 2 * np.pi * (c ** 2 + b * c ** 2 / d * F + b * d * E) return vol, surf scikit-image-0.13.1/skimage/draw/setup.py000066400000000000000000000017251316256236300202450ustar00rootroot00000000000000#!/usr/bin/env python import os from skimage._build import cython base_path = os.path.abspath(os.path.dirname(__file__)) def configuration(parent_package='', top_path=None): from numpy.distutils.misc_util import Configuration, get_numpy_include_dirs config = Configuration('draw', parent_package, top_path) config.add_data_dir('tests') cython(['_draw.pyx'], working_path=base_path) config.add_extension('_draw', sources=['_draw.c'], include_dirs=[get_numpy_include_dirs(), '../_shared']) return config if __name__ == '__main__': from numpy.distutils.core import setup setup(maintainer='scikit-image developers', author='scikit-image developers', maintainer_email='scikit-image@python.org', description='Drawing', url='https://github.com/scikit-image/scikit-image', license='SciPy License (BSD Style)', **(configuration(top_path='').todict()) ) scikit-image-0.13.1/skimage/draw/tests/000077500000000000000000000000001316256236300176705ustar00rootroot00000000000000scikit-image-0.13.1/skimage/draw/tests/__init__.py000066400000000000000000000001771316256236300220060ustar00rootroot00000000000000from ..._shared.testing import setup_test, teardown_test def setup(): setup_test() def teardown(): teardown_test() scikit-image-0.13.1/skimage/draw/tests/test_draw.py000066400000000000000000000730251316256236300222450ustar00rootroot00000000000000from numpy.testing import assert_array_equal, assert_equal, assert_raises, \ assert_almost_equal import numpy as np from skimage._shared.testing import test_parallel from skimage.draw import (set_color, line, line_aa, polygon, polygon_perimeter, circle, circle_perimeter, circle_perimeter_aa, ellipse, ellipse_perimeter, _bezier_segment, bezier_curve) from skimage.measure import regionprops def test_set_color(): img = np.zeros((10, 10)) rr, cc = line(0, 0, 0, 30) set_color(img, (rr, cc), 1) img_ = np.zeros((10, 10)) img_[0, :] = 1 assert_array_equal(img, img_) def test_set_color_with_alpha(): img = np.zeros((10, 10)) rr, cc, alpha = line_aa(0, 0, 0, 30) set_color(img, (rr, cc), 1, alpha=alpha) # Wrong dimensionality color assert_raises(ValueError, set_color, img, (rr, cc), (255, 0, 0), alpha=alpha) img = np.zeros((10, 10, 3)) rr, cc, alpha = line_aa(0, 0, 0, 30) set_color(img, (rr, cc), (1, 0, 0), alpha=alpha) @test_parallel() def test_line_horizontal(): img = np.zeros((10, 10)) rr, cc = line(0, 0, 0, 9) img[rr, cc] = 1 img_ = np.zeros((10, 10)) img_[0, :] = 1 assert_array_equal(img, img_) def test_line_vertical(): img = np.zeros((10, 10)) rr, cc = line(0, 0, 9, 0) img[rr, cc] = 1 img_ = np.zeros((10, 10)) img_[:, 0] = 1 assert_array_equal(img, img_) def test_line_reverse(): img = np.zeros((10, 10)) rr, cc = line(0, 9, 0, 0) img[rr, cc] = 1 img_ = np.zeros((10, 10)) img_[0, :] = 1 assert_array_equal(img, img_) def test_line_diag(): img = np.zeros((5, 5)) rr, cc = line(0, 0, 4, 4) img[rr, cc] = 1 img_ = np.eye(5) assert_array_equal(img, img_) def test_line_aa_horizontal(): img = np.zeros((10, 10)) rr, cc, val = line_aa(0, 0, 0, 9) set_color(img, (rr, cc), 1, alpha=val) img_ = np.zeros((10, 10)) img_[0, :] = 1 assert_array_equal(img, img_) def test_line_aa_vertical(): img = np.zeros((10, 10)) rr, cc, val = line_aa(0, 0, 9, 0) img[rr, cc] = val img_ = np.zeros((10, 10)) img_[:, 0] = 1 assert_array_equal(img, img_) def test_line_aa_diagonal(): img = np.zeros((10, 10)) rr, cc, val = line_aa(0, 0, 9, 6) img[rr, cc] = 1 # Check that each pixel belonging to line, # also belongs to line_aa r, c = line(0, 0, 9, 6) for r_i, c_i in zip(r, c): assert_equal(img[r_i, c_i], 1) def test_line_equal_aliasing_horizontally_vertically(): img0 = np.zeros((25, 25)) img1 = np.zeros((25, 25)) # Near-horizontal line rr, cc, val = line_aa(10, 2, 12, 20) img0[rr, cc] = val # Near-vertical (transpose of prior) rr, cc, val = line_aa(2, 10, 20, 12) img1[rr, cc] = val # Difference - should be zero assert_array_equal(img0, img1.T) def test_polygon_rectangle(): img = np.zeros((10, 10), 'uint8') rr, cc = polygon((1, 4, 4, 1, 1), (1, 1, 4, 4, 1)) img[rr, cc] = 1 img_ = np.zeros((10, 10)) img_[1:4, 1:4] = 1 assert_array_equal(img, img_) def test_polygon_rectangle_angular(): img = np.zeros((10, 10), 'uint8') poly = np.array(((0, 3), (4, 7), (7, 4), (3, 0), (0, 3))) rr, cc = polygon(poly[:, 0], poly[:, 1]) img[rr, cc] = 1 img_ = np.array( [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]] ) assert_array_equal(img, img_) def test_polygon_parallelogram(): img = np.zeros((10, 10), 'uint8') poly = np.array(((1, 1), (5, 1), (7, 6), (3, 6), (1, 1))) rr, cc = polygon(poly[:, 0], poly[:, 1]) img[rr, cc] = 1 img_ = np.array( [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]] ) assert_array_equal(img, img_) def test_polygon_exceed(): img = np.zeros((10, 10), 'uint8') poly = np.array(((1, -1), (100, -1), (100, 100), (1, 100), (1, 1))) rr, cc = polygon(poly[:, 0], poly[:, 1], img.shape) img[rr, cc] = 1 img_ = np.zeros((10, 10)) img_[1:, :] = 1 assert_array_equal(img, img_) def test_circle(): img = np.zeros((15, 15), 'uint8') rr, cc = circle(7, 7, 6) img[rr, cc] = 1 img_ = np.array( [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]] ) assert_array_equal(img, img_) def test_circle_perimeter_bresenham(): img = np.zeros((15, 15), 'uint8') rr, cc = circle_perimeter(7, 7, 0, method='bresenham') img[rr, cc] = 1 assert(np.sum(img) == 1) assert(img[7][7] == 1) img = np.zeros((17, 15), 'uint8') rr, cc = circle_perimeter(7, 7, 7, method='bresenham') img[rr, cc] = 1 img_ = np.array( [[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]] ) assert_array_equal(img, img_) def test_circle_perimeter_bresenham_shape(): img = np.zeros((15, 20), 'uint8') rr, cc = circle_perimeter(7, 10, 9, method='bresenham', shape=(15, 20)) img[rr, cc] = 1 shift = 5 img_ = np.zeros((15 + 2 * shift, 20), 'uint8') rr, cc = circle_perimeter(7 + shift, 10, 9, method='bresenham', shape=None) img_[rr, cc] = 1 assert_array_equal(img, img_[shift:-shift, :]) def test_circle_perimeter_andres(): img = np.zeros((15, 15), 'uint8') rr, cc = circle_perimeter(7, 7, 0, method='andres') img[rr, cc] = 1 assert(np.sum(img) == 1) assert(img[7][7] == 1) img = np.zeros((17, 15), 'uint8') rr, cc = circle_perimeter(7, 7, 7, method='andres') img[rr, cc] = 1 img_ = np.array( [[0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0], [0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]] ) assert_array_equal(img, img_) def test_circle_perimeter_aa(): img = np.zeros((15, 15), 'uint8') rr, cc, val = circle_perimeter_aa(7, 7, 0) img[rr, cc] = 1 assert(np.sum(img) == 1) assert(img[7][7] == 1) img = np.zeros((17, 17), 'uint8') rr, cc, val = circle_perimeter_aa(8, 8, 7) img[rr, cc] = val * 255 img_ = np.array( [[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 82, 180, 236, 255, 236, 180, 82, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 189, 172, 74, 18, 0, 18, 74, 172, 189, 0, 0, 0, 0], [ 0, 0, 0, 229, 25, 0, 0, 0, 0, 0, 0, 0, 25, 229, 0, 0, 0], [ 0, 0, 189, 25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 25, 189, 0, 0], [ 0, 82, 172, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 172, 82, 0], [ 0, 180, 74, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 74, 180, 0], [ 0, 236, 18, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 18, 236, 0], [ 0, 255, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 255, 0], [ 0, 236, 18, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 18, 236, 0], [ 0, 180, 74, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 74, 180, 0], [ 0, 82, 172, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 172, 82, 0], [ 0, 0, 189, 25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 25, 189, 0, 0], [ 0, 0, 0, 229, 25, 0, 0, 0, 0, 0, 0, 0, 25, 229, 0, 0, 0], [ 0, 0, 0, 0, 189, 172, 74, 18, 0, 18, 74, 172, 189, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 82, 180, 236, 255, 236, 180, 82, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]] ) assert_array_equal(img, img_) def test_circle_perimeter_aa_shape(): img = np.zeros((15, 20), 'uint8') rr, cc, val = circle_perimeter_aa(7, 10, 9, shape=(15, 20)) img[rr, cc] = val * 255 shift = 5 img_ = np.zeros((15 + 2 * shift, 20), 'uint8') rr, cc, val = circle_perimeter_aa(7 + shift, 10, 9, shape=None) img_[rr, cc] = val * 255 assert_array_equal(img, img_[shift:-shift, :]) def test_ellipse_trivial(): img = np.zeros((2, 2), 'uint8') rr, cc = ellipse(0.5, 0.5, 0.5, 0.5) img[rr, cc] = 1 img_correct = np.array([ [0, 0], [0, 0] ]) assert_array_equal(img, img_correct) img = np.zeros((2, 2), 'uint8') rr, cc = ellipse(0.5, 0.5, 1.1, 1.1) img[rr, cc] = 1 img_correct = np.array([ [1, 1], [1, 1], ]) assert_array_equal(img, img_correct) img = np.zeros((3, 3), 'uint8') rr, cc = ellipse(1, 1, 0.9, 0.9) img[rr, cc] = 1 img_correct = np.array([ [0, 0, 0], [0, 1, 0], [0, 0, 0], ]) assert_array_equal(img, img_correct) img = np.zeros((3, 3), 'uint8') rr, cc = ellipse(1, 1, 1.1, 1.1) img[rr, cc] = 1 img_correct = np.array([ [0, 1, 0], [1, 1, 1], [0, 1, 0], ]) assert_array_equal(img, img_correct) img = np.zeros((3, 3), 'uint8') rr, cc = ellipse(1, 1, 1.5, 1.5) img[rr, cc] = 1 img_correct = np.array([ [1, 1, 1], [1, 1, 1], [1, 1, 1], ]) assert_array_equal(img, img_correct) def test_ellipse_generic(): img = np.zeros((4, 4), 'uint8') rr, cc = ellipse(1.5, 1.5, 1.1, 1.7) img[rr, cc] = 1 img_ = np.array([ [0, 0, 0, 0], [1, 1, 1, 1], [1, 1, 1, 1], [0, 0, 0, 0], ]) assert_array_equal(img, img_) img = np.zeros((5, 5), 'uint8') rr, cc = ellipse(2, 2, 1.7, 1.7) img[rr, cc] = 1 img_ = np.array([ [0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 1, 1, 0], [0, 1, 1, 1, 0], [0, 0, 0, 0, 0], ]) assert_array_equal(img, img_) img = np.zeros((10, 10), 'uint8') rr, cc = ellipse(5, 5, 3, 4) img[rr, cc] = 1 img_ = np.array([ [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 0], [0, 0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], ]) assert_array_equal(img, img_) img = np.zeros((10, 10), 'uint8') rr, cc = ellipse(4.5, 5, 3.5, 4) img[rr, cc] = 1 img_ = np.array([ [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 0], [0, 0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], ]) assert_array_equal(img, img_) img = np.zeros((15, 15), 'uint8') rr, cc = ellipse(7, 7, 3, 7) img[rr, cc] = 1 img_ = np.array([ [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], ]) assert_array_equal(img, img_) def test_ellipse_with_shape(): img = np.zeros((15, 15), 'uint8') rr, cc = ellipse(7, 7, 3, 10, shape=img.shape) img[rr, cc] = 1 img_ = np.array( [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]] ) assert_array_equal(img, img_) def test_ellipse_negative(): rr, cc = ellipse(-3, -3, 1.7, 1.7) rr_, cc_ = np.nonzero(np.array([ [0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 1, 1, 0], [0, 1, 1, 1, 0], [0, 0, 0, 0, 0], ])) assert_array_equal(rr, rr_ - 5) assert_array_equal(cc, cc_ - 5) def test_ellipse_rotation_symmetry(): img1 = np.zeros((150, 150), dtype=np.uint8) img2 = np.zeros((150, 150), dtype=np.uint8) for angle in range(0, 180, 15): img1.fill(0) rr, cc = ellipse(80, 70, 60, 40, rotation=np.deg2rad(angle)) img1[rr, cc] = 1 img2.fill(0) rr, cc = ellipse(80, 70, 60, 40, rotation=np.deg2rad(angle + 180)) img2[rr, cc] = 1 assert_array_equal(img1, img2) def test_ellipse_rotated(): img = np.zeros((1000, 1200), dtype=np.uint8) for rot in range(0, 180, 10): img.fill(0) angle = np.deg2rad(rot) rr, cc = ellipse(500, 600, 200, 400, rotation=angle) img[rr, cc] = 1 # estimate orientation of ellipse angle_estim = np.round(regionprops(img)[0].orientation, 3) % (np.pi / 2) assert_almost_equal(angle_estim, angle % (np.pi / 2), 2) def test_ellipse_perimeter_dot_zeroangle(): # dot, angle == 0 img = np.zeros((30, 15), 'uint8') rr, cc = ellipse_perimeter(15, 7, 0, 0, 0) img[rr, cc] = 1 assert(np.sum(img) == 1) assert(img[15][7] == 1) def test_ellipse_perimeter_dot_nzeroangle(): # dot, angle != 0 img = np.zeros((30, 15), 'uint8') rr, cc = ellipse_perimeter(15, 7, 0, 0, 1) img[rr, cc] = 1 assert(np.sum(img) == 1) assert(img[15][7] == 1) def test_ellipse_perimeter_flat_zeroangle(): # flat ellipse img = np.zeros((20, 18), 'uint8') img_ = np.zeros((20, 18), 'uint8') rr, cc = ellipse_perimeter(6, 7, 0, 5, 0) img[rr, cc] = 1 rr, cc = line(6, 2, 6, 12) img_[rr, cc] = 1 assert_array_equal(img, img_) def test_ellipse_perimeter_zeroangle(): # angle == 0 img = np.zeros((30, 15), 'uint8') rr, cc = ellipse_perimeter(15, 7, 14, 6, 0) img[rr, cc] = 1 img_ = np.array( [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0]] ) assert_array_equal(img, img_) def test_ellipse_perimeter_nzeroangle(): # angle != 0 img = np.zeros((30, 25), 'uint8') rr, cc = ellipse_perimeter(15, 11, 12, 6, 1.1) img[rr, cc] = 1 img_ = np.array( [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]] ) assert_array_equal(img, img_) def test_ellipse_perimeter_shape(): img = np.zeros((15, 20), 'uint8') rr, cc = ellipse_perimeter(7, 10, 9, 9, 0, shape=(15, 20)) img[rr, cc] = 1 shift = 5 img_ = np.zeros((15 + 2 * shift, 20), 'uint8') rr, cc = ellipse_perimeter(7 + shift, 10, 9, 9, 0, shape=None) img_[rr, cc] = 1 assert_array_equal(img, img_[shift:-shift, :]) def test_bezier_segment_straight(): image = np.zeros((200, 200), dtype=int) r0, r1, r2 = 50, 150, 150 c0, c1, c2 = 50, 50, 150 rr, cc = _bezier_segment(r0, c0, r1, c1, r2, c2, 0) image[rr, cc] = 1 image2 = np.zeros((200, 200), dtype=int) rr, cc = line(r0, c0, r2, c2) image2[rr, cc] = 1 assert_array_equal(image, image2) def test_bezier_segment_curved(): img = np.zeros((25, 25), 'uint8') r0, c0 = 20, 20 r1, c1 = 20, 2 r2, c2 = 2, 2 rr, cc = _bezier_segment(r0, c0, r1, c1, r2, c2, 1) img[rr, cc] = 1 img_ = np.array( [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]] ) assert_equal(img[r0, c0], 1) assert_equal(img[r2, c2], 1) assert_array_equal(img, img_) def test_bezier_curve_straight(): image = np.zeros((200, 200), dtype=int) r0, c0 = 50, 50 r1, c1 = 150, 50 r2, c2 = 150, 150 rr, cc = bezier_curve(r0, c0, r1, c1, r2, c2, 0) image[rr, cc] = 1 image2 = np.zeros((200, 200), dtype=int) rr, cc = line(r0, c0, r2, c2) image2[rr, cc] = 1 assert_array_equal(image, image2) def test_bezier_curved_weight_eq_1(): img = np.zeros((23, 8), 'uint8') r0, c0 = 1, 1 r1, c1 = 11, 11 r2, c2 = 21, 1 rr, cc = bezier_curve(r0, c0, r1, c1, r2, c2, 1) img[rr, cc] = 1 assert_equal(img[r0, c0], 1) assert_equal(img[r2, c2], 1) img_ = np.array( [[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] ) assert_equal(img, img_) def test_bezier_curved_weight_neq_1(): img = np.zeros((23, 10), 'uint8') r0, c0 = 1, 1 r1, c1 = 11, 11 r2, c2 = 21, 1 rr, cc = bezier_curve(r0, c0, r1, c1, r2, c2, 2) img[rr, cc] = 1 assert_equal(img[r0, c0], 1) assert_equal(img[r2, c2], 1) img_ = np.array( [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]] ) assert_equal(img, img_) def test_bezier_curve_shape(): img = np.zeros((15, 20), 'uint8') r0, c0 = 1, 5 r1, c1 = 6, 11 r2, c2 = 1, 14 rr, cc = bezier_curve(r0, c0, r1, c1, r2, c2, 2, shape=(15, 20)) img[rr, cc] = 1 shift = 5 img_ = np.zeros((15 + 2 * shift, 20), 'uint8') r0, c0 = 1 + shift, 5 r1, c1 = 6 + shift, 11 r2, c2 = 1 + shift, 14 rr, cc = bezier_curve(r0, c0, r1, c1, r2, c2, 2, shape=None) img_[rr, cc] = 1 assert_array_equal(img, img_[shift:-shift, :]) def test_polygon_perimeter(): expected = np.array( [[1, 1, 1, 1], [1, 0, 0, 1], [1, 1, 1, 1]] ) out = np.zeros_like(expected) rr, cc = polygon_perimeter([0, 2, 2, 0], [0, 0, 3, 3]) out[rr, cc] = 1 assert_array_equal(out, expected) out = np.zeros_like(expected) rr, cc = polygon_perimeter([-1, -1, 3, 3], [-1, 4, 4, -1], shape=out.shape, clip=True) out[rr, cc] = 1 assert_array_equal(out, expected) assert_raises(ValueError, polygon_perimeter, [0], [1], clip=True) def test_polygon_perimeter_outside_image(): rr, cc = polygon_perimeter([-1, -1, 3, 3], [-1, 4, 4, -1], shape=(3, 4)) assert_equal(len(rr), 0) assert_equal(len(cc), 0) if __name__ == "__main__": from numpy.testing import run_module_suite run_module_suite() scikit-image-0.13.1/skimage/draw/tests/test_draw3d.py000066400000000000000000000105151316256236300224670ustar00rootroot00000000000000import numpy as np from numpy.testing import assert_array_equal, assert_allclose from nose.tools import raises from skimage.draw import ellipsoid, ellipsoid_stats @raises(ValueError) def test_ellipsoid_sign_parameters1(): ellipsoid(-1, 2, 2) @raises(ValueError) def test_ellipsoid_sign_parameters2(): ellipsoid(0, 2, 2) @raises(ValueError) def test_ellipsoid_sign_parameters3(): ellipsoid(-3, -2, 2) def test_ellipsoid_bool(): test = ellipsoid(2, 2, 2)[1:-1, 1:-1, 1:-1] test_anisotropic = ellipsoid(2, 2, 4, spacing=(1., 1., 2.)) test_anisotropic = test_anisotropic[1:-1, 1:-1, 1:-1] expected = np.array([[[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 1, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]], [[0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 1, 1, 0], [0, 1, 1, 1, 0], [0, 0, 0, 0, 0]], [[0, 0, 1, 0, 0], [0, 1, 1, 1, 0], [1, 1, 1, 1, 1], [0, 1, 1, 1, 0], [0, 0, 1, 0, 0]], [[0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 1, 1, 0], [0, 1, 1, 1, 0], [0, 0, 0, 0, 0]], [[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 1, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]]) assert_array_equal(test, expected.astype(bool)) assert_array_equal(test_anisotropic, expected.astype(bool)) def test_ellipsoid_levelset(): test = ellipsoid(2, 2, 2, levelset=True)[1:-1, 1:-1, 1:-1] test_anisotropic = ellipsoid(2, 2, 4, spacing=(1., 1., 2.), levelset=True) test_anisotropic = test_anisotropic[1:-1, 1:-1, 1:-1] expected = np.array([[[ 2. , 1.25, 1. , 1.25, 2. ], [ 1.25, 0.5 , 0.25, 0.5 , 1.25], [ 1. , 0.25, 0. , 0.25, 1. ], [ 1.25, 0.5 , 0.25, 0.5 , 1.25], [ 2. , 1.25, 1. , 1.25, 2. ]], [[ 1.25, 0.5 , 0.25, 0.5 , 1.25], [ 0.5 , -0.25, -0.5 , -0.25, 0.5 ], [ 0.25, -0.5 , -0.75, -0.5 , 0.25], [ 0.5 , -0.25, -0.5 , -0.25, 0.5 ], [ 1.25, 0.5 , 0.25, 0.5 , 1.25]], [[ 1. , 0.25, 0. , 0.25, 1. ], [ 0.25, -0.5 , -0.75, -0.5 , 0.25], [ 0. , -0.75, -1. , -0.75, 0. ], [ 0.25, -0.5 , -0.75, -0.5 , 0.25], [ 1. , 0.25, 0. , 0.25, 1. ]], [[ 1.25, 0.5 , 0.25, 0.5 , 1.25], [ 0.5 , -0.25, -0.5 , -0.25, 0.5 ], [ 0.25, -0.5 , -0.75, -0.5 , 0.25], [ 0.5 , -0.25, -0.5 , -0.25, 0.5 ], [ 1.25, 0.5 , 0.25, 0.5 , 1.25]], [[ 2. , 1.25, 1. , 1.25, 2. ], [ 1.25, 0.5 , 0.25, 0.5 , 1.25], [ 1. , 0.25, 0. , 0.25, 1. ], [ 1.25, 0.5 , 0.25, 0.5 , 1.25], [ 2. , 1.25, 1. , 1.25, 2. ]]]) assert_allclose(test, expected) assert_allclose(test_anisotropic, expected) def test_ellipsoid_stats(): # Test comparison values generated by Wolfram Alpha vol, surf = ellipsoid_stats(6, 10, 16) assert_allclose(1280 * np.pi, vol, atol=1e-4) assert_allclose(1383.28, surf, atol=1e-2) # Test when a <= b <= c does not hold vol, surf = ellipsoid_stats(16, 6, 10) assert_allclose(1280 * np.pi, vol, atol=1e-4) assert_allclose(1383.28, surf, atol=1e-2) # Larger test to ensure reliability over broad range vol, surf = ellipsoid_stats(17, 27, 169) assert_allclose(103428 * np.pi, vol, atol=1e-4) assert_allclose(37426.3, surf, atol=1e-1) if __name__ == "__main__": np.testing.run_module_suite() scikit-image-0.13.1/skimage/exposure/000077500000000000000000000000001316256236300174435ustar00rootroot00000000000000scikit-image-0.13.1/skimage/exposure/__init__.py000066400000000000000000000010261316256236300215530ustar00rootroot00000000000000from .exposure import histogram, equalize_hist, \ rescale_intensity, cumulative_distribution, \ adjust_gamma, adjust_sigmoid, adjust_log, \ is_low_contrast from ._adapthist import equalize_adapthist __all__ = ['histogram', 'equalize_hist', 'equalize_adapthist', 'rescale_intensity', 'cumulative_distribution', 'adjust_gamma', 'adjust_sigmoid', 'adjust_log', 'is_low_contrast'] scikit-image-0.13.1/skimage/exposure/_adapthist.py000066400000000000000000000257551316256236300221530ustar00rootroot00000000000000""" Adapted code from "Contrast Limited Adaptive Histogram Equalization" by Karel Zuiderveld , Graphics Gems IV, Academic Press, 1994. http://tog.acm.org/resources/GraphicsGems/ The Graphics Gems code is copyright-protected. In other words, you cannot claim the text of the code as your own and resell it. Using the code is permitted in any program, product, or library, non-commercial or commercial. Giving credit is not required, though is a nice gesture. The code comes as-is, and if there are any flaws or problems with any Gems code, nobody involved with Gems - authors, editors, publishers, or webmasters - are to be held responsible. Basically, don't be a jerk, and remember that anything free comes with no guarantee. """ from __future__ import division import numbers import numpy as np from .. import img_as_float, img_as_uint from ..color.adapt_rgb import adapt_rgb, hsv_value from ..exposure import rescale_intensity NR_OF_GREY = 2 ** 14 # number of grayscale levels to use in CLAHE algorithm @adapt_rgb(hsv_value) def equalize_adapthist(image, kernel_size=None, clip_limit=0.01, nbins=256, **kwargs): """Contrast Limited Adaptive Histogram Equalization (CLAHE). An algorithm for local contrast enhancement, that uses histograms computed over different tile regions of the image. Local details can therefore be enhanced even in regions that are darker or lighter than most of the image. Parameters ---------- image : (M, N[, C]) ndarray Input image. kernel_size: integer or list-like, optional Defines the shape of contextual regions used in the algorithm. If iterable is passed, it must have the same number of elements as ``image.ndim`` (without color channel). If integer, it is broadcasted to each `image` dimension. By default, ``kernel_size`` is 1/8 of ``image`` height by 1/8 of its width. clip_limit : float, optional Clipping limit, normalized between 0 and 1 (higher values give more contrast). nbins : int, optional Number of gray bins for histogram ("data range"). Returns ------- out : (M, N[, C]) ndarray Equalized image. See Also -------- equalize_hist, rescale_intensity Notes ----- * For color images, the following steps are performed: - The image is converted to HSV color space - The CLAHE algorithm is run on the V (Value) channel - The image is converted back to RGB space and returned * For RGBA images, the original alpha channel is removed. References ---------- .. [1] http://tog.acm.org/resources/GraphicsGems/ .. [2] https://en.wikipedia.org/wiki/CLAHE#CLAHE """ image = img_as_uint(image) image = rescale_intensity(image, out_range=(0, NR_OF_GREY - 1)) if kwargs: if 'ntiles_x' in kwargs or 'ntiles_y' in kwargs: msg = '`ntiles_*` have been deprecated in favor of `kernel_size`' raise ValueError(msg) if kernel_size is None: kernel_size = (image.shape[0] // 8, image.shape[1] // 8) elif isinstance(kernel_size, numbers.Number): kernel_size = (kernel_size,) * image.ndim elif len(kernel_size) != image.ndim: ValueError('Incorrect value of `kernel_size`: {}'.format(kernel_size)) kernel_size = [int(k) for k in kernel_size] image = _clahe(image, kernel_size, clip_limit * nbins, nbins) image = img_as_float(image) return rescale_intensity(image) def _clahe(image, kernel_size, clip_limit, nbins=128): """Contrast Limited Adaptive Histogram Equalization. Parameters ---------- image : (M, N) ndarray Input image. kernel_size: 2-tuple of int Defines the shape of contextual regions used in the algorithm. clip_limit : float Normalized clipping limit (higher values give more contrast). nbins : int, optional Number of gray bins for histogram ("data range"). Returns ------- out : (M, N) ndarray Equalized image. The number of "effective" greylevels in the output image is set by `nbins`; selecting a small value (eg. 128) speeds up processing and still produce an output image of good quality. The output image will have the same minimum and maximum value as the input image. A clip limit smaller than 1 results in standard (non-contrast limited) AHE. """ if clip_limit == 1.0: return image # is OK, immediately returns original image. nr = int(np.ceil(image.shape[0] / kernel_size[0])) nc = int(np.ceil(image.shape[1] / kernel_size[1])) row_step = int(np.floor(image.shape[0] / nr)) col_step = int(np.floor(image.shape[1] / nc)) bin_size = 1 + NR_OF_GREY // nbins lut = np.arange(NR_OF_GREY) lut //= bin_size map_array = np.zeros((nr, nc, nbins), dtype=int) # Calculate greylevel mappings for each contextual region for r in range(nr): for c in range(nc): sub_img = image[r * row_step: (r + 1) * row_step, c * col_step: (c + 1) * col_step] if clip_limit > 0.0: # Calculate actual cliplimit clim = int(clip_limit * sub_img.size / nbins) if clim < 1: clim = 1 else: clim = NR_OF_GREY # Large value, do not clip (AHE) hist = lut[sub_img.ravel()] hist = np.bincount(hist) hist = np.append(hist, np.zeros(nbins - hist.size, dtype=int)) hist = clip_histogram(hist, clim) hist = map_histogram(hist, 0, NR_OF_GREY - 1, sub_img.size) map_array[r, c] = hist # Interpolate greylevel mappings to get CLAHE image rstart = 0 for r in range(nr + 1): cstart = 0 if r == 0: # special case: top row r_offset = row_step / 2.0 rU = 0 rB = 0 elif r == nr: # special case: bottom row r_offset = row_step / 2.0 rU = nr - 1 rB = rU else: # default values r_offset = row_step rU = r - 1 rB = rB + 1 for c in range(nc + 1): if c == 0: # special case: left column c_offset = col_step / 2.0 cL = 0 cR = 0 elif c == nc: # special case: right column c_offset = col_step / 2.0 cL = nc - 1 cR = cL else: # default values c_offset = col_step cL = c - 1 cR = cL + 1 mapLU = map_array[rU, cL] mapRU = map_array[rU, cR] mapLB = map_array[rB, cL] mapRB = map_array[rB, cR] cslice = np.arange(cstart, cstart + c_offset) rslice = np.arange(rstart, rstart + r_offset) interpolate(image, cslice, rslice, mapLU, mapRU, mapLB, mapRB, lut) cstart += c_offset # set pointer on next matrix */ rstart += r_offset return image def clip_histogram(hist, clip_limit): """Perform clipping of the histogram and redistribution of bins. The histogram is clipped and the number of excess pixels is counted. Afterwards the excess pixels are equally redistributed across the whole histogram (providing the bin count is smaller than the cliplimit). Parameters ---------- hist : ndarray Histogram array. clip_limit : int Maximum allowed bin count. Returns ------- hist : ndarray Clipped histogram. """ # calculate total number of excess pixels excess_mask = hist > clip_limit excess = hist[excess_mask] n_excess = excess.sum() - excess.size * clip_limit # Second part: clip histogram and redistribute excess pixels in each bin bin_incr = int(n_excess / hist.size) # average binincrement upper = clip_limit - bin_incr # Bins larger than upper set to cliplimit hist[excess_mask] = clip_limit low_mask = hist < upper n_excess -= hist[low_mask].size * bin_incr hist[low_mask] += bin_incr mid_mask = (hist >= upper) & (hist < clip_limit) mid = hist[mid_mask] n_excess -= mid.size * clip_limit - mid.sum() hist[mid_mask] = clip_limit prev_n_excess = n_excess while n_excess > 0: # Redistribute remaining excess index = 0 while n_excess > 0 and index < hist.size: under_mask = hist < 0 step_size = int(hist[hist < clip_limit].size / n_excess) step_size = max(step_size, 1) indices = np.arange(index, hist.size, step_size) under_mask[indices] = True under_mask = (under_mask) & (hist < clip_limit) hist[under_mask] += 1 n_excess -= under_mask.sum() index += 1 # bail if we have not distributed any excess if prev_n_excess == n_excess: break prev_n_excess = n_excess return hist def map_histogram(hist, min_val, max_val, n_pixels): """Calculate the equalized lookup table (mapping). It does so by cumulating the input histogram. Parameters ---------- hist : ndarray Clipped histogram. min_val : int Minimum value for mapping. max_val : int Maximum value for mapping. n_pixels : int Number of pixels in the region. Returns ------- out : ndarray Mapped intensity LUT. """ out = np.cumsum(hist).astype(float) scale = ((float)(max_val - min_val)) / n_pixels out *= scale out += min_val out[out > max_val] = max_val return out.astype(int) def interpolate(image, xslice, yslice, mapLU, mapRU, mapLB, mapRB, lut): """Find the new grayscale level for a region using bilinear interpolation. Parameters ---------- image : ndarray Full image. xslice, yslice : array-like Indices of the region. map* : ndarray Mappings of greylevels from histograms. lut : ndarray Maps grayscale levels in image to histogram levels. Returns ------- out : ndarray Original image with the subregion replaced. Notes ----- This function calculates the new greylevel assignments of pixels within a submatrix of the image. This is done by a bilinear interpolation between four different mappings in order to eliminate boundary artifacts. """ norm = xslice.size * yslice.size # Normalization factor # interpolation weight matrices x_coef, y_coef = np.meshgrid(np.arange(xslice.size), np.arange(yslice.size)) x_inv_coef, y_inv_coef = x_coef[:, ::-1] + 1, y_coef[::-1] + 1 view = image[int(yslice[0]):int(yslice[-1] + 1), int(xslice[0]):int(xslice[-1] + 1)] im_slice = lut[view] new = ((y_inv_coef * (x_inv_coef * mapLU[im_slice] + x_coef * mapRU[im_slice]) + y_coef * (x_inv_coef * mapLB[im_slice] + x_coef * mapRB[im_slice])) / norm) view[:, :] = new return image scikit-image-0.13.1/skimage/exposure/exposure.py000066400000000000000000000371051316256236300216750ustar00rootroot00000000000000from __future__ import division import numpy as np from ..color import rgb2gray from ..util.dtype import dtype_range, dtype_limits from .._shared.utils import warn __all__ = ['histogram', 'cumulative_distribution', 'equalize_hist', 'rescale_intensity', 'adjust_gamma', 'adjust_log', 'adjust_sigmoid'] DTYPE_RANGE = dtype_range.copy() DTYPE_RANGE.update((d.__name__, limits) for d, limits in dtype_range.items()) DTYPE_RANGE.update({'uint10': (0, 2 ** 10 - 1), 'uint12': (0, 2 ** 12 - 1), 'uint14': (0, 2 ** 14 - 1), 'bool': dtype_range[np.bool_], 'float': dtype_range[np.float64]}) def histogram(image, nbins=256): """Return histogram of image. Unlike `numpy.histogram`, this function returns the centers of bins and does not rebin integer arrays. For integer arrays, each integer value has its own bin, which improves speed and intensity-resolution. The histogram is computed on the flattened image: for color images, the function should be used separately on each channel to obtain a histogram for each color channel. Parameters ---------- image : array Input image. nbins : int Number of bins used to calculate histogram. This value is ignored for integer arrays. Returns ------- hist : array The values of the histogram. bin_centers : array The values at the center of the bins. See Also -------- cumulative_distribution Examples -------- >>> from skimage import data, exposure, img_as_float >>> image = img_as_float(data.camera()) >>> np.histogram(image, bins=2) (array([107432, 154712]), array([ 0. , 0.5, 1. ])) >>> exposure.histogram(image, nbins=2) (array([107432, 154712]), array([ 0.25, 0.75])) """ sh = image.shape if len(sh) == 3 and sh[-1] < 4: warn("This might be a color image. The histogram will be " "computed on the flattened image. You can instead " "apply this function to each color channel.") # For integer types, histogramming with bincount is more efficient. if np.issubdtype(image.dtype, np.integer): offset = 0 image_min = np.min(image) if image_min < 0: offset = image_min image_range = np.max(image).astype(np.int64) - image_min # get smallest dtype that can hold both minimum and offset maximum offset_dtype = np.promote_types(np.min_scalar_type(image_range), np.min_scalar_type(image_min)) if image.dtype != offset_dtype: # prevent overflow errors when offsetting image = image.astype(offset_dtype) image = image - offset hist = np.bincount(image.ravel()) bin_centers = np.arange(len(hist)) + offset # clip histogram to start with a non-zero bin idx = np.nonzero(hist)[0][0] return hist[idx:], bin_centers[idx:] else: hist, bin_edges = np.histogram(image.flat, bins=nbins) bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2. return hist, bin_centers def cumulative_distribution(image, nbins=256): """Return cumulative distribution function (cdf) for the given image. Parameters ---------- image : array Image array. nbins : int Number of bins for image histogram. Returns ------- img_cdf : array Values of cumulative distribution function. bin_centers : array Centers of bins. See Also -------- histogram References ---------- .. [1] http://en.wikipedia.org/wiki/Cumulative_distribution_function Examples -------- >>> from skimage import data, exposure, img_as_float >>> image = img_as_float(data.camera()) >>> hi = exposure.histogram(image) >>> cdf = exposure.cumulative_distribution(image) >>> np.alltrue(cdf[0] == np.cumsum(hi[0])/float(image.size)) True """ hist, bin_centers = histogram(image, nbins) img_cdf = hist.cumsum() img_cdf = img_cdf / float(img_cdf[-1]) return img_cdf, bin_centers def equalize_hist(image, nbins=256, mask=None): """Return image after histogram equalization. Parameters ---------- image : array Image array. nbins : int, optional Number of bins for image histogram. Note: this argument is ignored for integer images, for which each integer is its own bin. mask: ndarray of bools or 0s and 1s, optional Array of same shape as `image`. Only points at which mask == True are used for the equalization, which is applied to the whole image. Returns ------- out : float array Image array after histogram equalization. Notes ----- This function is adapted from [1]_ with the author's permission. References ---------- .. [1] http://www.janeriksolem.net/2009/06/histogram-equalization-with-python-and.html .. [2] http://en.wikipedia.org/wiki/Histogram_equalization """ if mask is not None: mask = np.array(mask, dtype=bool) cdf, bin_centers = cumulative_distribution(image[mask], nbins) else: cdf, bin_centers = cumulative_distribution(image, nbins) out = np.interp(image.flat, bin_centers, cdf) return out.reshape(image.shape) def intensity_range(image, range_values='image', clip_negative=False): """Return image intensity range (min, max) based on desired value type. Parameters ---------- image : array Input image. range_values : str or 2-tuple The image intensity range is configured by this parameter. The possible values for this parameter are enumerated below. 'image' Return image min/max as the range. 'dtype' Return min/max of the image's dtype as the range. dtype-name Return intensity range based on desired `dtype`. Must be valid key in `DTYPE_RANGE`. Note: `image` is ignored for this range type. 2-tuple Return `range_values` as min/max intensities. Note that there's no reason to use this function if you just want to specify the intensity range explicitly. This option is included for functions that use `intensity_range` to support all desired range types. clip_negative : bool If True, clip the negative range (i.e. return 0 for min intensity) even if the image dtype allows negative values. """ if range_values == 'dtype': range_values = image.dtype.type if range_values == 'image': i_min = np.min(image) i_max = np.max(image) elif range_values in DTYPE_RANGE: i_min, i_max = DTYPE_RANGE[range_values] if clip_negative: i_min = 0 else: i_min, i_max = range_values return i_min, i_max def rescale_intensity(image, in_range='image', out_range='dtype'): """Return image after stretching or shrinking its intensity levels. The desired intensity range of the input and output, `in_range` and `out_range` respectively, are used to stretch or shrink the intensity range of the input image. See examples below. Parameters ---------- image : array Image array. in_range, out_range : str or 2-tuple Min and max intensity values of input and output image. The possible values for this parameter are enumerated below. 'image' Use image min/max as the intensity range. 'dtype' Use min/max of the image's dtype as the intensity range. dtype-name Use intensity range based on desired `dtype`. Must be valid key in `DTYPE_RANGE`. 2-tuple Use `range_values` as explicit min/max intensities. Returns ------- out : array Image array after rescaling its intensity. This image is the same dtype as the input image. See Also -------- equalize_hist Examples -------- By default, the min/max intensities of the input image are stretched to the limits allowed by the image's dtype, since `in_range` defaults to 'image' and `out_range` defaults to 'dtype': >>> image = np.array([51, 102, 153], dtype=np.uint8) >>> rescale_intensity(image) array([ 0, 127, 255], dtype=uint8) It's easy to accidentally convert an image dtype from uint8 to float: >>> 1.0 * image array([ 51., 102., 153.]) Use `rescale_intensity` to rescale to the proper range for float dtypes: >>> image_float = 1.0 * image >>> rescale_intensity(image_float) array([ 0. , 0.5, 1. ]) To maintain the low contrast of the original, use the `in_range` parameter: >>> rescale_intensity(image_float, in_range=(0, 255)) array([ 0.2, 0.4, 0.6]) If the min/max value of `in_range` is more/less than the min/max image intensity, then the intensity levels are clipped: >>> rescale_intensity(image_float, in_range=(0, 102)) array([ 0.5, 1. , 1. ]) If you have an image with signed integers but want to rescale the image to just the positive range, use the `out_range` parameter: >>> image = np.array([-10, 0, 10], dtype=np.int8) >>> rescale_intensity(image, out_range=(0, 127)) array([ 0, 63, 127], dtype=int8) """ dtype = image.dtype.type imin, imax = intensity_range(image, in_range) omin, omax = intensity_range(image, out_range, clip_negative=(imin >= 0)) image = np.clip(image, imin, imax) image = (image - imin) / float(imax - imin) return dtype(image * (omax - omin) + omin) def _assert_non_negative(image): if np.any(image < 0): raise ValueError('Image Correction methods work correctly only on ' 'images with non-negative values. Use ' 'skimage.exposure.rescale_intensity.') def adjust_gamma(image, gamma=1, gain=1): """Performs Gamma Correction on the input image. Also known as Power Law Transform. This function transforms the input image pixelwise according to the equation ``O = I**gamma`` after scaling each pixel to the range 0 to 1. Parameters ---------- image : ndarray Input image. gamma : float Non negative real number. Default value is 1. gain : float The constant multiplier. Default value is 1. Returns ------- out : ndarray Gamma corrected output image. See Also -------- adjust_log Notes ----- For gamma greater than 1, the histogram will shift towards left and the output image will be darker than the input image. For gamma less than 1, the histogram will shift towards right and the output image will be brighter than the input image. References ---------- .. [1] http://en.wikipedia.org/wiki/Gamma_correction Examples -------- >>> from skimage import data, exposure, img_as_float >>> image = img_as_float(data.moon()) >>> gamma_corrected = exposure.adjust_gamma(image, 2) >>> # Output is darker for gamma > 1 >>> image.mean() > gamma_corrected.mean() True """ _assert_non_negative(image) dtype = image.dtype.type if gamma < 0: raise ValueError("Gamma should be a non-negative real number.") scale = float(dtype_limits(image, True)[1] - dtype_limits(image, True)[0]) out = ((image / scale) ** gamma) * scale * gain return dtype(out) def adjust_log(image, gain=1, inv=False): """Performs Logarithmic correction on the input image. This function transforms the input image pixelwise according to the equation ``O = gain*log(1 + I)`` after scaling each pixel to the range 0 to 1. For inverse logarithmic correction, the equation is ``O = gain*(2**I - 1)``. Parameters ---------- image : ndarray Input image. gain : float The constant multiplier. Default value is 1. inv : float If True, it performs inverse logarithmic correction, else correction will be logarithmic. Defaults to False. Returns ------- out : ndarray Logarithm corrected output image. See Also -------- adjust_gamma References ---------- .. [1] http://www.ece.ucsb.edu/Faculty/Manjunath/courses/ece178W03/EnhancePart1.pdf """ _assert_non_negative(image) dtype = image.dtype.type scale = float(dtype_limits(image, True)[1] - dtype_limits(image, True)[0]) if inv: out = (2 ** (image / scale) - 1) * scale * gain return dtype(out) out = np.log2(1 + image / scale) * scale * gain return dtype(out) def adjust_sigmoid(image, cutoff=0.5, gain=10, inv=False): """Performs Sigmoid Correction on the input image. Also known as Contrast Adjustment. This function transforms the input image pixelwise according to the equation ``O = 1/(1 + exp*(gain*(cutoff - I)))`` after scaling each pixel to the range 0 to 1. Parameters ---------- image : ndarray Input image. cutoff : float Cutoff of the sigmoid function that shifts the characteristic curve in horizontal direction. Default value is 0.5. gain : float The constant multiplier in exponential's power of sigmoid function. Default value is 10. inv : bool If True, returns the negative sigmoid correction. Defaults to False. Returns ------- out : ndarray Sigmoid corrected output image. See Also -------- adjust_gamma References ---------- .. [1] Gustav J. Braun, "Image Lightness Rescaling Using Sigmoidal Contrast Enhancement Functions", http://www.cis.rit.edu/fairchild/PDFs/PAP07.pdf """ _assert_non_negative(image) dtype = image.dtype.type scale = float(dtype_limits(image, True)[1] - dtype_limits(image, True)[0]) if inv: out = (1 - 1 / (1 + np.exp(gain * (cutoff - image / scale)))) * scale return dtype(out) out = (1 / (1 + np.exp(gain * (cutoff - image / scale)))) * scale return dtype(out) def is_low_contrast(image, fraction_threshold=0.05, lower_percentile=1, upper_percentile=99, method='linear'): """Detemine if an image is low contrast. Parameters ---------- image : array-like The image under test. fraction_threshold : float, optional The low contrast fraction threshold. An image is considered low- contrast when its range of brightness spans less than this fraction of its data type's full range. [1]_ lower_bound : float, optional Disregard values below this percentile when computing image contrast. upper_bound : float, optional Disregard values above this percentile when computing image contrast. method : str, optional The contrast determination method. Right now the only available option is "linear". Returns ------- out : bool True when the image is determined to be low contrast. References ---------- .. [1] http://scikit-image.org/docs/dev/user_guide/data_types.html Examples -------- >>> image = np.linspace(0, 0.04, 100) >>> is_low_contrast(image) True >>> image[-1] = 1 >>> is_low_contrast(image) True >>> is_low_contrast(image, upper_percentile=100) False """ image = np.asanyarray(image) if image.ndim == 3 and image.shape[2] in [3, 4]: image = rgb2gray(image) dlimits = dtype_limits(image, clip_negative=False) limits = np.percentile(image, [lower_percentile, upper_percentile]) ratio = (limits[1] - limits[0]) / (dlimits[1] - dlimits[0]) return ratio < fraction_threshold scikit-image-0.13.1/skimage/exposure/setup.py000066400000000000000000000013611316256236300211560ustar00rootroot00000000000000#!/usr/bin/env python import os base_path = os.path.abspath(os.path.dirname(__file__)) def configuration(parent_package='', top_path=None): from numpy.distutils.misc_util import Configuration config = Configuration('exposure', parent_package, top_path) config.add_data_dir('tests') return config if __name__ == '__main__': from numpy.distutils.core import setup setup(maintainer='scikit-image Developers', author='scikit-image Developers', maintainer_email='scikit-image@python.org', description='Exposure corrections', url='https://github.com/scikit-image/scikit-image', license='SciPy License (BSD Style)', **(configuration(top_path='').todict()) ) scikit-image-0.13.1/skimage/exposure/tests/000077500000000000000000000000001316256236300206055ustar00rootroot00000000000000scikit-image-0.13.1/skimage/exposure/tests/__init__.py000066400000000000000000000001771316256236300227230ustar00rootroot00000000000000from ..._shared.testing import setup_test, teardown_test def setup(): setup_test() def teardown(): teardown_test() scikit-image-0.13.1/skimage/exposure/tests/test_exposure.py000066400000000000000000000415241316256236300240760ustar00rootroot00000000000000import warnings import numpy as np from numpy.testing import (assert_array_equal, assert_array_almost_equal, assert_raises, assert_almost_equal) import skimage from skimage import data from skimage import exposure from skimage.exposure.exposure import intensity_range from skimage.color import rgb2gray from skimage.util.dtype import dtype_range from skimage._shared._warnings import expected_warnings # Test integer histograms # ======================= def test_negative_overflow(): im = np.array([-1, 127], dtype=np.int8) frequencies, bin_centers = exposure.histogram(im) assert_array_equal(bin_centers, np.arange(-1, 128)) assert frequencies[0] == 1 assert frequencies[-1] == 1 assert_array_equal(frequencies[1:-1], 0) def test_all_negative_image(): im = np.array([-128, -1], dtype=np.int8) frequencies, bin_centers = exposure.histogram(im) assert_array_equal(bin_centers, np.arange(-128, 0)) assert frequencies[0] == 1 assert frequencies[-1] == 1 assert_array_equal(frequencies[1:-1], 0) # Test histogram equalization # =========================== np.random.seed(0) test_img_int = data.camera() # squeeze image intensities to lower image contrast test_img = skimage.img_as_float(test_img_int) test_img = exposure.rescale_intensity(test_img / 5. + 100) def test_equalize_uint8_approx(): """Check integer bins used for uint8 images.""" img_eq0 = exposure.equalize_hist(test_img_int) img_eq1 = exposure.equalize_hist(test_img_int, nbins=3) np.testing.assert_allclose(img_eq0, img_eq1) def test_equalize_ubyte(): with expected_warnings(['precision loss']): img = skimage.img_as_ubyte(test_img) img_eq = exposure.equalize_hist(img) cdf, bin_edges = exposure.cumulative_distribution(img_eq) check_cdf_slope(cdf) def test_equalize_float(): img = skimage.img_as_float(test_img) img_eq = exposure.equalize_hist(img) cdf, bin_edges = exposure.cumulative_distribution(img_eq) check_cdf_slope(cdf) def test_equalize_masked(): img = skimage.img_as_float(test_img) mask = np.zeros(test_img.shape) mask[50:150, 50:250] = 1 img_mask_eq = exposure.equalize_hist(img, mask=mask) img_eq = exposure.equalize_hist(img) cdf, bin_edges = exposure.cumulative_distribution(img_mask_eq) check_cdf_slope(cdf) assert not (img_eq == img_mask_eq).all() def check_cdf_slope(cdf): """Slope of cdf which should equal 1 for an equalized histogram.""" norm_intensity = np.linspace(0, 1, len(cdf)) slope, intercept = np.polyfit(norm_intensity, cdf, 1) assert 0.9 < slope < 1.1 # Test intensity range # ==================== def test_intensity_range_uint8(): image = np.array([0, 1], dtype=np.uint8) input_and_expected = [('image', [0, 1]), ('dtype', [0, 255]), ((10, 20), [10, 20])] for range_values, expected_values in input_and_expected: out = intensity_range(image, range_values=range_values) yield assert_array_equal, out, expected_values def test_intensity_range_float(): image = np.array([0.1, 0.2], dtype=np.float64) input_and_expected = [('image', [0.1, 0.2]), ('dtype', [-1, 1]), ((0.3, 0.4), [0.3, 0.4])] for range_values, expected_values in input_and_expected: out = intensity_range(image, range_values=range_values) yield assert_array_equal, out, expected_values def test_intensity_range_clipped_float(): image = np.array([0.1, 0.2], dtype=np.float64) out = intensity_range(image, range_values='dtype', clip_negative=True) assert_array_equal(out, (0, 1)) # Test rescale intensity # ====================== uint10_max = 2**10 - 1 uint12_max = 2**12 - 1 uint14_max = 2**14 - 1 uint16_max = 2**16 - 1 def test_rescale_stretch(): image = np.array([51, 102, 153], dtype=np.uint8) out = exposure.rescale_intensity(image) assert out.dtype == np.uint8 assert_array_almost_equal(out, [0, 127, 255]) def test_rescale_shrink(): image = np.array([51., 102., 153.]) out = exposure.rescale_intensity(image) assert_array_almost_equal(out, [0, 0.5, 1]) def test_rescale_in_range(): image = np.array([51., 102., 153.]) out = exposure.rescale_intensity(image, in_range=(0, 255)) assert_array_almost_equal(out, [0.2, 0.4, 0.6]) def test_rescale_in_range_clip(): image = np.array([51., 102., 153.]) out = exposure.rescale_intensity(image, in_range=(0, 102)) assert_array_almost_equal(out, [0.5, 1, 1]) def test_rescale_out_range(): image = np.array([-10, 0, 10], dtype=np.int8) out = exposure.rescale_intensity(image, out_range=(0, 127)) assert out.dtype == np.int8 assert_array_almost_equal(out, [0, 63, 127]) def test_rescale_named_in_range(): image = np.array([0, uint10_max, uint10_max + 100], dtype=np.uint16) out = exposure.rescale_intensity(image, in_range='uint10') assert_array_almost_equal(out, [0, uint16_max, uint16_max]) def test_rescale_named_out_range(): image = np.array([0, uint16_max], dtype=np.uint16) out = exposure.rescale_intensity(image, out_range='uint10') assert_array_almost_equal(out, [0, uint10_max]) def test_rescale_uint12_limits(): image = np.array([0, uint16_max], dtype=np.uint16) out = exposure.rescale_intensity(image, out_range='uint12') assert_array_almost_equal(out, [0, uint12_max]) def test_rescale_uint14_limits(): image = np.array([0, uint16_max], dtype=np.uint16) out = exposure.rescale_intensity(image, out_range='uint14') assert_array_almost_equal(out, [0, uint14_max]) # Test adaptive histogram equalization # ==================================== def test_adapthist_scalar(): """Test a scalar uint8 image """ img = skimage.img_as_ubyte(data.moon()) adapted = exposure.equalize_adapthist(img, kernel_size=64, clip_limit=0.02) assert adapted.min() == 0.0 assert adapted.max() == 1.0 assert img.shape == adapted.shape full_scale = skimage.exposure.rescale_intensity(skimage.img_as_float(img)) assert_almost_equal(peak_snr(full_scale, adapted), 102.066, 3) assert_almost_equal(norm_brightness_err(full_scale, adapted), 0.038, 3) def test_adapthist_grayscale(): """Test a grayscale float image """ img = skimage.img_as_float(data.astronaut()) img = rgb2gray(img) img = np.dstack((img, img, img)) with expected_warnings(['precision loss|non-contiguous input']): adapted = exposure.equalize_adapthist(img, kernel_size=(57, 51), clip_limit=0.01, nbins=128) assert img.shape == adapted.shape assert_almost_equal(peak_snr(img, adapted), 102.078, 3) assert_almost_equal(norm_brightness_err(img, adapted), 0.0529, 3) def test_adapthist_color(): """Test an RGB color uint16 image """ img = skimage.img_as_uint(data.astronaut()) with warnings.catch_warnings(record=True) as w: warnings.simplefilter('always') hist, bin_centers = exposure.histogram(img) assert len(w) > 0 with expected_warnings(['precision loss']): adapted = exposure.equalize_adapthist(img, clip_limit=0.01) assert adapted.min() == 0 assert adapted.max() == 1.0 assert img.shape == adapted.shape full_scale = skimage.exposure.rescale_intensity(img) assert_almost_equal(peak_snr(full_scale, adapted), 109.393, 1) assert_almost_equal(norm_brightness_err(full_scale, adapted), 0.02, 2) return data, adapted def test_adapthist_alpha(): """Test an RGBA color image """ img = skimage.img_as_float(data.astronaut()) alpha = np.ones((img.shape[0], img.shape[1]), dtype=float) img = np.dstack((img, alpha)) with expected_warnings(['precision loss']): adapted = exposure.equalize_adapthist(img) assert adapted.shape != img.shape img = img[:, :, :3] full_scale = skimage.exposure.rescale_intensity(img) assert img.shape == adapted.shape assert_almost_equal(peak_snr(full_scale, adapted), 109.393, 2) assert_almost_equal(norm_brightness_err(full_scale, adapted), 0.0248, 3) def test_adapthist_ntiles_raises(): img = skimage.img_as_ubyte(data.moon()) assert_raises(ValueError, exposure.equalize_adapthist, img, ntiles_x=8) assert_raises(ValueError, exposure.equalize_adapthist, img, ntiles_y=8) assert_raises(ValueError, exposure.equalize_adapthist, img, ntiles_x=8, ntiles_y=8) def peak_snr(img1, img2): """Peak signal to noise ratio of two images Parameters ---------- img1 : array-like img2 : array-like Returns ------- peak_snr : float Peak signal to noise ratio """ if img1.ndim == 3: img1, img2 = rgb2gray(img1.copy()), rgb2gray(img2.copy()) img1 = skimage.img_as_float(img1) img2 = skimage.img_as_float(img2) mse = 1. / img1.size * np.square(img1 - img2).sum() _, max_ = dtype_range[img1.dtype.type] return 20 * np.log(max_ / mse) def norm_brightness_err(img1, img2): """Normalized Absolute Mean Brightness Error between two images Parameters ---------- img1 : array-like img2 : array-like Returns ------- norm_brightness_error : float Normalized absolute mean brightness error """ if img1.ndim == 3: img1, img2 = rgb2gray(img1), rgb2gray(img2) ambe = np.abs(img1.mean() - img2.mean()) nbe = ambe / dtype_range[img1.dtype.type][1] return nbe # Test Gamma Correction # ===================== def test_adjust_gamma_one(): """Same image should be returned for gamma equal to one""" image = np.random.uniform(0, 255, (8, 8)) result = exposure.adjust_gamma(image, 1) assert_array_equal(result, image) def test_adjust_gamma_zero(): """White image should be returned for gamma equal to zero""" image = np.random.uniform(0, 255, (8, 8)) result = exposure.adjust_gamma(image, 0) dtype = image.dtype.type assert_array_equal(result, dtype_range[dtype][1]) def test_adjust_gamma_less_one(): """Verifying the output with expected results for gamma correction with gamma equal to half""" image = np.arange(0, 255, 4, np.uint8).reshape((8, 8)) expected = np.array([ [ 0, 31, 45, 55, 63, 71, 78, 84], [ 90, 95, 100, 105, 110, 115, 119, 123], [127, 131, 135, 139, 142, 146, 149, 153], [156, 159, 162, 165, 168, 171, 174, 177], [180, 183, 186, 188, 191, 194, 196, 199], [201, 204, 206, 209, 211, 214, 216, 218], [221, 223, 225, 228, 230, 232, 234, 236], [238, 241, 243, 245, 247, 249, 251, 253]], dtype=np.uint8) result = exposure.adjust_gamma(image, 0.5) assert_array_equal(result, expected) def test_adjust_gamma_greater_one(): """Verifying the output with expected results for gamma correction with gamma equal to two""" image = np.arange(0, 255, 4, np.uint8).reshape((8, 8)) expected = np.array([ [ 0, 0, 0, 0, 1, 1, 2, 3], [ 4, 5, 6, 7, 9, 10, 12, 14], [ 16, 18, 20, 22, 25, 27, 30, 33], [ 36, 39, 42, 45, 49, 52, 56, 60], [ 64, 68, 72, 76, 81, 85, 90, 95], [100, 105, 110, 116, 121, 127, 132, 138], [144, 150, 156, 163, 169, 176, 182, 189], [196, 203, 211, 218, 225, 233, 241, 249]], dtype=np.uint8) result = exposure.adjust_gamma(image, 2) assert_array_equal(result, expected) def test_adjust_gamma_neggative(): image = np.arange(0, 255, 4, np.uint8).reshape((8, 8)) assert_raises(ValueError, exposure.adjust_gamma, image, -1) # Test Logarithmic Correction # =========================== def test_adjust_log(): """Verifying the output with expected results for logarithmic correction with multiplier constant multiplier equal to unity""" image = np.arange(0, 255, 4, np.uint8).reshape((8, 8)) expected = np.array([ [ 0, 5, 11, 16, 22, 27, 33, 38], [ 43, 48, 53, 58, 63, 68, 73, 77], [ 82, 86, 91, 95, 100, 104, 109, 113], [117, 121, 125, 129, 133, 137, 141, 145], [149, 153, 157, 160, 164, 168, 172, 175], [179, 182, 186, 189, 193, 196, 199, 203], [206, 209, 213, 216, 219, 222, 225, 228], [231, 234, 238, 241, 244, 246, 249, 252]], dtype=np.uint8) result = exposure.adjust_log(image, 1) assert_array_equal(result, expected) def test_adjust_inv_log(): """Verifying the output with expected results for inverse logarithmic correction with multiplier constant multiplier equal to unity""" image = np.arange(0, 255, 4, np.uint8).reshape((8, 8)) expected = np.array([ [ 0, 2, 5, 8, 11, 14, 17, 20], [ 23, 26, 29, 32, 35, 38, 41, 45], [ 48, 51, 55, 58, 61, 65, 68, 72], [ 76, 79, 83, 87, 90, 94, 98, 102], [106, 110, 114, 118, 122, 126, 130, 134], [138, 143, 147, 151, 156, 160, 165, 170], [174, 179, 184, 188, 193, 198, 203, 208], [213, 218, 224, 229, 234, 239, 245, 250]], dtype=np.uint8) result = exposure.adjust_log(image, 1, True) assert_array_equal(result, expected) # Test Sigmoid Correction # ======================= def test_adjust_sigmoid_cutoff_one(): """Verifying the output with expected results for sigmoid correction with cutoff equal to one and gain of 5""" image = np.arange(0, 255, 4, np.uint8).reshape((8, 8)) expected = np.array([ [ 1, 1, 1, 2, 2, 2, 2, 2], [ 3, 3, 3, 4, 4, 4, 5, 5], [ 5, 6, 6, 7, 7, 8, 9, 10], [ 10, 11, 12, 13, 14, 15, 16, 18], [ 19, 20, 22, 24, 25, 27, 29, 32], [ 34, 36, 39, 41, 44, 47, 50, 54], [ 57, 61, 64, 68, 72, 76, 80, 85], [ 89, 94, 99, 104, 108, 113, 118, 123]], dtype=np.uint8) result = exposure.adjust_sigmoid(image, 1, 5) assert_array_equal(result, expected) def test_adjust_sigmoid_cutoff_zero(): """Verifying the output with expected results for sigmoid correction with cutoff equal to zero and gain of 10""" image = np.arange(0, 255, 4, np.uint8).reshape((8, 8)) expected = np.array([ [127, 137, 147, 156, 166, 175, 183, 191], [198, 205, 211, 216, 221, 225, 229, 232], [235, 238, 240, 242, 244, 245, 247, 248], [249, 250, 250, 251, 251, 252, 252, 253], [253, 253, 253, 253, 254, 254, 254, 254], [254, 254, 254, 254, 254, 254, 254, 254], [254, 254, 254, 254, 254, 254, 254, 254], [254, 254, 254, 254, 254, 254, 254, 254]], dtype=np.uint8) result = exposure.adjust_sigmoid(image, 0, 10) assert_array_equal(result, expected) def test_adjust_sigmoid_cutoff_half(): """Verifying the output with expected results for sigmoid correction with cutoff equal to half and gain of 10""" image = np.arange(0, 255, 4, np.uint8).reshape((8, 8)) expected = np.array([ [ 1, 1, 2, 2, 3, 3, 4, 5], [ 5, 6, 7, 9, 10, 12, 14, 16], [ 19, 22, 25, 29, 34, 39, 44, 50], [ 57, 64, 72, 80, 89, 99, 108, 118], [128, 138, 148, 158, 167, 176, 184, 192], [199, 205, 211, 217, 221, 226, 229, 233], [236, 238, 240, 242, 244, 246, 247, 248], [249, 250, 250, 251, 251, 252, 252, 253]], dtype=np.uint8) result = exposure.adjust_sigmoid(image, 0.5, 10) assert_array_equal(result, expected) def test_adjust_inv_sigmoid_cutoff_half(): """Verifying the output with expected results for inverse sigmoid correction with cutoff equal to half and gain of 10""" image = np.arange(0, 255, 4, np.uint8).reshape((8, 8)) expected = np.array([ [253, 253, 252, 252, 251, 251, 250, 249], [249, 248, 247, 245, 244, 242, 240, 238], [235, 232, 229, 225, 220, 215, 210, 204], [197, 190, 182, 174, 165, 155, 146, 136], [126, 116, 106, 96, 87, 78, 70, 62], [ 55, 49, 43, 37, 33, 28, 25, 21], [ 18, 16, 14, 12, 10, 8, 7, 6], [ 5, 4, 4, 3, 3, 2, 2, 1]], dtype=np.uint8) result = exposure.adjust_sigmoid(image, 0.5, 10, True) assert_array_equal(result, expected) def test_negative(): image = np.arange(-10, 245, 4).reshape((8, 8)).astype(np.double) assert_raises(ValueError, exposure.adjust_gamma, image) def test_is_low_contrast(): image = np.linspace(0, 0.04, 100) assert exposure.is_low_contrast(image) image[-1] = 1 assert exposure.is_low_contrast(image) assert not exposure.is_low_contrast(image, upper_percentile=100) image = (image * 255).astype(np.uint8) assert exposure.is_low_contrast(image) assert not exposure.is_low_contrast(image, upper_percentile=100) image = (image.astype(np.uint16)) * 2**8 assert exposure.is_low_contrast(image) assert not exposure.is_low_contrast(image, upper_percentile=100) if __name__ == '__main__': from numpy import testing testing.run_module_suite() scikit-image-0.13.1/skimage/external/000077500000000000000000000000001316256236300174135ustar00rootroot00000000000000scikit-image-0.13.1/skimage/external/LICENSE.txt000066400000000000000000000003601316256236300212350ustar00rootroot00000000000000 tifffile Copyright (c) 2008-2014, Christoph Gohlke Copyright (c) 2008-2014, The Regents of the University of California Produced at the Laboratory for Fluorescence Dynamics Distributed under the terms of the BSD License. scikit-image-0.13.1/skimage/external/__init__.py000066400000000000000000000000001316256236300215120ustar00rootroot00000000000000scikit-image-0.13.1/skimage/external/setup.py000066400000000000000000000015331316256236300211270ustar00rootroot00000000000000#!/usr/bin/env python import os.path base_path = os.path.abspath(os.path.dirname(__file__)) def configuration(parent_package='', top_path=None): from numpy.distutils.misc_util import Configuration, get_numpy_include_dirs config = Configuration('external', parent_package, top_path) config.add_extension('tifffile._tifffile', sources=['tifffile/tifffile.c'], include_dirs=[get_numpy_include_dirs()]) return config if __name__ == '__main__': from numpy.distutils.core import setup setup(maintainer='scikit-image Developers', maintainer_email='scikit-image@python.org', description='External Libaries', url='https://github.com/scikit-image/scikit-image', license='Modified BSD', **(configuration(top_path='').todict()) ) scikit-image-0.13.1/skimage/external/test_tifffile.py000066400000000000000000000042551316256236300226220ustar00rootroot00000000000000import os import numpy as np try: import skimage as si except Exception: si = None from numpy.testing import ( assert_array_equal, assert_array_almost_equal, run_module_suite) from numpy.testing.decorators import skipif from tempfile import NamedTemporaryFile from .tifffile import imread, imsave np.random.seed(0) @skipif(si is None) def test_imread_uint16(): expected = np.load(os.path.join(si.data_dir, 'chessboard_GRAY_U8.npy')) img = imread(os.path.join(si.data_dir, 'chessboard_GRAY_U16.tif')) assert img.dtype == np.uint16 assert_array_almost_equal(img, expected) @skipif(si is None) def test_imread_uint16_big_endian(): expected = np.load(os.path.join(si.data_dir, 'chessboard_GRAY_U8.npy')) img = imread(os.path.join(si.data_dir, 'chessboard_GRAY_U16B.tif')) assert img.dtype == np.uint16 assert_array_almost_equal(img, expected) def test_extension(): from .tifffile.tifffile import decode_packbits import types assert isinstance(decode_packbits, types.BuiltinFunctionType), type(decode_packbits) class TestSave: def roundtrip(self, dtype, x): # input: file name f = NamedTemporaryFile(suffix='.tif') fname = f.name f.close() imsave(fname, x) y = imread(fname) assert_array_equal(x, y) # input: open file object f = NamedTemporaryFile(suffix='.tif') imsave(f, x) f.seek(0) y = imread(f) assert_array_equal(x, y) f.close() #input: byte stream from io import BytesIO b = BytesIO() imsave(b, x) b.seek(0) y = imread(b) assert_array_equal(x, y) def test_imsave_roundtrip(self): for shape in [(10, 10), (10, 10, 3), (10, 10, 4)]: for dtype in (np.uint8, np.uint16, np.float32, np.int16, np.float64): x = np.random.rand(*shape) if not np.issubdtype(dtype, float): x = (x * np.iinfo(dtype).max).astype(dtype) else: x = x.astype(dtype) yield self.roundtrip, dtype, x if __name__ == "__main__": run_module_suite() scikit-image-0.13.1/skimage/external/tifffile/000077500000000000000000000000001316256236300212035ustar00rootroot00000000000000scikit-image-0.13.1/skimage/external/tifffile/__init__.py000066400000000000000000000003061316256236300233130ustar00rootroot00000000000000from .tifffile import imsave, imread, imshow, TiffFile, TiffWriter, TiffSequence __version__ = '0.6.2' __all__ = ('imsave', 'imread', 'imshow', 'TiffFile', 'TiffWriter', 'TiffSequence') scikit-image-0.13.1/skimage/external/tifffile/tifffile.c000066400000000000000000000721711316256236300231470ustar00rootroot00000000000000/* tifffile.c A Python C extension module for decoding PackBits and LZW encoded TIFF data. Refer to the tifffile.py module for documentation and tests. :Author: `Christoph Gohlke `_ :Organization: Laboratory for Fluorescence Dynamics, University of California, Irvine :Version: 2017.01.10 Requirements ------------ * `CPython 2.7 or 3.5 `_ * `Numpy 1.11 `_ * A Python distutils compatible C compiler (build) * `stdint.h `_ for msvc9 compiler Install ------- Use this Python distutils setup script to build the extension module:: # setup.py # Usage: ``python setup.py build_ext --inplace`` from distutils.core import setup, Extension import numpy setup(name='_tifffile', ext_modules=[Extension('_tifffile', ['tifffile.c'], include_dirs=[numpy.get_include()])]) License ------- Copyright (c) 2008-2017, Christoph Gohlke Copyright (c) 2008-2017, The Regents of the University of California Produced at the Laboratory for Fluorescence Dynamics All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holders nor the names of any contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. */ #define _VERSION_ "2017.01.10" #define WIN32_LEAN_AND_MEAN #define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION #include "Python.h" #include "string.h" #include "numpy/arrayobject.h" #include "stdint.h" /* little endian by default */ #ifndef MSB #define MSB 1 #endif #if MSB #define LSB 0 #define BOC '<' #else #define LSB 1 #define BOC '>' #endif #define NO_ERROR 0 #define VALUE_ERROR -1 #ifndef SSIZE_MAX #define SSIZE_MAX INTPTR_MAX #endif #define SWAP2BYTES(x) \ ((((x) >> 8) & 0x00FF) | (((x) & 0x00FF) << 8)) #define SWAP4BYTES(x) \ ((((x) >> 24) & 0x00FF) | (((x)&0x00FF) << 24) | \ (((x) >> 8 ) & 0xFF00) | (((x)&0xFF00) << 8)) #define SWAP8BYTES(x) \ ((((x) >> 56) & 0x00000000000000FF) | (((x) >> 40) & 0x000000000000FF00) | \ (((x) >> 24) & 0x0000000000FF0000) | (((x) >> 8) & 0x00000000FF000000) | \ (((x) << 8) & 0x000000FF00000000) | (((x) << 24) & 0x0000FF0000000000) | \ (((x) << 40) & 0x00FF000000000000) | (((x) << 56) & 0xFF00000000000000)) typedef union { uint8_t b[2]; uint16_t i; } u_uint16; typedef union { uint8_t b[4]; uint32_t i; } u_uint32; typedef union { uint8_t b[8]; uint64_t i; } u_uint64; typedef struct { ssize_t len; char* str; } string_t; /*****************************************************************************/ /* C functions */ /* Return mask for itemsize bits */ unsigned char bitmask(const int itemsize) { unsigned char result = 0; unsigned char power = 1; int i; for (i = 0; i < itemsize; i++) { result += power; power *= 2; } return result << (8 - itemsize); } /** Unpack sequence of tigthly packed 1-32 bit integers. Native byte order will be returned. Input data array should be padded to the next 16, 32 or 64-bit boundary if itemsize not in (1, 2, 4, 8, 16, 24, 32, 64). */ int unpackbits( unsigned char* data, const ssize_t size, /** size of data in bytes */ const int itemsize, /** number of bits in integer */ ssize_t numitems, /** number of items to unpack */ unsigned char* result /** buffer to store unpacked items */ ) { ssize_t i, j, k, storagesize; unsigned char value; /* Input validation is done in wrapper function */ storagesize = (ssize_t)(ceil(itemsize / 8.0)); storagesize = storagesize < 3 ? storagesize : storagesize > 4 ? 8 : 4; switch (itemsize) { case 8: case 16: case 32: case 64: memcpy(result, data, numitems*storagesize); return NO_ERROR; case 1: for (i = 0, j = 0; i < numitems/8; i++) { value = data[i]; result[j++] = (value & (unsigned char)(128)) >> 7; result[j++] = (value & (unsigned char)(64)) >> 6; result[j++] = (value & (unsigned char)(32)) >> 5; result[j++] = (value & (unsigned char)(16)) >> 4; result[j++] = (value & (unsigned char)(8)) >> 3; result[j++] = (value & (unsigned char)(4)) >> 2; result[j++] = (value & (unsigned char)(2)) >> 1; result[j++] = (value & (unsigned char)(1)); } if (numitems % 8) { value = data[i]; switch (numitems % 8) { case 7: result[j+6] = (value & (unsigned char)(2)) >> 1; case 6: result[j+5] = (value & (unsigned char)(4)) >> 2; case 5: result[j+4] = (value & (unsigned char)(8)) >> 3; case 4: result[j+3] = (value & (unsigned char)(16)) >> 4; case 3: result[j+2] = (value & (unsigned char)(32)) >> 5; case 2: result[j+1] = (value & (unsigned char)(64)) >> 6; case 1: result[j] = (value & (unsigned char)(128)) >> 7; } } return NO_ERROR; case 2: for (i = 0, j = 0; i < numitems/4; i++) { value = data[i]; result[j++] = (value & (unsigned char)(192)) >> 6; result[j++] = (value & (unsigned char)(48)) >> 4; result[j++] = (value & (unsigned char)(12)) >> 2; result[j++] = (value & (unsigned char)(3)); } if (numitems % 4) { value = data[i]; switch (numitems % 4) { case 3: result[j+2] = (value & (unsigned char)(12)) >> 2; case 2: result[j+1] = (value & (unsigned char)(48)) >> 4; case 1: result[j] = (value & (unsigned char)(192)) >> 6; } } return NO_ERROR; case 4: for (i = 0, j = 0; i < numitems/2; i++) { value = data[i]; result[j++] = (value & (unsigned char)(240)) >> 4; result[j++] = (value & (unsigned char)(15)); } if (numitems % 2) { value = data[i]; result[j] = (value & (unsigned char)(240)) >> 4; } return NO_ERROR; case 24: j = k = 0; for (i = 0; i < numitems; i++) { result[j++] = 0; result[j++] = data[k++]; result[j++] = data[k++]; result[j++] = data[k++]; } return NO_ERROR; } /* 3, 5, 6, 7 */ if (itemsize < 8) { int shr = 16; u_uint16 value, mask, tmp; j = k = 0; value.b[MSB] = data[j++]; value.b[LSB] = data[j++]; mask.b[MSB] = bitmask(itemsize); mask.b[LSB] = 0; for (i = 0; i < numitems; i++) { shr -= itemsize; tmp.i = (value.i & mask.i) >> shr; result[k++] = tmp.b[LSB]; if (shr < itemsize) { value.b[MSB] = value.b[LSB]; value.b[LSB] = data[j++]; mask.i <<= 8 - itemsize; shr += 8; } else { mask.i >>= itemsize; } } return NO_ERROR; } /* 9, 10, 11, 12, 13, 14, 15 */ if (itemsize < 16) { int shr = 32; u_uint32 value, mask, tmp; mask.i = 0; j = k = 0; #if MSB for (i = 3; i >= 0; i--) { value.b[i] = data[j++]; } mask.b[3] = 0xFF; mask.b[2] = bitmask(itemsize-8); for (i = 0; i < numitems; i++) { shr -= itemsize; tmp.i = (value.i & mask.i) >> shr; result[k++] = tmp.b[0]; /* swap bytes */ result[k++] = tmp.b[1]; if (shr < itemsize) { value.b[3] = value.b[1]; value.b[2] = value.b[0]; value.b[1] = data[j++]; value.b[0] = data[j++]; mask.i <<= 16 - itemsize; shr += 16; } else { mask.i >>= itemsize; } } #else /* not implemented */ #endif return NO_ERROR; } /* 17, 18, 19, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31 */ if (itemsize < 32) { int shr = 64; u_uint64 value, mask, tmp; mask.i = 0; j = k = 0; #if MSB for (i = 7; i >= 0; i--) { value.b[i] = data[j++]; } mask.b[7] = 0xFF; mask.b[6] = 0xFF; mask.b[5] = itemsize > 23 ? 0xFF : bitmask(itemsize-16); mask.b[4] = itemsize < 24 ? 0x00 : bitmask(itemsize-24); for (i = 0; i < numitems; i++) { shr -= itemsize; tmp.i = (value.i & mask.i) >> shr; result[k++] = tmp.b[0]; /* swap bytes */ result[k++] = tmp.b[1]; result[k++] = tmp.b[2]; result[k++] = tmp.b[3]; if (shr < itemsize) { value.b[7] = value.b[3]; value.b[6] = value.b[2]; value.b[5] = value.b[1]; value.b[4] = value.b[0]; value.b[3] = data[j++]; value.b[2] = data[j++]; value.b[1] = data[j++]; value.b[0] = data[j++]; mask.i <<= 32 - itemsize; shr += 32; } else { mask.i >>= itemsize; } } #else /* Not implemented */ #endif return NO_ERROR; } return VALUE_ERROR; } /*****************************************************************************/ /* Python functions */ /** Reverse bits in bytes of byte string or ndarray. */ char py_reverse_bitorder_doc[] = "Reverse bits in each byte of byte string or numpy array."; static PyObject* py_reverse_bitorder(PyObject* obj, PyObject* args) { PyObject* dataobj = NULL; PyObject* result = NULL; PyArray_Descr* dtype = NULL; PyArrayIterObject* iter = NULL; unsigned char* dataptr = NULL; unsigned char* resultptr = NULL; Py_ssize_t size, stride; Py_ssize_t i, j; int axis = -1; if (!PyArg_ParseTuple(args, "O", &dataobj)) return NULL; Py_INCREF(dataobj); if (PyBytes_Check(dataobj)) { dataptr = (unsigned char*)PyBytes_AS_STRING(dataobj); size = PyBytes_GET_SIZE(dataobj); result = PyBytes_FromStringAndSize(NULL, size); if (result == NULL) { PyErr_Format(PyExc_MemoryError, "unable to allocate result"); goto _fail; } resultptr = (unsigned char*)PyBytes_AS_STRING(result); Py_BEGIN_ALLOW_THREADS for (i = 0; i < size; i++) { /* http://graphics.stanford.edu/~seander/bithacks.html #ReverseByteWith64Bits */ *resultptr++ = (unsigned char)(((*dataptr++ * 0x80200802ULL) & 0x0884422110ULL) * 0x0101010101ULL >> 32); } Py_END_ALLOW_THREADS Py_DECREF(dataobj); return result; } else if (PyArray_Check(dataobj)) { dtype = PyArray_DTYPE((PyArrayObject*) dataobj); if (dtype->elsize == 0) { PyErr_Format(PyExc_ValueError, "can not handle dtype"); goto _fail; } iter = (PyArrayIterObject*)PyArray_IterAllButAxis(dataobj, &axis); size = PyArray_DIM((PyArrayObject*)dataobj, axis); stride = PyArray_STRIDE((PyArrayObject*)dataobj, axis); stride -= dtype->elsize; Py_BEGIN_ALLOW_THREADS while (iter->index < iter->size) { dataptr = (unsigned char*)iter->dataptr; for(i = 0; i < size; i++) { for(j = 0; j < dtype->elsize; j++) { *dataptr = (unsigned char)(((*dataptr * 0x80200802ULL) & 0x0884422110ULL) * 0x0101010101ULL >> 32); dataptr++; } dataptr += stride; } PyArray_ITER_NEXT(iter); } Py_END_ALLOW_THREADS Py_DECREF(iter); Py_DECREF(dataobj); Py_RETURN_NONE; } else { PyErr_Format(PyExc_TypeError, "not a byte string or ndarray"); goto _fail; } _fail: Py_XDECREF(dataobj); Py_XDECREF(result); Py_XDECREF(iter); return NULL; } /** Unpack tightly packed integers. */ char py_unpackints_doc[] = "Unpack groups of bits into numpy array."; static PyObject* py_unpackints(PyObject* obj, PyObject* args, PyObject* kwds) { PyObject* byteobj = NULL; PyArrayObject* result = NULL; PyArray_Descr* dtype = NULL; char* encoded = NULL; char* decoded = NULL; Py_ssize_t encoded_len = 0; Py_ssize_t decoded_len = 0; Py_ssize_t runlen = 0; Py_ssize_t i; int storagesize, bytesize; int itemsize = 0; int skipbits = 0; static char* kwlist[] = {"data", "dtype", "itemsize", "runlen", NULL}; if (!PyArg_ParseTupleAndKeywords(args, kwds, "OO&i|i", kwlist, &byteobj, PyArray_DescrConverter, &dtype, &itemsize, &runlen)) return NULL; Py_INCREF(byteobj); if (((itemsize < 1) || (itemsize > 32)) && (itemsize != 64)) { PyErr_Format(PyExc_ValueError, "itemsize out of range"); goto _fail; } if (!PyBytes_Check(byteobj)) { PyErr_Format(PyExc_TypeError, "expected byte string as input"); goto _fail; } encoded = PyBytes_AS_STRING(byteobj); encoded_len = PyBytes_GET_SIZE(byteobj); bytesize = (int)ceil(itemsize / 8.0); storagesize = bytesize < 3 ? bytesize : bytesize > 4 ? 8 : 4; if ((encoded_len < bytesize) || (encoded_len > SSIZE_MAX / storagesize)) { PyErr_Format(PyExc_ValueError, "data size out of range"); goto _fail; } if (dtype->elsize != storagesize) { PyErr_Format(PyExc_TypeError, "dtype.elsize does not fit itemsize"); goto _fail; } if (runlen == 0) { runlen = (Py_ssize_t)(((uint64_t)encoded_len*8) / (uint64_t)itemsize); } skipbits = (Py_ssize_t)(((uint64_t)runlen * (uint64_t)itemsize) % 8); if (skipbits > 0) { skipbits = 8 - skipbits; } decoded_len = (Py_ssize_t)((uint64_t)runlen * (((uint64_t)encoded_len*8) / ((uint64_t)runlen*(uint64_t)itemsize + (uint64_t)skipbits))); result = (PyArrayObject*)PyArray_SimpleNew(1, &decoded_len, dtype->type_num); if (result == NULL) { PyErr_Format(PyExc_MemoryError, "unable to allocate output array"); goto _fail; } decoded = (char*)PyArray_DATA(result); for (i = 0; i < decoded_len; i+=runlen) { if (NO_ERROR != unpackbits((unsigned char*) encoded, (ssize_t) encoded_len, (int) itemsize, (ssize_t) runlen, (unsigned char*) decoded)) { PyErr_Format(PyExc_ValueError, "unpackbits() failed"); goto _fail; } encoded += (Py_ssize_t)(((uint64_t)runlen * (uint64_t)itemsize + (uint64_t)skipbits) / 8); decoded += runlen * storagesize; } if ((dtype->byteorder != BOC) && (itemsize % 8 == 0)) { switch (dtype->elsize) { case 2: { uint16_t* d = (uint16_t*)PyArray_DATA(result); for (i = 0; i < PyArray_SIZE(result); i++) { *d = SWAP2BYTES(*d); d++; } break; } case 4: { uint32_t* d = (uint32_t*)PyArray_DATA(result); for (i = 0; i < PyArray_SIZE(result); i++) { *d = SWAP4BYTES(*d); d++; } break; } case 8: { uint64_t* d = (uint64_t*)PyArray_DATA(result); for (i = 0; i < PyArray_SIZE(result); i++) { *d = SWAP8BYTES(*d); d++; } break; } } } Py_DECREF(byteobj); Py_DECREF(dtype); return PyArray_Return(result); _fail: Py_XDECREF(byteobj); Py_XDECREF(result); Py_XDECREF(dtype); return NULL; } /** Decode TIFF PackBits encoded string. */ char py_decodepackbits_doc[] = "Return TIFF PackBits decoded string."; static PyObject* py_decodepackbits(PyObject* obj, PyObject* args) { int n; char e; char* decoded = NULL; char* encoded = NULL; char* encoded_end = NULL; char* encoded_pos = NULL; unsigned int encoded_len; unsigned int decoded_len; PyObject* byteobj = NULL; PyObject* result = NULL; if (!PyArg_ParseTuple(args, "O", &byteobj)) return NULL; if (!PyBytes_Check(byteobj)) { PyErr_Format(PyExc_TypeError, "expected byte string as input"); goto _fail; } Py_INCREF(byteobj); encoded = PyBytes_AS_STRING(byteobj); encoded_len = (unsigned int)PyBytes_GET_SIZE(byteobj); /* release GIL: byte/string objects are immutable */ Py_BEGIN_ALLOW_THREADS /* determine size of decoded string */ encoded_pos = encoded; encoded_end = encoded + encoded_len; decoded_len = 0; while (encoded_pos < encoded_end) { n = (int)*encoded_pos++; if (n >= 0) { n++; if (encoded_pos+n > encoded_end) n = (int)(encoded_end - encoded_pos); encoded_pos += n; decoded_len += n; } else if (n > -128) { encoded_pos++; decoded_len += 1-n; } } Py_END_ALLOW_THREADS result = PyBytes_FromStringAndSize(NULL, decoded_len); if (result == NULL) { PyErr_Format(PyExc_MemoryError, "failed to allocate decoded string"); goto _fail; } decoded = PyBytes_AS_STRING(result); Py_BEGIN_ALLOW_THREADS /* decode string */ encoded_end = encoded + encoded_len; while (encoded < encoded_end) { n = (int)*encoded++; if (n >= 0) { n++; if (encoded+n > encoded_end) n = (int)(encoded_end - encoded); /* memmove(decoded, encoded, n); decoded += n; encoded += n; */ while (n--) *decoded++ = *encoded++; } else if (n > -128) { n = 1 - n; e = *encoded++; /* memset(decoded, e, n); decoded += n; */ while (n--) *decoded++ = e; } } Py_END_ALLOW_THREADS Py_DECREF(byteobj); return result; _fail: Py_XDECREF(byteobj); Py_XDECREF(result); return NULL; } /** Decode TIFF LZW encoded string. */ char py_decodelzw_doc[] = "Return TIFF LZW decoded string."; #define GET_NEXT_CODE \ code = *((uint32_t*)((void*)(encoded + (bitcount >> 3)))); \ if (little_endian) \ code = SWAP4BYTES(code); \ code <<= (uint32_t)(bitcount % 8); \ code &= mask; \ code >>= shr; \ bitcount += bitw; \ static PyObject* py_decodelzw(PyObject* obj, PyObject* args) { PyObject* byteobj = NULL; PyObject* decoded = NULL; string_t* table = NULL; char* encoded = NULL; char* buffer = NULL; char* pbuffer = NULL; char* pdecoded = NULL; char* pstr = NULL; int little_endian; uint32_t code, oldcode, mask, shr, table_size; ssize_t i, decoded_size, buffersize, buffer_size; uint64_t bitcount, bitw, encoded_size; if (!PyArg_ParseTuple(args, "O", &byteobj)) return NULL; if (!PyBytes_Check(byteobj)) { PyErr_Format(PyExc_TypeError, "expected byte string as input"); goto _fail; } Py_INCREF(byteobj); encoded = PyBytes_AS_STRING(byteobj); encoded_size = (uint64_t)PyBytes_GET_SIZE(byteobj); /* if (encoded_size >= 512 * 1024 * 1024) { PyErr_Format(PyExc_ValueError, "encoded data > 512 MB not supported"); goto _fail; } */ encoded_size *= 8; /* bits */ if ((*encoded != -128) || ((*(encoded+1) & 128))) { PyErr_Format(PyExc_ValueError, "strip must begin with CLEAR code"); goto _fail; } little_endian = (*(uint16_t*)encoded) & 128; table = PyMem_Malloc(4096 * sizeof(string_t)); if (table == NULL) { PyErr_Format(PyExc_MemoryError, "failed to allocate table"); goto _fail; } for (i = 0; i < 4096; i++) { table[i].len = 1; } /* determine length of output and string buffer */ table_size = 258; bitw = 9; shr = 23; mask = 4286578688u; bitcount = 0; decoded_size = 0; buffer_size = 0; buffersize = 0; code = 0; oldcode = 0; Py_BEGIN_ALLOW_THREADS while ((bitcount + bitw) <= encoded_size) { /* read next code */ GET_NEXT_CODE if (code == 257) break; /* end of information */ if (code == 256) { /* clearcode */ /* initialize table and switch to 9-bit */ table_size = 258; bitw = 9; shr = 23; mask = 4286578688u; if (buffersize > buffer_size) buffer_size = buffersize; buffersize = 0; /* read next code, skip clearcodes */ do { GET_NEXT_CODE } while (code == 256); if (code == 257) break; /* end of information */ decoded_size++; } else { if (code < table_size) { /* code is in table */ decoded_size += table[code].len; buffersize += table[oldcode].len + 1; } else { /* code is not in table */ decoded_size += table[oldcode].len + 1; } table[table_size++].len = table[oldcode].len + 1; /* increase bit-width if necessary */ switch (table_size) { case 511: bitw = 10; shr = 22; mask = 4290772992u; break; case 1023: bitw = 11; shr = 21; mask = 4292870144u; break; case 2047: bitw = 12; shr = 20; mask = 4293918720u; } } oldcode = code; } Py_END_ALLOW_THREADS if (buffersize > buffer_size) buffer_size = buffersize; if (code != 257) { PyErr_WarnEx( NULL, "py_decodelzw encountered unexpected end of stream", 1); } /* allocate output and buffer string */ decoded = PyBytes_FromStringAndSize(0, decoded_size); if (decoded == NULL) { PyErr_Format(PyExc_MemoryError, "failed to allocate decoded string"); goto _fail; } pdecoded = PyBytes_AS_STRING(decoded); buffer = PyMem_Malloc(buffer_size); if (buffer == NULL) { PyErr_Format(PyExc_MemoryError, "failed to allocate string buffer"); goto _fail; } /* decode input to output string */ table_size = 258; bitw = 9; shr = 23; mask = 4286578688u; bitcount = 0; pbuffer = buffer; Py_BEGIN_ALLOW_THREADS while ((bitcount + bitw) <= encoded_size) { /* read next code */ GET_NEXT_CODE if (code == 257) break; /* end of information */ if (code == 256) { /* clearcode */ /* initialize table and switch to 9-bit */ table_size = 258; bitw = 9; shr = 23; mask = 4286578688u; pbuffer = buffer; /* read next code, skip clearcodes */ do { GET_NEXT_CODE } while (code == 256); if (code == 257) break; /* end of information */ *pdecoded++ = code; } else { if (code < table_size) { /* code is in table */ /* decoded.append(table[code]) */ if (code < 256) { *pdecoded++ = code; } else { pstr = table[code].str; for (i = 0; i < table[code].len; i++) { *pdecoded++ = *pstr++; } } /* table.append(table[oldcode] + table[code][0]) */ table[table_size].str = pbuffer; if (oldcode < 256) { *pbuffer++ = oldcode; } else { pstr = table[oldcode].str; for (i = 0; i < table[oldcode].len; i++) { *pbuffer++ = *pstr++; } } if (code < 256) { *pbuffer++ = code; } else { *pbuffer++ = table[code].str[0]; } } else { /* code is not in table */ /* outstring = table[oldcode] + table[oldcode][0] */ /* decoded.append(outstring) */ /* table.append(outstring) */ table[table_size].str = pdecoded; if (oldcode < 256) { *pdecoded++ = oldcode; *pdecoded++ = oldcode; } else { pstr = table[oldcode].str; for (i = 0; i < table[oldcode].len; i++) { *pdecoded++ = *pstr++; } *pdecoded++ = table[oldcode].str[0]; } } table[table_size++].len = table[oldcode].len + 1; /* increase bit-width if necessary */ switch (table_size) { case 511: bitw = 10; shr = 22; mask = 4290772992u; break; case 1023: bitw = 11; shr = 21; mask = 4292870144u; break; case 2047: bitw = 12; shr = 20; mask = 4293918720u; } } oldcode = code; } Py_END_ALLOW_THREADS PyMem_Free(buffer); PyMem_Free(table); Py_DECREF(byteobj); return decoded; _fail: if (table) PyMem_Free(table); if (buffer) PyMem_Free(buffer); Py_XDECREF(byteobj); Py_XDECREF(decoded); return NULL; } /*****************************************************************************/ /* Create Python module */ char module_doc[] = "A Python C extension module for decoding PackBits and LZW encoded " "TIFF data.\n\n" "Refer to the tifffile.py module for documentation and tests.\n\n" "Authors:\n Christoph Gohlke \n" " Laboratory for Fluorescence Dynamics, University of California, Irvine." "\n\nVersion: %s\n"; static PyMethodDef module_methods[] = { #if MSB {"unpack_ints", (PyCFunction)py_unpackints, METH_VARARGS|METH_KEYWORDS, py_unpackints_doc}, #endif {"decode_lzw", (PyCFunction)py_decodelzw, METH_VARARGS, py_decodelzw_doc}, {"decode_packbits", (PyCFunction)py_decodepackbits, METH_VARARGS, py_decodepackbits_doc}, {"reverse_bitorder", (PyCFunction)py_reverse_bitorder, METH_VARARGS, py_reverse_bitorder_doc}, {NULL, NULL, 0, NULL} /* Sentinel */ }; #if PY_MAJOR_VERSION >= 3 struct module_state { PyObject* error; }; #define GETSTATE(m) ((struct module_state*)PyModule_GetState(m)) static int module_traverse(PyObject* m, visitproc visit, void *arg) { Py_VISIT(GETSTATE(m)->error); return 0; } static int module_clear(PyObject* m) { Py_CLEAR(GETSTATE(m)->error); return 0; } static struct PyModuleDef moduledef = { PyModuleDef_HEAD_INIT, "_tifffile", NULL, sizeof(struct module_state), module_methods, NULL, module_traverse, module_clear, NULL }; #define INITERROR return NULL PyMODINIT_FUNC PyInit__tifffile(void) #else #define INITERROR return PyMODINIT_FUNC init_tifffile(void) #endif { PyObject* module; char* doc = (char*)PyMem_Malloc(sizeof(module_doc) + sizeof(_VERSION_)); PyOS_snprintf(doc, sizeof(module_doc) + sizeof(_VERSION_), module_doc, _VERSION_); #if PY_MAJOR_VERSION >= 3 moduledef.m_doc = doc; module = PyModule_Create(&moduledef); #else module = Py_InitModule3("_tifffile", module_methods, doc); #endif PyMem_Free(doc); if (module == NULL) INITERROR; if (_import_array() < 0) { Py_DECREF(module); INITERROR; } { #if PY_MAJOR_VERSION < 3 PyObject* s = PyString_FromString(_VERSION_); #else PyObject* s = PyUnicode_FromString(_VERSION_); #endif PyObject* dict = PyModule_GetDict(module); PyDict_SetItemString(dict, "__version__", s); Py_DECREF(s); } #if PY_MAJOR_VERSION >= 3 return module; #endif } scikit-image-0.13.1/skimage/external/tifffile/tifffile.py000066400000000000000000007054211316256236300233560ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # tifffile.py # Copyright (c) 2008-2017, Christoph Gohlke # Copyright (c) 2008-2017, The Regents of the University of California # Produced at the Laboratory for Fluorescence Dynamics # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # * Neither the name of the copyright holders nor the names of any # contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. """Read image and meta data from (bio)TIFF files. Save numpy arrays as TIFF. Image and metadata can be read from TIFF, BigTIFF, OME-TIFF, STK, LSM, NIH, SGI, ImageJ, MicroManager, FluoView, SEQ and GEL files. Only a subset of the TIFF specification is supported, mainly uncompressed and losslessly compressed 2**(0 to 6) bit integer, 16, 32 and 64-bit float, grayscale and RGB(A) images, which are commonly used in bio-scientific imaging. Specifically, reading JPEG and CCITT compressed image data, chroma subsampling, or EXIF, IPTC, GPS, and XMP metadata is not implemented. Only primary info records are read for STK, FluoView, MicroManager, and NIH Image formats. TIFF, the Tagged Image File Format aka Thousands of Incompatible File Formats, is under the control of Adobe Systems. BigTIFF allows for files greater than 4 GB. STK, LSM, FluoView, SGI, SEQ, GEL, and OME-TIFF, are custom extensions defined by Molecular Devices (Universal Imaging Corporation), Carl Zeiss MicroImaging, Olympus, Silicon Graphics International, Media Cybernetics, Molecular Dynamics, and the Open Microscopy Environment consortium respectively. For command line usage run `python tifffile.py --help` :Author: `Christoph Gohlke `_ :Organization: Laboratory for Fluorescence Dynamics, University of California, Irvine :Version: 2017.01.12 Requirements ------------ * `CPython 2.7 or 3.5 `_ (64-bit recommended) * `Numpy 1.11 `_ * `Matplotlib 1.5 `_ (optional for plotting) * `Tifffile.c 2017.01.10 `_ (recommended for faster decoding of PackBits and LZW encoded strings) Revisions --------- 2017.01.12 Read Zeiss SEM metadata. Read OME-TIFF with invalid references to external files. Rewrite C LZW decoder (5x faster). Read corrupted LSM files missing EOI code in LZW stream. 2017.01.01 Add option to append images to existing TIFF files. Read files without pages. Read S-FEG and Helios NanoLab tags created by FEI software. Allow saving Color Filter Array (CFA) images. Add info functions returning more information about TiffFile and TiffPage. Add option to read specific pages only. Remove maxpages argument (backwards incompatible). Remove test_tifffile function. 2016.10.28 Pass 1944 tests. Improve detection of ImageJ hyperstacks. Read TVIPS metadata created by EM-MENU (by Marco Oster). Add option to disable using OME-XML metadata. Allow non-integer range attributes in modulo tags (by Stuart Berg). 2016.06.21 Do not always memmap contiguous data in page series. 2016.05.13 Add option to specify resolution unit. Write grayscale images with extra samples when planarconfig is specified. Do not write RGB color images with 2 samples. Reorder TiffWriter.save keyword arguments (backwards incompatible). 2016.04.18 Pass 1932 tests. TiffWriter, imread, and imsave accept open binary file streams. 2016.04.13 Correctly handle reversed fill order in 2 and 4 bps images (bug fix). Implement reverse_bitorder in C. 2016.03.18 Fix saving additional ImageJ metadata. 2016.02.22 Pass 1920 tests. Write 8 bytes double tag values using offset if necessary (bug fix). Add option to disable writing second image description tag. Detect tags with incorrect counts. Disable color mapping for LSM. 2015.11.13 Read LSM 6 mosaics. Add option to specify directory of memory-mapped files. Add command line options to specify vmin and vmax values for colormapping. 2015.10.06 New helper function to apply colormaps. Renamed is_palette attributes to is_indexed (backwards incompatible). Color-mapped samples are now contiguous (backwards incompatible). Do not color-map ImageJ hyperstacks (backwards incompatible). Towards supporting Leica SCN. 2015.09.25 Read images with reversed bit order (fill_order is lsb2msb). 2015.09.21 Read RGB OME-TIFF. Warn about malformed OME-XML. 2015.09.16 Detect some corrupted ImageJ metadata. Better axes labels for 'shaped' files. Do not create TiffTags for default values. Chroma subsampling is not supported. Memory-map data in TiffPageSeries if possible (optional). 2015.08.17 Pass 1906 tests. Write ImageJ hyperstacks (optional). Read and write LZMA compressed data. Specify datetime when saving (optional). Save tiled and color-mapped images (optional). Ignore void byte_counts and offsets if possible. Ignore bogus image_depth tag created by ISS Vista software. Decode floating point horizontal differencing (not tiled). Save image data contiguously if possible. Only read first IFD from ImageJ files if possible. Read ImageJ 'raw' format (files larger than 4 GB). TiffPageSeries class for pages with compatible shape and data type. Try to read incomplete tiles. Open file dialog if no filename is passed on command line. Ignore errors when decoding OME-XML. Rename decoder functions (backwards incompatible) 2014.08.24 TiffWriter class for incremental writing images. Simplify examples. 2014.08.19 Add memmap function to FileHandle. Add function to determine if image data in TiffPage is memory-mappable. Do not close files if multifile_close parameter is False. 2014.08.10 Pass 1730 tests. Return all extrasamples by default (backwards incompatible). Read data from series of pages into memory-mapped array (optional). Squeeze OME dimensions (backwards incompatible). Workaround missing EOI code in strips. Support image and tile depth tags (SGI extension). Better handling of STK/UIC tags (backwards incompatible). Disable color mapping for STK. Julian to datetime converter. TIFF ASCII type may be NULL separated. Unwrap strip offsets for LSM files greater than 4 GB. Correct strip byte counts in compressed LSM files. Skip missing files in OME series. Read embedded TIFF files. 2014.02.05 Save rational numbers as type 5 (bug fix). 2013.12.20 Keep other files in OME multi-file series closed. FileHandle class to abstract binary file handle. Disable color mapping for bad OME-TIFF produced by bio-formats. Read bad OME-XML produced by ImageJ when cropping. 2013.11.03 Allow zlib compress data in imsave function (optional). Memory-map contiguous image data (optional). 2013.10.28 Read MicroManager metadata and little endian ImageJ tag. Save extra tags in imsave function. Save tags in ascending order by code (bug fix). 2012.10.18 Accept file like objects (read from OIB files). 2012.08.21 Rename TIFFfile to TiffFile and TIFFpage to TiffPage. TiffSequence class for reading sequence of TIFF files. Read UltraQuant tags. Allow float numbers as resolution in imsave function. 2012.08.03 Read MD GEL tags and NIH Image header. 2012.07.25 Read ImageJ tags. ... Notes ----- The API is not stable yet and might change between revisions. Tested on little-endian platforms only. Other Python packages and modules for reading bio-scientific TIFF files: * `python-bioformats `_ * `Imread `_ * `PyLibTiff `_ * `SimpleITK `_ * `PyLSM `_ * `PyMca.TiffIO.py `_ (same as fabio.TiffIO) * `BioImageXD.Readers `_ * `Cellcognition.io `_ * `pymimage `_ Acknowledgements ---------------- * Egor Zindy, University of Manchester, for cz_lsm_scan_info specifics. * Wim Lewis for a bug fix and some read_cz_lsm functions. * Hadrien Mary for help on reading MicroManager files. * Christian Kliche for help writing tiled and color-mapped files. References ---------- (1) TIFF 6.0 Specification and Supplements. Adobe Systems Incorporated. http://partners.adobe.com/public/developer/tiff/ (2) TIFF File Format FAQ. http://www.awaresystems.be/imaging/tiff/faq.html (3) MetaMorph Stack (STK) Image File Format. http://support.meta.moleculardevices.com/docs/t10243.pdf (4) Image File Format Description LSM 5/7 Release 6.0 (ZEN 2010). Carl Zeiss MicroImaging GmbH. BioSciences. May 10, 2011 (5) File Format Description - LSM 5xx Release 2.0. http://ibb.gsf.de/homepage/karsten.rodenacker/IDL/Lsmfile.doc (6) The OME-TIFF format. http://www.openmicroscopy.org/site/support/file-formats/ome-tiff (7) UltraQuant(r) Version 6.0 for Windows Start-Up Guide. http://www.ultralum.com/images%20ultralum/pdf/UQStart%20Up%20Guide.pdf (8) Micro-Manager File Formats. http://www.micro-manager.org/wiki/Micro-Manager_File_Formats (9) Tags for TIFF and Related Specifications. Digital Preservation. http://www.digitalpreservation.gov/formats/content/tiff_tags.shtml Examples -------- >>> data = numpy.random.rand(5, 301, 219) >>> imsave('temp.tif', data) >>> image = imread('temp.tif') >>> numpy.testing.assert_array_equal(image, data) >>> with TiffFile('temp.tif') as tif: ... images = tif.asarray() ... for page in tif: ... for tag in page.tags.values(): ... t = tag.name, tag.value ... image = page.asarray() """ from __future__ import division, print_function import sys import os import re import glob import math import zlib import time import json import struct import warnings import tempfile import datetime import collections from fractions import Fraction from xml.etree import cElementTree as etree import numpy try: import lzma except ImportError: try: import backports.lzma as lzma except ImportError: lzma = None try: if __package__: from . import _tifffile else: import _tifffile except ImportError: warnings.warn( "ImportError: No module named '_tifffile'. " "Loading of some compressed images will be very slow. " "Tifffile.c can be obtained at http://www.lfd.uci.edu/~gohlke/") __version__ = '2017.01.12' __docformat__ = 'restructuredtext en' __all__ = ( 'imsave', 'imread', 'imshow', 'TiffFile', 'TiffWriter', 'TiffSequence', # utility functions used in oiffile and czifile 'FileHandle', 'lazyattr', 'natural_sorted', 'decode_lzw', 'stripnull') def imsave(file, data, **kwargs): """Write image data to TIFF file. Refer to the TiffWriter class and member functions for documentation. Parameters ---------- file : str or binary stream File name or writable binary stream, such as a open file or BytesIO. data : array_like Input image. The last dimensions are assumed to be image depth, height, width, and samples. kwargs : dict Parameters 'append', 'byteorder', 'bigtiff', 'software', and 'imagej', are passed to the TiffWriter class. Parameters 'photometric', 'planarconfig', 'resolution', 'compress', 'colormap', 'tile', 'description', 'datetime', 'metadata', 'contiguous' and 'extratags' are passed to the TiffWriter.save function. Examples -------- >>> data = numpy.random.rand(2, 5, 3, 301, 219) >>> imsave('temp.tif', data, compress=6, metadata={'axes': 'TZCYX'}) """ tifargs = parse_kwargs(kwargs, 'append', 'bigtiff', 'byteorder', 'software', 'imagej') if 'bigtiff' not in tifargs and 'imagej' not in tifargs and ( data.size*data.dtype.itemsize > 2000*2**20): tifargs['bigtiff'] = True with TiffWriter(file, **tifargs) as tif: tif.save(data, **kwargs) class TiffWriter(object): """Write image data to TIFF file. TiffWriter instances must be closed using the 'close' method, which is automatically called when using the 'with' context manager. Examples -------- >>> data = numpy.random.rand(2, 5, 3, 301, 219) >>> with TiffWriter('temp.tif', bigtiff=True) as tif: ... for i in range(data.shape[0]): ... tif.save(data[i], compress=6) """ TYPES = {'B': 1, 's': 2, 'H': 3, 'I': 4, '2I': 5, 'b': 6, 'h': 8, 'i': 9, '2i': 10, 'f': 11, 'd': 12, 'Q': 16, 'q': 17} TAGS = { 'new_subfile_type': 254, 'subfile_type': 255, 'image_width': 256, 'image_length': 257, 'bits_per_sample': 258, 'compression': 259, 'photometric': 262, 'document_name': 269, 'image_description': 270, 'strip_offsets': 273, 'orientation': 274, 'samples_per_pixel': 277, 'rows_per_strip': 278, 'strip_byte_counts': 279, 'x_resolution': 282, 'y_resolution': 283, 'planar_configuration': 284, 'page_name': 285, 'resolution_unit': 296, 'software': 305, 'datetime': 306, 'predictor': 317, 'color_map': 320, 'tile_width': 322, 'tile_length': 323, 'tile_offsets': 324, 'tile_byte_counts': 325, 'extra_samples': 338, 'sample_format': 339, 'smin_sample_value': 340, 'smax_sample_value': 341, 'image_depth': 32997, 'tile_depth': 32998} def __init__(self, file, append=False, bigtiff=False, byteorder=None, software='tifffile.py', imagej=False): """Open a TIFF file for writing. Existing files are overwritten by default. Use bigtiff=True when creating files larger than 2 GB. Parameters ---------- file : str, binary stream, or FileHandle File name or writable binary stream, such as a open file or BytesIO. The file is created if it does not exist. append : bool If True and 'file' is an existing standard TIFF file, image data and tags are appended to the file. Appending data may corrupt specifically formatted TIFF files such as LSM, STK, ImageJ, NIH, or FluoView. bigtiff : bool If True, the BigTIFF format is used. byteorder : {'<', '>'} The endianness of the data in the file. By default this is the system's native byte order. software : str Name of the software used to create the file. Saved with the first page in the file only. imagej : bool If True, write an ImageJ hyperstack compatible file. This format can handle data types uint8, uint16, or float32 and data shapes up to 6 dimensions in TZCYXS order. RGB images (S=3 or S=4) must be uint8. ImageJ's default byte order is big endian but this implementation uses the system's native byte order by default. ImageJ does not support BigTIFF format or LZMA compression. The ImageJ file format is undocumented. """ if append: # determine if file is an existing TIFF file that can be extended try: with FileHandle(file, mode='rb', size=0) as fh: pos = fh.tell() try: with TiffFile(fh, pages=[0]) as tif: if (append != 'force' and any(getattr(tif, 'is_'+a) for a in ('lsm', 'stk', 'imagej', 'nih', 'fluoview', 'micromanager'))): raise ValueError("contains metadata") byteorder = tif.byteorder bigtiff = tif.is_bigtiff imagej = tif.is_imagej self._ifd_offset = tif._ifd_offset if tif.pages: software = None except Exception as e: raise ValueError("can not append to file: %s" % str(e)) finally: fh.seek(pos) except (IOError, FileNotFoundError): append = False if byteorder not in (None, '<', '>'): raise ValueError("invalid byteorder %s" % byteorder) if byteorder is None: byteorder = '<' if sys.byteorder == 'little' else '>' if imagej and bigtiff: warnings.warn("writing incompatible bigtiff ImageJ") self._byteorder = byteorder self._software = software self._imagej = bool(imagej) self._metadata = None self._colormap = None self._description_offset = 0 self._description_len_offset = 0 self._description_len = 0 self._tags = None self._shape = None # normalized shape of data in consecutive pages self._data_shape = None # shape of data in consecutive pages self._data_dtype = None # data type self._data_offset = None # offset to data self._data_byte_counts = None # byte counts per plane self._tag_offsets = None # strip or tile offset tag code if bigtiff: self._bigtiff = True self._offset_size = 8 self._tag_size = 20 self._numtag_format = 'Q' self._offset_format = 'Q' self._value_format = '8s' else: self._bigtiff = False self._offset_size = 4 self._tag_size = 12 self._numtag_format = 'H' self._offset_format = 'I' self._value_format = '4s' if append: self._fh = FileHandle(file, mode='r+b', size=0) self._fh.seek(0, 2) else: self._fh = FileHandle(file, mode='wb', size=0) self._fh.write({'<': b'II', '>': b'MM'}[byteorder]) if bigtiff: self._fh.write(struct.pack(byteorder+'HHH', 43, 8, 0)) else: self._fh.write(struct.pack(byteorder+'H', 42)) # first IFD self._ifd_offset = self._fh.tell() self._fh.write(struct.pack(byteorder+self._offset_format, 0)) def save(self, data, photometric=None, planarconfig=None, tile=None, contiguous=True, compress=0, colormap=None, description=None, datetime=None, resolution=None, metadata={}, extratags=()): """Write image data and tags to TIFF file. Image data are written in one stripe per plane by default. Dimensions larger than 2 to 4 (depending on photometric mode, planar configuration, and SGI mode) are flattened and saved as separate pages. The 'sample_format' and 'bits_per_sample' tags are derived from the data type. Parameters ---------- data : numpy.ndarray Input image. The last dimensions are assumed to be image depth, height (length), width, and samples. If a colormap is provided, the dtype must be uint8 or uint16 and the data values are indices into the last dimension of the colormap. photometric : {'minisblack', 'miniswhite', 'rgb', 'palette', 'cfa'} The color space of the image data. By default this setting is inferred from the data shape and the value of colormap. For CFA images, DNG tags must be specified in extratags. planarconfig : {'contig', 'planar'} Specifies if samples are stored contiguous or in separate planes. By default this setting is inferred from the data shape. If this parameter is set, extra samples are used to store grayscale images. 'contig': last dimension contains samples. 'planar': third last dimension contains samples. tile : tuple of int The shape (depth, length, width) of image tiles to write. If None (default), image data are written in one stripe per plane. The tile length and width must be a multiple of 16. If the tile depth is provided, the SGI image_depth and tile_depth tags are used to save volume data. Few software can read the SGI format, e.g. MeVisLab. contiguous : bool If True (default) and the data and parameters are compatible with previous ones, if any, the data are stored contiguously after the previous one. Parameters 'photometric' and 'planarconfig' are ignored. compress : int or 'lzma' Values from 0 to 9 controlling the level of zlib compression. If 0, data are written uncompressed (default). Compression cannot be used to write contiguous files. If 'lzma', LZMA compression is used, which is not available on all platforms. colormap : numpy.ndarray RGB color values for the corresponding data value. Must be of shape (3, 2**(data.itemsize*8)) and dtype uint16. description : str The subject of the image. Saved with the first page only. Cannot be used with the ImageJ format. datetime : datetime Date and time of image creation. Saved with the first page only. If None (default), the current date and time is used. resolution : (float, float[, str]) or ((int, int), (int, int)[, str]) X and Y resolutions in pixels per resolution unit as float or rational numbers. A third, optional parameter specifies the resolution unit, which must be None (default for ImageJ), 'inch' (default), or 'cm'. metadata : dict Additional meta data to be saved along with shape information in JSON or ImageJ formats in an image_description tag. If None, do not write a second image_description tag. extratags : sequence of tuples Additional tags as [(code, dtype, count, value, writeonce)]. code : int The TIFF tag Id. dtype : str Data type of items in 'value' in Python struct format. One of B, s, H, I, 2I, b, h, i, 2i, f, d, Q, or q. count : int Number of data values. Not used for string values. value : sequence 'Count' values compatible with 'dtype'. writeonce : bool If True, the tag is written to the first page only. """ # TODO: refactor this function fh = self._fh byteorder = self._byteorder numtag_format = self._numtag_format value_format = self._value_format offset_format = self._offset_format offset_size = self._offset_size tag_size = self._tag_size data = numpy.asarray(data, dtype=byteorder+data.dtype.char, order='C') if data.size == 0: raise ValueError("can not save empty array") # just append contiguous data if possible if self._data_shape: if (not contiguous or self._data_shape[1:] != data.shape or self._data_dtype != data.dtype or (compress and self._tags) or tile or not numpy.array_equal(colormap, self._colormap)): # incompatible shape, dtype, compression mode, or colormap self._write_remaining_pages() self._write_image_description() self._description_offset = 0 self._description_len_offset = 0 self._data_shape = None self._colormap = None if self._imagej: raise ValueError( "ImageJ does not support non-contiguous data") else: # consecutive mode self._data_shape = (self._data_shape[0] + 1,) + data.shape if not compress: # write contiguous data, write ifds/tags later fh.write_array(data) return if photometric not in (None, 'minisblack', 'miniswhite', 'rgb', 'palette', 'cfa'): raise ValueError("invalid photometric %s" % photometric) if planarconfig not in (None, 'contig', 'planar'): raise ValueError("invalid planarconfig %s" % planarconfig) # prepare compression if not compress: compress = False compress_tag = 1 elif compress == 'lzma': compress = lzma.compress compress_tag = 34925 if self._imagej: raise ValueError("ImageJ can not handle LZMA compression") elif not 0 <= compress <= 9: raise ValueError("invalid compression level %s" % compress) elif compress: def compress(data, level=compress): return zlib.compress(data, level) compress_tag = 32946 # prepare ImageJ format if self._imagej: if description: warnings.warn("not writing description to ImageJ file") description = None volume = False if data.dtype.char not in 'BHhf': raise ValueError("ImageJ does not support data type '%s'" % data.dtype.char) ijrgb = photometric == 'rgb' if photometric else None if data.dtype.char not in 'B': ijrgb = False ijshape = imagej_shape(data.shape, ijrgb) if ijshape[-1] in (3, 4): photometric = 'rgb' if data.dtype.char not in 'B': raise ValueError("ImageJ does not support data type '%s' " "for RGB" % data.dtype.char) elif photometric is None: photometric = 'minisblack' planarconfig = None if planarconfig == 'planar': raise ValueError("ImageJ does not support planar images") else: planarconfig = 'contig' if ijrgb else None # verify colormap and indices if colormap is not None: if data.dtype.char not in 'BH': raise ValueError("invalid data dtype for palette mode") colormap = numpy.asarray(colormap, dtype=byteorder+'H') if colormap.shape != (3, 2**(data.itemsize * 8)): raise ValueError("invalid color map shape") self._colormap = colormap # verify tile shape if tile: tile = tuple(int(i) for i in tile[:3]) volume = len(tile) == 3 if (len(tile) < 2 or tile[-1] % 16 or tile[-2] % 16 or any(i < 1 for i in tile)): raise ValueError("invalid tile shape") else: tile = () volume = False # normalize data shape to 5D or 6D, depending on volume: # (pages, planar_samples, [depth,] height, width, contig_samples) data_shape = data.shape if photometric == 'rgb': data = reshape_nd(data, 3) else: data = reshape_nd(data, 2) shape = data.shape samplesperpixel = 1 extrasamples = 0 if volume and data.ndim < 3: volume = False if colormap is not None: photometric = 'palette' planarconfig = None if photometric is None: photometric = 'minisblack' if planarconfig == 'contig': if data.ndim > 2 and shape[-1] in (3, 4): photometric = 'rgb' elif planarconfig == 'planar': if volume and data.ndim > 3 and shape[-4] in (3, 4): photometric = 'rgb' elif data.ndim > 2 and shape[-3] in (3, 4): photometric = 'rgb' elif data.ndim > 2 and shape[-1] in (3, 4): photometric = 'rgb' elif self._imagej: photometric = 'minisblack' elif volume and data.ndim > 3 and shape[-4] in (3, 4): photometric = 'rgb' elif data.ndim > 2 and shape[-3] in (3, 4): photometric = 'rgb' if planarconfig and len(shape) <= (3 if volume else 2): planarconfig = None photometric = 'minisblack' if photometric == 'rgb': if len(shape) < 3: raise ValueError("not a RGB(A) image") if len(shape) < 4: volume = False if planarconfig is None: if shape[-1] in (3, 4): planarconfig = 'contig' elif shape[-4 if volume else -3] in (3, 4): planarconfig = 'planar' elif shape[-1] > shape[-4 if volume else -3]: planarconfig = 'planar' else: planarconfig = 'contig' if planarconfig == 'contig': data = data.reshape((-1, 1) + shape[(-4 if volume else -3):]) samplesperpixel = data.shape[-1] else: data = data.reshape( (-1,) + shape[(-4 if volume else -3):] + (1,)) samplesperpixel = data.shape[1] if samplesperpixel > 3: extrasamples = samplesperpixel - 3 elif photometric == 'cfa': if len(shape) != 2: raise ValueError("invalid CFA image") volume = False planarconfig = None data = data.reshape((-1, 1) + shape[-2:] + (1,)) if 50706 not in (et[0] for et in extratags): raise ValueError("must specify DNG tags for CFA image") elif planarconfig and len(shape) > (3 if volume else 2): if planarconfig == 'contig': data = data.reshape((-1, 1) + shape[(-4 if volume else -3):]) samplesperpixel = data.shape[-1] else: data = data.reshape( (-1,) + shape[(-4 if volume else -3):] + (1,)) samplesperpixel = data.shape[1] extrasamples = samplesperpixel - 1 else: planarconfig = None # remove trailing 1s while len(shape) > 2 and shape[-1] == 1: shape = shape[:-1] if len(shape) < 3: volume = False data = data.reshape( (-1, 1) + shape[(-3 if volume else -2):] + (1,)) # normalize shape to 6D assert len(data.shape) in (5, 6) if len(data.shape) == 5: data = data.reshape(data.shape[:2] + (1,) + data.shape[2:]) shape = data.shape if tile and not volume: tile = (1, tile[-2], tile[-1]) if photometric == 'palette': if (samplesperpixel != 1 or extrasamples or shape[1] != 1 or shape[-1] != 1): raise ValueError("invalid data shape for palette mode") if photometric == 'rgb' and samplesperpixel == 2: raise ValueError("not a RGB image (samplesperpixel=2)") bytestr = bytes if sys.version[0] == '2' else ( lambda x: bytes(x, 'utf-8') if isinstance(x, str) else x) tags = [] # list of (code, ifdentry, ifdvalue, writeonce) strip_or_tile = 'tile' if tile else 'strip' tag_byte_counts = TiffWriter.TAGS[strip_or_tile + '_byte_counts'] tag_offsets = TiffWriter.TAGS[strip_or_tile + '_offsets'] self._tag_offsets = tag_offsets def pack(fmt, *val): return struct.pack(byteorder+fmt, *val) def addtag(code, dtype, count, value, writeonce=False): # Compute ifdentry & ifdvalue bytes from code, dtype, count, value # Append (code, ifdentry, ifdvalue, writeonce) to tags list code = int(TiffWriter.TAGS.get(code, code)) try: tifftype = TiffWriter.TYPES[dtype] except KeyError: raise ValueError("unknown dtype %s" % dtype) rawcount = count if dtype == 's': value = bytestr(value) + b'\0' count = rawcount = len(value) rawcount = value.find(b'\0\0') if rawcount < 0: rawcount = count else: rawcount += 1 # length of string without buffer value = (value,) if len(dtype) > 1: count *= int(dtype[:-1]) dtype = dtype[-1] ifdentry = [pack('HH', code, tifftype), pack(offset_format, rawcount)] ifdvalue = None if struct.calcsize(dtype) * count <= offset_size: # value(s) can be written directly if count == 1: if isinstance(value, (tuple, list, numpy.ndarray)): value = value[0] ifdentry.append(pack(value_format, pack(dtype, value))) else: ifdentry.append(pack(value_format, pack(str(count)+dtype, *value))) else: # use offset to value(s) ifdentry.append(pack(offset_format, 0)) if isinstance(value, numpy.ndarray): assert value.size == count assert value.dtype.char == dtype ifdvalue = value.tostring() elif isinstance(value, (tuple, list)): ifdvalue = pack(str(count)+dtype, *value) else: ifdvalue = pack(dtype, value) tags.append((code, b''.join(ifdentry), ifdvalue, writeonce)) def rational(arg, max_denominator=1000000): # return nominator and denominator from float or two integers try: f = Fraction.from_float(arg) except TypeError: f = Fraction(arg[0], arg[1]) f = f.limit_denominator(max_denominator) return f.numerator, f.denominator if description: # user provided description addtag('image_description', 's', 0, description, writeonce=True) # write shape and metadata to image_description self._metadata = {} if not metadata else metadata if self._imagej: description = imagej_description( data_shape, shape[-1] in (3, 4), self._colormap is not None, **self._metadata) elif metadata or metadata == {}: description = image_description( data_shape, self._colormap is not None, **self._metadata) else: description = None if description: # add 32 bytes buffer # the image description might be updated later with the final shape description += b'\0'*32 self._description_len = len(description) addtag('image_description', 's', 0, description, writeonce=True) if self._software: addtag('software', 's', 0, self._software, writeonce=True) self._software = None # only save to first page in file if datetime is None: datetime = self._now() addtag('datetime', 's', 0, datetime.strftime("%Y:%m:%d %H:%M:%S"), writeonce=True) addtag('compression', 'H', 1, compress_tag) addtag('image_width', 'I', 1, shape[-2]) addtag('image_length', 'I', 1, shape[-3]) if tile: addtag('tile_width', 'I', 1, tile[-1]) addtag('tile_length', 'I', 1, tile[-2]) if tile[0] > 1: addtag('image_depth', 'I', 1, shape[-4]) addtag('tile_depth', 'I', 1, tile[0]) addtag('new_subfile_type', 'I', 1, 0) addtag('sample_format', 'H', 1, {'u': 1, 'i': 2, 'f': 3, 'c': 6}[data.dtype.kind]) addtag('photometric', 'H', 1, {'miniswhite': 0, 'minisblack': 1, 'rgb': 2, 'palette': 3, 'cfa': 32803}[photometric]) if colormap is not None: addtag('color_map', 'H', colormap.size, colormap) addtag('samples_per_pixel', 'H', 1, samplesperpixel) if planarconfig and samplesperpixel > 1: addtag('planar_configuration', 'H', 1, 1 if planarconfig == 'contig' else 2) addtag('bits_per_sample', 'H', samplesperpixel, (data.dtype.itemsize * 8,) * samplesperpixel) else: addtag('bits_per_sample', 'H', 1, data.dtype.itemsize * 8) if extrasamples: if photometric == 'rgb' and extrasamples == 1: addtag('extra_samples', 'H', 1, 1) # associated alpha channel else: addtag('extra_samples', 'H', extrasamples, (0,) * extrasamples) if resolution: addtag('x_resolution', '2I', 1, rational(resolution[0])) addtag('y_resolution', '2I', 1, rational(resolution[1])) if len(resolution) > 2: resolution_unit = {None: 1, 'inch': 2, 'cm': 3}[resolution[2]] elif self._imagej: resolution_unit = 1 else: resolution_unit = 2 addtag('resolution_unit', 'H', 1, resolution_unit) if not tile: addtag('rows_per_strip', 'I', 1, shape[-3]) # * shape[-4] if tile: # use one chunk per tile per plane tiles = ((shape[2] + tile[0] - 1) // tile[0], (shape[3] + tile[1] - 1) // tile[1], (shape[4] + tile[2] - 1) // tile[2]) numtiles = product(tiles) * shape[1] strip_byte_counts = [ product(tile) * shape[-1] * data.dtype.itemsize] * numtiles addtag(tag_byte_counts, offset_format, numtiles, strip_byte_counts) addtag(tag_offsets, offset_format, numtiles, [0] * numtiles) # allocate tile buffer chunk = numpy.empty(tile + (shape[-1],), dtype=data.dtype) else: # use one strip per plane strip_byte_counts = [ data[0, 0].size * data.dtype.itemsize] * shape[1] addtag(tag_byte_counts, offset_format, shape[1], strip_byte_counts) addtag(tag_offsets, offset_format, shape[1], [0] * shape[1]) # add extra tags from user for t in extratags: addtag(*t) # TODO: check TIFFReadDirectoryCheckOrder warning in files containing # multiple tags of same code # the entries in an IFD must be sorted in ascending order by tag code tags = sorted(tags, key=lambda x: x[0]) if not (self._bigtiff or self._imagej) and ( fh.tell() + data.size*data.dtype.itemsize > 2**31-1): raise ValueError("data too large for standard TIFF file") # if not compressed or tiled, write the first ifd and then all data # contiguously; else, write all ifds and data interleaved for pageindex in range(shape[0] if (compress or tile) else 1): # update pointer at ifd_offset pos = fh.tell() fh.seek(self._ifd_offset) fh.write(pack(offset_format, pos)) fh.seek(pos) # write ifdentries fh.write(pack(numtag_format, len(tags))) tag_offset = fh.tell() fh.write(b''.join(t[1] for t in tags)) self._ifd_offset = fh.tell() fh.write(pack(offset_format, 0)) # offset to next IFD # write tag values and patch offsets in ifdentries, if necessary for tagindex, tag in enumerate(tags): if tag[2]: pos = fh.tell() fh.seek(tag_offset + tagindex*tag_size + offset_size + 4) fh.write(pack(offset_format, pos)) fh.seek(pos) if tag[0] == tag_offsets: strip_offsets_offset = pos elif tag[0] == tag_byte_counts: strip_byte_counts_offset = pos elif tag[0] == 270 and tag[2].endswith(b'\0\0\0\0'): # image description buffer self._description_offset = pos self._description_len_offset = ( tag_offset + tagindex * tag_size + 4) fh.write(tag[2]) # write image data data_offset = fh.tell() if compress: strip_byte_counts = [] if tile: for plane in data[pageindex]: for tz in range(tiles[0]): for ty in range(tiles[1]): for tx in range(tiles[2]): c0 = min(tile[0], shape[2] - tz*tile[0]) c1 = min(tile[1], shape[3] - ty*tile[1]) c2 = min(tile[2], shape[4] - tx*tile[2]) chunk[c0:, c1:, c2:] = 0 chunk[:c0, :c1, :c2] = plane[ tz*tile[0]:tz*tile[0]+c0, ty*tile[1]:ty*tile[1]+c1, tx*tile[2]:tx*tile[2]+c2] if compress: t = compress(chunk) strip_byte_counts.append(len(t)) fh.write(t) else: fh.write_array(chunk) fh.flush() elif compress: for plane in data[pageindex]: plane = compress(plane) strip_byte_counts.append(len(plane)) fh.write(plane) else: fh.write_array(data) # update strip/tile offsets and byte_counts if necessary pos = fh.tell() for tagindex, tag in enumerate(tags): if tag[0] == tag_offsets: # strip/tile offsets if tag[2]: fh.seek(strip_offsets_offset) strip_offset = data_offset for size in strip_byte_counts: fh.write(pack(offset_format, strip_offset)) strip_offset += size else: fh.seek(tag_offset + tagindex*tag_size + offset_size + 4) fh.write(pack(offset_format, data_offset)) elif tag[0] == tag_byte_counts: # strip/tile byte_counts if compress: if tag[2]: fh.seek(strip_byte_counts_offset) for size in strip_byte_counts: fh.write(pack(offset_format, size)) else: fh.seek(tag_offset + tagindex*tag_size + offset_size + 4) fh.write(pack(offset_format, strip_byte_counts[0])) break fh.seek(pos) fh.flush() # remove tags that should be written only once if pageindex == 0: tags = [tag for tag in tags if not tag[-1]] # if uncompressed, write remaining ifds/tags later if not (compress or tile): self._tags = tags self._shape = shape self._data_shape = (1,) + data_shape self._data_dtype = data.dtype self._data_offset = data_offset self._data_byte_counts = strip_byte_counts def _write_remaining_pages(self): """Write outstanding IFDs and tags to file.""" if not self._tags: return fh = self._fh byteorder = self._byteorder numtag_format = self._numtag_format offset_format = self._offset_format offset_size = self._offset_size tag_size = self._tag_size data_offset = self._data_offset page_data_size = sum(self._data_byte_counts) tag_bytes = b''.join(t[1] for t in self._tags) numpages = self._shape[0] * self._data_shape[0] - 1 pos = fh.tell() if not self._bigtiff and pos + len(tag_bytes) * numpages > 2**32 - 256: if self._imagej: warnings.warn("truncating ImageJ file") return raise ValueError("data too large for non-bigtiff file") def pack(fmt, *val): return struct.pack(byteorder+fmt, *val) for _ in range(numpages): # update pointer at ifd_offset pos = fh.tell() fh.seek(self._ifd_offset) fh.write(pack(offset_format, pos)) fh.seek(pos) # write ifd entries fh.write(pack(numtag_format, len(self._tags))) tag_offset = fh.tell() fh.write(tag_bytes) self._ifd_offset = fh.tell() fh.write(pack(offset_format, 0)) # offset to next IFD # offset to image data data_offset += page_data_size # write tag values and patch offsets in ifdentries, if necessary for tagindex, tag in enumerate(self._tags): if tag[2]: pos = fh.tell() fh.seek(tag_offset + tagindex*tag_size + offset_size + 4) fh.write(pack(offset_format, pos)) fh.seek(pos) if tag[0] == self._tag_offsets: strip_offsets_offset = pos fh.write(tag[2]) # update strip/tile offsets if necessary pos = fh.tell() for tagindex, tag in enumerate(self._tags): if tag[0] == self._tag_offsets: # strip/tile offsets if tag[2]: fh.seek(strip_offsets_offset) strip_offset = data_offset for size in self._data_byte_counts: fh.write(pack(offset_format, strip_offset)) strip_offset += size else: fh.seek(tag_offset + tagindex*tag_size + offset_size + 4) fh.write(pack(offset_format, data_offset)) break fh.seek(pos) self._tags = None self._data_dtype = None self._data_offset = None self._data_byte_counts = None # do not reset _shape or _data_shape def _write_image_description(self): """Write meta data to image_description tag.""" if (not self._data_shape or self._data_shape[0] == 1 or self._description_offset <= 0): return colormapped = self._colormap is not None if self._imagej: isrgb = self._shape[-1] in (3, 4) description = imagej_description( self._data_shape, isrgb, colormapped, **self._metadata) else: description = image_description( self._data_shape, colormapped, **self._metadata) # rewrite description and its length to file description = description[:self._description_len-1] pos = self._fh.tell() self._fh.seek(self._description_offset) self._fh.write(description) self._fh.seek(self._description_len_offset) self._fh.write(struct.pack(self._byteorder+self._offset_format, len(description)+1)) self._fh.seek(pos) self._description_offset = 0 self._description_len_offset = 0 self._description_len = 0 def _now(self): """Return current date and time.""" return datetime.datetime.now() def close(self, truncate=False): """Write remaining pages (if not truncate) and close file handle.""" if not truncate: self._write_remaining_pages() self._write_image_description() self._fh.close() def __enter__(self): return self def __exit__(self, exc_type, exc_value, traceback): self.close() def imread(files, **kwargs): """Return image data from TIFF file(s) as numpy array. Refer to the TiffFile class and member functions for documentation. Parameters ---------- files : str, binary stream, or sequence File name, seekable binary stream, glob pattern, or sequence of file names. kwargs : dict Parameters 'multifile', 'multifile_close', 'pages', 'fastij', and 'is_ome' are passed to the TiffFile class. The 'pattern' parameter is passed to the TiffSequence class. Other parameters are passed to the asarray functions. The first image series is returned if no arguments are provided. Examples -------- >>> imsave('temp.tif', numpy.random.rand(3, 4, 301, 219)) >>> im = imread('temp.tif', key=0) >>> im.shape (4, 301, 219) >>> ims = imread(['temp.tif', 'temp.tif']) >>> ims.shape (2, 3, 4, 301, 219) """ kwargs_file = parse_kwargs(kwargs, 'multifile', 'multifile_close', 'pages', 'fastij', 'is_ome') kwargs_seq = parse_kwargs(kwargs, 'pattern') if isinstance(files, basestring) and any(i in files for i in '?*'): files = glob.glob(files) if not files: raise ValueError('no files found') if not hasattr(files, 'seek') and len(files) == 1: files = files[0] if isinstance(files, basestring) or hasattr(files, 'seek'): with TiffFile(files, **kwargs_file) as tif: return tif.asarray(**kwargs) else: with TiffSequence(files, **kwargs_seq) as imseq: return imseq.asarray(**kwargs) class lazyattr(object): """Lazy object attribute whose value is computed on first access.""" __slots__ = ('func',) def __init__(self, func): self.func = func def __get__(self, instance, owner): if instance is None: return self value = self.func(instance) if value is NotImplemented: return getattr(super(owner, instance), self.func.__name__) setattr(instance, self.func.__name__, value) return value class TiffFile(object): """Read image and metadata from TIFF, STK, LSM, and FluoView files. TiffFile instances must be closed using the 'close' method, which is automatically called when using the 'with' context manager. Attributes ---------- pages : list of TiffPage All TIFF pages in file. series : list of TiffPageSeries TIFF pages with compatible shapes and types. micromanager_metadata: dict Extra MicroManager non-TIFF metadata in the file, if exists. All attributes are read-only. Examples -------- >>> with TiffFile('temp.tif') as tif: ... data = tif.asarray() ... data.shape (5, 301, 219) """ def __init__(self, arg, name=None, offset=None, size=None, multifile=True, multifile_close=True, pages=None, fastij=True, is_ome=None): """Initialize instance from file. Parameters ---------- arg : str or open file Name of file or open file object. The file objects are closed in TiffFile.close(). name : str Optional name of file in case 'arg' is a file handle. offset : int Optional start position of embedded file. By default this is the current file position. size : int Optional size of embedded file. By default this is the number of bytes from the 'offset' to the end of the file. multifile : bool If True (default), series may include pages from multiple files. Currently applies to OME-TIFF only. multifile_close : bool If True (default), keep the handles of other files in multifile series closed. This is inefficient when few files refer to many pages. If False, the C runtime may run out of resources. pages : sequence of int Indices of the pages to read. If None (default) all pages are read. Can be used to read only the first page with pages=[0]. Specifying pages might invalidate series based on metadata. fastij : bool If True (default), try to use only the metadata from the first page of ImageJ files. Significantly speeds up loading movies with thousands of pages. is_ome : bool If False, disable processing of OME-XML metadata. """ if is_ome is False: self.is_ome = False self._fh = FileHandle(arg, mode='rb', name=name, offset=offset, size=size) self.offset_size = None self.pages = [] self._multifile = bool(multifile) self._multifile_close = bool(multifile_close) self._files = {self._fh.name: self} # cache of TiffFiles self._ifd_offset = 0 # offset to offset of next IFD try: self._fromfile(pages, fastij) except Exception: self._fh.close() raise @property def filehandle(self): """Return file handle.""" return self._fh @property def filename(self): """Return name of file handle.""" return self._fh.name def close(self): """Close open file handle(s).""" for tif in self._files.values(): tif._fh.close() self._files = {} def _fromfile(self, pages=None, fastij=True): """Read TIFF header and all page records from file.""" self._fh.seek(0) try: self.byteorder = {b'II': '<', b'MM': '>'}[self._fh.read(2)] except KeyError: raise ValueError("invalid TIFF file") self._is_native = self.byteorder == {'big': '>', 'little': '<'}[sys.byteorder] version = struct.unpack(self.byteorder+'H', self._fh.read(2))[0] if version == 43: # BigTiff self.offset_size, zero = struct.unpack(self.byteorder+'HH', self._fh.read(4)) if zero or self.offset_size != 8: raise ValueError("invalid BigTIFF file") elif version == 42: self.offset_size = 4 else: raise ValueError("not a TIFF file") self._ifd_offset = self._fh.tell() self.pages = [] pageindex = -1 while True: pageindex += 1 skip = pages and pageindex not in pages try: page = TiffPage(self, skip) except StopIteration: break if skip: continue self.pages.append(page) if fastij: if page._patch_imagej(): break # only read the first page of ImageJ files fastij = False # TiffPage() leaves the file cursor at offset to offset of next IFD self._ifd_offset = self._fh.tell() if self.is_micromanager: # MicroManager files contain metadata not stored in TIFF tags. self.micromanager_metadata = read_micromanager_metadata(self._fh) if self.is_lsm: self._fix_lsm_strip_offsets() self._fix_lsm_strip_byte_counts() def _fix_lsm_strip_offsets(self): """Unwrap strip offsets for LSM files greater than 4 GB.""" # each series and position require separate unwrapping (undocumented) for series in self.series: positions = 1 for i in 0, 1: if series.axes[i] in 'PM': positions *= series.shape[i] positions = len(series.pages) // positions for i, page in enumerate(series.pages): if not i % positions: wrap = 0 previous_offset = 0 strip_offsets = [] for current_offset in page.strip_offsets: if current_offset < previous_offset: wrap += 2**32 strip_offsets.append(current_offset + wrap) previous_offset = current_offset page.strip_offsets = tuple(strip_offsets) def _fix_lsm_strip_byte_counts(self): """Set strip_byte_counts to size of compressed data. The strip_byte_counts tag in LSM files contains the number of bytes for the uncompressed data. """ if not self.pages: return strips = {} for page in self.pages: assert len(page.strip_offsets) == len(page.strip_byte_counts) for offset, bytecount in zip(page.strip_offsets, page.strip_byte_counts): strips[offset] = bytecount offsets = sorted(strips.keys()) offsets.append(min(offsets[-1] + strips[offsets[-1]], self._fh.size)) for i, offset in enumerate(offsets[:-1]): strips[offset] = min(strips[offset], offsets[i+1] - offset) for page in self.pages: if page.compression: page.strip_byte_counts = tuple( strips[offset] for offset in page.strip_offsets) def asarray(self, key=None, series=None, memmap=False, tempdir=None): """Return image data from multiple TIFF pages as numpy array. By default the first image series is returned. Parameters ---------- key : int, slice, or sequence of page indices Defines which pages to return as array. series : int or TiffPageSeries Defines which series of pages to return as array. memmap : bool If True, return an read-only array stored in a binary file on disk if possible. The TIFF file is used if possible, else a temporary file is created. tempdir : str The directory where the memory-mapped file will be created. """ if not self.pages: return numpy.array([]) if key is None and series is None: series = 0 if series is not None: try: series = self.series[series] except (KeyError, TypeError): pass pages = series.pages else: pages = self.pages if key is None: pass elif isinstance(key, int): pages = [pages[key]] elif isinstance(key, slice): pages = pages[key] elif isinstance(key, collections.Iterable): pages = [pages[k] for k in key] else: raise TypeError("key must be an int, slice, or sequence") if not len(pages): raise ValueError("no pages selected") if self.is_nih: if pages[0].is_indexed: result = stack_pages(pages, colormapped=False, squeeze=False) result = apply_colormap(result, pages[0].color_map) else: result = stack_pages(pages, memmap=memmap, tempdir=tempdir, colormapped=False, squeeze=False) elif len(pages) == 1: result = pages[0].asarray(memmap=memmap) elif self.is_ome: assert not self.is_indexed, "color mapping disabled for ome-tiff" if any(p is None for p in pages): # zero out missing pages firstpage = next(p for p in pages if p) nopage = numpy.zeros_like( firstpage.asarray(memmap=False)) if memmap: with tempfile.NamedTemporaryFile() as fh: result = numpy.memmap(fh, series.dtype, shape=series.shape) result = result.reshape(-1) else: result = numpy.empty(series.shape, series.dtype).reshape(-1) index = 0 class KeepOpen: # keep Tiff files open between consecutive pages def __init__(self, parent, close): self.master = parent self.parent = parent self._close = close def open(self, page): if self._close and page and page.parent != self.parent: if self.parent != self.master: self.parent.filehandle.close() self.parent = page.parent self.parent.filehandle.open() def close(self): if self._close and self.parent != self.master: self.parent.filehandle.close() keep = KeepOpen(self, self._multifile_close) for page in pages: keep.open(page) if page: a = page.asarray(memmap=False, colormapped=False, reopen=False) else: a = nopage try: result[index:index + a.size] = a.reshape(-1) except ValueError as e: warnings.warn("ome-tiff: %s" % e) break index += a.size keep.close() elif key is None and series and series.offset: if memmap: result = self.filehandle.memmap_array( series.dtype, series.shape, series.offset) else: self.filehandle.seek(series.offset) result = self.filehandle.read_array( series.dtype, product(series.shape)) else: result = stack_pages(pages, memmap=memmap, tempdir=tempdir) if key is None: try: result.shape = series.shape except ValueError: try: warnings.warn("failed to reshape %s to %s" % ( result.shape, series.shape)) # try series of expected shapes result.shape = (-1,) + series.shape except ValueError: # revert to generic shape result.shape = (-1,) + pages[0].shape elif len(pages) == 1: result.shape = pages[0].shape else: result.shape = (-1,) + pages[0].shape return result @lazyattr def series(self): """Return pages with compatible properties as TiffPageSeries.""" if not self.pages: return [] series = [] if self.is_ome: series = self._ome_series() elif self.is_fluoview: series = self._fluoview_series() elif self.is_lsm: series = self._lsm_series() elif self.is_imagej: series = self._imagej_series() elif self.is_nih: series = self._nih_series() if not series: # generic detection of series shapes = [] pages = {} index = 0 for page in self.pages: if not page.shape: continue if page.is_shaped: index += 1 # shape starts a new series shape = page.shape + (index, page.axes, page.compression in TIFF_DECOMPESSORS) if shape in pages: pages[shape].append(page) else: shapes.append(shape) pages[shape] = [page] series = [] for s in shapes: shape = ((len(pages[s]),) + s[:-3] if len(pages[s]) > 1 else s[:-3]) axes = (('I' + s[-2]) if len(pages[s]) > 1 else s[-2]) page0 = pages[s][0] if page0.is_shaped: metadata = image_description_dict(page0.is_shaped) reshape = metadata['shape'] if 'axes' in metadata: reaxes = metadata['axes'] if len(reaxes) == len(reshape): axes = reaxes shape = reshape else: warnings.warn("axes do not match shape") try: axes = reshape_axes(axes, shape, reshape) shape = reshape except ValueError as e: warnings.warn(str(e)) series.append( TiffPageSeries(pages[s], shape, page0.dtype, axes)) for i, s in enumerate(series): s.index = i # remove empty series, e.g. in MD Gel files series = [s for s in series if sum(s.shape) > 0] return series def _fluoview_series(self): """Return image series in FluoView file.""" page0 = self.pages[0] dims = { b'X': 'X', b'Y': 'Y', b'Z': 'Z', b'T': 'T', b'WAVELENGTH': 'C', b'TIME': 'T', b'XY': 'R', b'EVENT': 'V', b'EXPOSURE': 'L'} mmhd = list(reversed(page0.mm_header.dimensions)) axes = ''.join(dims.get(i[0].strip().upper(), 'Q') for i in mmhd if i[1] > 1) shape = tuple(int(i[1]) for i in mmhd if i[1] > 1) return [TiffPageSeries(self.pages, shape, page0.dtype, axes)] def _lsm_series(self): """Return image series in LSM file.""" page0 = self.pages[0] lsmi = page0.cz_lsm_info axes = CZ_SCAN_TYPES[lsmi.scan_type] if page0.is_rgb: axes = axes.replace('C', '').replace('XY', 'XYC') if hasattr(lsmi, 'dimension_p') and lsmi.dimension_p > 1: axes += 'P' if hasattr(lsmi, 'dimension_m') and lsmi.dimension_m > 1: axes += 'M' axes = axes[::-1] shape = tuple(getattr(lsmi, CZ_DIMENSIONS[i]) for i in axes) pages = [p for p in self.pages if not p.is_reduced] dtype = pages[0].dtype series = [TiffPageSeries(pages, shape, dtype, axes)] if len(pages) != len(self.pages): # reduced RGB pages pages = [p for p in self.pages if p.is_reduced] cp = 1 i = 0 while cp < len(pages) and i < len(shape)-2: cp *= shape[i] i += 1 shape = shape[:i] + pages[0].shape axes = axes[:i] + 'CYX' dtype = pages[0].dtype series.append(TiffPageSeries(pages, shape, dtype, axes)) return series def _imagej_series(self): """Return image series in ImageJ file.""" # ImageJ's dimension order is always TZCYXS # TODO: fix loading of color, composite or palette images shape = [] axes = [] page0 = self.pages[0] ij = page0.imagej_tags if 'frames' in ij: shape.append(ij['frames']) axes.append('T') if 'slices' in ij: shape.append(ij['slices']) axes.append('Z') if 'channels' in ij and not (self.is_rgb and not ij.get('hyperstack', False)): shape.append(ij['channels']) axes.append('C') remain = ij.get('images', len(self.pages)) // (product(shape) if shape else 1) if remain > 1: shape.append(remain) axes.append('I') if page0.axes[0] == 'I': # contiguous multiple images shape.extend(page0.shape[1:]) axes.extend(page0.axes[1:]) elif page0.axes[:2] == 'SI': # color-mapped contiguous multiple images shape = page0.shape[0:1] + tuple(shape) + page0.shape[2:] axes = list(page0.axes[0]) + axes + list(page0.axes[2:]) else: shape.extend(page0.shape) axes.extend(page0.axes) return [TiffPageSeries(self.pages, shape, page0.dtype, axes)] def _nih_series(self): """Return image series in NIH file.""" page0 = self.pages[0] if len(self.pages) == 1: shape = page0.shape axes = page0.axes else: shape = (len(self.pages),) + page0.shape axes = 'I' + page0.axes return [TiffPageSeries(self.pages, shape, page0.dtype, axes)] def _ome_series(self): """Return image series in OME-TIFF file(s).""" omexml = self.pages[0].tags['image_description'].value try: root = etree.fromstring(omexml) except etree.ParseError as e: # TODO: test this warnings.warn("ome-xml: %s" % e) omexml = omexml.decode('utf-8', 'ignore').encode('utf-8') root = etree.fromstring(omexml) uuid = root.attrib.get('UUID', None) self._files = {uuid: self} dirname = self._fh.dirname modulo = {} series = [] for element in root: if element.tag.endswith('BinaryOnly'): warnings.warn("ome-xml: not an ome-tiff master file") break if element.tag.endswith('StructuredAnnotations'): for annot in element: if not annot.attrib.get('Namespace', '').endswith('modulo'): continue for value in annot: for modul in value: for along in modul: if not along.tag[:-1].endswith('Along'): continue axis = along.tag[-1] newaxis = along.attrib.get('Type', 'other') newaxis = AXES_LABELS[newaxis] if 'Start' in along.attrib: step = float(along.attrib.get('Step', 1)) start = float(along.attrib['Start']) stop = float(along.attrib['End']) + step labels = numpy.arange(start, stop, step) else: labels = [label.text for label in along if label.tag.endswith('Label')] modulo[axis] = (newaxis, labels) if not element.tag.endswith('Image'): continue for pixels in element: if not pixels.tag.endswith('Pixels'): continue atr = pixels.attrib dtype = atr.get('Type', None) axes = ''.join(reversed(atr['DimensionOrder'])) shape = list(int(atr['Size'+ax]) for ax in axes) size = product(shape[:-2]) ifds = [None] * (size // self.pages[0].samples_per_pixel) for data in pixels: if not data.tag.endswith('TiffData'): continue atr = data.attrib ifd = int(atr.get('IFD', 0)) num = int(atr.get('NumPlanes', 1 if 'IFD' in atr else 0)) num = int(atr.get('PlaneCount', num)) idx = [int(atr.get('First'+ax, 0)) for ax in axes[:-2]] try: idx = numpy.ravel_multi_index(idx, shape[:-2]) except ValueError: # ImageJ produces invalid ome-xml when cropping warnings.warn("ome-xml: invalid TiffData index") continue for uuid in data: if not uuid.tag.endswith('UUID'): continue if uuid.text not in self._files: if not self._multifile: # abort reading multifile OME series # and fall back to generic series return [] fname = uuid.attrib['FileName'] try: tif = TiffFile(os.path.join(dirname, fname)) except (IOError, FileNotFoundError, ValueError): # TODO: close open file handle warnings.warn( "ome-xml: failed to read '%s'" % fname) break self._files[uuid.text] = tif if self._multifile_close: tif.close() pages = self._files[uuid.text].pages try: for i in range(num if num else len(pages)): ifds[idx + i] = pages[ifd + i] except IndexError: warnings.warn("ome-xml: index out of range") # only process first uuid break else: pages = self.pages try: for i in range(num if num else len(pages)): ifds[idx + i] = pages[ifd + i] except IndexError: warnings.warn("ome-xml: index out of range") if all(i is None for i in ifds): # skip images without data continue dtype = next(i for i in ifds if i).dtype series.append(TiffPageSeries(ifds, shape, dtype, axes, self)) for serie in series: shape = list(serie.shape) for axis, (newaxis, labels) in modulo.items(): i = serie.axes.index(axis) size = len(labels) if shape[i] == size: serie.axes = serie.axes.replace(axis, newaxis, 1) else: shape[i] //= size shape.insert(i+1, size) serie.axes = serie.axes.replace(axis, axis+newaxis, 1) serie.shape = tuple(shape) # squeeze dimensions for serie in series: serie.shape, serie.axes = squeeze_axes(serie.shape, serie.axes) return series def __len__(self): """Return number of image pages in file.""" return len(self.pages) def __getitem__(self, key): """Return specified page.""" return self.pages[key] def __iter__(self): """Return iterator over pages.""" return iter(self.pages) def __enter__(self): return self def __exit__(self, exc_type, exc_value, traceback): self.close() def __str__(self): """Return string containing information about file.""" result = [ "TIFF file: %s" % self._fh.name, format_size(self._fh.size), {'<': 'little endian', '>': 'big endian'}[self.byteorder]] if self.is_bigtiff: result.append("bigtiff") result.extend((attr for attr in ( 'mdgel', 'mediacy', 'stk', 'lsm', 'vista', 'imagej', 'fluoview', 'micromanager', 'nih', 'ome', 'scn', 'tvips', 'fei', 'sem') if getattr(self, 'is_'+attr))) if len(self.pages) > 1: result.append("%i pages" % len(self.pages)) if len(self.series) > 1: result.append("%i series" % len(self.series)) if len(self._files) > 1: result.append("%i files" % (len(self._files))) return ", ".join(result) def info(self, series=None, pages=None): """Return string with detailed information about file.""" if series is None: series = self.series else: series = [self.series[i] for i in sequence(series)] result = [str(self)] for s in series: result.append(str(s)) if pages is None: result.append(s.pages[0].info()) if pages is not None: if pages == 'all': pages = self.pages else: pages = [self.pages[i] for i in sequence(pages)] for p in pages: result.append(p.info()) return '\n\n'.join(result) @lazyattr def fstat(self): try: return os.fstat(self._fh.fileno()) except Exception: # io.UnsupportedOperation return None @lazyattr def is_bigtiff(self): """File has BigTIFF format.""" return self.offset_size != 4 @lazyattr def is_rgb(self): """File contains only RGB images.""" return self.pages and all(p.is_rgb for p in self.pages) @lazyattr def is_indexed(self): """File contains only indexed images.""" return self.pages and all(p.is_indexed for p in self.pages) @lazyattr def is_mdgel(self): """File has MD Gel format.""" return any(p.is_mdgel for p in self.pages) @lazyattr def is_mediacy(self): """File was created by Media Cybernetics software.""" return any(p.is_mediacy for p in self.pages) @lazyattr def is_stk(self): """File has MetaMorph STK format.""" return self.pages and all(p.is_stk for p in self.pages) @lazyattr def is_lsm(self): """File was created by Carl Zeiss software.""" return len(self.pages) and self.pages[0].is_lsm @lazyattr def is_vista(self): """File was created by ISS Vista.""" return len(self.pages) and self.pages[0].is_vista @lazyattr def is_imagej(self): """File has ImageJ format.""" return len(self.pages) and self.pages[0].is_imagej @lazyattr def is_micromanager(self): """File was created by MicroManager.""" return len(self.pages) and self.pages[0].is_micromanager @lazyattr def is_nih(self): """File has NIH Image format.""" return len(self.pages) and self.pages[0].is_nih @lazyattr def is_fluoview(self): """File was created by Olympus FluoView.""" return len(self.pages) and self.pages[0].is_fluoview @lazyattr def is_ome(self): """File has OME-TIFF format.""" return len(self.pages) and self.pages[0].is_ome @lazyattr def is_scn(self): """File has Leica SCN format.""" return len(self.pages) and self.pages[0].is_scn @lazyattr def is_tvips(self): """File was created using EM-MENU software.""" return len(self.pages) and self.pages[0].is_tvips @lazyattr def is_fei(self): """File was created using FEI software.""" return len(self.pages) and self.pages[0].is_fei @lazyattr def is_sem(self): """File contains Zeiss SEM metadata.""" return len(self.pages) and self.pages[0].is_sem class TiffPage(object): """A TIFF image file directory (IFD). Attributes ---------- index : int Index of page in file. dtype : str {TIFF_SAMPLE_DTYPES} Data type of image, color-mapped if applicable. shape : tuple Dimensions of the image array in TIFF page, color-mapped and with extra samples if applicable. axes : str Axes label codes: 'X' width, 'Y' height, 'S' sample, 'I' image series|page|plane, 'Z' depth, 'C' color|em-wavelength|channel, 'E' ex-wavelength|lambda, 'T' time, 'R' region|tile, 'A' angle, 'P' phase, 'H' lifetime, 'L' exposure, 'V' event, 'Q' unknown, '_' missing tags : TiffTags Dictionary of tags in page. Tag values are also directly accessible as attributes. color_map : numpy.ndarray Color look up table, if exists. cz_lsm_scan_info: Record(dict) LSM scan info attributes, if exists. imagej_tags: Record(dict) Consolidated ImageJ description and metadata tags, if exists. uic_tags: Record(dict) Consolidated MetaMorph STK/UIC tags, if exists. All attributes are read-only. Notes ----- The internal, normalized '_shape' attribute is 6 dimensional: 0. number planes/images (stk, ij). 1. planar samples_per_pixel. 2. image_depth Z (sgi). 3. image_length Y. 4. image_width X. 5. contig samples_per_pixel. """ def __init__(self, parent, skip=False): """Initialize instance from file. If skip, seek to next IFD offset without reading tags. """ self.parent = parent self.index = len(parent.pages) self.shape = self._shape = () self.dtype = self._dtype = None self.axes = "" self.tags = TiffTags() self._offset = 0 # offset to this IDF self._fromfile(skip) if skip: return self._process_tags() def _fromfile(self, skip=False): """Read TIFF IFD structure and its tags from file. The file cursor is left at the storage position of the offset to the next IFD (if any). Raises StopIteration if offset (first bytes read) is 0 or a corrupted page list is encountered. """ fh = self.parent.filehandle byteorder = self.parent.byteorder offset_size = self.parent.offset_size pos = fh.tell() # read offset to this IFD fmt = {4: 'I', 8: 'Q'}[offset_size] offset = struct.unpack(byteorder + fmt, fh.read(offset_size))[0] if not offset: fh.seek(pos) raise StopIteration() if offset >= fh.size: warnings.warn("invalid page offset > file size") fh.seek(pos) raise StopIteration() self._offset = offset # read standard tags fh.seek(offset) fmt, size, tagsize = {4: ('H', 2, 12), 8: ('Q', 8, 20)}[offset_size] try: numtags = struct.unpack(byteorder + fmt, fh.read(size))[0] if numtags > 4096: raise ValueError("suspicious number of tags") except Exception: warnings.warn("corrupted page list at offset %i" % offset) fh.seek(pos) raise StopIteration() if skip: fh.seek(offset + size + numtags * tagsize) return tags = self.tags tagcode = 0 for _ in range(numtags): try: tag = TiffTag(self.parent) except TiffTag.Error as e: warnings.warn(str(e)) continue if tagcode > tag.code: # expected for early LSM and tifffile versions warnings.warn("tags are not ordered by code") tagcode = tag.code if tag.name not in tags: tags[tag.name] = tag else: # some files contain multiple tags with same code # e.g. MicroManager files contain two image_description tags i = 1 while True: name = "%s_%i" % (tag.name, i) if name not in tags: tags[name] = tag break pos = fh.tell() # where offset to next IFD is stored if self.is_lsm or (self.index and self.parent.is_lsm): # correct non standard LSM bitspersample tags self.tags['bits_per_sample']._fix_lsm_bitspersample(self) if self.is_lsm: # read LSM info subrecords for name, reader in CZ_LSM_INFO_READERS.items(): try: offset = self.cz_lsm_info['offset_'+name] except KeyError: continue if offset < 8: # older LSM revision continue fh.seek(offset) try: setattr(self, 'cz_lsm_'+name, reader(fh)) except ValueError: pass elif self.is_stk and 'uic1tag' in tags and not tags['uic1tag'].value: # read uic1tag now that plane count is known uic1tag = tags['uic1tag'] fh.seek(uic1tag.value_offset) tags['uic1tag'].value = Record( read_uic1tag(fh, byteorder, uic1tag.dtype, uic1tag.count, tags['uic2tag'].count)) fh.seek(pos) def _process_tags(self): """Validate standard tags and initialize attributes. Raise ValueError if tag values are not supported. """ tags = self.tags for code, (name, default, dtype, count, validate) in TIFF_TAGS.items(): if name in tags: #tags[name] = TiffTag(code, dtype=dtype, count=count, # value=default, name=name) if validate: try: if tags[name].count == 1: setattr(self, name, validate[tags[name].value]) else: setattr(self, name, tuple( validate[value] for value in tags[name].value)) except KeyError: raise ValueError("%s.value (%s) not supported" % (name, tags[name].value)) elif default is not None: setattr(self, name, validate[default] if validate else default) if 'bits_per_sample' in tags: tag = tags['bits_per_sample'] if tag.count == 1: self.bits_per_sample = tag.value else: # LSM might list more items than samples_per_pixel value = tag.value[:self.samples_per_pixel] if any((v-value[0] for v in value)): self.bits_per_sample = value else: self.bits_per_sample = value[0] if 'sample_format' in tags: tag = tags['sample_format'] if tag.count == 1: self.sample_format = TIFF_SAMPLE_FORMATS[tag.value] else: value = tag.value[:self.samples_per_pixel] if any((v-value[0] for v in value)): self.sample_format = [TIFF_SAMPLE_FORMATS[v] for v in value] else: self.sample_format = TIFF_SAMPLE_FORMATS[value[0]] if 'photometric' not in tags: self.photometric = None if 'image_length' in tags: if 'rows_per_strip' not in tags: self.rows_per_strip = self.image_length self.strips_per_image = int(math.floor( float(self.image_length + self.rows_per_strip - 1) / self.rows_per_strip)) else: self.strips_per_image = 0 key = (self.sample_format, self.bits_per_sample) self.dtype = self._dtype = TIFF_SAMPLE_DTYPES.get(key, None) if 'image_length' not in self.tags or 'image_width' not in self.tags: # some GEL file pages are missing image data self.image_length = 0 self.image_width = 0 self.image_depth = 0 self.strip_offsets = 0 self._shape = () self.shape = () self.axes = '' if self.is_vista or self.parent.is_vista: # ISS Vista writes wrong image_depth tag self.image_depth = 1 if self.is_indexed: self.dtype = self.tags['color_map'].dtype[1] self.color_map = numpy.array(self.color_map, self.dtype) dmax = self.color_map.max() if dmax < 256: self.dtype = numpy.uint8 self.color_map = self.color_map.astype(self.dtype) #else: # self.dtype = numpy.uint8 # self.color_map >>= 8 # self.color_map = self.color_map.astype(self.dtype) # TODO: support other photometric modes than RGB self.color_map.shape = (3, -1) # determine shape of data image_length = self.image_length image_width = self.image_width image_depth = self.image_depth samples_per_pixel = self.samples_per_pixel if self.is_stk: assert self.image_depth == 1 planes = self.tags['uic2tag'].count if self.is_contig: self._shape = (planes, 1, 1, image_length, image_width, samples_per_pixel) if samples_per_pixel == 1: self.shape = (planes, image_length, image_width) self.axes = 'YX' else: self.shape = (planes, image_length, image_width, samples_per_pixel) self.axes = 'YXS' else: self._shape = (planes, samples_per_pixel, 1, image_length, image_width, 1) if samples_per_pixel == 1: self.shape = (planes, image_length, image_width) self.axes = 'YX' else: self.shape = (planes, samples_per_pixel, image_length, image_width) self.axes = 'SYX' # detect type of series if planes == 1: self.shape = self.shape[1:] elif numpy.all(self.uic2tag.z_distance != 0): self.axes = 'Z' + self.axes elif numpy.all(numpy.diff(self.uic2tag.time_created) != 0): self.axes = 'T' + self.axes else: self.axes = 'I' + self.axes # DISABLED if self.is_indexed: assert False, "color mapping disabled for stk" if self.color_map.shape[1] >= 2**self.bits_per_sample: if image_depth == 1: self.shape = (planes, image_length, image_width, self.color_map.shape[0]) else: self.shape = (planes, image_depth, image_length, image_width, self.color_map.shape[0]) self.axes = self.axes + 'S' else: warnings.warn("palette cannot be applied") self.is_indexed = False elif self.is_indexed: samples = 1 if 'extra_samples' in self.tags: samples += self.tags['extra_samples'].count if self.is_contig: self._shape = (1, 1, image_depth, image_length, image_width, samples) else: self._shape = (1, samples, image_depth, image_length, image_width, 1) if self.color_map.shape[1] >= 2**self.bits_per_sample: if image_depth == 1: self.shape = (image_length, image_width, self.color_map.shape[0]) self.axes = 'YXS' else: self.shape = (image_depth, image_length, image_width, self.color_map.shape[0]) self.axes = 'ZYXS' else: warnings.warn("palette cannot be applied") self.is_indexed = False if image_depth == 1: self.shape = (image_length, image_width) self.axes = 'YX' else: self.shape = (image_depth, image_length, image_width) self.axes = 'ZYX' elif self.is_rgb or samples_per_pixel > 1: if self.is_contig: self._shape = (1, 1, image_depth, image_length, image_width, samples_per_pixel) if image_depth == 1: self.shape = (image_length, image_width, samples_per_pixel) self.axes = 'YXS' else: self.shape = (image_depth, image_length, image_width, samples_per_pixel) self.axes = 'ZYXS' else: self._shape = (1, samples_per_pixel, image_depth, image_length, image_width, 1) if image_depth == 1: self.shape = (samples_per_pixel, image_length, image_width) self.axes = 'SYX' else: self.shape = (samples_per_pixel, image_depth, image_length, image_width) self.axes = 'SZYX' if False and self.is_rgb and 'extra_samples' in self.tags: # DISABLED: only use RGB and first alpha channel if exists extra_samples = self.extra_samples if self.tags['extra_samples'].count == 1: extra_samples = (extra_samples,) for exs in extra_samples: if exs in ('unassalpha', 'assocalpha', 'unspecified'): if self.is_contig: self.shape = self.shape[:-1] + (4,) else: self.shape = (4,) + self.shape[1:] break else: self._shape = (1, 1, image_depth, image_length, image_width, 1) if image_depth == 1: self.shape = (image_length, image_width) self.axes = 'YX' else: self.shape = (image_depth, image_length, image_width) self.axes = 'ZYX' if not self.compression and 'strip_byte_counts' not in tags: self.strip_byte_counts = ( product(self.shape) * (self.bits_per_sample // 8),) assert len(self.shape) == len(self.axes) def _patch_imagej(self): """Return if ImageJ data are contiguous and adjust page attributes. Patch 'strip_offsets' and 'strip_byte_counts' tags to span the complete contiguous data. ImageJ stores all image metadata in the first page and image data is stored contiguously before the second page, if any. No need to read other pages. """ if not self.is_imagej or not self.is_contiguous or self.parent.is_ome: return images = self.imagej_tags.get('images', 0) if images <= 1: return offset, count = self.is_contiguous shape = self.shape if self.is_indexed: shape = shape[:-1] fh = self.parent.filehandle if (count != product(shape) * self.bits_per_sample // 8 or offset + count*images > fh.size): self.is_imagej = False warnings.warn("invalid ImageJ metadata or corrupted file") return # check that next page is stored after data byteorder = self.parent.byteorder offset_size = self.parent.offset_size pos = fh.tell() fmt = {4: 'I', 8: 'Q'}[offset_size] nextpage = struct.unpack(byteorder + fmt, fh.read(offset_size))[0] fh.seek(pos) if nextpage and offset + count*images > nextpage: return # patch metadata pre = 'tile' if self.is_tiled else 'strip' self.tags[pre+'_offsets'].value = (offset,) self.tags[pre+'_byte_counts'].value = (count * images,) self.shape = (images,) + self.shape self._shape = (images,) + self._shape[1:] self.axes = 'I' + self.axes return True def asarray(self, squeeze=True, colormapped=True, rgbonly=False, scale_mdgel=False, memmap=False, reopen=True, maxsize=64*2**30): """Read image data from file and return as numpy array. Raise ValueError if format is unsupported. If any of 'squeeze', 'colormapped', or 'rgbonly' are not the default, the shape of the returned array might be different from the page shape. Parameters ---------- squeeze : bool If True, all length-1 dimensions (except X and Y) are squeezed out from result. colormapped : bool If True, color mapping is applied for palette-indexed images. rgbonly : bool If True, return RGB(A) image without additional extra samples. memmap : bool If True, use numpy.memmap to read arrays from file if possible. For use on 64-bit systems and files with few huge contiguous data. reopen : bool If True and the parent file handle is closed, the file is temporarily re-opened (and closed if no exception occurs). scale_mdgel : bool If True, MD Gel data will be scaled according to the private metadata in the second TIFF page. The dtype will be float32. maxsize: int or None Maximum size of data before a ValueError is raised. Can be used to catch DOS. Default: 64 GB. """ if not self._shape: return if maxsize and product(self._shape) > maxsize: raise ValueError("data is too large %s" % str(self._shape)) if self.dtype is None: raise ValueError("data type not supported: %s%i" % ( self.sample_format, self.bits_per_sample)) if self.compression not in TIFF_DECOMPESSORS: raise ValueError("cannot decompress %s" % self.compression) if 'sample_format' in self.tags: tag = self.tags['sample_format'] if tag.count != 1 and any((i-tag.value[0] for i in tag.value)): raise ValueError("sample formats do not match %s" % tag.value) if self.is_chroma_subsampled: # TODO: implement chroma subsampling raise NotImplementedError("chroma subsampling not supported") fh = self.parent.filehandle closed = fh.closed if closed: if reopen: fh.open() else: raise IOError("file handle is closed") dtype = self._dtype shape = self._shape image_width = self.image_width image_length = self.image_length image_depth = self.image_depth typecode = self.parent.byteorder + dtype bits_per_sample = self.bits_per_sample lsb2msb = self.fill_order == 'lsb2msb' byte_counts, offsets = self._byte_counts_offsets if self.is_tiled: tile_width = self.tile_width tile_length = self.tile_length tile_depth = self.tile_depth if 'tile_depth' in self.tags else 1 tw = (image_width + tile_width - 1) // tile_width tl = (image_length + tile_length - 1) // tile_length td = (image_depth + tile_depth - 1) // tile_depth shape = (shape[0], shape[1], td*tile_depth, tl*tile_length, tw*tile_width, shape[-1]) tile_shape = (tile_depth, tile_length, tile_width, shape[-1]) runlen = tile_width else: runlen = image_width if memmap and self._is_memmappable(rgbonly, colormapped): result = fh.memmap_array(typecode, shape, offset=offsets[0]) elif self.is_contiguous: fh.seek(offsets[0]) result = fh.read_array(typecode, product(shape)) result = result.astype('=' + dtype) if lsb2msb: reverse_bitorder(result) else: if self.is_contig: runlen *= self.samples_per_pixel if bits_per_sample in (8, 16, 32, 64, 128): if (bits_per_sample * runlen) % 8: raise ValueError("data and sample size mismatch") def unpack(x, typecode=typecode): if self.predictor == 'float': # the floating point horizontal differencing decoder # needs the raw byte order typecode = dtype try: return numpy.fromstring(x, typecode) except ValueError as e: # strips may be missing EOI warnings.warn("unpack: %s" % e) xlen = ((len(x) // (bits_per_sample // 8)) * (bits_per_sample // 8)) return numpy.fromstring(x[:xlen], typecode) elif isinstance(bits_per_sample, tuple): def unpack(x): return unpack_rgb(x, typecode, bits_per_sample) else: def unpack(x): return unpack_ints(x, typecode, bits_per_sample, runlen) decompress = TIFF_DECOMPESSORS[self.compression] if self.compression == 'jpeg': table = self.jpeg_tables if 'jpeg_tables' in self.tags else b'' def decompress(x): return decode_jpeg(x, table, self.photometric) if self.is_tiled: result = numpy.empty(shape, dtype) tw, tl, td, pl = 0, 0, 0, 0 for offset, bytecount in zip(offsets, byte_counts): fh.seek(offset) tile = fh.read(bytecount) if lsb2msb: tile = reverse_bitorder(tile) tile = decompress(tile) tile = unpack(tile) try: tile.shape = tile_shape except ValueError: # incomplete tiles; see gdal issue #1179 warnings.warn("invalid tile data") t = numpy.zeros(tile_shape, dtype).reshape(-1) s = min(tile.size, t.size) t[:s] = tile[:s] tile = t.reshape(tile_shape) if self.predictor == 'horizontal': numpy.cumsum(tile, axis=-2, dtype=dtype, out=tile) elif self.predictor == 'float': raise NotImplementedError() result[0, pl, td:td+tile_depth, tl:tl+tile_length, tw:tw+tile_width, :] = tile del tile tw += tile_width if tw >= shape[4]: tw, tl = 0, tl + tile_length if tl >= shape[3]: tl, td = 0, td + tile_depth if td >= shape[2]: td, pl = 0, pl + 1 result = result[..., :image_depth, :image_length, :image_width, :] else: strip_size = self.rows_per_strip * self.image_width if self.planar_configuration == 'contig': strip_size *= self.samples_per_pixel result = numpy.empty(shape, dtype).reshape(-1) index = 0 for offset, bytecount in zip(offsets, byte_counts): fh.seek(offset) strip = fh.read(bytecount) if lsb2msb: strip = reverse_bitorder(strip) strip = decompress(strip) strip = unpack(strip) size = min(result.size, strip.size, strip_size, result.size - index) result[index:index+size] = strip[:size] del strip index += size result.shape = self._shape if self.predictor and not (self.is_tiled and not self.is_contiguous): if self.parent.is_lsm and not self.compression: pass # work around bug in LSM510 software elif self.predictor == 'horizontal': numpy.cumsum(result, axis=-2, dtype=dtype, out=result) elif self.predictor == 'float': result = decode_floats(result) if colormapped and self.is_indexed: if self.color_map.shape[1] >= 2**bits_per_sample: # FluoView and LSM might fail here result = apply_colormap(result[:, 0:1, :, :, :, 0:1], self.color_map) elif rgbonly and self.is_rgb and 'extra_samples' in self.tags: # return only RGB and first alpha channel if exists extra_samples = self.extra_samples if self.tags['extra_samples'].count == 1: extra_samples = (extra_samples,) for i, exs in enumerate(extra_samples): if exs in ('unassalpha', 'assocalpha', 'unspecified'): if self.is_contig: result = result[..., [0, 1, 2, 3+i]] else: result = result[:, [0, 1, 2, 3+i]] break else: if self.is_contig: result = result[..., :3] else: result = result[:, :3] if squeeze: try: result.shape = self.shape except ValueError: warnings.warn("failed to reshape from %s to %s" % ( str(result.shape), str(self.shape))) if scale_mdgel and self.parent.is_mdgel: # MD Gel stores private metadata in the second page tags = self.parent.pages[1] if tags.md_file_tag in (2, 128): scale = tags.md_scale_pixel scale = scale[0] / scale[1] # rational result = result.astype('float32') if tags.md_file_tag == 2: result **= 2 # squary root data format result *= scale if closed: # TODO: file should remain open if an exception occurred above fh.close() return result @lazyattr def _byte_counts_offsets(self): """Return simplified byte_counts and offsets.""" if 'tile_offsets' in self.tags: byte_counts = self.tile_byte_counts offsets = self.tile_offsets else: byte_counts = self.strip_byte_counts offsets = self.strip_offsets j = 0 for i, (b, o) in enumerate(zip(byte_counts, offsets)): if b > 0 and o > 0: if i > j: byte_counts[j] = b offsets[j] = o j += 1 elif b > 0 and o <= 0: raise ValueError("invalid offset") else: warnings.warn("empty byte count") if j == 0: j = 1 return byte_counts[:j], offsets[:j] def _is_memmappable(self, rgbonly, colormapped): """Return if page's image data in file can be memory-mapped.""" return (self.parent.filehandle.is_file and self.is_contiguous and (self.bits_per_sample == 8 or self.parent._is_native) and self.fill_order == 'msb2lsb' and not self.predictor and not self.is_chroma_subsampled and not (rgbonly and 'extra_samples' in self.tags) and not (colormapped and self.is_indexed)) @lazyattr def is_contiguous(self): """Return offset and size of contiguous data, else None. Excludes prediction, fill_order, and colormapping. """ if self.compression or self.bits_per_sample not in (8, 16, 32, 64): return if self.is_tiled: if (self.image_width != self.tile_width or self.image_length % self.tile_length or self.tile_width % 16 or self.tile_length % 16): return if ('image_depth' in self.tags and 'tile_depth' in self.tags and (self.image_length != self.tile_length or self.image_depth % self.tile_depth)): return offsets = self.tile_offsets byte_counts = self.tile_byte_counts else: offsets = self.strip_offsets byte_counts = self.strip_byte_counts if len(offsets) == 1: return offsets[0], byte_counts[0] if self.is_stk or all(offsets[i] + byte_counts[i] == offsets[i+1] or byte_counts[i+1] == 0 # no data/ignore offset for i in range(len(offsets)-1)): return offsets[0], sum(byte_counts) def __getattr__(self, name): """Return tag value.""" if name in self.tags: value = self.tags[name].value setattr(self, name, value) return value raise AttributeError(name) def __str__(self): """Return string containing information about page.""" s = ', '.join(s for s in ( 'x'.join(str(i) for i in self.shape), str(numpy.dtype(self.dtype)), '%s bit' % str(self.bits_per_sample), self.photometric if 'photometric' in self.tags else '', self.compression if self.compression else 'raw', '|'.join(t[3:] for t in ( 'is_stk', 'is_lsm', 'is_nih', 'is_ome', 'is_imagej', 'is_micromanager', 'is_fluoview', 'is_mdgel', 'is_mediacy', 'is_scn', 'is_sgi', 'is_reduced', 'is_tiled', 'is_contiguous') if getattr(self, t))) if s) return "Page %i: %s" % (self.index, s) def info(self): """Return string with detailed information about page.""" result = ['\n'.join((str(self), str(self.tags)))] if self.is_indexed: result.append('Color Map: %s, %s' % (self.color_map.shape, self.color_map.dtype)) for attr in ('cz_lsm_info', 'cz_lsm_scan_info', 'uic_tags', 'mm_header', 'imagej_tags', 'micromanager_metadata', 'nih_image_header', 'tvips_metadata', 'sfeg_metadata', 'helios_metadata', 'sem_metadata'): if hasattr(self, attr): result.append('\n'.join(( attr.upper(), str(Record(getattr(self, attr)))))) if self.is_micromanager: result.append('MICROMANAGER_FILE_METADATA\n%s' % Record(self.micromanager_metadata)) return '\n\n'.join(result) @lazyattr def uic_tags(self): """Consolidate UIC tags.""" if not self.is_stk: raise AttributeError("uic_tags") tags = self.tags result = Record() result.number_planes = tags['uic2tag'].count if 'image_description' in tags: result.plane_descriptions = self.image_description.split(b'\x00') if 'uic1tag' in tags: result.update(tags['uic1tag'].value) if 'uic3tag' in tags: result.update(tags['uic3tag'].value) # wavelengths if 'uic4tag' in tags: result.update(tags['uic4tag'].value) # override uic1 tags uic2tag = tags['uic2tag'].value result.z_distance = uic2tag.z_distance result.time_created = uic2tag.time_created result.time_modified = uic2tag.time_modified try: result.datetime_created = [ julian_datetime(*dt) for dt in zip(uic2tag.date_created, uic2tag.time_created)] result.datetime_modified = [ julian_datetime(*dt) for dt in zip(uic2tag.date_modified, uic2tag.time_modified)] except ValueError as e: warnings.warn("uic_tags: %s" % e) return result @lazyattr def imagej_tags(self): """Consolidate ImageJ metadata.""" if not self.is_imagej: raise AttributeError("imagej_tags") result = imagej_description_dict(self.is_imagej) if 'imagej_metadata' in self.tags: try: result.update(imagej_metadata( self.tags['imagej_metadata'].value, self.tags['imagej_byte_counts'].value, self.parent.byteorder)) except Exception as e: warnings.warn(str(e)) return Record(result) @lazyattr def is_rgb(self): """Page contains a RGB image.""" return ('photometric' in self.tags and self.tags['photometric'].value == 2) @lazyattr def is_contig(self): """Page contains contiguous image.""" if 'planar_configuration' in self.tags: return self.tags['planar_configuration'].value == 1 return True @lazyattr def is_indexed(self): """Page contains indexed, palette-colored image. Disable color-mapping for OME, LSM, STK, and ImageJ hyperstacks. """ if (self.is_stk or self.is_lsm or self.parent.is_lsm or self.is_ome or self.parent.is_ome): return False if self.is_imagej: if b'mode' in self.is_imagej: return False elif self.parent.is_imagej: return self.parent.is_indexed return ('photometric' in self.tags and self.tags['photometric'].value == 3) @lazyattr def is_tiled(self): """Page contains tiled image.""" return 'tile_width' in self.tags @lazyattr def is_reduced(self): """Page is reduced image of another image.""" return ('new_subfile_type' in self.tags and self.tags['new_subfile_type'].value & 1) @lazyattr def is_chroma_subsampled(self): """Page contains chroma subsampled image.""" return ('ycbcr_subsampling' in self.tags and self.tags['ycbcr_subsampling'].value != (1, 1)) @lazyattr def is_mdgel(self): """Page contains md_file_tag tag.""" return 'md_file_tag' in self.tags @lazyattr def is_mediacy(self): """Page contains Media Cybernetics Id tag.""" return ('mc_id' in self.tags and self.tags['mc_id'].value.startswith(b'MC TIFF')) @lazyattr def is_stk(self): """Page contains UIC2Tag tag.""" return 'uic2tag' in self.tags @lazyattr def is_lsm(self): """Page contains LSM CZ_LSM_INFO tag.""" return 'cz_lsm_info' in self.tags @lazyattr def is_fluoview(self): """Page contains FluoView MM_STAMP tag.""" return 'mm_stamp' in self.tags @lazyattr def is_nih(self): """Page contains NIH image header.""" return 'nih_image_header' in self.tags @lazyattr def is_sgi(self): """Page contains SGI image and tile depth tags.""" return 'image_depth' in self.tags and 'tile_depth' in self.tags @lazyattr def is_vista(self): """Software tag is 'ISS Vista'.""" return ('software' in self.tags and self.tags['software'].value == b'ISS Vista') @lazyattr def is_ome(self): """Page contains OME-XML in image_description tag.""" if 'image_description' not in self.tags: return False d = self.tags['image_description'].value.strip() return d.startswith(b'') @lazyattr def is_scn(self): """Page contains Leica SCN XML in image_description tag.""" if 'image_description' not in self.tags: return False d = self.tags['image_description'].value.strip() return d.startswith(b'') @lazyattr def is_shaped(self): """Return description containing shape if exists, else None.""" if 'image_description' in self.tags: description = self.tags['image_description'].value if b'"shape":' in description or b'shape=(' in description: return description if 'image_description_1' in self.tags: description = self.tags['image_description_1'].value if b'"shape":' in description or b'shape=(' in description: return description @lazyattr def is_imagej(self): """Return ImageJ description if exists, else None.""" if 'image_description' in self.tags: description = self.tags['image_description'].value if description.startswith(b'ImageJ='): return description if 'image_description_1' in self.tags: # Micromanager description = self.tags['image_description_1'].value if description.startswith(b'ImageJ='): return description @lazyattr def is_micromanager(self): """Page contains Micro-Manager metadata.""" return 'micromanager_metadata' in self.tags @lazyattr def is_tvips(self): """Page contains TVIPS metadata.""" return 'tvips_metadata' in self.tags @lazyattr def is_fei(self): """Page contains SFEG or HELIOS metadata.""" return 'sfeg_metadata' in self.tags or 'helios_metadata' in self.tags @lazyattr def is_sem(self): """Page contains Zeiss SEM metadata.""" return 'sem_metadata' in self.tags class TiffTag(object): """A TIFF tag structure. Attributes ---------- name : string Attribute name of tag. code : int Decimal code of tag. dtype : str Datatype of tag data. One of TIFF_DATA_TYPES. count : int Number of values. value : various types Tag data as Python object. value_offset : int Location of value in file, if any. All attributes are read-only. """ __slots__ = ('code', 'name', 'count', 'dtype', 'value', 'value_offset', '_offset', '_value', '_type') class Error(Exception): pass def __init__(self, arg, **kwargs): """Initialize instance from file or arguments.""" self._offset = None if hasattr(arg, '_fh'): self._fromfile(arg, **kwargs) else: self._fromdata(arg, **kwargs) def _fromdata(self, code, dtype, count, value, name=None): """Initialize instance from arguments.""" self.code = int(code) self.name = name if name else str(code) self.dtype = TIFF_DATA_TYPES[dtype] self.count = int(count) self.value = value self._value = value self._type = dtype def _fromfile(self, parent): """Read tag structure from open file. Advance file cursor.""" fh = parent.filehandle byteorder = parent.byteorder self._offset = fh.tell() self.value_offset = self._offset + parent.offset_size + 4 fmt, size = {4: ('HHI4s', 12), 8: ('HHQ8s', 20)}[parent.offset_size] data = fh.read(size) code, dtype = struct.unpack(byteorder + fmt[:2], data[:4]) count, value = struct.unpack(byteorder + fmt[2:], data[4:]) self._value = value self._type = dtype if code in TIFF_TAGS: name, _, _, cout_, _ = TIFF_TAGS[code] if cout_ and cout_ != count: count = cout_ warnings.warn("incorrect count for tag '%s'" % name) elif code in CUSTOM_TAGS: name = CUSTOM_TAGS[code][0] else: name = str(code) try: dtype = TIFF_DATA_TYPES[self._type] except KeyError: raise TiffTag.Error("unknown tag data type %i" % self._type) fmt = '%s%i%s' % (byteorder, count*int(dtype[0]), dtype[1]) size = struct.calcsize(fmt) if size > parent.offset_size or code in CUSTOM_TAGS: pos = fh.tell() tof = {4: 'I', 8: 'Q'}[parent.offset_size] self.value_offset = offset = struct.unpack(byteorder+tof, value)[0] if offset < 0 or offset > parent.filehandle.size: raise TiffTag.Error("corrupt file - invalid tag value offset") elif offset < 4: raise TiffTag.Error("corrupt value offset for tag %i" % code) fh.seek(offset) if code in CUSTOM_TAGS: readfunc = CUSTOM_TAGS[code][1] value = readfunc(fh, byteorder, dtype, count) if isinstance(value, dict): # numpy.core.records.record value = Record(value) elif code in TIFF_TAGS or dtype[-1] == 's': value = struct.unpack(fmt, fh.read(size)) else: value = read_numpy(fh, byteorder, dtype, count) fh.seek(pos) else: value = struct.unpack(fmt, value[:size]) if code not in CUSTOM_TAGS and code not in ( 273, 279, 324, 325, 530, 531): # scalar value if not strip/tile offsets/byte_counts or subsampling if len(value) == 1: value = value[0] if (dtype.endswith('s') and isinstance(value, bytes) and self._type != 7): # TIFF ASCII fields can contain multiple strings, # each terminated with a NUL value = stripascii(value) self.code = code self.name = name self.dtype = dtype self.count = count self.value = value def _fix_lsm_bitspersample(self, parent): """Correct LSM bitspersample tag. Old LSM writers may use a separate region for two 16-bit values, although they fit into the tag value element of the tag. """ if self.code == 258 and self.count == 2: # TODO: test this case; need example file warnings.warn("correcting LSM bitspersample tag") fh = parent.filehandle tof = {4: '') def __str__(self): """Return string containing information about tag.""" return ' '.join(str(getattr(self, s)) for s in self.__slots__) class TiffPageSeries(object): """Series of TIFF pages with compatible shape and data type. Attributes ---------- pages : list of TiffPage Sequence of TiffPages in series. dtype : numpy.dtype or str Data type of the image array in series. shape : tuple Dimensions of the image array in series. axes : str Labels of axes in shape. See TiffPage.axes. offset : int or None Position of image data in file if memory-mappable, else None. """ #__slots__ = 'pages', 'shape', 'dtype', 'axes', 'parent' def __init__(self, pages, shape, dtype, axes, parent=None): # TODO? sort pages by page number? self.index = 0 self.pages = pages self.shape = tuple(shape) self.axes = ''.join(axes) self.dtype = numpy.dtype(dtype) if parent: self.parent = parent elif len(pages): self.parent = pages[0].parent else: self.parent = None def asarray(self, memmap=False): """Return image data from series of TIFF pages as numpy array. Parameters ---------- memmap : bool If True, return an array stored in a binary file on disk if possible. """ if self.parent: return self.parent.asarray(series=self, memmap=memmap) @lazyattr def offset(self): """Return offset to memory-mappable data in page series.""" if len(self.pages) == 0: return rgbonly = False colormapped = self.pages[0].is_indexed pos = 0 for page in self.pages: if page is None: return if not page._is_memmappable(rgbonly, colormapped): return if not pos: pos = page.is_contiguous[0] + page.is_contiguous[1] continue if pos != page.is_contiguous[0]: return pos += page.is_contiguous[1] offset = self.pages[0].is_contiguous[0] if (pos != offset + product(self.shape) * self.dtype.itemsize and not self.pages[0].is_imagej): return return offset def __len__(self): """Return number of TiffPages in series.""" return len(self.pages) def __getitem__(self, key): """Return specified TiffPage.""" return self.pages[key] def __iter__(self): """Return iterator over TiffPages in series.""" return iter(self.pages) def __str__(self): """Return string with information about series.""" s = ', '.join(s for s in ( 'x'.join(str(i) for i in self.shape), str(numpy.dtype(self.dtype)), self.axes, '%i pages' % len(self.pages), ('memmap-offset=%i' % self.offset) if self.offset else 'not mem-mappable')) return 'Series %i: %s' % (self.index, s) class TiffSequence(object): """Sequence of image files. The data shape and dtype of all files must match. Attributes ---------- files : list List of file names. shape : tuple Shape of image sequence. axes : str Labels of axes in shape. """ _patterns = { 'axes': r""" # matches Olympus OIF and Leica TIFF series _?(?:(q|l|p|a|c|t|x|y|z|ch|tp)(\d{1,4})) _?(?:(q|l|p|a|c|t|x|y|z|ch|tp)(\d{1,4}))? _?(?:(q|l|p|a|c|t|x|y|z|ch|tp)(\d{1,4}))? _?(?:(q|l|p|a|c|t|x|y|z|ch|tp)(\d{1,4}))? _?(?:(q|l|p|a|c|t|x|y|z|ch|tp)(\d{1,4}))? _?(?:(q|l|p|a|c|t|x|y|z|ch|tp)(\d{1,4}))? _?(?:(q|l|p|a|c|t|x|y|z|ch|tp)(\d{1,4}))? """} class ParseError(Exception): pass def __init__(self, files, imread=TiffFile, pattern='axes', *args, **kwargs): """Initialize instance from multiple files. Parameters ---------- files : str, or sequence of str Glob pattern or sequence of file names. Binary streams are not supported. imread : function or class Image read function or class with asarray function returning numpy array from single file. pattern : str Regular expression pattern that matches axes names and sequence indices in file names. By default this matches Olympus OIF and Leica TIFF series. """ if isinstance(files, basestring): files = natural_sorted(glob.glob(files)) files = list(files) if not files: raise ValueError("no files found") if not isinstance(files[0], basestring): raise ValueError("not a file name") #if not os.path.isfile(files[0]): # raise ValueError("file not found") self.files = files if hasattr(imread, 'asarray'): # redefine imread _imread = imread def imread(fname, *args, **kwargs): with _imread(fname) as im: return im.asarray(*args, **kwargs) self.imread = imread self.pattern = self._patterns.get(pattern, pattern) try: self._parse() if not self.axes: self.axes = 'I' except self.ParseError: self.axes = 'I' self.shape = (len(files),) self._start_index = (0,) self._indices = tuple((i,) for i in range(len(files))) def __str__(self): """Return string with information about image sequence.""" return "\n".join([ self.files[0], '* files: %i' % len(self.files), '* axes: %s' % self.axes, '* shape: %s' % str(self.shape)]) def __len__(self): return len(self.files) def __enter__(self): return self def __exit__(self, exc_type, exc_value, traceback): self.close() def close(self): pass def asarray(self, memmap=False, tempdir=None, *args, **kwargs): """Read image data from all files and return as single numpy array. If memmap is True, return an array stored in a binary file on disk. The args and kwargs parameters are passed to the imread function. Raise IndexError or ValueError if image shapes do not match. """ im = self.imread(self.files[0], *args, **kwargs) shape = self.shape + im.shape if memmap: with tempfile.NamedTemporaryFile(dir=tempdir) as fh: result = numpy.memmap(fh, dtype=im.dtype, shape=shape) else: result = numpy.zeros(shape, dtype=im.dtype) result = result.reshape(-1, *im.shape) for index, fname in zip(self._indices, self.files): index = [i-j for i, j in zip(index, self._start_index)] index = numpy.ravel_multi_index(index, self.shape) im = self.imread(fname, *args, **kwargs) result[index] = im result.shape = shape return result def _parse(self): """Get axes and shape from file names.""" if not self.pattern: raise self.ParseError("invalid pattern") pattern = re.compile(self.pattern, re.IGNORECASE | re.VERBOSE) matches = pattern.findall(self.files[0]) if not matches: raise self.ParseError("pattern does not match file names") matches = matches[-1] if len(matches) % 2: raise self.ParseError("pattern does not match axis name and index") axes = ''.join(m for m in matches[::2] if m) if not axes: raise self.ParseError("pattern does not match file names") indices = [] for fname in self.files: matches = pattern.findall(fname)[-1] if axes != ''.join(m for m in matches[::2] if m): raise ValueError("axes do not match within the image sequence") indices.append([int(m) for m in matches[1::2] if m]) shape = tuple(numpy.max(indices, axis=0)) start_index = tuple(numpy.min(indices, axis=0)) shape = tuple(i-j+1 for i, j in zip(shape, start_index)) if product(shape) != len(self.files): warnings.warn("files are missing. Missing data are zeroed") self.axes = axes.upper() self.shape = shape self._indices = indices self._start_index = start_index class Record(dict): """Dictionary with attribute access. Can also be initialized with numpy.core.records.record. """ __slots__ = () def __init__(self, arg=None, **kwargs): if kwargs: arg = kwargs elif arg is None: arg = {} try: dict.__init__(self, arg) except (TypeError, ValueError): # numpy records for i, name in enumerate(arg.dtype.names): v = arg[i] self[name] = v if v.dtype.char != 'S' else stripnull(v) def __getattr__(self, name): return self[name] def __setattr__(self, name, value): self.__setitem__(name, value) def __str__(self): """Pretty print Record.""" s = [] lists = [] for k in sorted(self): try: if k.startswith('_'): # does not work with byte continue except AttributeError: pass v = self[k] if isinstance(v, (list, tuple)) and len(v): if isinstance(v[0], Record): lists.append((k, v)) continue elif isinstance(v[0], TiffPage): v = [i.index for i in v if i] elif isinstance(v, Record): s.append(("* %s:\n%s" % (k, str(v).replace('*', ' *')))) continue s.append( ("* %s: %s" % (k, str(v))).split("\n", 1)[0] [:PRINT_LINE_LEN].rstrip()) for k, v in lists: l = [] for i, w in enumerate(v): l.append("* %s[%i]\n %s" % (k, i, str(w).replace("\n", "\n "))) s.append('\n'.join(l)) return '\n'.join(s) class TiffTags(Record): """Dictionary of TiffTag with attribute access.""" def __str__(self): """Return string with information about all tags.""" s = [] for tag in sorted(self.values(), key=lambda x: x.code): typecode = "%i%s" % (tag.count * int(tag.dtype[0]), tag.dtype[1]) line = "* %i %s (%s) %s" % ( tag.code, tag.name, typecode, tag.as_str()) s.append(line[:PRINT_LINE_LEN].lstrip()) return '\n'.join(s) class FileHandle(object): """Binary file handle. A limited, special purpose file handler that can: * handle embedded files (for CZI within CZI files) * re-open closed files (for multi file formats, such as OME-TIFF) * read and write numpy arrays and records from file like objects Only 'rb' and 'wb' modes are supported. Concurrently reading and writing of the same stream is untested. When initialized from another file handle, do not use it unless this FileHandle is closed. Attributes ---------- name : str Name of the file. path : str Absolute path to file. size : int Size of file in bytes. is_file : bool If True, file has a filno and can be memory-mapped. All attributes are read-only. """ __slots__ = ('_fh', '_file', '_mode', '_name', '_dir', '_offset', '_size', '_close', 'is_file') def __init__(self, file, mode='rb', name=None, offset=None, size=None): """Initialize file handle from file name or another file handle. Parameters ---------- file : str, binary stream, or FileHandle File name or seekable binary stream, such as a open file or BytesIO. mode : str File open mode in case 'file' is a file name. Must be 'rb' or 'wb'. name : str Optional name of file in case 'file' is a binary stream. offset : int Optional start position of embedded file. By default this is the current file position. size : int Optional size of embedded file. By default this is the number of bytes from the 'offset' to the end of the file. """ self._fh = None self._file = file self._mode = mode self._name = name self._dir = '' self._offset = offset self._size = size self._close = True self.is_file = False self.open() def open(self): """Open or re-open file.""" if self._fh: return # file is open if isinstance(self._file, basestring): # file name self._file = os.path.realpath(self._file) self._dir, self._name = os.path.split(self._file) self._fh = open(self._file, self._mode) self._close = True if self._offset is None: self._offset = 0 elif isinstance(self._file, FileHandle): # FileHandle self._fh = self._file._fh if self._offset is None: self._offset = 0 self._offset += self._file._offset self._close = False if not self._name: if self._offset: name, ext = os.path.splitext(self._file._name) self._name = "%s@%i%s" % (name, self._offset, ext) else: self._name = self._file._name if self._mode and self._mode != self._file._mode: raise ValueError('FileHandle has wrong mode') self._mode = self._file._mode self._dir = self._file._dir elif hasattr(self._file, 'seek'): # binary stream: open file, BytesIO try: self._file.tell() except Exception: raise ValueError("binary stream is not seekable") self._fh = self._file if self._offset is None: self._offset = self._file.tell() self._close = False if not self._name: try: self._dir, self._name = os.path.split(self._fh.name) except AttributeError: self._name = "Unnamed binary stream" try: self._mode = self._fh.mode except AttributeError: pass else: raise ValueError("The first parameter must be a file name, " "seekable binary stream, or FileHandle") if self._offset: self._fh.seek(self._offset) if self._size is None: pos = self._fh.tell() self._fh.seek(self._offset, 2) self._size = self._fh.tell() self._fh.seek(pos) try: self._fh.fileno() self.is_file = True except Exception: self.is_file = False def read(self, size=-1): """Read 'size' bytes from file, or until EOF is reached.""" if size < 0 and self._offset: size = self._size return self._fh.read(size) def write(self, bytestring): """Write bytestring to file.""" return self._fh.write(bytestring) def flush(self): """Flush write buffers if applicable.""" return self._fh.flush() def memmap_array(self, dtype, shape, offset=0, mode='r', order='C'): """Return numpy.memmap of data stored in file.""" if not self.is_file: raise ValueError("Can not memory-map file without fileno") return numpy.memmap(self._fh, dtype=dtype, mode=mode, offset=self._offset + offset, shape=shape, order=order) def read_array(self, dtype, count=-1, sep=""): """Return numpy array from file. Work around numpy issue #2230, "numpy.fromfile does not accept StringIO object" https://github.com/numpy/numpy/issues/2230. """ try: return numpy.fromfile(self._fh, dtype, count, sep) except IOError: if count < 0: size = self._size else: size = count * numpy.dtype(dtype).itemsize data = self._fh.read(size) return numpy.fromstring(data, dtype, count, sep) def read_record(self, dtype, shape=1, byteorder=None): """Return numpy record from file.""" try: rec = numpy.rec.fromfile(self._fh, dtype, shape, byteorder=byteorder) except Exception: dtype = numpy.dtype(dtype) if shape is None: shape = self._size // dtype.itemsize size = product(sequence(shape)) * dtype.itemsize data = self._fh.read(size) return numpy.rec.fromstring(data, dtype, shape, byteorder=byteorder) return rec[0] if shape == 1 else rec def write_array(self, data): """Write numpy array to binary file.""" try: data.tofile(self._fh) except Exception: # BytesIO self._fh.write(data.tostring()) def tell(self): """Return file's current position.""" return self._fh.tell() - self._offset def seek(self, offset, whence=0): """Set file's current position.""" if self._offset: if whence == 0: self._fh.seek(self._offset + offset, whence) return elif whence == 2 and self._size > 0: self._fh.seek(self._offset + self._size + offset, 0) return self._fh.seek(offset, whence) def close(self): """Close file.""" if self._close and self._fh: self._fh.close() self._fh = None def __enter__(self): return self def __exit__(self, exc_type, exc_value, traceback): self.close() def __getattr__(self, name): """Return attribute from underlying file object.""" if self._offset: warnings.warn( "FileHandle: '%s' not implemented for embedded files" % name) return getattr(self._fh, name) @property def name(self): return self._name @property def dirname(self): return self._dir @property def path(self): return os.path.join(self._dir, self._name) @property def size(self): return self._size @property def closed(self): return self._fh is None def read_bytes(fh, byteorder, dtype, count): """Read tag data from file and return as byte string.""" dtype = 'b' if dtype[-1] == 's' else byteorder+dtype[-1] return fh.read_array(dtype, count).tostring() def read_numpy(fh, byteorder, dtype, count): """Read tag data from file and return as numpy array.""" dtype = 'b' if dtype[-1] == 's' else byteorder+dtype[-1] return fh.read_array(dtype, count) def read_json(fh, byteorder, dtype, count): """Read JSON tag data from file and return as object.""" data = fh.read(count) try: return json.loads(unicode(stripnull(data), 'utf-8')) except ValueError: warnings.warn("invalid JSON '%s'" % data) def read_mm_header(fh, byteorder, dtype, count): """Read MM_HEADER tag from file and return as numpy.rec.array.""" return fh.read_record(MM_HEADER, byteorder=byteorder) def read_mm_stamp(fh, byteorder, dtype, count): """Read MM_STAMP tag from file and return as numpy.ndarray.""" return fh.read_array(byteorder+'f8', 8) def read_uic1tag(fh, byteorder, dtype, count, plane_count=None): """Read MetaMorph STK UIC1Tag from file and return as dictionary. Return empty dictionary if plane_count is unknown. """ assert dtype in ('2I', '1I') and byteorder == '<' result = {} if dtype == '2I': # pre MetaMorph 2.5 (not tested) values = fh.read_array(' structure_size: break cz_lsm_info.append((name, dtype)) else: cz_lsm_info = CZ_LSM_INFO return fh.read_record(cz_lsm_info, byteorder=byteorder) def read_cz_lsm_floatpairs(fh): """Read LSM sequence of float pairs from file and return as list.""" size = struct.unpack(' 0: esize, etime, etype = struct.unpack(''}[fh.read(2)] except IndexError: raise ValueError("not a MicroManager TIFF file") result = {} fh.seek(8) (index_header, index_offset, display_header, display_offset, comments_header, comments_offset, summary_header, summary_length ) = struct.unpack(byteorder + "IIIIIIII", fh.read(32)) if summary_header != 2355492: raise ValueError("invalid MicroManager summary_header") result['summary'] = read_json(fh, byteorder, None, summary_length) if index_header != 54773648: raise ValueError("invalid MicroManager index_header") fh.seek(index_offset) header, count = struct.unpack(byteorder + "II", fh.read(8)) if header != 3453623: raise ValueError("invalid MicroManager index_header") data = struct.unpack(byteorder + "IIIII"*count, fh.read(20*count)) result['index_map'] = { 'channel': data[::5], 'slice': data[1::5], 'frame': data[2::5], 'position': data[3::5], 'offset': data[4::5]} if display_header != 483765892: raise ValueError("invalid MicroManager display_header") fh.seek(display_offset) header, count = struct.unpack(byteorder + "II", fh.read(8)) if header != 347834724: raise ValueError("invalid MicroManager display_header") result['display_settings'] = read_json(fh, byteorder, None, count) if comments_header != 99384722: raise ValueError("invalid MicroManager comments_header") fh.seek(comments_offset) header, count = struct.unpack(byteorder + "II", fh.read(8)) if header != 84720485: raise ValueError("invalid MicroManager comments_header") result['comments'] = read_json(fh, byteorder, None, count) return result def imagej_metadata(data, bytecounts, byteorder): """Return dictionary from ImageJ metadata tag value.""" _str = str if sys.version_info[0] < 3 else lambda x: str(x, 'cp1252') def read_string(data, byteorder): return _str(stripnull(data[0 if byteorder == '<' else 1::2])) def read_double(data, byteorder): return struct.unpack(byteorder+('d' * (len(data) // 8)), data) def read_bytes(data, byteorder): #return struct.unpack('b' * len(data), data) return numpy.fromstring(data, 'uint8') metadata_types = { # big endian b'info': ('info', read_string), b'labl': ('labels', read_string), b'rang': ('ranges', read_double), b'luts': ('luts', read_bytes), b'roi ': ('roi', read_bytes), b'over': ('overlays', read_bytes)} metadata_types.update( # little endian dict((k[::-1], v) for k, v in metadata_types.items())) if not bytecounts: raise ValueError("no ImageJ metadata") if not data[:4] in (b'IJIJ', b'JIJI'): raise ValueError("invalid ImageJ metadata") header_size = bytecounts[0] if header_size < 12 or header_size > 804: raise ValueError("invalid ImageJ metadata header size") ntypes = (header_size - 4) // 8 header = struct.unpack(byteorder+'4sI'*ntypes, data[4:4+ntypes*8]) pos = 4 + ntypes * 8 counter = 0 result = {} for mtype, count in zip(header[::2], header[1::2]): values = [] name, func = metadata_types.get(mtype, (_str(mtype), read_bytes)) for _ in range(count): counter += 1 pos1 = pos + bytecounts[counter] values.append(func(data[pos:pos1], byteorder)) pos = pos1 result[name.strip()] = values[0] if count == 1 else values return result def imagej_description_dict(description): """Return dictionary from ImageJ image description byte string. Raise ValueError if not a valid ImageJ description. >>> description = b'ImageJ=1.11a\\nimages=510\\nhyperstack=true\\n' >>> imagej_description_dict(description) # doctest: +SKIP {'ImageJ': '1.11a', 'images': 510, 'hyperstack': True} """ def _bool(val): return {b'true': True, b'false': False}[val.lower()] _str = str if sys.version_info[0] < 3 else lambda x: str(x, 'cp1252') result = {} for line in description.splitlines(): try: key, val = line.split(b'=') except Exception: continue key = key.strip() val = val.strip() for dtype in (int, float, _bool, _str): try: val = dtype(val) break except Exception: pass result[_str(key)] = val if 'ImageJ' not in result: raise ValueError("not a ImageJ image description") return result def imagej_description(shape, rgb=None, colormaped=False, version='1.11a', hyperstack=None, mode=None, loop=None, **kwargs): """Return ImageJ image decription from data shape as byte string. ImageJ can handle up to 6 dimensions in order TZCYXS. >>> imagej_description((51, 5, 2, 196, 171)) # doctest: +SKIP ImageJ=1.11a images=510 channels=2 slices=5 frames=51 hyperstack=true mode=grayscale loop=false """ if colormaped: raise NotImplementedError("ImageJ colormapping not supported") shape = imagej_shape(shape, rgb=rgb) rgb = shape[-1] in (3, 4) result = ['ImageJ=%s' % version] append = [] result.append('images=%i' % product(shape[:-3])) if hyperstack is None: #if product(shape[:-3]) > 1: hyperstack = True append.append('hyperstack=true') else: append.append('hyperstack=%s' % bool(hyperstack)) if shape[2] > 1: result.append('channels=%i' % shape[2]) if mode is None and not rgb: mode = 'grayscale' if hyperstack and mode: append.append('mode=%s' % mode) if shape[1] > 1: result.append('slices=%i' % shape[1]) if shape[0] > 1: result.append("frames=%i" % shape[0]) if loop is None: append.append('loop=false') if loop is not None: append.append('loop=%s' % bool(loop)) for key, value in kwargs.items(): append.append('%s=%s' % (key.lower(), value)) return str2bytes('\n'.join(result + append + [''])) def imagej_shape(shape, rgb=None): """Return shape normalized to 6D ImageJ hyperstack TZCYXS. Raise ValueError if not a valid ImageJ hyperstack shape. >>> imagej_shape((2, 3, 4, 5, 3), False) (2, 3, 4, 5, 3, 1) """ shape = tuple(int(i) for i in shape) ndim = len(shape) if 1 > ndim > 6: raise ValueError("invalid ImageJ hyperstack: not 2 to 6 dimensional") if rgb is None: rgb = shape[-1] in (3, 4) and ndim > 2 if rgb and shape[-1] not in (3, 4): raise ValueError("invalid ImageJ hyperstack: not a RGB image") if not rgb and ndim == 6 and shape[-1] != 1: raise ValueError("invalid ImageJ hyperstack: not a non-RGB image") if rgb or shape[-1] == 1: return (1, ) * (6 - ndim) + shape else: return (1, ) * (5 - ndim) + shape + (1,) def image_description_dict(description): """Return dictionary from image description byte string. Raise ValuError if description is of unknown format. >>> image_description_dict(b'shape=(256, 256, 3)') {'shape': (256, 256, 3)} >>> description = b'{"shape": [256, 256, 3], "axes": "YXS"}' >>> image_description_dict(description) # doctest: +SKIP {'shape': [256, 256, 3], 'axes': 'YXS'} """ if description.startswith(b'shape='): # old style 'shaped' description shape = tuple(int(i) for i in description[7:-1].split(b',')) return dict(shape=shape) if description.startswith(b'{') and description.endswith(b'}'): # JSON description return json.loads(description.decode('utf-8')) raise ValueError("unknown image description") def image_description(shape, colormaped=False, **metadata): """Return image description from data shape and meta data. Return UTF-8 encoded JSON. >>> image_description((256, 256, 3), axes='YXS') # doctest: +SKIP b'{"shape": [256, 256, 3], "axes": "YXS"}' """ if colormaped: shape = shape + (3,) metadata.update({'shape': shape}) return json.dumps(metadata).encode('utf-8') def _replace_by(module_function, package=__package__, warn=False): """Try replace decorated function by module.function.""" try: from importlib import import_module except ImportError: warnings.warn('could not import module importlib') return lambda func: func def decorate(func, module_function=module_function, warn=warn): try: module, function = module_function.split('.') if package: module = import_module('.' + module, package=package) else: module = import_module(module) func, oldfunc = getattr(module, function), func globals()['__old_' + func.__name__] = oldfunc except Exception: if warn: warnings.warn("failed to import %s" % module_function) return func return decorate def decode_floats(data): """Decode floating point horizontal differencing. The TIFF predictor type 3 reorders the bytes of the image values and applies horizontal byte differencing to improve compression of floating point images. The ordering of interleaved color channels is preserved. Parameters ---------- data : numpy.ndarray The image to be decoded. The dtype must be a floating point. The shape must include the number of contiguous samples per pixel even if 1. """ shape = data.shape dtype = data.dtype if len(shape) < 3: raise ValueError('invalid data shape') if dtype.char not in 'dfe': raise ValueError('not a floating point image') littleendian = data.dtype.byteorder == '<' or ( sys.byteorder == 'little' and data.dtype.byteorder == '=') # undo horizontal byte differencing data = data.view('uint8') data.shape = shape[:-2] + (-1,) + shape[-1:] numpy.cumsum(data, axis=-2, dtype='uint8', out=data) # reorder bytes if littleendian: data.shape = shape[:-2] + (-1,) + shape[-2:] data = numpy.swapaxes(data, -3, -2) data = numpy.swapaxes(data, -2, -1) data = data[..., ::-1] # back to float data = numpy.ascontiguousarray(data) data = data.view(dtype) data.shape = shape return data def decode_jpeg(encoded, tables=b'', photometric=None, ycbcr_subsampling=None, ycbcr_positioning=None): """Decode JPEG encoded byte string (using _czifile extension module).""" from czifile import _czifile image = _czifile.decode_jpeg(encoded, tables) if photometric == 'rgb' and ycbcr_subsampling and ycbcr_positioning: # TODO: convert YCbCr to RGB pass return image.tostring() @_replace_by('_tifffile.decode_packbits') def decode_packbits(encoded): """Decompress PackBits encoded byte string. PackBits is a simple byte-oriented run-length compression scheme. """ func = ord if sys.version[0] == '2' else lambda x: x result = [] result_extend = result.extend i = 0 try: while True: n = func(encoded[i]) + 1 i += 1 if n < 129: result_extend(encoded[i:i+n]) i += n elif n > 129: result_extend(encoded[i:i+1] * (258-n)) i += 1 except IndexError: pass return b''.join(result) if sys.version[0] == '2' else bytes(result) @_replace_by('_tifffile.decode_lzw') def decode_lzw(encoded): """Decompress LZW (Lempel-Ziv-Welch) encoded TIFF strip (byte string). The strip must begin with a CLEAR code and end with an EOI code. This is an implementation of the LZW decoding algorithm described in (1). It is not compatible with old style LZW compressed files like quad-lzw.tif. """ len_encoded = len(encoded) bitcount_max = len_encoded * 8 unpack = struct.unpack if sys.version[0] == '2': newtable = [chr(i) for i in range(256)] else: newtable = [bytes([i]) for i in range(256)] newtable.extend((0, 0)) def next_code(): """Return integer of 'bitw' bits at 'bitcount' position in encoded.""" start = bitcount // 8 s = encoded[start:start+4] try: code = unpack('>I', s)[0] except Exception: code = unpack('>I', s + b'\x00'*(4-len(s)))[0] code <<= bitcount % 8 code &= mask return code >> shr switchbitch = { # code: bit-width, shr-bits, bit-mask 255: (9, 23, int(9*'1'+'0'*23, 2)), 511: (10, 22, int(10*'1'+'0'*22, 2)), 1023: (11, 21, int(11*'1'+'0'*21, 2)), 2047: (12, 20, int(12*'1'+'0'*20, 2)), } bitw, shr, mask = switchbitch[255] bitcount = 0 if len_encoded < 4: raise ValueError("strip must be at least 4 characters long") if next_code() != 256: raise ValueError("strip must begin with CLEAR code") code = 0 oldcode = 0 result = [] result_append = result.append while True: code = next_code() # ~5% faster when inlining this function bitcount += bitw if code == 257 or bitcount >= bitcount_max: # EOI break if code == 256: # CLEAR table = newtable[:] table_append = table.append lentable = 258 bitw, shr, mask = switchbitch[255] code = next_code() bitcount += bitw if code == 257: # EOI break result_append(table[code]) else: if code < lentable: decoded = table[code] newcode = table[oldcode] + decoded[:1] else: newcode = table[oldcode] newcode += newcode[:1] decoded = newcode result_append(decoded) table_append(newcode) lentable += 1 oldcode = code if lentable in switchbitch: bitw, shr, mask = switchbitch[lentable] if code != 257: warnings.warn("unexpected end of lzw stream (code %i)" % code) return b''.join(result) @_replace_by('_tifffile.unpack_ints') def unpack_ints(data, dtype, itemsize, runlen=0): """Decompress byte string to array of integers of any bit size <= 32. Parameters ---------- data : byte str Data to decompress. dtype : numpy.dtype or str A numpy boolean or integer type. itemsize : int Number of bits per integer. runlen : int Number of consecutive integers, after which to start at next byte. """ if itemsize == 1: # bitarray data = numpy.fromstring(data, '|B') data = numpy.unpackbits(data) if runlen % 8: data = data.reshape(-1, runlen + (8 - runlen % 8)) data = data[:, :runlen].reshape(-1) return data.astype(dtype) dtype = numpy.dtype(dtype) if itemsize in (8, 16, 32, 64): return numpy.fromstring(data, dtype) if itemsize < 1 or itemsize > 32: raise ValueError("itemsize out of range: %i" % itemsize) if dtype.kind not in "biu": raise ValueError("invalid dtype") itembytes = next(i for i in (1, 2, 4, 8) if 8 * i >= itemsize) if itembytes != dtype.itemsize: raise ValueError("dtype.itemsize too small") if runlen == 0: runlen = len(data) // itembytes skipbits = runlen*itemsize % 8 if skipbits: skipbits = 8 - skipbits shrbits = itembytes*8 - itemsize bitmask = int(itemsize*'1'+'0'*shrbits, 2) dtypestr = '>' + dtype.char # dtype always big endian? unpack = struct.unpack l = runlen * (len(data)*8 // (runlen*itemsize + skipbits)) result = numpy.empty((l,), dtype) bitcount = 0 for i in range(len(result)): start = bitcount // 8 s = data[start:start+itembytes] try: code = unpack(dtypestr, s)[0] except Exception: code = unpack(dtypestr, s + b'\x00'*(itembytes-len(s)))[0] code <<= bitcount % 8 code &= bitmask result[i] = code >> shrbits bitcount += itemsize if (i+1) % runlen == 0: bitcount += skipbits return result def unpack_rgb(data, dtype='>> data = struct.pack('BBBB', 0x21, 0x08, 0xff, 0xff) >>> print(unpack_rgb(data, '>> print(unpack_rgb(data, '>> print(unpack_rgb(data, '= bits) data = numpy.fromstring(data, dtype.byteorder+dt) result = numpy.empty((data.size, len(bitspersample)), dtype.char) for i, bps in enumerate(bitspersample): t = data >> int(numpy.sum(bitspersample[i+1:])) t &= int('0b'+'1'*bps, 2) if rescale: o = ((dtype.itemsize * 8) // bps + 1) * bps if o > data.dtype.itemsize * 8: t = t.astype('I') t *= (2**o - 1) // (2**bps - 1) t //= 2**(o - (dtype.itemsize * 8)) result[:, i] = t return result.reshape(-1) @_replace_by('_tifffile.reverse_bitorder') def reverse_bitorder(data): """Reverse bits in each byte of byte string or numpy array. Decode data where pixels with lower column values are stored in the lower-order bits of the bytes (fill_order == 'lsb2msb'). Parameters ---------- data : byte string or ndarray The data to be bit reversed. If byte string, a new bit-reversed byte string is returned. Numpy arrays are bit-reversed in-place. """ table = ( b'\x00\x80@\xc0 \xa0`\xe0\x10\x90P\xd00\xb0p\xf0\x08\x88H\xc8(\xa8h' b'\xe8\x18\x98X\xd88\xb8x\xf8\x04\x84D\xc4$\xa4d\xe4\x14\x94T\xd44' b'\xb4t\xf4\x0c\x8cL\xcc,\xacl\xec\x1c\x9c\\\xdc<\xbc|\xfc\x02\x82B' b'\xc2"\xa2b\xe2\x12\x92R\xd22\xb2r\xf2\n\x8aJ\xca*\xaaj\xea\x1a' b'\x9aZ\xda:\xbaz\xfa\x06\x86F\xc6&\xa6f\xe6\x16\x96V\xd66\xb6v\xf6' b'\x0e\x8eN\xce.\xaen\xee\x1e\x9e^\xde>\xbe~\xfe\x01\x81A\xc1!\xa1a' b'\xe1\x11\x91Q\xd11\xb1q\xf1\t\x89I\xc9)\xa9i\xe9\x19\x99Y\xd99' b'\xb9y\xf9\x05\x85E\xc5%\xa5e\xe5\x15\x95U\xd55\xb5u\xf5\r\x8dM' b'\xcd-\xadm\xed\x1d\x9d]\xdd=\xbd}\xfd\x03\x83C\xc3#\xa3c\xe3\x13' b'\x93S\xd33\xb3s\xf3\x0b\x8bK\xcb+\xabk\xeb\x1b\x9b[\xdb;\xbb{\xfb' b'\x07\x87G\xc7\'\xa7g\xe7\x17\x97W\xd77\xb7w\xf7\x0f\x8fO\xcf/\xafo' b'\xef\x1f\x9f_\xdf?\xbf\x7f\xff') try: view = data.view('uint8') numpy.take(numpy.fromstring(table, dtype='uint8'), view, out=view) except AttributeError: return data.translate(table) except ValueError: raise NotImplementedError("slices of arrays not supported") def apply_colormap(image, colormap, contig=True): """Return palette-colored image. The image values are used to index the colormap on axis 1. The returned image is of shape image.shape+colormap.shape[0] and dtype colormap.dtype. Parameters ---------- image : numpy.ndarray Indexes into the colormap. colormap : numpy.ndarray RGB lookup table aka palette of shape (3, 2**bits_per_sample). contig : bool If True, return a contiguous array. Examples -------- >>> image = numpy.arange(256, dtype='uint8') >>> colormap = numpy.vstack([image, image, image]).astype('uint16') * 256 >>> apply_colormap(image, colormap)[-1] array([65280, 65280, 65280], dtype=uint16) """ image = numpy.take(colormap, image, axis=1) image = numpy.rollaxis(image, 0, image.ndim) if contig: image = numpy.ascontiguousarray(image) return image def reorient(image, orientation): """Return reoriented view of image array. Parameters ---------- image : numpy.ndarray Non-squeezed output of asarray() functions. Axes -3 and -2 must be image length and width respectively. orientation : int or str One of TIFF_ORIENTATIONS keys or values. """ o = TIFF_ORIENTATIONS.get(orientation, orientation) if o == 'top_left': return image elif o == 'top_right': return image[..., ::-1, :] elif o == 'bottom_left': return image[..., ::-1, :, :] elif o == 'bottom_right': return image[..., ::-1, ::-1, :] elif o == 'left_top': return numpy.swapaxes(image, -3, -2) elif o == 'right_top': return numpy.swapaxes(image, -3, -2)[..., ::-1, :] elif o == 'left_bottom': return numpy.swapaxes(image, -3, -2)[..., ::-1, :, :] elif o == 'right_bottom': return numpy.swapaxes(image, -3, -2)[..., ::-1, ::-1, :] def reshape_nd(image, ndim): """Return image array with at least ndim dimensions. Prepend 1s to image shape as necessary. >>> reshape_nd(numpy.empty(0), 1).shape (0,) >>> reshape_nd(numpy.empty(1), 2).shape (1, 1) >>> reshape_nd(numpy.empty((2, 3)), 3).shape (1, 2, 3) >>> reshape_nd(numpy.empty((3, 4, 5)), 3).shape (3, 4, 5) """ if image.ndim >= ndim: return image image = image.reshape((1,) * (ndim - image.ndim) + image.shape) return image def squeeze_axes(shape, axes, skip='XY'): """Return shape and axes with single-dimensional entries removed. Remove unused dimensions unless their axes are listed in 'skip'. >>> squeeze_axes((5, 1, 2, 1, 1), 'TZYXC') ((5, 2, 1), 'TYX') """ if len(shape) != len(axes): raise ValueError("dimensions of axes and shape do not match") shape, axes = zip(*(i for i in zip(shape, axes) if i[0] > 1 or i[1] in skip)) return tuple(shape), ''.join(axes) def transpose_axes(image, axes, asaxes='CTZYX'): """Return image with its axes permuted to match specified axes. A view is returned if possible. >>> transpose_axes(numpy.zeros((2, 3, 4, 5)), 'TYXC', asaxes='CTZYX').shape (5, 2, 1, 3, 4) """ for ax in axes: if ax not in asaxes: raise ValueError("unknown axis %s" % ax) # add missing axes to image shape = image.shape for ax in reversed(asaxes): if ax not in axes: axes = ax + axes shape = (1,) + shape image = image.reshape(shape) # transpose axes image = image.transpose([axes.index(ax) for ax in asaxes]) return image def reshape_axes(axes, shape, newshape): """Return axes matching new shape. Unknown dimensions are labelled 'Q'. >>> reshape_axes('YXS', (219, 301, 1), (219, 301)) 'YX' >>> reshape_axes('IYX', (12, 219, 301), (3, 4, 219, 1, 301, 1)) 'QQYQXQ' """ shape = tuple(shape) newshape = tuple(newshape) if len(axes) != len(shape): raise ValueError("axes do not match shape") if product(shape) != product(newshape): raise ValueError("can not reshape %s to %s" % (shape, newshape)) if not axes or not newshape: return '' lendiff = max(0, len(shape) - len(newshape)) if lendiff: newshape = newshape + (1,) * lendiff i = len(shape)-1 prodns = 1 prods = 1 result = [] for ns in newshape[::-1]: prodns *= ns while i > 0 and shape[i] == 1 and ns != 1: i -= 1 if ns == shape[i] and prodns == prods*shape[i]: prods *= shape[i] result.append(axes[i]) i -= 1 else: result.append('Q') return ''.join(reversed(result[lendiff:])) def stack_pages(pages, memmap=False, tempdir=None, *args, **kwargs): """Read data from sequence of TiffPage and stack them vertically. If memmap is True, return an array stored in a binary file on disk. Additional parameters are passsed to the page asarray function. """ if len(pages) == 0: raise ValueError("no pages") if len(pages) == 1: return pages[0].asarray(memmap=memmap, *args, **kwargs) data0 = pages[0].asarray(*args, **kwargs) shape = (len(pages),) + data0.shape if memmap: with tempfile.NamedTemporaryFile(dir=tempdir) as fh: data = numpy.memmap(fh, dtype=data0.dtype, shape=shape) else: data = numpy.empty(shape, dtype=data0.dtype) data[0] = data0 if memmap: data.flush() del data0 for i, page in enumerate(pages[1:]): data[i+1] = page.asarray(*args, **kwargs) if memmap: data.flush() return data def stripnull(string, null=b'\x00'): """Return string truncated at first null character. Clean NULL terminated C strings. For unicode strings use null='\\0'. """ i = string.find(null) return string if (i < 0) else string[:i] def stripascii(string): """Return string truncated at last byte that is 7bit ASCII. Clean NULL separated and terminated TIFF strings. """ # TODO: pythonize this ord_ = ord if sys.version_info[0] < 3 else lambda x: x i = len(string) while i: i -= 1 if 8 < ord_(string[i]) < 127: break else: i = -1 return string[:i+1] def astype(value, types=None): """Return argument as one of types if possible.""" if types is None: types = int, float, bytes2str for typ in types: try: return typ(value) except (ValueError, TypeError, UnicodeEncodeError): pass return value def format_size(size): """Return file size as string from byte size.""" for unit in ('B', 'KiB', 'MiB', 'GiB', 'TiB', 'PiB'): if size < 2048: return "%.f %s" % (size, unit) size /= 1024.0 def sequence(value): """Return tuple containing value if value is not a sequence. >>> sequence(1) (1,) >>> sequence([1]) [1] """ try: len(value) return value except TypeError: return (value,) def product(iterable): """Return product of sequence of numbers. Equivalent of functools.reduce(operator.mul, iterable, 1). >>> product([2**8, 2**30]) 274877906944 >>> product([]) 1 """ prod = 1 for i in iterable: prod *= i return prod def natural_sorted(iterable): """Return human sorted list of strings. E.g. for sorting file names. >>> natural_sorted(['f1', 'f2', 'f10']) ['f1', 'f2', 'f10'] """ def sortkey(x): return [(int(c) if c.isdigit() else c) for c in re.split(numbers, x)] numbers = re.compile(r'(\d+)') return sorted(iterable, key=sortkey) def excel_datetime(timestamp, epoch=datetime.datetime.fromordinal(693594)): """Return datetime object from timestamp in Excel serial format. Convert LSM time stamps. >>> excel_datetime(40237.029999999795) datetime.datetime(2010, 2, 28, 0, 43, 11, 999982) """ return epoch + datetime.timedelta(timestamp) def julian_datetime(julianday, milisecond=0): """Return datetime from days since 1/1/4713 BC and ms since midnight. Convert Julian dates according to MetaMorph. >>> julian_datetime(2451576, 54362783) datetime.datetime(2000, 2, 2, 15, 6, 2, 783) """ if julianday <= 1721423: # no datetime before year 1 return None a = julianday + 1 if a > 2299160: alpha = math.trunc((a - 1867216.25) / 36524.25) a += 1 + alpha - alpha // 4 b = a + (1524 if a > 1721423 else 1158) c = math.trunc((b - 122.1) / 365.25) d = math.trunc(365.25 * c) e = math.trunc((b - d) / 30.6001) day = b - d - math.trunc(30.6001 * e) month = e - (1 if e < 13.5 else 13) year = c - (4716 if month > 2.5 else 4715) hour, milisecond = divmod(milisecond, 1000 * 60 * 60) minute, milisecond = divmod(milisecond, 1000 * 60) second, milisecond = divmod(milisecond, 1000) return datetime.datetime(year, month, day, hour, minute, second, milisecond) def parse_kwargs(kwargs, *keys, **keyvalues): """Return dict with keys from keys|keyvals and values from kwargs|keyvals. Existing keys are deleted from kwargs. >>> kwargs = {'one': 1, 'two': 2, 'four': 4} >>> kwargs2 = parse_kwargs(kwargs, 'two', 'three', four=None, five=5) >>> kwargs == {'one': 1} True >>> kwargs2 == {'two': 2, 'four': 4, 'five': 5} True """ result = {} for key in keys: if key in kwargs: result[key] = kwargs[key] del kwargs[key] for key, value in keyvalues.items(): if key in kwargs: result[key] = kwargs[key] del kwargs[key] else: result[key] = value return result def update_kwargs(kwargs, **keyvalues): """Update dict with keys and values if keys do not already exist. >>> kwargs = {'one': 1, } >>> update_kwargs(kwargs, one=None, two=2) >>> kwargs == {'one': 1, 'two': 2} True """ for key, value in keyvalues.items(): if key not in kwargs: kwargs[key] = value class TIFF_SUBFILE_TYPES(object): def __getitem__(self, key): result = [] if key & 1: result.append('reduced_image') if key & 2: result.append('page') if key & 4: result.append('mask') return tuple(result) TIFF_PHOTOMETRICS = { 0: 'miniswhite', 1: 'minisblack', 2: 'rgb', 3: 'palette', 4: 'mask', 5: 'separated', # CMYK 6: 'ycbcr', 8: 'cielab', 9: 'icclab', 10: 'itulab', 32803: 'cfa', # Color Filter Array 32844: 'logl', 32845: 'logluv', 34892: 'linear_raw' } TIFF_COMPESSIONS = { 1: None, 2: 'ccittrle', 3: 'ccittfax3', 4: 'ccittfax4', 5: 'lzw', 6: 'ojpeg', 7: 'jpeg', 8: 'adobe_deflate', 9: 't85', 10: 't43', 32766: 'next', 32771: 'ccittrlew', 32773: 'packbits', 32809: 'thunderscan', 32895: 'it8ctpad', 32896: 'it8lw', 32897: 'it8mp', 32898: 'it8bl', 32908: 'pixarfilm', 32909: 'pixarlog', 32946: 'deflate', 32947: 'dcs', 34661: 'jbig', 34676: 'sgilog', 34677: 'sgilog24', 34712: 'jp2000', 34713: 'nef', 34925: 'lzma', } TIFF_DECOMPESSORS = { None: lambda x: x, 'adobe_deflate': zlib.decompress, 'deflate': zlib.decompress, 'packbits': decode_packbits, 'lzw': decode_lzw, # 'jpeg': decode_jpeg } if lzma: TIFF_DECOMPESSORS['lzma'] = lzma.decompress TIFF_DATA_TYPES = { 1: '1B', # BYTE 8-bit unsigned integer. 2: '1s', # ASCII 8-bit byte that contains a 7-bit ASCII code; # the last byte must be NULL (binary zero). 3: '1H', # SHORT 16-bit (2-byte) unsigned integer 4: '1I', # LONG 32-bit (4-byte) unsigned integer. 5: '2I', # RATIONAL Two LONGs: the first represents the numerator of # a fraction; the second, the denominator. 6: '1b', # SBYTE An 8-bit signed (twos-complement) integer. 7: '1s', # UNDEFINED An 8-bit byte that may contain anything, # depending on the definition of the field. 8: '1h', # SSHORT A 16-bit (2-byte) signed (twos-complement) integer. 9: '1i', # SLONG A 32-bit (4-byte) signed (twos-complement) integer. 10: '2i', # SRATIONAL Two SLONGs: the first represents the numerator # of a fraction, the second the denominator. 11: '1f', # FLOAT Single precision (4-byte) IEEE format. 12: '1d', # DOUBLE Double precision (8-byte) IEEE format. 13: '1I', # IFD unsigned 4 byte IFD offset. #14: '', # UNICODE #15: '', # COMPLEX 16: '1Q', # LONG8 unsigned 8 byte integer (BigTiff) 17: '1q', # SLONG8 signed 8 byte integer (BigTiff) 18: '1Q', # IFD8 unsigned 8 byte IFD offset (BigTiff) } TIFF_SAMPLE_FORMATS = { 1: 'uint', 2: 'int', 3: 'float', #4: 'void', #5: 'complex_int', 6: 'complex', } TIFF_SAMPLE_DTYPES = { ('uint', 1): '?', # bitmap ('uint', 2): 'B', ('uint', 3): 'B', ('uint', 4): 'B', ('uint', 5): 'B', ('uint', 6): 'B', ('uint', 7): 'B', ('uint', 8): 'B', ('uint', 9): 'H', ('uint', 10): 'H', ('uint', 11): 'H', ('uint', 12): 'H', ('uint', 13): 'H', ('uint', 14): 'H', ('uint', 15): 'H', ('uint', 16): 'H', ('uint', 17): 'I', ('uint', 18): 'I', ('uint', 19): 'I', ('uint', 20): 'I', ('uint', 21): 'I', ('uint', 22): 'I', ('uint', 23): 'I', ('uint', 24): 'I', ('uint', 25): 'I', ('uint', 26): 'I', ('uint', 27): 'I', ('uint', 28): 'I', ('uint', 29): 'I', ('uint', 30): 'I', ('uint', 31): 'I', ('uint', 32): 'I', ('uint', 64): 'Q', ('int', 8): 'b', ('int', 16): 'h', ('int', 32): 'i', ('int', 64): 'q', ('float', 16): 'e', ('float', 32): 'f', ('float', 64): 'd', ('complex', 64): 'F', ('complex', 128): 'D', ('uint', (5, 6, 5)): 'B', } TIFF_ORIENTATIONS = { 1: 'top_left', 2: 'top_right', 3: 'bottom_right', 4: 'bottom_left', 5: 'left_top', 6: 'right_top', 7: 'right_bottom', 8: 'left_bottom', } # TODO: is there a standard for character axes labels? AXES_LABELS = { 'X': 'width', 'Y': 'height', 'Z': 'depth', 'S': 'sample', # rgb(a) 'I': 'series', # general sequence, plane, page, IFD 'T': 'time', 'C': 'channel', # color, emission wavelength 'A': 'angle', 'P': 'phase', # formerly F # P is Position in LSM! 'R': 'tile', # region, point, mosaic 'H': 'lifetime', # histogram 'E': 'lambda', # excitation wavelength 'L': 'exposure', # lux 'V': 'event', 'Q': 'other', 'M': 'mosaic', # LSM 6 } AXES_LABELS.update(dict((v, k) for k, v in AXES_LABELS.items())) # Map OME pixel types to numpy dtype OME_PIXEL_TYPES = { 'int8': 'i1', 'int16': 'i2', 'int32': 'i4', 'uint8': 'u1', 'uint16': 'u2', 'uint32': 'u4', 'float': 'f4', # 'bit': 'bit', 'double': 'f8', 'complex': 'c8', 'double-complex': 'c16', } # NIH Image PicHeader v1.63 NIH_IMAGE_HEADER = [ ('fileid', 'a8'), ('nlines', 'i2'), ('pixelsperline', 'i2'), ('version', 'i2'), ('oldlutmode', 'i2'), ('oldncolors', 'i2'), ('colors', 'u1', (3, 32)), ('oldcolorstart', 'i2'), ('colorwidth', 'i2'), ('extracolors', 'u2', (6, 3)), ('nextracolors', 'i2'), ('foregroundindex', 'i2'), ('backgroundindex', 'i2'), ('xscale', 'f8'), ('_x0', 'i2'), ('_x1', 'i2'), ('units_t', 'i2'), # NIH_UNITS_TYPE ('p1', [('x', 'i2'), ('y', 'i2')]), ('p2', [('x', 'i2'), ('y', 'i2')]), ('curvefit_t', 'i2'), # NIH_CURVEFIT_TYPE ('ncoefficients', 'i2'), ('coeff', 'f8', 6), ('_um_len', 'u1'), ('um', 'a15'), ('_x2', 'u1'), ('binarypic', 'b1'), ('slicestart', 'i2'), ('sliceend', 'i2'), ('scalemagnification', 'f4'), ('nslices', 'i2'), ('slicespacing', 'f4'), ('currentslice', 'i2'), ('frameinterval', 'f4'), ('pixelaspectratio', 'f4'), ('colorstart', 'i2'), ('colorend', 'i2'), ('ncolors', 'i2'), ('fill1', '3u2'), ('fill2', '3u2'), ('colortable_t', 'u1'), # NIH_COLORTABLE_TYPE ('lutmode_t', 'u1'), # NIH_LUTMODE_TYPE ('invertedtable', 'b1'), ('zeroclip', 'b1'), ('_xunit_len', 'u1'), ('xunit', 'a11'), ('stacktype_t', 'i2'), # NIH_STACKTYPE_TYPE ] NIH_COLORTABLE_TYPE = ( 'CustomTable', 'AppleDefault', 'Pseudo20', 'Pseudo32', 'Rainbow', 'Fire1', 'Fire2', 'Ice', 'Grays', 'Spectrum') NIH_LUTMODE_TYPE = ( 'PseudoColor', 'OldAppleDefault', 'OldSpectrum', 'GrayScale', 'ColorLut', 'CustomGrayscale') NIH_CURVEFIT_TYPE = ( 'StraightLine', 'Poly2', 'Poly3', 'Poly4', 'Poly5', 'ExpoFit', 'PowerFit', 'LogFit', 'RodbardFit', 'SpareFit1', 'Uncalibrated', 'UncalibratedOD') NIH_UNITS_TYPE = ( 'Nanometers', 'Micrometers', 'Millimeters', 'Centimeters', 'Meters', 'Kilometers', 'Inches', 'Feet', 'Miles', 'Pixels', 'OtherUnits') NIH_STACKTYPE_TYPE = ( 'VolumeStack', 'RGBStack', 'MovieStack', 'HSVStack') # Map Universal Imaging Corporation MetaMorph internal tag ids to name and type UIC_TAGS = { 0: ('auto_scale', int), 1: ('min_scale', int), 2: ('max_scale', int), 3: ('spatial_calibration', int), 4: ('x_calibration', Fraction), 5: ('y_calibration', Fraction), 6: ('calibration_units', str), 7: ('name', str), 8: ('thresh_state', int), 9: ('thresh_state_red', int), 10: ('tagid_10', None), # undefined 11: ('thresh_state_green', int), 12: ('thresh_state_blue', int), 13: ('thresh_state_lo', int), 14: ('thresh_state_hi', int), 15: ('zoom', int), 16: ('create_time', julian_datetime), 17: ('last_saved_time', julian_datetime), 18: ('current_buffer', int), 19: ('gray_fit', None), 20: ('gray_point_count', None), 21: ('gray_x', Fraction), 22: ('gray_y', Fraction), 23: ('gray_min', Fraction), 24: ('gray_max', Fraction), 25: ('gray_unit_name', str), 26: ('standard_lut', int), 27: ('wavelength', int), 28: ('stage_position', '(%i,2,2)u4'), # N xy positions as fractions 29: ('camera_chip_offset', '(%i,2,2)u4'), # N xy offsets as fractions 30: ('overlay_mask', None), 31: ('overlay_compress', None), 32: ('overlay', None), 33: ('special_overlay_mask', None), 34: ('special_overlay_compress', None), 35: ('special_overlay', None), 36: ('image_property', read_uic_image_property), 37: ('stage_label', '%ip'), # N str 38: ('autoscale_lo_info', Fraction), 39: ('autoscale_hi_info', Fraction), 40: ('absolute_z', '(%i,2)u4'), # N fractions 41: ('absolute_z_valid', '(%i,)u4'), # N long 42: ('gamma', int), 43: ('gamma_red', int), 44: ('gamma_green', int), 45: ('gamma_blue', int), 46: ('camera_bin', int), 47: ('new_lut', int), 48: ('image_property_ex', None), 49: ('plane_property', int), 50: ('user_lut_table', '(256,3)u1'), 51: ('red_autoscale_info', int), 52: ('red_autoscale_lo_info', Fraction), 53: ('red_autoscale_hi_info', Fraction), 54: ('red_minscale_info', int), 55: ('red_maxscale_info', int), 56: ('green_autoscale_info', int), 57: ('green_autoscale_lo_info', Fraction), 58: ('green_autoscale_hi_info', Fraction), 59: ('green_minscale_info', int), 60: ('green_maxscale_info', int), 61: ('blue_autoscale_info', int), 62: ('blue_autoscale_lo_info', Fraction), 63: ('blue_autoscale_hi_info', Fraction), 64: ('blue_min_scale_info', int), 65: ('blue_max_scale_info', int), #66: ('overlay_plane_color', read_uic_overlay_plane_color), } # Olympus FluoView MM_DIMENSION = [ ('name', 'a16'), ('size', 'i4'), ('origin', 'f8'), ('resolution', 'f8'), ('unit', 'a64'), ] MM_HEADER = [ ('header_flag', 'i2'), ('image_type', 'u1'), ('image_name', 'a257'), ('offset_data', 'u4'), ('palette_size', 'i4'), ('offset_palette0', 'u4'), ('offset_palette1', 'u4'), ('comment_size', 'i4'), ('offset_comment', 'u4'), ('dimensions', MM_DIMENSION, 10), ('offset_position', 'u4'), ('map_type', 'i2'), ('map_min', 'f8'), ('map_max', 'f8'), ('min_value', 'f8'), ('max_value', 'f8'), ('offset_map', 'u4'), ('gamma', 'f8'), ('offset', 'f8'), ('gray_channel', MM_DIMENSION), ('offset_thumbnail', 'u4'), ('voice_field', 'i4'), ('offset_voice_field', 'u4'), ] # Carl Zeiss LSM CZ_LSM_INFO = [ ('magic_number', 'u4'), ('structure_size', 'i4'), ('dimension_x', 'i4'), ('dimension_y', 'i4'), ('dimension_z', 'i4'), ('dimension_channels', 'i4'), ('dimension_time', 'i4'), ('data_type', 'i4'), # CZ_DATA_TYPES ('thumbnail_x', 'i4'), ('thumbnail_y', 'i4'), ('voxel_size_x', 'f8'), ('voxel_size_y', 'f8'), ('voxel_size_z', 'f8'), ('origin_x', 'f8'), ('origin_y', 'f8'), ('origin_z', 'f8'), ('scan_type', 'u2'), ('spectral_scan', 'u2'), ('type_of_data', 'u4'), # CZ_TYPE_OF_DATA ('offset_vector_overlay', 'u4'), ('offset_input_lut', 'u4'), ('offset_output_lut', 'u4'), ('offset_channel_colors', 'u4'), ('time_interval', 'f8'), ('offset_channel_data_types', 'u4'), ('offset_scan_info', 'u4'), # CZ_LSM_SCAN_INFO ('offset_ks_data', 'u4'), ('offset_time_stamps', 'u4'), ('offset_event_list', 'u4'), ('offset_roi', 'u4'), ('offset_bleach_roi', 'u4'), ('offset_next_recording', 'u4'), # LSM 2.0 ends here ('display_aspect_x', 'f8'), ('display_aspect_y', 'f8'), ('display_aspect_z', 'f8'), ('display_aspect_time', 'f8'), ('offset_mean_of_roi_overlay', 'u4'), ('offset_topo_isoline_overlay', 'u4'), ('offset_topo_profile_overlay', 'u4'), ('offset_linescan_overlay', 'u4'), ('offset_toolbar_flags', 'u4'), ('offset_channel_wavelength', 'u4'), ('offset_channel_factors', 'u4'), ('objective_sphere_correction', 'f8'), ('offset_unmix_parameters', 'u4'), # LSM 3.2, 4.0 end here ('offset_acquisition_parameters', 'u4'), ('offset_characteristics', 'u4'), ('offset_palette', 'u4'), ('time_difference_x', 'f8'), ('time_difference_y', 'f8'), ('time_difference_z', 'f8'), ('internal_use_1', 'u4'), ('dimension_p', 'i4'), ('dimension_m', 'i4'), ('dimensions_reserved', '16i4'), ('offset_tile_positions', 'u4'), ('reserved_1', '9u4'), ('offset_positions', 'u4'), ('reserved_2', '21u4'), # must be 0 ] # Import functions for LSM_INFO sub-records CZ_LSM_INFO_READERS = { 'scan_info': read_cz_lsm_scan_info, 'time_stamps': read_cz_lsm_time_stamps, 'event_list': read_cz_lsm_event_list, 'channel_colors': read_cz_lsm_floatpairs, 'positions': read_cz_lsm_floatpairs, 'tile_positions': read_cz_lsm_floatpairs, } # Map cz_lsm_info.scan_type to dimension order CZ_SCAN_TYPES = { 0: 'XYZCT', # x-y-z scan 1: 'XYZCT', # z scan (x-z plane) 2: 'XYZCT', # line scan 3: 'XYTCZ', # time series x-y 4: 'XYZTC', # time series x-z 5: 'XYTCZ', # time series 'Mean of ROIs' 6: 'XYZTC', # time series x-y-z 7: 'XYCTZ', # spline scan 8: 'XYCZT', # spline scan x-z 9: 'XYTCZ', # time series spline plane x-z 10: 'XYZCT', # point mode } # Map dimension codes to cz_lsm_info attribute CZ_DIMENSIONS = { 'X': 'dimension_x', 'Y': 'dimension_y', 'Z': 'dimension_z', 'C': 'dimension_channels', 'T': 'dimension_time', 'P': 'dimension_p', 'M': 'dimension_m', } # Description of cz_lsm_info.data_type CZ_DATA_TYPES = { 0: 'varying data types', 1: '8 bit unsigned integer', 2: '12 bit unsigned integer', 5: '32 bit float', } # Description of cz_lsm_info.type_of_data CZ_TYPE_OF_DATA = { 0: 'Original scan data', 1: 'Calculated data', 2: '3D reconstruction', 3: 'Topography height map', } CZ_LSM_SCAN_INFO_ARRAYS = { 0x20000000: "tracks", 0x30000000: "lasers", 0x60000000: "detection_channels", 0x80000000: "illumination_channels", 0xa0000000: "beam_splitters", 0xc0000000: "data_channels", 0x11000000: "timers", 0x13000000: "markers", } CZ_LSM_SCAN_INFO_STRUCTS = { # 0x10000000: "recording", 0x40000000: "track", 0x50000000: "laser", 0x70000000: "detection_channel", 0x90000000: "illumination_channel", 0xb0000000: "beam_splitter", 0xd0000000: "data_channel", 0x12000000: "timer", 0x14000000: "marker", } CZ_LSM_SCAN_INFO_ATTRIBUTES = { # recording 0x10000001: "name", 0x10000002: "description", 0x10000003: "notes", 0x10000004: "objective", 0x10000005: "processing_summary", 0x10000006: "special_scan_mode", 0x10000007: "scan_type", 0x10000008: "scan_mode", 0x10000009: "number_of_stacks", 0x1000000a: "lines_per_plane", 0x1000000b: "samples_per_line", 0x1000000c: "planes_per_volume", 0x1000000d: "images_width", 0x1000000e: "images_height", 0x1000000f: "images_number_planes", 0x10000010: "images_number_stacks", 0x10000011: "images_number_channels", 0x10000012: "linscan_xy_size", 0x10000013: "scan_direction", 0x10000014: "time_series", 0x10000015: "original_scan_data", 0x10000016: "zoom_x", 0x10000017: "zoom_y", 0x10000018: "zoom_z", 0x10000019: "sample_0x", 0x1000001a: "sample_0y", 0x1000001b: "sample_0z", 0x1000001c: "sample_spacing", 0x1000001d: "line_spacing", 0x1000001e: "plane_spacing", 0x1000001f: "plane_width", 0x10000020: "plane_height", 0x10000021: "volume_depth", 0x10000023: "nutation", 0x10000034: "rotation", 0x10000035: "precession", 0x10000036: "sample_0time", 0x10000037: "start_scan_trigger_in", 0x10000038: "start_scan_trigger_out", 0x10000039: "start_scan_event", 0x10000040: "start_scan_time", 0x10000041: "stop_scan_trigger_in", 0x10000042: "stop_scan_trigger_out", 0x10000043: "stop_scan_event", 0x10000044: "stop_scan_time", 0x10000045: "use_rois", 0x10000046: "use_reduced_memory_rois", 0x10000047: "user", 0x10000048: "use_bc_correction", 0x10000049: "position_bc_correction1", 0x10000050: "position_bc_correction2", 0x10000051: "interpolation_y", 0x10000052: "camera_binning", 0x10000053: "camera_supersampling", 0x10000054: "camera_frame_width", 0x10000055: "camera_frame_height", 0x10000056: "camera_offset_x", 0x10000057: "camera_offset_y", 0x10000059: "rt_binning", 0x1000005a: "rt_frame_width", 0x1000005b: "rt_frame_height", 0x1000005c: "rt_region_width", 0x1000005d: "rt_region_height", 0x1000005e: "rt_offset_x", 0x1000005f: "rt_offset_y", 0x10000060: "rt_zoom", 0x10000061: "rt_line_period", 0x10000062: "prescan", 0x10000063: "scan_direction_z", # track 0x40000001: "multiplex_type", # 0 after line; 1 after frame 0x40000002: "multiplex_order", 0x40000003: "sampling_mode", # 0 sample; 1 line average; 2 frame average 0x40000004: "sampling_method", # 1 mean; 2 sum 0x40000005: "sampling_number", 0x40000006: "acquire", 0x40000007: "sample_observation_time", 0x4000000b: "time_between_stacks", 0x4000000c: "name", 0x4000000d: "collimator1_name", 0x4000000e: "collimator1_position", 0x4000000f: "collimator2_name", 0x40000010: "collimator2_position", 0x40000011: "is_bleach_track", 0x40000012: "is_bleach_after_scan_number", 0x40000013: "bleach_scan_number", 0x40000014: "trigger_in", 0x40000015: "trigger_out", 0x40000016: "is_ratio_track", 0x40000017: "bleach_count", 0x40000018: "spi_center_wavelength", 0x40000019: "pixel_time", 0x40000021: "condensor_frontlens", 0x40000023: "field_stop_value", 0x40000024: "id_condensor_aperture", 0x40000025: "condensor_aperture", 0x40000026: "id_condensor_revolver", 0x40000027: "condensor_filter", 0x40000028: "id_transmission_filter1", 0x40000029: "id_transmission1", 0x40000030: "id_transmission_filter2", 0x40000031: "id_transmission2", 0x40000032: "repeat_bleach", 0x40000033: "enable_spot_bleach_pos", 0x40000034: "spot_bleach_posx", 0x40000035: "spot_bleach_posy", 0x40000036: "spot_bleach_posz", 0x40000037: "id_tubelens", 0x40000038: "id_tubelens_position", 0x40000039: "transmitted_light", 0x4000003a: "reflected_light", 0x4000003b: "simultan_grab_and_bleach", 0x4000003c: "bleach_pixel_time", # laser 0x50000001: "name", 0x50000002: "acquire", 0x50000003: "power", # detection_channel 0x70000001: "integration_mode", 0x70000002: "special_mode", 0x70000003: "detector_gain_first", 0x70000004: "detector_gain_last", 0x70000005: "amplifier_gain_first", 0x70000006: "amplifier_gain_last", 0x70000007: "amplifier_offs_first", 0x70000008: "amplifier_offs_last", 0x70000009: "pinhole_diameter", 0x7000000a: "counting_trigger", 0x7000000b: "acquire", 0x7000000c: "point_detector_name", 0x7000000d: "amplifier_name", 0x7000000e: "pinhole_name", 0x7000000f: "filter_set_name", 0x70000010: "filter_name", 0x70000013: "integrator_name", 0x70000014: "channel_name", 0x70000015: "detector_gain_bc1", 0x70000016: "detector_gain_bc2", 0x70000017: "amplifier_gain_bc1", 0x70000018: "amplifier_gain_bc2", 0x70000019: "amplifier_offset_bc1", 0x70000020: "amplifier_offset_bc2", 0x70000021: "spectral_scan_channels", 0x70000022: "spi_wavelength_start", 0x70000023: "spi_wavelength_stop", 0x70000026: "dye_name", 0x70000027: "dye_folder", # illumination_channel 0x90000001: "name", 0x90000002: "power", 0x90000003: "wavelength", 0x90000004: "aquire", 0x90000005: "detchannel_name", 0x90000006: "power_bc1", 0x90000007: "power_bc2", # beam_splitter 0xb0000001: "filter_set", 0xb0000002: "filter", 0xb0000003: "name", # data_channel 0xd0000001: "name", 0xd0000003: "acquire", 0xd0000004: "color", 0xd0000005: "sample_type", 0xd0000006: "bits_per_sample", 0xd0000007: "ratio_type", 0xd0000008: "ratio_track1", 0xd0000009: "ratio_track2", 0xd000000a: "ratio_channel1", 0xd000000b: "ratio_channel2", 0xd000000c: "ratio_const1", 0xd000000d: "ratio_const2", 0xd000000e: "ratio_const3", 0xd000000f: "ratio_const4", 0xd0000010: "ratio_const5", 0xd0000011: "ratio_const6", 0xd0000012: "ratio_first_images1", 0xd0000013: "ratio_first_images2", 0xd0000014: "dye_name", 0xd0000015: "dye_folder", 0xd0000016: "spectrum", 0xd0000017: "acquire", # timer 0x12000001: "name", 0x12000002: "description", 0x12000003: "interval", 0x12000004: "trigger_in", 0x12000005: "trigger_out", 0x12000006: "activation_time", 0x12000007: "activation_number", # marker 0x14000001: "name", 0x14000002: "description", 0x14000003: "trigger_in", 0x14000004: "trigger_out", } # TVIPS metadata from EMMENU Help file TVIPS_HEADER_V1 = [ ('version', 'i4'), ('comment_v1', 'a80'), ('high_tension', 'i4'), ('spherical_aberration', 'i4'), ('illumination_aperture', 'i4'), ('magnification', 'i4'), ('post-magnification', 'i4'), ('focal_length', 'i4'), ('defocus', 'i4'), ('astigmatism', 'i4'), ('astigmatism_direction', 'i4'), ('biprism_voltage', 'i4'), ('specimen_tilt_angle', 'i4'), ('specimen_tilt_direction', 'i4'), ('illumination_tilt_direction', 'i4'), ('illumination_tilt_angle', 'i4'), ('image_mode', 'i4'), ('energy_spread', 'i4'), ('chromatic_aberration', 'i4'), ('shutter_type', 'i4'), ('defocus_spread', 'i4'), ('ccd_number', 'i4'), ('ccd_size', 'i4'), ('offset_x_v1', 'i4'), ('offset_y_v1', 'i4'), ('physical_pixel_size', 'i4'), ('binning', 'i4'), ('readout_speed', 'i4'), ('gain_v1', 'i4'), ('sensitivity_v1', 'i4'), ('exposure_time_v1', 'i4'), ('flat_corrected', 'i4'), ('dead_px_corrected', 'i4'), ('image_mean', 'i4'), ('image_std', 'i4'), ('displacement_x', 'i4'), ('displacement_y', 'i4'), ('date_v1', 'i4'), ('time_v1', 'i4'), ('image_min', 'i4'), ('image_max', 'i4'), ('image_statistics_quality', 'i4'), ] TVIPS_HEADER_V2 = [ ('image_name', 'V160'), # utf16 ('image_folder', 'V160'), ('image_size_x', 'i4'), ('image_size_y', 'i4'), ('image_size_z', 'i4'), ('image_size_e', 'i4'), ('image_data_type', 'i4'), ('date', 'i4'), ('time', 'i4'), ('comment', 'V1024'), ('image_history', 'V1024'), ('scaling', '16f4'), ('image_statistics', '16c16'), ('image_type', 'i4'), ('image_display_type', 'i4'), ('pixel_size_x', 'f4'), # distance between two px in x, [nm] ('pixel_size_y', 'f4'), # distance between two px in y, [nm] ('image_distance_z', 'f4'), ('image_distance_e', 'f4'), ('image_misc', '32f4'), ('tem_type', 'V160'), ('tem_high_tension', 'f4'), ('tem_aberrations', '32f4'), ('tem_energy', '32f4'), ('tem_mode', 'i4'), ('tem_magnification', 'f4'), ('tem_magnification_correction', 'f4'), ('post_magnification', 'f4'), ('tem_stage_type', 'i4'), ('tem_stage_position', '5f4'), # x, y, z, a, b ('tem_image_shift', '2f4'), ('tem_beam_shift', '2f4'), ('tem_beam_tilt', '2f4'), ('tiling_parameters', '7f4'), # 0: tiling? 1:x 2:y 3: max x 4: max y # 5: overlap x 6: overlap y ('tem_illumination', '3f4'), # 0: spotsize 1: intensity ('tem_shutter', 'i4'), ('tem_misc', '32f4'), ('camera_type', 'V160'), ('physical_pixel_size_x', 'f4'), ('physical_pixel_size_y', 'f4'), ('offset_x', 'i4'), ('offset_y', 'i4'), ('binning_x', 'i4'), ('binning_y', 'i4'), ('exposure_time', 'f4'), ('gain', 'f4'), ('readout_rate', 'f4'), ('flatfield_description', 'V160'), ('sensitivity', 'f4'), ('dose', 'f4'), ('cam_misc', '32f4'), ('fei_microscope_information', 'V1024'), ('fei_specimen_information', 'V1024'), ('magic', 'u4'), ] # Map TIFF tag code to attribute name, default value, type, count, validator TIFF_TAGS = { 254: ('new_subfile_type', 0, 4, 1, TIFF_SUBFILE_TYPES()), 255: ('subfile_type', None, 3, 1, {0: 'undefined', 1: 'image', 2: 'reduced_image', 3: 'page'}), 256: ('image_width', None, 4, 1, None), 257: ('image_length', None, 4, 1, None), 258: ('bits_per_sample', 1, 3, None, None), 259: ('compression', 1, 3, 1, TIFF_COMPESSIONS), 262: ('photometric', None, 3, 1, TIFF_PHOTOMETRICS), 266: ('fill_order', 1, 3, 1, {1: 'msb2lsb', 2: 'lsb2msb'}), 269: ('document_name', None, 2, None, None), 270: ('image_description', None, 2, None, None), 271: ('make', None, 2, None, None), 272: ('model', None, 2, None, None), 273: ('strip_offsets', None, 4, None, None), 274: ('orientation', 1, 3, 1, TIFF_ORIENTATIONS), 277: ('samples_per_pixel', 1, 3, 1, None), 278: ('rows_per_strip', 2**32-1, 4, 1, None), 279: ('strip_byte_counts', None, 4, None, None), 280: ('min_sample_value', None, 3, None, None), 281: ('max_sample_value', None, 3, None, None), # 2**bits_per_sample 282: ('x_resolution', None, 5, 1, None), 283: ('y_resolution', None, 5, 1, None), 284: ('planar_configuration', 1, 3, 1, {1: 'contig', 2: 'separate'}), 285: ('page_name', None, 2, None, None), 286: ('x_position', None, 5, 1, None), 287: ('y_position', None, 5, 1, None), 296: ('resolution_unit', 2, 4, 1, {1: None, 2: 'inch', 3: 'centimeter'}), 297: ('page_number', None, 3, 2, None), 305: ('software', None, 2, None, None), 306: ('datetime', None, 2, None, None), 315: ('artist', None, 2, None, None), 316: ('host_computer', None, 2, None, None), 317: ('predictor', 1, 3, 1, {1: None, 2: 'horizontal', 3: 'float'}), 318: ('white_point', None, 5, 2, None), 319: ('primary_chromaticities', None, 5, 6, None), 320: ('color_map', None, 3, None, None), 322: ('tile_width', None, 4, 1, None), 323: ('tile_length', None, 4, 1, None), 324: ('tile_offsets', None, 4, None, None), 325: ('tile_byte_counts', None, 4, None, None), 330: ('sub_ifds', None, 4, None, None), 338: ('extra_samples', None, 3, None, {0: 'unspecified', 1: 'assocalpha', 2: 'unassalpha'}), 339: ('sample_format', 1, 3, None, TIFF_SAMPLE_FORMATS), 340: ('smin_sample_value', None, None, None, None), 341: ('smax_sample_value', None, None, None, None), 346: ('indexed', 0, 3, 1, None), 347: ('jpeg_tables', None, 7, None, None), 530: ('ycbcr_subsampling', (1, 1), 3, 2, None), 531: ('ycbcr_positioning', (1, 1), 3, 1, None), 532: ('reference_black_white', None, 5, 1, None), 32995: ('sgi_matteing', None, None, 1, None), # use extra_samples 32996: ('sgi_datatype', None, None, None, None), # use sample_format 32997: ('image_depth', 1, 4, 1, None), 32998: ('tile_depth', None, 4, 1, None), 33432: ('copyright', None, 1, None, None), 33445: ('md_file_tag', None, 4, 1, None), 33446: ('md_scale_pixel', None, 5, 1, None), 33447: ('md_color_table', None, 3, None, None), 33448: ('md_lab_name', None, 2, None, None), 33449: ('md_sample_info', None, 2, None, None), 33450: ('md_prep_date', None, 2, None, None), 33451: ('md_prep_time', None, 2, None, None), 33452: ('md_file_units', None, 2, None, None), 33550: ('model_pixel_scale', None, 12, 3, None), 33922: ('model_tie_point', None, 12, None, None), 34665: ('exif_ifd', None, None, 1, None), 34735: ('geo_key_directory', None, 3, None, None), 34736: ('geo_double_params', None, 12, None, None), 34737: ('geo_ascii_params', None, 2, None, None), 34853: ('gps_ifd', None, None, 1, None), 37510: ('user_comment', None, None, None, None), 42112: ('gdal_metadata', None, 2, None, None), 42113: ('gdal_nodata', None, 2, None, None), 50289: ('mc_xy_position', None, 12, 2, None), 50290: ('mc_z_position', None, 12, 1, None), 50291: ('mc_xy_calibration', None, 12, 3, None), 50292: ('mc_lens_lem_na_n', None, 12, 3, None), 50293: ('mc_channel_name', None, 1, None, None), 50294: ('mc_ex_wavelength', None, 12, 1, None), 50295: ('mc_time_stamp', None, 12, 1, None), 50838: ('imagej_byte_counts', None, None, None, None), 51023: ('fibics_xml', None, 2, None, None), 65200: ('flex_xml', None, 2, None, None), # code: (attribute name, default value, type, count, validator) } # Map custom TIFF tag codes to attribute names and import functions CUSTOM_TAGS = { 700: ('xmp', read_bytes), 34377: ('photoshop', read_numpy), 33723: ('iptc', read_bytes), 34675: ('icc_profile', read_bytes), 33628: ('uic1tag', read_uic1tag), # Universal Imaging Corporation STK 33629: ('uic2tag', read_uic2tag), 33630: ('uic3tag', read_uic3tag), 33631: ('uic4tag', read_uic4tag), 34118: ('sem_metadata', read_sem_metadata), # Zeiss SEM 34361: ('mm_header', read_mm_header), # Olympus FluoView 34362: ('mm_stamp', read_mm_stamp), 34386: ('mm_user_block', read_bytes), 34412: ('cz_lsm_info', read_cz_lsm_info), # Carl Zeiss LSM 34680: ('sfeg_metadata', read_fei_metadata), # S-FEG 34682: ('helios_metadata', read_fei_metadata), # Helios NanoLab 37706: ('tvips_metadata', read_tvips_header), # TVIPS EMMENU 43314: ('nih_image_header', read_nih_image_header), # 40001: ('mc_ipwinscal', read_bytes), 40100: ('mc_id_old', read_bytes), 50288: ('mc_id', read_bytes), 50296: ('mc_frame_properties', read_bytes), 50839: ('imagej_metadata', read_bytes), 51123: ('micromanager_metadata', read_json), } # Max line length of printed output PRINT_LINE_LEN = 79 def imshow(data, title=None, vmin=0, vmax=None, cmap=None, bitspersample=None, photometric='rgb', interpolation=None, dpi=96, figure=None, subplot=111, maxdim=8192, **kwargs): """Plot n-dimensional images using matplotlib.pyplot. Return figure, subplot and plot axis. Requires pyplot already imported `from matplotlib import pyplot`. Parameters ---------- bitspersample : int or None Number of bits per channel in integer RGB images. photometric : {'miniswhite', 'minisblack', 'rgb', or 'palette'} The color space of the image data. title : str Window and subplot title. figure : matplotlib.figure.Figure (optional). Matplotlib to use for plotting. subplot : int A matplotlib.pyplot.subplot axis. maxdim : int maximum image width and length. kwargs : optional Arguments for matplotlib.pyplot.imshow. """ #if photometric not in ('miniswhite', 'minisblack', 'rgb', 'palette'): # raise ValueError("Can not handle %s photometrics" % photometric) # TODO: handle photometric == 'separated' (CMYK) isrgb = photometric in ('rgb', 'palette') data = data.squeeze() if photometric in ('miniswhite', 'minisblack'): data = reshape_nd(data, 2) else: data = reshape_nd(data, 3) dims = data.ndim if dims < 2: raise ValueError("not an image") elif dims == 2: dims = 0 isrgb = False else: if isrgb and data.shape[-3] in (3, 4): data = numpy.swapaxes(data, -3, -2) data = numpy.swapaxes(data, -2, -1) elif not isrgb and (data.shape[-1] < data.shape[-2] // 8 and data.shape[-1] < data.shape[-3] // 8 and data.shape[-1] < 5): data = numpy.swapaxes(data, -3, -1) data = numpy.swapaxes(data, -2, -1) isrgb = isrgb and data.shape[-1] in (3, 4) dims -= 3 if isrgb else 2 if isrgb: data = data[..., :maxdim, :maxdim, :maxdim] else: data = data[..., :maxdim, :maxdim] if photometric == 'palette' and isrgb: datamax = data.max() if datamax > 255: data >>= 8 # possible precision loss data = data.astype('B') elif data.dtype.kind in 'ui': if not (isrgb and data.dtype.itemsize <= 1) or bitspersample is None: try: bitspersample = int(math.ceil(math.log(data.max(), 2))) except Exception: bitspersample = data.dtype.itemsize * 8 elif not isinstance(bitspersample, int): # bitspersample can be tuple, e.g. (5, 6, 5) bitspersample = data.dtype.itemsize * 8 datamax = 2**bitspersample if isrgb: if bitspersample < 8: data <<= 8 - bitspersample elif bitspersample > 8: data >>= bitspersample - 8 # precision loss data = data.astype('B') elif data.dtype.kind == 'f': datamax = data.max() if isrgb and datamax > 1.0: if data.dtype.char == 'd': data = data.astype('f') data /= datamax elif data.dtype.kind == 'b': datamax = 1 elif data.dtype.kind == 'c': # TODO: handle complex types raise NotImplementedError("complex type") if not isrgb: if vmax is None: vmax = datamax if vmin is None: if data.dtype.kind == 'i': dtmin = numpy.iinfo(data.dtype).min vmin = numpy.min(data) if vmin == dtmin: vmin = numpy.min(data > dtmin) if data.dtype.kind == 'f': dtmin = numpy.finfo(data.dtype).min vmin = numpy.min(data) if vmin == dtmin: vmin = numpy.min(data > dtmin) else: vmin = 0 pyplot = sys.modules['matplotlib.pyplot'] if figure is None: pyplot.rc('font', family='sans-serif', weight='normal', size=8) figure = pyplot.figure(dpi=dpi, figsize=(10.3, 6.3), frameon=True, facecolor='1.0', edgecolor='w') try: figure.canvas.manager.window.title(title) except Exception: pass pyplot.subplots_adjust(bottom=0.03*(dims+2), top=0.9, left=0.1, right=0.95, hspace=0.05, wspace=0.0) subplot = pyplot.subplot(subplot) if title: try: title = unicode(title, 'Windows-1252') except TypeError: pass pyplot.title(title, size=11) if cmap is None: if data.dtype.kind in 'ubf' or vmin == 0: cmap = 'cubehelix' else: cmap = 'coolwarm' if photometric == 'miniswhite': cmap += '_r' image = pyplot.imshow(data[(0,) * dims].squeeze(), vmin=vmin, vmax=vmax, cmap=cmap, interpolation=interpolation, **kwargs) if not isrgb: pyplot.colorbar() # panchor=(0.55, 0.5), fraction=0.05 def format_coord(x, y): # callback function to format coordinate display in toolbar x = int(x + 0.5) y = int(y + 0.5) try: if dims: return "%s @ %s [%4i, %4i]" % (cur_ax_dat[1][y, x], current, x, y) else: return "%s @ [%4i, %4i]" % (data[y, x], x, y) except IndexError: return "" pyplot.gca().format_coord = format_coord if dims: current = list((0,) * dims) cur_ax_dat = [0, data[tuple(current)].squeeze()] sliders = [pyplot.Slider( pyplot.axes([0.125, 0.03*(axis+1), 0.725, 0.025]), 'Dimension %i' % axis, 0, data.shape[axis]-1, 0, facecolor='0.5', valfmt='%%.0f [%i]' % data.shape[axis]) for axis in range(dims)] for slider in sliders: slider.drawon = False def set_image(current, sliders=sliders, data=data): # change image and redraw canvas cur_ax_dat[1] = data[tuple(current)].squeeze() image.set_data(cur_ax_dat[1]) for ctrl, index in zip(sliders, current): ctrl.eventson = False ctrl.set_val(index) ctrl.eventson = True figure.canvas.draw() def on_changed(index, axis, data=data, current=current): # callback function for slider change event index = int(round(index)) cur_ax_dat[0] = axis if index == current[axis]: return if index >= data.shape[axis]: index = 0 elif index < 0: index = data.shape[axis] - 1 current[axis] = index set_image(current) def on_keypressed(event, data=data, current=current): # callback function for key press event key = event.key axis = cur_ax_dat[0] if str(key) in '0123456789': on_changed(key, axis) elif key == 'right': on_changed(current[axis] + 1, axis) elif key == 'left': on_changed(current[axis] - 1, axis) elif key == 'up': cur_ax_dat[0] = 0 if axis == len(data.shape)-1 else axis + 1 elif key == 'down': cur_ax_dat[0] = len(data.shape)-1 if axis == 0 else axis - 1 elif key == 'end': on_changed(data.shape[axis] - 1, axis) elif key == 'home': on_changed(0, axis) figure.canvas.mpl_connect('key_press_event', on_keypressed) for axis, ctrl in enumerate(sliders): ctrl.on_changed(lambda k, a=axis: on_changed(k, a)) return figure, subplot, image def _app_show(): """Block the GUI. For use as skimage plugin.""" pyplot = sys.modules['matplotlib.pyplot'] pyplot.show() def askopenfilename(**kwargs): """Return file name(s) from Tkinter's file open dialog.""" try: from Tkinter import Tk import tkFileDialog as filedialog except ImportError: from tkinter import Tk, filedialog root = Tk() root.withdraw() root.update() filenames = filedialog.askopenfilename(**kwargs) root.destroy() return filenames def main(argv=None): """Command line usage main function.""" if float(sys.version[0:3]) < 2.7: print("This script requires Python version 2.7 or better.") print("This is Python version %s" % sys.version) return 0 if argv is None: argv = sys.argv import optparse parser = optparse.OptionParser( usage="usage: %prog [options] path", description="Display image data in TIFF files.", version="%%prog %s" % __version__) opt = parser.add_option opt('-p', '--page', dest='page', type='int', default=-1, help="display single page") opt('-s', '--series', dest='series', type='int', default=-1, help="display series of pages of same shape") opt('--nomultifile', dest='nomultifile', action='store_true', default=False, help="do not read OME series from multiple files") opt('--noplot', dest='noplot', action='store_true', default=False, help="do not display images") opt('--interpol', dest='interpol', metavar='INTERPOL', default='bilinear', help="image interpolation method") opt('--dpi', dest='dpi', type='int', default=96, help="set plot resolution") opt('--vmin', dest='vmin', type='int', default=None, help="set minimum value for colormapping") opt('--vmax', dest='vmax', type='int', default=None, help="set maximum value for colormapping") opt('--debug', dest='debug', action='store_true', default=False, help="raise exception on failures") opt('--doctest', dest='doctest', action='store_true', default=False, help="runs the docstring examples") opt('-v', '--verbose', dest='verbose', action='store_true', default=True) opt('-q', '--quiet', dest='verbose', action='store_false') settings, path = parser.parse_args() path = ' '.join(path) if settings.doctest: import doctest doctest.testmod() return 0 if not path: path = askopenfilename( title="Select a TIFF file", filetypes=[("TIF files", "*.tif"), ("LSM files", "*.lsm"), ("STK files", "*.stk"), ("allfiles", "*")]) if not path: parser.error("No file specified") if any(i in path for i in '?*'): path = glob.glob(path) if not path: print('no files match the pattern') return 0 # TODO: handle image sequences #if len(path) == 1: path = path[0] print("Reading file structure...", end=' ') start = time.time() try: tif = TiffFile(path, multifile=not settings.nomultifile) except Exception as e: if settings.debug: raise else: print("\n", e) sys.exit(0) print("%.3f ms" % ((time.time()-start) * 1e3)) if tif.is_ome: settings.norgb = True images = [] if not settings.noplot: print("Reading image data... ", end=' ') def notnone(x): return next(i for i in x if i is not None) start = time.time() try: if settings.page >= 0: images = [(tif.asarray(key=settings.page), tif[settings.page])] elif settings.series >= 0: images = [(tif.asarray(series=settings.series), notnone(tif.series[settings.series].pages))] else: images = [] for i, s in enumerate(tif.series): try: images.append( (tif.asarray(series=i), notnone(s.pages))) except ValueError as e: images.append((None, notnone(s.pages))) if settings.debug: raise else: print("\n* series %i failed: %s... " % (i, e), end='') print("%.3f ms" % ((time.time()-start) * 1e3)) except Exception as e: if settings.debug: raise else: print(e) tif.close() print() print(tif.info()) print() if images and not settings.noplot: try: import matplotlib matplotlib.use('TkAgg') from matplotlib import pyplot except ImportError as e: warnings.warn("failed to import matplotlib.\n%s" % e) else: for img, page in images: if img is None: continue vmin, vmax = settings.vmin, settings.vmax if 'gdal_nodata' in page.tags: try: vmin = numpy.min(img[img > float(page.gdal_nodata)]) except ValueError: pass if page.is_stk: try: vmin = page.uic_tags['min_scale'] vmax = page.uic_tags['max_scale'] except KeyError: pass else: if vmax <= vmin: vmin, vmax = settings.vmin, settings.vmax title = "%s\n %s" % (str(tif), str(page)) imshow(img, title=title, vmin=vmin, vmax=vmax, bitspersample=page.bits_per_sample, photometric=page.photometric, interpolation=settings.interpol, dpi=settings.dpi) pyplot.show() TIFFfile = TiffFile # backwards compatibility if sys.version_info[0] > 2: basestring = str, bytes unicode = str def bytes2str(b): return str(b, 'cp1252') def str2bytes(s, encoding="latin-1"): return s.encode(encoding) else: bytes2str = str def str2bytes(s): return s class FileNotFoundError(IOError): pass if __name__ == "__main__": sys.exit(main()) scikit-image-0.13.1/skimage/feature/000077500000000000000000000000001316256236300172245ustar00rootroot00000000000000scikit-image-0.13.1/skimage/feature/__init__.py000066400000000000000000000036321316256236300213410ustar00rootroot00000000000000from ._canny import canny from ._daisy import daisy from ._hog import hog from .texture import (greycomatrix, greycoprops, local_binary_pattern, multiblock_lbp, draw_multiblock_lbp) from .peak import peak_local_max from .corner import (corner_kitchen_rosenfeld, corner_harris, corner_shi_tomasi, corner_foerstner, corner_subpix, corner_peaks, corner_fast, structure_tensor, structure_tensor_eigvals, hessian_matrix, hessian_matrix_eigvals, hessian_matrix_det, corner_moravec, corner_orientations, shape_index) from .template import match_template from .register_translation import register_translation from .brief import BRIEF from .censure import CENSURE from .orb import ORB from .match import match_descriptors from .util import plot_matches from .blob import blob_dog, blob_log, blob_doh __all__ = ['canny', 'daisy', 'hog', 'greycomatrix', 'greycoprops', 'local_binary_pattern', 'multiblock_lbp', 'draw_multiblock_lbp', 'peak_local_max', 'structure_tensor', 'structure_tensor_eigvals', 'hessian_matrix', 'hessian_matrix_det', 'hessian_matrix_eigvals', 'shape_index', 'corner_kitchen_rosenfeld', 'corner_harris', 'corner_shi_tomasi', 'corner_foerstner', 'corner_subpix', 'corner_peaks', 'corner_moravec', 'corner_fast', 'corner_orientations', 'match_template', 'register_translation', 'BRIEF', 'CENSURE', 'ORB', 'match_descriptors', 'plot_matches', 'blob_dog', 'blob_doh', 'blob_log'] scikit-image-0.13.1/skimage/feature/_canny.py000066400000000000000000000257111316256236300210530ustar00rootroot00000000000000""" canny.py - Canny Edge detector Reference: Canny, J., A Computational Approach To Edge Detection, IEEE Trans. Pattern Analysis and Machine Intelligence, 8:679-714, 1986 Originally part of CellProfiler, code licensed under both GPL and BSD licenses. Website: http://www.cellprofiler.org Copyright (c) 2003-2009 Massachusetts Institute of Technology Copyright (c) 2009-2011 Broad Institute All rights reserved. Original author: Lee Kamentsky """ import numpy as np import scipy.ndimage as ndi from scipy.ndimage import (gaussian_filter, generate_binary_structure, binary_erosion, label) from .. import dtype_limits from .._shared.utils import assert_nD def smooth_with_function_and_mask(image, function, mask): """Smooth an image with a linear function, ignoring masked pixels Parameters ---------- image : array Image you want to smooth. function : callable A function that does image smoothing. mask : array Mask with 1's for significant pixels, 0's for masked pixels. Notes ------ This function calculates the fractional contribution of masked pixels by applying the function to the mask (which gets you the fraction of the pixel data that's due to significant points). We then mask the image and apply the function. The resulting values will be lower by the bleed-over fraction, so you can recalibrate by dividing by the function on the mask to recover the effect of smoothing from just the significant pixels. """ bleed_over = function(mask.astype(float)) masked_image = np.zeros(image.shape, image.dtype) masked_image[mask] = image[mask] smoothed_image = function(masked_image) output_image = smoothed_image / (bleed_over + np.finfo(float).eps) return output_image def canny(image, sigma=1., low_threshold=None, high_threshold=None, mask=None, use_quantiles=False): """Edge filter an image using the Canny algorithm. Parameters ----------- image : 2D array Greyscale input image to detect edges on; can be of any dtype. sigma : float Standard deviation of the Gaussian filter. low_threshold : float Lower bound for hysteresis thresholding (linking edges). If None, low_threshold is set to 10% of dtype's max. high_threshold : float Upper bound for hysteresis thresholding (linking edges). If None, high_threshold is set to 20% of dtype's max. mask : array, dtype=bool, optional Mask to limit the application of Canny to a certain area. use_quantiles : bool, optional If True then treat low_threshold and high_threshold as quantiles of the edge magnitude image, rather than absolute edge magnitude values. If True then the thresholds must be in the range [0, 1]. Returns ------- output : 2D array (image) The binary edge map. See also -------- skimage.sobel Notes ----- The steps of the algorithm are as follows: * Smooth the image using a Gaussian with ``sigma`` width. * Apply the horizontal and vertical Sobel operators to get the gradients within the image. The edge strength is the norm of the gradient. * Thin potential edges to 1-pixel wide curves. First, find the normal to the edge at each point. This is done by looking at the signs and the relative magnitude of the X-Sobel and Y-Sobel to sort the points into 4 categories: horizontal, vertical, diagonal and antidiagonal. Then look in the normal and reverse directions to see if the values in either of those directions are greater than the point in question. Use interpolation to get a mix of points instead of picking the one that's the closest to the normal. * Perform a hysteresis thresholding: first label all points above the high threshold as edges. Then recursively label any point above the low threshold that is 8-connected to a labeled point as an edge. References ----------- .. [1] Canny, J., A Computational Approach To Edge Detection, IEEE Trans. Pattern Analysis and Machine Intelligence, 8:679-714, 1986 .. [2] William Green's Canny tutorial http://dasl.mem.drexel.edu/alumni/bGreen/www.pages.drexel.edu/_weg22/can_tut.html Examples -------- >>> from skimage import feature >>> # Generate noisy image of a square >>> im = np.zeros((256, 256)) >>> im[64:-64, 64:-64] = 1 >>> im += 0.2 * np.random.rand(*im.shape) >>> # First trial with the Canny filter, with the default smoothing >>> edges1 = feature.canny(im) >>> # Increase the smoothing for better results >>> edges2 = feature.canny(im, sigma=3) """ # # The steps involved: # # * Smooth using the Gaussian with sigma above. # # * Apply the horizontal and vertical Sobel operators to get the gradients # within the image. The edge strength is the sum of the magnitudes # of the gradients in each direction. # # * Find the normal to the edge at each point using the arctangent of the # ratio of the Y sobel over the X sobel - pragmatically, we can # look at the signs of X and Y and the relative magnitude of X vs Y # to sort the points into 4 categories: horizontal, vertical, # diagonal and antidiagonal. # # * Look in the normal and reverse directions to see if the values # in either of those directions are greater than the point in question. # Use interpolation to get a mix of points instead of picking the one # that's the closest to the normal. # # * Label all points above the high threshold as edges. # * Recursively label any point above the low threshold that is 8-connected # to a labeled point as an edge. # # Regarding masks, any point touching a masked point will have a gradient # that is "infected" by the masked point, so it's enough to erode the # mask by one and then mask the output. We also mask out the border points # because who knows what lies beyond the edge of the image? # assert_nD(image, 2) if low_threshold is None: low_threshold = 0.1 * dtype_limits(image, clip_negative=False)[1] if high_threshold is None: high_threshold = 0.2 * dtype_limits(image, clip_negative=False)[1] if mask is None: mask = np.ones(image.shape, dtype=bool) def fsmooth(x): return gaussian_filter(x, sigma, mode='constant') smoothed = smooth_with_function_and_mask(image, fsmooth, mask) jsobel = ndi.sobel(smoothed, axis=1) isobel = ndi.sobel(smoothed, axis=0) abs_isobel = np.abs(isobel) abs_jsobel = np.abs(jsobel) magnitude = np.hypot(isobel, jsobel) # # Make the eroded mask. Setting the border value to zero will wipe # out the image edges for us. # s = generate_binary_structure(2, 2) eroded_mask = binary_erosion(mask, s, border_value=0) eroded_mask = eroded_mask & (magnitude > 0) # #--------- Find local maxima -------------- # # Assign each point to have a normal of 0-45 degrees, 45-90 degrees, # 90-135 degrees and 135-180 degrees. # local_maxima = np.zeros(image.shape, bool) #----- 0 to 45 degrees ------ pts_plus = (isobel >= 0) & (jsobel >= 0) & (abs_isobel >= abs_jsobel) pts_minus = (isobel <= 0) & (jsobel <= 0) & (abs_isobel >= abs_jsobel) pts = pts_plus | pts_minus pts = eroded_mask & pts # Get the magnitudes shifted left to make a matrix of the points to the # right of pts. Similarly, shift left and down to get the points to the # top right of pts. c1 = magnitude[1:, :][pts[:-1, :]] c2 = magnitude[1:, 1:][pts[:-1, :-1]] m = magnitude[pts] w = abs_jsobel[pts] / abs_isobel[pts] c_plus = c2 * w + c1 * (1 - w) <= m c1 = magnitude[:-1, :][pts[1:, :]] c2 = magnitude[:-1, :-1][pts[1:, 1:]] c_minus = c2 * w + c1 * (1 - w) <= m local_maxima[pts] = c_plus & c_minus #----- 45 to 90 degrees ------ # Mix diagonal and vertical # pts_plus = (isobel >= 0) & (jsobel >= 0) & (abs_isobel <= abs_jsobel) pts_minus = (isobel <= 0) & (jsobel <= 0) & (abs_isobel <= abs_jsobel) pts = pts_plus | pts_minus pts = eroded_mask & pts c1 = magnitude[:, 1:][pts[:, :-1]] c2 = magnitude[1:, 1:][pts[:-1, :-1]] m = magnitude[pts] w = abs_isobel[pts] / abs_jsobel[pts] c_plus = c2 * w + c1 * (1 - w) <= m c1 = magnitude[:, :-1][pts[:, 1:]] c2 = magnitude[:-1, :-1][pts[1:, 1:]] c_minus = c2 * w + c1 * (1 - w) <= m local_maxima[pts] = c_plus & c_minus #----- 90 to 135 degrees ------ # Mix anti-diagonal and vertical # pts_plus = (isobel <= 0) & (jsobel >= 0) & (abs_isobel <= abs_jsobel) pts_minus = (isobel >= 0) & (jsobel <= 0) & (abs_isobel <= abs_jsobel) pts = pts_plus | pts_minus pts = eroded_mask & pts c1a = magnitude[:, 1:][pts[:, :-1]] c2a = magnitude[:-1, 1:][pts[1:, :-1]] m = magnitude[pts] w = abs_isobel[pts] / abs_jsobel[pts] c_plus = c2a * w + c1a * (1.0 - w) <= m c1 = magnitude[:, :-1][pts[:, 1:]] c2 = magnitude[1:, :-1][pts[:-1, 1:]] c_minus = c2 * w + c1 * (1.0 - w) <= m local_maxima[pts] = c_plus & c_minus #----- 135 to 180 degrees ------ # Mix anti-diagonal and anti-horizontal # pts_plus = (isobel <= 0) & (jsobel >= 0) & (abs_isobel >= abs_jsobel) pts_minus = (isobel >= 0) & (jsobel <= 0) & (abs_isobel >= abs_jsobel) pts = pts_plus | pts_minus pts = eroded_mask & pts c1 = magnitude[:-1, :][pts[1:, :]] c2 = magnitude[:-1, 1:][pts[1:, :-1]] m = magnitude[pts] w = abs_jsobel[pts] / abs_isobel[pts] c_plus = c2 * w + c1 * (1 - w) <= m c1 = magnitude[1:, :][pts[:-1, :]] c2 = magnitude[1:, :-1][pts[:-1, 1:]] c_minus = c2 * w + c1 * (1 - w) <= m local_maxima[pts] = c_plus & c_minus # #---- If use_quantiles is set then calculate the thresholds to use # if use_quantiles: if high_threshold > 1.0 or low_threshold > 1.0: raise ValueError("Quantile thresholds must not be > 1.0") if high_threshold < 0.0 or low_threshold < 0.0: raise ValueError("Quantile thresholds must not be < 0.0") high_threshold = np.percentile(magnitude, 100.0 * high_threshold) low_threshold = np.percentile(magnitude, 100.0 * low_threshold) # #---- Create two masks at the two thresholds. # high_mask = local_maxima & (magnitude >= high_threshold) low_mask = local_maxima & (magnitude >= low_threshold) # # Segment the low-mask, then only keep low-segments that have # some high_mask component in them # strel = np.ones((3, 3), bool) labels, count = label(low_mask, strel) if count == 0: return low_mask sums = (np.array(ndi.sum(high_mask, labels, np.arange(count, dtype=np.int32) + 1), copy=False, ndmin=1)) good_label = np.zeros((count + 1,), bool) good_label[1:] = sums > 0 output_mask = good_label[labels] return output_mask scikit-image-0.13.1/skimage/feature/_daisy.py000066400000000000000000000233251316256236300210530ustar00rootroot00000000000000import numpy as np from scipy import sqrt, pi, arctan2, cos, sin, exp from scipy.ndimage import gaussian_filter from .. import img_as_float, draw from ..color import gray2rgb from .._shared.utils import assert_nD def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8, normalization='l1', sigmas=None, ring_radii=None, visualize=False): '''Extract DAISY feature descriptors densely for the given image. DAISY is a feature descriptor similar to SIFT formulated in a way that allows for fast dense extraction. Typically, this is practical for bag-of-features image representations. The implementation follows Tola et al. [1]_ but deviate on the following points: * Histogram bin contribution are smoothed with a circular Gaussian window over the tonal range (the angular range). * The sigma values of the spatial Gaussian smoothing in this code do not match the sigma values in the original code by Tola et al. [2]_. In their code, spatial smoothing is applied to both the input image and the center histogram. However, this smoothing is not documented in [1]_ and, therefore, it is omitted. Parameters ---------- img : (M, N) array Input image (greyscale). step : int, optional Distance between descriptor sampling points. radius : int, optional Radius (in pixels) of the outermost ring. rings : int, optional Number of rings. histograms : int, optional Number of histograms sampled per ring. orientations : int, optional Number of orientations (bins) per histogram. normalization : [ 'l1' | 'l2' | 'daisy' | 'off' ], optional How to normalize the descriptors * 'l1': L1-normalization of each descriptor. * 'l2': L2-normalization of each descriptor. * 'daisy': L2-normalization of individual histograms. * 'off': Disable normalization. sigmas : 1D array of float, optional Standard deviation of spatial Gaussian smoothing for the center histogram and for each ring of histograms. The array of sigmas should be sorted from the center and out. I.e. the first sigma value defines the spatial smoothing of the center histogram and the last sigma value defines the spatial smoothing of the outermost ring. Specifying sigmas overrides the following parameter. ``rings = len(sigmas) - 1`` ring_radii : 1D array of int, optional Radius (in pixels) for each ring. Specifying ring_radii overrides the following two parameters. ``rings = len(ring_radii)`` ``radius = ring_radii[-1]`` If both sigmas and ring_radii are given, they must satisfy the following predicate since no radius is needed for the center histogram. ``len(ring_radii) == len(sigmas) + 1`` visualize : bool, optional Generate a visualization of the DAISY descriptors Returns ------- descs : array Grid of DAISY descriptors for the given image as an array dimensionality (P, Q, R) where ``P = ceil((M - radius*2) / step)`` ``Q = ceil((N - radius*2) / step)`` ``R = (rings * histograms + 1) * orientations`` descs_img : (M, N, 3) array (only if visualize==True) Visualization of the DAISY descriptors. References ---------- .. [1] Tola et al. "Daisy: An efficient dense descriptor applied to wide- baseline stereo." Pattern Analysis and Machine Intelligence, IEEE Transactions on 32.5 (2010): 815-830. .. [2] http://cvlab.epfl.ch/software/daisy ''' assert_nD(img, 2, 'img') img = img_as_float(img) # Validate parameters. if sigmas is not None and ring_radii is not None \ and len(sigmas) - 1 != len(ring_radii): raise ValueError('`len(sigmas)-1 != len(ring_radii)`') if ring_radii is not None: rings = len(ring_radii) radius = ring_radii[-1] if sigmas is not None: rings = len(sigmas) - 1 if sigmas is None: sigmas = [radius * (i + 1) / float(2 * rings) for i in range(rings)] if ring_radii is None: ring_radii = [radius * (i + 1) / float(rings) for i in range(rings)] if normalization not in ['l1', 'l2', 'daisy', 'off']: raise ValueError('Invalid normalization method.') # Compute image derivatives. dx = np.zeros(img.shape) dy = np.zeros(img.shape) dx[:, :-1] = np.diff(img, n=1, axis=1) dy[:-1, :] = np.diff(img, n=1, axis=0) # Compute gradient orientation and magnitude and their contribution # to the histograms. grad_mag = sqrt(dx ** 2 + dy ** 2) grad_ori = arctan2(dy, dx) orientation_kappa = orientations / pi orientation_angles = [2 * o * pi / orientations - pi for o in range(orientations)] hist = np.empty((orientations,) + img.shape, dtype=float) for i, o in enumerate(orientation_angles): # Weigh bin contribution by the circular normal distribution hist[i, :, :] = exp(orientation_kappa * cos(grad_ori - o)) # Weigh bin contribution by the gradient magnitude hist[i, :, :] = np.multiply(hist[i, :, :], grad_mag) # Smooth orientation histograms for the center and all rings. sigmas = [sigmas[0]] + sigmas hist_smooth = np.empty((rings + 1,) + hist.shape, dtype=float) for i in range(rings + 1): for j in range(orientations): hist_smooth[i, j, :, :] = gaussian_filter(hist[j, :, :], sigma=sigmas[i]) # Assemble descriptor grid. theta = [2 * pi * j / histograms for j in range(histograms)] desc_dims = (rings * histograms + 1) * orientations descs = np.empty((desc_dims, img.shape[0] - 2 * radius, img.shape[1] - 2 * radius)) descs[:orientations, :, :] = hist_smooth[0, :, radius:-radius, radius:-radius] idx = orientations for i in range(rings): for j in range(histograms): y_min = radius + int(round(ring_radii[i] * sin(theta[j]))) y_max = descs.shape[1] + y_min x_min = radius + int(round(ring_radii[i] * cos(theta[j]))) x_max = descs.shape[2] + x_min descs[idx:idx + orientations, :, :] = hist_smooth[i + 1, :, y_min:y_max, x_min:x_max] idx += orientations descs = descs[:, ::step, ::step] descs = descs.swapaxes(0, 1).swapaxes(1, 2) # Normalize descriptors. if normalization != 'off': descs += 1e-10 if normalization == 'l1': descs /= np.sum(descs, axis=2)[:, :, np.newaxis] elif normalization == 'l2': descs /= sqrt(np.sum(descs ** 2, axis=2))[:, :, np.newaxis] elif normalization == 'daisy': for i in range(0, desc_dims, orientations): norms = sqrt(np.sum(descs[:, :, i:i + orientations] ** 2, axis=2)) descs[:, :, i:i + orientations] /= norms[:, :, np.newaxis] if visualize: descs_img = gray2rgb(img) for i in range(descs.shape[0]): for j in range(descs.shape[1]): # Draw center histogram sigma color = [1, 0, 0] desc_y = i * step + radius desc_x = j * step + radius rows, cols, val = draw.circle_perimeter_aa(desc_y, desc_x, int(sigmas[0])) draw.set_color(descs_img, (rows, cols), color, alpha=val) max_bin = np.max(descs[i, j, :]) for o_num, o in enumerate(orientation_angles): # Draw center histogram bins bin_size = descs[i, j, o_num] / max_bin dy = sigmas[0] * bin_size * sin(o) dx = sigmas[0] * bin_size * cos(o) rows, cols, val = draw.line_aa(desc_y, desc_x, int(desc_y + dy), int(desc_x + dx)) draw.set_color(descs_img, (rows, cols), color, alpha=val) for r_num, r in enumerate(ring_radii): color_offset = float(1 + r_num) / rings color = (1 - color_offset, 1, color_offset) for t_num, t in enumerate(theta): # Draw ring histogram sigmas hist_y = desc_y + int(round(r * sin(t))) hist_x = desc_x + int(round(r * cos(t))) rows, cols, val = draw.circle_perimeter_aa(hist_y, hist_x, int(sigmas[r_num + 1])) draw.set_color(descs_img, (rows, cols), color, alpha=val) for o_num, o in enumerate(orientation_angles): # Draw histogram bins bin_size = descs[i, j, orientations + r_num * histograms * orientations + t_num * orientations + o_num] bin_size /= max_bin dy = sigmas[r_num + 1] * bin_size * sin(o) dx = sigmas[r_num + 1] * bin_size * cos(o) rows, cols, val = draw.line_aa(hist_y, hist_x, int(hist_y + dy), int(hist_x + dx)) draw.set_color(descs_img, (rows, cols), color, alpha=val) return descs, descs_img else: return descs scikit-image-0.13.1/skimage/feature/_hessian_det_appx.pyx000066400000000000000000000105611316256236300234460ustar00rootroot00000000000000# cython: cdivision=True # cython: boundscheck=False # cython: nonecheck=False # cython: wraparound=False import numpy as np cimport numpy as cnp cdef inline Py_ssize_t _clip(Py_ssize_t x, Py_ssize_t low, Py_ssize_t high) nogil: """Clips coordinate between high and low. This method was created so that `hessian_det_appx` does not have to make a Python call. Parameters ---------- x : int Coordinate to be clipped. low : int The lower bound. high : int The higher bound. Returns ------- x : int `x` clipped between `high` and `low`. """ if(x > high): return high if(x < low): return low return x cdef inline cnp.double_t _integ( cnp.double_t[:, ::1] img, Py_ssize_t r, Py_ssize_t c, Py_ssize_t rl, Py_ssize_t cl) nogil: """Integrate over the integral image in the given window This method was created so that `hessian_det_appx` does not have to make a Python call. Parameters ---------- img : array The integral image over which to integrate. r : int The row number of the top left corner. c : int The column number of the top left corner. rl : int The number of rows over which to integrate. cl : int The number of columns over which to integrate. Returns ------- ans : int The integral over the given window. """ r = _clip(r, 0, img.shape[0] - 1) c = _clip(c, 0, img.shape[1] - 1) r2 = _clip(r + rl, 0, img.shape[0] - 1) c2 = _clip(c + cl, 0, img.shape[1] - 1) cdef cnp.double_t ans = img[r, c] + img[r2, c2] - img[r, c2] - img[r2, c] if (ans < 0): return 0 return ans def _hessian_matrix_det(cnp.double_t[:, ::1] img, double sigma): """Computes the approximate Hessian Determinant over an image. This method uses box filters over integral images to compute the approximate Hessian Determinant as described in [1]_. Parameters ---------- img : array The integral image over which to compute Hessian Determinant. sigma : float Standard deviation used for the Gaussian kernel, used for the Hessian matrix Returns ------- out : array The array of the Determinant of Hessians. References ---------- .. [1] Herbert Bay, Andreas Ess, Tinne Tuytelaars, Luc Van Gool, "SURF: Speeded Up Robust Features" ftp://ftp.vision.ee.ethz.ch/publications/articles/eth_biwi_00517.pdf Notes ----- The running time of this method only depends on size of the image. It is independent of `sigma` as one would expect. The downside is that the result for `sigma` less than `3` is not accurate, i.e., not similar to the result obtained if someone computed the Hessian and took it's determinant. """ cdef Py_ssize_t size = int(3 * sigma) cdef Py_ssize_t height = img.shape[0] cdef Py_ssize_t width = img.shape[1] cdef Py_ssize_t r, c cdef Py_ssize_t s2 = (size - 1) / 2 cdef Py_ssize_t s3 = size / 3 cdef Py_ssize_t l = size / 3 cdef Py_ssize_t w = size cdef Py_ssize_t b = (size - 1) / 2 cdef cnp.double_t mid, side, tl, tr, bl, br cdef cnp.double_t[:, ::1] out = np.zeros_like(img, dtype=np.double) cdef cnp.double_t w_i = 1.0 / size / size cdef float dxx, dyy, dxy with nogil: if size % 2 == 0: size += 1 for r in range(height): for c in range(width): tl = _integ(img, r - s3, c - s3, s3, s3) # top left br = _integ(img, r + 1, c + 1, s3, s3) # bottom right bl = _integ(img, r - s3, c + 1, s3, s3) # bottom left tr = _integ(img, r + 1, c - s3, s3, s3) # top right dxy = bl + tr - tl - br dxy = -dxy * w_i mid = _integ(img, r - s3 + 1, c - s2, 2 * s3 - 1, w) # middle box side = _integ(img, r - s3 + 1, c - s3 / 2, 2 * s3 - 1, s3) # sides dxx = mid - 3 * side dxx = -dxx * w_i mid = _integ(img, r - s2, c - s3 + 1, w, 2 * s3 - 1) side = _integ(img, r - s3 / 2, c - s3 + 1, s3, 2 * s3 - 1) dyy = mid - 3 * side dyy = -dyy * w_i out[r, c] = (dxx * dyy - 0.81 * (dxy * dxy)) return out scikit-image-0.13.1/skimage/feature/_hog.py000066400000000000000000000244221316256236300205160ustar00rootroot00000000000000from __future__ import division import numpy as np from .._shared.utils import assert_nD from .._shared.utils import skimage_deprecation, warn from . import _hoghistogram def _hog_normalize_block(block, method, eps=1e-5): if method == 'L1': out = block / (np.sum(np.abs(block)) + eps) elif method == 'L1-sqrt': out = np.sqrt(block / (np.sum(np.abs(block)) + eps)) elif method == 'L2': out = block / np.sqrt(np.sum(block ** 2) + eps ** 2) elif method == 'L2-Hys': out = block / np.sqrt(np.sum(block ** 2) + eps ** 2) out = np.minimum(out, 0.2) out = out / np.sqrt(np.sum(block ** 2) + eps ** 2) else: raise ValueError('Selected block normalization method is invalid.') return out def hog(image, orientations=9, pixels_per_cell=(8, 8), cells_per_block=(3, 3), block_norm='L1', visualise=False, transform_sqrt=False, feature_vector=True, normalise=None): """Extract Histogram of Oriented Gradients (HOG) for a given image. Compute a Histogram of Oriented Gradients (HOG) by 1. (optional) global image normalization 2. computing the gradient image in x and y 3. computing gradient histograms 4. normalizing across blocks 5. flattening into a feature vector Parameters ---------- image : (M, N) ndarray Input image (greyscale). orientations : int, optional Number of orientation bins. pixels_per_cell : 2-tuple (int, int), optional Size (in pixels) of a cell. cells_per_block : 2-tuple (int, int), optional Number of cells in each block. block_norm : str {'L1', 'L1-sqrt', 'L2', 'L2-Hys'}, optional Block normalization method: ``L1`` Normalization using L1-norm. (default) ``L1-sqrt`` Normalization using L1-norm, followed by square root. ``L2`` Normalization using L2-norm. ``L2-Hys`` Normalization using L2-norm, followed by limiting the maximum values to 0.2 (`Hys` stands for `hysteresis`) and renormalization using L2-norm. For details, see [3]_, [4]_. visualise : bool, optional Also return an image of the HOG. transform_sqrt : bool, optional Apply power law compression to normalize the image before processing. DO NOT use this if the image contains negative values. Also see `notes` section below. feature_vector : bool, optional Return the data as a feature vector by calling .ravel() on the result just before returning. normalise : bool, deprecated The parameter is deprecated. Use `transform_sqrt` for power law compression. `normalise` has been deprecated. Returns ------- newarr : ndarray HOG for the image as a 1D (flattened) array. hog_image : ndarray (if visualise=True) A visualisation of the HOG image. References ---------- .. [1] http://en.wikipedia.org/wiki/Histogram_of_oriented_gradients .. [2] Dalal, N and Triggs, B, Histograms of Oriented Gradients for Human Detection, IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2005 San Diego, CA, USA, https://lear.inrialpes.fr/people/triggs/pubs/Dalal-cvpr05.pdf, DOI:10.1109/CVPR.2005.177 .. [3] Lowe, D.G., Distinctive image features from scale-invatiant keypoints, International Journal of Computer Vision (2004) 60: 91, http://www.cs.ubc.ca/~lowe/papers/ijcv04.pdf, DOI:10.1023/B:VISI.0000029664.99615.94 .. [4] Dalal, N, Finding People in Images and Videos, Human-Computer Interaction [cs.HC], Institut National Polytechnique de Grenoble - INPG, 2006, https://tel.archives-ouvertes.fr/tel-00390303/file/NavneetDalalThesis.pdf Notes ----- The presented code implements the HOG extraction method from [2]_ with the following changes: (I) blocks of (3, 3) cells are used ((2, 2) in the paper; (II) no smoothing within cells (Gaussian spatial window with sigma=8pix in the paper); (III) L1 block normalization is used (L2-Hys in the paper). Power law compression, also known as Gamma correction, is used to reduce the effects of shadowing and illumination variations. The compression makes the dark regions lighter. When the kwarg `transform_sqrt` is set to ``True``, the function computes the square root of each color channel and then applies the hog algorithm to the image. """ if block_norm == 'L1': warn('Default value of `block_norm`==`L1` is deprecated and will ' 'be changed to `L2-Hys` in v0.15', skimage_deprecation) image = np.atleast_2d(image) """ The first stage applies an optional global image normalization equalisation that is designed to reduce the influence of illumination effects. In practice we use gamma (power law) compression, either computing the square root or the log of each colour channel. Image texture strength is typically proportional to the local surface illumination so this compression helps to reduce the effects of local shadowing and illumination variations. """ assert_nD(image, 2) if normalise is not None: raise ValueError("The ``normalise`` parameter was removed due to " "incorrect behavior: it only applied a square root " "instead of a true normalization. " "If you wish to duplicate the old behavior, set " "``transform_sqrt=True``. ``normalise`` will be " "completely removed in v0.14.") if transform_sqrt: image = np.sqrt(image) """ The second stage computes first order image gradients. These capture contour, silhouette and some texture information, while providing further resistance to illumination variations. The locally dominant colour channel is used, which provides colour invariance to a large extent. Variant methods may also include second order image derivatives, which act as primitive bar detectors - a useful feature for capturing, e.g. bar like structures in bicycles and limbs in humans. """ if image.dtype.kind == 'u': # convert uint image to float # to avoid problems with subtracting unsigned numbers image = image.astype('float') gy, gx = [np.ascontiguousarray(g, dtype=np.double) for g in np.gradient(image)] """ The third stage aims to produce an encoding that is sensitive to local image content while remaining resistant to small changes in pose or appearance. The adopted method pools gradient orientation information locally in the same way as the SIFT [Lowe 2004] feature. The image window is divided into small spatial regions, called "cells". For each cell we accumulate a local 1-D histogram of gradient or edge orientations over all the pixels in the cell. This combined cell-level 1-D histogram forms the basic "orientation histogram" representation. Each orientation histogram divides the gradient angle range into a fixed number of predetermined bins. The gradient magnitudes of the pixels in the cell are used to vote into the orientation histogram. """ sy, sx = image.shape cx, cy = pixels_per_cell bx, by = cells_per_block n_cellsx = int(sx // cx) # number of cells in x n_cellsy = int(sy // cy) # number of cells in y # compute orientations integral images orientation_histogram = np.zeros((n_cellsy, n_cellsx, orientations)) _hoghistogram.hog_histograms(gx, gy, cx, cy, sx, sy, n_cellsx, n_cellsy, orientations, orientation_histogram) # now compute the histogram for each cell hog_image = None if visualise: from .. import draw radius = min(cx, cy) // 2 - 1 orientations_arr = np.arange(orientations) dx_arr = radius * np.cos(orientations_arr / orientations * np.pi) dy_arr = radius * np.sin(orientations_arr / orientations * np.pi) hog_image = np.zeros((sy, sx), dtype=float) for x in range(n_cellsx): for y in range(n_cellsy): for o, dx, dy in zip(orientations_arr, dx_arr, dy_arr): centre = tuple([y * cy + cy // 2, x * cx + cx // 2]) rr, cc = draw.line(int(centre[0] - dx), int(centre[1] + dy), int(centre[0] + dx), int(centre[1] - dy)) hog_image[rr, cc] += orientation_histogram[y, x, o] """ The fourth stage computes normalization, which takes local groups of cells and contrast normalizes their overall responses before passing to next stage. Normalization introduces better invariance to illumination, shadowing, and edge contrast. It is performed by accumulating a measure of local histogram "energy" over local groups of cells that we call "blocks". The result is used to normalize each cell in the block. Typically each individual cell is shared between several blocks, but its normalizations are block dependent and thus different. The cell thus appears several times in the final output vector with different normalizations. This may seem redundant but it improves the performance. We refer to the normalized block descriptors as Histogram of Oriented Gradient (HOG) descriptors. """ n_blocksx = (n_cellsx - bx) + 1 n_blocksy = (n_cellsy - by) + 1 normalized_blocks = np.zeros((n_blocksy, n_blocksx, by, bx, orientations)) for x in range(n_blocksx): for y in range(n_blocksy): block = orientation_histogram[y:y + by, x:x + bx, :] normalized_blocks[y, x, :] = \ _hog_normalize_block(block, method=block_norm) """ The final step collects the HOG descriptors from all blocks of a dense overlapping grid of blocks covering the detection window into a combined feature vector for use in the window classifier. """ if feature_vector: normalized_blocks = normalized_blocks.ravel() if visualise: return normalized_blocks, hog_image else: return normalized_blocks scikit-image-0.13.1/skimage/feature/_hoghistogram.pyx000066400000000000000000000124531316256236300226250ustar00rootroot00000000000000# cython: cdivision=True # cython: boundscheck=False # cython: wraparound=False import numpy as np cimport numpy as cnp cdef float cell_hog(double[:, ::1] magnitude, double[:, ::1] orientation, float orientation_start, float orientation_end, int cell_columns, int cell_rows, int column_index, int row_index, int size_columns, int size_rows, int range_rows_start, int range_rows_stop, int range_columns_start, int range_columns_stop) nogil: """Calculation of the cell's HOG value Parameters ---------- magnitude : ndarray The gradient magnitudes of the pixels. orientation : ndarray Lookup table for orientations. orientation_start : float Orientation range start. orientation_end : float Orientation range end. cell_columns : int Pixels per cell (rows). cell_rows : int Pixels per cell (columns). column_index : int Block column index. row_index : int Block row index. size_columns : int Number of columns. size_rows : int Number of rows. range_rows_start : int Start row of cell. range_rows_stop : int Stop row of cell. range_columns_start : int Start column of cell. range_columns_stop : int Stop column of cell Returns ------- total : float The total HOG value. """ cdef int cell_column, cell_row, cell_row_index, cell_column_index cdef float total = 0. for cell_row in range(range_rows_start, range_rows_stop): cell_row_index = row_index + cell_row if (cell_row_index < 0 or cell_row_index >= size_rows): continue for cell_column in range(range_columns_start, range_columns_stop): cell_column_index = column_index + cell_column if (cell_column_index < 0 or cell_column_index >= size_columns or orientation[cell_row_index, cell_column_index] >= orientation_start or orientation[cell_row_index, cell_column_index] < orientation_end): continue total += magnitude[cell_row_index, cell_column_index] return total / (cell_rows * cell_columns) def hog_histograms(double[:, ::1] gradient_columns, double[:, ::1] gradient_rows, int cell_columns, int cell_rows, int size_columns, int size_rows, int number_of_cells_columns, int number_of_cells_rows, int number_of_orientations, cnp.float64_t[:, :, :] orientation_histogram): """Extract Histogram of Oriented Gradients (HOG) for a given image. Parameters ---------- gradient_columns : ndarray First order image gradients (rows). gradient_rows : ndarray First order image gradients (columns). cell_columns : int Pixels per cell (rows). cell_rows : int Pixels per cell (columns). size_columns : int Number of columns. size_rows : int Number of rows. number_of_cells_columns : int Number of cells (rows). number_of_cells_rows : int Number of cells (columns). number_of_orientations : int Number of orientation bins. orientation_histogram : ndarray The histogram array which is modified in place. """ cdef double[:, ::1] magnitude = np.hypot(gradient_columns, gradient_rows) cdef double[:, ::1] orientation = \ np.rad2deg(np.arctan2(gradient_rows, gradient_columns)) % 180 cdef int i, x, y, o, yi, xi, cc, cr, x0, y0, \ range_rows_start, range_rows_stop, \ range_columns_start, range_columns_stop cdef float orientation_start, orientation_end, \ number_of_orientations_per_180 y0 = cell_rows / 2 x0 = cell_columns / 2 cc = cell_rows * number_of_cells_rows cr = cell_columns * number_of_cells_columns range_rows_stop = cell_rows / 2 range_rows_start = -range_rows_stop range_columns_stop = cell_columns / 2 range_columns_start = -range_columns_stop number_of_orientations_per_180 = 180. / number_of_orientations with nogil: # compute orientations integral images for i in range(number_of_orientations): # isolate orientations in this range orientation_start = number_of_orientations_per_180 * (i + 1) orientation_end = number_of_orientations_per_180 * i x = x0 y = y0 yi = 0 xi = 0 while y < cc: xi = 0 x = x0 while x < cr: orientation_histogram[yi, xi, i] = \ cell_hog(magnitude, orientation, orientation_start, orientation_end, cell_columns, cell_rows, x, y, size_columns, size_rows, range_rows_start, range_rows_stop, range_columns_start, range_columns_stop) xi += 1 x += cell_columns yi += 1 y += cell_rows scikit-image-0.13.1/skimage/feature/_texture.pyx000066400000000000000000000334101316256236300216260ustar00rootroot00000000000000#cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False #cython: wraparound=False import numpy as np cimport numpy as cnp from libc.math cimport sin, cos, abs from .._shared.interpolation cimport bilinear_interpolation, round from .._shared.transform cimport integrate import cython cdef extern from "numpy/npy_math.h": double NAN "NPY_NAN" ctypedef fused any_int: cnp.uint8_t cnp.uint16_t cnp.uint32_t cnp.uint64_t cnp.int8_t cnp.int16_t cnp.int32_t cnp.int64_t def _glcm_loop(any_int[:, ::1] image, double[:] distances, double[:] angles, Py_ssize_t levels, cnp.uint32_t[:, :, :, ::1] out): """Perform co-occurrence matrix accumulation. Parameters ---------- image : ndarray Integer typed input image. Only positive valued images are supported. If type is other than uint8, the argument `levels` needs to be set. distances : ndarray List of pixel pair distance offsets. angles : ndarray List of pixel pair angles in radians. levels : int The input image should contain integers in [0, `levels`-1], where levels indicate the number of grey-levels counted (typically 256 for an 8-bit image). out : ndarray On input a 4D array of zeros, and on output it contains the results of the GLCM computation. """ cdef: Py_ssize_t a_idx, d_idx, r, c, rows, cols, row, col any_int i, j cnp.float64_t angle, distance with nogil: rows = image.shape[0] cols = image.shape[1] for a_idx in range(angles.shape[0]): angle = angles[a_idx] for d_idx in range(distances.shape[0]): distance = distances[d_idx] for r in range(rows): for c in range(cols): i = image[r, c] # compute the location of the offset pixel row = r + round(sin(angle) * distance) col = c + round(cos(angle) * distance) # make sure the offset is within bounds if row >= 0 and row < rows and \ col >= 0 and col < cols: j = image[row, col] if i >= 0 and i < levels and \ j >= 0 and j < levels: out[i, j, d_idx, a_idx] += 1 cdef inline int _bit_rotate_right(int value, int length) nogil: """Cyclic bit shift to the right. Parameters ---------- value : int integer value to shift length : int number of bits of integer """ return (value >> 1) | ((value & 1) << (length - 1)) def _local_binary_pattern(double[:, ::1] image, int P, float R, char method='D'): """Gray scale and rotation invariant LBP (Local Binary Patterns). LBP is an invariant descriptor that can be used for texture classification. Parameters ---------- image : (N, M) double array Graylevel image. P : int Number of circularly symmetric neighbour set points (quantization of the angular space). R : float Radius of circle (spatial resolution of the operator). method : {'D', 'R', 'U', 'N', 'V'} Method to determine the pattern. * 'D': 'default' * 'R': 'ror' * 'U': 'uniform' * 'N': 'nri_uniform' * 'V': 'var' Returns ------- output : (N, M) array LBP image. """ # texture weights cdef int[::1] weights = 2 ** np.arange(P, dtype=np.int32) # local position of texture elements rr = - R * np.sin(2 * np.pi * np.arange(P, dtype=np.double) / P) cc = R * np.cos(2 * np.pi * np.arange(P, dtype=np.double) / P) cdef double[::1] rp = np.round(rr, 5) cdef double[::1] cp = np.round(cc, 5) # pre-allocate arrays for computation cdef double[::1] texture = np.zeros(P, dtype=np.double) cdef signed char[::1] signed_texture = np.zeros(P, dtype=np.int8) cdef int[::1] rotation_chain = np.zeros(P, dtype=np.int32) output_shape = (image.shape[0], image.shape[1]) cdef double[:, ::1] output = np.zeros(output_shape, dtype=np.double) cdef Py_ssize_t rows = image.shape[0] cdef Py_ssize_t cols = image.shape[1] cdef double lbp cdef Py_ssize_t r, c, changes, i cdef Py_ssize_t rot_index, n_ones cdef cnp.int8_t first_zero, first_one # To compute the variance features cdef double sum_, var_, texture_i with nogil: for r in range(image.shape[0]): for c in range(image.shape[1]): for i in range(P): texture[i] = bilinear_interpolation(&image[0, 0], rows, cols, r + rp[i], c + cp[i], 'C', 0) # signed / thresholded texture for i in range(P): if texture[i] - image[r, c] >= 0: signed_texture[i] = 1 else: signed_texture[i] = 0 lbp = 0 # if method == 'var': if method == 'V': # Compute the variance without passing from numpy. # Following the LBP paper, we're taking a biased estimate # of the variance (ddof=0) sum_ = 0.0 var_ = 0.0 for i in range(P): texture_i = texture[i] sum_ += texture_i var_ += texture_i * texture_i var_ = (var_ - (sum_ * sum_) / P) / P if var_ != 0: lbp = var_ else: lbp = NAN # if method == 'uniform': elif method == 'U' or method == 'N': # determine number of 0 - 1 changes changes = 0 for i in range(P - 1): changes += (signed_texture[i] - signed_texture[i + 1]) != 0 if method == 'N': # Uniform local binary patterns are defined as patterns # with at most 2 value changes (from 0 to 1 or from 1 to # 0). Uniform patterns can be characterized by their # number `n_ones` of 1. The possible values for # `n_ones` range from 0 to P. # # Here is an example for P = 4: # n_ones=0: 0000 # n_ones=1: 0001, 1000, 0100, 0010 # n_ones=2: 0011, 1001, 1100, 0110 # n_ones=3: 0111, 1011, 1101, 1110 # n_ones=4: 1111 # # For a pattern of size P there are 2 constant patterns # corresponding to n_ones=0 and n_ones=P. For each other # value of `n_ones` , i.e n_ones=[1..P-1], there are P # possible patterns which are related to each other # through circular permutations. The total number of # uniform patterns is thus (2 + P * (P - 1)). # Given any pattern (uniform or not) we must be able to # associate a unique code: # # 1. Constant patterns patterns (with n_ones=0 and # n_ones=P) and non uniform patterns are given fixed # code values. # # 2. Other uniform patterns are indexed considering the # value of n_ones, and an index called 'rot_index' # reprenting the number of circular right shifts # required to obtain the pattern starting from a # reference position (corresponding to all zeros stacked # on the right). This number of rotations (or circular # right shifts) 'rot_index' is efficiently computed by # considering the positions of the first 1 and the first # 0 found in the pattern. if changes <= 2: # We have a uniform pattern n_ones = 0 # determines the number of ones first_one = -1 # position was the first one first_zero = -1 # position of the first zero for i in range(P): if signed_texture[i]: n_ones += 1 if first_one == -1: first_one = i else: if first_zero == -1: first_zero = i if n_ones == 0: lbp = 0 elif n_ones == P: lbp = P * (P - 1) + 1 else: if first_one == 0: rot_index = n_ones - first_zero else: rot_index = P - first_one lbp = 1 + (n_ones - 1) * P + rot_index else: # changes > 2 lbp = P * (P - 1) + 2 else: # method != 'N' if changes <= 2: for i in range(P): lbp += signed_texture[i] else: lbp = P + 1 else: # method == 'default' for i in range(P): lbp += signed_texture[i] * weights[i] # method == 'ror' if method == 'R': # shift LBP P times to the right and get minimum value rotation_chain[0] = lbp for i in range(1, P): rotation_chain[i] = \ _bit_rotate_right(rotation_chain[i - 1], P) lbp = rotation_chain[0] for i in range(1, P): lbp = min(lbp, rotation_chain[i]) output[r, c] = lbp return np.asarray(output) # Constant values that are used by `_multiblock_lbp` function. # Values represent offsets of neighbour rectangles relative to central one. # It has order starting from top left and going clockwise. cdef: Py_ssize_t[::1] mlbp_r_offsets = np.asarray([-1, -1, -1, 0, 1, 1, 1, 0], dtype=np.intp) Py_ssize_t[::1] mlbp_c_offsets = np.asarray([-1, 0, 1, 1, 1, 0, -1, -1], dtype=np.intp) def _multiblock_lbp(float[:, ::1] int_image, Py_ssize_t r, Py_ssize_t c, Py_ssize_t width, Py_ssize_t height): """Multi-block local binary pattern (MB-LBP) [1]_. Parameters ---------- int_image : (N, M) float array Integral image. r : int Row-coordinate of top left corner of a rectangle containing feature. c : int Column-coordinate of top left corner of a rectangle containing feature. width : int Width of one of 9 equal rectangles that will be used to compute a feature. height : int Height of one of 9 equal rectangles that will be used to compute a feature. Returns ------- output : int 8-bit MB-LBP feature descriptor. References ---------- .. [1] Face Detection Based on Multi-Block LBP Representation. Lun Zhang, Rufeng Chu, Shiming Xiang, Shengcai Liao, Stan Z. Li http://www.cbsr.ia.ac.cn/users/scliao/papers/Zhang-ICB07-MBLBP.pdf """ cdef: # Top-left coordinates of central rectangle. Py_ssize_t central_rect_r = r + height Py_ssize_t central_rect_c = c + width Py_ssize_t r_shift = height - 1 Py_ssize_t c_shift = width - 1 # Copy offset array to multiply it by width and height later. Py_ssize_t[::1] r_offsets = mlbp_r_offsets.copy() Py_ssize_t[::1] c_offsets = mlbp_c_offsets.copy() Py_ssize_t current_rect_r, current_rect_c Py_ssize_t element_num, i double current_rect_val int has_greater_value int lbp_code = 0 # Pre-multiply offsets with width and height. for i in range(8): r_offsets[i] = r_offsets[i]*height c_offsets[i] = c_offsets[i]*width # Sum of intensity values of central rectangle. cdef float central_rect_val = integrate(int_image, central_rect_r, central_rect_c, central_rect_r + r_shift, central_rect_c + c_shift) for element_num in range(8): current_rect_r = central_rect_r + r_offsets[element_num] current_rect_c = central_rect_c + c_offsets[element_num] current_rect_val = integrate(int_image, current_rect_r, current_rect_c, current_rect_r + r_shift, current_rect_c + c_shift) has_greater_value = current_rect_val >= central_rect_val # If current rectangle's intensity value is bigger # make corresponding bit to 1. lbp_code |= has_greater_value << (7 - element_num) return lbp_code scikit-image-0.13.1/skimage/feature/blob.py000066400000000000000000000407441316256236300205250ustar00rootroot00000000000000 import numpy as np from scipy.ndimage import gaussian_filter, gaussian_laplace import itertools as itt import math from math import sqrt, hypot, log from numpy import arccos from ..util import img_as_float from .peak import peak_local_max from ._hessian_det_appx import _hessian_matrix_det from ..transform import integral_image from .._shared.utils import assert_nD # This basic blob detection algorithm is based on: # http://www.cs.utah.edu/~jfishbau/advimproc/project1/ (04.04.2013) # Theory behind: http://en.wikipedia.org/wiki/Blob_detection (04.04.2013) def _blob_overlap(blob1, blob2): """Finds the overlapping area fraction between two blobs. Returns a float representing fraction of overlapped area. Parameters ---------- blob1 : sequence A sequence of ``(y,x,sigma)``, where ``x,y`` are coordinates of blob and sigma is the standard deviation of the Gaussian kernel which detected the blob. blob2 : sequence A sequence of ``(y,x,sigma)``, where ``x,y`` are coordinates of blob and sigma is the standard deviation of the Gaussian kernel which detected the blob. Returns ------- f : float Fraction of overlapped area. """ root2 = sqrt(2) # extent of the blob is given by sqrt(2)*scale r1 = blob1[2] * root2 r2 = blob2[2] * root2 d = hypot(blob1[0] - blob2[0], blob1[1] - blob2[1]) if d > r1 + r2: return 0 # one blob is inside the other, the smaller blob must die if d <= abs(r1 - r2): return 1 ratio1 = (d ** 2 + r1 ** 2 - r2 ** 2) / (2 * d * r1) ratio1 = np.clip(ratio1, -1, 1) acos1 = arccos(ratio1) ratio2 = (d ** 2 + r2 ** 2 - r1 ** 2) / (2 * d * r2) ratio2 = np.clip(ratio2, -1, 1) acos2 = arccos(ratio2) a = -d + r2 + r1 b = d - r2 + r1 c = d + r2 - r1 d = d + r2 + r1 area = r1 ** 2 * acos1 + r2 ** 2 * acos2 - 0.5 * sqrt(abs(a * b * c * d)) return area / (math.pi * (min(r1, r2) ** 2)) def _prune_blobs(blobs_array, overlap): """Eliminated blobs with area overlap. Parameters ---------- blobs_array : ndarray A 2d array with each row representing 3 values, ``(y,x,sigma)`` where ``(y,x)`` are coordinates of the blob and ``sigma`` is the standard deviation of the Gaussian kernel which detected the blob. overlap : float A value between 0 and 1. If the fraction of area overlapping for 2 blobs is greater than `overlap` the smaller blob is eliminated. Returns ------- A : ndarray `array` with overlapping blobs removed. """ # iterating again might eliminate more blobs, but one iteration suffices # for most cases for blob1, blob2 in itt.combinations(blobs_array, 2): if _blob_overlap(blob1, blob2) > overlap: if blob1[2] > blob2[2]: blob2[2] = -1 else: blob1[2] = -1 # return blobs_array[blobs_array[:, 2] > 0] return np.array([b for b in blobs_array if b[2] > 0]) def blob_dog(image, min_sigma=1, max_sigma=50, sigma_ratio=1.6, threshold=2.0, overlap=.5,): """Finds blobs in the given grayscale image. Blobs are found using the Difference of Gaussian (DoG) method [1]_. For each blob found, the method returns its coordinates and the standard deviation of the Gaussian kernel that detected the blob. Parameters ---------- image : ndarray Input grayscale image, blobs are assumed to be light on dark background (white on black). min_sigma : float, optional The minimum standard deviation for Gaussian Kernel. Keep this low to detect smaller blobs. max_sigma : float, optional The maximum standard deviation for Gaussian Kernel. Keep this high to detect larger blobs. sigma_ratio : float, optional The ratio between the standard deviation of Gaussian Kernels used for computing the Difference of Gaussians threshold : float, optional. The absolute lower bound for scale space maxima. Local maxima smaller than thresh are ignored. Reduce this to detect blobs with less intensities. overlap : float, optional A value between 0 and 1. If the area of two blobs overlaps by a fraction greater than `threshold`, the smaller blob is eliminated. Returns ------- A : (n, 3) ndarray A 2d array with each row representing 3 values, ``(y,x,sigma)`` where ``(y,x)`` are coordinates of the blob and ``sigma`` is the standard deviation of the Gaussian kernel which detected the blob. References ---------- .. [1] http://en.wikipedia.org/wiki/Blob_detection#The_difference_of_Gaussians_approach Examples -------- >>> from skimage import data, feature >>> feature.blob_dog(data.coins(), threshold=.5, max_sigma=40) array([[ 267. , 359. , 16.777216], [ 267. , 115. , 10.48576 ], [ 263. , 302. , 16.777216], [ 263. , 245. , 16.777216], [ 261. , 173. , 16.777216], [ 260. , 46. , 16.777216], [ 198. , 155. , 10.48576 ], [ 196. , 43. , 10.48576 ], [ 195. , 102. , 16.777216], [ 194. , 277. , 16.777216], [ 193. , 213. , 16.777216], [ 185. , 347. , 16.777216], [ 128. , 154. , 10.48576 ], [ 127. , 102. , 10.48576 ], [ 125. , 208. , 10.48576 ], [ 125. , 45. , 16.777216], [ 124. , 337. , 10.48576 ], [ 120. , 272. , 16.777216], [ 58. , 100. , 10.48576 ], [ 54. , 276. , 10.48576 ], [ 54. , 42. , 16.777216], [ 52. , 216. , 16.777216], [ 52. , 155. , 16.777216], [ 45. , 336. , 16.777216]]) Notes ----- The radius of each blob is approximately :math:`\sqrt{2}sigma`. """ assert_nD(image, 2) image = img_as_float(image) # k such that min_sigma*(sigma_ratio**k) > max_sigma k = int(log(float(max_sigma) / min_sigma, sigma_ratio)) + 1 # a geometric progression of standard deviations for gaussian kernels sigma_list = np.array([min_sigma * (sigma_ratio ** i) for i in range(k + 1)]) gaussian_images = [gaussian_filter(image, s) for s in sigma_list] # computing difference between two successive Gaussian blurred images # multiplying with standard deviation provides scale invariance dog_images = [(gaussian_images[i] - gaussian_images[i + 1]) * sigma_list[i] for i in range(k)] image_cube = np.dstack(dog_images) # local_maxima = get_local_maxima(image_cube, threshold) local_maxima = peak_local_max(image_cube, threshold_abs=threshold, footprint=np.ones((3, 3, 3)), threshold_rel=0.0, exclude_border=False) # Catch no peaks if local_maxima.size == 0: return np.empty((0,3)) # Convert local_maxima to float64 lm = local_maxima.astype(np.float64) # Convert the last index to its corresponding scale value lm[:, 2] = sigma_list[local_maxima[:, 2]] local_maxima = lm return _prune_blobs(local_maxima, overlap) def blob_log(image, min_sigma=1, max_sigma=50, num_sigma=10, threshold=.2, overlap=.5, log_scale=False): """Finds blobs in the given grayscale image. Blobs are found using the Laplacian of Gaussian (LoG) method [1]_. For each blob found, the method returns its coordinates and the standard deviation of the Gaussian kernel that detected the blob. Parameters ---------- image : ndarray Input grayscale image, blobs are assumed to be light on dark background (white on black). min_sigma : float, optional The minimum standard deviation for Gaussian Kernel. Keep this low to detect smaller blobs. max_sigma : float, optional The maximum standard deviation for Gaussian Kernel. Keep this high to detect larger blobs. num_sigma : int, optional The number of intermediate values of standard deviations to consider between `min_sigma` and `max_sigma`. threshold : float, optional. The absolute lower bound for scale space maxima. Local maxima smaller than thresh are ignored. Reduce this to detect blobs with less intensities. overlap : float, optional A value between 0 and 1. If the area of two blobs overlaps by a fraction greater than `threshold`, the smaller blob is eliminated. log_scale : bool, optional If set intermediate values of standard deviations are interpolated using a logarithmic scale to the base `10`. If not, linear interpolation is used. Returns ------- A : (n, 3) ndarray A 2d array with each row representing 3 values, ``(y,x,sigma)`` where ``(y,x)`` are coordinates of the blob and ``sigma`` is the standard deviation of the Gaussian kernel which detected the blob. References ---------- .. [1] http://en.wikipedia.org/wiki/Blob_detection#The_Laplacian_of_Gaussian Examples -------- >>> from skimage import data, feature, exposure >>> img = data.coins() >>> img = exposure.equalize_hist(img) # improves detection >>> feature.blob_log(img, threshold = .3) array([[ 266. , 115. , 11.88888889], [ 263. , 302. , 17.33333333], [ 263. , 244. , 17.33333333], [ 260. , 174. , 17.33333333], [ 198. , 155. , 11.88888889], [ 198. , 103. , 11.88888889], [ 197. , 44. , 11.88888889], [ 194. , 276. , 17.33333333], [ 194. , 213. , 17.33333333], [ 185. , 344. , 17.33333333], [ 128. , 154. , 11.88888889], [ 127. , 102. , 11.88888889], [ 126. , 208. , 11.88888889], [ 126. , 46. , 11.88888889], [ 124. , 336. , 11.88888889], [ 121. , 272. , 17.33333333], [ 113. , 323. , 1. ]]) Notes ----- The radius of each blob is approximately :math:`\sqrt{2}sigma`. """ assert_nD(image, 2) image = img_as_float(image) if log_scale: start, stop = log(min_sigma, 10), log(max_sigma, 10) sigma_list = np.logspace(start, stop, num_sigma) else: sigma_list = np.linspace(min_sigma, max_sigma, num_sigma) # computing gaussian laplace # s**2 provides scale invariance gl_images = [-gaussian_laplace(image, s) * s ** 2 for s in sigma_list] image_cube = np.dstack(gl_images) local_maxima = peak_local_max(image_cube, threshold_abs=threshold, footprint=np.ones((3, 3, 3)), threshold_rel=0.0, exclude_border=False) # Catch no peaks if local_maxima.size == 0: return np.empty((0,3)) # Convert local_maxima to float64 lm = local_maxima.astype(np.float64) # Convert the last index to its corresponding scale value lm[:, 2] = sigma_list[local_maxima[:, 2]] local_maxima = lm return _prune_blobs(local_maxima, overlap) def blob_doh(image, min_sigma=1, max_sigma=30, num_sigma=10, threshold=0.01, overlap=.5, log_scale=False): """Finds blobs in the given grayscale image. Blobs are found using the Determinant of Hessian method [1]_. For each blob found, the method returns its coordinates and the standard deviation of the Gaussian Kernel used for the Hessian matrix whose determinant detected the blob. Determinant of Hessians is approximated using [2]_. Parameters ---------- image : ndarray Input grayscale image.Blobs can either be light on dark or vice versa. min_sigma : float, optional The minimum standard deviation for Gaussian Kernel used to compute Hessian matrix. Keep this low to detect smaller blobs. max_sigma : float, optional The maximum standard deviation for Gaussian Kernel used to compute Hessian matrix. Keep this high to detect larger blobs. num_sigma : int, optional The number of intermediate values of standard deviations to consider between `min_sigma` and `max_sigma`. threshold : float, optional. The absolute lower bound for scale space maxima. Local maxima smaller than thresh are ignored. Reduce this to detect less prominent blobs. overlap : float, optional A value between 0 and 1. If the area of two blobs overlaps by a fraction greater than `threshold`, the smaller blob is eliminated. log_scale : bool, optional If set intermediate values of standard deviations are interpolated using a logarithmic scale to the base `10`. If not, linear interpolation is used. Returns ------- A : (n, 3) ndarray A 2d array with each row representing 3 values, ``(y,x,sigma)`` where ``(y,x)`` are coordinates of the blob and ``sigma`` is the standard deviation of the Gaussian kernel of the Hessian Matrix whose determinant detected the blob. References ---------- .. [1] http://en.wikipedia.org/wiki/Blob_detection#The_determinant_of_the_Hessian .. [2] Herbert Bay, Andreas Ess, Tinne Tuytelaars, Luc Van Gool, "SURF: Speeded Up Robust Features" ftp://ftp.vision.ee.ethz.ch/publications/articles/eth_biwi_00517.pdf Examples -------- >>> from skimage import data, feature >>> img = data.coins() >>> feature.blob_doh(img) array([[ 270. , 363. , 30. ], [ 265. , 113. , 23.55555556], [ 262. , 243. , 23.55555556], [ 260. , 173. , 30. ], [ 197. , 153. , 20.33333333], [ 197. , 44. , 20.33333333], [ 195. , 100. , 23.55555556], [ 193. , 275. , 23.55555556], [ 192. , 212. , 23.55555556], [ 185. , 348. , 30. ], [ 156. , 302. , 30. ], [ 126. , 153. , 20.33333333], [ 126. , 101. , 20.33333333], [ 124. , 336. , 20.33333333], [ 123. , 205. , 20.33333333], [ 123. , 44. , 23.55555556], [ 121. , 271. , 30. ]]) Notes ----- The radius of each blob is approximately `sigma`. Computation of Determinant of Hessians is independent of the standard deviation. Therefore detecting larger blobs won't take more time. In methods line :py:meth:`blob_dog` and :py:meth:`blob_log` the computation of Gaussians for larger `sigma` takes more time. The downside is that this method can't be used for detecting blobs of radius less than `3px` due to the box filters used in the approximation of Hessian Determinant. """ assert_nD(image, 2) image = img_as_float(image) image = integral_image(image) if log_scale: start, stop = log(min_sigma, 10), log(max_sigma, 10) sigma_list = np.logspace(start, stop, num_sigma) else: sigma_list = np.linspace(min_sigma, max_sigma, num_sigma) hessian_images = [_hessian_matrix_det(image, s) for s in sigma_list] image_cube = np.dstack(hessian_images) local_maxima = peak_local_max(image_cube, threshold_abs=threshold, footprint=np.ones((3, 3, 3)), threshold_rel=0.0, exclude_border=False) # Catch no peaks if local_maxima.size == 0: return np.empty((0,3)) # Convert local_maxima to float64 lm = local_maxima.astype(np.float64) # Convert the last index to its corresponding scale value lm[:, 2] = sigma_list[local_maxima[:, 2]] local_maxima = lm return _prune_blobs(local_maxima, overlap) scikit-image-0.13.1/skimage/feature/brief.py000066400000000000000000000157101316256236300206710ustar00rootroot00000000000000import numpy as np from scipy.ndimage import gaussian_filter from .util import (DescriptorExtractor, _mask_border_keypoints, _prepare_grayscale_input_2D) from .brief_cy import _brief_loop from .._shared.utils import assert_nD class BRIEF(DescriptorExtractor): """BRIEF binary descriptor extractor. BRIEF (Binary Robust Independent Elementary Features) is an efficient feature point descriptor. It is highly discriminative even when using relatively few bits and is computed using simple intensity difference tests. For each keypoint, intensity comparisons are carried out for a specifically distributed number N of pixel-pairs resulting in a binary descriptor of length N. For binary descriptors the Hamming distance can be used for feature matching, which leads to lower computational cost in comparison to the L2 norm. Parameters ---------- descriptor_size : int, optional Size of BRIEF descriptor for each keypoint. Sizes 128, 256 and 512 recommended by the authors. Default is 256. patch_size : int, optional Length of the two dimensional square patch sampling region around the keypoints. Default is 49. mode : {'normal', 'uniform'}, optional Probability distribution for sampling location of decision pixel-pairs around keypoints. sample_seed : int, optional Seed for the random sampling of the decision pixel-pairs. From a square window with length `patch_size`, pixel pairs are sampled using the `mode` parameter to build the descriptors using intensity comparison. The value of `sample_seed` must be the same for the images to be matched while building the descriptors. sigma : float, optional Standard deviation of the Gaussian low-pass filter applied to the image to alleviate noise sensitivity, which is strongly recommended to obtain discriminative and good descriptors. Attributes ---------- descriptors : (Q, `descriptor_size`) array of dtype bool 2D ndarray of binary descriptors of size `descriptor_size` for Q keypoints after filtering out border keypoints with value at an index ``(i, j)`` either being ``True`` or ``False`` representing the outcome of the intensity comparison for i-th keypoint on j-th decision pixel-pair. It is ``Q == np.sum(mask)``. mask : (N, ) array of dtype bool Mask indicating whether a keypoint has been filtered out (``False``) or is described in the `descriptors` array (``True``). Examples -------- >>> from skimage.feature import (corner_harris, corner_peaks, BRIEF, ... match_descriptors) >>> import numpy as np >>> square1 = np.zeros((8, 8), dtype=np.int32) >>> square1[2:6, 2:6] = 1 >>> square1 array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], dtype=int32) >>> square2 = np.zeros((9, 9), dtype=np.int32) >>> square2[2:7, 2:7] = 1 >>> square2 array([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=int32) >>> keypoints1 = corner_peaks(corner_harris(square1), min_distance=1) >>> keypoints2 = corner_peaks(corner_harris(square2), min_distance=1) >>> extractor = BRIEF(patch_size=5) >>> extractor.extract(square1, keypoints1) >>> descriptors1 = extractor.descriptors >>> extractor.extract(square2, keypoints2) >>> descriptors2 = extractor.descriptors >>> matches = match_descriptors(descriptors1, descriptors2) >>> matches array([[0, 0], [1, 1], [2, 2], [3, 3]]) >>> keypoints1[matches[:, 0]] array([[2, 2], [2, 5], [5, 2], [5, 5]]) >>> keypoints2[matches[:, 1]] array([[2, 2], [2, 6], [6, 2], [6, 6]]) """ def __init__(self, descriptor_size=256, patch_size=49, mode='normal', sigma=1, sample_seed=1): mode = mode.lower() if mode not in ('normal', 'uniform'): raise ValueError("`mode` must be 'normal' or 'uniform'.") self.descriptor_size = descriptor_size self.patch_size = patch_size self.mode = mode self.sigma = sigma self.sample_seed = sample_seed self.descriptors = None self.mask = None def extract(self, image, keypoints): """Extract BRIEF binary descriptors for given keypoints in image. Parameters ---------- image : 2D array Input image. keypoints : (N, 2) array Keypoint coordinates as ``(row, col)``. """ assert_nD(image, 2) random = np.random.RandomState() random.seed(self.sample_seed) image = _prepare_grayscale_input_2D(image) # Gaussian low-pass filtering to alleviate noise sensitivity image = np.ascontiguousarray(gaussian_filter(image, self.sigma)) # Sampling pairs of decision pixels in patch_size x patch_size window desc_size = self.descriptor_size patch_size = self.patch_size if self.mode == 'normal': samples = (patch_size / 5.0) * random.randn(desc_size * 8) samples = np.array(samples, dtype=np.int32) samples = samples[(samples < (patch_size // 2)) & (samples > - (patch_size - 2) // 2)] pos1 = samples[:desc_size * 2].reshape(desc_size, 2) pos2 = samples[desc_size * 2:desc_size * 4].reshape(desc_size, 2) elif self.mode == 'uniform': samples = random.randint(-(patch_size - 2) // 2, (patch_size // 2) + 1, (desc_size * 2, 2)) samples = np.array(samples, dtype=np.int32) pos1, pos2 = np.split(samples, 2) pos1 = np.ascontiguousarray(pos1) pos2 = np.ascontiguousarray(pos2) # Removing keypoints that are within (patch_size / 2) distance from the # image border self.mask = _mask_border_keypoints(image.shape, keypoints, patch_size // 2) keypoints = np.array(keypoints[self.mask, :], dtype=np.intp, order='C', copy=False) self.descriptors = np.zeros((keypoints.shape[0], desc_size), dtype=bool, order='C') _brief_loop(image, self.descriptors.view(np.uint8), keypoints, pos1, pos2) scikit-image-0.13.1/skimage/feature/brief_cy.pyx000066400000000000000000000013751316256236300215560ustar00rootroot00000000000000#cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False #cython: wraparound=False cimport numpy as cnp def _brief_loop(double[:, ::1] image, unsigned char[:, ::1] descriptors, Py_ssize_t[:, ::1] keypoints, int[:, ::1] pos0, int[:, ::1] pos1): cdef Py_ssize_t k, d, kr, kc, pr0, pr1, pc0, pc1 with nogil: for p in range(pos0.shape[0]): pr0 = pos0[p, 0] pc0 = pos0[p, 1] pr1 = pos1[p, 0] pc1 = pos1[p, 1] for k in range(keypoints.shape[0]): kr = keypoints[k, 0] kc = keypoints[k, 1] if image[kr + pr0, kc + pc0] < image[kr + pr1, kc + pc1]: descriptors[k, p] = True scikit-image-0.13.1/skimage/feature/censure.py000066400000000000000000000270611316256236300212500ustar00rootroot00000000000000import numpy as np from scipy.ndimage import maximum_filter, minimum_filter, convolve from ..transform import integral_image from ..feature import structure_tensor from ..morphology import octagon, star from ..feature.censure_cy import _censure_dob_loop from ..feature.util import (FeatureDetector, _prepare_grayscale_input_2D, _mask_border_keypoints) from .._shared.utils import assert_nD # The paper(Reference [1]) mentions the sizes of the Octagon shaped filter # kernel for the first seven scales only. The sizes of the later scales # have been extrapolated based on the following statement in the paper. # "These octagons scale linearly and were experimentally chosen to correspond # to the seven DOBs described in the previous section." OCTAGON_OUTER_SHAPE = [(5, 2), (5, 3), (7, 3), (9, 4), (9, 7), (13, 7), (15, 10), (15, 11), (15, 12), (17, 13), (17, 14)] OCTAGON_INNER_SHAPE = [(3, 0), (3, 1), (3, 2), (5, 2), (5, 3), (5, 4), (5, 5), (7, 5), (7, 6), (9, 6), (9, 7)] # The sizes for the STAR shaped filter kernel for different scales have been # taken from the OpenCV implementation. STAR_SHAPE = [1, 2, 3, 4, 6, 8, 11, 12, 16, 22, 23, 32, 45, 46, 64, 90, 128] STAR_FILTER_SHAPE = [(1, 0), (3, 1), (4, 2), (5, 3), (7, 4), (8, 5), (9, 6), (11, 8), (13, 10), (14, 11), (15, 12), (16, 14)] def _filter_image(image, min_scale, max_scale, mode): response = np.zeros((image.shape[0], image.shape[1], max_scale - min_scale + 1), dtype=np.double) if mode == 'dob': # make response[:, :, i] contiguous memory block item_size = response.itemsize response.strides = (item_size * response.shape[1], item_size, item_size * response.shape[0] * response.shape[1]) integral_img = integral_image(image) for i in range(max_scale - min_scale + 1): n = min_scale + i # Constant multipliers for the outer region and the inner region # of the bi-level filters with the constraint of keeping the # DC bias 0. inner_weight = (1.0 / (2 * n + 1) ** 2) outer_weight = (1.0 / (12 * n ** 2 + 4 * n)) _censure_dob_loop(n, integral_img, response[:, :, i], inner_weight, outer_weight) # NOTE : For the Octagon shaped filter, we implemented and evaluated the # slanted integral image based image filtering but the performance was # more or less equal to image filtering using # scipy.ndimage.filters.convolve(). Hence we have decided to use the # later for a much cleaner implementation. elif mode == 'octagon': # TODO : Decide the shapes of Octagon filters for scales > 7 for i in range(max_scale - min_scale + 1): mo, no = OCTAGON_OUTER_SHAPE[min_scale + i - 1] mi, ni = OCTAGON_INNER_SHAPE[min_scale + i - 1] response[:, :, i] = convolve(image, _octagon_kernel(mo, no, mi, ni)) elif mode == 'star': for i in range(max_scale - min_scale + 1): m = STAR_SHAPE[STAR_FILTER_SHAPE[min_scale + i - 1][0]] n = STAR_SHAPE[STAR_FILTER_SHAPE[min_scale + i - 1][1]] response[:, :, i] = convolve(image, _star_kernel(m, n)) return response def _octagon_kernel(mo, no, mi, ni): outer = (mo + 2 * no) ** 2 - 2 * no * (no + 1) inner = (mi + 2 * ni) ** 2 - 2 * ni * (ni + 1) outer_weight = 1.0 / (outer - inner) inner_weight = 1.0 / inner c = ((mo + 2 * no) - (mi + 2 * ni)) // 2 outer_oct = octagon(mo, no) inner_oct = np.zeros((mo + 2 * no, mo + 2 * no)) inner_oct[c: -c, c: -c] = octagon(mi, ni) bfilter = (outer_weight * outer_oct - (outer_weight + inner_weight) * inner_oct) return bfilter def _star_kernel(m, n): c = m + m // 2 - n - n // 2 outer_star = star(m) inner_star = np.zeros_like(outer_star) inner_star[c: -c, c: -c] = star(n) outer_weight = 1.0 / (np.sum(outer_star - inner_star)) inner_weight = 1.0 / np.sum(inner_star) bfilter = (outer_weight * outer_star - (outer_weight + inner_weight) * inner_star) return bfilter def _suppress_lines(feature_mask, image, sigma, line_threshold): Axx, Axy, Ayy = structure_tensor(image, sigma) feature_mask[(Axx + Ayy) ** 2 > line_threshold * (Axx * Ayy - Axy ** 2)] = False class CENSURE(FeatureDetector): """CENSURE keypoint detector. min_scale : int, optional Minimum scale to extract keypoints from. max_scale : int, optional Maximum scale to extract keypoints from. The keypoints will be extracted from all the scales except the first and the last i.e. from the scales in the range [min_scale + 1, max_scale - 1]. The filter sizes for different scales is such that the two adjacent scales comprise of an octave. mode : {'DoB', 'Octagon', 'STAR'}, optional Type of bi-level filter used to get the scales of the input image. Possible values are 'DoB', 'Octagon' and 'STAR'. The three modes represent the shape of the bi-level filters i.e. box(square), octagon and star respectively. For instance, a bi-level octagon filter consists of a smaller inner octagon and a larger outer octagon with the filter weights being uniformly negative in both the inner octagon while uniformly positive in the difference region. Use STAR and Octagon for better features and DoB for better performance. non_max_threshold : float, optional Threshold value used to suppress maximas and minimas with a weak magnitude response obtained after Non-Maximal Suppression. line_threshold : float, optional Threshold for rejecting interest points which have ratio of principal curvatures greater than this value. Attributes ---------- keypoints : (N, 2) array Keypoint coordinates as ``(row, col)``. scales : (N, ) array Corresponding scales. References ---------- .. [1] Motilal Agrawal, Kurt Konolige and Morten Rufus Blas "CENSURE: Center Surround Extremas for Realtime Feature Detection and Matching", http://link.springer.com/content/pdf/10.1007%2F978-3-540-88693-8_8.pdf .. [2] Adam Schmidt, Marek Kraft, Michal Fularz and Zuzanna Domagala "Comparative Assessment of Point Feature Detectors and Descriptors in the Context of Robot Navigation" http://www.jamris.org/01_2013/saveas.php?QUEST=JAMRIS_No01_2013_P_11-20.pdf Examples -------- >>> from skimage.data import astronaut >>> from skimage.color import rgb2gray >>> from skimage.feature import CENSURE >>> img = rgb2gray(astronaut()[100:300, 100:300]) >>> censure = CENSURE() >>> censure.detect(img) >>> censure.keypoints array([[ 4, 148], [ 12, 73], [ 21, 176], [ 91, 22], [ 93, 56], [ 94, 22], [ 95, 54], [100, 51], [103, 51], [106, 67], [108, 15], [117, 20], [122, 60], [125, 37], [129, 37], [133, 76], [145, 44], [146, 94], [150, 114], [153, 33], [154, 156], [155, 151], [184, 63]]) >>> censure.scales array([2, 6, 6, 2, 4, 3, 2, 3, 2, 6, 3, 2, 2, 3, 2, 2, 2, 3, 2, 2, 4, 2, 2]) """ def __init__(self, min_scale=1, max_scale=7, mode='DoB', non_max_threshold=0.15, line_threshold=10): mode = mode.lower() if mode not in ('dob', 'octagon', 'star'): raise ValueError("`mode` must be one of 'DoB', 'Octagon', 'STAR'.") if min_scale < 1 or max_scale < 1 or max_scale - min_scale < 2: raise ValueError('The scales must be >= 1 and the number of ' 'scales should be >= 3.') self.min_scale = min_scale self.max_scale = max_scale self.mode = mode self.non_max_threshold = non_max_threshold self.line_threshold = line_threshold self.keypoints = None self.scales = None def detect(self, image): """Detect CENSURE keypoints along with the corresponding scale. Parameters ---------- image : 2D ndarray Input image. """ # (1) First we generate the required scales on the input grayscale # image using a bi-level filter and stack them up in `filter_response`. # (2) We then perform Non-Maximal suppression in 3 x 3 x 3 window on # the filter_response to suppress points that are neither minima or # maxima in 3 x 3 x 3 neighbourhood. We obtain a boolean ndarray # `feature_mask` containing all the minimas and maximas in # `filter_response` as True. # (3) Then we suppress all the points in the `feature_mask` for which # the corresponding point in the image at a particular scale has the # ratio of principal curvatures greater than `line_threshold`. # (4) Finally, we remove the border keypoints and return the keypoints # along with its corresponding scale. assert_nD(image, 2) num_scales = self.max_scale - self.min_scale image = np.ascontiguousarray(_prepare_grayscale_input_2D(image)) # Generating all the scales filter_response = _filter_image(image, self.min_scale, self.max_scale, self.mode) # Suppressing points that are neither minima or maxima in their # 3 x 3 x 3 neighborhood to zero minimas = minimum_filter(filter_response, (3, 3, 3)) == filter_response maximas = maximum_filter(filter_response, (3, 3, 3)) == filter_response feature_mask = minimas | maximas feature_mask[filter_response < self.non_max_threshold] = False for i in range(1, num_scales): # sigma = (window_size - 1) / 6.0, so the window covers > 99% of # the kernel's distribution # window_size = 7 + 2 * (min_scale - 1 + i) # Hence sigma = 1 + (min_scale - 1 + i)/ 3.0 _suppress_lines(feature_mask[:, :, i], image, (1 + (self.min_scale + i - 1) / 3.0), self.line_threshold) rows, cols, scales = np.nonzero(feature_mask[..., 1:num_scales]) keypoints = np.column_stack([rows, cols]) scales = scales + self.min_scale + 1 if self.mode == 'dob': self.keypoints = keypoints self.scales = scales return cumulative_mask = np.zeros(keypoints.shape[0], dtype=np.bool) if self.mode == 'octagon': for i in range(self.min_scale + 1, self.max_scale): c = (OCTAGON_OUTER_SHAPE[i - 1][0] - 1) // 2 \ + OCTAGON_OUTER_SHAPE[i - 1][1] cumulative_mask |= ( _mask_border_keypoints(image.shape, keypoints, c) & (scales == i)) elif self.mode == 'star': for i in range(self.min_scale + 1, self.max_scale): c = STAR_SHAPE[STAR_FILTER_SHAPE[i - 1][0]] \ + STAR_SHAPE[STAR_FILTER_SHAPE[i - 1][0]] // 2 cumulative_mask |= ( _mask_border_keypoints(image.shape, keypoints, c) & (scales == i)) self.keypoints = keypoints[cumulative_mask] self.scales = scales[cumulative_mask] scikit-image-0.13.1/skimage/feature/censure_cy.pyx000066400000000000000000000056341316256236300221350ustar00rootroot00000000000000#cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False #cython: wraparound=False def _censure_dob_loop(Py_ssize_t n, double[:, ::1] integral_img, double[:, ::1] filtered_image, double inner_weight, double outer_weight): # This function calculates the value in the DoB filtered image using # integral images. If r = right. l = left, u = up, d = down, the sum of # pixel values in the rectangle formed by (u, l), (u, r), (d, r), (d, l) # is calculated as I(d, r) + I(u - 1, l - 1) - I(u - 1, r) - I(d, l - 1). cdef Py_ssize_t i, j cdef double inner, outer cdef Py_ssize_t n2 = 2 * n cdef double total_weight = inner_weight + outer_weight with nogil: # top-left pixel inner = (integral_img[n2 + n, n2 + n] + integral_img[n2 - n - 1, n2 - n - 1] - integral_img[n2 + n, n2 - n - 1] - integral_img[n2 - n - 1, n2 + n]) outer = integral_img[2 * n2, 2 * n2] filtered_image[n2, n2] = (outer_weight * outer - total_weight * inner) # left column for i in range(n2 + 1, integral_img.shape[0] - n2): inner = (integral_img[i + n, n2 + n] + integral_img[i - n - 1, n2 - n - 1] - integral_img[i + n, n2 - n - 1] - integral_img[i - n - 1, n2 + n]) outer = (integral_img[i + n2, 2 * n2] - integral_img[i - n2 - 1, 2 * n2]) filtered_image[i, n2] = (outer_weight * outer - total_weight * inner) # top row for j in range(n2 + 1, integral_img.shape[1] - n2): inner = (integral_img[n2 + n, j + n] + integral_img[n2 - n - 1, j - n - 1] - integral_img[n2 + n, j - n - 1] - integral_img[n2 - n - 1, j + n]) outer = (integral_img[2 * n2, j + n2] - integral_img[2 * n2, j - n2 - 1]) filtered_image[n2, j] = (outer_weight * outer - total_weight * inner) # remaining block for i in range(n2 + 1, integral_img.shape[0] - n2): for j in range(n2 + 1, integral_img.shape[1] - n2): inner = (integral_img[i + n, j + n] + integral_img[i - n - 1, j - n - 1] - integral_img[i + n, j - n - 1] - integral_img[i - n - 1, j + n]) outer = (integral_img[i + n2, j + n2] + integral_img[i - n2 - 1, j - n2 - 1] - integral_img[i + n2, j - n2 - 1] - integral_img[i - n2 - 1, j + n2]) filtered_image[i, j] = (outer_weight * outer - total_weight * inner) scikit-image-0.13.1/skimage/feature/corner.py000066400000000000000000001017201316256236300210670ustar00rootroot00000000000000from itertools import combinations_with_replacement import numpy as np from scipy import ndimage as ndi from scipy import stats from ..util import img_as_float from ..feature import peak_local_max from ..feature.util import _prepare_grayscale_input_2D from ..feature.corner_cy import _corner_fast from ._hessian_det_appx import _hessian_matrix_det from ..transform import integral_image from .._shared.utils import safe_as_int from .corner_cy import _corner_moravec, _corner_orientations from warnings import warn def _compute_derivatives(image, mode='constant', cval=0): """Compute derivatives in x and y direction using the Sobel operator. Parameters ---------- image : ndarray Input image. mode : {'constant', 'reflect', 'wrap', 'nearest', 'mirror'}, optional How to handle values outside the image borders. cval : float, optional Used in conjunction with mode 'constant', the value outside the image boundaries. Returns ------- imx : ndarray Derivative in x-direction. imy : ndarray Derivative in y-direction. """ imy = ndi.sobel(image, axis=0, mode=mode, cval=cval) imx = ndi.sobel(image, axis=1, mode=mode, cval=cval) return imx, imy def structure_tensor(image, sigma=1, mode='constant', cval=0): """Compute structure tensor using sum of squared differences. The structure tensor A is defined as:: A = [Axx Axy] [Axy Ayy] which is approximated by the weighted sum of squared differences in a local window around each pixel in the image. Parameters ---------- image : ndarray Input image. sigma : float, optional Standard deviation used for the Gaussian kernel, which is used as a weighting function for the local summation of squared differences. mode : {'constant', 'reflect', 'wrap', 'nearest', 'mirror'}, optional How to handle values outside the image borders. cval : float, optional Used in conjunction with mode 'constant', the value outside the image boundaries. Returns ------- Axx : ndarray Element of the structure tensor for each pixel in the input image. Axy : ndarray Element of the structure tensor for each pixel in the input image. Ayy : ndarray Element of the structure tensor for each pixel in the input image. Examples -------- >>> from skimage.feature import structure_tensor >>> square = np.zeros((5, 5)) >>> square[2, 2] = 1 >>> Axx, Axy, Ayy = structure_tensor(square, sigma=0.1) >>> Axx array([[ 0., 0., 0., 0., 0.], [ 0., 1., 0., 1., 0.], [ 0., 4., 0., 4., 0.], [ 0., 1., 0., 1., 0.], [ 0., 0., 0., 0., 0.]]) """ image = _prepare_grayscale_input_2D(image) imx, imy = _compute_derivatives(image, mode=mode, cval=cval) # structure tensore Axx = ndi.gaussian_filter(imx * imx, sigma, mode=mode, cval=cval) Axy = ndi.gaussian_filter(imx * imy, sigma, mode=mode, cval=cval) Ayy = ndi.gaussian_filter(imy * imy, sigma, mode=mode, cval=cval) return Axx, Axy, Ayy def hessian_matrix(image, sigma=1, mode='constant', cval=0, order=None): """Compute Hessian matrix. The Hessian matrix is defined as:: H = [Hrr Hrc] [Hrc Hcc] which is computed by convolving the image with the second derivatives of the Gaussian kernel in the respective x- and y-directions. Parameters ---------- image : ndarray Input image. sigma : float Standard deviation used for the Gaussian kernel, which is used as weighting function for the auto-correlation matrix. mode : {'constant', 'reflect', 'wrap', 'nearest', 'mirror'}, optional How to handle values outside the image borders. cval : float, optional Used in conjunction with mode 'constant', the value outside the image boundaries. order : {'xy', 'rc'}, optional This parameter allows for the use of reverse or forward order of the image axes in gradient computation. 'xy' indicates the usage of the last axis initially (Hxx, Hxy, Hyy), whilst 'rc' indicates the use of the first axis initially (Hrr, Hrc, Hcc). Returns ------- Hrr : ndarray Element of the Hessian matrix for each pixel in the input image. Hrc : ndarray Element of the Hessian matrix for each pixel in the input image. Hcc : ndarray Element of the Hessian matrix for each pixel in the input image. Examples -------- >>> from skimage.feature import hessian_matrix >>> square = np.zeros((5, 5)) >>> square[2, 2] = 4 >>> Hrr, Hrc, Hcc = hessian_matrix(square, sigma=0.1, order = 'rc') >>> Hrc array([[ 0., 0., 0., 0., 0.], [ 0., 1., 0., -1., 0.], [ 0., 0., 0., 0., 0.], [ 0., -1., 0., 1., 0.], [ 0., 0., 0., 0., 0.]]) """ image = img_as_float(image) gaussian_filtered = ndi.gaussian_filter(image, sigma=sigma, mode=mode, cval=cval) if order is None: if image.ndim == 2: # The legacy 2D code followed (x, y) convention, so we swap the axis # order to maintain compatibility with old code warn('deprecation warning: the default order of the hessian matrix values ' 'will be "row-column" instead of "xy" starting in skimage version 0.15. ' 'Use order="rc" or order="xy" to set this explicitly') order = 'xy' else: order = 'rc' gradients = np.gradient(gaussian_filtered) axes = range(image.ndim) if order == 'rc': axes = reversed(axes) H_elems = [np.gradient(gradients[ax0], axis=ax1) for ax0, ax1 in combinations_with_replacement(axes, 2)] return H_elems def hessian_matrix_det(image, sigma=1): """Computes the approximate Hessian Determinant over an image. This method uses box filters over integral images to compute the approximate Hessian Determinant as described in [1]_. Parameters ---------- image : array The image over which to compute Hessian Determinant. sigma : float, optional Standard deviation used for the Gaussian kernel, used for the Hessian matrix. Returns ------- out : array The array of the Determinant of Hessians. References ---------- .. [1] Herbert Bay, Andreas Ess, Tinne Tuytelaars, Luc Van Gool, "SURF: Speeded Up Robust Features" ftp://ftp.vision.ee.ethz.ch/publications/articles/eth_biwi_00517.pdf Notes ----- The running time of this method only depends on size of the image. It is independent of `sigma` as one would expect. The downside is that the result for `sigma` less than `3` is not accurate, i.e., not similar to the result obtained if someone computed the Hessian and took it's determinant. """ image = img_as_float(image) image = integral_image(image) return np.array(_hessian_matrix_det(image, sigma)) def _image_orthogonal_matrix22_eigvals(M00, M01, M11): l1 = (M00 + M11) / 2 + np.sqrt(4 * M01 ** 2 + (M00 - M11) ** 2) / 2 l2 = (M00 + M11) / 2 - np.sqrt(4 * M01 ** 2 + (M00 - M11) ** 2) / 2 return l1, l2 def structure_tensor_eigvals(Axx, Axy, Ayy): """Compute Eigen values of structure tensor. Parameters ---------- Axx : ndarray Element of the structure tensor for each pixel in the input image. Axy : ndarray Element of the structure tensor for each pixel in the input image. Ayy : ndarray Element of the structure tensor for each pixel in the input image. Returns ------- l1 : ndarray Larger eigen value for each input matrix. l2 : ndarray Smaller eigen value for each input matrix. Examples -------- >>> from skimage.feature import structure_tensor, structure_tensor_eigvals >>> square = np.zeros((5, 5)) >>> square[2, 2] = 1 >>> Axx, Axy, Ayy = structure_tensor(square, sigma=0.1) >>> structure_tensor_eigvals(Axx, Axy, Ayy)[0] array([[ 0., 0., 0., 0., 0.], [ 0., 2., 4., 2., 0.], [ 0., 4., 0., 4., 0.], [ 0., 2., 4., 2., 0.], [ 0., 0., 0., 0., 0.]]) """ return _image_orthogonal_matrix22_eigvals(Axx, Axy, Ayy) def hessian_matrix_eigvals(Hxx, Hxy, Hyy): """Compute Eigenvalues of Hessian matrix. Parameters ---------- Hxx : ndarray Element of the Hessian matrix for each pixel in the input image. Hxy : ndarray Element of the Hessian matrix for each pixel in the input image. Hyy : ndarray Element of the Hessian matrix for each pixel in the input image. Returns ------- l1 : ndarray Larger eigen value for each input matrix. l2 : ndarray Smaller eigen value for each input matrix. Examples -------- >>> from skimage.feature import hessian_matrix, hessian_matrix_eigvals >>> square = np.zeros((5, 5)) >>> square[2, 2] = 4 >>> Hxx, Hxy, Hyy = hessian_matrix(square, sigma=0.1, order='rc') >>> hessian_matrix_eigvals(Hxx, Hxy, Hyy)[0] array([[ 0., 0., 2., 0., 0.], [ 0., 1., 0., 1., 0.], [ 2., 0., -2., 0., 2.], [ 0., 1., 0., 1., 0.], [ 0., 0., 2., 0., 0.]]) """ return _image_orthogonal_matrix22_eigvals(Hxx, Hxy, Hyy) def shape_index(image, sigma=1, mode='constant', cval=0): """Compute the shape index. The shape index, as defined by Koenderink & van Doorn [1]_, is a single valued measure of local curvature, assuming the image as a 3D plane with intensities representing heights. It is derived from the eigen values of the Hessian, and its value ranges from -1 to 1 (and is undefined (=NaN) in *flat* regions), with following ranges representing following shapes: .. table:: Ranges of the shape index and corresponding shapes. =================== ============= Interval (s in ...) Shape =================== ============= [ -1, -7/8) Spherical cup [-7/8, -5/8) Through [-5/8, -3/8) Rut [-3/8, -1/8) Saddle rut [-1/8, +1/8) Saddle [+1/8, +3/8) Saddle ridge [+3/8, +5/8) Ridge [+5/8, +7/8) Dome [+7/8, +1] Spherical cap =================== ============= Parameters ---------- image : ndarray Input image. sigma : float, optional Standard deviation used for the Gaussian kernel, which is used for smoothing the input data before Hessian eigen value calculation. mode : {'constant', 'reflect', 'wrap', 'nearest', 'mirror'}, optional How to handle values outside the image borders cval : float, optional Used in conjunction with mode 'constant', the value outside the image boundaries. Returns ------- s : ndarray Shape index References ---------- .. [1] Koenderink, J. J. & van Doorn, A. J., "Surface shape and curvature scales", Image and Vision Computing, 1992, 10, 557-564. DOI:10.1016/0262-8856(92)90076-F Examples -------- >>> from skimage.feature import shape_index >>> square = np.zeros((5, 5)) >>> square[2, 2] = 4 >>> s = shape_index(square, sigma=0.1) >>> s array([[ nan, nan, -0.5, nan, nan], [ nan, -0. , nan, -0. , nan], [-0.5, nan, -1. , nan, -0.5], [ nan, -0. , nan, -0. , nan], [ nan, nan, -0.5, nan, nan]]) """ Hxx, Hxy, Hyy = hessian_matrix(image, sigma=sigma, mode=mode, cval=cval, order='rc') l1, l2 = hessian_matrix_eigvals(Hxx, Hxy, Hyy) return (2.0 / np.pi) * np.arctan((l2 + l1) / (l2 - l1)) def corner_kitchen_rosenfeld(image, mode='constant', cval=0): """Compute Kitchen and Rosenfeld corner measure response image. The corner measure is calculated as follows:: (imxx * imy**2 + imyy * imx**2 - 2 * imxy * imx * imy) / (imx**2 + imy**2) Where imx and imy are the first and imxx, imxy, imyy the second derivatives. Parameters ---------- image : ndarray Input image. mode : {'constant', 'reflect', 'wrap', 'nearest', 'mirror'}, optional How to handle values outside the image borders. cval : float, optional Used in conjunction with mode 'constant', the value outside the image boundaries. Returns ------- response : ndarray Kitchen and Rosenfeld response image. """ imx, imy = _compute_derivatives(image, mode=mode, cval=cval) imxx, imxy = _compute_derivatives(imx, mode=mode, cval=cval) imyx, imyy = _compute_derivatives(imy, mode=mode, cval=cval) numerator = (imxx * imy ** 2 + imyy * imx ** 2 - 2 * imxy * imx * imy) denominator = (imx ** 2 + imy ** 2) response = np.zeros_like(image, dtype=np.double) mask = denominator != 0 response[mask] = numerator[mask] / denominator[mask] return response def corner_harris(image, method='k', k=0.05, eps=1e-6, sigma=1): """Compute Harris corner measure response image. This corner detector uses information from the auto-correlation matrix A:: A = [(imx**2) (imx*imy)] = [Axx Axy] [(imx*imy) (imy**2)] [Axy Ayy] Where imx and imy are first derivatives, averaged with a gaussian filter. The corner measure is then defined as:: det(A) - k * trace(A)**2 or:: 2 * det(A) / (trace(A) + eps) Parameters ---------- image : ndarray Input image. method : {'k', 'eps'}, optional Method to compute the response image from the auto-correlation matrix. k : float, optional Sensitivity factor to separate corners from edges, typically in range `[0, 0.2]`. Small values of k result in detection of sharp corners. eps : float, optional Normalisation factor (Noble's corner measure). sigma : float, optional Standard deviation used for the Gaussian kernel, which is used as weighting function for the auto-correlation matrix. Returns ------- response : ndarray Harris response image. References ---------- .. [1] http://kiwi.cs.dal.ca/~dparks/CornerDetection/harris.htm .. [2] http://en.wikipedia.org/wiki/Corner_detection Examples -------- >>> from skimage.feature import corner_harris, corner_peaks >>> square = np.zeros([10, 10]) >>> square[2:8, 2:8] = 1 >>> square.astype(int) array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]) >>> corner_peaks(corner_harris(square), min_distance=1) array([[2, 2], [2, 7], [7, 2], [7, 7]]) """ Axx, Axy, Ayy = structure_tensor(image, sigma) # determinant detA = Axx * Ayy - Axy ** 2 # trace traceA = Axx + Ayy if method == 'k': response = detA - k * traceA ** 2 else: response = 2 * detA / (traceA + eps) return response def corner_shi_tomasi(image, sigma=1): """Compute Shi-Tomasi (Kanade-Tomasi) corner measure response image. This corner detector uses information from the auto-correlation matrix A:: A = [(imx**2) (imx*imy)] = [Axx Axy] [(imx*imy) (imy**2)] [Axy Ayy] Where imx and imy are first derivatives, averaged with a gaussian filter. The corner measure is then defined as the smaller eigenvalue of A:: ((Axx + Ayy) - sqrt((Axx - Ayy)**2 + 4 * Axy**2)) / 2 Parameters ---------- image : ndarray Input image. sigma : float, optional Standard deviation used for the Gaussian kernel, which is used as weighting function for the auto-correlation matrix. Returns ------- response : ndarray Shi-Tomasi response image. References ---------- .. [1] http://kiwi.cs.dal.ca/~dparks/CornerDetection/harris.htm .. [2] http://en.wikipedia.org/wiki/Corner_detection Examples -------- >>> from skimage.feature import corner_shi_tomasi, corner_peaks >>> square = np.zeros([10, 10]) >>> square[2:8, 2:8] = 1 >>> square.astype(int) array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]) >>> corner_peaks(corner_shi_tomasi(square), min_distance=1) array([[2, 2], [2, 7], [7, 2], [7, 7]]) """ Axx, Axy, Ayy = structure_tensor(image, sigma) # minimum eigenvalue of A response = ((Axx + Ayy) - np.sqrt((Axx - Ayy) ** 2 + 4 * Axy ** 2)) / 2 return response def corner_foerstner(image, sigma=1): """Compute Foerstner corner measure response image. This corner detector uses information from the auto-correlation matrix A:: A = [(imx**2) (imx*imy)] = [Axx Axy] [(imx*imy) (imy**2)] [Axy Ayy] Where imx and imy are first derivatives, averaged with a gaussian filter. The corner measure is then defined as:: w = det(A) / trace(A) (size of error ellipse) q = 4 * det(A) / trace(A)**2 (roundness of error ellipse) Parameters ---------- image : ndarray Input image. sigma : float, optional Standard deviation used for the Gaussian kernel, which is used as weighting function for the auto-correlation matrix. Returns ------- w : ndarray Error ellipse sizes. q : ndarray Roundness of error ellipse. References ---------- .. [1] http://www.ipb.uni-bonn.de/uploads/tx_ikgpublication/foerstner87.fast.pdf .. [2] http://en.wikipedia.org/wiki/Corner_detection Examples -------- >>> from skimage.feature import corner_foerstner, corner_peaks >>> square = np.zeros([10, 10]) >>> square[2:8, 2:8] = 1 >>> square.astype(int) array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]) >>> w, q = corner_foerstner(square) >>> accuracy_thresh = 0.5 >>> roundness_thresh = 0.3 >>> foerstner = (q > roundness_thresh) * (w > accuracy_thresh) * w >>> corner_peaks(foerstner, min_distance=1) array([[2, 2], [2, 7], [7, 2], [7, 7]]) """ Axx, Axy, Ayy = structure_tensor(image, sigma) # determinant detA = Axx * Ayy - Axy ** 2 # trace traceA = Axx + Ayy w = np.zeros_like(image, dtype=np.double) q = np.zeros_like(image, dtype=np.double) mask = traceA != 0 w[mask] = detA[mask] / traceA[mask] q[mask] = 4 * detA[mask] / traceA[mask] ** 2 return w, q def corner_fast(image, n=12, threshold=0.15): """Extract FAST corners for a given image. Parameters ---------- image : 2D ndarray Input image. n : int Minimum number of consecutive pixels out of 16 pixels on the circle that should all be either brighter or darker w.r.t testpixel. A point c on the circle is darker w.r.t test pixel p if `Ic < Ip - threshold` and brighter if `Ic > Ip + threshold`. Also stands for the n in `FAST-n` corner detector. threshold : float Threshold used in deciding whether the pixels on the circle are brighter, darker or similar w.r.t. the test pixel. Decrease the threshold when more corners are desired and vice-versa. Returns ------- response : ndarray FAST corner response image. References ---------- .. [1] Edward Rosten and Tom Drummond "Machine Learning for high-speed corner detection", http://www.edwardrosten.com/work/rosten_2006_machine.pdf .. [2] Wikipedia, "Features from accelerated segment test", https://en.wikipedia.org/wiki/Features_from_accelerated_segment_test Examples -------- >>> from skimage.feature import corner_fast, corner_peaks >>> square = np.zeros((12, 12)) >>> square[3:9, 3:9] = 1 >>> square.astype(int) array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]) >>> corner_peaks(corner_fast(square, 9), min_distance=1) array([[3, 3], [3, 8], [8, 3], [8, 8]]) """ image = _prepare_grayscale_input_2D(image) image = np.ascontiguousarray(image) response = _corner_fast(image, n, threshold) return response def corner_subpix(image, corners, window_size=11, alpha=0.99): """Determine subpixel position of corners. A statistical test decides whether the corner is defined as the intersection of two edges or a single peak. Depending on the classification result, the subpixel corner location is determined based on the local covariance of the grey-values. If the significance level for either statistical test is not sufficient, the corner cannot be classified, and the output subpixel position is set to NaN. Parameters ---------- image : ndarray Input image. corners : (N, 2) ndarray Corner coordinates `(row, col)`. window_size : int, optional Search window size for subpixel estimation. alpha : float, optional Significance level for corner classification. Returns ------- positions : (N, 2) ndarray Subpixel corner positions. NaN for "not classified" corners. References ---------- .. [1] http://www.ipb.uni-bonn.de/uploads/tx_ikgpublication/\ foerstner87.fast.pdf .. [2] http://en.wikipedia.org/wiki/Corner_detection Examples -------- >>> from skimage.feature import corner_harris, corner_peaks, corner_subpix >>> img = np.zeros((10, 10)) >>> img[:5, :5] = 1 >>> img[5:, 5:] = 1 >>> img.astype(int) array([[1, 1, 1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]]) >>> coords = corner_peaks(corner_harris(img), min_distance=2) >>> coords_subpix = corner_subpix(img, coords, window_size=7) >>> coords_subpix array([[ 4.5, 4.5]]) """ # window extent in one direction wext = (window_size - 1) // 2 image = np.pad(image, pad_width=wext, mode='constant', constant_values=0) # add pad width, make sure to not modify the input values in-place corners = safe_as_int(corners + wext) # normal equation arrays N_dot = np.zeros((2, 2), dtype=np.double) N_edge = np.zeros((2, 2), dtype=np.double) b_dot = np.zeros((2, ), dtype=np.double) b_edge = np.zeros((2, ), dtype=np.double) # critical statistical test values redundancy = window_size ** 2 - 2 t_crit_dot = stats.f.isf(1 - alpha, redundancy, redundancy) t_crit_edge = stats.f.isf(alpha, redundancy, redundancy) # coordinates of pixels within window y, x = np.mgrid[- wext:wext + 1, - wext:wext + 1] corners_subpix = np.zeros_like(corners, dtype=np.double) for i, (y0, x0) in enumerate(corners): # crop window around corner + border for sobel operator miny = y0 - wext - 1 maxy = y0 + wext + 2 minx = x0 - wext - 1 maxx = x0 + wext + 2 window = image[miny:maxy, minx:maxx] winx, winy = _compute_derivatives(window, mode='constant', cval=0) # compute gradient suares and remove border winx_winx = (winx * winx)[1:-1, 1:-1] winx_winy = (winx * winy)[1:-1, 1:-1] winy_winy = (winy * winy)[1:-1, 1:-1] # sum of squared differences (mean instead of gaussian filter) Axx = np.sum(winx_winx) Axy = np.sum(winx_winy) Ayy = np.sum(winy_winy) # sum of squared differences weighted with coordinates # (mean instead of gaussian filter) bxx_x = np.sum(winx_winx * x) bxx_y = np.sum(winx_winx * y) bxy_x = np.sum(winx_winy * x) bxy_y = np.sum(winx_winy * y) byy_x = np.sum(winy_winy * x) byy_y = np.sum(winy_winy * y) # normal equations for subpixel position N_dot[0, 0] = Axx N_dot[0, 1] = N_dot[1, 0] = - Axy N_dot[1, 1] = Ayy N_edge[0, 0] = Ayy N_edge[0, 1] = N_edge[1, 0] = Axy N_edge[1, 1] = Axx b_dot[:] = bxx_y - bxy_x, byy_x - bxy_y b_edge[:] = byy_y + bxy_x, bxx_x + bxy_y # estimated positions try: est_dot = np.linalg.solve(N_dot, b_dot) est_edge = np.linalg.solve(N_edge, b_edge) except np.linalg.LinAlgError: # if image is constant the system is singular corners_subpix[i, :] = np.nan, np.nan continue # residuals ry_dot = y - est_dot[0] rx_dot = x - est_dot[1] ry_edge = y - est_edge[0] rx_edge = x - est_edge[1] # squared residuals rxx_dot = rx_dot * rx_dot rxy_dot = rx_dot * ry_dot ryy_dot = ry_dot * ry_dot rxx_edge = rx_edge * rx_edge rxy_edge = rx_edge * ry_edge ryy_edge = ry_edge * ry_edge # determine corner class (dot or edge) # variance for different models var_dot = np.sum(winx_winx * ryy_dot - 2 * winx_winy * rxy_dot + winy_winy * rxx_dot) var_edge = np.sum(winy_winy * ryy_edge + 2 * winx_winy * rxy_edge + winx_winx * rxx_edge) # test value (F-distributed) if var_dot < np.spacing(1) and var_edge < np.spacing(1): t = np.nan elif var_dot == 0: t = np.inf else: t = var_edge / var_dot # 1 for edge, -1 for dot, 0 for "not classified" corner_class = int(t < t_crit_edge) - int(t > t_crit_dot) if corner_class == -1: corners_subpix[i, :] = y0 + est_dot[0], x0 + est_dot[1] elif corner_class == 0: corners_subpix[i, :] = np.nan, np.nan elif corner_class == 1: corners_subpix[i, :] = y0 + est_edge[0], x0 + est_edge[1] # subtract pad width corners_subpix -= wext return corners_subpix def corner_peaks(image, min_distance=1, threshold_abs=None, threshold_rel=0.1, exclude_border=True, indices=True, num_peaks=np.inf, footprint=None, labels=None): """Find corners in corner measure response image. This differs from `skimage.feature.peak_local_max` in that it suppresses multiple connected peaks with the same accumulator value. Parameters ---------- * : * See :py:meth:`skimage.feature.peak_local_max`. Examples -------- >>> from skimage.feature import peak_local_max >>> response = np.zeros((5, 5)) >>> response[2:4, 2:4] = 1 >>> response array([[ 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0.], [ 0., 0., 1., 1., 0.], [ 0., 0., 1., 1., 0.], [ 0., 0., 0., 0., 0.]]) >>> peak_local_max(response) array([[3, 3], [3, 2], [2, 3], [2, 2]]) >>> corner_peaks(response) array([[2, 2]]) """ peaks = peak_local_max(image, min_distance=min_distance, threshold_abs=threshold_abs, threshold_rel=threshold_rel, exclude_border=exclude_border, indices=False, num_peaks=num_peaks, footprint=footprint, labels=labels) if min_distance > 0: coords = np.transpose(peaks.nonzero()) for r, c in coords: if peaks[r, c]: peaks[r - min_distance:r + min_distance + 1, c - min_distance:c + min_distance + 1] = False peaks[r, c] = True if indices is True: return np.transpose(peaks.nonzero()) else: return peaks def corner_moravec(image, window_size=1): """Compute Moravec corner measure response image. This is one of the simplest corner detectors and is comparatively fast but has several limitations (e.g. not rotation invariant). Parameters ---------- image : ndarray Input image. window_size : int, optional Window size. Returns ------- response : ndarray Moravec response image. References ---------- .. [1] http://kiwi.cs.dal.ca/~dparks/CornerDetection/moravec.htm .. [2] http://en.wikipedia.org/wiki/Corner_detection Examples -------- >>> from skimage.feature import corner_moravec >>> square = np.zeros([7, 7]) >>> square[3, 3] = 1 >>> square.astype(int) array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]]) >>> corner_moravec(square).astype(int) array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 2, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]]) """ return _corner_moravec(image, window_size) def corner_orientations(image, corners, mask): """Compute the orientation of corners. The orientation of corners is computed using the first order central moment i.e. the center of mass approach. The corner orientation is the angle of the vector from the corner coordinate to the intensity centroid in the local neighborhood around the corner calculated using first order central moment. Parameters ---------- image : 2D array Input grayscale image. corners : (N, 2) array Corner coordinates as ``(row, col)``. mask : 2D array Mask defining the local neighborhood of the corner used for the calculation of the central moment. Returns ------- orientations : (N, 1) array Orientations of corners in the range [-pi, pi]. References ---------- .. [1] Ethan Rublee, Vincent Rabaud, Kurt Konolige and Gary Bradski "ORB : An efficient alternative to SIFT and SURF" http://www.vision.cs.chubu.ac.jp/CV-R/pdf/Rublee_iccv2011.pdf .. [2] Paul L. Rosin, "Measuring Corner Properties" http://users.cs.cf.ac.uk/Paul.Rosin/corner2.pdf Examples -------- >>> from skimage.morphology import octagon >>> from skimage.feature import (corner_fast, corner_peaks, ... corner_orientations) >>> square = np.zeros((12, 12)) >>> square[3:9, 3:9] = 1 >>> square.astype(int) array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]) >>> corners = corner_peaks(corner_fast(square, 9), min_distance=1) >>> corners array([[3, 3], [3, 8], [8, 3], [8, 8]]) >>> orientations = corner_orientations(square, corners, octagon(3, 2)) >>> np.rad2deg(orientations) array([ 45., 135., -45., -135.]) """ return _corner_orientations(image, corners, mask) scikit-image-0.13.1/skimage/feature/corner_cy.pyx000066400000000000000000000230721316256236300217550ustar00rootroot00000000000000#cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False #cython: wraparound=False import numpy as np cimport numpy as cnp from libc.float cimport DBL_MAX from libc.math cimport atan2, fabs from ..util import img_as_float from ..color import rgb2grey from .util import _prepare_grayscale_input_2D def _corner_moravec(image, Py_ssize_t window_size=1): """Compute Moravec corner measure response image. This is one of the simplest corner detectors and is comparatively fast but has several limitations (e.g. not rotation invariant). Parameters ---------- image : ndarray Input image. window_size : int, optional (default 1) Window size. Returns ------- response : ndarray Moravec response image. References ---------- .. [1] http://kiwi.cs.dal.ca/~dparks/CornerDetection/moravec.htm .. [2] http://en.wikipedia.org/wiki/Corner_detection Examples -------- >>> from skimage.feature import corner_moravec >>> square = np.zeros([7, 7]) >>> square[3, 3] = 1 >>> square.astype(int) array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]]) >>> corner_moravec(square).astype(int) array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 2, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]]) """ cdef Py_ssize_t rows = image.shape[0] cdef Py_ssize_t cols = image.shape[1] cdef double[:, ::1] cimage = np.ascontiguousarray(img_as_float(image)) cdef double[:, ::1] out = np.zeros(image.shape, dtype=np.double) cdef double msum, min_msum cdef Py_ssize_t r, c, br, bc, mr, mc, a, b with nogil: for r in range(2 * window_size, rows - 2 * window_size): for c in range(2 * window_size, cols - 2 * window_size): min_msum = DBL_MAX for br in range(r - window_size, r + window_size + 1): for bc in range(c - window_size, c + window_size + 1): if br != r and bc != c: msum = 0 for mr in range(- window_size, window_size + 1): for mc in range(- window_size, window_size + 1): msum += (cimage[r + mr, c + mc] - cimage[br + mr, bc + mc]) ** 2 min_msum = min(msum, min_msum) out[r, c] = min_msum return np.asarray(out) cdef inline double _corner_fast_response(double curr_pixel, double* circle_intensities, signed char* bins, signed char state, char n) nogil: cdef char consecutive_count = 0 cdef double curr_response cdef Py_ssize_t l, m for l in range(15 + n): if bins[l % 16] == state: consecutive_count += 1 if consecutive_count == n: curr_response = 0 for m in range(16): curr_response += fabs(circle_intensities[m] - curr_pixel) return curr_response else: consecutive_count = 0 return 0 def _corner_fast(double[:, ::1] image, signed char n, double threshold): cdef Py_ssize_t rows = image.shape[0] cdef Py_ssize_t cols = image.shape[1] cdef Py_ssize_t i, j, k cdef signed char speed_sum_b, speed_sum_d cdef double curr_pixel cdef double lower_threshold, upper_threshold cdef double[:, ::1] corner_response = np.zeros((rows, cols), dtype=np.double) cdef signed char *rp = [0, 1, 2, 3, 3, 3, 2, 1, 0, -1, -2, -3, -3, -3, -2, -1] cdef signed char *cp = [3, 3, 2, 1, 0, -1, -2, -3, -3, -3, -2, -1, 0, 1, 2, 3] cdef signed char bins[16] cdef double circle_intensities[16] cdef double curr_response with nogil: for i in range(3, rows - 3): for j in range(3, cols - 3): curr_pixel = image[i, j] lower_threshold = curr_pixel - threshold upper_threshold = curr_pixel + threshold for k in range(16): circle_intensities[k] = image[i + rp[k], j + cp[k]] if circle_intensities[k] > upper_threshold: # Brighter pixel bins[k] = 'b' elif circle_intensities[k] < lower_threshold: # Darker pixel bins[k] = 'd' else: # Similar pixel bins[k] = 's' # High speed test for n >= 12 if n >= 12: speed_sum_b = 0 speed_sum_d = 0 for k in range(0, 16, 4): if bins[k] == 'b': speed_sum_b += 1 elif bins[k] == 'd': speed_sum_d += 1 if speed_sum_d < 3 and speed_sum_b < 3: continue # Test for bright pixels curr_response = \ _corner_fast_response(curr_pixel, circle_intensities, bins, 'b', n) # Test for dark pixels if curr_response == 0: curr_response = \ _corner_fast_response(curr_pixel, circle_intensities, bins, 'd', n) corner_response[i, j] = curr_response return np.asarray(corner_response) def _corner_orientations(image, Py_ssize_t[:, :] corners, mask): """Compute the orientation of corners. The orientation of corners is computed using the first order central moment i.e. the center of mass approach. The corner orientation is the angle of the vector from the corner coordinate to the intensity centroid in the local neighborhood around the corner calculated using first order central moment. Parameters ---------- image : 2D array Input grayscale image. corners : (N, 2) array Corner coordinates as ``(row, col)``. mask : 2D array Mask defining the local neighborhood of the corner used for the calculation of the central moment. Returns ------- orientations : (N, 1) array Orientations of corners in the range [-pi, pi]. References ---------- .. [1] Ethan Rublee, Vincent Rabaud, Kurt Konolige and Gary Bradski "ORB : An efficient alternative to SIFT and SURF" http://www.vision.cs.chubu.ac.jp/CV-R/pdf/Rublee_iccv2011.pdf .. [2] Paul L. Rosin, "Measuring Corner Properties" http://users.cs.cf.ac.uk/Paul.Rosin/corner2.pdf Examples -------- >>> from skimage.morphology import octagon >>> from skimage.feature import (corner_fast, corner_peaks, ... corner_orientations) >>> square = np.zeros((12, 12)) >>> square[3:9, 3:9] = 1 >>> square.astype(int) array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]) >>> corners = corner_peaks(corner_fast(square, 9), min_distance=1) >>> corners array([[3, 3], [3, 8], [8, 3], [8, 8]]) >>> orientations = corner_orientations(square, corners, octagon(3, 2)) >>> np.rad2deg(orientations) array([ 45., 135., -45., -135.]) """ image = _prepare_grayscale_input_2D(image) if mask.shape[0] % 2 != 1 or mask.shape[1] % 2 != 1: raise ValueError("Size of mask must be uneven.") cdef unsigned char[:, ::1] cmask = np.ascontiguousarray(mask != 0, dtype=np.uint8) cdef Py_ssize_t i, r, c, r0, c0 cdef Py_ssize_t mrows = mask.shape[0] cdef Py_ssize_t mcols = mask.shape[1] cdef Py_ssize_t mrows2 = (mrows - 1) / 2 cdef Py_ssize_t mcols2 = (mcols - 1) / 2 cdef double[:, :] cimage = np.pad(image, (mrows2, mcols2), mode='constant', constant_values=0) cdef double[:] orientations = np.zeros(corners.shape[0], dtype=np.double) cdef double curr_pixel cdef double m01, m10, m01_tmp with nogil: for i in range(corners.shape[0]): r0 = corners[i, 0] c0 = corners[i, 1] m01 = 0 m10 = 0 for r in range(mrows): m01_tmp = 0 for c in range(mcols): if cmask[r, c]: curr_pixel = cimage[r0 + r, c0 + c] m10 += curr_pixel * (c - mcols2) m01_tmp += curr_pixel m01 += m01_tmp * (r - mrows2) orientations[i] = atan2(m01, m10) return np.asarray(orientations) scikit-image-0.13.1/skimage/feature/match.py000066400000000000000000000051621316256236300206760ustar00rootroot00000000000000import numpy as np from scipy.spatial.distance import cdist def match_descriptors(descriptors1, descriptors2, metric=None, p=2, max_distance=np.inf, cross_check=True): """Brute-force matching of descriptors. For each descriptor in the first set this matcher finds the closest descriptor in the second set (and vice-versa in the case of enabled cross-checking). Parameters ---------- descriptors1 : (M, P) array Binary descriptors of size P about M keypoints in the first image. descriptors2 : (N, P) array Binary descriptors of size P about N keypoints in the second image. metric : {'euclidean', 'cityblock', 'minkowski', 'hamming', ...} The metric to compute the distance between two descriptors. See `scipy.spatial.distance.cdist` for all possible types. The hamming distance should be used for binary descriptors. By default the L2-norm is used for all descriptors of dtype float or double and the Hamming distance is used for binary descriptors automatically. p : int The p-norm to apply for ``metric='minkowski'``. max_distance : float Maximum allowed distance between descriptors of two keypoints in separate images to be regarded as a match. cross_check : bool If True, the matched keypoints are returned after cross checking i.e. a matched pair (keypoint1, keypoint2) is returned if keypoint2 is the best match for keypoint1 in second image and keypoint1 is the best match for keypoint2 in first image. Returns ------- matches : (Q, 2) array Indices of corresponding matches in first and second set of descriptors, where ``matches[:, 0]`` denote the indices in the first and ``matches[:, 1]`` the indices in the second set of descriptors. """ if descriptors1.shape[1] != descriptors2.shape[1]: raise ValueError("Descriptor length must equal.") if metric is None: if np.issubdtype(descriptors1.dtype, np.bool): metric = 'hamming' else: metric = 'euclidean' distances = cdist(descriptors1, descriptors2, metric=metric, p=p) indices1 = np.arange(descriptors1.shape[0]) indices2 = np.argmin(distances, axis=1) if cross_check: matches1 = np.argmin(distances, axis=0) mask = indices1 == matches1[indices2] indices1 = indices1[mask] indices2 = indices2[mask] matches = np.column_stack((indices1, indices2)) if max_distance < np.inf: matches = matches[distances[indices1, indices2] < max_distance] return matches scikit-image-0.13.1/skimage/feature/orb.py000066400000000000000000000312201316256236300203560ustar00rootroot00000000000000import numpy as np from ..feature.util import (FeatureDetector, DescriptorExtractor, _mask_border_keypoints, _prepare_grayscale_input_2D) from ..feature import (corner_fast, corner_orientations, corner_peaks, corner_harris) from ..transform import pyramid_gaussian from .._shared.utils import assert_nD from .orb_cy import _orb_loop OFAST_MASK = np.zeros((31, 31)) OFAST_UMAX = [15, 15, 15, 15, 14, 14, 14, 13, 13, 12, 11, 10, 9, 8, 6, 3] for i in range(-15, 16): for j in range(-OFAST_UMAX[abs(i)], OFAST_UMAX[abs(i)] + 1): OFAST_MASK[15 + j, 15 + i] = 1 class ORB(FeatureDetector, DescriptorExtractor): """Oriented FAST and rotated BRIEF feature detector and binary descriptor extractor. Parameters ---------- n_keypoints : int, optional Number of keypoints to be returned. The function will return the best `n_keypoints` according to the Harris corner response if more than `n_keypoints` are detected. If not, then all the detected keypoints are returned. fast_n : int, optional The `n` parameter in `skimage.feature.corner_fast`. Minimum number of consecutive pixels out of 16 pixels on the circle that should all be either brighter or darker w.r.t test-pixel. A point c on the circle is darker w.r.t test pixel p if ``Ic < Ip - threshold`` and brighter if ``Ic > Ip + threshold``. Also stands for the n in ``FAST-n`` corner detector. fast_threshold : float, optional The ``threshold`` parameter in ``feature.corner_fast``. Threshold used to decide whether the pixels on the circle are brighter, darker or similar w.r.t. the test pixel. Decrease the threshold when more corners are desired and vice-versa. harris_k : float, optional The `k` parameter in `skimage.feature.corner_harris`. Sensitivity factor to separate corners from edges, typically in range ``[0, 0.2]``. Small values of `k` result in detection of sharp corners. downscale : float, optional Downscale factor for the image pyramid. Default value 1.2 is chosen so that there are more dense scales which enable robust scale invariance for a subsequent feature description. n_scales : int, optional Maximum number of scales from the bottom of the image pyramid to extract the features from. Attributes ---------- keypoints : (N, 2) array Keypoint coordinates as ``(row, col)``. scales : (N, ) array Corresponding scales. orientations : (N, ) array Corresponding orientations in radians. responses : (N, ) array Corresponding Harris corner responses. descriptors : (Q, `descriptor_size`) array of dtype bool 2D array of binary descriptors of size `descriptor_size` for Q keypoints after filtering out border keypoints with value at an index ``(i, j)`` either being ``True`` or ``False`` representing the outcome of the intensity comparison for i-th keypoint on j-th decision pixel-pair. It is ``Q == np.sum(mask)``. References ---------- .. [1] Ethan Rublee, Vincent Rabaud, Kurt Konolige and Gary Bradski "ORB: An efficient alternative to SIFT and SURF" http://www.vision.cs.chubu.ac.jp/CV-R/pdf/Rublee_iccv2011.pdf Examples -------- >>> from skimage.feature import ORB, match_descriptors >>> img1 = np.zeros((100, 100)) >>> img2 = np.zeros_like(img1) >>> np.random.seed(1) >>> square = np.random.rand(20, 20) >>> img1[40:60, 40:60] = square >>> img2[53:73, 53:73] = square >>> detector_extractor1 = ORB(n_keypoints=5) >>> detector_extractor2 = ORB(n_keypoints=5) >>> detector_extractor1.detect_and_extract(img1) >>> detector_extractor2.detect_and_extract(img2) >>> matches = match_descriptors(detector_extractor1.descriptors, ... detector_extractor2.descriptors) >>> matches array([[0, 0], [1, 1], [2, 2], [3, 3], [4, 4]]) >>> detector_extractor1.keypoints[matches[:, 0]] array([[ 42., 40.], [ 47., 58.], [ 44., 40.], [ 59., 42.], [ 45., 44.]]) >>> detector_extractor2.keypoints[matches[:, 1]] array([[ 55., 53.], [ 60., 71.], [ 57., 53.], [ 72., 55.], [ 58., 57.]]) """ def __init__(self, downscale=1.2, n_scales=8, n_keypoints=500, fast_n=9, fast_threshold=0.08, harris_k=0.04): self.downscale = downscale self.n_scales = n_scales self.n_keypoints = n_keypoints self.fast_n = fast_n self.fast_threshold = fast_threshold self.harris_k = harris_k self.keypoints = None self.scales = None self.responses = None self.orientations = None self.descriptors = None def _build_pyramid(self, image): image = _prepare_grayscale_input_2D(image) return list(pyramid_gaussian(image, self.n_scales - 1, self.downscale)) def _detect_octave(self, octave_image): # Extract keypoints for current octave fast_response = corner_fast(octave_image, self.fast_n, self.fast_threshold) keypoints = corner_peaks(fast_response, min_distance=1) if len(keypoints) == 0: return (np.zeros((0, 2), dtype=np.double), np.zeros((0, ), dtype=np.double), np.zeros((0, ), dtype=np.double)) mask = _mask_border_keypoints(octave_image.shape, keypoints, distance=16) keypoints = keypoints[mask] orientations = corner_orientations(octave_image, keypoints, OFAST_MASK) harris_response = corner_harris(octave_image, method='k', k=self.harris_k) responses = harris_response[keypoints[:, 0], keypoints[:, 1]] return keypoints, orientations, responses def detect(self, image): """Detect oriented FAST keypoints along with the corresponding scale. Parameters ---------- image : 2D array Input image. """ assert_nD(image, 2) pyramid = self._build_pyramid(image) keypoints_list = [] orientations_list = [] scales_list = [] responses_list = [] for octave in range(len(pyramid)): octave_image = np.ascontiguousarray(pyramid[octave]) keypoints, orientations, responses = \ self._detect_octave(octave_image) keypoints_list.append(keypoints * self.downscale ** octave) orientations_list.append(orientations) scales_list.append(self.downscale ** octave * np.ones(keypoints.shape[0], dtype=np.intp)) responses_list.append(responses) keypoints = np.vstack(keypoints_list) orientations = np.hstack(orientations_list) scales = np.hstack(scales_list) responses = np.hstack(responses_list) if keypoints.shape[0] < self.n_keypoints: self.keypoints = keypoints self.scales = scales self.orientations = orientations self.responses = responses else: # Choose best n_keypoints according to Harris corner response best_indices = responses.argsort()[::-1][:self.n_keypoints] self.keypoints = keypoints[best_indices] self.scales = scales[best_indices] self.orientations = orientations[best_indices] self.responses = responses[best_indices] def _extract_octave(self, octave_image, keypoints, orientations): mask = _mask_border_keypoints(octave_image.shape, keypoints, distance=20) keypoints = np.array(keypoints[mask], dtype=np.intp, order='C', copy=False) orientations = np.array(orientations[mask], dtype=np.double, order='C', copy=False) descriptors = _orb_loop(octave_image, keypoints, orientations) return descriptors, mask def extract(self, image, keypoints, scales, orientations): """Extract rBRIEF binary descriptors for given keypoints in image. Note that the keypoints must be extracted using the same `downscale` and `n_scales` parameters. Additionally, if you want to extract both keypoints and descriptors you should use the faster `detect_and_extract`. Parameters ---------- image : 2D array Input image. keypoints : (N, 2) array Keypoint coordinates as ``(row, col)``. scales : (N, ) array Corresponding scales. orientations : (N, ) array Corresponding orientations in radians. """ assert_nD(image, 2) pyramid = self._build_pyramid(image) descriptors_list = [] mask_list = [] # Determine octaves from scales octaves = (np.log(scales) / np.log(self.downscale)).astype(np.intp) for octave in range(len(pyramid)): # Mask for all keypoints in current octave octave_mask = octaves == octave if np.sum(octave_mask) > 0: octave_image = np.ascontiguousarray(pyramid[octave]) octave_keypoints = keypoints[octave_mask] octave_keypoints /= self.downscale ** octave octave_orientations = orientations[octave_mask] descriptors, mask = self._extract_octave(octave_image, octave_keypoints, octave_orientations) descriptors_list.append(descriptors) mask_list.append(mask) self.descriptors = np.vstack(descriptors_list).view(np.bool) self.mask_ = np.hstack(mask_list) def detect_and_extract(self, image): """Detect oriented FAST keypoints and extract rBRIEF descriptors. Note that this is faster than first calling `detect` and then `extract`. Parameters ---------- image : 2D array Input image. """ assert_nD(image, 2) pyramid = self._build_pyramid(image) keypoints_list = [] responses_list = [] scales_list = [] orientations_list = [] descriptors_list = [] for octave in range(len(pyramid)): octave_image = np.ascontiguousarray(pyramid[octave]) keypoints, orientations, responses = \ self._detect_octave(octave_image) if len(keypoints) == 0: keypoints_list.append(keypoints) responses_list.append(responses) descriptors_list.append(np.zeros((0, 256), dtype=np.bool)) continue descriptors, mask = self._extract_octave(octave_image, keypoints, orientations) keypoints_list.append(keypoints[mask] * self.downscale ** octave) responses_list.append(responses[mask]) orientations_list.append(orientations[mask]) scales_list.append(self.downscale ** octave * np.ones(keypoints.shape[0], dtype=np.intp)) descriptors_list.append(descriptors) if len(scales_list) == 0: raise RuntimeError("ORB found no features. Try passing in an image " "containing greater intensity contrasts between " "adjacent pixels.") keypoints = np.vstack(keypoints_list) responses = np.hstack(responses_list) scales = np.hstack(scales_list) orientations = np.hstack(orientations_list) descriptors = np.vstack(descriptors_list).view(np.bool) if keypoints.shape[0] < self.n_keypoints: self.keypoints = keypoints self.scales = scales self.orientations = orientations self.responses = responses self.descriptors = descriptors else: # Choose best n_keypoints according to Harris corner response best_indices = responses.argsort()[::-1][:self.n_keypoints] self.keypoints = keypoints[best_indices] self.scales = scales[best_indices] self.orientations = orientations[best_indices] self.responses = responses[best_indices] self.descriptors = descriptors[best_indices] scikit-image-0.13.1/skimage/feature/orb_cy.pyx000066400000000000000000000033211316256236300212420ustar00rootroot00000000000000#cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False #cython: wraparound=False import os import numpy as np from .. import data_dir cimport numpy as cnp from libc.math cimport sin, cos from .._shared.interpolation cimport round POS = np.loadtxt(os.path.join(data_dir, "orb_descriptor_positions.txt"), dtype=np.int8) POS0 = np.ascontiguousarray(POS[:, :2]) POS1 = np.ascontiguousarray(POS[:, 2:]) def _orb_loop(double[:, ::1] image, Py_ssize_t[:, ::1] keypoints, double[:] orientations): cdef Py_ssize_t i, d, kr, kc, pr0, pr1, pc0, pc1, spr0, spc0, spr1, spc1 cdef int[:, ::1] steered_pos0, steered_pos1 cdef double angle cdef unsigned char[:, ::1] descriptors = \ np.zeros((keypoints.shape[0], POS.shape[0]), dtype=np.uint8) cdef signed char[:, ::1] cpos0 = POS0 cdef signed char[:, ::1] cpos1 = POS1 with nogil: for i in range(descriptors.shape[0]): angle = orientations[i] sin_a = sin(angle) cos_a = cos(angle) kr = keypoints[i, 0] kc = keypoints[i, 1] for j in range(descriptors.shape[1]): pr0 = cpos0[j, 0] pc0 = cpos0[j, 1] pr1 = cpos1[j, 0] pc1 = cpos1[j, 1] spr0 = round(sin_a * pr0 + cos_a * pc0) spc0 = round(cos_a * pr0 - sin_a * pc0) spr1 = round(sin_a * pr1 + cos_a * pc1) spc1 = round(cos_a * pr1 - sin_a * pc1) if image[kr + spr0, kc + spc0] < image[kr + spr1, kc + spc1]: descriptors[i, j] = True return np.asarray(descriptors) scikit-image-0.13.1/skimage/feature/peak.py000066400000000000000000000260251316256236300205230ustar00rootroot00000000000000import numpy as np import scipy.ndimage as ndi from ..segmentation import relabel_sequential from .. import measure from ..filters import rank_order def _get_high_intensity_peaks(image, mask, num_peaks): """ Return the highest intensity peak coordinates. """ # get coordinates of peaks coord = np.nonzero(mask) # select num_peaks peaks if len(coord[0]) > num_peaks: intensities = image[coord] idx_maxsort = np.argsort(intensities) coord = np.transpose(coord)[idx_maxsort][-num_peaks:] else: coord = np.column_stack(coord) # Higest peak first return coord[::-1] def peak_local_max(image, min_distance=1, threshold_abs=None, threshold_rel=None, exclude_border=True, indices=True, num_peaks=np.inf, footprint=None, labels=None, num_peaks_per_label=np.inf): """Find peaks in an image as coordinate list or boolean mask. Peaks are the local maxima in a region of `2 * min_distance + 1` (i.e. peaks are separated by at least `min_distance`). If peaks are flat (i.e. multiple adjacent pixels have identical intensities), the coordinates of all such pixels are returned. If both `threshold_abs` and `threshold_rel` are provided, the maximum of the two is chosen as the minimum intensity threshold of peaks. Parameters ---------- image : ndarray Input image. min_distance : int, optional Minimum number of pixels separating peaks in a region of `2 * min_distance + 1` (i.e. peaks are separated by at least `min_distance`). To find the maximum number of peaks, use `min_distance=1`. threshold_abs : float, optional Minimum intensity of peaks. By default, the absolute threshold is the minimum intensity of the image. threshold_rel : float, optional Minimum intensity of peaks, calculated as `max(image) * threshold_rel`. exclude_border : int, optional If nonzero, `exclude_border` excludes peaks from within `exclude_border`-pixels of the border of the image. indices : bool, optional If True, the output will be an array representing peak coordinates. If False, the output will be a boolean array shaped as `image.shape` with peaks present at True elements. num_peaks : int, optional Maximum number of peaks. When the number of peaks exceeds `num_peaks`, return `num_peaks` peaks based on highest peak intensity. footprint : ndarray of bools, optional If provided, `footprint == 1` represents the local region within which to search for peaks at every point in `image`. Overrides `min_distance` (also for `exclude_border`). labels : ndarray of ints, optional If provided, each unique region `labels == value` represents a unique region to search for peaks. Zero is reserved for background. num_peaks_per_label : int, optional Maximum number of peaks for each label. Returns ------- output : ndarray or ndarray of bools * If `indices = True` : (row, column, ...) coordinates of peaks. * If `indices = False` : Boolean array shaped like `image`, with peaks represented by True values. Notes ----- The peak local maximum function returns the coordinates of local peaks (maxima) in an image. A maximum filter is used for finding local maxima. This operation dilates the original image. After comparison of the dilated and original image, this function returns the coordinates or a mask of the peaks where the dilated image equals the original image. Examples -------- >>> img1 = np.zeros((7, 7)) >>> img1[3, 4] = 1 >>> img1[3, 2] = 1.5 >>> img1 array([[ 0. , 0. , 0. , 0. , 0. , 0. , 0. ], [ 0. , 0. , 0. , 0. , 0. , 0. , 0. ], [ 0. , 0. , 0. , 0. , 0. , 0. , 0. ], [ 0. , 0. , 1.5, 0. , 1. , 0. , 0. ], [ 0. , 0. , 0. , 0. , 0. , 0. , 0. ], [ 0. , 0. , 0. , 0. , 0. , 0. , 0. ], [ 0. , 0. , 0. , 0. , 0. , 0. , 0. ]]) >>> peak_local_max(img1, min_distance=1) array([[3, 4], [3, 2]]) >>> peak_local_max(img1, min_distance=2) array([[3, 2]]) >>> img2 = np.zeros((20, 20, 20)) >>> img2[10, 10, 10] = 1 >>> peak_local_max(img2, exclude_border=0) array([[10, 10, 10]]) """ if type(exclude_border) == bool: exclude_border = min_distance if exclude_border else 0 out = np.zeros_like(image, dtype=np.bool) # In the case of labels, recursively build and return an output # operating on each label separately if labels is not None: label_values = np.unique(labels) # Reorder label values to have consecutive integers (no gaps) if np.any(np.diff(label_values) != 1): mask = labels >= 1 labels[mask] = 1 + rank_order(labels[mask])[0].astype(labels.dtype) labels = labels.astype(np.int32) # New values for new ordering label_values = np.unique(labels) for label in label_values[label_values != 0]: maskim = (labels == label) out += peak_local_max(image * maskim, min_distance=min_distance, threshold_abs=threshold_abs, threshold_rel=threshold_rel, exclude_border=exclude_border, indices=False, num_peaks=num_peaks_per_label, footprint=footprint, labels=None) # Select highest intensities (num_peaks) coordinates = _get_high_intensity_peaks(image, out, num_peaks) if indices is True: return coordinates else: nd_indices = tuple(coordinates.T) out[nd_indices] = True return out if np.all(image == image.flat[0]): if indices is True: return np.empty((0, 2), np.int) else: return out # Non maximum filter if footprint is not None: image_max = ndi.maximum_filter(image, footprint=footprint, mode='constant') else: size = 2 * min_distance + 1 image_max = ndi.maximum_filter(image, size=size, mode='constant') mask = image == image_max if exclude_border: # zero out the image borders for i in range(mask.ndim): mask = mask.swapaxes(0, i) remove = (footprint.shape[i] if footprint is not None else 2 * exclude_border) mask[:remove // 2] = mask[-remove // 2:] = False mask = mask.swapaxes(0, i) # find top peak candidates above a threshold thresholds = [] if threshold_abs is None: threshold_abs = image.min() thresholds.append(threshold_abs) if threshold_rel is not None: thresholds.append(threshold_rel * image.max()) if thresholds: mask &= image > max(thresholds) # Select highest intensities (num_peaks) coordinates = _get_high_intensity_peaks(image, mask, num_peaks) if indices is True: return coordinates else: nd_indices = tuple(coordinates.T) out[nd_indices] = True return out def _prominent_peaks(image, min_xdistance=1, min_ydistance=1, threshold=None, num_peaks=np.inf): """Return peaks with non-maximum suppression. Identifies most prominent features separated by certain distances. Non-maximum suppression with different sizes is applied separately in the first and second dimension of the image to identify peaks. Parameters ---------- image : (M, N) ndarray Input image. min_xdistance : int Minimum distance separating features in the x dimension. min_ydistance : int Minimum distance separating features in the y dimension. threshold : float Minimum intensity of peaks. Default is `0.5 * max(image)`. num_peaks : int Maximum number of peaks. When the number of peaks exceeds `num_peaks`, return `num_peaks` coordinates based on peak intensity. Returns ------- intensity, xcoords, ycoords : tuple of array Peak intensity values, x and y indices. """ img = image.copy() rows, cols = img.shape if threshold is None: threshold = 0.5 * np.max(img) ycoords_size = 2 * min_ydistance + 1 xcoords_size = 2 * min_xdistance + 1 img_max = ndi.maximum_filter1d(img, size=ycoords_size, axis=0, mode='constant', cval=0) img_max = ndi.maximum_filter1d(img_max, size=xcoords_size, axis=1, mode='constant', cval=0) mask = (img == img_max) img *= mask img_t = img > threshold label_img = measure.label(img_t) props = measure.regionprops(label_img, img_max) # Sort the list of peaks by intensity, not left-right, so larger peaks # in Hough space cannot be arbitrarily suppressed by smaller neighbors props = sorted(props, key=lambda x: x.max_intensity)[::-1] coords = np.array([np.round(p.centroid) for p in props], dtype=int) img_peaks = [] ycoords_peaks = [] xcoords_peaks = [] # relative coordinate grid for local neighbourhood suppression ycoords_ext, xcoords_ext = np.mgrid[-min_ydistance:min_ydistance + 1, -min_xdistance:min_xdistance + 1] for ycoords_idx, xcoords_idx in coords: accum = img_max[ycoords_idx, xcoords_idx] if accum > threshold: # absolute coordinate grid for local neighbourhood suppression ycoords_nh = ycoords_idx + ycoords_ext xcoords_nh = xcoords_idx + xcoords_ext # no reflection for distance neighbourhood ycoords_in = np.logical_and(ycoords_nh > 0, ycoords_nh < rows) ycoords_nh = ycoords_nh[ycoords_in] xcoords_nh = xcoords_nh[ycoords_in] # reflect xcoords and assume xcoords are continuous, # e.g. for angles: # (..., 88, 89, -90, -89, ..., 89, -90, -89, ...) xcoords_low = xcoords_nh < 0 ycoords_nh[xcoords_low] = rows - ycoords_nh[xcoords_low] xcoords_nh[xcoords_low] += cols xcoords_high = xcoords_nh >= cols ycoords_nh[xcoords_high] = rows - ycoords_nh[xcoords_high] xcoords_nh[xcoords_high] -= cols # suppress neighbourhood img_max[ycoords_nh, xcoords_nh] = 0 # add current feature to peaks img_peaks.append(accum) ycoords_peaks.append(ycoords_idx) xcoords_peaks.append(xcoords_idx) img_peaks = np.array(img_peaks) ycoords_peaks = np.array(ycoords_peaks) xcoords_peaks = np.array(xcoords_peaks) if num_peaks < len(img_peaks): idx_maxsort = np.argsort(img_peaks)[::-1][:num_peaks] img_peaks = img_peaks[idx_maxsort] ycoords_peaks = ycoords_peaks[idx_maxsort] xcoords_peaks = xcoords_peaks[idx_maxsort] return img_peaks, xcoords_peaks, ycoords_peaks scikit-image-0.13.1/skimage/feature/register_translation.py000066400000000000000000000233511316256236300240440ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ """ Port of Manuel Guizar's code from: http://www.mathworks.com/matlabcentral/fileexchange/18401-efficient-subpixel-image-registration-by-cross-correlation """ import numpy as np def _upsampled_dft(data, upsampled_region_size, upsample_factor=1, axis_offsets=None): """ Upsampled DFT by matrix multiplication. This code is intended to provide the same result as if the following operations were performed: - Embed the array "data" in an array that is ``upsample_factor`` times larger in each dimension. ifftshift to bring the center of the image to (1,1). - Take the FFT of the larger array. - Extract an ``[upsampled_region_size]`` region of the result, starting with the ``[axis_offsets+1]`` element. It achieves this result by computing the DFT in the output array without the need to zeropad. Much faster and memory efficient than the zero-padded FFT approach if ``upsampled_region_size`` is much smaller than ``data.size * upsample_factor``. Parameters ---------- data : 2D ndarray The input data array (DFT of original data) to upsample. upsampled_region_size : integer or tuple of integers, optional The size of the region to be sampled. If one integer is provided, it is duplicated up to the dimensionality of ``data``. upsample_factor : integer, optional The upsampling factor. Defaults to 1. axis_offsets : tuple of integers, optional The offsets of the region to be sampled. Defaults to None (uses image center) Returns ------- output : 2D ndarray The upsampled DFT of the specified region. """ # if people pass in an integer, expand it to a list of equal-sized sections if not hasattr(upsampled_region_size, "__iter__"): upsampled_region_size = [upsampled_region_size, ] * data.ndim else: if len(upsampled_region_size) != data.ndim: raise ValueError("shape of upsampled region sizes must be equal " "to input data's number of dimensions.") if axis_offsets is None: axis_offsets = [0, ] * data.ndim else: if len(axis_offsets) != data.ndim: raise ValueError("number of axis offsets must be equal to input " "data's number of dimensions.") col_kernel = np.exp( (-1j * 2 * np.pi / (data.shape[1] * upsample_factor)) * (np.fft.ifftshift(np.arange(data.shape[1]))[:, None] - np.floor(data.shape[1] / 2)).dot( np.arange(upsampled_region_size[1])[None, :] - axis_offsets[1]) ) row_kernel = np.exp( (-1j * 2 * np.pi / (data.shape[0] * upsample_factor)) * (np.arange(upsampled_region_size[0])[:, None] - axis_offsets[0]).dot( np.fft.ifftshift(np.arange(data.shape[0]))[None, :] - np.floor(data.shape[0] / 2)) ) return row_kernel.dot(data).dot(col_kernel) def _compute_phasediff(cross_correlation_max): """ Compute global phase difference between the two images (should be zero if images are non-negative). Parameters ---------- cross_correlation_max : complex The complex value of the cross correlation at its maximum point. """ return np.arctan2(cross_correlation_max.imag, cross_correlation_max.real) def _compute_error(cross_correlation_max, src_amp, target_amp): """ Compute RMS error metric between ``src_image`` and ``target_image``. Parameters ---------- cross_correlation_max : complex The complex value of the cross correlation at its maximum point. src_amp : float The normalized average image intensity of the source image target_amp : float The normalized average image intensity of the target image """ error = 1.0 - cross_correlation_max * cross_correlation_max.conj() /\ (src_amp * target_amp) return np.sqrt(np.abs(error)) def register_translation(src_image, target_image, upsample_factor=1, space="real"): """ Efficient subpixel image translation registration by cross-correlation. This code gives the same precision as the FFT upsampled cross-correlation in a fraction of the computation time and with reduced memory requirements. It obtains an initial estimate of the cross-correlation peak by an FFT and then refines the shift estimation by upsampling the DFT only in a small neighborhood of that estimate by means of a matrix-multiply DFT. Parameters ---------- src_image : ndarray Reference image. target_image : ndarray Image to register. Must be same dimensionality as ``src_image``. upsample_factor : int, optional Upsampling factor. Images will be registered to within ``1 / upsample_factor`` of a pixel. For example ``upsample_factor == 20`` means the images will be registered within 1/20th of a pixel. Default is 1 (no upsampling) space : string, one of "real" or "fourier", optional Defines how the algorithm interprets input data. "real" means data will be FFT'd to compute the correlation, while "fourier" data will bypass FFT of input data. Case insensitive. Returns ------- shifts : ndarray Shift vector (in pixels) required to register ``target_image`` with ``src_image``. Axis ordering is consistent with numpy (e.g. Z, Y, X) error : float Translation invariant normalized RMS error between ``src_image`` and ``target_image``. phasediff : float Global phase difference between the two images (should be zero if images are non-negative). References ---------- .. [1] Manuel Guizar-Sicairos, Samuel T. Thurman, and James R. Fienup, "Efficient subpixel image registration algorithms," Optics Letters 33, 156-158 (2008). DOI:10.1364/OL.33.000156 .. [2] James R. Fienup, "Invariant error metrics for image reconstruction" Optics Letters 36, 8352-8357 (1997). DOI:10.1364/AO.36.008352 """ # images must be the same shape if src_image.shape != target_image.shape: raise ValueError("Error: images must be same size for " "register_translation") # only 2D data makes sense right now if src_image.ndim != 2 and upsample_factor > 1: raise NotImplementedError("Error: register_translation only supports " "subpixel registration for 2D images") # assume complex data is already in Fourier space if space.lower() == 'fourier': src_freq = src_image target_freq = target_image # real data needs to be fft'd. elif space.lower() == 'real': src_image = np.array(src_image, dtype=np.complex128, copy=False) target_image = np.array(target_image, dtype=np.complex128, copy=False) src_freq = np.fft.fftn(src_image) target_freq = np.fft.fftn(target_image) else: raise ValueError("Error: register_translation only knows the \"real\" " "and \"fourier\" values for the ``space`` argument.") # Whole-pixel shift - Compute cross-correlation by an IFFT shape = src_freq.shape image_product = src_freq * target_freq.conj() cross_correlation = np.fft.ifftn(image_product) # Locate maximum maxima = np.unravel_index(np.argmax(np.abs(cross_correlation)), cross_correlation.shape) midpoints = np.array([np.fix(axis_size / 2) for axis_size in shape]) shifts = np.array(maxima, dtype=np.float64) shifts[shifts > midpoints] -= np.array(shape)[shifts > midpoints] if upsample_factor == 1: src_amp = np.sum(np.abs(src_freq) ** 2) / src_freq.size target_amp = np.sum(np.abs(target_freq) ** 2) / target_freq.size CCmax = cross_correlation.max() # If upsampling > 1, then refine estimate with matrix multiply DFT else: # Initial shift estimate in upsampled grid shifts = np.round(shifts * upsample_factor) / upsample_factor upsampled_region_size = np.ceil(upsample_factor * 1.5) # Center of output array at dftshift + 1 dftshift = np.fix(upsampled_region_size / 2.0) upsample_factor = np.array(upsample_factor, dtype=np.float64) normalization = (src_freq.size * upsample_factor ** 2) # Matrix multiply DFT around the current shift estimate sample_region_offset = dftshift - shifts*upsample_factor cross_correlation = _upsampled_dft(image_product.conj(), upsampled_region_size, upsample_factor, sample_region_offset).conj() cross_correlation /= normalization # Locate maximum and map back to original pixel grid maxima = np.array(np.unravel_index( np.argmax(np.abs(cross_correlation)), cross_correlation.shape), dtype=np.float64) maxima -= dftshift shifts = shifts + maxima / upsample_factor CCmax = cross_correlation.max() src_amp = _upsampled_dft(src_freq * src_freq.conj(), 1, upsample_factor)[0, 0] src_amp /= normalization target_amp = _upsampled_dft(target_freq * target_freq.conj(), 1, upsample_factor)[0, 0] target_amp /= normalization # If its only one row or column the shift along that dimension has no # effect. We set to zero. for dim in range(src_freq.ndim): if shape[dim] == 1: shifts[dim] = 0 return shifts, _compute_error(CCmax, src_amp, target_amp),\ _compute_phasediff(CCmax) scikit-image-0.13.1/skimage/feature/setup.py000066400000000000000000000041241316256236300207370ustar00rootroot00000000000000#!/usr/bin/env python import os from skimage._build import cython base_path = os.path.abspath(os.path.dirname(__file__)) def configuration(parent_package='', top_path=None): from numpy.distutils.misc_util import Configuration, get_numpy_include_dirs config = Configuration('feature', parent_package, top_path) config.add_data_dir('tests') cython(['corner_cy.pyx'], working_path=base_path) cython(['censure_cy.pyx'], working_path=base_path) cython(['orb_cy.pyx'], working_path=base_path) cython(['brief_cy.pyx'], working_path=base_path) cython(['_texture.pyx'], working_path=base_path) cython(['_hessian_det_appx.pyx'], working_path=base_path) cython(['_hoghistogram.pyx'], working_path=base_path) config.add_extension('corner_cy', sources=['corner_cy.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension('censure_cy', sources=['censure_cy.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension('orb_cy', sources=['orb_cy.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension('brief_cy', sources=['brief_cy.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension('_texture', sources=['_texture.c'], include_dirs=[get_numpy_include_dirs(), '../_shared']) config.add_extension('_hessian_det_appx', sources=['_hessian_det_appx.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension('_hoghistogram', sources=['_hoghistogram.c'], include_dirs=[get_numpy_include_dirs(), '../_shared']) return config if __name__ == '__main__': from numpy.distutils.core import setup setup(maintainer='scikit-image Developers', author='scikit-image Developers', maintainer_email='scikit-image@python.org', description='Features', url='https://github.com/scikit-image/scikit-image', license='SciPy License (BSD Style)', **(configuration(top_path='').todict()) ) scikit-image-0.13.1/skimage/feature/template.py000066400000000000000000000146571316256236300214260ustar00rootroot00000000000000from __future__ import division import numpy as np from scipy.signal import fftconvolve from .._shared.utils import assert_nD def _window_sum_2d(image, window_shape): window_sum = np.cumsum(image, axis=0) window_sum = (window_sum[window_shape[0]:-1] - window_sum[:-window_shape[0] - 1]) window_sum = np.cumsum(window_sum, axis=1) window_sum = (window_sum[:, window_shape[1]:-1] - window_sum[:, :-window_shape[1] - 1]) return window_sum def _window_sum_3d(image, window_shape): window_sum = _window_sum_2d(image, window_shape) window_sum = np.cumsum(window_sum, axis=2) window_sum = (window_sum[:, :, window_shape[2]:-1] - window_sum[:, :, :-window_shape[2] - 1]) return window_sum def match_template(image, template, pad_input=False, mode='constant', constant_values=0): """Match a template to a 2-D or 3-D image using normalized correlation. The output is an array with values between -1.0 and 1.0. The value at a given position corresponds to the correlation coefficient between the image and the template. For `pad_input=True` matches correspond to the center and otherwise to the top-left corner of the template. To find the best match you must search for peaks in the response (output) image. Parameters ---------- image : (M, N[, D]) array 2-D or 3-D input image. template : (m, n[, d]) array Template to locate. It must be `(m <= M, n <= N[, d <= D])`. pad_input : bool If True, pad `image` so that output is the same size as the image, and output values correspond to the template center. Otherwise, the output is an array with shape `(M - m + 1, N - n + 1)` for an `(M, N)` image and an `(m, n)` template, and matches correspond to origin (top-left corner) of the template. mode : see `numpy.pad`, optional Padding mode. constant_values : see `numpy.pad`, optional Constant values used in conjunction with ``mode='constant'``. Returns ------- output : array Response image with correlation coefficients. Notes ----- Details on the cross-correlation are presented in [1]_. This implementation uses FFT convolutions of the image and the template. Reference [2]_ presents similar derivations but the approximation presented in this reference is not used in our implementation. References ---------- .. [1] J. P. Lewis, "Fast Normalized Cross-Correlation", Industrial Light and Magic. .. [2] Briechle and Hanebeck, "Template Matching using Fast Normalized Cross Correlation", Proceedings of the SPIE (2001). DOI:10.1117/12.421129 Examples -------- >>> template = np.zeros((3, 3)) >>> template[1, 1] = 1 >>> template array([[ 0., 0., 0.], [ 0., 1., 0.], [ 0., 0., 0.]]) >>> image = np.zeros((6, 6)) >>> image[1, 1] = 1 >>> image[4, 4] = -1 >>> image array([[ 0., 0., 0., 0., 0., 0.], [ 0., 1., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., -1., 0.], [ 0., 0., 0., 0., 0., 0.]]) >>> result = match_template(image, template) >>> np.round(result, 3) array([[ 1. , -0.125, 0. , 0. ], [-0.125, -0.125, 0. , 0. ], [ 0. , 0. , 0.125, 0.125], [ 0. , 0. , 0.125, -1. ]]) >>> result = match_template(image, template, pad_input=True) >>> np.round(result, 3) array([[-0.125, -0.125, -0.125, 0. , 0. , 0. ], [-0.125, 1. , -0.125, 0. , 0. , 0. ], [-0.125, -0.125, -0.125, 0. , 0. , 0. ], [ 0. , 0. , 0. , 0.125, 0.125, 0.125], [ 0. , 0. , 0. , 0.125, -1. , 0.125], [ 0. , 0. , 0. , 0.125, 0.125, 0.125]]) """ assert_nD(image, (2, 3)) if image.ndim < template.ndim: raise ValueError("Dimensionality of template must be less than or " "equal to the dimensionality of image.") if np.any(np.less(image.shape, template.shape)): raise ValueError("Image must be larger than template.") image_shape = image.shape image = np.array(image, dtype=np.float64, copy=False) pad_width = tuple((width, width) for width in template.shape) if mode == 'constant': image = np.pad(image, pad_width=pad_width, mode=mode, constant_values=constant_values) else: image = np.pad(image, pad_width=pad_width, mode=mode) # Use special case for 2-D images for much better performance in # computation of integral images if image.ndim == 2: image_window_sum = _window_sum_2d(image, template.shape) image_window_sum2 = _window_sum_2d(image ** 2, template.shape) elif image.ndim == 3: image_window_sum = _window_sum_3d(image, template.shape) image_window_sum2 = _window_sum_3d(image ** 2, template.shape) template_mean = template.mean() template_volume = np.prod(template.shape) template_ssd = np.sum((template - template_mean) ** 2) if image.ndim == 2: xcorr = fftconvolve(image, template[::-1, ::-1], mode="valid")[1:-1, 1:-1] elif image.ndim == 3: xcorr = fftconvolve(image, template[::-1, ::-1, ::-1], mode="valid")[1:-1, 1:-1, 1:-1] numerator = xcorr - image_window_sum * template_mean denominator = image_window_sum2 np.multiply(image_window_sum, image_window_sum, out=image_window_sum) np.divide(image_window_sum, template_volume, out=image_window_sum) denominator -= image_window_sum denominator *= template_ssd np.maximum(denominator, 0, out=denominator) # sqrt of negative number not allowed np.sqrt(denominator, out=denominator) response = np.zeros_like(xcorr, dtype=np.float64) # avoid zero-division mask = denominator > np.finfo(np.float64).eps response[mask] = numerator[mask] / denominator[mask] slices = [] for i in range(template.ndim): if pad_input: d0 = (template.shape[i] - 1) // 2 d1 = d0 + image_shape[i] else: d0 = template.shape[i] - 1 d1 = d0 + image_shape[i] - template.shape[i] + 1 slices.append(slice(d0, d1)) return response[slices] scikit-image-0.13.1/skimage/feature/tests/000077500000000000000000000000001316256236300203665ustar00rootroot00000000000000scikit-image-0.13.1/skimage/feature/tests/__init__.py000066400000000000000000000001771316256236300225040ustar00rootroot00000000000000from ..._shared.testing import setup_test, teardown_test def setup(): setup_test() def teardown(): teardown_test() scikit-image-0.13.1/skimage/feature/tests/test_blob.py000066400000000000000000000117761316256236300227310ustar00rootroot00000000000000import numpy as np from skimage.draw import circle from skimage.feature import blob_dog, blob_log, blob_doh import math from numpy.testing import assert_raises def test_blob_dog(): r2 = math.sqrt(2) img = np.ones((512, 512)) img3 = np.ones((5, 5, 5)) xs, ys = circle(400, 130, 5) img[xs, ys] = 255 xs, ys = circle(100, 300, 25) img[xs, ys] = 255 xs, ys = circle(200, 350, 45) img[xs, ys] = 255 blobs = blob_dog(img, min_sigma=5, max_sigma=50) radius = lambda x: r2 * x[2] s = sorted(blobs, key=radius) thresh = 5 b = s[0] assert abs(b[0] - 400) <= thresh assert abs(b[1] - 130) <= thresh assert abs(radius(b) - 5) <= thresh b = s[1] assert abs(b[0] - 100) <= thresh assert abs(b[1] - 300) <= thresh assert abs(radius(b) - 25) <= thresh b = s[2] assert abs(b[0] - 200) <= thresh assert abs(b[1] - 350) <= thresh assert abs(radius(b) - 45) <= thresh assert_raises(ValueError, blob_dog, img3) # Testing no peaks img_empty = np.zeros((100,100)) assert blob_dog(img_empty).size == 0 def test_blob_log(): r2 = math.sqrt(2) img = np.ones((256, 256)) img3 = np.ones((5, 5, 5)) xs, ys = circle(200, 65, 5) img[xs, ys] = 255 xs, ys = circle(80, 25, 15) img[xs, ys] = 255 xs, ys = circle(50, 150, 25) img[xs, ys] = 255 xs, ys = circle(100, 175, 30) img[xs, ys] = 255 blobs = blob_log(img, min_sigma=5, max_sigma=20, threshold=1) radius = lambda x: r2 * x[2] s = sorted(blobs, key=radius) thresh = 3 b = s[0] assert abs(b[0] - 200) <= thresh assert abs(b[1] - 65) <= thresh assert abs(radius(b) - 5) <= thresh b = s[1] assert abs(b[0] - 80) <= thresh assert abs(b[1] - 25) <= thresh assert abs(radius(b) - 15) <= thresh b = s[2] assert abs(b[0] - 50) <= thresh assert abs(b[1] - 150) <= thresh assert abs(radius(b) - 25) <= thresh b = s[3] assert abs(b[0] - 100) <= thresh assert abs(b[1] - 175) <= thresh assert abs(radius(b) - 30) <= thresh # Testing log scale blobs = blob_log( img, min_sigma=5, max_sigma=20, threshold=1, log_scale=True) b = s[0] assert abs(b[0] - 200) <= thresh assert abs(b[1] - 65) <= thresh assert abs(radius(b) - 5) <= thresh b = s[1] assert abs(b[0] - 80) <= thresh assert abs(b[1] - 25) <= thresh assert abs(radius(b) - 15) <= thresh b = s[2] assert abs(b[0] - 50) <= thresh assert abs(b[1] - 150) <= thresh assert abs(radius(b) - 25) <= thresh b = s[3] assert abs(b[0] - 100) <= thresh assert abs(b[1] - 175) <= thresh assert abs(radius(b) - 30) <= thresh assert_raises(ValueError, blob_log, img3) # Testing no peaks img_empty = np.zeros((100,100)) assert blob_log(img_empty).size == 0 def test_blob_doh(): img = np.ones((512, 512), dtype=np.uint8) img3 = np.ones((5, 5, 5)) xs, ys = circle(400, 130, 20) img[xs, ys] = 255 xs, ys = circle(460, 50, 30) img[xs, ys] = 255 xs, ys = circle(100, 300, 40) img[xs, ys] = 255 xs, ys = circle(200, 350, 50) img[xs, ys] = 255 blobs = blob_doh( img, min_sigma=1, max_sigma=60, num_sigma=10, threshold=.05) radius = lambda x: x[2] s = sorted(blobs, key=radius) thresh = 4 b = s[0] assert abs(b[0] - 400) <= thresh assert abs(b[1] - 130) <= thresh assert abs(radius(b) - 20) <= thresh b = s[1] assert abs(b[0] - 460) <= thresh assert abs(b[1] - 50) <= thresh assert abs(radius(b) - 30) <= thresh b = s[2] assert abs(b[0] - 100) <= thresh assert abs(b[1] - 300) <= thresh assert abs(radius(b) - 40) <= thresh b = s[3] assert abs(b[0] - 200) <= thresh assert abs(b[1] - 350) <= thresh assert abs(radius(b) - 50) <= thresh # Testing log scale blobs = blob_doh( img, min_sigma=1, max_sigma=60, num_sigma=10, log_scale=True, threshold=.05) b = s[0] assert abs(b[0] - 400) <= thresh assert abs(b[1] - 130) <= thresh assert abs(radius(b) - 20) <= thresh b = s[1] assert abs(b[0] - 460) <= thresh assert abs(b[1] - 50) <= thresh assert abs(radius(b) - 30) <= thresh b = s[2] assert abs(b[0] - 100) <= thresh assert abs(b[1] - 300) <= thresh assert abs(radius(b) - 40) <= thresh b = s[3] assert abs(b[0] - 200) <= thresh assert abs(b[1] - 350) <= thresh assert abs(radius(b) - 50) <= thresh assert_raises(ValueError, blob_doh, img3) # Testing no peaks img_empty = np.zeros((100,100)) assert blob_doh(img_empty).size == 0 def test_blob_overlap(): img = np.ones((512, 512), dtype=np.uint8) xs, ys = circle(100, 100, 20) img[xs, ys] = 255 xs, ys = circle(120, 100, 30) img[xs, ys] = 255 blobs = blob_doh( img, min_sigma=1, max_sigma=60, num_sigma=10, threshold=.05) assert len(blobs) == 1 scikit-image-0.13.1/skimage/feature/tests/test_brief.py000066400000000000000000000061151316256236300230710ustar00rootroot00000000000000import numpy as np from numpy.testing import assert_array_equal, assert_raises from skimage import data from skimage.feature import BRIEF, corner_peaks, corner_harris from skimage._shared.testing import test_parallel def test_color_image_unsupported_error(): """Brief descriptors can be evaluated on gray-scale images only.""" img = np.zeros((20, 20, 3)) keypoints = np.asarray([[7, 5], [11, 13]]) assert_raises(ValueError, BRIEF().extract, img, keypoints) def test_normal_mode(): """Verify the computed BRIEF descriptors with expected for normal mode.""" img = data.coins() keypoints = corner_peaks(corner_harris(img), min_distance=5, threshold_abs=0, threshold_rel=0.1) extractor = BRIEF(descriptor_size=8, sigma=2) extractor.extract(img, keypoints[:8]) expected = np.array([[False, True, False, False, True, False, True, False], [ True, False, True, True, False, True, False, False], [ True, False, False, True, False, True, False, True], [ True, True, True, True, False, True, False, True], [ True, True, True, False, False, True, True, True], [False, False, False, False, True, False, False, False], [False, True, False, False, True, False, True, False], [False, False, False, False, False, False, False, False]], dtype=bool) assert_array_equal(extractor.descriptors, expected) def test_uniform_mode(): """Verify the computed BRIEF descriptors with expected for uniform mode.""" img = data.coins() keypoints = corner_peaks(corner_harris(img), min_distance=5, threshold_abs=0, threshold_rel=0.1) extractor = BRIEF(descriptor_size=8, sigma=2, mode='uniform') extractor.extract(img, keypoints[:8]) expected = np.array([[False, False, False, True, True, True, False, False], [ True, True, True, False, True, False, False, True], [ True, True, True, False, True, True, False, True], [ True, True, True, True, False, True, False, True], [ True, True, True, True, True, True, False, False], [ True, True, True, True, True, True, True, True], [False, False, False, True, True, True, True, True], [False, True, False, True, False, True, True, True]], dtype=bool) assert_array_equal(extractor.descriptors, expected) def test_unsupported_mode(): assert_raises(ValueError, BRIEF, mode='foobar') def test_border(): img = np.zeros((100, 100)) keypoints = np.array([[1, 1], [20, 20], [50, 50], [80, 80]]) extractor = BRIEF(patch_size=41) extractor.extract(img, keypoints) assert extractor.descriptors.shape[0] == 3 assert_array_equal(extractor.mask, (False, True, True, True)) if __name__ == '__main__': from numpy import testing testing.run_module_suite() scikit-image-0.13.1/skimage/feature/tests/test_canny.py000066400000000000000000000106231316256236300231110ustar00rootroot00000000000000import unittest import numpy as np from numpy.testing import assert_equal from scipy.ndimage import binary_dilation, binary_erosion import skimage.feature as F from skimage import data, img_as_float class TestCanny(unittest.TestCase): def test_00_00_zeros(self): '''Test that the Canny filter finds no points for a blank field''' result = F.canny(np.zeros((20, 20)), 4, 0, 0, np.ones((20, 20), bool)) self.assertFalse(np.any(result)) def test_00_01_zeros_mask(self): '''Test that the Canny filter finds no points in a masked image''' result = (F.canny(np.random.uniform(size=(20, 20)), 4, 0, 0, np.zeros((20, 20), bool))) self.assertFalse(np.any(result)) def test_01_01_circle(self): '''Test that the Canny filter finds the outlines of a circle''' i, j = np.mgrid[-200:200, -200:200].astype(float) / 200 c = np.abs(np.sqrt(i * i + j * j) - .5) < .02 result = F.canny(c.astype(float), 4, 0, 0, np.ones(c.shape, bool)) # # erode and dilate the circle to get rings that should contain the # outlines # cd = binary_dilation(c, iterations=3) ce = binary_erosion(c, iterations=3) cde = np.logical_and(cd, np.logical_not(ce)) self.assertTrue(np.all(cde[result])) # # The circle has a radius of 100. There are two rings here, one # for the inside edge and one for the outside. So that's # 100 * 2 * 2 * 3 for those places where pi is still 3. # The edge contains both pixels if there's a tie, so we # bump the count a little. point_count = np.sum(result) self.assertTrue(point_count > 1200) self.assertTrue(point_count < 1600) def test_01_02_circle_with_noise(self): '''Test that the Canny filter finds the circle outlines in a noisy image''' np.random.seed(0) i, j = np.mgrid[-200:200, -200:200].astype(float) / 200 c = np.abs(np.sqrt(i * i + j * j) - .5) < .02 cf = c.astype(float) * .5 + np.random.uniform(size=c.shape) * .5 result = F.canny(cf, 4, .1, .2, np.ones(c.shape, bool)) # # erode and dilate the circle to get rings that should contain the # outlines # cd = binary_dilation(c, iterations=4) ce = binary_erosion(c, iterations=4) cde = np.logical_and(cd, np.logical_not(ce)) self.assertTrue(np.all(cde[result])) point_count = np.sum(result) self.assertTrue(point_count > 1200) self.assertTrue(point_count < 1600) def test_image_shape(self): self.assertRaises(ValueError, F.canny, np.zeros((20, 20, 20)), 4, 0, 0) def test_mask_none(self): result1 = F.canny(np.zeros((20, 20)), 4, 0, 0, np.ones((20, 20), bool)) result2 = F.canny(np.zeros((20, 20)), 4, 0, 0) self.assertTrue(np.all(result1 == result2)) def test_use_quantiles(self): image = img_as_float(data.camera()[::50, ::50]) # Correct output produced manually with quantiles # of 0.8 and 0.6 for high and low respectively correct_output = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0], [0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0], [0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0], [0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=bool) result = F.canny(image, low_threshold=0.6, high_threshold=0.8, use_quantiles=True) assert_equal(result, correct_output) def test_invalid_use_quantiles(self): image = img_as_float(data.camera()[::50, ::50]) self.assertRaises(ValueError, F.canny, image, use_quantiles=True, low_threshold=0.5, high_threshold=3.6) self.assertRaises(ValueError, F.canny, image, use_quantiles=True, low_threshold=-5, high_threshold=0.5) self.assertRaises(ValueError, F.canny, image, use_quantiles=True, low_threshold=99, high_threshold=0.9) self.assertRaises(ValueError, F.canny, image, use_quantiles=True, low_threshold=0.5, high_threshold=-100) scikit-image-0.13.1/skimage/feature/tests/test_censure.py000066400000000000000000000070261316256236300234500ustar00rootroot00000000000000import numpy as np from numpy.testing import assert_array_equal, assert_raises from skimage.data import moon from skimage.feature import CENSURE from skimage._shared.testing import test_parallel from skimage.transform import rescale img = moon() np.random.seed(0) def test_censure_on_rectangular_images(): """Censure feature detector should work on 2D image of any shape.""" rect_image = np.random.rand(300, 200) square_image = np.random.rand(200, 200) CENSURE().detect((square_image)) CENSURE().detect((rect_image)) def test_keypoints_censure_color_image_unsupported_error(): """Censure keypoints can be extracted from gray-scale images only.""" assert_raises(ValueError, CENSURE().detect, np.zeros((20, 20, 3))) def test_keypoints_censure_mode_validity_error(): """Mode argument in keypoints_censure can be either DoB, Octagon or STAR.""" assert_raises(ValueError, CENSURE, mode='dummy') def test_keypoints_censure_scale_range_error(): """Difference between the the max_scale and min_scale parameters in keypoints_censure should be greater than or equal to two.""" assert_raises(ValueError, CENSURE, min_scale=1, max_scale=2) def test_keypoints_censure_moon_image_dob(): """Verify the actual Censure keypoints and their corresponding scale with the expected values for DoB filter.""" detector = CENSURE() detector.detect(img) expected_keypoints = np.array([[ 21, 497], [ 36, 46], [119, 350], [185, 177], [287, 250], [357, 239], [463, 116], [464, 132], [467, 260]]) expected_scales = np.array([3, 4, 4, 2, 2, 3, 2, 2, 2]) assert_array_equal(expected_keypoints, detector.keypoints) assert_array_equal(expected_scales, detector.scales) @test_parallel() def test_keypoints_censure_moon_image_octagon(): """Verify the actual Censure keypoints and their corresponding scale with the expected values for Octagon filter.""" detector = CENSURE(mode='octagon') detector.detect(rescale(img, 0.25)) # quarter scale image for speed expected_keypoints = np.array([[ 23, 27], [ 29, 89], [ 31, 87], [106, 59], [111, 67]]) expected_scales = np.array([3, 2, 5, 2, 4]) assert_array_equal(expected_keypoints, detector.keypoints) assert_array_equal(expected_scales, detector.scales) def test_keypoints_censure_moon_image_star(): """Verify the actual Censure keypoints and their corresponding scale with the expected values for STAR filter.""" detector = CENSURE(mode='star') detector.detect(rescale(img, 0.25)) # quarter scale image for speed expected_keypoints = np.array([[ 23, 27], [ 29, 89], [ 30, 86], [107, 59], [109, 64], [111, 67], [113, 70]]) expected_scales = np.array([3, 2, 4, 2, 5, 3, 2]) assert_array_equal(expected_keypoints, detector.keypoints) assert_array_equal(expected_scales, detector.scales) if __name__ == '__main__': from numpy import testing testing.run_module_suite() scikit-image-0.13.1/skimage/feature/tests/test_corner.py000066400000000000000000000403121316256236300232670ustar00rootroot00000000000000import numpy as np from numpy.testing import (assert_array_equal, assert_raises, assert_almost_equal, assert_warns) from skimage import data from skimage import img_as_float from skimage.color import rgb2gray from skimage.morphology import octagon from skimage._shared.testing import test_parallel from skimage.feature import (corner_moravec, corner_harris, corner_shi_tomasi, corner_subpix, peak_local_max, corner_peaks, corner_kitchen_rosenfeld, corner_foerstner, corner_fast, corner_orientations, structure_tensor, structure_tensor_eigvals, hessian_matrix, hessian_matrix_eigvals, hessian_matrix_det, shape_index) def test_structure_tensor(): square = np.zeros((5, 5)) square[2, 2] = 1 Axx, Axy, Ayy = structure_tensor(square, sigma=0.1) assert_array_equal(Axx, np.array([[ 0, 0, 0, 0, 0], [ 0, 1, 0, 1, 0], [ 0, 4, 0, 4, 0], [ 0, 1, 0, 1, 0], [ 0, 0, 0, 0, 0]])) assert_array_equal(Axy, np.array([[ 0, 0, 0, 0, 0], [ 0, 1, 0, -1, 0], [ 0, 0, 0, -0, 0], [ 0, -1, -0, 1, 0], [ 0, 0, 0, 0, 0]])) assert_array_equal(Ayy, np.array([[ 0, 0, 0, 0, 0], [ 0, 1, 4, 1, 0], [ 0, 0, 0, 0, 0], [ 0, 1, 4, 1, 0], [ 0, 0, 0, 0, 0]])) def test_hessian_matrix(): square = np.zeros((5, 5)) square[2, 2] = 4 Hrr, Hrc, Hcc = hessian_matrix(square, sigma=0.1, order='rc') assert_almost_equal(Hrr, np.array([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [2, 0, -2, 0, 2], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]])) assert_almost_equal(Hrc, np.array([[0, 0, 0, 0, 0], [0, 1, 0, -1, 0], [0, 0, 0, 0, 0], [0, -1, 0, 1, 0], [0, 0, 0, 0, 0]])) assert_almost_equal(Hcc, np.array([[0, 0, 2, 0, 0], [0, 0, 0, 0, 0], [0, 0, -2, 0, 0], [0, 0, 0, 0, 0], [0, 0, 2, 0, 0]])) matrix2d = np.random.rand(3, 3) assert_warns(UserWarning, hessian_matrix, matrix2d, sigma=0.1) def test_hessian_matrix_3d(): cube = np.zeros((5, 5, 5)) cube[2, 2, 2] = 4 Hs = hessian_matrix(cube, sigma=0.1, order='rc') assert len(Hs) == 6, ("incorrect number of Hessian images (%i) for 3D" % len(Hs)) assert_almost_equal(Hs[2][:, 2, :], np.array([[0, 0, 0, 0, 0], [0, 1, 0, -1, 0], [0, 0, 0, 0, 0], [0, -1, 0, 1, 0], [0, 0, 0, 0, 0]])) def test_structure_tensor_eigvals(): square = np.zeros((5, 5)) square[2, 2] = 1 Axx, Axy, Ayy = structure_tensor(square, sigma=0.1) l1, l2 = structure_tensor_eigvals(Axx, Axy, Ayy) assert_array_equal(l1, np.array([[0, 0, 0, 0, 0], [0, 2, 4, 2, 0], [0, 4, 0, 4, 0], [0, 2, 4, 2, 0], [0, 0, 0, 0, 0]])) assert_array_equal(l2, np.array([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]])) def test_hessian_matrix_eigvals(): square = np.zeros((5, 5)) square[2, 2] = 4 Hrr, Hrc, Hcc = hessian_matrix(square, sigma=0.1, order='rc') l1, l2 = hessian_matrix_eigvals(Hrr, Hrc, Hcc) assert_almost_equal(l1, np.array([[0, 0, 2, 0, 0], [0, 1, 0, 1, 0], [2, 0, -2, 0, 2], [0, 1, 0, 1, 0], [0, 0, 2, 0, 0]])) assert_almost_equal(l2, np.array([[0, 0, 0, 0, 0], [0, -1, 0, -1, 0], [0, 0, -2, 0, 0], [0, -1, 0, -1, 0], [0, 0, 0, 0, 0]])) @test_parallel() def test_hessian_matrix_det(): image = np.zeros((5, 5)) image[2, 2] = 1 det = hessian_matrix_det(image, 5) assert_almost_equal(det, 0, decimal=3) def test_shape_index(): square = np.zeros((5, 5)) square[2, 2] = 4 s = shape_index(square, sigma=0.1) assert_almost_equal( s, np.array([[ np.nan, np.nan, -0.5, np.nan, np.nan], [ np.nan, 0, np.nan, 0, np.nan], [ -0.5, np.nan, -1, np.nan, -0.5], [ np.nan, 0, np.nan, 0, np.nan], [ np.nan, np.nan, -0.5, np.nan, np.nan]]) ) @test_parallel() def test_square_image(): im = np.zeros((50, 50)).astype(float) im[:25, :25] = 1. # Moravec results = peak_local_max(corner_moravec(im), min_distance=10, threshold_rel=0) # interest points along edge assert len(results) == 57 # Harris results = peak_local_max(corner_harris(im, method='k'), min_distance=10, threshold_rel=0) # interest at corner assert len(results) == 1 results = peak_local_max(corner_harris(im, method='eps'), min_distance=10, threshold_rel=0) # interest at corner assert len(results) == 1 # Shi-Tomasi results = peak_local_max(corner_shi_tomasi(im), min_distance=10, threshold_rel=0) # interest at corner assert len(results) == 1 def test_noisy_square_image(): im = np.zeros((50, 50)).astype(float) im[:25, :25] = 1. np.random.seed(seed=1234) im = im + np.random.uniform(size=im.shape) * .2 # Moravec results = peak_local_max(corner_moravec(im), min_distance=10, threshold_rel=0) # undefined number of interest points assert results.any() # Harris results = peak_local_max(corner_harris(im, method='k'), min_distance=10, threshold_rel=0) assert len(results) == 1 results = peak_local_max(corner_harris(im, method='eps'), min_distance=10, threshold_rel=0) assert len(results) == 1 # Shi-Tomasi results = peak_local_max(corner_shi_tomasi(im, sigma=1.5), min_distance=10, threshold_rel=0) assert len(results) == 1 def test_squared_dot(): im = np.zeros((50, 50)) im[4:8, 4:8] = 1 im = img_as_float(im) # Moravec fails # Harris results = peak_local_max(corner_harris(im), min_distance=10, threshold_rel=0) assert (results == np.array([[6, 6]])).all() # Shi-Tomasi results = peak_local_max(corner_shi_tomasi(im), min_distance=10, threshold_rel=0) assert (results == np.array([[6, 6]])).all() def test_rotated_img(): """ The harris filter should yield the same results with an image and it's rotation. """ im = img_as_float(data.astronaut().mean(axis=2)) im_rotated = im.T # Moravec results = peak_local_max(corner_moravec(im), min_distance=10, threshold_rel=0) results_rotated = peak_local_max(corner_moravec(im_rotated), min_distance=10, threshold_rel=0) assert (np.sort(results[:, 0]) == np.sort(results_rotated[:, 1])).all() assert (np.sort(results[:, 1]) == np.sort(results_rotated[:, 0])).all() # Harris results = peak_local_max(corner_harris(im), min_distance=10, threshold_rel=0) results_rotated = peak_local_max(corner_harris(im_rotated), min_distance=10, threshold_rel=0) assert (np.sort(results[:, 0]) == np.sort(results_rotated[:, 1])).all() assert (np.sort(results[:, 1]) == np.sort(results_rotated[:, 0])).all() # Shi-Tomasi results = peak_local_max(corner_shi_tomasi(im), min_distance=10, threshold_rel=0) results_rotated = peak_local_max(corner_shi_tomasi(im_rotated), min_distance=10, threshold_rel=0) assert (np.sort(results[:, 0]) == np.sort(results_rotated[:, 1])).all() assert (np.sort(results[:, 1]) == np.sort(results_rotated[:, 0])).all() def test_subpix_edge(): img = np.zeros((50, 50)) img[:25, :25] = 255 img[25:, 25:] = 255 corner = peak_local_max(corner_harris(img), min_distance=10, threshold_rel=0, num_peaks=1) subpix = corner_subpix(img, corner) assert_array_equal(subpix[0], (24.5, 24.5)) def test_subpix_dot(): img = np.zeros((50, 50)) img[25, 25] = 255 corner = peak_local_max(corner_harris(img), min_distance=10, threshold_rel=0, num_peaks=1) subpix = corner_subpix(img, corner) assert_array_equal(subpix[0], (25, 25)) def test_subpix_no_class(): img = np.zeros((50, 50)) subpix = corner_subpix(img, np.array([[25, 25]])) assert_array_equal(subpix[0], (np.nan, np.nan)) img[25, 25] = 1e-10 corner = peak_local_max(corner_harris(img), min_distance=10, threshold_rel=0, num_peaks=1) subpix = corner_subpix(img, np.array([[25, 25]])) assert_array_equal(subpix[0], (np.nan, np.nan)) def test_subpix_border(): img = np.zeros((50, 50)) img[1:25,1:25] = 255 img[25:-1,25:-1] = 255 corner = corner_peaks(corner_harris(img), threshold_rel=0) subpix = corner_subpix(img, corner, window_size=11) ref = np.array([[ 0.52040816, 0.52040816], [ 0.52040816, 24.47959184], [24.47959184, 0.52040816], [24.5 , 24.5 ], [24.52040816, 48.47959184], [48.47959184, 24.52040816], [48.47959184, 48.47959184]]) assert_almost_equal(subpix, ref) def test_num_peaks(): """For a bunch of different values of num_peaks, check that peak_local_max returns exactly the right amount of peaks. Test is run on the astronaut image in order to produce a sufficient number of corners""" img_corners = corner_harris(rgb2gray(data.astronaut())) for i in range(20): n = np.random.randint(1, 21) results = peak_local_max(img_corners, min_distance=10, threshold_rel=0, num_peaks=n) assert (results.shape[0] == n) def test_corner_peaks(): response = np.zeros((10, 10)) response[2:5, 2:5] = 1 corners = corner_peaks(response, exclude_border=False, min_distance=10, threshold_rel=0) assert len(corners) == 1 corners = corner_peaks(response, exclude_border=False, min_distance=1) assert len(corners) == 4 corners = corner_peaks(response, exclude_border=False, min_distance=1, indices=False) assert np.sum(corners) == 4 def test_blank_image_nans(): """Some of the corner detectors had a weakness in terms of returning NaN when presented with regions of constant intensity. This should be fixed by now. We test whether each detector returns something finite in the case of constant input""" detectors = [corner_moravec, corner_harris, corner_shi_tomasi, corner_kitchen_rosenfeld, corner_foerstner] constant_image = np.zeros((20, 20)) for det in detectors: response = det(constant_image) assert np.all(np.isfinite(response)) def test_corner_fast_image_unsupported_error(): img = np.zeros((20, 20, 3)) assert_raises(ValueError, corner_fast, img) @test_parallel() def test_corner_fast_astronaut(): img = rgb2gray(data.astronaut()) expected = np.array([[101, 198], [140, 205], [141, 242], [177, 156], [188, 113], [197, 148], [213, 117], [223, 375], [232, 266], [245, 137], [249, 171], [300, 244], [305, 57], [325, 245], [339, 242], [346, 279], [353, 172], [358, 307], [362, 252], [362, 328], [363, 192], [364, 147], [369, 159], [374, 171], [379, 183], [387, 195], [390, 149], [401, 197], [403, 162], [413, 181], [444, 310], [464, 251], [476, 250], [489, 155], [492, 139], [494, 169], [496, 266]]) actual = corner_peaks(corner_fast(img, 12, 0.3), min_distance=10, threshold_rel=0) assert_array_equal(actual, expected) def test_corner_orientations_image_unsupported_error(): img = np.zeros((20, 20, 3)) assert_raises(ValueError, corner_orientations, img, np.asarray([[7, 7]]), np.ones((3, 3))) def test_corner_orientations_even_shape_error(): img = np.zeros((20, 20)) assert_raises(ValueError, corner_orientations, img, np.asarray([[7, 7]]), np.ones((4, 4))) @test_parallel() def test_corner_orientations_astronaut(): img = rgb2gray(data.astronaut()) corners = corner_peaks(corner_fast(img, 11, 0.35), min_distance=10, threshold_abs=0, threshold_rel=0.1) expected = np.array([-1.75220190e+00, 2.01197383e+00, -2.01162417e+00, -1.88247204e-01, 1.19134149e+00, -6.61151410e-01, -2.99143370e+00, 2.17103132e+00, -7.52950306e-04, 1.25854853e+00, 2.43573659e+00, -1.69230287e+00, -9.88548213e-01, 1.47154532e+00, -1.65449964e+00, 1.09650167e+00, 1.07812134e+00, -1.68885773e+00, -1.64397304e+00, 3.09780364e+00, -3.49561988e-01, -1.46554357e+00, -2.81524886e+00, 8.12701702e-01, 2.47305654e+00, -1.63869275e+00, 5.46905279e-02, -4.40598471e-01, 3.14918803e-01, -1.76069982e+00, 3.05330950e+00, 2.39291733e+00, -1.22091334e-01, -3.09279990e-01, 1.45931342e+00]) actual = corner_orientations(img, corners, octagon(3, 2)) assert_almost_equal(actual, expected) def test_corner_orientations_square(): square = np.zeros((12, 12)) square[3:9, 3:9] = 1 corners = corner_peaks(corner_fast(square, 9), min_distance=1, threshold_rel=0) actual_orientations = corner_orientations(square, corners, octagon(3, 2)) actual_orientations_degrees = np.rad2deg(actual_orientations) expected_orientations_degree = np.array([ 45., 135., -45., -135.]) assert_array_equal(actual_orientations_degrees, expected_orientations_degree) if __name__ == '__main__': from numpy import testing testing.run_module_suite() scikit-image-0.13.1/skimage/feature/tests/test_daisy.py000066400000000000000000000064241316256236300231160ustar00rootroot00000000000000import numpy as np from numpy.testing import assert_raises, assert_almost_equal from numpy import sqrt, ceil from skimage import data from skimage import img_as_float from skimage.feature import daisy def test_daisy_color_image_unsupported_error(): img = np.zeros((20, 20, 3)) assert_raises(ValueError, daisy, img) def test_daisy_desc_dims(): img = img_as_float(data.astronaut()[:128, :128].mean(axis=2)) rings = 2 histograms = 4 orientations = 3 descs = daisy(img, rings=rings, histograms=histograms, orientations=orientations) assert(descs.shape[2] == (rings * histograms + 1) * orientations) rings = 4 histograms = 5 orientations = 13 descs = daisy(img, rings=rings, histograms=histograms, orientations=orientations) assert(descs.shape[2] == (rings * histograms + 1) * orientations) def test_descs_shape(): img = img_as_float(data.astronaut()[:256, :256].mean(axis=2)) radius = 20 step = 8 descs = daisy(img, radius=radius, step=step) assert(descs.shape[0] == ceil((img.shape[0] - radius * 2) / float(step))) assert(descs.shape[1] == ceil((img.shape[1] - radius * 2) / float(step))) img = img[:-1, :-2] radius = 5 step = 3 descs = daisy(img, radius=radius, step=step) assert(descs.shape[0] == ceil((img.shape[0] - radius * 2) / float(step))) assert(descs.shape[1] == ceil((img.shape[1] - radius * 2) / float(step))) def test_daisy_sigmas_and_radii(): img = img_as_float(data.astronaut()[:64, :64].mean(axis=2)) sigmas = [1, 2, 3] radii = [1, 2] daisy(img, sigmas=sigmas, ring_radii=radii) def test_daisy_incompatible_sigmas_and_radii(): img = img_as_float(data.astronaut()[:64, :64].mean(axis=2)) sigmas = [1, 2] radii = [1, 2] assert_raises(ValueError, daisy, img, sigmas=sigmas, ring_radii=radii) def test_daisy_normalization(): img = img_as_float(data.astronaut()[:64, :64].mean(axis=2)) descs = daisy(img, normalization='l1') for i in range(descs.shape[0]): for j in range(descs.shape[1]): assert_almost_equal(np.sum(descs[i, j, :]), 1) descs_ = daisy(img) assert_almost_equal(descs, descs_) descs = daisy(img, normalization='l2') for i in range(descs.shape[0]): for j in range(descs.shape[1]): assert_almost_equal(sqrt(np.sum(descs[i, j, :] ** 2)), 1) orientations = 8 descs = daisy(img, orientations=orientations, normalization='daisy') desc_dims = descs.shape[2] for i in range(descs.shape[0]): for j in range(descs.shape[1]): for k in range(0, desc_dims, orientations): assert_almost_equal(sqrt(np.sum( descs[i, j, k:k + orientations] ** 2)), 1) img = np.zeros((50, 50)) descs = daisy(img, normalization='off') for i in range(descs.shape[0]): for j in range(descs.shape[1]): assert_almost_equal(np.sum(descs[i, j, :]), 0) assert_raises(ValueError, daisy, img, normalization='does_not_exist') def test_daisy_visualization(): img = img_as_float(data.astronaut()[:32, :32].mean(axis=2)) descs, descs_img = daisy(img, visualize=True) assert(descs_img.shape == (32, 32, 3)) if __name__ == '__main__': from numpy import testing testing.run_module_suite() scikit-image-0.13.1/skimage/feature/tests/test_hog.py000066400000000000000000000177541316256236300225720ustar00rootroot00000000000000import os import numpy as np from scipy import ndimage as ndi from skimage import color from skimage import data, data_dir from skimage import feature from skimage import img_as_float from skimage import draw from numpy.testing import (assert_raises, assert_almost_equal) def test_hog_output_size(): img = img_as_float(data.astronaut()[:256, :].mean(axis=2)) fd = feature.hog(img, orientations=9, pixels_per_cell=(8, 8), cells_per_block=(1, 1)) assert len(fd) == 9 * (256 // 8) * (512 // 8) def test_hog_output_correctness_l1_norm(): img = color.rgb2gray(data.astronaut()) correct_output = np.load( os.path.join(data_dir, 'astronaut_GRAY_hog_L1.npy')) output = feature.hog(img, orientations=9, pixels_per_cell=(8, 8), cells_per_block=(3, 3), block_norm='L1', feature_vector=True, transform_sqrt=False, visualise=False) assert_almost_equal(output, correct_output) def test_hog_output_correctness_l2hys_norm(): img = color.rgb2gray(data.astronaut()) correct_output = np.load( os.path.join(data_dir, 'astronaut_GRAY_hog_L2-Hys.npy')) output = feature.hog(img, orientations=9, pixels_per_cell=(8, 8), cells_per_block=(3, 3), block_norm='L2-Hys', feature_vector=True, transform_sqrt=False, visualise=False) assert_almost_equal(output, correct_output) def test_hog_image_size_cell_size_mismatch(): image = data.camera()[:150, :200] fd = feature.hog(image, orientations=9, pixels_per_cell=(8, 8), cells_per_block=(1, 1)) assert len(fd) == 9 * (150 // 8) * (200 // 8) def test_hog_color_image_unsupported_error(): image = np.zeros((20, 20, 3)) assert_raises(ValueError, feature.hog, image) def test_hog_basic_orientations_and_data_types(): # scenario: # 1) create image (with float values) where upper half is filled by # zeros, bottom half by 100 # 2) create unsigned integer version of this image # 3) calculate feature.hog() for both images, both with 'transform_sqrt' # option enabled and disabled # 4) verify that all results are equal where expected # 5) verify that computed feature vector is as expected # 6) repeat the scenario for 90, 180 and 270 degrees rotated images # size of testing image width = height = 35 image0 = np.zeros((height, width), dtype='float') image0[height // 2:] = 100 for rot in range(4): # rotate by 0, 90, 180 and 270 degrees image_float = np.rot90(image0, rot) # create uint8 image from image_float image_uint8 = image_float.astype('uint8') (hog_float, hog_img_float) = feature.hog( image_float, orientations=4, pixels_per_cell=(8, 8), cells_per_block=(1, 1), visualise=True, transform_sqrt=False) (hog_uint8, hog_img_uint8) = feature.hog( image_uint8, orientations=4, pixels_per_cell=(8, 8), cells_per_block=(1, 1), visualise=True, transform_sqrt=False) (hog_float_norm, hog_img_float_norm) = feature.hog( image_float, orientations=4, pixels_per_cell=(8, 8), cells_per_block=(1, 1), visualise=True, transform_sqrt=True) (hog_uint8_norm, hog_img_uint8_norm) = feature.hog( image_uint8, orientations=4, pixels_per_cell=(8, 8), cells_per_block=(1, 1), visualise=True, transform_sqrt=True) # set to True to enable manual debugging with graphical output, # must be False for automatic testing if False: import matplotlib.pyplot as plt plt.figure() plt.subplot(2, 3, 1) plt.imshow(image_float) plt.colorbar() plt.title('image') plt.subplot(2, 3, 2) plt.imshow(hog_img_float) plt.colorbar() plt.title('HOG result visualisation (float img)') plt.subplot(2, 3, 5) plt.imshow(hog_img_uint8) plt.colorbar() plt.title('HOG result visualisation (uint8 img)') plt.subplot(2, 3, 3) plt.imshow(hog_img_float_norm) plt.colorbar() plt.title('HOG result (transform_sqrt) visualisation (float img)') plt.subplot(2, 3, 6) plt.imshow(hog_img_uint8_norm) plt.colorbar() plt.title('HOG result (transform_sqrt) visualisation (uint8 img)') plt.show() # results (features and visualisation) for float and uint8 images must # be almost equal assert_almost_equal(hog_float, hog_uint8) assert_almost_equal(hog_img_float, hog_img_uint8) # resulting features should be almost equal when 'transform_sqrt' is enabled # or disabled (for current simple testing image) assert_almost_equal(hog_float, hog_float_norm, decimal=4) assert_almost_equal(hog_float, hog_uint8_norm, decimal=4) # reshape resulting feature vector to matrix with 4 columns (each # corresponding to one of 4 directions); only one direction should # contain nonzero values (this is manually determined for testing # image) actual = np.max(hog_float.reshape(-1, 4), axis=0) if rot in [0, 2]: # image is rotated by 0 and 180 degrees desired = [0, 0, 1, 0] elif rot in [1, 3]: # image is rotated by 90 and 270 degrees desired = [1, 0, 0, 0] else: raise Exception('Result is not determined for this rotation.') assert_almost_equal(actual, desired, decimal=2) def test_hog_orientations_circle(): # scenario: # 1) create image with blurred circle in the middle # 2) calculate feature.hog() # 3) verify that the resulting feature vector contains uniformly # distributed values for all orientations, i.e. no orientation is # lost or emphasized # 4) repeat the scenario for other 'orientations' option # size of testing image width = height = 100 image = np.zeros((height, width)) rr, cc = draw.circle(int(height / 2), int(width / 2), int(width / 3)) image[rr, cc] = 100 image = ndi.gaussian_filter(image, 2) for orientations in range(2, 15): (hog, hog_img) = feature.hog(image, orientations=orientations, pixels_per_cell=(8, 8), cells_per_block=(1, 1), visualise=True, transform_sqrt=False) # set to True to enable manual debugging with graphical output, # must be False for automatic testing if False: import matplotlib.pyplot as plt plt.figure() plt.subplot(1, 2, 1) plt.imshow(image) plt.colorbar() plt.title('image_float') plt.subplot(1, 2, 2) plt.imshow(hog_img) plt.colorbar() plt.title('HOG result visualisation, ' 'orientations=%d' % (orientations)) plt.show() # reshape resulting feature vector to matrix with N columns (each # column corresponds to one direction), hog_matrix = hog.reshape(-1, orientations) # compute mean values in the resulting feature vector for each # direction, these values should be almost equal to the global mean # value (since the image contains a circle), i.e., all directions have # same contribution to the result actual = np.mean(hog_matrix, axis=0) desired = np.mean(hog_matrix) assert_almost_equal(actual, desired, decimal=1) def test_hog_normalise_none_error_raised(): img = np.array([1, 2, 3]) assert_raises(ValueError, feature.hog, img, normalise=True) def test_hog_block_normalization_incorrect_error(): img = np.eye(4) assert_raises(ValueError, feature.hog, img, block_norm='Linf') if __name__ == '__main__': np.testing.run_module_suite() scikit-image-0.13.1/skimage/feature/tests/test_match.py000066400000000000000000000122531316256236300230760ustar00rootroot00000000000000import numpy as np from numpy.testing import assert_equal, assert_raises from skimage import data from skimage import transform as tf from skimage.color import rgb2gray from skimage.feature import (BRIEF, match_descriptors, corner_peaks, corner_harris) def test_binary_descriptors_unequal_descriptor_sizes_error(): """Sizes of descriptors of keypoints to be matched should be equal.""" descs1 = np.array([[True, True, False, True], [False, True, False, True]]) descs2 = np.array([[True, False, False, True, False], [False, True, True, True, False]]) assert_raises(ValueError, match_descriptors, descs1, descs2) def test_binary_descriptors(): descs1 = np.array([[True, True, False, True, True], [False, True, False, True, True]]) descs2 = np.array([[True, False, False, True, False], [False, False, True, True, True]]) matches = match_descriptors(descs1, descs2) assert_equal(matches, [[0, 0], [1, 1]]) def test_binary_descriptors_rotation_crosscheck_false(): """Verify matched keypoints and their corresponding masks results between image and its rotated version with the expected keypoint pairs with cross_check disabled.""" img = data.astronaut() img = rgb2gray(img) tform = tf.SimilarityTransform(scale=1, rotation=0.15, translation=(0, 0)) rotated_img = tf.warp(img, tform, clip=False) extractor = BRIEF(descriptor_size=512) keypoints1 = corner_peaks(corner_harris(img), min_distance=5, threshold_abs=0, threshold_rel=0.1) extractor.extract(img, keypoints1) descriptors1 = extractor.descriptors keypoints2 = corner_peaks(corner_harris(rotated_img), min_distance=5, threshold_abs=0, threshold_rel=0.1) extractor.extract(rotated_img, keypoints2) descriptors2 = extractor.descriptors matches = match_descriptors(descriptors1, descriptors2, cross_check=False) exp_matches1 = np.array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46]) exp_matches2 = np.array([ 0, 31, 2, 3, 1, 4, 6, 4, 38, 5, 27, 7, 13, 10, 9, 27, 7, 11, 15, 8, 23, 14, 12, 16, 10, 25, 18, 19, 21, 20, 41, 24, 25, 26, 28, 27, 22, 23, 29, 30, 31, 32, 35, 33, 34, 30, 36]) assert_equal(matches[:, 0], exp_matches1) assert_equal(matches[:, 1], exp_matches2) def test_binary_descriptors_rotation_crosscheck_true(): """Verify matched keypoints and their corresponding masks results between image and its rotated version with the expected keypoint pairs with cross_check enabled.""" img = data.astronaut() img = rgb2gray(img) tform = tf.SimilarityTransform(scale=1, rotation=0.15, translation=(0, 0)) rotated_img = tf.warp(img, tform, clip=False) extractor = BRIEF(descriptor_size=512) keypoints1 = corner_peaks(corner_harris(img), min_distance=5, threshold_abs=0, threshold_rel=0.1) extractor.extract(img, keypoints1) descriptors1 = extractor.descriptors keypoints2 = corner_peaks(corner_harris(rotated_img), min_distance=5, threshold_abs=0, threshold_rel=0.1) extractor.extract(rotated_img, keypoints2) descriptors2 = extractor.descriptors matches = match_descriptors(descriptors1, descriptors2, cross_check=True) exp_matches1 = np.array([ 0, 2, 3, 4, 5, 6, 9, 11, 12, 13, 14, 17, 18, 19, 21, 22, 23, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46]) exp_matches2 = np.array([ 0, 2, 3, 1, 4, 6, 5, 7, 13, 10, 9, 11, 15, 8, 14, 12, 16, 18, 19, 21, 20, 24, 25, 26, 28, 27, 22, 23, 29, 30, 31, 32, 35, 33, 34, 36]) assert_equal(matches[:, 0], exp_matches1) assert_equal(matches[:, 1], exp_matches2) def test_max_distance(): descs1 = np.zeros((10, 128)) descs2 = np.zeros((15, 128)) descs1[0, :] = 1 matches = match_descriptors(descs1, descs2, metric='euclidean', max_distance=0.1, cross_check=False) assert len(matches) == 9 matches = match_descriptors(descs1, descs2, metric='euclidean', max_distance=np.sqrt(128.1), cross_check=False) assert len(matches) == 10 matches = match_descriptors(descs1, descs2, metric='euclidean', max_distance=0.1, cross_check=True) assert_equal(matches, [[1, 0]]) matches = match_descriptors(descs1, descs2, metric='euclidean', max_distance=np.sqrt(128.1), cross_check=True) assert_equal(matches, [[1, 0]]) if __name__ == '__main__': from numpy import testing testing.run_module_suite() scikit-image-0.13.1/skimage/feature/tests/test_orb.py000066400000000000000000000121541316256236300225640ustar00rootroot00000000000000import numpy as np from numpy.testing import (assert_equal, assert_almost_equal, run_module_suite, assert_raises) from skimage.feature import ORB from skimage import data from skimage._shared.testing import test_parallel img = data.coins() @test_parallel() def test_keypoints_orb_desired_no_of_keypoints(): detector_extractor = ORB(n_keypoints=10, fast_n=12, fast_threshold=0.20) detector_extractor.detect(img) exp_rows = np.array([ 141. , 108. , 214.56 , 131. , 214.272, 67. , 206. , 177. , 108. , 141. ]) exp_cols = np.array([ 323. , 328. , 282.24 , 292. , 281.664, 85. , 260. , 284. , 328.8 , 267. ]) exp_scales = np.array([1, 1, 1.44, 1, 1.728, 1, 1, 1, 1.2, 1]) exp_orientations = np.array([ -53.97446153, 59.5055285 , -96.01885186, -149.70789506, -94.70171899, -45.76429535, -51.49752849, 113.57081195, 63.30428063, -79.56091118]) exp_response = np.array([ 1.01168357, 0.82934145, 0.67784179, 0.57176438, 0.56637459, 0.52248355, 0.43696175, 0.42992376, 0.37700486, 0.36126832]) assert_almost_equal(exp_rows, detector_extractor.keypoints[:, 0]) assert_almost_equal(exp_cols, detector_extractor.keypoints[:, 1]) assert_almost_equal(exp_scales, detector_extractor.scales) assert_almost_equal(exp_response, detector_extractor.responses) assert_almost_equal(exp_orientations, np.rad2deg(detector_extractor.orientations), 5) detector_extractor.detect_and_extract(img) assert_almost_equal(exp_rows, detector_extractor.keypoints[:, 0]) assert_almost_equal(exp_cols, detector_extractor.keypoints[:, 1]) def test_keypoints_orb_less_than_desired_no_of_keypoints(): detector_extractor = ORB(n_keypoints=15, fast_n=12, fast_threshold=0.33, downscale=2, n_scales=2) detector_extractor.detect(img) exp_rows = np.array([ 58., 65., 108., 140., 203.]) exp_cols = np.array([ 291., 130., 293., 202., 267.]) exp_scales = np.array([1., 1., 1., 1., 1.]) exp_orientations = np.array([-158.26941428, -59.42996346, 151.93905955, -79.46341354, -56.90052451]) exp_response = np.array([ 0.2667641 , 0.04009017, -0.17641695, -0.03243431, 0.26521259]) assert_almost_equal(exp_rows, detector_extractor.keypoints[:, 0]) assert_almost_equal(exp_cols, detector_extractor.keypoints[:, 1]) assert_almost_equal(exp_scales, detector_extractor.scales) assert_almost_equal(exp_response, detector_extractor.responses) assert_almost_equal(exp_orientations, np.rad2deg(detector_extractor.orientations), 5) detector_extractor.detect_and_extract(img) assert_almost_equal(exp_rows, detector_extractor.keypoints[:, 0]) assert_almost_equal(exp_cols, detector_extractor.keypoints[:, 1]) def test_descriptor_orb(): detector_extractor = ORB(fast_n=12, fast_threshold=0.20) exp_descriptors = np.array([[0, 1, 1, 1, 0, 1, 0, 1, 0, 1], [1, 1, 1, 0, 0, 1, 0, 0, 1, 1], [1, 0, 1, 1, 0, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 1, 0, 0, 0, 0, 0, 0, 1, 0], [1, 1, 0, 1, 1, 1, 0, 0, 1, 1], [1, 1, 0, 1, 0, 0, 1, 0, 1, 1], [0, 0, 1, 0, 1, 0, 0, 1, 1, 0], [1, 0, 0, 0, 1, 0, 0, 0, 0, 1], [0, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 0, 1, 0, 1, 0, 0, 1, 1], [1, 1, 1, 0, 0, 0, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1, 0, 0, 0, 0], [1, 1, 1, 0, 1, 1, 1, 1, 0, 0], [1, 1, 0, 0, 1, 0, 0, 1, 0, 1], [1, 1, 0, 0, 0, 0, 1, 0, 0, 1], [0, 0, 0, 0, 1, 1, 1, 0, 1, 0], [0, 0, 0, 0, 1, 1, 1, 0, 0, 1], [0, 0, 0, 0, 0, 1, 1, 0, 1, 1], [0, 0, 0, 0, 1, 0, 1, 0, 1, 1]], dtype=bool) detector_extractor.detect(img) detector_extractor.extract(img, detector_extractor.keypoints, detector_extractor.scales, detector_extractor.orientations) assert_equal(exp_descriptors, detector_extractor.descriptors[100:120, 10:20]) detector_extractor.detect_and_extract(img) assert_equal(exp_descriptors, detector_extractor.descriptors[100:120, 10:20]) def test_no_descriptors_extracted_orb(): img = np.ones((128, 128)) detector_extractor = ORB() assert_raises(RuntimeError, detector_extractor.detect_and_extract, img) if __name__ == '__main__': run_module_suite() scikit-image-0.13.1/skimage/feature/tests/test_peak.py000066400000000000000000000445101316256236300227230ustar00rootroot00000000000000import numpy as np import unittest from numpy.testing import (assert_array_almost_equal as assert_close, assert_equal, assert_raises) from scipy import ndimage as ndi from skimage.feature import peak np.random.seed(21) class TestPeakLocalMax(): def test_trivial_case(self): trivial = np.zeros((25, 25)) peak_indices = peak.peak_local_max(trivial, min_distance=1, indices=True) assert type(peak_indices) is np.ndarray assert not peak_indices # inherent boolean-ness of empty list peaks = peak.peak_local_max(trivial, min_distance=1, indices=False) assert (peaks.astype(np.bool) == trivial).all() def test_noisy_peaks(self): peak_locations = [(7, 7), (7, 13), (13, 7), (13, 13)] # image with noise of amplitude 0.8 and peaks of amplitude 1 image = 0.8 * np.random.rand(20, 20) for r, c in peak_locations: image[r, c] = 1 peaks_detected = peak.peak_local_max(image, min_distance=5) assert len(peaks_detected) == len(peak_locations) for loc in peaks_detected: assert tuple(loc) in peak_locations def test_relative_threshold(self): image = np.zeros((5, 5), dtype=np.uint8) image[1, 1] = 10 image[3, 3] = 20 peaks = peak.peak_local_max(image, min_distance=1, threshold_rel=0.5) assert len(peaks) == 1 assert_close(peaks, [(3, 3)]) def test_absolute_threshold(self): image = np.zeros((5, 5), dtype=np.uint8) image[1, 1] = 10 image[3, 3] = 20 peaks = peak.peak_local_max(image, min_distance=1, threshold_abs=10) assert len(peaks) == 1 assert_close(peaks, [(3, 3)]) def test_constant_image(self): image = 128 * np.ones((20, 20), dtype=np.uint8) peaks = peak.peak_local_max(image, min_distance=1) assert len(peaks) == 0 def test_flat_peak(self): image = np.zeros((5, 5), dtype=np.uint8) image[1:3, 1:3] = 10 peaks = peak.peak_local_max(image, min_distance=1) assert len(peaks) == 4 def test_sorted_peaks(self): image = np.zeros((5, 5), dtype=np.uint8) image[1, 1] = 20 image[3, 3] = 10 peaks = peak.peak_local_max(image, min_distance=1) assert peaks.tolist() == [[3, 3], [1, 1]] image = np.zeros((3, 10)) image[1, (1, 3, 5, 7)] = (1, 3, 2, 4) peaks = peak.peak_local_max(image, min_distance=1) assert peaks.tolist() == [[1, 7], [1, 5], [1, 3], [1, 1]] def test_num_peaks(self): image = np.zeros((7, 7), dtype=np.uint8) image[1, 1] = 10 image[1, 3] = 11 image[1, 5] = 12 image[3, 5] = 8 image[5, 3] = 7 assert len(peak.peak_local_max(image, min_distance=1, threshold_abs=0)) == 5 peaks_limited = peak.peak_local_max( image, min_distance=1, threshold_abs=0, num_peaks=2) assert len(peaks_limited) == 2 assert (1, 3) in peaks_limited assert (1, 5) in peaks_limited peaks_limited = peak.peak_local_max( image, min_distance=1, threshold_abs=0, num_peaks=4) assert len(peaks_limited) == 4 assert (1, 3) in peaks_limited assert (1, 5) in peaks_limited assert (1, 1) in peaks_limited assert (3, 5) in peaks_limited def test_num_peaks_and_labels(self): image = np.zeros((7, 7), dtype=np.uint8) labels = np.zeros((7, 7), dtype=np.uint8) + 20 image[1, 1] = 10 image[1, 3] = 11 image[1, 5] = 12 image[3, 5] = 8 image[5, 3] = 7 peaks_limited = peak.peak_local_max( image, min_distance=1, threshold_abs=0, labels=labels) assert len(peaks_limited) == 5 peaks_limited = peak.peak_local_max( image, min_distance=1, threshold_abs=0, labels=labels, num_peaks=2) assert len(peaks_limited) == 2 def test_num_peaks_tot_vs_labels_4quadrants(self): np.random.seed(21) image = np.random.uniform(size=(20, 30)) i, j = np.mgrid[0:20, 0:30] labels = 1 + (i >= 10) + (j >= 15) * 2 result = peak.peak_local_max(image, labels=labels, min_distance=1, threshold_rel=0, indices=True, num_peaks=np.inf, num_peaks_per_label=2) assert len(result) == 8 result = peak.peak_local_max(image, labels=labels, min_distance=1, threshold_rel=0, indices=True, num_peaks=np.inf, num_peaks_per_label=1) assert len(result) == 4 result = peak.peak_local_max(image, labels=labels, min_distance=1, threshold_rel=0, indices=True, num_peaks=2, num_peaks_per_label=2) assert len(result) == 2 def test_num_peaks3D(self): # Issue 1354: the old code only hold for 2D arrays # and this code would die with IndexError image = np.zeros((10, 10, 100)) image[5,5,::5] = np.arange(20) peaks_limited = peak.peak_local_max(image, min_distance=1, num_peaks=2) assert len(peaks_limited) == 2 def test_reorder_labels(self): image = np.random.uniform(size=(40, 60)) i, j = np.mgrid[0:40, 0:60] labels = 1 + (i >= 20) + (j >= 30) * 2 labels[labels == 4] = 5 i, j = np.mgrid[-3:4, -3:4] footprint = (i * i + j * j <= 9) expected = np.zeros(image.shape, float) for imin, imax in ((0, 20), (20, 40)): for jmin, jmax in ((0, 30), (30, 60)): expected[imin:imax, jmin:jmax] = ndi.maximum_filter( image[imin:imax, jmin:jmax], footprint=footprint) expected = (expected == image) result = peak.peak_local_max(image, labels=labels, min_distance=1, threshold_rel=0, footprint=footprint, indices=False, exclude_border=False) assert (result == expected).all() def test_indices_with_labels(self): image = np.random.uniform(size=(40, 60)) i, j = np.mgrid[0:40, 0:60] labels = 1 + (i >= 20) + (j >= 30) * 2 i, j = np.mgrid[-3:4, -3:4] footprint = (i * i + j * j <= 9) expected = np.zeros(image.shape, float) for imin, imax in ((0, 20), (20, 40)): for jmin, jmax in ((0, 30), (30, 60)): expected[imin:imax, jmin:jmax] = ndi.maximum_filter( image[imin:imax, jmin:jmax], footprint=footprint) expected = np.transpose(np.nonzero(expected == image)) expected = expected[np.argsort(image[tuple(expected.T)])[::-1]] result = peak.peak_local_max(image, labels=labels, min_distance=1, threshold_rel=0, footprint=footprint, indices=True, exclude_border=False) result = result[np.argsort(image[tuple(result.T)])[::-1]] assert (result == expected).all() def test_ndarray_indices_false(self): nd_image = np.zeros((5, 5, 5)) nd_image[2, 2, 2] = 1 peaks = peak.peak_local_max(nd_image, min_distance=1, indices=False) assert (peaks == nd_image.astype(np.bool)).all() def test_ndarray_exclude_border(self): nd_image = np.zeros((5, 5, 5)) nd_image[[1, 0, 0], [0, 1, 0], [0, 0, 1]] = 1 nd_image[3, 0, 0] = 1 nd_image[2, 2, 2] = 1 expected = np.zeros_like(nd_image, dtype=np.bool) expected[2, 2, 2] = True expectedNoBorder = nd_image > 0 result = peak.peak_local_max(nd_image, min_distance=2, exclude_border=2, indices=False) assert_equal(result, expected) # Check that bools work as expected assert_equal( peak.peak_local_max(nd_image, min_distance=2, exclude_border=2, indices=False), peak.peak_local_max(nd_image, min_distance=2, exclude_border=True, indices=False) ) assert_equal( peak.peak_local_max(nd_image, min_distance=2, exclude_border=0, indices=False), peak.peak_local_max(nd_image, min_distance=2, exclude_border=False, indices=False) ) # Check both versions with no border assert_equal( peak.peak_local_max(nd_image, min_distance=2, exclude_border=0, indices=False), expectedNoBorder, ) assert_equal( peak.peak_local_max(nd_image, exclude_border=False, indices=False), expectedNoBorder, ) def test_empty(self): image = np.zeros((10, 20)) labels = np.zeros((10, 20), int) result = peak.peak_local_max(image, labels=labels, footprint=np.ones((3, 3), bool), min_distance=1, threshold_rel=0, indices=False, exclude_border=False) assert np.all(~ result) def test_one_point(self): image = np.zeros((10, 20)) labels = np.zeros((10, 20), int) image[5, 5] = 1 labels[5, 5] = 1 result = peak.peak_local_max(image, labels=labels, footprint=np.ones((3, 3), bool), min_distance=1, threshold_rel=0, indices=False, exclude_border=False) assert np.all(result == (labels == 1)) def test_adjacent_and_same(self): image = np.zeros((10, 20)) labels = np.zeros((10, 20), int) image[5, 5:6] = 1 labels[5, 5:6] = 1 result = peak.peak_local_max(image, labels=labels, footprint=np.ones((3, 3), bool), min_distance=1, threshold_rel=0, indices=False, exclude_border=False) assert np.all(result == (labels == 1)) def test_adjacent_and_different(self): image = np.zeros((10, 20)) labels = np.zeros((10, 20), int) image[5, 5] = 1 image[5, 6] = .5 labels[5, 5:6] = 1 expected = (image == 1) result = peak.peak_local_max(image, labels=labels, footprint=np.ones((3, 3), bool), min_distance=1, threshold_rel=0, indices=False, exclude_border=False) assert np.all(result == expected) result = peak.peak_local_max(image, labels=labels, min_distance=1, threshold_rel=0, indices=False, exclude_border=False) assert np.all(result == expected) def test_not_adjacent_and_different(self): image = np.zeros((10, 20)) labels = np.zeros((10, 20), int) image[5, 5] = 1 image[5, 8] = .5 labels[image > 0] = 1 expected = (labels == 1) result = peak.peak_local_max(image, labels=labels, footprint=np.ones((3, 3), bool), min_distance=1, threshold_rel=0, indices=False, exclude_border=False) assert np.all(result == expected) def test_two_objects(self): image = np.zeros((10, 20)) labels = np.zeros((10, 20), int) image[5, 5] = 1 image[5, 15] = .5 labels[5, 5] = 1 labels[5, 15] = 2 expected = (labels > 0) result = peak.peak_local_max(image, labels=labels, footprint=np.ones((3, 3), bool), min_distance=1, threshold_rel=0, indices=False, exclude_border=False) assert np.all(result == expected) def test_adjacent_different_objects(self): image = np.zeros((10, 20)) labels = np.zeros((10, 20), int) image[5, 5] = 1 image[5, 6] = .5 labels[5, 5] = 1 labels[5, 6] = 2 expected = (labels > 0) result = peak.peak_local_max(image, labels=labels, footprint=np.ones((3, 3), bool), min_distance=1, threshold_rel=0, indices=False, exclude_border=False) assert np.all(result == expected) def test_four_quadrants(self): image = np.random.uniform(size=(20, 30)) i, j = np.mgrid[0:20, 0:30] labels = 1 + (i >= 10) + (j >= 15) * 2 i, j = np.mgrid[-3:4, -3:4] footprint = (i * i + j * j <= 9) expected = np.zeros(image.shape, float) for imin, imax in ((0, 10), (10, 20)): for jmin, jmax in ((0, 15), (15, 30)): expected[imin:imax, jmin:jmax] = ndi.maximum_filter( image[imin:imax, jmin:jmax], footprint=footprint) expected = (expected == image) result = peak.peak_local_max(image, labels=labels, footprint=footprint, min_distance=1, threshold_rel=0, indices=False, exclude_border=False) assert np.all(result == expected) def test_disk(self): '''regression test of img-1194, footprint = [1] Test peak.peak_local_max when every point is a local maximum ''' image = np.random.uniform(size=(10, 20)) footprint = np.array([[1]]) result = peak.peak_local_max(image, labels=np.ones((10, 20)), footprint=footprint, min_distance=1, threshold_rel=0, threshold_abs=-1, indices=False, exclude_border=False) assert np.all(result) result = peak.peak_local_max(image, footprint=footprint, threshold_abs=-1, indices=False, exclude_border=False) assert np.all(result) def test_3D(self): image = np.zeros((30, 30, 30)) image[15, 15, 15] = 1 image[5, 5, 5] = 1 assert_equal(peak.peak_local_max(image, min_distance=10, threshold_rel=0), [[15, 15, 15]]) assert_equal(peak.peak_local_max(image, min_distance=6, threshold_rel=0), [[15, 15, 15]]) assert sorted(peak.peak_local_max(image, min_distance=10, threshold_rel=0, exclude_border=False).tolist()) == \ [[5, 5, 5], [15, 15, 15]] assert sorted(peak.peak_local_max(image, min_distance=5, threshold_rel=0).tolist()) == \ [[5, 5, 5], [15, 15, 15]] def test_4D(self): image = np.zeros((30, 30, 30, 30)) image[15, 15, 15, 15] = 1 image[5, 5, 5, 5] = 1 assert_equal(peak.peak_local_max(image, min_distance=10, threshold_rel=0), [[15, 15, 15, 15]]) assert_equal(peak.peak_local_max(image, min_distance=6, threshold_rel=0), [[15, 15, 15, 15]]) assert sorted(peak.peak_local_max(image, min_distance=10, threshold_rel=0, exclude_border=False).tolist()) == \ [[5, 5, 5, 5], [15, 15, 15, 15]] assert sorted(peak.peak_local_max(image, min_distance=5, threshold_rel=0).tolist()) == \ [[5, 5, 5, 5], [15, 15, 15, 15]] def test_threshold_rel_default(self): image = np.ones((5, 5)) image[2, 2] = 1 assert len(peak.peak_local_max(image)) == 0 image[2, 2] = 2 assert_equal(peak.peak_local_max(image), [[2, 2]]) image[2, 2] = 0 assert len(peak.peak_local_max(image, min_distance=0)) == image.size - 1 class TestProminentPeaks(unittest.TestCase): def test_isolated_peaks(self): image = np.zeros((15, 15)) x0, y0, i0 = (12, 8, 1) x1, y1, i1 = (2, 2, 1) x2, y2, i2 = (5, 13, 1) image[y0, x0] = i0 image[y1, x1] = i1 image[y2, x2] = i2 out = peak._prominent_peaks(image) assert len(out[0]) == 3 for i, x, y in zip (out[0], out[1], out[2]): self.assertTrue(i in (i0, i1, i2)) self.assertTrue(x in (x0, x1, x2)) self.assertTrue(y in (y0, y1, y2)) def test_threshold(self): image = np.zeros((15, 15)) x0, y0, i0 = (12, 8, 10) x1, y1, i1 = (2, 2, 8) x2, y2, i2 = (5, 13, 10) image[y0, x0] = i0 image[y1, x1] = i1 image[y2, x2] = i2 out = peak._prominent_peaks(image, threshold=None) assert len(out[0]) == 3 for i, x, y in zip (out[0], out[1], out[2]): self.assertTrue(i in (i0, i1, i2)) self.assertTrue(x in (x0, x1, x2)) out = peak._prominent_peaks(image, threshold=9) assert len(out[0]) == 2 for i, x, y in zip (out[0], out[1], out[2]): self.assertTrue(i in (i0, i2)) self.assertTrue(x in (x0, x2)) self.assertTrue(y in (y0, y2)) def test_peaks_in_contact(self): image = np.zeros((15, 15)) x0, y0, i0 = (8, 8, 1) x1, y1, i1 = (7, 7, 1) # prominent peak x2, y2, i2 = (6, 6, 1) image[y0, x0] = i0 image[y1, x1] = i1 image[y2, x2] = i2 out = peak._prominent_peaks(image, min_xdistance=3, min_ydistance=3,) assert_equal(out[0], np.array((i1,))) assert_equal(out[1], np.array((x1,))) assert_equal(out[2], np.array((y1,))) def test_input_labels_unmodified(self): image = np.zeros((10, 20)) labels = np.zeros((10, 20), int) image[5, 5] = 1 labels[5, 5] = 1 labelsin = labels.copy() result = peak.peak_local_max(image, labels=labels, footprint=np.ones((3, 3), bool), min_distance=1, threshold_rel=0, indices=False, exclude_border=False) assert np.all(labels == labelsin) if __name__ == '__main__': np.testing.run_module_suite() scikit-image-0.13.1/skimage/feature/tests/test_register_translation.py000066400000000000000000000106501316256236300262430ustar00rootroot00000000000000import numpy as np from numpy.testing import assert_allclose, assert_raises from skimage.feature.register_translation import (register_translation, _upsampled_dft) from skimage.data import camera, binary_blobs from scipy.ndimage import fourier_shift from skimage import img_as_float def test_correlation(): reference_image = np.fft.fftn(camera()) shift = (-7, 12) shifted_image = fourier_shift(reference_image, shift) # pixel precision result, error, diffphase = register_translation(reference_image, shifted_image, space="fourier") assert_allclose(result[:2], -np.array(shift)) def test_subpixel_precision(): reference_image = np.fft.fftn(camera()) subpixel_shift = (-2.4, 1.32) shifted_image = fourier_shift(reference_image, subpixel_shift) # subpixel precision result, error, diffphase = register_translation(reference_image, shifted_image, 100, space="fourier") assert_allclose(result[:2], -np.array(subpixel_shift), atol=0.05) def test_real_input(): reference_image = camera() subpixel_shift = (-2.4, 1.32) shifted_image = fourier_shift(np.fft.fftn(reference_image), subpixel_shift) shifted_image = np.fft.ifftn(shifted_image) # subpixel precision result, error, diffphase = register_translation(reference_image, shifted_image, 100) assert_allclose(result[:2], -np.array(subpixel_shift), atol=0.05) def test_size_one_dimension_input(): # take a strip of the input image reference_image = np.fft.fftn(camera()[:, 15]).reshape((-1, 1)) subpixel_shift = (-2.4, 4) shifted_image = fourier_shift(reference_image, subpixel_shift) # subpixel precision result, error, diffphase = register_translation(reference_image, shifted_image, 100, space="fourier") assert_allclose(result[:2], -np.array((-2.4, 0)), atol=0.05) def test_3d_input(): phantom = img_as_float(binary_blobs(length=32, n_dim=3)) reference_image = np.fft.fftn(phantom) shift = (-2., 1., 5.) shifted_image = fourier_shift(reference_image, shift) result, error, diffphase = register_translation(reference_image, shifted_image, space="fourier") assert_allclose(result, -np.array(shift), atol=0.05) # subpixel precision not available for 3-D data subpixel_shift = (-2.3, 1., 5.) shifted_image = fourier_shift(reference_image, subpixel_shift) result, error, diffphase = register_translation(reference_image, shifted_image, space="fourier") assert_allclose(result, -np.array(shift), atol=0.5) assert_raises(NotImplementedError, register_translation, reference_image, shifted_image, upsample_factor=100, space="fourier") def test_unknown_space_input(): image = np.ones((5, 5)) assert_raises(ValueError, register_translation, image, image, space="frank") def test_wrong_input(): # Dimensionality mismatch image = np.ones((5, 5, 1)) template = np.ones((5, 5)) assert_raises(ValueError, register_translation, template, image) # Greater than 2 dimensions does not support subpixel precision # (TODO: should support 3D at some point.) image = np.ones((5, 5, 5)) template = np.ones((5, 5, 5)) assert_raises(NotImplementedError, register_translation, template, image, 2) # Size mismatch image = np.ones((5, 5)) template = np.ones((4, 4)) assert_raises(ValueError, register_translation, template, image) def test_mismatch_upsampled_region_size(): assert_raises(ValueError, _upsampled_dft, np.ones((4, 4)), upsampled_region_size=[3, 2, 1, 4]) def test_mismatch_offsets_size(): assert_raises(ValueError, _upsampled_dft, np.ones((4, 4)), 3, axis_offsets=[3, 2, 1, 4]) if __name__ == "__main__": from numpy import testing testing.run_module_suite() scikit-image-0.13.1/skimage/feature/tests/test_template.py000066400000000000000000000136101316256236300236130ustar00rootroot00000000000000import numpy as np from numpy.testing import assert_almost_equal, assert_equal, assert_raises from skimage import data, img_as_float from skimage.morphology import diamond from skimage.feature import match_template, peak_local_max def test_template(): size = 100 # Float prefactors ensure that image range is between 0 and 1 image = 0.5 * np.ones((400, 400)) target = 0.1 * (np.tri(size) + np.tri(size)[::-1]) target_positions = [(50, 50), (200, 200)] for x, y in target_positions: image[x:x + size, y:y + size] = target np.random.seed(1) image += 0.1 * np.random.uniform(size=(400, 400)) result = match_template(image, target) delta = 5 positions = peak_local_max(result, min_distance=delta) if len(positions) > 2: # Keep the two maximum peaks. intensities = result[tuple(positions.T)] i_maxsort = np.argsort(intensities)[::-1] positions = positions[i_maxsort][:2] # Sort so that order matches `target_positions`. positions = positions[np.argsort(positions[:, 0])] for xy_target, xy in zip(target_positions, positions): yield assert_almost_equal, xy, xy_target def test_normalization(): """Test that `match_template` gives the correct normalization. Normalization gives 1 for a perfect match and -1 for an inverted-match. This test adds positive and negative squares to a zero-array and matches the array with a positive template. """ n = 5 N = 20 ipos, jpos = (2, 3) ineg, jneg = (12, 11) image = 0.5 * np.ones((N, N)) image[ipos:ipos + n, jpos:jpos + n] = 1 image[ineg:ineg + n, jneg:jneg + n] = 0 # white square with a black border template = np.zeros((n + 2, n + 2)) template[1:1 + n, 1:1 + n] = 1 result = match_template(image, template) # get the max and min results. sorted_result = np.argsort(result.flat) iflat_min = sorted_result[0] iflat_max = sorted_result[-1] min_result = np.unravel_index(iflat_min, result.shape) max_result = np.unravel_index(iflat_max, result.shape) # shift result by 1 because of template border assert np.all((np.array(min_result) + 1) == (ineg, jneg)) assert np.all((np.array(max_result) + 1) == (ipos, jpos)) assert np.allclose(result.flat[iflat_min], -1) assert np.allclose(result.flat[iflat_max], 1) def test_no_nans(): """Test that `match_template` doesn't return NaN values. When image values are only slightly different, floating-point errors can cause a subtraction inside of a square root to go negative (without an explicit check that was added to `match_template`). """ np.random.seed(1) image = 0.5 + 1e-9 * np.random.normal(size=(20, 20)) template = np.ones((6, 6)) template[:3, :] = 0 result = match_template(image, template) assert not np.any(np.isnan(result)) def test_switched_arguments(): image = np.ones((5, 5)) template = np.ones((3, 3)) assert_raises(ValueError, match_template, template, image) def test_pad_input(): """Test `match_template` when `pad_input=True`. This test places two full templates (one with values lower than the image mean, the other higher) and two half templates, which are on the edges of the image. The two full templates should score the top (positive and negative) matches and the centers of the half templates should score 2nd. """ # Float prefactors ensure that image range is between 0 and 1 template = 0.5 * diamond(2) image = 0.5 * np.ones((9, 19)) mid = slice(2, 7) image[mid, :3] -= template[:, -3:] # half min template centered at 0 image[mid, 4:9] += template # full max template centered at 6 image[mid, -9:-4] -= template # full min template centered at 12 image[mid, -3:] += template[:, :3] # half max template centered at 18 result = match_template(image, template, pad_input=True, constant_values=image.mean()) # get the max and min results. sorted_result = np.argsort(result.flat) i, j = np.unravel_index(sorted_result[:2], result.shape) assert_equal(j, (12, 0)) i, j = np.unravel_index(sorted_result[-2:], result.shape) assert_equal(j, (18, 6)) def test_3d(): np.random.seed(1) template = np.random.rand(3, 3, 3) image = np.zeros((12, 12, 12)) image[3:6, 5:8, 4:7] = template result = match_template(image, template) assert_equal(result.shape, (10, 10, 10)) assert_equal(np.unravel_index(result.argmax(), result.shape), (3, 5, 4)) def test_3d_pad_input(): np.random.seed(1) template = np.random.rand(3, 3, 3) image = np.zeros((12, 12, 12)) image[3:6, 5:8, 4:7] = template result = match_template(image, template, pad_input=True) assert_equal(result.shape, (12, 12, 12)) assert_equal(np.unravel_index(result.argmax(), result.shape), (4, 6, 5)) def test_padding_reflect(): template = diamond(2) image = np.zeros((10, 10)) image[2:7, :3] = template[:, -3:] result = match_template(image, template, pad_input=True, mode='reflect') assert_equal(np.unravel_index(result.argmax(), result.shape), (4, 0)) def test_wrong_input(): image = np.ones((5, 5, 1)) template = np.ones((3, 3)) assert_raises(ValueError, match_template, template, image) image = np.ones((5, 5)) template = np.ones((3, 3, 2)) assert_raises(ValueError, match_template, template, image) image = np.ones((5, 5, 3, 3)) template = np.ones((3, 3, 2)) assert_raises(ValueError, match_template, template, image) def test_bounding_values(): image = img_as_float(data.page()) template = np.zeros((3, 3)) template[1, 1] = 1 result = match_template(img_as_float(data.page()), template) print(result.max()) assert result.max() < 1 + 1e-7 assert result.min() > -1 - 1e-7 if __name__ == "__main__": from numpy import testing testing.run_module_suite() scikit-image-0.13.1/skimage/feature/tests/test_texture.py000066400000000000000000000275141316256236300235100ustar00rootroot00000000000000import numpy as np from skimage.feature import (greycomatrix, greycoprops, local_binary_pattern, multiblock_lbp) from skimage._shared.testing import test_parallel from skimage.transform import integral_image class TestGLCM(): def setup(self): self.image = np.array([[0, 0, 1, 1], [0, 0, 1, 1], [0, 2, 2, 2], [2, 2, 3, 3]], dtype=np.uint8) @test_parallel() def test_output_angles(self): result = greycomatrix(self.image, [1], [0, np.pi / 4, np.pi / 2, 3 * np.pi / 4], 4) assert result.shape == (4, 4, 1, 4) expected1 = np.array([[2, 2, 1, 0], [0, 2, 0, 0], [0, 0, 3, 1], [0, 0, 0, 1]], dtype=np.uint32) np.testing.assert_array_equal(result[:, :, 0, 0], expected1) expected2 = np.array([[1, 1, 3, 0], [0, 1, 1, 0], [0, 0, 0, 2], [0, 0, 0, 0]], dtype=np.uint32) np.testing.assert_array_equal(result[:, :, 0, 1], expected2) expected3 = np.array([[3, 0, 2, 0], [0, 2, 2, 0], [0, 0, 1, 2], [0, 0, 0, 0]], dtype=np.uint32) np.testing.assert_array_equal(result[:, :, 0, 2], expected3) expected4 = np.array([[2, 0, 0, 0], [1, 1, 2, 0], [0, 0, 2, 1], [0, 0, 0, 0]], dtype=np.uint32) np.testing.assert_array_equal(result[:, :, 0, 3], expected4) def test_output_symmetric_1(self): result = greycomatrix(self.image, [1], [np.pi / 2], 4, symmetric=True) assert result.shape == (4, 4, 1, 1) expected = np.array([[6, 0, 2, 0], [0, 4, 2, 0], [2, 2, 2, 2], [0, 0, 2, 0]], dtype=np.uint32) np.testing.assert_array_equal(result[:, :, 0, 0], expected) def test_error_raise_float(self): for dtype in [np.float, np.double, np.float16, np.float32, np.float64]: np.testing.assert_raises(ValueError, greycomatrix, self.image.astype(dtype), [1], [np.pi], 4) def test_error_raise_int_types(self): for dtype in [np.int16, np.int32, np.int64, np.uint16, np.uint32, np.uint64]: np.testing.assert_raises(ValueError, greycomatrix, self.image.astype(dtype), [1], [np.pi]) def test_error_raise_negative(self): np.testing.assert_raises(ValueError, greycomatrix, self.image.astype(np.int16) - 1, [1], [np.pi], 4) def test_error_raise_levels_smaller_max(self): np.testing.assert_raises(ValueError, greycomatrix, self.image - 1, [1], [np.pi], 3) def test_image_data_types(self): for dtype in [np.uint16, np.uint32, np.uint64, np.int16, np.int32, np.int64]: img = self.image.astype(dtype) result = greycomatrix(img, [1], [np.pi / 2], 4, symmetric=True) assert result.shape == (4, 4, 1, 1) expected = np.array([[6, 0, 2, 0], [0, 4, 2, 0], [2, 2, 2, 2], [0, 0, 2, 0]], dtype=np.uint32) np.testing.assert_array_equal(result[:, :, 0, 0], expected) return def test_output_distance(self): im = np.array([[0, 0, 0, 0], [1, 0, 0, 1], [2, 0, 0, 2], [3, 0, 0, 3]], dtype=np.uint8) result = greycomatrix(im, [3], [0], 4, symmetric=False) expected = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]], dtype=np.uint32) np.testing.assert_array_equal(result[:, :, 0, 0], expected) def test_output_combo(self): im = np.array([[0], [1], [2], [3]], dtype=np.uint8) result = greycomatrix(im, [1, 2], [0, np.pi / 2], 4) assert result.shape == (4, 4, 2, 2) z = np.zeros((4, 4), dtype=np.uint32) e1 = np.array([[0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 0, 0]], dtype=np.uint32) e2 = np.array([[0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 0, 0], [0, 0, 0, 0]], dtype=np.uint32) np.testing.assert_array_equal(result[:, :, 0, 0], z) np.testing.assert_array_equal(result[:, :, 1, 0], z) np.testing.assert_array_equal(result[:, :, 0, 1], e1) np.testing.assert_array_equal(result[:, :, 1, 1], e2) def test_output_empty(self): result = greycomatrix(self.image, [10], [0], 4) np.testing.assert_array_equal(result[:, :, 0, 0], np.zeros((4, 4), dtype=np.uint32)) result = greycomatrix(self.image, [10], [0], 4, normed=True) np.testing.assert_array_equal(result[:, :, 0, 0], np.zeros((4, 4), dtype=np.uint32)) def test_normed_symmetric(self): result = greycomatrix(self.image, [1, 2, 3], [0, np.pi / 2, np.pi], 4, normed=True, symmetric=True) for d in range(result.shape[2]): for a in range(result.shape[3]): np.testing.assert_almost_equal(result[:, :, d, a].sum(), 1.0) np.testing.assert_array_equal(result[:, :, d, a], result[:, :, d, a].transpose()) def test_contrast(self): result = greycomatrix(self.image, [1, 2], [0], 4, normed=True, symmetric=True) result = np.round(result, 3) contrast = greycoprops(result, 'contrast') np.testing.assert_almost_equal(contrast[0, 0], 0.586) def test_dissimilarity(self): result = greycomatrix(self.image, [1], [0, np.pi / 2], 4, normed=True, symmetric=True) result = np.round(result, 3) dissimilarity = greycoprops(result, 'dissimilarity') np.testing.assert_almost_equal(dissimilarity[0, 0], 0.418) def test_dissimilarity_2(self): result = greycomatrix(self.image, [1, 3], [np.pi / 2], 4, normed=True, symmetric=True) result = np.round(result, 3) dissimilarity = greycoprops(result, 'dissimilarity')[0, 0] np.testing.assert_almost_equal(dissimilarity, 0.664) def test_invalid_property(self): result = greycomatrix(self.image, [1], [0], 4) np.testing.assert_raises(ValueError, greycoprops, result, 'ABC') def test_homogeneity(self): result = greycomatrix(self.image, [1], [0, 6], 4, normed=True, symmetric=True) homogeneity = greycoprops(result, 'homogeneity')[0, 0] np.testing.assert_almost_equal(homogeneity, 0.80833333) def test_energy(self): result = greycomatrix(self.image, [1], [0, 4], 4, normed=True, symmetric=True) energy = greycoprops(result, 'energy')[0, 0] np.testing.assert_almost_equal(energy, 0.38188131) def test_correlation(self): result = greycomatrix(self.image, [1, 2], [0], 4, normed=True, symmetric=True) energy = greycoprops(result, 'correlation') np.testing.assert_almost_equal(energy[0, 0], 0.71953255) np.testing.assert_almost_equal(energy[1, 0], 0.41176470) def test_uniform_properties(self): im = np.ones((4, 4), dtype=np.uint8) result = greycomatrix(im, [1, 2, 8], [0, np.pi / 2], 4, normed=True, symmetric=True) for prop in ['contrast', 'dissimilarity', 'homogeneity', 'energy', 'correlation', 'ASM']: greycoprops(result, prop) class TestLBP(): def setup(self): self.image = np.array([[255, 6, 255, 0, 141, 0], [ 48, 250, 204, 166, 223, 63], [ 8, 0, 159, 50, 255, 30], [167, 255, 63, 40, 128, 255], [ 0, 255, 30, 34, 255, 24], [146, 241, 255, 0, 189, 126]], dtype='double') @test_parallel() def test_default(self): lbp = local_binary_pattern(self.image, 8, 1, 'default') ref = np.array([[ 0, 251, 0, 255, 96, 255], [143, 0, 20, 153, 64, 56], [238, 255, 12, 191, 0, 252], [129, 64., 62, 159, 199, 0], [255, 4, 255, 175, 0, 254], [ 3, 5, 0, 255, 4, 24]]) np.testing.assert_array_equal(lbp, ref) def test_ror(self): lbp = local_binary_pattern(self.image, 8, 1, 'ror') ref = np.array([[ 0, 127, 0, 255, 3, 255], [ 31, 0, 5, 51, 1, 7], [119, 255, 3, 127, 0, 63], [ 3, 1, 31, 63, 31, 0], [255, 1, 255, 95, 0, 127], [ 3, 5, 0, 255, 1, 3]]) np.testing.assert_array_equal(lbp, ref) def test_uniform(self): lbp = local_binary_pattern(self.image, 8, 1, 'uniform') ref = np.array([[0, 7, 0, 8, 2, 8], [5, 0, 9, 9, 1, 3], [9, 8, 2, 7, 0, 6], [2, 1, 5, 6, 5, 0], [8, 1, 8, 9, 0, 7], [2, 9, 0, 8, 1, 2]]) np.testing.assert_array_equal(lbp, ref) def test_var(self): # Test idea: mean of variance is estimate of overall variance. # Fix random seed for test stability. np.random.seed(13141516) # Create random image with known variance. image = np.random.rand(500, 500) target_std = 0.3 image = image / image.std() * target_std # Use P=4 to avoid interpolation effects P, R = 4, 1 lbp = local_binary_pattern(image, P, R, 'var') # Take central part to avoid border effect. lbp = lbp[5:-5, 5:-5] # The LBP variance is biased (ddof=0), correct for that. expected = target_std**2 * (P-1)/P np.testing.assert_almost_equal(lbp.mean(), expected, 4) def test_nri_uniform(self): lbp = local_binary_pattern(self.image, 8, 1, 'nri_uniform') ref = np.array([[ 0, 54, 0, 57, 12, 57], [34, 0, 58, 58, 3, 22], [58, 57, 15, 50, 0, 47], [10, 3, 40, 42, 35, 0], [57, 7, 57, 58, 0, 56], [ 9, 58, 0, 57, 7, 14]]) np.testing.assert_array_almost_equal(lbp, ref) class TestMBLBP(): def test_single_mblbp(self): # Create dummy matrix where first and fifth rectangles have greater # value than the central one. Therefore, the following bits # should be 1. test_img = np.zeros((9, 9), dtype='uint8') test_img[3:6, 3:6] = 1 test_img[:3, :3] = 255 test_img[6:, 6:] = 255 # MB-LBP is filled in reverse order. So the first and fifth bits from # the end should be filled. correct_answer = 0b10001000 int_img = integral_image(test_img) lbp_code = multiblock_lbp(int_img, 0, 0, 3, 3) np.testing.assert_equal(lbp_code, correct_answer) if __name__ == '__main__': np.testing.run_module_suite() scikit-image-0.13.1/skimage/feature/tests/test_util.py000066400000000000000000000054061316256236300227610ustar00rootroot00000000000000import numpy as np try: import matplotlib.pyplot as plt except ImportError: plt = None from numpy.testing import assert_equal, assert_raises from skimage.feature.util import (FeatureDetector, DescriptorExtractor, _prepare_grayscale_input_2D, _mask_border_keypoints, plot_matches) def test_feature_detector(): assert_raises(NotImplementedError, FeatureDetector().detect, None) def test_descriptor_extractor(): assert_raises(NotImplementedError, DescriptorExtractor().extract, None, None) def test_prepare_grayscale_input_2D(): assert_raises(ValueError, _prepare_grayscale_input_2D, np.zeros((3, 3, 3))) assert_raises(ValueError, _prepare_grayscale_input_2D, np.zeros((3, 1))) assert_raises(ValueError, _prepare_grayscale_input_2D, np.zeros((3, 1, 1))) img = _prepare_grayscale_input_2D(np.zeros((3, 3))) img = _prepare_grayscale_input_2D(np.zeros((3, 3, 1))) img = _prepare_grayscale_input_2D(np.zeros((1, 3, 3))) def test_mask_border_keypoints(): keypoints = np.array([[0, 0], [1, 1], [2, 2], [3, 3], [4, 4]]) assert_equal(_mask_border_keypoints((10, 10), keypoints, 0), [1, 1, 1, 1, 1]) assert_equal(_mask_border_keypoints((10, 10), keypoints, 2), [0, 0, 1, 1, 1]) assert_equal(_mask_border_keypoints((4, 4), keypoints, 2), [0, 0, 1, 0, 0]) assert_equal(_mask_border_keypoints((10, 10), keypoints, 5), [0, 0, 0, 0, 0]) assert_equal(_mask_border_keypoints((10, 10), keypoints, 4), [0, 0, 0, 0, 1]) @np.testing.decorators.skipif(plt is None) def test_plot_matches(): fig, ax = plt.subplots(nrows=1, ncols=1) shapes = (((10, 10), (10, 10)), ((10, 10), (12, 10)), ((10, 10), (10, 12)), ((10, 10), (12, 12)), ((12, 10), (10, 10)), ((10, 12), (10, 10)), ((12, 12), (10, 10))) keypoints1 = 10 * np.random.rand(10, 2) keypoints2 = 10 * np.random.rand(10, 2) idxs1 = np.random.randint(10, size=10) idxs2 = np.random.randint(10, size=10) matches = np.column_stack((idxs1, idxs2)) for shape1, shape2 in shapes: img1 = np.zeros(shape1) img2 = np.zeros(shape2) plot_matches(ax, img1, img2, keypoints1, keypoints2, matches) plot_matches(ax, img1, img2, keypoints1, keypoints2, matches, only_matches=True) plot_matches(ax, img1, img2, keypoints1, keypoints2, matches, keypoints_color='r') plot_matches(ax, img1, img2, keypoints1, keypoints2, matches, matches_color='r') if __name__ == '__main__': from numpy import testing testing.run_module_suite() scikit-image-0.13.1/skimage/feature/texture.py000066400000000000000000000424341316256236300213050ustar00rootroot00000000000000""" Methods to characterize image textures. """ import numpy as np from .._shared.utils import assert_nD from ..util import img_as_float from ..color import gray2rgb from ._texture import (_glcm_loop, _local_binary_pattern, _multiblock_lbp) def greycomatrix(image, distances, angles, levels=None, symmetric=False, normed=False): """Calculate the grey-level co-occurrence matrix. A grey level co-occurrence matrix is a histogram of co-occurring greyscale values at a given offset over an image. Parameters ---------- image : array_like Integer typed input image. Only positive valued images are supported. If type is other than uint8, the argument `levels` needs to be set. distances : array_like List of pixel pair distance offsets. angles : array_like List of pixel pair angles in radians. levels : int, optional The input image should contain integers in [0, `levels`-1], where levels indicate the number of grey-levels counted (typically 256 for an 8-bit image). This argument is required for 16-bit images or higher and is typically the maximum of the image. As the output matrix is at least `levels` x `levels`, it might be preferable to use binning of the input image rather than large values for `levels`. symmetric : bool, optional If True, the output matrix `P[:, :, d, theta]` is symmetric. This is accomplished by ignoring the order of value pairs, so both (i, j) and (j, i) are accumulated when (i, j) is encountered for a given offset. The default is False. normed : bool, optional If True, normalize each matrix `P[:, :, d, theta]` by dividing by the total number of accumulated co-occurrences for the given offset. The elements of the resulting matrix sum to 1. The default is False. Returns ------- P : 4-D ndarray The grey-level co-occurrence histogram. The value `P[i,j,d,theta]` is the number of times that grey-level `j` occurs at a distance `d` and at an angle `theta` from grey-level `i`. If `normed` is `False`, the output is of type uint32, otherwise it is float64. The dimensions are: levels x levels x number of distances x number of angles. References ---------- .. [1] The GLCM Tutorial Home Page, http://www.fp.ucalgary.ca/mhallbey/tutorial.htm .. [2] Pattern Recognition Engineering, Morton Nadler & Eric P. Smith .. [3] Wikipedia, http://en.wikipedia.org/wiki/Co-occurrence_matrix Examples -------- Compute 2 GLCMs: One for a 1-pixel offset to the right, and one for a 1-pixel offset upwards. >>> image = np.array([[0, 0, 1, 1], ... [0, 0, 1, 1], ... [0, 2, 2, 2], ... [2, 2, 3, 3]], dtype=np.uint8) >>> result = greycomatrix(image, [1], [0, np.pi/4, np.pi/2, 3*np.pi/4], ... levels=4) >>> result[:, :, 0, 0] array([[2, 2, 1, 0], [0, 2, 0, 0], [0, 0, 3, 1], [0, 0, 0, 1]], dtype=uint32) >>> result[:, :, 0, 1] array([[1, 1, 3, 0], [0, 1, 1, 0], [0, 0, 0, 2], [0, 0, 0, 0]], dtype=uint32) >>> result[:, :, 0, 2] array([[3, 0, 2, 0], [0, 2, 2, 0], [0, 0, 1, 2], [0, 0, 0, 0]], dtype=uint32) >>> result[:, :, 0, 3] array([[2, 0, 0, 0], [1, 1, 2, 0], [0, 0, 2, 1], [0, 0, 0, 0]], dtype=uint32) """ assert_nD(image, 2) assert_nD(distances, 1, 'distances') assert_nD(angles, 1, 'angles') image = np.ascontiguousarray(image) image_max = image.max() if np.issubdtype(image.dtype, np.float): raise ValueError("Float images are not supported by greycomatrix. " "Convert the image to an unsigned integer type.") # for image type > 8bit, levels must be set. if image.dtype not in (np.uint8, np.int8) and levels is None: raise ValueError("The levels argument is required for data types " "other than uint8. The resulting matrix will be at " "least levels ** 2 in size.") if np.issubdtype(image.dtype, np.signedinteger) and np.any(image < 0): raise ValueError("Negative-valued images are not supported.") if levels is None: levels = 256 if image_max >= levels: raise ValueError("The maximum grayscale value in the image should be " "smaller than the number of levels.") distances = np.ascontiguousarray(distances, dtype=np.float64) angles = np.ascontiguousarray(angles, dtype=np.float64) P = np.zeros((levels, levels, len(distances), len(angles)), dtype=np.uint32, order='C') # count co-occurences _glcm_loop(image, distances, angles, levels, P) # make each GLMC symmetric if symmetric: Pt = np.transpose(P, (1, 0, 2, 3)) P = P + Pt # normalize each GLMC if normed: P = P.astype(np.float64) glcm_sums = np.apply_over_axes(np.sum, P, axes=(0, 1)) glcm_sums[glcm_sums == 0] = 1 P /= glcm_sums return P def greycoprops(P, prop='contrast'): """Calculate texture properties of a GLCM. Compute a feature of a grey level co-occurrence matrix to serve as a compact summary of the matrix. The properties are computed as follows: - 'contrast': :math:`\\sum_{i,j=0}^{levels-1} P_{i,j}(i-j)^2` - 'dissimilarity': :math:`\\sum_{i,j=0}^{levels-1}P_{i,j}|i-j|` - 'homogeneity': :math:`\\sum_{i,j=0}^{levels-1}\\frac{P_{i,j}}{1+(i-j)^2}` - 'ASM': :math:`\\sum_{i,j=0}^{levels-1} P_{i,j}^2` - 'energy': :math:`\\sqrt{ASM}` - 'correlation': .. math:: \\sum_{i,j=0}^{levels-1} P_{i,j}\\left[\\frac{(i-\\mu_i) \\ (j-\\mu_j)}{\\sqrt{(\\sigma_i^2)(\\sigma_j^2)}}\\right] Parameters ---------- P : ndarray Input array. `P` is the grey-level co-occurrence histogram for which to compute the specified property. The value `P[i,j,d,theta]` is the number of times that grey-level j occurs at a distance d and at an angle theta from grey-level i. prop : {'contrast', 'dissimilarity', 'homogeneity', 'energy', \ 'correlation', 'ASM'}, optional The property of the GLCM to compute. The default is 'contrast'. Returns ------- results : 2-D ndarray 2-dimensional array. `results[d, a]` is the property 'prop' for the d'th distance and the a'th angle. References ---------- .. [1] The GLCM Tutorial Home Page, http://www.fp.ucalgary.ca/mhallbey/tutorial.htm Examples -------- Compute the contrast for GLCMs with distances [1, 2] and angles [0 degrees, 90 degrees] >>> image = np.array([[0, 0, 1, 1], ... [0, 0, 1, 1], ... [0, 2, 2, 2], ... [2, 2, 3, 3]], dtype=np.uint8) >>> g = greycomatrix(image, [1, 2], [0, np.pi/2], levels=4, ... normed=True, symmetric=True) >>> contrast = greycoprops(g, 'contrast') >>> contrast array([[ 0.58333333, 1. ], [ 1.25 , 2.75 ]]) """ assert_nD(P, 4, 'P') (num_level, num_level2, num_dist, num_angle) = P.shape assert num_level == num_level2 assert num_dist > 0 assert num_angle > 0 # create weights for specified property I, J = np.ogrid[0:num_level, 0:num_level] if prop == 'contrast': weights = (I - J) ** 2 elif prop == 'dissimilarity': weights = np.abs(I - J) elif prop == 'homogeneity': weights = 1. / (1. + (I - J) ** 2) elif prop in ['ASM', 'energy', 'correlation']: pass else: raise ValueError('%s is an invalid property' % (prop)) # compute property for each GLCM if prop == 'energy': asm = np.apply_over_axes(np.sum, (P ** 2), axes=(0, 1))[0, 0] results = np.sqrt(asm) elif prop == 'ASM': results = np.apply_over_axes(np.sum, (P ** 2), axes=(0, 1))[0, 0] elif prop == 'correlation': results = np.zeros((num_dist, num_angle), dtype=np.float64) I = np.array(range(num_level)).reshape((num_level, 1, 1, 1)) J = np.array(range(num_level)).reshape((1, num_level, 1, 1)) diff_i = I - np.apply_over_axes(np.sum, (I * P), axes=(0, 1))[0, 0] diff_j = J - np.apply_over_axes(np.sum, (J * P), axes=(0, 1))[0, 0] std_i = np.sqrt(np.apply_over_axes(np.sum, (P * (diff_i) ** 2), axes=(0, 1))[0, 0]) std_j = np.sqrt(np.apply_over_axes(np.sum, (P * (diff_j) ** 2), axes=(0, 1))[0, 0]) cov = np.apply_over_axes(np.sum, (P * (diff_i * diff_j)), axes=(0, 1))[0, 0] # handle the special case of standard deviations near zero mask_0 = std_i < 1e-15 mask_0[std_j < 1e-15] = True results[mask_0] = 1 # handle the standard case mask_1 = mask_0 == False results[mask_1] = cov[mask_1] / (std_i[mask_1] * std_j[mask_1]) elif prop in ['contrast', 'dissimilarity', 'homogeneity']: weights = weights.reshape((num_level, num_level, 1, 1)) results = np.apply_over_axes(np.sum, (P * weights), axes=(0, 1))[0, 0] return results def local_binary_pattern(image, P, R, method='default'): """Gray scale and rotation invariant LBP (Local Binary Patterns). LBP is an invariant descriptor that can be used for texture classification. Parameters ---------- image : (N, M) array Graylevel image. P : int Number of circularly symmetric neighbour set points (quantization of the angular space). R : float Radius of circle (spatial resolution of the operator). method : {'default', 'ror', 'uniform', 'var'} Method to determine the pattern. * 'default': original local binary pattern which is gray scale but not rotation invariant. * 'ror': extension of default implementation which is gray scale and rotation invariant. * 'uniform': improved rotation invariance with uniform patterns and finer quantization of the angular space which is gray scale and rotation invariant. * 'nri_uniform': non rotation-invariant uniform patterns variant which is only gray scale invariant [2]_. * 'var': rotation invariant variance measures of the contrast of local image texture which is rotation but not gray scale invariant. Returns ------- output : (N, M) array LBP image. References ---------- .. [1] Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns. Timo Ojala, Matti Pietikainen, Topi Maenpaa. http://www.rafbis.it/biplab15/images/stories/docenti/Danielriccio/Articoliriferimento/LBP.pdf, 2002. .. [2] Face recognition with local binary patterns. Timo Ahonen, Abdenour Hadid, Matti Pietikainen, http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.214.6851, 2004. """ assert_nD(image, 2) methods = { 'default': ord('D'), 'ror': ord('R'), 'uniform': ord('U'), 'nri_uniform': ord('N'), 'var': ord('V') } image = np.ascontiguousarray(image, dtype=np.double) output = _local_binary_pattern(image, P, R, methods[method.lower()]) return output def multiblock_lbp(int_image, r, c, width, height): """Multi-block local binary pattern (MB-LBP). The features are calculated similarly to local binary patterns (LBPs), (See :py:meth:`local_binary_pattern`) except that summed blocks are used instead of individual pixel values. MB-LBP is an extension of LBP that can be computed on multiple scales in constant time using the integral image. Nine equally-sized rectangles are used to compute a feature. For each rectangle, the sum of the pixel intensities is computed. Comparisons of these sums to that of the central rectangle determine the feature, similarly to LBP. Parameters ---------- int_image : (N, M) array Integral image. r : int Row-coordinate of top left corner of a rectangle containing feature. c : int Column-coordinate of top left corner of a rectangle containing feature. width : int Width of one of the 9 equal rectangles that will be used to compute a feature. height : int Height of one of the 9 equal rectangles that will be used to compute a feature. Returns ------- output : int 8-bit MB-LBP feature descriptor. References ---------- .. [1] Face Detection Based on Multi-Block LBP Representation. Lun Zhang, Rufeng Chu, Shiming Xiang, Shengcai Liao, Stan Z. Li http://www.cbsr.ia.ac.cn/users/scliao/papers/Zhang-ICB07-MBLBP.pdf """ int_image = np.ascontiguousarray(int_image, dtype=np.float32) lbp_code = _multiblock_lbp(int_image, r, c, width, height) return lbp_code def draw_multiblock_lbp(img, r, c, width, height, lbp_code=0, color_greater_block=[1, 1, 1], color_less_block=[0, 0.69, 0.96], alpha=0.5 ): """Multi-block local binary pattern visualization. Blocks with higher sums are colored with alpha-blended white rectangles, whereas blocks with lower sums are colored alpha-blended cyan. Colors and the `alpha` parameter can be changed. Parameters ---------- img : ndarray of float or uint Image on which to visualize the pattern. r : int Row-coordinate of top left corner of a rectangle containing feature. c : int Column-coordinate of top left corner of a rectangle containing feature. width : int Width of one of 9 equal rectangles that will be used to compute a feature. height : int Height of one of 9 equal rectangles that will be used to compute a feature. lbp_code : int The descriptor of feature to visualize. If not provided, the descriptor with 0 value will be used. color_greater_block : list of 3 floats Floats specifying the color for the block that has greater intensity value. They should be in the range [0, 1]. Corresponding values define (R, G, B) values. Default value is white [1, 1, 1]. color_greater_block : list of 3 floats Floats specifying the color for the block that has greater intensity value. They should be in the range [0, 1]. Corresponding values define (R, G, B) values. Default value is cyan [0, 0.69, 0.96]. alpha : float Value in the range [0, 1] that specifies opacity of visualization. 1 - fully transparent, 0 - opaque. Returns ------- output : ndarray of float Image with MB-LBP visualization. References ---------- .. [1] Face Detection Based on Multi-Block LBP Representation. Lun Zhang, Rufeng Chu, Shiming Xiang, Shengcai Liao, Stan Z. Li http://www.cbsr.ia.ac.cn/users/scliao/papers/Zhang-ICB07-MBLBP.pdf """ # Default colors for regions. # White is for the blocks that are brighter. # Cyan is for the blocks that has less intensity. color_greater_block = np.asarray(color_greater_block, dtype=np.float64) color_less_block = np.asarray(color_less_block, dtype=np.float64) # Copy array to avoid the changes to the original one. output = np.copy(img) # As the visualization uses RGB color we need 3 bands. if len(img.shape) < 3: output = gray2rgb(img) # Colors are specified in floats. output = img_as_float(output) # Offsets of neighbour rectangles relative to central one. # It has order starting from top left and going clockwise. neighbour_rect_offsets = ((-1, -1), (-1, 0), (-1, 1), (0, 1), (1, 1), (1, 0), (1, -1), (0, -1)) # Pre-multiply the offsets with width and height. neighbour_rect_offsets = np.array(neighbour_rect_offsets) neighbour_rect_offsets[:, 0] *= height neighbour_rect_offsets[:, 1] *= width # Top-left coordinates of central rectangle. central_rect_r = r + height central_rect_c = c + width for element_num, offset in enumerate(neighbour_rect_offsets): offset_r, offset_c = offset curr_r = central_rect_r + offset_r curr_c = central_rect_c + offset_c has_greater_value = lbp_code & (1 << (7-element_num)) # Mix-in the visualization colors. if has_greater_value: new_value = ((1-alpha) * output[curr_r:curr_r+height, curr_c:curr_c+width] + alpha * color_greater_block) output[curr_r:curr_r+height, curr_c:curr_c+width] = new_value else: new_value = ((1-alpha) * output[curr_r:curr_r+height, curr_c:curr_c+width] + alpha * color_less_block) output[curr_r:curr_r+height, curr_c:curr_c+width] = new_value return output scikit-image-0.13.1/skimage/feature/util.py000066400000000000000000000111661316256236300205600ustar00rootroot00000000000000import numpy as np from ..util import img_as_float from .._shared.utils import assert_nD class FeatureDetector(object): def __init__(self): self.keypoints_ = np.array([]) def detect(self, image): """Detect keypoints in image. Parameters ---------- image : 2D array Input image. """ raise NotImplementedError() class DescriptorExtractor(object): def __init__(self): self.descriptors_ = np.array([]) def extract(self, image, keypoints): """Extract feature descriptors in image for given keypoints. Parameters ---------- image : 2D array Input image. keypoints : (N, 2) array Keypoint locations as ``(row, col)``. """ raise NotImplementedError() def plot_matches(ax, image1, image2, keypoints1, keypoints2, matches, keypoints_color='k', matches_color=None, only_matches=False): """Plot matched features. Parameters ---------- ax : matplotlib.axes.Axes Matches and image are drawn in this ax. image1 : (N, M [, 3]) array First grayscale or color image. image2 : (N, M [, 3]) array Second grayscale or color image. keypoints1 : (K1, 2) array First keypoint coordinates as ``(row, col)``. keypoints2 : (K2, 2) array Second keypoint coordinates as ``(row, col)``. matches : (Q, 2) array Indices of corresponding matches in first and second set of descriptors, where ``matches[:, 0]`` denote the indices in the first and ``matches[:, 1]`` the indices in the second set of descriptors. keypoints_color : matplotlib color, optional Color for keypoint locations. matches_color : matplotlib color, optional Color for lines which connect keypoint matches. By default the color is chosen randomly. only_matches : bool, optional Whether to only plot matches and not plot the keypoint locations. """ image1 = img_as_float(image1) image2 = img_as_float(image2) new_shape1 = list(image1.shape) new_shape2 = list(image2.shape) if image1.shape[0] < image2.shape[0]: new_shape1[0] = image2.shape[0] elif image1.shape[0] > image2.shape[0]: new_shape2[0] = image1.shape[0] if image1.shape[1] < image2.shape[1]: new_shape1[1] = image2.shape[1] elif image1.shape[1] > image2.shape[1]: new_shape2[1] = image1.shape[1] if new_shape1 != image1.shape: new_image1 = np.zeros(new_shape1, dtype=image1.dtype) new_image1[:image1.shape[0], :image1.shape[1]] = image1 image1 = new_image1 if new_shape2 != image2.shape: new_image2 = np.zeros(new_shape2, dtype=image2.dtype) new_image2[:image2.shape[0], :image2.shape[1]] = image2 image2 = new_image2 image = np.concatenate([image1, image2], axis=1) offset = image1.shape if not only_matches: ax.scatter(keypoints1[:, 1], keypoints1[:, 0], facecolors='none', edgecolors=keypoints_color) ax.scatter(keypoints2[:, 1] + offset[1], keypoints2[:, 0], facecolors='none', edgecolors=keypoints_color) ax.imshow(image, interpolation='nearest', cmap='gray') ax.axis((0, 2 * offset[1], offset[0], 0)) for i in range(matches.shape[0]): idx1 = matches[i, 0] idx2 = matches[i, 1] if matches_color is None: color = np.random.rand(3) else: color = matches_color ax.plot((keypoints1[idx1, 1], keypoints2[idx2, 1] + offset[1]), (keypoints1[idx1, 0], keypoints2[idx2, 0]), '-', color=color) def _prepare_grayscale_input_2D(image): image = np.squeeze(image) assert_nD(image, 2) return img_as_float(image) def _mask_border_keypoints(image_shape, keypoints, distance): """Mask coordinates that are within certain distance from the image border. Parameters ---------- image_shape : (2, ) array_like Shape of the image as ``(rows, cols)``. keypoints : (N, 2) array Keypoint coordinates as ``(rows, cols)``. distance : int Image border distance. Returns ------- mask : (N, ) bool array Mask indicating if pixels are within the image (``True``) or in the border region of the image (``False``). """ rows = image_shape[0] cols = image_shape[1] mask = (((distance - 1) < keypoints[:, 0]) & (keypoints[:, 0] < (rows - distance + 1)) & ((distance - 1) < keypoints[:, 1]) & (keypoints[:, 1] < (cols - distance + 1))) return mask scikit-image-0.13.1/skimage/filters/000077500000000000000000000000001316256236300172415ustar00rootroot00000000000000scikit-image-0.13.1/skimage/filters/LICENSE.txt000066400000000000000000000027201316256236300210650ustar00rootroot00000000000000threshold_li based on imagej code: Copyright (c) 2009 - 2015, Board of Regents of the University of Wisconsin-Madison, Broad Institute of MIT and Harvard, and Max Planck Institute of Molecular Cell Biology and Genetics. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. scikit-image-0.13.1/skimage/filters/__init__.py000066400000000000000000000041541316256236300213560ustar00rootroot00000000000000from .lpi_filter import inverse, wiener, LPIFilter2D from ._gaussian import gaussian from .edges import (sobel, sobel_h, sobel_v, scharr, scharr_h, scharr_v, prewitt, prewitt_h, prewitt_v, roberts, roberts_pos_diag, roberts_neg_diag, laplace) from ._rank_order import rank_order from ._gabor import gabor_kernel, gabor from ._frangi import frangi, hessian from .thresholding import (threshold_local, threshold_adaptive, threshold_otsu, threshold_yen, threshold_isodata, threshold_li, threshold_minimum, threshold_mean, threshold_triangle, threshold_niblack, threshold_sauvola, try_all_threshold) from . import rank from .rank import median from .._shared.utils import deprecated, copy_func gaussian_filter = copy_func(gaussian, name='gaussian_filter') gaussian_filter = deprecated('skimage.filters.gaussian')(gaussian_filter) gabor_filter = copy_func(gabor, name='gabor_filter') gabor_filter = deprecated('skimage.filters.gabor')(gabor_filter) __all__ = ['inverse', 'wiener', 'LPIFilter2D', 'gaussian', 'median', 'sobel', 'sobel_h', 'sobel_v', 'scharr', 'scharr_h', 'scharr_v', 'prewitt', 'prewitt_h', 'prewitt_v', 'roberts', 'roberts_pos_diag', 'roberts_neg_diag', 'laplace', 'denoise_tv_chambolle', 'denoise_bilateral', 'denoise_tv_bregman', 'rank_order', 'gabor_kernel', 'gabor', 'try_all_threshold', 'frangi', 'hessian', 'threshold_adaptive', 'threshold_otsu', 'threshold_yen', 'threshold_isodata', 'threshold_li', 'threshold_minimum', 'threshold_mean', 'threshold_niblack', 'threshold_sauvola', 'threshold_triangle', 'rank'] scikit-image-0.13.1/skimage/filters/_ctmf.pyx000066400000000000000000000722161316256236300211030ustar00rootroot00000000000000'''_ctmf.pyx - constant time per pixel median filtering Reference: S. Perreault and P. Hebert, "Median Filtering in Constant Time", IEEE Transactions on Image Processing, September 2007. Originally part of CellProfiler, code licensed under both GPL and BSD licenses. Website: http://www.cellprofiler.org Copyright (c) 2003-2009 Massachusetts Institute of Technology Copyright (c) 2009-2011 Broad Institute All rights reserved. Original author: Lee Kamentsky ''' import numpy as np cimport numpy as cnp cimport cython from libc.stdlib cimport malloc, free from libc.string cimport memset cdef extern from "../_shared/vectorized_ops.h": void add16(cnp.uint16_t *dest, cnp.uint16_t *src) nogil void sub16(cnp.uint16_t *dest, cnp.uint16_t *src) nogil ############################################################################## # # median_filter - implementation of constant-time median filter with # octagonal shape. The algorithm is derived from # Perreault, "Median Filtering in Constant Time", # IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 16, NO. 9, # SEPTEMBER 2007. # # Inputs: # a 2d array of uint8's to be median filtered # a similarly shaped uint8 masking array with "1" indicating a significant # pixel and "0" indicating a pixel to be masked. # # Outputs: # a 2d median-filtered array # ############################################################################## DTYPE_UINT32 = np.uint32 DTYPE_BOOL = np.bool ctypedef cnp.uint16_t pixel_count_t ########### # # Histograms # # There are five separate histograms for the octagonal filter and # there are two levels (coarse = 16 values, fine = 256 values) # per histogram. There are four histograms to maintain per position # representing the four diagonals of the histogram plus one histogram # for the straight side (which is used for adding and subtracting) # ########### cdef struct HistogramPiece: cnp.uint16_t coarse[16] cnp.uint16_t fine[256] cdef struct Histogram: HistogramPiece top_left # top-left corner HistogramPiece top_right # top-right corner HistogramPiece edge # leading/trailing edge HistogramPiece bottom_left # bottom-left corner HistogramPiece bottom_right # bottom-right corner # The pixel count has the number of pixels histogrammed in # each of the five compartments for this position. This changes # because of the mask # cdef struct PixelCount: pixel_count_t top_left pixel_count_t top_right pixel_count_t edge pixel_count_t bottom_left pixel_count_t bottom_right # # Stride + coordinates: the info we need when computing # relative offsets from the octagon center # cdef struct SCoord: Py_ssize_t stride # add the stride to the memory location Py_ssize_t x Py_ssize_t y cdef struct Histograms: HistogramPiece accumulator # running histogram (32-byte aligned) void *memory # pointer to the unaligned allocated memory Histogram *histogram # pointer to the histogram memory (aligned) PixelCount *pixel_count # pointer to the pixel count memory cnp.uint8_t *data # pointer to the image data cnp.uint8_t *mask # pointer to the image mask cnp.uint8_t *output # pointer to the output array Py_ssize_t column_count # number of columns represented by this # structure Py_ssize_t stripe_length # number of columns including "radius" before # and after Py_ssize_t row_count # number of rows available in image Py_ssize_t current_column # the column being processed Py_ssize_t current_row # the row being processed Py_ssize_t current_stride # offset in data and mask to current location Py_ssize_t radius # the "radius" of the octagon Py_ssize_t a_2 # 1/2 of the length of a side of the octagon # # # The strides are the offsets in the array to the points that need to # be added or removed from a histogram to shift from the previous row # to the current row. # Listed here going clockwise from the trailing edge's top. # (-) = needs to be removed # (+) = needs to be added # # - - # 1.=========2 # 1. 2 # +. +- Y # |. 3 | # |. 3 | # -. X \|/ # 5. 4 v # 5. 4 # +.=========+ # # x --> # SCoord last_top_left # (-) left side of octagon's top - 1 row SCoord top_left # (+) -1 row from trailing edge top SCoord last_top_right # (-) right side of octagon's top - 1 col - 1 row SCoord top_right # (+) -1 col -1 row from leading edge top SCoord last_leading_edge # (-) leading edge (right) top stride - 1 row SCoord leading_edge # (+) leading edge bottom stride SCoord last_bottom_right # (-) leading edge bottom - 1 col SCoord bottom_right # (+) right side of octagon's bottom - 1 col SCoord last_bottom_left # (-) trailing edge bottom - 1 col SCoord bottom_left # (+) left side of octagon's bottom - 1 col Py_ssize_t row_stride # stride between one row and the next Py_ssize_t col_stride # stride between one column and the next # # The running count of pixels in the accumulator # Py_ssize_t accumulator_count # # The percent of pixels within the octagon whose value is # less than or equal to the median-filtered value (e.g. for # median, this is 50, for lower quartile it's 25) # Py_ssize_t percent # # last_update_column keeps track of the column # of the last update # to the fine histogram accumulator. Short-term, the median # stays in one coarse block so only one fine histogram might # need to be updated # Py_ssize_t last_update_column[16] ############################################################################ # # allocate_histograms - allocates the Histograms structure for the run # ############################################################################ cdef Histograms *allocate_histograms(Py_ssize_t rows, Py_ssize_t columns, Py_ssize_t row_stride, Py_ssize_t col_stride, Py_ssize_t radius, Py_ssize_t percent, cnp.uint8_t *data, cnp.uint8_t *mask, cnp.uint8_t *output) nogil: cdef: Py_ssize_t adjusted_stripe_length = columns + 2*radius + 1 Py_ssize_t memory_size void *ptr Histograms *ph Py_ssize_t roundoff Py_ssize_t a, a_2 SCoord *psc memory_size = (adjusted_stripe_length * (sizeof(Histogram) + sizeof(PixelCount)) + sizeof(Histograms) + 64) ptr = malloc(memory_size) memset(ptr, 0, memory_size) # align ph.accumulator to 32-byte boundary roundoff = ( ptr + 31) % 32 ph = ( ptr + 31 - roundoff) if not ptr: return ph ph.memory = ptr ptr = (ph + 1) ph.pixel_count = ptr ptr = (ph.pixel_count + adjusted_stripe_length) # # Align histogram memory to a 32-byte boundary # roundoff = ( ptr + 31) % 32 ptr = ( ptr + 31 - roundoff) ph.histogram = ptr # # Fill in the statistical things we keep around # ph.column_count = columns ph.row_count = rows ph.current_column = -radius ph.stripe_length = adjusted_stripe_length ph.current_row = 0 ph.radius = radius ph.percent = percent ph.row_stride = row_stride ph.col_stride = col_stride ph.data = data ph.mask = mask ph.output = output # # Compute the coordinates of the significant points # (the SCoords) # # First, the length of a side of an octagon, compared # to what we call the radius is: # 2*r # ----------- = a # (1+sqrt(2)) # # a_2 is the offset from the center to each of the octagon # corners # a = (radius * 2.0 / 2.414213) a_2 = a / 2 if a_2 == 0: a_2 = 1 ph.a_2 = a_2 if radius <= a_2: radius = a_2+1 ph.radius = radius ph.last_top_left.x = -a_2 ph.last_top_left.y = -radius - 1 ph.top_left.x = -radius ph.top_left.y = -a_2 - 1 ph.last_top_right.x = a_2 - 1 ph.last_top_right.y = -radius - 1 ph.top_right.x = radius - 1 ph.top_right.y = -a_2 - 1 ph.last_leading_edge.x = radius ph.last_leading_edge.y = -a_2 - 1 ph.leading_edge.x = radius ph.leading_edge.y = a_2 ph.last_bottom_right.x = radius ph.last_bottom_right.y = a_2 ph.bottom_right.x = a_2 ph.bottom_right.y = radius ph.last_bottom_left.x = -radius-1 ph.last_bottom_left.y = a_2 ph.bottom_left.x = -a_2-1 ph.bottom_left.y = radius # # Set the stride of each SCoord based on its x and y # set_stride(ph, &ph.last_top_left) set_stride(ph, &ph.top_left) set_stride(ph, &ph.last_top_right) set_stride(ph, &ph.top_right) set_stride(ph, &ph.last_leading_edge) set_stride(ph, &ph.leading_edge) set_stride(ph, &ph.last_bottom_left) set_stride(ph, &ph.bottom_left) set_stride(ph, &ph.last_bottom_right) set_stride(ph, &ph.bottom_right) return ph ############################################################################ # # free_histograms - frees the Histograms structure # ############################################################################ cdef void free_histograms(Histograms *ph) nogil: free(ph.memory) ############################################################################ # # set_stride - set the stride of a SCoord from its X and Y # ############################################################################ cdef void set_stride(Histograms *ph, SCoord *psc) nogil: psc.stride = psc.x * ph.col_stride + psc.y * ph.row_stride ############################################################################ # # _colidx - convert a column index into the histogram # index for a diagonal # # The top-right and bottom left diagonals for one row at one column # become the diagonals for the next column to the right for the next row. # Conversely, the top-left and bottom right become the diagonals for the # previous column. # # These functions use the current row number to find the index of # a particular histogram taking this into account. The indices progress # forward or backward as you go to successive rows. # # The histogram array is, in effect, a circular buffer, so the start # offset is immaterial - we take advantage of this to make sure that # the numbers computed before taking the modulus are all positive, including # those that might be done for columns to the left of 0. We add 3* the radius # here to account for a row of -radius, a column of -radius and a request for # a column that is "radius" to the left. # ############################################################################ @cython.cdivision(True) cdef inline Py_ssize_t tl_br_colidx(Histograms *ph, Py_ssize_t colidx) nogil: return (colidx + 3*ph.radius + ph.current_row) % ph.stripe_length @cython.cdivision(True) cdef inline Py_ssize_t tr_bl_colidx(Histograms *ph, Py_ssize_t colidx) nogil: return (colidx + 3*ph.radius + ph.row_count-ph.current_row) % \ ph.stripe_length @cython.cdivision(True) cdef inline Py_ssize_t leading_edge_colidx(Histograms *ph, Py_ssize_t colidx) nogil: return (colidx + 5*ph.radius) % ph.stripe_length @cython.cdivision(True) cdef inline Py_ssize_t trailing_edge_colidx(Histograms *ph, Py_ssize_t colidx) nogil: return (colidx + 3*ph.radius - 1) % ph.stripe_length ############################################################################ # # accumulate_coarse_histogram - accumulate the coarse histogram # at an index into the accumulator # # ph - the Histograms structure that holds the accumulator # colidx - the index of the column to add # ############################################################################ cdef inline void accumulate_coarse_histogram(Histograms *ph, Py_ssize_t colidx) nogil: cdef Py_ssize_t offset offset = tr_bl_colidx(ph, colidx) if ph.pixel_count[offset].top_right > 0: add16(ph.accumulator.coarse, ph.histogram[offset].top_right.coarse) ph.accumulator_count += ph.pixel_count[offset].top_right offset = leading_edge_colidx(ph, colidx) if ph.pixel_count[offset].edge > 0: add16(ph.accumulator.coarse, ph.histogram[offset].edge.coarse) ph.accumulator_count += ph.pixel_count[offset].edge offset = tl_br_colidx(ph, colidx) if ph.pixel_count[offset].bottom_right > 0: add16(ph.accumulator.coarse, ph.histogram[offset].bottom_right.coarse) ph.accumulator_count += ph.pixel_count[offset].bottom_right ############################################################################ # # deaccumulate_coarse_histogram - subtract the coarse histogram # for a given column # ############################################################################ cdef inline void deaccumulate_coarse_histogram(Histograms *ph, Py_ssize_t colidx) nogil: cdef Py_ssize_t offset # # The trailing diagonals don't appear until here # if colidx <= ph.a_2: return offset = tl_br_colidx(ph, colidx) if ph.pixel_count[offset].top_left > 0: sub16(ph.accumulator.coarse, ph.histogram[offset].top_left.coarse) ph.accumulator_count -= ph.pixel_count[offset].top_left # # The trailing edge doesn't appear from the border until here # if colidx > ph.radius: offset = trailing_edge_colidx(ph, colidx) if ph.pixel_count[offset].edge > 0: sub16(ph.accumulator.coarse, ph.histogram[offset].edge.coarse) ph.accumulator_count -= ph.pixel_count[offset].edge offset = tr_bl_colidx(ph, colidx) if ph.pixel_count[offset].bottom_left > 0: sub16(ph.accumulator.coarse, ph.histogram[offset].bottom_left.coarse) ph.accumulator_count -= ph.pixel_count[offset].bottom_left ############################################################################ # # accumulate_fine_histogram - accumulate one of the 16 fine histograms # ############################################################################ cdef inline void accumulate_fine_histogram(Histograms *ph, Py_ssize_t colidx, Py_ssize_t fineidx) nogil: cdef: Py_ssize_t fineoffset = fineidx * 16 Py_ssize_t offset offset = tr_bl_colidx(ph, colidx) add16(ph.accumulator.fine + fineoffset, ph.histogram[offset].top_right.fine + fineoffset) offset = leading_edge_colidx(ph, colidx) add16(ph.accumulator.fine + fineoffset, ph.histogram[offset].edge.fine + fineoffset) offset = tl_br_colidx(ph, colidx) add16(ph.accumulator.fine + fineoffset, ph.histogram[offset].bottom_right.fine + fineoffset) ############################################################################ # # deaccumulate_fine_histogram - subtract one of the 16 fine histograms # ############################################################################ cdef inline void deaccumulate_fine_histogram(Histograms *ph, Py_ssize_t colidx, Py_ssize_t fineidx) nogil: cdef: Py_ssize_t fineoffset = fineidx * 16 Py_ssize_t offset # # The trailing diagonals don't appear until here # if colidx < ph.a_2: return offset = tl_br_colidx(ph, colidx) sub16(ph.accumulator.fine + fineoffset, ph.histogram[offset].top_left.fine + fineoffset) if colidx >= ph.radius: offset = trailing_edge_colidx(ph, colidx) sub16(ph.accumulator.fine+fineoffset, ph.histogram[offset].edge.fine + fineoffset) offset = tr_bl_colidx(ph, colidx) sub16(ph.accumulator.fine + fineoffset, ph.histogram[offset].bottom_left.fine + fineoffset) ############################################################################ # # accumulate - add the leading edge and subtract the trailing edge # ############################################################################ cdef inline void accumulate(Histograms *ph) nogil: cdef cnp.int32_t accumulator accumulate_coarse_histogram(ph, ph.current_column) deaccumulate_coarse_histogram(ph, ph.current_column) ############################################################################ # # update_fine - update one of the fine histograms to the current column # # The code has two choices: # redo the fine histogram from scratch - this involves accumulating # the entire histogram from the top_left.x to the top_right.x, # the center (edge) histogram from the trailing edge x to the # top_left.x and then computing a histogram of all points between # the trailing edge top, the point, (top_left.x,trailing edge top.y) # and the top_right and the corresponding triangle in the octagon's # lower half. # # accumulate and deaccumulate within the fine histogram from the last # column computed. # # The code below only implements the accumulate; redo and the code # to choose remains to be done. ############################################################################ cdef inline void update_fine(Histograms *ph, Py_ssize_t fineidx) nogil: cdef: Py_ssize_t first_update_column = ph.last_update_column[fineidx]+1 Py_ssize_t update_limit = ph.current_column+1 Py_ssize_t i for i in range(first_update_column, update_limit): accumulate_fine_histogram(ph, i, fineidx) deaccumulate_fine_histogram(ph, i, fineidx) ph.last_update_column[fineidx] = ph.current_column ############################################################################ # # update_histogram - update the coarse and fine levels of a histogram # based on addition of one value and subtraction of another # # ph - Histograms pointer (for access to row_count, column_count) # hist_piece - coarse and fine histogram to update # pixel_count- pointer to pixel counter for histogram # last_coord - coordinate and stride of pixel to remove # coord - coordinate and stride of pixel to add # ############################################################################ cdef inline void update_histogram(Histograms *ph, HistogramPiece *hist_piece, pixel_count_t *pixel_count, SCoord *last_coord, SCoord *coord) nogil: cdef: Py_ssize_t current_column = ph.current_column Py_ssize_t current_row = ph.current_row Py_ssize_t current_stride = ph.current_stride Py_ssize_t column_count = ph.column_count Py_ssize_t row_count = ph.row_count cnp.uint8_t value Py_ssize_t stride Py_ssize_t x Py_ssize_t y x = last_coord.x + current_column y = last_coord.y + current_row stride = current_stride+last_coord.stride if (x >= 0 and x < column_count and \ y >= 0 and y < row_count and \ ph.mask[stride]): value = ph.data[stride] pixel_count[0] -= 1 hist_piece.fine[value] -= 1 hist_piece.coarse[value / 16] -= 1 x = coord.x + current_column y = coord.y + current_row stride = current_stride + coord.stride if (x >= 0 and x < column_count and \ y >= 0 and y < row_count and \ ph.mask[stride]): value = ph.data[stride] pixel_count[0] += 1 hist_piece.fine[value] += 1 hist_piece.coarse[value / 16] += 1 ############################################################################ # # update_current_location - update the histograms at the current location # ############################################################################ cdef inline void update_current_location(Histograms *ph) nogil: cdef: Py_ssize_t current_column = ph.current_column Py_ssize_t radius = ph.radius Py_ssize_t top_left_off = tl_br_colidx(ph, current_column) Py_ssize_t top_right_off = tr_bl_colidx(ph, current_column) Py_ssize_t bottom_left_off = tr_bl_colidx(ph, current_column) Py_ssize_t bottom_right_off = tl_br_colidx(ph, current_column) Py_ssize_t leading_edge_off = leading_edge_colidx(ph, current_column) cnp.int32_t *coarse_histogram cnp.int32_t *fine_histogram Py_ssize_t last_xoff Py_ssize_t last_yoff Py_ssize_t last_stride Py_ssize_t xoff Py_ssize_t yoff Py_ssize_t stride update_histogram(ph, &ph.histogram[top_left_off].top_left, &ph.pixel_count[top_left_off].top_left, &ph.last_top_left, &ph.top_left) update_histogram(ph, &ph.histogram[top_right_off].top_right, &ph.pixel_count[top_right_off].top_right, &ph.last_top_right, &ph.top_right) update_histogram(ph, &ph.histogram[bottom_left_off].bottom_left, &ph.pixel_count[bottom_left_off].bottom_left, &ph.last_bottom_left, &ph.bottom_left) update_histogram(ph, &ph.histogram[bottom_right_off].bottom_right, &ph.pixel_count[bottom_right_off].bottom_right, &ph.last_bottom_right, &ph.bottom_right) update_histogram(ph, &ph.histogram[leading_edge_off].edge, &ph.pixel_count[leading_edge_off].edge, &ph.last_leading_edge, &ph.leading_edge) ############################################################################ # # find_median - search the current accumulator for the median # ############################################################################ cdef inline cnp.uint8_t find_median(Histograms *ph) nogil: cdef: Py_ssize_t pixels_below # of pixels below the median Py_ssize_t i Py_ssize_t j Py_ssize_t k cnp.uint32_t accumulator if ph.accumulator_count == 0: return 0 # +50 for roundoff pixels_below = (ph.accumulator_count * ph.percent + 50) / 100 if pixels_below > 0: pixels_below -= 1 accumulator = 0 for i in range(16): accumulator += ph.accumulator.coarse[i] if accumulator > pixels_below: break accumulator -= ph.accumulator.coarse[i] update_fine(ph, i) for j in range(i*16, (i + 1)*16): accumulator += ph.accumulator.fine[j] if accumulator > pixels_below: return j return 0 ############################################################################ # # c_median_filter - median filter algorithm # # rows - # of rows in each array # columns - # of columns in each array # row_stride - stride from one row to the next in each array # col_stride - stride from one column to the next in each array # radius - radius of circle inscribed into octagon # percent - "median" cutoff: 50 = median, 25 = lower quartile, etc # data - array of image pixels to be filtered # mask - mask of significant pixels # output - array to be filled with filtered pixels # ############################################################################ cdef int c_median_filter(Py_ssize_t rows, Py_ssize_t columns, Py_ssize_t row_stride, Py_ssize_t col_stride, Py_ssize_t radius, Py_ssize_t percent, cnp.uint8_t *data, cnp.uint8_t *mask, cnp.uint8_t *output) nogil: cdef: Histograms *ph Histogram *phistogram Py_ssize_t row Py_ssize_t col Py_ssize_t end_col Py_ssize_t i Py_ssize_t top_left_off Py_ssize_t top_right_off Py_ssize_t bottom_left_off Py_ssize_t bottom_right_off ph = allocate_histograms(rows, columns, row_stride, col_stride, radius, percent, data, mask, output) if not ph: return 1 for row in range(-radius, rows): # # Initialize the starting diagonal histograms to zero. The leading # and trailing histograms descend from above and so are initialized # when memory is initially set to zero. The diagonals move in # from the left (top left and bottom right) and right (top right # and bottom left). One of each needs to be initialized at the # start of each row # tl_br_off = tl_br_colidx(ph, -radius) tr_bl_off = tr_bl_colidx(ph, columns + radius - 1) memset(&ph.histogram[tl_br_off].top_left, 0, sizeof(HistogramPiece)) memset(&ph.histogram[tl_br_off].bottom_right, 0, sizeof(HistogramPiece)) memset(&ph.histogram[tr_bl_off].top_right, 0, sizeof(HistogramPiece)) memset(&ph.histogram[tr_bl_off].bottom_left, 0, sizeof(HistogramPiece)) ph.pixel_count[tl_br_off].top_left = 0 ph.pixel_count[tl_br_off].bottom_right = 0 ph.pixel_count[tr_bl_off].top_right = 0 ph.pixel_count[tr_bl_off].bottom_left = 0 # # Initialize the accumulator (octagon histogram) to zero # memset(&(ph.accumulator), 0, sizeof(ph.accumulator)) ph.accumulator_count = 0 for i in range(16): ph.last_update_column[i] = -radius-1 # # Initialize the current stride to the beginning of the row # ph.current_row = row # # Update locations and coarse accumulator for the octagon # for points before 0 # end_col = 0 if row >= 0 else columns + radius for col in range(-radius, end_col): ph.current_column = col ph.current_stride = row * row_stride + col * col_stride update_current_location(ph) accumulate(ph) # # Update locations and coarse accumulator and compute # the median for points between 0 and "columns" # if row >= 0: for col in range(0, columns): ph.current_column = col ph.current_stride = row * row_stride + col * col_stride update_current_location(ph) accumulate(ph) ph.output[ph.current_stride] = find_median(ph) for col in range(columns, columns+radius): ph.current_column = col ph.current_stride = row * row_stride + col * col_stride update_current_location(ph) free_histograms(ph) return 0 def median_filter(cnp.uint8_t[:, ::1] data, cnp.uint8_t[:, ::1] mask, cnp.uint8_t[:, ::1] output, int radius, cnp.int32_t percent): """Median filter with octagon shape and masking. Parameters ---------- data : (M,N) ndarray, dtype uint8 Input image. mask : (M,N) array, dtype uint8 A value of 1 indicates a significant pixel, 0 that a pixel is masked. output : (M,N) array, dtype uint8 Array of same size as the input in which to store the filtered image. radius : int Radius of the inscribed circle to the octagon. percent : int, optional The unmasked pixels within the octagon are sorted, and the value at the `percent`-th index chosen. For example, the default value of 50 chooses the median pixel. """ if percent < 0: raise ValueError('Median filter percent = %d is less than zero' % percent) if percent > 100: raise ValueError('Median filter percent = %d is greater than 100' % percent) if data.shape[0] != mask.shape[0] or data.shape[1] != mask.shape[1]: raise ValueError('Data shape (%d, %d) is not mask shape (%d, %d)' % (data.shape[0], data.shape[1], mask.shape[0], mask.shape[1])) if data.shape[0] != output.shape[0] or data.shape[1] != output.shape[1]: raise ValueError('Data shape (%d, %d) is not output shape (%d, %d)' % (data.shape[0], data.shape[1], output.shape[0], output.shape[1])) if c_median_filter(data.shape[0], data.shape[1], data.strides[0], data.strides[1], radius, percent, &data[0, 0], &mask[0, 0], &output[0, 0]): raise MemoryError('Failed to allocate scratchpad memory') scikit-image-0.13.1/skimage/filters/_frangi.py000066400000000000000000000143511316256236300212240ustar00rootroot00000000000000import numpy as np __all__ = ['frangi', 'hessian'] def _frangi_hessian_common_filter(image, scale_range, scale_step, beta1, beta2): """This is an intermediate function for Frangi and Hessian filters. Shares the common code for Frangi and Hessian functions. Parameters ---------- image : (N, M) ndarray Array with input image data. scale_range : 2-tuple of floats, optional The range of sigmas used. scale_step : float, optional Step size between sigmas. beta1 : float, optional Frangi correction constant that adjusts the filter's sensitivity to deviation from a blob-like structure. beta2 : float, optional Frangi correction constant that adjusts the filter's sensitivity to areas of high variance/texture/structure. Returns ------- filtered_list : list List of pre-filtered images. """ # Import has to be here due to circular import error from ..feature import hessian_matrix, hessian_matrix_eigvals sigmas = np.arange(scale_range[0], scale_range[1], scale_step) if np.any(np.asarray(sigmas) < 0.0): raise ValueError("Sigma values less than zero are not valid") beta1 = 2 * beta1 ** 2 beta2 = 2 * beta2 ** 2 filtered_array = np.zeros(sigmas.shape + image.shape) lambdas_array = np.zeros(sigmas.shape + image.shape) # Filtering for all sigmas for i, sigma in enumerate(sigmas): # Make 2D hessian (Drr, Drc, Dcc) = hessian_matrix(image, sigma, order='rc') # Correct for scale Drr = (sigma ** 2) * Drr Drc = (sigma ** 2) * Drc Dcc = (sigma ** 2) * Dcc # Calculate (abs sorted) eigenvalues and vectors (lambda1, lambda2) = hessian_matrix_eigvals(Drr, Drc, Dcc) # Compute some similarity measures lambda1[lambda1 == 0] = 1e-10 rb = (lambda2 / lambda1) ** 2 s2 = lambda1 ** 2 + lambda2 ** 2 # Compute the output image filtered = np.exp(-rb / beta1) * (np.ones(np.shape(image)) - np.exp(-s2 / beta2)) # Store the results in 3D matrices filtered_array[i] = filtered lambdas_array[i] = lambda1 return filtered_array, lambdas_array def frangi(image, scale_range=(1, 10), scale_step=2, beta1=0.5, beta2=15, black_ridges=True): """Filter an image with the Frangi filter. This filter can be used to detect continuous edges, e.g. vessels, wrinkles, rivers. It can be used to calculate the fraction of the whole image containing such objects. Calculates the eigenvectors of the Hessian to compute the similarity of an image region to vessels, according to the method described in _[1]. Parameters ---------- image : (N, M) ndarray Array with input image data. scale_range : 2-tuple of floats, optional The range of sigmas used. scale_step : float, optional Step size between sigmas. beta1 : float, optional Frangi correction constant that adjusts the filter's sensitivity to deviation from a blob-like structure. beta2 : float, optional Frangi correction constant that adjusts the filter's sensitivity to areas of high variance/texture/structure. black_ridges : boolean, optional When True (the default), the filter detects black ridges; when False, it detects white ridges. Returns ------- out : (N, M) ndarray Filtered image (maximum of pixels across all scales). Notes ----- Written by Marc Schrijver, 2/11/2001 Re-Written by D. J. Kroon University of Twente (May 2009) References ---------- .. [1] A. Frangi, W. Niessen, K. Vincken, and M. Viergever. "Multiscale vessel enhancement filtering," In LNCS, vol. 1496, pages 130-137, Germany, 1998. Springer-Verlag. .. [2] Kroon, D.J.: Hessian based Frangi vesselness filter. .. [3] http://mplab.ucsd.edu/tutorials/gabor.pdf. """ filtered, lambdas = _frangi_hessian_common_filter(image, scale_range, scale_step, beta1, beta2) if black_ridges: filtered[lambdas < 0] = 0 else: filtered[lambdas > 0] = 0 # Return for every pixel the value of the scale(sigma) with the maximum # output pixel value return np.max(filtered, axis=0) def hessian(image, scale_range=(1, 10), scale_step=2, beta1=0.5, beta2=15): """Filter an image with the Hessian filter. This filter can be used to detect continuous edges, e.g. vessels, wrinkles, rivers. It can be used to calculate the fraction of the whole image containing such objects. Almost equal to Frangi filter, but uses alternative method of smoothing. Refer to _[1] to find the differences between Frangi and Hessian filters. Parameters ---------- image : (N, M) ndarray Array with input image data. scale_range : 2-tuple of floats, optional The range of sigmas used. scale_step : float, optional Step size between sigmas. beta1 : float, optional Frangi correction constant that adjusts the filter's sensitivity to deviation from a blob-like structure. beta2 : float, optional Frangi correction constant that adjusts the filter's sensitivity to areas of high variance/texture/structure. Returns ------- out : (N, M) ndarray Filtered image (maximum of pixels across all scales). Notes ----- Written by Marc Schrijver, 2/11/2001 Re-Written by D. J. Kroon University of Twente (May 2009) References ---------- .. [1] Choon-Ching Ng, Moi Hoon Yap, Nicholas Costen and Baihua Li, "Automatic Wrinkle Detection using Hybrid Hessian Filter". """ filtered, lambdas = _frangi_hessian_common_filter(image, scale_range, scale_step, beta1, beta2) filtered[lambdas < 0] = 0 # Return for every pixel the value of the scale(sigma) with the maximum # output pixel value out = np.max(filtered, axis=0) out[out <= 0] = 1 return out scikit-image-0.13.1/skimage/filters/_gabor.py000066400000000000000000000154161316256236300210530ustar00rootroot00000000000000import numpy as np from scipy import ndimage as ndi from .._shared.utils import assert_nD __all__ = ['gabor_kernel', 'gabor'] def _sigma_prefactor(bandwidth): b = bandwidth # See http://www.cs.rug.nl/~imaging/simplecell.html return 1.0 / np.pi * np.sqrt(np.log(2) / 2.0) * \ (2.0 ** b + 1) / (2.0 ** b - 1) def gabor_kernel(frequency, theta=0, bandwidth=1, sigma_x=None, sigma_y=None, n_stds=3, offset=0): """Return complex 2D Gabor filter kernel. Gabor kernel is a Gaussian kernel modulated by a complex harmonic function. Harmonic function consists of an imaginary sine function and a real cosine function. Spatial frequency is inversely proportional to the wavelength of the harmonic and to the standard deviation of a Gaussian kernel. The bandwidth is also inversely proportional to the standard deviation. Parameters ---------- frequency : float Spatial frequency of the harmonic function. Specified in pixels. theta : float, optional Orientation in radians. If 0, the harmonic is in the x-direction. bandwidth : float, optional The bandwidth captured by the filter. For fixed bandwidth, `sigma_x` and `sigma_y` will decrease with increasing frequency. This value is ignored if `sigma_x` and `sigma_y` are set by the user. sigma_x, sigma_y : float, optional Standard deviation in x- and y-directions. These directions apply to the kernel *before* rotation. If `theta = pi/2`, then the kernel is rotated 90 degrees so that `sigma_x` controls the *vertical* direction. n_stds : scalar, optional The linear size of the kernel is n_stds (3 by default) standard deviations offset : float, optional Phase offset of harmonic function in radians. Returns ------- g : complex array Complex filter kernel. References ---------- .. [1] http://en.wikipedia.org/wiki/Gabor_filter .. [2] http://mplab.ucsd.edu/tutorials/gabor.pdf Examples -------- >>> from skimage.filters import gabor_kernel >>> from skimage import io >>> from matplotlib import pyplot as plt # doctest: +SKIP >>> gk = gabor_kernel(frequency=0.2) >>> plt.figure() # doctest: +SKIP >>> io.imshow(gk.real) # doctest: +SKIP >>> io.show() # doctest: +SKIP >>> # more ripples (equivalent to increasing the size of the >>> # Gaussian spread) >>> gk = gabor_kernel(frequency=0.2, bandwidth=0.1) >>> plt.figure() # doctest: +SKIP >>> io.imshow(gk.real) # doctest: +SKIP >>> io.show() # doctest: +SKIP """ if sigma_x is None: sigma_x = _sigma_prefactor(bandwidth) / frequency if sigma_y is None: sigma_y = _sigma_prefactor(bandwidth) / frequency x0 = np.ceil(max(np.abs(n_stds * sigma_x * np.cos(theta)), np.abs(n_stds * sigma_y * np.sin(theta)), 1)) y0 = np.ceil(max(np.abs(n_stds * sigma_y * np.cos(theta)), np.abs(n_stds * sigma_x * np.sin(theta)), 1)) y, x = np.mgrid[-y0:y0 + 1, -x0:x0 + 1] rotx = x * np.cos(theta) + y * np.sin(theta) roty = -x * np.sin(theta) + y * np.cos(theta) g = np.zeros(y.shape, dtype=np.complex) g[:] = np.exp(-0.5 * (rotx ** 2 / sigma_x ** 2 + roty ** 2 / sigma_y ** 2)) g /= 2 * np.pi * sigma_x * sigma_y g *= np.exp(1j * (2 * np.pi * frequency * rotx + offset)) return g def gabor(image, frequency, theta=0, bandwidth=1, sigma_x=None, sigma_y=None, n_stds=3, offset=0, mode='reflect', cval=0): """Return real and imaginary responses to Gabor filter. The real and imaginary parts of the Gabor filter kernel are applied to the image and the response is returned as a pair of arrays. Gabor filter is a linear filter with a Gaussian kernel which is modulated by a sinusoidal plane wave. Frequency and orientation representations of the Gabor filter are similar to those of the human visual system. Gabor filter banks are commonly used in computer vision and image processing. They are especially suitable for edge detection and texture classification. Parameters ---------- image : 2-D array Input image. frequency : float Spatial frequency of the harmonic function. Specified in pixels. theta : float, optional Orientation in radians. If 0, the harmonic is in the x-direction. bandwidth : float, optional The bandwidth captured by the filter. For fixed bandwidth, `sigma_x` and `sigma_y` will decrease with increasing frequency. This value is ignored if `sigma_x` and `sigma_y` are set by the user. sigma_x, sigma_y : float, optional Standard deviation in x- and y-directions. These directions apply to the kernel *before* rotation. If `theta = pi/2`, then the kernel is rotated 90 degrees so that `sigma_x` controls the *vertical* direction. n_stds : scalar, optional The linear size of the kernel is n_stds (3 by default) standard deviations. offset : float, optional Phase offset of harmonic function in radians. mode : {'constant', 'nearest', 'reflect', 'mirror', 'wrap'}, optional Mode used to convolve image with a kernel, passed to `ndi.convolve` cval : scalar, optional Value to fill past edges of input if `mode` of convolution is 'constant'. The parameter is passed to `ndi.convolve`. Returns ------- real, imag : arrays Filtered images using the real and imaginary parts of the Gabor filter kernel. Images are of the same dimensions as the input one. References ---------- .. [1] http://en.wikipedia.org/wiki/Gabor_filter .. [2] http://mplab.ucsd.edu/tutorials/gabor.pdf Examples -------- >>> from skimage.filters import gabor >>> from skimage import data, io >>> from matplotlib import pyplot as plt # doctest: +SKIP >>> image = data.coins() >>> # detecting edges in a coin image >>> filt_real, filt_imag = gabor(image, frequency=0.6) >>> plt.figure() # doctest: +SKIP >>> io.imshow(filt_real) # doctest: +SKIP >>> io.show() # doctest: +SKIP >>> # less sensitivity to finer details with the lower frequency kernel >>> filt_real, filt_imag = gabor(image, frequency=0.1) >>> plt.figure() # doctest: +SKIP >>> io.imshow(filt_real) # doctest: +SKIP >>> io.show() # doctest: +SKIP """ assert_nD(image, 2) g = gabor_kernel(frequency, theta, bandwidth, sigma_x, sigma_y, n_stds, offset) filtered_real = ndi.convolve(image, np.real(g), mode=mode, cval=cval) filtered_imag = ndi.convolve(image, np.imag(g), mode=mode, cval=cval) return filtered_real, filtered_imag scikit-image-0.13.1/skimage/filters/_gaussian.py000066400000000000000000000110571316256236300215700ustar00rootroot00000000000000import collections as coll import numpy as np from scipy import ndimage as ndi from ..util import img_as_float from ..color import guess_spatial_dimensions from .._shared.utils import warn, convert_to_float __all__ = ['gaussian'] def _convert_input(image, preserve_range): if preserve_range: image = image.astype(np.double) else: image = img_as_float(image) return image def gaussian(image, sigma=1, output=None, mode='nearest', cval=0, multichannel=None, preserve_range=False, truncate=4.0): """Multi-dimensional Gaussian filter. Parameters ---------- image : array-like Input image (grayscale or color) to filter. sigma : scalar or sequence of scalars, optional Standard deviation for Gaussian kernel. The standard deviations of the Gaussian filter are given for each axis as a sequence, or as a single number, in which case it is equal for all axes. output : array, optional The ``output`` parameter passes an array in which to store the filter output. mode : {'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional The `mode` parameter determines how the array borders are handled, where `cval` is the value when mode is equal to 'constant'. Default is 'nearest'. cval : scalar, optional Value to fill past edges of input if `mode` is 'constant'. Default is 0.0 multichannel : bool, optional (default: None) Whether the last axis of the image is to be interpreted as multiple channels. If True, each channel is filtered separately (channels are not mixed together). Only 3 channels are supported. If `None`, the function will attempt to guess this, and raise a warning if ambiguous, when the array has shape (M, N, 3). preserve_range : bool, optional Whether to keep the original range of values. Otherwise, the input image is converted according to the conventions of `img_as_float`. truncate : float, optional Truncate the filter at this many standard deviations. Returns ------- filtered_image : ndarray the filtered array Notes ----- This function is a wrapper around :func:`scipy.ndi.gaussian_filter`. Integer arrays are converted to float. The multi-dimensional filter is implemented as a sequence of one-dimensional convolution filters. The intermediate arrays are stored in the same data type as the output. Therefore, for output types with a limited precision, the results may be imprecise because intermediate results may be stored with insufficient precision. Examples -------- >>> a = np.zeros((3, 3)) >>> a[1, 1] = 1 >>> a array([[ 0., 0., 0.], [ 0., 1., 0.], [ 0., 0., 0.]]) >>> gaussian(a, sigma=0.4) # mild smoothing array([[ 0.00163116, 0.03712502, 0.00163116], [ 0.03712502, 0.84496158, 0.03712502], [ 0.00163116, 0.03712502, 0.00163116]]) >>> gaussian(a, sigma=1) # more smoothing array([[ 0.05855018, 0.09653293, 0.05855018], [ 0.09653293, 0.15915589, 0.09653293], [ 0.05855018, 0.09653293, 0.05855018]]) >>> # Several modes are possible for handling boundaries >>> gaussian(a, sigma=1, mode='reflect') array([[ 0.08767308, 0.12075024, 0.08767308], [ 0.12075024, 0.16630671, 0.12075024], [ 0.08767308, 0.12075024, 0.08767308]]) >>> # For RGB images, each is filtered separately >>> from skimage.data import astronaut >>> image = astronaut() >>> filtered_img = gaussian(image, sigma=1, multichannel=True) """ spatial_dims = guess_spatial_dimensions(image) if spatial_dims is None and multichannel is None: msg = ("Images with dimensions (M, N, 3) are interpreted as 2D+RGB " "by default. Use `multichannel=False` to interpret as " "3D image with last dimension of length 3.") warn(RuntimeWarning(msg)) multichannel = True if np.any(np.asarray(sigma) < 0.0): raise ValueError("Sigma values less than zero are not valid") if multichannel: # do not filter across channels if not isinstance(sigma, coll.Iterable): sigma = [sigma] * (image.ndim - 1) if len(sigma) != image.ndim: sigma = np.concatenate((np.asarray(sigma), [0])) image = convert_to_float(image, preserve_range) return ndi.gaussian_filter(image, sigma, mode=mode, cval=cval, truncate=truncate) scikit-image-0.13.1/skimage/filters/_rank_order.py000066400000000000000000000040221316256236300220760ustar00rootroot00000000000000"""rankorder.py - convert an image of any type to an image of ints whose pixels have an identical rank order compared to the original image Originally part of CellProfiler, code licensed under both GPL and BSD licenses. Website: http://www.cellprofiler.org Copyright (c) 2003-2009 Massachusetts Institute of Technology Copyright (c) 2009-2011 Broad Institute All rights reserved. Original author: Lee Kamentstky """ import numpy as np def rank_order(image): """Return an image of the same shape where each pixel is the index of the pixel value in the ascending order of the unique values of `image`, aka the rank-order value. Parameters ---------- image: ndarray Returns ------- labels: ndarray of type np.uint32, of shape image.shape New array where each pixel has the rank-order value of the corresponding pixel in `image`. Pixel values are between 0 and n - 1, where n is the number of distinct unique values in `image`. original_values: 1-D ndarray Unique original values of `image` Examples -------- >>> a = np.array([[1, 4, 5], [4, 4, 1], [5, 1, 1]]) >>> a array([[1, 4, 5], [4, 4, 1], [5, 1, 1]]) >>> rank_order(a) (array([[0, 1, 2], [1, 1, 0], [2, 0, 0]], dtype=uint32), array([1, 4, 5])) >>> b = np.array([-1., 2.5, 3.1, 2.5]) >>> rank_order(b) (array([0, 1, 2, 1], dtype=uint32), array([-1. , 2.5, 3.1])) """ flat_image = image.ravel() sort_order = flat_image.argsort().astype(np.uint32) flat_image = flat_image[sort_order] sort_rank = np.zeros_like(sort_order) is_different = flat_image[:-1] != flat_image[1:] np.cumsum(is_different, out=sort_rank[1:]) original_values = np.zeros((sort_rank[-1] + 1,), image.dtype) original_values[0] = flat_image[0] original_values[1:] = flat_image[1:][is_different] int_image = np.zeros_like(sort_order) int_image[sort_order] = sort_rank return (int_image.reshape(image.shape), original_values) scikit-image-0.13.1/skimage/filters/edges.py000066400000000000000000000341771316256236300207160ustar00rootroot00000000000000""" Sobel and Prewitt filters originally part of CellProfiler, code licensed under both GPL and BSD licenses. Website: http://www.cellprofiler.org Copyright (c) 2003-2009 Massachusetts Institute of Technology Copyright (c) 2009-2011 Broad Institute All rights reserved. Original author: Lee Kamentsky """ import numpy as np from .. import img_as_float from .._shared.utils import assert_nD from scipy.ndimage import convolve, binary_erosion, generate_binary_structure from ..restoration.uft import laplacian EROSION_SELEM = generate_binary_structure(2, 2) HSOBEL_WEIGHTS = np.array([[ 1, 2, 1], [ 0, 0, 0], [-1,-2,-1]]) / 4.0 VSOBEL_WEIGHTS = HSOBEL_WEIGHTS.T HSCHARR_WEIGHTS = np.array([[ 3, 10, 3], [ 0, 0, 0], [-3, -10, -3]]) / 16.0 VSCHARR_WEIGHTS = HSCHARR_WEIGHTS.T HPREWITT_WEIGHTS = np.array([[ 1, 1, 1], [ 0, 0, 0], [-1,-1,-1]]) / 3.0 VPREWITT_WEIGHTS = HPREWITT_WEIGHTS.T ROBERTS_PD_WEIGHTS = np.array([[1, 0], [0, -1]], dtype=np.double) ROBERTS_ND_WEIGHTS = np.array([[0, 1], [-1, 0]], dtype=np.double) def _mask_filter_result(result, mask): """Return result after masking. Input masks are eroded so that mask areas in the original image don't affect values in the result. """ if mask is None: result[0, :] = 0 result[-1, :] = 0 result[:, 0] = 0 result[:, -1] = 0 return result else: mask = binary_erosion(mask, EROSION_SELEM, border_value=0) return result * mask def sobel(image, mask=None): """Find the edge magnitude using the Sobel transform. Parameters ---------- image : 2-D array Image to process. mask : 2-D array, optional An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result. Returns ------- output : 2-D array The Sobel edge map. See also -------- scharr, prewitt, roberts, feature.canny Notes ----- Take the square root of the sum of the squares of the horizontal and vertical Sobels to get a magnitude that's somewhat insensitive to direction. The 3x3 convolution kernel used in the horizontal and vertical Sobels is an approximation of the gradient of the image (with some slight blurring since 9 pixels are used to compute the gradient at a given pixel). As an approximation of the gradient, the Sobel operator is not completely rotation-invariant. The Scharr operator should be used for a better rotation invariance. Note that ``scipy.ndimage.sobel`` returns a directional Sobel which has to be further processed to perform edge detection. Examples -------- >>> from skimage import data >>> camera = data.camera() >>> from skimage import filters >>> edges = filters.sobel(camera) """ assert_nD(image, 2) out = np.sqrt(sobel_h(image, mask)**2 + sobel_v(image, mask)**2) out /= np.sqrt(2) return out def sobel_h(image, mask=None): """Find the horizontal edges of an image using the Sobel transform. Parameters ---------- image : 2-D array Image to process. mask : 2-D array, optional An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result. Returns ------- output : 2-D array The Sobel edge map. Notes ----- We use the following kernel:: 1 2 1 0 0 0 -1 -2 -1 """ assert_nD(image, 2) image = img_as_float(image) result = convolve(image, HSOBEL_WEIGHTS) return _mask_filter_result(result, mask) def sobel_v(image, mask=None): """Find the vertical edges of an image using the Sobel transform. Parameters ---------- image : 2-D array Image to process. mask : 2-D array, optional An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result. Returns ------- output : 2-D array The Sobel edge map. Notes ----- We use the following kernel:: 1 0 -1 2 0 -2 1 0 -1 """ assert_nD(image, 2) image = img_as_float(image) result = convolve(image, VSOBEL_WEIGHTS) return _mask_filter_result(result, mask) def scharr(image, mask=None): """Find the edge magnitude using the Scharr transform. Parameters ---------- image : 2-D array Image to process. mask : 2-D array, optional An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result. Returns ------- output : 2-D array The Scharr edge map. See also -------- sobel, prewitt, canny Notes ----- Take the square root of the sum of the squares of the horizontal and vertical Scharrs to get a magnitude that is somewhat insensitive to direction. The Scharr operator has a better rotation invariance than other edge filters such as the Sobel or the Prewitt operators. References ---------- .. [1] D. Kroon, 2009, Short Paper University Twente, Numerical Optimization of Kernel Based Image Derivatives. .. [2] http://en.wikipedia.org/wiki/Sobel_operator#Alternative_operators Examples -------- >>> from skimage import data >>> camera = data.camera() >>> from skimage import filters >>> edges = filters.scharr(camera) """ out = np.sqrt(scharr_h(image, mask)**2 + scharr_v(image, mask)**2) out /= np.sqrt(2) return out def scharr_h(image, mask=None): """Find the horizontal edges of an image using the Scharr transform. Parameters ---------- image : 2-D array Image to process. mask : 2-D array, optional An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result. Returns ------- output : 2-D array The Scharr edge map. Notes ----- We use the following kernel:: 3 10 3 0 0 0 -3 -10 -3 References ---------- .. [1] D. Kroon, 2009, Short Paper University Twente, Numerical Optimization of Kernel Based Image Derivatives. """ assert_nD(image, 2) image = img_as_float(image) result = convolve(image, HSCHARR_WEIGHTS) return _mask_filter_result(result, mask) def scharr_v(image, mask=None): """Find the vertical edges of an image using the Scharr transform. Parameters ---------- image : 2-D array Image to process mask : 2-D array, optional An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result. Returns ------- output : 2-D array The Scharr edge map. Notes ----- We use the following kernel:: 3 0 -3 10 0 -10 3 0 -3 References ---------- .. [1] D. Kroon, 2009, Short Paper University Twente, Numerical Optimization of Kernel Based Image Derivatives. """ assert_nD(image, 2) image = img_as_float(image) result = convolve(image, VSCHARR_WEIGHTS) return _mask_filter_result(result, mask) def prewitt(image, mask=None): """Find the edge magnitude using the Prewitt transform. Parameters ---------- image : 2-D array Image to process. mask : 2-D array, optional An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result. Returns ------- output : 2-D array The Prewitt edge map. See also -------- sobel, scharr Notes ----- Return the square root of the sum of squares of the horizontal and vertical Prewitt transforms. The edge magnitude depends slightly on edge directions, since the approximation of the gradient operator by the Prewitt operator is not completely rotation invariant. For a better rotation invariance, the Scharr operator should be used. The Sobel operator has a better rotation invariance than the Prewitt operator, but a worse rotation invariance than the Scharr operator. Examples -------- >>> from skimage import data >>> camera = data.camera() >>> from skimage import filters >>> edges = filters.prewitt(camera) """ assert_nD(image, 2) out = np.sqrt(prewitt_h(image, mask)**2 + prewitt_v(image, mask)**2) out /= np.sqrt(2) return out def prewitt_h(image, mask=None): """Find the horizontal edges of an image using the Prewitt transform. Parameters ---------- image : 2-D array Image to process. mask : 2-D array, optional An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result. Returns ------- output : 2-D array The Prewitt edge map. Notes ----- We use the following kernel:: 1 1 1 0 0 0 -1 -1 -1 """ assert_nD(image, 2) image = img_as_float(image) result = convolve(image, HPREWITT_WEIGHTS) return _mask_filter_result(result, mask) def prewitt_v(image, mask=None): """Find the vertical edges of an image using the Prewitt transform. Parameters ---------- image : 2-D array Image to process. mask : 2-D array, optional An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result. Returns ------- output : 2-D array The Prewitt edge map. Notes ----- We use the following kernel:: 1 0 -1 1 0 -1 1 0 -1 """ assert_nD(image, 2) image = img_as_float(image) result = convolve(image, VPREWITT_WEIGHTS) return _mask_filter_result(result, mask) def roberts(image, mask=None): """Find the edge magnitude using Roberts' cross operator. Parameters ---------- image : 2-D array Image to process. mask : 2-D array, optional An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result. Returns ------- output : 2-D array The Roberts' Cross edge map. See also -------- sobel, scharr, prewitt, feature.canny Examples -------- >>> from skimage import data >>> camera = data.camera() >>> from skimage import filters >>> edges = filters.roberts(camera) """ assert_nD(image, 2) out = np.sqrt(roberts_pos_diag(image, mask)**2 + roberts_neg_diag(image, mask)**2) out /= np.sqrt(2) return out def roberts_pos_diag(image, mask=None): """Find the cross edges of an image using Roberts' cross operator. The kernel is applied to the input image to produce separate measurements of the gradient component one orientation. Parameters ---------- image : 2-D array Image to process. mask : 2-D array, optional An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result. Returns ------- output : 2-D array The Robert's edge map. Notes ----- We use the following kernel:: 1 0 0 -1 """ assert_nD(image, 2) image = img_as_float(image) result = convolve(image, ROBERTS_PD_WEIGHTS) return _mask_filter_result(result, mask) def roberts_neg_diag(image, mask=None): """Find the cross edges of an image using the Roberts' Cross operator. The kernel is applied to the input image to produce separate measurements of the gradient component one orientation. Parameters ---------- image : 2-D array Image to process. mask : 2-D array, optional An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result. Returns ------- output : 2-D array The Robert's edge map. Notes ----- We use the following kernel:: 0 1 -1 0 """ assert_nD(image, 2) image = img_as_float(image) result = convolve(image, ROBERTS_ND_WEIGHTS) return _mask_filter_result(result, mask) def laplace(image, ksize=3, mask=None): """Find the edges of an image using the Laplace operator. Parameters ---------- image : ndarray Image to process. ksize : int, optional Define the size of the discrete Laplacian operator such that it will have a size of (ksize,) * image.ndim. mask : ndarray, optional An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result. Returns ------- output : ndarray The Laplace edge map. Notes ----- The Laplacian operator is generated using the function skimage.restoration.uft.laplacian(). """ image = img_as_float(image) # Create the discrete Laplacian operator - We keep only the real part of the filter _, laplace_op = laplacian(image.ndim, (ksize, ) * image.ndim) result = convolve(image, laplace_op) return _mask_filter_result(result, mask) scikit-image-0.13.1/skimage/filters/lpi_filter.py000066400000000000000000000162651316256236300217560ustar00rootroot00000000000000""" :author: Stefan van der Walt, 2008 :license: modified BSD """ import numpy as np from scipy.fftpack import ifftshift from .._shared.utils import assert_nD eps = np.finfo(float).eps def _min_limit(x, val=eps): mask = np.abs(x) < eps x[mask] = np.sign(x[mask]) * eps def _centre(x, oshape): """Return an array of oshape from the centre of x. """ start = (np.array(x.shape) - np.array(oshape)) // 2 + 1 out = x[[slice(s, s + n) for s, n in zip(start, oshape)]] return out def _pad(data, shape): """Pad the data to the given shape with zeros. Parameters ---------- data : 2-d ndarray Input data shape : (2,) tuple """ out = np.zeros(shape) out[[slice(0, n) for n in data.shape]] = data return out class LPIFilter2D(object): """Linear Position-Invariant Filter (2-dimensional) """ def __init__(self, impulse_response, **filter_params): """ Parameters ---------- impulse_response : callable `f(r, c, **filter_params)` Function that yields the impulse response. `r` and `c` are 1-dimensional vectors that represent row and column positions, in other words coordinates are (r[0],c[0]),(r[0],c[1]) etc. `**filter_params` are passed through. In other words, `impulse_response` would be called like this: >>> def impulse_response(r, c, **filter_params): ... pass >>> >>> r = [0,0,0,1,1,1,2,2,2] >>> c = [0,1,2,0,1,2,0,1,2] >>> filter_params = {'kw1': 1, 'kw2': 2, 'kw3': 3} >>> impulse_response(r, c, **filter_params) Examples -------- Gaussian filter: Use a 1-D gaussian in each direction without normalization coefficients. >>> def filt_func(r, c, sigma = 1): ... return np.exp(-np.hypot(r, c)/sigma) >>> filter = LPIFilter2D(filt_func) """ if not callable(impulse_response): raise ValueError("Impulse response must be a callable.") self.impulse_response = impulse_response self.filter_params = filter_params self._cache = None def _prepare(self, data): """Calculate filter and data FFT in preparation for filtering. """ dshape = np.array(data.shape) dshape += (dshape % 2 == 0) # all filter dimensions must be uneven oshape = np.array(data.shape) * 2 - 1 if self._cache is None or np.any(self._cache.shape != oshape): coords = np.mgrid[[slice(0, float(n)) for n in dshape]] # this steps over two sets of coordinates, # not over the coordinates individually for k, coord in enumerate(coords): coord -= (dshape[k] - 1) / 2. coords = coords.reshape(2, -1).T # coordinate pairs (r,c) f = self.impulse_response(coords[:, 0], coords[:, 1], **self.filter_params).reshape(dshape) f = _pad(f, oshape) F = np.dual.fftn(f) self._cache = F else: F = self._cache data = _pad(data, oshape) G = np.dual.fftn(data) return F, G def __call__(self, data): """Apply the filter to the given data. Parameters ---------- data : (M,N) ndarray """ assert_nD(data, 2, 'data') F, G = self._prepare(data) out = np.dual.ifftn(F * G) out = np.abs(_centre(out, data.shape)) return out def forward(data, impulse_response=None, filter_params={}, predefined_filter=None): """Apply the given filter to data. Parameters ---------- data : (M,N) ndarray Input data. impulse_response : callable `f(r, c, **filter_params)` Impulse response of the filter. See LPIFilter2D.__init__. filter_params : dict Additional keyword parameters to the impulse_response function. Other Parameters ---------------- predefined_filter : LPIFilter2D If you need to apply the same filter multiple times over different images, construct the LPIFilter2D and specify it here. Examples -------- Gaussian filter: >>> def filt_func(r, c): ... return np.exp(-np.hypot(r, c)/1) >>> >>> from skimage import data >>> filtered = forward(data.coins(), filt_func) """ assert_nD(data, 2, 'data') if predefined_filter is None: predefined_filter = LPIFilter2D(impulse_response, **filter_params) return predefined_filter(data) def inverse(data, impulse_response=None, filter_params={}, max_gain=2, predefined_filter=None): """Apply the filter in reverse to the given data. Parameters ---------- data : (M,N) ndarray Input data. impulse_response : callable `f(r, c, **filter_params)` Impulse response of the filter. See LPIFilter2D.__init__. filter_params : dict Additional keyword parameters to the impulse_response function. max_gain : float Limit the filter gain. Often, the filter contains zeros, which would cause the inverse filter to have infinite gain. High gain causes amplification of artefacts, so a conservative limit is recommended. Other Parameters ---------------- predefined_filter : LPIFilter2D If you need to apply the same filter multiple times over different images, construct the LPIFilter2D and specify it here. """ assert_nD(data, 2, 'data') if predefined_filter is None: filt = LPIFilter2D(impulse_response, **filter_params) else: filt = predefined_filter F, G = filt._prepare(data) _min_limit(F) F = 1 / F mask = np.abs(F) > max_gain F[mask] = np.sign(F[mask]) * max_gain return _centre(np.abs(ifftshift(np.dual.ifftn(G * F))), data.shape) def wiener(data, impulse_response=None, filter_params={}, K=0.25, predefined_filter=None): """Minimum Mean Square Error (Wiener) inverse filter. Parameters ---------- data : (M,N) ndarray Input data. K : float or (M,N) ndarray Ratio between power spectrum of noise and undegraded image. impulse_response : callable `f(r, c, **filter_params)` Impulse response of the filter. See LPIFilter2D.__init__. filter_params : dict Additional keyword parameters to the impulse_response function. Other Parameters ---------------- predefined_filter : LPIFilter2D If you need to apply the same filter multiple times over different images, construct the LPIFilter2D and specify it here. """ assert_nD(data, 2, 'data') if not isinstance(K, float): assert_nD(K, 2, 'K') if predefined_filter is None: filt = LPIFilter2D(impulse_response, **filter_params) else: filt = predefined_filter F, G = filt._prepare(data) _min_limit(F) H_mag_sqr = np.abs(F) ** 2 F = 1 / F * H_mag_sqr / (H_mag_sqr + K) return _centre(np.abs(ifftshift(np.dual.ifftn(G * F))), data.shape) def constrained_least_squares(data, lam, impulse_response=None, filter_params={}): raise NotImplementedError scikit-image-0.13.1/skimage/filters/rank/000077500000000000000000000000001316256236300201745ustar00rootroot00000000000000scikit-image-0.13.1/skimage/filters/rank/.gitignore000066400000000000000000000000051316256236300221570ustar00rootroot00000000000000demo/scikit-image-0.13.1/skimage/filters/rank/__init__.py000066400000000000000000000027361316256236300223150ustar00rootroot00000000000000from .generic import (autolevel, bottomhat, equalize, gradient, maximum, mean, geometric_mean, subtract_mean, median, minimum, modal, enhance_contrast, pop, threshold, tophat, noise_filter, entropy, otsu, sum, windowed_histogram) from ._percentile import (autolevel_percentile, gradient_percentile, mean_percentile, subtract_mean_percentile, enhance_contrast_percentile, percentile, pop_percentile, sum_percentile, threshold_percentile) from .bilateral import mean_bilateral, pop_bilateral, sum_bilateral __all__ = ['autolevel', 'autolevel_percentile', 'bottomhat', 'equalize', 'gradient', 'gradient_percentile', 'maximum', 'mean', 'geometric_mean', 'mean_percentile', 'mean_bilateral', 'subtract_mean', 'subtract_mean_percentile', 'median', 'minimum', 'modal', 'enhance_contrast', 'enhance_contrast_percentile', 'pop', 'pop_percentile', 'pop_bilateral', 'sum', 'sum_bilateral', 'sum_percentile', 'threshold', 'threshold_percentile', 'tophat', 'noise_filter', 'entropy', 'otsu', 'percentile', 'windowed_histogram'] scikit-image-0.13.1/skimage/filters/rank/_percentile.py000066400000000000000000000330461316256236300230450ustar00rootroot00000000000000"""Inferior and superior ranks, provided by the user, are passed to the kernel function to provide a softer version of the rank filters. E.g. ``autolevel_percentile`` will stretch image levels between percentile [p0, p1] instead of using [min, max]. It means that isolated bright or dark pixels will not produce halos. The local histogram is computed using a sliding window similar to the method described in [1]_. Input image can be 8-bit or 16-bit, for 16-bit input images, the number of histogram bins is determined from the maximum value present in the image. Result image is 8-/16-bit or double with respect to the input image and the rank filter operation. References ---------- .. [1] Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional median filtering algorithm", IEEE Transactions on Acoustics, Speech and Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18. """ from ..._shared.utils import assert_nD from . import percentile_cy from .generic import _handle_input __all__ = ['autolevel_percentile', 'gradient_percentile', 'mean_percentile', 'subtract_mean_percentile', 'enhance_contrast_percentile', 'percentile', 'pop_percentile', 'threshold_percentile'] def _apply(func, image, selem, out, mask, shift_x, shift_y, p0, p1, out_dtype=None): assert_nD(image, 2) image, selem, out, mask, max_bin = _handle_input(image, selem, out, mask, out_dtype) func(image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask, out=out, max_bin=max_bin, p0=p0, p1=p1) return out.reshape(out.shape[:2]) def autolevel_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1): """Return greyscale local autolevel of an image. This filter locally stretches the histogram of greyvalues to cover the entire range of values from "white" to "black". Only greyvalues between percentiles [p0, p1] are considered in the filter. Parameters ---------- image : 2-D array (uint8, uint16) Input image. selem : 2-D array The neighborhood expressed as a 2-D array of 1's and 0's. out : 2-D array (same dtype as input) If None, a new array is allocated. mask : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). shift_x, shift_y : int Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element). p0, p1 : float in [0, ..., 1] Define the [p0, p1] percentile interval to be considered for computing the value. Returns ------- out : 2-D array (same dtype as input image) Output image. """ return _apply(percentile_cy._autolevel, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) def gradient_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1): """Return local gradient of an image (i.e. local maximum - local minimum). Only greyvalues between percentiles [p0, p1] are considered in the filter. Parameters ---------- image : 2-D array (uint8, uint16) Input image. selem : 2-D array The neighborhood expressed as a 2-D array of 1's and 0's. out : 2-D array (same dtype as input) If None, a new array is allocated. mask : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). shift_x, shift_y : int Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element). p0, p1 : float in [0, ..., 1] Define the [p0, p1] percentile interval to be considered for computing the value. Returns ------- out : 2-D array (same dtype as input image) Output image. """ return _apply(percentile_cy._gradient, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) def mean_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1): """Return local mean of an image. Only greyvalues between percentiles [p0, p1] are considered in the filter. Parameters ---------- image : 2-D array (uint8, uint16) Input image. selem : 2-D array The neighborhood expressed as a 2-D array of 1's and 0's. out : 2-D array (same dtype as input) If None, a new array is allocated. mask : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). shift_x, shift_y : int Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element). p0, p1 : float in [0, ..., 1] Define the [p0, p1] percentile interval to be considered for computing the value. Returns ------- out : 2-D array (same dtype as input image) Output image. """ return _apply(percentile_cy._mean, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) def subtract_mean_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1): """Return image subtracted from its local mean. Only greyvalues between percentiles [p0, p1] are considered in the filter. Parameters ---------- image : 2-D array (uint8, uint16) Input image. selem : 2-D array The neighborhood expressed as a 2-D array of 1's and 0's. out : 2-D array (same dtype as input) If None, a new array is allocated. mask : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). shift_x, shift_y : int Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element). p0, p1 : float in [0, ..., 1] Define the [p0, p1] percentile interval to be considered for computing the value. Returns ------- out : 2-D array (same dtype as input image) Output image. """ return _apply(percentile_cy._subtract_mean, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) def enhance_contrast_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1): """Enhance contrast of an image. This replaces each pixel by the local maximum if the pixel greyvalue is closer to the local maximum than the local minimum. Otherwise it is replaced by the local minimum. Only greyvalues between percentiles [p0, p1] are considered in the filter. Parameters ---------- image : 2-D array (uint8, uint16) Input image. selem : 2-D array The neighborhood expressed as a 2-D array of 1's and 0's. out : 2-D array (same dtype as input) If None, a new array is allocated. mask : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). shift_x, shift_y : int Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element). p0, p1 : float in [0, ..., 1] Define the [p0, p1] percentile interval to be considered for computing the value. Returns ------- out : 2-D array (same dtype as input image) Output image. """ return _apply(percentile_cy._enhance_contrast, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) def percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0): """Return local percentile of an image. Returns the value of the p0 lower percentile of the local greyvalue distribution. Only greyvalues between percentiles [p0, p1] are considered in the filter. Parameters ---------- image : 2-D array (uint8, uint16) Input image. selem : 2-D array The neighborhood expressed as a 2-D array of 1's and 0's. out : 2-D array (same dtype as input) If None, a new array is allocated. mask : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). shift_x, shift_y : int Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element). p0 : float in [0, ..., 1] Set the percentile value. Returns ------- out : 2-D array (same dtype as input image) Output image. """ return _apply(percentile_cy._percentile, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=0.) def pop_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1): """Return the local number (population) of pixels. The number of pixels is defined as the number of pixels which are included in the structuring element and the mask. Only greyvalues between percentiles [p0, p1] are considered in the filter. Parameters ---------- image : 2-D array (uint8, uint16) Input image. selem : 2-D array The neighborhood expressed as a 2-D array of 1's and 0's. out : 2-D array (same dtype as input) If None, a new array is allocated. mask : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). shift_x, shift_y : int Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element). p0, p1 : float in [0, ..., 1] Define the [p0, p1] percentile interval to be considered for computing the value. Returns ------- out : 2-D array (same dtype as input image) Output image. """ return _apply(percentile_cy._pop, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) def sum_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1): """Return the local sum of pixels. Only greyvalues between percentiles [p0, p1] are considered in the filter. Note that the sum may overflow depending on the data type of the input array. Parameters ---------- image : 2-D array (uint8, uint16) Input image. selem : 2-D array The neighborhood expressed as a 2-D array of 1's and 0's. out : 2-D array (same dtype as input) If None, a new array is allocated. mask : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). shift_x, shift_y : int Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element). p0, p1 : float in [0, ..., 1] Define the [p0, p1] percentile interval to be considered for computing the value. Returns ------- out : 2-D array (same dtype as input image) Output image. """ return _apply(percentile_cy._sum, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=p1) def threshold_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0): """Local threshold of an image. The resulting binary mask is True if the greyvalue of the center pixel is greater than the local mean. Only greyvalues between percentiles [p0, p1] are considered in the filter. Parameters ---------- image : 2-D array (uint8, uint16) Input image. selem : 2-D array The neighborhood expressed as a 2-D array of 1's and 0's. out : 2-D array (same dtype as input) If None, a new array is allocated. mask : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). shift_x, shift_y : int Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element). p0 : float in [0, ..., 1] Set the percentile value. Returns ------- out : 2-D array (same dtype as input image) Output image. """ return _apply(percentile_cy._threshold, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, p0=p0, p1=0) scikit-image-0.13.1/skimage/filters/rank/bilateral.py000066400000000000000000000167121316256236300225140ustar00rootroot00000000000000"""Approximate bilateral rank filter for local (custom kernel) mean. The local histogram is computed using a sliding window similar to the method described in [1]_. The pixel neighborhood is defined by: * the given structuring element * an interval [g-s0, g+s1] in greylevel around g the processed pixel greylevel The kernel is flat (i.e. each pixel belonging to the neighborhood contributes equally). Result image is 8-/16-bit or double with respect to the input image and the rank filter operation. References ---------- .. [1] Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional median filtering algorithm", IEEE Transactions on Acoustics, Speech and Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18. """ import numpy as np from ..._shared.utils import assert_nD from . import bilateral_cy from .generic import _handle_input __all__ = ['mean_bilateral', 'pop_bilateral', 'sum_bilateral'] def _apply(func, image, selem, out, mask, shift_x, shift_y, s0, s1, out_dtype=None): assert_nD(image, 2) image, selem, out, mask, max_bin = _handle_input(image, selem, out, mask, out_dtype) func(image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask, out=out, max_bin=max_bin, s0=s0, s1=s1) return out.reshape(out.shape[:2]) def mean_bilateral(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10): """Apply a flat kernel bilateral filter. This is an edge-preserving and noise reducing denoising filter. It averages pixels based on their spatial closeness and radiometric similarity. Spatial closeness is measured by considering only the local pixel neighborhood given by a structuring element. Radiometric similarity is defined by the greylevel interval [g-s0, g+s1] where g is the current pixel greylevel. Only pixels belonging to the structuring element and having a greylevel inside this interval are averaged. Parameters ---------- image : 2-D array (uint8, uint16) Input image. selem : 2-D array The neighborhood expressed as a 2-D array of 1's and 0's. out : 2-D array (same dtype as input) If None, a new array is allocated. mask : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). shift_x, shift_y : int Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element). s0, s1 : int Define the [s0, s1] interval around the greyvalue of the center pixel to be considered for computing the value. Returns ------- out : 2-D array (same dtype as input image) Output image. See also -------- skimage.filters.denoise_bilateral for a Gaussian bilateral filter. Examples -------- >>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import mean_bilateral >>> img = data.camera().astype(np.uint16) >>> bilat_img = mean_bilateral(img, disk(20), s0=10,s1=10) """ return _apply(bilateral_cy._mean, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, s0=s0, s1=s1) def pop_bilateral(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10): """Return the local number (population) of pixels. The number of pixels is defined as the number of pixels which are included in the structuring element and the mask. Additionally pixels must have a greylevel inside the interval [g-s0, g+s1] where g is the greyvalue of the center pixel. Parameters ---------- image : 2-D array (uint8, uint16) Input image. selem : 2-D array The neighborhood expressed as a 2-D array of 1's and 0's. out : 2-D array (same dtype as input) If None, a new array is allocated. mask : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). shift_x, shift_y : int Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element). s0, s1 : int Define the [s0, s1] interval around the greyvalue of the center pixel to be considered for computing the value. Returns ------- out : 2-D array (same dtype as input image) Output image. Examples -------- >>> from skimage.morphology import square >>> import skimage.filters.rank as rank >>> img = 255 * np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) >>> rank.pop_bilateral(img, square(3), s0=10, s1=10) array([[3, 4, 3, 4, 3], [4, 4, 6, 4, 4], [3, 6, 9, 6, 3], [4, 4, 6, 4, 4], [3, 4, 3, 4, 3]], dtype=uint16) """ return _apply(bilateral_cy._pop, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, s0=s0, s1=s1) def sum_bilateral(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10): """Apply a flat kernel bilateral filter. This is an edge-preserving and noise reducing denoising filter. It averages pixels based on their spatial closeness and radiometric similarity. Spatial closeness is measured by considering only the local pixel neighborhood given by a structuring element (selem). Radiometric similarity is defined by the greylevel interval [g-s0, g+s1] where g is the current pixel greylevel. Only pixels belonging to the structuring element AND having a greylevel inside this interval are summed. Note that the sum may overflow depending on the data type of the input array. Parameters ---------- image : 2-D array (uint8, uint16) Input image. selem : 2-D array The neighborhood expressed as a 2-D array of 1's and 0's. out : 2-D array (same dtype as input) If None, a new array is allocated. mask : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). shift_x, shift_y : int Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element). s0, s1 : int Define the [s0, s1] interval around the greyvalue of the center pixel to be considered for computing the value. Returns ------- out : 2-D array (same dtype as input image) Output image. See also -------- skimage.filters.denoise_bilateral for a Gaussian bilateral filter. Examples -------- >>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import sum_bilateral >>> img = data.camera().astype(np.uint16) >>> bilat_img = sum_bilateral(img, disk(10), s0=10, s1=10) """ return _apply(bilateral_cy._sum, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, s0=s0, s1=s1) scikit-image-0.13.1/skimage/filters/rank/bilateral_cy.pyx000066400000000000000000000066101316256236300233730ustar00rootroot00000000000000#cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False #cython: wraparound=False cimport numpy as cnp from libc.math cimport log from .core_cy cimport dtype_t, dtype_t_out, _core cdef inline void _kernel_mean(dtype_t_out* out, Py_ssize_t odepth, Py_ssize_t* histo, double pop, dtype_t g, Py_ssize_t max_bin, Py_ssize_t mid_bin, double p0, double p1, Py_ssize_t s0, Py_ssize_t s1) nogil: cdef Py_ssize_t i cdef Py_ssize_t bilat_pop = 0 cdef Py_ssize_t mean = 0 if pop: for i in range(max_bin): if (g > (i - s0)) and (g < (i + s1)): bilat_pop += histo[i] mean += histo[i] * i if bilat_pop: out[0] = (mean / bilat_pop) else: out[0] = 0 else: out[0] = 0 cdef inline void _kernel_pop(dtype_t_out* out, Py_ssize_t odepth, Py_ssize_t* histo, double pop, dtype_t g, Py_ssize_t max_bin, Py_ssize_t mid_bin, double p0, double p1, Py_ssize_t s0, Py_ssize_t s1) nogil: cdef Py_ssize_t i cdef Py_ssize_t bilat_pop = 0 if pop: for i in range(max_bin): if (g > (i - s0)) and (g < (i + s1)): bilat_pop += histo[i] out[0] = bilat_pop else: out[0] = 0 cdef inline void _kernel_sum(dtype_t_out* out, Py_ssize_t odepth, Py_ssize_t* histo, double pop, dtype_t g, Py_ssize_t max_bin, Py_ssize_t mid_bin, double p0, double p1, Py_ssize_t s0, Py_ssize_t s1) nogil: cdef Py_ssize_t i cdef Py_ssize_t bilat_pop = 0 cdef Py_ssize_t sum = 0 if pop: for i in range(max_bin): if (g > (i - s0)) and (g < (i + s1)): bilat_pop += histo[i] sum += histo[i] * i if bilat_pop: out[0] = sum else: out[0] = 0 else: out[0] = 0 def _mean(dtype_t[:, ::1] image, char[:, ::1] selem, char[:, ::1] mask, dtype_t_out[:, :, ::1] out, signed char shift_x, signed char shift_y, Py_ssize_t s0, Py_ssize_t s1, Py_ssize_t max_bin): _core(_kernel_mean[dtype_t_out, dtype_t], image, selem, mask, out, shift_x, shift_y, 0, 0, s0, s1, max_bin) def _pop(dtype_t[:, ::1] image, char[:, ::1] selem, char[:, ::1] mask, dtype_t_out[:, :, ::1] out, signed char shift_x, signed char shift_y, Py_ssize_t s0, Py_ssize_t s1, Py_ssize_t max_bin): _core(_kernel_pop[dtype_t_out, dtype_t], image, selem, mask, out, shift_x, shift_y, 0, 0, s0, s1, max_bin) def _sum(dtype_t[:, ::1] image, char[:, ::1] selem, char[:, ::1] mask, dtype_t_out[:, :, ::1] out, signed char shift_x, signed char shift_y, Py_ssize_t s0, Py_ssize_t s1, Py_ssize_t max_bin): _core(_kernel_sum[dtype_t_out, dtype_t], image, selem, mask, out, shift_x, shift_y, 0, 0, s0, s1, max_bin) scikit-image-0.13.1/skimage/filters/rank/core_cy.pxd000066400000000000000000000014551316256236300223410ustar00rootroot00000000000000from numpy cimport uint8_t, uint16_t, double_t ctypedef fused dtype_t: uint8_t uint16_t ctypedef fused dtype_t_out: uint8_t uint16_t double_t cdef dtype_t _max(dtype_t a, dtype_t b) nogil cdef dtype_t _min(dtype_t a, dtype_t b) nogil cdef void _core(void kernel(dtype_t_out*, Py_ssize_t, Py_ssize_t*, double, dtype_t, Py_ssize_t, Py_ssize_t, double, double, Py_ssize_t, Py_ssize_t) nogil, dtype_t[:, ::1] image, char[:, ::1] selem, char[:, ::1] mask, dtype_t_out[:, :, ::1] out, signed char shift_x, signed char shift_y, double p0, double p1, Py_ssize_t s0, Py_ssize_t s1, Py_ssize_t max_bin) except * scikit-image-0.13.1/skimage/filters/rank/core_cy.pyx000066400000000000000000000214361316256236300223670ustar00rootroot00000000000000#cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False #cython: wraparound=False import numpy as np cimport numpy as cnp from libc.stdlib cimport malloc, free cdef inline dtype_t _max(dtype_t a, dtype_t b) nogil: return a if a >= b else b cdef inline dtype_t _min(dtype_t a, dtype_t b) nogil: return a if a <= b else b cdef inline void histogram_increment(Py_ssize_t* histo, double* pop, dtype_t value) nogil: histo[value] += 1 pop[0] += 1 cdef inline void histogram_decrement(Py_ssize_t* histo, double* pop, dtype_t value) nogil: histo[value] -= 1 pop[0] -= 1 cdef inline char is_in_mask(Py_ssize_t rows, Py_ssize_t cols, Py_ssize_t r, Py_ssize_t c, char* mask) nogil: """Check whether given coordinate is within image and mask is true.""" if r < 0 or r > rows - 1 or c < 0 or c > cols - 1: return 0 else: if mask[r * cols + c]: return 1 else: return 0 cdef void _core(void kernel(dtype_t_out*, Py_ssize_t, Py_ssize_t*, double, dtype_t, Py_ssize_t, Py_ssize_t, double, double, Py_ssize_t, Py_ssize_t) nogil, dtype_t[:, ::1] image, char[:, ::1] selem, char[:, ::1] mask, dtype_t_out[:, :, ::1] out, signed char shift_x, signed char shift_y, double p0, double p1, Py_ssize_t s0, Py_ssize_t s1, Py_ssize_t max_bin) except *: """Compute histogram for each pixel neighborhood, apply kernel function and use kernel function return value for output image. """ cdef Py_ssize_t rows = image.shape[0] cdef Py_ssize_t cols = image.shape[1] cdef Py_ssize_t srows = selem.shape[0] cdef Py_ssize_t scols = selem.shape[1] cdef Py_ssize_t odepth = out.shape[2] cdef Py_ssize_t centre_r = (selem.shape[0] / 2) + shift_y cdef Py_ssize_t centre_c = (selem.shape[1] / 2) + shift_x # check that structuring element center is inside the element bounding box assert centre_r >= 0 assert centre_c >= 0 assert centre_r < srows assert centre_c < scols # add 1 to ensure maximum value is included in histogram -> range(max_bin) max_bin += 1 cdef Py_ssize_t mid_bin = max_bin / 2 # define pointers to the data cdef char* mask_data = &mask[0, 0] # define local variable types cdef Py_ssize_t r, c, rr, cc, s, value, local_max, i, even_row # number of pixels actually inside the neighborhood (double) cdef double pop = 0 # the current local histogram distribution cdef Py_ssize_t* histo = malloc(max_bin * sizeof(Py_ssize_t)) for i in range(max_bin): histo[i] = 0 # these lists contain the relative pixel row and column for each of the 4 # attack borders east, west, north and south e.g. se_e_r lists the rows of # the east structuring element border cdef Py_ssize_t max_se = srows * scols # number of element in each attack border cdef Py_ssize_t num_se_n, num_se_s, num_se_e, num_se_w num_se_n = num_se_s = num_se_e = num_se_w = 0 cdef Py_ssize_t* se_e_r = malloc(max_se * sizeof(Py_ssize_t)) cdef Py_ssize_t* se_e_c = malloc(max_se * sizeof(Py_ssize_t)) cdef Py_ssize_t* se_w_r = malloc(max_se * sizeof(Py_ssize_t)) cdef Py_ssize_t* se_w_c = malloc(max_se * sizeof(Py_ssize_t)) cdef Py_ssize_t* se_n_r = malloc(max_se * sizeof(Py_ssize_t)) cdef Py_ssize_t* se_n_c = malloc(max_se * sizeof(Py_ssize_t)) cdef Py_ssize_t* se_s_r = malloc(max_se * sizeof(Py_ssize_t)) cdef Py_ssize_t* se_s_c = malloc(max_se * sizeof(Py_ssize_t)) # build attack and release borders by using difference along axis t = np.hstack((selem, np.zeros((selem.shape[0], 1)))) cdef unsigned char[:, :] t_e = (np.diff(t, axis=1) < 0).view(np.uint8) t = np.hstack((np.zeros((selem.shape[0], 1)), selem)) cdef unsigned char[:, :] t_w = (np.diff(t, axis=1) > 0).view(np.uint8) t = np.vstack((selem, np.zeros((1, selem.shape[1])))) cdef unsigned char[:, :] t_s = (np.diff(t, axis=0) < 0).view(np.uint8) t = np.vstack((np.zeros((1, selem.shape[1])), selem)) cdef unsigned char[:, :] t_n = (np.diff(t, axis=0) > 0).view(np.uint8) with nogil: for r in range(srows): for c in range(scols): if t_e[r, c]: se_e_r[num_se_e] = r - centre_r se_e_c[num_se_e] = c - centre_c num_se_e += 1 if t_w[r, c]: se_w_r[num_se_w] = r - centre_r se_w_c[num_se_w] = c - centre_c num_se_w += 1 if t_n[r, c]: se_n_r[num_se_n] = r - centre_r se_n_c[num_se_n] = c - centre_c num_se_n += 1 if t_s[r, c]: se_s_r[num_se_s] = r - centre_r se_s_c[num_se_s] = c - centre_c num_se_s += 1 for r in range(srows): for c in range(scols): rr = r - centre_r cc = c - centre_c if selem[r, c]: if is_in_mask(rows, cols, rr, cc, mask_data): histogram_increment(histo, &pop, image[rr, cc]) r = 0 c = 0 kernel(&out[r, c, 0], odepth, histo, pop, image[r, c], max_bin, mid_bin, p0, p1, s0, s1) # main loop r = 0 for even_row in range(0, rows, 2): # ---> west to east for c in range(1, cols): for s in range(num_se_e): rr = r + se_e_r[s] cc = c + se_e_c[s] if is_in_mask(rows, cols, rr, cc, mask_data): histogram_increment(histo, &pop, image[rr, cc]) for s in range(num_se_w): rr = r + se_w_r[s] cc = c + se_w_c[s] - 1 if is_in_mask(rows, cols, rr, cc, mask_data): histogram_decrement(histo, &pop, image[rr, cc]) kernel(&out[r, c, 0], odepth, histo, pop, image[r, c], max_bin, mid_bin, p0, p1, s0, s1) r += 1 # pass to the next row if r >= rows: break # ---> north to south for s in range(num_se_s): rr = r + se_s_r[s] cc = c + se_s_c[s] if is_in_mask(rows, cols, rr, cc, mask_data): histogram_increment(histo, &pop, image[rr, cc]) for s in range(num_se_n): rr = r + se_n_r[s] - 1 cc = c + se_n_c[s] if is_in_mask(rows, cols, rr, cc, mask_data): histogram_decrement(histo, &pop, image[rr, cc]) kernel(&out[r, c, 0], odepth, histo, pop, image[r, c], max_bin, mid_bin, p0, p1, s0, s1) # ---> east to west for c in range(cols - 2, -1, -1): for s in range(num_se_w): rr = r + se_w_r[s] cc = c + se_w_c[s] if is_in_mask(rows, cols, rr, cc, mask_data): histogram_increment(histo, &pop, image[rr, cc]) for s in range(num_se_e): rr = r + se_e_r[s] cc = c + se_e_c[s] + 1 if is_in_mask(rows, cols, rr, cc, mask_data): histogram_decrement(histo, &pop, image[rr, cc]) kernel(&out[r, c, 0], odepth, histo, pop, image[r, c], max_bin, mid_bin, p0, p1, s0, s1) r += 1 # pass to the next row if r >= rows: break # ---> north to south for s in range(num_se_s): rr = r + se_s_r[s] cc = c + se_s_c[s] if is_in_mask(rows, cols, rr, cc, mask_data): histogram_increment(histo, &pop, image[rr, cc]) for s in range(num_se_n): rr = r + se_n_r[s] - 1 cc = c + se_n_c[s] if is_in_mask(rows, cols, rr, cc, mask_data): histogram_decrement(histo, &pop, image[rr, cc]) kernel(&out[r, c, 0], odepth, histo, pop, image[r, c], max_bin, mid_bin, p0, p1, s0, s1) # release memory allocated by malloc free(se_e_r) free(se_e_c) free(se_w_r) free(se_w_c) free(se_n_r) free(se_n_c) free(se_s_r) free(se_s_c) free(histo) scikit-image-0.13.1/skimage/filters/rank/generic.py000066400000000000000000001037461316256236300221750ustar00rootroot00000000000000""" General Description ------------------- These filters compute the local histogram at each pixel, using a sliding window similar to the method described in [1]_. A histogram is built using a moving window in order to limit redundant computation. The moving window follows a snake-like path: ...------------------------\ /--------------------------/ \--------------------------... The local histogram is updated at each pixel as the structuring element window moves by, i.e. only those pixels entering and leaving the structuring element update the local histogram. The histogram size is 8-bit (256 bins) for 8-bit images and 2- to 16-bit for 16-bit images depending on the maximum value of the image. The filter is applied up to the image border, the neighborhood used is adjusted accordingly. The user may provide a mask image (same size as input image) where non zero values are the part of the image participating in the histogram computation. By default the entire image is filtered. This implementation outperforms grey.dilation for large structuring elements. Input image can be 8-bit or 16-bit, for 16-bit input images, the number of histogram bins is determined from the maximum value present in the image. Result image is 8-/16-bit or double with respect to the input image and the rank filter operation. To do ----- * add simple examples, adapt documentation on existing examples * add/check existing doc * adapting tests for each type of filter References ---------- .. [1] Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional median filtering algorithm", IEEE Transactions on Acoustics, Speech and Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18. """ import functools import numpy as np from scipy import ndimage as ndi from ... import img_as_ubyte from ..._shared.utils import assert_nD, warn from . import generic_cy __all__ = ['autolevel', 'bottomhat', 'equalize', 'gradient', 'maximum', 'mean', 'geometric_mean', 'subtract_mean', 'median', 'minimum', 'modal', 'enhance_contrast', 'pop', 'threshold', 'tophat', 'noise_filter', 'entropy', 'otsu'] def _handle_input(image, selem, out, mask, out_dtype=None, pixel_size=1): assert_nD(image, 2) if image.dtype not in (np.uint8, np.uint16): image = img_as_ubyte(image) selem = np.ascontiguousarray(img_as_ubyte(selem > 0)) image = np.ascontiguousarray(image) if mask is None: mask = np.ones(image.shape, dtype=np.uint8) else: mask = img_as_ubyte(mask) mask = np.ascontiguousarray(mask) if image is out: raise NotImplementedError("Cannot perform rank operation in place.") if out is None: if out_dtype is None: out_dtype = image.dtype out = np.empty(image.shape+(pixel_size,), dtype=out_dtype) else: if len(out.shape) == 2: out = out.reshape(out.shape+(pixel_size,)) is_8bit = image.dtype in (np.uint8, np.int8) if is_8bit: max_bin = 255 else: max_bin = max(4, image.max()) bitdepth = int(np.log2(max_bin)) if bitdepth > 10: warn("Bitdepth of %d may result in bad rank filter " "performance due to large number of bins." % bitdepth) return image, selem, out, mask, max_bin def _apply_scalar_per_pixel(func, image, selem, out, mask, shift_x, shift_y, out_dtype=None): image, selem, out, mask, max_bin = _handle_input(image, selem, out, mask, out_dtype) func(image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask, out=out, max_bin=max_bin) return out.reshape(out.shape[:2]) def _apply_vector_per_pixel(func, image, selem, out, mask, shift_x, shift_y, out_dtype=None, pixel_size=1): image, selem, out, mask, max_bin = _handle_input(image, selem, out, mask, out_dtype, pixel_size=pixel_size) func(image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask, out=out, max_bin=max_bin) return out def _default_selem(func): """Decorator to add a default structuring element to morphology functions. Parameters ---------- func : function A morphology function such as erosion, dilation, opening, closing, white_tophat, or black_tophat. Returns ------- func_out : function The function, using a default structuring element of same dimension as the input image with connectivity 1. """ @functools.wraps(func) def func_out(image, selem=None, *args, **kwargs): if selem is None: selem = ndi.generate_binary_structure(image.ndim, image.ndim) return func(image, selem=selem, *args, **kwargs) return func_out def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Auto-level image using local histogram. This filter locally stretches the histogram of greyvalues to cover the entire range of values from "white" to "black". Parameters ---------- image : 2-D array (uint8, uint16) Input image. selem : 2-D array The neighborhood expressed as a 2-D array of 1's and 0's. out : 2-D array (same dtype as input) If None, a new array is allocated. mask : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). shift_x, shift_y : int Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- out : 2-D array (same dtype as input image) Output image. Examples -------- >>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import autolevel >>> img = data.camera() >>> auto = autolevel(img, disk(5)) """ return _apply_scalar_per_pixel(generic_cy._autolevel, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Local bottom-hat of an image. This filter computes the morphological closing of the image and then subtracts the result from the original image. Parameters ---------- image : 2-D array (uint8, uint16) Input image. selem : 2-D array The neighborhood expressed as a 2-D array of 1's and 0's. out : 2-D array (same dtype as input) If None, a new array is allocated. mask : 2-D array Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). shift_x, shift_y : int Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- out : 2-D array (same dtype as input image) Output image. Examples -------- >>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import bottomhat >>> img = data.camera() >>> out = bottomhat(img, disk(5)) """ return _apply_scalar_per_pixel(generic_cy._bottomhat, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Equalize image using local histogram. Parameters ---------- image : 2-D array (uint8, uint16) Input image. selem : 2-D array The neighborhood expressed as a 2-D array of 1's and 0's. out : 2-D array (same dtype as input) If None, a new array is allocated. mask : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). shift_x, shift_y : int Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- out : 2-D array (same dtype as input image) Output image. Examples -------- >>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import equalize >>> img = data.camera() >>> equ = equalize(img, disk(5)) """ return _apply_scalar_per_pixel(generic_cy._equalize, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return local gradient of an image (i.e. local maximum - local minimum). Parameters ---------- image : 2-D array (uint8, uint16) Input image. selem : 2-D array The neighborhood expressed as a 2-D array of 1's and 0's. out : 2-D array (same dtype as input) If None, a new array is allocated. mask : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). shift_x, shift_y : int Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- out : 2-D array (same dtype as input image) Output image. Examples -------- >>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import gradient >>> img = data.camera() >>> out = gradient(img, disk(5)) """ return _apply_scalar_per_pixel(generic_cy._gradient, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return local maximum of an image. Parameters ---------- image : 2-D array (uint8, uint16) Input image. selem : 2-D array The neighborhood expressed as a 2-D array of 1's and 0's. out : 2-D array (same dtype as input) If None, a new array is allocated. mask : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). shift_x, shift_y : int Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- out : 2-D array (same dtype as input image) Output image. See also -------- skimage.morphology.dilation Notes ----- The lower algorithm complexity makes `skimage.filters.rank.maximum` more efficient for larger images and structuring elements. Examples -------- >>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import maximum >>> img = data.camera() >>> out = maximum(img, disk(5)) """ return _apply_scalar_per_pixel(generic_cy._maximum, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return local mean of an image. Parameters ---------- image : 2-D array (uint8, uint16) Input image. selem : 2-D array The neighborhood expressed as a 2-D array of 1's and 0's. out : 2-D array (same dtype as input) If None, a new array is allocated. mask : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). shift_x, shift_y : int Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- out : 2-D array (same dtype as input image) Output image. Examples -------- >>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import mean >>> img = data.camera() >>> avg = mean(img, disk(5)) """ return _apply_scalar_per_pixel(generic_cy._mean, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def geometric_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return local geometric mean of an image. Parameters ---------- image : 2-D array (uint8, uint16) Input image. selem : 2-D array The neighborhood expressed as a 2-D array of 1's and 0's. out : 2-D array (same dtype as input) If None, a new array is allocated. mask : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). shift_x, shift_y : int Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- out : 2-D array (same dtype as input image) Output image. Examples -------- >>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import mean >>> img = data.camera() >>> avg = geometric_mean(img, disk(5)) References ---------- .. [1] Gonzalez, R. C. and Wood, R. E. "Digital Image Processing (3rd Edition)." Prentice-Hall Inc, 2006. """ return _apply_scalar_per_pixel(generic_cy._geometric_mean, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def subtract_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return image subtracted from its local mean. Parameters ---------- image : 2-D array (uint8, uint16) Input image. selem : 2-D array The neighborhood expressed as a 2-D array of 1's and 0's. out : 2-D array (same dtype as input) If None, a new array is allocated. mask : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). shift_x, shift_y : int Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- out : 2-D array (same dtype as input image) Output image. Examples -------- >>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import subtract_mean >>> img = data.camera() >>> out = subtract_mean(img, disk(5)) """ return _apply_scalar_per_pixel(generic_cy._subtract_mean, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) @_default_selem def median(image, selem=None, out=None, mask=None, shift_x=False, shift_y=False): """Return local median of an image. Parameters ---------- image : 2-D array (uint8, uint16) Input image. selem : 2-D array, optional The neighborhood expressed as a 2-D array of 1's and 0's. If None, a full square of size 3 is used. out : 2-D array (same dtype as input) If None, a new array is allocated. mask : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). shift_x, shift_y : int Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- out : 2-D array (same dtype as input image) Output image. Examples -------- >>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import median >>> img = data.camera() >>> med = median(img, disk(5)) """ return _apply_scalar_per_pixel(generic_cy._median, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return local minimum of an image. Parameters ---------- image : 2-D array (uint8, uint16) Input image. selem : 2-D array The neighborhood expressed as a 2-D array of 1's and 0's. out : 2-D array (same dtype as input) If None, a new array is allocated. mask : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). shift_x, shift_y : int Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- out : 2-D array (same dtype as input image) Output image. See also -------- skimage.morphology.erosion Notes ----- The lower algorithm complexity makes `skimage.filters.rank.minimum` more efficient for larger images and structuring elements. Examples -------- >>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import minimum >>> img = data.camera() >>> out = minimum(img, disk(5)) """ return _apply_scalar_per_pixel(generic_cy._minimum, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return local mode of an image. The mode is the value that appears most often in the local histogram. Parameters ---------- image : 2-D array (uint8, uint16) Input image. selem : 2-D array The neighborhood expressed as a 2-D array of 1's and 0's. out : 2-D array (same dtype as input) If None, a new array is allocated. mask : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). shift_x, shift_y : int Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- out : 2-D array (same dtype as input image) Output image. Examples -------- >>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import modal >>> img = data.camera() >>> out = modal(img, disk(5)) """ return _apply_scalar_per_pixel(generic_cy._modal, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def enhance_contrast(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Enhance contrast of an image. This replaces each pixel by the local maximum if the pixel greyvalue is closer to the local maximum than the local minimum. Otherwise it is replaced by the local minimum. Parameters ---------- image : 2-D array (uint8, uint16) Input image. selem : 2-D array The neighborhood expressed as a 2-D array of 1's and 0's. out : 2-D array (same dtype as input) If None, a new array is allocated. mask : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). shift_x, shift_y : int Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns Output image. out : 2-D array (same dtype as input image) The result of the local enhance_contrast. Examples -------- >>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import enhance_contrast >>> img = data.camera() >>> out = enhance_contrast(img, disk(5)) """ return _apply_scalar_per_pixel(generic_cy._enhance_contrast, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return the local number (population) of pixels. The number of pixels is defined as the number of pixels which are included in the structuring element and the mask. Parameters ---------- image : 2-D array (uint8, uint16) Input image. selem : 2-D array The neighborhood expressed as a 2-D array of 1's and 0's. out : 2-D array (same dtype as input) If None, a new array is allocated. mask : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). shift_x, shift_y : int Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- out : 2-D array (same dtype as input image) Output image. Examples -------- >>> from skimage.morphology import square >>> import skimage.filters.rank as rank >>> img = 255 * np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) >>> rank.pop(img, square(3)) array([[4, 6, 6, 6, 4], [6, 9, 9, 9, 6], [6, 9, 9, 9, 6], [6, 9, 9, 9, 6], [4, 6, 6, 6, 4]], dtype=uint8) """ return _apply_scalar_per_pixel(generic_cy._pop, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def sum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Return the local sum of pixels. Note that the sum may overflow depending on the data type of the input array. Parameters ---------- image : 2-D array (uint8, uint16) Input image. selem : 2-D array The neighborhood expressed as a 2-D array of 1's and 0's. out : 2-D array (same dtype as input) If None, a new array is allocated. mask : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). shift_x, shift_y : int Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- out : 2-D array (same dtype as input image) Output image. Examples -------- >>> from skimage.morphology import square >>> import skimage.filters.rank as rank >>> img = np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) >>> rank.sum(img, square(3)) array([[1, 2, 3, 2, 1], [2, 4, 6, 4, 2], [3, 6, 9, 6, 3], [2, 4, 6, 4, 2], [1, 2, 3, 2, 1]], dtype=uint8) """ return _apply_scalar_per_pixel(generic_cy._sum, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Local threshold of an image. The resulting binary mask is True if the greyvalue of the center pixel is greater than the local mean. Parameters ---------- image : 2-D array (uint8, uint16) Input image. selem : 2-D array The neighborhood expressed as a 2-D array of 1's and 0's. out : 2-D array (same dtype as input) If None, a new array is allocated. mask : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). shift_x, shift_y : int Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- out : 2-D array (same dtype as input image) Output image. Examples -------- >>> from skimage.morphology import square >>> from skimage.filters.rank import threshold >>> img = 255 * np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) >>> threshold(img, square(3)) array([[0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 0, 1, 0], [0, 1, 1, 1, 0], [0, 0, 0, 0, 0]], dtype=uint8) """ return _apply_scalar_per_pixel(generic_cy._threshold, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Local top-hat of an image. This filter computes the morphological opening of the image and then subtracts the result from the original image. Parameters ---------- image : 2-D array (uint8, uint16) Input image. selem : 2-D array The neighborhood expressed as a 2-D array of 1's and 0's. out : 2-D array (same dtype as input) If None, a new array is allocated. mask : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). shift_x, shift_y : int Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- out : 2-D array (same dtype as input image) Output image. Examples -------- >>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import tophat >>> img = data.camera() >>> out = tophat(img, disk(5)) """ return _apply_scalar_per_pixel(generic_cy._tophat, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def noise_filter(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Noise feature. Parameters ---------- image : 2-D array (uint8, uint16) Input image. selem : 2-D array The neighborhood expressed as a 2-D array of 1's and 0's. out : 2-D array (same dtype as input) If None, a new array is allocated. mask : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). shift_x, shift_y : int Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element). References ---------- .. [1] N. Hashimoto et al. Referenceless image quality evaluation for whole slide imaging. J Pathol Inform 2012;3:9. Returns ------- out : 2-D array (same dtype as input image) Output image. Examples -------- >>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import noise_filter >>> img = data.camera() >>> out = noise_filter(img, disk(5)) """ # ensure that the central pixel in the structuring element is empty centre_r = int(selem.shape[0] / 2) + shift_y centre_c = int(selem.shape[1] / 2) + shift_x # make a local copy selem_cpy = selem.copy() selem_cpy[centre_r, centre_c] = 0 return _apply_scalar_per_pixel(generic_cy._noise_filter, image, selem_cpy, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def entropy(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Local entropy. The entropy is computed using base 2 logarithm i.e. the filter returns the minimum number of bits needed to encode the local greylevel distribution. Parameters ---------- image : 2-D array (uint8, uint16) Input image. selem : 2-D array The neighborhood expressed as a 2-D array of 1's and 0's. out : 2-D array (same dtype as input) If None, a new array is allocated. mask : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). shift_x, shift_y : int Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- out : ndarray (double) Output image. References ---------- .. [1] http://en.wikipedia.org/wiki/Entropy_(information_theory) Examples -------- >>> from skimage import data >>> from skimage.filters.rank import entropy >>> from skimage.morphology import disk >>> img = data.camera() >>> ent = entropy(img, disk(5)) """ return _apply_scalar_per_pixel(generic_cy._entropy, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, out_dtype=np.double) def otsu(image, selem, out=None, mask=None, shift_x=False, shift_y=False): """Local Otsu's threshold value for each pixel. Parameters ---------- image : ndarray Image array (uint8 array). selem : 2-D array The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray If None, a new array will be allocated. mask : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). shift_x, shift_y : int Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns ------- out : 2-D array (same dtype as input image) Output image. References ---------- .. [1] http://en.wikipedia.org/wiki/Otsu's_method Examples -------- >>> from skimage import data >>> from skimage.filters.rank import otsu >>> from skimage.morphology import disk >>> img = data.camera() >>> local_otsu = otsu(img, disk(5)) >>> thresh_image = img >= local_otsu """ return _apply_scalar_per_pixel(generic_cy._otsu, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y) def windowed_histogram(image, selem, out=None, mask=None, shift_x=False, shift_y=False, n_bins=None): """Normalized sliding window histogram Parameters ---------- image : ndarray Image array (uint8 array). selem : 2-D array The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray If None, a new array will be allocated. mask : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default). shift_x, shift_y : int Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element). n_bins : int or None The number of histogram bins. Will default to ``image.max() + 1`` if None is passed. Returns ------- out : 3-D array with float dtype of dimensions (H,W,N), where (H,W) are the dimensions of the input image and N is n_bins or ``image.max() + 1`` if no value is provided as a parameter. Effectively, each pixel is a N-D feature vector that is the histogram. The sum of the elements in the feature vector will be 1, unless no pixels in the window were covered by both selem and mask, in which case all elements will be 0. Examples -------- >>> from skimage import data >>> from skimage.filters.rank import windowed_histogram >>> from skimage.morphology import disk >>> img = data.camera() >>> hist_img = windowed_histogram(img, disk(5)) """ if n_bins is None: n_bins = int(image.max()) + 1 return _apply_vector_per_pixel(generic_cy._windowed_hist, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y, out_dtype=np.double, pixel_size=n_bins) scikit-image-0.13.1/skimage/filters/rank/generic_cy.pyx000066400000000000000000000510031316256236300230440ustar00rootroot00000000000000#cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False #cython: wraparound=False cimport numpy as cnp from libc.math cimport log, exp from .core_cy cimport dtype_t, dtype_t_out, _core from ..._shared.interpolation cimport round cdef inline void _kernel_autolevel(dtype_t_out* out, Py_ssize_t odepth, Py_ssize_t* histo, double pop, dtype_t g, Py_ssize_t max_bin, Py_ssize_t mid_bin, double p0, double p1, Py_ssize_t s0, Py_ssize_t s1) nogil: cdef Py_ssize_t i, imin, imax, delta if pop: for i in range(max_bin - 1, -1, -1): if histo[i]: imax = i break for i in range(max_bin): if histo[i]: imin = i break delta = imax - imin if delta > 0: out[0] = ((max_bin - 1) * (g - imin) / delta) else: out[0] = 0 else: out[0] = 0 cdef inline void _kernel_bottomhat(dtype_t_out* out, Py_ssize_t odepth, Py_ssize_t* histo, double pop, dtype_t g, Py_ssize_t max_bin, Py_ssize_t mid_bin, double p0, double p1, Py_ssize_t s0, Py_ssize_t s1) nogil: cdef Py_ssize_t i if pop: for i in range(max_bin): if histo[i]: break out[0] = (g - i) else: out[0] = 0 cdef inline void _kernel_equalize(dtype_t_out* out, Py_ssize_t odepth, Py_ssize_t* histo, double pop, dtype_t g, Py_ssize_t max_bin, Py_ssize_t mid_bin, double p0, double p1, Py_ssize_t s0, Py_ssize_t s1) nogil: cdef Py_ssize_t i cdef Py_ssize_t sum = 0 if pop: for i in range(max_bin): sum += histo[i] if i >= g: break out[0] = (((max_bin - 1) * sum) / pop) else: out[0] = 0 cdef inline void _kernel_gradient(dtype_t_out* out, Py_ssize_t odepth, Py_ssize_t* histo, double pop, dtype_t g, Py_ssize_t max_bin, Py_ssize_t mid_bin, double p0, double p1, Py_ssize_t s0, Py_ssize_t s1) nogil: cdef Py_ssize_t i, imin, imax if pop: for i in range(max_bin - 1, -1, -1): if histo[i]: imax = i break for i in range(max_bin): if histo[i]: imin = i break out[0] = (imax - imin) else: out[0] = 0 cdef inline void _kernel_maximum(dtype_t_out* out, Py_ssize_t odepth, Py_ssize_t* histo, double pop, dtype_t g, Py_ssize_t max_bin, Py_ssize_t mid_bin, double p0, double p1, Py_ssize_t s0, Py_ssize_t s1) nogil: cdef Py_ssize_t i if pop: for i in range(max_bin - 1, -1, -1): if histo[i]: out[0] = i return else: out[0] = 0 cdef inline void _kernel_mean(dtype_t_out* out, Py_ssize_t odepth, Py_ssize_t* histo, double pop, dtype_t g, Py_ssize_t max_bin, Py_ssize_t mid_bin, double p0, double p1, Py_ssize_t s0, Py_ssize_t s1) nogil: cdef Py_ssize_t i cdef Py_ssize_t mean = 0 if pop: for i in range(max_bin): mean += histo[i] * i out[0] = (mean / pop) else: out[0] = 0 cdef inline void _kernel_geometric_mean(dtype_t_out* out, Py_ssize_t odepth, Py_ssize_t* histo, double pop, dtype_t g, Py_ssize_t max_bin, Py_ssize_t mid_bin, double p0, double p1, Py_ssize_t s0, Py_ssize_t s1) nogil: cdef Py_ssize_t i cdef double mean = 0. if pop: for i in range(max_bin): if histo[i]: mean += (histo[i] * log(i+1)) out[0] = round(exp(mean / pop)-1) else: out[0] = 0 cdef inline void _kernel_subtract_mean(dtype_t_out* out, Py_ssize_t odepth, Py_ssize_t* histo, double pop, dtype_t g, Py_ssize_t max_bin, Py_ssize_t mid_bin, double p0, double p1, Py_ssize_t s0, Py_ssize_t s1) nogil: cdef Py_ssize_t i cdef Py_ssize_t mean = 0 if pop: for i in range(max_bin): mean += histo[i] * i out[0] = ((g - mean / pop) / 2. + 127) else: out[0] = 0 cdef inline void _kernel_median(dtype_t_out* out, Py_ssize_t odepth, Py_ssize_t* histo, double pop, dtype_t g, Py_ssize_t max_bin, Py_ssize_t mid_bin, double p0, double p1, Py_ssize_t s0, Py_ssize_t s1) nogil: cdef Py_ssize_t i cdef double sum = pop / 2.0 if pop: for i in range(max_bin): if histo[i]: sum -= histo[i] if sum < 0: out[0] = i return else: out[0] = 0 cdef inline void _kernel_minimum(dtype_t_out* out, Py_ssize_t odepth, Py_ssize_t* histo, double pop, dtype_t g, Py_ssize_t max_bin, Py_ssize_t mid_bin, double p0, double p1, Py_ssize_t s0, Py_ssize_t s1) nogil: cdef Py_ssize_t i if pop: for i in range(max_bin): if histo[i]: out[0] = i return else: out[0] = 0 cdef inline void _kernel_modal(dtype_t_out* out, Py_ssize_t odepth, Py_ssize_t* histo, double pop, dtype_t g, Py_ssize_t max_bin, Py_ssize_t mid_bin, double p0, double p1, Py_ssize_t s0, Py_ssize_t s1) nogil: cdef Py_ssize_t hmax = 0, imax = 0 if pop: for i in range(max_bin): if histo[i] > hmax: hmax = histo[i] imax = i out[0] = imax else: out[0] = 0 cdef inline void _kernel_enhance_contrast(dtype_t_out* out, Py_ssize_t odepth, Py_ssize_t* histo, double pop, dtype_t g, Py_ssize_t max_bin, Py_ssize_t mid_bin, double p0, double p1, Py_ssize_t s0, Py_ssize_t s1) nogil: cdef Py_ssize_t i, imin, imax if pop: for i in range(max_bin - 1, -1, -1): if histo[i]: imax = i break for i in range(max_bin): if histo[i]: imin = i break if imax - g < g - imin: out[0] = imax else: out[0] = imin else: out[0] = 0 cdef inline void _kernel_pop(dtype_t_out* out, Py_ssize_t odepth, Py_ssize_t* histo, double pop, dtype_t g, Py_ssize_t max_bin, Py_ssize_t mid_bin, double p0, double p1, Py_ssize_t s0, Py_ssize_t s1) nogil: out[0] = pop cdef inline void _kernel_sum(dtype_t_out* out, Py_ssize_t odepth, Py_ssize_t* histo, double pop, dtype_t g, Py_ssize_t max_bin, Py_ssize_t mid_bin, double p0, double p1, Py_ssize_t s0, Py_ssize_t s1) nogil: cdef Py_ssize_t i cdef Py_ssize_t sum = 0 if pop: for i in range(max_bin): sum += histo[i] * i out[0] = sum else: out[0] = 0 cdef inline void _kernel_threshold(dtype_t_out* out, Py_ssize_t odepth, Py_ssize_t* histo, double pop, dtype_t g, Py_ssize_t max_bin, Py_ssize_t mid_bin, double p0, double p1, Py_ssize_t s0, Py_ssize_t s1) nogil: cdef Py_ssize_t i cdef Py_ssize_t mean = 0 if pop: for i in range(max_bin): mean += histo[i] * i out[0] = (g > (mean / pop)) else: out[0] = 0 cdef inline void _kernel_tophat(dtype_t_out* out, Py_ssize_t odepth, Py_ssize_t* histo, double pop, dtype_t g, Py_ssize_t max_bin, Py_ssize_t mid_bin, double p0, double p1, Py_ssize_t s0, Py_ssize_t s1) nogil: cdef Py_ssize_t i if pop: for i in range(max_bin - 1, -1, -1): if histo[i]: break out[0] = (i - g) else: out[0] = 0 cdef inline void _kernel_noise_filter(dtype_t_out* out, Py_ssize_t odepth, Py_ssize_t* histo, double pop, dtype_t g, Py_ssize_t max_bin, Py_ssize_t mid_bin, double p0, double p1, Py_ssize_t s0, Py_ssize_t s1) nogil: cdef Py_ssize_t i cdef Py_ssize_t min_i # early stop if at least one pixel of the neighborhood has the same g if histo[g] > 0: out[0] = 0 for i in range(g, -1, -1): if histo[i]: break min_i = g - i for i in range(g, max_bin): if histo[i]: break if i - g < min_i: out[0] = (i - g) else: out[0] = min_i cdef inline void _kernel_entropy(dtype_t_out* out, Py_ssize_t odepth, Py_ssize_t* histo, double pop, dtype_t g, Py_ssize_t max_bin, Py_ssize_t mid_bin, double p0, double p1, Py_ssize_t s0, Py_ssize_t s1) nogil: cdef Py_ssize_t i cdef double e, p if pop: e = 0. for i in range(max_bin): p = histo[i] / pop if p > 0: e -= p * log(p) / 0.6931471805599453 out[0] = e else: out[0] = 0 cdef inline void _kernel_otsu(dtype_t_out* out, Py_ssize_t odepth, Py_ssize_t* histo, double pop, dtype_t g, Py_ssize_t max_bin, Py_ssize_t mid_bin, double p0, double p1, Py_ssize_t s0, Py_ssize_t s1) nogil: cdef Py_ssize_t i cdef Py_ssize_t max_i cdef double P, mu1, mu2, q1, new_q1, sigma_b, max_sigma_b cdef double mu = 0. # compute local mean if pop: for i in range(max_bin): mu += histo[i] * i mu = mu / pop else: out[0] = 0 # maximizing the between class variance max_i = 0 q1 = histo[0] / pop mu1 = 0. max_sigma_b = 0. for i in range(1, max_bin): P = histo[i] / pop new_q1 = q1 + P if new_q1 > 0: mu1 = (q1 * mu1 + i * P) / new_q1 mu2 = (mu - new_q1 * mu1) / (1. - new_q1) sigma_b = new_q1 * (1. - new_q1) * (mu1 - mu2) ** 2 if sigma_b > max_sigma_b: max_sigma_b = sigma_b max_i = i q1 = new_q1 out[0] = max_i cdef inline void _kernel_win_hist(dtype_t_out* out, Py_ssize_t odepth, Py_ssize_t* histo, double pop, dtype_t g, Py_ssize_t max_bin, Py_ssize_t mid_bin, double p0, double p1, Py_ssize_t s0, Py_ssize_t s1) nogil: cdef Py_ssize_t i cdef Py_ssize_t max_i cdef double scale if pop: scale = 1.0 / pop for i in xrange(odepth): out[i] = (histo[i] * scale) else: for i in xrange(odepth): out[i] = 0 def _autolevel(dtype_t[:, ::1] image, char[:, ::1] selem, char[:, ::1] mask, dtype_t_out[:, :, ::1] out, signed char shift_x, signed char shift_y, Py_ssize_t max_bin): _core(_kernel_autolevel[dtype_t_out, dtype_t], image, selem, mask, out, shift_x, shift_y, 0, 0, 0, 0, max_bin) def _bottomhat(dtype_t[:, ::1] image, char[:, ::1] selem, char[:, ::1] mask, dtype_t_out[:, :, ::1] out, signed char shift_x, signed char shift_y, Py_ssize_t max_bin): _core(_kernel_bottomhat[dtype_t_out, dtype_t], image, selem, mask, out, shift_x, shift_y, 0, 0, 0, 0, max_bin) def _equalize(dtype_t[:, ::1] image, char[:, ::1] selem, char[:, ::1] mask, dtype_t_out[:, :, ::1] out, signed char shift_x, signed char shift_y, Py_ssize_t max_bin): _core(_kernel_equalize[dtype_t_out, dtype_t], image, selem, mask, out, shift_x, shift_y, 0, 0, 0, 0, max_bin) def _gradient(dtype_t[:, ::1] image, char[:, ::1] selem, char[:, ::1] mask, dtype_t_out[:, :, ::1] out, signed char shift_x, signed char shift_y, Py_ssize_t max_bin): _core(_kernel_gradient[dtype_t_out, dtype_t], image, selem, mask, out, shift_x, shift_y, 0, 0, 0, 0, max_bin) def _maximum(dtype_t[:, ::1] image, char[:, ::1] selem, char[:, ::1] mask, dtype_t_out[:, :, ::1] out, signed char shift_x, signed char shift_y, Py_ssize_t max_bin): _core(_kernel_maximum[dtype_t_out, dtype_t], image, selem, mask, out, shift_x, shift_y, 0, 0, 0, 0, max_bin) def _mean(dtype_t[:, ::1] image, char[:, ::1] selem, char[:, ::1] mask, dtype_t_out[:, :, ::1] out, signed char shift_x, signed char shift_y, Py_ssize_t max_bin): _core(_kernel_mean[dtype_t_out, dtype_t], image, selem, mask, out, shift_x, shift_y, 0, 0, 0, 0, max_bin) def _geometric_mean(dtype_t[:, ::1] image, char[:, ::1] selem, char[:, ::1] mask, dtype_t_out[:, :, ::1] out, signed char shift_x, signed char shift_y, Py_ssize_t max_bin): _core(_kernel_geometric_mean[dtype_t_out, dtype_t], image, selem, mask, out, shift_x, shift_y, 0, 0, 0, 0, max_bin) def _subtract_mean(dtype_t[:, ::1] image, char[:, ::1] selem, char[:, ::1] mask, dtype_t_out[:, :, ::1] out, signed char shift_x, signed char shift_y, Py_ssize_t max_bin): _core(_kernel_subtract_mean[dtype_t_out, dtype_t], image, selem, mask, out, shift_x, shift_y, 0, 0, 0, 0, max_bin) def _median(dtype_t[:, ::1] image, char[:, ::1] selem, char[:, ::1] mask, dtype_t_out[:, :, ::1] out, signed char shift_x, signed char shift_y, Py_ssize_t max_bin): _core(_kernel_median[dtype_t_out, dtype_t], image, selem, mask, out, shift_x, shift_y, 0, 0, 0, 0, max_bin) def _minimum(dtype_t[:, ::1] image, char[:, ::1] selem, char[:, ::1] mask, dtype_t_out[:, :, ::1] out, signed char shift_x, signed char shift_y, Py_ssize_t max_bin): _core(_kernel_minimum[dtype_t_out, dtype_t], image, selem, mask, out, shift_x, shift_y, 0, 0, 0, 0, max_bin) def _enhance_contrast(dtype_t[:, ::1] image, char[:, ::1] selem, char[:, ::1] mask, dtype_t_out[:, :, ::1] out, signed char shift_x, signed char shift_y, Py_ssize_t max_bin): _core(_kernel_enhance_contrast[dtype_t_out, dtype_t], image, selem, mask, out, shift_x, shift_y, 0, 0, 0, 0, max_bin) def _modal(dtype_t[:, ::1] image, char[:, ::1] selem, char[:, ::1] mask, dtype_t_out[:, :, ::1] out, signed char shift_x, signed char shift_y, Py_ssize_t max_bin): _core(_kernel_modal[dtype_t_out, dtype_t], image, selem, mask, out, shift_x, shift_y, 0, 0, 0, 0, max_bin) def _pop(dtype_t[:, ::1] image, char[:, ::1] selem, char[:, ::1] mask, dtype_t_out[:, :, ::1] out, signed char shift_x, signed char shift_y, Py_ssize_t max_bin): _core(_kernel_pop[dtype_t_out, dtype_t], image, selem, mask, out, shift_x, shift_y, 0, 0, 0, 0, max_bin) def _sum(dtype_t[:, ::1] image, char[:, ::1] selem, char[:, ::1] mask, dtype_t_out[:, :, ::1] out, signed char shift_x, signed char shift_y, Py_ssize_t max_bin): _core(_kernel_sum[dtype_t_out, dtype_t], image, selem, mask, out, shift_x, shift_y, 0, 0, 0, 0, max_bin) def _threshold(dtype_t[:, ::1] image, char[:, ::1] selem, char[:, ::1] mask, dtype_t_out[:, :, ::1] out, signed char shift_x, signed char shift_y, Py_ssize_t max_bin): _core(_kernel_threshold[dtype_t_out, dtype_t], image, selem, mask, out, shift_x, shift_y, 0, 0, 0, 0, max_bin) def _tophat(dtype_t[:, ::1] image, char[:, ::1] selem, char[:, ::1] mask, dtype_t_out[:, :, ::1] out, signed char shift_x, signed char shift_y, Py_ssize_t max_bin): _core(_kernel_tophat[dtype_t_out, dtype_t], image, selem, mask, out, shift_x, shift_y, 0, 0, 0, 0, max_bin) def _noise_filter(dtype_t[:, ::1] image, char[:, ::1] selem, char[:, ::1] mask, dtype_t_out[:, :, ::1] out, signed char shift_x, signed char shift_y, Py_ssize_t max_bin): _core(_kernel_noise_filter[dtype_t_out, dtype_t], image, selem, mask, out, shift_x, shift_y, 0, 0, 0, 0, max_bin) def _entropy(dtype_t[:, ::1] image, char[:, ::1] selem, char[:, ::1] mask, dtype_t_out[:, :, ::1] out, signed char shift_x, signed char shift_y, Py_ssize_t max_bin): _core(_kernel_entropy[dtype_t_out, dtype_t], image, selem, mask, out, shift_x, shift_y, 0, 0, 0, 0, max_bin) def _otsu(dtype_t[:, ::1] image, char[:, ::1] selem, char[:, ::1] mask, dtype_t_out[:, :, ::1] out, signed char shift_x, signed char shift_y, Py_ssize_t max_bin): _core(_kernel_otsu[dtype_t_out, dtype_t], image, selem, mask, out, shift_x, shift_y, 0, 0, 0, 0, max_bin) def _windowed_hist(dtype_t[:, ::1] image, char[:, ::1] selem, char[:, ::1] mask, dtype_t_out[:, :, ::1] out, signed char shift_x, signed char shift_y, Py_ssize_t max_bin): _core(_kernel_win_hist[dtype_t_out, dtype_t], image, selem, mask, out, shift_x, shift_y, 0, 0, 0, 0, max_bin) scikit-image-0.13.1/skimage/filters/rank/percentile_cy.pyx000066400000000000000000000264031316256236300235700ustar00rootroot00000000000000#cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False #cython: wraparound=False cimport numpy as cnp from .core_cy cimport dtype_t, dtype_t_out, _core, _min, _max cdef inline void _kernel_autolevel(dtype_t_out* out, Py_ssize_t odepth, Py_ssize_t* histo, double pop, dtype_t g, Py_ssize_t max_bin, Py_ssize_t mid_bin, double p0, double p1, Py_ssize_t s0, Py_ssize_t s1) nogil: cdef Py_ssize_t i, imin, imax, sum, delta if pop: sum = 0 p1 = 1.0 - p1 for i in range(max_bin): sum += histo[i] if sum > p0 * pop: imin = i break sum = 0 for i in range(max_bin - 1, -1, -1): sum += histo[i] if sum > p1 * pop: imax = i break delta = imax - imin if delta > 0: out[0] = ((max_bin - 1) * (_min(_max(imin, g), imax) - imin) / delta) else: out[0] = (imax - imin) else: out[0] = 0 cdef inline void _kernel_gradient(dtype_t_out* out, Py_ssize_t odepth, Py_ssize_t* histo, double pop, dtype_t g, Py_ssize_t max_bin, Py_ssize_t mid_bin, double p0, double p1, Py_ssize_t s0, Py_ssize_t s1) nogil: cdef Py_ssize_t i, imin, imax, sum, delta if pop: sum = 0 p1 = 1.0 - p1 for i in range(max_bin): sum += histo[i] if sum >= p0 * pop: imin = i break sum = 0 for i in range(max_bin - 1, -1, -1): sum += histo[i] if sum >= p1 * pop: imax = i break out[0] = (imax - imin) else: out[0] = 0 cdef inline void _kernel_mean(dtype_t_out* out, Py_ssize_t odepth, Py_ssize_t* histo, double pop, dtype_t g, Py_ssize_t max_bin, Py_ssize_t mid_bin, double p0, double p1, Py_ssize_t s0, Py_ssize_t s1) nogil: cdef Py_ssize_t i, sum, mean, n if pop: sum = 0 mean = 0 n = 0 for i in range(max_bin): sum += histo[i] if (sum >= p0 * pop) and (sum <= p1 * pop): n += histo[i] mean += histo[i] * i if n > 0: out[0] = (mean / n) else: out[0] = 0 else: out[0] = 0 cdef inline void _kernel_sum(dtype_t_out* out, Py_ssize_t odepth, Py_ssize_t* histo, double pop, dtype_t g, Py_ssize_t max_bin, Py_ssize_t mid_bin, double p0, double p1, Py_ssize_t s0, Py_ssize_t s1) nogil: cdef Py_ssize_t i, sum, sum_g, n if pop: sum = 0 sum_g = 0 n = 0 for i in range(max_bin): sum += histo[i] if (sum >= p0 * pop) and (sum <= p1 * pop): n += histo[i] sum_g += histo[i] * i if n > 0: out[0] = sum_g else: out[0] = 0 else: out[0] = 0 cdef inline void _kernel_subtract_mean(dtype_t_out* out, Py_ssize_t odepth, Py_ssize_t* histo, double pop, dtype_t g, Py_ssize_t max_bin, Py_ssize_t mid_bin, double p0, double p1, Py_ssize_t s0, Py_ssize_t s1) nogil: cdef Py_ssize_t i, sum, mean, n if pop: sum = 0 mean = 0 n = 0 for i in range(max_bin): sum += histo[i] if (sum >= p0 * pop) and (sum <= p1 * pop): n += histo[i] mean += histo[i] * i if n > 0: out[0] = ((g - (mean / n)) * .5 + mid_bin) else: out[0] = 0 else: out[0] = 0 cdef inline void _kernel_enhance_contrast(dtype_t_out* out, Py_ssize_t odepth, Py_ssize_t* histo, double pop, dtype_t g, Py_ssize_t max_bin, Py_ssize_t mid_bin, double p0, double p1, Py_ssize_t s0, Py_ssize_t s1) nogil: cdef Py_ssize_t i, imin, imax, sum, delta if pop: sum = 0 p1 = 1.0 - p1 for i in range(max_bin): sum += histo[i] if sum > p0 * pop: imin = i break sum = 0 for i in range(max_bin - 1, -1, -1): sum += histo[i] if sum > p1 * pop: imax = i break if g > imax: out[0] = imax if g < imin: out[0] = imin if imax - g < g - imin: out[0] = imax else: out[0] = imin else: out[0] = 0 cdef inline void _kernel_percentile(dtype_t_out* out, Py_ssize_t odepth, Py_ssize_t* histo, double pop, dtype_t g, Py_ssize_t max_bin, Py_ssize_t mid_bin, double p0, double p1, Py_ssize_t s0, Py_ssize_t s1) nogil: cdef Py_ssize_t i cdef Py_ssize_t sum = 0 if pop: if p0 == 1: # make sure p0 = 1 returns the maximum filter for i in range(max_bin - 1, -1, -1): if histo[i]: break else: for i in range(max_bin): sum += histo[i] if sum > p0 * pop: break out[0] = i else: out[0] = 0 cdef inline void _kernel_pop(dtype_t_out* out, Py_ssize_t odepth, Py_ssize_t* histo, double pop, dtype_t g, Py_ssize_t max_bin, Py_ssize_t mid_bin, double p0, double p1, Py_ssize_t s0, Py_ssize_t s1) nogil: cdef Py_ssize_t i, sum, n if pop: sum = 0 n = 0 for i in range(max_bin): sum += histo[i] if (sum >= p0 * pop) and (sum <= p1 * pop): n += histo[i] out[0] = n else: out[0] = 0 cdef inline void _kernel_threshold(dtype_t_out* out, Py_ssize_t odepth, Py_ssize_t* histo, double pop, dtype_t g, Py_ssize_t max_bin, Py_ssize_t mid_bin, double p0, double p1, Py_ssize_t s0, Py_ssize_t s1) nogil: cdef int i cdef Py_ssize_t sum = 0 if pop: for i in range(max_bin): sum += histo[i] if sum >= p0 * pop: break out[0] = ((max_bin - 1) * (g >= i)) else: out[0] = 0 def _autolevel(dtype_t[:, ::1] image, char[:, ::1] selem, char[:, ::1] mask, dtype_t_out[:, :, ::1] out, signed char shift_x, signed char shift_y, double p0, double p1, Py_ssize_t max_bin): _core(_kernel_autolevel[dtype_t_out, dtype_t], image, selem, mask, out, shift_x, shift_y, p0, p1, 0, 0, max_bin) def _gradient(dtype_t[:, ::1] image, char[:, ::1] selem, char[:, ::1] mask, dtype_t_out[:, :, ::1] out, signed char shift_x, signed char shift_y, double p0, double p1, Py_ssize_t max_bin): _core(_kernel_gradient[dtype_t_out, dtype_t], image, selem, mask, out, shift_x, shift_y, p0, p1, 0, 0, max_bin) def _mean(dtype_t[:, ::1] image, char[:, ::1] selem, char[:, ::1] mask, dtype_t_out[:, :, ::1] out, signed char shift_x, signed char shift_y, double p0, double p1, Py_ssize_t max_bin): _core(_kernel_mean[dtype_t_out, dtype_t], image, selem, mask, out, shift_x, shift_y, p0, p1, 0, 0, max_bin) def _sum(dtype_t[:, ::1] image, char[:, ::1] selem, char[:, ::1] mask, dtype_t_out[:, :, ::1] out, signed char shift_x, signed char shift_y, double p0, double p1, Py_ssize_t max_bin): _core(_kernel_sum[dtype_t_out, dtype_t], image, selem, mask, out, shift_x, shift_y, p0, p1, 0, 0, max_bin) def _subtract_mean(dtype_t[:, ::1] image, char[:, ::1] selem, char[:, ::1] mask, dtype_t_out[:, :, ::1] out, signed char shift_x, signed char shift_y, double p0, double p1, Py_ssize_t max_bin): _core(_kernel_subtract_mean[dtype_t_out, dtype_t], image, selem, mask, out, shift_x, shift_y, p0, p1, 0, 0, max_bin) def _enhance_contrast(dtype_t[:, ::1] image, char[:, ::1] selem, char[:, ::1] mask, dtype_t_out[:, :, ::1] out, signed char shift_x, signed char shift_y, double p0, double p1, Py_ssize_t max_bin): _core(_kernel_enhance_contrast[dtype_t_out, dtype_t], image, selem, mask, out, shift_x, shift_y, p0, p1, 0, 0, max_bin) def _percentile(dtype_t[:, ::1] image, char[:, ::1] selem, char[:, ::1] mask, dtype_t_out[:, :, ::1] out, signed char shift_x, signed char shift_y, double p0, double p1, Py_ssize_t max_bin): _core(_kernel_percentile[dtype_t_out, dtype_t], image, selem, mask, out, shift_x, shift_y, p0, 1, 0, 0, max_bin) def _pop(dtype_t[:, ::1] image, char[:, ::1] selem, char[:, ::1] mask, dtype_t_out[:, :, ::1] out, signed char shift_x, signed char shift_y, double p0, double p1, Py_ssize_t max_bin): _core(_kernel_pop[dtype_t_out, dtype_t], image, selem, mask, out, shift_x, shift_y, p0, p1, 0, 0, max_bin) def _threshold(dtype_t[:, ::1] image, char[:, ::1] selem, char[:, ::1] mask, dtype_t_out[:, :, ::1] out, signed char shift_x, signed char shift_y, double p0, double p1, Py_ssize_t max_bin): _core(_kernel_threshold[dtype_t_out, dtype_t], image, selem, mask, out, shift_x, shift_y, p0, 1, 0, 0, max_bin) scikit-image-0.13.1/skimage/filters/rank/tests/000077500000000000000000000000001316256236300213365ustar00rootroot00000000000000scikit-image-0.13.1/skimage/filters/rank/tests/__init__.py000066400000000000000000000002001316256236300234370ustar00rootroot00000000000000from ...._shared.testing import setup_test, teardown_test def setup(): setup_test() def teardown(): teardown_test() scikit-image-0.13.1/skimage/filters/rank/tests/test_rank.py000066400000000000000000000704771316256236300237210ustar00rootroot00000000000000import os import numpy as np from numpy.testing import run_module_suite, assert_equal, assert_raises import skimage from skimage import img_as_ubyte, img_as_float from skimage import data, util, morphology from skimage.morphology import grey, disk from skimage.filters import rank from skimage._shared._warnings import expected_warnings from skimage._shared.testing import test_parallel class TestRank(): def setup(self): np.random.seed(0) # This image is used along with @test_parallel # to ensure that the same seed is used for each thread. self.image = np.random.rand(25, 25) # Set again the seed for the other tests. np.random.seed(0) def test_all(self): @test_parallel() def check_all(): selem = morphology.disk(1) refs = np.load(os.path.join(skimage.data_dir, "rank_filter_tests.npz")) assert_equal(refs["autolevel"], rank.autolevel(self.image, selem)) assert_equal(refs["autolevel_percentile"], rank.autolevel_percentile(self.image, selem)) assert_equal(refs["bottomhat"], rank.bottomhat(self.image, selem)) assert_equal(refs["equalize"], rank.equalize(self.image, selem)) assert_equal(refs["gradient"], rank.gradient(self.image, selem)) assert_equal(refs["gradient_percentile"], rank.gradient_percentile(self.image, selem)) assert_equal(refs["maximum"], rank.maximum(self.image, selem)) assert_equal(refs["mean"], rank.mean(self.image, selem)) assert_equal(refs["geometric_mean"], rank.geometric_mean(self.image, selem)), assert_equal(refs["mean_percentile"], rank.mean_percentile(self.image, selem)) assert_equal(refs["mean_bilateral"], rank.mean_bilateral(self.image, selem)) assert_equal(refs["subtract_mean"], rank.subtract_mean(self.image, selem)) assert_equal(refs["subtract_mean_percentile"], rank.subtract_mean_percentile(self.image, selem)) assert_equal(refs["median"], rank.median(self.image, selem)) assert_equal(refs["minimum"], rank.minimum(self.image, selem)) assert_equal(refs["modal"], rank.modal(self.image, selem)) assert_equal(refs["enhance_contrast"], rank.enhance_contrast(self.image, selem)) assert_equal(refs["enhance_contrast_percentile"], rank.enhance_contrast_percentile(self.image, selem)) assert_equal(refs["pop"], rank.pop(self.image, selem)) assert_equal(refs["pop_percentile"], rank.pop_percentile(self.image, selem)) assert_equal(refs["pop_bilateral"], rank.pop_bilateral(self.image, selem)) assert_equal(refs["sum"], rank.sum(self.image, selem)) assert_equal(refs["sum_bilateral"], rank.sum_bilateral(self.image, selem)) assert_equal(refs["sum_percentile"], rank.sum_percentile(self.image, selem)) assert_equal(refs["threshold"], rank.threshold(self.image, selem)) assert_equal(refs["threshold_percentile"], rank.threshold_percentile(self.image, selem)) assert_equal(refs["tophat"], rank.tophat(self.image, selem)) assert_equal(refs["noise_filter"], rank.noise_filter(self.image, selem)) assert_equal(refs["entropy"], rank.entropy(self.image, selem)) assert_equal(refs["otsu"], rank.otsu(self.image, selem)) assert_equal(refs["percentile"], rank.percentile(self.image, selem)) assert_equal(refs["windowed_histogram"], rank.windowed_histogram(self.image, selem)) with expected_warnings(['precision loss', 'non-integer|\A\Z']): check_all() def test_random_sizes(self): # make sure the size is not a problem elem = np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1]], dtype=np.uint8) for m, n in np.random.randint(1, 101, size=(10, 2)): mask = np.ones((m, n), dtype=np.uint8) image8 = np.ones((m, n), dtype=np.uint8) out8 = np.empty_like(image8) rank.mean(image=image8, selem=elem, mask=mask, out=out8, shift_x=0, shift_y=0) assert_equal(image8.shape, out8.shape) rank.mean(image=image8, selem=elem, mask=mask, out=out8, shift_x=+1, shift_y=+1) assert_equal(image8.shape, out8.shape) rank.geometric_mean(image=image8, selem=elem, mask=mask, out=out8, shift_x=0, shift_y=0) assert_equal(image8.shape, out8.shape) rank.geometric_mean(image=image8, selem=elem, mask=mask, out=out8, shift_x=+1, shift_y=+1) assert_equal(image8.shape, out8.shape) image16 = np.ones((m, n), dtype=np.uint16) out16 = np.empty_like(image8, dtype=np.uint16) rank.mean(image=image16, selem=elem, mask=mask, out=out16, shift_x=0, shift_y=0) assert_equal(image16.shape, out16.shape) rank.mean(image=image16, selem=elem, mask=mask, out=out16, shift_x=+1, shift_y=+1) assert_equal(image16.shape, out16.shape) rank.geometric_mean(image=image16, selem=elem, mask=mask, out=out16, shift_x=0, shift_y=0) assert_equal(image16.shape, out16.shape) rank.geometric_mean(image=image16, selem=elem, mask=mask, out=out16, shift_x=+1, shift_y=+1) assert_equal(image16.shape, out16.shape) rank.mean_percentile(image=image16, mask=mask, out=out16, selem=elem, shift_x=0, shift_y=0, p0=.1, p1=.9) assert_equal(image16.shape, out16.shape) rank.mean_percentile(image=image16, mask=mask, out=out16, selem=elem, shift_x=+1, shift_y=+1, p0=.1, p1=.9) assert_equal(image16.shape, out16.shape) def test_compare_with_grey_dilation(self): # compare the result of maximum filter with dilate image = (np.random.rand(100, 100) * 256).astype(np.uint8) out = np.empty_like(image) mask = np.ones(image.shape, dtype=np.uint8) for r in range(3, 20, 2): elem = np.ones((r, r), dtype=np.uint8) rank.maximum(image=image, selem=elem, out=out, mask=mask) cm = grey.dilation(image=image, selem=elem) assert_equal(out, cm) def test_compare_with_grey_erosion(self): # compare the result of maximum filter with erode image = (np.random.rand(100, 100) * 256).astype(np.uint8) out = np.empty_like(image) mask = np.ones(image.shape, dtype=np.uint8) for r in range(3, 20, 2): elem = np.ones((r, r), dtype=np.uint8) rank.minimum(image=image, selem=elem, out=out, mask=mask) cm = grey.erosion(image=image, selem=elem) assert_equal(out, cm) def test_bitdepth(self): # test the different bit depth for rank16 elem = np.ones((3, 3), dtype=np.uint8) out = np.empty((100, 100), dtype=np.uint16) mask = np.ones((100, 100), dtype=np.uint8) for i in range(5): image = np.ones((100, 100), dtype=np.uint16) * 255 * 2 ** i if i > 3: expected = ["Bitdepth of"] else: expected = [] with expected_warnings(expected): rank.mean_percentile(image=image, selem=elem, mask=mask, out=out, shift_x=0, shift_y=0, p0=.1, p1=.9) def test_population(self): # check the number of valid pixels in the neighborhood image = np.zeros((5, 5), dtype=np.uint8) elem = np.ones((3, 3), dtype=np.uint8) out = np.empty_like(image) mask = np.ones(image.shape, dtype=np.uint8) rank.pop(image=image, selem=elem, out=out, mask=mask) r = np.array([[4, 6, 6, 6, 4], [6, 9, 9, 9, 6], [6, 9, 9, 9, 6], [6, 9, 9, 9, 6], [4, 6, 6, 6, 4]]) assert_equal(r, out) def test_structuring_element8(self): # check the output for a custom structuring element r = np.array([[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 255, 0, 0, 0], [0, 0, 255, 255, 255, 0], [0, 0, 0, 255, 255, 0], [0, 0, 0, 0, 0, 0]]) # 8-bit image = np.zeros((6, 6), dtype=np.uint8) image[2, 2] = 255 elem = np.asarray([[1, 1, 0], [1, 1, 1], [0, 0, 1]], dtype=np.uint8) out = np.empty_like(image) mask = np.ones(image.shape, dtype=np.uint8) rank.maximum(image=image, selem=elem, out=out, mask=mask, shift_x=1, shift_y=1) assert_equal(r, out) # 16-bit image = np.zeros((6, 6), dtype=np.uint16) image[2, 2] = 255 out = np.empty_like(image) rank.maximum(image=image, selem=elem, out=out, mask=mask, shift_x=1, shift_y=1) assert_equal(r, out) def test_pass_on_bitdepth(self): # should pass because data bitdepth is not too high for the function image = np.ones((100, 100), dtype=np.uint16) * 2 ** 11 elem = np.ones((3, 3), dtype=np.uint8) out = np.empty_like(image) mask = np.ones(image.shape, dtype=np.uint8) with expected_warnings(["Bitdepth of"]): rank.maximum(image=image, selem=elem, out=out, mask=mask) def test_inplace_output(self): # rank filters are not supposed to filter inplace selem = disk(20) image = (np.random.rand(500, 500) * 256).astype(np.uint8) out = image assert_raises(NotImplementedError, rank.mean, image, selem, out=out) def test_compare_autolevels(self): # compare autolevel and percentile autolevel with p0=0.0 and p1=1.0 # should returns the same arrays image = util.img_as_ubyte(data.camera()) selem = disk(20) loc_autolevel = rank.autolevel(image, selem=selem) loc_perc_autolevel = rank.autolevel_percentile(image, selem=selem, p0=.0, p1=1.) assert_equal(loc_autolevel, loc_perc_autolevel) def test_compare_autolevels_16bit(self): # compare autolevel(16-bit) and percentile autolevel(16-bit) with p0=0.0 # and p1=1.0 should returns the same arrays image = data.camera().astype(np.uint16) * 4 selem = disk(20) loc_autolevel = rank.autolevel(image, selem=selem) loc_perc_autolevel = rank.autolevel_percentile(image, selem=selem, p0=.0, p1=1.) assert_equal(loc_autolevel, loc_perc_autolevel) def test_compare_ubyte_vs_float(self): # Create signed int8 image that and convert it to uint8 image_uint = img_as_ubyte(data.camera()[:50, :50]) image_float = img_as_float(image_uint) methods = ['autolevel', 'bottomhat', 'equalize', 'gradient', 'threshold', 'subtract_mean', 'enhance_contrast', 'pop', 'tophat'] for method in methods: func = getattr(rank, method) out_u = func(image_uint, disk(3)) with expected_warnings(['precision loss']): out_f = func(image_float, disk(3)) assert_equal(out_u, out_f) def test_compare_8bit_unsigned_vs_signed(self): # filters applied on 8-bit image ore 16-bit image (having only real 8-bit # of dynamic) should be identical # Create signed int8 image that and convert it to uint8 image = img_as_ubyte(data.camera())[::2, ::2] image[image > 127] = 0 image_s = image.astype(np.int8) with expected_warnings(['sign loss', 'precision loss']): image_u = img_as_ubyte(image_s) assert_equal(image_u, img_as_ubyte(image_s)) methods = ['autolevel', 'bottomhat', 'equalize', 'gradient', 'maximum', 'mean', 'geometric_mean', 'subtract_mean', 'median', 'minimum', 'modal', 'enhance_contrast', 'pop', 'threshold', 'tophat'] for method in methods: func = getattr(rank, method) with expected_warnings(['sign loss', 'precision loss']): out_u = func(image_u, disk(3)) out_s = func(image_s, disk(3)) assert_equal(out_u, out_s) def test_compare_8bit_vs_16bit(self): # filters applied on 8-bit image ore 16-bit image (having only real 8-bit # of dynamic) should be identical image8 = util.img_as_ubyte(data.camera())[::2, ::2] image16 = image8.astype(np.uint16) assert_equal(image8, image16) methods = ['autolevel', 'bottomhat', 'equalize', 'gradient', 'maximum', 'mean', 'subtract_mean', 'median', 'minimum', 'modal', 'enhance_contrast', 'pop', 'threshold', 'tophat'] for method in methods: func = getattr(rank, method) f8 = func(image8, disk(3)) f16 = func(image16, disk(3)) assert_equal(f8, f16) def test_trivial_selem8(self): # check that min, max and mean returns identity if structuring element # contains only central pixel image = np.zeros((5, 5), dtype=np.uint8) out = np.zeros_like(image) mask = np.ones_like(image, dtype=np.uint8) image[2, 2] = 255 image[2, 3] = 128 image[1, 2] = 16 elem = np.array([[0, 0, 0], [0, 1, 0], [0, 0, 0]], dtype=np.uint8) rank.mean(image=image, selem=elem, out=out, mask=mask, shift_x=0, shift_y=0) assert_equal(image, out) rank.geometric_mean(image=image, selem=elem, out=out, mask=mask, shift_x=0, shift_y=0) assert_equal(image, out) rank.minimum(image=image, selem=elem, out=out, mask=mask, shift_x=0, shift_y=0) assert_equal(image, out) rank.maximum(image=image, selem=elem, out=out, mask=mask, shift_x=0, shift_y=0) assert_equal(image, out) def test_trivial_selem16(self): # check that min, max and mean returns identity if structuring element # contains only central pixel image = np.zeros((5, 5), dtype=np.uint16) out = np.zeros_like(image) mask = np.ones_like(image, dtype=np.uint8) image[2, 2] = 255 image[2, 3] = 128 image[1, 2] = 16 elem = np.array([[0, 0, 0], [0, 1, 0], [0, 0, 0]], dtype=np.uint8) rank.mean(image=image, selem=elem, out=out, mask=mask, shift_x=0, shift_y=0) assert_equal(image, out) rank.geometric_mean(image=image, selem=elem, out=out, mask=mask, shift_x=0, shift_y=0) assert_equal(image, out) rank.minimum(image=image, selem=elem, out=out, mask=mask, shift_x=0, shift_y=0) assert_equal(image, out) rank.maximum(image=image, selem=elem, out=out, mask=mask, shift_x=0, shift_y=0) assert_equal(image, out) def test_smallest_selem8(self): # check that min, max and mean returns identity if structuring element # contains only central pixel image = np.zeros((5, 5), dtype=np.uint8) out = np.zeros_like(image) mask = np.ones_like(image, dtype=np.uint8) image[2, 2] = 255 image[2, 3] = 128 image[1, 2] = 16 elem = np.array([[1]], dtype=np.uint8) rank.mean(image=image, selem=elem, out=out, mask=mask, shift_x=0, shift_y=0) assert_equal(image, out) rank.minimum(image=image, selem=elem, out=out, mask=mask, shift_x=0, shift_y=0) assert_equal(image, out) rank.maximum(image=image, selem=elem, out=out, mask=mask, shift_x=0, shift_y=0) assert_equal(image, out) def test_smallest_selem16(self): # check that min, max and mean returns identity if structuring element # contains only central pixel image = np.zeros((5, 5), dtype=np.uint16) out = np.zeros_like(image) mask = np.ones_like(image, dtype=np.uint8) image[2, 2] = 255 image[2, 3] = 128 image[1, 2] = 16 elem = np.array([[1]], dtype=np.uint8) rank.mean(image=image, selem=elem, out=out, mask=mask, shift_x=0, shift_y=0) assert_equal(image, out) rank.geometric_mean(image=image, selem=elem, out=out, mask=mask, shift_x=0, shift_y=0) assert_equal(image, out) rank.minimum(image=image, selem=elem, out=out, mask=mask, shift_x=0, shift_y=0) assert_equal(image, out) rank.maximum(image=image, selem=elem, out=out, mask=mask, shift_x=0, shift_y=0) assert_equal(image, out) def test_empty_selem(self): # check that min, max and mean returns zeros if structuring element is # empty image = np.zeros((5, 5), dtype=np.uint16) out = np.zeros_like(image) mask = np.ones_like(image, dtype=np.uint8) res = np.zeros_like(image) image[2, 2] = 255 image[2, 3] = 128 image[1, 2] = 16 elem = np.array([[0, 0, 0], [0, 0, 0]], dtype=np.uint8) rank.mean(image=image, selem=elem, out=out, mask=mask, shift_x=0, shift_y=0) assert_equal(res, out) rank.geometric_mean(image=image, selem=elem, out=out, mask=mask, shift_x=0, shift_y=0) assert_equal(res, out) rank.minimum(image=image, selem=elem, out=out, mask=mask, shift_x=0, shift_y=0) assert_equal(res, out) rank.maximum(image=image, selem=elem, out=out, mask=mask, shift_x=0, shift_y=0) assert_equal(res, out) def test_otsu(self): # test the local Otsu segmentation on a synthetic image # (left to right ramp * sinus) test = np.tile([128, 145, 103, 127, 165, 83, 127, 185, 63, 127, 205, 43, 127, 225, 23, 127], (16, 1)) test = test.astype(np.uint8) res = np.tile([1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1], (16, 1)) selem = np.ones((6, 6), dtype=np.uint8) th = 1 * (test >= rank.otsu(test, selem)) assert_equal(th, res) def test_entropy(self): # verify that entropy is coherent with bitdepth of the input data selem = np.ones((16, 16), dtype=np.uint8) # 1 bit per pixel data = np.tile(np.asarray([0, 1]), (100, 100)).astype(np.uint8) assert(np.max(rank.entropy(data, selem)) == 1) # 2 bit per pixel data = np.tile(np.asarray([[0, 1], [2, 3]]), (10, 10)).astype(np.uint8) assert(np.max(rank.entropy(data, selem)) == 2) # 3 bit per pixel data = np.tile( np.asarray([[0, 1, 2, 3], [4, 5, 6, 7]]), (10, 10)).astype(np.uint8) assert(np.max(rank.entropy(data, selem)) == 3) # 4 bit per pixel data = np.tile( np.reshape(np.arange(16), (4, 4)), (10, 10)).astype(np.uint8) assert(np.max(rank.entropy(data, selem)) == 4) # 6 bit per pixel data = np.tile( np.reshape(np.arange(64), (8, 8)), (10, 10)).astype(np.uint8) assert(np.max(rank.entropy(data, selem)) == 6) # 8-bit per pixel data = np.tile( np.reshape(np.arange(256), (16, 16)), (10, 10)).astype(np.uint8) assert(np.max(rank.entropy(data, selem)) == 8) # 12 bit per pixel selem = np.ones((64, 64), dtype=np.uint8) data = np.zeros((65, 65), dtype=np.uint16) data[:64, :64] = np.reshape(np.arange(4096), (64, 64)) with expected_warnings(['Bitdepth of 11']): assert(np.max(rank.entropy(data, selem)) == 12) # make sure output is of dtype double with expected_warnings(['Bitdepth of 11']): out = rank.entropy(data, np.ones((16, 16), dtype=np.uint8)) assert out.dtype == np.double def test_selem_dtypes(self): image = np.zeros((5, 5), dtype=np.uint8) out = np.zeros_like(image) mask = np.ones_like(image, dtype=np.uint8) image[2, 2] = 255 image[2, 3] = 128 image[1, 2] = 16 for dtype in (np.uint8, np.uint16, np.int32, np.int64, np.float32, np.float64): elem = np.array([[0, 0, 0], [0, 1, 0], [0, 0, 0]], dtype=dtype) rank.mean(image=image, selem=elem, out=out, mask=mask, shift_x=0, shift_y=0) assert_equal(image, out) rank.geometric_mean(image=image, selem=elem, out=out, mask=mask, shift_x=0, shift_y=0) assert_equal(image, out) rank.mean_percentile(image=image, selem=elem, out=out, mask=mask, shift_x=0, shift_y=0) assert_equal(image, out) def test_16bit(self): image = np.zeros((21, 21), dtype=np.uint16) selem = np.ones((3, 3), dtype=np.uint8) for bitdepth in range(17): value = 2 ** bitdepth - 1 image[10, 10] = value if bitdepth > 11: expected = ['Bitdepth of %s' % (bitdepth - 1)] else: expected = [] with expected_warnings(expected): assert rank.minimum(image, selem)[10, 10] == 0 assert rank.maximum(image, selem)[10, 10] == value assert rank.mean(image, selem)[10, 10] == int(value / selem.size) def test_bilateral(self): image = np.zeros((21, 21), dtype=np.uint16) selem = np.ones((3, 3), dtype=np.uint8) image[10, 10] = 1000 image[10, 11] = 1010 image[10, 9] = 900 assert rank.mean_bilateral(image, selem, s0=1, s1=1)[10, 10] == 1000 assert rank.pop_bilateral(image, selem, s0=1, s1=1)[10, 10] == 1 assert rank.mean_bilateral(image, selem, s0=11, s1=11)[10, 10] == 1005 assert rank.pop_bilateral(image, selem, s0=11, s1=11)[10, 10] == 2 def test_percentile_min(self): # check that percentile p0 = 0 is identical to local min img = data.camera() img16 = img.astype(np.uint16) selem = disk(15) # check for 8bit img_p0 = rank.percentile(img, selem=selem, p0=0) img_min = rank.minimum(img, selem=selem) assert_equal(img_p0, img_min) # check for 16bit img_p0 = rank.percentile(img16, selem=selem, p0=0) img_min = rank.minimum(img16, selem=selem) assert_equal(img_p0, img_min) def test_percentile_max(self): # check that percentile p0 = 1 is identical to local max img = data.camera() img16 = img.astype(np.uint16) selem = disk(15) # check for 8bit img_p0 = rank.percentile(img, selem=selem, p0=1.) img_max = rank.maximum(img, selem=selem) assert_equal(img_p0, img_max) # check for 16bit img_p0 = rank.percentile(img16, selem=selem, p0=1.) img_max = rank.maximum(img16, selem=selem) assert_equal(img_p0, img_max) def test_percentile_median(self): # check that percentile p0 = 0.5 is identical to local median img = data.camera() img16 = img.astype(np.uint16) selem = disk(15) # check for 8bit img_p0 = rank.percentile(img, selem=selem, p0=.5) img_max = rank.median(img, selem=selem) assert_equal(img_p0, img_max) # check for 16bit img_p0 = rank.percentile(img16, selem=selem, p0=.5) img_max = rank.median(img16, selem=selem) assert_equal(img_p0, img_max) def test_sum(self): # check the number of valid pixels in the neighborhood image8 = np.array([[0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 1, 1, 0], [0, 1, 1, 1, 0], [0, 0, 0, 0, 0]], dtype=np.uint8) image16 = 400 * np.array([[0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 1, 1, 0], [0, 1, 1, 1, 0], [0, 0, 0, 0, 0]], dtype=np.uint16) elem = np.ones((3, 3), dtype=np.uint8) out8 = np.empty_like(image8) out16 = np.empty_like(image16) mask = np.ones(image8.shape, dtype=np.uint8) r = np.array([[1, 2, 3, 2, 1], [2, 4, 6, 4, 2], [3, 6, 9, 6, 3], [2, 4, 6, 4, 2], [1, 2, 3, 2, 1]], dtype=np.uint8) rank.sum(image=image8, selem=elem, out=out8, mask=mask) assert_equal(r, out8) rank.sum_percentile( image=image8, selem=elem, out=out8, mask=mask, p0=.0, p1=1.) assert_equal(r, out8) rank.sum_bilateral( image=image8, selem=elem, out=out8, mask=mask, s0=255, s1=255) assert_equal(r, out8) r = 400 * np.array([[1, 2, 3, 2, 1], [2, 4, 6, 4, 2], [3, 6, 9, 6, 3], [2, 4, 6, 4, 2], [1, 2, 3, 2, 1]], dtype=np.uint16) rank.sum(image=image16, selem=elem, out=out16, mask=mask) assert_equal(r, out16) rank.sum_percentile( image=image16, selem=elem, out=out16, mask=mask, p0=.0, p1=1.) assert_equal(r, out16) rank.sum_bilateral( image=image16, selem=elem, out=out16, mask=mask, s0=1000, s1=1000) assert_equal(r, out16) def test_windowed_histogram(self): # check the number of valid pixels in the neighborhood image8 = np.array([[0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 1, 1, 0], [0, 1, 1, 1, 0], [0, 0, 0, 0, 0]], dtype=np.uint8) elem = np.ones((3, 3), dtype=np.uint8) outf = np.empty(image8.shape + (2,), dtype=float) mask = np.ones(image8.shape, dtype=np.uint8) # Population so we can normalize the expected output while maintaining # code readability pop = np.array([[4, 6, 6, 6, 4], [6, 9, 9, 9, 6], [6, 9, 9, 9, 6], [6, 9, 9, 9, 6], [4, 6, 6, 6, 4]], dtype=float) r0 = np.array([[3, 4, 3, 4, 3], [4, 5, 3, 5, 4], [3, 3, 0, 3, 3], [4, 5, 3, 5, 4], [3, 4, 3, 4, 3]], dtype=float) / pop r1 = np.array([[1, 2, 3, 2, 1], [2, 4, 6, 4, 2], [3, 6, 9, 6, 3], [2, 4, 6, 4, 2], [1, 2, 3, 2, 1]], dtype=float) / pop rank.windowed_histogram(image=image8, selem=elem, out=outf, mask=mask) assert_equal(r0, outf[:, :, 0]) assert_equal(r1, outf[:, :, 1]) # Test n_bins parameter larger_output = rank.windowed_histogram(image=image8, selem=elem, mask=mask, n_bins=5) assert larger_output.shape[2] == 5 def test_median_default_value(self): a = np.zeros((3, 3), dtype=np.uint8) a[1] = 1 full_selem = np.ones((3, 3), dtype=np.uint8) assert_equal(rank.median(a), rank.median(a, full_selem)) assert rank.median(a)[1, 1] == 0 assert rank.median(a, disk(1))[1, 1] == 1 if __name__ == "__main__": run_module_suite() scikit-image-0.13.1/skimage/filters/setup.py000066400000000000000000000033501316256236300207540ustar00rootroot00000000000000#!/usr/bin/env python import os from skimage._build import cython base_path = os.path.abspath(os.path.dirname(__file__)) def configuration(parent_package='', top_path=None): from numpy.distutils.misc_util import Configuration, get_numpy_include_dirs config = Configuration('filters', parent_package, top_path) config.add_data_dir('tests') config.add_data_dir('rank/tests') cython(['_ctmf.pyx'], working_path=base_path) cython(['rank/core_cy.pyx'], working_path=base_path) cython(['rank/generic_cy.pyx'], working_path=base_path) cython(['rank/percentile_cy.pyx'], working_path=base_path) cython(['rank/bilateral_cy.pyx'], working_path=base_path) config.add_extension('_ctmf', sources=['_ctmf.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension('rank.core_cy', sources=['rank/core_cy.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension('rank.generic_cy', sources=['rank/generic_cy.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension( 'rank.percentile_cy', sources=['rank/percentile_cy.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension( 'rank.bilateral_cy', sources=['rank/bilateral_cy.c'], include_dirs=[get_numpy_include_dirs()]) return config if __name__ == '__main__': from numpy.distutils.core import setup setup(maintainer='scikit-image Developers', author='scikit-image Developers', maintainer_email='scikit-image@python.org', description='Filters', url='https://github.com/scikit-image/scikit-image', license='SciPy License (BSD Style)', **(configuration(top_path='').todict()) ) scikit-image-0.13.1/skimage/filters/tests/000077500000000000000000000000001316256236300204035ustar00rootroot00000000000000scikit-image-0.13.1/skimage/filters/tests/__init__.py000066400000000000000000000001771316256236300225210ustar00rootroot00000000000000from ..._shared.testing import setup_test, teardown_test def setup(): setup_test() def teardown(): teardown_test() scikit-image-0.13.1/skimage/filters/tests/test_edges.py000066400000000000000000000332401316256236300231050ustar00rootroot00000000000000import numpy as np from numpy.testing import (assert_array_almost_equal as assert_close, assert_, assert_allclose) from skimage import filters from skimage.filters.edges import _mask_filter_result def test_roberts_zeros(): """Roberts' filter on an array of all zeros.""" result = filters.roberts(np.zeros((10, 10)), np.ones((10, 10), bool)) assert (np.all(result == 0)) def test_roberts_diagonal1(): """Roberts' filter on a diagonal edge should be a diagonal line.""" image = np.tri(10, 10, 0) expected = ~(np.tri(10, 10, -1).astype(bool) | np.tri(10, 10, -2).astype(bool).transpose()) expected = _mask_filter_result(expected, None) result = filters.roberts(image).astype(bool) assert_close(result, expected) def test_roberts_diagonal2(): """Roberts' filter on a diagonal edge should be a diagonal line.""" image = np.rot90(np.tri(10, 10, 0), 3) expected = ~np.rot90(np.tri(10, 10, -1).astype(bool) | np.tri(10, 10, -2).astype(bool).transpose()) expected = _mask_filter_result(expected, None) result = filters.roberts(image).astype(bool) assert_close(result, expected) def test_sobel_zeros(): """Sobel on an array of all zeros.""" result = filters.sobel(np.zeros((10, 10)), np.ones((10, 10), bool)) assert (np.all(result == 0)) def test_sobel_mask(): """Sobel on a masked array should be zero.""" np.random.seed(0) result = filters.sobel(np.random.uniform(size=(10, 10)), np.zeros((10, 10), bool)) assert (np.all(result == 0)) def test_sobel_horizontal(): """Sobel on a horizontal edge should be a horizontal line.""" i, j = np.mgrid[-5:6, -5:6] image = (i >= 0).astype(float) result = filters.sobel(image) * np.sqrt(2) # Fudge the eroded points i[np.abs(j) == 5] = 10000 assert_allclose(result[i == 0], 1) assert (np.all(result[np.abs(i) > 1] == 0)) def test_sobel_vertical(): """Sobel on a vertical edge should be a vertical line.""" i, j = np.mgrid[-5:6, -5:6] image = (j >= 0).astype(float) result = filters.sobel(image) * np.sqrt(2) j[np.abs(i) == 5] = 10000 assert (np.all(result[j == 0] == 1)) assert (np.all(result[np.abs(j) > 1] == 0)) def test_sobel_h_zeros(): """Horizontal sobel on an array of all zeros.""" result = filters.sobel_h(np.zeros((10, 10)), np.ones((10, 10), bool)) assert (np.all(result == 0)) def test_sobel_h_mask(): """Horizontal Sobel on a masked array should be zero.""" np.random.seed(0) result = filters.sobel_h(np.random.uniform(size=(10, 10)), np.zeros((10, 10), bool)) assert (np.all(result == 0)) def test_sobel_h_horizontal(): """Horizontal Sobel on an edge should be a horizontal line.""" i, j = np.mgrid[-5:6, -5:6] image = (i >= 0).astype(float) result = filters.sobel_h(image) # Fudge the eroded points i[np.abs(j) == 5] = 10000 assert (np.all(result[i == 0] == 1)) assert (np.all(result[np.abs(i) > 1] == 0)) def test_sobel_h_vertical(): """Horizontal Sobel on a vertical edge should be zero.""" i, j = np.mgrid[-5:6, -5:6] image = (j >= 0).astype(float) * np.sqrt(2) result = filters.sobel_h(image) assert_allclose(result, 0, atol=1e-10) def test_sobel_v_zeros(): """Vertical sobel on an array of all zeros.""" result = filters.sobel_v(np.zeros((10, 10)), np.ones((10, 10), bool)) assert_allclose(result, 0) def test_sobel_v_mask(): """Vertical Sobel on a masked array should be zero.""" np.random.seed(0) result = filters.sobel_v(np.random.uniform(size=(10, 10)), np.zeros((10, 10), bool)) assert_allclose(result, 0) def test_sobel_v_vertical(): """Vertical Sobel on an edge should be a vertical line.""" i, j = np.mgrid[-5:6, -5:6] image = (j >= 0).astype(float) result = filters.sobel_v(image) # Fudge the eroded points j[np.abs(i) == 5] = 10000 assert (np.all(result[j == 0] == 1)) assert (np.all(result[np.abs(j) > 1] == 0)) def test_sobel_v_horizontal(): """vertical Sobel on a horizontal edge should be zero.""" i, j = np.mgrid[-5:6, -5:6] image = (i >= 0).astype(float) result = filters.sobel_v(image) assert_allclose(result, 0) def test_scharr_zeros(): """Scharr on an array of all zeros.""" result = filters.scharr(np.zeros((10, 10)), np.ones((10, 10), bool)) assert (np.all(result < 1e-16)) def test_scharr_mask(): """Scharr on a masked array should be zero.""" np.random.seed(0) result = filters.scharr(np.random.uniform(size=(10, 10)), np.zeros((10, 10), bool)) assert_allclose(result, 0) def test_scharr_horizontal(): """Scharr on an edge should be a horizontal line.""" i, j = np.mgrid[-5:6, -5:6] image = (i >= 0).astype(float) result = filters.scharr(image) * np.sqrt(2) # Fudge the eroded points i[np.abs(j) == 5] = 10000 assert_allclose(result[i == 0], 1) assert (np.all(result[np.abs(i) > 1] == 0)) def test_scharr_vertical(): """Scharr on a vertical edge should be a vertical line.""" i, j = np.mgrid[-5:6, -5:6] image = (j >= 0).astype(float) result = filters.scharr(image) * np.sqrt(2) j[np.abs(i) == 5] = 10000 assert_allclose(result[j == 0], 1) assert (np.all(result[np.abs(j) > 1] == 0)) def test_scharr_h_zeros(): """Horizontal Scharr on an array of all zeros.""" result = filters.scharr_h(np.zeros((10, 10)), np.ones((10, 10), bool)) assert_allclose(result, 0) def test_scharr_h_mask(): """Horizontal Scharr on a masked array should be zero.""" np.random.seed(0) result = filters.scharr_h(np.random.uniform(size=(10, 10)), np.zeros((10, 10), bool)) assert_allclose(result, 0) def test_scharr_h_horizontal(): """Horizontal Scharr on an edge should be a horizontal line.""" i, j = np.mgrid[-5:6, -5:6] image = (i >= 0).astype(float) result = filters.scharr_h(image) # Fudge the eroded points i[np.abs(j) == 5] = 10000 assert (np.all(result[i == 0] == 1)) assert (np.all(result[np.abs(i) > 1] == 0)) def test_scharr_h_vertical(): """Horizontal Scharr on a vertical edge should be zero.""" i, j = np.mgrid[-5:6, -5:6] image = (j >= 0).astype(float) result = filters.scharr_h(image) assert_allclose(result, 0) def test_scharr_v_zeros(): """Vertical Scharr on an array of all zeros.""" result = filters.scharr_v(np.zeros((10, 10)), np.ones((10, 10), bool)) assert_allclose(result, 0) def test_scharr_v_mask(): """Vertical Scharr on a masked array should be zero.""" np.random.seed(0) result = filters.scharr_v(np.random.uniform(size=(10, 10)), np.zeros((10, 10), bool)) assert_allclose(result, 0) def test_scharr_v_vertical(): """Vertical Scharr on an edge should be a vertical line.""" i, j = np.mgrid[-5:6, -5:6] image = (j >= 0).astype(float) result = filters.scharr_v(image) # Fudge the eroded points j[np.abs(i) == 5] = 10000 assert (np.all(result[j == 0] == 1)) assert (np.all(result[np.abs(j) > 1] == 0)) def test_scharr_v_horizontal(): """vertical Scharr on a horizontal edge should be zero.""" i, j = np.mgrid[-5:6, -5:6] image = (i >= 0).astype(float) result = filters.scharr_v(image) assert_allclose(result, 0) def test_prewitt_zeros(): """Prewitt on an array of all zeros.""" result = filters.prewitt(np.zeros((10, 10)), np.ones((10, 10), bool)) assert_allclose(result, 0) def test_prewitt_mask(): """Prewitt on a masked array should be zero.""" np.random.seed(0) result = filters.prewitt(np.random.uniform(size=(10, 10)), np.zeros((10, 10), bool)) assert_allclose(np.abs(result), 0) def test_prewitt_horizontal(): """Prewitt on an edge should be a horizontal line.""" i, j = np.mgrid[-5:6, -5:6] image = (i >= 0).astype(float) result = filters.prewitt(image) * np.sqrt(2) # Fudge the eroded points i[np.abs(j) == 5] = 10000 assert (np.all(result[i == 0] == 1)) assert_allclose(result[np.abs(i) > 1], 0, atol=1e-10) def test_prewitt_vertical(): """Prewitt on a vertical edge should be a vertical line.""" i, j = np.mgrid[-5:6, -5:6] image = (j >= 0).astype(float) result = filters.prewitt(image) * np.sqrt(2) j[np.abs(i) == 5] = 10000 assert_allclose(result[j == 0], 1) assert_allclose(result[np.abs(j) > 1], 0, atol=1e-10) def test_prewitt_h_zeros(): """Horizontal prewitt on an array of all zeros.""" result = filters.prewitt_h(np.zeros((10, 10)), np.ones((10, 10), bool)) assert_allclose(result, 0) def test_prewitt_h_mask(): """Horizontal prewitt on a masked array should be zero.""" np.random.seed(0) result = filters.prewitt_h(np.random.uniform(size=(10, 10)), np.zeros((10, 10), bool)) assert_allclose(result, 0) def test_prewitt_h_horizontal(): """Horizontal prewitt on an edge should be a horizontal line.""" i, j = np.mgrid[-5:6, -5:6] image = (i >= 0).astype(float) result = filters.prewitt_h(image) # Fudge the eroded points i[np.abs(j) == 5] = 10000 assert (np.all(result[i == 0] == 1)) assert_allclose(result[np.abs(i) > 1], 0, atol=1e-10) def test_prewitt_h_vertical(): """Horizontal prewitt on a vertical edge should be zero.""" i, j = np.mgrid[-5:6, -5:6] image = (j >= 0).astype(float) result = filters.prewitt_h(image) assert_allclose(result, 0, atol=1e-10) def test_prewitt_v_zeros(): """Vertical prewitt on an array of all zeros.""" result = filters.prewitt_v(np.zeros((10, 10)), np.ones((10, 10), bool)) assert_allclose(result, 0) def test_prewitt_v_mask(): """Vertical prewitt on a masked array should be zero.""" np.random.seed(0) result = filters.prewitt_v(np.random.uniform(size=(10, 10)), np.zeros((10, 10), bool)) assert_allclose(result, 0) def test_prewitt_v_vertical(): """Vertical prewitt on an edge should be a vertical line.""" i, j = np.mgrid[-5:6, -5:6] image = (j >= 0).astype(float) result = filters.prewitt_v(image) # Fudge the eroded points j[np.abs(i) == 5] = 10000 assert (np.all(result[j == 0] == 1)) assert_allclose(result[np.abs(j) > 1], 0, atol=1e-10) def test_prewitt_v_horizontal(): """Vertical prewitt on a horizontal edge should be zero.""" i, j = np.mgrid[-5:6, -5:6] image = (i >= 0).astype(float) result = filters.prewitt_v(image) assert_allclose(result, 0) def test_laplace_zeros(): """Laplace on a square image.""" # Create a synthetic 2D image image = np.zeros((9, 9)) image[3:-3, 3:-3] = 1 result = filters.laplace(image) res_chk = np.array([[ 0., 0., 0., 0., 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0., 0., 0., 0., 0.], [ 0., 0., 0., -1., -1., -1., 0., 0., 0.], [ 0., 0., -1., 2., 1., 2., -1., 0., 0.], [ 0., 0., -1., 1., 0., 1., -1., 0., 0.], [ 0., 0., -1., 2., 1., 2., -1., 0., 0.], [ 0., 0., 0., -1., -1., -1., 0., 0., 0.], [ 0., 0., 0., 0., 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0., 0., 0., 0., 0.]]) assert_allclose(result, res_chk) def test_laplace_mask(): """Laplace on a masked array should be zero.""" # Create a synthetic 2D image image = np.zeros((9, 9)) image[3:-3, 3:-3] = 1 # Define the mask result = filters.laplace(image, ksize=3, mask=np.zeros((9, 9), bool)) assert (np.all(result == 0)) def test_horizontal_mask_line(): """Horizontal edge filters mask pixels surrounding input mask.""" vgrad, _ = np.mgrid[:1:11j, :1:11j] # vertical gradient with spacing 0.1 vgrad[5, :] = 1 # bad horizontal line mask = np.ones_like(vgrad) mask[5, :] = 0 # mask bad line expected = np.zeros_like(vgrad) expected[1:-1, 1:-1] = 0.2 # constant gradient for most of image, expected[4:7, 1:-1] = 0 # but line and neighbors masked for grad_func in (filters.prewitt_h, filters.sobel_h, filters.scharr_h): result = grad_func(vgrad, mask) yield assert_close, result, expected def test_vertical_mask_line(): """Vertical edge filters mask pixels surrounding input mask.""" _, hgrad = np.mgrid[:1:11j, :1:11j] # horizontal gradient with spacing 0.1 hgrad[:, 5] = 1 # bad vertical line mask = np.ones_like(hgrad) mask[:, 5] = 0 # mask bad line expected = np.zeros_like(hgrad) expected[1:-1, 1:-1] = 0.2 # constant gradient for most of image, expected[1:-1, 4:7] = 0 # but line and neighbors masked for grad_func in (filters.prewitt_v, filters.sobel_v, filters.scharr_v): result = grad_func(hgrad, mask) yield assert_close, result, expected def test_range(): """Output of edge detection should be in [0, 1]""" image = np.random.random((100, 100)) for detector in (filters.sobel, filters.scharr, filters.prewitt, filters.roberts): out = detector(image) assert_(out.min() >= 0, "Minimum of `{0}` is smaller than zero".format( detector.__name__) ) assert_(out.max() <= 1, "Maximum of `{0}` is larger than 1".format( detector.__name__) ) if __name__ == "__main__": from numpy import testing testing.run_module_suite() scikit-image-0.13.1/skimage/filters/tests/test_frangi.py000066400000000000000000000022561316256236300232670ustar00rootroot00000000000000import numpy as np from numpy.testing import assert_equal, assert_almost_equal, assert_allclose from skimage.filters import frangi, hessian from skimage.data import camera from skimage.util import crop def test_null_matrix(): a = np.zeros((3, 3)) assert_almost_equal(frangi(a), np.zeros((3, 3))) assert_almost_equal(frangi(a, black_ridges=False), np.zeros((3, 3))) assert_equal(hessian(a), np.ones((3, 3))) def test_energy_decrease(): a = np.zeros((5, 5)) a[2, 2] = 1. assert frangi(a).std() < a.std() assert frangi(a, black_ridges=False).std() < a.std() assert hessian(a).std() > a.std() def test_values_decreased(): a = np.multiply(np.ones((3, 3)), 10) assert_equal(frangi(a), np.zeros((3, 3))) assert_equal(hessian(a), np.ones((3, 3))) def test_cropped_camera_image(): image = crop(camera(), ((206, 206), (206, 206))) assert_allclose(frangi(image), np.zeros((100, 100)), atol=1e-03) assert_allclose(frangi(image, black_ridges=True), np.zeros((100,100)), atol=1e-03) assert_allclose(hessian(image), np.ones((100, 100)), atol=1-1e-07) if __name__ == "__main__": from numpy import testing testing.run_module_suite() scikit-image-0.13.1/skimage/filters/tests/test_gabor.py000066400000000000000000000055101316256236300231070ustar00rootroot00000000000000import numpy as np from numpy.testing import (assert_equal, assert_almost_equal, assert_array_almost_equal) from skimage.filters._gabor import gabor_kernel, gabor, _sigma_prefactor def test_gabor_kernel_size(): sigma_x = 5 sigma_y = 10 # Sizes cut off at +/- three sigma + 1 for the center size_x = sigma_x * 6 + 1 size_y = sigma_y * 6 + 1 kernel = gabor_kernel(0, theta=0, sigma_x=sigma_x, sigma_y=sigma_y) assert_equal(kernel.shape, (size_y, size_x)) kernel = gabor_kernel(0, theta=np.pi / 2, sigma_x=sigma_x, sigma_y=sigma_y) assert_equal(kernel.shape, (size_x, size_y)) def test_gabor_kernel_bandwidth(): kernel = gabor_kernel(1, bandwidth=1) assert_equal(kernel.shape, (5, 5)) kernel = gabor_kernel(1, bandwidth=0.5) assert_equal(kernel.shape, (9, 9)) kernel = gabor_kernel(0.5, bandwidth=1) assert_equal(kernel.shape, (9, 9)) def test_sigma_prefactor(): assert_almost_equal(_sigma_prefactor(1), 0.56, 2) assert_almost_equal(_sigma_prefactor(0.5), 1.09, 2) def test_gabor_kernel_sum(): for sigma_x in range(1, 10, 2): for sigma_y in range(1, 10, 2): for frequency in range(0, 10, 2): kernel = gabor_kernel(frequency + 0.1, theta=0, sigma_x=sigma_x, sigma_y=sigma_y) # make sure gaussian distribution is covered nearly 100% assert_almost_equal(np.abs(kernel).sum(), 1, 2) def test_gabor_kernel_theta(): for sigma_x in range(1, 10, 2): for sigma_y in range(1, 10, 2): for frequency in range(0, 10, 2): for theta in range(0, 10, 2): kernel0 = gabor_kernel(frequency + 0.1, theta=theta, sigma_x=sigma_x, sigma_y=sigma_y) kernel180 = gabor_kernel(frequency, theta=theta + np.pi, sigma_x=sigma_x, sigma_y=sigma_y) assert_array_almost_equal(np.abs(kernel0), np.abs(kernel180)) def test_gabor(): Y, X = np.mgrid[:40, :40] frequencies = (0.1, 0.3) wave_images = [np.sin(2 * np.pi * X * f) for f in frequencies] def match_score(image, frequency): gabor_responses = gabor(image, frequency) return np.mean(np.hypot(*gabor_responses)) # Gabor scores: diagonals are frequency-matched, off-diagonals are not. responses = np.array([[match_score(image, f) for f in frequencies] for image in wave_images]) assert responses[0, 0] > responses[0, 1] assert responses[1, 1] > responses[0, 1] assert responses[0, 0] > responses[1, 0] assert responses[1, 1] > responses[1, 0] if __name__ == "__main__": from numpy import testing testing.run_module_suite() scikit-image-0.13.1/skimage/filters/tests/test_gaussian.py000066400000000000000000000041371316256236300236330ustar00rootroot00000000000000import numpy as np from numpy.testing import assert_raises from skimage.filters._gaussian import gaussian from skimage._shared._warnings import expected_warnings def test_negative_sigma(): a = np.zeros((3, 3)) a[1, 1] = 1. assert_raises(ValueError, gaussian, a, sigma=-1.0) assert_raises(ValueError, gaussian, a, sigma=[-1.0, 1.0]) assert_raises(ValueError, gaussian, a, sigma=np.asarray([-1.0, 1.0])) def test_null_sigma(): a = np.zeros((3, 3)) a[1, 1] = 1. assert np.all(gaussian(a, 0) == a) def test_default_sigma(): a = np.zeros((3, 3)) a[1, 1] = 1. assert np.all(gaussian(a) == gaussian(a, sigma=1)) def test_energy_decrease(): a = np.zeros((3, 3)) a[1, 1] = 1. gaussian_a = gaussian(a, sigma=1, mode='reflect') assert gaussian_a.std() < a.std() def test_multichannel(): a = np.zeros((5, 5, 3)) a[1, 1] = np.arange(1, 4) gaussian_rgb_a = gaussian(a, sigma=1, mode='reflect', multichannel=True) # Check that the mean value is conserved in each channel # (color channels are not mixed together) assert np.allclose([a[..., i].mean() for i in range(3)], [gaussian_rgb_a[..., i].mean() for i in range(3)]) # Test multichannel = None with expected_warnings(['multichannel']): gaussian_rgb_a = gaussian(a, sigma=1, mode='reflect') # Check that the mean value is conserved in each channel # (color channels are not mixed together) assert np.allclose([a[..., i].mean() for i in range(3)], [gaussian_rgb_a[..., i].mean() for i in range(3)]) # Iterable sigma gaussian_rgb_a = gaussian(a, sigma=[1, 2], mode='reflect', multichannel=True) assert np.allclose([a[..., i].mean() for i in range(3)], [gaussian_rgb_a[..., i].mean() for i in range(3)]) def test_preserve_range(): img = np.array([[10.0, -10.0], [-4, 3]], dtype=np.float32) gaussian(img, 1, preserve_range=True) if __name__ == "__main__": from numpy import testing testing.run_module_suite() scikit-image-0.13.1/skimage/filters/tests/test_lpi_filter.py000066400000000000000000000033351316256236300241510ustar00rootroot00000000000000import numpy as np from numpy.testing import (assert_raises, assert_, assert_equal, run_module_suite) from skimage import data from skimage.filters import LPIFilter2D, inverse, wiener class TestLPIFilter2D(object): img = data.camera()[:50, :50] def filt_func(self, r, c): return np.exp(-np.hypot(r, c) / 1) def setUp(self): self.f = LPIFilter2D(self.filt_func) def tst_shape(self, x): X = self.f(x) assert_equal(X.shape, x.shape) def test_ip_shape(self): rows, columns = self.img.shape[:2] for c_slice in [slice(0, columns), slice(0, columns - 5), slice(0, columns - 20)]: yield (self.tst_shape, self.img[:, c_slice]) def test_inverse(self): F = self.f(self.img) g = inverse(F, predefined_filter=self.f) assert_equal(g.shape, self.img.shape) g1 = inverse(F[::-1, ::-1], predefined_filter=self.f) assert_((g - g1[::-1, ::-1]).sum() < 55) # test cache g1 = inverse(F[::-1, ::-1], predefined_filter=self.f) assert_((g - g1[::-1, ::-1]).sum() < 55) g1 = inverse(F[::-1, ::-1], self.filt_func) assert_((g - g1[::-1, ::-1]).sum() < 55) def test_wiener(self): F = self.f(self.img) g = wiener(F, predefined_filter=self.f) assert_equal(g.shape, self.img.shape) g1 = wiener(F[::-1, ::-1], predefined_filter=self.f) assert_((g - g1[::-1, ::-1]).sum() < 1) g1 = wiener(F[::-1, ::-1], self.filt_func) assert_((g - g1[::-1, ::-1]).sum() < 1) def test_non_callable(self): assert_raises(ValueError, LPIFilter2D, None) if __name__ == "__main__": run_module_suite() scikit-image-0.13.1/skimage/filters/tests/test_median.py000066400000000000000000000001051316256236300232450ustar00rootroot00000000000000def test_median_in_filters(): from skimage.filters import median scikit-image-0.13.1/skimage/filters/tests/test_thresholding.py000066400000000000000000000343321316256236300245130ustar00rootroot00000000000000import numpy as np from scipy import ndimage as ndi from numpy.testing import (assert_equal, assert_almost_equal, assert_raises) import skimage from skimage import data from skimage._shared._warnings import expected_warnings from skimage.filters.thresholding import (threshold_local, threshold_adaptive, threshold_otsu, threshold_li, threshold_yen, threshold_isodata, threshold_niblack, threshold_sauvola, threshold_mean, threshold_triangle, threshold_minimum, _mean_std) class TestSimpleImage(): def setup(self): self.image = np.array([[0, 0, 1, 3, 5], [0, 1, 4, 3, 4], [1, 2, 5, 4, 1], [2, 4, 5, 2, 1], [4, 5, 1, 0, 0]], dtype=int) def test_otsu(self): assert threshold_otsu(self.image) == 2 def test_otsu_negative_int(self): image = self.image - 2 assert threshold_otsu(image) == 0 def test_otsu_float_image(self): image = np.float64(self.image) assert 2 <= threshold_otsu(image) < 3 def test_li(self): assert int(threshold_li(self.image)) == 2 def test_li_negative_int(self): image = self.image - 2 assert int(threshold_li(image)) == 0 def test_li_float_image(self): image = np.float64(self.image) assert 2 <= threshold_li(image) < 3 def test_li_constant_image(self): assert_raises(ValueError, threshold_li, np.ones((10,10))) def test_yen(self): assert threshold_yen(self.image) == 2 def test_yen_negative_int(self): image = self.image - 2 assert threshold_yen(image) == 0 def test_yen_float_image(self): image = np.float64(self.image) assert 2 <= threshold_yen(image) < 3 def test_yen_arange(self): image = np.arange(256) assert threshold_yen(image) == 127 def test_yen_binary(self): image = np.zeros([2, 256], dtype=np.uint8) image[0] = 255 assert threshold_yen(image) < 1 def test_yen_blank_zero(self): image = np.zeros((5, 5), dtype=np.uint8) assert threshold_yen(image) == 0 def test_yen_blank_max(self): image = np.empty((5, 5), dtype=np.uint8) image.fill(255) assert threshold_yen(image) == 255 def test_isodata(self): assert threshold_isodata(self.image) == 2 assert threshold_isodata(self.image, return_all=True) == [2] def test_isodata_blank_zero(self): image = np.zeros((5, 5), dtype=np.uint8) assert threshold_isodata(image) == 0 assert threshold_isodata(image, return_all=True) == [0] def test_isodata_linspace(self): image = np.linspace(-127, 0, 256) assert -63.8 < threshold_isodata(image) < -63.6 assert_almost_equal(threshold_isodata(image, return_all=True), [-63.74804688, -63.25195312]) def test_isodata_16bit(self): np.random.seed(0) imfloat = np.random.rand(256, 256) assert 0.49 < threshold_isodata(imfloat, nbins=1024) < 0.51 assert all(0.49 < threshold_isodata(imfloat, nbins=1024, return_all=True)) def test_threshold_local_equals_adaptive(self): def func(arr): return arr.sum() / arr.shape[0] with expected_warnings(['deprecated', 'return value']): thresholded_original = threshold_adaptive(self.image, 3, method='generic', param=func) threshold_new = threshold_local(self.image, 3, method='generic', param=func) assert_equal(thresholded_original, self.image > threshold_new) def test_threshold_adaptive_generic(self): def func(arr): return arr.sum() / arr.shape[0] ref = np.array( [[False, False, False, False, True], [False, False, True, False, True], [False, False, True, True, False], [False, True, True, False, False], [ True, True, False, False, False]] ) with expected_warnings(['deprecated', 'return value']): out = threshold_adaptive(self.image, 3, method='generic', param=func) assert_equal(ref, out) def test_threshold_local_gaussian(self): ref = np.array( [[False, False, False, False, True], [False, False, True, False, True], [False, False, True, True, False], [False, True, True, False, False], [ True, True, False, False, False]] ) out = threshold_local(self.image, 3, method='gaussian') assert_equal(ref, self.image > out) out = threshold_local(self.image, 3, method='gaussian', param=1./3.) assert_equal(ref, self.image > out) def test_threshold_local_mean(self): ref = np.array( [[False, False, False, False, True], [False, False, True, False, True], [False, False, True, True, False], [False, True, True, False, False], [ True, True, False, False, False]] ) out = threshold_local(self.image, 3, method='mean') assert_equal(ref, self.image > out) def test_threshold_local_median(self): ref = np.array( [[False, False, False, False, True], [False, False, True, False, False], [False, False, True, False, False], [False, False, True, True, False], [False, True, False, False, False]] ) out = threshold_local(self.image, 3, method='median') assert_equal(ref, self.image > out) def test_threshold_niblack(self): ref = np.array( [[False, False, False, True, True], [False, True, True, True, True], [False, True, True, True, False], [False, True, True, True, True], [True, True, False, False, False]] ) thres = threshold_niblack(self.image, window_size=3, k=0.5) out = self.image > thres assert_equal(ref, out) def test_threshold_sauvola(self): ref = np.array( [[False, False, False, True, True], [False, False, True, True, True], [False, False, True, True, False], [False, True, True, True, False], [True, True, False, False, False]] ) thres = threshold_sauvola(self.image, window_size=3, k=0.2, r=128) out = self.image > thres assert_equal(ref, out) def test_otsu_camera_image(): camera = skimage.img_as_ubyte(data.camera()) assert 86 < threshold_otsu(camera) < 88 def test_otsu_coins_image(): coins = skimage.img_as_ubyte(data.coins()) assert 106 < threshold_otsu(coins) < 108 def test_otsu_coins_image_as_float(): coins = skimage.img_as_float(data.coins()) assert 0.41 < threshold_otsu(coins) < 0.42 def test_otsu_astro_image(): img = skimage.img_as_ubyte(data.astronaut()) with expected_warnings(['grayscale']): assert 109 < threshold_otsu(img) < 111 def test_otsu_one_color_image(): img = np.ones((10, 10), dtype=np.uint8) assert_raises(ValueError, threshold_otsu, img) def test_li_camera_image(): camera = skimage.img_as_ubyte(data.camera()) assert 63 < threshold_li(camera) < 65 def test_li_coins_image(): coins = skimage.img_as_ubyte(data.coins()) assert 95 < threshold_li(coins) < 97 def test_li_coins_image_as_float(): coins = skimage.img_as_float(data.coins()) assert 0.37 < threshold_li(coins) < 0.38 def test_li_astro_image(): img = skimage.img_as_ubyte(data.astronaut()) assert 66 < threshold_li(img) < 68 def test_yen_camera_image(): camera = skimage.img_as_ubyte(data.camera()) assert 197 < threshold_yen(camera) < 199 def test_yen_coins_image(): coins = skimage.img_as_ubyte(data.coins()) assert 109 < threshold_yen(coins) < 111 def test_yen_coins_image_as_float(): coins = skimage.img_as_float(data.coins()) assert 0.43 < threshold_yen(coins) < 0.44 def test_adaptive_even_block_size_error(): img = data.camera() assert_raises(ValueError, threshold_local, img, block_size=4) def test_isodata_camera_image(): camera = skimage.img_as_ubyte(data.camera()) threshold = threshold_isodata(camera) assert np.floor((camera[camera <= threshold].mean() + camera[camera > threshold].mean()) / 2.0) == threshold assert threshold == 87 assert threshold_isodata(camera, return_all=True) == [87] def test_isodata_coins_image(): coins = skimage.img_as_ubyte(data.coins()) threshold = threshold_isodata(coins) assert np.floor((coins[coins <= threshold].mean() + coins[coins > threshold].mean()) / 2.0) == threshold assert threshold == 107 assert threshold_isodata(coins, return_all=True) == [107] def test_isodata_moon_image(): moon = skimage.img_as_ubyte(data.moon()) threshold = threshold_isodata(moon) assert np.floor((moon[moon <= threshold].mean() + moon[moon > threshold].mean()) / 2.0) == threshold assert threshold == 86 thresholds = threshold_isodata(moon, return_all=True) for threshold in thresholds: assert np.floor((moon[moon <= threshold].mean() + moon[moon > threshold].mean()) / 2.0) == threshold assert_equal(thresholds, [86, 87, 88, 122, 123, 124, 139, 140]) def test_isodata_moon_image_negative_int(): moon = skimage.img_as_ubyte(data.moon()).astype(np.int32) moon -= 100 threshold = threshold_isodata(moon) assert np.floor((moon[moon <= threshold].mean() + moon[moon > threshold].mean()) / 2.0) == threshold assert threshold == -14 thresholds = threshold_isodata(moon, return_all=True) for threshold in thresholds: assert np.floor((moon[moon <= threshold].mean() + moon[moon > threshold].mean()) / 2.0) == threshold assert_equal(thresholds, [-14, -13, -12, 22, 23, 24, 39, 40]) def test_isodata_moon_image_negative_float(): moon = skimage.img_as_ubyte(data.moon()).astype(np.float64) moon -= 100 assert -14 < threshold_isodata(moon) < -13 thresholds = threshold_isodata(moon, return_all=True) assert_almost_equal(thresholds, [-13.83789062, -12.84179688, -11.84570312, 22.02148438, 23.01757812, 24.01367188, 38.95507812, 39.95117188]) def test_threshold_minimum(): camera = skimage.img_as_ubyte(data.camera()) threshold = threshold_minimum(camera) assert_equal(threshold, 76) astronaut = skimage.img_as_ubyte(data.astronaut()) threshold = threshold_minimum(astronaut) assert_equal(threshold, 114) def test_threshold_minimum_synthetic(): img = np.arange(25*25, dtype=np.uint8).reshape((25, 25)) img[0:9, :] = 50 img[14:25, :] = 250 threshold = threshold_minimum(img) assert_equal(threshold, 95) def test_threshold_minimum_failure(): img = np.zeros((16*16), dtype=np.uint8) assert_raises(RuntimeError, threshold_minimum, img) def test_mean(): img = np.zeros((2, 6)) img[:, 2:4] = 1 img[:, 4:] = 2 assert(threshold_mean(img) == 1.) def test_triangle_uint_images(): assert(threshold_triangle(np.invert(data.text())) == 151) assert(threshold_triangle(data.text()) == 104) assert(threshold_triangle(data.coins()) == 80) assert(threshold_triangle(np.invert(data.coins())) == 175) def test_triangle_float_images(): text = data.text() int_bins = text.max() - text.min() + 1 # Set nbins to match the uint case and threshold as float. assert(round(threshold_triangle( text.astype(np.float), nbins=int_bins)) == 104) # Check that rescaling image to floats in unit interval is equivalent. assert(round(threshold_triangle(text / 255., nbins=int_bins) * 255) == 104) # Repeat for inverted image. assert(round(threshold_triangle( np.invert(text).astype(np.float), nbins=int_bins)) == 151) assert (round(threshold_triangle( np.invert(text) / 255., nbins=int_bins) * 255) == 151) def test_triangle_flip(): # Depending on the skewness, the algorithm flips the histogram. # We check that the flip doesn't affect too much the result. img = data.camera() inv_img = np.invert(img) t = threshold_triangle(inv_img) t_inv_img = inv_img > t t_inv_inv_img = np.invert(t_inv_img) t = threshold_triangle(img) t_img = img > t # Check that most of the pixels are identical # See numpy #7685 for a future np.testing API unequal_pos = np.where(t_img.ravel() != t_inv_inv_img.ravel()) assert(len(unequal_pos[0]) / t_img.size < 1e-2) def test_mean_std_2d(): image = np.random.rand(256, 256) window_size = 11 m, s = _mean_std(image, w=window_size) mean_kernel = np.ones((window_size,) * 2) / window_size**2 expected_m = ndi.convolve(image, mean_kernel, mode='mirror') np.testing.assert_allclose(m, expected_m) expected_s = ndi.generic_filter(image, np.std, size=window_size, mode='mirror') np.testing.assert_allclose(s, expected_s) def test_mean_std_3d(): image = np.random.rand(40, 40, 40) window_size = 5 mean_kernel = np.ones((window_size,) * 3) / window_size**3 m, s = _mean_std(image, w=window_size) expected_m = ndi.convolve(image, mean_kernel, mode='mirror') np.testing.assert_allclose(m, expected_m) expected_s = ndi.generic_filter(image, np.std, size=window_size, mode='mirror') np.testing.assert_allclose(s, expected_s) if __name__ == '__main__': np.testing.run_module_suite() scikit-image-0.13.1/skimage/filters/thresholding.py000066400000000000000000000731101316256236300223070ustar00rootroot00000000000000import itertools import math import numpy as np from scipy import ndimage as ndi from scipy.ndimage import filters as ndif from collections import OrderedDict from ..exposure import histogram from .._shared.utils import assert_nD, warn, deprecated from ..transform import integral_image from .. import util from skimage import dtype_limits __all__ = ['try_all_threshold', 'threshold_adaptive', 'threshold_otsu', 'threshold_yen', 'threshold_isodata', 'threshold_li', 'threshold_minimum', 'threshold_mean', 'threshold_niblack', 'threshold_sauvola', 'threshold_triangle'] def _try_all(image, methods=None, figsize=None, num_cols=2, verbose=True): """Returns a figure comparing the outputs of different methods. Parameters ---------- image : (N, M) ndarray Input image. methods : dict, optional Names and associated functions. Functions must take and return an image. figsize : tuple, optional Figure size (in inches). num_cols : int, optional Number of columns. verbose : bool, optional Print function name for each method. Returns ------- fig, ax : tuple Matplotlib figure and axes. """ from matplotlib import pyplot as plt num_rows = math.ceil((len(methods) + 1.) / num_cols) num_rows = int(num_rows) # Python 2.7 support fig, ax = plt.subplots(num_rows, num_cols, figsize=figsize, sharex=True, sharey=True, subplot_kw={'adjustable': 'box-forced'}) ax = ax.ravel() ax[0].imshow(image, cmap=plt.cm.gray) ax[0].set_title('Original') i = 1 for name, func in methods.items(): ax[i].imshow(func(image), cmap=plt.cm.gray) ax[i].set_title(name) i += 1 if verbose: print(func.__orifunc__) for a in ax: a.axis('off') fig.tight_layout() return fig, ax def try_all_threshold(image, figsize=(8, 5), verbose=True): """Returns a figure comparing the outputs of different thresholding methods. Parameters ---------- image : (N, M) ndarray Input image. figsize : tuple, optional Figure size (in inches). verbose : bool, optional Print function name for each method. Returns ------- fig, ax : tuple Matplotlib figure and axes. Notes ----- The following algorithms are used: * isodata * li * mean * minimum * otsu * triangle * yen Examples -------- >>> from skimage.data import text >>> fig, ax = try_all_threshold(text(), figsize=(10, 6), verbose=False) """ def thresh(func): """ A wrapper function to return a thresholded image. """ def wrapper(im): return im > func(im) try: wrapper.__orifunc__ = func.__orifunc__ except AttributeError: wrapper.__orifunc__ = func.__module__ + '.' + func.__name__ return wrapper # Global algorithms. methods = OrderedDict({'Isodata': thresh(threshold_isodata), 'Li': thresh(threshold_li), 'Mean': thresh(threshold_mean), 'Minimum': thresh(threshold_minimum), 'Otsu': thresh(threshold_otsu), 'Triangle': thresh(threshold_triangle), 'Yen': thresh(threshold_yen)}) return _try_all(image, figsize=figsize, methods=methods, verbose=verbose) def threshold_local(image, block_size, method='gaussian', offset=0, mode='reflect', param=None): """Compute a threshold mask image based on local pixel neighborhood. Also known as adaptive or dynamic thresholding. The threshold value is the weighted mean for the local neighborhood of a pixel subtracted by a constant. Alternatively the threshold can be determined dynamically by a given function, using the 'generic' method. Parameters ---------- image : (N, M) ndarray Input image. block_size : int Odd size of pixel neighborhood which is used to calculate the threshold value (e.g. 3, 5, 7, ..., 21, ...). method : {'generic', 'gaussian', 'mean', 'median'}, optional Method used to determine adaptive threshold for local neighbourhood in weighted mean image. * 'generic': use custom function (see `param` parameter) * 'gaussian': apply gaussian filter (see `param` parameter for custom\ sigma value) * 'mean': apply arithmetic mean filter * 'median': apply median rank filter By default the 'gaussian' method is used. offset : float, optional Constant subtracted from weighted mean of neighborhood to calculate the local threshold value. Default offset is 0. mode : {'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional The mode parameter determines how the array borders are handled, where cval is the value when mode is equal to 'constant'. Default is 'reflect'. param : {int, function}, optional Either specify sigma for 'gaussian' method or function object for 'generic' method. This functions takes the flat array of local neighbourhood as a single argument and returns the calculated threshold for the centre pixel. Returns ------- threshold : (N, M) ndarray Threshold image. All pixels in the input image higher than the corresponding pixel in the threshold image are considered foreground. References ---------- .. [1] http://docs.opencv.org/modules/imgproc/doc/miscellaneous_transformations.html?highlight=threshold#adaptivethreshold Examples -------- >>> from skimage.data import camera >>> image = camera()[:50, :50] >>> binary_image1 = image > threshold_local(image, 15, 'mean') >>> func = lambda arr: arr.mean() >>> binary_image2 = image > threshold_local(image, 15, 'generic', ... param=func) """ if block_size % 2 == 0: raise ValueError("The kwarg ``block_size`` must be odd! Given " "``block_size`` {0} is even.".format(block_size)) assert_nD(image, 2) thresh_image = np.zeros(image.shape, 'double') if method == 'generic': ndi.generic_filter(image, param, block_size, output=thresh_image, mode=mode) elif method == 'gaussian': if param is None: # automatically determine sigma which covers > 99% of distribution sigma = (block_size - 1) / 6.0 else: sigma = param ndi.gaussian_filter(image, sigma, output=thresh_image, mode=mode) elif method == 'mean': mask = 1. / block_size * np.ones((block_size,)) # separation of filters to speedup convolution ndi.convolve1d(image, mask, axis=0, output=thresh_image, mode=mode) ndi.convolve1d(thresh_image, mask, axis=1, output=thresh_image, mode=mode) elif method == 'median': ndi.median_filter(image, block_size, output=thresh_image, mode=mode) return thresh_image - offset @deprecated('threshold_local', removed_version='0.15') def threshold_adaptive(image, block_size, method='gaussian', offset=0, mode='reflect', param=None): warn('The return value of `threshold_local` is a threshold image, while ' '`threshold_adaptive` returned the *thresholded* image.') return image > threshold_local(image, block_size=block_size, method=method, offset=offset, mode=mode, param=param) def threshold_otsu(image, nbins=256): """Return threshold value based on Otsu's method. Parameters ---------- image : (N, M) ndarray Grayscale input image. nbins : int, optional Number of bins used to calculate histogram. This value is ignored for integer arrays. Returns ------- threshold : float Upper threshold value. All pixels with an intensity higher than this value are assumed to be foreground. Raises ------ ValueError If `image` only contains a single grayscale value. References ---------- .. [1] Wikipedia, http://en.wikipedia.org/wiki/Otsu's_Method Examples -------- >>> from skimage.data import camera >>> image = camera() >>> thresh = threshold_otsu(image) >>> binary = image <= thresh Notes ----- The input image must be grayscale. """ if len(image.shape) > 2 and image.shape[-1] in (3, 4): msg = "threshold_otsu is expected to work correctly only for " \ "grayscale images; image shape {0} looks like an RGB image" warn(msg.format(image.shape)) # Check if the image is multi-colored or not if image.min() == image.max(): raise ValueError("threshold_otsu is expected to work with images " "having more than one color. The input image seems " "to have just one color {0}.".format(image.min())) hist, bin_centers = histogram(image.ravel(), nbins) hist = hist.astype(float) # class probabilities for all possible thresholds weight1 = np.cumsum(hist) weight2 = np.cumsum(hist[::-1])[::-1] # class means for all possible thresholds mean1 = np.cumsum(hist * bin_centers) / weight1 mean2 = (np.cumsum((hist * bin_centers)[::-1]) / weight2[::-1])[::-1] # Clip ends to align class 1 and class 2 variables: # The last value of `weight1`/`mean1` should pair with zero values in # `weight2`/`mean2`, which do not exist. variance12 = weight1[:-1] * weight2[1:] * (mean1[:-1] - mean2[1:]) ** 2 idx = np.argmax(variance12) threshold = bin_centers[:-1][idx] return threshold def threshold_yen(image, nbins=256): """Return threshold value based on Yen's method. Parameters ---------- image : (N, M) ndarray Input image. nbins : int, optional Number of bins used to calculate histogram. This value is ignored for integer arrays. Returns ------- threshold : float Upper threshold value. All pixels with an intensity higher than this value are assumed to be foreground. References ---------- .. [1] Yen J.C., Chang F.J., and Chang S. (1995) "A New Criterion for Automatic Multilevel Thresholding" IEEE Trans. on Image Processing, 4(3): 370-378. DOI:10.1109/83.366472 .. [2] Sezgin M. and Sankur B. (2004) "Survey over Image Thresholding Techniques and Quantitative Performance Evaluation" Journal of Electronic Imaging, 13(1): 146-165, DOI:10.1117/1.1631315 http://www.busim.ee.boun.edu.tr/~sankur/SankurFolder/Threshold_survey.pdf .. [3] ImageJ AutoThresholder code, http://fiji.sc/wiki/index.php/Auto_Threshold Examples -------- >>> from skimage.data import camera >>> image = camera() >>> thresh = threshold_yen(image) >>> binary = image <= thresh """ hist, bin_centers = histogram(image.ravel(), nbins) # On blank images (e.g. filled with 0) with int dtype, `histogram()` # returns `bin_centers` containing only one value. Speed up with it. if bin_centers.size == 1: return bin_centers[0] # Calculate probability mass function pmf = hist.astype(np.float32) / hist.sum() P1 = np.cumsum(pmf) # Cumulative normalized histogram P1_sq = np.cumsum(pmf ** 2) # Get cumsum calculated from end of squared array: P2_sq = np.cumsum(pmf[::-1] ** 2)[::-1] # P2_sq indexes is shifted +1. I assume, with P1[:-1] it's help avoid '-inf' # in crit. ImageJ Yen implementation replaces those values by zero. crit = np.log(((P1_sq[:-1] * P2_sq[1:]) ** -1) * (P1[:-1] * (1.0 - P1[:-1])) ** 2) return bin_centers[crit.argmax()] def threshold_isodata(image, nbins=256, return_all=False): """Return threshold value(s) based on ISODATA method. Histogram-based threshold, known as Ridler-Calvard method or inter-means. Threshold values returned satisfy the following equality: `threshold = (image[image <= threshold].mean() +` `image[image > threshold].mean()) / 2.0` That is, returned thresholds are intensities that separate the image into two groups of pixels, where the threshold intensity is midway between the mean intensities of these groups. For integer images, the above equality holds to within one; for floating- point images, the equality holds to within the histogram bin-width. Parameters ---------- image : (N, M) ndarray Input image. nbins : int, optional Number of bins used to calculate histogram. This value is ignored for integer arrays. return_all: bool, optional If False (default), return only the lowest threshold that satisfies the above equality. If True, return all valid thresholds. Returns ------- threshold : float or int or array Threshold value(s). References ---------- .. [1] Ridler, TW & Calvard, S (1978), "Picture thresholding using an iterative selection method" IEEE Transactions on Systems, Man and Cybernetics 8: 630-632, DOI:10.1109/TSMC.1978.4310039 .. [2] Sezgin M. and Sankur B. (2004) "Survey over Image Thresholding Techniques and Quantitative Performance Evaluation" Journal of Electronic Imaging, 13(1): 146-165, http://www.busim.ee.boun.edu.tr/~sankur/SankurFolder/Threshold_survey.pdf DOI:10.1117/1.1631315 .. [3] ImageJ AutoThresholder code, http://fiji.sc/wiki/index.php/Auto_Threshold Examples -------- >>> from skimage.data import coins >>> image = coins() >>> thresh = threshold_isodata(image) >>> binary = image > thresh """ hist, bin_centers = histogram(image.ravel(), nbins) # image only contains one unique value if len(bin_centers) == 1: if return_all: return bin_centers else: return bin_centers[0] hist = hist.astype(np.float32) # csuml and csumh contain the count of pixels in that bin or lower, and # in all bins strictly higher than that bin, respectively csuml = np.cumsum(hist) csumh = np.cumsum(hist[::-1])[::-1] - hist # intensity_sum contains the total pixel intensity from each bin intensity_sum = hist * bin_centers # l and h contain average value of all pixels in that bin or lower, and # in all bins strictly higher than that bin, respectively. # Note that since exp.histogram does not include empty bins at the low or # high end of the range, csuml and csumh are strictly > 0, except in the # last bin of csumh, which is zero by construction. # So no worries about division by zero in the following lines, except # for the last bin, but we can ignore that because no valid threshold # can be in the top bin. So we just patch up csumh[-1] to not cause 0/0 # errors. csumh[-1] = 1 l = np.cumsum(intensity_sum) / csuml h = (np.cumsum(intensity_sum[::-1])[::-1] - intensity_sum) / csumh # isodata finds threshold values that meet the criterion t = (l + m)/2 # where l is the mean of all pixels <= t and h is the mean of all pixels # > t, as calculated above. So we are looking for places where # (l + m) / 2 equals the intensity value for which those l and m figures # were calculated -- which is, of course, the histogram bin centers. # We only require this equality to be within the precision of the bin # width, of course. all_mean = (l + h) / 2.0 bin_width = bin_centers[1] - bin_centers[0] # Look only at thresholds that are below the actual all_mean value, # for consistency with the threshold being included in the lower pixel # group. Otherwise can get thresholds that are not actually fixed-points # of the isodata algorithm. For float images, this matters less, since # there really can't be any guarantees anymore anyway. distances = all_mean - bin_centers thresholds = bin_centers[(distances >= 0) & (distances < bin_width)] if return_all: return thresholds else: return thresholds[0] def threshold_li(image): """Return threshold value based on adaptation of Li's Minimum Cross Entropy method. Parameters ---------- image : (N, M) ndarray Input image. Returns ------- threshold : float Upper threshold value. All pixels with an intensity higher than this value are assumed to be foreground. References ---------- .. [1] Li C.H. and Lee C.K. (1993) "Minimum Cross Entropy Thresholding" Pattern Recognition, 26(4): 617-625 DOI:10.1016/0031-3203(93)90115-D .. [2] Li C.H. and Tam P.K.S. (1998) "An Iterative Algorithm for Minimum Cross Entropy Thresholding" Pattern Recognition Letters, 18(8): 771-776 DOI:10.1016/S0167-8655(98)00057-9 .. [3] Sezgin M. and Sankur B. (2004) "Survey over Image Thresholding Techniques and Quantitative Performance Evaluation" Journal of Electronic Imaging, 13(1): 146-165 DOI:10.1117/1.1631315 .. [4] ImageJ AutoThresholder code, http://fiji.sc/wiki/index.php/Auto_Threshold Examples -------- >>> from skimage.data import camera >>> image = camera() >>> thresh = threshold_li(image) >>> binary = image > thresh """ # Make sure image has more than one value if np.all(image == image.flat[0]): raise ValueError("threshold_li is expected to work with images " "having more than one value. The input image seems " "to have just one value {0}.".format(image.flat[0])) # Copy to ensure input image is not modified image = image.copy() # Requires positive image (because of log(mean)) immin = np.min(image) image -= immin imrange = np.max(image) tolerance = 0.5 * imrange / 256 # Calculate the mean gray-level mean = np.mean(image) # Initial estimate new_thresh = mean old_thresh = new_thresh + 2 * tolerance # Stop the iterations when the difference between the # new and old threshold values is less than the tolerance while abs(new_thresh - old_thresh) > tolerance: old_thresh = new_thresh threshold = old_thresh + tolerance # range # Calculate the means of background and object pixels mean_back = image[image <= threshold].mean() mean_obj = image[image > threshold].mean() temp = (mean_back - mean_obj) / (np.log(mean_back) - np.log(mean_obj)) if temp < 0: new_thresh = temp - tolerance else: new_thresh = temp + tolerance return threshold + immin def threshold_minimum(image, nbins=256, max_iter=10000): """Return threshold value based on minimum method. The histogram of the input `image` is computed and smoothed until there are only two maxima. Then the minimum in between is the threshold value. Parameters ---------- image : (M, N) ndarray Input image. nbins : int, optional Number of bins used to calculate histogram. This value is ignored for integer arrays. max_iter: int, optional Maximum number of iterations to smooth the histogram. Returns ------- threshold : float Upper threshold value. All pixels with an intensity higher than this value are assumed to be foreground. Raises ------ RuntimeError If unable to find two local maxima in the histogram or if the smoothing takes more than 1e4 iterations. References ---------- .. [1] C. A. Glasbey, "An analysis of histogram-based thresholding algorithms," CVGIP: Graphical Models and Image Processing, vol. 55, pp. 532-537, 1993. .. [2] Prewitt, JMS & Mendelsohn, ML (1966), "The analysis of cell images", Annals of the New York Academy of Sciences 128: 1035-1053 DOI:10.1111/j.1749-6632.1965.tb11715.x Examples -------- >>> from skimage.data import camera >>> image = camera() >>> thresh = threshold_minimum(image) >>> binary = image > thresh """ def find_local_maxima_idx(hist): # We can't use scipy.signal.argrelmax # as it fails on plateaus maximum_idxs = list() direction = 1 for i in range(hist.shape[0] - 1): if direction > 0: if hist[i + 1] < hist[i]: direction = -1 maximum_idxs.append(i) else: if hist[i + 1] > hist[i]: direction = 1 return maximum_idxs hist, bin_centers = histogram(image.ravel(), nbins) smooth_hist = np.copy(hist).astype(np.float64) for counter in range(max_iter): smooth_hist = ndif.uniform_filter1d(smooth_hist, 3) maximum_idxs = find_local_maxima_idx(smooth_hist) if len(maximum_idxs) < 3: break if len(maximum_idxs) != 2: raise RuntimeError('Unable to find two maxima in histogram') elif counter == max_iter - 1: raise RuntimeError('Maximum iteration reached for histogram' 'smoothing') # Find lowest point between the maxima threshold_idx = np.argmin(smooth_hist[maximum_idxs[0]:maximum_idxs[1] + 1]) return bin_centers[maximum_idxs[0] + threshold_idx] def threshold_mean(image): """Return threshold value based on the mean of grayscale values. Parameters ---------- image : (N, M[, ..., P]) ndarray Grayscale input image. Returns ------- threshold : float Upper threshold value. All pixels with an intensity higher than this value are assumed to be foreground. References ---------- .. [1] C. A. Glasbey, "An analysis of histogram-based thresholding algorithms," CVGIP: Graphical Models and Image Processing, vol. 55, pp. 532-537, 1993. DOI:10.1006/cgip.1993.1040 Examples -------- >>> from skimage.data import camera >>> image = camera() >>> thresh = threshold_mean(image) >>> binary = image > thresh """ return np.mean(image) def threshold_triangle(image, nbins=256): """Return threshold value based on the triangle algorithm. Parameters ---------- image : (N, M[, ..., P]) ndarray Grayscale input image. nbins : int, optional Number of bins used to calculate histogram. This value is ignored for integer arrays. Returns ------- threshold : float Upper threshold value. All pixels with an intensity higher than this value are assumed to be foreground. References ---------- .. [1] Zack, G. W., Rogers, W. E. and Latt, S. A., 1977, Automatic Measurement of Sister Chromatid Exchange Frequency, Journal of Histochemistry and Cytochemistry 25 (7), pp. 741-753 DOI:10.1177/25.7.70454 .. [2] ImageJ AutoThresholder code, http://fiji.sc/wiki/index.php/Auto_Threshold Examples -------- >>> from skimage.data import camera >>> image = camera() >>> thresh = threshold_triangle(image) >>> binary = image > thresh """ # nbins is ignored for integer arrays # so, we recalculate the effective nbins. hist, bin_centers = histogram(image.ravel(), nbins) nbins = len(hist) # Find peak, lowest and highest gray levels. arg_peak_height = np.argmax(hist) peak_height = hist[arg_peak_height] arg_low_level, arg_high_level = np.where(hist>0)[0][[0, -1]] # Flip is True if left tail is shorter. flip = arg_peak_height - arg_low_level < arg_high_level - arg_peak_height if flip: hist = hist[::-1] arg_low_level = nbins - arg_high_level - 1 arg_peak_height = nbins - arg_peak_height - 1 # If flip == True, arg_high_level becomes incorrect # but we don't need it anymore. del(arg_high_level) # Set up the coordinate system. width = arg_peak_height - arg_low_level x1 = np.arange(width) y1 = hist[x1 + arg_low_level] # Normalize. norm = np.sqrt(peak_height**2 + width**2) peak_height /= norm width /= norm # Maximize the length. # The ImageJ implementation includes an additional constant when calculating # the length, but here we omit it as it does not affect the location of the # minimum. length = peak_height * x1 - width * y1 arg_level = np.argmax(length) + arg_low_level if flip: arg_level = nbins - arg_level - 1 return bin_centers[arg_level] def _mean_std(image, w): """Return local mean and standard deviation of each pixel using a neighborhood defined by a rectangular window with size w times w. The algorithm uses integral images to speedup computation. This is used by threshold_niblack and threshold_sauvola. Parameters ---------- image : ndarray Input image. w : int Odd window size (e.g. 3, 5, 7, ..., 21, ...). Returns ------- m : 2-D array of same size of image with local mean values. s : 2-D array of same size of image with local standard deviation values. References ---------- .. [1] F. Shafait, D. Keysers, and T. M. Breuel, "Efficient implementation of local adaptive thresholding techniques using integral images." in Document Recognition and Retrieval XV, (San Jose, USA), Jan. 2008. DOI:10.1117/12.767755 """ if w == 1 or w % 2 == 0: raise ValueError( "Window size w = %s must be odd and greater than 1." % w) left_pad = w // 2 + 1 right_pad = w // 2 padded = np.pad(image.astype('float'), (left_pad, right_pad), mode='reflect') padded_sq = padded * padded integral = integral_image(padded) integral_sq = integral_image(padded_sq) kern = np.zeros((w + 1,) * image.ndim) for indices in itertools.product(*([[0, -1]] * image.ndim)): kern[indices] = (-1) ** (image.ndim % 2 != np.sum(indices) % 2) sum_full = ndi.correlate(integral, kern, mode='constant') m = util.crop(sum_full, (left_pad, right_pad)) / (w ** image.ndim) sum_sq_full = ndi.correlate(integral_sq, kern, mode='constant') g2 = util.crop(sum_sq_full, (left_pad, right_pad)) / (w ** image.ndim) s = np.sqrt(g2 - m * m) return m, s def threshold_niblack(image, window_size=15, k=0.2): """Applies Niblack local threshold to an array. A threshold T is calculated for every pixel in the image using the following formula: T = m(x,y) - k * s(x,y) where m(x,y) and s(x,y) are the mean and standard deviation of pixel (x,y) neighborhood defined by a rectangular window with size w times w centered around the pixel. k is a configurable parameter that weights the effect of standard deviation. Parameters ---------- image: (N, M) ndarray Grayscale input image. window_size : int, optional Odd size of pixel neighborhood window (e.g. 3, 5, 7...). k : float, optional Value of parameter k in threshold formula. Returns ------- threshold : (N, M) ndarray Threshold mask. All pixels with an intensity higher than this value are assumed to be foreground. Notes ----- This algorithm is originally designed for text recognition. References ---------- .. [1] Niblack, W (1986), An introduction to Digital Image Processing, Prentice-Hall. Examples -------- >>> from skimage import data >>> image = data.page() >>> binary_image = threshold_niblack(image, window_size=7, k=0.1) """ m, s = _mean_std(image, window_size) return m - k * s def threshold_sauvola(image, window_size=15, k=0.2, r=None): """Applies Sauvola local threshold to an array. Sauvola is a modification of Niblack technique. In the original method a threshold T is calculated for every pixel in the image using the following formula: T = m(x,y) * (1 + k * ((s(x,y) / R) - 1)) where m(x,y) and s(x,y) are the mean and standard deviation of pixel (x,y) neighborhood defined by a rectangular window with size w times w centered around the pixel. k is a configurable parameter that weights the effect of standard deviation. R is the maximum standard deviation of a greyscale image. Parameters ---------- image: (N, M) ndarray Grayscale input image. window_size : int, optional Odd size of pixel neighborhood window (e.g. 3, 5, 7...). k : float, optional Value of the positive parameter k. r : float, optional Value of R, the dynamic range of standard deviation. If None, set to the half of the image dtype range. Returns ------- threshold : (N, M) ndarray Threshold mask. All pixels with an intensity higher than this value are assumed to be foreground. Notes ----- This algorithm is originally designed for text recognition. References ---------- .. [1] J. Sauvola and M. Pietikainen, "Adaptive document image binarization," Pattern Recognition 33(2), pp. 225-236, 2000. DOI:10.1016/S0031-3203(99)00055-2 Examples -------- >>> from skimage import data >>> image = data.page() >>> binary_sauvola = threshold_sauvola(image, ... window_size=15, k=0.2) """ if r is None: imin, imax = dtype_limits(image, clip_negative=False) r = 0.5 * (imax - imin) m, s = _mean_std(image, window_size) return m * (1 + k * ((s / r) - 1)) scikit-image-0.13.1/skimage/future/000077500000000000000000000000001316256236300171035ustar00rootroot00000000000000scikit-image-0.13.1/skimage/future/__init__.py000066400000000000000000000006361316256236300212210ustar00rootroot00000000000000"""Functionality with an experimental API. Although you can count on the functions in this package being around in the future, the API may change with any version update **and will not follow the skimage two-version deprecation path**. Therefore, use the functions herein with care, and do not use them in production code that will depend on updated skimage versions. """ from . import graph __all__ = ['graph'] scikit-image-0.13.1/skimage/future/graph/000077500000000000000000000000001316256236300202045ustar00rootroot00000000000000scikit-image-0.13.1/skimage/future/graph/__init__.py000066400000000000000000000006031316256236300223140ustar00rootroot00000000000000from .graph_cut import cut_threshold, cut_normalized from .rag import rag_mean_color, RAG, show_rag, rag_boundary from .graph_merge import merge_hierarchical ncut = cut_normalized __all__ = ['rag_mean_color', 'cut_threshold', 'cut_normalized', 'ncut', 'show_rag', 'merge_hierarchical', 'rag_boundary', 'RAG'] scikit-image-0.13.1/skimage/future/graph/_ncut.py000066400000000000000000000035351316256236300216740ustar00rootroot00000000000000try: import networkx as nx except ImportError: from ..._shared.utils import warn warn('RAGs require networkx') import numpy as np from scipy import sparse from . import _ncut_cy def DW_matrices(graph): """Returns the diagonal and weight matrices of a graph. Parameters ---------- graph : RAG A Region Adjacency Graph. Returns ------- D : csc_matrix The diagonal matrix of the graph. ``D[i, i]`` is the sum of weights of all edges incident on `i`. All other entries are `0`. W : csc_matrix The weight matrix of the graph. ``W[i, j]`` is the weight of the edge joining `i` to `j`. """ # sparse.eighsh is most efficient with CSC-formatted input W = nx.to_scipy_sparse_matrix(graph, format='csc') entries = W.sum(axis=0) D = sparse.dia_matrix((entries, 0), shape=W.shape).tocsc() return D, W def ncut_cost(cut, D, W): """Returns the N-cut cost of a bi-partition of a graph. Parameters ---------- cut : ndarray The mask for the nodes in the graph. Nodes corresponding to a `True` value are in one set. D : csc_matrix The diagonal matrix of the graph. W : csc_matrix The weight matrix of the graph. Returns ------- cost : float The cost of performing the N-cut. References ---------- .. [1] Normalized Cuts and Image Segmentation, Jianbo Shi and Jitendra Malik, IEEE Transactions on Pattern Analysis and Machine Intelligence, Page 889, Equation 2. """ cut = np.array(cut) cut_cost = _ncut_cy.cut_cost(cut, W) # D has elements only along the diagonal, one per node, so we can directly # index the data attribute with cut. assoc_a = D.data[cut].sum() assoc_b = D.data[~cut].sum() return (cut_cost / assoc_a) + (cut_cost / assoc_b) scikit-image-0.13.1/skimage/future/graph/_ncut_cy.pyx000066400000000000000000000036041316256236300225540ustar00rootroot00000000000000# cython: cdivision=True # cython: boundscheck=False # cython: nonecheck=False # cython: wraparound=False cimport numpy as cnp import numpy as np def argmin2(cnp.double_t[:] array): """Return the index of the 2nd smallest value in an array. Parameters ---------- array : array The array to process. Returns ------- min_idx2 : int The index of the second smallest value. """ cdef cnp.float64_t min1 = np.inf cdef cnp.float64_t min2 = np.inf cdef Py_ssize_t min_idx1 = 0 cdef Py_ssize_t min_idx2 = 0 cdef Py_ssize_t i = 0 cdef Py_ssize_t n = array.shape[0] for i in range(n): x = array[i] if x < min1: min2 = min1 min_idx2 = min_idx1 min1 = x min_idx1 = i elif x > min1 and x < min2: min2 = x min_idx2 = i i += 1 return min_idx2 def cut_cost(cut, W): """Return the total weight of crossing edges in a bi-partition. Parameters ---------- cut : array A array of booleans. Elements set to `True` belong to one set. W : array The weight matrix of the graph. Returns ------- cost : float The total weight of crossing edges. """ cdef cnp.ndarray[cnp.uint8_t, cast = True] cut_mask = np.array(cut) cdef Py_ssize_t num_rows, num_cols cdef cnp.int32_t row, col cdef cnp.int32_t[:] indices = W.indices cdef cnp.int32_t[:] indptr = W.indptr cdef cnp.double_t[:] data = W.data.astype(np.double) cdef cnp.int32_t row_index cdef cnp.double_t cost = 0 num_rows = W.shape[0] num_cols = W.shape[1] for col in range(num_cols): for row_index in range(indptr[col], indptr[col + 1]): row = indices[row_index] if cut_mask[row] != cut_mask[col]: cost += data[row_index] return cost * 0.5 scikit-image-0.13.1/skimage/future/graph/graph_cut.py000066400000000000000000000221241316256236300225330ustar00rootroot00000000000000try: import networkx as nx except ImportError: from ..._shared.utils import warn warn('RAGs require networkx') import numpy as np from . import _ncut from . import _ncut_cy from scipy.sparse import linalg def cut_threshold(labels, rag, thresh, in_place=True): """Combine regions separated by weight less than threshold. Given an image's labels and its RAG, output new labels by combining regions whose nodes are separated by a weight less than the given threshold. Parameters ---------- labels : ndarray The array of labels. rag : RAG The region adjacency graph. thresh : float The threshold. Regions connected by edges with smaller weights are combined. in_place : bool If set, modifies `rag` in place. The function will remove the edges with weights less that `thresh`. If set to `False` the function makes a copy of `rag` before proceeding. Returns ------- out : ndarray The new labelled array. Examples -------- >>> from skimage import data, segmentation >>> from skimage.future import graph >>> img = data.astronaut() >>> labels = segmentation.slic(img) >>> rag = graph.rag_mean_color(img, labels) >>> new_labels = graph.cut_threshold(labels, rag, 10) References ---------- .. [1] Alain Tremeau and Philippe Colantoni "Regions Adjacency Graph Applied To Color Image Segmentation" http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.11.5274 """ if not in_place: rag = rag.copy() # Because deleting edges while iterating through them produces an error. to_remove = [(x, y) for x, y, d in rag.edges(data=True) if d['weight'] >= thresh] rag.remove_edges_from(to_remove) comps = nx.connected_components(rag) # We construct an array which can map old labels to the new ones. # All the labels within a connected component are assigned to a single # label in the output. map_array = np.arange(labels.max() + 1, dtype=labels.dtype) for i, nodes in enumerate(comps): for node in nodes: for label in rag.node[node]['labels']: map_array[label] = i return map_array[labels] def cut_normalized(labels, rag, thresh=0.001, num_cuts=10, in_place=True, max_edge=1.0): """Perform Normalized Graph cut on the Region Adjacency Graph. Given an image's labels and its similarity RAG, recursively perform a 2-way normalized cut on it. All nodes belonging to a subgraph that cannot be cut further are assigned a unique label in the output. Parameters ---------- labels : ndarray The array of labels. rag : RAG The region adjacency graph. thresh : float The threshold. A subgraph won't be further subdivided if the value of the N-cut exceeds `thresh`. num_cuts : int The number or N-cuts to perform before determining the optimal one. in_place : bool If set, modifies `rag` in place. For each node `n` the function will set a new attribute ``rag.node[n]['ncut label']``. max_edge : float, optional The maximum possible value of an edge in the RAG. This corresponds to an edge between identical regions. This is used to put self edges in the RAG. Returns ------- out : ndarray The new labeled array. Examples -------- >>> from skimage import data, segmentation >>> from skimage.future import graph >>> img = data.astronaut() >>> labels = segmentation.slic(img, compactness=30, n_segments=400) >>> rag = graph.rag_mean_color(img, labels, mode='similarity') >>> new_labels = graph.cut_normalized(labels, rag) References ---------- .. [1] Shi, J.; Malik, J., "Normalized cuts and image segmentation", Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 22, no. 8, pp. 888-905, August 2000. """ if not in_place: rag = rag.copy() for node in rag.nodes(): rag.add_edge(node, node, weight=max_edge) _ncut_relabel(rag, thresh, num_cuts) map_array = np.zeros(labels.max() + 1, dtype=labels.dtype) # Mapping from old labels to new for n, d in rag.nodes(data=True): map_array[d['labels']] = d['ncut label'] return map_array[labels] def partition_by_cut(cut, rag): """Compute resulting subgraphs from given bi-parition. Parameters ---------- cut : array A array of booleans. Elements set to `True` belong to one set. rag : RAG The Region Adjacency Graph. Returns ------- sub1, sub2 : RAG The two resulting subgraphs from the bi-partition. """ # `cut` is derived from `D` and `W` matrices, which also follow the # ordering returned by `rag.nodes()` because we use # nx.to_scipy_sparse_matrix. # Example # rag.nodes() = [3, 7, 9, 13] # cut = [True, False, True, False] # nodes1 = [3, 9] # nodes2 = [7, 10] nodes1 = [n for i, n in enumerate(rag.nodes()) if cut[i]] nodes2 = [n for i, n in enumerate(rag.nodes()) if not cut[i]] sub1 = rag.subgraph(nodes1) sub2 = rag.subgraph(nodes2) return sub1, sub2 def get_min_ncut(ev, d, w, num_cuts): """Threshold an eigenvector evenly, to determine minimum ncut. Parameters ---------- ev : array The eigenvector to threshold. d : ndarray The diagonal matrix of the graph. w : ndarray The weight matrix of the graph. num_cuts : int The number of evenly spaced thresholds to check for. Returns ------- mask : array The array of booleans which denotes the bi-partition. mcut : float The value of the minimum ncut. """ mcut = np.inf mn = ev.min() mx = ev.max() # If all values in `ev` are equal, it implies that the graph can't be # further sub-divided. In this case the bi-partition is the the graph # itself and an empty set. min_mask = np.zeros_like(ev, dtype=np.bool) if np.allclose(mn, mx): return min_mask, mcut # Refer Shi & Malik 2001, Section 3.1.3, Page 892 # Perform evenly spaced n-cuts and determine the optimal one. for t in np.linspace(mn, mx, num_cuts, endpoint=False): mask = ev > t cost = _ncut.ncut_cost(mask, d, w) if cost < mcut: min_mask = mask mcut = cost return min_mask, mcut def _label_all(rag, attr_name): """Assign a unique integer to the given attribute in the RAG. This function assumes that all labels in `rag` are unique. It picks up a random label from them and assigns it to the `attr_name` attribute of all the nodes. rag : RAG The Region Adjacency Graph. attr_name : string The attribute to which a unique integer is assigned. """ node = min(rag.nodes()) new_label = rag.node[node]['labels'][0] for n, d in rag.nodes(data=True): d[attr_name] = new_label def _ncut_relabel(rag, thresh, num_cuts): """Perform Normalized Graph cut on the Region Adjacency Graph. Recursively partition the graph into 2, until further subdivision yields a cut greater than `thresh` or such a cut cannot be computed. For such a subgraph, indices to labels of all its nodes map to a single unique value. Parameters ---------- labels : ndarray The array of labels. rag : RAG The region adjacency graph. thresh : float The threshold. A subgraph won't be further subdivided if the value of the N-cut exceeds `thresh`. num_cuts : int The number or N-cuts to perform before determining the optimal one. map_array : array The array which maps old labels to new ones. This is modified inside the function. """ d, w = _ncut.DW_matrices(rag) m = w.shape[0] if m > 2: d2 = d.copy() # Since d is diagonal, we can directly operate on its data # the inverse of the square root d2.data = np.reciprocal(np.sqrt(d2.data, out=d2.data), out=d2.data) # Refer Shi & Malik 2001, Equation 7, Page 891 vals, vectors = linalg.eigsh(d2 * (d - w) * d2, which='SM', k=min(100, m - 2)) # Pick second smallest eigenvector. # Refer Shi & Malik 2001, Section 3.2.3, Page 893 vals, vectors = np.real(vals), np.real(vectors) index2 = _ncut_cy.argmin2(vals) ev = vectors[:, index2] cut_mask, mcut = get_min_ncut(ev, d, w, num_cuts) if (mcut < thresh): # Sub divide and perform N-cut again # Refer Shi & Malik 2001, Section 3.2.5, Page 893 sub1, sub2 = partition_by_cut(cut_mask, rag) _ncut_relabel(sub1, thresh, num_cuts) _ncut_relabel(sub2, thresh, num_cuts) return # The N-cut wasn't small enough, or could not be computed. # The remaining graph is a region. # Assign `ncut label` by picking any label from the existing nodes, since # `labels` are unique, `new_label` is also unique. _label_all(rag, 'ncut label') scikit-image-0.13.1/skimage/future/graph/graph_merge.py000066400000000000000000000103421316256236300230360ustar00rootroot00000000000000import numpy as np import heapq def _revalidate_node_edges(rag, node, heap_list): """Handles validation and invalidation of edges incident to a node. This function invalidates all existing edges incident on `node` and inserts new items in `heap_list` updated with the valid weights. rag : RAG The Region Adjacency Graph. node : int The id of the node whose incident edges are to be validated/invalidated . heap_list : list The list containing the existing heap of edges. """ # networkx updates data dictionary if edge exists # this would mean we have to reposition these edges in # heap if their weight is updated. # instead we invalidate them for nbr in rag.neighbors(node): data = rag[node][nbr] try: # invalidate edges incident on `dst`, they have new weights data['heap item'][3] = False _invalidate_edge(rag, node, nbr) except KeyError: # will handle the case where the edge did not exist in the existing # graph pass wt = data['weight'] heap_item = [wt, node, nbr, True] data['heap item'] = heap_item heapq.heappush(heap_list, heap_item) def _rename_node(graph, node_id, copy_id): """ Rename `node_id` in `graph` to `copy_id`. """ graph._add_node_silent(copy_id) graph.node[copy_id].update(graph.node[node_id]) for nbr in graph.neighbors(node_id): wt = graph[node_id][nbr]['weight'] graph.add_edge(nbr, copy_id, {'weight': wt}) graph.remove_node(node_id) def _invalidate_edge(graph, n1, n2): """ Invalidates the edge (n1, n2) in the heap. """ graph[n1][n2]['heap item'][3] = False def merge_hierarchical(labels, rag, thresh, rag_copy, in_place_merge, merge_func, weight_func): """Perform hierarchical merging of a RAG. Greedily merges the most similar pair of nodes until no edges lower than `thresh` remain. Parameters ---------- labels : ndarray The array of labels. rag : RAG The Region Adjacency Graph. thresh : float Regions connected by an edge with weight smaller than `thresh` are merged. rag_copy : bool If set, the RAG copied before modifying. in_place_merge : bool If set, the nodes are merged in place. Otherwise, a new node is created for each merge.. merge_func : callable This function is called before merging two nodes. For the RAG `graph` while merging `src` and `dst`, it is called as follows ``merge_func(graph, src, dst)``. weight_func : callable The function to compute the new weights of the nodes adjacent to the merged node. This is directly supplied as the argument `weight_func` to `merge_nodes`. Returns ------- out : ndarray The new labeled array. """ if rag_copy: rag = rag.copy() edge_heap = [] for n1, n2, data in rag.edges(data=True): # Push a valid edge in the heap wt = data['weight'] heap_item = [wt, n1, n2, True] heapq.heappush(edge_heap, heap_item) # Reference to the heap item in the graph data['heap item'] = heap_item while len(edge_heap) > 0 and edge_heap[0][0] < thresh: _, n1, n2, valid = heapq.heappop(edge_heap) # Ensure popped edge is valid, if not, the edge is discarded if valid: # Invalidate all neigbors of `src` before its deleted for nbr in rag.neighbors(n1): _invalidate_edge(rag, n1, nbr) for nbr in rag.neighbors(n2): _invalidate_edge(rag, n2, nbr) if not in_place_merge: next_id = rag.next_id() _rename_node(rag, n2, next_id) src, dst = n1, next_id else: src, dst = n1, n2 merge_func(rag, src, dst) new_id = rag.merge_nodes(src, dst, weight_func) _revalidate_node_edges(rag, new_id, edge_heap) label_map = np.arange(labels.max() + 1) for ix, (n, d) in enumerate(rag.nodes(data=True)): for label in d['labels']: label_map[label] = ix return label_map[labels] scikit-image-0.13.1/skimage/future/graph/rag.py000066400000000000000000000505451316256236300213400ustar00rootroot00000000000000import networkx as nx import numpy as np from numpy.lib.stride_tricks import as_strided from scipy import ndimage as ndi from scipy import sparse import math from ... import measure, segmentation, util, color from matplotlib import colors, cm from matplotlib import pyplot as plt from matplotlib.collections import LineCollection def _edge_generator_from_csr(csr_matrix): """Yield weighted edge triples for use by NetworkX from a CSR matrix. This function is a straight rewrite of `networkx.convert_matrix._csr_gen_triples`. Since that is a private function, it is safer to include our own here. Parameters ---------- csr_matrix : scipy.sparse.csr_matrix The input matrix. An edge (i, j, w) will be yielded if there is a data value for coordinates (i, j) in the matrix, even if that value is 0. Yields ------ i, j, w : (int, int, float) tuples Each value `w` in the matrix along with its coordinates (i, j). Examples -------- >>> dense = np.eye(2, dtype=np.float) >>> csr = sparse.csr_matrix(dense) >>> edges = _edge_generator_from_csr(csr) >>> list(edges) [(0, 0, 1.0), (1, 1, 1.0)] """ nrows = csr_matrix.shape[0] values = csr_matrix.data indptr = csr_matrix.indptr col_indices = csr_matrix.indices for i in range(nrows): for j in range(indptr[i], indptr[i + 1]): yield i, col_indices[j], values[j] def min_weight(graph, src, dst, n): """Callback to handle merging nodes by choosing minimum weight. Returns a dictionary with `"weight"` set as either the weight between (`src`, `n`) or (`dst`, `n`) in `graph` or the minimum of the two when both exist. Parameters ---------- graph : RAG The graph under consideration. src, dst : int The verices in `graph` to be merged. n : int A neighbor of `src` or `dst` or both. Returns ------- data : dict A dict with the `"weight"` attribute set the weight between (`src`, `n`) or (`dst`, `n`) in `graph` or the minimum of the two when both exist. """ # cover the cases where n only has edge to either `src` or `dst` default = {'weight': np.inf} w1 = graph[n].get(src, default)['weight'] w2 = graph[n].get(dst, default)['weight'] return {'weight': min(w1, w2)} def _add_edge_filter(values, graph): """Create edge in `graph` between central element of `values` and the rest. Add an edge between the middle element in `values` and all other elements of `values` into `graph`. ``values[len(values) // 2]`` is expected to be the central value of the footprint used. Parameters ---------- values : array The array to process. graph : RAG The graph to add edges in. Returns ------- 0 : float Always returns 0. The return value is required so that `generic_filter` can put it in the output array, but it is ignored by this filter. """ values = values.astype(int) center = values[len(values) // 2] for value in values: if value != center and not graph.has_edge(center, value): graph.add_edge(center, value) return 0. class RAG(nx.Graph): """ The Region Adjacency Graph (RAG) of an image, subclasses `networx.Graph `_ Parameters ---------- label_image : array of int An initial segmentation, with each region labeled as a different integer. Every unique value in ``label_image`` will correspond to a node in the graph. connectivity : int in {1, ..., ``label_image.ndim``}, optional The connectivity between pixels in ``label_image``. For a 2D image, a connectivity of 1 corresponds to immediate neighbors up, down, left, and right, while a connectivity of 2 also includes diagonal neighbors. See `scipy.ndimage.generate_binary_structure`. data : networkx Graph specification, optional Initial or additional edges to pass to the NetworkX Graph constructor. See `networkx.Graph`. Valid edge specifications include edge list (list of tuples), NumPy arrays, and SciPy sparse matrices. **attr : keyword arguments, optional Additional attributes to add to the graph. """ def __init__(self, label_image=None, connectivity=1, data=None, **attr): super(RAG, self).__init__(data, **attr) if self.number_of_nodes() == 0: self.max_id = 0 else: self.max_id = max(self.nodes()) if label_image is not None: fp = ndi.generate_binary_structure(label_image.ndim, connectivity) # In the next ``ndi.generic_filter`` function, the kwarg # ``output`` is used to provide a strided array with a single # 64-bit floating point number, to which the function repeatedly # writes. This is done because even if we don't care about the # output, without this, a float array of the same shape as the # input image will be created and that could be expensive in # memory consumption. ndi.generic_filter( label_image, function=_add_edge_filter, footprint=fp, mode='nearest', output=as_strided(np.empty((1,), dtype=np.float_), shape=label_image.shape, strides=((0,) * label_image.ndim)), extra_arguments=(self,)) def merge_nodes(self, src, dst, weight_func=min_weight, in_place=True, extra_arguments=[], extra_keywords={}): """Merge node `src` and `dst`. The new combined node is adjacent to all the neighbors of `src` and `dst`. `weight_func` is called to decide the weight of edges incident on the new node. Parameters ---------- src, dst : int Nodes to be merged. weight_func : callable, optional Function to decide the attributes of edges incident on the new node. For each neighbor `n` for `src and `dst`, `weight_func` will be called as follows: `weight_func(src, dst, n, *extra_arguments, **extra_keywords)`. `src`, `dst` and `n` are IDs of vertices in the RAG object which is in turn a subclass of `networkx.Graph`. It is expected to return a dict of attributes of the resulting edge. in_place : bool, optional If set to `True`, the merged node has the id `dst`, else merged node has a new id which is returned. extra_arguments : sequence, optional The sequence of extra positional arguments passed to `weight_func`. extra_keywords : dictionary, optional The dict of keyword arguments passed to the `weight_func`. Returns ------- id : int The id of the new node. Notes ----- If `in_place` is `False` the resulting node has a new id, rather than `dst`. """ src_nbrs = set(self.neighbors(src)) dst_nbrs = set(self.neighbors(dst)) neighbors = (src_nbrs | dst_nbrs) - set([src, dst]) if in_place: new = dst else: new = self.next_id() self.add_node(new) for neighbor in neighbors: data = weight_func(self, src, new, neighbor, *extra_arguments, **extra_keywords) self.add_edge(neighbor, new, attr_dict=data) self.node[new]['labels'] = (self.node[src]['labels'] + self.node[dst]['labels']) self.remove_node(src) if not in_place: self.remove_node(dst) return new def add_node(self, n, attr_dict=None, **attr): """Add node `n` while updating the maximum node id. .. seealso:: :func:`networkx.Graph.add_node`.""" if attr_dict is None: # compatibility with old networkx attr_dict = attr else: attr_dict.update(attr) super(RAG, self).add_node(n, **attr_dict) self.max_id = max(n, self.max_id) def add_edge(self, u, v, attr_dict=None, **attr): """Add an edge between `u` and `v` while updating max node id. .. seealso:: :func:`networkx.Graph.add_edge`.""" if attr_dict is None: # compatibility with old networkx attr_dict = attr else: attr_dict.update(attr) super(RAG, self).add_edge(u, v, **attr_dict) self.max_id = max(u, v, self.max_id) def copy(self): """Copy the graph with its max node id. .. seealso:: :func:`networkx.Graph.copy`.""" g = super(RAG, self).copy() g.max_id = self.max_id return g def fresh_copy(self): """Return a fresh copy graph with the same data structure. A fresh copy has no nodes, edges or graph attributes. It is the same data structure as the current graph. This method is typically used to create an empty version of the graph. This is required when subclassing Graph with networkx v2 and does not cause problems for v1. Here is more detail from the network migrating from 1.x to 2.x document:: With the new GraphViews (SubGraph, ReversedGraph, etc) you can't assume that ``G.__class__()`` will create a new instance of the same graph type as ``G``. In fact, the call signature for ``__class__`` differs depending on whether ``G`` is a view or a base class. For v2.x you should use ``G.fresh_copy()`` to create a null graph of the correct type---ready to fill with nodes and edges. """ return RAG() def next_id(self): """Returns the `id` for the new node to be inserted. The current implementation returns one more than the maximum `id`. Returns ------- id : int The `id` of the new node to be inserted. """ return self.max_id + 1 def _add_node_silent(self, n): """Add node `n` without updating the maximum node id. This is a convenience method used internally. .. seealso:: :func:`networkx.Graph.add_node`.""" super(RAG, self).add_node(n) def rag_mean_color(image, labels, connectivity=2, mode='distance', sigma=255.0): """Compute the Region Adjacency Graph using mean colors. Given an image and its initial segmentation, this method constructs the corresponding Region Adjacency Graph (RAG). Each node in the RAG represents a set of pixels within `image` with the same label in `labels`. The weight between two adjacent regions represents how similar or dissimilar two regions are depending on the `mode` parameter. Parameters ---------- image : ndarray, shape(M, N, [..., P,] 3) Input image. labels : ndarray, shape(M, N, [..., P]) The labelled image. This should have one dimension less than `image`. If `image` has dimensions `(M, N, 3)` `labels` should have dimensions `(M, N)`. connectivity : int, optional Pixels with a squared distance less than `connectivity` from each other are considered adjacent. It can range from 1 to `labels.ndim`. Its behavior is the same as `connectivity` parameter in ``scipy.ndimage.generate_binary_structure``. mode : {'distance', 'similarity'}, optional The strategy to assign edge weights. 'distance' : The weight between two adjacent regions is the :math:`|c_1 - c_2|`, where :math:`c_1` and :math:`c_2` are the mean colors of the two regions. It represents the Euclidean distance in their average color. 'similarity' : The weight between two adjacent is :math:`e^{-d^2/sigma}` where :math:`d=|c_1 - c_2|`, where :math:`c_1` and :math:`c_2` are the mean colors of the two regions. It represents how similar two regions are. sigma : float, optional Used for computation when `mode` is "similarity". It governs how close to each other two colors should be, for their corresponding edge weight to be significant. A very large value of `sigma` could make any two colors behave as though they were similar. Returns ------- out : RAG The region adjacency graph. Examples -------- >>> from skimage import data, segmentation >>> from skimage.future import graph >>> img = data.astronaut() >>> labels = segmentation.slic(img) >>> rag = graph.rag_mean_color(img, labels) References ---------- .. [1] Alain Tremeau and Philippe Colantoni "Regions Adjacency Graph Applied To Color Image Segmentation" http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.11.5274 """ graph = RAG(labels, connectivity=connectivity) for n in graph: graph.node[n].update({'labels': [n], 'pixel count': 0, 'total color': np.array([0, 0, 0], dtype=np.double)}) for index in np.ndindex(labels.shape): current = labels[index] graph.node[current]['pixel count'] += 1 graph.node[current]['total color'] += image[index] for n in graph: graph.node[n]['mean color'] = (graph.node[n]['total color'] / graph.node[n]['pixel count']) for x, y, d in graph.edges(data=True): diff = graph.node[x]['mean color'] - graph.node[y]['mean color'] diff = np.linalg.norm(diff) if mode == 'similarity': d['weight'] = math.e ** (-(diff ** 2) / sigma) elif mode == 'distance': d['weight'] = diff else: raise ValueError("The mode '%s' is not recognised" % mode) return graph def rag_boundary(labels, edge_map, connectivity=2): """ Comouter RAG based on region boundaries Given an image's initial segmentation and its edge map this method constructs the corresponding Region Adjacency Graph (RAG). Each node in the RAG represents a set of pixels within the image with the same label in `labels`. The weight between two adjacent regions is the average value in `edge_map` along their boundary. labels : ndarray The labelled image. edge_map : ndarray This should have the same shape as that of `labels`. For all pixels along the boundary between 2 adjacent regions, the average value of the corresponding pixels in `edge_map` is the edge weight between them. connectivity : int, optional Pixels with a squared distance less than `connectivity` from each other are considered adjacent. It can range from 1 to `labels.ndim`. Its behavior is the same as `connectivity` parameter in `scipy.ndimage.filters.generate_binary_structure`. Examples -------- >>> from skimage import data, segmentation, filters, color >>> from skimage.future import graph >>> img = data.chelsea() >>> labels = segmentation.slic(img) >>> edge_map = filters.sobel(color.rgb2gray(img)) >>> rag = graph.rag_boundary(labels, edge_map) """ conn = ndi.generate_binary_structure(labels.ndim, connectivity) eroded = ndi.grey_erosion(labels, footprint=conn) dilated = ndi.grey_dilation(labels, footprint=conn) boundaries0 = (eroded != labels) boundaries1 = (dilated != labels) labels_small = np.concatenate((eroded[boundaries0], labels[boundaries1])) labels_large = np.concatenate((labels[boundaries0], dilated[boundaries1])) n = np.max(labels_large) + 1 # use a dummy broadcast array as data for RAG ones = as_strided(np.ones((1,), dtype=np.float), shape=labels_small.shape, strides=(0,)) count_matrix = sparse.coo_matrix((ones, (labels_small, labels_large)), dtype=np.int_, shape=(n, n)).tocsr() data = np.concatenate((edge_map[boundaries0], edge_map[boundaries1])) data_coo = sparse.coo_matrix((data, (labels_small, labels_large))) graph_matrix = data_coo.tocsr() graph_matrix.data /= count_matrix.data rag = RAG() rag.add_weighted_edges_from(_edge_generator_from_csr(graph_matrix), weight='weight') rag.add_weighted_edges_from(_edge_generator_from_csr(count_matrix), weight='count') for n in rag.nodes(): rag.node[n].update({'labels': [n]}) return rag def show_rag(labels, rag, img, border_color='black', edge_width=1.5, edge_cmap='magma', img_cmap='bone', in_place=True, ax=None): """Show a Region Adjacency Graph on an image. Given a labelled image and its corresponding RAG, show the nodes and edges of the RAG on the image with the specified colors. Edges are displayed between the centroid of the 2 adjacent regions in the image. Parameters ---------- labels : ndarray, shape (M, N) The labelled image. rag : RAG The Region Adjacency Graph. img : ndarray, shape (M, N[, 3]) Input image. If `colormap` is `None`, the image should be in RGB format. border_color : color spec, optional Color with which the borders between regions are drawn. edge_width : float, optional The thickness with which the RAG edges are drawn. edge_cmap : :py:class:`matplotlib.colors.Colormap`, optional Any matplotlib colormap with which the edges are drawn. img_cmap : :py:class:`matplotlib.colors.Colormap`, optional Any matplotlib colormap with which the image is draw. If set to `None` the image is drawn as it is. in_place : bool, optional If set, the RAG is modified in place. For each node `n` the function will set a new attribute ``rag.node[n]['centroid']``. ax : :py:class:`matplotlib.axes.Axes`, optional The axes to draw on. If not specified, new axes are created and drawn on. Returns ------- lc : :py:class:`matplotlib.collections.LineCollection` A colection of lines that represent the edges of the graph. It can be passed to the :meth:`matplotlib.figure.Figure.colorbar` function. Examples -------- >>> from skimage import data, segmentation >>> from skimage.future import graph >>> img = data.coffee() >>> labels = segmentation.slic(img) >>> g = graph.rag_mean_color(img, labels) >>> lc = graph.show_rag(labels, g, img) >>> cbar = plt.colorbar(lc) """ if not in_place: rag = rag.copy() if ax is None: fig, ax = plt.subplots() out = util.img_as_float(img, force_copy=True) if img_cmap is None: if img.ndim < 3 or img.shape[2] not in [3, 4]: msg = 'If colormap is `None`, an RGB or RGBA image should be given' raise ValueError(msg) # Ignore the alpha channel out = img[:, :, :3] else: img_cmap = cm.get_cmap(img_cmap) out = color.rgb2gray(img) # Ignore the alpha channel out = img_cmap(out)[:, :, :3] edge_cmap = cm.get_cmap(edge_cmap) # Handling the case where one node has multiple labels # offset is 1 so that regionprops does not ignore 0 offset = 1 map_array = np.arange(labels.max() + 1) for n, d in rag.nodes(data=True): for label in d['labels']: map_array[label] = offset offset += 1 rag_labels = map_array[labels] regions = measure.regionprops(rag_labels) for (n, data), region in zip(rag.nodes(data=True), regions): data['centroid'] = tuple(map(int, region['centroid'])) cc = colors.ColorConverter() if border_color is not None: border_color = cc.to_rgb(border_color) out = segmentation.mark_boundaries(out, rag_labels, color=border_color) ax.imshow(out) # Defining the end points of the edges # The tuple[::-1] syntax reverses a tuple as matplotlib uses (x,y) # convention while skimage uses (row, column) lines = [[rag.node[n1]['centroid'][::-1], rag.node[n2]['centroid'][::-1]] for (n1, n2) in rag.edges()] lc = LineCollection(lines, linewidths=edge_width, cmap=edge_cmap) edge_weights = [d['weight'] for x, y, d in rag.edges(data=True)] lc.set_array(np.array(edge_weights)) ax.add_collection(lc) return lc scikit-image-0.13.1/skimage/future/graph/setup.py000066400000000000000000000020771316256236300217240ustar00rootroot00000000000000#!/usr/bin/env python from skimage._build import cython import os.path base_path = os.path.abspath(os.path.dirname(__file__)) def configuration(parent_package='', top_path=None): from numpy.distutils.misc_util import Configuration, get_numpy_include_dirs config = Configuration('graph', parent_package, top_path) config.add_data_dir('tests') # This function tries to create C files from the given .pyx files. If # it fails, try to build with pre-generated .c files. cython(['_ncut_cy.pyx'], working_path=base_path) config.add_extension('_ncut_cy', sources=['_ncut_cy.c'], include_dirs=[get_numpy_include_dirs()]) return config if __name__ == '__main__': from numpy.distutils.core import setup setup(maintainer='scikit-image Developers', maintainer_email='scikit-image@python.org', description='Graph-based Image-processing Algorithms', url='https://github.com/scikit-image/scikit-image', license='Modified BSD', **(configuration(top_path='').todict()) ) scikit-image-0.13.1/skimage/future/graph/tests/000077500000000000000000000000001316256236300213465ustar00rootroot00000000000000scikit-image-0.13.1/skimage/future/graph/tests/test_rag.py000066400000000000000000000151321316256236300235320ustar00rootroot00000000000000import numpy as np from skimage.future import graph from skimage._shared.version_requirements import is_installed from numpy.testing.decorators import skipif from skimage import segmentation from numpy import testing def max_edge(g, src, dst, n): default = {'weight': -np.inf} w1 = g[n].get(src, default)['weight'] w2 = g[n].get(dst, default)['weight'] return {'weight': max(w1, w2)} @skipif(not is_installed('networkx')) def test_rag_merge(): g = graph.rag.RAG() for i in range(5): g.add_node(i, {'labels': [i]}) g.add_edge(0, 1, {'weight': 10}) g.add_edge(1, 2, {'weight': 20}) g.add_edge(2, 3, {'weight': 30}) g.add_edge(3, 0, {'weight': 40}) g.add_edge(0, 2, {'weight': 50}) g.add_edge(3, 4, {'weight': 60}) gc = g.copy() # We merge nodes and ensure that the minimum weight is chosen # when there is a conflict. g.merge_nodes(0, 2) assert g.adj[1][2]['weight'] == 10 assert g.adj[2][3]['weight'] == 30 # We specify `max_edge` as `weight_func` as ensure that maximum # weight is chosen in case on conflict gc.merge_nodes(0, 2, weight_func=max_edge) assert gc.adj[1][2]['weight'] == 20 assert gc.adj[2][3]['weight'] == 40 g.merge_nodes(1, 4) g.merge_nodes(2, 3) n = g.merge_nodes(3, 4, in_place=False) assert sorted(g.node[n]['labels']) == list(range(5)) assert list(g.edges()) == [] @skipif(not is_installed('networkx')) def test_threshold_cut(): img = np.zeros((100, 100, 3), dtype='uint8') img[:50, :50] = 255, 255, 255 img[:50, 50:] = 254, 254, 254 img[50:, :50] = 2, 2, 2 img[50:, 50:] = 1, 1, 1 labels = np.zeros((100, 100), dtype='uint8') labels[:50, :50] = 0 labels[:50, 50:] = 1 labels[50:, :50] = 2 labels[50:, 50:] = 3 rag = graph.rag_mean_color(img, labels) new_labels = graph.cut_threshold(labels, rag, 10, in_place=False) # Two labels assert new_labels.max() == 1 new_labels = graph.cut_threshold(labels, rag, 10) # Two labels assert new_labels.max() == 1 @skipif(not is_installed('networkx')) def test_cut_normalized(): img = np.zeros((100, 100, 3), dtype='uint8') img[:50, :50] = 255, 255, 255 img[:50, 50:] = 254, 254, 254 img[50:, :50] = 2, 2, 2 img[50:, 50:] = 1, 1, 1 labels = np.zeros((100, 100), dtype='uint8') labels[:50, :50] = 0 labels[:50, 50:] = 1 labels[50:, :50] = 2 labels[50:, 50:] = 3 rag = graph.rag_mean_color(img, labels, mode='similarity') new_labels = graph.cut_normalized(labels, rag, in_place=False) new_labels, _, _ = segmentation.relabel_sequential(new_labels) # Two labels assert new_labels.max() == 1 new_labels = graph.cut_normalized(labels, rag) new_labels, _, _ = segmentation.relabel_sequential(new_labels) assert new_labels.max() == 1 @skipif(not is_installed('networkx')) def test_rag_error(): img = np.zeros((10, 10, 3), dtype='uint8') labels = np.zeros((10, 10), dtype='uint8') labels[:5, :] = 0 labels[5:, :] = 1 testing.assert_raises(ValueError, graph.rag_mean_color, img, labels, 2, 'non existant mode') def _weight_mean_color(graph, src, dst, n): diff = graph.node[dst]['mean color'] - graph.node[n]['mean color'] diff = np.linalg.norm(diff) return {'weight': diff} def _pre_merge_mean_color(graph, src, dst): graph.node[dst]['total color'] += graph.node[src]['total color'] graph.node[dst]['pixel count'] += graph.node[src]['pixel count'] graph.node[dst]['mean color'] = (graph.node[dst]['total color'] / graph.node[dst]['pixel count']) def merge_hierarchical_mean_color(labels, rag, thresh, rag_copy=True, in_place_merge=False): return graph.merge_hierarchical(labels, rag, thresh, rag_copy, in_place_merge, _pre_merge_mean_color, _weight_mean_color) @skipif(not is_installed('networkx')) def test_rag_hierarchical(): img = np.zeros((8, 8, 3), dtype='uint8') labels = np.zeros((8, 8), dtype='uint8') img[:, :, :] = 31 labels[:, :] = 1 img[0:4, 0:4, :] = 10, 10, 10 labels[0:4, 0:4] = 2 img[4:, 0:4, :] = 20, 20, 20 labels[4:, 0:4] = 3 g = graph.rag_mean_color(img, labels) g2 = g.copy() thresh = 20 # more than 11*sqrt(3) but less than result = merge_hierarchical_mean_color(labels, g, thresh) assert(np.all(result[:, :4] == result[0, 0])) assert(np.all(result[:, 4:] == result[-1, -1])) result = merge_hierarchical_mean_color(labels, g2, thresh, in_place_merge=True) assert(np.all(result[:, :4] == result[0, 0])) assert(np.all(result[:, 4:] == result[-1, -1])) result = graph.cut_threshold(labels, g, thresh) assert np.all(result == result[0, 0]) @skipif(not is_installed('networkx')) def test_ncut_stable_subgraph(): """ Test to catch an error thrown when subgraph has all equal edges. """ img = np.zeros((100, 100, 3), dtype='uint8') labels = np.zeros((100, 100), dtype='uint8') labels[:50, :50] = 1 labels[:50, 50:] = 2 rag = graph.rag_mean_color(img, labels, mode='similarity') new_labels = graph.cut_normalized(labels, rag, in_place=False) new_labels, _, _ = segmentation.relabel_sequential(new_labels) assert new_labels.max() == 0 def test_generic_rag_2d(): labels = np.array([[1, 2], [3, 4]], dtype=np.uint8) g = graph.RAG(labels) assert g.has_edge(1, 2) and g.has_edge(2, 4) and not g.has_edge(1, 4) h = graph.RAG(labels, connectivity=2) assert h.has_edge(1, 2) and h.has_edge(1, 4) and h.has_edge(2, 3) def test_generic_rag_3d(): labels = np.arange(8, dtype=np.uint8).reshape((2, 2, 2)) g = graph.RAG(labels) assert g.has_edge(0, 1) and g.has_edge(1, 3) and not g.has_edge(0, 3) h = graph.RAG(labels, connectivity=2) assert h.has_edge(0, 1) and h.has_edge(0, 3) and not h.has_edge(0, 7) k = graph.RAG(labels, connectivity=3) assert k.has_edge(0, 1) and k.has_edge(1, 2) and k.has_edge(2, 5) def test_rag_boundary(): labels = np.zeros((16, 16), dtype='uint8') edge_map = np.zeros_like(labels, dtype=float) edge_map[8, :] = 0.5 edge_map[:, 8] = 1.0 labels[:8, :8] = 1 labels[:8, 8:] = 2 labels[8:, :8] = 3 labels[8:, 8:] = 4 g = graph.rag_boundary(labels, edge_map, connectivity=1) assert set(g.nodes()) == set([1, 2, 3, 4]) assert set(g.edges()) == set([(1, 2), (1, 3), (2, 4), (3, 4)]) assert g[1][3]['weight'] == 0.25 assert g[2][4]['weight'] == 0.34375 assert g[1][3]['count'] == 16 scikit-image-0.13.1/skimage/future/setup.py000066400000000000000000000005661316256236300206240ustar00rootroot00000000000000 def configuration(parent_package='skimage', top_path=None): from numpy.distutils.misc_util import Configuration config = Configuration('future', parent_package, top_path) config.add_subpackage('graph') return config if __name__ == "__main__": from numpy.distutils.core import setup config = configuration(top_path='').todict() setup(**config) scikit-image-0.13.1/skimage/graph/000077500000000000000000000000001316256236300166725ustar00rootroot00000000000000scikit-image-0.13.1/skimage/graph/__init__.py000066400000000000000000000007131316256236300210040ustar00rootroot00000000000000from .spath import shortest_path from .mcp import MCP, MCP_Geometric, MCP_Connect, MCP_Flexible, route_through_array __all__ = ['shortest_path', 'MCP', 'MCP_Geometric', 'MCP_Connect', 'MCP_Flexible', 'route_through_array', 'rag_mean_color', 'cut_threshold', 'cut_normalized', 'ncut', 'draw_rag', 'merge_hierarchical', 'RAG'] scikit-image-0.13.1/skimage/graph/_mcp.pxd000066400000000000000000000027161316256236300203330ustar00rootroot00000000000000""" This is the definition file for mcp.pyx. It contains the definitions of the mcp class, such that other cython modules can "cimport mcp" and subclass it. """ cimport heap cimport numpy as cnp ctypedef heap.BOOL_T BOOL_T ctypedef unsigned char DIM_T ctypedef cnp.float64_t FLOAT_T ctypedef cnp.intp_t INDEX_T ctypedef cnp.int8_t EDGE_T ctypedef cnp.int8_t OFFSET_T ctypedef cnp.int16_t OFFSETS_INDEX_T cdef class MCP: cdef heap.FastUpdateBinaryHeap costs_heap cdef object costs_shape cdef object _starts cdef object _ends cdef DIM_T dim cdef BOOL_T dirty cdef BOOL_T use_start_cost # if use_start_cost is true, the cost of the starting element is added to # the cost of the path. Set to true by default in the base class... # Arrays used during front propagation cdef FLOAT_T [:] flat_costs cdef FLOAT_T [:] flat_cumulative_costs cdef OFFSETS_INDEX_T [:] traceback_offsets cdef EDGE_T [:,:] flat_pos_edge_map cdef EDGE_T [:,:] flat_neg_edge_map cdef OFFSET_T [:,:] offsets cdef INDEX_T [:] flat_offsets cdef FLOAT_T [:] offset_lengths # Methods cpdef int goal_reached(self, INDEX_T index, FLOAT_T cumcost) cdef FLOAT_T _travel_cost(self, FLOAT_T old_cost, FLOAT_T new_cost, FLOAT_T offset_length) cdef void _examine_neighbor(self, INDEX_T index, INDEX_T new_index, FLOAT_T offset_length) cdef void _update_node(self, INDEX_T index, INDEX_T new_index, FLOAT_T offset_length) scikit-image-0.13.1/skimage/graph/_mcp.pyx000066400000000000000000001101171316256236300203530ustar00rootroot00000000000000#cython: cdivision=True #cython: nonecheck=False """Cython implementation of Dijkstra's minimum cost path algorithm, for use with data on a n-dimensional lattice. Original author: Zachary Pincus Inspired by code from Almar Klein Later modifications by Almar Klein (Dec 2013) License: BSD Copyright 2009 Zachary Pincus Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. """ import cython import numpy as np import heap from .._shared.utils import warn cimport numpy as cnp cimport heap OFFSET_D = np.int8 OFFSETS_INDEX_D = np.int16 EDGE_D = np.int8 INDEX_D = np.intp FLOAT_D = np.float64 @cython.boundscheck(False) @cython.wraparound(False) def _get_edge_map(shape): """Return an array with edge points/lines/planes/hyperplanes marked. Given a shape (of length n), return an edge_map array with a shape of original_shape + (n,), where, for each dimension, edge_map[...,dim] will have zeros at indices not along an edge in that dimension, -1s at indices along the lower boundary, and +1s on the upper boundary. This allows one to, given an nd index, calculate not only if the index is at the edge of the array, but if so, which edge(s) it lies along. """ d = len(shape) edges = np.zeros(shape+(d,), order='F', dtype=EDGE_D) for i in range(d): slices = [slice(None)] * (d+1) slices[d] = i slices[i] = 0 edges[tuple(slices)] = -1 slices[i] = -1 edges[tuple(slices)] = 1 return edges @cython.boundscheck(False) @cython.wraparound(False) def _offset_edge_map(shape, offsets): """Return an array with positions marked where offsets will step out of bounds. Given a shape (of length n) and a list of n-d offsets, return a two arrays of (n,) + shape: pos_edge_map and neg_edge_map. For each dimension xxx_edge_map[dim, ...] has zeros at indices at which none of the given offsets (in that dimension) of the given sign (positive or negative, respectively) will step out of bounds. If the value is nonzero, it gives the largest offset (in terms of absolute value) that will step out of bounds in that direction. An example will be explanatory: >>> offsets = [[-2,0], [1,1], [0,2]] >>> pos_edge_map, neg_edge_map = _offset_edge_map((4,4), offsets) >>> neg_edge_map[0] array([[-1, -1, -1, -1], [-2, -2, -2, -2], [ 0, 0, 0, 0], [ 0, 0, 0, 0]], dtype=int8) >>> pos_edge_map[1] array([[0, 0, 2, 1], [0, 0, 2, 1], [0, 0, 2, 1], [0, 0, 2, 1]], dtype=int8) """ indices = np.indices(shape) # indices.shape = (n,)+shape #get the distance from each index to the upper or lower edge in each dim pos_edges = (shape - indices.T).T neg_edges = -1 - indices # now set the distances to zero if none of the given offsets could reach offsets = np.asarray(offsets) maxes = offsets.max(axis=0) mins = offsets.min(axis=0) for pos, neg, mx, mn in zip(pos_edges, neg_edges, maxes, mins): pos[pos > mx] = 0 neg[neg < mn] = 0 return pos_edges.astype(EDGE_D), neg_edges.astype(EDGE_D) @cython.boundscheck(False) @cython.wraparound(False) def make_offsets(d, fully_connected): """Make a list of offsets from a center point defining a n-dim neighborhood. Parameters ---------- d : int dimension of the offsets to produce fully_connected : bool whether the neighborhood should be singly- of fully-connected Returns ------- offsets : list of tuples of length `d` Examples -------- The singly-connected 2-d neighborhood is four offsets: >>> make_offsets(2, False) [(-1,0), (1,0), (0,-1), (0,1)] While the fully-connected 2-d neighborhood is the full cartesian product of {-1, 0, 1} (less the origin (0,0)). """ if fully_connected: mask = np.ones([3]*d, dtype=np.uint8) mask[tuple([1]*d)] = 0 else: mask = np.zeros([3]*d, dtype=np.uint8) for i in range(d): indices = [1]*d indices[i] = (0, -1) mask[tuple(indices)] = 1 offsets = [] for indices, value in np.ndenumerate(mask): if value == 1: indices = np.array(indices) - 1 offsets.append(indices) return offsets @cython.boundscheck(True) @cython.wraparound(True) def _unravel_index_fortran(flat_indices, shape): """_unravel_index_fortran(flat_indices, shape) Given a flat index into an n-d fortran-strided array, return an index tuple. """ strides = np.multiply.accumulate([1] + list(shape[:-1])) indices = [tuple((idx // strides) % shape) for idx in flat_indices] return indices @cython.boundscheck(True) @cython.wraparound(True) def _ravel_index_fortran(indices, shape): """_ravel_index_fortran(flat_indices, shape) Given an index tuple into an n-d fortran-strided array, return a flat index. """ strides = np.multiply.accumulate([1] + list(shape[:-1])) flat_indices = [np.sum(strides * idx) for idx in indices] return flat_indices @cython.boundscheck(False) @cython.wraparound(False) def _normalize_indices(indices, shape): """_normalize_indices(indices, shape) Make all indices positive. If an index is out-of-bounds, return None. """ new_indices = [] for index in indices: if len(index) != len(shape): return None new_index = [] for i, s in zip(index, shape): i = int(i) if i < 0: i = s + i if not (0 <= i < s): return None new_index.append(i) new_indices.append(new_index) return new_indices @cython.boundscheck(True) @cython.wraparound(True) def _reverse(arr): """Reverse index an array safely, with bounds/wraparound checks on. """ return arr[::-1] @cython.boundscheck(False) @cython.wraparound(False) cdef class MCP: """MCP(costs, offsets=None, fully_connected=True, sampling=None) A class for finding the minimum cost path through a given n-d costs array. Given an n-d costs array, this class can be used to find the minimum-cost path through that array from any set of points to any other set of points. Basic usage is to initialize the class and call find_costs() with a one or more starting indices (and an optional list of end indices). After that, call traceback() one or more times to find the path from any given end-position to the closest starting index. New paths through the same costs array can be found by calling find_costs() repeatedly. The cost of a path is calculated simply as the sum of the values of the `costs` array at each point on the path. The class MCP_Geometric, on the other hand, accounts for the fact that diagonal vs. axial moves are of different lengths, and weights the path cost accordingly. Array elements with infinite or negative costs will simply be ignored, as will paths whose cumulative cost overflows to infinite. Parameters ---------- costs : ndarray offsets : iterable, optional A list of offset tuples: each offset specifies a valid move from a given n-d position. If not provided, offsets corresponding to a singly- or fully-connected n-d neighborhood will be constructed with make_offsets(), using the `fully_connected` parameter value. fully_connected : bool, optional If no `offsets` are provided, this determines the connectivity of the generated neighborhood. If true, the path may go along diagonals between elements of the `costs` array; otherwise only axial moves are permitted. sampling : tuple, optional For each dimension, specifies the distance between two cells/voxels. If not given or None, the distance is assumed unit. Attributes ---------- offsets : ndarray Equivalent to the `offsets` provided to the constructor, or if none were so provided, the offsets created for the requested n-d neighborhood. These are useful for interpreting the `traceback` array returned by the find_costs() method. """ def __init__(self, costs, offsets=None, fully_connected=True, sampling=None): """__init__(costs, offsets=None, fully_connected=True, sampling=None) See class documentation. """ costs = np.asarray(costs) if not np.can_cast(costs.dtype, FLOAT_D): raise TypeError('cannot cast costs array to ' + str(FLOAT_D)) # Check sampling if sampling is None: sampling = np.array([1.0 for s in costs.shape], FLOAT_D) elif isinstance(sampling, (list, tuple)): sampling = np.array(sampling, FLOAT_D) if sampling.ndim != 1 or len(sampling) != costs.ndim: raise ValueError('Need one sampling element per dimension.') else: raise ValueError('Invalid type for sampling: %r.' % type(sampling)) # We use flat, fortran-style indexing here (could use C-style, # but this is my code and I like fortran-style! Also, it's # faster when working with image arrays, which are often # already fortran-strided.) try: self.flat_costs = costs.astype(FLOAT_D, copy=False).ravel('F') except TypeError: self.flat_costs = costs.astype(FLOAT_D).flatten('F') warn('Upgrading NumPy should decrease memory usage and increase' ' speed.') size = self.flat_costs.shape[0] self.flat_cumulative_costs = np.empty(size, dtype=FLOAT_D) self.dim = len(costs.shape) self.costs_shape = costs.shape self.costs_heap = heap.FastUpdateBinaryHeap(initial_capacity=128, max_reference=size-1) # This array stores, for each point, the index into the offset # array (see below) that leads to that point from the # predecessor point. self.traceback_offsets = np.empty(size, dtype=OFFSETS_INDEX_D) # The offsets are a list of relative offsets from a central # point to each point in the relevant neighborhood. (e.g. (-1, # 0) might be a 2d offset). # These offsets are raveled to provide flat, 1d offsets that can be # used in the same way for flat indices to move to neighboring points. if offsets is None: offsets = make_offsets(self.dim, fully_connected) self.offsets = np.array(offsets, dtype=OFFSET_D) self.flat_offsets = np.array( _ravel_index_fortran(self.offsets, self.costs_shape), dtype=INDEX_D) # Instead of unraveling each index during the pathfinding algorithm, we # will use a pre-computed "edge map" that specifies for each dimension # whether a given index is on a lower or upper boundary (or none at # all). Flatten this map to get something that can be indexed as by the # same flat indices as elsewhere. # The edge map stores more than a boolean "on some edge" flag so as to # allow us to examine the non-out-of-bounds neighbors for a given edge # point while excluding the neighbors which are outside the array. pos, neg = _offset_edge_map(costs.shape, self.offsets) self.flat_pos_edge_map = pos.reshape((self.dim, size), order='F') self.flat_neg_edge_map = neg.reshape((self.dim, size), order='F') # The offset lengths are the distances traveled along each offset self.offset_lengths = np.sqrt(np.sum((sampling * self.offsets)**2, axis=1)).astype(FLOAT_D) self.dirty = 0 self.use_start_cost = 1 def _reset(self): """_reset() Clears paths found by find_costs(). """ cdef INDEX_T start self.costs_heap.reset() self.traceback_offsets[...] = -2 # -2 is not reached, -1 is start self.flat_cumulative_costs[...] = np.inf self.dirty = 0 # Get starts and ends # We do not pass them in as arguments for backwards compat starts, ends = self._starts, self._ends # push each start point into the heap. Note that we use flat indexing! for start in _ravel_index_fortran(starts, self.costs_shape): self.traceback_offsets[start] = -1 if self.use_start_cost: self.costs_heap.push_fast(self.flat_costs[start], start) else: self.costs_heap.push_fast(0, start) cdef FLOAT_T _travel_cost(self, FLOAT_T old_cost, FLOAT_T new_cost, FLOAT_T offset_length): """ float _travel_cost(float old_cost, float new_cost, float offset_length) The travel cost for going from the current node to the next. Default is simply the cost of the next node. """ return new_cost cpdef int goal_reached(self, INDEX_T index, FLOAT_T cumcost): """ int goal_reached(int index, float cumcost) This method is called each iteration after popping an index from the heap, before examining the neighbours. This method can be overloaded to modify the behavior of the MCP algorithm. An example might be to stop the algorithm when a certain cumulative cost is reached, or when the front is a certain distance away from the seed point. This method should return 1 if the algorithm should not check the current point's neighbours and 2 if the algorithm is now done. """ return 0 cdef void _examine_neighbor(self, INDEX_T index, INDEX_T new_index, FLOAT_T offset_length): """ _examine_neighbor(int index, int new_index, float offset_length) This method is called once for every pair of neighboring nodes, as soon as both nodes become frozen. """ pass cdef void _update_node(self, INDEX_T index, INDEX_T new_index, FLOAT_T offset_length): """ _update_node(int index, int new_index, float offset_length) This method is called when a node is updated. """ pass def find_costs(self, starts, ends=None, find_all_ends=True, max_coverage=1.0, max_cumulative_cost=None, max_cost=None): """ Find the minimum-cost path from the given starting points. This method finds the minimum-cost path to the specified ending indices from any one of the specified starting indices. If no end positions are given, then the minimum-cost path to every position in the costs array will be found. Parameters ---------- starts : iterable A list of n-d starting indices (where n is the dimension of the `costs` array). The minimum cost path to the closest/cheapest starting point will be found. ends : iterable, optional A list of n-d ending indices. find_all_ends : bool, optional If 'True' (default), the minimum-cost-path to every specified end-position will be found; otherwise the algorithm will stop when a a path is found to any end-position. (If no `ends` were specified, then this parameter has no effect.) Returns ------- cumulative_costs : ndarray Same shape as the `costs` array; this array records the minimum cost path from the nearest/cheapest starting index to each index considered. (If `ends` were specified, not all elements in the array will necessarily be considered: positions not evaluated will have a cumulative cost of inf. If `find_all_ends` is 'False', only one of the specified end-positions will have a finite cumulative cost.) traceback : ndarray Same shape as the `costs` array; this array contains the offset to any given index from its predecessor index. The offset indices index into the `offsets` attribute, which is a array of n-d offsets. In the 2-d case, if offsets[traceback[x, y]] is (-1, -1), that means that the predecessor of [x, y] in the minimum cost path to some start position is [x+1, y+1]. Note that if the offset_index is -1, then the given index was not considered. """ # basic variables to use for end-finding; also fix up the start and end # lists cdef BOOL_T use_ends = 0 cdef INDEX_T num_ends cdef BOOL_T all_ends = find_all_ends cdef INDEX_T[:] flat_ends starts = _normalize_indices(starts, self.costs_shape) if starts is None: raise ValueError('start points must all be within the costs array') elif not starts: raise ValueError('no valid start points to start front' + 'propagation') if ends is not None: ends = _normalize_indices(ends, self.costs_shape) if ends is None: raise ValueError('end points must all be within ' 'the costs array') use_ends = 1 num_ends = len(ends) flat_ends = np.array(_ravel_index_fortran( ends, self.costs_shape), dtype=INDEX_D) # Always perform a reset to (re)initialize our arrays and start # positions self._starts, self._ends = starts, ends self._reset() # Get shorter names for arrays cdef FLOAT_T[:] flat_costs = self.flat_costs cdef FLOAT_T[:] flat_cumulative_costs = self.flat_cumulative_costs cdef OFFSETS_INDEX_T[:] traceback_offsets = self.traceback_offsets cdef EDGE_T[:, :] flat_pos_edge_map = self.flat_pos_edge_map cdef EDGE_T[:, :] flat_neg_edge_map = self.flat_neg_edge_map cdef OFFSET_T[:, :] offsets = self.offsets cdef INDEX_T[:] flat_offsets = self.flat_offsets cdef FLOAT_T[:] offset_lengths = self.offset_lengths # Short names for other attributes cdef heap.FastUpdateBinaryHeap costs_heap = self.costs_heap cdef DIM_T dim = self.dim cdef int num_offsets = len(flat_offsets) # Variables used during front propagation cdef FLOAT_T cost, new_cost, cumcost, new_cumcost, offset_length cdef INDEX_T index, new_index cdef BOOL_T is_at_edge, use_offset cdef INDEX_T d, i, iter cdef OFFSET_T offset cdef EDGE_T pos_edge_val, neg_edge_val cdef int num_ends_found = 0 cdef FLOAT_T inf = np.inf cdef int goal_reached cdef INDEX_T maxiter = int(max_coverage * flat_costs.size) for iter in range(maxiter): # This is rather like a while loop, except we are guaranteed to # exit, which is nice during developing to prevent eternal loops. # Find the point with the minimum cost in the heap. Once # popped, this point's minimum cost path has been found. if costs_heap.count == 0: # nothing in the heap: we've found paths to every # point in the array break # Get current cumulative cost and index from the heap cumcost = costs_heap.pop_fast() index = costs_heap._popped_ref # Record the cost we found to this point flat_cumulative_costs[index] = cumcost # Check if goal is reached goal_reached = self.goal_reached(index, cumcost) if goal_reached > 0: if goal_reached == 1: continue # Skip neighbours else: break # Done completely if use_ends: # If we're only tracing out a path to one or more # endpoints, check to see if this is an endpoint, and # if so, if we're done pathfinding. for i in range(num_ends): if index == flat_ends[i]: num_ends_found += 1 break if (num_ends_found and not all_ends) or \ num_ends_found == num_ends: # if we've found one or all of the end points (as # requested), stop searching break # Look into the edge map to see if this point is at an # edge along any axis is_at_edge = 0 for d in range(dim): if (flat_pos_edge_map[d, index] != 0 or flat_neg_edge_map[d, index] != 0): is_at_edge = 1 break # Now examine the points neighboring the given point for i in range(num_offsets): # First, if we're at some edge, scrutinize the offset # to ensure that it won't put us out-of-bounds. If, # for example, the edge_map at (x, y) is (-1, 0) -- # though of course we use flat indexing below -- that # means that (x, y) is along the lower edge of the # array; thus offsets with -1 or more negative in the # x-dimension should not be used! use_offset = 1 if is_at_edge: for d in range(dim): offset = offsets[i, d] pos_edge_val = flat_pos_edge_map[d, index] neg_edge_val = flat_neg_edge_map[d, index] if (pos_edge_val > 0 and offset >= pos_edge_val) or \ (neg_edge_val < 0 and offset <= neg_edge_val): # the offset puts us out of bounds... use_offset = 0 break # If not at an edge, or the specific offset doesn't # push over the edge, then we go on. if not use_offset: continue # using the flat offsets, calculate the new flat index new_index = index + flat_offsets[i] # Get offset length offset_length = offset_lengths[i] # If we have already found the best path here then # ignore this point if flat_cumulative_costs[new_index] != inf: # Give subclass the oportunity to examine these two nodes # Note that only when both nodes are "frozen" their # cumulative cost is set. By doing the check here, each # pair of nodes is checked exactly once. self._examine_neighbor(index, new_index, offset_length) continue # Get cost and new cost cost = flat_costs[index] new_cost = flat_costs[new_index] # If the cost at this point is negative or infinite, ignore it if new_cost < 0 or new_cost == inf: continue # Calculate new cumulative cost new_cumcost = cumcost + self._travel_cost(cost, new_cost, offset_length) # Now we ask the heap to append or update the cost to # this new point, but only if that point isn't already # in the heap, or it is but the new cost is lower. # don't push infs into the heap though! if new_cumcost != inf: costs_heap.push_if_lower_fast(new_cumcost, new_index) # If we did perform an append or update, we should # record the offset from the predecessor to this new # point if costs_heap._pushed: traceback_offsets[new_index] = i self._update_node(index, new_index, offset_length) # Un-flatten the costs and traceback arrays for human consumption. cumulative_costs = np.asarray(flat_cumulative_costs) cumulative_costs = cumulative_costs.reshape(self.costs_shape, order='F') traceback = np.asarray(traceback_offsets) traceback = traceback.reshape(self.costs_shape, order='F') self.dirty = 1 return cumulative_costs, traceback def traceback(self, end): """traceback(end) Trace a minimum cost path through the pre-calculated traceback array. This convenience function reconstructs the the minimum cost path to a given end position from one of the starting indices provided to find_costs(), which must have been called previously. This function can be called as many times as desired after find_costs() has been run. Parameters ---------- end : iterable An n-d index into the `costs` array. Returns ------- traceback : list of n-d tuples A list of indices into the `costs` array, starting with one of the start positions passed to find_costs(), and ending with the given `end` index. These indices specify the minimum-cost path from any given start index to the `end` index. (The total cost of that path can be read out from the `cumulative_costs` array returned by find_costs().) """ if not self.dirty: raise Exception('find_costs() must be run before traceback()') ends = _normalize_indices([end], self.costs_shape) if ends is None: raise ValueError('the specified end point must be ' 'within the costs array') traceback = [tuple(ends[0])] cdef INDEX_T flat_position =\ _ravel_index_fortran(ends, self.costs_shape)[0] if self.flat_cumulative_costs[flat_position] == np.inf: raise ValueError('no minimum-cost path was found ' 'to the specified end point') # Short names for arrays cdef OFFSETS_INDEX_T [:] traceback_offsets = self.traceback_offsets cdef OFFSET_T [:,:] offsets = self.offsets cdef INDEX_T [:] flat_offsets = self.flat_offsets # New array cdef INDEX_T [:] position = np.array(ends[0], dtype=INDEX_D) cdef OFFSETS_INDEX_T offset cdef DIM_T d cdef DIM_T dim = self.dim while 1: offset = traceback_offsets[flat_position] if offset == -1: # At a point where we can go no further: probably a start point break flat_position -= flat_offsets[offset] for d in range(dim): position[d] -= offsets[offset, d] traceback.append(tuple(position)) return _reverse(traceback) @cython.boundscheck(False) @cython.wraparound(False) cdef class MCP_Geometric(MCP): """MCP_Geometric(costs, offsets=None, fully_connected=True) Find distance-weighted minimum cost paths through an n-d costs array. See the documentation for MCP for full details. This class differs from MCP in that the cost of a path is not simply the sum of the costs along that path. This class instead assumes that the costs array contains at each position the "cost" of a unit distance of travel through that position. For example, a move (in 2-d) from (1, 1) to (1, 2) is assumed to originate in the center of the pixel (1, 1) and terminate in the center of (1, 2). The entire move is of distance 1, half through (1, 1) and half through (1, 2); thus the cost of that move is `(1/2)*costs[1,1] + (1/2)*costs[1,2]`. On the other hand, a move from (1, 1) to (2, 2) is along the diagonal and is sqrt(2) in length. Half of this move is within the pixel (1, 1) and the other half in (2, 2), so the cost of this move is calculated as `(sqrt(2)/2)*costs[1,1] + (sqrt(2)/2)*costs[2,2]`. These calculations don't make a lot of sense with offsets of magnitude greater than 1. Use the `sampling` argument in order to deal with anisotropic data. """ def __init__(self, costs, offsets=None, fully_connected=True, sampling=None): """__init__(costs, offsets=None, fully_connected=True, sampling=None) See class documentation. """ MCP.__init__(self, costs, offsets, fully_connected, sampling) if np.absolute(self.offsets).max() > 1: raise ValueError('all offset components must be 0, 1, or -1') self.use_start_cost = 0 cdef FLOAT_T _travel_cost(self, FLOAT_T old_cost, FLOAT_T new_cost, FLOAT_T offset_length): return offset_length * 0.5 * (old_cost + new_cost) @cython.boundscheck(True) @cython.wraparound(True) cdef class MCP_Connect(MCP): """MCP_Connect(costs, offsets=None, fully_connected=True) Connect source points using the distance-weighted minimum cost function. A front is grown from each seed point simultaneously, while the origin of the front is tracked as well. When two fronts meet, create_connection() is called. This method must be overloaded to deal with the found edges in a way that is appropriate for the application. """ cdef INDEX_T [:] flat_idmap def __init__(self, costs, offsets=None, fully_connected=True, sampling=None): MCP.__init__(self, costs, offsets, fully_connected, sampling) # Create id map to keep track of origin of nodes self.flat_idmap = np.zeros(self.costs_shape, INDEX_D).ravel('F') def _reset(self): """ Reset the id map. """ cdef INDEX_T start MCP._reset(self) starts, ends = self._starts, self._ends # Reset idmap self.flat_idmap[...] = -1 id = 0 for start in _ravel_index_fortran(starts, self.costs_shape): self.flat_idmap[start] = id id += 1 cdef FLOAT_T _travel_cost(self, FLOAT_T old_cost, FLOAT_T new_cost, FLOAT_T offset_length): """ Equivalent to MCP_Geometric. """ return offset_length * 0.5 * (old_cost + new_cost) cdef void _examine_neighbor(self, INDEX_T index, INDEX_T new_index, FLOAT_T offset_length): """ Check whether two fronts are meeting. If so, the flat_traceback is obtained and a connection is created. """ # Short names cdef INDEX_T [:] flat_idmap = self.flat_idmap cdef FLOAT_T [:] flat_cumulative_costs = self.flat_cumulative_costs # Get ids cdef INDEX_T id1 = flat_idmap[index] cdef INDEX_T id2 = flat_idmap[new_index] if id2 < 0 or id1 < 0: pass elif id2 != id1: # We reached the 'front' of another seed point! # Get position/coordinates pos1, pos2 = _unravel_index_fortran([index, new_index], self.costs_shape) # Also get the costs, so we can keep the path with the least cost cost1 = flat_cumulative_costs[index] cost2 = flat_cumulative_costs[new_index] # Create connection self.create_connection(id1, id2, pos1, pos2, cost1, cost2) def create_connection(self, id1, id2, tb1, tb2, cost1, cost2): """ create_connection id1, id2, pos1, pos2, cost1, cost2) Overload this method to keep track of the connections that are found during MCP processing. Note that a connection with the same ids can be found multiple times (but with different positions and costs). At the time that this method is called, both points are "frozen" and will not be visited again by the MCP algorithm. Parameters ---------- id1 : int The seed point id where the first neighbor originated from. id2 : int The seed point id where the second neighbor originated from. pos1 : tuple The index of of the first neighbour in the connection. pos2 : tuple The index of of the second neighbour in the connection. cost1 : float The cumulative cost at `pos1`. cost2 : float The cumulative costs at `pos2`. """ pass cdef void _update_node(self, INDEX_T index, INDEX_T new_index, FLOAT_T offset_length): """ Keep track of the id map so that we know which seed point a certain front originates from. """ self.flat_idmap[new_index] = self.flat_idmap[index] @cython.boundscheck(False) @cython.wraparound(False) cdef class MCP_Flexible(MCP): """MCP_Flexible(costs, offsets=None, fully_connected=True) Find minimum cost paths through an N-d costs array. See the documentation for MCP for full details. This class differs from MCP in that several methods can be overloaded (from pure Python) to modify the behavior of the algorithm and/or create custom algorithms based on MCP. Note that goal_reached can also be overloaded in the MCP class. """ def travel_cost(self, FLOAT_T old_cost, FLOAT_T new_cost, FLOAT_T offset_length): """ travel_cost(old_cost, new_cost, offset_length) This method calculates the travel cost for going from the current node to the next. The default implementation returns new_cost. Overload this method to adapt the behaviour of the algorithm. """ return new_cost def examine_neighbor(self, INDEX_T index, INDEX_T new_index, FLOAT_T offset_length): """ examine_neighbor(index, new_index, offset_length) This method is called once for every pair of neighboring nodes, as soon as both nodes are frozen. This method can be overloaded to obtain information about neightboring nodes, and/or to modify the behavior of the MCP algorithm. One example is the MCP_Connect class, which checks for meeting fronts using this hook. """ pass def update_node(self, INDEX_T index, INDEX_T new_index, FLOAT_T offset_length): """ update_node(index, new_index, offset_length) This method is called when a node is updated, right after new_index is pushed onto the heap and the traceback map is updated. This method can be overloaded to keep track of other arrays that are used by a specific implementation of the algorithm. For instance the MCP_Connect class uses it to update an id map. """ pass cdef FLOAT_T _travel_cost(self, FLOAT_T old_cost, FLOAT_T new_cost, FLOAT_T offset_length): return self.travel_cost(old_cost, new_cost, offset_length) cdef void _examine_neighbor(self, INDEX_T index, INDEX_T new_index, FLOAT_T offset_length): self.examine_neighbor(index, new_index, offset_length) cdef void _update_node(self, INDEX_T index, INDEX_T new_index, FLOAT_T offset_length): self.update_node(index, new_index, offset_length) scikit-image-0.13.1/skimage/graph/_spath.pyx000066400000000000000000000020601316256236300207100ustar00rootroot00000000000000# -*- python -*- import _mcp cimport _mcp from libc.math cimport fabs cdef class MCP_Diff(_mcp.MCP): """MCP_Diff(costs, offsets=None, fully_connected=True) Find minimum-difference paths through an n-d costs array. See the documentation for MCP for full details. This class differs from MCP in that the cost of a path is not simply the sum of the costs along that path. This class instead assumes that the cost of moving from one point to another is the absolute value of the difference in the costs between the two points. """ def __init__(self, costs, offsets=None, fully_connected=True): """__init__(costs, offsets=None, fully_connected=True) See class documentation. """ _mcp.MCP.__init__(self, costs, offsets, fully_connected) self.use_start_cost = 0 cdef _mcp.FLOAT_T _travel_cost(self, _mcp.FLOAT_T old_cost, _mcp.FLOAT_T new_cost, _mcp.FLOAT_T offset_length): return fabs(old_cost - new_cost) scikit-image-0.13.1/skimage/graph/heap.pxd000066400000000000000000000024231316256236300203250ustar00rootroot00000000000000""" This is the definition file for heap.pyx. It contains the definitions of the heap classes, such that other cython modules can "cimport heap" and thus use the C versions of pop(), push(), and value_of(): pop_fast(), push_fast() and value_of_fast() """ # determine datatypes for heap ctypedef double VALUE_T ctypedef Py_ssize_t REFERENCE_T ctypedef REFERENCE_T INDEX_T ctypedef unsigned char BOOL_T ctypedef unsigned char LEVELS_T cdef class BinaryHeap: cdef readonly INDEX_T count cdef readonly LEVELS_T levels, min_levels cdef VALUE_T *_values cdef REFERENCE_T *_references cdef REFERENCE_T _popped_ref cdef void _add_or_remove_level(self, LEVELS_T add_or_remove) nogil cdef void _update(self) nogil cdef void _update_one(self, INDEX_T i) nogil cdef void _remove(self, INDEX_T i) nogil cdef INDEX_T push_fast(self, VALUE_T value, REFERENCE_T reference) nogil cdef VALUE_T pop_fast(self) nogil cdef class FastUpdateBinaryHeap(BinaryHeap): cdef readonly REFERENCE_T max_reference cdef INDEX_T *_crossref cdef BOOL_T _invalid_ref cdef BOOL_T _pushed cdef VALUE_T value_of_fast(self, REFERENCE_T reference) cdef INDEX_T push_if_lower_fast(self, VALUE_T value, REFERENCE_T reference) nogil scikit-image-0.13.1/skimage/graph/heap.pyx000066400000000000000000000577061316256236300203700ustar00rootroot00000000000000# -*- python -*- """Cython implementation of a binary min heap. Original author: Almar Klein Modified by: Zachary Pincus License: BSD Copyright 2009 Almar Klein Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. """ from __future__ import division # cython specific imports import cython from libc.stdlib cimport malloc, free cdef extern from "pyport.h": double Py_HUGE_VAL cdef VALUE_T inf = Py_HUGE_VAL # this is handy cdef inline INDEX_T index_min(INDEX_T a, INDEX_T b) nogil: return a if a <= b else b cdef class BinaryHeap: """BinaryHeap(initial_capacity=128) A binary heap class that can store values and an integer reference. A binary heap is an object to store values in, optimized in such a way that the minimum (or maximum, but a minimum in this implementation) value can be found in O(log2(N)) time. In this implementation, a reference value (a single integer) can also be stored with each value. Use the methods push() and pop() to put in or extract values. In C, use the corresponding push_fast() and pop_fast(). Parameters ---------- initial_capacity : int Estimate of the size of the heap, if known in advance. (In any case, the heap will dynamically grow and shrink as required, though never below the `initial_capacity`.) Attributes ---------- count : int The number of values in the heap levels : int The number of levels in the binary heap (see Notes below). The values are stored in the last level, so 2**levels is the capacity of the heap before another resize is required. min_levels : int The minimum number of levels in the heap (relates to the `initial_capacity` parameter.) Notes ----- This implementation stores the binary heap in an array twice as long as the number of elements in the heap. The array is structured in levels, starting at level 0 with a single value, doubling the amount of values in each level. The final level contains the actual values, the level before it contains the pairwise minimum values. The level before that contains the pairwise minimum values of that level, etc. Take a look at this illustration: level: 0 11 2222 33333333 4444444444444444 index: 0 12 3456 78901234 5678901234567890 1 2 3 The actual values are stored in level 4. The minimum value of position 15 and 16 is stored in position 7. min(17,18)->8, min(7,8)->3, min(3,4)->1. When adding a value, only the path to the top has to be updated, which takesO(log2(N)) time. The advantage of this implementation relative to more common implementations that swap values when pushing to the array is that data only needs to be swapped once when an element is removed. This means that keeping an array of references along with the values is very inexpensive. Th disadvantage is that if you pop the minimum value, the tree has to be traced from top to bottom and back. So if you only want values and no references, this implementation will probably be slower. If you need references (and maybe cross references to be kept up to date) this implementation will be faster. """ ## Basic methods # The following lines are always "inlined", but documented here for # clarity: # # To calculate the start index of a certain level: # 2**l-1 # LevelStart # Note that in inner loops, this may also be represented as (1<malloc( 2*number * sizeof(VALUE_T)) self._references = malloc(number * sizeof(REFERENCE_T)) def __init__(self, INDEX_T initial_capacity=128): """__init__(initial_capacity=128) Class constructor. Takes an optional parameter 'initial_capacity' so that if the required heap capacity is known or can be estimated in advance, there will need to be fewer resize operations on the heap.""" if self._values is NULL or self._references is NULL: raise MemoryError() self.reset() def reset(self): """reset() Reset the heap to default, empty state. """ cdef INDEX_T number = 2**self.levels cdef INDEX_T i cdef VALUE_T *values = self._values for i in range(number*2): values[i] = inf def __dealloc__(self): if self._values is not NULL: free(self._values) if self._references is not NULL: free(self._references) def __str__(self): s = '' for level in range(1,self.levels+1): i0 = 2**level-1 # LevelStart s+= 'level %i: ' % level for i in range(i0,i0+2**level): s += '%g, ' % self._values[i] s = s[:-1] + '\n' return s ## C Maintanance methods cdef void _add_or_remove_level(self, LEVELS_T add_or_remove) nogil: # init indexing ints cdef INDEX_T i, i1, i2, n # new amount of levels cdef LEVELS_T new_levels = self.levels + add_or_remove # allocate new arrays cdef INDEX_T number = 2**new_levels cdef VALUE_T *values cdef REFERENCE_T *references values = malloc(number*2 * sizeof(VALUE_T)) references = malloc(number * sizeof(REFERENCE_T)) # init arrays for i in range(number*2): values[i] = inf for i in range(number): references[i] = -1 # copy data cdef VALUE_T *old_values = self._values cdef REFERENCE_T *old_references = self._references if self.count: i1 = 2**new_levels-1 # LevelStart i2 = 2**self.levels-1 # LevelStart n = index_min(2**new_levels, 2**self.levels) for i in range(n): values[i1+i] = old_values[i2+i] for i in range(n): references[i] = old_references[i] # make current free(self._values) free(self._references) self._values = values self._references = references # we need a full update self.levels = new_levels self._update() cdef void _update(self) nogil: """Update the full tree from the bottom up. This should be done after resizing. """ # shorter name for values cdef VALUE_T *values = self._values # Note that i represents an absolute index here cdef INDEX_T i0, i, ii, n cdef LEVELS_T level # track tree for level in range(self.levels,1,-1): i0 = (1 << level) - 1 #2**level-1 = LevelStart n = i0 + 1 #2**level for i in range(i0,i0+n,2): ii = (i-1)//2 # CalcPrevAbs if values[i] < values[i+1]: values[ii] = values[i] else: values[ii] = values[i+1] cdef void _update_one(self, INDEX_T i) nogil: """Update the tree for one value.""" # shorter name for values cdef VALUE_T *values = self._values # make index uneven if i % 2==0: i = i-1 # track tree cdef INDEX_T ii cdef LEVELS_T level for level in range(self.levels,1,-1): ii = (i-1)//2 # CalcPrevAbs # test if values[i] < values[i+1]: values[ii] = values[i] else: values[ii] = values[i+1] # next if ii % 2: i = ii else: i = ii-1 cdef void _remove(self, INDEX_T i1) nogil: """Remove a value from the heap. By index.""" cdef LEVELS_T levels = self.levels cdef INDEX_T count = self.count # get indices cdef INDEX_T i0 = (1 << levels) - 1 #2**self.levels - 1 # LevelStart cdef INDEX_T i2 = i0 + count - 1 # get relative indices cdef INDEX_T r1 = i1 - i0 cdef INDEX_T r2 = count - 1 cdef VALUE_T *values = self._values cdef REFERENCE_T *references = self._references # swap with last values[i1] = values[i2] references[r1] = references[r2] # make last Null values[i2] = inf # update self.count -= 1 count -= 1 if (levels>self.min_levels) & (count < (1 << (levels-2))): self._add_or_remove_level(-1) else: self._update_one(i1) self._update_one(i2) ## C Public methods cdef INDEX_T push_fast(self, VALUE_T value, REFERENCE_T reference) nogil: """The c-method for fast pushing. Returns the index relative to the start of the last level in the heap.""" # We need to resize if currently it just fits. cdef LEVELS_T levels = self.levels cdef INDEX_T count = self.count if count >= (1 << levels):#2**self.levels: self._add_or_remove_level(+1) levels += 1 # insert new value cdef INDEX_T i = ((1 << levels) - 1) + count # LevelStart + n self._values[i] = value self._references[count] = reference # update self.count += 1 self._update_one(i) # return return count cdef VALUE_T pop_fast(self) nogil: """The c-method for fast popping. Returns the minimum value. The reference is put in self._popped_ref""" # shorter name for values cdef VALUE_T *values = self._values # init index. start at 1 because we start in level 1 cdef LEVELS_T level cdef INDEX_T i = 1 cdef LEVELS_T levels = self.levels # search tree (using absolute indices) for level in range(1, levels): if values[i] <= values[i+1]: i = i*2+1 # CalcNextAbs else: i = (i+1)*2+1 # CalcNextAbs # select best one in last level if values[i] <= values[i+1]: i = i else: i = i+1 # get values cdef INDEX_T ir = i - ((1 << levels) - 1) #(2**self.levels-1) # LevelStart cdef VALUE_T value = values[i] self._popped_ref = self._references[ir] # remove it if self.count: self._remove(i) # return return value ## Python Public methods (that do not need to be VERY fast) def push(self, VALUE_T value, REFERENCE_T reference=-1): """push(value, reference=-1) Append a value to the heap, with optional reference. Parameters ---------- value : float Value to push onto the heap reference : int, optional Reference to associate with the given value. """ self.push_fast(value, reference) def min_val(self): """min_val() Get the minimum value on the heap. Returns only the value, and does not remove it from the heap. """ # shorter name for values cdef VALUE_T *values = self._values # select best one in last level if values[1] < values[2]: return values[1] else: return values[2] def values(self): """values() Get the values in the heap as a list. """ out = [] cdef INDEX_T i, i0 i0 = 2**self.levels-1 # LevelStart for i in range(self.count): out.append(self._values[i0+i]) return out def references(self): """references() Get the references in the heap as a list. """ out = [] cdef INDEX_T i for i in range(self.count): out.append(self._references[i]) return out def pop(self): """pop() Get the minimum value and remove it from the list. Returns ------- value : float reference : int If no reference was provided, -1 is returned here. Raises ------ IndexError On attempt to pop from an empty heap """ if self.count == 0: raise IndexError('pop from an empty heap') value = self.pop_fast() ref = self._popped_ref return value, ref cdef class FastUpdateBinaryHeap(BinaryHeap): """FastUpdateBinaryHeap(initial_capacity=128, max_reference=None) Binary heap that allows the value of a reference to be updated quickly. This heap class keeps cross-references so that the value associated with a given reference can be quickly queried (O(1) time) or updated (O(log2(N)) time). This is ideal for pathfinding algorithms that implement some variant of Dijkstra's algorithm. Parameters ---------- initial_capacity : int Estimate of the size of the heap, if known in advance. (In any case, the heap will dynamically grow and shrink as required, though never below the `initial_capacity`.) max_reference : int, optional Largest reference value that might be pushed to the heap. (Pushing a larger value will result in an error.) If no value is provided, `1-initial_capacity` will be used. For the cross-reference index to work, all references must be in the range [0, max_reference]; references pushed outside of that range will not be added to the heap. The cross-references are kept as a 1-d array of length `max_reference+1', so memory use of this heap is effectively O(max_reference) Attributes ---------- count : int The number of values in the heap levels : int The number of levels in the binary heap (see Notes below). The values are stored in the last level, so 2**levels is the capacity of the heap before another resize is required. min_levels : int The minimum number of levels in the heap (relates to the `initial_capacity` parameter.) max_reference : int The provided or calculated maximum allowed reference value. Notes ----- The cross-references map data[reference]->internalindex, such that the value corresponding to a given reference can be found efficiently. This can be queried with the value_of() method. A special method, push_if_lower() is provided that will update the heap if the given reference is not in the heap, or if it is and the provided value is lower than the current value in the heap. This is again useful for pathfinding algorithms. """ def __cinit__(self, INDEX_T initial_capacity=128, max_reference=None): if max_reference is None: max_reference = initial_capacity - 1 self.max_reference = max_reference self._crossref = malloc((self.max_reference+1) * \ sizeof(INDEX_T)) def __init__(self, INDEX_T initial_capacity=128, max_reference=None): """__init__(initial_capacity=128, max_reference=None) Class constructor. """ # below will call self.reset BinaryHeap.__init__(self, initial_capacity) def __dealloc__(self): if self._crossref is not NULL: free(self._crossref) def reset(self): """reset() Reset the heap to default, empty state. """ BinaryHeap.reset(self) # set default values of crossrefs cdef INDEX_T i for i in range(self.max_reference+1): self._crossref[i] = -1 cdef void _remove(self, INDEX_T i1) nogil: """ Remove a value from the heap. By index. """ cdef LEVELS_T levels = self.levels cdef INDEX_T count = self.count # get indices cdef INDEX_T i0 = (1 << levels) - 1 #2**self.levels - 1 # LevelStart cdef INDEX_T i2 = i0 + count - 1 # get relative indices cdef INDEX_T r1 = i1 - i0 cdef INDEX_T r2 = count - 1 cdef VALUE_T *values = self._values cdef REFERENCE_T *references = self._references cdef INDEX_T *crossref = self._crossref # update cross reference crossref[references[r2]]=r1 crossref[references[r1]]=-1 # disable removed item # swap with last values[i1] = values[i2] references[r1] = references[r2] # make last Null values[i2] = inf # update self.count -= 1 count -= 1 if (levels > self.min_levels) & (count < (1 << (levels-2))): self._add_or_remove_level(-1) else: self._update_one(i1) self._update_one(i2) cdef INDEX_T push_fast(self, VALUE_T value, REFERENCE_T reference) nogil: """The c method for fast pushing. If the reference is already present, will update its value, otherwise will append it. If -1 is returned, the provided reference was out-of-bounds and no value was pushed to the heap.""" if not (0 <= reference <= self.max_reference): return -1 # init variable to store the index-in-the-heap cdef INDEX_T i # Reference is the index in the array where MCP is applied to. # Find the index-in-the-heap using the crossref array. cdef INDEX_T ir = self._crossref[reference] if ir != -1: # update i = (1 << self.levels) - 1 + ir self._values[i] = value self._update_one(i) return ir # if not updated: append normally and store reference ir = BinaryHeap.push_fast(self, value, reference) self._crossref[reference] = ir return ir cdef INDEX_T push_if_lower_fast(self, VALUE_T value, REFERENCE_T reference) nogil: """If the reference is already present, will update its value ONLY if the new value is lower than the old one. If the reference is not present, this append it. If a value was appended, self._pushed is set to 1. If -1 is returned, the provided reference was out-of-bounds and no value was pushed to the heap. """ if not (0 <= reference <= self.max_reference): return -1 # init variable to store the index-in-the-heap cdef INDEX_T i # Reference is the index in the array where MCP is applied to. # Find the index-in-the-heap using the crossref array. cdef INDEX_T ir = self._crossref[reference] cdef VALUE_T *values = self._values self._pushed = 1 if ir != -1: # update i = (1 << self.levels) - 1 + ir if values[i] > value: values[i] = value self._update_one(i) else: self._pushed = 0 return ir # if not updated: append normally and store reference ir = BinaryHeap.push_fast(self, value, reference) self._crossref[reference] = ir return ir cdef VALUE_T value_of_fast(self, REFERENCE_T reference): """Return the value corresponding to the given reference. If inf is returned, the reference may be invalid: check the _invaild_ref field in this case.""" if not (0 <= reference <= self.max_reference): self.invalid_ref = 1 return inf # init variable to store the index-in-the-heap cdef INDEX_T i # Reference is the index in the array where MCP is applied to. # Find the index-in-the-heap using the crossref array. cdef INDEX_T ir = self._crossref[reference] self._invalid_ref = 0 if ir == -1: self._invalid_ref = 1 return inf i = (1 << self.levels) - 1 + ir return self._values[i] def push(self, double value, int reference): """push(value, reference) Append/update a value in the heap. Parameters ---------- value : float reference : int If the reference is already present in the array, the value for that reference will be updated, otherwise the (value, reference) pair will be added to the heap. Raises ------ ValueError On pushing a reference outside the range [0, max_reference]. """ if self.push_fast(value, reference) == -1: raise ValueError("reference outside of range [0, max_reference]") def push_if_lower(self, double value, int reference): """push_if_lower(value, reference) Append/update a value in the heap if the extant value is lower. If the reference is already in the heap, update only of the new value is lower than the current one. If the reference is not present, the value will always be pushed to the heap. Parameters ---------- value : float reference : int If the reference is already present in the array, the value for that reference will be updated, otherwise the (value, reference) pair will be added to the heap. Returns ------- pushed : bool True if an append/update occurred, False if otherwise. Raises ------ ValueError On pushing a reference outside the range [0, max_reference]. """ if self.push_if_lower_fast(value, reference) == -1: raise ValueError("reference outside of range [0, max_reference]") return self._pushed == 1 def value_of(self, int reference): """value_of(reference) Get the value corresponding to a given reference. Parameters ---------- reference : int A reference already pushed to the heap. Returns ------- value : float Raises ------ ValueError On querying a reference outside the range [0, max_reference], or not already pushed to the heap. """ value = self.value_of_fast(reference) if self._invalid_ref: raise ValueError('invalid reference') return value def cross_references(self): """Get the cross references in the heap as a list.""" out = [] cdef INDEX_T i for i in range(self.max_reference+1): out.append( self._crossref[i] ) return out scikit-image-0.13.1/skimage/graph/mcp.py000066400000000000000000000061631316256236300200310ustar00rootroot00000000000000from ._mcp import MCP, MCP_Geometric, MCP_Connect, MCP_Flexible def route_through_array(array, start, end, fully_connected=True, geometric=True): """Simple example of how to use the MCP and MCP_Geometric classes. See the MCP and MCP_Geometric class documentation for explanation of the path-finding algorithm. Parameters ---------- array : ndarray Array of costs. start : iterable n-d index into `array` defining the starting point end : iterable n-d index into `array` defining the end point fully_connected : bool (optional) If True, diagonal moves are permitted, if False, only axial moves. geometric : bool (optional) If True, the MCP_Geometric class is used to calculate costs, if False, the MCP base class is used. See the class documentation for an explanation of the differences between MCP and MCP_Geometric. Returns ------- path : list List of n-d index tuples defining the path from `start` to `end`. cost : float Cost of the path. If `geometric` is False, the cost of the path is the sum of the values of `array` along the path. If `geometric` is True, a finer computation is made (see the documentation of the MCP_Geometric class). See Also -------- MCP, MCP_Geometric Examples -------- >>> import numpy as np >>> from skimage.graph import route_through_array >>> >>> image = np.array([[1, 3], [10, 12]]) >>> image array([[ 1, 3], [10, 12]]) >>> # Forbid diagonal steps >>> route_through_array(image, [0, 0], [1, 1], fully_connected=False) ([(0, 0), (0, 1), (1, 1)], 9.5) >>> # Now allow diagonal steps: the path goes directly from start to end >>> route_through_array(image, [0, 0], [1, 1]) ([(0, 0), (1, 1)], 9.1923881554251192) >>> # Cost is the sum of array values along the path (16 = 1 + 3 + 12) >>> route_through_array(image, [0, 0], [1, 1], fully_connected=False, ... geometric=False) ([(0, 0), (0, 1), (1, 1)], 16.0) >>> # Larger array where we display the path that is selected >>> image = np.arange((36)).reshape((6, 6)) >>> image array([[ 0, 1, 2, 3, 4, 5], [ 6, 7, 8, 9, 10, 11], [12, 13, 14, 15, 16, 17], [18, 19, 20, 21, 22, 23], [24, 25, 26, 27, 28, 29], [30, 31, 32, 33, 34, 35]]) >>> # Find the path with lowest cost >>> indices, weight = route_through_array(image, (0, 0), (5, 5)) >>> indices = np.array(indices).T >>> path = np.zeros_like(image) >>> path[indices[0], indices[1]] = 1 >>> path array([[1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 1]]) """ start, end = tuple(start), tuple(end) if geometric: mcp_class = MCP_Geometric else: mcp_class = MCP m = mcp_class(array, fully_connected=fully_connected) costs, traceback_array = m.find_costs([start], [end]) return m.traceback(end), costs[end] scikit-image-0.13.1/skimage/graph/setup.py000066400000000000000000000026121316256236300204050ustar00rootroot00000000000000#!/usr/bin/env python from skimage._build import cython import os.path base_path = os.path.abspath(os.path.dirname(__file__)) def configuration(parent_package='', top_path=None): from numpy.distutils.misc_util import Configuration, get_numpy_include_dirs config = Configuration('graph', parent_package, top_path) config.add_data_dir('tests') # This function tries to create C files from the given .pyx files. If # it fails, try to build with pre-generated .c files. cython(['_spath.pyx'], working_path=base_path) cython(['_mcp.pyx'], working_path=base_path) cython(['heap.pyx'], working_path=base_path) config.add_extension('_spath', sources=['_spath.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension('_mcp', sources=['_mcp.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension('heap', sources=['heap.c'], include_dirs=[get_numpy_include_dirs()]) return config if __name__ == '__main__': from numpy.distutils.core import setup setup(maintainer='scikit-image Developers', maintainer_email='scikit-image@python.org', description='Graph-based Image-processing Algorithms', url='https://github.com/scikit-image/scikit-image', license='Modified BSD', **(configuration(top_path='').todict()) ) scikit-image-0.13.1/skimage/graph/spath.py000066400000000000000000000065431316256236300203730ustar00rootroot00000000000000import numpy as np from . import _spath def shortest_path(arr, reach=1, axis=-1, output_indexlist=False): """Find the shortest path through an n-d array from one side to another. Parameters ---------- arr : ndarray of float64 reach : int, optional By default (``reach = 1``), the shortest path can only move one row up or down for every step it moves forward (i.e., the path gradient is limited to 1). `reach` defines the number of elements that can be skipped along each non-axis dimension at each step. axis : int, optional The axis along which the path must always move forward (default -1) output_indexlist: bool, optional See return value `p` for explanation. Returns ------- p : iterable of int For each step along `axis`, the coordinate of the shortest path. If `output_indexlist` is True, then the path is returned as a list of n-d tuples that index into `arr`. If False, then the path is returned as an array listing the coordinates of the path along the non-axis dimensions for each step along the axis dimension. That is, `p.shape == (arr.shape[axis], arr.ndim-1)` except that p is squeezed before returning so if `arr.ndim == 2`, then `p.shape == (arr.shape[axis],)` cost : float Cost of path. This is the absolute sum of all the differences along the path. """ # First: calculate the valid moves from any given position. Basically, # always move +1 along the given axis, and then can move anywhere within # a grid defined by the reach. if axis < 0: axis += arr.ndim offset_ind_shape = (2 * reach + 1,) * (arr.ndim - 1) offset_indices = np.indices(offset_ind_shape) - reach offset_indices = np.insert(offset_indices, axis, np.ones(offset_ind_shape), axis=0) offset_size = np.multiply.reduce(offset_ind_shape) offsets = np.reshape(offset_indices, (arr.ndim, offset_size), order='F').T # Valid starting positions are anywhere on the hyperplane defined by # position 0 on the given axis. Ending positions are anywhere on the # hyperplane at position -1 along the same. non_axis_shape = arr.shape[:axis] + arr.shape[axis + 1:] non_axis_indices = np.indices(non_axis_shape) non_axis_size = np.multiply.reduce(non_axis_shape) start_indices = np.insert(non_axis_indices, axis, np.zeros(non_axis_shape), axis=0) starts = np.reshape(start_indices, (arr.ndim, non_axis_size), order='F').T end_indices = np.insert(non_axis_indices, axis, -np.ones(non_axis_shape), axis=0) ends = np.reshape(end_indices, (arr.ndim, non_axis_size), order='F').T # Find the minimum-cost path to one of the end-points m = _spath.MCP_Diff(arr, offsets=offsets) costs, traceback = m.find_costs(starts, ends, find_all_ends=False) # Figure out which end-point was found for end in ends: cost = costs[tuple(end)] if cost != np.inf: break traceback = m.traceback(end) if not output_indexlist: traceback = np.array(traceback) traceback = np.concatenate([traceback[:, :axis], traceback[:, axis + 1:]], axis=1) traceback = np.squeeze(traceback) return traceback, cost scikit-image-0.13.1/skimage/graph/tests/000077500000000000000000000000001316256236300200345ustar00rootroot00000000000000scikit-image-0.13.1/skimage/graph/tests/__init__.py000066400000000000000000000001771316256236300221520ustar00rootroot00000000000000from ..._shared.testing import setup_test, teardown_test def setup(): setup_test() def teardown(): teardown_test() scikit-image-0.13.1/skimage/graph/tests/test_anisotropy.py000066400000000000000000000043601316256236300236570ustar00rootroot00000000000000import skimage.graph.mcp as mcp from numpy.testing import (assert_array_equal, assert_almost_equal, ) import numpy as np a = np.ones((8, 8), dtype=np.float32) horizontal_ramp = np.array([[ 0., 1., 2., 3., 4., 5., 6., 7.,], [ 0., 1., 2., 3., 4., 5., 6., 7.,], [ 0., 1., 2., 3., 4., 5., 6., 7.,], [ 0., 1., 2., 3., 4., 5., 6., 7.,], [ 0., 1., 2., 3., 4., 5., 6., 7.,], [ 0., 1., 2., 3., 4., 5., 6., 7.,], [ 0., 1., 2., 3., 4., 5., 6., 7.,], [ 0., 1., 2., 3., 4., 5., 6., 7.,]]) vertical_ramp = np.array( [[ 0., 0., 0., 0., 0., 0., 0., 0.,], [ 1., 1., 1., 1., 1., 1., 1., 1.,], [ 2., 2., 2., 2., 2., 2., 2., 2.,], [ 3., 3., 3., 3., 3., 3., 3., 3.,], [ 4., 4., 4., 4., 4., 4., 4., 4.,], [ 5., 5., 5., 5., 5., 5., 5., 5.,], [ 6., 6., 6., 6., 6., 6., 6., 6.,], [ 7., 7., 7., 7., 7., 7., 7., 7.,]]) def test_anisotropy(): # Create seeds; vertical seeds create a horizonral ramp seeds_for_horizontal = [(i, 0) for i in range(8) ] seeds_for_vertcal = [(0, i) for i in range(8) ] for sy in range(1, 5): for sx in range(1,5): sampling = sy, sx # Trace horizontally m1 = mcp.MCP_Geometric(a, sampling=sampling, fully_connected=True) costs1, traceback = m1.find_costs(seeds_for_horizontal) # Trace vertically m2 = mcp.MCP_Geometric(a, sampling=sampling, fully_connected=True) costs2, traceback = m2.find_costs(seeds_for_vertcal) # Check assert_array_equal(costs1, horizontal_ramp * sx) assert_array_equal(costs2, vertical_ramp * sy) if __name__ == "__main__": np.testing.run_module_suite() scikit-image-0.13.1/skimage/graph/tests/test_connect.py000066400000000000000000000051101316256236300230730ustar00rootroot00000000000000import skimage.graph.mcp as mcp # import stentseg.graph._mcp as mcp from numpy.testing import (assert_array_equal, assert_almost_equal, ) import numpy as np a = np.ones((8, 8), dtype=np.float32) count = 0 class MCP(mcp.MCP_Connect): def _reset(self): """ Reset the id map. """ mcp.MCP_Connect._reset(self) self._conn = {} self._bestconn = {} def create_connection(self, id1, id2, pos1, pos2, cost1, cost2): # Process data hash = min(id1, id2), max(id1, id2) val = min(pos1, pos2), max(pos1, pos2) cost = min(cost1, cost2) # Add to total list self._conn.setdefault(hash, []).append(val) # Keep track of connection with lowest cost curcost = self._bestconn.get(hash, (np.inf,))[0] if cost < curcost: self._bestconn[hash] = (cost,) + val def test_connections(): # Create MCP object with three seed points mcp = MCP(a) costs, traceback = mcp.find_costs([ (1,1), (7,7), (1,7) ]) # Test that all three seed points are connected connections = set(mcp._conn.keys()) assert (0, 1) in connections assert (1, 2) in connections assert (0, 2) in connections # Test that any two neighbors have only been connected once for position_tuples in mcp._conn.values(): n1 = len(position_tuples) n2 = len(set(position_tuples)) assert n1 == n2 # For seed 0 and 1 cost, pos1, pos2 = mcp._bestconn[(0,1)] # Test meeting points assert (pos1, pos2) == ( (3,3), (4,4) ) # Test the whole path path = mcp.traceback(pos1) + list(reversed(mcp.traceback(pos2))) assert_array_equal(path, [(1, 1), (2, 2), (3, 3), (4, 4), (5, 5), (6, 6), (7, 7)]) # For seed 1 and 2 cost, pos1, pos2 = mcp._bestconn[(1,2)] # Test meeting points assert (pos1, pos2) == ( (3,7), (4,7) ) # Test the whole path path = mcp.traceback(pos1) + list(reversed(mcp.traceback(pos2))) assert_array_equal(path, [(1, 7), (2, 7), (3, 7), (4, 7), (5, 7), (6, 7), (7, 7)]) # For seed 0 and 2 cost, pos1, pos2 = mcp._bestconn[(0,2)] # Test meeting points assert (pos1, pos2) == ( (1,3), (1,4) ) # Test the whole path path = mcp.traceback(pos1) + list(reversed(mcp.traceback(pos2))) assert_array_equal(path, [(1, 1), (1, 2), (1, 3), (1, 4), (1, 5), (1, 6), (1, 7)]) if __name__ == "__main__": np.testing.run_module_suite()scikit-image-0.13.1/skimage/graph/tests/test_flexible.py000066400000000000000000000035321316256236300232420ustar00rootroot00000000000000import skimage.graph.mcp as mcp from numpy.testing import (assert_array_equal, assert_almost_equal, ) import numpy as np a = np.ones((8, 8), dtype=np.float32) a[1::2] *= 2.0 class FlexibleMCP(mcp.MCP_Flexible): """ Simple MCP subclass that allows the front to travel a certain distance from the seed point, and uses a constant cost factor that is independant of the cost array. """ def _reset(self): mcp.MCP_Flexible._reset(self) self._distance = np.zeros((8, 8), dtype=np.float32).ravel() def goal_reached(self, index, cumcost): if self._distance[index] > 4: return 2 else: return 0 def travel_cost(self, index, new_index, offset_length): return 1.0 # fixed cost def examine_neighbor(self, index, new_index, offset_length): pass # We do not test this def update_node(self, index, new_index, offset_length): self._distance[new_index] = self._distance[index] + 1 def test_flexible(): # Create MCP and do a traceback mcp = FlexibleMCP(a) costs, traceback = mcp.find_costs([(0, 0)]) # Check that inner part is correct. This basically # tests whether travel_cost works. assert_array_equal(costs[:4,:4], [[1, 2, 3, 4], [2, 2, 3, 4], [3, 3, 3, 4], [4, 4, 4, 4]]) # Test that the algorithm stopped at the right distance. # Note that some of the costs are filled in but not yet frozen, # so we take a bit of margin assert np.all( costs[-2:,:] == np.inf ) assert np.all( costs[:,-2:] == np.inf ) #print(costs) if __name__ == "__main__": np.testing.run_module_suite() scikit-image-0.13.1/skimage/graph/tests/test_heap.py000066400000000000000000000022411316256236300223610ustar00rootroot00000000000000import time import random import skimage.graph.heap as heap from skimage._shared.testing import test_parallel @test_parallel() def test_heap(): _test_heap(100000, True) _test_heap(100000, False) def _test_heap(n, fast_update): # generate random numbers with duplicates random.seed(0) a = [random.uniform(1.0, 100.0) for i in range(n // 2)] a = a + a t0 = time.clock() # insert in heap with random removals if fast_update: h = heap.FastUpdateBinaryHeap(128, n) else: h = heap.BinaryHeap(128) for i in range(len(a)): h.push(a[i], i) if a[i] < 25: # double-push same ref sometimes to test fast update codepaths h.push(2 * a[i], i) if 25 < a[i] < 50: # pop some to test random removal h.pop() # pop from heap b = [] while True: try: b.append(h.pop()[0]) except IndexError: break t1 = time.clock() # verify for i in range(1, len(b)): assert(b[i] >= b[i - 1]) return t1 - t0 if __name__ == "__main__": from numpy.testing import run_module_suite run_module_suite() scikit-image-0.13.1/skimage/graph/tests/test_mcp.py000066400000000000000000000137351316256236300222350ustar00rootroot00000000000000import numpy as np from numpy.testing import (assert_array_equal, assert_almost_equal, ) import skimage.graph.mcp as mcp from skimage._shared._warnings import expected_warnings np.random.seed(0) a = np.ones((8, 8), dtype=np.float32) a[1:-1, 1] = 0 a[1, 1:-1] = 0 warning_optional = r'|\A\Z' def test_basic(): with expected_warnings(['Upgrading NumPy' + warning_optional]): m = mcp.MCP(a, fully_connected=True) costs, traceback = m.find_costs([(1, 6)]) return_path = m.traceback((7, 2)) assert_array_equal(costs, [[1., 1., 1., 1., 1., 1., 1., 1.], [1., 0., 0., 0., 0., 0., 0., 1.], [1., 0., 1., 1., 1., 1., 1., 1.], [1., 0., 1., 2., 2., 2., 2., 2.], [1., 0., 1., 2., 3., 3., 3., 3.], [1., 0., 1., 2., 3., 4., 4., 4.], [1., 0., 1., 2., 3., 4., 5., 5.], [1., 1., 1., 2., 3., 4., 5., 6.]]) assert_array_equal(return_path, [(1, 6), (1, 5), (1, 4), (1, 3), (1, 2), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 2)]) def test_neg_inf(): expected_costs = np.where(a == 1, np.inf, 0) expected_path = [(1, 6), (1, 5), (1, 4), (1, 3), (1, 2), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1)] test_neg = np.where(a == 1, -1, 0) test_inf = np.where(a == 1, np.inf, 0) with expected_warnings(['Upgrading NumPy' + warning_optional]): m = mcp.MCP(test_neg, fully_connected=True) costs, traceback = m.find_costs([(1, 6)]) return_path = m.traceback((6, 1)) assert_array_equal(costs, expected_costs) assert_array_equal(return_path, expected_path) with expected_warnings(['Upgrading NumPy' + warning_optional]): m = mcp.MCP(test_inf, fully_connected=True) costs, traceback = m.find_costs([(1, 6)]) return_path = m.traceback((6, 1)) assert_array_equal(costs, expected_costs) assert_array_equal(return_path, expected_path) def test_route(): with expected_warnings(['Upgrading NumPy' + warning_optional]): return_path, cost = mcp.route_through_array(a, (1, 6), (7, 2), geometric=True) assert_almost_equal(cost, np.sqrt(2) / 2) assert_array_equal(return_path, [(1, 6), (1, 5), (1, 4), (1, 3), (1, 2), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 2)]) def test_no_diagonal(): with expected_warnings(['Upgrading NumPy' + warning_optional]): m = mcp.MCP(a, fully_connected=False) costs, traceback = m.find_costs([(1, 6)]) return_path = m.traceback((7, 2)) assert_array_equal(costs, [[2., 1., 1., 1., 1., 1., 1., 2.], [1., 0., 0., 0., 0., 0., 0., 1.], [1., 0., 1., 1., 1., 1., 1., 2.], [1., 0., 1., 2., 2., 2., 2., 3.], [1., 0., 1., 2., 3., 3., 3., 4.], [1., 0., 1., 2., 3., 4., 4., 5.], [1., 0., 1., 2., 3., 4., 5., 6.], [2., 1., 2., 3., 4., 5., 6., 7.]]) assert_array_equal(return_path, [(1, 6), (1, 5), (1, 4), (1, 3), (1, 2), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1), (7, 2)]) def test_offsets(): offsets = [(1, i) for i in range(10)] + [(1, -i) for i in range(1, 10)] with expected_warnings(['Upgrading NumPy' + warning_optional]): m = mcp.MCP(a, offsets=offsets) costs, traceback = m.find_costs([(1, 6)]) assert_array_equal(traceback, [[-2, -2, -2, -2, -2, -2, -2, -2], [-2, -2, -2, -2, -2, -2, -1, -2], [15, 14, 13, 12, 11, 10, 0, 1], [10, 0, 1, 2, 3, 4, 5, 6], [10, 0, 1, 2, 3, 4, 5, 6], [10, 0, 1, 2, 3, 4, 5, 6], [10, 0, 1, 2, 3, 4, 5, 6], [10, 0, 1, 2, 3, 4, 5, 6]]) def test_crashing(): for shape in [(100, 100), (5, 8, 13, 17)] * 5: yield _test_random, shape def _test_random(shape): # Just tests for crashing -- not for correctness. a = np.random.rand(*shape).astype(np.float32) starts = [[0] * len(shape), [-1] * len(shape), (np.random.rand(len(shape)) * shape).astype(int)] ends = [(np.random.rand(len(shape)) * shape).astype(int) for i in range(4)] with expected_warnings(['Upgrading NumPy' + warning_optional]): m = mcp.MCP(a, fully_connected=True) costs, offsets = m.find_costs(starts) for point in [(np.random.rand(len(shape)) * shape).astype(int) for i in range(4)]: m.traceback(point) m._reset() m.find_costs(starts, ends) for end in ends: m.traceback(end) return a, costs, offsets if __name__ == "__main__": np.testing.run_module_suite() scikit-image-0.13.1/skimage/graph/tests/test_spath.py000066400000000000000000000016441316256236300225710ustar00rootroot00000000000000import numpy as np from numpy.testing import assert_equal, assert_array_equal import skimage.graph.spath as spath def test_basic(): x = np.array([[1, 1, 3], [0, 2, 0], [4, 3, 1]]) path, cost = spath.shortest_path(x) assert_array_equal(path, [0, 0, 1]) assert_equal(cost, 1) def test_reach(): x = np.array([[1, 1, 3], [0, 2, 0], [4, 3, 1]]) path, cost = spath.shortest_path(x, reach=2) assert_array_equal(path, [0, 0, 2]) assert_equal(cost, 0) def test_non_square(): x = np.array([[1, 1, 1, 1, 5, 5, 5], [5, 0, 0, 5, 9, 1, 1], [0, 5, 1, 0, 5, 5, 0], [6, 1, 1, 5, 0, 0, 1]]) path, cost = spath.shortest_path(x, reach=2) assert_array_equal(path, [2, 1, 1, 2, 3, 3, 2]) assert_equal(cost, 0) if __name__ == "__main__": np.testing.run_module_suite() scikit-image-0.13.1/skimage/io/000077500000000000000000000000001316256236300162005ustar00rootroot00000000000000scikit-image-0.13.1/skimage/io/__init__.py000066400000000000000000000033511316256236300203130ustar00rootroot00000000000000"""Utilities to read and write images in various formats. The following plug-ins are available: """ from .manage_plugins import * from .sift import * from .collection import * from ._io import * from ._image_stack import * reset_plugins() WRAP_LEN = 73 def _separator(char, lengths): return [char * separator_length for separator_length in lengths] def _format_plugin_info_table(info_table, column_lengths): """Add separators and column titles to plugin info table.""" info_table.insert(0, _separator('=', column_lengths)) info_table.insert(1, ('Plugin', 'Description')) info_table.insert(2, _separator('-', column_lengths)) info_table.append(_separator('=', column_lengths)) def _update_doc(doc): """Add a list of plugins to the module docstring, formatted as a ReStructuredText table. """ from textwrap import wrap info_table = [(p, plugin_info(p).get('description', 'no description')) for p in available_plugins if not p == 'test'] if len(info_table) > 0: name_length = max([len(n) for (n, _) in info_table]) else: name_length = 0 description_length = WRAP_LEN - 1 - name_length column_lengths = [name_length, description_length] _format_plugin_info_table(info_table, column_lengths) for (name, plugin_description) in info_table: description_lines = wrap(plugin_description, description_length) name_column = [name] name_column.extend(['' for _ in range(len(description_lines) - 1)]) for name, description in zip(name_column, description_lines): doc += "%s %s\n" % (name.ljust(name_length), description) doc = doc.strip() return doc if __doc__ is not None: __doc__ = _update_doc(__doc__) scikit-image-0.13.1/skimage/io/_image_stack.py000066400000000000000000000010721316256236300211600ustar00rootroot00000000000000import numpy as np __all__ = ['image_stack', 'push', 'pop'] # Shared image queue image_stack = [] def push(img): """Push an image onto the shared image stack. Parameters ---------- img : ndarray Image to push. """ if not isinstance(img, np.ndarray): raise ValueError("Can only push ndarrays to the image stack.") image_stack.append(img) def pop(): """Pop an image from the shared image stack. Returns ------- img : ndarray Image popped from the stack. """ return image_stack.pop() scikit-image-0.13.1/skimage/io/_io.py000066400000000000000000000130341316256236300173210ustar00rootroot00000000000000from io import BytesIO import numpy as np import six from ..io.manage_plugins import call_plugin from ..color import rgb2grey from .util import file_or_url_context from ..exposure import is_low_contrast from .._shared.utils import all_warnings, warn __all__ = ['imread', 'imsave', 'imshow', 'show', 'imread_collection', 'imshow_collection'] def imread(fname, as_grey=False, plugin=None, flatten=None, **plugin_args): """Load an image from file. Parameters ---------- fname : string Image file name, e.g. ``test.jpg`` or URL. as_grey : bool If True, convert color images to grey-scale (64-bit floats). Images that are already in grey-scale format are not converted. plugin : str Name of plugin to use. By default, the different plugins are tried (starting with the Python Imaging Library) until a suitable candidate is found. If not given and fname is a tiff file, the tifffile plugin will be used. Other Parameters ---------------- plugin_args : keywords Passed to the given plugin. flatten : bool Backward compatible keyword, superseded by `as_grey`. plugin_args : keywords Passed to the given plugin. Returns ------- img_array : ndarray The different colour bands/channels are stored in the third dimension, such that a grey-image is MxN, an RGB-image MxNx3 and an RGBA-image MxNx4. """ # Backward compatibility if flatten is not None: as_grey = flatten if plugin is None and hasattr(fname, 'lower'): if fname.lower().endswith(('.tiff', '.tif')): plugin = 'tifffile' with file_or_url_context(fname) as fname: img = call_plugin('imread', fname, plugin=plugin, **plugin_args) if not hasattr(img, 'ndim'): return img if img.ndim > 2: if img.shape[-1] not in (3, 4) and img.shape[-3] in (3, 4): img = np.swapaxes(img, -1, -3) img = np.swapaxes(img, -2, -3) if as_grey: img = rgb2grey(img) return img def imread_collection(load_pattern, conserve_memory=True, plugin=None, **plugin_args): """ Load a collection of images. Parameters ---------- load_pattern : str or list List of objects to load. These are usually filenames, but may vary depending on the currently active plugin. See the docstring for ``ImageCollection`` for the default behaviour of this parameter. conserve_memory : bool, optional If True, never keep more than one in memory at a specific time. Otherwise, images will be cached once they are loaded. Returns ------- ic : ImageCollection Collection of images. Other parameters ---------------- plugin_args : keywords Passed to the given plugin. """ return call_plugin('imread_collection', load_pattern, conserve_memory, plugin=plugin, **plugin_args) def imsave(fname, arr, plugin=None, **plugin_args): """Save an image to file. Parameters ---------- fname : str Target filename. arr : ndarray of shape (M,N) or (M,N,3) or (M,N,4) Image data. plugin : str Name of plugin to use. By default, the different plugins are tried (starting with the Python Imaging Library) until a suitable candidate is found. If not given and fname is a tiff file, the tifffile plugin will be used. Other parameters ---------------- plugin_args : keywords Passed to the given plugin. """ if plugin is None and hasattr(fname, 'lower'): if fname.lower().endswith(('.tiff', '.tif')): plugin = 'tifffile' if is_low_contrast(arr): warn('%s is a low contrast image' % fname) return call_plugin('imsave', fname, arr, plugin=plugin, **plugin_args) def imshow(arr, plugin=None, **plugin_args): """Display an image. Parameters ---------- arr : ndarray or str Image data or name of image file. plugin : str Name of plugin to use. By default, the different plugins are tried (starting with the Python Imaging Library) until a suitable candidate is found. Other parameters ---------------- plugin_args : keywords Passed to the given plugin. """ if isinstance(arr, six.string_types): arr = call_plugin('imread', arr, plugin=plugin) return call_plugin('imshow', arr, plugin=plugin, **plugin_args) def imshow_collection(ic, plugin=None, **plugin_args): """Display a collection of images. Parameters ---------- ic : ImageCollection Collection to display. plugin : str Name of plugin to use. By default, the different plugins are tried until a suitable candidate is found. Other parameters ---------------- plugin_args : keywords Passed to the given plugin. """ return call_plugin('imshow_collection', ic, plugin=plugin, **plugin_args) def show(): '''Display pending images. Launch the event loop of the current gui plugin, and display all pending images, queued via `imshow`. This is required when using `imshow` from non-interactive scripts. A call to `show` will block execution of code until all windows have been closed. Examples -------- >>> import skimage.io as io >>> for i in range(4): ... ax_im = io.imshow(np.random.rand(50, 50)) >>> io.show() # doctest: +SKIP ''' return call_plugin('_app_show') scikit-image-0.13.1/skimage/io/_plugins/000077500000000000000000000000001316256236300200205ustar00rootroot00000000000000scikit-image-0.13.1/skimage/io/_plugins/__init__.py000066400000000000000000000000001316256236300221170ustar00rootroot00000000000000scikit-image-0.13.1/skimage/io/_plugins/_colormixer.pyx000066400000000000000000000307261316256236300231140ustar00rootroot00000000000000#cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False #cython: wraparound=False """Colour Mixer NumPy does not do overflow checking when adding or multiplying integers, so currently the only way to clip results efficiently (without making copies of the data) is with an extension such as this one. """ import cython cimport numpy as cnp from libc.math cimport exp, pow def add(cnp.ndarray[cnp.uint8_t, ndim=3] img, cnp.ndarray[cnp.uint8_t, ndim=3] stateimg, Py_ssize_t channel, Py_ssize_t amount): """Add a given amount to a colour channel of `stateimg`, and store the result in `img`. Overflow is clipped. Parameters ---------- img : (M, N, 3) ndarray of uint8 Output image. stateimg : (M, N, 3) ndarray of uint8 Input image. channel : int Channel (0 for "red", 1 for "green", 2 for "blue"). amount : int Value to add. """ cdef Py_ssize_t height = img.shape[0] cdef Py_ssize_t width = img.shape[1] cdef Py_ssize_t k = channel cdef Py_ssize_t n = amount cdef cnp.int16_t op_result cdef cnp.uint8_t lut[256] cdef Py_ssize_t i, j, l with nogil: for l from 0 <= l < 256: op_result = (l + n) if op_result > 255: op_result = 255 elif op_result < 0: op_result = 0 else: pass lut[l] = op_result for i from 0 <= i < height: for j from 0 <= j < width: img[i, j, k] = lut[stateimg[i,j,k]] def multiply(cnp.ndarray[cnp.uint8_t, ndim=3] img, cnp.ndarray[cnp.uint8_t, ndim=3] stateimg, Py_ssize_t channel, float amount): """Multiply a colour channel of `stateimg` by a certain amount, and store the result in `img`. Overflow is clipped. Parameters ---------- img : (M, N, 3) ndarray of uint8 Output image. stateimg : (M, N, 3) ndarray of uint8 Input image. channel : int Channel (0 for "red", 1 for "green", 2 for "blue"). amount : float Multiplication factor. """ cdef Py_ssize_t height = img.shape[0] cdef Py_ssize_t width = img.shape[1] cdef Py_ssize_t k = channel cdef float n = amount cdef float op_result cdef cnp.uint8_t lut[256] cdef Py_ssize_t i, j, l with nogil: for l from 0 <= l < 256: op_result = l * n if op_result > 255: op_result = 255 elif op_result < 0: op_result = 0 else: pass lut[l] = op_result for i from 0 <= i < height: for j from 0 <= j < width: img[i,j,k] = lut[stateimg[i,j,k]] def brightness(cnp.ndarray[cnp.uint8_t, ndim=3] img, cnp.ndarray[cnp.uint8_t, ndim=3] stateimg, float factor, Py_ssize_t offset): """Modify the brightness of an image. 'factor' is multiplied to all channels, which are then added by 'amount'. Overflow is clipped. Parameters ---------- img : (M, N, 3) ndarray of uint8 Output image. stateimg : (M, N, 3) ndarray of uint8 Input image. factor : float Multiplication factor. offset : int Ammount to add to each channel. """ cdef Py_ssize_t height = img.shape[0] cdef Py_ssize_t width = img.shape[1] cdef float op_result cdef cnp.uint8_t lut[256] cdef Py_ssize_t i, j, k with nogil: for k from 0 <= k < 256: op_result = k * factor + offset if op_result > 255: op_result = 255 elif op_result < 0: op_result = 0 else: pass lut[k] = op_result for i from 0 <= i < height: for j from 0 <= j < width: img[i,j,0] = lut[stateimg[i,j,0]] img[i,j,1] = lut[stateimg[i,j,1]] img[i,j,2] = lut[stateimg[i,j,2]] def sigmoid_gamma(cnp.ndarray[cnp.uint8_t, ndim=3] img, cnp.ndarray[cnp.uint8_t, ndim=3] stateimg, float alpha, float beta): cdef Py_ssize_t height = img.shape[0] cdef Py_ssize_t width = img.shape[1] cdef Py_ssize_t i, j, k cdef float c1 = 1 / (1 + exp(beta)) cdef float c2 = 1 / (1 + exp(beta - alpha)) - c1 cdef cnp.uint8_t lut[256] with nogil: # compute the lut for k from 0 <= k < 256: lut[k] = (((1 / (1 + exp(beta - (k / 255.) * alpha))) - c1) * 255 / c2) for i from 0 <= i < height: for j from 0 <= j < width: img[i,j,0] = lut[stateimg[i,j,0]] img[i,j,1] = lut[stateimg[i,j,1]] img[i,j,2] = lut[stateimg[i,j,2]] def gamma(cnp.ndarray[cnp.uint8_t, ndim=3] img, cnp.ndarray[cnp.uint8_t, ndim=3] stateimg, float gamma): cdef Py_ssize_t height = img.shape[0] cdef Py_ssize_t width = img.shape[1] cdef cnp.uint8_t lut[256] cdef Py_ssize_t i, j, k if gamma == 0: gamma = 0.00000000000000000001 gamma = 1./gamma with nogil: # compute the lut for k from 0 <= k < 256: lut[k] = ((pow((k / 255.), gamma) * 255)) for i from 0 <= i < height: for j from 0 <= j < width: img[i,j,0] = lut[stateimg[i,j,0]] img[i,j,1] = lut[stateimg[i,j,1]] img[i,j,2] = lut[stateimg[i,j,2]] cdef void rgb_2_hsv(float* RGB, float* HSV) nogil: cdef float R, G, B, H, S, V, MAX, MIN R = RGB[0] G = RGB[1] B = RGB[2] if R > 255: R = 255 elif R < 0: R = 0 else: pass if G > 255: G = 255 elif G < 0: G = 0 else: pass if B > 255: B = 255 elif B < 0: B = 0 else: pass if R < G: MIN = R MAX = G else: MIN = G MAX = R if B < MIN: MIN = B elif B > MAX: MAX = B else: pass V = MAX / 255. if MAX == MIN: H = 0. elif MAX == R: H = (60 * (G - B) / (MAX - MIN) + 360) % 360 elif MAX == G: H = 60 * (B - R) / (MAX - MIN) + 120 else: H = 60 * (R - G) / (MAX - MIN) + 240 if MAX == 0: S = 0 else: S = 1 - MIN / MAX HSV[0] = H HSV[1] = S HSV[2] = V cdef void hsv_2_rgb(float* HSV, float* RGB) nogil: cdef float H, S, V cdef float f, p, q, t, r, g, b cdef Py_ssize_t hi H = HSV[0] S = HSV[1] V = HSV[2] if H > 360: H = H % 360 elif H < 0: H = 360 - ((-1 * H) % 360) else: pass if S > 1: S = 1 elif S < 0: S = 0 else: pass if V > 1: V = 1 elif V < 0: V = 0 else: pass hi = ((H / 60.)) % 6 f = (H / 60.) - ((H / 60.)) p = V * (1 - S) q = V * (1 - f * S) t = V * (1 - (1 -f) * S) if hi == 0: r = V g = t b = p elif hi == 1: r = q g = V b = p elif hi == 2: r = p g = V b = t elif hi == 3: r = p g = q b = V elif hi == 4: r = t g = p b = V else: r = V g = p b = q RGB[0] = r RGB[1] = g RGB[2] = b def py_hsv_2_rgb(H, S, V): '''Convert an HSV value to RGB. Automatic clipping. Parameters ---------- H : float From 0. - 360. S : float From 0. - 1. V : float From 0. - 1. Returns ------- out : (R, G, B) ints Each from 0 - 255 conversion convention from here: http://en.wikipedia.org/wiki/HSL_and_HSV ''' cdef float HSV[3] cdef float RGB[3] HSV[0] = H HSV[1] = S HSV[2] = V hsv_2_rgb(HSV, RGB) R = int(RGB[0] * 255) G = int(RGB[1] * 255) B = int(RGB[2] * 255) return (R, G, B) def py_rgb_2_hsv(R, G, B): '''Convert an HSV value to RGB. Automatic clipping. Parameters ---------- R : int From 0. - 255. G : int From 0. - 255. B : int From 0. - 255. Returns ------- out : (H, S, V) floats Ranges (0...360), (0...1), (0...1) conversion convention from here: http://en.wikipedia.org/wiki/HSL_and_HSV ''' cdef float HSV[3] cdef float RGB[3] RGB[0] = R RGB[1] = G RGB[2] = B rgb_2_hsv(RGB, HSV) H = HSV[0] S = HSV[1] V = HSV[2] return (H, S, V) def hsv_add(cnp.ndarray[cnp.uint8_t, ndim=3] img, cnp.ndarray[cnp.uint8_t, ndim=3] stateimg, float h_amt, float s_amt, float v_amt): """Modify the image color by specifying additive HSV Values. Since the underlying images are RGB, all three values HSV must be specified at the same time. The RGB triplet in the image is converted to HSV, the operation is applied, and then the HSV triplet is converted back to RGB HSV values are scaled to H(0. - 360.), S(0. - 1.), V(0. - 1.) then the operation is performed and any overflow is clipped, then the reverse transform is performed. Those are the ranges to keep in mind, when passing in values. Parameters ---------- img : (M, N, 3) ndarray of uint8 Output image. stateimg : (M, N, 3) ndarray of uint8 Input image. h_amt : float Ammount to add to H channel. s_amt : float Ammount to add to S channel. v_amt : float Ammount to add to V channel. """ cdef Py_ssize_t height = img.shape[0] cdef Py_ssize_t width = img.shape[1] cdef float HSV[3] cdef float RGB[3] cdef Py_ssize_t i, j with nogil: for i from 0 <= i < height: for j from 0 <= j < width: RGB[0] = stateimg[i, j, 0] RGB[1] = stateimg[i, j, 1] RGB[2] = stateimg[i, j, 2] rgb_2_hsv(RGB, HSV) # Add operation HSV[0] += h_amt HSV[1] += s_amt HSV[2] += v_amt hsv_2_rgb(HSV, RGB) RGB[0] *= 255 RGB[1] *= 255 RGB[2] *= 255 img[i, j, 0] = RGB[0] img[i, j, 1] = RGB[1] img[i, j, 2] = RGB[2] def hsv_multiply(cnp.ndarray[cnp.uint8_t, ndim=3] img, cnp.ndarray[cnp.uint8_t, ndim=3] stateimg, float h_amt, float s_amt, float v_amt): """Modify the image color by specifying multiplicative HSV Values. Since the underlying images are RGB, all three values HSV must be specified at the same time. The RGB triplet in the image is converted to HSV, the operation is applied, and then the HSV triplet is converted back to RGB HSV values are scaled to H(0. - 360.), S(0. - 1.), V(0. - 1.) then the operation is performed and any overflow is clipped, then the reverse transform is performed. Those are the ranges to keep in mind, when passing in values. Note that since hue is in degrees, it makes no sense to multiply that channel, thus an add operation is performed on the hue. And the values given for h_amt, should be the same as for hsv_add Parameters ---------- img : (M, N, 3) ndarray of uint8 Output image. stateimg : (M, N, 3) ndarray of uint8 Input image. h_amt : float Ammount to add to H channel. s_amt : float Ammount by which to multiply S channel. v_amt : float Ammount by which to multiply V channel. """ cdef Py_ssize_t height = img.shape[0] cdef Py_ssize_t width = img.shape[1] cdef float HSV[3] cdef float RGB[3] cdef Py_ssize_t i, j with nogil: for i from 0 <= i < height: for j from 0 <= j < width: RGB[0] = stateimg[i, j, 0] RGB[1] = stateimg[i, j, 1] RGB[2] = stateimg[i, j, 2] rgb_2_hsv(RGB, HSV) # Multiply operation HSV[0] += h_amt HSV[1] *= s_amt HSV[2] *= v_amt hsv_2_rgb(HSV, RGB) RGB[0] *= 255 RGB[1] *= 255 RGB[2] *= 255 img[i, j, 0] = RGB[0] img[i, j, 1] = RGB[1] img[i, j, 2] = RGB[2] scikit-image-0.13.1/skimage/io/_plugins/_histograms.pyx000066400000000000000000000037001316256236300231010ustar00rootroot00000000000000#cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False #cython: wraparound=False import numpy as np cimport numpy as cnp cdef inline float tri_max(float a, float b, float c): cdef float MAX if a > b: MAX = a else: MAX = b if MAX > c: return MAX else: return c def histograms(cnp.ndarray[cnp.uint8_t, ndim=3] img, int nbins): '''Calculate the channel histograms of the current image. Parameters ---------- img : ndarray, uint8, ndim=3 The image to calculate the histogram. nbins : int The number of bins. Returns ------- out : (rcounts, gcounts, bcounts, vcounts) The binned histograms of the RGB channels and grayscale intensity. This is a NAIVE histogram routine, meant primarily for fast display. ''' cdef int width = img.shape[1] cdef int height = img.shape[0] cdef cnp.ndarray[cnp.int32_t, ndim=1] r, g, b, v r = np.zeros((nbins,), dtype=np.int32) g = np.zeros((nbins,), dtype=np.int32) b = np.zeros((nbins,), dtype=np.int32) v = np.zeros((nbins,), dtype=np.int32) cdef int i, j, k, rbin, gbin, bbin, vbin cdef float bin_width = 255./ nbins cdef float R, G, B, V for i in range(height): for j in range(width): R = img[i, j, 0] G = img[i, j, 1] B = img[i, j, 2] V = tri_max(R, G, B) rbin = (R / bin_width) gbin = (G / bin_width) bbin = (B / bin_width) vbin = (V / bin_width) # fully open last bin if R == 255: rbin -= 1 if G == 255: gbin -= 1 if B == 255: bbin -= 1 if V == 255: vbin -= 1 r[rbin] += 1 g[gbin] += 1 b[bbin] += 1 v[vbin] += 1 return (r, g, b, v) scikit-image-0.13.1/skimage/io/_plugins/fits_plugin.ini000066400000000000000000000001311316256236300230370ustar00rootroot00000000000000[fits] description = FITS image reading via PyFITS provides = imread, imread_collection scikit-image-0.13.1/skimage/io/_plugins/fits_plugin.py000066400000000000000000000111771316256236300227240ustar00rootroot00000000000000__all__ = ['imread', 'imread_collection'] import skimage.io as io try: from astropy.io import fits as pyfits except ImportError: try: import pyfits except ImportError: raise ImportError( "PyFITS could not be found. Please refer to\n" "http://www.stsci.edu/resources/software_hardware/pyfits\n" "for further instructions.") def imread(fname, dtype=None): """Load an image from a FITS file. Parameters ---------- fname : string Image file name, e.g. ``test.fits``. dtype : dtype, optional For FITS, this argument is ignored because Stefan is planning on removing the dtype argument from imread anyway. Returns ------- img_array : ndarray Unlike plugins such as PIL, where different colour bands/channels are stored in the third dimension, FITS images are greyscale-only and can be N-dimensional, so an array of the native FITS dimensionality is returned, without colour channels. Currently if no image is found in the file, None will be returned Notes ----- Currently FITS ``imread()`` always returns the first image extension when given a Multi-Extension FITS file; use ``imread_collection()`` (which does lazy loading) to get all the extensions at once. """ hdulist = pyfits.open(fname) # Iterate over FITS image extensions, ignoring any other extension types # such as binary tables, and get the first image data array: img_array = None for hdu in hdulist: if isinstance(hdu, pyfits.ImageHDU) or \ isinstance(hdu, pyfits.PrimaryHDU): if hdu.data is not None: img_array = hdu.data break hdulist.close() return img_array def imread_collection(load_pattern, conserve_memory=True): """Load a collection of images from one or more FITS files Parameters ---------- load_pattern : str or list List of extensions to load. Filename globbing is currently unsupported. converve_memory : bool If True, never keep more than one in memory at a specific time. Otherwise, images will be cached once they are loaded. Returns ------- ic : ImageCollection Collection of images. """ intype = type(load_pattern) if intype is not list and intype is not str: raise TypeError("Input must be a filename or list of filenames") # Ensure we have a list, otherwise we'll end up iterating over the string: if intype is not list: load_pattern = [load_pattern] # Generate a list of filename/extension pairs by opening the list of # files and finding the image extensions in each one: ext_list = [] for filename in load_pattern: hdulist = pyfits.open(filename) for n, hdu in zip(range(len(hdulist)), hdulist): if isinstance(hdu, pyfits.ImageHDU) or \ isinstance(hdu, pyfits.PrimaryHDU): # Ignore (primary) header units with no data (use '.size' # rather than '.data' to avoid actually loading the image): try: data_size = hdu.size() except TypeError: # (size changed to int in PyFITS 3.1) data_size = hdu.size if data_size > 0: ext_list.append((filename, n)) hdulist.close() return io.ImageCollection(ext_list, load_func=FITSFactory, conserve_memory=conserve_memory) def FITSFactory(image_ext): """Load an image extension from a FITS file and return a NumPy array Parameters ---------- image_ext : tuple FITS extension to load, in the format ``(filename, ext_num)``. The FITS ``(extname, extver)`` format is unsupported, since this function is not called directly by the user and ``imread_collection()`` does the work of figuring out which extensions need loading. """ # Expect a length-2 tuple with a filename as the first element: if not isinstance(image_ext, tuple): raise TypeError("Expected a tuple") if len(image_ext) != 2: raise ValueError("Expected a tuple of length 2") filename = image_ext[0] extnum = image_ext[1] if type(filename) is not str or type(extnum) is not int: raise ValueError("Expected a (filename, extension) tuple") hdulist = pyfits.open(filename) data = hdulist[extnum].data hdulist.close() if data is None: raise RuntimeError( "Extension %d of %s has no data" % (extnum, filename)) return data scikit-image-0.13.1/skimage/io/_plugins/freeimage_plugin.ini000066400000000000000000000001341316256236300240210ustar00rootroot00000000000000[freeimage] description = Load images using the FreeImage library provides = imread, imsave scikit-image-0.13.1/skimage/io/_plugins/freeimage_plugin.py000066400000000000000000000012761316256236300237020ustar00rootroot00000000000000from imageio import imread as imread_imageio, imsave as imsave_imageio from ..._shared.utils import deprecated @deprecated('imageio plugin') def imread(filename): """ img = imread(filename) Reads an image from file `filename` Parameters ---------- filename : file name Returns ------- img : ndarray """ img = imread_imageio(filename) return img @deprecated('imageio plugin') def imsave(filename, img): ''' imsave(filename, img) Save image to disk Image type is inferred from filename Parameters ---------- filename : file name img : image to be saved as nd array ''' imsave_imageio(filename, img) scikit-image-0.13.1/skimage/io/_plugins/gdal_plugin.ini000066400000000000000000000001321316256236300230020ustar00rootroot00000000000000[gdal] description = Image reading via the GDAL Library (www.gdal.org) provides = imread scikit-image-0.13.1/skimage/io/_plugins/gdal_plugin.py000066400000000000000000000006131316256236300226570ustar00rootroot00000000000000__all__ = ['imread'] try: import osgeo.gdal as gdal except ImportError: raise ImportError("The GDAL Library could not be found. " "Please refer to http://www.gdal.org/ " "for further instructions.") def imread(fname, dtype=None): """Load an image from file. """ ds = gdal.Open(fname) return ds.ReadAsArray().astype(dtype) scikit-image-0.13.1/skimage/io/_plugins/gtk_plugin.ini000066400000000000000000000001331316256236300226610ustar00rootroot00000000000000[gtk] description = Fast image display using the GTK library provides = imshow, _app_show scikit-image-0.13.1/skimage/io/_plugins/gtk_plugin.py000066400000000000000000000032641316256236300225420ustar00rootroot00000000000000from .util import prepare_for_display, window_manager, GuiLockError try: # we try to aquire the gui lock first # or else the gui import might trample another # gui's pyos_inputhook. window_manager.acquire('gtk') except GuiLockError as gle: print(gle) else: try: import gtk except ImportError: print('pygtk libraries not installed.') print('plugin not loaded.') window_manager._release('gtk') else: class ImageWindow(gtk.Window): def __init__(self, arr, mgr): gtk.Window.__init__(self) self.mgr = mgr self.mgr.add_window(self) self.connect("destroy", self.destroy) width = arr.shape[1] height = arr.shape[0] rstride = arr.strides[0] pb = gtk.gdk.pixbuf_new_from_data(arr.data, gtk.gdk.COLORSPACE_RGB, False, 8, width, height, rstride) self.img = gtk.Image() self.img.set_from_pixbuf(pb) self.add(self.img) self.img.show() def destroy(self, widget, data=None): self.mgr.remove_window(self) def imshow(arr): arr = prepare_for_display(arr) iw = ImageWindow(arr, window_manager) iw.show() def _app_show(): if window_manager.has_windows(): window_manager.register_callback(gtk.main_quit) gtk.main() else: print('no images to display') scikit-image-0.13.1/skimage/io/_plugins/imageio_plugin.ini000066400000000000000000000001301316256236300235030ustar00rootroot00000000000000[imageio] description = Image reading via the ImageIO Library provides = imread, imsave scikit-image-0.13.1/skimage/io/_plugins/imageio_plugin.py000066400000000000000000000001031316256236300233540ustar00rootroot00000000000000__all__ = ['imread', 'imsave'] from imageio import imread, imsave scikit-image-0.13.1/skimage/io/_plugins/imread_plugin.ini000066400000000000000000000001261316256236300233370ustar00rootroot00000000000000[imread] description = Image reading and writing via imread provides = imread, imsave scikit-image-0.13.1/skimage/io/_plugins/imread_plugin.py000066400000000000000000000017071316256236300232160ustar00rootroot00000000000000__all__ = ['imread', 'imsave'] from ...util.dtype import convert try: import imread as _imread except ImportError: raise ImportError("Imread could not be found" "Please refer to http://pypi.python.org/pypi/imread/ " "for further instructions.") def imread(fname, dtype=None): """Load an image from file. Parameters ---------- fname : str Name of input file """ im = _imread.imread(fname) if dtype is not None: im = convert(im, dtype) return im def imsave(fname, arr, format_str=None): """Save an image to disk. Parameters ---------- fname : str Name of destination file. arr : ndarray of uint8 or uint16 Array (image) to save. format_str: str,optional Format to save as. Notes ----- Currently, only 8-bit precision is supported. """ return _imread.imsave(fname, arr, formatstr=format_str) scikit-image-0.13.1/skimage/io/_plugins/matplotlib_plugin.ini000066400000000000000000000001731316256236300242470ustar00rootroot00000000000000[matplotlib] description = Display or save images using Matplotlib provides = imshow, imread, imshow_collection, _app_show scikit-image-0.13.1/skimage/io/_plugins/matplotlib_plugin.py000066400000000000000000000130421316256236300241170ustar00rootroot00000000000000from collections import namedtuple import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.axes_grid1 import make_axes_locatable from ...util import dtype as dtypes from ...exposure import is_low_contrast from ...util.colormap import viridis from ..._shared.utils import warn _default_colormap = 'gray' _nonstandard_colormap = viridis _diverging_colormap = 'RdBu' ImageProperties = namedtuple('ImageProperties', ['signed', 'out_of_range_float', 'low_data_range', 'unsupported_dtype']) def _get_image_properties(image): """Determine nonstandard properties of an input image. Parameters ---------- image : array The input image. Returns ------- ip : ImageProperties named tuple The properties of the image: - signed: whether the image has negative values. - out_of_range_float: if the image has floating point data outside of [-1, 1]. - low_data_range: if the image is in the standard image range (e.g. [0, 1] for a floating point image) but its data range would be too small to display with standard image ranges. - unsupported_dtype: if the image data type is not a standard skimage type, e.g. ``numpy.uint64``. """ immin, immax = np.min(image), np.max(image) imtype = image.dtype.type try: lo, hi = dtypes.dtype_range[imtype] except KeyError: lo, hi = immin, immax signed = immin < 0 out_of_range_float = (np.issubdtype(image.dtype, np.float) and (immin < lo or immax > hi)) low_data_range = (immin != immax and is_low_contrast(image)) unsupported_dtype = image.dtype not in dtypes._supported_types return ImageProperties(signed, out_of_range_float, low_data_range, unsupported_dtype) def _raise_warnings(image_properties): """Raise the appropriate warning for each nonstandard image type. Parameters ---------- image_properties : ImageProperties named tuple The properties of the considered image. """ ip = image_properties if ip.unsupported_dtype: warn("Non-standard image type; displaying image with " "stretched contrast.") if ip.low_data_range: warn("Low image data range; displaying image with " "stretched contrast.") if ip.out_of_range_float: warn("Float image out of standard range; displaying " "image with stretched contrast.") def _get_display_range(image): """Return the display range for a given set of image properties. Parameters ---------- image : array The input image. Returns ------- lo, hi : same type as immin, immax The display range to be used for the input image. cmap : string The name of the colormap to use. """ ip = _get_image_properties(image) immin, immax = np.min(image), np.max(image) if ip.signed: magnitude = max(abs(immin), abs(immax)) lo, hi = -magnitude, magnitude cmap = _diverging_colormap elif any(ip): _raise_warnings(ip) lo, hi = immin, immax cmap = _nonstandard_colormap else: lo = 0 imtype = image.dtype.type hi = dtypes.dtype_range[imtype][1] cmap = _default_colormap return lo, hi, cmap def imshow(im, ax=None, show_cbar=None, **kwargs): """Show the input image and return the current axes. By default, the image is displayed in greyscale, rather than the matplotlib default colormap. Images are assumed to have standard range for their type. For example, if a floating point image has values in [0, 0.5], the most intense color will be gray50, not white. If the image exceeds the standard range, or if the range is too small to display, we fall back on displaying exactly the range of the input image, along with a colorbar to clearly indicate that this range transformation has occurred. For signed images, we use a diverging colormap centered at 0. Parameters ---------- im : array, shape (M, N[, 3]) The image to display. ax: `matplotlib.axes.Axes`, optional The axis to use for the image, defaults to plt.gca(). show_cbar: boolean, optional. Whether to show the colorbar (used to override default behavior). **kwargs : Keyword arguments These are passed directly to `matplotlib.pyplot.imshow`. Returns ------- ax_im : `matplotlib.pyplot.AxesImage` The `AxesImage` object returned by `plt.imshow`. """ if kwargs.get('cmap', None) == 'viridis': kwargs['cmap'] = viridis lo, hi, cmap = _get_display_range(im) kwargs.setdefault('interpolation', 'nearest') kwargs.setdefault('cmap', cmap) kwargs.setdefault('vmin', lo) kwargs.setdefault('vmax', hi) ax = ax or plt.gca() ax_im = ax.imshow(im, **kwargs) if (cmap != _default_colormap and show_cbar is not False) or show_cbar: divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) plt.colorbar(ax_im, cax=cax) ax.set_adjustable('box-forced') ax.get_figure().tight_layout() return ax_im def imshow_collection(ic, *args, **kwargs): """Display all images in the collection. """ fig, axes = plt.subplots(1, len(ic)) for n, image in enumerate(ic): kwargs['ax'] = axes[n] imshow(image, *args, **kwargs) imread = plt.imread show = plt.show def _app_show(): show() scikit-image-0.13.1/skimage/io/_plugins/pil_plugin.ini000066400000000000000000000001331316256236300226600ustar00rootroot00000000000000[pil] description = Image reading via the Python Imaging Library provides = imread, imsave scikit-image-0.13.1/skimage/io/_plugins/pil_plugin.py000066400000000000000000000170621316256236300225420ustar00rootroot00000000000000__all__ = ['imread', 'imsave'] import numpy as np from six import string_types from PIL import Image from ...util import img_as_ubyte, img_as_uint def imread(fname, dtype=None, img_num=None, **kwargs): """Load an image from file. Parameters ---------- fname : str or file File name or file-like-object. dtype : numpy dtype object or string specifier Specifies data type of array elements. img_num : int, optional Specifies which image to read in a file with multiple images (zero-indexed). kwargs : keyword pairs, optional Addition keyword arguments to pass through. Notes ----- Files are read using the Python Imaging Libary. See PIL docs [1]_ for a list of supported formats. References ---------- .. [1] http://pillow.readthedocs.org/en/latest/handbook/image-file-formats.html """ if isinstance(fname, string_types): with open(fname, 'rb') as f: im = Image.open(f) return pil_to_ndarray(im, dtype=dtype, img_num=img_num) else: im = Image.open(fname) return pil_to_ndarray(im, dtype=dtype, img_num=img_num) def pil_to_ndarray(im, dtype=None, img_num=None): """Import a PIL Image object to an ndarray, in memory. Parameters ---------- Refer to ``imread``. """ try: # this will raise an IOError if the file is not readable im.getdata()[0] except IOError as e: site = "http://pillow.readthedocs.org/en/latest/installation.html#external-libraries" pillow_error_message = str(e) error_message = ('Could not load "%s" \n' 'Reason: "%s"\n' 'Please see documentation at: %s' % (im.filename, pillow_error_message, site)) raise ValueError(error_message) frames = [] grayscale = None i = 0 while 1: try: im.seek(i) except EOFError: break frame = im if img_num is not None and img_num != i: im.getdata()[0] i += 1 continue if im.format == 'PNG' and im.mode == 'I' and dtype is None: dtype = 'uint16' if im.mode == 'P': if grayscale is None: grayscale = _palette_is_grayscale(im) if grayscale: frame = im.convert('L') else: if im.format == 'PNG' and 'transparency' in im.info: frame = im.convert('RGBA') else: frame = im.convert('RGB') elif im.mode == '1': frame = im.convert('L') elif 'A' in im.mode: frame = im.convert('RGBA') elif im.mode == 'CMYK': frame = im.convert('RGB') if im.mode.startswith('I;16'): shape = im.size dtype = '>u2' if im.mode.endswith('B') else ' 1: return np.array(frames) elif frames: return frames[0] elif img_num: raise IndexError('Could not find image #%s' % img_num) def _palette_is_grayscale(pil_image): """Return True if PIL image in palette mode is grayscale. Parameters ---------- pil_image : PIL image PIL Image that is in Palette mode. Returns ------- is_grayscale : bool True if all colors in image palette are gray. """ assert pil_image.mode == 'P' # get palette as an array with R, G, B columns palette = np.asarray(pil_image.getpalette()).reshape((256, 3)) # Not all palette colors are used; unused colors have junk values. start, stop = pil_image.getextrema() valid_palette = palette[start:stop] # Image is grayscale if channel differences (R - G and G - B) # are all zero. return np.allclose(np.diff(valid_palette), 0) def ndarray_to_pil(arr, format_str=None): """Export an ndarray to a PIL object. Parameters ---------- Refer to ``imsave``. """ if arr.ndim == 3: arr = img_as_ubyte(arr) mode = {3: 'RGB', 4: 'RGBA'}[arr.shape[2]] elif format_str in ['png', 'PNG']: mode = 'I;16' mode_base = 'I' if arr.dtype.kind == 'f': arr = img_as_uint(arr) elif arr.max() < 256 and arr.min() >= 0: arr = arr.astype(np.uint8) mode = mode_base = 'L' else: arr = img_as_uint(arr) else: arr = img_as_ubyte(arr) mode = 'L' mode_base = 'L' try: array_buffer = arr.tobytes() except AttributeError: array_buffer = arr.tostring() # Numpy < 1.9 if arr.ndim == 2: im = Image.new(mode_base, arr.T.shape) try: im.frombytes(array_buffer, 'raw', mode) except AttributeError: im.fromstring(array_buffer, 'raw', mode) # PIL 1.1.7 else: image_shape = (arr.shape[1], arr.shape[0]) try: im = Image.frombytes(mode, image_shape, array_buffer) except AttributeError: im = Image.fromstring(mode, image_shape, array_buffer) # PIL 1.1.7 return im def imsave(fname, arr, format_str=None, **kwargs): """Save an image to disk. Parameters ---------- fname : str or file-like object Name of destination file. arr : ndarray of uint8 or float Array (image) to save. Arrays of data-type uint8 should have values in [0, 255], whereas floating-point arrays must be in [0, 1]. format_str: str Format to save as, this is defaulted to PNG if using a file-like object; this will be derived from the extension if fname is a string kwargs: dict Keyword arguments to the Pillow save function (or tifffile save function, for Tiff files). These are format dependent. For example, Pillow's JPEG save function supports an integer ``quality`` argument with values in [1, 95], while TIFFFile supports a ``compress`` integer argument with values in [0, 9]. Notes ----- Use the Python Imaging Libary. See PIL docs [1]_ for a list of other supported formats. All images besides single channel PNGs are converted using `img_as_uint8`. Single Channel PNGs have the following behavior: - Integer values in [0, 255] and Boolean types -> img_as_uint8 - Floating point and other integers -> img_as_uint16 References ---------- .. [1] http://pillow.readthedocs.org/en/latest/handbook/image-file-formats.html """ # default to PNG if file-like object if not isinstance(fname, string_types) and format_str is None: format_str = "PNG" # Check for png in filename if (isinstance(fname, string_types) and fname.lower().endswith(".png")): format_str = "PNG" arr = np.asanyarray(arr) if arr.dtype.kind == 'b': arr = arr.astype(np.uint8) if arr.ndim not in (2, 3): raise ValueError("Invalid shape for image array: %s" % (arr.shape, )) if arr.ndim == 3: if arr.shape[2] not in (3, 4): raise ValueError("Invalid number of channels in image array.") img = ndarray_to_pil(arr, format_str=format_str) img.save(fname, format=format_str, **kwargs) scikit-image-0.13.1/skimage/io/_plugins/q_color_mixer.py000066400000000000000000000273251316256236300232450ustar00rootroot00000000000000# the module for the qt color_mixer plugin from PyQt4 import QtGui, QtCore from PyQt4.QtGui import (QWidget, QStackedWidget, QSlider, QGridLayout, QLabel) from .util import ColorMixer class IntelligentSlider(QWidget): ''' A slider that adds a 'name' attribute and calls a callback with 'name' as an argument to the registerd callback. This allows you to create large groups of sliders in a loop, but still keep track of the individual events It also prints a label below the slider. The range of the slider is hardcoded from zero - 1000, but it supports a conversion factor so you can scale the results''' def __init__(self, name, a, b, callback): QWidget.__init__(self) self.name = name self.callback = callback self.a = a self.b = b self.manually_triggered = False self.slider = QSlider() self.slider.setRange(0, 1000) self.slider.setValue(500) self.slider.valueChanged.connect(self.slider_changed) self.name_label = QLabel() self.name_label.setText(self.name) self.name_label.setAlignment(QtCore.Qt.AlignCenter) self.value_label = QLabel() self.value_label.setText('%2.2f' % (self.slider.value() * self.a + self.b)) self.value_label.setAlignment(QtCore.Qt.AlignCenter) self.layout = QGridLayout(self) self.layout.addWidget(self.name_label, 0, 0) self.layout.addWidget(self.slider, 1, 0, QtCore.Qt.AlignHCenter) self.layout.addWidget(self.value_label, 2, 0) # bind this to the valueChanged signal of the slider def slider_changed(self, val): val = self.val() self.value_label.setText(str(val)[:4]) if not self.manually_triggered: self.callback(self.name, val) def set_conv_fac(self, a, b): self.a = a self.b = b def set_value(self, val): self.manually_triggered = True self.slider.setValue(int((val - self.b) / self.a)) self.value_label.setText('%2.2f' % val) self.manually_triggered = False def val(self): return self.slider.value() * self.a + self.b class MixerPanel(QtGui.QFrame): '''A color mixer to hook up to an image. You pass the image you the panel to operate on and it operates on that image in place. You also pass a callback to be called to trigger a refresh. This callback is called every time the mixer modifies your image.''' def __init__(self, img): QtGui.QFrame.__init__(self) #self.setFrameStyle(QtGui.QFrame.Box|QtGui.QFrame.Sunken) self.img = img self.mixer = ColorMixer(self.img) self.callback = None #--------------------------------------------------------------- # ComboBox #--------------------------------------------------------------- self.combo_box_entries = ['RGB Color', 'HSV Color', 'Brightness/Contrast', 'Gamma', 'Gamma (Sigmoidal)'] self.combo_box = QtGui.QComboBox() for entry in self.combo_box_entries: self.combo_box.addItem(entry) self.combo_box.currentIndexChanged.connect(self.combo_box_changed) #--------------------------------------------------------------- # RGB color sliders #--------------------------------------------------------------- # radio buttons self.rgb_add = QtGui.QRadioButton('Additive') self.rgb_mul = QtGui.QRadioButton('Multiplicative') self.rgb_mul.toggled.connect(self.rgb_radio_changed) self.rgb_add.toggled.connect(self.rgb_radio_changed) # sliders rs = IntelligentSlider('R', 0.51, -255, self.rgb_changed) gs = IntelligentSlider('G', 0.51, -255, self.rgb_changed) bs = IntelligentSlider('B', 0.51, -255, self.rgb_changed) self.rs = rs self.gs = gs self.bs = bs self.rgb_widget = QWidget() self.rgb_widget.layout = QGridLayout(self.rgb_widget) self.rgb_widget.layout.addWidget(self.rgb_add, 0, 0, 1, 3) self.rgb_widget.layout.addWidget(self.rgb_mul, 1, 0, 1, 3) self.rgb_widget.layout.addWidget(self.rs, 2, 0) self.rgb_widget.layout.addWidget(self.gs, 2, 1) self.rgb_widget.layout.addWidget(self.bs, 2, 2) #--------------------------------------------------------------- # HSV sliders #--------------------------------------------------------------- # radio buttons self.hsv_add = QtGui.QRadioButton('Additive') self.hsv_mul = QtGui.QRadioButton('Multiplicative') self.hsv_mul.toggled.connect(self.hsv_radio_changed) self.hsv_mul.toggled.connect(self.hsv_radio_changed) # sliders hs = IntelligentSlider('H', 0.36, -180, self.hsv_changed) ss = IntelligentSlider('S', 0.002, 0, self.hsv_changed) vs = IntelligentSlider('V', 0.002, 0, self.hsv_changed) self.hs = hs self.ss = ss self.vs = vs self.hsv_widget = QWidget() self.hsv_widget.layout = QGridLayout(self.hsv_widget) self.hsv_widget.layout.addWidget(self.hsv_add, 0, 0, 1, 3) self.hsv_widget.layout.addWidget(self.hsv_mul, 1, 0, 1, 3) self.hsv_widget.layout.addWidget(self.hs, 2, 0) self.hsv_widget.layout.addWidget(self.ss, 2, 1) self.hsv_widget.layout.addWidget(self.vs, 2, 2) #--------------------------------------------------------------- # Brightness/Contrast sliders #--------------------------------------------------------------- # sliders cont = IntelligentSlider('x', 0.002, 0, self.bright_changed) bright = IntelligentSlider('+', 0.51, -255, self.bright_changed) self.cont = cont self.bright = bright # layout self.bright_widget = QWidget() self.bright_widget.layout = QtGui.QGridLayout(self.bright_widget) self.bright_widget.layout.addWidget(self.cont, 0, 0) self.bright_widget.layout.addWidget(self.bright, 0, 1) #---------------------------------------------------------------------- # Gamma Slider #---------------------------------------------------------------------- gamma = IntelligentSlider('gamma', 0.005, 0, self.gamma_changed) self.gamma = gamma # layout self.gamma_widget = QWidget() self.gamma_widget.layout = QtGui.QGridLayout(self.gamma_widget) self.gamma_widget.layout.addWidget(self.gamma, 0, 0) #--------------------------------------------------------------- # Sigmoid Gamma sliders #--------------------------------------------------------------- # sliders alpha = IntelligentSlider('alpha', 0.011, 1, self.sig_gamma_changed) beta = IntelligentSlider('beta', 0.012, 0, self.sig_gamma_changed) self.a_gamma = alpha self.b_gamma = beta # layout self.sig_gamma_widget = QWidget() self.sig_gamma_widget.layout = QtGui.QGridLayout(self.sig_gamma_widget) self.sig_gamma_widget.layout.addWidget(self.a_gamma, 0, 0) self.sig_gamma_widget.layout.addWidget(self.b_gamma, 0, 1) #--------------------------------------------------------------- # Buttons #--------------------------------------------------------------- self.commit_button = QtGui.QPushButton('Commit') self.commit_button.clicked.connect(self.commit_changes) self.revert_button = QtGui.QPushButton('Revert') self.revert_button.clicked.connect(self.revert_changes) #--------------------------------------------------------------- # Mixer Layout #--------------------------------------------------------------- self.sliders = QStackedWidget() self.sliders.addWidget(self.rgb_widget) self.sliders.addWidget(self.hsv_widget) self.sliders.addWidget(self.bright_widget) self.sliders.addWidget(self.gamma_widget) self.sliders.addWidget(self.sig_gamma_widget) self.layout = QtGui.QGridLayout(self) self.layout.addWidget(self.combo_box, 0, 0) self.layout.addWidget(self.sliders, 1, 0) self.layout.addWidget(self.commit_button, 2, 0) self.layout.addWidget(self.revert_button, 3, 0) #--------------------------------------------------------------- # State Initialization #--------------------------------------------------------------- self.combo_box.setCurrentIndex(0) self.rgb_mul.setChecked(True) self.hsv_mul.setChecked(True) def set_callback(self, callback): self.callback = callback def combo_box_changed(self, index): self.sliders.setCurrentIndex(index) self.reset() def rgb_radio_changed(self): self.reset() def hsv_radio_changed(self): self.reset() def reset(self): self.reset_sliders() self.mixer.set_to_stateimg() if self.callback: self.callback() def reset_sliders(self): # handle changing the conversion factors necessary if self.rgb_add.isChecked(): self.rs.set_conv_fac(0.51, -255) self.rs.set_value(0) self.gs.set_conv_fac(0.51, -255) self.gs.set_value(0) self.bs.set_conv_fac(0.51, -255) self.bs.set_value(0) else: self.rs.set_conv_fac(0.002, 0) self.rs.set_value(1.) self.gs.set_conv_fac(0.002, 0) self.gs.set_value(1.) self.bs.set_conv_fac(0.002, 0) self.bs.set_value(1.) self.hs.set_value(0) if self.hsv_add.isChecked(): self.ss.set_conv_fac(0.002, -1) self.ss.set_value(0) self.vs.set_conv_fac(0.002, -1) self.vs.set_value(0) else: self.ss.set_conv_fac(0.002, 0) self.ss.set_value(1.) self.vs.set_conv_fac(0.002, 0) self.vs.set_value(1.) self.bright.set_value(0) self.cont.set_value(1.) self.gamma.set_value(1) self.a_gamma.set_value(1) self.b_gamma.set_value(0.5) def rgb_changed(self, name, val): if name == 'R': channel = self.mixer.RED elif name == 'G': channel = self.mixer.GREEN else: channel = self.mixer.BLUE if self.rgb_mul.isChecked(): self.mixer.multiply(channel, val) elif self.rgb_add.isChecked(): self.mixer.add(channel, val) else: pass if self.callback: self.callback() def hsv_changed(self, name, val): h = self.hs.val() s = self.ss.val() v = self.vs.val() if self.hsv_mul.isChecked(): self.mixer.hsv_multiply(h, s, v) elif self.hsv_add.isChecked(): self.mixer.hsv_add(h, s, v) else: pass if self.callback: self.callback() def bright_changed(self, name, val): b = self.bright.val() c = self.cont.val() self.mixer.brightness(c, b) if self.callback: self.callback() def gamma_changed(self, name, val): self.mixer.gamma(val) if self.callback: self.callback() def sig_gamma_changed(self, name, val): ag = self.a_gamma.val() bg = self.b_gamma.val() self.mixer.sigmoid_gamma(ag, bg) if self.callback: self.callback() def commit_changes(self): self.mixer.commit_changes() self.reset_sliders() def revert_changes(self): self.mixer.revert() self.reset_sliders() if self.callback: self.callback() scikit-image-0.13.1/skimage/io/_plugins/q_histogram.py000066400000000000000000000113311316256236300227060ustar00rootroot00000000000000import numpy as np from PyQt4.QtGui import QWidget, QPainter, QGridLayout, QColor, QFrame from .util import histograms class ColorHistogram(QWidget): '''A Class which draws a scaling histogram in a widget. Where counts are the bin values in the histogram and colormap is a tuple of (R, G, B) tuples the same length as counts. These are the colors to apply to the histogram bars. Colormap can also contain a single tuple (R, G, B), in which case this is the color applied to all bars of that histogram. The histogram assumes the bins were evenly spaced. ''' def __init__(self, counts, colormap): QWidget.__init__(self) self._validate_input(counts, colormap) self.counts = counts self.n = np.sum(self.counts) self.colormap = colormap self.setMinimumSize(100, 50) def _validate_input(self, counts, colormap): if len(counts) != len(colormap): if len(colormap) != 3: msg = '''Colormap must be a list of 3-tuples the same length as counts or a 3-tuple''' raise ValueError(msg) def paintEvent(self, evt): # get the widget dimensions orig_width = self.width() orig_height = self.height() # fill perc % of the widget perc = 1 width = int(orig_width * perc) height = int(orig_height * perc) # get the starting origin x_orig = int((orig_width - width) / 2) # we want to start at the bottom and draw up. y_orig = orig_height - int((orig_height - height) / 2) # a running x-position running_pos = x_orig # calculate to number of bars nbars = len(self.counts) # calculate the bar widths, this compilcation is # necessary because integer trunction severly cripples # the layout. remainder = width % nbars bar_width = [int(width / nbars)] * nbars for i in range(remainder): bar_width[i] += 1 paint = QPainter() paint.begin(self) # determine the scaling factor max_val = np.max(self.counts) scale = 1. * height / max_val # determine if we have a colormap and drop into the appopriate # loop. if hasattr(self.colormap[0], '__iter__'): # assume we have a colormap for i in range(len(self.counts)): bar_height = self.counts[i] r, g, b = self.colormap[i] paint.setPen(QColor(r, g, b)) paint.setBrush(QColor(r, g, b)) paint.drawRect(running_pos, y_orig, bar_width[i], -bar_height) running_pos += bar_width[i] else: # we have a tuple r, g, b = self.colormap paint.setPen(QColor(r, g, b)) paint.setBrush(QColor(r, g, b)) for i in range(len(self.counts)): bar_height = self.counts[i] * scale paint.drawRect(running_pos, y_orig, bar_width[i], -bar_height) running_pos += bar_width[i] paint.end() def update_hist(self, counts, cmap): self._validate_input(counts, cmap) self.counts = counts self.colormap = cmap self.repaint() class QuadHistogram(QFrame): '''A class which uses ColorHistogram to draw the 4 histograms of an image. R, G, B, and Value. The 4 histograms are layout out in a grid, and can be specified horizontal or vertical, and in which order ie. ['R', 'G', 'B', 'V'] ''' def __init__(self, img, layout='vertical', order=['R', 'G', 'B', 'V']): QFrame.__init__(self) r, g, b, v = histograms(img, 100) self.r_hist = ColorHistogram(r, (255, 0, 0)) self.g_hist = ColorHistogram(g, (0, 255, 0)) self.b_hist = ColorHistogram(b, (0, 0, 255)) self.v_hist = ColorHistogram(v, (0, 0, 0)) self.setFrameStyle(QFrame.StyledPanel | QFrame.Sunken) self.layout = QGridLayout(self) self.layout.setMargin(0) order_map = {'R': self.r_hist, 'G': self.g_hist, 'B': self.b_hist, 'V': self.v_hist} if layout == 'vertical': for i in range(len(order)): self.layout.addWidget(order_map[order[i]], i, 0) elif layout == 'horizontal': for i in range(len(order)): self.layout.addWidget(order_map[order[i]], 0, i) def update_hists(self, img): r, g, b, v = histograms(img, 100) self.r_hist.update_hist(r, (255, 0, 0)) self.g_hist.update_hist(g, (0, 255, 0)) self.b_hist.update_hist(b, (0, 0, 255)) self.v_hist.update_hist(v, (0, 0, 0)) scikit-image-0.13.1/skimage/io/_plugins/qt_plugin.ini000066400000000000000000000001511316256236300225200ustar00rootroot00000000000000[qt] description = Fast image display using the Qt library provides = imshow, _app_show, imsave, imread scikit-image-0.13.1/skimage/io/_plugins/qt_plugin.py000066400000000000000000000126331316256236300224010ustar00rootroot00000000000000import numpy as np from .util import prepare_for_display, window_manager from ..._shared.utils import warn # We try to aquire the gui lock first or else the gui import might # trample another GUI's PyOS_InputHook. window_manager.acquire('qt') try: from PyQt4.QtGui import (QApplication, QImage, QLabel, QMainWindow, QPixmap, QWidget) from PyQt4 import QtCore, QtGui import sip except ImportError: window_manager._release('qt') raise ImportError("""\ PyQt4 libraries not installed. Please refer to http://www.riverbankcomputing.co.uk/software/pyqt/intro for more information. PyQt4 is GPL licensed. For an LGPL equivalent, see http://www.pyside.org """) app = None class ImageLabel(QLabel): def __init__(self, parent, arr): QLabel.__init__(self) # we need to hold a reference to # arr because QImage doesn't copy the data # and the buffer must be alive as long # as the image is alive. self.arr = arr # we also need to pass in the row-stride to # the constructor, because we can't guarantee # that every row of the numpy data is # 4-byte aligned. Which Qt would require # if we didnt pass the stride. self.img = QImage(arr.data, arr.shape[1], arr.shape[0], arr.strides[0], QImage.Format_RGB888) self.pm = QPixmap.fromImage(self.img) self.setPixmap(self.pm) self.setAlignment(QtCore.Qt.AlignTop) self.setMinimumSize(100, 100) def resizeEvent(self, evt): width = self.width() pm = QPixmap.fromImage(self.img) self.pm = pm.scaledToWidth(width) self.setPixmap(self.pm) class ImageWindow(QMainWindow): def __init__(self, arr, mgr): QMainWindow.__init__(self) self.setWindowTitle('skimage') self.mgr = mgr self.main_widget = QWidget() self.layout = QtGui.QGridLayout(self.main_widget) self.setCentralWidget(self.main_widget) self.label = ImageLabel(self, arr) self.layout.addWidget(self.label, 0, 0) self.layout.addLayout self.mgr.add_window(self) self.main_widget.show() def closeEvent(self, event): # Allow window to be destroyed by removing any # references to it self.mgr.remove_window(self) def imread_qt(filename): """ Read an image using QT's QImage.load """ qtimg = QImage() if not qtimg.load(filename): # QImage.load() returns false on failure, so raise an exception raise IOError('Unable to load file %s' % filename) if qtimg.depth() == 1: raise IOError('1-bit images currently not supported') # TODO: Warn about other odd formats we don't currently handle properly, # such as the odd 16-bit packed formats QT supports arrayptr = qtimg.bits() # QT may pad the image, so we need to use bytesPerLine, not width for # the conversion to a numpy array bytes_per_pixel = qtimg.depth() // 8 pixels_per_line = qtimg.bytesPerLine() // bytes_per_pixel img_size = pixels_per_line * qtimg.height() * bytes_per_pixel arrayptr.setsize(img_size) img = np.array(arrayptr) # Reshape and trim down to correct dimensions if bytes_per_pixel > 1: img = img.reshape((qtimg.height(), pixels_per_line, bytes_per_pixel)) img = img[:, :qtimg.width(), :] else: img = img.reshape((qtimg.height(), pixels_per_line)) img = img[:, :qtimg.width()] # Strip qt's false alpha channel if needed # and reorder color axes as required if bytes_per_pixel == 4 and not qtimg.hasAlphaChannel(): img = img[:, :, 2::-1] elif bytes_per_pixel == 4: img[:, :, 0:3] = img[:, :, 2::-1] return img if sip.SIP_VERSION >= 0x040c00: # sip.voidptr only acquired a buffer view in 4.12.0, so our imread # doesn't work with earlier versions imread = imread_qt else: warn(RuntimeWarning("sip version too old. QT imread disabled")) def imshow(arr, fancy=False): global app if not app: app = QApplication([]) arr = prepare_for_display(arr) if not fancy: iw = ImageWindow(arr, window_manager) else: from .skivi import SkiviImageWindow iw = SkiviImageWindow(arr, window_manager) iw.show() def _app_show(): global app if app and window_manager.has_windows(): app.exec_() else: print('No images to show. See `imshow`.') def imsave(filename, img, format_str=None): # we can add support for other than 3D uint8 here... img = prepare_for_display(img) qimg = QImage(img.data, img.shape[1], img.shape[0], img.strides[0], QImage.Format_RGB888) if _is_filelike(filename): byte_array = QtCore.QByteArray() qbuffer = QtCore.QBuffer(byte_array) qbuffer.open(QtCore.QIODevice.ReadWrite) saved = qimg.save(qbuffer, format_str.upper()) qbuffer.seek(0) filename.write(qbuffer.readAll().data()) qbuffer.close() else: saved = qimg.save(filename) if not saved: from textwrap import dedent msg = dedent( '''The image was not saved. Allowable file formats for the QT imsave plugin are: BMP, JPG, JPEG, PNG, PPM, TIFF, XBM, XPM''') raise RuntimeError(msg) def _is_filelike(possible_filelike): return callable(getattr(possible_filelike, 'write', None)) scikit-image-0.13.1/skimage/io/_plugins/simpleitk_plugin.ini000066400000000000000000000001341316256236300240760ustar00rootroot00000000000000[simpleitk] description = Image reading and writing via SimpleITK provides = imread, imsave scikit-image-0.13.1/skimage/io/_plugins/simpleitk_plugin.py000066400000000000000000000011131316256236300237450ustar00rootroot00000000000000__all__ = ['imread', 'imsave'] try: import SimpleITK as sitk except ImportError: raise ImportError("SimpleITK could not be found. " "Please try " " easy_install SimpleITK " "or refer to " " http://simpleitk.org/ " "for further instructions.") def imread(fname): sitk_img = sitk.ReadImage(fname) return sitk.GetArrayFromImage(sitk_img) def imsave(fname, arr): sitk_img = sitk.GetImageFromArray(arr, isVector=True) sitk.WriteImage(sitk_img, fname) scikit-image-0.13.1/skimage/io/_plugins/skivi.py000066400000000000000000000173161316256236300215270ustar00rootroot00000000000000''' Skivi is written/maintained/developed by: S. Chris Colbert - sccolbert@gmail.com Skivi is free software and is part of the scikit-image project. Skivi is governed by the licenses of the scikit-image project. Please report any bugs to the author. The skivi module is not meant to be used directly. Use skimage.io.imshow(img, fancy=True)''' from textwrap import dedent from PyQt4 import QtCore, QtGui from PyQt4.QtGui import QMainWindow, QImage, QPixmap, QLabel, QWidget, QFrame from .q_color_mixer import MixerPanel from .q_histogram import QuadHistogram class ImageLabel(QLabel): def __init__(self, parent, arr): QLabel.__init__(self) self.parent = parent # we need to hold a reference to # arr because QImage doesn't copy the data # and the buffer must be alive as long # as the image is alive. self.arr = arr # we also need to pass in the row-stride to # the constructor, because we can't guarantee # that every row of the numpy data is # 4-byte aligned. Which Qt would require # if we didnt pass the stride. self.img = QImage(arr.data, arr.shape[1], arr.shape[0], arr.strides[0], QImage.Format_RGB888) self.pm = QPixmap.fromImage(self.img) self.setPixmap(self.pm) self.setAlignment(QtCore.Qt.AlignTop) self.setMinimumSize(100, 100) self.setMouseTracking(True) def mouseMoveEvent(self, evt): self.parent.label_mouseMoveEvent(evt) def resizeEvent(self, evt): width = self.width() pm = QPixmap.fromImage(self.img) self.pm = pm.scaledToWidth(width) self.setPixmap(self.pm) def update_image(self): width = self.width() pm = QPixmap.fromImage(self.img) pm = pm.scaledToWidth(width) self.setPixmap(pm) class RGBHSVDisplay(QFrame): def __init__(self): QFrame.__init__(self) self.setFrameStyle(QtGui.QFrame.Box | QtGui.QFrame.Sunken) self.posx_label = QLabel('X-pos:') self.posx_value = QLabel() self.posy_label = QLabel('Y-pos:') self.posy_value = QLabel() self.r_label = QLabel('R:') self.r_value = QLabel() self.g_label = QLabel('G:') self.g_value = QLabel() self.b_label = QLabel('B:') self.b_value = QLabel() self.h_label = QLabel('H:') self.h_value = QLabel() self.s_label = QLabel('S:') self.s_value = QLabel() self.v_label = QLabel('V:') self.v_value = QLabel() self.layout = QtGui.QGridLayout(self) self.layout.addWidget(self.posx_label, 0, 0) self.layout.addWidget(self.posx_value, 0, 1) self.layout.addWidget(self.posy_label, 1, 0) self.layout.addWidget(self.posy_value, 1, 1) self.layout.addWidget(self.r_label, 0, 2) self.layout.addWidget(self.r_value, 0, 3) self.layout.addWidget(self.g_label, 1, 2) self.layout.addWidget(self.g_value, 1, 3) self.layout.addWidget(self.b_label, 2, 2) self.layout.addWidget(self.b_value, 2, 3) self.layout.addWidget(self.h_label, 0, 4) self.layout.addWidget(self.h_value, 0, 5) self.layout.addWidget(self.s_label, 1, 4) self.layout.addWidget(self.s_value, 1, 5) self.layout.addWidget(self.v_label, 2, 4) self.layout.addWidget(self.v_value, 2, 5) def update_vals(self, data): xpos, ypos, r, g, b, h, s, v = data self.posx_value.setText(str(xpos)[:5]) self.posy_value.setText(str(ypos)[:5]) self.r_value.setText(str(r)[:5]) self.g_value.setText(str(g)[:5]) self.b_value.setText(str(b)[:5]) self.h_value.setText(str(h)[:5]) self.s_value.setText(str(s)[:5]) self.v_value.setText(str(v)[:5]) class SkiviImageWindow(QMainWindow): def __init__(self, arr, mgr): QMainWindow.__init__(self) self.arr = arr self.mgr = mgr self.main_widget = QWidget() self.layout = QtGui.QGridLayout(self.main_widget) self.setCentralWidget(self.main_widget) self.label = ImageLabel(self, arr) self.label_container = QFrame() self.label_container.setFrameShape( QtGui.QFrame.StyledPanel | QtGui.QFrame.Sunken) self.label_container.setLineWidth(1) self.label_container.layout = QtGui.QGridLayout(self.label_container) self.label_container.layout.setMargin(0) self.label_container.layout.addWidget(self.label, 0, 0) self.layout.addWidget(self.label_container, 0, 0) self.mgr.add_window(self) self.main_widget.show() self.setWindowTitle('Skivi - The skimage viewer.') self.mixer_panel = MixerPanel(self.arr) self.layout.addWidget(self.mixer_panel, 0, 2) self.mixer_panel.show() self.mixer_panel.set_callback(self.refresh_image) self.rgbv_hist = QuadHistogram(self.arr) self.layout.addWidget(self.rgbv_hist, 0, 1) self.rgbv_hist.show() self.rgb_hsv_disp = RGBHSVDisplay() self.layout.addWidget(self.rgb_hsv_disp, 1, 0) self.rgb_hsv_disp.show() self.layout.setColumnStretch(0, 1) self.layout.setRowStretch(0, 1) self.save_file = QtGui.QPushButton('Save to File') self.save_file.clicked.connect(self.save_to_file) self.save_stack = QtGui.QPushButton('Save to Stack') self.save_stack.clicked.connect(self.save_to_stack) self.save_file.show() self.save_stack.show() self.layout.addWidget(self.save_stack, 1, 1) self.layout.addWidget(self.save_file, 1, 2) def closeEvent(self, event): # Allow window to be destroyed by removing any # references to it self.mgr.remove_window(self) def update_histograms(self): self.rgbv_hist.update_hists(self.arr) def refresh_image(self): self.label.update_image() self.update_histograms() def scale_mouse_pos(self, x, y): width = self.label.pm.width() height = self.label.pm.height() x_frac = 1. * x / width y_frac = 1. * y / height width = self.arr.shape[1] height = self.arr.shape[0] new_x = int(width * x_frac) new_y = int(height * y_frac) return(new_x, new_y) def label_mouseMoveEvent(self, evt): x = evt.x() y = evt.y() x, y = self.scale_mouse_pos(x, y) # handle tracking out of array bounds maxw = self.arr.shape[1] maxh = self.arr.shape[0] if x >= maxw or y >= maxh or x < 0 or y < 0: r = g = b = h = s = v = '' else: r = self.arr[y, x, 0] g = self.arr[y, x, 1] b = self.arr[y, x, 2] h, s, v = self.mixer_panel.mixer.rgb_2_hsv_pixel(r, g, b) self.rgb_hsv_disp.update_vals((x, y, r, g, b, h, s, v)) def save_to_stack(self): from ... import io img = self.arr.copy() io.push(img) msg = dedent(''' The image has been pushed to the io stack. Use io.pop() to retrieve the most recently pushed image.''') msglabel = QLabel(msg) dialog = QtGui.QDialog() ok = QtGui.QPushButton('OK', dialog) ok.clicked.connect(dialog.accept) ok.setDefault(True) dialog.layout = QtGui.QGridLayout(dialog) dialog.layout.addWidget(msglabel, 0, 0, 1, 3) dialog.layout.addWidget(ok, 1, 1) dialog.exec_() def save_to_file(self): from ... import io filename = str(QtGui.QFileDialog.getSaveFileName()) if len(filename) == 0: return io.imsave(filename, self.arr) scikit-image-0.13.1/skimage/io/_plugins/test_plugin.ini000066400000000000000000000001511316256236300230530ustar00rootroot00000000000000[test] description = Test plugin provides = imsave, imshow, imread, imread_collection, imshow_collection scikit-image-0.13.1/skimage/io/_plugins/test_plugin.py000066400000000000000000000011741316256236300227320ustar00rootroot00000000000000# This mock-up is called by ../tests/test_plugin.py # to verify the behaviour of the plugin infrastructure from skimage.io import ImageCollection def imread(fname, dtype=None): assert fname == 'test.png' assert dtype == 'i4' def imsave(fname, arr): assert fname == 'test.png' assert arr == [1, 2, 3] def imshow(arr, plugin_arg=None): assert arr == [1, 2, 3] assert plugin_arg == (1, 2) def imread_collection(x, conserve_memory=True): assert conserve_memory == False assert x == '*.png' return ImageCollection([0, 1], load_func=lambda x: x) def imshow_collection(x): assert len(x) == 2 scikit-image-0.13.1/skimage/io/_plugins/tifffile_plugin.ini000066400000000000000000000001561316256236300236710ustar00rootroot00000000000000[tifffile] description = Load and save TIFF and TIFF-based images using tifffile.py provides = imread, imsave scikit-image-0.13.1/skimage/io/_plugins/tifffile_plugin.py000066400000000000000000000016141316256236300235420ustar00rootroot00000000000000from ...external.tifffile import TiffFile, imsave def imread(fname, dtype=None, **kwargs): """Load a tiff image from file. Parameters ---------- fname : str or file File name or file-like-object. dtype : numpy dtype object or string specifier Specifies data type of array elements (Not currently used). kwargs : keyword pairs, optional Additional keyword arguments to pass through (see ``tifffile``'s ``imread`` function). Notes ----- Provided by Christophe Golhke's tifffile.py [1]_, and supports many advanced image types including multi-page and floating point. References ---------- .. [1] http://www.lfd.uci.edu/~gohlke/code/tifffile.py """ if 'img_num' in kwargs: kwargs['key'] = kwargs.pop('img_num') with open(fname, 'rb') as f: tif = TiffFile(f) return tif.asarray(**kwargs) scikit-image-0.13.1/skimage/io/_plugins/util.py000066400000000000000000000313631316256236300213550ustar00rootroot00000000000000import numpy as np from . import _colormixer from . import _histograms import threading from ...util import img_as_ubyte # utilities to make life easier for plugin writers. try: import multiprocessing CPU_COUNT = multiprocessing.cpu_count() except: CPU_COUNT = 2 class GuiLockError(Exception): def __init__(self, msg): self.msg = msg def __str__(self): return self.msg class WindowManager(object): ''' A class to keep track of spawned windows, and make any needed callback once all the windows, are closed.''' def __init__(self): self._windows = [] self._callback = None self._callback_args = () self._callback_kwargs = {} self._gui_lock = False self._guikit = '' def _check_locked(self): if not self._gui_lock: raise GuiLockError(\ 'Must first acquire the gui lock before using this image manager') def _exec_callback(self): if self._callback: self._callback(*self._callback_args, **self._callback_kwargs) def acquire(self, kit): if self._gui_lock: raise GuiLockError(\ 'The gui lock can only be acquired by one toolkit per session. \ The lock is already aquired by %s' % self._guikit) else: self._gui_lock = True self._guikit = str(kit) def _release(self, kit): # releaseing the lock will lose all references to currently # tracked images and the callback. # this function is private for reason! self._check_locked() if str(kit) == self._guikit: self._windows = [] self._callback = None self._callback_args = () self._callback_kwargs = {} self._gui_lock = False self._guikit = '' else: raise RuntimeError('Only the toolkit that owns the lock may ' 'release it') def add_window(self, win): self._check_locked() self._windows.append(win) def remove_window(self, win): self._check_locked() try: self._windows.remove(win) except ValueError: print('Unable to find referenced window in tracked windows.') print('Ignoring...') else: if len(self._windows) == 0: self._exec_callback() def register_callback(self, cb, *cbargs, **cbkwargs): self._check_locked() self._callback = cb self._callback_args = cbargs self._callback_kwargs = cbkwargs def has_windows(self): if len(self._windows) > 0: return True else: return False window_manager = WindowManager() def prepare_for_display(npy_img): '''Convert a 2D or 3D numpy array of any dtype into a 3D numpy array with dtype uint8. This array will be suitable for use in passing to gui toolkits for image display purposes. Parameters ---------- npy_img : ndarray, 2D or 3D The image to convert for display Returns ------- out : ndarray, 3D dtype=np.uint8 The converted image. This is guaranteed to be a contiguous array. Notes ----- If the input image is floating point, it is assumed that the data is in the range of 0.0 - 1.0. No check is made to assert this condition. The image is then scaled to be in the range 0 - 255 and then cast to np.uint8 For all other dtypes, the array is simply cast to np.uint8 If a 2D array is passed, the single channel is replicated to the 2nd and 3rd channels. If the array contains an alpha channel, this channel is ignored. ''' if npy_img.ndim < 2: raise ValueError('Image must be 2D or 3D array') height = npy_img.shape[0] width = npy_img.shape[1] out = np.empty((height, width, 3), dtype=np.uint8) npy_img = img_as_ubyte(npy_img) if npy_img.ndim == 2 or \ (npy_img.ndim == 3 and npy_img.shape[2] == 1): npy_plane = npy_img.reshape((height, width)) out[:, :, 0] = npy_plane out[:, :, 1] = npy_plane out[:, :, 2] = npy_plane elif npy_img.ndim == 3: if npy_img.shape[2] == 3 or npy_img.shape[2] == 4: out[:, :, :3] = npy_img[:, :, :3] else: raise ValueError('Image must have 1, 3, or 4 channels') else: raise ValueError('Image must have 2 or 3 dimensions') return out def histograms(img, nbins): '''Calculate the channel histograms of the current image. Parameters ---------- img : ndarray, ndim=3, dtype=np.uint8 nbins : int The number of bins. Returns ------- out : (rcounts, gcounts, bcounts, vcounts) The binned histograms of the RGB channels and intensity values. This is a NAIVE histogram routine, meant primarily for fast display. ''' return _histograms.histograms(img, nbins) class ImgThread(threading.Thread): def __init__(self, func, *args): super(ImgThread, self).__init__() self.func = func self.args = args def run(self): self.func(*self.args) class ThreadDispatch(object): def __init__(self, img, stateimg, func, *args): height = img.shape[0] self.cores = CPU_COUNT self.threads = [] self.chunks = [] if self.cores == 1: self.chunks.append((img, stateimg)) elif self.cores >= 4: self.chunks.append((img[:(height / 4), :, :], stateimg[:(height / 4), :, :])) self.chunks.append((img[(height / 4):(height / 2), :, :], stateimg[(height / 4):(height / 2), :, :])) self.chunks.append((img[(height / 2):(3 * height / 4), :, :], stateimg[(height / 2):(3 * height / 4), :, :])) self.chunks.append((img[(3 * height / 4):, :, :], stateimg[(3 * height / 4):, :, :])) # if they dont have 1, or 4 or more, 2 is good. else: self.chunks.append((img[:(height / 2), :, :], stateimg[:(height / 2), :, :])) self.chunks.append((img[(height / 2):, :, :], stateimg[(height / 2):, :, :])) for i in range(len(self.chunks)): self.threads.append(ImgThread(func, self.chunks[i][0], self.chunks[i][1], *args)) def run(self): for t in self.threads: t.start() for t in self.threads: t.join() class ColorMixer(object): ''' a class to manage mixing colors in an image. The input array must be an RGB uint8 image. The mixer maintains an original copy of the image, and uses this copy to query the pixel data for operations. It also makes a copy for sharing state across operations. That is, if you add to a channel, and multiply to same channel, the two operations are carried separately and the results averaged together. it modifies your array in place. This ensures that if you bust over a threshold, you can always come back down. The passed values to a function are always considered absolute. Thus to threshold a channel completely you can do mixer.add(RED, 255). Or to double the intensity of the blue channel: mixer.multiply(BLUE, 2.) To reverse these operations, respectively: mixer.add(RED, 0), mixer.multiply(BLUE, 1.) The majority of the backend is implemented in Cython, so it should be quite quick. ''' RED = 0 GREEN = 1 BLUE = 2 valid_channels = [RED, GREEN, BLUE] def __init__(self, img): if type(img) != np.ndarray: raise ValueError('Image must be a numpy array') if img.dtype != np.uint8: raise ValueError('Image must have dtype uint8') if img.ndim != 3 or img.shape[2] != 3: raise ValueError('Image must be 3 channel MxNx3') self.img = img self.origimg = img.copy() self.stateimg = img.copy() def get_stateimage(self): return self.stateimg def commit_changes(self): self.stateimg[:] = self.img[:] def revert(self): self.stateimg[:] = self.origimg[:] self.img[:] = self.stateimg[:] def set_to_stateimg(self): self.img[:] = self.stateimg[:] def add(self, channel, ammount): '''Add the specified ammount to the specified channel. Parameters ---------- channel : flag the color channel to operate on RED, GREED, or BLUE ammount : integer the ammount of color to add to the channel, can be positive or negative. ''' assert channel in self.valid_channels pool = ThreadDispatch(self.img, self.stateimg, _colormixer.add, channel, ammount) pool.run() def multiply(self, channel, ammount): '''Mutliply the indicated channel by the specified value. Parameters ---------- channel : flag the color channel to operate on RED, GREED, or BLUE ammount : integer the ammount of color to add to the channel, can be positive or negative. ''' assert channel in self.valid_channels pool = ThreadDispatch(self.img, self.stateimg, _colormixer.multiply, channel, ammount) pool.run() def brightness(self, factor, offset): '''Adjust the brightness off an image with an offset and factor. Parameters ---------- offset : integer The ammount to add to each channel. factor : float The factor to multiply each channel by. result = clip((pixel + offset)*factor) ''' pool = ThreadDispatch(self.img, self.stateimg, _colormixer.brightness, factor, offset) pool.run() def sigmoid_gamma(self, alpha, beta): pool = ThreadDispatch(self.img, self.stateimg, _colormixer.sigmoid_gamma, alpha, beta) pool.run() def gamma(self, gamma): pool = ThreadDispatch(self.img, self.stateimg, _colormixer.gamma, gamma) pool.run() def hsv_add(self, h_amt, s_amt, v_amt): '''Adjust the H, S, V channels of an image by a constant ammount. This is similar to the add() mixer function, but operates over the entire image at once. Thus all three additive values, H, S, V, must be supplied simultaneously. Parameters ---------- h_amt : float The ammount to add to the hue (-180..180) s_amt : float The ammount to add to the saturation (-1..1) v_amt : float The ammount to add to the value (-1..1) ''' pool = ThreadDispatch(self.img, self.stateimg, _colormixer.hsv_add, h_amt, s_amt, v_amt) pool.run() def hsv_multiply(self, h_amt, s_amt, v_amt): '''Adjust the H, S, V channels of an image by a constant ammount. This is similar to the add() mixer function, but operates over the entire image at once. Thus all three additive values, H, S, V, must be supplied simultaneously. Note that since hue is in degrees, it makes no sense to multiply that channel, thus an add operation is performed on the hue. And the values given for h_amt, should be the same as for hsv_add Parameters ---------- h_amt : float The ammount to to add to the hue (-180..180) s_amt : float The ammount to multiply to the saturation (0..1) v_amt : float The ammount to multiply to the value (0..1) ''' pool = ThreadDispatch(self.img, self.stateimg, _colormixer.hsv_multiply, h_amt, s_amt, v_amt) pool.run() def rgb_2_hsv_pixel(self, R, G, B): '''Convert an RGB value to HSV Parameters ---------- R : int Red value G : int Green value B : int Blue value Returns ------- out : (H, S, V) Floats The HSV values ''' H, S, V = _colormixer.py_rgb_2_hsv(R, G, B) return (H, S, V) def hsv_2_rgb_pixel(self, H, S, V): '''Convert an HSV value to RGB Parameters ---------- H : float Hue value S : float Saturation value V : float Intensity value Returns ------- out : (R, G, B) ints The RGB values ''' R, G, B = _colormixer.py_hsv_2_rgb(H, S, V) return (R, G, B) scikit-image-0.13.1/skimage/io/collection.py000066400000000000000000000317421316256236300207140ustar00rootroot00000000000000"""Data structures to hold collections of images, with optional caching.""" from __future__ import with_statement import os from glob import glob import re from copy import copy import numpy as np import six from PIL import Image from ..external.tifffile import TiffFile __all__ = ['MultiImage', 'ImageCollection', 'concatenate_images', 'imread_collection_wrapper'] def concatenate_images(ic): """Concatenate all images in the image collection into an array. Parameters ---------- ic: an iterable of images (including ImageCollection and MultiImage) The images to be concatenated. Returns ------- ar : np.ndarray An array having one more dimension than the images in `ic`. See Also -------- ImageCollection.concatenate, MultiImage.concatenate Raises ------ ValueError If images in `ic` don't have identical shapes. """ all_images = [img[np.newaxis, ...] for img in ic] try: ar = np.concatenate(all_images) except ValueError: raise ValueError('Image dimensions must agree.') return ar def alphanumeric_key(s): """Convert string to list of strings and ints that gives intuitive sorting. Parameters ---------- s: string Returns ------- k: a list of strings and ints Examples -------- >>> alphanumeric_key('z23a') ['z', 23, 'a'] >>> filenames = ['f9.10.png', 'e10.png', 'f9.9.png', 'f10.10.png', ... 'f10.9.png'] >>> sorted(filenames) ['e10.png', 'f10.10.png', 'f10.9.png', 'f9.10.png', 'f9.9.png'] >>> sorted(filenames, key=alphanumeric_key) ['e10.png', 'f9.9.png', 'f9.10.png', 'f10.9.png', 'f10.10.png'] """ k = [int(c) if c.isdigit() else c for c in re.split('([0-9]+)', s)] return k class ImageCollection(object): """Load and manage a collection of image files. Note that files are always stored in alphabetical order. Also note that slicing returns a new ImageCollection, *not* a view into the data. Parameters ---------- load_pattern : str or list Pattern glob or filenames to load. The path can be absolute or relative. Multiple patterns should be separated by os.pathsep, e.g. '/tmp/work/*.png:/tmp/other/*.jpg'. Also see implementation notes below. conserve_memory : bool, optional If True, never keep more than one in memory at a specific time. Otherwise, images will be cached once they are loaded. Other parameters ---------------- load_func : callable ``imread`` by default. See notes below. Attributes ---------- files : list of str If a glob string is given for `load_pattern`, this attribute stores the expanded file list. Otherwise, this is simply equal to `load_pattern`. Notes ----- ImageCollection can be modified to load images from an arbitrary source by specifying a combination of `load_pattern` and `load_func`. For an ImageCollection ``ic``, ``ic[5]`` uses ``load_func(file_pattern[5])`` to load the image. Imagine, for example, an ImageCollection that loads every tenth frame from a video file:: class AVILoader: video_file = 'myvideo.avi' def __call__(self, frame): return video_read(self.video_file, frame) avi_load = AVILoader() frames = range(0, 1000, 10) # 0, 10, 20, ... ic = ImageCollection(frames, load_func=avi_load) x = ic[5] # calls avi_load(frames[5]) or equivalently avi_load(50) Another use of ``load_func`` would be to convert all images to ``uint8``:: def imread_convert(f): return imread(f).astype(np.uint8) ic = ImageCollection('/tmp/*.png', load_func=imread_convert) For files with multiple images, the images will be flattened into a list and added to the list of available images. In this case, ``load_func`` should accept the keyword argument ``img_num``. Examples -------- >>> import skimage.io as io >>> from skimage import data_dir >>> coll = io.ImageCollection(data_dir + '/chess*.png') >>> len(coll) 2 >>> coll[0].shape (200, 200) >>> ic = io.ImageCollection('/tmp/work/*.png:/tmp/other/*.jpg') """ def __init__(self, load_pattern, conserve_memory=True, load_func=None, **load_func_kwargs): """Load and manage a collection of images.""" if isinstance(load_pattern, six.string_types): load_pattern = load_pattern.split(os.pathsep) self._files = [] for pattern in load_pattern: self._files.extend(glob(pattern)) self._files = sorted(self._files, key=alphanumeric_key) self._numframes = self._find_images() else: self._files = load_pattern self._numframes = len(self._files) self._frame_index = None if conserve_memory: memory_slots = 1 else: memory_slots = self._numframes self._conserve_memory = conserve_memory self._cached = None if load_func is None: from ._io import imread self.load_func = imread else: self.load_func = load_func self.load_func_kwargs = load_func_kwargs self.data = np.empty(memory_slots, dtype=object) @property def files(self): return self._files @property def conserve_memory(self): return self._conserve_memory def _find_images(self): index = [] for fname in self._files: if fname.lower().endswith(('.tiff', '.tif')): with open(fname, 'rb') as f: img = TiffFile(f) index += [(fname, i) for i in range(len(img.pages))] else: try: im = Image.open(fname) im.seek(0) except (IOError, OSError): continue i = 0 while True: try: im.seek(i) except EOFError: break index.append((fname, i)) i += 1 if hasattr(im, 'fp') and im.fp: im.fp.close() self._frame_index = index return len(index) def __getitem__(self, n): """Return selected image(s) in the collection. Loading is done on demand. Parameters ---------- n : int or slice The image number to be returned, or a slice selecting the images and ordering to be returned in a new ImageCollection. Returns ------- img : ndarray or ImageCollection. The `n`-th image in the collection, or a new ImageCollection with the selected images. """ if hasattr(n, '__index__'): n = n.__index__() if type(n) not in [int, slice]: raise TypeError('slicing must be with an int or slice object') if type(n) is int: n = self._check_imgnum(n) idx = n % len(self.data) if ((self.conserve_memory and n != self._cached) or (self.data[idx] is None)): kwargs = self.load_func_kwargs if self._frame_index: fname, img_num = self._frame_index[n] if img_num is not None: kwargs['img_num'] = img_num try: self.data[idx] = self.load_func(fname, **kwargs) # Account for functions that do not accept an img_num kwarg except TypeError as e: if "unexpected keyword argument 'img_num'" in str(e): del kwargs['img_num'] self.data[idx] = self.load_func(fname, **kwargs) else: raise else: self.data[idx] = self.load_func(self.files[n], **kwargs) self._cached = n return self.data[idx] else: # A slice object was provided, so create a new ImageCollection # object. Any loaded image data in the original ImageCollection # will be copied by reference to the new object. Image data # loaded after this creation is not linked. fidx = range(self._numframes)[n] new_ic = copy(self) if self._frame_index: new_ic._files = [self._frame_index[i][0] for i in fidx] new_ic._frame_index = [self._frame_index[i] for i in fidx] else: new_ic._files = [self._files[i] for i in fidx] new_ic._numframes = len(fidx) if self.conserve_memory: if self._cached in fidx: new_ic._cached = fidx.index(self._cached) new_ic.data = np.copy(self.data) else: new_ic.data = np.empty(1, dtype=object) else: new_ic.data = self.data[fidx] return new_ic def _check_imgnum(self, n): """Check that the given image number is valid.""" num = self._numframes if -num <= n < num: n = n % num else: raise IndexError("There are only %s images in the collection" % num) return n def __iter__(self): """Iterate over the images.""" for i in range(len(self)): yield self[i] def __len__(self): """Number of images in collection.""" return self._numframes def __str__(self): return str(self.files) def reload(self, n=None): """Clear the image cache. Parameters ---------- n : None or int Clear the cache for this image only. By default, the entire cache is erased. """ self.data = np.empty_like(self.data) def concatenate(self): """Concatenate all images in the collection into an array. Returns ------- ar : np.ndarray An array having one more dimension than the images in `self`. See Also -------- concatenate_images Raises ------ ValueError If images in the `ImageCollection` don't have identical shapes. """ return concatenate_images(self) def imread_collection_wrapper(imread): def imread_collection(load_pattern, conserve_memory=True): """Return an `ImageCollection` from files matching the given pattern. Note that files are always stored in alphabetical order. Also note that slicing returns a new ImageCollection, *not* a view into the data. See `skimage.io.ImageCollection` for details. Parameters ---------- load_pattern : str or list Pattern glob or filenames to load. The path can be absolute or relative. Multiple patterns should be separated by a colon, e.g. '/tmp/work/*.png:/tmp/other/*.jpg'. Also see implementation notes below. conserve_memory : bool, optional If True, never keep more than one in memory at a specific time. Otherwise, images will be cached once they are loaded. """ return ImageCollection(load_pattern, conserve_memory=conserve_memory, load_func=imread) return imread_collection class MultiImage(ImageCollection): """A class containing a single multi-frame image. Parameters ---------- filename : str The complete path to the image file. conserve_memory : bool, optional Whether to conserve memory by only caching a single frame. Default is True. Notes ----- If ``conserve_memory=True`` the memory footprint can be reduced, however the performance can be affected because frames have to be read from file more often. The last accessed frame is cached, all other frames will have to be read from file. The current implementation makes use of ``tifffile`` for Tiff files and PIL otherwise. Examples -------- >>> from skimage import data_dir >>> img = MultiImage(data_dir + '/multipage.tif') # doctest: +SKIP >>> len(img) # doctest: +SKIP 2 >>> for frame in img: # doctest: +SKIP ... print(frame.shape) # doctest: +SKIP (15, 10) (15, 10) """ def __init__(self, filename, conserve_memory=True, dtype=None, **imread_kwargs): """Load a multi-img.""" from ._io import imread def load_func(fname, **kwargs): kwargs.setdefault('dtype', dtype) return imread(fname, **kwargs) self._filename = filename super(MultiImage, self).__init__(filename, conserve_memory, load_func=load_func, **imread_kwargs) @property def filename(self): return self._filename scikit-image-0.13.1/skimage/io/manage_plugins.py000066400000000000000000000243771316256236300215600ustar00rootroot00000000000000"""Handle image reading, writing and plotting plugins. To improve performance, plugins are only loaded as needed. As a result, there can be multiple states for a given plugin: available: Defined in an *ini file located in `skimage.io._plugins`. See also `skimage.io.available_plugins`. partial definition: Specified in an *ini file, but not defined in the corresponding plugin module. This will raise an error when loaded. available but not on this system: Defined in `skimage.io._plugins`, but a dependent library (e.g. Qt, PIL) is not available on your system. This will raise an error when loaded. loaded: The real availability is determined when it's explicitly loaded, either because it's one of the default plugins, or because it's loaded explicitly by the user. """ import sys if sys.version.startswith('3'): from configparser import ConfigParser # Python 3 else: from ConfigParser import ConfigParser # Python 2 import os.path from glob import glob from .collection import imread_collection_wrapper __all__ = ['use_plugin', 'call_plugin', 'plugin_info', 'plugin_order', 'reset_plugins', 'find_available_plugins', 'available_plugins'] # The plugin store will save a list of *loaded* io functions for each io type # (e.g. 'imread', 'imsave', etc.). Plugins are loaded as requested. plugin_store = None # Dictionary mapping plugin names to a list of functions they provide. plugin_provides = {} # The module names for the plugins in `skimage.io._plugins`. plugin_module_name = {} # Meta-data about plugins provided by *.ini files. plugin_meta_data = {} # For each plugin type, default to the first available plugin as defined by # the following preferences. preferred_plugins = { # Default plugins for all types (overridden by specific types below). 'all': ['pil', 'matplotlib', 'qt', 'freeimage'], 'imshow': ['matplotlib'], 'imshow_collection': ['matplotlib'] } def _clear_plugins(): """Clear the plugin state to the default, i.e., where no plugins are loaded """ global plugin_store plugin_store = {'imread': [], 'imsave': [], 'imshow': [], 'imread_collection': [], 'imshow_collection': [], '_app_show': []} _clear_plugins() def _load_preferred_plugins(): # Load preferred plugin for each io function. io_types = ['imsave', 'imshow', 'imread_collection', 'imshow_collection', 'imread'] for p_type in io_types: _set_plugin(p_type, preferred_plugins['all']) plugin_types = (p for p in preferred_plugins.keys() if p != 'all') for p_type in plugin_types: _set_plugin(p_type, preferred_plugins[p_type]) def _set_plugin(plugin_type, plugin_list): for plugin in plugin_list: if plugin not in available_plugins: continue try: use_plugin(plugin, kind=plugin_type) break except (ImportError, RuntimeError, OSError): pass def reset_plugins(): _clear_plugins() _load_preferred_plugins() def _parse_config_file(filename): """Return plugin name and meta-data dict from plugin config file.""" parser = ConfigParser() parser.read(filename) name = parser.sections()[0] meta_data = {} for opt in parser.options(name): meta_data[opt] = parser.get(name, opt) return name, meta_data def _scan_plugins(): """Scan the plugins directory for .ini files and parse them to gather plugin meta-data. """ pd = os.path.dirname(__file__) config_files = glob(os.path.join(pd, '_plugins', '*.ini')) for filename in config_files: name, meta_data = _parse_config_file(filename) plugin_meta_data[name] = meta_data provides = [s.strip() for s in meta_data['provides'].split(',')] valid_provides = [p for p in provides if p in plugin_store] for p in provides: if not p in plugin_store: print("Plugin `%s` wants to provide non-existent `%s`." " Ignoring." % (name, p)) # Add plugins that provide 'imread' as provider of 'imread_collection'. need_to_add_collection = ('imread_collection' not in valid_provides and 'imread' in valid_provides) if need_to_add_collection: valid_provides.append('imread_collection') plugin_provides[name] = valid_provides plugin_module_name[name] = os.path.basename(filename)[:-4] _scan_plugins() def find_available_plugins(loaded=False): """List available plugins. Parameters ---------- loaded : bool If True, show only those plugins currently loaded. By default, all plugins are shown. Returns ------- p : dict Dictionary with plugin names as keys and exposed functions as values. """ active_plugins = set() for plugin_func in plugin_store.values(): for plugin, func in plugin_func: active_plugins.add(plugin) d = {} for plugin in plugin_provides: if not loaded or plugin in active_plugins: d[plugin] = [f for f in plugin_provides[plugin] if not f.startswith('_')] return d available_plugins = find_available_plugins() def call_plugin(kind, *args, **kwargs): """Find the appropriate plugin of 'kind' and execute it. Parameters ---------- kind : {'imshow', 'imsave', 'imread', 'imread_collection'} Function to look up. plugin : str, optional Plugin to load. Defaults to None, in which case the first matching plugin is used. *args, **kwargs : arguments and keyword arguments Passed to the plugin function. """ if not kind in plugin_store: raise ValueError('Invalid function (%s) requested.' % kind) plugin_funcs = plugin_store[kind] if len(plugin_funcs) == 0: msg = ("No suitable plugin registered for %s.\n\n" "You may load I/O plugins with the `skimage.io.use_plugin` " "command. A list of all available plugins are shown in the " "`skimage.io` docstring.") raise RuntimeError(msg % kind) plugin = kwargs.pop('plugin', None) if plugin is None: _, func = plugin_funcs[0] else: _load(plugin) try: func = [f for (p, f) in plugin_funcs if p == plugin][0] except IndexError: raise RuntimeError('Could not find the plugin "%s" for %s.' % (plugin, kind)) return func(*args, **kwargs) def use_plugin(name, kind=None): """Set the default plugin for a specified operation. The plugin will be loaded if it hasn't been already. Parameters ---------- name : str Name of plugin. kind : {'imsave', 'imread', 'imshow', 'imread_collection', 'imshow_collection'}, optional Set the plugin for this function. By default, the plugin is set for all functions. See Also -------- available_plugins : List of available plugins Examples -------- To use Matplotlib as the default image reader, you would write: >>> from skimage import io >>> io.use_plugin('matplotlib', 'imread') To see a list of available plugins run ``io.available_plugins``. Note that this lists plugins that are defined, but the full list may not be usable if your system does not have the required libraries installed. """ if kind is None: kind = plugin_store.keys() else: if not kind in plugin_provides[name]: raise RuntimeError("Plugin %s does not support `%s`." % (name, kind)) if kind == 'imshow': kind = [kind, '_app_show'] else: kind = [kind] _load(name) for k in kind: if not k in plugin_store: raise RuntimeError("'%s' is not a known plugin function." % k) funcs = plugin_store[k] # Shuffle the plugins so that the requested plugin stands first # in line funcs = [(n, f) for (n, f) in funcs if n == name] + \ [(n, f) for (n, f) in funcs if n != name] plugin_store[k] = funcs def _inject_imread_collection_if_needed(module): """Add `imread_collection` to module if not already present.""" if not hasattr(module, 'imread_collection') and hasattr(module, 'imread'): imread = getattr(module, 'imread') func = imread_collection_wrapper(imread) setattr(module, 'imread_collection', func) def _load(plugin): """Load the given plugin. Parameters ---------- plugin : str Name of plugin to load. See Also -------- plugins : List of available plugins """ if plugin in find_available_plugins(loaded=True): return if not plugin in plugin_module_name: raise ValueError("Plugin %s not found." % plugin) else: modname = plugin_module_name[plugin] plugin_module = __import__('skimage.io._plugins.' + modname, fromlist=[modname]) provides = plugin_provides[plugin] for p in provides: if p == 'imread_collection': _inject_imread_collection_if_needed(plugin_module) elif not hasattr(plugin_module, p): print("Plugin %s does not provide %s as advertised. Ignoring." % (plugin, p)) continue store = plugin_store[p] func = getattr(plugin_module, p) if not (plugin, func) in store: store.append((plugin, func)) def plugin_info(plugin): """Return plugin meta-data. Parameters ---------- plugin : str Name of plugin. Returns ------- m : dict Meta data as specified in plugin ``.ini``. """ try: return plugin_meta_data[plugin] except KeyError: raise ValueError('No information on plugin "%s"' % plugin) def plugin_order(): """Return the currently preferred plugin order. Returns ------- p : dict Dictionary of preferred plugin order, with function name as key and plugins (in order of preference) as value. """ p = {} for func in plugin_store: p[func] = [plugin_name for (plugin_name, f) in plugin_store[func]] return p scikit-image-0.13.1/skimage/io/setup.py000066400000000000000000000025431316256236300177160ustar00rootroot00000000000000#!/usr/bin/env python from skimage._build import cython import os.path base_path = os.path.abspath(os.path.dirname(__file__)) def configuration(parent_package='', top_path=None): from numpy.distutils.misc_util import Configuration, get_numpy_include_dirs config = Configuration('io', parent_package, top_path) config.add_data_dir('tests') config.add_data_files('_plugins/*.ini') # This function tries to create C files from the given .pyx files. If # it fails, we build the checked-in .c files. cython(['_plugins/_colormixer.pyx', '_plugins/_histograms.pyx'], working_path=base_path) config.add_extension('_plugins._colormixer', sources=['_plugins/_colormixer.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension('_plugins._histograms', sources=['_plugins/_histograms.c'], include_dirs=[get_numpy_include_dirs()]) return config if __name__ == '__main__': from numpy.distutils.core import setup setup(maintainer='scikit-image Developers', maintainer_email='scikit-image@python.org', description='Image I/O Routines', url='https://github.com/scikit-image/scikit-image', license='Modified BSD', **(configuration(top_path='').todict()) ) scikit-image-0.13.1/skimage/io/sift.py000066400000000000000000000043131316256236300175200ustar00rootroot00000000000000"""Read SIFT and SURF feature files. See Also -------- http://people.cs.ubc.ca/~lowe/keypoints/ http://www.vision.ee.ethz.ch/~surf/ """ __all__ = ['load_sift', 'load_surf'] import numpy as np def _sift_read(f, mode='SIFT'): """Read SIFT or SURF features from externally generated file. This routine reads SIFT or SURF files generated by binary utilities from http://people.cs.ubc.ca/~lowe/keypoints/ and http://www.vision.ee.ethz.ch/~surf/. This routine *does not* generate SIFT/SURF features from an image. These algorithms are patent encumbered. Please use `skimage.feature.CENSURE` instead. Parameters ---------- f : string or open file Input file generated by the feature detectors from http://people.cs.ubc.ca/~lowe/keypoints/ or http://www.vision.ee.ethz.ch/~surf/ Returns ------- data : record array with fields - row: int row position of feature - column: int column position of feature - scale: float feature scale - orientation: float feature orientation - data: array feature values """ if not hasattr(f, 'readline'): f = open(f, 'r') if mode == 'SIFT': nr_features, feature_len = map(int, f.readline().split()) datatype = np.dtype([('row', float), ('column', float), ('scale', float), ('orientation', float), ('data', (float, feature_len))]) else: mode = 'SURF' feature_len = int(f.readline()) - 1 nr_features = int(f.readline()) datatype = np.dtype([('column', float), ('row', float), ('second_moment', (float, 3)), ('sign', float), ('data', (float, feature_len))]) data = np.fromfile(f, sep=' ') if data.size != nr_features * datatype.itemsize / np.dtype(float).itemsize: raise IOError("Invalid %s feature file." % mode) f.close() return data.view(datatype) def load_sift(f): return _sift_read(f, mode='SIFT') def load_surf(f): return _sift_read(f, mode='SURF') load_sift.__doc__ = _sift_read.__doc__ load_surf.__doc__ = _sift_read.__doc__ scikit-image-0.13.1/skimage/io/tests/000077500000000000000000000000001316256236300173425ustar00rootroot00000000000000scikit-image-0.13.1/skimage/io/tests/__init__.py000066400000000000000000000001771316256236300214600ustar00rootroot00000000000000from ..._shared.testing import setup_test, teardown_test def setup(): setup_test() def teardown(): teardown_test() scikit-image-0.13.1/skimage/io/tests/test_collection.py000066400000000000000000000065121316256236300231120ustar00rootroot00000000000000import os.path import numpy as np from numpy.testing import assert_raises, assert_equal, assert_allclose from skimage import data_dir from skimage.io.collection import ImageCollection, alphanumeric_key from skimage.io import reset_plugins def test_string_split(): test_string = 'z23a' test_str_result = ['z', 23, 'a'] assert_equal(alphanumeric_key(test_string), test_str_result) def test_string_sort(): filenames = ['f9.10.png', 'f9.9.png', 'f10.10.png', 'f10.9.png', 'e9.png', 'e10.png', 'em.png'] sorted_filenames = ['e9.png', 'e10.png', 'em.png', 'f9.9.png', 'f9.10.png', 'f10.9.png', 'f10.10.png'] sorted_filenames = sorted(filenames, key=alphanumeric_key) assert_equal(sorted_filenames, sorted_filenames) class TestImageCollection(): pattern = [os.path.join(data_dir, pic) for pic in ['camera.png', 'color.png']] pattern_matched = [os.path.join(data_dir, pic) for pic in ['camera.png', 'moon.png']] def setUp(self): reset_plugins() # Generic image collection with images of different shapes. self.images = ImageCollection(self.pattern) # Image collection with images having shapes that match. self.images_matched = ImageCollection(self.pattern_matched) def test_len(self): assert len(self.images) == 2 def test_getitem(self): num = len(self.images) for i in range(-num, num): assert type(self.images[i]) is np.ndarray assert_allclose(self.images[0], self.images[-num]) # assert_raises expects a callable, hence this thin wrapper function. def return_img(n): return self.images[n] assert_raises(IndexError, return_img, num) assert_raises(IndexError, return_img, -num - 1) def test_slicing(self): assert type(self.images[:]) is ImageCollection assert len(self.images[:]) == 2 assert len(self.images[:1]) == 1 assert len(self.images[1:]) == 1 assert_allclose(self.images[0], self.images[:1][0]) assert_allclose(self.images[1], self.images[1:][0]) assert_allclose(self.images[1], self.images[::-1][0]) assert_allclose(self.images[0], self.images[::-1][1]) def test_files_property(self): assert isinstance(self.images.files, list) def set_files(f): self.images.files = f assert_raises(AttributeError, set_files, 'newfiles') def test_custom_load(self): load_pattern = [(1, 'one'), (2, 'two')] def load_fn(x): return x ic = ImageCollection(load_pattern, load_func=load_fn) assert_equal(ic[1], (2, 'two')) def test_custom_load_func(self): def load_fn(x): return x ic = ImageCollection(os.pathsep.join(self.pattern), load_func=load_fn) assert_equal(ic[0], self.pattern[0]) def test_concatenate(self): array = self.images_matched.concatenate() expected_shape = (len(self.images_matched),) + self.images[0].shape assert_equal(array.shape, expected_shape) def test_concatentate_mismatched_image_shapes(self): assert_raises(ValueError, self.images.concatenate) if __name__ == "__main__": from numpy.testing import run_module_suite run_module_suite() scikit-image-0.13.1/skimage/io/tests/test_colormixer.py000066400000000000000000000110131316256236300231320ustar00rootroot00000000000000from numpy.testing import (assert_array_equal, assert_almost_equal, assert_equal, assert_array_almost_equal, ) import numpy as np import skimage.io._plugins._colormixer as cm class ColorMixerTest(object): def setup(self): self.state = np.ones((18, 33, 3), dtype=np.uint8) * 200 self.img = np.zeros_like(self.state) def test_basic(self): self.op(self.img, self.state, 0, self.positive) assert_array_equal(self.img[..., 0], self.py_op(self.state[..., 0], self.positive)) def test_clip(self): self.op(self.img, self.state, 0, self.positive_clip) assert_array_equal(self.img[..., 0], np.ones_like(self.img[..., 0]) * 255) def test_negative(self): self.op(self.img, self.state, 0, self.negative) assert_array_equal(self.img[..., 0], self.py_op(self.state[..., 0], self.negative)) def test_negative_clip(self): self.op(self.img, self.state, 0, self.negative_clip) assert_array_equal(self.img[..., 0], np.zeros_like(self.img[..., 0])) class TestColorMixerAdd(ColorMixerTest): op = cm.add py_op = np.add positive = 50 positive_clip = 56 negative = -50 negative_clip = -220 class TestColorMixerMul(ColorMixerTest): op = cm.multiply py_op = np.multiply positive = 1.2 positive_clip = 2 negative = 0.5 negative_clip = -0.5 class TestColorMixerBright(object): def setup(self): self.state = np.ones((18, 33, 3), dtype=np.uint8) * 200 self.img = np.zeros_like(self.state) def test_brightness_pos(self): cm.brightness(self.img, self.state, 1.25, 1) assert_array_equal(self.img, np.ones_like(self.img) * 251) def test_brightness_neg(self): cm.brightness(self.img, self.state, 0.5, -50) assert_array_equal(self.img, np.ones_like(self.img) * 50) def test_brightness_pos_clip(self): cm.brightness(self.img, self.state, 2, 0) assert_array_equal(self.img, np.ones_like(self.img) * 255) def test_brightness_neg_clip(self): cm.brightness(self.img, self.state, 0, 0) assert_array_equal(self.img, np.zeros_like(self.img)) class TestColorMixer(object): def setup(self): self.state = np.ones((18, 33, 3), dtype=np.uint8) * 50 self.img = np.zeros_like(self.state) def test_sigmoid(self): import math alpha = 1.5 beta = 1.5 c1 = 1 / (1 + math.exp(beta)) c2 = 1 / (1 + math.exp(beta - alpha)) - c1 state = self.state / 255. cm.sigmoid_gamma(self.img, self.state, alpha, beta) img = 1 / (1 + np.exp(beta - state * alpha)) img = np.asarray((img - c1) / c2 * 255, dtype='uint8') assert_almost_equal(img, self.img) def test_gamma(self): gamma = 1.5 cm.gamma(self.img, self.state, gamma) img = np.asarray(((self.state / 255.)**(1 / gamma)) * 255, dtype='uint8') assert_array_almost_equal(img, self.img) def test_rgb_2_hsv(self): r = 255 g = 0 b = 0 h, s, v = cm.py_rgb_2_hsv(r, g, b) assert_almost_equal(np.array([h]), np.array([0])) assert_almost_equal(np.array([s]), np.array([1])) assert_almost_equal(np.array([v]), np.array([1])) def test_hsv_2_rgb(self): h = 0 s = 1 v = 1 r, g, b = cm.py_hsv_2_rgb(h, s, v) assert_almost_equal(np.array([r]), np.array([255])) assert_almost_equal(np.array([g]), np.array([0])) assert_almost_equal(np.array([b]), np.array([0])) def test_hsv_add(self): cm.hsv_add(self.img, self.state, 360, 0, 0) assert_almost_equal(self.img, self.state) def test_hsv_add_clip_neg(self): cm.hsv_add(self.img, self.state, 0, 0, -1) assert_equal(self.img, np.zeros_like(self.state)) def test_hsv_add_clip_pos(self): cm.hsv_add(self.img, self.state, 0, 0, 1) assert_equal(self.img, np.ones_like(self.state) * 255) def test_hsv_mul(self): cm.hsv_multiply(self.img, self.state, 360, 1, 1) assert_almost_equal(self.img, self.state) def test_hsv_mul_clip_neg(self): cm.hsv_multiply(self.img, self.state, 0, 0, 0) assert_equal(self.img, np.zeros_like(self.state)) if __name__ == "__main__": from numpy.testing import run_module_suite run_module_suite() scikit-image-0.13.1/skimage/io/tests/test_fits.py000066400000000000000000000047761316256236300217360ustar00rootroot00000000000000import os.path import numpy as np from numpy.testing import run_module_suite from numpy.testing.decorators import skipif import skimage.io as io from skimage import data_dir pyfits_available = True try: from astropy.io import fits as pyfits except ImportError: try: import pyfits except ImportError: pyfits_available = False if pyfits_available: import skimage.io._plugins.fits_plugin as fplug def test_fits_plugin_import(): # Make sure we get an import exception if PyFITS isn't there # (not sure how useful this is, but it ensures there isn't some other # error when trying to load the plugin) try: io.use_plugin('fits') except ImportError: assert pyfits_available == False else: assert pyfits_available == True def teardown(): io.reset_plugins() @skipif(not pyfits_available) def test_imread_MEF(): io.use_plugin('fits') testfile = os.path.join(data_dir, 'multi.fits') img = io.imread(testfile) assert np.all(img == pyfits.getdata(testfile, 1)) @skipif(not pyfits_available) def test_imread_simple(): io.use_plugin('fits') testfile = os.path.join(data_dir, 'simple.fits') img = io.imread(testfile) assert np.all(img == pyfits.getdata(testfile, 0)) @skipif(not pyfits_available) def test_imread_collection_single_MEF(): io.use_plugin('fits') testfile = os.path.join(data_dir, 'multi.fits') ic1 = io.imread_collection(testfile) ic2 = io.ImageCollection([(testfile, 1), (testfile, 2), (testfile, 3)], load_func=fplug.FITSFactory) assert _same_ImageCollection(ic1, ic2) @skipif(not pyfits_available) def test_imread_collection_MEF_and_simple(): io.use_plugin('fits') testfile1 = os.path.join(data_dir, 'multi.fits') testfile2 = os.path.join(data_dir, 'simple.fits') ic1 = io.imread_collection([testfile1, testfile2]) ic2 = io.ImageCollection([(testfile1, 1), (testfile1, 2), (testfile1, 3), (testfile2, 0)], load_func=fplug.FITSFactory) assert _same_ImageCollection(ic1, ic2) def _same_ImageCollection(collection1, collection2): """Ancillary function to compare two ImageCollection objects, checking that their constituent arrays are equal. """ if len(collection1) != len(collection2): return False for ext1, ext2 in zip(collection1, collection2): if not np.all(ext1 == ext2): return False return True if __name__ == '__main__': run_module_suite() scikit-image-0.13.1/skimage/io/tests/test_histograms.py000066400000000000000000000014231316256236300231330ustar00rootroot00000000000000from numpy.testing import * import numpy as np from skimage.io._plugins._histograms import histograms class TestHistogram: def test_basic(self): img = np.ones((50, 50, 3), dtype=np.uint8) r, g, b, v = histograms(img, 255) for band in (r, g, b, v): yield assert_equal, band.sum(), 50 * 50 def test_counts(self): channel = np.arange(255).reshape(51, 5) img = np.empty((51, 5, 3), dtype='uint8') img[:, :, 0] = channel img[:, :, 1] = channel img[:, :, 2] = channel r, g, b, v = histograms(img, 255) assert_array_equal(r, g) assert_array_equal(r, b) assert_array_equal(r, v) assert_array_equal(r, np.ones(255)) if __name__ == "__main__": run_module_suite() scikit-image-0.13.1/skimage/io/tests/test_imageio.py000066400000000000000000000042001316256236300223610ustar00rootroot00000000000000import os import os.path import numpy as np from numpy.testing import * from numpy.testing.decorators import skipif from tempfile import NamedTemporaryFile from skimage import data_dir from skimage.io import imread, imsave, use_plugin, reset_plugins try: import imageio as _imageio except ImportError: imageio_available = False else: imageio_available = True def setup(): if imageio_available: np.random.seed(0) use_plugin('imageio') def teardown(): reset_plugins() @skipif(not imageio_available) def test_imageio_flatten(): # a color image is flattened img = imread(os.path.join(data_dir, 'color.png'), flatten=True) assert img.ndim == 2 assert img.dtype == np.float64 img = imread(os.path.join(data_dir, 'camera.png'), flatten=True) # check that flattening does not occur for an image that is grey already. assert np.sctype2char(img.dtype) in np.typecodes['AllInteger'] @skipif(not imageio_available) def test_imageio_palette(): img = imread(os.path.join(data_dir, 'palette_color.png')) assert img.ndim == 3 @skipif(not imageio_available) def test_imageio_truncated_jpg(): # imageio>2.0 uses Pillow / PIL to try and load the file. # Oddly, PIL explicitly raises a SyntaxError when the file read fails. assert_raises((RuntimeError, ValueError, SyntaxError), imread, os.path.join(data_dir, 'truncated.jpg')) class TestSave: def roundtrip(self, x, scaling=1): f = NamedTemporaryFile(suffix='.png') fname = f.name f.close() imsave(fname, x) y = imread(fname) assert_array_almost_equal((x * scaling).astype(np.int32), y) @skipif(not imageio_available) def test_imsave_roundtrip(self): dtype = np.uint8 for shape in [(10, 10), (10, 10, 3), (10, 10, 4)]: x = np.ones(shape, dtype=dtype) * np.random.rand(*shape) if np.issubdtype(dtype, float): yield self.roundtrip, x, 255 else: x = (x * 255).astype(dtype) yield self.roundtrip, x if __name__ == "__main__": run_module_suite() scikit-image-0.13.1/skimage/io/tests/test_imread.py000066400000000000000000000043371316256236300222230ustar00rootroot00000000000000import os import os.path import numpy as np from numpy.testing import * from numpy.testing.decorators import skipif from tempfile import NamedTemporaryFile from skimage import data_dir from skimage.io import imread, imsave, use_plugin, reset_plugins import skimage.io as sio try: import imread as _imread except ImportError: imread_available = False else: imread_available = True def setup(): if imread_available: np.random.seed(0) use_plugin('imread') def teardown(): reset_plugins() @skipif(not imread_available) def test_imread_flatten(): # a color image is flattened img = imread(os.path.join(data_dir, 'color.png'), flatten=True) assert img.ndim == 2 assert img.dtype == np.float64 img = imread(os.path.join(data_dir, 'camera.png'), flatten=True) # check that flattening does not occur for an image that is grey already. assert np.sctype2char(img.dtype) in np.typecodes['AllInteger'] @skipif(not imread_available) def test_imread_palette(): img = imread(os.path.join(data_dir, 'palette_color.png')) assert img.ndim == 3 @skipif(not imread_available) def test_imread_truncated_jpg(): assert_raises((RuntimeError, ValueError), sio.imread, os.path.join(data_dir, 'truncated.jpg')) @skipif(not imread_available) def test_bilevel(): expected = np.zeros((10, 10), bool) expected[::2] = 1 img = imread(os.path.join(data_dir, 'checker_bilevel.png')) assert_array_equal(img.astype(bool), expected) class TestSave: def roundtrip(self, x, scaling=1): f = NamedTemporaryFile(suffix='.png') fname = f.name f.close() imsave(fname, x) y = imread(fname) assert_array_almost_equal((x * scaling).astype(np.int32), y) @skipif(not imread_available) def test_imsave_roundtrip(self): dtype = np.uint8 for shape in [(10, 10), (10, 10, 3), (10, 10, 4)]: x = np.ones(shape, dtype=dtype) * np.random.rand(*shape) if np.issubdtype(dtype, float): yield self.roundtrip, x, 255 else: x = (x * 255).astype(dtype) yield self.roundtrip, x if __name__ == "__main__": run_module_suite() scikit-image-0.13.1/skimage/io/tests/test_io.py000066400000000000000000000014011316256236300213560ustar00rootroot00000000000000import os from numpy.testing import assert_array_equal, raises, run_module_suite import numpy as np import skimage.io as io from skimage.io.manage_plugins import plugin_store from skimage import data_dir def test_stack_basic(): x = np.arange(12).reshape(3, 4) io.push(x) assert_array_equal(io.pop(), x) @raises(ValueError) def test_stack_non_array(): io.push([[1, 2, 3]]) def test_imread_url(): # tweak data path so that file URI works on both unix and windows. data_path = data_dir.lstrip(os.path.sep) data_path = data_path.replace(os.path.sep, '/') image_url = 'file:///{0}/camera.png'.format(data_path) image = io.imread(image_url) assert image.shape == (512, 512) if __name__ == "__main__": run_module_suite() scikit-image-0.13.1/skimage/io/tests/test_mpl_imshow.py000066400000000000000000000077171316256236300231450ustar00rootroot00000000000000from __future__ import division import numpy as np from skimage import io from skimage._shared._warnings import expected_warnings import matplotlib.pyplot as plt def setup(): io.reset_plugins() # test images. Note that they don't have their full range for their dtype, # but we still expect the display range to equal the full dtype range. im8 = np.array([[0, 64], [128, 240]], np.uint8) im16 = im8.astype(np.uint16) * 256 im64 = im8.astype(np.uint64) imf = im8 / 255 im_lo = imf / 1000 im_hi = imf + 10 def n_subplots(ax_im): """Return the number of subplots in the figure containing an ``AxesImage``. Parameters ---------- ax_im : matplotlib.pyplot.AxesImage object The input ``AxesImage``. Returns ------- n : int The number of subplots in the corresponding figure. Notes ----- This function is intended to check whether a colorbar was drawn, in which case two subplots are expected. For standard imshows, one subplot is expected. """ return len(ax_im.get_figure().get_axes()) def test_uint8(): plt.figure() with expected_warnings(["tight_layout : falling back to Agg|\A\Z", "CObject type is marked|\A\Z"]): ax_im = io.imshow(im8) assert ax_im.cmap.name == 'gray' assert ax_im.get_clim() == (0, 255) assert n_subplots(ax_im) == 1 assert ax_im.colorbar is None def test_uint16(): plt.figure() with expected_warnings(["tight_layout : falling back to Agg|\A\Z", "CObject type is marked|\A\Z"]): ax_im = io.imshow(im16) assert ax_im.cmap.name == 'gray' assert ax_im.get_clim() == (0, 65535) assert n_subplots(ax_im) == 1 assert ax_im.colorbar is None def test_float(): plt.figure() with expected_warnings(["tight_layout : falling back to Agg|\A\Z", "CObject type is marked|\A\Z"]): ax_im = io.imshow(imf) assert ax_im.cmap.name == 'gray' assert ax_im.get_clim() == (0, 1) assert n_subplots(ax_im) == 1 assert ax_im.colorbar is None def test_low_data_range(): with expected_warnings(["Low image data range|CObject type is marked", "tight_layout : falling back to Agg|\A\Z"]): ax_im = io.imshow(im_lo) assert ax_im.get_clim() == (im_lo.min(), im_lo.max()) # check that a colorbar was created assert ax_im.colorbar is not None def test_outside_standard_range(): plt.figure() # Warning raised by matplotlib on Windows: # "The CObject type is marked Pending Deprecation in Python 2.7. # Please use capsule objects instead." # Ref: https://docs.python.org/2/c-api/cobject.html with expected_warnings(["out of standard range|CObject type is marked", "tight_layout : falling back to Agg|\A\Z"]): ax_im = io.imshow(im_hi) assert ax_im.get_clim() == (im_hi.min(), im_hi.max()) assert n_subplots(ax_im) == 2 assert ax_im.colorbar is not None def test_nonstandard_type(): plt.figure() # Warning raised by matplotlib on Windows: # "The CObject type is marked Pending Deprecation in Python 2.7. # Please use capsule objects instead." # Ref: https://docs.python.org/2/c-api/cobject.html with expected_warnings(["Low image data range|CObject type is marked", "tight_layout : falling back to Agg|\A\Z"]): ax_im = io.imshow(im64) assert ax_im.get_clim() == (im64.min(), im64.max()) assert n_subplots(ax_im) == 2 assert ax_im.colorbar is not None def test_signed_image(): plt.figure() im_signed = np.array([[-0.5, -0.2], [0.1, 0.4]]) with expected_warnings(["tight_layout : falling back to Agg|\A\Z", "CObject type is marked|\A\Z"]): ax_im = io.imshow(im_signed) assert ax_im.get_clim() == (-0.5, 0.5) assert n_subplots(ax_im) == 2 assert ax_im.colorbar is not None if __name__ == '__main__': np.testing.run_module_suite() scikit-image-0.13.1/skimage/io/tests/test_multi_image.py000066400000000000000000000065301316256236300232530ustar00rootroot00000000000000import os import numpy as np from numpy.testing import assert_raises, assert_equal, assert_allclose from skimage.io import use_plugin from skimage import data_dir from skimage.io.collection import MultiImage, ImageCollection import six class TestMultiImage(): def setUp(self): # This multipage TIF file was created with imagemagick: # convert im1.tif im2.tif -adjoin multipage.tif use_plugin('pil') paths = [os.path.join(data_dir, 'multipage_rgb.tif'), os.path.join(data_dir, 'no_time_for_that_tiny.gif')] self.imgs = [MultiImage(paths[0]), MultiImage(paths[0], conserve_memory=False), MultiImage(paths[1]), MultiImage(paths[1], conserve_memory=False), ImageCollection(paths[0]), ImageCollection(paths[1], conserve_memory=False), ImageCollection(os.pathsep.join(paths))] def test_shapes(self): img = self.imgs[-1] imgs = img[:] assert imgs[0].shape == imgs[1].shape assert imgs[0].shape == (10, 10, 3) def test_len(self): assert len(self.imgs[0]) == len(self.imgs[1]) == 2 assert len(self.imgs[2]) == len(self.imgs[3]) == 24 assert len(self.imgs[4]) == 2 assert len(self.imgs[5]) == 24 assert len(self.imgs[6]) == 26, len(self.imgs[6]) def test_slicing(self): img = self.imgs[-1] assert type(img[:]) is ImageCollection assert len(img[:]) == 26, len(img[:]) assert len(img[:1]) == 1 assert len(img[1:]) == 25 assert_allclose(img[0], img[:1][0]) assert_allclose(img[1], img[1:][0]) assert_allclose(img[-1], img[::-1][0]) assert_allclose(img[0], img[::-1][-1]) def test_getitem(self): for img in self.imgs: num = len(img) for i in range(-num, num): assert type(img[i]) is np.ndarray assert_allclose(img[0], img[-num]) assert_raises(AssertionError, assert_allclose, img[0], img[1]) # assert_raises expects a callable, hence this thin wrapper function. def return_img(n): return img[n] assert_raises(IndexError, return_img, num) assert_raises(IndexError, return_img, -num - 1) def test_files_property(self): for img in self.imgs: if isinstance(img, ImageCollection): continue assert isinstance(img.filename, six.string_types) def set_filename(f): img.filename = f assert_raises(AttributeError, set_filename, 'newfile') def test_conserve_memory_property(self): for img in self.imgs: assert isinstance(img.conserve_memory, bool) def set_mem(val): img.conserve_memory = val assert_raises(AttributeError, set_mem, True) def test_concatenate(self): for img in self.imgs: if img[0].shape != img[-1].shape: assert_raises(ValueError, img.concatenate) continue array = img.concatenate() assert_equal(array.shape, (len(img),) + img[0].shape) if __name__ == "__main__": from numpy.testing import run_module_suite run_module_suite() scikit-image-0.13.1/skimage/io/tests/test_pil.py000066400000000000000000000174271316256236300215520ustar00rootroot00000000000000import os.path import numpy as np from numpy.testing import ( assert_array_equal, assert_array_almost_equal, assert_raises, assert_allclose, run_module_suite) from tempfile import NamedTemporaryFile from ... import data_dir, img_as_float from .. import imread, imsave, use_plugin, reset_plugins from ..._shared.testing import mono_check, color_check from ..._shared._warnings import expected_warnings from ..._shared._tempfile import temporary_file from six import BytesIO from PIL import Image from .._plugins.pil_plugin import ( pil_to_ndarray, ndarray_to_pil, _palette_is_grayscale) from ...measure import compare_ssim as ssim from ...color import rgb2lab def setup(): use_plugin('pil') def teardown(): reset_plugins() def setup_module(self): """The effect of the `plugin.use` call may be overridden by later imports. Call `use_plugin` directly before the tests to ensure that PIL is used. """ try: use_plugin('pil') except ImportError: pass def test_png_round_trip(): f = NamedTemporaryFile(suffix='.png') fname = f.name f.close() I = np.eye(3) imsave(fname, I) Ip = img_as_float(imread(fname)) os.remove(fname) assert np.sum(np.abs(Ip-I)) < 1e-3 def test_imread_flatten(): # a color image is flattened img = imread(os.path.join(data_dir, 'color.png'), flatten=True) assert img.ndim == 2 assert img.dtype == np.float64 img = imread(os.path.join(data_dir, 'camera.png'), flatten=True) # check that flattening does not occur for an image that is grey already. assert np.sctype2char(img.dtype) in np.typecodes['AllInteger'] def test_imread_separate_channels(): # Test that imread returns RGBA values contiguously even when they are # stored in separate planes. x = np.random.rand(3, 16, 8) f = NamedTemporaryFile(suffix='.tif') fname = f.name f.close() imsave(fname, x) img = imread(fname) os.remove(fname) assert img.shape == (16, 8, 3), img.shape def test_imread_multipage_rgb_tif(): img = imread(os.path.join(data_dir, 'multipage_rgb.tif')) assert img.shape == (2, 10, 10, 3), img.shape def test_imread_palette(): img = imread(os.path.join(data_dir, 'palette_gray.png')) assert img.ndim == 2 img = imread(os.path.join(data_dir, 'palette_color.png')) assert img.ndim == 3 def test_imread_index_png_with_alpha(): # The file `foo3x5x4indexed.png` was created with this array # (3x5 is (height)x(width)): data = np.array([[[127, 0, 255, 255], [127, 0, 255, 255], [127, 0, 255, 255], [127, 0, 255, 255], [127, 0, 255, 255]], [[192, 192, 255, 0], [192, 192, 255, 0], [0, 0, 255, 0], [0, 0, 255, 0], [0, 0, 255, 0]], [[0, 31, 255, 255], [0, 31, 255, 255], [0, 31, 255, 255], [0, 31, 255, 255], [0, 31, 255, 255]]], dtype=np.uint8) img = imread(os.path.join(data_dir, 'foo3x5x4indexed.png')) assert_array_equal(img, data) def test_palette_is_gray(): gray = Image.open(os.path.join(data_dir, 'palette_gray.png')) assert _palette_is_grayscale(gray) color = Image.open(os.path.join(data_dir, 'palette_color.png')) assert not _palette_is_grayscale(color) def test_bilevel(): expected = np.zeros((10, 10)) expected[::2] = 255 img = imread(os.path.join(data_dir, 'checker_bilevel.png')) assert_array_equal(img, expected) def test_imread_uint16(): expected = np.load(os.path.join(data_dir, 'chessboard_GRAY_U8.npy')) img = imread(os.path.join(data_dir, 'chessboard_GRAY_U16.tif')) assert np.issubdtype(img.dtype, np.uint16) assert_array_almost_equal(img, expected) def test_imread_truncated_jpg(): assert_raises((IOError, ValueError), imread, os.path.join(data_dir, 'truncated.jpg')) def test_jpg_quality_arg(): chessboard = np.load(os.path.join(data_dir, 'chessboard_GRAY_U8.npy')) with temporary_file(suffix='.jpg') as jpg: imsave(jpg, chessboard, quality=95) im = imread(jpg) sim = ssim(chessboard, im, data_range=chessboard.max() - chessboard.min()) assert sim > 0.99 def test_imread_uint16_big_endian(): expected = np.load(os.path.join(data_dir, 'chessboard_GRAY_U8.npy')) img = imread(os.path.join(data_dir, 'chessboard_GRAY_U16B.tif')) assert img.dtype == np.uint16 assert_array_almost_equal(img, expected) class TestSave: def roundtrip_file(self, x): with temporary_file(suffix='.png') as fname: imsave(fname, x) y = imread(fname) return y def roundtrip_pil_image(self, x): pil_image = ndarray_to_pil(x) y = pil_to_ndarray(pil_image) return y def verify_roundtrip(self, dtype, x, y, scaling=1): assert_array_almost_equal((x * scaling).astype(np.int32), y) def verify_imsave_roundtrip(self, roundtrip_function): for shape in [(10, 10), (10, 10, 3), (10, 10, 4)]: for dtype in (np.uint8, np.uint16, np.float32, np.float64): x = np.ones(shape, dtype=dtype) * np.random.rand(*shape) if np.issubdtype(dtype, float): yield (self.verify_roundtrip, dtype, x, roundtrip_function(x), 255) else: x = (x * 255).astype(dtype) yield (self.verify_roundtrip, dtype, x, roundtrip_function(x)) def test_imsave_roundtrip_file(self): self.verify_imsave_roundtrip(self.roundtrip_file) def test_imsave_roundtrip_pil_image(self): self.verify_imsave_roundtrip(self.roundtrip_pil_image) def test_imsave_incorrect_dimension(): with temporary_file(suffix='.png') as fname: assert_raises(ValueError, imsave, fname, np.zeros((2, 3, 3, 1))) assert_raises(ValueError, imsave, fname, np.zeros((2, 3, 2))) def test_imsave_filelike(): shape = (2, 2) image = np.zeros(shape) s = BytesIO() # save to file-like object with expected_warnings(['precision loss', 'is a low contrast image']): imsave(s, image) # read from file-like object s.seek(0) out = imread(s) assert out.shape == shape assert_allclose(out, image) def test_imexport_imimport(): shape = (2, 2) image = np.zeros(shape) with expected_warnings(['precision loss']): pil_image = ndarray_to_pil(image) out = pil_to_ndarray(pil_image) assert out.shape == shape def test_all_color(): color_check('pil') color_check('pil', 'bmp') def test_all_mono(): mono_check('pil') def test_multi_page_gif(): img = imread(os.path.join(data_dir, 'no_time_for_that_tiny.gif')) assert img.shape == (24, 25, 14, 3), img.shape img2 = imread(os.path.join(data_dir, 'no_time_for_that_tiny.gif'), img_num=5) assert img2.shape == (25, 14, 3) assert_allclose(img[5], img2) def test_cmyk(): ref = imread(os.path.join(data_dir, 'color.png')) img = Image.open(os.path.join(data_dir, 'color.png')) img = img.convert('CMYK') f = NamedTemporaryFile(suffix='.jpg') fname = f.name f.close() img.save(fname) try: img.close() except AttributeError: # `close` not available on PIL pass new = imread(fname) ref_lab = rgb2lab(ref) new_lab = rgb2lab(new) for i in range(3): newi = np.ascontiguousarray(new_lab[:, :, i]) refi = np.ascontiguousarray(ref_lab[:, :, i]) sim = ssim(refi, newi, data_range=refi.max() - refi.min()) assert sim > 0.99 if __name__ == "__main__": run_module_suite() scikit-image-0.13.1/skimage/io/tests/test_plugin.py000066400000000000000000000065431316256236300222610ustar00rootroot00000000000000from contextlib import contextmanager from numpy.testing import assert_equal, raises from skimage import io from skimage.io import manage_plugins io.use_plugin('pil') priority_plugin = 'pil' def setup_module(): manage_plugins.use_plugin('test') # see ../_plugins/test_plugin.py def teardown_module(): io.reset_plugins() @contextmanager def protect_preferred_plugins(): """Contexts where `preferred_plugins` can be modified w/o side-effects.""" preferred_plugins = manage_plugins.preferred_plugins.copy() try: yield finally: manage_plugins.preferred_plugins = preferred_plugins def test_read(): io.imread('test.png', as_grey=True, dtype='i4', plugin='test') def test_save(): io.imsave('test.png', [1, 2, 3], plugin='test') def test_show(): io.imshow([1, 2, 3], plugin_arg=(1, 2), plugin='test') def test_collection(): ic = io.imread_collection('*.png', conserve_memory=False, plugin='test') io.imshow_collection(ic) def test_use(): manage_plugins.use_plugin('test') manage_plugins.use_plugin('test', 'imshow') @raises(ValueError) def test_failed_use(): manage_plugins.use_plugin('asd') def test_use_priority(): manage_plugins.use_plugin(priority_plugin) plug, func = manage_plugins.plugin_store['imread'][0] assert_equal(plug, priority_plugin) manage_plugins.use_plugin('test') plug, func = manage_plugins.plugin_store['imread'][0] assert_equal(plug, 'test') def test_use_priority_with_func(): manage_plugins.use_plugin('pil') plug, func = manage_plugins.plugin_store['imread'][0] assert_equal(plug, 'pil') manage_plugins.use_plugin('test', 'imread') plug, func = manage_plugins.plugin_store['imread'][0] assert_equal(plug, 'test') plug, func = manage_plugins.plugin_store['imsave'][0] assert_equal(plug, 'pil') manage_plugins.use_plugin('test') plug, func = manage_plugins.plugin_store['imsave'][0] assert_equal(plug, 'test') def test_plugin_order(): p = io.plugin_order() assert 'imread' in p assert 'test' in p['imread'] def test_available(): assert 'qt' in io.available_plugins assert 'test' in io.find_available_plugins(loaded=True) def test_load_preferred_plugins_all(): from skimage.io._plugins import pil_plugin, matplotlib_plugin with protect_preferred_plugins(): manage_plugins.preferred_plugins = {'all': ['pil'], 'imshow': ['matplotlib']} manage_plugins.reset_plugins() for plugin_type in ('imread', 'imsave'): plug, func = manage_plugins.plugin_store[plugin_type][0] assert func == getattr(pil_plugin, plugin_type) plug, func = manage_plugins.plugin_store['imshow'][0] assert func == getattr(matplotlib_plugin, 'imshow') def test_load_preferred_plugins_imread(): from skimage.io._plugins import pil_plugin, matplotlib_plugin with protect_preferred_plugins(): manage_plugins.preferred_plugins['imread'] = ['pil'] manage_plugins.reset_plugins() plug, func = manage_plugins.plugin_store['imread'][0] assert func == pil_plugin.imread plug, func = manage_plugins.plugin_store['imshow'][0] assert func == matplotlib_plugin.imshow, func.__module__ if __name__ == "__main__": from numpy.testing import run_module_suite run_module_suite() scikit-image-0.13.1/skimage/io/tests/test_plugin_util.py000066400000000000000000000042661316256236300233160ustar00rootroot00000000000000from skimage.io._plugins.util import prepare_for_display, WindowManager from skimage._shared._warnings import expected_warnings from numpy.testing import * import numpy as np np.random.seed(0) class TestPrepareForDisplay: def test_basic(self): with expected_warnings(['precision loss']): prepare_for_display(np.random.rand(10, 10)) def test_dtype(self): with expected_warnings(['precision loss']): x = prepare_for_display(np.random.rand(10, 15)) assert x.dtype == np.dtype(np.uint8) def test_grey(self): with expected_warnings(['precision loss']): tmp = np.arange(12, dtype=float).reshape((4, 3)) / 11 x = prepare_for_display(tmp) assert_array_equal(x[..., 0], x[..., 2]) assert x[0, 0, 0] == 0 assert x[3, 2, 0] == 255 def test_colour(self): with expected_warnings(['precision loss']): prepare_for_display(np.random.rand(10, 10, 3)) def test_alpha(self): with expected_warnings(['precision loss']): prepare_for_display(np.random.rand(10, 10, 4)) @raises(ValueError) def test_wrong_dimensionality(self): with expected_warnings(['precision loss']): prepare_for_display(np.random.rand(10, 10, 1, 1)) @raises(ValueError) def test_wrong_depth(self): with expected_warnings(['precision loss']): prepare_for_display(np.random.rand(10, 10, 5)) class TestWindowManager: callback_called = False def setup(self): self.wm = WindowManager() self.wm.acquire('test') def test_add_window(self): self.wm.add_window('window1') self.wm.remove_window('window1') def callback(self): self.callback_called = True def test_callback(self): self.wm.register_callback(self.callback) self.wm.add_window('window') self.wm.remove_window('window') assert self.callback_called def test_has_images(self): assert not self.wm.has_windows() self.wm.add_window('window') assert self.wm.has_windows() def teardown(self): self.wm._release('test') if __name__ == "__main__": run_module_suite() scikit-image-0.13.1/skimage/io/tests/test_sift.py000066400000000000000000000064061316256236300217260ustar00rootroot00000000000000from nose.tools import * from numpy.testing import assert_equal, run_module_suite from tempfile import NamedTemporaryFile import os from skimage.io import load_sift, load_surf def test_load_sift(): f = NamedTemporaryFile(delete=False) fname = f.name f.close() f = open(fname, 'wb') f.write(b'''2 128 133.92 135.88 14.38 -2.732 3 12 23 38 10 15 78 20 39 67 42 8 12 8 39 35 118 43 17 0 0 1 12 109 9 2 6 0 0 21 46 22 14 18 51 19 5 9 41 52 65 30 3 21 55 49 26 30 118 118 25 12 8 3 2 60 53 56 72 20 7 10 16 7 88 23 13 15 12 11 11 71 45 7 4 49 82 38 38 91 118 15 2 16 33 3 5 118 98 38 6 19 36 1 0 15 64 22 1 2 6 11 18 61 31 3 0 6 15 23 118 118 13 0 0 35 38 18 40 96 24 1 0 13 17 3 24 98 132.36 99.75 11.45 -2.910 94 32 7 2 13 7 5 23 121 94 13 5 0 0 4 59 13 30 71 32 0 6 32 11 25 32 13 0 0 16 51 5 44 50 0 3 33 55 11 9 121 121 12 9 6 3 0 18 55 60 48 44 44 9 0 2 106 117 13 2 1 0 1 1 37 1 1 25 80 35 15 41 121 3 0 2 14 3 2 121 51 11 0 20 93 6 0 20 109 57 3 4 5 0 0 28 21 2 0 5 13 12 75 119 35 0 0 13 28 14 37 121 12 0 0 21 46 5 11 93 29 0 0 3 14 4 11 99''') f.close() # Check whether loading by filename works load_sift(fname) with open(fname, 'r') as f: features = load_sift(f) os.remove(fname) assert_equal(len(features), 2) assert_equal(len(features['data'][0]), 128) assert_equal(features['row'][0], 133.92) assert_equal(features['column'][1], 99.75) def test_load_surf(): f = NamedTemporaryFile(delete=False) fname = f.name f.close() f = open(fname, 'wb') f.write(b'''65 2 38.3727 62.0491 0.0371343 0 0.0371343 -1 -0.0705589 0.0130983 -0.00460534 0.132168 -0.0718833 0.0320583 -0.0134032 0.0988654 -0.0542241 0.0171002 -0.00135754 0.105755 -0.0362088 0.0151748 -0.00694272 0.0610017 -0.247091 0.109605 -0.0337623 0.0813307 -0.24185 0.278548 -0.0494523 0.107804 -0.166312 0.0691584 -0.0288199 0.138476 -0.110956 0.0280772 -0.0752509 0.0736344 -0.22667 0.164226 -0.0544717 0.0388139 -0.30194 0.33065 -0.0537507 0.0596398 -0.245395 0.110925 -0.0603322 0.0239389 -0.18726 0.0374145 -0.0355872 0.0140762 -0.129022 0.135104 -0.0703396 0.0374049 -0.24256 0.222544 -0.0536354 0.0501252 -0.209004 0.0971316 -0.0550094 0.0229767 -0.125547 0.0317879 -0.0291574 0.0124569 68.5773 61.474 0.0313267 0 0.0313267 1 -0.10198 0.130987 -0.0321845 0.0487543 -0.0900435 0.121113 -0.100917 0.0444702 -0.0151742 0.107604 -0.0542035 0.014069 -0.00594097 0.0339933 -0.00994295 0.0127262 -0.125613 0.192551 -0.0174399 0.0433488 -0.272698 0.164641 -0.0676735 0.0467444 -0.0527907 0.258005 -0.0818114 0.0440569 -0.0104433 0.0548934 -0.0323454 0.0145296 -0.112357 0.199223 -0.0532903 0.0332622 -0.342481 0.207469 -0.0526129 0.0741355 -0.256234 0.402708 -0.108296 0.117362 -0.0560274 0.128856 -0.123509 0.0510046 -0.0198793 0.0775934 -0.103863 0.00406679 -0.10264 0.1312 -0.108244 0.0812913 -0.127868 0.182924 -0.0680942 0.071913 -0.0858004 0.144806 -0.0176522 0.0686146''') f.close() # Check whether loading by filename works load_surf(fname) f = open(fname, 'r') features = load_surf(f) f.close() os.remove(fname) assert_equal(len(features), 2) assert_equal(len(features['data'][0]), 64) assert_equal(features['column'][1], 68.5773) assert_equal(features['row'][0], 62.0491) if __name__ == '__main__': run_module_suite() scikit-image-0.13.1/skimage/io/tests/test_simpleitk.py000066400000000000000000000057171316256236300227660ustar00rootroot00000000000000import os.path import numpy as np from numpy.testing.decorators import skipif from numpy.testing import assert_raises from tempfile import NamedTemporaryFile from skimage import data_dir from skimage.io import imread, imsave, use_plugin, reset_plugins try: import SimpleITK as sitk use_plugin('simpleitk') except ImportError: sitk_available = False else: sitk_available = True np.random.seed(0) def teardown(): reset_plugins() def setup_module(self): """The effect of the `plugin.use` call may be overridden by later imports. Call `use_plugin` directly before the tests to ensure that sitk is used. """ try: use_plugin('simpleitk') except ImportError: pass @skipif(not sitk_available) def test_imread_flatten(): # a color image is flattened img = imread(os.path.join(data_dir, 'color.png'), flatten=True) assert img.ndim == 2 assert img.dtype == np.float64 img = imread(os.path.join(data_dir, 'camera.png'), flatten=True) # check that flattening does not occur for an image that is grey already. assert np.sctype2char(img.dtype) in np.typecodes['AllInteger'] @skipif(not sitk_available) def test_bilevel(): expected = np.zeros((10, 10)) expected[::2] = 255 img = imread(os.path.join(data_dir, 'checker_bilevel.png')) np.testing.assert_array_equal(img, expected) """ #TODO: This test causes a Segmentation fault @skipif(not sitk_available) def test_imread_truncated_jpg(): assert_raises((RuntimeError, ValueError), imread, os.path.join(data_dir, 'truncated.jpg')) """ @skipif(not sitk_available) def test_imread_uint16(): expected = np.load(os.path.join(data_dir, 'chessboard_GRAY_U8.npy')) img = imread(os.path.join(data_dir, 'chessboard_GRAY_U16.tif')) assert np.issubdtype(img.dtype, np.uint16) np.testing.assert_array_almost_equal(img, expected) @skipif(not sitk_available) def test_imread_uint16_big_endian(): expected = np.load(os.path.join(data_dir, 'chessboard_GRAY_U8.npy')) img = imread(os.path.join(data_dir, 'chessboard_GRAY_U16B.tif')) np.testing.assert_array_almost_equal(img, expected) class TestSave: def roundtrip(self, dtype, x): f = NamedTemporaryFile(suffix='.mha') fname = f.name f.close() imsave(fname, x) y = imread(fname) np.testing.assert_array_almost_equal(x, y) @skipif(not sitk_available) def test_imsave_roundtrip(self): for shape in [(10, 10), (10, 10, 3), (10, 10, 4)]: for dtype in (np.uint8, np.uint16, np.float32, np.float64): x = np.ones(shape, dtype=dtype) * np.random.rand(*shape) if np.issubdtype(dtype, float): yield self.roundtrip, dtype, x else: x = (x * 255).astype(dtype) yield self.roundtrip, dtype, x if __name__ == "__main__": from numpy.testing import run_module_suite run_module_suite() scikit-image-0.13.1/skimage/io/tests/test_tifffile.py000066400000000000000000000033411316256236300225440ustar00rootroot00000000000000import os from ... import data_dir from .. import imread, imsave, use_plugin, reset_plugins import numpy as np from numpy.testing import ( assert_array_equal, assert_array_almost_equal, run_module_suite) from tempfile import NamedTemporaryFile def setup(): use_plugin('tifffile') np.random.seed(0) def teardown(): reset_plugins() def test_imread_uint16(): expected = np.load(os.path.join(data_dir, 'chessboard_GRAY_U8.npy')) img = imread(os.path.join(data_dir, 'chessboard_GRAY_U16.tif')) assert img.dtype == np.uint16 assert_array_almost_equal(img, expected) def test_imread_uint16_big_endian(): expected = np.load(os.path.join(data_dir, 'chessboard_GRAY_U8.npy')) img = imread(os.path.join(data_dir, 'chessboard_GRAY_U16B.tif')) assert img.dtype == np.uint16 assert_array_almost_equal(img, expected) def test_imread_multipage_rgb_tif(): img = imread(os.path.join(data_dir, 'multipage_rgb.tif')) assert img.shape == (2, 10, 10, 3), img.shape class TestSave: def roundtrip(self, dtype, x): f = NamedTemporaryFile(suffix='.tif') fname = f.name f.close() imsave(fname, x) y = imread(fname) assert_array_equal(x, y) def test_imsave_roundtrip(self): for shape in [(10, 10), (10, 10, 3), (10, 10, 4)]: for dtype in (np.uint8, np.uint16, np.float32, np.int16, np.float64): x = np.random.rand(*shape) if not np.issubdtype(dtype, float): x = (x * np.iinfo(dtype).max).astype(dtype) else: x = x.astype(dtype) yield self.roundtrip, dtype, x if __name__ == "__main__": run_module_suite() scikit-image-0.13.1/skimage/io/util.py000066400000000000000000000017431316256236300175340ustar00rootroot00000000000000try: from urllib.request import urlopen # Python 3 except ImportError: from urllib2 import urlopen # Python 2 import os import re import tempfile from contextlib import contextmanager import six URL_REGEX = re.compile(r'http://|https://|ftp://|file://|file:\\') def is_url(filename): """Return True if string is an http or ftp path.""" return (isinstance(filename, six.string_types) and URL_REGEX.match(filename) is not None) @contextmanager def file_or_url_context(resource_name): """Yield name of file from the given resource (i.e. file or url).""" if is_url(resource_name): _, ext = os.path.splitext(resource_name) try: with tempfile.NamedTemporaryFile(delete=False, suffix=ext) as f: u = urlopen(resource_name) f.write(u.read()) # f must be closed before yielding yield f.name finally: os.remove(f.name) else: yield resource_name scikit-image-0.13.1/skimage/measure/000077500000000000000000000000001316256236300172325ustar00rootroot00000000000000scikit-image-0.13.1/skimage/measure/__init__.py000077500000000000000000000033511316256236300213500ustar00rootroot00000000000000from ._find_contours import find_contours from ._marching_cubes_lewiner import marching_cubes, marching_cubes_lewiner from ._marching_cubes_classic import (marching_cubes_classic, mesh_surface_area, correct_mesh_orientation) from ._regionprops import regionprops, perimeter from .simple_metrics import compare_mse, compare_nrmse, compare_psnr from ._structural_similarity import compare_ssim, structural_similarity from ._polygon import approximate_polygon, subdivide_polygon from .pnpoly import points_in_poly, grid_points_in_poly from ._moments import moments, moments_central, moments_normalized, moments_hu from .profile import profile_line from .fit import LineModel, LineModelND, CircleModel, EllipseModel, ransac from .block import block_reduce from ._label import label from .entropy import shannon_entropy __all__ = ['find_contours', 'regionprops', 'perimeter', 'approximate_polygon', 'subdivide_polygon', 'LineModel', 'LineModelND', 'CircleModel', 'EllipseModel', 'ransac', 'block_reduce', 'moments', 'moments_central', 'moments_normalized', 'moments_hu', 'marching_cubes', 'marching_cubes_lewiner', 'marching_cubes_classic', 'mesh_surface_area', 'correct_mesh_orientation', 'profile_line', 'label', 'points_in_poly', 'grid_points_in_poly', 'structural_similarity', 'compare_ssim', 'compare_mse', 'compare_nrmse', 'compare_psnr', 'shannon_entropy', ] scikit-image-0.13.1/skimage/measure/_ccomp.pxd000066400000000000000000000004301316256236300212040ustar00rootroot00000000000000"""Export fast union find in Cython""" cimport numpy as cnp ctypedef cnp.intp_t DTYPE_t cdef DTYPE_t find_root(DTYPE_t *forest, DTYPE_t n) nogil cdef void set_root(DTYPE_t *forest, DTYPE_t n, DTYPE_t root) nogil cdef void join_trees(DTYPE_t *forest, DTYPE_t n, DTYPE_t m) nogil scikit-image-0.13.1/skimage/measure/_ccomp.pyx000066400000000000000000000652551316256236300212510ustar00rootroot00000000000000#cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False #cython: wraparound=False import numpy as np from .._shared.utils import warn cimport numpy as cnp DTYPE = np.intp BG_NODE_NULL = -999 # Short int - could be more graceful to the CPU cache ctypedef cnp.int32_t INTS_t cdef struct s_shpinfo ctypedef s_shpinfo shape_info ctypedef int (* fun_ravel)(int, int, int, shape_info *) # For having stuff concerning background in one place ctypedef struct bginfo: ## The value in the image (i.e. not the label!) that identifies ## the background. DTYPE_t background_val DTYPE_t background_node ## Identification of the background in the labelled image DTYPE_t background_label cdef void get_bginfo(background_val, bginfo *ret) except *: if background_val is None: ret.background_val = 0 else: ret.background_val = background_val # The node -999 doesn't exist, it will get subsituted by a meaningful value # upon the first background pixel occurence ret.background_node = BG_NODE_NULL ret.background_label = 0 # A pixel has neighbors that have already been scanned. # In the paper, the pixel is denoted by E and its neighbors: # # z=1 z=0 x # ----------------------> # | A B C F G H # | D E . I J K # | . . . L M N # | # y V # # D_ea represents offset of A from E etc. - see the definition of # get_shape_info cdef enum: # the 0D neighbor # D_ee, # We don't need D_ee # the 1D neighbor D_ed, # 2D neighbors D_ea, D_eb, D_ec, # 3D neighbors D_ef, D_eg, D_eh, D_ei, D_ej, D_ek, D_el, D_em, D_en, D_COUNT # Structure for centralised access to shape data # Contains information related to the shape of the input array cdef struct s_shpinfo: INTS_t x INTS_t y INTS_t z # Number of elements DTYPE_t numels # Number of of the input array INTS_t ndim # Offsets between elements recalculated to linear index increments # DEX[D_ea] is offset between E and A (i.e. to the point to upper left) # The name DEX is supposed to evoke DE., where . = A, B, C, D, F etc. INTS_t DEX[D_COUNT] # Function pointer to a function that recalculates multiindex to linear # index. Heavily depends on dimensions of the input array. fun_ravel ravel_index cdef void get_shape_info(inarr_shape, shape_info *res) except *: """ Precalculates all the needed data from the input array shape and stores them in the shape_info struct. """ res.y = 1 res.z = 1 res.ravel_index = ravel_index2D # A shape (3, 1, 4) would make the algorithm crash, but the corresponding # good_shape (i.e. the array with axis swapped) (1, 3, 4) is OK. # Having an axis length of 1 when an axis on the left is longer than 1 # (in this case, it has length of 3) is NOT ALLOWED. good_shape = tuple(sorted(inarr_shape)) res.ndim = len(inarr_shape) if res.ndim == 1: res.x = inarr_shape[0] res.ravel_index = ravel_index1D elif res.ndim == 2: res.x = inarr_shape[1] res.y = inarr_shape[0] res.ravel_index = ravel_index2D if res.x == 1 and res.y > 1: # Should not occur, but better be safe than sorry raise ValueError( "Swap axis of your %s array so it has a %s shape" % (inarr_shape, good_shape)) elif res.ndim == 3: res.x = inarr_shape[2] res.y = inarr_shape[1] res.z = inarr_shape[0] res.ravel_index = ravel_index3D if ((res.x == 1 and res.y > 1) or res.y == 1 and res.z > 1): # Should not occur, but better be safe than sorry raise ValueError( "Swap axes of your %s array so it has a %s shape" % (inarr_shape, good_shape)) else: raise NotImplementedError( "Only for images of dimension 1-3 are supported, got a %sD one" % res.ndim) res.numels = res.x * res.y * res.z # When reading this for the first time, look at the diagram by the enum # definition above (keyword D_ee) # Difference between E and G is (x=0, y=-1, z=-1), E and A (-1, -1, 0) etc. # Here, it is recalculated to linear (raveled) indices of flattened arrays # with their last (=contiguous) dimension is x. # So now the 1st (needed for 1D, 2D and 3D) part, y = 1, z = 1 res.DEX[D_ed] = -1 # Not needed, just for illustration # res.DEX[D_ee] = 0 # So now the 2nd (needed for 2D and 3D) part, y = 0, z = 1 res.DEX[D_ea] = res.ravel_index(-1, -1, 0, res) res.DEX[D_eb] = res.DEX[D_ea] + 1 res.DEX[D_ec] = res.DEX[D_eb] + 1 # And now the 3rd (needed only for 3D) part, z = 0 res.DEX[D_ef] = res.ravel_index(-1, -1, -1, res) res.DEX[D_eg] = res.DEX[D_ef] + 1 res.DEX[D_eh] = res.DEX[D_ef] + 2 res.DEX[D_ei] = res.DEX[D_ef] - res.DEX[D_eb] # DEX[D_eb] = one row up, remember? res.DEX[D_ej] = res.DEX[D_ei] + 1 res.DEX[D_ek] = res.DEX[D_ei] + 2 res.DEX[D_el] = res.DEX[D_ei] - res.DEX[D_eb] res.DEX[D_em] = res.DEX[D_el] + 1 res.DEX[D_en] = res.DEX[D_el] + 2 cdef inline void join_trees_wrapper(DTYPE_t *data_p, DTYPE_t *forest_p, DTYPE_t rindex, INTS_t idxdiff): if data_p[rindex] == data_p[rindex + idxdiff]: join_trees(forest_p, rindex, rindex + idxdiff) cdef int ravel_index1D(int x, int y, int z, shape_info *shapeinfo): """ Ravel index of a 1D array - trivial. y and z are ignored. """ return x cdef int ravel_index2D(int x, int y, int z, shape_info *shapeinfo): """ Ravel index of a 2D array. z is ignored """ cdef int ret = x + y * shapeinfo.x return ret cdef int ravel_index3D(int x, int y, int z, shape_info *shapeinfo): """ Ravel index of a 3D array """ cdef int ret = x + y * shapeinfo.x + z * shapeinfo.y * shapeinfo.x return ret # Tree operations implemented by an array as described in Wu et al. # The term "forest" is used to indicate an array that stores one or more trees # Consider a following tree: # # 5 ----> 3 ----> 2 ----> 1 <---- 6 <---- 7 # | | # 4 >----/ \----< 8 <---- 9 # # The vertices are a unique number, so the tree can be represented by an # array where a the tuple (index, array[index]) represents an edge, # so for our example, array[2] == 1, array[7] == 6 and array[1] == 1, because # 1 is the root. # Last but not least, one array can hold more than one tree as long as their # indices are different. It is the case in this algorithm, so for that reason # the array is referred to as the "forest" = multiple trees next to each # other. # # In this algorithm, there are as many indices as there are elements in the # array to label and array[x] == x for all x. As the labelling progresses, # equivalence between so-called provisional (i.e. not final) labels is # discovered and trees begin to surface. # When we found out that label 5 and 3 are the same, we assign array[5] = 3. cdef DTYPE_t find_root(DTYPE_t *forest, DTYPE_t n) nogil: """Find the root of node n. Given the example above, for any integer from 1 to 9, 1 is always returned """ cdef DTYPE_t root = n while (forest[root] < root): root = forest[root] return root cdef inline void set_root(DTYPE_t *forest, DTYPE_t n, DTYPE_t root) nogil: """ Set all nodes on a path to point to new_root. Given the example above, given n=9, root=6, it would "reconnect" the tree. so forest[9] = 6 and forest[8] = 6 The ultimate goal is that all tree nodes point to the real root, which is element 1 in this case. """ cdef DTYPE_t j while (forest[n] < n): j = forest[n] forest[n] = root n = j forest[n] = root cdef inline void join_trees(DTYPE_t *forest, DTYPE_t n, DTYPE_t m) nogil: """Join two trees containing nodes n and m. If we imagine that in the example tree, the root 1 is not known, we rather have two disjoint trees with roots 2 and 6. Joining them would mean that all elements of both trees become connected to the element 2, so forest[9] == 2, forest[6] == 2 etc. However, when the relationship between 1 and 2 can still be discovered later. """ cdef DTYPE_t root cdef DTYPE_t root_m if (n != m): root = find_root(forest, n) root_m = find_root(forest, m) if (root > root_m): root = root_m set_root(forest, n, root) set_root(forest, m, root) def _get_swaps(shp): """ What axes to swap if we want to convert an illegal array shape to a legal one. Args: shp (tuple-like): The array shape Returns: list: List of tuples to be passed to np.swapaxes """ shp = np.array(shp) swaps = [] # Dimensions where the array is "flat" ones = np.where(shp == 1)[0][::-1] # The other dimensions bigs = np.where(shp > 1)[0] # We now go from opposite directions and swap axes if an index of a flat # axis is higher than the one of a thick axis for one, big in zip(ones, bigs): if one < big: # We are ordered already break else: swaps.append((one, big)) # TODO: Add more swaps so the array is longer along x and shorter along y return swaps def _apply_swaps(arr, swaps): arr2 = arr for one, two in swaps: arr2 = arr.swapaxes(one, two) return arr2 def reshape_array(arr): """ "Rotates" the array so it gains a shape that the algorithm can work with. An illegal shape is (3, 1, 4), and correct one is (1, 3, 4) or (1, 4, 3). The point is to have all 1s of the shape at the beginning, not in the middle of the shape tuple. Note: The greater-than-one shape component should go from greatest to lowest numbers since it is more friendly to the CPU cache (so (1, 3, 4) is less optimal than (1, 4, 3). Keyword to this is "memory spatial locality" Args: arr (np.ndarray): The array we want to fix Returns: tuple (result, swaps): The result is the "fixed" array and a record of what has been done with it. """ swaps = _get_swaps(arr.shape) reshaped = _apply_swaps(arr, swaps) return reshaped, swaps def undo_reshape_array(arr, swaps): """ Does the opposite of what :func:`reshape_array` does Args: arr (np.ndarray): The array to "revert" swaps (list): The second result of :func:`reshape_array` Returns: np.ndarray: The array of the original shape """ # It is safer to undo axes swaps in the opposite order # than the application order reshaped = _apply_swaps(arr, swaps[::-1]) return reshaped def label_cython(input, neighbors=None, background=None, return_num=False, connectivity=None): # Connected components search as described in Fiorio et al. # We have to ensure that the shape of the input can be handled by the # algorithm the input if it is the case input_corrected, swaps = reshape_array(input) cdef cnp.ndarray[DTYPE_t, ndim=1] data cdef cnp.ndarray[DTYPE_t, ndim=1] forest # Having data a 2D array slows down access considerably using linear # indices even when using the data_p pointer :-( data = np.copy(input_corrected.flatten().astype(DTYPE)) forest = np.arange(data.size, dtype=DTYPE) cdef DTYPE_t *forest_p = forest.data cdef DTYPE_t *data_p = data.data cdef shape_info shapeinfo cdef bginfo bg get_shape_info(input_corrected.shape, &shapeinfo) get_bginfo(background, &bg) if neighbors is None and connectivity is None: # use the full connectivity by default connectivity = input_corrected.ndim elif neighbors is not None: DeprecationWarning("The argument 'neighbors' is deprecated, use " "'connectivity' instead") # backwards-compatible neighbors recalc to connectivity, if neighbors == 4: connectivity = 1 elif neighbors == 8: connectivity = input_corrected.ndim else: raise ValueError("Neighbors must be either 4 or 8, got '%d'.\n" % neighbors) if not 1 <= connectivity <= input_corrected.ndim: raise ValueError( "Connectivity below 1 or above %d is illegal." % input_corrected.ndim) scanBG(data_p, forest_p, &shapeinfo, &bg) # the data are treated as degenerated 3D arrays if needed # witout any performance sacrifice scan3D(data_p, forest_p, &shapeinfo, &bg, connectivity) # Label output cdef DTYPE_t ctr ctr = resolve_labels(data_p, forest_p, &shapeinfo, &bg) # Work around a bug in ndimage's type checking on 32-bit platforms if data.dtype == np.int32: data = data.view(np.int32) res = data.reshape(input_corrected.shape) res_orig = undo_reshape_array(res, swaps) if return_num: return res_orig, ctr else: return res_orig cdef DTYPE_t resolve_labels(DTYPE_t *data_p, DTYPE_t *forest_p, shape_info *shapeinfo, bginfo *bg): """ We iterate through the provisional labels and assign final labels based on our knowledge of prov. labels relationship. We also track how many distinct final labels we have. """ cdef DTYPE_t counter = 1, i for i in range(shapeinfo.numels): if i == forest_p[i]: # We have stumbled across a root which is something new to us (root # is the LOWEST of all prov. labels that are equivalent to it) # If the root happens to be the background, # assign the background label instead of a # new label from the counter if i == bg.background_node: # Also, if there is no background in the image, # bg.background_node == BG_NODE_NULL < 0 and this never occurs. data_p[i] = bg.background_label else: data_p[i] = counter # The background label is basically hardcoded to 0, so no need # to check that the new counter != bg.background_label counter += 1 else: data_p[i] = data_p[forest_p[i]] return counter - 1 cdef void scanBG(DTYPE_t *data_p, DTYPE_t *forest_p, shape_info *shapeinfo, bginfo *bg): """ Settle all background pixels now and don't bother with them later. Since this only requires one linar sweep through the array, it is fast and it makes sense to do it separately. The purpose of this function is update of forest_p and bg parameter inplace. """ cdef DTYPE_t i, bgval = bg.background_val, firstbg = shapeinfo.numels # We find the provisional label of the background, which is the index of # the first background pixel for i in range(shapeinfo.numels): if data_p[i] == bgval: firstbg = i bg.background_node = firstbg break # There is no background, therefore the first background element # is not defined. # Since BG_NODE_NULL < 0, this is enough to ensure # that resolve_labels doesn't worry about background. if bg.background_node == BG_NODE_NULL: return # And then we apply this provisional label to the whole background for i in range(firstbg, shapeinfo.numels): if data_p[i] == bgval: forest_p[i] = firstbg # Here, we work with flat arrays regardless whether the data is 1, 2 or 3D. # The lookup to the neighbor in a 2D array is achieved by precalculating an # offset and adding it to the index. # The forward scan mask looks like this (the center point is actually E): # (take a look at shape_info docs for more exhaustive info) # A B C # D E # # So if I am in the point E and want to take a look to A, I take the index of # E and add shapeinfo.DEX[D_ea] to it and get the index of A. # The 1D indices are "raveled" or "linear", that's where "rindex" comes from. cdef void scan1D(DTYPE_t *data_p, DTYPE_t *forest_p, shape_info *shapeinfo, bginfo *bg, DTYPE_t connectivity, DTYPE_t y, DTYPE_t z): """ Perform forward scan on a 1D object, usually the first row of an image """ # Initialize the first row cdef DTYPE_t x, rindex, bgval = bg.background_val cdef INTS_t *DEX = shapeinfo.DEX rindex = shapeinfo.ravel_index(0, y, z, shapeinfo) for x in range(1, shapeinfo.x): rindex += 1 # Handle the first row if data_p[rindex] == bgval: # Nothing to do if we are background continue join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ed]) cdef void scan2D(DTYPE_t *data_p, DTYPE_t *forest_p, shape_info *shapeinfo, bginfo *bg, DTYPE_t connectivity, DTYPE_t z): """ Perform forward scan on a 2D array. """ cdef DTYPE_t x, y, rindex, bgval = bg.background_val cdef INTS_t *DEX = shapeinfo.DEX scan1D(data_p, forest_p, shapeinfo, bg, connectivity, 0, z) for y in range(1, shapeinfo.y): # BEGINNING of x = 0 rindex = shapeinfo.ravel_index(0, y, 0, shapeinfo) # Handle the first column if data_p[rindex] != bgval: # Nothing to do if we are background join_trees_wrapper(data_p, forest_p, rindex, DEX[D_eb]) if connectivity >= 2: join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ec]) # END of x = 0 for x in range(1, shapeinfo.x - 1): # We have just moved to another column (of the same row) # so we increment the raveled index. It will be reset when we get # to another row, so we don't have to worry about altering it here. rindex += 1 if data_p[rindex] == bgval: # Nothing to do if we are background continue join_trees_wrapper(data_p, forest_p, rindex, DEX[D_eb]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ed]) if connectivity >= 2: join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ea]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ec]) # Finally, the last column # BEGINNING of x = max rindex += 1 if data_p[rindex] != bgval: # Nothing to do if we are background join_trees_wrapper(data_p, forest_p, rindex, DEX[D_eb]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ed]) if connectivity >= 2: join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ea]) # END of x = max cdef void scan3D(DTYPE_t *data_p, DTYPE_t *forest_p, shape_info *shapeinfo, bginfo *bg, DTYPE_t connectivity): """ Perform forward scan on a 2D array. """ cdef DTYPE_t x, y, z, rindex, bgval = bg.background_val cdef INTS_t *DEX = shapeinfo.DEX # Handle first plane scan2D(data_p, forest_p, shapeinfo, bg, connectivity, 0) for z in range(1, shapeinfo.z): # Handle first row in 3D manner # BEGINNING of y = 0 # BEGINNING of x = 0 rindex = shapeinfo.ravel_index(0, 0, z, shapeinfo) if data_p[rindex] != bgval: # Nothing to do if we are background # Now we have pixels below join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ej]) if connectivity >= 2: join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ek]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_em]) if connectivity >= 3: join_trees_wrapper(data_p, forest_p, rindex, DEX[D_en]) # END of x = 0 for x in range(1, shapeinfo.x - 1): rindex += 1 # Handle the first row if data_p[rindex] == bgval: # Nothing to do if we are background continue join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ed]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ej]) if connectivity >= 2: join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ei]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ek]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_em]) if connectivity >= 3: join_trees_wrapper(data_p, forest_p, rindex, DEX[D_el]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_en]) # BEGINNING of x = max rindex += 1 # Handle the last element of the first row if data_p[rindex] != bgval: # Nothing to do if we are background join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ed]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ej]) if connectivity >= 2: join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ei]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_em]) if connectivity >= 3: join_trees_wrapper(data_p, forest_p, rindex, DEX[D_el]) # END of x = max # END of y = 0 # BEGINNING of y = ... for y in range(1, shapeinfo.y - 1): # BEGINNING of x = 0 rindex = shapeinfo.ravel_index(0, y, z, shapeinfo) # Handle the first column in 3D manner if data_p[rindex] != bgval: # Nothing to do if we are background join_trees_wrapper(data_p, forest_p, rindex, DEX[D_eb]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ej]) if connectivity >= 2: join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ec]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_eg]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ek]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_em]) if connectivity >= 3: join_trees_wrapper(data_p, forest_p, rindex, DEX[D_eh]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_en]) # END of x = 0 # Handle the rest of columns for x in range(1, shapeinfo.x - 1): rindex += 1 if data_p[rindex] == bgval: # Nothing to do if we are background continue join_trees_wrapper(data_p, forest_p, rindex, DEX[D_eb]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ed]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ej]) if connectivity >= 2: join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ea]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ec]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_eg]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ei]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ek]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_em]) if connectivity >= 3: join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ef]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_eh]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_el]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_en]) # BEGINNING of x = max rindex += 1 if data_p[rindex] != bgval: # Nothing to do if we are background join_trees_wrapper(data_p, forest_p, rindex, DEX[D_eb]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ed]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ej]) if connectivity >= 2: join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ea]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_eg]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ei]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_em]) if connectivity >= 3: join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ef]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_el]) # END of x = max # END of y = ... # BEGINNING of y = max # BEGINNING of x = 0 rindex = shapeinfo.ravel_index(0, shapeinfo.y - 1, z, shapeinfo) # Handle the first column in 3D manner if data_p[rindex] != bgval: # Nothing to do if we are background join_trees_wrapper(data_p, forest_p, rindex, DEX[D_eb]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ej]) if connectivity >= 2: join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ec]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_eg]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ek]) if connectivity >= 3: join_trees_wrapper(data_p, forest_p, rindex, DEX[D_eh]) # END of x = 0 # Handle the rest of columns for x in range(1, shapeinfo.x - 1): rindex += 1 if data_p[rindex] == bgval: # Nothing to do if we are background continue join_trees_wrapper(data_p, forest_p, rindex, DEX[D_eb]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ed]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ej]) if connectivity >= 2: join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ea]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ec]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_eg]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ei]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ek]) if connectivity >= 3: join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ef]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_eh]) # BEGINNING of x = max rindex += 1 if data_p[rindex] != bgval: # Nothing to do if we are background join_trees_wrapper(data_p, forest_p, rindex, DEX[D_eb]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ed]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ej]) if connectivity >= 2: join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ea]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_eg]) join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ei]) if connectivity >= 3: join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ef]) # END of x = max # END of y = max scikit-image-0.13.1/skimage/measure/_find_contours.py000077500000000000000000000220011316256236300226150ustar00rootroot00000000000000import numpy as np from . import _find_contours_cy from collections import deque _param_options = ('high', 'low') def find_contours(array, level, fully_connected='low', positive_orientation='low'): """Find iso-valued contours in a 2D array for a given level value. Uses the "marching squares" method to compute a the iso-valued contours of the input 2D array for a particular level value. Array values are linearly interpolated to provide better precision for the output contours. Parameters ---------- array : 2D ndarray of double Input data in which to find contours. level : float Value along which to find contours in the array. fully_connected : str, {'low', 'high'} Indicates whether array elements below the given level value are to be considered fully-connected (and hence elements above the value will only be face connected), or vice-versa. (See notes below for details.) positive_orientation : either 'low' or 'high' Indicates whether the output contours will produce positively-oriented polygons around islands of low- or high-valued elements. If 'low' then contours will wind counter- clockwise around elements below the iso-value. Alternately, this means that low-valued elements are always on the left of the contour. (See below for details.) Returns ------- contours : list of (n,2)-ndarrays Each contour is an ndarray of shape ``(n, 2)``, consisting of n ``(row, column)`` coordinates along the contour. Notes ----- The marching squares algorithm is a special case of the marching cubes algorithm [1]_. A simple explanation is available here:: http://www.essi.fr/~lingrand/MarchingCubes/algo.html There is a single ambiguous case in the marching squares algorithm: when a given ``2 x 2``-element square has two high-valued and two low-valued elements, each pair diagonally adjacent. (Where high- and low-valued is with respect to the contour value sought.) In this case, either the high-valued elements can be 'connected together' via a thin isthmus that separates the low-valued elements, or vice-versa. When elements are connected together across a diagonal, they are considered 'fully connected' (also known as 'face+vertex-connected' or '8-connected'). Only high-valued or low-valued elements can be fully-connected, the other set will be considered as 'face-connected' or '4-connected'. By default, low-valued elements are considered fully-connected; this can be altered with the 'fully_connected' parameter. Output contours are not guaranteed to be closed: contours which intersect the array edge will be left open. All other contours will be closed. (The closed-ness of a contours can be tested by checking whether the beginning point is the same as the end point.) Contours are oriented. By default, array values lower than the contour value are to the left of the contour and values greater than the contour value are to the right. This means that contours will wind counter-clockwise (i.e. in 'positive orientation') around islands of low-valued pixels. This behavior can be altered with the 'positive_orientation' parameter. The order of the contours in the output list is determined by the position of the smallest ``x,y`` (in lexicographical order) coordinate in the contour. This is a side-effect of how the input array is traversed, but can be relied upon. .. warning:: Array coordinates/values are assumed to refer to the *center* of the array element. Take a simple example input: ``[0, 1]``. The interpolated position of 0.5 in this array is midway between the 0-element (at ``x=0``) and the 1-element (at ``x=1``), and thus would fall at ``x=0.5``. This means that to find reasonable contours, it is best to find contours midway between the expected "light" and "dark" values. In particular, given a binarized array, *do not* choose to find contours at the low or high value of the array. This will often yield degenerate contours, especially around structures that are a single array element wide. Instead choose a middle value, as above. References ---------- .. [1] Lorensen, William and Harvey E. Cline. Marching Cubes: A High Resolution 3D Surface Construction Algorithm. Computer Graphics (SIGGRAPH 87 Proceedings) 21(4) July 1987, p. 163-170). Examples -------- >>> a = np.zeros((3, 3)) >>> a[0, 0] = 1 >>> a array([[ 1., 0., 0.], [ 0., 0., 0.], [ 0., 0., 0.]]) >>> find_contours(a, 0.5) [array([[ 0. , 0.5], [ 0.5, 0. ]])] """ array = np.asarray(array, dtype=np.double) if array.ndim != 2: raise ValueError('Only 2D arrays are supported.') level = float(level) if (fully_connected not in _param_options or positive_orientation not in _param_options): raise ValueError('Parameters "fully_connected" and' ' "positive_orientation" must be either "high" or "low".') point_list = _find_contours_cy.iterate_and_store(array, level, fully_connected == 'high') contours = _assemble_contours(_take_2(point_list)) if positive_orientation == 'high': contours = [c[::-1] for c in contours] return contours def _take_2(seq): iterator = iter(seq) while(True): n1 = next(iterator) n2 = next(iterator) yield (n1, n2) def _assemble_contours(points_iterator): current_index = 0 contours = {} starts = {} ends = {} for from_point, to_point in points_iterator: # Ignore degenerate segments. # This happens when (and only when) one vertex of the square is # exactly the contour level, and the rest are above or below. # This degnerate vertex will be picked up later by neighboring squares. if from_point == to_point: continue tail_data = starts.get(to_point) head_data = ends.get(from_point) if tail_data is not None and head_data is not None: tail, tail_num = tail_data head, head_num = head_data # We need to connect these two contours. if tail is head: # We need to closed a contour. # Add the end point, and remove the contour from the # 'starts' and 'ends' dicts. head.append(to_point) del starts[to_point] del ends[from_point] else: # tail is not head # We need to join two distinct contours. # We want to keep the first contour segment created, so that # the final contours are ordered left->right, top->bottom. if tail_num > head_num: # tail was created second. Append tail to head. head.extend(tail) # remove all traces of tail: del starts[to_point] try: del ends[tail[-1]] except KeyError: pass del contours[tail_num] # remove the old end of head and add the new end. del ends[from_point] ends[head[-1]] = (head, head_num) else: # tail_num <= head_num # head was created second. Prepend head to tail. tail.extendleft(reversed(head)) # remove all traces of head: del starts[head[0]] del ends[from_point] del contours[head_num] # remove the old start of tail and add the new start. del starts[to_point] starts[tail[0]] = (tail, tail_num) elif tail_data is None and head_data is None: # we need to add a new contour current_index += 1 new_num = current_index new_contour = deque((from_point, to_point)) contours[new_num] = new_contour starts[from_point] = (new_contour, new_num) ends[to_point] = (new_contour, new_num) elif tail_data is not None and head_data is None: tail, tail_num = tail_data # We've found a single contour to which the new segment should be # prepended. tail.appendleft(from_point) del starts[to_point] starts[from_point] = (tail, tail_num) elif tail_data is None and head_data is not None: head, head_num = head_data # We've found a single contour to which the new segment should be # appended head.append(to_point) del ends[from_point] ends[to_point] = (head, head_num) # end iteration over from_ and to_ points return [np.array(contour) for (num, contour) in sorted(contours.items())] scikit-image-0.13.1/skimage/measure/_find_contours_cy.pyx000066400000000000000000000157361316256236300235160ustar00rootroot00000000000000#cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False #cython: wraparound=False import numpy as np cdef inline double _get_fraction(double from_value, double to_value, double level): if (to_value == from_value): return 0 return ((level - from_value) / (to_value - from_value)) def iterate_and_store(double[:, :] array, double level, Py_ssize_t vertex_connect_high): """Iterate across the given array in a marching-squares fashion, looking for segments that cross 'level'. If such a segment is found, its coordinates are added to a growing list of segments, which is returned by the function. if vertex_connect_high is nonzero, high-values pixels are considered to be face+vertex connected into objects; otherwise low-valued pixels are. """ if array.shape[0] < 2 or array.shape[1] < 2: raise ValueError("Input array must be at least 2x2.") cdef list arc_list = [] cdef Py_ssize_t n # The plan is to iterate a 2x2 square across the input array. This means # that the upper-left corner of the square needs to iterate across a # sub-array that's one-less-large in each direction (so that the square # never steps out of bounds). The square is represented by four pointers: # ul, ur, ll, and lr (for 'upper left', etc.). We also maintain the current # 2D coordinates for the position of the upper-left pointer. Note that we # ensured that the array is of type 'double' and is C-contiguous (last # index varies the fastest). # Current coords start at 0,0. cdef Py_ssize_t[2] coords coords[0] = 0 coords[1] = 0 # Calculate the number of iterations we'll need cdef Py_ssize_t num_square_steps = (array.shape[0] - 1) \ * (array.shape[1] - 1) cdef unsigned char square_case = 0 cdef tuple top, bottom, left, right cdef double ul, ur, ll, lr cdef Py_ssize_t r0, r1, c0, c1 for n in range(num_square_steps): # There are sixteen different possible square types, diagramed below. # A + indicates that the vertex is above the contour value, and a - # indicates that the vertex is below or equal to the contour value. # The vertices of each square are: # ul ur # ll lr # and can be treated as a binary value with the bits in that order. Thus # each square case can be numbered: # 0-- 1+- 2-+ 3++ 4-- 5+- 6-+ 7++ # -- -- -- -- +- +- +- +- # # 8-- 9+- 10-+ 11++ 12-- 13+- 14-+ 15++ # -+ -+ -+ -+ ++ ++ ++ ++ # # The position of the line segment that cuts through (or # doesn't, in case 0 and 15) each square is clear, except in # cases 6 and 9. In this case, where the segments are placed # is determined by vertex_connect_high. If # vertex_connect_high is false, then lines like \\ are drawn # through square 6, and lines like // are drawn through square # 9. Otherwise, the situation is reversed. # Finally, recall that we draw the lines so that (moving from tail to # head) the lower-valued pixels are on the left of the line. So, for # example, case 1 entails a line slanting from the middle of the top of # the square to the middle of the left side of the square. r0, c0 = coords[0], coords[1] r1, c1 = r0 + 1, c0 + 1 ul = array[r0, c0] ur = array[r0, c1] ll = array[r1, c0] lr = array[r1, c1] # now in advance the coords indices if coords[1] < array.shape[1] - 2: coords[1] += 1 else: coords[0] += 1 coords[1] = 0 square_case = 0 if (ul > level): square_case += 1 if (ur > level): square_case += 2 if (ll > level): square_case += 4 if (lr > level): square_case += 8 if (square_case != 0 and square_case != 15): # only do anything if there's a line passing through the # square. Cases 0 and 15 are entirely below/above the contour. top = r0, c0 + _get_fraction(ul, ur, level) bottom = r1, c0 + _get_fraction(ll, lr, level) left = r0 + _get_fraction(ul, ll, level), c0 right = r0 + _get_fraction(ur, lr, level), c1 if (square_case == 1): # top to left arc_list.append(top) arc_list.append(left) elif (square_case == 2): # right to top arc_list.append(right) arc_list.append(top) elif (square_case == 3): # right to left arc_list.append(right) arc_list.append(left) elif (square_case == 4): # left to bottom arc_list.append(left) arc_list.append(bottom) elif (square_case == 5): # top to bottom arc_list.append(top) arc_list.append(bottom) elif (square_case == 6): if vertex_connect_high: arc_list.append(left) arc_list.append(top) arc_list.append(right) arc_list.append(bottom) else: arc_list.append(right) arc_list.append(top) arc_list.append(left) arc_list.append(bottom) elif (square_case == 7): # right to bottom arc_list.append(right) arc_list.append(bottom) elif (square_case == 8): # bottom to right arc_list.append(bottom) arc_list.append(right) elif (square_case == 9): if vertex_connect_high: arc_list.append(top) arc_list.append(right) arc_list.append(bottom) arc_list.append(left) else: arc_list.append(top) arc_list.append(left) arc_list.append(bottom) arc_list.append(right) elif (square_case == 10): # bottom to top arc_list.append(bottom) arc_list.append(top) elif (square_case == 11): # bottom to left arc_list.append(bottom) arc_list.append(left) elif (square_case == 12): # lef to right arc_list.append(left) arc_list.append(right) elif (square_case == 13): # top to right arc_list.append(top) arc_list.append(right) elif (square_case == 14): # left to top arc_list.append(left) arc_list.append(top) return arc_list scikit-image-0.13.1/skimage/measure/_label.py000066400000000000000000000061261316256236300210270ustar00rootroot00000000000000from ._ccomp import label_cython as clabel def label(input, neighbors=None, background=None, return_num=False, connectivity=None): r"""Label connected regions of an integer array. Two pixels are connected when they are neighbors and have the same value. In 2D, they can be neighbors either in a 1- or 2-connected sense. The value refers to the maximum number of orthogonal hops to consider a pixel/voxel a neighbor:: 1-connectivity 2-connectivity diagonal connection close-up [ ] [ ] [ ] [ ] [ ] | \ | / | <- hop 2 [ ]--[x]--[ ] [ ]--[x]--[ ] [x]--[ ] | / | \ hop 1 [ ] [ ] [ ] [ ] Parameters ---------- input : ndarray of dtype int Image to label. neighbors : {4, 8}, int, optional Whether to use 4- or 8-"connectivity". In 3D, 4-"connectivity" means connected pixels have to share face, whereas with 8-"connectivity", they have to share only edge or vertex. **Deprecated, use ``connectivity`` instead.** background : int, optional Consider all pixels with this value as background pixels, and label them as 0. By default, 0-valued pixels are considered as background pixels. return_num : bool, optional Whether to return the number of assigned labels. connectivity : int, optional Maximum number of orthogonal hops to consider a pixel/voxel as a neighbor. Accepted values are ranging from 1 to input.ndim. If ``None``, a full connectivity of ``input.ndim`` is used. Returns ------- labels : ndarray of dtype int Labeled array, where all connected regions are assigned the same integer value. num : int, optional Number of labels, which equals the maximum label index and is only returned if return_num is `True`. See Also -------- regionprops References ---------- .. [1] Christophe Fiorio and Jens Gustedt, "Two linear time Union-Find strategies for image processing", Theoretical Computer Science 154 (1996), pp. 165-181. .. [2] Kensheng Wu, Ekow Otoo and Arie Shoshani, "Optimizing connected component labeling algorithms", Paper LBNL-56864, 2005, Lawrence Berkeley National Laboratory (University of California), http://repositories.cdlib.org/lbnl/LBNL-56864 Examples -------- >>> import numpy as np >>> x = np.eye(3).astype(int) >>> print(x) [[1 0 0] [0 1 0] [0 0 1]] >>> print(label(x, connectivity=1)) [[1 0 0] [0 2 0] [0 0 3]] >>> print(label(x, connectivity=2)) [[1 0 0] [0 1 0] [0 0 1]] >>> print(label(x, background=-1)) [[1 2 2] [2 1 2] [2 2 1]] >>> x = np.array([[1, 0, 0], ... [1, 1, 5], ... [0, 0, 0]]) >>> print(label(x)) [[1 0 0] [1 1 2] [0 0 0]] """ return clabel(input, neighbors, background, return_num, connectivity) scikit-image-0.13.1/skimage/measure/_marching_cubes_classic.py000066400000000000000000000345021316256236300244210ustar00rootroot00000000000000import numpy as np import scipy.ndimage as ndi from .._shared.utils import warn from . import _marching_cubes_classic_cy def marching_cubes_classic(volume, level=None, spacing=(1., 1., 1.), gradient_direction='descent'): """ Classic marching cubes algorithm to find surfaces in 3d volumetric data. Note that the ``marching_cubes()`` algorithm is recommended over this algorithm, because it's faster and produces better results. Parameters ---------- volume : (M, N, P) array of doubles Input data volume to find isosurfaces. Will be cast to `np.float64`. level : float Contour value to search for isosurfaces in `volume`. If not given or None, the average of the min and max of vol is used. spacing : length-3 tuple of floats Voxel spacing in spatial dimensions corresponding to numpy array indexing dimensions (M, N, P) as in `volume`. gradient_direction : string Controls if the mesh was generated from an isosurface with gradient descent toward objects of interest (the default), or the opposite. The two options are: * descent : Object was greater than exterior * ascent : Exterior was greater than object Returns ------- verts : (V, 3) array Spatial coordinates for V unique mesh vertices. Coordinate order matches input `volume` (M, N, P). faces : (F, 3) array Define triangular faces via referencing vertex indices from ``verts``. This algorithm specifically outputs triangles, so each face has exactly three indices. Notes ----- The marching cubes algorithm is implemented as described in [1]_. A simple explanation is available here:: http://www.essi.fr/~lingrand/MarchingCubes/algo.html There are several known ambiguous cases in the marching cubes algorithm. Using point labeling as in [1]_, Figure 4, as shown:: v8 ------ v7 / | / | y / | / | ^ z v4 ------ v3 | | / | v5 ----|- v6 |/ (note: NOT right handed!) | / | / ----> x | / | / v1 ------ v2 Most notably, if v4, v8, v2, and v6 are all >= `level` (or any generalization of this case) two parallel planes are generated by this algorithm, separating v4 and v8 from v2 and v6. An equally valid interpretation would be a single connected thin surface enclosing all four points. This is the best known ambiguity, though there are others. This algorithm does not attempt to resolve such ambiguities; it is a naive implementation of marching cubes as in [1]_, but may be a good beginning for work with more recent techniques (Dual Marching Cubes, Extended Marching Cubes, Cubic Marching Squares, etc.). Because of interactions between neighboring cubes, the isosurface(s) generated by this algorithm are NOT guaranteed to be closed, particularly for complicated contours. Furthermore, this algorithm does not guarantee a single contour will be returned. Indeed, ALL isosurfaces which cross `level` will be found, regardless of connectivity. The output is a triangular mesh consisting of a set of unique vertices and connecting triangles. The order of these vertices and triangles in the output list is determined by the position of the smallest ``x,y,z`` (in lexicographical order) coordinate in the contour. This is a side-effect of how the input array is traversed, but can be relied upon. The generated mesh guarantees coherent orientation as of version 0.12. To quantify the area of an isosurface generated by this algorithm, pass outputs directly into `skimage.measure.mesh_surface_area`. References ---------- .. [1] Lorensen, William and Harvey E. Cline. Marching Cubes: A High Resolution 3D Surface Construction Algorithm. Computer Graphics (SIGGRAPH 87 Proceedings) 21(4) July 1987, p. 163-170). DOI: 10.1145/37401.37422 See Also -------- skimage.measure.marching_cubes skimage.measure.mesh_surface_area """ # Check inputs and ensure `volume` is C-contiguous for memoryviews if volume.ndim != 3: raise ValueError("Input volume must have 3 dimensions.") if level is None: level = 0.5 * (volume.min() + volume.max()) else: level = float(level) if level < volume.min() or level > volume.max(): raise ValueError("Surface level must be within volume data range.") if len(spacing) != 3: raise ValueError("`spacing` must consist of three floats.") volume = np.array(volume, dtype=np.float64, order="C") # Extract raw triangles using marching cubes in Cython # Returns a list of length-3 lists, each sub-list containing three # tuples. The tuples hold (x, y, z) coordinates for triangle vertices. # Note: this algorithm is fast, but returns degenerate "triangles" which # have repeated vertices - and equivalent vertices are redundantly # placed in every triangle they connect with. raw_faces = _marching_cubes_classic_cy.iterate_and_store_3d(volume, float(level)) # Find and collect unique vertices, storing triangle verts as indices. # Returns a true mesh with no degenerate faces. verts, faces = _marching_cubes_classic_cy.unpack_unique_verts(raw_faces) verts = np.asarray(verts) faces = np.asarray(faces) # Fancy indexing to define two vector arrays from triangle vertices faces = _correct_mesh_orientation(volume, verts[faces], faces, spacing, gradient_direction) # Adjust for non-isotropic spacing in `verts` at time of return return verts * np.r_[spacing], faces def mesh_surface_area(verts, faces): """ Compute surface area, given vertices & triangular faces Parameters ---------- verts : (V, 3) array of floats Array containing (x, y, z) coordinates for V unique mesh vertices. faces : (F, 3) array of ints List of length-3 lists of integers, referencing vertex coordinates as provided in `verts` Returns ------- area : float Surface area of mesh. Units now [coordinate units] ** 2. Notes ----- The arguments expected by this function are the first two outputs from `skimage.measure.marching_cubes`. For unit correct output, ensure correct `spacing` was passed to `skimage.measure.marching_cubes`. This algorithm works properly only if the ``faces`` provided are all triangles. See Also -------- skimage.measure.marching_cubes skimage.measure.marching_cubes_classic skimage.measure.correct_mesh_orientation """ # Fancy indexing to define two vector arrays from triangle vertices actual_verts = verts[faces] a = actual_verts[:, 0, :] - actual_verts[:, 1, :] b = actual_verts[:, 0, :] - actual_verts[:, 2, :] del actual_verts # Area of triangle in 3D = 1/2 * Euclidean norm of cross product return ((np.cross(a, b) ** 2).sum(axis=1) ** 0.5).sum() / 2. def correct_mesh_orientation(volume, verts, faces, spacing=(1., 1., 1.), gradient_direction='descent'): """ Correct orientations of mesh faces. Parameters ---------- volume : (M, N, P) array of doubles Input data volume to find isosurfaces. Will be cast to `np.float64`. verts : (V, 3) array of floats Array containing (x, y, z) coordinates for V unique mesh vertices. faces : (F, 3) array of ints List of length-3 lists of integers, referencing vertex coordinates as provided in `verts`. spacing : length-3 tuple of floats Voxel spacing in spatial dimensions corresponding to numpy array indexing dimensions (M, N, P) as in `volume`. gradient_direction : string Controls if the mesh was generated from an isosurface with gradient descent toward objects of interest (the default), or the opposite. The two options are: * descent : Object was greater than exterior * ascent : Exterior was greater than object Returns ------- faces_corrected (F, 3) array of ints Corrected list of faces referencing vertex coordinates in `verts`. Notes ----- Certain applications and mesh processing algorithms require all faces to be oriented in a consistent way. Generally, this means a normal vector points "out" of the meshed shapes. This algorithm corrects the output from `skimage.measure.marching_cubes_classic` by flipping the orientation of mis-oriented faces. Because marching cubes could be used to find isosurfaces either on gradient descent (where the desired object has greater values than the exterior) or ascent (where the desired object has lower values than the exterior), the ``gradient_direction`` kwarg allows the user to inform this algorithm which is correct. If the resulting mesh appears to be oriented completely incorrectly, try changing this option. The arguments expected by this function are the exact outputs from `skimage.measure.marching_cubes_classic`. Only `faces` is corrected and returned, as the vertices do not change; only the order in which they are referenced. This algorithm assumes ``faces`` provided are all triangles. See Also -------- skimage.measure.marching_cubes_classic skimage.measure.mesh_surface_area """ warn(DeprecationWarning("`correct_mesh_orientation` is deprecated for " "removal as `marching_cubes_classic` now " "guarantees correct mesh orientation.")) verts = verts.copy() verts[:, 0] /= spacing[0] verts[:, 1] /= spacing[1] verts[:, 2] /= spacing[2] # Fancy indexing to define two vector arrays from triangle vertices actual_verts = verts[faces] return _correct_mesh_orientation(volume, actual_verts, faces, spacing, gradient_direction) def _correct_mesh_orientation(volume, actual_verts, faces, spacing=(1., 1., 1.), gradient_direction='descent'): """ Correct orientations of mesh faces. Parameters ---------- volume : (M, N, P) array of doubles Input data volume to find isosurfaces. Will be cast to `np.float64`. actual_verts : (F, 3, 3) array of floats Array with (face, vertex, coords) index coordinates. faces : (F, 3) array of ints List of length-3 lists of integers, referencing vertex coordinates as provided in `verts`. spacing : length-3 tuple of floats Voxel spacing in spatial dimensions corresponding to numpy array indexing dimensions (M, N, P) as in `volume`. gradient_direction : string Controls if the mesh was generated from an isosurface with gradient descent toward objects of interest (the default), or the opposite. The two options are: * descent : Object was greater than exterior * ascent : Exterior was greater than object Returns ------- faces_corrected (F, 3) array of ints Corrected list of faces referencing vertex coordinates in `verts`. Notes ----- Certain applications and mesh processing algorithms require all faces to be oriented in a consistent way. Generally, this means a normal vector points "out" of the meshed shapes. This algorithm corrects the output from `skimage.measure.marching_cubes_classic` by flipping the orientation of mis-oriented faces. Because marching cubes could be used to find isosurfaces either on gradient descent (where the desired object has greater values than the exterior) or ascent (where the desired object has lower values than the exterior), the ``gradient_direction`` kwarg allows the user to inform this algorithm which is correct. If the resulting mesh appears to be oriented completely incorrectly, try changing this option. The arguments expected by this function are the exact outputs from `skimage.measure.marching_cubes_classic` except `actual_verts`, which is an uncorrected version of the fancy indexing operation `verts[faces]`. Only `faces` is corrected and returned as the vertices do not change, only the order in which they are referenced. This algorithm assumes ``faces`` provided are exclusively triangles. See Also -------- skimage.measure.marching_cubes_classic skimage.measure.mesh_surface_area """ # Calculate gradient of `volume`, then interpolate to vertices in `verts` grad_x, grad_y, grad_z = np.gradient(volume) a = actual_verts[:, 0, :] - actual_verts[:, 1, :] b = actual_verts[:, 0, :] - actual_verts[:, 2, :] # Find triangle centroids centroids = (actual_verts.sum(axis=1) / 3.).T del actual_verts # Interpolate face centroids into each gradient axis grad_centroids_x = ndi.map_coordinates(grad_x, centroids) grad_centroids_y = ndi.map_coordinates(grad_y, centroids) grad_centroids_z = ndi.map_coordinates(grad_z, centroids) # Combine and normalize interpolated gradients grad_centroids = np.c_[grad_centroids_x, grad_centroids_y, grad_centroids_z] grad_centroids = (grad_centroids / (np.sum(grad_centroids ** 2, axis=1) ** 0.5)[:, np.newaxis]) # Find normal vectors for each face via cross product crosses = np.cross(a, b) crosses = crosses / (np.sum(crosses ** 2, axis=1) ** (0.5))[:, np.newaxis] # Take dot product dotproducts = (grad_centroids * crosses).sum(axis=1) # Find mis-oriented faces if 'descent' in gradient_direction: # Faces with incorrect orientations have dot product < 0 indices = (dotproducts < 0).nonzero()[0] elif 'ascent' in gradient_direction: # Faces with incorrection orientation have dot product > 0 indices = (dotproducts > 0).nonzero()[0] else: raise ValueError("Incorrect input %s in `gradient_direction`, see " "docstring." % (gradient_direction)) # Correct orientation and return, without modifying original data faces_corrected = faces.copy() faces_corrected[indices] = faces_corrected[indices, ::-1] return faces_corrected scikit-image-0.13.1/skimage/measure/_marching_cubes_classic_cy.pyx000066400000000000000000001154301316256236300253040ustar00rootroot00000000000000#cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False #cython: wraparound=False import numpy as np cimport numpy as cnp cdef inline double _get_fraction(double from_value, double to_value, double level): if (to_value == from_value): return 0 return ((level - from_value) / (to_value - from_value)) def unpack_unique_verts(list trilist): """ Convert a list of lists of tuples corresponding to triangle vertices into a unique vertex list, and a list of triangle faces w/indices corresponding to entries of the vertex list. """ cdef Py_ssize_t idx = 0 cdef Py_ssize_t n_tris = len(trilist) cdef Py_ssize_t i, j cdef dict vert_index = {} cdef list vert_list = [] cdef list face_list = [] cdef list templist # Iterate over triangles for i in range(n_tris): templist = [] # Only parse vertices from non-degenerate triangles if not ((trilist[i][0] == trilist[i][1]) or (trilist[i][0] == trilist[i][2]) or (trilist[i][1] == trilist[i][2])): # Iterate over vertices within each triangle for j in range(3): vert = trilist[i][j] # Check if a new unique vertex found if vert not in vert_index: vert_index[vert] = idx templist.append(idx) vert_list.append(vert) idx += 1 else: templist.append(vert_index[vert]) face_list.append(templist) return vert_list, face_list def iterate_and_store_3d(double[:, :, ::1] arr, double level): """Iterate across the given array in a marching-cubes fashion, looking for volumes with edges that cross 'level'. If such a volume is found, appropriate triangulations are added to a growing list of faces to be returned by this function. """ if arr.shape[0] < 2 or arr.shape[1] < 2 or arr.shape[2] < 2: raise ValueError("Input array must be at least 2x2x2.") cdef list face_list = [] cdef list norm_list = [] cdef Py_ssize_t n cdef bint plus_z plus_z = False # The plan is to iterate a 2x2x2 cube across the input array. This means # the upper-left corner of the cube needs to iterate across a sub-array # of size one-less-large in each direction (so we can get away with no # bounds checking in Cython). The cube is represented by eight vertices: # v1, v2, ..., v8, oriented thus (see Lorensen, Figure 4): # # v8 ------ v7 # / | / | y # / | / | ^ z # v4 ------ v3 | | / # | v5 ----|- v6 |/ (note: NOT right handed!) # | / | / ----> x # | / | / # v1 ------ v2 # # We also maintain the current 2D coordinates for v1, and ensure the array # is of type 'double' and is C-contiguous (last index varies fastest). # Coords start at (0, 0, 0). cdef Py_ssize_t[3] coords coords[0] = 0 coords[1] = 0 coords[2] = 0 # Calculate the number of iterations we'll need cdef Py_ssize_t num_cube_steps = ((arr.shape[0] - 1) * (arr.shape[1] - 1) * (arr.shape[2] - 1)) cdef unsigned char cube_case = 0 cdef tuple e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12 cdef double v1, v2, v3, v4, v5, v6, v7, v8 cdef Py_ssize_t x0, y0, z0, x1, y1, z1 e5, e6, e7, e8 = (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0) for n in range(num_cube_steps): # There are 255 unique values for `cube_case`. This algorithm follows # the Lorensen paper in vertex and edge labeling, however, it should # be noted that Lorensen used a left-handed coordinate system while # NumPy uses a proper right handed system. Transforming between these # coordinate systems was handled in the definitions of the cube # vertices v1, v2, ..., v8. # # Refer to the paper, figure 4, for cube edge designations e1, ... e12 # Standard Py_ssize_t coordinates for indexing x0, y0, z0 = coords[0], coords[1], coords[2] x1, y1, z1 = x0 + 1, y0 + 1, z0 + 1 # We use a right-handed coordinate system, UNlike the paper, but want # to index in agreement - the coordinate adjustment takes place here. v1 = arr[x0, y0, z0] v2 = arr[x1, y0, z0] v3 = arr[x1, y1, z0] v4 = arr[x0, y1, z0] v5 = arr[x0, y0, z1] v6 = arr[x1, y0, z1] v7 = arr[x1, y1, z1] v8 = arr[x0, y1, z1] # Unique triangulation cases cube_case = 0 if (v1 > level): cube_case += 1 if (v2 > level): cube_case += 2 if (v3 > level): cube_case += 4 if (v4 > level): cube_case += 8 if (v5 > level): cube_case += 16 if (v6 > level): cube_case += 32 if (v7 > level): cube_case += 64 if (v8 > level): cube_case += 128 if (cube_case != 0 and cube_case != 255): # Only do anything if there's a plane intersecting the cube. # Cases 0 and 255 are entirely below/above the contour. if cube_case > 127: if ((cube_case != 150) and (cube_case != 170) and (cube_case != 195)): cube_case = 255 - cube_case # Calculate cube edges, to become triangulation vertices. # If we moved in a convenient direction, save 1/3 of the effort by # re-assigning prior results. if plus_z: # Reassign prior calculated edges e1 = e5 e2 = e6 e3 = e7 e4 = e8 else: # Calculate these edges normally e1 = x0 + _get_fraction(v1, v2, level), y0, z0 e2 = x1, y0 + _get_fraction(v2, v3, level), z0 e3 = x0 + _get_fraction(v4, v3, level), y1, z0 e4 = x0, y0 + _get_fraction(v1, v4, level), z0 # These must be calculated at each point unless we implemented a # large, growing lookup table for all adjacent values; could save # ~30% in terms of runtime at the expense of memory usage and # much greater complexity. e5 = x0 + _get_fraction(v5, v6, level), y0, z1 e6 = x1, y0 + _get_fraction(v6, v7, level), z1 e7 = x0 + _get_fraction(v8, v7, level), y1, z1 e8 = x0, y0 + _get_fraction(v5, v8, level), z1 e9 = x0, y0, z0 + _get_fraction(v1, v5, level) e10 = x1, y0, z0 + _get_fraction(v2, v6, level) e11 = x0, y1, z0 + _get_fraction(v4, v8, level) e12 = x1, y1, z0 + _get_fraction(v3, v7, level) # Append appropriate triangles to the growing output `face_list` _append_tris(face_list, cube_case, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) # Advance the coords indices if coords[2] < arr.shape[2] - 2: coords[2] += 1 plus_z = True elif coords[1] < arr.shape[1] - 2: coords[1] += 1 coords[2] = 0 plus_z = False else: coords[0] += 1 coords[1] = 0 coords[2] = 0 plus_z = False return face_list def _append_tris(list face_list, unsigned char case, tuple e1, tuple e2, tuple e3, tuple e4, tuple e5, tuple e6, tuple e7, tuple e8, tuple e9, tuple e10, tuple e11, tuple e12): # Permits recursive use for duplicated planes to conserve code - it's # quite long enough as-is. if (case == 1): # front lower left corner face_list.append([e1, e4, e9]) elif (case == 2): # front lower right corner face_list.append([e10, e2, e1]) elif (case == 3): # front lower plane face_list.append([e2, e4, e9]) face_list.append([e2, e9, e10]) elif (case == 4): # front upper right corner face_list.append([e12, e3, e2]) elif (case == 5): # lower left, upper right corners _append_tris(face_list, 1, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 4, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 6): # front right plane face_list.append([e12, e3, e1]) face_list.append([e12, e1, e10]) elif (case == 7): # Shelf including v1, v2, v3 face_list.append([e3, e4, e12]) face_list.append([e4, e9, e12]) face_list.append([e12, e9, e10]) elif (case == 8): # front upper left corner face_list.append([e3, e11, e4]) elif (case == 9): # front left plane face_list.append([e3, e11, e9]) face_list.append([e3, e9, e1]) elif (case == 10): # upper left, lower right corners _append_tris(face_list, 2, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 8, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 11): # Shelf including v4, v1, v2 face_list.append([e3, e11, e2]) face_list.append([e11, e10, e2]) face_list.append([e11, e9, e10]) elif (case == 12): # front upper plane face_list.append([e11, e4, e12]) face_list.append([e2, e4, e12]) elif (case == 13): # Shelf including v1, v4, v3 face_list.append([e11, e9, e12]) face_list.append([e12, e9, e1]) face_list.append([e12, e1, e2]) elif (case == 14): # Shelf including v2, v3, v4 face_list.append([e11, e10, e12]) face_list.append([e11, e4, e10]) face_list.append([e4, e1, e10]) elif (case == 15): # Plane parallel to x-axis through middle face_list.append([e11, e9, e12]) face_list.append([e12, e9, e10]) elif (case == 16): # back lower left corner face_list.append([e8, e9, e5]) elif (case == 17): # lower left plane face_list.append([e4, e1, e8]) face_list.append([e8, e1, e5]) elif (case == 18): # lower left back, lower right front corners _append_tris(face_list, 16, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 2, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 19): # Shelf including v1, v2, v5 face_list.append([e8, e4, e2]) face_list.append([e8, e2, e10]) face_list.append([e8, e10, e5]) elif (case == 20): # lower left back, upper right front corners _append_tris(face_list, 16, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 4, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 21): # lower left plane + upper right front corner, v1, v3, v5 _append_tris(face_list, 17, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 4, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 22): # front right plane + lower left back corner, v2, v3, v5 _append_tris(face_list, 16, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 6, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 23): # Rotated case 14 in the paper face_list.append([e3, e10, e8]) face_list.append([e3, e10, e12]) face_list.append([e8, e10, e5]) face_list.append([e3, e4, e8]) elif (case == 24): # upper front left, lower back left corners _append_tris(face_list, 16, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 8, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 25): # Shelf including v1, v4, v5 face_list.append([e1, e5, e3]) face_list.append([e3, e8, e11]) face_list.append([e3, e5, e8]) elif (case == 26): # Three isolated corners _append_tris(face_list, 2, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 8, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 16, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 27): # Full corner v1, case 9 in paper: (v1, v2, v4, v5) face_list.append([e11, e3, e2]) face_list.append([e11, e2, e10]) face_list.append([e10, e11, e8]) face_list.append([e8, e5, e10]) elif (case == 28): # upper front plane + corner v5 _append_tris(face_list, 16, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 12, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 29): # special case of 11 in the paper: (v1, v3, v4, v5) face_list.append([e11, e5, e2]) face_list.append([e11, e12, e2]) face_list.append([e11, e5, e8]) face_list.append([e2, e1, e5]) elif (case == 30): # Shelf (v2, v3, v4) and lower left back corner _append_tris(face_list, 14, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 16, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 31): # Shelf: (v6, v7, v8) by inversion face_list.append([e11, e12, e10]) face_list.append([e11, e8, e10]) face_list.append([e8, e10, e5]) elif (case == 32): # lower right back corner face_list.append([e6, e5, e10]) elif (case == 33): # lower right back, lower left front corners _append_tris(face_list, 1, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 32, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 34): # lower right plane face_list.append([e1, e2, e5]) face_list.append([e2, e6, e5]) elif (case == 35): # Shelf: v1, v2, v6 face_list.append([e4, e2, e6]) face_list.append([e4, e9, e6]) face_list.append([e6, e9, e5]) elif (case == 36): # upper right front, lower right back corners _append_tris(face_list, 32, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 4, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 37): # lower left front, upper right front, lower right back corners _append_tris(face_list, 32, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 4, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 1, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 38): # Shelf: v2, v3, v6 face_list.append([e3, e1, e5]) face_list.append([e3, e5, e12]) face_list.append([e12, e5, e6]) elif (case == 39): # Full corner v2: (v1, v2, v3, v6) face_list.append([e3, e4, e5]) face_list.append([e4, e9, e5]) face_list.append([e3, e5, e6]) face_list.append([e3, e12, e6]) elif (case == 40): # upper left front, lower right back corners _append_tris(face_list, 32, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 8, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 41): # front left plane, lower right back corner _append_tris(face_list, 32, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 9, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 42): # lower right plane, upper front left corner _append_tris(face_list, 8, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 34, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 43): # Rotated case 11 in paper face_list.append([e11, e3, e9]) face_list.append([e3, e9, e6]) face_list.append([e3, e2, e6]) face_list.append([e9, e5, e6]) elif (case == 44): # upper front plane, lower right back corner _append_tris(face_list, 12, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 32, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 45): # Shelf: (v1, v3, v4) + lower right back corner _append_tris(face_list, 13, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 32, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 46): # Rotated case 14 in paper face_list.append([e4, e11, e12]) face_list.append([e4, e12, e5]) face_list.append([e12, e5, e6]) face_list.append([e4, e5, e1]) elif (case == 47): # Shelf: (v5, v8, v7) by inversion face_list.append([e11, e9, e12]) face_list.append([e12, e9, e5]) face_list.append([e12, e5, e6]) elif (case == 48): # Back lower plane face_list.append([e9, e10, e6]) face_list.append([e9, e6, e8]) elif (case == 49): # Shelf: (v1, v5, v6) face_list.append([e4, e8, e6]) face_list.append([e4, e6, e1]) face_list.append([e6, e1, e10]) elif (case == 50): # Shelf: (v2, v5, v6) face_list.append([e8, e6, e2]) face_list.append([e8, e2, e1]) face_list.append([e8, e9, e1]) elif (case == 51): # Plane through middle of cube, parallel to x-z axis face_list.append([e4, e8, e2]) face_list.append([e8, e2, e6]) elif (case == 52): # Back lower plane, and front upper right corner _append_tris(face_list, 48, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 4, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 53): # Shelf (v1, v5, v6) and front upper right corner _append_tris(face_list, 49, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 4, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 54): # Rotated case 11 from paper (v2, v3, v5, v6) face_list.append([e1, e9, e3]) face_list.append([e9, e3, e6]) face_list.append([e9, e8, e6]) face_list.append([e12, e3, e6]) elif (case == 55): # Shelf: (v4, v8, v7) by inversion face_list.append([e4, e8, e6]) face_list.append([e4, e6, e3]) face_list.append([e6, e3, e12]) elif (case == 56): # Back lower plane + upper left front corner _append_tris(face_list, 48, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 8, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 57): # Rotated case 14 from paper (v4, v1, v5, v6) face_list.append([e3, e11, e8]) face_list.append([e3, e8, e10]) face_list.append([e10, e6, e8]) face_list.append([e3, e1, e10]) elif (case == 58): # Shelf: (v2, v6, v5) + upper left front corner _append_tris(face_list, 50, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 8, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 59): # Shelf: (v3, v7, v8) by inversion face_list.append([e2, e6, e8]) face_list.append([e8, e2, e3]) face_list.append([e8, e3, e11]) elif (case == 60): # AMBIGUOUS CASE: parallel planes (front upper, back lower) _append_tris(face_list, 48, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 12, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 61): # Upper back plane + lower right front corner by inversion _append_tris(face_list, 63, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 2, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 62): # Upper back plane + lower left front corner by inversion _append_tris(face_list, 63, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 1, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 63): # Upper back plane face_list.append([e11, e12, e6]) face_list.append([e11, e8, e6]) elif (case == 64): # Upper right back corner face_list.append([e12, e7, e6]) elif (case == 65): # upper right back, lower left front corners _append_tris(face_list, 64, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 1, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 66): # upper right back, lower right front corners _append_tris(face_list, 64, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 2, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 67): # lower front plane + upper right back corner _append_tris(face_list, 64, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 3, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 68): # upper right plane face_list.append([e3, e2, e6]) face_list.append([e3, e7, e6]) elif (case == 69): # Upper right plane, lower left front corner _append_tris(face_list, 68, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 1, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 70): # Shelf: (v2, v3, v7) face_list.append([e1, e3, e7]) face_list.append([e1, e10, e7]) face_list.append([e7, e10, e6]) elif (case == 71): # Rotated version of case 11 in paper (v1, v2, v3, v7) face_list.append([e10, e7, e4]) face_list.append([e4, e3, e7]) face_list.append([e10, e4, e9]) face_list.append([e7, e10, e6]) elif (case == 72): # upper left front, upper right back corners _append_tris(face_list, 64, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 8, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 73): # front left plane, upper right back corner _append_tris(face_list, 64, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 9, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 74): # Three isolated corners, exactly case 7 in paper _append_tris(face_list, 64, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 8, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 2, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 75): # Shelf: (v1, v2, v4) + upper right back corner _append_tris(face_list, 64, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 11, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 76): # Shelf: (v4, v3, v7) face_list.append([e4, e2, e6]) face_list.append([e4, e11, e7]) face_list.append([e4, e7, e6]) elif (case == 77): # Rotated case 14 in paper (v1, v4, v3, v7) face_list.append([e11, e9, e1]) face_list.append([e11, e1, e6]) face_list.append([e1, e6, e2]) face_list.append([e11, e6, e7]) elif (case == 78): # Full corner v3: (v2, v3, v4, v7) face_list.append([e1, e4, e7]) face_list.append([e1, e7, e6]) face_list.append([e4, e11, e7]) face_list.append([e1, e10, e6]) elif (case == 79): # Shelf: (v6, v5, v8) by inversion face_list.append([e9, e11, e10]) face_list.append([e11, e7, e10]) face_list.append([e7, e10, e6]) elif (case == 80): # lower left back, upper right back corners (v5, v7) _append_tris(face_list, 64, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 16, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 81): # lower left plane, upper right back corner _append_tris(face_list, 64, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 17, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 82): # isolated corners (v2, v5, v7) _append_tris(face_list, 64, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 16, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 2, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 83): # Shelf: (v1, v2, v5) + upper right back corner _append_tris(face_list, 64, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 19, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 84): # upper right plane, lower left back corner _append_tris(face_list, 16, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 68, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 85): # AMBIGUOUS CASE: upper right and lower left parallel planes _append_tris(face_list, 17, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 68, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 86): # Shelf: (v2, v3, v7) + lower left back corner _append_tris(face_list, 70, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 16, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 87): # Upper left plane + lower right back corner, by inversion _append_tris(face_list, 119, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 32, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 88): # Isolated corners v4, v5, v7 _append_tris(face_list, 64, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 16, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 8, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 89): # Shelf: (v1, v4, v5) + isolated corner v7 _append_tris(face_list, 25, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 64, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 90): # Four isolated corners v2, v4, v5, v7 _append_tris(face_list, 2, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 8, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 16, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 64, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 91): # Three isolated corners, v3, v6, v8 by inversion _append_tris(face_list, 4, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 32, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 127, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 92): # Shelf (v4, v3, v7) + isolated corner v5 _append_tris(face_list, 76, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 16, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 93): # Lower right plane + isolated corner v8 by inversion _append_tris(face_list, 127, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 34, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 94): # Isolated corners v1, v6, v8 by inversion _append_tris(face_list, 1, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 32, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 127, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 95): # Isolated corners v6, v8 by inversion _append_tris(face_list, 32, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 127, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 96): # back right plane face_list.append([e7, e12, e5]) face_list.append([e5, e10, e12]) elif (case == 97): # back right plane + isolated corner v1 _append_tris(face_list, 96, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 1, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 98): # Shelf: (v2, v6, v7) face_list.append([e1, e7, e5]) face_list.append([e7, e1, e12]) face_list.append([e1, e12, e2]) elif (case == 99): # Rotated case 14 in paper: (v1, v2, v6, v7) face_list.append([e9, e2, e7]) face_list.append([e9, e2, e4]) face_list.append([e2, e7, e12]) face_list.append([e7, e9, e5]) elif (case == 100): # Shelf: (v3, v6, v7) face_list.append([e3, e7, e5]) face_list.append([e3, e5, e2]) face_list.append([e2, e5, e10]) elif (case == 101): # Shelf: (v3, v6, v7) + isolated corner v1 _append_tris(face_list, 100, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 1, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 102): # Plane bisecting left-right halves of cube face_list.append([e1, e3, e7]) face_list.append([e1, e7, e5]) elif (case == 103): # Shelf: (v4, v5, v8) by inversion face_list.append([e3, e7, e5]) face_list.append([e3, e5, e4]) face_list.append([e4, e5, e9]) elif (case == 104): # Back right plane + isolated corner v4 _append_tris(face_list, 8, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 96, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 105): # AMBIGUOUS CASE: back right and front left planes _append_tris(face_list, 96, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 9, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 106): # Shelf: (v2, v6, v7) + isolated corner v4 _append_tris(face_list, 98, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 8, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 107): # Back left plane + isolated corner v3 by inversion _append_tris(face_list, 4, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 111, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 108): # Rotated case 11 from paper: (v4, v3, v7, v6) face_list.append([e4, e10, e7]) face_list.append([e4, e10, e2]) face_list.append([e4, e11, e7]) face_list.append([e7, e10, e5]) elif (case == 109): # Back left plane + isolated corner v2 by inversion _append_tris(face_list, 111, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 2, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 110): # Shelf: (v1, v5, v8) by inversion face_list.append([e1, e5, e7]) face_list.append([e1, e7, e11]) face_list.append([e1, e11, e4]) elif (case == 111): # Back left plane face_list.append([e11, e9, e7]) face_list.append([e9, e7, e5]) elif (case == 112): # Shelf: (v5, v6, v7) face_list.append([e9, e10, e12]) face_list.append([e9, e12, e7]) face_list.append([e9, e7, e8]) elif (case == 113): # Exactly case 11 from paper: (v1, v5, v6, v7) face_list.append([e1, e8, e12]) face_list.append([e1, e8, e4]) face_list.append([e8, e7, e12]) face_list.append([e12, e1, e10]) elif (case == 114): # Full corner v6: (v2, v6, v7, v5) face_list.append([e1, e9, e7]) face_list.append([e1, e7, e12]) face_list.append([e1, e12, e2]) face_list.append([e9, e8, e7]) elif (case == 115): # Shelf: (v3, v4, v8) face_list.append([e2, e4, e8]) face_list.append([e2, e12, e7]) face_list.append([e2, e8, e7]) elif (case == 116): # Rotated case 14 in paper: (v5, v6, v7, v3) face_list.append([e9, e2, e7]) face_list.append([e9, e2, e10]) face_list.append([e9, e8, e7]) face_list.append([e2, e3, e7]) elif (case == 117): # upper left plane + isolated corner v2 by inversion _append_tris(face_list, 2, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 119, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 118): # Shelf: (v1, v4, v8) face_list.append([e1, e3, e7]) face_list.append([e7, e1, e8]) face_list.append([e1, e8, e9]) elif (case == 119): # Upper left plane face_list.append([e4, e3, e7]) face_list.append([e4, e8, e7]) elif (case == 120): # Shelf: (v5, v6, v7) + isolated corner v4 _append_tris(face_list, 112, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 8, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 121): # Front right plane + isolated corner v8 _append_tris(face_list, 6, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 127, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 122): # Isolated corners v1, v3, v8 _append_tris(face_list, 1, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 4, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 127, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 123): # Isolated corners v3, v8 _append_tris(face_list, 4, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 127, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 124): # Front lower plane + isolated corner v8 _append_tris(face_list, 3, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 127, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 125): # Isolated corners v2, v8 _append_tris(face_list, 2, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 127, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 126): # Isolated corners v1, v8 _append_tris(face_list, 1, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 127, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 127): # Isolated corner v8 face_list.append([e11, e7, e8]) elif (case == 150): # AMBIGUOUS CASE: back right and front left planes # In these cube_case > 127 cases, the vertices are identical BUT # they are connected in the opposite fashion. _append_tris(face_list, 6, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 111, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 170): # AMBIGUOUS CASE: upper left and lower right planes # In these cube_case > 127 cases, the vertices are identical BUT # they are connected in the opposite fashion. _append_tris(face_list, 119, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 34, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) elif (case == 195): # AMBIGUOUS CASE: back upper and front lower planes # In these cube_case > 127 cases, the vertices are identical BUT # they are connected in the opposite fashion. _append_tris(face_list, 63, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) _append_tris(face_list, 3, e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12) return scikit-image-0.13.1/skimage/measure/_marching_cubes_lewiner.py000066400000000000000000000306031316256236300244430ustar00rootroot00000000000000import sys import base64 import dis import inspect import numpy as np if sys.version_info >= (3, ): base64decode = base64.decodebytes ordornot = lambda x: x else: base64decode = base64.decodestring ordornot = ord from . import _marching_cubes_lewiner_luts as mcluts from . import _marching_cubes_lewiner_cy from .._shared.utils import skimage_deprecation, warn def _expected_output_args(): """ Get number of expected output args. Please don't use this to influence the algorithmic bahaviour of a function. For ``a, b, rest*, c = ...`` syntax, returns n + 0.1 (3.1 in this example). """ offset = 2 if sys.version_info >= (3, 6) else 3 f = inspect.currentframe().f_back.f_back i = f.f_lasti + offset bytecode = f.f_code.co_code instruction = ordornot(bytecode[i]) while True: if instruction == dis.opmap['DUP_TOP']: if ordornot(bytecode[i + 1]) == dis.opmap['UNPACK_SEQUENCE']: return ordornot(bytecode[i + 2]) i += 4 instruction = ordornot(bytecode[i]) continue if instruction == dis.opmap['STORE_NAME']: return 1 if instruction == dis.opmap['UNPACK_SEQUENCE']: return ordornot(bytecode[i + 1]) if instruction == dis.opmap.get('UNPACK_EX', -1): # py3k if ordornot(bytecode[i + 2]) < 10: return ordornot(bytecode[i + 1]) + ordornot(bytecode[i + 2]) + 0.1 else: # 3.6 return ordornot(bytecode[i + 1]) + 0.1 if instruction == dis.opmap.get('EXTENDED_ARG', -1): # py 3.6 if ordornot(bytecode[i + 2]) == dis.opmap.get('UNPACK_EX', -1): return ordornot(bytecode[i + 1]) + ordornot(bytecode[i + 3]) + 0.1 i += 4 instruction = ordornot(bytecode[i]) continue return 0 def marching_cubes(volume, level=None, spacing=(1., 1., 1.), gradient_direction='descent', step_size=1, allow_degenerate=True, use_classic=False): """ Lewiner marching cubes algorithm to find surfaces in 3d volumetric data. In contrast to ``marching_cubes_classic()``, this algorithm is faster, resolves ambiguities, and guarantees topologically correct results. Therefore, this algorithm generally a better choice, unless there is a specific need for the classic algorithm. Parameters ---------- volume : (M, N, P) array Input data volume to find isosurfaces. Will internally be converted to float32 if necessary. level : float Contour value to search for isosurfaces in `volume`. If not given or None, the average of the min and max of vol is used. spacing : length-3 tuple of floats Voxel spacing in spatial dimensions corresponding to numpy array indexing dimensions (M, N, P) as in `volume`. gradient_direction : string Controls if the mesh was generated from an isosurface with gradient descent toward objects of interest (the default), or the opposite, considering the *left-hand* rule. The two options are: * descent : Object was greater than exterior * ascent : Exterior was greater than object step_size : int Step size in voxels. Default 1. Larger steps yield faster but coarser results. The result will always be topologically correct though. allow_degenerate : bool Whether to allow degenerate (i.e. zero-area) triangles in the end-result. Default True. If False, degenerate triangles are removed, at the cost of making the algorithm slower. use_classic : bool If given and True, the classic marching cubes by Lorensen (1987) is used. This option is included for reference purposes. Note that this algorithm has ambiguities and is not guaranteed to produce a topologically correct result. The results with using this option are *not* generally the same as the ``marching_cubes_classic()`` function. Returns ------- verts : (V, 3) array Spatial coordinates for V unique mesh vertices. Coordinate order matches input `volume` (M, N, P). faces : (F, 3) array Define triangular faces via referencing vertex indices from ``verts``. This algorithm specifically outputs triangles, so each face has exactly three indices. normals : (V, 3) array The normal direction at each vertex, as calculated from the data. values : (V, ) array Gives a measure for the maximum value of the data in the local region near each vertex. This can be used by visualization tools to apply a colormap to the mesh. Notes ----- The algorithm [1] is an improved version of Chernyaev's Marching Cubes 33 algorithm. It is an efficient algorithm that relies on heavy use of lookup tables to handle the many different cases, keeping the algorithm relatively easy. This implementation is written in Cython, ported from Lewiner's C++ implementation. To quantify the area of an isosurface generated by this algorithm, pass verts and faces to `skimage.measure.mesh_surface_area`. Regarding visualization of algorithm output, to contour a volume named `myvolume` about the level 0.0, using the ``mayavi`` package:: >>> from mayavi import mlab # doctest: +SKIP >>> verts, faces, normals, values = marching_cubes(myvolume, 0.0) # doctest: +SKIP >>> mlab.triangular_mesh([vert[0] for vert in verts], ... [vert[1] for vert in verts], ... [vert[2] for vert in verts], ... faces) # doctest: +SKIP >>> mlab.show() # doctest: +SKIP Similarly using the ``visvis`` package:: >>> import visvis as vv # doctest: +SKIP >>> verts, faces, normals, values = marching_cubes_classic(myvolume, 0.0) # doctest: +SKIP >>> vv.mesh(np.fliplr(verts), faces, normals, values) # doctest: +SKIP >>> vv.use().Run() # doctest: +SKIP References ---------- .. [1] Thomas Lewiner, Helio Lopes, Antonio Wilson Vieira and Geovan Tavares. Efficient implementation of Marching Cubes' cases with topological guarantees. Journal of Graphics Tools 8(2) pp. 1-15 (december 2003). DOI: 10.1080/10867651.2003.10487582 See Also -------- skimage.measure.marching_cubes_classic skimage.measure.mesh_surface_area """ # This signature (output args) of this func changed after 0.12 try: nout = _expected_output_args() except Exception: nout = 0 # always warn if, for whaterver reason, the black magic in above call fails if nout <= 2: warn(skimage_deprecation('`marching_cubes` now uses a better and ' 'faster algorithm, and returns four instead ' 'of two outputs (see docstring for details). ' 'Backwards compatibility with 0.12 and prior ' 'is available with `marching_cubes_classic`. ' 'This function will be removed in 0.14, ' 'consider switching to `marching_cubes_lewiner`.')) return marching_cubes_lewiner(volume, level, spacing, gradient_direction, step_size, allow_degenerate, use_classic) def marching_cubes_lewiner(volume, level=None, spacing=(1., 1., 1.), gradient_direction='descent', step_size=1, allow_degenerate=True, use_classic=False): """ Alias for ``marching_cubes()``. """ # Check volume and ensure its in the format that the alg needs if not isinstance(volume, np.ndarray) or (volume.ndim != 3): raise ValueError('Input volume should be a 3D numpy array.') if volume.shape[0] < 2 or volume.shape[1] < 2 or volume.shape[2] < 2: raise ValueError("Input array must be at least 2x2x2.") volume = np.ascontiguousarray(volume, np.float32) # no copy if not necessary # Check/convert other inputs: # level if level is None: level = 0.5 * (volume.min() + volume.max()) else: level = float(level) if level < volume.min() or level > volume.max(): raise ValueError("Surface level must be within volume data range.") # spacing if len(spacing) != 3: raise ValueError("`spacing` must consist of three floats.") # step_size step_size = int(step_size) if step_size < 1: raise ValueError('step_size must be at least one.') # use_classic use_classic = bool(use_classic) # Get LutProvider class (reuse if possible) L = _get_mc_luts() # Apply algorithm func = _marching_cubes_lewiner_cy.marching_cubes vertices, faces , normals, values = func(volume, level, L, step_size, use_classic) if not len(vertices): raise RuntimeError('No surface found at the given iso value.') # Output in z-y-x order, as is common in skimage vertices = np.fliplr(vertices) normals = np.fliplr(normals) # Finishing touches to output faces.shape = -1, 3 if gradient_direction == 'descent': # MC implementation is right-handed, but gradient_direction is left-handed faces = np.fliplr(faces) elif not gradient_direction == 'ascent': raise ValueError("Incorrect input %s in `gradient_direction`, see " "docstring." % (gradient_direction)) if spacing != (1, 1, 1): vertices = vertices * np.r_[spacing] if allow_degenerate: return vertices, faces, normals, values else: fun = _marching_cubes_lewiner_cy.remove_degenerate_faces return fun(vertices, faces, normals, values) def _to_array(args): shape, text = args byts = base64decode(text.encode('utf-8')) ar = np.frombuffer(byts, dtype='int8') ar.shape = shape return ar # Map an edge-index to two relative pixel positions. The ege index # represents a point that lies somewhere in between these pixels. # Linear interpolation should be used to determine where it is exactly. # 0 # 3 1 -> 0x # 2 xx EDGETORELATIVEPOSX = np.array([ [0,1],[1,1],[1,0],[0,0], [0,1],[1,1],[1,0],[0,0], [0,0],[1,1],[1,1],[0,0] ], 'int8') EDGETORELATIVEPOSY = np.array([ [0,0],[0,1],[1,1],[1,0], [0,0],[0,1],[1,1],[1,0], [0,0],[0,0],[1,1],[1,1] ], 'int8') EDGETORELATIVEPOSZ = np.array([ [0,0],[0,0],[0,0],[0,0], [1,1],[1,1],[1,1],[1,1], [0,1],[0,1],[0,1],[0,1] ], 'int8') def _get_mc_luts(): """ Kind of lazy obtaining of the luts. """ if not hasattr(mcluts, 'THE_LUTS'): mcluts.THE_LUTS = _marching_cubes_lewiner_cy.LutProvider( EDGETORELATIVEPOSX, EDGETORELATIVEPOSY, EDGETORELATIVEPOSZ, _to_array(mcluts.CASESCLASSIC), _to_array(mcluts.CASES), _to_array(mcluts.TILING1), _to_array(mcluts.TILING2), _to_array(mcluts.TILING3_1), _to_array(mcluts.TILING3_2), _to_array(mcluts.TILING4_1), _to_array(mcluts.TILING4_2), _to_array(mcluts.TILING5), _to_array(mcluts.TILING6_1_1), _to_array(mcluts.TILING6_1_2), _to_array(mcluts.TILING6_2), _to_array(mcluts.TILING7_1), _to_array(mcluts.TILING7_2), _to_array(mcluts.TILING7_3), _to_array(mcluts.TILING7_4_1), _to_array(mcluts.TILING7_4_2), _to_array(mcluts.TILING8), _to_array(mcluts.TILING9), _to_array(mcluts.TILING10_1_1), _to_array(mcluts.TILING10_1_1_), _to_array(mcluts.TILING10_1_2), _to_array(mcluts.TILING10_2), _to_array(mcluts.TILING10_2_), _to_array(mcluts.TILING11), _to_array(mcluts.TILING12_1_1), _to_array(mcluts.TILING12_1_1_), _to_array(mcluts.TILING12_1_2), _to_array(mcluts.TILING12_2), _to_array(mcluts.TILING12_2_), _to_array(mcluts.TILING13_1), _to_array(mcluts.TILING13_1_), _to_array(mcluts.TILING13_2), _to_array(mcluts.TILING13_2_), _to_array(mcluts.TILING13_3), _to_array(mcluts.TILING13_3_), _to_array(mcluts.TILING13_4), _to_array(mcluts.TILING13_5_1), _to_array(mcluts.TILING13_5_2), _to_array(mcluts.TILING14), _to_array(mcluts.TEST3), _to_array(mcluts.TEST4), _to_array(mcluts.TEST6), _to_array(mcluts.TEST7), _to_array(mcluts.TEST10), _to_array(mcluts.TEST12), _to_array(mcluts.TEST13), _to_array(mcluts.SUBCONFIG13), ) return mcluts.THE_LUTS scikit-image-0.13.1/skimage/measure/_marching_cubes_lewiner_cy.pyx000066400000000000000000001541241316256236300253330ustar00rootroot00000000000000# -*- coding: utf-8 -*- # #cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False #cython: wraparound=False """ This is an implementation of the marching cubes algorithm proposed in: Efficient implementation of Marching Cubes' cases with topological guarantees. Thomas Lewiner, Helio Lopes, Antonio Wilson Vieira and Geovan Tavares. Journal of Graphics Tools 8(2): pp. 1-15 (december 2003) This algorithm has the advantage that it provides topologically correct results, and the algorithms implementation is relatively simple. Most of the magic is in the lookup tables, which are provided as open source. Originally implemented in C++ by Thomas Lewiner in 2002, ported to Cython by Almar Klein in 2012. Adapted for scikit-image in 2016. """ # Cython specific imports import numpy as np cimport numpy as np import cython # Enable low level memory management # from libc.stdlib cimport malloc, free cdef extern from "stdlib.h": # The cimport does not work on my Linux Laptop void free(void* ptr) void* malloc(size_t size) # Define tiny winy number cdef double FLT_EPSILON = np.spacing(1.0) #0.0000001 # Define abs function for doubles cdef inline double dabs(double a): return a if a>=0 else -a cdef inline int imin(int a, int b): return a if a0]]] vertices2 = vertices[vertices_ok] arrays2 = [arr[vertices_ok] for arr in arrays] return (vertices2, faces2) + tuple(arrays2) cdef class Cell: """ Class to keep track of some stuff during the whole cube marching procedure. This "struct" keeps track of the current cell location, and the values of corners of the cube. Gradients for the cube corners are calculated when needed. Additionally, it keeps track of the array of vertices, faces and normals. Notes on vertices ----------------- The vertices are stored in a C-array that is increased in size with factors of two if needed. The same applies to the faces and normals. Notes on faces -------------- To keep track of the vertices already defined, this class maintains two faceLayer arrays. faceLayer1 is of the current layer (z-value) and faceLayer2 is of the next. Both face layers have 4 elements per cell in that layer, 1 for each unique edge per cell (see get_index_in_facelayer). These are initialized as -1, and set to the index in the vertex array when a new vertex is created. In summary, this allows us to keep track of the already created vertices without keeping a very big array. Notes on normals ---------------- The normal is simply defined as the gradient. Each time that a face is created, we also add the gradient of that vertex position to the normals array. The gradients are all calculated from the differences between the 8 corners of the current cube, but because the final value of a normal was contributed from multiple cells, the normals are quite accurate. """ # Reference to LUTS object cdef LutProvider luts # Location of cube cdef int x cdef int y cdef int z # Stepsize cdef int step # Values of cube corners (isovalue subtracted) cdef double v0 cdef double v1 cdef double v2 cdef double v3 cdef double v4 cdef double v5 cdef double v6 cdef double v7 # Small arrays to store the above values in (allowing indexing) # and also the gradient at these points cdef double *vv cdef double *vg # Max value of the eight corners cdef double vmax # Vertex position of center of cube (only calculated if needed) cdef double v12_x cdef double v12_y cdef double v12_z # And corresponding gradient cdef double v12_xg cdef double v12_yg cdef double v12_zg cdef int v12_calculated # a boolean # The index value, our magic 256 bit word cdef int index # Dimensions of the total volume cdef int nx cdef int ny cdef int nz # Arrays with face information cdef int *faceLayer # The current facelayer (reference-copy of one of the below) cdef int *faceLayer1 # The actual first face layer cdef int *faceLayer2 # The actual second face layer # Stuff to store the output vertices cdef float *_vertices cdef float *_normals cdef float *_values cdef int _vertexCount cdef int _vertexMaxCount # Stuff to store the output faces cdef int *_faces cdef int _faceCount cdef int _faceMaxCount def __init__(self, LutProvider luts, int nx, int ny, int nz): self.luts = luts self.nx, self.ny, self.nz = nx, ny, nz # Allocate face layers self.faceLayer1 = malloc(self.nx*self.ny*4 * sizeof(int)) self.faceLayer2 = malloc(self.nx*self.ny*4 * sizeof(int)) cdef int i for i in range(self.nx*self.ny*4): self.faceLayer1[i] = -1 self.faceLayer2[i] = -1 self.faceLayer = self.faceLayer1 def __cinit__(self): # Init tiny arrays for vertices and gradients at the vertices self.vv = malloc(8 * sizeof(double)) self.vg = malloc(8*3 * sizeof(double)) # Init face layers self.faceLayer1 = NULL self.faceLayer2 = NULL # Init vertices self._vertexCount = 0 self._vertexMaxCount = 8 self._vertices = malloc(self._vertexMaxCount*3 * sizeof(float)) self._normals = malloc(self._vertexMaxCount*3 * sizeof(float)) self._values = malloc(self._vertexMaxCount * sizeof(float)) # Clear normals and values cdef int i, j for i in range(self._vertexMaxCount): self._values[i] = 0.0 for j in range(3): self._normals[i*3+j] = 0.0 # Init faces self._faceCount = 0 self._faceMaxCount = 8 self._faces = malloc(self._faceMaxCount * sizeof(int)) def __dealloc__(self): if self.vv is not NULL: free(self.vv) if self.vg is not NULL: free(self.vg) if self.faceLayer1 is not NULL: free(self.faceLayer1) if self.faceLayer2 is not NULL: free(self.faceLayer2) if self._vertices is not NULL: free(self._vertices) if self._normals is not NULL: free(self._normals) if self._values is not NULL: free(self._values) if self._faces is not NULL: free(self._faces) cdef void _increase_size_vertices(self): """ Increase the size of the vertices array by a factor two. """ # Allocate new array cdef int newMaxCount = self._vertexMaxCount * 2 cdef float *newVertices = malloc(newMaxCount*3 * sizeof(float)) cdef float *newNormals = malloc(newMaxCount*3 * sizeof(float)) cdef float *newValues = malloc(newMaxCount * sizeof(float)) # Clear cdef int i, j for i in range(self._vertexCount, newMaxCount): newValues[i] = 0.0 for j in range(3): newNormals[i*3+j] = 0.0 # Copy for i in range(self._vertexCount): newValues[i] = self._values[i] for j in range(3): newVertices[i*3+j] = self._vertices[i*3+j] newNormals[i*3+j] = self._normals[i*3+j] # Apply free(self._vertices); self._vertices = newVertices free(self._normals); self._normals = newNormals free(self._values); self._values = newValues self._vertexMaxCount = newMaxCount cdef void _increase_size_faces(self): """ Increase the size of the faces array by a factor two. """ # Allocate new array cdef int newMaxCount = self._faceMaxCount * 2 cdef int *newFaces = malloc(newMaxCount * sizeof(int)) # Copy cdef int i for i in range(self._faceCount): newFaces[i] = self._faces[i] # Apply free(self._faces) self._faces = newFaces self._faceMaxCount = newMaxCount ## Adding results cdef int add_vertex(self, float x, float y, float z): """ Add a vertex to the result. Return index in vertex array. """ # Check if array is large enough if self._vertexCount >= self._vertexMaxCount: self._increase_size_vertices() # Add vertex self._vertices[self._vertexCount*3+0] = x self._vertices[self._vertexCount*3+1] = y self._vertices[self._vertexCount*3+2] = z self._vertexCount += 1 return self._vertexCount -1 cdef void add_gradient(self, int vertexIndex, float gx, float gy, float gz): """ Add a gradient value to the vertex corresponding to the given index. """ self._normals[vertexIndex*3+0] += gx self._normals[vertexIndex*3+1] += gy self._normals[vertexIndex*3+2] += gz cdef void add_gradient_from_index(self, int vertexIndex, int i, float strength): """ Add a gradient value to the vertex corresponding to the given index. vertexIndex is the index in the large array of vertices that is returned. i is the index of the array of vertices 0-7 for the current cell. """ self.add_gradient(vertexIndex, self.vg[i*3+0] * strength, self.vg[i*3+1] * strength, self.vg[i*3+2] * strength) cdef add_face(self, int index): """ Add a face to the result. Also updates the value. """ # Check if array is large enough if self._faceCount >= self._faceMaxCount: self._increase_size_faces() # Add face self._faces[self._faceCount] = index self._faceCount += 1 # Also update value if self.vmax > self._values[index]: self._values[index] = self.vmax ## Getting results def get_vertices(self): """ Get the final vertex array. """ vertices = np.empty((self._vertexCount,3), np.float32) cdef float [:, :] vertices_ = vertices cdef int i, j for i in range(self._vertexCount): for j in range(3): vertices_[i, j] = self._vertices[i*3+j] return vertices def get_normals(self): """ Get the final normals array. The normals are normalized to unit length. """ normals = np.empty((self._vertexCount,3), np.float32) cdef float [:, :] normals_ = normals cdef int i, j cdef double length, dtmp for i in range(self._vertexCount): length = 0.0 for j in range(3): dtmp = self._normals[i*3+j] # Make it double before taking **2! length += dtmp*dtmp if length > 0.0: length = 1.0 / length**0.5 for j in range(3): normals_[i,j] = self._normals[i*3+j] * length return normals def get_faces(self): faces = np.empty((self._faceCount,), np.int32) cdef int [:] faces_ = faces cdef int i, j for i in range(self._faceCount): faces_[i] = self._faces[i] return faces def get_values(self): values = np.empty((self._vertexCount,), np.float32) cdef float [:] values_ = values cdef int i, j for i in range(self._vertexCount): values_[i] = self._values[i] return values ## Called from marching cube function cdef void new_z_value(self): """ This method should be called each time a new z layer is entered. We will swap the layers with face information and empty the second. """ # Swap layers self.faceLayer1, self.faceLayer2 = self.faceLayer2, self.faceLayer1 # Empty last cdef int i for i in range(self.nx*self.ny*4): self.faceLayer2[i] = -1 cdef void set_cube(self, double isovalue, int x, int y, int z, int step, double v0, double v1, double v2, double v3, double v4, double v5, double v6, double v7): """ Set the cube to the new location. Set the values of the cube corners. The isovalue is subtracted from them, such that in further calculations the isovalue can be taken as zero. This method also calculated the magic 256 word to identify the cases (i.e. cell.index). """ # Set location and step self.x = x self.y = y self.z = z self.step = step # Set values self.v0 = v0 - isovalue self.v1 = v1 - isovalue self.v2 = v2 - isovalue self.v3 = v3 - isovalue self.v4 = v4 - isovalue self.v5 = v5 - isovalue self.v6 = v6 - isovalue self.v7 = v7 - isovalue # Calculate index cdef int index = 0 if self.v0 > 0.0: index += 1 if self.v1 > 0.0: index += 2 if self.v2 > 0.0: index += 4 if self.v3 > 0.0: index += 8 if self.v4 > 0.0: index += 16 if self.v5 > 0.0: index += 32 if self.v6 > 0.0: index += 64 if self.v7 > 0.0: index += 128 self.index = index # Reset c12 self.v12_calculated = 0 cdef void add_triangles(self, Lut lut, int lutIndex, int nt): """ Add triangles. The vertices for the triangles are specified in the given Lut at the specified index. There are nt triangles. The reason that nt should be given is because it is often known beforehand. """ cdef int i, j cdef int vi self.prepare_for_adding_triangles() for i in range(nt): for j in range(3): # Get two sides for each element in this vertex vi = lut.get2(lutIndex, i*3+j) self._add_face_from_edge_index(vi) cdef void add_triangles2(self, Lut lut, int lutIndex, int lutIndex2, int nt): """ Same as add_triangles, except that now the geometry is in a LUT with 3 dimensions, and an extra index is provided. """ cdef int i, j cdef int vi self.prepare_for_adding_triangles() for i in range(nt): for j in range(3): # Get two sides for each element in this vertex vi = lut.get3(lutIndex, lutIndex2, i*3+j) self._add_face_from_edge_index(vi) ## Used internally cdef void _add_face_from_edge_index(self, int vi): """ Add one face from an edge index. Only adds a face if the vertex already exists. Otherwise also adds a vertex and applies interpolation. """ # typedefs cdef int indexInVertexArray, indexInFaceLayer cdef int dx1, dy1, dz1 cdef int dx2, dy2, dz2 cdef int index1, index2 cdef double tmpf1, tmpf2 cdef double fx, fy, fz, ff cdef double stp = self.step # Get index in the face layer and corresponding vertex number indexInFaceLayer = self.get_index_in_facelayer(vi) indexInVertexArray = self.faceLayer[indexInFaceLayer] # If we have the center vertex, we have things pre-calculated, # otherwise we need to interpolate. # In both cases we distinguish between having this vertex already # or not. if vi == 12: # center vertex if self.v12_calculated == 0: self.calculate_center_vertex() if indexInVertexArray >= 0: # Vertex already calculated, only need to add face and gradient self.add_face(indexInVertexArray) self.add_gradient(indexInVertexArray, self.v12_xg, self.v12_yg, self.v12_zg) else: # Add precalculated center vertex position (is interpolated) indexInVertexArray = self.add_vertex( self.v12_x, self.v12_y, self.v12_z) # Update face layer self.faceLayer[indexInFaceLayer] = indexInVertexArray # Add face and gradient self.add_face(indexInVertexArray) self.add_gradient(indexInVertexArray, self.v12_xg, self.v12_yg, self.v12_zg) else: # Get relative edge indices for x, y and z dx1, dx2 = self.luts.EDGESRELX.get2(vi,0), self.luts.EDGESRELX.get2(vi,1) dy1, dy2 = self.luts.EDGESRELY.get2(vi,0), self.luts.EDGESRELY.get2(vi,1) dz1, dz2 = self.luts.EDGESRELZ.get2(vi,0), self.luts.EDGESRELZ.get2(vi,1) # Make two vertex indices index1 = dz1*4 + dy1*2 + dx1 index2 = dz2*4 + dy2*2 + dx2 # Define strength of both corners tmpf1 = 1.0 / (FLT_EPSILON + dabs(self.vv[index1])) tmpf2 = 1.0 / (FLT_EPSILON + dabs(self.vv[index2])) # print('indexInVertexArray', self.x, self.y, self.z, '-', vi, indexInVertexArray, indexInFaceLayer) if indexInVertexArray >= 0: # Vertex already calculated, only need to add face and gradient self.add_face(indexInVertexArray) self.add_gradient_from_index(indexInVertexArray, index1, tmpf1) self.add_gradient_from_index(indexInVertexArray, index2, tmpf2) else: # Interpolate by applying a kind of center-of-mass method fx, fy, fz, ff = 0.0, 0.0, 0.0, 0.0 fx += dx1 * tmpf1; fy += dy1 * tmpf1; fz += dz1 * tmpf1; ff += tmpf1 fx += dx2 * tmpf2; fy += dy2 * tmpf2; fz += dz2 * tmpf2; ff += tmpf2 # Add vertex indexInVertexArray = self.add_vertex( self.x + stp*fx/ff, self.y + stp*fy/ff, self.z + stp*fz/ff ) # Update face layer self.faceLayer[indexInFaceLayer] = indexInVertexArray # Add face and gradient self.add_face(indexInVertexArray) self.add_gradient_from_index(indexInVertexArray, index1, tmpf1) self.add_gradient_from_index(indexInVertexArray, index2, tmpf2) # # Create vertex non-interpolated # self.add_vertex( self.x + 0.5* dx1 + 0.5 * dx2, # self.y + 0.5* dy1 + 0.5 * dy2, # self.z + 0.5* dz1 + 0.5 * dz2 ) cdef int get_index_in_facelayer(self, int vi): """ Get the index of a vertex position, given the edge on which it lies. We keep a list of faces so we can reuse vertices. This improves speed because we need less interpolation, and the result is more compact and can be visualized better because normals can be interpolated. For each cell, we store 4 vertex indices; all other edges can be represented as the edge of another cell. The fourth is the center vertex. This method returns -1 if no vertex has been defined yet. vertices edges edge-indices per cell * 7 ________ 6 _____6__ ________ * /| /| 7/| /| /| /| * / | / | / | /5 | / | / | * 4 /_______ / | /__4____ / 10 /_______ / | * | | |5 | | 11 | | | | | | * | 3|__|_____|2 | |__|__2__| | |__|_____| * | / | / 8 3/ 9 / 2 / | / * | / | / | / | /1 | 1 | / * |/_______|/ |/___0___|/ |/___0___|/ * 0 1 */ """ # Init indices, both are corrected below cdef int i = self.nx * self.y + self.x # Index of cube to get vertex at cdef int j = 0 # Vertex number for that cell cdef int vi_ = vi cdef int *faceLayer # Select either upper or lower half if vi < 8: # 8 horizontal edges if vi < 4: faceLayer = self.faceLayer1 else: vi -= 4 faceLayer = self.faceLayer2 # Calculate actual index based on edge #if vi == 0: pass # no step if vi == 1: # step in x i += self.step j = 1 elif vi == 2: # step in y i += self.nx * self.step elif vi == 3: # no step j = 1 elif vi < 12: # 4 vertical edges faceLayer = self.faceLayer1 j = 2 #if vi == 8: pass # no step if vi == 9: # step in x i += self.step elif vi == 10: # step in x and y i += self.nx * self.step + self.step elif vi == 11: # step in y i += self.nx * self.step else: # center vertex faceLayer = self.faceLayer1 j = 3 # Store facelayer and return index self.faceLayer = faceLayer # Dirty way of returning a value return 4*i + j cdef void prepare_for_adding_triangles(self): """ Calculates some things to help adding the triangles: array with corner values, max corner value, gradient at each corner. """ cdef int i # Copy values in array so we can index them. Note the misalignment # because the numbering does not correspond with bitwise OR of xyz. self.vv[0] = self.v0 self.vv[1] = self.v1 self.vv[2] = self.v3# self.vv[3] = self.v2# self.vv[4] = self.v4 self.vv[5] = self.v5 self.vv[6] = self.v7# self.vv[7] = self.v6# # Calculate max cdef double vmin, vmax vmin, vmax = 0.0, 0.0 for i in range(8): if self.vv[i] > vmax: vmax = self.vv[i] if self.vv[i] < vmin: vmin = self.vv[i] self.vmax = vmax-vmin # Calculate gradients # Derivatives, selected to always point in same direction. # Note that many corners have the same components as other points, # by interpolating and averaging the normals this is solved. # todo: we can potentially reuse these similar to how we store vertex indices in face layers self.vg[0*3+0], self.vg[0*3+1], self.vg[0*3+2] = self.v0-self.v1, self.v0-self.v3, self.v0-self.v4 self.vg[1*3+0], self.vg[1*3+1], self.vg[1*3+2] = self.v0-self.v1, self.v1-self.v2, self.v1-self.v5 self.vg[2*3+0], self.vg[2*3+1], self.vg[2*3+2] = self.v3-self.v2, self.v1-self.v2, self.v2-self.v6 self.vg[3*3+0], self.vg[3*3+1], self.vg[3*3+2] = self.v3-self.v2, self.v0-self.v3, self.v3-self.v7 self.vg[4*3+0], self.vg[4*3+1], self.vg[4*3+2] = self.v4-self.v5, self.v4-self.v7, self.v0-self.v4 self.vg[5*3+0], self.vg[5*3+1], self.vg[5*3+2] = self.v4-self.v5, self.v5-self.v6, self.v1-self.v5 self.vg[6*3+0], self.vg[6*3+1], self.vg[6*3+2] = self.v7-self.v6, self.v5-self.v6, self.v2-self.v6 self.vg[7*3+0], self.vg[7*3+1], self.vg[7*3+2] = self.v7-self.v6, self.v4-self.v7, self.v3-self.v7 cdef void calculate_center_vertex(self): """ Calculate interpolated center vertex and its gradient. """ cdef double v0, v1, v2, v3, v4, v5, v6, v7 cdef double fx, fy, fz, ff fx, fy, fz, ff = 0.0, 0.0, 0.0, 0.0 # Define "strength" of each corner of the cube that we need v0 = 1.0 / (FLT_EPSILON + dabs(self.v0)) v1 = 1.0 / (FLT_EPSILON + dabs(self.v1)) v2 = 1.0 / (FLT_EPSILON + dabs(self.v2)) v3 = 1.0 / (FLT_EPSILON + dabs(self.v3)) v4 = 1.0 / (FLT_EPSILON + dabs(self.v4)) v5 = 1.0 / (FLT_EPSILON + dabs(self.v5)) v6 = 1.0 / (FLT_EPSILON + dabs(self.v6)) v7 = 1.0 / (FLT_EPSILON + dabs(self.v7)) # Apply a kind of center-of-mass method fx += 0.0*v0; fy += 0.0*v0; fz += 0.0*v0; ff += v0 fx += 1.0*v1; fy += 0.0*v1; fz += 0.0*v1; ff += v1 fx += 1.0*v2; fy += 1.0*v2; fz += 0.0*v2; ff += v2 fx += 0.0*v3; fy += 1.0*v3; fz += 0.0*v3; ff += v3 fx += 0.0*v4; fy += 0.0*v4; fz += 1.0*v4; ff += v4 fx += 1.0*v5; fy += 0.0*v5; fz += 1.0*v5; ff += v5 fx += 1.0*v6; fy += 1.0*v6; fz += 1.0*v6; ff += v6 fx += 0.0*v7; fy += 1.0*v7; fz += 1.0*v7; ff += v7 # Store cdef double stp = self.step self.v12_x = self.x + stp * fx / ff self.v12_y = self.y + stp * fy / ff self.v12_z = self.z + stp * fz / ff # Also pre-calculate gradient of center # note that prepare_for_adding_triangles() must have been called for # the gradient data to exist. self.v12_xg = ( v0*self.vg[0*3+0] + v1*self.vg[1*3+0] + v2*self.vg[2*3+0] + v3*self.vg[3*3+0] + v4*self.vg[4*3+0] + v5*self.vg[5*3+0] + v6*self.vg[6*3+0] + v7*self.vg[7*3+0] ) self.v12_yg = ( v0*self.vg[0*3+1] + v1*self.vg[1*3+1] + v2*self.vg[2*3+1] + v3*self.vg[3*3+1] + v4*self.vg[4*3+1] + v5*self.vg[5*3+1] + v6*self.vg[6*3+1] + v7*self.vg[7*3+1] ) self.v12_xg = ( v0*self.vg[0*3+2] + v1*self.vg[1*3+2] + v2*self.vg[2*3+2] + v3*self.vg[3*3+2] + v4*self.vg[4*3+2] + v5*self.vg[5*3+2] + v6*self.vg[6*3+2] + v7*self.vg[7*3+2] ) # Set flag that this stuff is calculated self.v12_calculated = 1 cdef class Lut: """ Representation of a lookup table. The tables are initially defined as numpy arrays. On initialization, this class converts them to a C array for fast access. This class defines functions to look up values using 1, 2 or 3 indices. """ cdef char* VALUES cdef int L0 # Length cdef int L1 # size of tuple cdef int L2 # size of tuple in tuple (if any) def __init__(self, array): # Get the shape of the LUT self.L1 = 1 self.L2 = 1 # self.L0 = array.shape[0] if array.ndim > 1: self.L1 = array.shape[1] if array.ndim > 2: self.L2 = array.shape[2] # Copy the contents array = array.ravel() cdef int n, N N = self.L0 * self.L1 * self.L2 self.VALUES = malloc(N * sizeof(char)) for n in range(N): self.VALUES[n] = array[n] def __cinit__(self): self.VALUES = NULL def __dealloc__(self): if self.VALUES is not NULL: free(self.VALUES) cdef int get1(self, int i0): return self.VALUES[i0] cdef int get2(self, int i0, int i1): return self.VALUES[i0*self.L1 + i1] cdef int get3(self, int i0, int i1, int i2): return self.VALUES[i0*self.L1*self.L2 + i1*self.L2 + i2] cdef class LutProvider: """ Class that provides a common interface to the many lookup tables used by the algorithm. All the lists of lut names are autogenerated to prevent human error. """ cdef Lut EDGESRELX # Edges relative X cdef Lut EDGESRELY cdef Lut EDGESRELZ cdef Lut CASESCLASSIC cdef Lut CASES cdef Lut TILING1 cdef Lut TILING2 cdef Lut TILING3_1 cdef Lut TILING3_2 cdef Lut TILING4_1 cdef Lut TILING4_2 cdef Lut TILING5 cdef Lut TILING6_1_1 cdef Lut TILING6_1_2 cdef Lut TILING6_2 cdef Lut TILING7_1 cdef Lut TILING7_2 cdef Lut TILING7_3 cdef Lut TILING7_4_1 cdef Lut TILING7_4_2 cdef Lut TILING8 cdef Lut TILING9 cdef Lut TILING10_1_1 cdef Lut TILING10_1_1_ cdef Lut TILING10_1_2 cdef Lut TILING10_2 cdef Lut TILING10_2_ cdef Lut TILING11 cdef Lut TILING12_1_1 cdef Lut TILING12_1_1_ cdef Lut TILING12_1_2 cdef Lut TILING12_2 cdef Lut TILING12_2_ cdef Lut TILING13_1 cdef Lut TILING13_1_ cdef Lut TILING13_2 cdef Lut TILING13_2_ cdef Lut TILING13_3 cdef Lut TILING13_3_ cdef Lut TILING13_4 cdef Lut TILING13_5_1 cdef Lut TILING13_5_2 cdef Lut TILING14 cdef Lut TEST3 cdef Lut TEST4 cdef Lut TEST6 cdef Lut TEST7 cdef Lut TEST10 cdef Lut TEST12 cdef Lut TEST13 cdef Lut SUBCONFIG13 def __init__(self, EDGESRELX, EDGESRELY, EDGESRELZ, CASESCLASSIC, CASES, TILING1, TILING2, TILING3_1, TILING3_2, TILING4_1, TILING4_2, TILING5, TILING6_1_1, TILING6_1_2, TILING6_2, TILING7_1, TILING7_2, TILING7_3, TILING7_4_1, TILING7_4_2, TILING8, TILING9, TILING10_1_1, TILING10_1_1_, TILING10_1_2, TILING10_2, TILING10_2_, TILING11, TILING12_1_1, TILING12_1_1_, TILING12_1_2, TILING12_2, TILING12_2_, TILING13_1, TILING13_1_, TILING13_2, TILING13_2_, TILING13_3, TILING13_3_, TILING13_4, TILING13_5_1, TILING13_5_2, TILING14, TEST3, TEST4, TEST6, TEST7, TEST10, TEST12, TEST13, SUBCONFIG13, ): self.EDGESRELX = Lut(EDGESRELX) self.EDGESRELY = Lut(EDGESRELY) self.EDGESRELZ = Lut(EDGESRELZ) self.CASESCLASSIC = Lut(CASESCLASSIC) self.CASES = Lut(CASES) self.TILING1 = Lut(TILING1) self.TILING2 = Lut(TILING2) self.TILING3_1 = Lut(TILING3_1) self.TILING3_2 = Lut(TILING3_2) self.TILING4_1 = Lut(TILING4_1) self.TILING4_2 = Lut(TILING4_2) self.TILING5 = Lut(TILING5) self.TILING6_1_1 = Lut(TILING6_1_1) self.TILING6_1_2 = Lut(TILING6_1_2) self.TILING6_2 = Lut(TILING6_2) self.TILING7_1 = Lut(TILING7_1) self.TILING7_2 = Lut(TILING7_2) self.TILING7_3 = Lut(TILING7_3) self.TILING7_4_1 = Lut(TILING7_4_1) self.TILING7_4_2 = Lut(TILING7_4_2) self.TILING8 = Lut(TILING8) self.TILING9 = Lut(TILING9) self.TILING10_1_1 = Lut(TILING10_1_1) self.TILING10_1_1_ = Lut(TILING10_1_1_) self.TILING10_1_2 = Lut(TILING10_1_2) self.TILING10_2 = Lut(TILING10_2) self.TILING10_2_ = Lut(TILING10_2_) self.TILING11 = Lut(TILING11) self.TILING12_1_1 = Lut(TILING12_1_1) self.TILING12_1_1_ = Lut(TILING12_1_1_) self.TILING12_1_2 = Lut(TILING12_1_2) self.TILING12_2 = Lut(TILING12_2) self.TILING12_2_ = Lut(TILING12_2_) self.TILING13_1 = Lut(TILING13_1) self.TILING13_1_ = Lut(TILING13_1_) self.TILING13_2 = Lut(TILING13_2) self.TILING13_2_ = Lut(TILING13_2_) self.TILING13_3 = Lut(TILING13_3) self.TILING13_3_ = Lut(TILING13_3_) self.TILING13_4 = Lut(TILING13_4) self.TILING13_5_1 = Lut(TILING13_5_1) self.TILING13_5_2 = Lut(TILING13_5_2) self.TILING14 = Lut(TILING14) self.TEST3 = Lut(TEST3) self.TEST4 = Lut(TEST4) self.TEST6 = Lut(TEST6) self.TEST7 = Lut(TEST7) self.TEST10 = Lut(TEST10) self.TEST12 = Lut(TEST12) self.TEST13 = Lut(TEST13) self.SUBCONFIG13 = Lut(SUBCONFIG13) def marching_cubes(float [:, :, :] im not None, double isovalue, LutProvider luts, int st=1, int classic=0): """ marching_cubes(im, double isovalue, LutProvider luts, int st=1, int classic=0) This is the main entry to apply marching cubes. Returns (vertices, faces, normals, values) """ # Get dimemsnions cdef int Nx, Ny, Nz Nx, Ny, Nz = im.shape[2], im.shape[1], im.shape[0] # Create cell to use throughout cdef Cell cell = Cell(luts, Nx, Ny, Nz) # Typedef variables cdef int x, y, z, x_st, y_st, z_st cdef int nt cdef int case, config, subconfig # Unfortunately specifying a step in range() siginificantly degrades # performance. Therefore we use a while loop. # we have: max_x = Nx_bound + st + st - 1 # -> Nx_bound = max_allowable_x + 1 - 2 * st # -> Nx_bound = Nx - 2 * st assert st > 0 cdef int Nx_bound, Ny_bound, Nz_bound Nx_bound, Ny_bound, Nz_bound = Nx - 2 * st, Ny - 2 * st, Nz - 2 * st # precalculated index range z = -st while z < Nz_bound: z += st z_st = z + st cell.new_z_value() # Indicate that we enter a new layer y = -st while y < Ny_bound: y += st y_st = y + st x = -st while x < Nx_bound: x += st x_st = x + st # Initialize cell cell.set_cube(isovalue, x, y, z, st, im[z ,y, x], im[z ,y, x_st], im[z ,y_st, x_st], im[z ,y_st, x], im[z_st,y, x], im[z_st,y, x_st], im[z_st,y_st, x_st], im[z_st,y_st, x] ) # Do classic! if classic: # Determine number of vertices nt = 0 while luts.CASESCLASSIC.get2(cell.index, 3*nt) != -1: nt += 1 # Add triangles if nt > 0: cell.add_triangles(luts.CASESCLASSIC, cell.index, nt) else: # Get case, if non-nul, enter the big switch case = luts.CASES.get2(cell.index, 0) if case > 0: config = luts.CASES.get2(cell.index, 1) the_big_switch(luts, cell, case, config) # Done return cell.get_vertices(), cell.get_faces(), cell.get_normals(), cell.get_values() cdef void the_big_switch(LutProvider luts, Cell cell, int case, int config): """ The big switch (i.e. if-statement) that I meticulously ported from the source code provided by Lewiner et. al. Together with all the look-up tables, this is where the magic is ... """ cdef int subconfig = 0 # Sinatures for tests #test_face(cell, luts.TESTX.get1(config)): #test_internal(cell, luts, case, config, subconfig, luts.TESTX.get1(config)): #cell.add_triangles(luts.TILINGX, config, N) if case == 1: cell.add_triangles(luts.TILING1, config, 1) elif case == 2: cell.add_triangles(luts.TILING2, config, 2) elif case == 3: if test_face(cell, luts.TEST3.get1(config)): cell.add_triangles(luts.TILING3_2, config, 4) else: cell.add_triangles(luts.TILING3_1, config, 2) elif case == 4 : if test_internal(cell, luts, case, config, subconfig, luts.TEST4.get1(config)): cell.add_triangles(luts.TILING4_1, config, 2) else: cell.add_triangles(luts.TILING4_2, config, 6) elif case == 5 : cell.add_triangles(luts.TILING5, config, 3) elif case == 6 : if test_face(cell, luts.TEST6.get2(config,0)): cell.add_triangles(luts.TILING6_2, config, 5) else: if test_internal(cell, luts, case, config, subconfig, luts.TEST6.get2(config,1)): cell.add_triangles(luts.TILING6_1_1, config, 3) else: #cell.calculate_center_vertex() # v12 needed cell.add_triangles(luts.TILING6_1_2, config, 9) elif case == 7 : # Get subconfig if test_face(cell, luts.TEST7.get2(config,0)): subconfig += 1 if test_face(cell, luts.TEST7.get2(config,1)): subconfig += 2 if test_face(cell, luts.TEST7.get2(config,2)): subconfig += 4 # Behavior depends on subconfig if subconfig == 0: cell.add_triangles(luts.TILING7_1, config, 3) elif subconfig == 1: cell.add_triangles2(luts.TILING7_2, config, 0, 5) elif subconfig == 2: cell.add_triangles2(luts.TILING7_2, config, 1, 5) elif subconfig == 3: #cell.calculate_center_vertex() # v12 needed cell.add_triangles2(luts.TILING7_3, config, 0, 9) elif subconfig == 4: cell.add_triangles2(luts.TILING7_2, config, 2, 5) elif subconfig == 5: #cell.calculate_center_vertex() # v12 needed cell.add_triangles2(luts.TILING7_3, config, 1, 9) elif subconfig == 6: #cell.calculate_center_vertex() # v12 needed cell.add_triangles2(luts.TILING7_3, config, 2, 9) elif subconfig == 7: if test_internal(cell, luts, case, config, subconfig, luts.TEST7.get2(config,3)): cell.add_triangles(luts.TILING7_4_2, config, 9) else: cell.add_triangles(luts.TILING7_4_1, config, 5) elif case == 8 : cell.add_triangles(luts.TILING8, config, 2) elif case == 9 : cell.add_triangles(luts.TILING9, config, 4) elif case == 10 : if test_face(cell, luts.TEST10.get2(config,0)): if test_face(cell, luts.TEST10.get2(config,1)): cell.add_triangles(luts.TILING10_1_1_, config, 4) else: #cell.calculate_center_vertex() # v12 needed cell.add_triangles(luts.TILING10_2, config, 8) else: if test_face(cell, luts.TEST10.get2(config,1)): #cell.calculate_center_vertex() # v12 needed cell.add_triangles(luts.TILING10_2_, config, 8) else: if test_internal(cell, luts, case, config, subconfig, luts.TEST10.get2(config,2)): cell.add_triangles(luts.TILING10_1_1, config, 4) else: cell.add_triangles(luts.TILING10_1_2, config, 8) elif case == 11 : cell.add_triangles(luts.TILING11, config, 4) elif case == 12 : if test_face(cell, luts.TEST12.get2(config,0)): if test_face(cell, luts.TEST12.get2(config,1)): cell.add_triangles(luts.TILING12_1_1_, config, 4) else: #cell.calculate_center_vertex() # v12 needed cell.add_triangles(luts.TILING12_2, config, 8) else: if test_face(cell, luts.TEST12.get2(config,1)): #cell.calculate_center_vertex() # v12 needed cell.add_triangles(luts.TILING12_2_, config, 8) else: if test_internal(cell, luts, case, config, subconfig, luts.TEST12.get2(config,2)): cell.add_triangles(luts.TILING12_1_1, config, 4) else: cell.add_triangles(luts.TILING12_1_2, config, 8) elif case == 13 : # Calculate subconfig if test_face(cell, luts.TEST13.get2(config,0)): subconfig += 1 if test_face(cell, luts.TEST13.get2(config,1)): subconfig += 2 if test_face(cell, luts.TEST13.get2(config,2)): subconfig += 4 if test_face(cell, luts.TEST13.get2(config,3)): subconfig += 8 if test_face(cell, luts.TEST13.get2(config,4)): subconfig += 16 if test_face(cell, luts.TEST13.get2(config,5)): subconfig += 32 # Map via LUT subconfig = luts.SUBCONFIG13.get1(subconfig) # Behavior depends on subconfig if subconfig==0: cell.add_triangles(luts.TILING13_1, config, 4) elif subconfig==1: cell.add_triangles2(luts.TILING13_2, config, 0, 6) elif subconfig==2: cell.add_triangles2(luts.TILING13_2, config, 1, 6) elif subconfig==3: cell.add_triangles2(luts.TILING13_2, config, 2, 6) elif subconfig==4: cell.add_triangles2(luts.TILING13_2, config, 3, 6) elif subconfig==5: cell.add_triangles2(luts.TILING13_2, config, 4, 6) elif subconfig==6: cell.add_triangles2(luts.TILING13_2, config, 5, 6) # elif subconfig==7: #cell.calculate_center_vertex() # v12 needed cell.add_triangles2(luts.TILING13_3, config, 0, 10) elif subconfig==8: #cell.calculate_center_vertex() # v12 needed cell.add_triangles2(luts.TILING13_3, config, 1, 10) elif subconfig==9: #cell.calculate_center_vertex() # v12 needed cell.add_triangles2(luts.TILING13_3, config, 2, 10) elif subconfig==10: #cell.calculate_center_vertex() # v12 needed cell.add_triangles2(luts.TILING13_3, config, 3, 10) elif subconfig==11: #cell.calculate_center_vertex() # v12 needed cell.add_triangles2(luts.TILING13_3, config, 4, 10) elif subconfig==12: #cell.calculate_center_vertex() # v12 needed cell.add_triangles2(luts.TILING13_3, config, 5, 10) elif subconfig==13: #cell.calculate_center_vertex() # v12 needed cell.add_triangles2(luts.TILING13_3, config, 6, 10) elif subconfig==14: #cell.calculate_center_vertex() # v12 needed cell.add_triangles2(luts.TILING13_3, config, 7, 10) elif subconfig==15: #cell.calculate_center_vertex() # v12 needed cell.add_triangles2(luts.TILING13_3, config, 8, 10) elif subconfig==16: #cell.calculate_center_vertex() # v12 needed cell.add_triangles2(luts.TILING13_3, config, 9, 10) elif subconfig==17: #cell.calculate_center_vertex() # v12 needed cell.add_triangles2(luts.TILING13_3, config, 10, 10) elif subconfig==18: #cell.calculate_center_vertex() # v12 needed cell.add_triangles2(luts.TILING13_3, config, 11, 10) # elif subconfig==19: #cell.calculate_center_vertex() # v12 needed cell.add_triangles2(luts.TILING13_4, config, 0, 12) elif subconfig==20: #cell.calculate_center_vertex() # v12 needed cell.add_triangles2(luts.TILING13_4, config, 1, 12) elif subconfig==21: #cell.calculate_center_vertex() # v12 needed cell.add_triangles2(luts.TILING13_4, config, 2, 12) elif subconfig==22: #cell.calculate_center_vertex() # v12 needed cell.add_triangles2(luts.TILING13_4, config, 3, 12) # elif subconfig==23: subconfig = 0 # Note: the original source code sets the subconfig, without apparent reason if test_internal(cell, luts, case, config, subconfig, luts.TEST13.get2(config,6)): cell.add_triangles2(luts.TILING13_5_1, config, 0, 6) else: cell.add_triangles2(luts.TILING13_5_2, config, 0, 10) elif subconfig==24: subconfig = 1 if test_internal(cell, luts, case, config, subconfig, luts.TEST13.get2(config,6)): cell.add_triangles2(luts.TILING13_5_1, config, 1, 6) else: cell.add_triangles2(luts.TILING13_5_2, config, 1, 10) elif subconfig==25: subconfig = 2 ; if test_internal(cell, luts, case, config, subconfig, luts.TEST13.get2(config,6)): cell.add_triangles2(luts.TILING13_5_1, config, 2, 6) else: cell.add_triangles2(luts.TILING13_5_2, config, 2, 10) elif subconfig==26: subconfig = 3 ; if test_internal(cell, luts, case, config, subconfig, luts.TEST13.get2(config,6)): cell.add_triangles2(luts.TILING13_5_1, config, 3, 6) else: cell.add_triangles2(luts.TILING13_5_2, config, 3, 10) # elif subconfig==27: #cell.calculate_center_vertex() # v12 needed cell.add_triangles2(luts.TILING13_3_, config, 0, 10) elif subconfig==28: #cell.calculate_center_vertex() # v12 needed cell.add_triangles2(luts.TILING13_3_, config, 1, 10) elif subconfig==29: #cell.calculate_center_vertex() # v12 needed cell.add_triangles2(luts.TILING13_3_, config, 2, 10) elif subconfig==30: #cell.calculate_center_vertex() # v12 needed cell.add_triangles2(luts.TILING13_3_, config, 3, 10) elif subconfig==31: #cell.calculate_center_vertex() # v12 needed cell.add_triangles2(luts.TILING13_3_, config, 4, 10) elif subconfig==32: #cell.calculate_center_vertex() # v12 needed cell.add_triangles2(luts.TILING13_3_, config, 5, 10) elif subconfig==33: #cell.calculate_center_vertex() # v12 needed cell.add_triangles2(luts.TILING13_3_, config,6, 10) elif subconfig==34: #cell.calculate_center_vertex() # v12 needed cell.add_triangles2(luts.TILING13_3_, config, 7, 10) elif subconfig==35: #cell.calculate_center_vertex() # v12 needed cell.add_triangles2(luts.TILING13_3_, config, 8, 10) elif subconfig==36: #cell.calculate_center_vertex() # v12 needed cell.add_triangles2(luts.TILING13_3_, config, 9, 10) elif subconfig==37: #cell.calculate_center_vertex() # v12 needed cell.add_triangles2(luts.TILING13_3_, config, 10, 10) elif subconfig==38: #cell.calculate_center_vertex() # v12 needed cell.add_triangles2(luts.TILING13_3_, config, 11, 10) # elif subconfig==39: cell.add_triangles2(luts.TILING13_2_, config, 0, 6) elif subconfig==40: cell.add_triangles2(luts.TILING13_2_, config, 1, 6) elif subconfig==41: cell.add_triangles2(luts.TILING13_2_, config, 2, 6) elif subconfig==42: cell.add_triangles2(luts.TILING13_2_, config, 3, 6) elif subconfig==43: cell.add_triangles2(luts.TILING13_2_, config, 4, 6) elif subconfig==44: cell.add_triangles2(luts.TILING13_2_, config, 5, 6) # elif subconfig==45: cell.add_triangles(luts.TILING13_1_, config, 4) # else: print("Marching Cubes: Impossible case 13?" ) elif case == 14 : cell.add_triangles(luts.TILING14, config, 4) cdef int test_face(Cell cell, int face): """ Return True of the face contains part of the surface. """ # Get face absolute value cdef int absFace = face if face < 0: absFace *= -1 # Get values of corners A B C D cdef double A, B, C, D if absFace == 1: A, B, C, D = cell.v0, cell.v4, cell.v5, cell.v1 elif absFace == 2: A, B, C, D = cell.v1, cell.v5, cell.v6, cell.v2 elif absFace == 3: A, B, C, D = cell.v2, cell.v6, cell.v7, cell.v3 elif absFace == 4: A, B, C, D = cell.v3, cell.v7, cell.v4, cell.v0 elif absFace == 5: A, B, C, D = cell.v0, cell.v3, cell.v2, cell.v1 elif absFace == 6: A, B, C, D = cell.v4, cell.v7, cell.v6, cell.v5 # Return sign cdef double AC_BD = A*C - B*D if AC_BD > - FLT_EPSILON and AC_BD < FLT_EPSILON: return face >= 0 else: return face * A * AC_BD >= 0; # face and A invert signs cdef int test_internal(Cell cell, LutProvider luts, int case, int config, int subconfig, int s): """ Return True of the face contains part of the surface. """ # Typedefs cdef double t, At, Bt, Ct, Dt, a, b cdef int test = 0 cdef int edge = -1 # reference edge of the triangulation # Calculate At Bt Ct Dt a b # Select case 4, 10, 7, 12, 13 At, Bt, Ct, Dt = 0.0, 0.0, 0.0, 0.0 if case==4 or case==10: a = ( cell.v4 - cell.v0 ) * ( cell.v6 - cell.v2 ) - ( cell.v7 - cell.v3 ) * ( cell.v5 - cell.v1 ) b = cell.v2 * ( cell.v4 - cell.v0 ) + cell.v0 * ( cell.v6 - cell.v2 ) - cell.v1 * ( cell.v7 - cell.v3 ) - cell.v3 * ( cell.v5 - cell.v1 ) t = - b / (2*a + FLT_EPSILON) if t<0 or t>1: return s>0 ; At = cell.v0 + ( cell.v4 - cell.v0 ) * t Bt = cell.v3 + ( cell.v7 - cell.v3 ) * t Ct = cell.v2 + ( cell.v6 - cell.v2 ) * t Dt = cell.v1 + ( cell.v5 - cell.v1 ) * t elif case==6 or case==7 or case==12 or case==13: # Define edge if case == 6: edge = luts.TEST6.get2(config, 2) elif case == 7: edge = luts.TEST7.get2(config, 4) elif case == 12: edge = luts.TEST12.get2(config, 3) elif case == 13: edge = luts.TILING13_5_1.get3(config, subconfig, 0) if edge==0: t = cell.v0 / ( cell.v0 - cell.v1 + FLT_EPSILON ) At = 0 Bt = cell.v3 + ( cell.v2 - cell.v3 ) * t Ct = cell.v7 + ( cell.v6 - cell.v7 ) * t Dt = cell.v4 + ( cell.v5 - cell.v4 ) * t elif edge==1: t = cell.v1 / ( cell.v1 - cell.v2 + FLT_EPSILON ) At = 0 Bt = cell.v0 + ( cell.v3 - cell.v0 ) * t Ct = cell.v4 + ( cell.v7 - cell.v4 ) * t Dt = cell.v5 + ( cell.v6 - cell.v5 ) * t elif edge==2: t = cell.v2 / ( cell.v2 - cell.v3 + FLT_EPSILON ) At = 0 Bt = cell.v1 + ( cell.v0 - cell.v1 ) * t Ct = cell.v5 + ( cell.v4 - cell.v5 ) * t Dt = cell.v6 + ( cell.v7 - cell.v6 ) * t elif edge==3: t = cell.v3 / ( cell.v3 - cell.v0 + FLT_EPSILON ) At = 0 Bt = cell.v2 + ( cell.v1 - cell.v2 ) * t Ct = cell.v6 + ( cell.v5 - cell.v6 ) * t Dt = cell.v7 + ( cell.v4 - cell.v7 ) * t elif edge==4: t = cell.v4 / ( cell.v4 - cell.v5 + FLT_EPSILON ) At = 0 Bt = cell.v7 + ( cell.v6 - cell.v7 ) * t Ct = cell.v3 + ( cell.v2 - cell.v3 ) * t Dt = cell.v0 + ( cell.v1 - cell.v0 ) * t elif edge==5: t = cell.v5 / ( cell.v5 - cell.v6 + FLT_EPSILON ) At = 0 Bt = cell.v4 + ( cell.v7 - cell.v4 ) * t Ct = cell.v0 + ( cell.v3 - cell.v0 ) * t Dt = cell.v1 + ( cell.v2 - cell.v1 ) * t elif edge==6: t = cell.v6 / ( cell.v6 - cell.v7 + FLT_EPSILON ) At = 0 Bt = cell.v5 + ( cell.v4 - cell.v5 ) * t Ct = cell.v1 + ( cell.v0 - cell.v1 ) * t Dt = cell.v2 + ( cell.v3 - cell.v2 ) * t elif edge==7: t = cell.v7 / ( cell.v7 - cell.v4 + FLT_EPSILON ) At = 0 Bt = cell.v6 + ( cell.v5 - cell.v6 ) * t Ct = cell.v2 + ( cell.v1 - cell.v2 ) * t Dt = cell.v3 + ( cell.v0 - cell.v3 ) * t elif edge==8: t = cell.v0 / ( cell.v0 - cell.v4 + FLT_EPSILON ) At = 0 Bt = cell.v3 + ( cell.v7 - cell.v3 ) * t Ct = cell.v2 + ( cell.v6 - cell.v2 ) * t Dt = cell.v1 + ( cell.v5 - cell.v1 ) * t elif edge==9: t = cell.v1 / ( cell.v1 - cell.v5 + FLT_EPSILON ) At = 0 Bt = cell.v0 + ( cell.v4 - cell.v0 ) * t Ct = cell.v3 + ( cell.v7 - cell.v3 ) * t Dt = cell.v2 + ( cell.v6 - cell.v2 ) * t elif edge==10: t = cell.v2 / ( cell.v2 - cell.v6 + FLT_EPSILON ) At = 0 Bt = cell.v1 + ( cell.v5 - cell.v1 ) * t Ct = cell.v0 + ( cell.v4 - cell.v0 ) * t Dt = cell.v3 + ( cell.v7 - cell.v3 ) * t elif edge==11: t = cell.v3 / ( cell.v3 - cell.v7 + FLT_EPSILON ) At = 0 Bt = cell.v2 + ( cell.v6 - cell.v2 ) * t Ct = cell.v1 + ( cell.v5 - cell.v1 ) * t Dt = cell.v0 + ( cell.v4 - cell.v0 ) * t else: print( "Invalid edge %i." % edge ) else: print( "Invalid ambiguous case %i." % case ) # Process results if At >= 0: test += 1 if Bt >= 0: test += 2 if Ct >= 0: test += 4 if Dt >= 0: test += 8 # Determine what to return if test==0: return s>0 elif test==1: return s>0 elif test==2: return s>0 elif test==3: return s>0 elif test==4: return s>0 elif test==5: if At * Ct - Bt * Dt < FLT_EPSILON: return s>0 elif test==6: return s>0 elif test==7: return s<0 elif test==8: return s>0 elif test==9: return s>0 elif test==10: if At * Ct - Bt * Dt >= FLT_EPSILON: return s>0 elif test==11: return s<0 elif test==12: return s>0 elif test==13: return s<0 elif test==14: return s<0 elif test==15: return s<0 else: return s<0 scikit-image-0.13.1/skimage/measure/_marching_cubes_lewiner_luts.py000066400000000000000000000646541316256236300255270ustar00rootroot00000000000000# -*- coding: utf-8 -*- # This file was auto-generated from LookUpTable.h by createluts.py. # The luts are Copyright (C) 2002 by Thomas Lewiner #static const char casesClassic[256][16] CASESCLASSIC = (256, 16), """ /////////////////////wAIA/////////////////8AAQn/////////////////AQgDCQgB//// /////////wECCv////////////////8ACAMBAgr/////////////CQIKAAIJ/////////////wII AwIKCAoJCP////////8DCwL/////////////////AAsCCAsA/////////////wEJAAIDC/////// //////8BCwIBCQsJCAv/////////AwoBCwoD/////////////wAKAQAICggLCv////////8DCQAD CwkLCgn/////////CQgKCggL/////////////wQHCP////////////////8EAwAHAwT///////// ////AAEJCAQH/////////////wQBCQQHAQcDAf////////8BAgoIBAf/////////////AwQHAwAE AQIK/////////wkCCgkAAggEB/////////8CCgkCCQcCBwMHCQT/////CAQHAwsC//////////// 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#static const char cases[256][2] CASES = (256, 2), """ AP8BAAEBAgABAgMAAgMFAAEDAgEDAwUBAgUFBAUJCAABBAICAwQFAgQCBgIGCQsAAwgFBQcDCQEG EA4DDAwFGAEFAwECBAUDAwYHAAUKCQAEAwYEBgsOAQYRDAQLBgUZAggFBwUMCAEGEgwFDgcFHAYV CwQMDwUeCgUGIAYnAgwBBgQAAwUGAAIGBgMFCw4AAwkGBQcEDAEFDgsDCQQFGgMKBgYHBQwCBhMK AQwNBhgHBwwJDQEHCQwUBiEHDQMMAgoGBwUNCwIFEAwHCAMFHQYWCgIMEQYbDgkGIgUnAg4FFA4F CQUFIAsKBiMFKQIQDBcGJQcOAxAGLgQGAxUBCAEHAwIEAQYBAwcHAQYKDAACBwUGBgwLAQUPCQIO BgUbAgkFCAYNDgIGFAwGCgMGGQUSCAIMEAUfCwkFIgYoAg0DCwcCBg4MAwcGDQAMDgcIBhcMCgoE BhwMFQcKBikDDQUVCQMLCAUhDBYHCwYqAw4OCwUkBiwCEQYvAxIEBwEJAgsGCAYPCgAFEQwICwcG GgUTDgQMEgYdCAQFIwUoAg8FFgsFDBMGHg4KBiQGKwQECQcFJQcPAxEFLAITAxYBCgUXDAsOCAYf CQYHDAUqAw8LCwYmBi0EBQUtAxMCFQELCAUFJgUrAhIFLgMUAhYBDAUvAhQDFwENAhcBDgEPAP8= """ #static const char tiling1[16][3] TILING1 = (16, 3), """ AAgDAAEJAQIKAwsCBAcICQUECgYFBwYLBwsGCgUGCQQFBAgHAwILAQoCAAkBAAMI """ #static const char tiling2[24][6] TILING2 = (24, 6), """ AQgDCQgBAAsCCAsABAMABwMECQIKAAIJAAUEAQUAAwoBCwoDAQYFAgYBBwIDBgIHCQcIBQcJBggE CwgGCgQJBgQKCwUKBwULCwoFBwsFCgkEBgoEBgQICwYICQgHBQkHBwMCBgcCAQUGAgEGAwEKCwMK AAQFAQAFCQoCAAkCBAADBwQDAAILCAALAQMICQEI """ #static const char tiling3_1[24][6] TILING3_1 = (24, 6), """ AAgDAQIKCQUEAAgDAwAICwcGAQkAAgMLAAEJCAQHCQABBQoGAQIKCQUECgECBgsHCAQHAwsCAgML CgYFBQoGBAcIBAkFBwYLBQkECwYHBgoFCAcECwMCBQYKBwQIAgsDAgEKBwsGCgIBBAUJAQAJBgoF CQEABwQIAAkBCwMCCAADBgcLBAUJAwgAAwgACgIB """ #static const char tiling3_2[24][12] TILING3_2 = (24, 12), """ CgMCCggDCgEACAoAAwQIAwUEAwAJBQMJBggHBgAIBgsDAAYDCwADCwkACwIBCQsBBwkEBwEJBwgA AQcABgEKBgABCQAGCQYFBAoFBAIKBAkBAgQBBwILBwECBwYKAQcKAgcLAgQHAgMIBAIIBQsGBQML BQoCAwUCCAYHCAoGCAQFCggFCwUGCwkFCwcECQsEBgULBQkLBAcLBAsJBwYIBgoIBQQIBQgKBgsF CwMFAgoFAgUDCwcCBwQCCAMCCAIECwIHAgEHCgYHCgcBBQoECgIEAQkEAQQCCgEGAQAGBgAJBQYJ BAkHCQEHAAgHAAcBAwALAAkLAQILAQsJBwgGCAAGAwsGAwYACAQDBAUDCQADCQMFAgMKAwgKAAEK AAoI """ #static const char tiling4_1[8][6] TILING4_1 = (8, 6), """ AAgDBQoGAAEJCwcGAQIKCAQHCQUEAgMLBAUJCwMCCgIBBwQICQEABgcLAwgABgoF """ #static const char tiling4_2[8][18] TILING4_2 = (8, 18), """ CAUABQgGAwYIBgMKAAoDCgAFCQYBBgkHAAcJBwALAQsACwEGCgcCBwoEAQQKBAEIAggBCAIHCwQD BAsFAgULBQIJAwkCCQMEAwQLBQsECwUCCQIFAgkDBAMJAgcKBAoHCgQBCAEEAQgCBwIIAQYJBwkG CQcACwAHAAsBBgELAAUIBggFCAYDCgMGAwoABQAK """ #static const char tiling5[48][9] TILING5 = (48, 9), """ AggDAgoICgkIAQsCAQkLCQgLBAEJBAcBBwMBCAUECAMFAwEFAAoBAAgKCAsKCwQHCwIEAgAEBwAI BwYABgIACQMACQUDBQcDAwYLAwAGAAQGAwkAAwsJCwoJBQIKBQQCBAACCQYFCQAGAAIGAAcIAAEH AQUHCgABCgYABgQABgMLBgUDBQEDCgcGCgEHAQMHAQQJAQIEAgYECwECCwcBBwUBCAIDCAQCBAYC AgUKAgMFAwcFBwoGBwgKCAkKBgkFBgsJCwgJBQgEBQoICgsIBAsHBAkLCQoLBAcLBAsJCQsKBQQI BQgKCggLBgUJBgkLCwkIBwYKBwoICAoJAgoFAgUDAwUHCAMCCAIEBAIGCwIBCwEHBwEFAQkEAQQC AgQGCgYHCgcBAQcDBgsDBgMFBQMBCgEACgAGBgAEAAgHAAcBAQcFCQUGCQYAAAYCBQoCBQIEBAIA AwAJAwkLCwkKAwsGAwYAAAYECQADCQMFBQMHBwgABwAGBgACCwcECwQCAgQAAAEKAAoICAoLCAQF CAUDAwUBBAkBBAEHBwEDAQILAQsJCQsIAgMIAggKCggJ """ #static const char tiling6_1_1[48][9] TILING6_1_1 = (48, 9), """ BgUKAwEICQgBCwcGCQMBAwkIAQIKBwAEAAcDAwAIBQIGAgUBBQQJAgALCAsACgYFCAIAAggLCgYF AAQDBwMEAwAIBgQKCQoECAMACgcFBwoLCAQHCgACAAoJBwYLAAIJCgkCAgMLBAEFAQQAAAEJBgMH AwYCCQABCwQGBAsICwcGAQUABAAFAAEJBwULCgsFBAcIAQMKCwoDCQUECwEDAQsKCgECCAUHBQgJ CAQHAgYBBQEGAQIKBAYICwgGAgMLBQcJCAkHCwIDCQYEBgkKCQUEAwcCBgIHBAUJAgcDBwIGAwIL BAYJCgkGCwMCCQcFBwkICgIBCAYEBggLBwQIAQYCBgEFAgEKBwUICQgFBAUJAwELCgsBCAcECgMB AwoLCQEACwUHBQsKBgcLAAUBBQAEAQAJBgQLCAsECQEABwMGAgYDCwMCBQEEAAQBCwYHCQIAAgkK BwQIAgAKCQoAAAMIBQcKCwoHCAADCgQGBAoJBQYKAwQABAMHBQYKAAIICwgCCQQFCwACAAsICAAD BgIFAQUCCgIBBAAHAwcABgcLAQMJCAkDCgUGCAEDAQgJ """ #static const char tiling6_1_2[48][27] TILING6_1_2 = (48, 27), """ AQwDDAoDBgMKAwYIBQgGCAUMDAkIAQkMDAUKAQwDAQsMCwEGCQYBBgkHDAcJCQgMDAgDCwcMBAwA BAEMAQQKBwoECgcCDAIHBwMMDAMAAQIMBgwCBgMMAwYIBQgGCAUADAAFBQEMDAECAwAMAAwCDAkC BQIJAgULBAsFCwQMDAgLAAgMDAQJAAwCAAoMCgAFCAUABQgGDAYICAsMDAsCCgYMBAwADAUACgAF AAoDBgMKAwYMDAcDBAcMDAYFBAwGDAgGAwYIBgMKAAoDCgAMDAkKBAkMDAAIBQwHBQgMCAUACgAF AAoDDAMKCgsMDAsHCAMMAgwAAggMCAIHCgcCBwoEDAQKCgkMDAkACAQMAgwADAsABwALAAcJBgkH CQYMDAoJAgoMDAYLBQwBBQIMAgULBAsFCwQDDAMEBAAMDAABAgMMBwwDBwAMAAcJBgkHCQYBDAEG BgIMDAIDAAEMBgwEBgkMCQYBCwEGAQsADAALCwgMDAgECQAMBQwBDAYBCwEGAQsABwALAAcMDAQA BQQMDAcGBQwHDAkHAAcJBwALAQsACwEMDAoLBQoMDAEJAwwBDAgBBAEIAQQKBwoECgcMDAsKAwsM DAcIAwwBAwkMCQMECwQDBAsFDAULCwoMDAoBCQUMBwwFBwoMCgcCCAIHAggBDAEICAkMDAkFCgEM BgwCDAcCCAIHAggBBAEIAQQMDAUBBgUMDAQHBgwEDAoEAQQKBAEIAggBCAIMDAsIBgsMDAIKBwwF DAsFAgULBQIJAwkCCQMMDAgJBwgMDAMLBAwGBAsMCwQDCQMEAwkCDAIJCQoMDAoGCwIMBwwDDAQD CQMEAwkCBQIJAgUMDAYCBwYMDAUEAwwHAwQMBAMJAgkDCQIFDAUCAgYMDAYHBAUMBgwEDAsEAwQL BAMJAgkDCQIMDAoJBgoMDAILBQwHBQsMCwUCCQIFAgkDDAMJCQgMDAgHCwMMBAwGBAoMCgQBCAEE AQgCDAIICAsMDAsGCgIMAgwGAgcMBwIIAQgCCAEEDAQBAQUMDAUGBwQMBQwHDAoHAgcKBwIIAQgC CAEMDAkIBQkMDAEKAQwDDAkDBAMJAwQLBQsECwUMDAoLAQoMDAUJAQwDAQgMCAEECgQBBAoHDAcK CgsMDAsDCAcMBwwFBwkMCQcACwAHAAsBDAELCwoMDAoFCQEMAQwFAQYMBgELAAsBCwAHDAcAAAQM DAQFBgcMBAwGDAkGAQYJBgELAAsBCwAMDAgLBAgMDAAJAwwHDAAHCQcABwkGAQYJBgEMDAIGAwIM DAEAAQwFDAIFCwUCBQsEAwQLBAMMDAAEAQAMDAMCAAwCAAsMCwAHCQcABwkGDAYJCQoMDAoCCwYM AAwCDAgCBwIIAgcKBAoHCgQMDAkKAAkMDAQIBwwFDAgFAAUIBQAKAwoACgMMDAsKBwsMDAMIBgwE BggMCAYDCgMGAwoADAAKCgkMDAkECAAMAAwEAAUMBQAKAwoACgMGDAYDAwcMDAcEBQYMAgwADAoA BQAKAAUIBggFCAYMDAsIAgsMDAYKAgwAAgkMCQIFCwUCBQsEDAQLCwgMDAgACQQMAgwGDAMGCAYD BggFAAUIBQAMDAEFAgEMDAADAAwEDAEECgQBBAoHAgcKBwIMDAMHAAMMDAIBAwwBDAsBBgELAQYJ BwkGCQcMDAgJAwgMDAcLAwwBAwoMCgMGCAYDBggFDAUICAkMDAkBCgUM """ #static const char tiling6_2[48][15] TILING6_2 = (48, 15), """ AQoDBgMKAwYIBQgGCAUJAQsDCwEGCQYBBgkHCAcJBAEAAQQKBwoECgcCAwIHBgMCAwYIBQgGCAUA AQAFAAkCBQIJAgULBAsFCwQIAAoCCgAFCAUABQgGCwYIBAUACgAFAAoDBgMKAwYHBAgGAwYIBgMK AAoDCgAJBQgHCAUACgAFAAoDCwMKAggACAIHCgcCBwoECQQKAgsABwALAAcJBgkHCQYKBQIBAgUL BAsFCwQDAAMEBwADAAcJBgkHCQYBAgEGBgkECQYBCwEGAQsACAALBQYBCwEGAQsABwALAAcEBQkH AAcJBwALAQsACwEKAwgBBAEIAQQKBwoECgcLAwkBCQMECwQDBAsFCgULBwoFCgcCCAIHAggBCQEI BgcCCAIHAggBBAEIAQQFBgoEAQQKBAEIAggBCAILBwsFAgULBQIJAwkCCQMIBAsGCwQDCQMEAwkC CgIJBwQDCQMEAwkCBQIJAgUGAwQHBAMJAgkDCQIFBgUCBgsEAwQLBAMJAgkDCQIKBQsHCwUCCQIF AgkDCAMJBAoGCgQBCAEEAQgCCwIIAgcGBwIIAQgCCAEEBQQBBQoHAgcKBwIIAQgCCAEJAQkDBAMJ AwQLBQsECwUKAQgDCAEECgQBBAoHCwcKBwkFCQcACwAHAAsBCgELAQYFBgELAAsBCwAHBAcABAkG AQYJBgELAAsBCwAIAwAHCQcABwkGAQYJBgECAQIFCwUCBQsEAwQLBAMAAAsCCwAHCQcABwkGCgYJ AAgCBwIIAgcKBAoHCgQJBwgFAAUIBQAKAwoACgMLBggECAYDCgMGAwoACQAKAAUEBQAKAwoACgMG BwYDAgoABQAKAAUIBggFCAYLAgkACQIFCwUCBQsECAQLAgMGCAYDBggFAAUIBQABAAEECgQBBAoH AgcKBwIDAwsBBgELAQYJBwkGCQcIAwoBCgMGCAYDBggFCQUI """ #static const char tiling7_1[16][9] TILING7_1 = (16, 9), """ CQUECgECCAMACwcGCAMACgECAwAIBQQJBwYLCAQHCQABCwIDCgYFCwIDCQABAAEJBgUKBAcIAQIK BwYLBQQJAgMLBAcIBgUKCwMCCAcECgUGCgIBCwYHCQQFCQEACgUGCAcEBQYKAwILAQAJBwQIAQAJ AwILCAADCQQFCwYHBgcLAAMIAgEKBAUJAgEKAAMI """ #static const char tiling7_2[16][3][15] TILING7_2 = (16, 3, 15), """ AQIKAwQIBAMFAAUDBQAJAwAICQEEAgQBBAIFCgUCCQUEAAoBCgAICggCAwIIAwAIAQYKBgEHAgcB BwILAQIKCwMGAAYDBgAHCAcACwcGAggDCAIKCAoAAQAKCQUECwMGAAYDBgAHCAcACwcGAwQIBAMF AAUDBQAJAwAIBAkHCwcJBQsJCwUGAAEJAgcLBwIEAwQCBAMIAgMLCAAHAQcABwEECQQBCAQHAwkA CQMLCQsBAgELAgMLAAUJBQAGAQYABgEKAAEJCgIFAwUCBQMGCwYDBgUKAQsCCwEJCwkDAAMJBgUK CAAHAQcABwEECQQBCAQHAAUJBQAGAQYABgEKAAEJBQoECAQKBggKCAYHCwcGCQEEAgQBBAIFCgUC CQUEAQYKBgEHAgcBBwILAQIKBgsFCQULBwkLCQcECAQHCgIFAwUCBQMGCwYDBgUKAgcLBwIEAwQC BAMIAgMLBwgGCgYIBAoICgQFBwQIBQIKAgUDBgMFAwYLCgUGCwcCBAIHAgQDCAMECwMCBggHCAYK CAoEBQQKBgcLBAEJAQQCBQIEAgUKBAUJCgYBBwEGAQcCCwIHCgIBBQsGCwUJCwkHBAcJCgUGBwAI AAcBBAEHAQQJBwQICQUABgAFAAYBCgEGCQEABAoFCgQICggGBwYICwMCCQUABgAFAAYBCgEGCQEA BQIKAgUDBgMFAwYLCgUGAgsBCQELAwkLCQMACQEACwcCBAIHAgQDCAMECwMCBwAIAAcBBAEHAQQJ BwQIAAkDCwMJAQsJCwECBAUJBgMLAwYABwAGAAcIBgcLCAQDBQMEAwUACQAFCAADBwkECQcLCQsF BgULCAADCgYBBwEGAQcCCwIHCgIBBgMLAwYABwAGAAcIBgcLAwgCCgIIAAoICgABCgIBCAQDBQME AwUACQAFCAADBAEJAQQCBQIEAgUKBAUJAQoACAAKAggKCAID """ #static const char tiling7_3[16][3][27] TILING7_3 = (16, 3, 27), """ DAIKDAoFDAUEDAQIDAgDDAMADAAJDAkBDAECDAUEDAQIDAgDDAMCDAIKDAoBDAEADAAJDAkFBQQM CgUMAgoMAwIMCAMMAAgMAQAMCQEMBAkMDAAIDAgHDAcGDAYKDAoBDAECDAILDAsDDAMADAcGDAYK DAoBDAEADAAIDAgDDAMCDAILDAsHBwYMCAcMAAgMAQAMCgEMAgoMAwIMCwMMBgsMCQUMAAkMAwAM CwMMBgsMBwYMCAcMBAgMBQQMAwAMCwMMBgsMBQYMCQUMBAkMBwQMCAcMAAgMDAMADAAJDAkFDAUG DAYLDAsHDAcEDAQIDAgDDAEJDAkEDAQHDAcLDAsCDAIDDAMIDAgADAABDAQHDAcLDAsCDAIBDAEJ DAkADAADDAMIDAgEBAcMCQQMAQkMAgEMCwIMAwsMAAMMCAAMBwgMDAMLDAsGDAYFDAUJDAkADAAB DAEKDAoCDAIDDAYFDAUJDAkADAADDAMLDAsCDAIBDAEKDAoGBgUMCwYMAwsMAAMMCQAMAQkMAgEM CgIMBQoMCgYMAQoMAAEMCAAMBwgMBAcMCQQMBQkMBgUMAAEMCAAMBwgMBgcMCgYMBQoMBAUMCQQM AQkMDAABDAEKDAoGDAYHDAcIDAgEDAQFDAUJDAkACwcMAgsMAQIMCQEMBAkMBQQMCgUMBgoMBwYM AQIMCQEMBAkMBwQMCwcMBgsMBQYMCgUMAgoMDAECDAILDAsHDAcEDAQJDAkFDAUGDAYKDAoBCAQM AwgMAgMMCgIMBQoMBgUMCwYMBwsMBAcMAgMMCgIMBQoMBAUMCAQMBwgMBgcMCwYMAwsMDAIDDAMI DAgEDAQFDAUKDAoGDAYHDAcLDAsCDAQIDAgDDAMCDAIKDAoFDAUGDAYLDAsHDAcEDAMCDAIKDAoF DAUEDAQIDAgHDAcGDAYLDAsDAwIMCAMMBAgMBQQMCgUMBgoMBwYMCwcMAgsMDAcLDAsCDAIBDAEJ DAkEDAQFDAUKDAoGDAYHDAIBDAEJDAkEDAQHDAcLDAsGDAYFDAUKDAoCAgEMCwIMBwsMBAcMCQQM BQkMBgUMCgYMAQoMDAYKDAoBDAEADAAIDAgHDAcEDAQJDAkFDAUGDAEADAAIDAgHDAcGDAYKDAoF DAUEDAQJDAkBAQAMCgEMBgoMBwYMCAcMBAgMBQQMCQUMAAkMCwMMBgsMBQYMCQUMAAkMAQAMCgEM AgoMAwIMBQYMCQUMAAkMAwAMCwMMAgsMAQIMCgEMBgoMDAUGDAYLDAsDDAMADAAJDAkBDAECDAIK DAoFCQEMBAkMBwQMCwcMAgsMAwIMCAMMAAgMAQAMBwQMCwcMAgsMAQIMCQEMAAkMAwAMCAMMBAgM DAcEDAQJDAkBDAECDAILDAsDDAMADAAIDAgHDAUJDAkADAADDAMLDAsGDAYHDAcIDAgEDAQFDAAD DAMLDAsGDAYFDAUJDAkEDAQHDAcIDAgAAAMMCQAMBQkMBgUMCwYMBwsMBAcMCAQMAwgMCAAMBwgM BgcMCgYMAQoMAgEMCwIMAwsMAAMMBgcMCgYMAQoMAAEMCAAMAwgMAgMMCwIMBwsMDAYHDAcIDAgA DAABDAEKDAoCDAIDDAMLDAsGCgIMBQoMBAUMCAQMAwgMAAMMCQAMAQkMAgEMBAUMCAQMAwgMAgMM CgIMAQoMAAEMCQAMBQkMDAQFDAUKDAoCDAIDDAMIDAgADAABDAEJDAkE """ #static const char tiling7_4_1[16][15] TILING7_4_1 = (16, 15), """ AwQIBAMKAgoDBAoFCQEAAQYKBgEIAAgBBggHCwMCCwMGCQYDBgkFAAkDBwQIAgcLBwIJAQkCBwkE CAADAAUJBQALAwsABQsGCgIBCAAHCgcABwoGAQoABAUJCQEECwQBBAsHAgsBBQYKCgIFCAUCBQgE AwgCBgcLBQIKAgUIBAgFAggDCwcGBAEJAQQLBwsEAQsCCgYFBwAIAAcKBgoHAAoBCQUECQUACwAF AAsDBgsFAQIKCwcCCQIHAgkBBAkHAwAIBgMLAwYJBQkGAwkACAQHCgYBCAEGAQgABwgGAgMLCAQD CgMEAwoCBQoEAAEJ """ #static const char tiling7_4_2[16][27] TILING7_4_2 = (16, 27), """ CQQIBAkFCgUJAQoJCgECAAIBAgADCAMACQgACwYKBgsHCAcLAwgLCAMAAgADAAIBCgECCwoCCwMI AAgDCAAJCAkEBQQJBAUHBgcFBwYLBwsICAcLBwgECQQIAAkICQABAwEAAQMCCwIDCAsDCgUJBQoG CwYKAgsKCwIDAQMCAwEACQABCgkBCAAJAQkACQEKCQoFBgUKBQYEBwQGBAcIBAgJCQEKAgoBCgIL CgsGBwYLBgcFBAUHBQQJBQkKCgILAwsCCwMICwgHBAcIBwQGBQYEBgUKBgoLCwIKAgsDCAMLBwgL CAcEBgQHBAYFCgUGCwoGCgEJAQoCCwIKBgsKCwYHBQcGBwUECQQFCgkFCQAIAAkBCgEJBQoJCgUG BAYFBgQHCAcECQgECQUKBgoFCgYLCgsCAwILAgMBAAEDAQAJAQkKCwcIBAgHCAQJCAkAAQAJAAED AgMBAwILAwsICAMLAwgACQAIBAkICQQFBwUEBQcGCwYHCAsHCgYLBwsGCwcICwgDAAMIAwACAQIA AgEKAgoLCAQJBQkECQUKCQoBAgEKAQIAAwACAAMIAAgJ """ #static const char tiling8[6][6] TILING8 = (6, 6), """ CQgKCggLAQUDAwUHAAQCBAYCAAIEBAIGAQMFAwcFCQoICgsI """ #static const char tiling9[8][12] TILING9 = (8, 12), """ AgoFAwIFAwUEAwQIBAcLCQQLCQsCCQIBCgcGAQcKAQgHAQAIAwYLAAYDAAUGAAkFAwsGAAMGAAYF AAUJCgYHAQoHAQcIAQgABAsHCQsECQILCQECAgUKAwUCAwQFAwgE """ #static const char tiling10_1_1[6][12] TILING10_1_1 = (6, 12), """ BQoHCwcKCAEJAQgDAQIFBgUCBAMAAwQHCwAIAAsCBAkGCgYJCQAKAgoABggECAYLBwIDAgcGAAEE BQQBBwkFCQcICgELAwsB """ #static const char tiling10_1_1_[6][12] TILING10_1_1_ = (6, 12), """ BQkHCAcJCwEKAQsDAwIHBgcCBAEAAQQFCgAJAAoCBAgGCwYICAALAgsABgkECQYKBQIBAgUGAAME BwQDBwoFCgcLCQEIAwgB """ #static const char tiling10_1_2[6][24] TILING10_1_2 = (6, 24), """ AwsHAwcICQgHBQkHCQUKCQoBAwEKCwMKBwYFBwUEAAQFAQAFAAECAAIDBwMCBgcCCwIKBgsKCwYE CwQIAAgECQAEAAkKAAoCCwIKCwoGBAYKCQQKBAkABAAICwgAAgsABwYFBAcFBwQABwADAgMAAQIA AgEFAgUGBwgDCwcDBwsKBwoFCQUKAQkKCQEDCQMI """ #static const char tiling10_2[6][24] TILING10_2 = (6, 24), """ DAUJDAkIDAgDDAMBDAEKDAoLDAsHDAcFDAEADAAEDAQHDAcDDAMCDAIGDAYFDAUBBAgMBgQMCgYM CQoMAAkMAgAMCwIMCAsMDAkEDAQGDAYLDAsIDAgADAACDAIKDAoJAAMMBAAMBQQMAQUMAgEMBgIM BwYMAwcMCgUMCwoMAwsMAQMMCQEMCAkMBwgMBQcM """ #static const char tiling10_2_[6][24] TILING10_2_ = (6, 24), """ CAcMCQgMAQkMAwEMCwMMCgsMBQoMBwUMBAUMAAQMAwAMBwMMBgcMAgYMAQIMBQEMDAsGDAYEDAQJ DAkKDAoCDAIADAAIDAgLBgoMBAYMCAQMCwgMAgsMAAIMCQAMCgkMDAcEDAQADAABDAEFDAUGDAYC DAIDDAMHDAcLDAsKDAoBDAEDDAMIDAgJDAkFDAUH """ #static const char tiling11[12][12] TILING11 = (12, 12), """ AgoJAgkHAgcDBwkEAQYCAQgGAQkICAcGCAMBCAEGCAYEBgEKAAgLAAsFAAUBBQsGCQUHCQcCCQIA AgcLBQAEBQsABQoLCwMABQQABQALBQsKCwADCQcFCQIHCQACAgsHAAsIAAULAAEFBQYLCAEDCAYB CAQGBgoBAQIGAQYIAQgJCAYHAgkKAgcJAgMHBwQJ """ #static const char tiling12_1_1[24][12] TILING12_1_1 = (24, 12), """ BwYLCgMCAwoICQgKBgUKCQIBAgkLCAsJCgYFBwkECQcBAwEHBwYLBAgFAwUIBQMBBQQJCAEAAQgK CwoIAQIKAAkDBQMJAwUHCgECAAsDCwAGBAYACAMAAgkBCQIEBgQCAwAIAgsBBwELAQcFBgUKBwsE AgQLBAIACQUEBggHCAYAAgAGCAMABwQLCQsECwkKBAcICwADAAsJCgkLBAcIBQkGAAYJBgACCwcG BAoFCgQCAAIECwIDAQgACAEHBQcBAAEJAwgCBAIIAgQGAgMLAQoABgAKAAYECQABAwoCCgMFBwUD CQABBAUICggFCAoLCAQHBQsGCwUDAQMFBQQJBgoHAQcKBwEDCgECBQYJCwkGCQsICwIDBgcKCAoH CggJ """ #static const char tiling12_1_1_[24][12] TILING12_1_1_ = (24, 12), """ AwILCgcGBwoICQgKAgEKCQYFBgkLCAsJCQQFBwoGCgcBAwEHBwQIBgsFAwULBQMBAQAJCAUEBQgK CwoIAQAJAgoDBQMKAwUHCwMCAAoBCgAGBAYACQEAAggDCAIEBgQCAwILAAgBBwEIAQcFBgcLBQoE AgQKBAIACAcEBgkFCQYAAgAGCAcEAwALCQsACwkKAAMICwQHBAsJCgkLBAUJBwgGAAYIBgACCgUG BAsHCwQCAAIECAADAQsCCwEHBQcBAAMIAQkCBAIJAgQGAgEKAwsABgALAAYECgIBAwkACQMFBwUD CQQFAAEICggBCAoLCwYHBQgECAUDAQMFBQYKBAkHAQcJBwEDCgUGAQIJCwkCCQsICwYHAgMKCAoD CggJ """ #static const char tiling12_1_2[24][24] TILING12_1_2 = (24, 24), """ BwMLAwcICQgHBgkHCQYKAgoGCwIGAgsDBgIKAgYLCAsGBQgGCAUJAQkFCgEFAQoCCgkFCQoBAwEK BgMKAwYHBAcGBQQGBAUJBwgLAwsICwMBCwEGBQYBBgUEBgQHCAcEBQEJAQUKCwoFBAsFCwQIAAgE CQAEAAkBAQkKBQoJCgUHCgcCAwIHAgMAAgABCQEACgsCCwoGBAYKAQQKBAEAAwABAgMBAwILCAkA CQgEBgQIAwYIBgMCAQIDAAEDAQAJAwsIBwgLCAcFCAUAAQAFAAECAAIDCwMCBgsKAgoLCgIACgAF BAUABQQHBQcGCwYHCQgECAkAAgAJBQIJAgUGBwYFBAcFBwQICAQACQAEAAkKAAoDCwMKAwsHAwcI BAgHBAAIAAQJCgkEBwoECgcLAwsHCAMHAwgABAkIAAgJCAACCAIHBgcCBwYFBwUECQQFCwoGCgsC AAILBwALAAcEBQQHBgUHBQYKCwgDCAsHBQcLAgULBQIBAAECAwACAAMIAAgJBAkICQQGCQYBAgEG AQIDAQMACAADAgoLBgsKCwYECwQDAAMEAwABAwECCgIBCQoBCgkFBwUJAAcJBwADAgMAAQIAAgEK CQUBCgEFAQoLAQsACAALAAgEAAQJBQkECAsHCwgDAQMIBAEIAQQFBgUEBwYEBgcLBQoJAQkKCQED CQMEBwQDBAcGBAYFCgUGCgYCCwIGAgsIAggBCQEIAQkFAQUKBgoFCwcDCAMHAwgJAwkCCgIJAgoG AgYLBwsG """ #static const char tiling12_2[24][24] TILING12_2 = (24, 24), """ CQgMCgkMAgoMAwIMCwMMBgsMBwYMCAcMCAsMCQgMAQkMAgEMCgIMBQoMBgUMCwYMAwEMBwMMBAcM CQQMBQkMBgUMCgYMAQoMDAMBDAEFDAUGDAYLDAsHDAcEDAQIDAgDCwoMCAsMAAgMAQAMCQEMBAkM BQQMCgUMDAUHDAcDDAMCDAIKDAoBDAEADAAJDAkFBAYMAAQMAQAMCgEMAgoMAwIMCwMMBgsMBgQM AgYMAwIMCAMMAAgMAQAMCQEMBAkMDAcFDAUBDAEADAAIDAgDDAMCDAILDAsHDAIADAAEDAQFDAUK DAoGDAYHDAcLDAsCAgAMBgIMBwYMCAcMBAgMBQQMCQUMAAkMDAkKDAoLDAsHDAcEDAQIDAgDDAMA DAAJCgkMCwoMBwsMBAcMCAQMAwgMAAMMCQAMDAACDAIGDAYHDAcIDAgEDAQFDAUJDAkAAAIMBAAM BQQMCgUMBgoMBwYMCwcMAgsMBQcMAQUMAAEMCAAMAwgMAgMMCwIMBwsMDAQGDAYCDAIDDAMIDAgA DAABDAEJDAkEDAYEDAQADAABDAEKDAoCDAIDDAMLDAsGBwUMAwcMAgMMCgIMAQoMAAEMCQAMBQkM DAoLDAsIDAgADAABDAEJDAkEDAQFDAUKAQMMBQEMBgUMCwYMBwsMBAcMCAQMAwgMDAEDDAMHDAcE DAQJDAkFDAUGDAYKDAoBDAsIDAgJDAkBDAECDAIKDAoFDAUGDAYLDAgJDAkKDAoCDAIDDAMLDAsG DAYHDAcI """ #static const char tiling12_2_[24][24] TILING12_2_ = (24, 24), """ DAILDAsHDAcGDAYKDAoJDAkIDAgDDAMCDAEKDAoGDAYFDAUJDAkIDAgLDAsCDAIBDAQFDAUKDAoG DAYHDAcDDAMBDAEJDAkEBwYMCAcMBAgMBQQMAQUMAwEMCwMMBgsMDAAJDAkFDAUEDAQIDAgLDAsK DAoBDAEAAQIMCQEMAAkMAwAMBwMMBQcMCgUMAgoMDAECDAILDAsDDAMADAAEDAQGDAYKDAoBDAMA DAAJDAkBDAECDAIGDAYEDAQIDAgDAwAMCwMMAgsMAQIMBQEMBwUMCAcMAAgMBgUMCwYMBwsMBAcM AAQMAgAMCgIMBQoMDAcEDAQJDAkFDAUGDAYCDAIADAAIDAgHCAcMAAgMAwAMCwMMCgsMCQoMBAkM BwQMDAcIDAgADAADDAMLDAsKDAoJDAkEDAQHBAcMCQQMBQkMBgUMAgYMAAIMCAAMBwgMDAUGDAYL DAsHDAcEDAQADAACDAIKDAoFDAADDAMLDAsCDAIBDAEFDAUHDAcIDAgAAAMMCQAMAQkMAgEMBgIM BAYMCAQMAwgMAgEMCwIMAwsMAAMMBAAMBgQMCgYMAQoMDAIBDAEJDAkADAADDAMHDAcFDAUKDAoC CQAMBQkMBAUMCAQMCwgMCgsMAQoMAAEMDAYHDAcIDAgEDAQFDAUBDAEDDAMLDAsGBQQMCgUMBgoM BwYMAwcMAQMMCQEMBAkMCgEMBgoMBQYMCQUMCAkMCwgMAgsMAQIMCwIMBwsMBgcMCgYMCQoMCAkM AwgMAgMM """ #static const char tiling13_1[2][12] TILING13_1 = (2, 12), """ CwcGAQIKCAMACQUECAQHAgMLCQABCgYF """ #static const char tiling13_1_[2][12] TILING13_1_ = (2, 12), """ BwQICwMCAQAJBQYKBgcLCgIBAAMIBAUJ """ #static const char tiling13_2[2][6][18] TILING13_2 = (2, 6, 18), """ AQIKCwcGAwQIBAMFAAUDBQAJCAMACwcGCQEEAgQBBAIFCgUCCQUECAMAAQYKBgEHAgcBBwILCQUE AQIKCwMGAAYDBgAHCAcACQUECwcGAAoBCgAICggCAwIIAQIKAwAIBAkHCwcJBQsJCwUGAgMLCAQH AAUJBQAGAQYABgEKCQABCAQHCgIFAwUCBQMGCwYDBgUKCQABAgcLBwIEAwQCBAMIBgUKAgMLCAAH AQcABwEECQQBBgUKCAQHAQsCCwEJCwkDAAMJAgMLAAEJBQoECAQKBggKCAYH """ #static const char tiling13_2_[2][6][18] TILING13_2_ = (2, 6, 18), """ CgUGCwMCBwAIAAcBBAEHAQQJCwMCBwQICQUABgAFAAYBCgEGAQAJBwQIBQIKAgUDBgMFAwYLCgUG AQAJCwcCBAIHAgQDCAMECgUGBwQIAgsBCQELAwkLCQMACwMCCQEABAoFCgQICggGBwYIBgcLCAAD BAEJAQQCBQIEAgUKCAADBAUJCgYBBwEGAQcCCwIHAgEKBAUJBgMLAwYABwAGAAcIBgcLAgEKCAQD BQMEAwUACQAFBgcLBAUJAwgCCgIIAAoICgABCAADCgIBBQsGCwUJCwkHBAcJ """ #static const char tiling13_3[2][12][30] TILING13_3 = (2, 12, 30), """ CwcGDAIKDAoFDAUEDAQIDAgDDAMADAAJDAkBDAECAQIKCQUMAAkMAwAMCwMMBgsMBwYMCAcMBAgM BQQMCwcGDAUEDAQIDAgDDAMCDAIKDAoBDAEADAAJDAkFAQIKDAMADAAJDAkFDAUGDAYLDAsHDAcE DAQIDAgDCAMACwcMAgsMAQIMCQEMBAkMBQQMCgUMBgoMBwYMCwcGBQQMCgUMAgoMAwIMCAMMAAgM AQAMCQEMBAkMCAMAAQIMCQEMBAkMBwQMCwcMBgsMBQYMCgUMAgoMCQUEDAAIDAgHDAcGDAYKDAoB DAECDAILDAsDDAMACQUEDAcGDAYKDAoBDAEADAAIDAgDDAMCDAILDAsHCAMADAECDAILDAsHDAcE DAQJDAkFDAUGDAYKDAoBCQUEBwYMCAcMAAgMAQAMCgEMAgoMAwIMCwMMBgsMAQIKAwAMCwMMBgsM BQYMCQUMBAkMBwQMCAcMAAgMCAQHDAMLDAsGDAYFDAUJDAkADAABDAEKDAoCDAIDAgMLCgYMAQoM AAEMCAAMBwgMBAcMCQQMBQkMBgUMCAQHDAYFDAUJDAkADAADDAMLDAsCDAIBDAEKDAoGAgMLDAAB DAEKDAoGDAYHDAcIDAgEDAQFDAUJDAkAAAEJCAQMAwgMAgMMCgIMBQoMBgUMCwYMBwsMBAcMCAQH BgUMCwYMAwsMAAMMCQAMAQkMAgEMCgIMBQoMCQABAgMMCgIMBQoMBAUMCAQMBwgMBgcMCwYMAwsM BgUKDAEJDAkEDAQHDAcLDAsCDAIDDAMIDAgADAABBgUKDAQHDAcLDAsCDAIBDAEJDAkADAADDAMI DAgECQABDAIDDAMIDAgEDAQFDAUKDAoGDAYHDAcLDAsCBgUKBAcMCQQMAQkMAgEMCwIMAwsMAAMM CAAMBwgMAgMLAAEMCAAMBwgMBgcMCgYMBQoMBAUMCQQMAQkM """ #static const char tiling13_3_[2][12][30] TILING13_3_ = (2, 12, 30), """ AwILCAcMAAgMAQAMCgEMBgoMBQYMCQUMBAkMBwQMBQYKDAILDAsHDAcEDAQJDAkBDAEADAAIDAgD DAMCCgUGDAcEDAQJDAkBDAECDAILDAsDDAMADAAIDAgHCwMCDAEADAAIDAgHDAcGDAYKDAoFDAUE DAQJDAkBBwQICwMMBgsMBQYMCQUMAAkMAQAMCgEMAgoMAwIMBwQIBQYMCQUMAAkMAwAMCwMMAgsM AQIMCgEMBgoMCwMCAQAMCgEMBgoMBwYMCAcMBAgMBQQMCQUMAAkMAQAJDAQIDAgDDAMCDAIKDAoF DAUGDAYLDAsHDAcEBwQIDAUGDAYLDAsDDAMADAAJDAkBDAECDAIKDAoFAQAJDAMCDAIKDAoFDAUE DAQIDAgHDAcGDAYLDAsDCgUGBwQMCwcMAgsMAQIMCQEMAAkMAwAMCAMMBAgMCQEAAwIMCAMMBAgM BQQMCgUMBgoMBwYMCwcMAgsMAAMICQQMAQkMAgEMCwIMBwsMBgcMCgYMBQoMBAUMCwYHDAMIDAgE DAQFDAUKDAoCDAIBDAEJDAkADAADBgcLDAQFDAUKDAoCDAIDDAMIDAgADAABDAEJDAkECAADDAIB DAEJDAkEDAQHDAcLDAsGDAYFDAUKDAoCBAUJCAAMBwgMBgcMCgYMAQoMAgEMCwIMAwsMAAMMBAUJ BgcMCgYMAQoMAAEMCAAMAwgMAgMMCwIMBwsMCAADAgEMCwIMBwsMBAcMCQQMBQkMBgUMCgYMAQoM AgEKDAUJDAkADAADDAMLDAsGDAYHDAcIDAgEDAQFBAUJDAYHDAcIDAgADAABDAEKDAoCDAIDDAML DAsGAgEKDAADDAMLDAsGDAYFDAUJDAkEDAQHDAcIDAgABgcLBAUMCAQMAwgMAgMMCgIMAQoMAAEM CQAMBQkMCgIBAAMMCQAMBQkMBgUMCwYMBwsMBAcMCAQMAwgM """ #static const char tiling13_4[2][4][36] TILING13_4 = (2, 4, 36), """ DAIKDAoFDAUGDAYLDAsHDAcEDAQIDAgDDAMADAAJDAkBDAECCwMMBgsMBwYMCAcMBAgMBQQMCQUM AAkMAQAMCgEMAgoMAwIMCQEMBAkMBQQMCgUMBgoMBwYMCwcMAgsMAwIMCAMMAAgMAQAMDAAIDAgH DAcEDAQJDAkFDAUGDAYKDAoBDAECDAILDAsDDAMADAMLDAsGDAYHDAcIDAgEDAQFDAUJDAkADAAB DAEKDAoCDAIDCAAMBwgMBAcMCQQMBQkMBgUMCgYMAQoMAgEMCwIMAwsMAAMMCgIMBQoMBgUMCwYM BwsMBAcMCAQMAwgMAAMMCQAMAQkMAgEMDAEJDAkEDAQFDAUKDAoGDAYHDAcLDAsCDAIDDAMIDAgA DAAB """ #static const char tiling13_5_1[2][4][18] TILING13_5_1 = (2, 4, 18), """ BwYLAQAJCgMCAwoFAwUIBAgFAQIKBwQIAwALBgsACQYABgkFAwAIBQYKAQIJBAkCCwQCBAsHBQQJ AwILCAEAAQgHAQcKBgoHBAcIAgEKCwADAAsGAAYJBQkGAgMLBAUJAAEIBwgBCgcBBwoGAAEJBgcL AgMKBQoDCAUDBQgEBgUKAAMICQIBAgkEAgQLBwsE """ #static const char tiling13_5_2[2][4][30] TILING13_5_2 = (2, 4, 30), """ AQAJBwQIBwgDBwMLAgsDCwIKCwoGBQYKBgUHBAcFBwQICwMCBgsCCgYCBgoFCQUKAQkKCQEAAgAB AAIDBQYKCQEABAkACAQABAgHCwcIAwsICwMCAAIDAgABAwILBQYKBQoBBQEJAAkBCQAICQgEBAgH BAcFBgUHAgEKBAUJBAkABAAIAwgACAMLCAsHBgcLBwYEBQQGBAUJCAADBwgDCwcDBwsGCgYLAgoL CgIBAwECAQMABgcLCgIBBQoBCQUBBQkECAQJAAgJCAADAQMAAwECAAMIBgcLBgsCBgIKAQoCCgEJ CgkFBQkEBQQGBwYE """ #static const char tiling14[12][12] TILING14 = (12, 12), """ BQkIBQgCBQIGAwIIAgEFAgUIAggLBAgFCQQGCQYDCQMBCwMGAQsKAQQLAQAEBwsECAIACAUCCAcF CgIFAAcDAAoHAAkKBgcKAAMHAAcKAAoJBgoHCAACCAIFCAUHCgUCAQoLAQsEAQQABwQLCQYECQMG CQEDCwYDAgUBAggFAgsIBAUIBQgJBQIIBQYCAwgC """ #static const char test3[24] TEST3 = (24,), """ BQEEBQECAgMEAwYG+vr9/P3+/v/7/P/7 """ #static const char test4[8] TEST4 = (8,), """ BwcHB/n5+fk= """ #static const char test6[48][3] TEST6 = (48, 3), """ AgcKBAcLBQcBBQcDAQcJAwcKBgcFAQcIBAcIAQcIAwcLBQcCBQcAAQcJBgcGAgcJBAcIAgcJAgcK BgcHAwcKBAcLAwcLBgcE+vkE/fkL/PkL/fkK+vkH/vkK/vkJ/PkI/vkJ+vkG//kJ+/kA+/kC/fkL //kI/PkI//kI+vkF/fkK//kJ+/kD+/kB/PkL/vkK """ #static const char test7[16][5] TEST7 = (16, 5), """ AQIFBwEDBAUHAwQBBgcEBAEFBwACAwUHAgECBgcFAgMGBwYDBAYHB/38+vkH/v36+Qb//vr5Bf79 +/kC/P/7+QD8//r5BP38+/kD//77+QE= """ #static const char test10[6][3] TEST10 = (6, 3), """ AgQHBQYHAQMHAQMHBQYHAgQH """ #static const char test12[24][4] TEST12 = (24, 4), """ BAMHCwMCBwoCBgcFBgQHBwIBBwkFAgcBBQMHAgUBBwAFBAcDBgMHBgEGBwQBBAcIBAEHCAYBBwQD BgcGBAUHAwEFBwADBQcCAgUHAQECBwkEBgcHBgIHBQIDBwoDBAcL """ #static const char test13[2][7] TEST13 = (2, 7), """ AQIDBAUGBwIDBAEFBgc= """ #static const char subconfig13[64] SUBCONFIG13 = (64,), """ AAECBwP/C/8ECP//Dv///wUJDBcP/xUmERT/JBohHiwGCg0TEP8ZJRIY/yMWIB0r////Iv//HCr/ H/8pGygnLQ== """ scikit-image-0.13.1/skimage/measure/_moments.py000066400000000000000000000137511316256236300214340ustar00rootroot00000000000000# coding: utf-8 import numpy as np from . import _moments_cy def moments(image, order=3): """Calculate all raw image moments up to a certain order. The following properties can be calculated from raw image moments: * Area as: ``m[0, 0]``. * Centroid as: {``m[0, 1] / m[0, 0]``, ``m[1, 0] / m[0, 0]``}. Note that raw moments are neither translation, scale nor rotation invariant. Parameters ---------- image : 2D double or uint8 array Rasterized shape as image. order : int, optional Maximum order of moments. Default is 3. Returns ------- m : (``order + 1``, ``order + 1``) array Raw image moments. References ---------- .. [1] Wilhelm Burger, Mark Burge. Principles of Digital Image Processing: Core Algorithms. Springer-Verlag, London, 2009. .. [2] B. Jähne. Digital Image Processing. Springer-Verlag, Berlin-Heidelberg, 6. edition, 2005. .. [3] T. H. Reiss. Recognizing Planar Objects Using Invariant Image Features, from Lecture notes in computer science, p. 676. Springer, Berlin, 1993. .. [4] http://en.wikipedia.org/wiki/Image_moment Examples -------- >>> image = np.zeros((20, 20), dtype=np.double) >>> image[13:17, 13:17] = 1 >>> m = moments(image) >>> cr = m[0, 1] / m[0, 0] >>> cc = m[1, 0] / m[0, 0] >>> cr, cc (14.5, 14.5) """ return _moments_cy.moments_central(image, 0, 0, order) def moments_central(image, cr, cc, order=3): """Calculate all central image moments up to a certain order. The center coordinates (cr, cc) can be calculated from the raw moments as: {``m[0, 1] / m[0, 0]``, ``m[1, 0] / m[0, 0]``}. Note that central moments are translation invariant but not scale and rotation invariant. Parameters ---------- image : 2D double or uint8 array Rasterized shape as image. cr : double Center row coordinate. cc : double Center column coordinate. order : int, optional Maximum order of moments. Default is 3. Returns ------- mu : (``order + 1``, ``order + 1``) array Central image moments. References ---------- .. [1] Wilhelm Burger, Mark Burge. Principles of Digital Image Processing: Core Algorithms. Springer-Verlag, London, 2009. .. [2] B. Jähne. Digital Image Processing. Springer-Verlag, Berlin-Heidelberg, 6. edition, 2005. .. [3] T. H. Reiss. Recognizing Planar Objects Using Invariant Image Features, from Lecture notes in computer science, p. 676. Springer, Berlin, 1993. .. [4] http://en.wikipedia.org/wiki/Image_moment Examples -------- >>> image = np.zeros((20, 20), dtype=np.double) >>> image[13:17, 13:17] = 1 >>> m = moments(image) >>> cr = m[0, 1] / m[0, 0] >>> cc = m[1, 0] / m[0, 0] >>> moments_central(image, cr, cc) array([[ 16., 0., 20., 0.], [ 0., 0., 0., 0.], [ 20., 0., 25., 0.], [ 0., 0., 0., 0.]]) """ return _moments_cy.moments_central(image, cr, cc, order) def moments_normalized(mu, order=3): """Calculate all normalized central image moments up to a certain order. Note that normalized central moments are translation and scale invariant but not rotation invariant. Parameters ---------- mu : (M, M) array Central image moments, where M must be > ``order``. order : int, optional Maximum order of moments. Default is 3. Returns ------- nu : (``order + 1``, ``order + 1``) array Normalized central image moments. References ---------- .. [1] Wilhelm Burger, Mark Burge. Principles of Digital Image Processing: Core Algorithms. Springer-Verlag, London, 2009. .. [2] B. Jähne. Digital Image Processing. Springer-Verlag, Berlin-Heidelberg, 6. edition, 2005. .. [3] T. H. Reiss. Recognizing Planar Objects Using Invariant Image Features, from Lecture notes in computer science, p. 676. Springer, Berlin, 1993. .. [4] http://en.wikipedia.org/wiki/Image_moment Examples -------- >>> image = np.zeros((20, 20), dtype=np.double) >>> image[13:17, 13:17] = 1 >>> m = moments(image) >>> cr = m[0, 1] / m[0, 0] >>> cc = m[1, 0] / m[0, 0] >>> mu = moments_central(image, cr, cc) >>> moments_normalized(mu) array([[ nan, nan, 0.078125 , 0. ], [ nan, 0. , 0. , 0. ], [ 0.078125 , 0. , 0.00610352, 0. ], [ 0. , 0. , 0. , 0. ]]) """ if mu.ndim != 2: raise TypeError("Image moments must be 2-dimension") if mu.shape[0] <= order or mu.shape[1] <= order: raise TypeError("Shape of image moments must be >= `order`") return _moments_cy.moments_normalized(mu.astype(np.double), order) def moments_hu(nu): """Calculate Hu's set of image moments. Note that this set of moments is proofed to be translation, scale and rotation invariant. Parameters ---------- nu : (M, M) array Normalized central image moments, where M must be > 4. Returns ------- nu : (7, 1) array Hu's set of image moments. References ---------- .. [1] M. K. Hu, "Visual Pattern Recognition by Moment Invariants", IRE Trans. Info. Theory, vol. IT-8, pp. 179-187, 1962 .. [2] Wilhelm Burger, Mark Burge. Principles of Digital Image Processing: Core Algorithms. Springer-Verlag, London, 2009. .. [3] B. Jähne. Digital Image Processing. Springer-Verlag, Berlin-Heidelberg, 6. edition, 2005. .. [4] T. H. Reiss. Recognizing Planar Objects Using Invariant Image Features, from Lecture notes in computer science, p. 676. Springer, Berlin, 1993. .. [5] http://en.wikipedia.org/wiki/Image_moment """ return _moments_cy.moments_hu(nu.astype(np.double)) scikit-image-0.13.1/skimage/measure/_moments_cy.pyx000066400000000000000000000036221316256236300223130ustar00rootroot00000000000000#cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False #cython: wraparound=False import numpy as np import cython ctypedef fused image_t: cython.uchar[:, :] cython.double[:, :] cpdef moments_central(image_t image, double cr, double cc, Py_ssize_t order): cdef Py_ssize_t p, q, r, c cdef double[:, ::1] mu = np.zeros((order + 1, order + 1), dtype=np.double) cdef double val, dr, dc, dcp, drq for r in range(image.shape[0]): dr = r - cr for c in range(image.shape[1]): dc = c - cc val = image[r, c] dcp = 1 for p in range(order + 1): drq = 1 for q in range(order + 1): mu[p, q] += val * drq * dcp drq *= dr dcp *= dc return np.asarray(mu) def moments_normalized(double[:, :] mu, Py_ssize_t order=3): cdef Py_ssize_t p, q cdef double[:, ::1] nu = np.zeros((order + 1, order + 1), dtype=np.double) for p in range(order + 1): for q in range(order + 1): if p + q >= 2: nu[p, q] = mu[p, q] / mu[0, 0] ** ((p + q) / 2 + 1) else: nu[p, q] = np.nan return np.asarray(nu) def moments_hu(double[:, :] nu): cdef double[::1] hu = np.zeros((7, ), dtype=np.double) cdef double t0 = nu[3, 0] + nu[1, 2] cdef double t1 = nu[2, 1] + nu[0, 3] cdef double q0 = t0 * t0 cdef double q1 = t1 * t1 cdef double n4 = 4 * nu[1, 1] cdef double s = nu[2, 0] + nu[0, 2] cdef double d = nu[2, 0] - nu[0, 2] hu[0] = s hu[1] = d * d + n4 * nu[1, 1] hu[3] = q0 + q1 hu[5] = d * (q0 - q1) + n4 * t0 * t1 t0 *= q0 - 3 * q1 t1 *= 3 * q0 - q1 q0 = nu[3, 0]- 3 * nu[1, 2] q1 = 3 * nu[2, 1] - nu[0, 3] hu[2] = q0 * q0 + q1 * q1 hu[4] = q0 * t0 + q1 * t1 hu[6] = q1 * t0 - q0 * t1 return np.asarray(hu) scikit-image-0.13.1/skimage/measure/_pnpoly.pyx000066400000000000000000000046321316256236300214610ustar00rootroot00000000000000#cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False #cython: wraparound=False import numpy as np cimport numpy as cnp from .._shared.geometry cimport point_in_polygon, points_in_polygon def _grid_points_in_poly(shape, verts): """Test whether points on a specified grid are inside a polygon. For each ``(r, c)`` coordinate on a grid, i.e. ``(0, 0)``, ``(0, 1)`` etc., test whether that point lies inside a polygon. Parameters ---------- shape : tuple (M, N) Shape of the grid. verts : (V, 2) array Specify the V vertices of the polygon, sorted either clockwise or anti-clockwise. The first point may (but does not need to be) duplicated. See Also -------- points_in_poly Returns ------- mask : (M, N) ndarray of bool True where the grid falls inside the polygon. """ cdef double[:] vx, vy verts = np.asarray(verts) vx = verts[:, 0].astype(np.double) vy = verts[:, 1].astype(np.double) cdef Py_ssize_t V = vx.shape[0] cdef Py_ssize_t M = shape[0] cdef Py_ssize_t N = shape[1] cdef Py_ssize_t m, n cdef cnp.ndarray[dtype=cnp.uint8_t, ndim=2, mode="c"] out = \ np.zeros((M, N), dtype=np.uint8) for m in range(M): for n in range(N): out[m, n] = point_in_polygon(V, &vx[0], &vy[0], m, n) return out.view(bool) def _points_in_poly(points, verts): """Test whether points lie inside a polygon. Parameters ---------- points : (N, 2) array Input points, ``(x, y)``. verts : (M, 2) array Vertices of the polygon, sorted either clockwise or anti-clockwise. The first point may (but does not need to be) duplicated. See Also -------- grid_points_in_poly Returns ------- mask : (N,) array of bool True if corresponding point is inside the polygon. """ cdef double[:] x, y, vx, vy points = np.asarray(points) verts = np.asarray(verts) x = points[:, 0].astype(np.double) y = points[:, 1].astype(np.double) vx = verts[:, 0].astype(np.double) vy = verts[:, 1].astype(np.double) cdef cnp.ndarray[cnp.uint8_t, ndim=1] out = \ np.zeros(x.shape[0], dtype=np.uint8) points_in_polygon(vx.shape[0], &vx[0], &vy[0], x.shape[0], &x[0], &y[0], out.data) return out.astype(bool) scikit-image-0.13.1/skimage/measure/_polygon.py000066400000000000000000000123561316256236300214410ustar00rootroot00000000000000import numpy as np from scipy import signal def approximate_polygon(coords, tolerance): """Approximate a polygonal chain with the specified tolerance. It is based on the Douglas-Peucker algorithm. Note that the approximated polygon is always within the convex hull of the original polygon. Parameters ---------- coords : (N, 2) array Coordinate array. tolerance : float Maximum distance from original points of polygon to approximated polygonal chain. If tolerance is 0, the original coordinate array is returned. Returns ------- coords : (M, 2) array Approximated polygonal chain where M <= N. References ---------- .. [1] http://en.wikipedia.org/wiki/Ramer-Douglas-Peucker_algorithm """ if tolerance <= 0: return coords chain = np.zeros(coords.shape[0], 'bool') # pre-allocate distance array for all points dists = np.zeros(coords.shape[0]) chain[0] = True chain[-1] = True pos_stack = [(0, chain.shape[0] - 1)] end_of_chain = False while not end_of_chain: start, end = pos_stack.pop() # determine properties of current line segment r0, c0 = coords[start, :] r1, c1 = coords[end, :] dr = r1 - r0 dc = c1 - c0 segment_angle = - np.arctan2(dr, dc) segment_dist = c0 * np.sin(segment_angle) + r0 * np.cos(segment_angle) # select points in-between line segment segment_coords = coords[start + 1:end, :] segment_dists = dists[start + 1:end] # check whether to take perpendicular or euclidean distance with # inner product of vectors # vectors from points -> start and end dr0 = segment_coords[:, 0] - r0 dc0 = segment_coords[:, 1] - c0 dr1 = segment_coords[:, 0] - r1 dc1 = segment_coords[:, 1] - c1 # vectors points -> start and end projected on start -> end vector projected_lengths0 = dr0 * dr + dc0 * dc projected_lengths1 = - dr1 * dr - dc1 * dc perp = np.logical_and(projected_lengths0 > 0, projected_lengths1 > 0) eucl = np.logical_not(perp) segment_dists[perp] = np.abs( segment_coords[perp, 0] * np.cos(segment_angle) + segment_coords[perp, 1] * np.sin(segment_angle) - segment_dist ) segment_dists[eucl] = np.minimum( # distance to start point np.sqrt(dc0[eucl] ** 2 + dr0[eucl] ** 2), # distance to end point np.sqrt(dc1[eucl] ** 2 + dr1[eucl] ** 2) ) if np.any(segment_dists > tolerance): # select point with maximum distance to line new_end = start + np.argmax(segment_dists) + 1 pos_stack.append((new_end, end)) pos_stack.append((start, new_end)) chain[new_end] = True if len(pos_stack) == 0: end_of_chain = True return coords[chain, :] # B-Spline subdivision _SUBDIVISION_MASKS = { # degree: (mask_even, mask_odd) # extracted from (degree + 2)th row of Pascal's triangle 1: ([1, 1], [1, 1]), 2: ([3, 1], [1, 3]), 3: ([1, 6, 1], [0, 4, 4]), 4: ([5, 10, 1], [1, 10, 5]), 5: ([1, 15, 15, 1], [0, 6, 20, 6]), 6: ([7, 35, 21, 1], [1, 21, 35, 7]), 7: ([1, 28, 70, 28, 1], [0, 8, 56, 56, 8]), } def subdivide_polygon(coords, degree=2, preserve_ends=False): """Subdivision of polygonal curves using B-Splines. Note that the resulting curve is always within the convex hull of the original polygon. Circular polygons stay closed after subdivision. Parameters ---------- coords : (N, 2) array Coordinate array. degree : {1, 2, 3, 4, 5, 6, 7}, optional Degree of B-Spline. Default is 2. preserve_ends : bool, optional Preserve first and last coordinate of non-circular polygon. Default is False. Returns ------- coords : (M, 2) array Subdivided coordinate array. References ---------- .. [1] http://mrl.nyu.edu/publications/subdiv-course2000/coursenotes00.pdf """ if degree not in _SUBDIVISION_MASKS: raise ValueError("Invalid B-Spline degree. Only degree 1 - 7 is " "supported.") circular = np.all(coords[0, :] == coords[-1, :]) method = 'valid' if circular: # remove last coordinate because of wrapping coords = coords[:-1, :] # circular convolution by wrapping boundaries method = 'same' mask_even, mask_odd = _SUBDIVISION_MASKS[degree] # divide by total weight mask_even = np.array(mask_even, np.float) / (2 ** degree) mask_odd = np.array(mask_odd, np.float) / (2 ** degree) even = signal.convolve2d(coords.T, np.atleast_2d(mask_even), mode=method, boundary='wrap') odd = signal.convolve2d(coords.T, np.atleast_2d(mask_odd), mode=method, boundary='wrap') out = np.zeros((even.shape[1] + odd.shape[1], 2)) out[1::2] = even.T out[::2] = odd.T if circular: # close polygon out = np.vstack([out, out[0, :]]) if preserve_ends and not circular: out = np.vstack([coords[0, :], out, coords[-1, :]]) return out scikit-image-0.13.1/skimage/measure/_regionprops.py000066400000000000000000000477211316256236300223250ustar00rootroot00000000000000# coding: utf-8 from __future__ import division from math import sqrt, atan2, pi as PI import numpy as np from scipy import ndimage as ndi from ._label import label from . import _moments from functools import wraps __all__ = ['regionprops', 'perimeter'] STREL_4 = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]], dtype=np.uint8) STREL_8 = np.ones((3, 3), dtype=np.uint8) STREL_26_3D = np.ones((3, 3, 3), dtype=np.uint8) PROPS = { 'Area': 'area', 'BoundingBox': 'bbox', 'BoundingBoxArea': 'bbox_area', 'CentralMoments': 'moments_central', 'Centroid': 'centroid', 'ConvexArea': 'convex_area', # 'ConvexHull', 'ConvexImage': 'convex_image', 'Coordinates': 'coords', 'Eccentricity': 'eccentricity', 'EquivDiameter': 'equivalent_diameter', 'EulerNumber': 'euler_number', 'Extent': 'extent', # 'Extrema', 'FilledArea': 'filled_area', 'FilledImage': 'filled_image', 'HuMoments': 'moments_hu', 'Image': 'image', 'Label': 'label', 'MajorAxisLength': 'major_axis_length', 'MaxIntensity': 'max_intensity', 'MeanIntensity': 'mean_intensity', 'MinIntensity': 'min_intensity', 'MinorAxisLength': 'minor_axis_length', 'Moments': 'moments', 'NormalizedMoments': 'moments_normalized', 'Orientation': 'orientation', 'Perimeter': 'perimeter', # 'PixelIdxList', # 'PixelList', 'Solidity': 'solidity', # 'SubarrayIdx' 'WeightedCentralMoments': 'weighted_moments_central', 'WeightedCentroid': 'weighted_centroid', 'WeightedHuMoments': 'weighted_moments_hu', 'WeightedMoments': 'weighted_moments', 'WeightedNormalizedMoments': 'weighted_moments_normalized' } PROP_VALS = set(PROPS.values()) def _cached(f): @wraps(f) def wrapper(obj): cache = obj._cache prop = f.__name__ if not ((prop in cache) and obj._cache_active): cache[prop] = f(obj) return cache[prop] return wrapper def only2d(method): @wraps(method) def func2d(self, *args, **kwargs): if self._ndim > 2: raise NotImplementedError('Property %s is not implemented for ' '3D images' % method.__name__) return method(self, *args, **kwargs) return func2d class _RegionProperties(object): """Please refer to `skimage.measure.regionprops` for more information on the available region properties. """ def __init__(self, slice, label, label_image, intensity_image, cache_active): if intensity_image is not None: if not intensity_image.shape == label_image.shape: raise ValueError('Label and intensity image must have the' 'same shape.') self.label = label self._slice = slice self._label_image = label_image self._intensity_image = intensity_image self._cache_active = cache_active self._cache = {} self._ndim = label_image.ndim @_cached def area(self): return np.sum(self.image) def bbox(self): """ Returns ------- A tuple of the bounding box's start coordinates for each dimension, followed by the end coordinates for each dimension """ return tuple([self._slice[i].start for i in range(self._ndim)] + [self._slice[i].stop for i in range(self._ndim)]) def bbox_area(self): return self._label_image.size def centroid(self): return tuple(self.coords.mean(axis=0)) @only2d def convex_area(self): return np.sum(self.convex_image) @_cached @only2d def convex_image(self): from ..morphology.convex_hull import convex_hull_image return convex_hull_image(self.image) def coords(self): indices = np.nonzero(self.image) return np.vstack([indices[i] + self._slice[i].start for i in range(self._ndim)]).T @only2d def eccentricity(self): l1, l2 = self.inertia_tensor_eigvals if l1 == 0: return 0 return sqrt(1 - l2 / l1) def equivalent_diameter(self): if self._ndim == 2: return sqrt(4 * self.area / PI) elif self._ndim == 3: return (6 * self.area / PI) ** (1. / 3) @only2d def euler_number(self): euler_array = self.filled_image != self.image _, num = label(euler_array, neighbors=8, return_num=True, background=0) return -num + 1 def extent(self): return self.area / self.image.size def filled_area(self): return np.sum(self.filled_image) @_cached def filled_image(self): structure = STREL_8 if self._ndim == 2 else STREL_26_3D return ndi.binary_fill_holes(self.image, structure) @_cached def image(self): return self._label_image[self._slice] == self.label @_cached @only2d def inertia_tensor(self): mu = self.moments_central a = mu[2, 0] / mu[0, 0] b = -mu[1, 1] / mu[0, 0] c = mu[0, 2] / mu[0, 0] return np.array([[a, b], [b, c]]) @_cached @only2d def inertia_tensor_eigvals(self): a, b, b, c = self.inertia_tensor.flat # eigen values of inertia tensor l1 = (a + c) / 2 + sqrt(4 * b ** 2 + (a - c) ** 2) / 2 l2 = (a + c) / 2 - sqrt(4 * b ** 2 + (a - c) ** 2) / 2 return l1, l2 @_cached def intensity_image(self): if self._intensity_image is None: raise AttributeError('No intensity image specified.') return self._intensity_image[self._slice] * self.image def _intensity_image_double(self): return self.intensity_image.astype(np.double) @only2d def local_centroid(self): m = self.moments row = m[0, 1] / m[0, 0] col = m[1, 0] / m[0, 0] return row, col def max_intensity(self): return np.max(self.intensity_image[self.image]) def mean_intensity(self): return np.mean(self.intensity_image[self.image]) def min_intensity(self): return np.min(self.intensity_image[self.image]) @only2d def major_axis_length(self): l1, _ = self.inertia_tensor_eigvals return 4 * sqrt(l1) @only2d def minor_axis_length(self): _, l2 = self.inertia_tensor_eigvals return 4 * sqrt(l2) @_cached @only2d def moments(self): return _moments.moments(self.image.astype(np.uint8), 3) @_cached @only2d def moments_central(self): row, col = self.local_centroid return _moments.moments_central(self.image.astype(np.uint8), row, col, 3) @only2d def moments_hu(self): return _moments.moments_hu(self.moments_normalized) @_cached @only2d def moments_normalized(self): return _moments.moments_normalized(self.moments_central, 3) @only2d def orientation(self): a, b, b, c = self.inertia_tensor.flat b = -b if a - c == 0: if b > 0: return -PI / 4. else: return PI / 4. else: return - 0.5 * atan2(2 * b, (a - c)) @only2d def perimeter(self): return perimeter(self.image, 4) @only2d def solidity(self): return self.moments[0, 0] / np.sum(self.convex_image) @only2d def weighted_centroid(self): row, col = self.weighted_local_centroid return row + self._slice[0].start, col + self._slice[1].start @only2d def weighted_local_centroid(self): m = self.weighted_moments row = m[0, 1] / m[0, 0] col = m[1, 0] / m[0, 0] return row, col @_cached @only2d def weighted_moments(self): return _moments.moments_central(self._intensity_image_double(), 0, 0, 3) @_cached @only2d def weighted_moments_central(self): row, col = self.weighted_local_centroid return _moments.moments_central(self._intensity_image_double(), row, col, 3) @only2d def weighted_moments_hu(self): return _moments.moments_hu(self.weighted_moments_normalized) @_cached @only2d def weighted_moments_normalized(self): return _moments.moments_normalized(self.weighted_moments_central, 3) def __iter__(self): props = PROP_VALS if self._intensity_image is None: unavailable_props = ('intensity_image', 'max_intensity', 'mean_intensity', 'min_intensity', 'weighted_moments', 'weighted_moments_central', 'weighted_centroid', 'weighted_local_centroid', 'weighted_moments_hu', 'weighted_moments_normalized') props = props.difference(unavailable_props) return iter(sorted(props)) def __getitem__(self, key): value = getattr(self, key, None) if value is not None: return value else: # backwards compatability return getattr(self, PROPS[key]) def __eq__(self, other): if not isinstance(other, _RegionProperties): return False for key in PROP_VALS: try: # so that NaNs are equal np.testing.assert_equal(getattr(self, key, None), getattr(other, key, None)) except AssertionError: return False return True def regionprops(label_image, intensity_image=None, cache=True): """Measure properties of labeled image regions. Parameters ---------- label_image : (N, M) ndarray Labeled input image. Labels with value 0 are ignored. intensity_image : (N, M) ndarray, optional Intensity (i.e., input) image with same size as labeled image. Default is None. cache : bool, optional Determine whether to cache calculated properties. The computation is much faster for cached properties, whereas the memory consumption increases. Returns ------- properties : list of RegionProperties Each item describes one labeled region, and can be accessed using the attributes listed below. Notes ----- The following properties can be accessed as attributes or keys: **area** : int Number of pixels of region. **bbox** : tuple Bounding box ``(min_row, min_col, max_row, max_col)``. Pixels belonging to the bounding box are in the half-open interval ``[min_row; max_row)`` and ``[min_col; max_col)``. **bbox_area** : int Number of pixels of bounding box. **centroid** : array Centroid coordinate tuple ``(row, col)``. **convex_area** : int Number of pixels of convex hull image. **convex_image** : (H, J) ndarray Binary convex hull image which has the same size as bounding box. **coords** : (N, 2) ndarray Coordinate list ``(row, col)`` of the region. **eccentricity** : float Eccentricity of the ellipse that has the same second-moments as the region. The eccentricity is the ratio of the focal distance (distance between focal points) over the major axis length. The value is in the interval [0, 1). When it is 0, the ellipse becomes a circle. **equivalent_diameter** : float The diameter of a circle with the same area as the region. **euler_number** : int Euler characteristic of region. Computed as number of objects (= 1) subtracted by number of holes (8-connectivity). **extent** : float Ratio of pixels in the region to pixels in the total bounding box. Computed as ``area / (rows * cols)`` **filled_area** : int Number of pixels of filled region. **filled_image** : (H, J) ndarray Binary region image with filled holes which has the same size as bounding box. **image** : (H, J) ndarray Sliced binary region image which has the same size as bounding box. **inertia_tensor** : (2, 2) ndarray Inertia tensor of the region for the rotation around its mass. **inertia_tensor_eigvals** : tuple The two eigen values of the inertia tensor in decreasing order. **intensity_image** : ndarray Image inside region bounding box. **label** : int The label in the labeled input image. **local_centroid** : array Centroid coordinate tuple ``(row, col)``, relative to region bounding box. **major_axis_length** : float The length of the major axis of the ellipse that has the same normalized second central moments as the region. **max_intensity** : float Value with the greatest intensity in the region. **mean_intensity** : float Value with the mean intensity in the region. **min_intensity** : float Value with the least intensity in the region. **minor_axis_length** : float The length of the minor axis of the ellipse that has the same normalized second central moments as the region. **moments** : (3, 3) ndarray Spatial moments up to 3rd order:: m_ji = sum{ array(x, y) * x^j * y^i } where the sum is over the `x`, `y` coordinates of the region. **moments_central** : (3, 3) ndarray Central moments (translation invariant) up to 3rd order:: mu_ji = sum{ array(x, y) * (x - x_c)^j * (y - y_c)^i } where the sum is over the `x`, `y` coordinates of the region, and `x_c` and `y_c` are the coordinates of the region's centroid. **moments_hu** : tuple Hu moments (translation, scale and rotation invariant). **moments_normalized** : (3, 3) ndarray Normalized moments (translation and scale invariant) up to 3rd order:: nu_ji = mu_ji / m_00^[(i+j)/2 + 1] where `m_00` is the zeroth spatial moment. **orientation** : float Angle between the X-axis and the major axis of the ellipse that has the same second-moments as the region. Ranging from `-pi/2` to `pi/2` in counter-clockwise direction. **perimeter** : float Perimeter of object which approximates the contour as a line through the centers of border pixels using a 4-connectivity. **solidity** : float Ratio of pixels in the region to pixels of the convex hull image. **weighted_centroid** : array Centroid coordinate tuple ``(row, col)`` weighted with intensity image. **weighted_local_centroid** : array Centroid coordinate tuple ``(row, col)``, relative to region bounding box, weighted with intensity image. **weighted_moments** : (3, 3) ndarray Spatial moments of intensity image up to 3rd order:: wm_ji = sum{ array(x, y) * x^j * y^i } where the sum is over the `x`, `y` coordinates of the region. **weighted_moments_central** : (3, 3) ndarray Central moments (translation invariant) of intensity image up to 3rd order:: wmu_ji = sum{ array(x, y) * (x - x_c)^j * (y - y_c)^i } where the sum is over the `x`, `y` coordinates of the region, and `x_c` and `y_c` are the coordinates of the region's weighted centroid. **weighted_moments_hu** : tuple Hu moments (translation, scale and rotation invariant) of intensity image. **weighted_moments_normalized** : (3, 3) ndarray Normalized moments (translation and scale invariant) of intensity image up to 3rd order:: wnu_ji = wmu_ji / wm_00^[(i+j)/2 + 1] where ``wm_00`` is the zeroth spatial moment (intensity-weighted area). Each region also supports iteration, so that you can do:: for prop in region: print(prop, region[prop]) See Also -------- label References ---------- .. [1] Wilhelm Burger, Mark Burge. Principles of Digital Image Processing: Core Algorithms. Springer-Verlag, London, 2009. .. [2] B. Jähne. Digital Image Processing. Springer-Verlag, Berlin-Heidelberg, 6. edition, 2005. .. [3] T. H. Reiss. Recognizing Planar Objects Using Invariant Image Features, from Lecture notes in computer science, p. 676. Springer, Berlin, 1993. .. [4] http://en.wikipedia.org/wiki/Image_moment Examples -------- >>> from skimage import data, util >>> from skimage.measure import label >>> img = util.img_as_ubyte(data.coins()) > 110 >>> label_img = label(img, connectivity=img.ndim) >>> props = regionprops(label_img) >>> # centroid of first labeled object >>> props[0].centroid (22.729879860483141, 81.912285234465827) >>> # centroid of first labeled object >>> props[0]['centroid'] (22.729879860483141, 81.912285234465827) """ label_image = np.squeeze(label_image) if label_image.ndim not in (2, 3): raise TypeError('Only 2-D and 3-D images supported.') if not np.issubdtype(label_image.dtype, np.integer): raise TypeError('Label image must be of integral type.') regions = [] objects = ndi.find_objects(label_image) for i, sl in enumerate(objects): if sl is None: continue label = i + 1 props = _RegionProperties(sl, label, label_image, intensity_image, cache) regions.append(props) return regions def perimeter(image, neighbourhood=4): """Calculate total perimeter of all objects in binary image. Parameters ---------- image : array Binary image. neighbourhood : 4 or 8, optional Neighborhood connectivity for border pixel determination. Returns ------- perimeter : float Total perimeter of all objects in binary image. References ---------- .. [1] K. Benkrid, D. Crookes. Design and FPGA Implementation of a Perimeter Estimator. The Queen's University of Belfast. http://www.cs.qub.ac.uk/~d.crookes/webpubs/papers/perimeter.doc """ if neighbourhood == 4: strel = STREL_4 else: strel = STREL_8 image = image.astype(np.uint8) eroded_image = ndi.binary_erosion(image, strel, border_value=0) border_image = image - eroded_image perimeter_weights = np.zeros(50, dtype=np.double) perimeter_weights[[5, 7, 15, 17, 25, 27]] = 1 perimeter_weights[[21, 33]] = sqrt(2) perimeter_weights[[13, 23]] = (1 + sqrt(2)) / 2 perimeter_image = ndi.convolve(border_image, np.array([[10, 2, 10], [ 2, 1, 2], [10, 2, 10]]), mode='constant', cval=0) # You can also write # return perimeter_weights[perimeter_image].sum() # but that was measured as taking much longer than bincount + np.dot (5x # as much time) perimeter_histogram = np.bincount(perimeter_image.ravel(), minlength=50) total_perimeter = np.dot(perimeter_histogram, perimeter_weights) return total_perimeter def _parse_docs(): import re import textwrap doc = regionprops.__doc__ matches = re.finditer('\*\*(\w+)\*\* \:.*?\n(.*?)(?=\n [\*\S]+)', doc, flags=re.DOTALL) prop_doc = dict((m.group(1), textwrap.dedent(m.group(2))) for m in matches) return prop_doc def _install_properties_docs(): prop_doc = _parse_docs() for p in [member for member in dir(_RegionProperties) if not member.startswith('_')]: try: getattr(_RegionProperties, p).__doc__ = prop_doc[p] except AttributeError: # For Python 2.x getattr(_RegionProperties, p).im_func.__doc__ = prop_doc[p] setattr(_RegionProperties, p, property(getattr(_RegionProperties, p))) _install_properties_docs() scikit-image-0.13.1/skimage/measure/_structural_similarity.py000066400000000000000000000201661316256236300244260ustar00rootroot00000000000000from __future__ import division import numpy as np from scipy.ndimage import uniform_filter, gaussian_filter from ..util.dtype import dtype_range from ..util.arraycrop import crop from .._shared.utils import deprecated from .._shared.utils import skimage_deprecation, warn __all__ = ['compare_ssim'] def compare_ssim(X, Y, win_size=None, gradient=False, data_range=None, multichannel=False, gaussian_weights=False, full=False, dynamic_range=None, **kwargs): """Compute the mean structural similarity index between two images. Parameters ---------- X, Y : ndarray Image. Any dimensionality. win_size : int or None The side-length of the sliding window used in comparison. Must be an odd value. If `gaussian_weights` is True, this is ignored and the window size will depend on `sigma`. gradient : bool, optional If True, also return the gradient. data_range : int, optional The data range of the input image (distance between minimum and maximum possible values). By default, this is estimated from the image data-type. multichannel : bool, optional If True, treat the last dimension of the array as channels. Similarity calculations are done independently for each channel then averaged. gaussian_weights : bool, optional If True, each patch has its mean and variance spatially weighted by a normalized Gaussian kernel of width sigma=1.5. full : bool, optional If True, return the full structural similarity image instead of the mean value. Other Parameters ---------------- use_sample_covariance : bool if True, normalize covariances by N-1 rather than, N where N is the number of pixels within the sliding window. K1 : float algorithm parameter, K1 (small constant, see [1]_) K2 : float algorithm parameter, K2 (small constant, see [1]_) sigma : float sigma for the Gaussian when `gaussian_weights` is True. Returns ------- mssim : float The mean structural similarity over the image. grad : ndarray The gradient of the structural similarity index between X and Y [2]_. This is only returned if `gradient` is set to True. S : ndarray The full SSIM image. This is only returned if `full` is set to True. Notes ----- To match the implementation of Wang et. al. [1]_, set `gaussian_weights` to True, `sigma` to 1.5, and `use_sample_covariance` to False. References ---------- .. [1] Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13, 600-612. https://ece.uwaterloo.ca/~z70wang/publications/ssim.pdf, DOI:10.1.1.11.2477 .. [2] Avanaki, A. N. (2009). Exact global histogram specification optimized for structural similarity. Optical Review, 16, 613-621. http://arxiv.org/abs/0901.0065, DOI:10.1007/s10043-009-0119-z """ if not X.dtype == Y.dtype: raise ValueError('Input images must have the same dtype.') if not X.shape == Y.shape: raise ValueError('Input images must have the same dimensions.') if dynamic_range is not None: warn('`dynamic_range` has been deprecated in favor of ' '`data_range`. The `dynamic_range` keyword argument ' 'will be removed in v0.14', skimage_deprecation) data_range = dynamic_range if multichannel: # loop over channels args = dict(win_size=win_size, gradient=gradient, data_range=data_range, multichannel=False, gaussian_weights=gaussian_weights, full=full) args.update(kwargs) nch = X.shape[-1] mssim = np.empty(nch) if gradient: G = np.empty(X.shape) if full: S = np.empty(X.shape) for ch in range(nch): ch_result = compare_ssim(X[..., ch], Y[..., ch], **args) if gradient and full: mssim[..., ch], G[..., ch], S[..., ch] = ch_result elif gradient: mssim[..., ch], G[..., ch] = ch_result elif full: mssim[..., ch], S[..., ch] = ch_result else: mssim[..., ch] = ch_result mssim = mssim.mean() if gradient and full: return mssim, G, S elif gradient: return mssim, G elif full: return mssim, S else: return mssim K1 = kwargs.pop('K1', 0.01) K2 = kwargs.pop('K2', 0.03) sigma = kwargs.pop('sigma', 1.5) if K1 < 0: raise ValueError("K1 must be positive") if K2 < 0: raise ValueError("K2 must be positive") if sigma < 0: raise ValueError("sigma must be positive") use_sample_covariance = kwargs.pop('use_sample_covariance', True) if win_size is None: if gaussian_weights: win_size = 11 # 11 to match Wang et. al. 2004 else: win_size = 7 # backwards compatibility if np.any((np.asarray(X.shape) - win_size) < 0): raise ValueError( "win_size exceeds image extent. If the input is a multichannel " "(color) image, set multichannel=True.") if not (win_size % 2 == 1): raise ValueError('Window size must be odd.') if data_range is None: dmin, dmax = dtype_range[X.dtype.type] data_range = dmax - dmin ndim = X.ndim if gaussian_weights: # sigma = 1.5 to approximately match filter in Wang et. al. 2004 # this ends up giving a 13-tap rather than 11-tap Gaussian filter_func = gaussian_filter filter_args = {'sigma': sigma} else: filter_func = uniform_filter filter_args = {'size': win_size} # ndimage filters need floating point data X = X.astype(np.float64) Y = Y.astype(np.float64) NP = win_size ** ndim # filter has already normalized by NP if use_sample_covariance: cov_norm = NP / (NP - 1) # sample covariance else: cov_norm = 1.0 # population covariance to match Wang et. al. 2004 # compute (weighted) means ux = filter_func(X, **filter_args) uy = filter_func(Y, **filter_args) # compute (weighted) variances and covariances uxx = filter_func(X * X, **filter_args) uyy = filter_func(Y * Y, **filter_args) uxy = filter_func(X * Y, **filter_args) vx = cov_norm * (uxx - ux * ux) vy = cov_norm * (uyy - uy * uy) vxy = cov_norm * (uxy - ux * uy) R = data_range C1 = (K1 * R) ** 2 C2 = (K2 * R) ** 2 A1, A2, B1, B2 = ((2 * ux * uy + C1, 2 * vxy + C2, ux ** 2 + uy ** 2 + C1, vx + vy + C2)) D = B1 * B2 S = (A1 * A2) / D # to avoid edge effects will ignore filter radius strip around edges pad = (win_size - 1) // 2 # compute (weighted) mean of ssim mssim = crop(S, pad).mean() if gradient: # The following is Eqs. 7-8 of Avanaki 2009. grad = filter_func(A1 / D, **filter_args) * X grad += filter_func(-S / B2, **filter_args) * Y grad += filter_func((ux * (A2 - A1) - uy * (B2 - B1) * S) / D, **filter_args) grad *= (2 / X.size) if full: return mssim, grad, S else: return mssim, grad else: if full: return mssim, S else: return mssim @deprecated('compare_ssim', removed_version='0.14') def structural_similarity(X, Y, win_size=None, gradient=False, dynamic_range=None, multichannel=False, gaussian_weights=False, full=False, **kwargs): """""" + compare_ssim.__doc__ return compare_ssim(X, Y, win_size=win_size, gradient=gradient, data_range=dynamic_range, multichannel=multichannel, gaussian_weights=gaussian_weights, full=full, **kwargs) scikit-image-0.13.1/skimage/measure/block.py000066400000000000000000000050641316256236300207030ustar00rootroot00000000000000import numpy as np from ..util import view_as_blocks def block_reduce(image, block_size, func=np.sum, cval=0): """Down-sample image by applying function to local blocks. Parameters ---------- image : ndarray N-dimensional input image. block_size : array_like Array containing down-sampling integer factor along each axis. func : callable Function object which is used to calculate the return value for each local block. This function must implement an ``axis`` parameter such as ``numpy.sum`` or ``numpy.min``. cval : float Constant padding value if image is not perfectly divisible by the block size. Returns ------- image : ndarray Down-sampled image with same number of dimensions as input image. Examples -------- >>> from skimage.measure import block_reduce >>> image = np.arange(3*3*4).reshape(3, 3, 4) >>> image # doctest: +NORMALIZE_WHITESPACE array([[[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]], [[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23]], [[24, 25, 26, 27], [28, 29, 30, 31], [32, 33, 34, 35]]]) >>> block_reduce(image, block_size=(3, 3, 1), func=np.mean) array([[[ 16., 17., 18., 19.]]]) >>> image_max1 = block_reduce(image, block_size=(1, 3, 4), func=np.max) >>> image_max1 # doctest: +NORMALIZE_WHITESPACE array([[[11]], [[23]], [[35]]]) >>> image_max2 = block_reduce(image, block_size=(3, 1, 4), func=np.max) >>> image_max2 # doctest: +NORMALIZE_WHITESPACE array([[[27], [31], [35]]]) """ if len(block_size) != image.ndim: raise ValueError("`block_size` must have the same length " "as `image.shape`.") pad_width = [] for i in range(len(block_size)): if block_size[i] < 1: raise ValueError("Down-sampling factors must be >= 1. Use " "`skimage.transform.resize` to up-sample an " "image.") if image.shape[i] % block_size[i] != 0: after_width = block_size[i] - (image.shape[i] % block_size[i]) else: after_width = 0 pad_width.append((0, after_width)) image = np.pad(image, pad_width=pad_width, mode='constant', constant_values=cval) out = view_as_blocks(image, block_size) for i in range(len(out.shape) // 2): out = func(out, axis=-1) return out scikit-image-0.13.1/skimage/measure/entropy.py000066400000000000000000000016651316256236300213140ustar00rootroot00000000000000from scipy.stats import entropy as scipy_entropy def shannon_entropy(image, base=2): """Calculate the Shannon entropy of an image. The Shannon entropy is defined as S = -sum(pk * log(pk)), where pk are the number of pixels of value k. Parameters ---------- image : (N, M) ndarray Grayscale input image. base : float, optional The logarithmic base to use. Returns ------- entropy : float Notes ----- The returned value is measured in bits or shannon (Sh) for base=2, natural unit (nat) for base=np.e and hartley (Hart) for base=10. References ---------- .. [1] https://en.wikipedia.org/wiki/Entropy_(information_theory) .. [2] https://en.wiktionary.org/wiki/Shannon_entropy Examples -------- >>> from skimage import data >>> shannon_entropy(data.camera()) 17.732031303342747 """ return scipy_entropy(image.ravel(), base=base) scikit-image-0.13.1/skimage/measure/fit.py000066400000000000000000000726341316256236300204020ustar00rootroot00000000000000import math import numpy as np from scipy import optimize from .._shared.utils import check_random_state, skimage_deprecation, warn def _check_data_dim(data, dim): if data.ndim != 2 or data.shape[1] != dim: raise ValueError('Input data must have shape (N, %d).' % dim) def _check_data_atleast_2D(data): if data.ndim < 2 or data.shape[1] < 2: raise ValueError('Input data must be at least 2D.') def _norm_along_axis(x, axis): """NumPy < 1.8 does not support the `axis` argument for `np.linalg.norm`.""" return np.sqrt(np.einsum('ij,ij->i', x, x)) class BaseModel(object): def __init__(self): self.params = None class LineModel(BaseModel): """Total least squares estimator for 2D lines. Lines are parameterized using polar coordinates as functional model:: dist = x * cos(theta) + y * sin(theta) This parameterization is able to model vertical lines in contrast to the standard line model ``y = a*x + b``. This estimator minimizes the squared distances from all points to the line:: min{ sum((dist - x_i * cos(theta) + y_i * sin(theta))**2) } A minimum number of 2 points is required to solve for the parameters. **Deprecated class**. Use ``LineModelND`` instead. Attributes ---------- params : tuple Line model parameters in the following order `dist`, `theta`. """ def __init__(self): self.params = None warn(skimage_deprecation('`LineModel` is deprecated, ' 'use `LineModelND` instead.')) def estimate(self, data): """Estimate line model from data using total least squares. Parameters ---------- data : (N, 2) array N points with ``(x, y)`` coordinates, respectively. Returns ------- success : bool True, if model estimation succeeds. """ _check_data_dim(data, dim=2) X0 = data.mean(axis=0) if data.shape[0] == 2: # well determined theta = np.arctan2(data[1, 1] - data[0, 1], data[1, 0] - data[0, 0]) elif data.shape[0] > 2: # over-determined data = data - X0 # first principal component _, _, v = np.linalg.svd(data) theta = np.arctan2(v[0, 1], v[0, 0]) else: # under-determined raise ValueError('At least 2 input points needed.') # angle perpendicular to line angle theta = (theta + np.pi / 2) % np.pi # line always passes through mean dist = X0[0] * math.cos(theta) + X0[1] * math.sin(theta) self.params = (dist, theta) return True def residuals(self, data): """Determine residuals of data to model. For each point the shortest distance to the line is returned. Parameters ---------- data : (N, 2) array N points with ``(x, y)`` coordinates, respectively. Returns ------- residuals : (N, ) array Residual for each data point. """ _check_data_dim(data, dim=2) dist, theta = self.params x = data[:, 0] y = data[:, 1] return dist - (x * math.cos(theta) + y * math.sin(theta)) def predict_x(self, y, params=None): """Predict x-coordinates using the estimated model. Parameters ---------- y : array y-coordinates. params : (2, ) array, optional Optional custom parameter set. Returns ------- x : array Predicted x-coordinates. """ if params is None: params = self.params dist, theta = params return (dist - y * math.sin(theta)) / math.cos(theta) def predict_y(self, x, params=None): """Predict y-coordinates using the estimated model. Parameters ---------- x : array x-coordinates. params : (2, ) array, optional Optional custom parameter set. Returns ------- y : array Predicted y-coordinates. """ if params is None: params = self.params dist, theta = params return (dist - x * math.cos(theta)) / math.sin(theta) class LineModelND(BaseModel): """Total least squares estimator for N-dimensional lines. Lines are defined by a point (origin) and a unit vector (direction) according to the following vector equation:: X = origin + lambda * direction Attributes ---------- params : tuple Line model parameters in the following order `origin`, `direction`. """ def estimate(self, data): """Estimate line model from data. Parameters ---------- data : (N, dim) array N points in a space of dimensionality dim >= 2. Returns ------- success : bool True, if model estimation succeeds. """ _check_data_atleast_2D(data) X0 = data.mean(axis=0) if data.shape[0] == 2: # well determined u = data[1] - data[0] norm = np.linalg.norm(u) if norm > 0: u /= norm elif data.shape[0] > 2: # over-determined data = data - X0 # first principal component # Note: without full_matrices=False Python dies with joblib # parallel_for. _, _, u = np.linalg.svd(data, full_matrices=False) u = u[0] else: # under-determined raise ValueError('At least 2 input points needed.') self.params = (X0, u) return True def residuals(self, data): """Determine residuals of data to model. For each point the shortest distance to the line is returned. It is obtained by projecting the data onto the line. Parameters ---------- data : (N, dim) array N points in a space of dimension dim. Returns ------- residuals : (N, ) array Residual for each data point. """ X0, u = self.params return _norm_along_axis((data - X0) - np.dot(data - X0, u)[..., np.newaxis] * u, axis=1) def predict(self, x, axis=0, params=None): """Predict intersection of the estimated line model with a hyperplane orthogonal to a given axis. Parameters ---------- x : array coordinates along an axis. axis : int axis orthogonal to the hyperplane intersecting the line. params : (2, ) array, optional Optional custom parameter set in the form (`origin`, `direction`). Returns ------- y : array Predicted coordinates. If the line is parallel to the given axis, a ValueError is raised. """ if params is None: params = self.params X0, u = params if u[axis] == 0: # line parallel to axis raise ValueError('Line parallel to axis %s' % axis) l = (x - X0[axis]) / u[axis] return X0 + l[..., np.newaxis] * u def predict_x(self, y, params=None): """Predict x-coordinates for 2D lines using the estimated model. Alias for:: predict(y, axis=1)[:, 0] Parameters ---------- y : array y-coordinates. params : (2, ) array, optional Optional custom parameter set in the form (`origin`, `direction`). Returns ------- x : array Predicted x-coordinates. """ return self.predict(y, axis=1, params=params)[:, 0] def predict_y(self, x, params=None): """Predict y-coordinates for 2D lines using the estimated model. Alias for:: predict(x, axis=0)[:, 1] Parameters ---------- x : array x-coordinates. params : (2, ) array, optional Optional custom parameter set in the form (`origin`, `direction`). Returns ------- y : array Predicted y-coordinates. """ return self.predict(x, axis=0, params=params)[:, 1] class CircleModel(BaseModel): """Total least squares estimator for 2D circles. The functional model of the circle is:: r**2 = (x - xc)**2 + (y - yc)**2 This estimator minimizes the squared distances from all points to the circle:: min{ sum((r - sqrt((x_i - xc)**2 + (y_i - yc)**2))**2) } A minimum number of 3 points is required to solve for the parameters. Attributes ---------- params : tuple Circle model parameters in the following order `xc`, `yc`, `r`. """ def estimate(self, data): """Estimate circle model from data using total least squares. Parameters ---------- data : (N, 2) array N points with ``(x, y)`` coordinates, respectively. Returns ------- success : bool True, if model estimation succeeds. """ _check_data_dim(data, dim=2) x = data[:, 0] y = data[:, 1] # pre-allocate jacobian for all iterations A = np.zeros((3, data.shape[0]), dtype=np.double) # same for all iterations: r A[2, :] = -1 def dist(xc, yc): return np.sqrt((x - xc)**2 + (y - yc)**2) def fun(params): xc, yc, r = params return dist(xc, yc) - r def Dfun(params): xc, yc, r = params d = dist(xc, yc) A[0, :] = -(x - xc) / d A[1, :] = -(y - yc) / d # same for all iterations, so not changed in each iteration #A[2, :] = -1 return A xc0 = x.mean() yc0 = y.mean() r0 = dist(xc0, yc0).mean() params0 = (xc0, yc0, r0) params, _ = optimize.leastsq(fun, params0, Dfun=Dfun, col_deriv=True) self.params = params return True def residuals(self, data): """Determine residuals of data to model. For each point the shortest distance to the circle is returned. Parameters ---------- data : (N, 2) array N points with ``(x, y)`` coordinates, respectively. Returns ------- residuals : (N, ) array Residual for each data point. """ _check_data_dim(data, dim=2) xc, yc, r = self.params x = data[:, 0] y = data[:, 1] return r - np.sqrt((x - xc)**2 + (y - yc)**2) def predict_xy(self, t, params=None): """Predict x- and y-coordinates using the estimated model. Parameters ---------- t : array Angles in circle in radians. Angles start to count from positive x-axis to positive y-axis in a right-handed system. params : (3, ) array, optional Optional custom parameter set. Returns ------- xy : (..., 2) array Predicted x- and y-coordinates. """ if params is None: params = self.params xc, yc, r = params x = xc + r * np.cos(t) y = yc + r * np.sin(t) return np.concatenate((x[..., None], y[..., None]), axis=t.ndim) class EllipseModel(BaseModel): """Total least squares estimator for 2D ellipses. The functional model of the ellipse is:: xt = xc + a*cos(theta)*cos(t) - b*sin(theta)*sin(t) yt = yc + a*sin(theta)*cos(t) + b*cos(theta)*sin(t) d = sqrt((x - xt)**2 + (y - yt)**2) where ``(xt, yt)`` is the closest point on the ellipse to ``(x, y)``. Thus d is the shortest distance from the point to the ellipse. The estimator is based on a least squares minimization. The optimal solution is computed directly, no iterations are required. This leads to a simple, stable and robust fitting method. The ``params`` attribute contains the parameters in the following order:: xc, yc, a, b, theta Attributes ---------- params : tuple Ellipse model parameters in the following order `xc`, `yc`, `a`, `b`, `theta`. Examples -------- >>> xy = EllipseModel().predict_xy(np.linspace(0, 2 * np.pi, 25), ... params=(10, 15, 4, 8, np.deg2rad(30))) >>> ellipse = EllipseModel() >>> ellipse.estimate(xy) True >>> np.round(ellipse.params, 2) array([ 10. , 15. , 4. , 8. , 0.52]) >>> np.round(abs(ellipse.residuals(xy)), 5) array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]) """ def estimate(self, data): """Estimate circle model from data using total least squares. Parameters ---------- data : (N, 2) array N points with ``(x, y)`` coordinates, respectively. Returns ------- success : bool True, if model estimation succeeds. References ---------- .. [1] Halir, R.; Flusser, J. "Numerically stable direct least squares fitting of ellipses". In Proc. 6th International Conference in Central Europe on Computer Graphics and Visualization. WSCG (Vol. 98, pp. 125-132). """ # Original Implementation: Ben Hammel, Nick Sullivan-Molina # another REFERENCE: [2] http://mathworld.wolfram.com/Ellipse.html _check_data_dim(data, dim=2) x = data[:, 0] y = data[:, 1] # Quadratic part of design matrix [eqn. 15] from [1] D1 = np.vstack([x ** 2, x * y, y ** 2]).T # Linear part of design matrix [eqn. 16] from [1] D2 = np.vstack([x, y, np.ones(len(x))]).T # forming scatter matrix [eqn. 17] from [1] S1 = np.dot(D1.T, D1) S2 = np.dot(D1.T, D2) S3 = np.dot(D2.T, D2) # Constraint matrix [eqn. 18] C1 = np.array([[0., 0., 2.], [0., -1., 0.], [2., 0., 0.]]) try: # Reduced scatter matrix [eqn. 29] M = np.linalg.inv(C1).dot( S1 - np.dot(S2, np.linalg.inv(S3)).dot(S2.T)) except np.linalg.LinAlgError: # LinAlgError: Singular matrix return False # M*|a b c >=l|a b c >. Find eigenvalues and eigenvectors # from this equation [eqn. 28] eig_vals, eig_vecs = np.linalg.eig(M) # eigenvector must meet constraint 4ac - b^2 to be valid. cond = 4 * np.multiply(eig_vecs[0, :], eig_vecs[2, :]) \ - np.power(eig_vecs[1, :], 2) a1 = eig_vecs[:, (cond > 0)] # seeks for empty matrix if 0 in a1.shape or len(a1.ravel()) != 3: return False a, b, c = a1.ravel() # |d f g> = -S3^(-1)*S2^(T)*|a b c> [eqn. 24] a2 = np.dot(-np.linalg.inv(S3), S2.T).dot(a1) d, f, g = a2.ravel() # eigenvectors are the coefficients of an ellipse in general form # a*x^2 + 2*b*x*y + c*y^2 + 2*d*x + 2*f*y + g = 0 (eqn. 15) from [2] b /= 2. d /= 2. f /= 2. # finding center of ellipse [eqn.19 and 20] from [2] x0 = (c * d - b * f) / (b ** 2. - a * c) y0 = (a * f - b * d) / (b ** 2. - a * c) # Find the semi-axes lengths [eqn. 21 and 22] from [2] numerator = a * f ** 2 + c * d ** 2 + g * b ** 2 \ - 2 * b * d * f - a * c * g term = np.sqrt((a - c) ** 2 + 4 * b ** 2) denominator1 = (b ** 2 - a * c) * (term - (a + c)) denominator2 = (b ** 2 - a * c) * (- term - (a + c)) width = np.sqrt(2 * numerator / denominator1) height = np.sqrt(2 * numerator / denominator2) # angle of counterclockwise rotation of major-axis of ellipse # to x-axis [eqn. 23] from [2]. phi = 0.5 * np.arctan((2. * b) / (a - c)) if a > c: phi += 0.5 * np.pi self.params = np.nan_to_num([x0, y0, width, height, phi]).tolist() self.params = [float(np.real(x)) for x in self.params] return True def residuals(self, data): """Determine residuals of data to model. For each point the shortest distance to the ellipse is returned. Parameters ---------- data : (N, 2) array N points with ``(x, y)`` coordinates, respectively. Returns ------- residuals : (N, ) array Residual for each data point. """ _check_data_dim(data, dim=2) xc, yc, a, b, theta = self.params ctheta = math.cos(theta) stheta = math.sin(theta) x = data[:, 0] y = data[:, 1] N = data.shape[0] def fun(t, xi, yi): ct = math.cos(t) st = math.sin(t) xt = xc + a * ctheta * ct - b * stheta * st yt = yc + a * stheta * ct + b * ctheta * st return (xi - xt) ** 2 + (yi - yt) ** 2 # def Dfun(t, xi, yi): # ct = math.cos(t) # st = math.sin(t) # xt = xc + a * ctheta * ct - b * stheta * st # yt = yc + a * stheta * ct + b * ctheta * st # dfx_t = - 2 * (xi - xt) * (- a * ctheta * st # - b * stheta * ct) # dfy_t = - 2 * (yi - yt) * (- a * stheta * st # + b * ctheta * ct) # return [dfx_t + dfy_t] residuals = np.empty((N, ), dtype=np.double) # initial guess for parameter t of closest point on ellipse t0 = np.arctan2(y - yc, x - xc) - theta # determine shortest distance to ellipse for each point for i in range(N): xi = x[i] yi = y[i] # faster without Dfun, because of the python overhead t, _ = optimize.leastsq(fun, t0[i], args=(xi, yi)) residuals[i] = np.sqrt(fun(t, xi, yi)) return residuals def predict_xy(self, t, params=None): """Predict x- and y-coordinates using the estimated model. Parameters ---------- t : array Angles in circle in radians. Angles start to count from positive x-axis to positive y-axis in a right-handed system. params : (5, ) array, optional Optional custom parameter set. Returns ------- xy : (..., 2) array Predicted x- and y-coordinates. """ if params is None: params = self.params xc, yc, a, b, theta = params ct = np.cos(t) st = np.sin(t) ctheta = math.cos(theta) stheta = math.sin(theta) x = xc + a * ctheta * ct - b * stheta * st y = yc + a * stheta * ct + b * ctheta * st return np.concatenate((x[..., None], y[..., None]), axis=t.ndim) def _dynamic_max_trials(n_inliers, n_samples, min_samples, probability): """Determine number trials such that at least one outlier-free subset is sampled for the given inlier/outlier ratio. Parameters ---------- n_inliers : int Number of inliers in the data. n_samples : int Total number of samples in the data. min_samples : int Minimum number of samples chosen randomly from original data. probability : float Probability (confidence) that one outlier-free sample is generated. Returns ------- trials : int Number of trials. """ if n_inliers == 0: return np.inf nom = 1 - probability if nom == 0: return np.inf inlier_ratio = n_inliers / float(n_samples) denom = 1 - inlier_ratio ** min_samples if denom == 0: return 1 elif denom == 1: return np.inf nom = np.log(nom) denom = np.log(denom) if denom == 0: return 0 return int(np.ceil(nom / denom)) def ransac(data, model_class, min_samples, residual_threshold, is_data_valid=None, is_model_valid=None, max_trials=100, stop_sample_num=np.inf, stop_residuals_sum=0, stop_probability=1, random_state=None): """Fit a model to data with the RANSAC (random sample consensus) algorithm. RANSAC is an iterative algorithm for the robust estimation of parameters from a subset of inliers from the complete data set. Each iteration performs the following tasks: 1. Select `min_samples` random samples from the original data and check whether the set of data is valid (see `is_data_valid`). 2. Estimate a model to the random subset (`model_cls.estimate(*data[random_subset]`) and check whether the estimated model is valid (see `is_model_valid`). 3. Classify all data as inliers or outliers by calculating the residuals to the estimated model (`model_cls.residuals(*data)`) - all data samples with residuals smaller than the `residual_threshold` are considered as inliers. 4. Save estimated model as best model if number of inlier samples is maximal. In case the current estimated model has the same number of inliers, it is only considered as the best model if it has less sum of residuals. These steps are performed either a maximum number of times or until one of the special stop criteria are met. The final model is estimated using all inlier samples of the previously determined best model. Parameters ---------- data : [list, tuple of] (N, D) array Data set to which the model is fitted, where N is the number of data points and D the dimensionality of the data. If the model class requires multiple input data arrays (e.g. source and destination coordinates of ``skimage.transform.AffineTransform``), they can be optionally passed as tuple or list. Note, that in this case the functions ``estimate(*data)``, ``residuals(*data)``, ``is_model_valid(model, *random_data)`` and ``is_data_valid(*random_data)`` must all take each data array as separate arguments. model_class : object Object with the following object methods: * ``success = estimate(*data)`` * ``residuals(*data)`` where `success` indicates whether the model estimation succeeded (`True` or `None` for success, `False` for failure). min_samples : int The minimum number of data points to fit a model to. residual_threshold : float Maximum distance for a data point to be classified as an inlier. is_data_valid : function, optional This function is called with the randomly selected data before the model is fitted to it: `is_data_valid(*random_data)`. is_model_valid : function, optional This function is called with the estimated model and the randomly selected data: `is_model_valid(model, *random_data)`, . max_trials : int, optional Maximum number of iterations for random sample selection. stop_sample_num : int, optional Stop iteration if at least this number of inliers are found. stop_residuals_sum : float, optional Stop iteration if sum of residuals is less than or equal to this threshold. stop_probability : float in range [0, 1], optional RANSAC iteration stops if at least one outlier-free set of the training data is sampled with ``probability >= stop_probability``, depending on the current best model's inlier ratio and the number of trials. This requires to generate at least N samples (trials): N >= log(1 - probability) / log(1 - e**m) where the probability (confidence) is typically set to a high value such as 0.99, and e is the current fraction of inliers w.r.t. the total number of samples. random_state : int, RandomState instance or None, optional If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. Returns ------- model : object Best model with largest consensus set. inliers : (N, ) array Boolean mask of inliers classified as ``True``. References ---------- .. [1] "RANSAC", Wikipedia, http://en.wikipedia.org/wiki/RANSAC Examples -------- Generate ellipse data without tilt and add noise: >>> t = np.linspace(0, 2 * np.pi, 50) >>> xc, yc = 20, 30 >>> a, b = 5, 10 >>> x = xc + a * np.cos(t) >>> y = yc + b * np.sin(t) >>> data = np.column_stack([x, y]) >>> np.random.seed(seed=1234) >>> data += np.random.normal(size=data.shape) Add some faulty data: >>> data[0] = (100, 100) >>> data[1] = (110, 120) >>> data[2] = (120, 130) >>> data[3] = (140, 130) Estimate ellipse model using all available data: >>> model = EllipseModel() >>> model.estimate(data) True >>> np.round(model.params) # doctest: +SKIP array([ 72., 75., 77., 14., 1.]) Estimate ellipse model using RANSAC: >>> ransac_model, inliers = ransac(data, EllipseModel, 20, 3, max_trials=50) >>> abs(np.round(ransac_model.params)) array([ 20., 30., 5., 10., 0.]) >>> inliers # doctest: +SKIP array([False, False, False, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True], dtype=bool) >>> sum(inliers) > 40 True Robustly estimate geometric transformation: >>> from skimage.transform import SimilarityTransform >>> np.random.seed(0) >>> src = 100 * np.random.rand(50, 2) >>> model0 = SimilarityTransform(scale=0.5, rotation=1, ... translation=(10, 20)) >>> dst = model0(src) >>> dst[0] = (10000, 10000) >>> dst[1] = (-100, 100) >>> dst[2] = (50, 50) >>> model, inliers = ransac((src, dst), SimilarityTransform, 2, 10) >>> inliers array([False, False, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True], dtype=bool) """ best_model = None best_inlier_num = 0 best_inlier_residuals_sum = np.inf best_inliers = None random_state = check_random_state(random_state) if min_samples < 0: raise ValueError("`min_samples` must be greater than zero") if max_trials < 0: raise ValueError("`max_trials` must be greater than zero") if stop_probability < 0 or stop_probability > 1: raise ValueError("`stop_probability` must be in range [0, 1]") if not isinstance(data, list) and not isinstance(data, tuple): data = [data] # make sure data is list and not tuple, so it can be modified below data = list(data) # number of samples num_samples = data[0].shape[0] for num_trials in range(max_trials): # choose random sample set samples = [] random_idxs = random_state.randint(0, num_samples, min_samples) for d in data: samples.append(d[random_idxs]) # check if random sample set is valid if is_data_valid is not None and not is_data_valid(*samples): continue # estimate model for current random sample set sample_model = model_class() success = sample_model.estimate(*samples) if success is not None: # backwards compatibility if not success: continue # check if estimated model is valid if is_model_valid is not None \ and not is_model_valid(sample_model, *samples): continue sample_model_residuals = np.abs(sample_model.residuals(*data)) # consensus set / inliers sample_model_inliers = sample_model_residuals < residual_threshold sample_model_residuals_sum = np.sum(sample_model_residuals**2) # choose as new best model if number of inliers is maximal sample_inlier_num = np.sum(sample_model_inliers) if ( # more inliers sample_inlier_num > best_inlier_num # same number of inliers but less "error" in terms of residuals or (sample_inlier_num == best_inlier_num and sample_model_residuals_sum < best_inlier_residuals_sum) ): best_model = sample_model best_inlier_num = sample_inlier_num best_inlier_residuals_sum = sample_model_residuals_sum best_inliers = sample_model_inliers if ( best_inlier_num >= stop_sample_num or best_inlier_residuals_sum <= stop_residuals_sum or num_trials >= _dynamic_max_trials(best_inlier_num, num_samples, min_samples, stop_probability) ): break # estimate final model using all inliers if best_inliers is not None: # select inliers for each data array for i in range(len(data)): data[i] = data[i][best_inliers] best_model.estimate(*data) return best_model, best_inliers scikit-image-0.13.1/skimage/measure/mc_meta/000077500000000000000000000000001316256236300206375ustar00rootroot00000000000000scikit-image-0.13.1/skimage/measure/mc_meta/LookUpTable.h000066400000000000000000004663741316256236300232150ustar00rootroot00000000000000/** * @file LookUpTable.h * @author Thomas Lewiner * @author Math Dept, PUC-Rio * @version 0.2 * @date 12/08/2002 * * @brief LookUpTable for the MarchingCubes 33 Algorithm */ //________________________________________________ #ifndef _LOOKUPTABLE_H_ #define _LOOKUPTABLE_H_ //_____________________________________________________________________________ /** * \brief case mapping * For each of the possible vertex states listed in this table there is a * specific triangulation of the edge intersection points. The table lists * all of them in the form of 0-5 edge triples with the list terminated by * the invalid value -1. For example: case[3] list the 2 triangles * formed when cube[0] and cube[1] are inside of the surface, but the rest of * the cube is not. * * Cube description: * 7 ________ 6 _____6__ ________ * /| /| 7/| /| /| /| * / | / | / | /5 | / 6 / | * 4 /_______ / | /__4____ / 10 /_______3/ | * | | |5 | | 11 | | | | | 2 | * | 3|__|_____|2 | |__|__2__| | 4 |__|_____| * | / | / 8 3/ 9 / | / | / * | / | / | / | /1 | / 5 / * |/_______|/ |/___0___|/ |/_1_____|/ * 0 1 0 1 */ //----------------------------------------------------------------------------- static const char cases[256][2] = { /* 0: */ { 0, -1 }, /* 1: 0, */ { 1, 0 }, /* 2: 1, */ { 1, 1 }, /* 3: 0, 1, */ { 2, 0 }, /* 4: 2, */ { 1, 2 }, /* 5: 0, 2, */ { 3, 0 }, /* 6: 1, 2, */ { 2, 3 }, /* 7: 0, 1, 2, */ { 5, 0 }, /* 8: 3, */ { 1, 3 }, /* 9: 0, 3, */ { 2, 1 }, /* 10: 1, 3, */ { 3, 3 }, /* 11: 0, 1, 3, */ { 5, 1 }, /* 12: 2, 3, */ { 2, 5 }, /* 13: 0, 2, 3, */ { 5, 4 }, /* 14: 1, 2, 3, */ { 5, 9 }, /* 15: 0, 1, 2, 3, */ { 8, 0 }, /* 16: 4, */ { 1, 4 }, /* 17: 0, 4, */ { 2, 2 }, /* 18: 1, 4, */ { 3, 4 }, /* 19: 0, 1, 4, */ { 5, 2 }, /* 20: 2, 4, */ { 4, 2 }, /* 21: 0, 2, 4, */ { 6, 2 }, /* 22: 1, 2, 4, */ { 6, 9 }, /* 23: 0, 1, 2, 4, */ { 11, 0 }, /* 24: 3, 4, */ { 3, 8 }, /* 25: 0, 3, 4, */ { 5, 5 }, /* 26: 1, 3, 4, */ { 7, 3 }, /* 27: 0, 1, 3, 4, */ { 9, 1 }, /* 28: 2, 3, 4, */ { 6, 16 }, /* 29: 0, 2, 3, 4, */ { 14, 3 }, /* 30: 1, 2, 3, 4, */ { 12, 12 }, /* 31: 0, 1, 2, 3, 4, */ { 5, 24 }, /* 32: 5, */ { 1, 5 }, /* 33: 0, 5, */ { 3, 1 }, /* 34: 1, 5, */ { 2, 4 }, /* 35: 0, 1, 5, */ { 5, 3 }, /* 36: 2, 5, */ { 3, 6 }, /* 37: 0, 2, 5, */ { 7, 0 }, /* 38: 1, 2, 5, */ { 5, 10 }, /* 39: 0, 1, 2, 5, */ { 9, 0 }, /* 40: 3, 5, */ { 4, 3 }, /* 41: 0, 3, 5, */ { 6, 4 }, /* 42: 1, 3, 5, */ { 6, 11 }, /* 43: 0, 1, 3, 5, */ { 14, 1 }, /* 44: 2, 3, 5, */ { 6, 17 }, /* 45: 0, 2, 3, 5, */ { 12, 4 }, /* 46: 1, 2, 3, 5, */ { 11, 6 }, /* 47: 0, 1, 2, 3, 5, */ { 5, 25 }, /* 48: 4, 5, */ { 2, 8 }, /* 49: 0, 4, 5, */ { 5, 7 }, /* 50: 1, 4, 5, */ { 5, 12 }, /* 51: 0, 1, 4, 5, */ { 8, 1 }, /* 52: 2, 4, 5, */ { 6, 18 }, /* 53: 0, 2, 4, 5, */ { 12, 5 }, /* 54: 1, 2, 4, 5, */ { 14, 7 }, /* 55: 0, 1, 2, 4, 5, */ { 5, 28 }, /* 56: 3, 4, 5, */ { 6, 21 }, /* 57: 0, 3, 4, 5, */ { 11, 4 }, /* 58: 1, 3, 4, 5, */ { 12, 15 }, /* 59: 0, 1, 3, 4, 5, */ { 5, 30 }, /* 60: 2, 3, 4, 5, */ { 10, 5 }, /* 61: 0, 2, 3, 4, 5, */ { 6, 32 }, /* 62: 1, 2, 3, 4, 5, */ { 6, 39 }, /* 63: 0, 1, 2, 3, 4, 5, */ { 2, 12 }, /* 64: 6, */ { 1, 6 }, /* 65: 0, 6, */ { 4, 0 }, /* 66: 1, 6, */ { 3, 5 }, /* 67: 0, 1, 6, */ { 6, 0 }, /* 68: 2, 6, */ { 2, 6 }, /* 69: 0, 2, 6, */ { 6, 3 }, /* 70: 1, 2, 6, */ { 5, 11 }, /* 71: 0, 1, 2, 6, */ { 14, 0 }, /* 72: 3, 6, */ { 3, 9 }, /* 73: 0, 3, 6, */ { 6, 5 }, /* 74: 1, 3, 6, */ { 7, 4 }, /* 75: 0, 1, 3, 6, */ { 12, 1 }, /* 76: 2, 3, 6, */ { 5, 14 }, /* 77: 0, 2, 3, 6, */ { 11, 3 }, /* 78: 1, 2, 3, 6, */ { 9, 4 }, /* 79: 0, 1, 2, 3, 6, */ { 5, 26 }, /* 80: 4, 6, */ { 3, 10 }, /* 81: 0, 4, 6, */ { 6, 6 }, /* 82: 1, 4, 6, */ { 7, 5 }, /* 83: 0, 1, 4, 6, */ { 12, 2 }, /* 84: 2, 4, 6, */ { 6, 19 }, /* 85: 0, 2, 4, 6, */ { 10, 1 }, /* 86: 1, 2, 4, 6, */ { 12, 13 }, /* 87: 0, 1, 2, 4, 6, */ { 6, 24 }, /* 88: 3, 4, 6, */ { 7, 7 }, /* 89: 0, 3, 4, 6, */ { 12, 9 }, /* 90: 1, 3, 4, 6, */ { 13, 1 }, /* 91: 0, 1, 3, 4, 6, */ { 7, 9 }, /* 92: 2, 3, 4, 6, */ { 12, 20 }, /* 93: 0, 2, 3, 4, 6, */ { 6, 33 }, /* 94: 1, 2, 3, 4, 6, */ { 7, 13 }, /* 95: 0, 1, 2, 3, 4, 6, */ { 3, 12 }, /* 96: 5, 6, */ { 2, 10 }, /* 97: 0, 5, 6, */ { 6, 7 }, /* 98: 1, 5, 6, */ { 5, 13 }, /* 99: 0, 1, 5, 6, */ { 11, 2 }, /* 100: 2, 5, 6, */ { 5, 16 }, /* 101: 0, 2, 5, 6, */ { 12, 7 }, /* 102: 1, 2, 5, 6, */ { 8, 3 }, /* 103: 0, 1, 2, 5, 6, */ { 5, 29 }, /* 104: 3, 5, 6, */ { 6, 22 }, /* 105: 0, 3, 5, 6, */ { 10, 2 }, /* 106: 1, 3, 5, 6, */ { 12, 17 }, /* 107: 0, 1, 3, 5, 6, */ { 6, 27 }, /* 108: 2, 3, 5, 6, */ { 14, 9 }, /* 109: 0, 2, 3, 5, 6, */ { 6, 34 }, /* 110: 1, 2, 3, 5, 6, */ { 5, 39 }, /* 111: 0, 1, 2, 3, 5, 6, */ { 2, 14 }, /* 112: 4, 5, 6, */ { 5, 20 }, /* 113: 0, 4, 5, 6, */ { 14, 5 }, /* 114: 1, 4, 5, 6, */ { 9, 5 }, /* 115: 0, 1, 4, 5, 6, */ { 5, 32 }, /* 116: 2, 4, 5, 6, */ { 11, 10 }, /* 117: 0, 2, 4, 5, 6, */ { 6, 35 }, /* 118: 1, 2, 4, 5, 6, */ { 5, 41 }, /* 119: 0, 1, 2, 4, 5, 6, */ { 2, 16 }, /* 120: 3, 4, 5, 6, */ { 12, 23 }, /* 121: 0, 3, 4, 5, 6, */ { 6, 37 }, /* 122: 1, 3, 4, 5, 6, */ { 7, 14 }, /* 123: 0, 1, 3, 4, 5, 6, */ { 3, 16 }, /* 124: 2, 3, 4, 5, 6, */ { 6, 46 }, /* 125: 0, 2, 3, 4, 5, 6, */ { 4, 6 }, /* 126: 1, 2, 3, 4, 5, 6, */ { 3, 21 }, /* 127: 0, 1, 2, 3, 4, 5, 6, */ { 1, 8 }, /* 128: 7, */ { 1, 7 }, /* 129: 0, 7, */ { 3, 2 }, /* 130: 1, 7, */ { 4, 1 }, /* 131: 0, 1, 7, */ { 6, 1 }, /* 132: 2, 7, */ { 3, 7 }, /* 133: 0, 2, 7, */ { 7, 1 }, /* 134: 1, 2, 7, */ { 6, 10 }, /* 135: 0, 1, 2, 7, */ { 12, 0 }, /* 136: 3, 7, */ { 2, 7 }, /* 137: 0, 3, 7, */ { 5, 6 }, /* 138: 1, 3, 7, */ { 6, 12 }, /* 139: 0, 1, 3, 7, */ { 11, 1 }, /* 140: 2, 3, 7, */ { 5, 15 }, /* 141: 0, 2, 3, 7, */ { 9, 2 }, /* 142: 1, 2, 3, 7, */ { 14, 6 }, /* 143: 0, 1, 2, 3, 7, */ { 5, 27 }, /* 144: 4, 7, */ { 2, 9 }, /* 145: 0, 4, 7, */ { 5, 8 }, /* 146: 1, 4, 7, */ { 6, 13 }, /* 147: 0, 1, 4, 7, */ { 14, 2 }, /* 148: 2, 4, 7, */ { 6, 20 }, /* 149: 0, 2, 4, 7, */ { 12, 6 }, /* 150: 1, 2, 4, 7, */ { 10, 3 }, /* 151: 0, 1, 2, 4, 7, */ { 6, 25 }, /* 152: 3, 4, 7, */ { 5, 18 }, /* 153: 0, 3, 4, 7, */ { 8, 2 }, /* 154: 1, 3, 4, 7, */ { 12, 16 }, /* 155: 0, 1, 3, 4, 7, */ { 5, 31 }, /* 156: 2, 3, 4, 7, */ { 11, 9 }, /* 157: 0, 2, 3, 4, 7, */ { 5, 34 }, /* 158: 1, 2, 3, 4, 7, */ { 6, 40 }, /* 159: 0, 1, 2, 3, 4, 7, */ { 2, 13 }, /* 160: 5, 7, */ { 3, 11 }, /* 161: 0, 5, 7, */ { 7, 2 }, /* 162: 1, 5, 7, */ { 6, 14 }, /* 163: 0, 1, 5, 7, */ { 12, 3 }, /* 164: 2, 5, 7, */ { 7, 6 }, /* 165: 0, 2, 5, 7, */ { 13, 0 }, /* 166: 1, 2, 5, 7, */ { 12, 14 }, /* 167: 0, 1, 2, 5, 7, */ { 7, 8 }, /* 168: 3, 5, 7, */ { 6, 23 }, /* 169: 0, 3, 5, 7, */ { 12, 10 }, /* 170: 1, 3, 5, 7, */ { 10, 4 }, /* 171: 0, 1, 3, 5, 7, */ { 6, 28 }, /* 172: 2, 3, 5, 7, */ { 12, 21 }, /* 173: 0, 2, 3, 5, 7, */ { 7, 10 }, /* 174: 1, 2, 3, 5, 7, */ { 6, 41 }, /* 175: 0, 1, 2, 3, 5, 7, */ { 3, 13 }, /* 176: 4, 5, 7, */ { 5, 21 }, /* 177: 0, 4, 5, 7, */ { 9, 3 }, /* 178: 1, 4, 5, 7, */ { 11, 8 }, /* 179: 0, 1, 4, 5, 7, */ { 5, 33 }, /* 180: 2, 4, 5, 7, */ { 12, 22 }, /* 181: 0, 2, 4, 5, 7, */ { 7, 11 }, /* 182: 1, 2, 4, 5, 7, */ { 6, 42 }, /* 183: 0, 1, 2, 4, 5, 7, */ { 3, 14 }, /* 184: 3, 4, 5, 7, */ { 14, 11 }, /* 185: 0, 3, 4, 5, 7, */ { 5, 36 }, /* 186: 1, 3, 4, 5, 7, */ { 6, 44 }, /* 187: 0, 1, 3, 4, 5, 7, */ { 2, 17 }, /* 188: 2, 3, 4, 5, 7, */ { 6, 47 }, /* 189: 0, 2, 3, 4, 5, 7, */ { 3, 18 }, /* 190: 1, 2, 3, 4, 5, 7, */ { 4, 7 }, /* 191: 0, 1, 2, 3, 4, 5, 7, */ { 1, 9 }, /* 192: 6, 7, */ { 2, 11 }, /* 193: 0, 6, 7, */ { 6, 8 }, /* 194: 1, 6, 7, */ { 6, 15 }, /* 195: 0, 1, 6, 7, */ { 10, 0 }, /* 196: 2, 6, 7, */ { 5, 17 }, /* 197: 0, 2, 6, 7, */ { 12, 8 }, /* 198: 1, 2, 6, 7, */ { 11, 7 }, /* 199: 0, 1, 2, 6, 7, */ { 6, 26 }, /* 200: 3, 6, 7, */ { 5, 19 }, /* 201: 0, 3, 6, 7, */ { 14, 4 }, /* 202: 1, 3, 6, 7, */ { 12, 18 }, /* 203: 0, 1, 3, 6, 7, */ { 6, 29 }, /* 204: 2, 3, 6, 7, */ { 8, 4 }, /* 205: 0, 2, 3, 6, 7, */ { 5, 35 }, /* 206: 1, 2, 3, 6, 7, */ { 5, 40 }, /* 207: 0, 1, 2, 3, 6, 7, */ { 2, 15 }, /* 208: 4, 6, 7, */ { 5, 22 }, /* 209: 0, 4, 6, 7, */ { 11, 5 }, /* 210: 1, 4, 6, 7, */ { 12, 19 }, /* 211: 0, 1, 4, 6, 7, */ { 6, 30 }, /* 212: 2, 4, 6, 7, */ { 14, 10 }, /* 213: 0, 2, 4, 6, 7, */ { 6, 36 }, /* 214: 1, 2, 4, 6, 7, */ { 6, 43 }, /* 215: 0, 1, 2, 4, 6, 7, */ { 4, 4 }, /* 216: 3, 4, 6, 7, */ { 9, 7 }, /* 217: 0, 3, 4, 6, 7, */ { 5, 37 }, /* 218: 1, 3, 4, 6, 7, */ { 7, 15 }, /* 219: 0, 1, 3, 4, 6, 7, */ { 3, 17 }, /* 220: 2, 3, 4, 6, 7, */ { 5, 44 }, /* 221: 0, 2, 3, 4, 6, 7, */ { 2, 19 }, /* 222: 1, 2, 3, 4, 6, 7, */ { 3, 22 }, /* 223: 0, 1, 2, 3, 4, 6, 7, */ { 1, 10 }, /* 224: 5, 6, 7, */ { 5, 23 }, /* 225: 0, 5, 6, 7, */ { 12, 11 }, /* 226: 1, 5, 6, 7, */ { 14, 8 }, /* 227: 0, 1, 5, 6, 7, */ { 6, 31 }, /* 228: 2, 5, 6, 7, */ { 9, 6 }, /* 229: 0, 2, 5, 6, 7, */ { 7, 12 }, /* 230: 1, 2, 5, 6, 7, */ { 5, 42 }, /* 231: 0, 1, 2, 5, 6, 7, */ { 3, 15 }, /* 232: 3, 5, 6, 7, */ { 11, 11 }, /* 233: 0, 3, 5, 6, 7, */ { 6, 38 }, /* 234: 1, 3, 5, 6, 7, */ { 6, 45 }, /* 235: 0, 1, 3, 5, 6, 7, */ { 4, 5 }, /* 236: 2, 3, 5, 6, 7, */ { 5, 45 }, /* 237: 0, 2, 3, 5, 6, 7, */ { 3, 19 }, /* 238: 1, 2, 3, 5, 6, 7, */ { 2, 21 }, /* 239: 0, 1, 2, 3, 5, 6, 7, */ { 1, 11 }, /* 240: 4, 5, 6, 7, */ { 8, 5 }, /* 241: 0, 4, 5, 6, 7, */ { 5, 38 }, /* 242: 1, 4, 5, 6, 7, */ { 5, 43 }, /* 243: 0, 1, 4, 5, 6, 7, */ { 2, 18 }, /* 244: 2, 4, 5, 6, 7, */ { 5, 46 }, /* 245: 0, 2, 4, 5, 6, 7, */ { 3, 20 }, /* 246: 1, 2, 4, 5, 6, 7, */ { 2, 22 }, /* 247: 0, 1, 2, 4, 5, 6, 7, */ { 1, 12 }, /* 248: 3, 4, 5, 6, 7, */ { 5, 47 }, /* 249: 0, 3, 4, 5, 6, 7, */ { 2, 20 }, /* 250: 1, 3, 4, 5, 6, 7, */ { 3, 23 }, /* 251: 0, 1, 3, 4, 5, 6, 7, */ { 1, 13 }, /* 252: 2, 3, 4, 5, 6, 7, */ { 2, 23 }, /* 253: 0, 2, 3, 4, 5, 6, 7, */ { 1, 14 }, /* 254: 1, 2, 3, 4, 5, 6, 7, */ { 1, 15 }, /* 255: 0, 1, 2, 3, 4, 5, 6, 7, */ { 0, -1 } }; //_____________________________________________________________________________ //_____________________________________________________________________________ /** * \brief tiling table for case 1 * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- static const char tiling1[16][3] = { /* 1: 0, */ { 0, 8, 3 }, /* 2: 1, */ { 0, 1, 9 }, /* 4: 2, */ { 1, 2, 10 }, /* 8: 3, */ { 3, 11, 2 }, /* 16: 4, */ { 4, 7, 8 }, /* 32: 5, */ { 9, 5, 4 }, /* 64: 6, */ { 10, 6, 5 }, /* 128: 7, */ { 7, 6, 11 }, /* 127: 0, 1, 2, 3, 4, 5, 6, */ { 7, 11, 6 }, /* 191: 0, 1, 2, 3, 4, 5, 7, */ { 10, 5, 6 }, /* 223: 0, 1, 2, 3, 4, 6, 7, */ { 9, 4, 5 }, /* 239: 0, 1, 2, 3, 5, 6, 7, */ { 4, 8, 7 }, /* 247: 0, 1, 2, 4, 5, 6, 7, */ { 3, 2, 11 }, /* 251: 0, 1, 3, 4, 5, 6, 7, */ { 1, 10, 2 }, /* 253: 0, 2, 3, 4, 5, 6, 7, */ { 0, 9, 1 }, /* 254: 1, 2, 3, 4, 5, 6, 7, */ { 0, 3, 8 } }; //_____________________________________________________________________________ //_____________________________________________________________________________ /** * \brief tiling table for case 2 * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- static const char tiling2[24][6] = { /* 3: 0, 1, */ { 1, 8, 3, 9, 8, 1 }, /* 9: 0, 3, */ { 0, 11, 2, 8, 11, 0 }, /* 17: 0, 4, */ { 4, 3, 0, 7, 3, 4 }, /* 6: 1, 2, */ { 9, 2, 10, 0, 2, 9 }, /* 34: 1, 5, */ { 0, 5, 4, 1, 5, 0 }, /* 12: 2, 3, */ { 3, 10, 1, 11, 10, 3 }, /* 68: 2, 6, */ { 1, 6, 5, 2, 6, 1 }, /* 136: 3, 7, */ { 7, 2, 3, 6, 2, 7 }, /* 48: 4, 5, */ { 9, 7, 8, 5, 7, 9 }, /* 144: 4, 7, */ { 6, 8, 4, 11, 8, 6 }, /* 96: 5, 6, */ { 10, 4, 9, 6, 4, 10 }, /* 192: 6, 7, */ { 11, 5, 10, 7, 5, 11 }, /* 63: 0, 1, 2, 3, 4, 5, */ { 11, 10, 5, 7, 11, 5 }, /* 159: 0, 1, 2, 3, 4, 7, */ { 10, 9, 4, 6, 10, 4 }, /* 111: 0, 1, 2, 3, 5, 6, */ { 6, 4, 8, 11, 6, 8 }, /* 207: 0, 1, 2, 3, 6, 7, */ { 9, 8, 7, 5, 9, 7 }, /* 119: 0, 1, 2, 4, 5, 6, */ { 7, 3, 2, 6, 7, 2 }, /* 187: 0, 1, 3, 4, 5, 7, */ { 1, 5, 6, 2, 1, 6 }, /* 243: 0, 1, 4, 5, 6, 7, */ { 3, 1, 10, 11, 3, 10 }, /* 221: 0, 2, 3, 4, 6, 7, */ { 0, 4, 5, 1, 0, 5 }, /* 249: 0, 3, 4, 5, 6, 7, */ { 9, 10, 2, 0, 9, 2 }, /* 238: 1, 2, 3, 5, 6, 7, */ { 4, 0, 3, 7, 4, 3 }, /* 246: 1, 2, 4, 5, 6, 7, */ { 0, 2, 11, 8, 0, 11 }, /* 252: 2, 3, 4, 5, 6, 7, */ { 1, 3, 8, 9, 1, 8 } }; //_____________________________________________________________________________ //_____________________________________________________________________________ /** * \brief test table for case 3 * One face to test * When the test on the specified face is positive : 4 first triangles * When the test on the specified face is negative : 2 last triangles * * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- static const char test3[24] = { /* 5: 0, 2, */ 5, /* 33: 0, 5, */ 1, /* 129: 0, 7, */ 4, /* 10: 1, 3, */ 5, /* 18: 1, 4, */ 1, /* 66: 1, 6, */ 2, /* 36: 2, 5, */ 2, /* 132: 2, 7, */ 3, /* 24: 3, 4, */ 4, /* 72: 3, 6, */ 3, /* 80: 4, 6, */ 6, /* 160: 5, 7, */ 6, /* 95: 0, 1, 2, 3, 4, 6, */ -6, /* 175: 0, 1, 2, 3, 5, 7, */ -6, /* 183: 0, 1, 2, 4, 5, 7, */ -3, /* 231: 0, 1, 2, 5, 6, 7, */ -4, /* 123: 0, 1, 3, 4, 5, 6, */ -3, /* 219: 0, 1, 3, 4, 6, 7, */ -2, /* 189: 0, 2, 3, 4, 5, 7, */ -2, /* 237: 0, 2, 3, 5, 6, 7, */ -1, /* 245: 0, 2, 4, 5, 6, 7, */ -5, /* 126: 1, 2, 3, 4, 5, 6, */ -4, /* 222: 1, 2, 3, 4, 6, 7, */ -1, /* 250: 1, 3, 4, 5, 6, 7, */ -5 }; //_____________________________________________________________________________ /** * \brief tiling table for case 3.1 * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- static const char tiling3_1[24][6] = { /* 5: 0, 2, */ { 0, 8, 3, 1, 2, 10 }, /* 33: 0, 5, */ { 9, 5, 4, 0, 8, 3 }, /* 129: 0, 7, */ { 3, 0, 8, 11, 7, 6 }, /* 10: 1, 3, */ { 1, 9, 0, 2, 3, 11 }, /* 18: 1, 4, */ { 0, 1, 9, 8, 4, 7 }, /* 66: 1, 6, */ { 9, 0, 1, 5, 10, 6 }, /* 36: 2, 5, */ { 1, 2, 10, 9, 5, 4 }, /* 132: 2, 7, */ { 10, 1, 2, 6, 11, 7 }, /* 24: 3, 4, */ { 8, 4, 7, 3, 11, 2 }, /* 72: 3, 6, */ { 2, 3, 11, 10, 6, 5 }, /* 80: 4, 6, */ { 5, 10, 6, 4, 7, 8 }, /* 160: 5, 7, */ { 4, 9, 5, 7, 6, 11 }, /* 95: 0, 1, 2, 3, 4, 6, */ { 5, 9, 4, 11, 6, 7 }, /* 175: 0, 1, 2, 3, 5, 7, */ { 6, 10, 5, 8, 7, 4 }, /* 183: 0, 1, 2, 4, 5, 7, */ { 11, 3, 2, 5, 6, 10 }, /* 231: 0, 1, 2, 5, 6, 7, */ { 7, 4, 8, 2, 11, 3 }, /* 123: 0, 1, 3, 4, 5, 6, */ { 2, 1, 10, 7, 11, 6 }, /* 219: 0, 1, 3, 4, 6, 7, */ { 10, 2, 1, 4, 5, 9 }, /* 189: 0, 2, 3, 4, 5, 7, */ { 1, 0, 9, 6, 10, 5 }, /* 237: 0, 2, 3, 5, 6, 7, */ { 9, 1, 0, 7, 4, 8 }, /* 245: 0, 2, 4, 5, 6, 7, */ { 0, 9, 1, 11, 3, 2 }, /* 126: 1, 2, 3, 4, 5, 6, */ { 8, 0, 3, 6, 7, 11 }, /* 222: 1, 2, 3, 4, 6, 7, */ { 4, 5, 9, 3, 8, 0 }, /* 250: 1, 3, 4, 5, 6, 7, */ { 3, 8, 0, 10, 2, 1 } }; //_____________________________________________________________________________ /** * \brief tiling table for case 3.2 * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- static const char tiling3_2[24][12] = { /* 5: 0, 2, */ { 10, 3, 2, 10, 8, 3, 10, 1, 0, 8, 10, 0 }, /* 33: 0, 5, */ { 3, 4, 8, 3, 5, 4, 3, 0, 9, 5, 3, 9 }, /* 129: 0, 7, */ { 6, 8, 7, 6, 0, 8, 6, 11, 3, 0, 6, 3 }, /* 10: 1, 3, */ { 11, 0, 3, 11, 9, 0, 11, 2, 1, 9, 11, 1 }, /* 18: 1, 4, */ { 7, 9, 4, 7, 1, 9, 7, 8, 0, 1, 7, 0 }, /* 66: 1, 6, */ { 6, 1, 10, 6, 0, 1, 9, 0, 6, 9, 6, 5 }, /* 36: 2, 5, */ { 4, 10, 5, 4, 2, 10, 4, 9, 1, 2, 4, 1 }, /* 132: 2, 7, */ { 7, 2, 11, 7, 1, 2, 7, 6, 10, 1, 7, 10 }, /* 24: 3, 4, */ { 2, 7, 11, 2, 4, 7, 2, 3, 8, 4, 2, 8 }, /* 72: 3, 6, */ { 5, 11, 6, 5, 3, 11, 5, 10, 2, 3, 5, 2 }, /* 80: 4, 6, */ { 8, 6, 7, 8, 10, 6, 8, 4, 5, 10, 8, 5 }, /* 160: 5, 7, */ { 11, 5, 6, 11, 9, 5, 11, 7, 4, 9, 11, 4 }, /* 95: 0, 1, 2, 3, 4, 6, */ { 6, 5, 11, 5, 9, 11, 4, 7, 11, 4, 11, 9 }, /* 175: 0, 1, 2, 3, 5, 7, */ { 7, 6, 8, 6, 10, 8, 5, 4, 8, 5, 8, 10 }, /* 183: 0, 1, 2, 4, 5, 7, */ { 6, 11, 5, 11, 3, 5, 2, 10, 5, 2, 5, 3 }, /* 231: 0, 1, 2, 5, 6, 7, */ { 11, 7, 2, 7, 4, 2, 8, 3, 2, 8, 2, 4 }, /* 123: 0, 1, 3, 4, 5, 6, */ { 11, 2, 7, 2, 1, 7, 10, 6, 7, 10, 7, 1 }, /* 219: 0, 1, 3, 4, 6, 7, */ { 5, 10, 4, 10, 2, 4, 1, 9, 4, 1, 4, 2 }, /* 189: 0, 2, 3, 4, 5, 7, */ { 10, 1, 6, 1, 0, 6, 6, 0, 9, 5, 6, 9 }, /* 237: 0, 2, 3, 5, 6, 7, */ { 4, 9, 7, 9, 1, 7, 0, 8, 7, 0, 7, 1 }, /* 245: 0, 2, 4, 5, 6, 7, */ { 3, 0, 11, 0, 9, 11, 1, 2, 11, 1, 11, 9 }, /* 126: 1, 2, 3, 4, 5, 6, */ { 7, 8, 6, 8, 0, 6, 3, 11, 6, 3, 6, 0 }, /* 222: 1, 2, 3, 4, 6, 7, */ { 8, 4, 3, 4, 5, 3, 9, 0, 3, 9, 3, 5 }, /* 250: 1, 3, 4, 5, 6, 7, */ { 2, 3, 10, 3, 8, 10, 0, 1, 10, 0, 10, 8 } }; //_____________________________________________________________________________ //_____________________________________________________________________________ /** * \brief test table for case 4 * Interior to test * When the test on the interior is negative : 2 first triangles * When the test on the interior is positive : 6 last triangles * * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- static const char test4[8] = { /* 65: 0, 6, */ 7, /* 130: 1, 7, */ 7, /* 20: 2, 4, */ 7, /* 40: 3, 5, */ 7, /* 215: 0, 1, 2, 4, 6, 7, */ -7, /* 235: 0, 1, 3, 5, 6, 7, */ -7, /* 125: 0, 2, 3, 4, 5, 6, */ -7, /* 190: 1, 2, 3, 4, 5, 7, */ -7 }; //_____________________________________________________________________________ /** * \brief tiling table for case 4.1 * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- static const char tiling4_1[8][6] = { /* 65: 0, 6, */ { 0, 8, 3, 5, 10, 6 }, /* 130: 1, 7, */ { 0, 1, 9, 11, 7, 6 }, /* 20: 2, 4, */ { 1, 2, 10, 8, 4, 7 }, /* 40: 3, 5, */ { 9, 5, 4, 2, 3, 11 }, /* 215: 0, 1, 2, 4, 6, 7, */ { 4, 5, 9, 11, 3, 2 }, /* 235: 0, 1, 3, 5, 6, 7, */ { 10, 2, 1, 7, 4, 8 }, /* 125: 0, 2, 3, 4, 5, 6, */ { 9, 1, 0, 6, 7, 11 }, /* 190: 1, 2, 3, 4, 5, 7, */ { 3, 8, 0, 6, 10, 5 } }; //_____________________________________________________________________________ /** * \brief tiling table for case 4.2 * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- static const char tiling4_2[8][18] = { /* 65: 0, 6, */ { 8, 5, 0, 5, 8, 6, 3, 6, 8, 6, 3, 10, 0, 10, 3, 10, 0, 5 }, /* 130: 1, 7, */ { 9, 6, 1, 6, 9, 7, 0, 7, 9, 7, 0, 11, 1, 11, 0, 11, 1, 6 }, /* 20: 2, 4, */ { 10, 7, 2, 7, 10, 4, 1, 4, 10, 4, 1, 8, 2, 8, 1, 8, 2, 7 }, /* 40: 3, 5, */ { 11, 4, 3, 4, 11, 5, 2, 5, 11, 5, 2, 9, 3, 9, 2, 9, 3, 4 }, /* 215: 0, 1, 2, 4, 6, 7, */ { 3, 4, 11, 5, 11, 4, 11, 5, 2, 9, 2, 5, 2, 9, 3, 4, 3, 9 }, /* 235: 0, 1, 3, 5, 6, 7, */ { 2, 7, 10, 4, 10, 7, 10, 4, 1, 8, 1, 4, 1, 8, 2, 7, 2, 8 }, /* 125: 0, 2, 3, 4, 5, 6, */ { 1, 6, 9, 7, 9, 6, 9, 7, 0, 11, 0, 7, 0, 11, 1, 6, 1, 11 }, /* 190: 1, 2, 3, 4, 5, 7, */ { 0, 5, 8, 6, 8, 5, 8, 6, 3, 10, 3, 6, 3, 10, 0, 5, 0, 10 } }; //_____________________________________________________________________________ //_____________________________________________________________________________ /** * \brief tiling table for case 5 * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- static const char tiling5[48][9] = { /* 7: 0, 1, 2, */ { 2, 8, 3, 2, 10, 8, 10, 9, 8 }, /* 11: 0, 1, 3, */ { 1, 11, 2, 1, 9, 11, 9, 8, 11 }, /* 19: 0, 1, 4, */ { 4, 1, 9, 4, 7, 1, 7, 3, 1 }, /* 35: 0, 1, 5, */ { 8, 5, 4, 8, 3, 5, 3, 1, 5 }, /* 13: 0, 2, 3, */ { 0, 10, 1, 0, 8, 10, 8, 11, 10 }, /* 25: 0, 3, 4, */ { 11, 4, 7, 11, 2, 4, 2, 0, 4 }, /* 137: 0, 3, 7, */ { 7, 0, 8, 7, 6, 0, 6, 2, 0 }, /* 49: 0, 4, 5, */ { 9, 3, 0, 9, 5, 3, 5, 7, 3 }, /* 145: 0, 4, 7, */ { 3, 6, 11, 3, 0, 6, 0, 4, 6 }, /* 14: 1, 2, 3, */ { 3, 9, 0, 3, 11, 9, 11, 10, 9 }, /* 38: 1, 2, 5, */ { 5, 2, 10, 5, 4, 2, 4, 0, 2 }, /* 70: 1, 2, 6, */ { 9, 6, 5, 9, 0, 6, 0, 2, 6 }, /* 50: 1, 4, 5, */ { 0, 7, 8, 0, 1, 7, 1, 5, 7 }, /* 98: 1, 5, 6, */ { 10, 0, 1, 10, 6, 0, 6, 4, 0 }, /* 76: 2, 3, 6, */ { 6, 3, 11, 6, 5, 3, 5, 1, 3 }, /* 140: 2, 3, 7, */ { 10, 7, 6, 10, 1, 7, 1, 3, 7 }, /* 100: 2, 5, 6, */ { 1, 4, 9, 1, 2, 4, 2, 6, 4 }, /* 196: 2, 6, 7, */ { 11, 1, 2, 11, 7, 1, 7, 5, 1 }, /* 152: 3, 4, 7, */ { 8, 2, 3, 8, 4, 2, 4, 6, 2 }, /* 200: 3, 6, 7, */ { 2, 5, 10, 2, 3, 5, 3, 7, 5 }, /* 112: 4, 5, 6, */ { 7, 10, 6, 7, 8, 10, 8, 9, 10 }, /* 176: 4, 5, 7, */ { 6, 9, 5, 6, 11, 9, 11, 8, 9 }, /* 208: 4, 6, 7, */ { 5, 8, 4, 5, 10, 8, 10, 11, 8 }, /* 224: 5, 6, 7, */ { 4, 11, 7, 4, 9, 11, 9, 10, 11 }, /* 31: 0, 1, 2, 3, 4, */ { 4, 7, 11, 4, 11, 9, 9, 11, 10 }, /* 47: 0, 1, 2, 3, 5, */ { 5, 4, 8, 5, 8, 10, 10, 8, 11 }, /* 79: 0, 1, 2, 3, 6, */ { 6, 5, 9, 6, 9, 11, 11, 9, 8 }, /* 143: 0, 1, 2, 3, 7, */ { 7, 6, 10, 7, 10, 8, 8, 10, 9 }, /* 55: 0, 1, 2, 4, 5, */ { 2, 10, 5, 2, 5, 3, 3, 5, 7 }, /* 103: 0, 1, 2, 5, 6, */ { 8, 3, 2, 8, 2, 4, 4, 2, 6 }, /* 59: 0, 1, 3, 4, 5, */ { 11, 2, 1, 11, 1, 7, 7, 1, 5 }, /* 155: 0, 1, 3, 4, 7, */ { 1, 9, 4, 1, 4, 2, 2, 4, 6 }, /* 115: 0, 1, 4, 5, 6, */ { 10, 6, 7, 10, 7, 1, 1, 7, 3 }, /* 179: 0, 1, 4, 5, 7, */ { 6, 11, 3, 6, 3, 5, 5, 3, 1 }, /* 157: 0, 2, 3, 4, 7, */ { 10, 1, 0, 10, 0, 6, 6, 0, 4 }, /* 205: 0, 2, 3, 6, 7, */ { 0, 8, 7, 0, 7, 1, 1, 7, 5 }, /* 185: 0, 3, 4, 5, 7, */ { 9, 5, 6, 9, 6, 0, 0, 6, 2 }, /* 217: 0, 3, 4, 6, 7, */ { 5, 10, 2, 5, 2, 4, 4, 2, 0 }, /* 241: 0, 4, 5, 6, 7, */ { 3, 0, 9, 3, 9, 11, 11, 9, 10 }, /* 110: 1, 2, 3, 5, 6, */ { 3, 11, 6, 3, 6, 0, 0, 6, 4 }, /* 206: 1, 2, 3, 6, 7, */ { 9, 0, 3, 9, 3, 5, 5, 3, 7 }, /* 118: 1, 2, 4, 5, 6, */ { 7, 8, 0, 7, 0, 6, 6, 0, 2 }, /* 230: 1, 2, 5, 6, 7, */ { 11, 7, 4, 11, 4, 2, 2, 4, 0 }, /* 242: 1, 4, 5, 6, 7, */ { 0, 1, 10, 0, 10, 8, 8, 10, 11 }, /* 220: 2, 3, 4, 6, 7, */ { 8, 4, 5, 8, 5, 3, 3, 5, 1 }, /* 236: 2, 3, 5, 6, 7, */ { 4, 9, 1, 4, 1, 7, 7, 1, 3 }, /* 244: 2, 4, 5, 6, 7, */ { 1, 2, 11, 1, 11, 9, 9, 11, 8 }, /* 248: 3, 4, 5, 6, 7, */ { 2, 3, 8, 2, 8, 10, 10, 8, 9 } }; //_____________________________________________________________________________ //_____________________________________________________________________________ /** * \brief test table for case 6 * 1 face to test + eventually the interior * When the test on the specified face is positive : 5 first triangles * When the test on the specified face is negative : * - if the test on the interior is negative : 3 middle triangles * - if the test on the interior is positive : 8 last triangles * The support edge for the interior test is marked as the 3rd column. * * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- static const char test6[48][3] = { /* 67: 0, 1, 6, */ { 2, 7, 10 }, /* 131: 0, 1, 7, */ { 4, 7, 11 }, /* 21: 0, 2, 4, */ { 5, 7, 1 }, /* 69: 0, 2, 6, */ { 5, 7, 3 }, /* 41: 0, 3, 5, */ { 1, 7, 9 }, /* 73: 0, 3, 6, */ { 3, 7, 10 }, /* 81: 0, 4, 6, */ { 6, 7, 5 }, /* 97: 0, 5, 6, */ { 1, 7, 8 }, /* 193: 0, 6, 7, */ { 4, 7, 8 }, /* 22: 1, 2, 4, */ { 1, 7, 8 }, /* 134: 1, 2, 7, */ { 3, 7, 11 }, /* 42: 1, 3, 5, */ { 5, 7, 2 }, /* 138: 1, 3, 7, */ { 5, 7, 0 }, /* 146: 1, 4, 7, */ { 1, 7, 9 }, /* 162: 1, 5, 7, */ { 6, 7, 6 }, /* 194: 1, 6, 7, */ { 2, 7, 9 }, /* 28: 2, 3, 4, */ { 4, 7, 8 }, /* 44: 2, 3, 5, */ { 2, 7, 9 }, /* 52: 2, 4, 5, */ { 2, 7, 10 }, /* 84: 2, 4, 6, */ { 6, 7, 7 }, /* 148: 2, 4, 7, */ { 3, 7, 10 }, /* 56: 3, 4, 5, */ { 4, 7, 11 }, /* 104: 3, 5, 6, */ { 3, 7, 11 }, /* 168: 3, 5, 7, */ { 6, 7, 4 }, /* 87: 0, 1, 2, 4, 6, */ { -6, -7, 4 }, /* 151: 0, 1, 2, 4, 7, */ { -3, -7, 11 }, /* 199: 0, 1, 2, 6, 7, */ { -4, -7, 11 }, /* 107: 0, 1, 3, 5, 6, */ { -3, -7, 10 }, /* 171: 0, 1, 3, 5, 7, */ { -6, -7, 7 }, /* 203: 0, 1, 3, 6, 7, */ { -2, -7, 10 }, /* 211: 0, 1, 4, 6, 7, */ { -2, -7, 9 }, /* 227: 0, 1, 5, 6, 7, */ { -4, -7, 8 }, /* 61: 0, 2, 3, 4, 5, */ { -2, -7, 9 }, /* 93: 0, 2, 3, 4, 6, */ { -6, -7, 6 }, /* 109: 0, 2, 3, 5, 6, */ { -1, -7, 9 }, /* 117: 0, 2, 4, 5, 6, */ { -5, -7, 0 }, /* 213: 0, 2, 4, 6, 7, */ { -5, -7, 2 }, /* 121: 0, 3, 4, 5, 6, */ { -3, -7, 11 }, /* 233: 0, 3, 5, 6, 7, */ { -1, -7, 8 }, /* 62: 1, 2, 3, 4, 5, */ { -4, -7, 8 }, /* 158: 1, 2, 3, 4, 7, */ { -1, -7, 8 }, /* 174: 1, 2, 3, 5, 7, */ { -6, -7, 5 }, /* 182: 1, 2, 4, 5, 7, */ { -3, -7, 10 }, /* 214: 1, 2, 4, 6, 7, */ { -1, -7, 9 }, /* 186: 1, 3, 4, 5, 7, */ { -5, -7, 3 }, /* 234: 1, 3, 5, 6, 7, */ { -5, -7, 1 }, /* 124: 2, 3, 4, 5, 6, */ { -4, -7, 11 }, /* 188: 2, 3, 4, 5, 7, */ { -2, -7, 10 } }; //_____________________________________________________________________________ /** * \brief tiling table for case 6.1.1 * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- static const char tiling6_1_1[48][9] = { /* 67: 0, 1, 6, */ { 6, 5, 10, 3, 1, 8, 9, 8, 1 }, /* 131: 0, 1, 7, */ { 11, 7, 6, 9, 3, 1, 3, 9, 8 }, /* 21: 0, 2, 4, */ { 1, 2, 10, 7, 0, 4, 0, 7, 3 }, /* 69: 0, 2, 6, */ { 3, 0, 8, 5, 2, 6, 2, 5, 1 }, /* 41: 0, 3, 5, */ { 5, 4, 9, 2, 0, 11, 8, 11, 0 }, /* 73: 0, 3, 6, */ { 10, 6, 5, 8, 2, 0, 2, 8, 11 }, /* 81: 0, 4, 6, */ { 10, 6, 5, 0, 4, 3, 7, 3, 4 }, /* 97: 0, 5, 6, */ { 3, 0, 8, 6, 4, 10, 9, 10, 4 }, /* 193: 0, 6, 7, */ { 8, 3, 0, 10, 7, 5, 7, 10, 11 }, /* 22: 1, 2, 4, */ { 8, 4, 7, 10, 0, 2, 0, 10, 9 }, /* 134: 1, 2, 7, */ { 7, 6, 11, 0, 2, 9, 10, 9, 2 }, /* 42: 1, 3, 5, */ { 2, 3, 11, 4, 1, 5, 1, 4, 0 }, /* 138: 1, 3, 7, */ { 0, 1, 9, 6, 3, 7, 3, 6, 2 }, /* 146: 1, 4, 7, */ { 9, 0, 1, 11, 4, 6, 4, 11, 8 }, /* 162: 1, 5, 7, */ { 11, 7, 6, 1, 5, 0, 4, 0, 5 }, /* 194: 1, 6, 7, */ { 0, 1, 9, 7, 5, 11, 10, 11, 5 }, /* 28: 2, 3, 4, */ { 4, 7, 8, 1, 3, 10, 11, 10, 3 }, /* 44: 2, 3, 5, */ { 9, 5, 4, 11, 1, 3, 1, 11, 10 }, /* 52: 2, 4, 5, */ { 10, 1, 2, 8, 5, 7, 5, 8, 9 }, /* 84: 2, 4, 6, */ { 8, 4, 7, 2, 6, 1, 5, 1, 6 }, /* 148: 2, 4, 7, */ { 1, 2, 10, 4, 6, 8, 11, 8, 6 }, /* 56: 3, 4, 5, */ { 2, 3, 11, 5, 7, 9, 8, 9, 7 }, /* 104: 3, 5, 6, */ { 11, 2, 3, 9, 6, 4, 6, 9, 10 }, /* 168: 3, 5, 7, */ { 9, 5, 4, 3, 7, 2, 6, 2, 7 }, /* 87: 0, 1, 2, 4, 6, */ { 4, 5, 9, 2, 7, 3, 7, 2, 6 }, /* 151: 0, 1, 2, 4, 7, */ { 3, 2, 11, 4, 6, 9, 10, 9, 6 }, /* 199: 0, 1, 2, 6, 7, */ { 11, 3, 2, 9, 7, 5, 7, 9, 8 }, /* 107: 0, 1, 3, 5, 6, */ { 10, 2, 1, 8, 6, 4, 6, 8, 11 }, /* 171: 0, 1, 3, 5, 7, */ { 7, 4, 8, 1, 6, 2, 6, 1, 5 }, /* 203: 0, 1, 3, 6, 7, */ { 2, 1, 10, 7, 5, 8, 9, 8, 5 }, /* 211: 0, 1, 4, 6, 7, */ { 4, 5, 9, 3, 1, 11, 10, 11, 1 }, /* 227: 0, 1, 5, 6, 7, */ { 8, 7, 4, 10, 3, 1, 3, 10, 11 }, /* 61: 0, 2, 3, 4, 5, */ { 9, 1, 0, 11, 5, 7, 5, 11, 10 }, /* 93: 0, 2, 3, 4, 6, */ { 6, 7, 11, 0, 5, 1, 5, 0, 4 }, /* 109: 0, 2, 3, 5, 6, */ { 1, 0, 9, 6, 4, 11, 8, 11, 4 }, /* 117: 0, 2, 4, 5, 6, */ { 9, 1, 0, 7, 3, 6, 2, 6, 3 }, /* 213: 0, 2, 4, 6, 7, */ { 11, 3, 2, 5, 1, 4, 0, 4, 1 }, /* 121: 0, 3, 4, 5, 6, */ { 11, 6, 7, 9, 2, 0, 2, 9, 10 }, /* 233: 0, 3, 5, 6, 7, */ { 7, 4, 8, 2, 0, 10, 9, 10, 0 }, /* 62: 1, 2, 3, 4, 5, */ { 0, 3, 8, 5, 7, 10, 11, 10, 7 }, /* 158: 1, 2, 3, 4, 7, */ { 8, 0, 3, 10, 4, 6, 4, 10, 9 }, /* 174: 1, 2, 3, 5, 7, */ { 5, 6, 10, 3, 4, 0, 4, 3, 7 }, /* 182: 1, 2, 4, 5, 7, */ { 5, 6, 10, 0, 2, 8, 11, 8, 2 }, /* 214: 1, 2, 4, 6, 7, */ { 9, 4, 5, 11, 0, 2, 0, 11, 8 }, /* 186: 1, 3, 4, 5, 7, */ { 8, 0, 3, 6, 2, 5, 1, 5, 2 }, /* 234: 1, 3, 5, 6, 7, */ { 10, 2, 1, 4, 0, 7, 3, 7, 0 }, /* 124: 2, 3, 4, 5, 6, */ { 6, 7, 11, 1, 3, 9, 8, 9, 3 }, /* 188: 2, 3, 4, 5, 7, */ { 10, 5, 6, 8, 1, 3, 1, 8, 9 } }; //_____________________________________________________________________________ /** * \brief tiling table for case 6.1.2 * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- static const char tiling6_1_2[48][27] = { /* 67: 0, 1, 6, */ { 1, 12, 3, 12, 10, 3, 6, 3, 10, 3, 6, 8, 5, 8, 6, 8, 5, 12, 12, 9, 8, 1, 9, 12, 12, 5, 10 }, /* 131: 0, 1, 7, */ { 1, 12, 3, 1, 11, 12, 11, 1, 6, 9, 6, 1, 6, 9, 7, 12, 7, 9, 9, 8, 12, 12, 8, 3, 11, 7, 12 }, /* 21: 0, 2, 4, */ { 4, 12, 0, 4, 1, 12, 1, 4, 10, 7, 10, 4, 10, 7, 2, 12, 2, 7, 7, 3, 12, 12, 3, 0, 1, 2, 12 }, /* 69: 0, 2, 6, */ { 6, 12, 2, 6, 3, 12, 3, 6, 8, 5, 8, 6, 8, 5, 0, 12, 0, 5, 5, 1, 12, 12, 1, 2, 3, 0, 12 }, /* 41: 0, 3, 5, */ { 0, 12, 2, 12, 9, 2, 5, 2, 9, 2, 5, 11, 4, 11, 5, 11, 4, 12, 12, 8, 11, 0, 8, 12, 12, 4, 9 }, /* 73: 0, 3, 6, */ { 0, 12, 2, 0, 10, 12, 10, 0, 5, 8, 5, 0, 5, 8, 6, 12, 6, 8, 8, 11, 12, 12, 11, 2, 10, 6, 12 }, /* 81: 0, 4, 6, */ { 4, 12, 0, 12, 5, 0, 10, 0, 5, 0, 10, 3, 6, 3, 10, 3, 6, 12, 12, 7, 3, 4, 7, 12, 12, 6, 5 }, /* 97: 0, 5, 6, */ { 4, 12, 6, 12, 8, 6, 3, 6, 8, 6, 3, 10, 0, 10, 3, 10, 0, 12, 12, 9, 10, 4, 9, 12, 12, 0, 8 }, /* 193: 0, 6, 7, */ { 5, 12, 7, 5, 8, 12, 8, 5, 0, 10, 0, 5, 0, 10, 3, 12, 3, 10, 10, 11, 12, 12, 11, 7, 8, 3, 12 }, /* 22: 1, 2, 4, */ { 2, 12, 0, 2, 8, 12, 8, 2, 7, 10, 7, 2, 7, 10, 4, 12, 4, 10, 10, 9, 12, 12, 9, 0, 8, 4, 12 }, /* 134: 1, 2, 7, */ { 2, 12, 0, 12, 11, 0, 7, 0, 11, 0, 7, 9, 6, 9, 7, 9, 6, 12, 12, 10, 9, 2, 10, 12, 12, 6, 11 }, /* 42: 1, 3, 5, */ { 5, 12, 1, 5, 2, 12, 2, 5, 11, 4, 11, 5, 11, 4, 3, 12, 3, 4, 4, 0, 12, 12, 0, 1, 2, 3, 12 }, /* 138: 1, 3, 7, */ { 7, 12, 3, 7, 0, 12, 0, 7, 9, 6, 9, 7, 9, 6, 1, 12, 1, 6, 6, 2, 12, 12, 2, 3, 0, 1, 12 }, /* 146: 1, 4, 7, */ { 6, 12, 4, 6, 9, 12, 9, 6, 1, 11, 1, 6, 1, 11, 0, 12, 0, 11, 11, 8, 12, 12, 8, 4, 9, 0, 12 }, /* 162: 1, 5, 7, */ { 5, 12, 1, 12, 6, 1, 11, 1, 6, 1, 11, 0, 7, 0, 11, 0, 7, 12, 12, 4, 0, 5, 4, 12, 12, 7, 6 }, /* 194: 1, 6, 7, */ { 5, 12, 7, 12, 9, 7, 0, 7, 9, 7, 0, 11, 1, 11, 0, 11, 1, 12, 12, 10, 11, 5, 10, 12, 12, 1, 9 }, /* 28: 2, 3, 4, */ { 3, 12, 1, 12, 8, 1, 4, 1, 8, 1, 4, 10, 7, 10, 4, 10, 7, 12, 12, 11, 10, 3, 11, 12, 12, 7, 8 }, /* 44: 2, 3, 5, */ { 3, 12, 1, 3, 9, 12, 9, 3, 4, 11, 4, 3, 4, 11, 5, 12, 5, 11, 11, 10, 12, 12, 10, 1, 9, 5, 12 }, /* 52: 2, 4, 5, */ { 7, 12, 5, 7, 10, 12, 10, 7, 2, 8, 2, 7, 2, 8, 1, 12, 1, 8, 8, 9, 12, 12, 9, 5, 10, 1, 12 }, /* 84: 2, 4, 6, */ { 6, 12, 2, 12, 7, 2, 8, 2, 7, 2, 8, 1, 4, 1, 8, 1, 4, 12, 12, 5, 1, 6, 5, 12, 12, 4, 7 }, /* 148: 2, 4, 7, */ { 6, 12, 4, 12, 10, 4, 1, 4, 10, 4, 1, 8, 2, 8, 1, 8, 2, 12, 12, 11, 8, 6, 11, 12, 12, 2, 10 }, /* 56: 3, 4, 5, */ { 7, 12, 5, 12, 11, 5, 2, 5, 11, 5, 2, 9, 3, 9, 2, 9, 3, 12, 12, 8, 9, 7, 8, 12, 12, 3, 11 }, /* 104: 3, 5, 6, */ { 4, 12, 6, 4, 11, 12, 11, 4, 3, 9, 3, 4, 3, 9, 2, 12, 2, 9, 9, 10, 12, 12, 10, 6, 11, 2, 12 }, /* 168: 3, 5, 7, */ { 7, 12, 3, 12, 4, 3, 9, 3, 4, 3, 9, 2, 5, 2, 9, 2, 5, 12, 12, 6, 2, 7, 6, 12, 12, 5, 4 }, /* 87: 0, 1, 2, 4, 6, */ { 3, 12, 7, 3, 4, 12, 4, 3, 9, 2, 9, 3, 9, 2, 5, 12, 5, 2, 2, 6, 12, 12, 6, 7, 4, 5, 12 }, /* 151: 0, 1, 2, 4, 7, */ { 6, 12, 4, 12, 11, 4, 3, 4, 11, 4, 3, 9, 2, 9, 3, 9, 2, 12, 12, 10, 9, 6, 10, 12, 12, 2, 11 }, /* 199: 0, 1, 2, 6, 7, */ { 5, 12, 7, 5, 11, 12, 11, 5, 2, 9, 2, 5, 2, 9, 3, 12, 3, 9, 9, 8, 12, 12, 8, 7, 11, 3, 12 }, /* 107: 0, 1, 3, 5, 6, */ { 4, 12, 6, 4, 10, 12, 10, 4, 1, 8, 1, 4, 1, 8, 2, 12, 2, 8, 8, 11, 12, 12, 11, 6, 10, 2, 12 }, /* 171: 0, 1, 3, 5, 7, */ { 2, 12, 6, 2, 7, 12, 7, 2, 8, 1, 8, 2, 8, 1, 4, 12, 4, 1, 1, 5, 12, 12, 5, 6, 7, 4, 12 }, /* 203: 0, 1, 3, 6, 7, */ { 5, 12, 7, 12, 10, 7, 2, 7, 10, 7, 2, 8, 1, 8, 2, 8, 1, 12, 12, 9, 8, 5, 9, 12, 12, 1, 10 }, /* 211: 0, 1, 4, 6, 7, */ { 1, 12, 3, 12, 9, 3, 4, 3, 9, 3, 4, 11, 5, 11, 4, 11, 5, 12, 12, 10, 11, 1, 10, 12, 12, 5, 9 }, /* 227: 0, 1, 5, 6, 7, */ { 1, 12, 3, 1, 8, 12, 8, 1, 4, 10, 4, 1, 4, 10, 7, 12, 7, 10, 10, 11, 12, 12, 11, 3, 8, 7, 12 }, /* 61: 0, 2, 3, 4, 5, */ { 7, 12, 5, 7, 9, 12, 9, 7, 0, 11, 0, 7, 0, 11, 1, 12, 1, 11, 11, 10, 12, 12, 10, 5, 9, 1, 12 }, /* 93: 0, 2, 3, 4, 6, */ { 1, 12, 5, 1, 6, 12, 6, 1, 11, 0, 11, 1, 11, 0, 7, 12, 7, 0, 0, 4, 12, 12, 4, 5, 6, 7, 12 }, /* 109: 0, 2, 3, 5, 6, */ { 4, 12, 6, 12, 9, 6, 1, 6, 9, 6, 1, 11, 0, 11, 1, 11, 0, 12, 12, 8, 11, 4, 8, 12, 12, 0, 9 }, /* 117: 0, 2, 4, 5, 6, */ { 3, 12, 7, 12, 0, 7, 9, 7, 0, 7, 9, 6, 1, 6, 9, 6, 1, 12, 12, 2, 6, 3, 2, 12, 12, 1, 0 }, /* 213: 0, 2, 4, 6, 7, */ { 1, 12, 5, 12, 2, 5, 11, 5, 2, 5, 11, 4, 3, 4, 11, 4, 3, 12, 12, 0, 4, 1, 0, 12, 12, 3, 2 }, /* 121: 0, 3, 4, 5, 6, */ { 0, 12, 2, 0, 11, 12, 11, 0, 7, 9, 7, 0, 7, 9, 6, 12, 6, 9, 9, 10, 12, 12, 10, 2, 11, 6, 12 }, /* 233: 0, 3, 5, 6, 7, */ { 0, 12, 2, 12, 8, 2, 7, 2, 8, 2, 7, 10, 4, 10, 7, 10, 4, 12, 12, 9, 10, 0, 9, 12, 12, 4, 8 }, /* 62: 1, 2, 3, 4, 5, */ { 7, 12, 5, 12, 8, 5, 0, 5, 8, 5, 0, 10, 3, 10, 0, 10, 3, 12, 12, 11, 10, 7, 11, 12, 12, 3, 8 }, /* 158: 1, 2, 3, 4, 7, */ { 6, 12, 4, 6, 8, 12, 8, 6, 3, 10, 3, 6, 3, 10, 0, 12, 0, 10, 10, 9, 12, 12, 9, 4, 8, 0, 12 }, /* 174: 1, 2, 3, 5, 7, */ { 0, 12, 4, 0, 5, 12, 5, 0, 10, 3, 10, 0, 10, 3, 6, 12, 6, 3, 3, 7, 12, 12, 7, 4, 5, 6, 12 }, /* 182: 1, 2, 4, 5, 7, */ { 2, 12, 0, 12, 10, 0, 5, 0, 10, 0, 5, 8, 6, 8, 5, 8, 6, 12, 12, 11, 8, 2, 11, 12, 12, 6, 10 }, /* 214: 1, 2, 4, 6, 7, */ { 2, 12, 0, 2, 9, 12, 9, 2, 5, 11, 5, 2, 5, 11, 4, 12, 4, 11, 11, 8, 12, 12, 8, 0, 9, 4, 12 }, /* 186: 1, 3, 4, 5, 7, */ { 2, 12, 6, 12, 3, 6, 8, 6, 3, 6, 8, 5, 0, 5, 8, 5, 0, 12, 12, 1, 5, 2, 1, 12, 12, 0, 3 }, /* 234: 1, 3, 5, 6, 7, */ { 0, 12, 4, 12, 1, 4, 10, 4, 1, 4, 10, 7, 2, 7, 10, 7, 2, 12, 12, 3, 7, 0, 3, 12, 12, 2, 1 }, /* 124: 2, 3, 4, 5, 6, */ { 3, 12, 1, 12, 11, 1, 6, 1, 11, 1, 6, 9, 7, 9, 6, 9, 7, 12, 12, 8, 9, 3, 8, 12, 12, 7, 11 }, /* 188: 2, 3, 4, 5, 7, */ { 3, 12, 1, 3, 10, 12, 10, 3, 6, 8, 6, 3, 6, 8, 5, 12, 5, 8, 8, 9, 12, 12, 9, 1, 10, 5, 12 }, }; //_____________________________________________________________________________ /** * \brief tiling table for case 6.2 * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- static const char tiling6_2[48][15] = { /* 67: 0, 1, 6, */ { 1, 10, 3, 6, 3, 10, 3, 6, 8, 5, 8, 6, 8, 5, 9 }, /* 131: 0, 1, 7, */ { 1, 11, 3, 11, 1, 6, 9, 6, 1, 6, 9, 7, 8, 7, 9 }, /* 21: 0, 2, 4, */ { 4, 1, 0, 1, 4, 10, 7, 10, 4, 10, 7, 2, 3, 2, 7 }, /* 69: 0, 2, 6, */ { 6, 3, 2, 3, 6, 8, 5, 8, 6, 8, 5, 0, 1, 0, 5 }, /* 41: 0, 3, 5, */ { 0, 9, 2, 5, 2, 9, 2, 5, 11, 4, 11, 5, 11, 4, 8 }, /* 73: 0, 3, 6, */ { 0, 10, 2, 10, 0, 5, 8, 5, 0, 5, 8, 6, 11, 6, 8 }, /* 81: 0, 4, 6, */ { 4, 5, 0, 10, 0, 5, 0, 10, 3, 6, 3, 10, 3, 6, 7 }, /* 97: 0, 5, 6, */ { 4, 8, 6, 3, 6, 8, 6, 3, 10, 0, 10, 3, 10, 0, 9 }, /* 193: 0, 6, 7, */ { 5, 8, 7, 8, 5, 0, 10, 0, 5, 0, 10, 3, 11, 3, 10 }, /* 22: 1, 2, 4, */ { 2, 8, 0, 8, 2, 7, 10, 7, 2, 7, 10, 4, 9, 4, 10 }, /* 134: 1, 2, 7, */ { 2, 11, 0, 7, 0, 11, 0, 7, 9, 6, 9, 7, 9, 6, 10 }, /* 42: 1, 3, 5, */ { 5, 2, 1, 2, 5, 11, 4, 11, 5, 11, 4, 3, 0, 3, 4 }, /* 138: 1, 3, 7, */ { 7, 0, 3, 0, 7, 9, 6, 9, 7, 9, 6, 1, 2, 1, 6 }, /* 146: 1, 4, 7, */ { 6, 9, 4, 9, 6, 1, 11, 1, 6, 1, 11, 0, 8, 0, 11 }, /* 162: 1, 5, 7, */ { 5, 6, 1, 11, 1, 6, 1, 11, 0, 7, 0, 11, 0, 7, 4 }, /* 194: 1, 6, 7, */ { 5, 9, 7, 0, 7, 9, 7, 0, 11, 1, 11, 0, 11, 1, 10 }, /* 28: 2, 3, 4, */ { 3, 8, 1, 4, 1, 8, 1, 4, 10, 7, 10, 4, 10, 7, 11 }, /* 44: 2, 3, 5, */ { 3, 9, 1, 9, 3, 4, 11, 4, 3, 4, 11, 5, 10, 5, 11 }, /* 52: 2, 4, 5, */ { 7, 10, 5, 10, 7, 2, 8, 2, 7, 2, 8, 1, 9, 1, 8 }, /* 84: 2, 4, 6, */ { 6, 7, 2, 8, 2, 7, 2, 8, 1, 4, 1, 8, 1, 4, 5 }, /* 148: 2, 4, 7, */ { 6, 10, 4, 1, 4, 10, 4, 1, 8, 2, 8, 1, 8, 2, 11 }, /* 56: 3, 4, 5, */ { 7, 11, 5, 2, 5, 11, 5, 2, 9, 3, 9, 2, 9, 3, 8 }, /* 104: 3, 5, 6, */ { 4, 11, 6, 11, 4, 3, 9, 3, 4, 3, 9, 2, 10, 2, 9 }, /* 168: 3, 5, 7, */ { 7, 4, 3, 9, 3, 4, 3, 9, 2, 5, 2, 9, 2, 5, 6 }, /* 87: 0, 1, 2, 4, 6, */ { 3, 4, 7, 4, 3, 9, 2, 9, 3, 9, 2, 5, 6, 5, 2 }, /* 151: 0, 1, 2, 4, 7, */ { 6, 11, 4, 3, 4, 11, 4, 3, 9, 2, 9, 3, 9, 2, 10 }, /* 199: 0, 1, 2, 6, 7, */ { 5, 11, 7, 11, 5, 2, 9, 2, 5, 2, 9, 3, 8, 3, 9 }, /* 107: 0, 1, 3, 5, 6, */ { 4, 10, 6, 10, 4, 1, 8, 1, 4, 1, 8, 2, 11, 2, 8 }, /* 171: 0, 1, 3, 5, 7, */ { 2, 7, 6, 7, 2, 8, 1, 8, 2, 8, 1, 4, 5, 4, 1 }, /* 203: 0, 1, 3, 6, 7, */ { 5, 10, 7, 2, 7, 10, 7, 2, 8, 1, 8, 2, 8, 1, 9 }, /* 211: 0, 1, 4, 6, 7, */ { 1, 9, 3, 4, 3, 9, 3, 4, 11, 5, 11, 4, 11, 5, 10 }, /* 227: 0, 1, 5, 6, 7, */ { 1, 8, 3, 8, 1, 4, 10, 4, 1, 4, 10, 7, 11, 7, 10 }, /* 61: 0, 2, 3, 4, 5, */ { 7, 9, 5, 9, 7, 0, 11, 0, 7, 0, 11, 1, 10, 1, 11 }, /* 93: 0, 2, 3, 4, 6, */ { 1, 6, 5, 6, 1, 11, 0, 11, 1, 11, 0, 7, 4, 7, 0 }, /* 109: 0, 2, 3, 5, 6, */ { 4, 9, 6, 1, 6, 9, 6, 1, 11, 0, 11, 1, 11, 0, 8 }, /* 117: 0, 2, 4, 5, 6, */ { 3, 0, 7, 9, 7, 0, 7, 9, 6, 1, 6, 9, 6, 1, 2 }, /* 213: 0, 2, 4, 6, 7, */ { 1, 2, 5, 11, 5, 2, 5, 11, 4, 3, 4, 11, 4, 3, 0 }, /* 121: 0, 3, 4, 5, 6, */ { 0, 11, 2, 11, 0, 7, 9, 7, 0, 7, 9, 6, 10, 6, 9 }, /* 233: 0, 3, 5, 6, 7, */ { 0, 8, 2, 7, 2, 8, 2, 7, 10, 4, 10, 7, 10, 4, 9 }, /* 62: 1, 2, 3, 4, 5, */ { 7, 8, 5, 0, 5, 8, 5, 0, 10, 3, 10, 0, 10, 3, 11 }, /* 158: 1, 2, 3, 4, 7, */ { 6, 8, 4, 8, 6, 3, 10, 3, 6, 3, 10, 0, 9, 0, 10 }, /* 174: 1, 2, 3, 5, 7, */ { 0, 5, 4, 5, 0, 10, 3, 10, 0, 10, 3, 6, 7, 6, 3 }, /* 182: 1, 2, 4, 5, 7, */ { 2, 10, 0, 5, 0, 10, 0, 5, 8, 6, 8, 5, 8, 6, 11 }, /* 214: 1, 2, 4, 6, 7, */ { 2, 9, 0, 9, 2, 5, 11, 5, 2, 5, 11, 4, 8, 4, 11 }, /* 186: 1, 3, 4, 5, 7, */ { 2, 3, 6, 8, 6, 3, 6, 8, 5, 0, 5, 8, 5, 0, 1 }, /* 234: 1, 3, 5, 6, 7, */ { 0, 1, 4, 10, 4, 1, 4, 10, 7, 2, 7, 10, 7, 2, 3 }, /* 124: 2, 3, 4, 5, 6, */ { 3, 11, 1, 6, 1, 11, 1, 6, 9, 7, 9, 6, 9, 7, 8 }, /* 188: 2, 3, 4, 5, 7, */ { 3, 10, 1, 10, 3, 6, 8, 6, 3, 6, 8, 5, 9, 5, 8 } }; //_____________________________________________________________________________ //_____________________________________________________________________________ /** * \brief test table for case 7 * 3 faces to test + eventually the interior * When the tests on the 3 specified faces are positive : * - if the test on the interior is positive : 5 first triangles * - if the test on the interior is negative : 9 next triangles * When the tests on the first and the second specified faces are positive : 9 next triangles * When the tests on the first and the third specified faces are positive : 9 next triangles * When the tests on the second and the third specified faces are positive : 9 next triangles * When the test on the first specified face is positive : 5 next triangles * When the test on the second specified face is positive : 5 next triangles * When the test on the third specified face is positive : 5 next triangles * When the tests on the 3 specified faces are negative : 3 last triangles * The support edge for the interior test is marked as the 5th column. * * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- static const char test7[16][5] = { /* 37: 0, 2, 5, */ { 1, 2, 5, 7, 1 }, /* 133: 0, 2, 7, */ { 3, 4, 5, 7, 3 }, /* 161: 0, 5, 7, */ { 4, 1, 6, 7, 4 }, /* 26: 1, 3, 4, */ { 4, 1, 5, 7, 0 }, /* 74: 1, 3, 6, */ { 2, 3, 5, 7, 2 }, /* 82: 1, 4, 6, */ { 1, 2, 6, 7, 5 }, /* 164: 2, 5, 7, */ { 2, 3, 6, 7, 6 }, /* 88: 3, 4, 6, */ { 3, 4, 6, 7, 7 }, /* 167: 0, 1, 2, 5, 7, */ { -3, -4, -6, -7, 7 }, /* 91: 0, 1, 3, 4, 6, */ { -2, -3, -6, -7, 6 }, /* 173: 0, 2, 3, 5, 7, */ { -1, -2, -6, -7, 5 }, /* 181: 0, 2, 4, 5, 7, */ { -2, -3, -5, -7, 2 }, /* 229: 0, 2, 5, 6, 7, */ { -4, -1, -5, -7, 0 }, /* 94: 1, 2, 3, 4, 6, */ { -4, -1, -6, -7, 4 }, /* 122: 1, 3, 4, 5, 6, */ { -3, -4, -5, -7, 3 }, /* 218: 1, 3, 4, 6, 7, */ { -1, -2, -5, -7, 1 } }; //_____________________________________________________________________________ /** * \brief tiling table for case 7.1 * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- static const char tiling7_1[16][9] = { /* 37: 0, 2, 5, */ { 9, 5, 4, 10, 1, 2, 8, 3, 0 }, /* 133: 0, 2, 7, */ { 11, 7, 6, 8, 3, 0, 10, 1, 2 }, /* 161: 0, 5, 7, */ { 3, 0, 8, 5, 4, 9, 7, 6, 11 }, /* 26: 1, 3, 4, */ { 8, 4, 7, 9, 0, 1, 11, 2, 3 }, /* 74: 1, 3, 6, */ { 10, 6, 5, 11, 2, 3, 9, 0, 1 }, /* 82: 1, 4, 6, */ { 0, 1, 9, 6, 5, 10, 4, 7, 8 }, /* 164: 2, 5, 7, */ { 1, 2, 10, 7, 6, 11, 5, 4, 9 }, /* 88: 3, 4, 6, */ { 2, 3, 11, 4, 7, 8, 6, 5, 10 }, /* 167: 0, 1, 2, 5, 7, */ { 11, 3, 2, 8, 7, 4, 10, 5, 6 }, /* 91: 0, 1, 3, 4, 6, */ { 10, 2, 1, 11, 6, 7, 9, 4, 5 }, /* 173: 0, 2, 3, 5, 7, */ { 9, 1, 0, 10, 5, 6, 8, 7, 4 }, /* 181: 0, 2, 4, 5, 7, */ { 5, 6, 10, 3, 2, 11, 1, 0, 9 }, /* 229: 0, 2, 5, 6, 7, */ { 7, 4, 8, 1, 0, 9, 3, 2, 11 }, /* 94: 1, 2, 3, 4, 6, */ { 8, 0, 3, 9, 4, 5, 11, 6, 7 }, /* 122: 1, 3, 4, 5, 6, */ { 6, 7, 11, 0, 3, 8, 2, 1, 10 }, /* 218: 1, 3, 4, 6, 7, */ { 4, 5, 9, 2, 1, 10, 0, 3, 8 } }; //_____________________________________________________________________________ /** * \brief tiling table for case 7.2 * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- static const char tiling7_2[16][3][15] = { /* 37: 0, 2, 5, */ { /* 1,0 */ { 1, 2, 10, 3, 4, 8, 4, 3, 5, 0, 5, 3, 5, 0, 9 }, /* 0,1 */ { 3, 0, 8, 9, 1, 4, 2, 4, 1, 4, 2, 5, 10, 5, 2 }, /* 1,1 */ { 9, 5, 4, 0, 10, 1, 10, 0, 8, 10, 8, 2, 3, 2, 8 } }, /* 133: 0, 2, 7, */ { /* 1,0 */ { 3, 0, 8, 1, 6, 10, 6, 1, 7, 2, 7, 1, 7, 2, 11 }, /* 0,1 */ { 1, 2, 10, 11, 3, 6, 0, 6, 3, 6, 0, 7, 8, 7, 0 }, /* 1,1 */ { 11, 7, 6, 2, 8, 3, 8, 2, 10, 8, 10, 0, 1, 0, 10 } }, /* 161: 0, 5, 7, */ { /* 1,0 */ { 9, 5, 4, 11, 3, 6, 0, 6, 3, 6, 0, 7, 8, 7, 0 }, /* 0,1 */ { 11, 7, 6, 3, 4, 8, 4, 3, 5, 0, 5, 3, 5, 0, 9 }, /* 1,1 */ { 3, 0, 8, 4, 9, 7, 11, 7, 9, 5, 11, 9, 11, 5, 6 } }, /* 26: 1, 3, 4, */ { /* 1,0 */ { 0, 1, 9, 2, 7, 11, 7, 2, 4, 3, 4, 2, 4, 3, 8 }, /* 0,1 */ { 2, 3, 11, 8, 0, 7, 1, 7, 0, 7, 1, 4, 9, 4, 1 }, /* 1,1 */ { 8, 4, 7, 3, 9, 0, 9, 3, 11, 9, 11, 1, 2, 1, 11 } }, /* 74: 1, 3, 6, */ { /* 1,0 */ { 2, 3, 11, 0, 5, 9, 5, 0, 6, 1, 6, 0, 6, 1, 10 }, /* 0,1 */ { 0, 1, 9, 10, 2, 5, 3, 5, 2, 5, 3, 6, 11, 6, 3 }, /* 1,1 */ { 6, 5, 10, 1, 11, 2, 11, 1, 9, 11, 9, 3, 0, 3, 9 } }, /* 82: 1, 4, 6, */ { /* 1,0 */ { 6, 5, 10, 8, 0, 7, 1, 7, 0, 7, 1, 4, 9, 4, 1 }, /* 0,1 */ { 8, 4, 7, 0, 5, 9, 5, 0, 6, 1, 6, 0, 6, 1, 10 }, /* 1,1 */ { 0, 1, 9, 5, 10, 4, 8, 4, 10, 6, 8, 10, 8, 6, 7 } }, /* 164: 2, 5, 7, */ { /* 1,0 */ { 11, 7, 6, 9, 1, 4, 2, 4, 1, 4, 2, 5, 10, 5, 2 }, /* 0,1 */ { 9, 5, 4, 1, 6, 10, 6, 1, 7, 2, 7, 1, 7, 2, 11 }, /* 1,1 */ { 1, 2, 10, 6, 11, 5, 9, 5, 11, 7, 9, 11, 9, 7, 4 } }, /* 88: 3, 4, 6, */ { /* 1,0 */ { 8, 4, 7, 10, 2, 5, 3, 5, 2, 5, 3, 6, 11, 6, 3 }, /* 0,1 */ { 6, 5, 10, 2, 7, 11, 7, 2, 4, 3, 4, 2, 4, 3, 8 }, /* 1,1 */ { 2, 3, 11, 7, 8, 6, 10, 6, 8, 4, 10, 8, 10, 4, 5 } }, /* 167: 0, 1, 2, 5, 7, */ { /* 1,0 */ { 7, 4, 8, 5, 2, 10, 2, 5, 3, 6, 3, 5, 3, 6, 11 }, /* 0,1 */ { 10, 5, 6, 11, 7, 2, 4, 2, 7, 2, 4, 3, 8, 3, 4 }, /* 1,1 */ { 11, 3, 2, 6, 8, 7, 8, 6, 10, 8, 10, 4, 5, 4, 10 } }, /* 91: 0, 1, 3, 4, 6, */ { /* 1,0 */ { 6, 7, 11, 4, 1, 9, 1, 4, 2, 5, 2, 4, 2, 5, 10 }, /* 0,1 */ { 4, 5, 9, 10, 6, 1, 7, 1, 6, 1, 7, 2, 11, 2, 7 }, /* 1,1 */ { 10, 2, 1, 5, 11, 6, 11, 5, 9, 11, 9, 7, 4, 7, 9 } }, /* 173: 0, 2, 3, 5, 7, */ { /* 1,0 */ { 10, 5, 6, 7, 0, 8, 0, 7, 1, 4, 1, 7, 1, 4, 9 }, /* 0,1 */ { 7, 4, 8, 9, 5, 0, 6, 0, 5, 0, 6, 1, 10, 1, 6 }, /* 1,1 */ { 9, 1, 0, 4, 10, 5, 10, 4, 8, 10, 8, 6, 7, 6, 8 } }, /* 181: 0, 2, 4, 5, 7, */ { /* 1,0 */ { 11, 3, 2, 9, 5, 0, 6, 0, 5, 0, 6, 1, 10, 1, 6 }, /* 0,1 */ { 9, 1, 0, 5, 2, 10, 2, 5, 3, 6, 3, 5, 3, 6, 11 }, /* 1,1 */ { 10, 5, 6, 2, 11, 1, 9, 1, 11, 3, 9, 11, 9, 3, 0 } }, /* 229: 0, 2, 5, 6, 7, */ { /* 1,0 */ { 9, 1, 0, 11, 7, 2, 4, 2, 7, 2, 4, 3, 8, 3, 4 }, /* 0,1 */ { 11, 3, 2, 7, 0, 8, 0, 7, 1, 4, 1, 7, 1, 4, 9 }, /* 1,1 */ { 7, 4, 8, 0, 9, 3, 11, 3, 9, 1, 11, 9, 11, 1, 2 } }, /* 94: 1, 2, 3, 4, 6, */ { /* 1,0 */ { 4, 5, 9, 6, 3, 11, 3, 6, 0, 7, 0, 6, 0, 7, 8 }, /* 0,1 */ { 6, 7, 11, 8, 4, 3, 5, 3, 4, 3, 5, 0, 9, 0, 5 }, /* 1,1 */ { 8, 0, 3, 7, 9, 4, 9, 7, 11, 9, 11, 5, 6, 5, 11 } }, /* 122: 1, 3, 4, 5, 6, */ { /* 1,0 */ { 8, 0, 3, 10, 6, 1, 7, 1, 6, 1, 7, 2, 11, 2, 7 }, /* 0,1 */ { 10, 2, 1, 6, 3, 11, 3, 6, 0, 7, 0, 6, 0, 7, 8 }, /* 1,1 */ { 6, 7, 11, 3, 8, 2, 10, 2, 8, 0, 10, 8, 10, 0, 1 } }, /* 218: 1, 3, 4, 6, 7, */ { /* 1,0 */ { 10, 2, 1, 8, 4, 3, 5, 3, 4, 3, 5, 0, 9, 0, 5 }, /* 0,1 */ { 8, 0, 3, 4, 1, 9, 1, 4, 2, 5, 2, 4, 2, 5, 10 }, /* 1,1 */ { 4, 5, 9, 1, 10, 0, 8, 0, 10, 2, 8, 10, 8, 2, 3 } } }; //_____________________________________________________________________________ /** * \brief tiling table for case 7.3 * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- static const char tiling7_3[16][3][27] = { /* 37: 0, 2, 5, */ { /* 1,0 */ { 12, 2, 10, 12, 10, 5, 12, 5, 4, 12, 4, 8, 12, 8, 3, 12, 3, 0, 12, 0, 9, 12, 9, 1, 12, 1, 2 }, /* 0,1 */ { 12, 5, 4, 12, 4, 8, 12, 8, 3, 12, 3, 2, 12, 2, 10, 12, 10, 1, 12, 1, 0, 12, 0, 9, 12, 9, 5 }, /* 1,1 */ { 5, 4, 12, 10, 5, 12, 2, 10, 12, 3, 2, 12, 8, 3, 12, 0, 8, 12, 1, 0, 12, 9, 1, 12, 4, 9, 12 } }, /* 133: 0, 2, 7, */ { /* 1,0 */ { 12, 0, 8, 12, 8, 7, 12, 7, 6, 12, 6, 10, 12, 10, 1, 12, 1, 2, 12, 2, 11, 12, 11, 3, 12, 3, 0 }, /* 0,1 */ { 12, 7, 6, 12, 6, 10, 12, 10, 1, 12, 1, 0, 12, 0, 8, 12, 8, 3, 12, 3, 2, 12, 2, 11, 12, 11, 7 }, /* 1,1 */ { 7, 6, 12, 8, 7, 12, 0, 8, 12, 1, 0, 12, 10, 1, 12, 2, 10, 12, 3, 2, 12, 11, 3, 12, 6, 11, 12 } }, /* 161: 0, 5, 7, */ { /* 1,0 */ { 9, 5, 12, 0, 9, 12, 3, 0, 12, 11, 3, 12, 6, 11, 12, 7, 6, 12, 8, 7, 12, 4, 8, 12, 5, 4, 12 }, /* 0,1 */ { 3, 0, 12, 11, 3, 12, 6, 11, 12, 5, 6, 12, 9, 5, 12, 4, 9, 12, 7, 4, 12, 8, 7, 12, 0, 8, 12 }, /* 1,1 */ { 12, 3, 0, 12, 0, 9, 12, 9, 5, 12, 5, 6, 12, 6, 11, 12, 11, 7, 12, 7, 4, 12, 4, 8, 12, 8, 3 } }, /* 26: 1, 3, 4, */ { /* 1,0 */ { 12, 1, 9, 12, 9, 4, 12, 4, 7, 12, 7, 11, 12, 11, 2, 12, 2, 3, 12, 3, 8, 12, 8, 0, 12, 0, 1 }, /* 0,1 */ { 12, 4, 7, 12, 7, 11, 12, 11, 2, 12, 2, 1, 12, 1, 9, 12, 9, 0, 12, 0, 3, 12, 3, 8, 12, 8, 4 }, /* 1,1 */ { 4, 7, 12, 9, 4, 12, 1, 9, 12, 2, 1, 12, 11, 2, 12, 3, 11, 12, 0, 3, 12, 8, 0, 12, 7, 8, 12 } }, /* 74: 1, 3, 6, */ { /* 1,0 */ { 12, 3, 11, 12, 11, 6, 12, 6, 5, 12, 5, 9, 12, 9, 0, 12, 0, 1, 12, 1, 10, 12, 10, 2, 12, 2, 3 }, /* 0,1 */ { 12, 6, 5, 12, 5, 9, 12, 9, 0, 12, 0, 3, 12, 3, 11, 12, 11, 2, 12, 2, 1, 12, 1, 10, 12, 10, 6 }, /* 1,1 */ { 6, 5, 12, 11, 6, 12, 3, 11, 12, 0, 3, 12, 9, 0, 12, 1, 9, 12, 2, 1, 12, 10, 2, 12, 5, 10, 12 } }, /* 82: 1, 4, 6, */ { /* 1,0 */ { 10, 6, 12, 1, 10, 12, 0, 1, 12, 8, 0, 12, 7, 8, 12, 4, 7, 12, 9, 4, 12, 5, 9, 12, 6, 5, 12 }, /* 0,1 */ { 0, 1, 12, 8, 0, 12, 7, 8, 12, 6, 7, 12, 10, 6, 12, 5, 10, 12, 4, 5, 12, 9, 4, 12, 1, 9, 12 }, /* 1,1 */ { 12, 0, 1, 12, 1, 10, 12, 10, 6, 12, 6, 7, 12, 7, 8, 12, 8, 4, 12, 4, 5, 12, 5, 9, 12, 9, 0 } }, /* 164: 2, 5, 7, */ { /* 1,0 */ { 11, 7, 12, 2, 11, 12, 1, 2, 12, 9, 1, 12, 4, 9, 12, 5, 4, 12, 10, 5, 12, 6, 10, 12, 7, 6, 12 }, /* 0,1 */ { 1, 2, 12, 9, 1, 12, 4, 9, 12, 7, 4, 12, 11, 7, 12, 6, 11, 12, 5, 6, 12, 10, 5, 12, 2, 10, 12 }, /* 1,1 */ { 12, 1, 2, 12, 2, 11, 12, 11, 7, 12, 7, 4, 12, 4, 9, 12, 9, 5, 12, 5, 6, 12, 6, 10, 12, 10, 1 } }, /* 88: 3, 4, 6, */ { /* 1,0 */ { 8, 4, 12, 3, 8, 12, 2, 3, 12, 10, 2, 12, 5, 10, 12, 6, 5, 12, 11, 6, 12, 7, 11, 12, 4, 7, 12 }, /* 0,1 */ { 2, 3, 12, 10, 2, 12, 5, 10, 12, 4, 5, 12, 8, 4, 12, 7, 8, 12, 6, 7, 12, 11, 6, 12, 3, 11, 12 }, /* 1,1 */ { 12, 2, 3, 12, 3, 8, 12, 8, 4, 12, 4, 5, 12, 5, 10, 12, 10, 6, 12, 6, 7, 12, 7, 11, 12, 11, 2 } }, /* 167: 0, 1, 2, 5, 7, */ { /* 1,0 */ { 12, 4, 8, 12, 8, 3, 12, 3, 2, 12, 2, 10, 12, 10, 5, 12, 5, 6, 12, 6, 11, 12, 11, 7, 12, 7, 4 }, /* 0,1 */ { 12, 3, 2, 12, 2, 10, 12, 10, 5, 12, 5, 4, 12, 4, 8, 12, 8, 7, 12, 7, 6, 12, 6, 11, 12, 11, 3 }, /* 1,1 */ { 3, 2, 12, 8, 3, 12, 4, 8, 12, 5, 4, 12, 10, 5, 12, 6, 10, 12, 7, 6, 12, 11, 7, 12, 2, 11, 12 } }, /* 91: 0, 1, 3, 4, 6, */ { /* 1,0 */ { 12, 7, 11, 12, 11, 2, 12, 2, 1, 12, 1, 9, 12, 9, 4, 12, 4, 5, 12, 5, 10, 12, 10, 6, 12, 6, 7 }, /* 0,1 */ { 12, 2, 1, 12, 1, 9, 12, 9, 4, 12, 4, 7, 12, 7, 11, 12, 11, 6, 12, 6, 5, 12, 5, 10, 12, 10, 2 }, /* 1,1 */ { 2, 1, 12, 11, 2, 12, 7, 11, 12, 4, 7, 12, 9, 4, 12, 5, 9, 12, 6, 5, 12, 10, 6, 12, 1, 10, 12 } }, /* 173: 0, 2, 3, 5, 7, */ { /* 1,0 */ { 12, 6, 10, 12, 10, 1, 12, 1, 0, 12, 0, 8, 12, 8, 7, 12, 7, 4, 12, 4, 9, 12, 9, 5, 12, 5, 6 }, /* 0,1 */ { 12, 1, 0, 12, 0, 8, 12, 8, 7, 12, 7, 6, 12, 6, 10, 12, 10, 5, 12, 5, 4, 12, 4, 9, 12, 9, 1 }, /* 1,1 */ { 1, 0, 12, 10, 1, 12, 6, 10, 12, 7, 6, 12, 8, 7, 12, 4, 8, 12, 5, 4, 12, 9, 5, 12, 0, 9, 12 } }, /* 181: 0, 2, 4, 5, 7, */ { /* 1,0 */ { 11, 3, 12, 6, 11, 12, 5, 6, 12, 9, 5, 12, 0, 9, 12, 1, 0, 12, 10, 1, 12, 2, 10, 12, 3, 2, 12 }, /* 0,1 */ { 5, 6, 12, 9, 5, 12, 0, 9, 12, 3, 0, 12, 11, 3, 12, 2, 11, 12, 1, 2, 12, 10, 1, 12, 6, 10, 12 }, /* 1,1 */ { 12, 5, 6, 12, 6, 11, 12, 11, 3, 12, 3, 0, 12, 0, 9, 12, 9, 1, 12, 1, 2, 12, 2, 10, 12, 10, 5 } }, /* 229: 0, 2, 5, 6, 7, */ { /* 1,0 */ { 9, 1, 12, 4, 9, 12, 7, 4, 12, 11, 7, 12, 2, 11, 12, 3, 2, 12, 8, 3, 12, 0, 8, 12, 1, 0, 12 }, /* 0,1 */ { 7, 4, 12, 11, 7, 12, 2, 11, 12, 1, 2, 12, 9, 1, 12, 0, 9, 12, 3, 0, 12, 8, 3, 12, 4, 8, 12 }, /* 1,1 */ { 12, 7, 4, 12, 4, 9, 12, 9, 1, 12, 1, 2, 12, 2, 11, 12, 11, 3, 12, 3, 0, 12, 0, 8, 12, 8, 7 } }, /* 94: 1, 2, 3, 4, 6, */ { /* 1,0 */ { 12, 5, 9, 12, 9, 0, 12, 0, 3, 12, 3, 11, 12, 11, 6, 12, 6, 7, 12, 7, 8, 12, 8, 4, 12, 4, 5 }, /* 0,1 */ { 12, 0, 3, 12, 3, 11, 12, 11, 6, 12, 6, 5, 12, 5, 9, 12, 9, 4, 12, 4, 7, 12, 7, 8, 12, 8, 0 }, /* 1,1 */ { 0, 3, 12, 9, 0, 12, 5, 9, 12, 6, 5, 12, 11, 6, 12, 7, 11, 12, 4, 7, 12, 8, 4, 12, 3, 8, 12 } }, /* 122: 1, 3, 4, 5, 6, */ { /* 1,0 */ { 8, 0, 12, 7, 8, 12, 6, 7, 12, 10, 6, 12, 1, 10, 12, 2, 1, 12, 11, 2, 12, 3, 11, 12, 0, 3, 12 }, /* 0,1 */ { 6, 7, 12, 10, 6, 12, 1, 10, 12, 0, 1, 12, 8, 0, 12, 3, 8, 12, 2, 3, 12, 11, 2, 12, 7, 11, 12 }, /* 1,1 */ { 12, 6, 7, 12, 7, 8, 12, 8, 0, 12, 0, 1, 12, 1, 10, 12, 10, 2, 12, 2, 3, 12, 3, 11, 12, 11, 6 } }, /* 218: 1, 3, 4, 6, 7, */ { /* 1,0 */ { 10, 2, 12, 5, 10, 12, 4, 5, 12, 8, 4, 12, 3, 8, 12, 0, 3, 12, 9, 0, 12, 1, 9, 12, 2, 1, 12 }, /* 0,1 */ { 4, 5, 12, 8, 4, 12, 3, 8, 12, 2, 3, 12, 10, 2, 12, 1, 10, 12, 0, 1, 12, 9, 0, 12, 5, 9, 12 }, /* 1,1 */ { 12, 4, 5, 12, 5, 10, 12, 10, 2, 12, 2, 3, 12, 3, 8, 12, 8, 0, 12, 0, 1, 12, 1, 9, 12, 9, 4 } } }; //_____________________________________________________________________________ /** * \brief tiling table for case 7.4.1 * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- static const char tiling7_4_1[16][15] = { /* 37: 0, 2, 5, */ { 3, 4, 8, 4, 3, 10, 2, 10, 3, 4, 10, 5, 9, 1, 0 }, /* 133: 0, 2, 7, */ { 1, 6, 10, 6, 1, 8, 0, 8, 1, 6, 8, 7, 11, 3, 2 }, /* 161: 0, 5, 7, */ { 11, 3, 6, 9, 6, 3, 6, 9, 5, 0, 9, 3, 7, 4, 8 }, /* 26: 1, 3, 4, */ { 2, 7, 11, 7, 2, 9, 1, 9, 2, 7, 9, 4, 8, 0, 3 }, /* 74: 1, 3, 6, */ { 0, 5, 9, 5, 0, 11, 3, 11, 0, 5, 11, 6, 10, 2, 1 }, /* 82: 1, 4, 6, */ { 8, 0, 7, 10, 7, 0, 7, 10, 6, 1, 10, 0, 4, 5, 9 }, /* 164: 2, 5, 7, */ { 9, 1, 4, 11, 4, 1, 4, 11, 7, 2, 11, 1, 5, 6, 10 }, /* 88: 3, 4, 6, */ { 10, 2, 5, 8, 5, 2, 5, 8, 4, 3, 8, 2, 6, 7, 11 }, /* 167: 0, 1, 2, 5, 7, */ { 5, 2, 10, 2, 5, 8, 4, 8, 5, 2, 8, 3, 11, 7, 6 }, /* 91: 0, 1, 3, 4, 6, */ { 4, 1, 9, 1, 4, 11, 7, 11, 4, 1, 11, 2, 10, 6, 5 }, /* 173: 0, 2, 3, 5, 7, */ { 7, 0, 8, 0, 7, 10, 6, 10, 7, 0, 10, 1, 9, 5, 4 }, /* 181: 0, 2, 4, 5, 7, */ { 9, 5, 0, 11, 0, 5, 0, 11, 3, 6, 11, 5, 1, 2, 10 }, /* 229: 0, 2, 5, 6, 7, */ { 11, 7, 2, 9, 2, 7, 2, 9, 1, 4, 9, 7, 3, 0, 8 }, /* 94: 1, 2, 3, 4, 6, */ { 6, 3, 11, 3, 6, 9, 5, 9, 6, 3, 9, 0, 8, 4, 7 }, /* 122: 1, 3, 4, 5, 6, */ { 10, 6, 1, 8, 1, 6, 1, 8, 0, 7, 8, 6, 2, 3, 11 }, /* 218: 1, 3, 4, 6, 7, */ { 8, 4, 3, 10, 3, 4, 3, 10, 2, 5, 10, 4, 0, 1, 9 } }; //_____________________________________________________________________________ /** * \brief tiling table for case 7.4.2 * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- static const char tiling7_4_2[16][27] = { /* 37: 0, 2, 5, */ { 9, 4, 8, 4, 9, 5, 10, 5, 9, 1, 10, 9, 10, 1, 2, 0, 2, 1, 2, 0, 3, 8, 3, 0, 9, 8, 0 }, /* 133: 0, 2, 7, */ { 11, 6, 10, 6, 11, 7, 8, 7, 11, 3, 8, 11, 8, 3, 0, 2, 0, 3, 0, 2, 1, 10, 1, 2, 11, 10, 2 }, /* 161: 0, 5, 7, */ { 11, 3, 8, 0, 8, 3, 8, 0, 9, 8, 9, 4, 5, 4, 9, 4, 5, 7, 6, 7, 5, 7, 6, 11, 7, 11, 8 }, /* 26: 1, 3, 4, */ { 8, 7, 11, 7, 8, 4, 9, 4, 8, 0, 9, 8, 9, 0, 1, 3, 1, 0, 1, 3, 2, 11, 2, 3, 8, 11, 3 }, /* 74: 1, 3, 6, */ { 10, 5, 9, 5, 10, 6, 11, 6, 10, 2, 11, 10, 11, 2, 3, 1, 3, 2, 3, 1, 0, 9, 0, 1, 10, 9, 1 }, /* 82: 1, 4, 6, */ { 8, 0, 9, 1, 9, 0, 9, 1, 10, 9, 10, 5, 6, 5, 10, 5, 6, 4, 7, 4, 6, 4, 7, 8, 4, 8, 9 }, /* 164: 2, 5, 7, */ { 9, 1, 10, 2, 10, 1, 10, 2, 11, 10, 11, 6, 7, 6, 11, 6, 7, 5, 4, 5, 7, 5, 4, 9, 5, 9, 10 }, /* 88: 3, 4, 6, */ { 10, 2, 11, 3, 11, 2, 11, 3, 8, 11, 8, 7, 4, 7, 8, 7, 4, 6, 5, 6, 4, 6, 5, 10, 6, 10, 11 }, /* 167: 0, 1, 2, 5, 7, */ { 11, 2, 10, 2, 11, 3, 8, 3, 11, 7, 8, 11, 8, 7, 4, 6, 4, 7, 4, 6, 5, 10, 5, 6, 11, 10, 6 }, /* 91: 0, 1, 3, 4, 6, */ { 10, 1, 9, 1, 10, 2, 11, 2, 10, 6, 11, 10, 11, 6, 7, 5, 7, 6, 7, 5, 4, 9, 4, 5, 10, 9, 5 }, /* 173: 0, 2, 3, 5, 7, */ { 9, 0, 8, 0, 9, 1, 10, 1, 9, 5, 10, 9, 10, 5, 6, 4, 6, 5, 6, 4, 7, 8, 7, 4, 9, 8, 4 }, /* 181: 0, 2, 4, 5, 7, */ { 9, 5, 10, 6, 10, 5, 10, 6, 11, 10, 11, 2, 3, 2, 11, 2, 3, 1, 0, 1, 3, 1, 0, 9, 1, 9, 10 }, /* 229: 0, 2, 5, 6, 7, */ { 11, 7, 8, 4, 8, 7, 8, 4, 9, 8, 9, 0, 1, 0, 9, 0, 1, 3, 2, 3, 1, 3, 2, 11, 3, 11, 8 }, /* 94: 1, 2, 3, 4, 6, */ { 8, 3, 11, 3, 8, 0, 9, 0, 8, 4, 9, 8, 9, 4, 5, 7, 5, 4, 5, 7, 6, 11, 6, 7, 8, 11, 7 }, /* 122: 1, 3, 4, 5, 6, */ { 10, 6, 11, 7, 11, 6, 11, 7, 8, 11, 8, 3, 0, 3, 8, 3, 0, 2, 1, 2, 0, 2, 1, 10, 2, 10, 11 }, /* 218: 1, 3, 4, 6, 7, */ { 8, 4, 9, 5, 9, 4, 9, 5, 10, 9, 10, 1, 2, 1, 10, 1, 2, 0, 3, 0, 2, 0, 3, 8, 0, 8, 9 } }; //_____________________________________________________________________________ //_____________________________________________________________________________ /** * \brief tiling table for case 8 * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- static const char tiling8[6][6] = { /* 15: 0, 1, 2, 3, */ { 9, 8, 10, 10, 8, 11 }, /* 51: 0, 1, 4, 5, */ { 1, 5, 3, 3, 5, 7 }, /* 153: 0, 3, 4, 7, */ { 0, 4, 2, 4, 6, 2 }, /* 102: 1, 2, 5, 6, */ { 0, 2, 4, 4, 2, 6 }, /* 204: 2, 3, 6, 7, */ { 1, 3, 5, 3, 7, 5 }, /* 240: 4, 5, 6, 7, */ { 9, 10, 8, 10, 11, 8 } }; //_____________________________________________________________________________ //_____________________________________________________________________________ /** * \brief tiling table for case 9 * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- static const char tiling9[8][12] = { /* 39: 0, 1, 2, 5, */ { 2, 10, 5, 3, 2, 5, 3, 5, 4, 3, 4, 8 }, /* 27: 0, 1, 3, 4, */ { 4, 7, 11, 9, 4, 11, 9, 11, 2, 9, 2, 1 }, /* 141: 0, 2, 3, 7, */ { 10, 7, 6, 1, 7, 10, 1, 8, 7, 1, 0, 8 }, /* 177: 0, 4, 5, 7, */ { 3, 6, 11, 0, 6, 3, 0, 5, 6, 0, 9, 5 }, /* 78: 1, 2, 3, 6, */ { 3, 11, 6, 0, 3, 6, 0, 6, 5, 0, 5, 9 }, /* 114: 1, 4, 5, 6, */ { 10, 6, 7, 1, 10, 7, 1, 7, 8, 1, 8, 0 }, /* 228: 2, 5, 6, 7, */ { 4, 11, 7, 9, 11, 4, 9, 2, 11, 9, 1, 2 }, /* 216: 3, 4, 6, 7, */ { 2, 5, 10, 3, 5, 2, 3, 4, 5, 3, 8, 4 } }; //_____________________________________________________________________________ //_____________________________________________________________________________ /** * \brief test table for case 10 * 2 faces to test + eventually the interior * When the tests on both specified faces are positive : 4 middle triangles (1) * When the test on the first specified face is positive : 8 first triangles * When the test on the second specified face is positive : 8 next triangles * When the tests on both specified faces are negative : * - if the test on the interior is negative : 4 middle triangles * - if the test on the interior is positive : 8 last triangles * * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- static const char test10[6][3] = { /* 195: 0, 1, 6, 7, */ { 2, 4, 7 }, /* 85: 0, 2, 4, 6, */ { 5, 6, 7 }, /* 105: 0, 3, 5, 6, */ { 1, 3, 7 }, /* 150: 1, 2, 4, 7, */ { 1, 3, 7 }, /* 170: 1, 3, 5, 7, */ { 5, 6, 7 }, /* 60: 2, 3, 4, 5, */ { 2, 4, 7 } }; //_____________________________________________________________________________ /** * \brief tiling table for case 10.1.1 * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- static const char tiling10_1_1[6][12] = { /* 195: 0, 1, 6, 7, */ { 5, 10, 7, 11, 7, 10, 8, 1, 9, 1, 8, 3 }, /* 85: 0, 2, 4, 6, */ { 1, 2, 5, 6, 5, 2, 4, 3, 0, 3, 4, 7 }, /* 105: 0, 3, 5, 6, */ { 11, 0, 8, 0, 11, 2, 4, 9, 6, 10, 6, 9 }, /* 150: 1, 2, 4, 7, */ { 9, 0, 10, 2, 10, 0, 6, 8, 4, 8, 6, 11 }, /* 170: 1, 3, 5, 7, */ { 7, 2, 3, 2, 7, 6, 0, 1, 4, 5, 4, 1 }, /* 60: 2, 3, 4, 5, */ { 7, 9, 5, 9, 7, 8, 10, 1, 11, 3, 11, 1 } }; //_____________________________________________________________________________ /** * \brief tiling table for case 10.1.1 inverted * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- static const char tiling10_1_1_[6][12] = { /* 195: 0, 1, 6, 7, */ { 5, 9, 7, 8, 7, 9, 11, 1, 10, 1, 11, 3 }, /* 85: 0, 2, 4, 6, */ { 3, 2, 7, 6, 7, 2, 4, 1, 0, 1, 4, 5 }, /* 105: 0, 3, 5, 6, */ { 10, 0, 9, 0, 10, 2, 4, 8, 6, 11, 6, 8 }, /* 150: 1, 2, 4, 7, */ { 8, 0, 11, 2, 11, 0, 6, 9, 4, 9, 6, 10 }, /* 170: 1, 3, 5, 7, */ { 5, 2, 1, 2, 5, 6, 0, 3, 4, 7, 4, 3 }, /* 60: 2, 3, 4, 5, */ { 7, 10, 5, 10, 7, 11, 9, 1, 8, 3, 8, 1 } }; //_____________________________________________________________________________ /** * \brief tiling table for case 10.1.2 * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- static const char tiling10_1_2[6][24] = { /* 195: 0, 1, 6, 7, */ { 3, 11, 7, 3, 7, 8, 9, 8, 7, 5, 9, 7, 9, 5, 10, 9, 10, 1, 3, 1, 10, 11, 3, 10 }, /* 85: 0, 2, 4, 6, */ { 7, 6, 5, 7, 5, 4, 0, 4, 5, 1, 0, 5, 0, 1, 2, 0, 2, 3, 7, 3, 2, 6, 7, 2 }, /* 105: 0, 3, 5, 6, */ { 11, 2, 10, 6, 11, 10, 11, 6, 4, 11, 4, 8, 0, 8, 4, 9, 0, 4, 0, 9, 10, 0, 10, 2 }, /* 150: 1, 2, 4, 7, */ { 11, 2, 10, 11, 10, 6, 4, 6, 10, 9, 4, 10, 4, 9, 0, 4, 0, 8, 11, 8, 0, 2, 11, 0 }, /* 170: 1, 3, 5, 7, */ { 7, 6, 5, 4, 7, 5, 7, 4, 0, 7, 0, 3, 2, 3, 0, 1, 2, 0, 2, 1, 5, 2, 5, 6 }, /* 60: 2, 3, 4, 5, */ { 7, 8, 3, 11, 7, 3, 7, 11, 10, 7, 10, 5, 9, 5, 10, 1, 9, 10, 9, 1, 3, 9, 3, 8 } }; //_____________________________________________________________________________ /** * \brief tiling table for case 10.2 * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- static const char tiling10_2[6][24] = { /* 195: 0, 1, 6, 7, */ { 12, 5, 9, 12, 9, 8, 12, 8, 3, 12, 3, 1, 12, 1, 10, 12, 10, 11, 12, 11, 7, 12, 7, 5 }, /* 85: 0, 2, 4, 6, */ { 12, 1, 0, 12, 0, 4, 12, 4, 7, 12, 7, 3, 12, 3, 2, 12, 2, 6, 12, 6, 5, 12, 5, 1 }, /* 105: 0, 3, 5, 6, */ { 4, 8, 12, 6, 4, 12, 10, 6, 12, 9, 10, 12, 0, 9, 12, 2, 0, 12, 11, 2, 12, 8, 11, 12 }, /* 150: 1, 2, 4, 7, */ { 12, 9, 4, 12, 4, 6, 12, 6, 11, 12, 11, 8, 12, 8, 0, 12, 0, 2, 12, 2, 10, 12, 10, 9 }, /* 170: 1, 3, 5, 7, */ { 0, 3, 12, 4, 0, 12, 5, 4, 12, 1, 5, 12, 2, 1, 12, 6, 2, 12, 7, 6, 12, 3, 7, 12 }, /* 60: 2, 3, 4, 5, */ { 10, 5, 12, 11, 10, 12, 3, 11, 12, 1, 3, 12, 9, 1, 12, 8, 9, 12, 7, 8, 12, 5, 7, 12 } }; //_____________________________________________________________________________ /** * \brief tiling table for case 10.2 inverted * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- static const char tiling10_2_[6][24] = { /* 195: 0, 1, 6, 7, */ { 8, 7, 12, 9, 8, 12, 1, 9, 12, 3, 1, 12, 11, 3, 12, 10, 11, 12, 5, 10, 12, 7, 5, 12 }, /* 85: 0, 2, 4, 6, */ { 4, 5, 12, 0, 4, 12, 3, 0, 12, 7, 3, 12, 6, 7, 12, 2, 6, 12, 1, 2, 12, 5, 1, 12 }, /* 105: 0, 3, 5, 6, */ { 12, 11, 6, 12, 6, 4, 12, 4, 9, 12, 9, 10, 12, 10, 2, 12, 2, 0, 12, 0, 8, 12, 8, 11 }, /* 150: 1, 2, 4, 7, */ { 6, 10, 12, 4, 6, 12, 8, 4, 12, 11, 8, 12, 2, 11, 12, 0, 2, 12, 9, 0, 12, 10, 9, 12 }, /* 170: 1, 3, 5, 7, */ { 12, 7, 4, 12, 4, 0, 12, 0, 1, 12, 1, 5, 12, 5, 6, 12, 6, 2, 12, 2, 3, 12, 3, 7 }, /* 60: 2, 3, 4, 5, */ { 12, 7, 11, 12, 11, 10, 12, 10, 1, 12, 1, 3, 12, 3, 8, 12, 8, 9, 12, 9, 5, 12, 5, 7 } }; //_____________________________________________________________________________ //_____________________________________________________________________________ /** * \brief tiling table for case 11 * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- static const char tiling11[12][12] = { /* 23: 0, 1, 2, 4, */ { 2, 10, 9, 2, 9, 7, 2, 7, 3, 7, 9, 4 }, /* 139: 0, 1, 3, 7, */ { 1, 6, 2, 1, 8, 6, 1, 9, 8, 8, 7, 6 }, /* 99: 0, 1, 5, 6, */ { 8, 3, 1, 8, 1, 6, 8, 6, 4, 6, 1, 10 }, /* 77: 0, 2, 3, 6, */ { 0, 8, 11, 0, 11, 5, 0, 5, 1, 5, 11, 6 }, /* 57: 0, 3, 4, 5, */ { 9, 5, 7, 9, 7, 2, 9, 2, 0, 2, 7, 11 }, /* 209: 0, 4, 6, 7, */ { 5, 0, 4, 5, 11, 0, 5, 10, 11, 11, 3, 0 }, /* 46: 1, 2, 3, 5, */ { 5, 4, 0, 5, 0, 11, 5, 11, 10, 11, 0, 3 }, /* 198: 1, 2, 6, 7, */ { 9, 7, 5, 9, 2, 7, 9, 0, 2, 2, 11, 7 }, /* 178: 1, 4, 5, 7, */ { 0, 11, 8, 0, 5, 11, 0, 1, 5, 5, 6, 11 }, /* 156: 2, 3, 4, 7, */ { 8, 1, 3, 8, 6, 1, 8, 4, 6, 6, 10, 1 }, /* 116: 2, 4, 5, 6, */ { 1, 2, 6, 1, 6, 8, 1, 8, 9, 8, 6, 7 }, /* 232: 3, 5, 6, 7, */ { 2, 9, 10, 2, 7, 9, 2, 3, 7, 7, 4, 9 } }; //_____________________________________________________________________________ //_____________________________________________________________________________ /** * \brief test table for case 12 * 2 faces to test + eventually the interior * When the tests on both specified faces are positive : 4 middle triangles (1) * When the test on the first specified face is positive : 8 first triangles * When the test on the second specified face is positive : 8 next triangles * When the tests on both specified faces are negative : * - if the test on the interior is negative : 4 middle triangles * - if the test on the interior is positive : 8 last triangles * The support edge for the interior test is marked as the 4th column. * * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- static const char test12[24][4] = { /* 135: 0, 1, 2, 7, */ { 4, 3, 7, 11 }, /* 75: 0, 1, 3, 6, */ { 3, 2, 7, 10 }, /* 83: 0, 1, 4, 6, */ { 2, 6, 7, 5 }, /* 163: 0, 1, 5, 7, */ { 6, 4, 7, 7 }, /* 45: 0, 2, 3, 5, */ { 2, 1, 7, 9 }, /* 53: 0, 2, 4, 5, */ { 5, 2, 7, 1 }, /* 149: 0, 2, 4, 7, */ { 5, 3, 7, 2 }, /* 101: 0, 2, 5, 6, */ { 5, 1, 7, 0 }, /* 197: 0, 2, 6, 7, */ { 5, 4, 7, 3 }, /* 89: 0, 3, 4, 6, */ { 6, 3, 7, 6 }, /* 169: 0, 3, 5, 7, */ { 1, 6, 7, 4 }, /* 225: 0, 5, 6, 7, */ { 1, 4, 7, 8 }, /* 30: 1, 2, 3, 4, */ { 4, 1, 7, 8 }, /* 86: 1, 2, 4, 6, */ { 6, 1, 7, 4 }, /* 166: 1, 2, 5, 7, */ { 3, 6, 7, 6 }, /* 58: 1, 3, 4, 5, */ { 4, 5, 7, 3 }, /* 154: 1, 3, 4, 7, */ { 1, 5, 7, 0 }, /* 106: 1, 3, 5, 6, */ { 3, 5, 7, 2 }, /* 202: 1, 3, 6, 7, */ { 2, 5, 7, 1 }, /* 210: 1, 4, 6, 7, */ { 1, 2, 7, 9 }, /* 92: 2, 3, 4, 6, */ { 4, 6, 7, 7 }, /* 172: 2, 3, 5, 7, */ { 6, 2, 7, 5 }, /* 180: 2, 4, 5, 7, */ { 2, 3, 7, 10 }, /* 120: 3, 4, 5, 6, */ { 3, 4, 7, 11 } }; //_____________________________________________________________________________ /** * \brief tiling table for case 12.1.1 * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- static const char tiling12_1_1[24][12] = { /* 135: 0, 1, 2, 7, */ { 7, 6, 11, 10, 3, 2, 3, 10, 8, 9, 8, 10 }, /* 75: 0, 1, 3, 6, */ { 6, 5, 10, 9, 2, 1, 2, 9, 11, 8, 11, 9 }, /* 83: 0, 1, 4, 6, */ { 10, 6, 5, 7, 9, 4, 9, 7, 1, 3, 1, 7 }, /* 163: 0, 1, 5, 7, */ { 7, 6, 11, 4, 8, 5, 3, 5, 8, 5, 3, 1 }, /* 45: 0, 2, 3, 5, */ { 5, 4, 9, 8, 1, 0, 1, 8, 10, 11, 10, 8 }, /* 53: 0, 2, 4, 5, */ { 1, 2, 10, 0, 9, 3, 5, 3, 9, 3, 5, 7 }, /* 149: 0, 2, 4, 7, */ { 10, 1, 2, 0, 11, 3, 11, 0, 6, 4, 6, 0 }, /* 101: 0, 2, 5, 6, */ { 8, 3, 0, 2, 9, 1, 9, 2, 4, 6, 4, 2 }, /* 197: 0, 2, 6, 7, */ { 3, 0, 8, 2, 11, 1, 7, 1, 11, 1, 7, 5 }, /* 89: 0, 3, 4, 6, */ { 6, 5, 10, 7, 11, 4, 2, 4, 11, 4, 2, 0 }, /* 169: 0, 3, 5, 7, */ { 9, 5, 4, 6, 8, 7, 8, 6, 0, 2, 0, 6 }, /* 225: 0, 5, 6, 7, */ { 8, 3, 0, 7, 4, 11, 9, 11, 4, 11, 9, 10 }, /* 30: 1, 2, 3, 4, */ { 4, 7, 8, 11, 0, 3, 0, 11, 9, 10, 9, 11 }, /* 86: 1, 2, 4, 6, */ { 4, 7, 8, 5, 9, 6, 0, 6, 9, 6, 0, 2 }, /* 166: 1, 2, 5, 7, */ { 11, 7, 6, 4, 10, 5, 10, 4, 2, 0, 2, 4 }, /* 58: 1, 3, 4, 5, */ { 11, 2, 3, 1, 8, 0, 8, 1, 7, 5, 7, 1 }, /* 154: 1, 3, 4, 7, */ { 0, 1, 9, 3, 8, 2, 4, 2, 8, 2, 4, 6 }, /* 106: 1, 3, 5, 6, */ { 2, 3, 11, 1, 10, 0, 6, 0, 10, 0, 6, 4 }, /* 202: 1, 3, 6, 7, */ { 9, 0, 1, 3, 10, 2, 10, 3, 5, 7, 5, 3 }, /* 210: 1, 4, 6, 7, */ { 9, 0, 1, 4, 5, 8, 10, 8, 5, 8, 10, 11 }, /* 92: 2, 3, 4, 6, */ { 8, 4, 7, 5, 11, 6, 11, 5, 3, 1, 3, 5 }, /* 172: 2, 3, 5, 7, */ { 5, 4, 9, 6, 10, 7, 1, 7, 10, 7, 1, 3 }, /* 180: 2, 4, 5, 7, */ { 10, 1, 2, 5, 6, 9, 11, 9, 6, 9, 11, 8 }, /* 120: 3, 4, 5, 6, */ { 11, 2, 3, 6, 7, 10, 8, 10, 7, 10, 8, 9 } }; //_____________________________________________________________________________ /** * \brief tiling table for case 12.1.1 inverted * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- static const char tiling12_1_1_[24][12] = { /* 135: 0, 1, 2, 7, */ { 3, 2, 11, 10, 7, 6, 7, 10, 8, 9, 8, 10 }, /* 75: 0, 1, 3, 6, */ { 2, 1, 10, 9, 6, 5, 6, 9, 11, 8, 11, 9 }, /* 83: 0, 1, 4, 6, */ { 9, 4, 5, 7, 10, 6, 10, 7, 1, 3, 1, 7 }, /* 163: 0, 1, 5, 7, */ { 7, 4, 8, 6, 11, 5, 3, 5, 11, 5, 3, 1 }, /* 45: 0, 2, 3, 5, */ { 1, 0, 9, 8, 5, 4, 5, 8, 10, 11, 10, 8 }, /* 53: 0, 2, 4, 5, */ { 1, 0, 9, 2, 10, 3, 5, 3, 10, 3, 5, 7 }, /* 149: 0, 2, 4, 7, */ { 11, 3, 2, 0, 10, 1, 10, 0, 6, 4, 6, 0 }, /* 101: 0, 2, 5, 6, */ { 9, 1, 0, 2, 8, 3, 8, 2, 4, 6, 4, 2 }, /* 197: 0, 2, 6, 7, */ { 3, 2, 11, 0, 8, 1, 7, 1, 8, 1, 7, 5 }, /* 89: 0, 3, 4, 6, */ { 6, 7, 11, 5, 10, 4, 2, 4, 10, 4, 2, 0 }, /* 169: 0, 3, 5, 7, */ { 8, 7, 4, 6, 9, 5, 9, 6, 0, 2, 0, 6 }, /* 225: 0, 5, 6, 7, */ { 8, 7, 4, 3, 0, 11, 9, 11, 0, 11, 9, 10 }, /* 30: 1, 2, 3, 4, */ { 0, 3, 8, 11, 4, 7, 4, 11, 9, 10, 9, 11 }, /* 86: 1, 2, 4, 6, */ { 4, 5, 9, 7, 8, 6, 0, 6, 8, 6, 0, 2 }, /* 166: 1, 2, 5, 7, */ { 10, 5, 6, 4, 11, 7, 11, 4, 2, 0, 2, 4 }, /* 58: 1, 3, 4, 5, */ { 8, 0, 3, 1, 11, 2, 11, 1, 7, 5, 7, 1 }, /* 154: 1, 3, 4, 7, */ { 0, 3, 8, 1, 9, 2, 4, 2, 9, 2, 4, 6 }, /* 106: 1, 3, 5, 6, */ { 2, 1, 10, 3, 11, 0, 6, 0, 11, 0, 6, 4 }, /* 202: 1, 3, 6, 7, */ { 10, 2, 1, 3, 9, 0, 9, 3, 5, 7, 5, 3 }, /* 210: 1, 4, 6, 7, */ { 9, 4, 5, 0, 1, 8, 10, 8, 1, 8, 10, 11 }, /* 92: 2, 3, 4, 6, */ { 11, 6, 7, 5, 8, 4, 8, 5, 3, 1, 3, 5 }, /* 172: 2, 3, 5, 7, */ { 5, 6, 10, 4, 9, 7, 1, 7, 9, 7, 1, 3 }, /* 180: 2, 4, 5, 7, */ { 10, 5, 6, 1, 2, 9, 11, 9, 2, 9, 11, 8 }, /* 120: 3, 4, 5, 6, */ { 11, 6, 7, 2, 3, 10, 8, 10, 3, 10, 8, 9 } }; //_____________________________________________________________________________ /** * \brief tiling table for case 12.1.2 * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- static const char tiling12_1_2[24][24] = { /* 135: 0, 1, 2, 7, */ { 7, 3, 11, 3, 7, 8, 9, 8, 7, 6, 9, 7, 9, 6, 10, 2, 10, 6, 11, 2, 6, 2, 11, 3 }, /* 75: 0, 1, 3, 6, */ { 6, 2, 10, 2, 6, 11, 8, 11, 6, 5, 8, 6, 8, 5, 9, 1, 9, 5, 10, 1, 5, 1, 10, 2 }, /* 83: 0, 1, 4, 6, */ { 10, 9, 5, 9, 10, 1, 3, 1, 10, 6, 3, 10, 3, 6, 7, 4, 7, 6, 5, 4, 6, 4, 5, 9 }, /* 163: 0, 1, 5, 7, */ { 7, 8, 11, 3, 11, 8, 11, 3, 1, 11, 1, 6, 5, 6, 1, 6, 5, 4, 6, 4, 7, 8, 7, 4 }, /* 45: 0, 2, 3, 5, */ { 5, 1, 9, 1, 5, 10, 11, 10, 5, 4, 11, 5, 11, 4, 8, 0, 8, 4, 9, 0, 4, 0, 9, 1 }, /* 53: 0, 2, 4, 5, */ { 1, 9, 10, 5, 10, 9, 10, 5, 7, 10, 7, 2, 3, 2, 7, 2, 3, 0, 2, 0, 1, 9, 1, 0 }, /* 149: 0, 2, 4, 7, */ { 10, 11, 2, 11, 10, 6, 4, 6, 10, 1, 4, 10, 4, 1, 0, 3, 0, 1, 2, 3, 1, 3, 2, 11 }, /* 101: 0, 2, 5, 6, */ { 8, 9, 0, 9, 8, 4, 6, 4, 8, 3, 6, 8, 6, 3, 2, 1, 2, 3, 0, 1, 3, 1, 0, 9 }, /* 197: 0, 2, 6, 7, */ { 3, 11, 8, 7, 8, 11, 8, 7, 5, 8, 5, 0, 1, 0, 5, 0, 1, 2, 0, 2, 3, 11, 3, 2 }, /* 89: 0, 3, 4, 6, */ { 6, 11, 10, 2, 10, 11, 10, 2, 0, 10, 0, 5, 4, 5, 0, 5, 4, 7, 5, 7, 6, 11, 6, 7 }, /* 169: 0, 3, 5, 7, */ { 9, 8, 4, 8, 9, 0, 2, 0, 9, 5, 2, 9, 2, 5, 6, 7, 6, 5, 4, 7, 5, 7, 4, 8 }, /* 225: 0, 5, 6, 7, */ { 8, 4, 0, 9, 0, 4, 0, 9, 10, 0, 10, 3, 11, 3, 10, 3, 11, 7, 3, 7, 8, 4, 8, 7 }, /* 30: 1, 2, 3, 4, */ { 4, 0, 8, 0, 4, 9, 10, 9, 4, 7, 10, 4, 10, 7, 11, 3, 11, 7, 8, 3, 7, 3, 8, 0 }, /* 86: 1, 2, 4, 6, */ { 4, 9, 8, 0, 8, 9, 8, 0, 2, 8, 2, 7, 6, 7, 2, 7, 6, 5, 7, 5, 4, 9, 4, 5 }, /* 166: 1, 2, 5, 7, */ { 11, 10, 6, 10, 11, 2, 0, 2, 11, 7, 0, 11, 0, 7, 4, 5, 4, 7, 6, 5, 7, 5, 6, 10 }, /* 58: 1, 3, 4, 5, */ { 11, 8, 3, 8, 11, 7, 5, 7, 11, 2, 5, 11, 5, 2, 1, 0, 1, 2, 3, 0, 2, 0, 3, 8 }, /* 154: 1, 3, 4, 7, */ { 0, 8, 9, 4, 9, 8, 9, 4, 6, 9, 6, 1, 2, 1, 6, 1, 2, 3, 1, 3, 0, 8, 0, 3 }, /* 106: 1, 3, 5, 6, */ { 2, 10, 11, 6, 11, 10, 11, 6, 4, 11, 4, 3, 0, 3, 4, 3, 0, 1, 3, 1, 2, 10, 2, 1 }, /* 202: 1, 3, 6, 7, */ { 9, 10, 1, 10, 9, 5, 7, 5, 9, 0, 7, 9, 7, 0, 3, 2, 3, 0, 1, 2, 0, 2, 1, 10 }, /* 210: 1, 4, 6, 7, */ { 9, 5, 1, 10, 1, 5, 1, 10, 11, 1, 11, 0, 8, 0, 11, 0, 8, 4, 0, 4, 9, 5, 9, 4 }, /* 92: 2, 3, 4, 6, */ { 8, 11, 7, 11, 8, 3, 1, 3, 8, 4, 1, 8, 1, 4, 5, 6, 5, 4, 7, 6, 4, 6, 7, 11 }, /* 172: 2, 3, 5, 7, */ { 5, 10, 9, 1, 9, 10, 9, 1, 3, 9, 3, 4, 7, 4, 3, 4, 7, 6, 4, 6, 5, 10, 5, 6 }, /* 180: 2, 4, 5, 7, */ { 10, 6, 2, 11, 2, 6, 2, 11, 8, 2, 8, 1, 9, 1, 8, 1, 9, 5, 1, 5, 10, 6, 10, 5 }, /* 120: 3, 4, 5, 6, */ { 11, 7, 3, 8, 3, 7, 3, 8, 9, 3, 9, 2, 10, 2, 9, 2, 10, 6, 2, 6, 11, 7, 11, 6 } }; //_____________________________________________________________________________ /** * \brief tiling table for case 12.2 * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- static const char tiling12_2[24][24] = { /* 135: 0, 1, 2, 7, */ { 9, 8, 12, 10, 9, 12, 2, 10, 12, 3, 2, 12, 11, 3, 12, 6, 11, 12, 7, 6, 12, 8, 7, 12 }, /* 75: 0, 1, 3, 6, */ { 8, 11, 12, 9, 8, 12, 1, 9, 12, 2, 1, 12, 10, 2, 12, 5, 10, 12, 6, 5, 12, 11, 6, 12 }, /* 83: 0, 1, 4, 6, */ { 3, 1, 12, 7, 3, 12, 4, 7, 12, 9, 4, 12, 5, 9, 12, 6, 5, 12, 10, 6, 12, 1, 10, 12 }, /* 163: 0, 1, 5, 7, */ { 12, 3, 1, 12, 1, 5, 12, 5, 6, 12, 6, 11, 12, 11, 7, 12, 7, 4, 12, 4, 8, 12, 8, 3 }, /* 45: 0, 2, 3, 5, */ { 11, 10, 12, 8, 11, 12, 0, 8, 12, 1, 0, 12, 9, 1, 12, 4, 9, 12, 5, 4, 12, 10, 5, 12 }, /* 53: 0, 2, 4, 5, */ { 12, 5, 7, 12, 7, 3, 12, 3, 2, 12, 2, 10, 12, 10, 1, 12, 1, 0, 12, 0, 9, 12, 9, 5 }, /* 149: 0, 2, 4, 7, */ { 4, 6, 12, 0, 4, 12, 1, 0, 12, 10, 1, 12, 2, 10, 12, 3, 2, 12, 11, 3, 12, 6, 11, 12 }, /* 101: 0, 2, 5, 6, */ { 6, 4, 12, 2, 6, 12, 3, 2, 12, 8, 3, 12, 0, 8, 12, 1, 0, 12, 9, 1, 12, 4, 9, 12 }, /* 197: 0, 2, 6, 7, */ { 12, 7, 5, 12, 5, 1, 12, 1, 0, 12, 0, 8, 12, 8, 3, 12, 3, 2, 12, 2, 11, 12, 11, 7 }, /* 89: 0, 3, 4, 6, */ { 12, 2, 0, 12, 0, 4, 12, 4, 5, 12, 5, 10, 12, 10, 6, 12, 6, 7, 12, 7, 11, 12, 11, 2 }, /* 169: 0, 3, 5, 7, */ { 2, 0, 12, 6, 2, 12, 7, 6, 12, 8, 7, 12, 4, 8, 12, 5, 4, 12, 9, 5, 12, 0, 9, 12 }, /* 225: 0, 5, 6, 7, */ { 12, 9, 10, 12, 10, 11, 12, 11, 7, 12, 7, 4, 12, 4, 8, 12, 8, 3, 12, 3, 0, 12, 0, 9 }, /* 30: 1, 2, 3, 4, */ { 10, 9, 12, 11, 10, 12, 7, 11, 12, 4, 7, 12, 8, 4, 12, 3, 8, 12, 0, 3, 12, 9, 0, 12 }, /* 86: 1, 2, 4, 6, */ { 12, 0, 2, 12, 2, 6, 12, 6, 7, 12, 7, 8, 12, 8, 4, 12, 4, 5, 12, 5, 9, 12, 9, 0 }, /* 166: 1, 2, 5, 7, */ { 0, 2, 12, 4, 0, 12, 5, 4, 12, 10, 5, 12, 6, 10, 12, 7, 6, 12, 11, 7, 12, 2, 11, 12 }, /* 58: 1, 3, 4, 5, */ { 5, 7, 12, 1, 5, 12, 0, 1, 12, 8, 0, 12, 3, 8, 12, 2, 3, 12, 11, 2, 12, 7, 11, 12 }, /* 154: 1, 3, 4, 7, */ { 12, 4, 6, 12, 6, 2, 12, 2, 3, 12, 3, 8, 12, 8, 0, 12, 0, 1, 12, 1, 9, 12, 9, 4 }, /* 106: 1, 3, 5, 6, */ { 12, 6, 4, 12, 4, 0, 12, 0, 1, 12, 1, 10, 12, 10, 2, 12, 2, 3, 12, 3, 11, 12, 11, 6 }, /* 202: 1, 3, 6, 7, */ { 7, 5, 12, 3, 7, 12, 2, 3, 12, 10, 2, 12, 1, 10, 12, 0, 1, 12, 9, 0, 12, 5, 9, 12 }, /* 210: 1, 4, 6, 7, */ { 12, 10, 11, 12, 11, 8, 12, 8, 0, 12, 0, 1, 12, 1, 9, 12, 9, 4, 12, 4, 5, 12, 5, 10 }, /* 92: 2, 3, 4, 6, */ { 1, 3, 12, 5, 1, 12, 6, 5, 12, 11, 6, 12, 7, 11, 12, 4, 7, 12, 8, 4, 12, 3, 8, 12 }, /* 172: 2, 3, 5, 7, */ { 12, 1, 3, 12, 3, 7, 12, 7, 4, 12, 4, 9, 12, 9, 5, 12, 5, 6, 12, 6, 10, 12, 10, 1 }, /* 180: 2, 4, 5, 7, */ { 12, 11, 8, 12, 8, 9, 12, 9, 1, 12, 1, 2, 12, 2, 10, 12, 10, 5, 12, 5, 6, 12, 6, 11 }, /* 120: 3, 4, 5, 6, */ { 12, 8, 9, 12, 9, 10, 12, 10, 2, 12, 2, 3, 12, 3, 11, 12, 11, 6, 12, 6, 7, 12, 7, 8 } }; //_____________________________________________________________________________ /** * \brief tiling table for case 12.2 inverted * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- static const char tiling12_2_[24][24] = { /* 135: 0, 1, 2, 7, */ { 12, 2, 11, 12, 11, 7, 12, 7, 6, 12, 6, 10, 12, 10, 9, 12, 9, 8, 12, 8, 3, 12, 3, 2 }, /* 75: 0, 1, 3, 6, */ { 12, 1, 10, 12, 10, 6, 12, 6, 5, 12, 5, 9, 12, 9, 8, 12, 8, 11, 12, 11, 2, 12, 2, 1 }, /* 83: 0, 1, 4, 6, */ { 12, 4, 5, 12, 5, 10, 12, 10, 6, 12, 6, 7, 12, 7, 3, 12, 3, 1, 12, 1, 9, 12, 9, 4 }, /* 163: 0, 1, 5, 7, */ { 7, 6, 12, 8, 7, 12, 4, 8, 12, 5, 4, 12, 1, 5, 12, 3, 1, 12, 11, 3, 12, 6, 11, 12 }, /* 45: 0, 2, 3, 5, */ { 12, 0, 9, 12, 9, 5, 12, 5, 4, 12, 4, 8, 12, 8, 11, 12, 11, 10, 12, 10, 1, 12, 1, 0 }, /* 53: 0, 2, 4, 5, */ { 1, 2, 12, 9, 1, 12, 0, 9, 12, 3, 0, 12, 7, 3, 12, 5, 7, 12, 10, 5, 12, 2, 10, 12 }, /* 149: 0, 2, 4, 7, */ { 12, 1, 2, 12, 2, 11, 12, 11, 3, 12, 3, 0, 12, 0, 4, 12, 4, 6, 12, 6, 10, 12, 10, 1 }, /* 101: 0, 2, 5, 6, */ { 12, 3, 0, 12, 0, 9, 12, 9, 1, 12, 1, 2, 12, 2, 6, 12, 6, 4, 12, 4, 8, 12, 8, 3 }, /* 197: 0, 2, 6, 7, */ { 3, 0, 12, 11, 3, 12, 2, 11, 12, 1, 2, 12, 5, 1, 12, 7, 5, 12, 8, 7, 12, 0, 8, 12 }, /* 89: 0, 3, 4, 6, */ { 6, 5, 12, 11, 6, 12, 7, 11, 12, 4, 7, 12, 0, 4, 12, 2, 0, 12, 10, 2, 12, 5, 10, 12 }, /* 169: 0, 3, 5, 7, */ { 12, 7, 4, 12, 4, 9, 12, 9, 5, 12, 5, 6, 12, 6, 2, 12, 2, 0, 12, 0, 8, 12, 8, 7 }, /* 225: 0, 5, 6, 7, */ { 8, 7, 12, 0, 8, 12, 3, 0, 12, 11, 3, 12, 10, 11, 12, 9, 10, 12, 4, 9, 12, 7, 4, 12 }, /* 30: 1, 2, 3, 4, */ { 12, 7, 8, 12, 8, 0, 12, 0, 3, 12, 3, 11, 12, 11, 10, 12, 10, 9, 12, 9, 4, 12, 4, 7 }, /* 86: 1, 2, 4, 6, */ { 4, 7, 12, 9, 4, 12, 5, 9, 12, 6, 5, 12, 2, 6, 12, 0, 2, 12, 8, 0, 12, 7, 8, 12 }, /* 166: 1, 2, 5, 7, */ { 12, 5, 6, 12, 6, 11, 12, 11, 7, 12, 7, 4, 12, 4, 0, 12, 0, 2, 12, 2, 10, 12, 10, 5 }, /* 58: 1, 3, 4, 5, */ { 12, 0, 3, 12, 3, 11, 12, 11, 2, 12, 2, 1, 12, 1, 5, 12, 5, 7, 12, 7, 8, 12, 8, 0 }, /* 154: 1, 3, 4, 7, */ { 0, 3, 12, 9, 0, 12, 1, 9, 12, 2, 1, 12, 6, 2, 12, 4, 6, 12, 8, 4, 12, 3, 8, 12 }, /* 106: 1, 3, 5, 6, */ { 2, 1, 12, 11, 2, 12, 3, 11, 12, 0, 3, 12, 4, 0, 12, 6, 4, 12, 10, 6, 12, 1, 10, 12 }, /* 202: 1, 3, 6, 7, */ { 12, 2, 1, 12, 1, 9, 12, 9, 0, 12, 0, 3, 12, 3, 7, 12, 7, 5, 12, 5, 10, 12, 10, 2 }, /* 210: 1, 4, 6, 7, */ { 9, 0, 12, 5, 9, 12, 4, 5, 12, 8, 4, 12, 11, 8, 12, 10, 11, 12, 1, 10, 12, 0, 1, 12 }, /* 92: 2, 3, 4, 6, */ { 12, 6, 7, 12, 7, 8, 12, 8, 4, 12, 4, 5, 12, 5, 1, 12, 1, 3, 12, 3, 11, 12, 11, 6 }, /* 172: 2, 3, 5, 7, */ { 5, 4, 12, 10, 5, 12, 6, 10, 12, 7, 6, 12, 3, 7, 12, 1, 3, 12, 9, 1, 12, 4, 9, 12 }, /* 180: 2, 4, 5, 7, */ { 10, 1, 12, 6, 10, 12, 5, 6, 12, 9, 5, 12, 8, 9, 12, 11, 8, 12, 2, 11, 12, 1, 2, 12 }, /* 120: 3, 4, 5, 6, */ { 11, 2, 12, 7, 11, 12, 6, 7, 12, 10, 6, 12, 9, 10, 12, 8, 9, 12, 3, 8, 12, 2, 3, 12 } }; //_____________________________________________________________________________ //_____________________________________________________________________________ /** * \brief test table for case 13 * All faces are to be tested * * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- /* 13: face test */ static const char test13[2][7] = { /* 165: 0, 2, 5, 7, */ { 1,2,3,4,5,6,7 }, /* 90: 1, 3, 4, 6, */ { 2,3,4,1,5,6,7 }, }; //_____________________________________________________________________________ /** * \brief subconfiguration table for case 13 * Hard-coded tests for the subconfiguration determination * * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- /* 13: sub configs */ static const char subconfig13[64] = { /* 0: 0,0,0,0,0,0 */ 0, /* 1: 1,0,0,0,0,0 */ 1, /* 2: 0,1,0,0,0,0 */ 2, /* 3: 1,1,0,0,0,0 */ 7, /* 4: 0,0,1,0,0,0 */ 3, /* 5: 1,0,1,0,0,0 */ -1, /* 6: 0,1,1,0,0,0 */ 11, /* 7: 1,1,1,0,0,0 */ -1, /* 8: 0,0,0,1,0,0 */ 4, /* 9: 1,0,0,1,0,0 */ 8, /* 10: 0,1,0,1,0,0 */ -1, /* 11: 1,1,0,1,0,0 */ -1, /* 12: 0,0,1,1,0,0 */ 14, /* 13: 1,0,1,1,0,0 */ -1, /* 14: 0,1,1,1,0,0 */ -1, /* 15: 1,1,1,1,0,0 */ -1, /* 16: 0,0,0,0,1,0 */ 5, /* 17: 1,0,0,0,1,0 */ 9, /* 18: 0,1,0,0,1,0 */ 12, /* 19: 1,1,0,0,1,0 */ 23, /* 20: 0,0,1,0,1,0 */ 15, /* 21: 1,0,1,0,1,0 */ -1, /* 22: 0,1,1,0,1,0 */ 21, /* 23: 1,1,1,0,1,0 */ 38, /* 24: 0,0,0,1,1,0 */ 17, /* 25: 1,0,0,1,1,0 */ 20, /* 26: 0,1,0,1,1,0 */ -1, /* 27: 1,1,0,1,1,0 */ 36, /* 28: 0,0,1,1,1,0 */ 26, /* 29: 1,0,1,1,1,0 */ 33, /* 30: 0,1,1,1,1,0 */ 30, /* 31: 1,1,1,1,1,0 */ 44, /* 32: 0,0,0,0,0,1 */ 6, /* 33: 1,0,0,0,0,1 */ 10, /* 34: 0,1,0,0,0,1 */ 13, /* 35: 1,1,0,0,0,1 */ 19, /* 36: 0,0,1,0,0,1 */ 16, /* 37: 1,0,1,0,0,1 */ -1, /* 38: 0,1,1,0,0,1 */ 25, /* 39: 1,1,1,0,0,1 */ 37, /* 40: 0,0,0,1,0,1 */ 18, /* 41: 1,0,0,1,0,1 */ 24, /* 42: 0,1,0,1,0,1 */ -1, /* 43: 1,1,0,1,0,1 */ 35, /* 44: 0,0,1,1,0,1 */ 22, /* 45: 1,0,1,1,0,1 */ 32, /* 46: 0,1,1,1,0,1 */ 29, /* 47: 1,1,1,1,0,1 */ 43, /* 48: 0,0,0,0,1,1 */ -1, /* 49: 1,0,0,0,1,1 */ -1, /* 50: 0,1,0,0,1,1 */ -1, /* 51: 1,1,0,0,1,1 */ 34, /* 52: 0,0,1,0,1,1 */ -1, /* 53: 1,0,1,0,1,1 */ -1, /* 54: 0,1,1,0,1,1 */ 28, /* 55: 1,1,1,0,1,1 */ 42, /* 56: 0,0,0,1,1,1 */ -1, /* 57: 1,0,0,1,1,1 */ 31, /* 58: 0,1,0,1,1,1 */ -1, /* 59: 1,1,0,1,1,1 */ 41, /* 60: 0,0,1,1,1,1 */ 27, /* 61: 1,0,1,1,1,1 */ 40, /* 62: 0,1,1,1,1,1 */ 39, /* 63: 1,1,1,1,1,1 */ 45, }; //_____________________________________________________________________________ /** * \brief tiling table for case 13.1 * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- /* 13.1 */ static const char tiling13_1[2][12] = { /* 165: 0, 2, 5, 7, */ { 11, 7, 6, 1, 2, 10, 8, 3, 0, 9, 5, 4 }, /* 90: 1, 3, 4, 6, */ { 8, 4, 7, 2, 3, 11, 9, 0, 1, 10, 6, 5 } }; //_____________________________________________________________________________ /** * \brief tiling table for case 13.1 inverted * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- /* 13.1 */ static const char tiling13_1_[2][12] = { /* 165: 0, 2, 5, 7, */ { 7, 4, 8, 11, 3, 2, 1, 0, 9, 5, 6, 10 }, /* 90: 1, 3, 4, 6, */ { 6, 7, 11, 10, 2, 1, 0, 3, 8, 4, 5, 9 } }; //_____________________________________________________________________________ /** * \brief tiling table for case 13.2 * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- /* 13.2 */ static const char tiling13_2[2][6][18] = { /* 165: 0, 2, 5, 7, */ { /* 1 */ { 1, 2, 10, 11, 7, 6, 3, 4, 8, 4, 3, 5, 0, 5, 3, 5, 0, 9 }, /* 2 */ { 8, 3, 0, 11, 7, 6, 9, 1, 4, 2, 4, 1, 4, 2, 5, 10, 5, 2 }, /* 3 */ { 9, 5, 4, 8, 3, 0, 1, 6, 10, 6, 1, 7, 2, 7, 1, 7, 2, 11 }, /* 4 */ { 9, 5, 4, 1, 2, 10, 11, 3, 6, 0, 6, 3, 6, 0, 7, 8, 7, 0 }, /* 5 */ { 9, 5, 4, 11, 7, 6, 0, 10, 1, 10, 0, 8, 10, 8, 2, 3, 2, 8 }, /* 6 */ { 1, 2, 10, 3, 0, 8, 4, 9, 7, 11, 7, 9, 5, 11, 9, 11, 5, 6 } }, /* 90: 1, 3, 4, 6, */ { /* 1 */ { 2, 3, 11, 8, 4, 7, 0, 5, 9, 5, 0, 6, 1, 6, 0, 6, 1, 10 }, /* 2 */ { 9, 0, 1, 8, 4, 7, 10, 2, 5, 3, 5, 2, 5, 3, 6, 11, 6, 3 }, /* 3 */ { 6, 5, 10, 9, 0, 1, 2, 7, 11, 7, 2, 4, 3, 4, 2, 4, 3, 8 }, /* 4 */ { 6, 5, 10, 2, 3, 11, 8, 0, 7, 1, 7, 0, 7, 1, 4, 9, 4, 1 }, /* 5 */ { 6, 5, 10, 8, 4, 7, 1, 11, 2, 11, 1, 9, 11, 9, 3, 0, 3, 9 }, /* 6 */ { 2, 3, 11, 0, 1, 9, 5, 10, 4, 8, 4, 10, 6, 8, 10, 8, 6, 7 } } }; //_____________________________________________________________________________ /** * \brief tiling table for case 13.2 inverted * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- /* 13.2 */ static const char tiling13_2_[2][6][18] = { /* 165: 0, 2, 5, 7, */ { /* 1 */ { 10, 5, 6, 11, 3, 2, 7, 0, 8, 0, 7, 1, 4, 1, 7, 1, 4, 9 }, /* 2 */ { 11, 3, 2, 7, 4, 8, 9, 5, 0, 6, 0, 5, 0, 6, 1, 10, 1, 6 }, /* 3 */ { 1, 0, 9, 7, 4, 8, 5, 2, 10, 2, 5, 3, 6, 3, 5, 3, 6, 11 }, /* 4 */ { 10, 5, 6, 1, 0, 9, 11, 7, 2, 4, 2, 7, 2, 4, 3, 8, 3, 4 }, /* 5 */ { 10, 5, 6, 7, 4, 8, 2, 11, 1, 9, 1, 11, 3, 9, 11, 9, 3, 0 }, /* 6 */ { 11, 3, 2, 9, 1, 0, 4, 10, 5, 10, 4, 8, 10, 8, 6, 7, 6, 8 } }, /* 90: 1, 3, 4, 6, */ { /* 1 */ { 6, 7, 11, 8, 0, 3, 4, 1, 9, 1, 4, 2, 5, 2, 4, 2, 5, 10 }, /* 2 */ { 8, 0, 3, 4, 5, 9, 10, 6, 1, 7, 1, 6, 1, 7, 2, 11, 2, 7 }, /* 3 */ { 2, 1, 10, 4, 5, 9, 6, 3, 11, 3, 6, 0, 7, 0, 6, 0, 7, 8 }, /* 4 */ { 6, 7, 11, 2, 1, 10, 8, 4, 3, 5, 3, 4, 3, 5, 0, 9, 0, 5 }, /* 5 */ { 6, 7, 11, 4, 5, 9, 3, 8, 2, 10, 2, 8, 0, 10, 8, 10, 0, 1 }, /* 6 */ { 8, 0, 3, 10, 2, 1, 5, 11, 6, 11, 5, 9, 11, 9, 7, 4, 7, 9 } } }; //_____________________________________________________________________________ /** * \brief tiling table for case 13.3 * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- /* 13.3 */ static const char tiling13_3[2][12][30] = { /* 165: 0, 2, 5, 7, */ { /* 1,2 */ { 11, 7, 6, 12, 2, 10, 12, 10, 5, 12, 5, 4, 12, 4, 8, 12, 8, 3, 12, 3, 0, 12, 0, 9, 12, 9, 1, 12, 1, 2 }, /* 1,4 */ { 1, 2, 10, 9, 5, 12, 0, 9, 12, 3, 0, 12, 11, 3, 12, 6, 11, 12, 7, 6, 12, 8, 7, 12, 4, 8, 12, 5, 4, 12 }, /* 1,5 */ { 11, 7, 6, 12, 5, 4, 12, 4, 8, 12, 8, 3, 12, 3, 2, 12, 2, 10, 12, 10, 1, 12, 1, 0, 12, 0, 9, 12, 9, 5 }, /* 1,6 */ { 1, 2, 10, 12, 3, 0, 12, 0, 9, 12, 9, 5, 12, 5, 6, 12, 6, 11, 12, 11, 7, 12, 7, 4, 12, 4, 8, 12, 8, 3 }, /* 2,3 */ { 8, 3, 0, 11, 7, 12, 2, 11, 12, 1, 2, 12, 9, 1, 12, 4, 9, 12, 5, 4, 12, 10, 5, 12, 6, 10, 12, 7, 6, 12 }, /* 2,5 */ { 11, 7, 6, 5, 4, 12, 10, 5, 12, 2, 10, 12, 3, 2, 12, 8, 3, 12, 0, 8, 12, 1, 0, 12, 9, 1, 12, 4, 9, 12 }, /* 2,6 */ { 8, 3, 0, 1, 2, 12, 9, 1, 12, 4, 9, 12, 7, 4, 12, 11, 7, 12, 6, 11, 12, 5, 6, 12, 10, 5, 12, 2, 10, 12 }, /* 3,4 */ { 9, 5, 4, 12, 0, 8, 12, 8, 7, 12, 7, 6, 12, 6, 10, 12, 10, 1, 12, 1, 2, 12, 2, 11, 12, 11, 3, 12, 3, 0 }, /* 3,5 */ { 9, 5, 4, 12, 7, 6, 12, 6, 10, 12, 10, 1, 12, 1, 0, 12, 0, 8, 12, 8, 3, 12, 3, 2, 12, 2, 11, 12, 11, 7 }, /* 3,6 */ { 8, 3, 0, 12, 1, 2, 12, 2, 11, 12, 11, 7, 12, 7, 4, 12, 4, 9, 12, 9, 5, 12, 5, 6, 12, 6, 10, 12, 10, 1 }, /* 4,5 */ { 9, 5, 4, 7, 6, 12, 8, 7, 12, 0, 8, 12, 1, 0, 12, 10, 1, 12, 2, 10, 12, 3, 2, 12, 11, 3, 12, 6, 11, 12 }, /* 4,6 */ { 1, 2, 10, 3, 0, 12, 11, 3, 12, 6, 11, 12, 5, 6, 12, 9, 5, 12, 4, 9, 12, 7, 4, 12, 8, 7, 12, 0, 8, 12 } }, /* 90: 1, 3, 4, 6, */ { /* 1,2 */ { 8, 4, 7, 12, 3, 11, 12, 11, 6, 12, 6, 5, 12, 5, 9, 12, 9, 0, 12, 0, 1, 12, 1, 10, 12, 10, 2, 12, 2, 3 }, /* 1,4 */ { 2, 3, 11, 10, 6, 12, 1, 10, 12, 0, 1, 12, 8, 0, 12, 7, 8, 12, 4, 7, 12, 9, 4, 12, 5, 9, 12, 6, 5, 12 }, /* 1,5 */ { 8, 4, 7, 12, 6, 5, 12, 5, 9, 12, 9, 0, 12, 0, 3, 12, 3, 11, 12, 11, 2, 12, 2, 1, 12, 1, 10, 12, 10, 6 }, /* 1,6 */ { 2, 3, 11, 12, 0, 1, 12, 1, 10, 12, 10, 6, 12, 6, 7, 12, 7, 8, 12, 8, 4, 12, 4, 5, 12, 5, 9, 12, 9, 0 }, /* 2,3 */ { 0, 1, 9, 8, 4, 12, 3, 8, 12, 2, 3, 12, 10, 2, 12, 5, 10, 12, 6, 5, 12, 11, 6, 12, 7, 11, 12, 4, 7, 12 }, /* 2,5 */ { 8, 4, 7, 6, 5, 12, 11, 6, 12, 3, 11, 12, 0, 3, 12, 9, 0, 12, 1, 9, 12, 2, 1, 12, 10, 2, 12, 5, 10, 12 }, /* 2,6 */ { 9, 0, 1, 2, 3, 12, 10, 2, 12, 5, 10, 12, 4, 5, 12, 8, 4, 12, 7, 8, 12, 6, 7, 12, 11, 6, 12, 3, 11, 12 }, /* 3,4 */ { 6, 5, 10, 12, 1, 9, 12, 9, 4, 12, 4, 7, 12, 7, 11, 12, 11, 2, 12, 2, 3, 12, 3, 8, 12, 8, 0, 12, 0, 1 }, /* 3,5 */ { 6, 5, 10, 12, 4, 7, 12, 7, 11, 12, 11, 2, 12, 2, 1, 12, 1, 9, 12, 9, 0, 12, 0, 3, 12, 3, 8, 12, 8, 4 }, /* 3,6 */ { 9, 0, 1, 12, 2, 3, 12, 3, 8, 12, 8, 4, 12, 4, 5, 12, 5, 10, 12, 10, 6, 12, 6, 7, 12, 7, 11, 12, 11, 2 }, /* 4,5 */ { 6, 5, 10, 4, 7, 12, 9, 4, 12, 1, 9, 12, 2, 1, 12, 11, 2, 12, 3, 11, 12, 0, 3, 12, 8, 0, 12, 7, 8, 12 }, /* 4,6 */ { 2, 3, 11, 0, 1, 12, 8, 0, 12, 7, 8, 12, 6, 7, 12, 10, 6, 12, 5, 10, 12, 4, 5, 12, 9, 4, 12, 1, 9, 12 } } }; //_____________________________________________________________________________ /** * \brief tiling table for case 13.3, inverted * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- /* 13.3 */ static const char tiling13_3_[2][12][30] = { /* 165: 0, 2, 5, 7, */ { /* 1,2 */ { 3, 2, 11, 8, 7, 12, 0, 8, 12, 1, 0, 12, 10, 1, 12, 6, 10, 12, 5, 6, 12, 9, 5, 12, 4, 9, 12, 7, 4, 12 }, /* 1,4 */ { 5, 6, 10, 12, 2, 11, 12, 11, 7, 12, 7, 4, 12, 4, 9, 12, 9, 1, 12, 1, 0, 12, 0, 8, 12, 8, 3, 12, 3, 2 }, /* 1,5 */ { 10, 5, 6, 12, 7, 4, 12, 4, 9, 12, 9, 1, 12, 1, 2, 12, 2, 11, 12, 11, 3, 12, 3, 0, 12, 0, 8, 12, 8, 7 }, /* 1,6 */ { 11, 3, 2, 12, 1, 0, 12, 0, 8, 12, 8, 7, 12, 7, 6, 12, 6, 10, 12, 10, 5, 12, 5, 4, 12, 4, 9, 12, 9, 1 }, /* 2,3 */ { 7, 4, 8, 11, 3, 12, 6, 11, 12, 5, 6, 12, 9, 5, 12, 0, 9, 12, 1, 0, 12, 10, 1, 12, 2, 10, 12, 3, 2, 12 }, /* 2,5 */ { 7, 4, 8, 5, 6, 12, 9, 5, 12, 0, 9, 12, 3, 0, 12, 11, 3, 12, 2, 11, 12, 1, 2, 12, 10, 1, 12, 6, 10, 12 }, /* 2,6 */ { 11, 3, 2, 1, 0, 12, 10, 1, 12, 6, 10, 12, 7, 6, 12, 8, 7, 12, 4, 8, 12, 5, 4, 12, 9, 5, 12, 0, 9, 12 }, /* 3,4 */ { 1, 0, 9, 12, 4, 8, 12, 8, 3, 12, 3, 2, 12, 2, 10, 12, 10, 5, 12, 5, 6, 12, 6, 11, 12, 11, 7, 12, 7, 4 }, /* 3,5 */ { 7, 4, 8, 12, 5, 6, 12, 6, 11, 12, 11, 3, 12, 3, 0, 12, 0, 9, 12, 9, 1, 12, 1, 2, 12, 2, 10, 12, 10, 5 }, /* 3,6 */ { 1, 0, 9, 12, 3, 2, 12, 2, 10, 12, 10, 5, 12, 5, 4, 12, 4, 8, 12, 8, 7, 12, 7, 6, 12, 6, 11, 12, 11, 3 }, /* 4,5 */ { 10, 5, 6, 7, 4, 12, 11, 7, 12, 2, 11, 12, 1, 2, 12, 9, 1, 12, 0, 9, 12, 3, 0, 12, 8, 3, 12, 4, 8, 12 }, /* 4,6 */ { 9, 1, 0, 3, 2, 12, 8, 3, 12, 4, 8, 12, 5, 4, 12, 10, 5, 12, 6, 10, 12, 7, 6, 12, 11, 7, 12, 2, 11, 12 } }, /* 90: 1, 3, 4, 6, */ { /* 1,2 */ { 0, 3, 8, 9, 4, 12, 1, 9, 12, 2, 1, 12, 11, 2, 12, 7, 11, 12, 6, 7, 12, 10, 6, 12, 5, 10, 12, 4, 5, 12 }, /* 1,4 */ { 11, 6, 7, 12, 3, 8, 12, 8, 4, 12, 4, 5, 12, 5, 10, 12, 10, 2, 12, 2, 1, 12, 1, 9, 12, 9, 0, 12, 0, 3 }, /* 1,5 */ { 6, 7, 11, 12, 4, 5, 12, 5, 10, 12, 10, 2, 12, 2, 3, 12, 3, 8, 12, 8, 0, 12, 0, 1, 12, 1, 9, 12, 9, 4 }, /* 1,6 */ { 8, 0, 3, 12, 2, 1, 12, 1, 9, 12, 9, 4, 12, 4, 7, 12, 7, 11, 12, 11, 6, 12, 6, 5, 12, 5, 10, 12, 10, 2 }, /* 2,3 */ { 4, 5, 9, 8, 0, 12, 7, 8, 12, 6, 7, 12, 10, 6, 12, 1, 10, 12, 2, 1, 12, 11, 2, 12, 3, 11, 12, 0, 3, 12 }, /* 2,5 */ { 4, 5, 9, 6, 7, 12, 10, 6, 12, 1, 10, 12, 0, 1, 12, 8, 0, 12, 3, 8, 12, 2, 3, 12, 11, 2, 12, 7, 11, 12 }, /* 2,6 */ { 8, 0, 3, 2, 1, 12, 11, 2, 12, 7, 11, 12, 4, 7, 12, 9, 4, 12, 5, 9, 12, 6, 5, 12, 10, 6, 12, 1, 10, 12 }, /* 3,4 */ { 2, 1, 10, 12, 5, 9, 12, 9, 0, 12, 0, 3, 12, 3, 11, 12, 11, 6, 12, 6, 7, 12, 7, 8, 12, 8, 4, 12, 4, 5 }, /* 3,5 */ { 4, 5, 9, 12, 6, 7, 12, 7, 8, 12, 8, 0, 12, 0, 1, 12, 1, 10, 12, 10, 2, 12, 2, 3, 12, 3, 11, 12, 11, 6 }, /* 3,6 */ { 2, 1, 10, 12, 0, 3, 12, 3, 11, 12, 11, 6, 12, 6, 5, 12, 5, 9, 12, 9, 4, 12, 4, 7, 12, 7, 8, 12, 8, 0 }, /* 4,5 */ { 6, 7, 11, 4, 5, 12, 8, 4, 12, 3, 8, 12, 2, 3, 12, 10, 2, 12, 1, 10, 12, 0, 1, 12, 9, 0, 12, 5, 9, 12 }, /* 4,6 */ { 10, 2, 1, 0, 3, 12, 9, 0, 12, 5, 9, 12, 6, 5, 12, 11, 6, 12, 7, 11, 12, 4, 7, 12, 8, 4, 12, 3, 8, 12 } } }; //_____________________________________________________________________________ /** * \brief tiling table for case 13.4 * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- /* 13.4 */ static const char tiling13_4[2][4][36] = { /* 165: 0, 2, 5, 7, */ { /* 1,2,6 */ { 12, 2, 10, 12, 10, 5, 12, 5, 6, 12, 6, 11, 12, 11, 7, 12, 7, 4, 12, 4, 8, 12, 8, 3, 12, 3, 0, 12, 0, 9, 12, 9, 1, 12, 1, 2 }, /* 1,4,5 */ { 11, 3, 12, 6, 11, 12, 7, 6, 12, 8, 7, 12, 4, 8, 12, 5, 4, 12, 9, 5, 12, 0, 9, 12, 1, 0, 12, 10, 1, 12, 2, 10, 12, 3, 2, 12 }, /* 2,3,5 */ { 9, 1, 12, 4, 9, 12, 5, 4, 12, 10, 5, 12, 6, 10, 12, 7, 6, 12, 11, 7, 12, 2, 11, 12, 3, 2, 12, 8, 3, 12, 0, 8, 12, 1, 0, 12 }, /* 3,4,6 */ { 12, 0, 8, 12, 8, 7, 12, 7, 4, 12, 4, 9, 12, 9, 5, 12, 5, 6, 12, 6, 10, 12, 10, 1, 12, 1, 2, 12, 2, 11, 12, 11, 3, 12, 3, 0 } }, /* 90: 1, 3, 4, 6, */ { /* 1,2,6 */ { 12, 3, 11, 12, 11, 6, 12, 6, 7, 12, 7, 8, 12, 8, 4, 12, 4, 5, 12, 5, 9, 12, 9, 0, 12, 0, 1, 12, 1, 10, 12, 10, 2, 12, 2, 3 }, /* 1,4,5 */ { 8, 0, 12, 7, 8, 12, 4, 7, 12, 9, 4, 12, 5, 9, 12, 6, 5, 12, 10, 6, 12, 1, 10, 12, 2, 1, 12, 11, 2, 12, 3, 11, 12, 0, 3, 12 }, /* 2,3,5 */ { 10, 2, 12, 5, 10, 12, 6, 5, 12, 11, 6, 12, 7, 11, 12, 4, 7, 12, 8, 4, 12, 3, 8, 12, 0, 3, 12, 9, 0, 12, 1, 9, 12, 2, 1, 12 }, /* 3,4,6 */ { 12, 1, 9, 12, 9, 4, 12, 4, 5, 12, 5, 10, 12, 10, 6, 12, 6, 7, 12, 7, 11, 12, 11, 2, 12, 2, 3, 12, 3, 8, 12, 8, 0, 12, 0, 1 } } }; //_____________________________________________________________________________ /** * \brief tiling table for case 13.5.1 * The support edge for the interior test is marked as the 1st column. * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- /* 13.5.1 */ static const char tiling13_5_1[2][4][18] = { /* 165: 0, 2, 5, 7, */ { /* 1,2,5 */ { 7, 6, 11, 1, 0, 9, 10, 3, 2, 3, 10, 5, 3, 5, 8, 4, 8, 5 }, /* 1,4,6 */ { 1, 2, 10, 7, 4, 8, 3, 0, 11, 6, 11, 0, 9, 6, 0, 6, 9, 5 }, /* 2,3,6 */ { 3, 0, 8, 5, 6, 10, 1, 2, 9, 4, 9, 2, 11, 4, 2, 4, 11, 7 }, /* 3,4,5 */ { 5, 4, 9, 3, 2, 11, 8, 1, 0, 1, 8, 7, 1, 7, 10, 6, 10, 7 } }, /* 90: 1, 3, 4, 6, */ { /* 1,2,5 */ { 4, 7, 8, 2, 1, 10, 11, 0, 3, 0, 11, 6, 0, 6, 9, 5, 9, 6 }, /* 1,4,6 */ { 2, 3, 11, 4, 5, 9, 0, 1, 8, 7, 8, 1, 10, 7, 1, 7, 10, 6 }, /* 2,3,6 */ { 0, 1, 9, 6, 7, 11, 2, 3, 10, 5, 10, 3, 8, 5, 3, 5, 8, 4 }, /* 3,4,5 */ { 6, 5, 10, 0, 3, 8, 9, 2, 1, 2, 9, 4, 2, 4, 11, 7, 11, 4 } } }; //_____________________________________________________________________________ /** * \brief tiling table for case 13.5.2 * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- /* 13.5.2 */ static const char tiling13_5_2[2][4][30] = { /* 165: 0, 2, 5, 7, */ { /* 1,2,5 */ { 1, 0, 9, 7, 4, 8, 7, 8, 3, 7, 3, 11, 2, 11, 3, 11, 2, 10, 11, 10, 6, 5, 6, 10, 6, 5, 7, 4, 7, 5 }, /* 1,4,6 */ { 7, 4, 8, 11, 3, 2, 6, 11, 2, 10, 6, 2, 6, 10, 5, 9, 5, 10, 1, 9, 10, 9, 1, 0, 2, 0, 1, 0, 2, 3 }, /* 2,3,6 */ { 5, 6, 10, 9, 1, 0, 4, 9, 0, 8, 4, 0, 4, 8, 7, 11, 7, 8, 3, 11, 8, 11, 3, 2, 0, 2, 3, 2, 0, 1 }, /* 3,4,5 */ { 3, 2, 11, 5, 6, 10, 5, 10, 1, 5, 1, 9, 0, 9, 1, 9, 0, 8, 9, 8, 4, 4, 8, 7, 4, 7, 5, 6, 5, 7 } }, /* 90: 1, 3, 4, 6, */ { /* 1,2,5 */ { 2, 1, 10, 4, 5, 9, 4, 9, 0, 4, 0, 8, 3, 8, 0, 8, 3, 11, 8, 11, 7, 6, 7, 11, 7, 6, 4, 5, 4, 6 }, /* 1,4,6 */ { 4, 5, 9, 8, 0, 3, 7, 8, 3, 11, 7, 3, 7, 11, 6, 10, 6, 11, 2, 10, 11, 10, 2, 1, 3, 1, 2, 1, 3, 0 }, /* 2,3,6 */ { 6, 7, 11, 10, 2, 1, 5, 10, 1, 9, 5, 1, 5, 9, 4, 8, 4, 9, 0, 8, 9, 8, 0, 3, 1, 3, 0, 3, 1, 2 }, /* 3,4,5 */ { 0, 3, 8, 6, 7, 11, 6, 11, 2, 6, 2, 10, 1, 10, 2, 10, 1, 9, 10, 9, 5, 5, 9, 4, 5, 4, 6, 7, 6, 4 } } }; //_____________________________________________________________________________ //_____________________________________________________________________________ /** * \brief tiling table for case 14 * For each of the case above, the specific triangulation of the edge * intersection points is given. * When a case is ambiguous, there is an auxiliary table that contains * the face number to test and the tiling table contains the specific * triangulations depending on the results * A minus sign means to invert the result of the test. */ //----------------------------------------------------------------------------- static const char tiling14[12][12] = { /* 71: 0, 1, 2, 6, */ { 5, 9, 8, 5, 8, 2, 5, 2, 6, 3, 2, 8 }, /* 43: 0, 1, 3, 5, */ { 2, 1, 5, 2, 5, 8, 2, 8, 11, 4, 8, 5 }, /* 147: 0, 1, 4, 7, */ { 9, 4, 6, 9, 6, 3, 9, 3, 1, 11, 3, 6 }, /* 29: 0, 2, 3, 4, */ { 1, 11, 10, 1, 4, 11, 1, 0, 4, 7, 11, 4 }, /* 201: 0, 3, 6, 7, */ { 8, 2, 0, 8, 5, 2, 8, 7, 5, 10, 2, 5 }, /* 113: 0, 4, 5, 6, */ { 0, 7, 3, 0, 10, 7, 0, 9, 10, 6, 7, 10 }, /* 142: 1, 2, 3, 7, */ { 0, 3, 7, 0, 7, 10, 0, 10, 9, 6, 10, 7 }, /* 54: 1, 2, 4, 5, */ { 8, 0, 2, 8, 2, 5, 8, 5, 7, 10, 5, 2 }, /* 226: 1, 5, 6, 7, */ { 1, 10, 11, 1, 11, 4, 1, 4, 0, 7, 4, 11 }, /* 108: 2, 3, 5, 6, */ { 9, 6, 4, 9, 3, 6, 9, 1, 3, 11, 6, 3 }, /* 212: 2, 4, 6, 7, */ { 2, 5, 1, 2, 8, 5, 2, 11, 8, 4, 5, 8 }, /* 184: 3, 4, 5, 7, */ { 5, 8, 9, 5, 2, 8, 5, 6, 2, 3, 8, 2 } }; //_____________________________________________________________________________ //_____________________________________________________________________________ /** * \brief original Marching Cubes implementation * For each of the possible vertex states listed in this table there is a * specific triangulation of the edge intersection points. The table lists * all of them in the form of 0-5 edge triples with the list terminated by * the invalid value -1. For example: casesClassic[3] list the 2 triangles * formed when cube[0] and cube[1] are inside of the surface, but the rest of * the cube is not. */ //----------------------------------------------------------------------------- static const char casesClassic[256][16] = { /* 0: */ { -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 1: 0, */ { 0, 8, 3, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 2: 1, */ { 0, 1, 9, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 3: 0, 1, */ { 1, 8, 3, 9, 8, 1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 4: 2, */ { 1, 2, 10, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 5: 0, 2, */ { 0, 8, 3, 1, 2, 10, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 6: 1, 2, */ { 9, 2, 10, 0, 2, 9, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 7: 0, 1, 2, */ { 2, 8, 3, 2, 10, 8, 10, 9, 8, -1, -1, -1, -1, -1, -1, -1 }, /* 8: 3, */ { 3, 11, 2, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 9: 0, 3, */ { 0, 11, 2, 8, 11, 0, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 10: 1, 3, */ { 1, 9, 0, 2, 3, 11, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 11: 0, 1, 3, */ { 1, 11, 2, 1, 9, 11, 9, 8, 11, -1, -1, -1, -1, -1, -1, -1 }, /* 12: 2, 3, */ { 3, 10, 1, 11, 10, 3, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 13: 0, 2, 3, */ { 0, 10, 1, 0, 8, 10, 8, 11, 10, -1, -1, -1, -1, -1, -1, -1 }, /* 14: 1, 2, 3, */ { 3, 9, 0, 3, 11, 9, 11, 10, 9, -1, -1, -1, -1, -1, -1, -1 }, /* 15: 0, 1, 2, 3, */ { 9, 8, 10, 10, 8, 11, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 16: 4, */ { 4, 7, 8, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 17: 0, 4, */ { 4, 3, 0, 7, 3, 4, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 18: 1, 4, */ { 0, 1, 9, 8, 4, 7, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 19: 0, 1, 4, */ { 4, 1, 9, 4, 7, 1, 7, 3, 1, -1, -1, -1, -1, -1, -1, -1 }, /* 20: 2, 4, */ { 1, 2, 10, 8, 4, 7, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 21: 0, 2, 4, */ { 3, 4, 7, 3, 0, 4, 1, 2, 10, -1, -1, -1, -1, -1, -1, -1 }, /* 22: 1, 2, 4, */ { 9, 2, 10, 9, 0, 2, 8, 4, 7, -1, -1, -1, -1, -1, -1, -1 }, /* 23: 0, 1, 2, 4, */ { 2, 10, 9, 2, 9, 7, 2, 7, 3, 7, 9, 4, -1, -1, -1, -1 }, /* 24: 3, 4, */ { 8, 4, 7, 3, 11, 2, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 25: 0, 3, 4, */ { 11, 4, 7, 11, 2, 4, 2, 0, 4, -1, -1, -1, -1, -1, -1, -1 }, /* 26: 1, 3, 4, */ { 9, 0, 1, 8, 4, 7, 2, 3, 11, -1, -1, -1, -1, -1, -1, -1 }, /* 27: 0, 1, 3, 4, */ { 4, 7, 11, 9, 4, 11, 9, 11, 2, 9, 2, 1, -1, -1, -1, -1 }, /* 28: 2, 3, 4, */ { 3, 10, 1, 3, 11, 10, 7, 8, 4, -1, -1, -1, -1, -1, -1, -1 }, /* 29: 0, 2, 3, 4, */ { 1, 11, 10, 1, 4, 11, 1, 0, 4, 7, 11, 4, -1, -1, -1, -1 }, /* 30: 1, 2, 3, 4, */ { 4, 7, 8, 9, 0, 11, 9, 11, 10, 11, 0, 3, -1, -1, -1, -1 }, /* 31: 0, 1, 2, 3, 4, */ { 4, 7, 11, 4, 11, 9, 9, 11, 10, -1, -1, -1, -1, -1, -1, -1 }, /* 32: 5, */ { 9, 5, 4, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 33: 0, 5, */ { 9, 5, 4, 0, 8, 3, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 34: 1, 5, */ { 0, 5, 4, 1, 5, 0, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 35: 0, 1, 5, */ { 8, 5, 4, 8, 3, 5, 3, 1, 5, -1, -1, -1, -1, -1, -1, -1 }, /* 36: 2, 5, */ { 1, 2, 10, 9, 5, 4, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 37: 0, 2, 5, */ { 3, 0, 8, 1, 2, 10, 4, 9, 5, -1, -1, -1, -1, -1, -1, -1 }, /* 38: 1, 2, 5, */ { 5, 2, 10, 5, 4, 2, 4, 0, 2, -1, -1, -1, -1, -1, -1, -1 }, /* 39: 0, 1, 2, 5, */ { 2, 10, 5, 3, 2, 5, 3, 5, 4, 3, 4, 8, -1, -1, -1, -1 }, /* 40: 3, 5, */ { 9, 5, 4, 2, 3, 11, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 41: 0, 3, 5, */ { 0, 11, 2, 0, 8, 11, 4, 9, 5, -1, -1, -1, -1, -1, -1, -1 }, /* 42: 1, 3, 5, */ { 0, 5, 4, 0, 1, 5, 2, 3, 11, -1, -1, -1, -1, -1, -1, -1 }, /* 43: 0, 1, 3, 5, */ { 2, 1, 5, 2, 5, 8, 2, 8, 11, 4, 8, 5, -1, -1, -1, -1 }, /* 44: 2, 3, 5, */ { 10, 3, 11, 10, 1, 3, 9, 5, 4, -1, -1, -1, -1, -1, -1, -1 }, /* 45: 0, 2, 3, 5, */ { 4, 9, 5, 0, 8, 1, 8, 10, 1, 8, 11, 10, -1, -1, -1, -1 }, /* 46: 1, 2, 3, 5, */ { 5, 4, 0, 5, 0, 11, 5, 11, 10, 11, 0, 3, -1, -1, -1, -1 }, /* 47: 0, 1, 2, 3, 5, */ { 5, 4, 8, 5, 8, 10, 10, 8, 11, -1, -1, -1, -1, -1, -1, -1 }, /* 48: 4, 5, */ { 9, 7, 8, 5, 7, 9, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 49: 0, 4, 5, */ { 9, 3, 0, 9, 5, 3, 5, 7, 3, -1, -1, -1, -1, -1, -1, -1 }, /* 50: 1, 4, 5, */ { 0, 7, 8, 0, 1, 7, 1, 5, 7, -1, -1, -1, -1, -1, -1, -1 }, /* 51: 0, 1, 4, 5, */ { 1, 5, 3, 3, 5, 7, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 52: 2, 4, 5, */ { 9, 7, 8, 9, 5, 7, 10, 1, 2, -1, -1, -1, -1, -1, -1, -1 }, /* 53: 0, 2, 4, 5, */ { 10, 1, 2, 9, 5, 0, 5, 3, 0, 5, 7, 3, -1, -1, -1, -1 }, /* 54: 1, 2, 4, 5, */ { 8, 0, 2, 8, 2, 5, 8, 5, 7, 10, 5, 2, -1, -1, -1, -1 }, /* 55: 0, 1, 2, 4, 5, */ { 2, 10, 5, 2, 5, 3, 3, 5, 7, -1, -1, -1, -1, -1, -1, -1 }, /* 56: 3, 4, 5, */ { 7, 9, 5, 7, 8, 9, 3, 11, 2, -1, -1, -1, -1, -1, -1, -1 }, /* 57: 0, 3, 4, 5, */ { 9, 5, 7, 9, 7, 2, 9, 2, 0, 2, 7, 11, -1, -1, -1, -1 }, /* 58: 1, 3, 4, 5, */ { 2, 3, 11, 0, 1, 8, 1, 7, 8, 1, 5, 7, -1, -1, -1, -1 }, /* 59: 0, 1, 3, 4, 5, */ { 11, 2, 1, 11, 1, 7, 7, 1, 5, -1, -1, -1, -1, -1, -1, -1 }, /* 60: 2, 3, 4, 5, */ { 9, 5, 8, 8, 5, 7, 10, 1, 3, 10, 3, 11, -1, -1, -1, -1 }, /* 61: 0, 2, 3, 4, 5, */ { 5, 7, 0, 5, 0, 9, 7, 11, 0, 1, 0, 10, 11, 10, 0, -1 }, /* 62: 1, 2, 3, 4, 5, */ { 11, 10, 0, 11, 0, 3, 10, 5, 0, 8, 0, 7, 5, 7, 0, -1 }, /* 63: 0, 1, 2, 3, 4, 5, */ { 11, 10, 5, 7, 11, 5, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 64: 6, */ { 10, 6, 5, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 65: 0, 6, */ { 0, 8, 3, 5, 10, 6, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 66: 1, 6, */ { 9, 0, 1, 5, 10, 6, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 67: 0, 1, 6, */ { 1, 8, 3, 1, 9, 8, 5, 10, 6, -1, -1, -1, -1, -1, -1, -1 }, /* 68: 2, 6, */ { 1, 6, 5, 2, 6, 1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 69: 0, 2, 6, */ { 1, 6, 5, 1, 2, 6, 3, 0, 8, -1, -1, -1, -1, -1, -1, -1 }, /* 70: 1, 2, 6, */ { 9, 6, 5, 9, 0, 6, 0, 2, 6, -1, -1, -1, -1, -1, -1, -1 }, /* 71: 0, 1, 2, 6, */ { 5, 9, 8, 5, 8, 2, 5, 2, 6, 3, 2, 8, -1, -1, -1, -1 }, /* 72: 3, 6, */ { 2, 3, 11, 10, 6, 5, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 73: 0, 3, 6, */ { 11, 0, 8, 11, 2, 0, 10, 6, 5, -1, -1, -1, -1, -1, -1, -1 }, /* 74: 1, 3, 6, */ { 0, 1, 9, 2, 3, 11, 5, 10, 6, -1, -1, -1, -1, -1, -1, -1 }, /* 75: 0, 1, 3, 6, */ { 5, 10, 6, 1, 9, 2, 9, 11, 2, 9, 8, 11, -1, -1, -1, -1 }, /* 76: 2, 3, 6, */ { 6, 3, 11, 6, 5, 3, 5, 1, 3, -1, -1, -1, -1, -1, -1, -1 }, /* 77: 0, 2, 3, 6, */ { 0, 8, 11, 0, 11, 5, 0, 5, 1, 5, 11, 6, -1, -1, -1, -1 }, /* 78: 1, 2, 3, 6, */ { 3, 11, 6, 0, 3, 6, 0, 6, 5, 0, 5, 9, -1, -1, -1, -1 }, /* 79: 0, 1, 2, 3, 6, */ { 6, 5, 9, 6, 9, 11, 11, 9, 8, -1, -1, -1, -1, -1, -1, -1 }, /* 80: 4, 6, */ { 5, 10, 6, 4, 7, 8, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 81: 0, 4, 6, */ { 4, 3, 0, 4, 7, 3, 6, 5, 10, -1, -1, -1, -1, -1, -1, -1 }, /* 82: 1, 4, 6, */ { 1, 9, 0, 5, 10, 6, 8, 4, 7, -1, -1, -1, -1, -1, -1, -1 }, /* 83: 0, 1, 4, 6, */ { 10, 6, 5, 1, 9, 7, 1, 7, 3, 7, 9, 4, -1, -1, -1, -1 }, /* 84: 2, 4, 6, */ { 6, 1, 2, 6, 5, 1, 4, 7, 8, -1, -1, -1, -1, -1, -1, -1 }, /* 85: 0, 2, 4, 6, */ { 1, 2, 5, 5, 2, 6, 3, 0, 4, 3, 4, 7, -1, -1, -1, -1 }, /* 86: 1, 2, 4, 6, */ { 8, 4, 7, 9, 0, 5, 0, 6, 5, 0, 2, 6, -1, -1, -1, -1 }, /* 87: 0, 1, 2, 4, 6, */ { 7, 3, 9, 7, 9, 4, 3, 2, 9, 5, 9, 6, 2, 6, 9, -1 }, /* 88: 3, 4, 6, */ { 3, 11, 2, 7, 8, 4, 10, 6, 5, -1, -1, -1, -1, -1, -1, -1 }, /* 89: 0, 3, 4, 6, */ { 5, 10, 6, 4, 7, 2, 4, 2, 0, 2, 7, 11, -1, -1, -1, -1 }, /* 90: 1, 3, 4, 6, */ { 0, 1, 9, 4, 7, 8, 2, 3, 11, 5, 10, 6, -1, -1, -1, -1 }, /* 91: 0, 1, 3, 4, 6, */ { 9, 2, 1, 9, 11, 2, 9, 4, 11, 7, 11, 4, 5, 10, 6, -1 }, /* 92: 2, 3, 4, 6, */ { 8, 4, 7, 3, 11, 5, 3, 5, 1, 5, 11, 6, -1, -1, -1, -1 }, /* 93: 0, 2, 3, 4, 6, */ { 5, 1, 11, 5, 11, 6, 1, 0, 11, 7, 11, 4, 0, 4, 11, -1 }, /* 94: 1, 2, 3, 4, 6, */ { 0, 5, 9, 0, 6, 5, 0, 3, 6, 11, 6, 3, 8, 4, 7, -1 }, /* 95: 0, 1, 2, 3, 4, 6, */ { 6, 5, 9, 6, 9, 11, 4, 7, 9, 7, 11, 9, -1, -1, -1, -1 }, /* 96: 5, 6, */ { 10, 4, 9, 6, 4, 10, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 97: 0, 5, 6, */ { 4, 10, 6, 4, 9, 10, 0, 8, 3, -1, -1, -1, -1, -1, -1, -1 }, /* 98: 1, 5, 6, */ { 10, 0, 1, 10, 6, 0, 6, 4, 0, -1, -1, -1, -1, -1, -1, -1 }, /* 99: 0, 1, 5, 6, */ { 8, 3, 1, 8, 1, 6, 8, 6, 4, 6, 1, 10, -1, -1, -1, -1 }, /* 100: 2, 5, 6, */ { 1, 4, 9, 1, 2, 4, 2, 6, 4, -1, -1, -1, -1, -1, -1, -1 }, /* 101: 0, 2, 5, 6, */ { 3, 0, 8, 1, 2, 9, 2, 4, 9, 2, 6, 4, -1, -1, -1, -1 }, /* 102: 1, 2, 5, 6, */ { 0, 2, 4, 4, 2, 6, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 103: 0, 1, 2, 5, 6, */ { 8, 3, 2, 8, 2, 4, 4, 2, 6, -1, -1, -1, -1, -1, -1, -1 }, /* 104: 3, 5, 6, */ { 10, 4, 9, 10, 6, 4, 11, 2, 3, -1, -1, -1, -1, -1, -1, -1 }, /* 105: 0, 3, 5, 6, */ { 0, 8, 2, 2, 8, 11, 4, 9, 10, 4, 10, 6, -1, -1, -1, -1 }, /* 106: 1, 3, 5, 6, */ { 3, 11, 2, 0, 1, 6, 0, 6, 4, 6, 1, 10, -1, -1, -1, -1 }, /* 107: 0, 1, 3, 5, 6, */ { 6, 4, 1, 6, 1, 10, 4, 8, 1, 2, 1, 11, 8, 11, 1, -1 }, /* 108: 2, 3, 5, 6, */ { 9, 6, 4, 9, 3, 6, 9, 1, 3, 11, 6, 3, -1, -1, -1, -1 }, /* 109: 0, 2, 3, 5, 6, */ { 8, 11, 1, 8, 1, 0, 11, 6, 1, 9, 1, 4, 6, 4, 1, -1 }, /* 110: 1, 2, 3, 5, 6, */ { 3, 11, 6, 3, 6, 0, 0, 6, 4, -1, -1, -1, -1, -1, -1, -1 }, /* 111: 0, 1, 2, 3, 5, 6, */ { 6, 4, 8, 11, 6, 8, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 112: 4, 5, 6, */ { 7, 10, 6, 7, 8, 10, 8, 9, 10, -1, -1, -1, -1, -1, -1, -1 }, /* 113: 0, 4, 5, 6, */ { 0, 7, 3, 0, 10, 7, 0, 9, 10, 6, 7, 10, -1, -1, -1, -1 }, /* 114: 1, 4, 5, 6, */ { 10, 6, 7, 1, 10, 7, 1, 7, 8, 1, 8, 0, -1, -1, -1, -1 }, /* 115: 0, 1, 4, 5, 6, */ { 10, 6, 7, 10, 7, 1, 1, 7, 3, -1, -1, -1, -1, -1, -1, -1 }, /* 116: 2, 4, 5, 6, */ { 1, 2, 6, 1, 6, 8, 1, 8, 9, 8, 6, 7, -1, -1, -1, -1 }, /* 117: 0, 2, 4, 5, 6, */ { 2, 6, 9, 2, 9, 1, 6, 7, 9, 0, 9, 3, 7, 3, 9, -1 }, /* 118: 1, 2, 4, 5, 6, */ { 7, 8, 0, 7, 0, 6, 6, 0, 2, -1, -1, -1, -1, -1, -1, -1 }, /* 119: 0, 1, 2, 4, 5, 6, */ { 7, 3, 2, 6, 7, 2, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 120: 3, 4, 5, 6, */ { 2, 3, 11, 10, 6, 8, 10, 8, 9, 8, 6, 7, -1, -1, -1, -1 }, /* 121: 0, 3, 4, 5, 6, */ { 2, 0, 7, 2, 7, 11, 0, 9, 7, 6, 7, 10, 9, 10, 7, -1 }, /* 122: 1, 3, 4, 5, 6, */ { 1, 8, 0, 1, 7, 8, 1, 10, 7, 6, 7, 10, 2, 3, 11, -1 }, /* 123: 0, 1, 3, 4, 5, 6, */ { 11, 2, 1, 11, 1, 7, 10, 6, 1, 6, 7, 1, -1, -1, -1, -1 }, /* 124: 2, 3, 4, 5, 6, */ { 8, 9, 6, 8, 6, 7, 9, 1, 6, 11, 6, 3, 1, 3, 6, -1 }, /* 125: 0, 2, 3, 4, 5, 6, */ { 0, 9, 1, 11, 6, 7, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 126: 1, 2, 3, 4, 5, 6, */ { 7, 8, 0, 7, 0, 6, 3, 11, 0, 11, 6, 0, -1, -1, -1, -1 }, /* 127: 0, 1, 2, 3, 4, 5, 6, */ { 7, 11, 6, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 128: 7, */ { 7, 6, 11, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 129: 0, 7, */ { 3, 0, 8, 11, 7, 6, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 130: 1, 7, */ { 0, 1, 9, 11, 7, 6, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 131: 0, 1, 7, */ { 8, 1, 9, 8, 3, 1, 11, 7, 6, -1, -1, -1, -1, -1, -1, -1 }, /* 132: 2, 7, */ { 10, 1, 2, 6, 11, 7, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 133: 0, 2, 7, */ { 1, 2, 10, 3, 0, 8, 6, 11, 7, -1, -1, -1, -1, -1, -1, -1 }, /* 134: 1, 2, 7, */ { 2, 9, 0, 2, 10, 9, 6, 11, 7, -1, -1, -1, -1, -1, -1, -1 }, /* 135: 0, 1, 2, 7, */ { 6, 11, 7, 2, 10, 3, 10, 8, 3, 10, 9, 8, -1, -1, -1, -1 }, /* 136: 3, 7, */ { 7, 2, 3, 6, 2, 7, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 137: 0, 3, 7, */ { 7, 0, 8, 7, 6, 0, 6, 2, 0, -1, -1, -1, -1, -1, -1, -1 }, /* 138: 1, 3, 7, */ { 2, 7, 6, 2, 3, 7, 0, 1, 9, -1, -1, -1, -1, -1, -1, -1 }, /* 139: 0, 1, 3, 7, */ { 1, 6, 2, 1, 8, 6, 1, 9, 8, 8, 7, 6, -1, -1, -1, -1 }, /* 140: 2, 3, 7, */ { 10, 7, 6, 10, 1, 7, 1, 3, 7, -1, -1, -1, -1, -1, -1, -1 }, /* 141: 0, 2, 3, 7, */ { 10, 7, 6, 1, 7, 10, 1, 8, 7, 1, 0, 8, -1, -1, -1, -1 }, /* 142: 1, 2, 3, 7, */ { 0, 3, 7, 0, 7, 10, 0, 10, 9, 6, 10, 7, -1, -1, -1, -1 }, /* 143: 0, 1, 2, 3, 7, */ { 7, 6, 10, 7, 10, 8, 8, 10, 9, -1, -1, -1, -1, -1, -1, -1 }, /* 144: 4, 7, */ { 6, 8, 4, 11, 8, 6, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 145: 0, 4, 7, */ { 3, 6, 11, 3, 0, 6, 0, 4, 6, -1, -1, -1, -1, -1, -1, -1 }, /* 146: 1, 4, 7, */ { 8, 6, 11, 8, 4, 6, 9, 0, 1, -1, -1, -1, -1, -1, -1, -1 }, /* 147: 0, 1, 4, 7, */ { 9, 4, 6, 9, 6, 3, 9, 3, 1, 11, 3, 6, -1, -1, -1, -1 }, /* 148: 2, 4, 7, */ { 6, 8, 4, 6, 11, 8, 2, 10, 1, -1, -1, -1, -1, -1, -1, -1 }, /* 149: 0, 2, 4, 7, */ { 1, 2, 10, 3, 0, 11, 0, 6, 11, 0, 4, 6, -1, -1, -1, -1 }, /* 150: 1, 2, 4, 7, */ { 4, 11, 8, 4, 6, 11, 0, 2, 9, 2, 10, 9, -1, -1, -1, -1 }, /* 151: 0, 1, 2, 4, 7, */ { 10, 9, 3, 10, 3, 2, 9, 4, 3, 11, 3, 6, 4, 6, 3, -1 }, /* 152: 3, 4, 7, */ { 8, 2, 3, 8, 4, 2, 4, 6, 2, -1, -1, -1, -1, -1, -1, -1 }, /* 153: 0, 3, 4, 7, */ { 0, 4, 2, 4, 6, 2, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 154: 1, 3, 4, 7, */ { 1, 9, 0, 2, 3, 4, 2, 4, 6, 4, 3, 8, -1, -1, -1, -1 }, /* 155: 0, 1, 3, 4, 7, */ { 1, 9, 4, 1, 4, 2, 2, 4, 6, -1, -1, -1, -1, -1, -1, -1 }, /* 156: 2, 3, 4, 7, */ { 8, 1, 3, 8, 6, 1, 8, 4, 6, 6, 10, 1, -1, -1, -1, -1 }, /* 157: 0, 2, 3, 4, 7, */ { 10, 1, 0, 10, 0, 6, 6, 0, 4, -1, -1, -1, -1, -1, -1, -1 }, /* 158: 1, 2, 3, 4, 7, */ { 4, 6, 3, 4, 3, 8, 6, 10, 3, 0, 3, 9, 10, 9, 3, -1 }, /* 159: 0, 1, 2, 3, 4, 7, */ { 10, 9, 4, 6, 10, 4, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 160: 5, 7, */ { 4, 9, 5, 7, 6, 11, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 161: 0, 5, 7, */ { 0, 8, 3, 4, 9, 5, 11, 7, 6, -1, -1, -1, -1, -1, -1, -1 }, /* 162: 1, 5, 7, */ { 5, 0, 1, 5, 4, 0, 7, 6, 11, -1, -1, -1, -1, -1, -1, -1 }, /* 163: 0, 1, 5, 7, */ { 11, 7, 6, 8, 3, 4, 3, 5, 4, 3, 1, 5, -1, -1, -1, -1 }, /* 164: 2, 5, 7, */ { 9, 5, 4, 10, 1, 2, 7, 6, 11, -1, -1, -1, -1, -1, -1, -1 }, /* 165: 0, 2, 5, 7, */ { 6, 11, 7, 1, 2, 10, 0, 8, 3, 4, 9, 5, -1, -1, -1, -1 }, /* 166: 1, 2, 5, 7, */ { 7, 6, 11, 5, 4, 10, 4, 2, 10, 4, 0, 2, -1, -1, -1, -1 }, /* 167: 0, 1, 2, 5, 7, */ { 3, 4, 8, 3, 5, 4, 3, 2, 5, 10, 5, 2, 11, 7, 6, -1 }, /* 168: 3, 5, 7, */ { 7, 2, 3, 7, 6, 2, 5, 4, 9, -1, -1, -1, -1, -1, -1, -1 }, /* 169: 0, 3, 5, 7, */ { 9, 5, 4, 0, 8, 6, 0, 6, 2, 6, 8, 7, -1, -1, -1, -1 }, /* 170: 1, 3, 5, 7, */ { 3, 6, 2, 3, 7, 6, 1, 5, 0, 5, 4, 0, -1, -1, -1, -1 }, /* 171: 0, 1, 3, 5, 7, */ { 6, 2, 8, 6, 8, 7, 2, 1, 8, 4, 8, 5, 1, 5, 8, -1 }, /* 172: 2, 3, 5, 7, */ { 9, 5, 4, 10, 1, 6, 1, 7, 6, 1, 3, 7, -1, -1, -1, -1 }, /* 173: 0, 2, 3, 5, 7, */ { 1, 6, 10, 1, 7, 6, 1, 0, 7, 8, 7, 0, 9, 5, 4, -1 }, /* 174: 1, 2, 3, 5, 7, */ { 4, 0, 10, 4, 10, 5, 0, 3, 10, 6, 10, 7, 3, 7, 10, -1 }, /* 175: 0, 1, 2, 3, 5, 7, */ { 7, 6, 10, 7, 10, 8, 5, 4, 10, 4, 8, 10, -1, -1, -1, -1 }, /* 176: 4, 5, 7, */ { 6, 9, 5, 6, 11, 9, 11, 8, 9, -1, -1, -1, -1, -1, -1, -1 }, /* 177: 0, 4, 5, 7, */ { 3, 6, 11, 0, 6, 3, 0, 5, 6, 0, 9, 5, -1, -1, -1, -1 }, /* 178: 1, 4, 5, 7, */ { 0, 11, 8, 0, 5, 11, 0, 1, 5, 5, 6, 11, -1, -1, -1, -1 }, /* 179: 0, 1, 4, 5, 7, */ { 6, 11, 3, 6, 3, 5, 5, 3, 1, -1, -1, -1, -1, -1, -1, -1 }, /* 180: 2, 4, 5, 7, */ { 1, 2, 10, 9, 5, 11, 9, 11, 8, 11, 5, 6, -1, -1, -1, -1 }, /* 181: 0, 2, 4, 5, 7, */ { 0, 11, 3, 0, 6, 11, 0, 9, 6, 5, 6, 9, 1, 2, 10, -1 }, /* 182: 1, 2, 4, 5, 7, */ { 11, 8, 5, 11, 5, 6, 8, 0, 5, 10, 5, 2, 0, 2, 5, -1 }, /* 183: 0, 1, 2, 4, 5, 7, */ { 6, 11, 3, 6, 3, 5, 2, 10, 3, 10, 5, 3, -1, -1, -1, -1 }, /* 184: 3, 4, 5, 7, */ { 5, 8, 9, 5, 2, 8, 5, 6, 2, 3, 8, 2, -1, -1, -1, -1 }, /* 185: 0, 3, 4, 5, 7, */ { 9, 5, 6, 9, 6, 0, 0, 6, 2, -1, -1, -1, -1, -1, -1, -1 }, /* 186: 1, 3, 4, 5, 7, */ { 1, 5, 8, 1, 8, 0, 5, 6, 8, 3, 8, 2, 6, 2, 8, -1 }, /* 187: 0, 1, 3, 4, 5, 7, */ { 1, 5, 6, 2, 1, 6, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 188: 2, 3, 4, 5, 7, */ { 1, 3, 6, 1, 6, 10, 3, 8, 6, 5, 6, 9, 8, 9, 6, -1 }, /* 189: 0, 2, 3, 4, 5, 7, */ { 10, 1, 0, 10, 0, 6, 9, 5, 0, 5, 6, 0, -1, -1, -1, -1 }, /* 190: 1, 2, 3, 4, 5, 7, */ { 0, 3, 8, 5, 6, 10, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 191: 0, 1, 2, 3, 4, 5, 7, */ { 10, 5, 6, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 192: 6, 7, */ { 11, 5, 10, 7, 5, 11, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 193: 0, 6, 7, */ { 11, 5, 10, 11, 7, 5, 8, 3, 0, -1, -1, -1, -1, -1, -1, -1 }, /* 194: 1, 6, 7, */ { 5, 11, 7, 5, 10, 11, 1, 9, 0, -1, -1, -1, -1, -1, -1, -1 }, /* 195: 0, 1, 6, 7, */ { 10, 7, 5, 10, 11, 7, 9, 8, 1, 8, 3, 1, -1, -1, -1, -1 }, /* 196: 2, 6, 7, */ { 11, 1, 2, 11, 7, 1, 7, 5, 1, -1, -1, -1, -1, -1, -1, -1 }, /* 197: 0, 2, 6, 7, */ { 0, 8, 3, 1, 2, 7, 1, 7, 5, 7, 2, 11, -1, -1, -1, -1 }, /* 198: 1, 2, 6, 7, */ { 9, 7, 5, 9, 2, 7, 9, 0, 2, 2, 11, 7, -1, -1, -1, -1 }, /* 199: 0, 1, 2, 6, 7, */ { 7, 5, 2, 7, 2, 11, 5, 9, 2, 3, 2, 8, 9, 8, 2, -1 }, /* 200: 3, 6, 7, */ { 2, 5, 10, 2, 3, 5, 3, 7, 5, -1, -1, -1, -1, -1, -1, -1 }, /* 201: 0, 3, 6, 7, */ { 8, 2, 0, 8, 5, 2, 8, 7, 5, 10, 2, 5, -1, -1, -1, -1 }, /* 202: 1, 3, 6, 7, */ { 9, 0, 1, 5, 10, 3, 5, 3, 7, 3, 10, 2, -1, -1, -1, -1 }, /* 203: 0, 1, 3, 6, 7, */ { 9, 8, 2, 9, 2, 1, 8, 7, 2, 10, 2, 5, 7, 5, 2, -1 }, /* 204: 2, 3, 6, 7, */ { 1, 3, 5, 3, 7, 5, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 205: 0, 2, 3, 6, 7, */ { 0, 8, 7, 0, 7, 1, 1, 7, 5, -1, -1, -1, -1, -1, -1, -1 }, /* 206: 1, 2, 3, 6, 7, */ { 9, 0, 3, 9, 3, 5, 5, 3, 7, -1, -1, -1, -1, -1, -1, -1 }, /* 207: 0, 1, 2, 3, 6, 7, */ { 9, 8, 7, 5, 9, 7, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 208: 4, 6, 7, */ { 5, 8, 4, 5, 10, 8, 10, 11, 8, -1, -1, -1, -1, -1, -1, -1 }, /* 209: 0, 4, 6, 7, */ { 5, 0, 4, 5, 11, 0, 5, 10, 11, 11, 3, 0, -1, -1, -1, -1 }, /* 210: 1, 4, 6, 7, */ { 0, 1, 9, 8, 4, 10, 8, 10, 11, 10, 4, 5, -1, -1, -1, -1 }, /* 211: 0, 1, 4, 6, 7, */ { 10, 11, 4, 10, 4, 5, 11, 3, 4, 9, 4, 1, 3, 1, 4, -1 }, /* 212: 2, 4, 6, 7, */ { 2, 5, 1, 2, 8, 5, 2, 11, 8, 4, 5, 8, -1, -1, -1, -1 }, /* 213: 0, 2, 4, 6, 7, */ { 0, 4, 11, 0, 11, 3, 4, 5, 11, 2, 11, 1, 5, 1, 11, -1 }, /* 214: 1, 2, 4, 6, 7, */ { 0, 2, 5, 0, 5, 9, 2, 11, 5, 4, 5, 8, 11, 8, 5, -1 }, /* 215: 0, 1, 2, 4, 6, 7, */ { 9, 4, 5, 2, 11, 3, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 216: 3, 4, 6, 7, */ { 2, 5, 10, 3, 5, 2, 3, 4, 5, 3, 8, 4, -1, -1, -1, -1 }, /* 217: 0, 3, 4, 6, 7, */ { 5, 10, 2, 5, 2, 4, 4, 2, 0, -1, -1, -1, -1, -1, -1, -1 }, /* 218: 1, 3, 4, 6, 7, */ { 3, 10, 2, 3, 5, 10, 3, 8, 5, 4, 5, 8, 0, 1, 9, -1 }, /* 219: 0, 1, 3, 4, 6, 7, */ { 5, 10, 2, 5, 2, 4, 1, 9, 2, 9, 4, 2, -1, -1, -1, -1 }, /* 220: 2, 3, 4, 6, 7, */ { 8, 4, 5, 8, 5, 3, 3, 5, 1, -1, -1, -1, -1, -1, -1, -1 }, /* 221: 0, 2, 3, 4, 6, 7, */ { 0, 4, 5, 1, 0, 5, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 222: 1, 2, 3, 4, 6, 7, */ { 8, 4, 5, 8, 5, 3, 9, 0, 5, 0, 3, 5, -1, -1, -1, -1 }, /* 223: 0, 1, 2, 3, 4, 6, 7, */ { 9, 4, 5, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 224: 5, 6, 7, */ { 4, 11, 7, 4, 9, 11, 9, 10, 11, -1, -1, -1, -1, -1, -1, -1 }, /* 225: 0, 5, 6, 7, */ { 0, 8, 3, 4, 9, 7, 9, 11, 7, 9, 10, 11, -1, -1, -1, -1 }, /* 226: 1, 5, 6, 7, */ { 1, 10, 11, 1, 11, 4, 1, 4, 0, 7, 4, 11, -1, -1, -1, -1 }, /* 227: 0, 1, 5, 6, 7, */ { 3, 1, 4, 3, 4, 8, 1, 10, 4, 7, 4, 11, 10, 11, 4, -1 }, /* 228: 2, 5, 6, 7, */ { 4, 11, 7, 9, 11, 4, 9, 2, 11, 9, 1, 2, -1, -1, -1, -1 }, /* 229: 0, 2, 5, 6, 7, */ { 9, 7, 4, 9, 11, 7, 9, 1, 11, 2, 11, 1, 0, 8, 3, -1 }, /* 230: 1, 2, 5, 6, 7, */ { 11, 7, 4, 11, 4, 2, 2, 4, 0, -1, -1, -1, -1, -1, -1, -1 }, /* 231: 0, 1, 2, 5, 6, 7, */ { 11, 7, 4, 11, 4, 2, 8, 3, 4, 3, 2, 4, -1, -1, -1, -1 }, /* 232: 3, 5, 6, 7, */ { 2, 9, 10, 2, 7, 9, 2, 3, 7, 7, 4, 9, -1, -1, -1, -1 }, /* 233: 0, 3, 5, 6, 7, */ { 9, 10, 7, 9, 7, 4, 10, 2, 7, 8, 7, 0, 2, 0, 7, -1 }, /* 234: 1, 3, 5, 6, 7, */ { 3, 7, 10, 3, 10, 2, 7, 4, 10, 1, 10, 0, 4, 0, 10, -1 }, /* 235: 0, 1, 3, 5, 6, 7, */ { 1, 10, 2, 8, 7, 4, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 236: 2, 3, 5, 6, 7, */ { 4, 9, 1, 4, 1, 7, 7, 1, 3, -1, -1, -1, -1, -1, -1, -1 }, /* 237: 0, 2, 3, 5, 6, 7, */ { 4, 9, 1, 4, 1, 7, 0, 8, 1, 8, 7, 1, -1, -1, -1, -1 }, /* 238: 1, 2, 3, 5, 6, 7, */ { 4, 0, 3, 7, 4, 3, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 239: 0, 1, 2, 3, 5, 6, 7, */ { 4, 8, 7, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 240: 4, 5, 6, 7, */ { 9, 10, 8, 10, 11, 8, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 241: 0, 4, 5, 6, 7, */ { 3, 0, 9, 3, 9, 11, 11, 9, 10, -1, -1, -1, -1, -1, -1, -1 }, /* 242: 1, 4, 5, 6, 7, */ { 0, 1, 10, 0, 10, 8, 8, 10, 11, -1, -1, -1, -1, -1, -1, -1 }, /* 243: 0, 1, 4, 5, 6, 7, */ { 3, 1, 10, 11, 3, 10, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 244: 2, 4, 5, 6, 7, */ { 1, 2, 11, 1, 11, 9, 9, 11, 8, -1, -1, -1, -1, -1, -1, -1 }, /* 245: 0, 2, 4, 5, 6, 7, */ { 3, 0, 9, 3, 9, 11, 1, 2, 9, 2, 11, 9, -1, -1, -1, -1 }, /* 246: 1, 2, 4, 5, 6, 7, */ { 0, 2, 11, 8, 0, 11, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 247: 0, 1, 2, 4, 5, 6, 7, */ { 3, 2, 11, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 248: 3, 4, 5, 6, 7, */ { 2, 3, 8, 2, 8, 10, 10, 8, 9, -1, -1, -1, -1, -1, -1, -1 }, /* 249: 0, 3, 4, 5, 6, 7, */ { 9, 10, 2, 0, 9, 2, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 250: 1, 3, 4, 5, 6, 7, */ { 2, 3, 8, 2, 8, 10, 0, 1, 8, 1, 10, 8, -1, -1, -1, -1 }, /* 251: 0, 1, 3, 4, 5, 6, 7, */ { 1, 10, 2, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 252: 2, 3, 4, 5, 6, 7, */ { 1, 3, 8, 9, 1, 8, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 253: 0, 2, 3, 4, 5, 6, 7, */ { 0, 9, 1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 254: 1, 2, 3, 4, 5, 6, 7, */ { 0, 3, 8, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 }, /* 255: 0, 1, 2, 3, 4, 5, 6, 7, */ { -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 } }; //_____________________________________________________________________________ #endif // _LOOKUPTABLE_H_ scikit-image-0.13.1/skimage/measure/mc_meta/MarchingCubes.cpp000066400000000000000000001274211316256236300240640ustar00rootroot00000000000000/** * @file MarchingCubes.cpp * @author Thomas Lewiner * @author Math Dept, PUC-Rio * @version 0.2 * @date 12/08/2002 * * @brief MarchingCubes Algorithm */ //________________________________________________ #if !defined(WIN32) || defined(__CYGWIN__) #pragma implementation #endif // WIN32 #include #include #include #include #include #include "MarchingCubes.h" #include "ply.h" #include "LookUpTable.h" // step size of the arrays of vertices and triangles #define ALLOC_SIZE 65536 //_____________________________________________________________________________ // print cube for debug void MarchingCubes::print_cube() { printf( "\t%f %f %f %f %f %f %f %f\n", _cube[0], _cube[1], _cube[2], _cube[3], _cube[4], _cube[5], _cube[6], _cube[7]) ; } //_____________________________________________________________________________ //_____________________________________________________________________________ // Constructor MarchingCubes::MarchingCubes( const int size_x /*= -1*/, const int size_y /*= -1*/, const int size_z /*= -1*/ ) : //----------------------------------------------------------------------------- _originalMC(false), _ext_data (false), _size_x (size_x), _size_y (size_y), _size_z (size_z), _data ((real *)NULL), _x_verts (( int *)NULL), _y_verts (( int *)NULL), _z_verts (( int *)NULL), _nverts (0), _ntrigs (0), _Nverts (0), _Ntrigs (0), _vertices (( Vertex *)NULL), _triangles ((Triangle*)NULL) {} //_____________________________________________________________________________ //_____________________________________________________________________________ // Destructor MarchingCubes::~MarchingCubes() //----------------------------------------------------------------------------- { clean_all() ; } //_____________________________________________________________________________ //_____________________________________________________________________________ // main algorithm void MarchingCubes::run( real iso ) //----------------------------------------------------------------------------- { clock_t time = clock() ; compute_intersection_points( iso ) ; for( _k = 0 ; _k < _size_z-1 ; _k++ ) for( _j = 0 ; _j < _size_y-1 ; _j++ ) for( _i = 0 ; _i < _size_x-1 ; _i++ ) { _lut_entry = 0 ; for( int p = 0 ; p < 8 ; ++p ) { _cube[p] = get_data( _i+((p^(p>>1))&1), _j+((p>>1)&1), _k+((p>>2)&1) ) - iso ; if( fabs( _cube[p] ) < FLT_EPSILON ) _cube[p] = FLT_EPSILON ; if( _cube[p] > 0 ) _lut_entry += 1 << p ; } /* if( ( _cube[0] = get_data( _i , _j , _k ) ) > 0 ) _lut_entry += 1 ; if( ( _cube[1] = get_data(_i+1, _j , _k ) ) > 0 ) _lut_entry += 2 ; if( ( _cube[2] = get_data(_i+1,_j+1, _k ) ) > 0 ) _lut_entry += 4 ; if( ( _cube[3] = get_data( _i ,_j+1, _k ) ) > 0 ) _lut_entry += 8 ; if( ( _cube[4] = get_data( _i , _j ,_k+1) ) > 0 ) _lut_entry += 16 ; if( ( _cube[5] = get_data(_i+1, _j ,_k+1) ) > 0 ) _lut_entry += 32 ; if( ( _cube[6] = get_data(_i+1,_j+1,_k+1) ) > 0 ) _lut_entry += 64 ; if( ( _cube[7] = get_data( _i ,_j+1,_k+1) ) > 0 ) _lut_entry += 128 ; */ process_cube( ) ; } printf("Marching Cubes ran in %lf secs.\n", (double)(clock() - time)/CLOCKS_PER_SEC) ; } //_____________________________________________________________________________ //_____________________________________________________________________________ // init temporary structures (must set sizes before call) void MarchingCubes::init_temps() //----------------------------------------------------------------------------- { if( !_ext_data ) _data = new real [_size_x * _size_y * _size_z] ; _x_verts = new int [_size_x * _size_y * _size_z] ; _y_verts = new int [_size_x * _size_y * _size_z] ; _z_verts = new int [_size_x * _size_y * _size_z] ; memset( _x_verts, -1, _size_x * _size_y * _size_z * sizeof( int ) ) ; memset( _y_verts, -1, _size_x * _size_y * _size_z * sizeof( int ) ) ; memset( _z_verts, -1, _size_x * _size_y * _size_z * sizeof( int ) ) ; } //_____________________________________________________________________________ //_____________________________________________________________________________ // init all structures (must set sizes before call) void MarchingCubes::init_all () //----------------------------------------------------------------------------- { init_temps() ; _nverts = _ntrigs = 0 ; _Nverts = _Ntrigs = ALLOC_SIZE ; _vertices = new Vertex [_Nverts] ; _triangles = new Triangle[_Ntrigs] ; } //_____________________________________________________________________________ //_____________________________________________________________________________ // clean temporary structures void MarchingCubes::clean_temps() //----------------------------------------------------------------------------- { if( !_ext_data ) delete [] _data; delete [] _x_verts; delete [] _y_verts; delete [] _z_verts; if( !_ext_data ) _data = (real*)NULL ; _x_verts = (int*)NULL ; _y_verts = (int*)NULL ; _z_verts = (int*)NULL ; } //_____________________________________________________________________________ //_____________________________________________________________________________ // clean all structures void MarchingCubes::clean_all() //----------------------------------------------------------------------------- { clean_temps() ; delete [] _vertices ; delete [] _triangles ; _vertices = (Vertex *)NULL ; _triangles = (Triangle *)NULL ; _nverts = _ntrigs = 0 ; _Nverts = _Ntrigs = 0 ; _size_x = _size_y = _size_z = -1 ; } //_____________________________________________________________________________ //_____________________________________________________________________________ //_____________________________________________________________________________ //_____________________________________________________________________________ // Compute the intersection points void MarchingCubes::compute_intersection_points( real iso ) //----------------------------------------------------------------------------- { for( _k = 0 ; _k < _size_z ; _k++ ) for( _j = 0 ; _j < _size_y ; _j++ ) for( _i = 0 ; _i < _size_x ; _i++ ) { _cube[0] = get_data( _i, _j, _k ) - iso ; if( _i < _size_x - 1 ) _cube[1] = get_data(_i+1, _j , _k ) - iso ; else _cube[1] = _cube[0] ; if( _j < _size_y - 1 ) _cube[3] = get_data( _i ,_j+1, _k ) - iso ; else _cube[3] = _cube[0] ; if( _k < _size_z - 1 ) _cube[4] = get_data( _i , _j ,_k+1) - iso ; else _cube[4] = _cube[0] ; if( fabs( _cube[0] ) < FLT_EPSILON ) _cube[0] = FLT_EPSILON ; if( fabs( _cube[1] ) < FLT_EPSILON ) _cube[1] = FLT_EPSILON ; if( fabs( _cube[3] ) < FLT_EPSILON ) _cube[3] = FLT_EPSILON ; if( fabs( _cube[4] ) < FLT_EPSILON ) _cube[4] = FLT_EPSILON ; if( _cube[0] < 0 ) { if( _cube[1] > 0 ) set_x_vert( add_x_vertex( ), _i,_j,_k ) ; if( _cube[3] > 0 ) set_y_vert( add_y_vertex( ), _i,_j,_k ) ; if( _cube[4] > 0 ) set_z_vert( add_z_vertex( ), _i,_j,_k ) ; } else { if( _cube[1] < 0 ) set_x_vert( add_x_vertex( ), _i,_j,_k ) ; if( _cube[3] < 0 ) set_y_vert( add_y_vertex( ), _i,_j,_k ) ; if( _cube[4] < 0 ) set_z_vert( add_z_vertex( ), _i,_j,_k ) ; } } } //_____________________________________________________________________________ //_____________________________________________________________________________ // Test a face // if face>0 return true if the face contains a part of the surface bool MarchingCubes::test_face( schar face ) //----------------------------------------------------------------------------- { real A,B,C,D ; switch( face ) { case -1 : case 1 : A = _cube[0] ; B = _cube[4] ; C = _cube[5] ; D = _cube[1] ; break ; case -2 : case 2 : A = _cube[1] ; B = _cube[5] ; C = _cube[6] ; D = _cube[2] ; break ; case -3 : case 3 : A = _cube[2] ; B = _cube[6] ; C = _cube[7] ; D = _cube[3] ; break ; case -4 : case 4 : A = _cube[3] ; B = _cube[7] ; C = _cube[4] ; D = _cube[0] ; break ; case -5 : case 5 : A = _cube[0] ; B = _cube[3] ; C = _cube[2] ; D = _cube[1] ; break ; case -6 : case 6 : A = _cube[4] ; B = _cube[7] ; C = _cube[6] ; D = _cube[5] ; break ; default : printf( "Invalid face code %d\n", face ) ; print_cube() ; A = B = C = D = 0 ; }; if( fabs( A*C - B*D ) < FLT_EPSILON ) return face >= 0 ; return face * A * ( A*C - B*D ) >= 0 ; // face and A invert signs } //_____________________________________________________________________________ //_____________________________________________________________________________ // Test the interior of a cube // if s == 7, return true if the interior is empty // if s ==-7, return false if the interior is empty bool MarchingCubes::test_interior( schar s ) //----------------------------------------------------------------------------- { real t, At=0, Bt=0, Ct=0, Dt=0, a, b ; char test = 0 ; char edge = -1 ; // reference edge of the triangulation switch( _case ) { case 4 : case 10 : a = ( _cube[4] - _cube[0] ) * ( _cube[6] - _cube[2] ) - ( _cube[7] - _cube[3] ) * ( _cube[5] - _cube[1] ) ; b = _cube[2] * ( _cube[4] - _cube[0] ) + _cube[0] * ( _cube[6] - _cube[2] ) - _cube[1] * ( _cube[7] - _cube[3] ) - _cube[3] * ( _cube[5] - _cube[1] ) ; t = - b / (2*a) ; if( t<0 || t>1 ) return s>0 ; At = _cube[0] + ( _cube[4] - _cube[0] ) * t ; Bt = _cube[3] + ( _cube[7] - _cube[3] ) * t ; Ct = _cube[2] + ( _cube[6] - _cube[2] ) * t ; Dt = _cube[1] + ( _cube[5] - _cube[1] ) * t ; break ; case 6 : case 7 : case 12 : case 13 : switch( _case ) { case 6 : edge = test6 [_config][2] ; break ; case 7 : edge = test7 [_config][4] ; break ; case 12 : edge = test12[_config][3] ; break ; case 13 : edge = tiling13_5_1[_config][_subconfig][0] ; break ; } switch( edge ) { case 0 : t = _cube[0] / ( _cube[0] - _cube[1] ) ; At = 0 ; Bt = _cube[3] + ( _cube[2] - _cube[3] ) * t ; Ct = _cube[7] + ( _cube[6] - _cube[7] ) * t ; Dt = _cube[4] + ( _cube[5] - _cube[4] ) * t ; break ; case 1 : t = _cube[1] / ( _cube[1] - _cube[2] ) ; At = 0 ; Bt = _cube[0] + ( _cube[3] - _cube[0] ) * t ; Ct = _cube[4] + ( _cube[7] - _cube[4] ) * t ; Dt = _cube[5] + ( _cube[6] - _cube[5] ) * t ; break ; case 2 : t = _cube[2] / ( _cube[2] - _cube[3] ) ; At = 0 ; Bt = _cube[1] + ( _cube[0] - _cube[1] ) * t ; Ct = _cube[5] + ( _cube[4] - _cube[5] ) * t ; Dt = _cube[6] + ( _cube[7] - _cube[6] ) * t ; break ; case 3 : t = _cube[3] / ( _cube[3] - _cube[0] ) ; At = 0 ; Bt = _cube[2] + ( _cube[1] - _cube[2] ) * t ; Ct = _cube[6] + ( _cube[5] - _cube[6] ) * t ; Dt = _cube[7] + ( _cube[4] - _cube[7] ) * t ; break ; case 4 : t = _cube[4] / ( _cube[4] - _cube[5] ) ; At = 0 ; Bt = _cube[7] + ( _cube[6] - _cube[7] ) * t ; Ct = _cube[3] + ( _cube[2] - _cube[3] ) * t ; Dt = _cube[0] + ( _cube[1] - _cube[0] ) * t ; break ; case 5 : t = _cube[5] / ( _cube[5] - _cube[6] ) ; At = 0 ; Bt = _cube[4] + ( _cube[7] - _cube[4] ) * t ; Ct = _cube[0] + ( _cube[3] - _cube[0] ) * t ; Dt = _cube[1] + ( _cube[2] - _cube[1] ) * t ; break ; case 6 : t = _cube[6] / ( _cube[6] - _cube[7] ) ; At = 0 ; Bt = _cube[5] + ( _cube[4] - _cube[5] ) * t ; Ct = _cube[1] + ( _cube[0] - _cube[1] ) * t ; Dt = _cube[2] + ( _cube[3] - _cube[2] ) * t ; break ; case 7 : t = _cube[7] / ( _cube[7] - _cube[4] ) ; At = 0 ; Bt = _cube[6] + ( _cube[5] - _cube[6] ) * t ; Ct = _cube[2] + ( _cube[1] - _cube[2] ) * t ; Dt = _cube[3] + ( _cube[0] - _cube[3] ) * t ; break ; case 8 : t = _cube[0] / ( _cube[0] - _cube[4] ) ; At = 0 ; Bt = _cube[3] + ( _cube[7] - _cube[3] ) * t ; Ct = _cube[2] + ( _cube[6] - _cube[2] ) * t ; Dt = _cube[1] + ( _cube[5] - _cube[1] ) * t ; break ; case 9 : t = _cube[1] / ( _cube[1] - _cube[5] ) ; At = 0 ; Bt = _cube[0] + ( _cube[4] - _cube[0] ) * t ; Ct = _cube[3] + ( _cube[7] - _cube[3] ) * t ; Dt = _cube[2] + ( _cube[6] - _cube[2] ) * t ; break ; case 10 : t = _cube[2] / ( _cube[2] - _cube[6] ) ; At = 0 ; Bt = _cube[1] + ( _cube[5] - _cube[1] ) * t ; Ct = _cube[0] + ( _cube[4] - _cube[0] ) * t ; Dt = _cube[3] + ( _cube[7] - _cube[3] ) * t ; break ; case 11 : t = _cube[3] / ( _cube[3] - _cube[7] ) ; At = 0 ; Bt = _cube[2] + ( _cube[6] - _cube[2] ) * t ; Ct = _cube[1] + ( _cube[5] - _cube[1] ) * t ; Dt = _cube[0] + ( _cube[4] - _cube[0] ) * t ; break ; default : printf( "Invalid edge %d\n", edge ) ; print_cube() ; break ; } break ; default : printf( "Invalid ambiguous case %d\n", _case ) ; print_cube() ; break ; } if( At >= 0 ) test ++ ; if( Bt >= 0 ) test += 2 ; if( Ct >= 0 ) test += 4 ; if( Dt >= 0 ) test += 8 ; switch( test ) { case 0 : return s>0 ; case 1 : return s>0 ; case 2 : return s>0 ; case 3 : return s>0 ; case 4 : return s>0 ; case 5 : if( At * Ct - Bt * Dt < FLT_EPSILON ) return s>0 ; break ; case 6 : return s>0 ; case 7 : return s<0 ; case 8 : return s>0 ; case 9 : return s>0 ; case 10 : if( At * Ct - Bt * Dt >= FLT_EPSILON ) return s>0 ; break ; case 11 : return s<0 ; case 12 : return s>0 ; case 13 : return s<0 ; case 14 : return s<0 ; case 15 : return s<0 ; } return s<0 ; } //_____________________________________________________________________________ //_____________________________________________________________________________ // Process a unit cube void MarchingCubes::process_cube( ) //----------------------------------------------------------------------------- { if( _originalMC ) { char nt = 0 ; while( casesClassic[_lut_entry][3*nt] != -1 ) nt++ ; add_triangle( casesClassic[_lut_entry], nt ) ; return ; } int v12 = -1 ; _case = cases[_lut_entry][0] ; _config = cases[_lut_entry][1] ; _subconfig = 0 ; switch( _case ) { case 0 : break ; case 1 : add_triangle( tiling1[_config], 1 ) ; break ; case 2 : add_triangle( tiling2[_config], 2 ) ; break ; case 3 : if( test_face( test3[_config]) ) add_triangle( tiling3_2[_config], 4 ) ; // 3.2 else add_triangle( tiling3_1[_config], 2 ) ; // 3.1 break ; case 4 : if( test_interior( test4[_config]) ) add_triangle( tiling4_1[_config], 2 ) ; // 4.1.1 else add_triangle( tiling4_2[_config], 6 ) ; // 4.1.2 break ; case 5 : add_triangle( tiling5[_config], 3 ) ; break ; case 6 : if( test_face( test6[_config][0]) ) add_triangle( tiling6_2[_config], 5 ) ; // 6.2 else { if( test_interior( test6[_config][1]) ) add_triangle( tiling6_1_1[_config], 3 ) ; // 6.1.1 else { v12 = add_c_vertex() ; add_triangle( tiling6_1_2[_config], 9 , v12) ; // 6.1.2 } } break ; case 7 : if( test_face( test7[_config][0] ) ) _subconfig += 1 ; if( test_face( test7[_config][1] ) ) _subconfig += 2 ; if( test_face( test7[_config][2] ) ) _subconfig += 4 ; switch( _subconfig ) { case 0 : add_triangle( tiling7_1[_config], 3 ) ; break ; case 1 : add_triangle( tiling7_2[_config][0], 5 ) ; break ; case 2 : add_triangle( tiling7_2[_config][1], 5 ) ; break ; case 3 : v12 = add_c_vertex() ; add_triangle( tiling7_3[_config][0], 9, v12 ) ; break ; case 4 : add_triangle( tiling7_2[_config][2], 5 ) ; break ; case 5 : v12 = add_c_vertex() ; add_triangle( tiling7_3[_config][1], 9, v12 ) ; break ; case 6 : v12 = add_c_vertex() ; add_triangle( tiling7_3[_config][2], 9, v12 ) ; break ; case 7 : if( test_interior( test7[_config][3]) ) add_triangle( tiling7_4_2[_config], 9 ) ; else add_triangle( tiling7_4_1[_config], 5 ) ; break ; }; break ; case 8 : add_triangle( tiling8[_config], 2 ) ; break ; case 9 : add_triangle( tiling9[_config], 4 ) ; break ; case 10 : if( test_face( test10[_config][0]) ) { if( test_face( test10[_config][1]) ) add_triangle( tiling10_1_1_[_config], 4 ) ; // 10.1.1 else { v12 = add_c_vertex() ; add_triangle( tiling10_2[_config], 8, v12 ) ; // 10.2 } } else { if( test_face( test10[_config][1]) ) { v12 = add_c_vertex() ; add_triangle( tiling10_2_[_config], 8, v12 ) ; // 10.2 } else { if( test_interior( test10[_config][2]) ) add_triangle( tiling10_1_1[_config], 4 ) ; // 10.1.1 else add_triangle( tiling10_1_2[_config], 8 ) ; // 10.1.2 } } break ; case 11 : add_triangle( tiling11[_config], 4 ) ; break ; case 12 : if( test_face( test12[_config][0]) ) { if( test_face( test12[_config][1]) ) add_triangle( tiling12_1_1_[_config], 4 ) ; // 12.1.1 else { v12 = add_c_vertex() ; add_triangle( tiling12_2[_config], 8, v12 ) ; // 12.2 } } else { if( test_face( test12[_config][1]) ) { v12 = add_c_vertex() ; add_triangle( tiling12_2_[_config], 8, v12 ) ; // 12.2 } else { if( test_interior( test12[_config][2]) ) add_triangle( tiling12_1_1[_config], 4 ) ; // 12.1.1 else add_triangle( tiling12_1_2[_config], 8 ) ; // 12.1.2 } } break ; case 13 : if( test_face( test13[_config][0] ) ) _subconfig += 1 ; if( test_face( test13[_config][1] ) ) _subconfig += 2 ; if( test_face( test13[_config][2] ) ) _subconfig += 4 ; if( test_face( test13[_config][3] ) ) _subconfig += 8 ; if( test_face( test13[_config][4] ) ) _subconfig += 16 ; if( test_face( test13[_config][5] ) ) _subconfig += 32 ; switch( subconfig13[_subconfig] ) { case 0 :/* 13.1 */ add_triangle( tiling13_1[_config], 4 ) ; break ; case 1 :/* 13.2 */ add_triangle( tiling13_2[_config][0], 6 ) ; break ; case 2 :/* 13.2 */ add_triangle( tiling13_2[_config][1], 6 ) ; break ; case 3 :/* 13.2 */ add_triangle( tiling13_2[_config][2], 6 ) ; break ; case 4 :/* 13.2 */ add_triangle( tiling13_2[_config][3], 6 ) ; break ; case 5 :/* 13.2 */ add_triangle( tiling13_2[_config][4], 6 ) ; break ; case 6 :/* 13.2 */ add_triangle( tiling13_2[_config][5], 6 ) ; break ; case 7 :/* 13.3 */ v12 = add_c_vertex() ; add_triangle( tiling13_3[_config][0], 10, v12 ) ; break ; case 8 :/* 13.3 */ v12 = add_c_vertex() ; add_triangle( tiling13_3[_config][1], 10, v12 ) ; break ; case 9 :/* 13.3 */ v12 = add_c_vertex() ; add_triangle( tiling13_3[_config][2], 10, v12 ) ; break ; case 10 :/* 13.3 */ v12 = add_c_vertex() ; add_triangle( tiling13_3[_config][3], 10, v12 ) ; break ; case 11 :/* 13.3 */ v12 = add_c_vertex() ; add_triangle( tiling13_3[_config][4], 10, v12 ) ; break ; case 12 :/* 13.3 */ v12 = add_c_vertex() ; add_triangle( tiling13_3[_config][5], 10, v12 ) ; break ; case 13 :/* 13.3 */ v12 = add_c_vertex() ; add_triangle( tiling13_3[_config][6], 10, v12 ) ; break ; case 14 :/* 13.3 */ v12 = add_c_vertex() ; add_triangle( tiling13_3[_config][7], 10, v12 ) ; break ; case 15 :/* 13.3 */ v12 = add_c_vertex() ; add_triangle( tiling13_3[_config][8], 10, v12 ) ; break ; case 16 :/* 13.3 */ v12 = add_c_vertex() ; add_triangle( tiling13_3[_config][9], 10, v12 ) ; break ; case 17 :/* 13.3 */ v12 = add_c_vertex() ; add_triangle( tiling13_3[_config][10], 10, v12 ) ; break ; case 18 :/* 13.3 */ v12 = add_c_vertex() ; add_triangle( tiling13_3[_config][11], 10, v12 ) ; break ; case 19 :/* 13.4 */ v12 = add_c_vertex() ; add_triangle( tiling13_4[_config][0], 12, v12 ) ; break ; case 20 :/* 13.4 */ v12 = add_c_vertex() ; add_triangle( tiling13_4[_config][1], 12, v12 ) ; break ; case 21 :/* 13.4 */ v12 = add_c_vertex() ; add_triangle( tiling13_4[_config][2], 12, v12 ) ; break ; case 22 :/* 13.4 */ v12 = add_c_vertex() ; add_triangle( tiling13_4[_config][3], 12, v12 ) ; break ; case 23 :/* 13.5 */ _subconfig = 0 ; if( test_interior( test13[_config][6] ) ) add_triangle( tiling13_5_1[_config][0], 6 ) ; else add_triangle( tiling13_5_2[_config][0], 10 ) ; break ; case 24 :/* 13.5 */ _subconfig = 1 ; if( test_interior( test13[_config][6] ) ) add_triangle( tiling13_5_1[_config][1], 6 ) ; else add_triangle( tiling13_5_2[_config][1], 10 ) ; break ; case 25 :/* 13.5 */ _subconfig = 2 ; if( test_interior( test13[_config][6] ) ) add_triangle( tiling13_5_1[_config][2], 6 ) ; else add_triangle( tiling13_5_2[_config][2], 10 ) ; break ; case 26 :/* 13.5 */ _subconfig = 3 ; if( test_interior( test13[_config][6] ) ) add_triangle( tiling13_5_1[_config][3], 6 ) ; else add_triangle( tiling13_5_2[_config][3], 10 ) ; break ; case 27 :/* 13.3 */ v12 = add_c_vertex() ; add_triangle( tiling13_3_[_config][0], 10, v12 ) ; break ; case 28 :/* 13.3 */ v12 = add_c_vertex() ; add_triangle( tiling13_3_[_config][1], 10, v12 ) ; break ; case 29 :/* 13.3 */ v12 = add_c_vertex() ; add_triangle( tiling13_3_[_config][2], 10, v12 ) ; break ; case 30 :/* 13.3 */ v12 = add_c_vertex() ; add_triangle( tiling13_3_[_config][3], 10, v12 ) ; break ; case 31 :/* 13.3 */ v12 = add_c_vertex() ; add_triangle( tiling13_3_[_config][4], 10, v12 ) ; break ; case 32 :/* 13.3 */ v12 = add_c_vertex() ; add_triangle( tiling13_3_[_config][5], 10, v12 ) ; break ; case 33 :/* 13.3 */ v12 = add_c_vertex() ; add_triangle( tiling13_3_[_config][6], 10, v12 ) ; break ; case 34 :/* 13.3 */ v12 = add_c_vertex() ; add_triangle( tiling13_3_[_config][7], 10, v12 ) ; break ; case 35 :/* 13.3 */ v12 = add_c_vertex() ; add_triangle( tiling13_3_[_config][8], 10, v12 ) ; break ; case 36 :/* 13.3 */ v12 = add_c_vertex() ; add_triangle( tiling13_3_[_config][9], 10, v12 ) ; break ; case 37 :/* 13.3 */ v12 = add_c_vertex() ; add_triangle( tiling13_3_[_config][10], 10, v12 ) ; break ; case 38 :/* 13.3 */ v12 = add_c_vertex() ; add_triangle( tiling13_3_[_config][11], 10, v12 ) ; break ; case 39 :/* 13.2 */ add_triangle( tiling13_2_[_config][0], 6 ) ; break ; case 40 :/* 13.2 */ add_triangle( tiling13_2_[_config][1], 6 ) ; break ; case 41 :/* 13.2 */ add_triangle( tiling13_2_[_config][2], 6 ) ; break ; case 42 :/* 13.2 */ add_triangle( tiling13_2_[_config][3], 6 ) ; break ; case 43 :/* 13.2 */ add_triangle( tiling13_2_[_config][4], 6 ) ; break ; case 44 :/* 13.2 */ add_triangle( tiling13_2_[_config][5], 6 ) ; break ; case 45 :/* 13.1 */ add_triangle( tiling13_1_[_config], 4 ) ; break ; default : printf("Marching Cubes: Impossible case 13?\n" ) ; print_cube() ; } break ; case 14 : add_triangle( tiling14[_config], 4 ) ; break ; }; } //_____________________________________________________________________________ //_____________________________________________________________________________ // Adding triangles void MarchingCubes::add_triangle( const char* trig, char n, int v12 ) //----------------------------------------------------------------------------- { int tv[3] ; for( int t = 0 ; t < 3*n ; t++ ) { switch( trig[t] ) { case 0 : tv[ t % 3 ] = get_x_vert( _i , _j , _k ) ; break ; case 1 : tv[ t % 3 ] = get_y_vert(_i+1, _j , _k ) ; break ; case 2 : tv[ t % 3 ] = get_x_vert( _i ,_j+1, _k ) ; break ; case 3 : tv[ t % 3 ] = get_y_vert( _i , _j , _k ) ; break ; case 4 : tv[ t % 3 ] = get_x_vert( _i , _j ,_k+1) ; break ; case 5 : tv[ t % 3 ] = get_y_vert(_i+1, _j ,_k+1) ; break ; case 6 : tv[ t % 3 ] = get_x_vert( _i ,_j+1,_k+1) ; break ; case 7 : tv[ t % 3 ] = get_y_vert( _i , _j ,_k+1) ; break ; case 8 : tv[ t % 3 ] = get_z_vert( _i , _j , _k ) ; break ; case 9 : tv[ t % 3 ] = get_z_vert(_i+1, _j , _k ) ; break ; case 10 : tv[ t % 3 ] = get_z_vert(_i+1,_j+1, _k ) ; break ; case 11 : tv[ t % 3 ] = get_z_vert( _i ,_j+1, _k ) ; break ; case 12 : tv[ t % 3 ] = v12 ; break ; default : break ; } if( tv[t%3] == -1 ) { printf("Marching Cubes: invalid triangle %d\n", _ntrigs+1) ; print_cube() ; } if( t%3 == 2 ) { if( _ntrigs >= _Ntrigs ) { Triangle *temp = _triangles ; _triangles = new Triangle[ 2*_Ntrigs ] ; memcpy( _triangles, temp, _Ntrigs*sizeof(Triangle) ) ; delete[] temp ; printf("%d allocated triangles\n", _Ntrigs) ; _Ntrigs *= 2 ; } Triangle *T = _triangles + _ntrigs++ ; T->v1 = tv[0] ; T->v2 = tv[1] ; T->v3 = tv[2] ; } } } //_____________________________________________________________________________ //_____________________________________________________________________________ // Calculating gradient real MarchingCubes::get_x_grad( const int i, const int j, const int k ) const //----------------------------------------------------------------------------- { if( i > 0 ) { if ( i < _size_x - 1 ) return ( get_data( i+1, j, k ) - get_data( i-1, j, k ) ) / 2 ; else return get_data( i, j, k ) - get_data( i-1, j, k ) ; } else return get_data( i+1, j, k ) - get_data( i, j, k ) ; } //----------------------------------------------------------------------------- real MarchingCubes::get_y_grad( const int i, const int j, const int k ) const //----------------------------------------------------------------------------- { if( j > 0 ) { if ( j < _size_y - 1 ) return ( get_data( i, j+1, k ) - get_data( i, j-1, k ) ) / 2 ; else return get_data( i, j, k ) - get_data( i, j-1, k ) ; } else return get_data( i, j+1, k ) - get_data( i, j, k ) ; } //----------------------------------------------------------------------------- real MarchingCubes::get_z_grad( const int i, const int j, const int k ) const //----------------------------------------------------------------------------- { if( k > 0 ) { if ( k < _size_z - 1 ) return ( get_data( i, j, k+1 ) - get_data( i, j, k-1 ) ) / 2 ; else return get_data( i, j, k ) - get_data( i, j, k-1 ) ; } else return get_data( i, j, k+1 ) - get_data( i, j, k ) ; } //_____________________________________________________________________________ //_____________________________________________________________________________ // Adding vertices void MarchingCubes::test_vertex_addition() { if( _nverts >= _Nverts ) { Vertex *temp = _vertices ; _vertices = new Vertex[ _Nverts*2 ] ; memcpy( _vertices, temp, _Nverts*sizeof(Vertex) ) ; delete[] temp ; printf("%d allocated vertices\n", _Nverts) ; _Nverts *= 2 ; } } int MarchingCubes::add_x_vertex( ) //----------------------------------------------------------------------------- { test_vertex_addition() ; Vertex *vert = _vertices + _nverts++ ; real u = ( _cube[0] ) / ( _cube[0] - _cube[1] ) ; vert->x = (real)_i+u; vert->y = (real) _j ; vert->z = (real) _k ; vert->nx = (1-u)*get_x_grad(_i,_j,_k) + u*get_x_grad(_i+1,_j,_k) ; vert->ny = (1-u)*get_y_grad(_i,_j,_k) + u*get_y_grad(_i+1,_j,_k) ; vert->nz = (1-u)*get_z_grad(_i,_j,_k) + u*get_z_grad(_i+1,_j,_k) ; u = (real) sqrt( vert->nx * vert->nx + vert->ny * vert->ny +vert->nz * vert->nz ) ; if( u > 0 ) { vert->nx /= u ; vert->ny /= u ; vert->nz /= u ; } return _nverts-1 ; } //----------------------------------------------------------------------------- int MarchingCubes::add_y_vertex( ) //----------------------------------------------------------------------------- { test_vertex_addition() ; Vertex *vert = _vertices + _nverts++ ; real u = ( _cube[0] ) / ( _cube[0] - _cube[3] ) ; vert->x = (real) _i ; vert->y = (real)_j+u; vert->z = (real) _k ; vert->nx = (1-u)*get_x_grad(_i,_j,_k) + u*get_x_grad(_i,_j+1,_k) ; vert->ny = (1-u)*get_y_grad(_i,_j,_k) + u*get_y_grad(_i,_j+1,_k) ; vert->nz = (1-u)*get_z_grad(_i,_j,_k) + u*get_z_grad(_i,_j+1,_k) ; u = (real) sqrt( vert->nx * vert->nx + vert->ny * vert->ny +vert->nz * vert->nz ) ; if( u > 0 ) { vert->nx /= u ; vert->ny /= u ; vert->nz /= u ; } return _nverts-1 ; } //----------------------------------------------------------------------------- int MarchingCubes::add_z_vertex( ) //----------------------------------------------------------------------------- { test_vertex_addition() ; Vertex *vert = _vertices + _nverts++ ; real u = ( _cube[0] ) / ( _cube[0] - _cube[4] ) ; vert->x = (real) _i ; vert->y = (real) _j ; vert->z = (real)_k+u; vert->nx = (1-u)*get_x_grad(_i,_j,_k) + u*get_x_grad(_i,_j,_k+1) ; vert->ny = (1-u)*get_y_grad(_i,_j,_k) + u*get_y_grad(_i,_j,_k+1) ; vert->nz = (1-u)*get_z_grad(_i,_j,_k) + u*get_z_grad(_i,_j,_k+1) ; u = (real) sqrt( vert->nx * vert->nx + vert->ny * vert->ny +vert->nz * vert->nz ) ; if( u > 0 ) { vert->nx /= u ; vert->ny /= u ; vert->nz /= u ; } return _nverts-1 ; } int MarchingCubes::add_c_vertex( ) //----------------------------------------------------------------------------- { test_vertex_addition() ; Vertex *vert = _vertices + _nverts++ ; real u = 0 ; int vid ; vert->x = vert->y = vert->z = vert->nx = vert->ny = vert->nz = 0 ; // Computes the average of the intersection points of the cube vid = get_x_vert( _i , _j , _k ) ; if( vid != -1 ) { ++u ; const Vertex &v = _vertices[vid] ; vert->x += v.x ; vert->y += v.y ; vert->z += v.z ; vert->nx += v.nx ; vert->ny += v.ny ; vert->nz += v.nz ; } vid = get_y_vert(_i+1, _j , _k ) ; if( vid != -1 ) { ++u ; const Vertex &v = _vertices[vid] ; vert->x += v.x ; vert->y += v.y ; vert->z += v.z ; vert->nx += v.nx ; vert->ny += v.ny ; vert->nz += v.nz ; } vid = get_x_vert( _i ,_j+1, _k ) ; if( vid != -1 ) { ++u ; const Vertex &v = _vertices[vid] ; vert->x += v.x ; vert->y += v.y ; vert->z += v.z ; vert->nx += v.nx ; vert->ny += v.ny ; vert->nz += v.nz ; } vid = get_y_vert( _i , _j , _k ) ; if( vid != -1 ) { ++u ; const Vertex &v = _vertices[vid] ; vert->x += v.x ; vert->y += v.y ; vert->z += v.z ; vert->nx += v.nx ; vert->ny += v.ny ; vert->nz += v.nz ; } vid = get_x_vert( _i , _j ,_k+1) ; if( vid != -1 ) { ++u ; const Vertex &v = _vertices[vid] ; vert->x += v.x ; vert->y += v.y ; vert->z += v.z ; vert->nx += v.nx ; vert->ny += v.ny ; vert->nz += v.nz ; } vid = get_y_vert(_i+1, _j ,_k+1) ; if( vid != -1 ) { ++u ; const Vertex &v = _vertices[vid] ; vert->x += v.x ; vert->y += v.y ; vert->z += v.z ; vert->nx += v.nx ; vert->ny += v.ny ; vert->nz += v.nz ; } vid = get_x_vert( _i ,_j+1,_k+1) ; if( vid != -1 ) { ++u ; const Vertex &v = _vertices[vid] ; vert->x += v.x ; vert->y += v.y ; vert->z += v.z ; vert->nx += v.nx ; vert->ny += v.ny ; vert->nz += v.nz ; } vid = get_y_vert( _i , _j ,_k+1) ; if( vid != -1 ) { ++u ; const Vertex &v = _vertices[vid] ; vert->x += v.x ; vert->y += v.y ; vert->z += v.z ; vert->nx += v.nx ; vert->ny += v.ny ; vert->nz += v.nz ; } vid = get_z_vert( _i , _j , _k ) ; if( vid != -1 ) { ++u ; const Vertex &v = _vertices[vid] ; vert->x += v.x ; vert->y += v.y ; vert->z += v.z ; vert->nx += v.nx ; vert->ny += v.ny ; vert->nz += v.nz ; } vid = get_z_vert(_i+1, _j , _k ) ; if( vid != -1 ) { ++u ; const Vertex &v = _vertices[vid] ; vert->x += v.x ; vert->y += v.y ; vert->z += v.z ; vert->nx += v.nx ; vert->ny += v.ny ; vert->nz += v.nz ; } vid = get_z_vert(_i+1,_j+1, _k ) ; if( vid != -1 ) { ++u ; const Vertex &v = _vertices[vid] ; vert->x += v.x ; vert->y += v.y ; vert->z += v.z ; vert->nx += v.nx ; vert->ny += v.ny ; vert->nz += v.nz ; } vid = get_z_vert( _i ,_j+1, _k ) ; if( vid != -1 ) { ++u ; const Vertex &v = _vertices[vid] ; vert->x += v.x ; vert->y += v.y ; vert->z += v.z ; vert->nx += v.nx ; vert->ny += v.ny ; vert->nz += v.nz ; } vert->x /= u ; vert->y /= u ; vert->z /= u ; u = (real) sqrt( vert->nx * vert->nx + vert->ny * vert->ny +vert->nz * vert->nz ) ; if( u > 0 ) { vert->nx /= u ; vert->ny /= u ; vert->nz /= u ; } return _nverts-1 ; } //_____________________________________________________________________________ //_____________________________________________________________________________ //_____________________________________________________________________________ //_____________________________________________________________________________ // Grid exportation void MarchingCubes::writeISO(const char *fn ) //----------------------------------------------------------------------------- { unsigned char buf[sizeof(float)] ; FILE *fp = fopen( fn, "wb" ) ; // header * (int*) buf = _size_x ; fwrite(buf, sizeof(float), 1, fp); * (int*) buf = _size_y ; fwrite(buf, sizeof(float), 1, fp); * (int*) buf = _size_z ; fwrite(buf, sizeof(float), 1, fp); * (float*) buf = -1.0f ; fwrite(buf, sizeof(float), 1, fp); * (float*) buf = 1.0f ; fwrite(buf, sizeof(float), 1, fp); * (float*) buf = -1.0f ; fwrite(buf, sizeof(float), 1, fp); * (float*) buf = 1.0f ; fwrite(buf, sizeof(float), 1, fp); * (float*) buf = -1.0f ; fwrite(buf, sizeof(float), 1, fp); * (float*) buf = 1.0f ; fwrite(buf, sizeof(float), 1, fp); for( int i = 0 ; i < _size_x ; i++ ) { for( int j = 0 ; j < _size_y ; j++ ) { for( int k = 0 ; k < _size_z ; k++ ) { * (float*) buf = (float)get_data( i,j,k ) ; fwrite(buf, sizeof(float), 1, fp); } } } fclose(fp) ; } //_____________________________________________________________________________ //_____________________________________________________________________________ // PLY exportation void MarchingCubes::writePLY(const char *fn, bool bin ) //----------------------------------------------------------------------------- { typedef struct PlyFace { unsigned char nverts; /* number of Vertex indices in list */ int *verts; /* Vertex index list */ } PlyFace; PlyProperty vert_props[] = { /* list of property information for a PlyVertex */ {"x", Float32, Float32, offsetof( Vertex,x ), 0, 0, 0, 0}, {"y", Float32, Float32, offsetof( Vertex,y ), 0, 0, 0, 0}, {"z", Float32, Float32, offsetof( Vertex,z ), 0, 0, 0, 0}, {"nx", Float32, Float32, offsetof( Vertex,nx ), 0, 0, 0, 0}, {"ny", Float32, Float32, offsetof( Vertex,ny ), 0, 0, 0, 0}, {"nz", Float32, Float32, offsetof( Vertex,nz ), 0, 0, 0, 0} }; PlyProperty face_props[] = { /* list of property information for a PlyFace */ {"vertex_indices", Int32, Int32, offsetof( PlyFace,verts ), 1, Uint8, Uint8, offsetof( PlyFace,nverts )}, }; PlyFile *ply; FILE *fp = fopen( fn, "w" ); int i ; PlyFace face ; int verts[3] ; char *elem_names[] = { "vertex", "face" }; printf("Marching Cubes::writePLY(%s)...", fn ) ; ply = write_ply ( fp, 2, elem_names, bin? PLY_BINARY_LE : PLY_ASCII ); /* describe what properties go into the PlyVertex elements */ describe_element_ply ( ply, "vertex", _nverts ); describe_property_ply ( ply, &vert_props[0] ); describe_property_ply ( ply, &vert_props[1] ); describe_property_ply ( ply, &vert_props[2] ); describe_property_ply ( ply, &vert_props[3] ); describe_property_ply ( ply, &vert_props[4] ); describe_property_ply ( ply, &vert_props[5] ); /* describe PlyFace properties (just list of PlyVertex indices) */ describe_element_ply ( ply, "face", _ntrigs ); describe_property_ply ( ply, &face_props[0] ); header_complete_ply ( ply ); /* set up and write the PlyVertex elements */ put_element_setup_ply ( ply, "vertex" ); for ( i = 0; i < _nverts; i++ ) put_element_ply ( ply, ( void * ) &(_vertices[i]) ); printf(" %d vertices written\n", _nverts ) ; /* set up and write the PlyFace elements */ put_element_setup_ply ( ply, "face" ); face.nverts = 3 ; face.verts = verts ; for ( i = 0; i < _ntrigs; i++ ) { face.verts[0] = _triangles[i].v1 ; face.verts[1] = _triangles[i].v2 ; face.verts[2] = _triangles[i].v3 ; put_element_ply ( ply, ( void * ) &face ); } printf(" %d triangles written\n", _ntrigs ) ; close_ply ( ply ); free_ply ( ply ); fclose( fp ) ; } //_____________________________________________________________________________ //_____________________________________________________________________________ // PLY importation void MarchingCubes::readPLY(const char *fn ) //----------------------------------------------------------------------------- { typedef struct PlyFace { unsigned char nverts; /* number of Vertex indices in list */ int *verts; /* Vertex index list */ } PlyFace; PlyProperty vert_props[] = { /* list of property information for a PlyVertex */ {"x", Float32, Float32, offsetof( Vertex,x ), 0, 0, 0, 0}, {"y", Float32, Float32, offsetof( Vertex,y ), 0, 0, 0, 0}, {"z", Float32, Float32, offsetof( Vertex,z ), 0, 0, 0, 0}, {"nx", Float32, Float32, offsetof( Vertex,nx ), 0, 0, 0, 0}, {"ny", Float32, Float32, offsetof( Vertex,ny ), 0, 0, 0, 0}, {"nz", Float32, Float32, offsetof( Vertex,nz ), 0, 0, 0, 0} }; PlyProperty face_props[] = { /* list of property information for a PlyFace */ {"vertex_indices", Int32, Int32, offsetof( PlyFace,verts ), 1, Uint8, Uint8, offsetof( PlyFace,nverts )}, }; FILE *fp = fopen( fn, "r" ); if( !fp ) return ; PlyFile *ply = read_ply ( fp ); printf("Marching Cubes::readPLY(%s)...", fn ) ; //----------------------------------------------------------------------------- // gets the number of faces and vertices for ( int i = 0; i < ply->num_elem_types; ++i ) { int elem_count ; char *elem_name = setup_element_read_ply ( ply, i, &elem_count ); if ( equal_strings ( "vertex", elem_name ) ) _Nverts = _nverts = elem_count; if ( equal_strings ( "face", elem_name ) ) _Ntrigs = _ntrigs = elem_count; } delete [] _vertices ; _vertices = new Vertex [_Nverts] ; delete [] _triangles ; _triangles = new Triangle[_Ntrigs] ; //----------------------------------------------------------------------------- /* examine each element type that is in the file (PlyVertex, PlyFace) */ for ( int i = 0; i < ply->num_elem_types; ++i ) { /* prepare to read the i'th list of elements */ int elem_count ; char *elem_name = setup_element_read_ply ( ply, i, &elem_count ); //----------------------------------------------------------------------------- if ( equal_strings ( "vertex", elem_name ) ) { /* set up for getting PlyVertex elements */ setup_property_ply ( ply, &vert_props[0] ); setup_property_ply ( ply, &vert_props[1] ); setup_property_ply ( ply, &vert_props[2] ); setup_property_ply ( ply, &vert_props[3] ); setup_property_ply ( ply, &vert_props[4] ); setup_property_ply ( ply, &vert_props[5] ); for ( int j = 0; j < _nverts; ++j ) { get_element_ply ( ply, ( void * ) (_vertices + j) ); } printf(" %d vertices read\n", _nverts ) ; } //----------------------------------------------------------------------------- else if ( equal_strings ( "face", elem_name ) ) { /* set up for getting PlyFace elements */ /* (all we need are PlyVertex indices) */ setup_property_ply ( ply, &face_props[0] ) ; PlyFace face ; for ( int j = 0; j < _ntrigs; ++j ) { get_element_ply ( ply, ( void * ) &face ); if( face.nverts != 3 ) { printf( "not a triangulated surface: polygon %d has %d sides\n", j, face.nverts ) ; return ; } _triangles[j].v1 = face.verts[0] ; _triangles[j].v2 = face.verts[1] ; _triangles[j].v3 = face.verts[2] ; free( face.verts ) ; } printf(" %d triangles read\n", _ntrigs ) ; } //----------------------------------------------------------------------------- //----------------------------------------------------------------------------- else /* all non-PlyVertex and non-PlyFace elements are grabbed here */ get_other_element_ply ( ply ); //----------------------------------------------------------------------------- } close_ply ( ply ); free_ply ( ply ); // fit_to_bbox() ; fclose( fp ) ; } //_____________________________________________________________________________ //_____________________________________________________________________________ // Open Inventor / VRML 1.0 ascii exportation void MarchingCubes::writeIV(const char *fn ) //----------------------------------------------------------------------------- { FILE *fp = fopen( fn, "w" ) ; int i ; printf("Marching Cubes::exportIV(%s)...", fn) ; fprintf( fp, "#Inventor V2.1 ascii \n\nSeparator { \n ShapeHints {\n vertexOrdering COUNTERCLOCKWISE\n shapeType UNKNOWN_SHAPE_TYPE\n creaseAngle 0.0\n }\n Coordinate3 { \n point [ \n" ) ; for ( i = 0; i < _nverts; i++ ) fprintf( fp, " %f %f %f,\n", _vertices[i].x, _vertices[i].y, _vertices[i].z ) ; printf(" %d vertices written\n", _nverts ) ; fprintf( fp, "\n ] \n} \nNormal { \nvector [ \n" ) ; for ( i = 0; i < _nverts; i++ ) fprintf( fp, " %f %f %f,\n", _vertices[i].nx, _vertices[i].ny, _vertices[i].nz ) ; fprintf( fp, "\n ] \n} \nIndexedFaceSet { \ncoordIndex [ \n" ) ; for ( i = 0; i < _ntrigs; i++ ) fprintf( fp, "%d, %d, %d, -1,\n", _triangles[i].v1, _triangles[i].v2, _triangles[i].v3 ) ; fprintf( fp, " ] \n } \n } \n" ) ; fclose( fp ) ; printf(" %d triangles written\n", _ntrigs ) ; } //_____________________________________________________________________________ scikit-image-0.13.1/skimage/measure/mc_meta/README.md000066400000000000000000000007111316256236300221150ustar00rootroot00000000000000This directory contains meta-files related to the Lewiner marching cubes algorithm. These are not used by the algorithm, but can be convenient for development/maintenance: * MarchingCubes.cpp - the original algorithm, this is ported to Cython * LookupTable.h - the original LUTs, these are ported to Python * createluts.py - scrip to generate Python luts from the .h file * visual_test.py - script to compare visual results of marchingcubes algorithms scikit-image-0.13.1/skimage/measure/mc_meta/createluts.py000066400000000000000000000111161316256236300233640ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Create lookup tables for the marching cubes algorithm, by parsing the file "LookUpTable.h". This prints a text to the stdout wich can then be copied to luts.py. The luts are tuples of shape and base64 encoded bytes. """ import sys import base64 # Get base64 encode/decode functions if sys.version_info >= (3, ): base64encode = base64.encodebytes base64decode = base64.decodebytes else: base64encode = base64.encodestring base64decode = base64.decodestring def create_luts(fname): # Get the lines in the C header file text = open(fname,'rb').read().decode('utf-8') lines1 = [line.rstrip() for line in text.splitlines()] # Init lines for Python lines2 = [] # Get classic table more_lines, ii = get_table(lines1, 'static const char casesClassic', 0) lines2.extend(more_lines) # Get cases table more_lines, ii = get_table(lines1, 'static const char cases', 0) lines2.extend(more_lines) # Get tiling tables ii = 0 for casenr in range(99): # Get table more_lines, ii = get_table(lines1, 'static const char tiling', ii+1) if ii < 0: break else: lines2.extend(more_lines) # Get test tables ii = 0 for casenr in range(99): # Get table more_lines, ii = get_table(lines1, 'static const char test', ii+1) if ii < 0: break else: lines2.extend(more_lines) # Get subconfig tables ii = 0 for casenr in range(99): # Get table more_lines, ii = get_table(lines1, 'static const char subconfig', ii+1) if ii < 0: break else: lines2.extend(more_lines) return '\n'.join(lines2) def get_table(lines1, needle, i): # Try to find the start ii = search_line(lines1, needle, i) if ii < 0: return [], -1 # Init result lines2 = [] # Get size and name front, dummu, back = lines1[ii].partition('[') name = front.split(' ')[-1].upper() size = int(back.split(']',1)[0]) cdes = lines1[ii].rstrip(' {=') # Write name lines2.append('%s = np.array([' % name) # Get elements for i in range(ii+1, ii+1+9999999): line1 = lines1[i] front, dummy, back = line1.partition('*/') if not back: front, back = back, front line2 = ' ' line2 += back.strip().replace('{', '[').replace('}',']').replace(';','') line2 += front.replace('/*',' #').rstrip() lines2.append(line2) if line1.endswith('};'): break # Close and return lines2.append(" , 'int8')") lines2.append('') #return lines2, ii+size # Execute code and get array as base64 text code = '\n'.join(lines2) code = code.split('=',1)[1] array = eval(code) array64 = base64encode(array.tostring()).decode('utf-8') # Reverse: bytes = base64decode(text.encode('utf-8')) text = '%s = %s, """\n%s"""' % (name, str(array.shape), array64) # Build actual lines lines2 = [] #lines2.append( '# %s -> %s %s' % (cdes, str(array.dtype), str(array.shape)) ) lines2.append( '#' + cdes) lines2.append(text) lines2.append('') return lines2, ii+size def search_line(lines, refline, start=0): for i, line in enumerate(lines[start:]): if line.startswith(refline): return i + start return -1 def getLutNames(prefix): aa = [] for a in dir(luts): if a.startswith(prefix): aa.append(a) def sortkey(x): fullnr = x.split(prefix)[1] nr, us, subnr = fullnr.partition('_') if len(nr) == 1: nr = '0'+nr return nr + us + subnr return [a for a in sorted(aa, key=sortkey)] if __name__ == '__main__': import os fname = os.path.join(os.getcwd(), 'LookUpTable.h') if True: with open(os.path.join(os.getcwd(), 'mcluts.py'), 'w') as f: f.write('# -*- coding: utf-8 -*-\n') f.write('# Copyright (C) 2012, Almar Klein\n# Copyright (C) 2002, Thomas Lewiner\n\n') f.write('# This file was auto-generated from LookUpTable.h by createluts.py.\n\n') f.write(create_luts(fname)) else: for prefix in ['TILING', 'TEST']: tmp = ['luts.'+a for a in getLutNames(prefix)] print(', '.join(tmp)) print('') for name in getLutNames(prefix): print('self.%s = Lut(%s)' % (name, name)) print('') for name in getLutNames(prefix): print('cdef Lut %s' % name) print('') print('') scikit-image-0.13.1/skimage/measure/mc_meta/visual_test.py000066400000000000000000000045271316256236300235630ustar00rootroot00000000000000""" Script to show the results of the two marching cubes algorithms on different data. """ import time import numpy as np import visvis as vv from skimage.measure import marching_cubes_classic, marching_cubes_lewiner from skimage.draw import ellipsoid # Create test volume SELECT = 3 gradient_dir = 'descent' # ascent or descent if SELECT == 1: # Medical data vol = vv.volread('stent') isovalue = 800 elif SELECT == 2: # Blocky data vol = vv.aVolume(20, 128) # Different every time isovalue = 0.2 elif SELECT == 3: # Generate two donuts using a formula by Thomas Lewiner n = 48 a, b = 2.5 / n, -1.25 isovalue = 0.0 # vol = np.empty((n, n, n), 'float32') for iz in range(vol.shape[0]): for iy in range(vol.shape[1]): for ix in range(vol.shape[2]): z, y, x = float(iz)*a+b, float(iy)*a+b, float(ix)*a+b vol[iz, iy, ix] = ( ( (8*x)**2 + (8*y-2)**2 + (8*z)**2 + 16 - 1.85*1.85 ) * ( (8*x)**2 + (8*y-2)**2 + (8*z)**2 + 16 - 1.85*1.85 ) - 64 * ( (8*x)**2 + (8*y-2)**2 ) ) * ( ( (8*x)**2 + ((8*y-2)+4)*((8*y-2)+4) + (8*z)**2 + 16 - 1.85*1.85 ) * ( (8*x)**2 + ((8*y-2)+4)*((8*y-2)+4) + (8*z)**2 + 16 - 1.85*1.85 ) - 64 * ( ((8*y-2)+4)*((8*y-2)+4) + (8*z)**2 ) ) + 1025 # Uncommenting the line below will yield different results for classic MC #vol = -vol elif SELECT == 4: vol = ellipsoid(4, 3, 2, levelset=True) isovalue = 0 # Get surface meshes t0 = time.time() vertices1, faces1, *_ = marching_cubes_lewiner(vol, isovalue, gradient_direction=gradient_dir, use_classic=False) print('finding surface lewiner took %1.0f ms' % (1000*(time.time()-t0)) ) t0 = time.time() vertices2, faces2, *_ = marching_cubes_classic(vol, isovalue, gradient_direction=gradient_dir) print('finding surface classic took %1.0f ms' % (1000*(time.time()-t0)) ) # Show vv.figure(1); vv.clf() a1 = vv.subplot(121); m1 = vv.mesh(np.fliplr(vertices1), faces1) a2 = vv.subplot(122); m2 = vv.mesh(np.fliplr(vertices2), faces2) a1.camera = a2.camera # visvis uses right-hand rule, gradient_direction param uses left-hand rule m1.cullFaces = m2.cullFaces = 'front' # None, front or back vv.use().Run() scikit-image-0.13.1/skimage/measure/pnpoly.py000066400000000000000000000025031316256236300211250ustar00rootroot00000000000000from ._pnpoly import _grid_points_in_poly, _points_in_poly def grid_points_in_poly(shape, verts): """Test whether points on a specified grid are inside a polygon. For each ``(r, c)`` coordinate on a grid, i.e. ``(0, 0)``, ``(0, 1)`` etc., test whether that point lies inside a polygon. Parameters ---------- shape : tuple (M, N) Shape of the grid. verts : (V, 2) array Specify the V vertices of the polygon, sorted either clockwise or anti-clockwise. The first point may (but does not need to be) duplicated. See Also -------- points_in_poly Returns ------- mask : (M, N) ndarray of bool True where the grid falls inside the polygon. """ return _grid_points_in_poly(shape, verts) def points_in_poly(points, verts): """Test whether points lie inside a polygon. Parameters ---------- points : (N, 2) array Input points, ``(x, y)``. verts : (M, 2) array Vertices of the polygon, sorted either clockwise or anti-clockwise. The first point may (but does not need to be) duplicated. See Also -------- grid_points_in_poly Returns ------- mask : (N,) array of bool True if corresponding point is inside the polygon. """ return _points_in_poly(points, verts) scikit-image-0.13.1/skimage/measure/profile.py000066400000000000000000000112171316256236300212460ustar00rootroot00000000000000import numpy as np from scipy import ndimage as ndi def profile_line(img, src, dst, linewidth=1, order=1, mode='constant', cval=0.0): """Return the intensity profile of an image measured along a scan line. Parameters ---------- img : numeric array, shape (M, N[, C]) The image, either grayscale (2D array) or multichannel (3D array, where the final axis contains the channel information). src : 2-tuple of numeric scalar (float or int) The start point of the scan line. dst : 2-tuple of numeric scalar (float or int) The end point of the scan line. The destination point is *included* in the profile, in constrast to standard numpy indexing. linewidth : int, optional Width of the scan, perpendicular to the line order : int in {0, 1, 2, 3, 4, 5}, optional The order of the spline interpolation to compute image values at non-integer coordinates. 0 means nearest-neighbor interpolation. mode : {'constant', 'nearest', 'reflect', 'mirror', 'wrap'}, optional How to compute any values falling outside of the image. cval : float, optional If `mode` is 'constant', what constant value to use outside the image. Returns ------- return_value : array The intensity profile along the scan line. The length of the profile is the ceil of the computed length of the scan line. Examples -------- >>> x = np.array([[1, 1, 1, 2, 2, 2]]) >>> img = np.vstack([np.zeros_like(x), x, x, x, np.zeros_like(x)]) >>> img array([[0, 0, 0, 0, 0, 0], [1, 1, 1, 2, 2, 2], [1, 1, 1, 2, 2, 2], [1, 1, 1, 2, 2, 2], [0, 0, 0, 0, 0, 0]]) >>> profile_line(img, (2, 1), (2, 4)) array([ 1., 1., 2., 2.]) >>> profile_line(img, (1, 0), (1, 6), cval=4) array([ 1., 1., 1., 2., 2., 2., 4.]) The destination point is included in the profile, in contrast to standard numpy indexing. For example: >>> profile_line(img, (1, 0), (1, 6)) # The final point is out of bounds array([ 1., 1., 1., 2., 2., 2., 0.]) >>> profile_line(img, (1, 0), (1, 5)) # This accesses the full first row array([ 1., 1., 1., 2., 2., 2.]) """ perp_lines = _line_profile_coordinates(src, dst, linewidth=linewidth) if img.ndim == 3: pixels = [ndi.map_coordinates(img[..., i], perp_lines, order=order, mode=mode, cval=cval) for i in range(img.shape[2])] pixels = np.transpose(np.asarray(pixels), (1, 2, 0)) else: pixels = ndi.map_coordinates(img, perp_lines, order=order, mode=mode, cval=cval) intensities = pixels.mean(axis=1) return intensities def _line_profile_coordinates(src, dst, linewidth=1): """Return the coordinates of the profile of an image along a scan line. Parameters ---------- src : 2-tuple of numeric scalar (float or int) The start point of the scan line. dst : 2-tuple of numeric scalar (float or int) The end point of the scan line. linewidth : int, optional Width of the scan, perpendicular to the line Returns ------- coords : array, shape (2, N, C), float The coordinates of the profile along the scan line. The length of the profile is the ceil of the computed length of the scan line. Notes ----- This is a utility method meant to be used internally by skimage functions. The destination point is included in the profile, in contrast to standard numpy indexing. """ src_row, src_col = src = np.asarray(src, dtype=float) dst_row, dst_col = dst = np.asarray(dst, dtype=float) d_row, d_col = dst - src theta = np.arctan2(d_row, d_col) length = np.ceil(np.hypot(d_row, d_col) + 1) # we add one above because we include the last point in the profile # (in contrast to standard numpy indexing) line_col = np.linspace(src_col, dst_col, length) line_row = np.linspace(src_row, dst_row, length) # we subtract 1 from linewidth to change from pixel-counting # (make this line 3 pixels wide) to point distances (the # distance between pixel centers) col_width = (linewidth - 1) * np.sin(-theta) / 2 row_width = (linewidth - 1) * np.cos(theta) / 2 perp_rows = np.array([np.linspace(row_i - row_width, row_i + row_width, linewidth) for row_i in line_row]) perp_cols = np.array([np.linspace(col_i - col_width, col_i + col_width, linewidth) for col_i in line_col]) return np.array([perp_rows, perp_cols]) scikit-image-0.13.1/skimage/measure/setup.py000066400000000000000000000040041316256236300207420ustar00rootroot00000000000000#!/usr/bin/env python from skimage._build import cython import os base_path = os.path.abspath(os.path.dirname(__file__)) def configuration(parent_package='', top_path=None): from numpy.distutils.misc_util import Configuration, get_numpy_include_dirs config = Configuration('measure', parent_package, top_path) config.add_data_dir('tests') cython(['_ccomp.pyx'], working_path=base_path) cython(['_find_contours_cy.pyx'], working_path=base_path) cython(['_moments_cy.pyx'], working_path=base_path) cython(['_marching_cubes_classic_cy.pyx'], working_path=base_path) cython(['_marching_cubes_lewiner_cy.pyx'], working_path=base_path) cython(['_pnpoly.pyx'], working_path=base_path) config.add_extension('_ccomp', sources=['_ccomp.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension('_find_contours_cy', sources=['_find_contours_cy.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension('_moments_cy', sources=['_moments_cy.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension('_marching_cubes_classic_cy', sources=['_marching_cubes_classic_cy.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension('_marching_cubes_lewiner_cy', sources=['_marching_cubes_lewiner_cy.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension('_pnpoly', sources=['_pnpoly.c'], include_dirs=[get_numpy_include_dirs(), '../_shared']) return config if __name__ == '__main__': from numpy.distutils.core import setup setup(maintainer='scikit-image Developers', maintainer_email='scikit-image@python.org', description='Graph-based Image-processing Algorithms', url='https://github.com/scikit-image/scikit-image', license='Modified BSD', **(configuration(top_path='').todict()) ) scikit-image-0.13.1/skimage/measure/simple_metrics.py000066400000000000000000000103321316256236300226220ustar00rootroot00000000000000from __future__ import division import numpy as np from ..util.dtype import dtype_range from .._shared.utils import skimage_deprecation, warn __all__ = ['compare_mse', 'compare_nrmse', 'compare_psnr', ] def _assert_compatible(im1, im2): """Raise an error if the shape and dtype do not match.""" if not im1.dtype == im2.dtype: raise ValueError('Input images must have the same dtype.') if not im1.shape == im2.shape: raise ValueError('Input images must have the same dimensions.') return def _as_floats(im1, im2): """Promote im1, im2 to nearest appropriate floating point precision.""" float_type = np.result_type(im1.dtype, im2.dtype, np.float32) if im1.dtype != float_type: im1 = im1.astype(float_type) if im2.dtype != float_type: im2 = im2.astype(float_type) return im1, im2 def compare_mse(im1, im2): """Compute the mean-squared error between two images. Parameters ---------- im1, im2 : ndarray Image. Any dimensionality. Returns ------- mse : float The mean-squared error (MSE) metric. """ _assert_compatible(im1, im2) im1, im2 = _as_floats(im1, im2) return np.mean(np.square(im1 - im2), dtype=np.float64) def compare_nrmse(im_true, im_test, norm_type='Euclidean'): """Compute the normalized root mean-squared error (NRMSE) between two images. Parameters ---------- im_true : ndarray Ground-truth image. im_test : ndarray Test image. norm_type : {'Euclidean', 'min-max', 'mean'} Controls the normalization method to use in the denominator of the NRMSE. There is no standard method of normalization across the literature [1]_. The methods available here are as follows: - 'Euclidean' : normalize by the Euclidean norm of ``im_true``. - 'min-max' : normalize by the intensity range of ``im_true``. - 'mean' : normalize by the mean of ``im_true``. Returns ------- nrmse : float The NRMSE metric. References ---------- .. [1] https://en.wikipedia.org/wiki/Root-mean-square_deviation """ _assert_compatible(im_true, im_test) im_true, im_test = _as_floats(im_true, im_test) norm_type = norm_type.lower() if norm_type == 'euclidean': denom = np.sqrt(np.mean((im_true*im_true), dtype=np.float64)) elif norm_type == 'min-max': denom = im_true.max() - im_true.min() elif norm_type == 'mean': denom = im_true.mean() else: raise ValueError("Unsupported norm_type") return np.sqrt(compare_mse(im_true, im_test)) / denom def compare_psnr(im_true, im_test, data_range=None, dynamic_range=None): """ Compute the peak signal to noise ratio (PSNR) for an image. Parameters ---------- im_true : ndarray Ground-truth image. im_test : ndarray Test image. data_range : int The data range of the input image (distance between minimum and maximum possible values). By default, this is estimated from the image data-type. Returns ------- psnr : float The PSNR metric. References ---------- .. [1] https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio """ _assert_compatible(im_true, im_test) if dynamic_range is not None: warn('`dynamic_range` has been deprecated in favor of ' '`data_range`. The `dynamic_range` keyword argument ' 'will be removed in v0.14', skimage_deprecation) data_range = dynamic_range if data_range is None: dmin, dmax = dtype_range[im_true.dtype.type] true_min, true_max = np.min(im_true), np.max(im_true) if true_max > dmax or true_min < dmin: raise ValueError( "im_true has intensity values outside the range expected for " "its data type. Please manually specify the data_range") if true_min >= 0: # most common case (255 for uint8, 1 for float) data_range = dmax else: data_range = dmax - dmin im_true, im_test = _as_floats(im_true, im_test) err = compare_mse(im_true, im_test) return 10 * np.log10((data_range ** 2) / err) scikit-image-0.13.1/skimage/measure/tests/000077500000000000000000000000001316256236300203745ustar00rootroot00000000000000scikit-image-0.13.1/skimage/measure/tests/__init__.py000066400000000000000000000001771316256236300225120ustar00rootroot00000000000000from ..._shared.testing import setup_test, teardown_test def setup(): setup_test() def teardown(): teardown_test() scikit-image-0.13.1/skimage/measure/tests/test_block.py000066400000000000000000000052721316256236300231050ustar00rootroot00000000000000import numpy as np from numpy.testing import assert_equal, assert_raises from skimage.measure import block_reduce def test_block_reduce_sum(): image1 = np.arange(4 * 6).reshape(4, 6) out1 = block_reduce(image1, (2, 3)) expected1 = np.array([[ 24, 42], [ 96, 114]]) assert_equal(expected1, out1) image2 = np.arange(5 * 8).reshape(5, 8) out2 = block_reduce(image2, (3, 3)) expected2 = np.array([[ 81, 108, 87], [174, 192, 138]]) assert_equal(expected2, out2) def test_block_reduce_mean(): image1 = np.arange(4 * 6).reshape(4, 6) out1 = block_reduce(image1, (2, 3), func=np.mean) expected1 = np.array([[ 4., 7.], [ 16., 19.]]) assert_equal(expected1, out1) image2 = np.arange(5 * 8).reshape(5, 8) out2 = block_reduce(image2, (4, 5), func=np.mean) expected2 = np.array([[14. , 10.8], [ 8.5, 5.7]]) assert_equal(expected2, out2) def test_block_reduce_median(): image1 = np.arange(4 * 6).reshape(4, 6) out1 = block_reduce(image1, (2, 3), func=np.median) expected1 = np.array([[ 4., 7.], [ 16., 19.]]) assert_equal(expected1, out1) image2 = np.arange(5 * 8).reshape(5, 8) out2 = block_reduce(image2, (4, 5), func=np.median) expected2 = np.array([[ 14., 17.], [ 0., 0.]]) assert_equal(expected2, out2) image3 = np.array([[1, 5, 5, 5], [5, 5, 5, 1000]]) out3 = block_reduce(image3, (2, 4), func=np.median) assert_equal(5, out3) def test_block_reduce_min(): image1 = np.arange(4 * 6).reshape(4, 6) out1 = block_reduce(image1, (2, 3), func=np.min) expected1 = np.array([[ 0, 3], [12, 15]]) assert_equal(expected1, out1) image2 = np.arange(5 * 8).reshape(5, 8) out2 = block_reduce(image2, (4, 5), func=np.min) expected2 = np.array([[0, 0], [0, 0]]) assert_equal(expected2, out2) def test_block_reduce_max(): image1 = np.arange(4 * 6).reshape(4, 6) out1 = block_reduce(image1, (2, 3), func=np.max) expected1 = np.array([[ 8, 11], [20, 23]]) assert_equal(expected1, out1) image2 = np.arange(5 * 8).reshape(5, 8) out2 = block_reduce(image2, (4, 5), func=np.max) expected2 = np.array([[28, 31], [36, 39]]) assert_equal(expected2, out2) def test_invalid_block_size(): image = np.arange(4 * 6).reshape(4, 6) assert_raises(ValueError, block_reduce, image, [1, 2, 3]) assert_raises(ValueError, block_reduce, image, [1, 0.5]) if __name__ == "__main__": np.testing.run_module_suite() scikit-image-0.13.1/skimage/measure/tests/test_entropy.py000066400000000000000000000005361316256236300235110ustar00rootroot00000000000000from numpy.testing import assert_almost_equal import numpy as np from skimage.measure import shannon_entropy def test_shannon_ones(): img = np.ones((10, 10)) res = shannon_entropy(img, base=np.e) assert_almost_equal(res, np.log(10*10)) if __name__ == "__main__": from numpy.testing import run_module_suite run_module_suite() scikit-image-0.13.1/skimage/measure/tests/test_find_contours.py000066400000000000000000000034421316256236300246640ustar00rootroot00000000000000import numpy as np from numpy.testing import (assert_raises, assert_array_equal, ) from skimage.measure import find_contours a = np.ones((8, 8), dtype=np.float32) a[1:-1, 1] = 0 a[1, 1:-1] = 0 x, y = np.mgrid[-1:1:5j, -1:1:5j] r = np.sqrt(x**2 + y**2) def test_binary(): ref = [[6. , 1.5], [5. , 1.5], [4. , 1.5], [3. , 1.5], [2. , 1.5], [1.5, 2. ], [1.5, 3. ], [1.5, 4. ], [1.5, 5. ], [1.5, 6. ], [1. , 6.5], [0.5, 6. ], [0.5, 5. ], [0.5, 4. ], [0.5, 3. ], [0.5, 2. ], [0.5, 1. ], [1. , 0.5], [2. , 0.5], [3. , 0.5], [4. , 0.5], [5. , 0.5], [6. , 0.5], [6.5, 1. ], [6. , 1.5]] contours = find_contours(a, 0.5, positive_orientation='high') assert len(contours) == 1 assert_array_equal(contours[0][::-1], ref) def test_float(): contours = find_contours(r, 0.5) assert len(contours) == 1 assert_array_equal(contours[0], [[ 2., 3.], [ 1., 2.], [ 2., 1.], [ 3., 2.], [ 2., 3.]]) def test_memory_order(): contours = find_contours(np.ascontiguousarray(r), 0.5) assert len(contours) == 1 contours = find_contours(np.asfortranarray(r), 0.5) assert len(contours) == 1 def test_invalid_input(): assert_raises(ValueError, find_contours, r, 0.5, 'foo', 'bar') assert_raises(ValueError, find_contours, r[..., None], 0.5) if __name__ == '__main__': from numpy.testing import run_module_suite run_module_suite() scikit-image-0.13.1/skimage/measure/tests/test_fit.py000066400000000000000000000313521316256236300225730ustar00rootroot00000000000000import numpy as np from numpy.testing import assert_equal, assert_almost_equal, assert_array_less from numpy.testing import assert_raises, assert_array_less from skimage.measure import LineModelND, CircleModel, EllipseModel, ransac from skimage.transform import AffineTransform from skimage.measure.fit import _dynamic_max_trials from skimage._shared._warnings import expected_warnings def test_line_model_invalid_input(): assert_raises(ValueError, LineModelND().estimate, np.empty((1, 3))) def test_line_model_predict(): model = LineModelND() model.params = ((0, 0), (1, 1)) x = np.arange(-10, 10) y = model.predict_y(x) assert_almost_equal(x, model.predict_x(y)) def test_line_model_estimate(): # generate original data without noise model0 = LineModelND() model0.params = ((0, 0), (1, 1)) x0 = np.arange(-100, 100) y0 = model0.predict_y(x0) data = np.column_stack([x0, y0]) # estimate parameters of noisy data model_est = LineModelND() model_est.estimate(data) # test whether estimated parameters almost equal original parameters random_state = np.random.RandomState(1234) x = random_state.rand(100, 2) assert_almost_equal(model0.predict(x), model_est.predict(x), 1) def test_line_model_residuals(): model = LineModelND() model.params = (np.array([0, 0]), np.array([0, 1])) assert_equal(model.residuals(np.array([[0, 0]])), 0) assert_equal(model.residuals(np.array([[0, 10]])), 0) assert_equal(model.residuals(np.array([[10, 0]])), 10) model.params = (np.array([-2, 0]), np.array([1, 1]) / np.sqrt(2)) assert_equal(model.residuals(np.array([[0, 0]])), np.sqrt(2)) assert_almost_equal(model.residuals(np.array([[-4, 0]])), np.sqrt(2)) def test_line_model_under_determined(): data = np.empty((1, 2)) assert_raises(ValueError, LineModelND().estimate, data) def test_line_modelND_invalid_input(): assert_raises(ValueError, LineModelND().estimate, np.empty((5, 1))) def test_line_modelND_predict(): model = LineModelND() model.params = (np.array([0, 0]), np.array([0.2, 0.98])) x = np.arange(-10, 10) y = model.predict_y(x) assert_almost_equal(x, model.predict_x(y)) def test_line_modelND_estimate(): # generate original data without noise model0 = LineModelND() model0.params = (np.array([0,0,0], dtype='float'), np.array([1,1,1], dtype='float')/np.sqrt(3)) # we scale the unit vector with a factor 10 when generating points on the # line in order to compensate for the scale of the random noise data0 = (model0.params[0] + 10 * np.arange(-100,100)[...,np.newaxis] * model0.params[1]) # add gaussian noise to data random_state = np.random.RandomState(1234) data = data0 + random_state.normal(size=data0.shape) # estimate parameters of noisy data model_est = LineModelND() model_est.estimate(data) # test whether estimated parameters are correct # we use the following geometric property: two aligned vectors have # a cross-product equal to zero # test if direction vectors are aligned assert_almost_equal(np.linalg.norm(np.cross(model0.params[1], model_est.params[1])), 0, 1) # test if origins are aligned with the direction a = model_est.params[0] - model0.params[0] if np.linalg.norm(a) > 0: a /= np.linalg.norm(a) assert_almost_equal(np.linalg.norm(np.cross(model0.params[1], a)), 0, 1) def test_line_modelND_residuals(): model = LineModelND() model.params = (np.array([0, 0, 0]), np.array([0, 0, 1])) assert_equal(abs(model.residuals(np.array([[0, 0, 0]]))), 0) assert_equal(abs(model.residuals(np.array([[0, 0, 1]]))), 0) assert_equal(abs(model.residuals(np.array([[10, 0, 0]]))), 10) def test_line_modelND_under_determined(): data = np.empty((1, 3)) assert_raises(ValueError, LineModelND().estimate, data) def test_circle_model_invalid_input(): assert_raises(ValueError, CircleModel().estimate, np.empty((5, 3))) def test_circle_model_predict(): model = CircleModel() r = 5 model.params = (0, 0, r) t = np.arange(0, 2 * np.pi, np.pi / 2) xy = np.array(((5, 0), (0, 5), (-5, 0), (0, -5))) assert_almost_equal(xy, model.predict_xy(t)) def test_circle_model_estimate(): # generate original data without noise model0 = CircleModel() model0.params = (10, 12, 3) t = np.linspace(0, 2 * np.pi, 1000) data0 = model0.predict_xy(t) # add gaussian noise to data random_state = np.random.RandomState(1234) data = data0 + random_state.normal(size=data0.shape) # estimate parameters of noisy data model_est = CircleModel() model_est.estimate(data) # test whether estimated parameters almost equal original parameters assert_almost_equal(model0.params, model_est.params, 1) def test_circle_model_residuals(): model = CircleModel() model.params = (0, 0, 5) assert_almost_equal(abs(model.residuals(np.array([[5, 0]]))), 0) assert_almost_equal(abs(model.residuals(np.array([[6, 6]]))), np.sqrt(2 * 6**2) - 5) assert_almost_equal(abs(model.residuals(np.array([[10, 0]]))), 5) def test_ellipse_model_invalid_input(): assert_raises(ValueError, EllipseModel().estimate, np.empty((5, 3))) def test_ellipse_model_predict(): model = EllipseModel() model.params = (0, 0, 5, 10, 0) t = np.arange(0, 2 * np.pi, np.pi / 2) xy = np.array(((5, 0), (0, 10), (-5, 0), (0, -10))) assert_almost_equal(xy, model.predict_xy(t)) def test_ellipse_model_estimate(): for angle in range(0, 180, 15): rad = np.deg2rad(angle) # generate original data without noise model0 = EllipseModel() model0.params = (10, 20, 15, 25, rad) t = np.linspace(0, 2 * np.pi, 100) data0 = model0.predict_xy(t) # add gaussian noise to data random_state = np.random.RandomState(1234) data = data0 + random_state.normal(size=data0.shape) # estimate parameters of noisy data model_est = EllipseModel() model_est.estimate(data) # test whether estimated parameters almost equal original parameters assert_almost_equal(model0.params[:2], model_est.params[:2], 0) res = model_est.residuals(data0) assert_array_less(res, np.ones(res.shape)) def test_ellipse_model_estimate_from_data(): data = np.array([ [264, 854], [265, 875], [268, 863], [270, 857], [275, 905], [285, 915], [305, 925], [324, 934], [335, 764], [336, 915], [345, 925], [345, 945], [354, 933], [355, 745], [364, 936], [365, 754], [375, 745], [375, 735], [385, 736], [395, 735], [394, 935], [405, 727], [415, 736], [415, 727], [425, 727], [426, 929], [435, 735], [444, 933], [445, 735], [455, 724], [465, 934], [465, 735], [475, 908], [475, 726], [485, 753], [485, 728], [492, 762], [495, 745], [491, 910], [493, 909], [499, 904], [505, 905], [504, 747], [515, 743], [516, 752], [524, 855], [525, 844], [525, 885], [533, 845], [533, 873], [535, 883], [545, 874], [543, 864], [553, 865], [553, 845], [554, 825], [554, 835], [563, 845], [565, 826], [563, 855], [563, 795], [565, 735], [573, 778], [572, 815], [574, 804], [575, 665], [575, 685], [574, 705], [574, 745], [575, 875], [572, 732], [582, 795], [579, 709], [583, 805], [583, 854], [586, 755], [584, 824], [585, 655], [581, 718], [586, 844], [585, 915], [587, 905], [594, 824], [593, 855], [590, 891], [594, 776], [596, 767], [593, 763], [603, 785], [604, 775], [603, 885], [605, 753], [605, 655], [606, 935], [603, 761], [613, 802], [613, 945], [613, 965], [615, 693], [617, 665], [623, 962], [624, 972], [625, 995], [633, 673], [633, 965], [633, 683], [633, 692], [633, 954], [634, 1016], [635, 664], [641, 804], [637, 999], [641, 956], [643, 946], [643, 926], [644, 975], [643, 655], [646, 705], [651, 664], [651, 984], [647, 665], [651, 715], [651, 725], [651, 734], [647, 809], [651, 825], [651, 873], [647, 900], [652, 917], [651, 944], [652, 742], [648, 811], [651, 994], [652, 783], [650, 911], [654, 879]]) # estimate parameters of real data model = EllipseModel() model.estimate(data) # test whether estimated parameters are smaller then 1000, so means stable assert_array_less(np.abs(model.params[:4]), np.array([2e3] * 4)) def test_ellipse_model_estimate_failers(): # estimate parameters of real data model = EllipseModel() assert not model.estimate(np.ones((5, 2))) assert not model.estimate(np.array([[50, 80], [51, 81], [52, 80]])) def test_ellipse_model_residuals(): model = EllipseModel() # vertical line through origin model.params = (0, 0, 10, 5, 0) assert_almost_equal(abs(model.residuals(np.array([[10, 0]]))), 0) assert_almost_equal(abs(model.residuals(np.array([[0, 5]]))), 0) assert_almost_equal(abs(model.residuals(np.array([[0, 10]]))), 5) def test_ransac_shape(): # generate original data without noise model0 = CircleModel() model0.params = (10, 12, 3) t = np.linspace(0, 2 * np.pi, 1000) data0 = model0.predict_xy(t) # add some faulty data outliers = (10, 30, 200) data0[outliers[0], :] = (1000, 1000) data0[outliers[1], :] = (-50, 50) data0[outliers[2], :] = (-100, -10) # estimate parameters of corrupted data model_est, inliers = ransac(data0, CircleModel, 3, 5, random_state=1) # test whether estimated parameters equal original parameters assert_equal(model0.params, model_est.params) for outlier in outliers: assert outlier not in inliers def test_ransac_geometric(): random_state = np.random.RandomState(1) # generate original data without noise src = 100 * random_state.random_sample((50, 2)) model0 = AffineTransform(scale=(0.5, 0.3), rotation=1, translation=(10, 20)) dst = model0(src) # add some faulty data outliers = (0, 5, 20) dst[outliers[0]] = (10000, 10000) dst[outliers[1]] = (-100, 100) dst[outliers[2]] = (50, 50) # estimate parameters of corrupted data model_est, inliers = ransac((src, dst), AffineTransform, 2, 20, random_state=random_state) # test whether estimated parameters equal original parameters assert_almost_equal(model0.params, model_est.params) assert np.all(np.nonzero(inliers == False)[0] == outliers) def test_ransac_is_data_valid(): is_data_valid = lambda data: data.shape[0] > 2 model, inliers = ransac(np.empty((10, 2)), LineModelND, 2, np.inf, is_data_valid=is_data_valid, random_state=1) assert_equal(model, None) assert_equal(inliers, None) def test_ransac_is_model_valid(): def is_model_valid(model, data): return False model, inliers = ransac(np.empty((10, 2)), LineModelND, 2, np.inf, is_model_valid=is_model_valid, random_state=1) assert_equal(model, None) assert_equal(inliers, None) def test_ransac_dynamic_max_trials(): # Numbers hand-calculated and confirmed on page 119 (Table 4.3) in # Hartley, R.~I. and Zisserman, A., 2004, # Multiple View Geometry in Computer Vision, Second Edition, # Cambridge University Press, ISBN: 0521540518 # e = 0%, min_samples = X assert_equal(_dynamic_max_trials(100, 100, 2, 0.99), 1) # e = 5%, min_samples = 2 assert_equal(_dynamic_max_trials(95, 100, 2, 0.99), 2) # e = 10%, min_samples = 2 assert_equal(_dynamic_max_trials(90, 100, 2, 0.99), 3) # e = 30%, min_samples = 2 assert_equal(_dynamic_max_trials(70, 100, 2, 0.99), 7) # e = 50%, min_samples = 2 assert_equal(_dynamic_max_trials(50, 100, 2, 0.99), 17) # e = 5%, min_samples = 8 assert_equal(_dynamic_max_trials(95, 100, 8, 0.99), 5) # e = 10%, min_samples = 8 assert_equal(_dynamic_max_trials(90, 100, 8, 0.99), 9) # e = 30%, min_samples = 8 assert_equal(_dynamic_max_trials(70, 100, 8, 0.99), 78) # e = 50%, min_samples = 8 assert_equal(_dynamic_max_trials(50, 100, 8, 0.99), 1177) # e = 0%, min_samples = 5 assert_equal(_dynamic_max_trials(1, 100, 5, 0), 0) assert_equal(_dynamic_max_trials(1, 100, 5, 1), np.inf) def test_ransac_invalid_input(): assert_raises(ValueError, ransac, np.zeros((10, 2)), None, min_samples=2, residual_threshold=0, max_trials=-1) assert_raises(ValueError, ransac, np.zeros((10, 2)), None, min_samples=2, residual_threshold=0, stop_probability=-1) assert_raises(ValueError, ransac, np.zeros((10, 2)), None, min_samples=2, residual_threshold=0, stop_probability=1.01) if __name__ == "__main__": np.testing.run_module_suite() scikit-image-0.13.1/skimage/measure/tests/test_marching_cubes.py000066400000000000000000000172521316256236300247650ustar00rootroot00000000000000import sys import numpy as np from numpy.testing import assert_raises from skimage.draw import ellipsoid, ellipsoid_stats from skimage.measure import (marching_cubes, marching_cubes_classic, marching_cubes_lewiner, mesh_surface_area, correct_mesh_orientation) from skimage.measure._marching_cubes_lewiner import _expected_output_args def func_that_knows_about_its_outputs(r): # Must be defined in global scope to avoid syntax error on Python 2 *sigh* nout = _expected_output_args() print(nout) r.append(nout) return [nout] * int(nout) def test_expected_output_args(): foo = func_that_knows_about_its_outputs res = [] foo(res) a = foo(res) a, b = foo(res) a, b, c = foo(res) assert res == [0, 1, 2, 3] or res == [0, 0, 2, 3] # ``a = foo()`` somehow yields 0 in test, which is ok for us; # we only want to distinguish between > 2 args or not if sys.version_info >= (3, 3): res = [] exec('*a, b, c = foo(res)') exec('a, b, c, *d = foo(res)') exec('a, b, *c, d, e = foo(res)') assert res == [2.1, 3.1, 4.1] def test_marching_cubes_isotropic(): ellipsoid_isotropic = ellipsoid(6, 10, 16, levelset=True) _, surf = ellipsoid_stats(6, 10, 16) # Classic verts, faces = marching_cubes_classic(ellipsoid_isotropic, 0.) surf_calc = mesh_surface_area(verts, faces) # Test within 1% tolerance for isotropic. Will always underestimate. assert surf > surf_calc and surf_calc > surf * 0.99 # Lewiner verts, faces = marching_cubes_lewiner(ellipsoid_isotropic, 0.)[:2] surf_calc = mesh_surface_area(verts, faces) # Test within 1% tolerance for isotropic. Will always underestimate. assert surf > surf_calc and surf_calc > surf * 0.99 def test_marching_cubes_anisotropic(): spacing = (1., 10 / 6., 16 / 6.) ellipsoid_anisotropic = ellipsoid(6, 10, 16, spacing=spacing, levelset=True) _, surf = ellipsoid_stats(6, 10, 16) # Classic verts, faces = marching_cubes_classic(ellipsoid_anisotropic, 0., spacing=spacing) surf_calc = mesh_surface_area(verts, faces) # Test within 1.5% tolerance for anisotropic. Will always underestimate. assert surf > surf_calc and surf_calc > surf * 0.985 # Lewiner verts, faces = marching_cubes_lewiner(ellipsoid_anisotropic, 0., spacing=spacing)[:2] surf_calc = mesh_surface_area(verts, faces) # Test within 1.5% tolerance for anisotropic. Will always underestimate. assert surf > surf_calc and surf_calc > surf * 0.985 def test_invalid_input(): # Classic assert_raises(ValueError, marching_cubes_classic, np.zeros((2, 2, 1)), 0) assert_raises(ValueError, marching_cubes_classic, np.zeros((2, 2, 1)), 1) assert_raises(ValueError, marching_cubes_classic, np.ones((3, 3, 3)), 1, spacing=(1, 2)) assert_raises(ValueError, marching_cubes_classic, np.zeros((20, 20)), 0) # Lewiner assert_raises(ValueError, marching_cubes_lewiner, np.zeros((2, 2, 1)), 0) assert_raises(ValueError, marching_cubes_lewiner, np.zeros((2, 2, 1)), 1) assert_raises(ValueError, marching_cubes_lewiner, np.ones((3, 3, 3)), 1, spacing=(1, 2)) assert_raises(ValueError, marching_cubes_lewiner, np.zeros((20, 20)), 0) def test_correct_mesh_orientation(): sphere_small = ellipsoid(1, 1, 1, levelset=True) # Mesh with incorrectly oriented faces which was previously returned from # `marching_cubes`, before it guaranteed correct mesh orientation verts = np.array([[1., 2., 2.], [2., 2., 1.], [2., 1., 2.], [2., 2., 3.], [2., 3., 2.], [3., 2., 2.]]) faces = np.array([[0, 1, 2], [2, 0, 3], [1, 0, 4], [4, 0, 3], [1, 2, 5], [2, 3, 5], [1, 4, 5], [5, 4, 3]]) # Correct mesh orientation - descent corrected_faces1 = correct_mesh_orientation(sphere_small, verts, faces, gradient_direction='descent') corrected_faces2 = correct_mesh_orientation(sphere_small, verts, faces, gradient_direction='ascent') # Ensure ascent is opposite of descent for all faces np.testing.assert_array_equal(corrected_faces1, corrected_faces2[:, ::-1]) # Ensure correct faces have been reversed: 1, 4, and 5 idx = [1, 4, 5] expected = faces.copy() expected[idx] = expected[idx, ::-1] np.testing.assert_array_equal(expected, corrected_faces1) def test_both_algs_same_result_ellipse(): # Performing this test on data that does not have ambiguities sphere_small = ellipsoid(1, 1, 1, levelset=True) vertices1, faces1 = marching_cubes_classic(sphere_small, 0)[:2] vertices2, faces2 = marching_cubes_lewiner(sphere_small, 0, allow_degenerate=False)[:2] vertices3, faces3 = marching_cubes_lewiner(sphere_small, 0, allow_degenerate=False, use_classic=True)[:2] # Order is different, best we can do is test equal shape and same vertices present assert _same_mesh(vertices1, faces1, vertices2, faces2) assert _same_mesh(vertices1, faces1, vertices3, faces3) def _same_mesh(vertices1, faces1, vertices2, faces2, tol=1e-10): """ Compare two meshes, using a certain tolerance and invariant to the order of the faces. """ # Unwind vertices triangles1 = vertices1[np.array(faces1)] triangles2 = vertices2[np.array(faces2)] # Sort vertices within each triangle triang1 = [np.concatenate(sorted(t, key=lambda x:tuple(x))) for t in triangles1] triang2 = [np.concatenate(sorted(t, key=lambda x:tuple(x))) for t in triangles2] # Sort the resulting 9-element "tuples" triang1 = np.array(sorted([tuple(x) for x in triang1])) triang2 = np.array(sorted([tuple(x) for x in triang2])) return triang1.shape == triang2.shape and np.allclose(triang1, triang2, 0, tol) def test_both_algs_same_result_donut(): # Performing this test on data that does not have ambiguities n = 48 a, b = 2.5/n, -1.25 isovalue = 0.0 vol = np.empty((n,n,n), 'float32') for iz in range(vol.shape[0]): for iy in range(vol.shape[1]): for ix in range(vol.shape[2]): # Double-torii formula by Thomas Lewiner z, y, x = float(iz)*a+b, float(iy)*a+b, float(ix)*a+b vol[iz,iy,ix] = ( ( (8*x)**2 + (8*y-2)**2 + (8*z)**2 + 16 - 1.85*1.85 ) * ( (8*x)**2 + (8*y-2)**2 + (8*z)**2 + 16 - 1.85*1.85 ) - 64 * ( (8*x)**2 + (8*y-2)**2 ) ) * ( ( (8*x)**2 + ((8*y-2)+4)*((8*y-2)+4) + (8*z)**2 + 16 - 1.85*1.85 ) * ( (8*x)**2 + ((8*y-2)+4)*((8*y-2)+4) + (8*z)**2 + 16 - 1.85*1.85 ) - 64 * ( ((8*y-2)+4)*((8*y-2)+4) + (8*z)**2 ) ) + 1025 vertices1, faces1 = marching_cubes_classic(vol, 0)[:2] vertices2, faces2 = marching_cubes_lewiner(vol, 0)[:2] vertices3, faces3 = marching_cubes_lewiner(vol, 0, use_classic=True)[:2] # Old and new alg are different assert not _same_mesh(vertices1, faces1, vertices2, faces2) # New classic and new Lewiner are different assert not _same_mesh(vertices2, faces2, vertices3, faces3) # Would have been nice if old and new classic would have been the same # assert _same_mesh(vertices1, faces1, vertices3, faces3, 5) if __name__ == '__main__': np.testing.run_module_suite() scikit-image-0.13.1/skimage/measure/tests/test_moments.py000066400000000000000000000047501316256236300234750ustar00rootroot00000000000000from numpy.testing import assert_equal, assert_almost_equal, assert_raises import numpy as np from skimage.measure import (moments, moments_central, moments_normalized, moments_hu) def test_moments(): image = np.zeros((20, 20), dtype=np.double) image[14, 14] = 1 image[15, 15] = 1 image[14, 15] = 0.5 image[15, 14] = 0.5 m = moments(image) assert_equal(m[0, 0], 3) assert_almost_equal(m[0, 1] / m[0, 0], 14.5) assert_almost_equal(m[1, 0] / m[0, 0], 14.5) def test_moments_central(): image = np.zeros((20, 20), dtype=np.double) image[14, 14] = 1 image[15, 15] = 1 image[14, 15] = 0.5 image[15, 14] = 0.5 mu = moments_central(image, 14.5, 14.5) # shift image by dx=2, dy=2 image2 = np.zeros((20, 20), dtype=np.double) image2[16, 16] = 1 image2[17, 17] = 1 image2[16, 17] = 0.5 image2[17, 16] = 0.5 mu2 = moments_central(image2, 14.5 + 2, 14.5 + 2) # central moments must be translation invariant assert_equal(mu, mu2) def test_moments_normalized(): image = np.zeros((20, 20), dtype=np.double) image[13:17, 13:17] = 1 mu = moments_central(image, 14.5, 14.5) nu = moments_normalized(mu) # shift image by dx=-3, dy=-3 and scale by 0.5 image2 = np.zeros((20, 20), dtype=np.double) image2[11:13, 11:13] = 1 mu2 = moments_central(image2, 11.5, 11.5) nu2 = moments_normalized(mu2) # central moments must be translation and scale invariant assert_almost_equal(nu, nu2, decimal=1) def test_moments_normalized_invalid(): assert_raises(TypeError, moments_normalized, np.zeros((3, 3, 3))) assert_raises(TypeError, moments_normalized, np.zeros((3,))) assert_raises(TypeError, moments_normalized, np.zeros((3, 3)), 3) assert_raises(TypeError, moments_normalized, np.zeros((3, 3)), 4) def test_moments_hu(): image = np.zeros((20, 20), dtype=np.double) image[13:15, 13:17] = 1 mu = moments_central(image, 13.5, 14.5) nu = moments_normalized(mu) hu = moments_hu(nu) # shift image by dx=2, dy=3, scale by 0.5 and rotate by 90deg image2 = np.zeros((20, 20), dtype=np.double) image2[11, 11:13] = 1 image2 = image2.T mu2 = moments_central(image2, 11.5, 11) nu2 = moments_normalized(mu2) hu2 = moments_hu(nu2) # central moments must be translation and scale invariant assert_almost_equal(hu, hu2, decimal=1) if __name__ == "__main__": from numpy.testing import run_module_suite run_module_suite() scikit-image-0.13.1/skimage/measure/tests/test_pnpoly.py000066400000000000000000000021231316256236300233240ustar00rootroot00000000000000import numpy as np from numpy.testing import assert_array_equal from skimage.measure import points_in_poly, grid_points_in_poly class test_npnpoly(): def test_square(self): v = np.array([[0, 0], [0, 1], [1, 1], [1, 0]]) assert(points_in_poly([[0.5, 0.5]], v)[0]) assert(not points_in_poly([[-0.1, 0.1]], v)[0]) def test_triangle(self): v = np.array([[0, 0], [1, 0], [0.5, 0.75]]) assert(points_in_poly([[0.5, 0.7]], v)[0]) assert(not points_in_poly([[0.5, 0.76]], v)[0]) assert(not points_in_poly([[0.7, 0.5]], v)[0]) def test_type(self): assert(points_in_poly([[0, 0]], [[0, 0]]).dtype == np.bool) def test_grid_points_in_poly(): v = np.array([[0, 0], [5, 0], [5, 5]]) expected = np.tril(np.ones((5, 5), dtype=bool)) assert_array_equal(grid_points_in_poly((5, 5), v), expected) if __name__ == "__main__": np.testing.run_module_suite() scikit-image-0.13.1/skimage/measure/tests/test_polygon.py000066400000000000000000000045071316256236300235020ustar00rootroot00000000000000import numpy as np from skimage.measure import approximate_polygon, subdivide_polygon from skimage.measure._polygon import _SUBDIVISION_MASKS square = np.array([ [0, 0], [0, 1], [0, 2], [0, 3], [1, 3], [2, 3], [3, 3], [3, 2], [3, 1], [3, 0], [2, 0], [1, 0], [0, 0] ]) def test_approximate_polygon(): out = approximate_polygon(square, 0.1) np.testing.assert_array_equal(out, square[(0, 3, 6, 9, 12), :]) out = approximate_polygon(square, 2.2) np.testing.assert_array_equal(out, square[(0, 6, 12), :]) out = approximate_polygon(square[(0, 1, 3, 4, 5, 6, 7, 9, 11, 12), :], 0.1) np.testing.assert_array_equal(out, square[(0, 3, 6, 9, 12), :]) out = approximate_polygon(square, -1) np.testing.assert_array_equal(out, square) out = approximate_polygon(square, 0) np.testing.assert_array_equal(out, square) def test_subdivide_polygon(): new_square1 = square new_square2 = square[:-1] new_square3 = square[:-1] # test iterative subdvision for _ in range(10): square1, square2, square3 = new_square1, new_square2, new_square3 # test different B-Spline degrees for degree in range(1, 7): mask_len = len(_SUBDIVISION_MASKS[degree][0]) # test circular new_square1 = subdivide_polygon(square1, degree) np.testing.assert_array_equal(new_square1[-1], new_square1[0]) np.testing.assert_equal(new_square1.shape[0], 2 * square1.shape[0] - 1) # test non-circular new_square2 = subdivide_polygon(square2, degree) np.testing.assert_equal(new_square2.shape[0], 2 * (square2.shape[0] - mask_len + 1)) # test non-circular, preserve_ends new_square3 = subdivide_polygon(square3, degree, True) np.testing.assert_equal(new_square3[0], square3[0]) np.testing.assert_equal(new_square3[-1], square3[-1]) np.testing.assert_equal(new_square3.shape[0], 2 * (square3.shape[0] - mask_len + 2)) # not supported B-Spline degree np.testing.assert_raises(ValueError, subdivide_polygon, square, 0) np.testing.assert_raises(ValueError, subdivide_polygon, square, 8) if __name__ == "__main__": np.testing.run_module_suite() scikit-image-0.13.1/skimage/measure/tests/test_profile.py000066400000000000000000000070201316256236300234440ustar00rootroot00000000000000from numpy.testing import assert_equal, assert_almost_equal import numpy as np from skimage.measure import profile_line image = np.arange(100).reshape((10, 10)).astype(np.float) def test_horizontal_rightward(): prof = profile_line(image, (0, 2), (0, 8), order=0) expected_prof = np.arange(2, 9) assert_equal(prof, expected_prof) def test_horizontal_leftward(): prof = profile_line(image, (0, 8), (0, 2), order=0) expected_prof = np.arange(8, 1, -1) assert_equal(prof, expected_prof) def test_vertical_downward(): prof = profile_line(image, (2, 5), (8, 5), order=0) expected_prof = np.arange(25, 95, 10) assert_equal(prof, expected_prof) def test_vertical_upward(): prof = profile_line(image, (8, 5), (2, 5), order=0) expected_prof = np.arange(85, 15, -10) assert_equal(prof, expected_prof) def test_45deg_right_downward(): prof = profile_line(image, (2, 2), (8, 8), order=0) expected_prof = np.array([22, 33, 33, 44, 55, 55, 66, 77, 77, 88]) # repeats are due to aliasing using nearest neighbor interpolation. # to see this, imagine a diagonal line with markers every unit of # length traversing a checkerboard pattern of squares also of unit # length. Because the line is diagonal, sometimes more than one # marker will fall on the same checkerboard box. assert_almost_equal(prof, expected_prof) def test_45deg_right_downward_interpolated(): prof = profile_line(image, (2, 2), (8, 8), order=1) expected_prof = np.linspace(22, 88, 10) assert_almost_equal(prof, expected_prof) def test_45deg_right_upward(): prof = profile_line(image, (8, 2), (2, 8), order=1) expected_prof = np.arange(82, 27, -6) assert_almost_equal(prof, expected_prof) def test_45deg_left_upward(): prof = profile_line(image, (8, 8), (2, 2), order=1) expected_prof = np.arange(88, 21, -22. / 3) assert_almost_equal(prof, expected_prof) def test_45deg_left_downward(): prof = profile_line(image, (2, 8), (8, 2), order=1) expected_prof = np.arange(28, 83, 6) assert_almost_equal(prof, expected_prof) def test_pythagorean_triangle_right_downward(): prof = profile_line(image, (1, 1), (7, 9), order=0) expected_prof = np.array([11, 22, 23, 33, 34, 45, 56, 57, 67, 68, 79]) assert_equal(prof, expected_prof) def test_pythagorean_triangle_right_downward_interpolated(): prof = profile_line(image, (1, 1), (7, 9), order=1) expected_prof = np.linspace(11, 79, 11) assert_almost_equal(prof, expected_prof) pyth_image = np.zeros((6, 7), np.float) line = ((1, 2, 2, 3, 3, 4), (1, 2, 3, 3, 4, 5)) below = ((2, 2, 3, 4, 4, 5), (0, 1, 2, 3, 4, 4)) above = ((0, 1, 1, 2, 3, 3), (2, 2, 3, 4, 5, 6)) pyth_image[line] = 1.8 pyth_image[below] = 0.6 pyth_image[above] = 0.6 def test_pythagorean_triangle_right_downward_linewidth(): prof = profile_line(pyth_image, (1, 1), (4, 5), linewidth=3, order=0) expected_prof = np.ones(6) assert_almost_equal(prof, expected_prof) def test_pythagorean_triangle_right_upward_linewidth(): prof = profile_line(pyth_image[::-1, :], (4, 1), (1, 5), linewidth=3, order=0) expected_prof = np.ones(6) assert_almost_equal(prof, expected_prof) def test_pythagorean_triangle_transpose_left_down_linewidth(): prof = profile_line(pyth_image.T[:, ::-1], (1, 4), (5, 1), linewidth=3, order=0) expected_prof = np.ones(6) assert_almost_equal(prof, expected_prof) if __name__ == "__main__": from numpy.testing import run_module_suite run_module_suite() scikit-image-0.13.1/skimage/measure/tests/test_regionprops.py000066400000000000000000000347321316256236300243650ustar00rootroot00000000000000from numpy.testing import assert_array_equal, assert_almost_equal, \ assert_array_almost_equal, assert_raises, assert_equal import numpy as np import math from skimage.measure._regionprops import (regionprops, PROPS, perimeter, _parse_docs) SAMPLE = np.array( [[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1], [0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1]] ) INTENSITY_SAMPLE = SAMPLE.copy() INTENSITY_SAMPLE[1, 9:11] = 2 SAMPLE_3D = np.zeros((6, 6, 6), dtype=np.uint8) SAMPLE_3D[1:3, 1:3, 1:3] = 1 SAMPLE_3D[3, 2, 2] = 1 INTENSITY_SAMPLE_3D = SAMPLE_3D.copy() def test_all_props(): region = regionprops(SAMPLE, INTENSITY_SAMPLE)[0] for prop in PROPS: assert_almost_equal(region[prop], getattr(region, PROPS[prop])) def test_all_props_3d(): region = regionprops(SAMPLE_3D, INTENSITY_SAMPLE_3D)[0] for prop in PROPS: try: assert_almost_equal(region[prop], getattr(region, PROPS[prop])) except NotImplementedError: pass def test_dtype(): regionprops(np.zeros((10, 10), dtype=np.int)) regionprops(np.zeros((10, 10), dtype=np.uint)) assert_raises((TypeError), regionprops, np.zeros((10, 10), dtype=np.float)) assert_raises((TypeError), regionprops, np.zeros((10, 10), dtype=np.double)) def test_ndim(): regionprops(np.zeros((10, 10), dtype=np.int)) regionprops(np.zeros((10, 10, 1), dtype=np.int)) regionprops(np.zeros((10, 10, 1, 1), dtype=np.int)) regionprops(np.zeros((10, 10, 10), dtype=np.int)) assert_raises(TypeError, regionprops, np.zeros((10, 10, 10, 2), dtype=np.int)) def test_area(): area = regionprops(SAMPLE)[0].area assert area == np.sum(SAMPLE) area = regionprops(SAMPLE_3D)[0].area assert area == np.sum(SAMPLE_3D) def test_bbox(): bbox = regionprops(SAMPLE)[0].bbox assert_array_almost_equal(bbox, (0, 0, SAMPLE.shape[0], SAMPLE.shape[1])) SAMPLE_mod = SAMPLE.copy() SAMPLE_mod[:, -1] = 0 bbox = regionprops(SAMPLE_mod)[0].bbox assert_array_almost_equal(bbox, (0, 0, SAMPLE.shape[0], SAMPLE.shape[1]-1)) bbox = regionprops(SAMPLE_3D)[0].bbox assert_array_almost_equal(bbox, (1, 1, 1, 4, 3, 3)) def test_bbox_area(): bbox_area = regionprops(SAMPLE)[0].bbox_area assert_array_almost_equal(bbox_area, SAMPLE.shape[0] * SAMPLE.shape[1]) def test_moments_central(): mu = regionprops(SAMPLE)[0].moments_central # determined with OpenCV assert_almost_equal(mu[0,2], 436.00000000000045) # different from OpenCV results, bug in OpenCV assert_almost_equal(mu[0,3], -737.333333333333) assert_almost_equal(mu[1,1], -87.33333333333303) assert_almost_equal(mu[1,2], -127.5555555555593) assert_almost_equal(mu[2,0], 1259.7777777777774) assert_almost_equal(mu[2,1], 2000.296296296291) assert_almost_equal(mu[3,0], -760.0246913580195) def test_centroid(): centroid = regionprops(SAMPLE)[0].centroid # determined with MATLAB assert_array_almost_equal(centroid, (5.66666666666666, 9.444444444444444)) def test_centroid_3d(): centroid = regionprops(SAMPLE_3D)[0].centroid # determined by mean along axis 1 of SAMPLE_3D.nonzero() assert_array_almost_equal(centroid, (1.66666667, 1.55555556, 1.55555556)) def test_convex_area(): area = regionprops(SAMPLE)[0].convex_area # determined with MATLAB assert area == 124 def test_convex_image(): img = regionprops(SAMPLE)[0].convex_image # determined with MATLAB ref = np.array( [[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] ) assert_array_equal(img, ref) def test_coordinates(): sample = np.zeros((10, 10), dtype=np.int8) coords = np.array([[3, 2], [3, 3], [3, 4]]) sample[coords[:, 0], coords[:, 1]] = 1 prop_coords = regionprops(sample)[0].coords assert_array_equal(prop_coords, coords) sample = np.zeros((6, 6, 6), dtype=np.int8) coords = np.array([[1, 1, 1], [1, 2, 1], [1, 3, 1]]) sample[coords[:, 0], coords[:, 1], coords[:, 2]] = 1 prop_coords = regionprops(sample)[0].coords assert_array_equal(prop_coords, coords) def test_eccentricity(): eps = regionprops(SAMPLE)[0].eccentricity assert_almost_equal(eps, 0.814629313427) img = np.zeros((5, 5), dtype=np.int) img[2, 2] = 1 eps = regionprops(img)[0].eccentricity assert_almost_equal(eps, 0) def test_equiv_diameter(): diameter = regionprops(SAMPLE)[0].equivalent_diameter # determined with MATLAB assert_almost_equal(diameter, 9.57461472963) def test_euler_number(): en = regionprops(SAMPLE)[0].euler_number assert en == 1 SAMPLE_mod = SAMPLE.copy() SAMPLE_mod[7, -3] = 0 en = regionprops(SAMPLE_mod)[0].euler_number assert en == 0 def test_extent(): extent = regionprops(SAMPLE)[0].extent assert_almost_equal(extent, 0.4) def test_moments_hu(): hu = regionprops(SAMPLE)[0].moments_hu ref = np.array([ 3.27117627e-01, 2.63869194e-02, 2.35390060e-02, 1.23151193e-03, 1.38882330e-06, -2.72586158e-05, 6.48350653e-06 ]) # bug in OpenCV caused in Central Moments calculation? assert_array_almost_equal(hu, ref) def test_image(): img = regionprops(SAMPLE)[0].image assert_array_equal(img, SAMPLE) img = regionprops(SAMPLE_3D)[0].image assert_array_equal(img, SAMPLE_3D[1:4, 1:3, 1:3]) def test_label(): label = regionprops(SAMPLE)[0].label assert_array_equal(label, 1) label = regionprops(SAMPLE_3D)[0].label assert_array_equal(label, 1) def test_filled_area(): area = regionprops(SAMPLE)[0].filled_area assert area == np.sum(SAMPLE) SAMPLE_mod = SAMPLE.copy() SAMPLE_mod[7, -3] = 0 area = regionprops(SAMPLE_mod)[0].filled_area assert area == np.sum(SAMPLE) def test_filled_image(): img = regionprops(SAMPLE)[0].filled_image assert_array_equal(img, SAMPLE) def test_major_axis_length(): length = regionprops(SAMPLE)[0].major_axis_length # MATLAB has different interpretation of ellipse than found in literature, # here implemented as found in literature assert_almost_equal(length, 16.7924234999) def test_max_intensity(): intensity = regionprops(SAMPLE, intensity_image=INTENSITY_SAMPLE )[0].max_intensity assert_almost_equal(intensity, 2) def test_mean_intensity(): intensity = regionprops(SAMPLE, intensity_image=INTENSITY_SAMPLE )[0].mean_intensity assert_almost_equal(intensity, 1.02777777777777) def test_min_intensity(): intensity = regionprops(SAMPLE, intensity_image=INTENSITY_SAMPLE )[0].min_intensity assert_almost_equal(intensity, 1) def test_minor_axis_length(): length = regionprops(SAMPLE)[0].minor_axis_length # MATLAB has different interpretation of ellipse than found in literature, # here implemented as found in literature assert_almost_equal(length, 9.739302807263) def test_moments(): m = regionprops(SAMPLE)[0].moments # determined with OpenCV assert_almost_equal(m[0,0], 72.0) assert_almost_equal(m[0,1], 408.0) assert_almost_equal(m[0,2], 2748.0) assert_almost_equal(m[0,3], 19776.0) assert_almost_equal(m[1,0], 680.0) assert_almost_equal(m[1,1], 3766.0) assert_almost_equal(m[1,2], 24836.0) assert_almost_equal(m[2,0], 7682.0) assert_almost_equal(m[2,1], 43882.0) assert_almost_equal(m[3,0], 95588.0) def test_moments_normalized(): nu = regionprops(SAMPLE)[0].moments_normalized # determined with OpenCV assert_almost_equal(nu[0,2], 0.08410493827160502) assert_almost_equal(nu[1,1], -0.016846707818929982) assert_almost_equal(nu[1,2], -0.002899800614433943) assert_almost_equal(nu[2,0], 0.24301268861454037) assert_almost_equal(nu[2,1], 0.045473992910668816) assert_almost_equal(nu[3,0], -0.017278118992041805) def test_orientation(): orientation = regionprops(SAMPLE)[0].orientation # determined with MATLAB assert_almost_equal(orientation, 0.10446844651921) # test correct quadrant determination orientation2 = regionprops(SAMPLE.T)[0].orientation assert_almost_equal(orientation2, math.pi / 2 - orientation) # test diagonal regions diag = np.eye(10, dtype=int) orientation_diag = regionprops(diag)[0].orientation assert_almost_equal(orientation_diag, -math.pi / 4) orientation_diag = regionprops(np.flipud(diag))[0].orientation assert_almost_equal(orientation_diag, math.pi / 4) orientation_diag = regionprops(np.fliplr(diag))[0].orientation assert_almost_equal(orientation_diag, math.pi / 4) orientation_diag = regionprops(np.fliplr(np.flipud(diag)))[0].orientation assert_almost_equal(orientation_diag, -math.pi / 4) def test_perimeter(): per = regionprops(SAMPLE)[0].perimeter assert_almost_equal(per, 55.2487373415) per = perimeter(SAMPLE.astype('double'), neighbourhood=8) assert_almost_equal(per, 46.8284271247) def test_solidity(): solidity = regionprops(SAMPLE)[0].solidity # determined with MATLAB assert_almost_equal(solidity, 0.580645161290323) def test_weighted_moments_central(): wmu = regionprops(SAMPLE, intensity_image=INTENSITY_SAMPLE )[0].weighted_moments_central ref = np.array( [[ 7.4000000000e+01, -2.1316282073e-13, 4.7837837838e+02, -7.5943608473e+02], [ 3.7303493627e-14, -8.7837837838e+01, -1.4801314828e+02, -1.2714707125e+03], [ 1.2602837838e+03, 2.1571526662e+03, 6.6989799420e+03, 1.5304076361e+04], [ -7.6561796932e+02, -4.2385971907e+03, -9.9501164076e+03, -3.3156729271e+04]] ) np.set_printoptions(precision=10) assert_array_almost_equal(wmu, ref) def test_weighted_centroid(): centroid = regionprops(SAMPLE, intensity_image=INTENSITY_SAMPLE )[0].weighted_centroid assert_array_almost_equal(centroid, (5.540540540540, 9.445945945945)) def test_weighted_moments_hu(): whu = regionprops(SAMPLE, intensity_image=INTENSITY_SAMPLE )[0].weighted_moments_hu ref = np.array([ 3.1750587329e-01, 2.1417517159e-02, 2.3609322038e-02, 1.2565683360e-03, 8.3014209421e-07, -3.5073773473e-05, 6.7936409056e-06 ]) assert_array_almost_equal(whu, ref) def test_weighted_moments(): wm = regionprops(SAMPLE, intensity_image=INTENSITY_SAMPLE )[0].weighted_moments ref = np.array( [[ 7.4000000000e+01, 4.1000000000e+02, 2.7500000000e+03, 1.9778000000e+04], [ 6.9900000000e+02, 3.7850000000e+03, 2.4855000000e+04, 1.7500100000e+05], [ 7.8630000000e+03, 4.4063000000e+04, 2.9347700000e+05, 2.0810510000e+06], [ 9.7317000000e+04, 5.7256700000e+05, 3.9007170000e+06, 2.8078871000e+07]] ) assert_array_almost_equal(wm, ref) def test_weighted_moments_normalized(): wnu = regionprops(SAMPLE, intensity_image=INTENSITY_SAMPLE )[0].weighted_moments_normalized ref = np.array( [[ np.nan, np.nan, 0.0873590903, -0.0161217406], [ np.nan, -0.0160405109, -0.0031421072, -0.0031376984], [ 0.230146783, 0.0457932622, 0.0165315478, 0.0043903193], [-0.0162529732, -0.0104598869, -0.0028544152, -0.0011057191]] ) assert_array_almost_equal(wnu, ref) def test_label_sequence(): a = np.empty((2, 2), dtype=np.int) a[:, :] = 2 ps = regionprops(a) assert len(ps) == 1 assert ps[0].label == 2 def test_pure_background(): a = np.zeros((2, 2), dtype=np.int) ps = regionprops(a) assert len(ps) == 0 def test_invalid(): ps = regionprops(SAMPLE) def get_intensity_image(): ps[0].intensity_image assert_raises(AttributeError, get_intensity_image) def test_invalid_size(): wrong_intensity_sample = np.array([[1], [1]]) assert_raises(ValueError, regionprops, SAMPLE, wrong_intensity_sample) def test_equals(): arr = np.zeros((100, 100), dtype=np.int) arr[0:25, 0:25] = 1 arr[50:99, 50:99] = 2 regions = regionprops(arr) r1 = regions[0] regions = regionprops(arr) r2 = regions[0] r3 = regions[1] assert_equal(r1 == r2, True, "Same regionprops are not equal") assert_equal(r1 != r3, True, "Different regionprops are equal") def test_iterate_all_props(): region = regionprops(SAMPLE)[0] p0 = dict((p, region[p]) for p in region) region = regionprops(SAMPLE, intensity_image=INTENSITY_SAMPLE)[0] p1 = dict((p, region[p]) for p in region) assert len(p0) < len(p1) def test_cache(): region = regionprops(SAMPLE)[0] f0 = region.filled_image region._label_image[:10] = 1 f1 = region.filled_image # Changed underlying image, but cache keeps result the same assert_array_equal(f0, f1) # Now invalidate cache region._cache_active = False f1 = region.filled_image assert np.any(f0 != f1) def test_docstrings_and_props(): region = regionprops(SAMPLE)[0] docs = _parse_docs() props = [m for m in dir(region) if not m.startswith('_')] nr_docs_parsed = len(docs) nr_props = len(props) assert_equal(nr_docs_parsed, nr_props) ds = docs['weighted_moments_normalized'] assert 'iteration' not in ds assert len(ds.split('\n')) > 3 if __name__ == "__main__": from numpy.testing import run_module_suite run_module_suite() scikit-image-0.13.1/skimage/measure/tests/test_simple_metrics.py000066400000000000000000000046771316256236300250420ustar00rootroot00000000000000import numpy as np from numpy.testing import (run_module_suite, assert_equal, assert_raises, assert_almost_equal) from skimage.measure import compare_psnr, compare_nrmse, compare_mse import skimage.data from skimage._shared._warnings import expected_warnings np.random.seed(5) cam = skimage.data.camera() sigma = 20.0 cam_noisy = np.clip(cam + sigma * np.random.randn(*cam.shape), 0, 255) cam_noisy = cam_noisy.astype(cam.dtype) def test_PSNR_vs_IPOL(): # Tests vs. imdiff result from the following IPOL article and code: # http://www.ipol.im/pub/art/2011/g_lmii/ p_IPOL = 22.4497 p = compare_psnr(cam, cam_noisy) assert_almost_equal(p, p_IPOL, decimal=4) def test_PSNR_float(): p_uint8 = compare_psnr(cam, cam_noisy) p_float64 = compare_psnr(cam/255., cam_noisy/255., data_range=1) assert_almost_equal(p_uint8, p_float64, decimal=5) def test_PSNR_dynamic_range_and_data_range(): # Tests deprecation of "dynamic_range" in favor of "data_range" out1 = compare_psnr(cam/255., cam_noisy/255., data_range=1) with expected_warnings( '`dynamic_range` has been deprecated in favor of ' '`data_range`. The `dynamic_range` keyword argument ' 'will be removed in v0.14'): out2 = compare_psnr(cam/255., cam_noisy/255., dynamic_range=1) assert_equal(out1, out2) def test_PSNR_errors(): assert_raises(ValueError, compare_psnr, cam, cam.astype(np.float32)) assert_raises(ValueError, compare_psnr, cam, cam[:-1, :]) def test_NRMSE(): x = np.ones(4) y = np.asarray([0., 2., 2., 2.]) assert_equal(compare_nrmse(y, x, 'mean'), 1/np.mean(y)) assert_equal(compare_nrmse(y, x, 'Euclidean'), 1/np.sqrt(3)) assert_equal(compare_nrmse(y, x, 'min-max'), 1/(y.max()-y.min())) def test_NRMSE_no_int_overflow(): camf = cam.astype(np.float32) cam_noisyf = cam_noisy.astype(np.float32) assert_almost_equal(compare_mse(cam, cam_noisy), compare_mse(camf, cam_noisyf)) assert_almost_equal(compare_nrmse(cam, cam_noisy), compare_nrmse(camf, cam_noisyf)) def test_NRMSE_errors(): x = np.ones(4) assert_raises(ValueError, compare_nrmse, x.astype(np.uint8), x.astype(np.float32)) assert_raises(ValueError, compare_nrmse, x[:-1], x) # invalid normalization name assert_raises(ValueError, compare_nrmse, x, x, 'foo') if __name__ == "__main__": run_module_suite() scikit-image-0.13.1/skimage/measure/tests/test_structural_similarity.py000066400000000000000000000163041316256236300264670ustar00rootroot00000000000000import os import numpy as np import scipy.io from numpy.testing import (assert_equal, assert_raises, assert_almost_equal, assert_array_almost_equal) from skimage.measure import compare_ssim as ssim import skimage.data from skimage.io import imread from skimage import data_dir from skimage._shared._warnings import expected_warnings np.random.seed(5) cam = skimage.data.camera() sigma = 20.0 cam_noisy = np.clip(cam + sigma * np.random.randn(*cam.shape), 0, 255) cam_noisy = cam_noisy.astype(cam.dtype) np.random.seed(1234) # This test to be removed in 0.14, along with the structural_similarity alias # for compare_ssim def test_old_name_deprecated(): from skimage.measure import structural_similarity with expected_warnings('deprecated'): ssim_result = structural_similarity(cam, cam_noisy, win_size=31) def test_ssim_patch_range(): N = 51 X = (np.random.rand(N, N) * 255).astype(np.uint8) Y = (np.random.rand(N, N) * 255).astype(np.uint8) assert(ssim(X, Y, win_size=N) < 0.1) assert_equal(ssim(X, X, win_size=N), 1) def test_ssim_image(): N = 100 X = (np.random.rand(N, N) * 255).astype(np.uint8) Y = (np.random.rand(N, N) * 255).astype(np.uint8) S0 = ssim(X, X, win_size=3) assert_equal(S0, 1) S1 = ssim(X, Y, win_size=3) assert(S1 < 0.3) S2 = ssim(X, Y, win_size=11, gaussian_weights=True) assert(S1 < 0.3) mssim0, S3 = ssim(X, Y, full=True) assert_equal(S3.shape, X.shape) mssim = ssim(X, Y) assert_equal(mssim0, mssim) # ssim of image with itself should be 1.0 assert_equal(ssim(X, X), 1.0) # NOTE: This test is known to randomly fail on some systems (Mac OS X 10.6) def test_ssim_grad(): N = 30 X = np.random.rand(N, N) * 255 Y = np.random.rand(N, N) * 255 f = ssim(X, Y, data_range=255) g = ssim(X, Y, data_range=255, gradient=True) assert f < 0.05 assert g[0] < 0.05 assert np.all(g[1] < 0.05) mssim, grad, s = ssim(X, Y, data_range=255, gradient=True, full=True) assert np.all(grad < 0.05) # NOTE: This test is known to randomly fail on some systems (Mac OS X 10.6) def test_ssim_dynamic_range_and_data_range(): # Tests deprecation of "dynamic_range" in favor of "data_range" N = 30 X = np.random.rand(N, N) * 255 Y = np.random.rand(N, N) * 255 with expected_warnings( '`dynamic_range` has been deprecated in favor of ' '`data_range`. The `dynamic_range` keyword argument ' 'will be removed in v0.14'): out2 = ssim(X, Y, dynamic_range=255) out1 = ssim(X, Y, data_range=255) assert_equal(out1, out2) def test_ssim_dtype(): N = 30 X = np.random.rand(N, N) Y = np.random.rand(N, N) S1 = ssim(X, Y) X = (X * 255).astype(np.uint8) Y = (X * 255).astype(np.uint8) S2 = ssim(X, Y) assert S1 < 0.1 assert S2 < 0.1 def test_ssim_multichannel(): N = 100 X = (np.random.rand(N, N) * 255).astype(np.uint8) Y = (np.random.rand(N, N) * 255).astype(np.uint8) S1 = ssim(X, Y, win_size=3) # replicate across three channels. should get identical value Xc = np.tile(X[..., np.newaxis], (1, 1, 3)) Yc = np.tile(Y[..., np.newaxis], (1, 1, 3)) S2 = ssim(Xc, Yc, multichannel=True, win_size=3) assert_almost_equal(S1, S2) # full case should return an image as well m, S3 = ssim(Xc, Yc, multichannel=True, full=True) assert_equal(S3.shape, Xc.shape) # gradient case m, grad = ssim(Xc, Yc, multichannel=True, gradient=True) assert_equal(grad.shape, Xc.shape) # full and gradient case m, grad, S3 = ssim(Xc, Yc, multichannel=True, full=True, gradient=True) assert_equal(grad.shape, Xc.shape) assert_equal(S3.shape, Xc.shape) # fail if win_size exceeds any non-channel dimension assert_raises(ValueError, ssim, Xc, Yc, win_size=7, multichannel=False) def test_ssim_nD(): # test 1D through 4D on small random arrays N = 10 for ndim in range(1, 5): xsize = [N, ] * 5 X = (np.random.rand(*xsize) * 255).astype(np.uint8) Y = (np.random.rand(*xsize) * 255).astype(np.uint8) mssim = ssim(X, Y, win_size=3) assert mssim < 0.05 def test_ssim_multichannel_chelsea(): # color image example Xc = skimage.data.chelsea() sigma = 15.0 Yc = np.clip(Xc + sigma * np.random.randn(*Xc.shape), 0, 255) Yc = Yc.astype(Xc.dtype) # multichannel result should be mean of the individual channel results mssim = ssim(Xc, Yc, multichannel=True) mssim_sep = [ssim(Yc[..., c], Xc[..., c]) for c in range(Xc.shape[-1])] assert_almost_equal(mssim, np.mean(mssim_sep)) # ssim of image with itself should be 1.0 assert_equal(ssim(Xc, Xc, multichannel=True), 1.0) def test_gaussian_mssim_vs_IPOL(): # Tests vs. imdiff result from the following IPOL article and code: # http://www.ipol.im/pub/art/2011/g_lmii/ mssim_IPOL = 0.327309966087341 mssim = ssim(cam, cam_noisy, gaussian_weights=True, use_sample_covariance=False) assert_almost_equal(mssim, mssim_IPOL, decimal=3) def test_gaussian_mssim_vs_author_ref(): """ test vs. result from original author's Matlab implementation available at https://ece.uwaterloo.ca/~z70wang/research/ssim/ Matlab test code: img1 = imread('camera.png') img2 = imread('camera_noisy.png') mssim = ssim_index(img1, img2) """ mssim_matlab = 0.327314295673357 mssim = ssim(cam, cam_noisy, gaussian_weights=True, use_sample_covariance=False) assert_almost_equal(mssim, mssim_matlab, decimal=3) def test_gaussian_mssim_and_gradient_vs_Matlab(): # comparison to Matlab implementation of N. Avanaki: # https://ece.uwaterloo.ca/~nnikvand/Coderep/SHINE%20TOOLBOX/SHINEtoolbox/ # Note: final line of ssim_sens.m was modified to discard image borders ref = np.load(os.path.join(data_dir, 'mssim_matlab_output.npz')) grad_matlab = ref['grad_matlab'] mssim_matlab = float(ref['mssim_matlab']) mssim, grad = ssim(cam, cam_noisy, gaussian_weights=True, gradient=True, use_sample_covariance=False) assert_almost_equal(mssim, mssim_matlab, decimal=3) # check almost equal aside from object borders assert_array_almost_equal(grad_matlab[5:-5], grad[5:-5]) def test_mssim_vs_legacy(): # check that ssim with default options matches skimage 0.11 result mssim_skimage_0pt11 = 0.34192589699605191 mssim = ssim(cam, cam_noisy) assert_almost_equal(mssim, mssim_skimage_0pt11) def test_invalid_input(): X = np.zeros((3, 3), dtype=np.double) Y = np.zeros((3, 3), dtype=np.int) assert_raises(ValueError, ssim, X, Y) Y = np.zeros((4, 4), dtype=np.double) assert_raises(ValueError, ssim, X, Y) assert_raises(ValueError, ssim, X, X, win_size=8) # do not allow both image content weighting and gradient calculation assert_raises(ValueError, ssim, X, X, image_content_weighting=True, gradient=True) # some kwarg inputs must be non-negative assert_raises(ValueError, ssim, X, X, K1=-0.1) assert_raises(ValueError, ssim, X, X, K2=-0.1) assert_raises(ValueError, ssim, X, X, sigma=-1.0) if __name__ == "__main__": np.testing.run_module_suite() scikit-image-0.13.1/skimage/morphology/000077500000000000000000000000001316256236300177705ustar00rootroot00000000000000scikit-image-0.13.1/skimage/morphology/__init__.py000066400000000000000000000030621316256236300221020ustar00rootroot00000000000000from .binary import (binary_erosion, binary_dilation, binary_opening, binary_closing) from .grey import (erosion, dilation, opening, closing, white_tophat, black_tophat) from .selem import (square, rectangle, diamond, disk, cube, octahedron, ball, octagon, star) from ..measure._label import label from .watershed import watershed from ._skeletonize import skeletonize, medial_axis, thin from ._skeletonize_3d import skeletonize_3d from .convex_hull import convex_hull_image, convex_hull_object from .greyreconstruct import reconstruction from .misc import remove_small_objects, remove_small_holes from .extrema import (h_minima, h_maxima, local_maxima, local_minima) __all__ = ['binary_erosion', 'binary_dilation', 'binary_opening', 'binary_closing', 'erosion', 'dilation', 'opening', 'closing', 'white_tophat', 'black_tophat', 'square', 'rectangle', 'diamond', 'disk', 'cube', 'octahedron', 'ball', 'octagon', 'star', 'label', 'watershed', 'skeletonize', 'skeletonize_3d', 'thin', 'medial_axis', 'convex_hull_image', 'convex_hull_object', 'reconstruction', 'remove_small_objects', 'remove_small_holes', 'h_minima', 'h_maxima', 'local_maxima', 'local_minima'] scikit-image-0.13.1/skimage/morphology/_convex_hull.pyx000066400000000000000000000037561316256236300232320ustar00rootroot00000000000000#cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False #cython: wraparound=False import numpy as np cimport numpy as cnp def possible_hull(cnp.uint8_t[:, ::1] img): """Return positions of pixels that possibly belong to the convex hull. Parameters ---------- img : ndarray of bool Binary input image. Returns ------- coords : ndarray (cols, 2) The ``(row, column)`` coordinates of all pixels that possibly belong to the convex hull. """ cdef Py_ssize_t r, c cdef Py_ssize_t rows = img.shape[0] cdef Py_ssize_t cols = img.shape[1] # Output: rows storage slots for left boundary pixels # cols storage slots for top boundary pixels # rows storage slots for right boundary pixels # cols storage slots for bottom boundary pixels coords = np.ones((2 * (rows + cols), 2), dtype=np.intp) coords *= -1 cdef Py_ssize_t[:, ::1] nonzero = coords cdef Py_ssize_t rows_cols = rows + cols cdef Py_ssize_t rows_2_cols = 2 * rows + cols cdef Py_ssize_t rows_cols_r, rows_c for r in range(rows): rows_cols_r = rows_cols + r for c in range(cols): if img[r, c] != 0: rows_c = rows + c rows_2_cols_c = rows_2_cols + c # Left check if nonzero[r, 1] == -1: nonzero[r, 0] = r nonzero[r, 1] = c # Right check elif nonzero[rows_cols_r, 1] < c: nonzero[rows_cols_r, 0] = r nonzero[rows_cols_r, 1] = c # Top check if nonzero[rows_c, 1] == -1: nonzero[rows_c, 0] = r nonzero[rows_c, 1] = c # Bottom check elif nonzero[rows_2_cols_c, 0] < r: nonzero[rows_2_cols_c, 0] = r nonzero[rows_2_cols_c, 1] = c return coords[coords[:, 0] != -1] scikit-image-0.13.1/skimage/morphology/_greyreconstruct.pyx000066400000000000000000000103121316256236300241300ustar00rootroot00000000000000""" `reconstruction_loop` originally part of CellProfiler, code licensed under both GPL and BSD licenses. Website: http://www.cellprofiler.org Copyright (c) 2003-2009 Massachusetts Institute of Technology Copyright (c) 2009-2011 Broad Institute All rights reserved. Original author: Lee Kamentsky """ cimport numpy as cnp cimport cython @cython.boundscheck(False) def reconstruction_loop(cnp.ndarray[dtype=cnp.uint32_t, ndim=1, negative_indices=False, mode='c'] aranks, cnp.ndarray[dtype=cnp.int32_t, ndim=1, negative_indices=False, mode='c'] aprev, cnp.ndarray[dtype=cnp.int32_t, ndim=1, negative_indices=False, mode='c'] anext, cnp.ndarray[dtype=cnp.int32_t, ndim=1, negative_indices=False, mode='c'] astrides, Py_ssize_t current_idx, Py_ssize_t image_stride): """The inner loop for reconstruction. This algorithm uses the rank-order of pixels. If low intensity pixels have a low rank and high intensity pixels have a high rank, then this loop performs reconstruction by dilation. If this ranking is reversed, the result is reconstruction by erosion. For each pixel in the seed image, check its neighbors. If its neighbor's rank is below that of the current pixel, replace the neighbor's rank with the rank of the current pixel. This dilation is limited by the mask, i.e. the rank at each pixel cannot exceed the mask as that pixel. Parameters ---------- aranks : array The rank order of the flattened seed and mask images. aprev, anext: arrays Indices of previous and next pixels in rank sorted order. astrides : array Strides to neighbors of the current pixel. current_idx : int Index of highest-ranked pixel used as starting point in loop. image_stride : int Stride between seed image and mask image in `aranks`. """ cdef unsigned int neighbor_rank, current_rank, mask_rank cdef int i, neighbor_idx, current_link, nprev, nnext cdef int nstrides = astrides.shape[0] cdef cnp.uint32_t *ranks = (aranks.data) cdef cnp.int32_t *prev = (aprev.data) cdef cnp.int32_t *next = (anext.data) cdef cnp.int32_t *strides = (astrides.data) while current_idx != -1: if current_idx < image_stride: current_rank = ranks[current_idx] if current_rank == 0: break for i in range(nstrides): neighbor_idx = current_idx + strides[i] neighbor_rank = ranks[neighbor_idx] # Only propagate neighbors ranked below the current rank if neighbor_rank < current_rank: mask_rank = ranks[neighbor_idx + image_stride] # Only propagate neighbors ranked below the mask rank if neighbor_rank < mask_rank: # Raise the neighbor to the mask rank if # the mask ranked below the current rank if mask_rank < current_rank: current_link = neighbor_idx + image_stride ranks[neighbor_idx] = mask_rank else: current_link = current_idx ranks[neighbor_idx] = current_rank # unlink the neighbor nprev = prev[neighbor_idx] nnext = next[neighbor_idx] next[nprev] = nnext if nnext != -1: prev[nnext] = nprev # link to the neighbor after the current link nnext = next[current_link] next[neighbor_idx] = nnext prev[neighbor_idx] = current_link if nnext >= 0: prev[nnext] = neighbor_idx next[current_link] = neighbor_idx current_idx = next[current_idx] scikit-image-0.13.1/skimage/morphology/_skeletonize.py000066400000000000000000000425611316256236300230450ustar00rootroot00000000000000""" Algorithms for computing the skeleton of a binary image """ import sys from six.moves import range import numpy as np from scipy import ndimage as ndi from ._skeletonize_cy import (_fast_skeletonize, _skeletonize_loop, _table_lookup_index) from .._shared.utils import assert_nD # --------- Skeletonization by morphological thinning --------- def skeletonize(image): """Return the skeleton of a binary image. Thinning is used to reduce each connected component in a binary image to a single-pixel wide skeleton. Parameters ---------- image : numpy.ndarray A binary image containing the objects to be skeletonized. '1' represents foreground, and '0' represents background. It also accepts arrays of boolean values where True is foreground. Returns ------- skeleton : ndarray A matrix containing the thinned image. See also -------- medial_axis Notes ----- The algorithm [Zha84]_ works by making successive passes of the image, removing pixels on object borders. This continues until no more pixels can be removed. The image is correlated with a mask that assigns each pixel a number in the range [0...255] corresponding to each possible pattern of its 8 neighbouring pixels. A look up table is then used to assign the pixels a value of 0, 1, 2 or 3, which are selectively removed during the iterations. Note that this algorithm will give different results than a medial axis transform, which is also often referred to as "skeletonization". References ---------- .. [Zha84] A fast parallel algorithm for thinning digital patterns, T. Y. Zhang and C. Y. Suen, Communications of the ACM, March 1984, Volume 27, Number 3. Examples -------- >>> X, Y = np.ogrid[0:9, 0:9] >>> ellipse = (1./3 * (X - 4)**2 + (Y - 4)**2 < 3**2).astype(np.uint8) >>> ellipse array([[0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0]], dtype=uint8) >>> skel = skeletonize(ellipse) >>> skel.astype(np.uint8) array([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8) """ # convert to unsigned int (this should work for boolean values) image = image.astype(np.uint8) # check some properties of the input image: # - 2D # - binary image with only 0's and 1's if image.ndim != 2: raise ValueError('Skeletonize requires a 2D array') if not np.all(np.in1d(image.flat, (0, 1))): raise ValueError('Image contains values other than 0 and 1') return _fast_skeletonize(image) # --------- Skeletonization and thinning based on Guo and Hall 1989 --------- def _generate_thin_luts(): """generate LUTs for thinning algorithm (for reference)""" def nabe(n): return np.array([n >> i & 1 for i in range(0, 9)]).astype(np.bool) def G1(n): s = 0 bits = nabe(n) for i in (0, 2, 4, 6): if not(bits[i]) and (bits[i + 1] or bits[(i + 2) % 8]): s += 1 return s == 1 g1_lut = np.array([G1(n) for n in range(256)]) def G2(n): n1, n2 = 0, 0 bits = nabe(n) for k in (1, 3, 5, 7): if bits[k] or bits[k - 1]: n1 += 1 if bits[k] or bits[(k + 1) % 8]: n2 += 1 return min(n1, n2) in [2, 3] g2_lut = np.array([G2(n) for n in range(256)]) g12_lut = g1_lut & g2_lut def G3(n): bits = nabe(n) return not((bits[1] or bits[2] or not(bits[7])) and bits[0]) def G3p(n): bits = nabe(n) return not((bits[5] or bits[6] or not(bits[3])) and bits[4]) g3_lut = np.array([G3(n) for n in range(256)]) g3p_lut = np.array([G3p(n) for n in range(256)]) g123_lut = g12_lut & g3_lut g123p_lut = g12_lut & g3p_lut return g123_lut, g123p_lut G123_LUT = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0], dtype=np.bool) G123P_LUT = np.array([0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=np.bool) def thin(image, max_iter=None): """ Perform morphological thinning of a binary image. Parameters ---------- image : binary (M, N) ndarray The image to be thinned. max_iter : int, number of iterations, optional Regardless of the value of this parameter, the thinned image is returned immediately if an iteration produces no change. If this parameter is specified it thus sets an upper bound on the number of iterations performed. Returns ------- out : ndarray of bool Thinned image. See also -------- skeletonize, skeletonize_3d, medial_axis Notes ----- This algorithm [1]_ works by making multiple passes over the image, removing pixels matching a set of criteria designed to thin connected regions while preserving eight-connected components and 2 x 2 squares [2]_. In each of the two sub-iterations the algorithm correlates the intermediate skeleton image with a neighborhood mask, then looks up each neighborhood in a lookup table indicating whether the central pixel should be deleted in that sub-iteration. References ---------- .. [1] Z. Guo and R. W. Hall, "Parallel thinning with two-subiteration algorithms," Comm. ACM, vol. 32, no. 3, pp. 359-373, 1989. DOI:10.1145/62065.62074 .. [2] Lam, L., Seong-Whan Lee, and Ching Y. Suen, "Thinning Methodologies-A Comprehensive Survey," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 14, No. 9, p. 879, 1992. DOI:10.1109/34.161346 Examples -------- >>> square = np.zeros((7, 7), dtype=np.uint8) >>> square[1:-1, 2:-2] = 1 >>> square[0, 1] = 1 >>> square array([[0, 1, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0]], dtype=uint8) >>> skel = thin(square) >>> skel.astype(np.uint8) array([[0, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]], dtype=uint8) """ # check parameters max_iter = max_iter or sys.maxsize # check that image is 2d assert_nD(image, 2) # convert image to uint8 with values in {0, 1} skel = np.asanyarray(image, dtype=bool).astype(np.uint8) # neighborhood mask mask = np.array([[ 8, 4, 2], [16, 0, 1], [32, 64,128]], dtype=np.uint8) # iterate until convergence, up to the iteration limit for i in range(max_iter): before = np.sum(skel) # count points before thinning # perform the two "subiterations" described in the paper for lut in [G123_LUT, G123P_LUT]: # correlate image with neighborhood mask N = ndi.correlate(skel, mask, mode='constant') # take deletion decision from this subiteration's LUT D = np.take(lut, N) # perform deletion skel[D] = 0 after = np.sum(skel) # count points after thinning if before == after: # iteration had no effect: finish break return skel.astype(np.bool) # --------- Skeletonization by medial axis transform -------- _eight_connect = ndi.generate_binary_structure(2, 2) def medial_axis(image, mask=None, return_distance=False): """ Compute the medial axis transform of a binary image Parameters ---------- image : binary ndarray, shape (M, N) The image of the shape to be skeletonized. mask : binary ndarray, shape (M, N), optional If a mask is given, only those elements in `image` with a true value in `mask` are used for computing the medial axis. return_distance : bool, optional If true, the distance transform is returned as well as the skeleton. Returns ------- out : ndarray of bools Medial axis transform of the image dist : ndarray of ints, optional Distance transform of the image (only returned if `return_distance` is True) See also -------- skeletonize Notes ----- This algorithm computes the medial axis transform of an image as the ridges of its distance transform. The different steps of the algorithm are as follows * A lookup table is used, that assigns 0 or 1 to each configuration of the 3x3 binary square, whether the central pixel should be removed or kept. We want a point to be removed if it has more than one neighbor and if removing it does not change the number of connected components. * The distance transform to the background is computed, as well as the cornerness of the pixel. * The foreground (value of 1) points are ordered by the distance transform, then the cornerness. * A cython function is called to reduce the image to its skeleton. It processes pixels in the order determined at the previous step, and removes or maintains a pixel according to the lookup table. Because of the ordering, it is possible to process all pixels in only one pass. Examples -------- >>> square = np.zeros((7, 7), dtype=np.uint8) >>> square[1:-1, 2:-2] = 1 >>> square array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0]], dtype=uint8) >>> medial_axis(square).astype(np.uint8) array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0]], dtype=uint8) """ global _eight_connect if mask is None: masked_image = image.astype(np.bool) else: masked_image = image.astype(bool).copy() masked_image[~mask] = False # # Build lookup table - three conditions # 1. Keep only positive pixels (center_is_foreground array). # AND # 2. Keep if removing the pixel results in a different connectivity # (if the number of connected components is different with and # without the central pixel) # OR # 3. Keep if # pixels in neighbourhood is 2 or less # Note that table is independent of image center_is_foreground = (np.arange(512) & 2**4).astype(bool) table = (center_is_foreground # condition 1. & (np.array([ndi.label(_pattern_of(index), _eight_connect)[1] != ndi.label(_pattern_of(index & ~ 2**4), _eight_connect)[1] for index in range(512)]) # condition 2 | np.array([np.sum(_pattern_of(index)) < 3 for index in range(512)])) # condition 3 ) # Build distance transform distance = ndi.distance_transform_edt(masked_image) if return_distance: store_distance = distance.copy() # Corners # The processing order along the edge is critical to the shape of the # resulting skeleton: if you process a corner first, that corner will # be eroded and the skeleton will miss the arm from that corner. Pixels # with fewer neighbors are more "cornery" and should be processed last. # We use a cornerness_table lookup table where the score of a # configuration is the number of background (0-value) pixels in the # 3x3 neighbourhood cornerness_table = np.array([9 - np.sum(_pattern_of(index)) for index in range(512)]) corner_score = _table_lookup(masked_image, cornerness_table) # Define arrays for inner loop i, j = np.mgrid[0:image.shape[0], 0:image.shape[1]] result = masked_image.copy() distance = distance[result] i = np.ascontiguousarray(i[result], dtype=np.intp) j = np.ascontiguousarray(j[result], dtype=np.intp) result = np.ascontiguousarray(result, np.uint8) # Determine the order in which pixels are processed. # We use a random # for tiebreaking. Assign each pixel in the image a # predictable, random # so that masking doesn't affect arbitrary choices # of skeletons # generator = np.random.RandomState(0) tiebreaker = generator.permutation(np.arange(masked_image.sum())) order = np.lexsort((tiebreaker, corner_score[masked_image], distance)) order = np.ascontiguousarray(order, dtype=np.int32) table = np.ascontiguousarray(table, dtype=np.uint8) # Remove pixels not belonging to the medial axis _skeletonize_loop(result, i, j, order, table) result = result.astype(bool) if not mask is None: result[~mask] = image[~mask] if return_distance: return result, store_distance else: return result def _pattern_of(index): """ Return the pattern represented by an index value Byte decomposition of index """ return np.array([[index & 2**0, index & 2**1, index & 2**2], [index & 2**3, index & 2**4, index & 2**5], [index & 2**6, index & 2**7, index & 2**8]], bool) def _table_lookup(image, table): """ Perform a morphological transform on an image, directed by its neighbors Parameters ---------- image : ndarray A binary image table : ndarray A 512-element table giving the transform of each pixel given the values of that pixel and its 8-connected neighbors. border_value : bool The value of pixels beyond the border of the image. Returns ------- result : ndarray of same shape as `image` Transformed image Notes ----- The pixels are numbered like this:: 0 1 2 3 4 5 6 7 8 The index at a pixel is the sum of 2** for pixels that evaluate to true. """ # # We accumulate into the indexer to get the index into the table # at each point in the image # if image.shape[0] < 3 or image.shape[1] < 3: image = image.astype(bool) indexer = np.zeros(image.shape, int) indexer[1:, 1:] += image[:-1, :-1] * 2**0 indexer[1:, :] += image[:-1, :] * 2**1 indexer[1:, :-1] += image[:-1, 1:] * 2**2 indexer[:, 1:] += image[:, :-1] * 2**3 indexer[:, :] += image[:, :] * 2**4 indexer[:, :-1] += image[:, 1:] * 2**5 indexer[:-1, 1:] += image[1:, :-1] * 2**6 indexer[:-1, :] += image[1:, :] * 2**7 indexer[:-1, :-1] += image[1:, 1:] * 2**8 else: indexer = _table_lookup_index(np.ascontiguousarray(image, np.uint8)) image = table[indexer] return image scikit-image-0.13.1/skimage/morphology/_skeletonize_3d.py000066400000000000000000000047661316256236300234400ustar00rootroot00000000000000from __future__ import division, print_function, absolute_import import numpy as np from ..util import img_as_ubyte, crop from ._skeletonize_3d_cy import _compute_thin_image def skeletonize_3d(img): """Compute the skeleton of a binary image. Thinning is used to reduce each connected component in a binary image to a single-pixel wide skeleton. Parameters ---------- img : ndarray, 2D or 3D A binary image containing the objects to be skeletonized. Zeros represent background, nonzero values are foreground. Returns ------- skeleton : ndarray The thinned image. See also -------- skeletonize, medial_axis Notes ----- The method of [Lee94]_ uses an octree data structure to examine a 3x3x3 neighborhood of a pixel. The algorithm proceeds by iteratively sweeping over the image, and removing pixels at each iteration until the image stops changing. Each iteration consists of two steps: first, a list of candidates for removal is assembled; then pixels from this list are rechecked sequentially, to better preserve connectivity of the image. The algorithm this function implements is different from the algorithms used by either `skeletonize` or `medial_axis`, thus for 2D images the results produced by this function are generally different. References ---------- .. [Lee94] T.-C. Lee, R.L. Kashyap and C.-N. Chu, Building skeleton models via 3-D medial surface/axis thinning algorithms. Computer Vision, Graphics, and Image Processing, 56(6):462-478, 1994. """ # make sure the image is 3D or 2D if img.ndim < 2 or img.ndim > 3: raise ValueError("skeletonize_3d can only handle 2D or 3D images; " "got img.ndim = %s instead." % img.ndim) img = np.ascontiguousarray(img) img = img_as_ubyte(img, force_copy=False) # make an in image 3D and pad it w/ zeros to simplify dealing w/ boundaries # NB: careful here to not clobber the original *and* minimize copying img_o = img if img.ndim == 2: img_o = img[np.newaxis, ...] img_o = np.pad(img_o, pad_width=1, mode='constant') # normalize to binary maxval = img_o.max() img_o[img_o != 0] = 1 # do the computation img_o = np.asarray(_compute_thin_image(img_o)) # crop it back and restore the original intensity range img_o = crop(img_o, crop_width=1) if img.ndim == 2: img_o = img_o[0] img_o *= maxval return img_oscikit-image-0.13.1/skimage/morphology/_skeletonize_3d_cy.pyx.in000066400000000000000000000347741316256236300247320ustar00rootroot00000000000000""" This is an implementation of the 2D/3D thinning algorithm of [Lee94]_ of binary images, based on [IAC15]_. The original Java code [IAC15]_ carries the following message: * This work is an implementation by Ignacio Arganda-Carreras of the * 3D thinning algorithm from Lee et al. "Building skeleton models via 3-D * medial surface/axis thinning algorithms. Computer Vision, Graphics, and * Image Processing, 56(6):462-478, 1994." Based on the ITK version from * Hanno Homann http://hdl.handle.net/1926/1292 *

      * More information at Skeletonize3D homepage: * http://fiji.sc/Skeletonize3D * * @version 1.0 11/13/2015 (unique BSD licensed version for scikit-image) * @author Ignacio Arganda-Carreras (iargandacarreras at gmail.com) References ---------- .. [Lee94] T.-C. Lee, R.L. Kashyap and C.-N. Chu, Building skeleton models via 3-D medial surface/axis thinning algorithms. Computer Vision, Graphics, and Image Processing, 56(6):462-478, 1994. .. [IAC15] Ignacio Arganda-Carreras, 2015. Skeletonize3D plugin for ImageJ(C). http://fiji.sc/Skeletonize3D """ from __future__ import division, print_function, absolute_import from libc.string cimport memcpy import numpy as np from numpy cimport npy_intp, npy_uint8, ndarray cimport cython ctypedef npy_uint8 pixel_type @cython.boundscheck(False) @cython.wraparound(False) def _compute_thin_image(pixel_type[:, :, ::1] img not None): """Compute a thin image. Loop through the image multiple times, removing "simple" points, i.e. those point which can be removed without changing local connectivity in the 3x3x3 neighborhood of a point. This routine implements the two-pass algorithm of [Lee94]_. Namely, for each of the six border types (positive and negative x-, y- and z-), the algorithm first collects all possibly deletable points, and then performs a sequential rechecking. The input, `img`, is assumed to be a 3D binary image in the (p, r, c) format [i.e., C ordered array], filled by zeros (background) and ones. Furthermore, `img` is assumed to be padded by zeros from all directions --- this way the zero boundary conditions are automatic and there is need to guard against out-of-bounds access. """ cdef: int unchanged_borders = 0, curr_border, num_borders int borders[6] npy_intp p, r, c bint no_change list simple_border_points Py_ssize_t num_border_points, i, j (npy_intp, npy_intp, npy_intp) point pixel_type neighb[27] # loop over the six directions in this order (for consistency with ImageJ) borders[:] = [4, 3, 2, 1, 5, 6] # no need to worry about the z direction if the original image is 2D. if img.shape[0] == 3: num_borders = 4 else: num_borders = 6 # loop through the image several times until there is no change for all # the six border types while unchanged_borders < num_borders: unchanged_borders = 0 for j in range(num_borders): curr_border = borders[j] simple_border_points = find_simple_point_candidates(img, curr_border) # sequential re-checking to preserve connectivity when deleting # in a parallel way no_change = True num_border_points = len(simple_border_points) for i in range(num_border_points): point = simple_border_points[i] p, r, c = point get_neighborhood(img, p, r, c, neighb) if is_simple_point(neighb): img[p, r, c] = 0 no_change = False if no_change: unchanged_borders += 1 return np.asarray(img) @cython.boundscheck(False) @cython.wraparound(False) cdef list find_simple_point_candidates(pixel_type[:, :, ::1] img, int curr_border): """Inner loop of compute_thin_image. The algorithm of [Lee94]_ proceeds in two steps: (1) six directions are checked for simple border points to remove, and (2) these candidates are sequentially rechecked, see Sec 3 of [Lee94]_ for rationale and discussion. This routine implements the first step above: it loops over the image for a given direction and assembles candidates for removal. """ # This routine looks like it could be nogil, but actually it cannot be, # because of `simple_border_points` being a python list which is being # mutated. cdef: list simple_border_points = [] pixel_type neighborhood[27] npy_intp p, r, c bint is_border_pt (npy_intp, npy_intp, npy_intp) point # rebind a global name to avoid lookup. The table is filled in # at import time. int[::1] Euler_LUT = LUT # loop through the image # NB: each loop is from 1 to size-1: img is padded from all sides for p in range(1, img.shape[0] - 1): for r in range(1, img.shape[1] - 1): for c in range(1, img.shape[2] - 1): # check if pixel is foreground if img[p, r, c] != 1: continue is_border_pt = (curr_border == 1 and img[p, r, c-1] == 0 or #N curr_border == 2 and img[p, r, c+1] == 0 or #S curr_border == 3 and img[p, r+1, c] == 0 or #E curr_border == 4 and img[p, r-1, c] == 0 or #W curr_border == 5 and img[p+1, r, c] == 0 or #U curr_border == 6 and img[p-1, r, c] == 0) #B if not is_border_pt: # current point is not deletable continue get_neighborhood(img, p, r, c, neighborhood) # check if (p, r, c) can be deleted: # * it must not be an endpoint; # * it must be Euler invariant (condition 1 in [Lee94]_); and # * it must be simple (i.e., its deletion does not change # connectivity in the 3x3x3 neighborhood) # this is conditions 2 and 3 in [Lee94]_ if (is_endpoint(neighborhood) or not is_Euler_invariant(neighborhood, Euler_LUT) or not is_simple_point(neighborhood)): continue # ok, add (p, r, c) to the list of simple border points point = p, r, c simple_border_points.append(point) return simple_border_points @cython.boundscheck(False) @cython.wraparound(False) cdef void get_neighborhood(pixel_type[:, :, ::1] img, npy_intp p, npy_intp r, npy_intp c, pixel_type neighborhood[]): """Get the neighborhood of a pixel. Assume zero boundary conditions. Image is already padded, so no out-of-bounds checking. For the numbering of points see Fig. 1a. of [Lee94]_, where the numbers do *not* include the center point itself. OTOH, this numbering below includes it as number 13. The latter is consistent with [IAC15]_. """ neighborhood[0] = img[p-1, r-1, c-1] neighborhood[1] = img[p-1, r, c-1] neighborhood[2] = img[p-1, r+1, c-1] neighborhood[ 3] = img[p-1, r-1, c] neighborhood[ 4] = img[p-1, r, c] neighborhood[ 5] = img[p-1, r+1, c] neighborhood[ 6] = img[p-1, r-1, c+1] neighborhood[ 7] = img[p-1, r, c+1] neighborhood[ 8] = img[p-1, r+1, c+1] neighborhood[ 9] = img[p, r-1, c-1] neighborhood[10] = img[p, r, c-1] neighborhood[11] = img[p, r+1, c-1] neighborhood[12] = img[p, r-1, c] neighborhood[13] = img[p, r, c] neighborhood[14] = img[p, r+1, c] neighborhood[15] = img[p, r-1, c+1] neighborhood[16] = img[p, r, c+1] neighborhood[17] = img[p, r+1, c+1] neighborhood[18] = img[p+1, r-1, c-1] neighborhood[19] = img[p+1, r, c-1] neighborhood[20] = img[p+1, r+1, c-1] neighborhood[21] = img[p+1, r-1, c] neighborhood[22] = img[p+1, r, c] neighborhood[23] = img[p+1, r+1, c] neighborhood[24] = img[p+1, r-1, c+1] neighborhood[25] = img[p+1, r, c+1] neighborhood[26] = img[p+1, r+1, c+1] ###### look-up tables def fill_Euler_LUT(): """ Look-up table for preserving Euler characteristic. This is column $\delta G_{26}$ of Table 2 of [Lee94]_. """ cdef int arr[128] arr[:] = [1, -1, -1, 1, -3, -1, -1, 1, -1, 1, 1, -1, 3, 1, 1, -1, -3, -1, 3, 1, 1, -1, 3, 1, -1, 1, 1, -1, 3, 1, 1, -1, -3, 3, -1, 1, 1, 3, -1, 1, -1, 1, 1, -1, 3, 1, 1, -1, 1, 3, 3, 1, 5, 3, 3, 1, -1, 1, 1, -1, 3, 1, 1, -1, -7, -1, -1, 1, -3, -1, -1, 1, -1, 1, 1, -1, 3, 1, 1, -1, -3, -1, 3, 1, 1, -1, 3, 1, -1, 1, 1, -1, 3, 1, 1, -1, -3, 3, -1, 1, 1, 3, -1, 1, -1, 1, 1, -1, 3, 1, 1, -1, 1, 3, 3, 1, 5, 3, 3, 1, -1, 1, 1, -1, 3, 1, 1, -1] cdef ndarray LUT = np.zeros(256, dtype=np.intc) LUT[1::2] = arr return LUT cdef int[::1] LUT = fill_Euler_LUT() # Fill the look-up table for indexing octants for computing the Euler # characteristic. See is_Euler_invariant routine below. {{py: _neighb_idx = [[2, 1, 11, 10, 5, 4, 14], # NEB [0, 9, 3, 12, 1, 10, 4], # NWB [8, 7, 17, 16, 5, 4, 14], # SEB [6, 15, 7, 16, 3, 12, 4], # SWB [20, 23, 19, 22, 11, 14, 10], # NEU [18, 21, 9, 12, 19, 22, 10], # NWU [26, 23, 17, 14, 25, 22, 16], # SEU [24, 25, 15, 16, 21, 22, 12], # SWU ] }} @cython.boundscheck(False) @cython.wraparound(False) cdef bint is_Euler_invariant(pixel_type neighbors[], int[::1] lut): """Check if a point is Euler invariant. Calculate Euler characteristic for each octant and sum up. Parameters ---------- neighbors neighbors of a point lut The look-up table for preserving the Euler characteristic. Returns ------- bool (C bool, that is) """ cdef int n, euler_char = 0 {{for _octant in range(8)}} # octant {{_octant}}: n = 1 {{for _j in range(7):}} {{py: _idx = _neighb_idx[_octant][_j]}} if neighbors[{{_idx}}] == 1: n |= {{1 << (7 - _j)}} {{endfor}} euler_char += lut[n] {{endfor}} return euler_char == 0 cdef inline bint is_endpoint(pixel_type neighbors[]): """An endpoint has exactly one neighbor in the 26-neighborhood. """ # The center pixel is counted, thus r.h.s. is 2 cdef int s = 0, j for j in range(27): s += neighbors[j] return s == 2 cdef bint is_simple_point(pixel_type neighbors[]): """Check is a point is a Simple Point. A point is simple iff its deletion does not change connectivity in the 3x3x3 neighborhood. (cf conditions 2 and 3 in [Lee94]_). This method is named "N(v)_labeling" in [Lee94]_. Parameters ---------- neighbors : uint8 C array, shape(27,) neighbors of the point Returns ------- bool Whether the point is simple or not. """ # copy neighbors for labeling # ignore center pixel (i=13) when counting (see [Lee94]_) cdef pixel_type cube[26] memcpy(cube, neighbors, 13*sizeof(pixel_type)) memcpy(cube+13, neighbors+14, 13*sizeof(pixel_type)) # set initial label cdef int label = 2, i # for all point in the neighborhood for i in range(26): if cube[i] == 1: # voxel has not been labeled yet # start recursion with any octant that contains the point i if i in (0, 1, 3, 4, 9, 10, 12): octree_labeling(1, label, cube) elif i in (2, 5, 11, 13): octree_labeling(2, label, cube) elif i in (6, 7, 14, 15): octree_labeling(3, label, cube) elif i in (8, 16): octree_labeling(4, label, cube) elif i in (17, 18, 20, 21): octree_labeling(5, label, cube) elif i in (19, 22): octree_labeling(6, label, cube) elif i in (23, 24): octree_labeling(7, label, cube) elif i == 25: octree_labeling(8, label, cube) label += 1 if label - 2 >= 2: return False return True # Octree structure for labeling in `octree_labeling` routine below. # NB: this is only available at build time, and is used by Tempita templating. {{py: _octree = [ # octant 1 ([0, 1, 3, 4, 9, 10, 12], [[], [2], [3], [2, 3, 4], [5], [2, 5, 6], [3, 5, 7]]), # octant 2 ([1, 4, 10, 2, 5, 11, 13], [[1], [1, 3, 4], [1, 5, 6], [], [4], [6], [4, 6, 8]]), # octant 3 ([3, 4, 12, 6, 7, 14, 15], [[1], [1, 2, 4], [1, 5, 7], [], [4], [7], [4, 7, 8]]), # octant 4 ([4, 5, 13, 7, 15, 8, 16], [[1, 2, 3], [2], [2, 6, 8], [3], [3, 7, 8], [], [8]]), # octant 5 ([9, 10, 12, 17, 18, 20, 21], [[1], [1, 2, 6], [1, 3, 7], [], [6], [7], [6, 7, 8]]), # octant 6 ([10, 11, 13, 18, 21, 19, 22], [[1, 2, 5], [2], [2, 4, 8], [5], [5, 7, 8], [], [8]]), # octant 7 ([12, 14, 15, 20, 21, 23, 24], [[1, 3, 5], [3], [3, 4, 8], [5], [5, 6, 8], [], [8]]), # octant 8 ([13, 15, 16, 21, 22, 24, 25], [[2, 4, 6], [3, 4, 7], [4], [5, 6, 7], [6], [7], []]) ] }} @cython.boundscheck(False) @cython.wraparound(False) cdef void octree_labeling(int octant, int label, pixel_type cube[]): """This is a recursive method that calculates the number of connected components in the 3D neighborhood after the center pixel would have been removed. See Figs. 6 and 7 of [Lee94]_ for the values of indices. Parameters ---------- octant : int octant index label : int the current label of the center point cube : uint8 C array, shape(26,) local neighborhood of the point """ # This routine checks if there are points in the octant with value 1 # Then sets points in this octant to current label # and recursive labeling of adjacent octants. # # Below, leading underscore means build-time variables. {{for _oct in range(1, 9)}} if octant == {{_oct}}: {{py: _indices, _list_octants = _octree[_oct-1]}} {{for _idx, _new_octants in zip(_indices, _list_octants)}} if cube[{{_idx}}] == 1: cube[{{_idx}}] = label {{for _new_octant in _new_octants}} octree_labeling({{_new_octant}}, label, cube) {{endfor}} {{endfor}} {{endfor}} scikit-image-0.13.1/skimage/morphology/_skeletonize_cy.pyx000066400000000000000000000256461316256236300237350ustar00rootroot00000000000000#cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False #cython: wraparound=False import numpy as np cimport numpy as cnp def _fast_skeletonize(image): """Optimized parts of the Zhang-Suen [1]_ skeletonization. Iteratively, pixels meeting removal criteria are removed, till only the skeleton remains (that is, no further removable pixel was found). Performs a hard-coded correlation to assign every neighborhood of 8 a unique number, which in turn is used in conjunction with a look up table to select the appropriate thinning criteria. Parameters ---------- image : numpy.ndarray A binary image containing the objects to be skeletonized. '1' represents foreground, and '0' represents background. Returns ------- skeleton : ndarray A matrix containing the thinned image. References ---------- .. [1] A fast parallel algorithm for thinning digital patterns, T. Y. Zhang and C. Y. Suen, Communications of the ACM, March 1984, Volume 27, Number 3. """ # look up table - there is one entry for each of the 2^8=256 possible # combinations of 8 binary neighbours. 1's, 2's and 3's are candidates # for removal at each iteration of the algorithm. cdef int *lut = \ [0, 0, 0, 1, 0, 0, 1, 3, 0, 0, 3, 1, 1, 0, 1, 3, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 3, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 2, 0, 0, 0, 3, 1, 0, 0, 1, 3, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 1, 3, 0, 0, 1, 3, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 1, 0, 0, 0, 0, 2, 2, 0, 0, 2, 0, 0, 0] cdef int pixel_removed, first_pass, neighbors # indices for fast iteration cdef Py_ssize_t row, col, nrows = image.shape[0]+2, ncols = image.shape[1]+2 # we copy over the image into a larger version with a single pixel border # this removes the need to handle border cases below _skeleton = np.zeros((nrows, ncols), dtype=np.uint8) _skeleton[1:nrows-1, 1:ncols-1] = image > 0 _cleaned_skeleton = _skeleton.copy() # cdef'd numpy-arrays for fast, typed access cdef cnp.ndarray[cnp.uint8_t, ndim=2] skeleton, cleaned_skeleton skeleton = _skeleton cleaned_skeleton = _cleaned_skeleton pixel_removed = True # the algorithm reiterates the thinning till # no further thinning occurred (variable pixel_removed set) while pixel_removed: pixel_removed = False # there are two phases, in the first phase, pixels labeled (see below) # 1 and 3 are removed, in the second 2 and 3 for first_pass in (True, False): for row in range(1, nrows-1): for col in range(1, ncols-1): # all set pixels ... if skeleton[row, col]: # are correlated with a kernel (coefficients spread around here ...) # to apply a unique number to every possible neighborhood ... # which is used with the lut to find the "connectivity type" neighbors = lut[ 1*skeleton[row - 1, col - 1] + 2*skeleton[row - 1, col] +\ 4*skeleton[row - 1, col + 1] + 8*skeleton[row, col + 1] +\ 16*skeleton[row + 1, col + 1] + 32*skeleton[row + 1, col] +\ 64*skeleton[row + 1, col - 1] + 128*skeleton[row, col - 1]] # if the condition is met, the pixel is removed (unset) if ((neighbors == 1 and first_pass) or (neighbors == 2 and not first_pass) or (neighbors == 3)): cleaned_skeleton[row, col] = 0 pixel_removed = True # once a step has been processed, the original skeleton # is overwritten with the cleaned version _skeleton = _cleaned_skeleton.copy() skeleton = _skeleton return _skeleton[1:nrows-1, 1:ncols-1].astype(np.bool) """ Originally part of CellProfiler, code licensed under both GPL and BSD licenses. Website: http://www.cellprofiler.org Copyright (c) 2003-2009 Massachusetts Institute of Technology Copyright (c) 2009-2011 Broad Institute All rights reserved. Original author: Lee Kamentsky """ def _skeletonize_loop(cnp.uint8_t[:, ::1] result, Py_ssize_t[::1] i, Py_ssize_t[::1] j, cnp.int32_t[::1] order, cnp.uint8_t[::1] table): """ Inner loop of skeletonize function Parameters ---------- result : ndarray of uint8 On input, the image to be skeletonized, on output the skeletonized image. i, j : ndarrays The coordinates of each foreground pixel in the image order : ndarray The index of each pixel, in the order of processing (order[0] is the first pixel to process, etc.) table : ndarray The 512-element lookup table of values after transformation (whether to keep or not each configuration in a binary 3x3 array) Notes ----- The loop determines whether each pixel in the image can be removed without changing the Euler number of the image. The pixels are ordered by increasing distance from the background which means a point nearer to the quench-line of the brushfire will be evaluated later than a point closer to the edge. Note that the neighbourhood of a pixel may evolve before the loop arrives at this pixel. This is why it is possible to compute the skeleton in only one pass, thanks to an adapted ordering of the pixels. """ cdef: Py_ssize_t accumulator Py_ssize_t index, order_index Py_ssize_t ii, jj Py_ssize_t rows = result.shape[0] Py_ssize_t cols = result.shape[1] for index in range(order.shape[0]): accumulator = 16 order_index = order[index] ii = i[order_index] jj = j[order_index] # Compute the configuration around the pixel if ii > 0: if jj > 0 and result[ii - 1, jj - 1]: accumulator += 1 if result[ii - 1, jj]: accumulator += 2 if jj < cols - 1 and result[ii - 1, jj + 1]: accumulator += 4 if jj > 0 and result[ii, jj - 1]: accumulator += 8 if jj < cols - 1 and result[ii, jj + 1]: accumulator += 32 if ii < rows - 1: if jj > 0 and result[ii + 1, jj - 1]: accumulator += 64 if result[ii + 1, jj]: accumulator += 128 if jj < cols - 1 and result[ii + 1, jj + 1]: accumulator += 256 # Assign the value of table corresponding to the configuration result[ii, jj] = table[accumulator] def _table_lookup_index(cnp.uint8_t[:, ::1] image): """ Return an index into a table per pixel of a binary image Take the sum of true neighborhood pixel values where the neighborhood looks like this:: 1 2 4 8 16 32 64 128 256 This code could be replaced by a convolution with the kernel:: 256 128 64 32 16 8 4 2 1 but this runs about twice as fast because of inlining and the hardwired kernel. """ cdef: Py_ssize_t[:, ::1] indexer Py_ssize_t *p_indexer cnp.uint8_t *p_image Py_ssize_t i_stride Py_ssize_t i_shape Py_ssize_t j_shape Py_ssize_t i Py_ssize_t j Py_ssize_t offset i_shape = image.shape[0] j_shape = image.shape[1] indexer = np.zeros((i_shape, j_shape), dtype=np.intp) p_indexer = &indexer[0, 0] p_image = &image[0, 0] i_stride = image.strides[0] assert i_shape >= 3 and j_shape >= 3, \ "Please use the slow method for arrays < 3x3" for i in range(1, i_shape-1): offset = i_stride* i + 1 for j in range(1, j_shape - 1): if p_image[offset]: p_indexer[offset + i_stride + 1] += 1 p_indexer[offset + i_stride] += 2 p_indexer[offset + i_stride - 1] += 4 p_indexer[offset + 1] += 8 p_indexer[offset] += 16 p_indexer[offset - 1] += 32 p_indexer[offset - i_stride + 1] += 64 p_indexer[offset - i_stride] += 128 p_indexer[offset - i_stride - 1] += 256 offset += 1 # # Do the corner cases (literally) # if image[0, 0]: indexer[0, 0] += 16 indexer[0, 1] += 8 indexer[1, 0] += 2 indexer[1, 1] += 1 if image[0, j_shape - 1]: indexer[0, j_shape - 2] += 32 indexer[0, j_shape - 1] += 16 indexer[1, j_shape - 2] += 4 indexer[1, j_shape - 1] += 2 if image[i_shape - 1, 0]: indexer[i_shape - 2, 0] += 128 indexer[i_shape - 2, 1] += 64 indexer[i_shape - 1, 0] += 16 indexer[i_shape - 1, 1] += 8 if image[i_shape - 1, j_shape - 1]: indexer[i_shape - 2, j_shape - 2] += 256 indexer[i_shape - 2, j_shape - 1] += 128 indexer[i_shape - 1, j_shape - 2] += 32 indexer[i_shape - 1, j_shape - 1] += 16 # # Do the edges # for j in range(1, j_shape - 1): if image[0, j]: indexer[0, j - 1] += 32 indexer[0, j] += 16 indexer[0, j + 1] += 8 indexer[1, j - 1] += 4 indexer[1, j] += 2 indexer[1, j + 1] += 1 if image[i_shape - 1, j]: indexer[i_shape - 2, j - 1] += 256 indexer[i_shape - 2, j] += 128 indexer[i_shape - 2, j + 1] += 64 indexer[i_shape - 1, j - 1] += 32 indexer[i_shape - 1, j] += 16 indexer[i_shape - 1, j + 1] += 8 for i in range(1, i_shape - 1): if image[i, 0]: indexer[i - 1, 0] += 128 indexer[i, 0] += 16 indexer[i + 1, 0] += 2 indexer[i - 1, 1] += 64 indexer[i, 1] += 8 indexer[i + 1, 1] += 1 if image[i, j_shape - 1]: indexer[i - 1, j_shape - 2] += 256 indexer[i, j_shape - 2] += 32 indexer[i + 1, j_shape - 2] += 4 indexer[i - 1, j_shape - 1] += 128 indexer[i, j_shape - 1] += 16 indexer[i + 1, j_shape - 1] += 2 return np.asarray(indexer) scikit-image-0.13.1/skimage/morphology/_watershed.pyx000066400000000000000000000124001316256236300226540ustar00rootroot00000000000000"""watershed.pyx - scithon implementation of guts of watershed Originally part of CellProfiler, code licensed under both GPL and BSD licenses. Website: http://www.cellprofiler.org Copyright (c) 2003-2009 Massachusetts Institute of Technology Copyright (c) 2009-2011 Broad Institute All rights reserved. Original author: Lee Kamentsky """ import numpy as np from libc.math cimport sqrt cimport numpy as cnp cimport cython ctypedef cnp.int32_t DTYPE_INT32_t ctypedef cnp.int8_t DTYPE_BOOL_t include "heap_watershed.pxi" @cython.boundscheck(False) @cython.cdivision(True) @cython.overflowcheck(False) @cython.unraisable_tracebacks(False) cdef inline double _euclid_dist(cnp.int32_t pt0, cnp.int32_t pt1, cnp.int32_t[::1] strides): """Return the Euclidean distance between raveled points pt0 and pt1.""" cdef double result = 0 cdef double curr = 0 for i in range(strides.shape[0]): curr = (pt0 // strides[i]) - (pt1 // strides[i]) result += curr * curr pt0 = pt0 % strides[i] pt1 = pt1 % strides[i] return sqrt(result) @cython.boundscheck(False) def watershed_raveled(cnp.float64_t[::1] image, DTYPE_INT32_t[::1] marker_locations, DTYPE_INT32_t[::1] structure, DTYPE_BOOL_t[::1] mask, cnp.int32_t[::1] strides, cnp.double_t compactness, DTYPE_INT32_t[::1] output, DTYPE_BOOL_t wsl): """Perform watershed algorithm using a raveled image and neighborhood. Parameters ---------- image : array of float The flattened image pixels. marker_locations : array of int The raveled coordinates of the initial markers (aka seeds) for the watershed. NOTE: these should *all* point to nonzero entries in the output, or the algorithm will never terminate and blow up your memory! structure : array of int A list of coordinate offsets to compute the raveled coordinates of each neighbor from the raveled coordinates of the current pixel. mask : array of int An array of the same shape as `image` where each pixel contains a nonzero value if it is to be considered for flooding with watershed, zero otherwise. NOTE: it is *essential* that the border pixels (those with neighbors falling outside the volume) are all set to zero, or segfaults could occur. strides : array of int An array representing the number of steps to move along each dimension. This is used in computing the Euclidean distance between raveled indices. compactness : float A value greater than 0 implements the compact watershed algorithm (see .py file). output : array of int The output array, which must already contain nonzero entries at all the seed locations. wsl : bool Parameter indicating whether the watershed line is calculated. If wsl is set to True, the watershed line is calculated. """ cdef Heapitem elem cdef Heapitem new_elem cdef Py_ssize_t nneighbors = structure.shape[0] cdef Py_ssize_t i = 0 cdef Py_ssize_t age = 1 cdef Py_ssize_t index = 0 cdef DTYPE_INT32_t wsl_label = -1 cdef Heap *hp = heap_from_numpy2() for i in range(marker_locations.shape[0]): index = marker_locations[i] elem.value = image[index] elem.age = 0 elem.index = index elem.source = index heappush(hp, &elem) if wsl and wsl_label >= output[index]: wsl_label = output[index] - 1 while hp.items > 0: heappop(hp, &elem) # this can happen if the same pixel entered the queue # several times before being processed. if wsl and output[elem.index] == wsl_label: # wsl labels are not propagated. continue if output[elem.index] and elem.index != elem.source: # non-marker, already visited from another neighbor continue output[elem.index] = output[elem.source] for i in range(nneighbors): # get the flattened address of the neighbor index = structure[i] + elem.index if not mask[index]: # neighbor is not in mask continue if wsl and output[index] == wsl_label: continue if output[index]: # neighbor has a label (but not wsl_label): # the neighbor is not added to the queue. if wsl: # if the label of the neighbor is different # from the label of the pixel taken from the queue, # the latter takes the WSL label. if output[index] != output[elem.index]: output[elem.index] = wsl_label continue age += 1 new_elem.value = image[index] if compactness > 0: new_elem.value += (compactness * _euclid_dist(index, elem.source, strides)) new_elem.age = age new_elem.index = index new_elem.source = elem.source heappush(hp, &new_elem) heap_done(hp) scikit-image-0.13.1/skimage/morphology/binary.py000066400000000000000000000112301316256236300216230ustar00rootroot00000000000000""" Binary morphological operations """ import numpy as np from scipy import ndimage as ndi from .misc import default_selem # The default_selem decorator provides a diamond structuring element as default # with the same dimension as the input image and size 3 along each axis. @default_selem def binary_erosion(image, selem=None, out=None): """Return fast binary morphological erosion of an image. This function returns the same result as greyscale erosion but performs faster for binary images. Morphological erosion sets a pixel at ``(i,j)`` to the minimum over all pixels in the neighborhood centered at ``(i,j)``. Erosion shrinks bright regions and enlarges dark regions. Parameters ---------- image : ndarray Binary input image. selem : ndarray, optional The neighborhood expressed as a 2-D array of 1's and 0's. If None, use cross-shaped structuring element (connectivity=1). out : ndarray of bool, optional The array to store the result of the morphology. If None is passed, a new array will be allocated. Returns ------- eroded : ndarray of bool or uint The result of the morphological erosion taking values in ``[False, True]``. """ if out is None: out = np.empty(image.shape, dtype=np.bool) ndi.binary_erosion(image, structure=selem, output=out) return out @default_selem def binary_dilation(image, selem=None, out=None): """Return fast binary morphological dilation of an image. This function returns the same result as greyscale dilation but performs faster for binary images. Morphological dilation sets a pixel at ``(i,j)`` to the maximum over all pixels in the neighborhood centered at ``(i,j)``. Dilation enlarges bright regions and shrinks dark regions. Parameters ---------- image : ndarray Binary input image. selem : ndarray, optional The neighborhood expressed as a 2-D array of 1's and 0's. If None, use cross-shaped structuring element (connectivity=1). out : ndarray of bool, optional The array to store the result of the morphology. If None, is passed, a new array will be allocated. Returns ------- dilated : ndarray of bool or uint The result of the morphological dilation with values in ``[False, True]``. """ if out is None: out = np.empty(image.shape, dtype=np.bool) ndi.binary_dilation(image, structure=selem, output=out) return out @default_selem def binary_opening(image, selem=None, out=None): """Return fast binary morphological opening of an image. This function returns the same result as greyscale opening but performs faster for binary images. The morphological opening on an image is defined as an erosion followed by a dilation. Opening can remove small bright spots (i.e. "salt") and connect small dark cracks. This tends to "open" up (dark) gaps between (bright) features. Parameters ---------- image : ndarray Binary input image. selem : ndarray, optional The neighborhood expressed as a 2-D array of 1's and 0's. If None, use cross-shaped structuring element (connectivity=1). out : ndarray of bool, optional The array to store the result of the morphology. If None is passed, a new array will be allocated. Returns ------- opening : ndarray of bool The result of the morphological opening. """ eroded = binary_erosion(image, selem) out = binary_dilation(eroded, selem, out=out) return out @default_selem def binary_closing(image, selem=None, out=None): """Return fast binary morphological closing of an image. This function returns the same result as greyscale closing but performs faster for binary images. The morphological closing on an image is defined as a dilation followed by an erosion. Closing can remove small dark spots (i.e. "pepper") and connect small bright cracks. This tends to "close" up (dark) gaps between (bright) features. Parameters ---------- image : ndarray Binary input image. selem : ndarray, optional The neighborhood expressed as a 2-D array of 1's and 0's. If None, use cross-shaped structuring element (connectivity=1). out : ndarray of bool, optional The array to store the result of the morphology. If None, is passed, a new array will be allocated. Returns ------- closing : ndarray of bool The result of the morphological closing. """ dilated = binary_dilation(image, selem) out = binary_erosion(dilated, selem, out=out) return out scikit-image-0.13.1/skimage/morphology/cmorph.pyx000066400000000000000000000113621316256236300220250ustar00rootroot00000000000000#cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False #cython: wraparound=False import numpy as np cimport numpy as np from libc.stdlib cimport malloc, free def _dilate(np.uint8_t[:, :] image, np.uint8_t[:, :] selem, np.uint8_t[:, :] out=None, signed char shift_x=0, signed char shift_y=0): """Return greyscale morphological dilation of an image. Morphological dilation sets a pixel at (i,j) to the maximum over all pixels in the neighborhood centered at (i,j). Dilation enlarges bright regions and shrinks dark regions. Parameters ---------- image : ndarray Image array. selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray The array to store the result of the morphology. If None, is passed, a new array will be allocated. shift_x, shift_y : bool shift structuring element about center point. This only affects eccentric structuring elements (i.e. selem with even numbered sides). Returns ------- dilated : uint8 array The result of the morphological dilation. """ cdef Py_ssize_t rows = image.shape[0] cdef Py_ssize_t cols = image.shape[1] cdef Py_ssize_t srows = selem.shape[0] cdef Py_ssize_t scols = selem.shape[1] cdef Py_ssize_t centre_r = int(selem.shape[0] / 2) - shift_y cdef Py_ssize_t centre_c = int(selem.shape[1] / 2) - shift_x image = np.ascontiguousarray(image) if out is None: out = np.zeros((rows, cols), dtype=np.uint8) cdef Py_ssize_t r, c, rr, cc, s, value, local_max cdef Py_ssize_t selem_num = np.sum(np.asarray(selem) != 0) cdef Py_ssize_t* sr = malloc(selem_num * sizeof(Py_ssize_t)) cdef Py_ssize_t* sc = malloc(selem_num * sizeof(Py_ssize_t)) s = 0 for r in range(srows): for c in range(scols): if selem[r, c] != 0: sr[s] = r - centre_r sc[s] = c - centre_c s += 1 for r in range(rows): for c in range(cols): local_max = 0 for s in range(selem_num): rr = r + sr[s] cc = c + sc[s] if 0 <= rr < rows and 0 <= cc < cols: value = image[rr, cc] if value > local_max: local_max = value out[r, c] = local_max free(sr) free(sc) return np.asarray(out) def _erode(np.uint8_t[:, :] image, np.uint8_t[:, :] selem, np.uint8_t[:, :] out=None, signed char shift_x=0, signed char shift_y=0): """Return greyscale morphological erosion of an image. Morphological erosion sets a pixel at (i,j) to the minimum over all pixels in the neighborhood centered at (i,j). Erosion shrinks bright regions and enlarges dark regions. Parameters ---------- image : ndarray Image array. selem : ndarray The neighborhood expressed as a 2-D array of 1's and 0's. out : ndarray The array to store the result of the morphology. If None is passed, a new array will be allocated. shift_x, shift_y : bool shift structuring element about center point. This only affects eccentric structuring elements (i.e. selem with even numbered sides). Returns ------- eroded : uint8 array The result of the morphological erosion. """ cdef Py_ssize_t rows = image.shape[0] cdef Py_ssize_t cols = image.shape[1] cdef Py_ssize_t srows = selem.shape[0] cdef Py_ssize_t scols = selem.shape[1] cdef Py_ssize_t centre_r = int(selem.shape[0] / 2) - shift_y cdef Py_ssize_t centre_c = int(selem.shape[1] / 2) - shift_x image = np.ascontiguousarray(image) if out is None: out = np.zeros((rows, cols), dtype=np.uint8) cdef int r, c, rr, cc, s, value, local_min cdef Py_ssize_t selem_num = np.sum(np.asarray(selem) != 0) cdef Py_ssize_t* sr = malloc(selem_num * sizeof(Py_ssize_t)) cdef Py_ssize_t* sc = malloc(selem_num * sizeof(Py_ssize_t)) s = 0 for r in range(srows): for c in range(scols): if selem[r, c] != 0: sr[s] = r - centre_r sc[s] = c - centre_c s += 1 for r in range(rows): for c in range(cols): local_min = 255 for s in range(selem_num): rr = r + sr[s] cc = c + sc[s] if 0 <= rr < rows and 0 <= cc < cols: value = image[rr, cc] if value < local_min: local_min = value out[r, c] = local_min free(sr) free(sc) return np.asarray(out) scikit-image-0.13.1/skimage/morphology/convex_hull.py000066400000000000000000000074461316256236300227030ustar00rootroot00000000000000"""Convex Hull.""" import numpy as np from ..measure.pnpoly import grid_points_in_poly from ._convex_hull import possible_hull from ..measure._label import label from ..util import unique_rows __all__ = ['convex_hull_image', 'convex_hull_object'] try: from scipy.spatial import Delaunay except ImportError: Delaunay = None def convex_hull_image(image): """Compute the convex hull image of a binary image. The convex hull is the set of pixels included in the smallest convex polygon that surround all white pixels in the input image. Parameters ---------- image : (M, N) array Binary input image. This array is cast to bool before processing. Returns ------- hull : (M, N) array of bool Binary image with pixels in convex hull set to True. References ---------- .. [1] http://blogs.mathworks.com/steve/2011/10/04/binary-image-convex-hull-algorithm-notes/ """ if image.ndim > 2: raise ValueError("Input must be a 2D image") if Delaunay is None: raise ImportError("Could not import scipy.spatial.Delaunay, " "only available in scipy >= 0.9.") # Here we do an optimisation by choosing only pixels that are # the starting or ending pixel of a row or column. This vastly # limits the number of coordinates to examine for the virtual hull. coords = possible_hull(image.astype(np.uint8)) N = len(coords) # Add a vertex for the middle of each pixel edge coords_corners = np.empty((N * 4, 2)) for i, (x_offset, y_offset) in enumerate(zip((0, 0, -0.5, 0.5), (-0.5, 0.5, 0, 0))): coords_corners[i * N:(i + 1) * N] = coords + [x_offset, y_offset] # repeated coordinates can *sometimes* cause problems in # scipy.spatial.Delaunay, so we remove them. coords = unique_rows(coords_corners) # Subtract offset offset = coords.mean(axis=0) coords -= offset # Find the convex hull chull = Delaunay(coords).convex_hull v = coords[np.unique(chull)] # Sort vertices clock-wise v_centred = v - v.mean(axis=0) angles = np.arctan2(v_centred[:, 0], v_centred[:, 1]) v = v[np.argsort(angles)] # Add back offset v += offset # For each pixel coordinate, check whether that pixel # lies inside the convex hull mask = grid_points_in_poly(image.shape[:2], v) return mask def convex_hull_object(image, neighbors=8): """Compute the convex hull image of individual objects in a binary image. The convex hull is the set of pixels included in the smallest convex polygon that surround all white pixels in the input image. Parameters ---------- image : (M, N) array Binary input image. neighbors : {4, 8}, int Whether to use 4- or 8-connectivity. Returns ------- hull : ndarray of bool Binary image with pixels in convex hull set to True. Notes ----- This function uses skimage.morphology.label to define unique objects, finds the convex hull of each using convex_hull_image, and combines these regions with logical OR. Be aware the convex hulls of unconnected objects may overlap in the result. If this is suspected, consider using convex_hull_image separately on each object. """ if image.ndim > 2: raise ValueError("Input must be a 2D image") if neighbors != 4 and neighbors != 8: raise ValueError('Neighbors must be either 4 or 8.') labeled_im = label(image, neighbors, background=0) convex_obj = np.zeros(image.shape, dtype=bool) convex_img = np.zeros(image.shape, dtype=bool) for i in range(1, labeled_im.max() + 1): convex_obj = convex_hull_image(labeled_im == i) convex_img = np.logical_or(convex_img, convex_obj) return convex_img scikit-image-0.13.1/skimage/morphology/extrema.py000066400000000000000000000270231316256236300220130ustar00rootroot00000000000000"""extrema.py - local minima and maxima This module provides functions to find local maxima and minima of an image. Here, local maxima (minima) are defined as connected sets of pixels with equal grey level which is strictly greater (smaller) than the grey level of all pixels in direct neighborhood of the connected set. In addition, the module provides the closely related functions h-maxima and h-minima. Soille, P. (2003). Morphological Image Analysis: Principles and Applications (2nd ed.), Chapter 6. Springer-Verlag New York, Inc. """ from __future__ import absolute_import import numpy as np from . import greyreconstruct from ..util import dtype_limits def _add_constant_clip(img, const_value): """Add constant to the image while handling overflow issues gracefully. """ min_dtype, max_dtype = dtype_limits(img, clip_negative=False) if const_value > (max_dtype - min_dtype): raise ValueError("The added constant is not compatible" "with the image data type.") result = img + const_value result[img > max_dtype-const_value] = max_dtype return(result) def _subtract_constant_clip(img, const_value): """Subtract constant from image while handling underflow issues. """ min_dtype, max_dtype = dtype_limits(img, clip_negative=False) if const_value > (max_dtype-min_dtype): raise ValueError("The subtracted constant is not compatible" "with the image data type.") result = img - const_value result[img < (const_value + min_dtype)] = min_dtype return(result) def h_maxima(img, h, selem=None): """Determine all maxima of the image with height >= h. The local maxima are defined as connected sets of pixels with equal grey level strictly greater than the grey level of all pixels in direct neighborhood of the set. A local maximum M of height h is a local maximum for which there is at least one path joining M with a higher maximum on which the minimal value is f(M) - h (i.e. the values along the path are not decreasing by more than h with respect to the maximum's value) and no path for which the minimal value is greater. Parameters ---------- img : ndarray The input image for which the maxima are to be calculated. h : unsigned integer The minimal height of all extracted maxima. selem : ndarray, optional The neighborhood expressed as an n-D array of 1's and 0's. Default is the ball of radius 1 according to the maximum norm (i.e. a 3x3 square for 2D images, a 3x3x3 cube for 3D images, etc.) Returns ------- h_max : ndarray The maxima of height >= h. The result image is a binary image, where pixels belonging to the selected maxima take value 1, the others take value 0. See also -------- skimage.morphology.extrema.h_minima skimage.morphology.extrema.local_maxima skimage.morphology.extrema.local_minima References ---------- .. [1] Soille, P., "Morphological Image Analysis: Principles and Applications" (Chapter 6), 2nd edition (2003), ISBN 3540429883. Examples -------- >>> import numpy as np >>> from skimage.morphology import extrema We create an image (quadratic function with a maximum in the center and 4 additional constant maxima. The heights of the maxima are: 1, 21, 41, 61, 81, 101 >>> w = 10 >>> x, y = np.mgrid[0:w,0:w] >>> f = 20 - 0.2*((x - w/2)**2 + (y-w/2)**2) >>> f[2:4,2:4] = 40; f[2:4,7:9] = 60; f[7:9,2:4] = 80; f[7:9,7:9] = 100 >>> f = f.astype(np.int) We can calculate all maxima with a height of at least 40: >>> maxima = extrema.h_maxima(f, 40) The resulting image will contain 4 local maxima. """ if np.issubdtype(img.dtype, 'half'): resolution = 2 * np.finfo(img.dtype).resolution if h < resolution: h = resolution h_corrected = h - resolution / 2.0 shifted_img = img - h else: shifted_img = _subtract_constant_clip(img, h) h_corrected = h rec_img = greyreconstruct.reconstruction(shifted_img, img, method='dilation', selem=selem) residue_img = img - rec_img h_max = np.zeros(img.shape, dtype=np.uint8) h_max[residue_img >= h_corrected] = 1 return h_max def h_minima(img, h, selem=None): """Determine all minima of the image with depth >= h. The local minima are defined as connected sets of pixels with equal grey level strictly smaller than the grey levels of all pixels in direct neighborhood of the set. A local minimum M of depth h is a local minimum for which there is at least one path joining M with a deeper minimum on which the maximal value is f(M) + h (i.e. the values along the path are not increasing by more than h with respect to the minimum's value) and no path for which the maximal value is smaller. Parameters ---------- img : ndarray The input image for which the minima are to be calculated. h : unsigned integer The minimal depth of all extracted minima. selem : ndarray, optional The neighborhood expressed as an n-D array of 1's and 0's. Default is the ball of radius 1 according to the maximum norm (i.e. a 3x3 square for 2D images, a 3x3x3 cube for 3D images, etc.) Returns ------- h_min : ndarray The minima of depth >= h. The result image is a binary image, where pixels belonging to the selected minima take value 1, the other pixels take value 0. See also -------- skimage.morphology.extrema.h_maxima skimage.morphology.extrema.local_maxima skimage.morphology.extrema.local_minima References ---------- .. [1] Soille, P., "Morphological Image Analysis: Principles and Applications" (Chapter 6), 2nd edition (2003), ISBN 3540429883. Examples -------- >>> import numpy as np >>> from skimage.morphology import extrema We create an image (quadratic function with a minimum in the center and 4 additional constant maxima. The depth of the minima are: 1, 21, 41, 61, 81, 101 >>> w = 10 >>> x, y = np.mgrid[0:w,0:w] >>> f = 180 + 0.2*((x - w/2)**2 + (y-w/2)**2) >>> f[2:4,2:4] = 160; f[2:4,7:9] = 140; f[7:9,2:4] = 120; f[7:9,7:9] = 100 >>> f = f.astype(np.int) We can calculate all minima with a depth of at least 40: >>> minima = extrema.h_minima(f, 40) The resulting image will contain 4 local minima. """ if np.issubdtype(img.dtype, 'half'): resolution = 2 * np.finfo(img.dtype).resolution if h < resolution: h = resolution h_corrected = h - resolution / 2.0 shifted_img = img + h else: shifted_img = _add_constant_clip(img, h) h_corrected = h rec_img = greyreconstruct.reconstruction(shifted_img, img, method='erosion', selem=selem) residue_img = rec_img - img h_min = np.zeros(img.shape, dtype=np.uint8) h_min[residue_img >= h_corrected] = 1 return h_min def _find_min_diff(img): """ Find the minimal difference of grey levels inside the image. """ img_vec = np.unique(img.flatten()) min_diff = np.min(img_vec[1:] - img_vec[:-1]) return min_diff def local_maxima(img, selem=None): """Determine all local maxima of the image. The local maxima are defined as connected sets of pixels with equal grey level strictly greater than the grey levels of all pixels in direct neighborhood of the set. For integer typed images, this corresponds to the h-maxima with h=1. For float typed images, h is determined as the smallest difference between grey levels. Parameters ---------- img : ndarray The input image for which the maxima are to be calculated. selem : ndarray, optional The neighborhood expressed as an n-D array of 1's and 0's. Default is the ball of radius 1 according to the maximum norm (i.e. a 3x3 square for 2D images, a 3x3x3 cube for 3D images, etc.) Returns ------- local_max : ndarray All local maxima of the image. The result image is a binary image, where pixels belonging to local maxima take value 1, the other pixels take value 0. See also -------- skimage.morphology.extrema.h_minima skimage.morphology.extrema.h_maxima skimage.morphology.extrema.local_minima References ---------- .. [1] Soille, P., "Morphological Image Analysis: Principles and Applications" (Chapter 6), 2nd edition (2003), ISBN 3540429883. Examples -------- >>> import numpy as np >>> from skimage.morphology import extrema We create an image (quadratic function with a maximum in the center and 4 additional constant maxima. The heights of the maxima are: 1, 21, 41, 61, 81, 101 >>> w = 10 >>> x, y = np.mgrid[0:w,0:w] >>> f = 20 - 0.2*((x - w/2)**2 + (y-w/2)**2) >>> f[2:4,2:4] = 40; f[2:4,7:9] = 60; f[7:9,2:4] = 80; f[7:9,7:9] = 100 >>> f = f.astype(np.int) We can calculate all local maxima: >>> maxima = extrema.local_maxima(f) The resulting image will contain all 6 local maxima. """ if np.issubdtype(img.dtype, 'half'): # find the minimal grey level difference h = _find_min_diff(img) else: h = 1 local_max = h_maxima(img, h, selem=selem) return local_max def local_minima(img, selem=None): """Determine all local minima of the image. The local minima are defined as connected sets of pixels with equal grey level strictly smaller than the grey levels of all pixels in direct neighborhood of the set. For integer typed images, this corresponds to the h-minima with h=1. For float typed images, h is determined as the smallest difference between grey levels. Parameters ---------- img : ndarray The input image for which the minima are to be calculated. selem : ndarray, optional The neighborhood expressed as an n-D array of 1's and 0's. Default is the ball of radius 1 according to the maximum norm (i.e. a 3x3 square for 2D images, a 3x3x3 cube for 3D images, etc.) Returns ------- local_min : ndarray All local minima of the image. The result image is a binary image, where pixels belonging to local minima take value 1, the other pixels take value 0. See also -------- skimage.morphology.extrema.h_minima skimage.morphology.extrema.h_maxima skimage.morphology.extrema.local_maxima References ---------- .. [1] Soille, P., "Morphological Image Analysis: Principles and Applications" (Chapter 6), 2nd edition (2003), ISBN 3540429883. Examples -------- >>> import numpy as np >>> from skimage.morphology import extrema We create an image (quadratic function with a minimum in the center and 4 additional constant maxima. The depth of the minima are: 1, 21, 41, 61, 81, 101 >>> w = 10 >>> x, y = np.mgrid[0:w,0:w] >>> f = 180 + 0.2*((x - w/2)**2 + (y-w/2)**2) >>> f[2:4,2:4] = 160; f[2:4,7:9] = 140; f[7:9,2:4] = 120; f[7:9,7:9] = 100 >>> f = f.astype(np.int) We can calculate all local minima: >>> minima = extrema.local_minima(f) The resulting image will contain all 6 local minima. """ if np.issubdtype(img.dtype, 'half'): # find the minimal grey level difference h = _find_min_diff(img) else: h = 1 local_min = h_minima(img, h, selem=selem) return local_min scikit-image-0.13.1/skimage/morphology/grey.py000066400000000000000000000353161316256236300213200ustar00rootroot00000000000000""" Grayscale morphological operations """ import functools import numpy as np from scipy import ndimage as ndi from .misc import default_selem from ..util import crop __all__ = ['erosion', 'dilation', 'opening', 'closing', 'white_tophat', 'black_tophat'] def _shift_selem(selem, shift_x, shift_y): """Shift the binary image `selem` in the left and/or up. This only affects 2D structuring elements with even number of rows or columns. Parameters ---------- selem : 2D array, shape (M, N) The input structuring element. shift_x, shift_y : bool Whether to move `selem` along each axis. Returns ------- out : 2D array, shape (M + int(shift_x), N + int(shift_y)) The shifted structuring element. """ if selem.ndim > 2: # do nothing for 3D or higher structuring elements return selem m, n = selem.shape if m % 2 == 0: extra_row = np.zeros((1, n), selem.dtype) if shift_x: selem = np.vstack((selem, extra_row)) else: selem = np.vstack((extra_row, selem)) m += 1 if n % 2 == 0: extra_col = np.zeros((m, 1), selem.dtype) if shift_y: selem = np.hstack((selem, extra_col)) else: selem = np.hstack((extra_col, selem)) return selem def _invert_selem(selem): """Change the order of the values in `selem`. This is a patch for the *weird* footprint inversion in `ndi.grey_morphology` [1]_. Parameters ---------- selem : array The input structuring element. Returns ------- inverted : array, same shape and type as `selem` The structuring element, in opposite order. Examples -------- >>> selem = np.array([[0, 0, 0], [0, 1, 1], [0, 1, 1]], np.uint8) >>> _invert_selem(selem) array([[1, 1, 0], [1, 1, 0], [0, 0, 0]], dtype=uint8) References ---------- .. [1] https://github.com/scipy/scipy/blob/ec20ababa400e39ac3ffc9148c01ef86d5349332/scipy/ndimage/morphology.py#L1285 """ inverted = selem[(slice(None, None, -1),) * selem.ndim] return inverted def pad_for_eccentric_selems(func): """Pad input images for certain morphological operations. Parameters ---------- func : callable A morphological function, either opening or closing, that supports eccentric structuring elements. Its parameters must include at least `image`, `selem`, and `out`. Returns ------- func_out : callable The same function, but correctly padding the input image before applying the input function. See Also -------- opening, closing. """ @functools.wraps(func) def func_out(image, selem, out=None, *args, **kwargs): pad_widths = [] padding = False if out is None: out = np.empty_like(image) for axis_len in selem.shape: if axis_len % 2 == 0: axis_pad_width = axis_len - 1 padding = True else: axis_pad_width = 0 pad_widths.append((axis_pad_width,) * 2) if padding: image = np.pad(image, pad_widths, mode='edge') out_temp = np.empty_like(image) else: out_temp = out out_temp = func(image, selem, out=out_temp, *args, **kwargs) if padding: out[:] = crop(out_temp, pad_widths) else: out = out_temp return out return func_out @default_selem def erosion(image, selem=None, out=None, shift_x=False, shift_y=False): """Return greyscale morphological erosion of an image. Morphological erosion sets a pixel at (i,j) to the minimum over all pixels in the neighborhood centered at (i,j). Erosion shrinks bright regions and enlarges dark regions. Parameters ---------- image : ndarray Image array. selem : ndarray, optional The neighborhood expressed as an array of 1's and 0's. If None, use cross-shaped structuring element (connectivity=1). out : ndarrays, optional The array to store the result of the morphology. If None is passed, a new array will be allocated. shift_x, shift_y : bool, optional shift structuring element about center point. This only affects eccentric structuring elements (i.e. selem with even numbered sides). Returns ------- eroded : array, same shape as `image` The result of the morphological erosion. Notes ----- For ``uint8`` (and ``uint16`` up to a certain bit-depth) data, the lower algorithm complexity makes the `skimage.filters.rank.minimum` function more efficient for larger images and structuring elements. Examples -------- >>> # Erosion shrinks bright regions >>> import numpy as np >>> from skimage.morphology import square >>> bright_square = np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) >>> erosion(bright_square, square(3)) array([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 1, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]], dtype=uint8) """ selem = np.array(selem) selem = _shift_selem(selem, shift_x, shift_y) if out is None: out = np.empty_like(image) ndi.grey_erosion(image, footprint=selem, output=out) return out @default_selem def dilation(image, selem=None, out=None, shift_x=False, shift_y=False): """Return greyscale morphological dilation of an image. Morphological dilation sets a pixel at (i,j) to the maximum over all pixels in the neighborhood centered at (i,j). Dilation enlarges bright regions and shrinks dark regions. Parameters ---------- image : ndarray Image array. selem : ndarray, optional The neighborhood expressed as a 2-D array of 1's and 0's. If None, use cross-shaped structuring element (connectivity=1). out : ndarray, optional The array to store the result of the morphology. If None, is passed, a new array will be allocated. shift_x, shift_y : bool, optional shift structuring element about center point. This only affects eccentric structuring elements (i.e. selem with even numbered sides). Returns ------- dilated : uint8 array, same shape and type as `image` The result of the morphological dilation. Notes ----- For `uint8` (and `uint16` up to a certain bit-depth) data, the lower algorithm complexity makes the `skimage.filters.rank.maximum` function more efficient for larger images and structuring elements. Examples -------- >>> # Dilation enlarges bright regions >>> import numpy as np >>> from skimage.morphology import square >>> bright_pixel = np.array([[0, 0, 0, 0, 0], ... [0, 0, 0, 0, 0], ... [0, 0, 1, 0, 0], ... [0, 0, 0, 0, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) >>> dilation(bright_pixel, square(3)) array([[0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 1, 1, 0], [0, 1, 1, 1, 0], [0, 0, 0, 0, 0]], dtype=uint8) """ selem = np.array(selem) selem = _shift_selem(selem, shift_x, shift_y) # Inside ndimage.grey_dilation, the structuring element is inverted, # eg. `selem = selem[::-1, ::-1]` for 2D [1]_, for reasons unknown to # this author (@jni). To "patch" this behaviour, we invert our own # selem before passing it to `ndi.grey_dilation`. # [1] https://github.com/scipy/scipy/blob/ec20ababa400e39ac3ffc9148c01ef86d5349332/scipy/ndimage/morphology.py#L1285 selem = _invert_selem(selem) if out is None: out = np.empty_like(image) ndi.grey_dilation(image, footprint=selem, output=out) return out @default_selem @pad_for_eccentric_selems def opening(image, selem=None, out=None): """Return greyscale morphological opening of an image. The morphological opening on an image is defined as an erosion followed by a dilation. Opening can remove small bright spots (i.e. "salt") and connect small dark cracks. This tends to "open" up (dark) gaps between (bright) features. Parameters ---------- image : ndarray Image array. selem : ndarray, optional The neighborhood expressed as an array of 1's and 0's. If None, use cross-shaped structuring element (connectivity=1). out : ndarray, optional The array to store the result of the morphology. If None is passed, a new array will be allocated. Returns ------- opening : array, same shape and type as `image` The result of the morphological opening. Examples -------- >>> # Open up gap between two bright regions (but also shrink regions) >>> import numpy as np >>> from skimage.morphology import square >>> bad_connection = np.array([[1, 0, 0, 0, 1], ... [1, 1, 0, 1, 1], ... [1, 1, 1, 1, 1], ... [1, 1, 0, 1, 1], ... [1, 0, 0, 0, 1]], dtype=np.uint8) >>> opening(bad_connection, square(3)) array([[0, 0, 0, 0, 0], [1, 1, 0, 1, 1], [1, 1, 0, 1, 1], [1, 1, 0, 1, 1], [0, 0, 0, 0, 0]], dtype=uint8) """ eroded = erosion(image, selem) # note: shift_x, shift_y do nothing if selem side length is odd out = dilation(eroded, selem, out=out, shift_x=True, shift_y=True) return out @default_selem @pad_for_eccentric_selems def closing(image, selem=None, out=None): """Return greyscale morphological closing of an image. The morphological closing on an image is defined as a dilation followed by an erosion. Closing can remove small dark spots (i.e. "pepper") and connect small bright cracks. This tends to "close" up (dark) gaps between (bright) features. Parameters ---------- image : ndarray Image array. selem : ndarray, optional The neighborhood expressed as an array of 1's and 0's. If None, use cross-shaped structuring element (connectivity=1). out : ndarray, optional The array to store the result of the morphology. If None, is passed, a new array will be allocated. Returns ------- closing : array, same shape and type as `image` The result of the morphological closing. Examples -------- >>> # Close a gap between two bright lines >>> import numpy as np >>> from skimage.morphology import square >>> broken_line = np.array([[0, 0, 0, 0, 0], ... [0, 0, 0, 0, 0], ... [1, 1, 0, 1, 1], ... [0, 0, 0, 0, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) >>> closing(broken_line, square(3)) array([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [1, 1, 1, 1, 1], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]], dtype=uint8) """ dilated = dilation(image, selem) # note: shift_x, shift_y do nothing if selem side length is odd out = erosion(dilated, selem, out=out, shift_x=True, shift_y=True) return out @default_selem def white_tophat(image, selem=None, out=None): """Return white top hat of an image. The white top hat of an image is defined as the image minus its morphological opening. This operation returns the bright spots of the image that are smaller than the structuring element. Parameters ---------- image : ndarray Image array. selem : ndarray, optional The neighborhood expressed as an array of 1's and 0's. If None, use cross-shaped structuring element (connectivity=1). out : ndarray, optional The array to store the result of the morphology. If None is passed, a new array will be allocated. Returns ------- out : array, same shape and type as `image` The result of the morphological white top hat. Examples -------- >>> # Subtract grey background from bright peak >>> import numpy as np >>> from skimage.morphology import square >>> bright_on_grey = np.array([[2, 3, 3, 3, 2], ... [3, 4, 5, 4, 3], ... [3, 5, 9, 5, 3], ... [3, 4, 5, 4, 3], ... [2, 3, 3, 3, 2]], dtype=np.uint8) >>> white_tophat(bright_on_grey, square(3)) array([[0, 0, 0, 0, 0], [0, 0, 1, 0, 0], [0, 1, 5, 1, 0], [0, 0, 1, 0, 0], [0, 0, 0, 0, 0]], dtype=uint8) """ selem = np.array(selem) if out is image: opened = opening(image, selem) out -= opened return out elif out is None: out = np.empty_like(image) out = ndi.white_tophat(image, footprint=selem, output=out) return out @default_selem def black_tophat(image, selem=None, out=None): """Return black top hat of an image. The black top hat of an image is defined as its morphological closing minus the original image. This operation returns the dark spots of the image that are smaller than the structuring element. Note that dark spots in the original image are bright spots after the black top hat. Parameters ---------- image : ndarray Image array. selem : ndarray, optional The neighborhood expressed as a 2-D array of 1's and 0's. If None, use cross-shaped structuring element (connectivity=1). out : ndarray, optional The array to store the result of the morphology. If None is passed, a new array will be allocated. Returns ------- out : array, same shape and type as `image` The result of the morphological black top hat. Examples -------- >>> # Change dark peak to bright peak and subtract background >>> import numpy as np >>> from skimage.morphology import square >>> dark_on_grey = np.array([[7, 6, 6, 6, 7], ... [6, 5, 4, 5, 6], ... [6, 4, 0, 4, 6], ... [6, 5, 4, 5, 6], ... [7, 6, 6, 6, 7]], dtype=np.uint8) >>> black_tophat(dark_on_grey, square(3)) array([[0, 0, 0, 0, 0], [0, 0, 1, 0, 0], [0, 1, 5, 1, 0], [0, 0, 1, 0, 0], [0, 0, 0, 0, 0]], dtype=uint8) """ if out is image: original = image.copy() else: original = image out = closing(image, selem, out=out) out -= original return out scikit-image-0.13.1/skimage/morphology/greyreconstruct.py000066400000000000000000000205541316256236300236120ustar00rootroot00000000000000""" This morphological reconstruction routine was adapted from CellProfiler, code licensed under both GPL and BSD licenses. Website: http://www.cellprofiler.org Copyright (c) 2003-2009 Massachusetts Institute of Technology Copyright (c) 2009-2011 Broad Institute All rights reserved. Original author: Lee Kamentsky """ import numpy as np from ..filters._rank_order import rank_order def reconstruction(seed, mask, method='dilation', selem=None, offset=None): """Perform a morphological reconstruction of an image. Morphological reconstruction by dilation is similar to basic morphological dilation: high-intensity values will replace nearby low-intensity values. The basic dilation operator, however, uses a structuring element to determine how far a value in the input image can spread. In contrast, reconstruction uses two images: a "seed" image, which specifies the values that spread, and a "mask" image, which gives the maximum allowed value at each pixel. The mask image, like the structuring element, limits the spread of high-intensity values. Reconstruction by erosion is simply the inverse: low-intensity values spread from the seed image and are limited by the mask image, which represents the minimum allowed value. Alternatively, you can think of reconstruction as a way to isolate the connected regions of an image. For dilation, reconstruction connects regions marked by local maxima in the seed image: neighboring pixels less-than-or-equal-to those seeds are connected to the seeded region. Local maxima with values larger than the seed image will get truncated to the seed value. Parameters ---------- seed : ndarray The seed image (a.k.a. marker image), which specifies the values that are dilated or eroded. mask : ndarray The maximum (dilation) / minimum (erosion) allowed value at each pixel. method : {'dilation'|'erosion'} Perform reconstruction by dilation or erosion. In dilation (or erosion), the seed image is dilated (or eroded) until limited by the mask image. For dilation, each seed value must be less than or equal to the corresponding mask value; for erosion, the reverse is true. selem : ndarray The neighborhood expressed as an n-D array of 1's and 0's. Default is the ball of radius 1 according to the maximum norm (i.e. a 3x3 square for 2D images, a 3x3x3 cube for 3D images, etc.) Returns ------- reconstructed : ndarray The result of morphological reconstruction. Examples -------- >>> import numpy as np >>> from skimage.morphology import reconstruction First, we create a sinusoidal mask image with peaks at middle and ends. >>> x = np.linspace(0, 4 * np.pi) >>> y_mask = np.cos(x) Then, we create a seed image initialized to the minimum mask value (for reconstruction by dilation, min-intensity values don't spread) and add "seeds" to the left and right peak, but at a fraction of peak value (1). >>> y_seed = y_mask.min() * np.ones_like(x) >>> y_seed[0] = 0.5 >>> y_seed[-1] = 0 >>> y_rec = reconstruction(y_seed, y_mask) The reconstructed image (or curve, in this case) is exactly the same as the mask image, except that the peaks are truncated to 0.5 and 0. The middle peak disappears completely: Since there were no seed values in this peak region, its reconstructed value is truncated to the surrounding value (-1). As a more practical example, we try to extract the bright features of an image by subtracting a background image created by reconstruction. >>> y, x = np.mgrid[:20:0.5, :20:0.5] >>> bumps = np.sin(x) + np.sin(y) To create the background image, set the mask image to the original image, and the seed image to the original image with an intensity offset, `h`. >>> h = 0.3 >>> seed = bumps - h >>> background = reconstruction(seed, bumps) The resulting reconstructed image looks exactly like the original image, but with the peaks of the bumps cut off. Subtracting this reconstructed image from the original image leaves just the peaks of the bumps >>> hdome = bumps - background This operation is known as the h-dome of the image and leaves features of height `h` in the subtracted image. Notes ----- The algorithm is taken from [1]_. Applications for greyscale reconstruction are discussed in [2]_ and [3]_. References ---------- .. [1] Robinson, "Efficient morphological reconstruction: a downhill filter", Pattern Recognition Letters 25 (2004) 1759-1767. .. [2] Vincent, L., "Morphological Grayscale Reconstruction in Image Analysis: Applications and Efficient Algorithms", IEEE Transactions on Image Processing (1993) .. [3] Soille, P., "Morphological Image Analysis: Principles and Applications", Chapter 6, 2nd edition (2003), ISBN 3540429883. """ assert tuple(seed.shape) == tuple(mask.shape) if method == 'dilation' and np.any(seed > mask): raise ValueError("Intensity of seed image must be less than that " "of the mask image for reconstruction by dilation.") elif method == 'erosion' and np.any(seed < mask): raise ValueError("Intensity of seed image must be greater than that " "of the mask image for reconstruction by erosion.") try: from ._greyreconstruct import reconstruction_loop except ImportError: raise ImportError("_greyreconstruct extension not available.") if selem is None: selem = np.ones([3] * seed.ndim, dtype=bool) else: selem = selem.astype(bool) if offset is None: if not all([d % 2 == 1 for d in selem.shape]): raise ValueError("Footprint dimensions must all be odd") offset = np.array([d // 2 for d in selem.shape]) # Cross out the center of the selem selem[[slice(d, d + 1) for d in offset]] = False # Make padding for edges of reconstructed image so we can ignore boundaries padding = (np.array(selem.shape) / 2).astype(int) dims = np.zeros(seed.ndim + 1, dtype=int) dims[1:] = np.array(seed.shape) + 2 * padding dims[0] = 2 inside_slices = [slice(p, -p) for p in padding] # Set padded region to minimum image intensity and mask along first axis so # we can interleave image and mask pixels when sorting. if method == 'dilation': pad_value = np.min(seed) elif method == 'erosion': pad_value = np.max(seed) else: raise ValueError("Reconstruction method can be one of 'erosion' " "or 'dilation'. Got '%s'." % method) images = np.ones(dims) * pad_value images[[0] + inside_slices] = seed images[[1] + inside_slices] = mask # Create a list of strides across the array to get the neighbors within # a flattened array value_stride = np.array(images.strides[1:]) // images.dtype.itemsize image_stride = images.strides[0] // images.dtype.itemsize selem_mgrid = np.mgrid[[slice(-o, d - o) for d, o in zip(selem.shape, offset)]] selem_offsets = selem_mgrid[:, selem].transpose() nb_strides = np.array([np.sum(value_stride * selem_offset) for selem_offset in selem_offsets], np.int32) images = images.flatten() # Erosion goes smallest to largest; dilation goes largest to smallest. index_sorted = np.argsort(images).astype(np.int32) if method == 'dilation': index_sorted = index_sorted[::-1] # Make a linked list of pixels sorted by value. -1 is the list terminator. prev = -np.ones(len(images), np.int32) next = -np.ones(len(images), np.int32) prev[index_sorted[1:]] = index_sorted[:-1] next[index_sorted[:-1]] = index_sorted[1:] # Cython inner-loop compares the rank of pixel values. if method == 'dilation': value_rank, value_map = rank_order(images) elif method == 'erosion': value_rank, value_map = rank_order(-images) value_map = -value_map start = index_sorted[0] reconstruction_loop(value_rank, prev, next, nb_strides, start, image_stride) # Reshape reconstructed image to original image shape and remove padding. rec_img = value_map[value_rank[:image_stride]] rec_img.shape = np.array(seed.shape) + 2 * padding return rec_img[inside_slices] scikit-image-0.13.1/skimage/morphology/heap_general.pxi000066400000000000000000000074711316256236300231350ustar00rootroot00000000000000""" Originally part of CellProfiler, code licensed under both GPL and BSD licenses. Website: http://www.cellprofiler.org Copyright (c) 2003-2009 Massachusetts Institute of Technology Copyright (c) 2009-2011 Broad Institute All rights reserved. Original author: Lee Kamentsky """ from libc.stdlib cimport free, malloc, realloc cdef struct Heap: Py_ssize_t items Py_ssize_t space Heapitem *data Heapitem **ptrs cdef inline Heap *heap_from_numpy2(): cdef Py_ssize_t k cdef Heap *heap heap = malloc(sizeof (Heap)) heap.items = 0 heap.space = 1000 heap.data = malloc(heap.space * sizeof(Heapitem)) heap.ptrs = malloc(heap.space * sizeof(Heapitem *)) for k in range(heap.space): heap.ptrs[k] = heap.data + k return heap cdef inline void heap_done(Heap *heap): free(heap.data) free(heap.ptrs) free(heap) cdef inline void swap(Py_ssize_t a, Py_ssize_t b, Heap *h): h.ptrs[a], h.ptrs[b] = h.ptrs[b], h.ptrs[a] ###################################################### # heappop - inlined # # pop an element off the heap, maintaining heap invariant # # Note: heap ordering is the same as python heapq, i.e., smallest first. ###################################################### cdef inline void heappop(Heap *heap, Heapitem *dest): cdef Py_ssize_t i, smallest, l, r # heap indices # # Start by copying the first element to the destination # dest[0] = heap.ptrs[0][0] heap.items -= 1 # if the heap is now empty, we can return, no need to fix heap. if heap.items == 0: return # # Move the last element in the heap to the first. # swap(0, heap.items, heap) # # Restore the heap invariant. # i = 0 smallest = i while True: # loop invariant here: smallest == i # find smallest of (i, l, r), and swap it to i's position if necessary l = i * 2 + 1 #__left(i) r = i * 2 + 2 #__right(i) if l < heap.items: if smaller(heap.ptrs[l], heap.ptrs[i]): smallest = l if r < heap.items and smaller(heap.ptrs[r], heap.ptrs[smallest]): smallest = r else: # this is unnecessary, but trims 0.04 out of 0.85 seconds... break # the element at i is smaller than either of its children, heap # invariant restored. if smallest == i: break # swap swap(i, smallest, heap) i = smallest ################################################## # heappush - inlined # # push the element onto the heap, maintaining the heap invariant # # Note: heap ordering is the same as python heapq, i.e., smallest first. ################################################## cdef inline void heappush(Heap *heap, Heapitem *new_elem): cdef Py_ssize_t child = heap.items cdef Py_ssize_t parent cdef Py_ssize_t k cdef Heapitem *new_data # grow if necessary if heap.items == heap.space: heap.space = heap.space * 2 new_data = realloc(heap.data, (heap.space * sizeof(Heapitem))) heap.ptrs = realloc(heap.ptrs, (heap.space * sizeof(Heapitem *))) for k in range(heap.items): heap.ptrs[k] = new_data + (heap.ptrs[k] - heap.data) for k in range(heap.items, heap.space): heap.ptrs[k] = new_data + k heap.data = new_data # insert new data at child heap.ptrs[child][0] = new_elem[0] heap.items += 1 # restore heap invariant, all parents <= children while child > 0: parent = (child + 1) / 2 - 1 # __parent(i) if smaller(heap.ptrs[child], heap.ptrs[parent]): swap(parent, child, heap) child = parent else: break scikit-image-0.13.1/skimage/morphology/heap_watershed.pxi000066400000000000000000000011041316256236300234710ustar00rootroot00000000000000""" Originally part of CellProfiler, code licensed under both GPL and BSD licenses. Website: http://www.cellprofiler.org Copyright (c) 2003-2009 Massachusetts Institute of Technology Copyright (c) 2009-2011 Broad Institute All rights reserved. Original author: Lee Kamentsky """ cimport numpy as cnp cdef struct Heapitem: cnp.float64_t value cnp.int32_t age Py_ssize_t index Py_ssize_t source cdef inline int smaller(Heapitem *a, Heapitem *b): if a.value != b.value: return a.value < b.value return a.age < b.age include "heap_general.pxi" scikit-image-0.13.1/skimage/morphology/misc.py000066400000000000000000000162431316256236300213030ustar00rootroot00000000000000import numpy as np import functools from scipy import ndimage as ndi from .._shared.utils import warn from .selem import _default_selem # Our function names don't exactly correspond to ndimages. # This dictionary translates from our names to scipy's. funcs = ('erosion', 'dilation', 'opening', 'closing') skimage2ndimage = dict((x, 'grey_' + x) for x in funcs) # These function names are the same in ndimage. funcs = ('binary_erosion', 'binary_dilation', 'binary_opening', 'binary_closing', 'black_tophat', 'white_tophat') skimage2ndimage.update(dict((x, x) for x in funcs)) def default_selem(func): """Decorator to add a default structuring element to morphology functions. Parameters ---------- func : function A morphology function such as erosion, dilation, opening, closing, white_tophat, or black_tophat. Returns ------- func_out : function The function, using a default structuring element of same dimension as the input image with connectivity 1. """ @functools.wraps(func) def func_out(image, selem=None, *args, **kwargs): if selem is None: selem = _default_selem(image.ndim) return func(image, selem=selem, *args, **kwargs) return func_out def _check_dtype_supported(ar): # Should use `issubdtype` for bool below, but there's a bug in numpy 1.7 if not (ar.dtype == bool or np.issubdtype(ar.dtype, np.integer)): raise TypeError("Only bool or integer image types are supported. " "Got %s." % ar.dtype) def remove_small_objects(ar, min_size=64, connectivity=1, in_place=False): """Remove connected components smaller than the specified size. Parameters ---------- ar : ndarray (arbitrary shape, int or bool type) The array containing the connected components of interest. If the array type is int, it is assumed that it contains already-labeled objects. The ints must be non-negative. min_size : int, optional (default: 64) The smallest allowable connected component size. connectivity : int, {1, 2, ..., ar.ndim}, optional (default: 1) The connectivity defining the neighborhood of a pixel. in_place : bool, optional (default: False) If `True`, remove the connected components in the input array itself. Otherwise, make a copy. Raises ------ TypeError If the input array is of an invalid type, such as float or string. ValueError If the input array contains negative values. Returns ------- out : ndarray, same shape and type as input `ar` The input array with small connected components removed. Examples -------- >>> from skimage import morphology >>> a = np.array([[0, 0, 0, 1, 0], ... [1, 1, 1, 0, 0], ... [1, 1, 1, 0, 1]], bool) >>> b = morphology.remove_small_objects(a, 6) >>> b array([[False, False, False, False, False], [ True, True, True, False, False], [ True, True, True, False, False]], dtype=bool) >>> c = morphology.remove_small_objects(a, 7, connectivity=2) >>> c array([[False, False, False, True, False], [ True, True, True, False, False], [ True, True, True, False, False]], dtype=bool) >>> d = morphology.remove_small_objects(a, 6, in_place=True) >>> d is a True """ # Raising type error if not int or bool _check_dtype_supported(ar) if in_place: out = ar else: out = ar.copy() if min_size == 0: # shortcut for efficiency return out if out.dtype == bool: selem = ndi.generate_binary_structure(ar.ndim, connectivity) ccs = np.zeros_like(ar, dtype=np.int32) ndi.label(ar, selem, output=ccs) else: ccs = out try: component_sizes = np.bincount(ccs.ravel()) except ValueError: raise ValueError("Negative value labels are not supported. Try " "relabeling the input with `scipy.ndimage.label` or " "`skimage.morphology.label`.") if len(component_sizes) == 2: warn("Only one label was provided to `remove_small_objects`. " "Did you mean to use a boolean array?") too_small = component_sizes < min_size too_small_mask = too_small[ccs] out[too_small_mask] = 0 return out def remove_small_holes(ar, min_size=64, connectivity=1, in_place=False): """Remove continguous holes smaller than the specified size. Parameters ---------- ar : ndarray (arbitrary shape, int or bool type) The array containing the connected components of interest. min_size : int, optional (default: 64) The hole component size. connectivity : int, {1, 2, ..., ar.ndim}, optional (default: 1) The connectivity defining the neighborhood of a pixel. in_place : bool, optional (default: False) If `True`, remove the connected components in the input array itself. Otherwise, make a copy. Raises ------ TypeError If the input array is of an invalid type, such as float or string. ValueError If the input array contains negative values. Returns ------- out : ndarray, same shape and type as input `ar` The input array with small holes within connected components removed. Examples -------- >>> from skimage import morphology >>> a = np.array([[1, 1, 1, 1, 1, 0], ... [1, 1, 1, 0, 1, 0], ... [1, 0, 0, 1, 1, 0], ... [1, 1, 1, 1, 1, 0]], bool) >>> b = morphology.remove_small_holes(a, 2) >>> b array([[ True, True, True, True, True, False], [ True, True, True, True, True, False], [ True, False, False, True, True, False], [ True, True, True, True, True, False]], dtype=bool) >>> c = morphology.remove_small_holes(a, 2, connectivity=2) >>> c array([[ True, True, True, True, True, False], [ True, True, True, False, True, False], [ True, False, False, True, True, False], [ True, True, True, True, True, False]], dtype=bool) >>> d = morphology.remove_small_holes(a, 2, in_place=True) >>> d is a True Notes ----- If the array type is int, it is assumed that it contains already-labeled objects. The labels are not kept in the output image (this function always outputs a bool image). It is suggested that labeling is completed after using this function. """ _check_dtype_supported(ar) #Creates warning if image is an integer image if ar.dtype != bool: warn("Any labeled images will be returned as a boolean array. " "Did you mean to use a boolean array?", UserWarning) if in_place: out = ar else: out = ar.copy() #Creating the inverse of ar if in_place: out = np.logical_not(out,out) else: out = np.logical_not(out) #removing small objects from the inverse of ar out = remove_small_objects(out, min_size, connectivity, in_place) if in_place: out = np.logical_not(out,out) else: out = np.logical_not(out) return out scikit-image-0.13.1/skimage/morphology/selem.py000066400000000000000000000230251316256236300214510ustar00rootroot00000000000000import numpy as np from scipy import ndimage as ndi from .. import draw def square(width, dtype=np.uint8): """Generates a flat, square-shaped structuring element. Every pixel along the perimeter has a chessboard distance no greater than radius (radius=floor(width/2)) pixels. Parameters ---------- width : int The width and height of the square. Other Parameters ---------------- dtype : data-type The data type of the structuring element. Returns ------- selem : ndarray A structuring element consisting only of ones, i.e. every pixel belongs to the neighborhood. """ return np.ones((width, width), dtype=dtype) def rectangle(width, height, dtype=np.uint8): """Generates a flat, rectangular-shaped structuring element. Every pixel in the rectangle generated for a given width and given height belongs to the neighborhood. Parameters ---------- width : int The width of the rectangle. height : int The height of the rectangle. Other Parameters ---------------- dtype : data-type The data type of the structuring element. Returns ------- selem : ndarray A structuring element consisting only of ones, i.e. every pixel belongs to the neighborhood. """ return np.ones((width, height), dtype=dtype) def diamond(radius, dtype=np.uint8): """Generates a flat, diamond-shaped structuring element. A pixel is part of the neighborhood (i.e. labeled 1) if the city block/Manhattan distance between it and the center of the neighborhood is no greater than radius. Parameters ---------- radius : int The radius of the diamond-shaped structuring element. Other Parameters ---------------- dtype : data-type The data type of the structuring element. Returns ------- selem : ndarray The structuring element where elements of the neighborhood are 1 and 0 otherwise. """ L = np.arange(0, radius * 2 + 1) I, J = np.meshgrid(L, L) return np.array(np.abs(I - radius) + np.abs(J - radius) <= radius, dtype=dtype) def disk(radius, dtype=np.uint8): """Generates a flat, disk-shaped structuring element. A pixel is within the neighborhood if the euclidean distance between it and the origin is no greater than radius. Parameters ---------- radius : int The radius of the disk-shaped structuring element. Other Parameters ---------------- dtype : data-type The data type of the structuring element. Returns ------- selem : ndarray The structuring element where elements of the neighborhood are 1 and 0 otherwise. """ L = np.arange(-radius, radius + 1) X, Y = np.meshgrid(L, L) return np.array((X ** 2 + Y ** 2) <= radius ** 2, dtype=dtype) def ellipse(width, height, dtype=np.uint8): """Generates a flat, ellipse-shaped structuring element. Every pixel along the perimeter of ellipse satisfies the equation ``(x/width+1)**2 + (y/height+1)**2 = 1``. Parameters ---------- width : int The width of the ellipse-shaped structuring element. height : int The height of the ellipse-shaped structuring element. Other Parameters ---------------- dtype : data-type The data type of the structuring element. Returns ------- selem : ndarray The structuring element where elements of the neighborhood are 1 and 0 otherwise. Examples -------- >>> from skimage.morphology import selem >>> selem.ellipse(5, 3) array([[0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]], dtype=uint8) """ selem = np.zeros((2 * height + 1, 2 * width + 1), dtype=dtype) rows, cols = draw.ellipse(height, width, height + 1, width + 1) selem[rows, cols] = 1 return selem def cube(width, dtype=np.uint8): """ Generates a cube-shaped structuring element. This is the 3D equivalent of a square. Every pixel along the perimeter has a chessboard distance no greater than radius (radius=floor(width/2)) pixels. Parameters ---------- width : int The width, height and depth of the cube. Other Parameters ---------------- dtype : data-type The data type of the structuring element. Returns ------- selem : ndarray A structuring element consisting only of ones, i.e. every pixel belongs to the neighborhood. """ return np.ones((width, width, width), dtype=dtype) def octahedron(radius, dtype=np.uint8): """Generates a octahedron-shaped structuring element. This is the 3D equivalent of a diamond. A pixel is part of the neighborhood (i.e. labeled 1) if the city block/Manhattan distance between it and the center of the neighborhood is no greater than radius. Parameters ---------- radius : int The radius of the octahedron-shaped structuring element. Other Parameters ---------------- dtype : data-type The data type of the structuring element. Returns ------- selem : ndarray The structuring element where elements of the neighborhood are 1 and 0 otherwise. """ # note that in contrast to diamond(), this method allows non-integer radii n = 2 * radius + 1 Z, Y, X = np.mgrid[-radius:radius:n * 1j, -radius:radius:n * 1j, -radius:radius:n * 1j] s = np.abs(X) + np.abs(Y) + np.abs(Z) return np.array(s <= radius, dtype=dtype) def ball(radius, dtype=np.uint8): """Generates a ball-shaped structuring element. This is the 3D equivalent of a disk. A pixel is within the neighborhood if the euclidean distance between it and the origin is no greater than radius. Parameters ---------- radius : int The radius of the ball-shaped structuring element. Other Parameters ---------------- dtype : data-type The data type of the structuring element. Returns ------- selem : ndarray The structuring element where elements of the neighborhood are 1 and 0 otherwise. """ n = 2 * radius + 1 Z, Y, X = np.mgrid[-radius:radius:n * 1j, -radius:radius:n * 1j, -radius:radius:n * 1j] s = X ** 2 + Y ** 2 + Z ** 2 return np.array(s <= radius * radius, dtype=dtype) def octagon(m, n, dtype=np.uint8): """Generates an octagon shaped structuring element. For a given size of (m) horizontal and vertical sides and a given (n) height or width of slanted sides octagon is generated. The slanted sides are 45 or 135 degrees to the horizontal axis and hence the widths and heights are equal. Parameters ---------- m : int The size of the horizontal and vertical sides. n : int The height or width of the slanted sides. Other Parameters ---------------- dtype : data-type The data type of the structuring element. Returns ------- selem : ndarray The structuring element where elements of the neighborhood are 1 and 0 otherwise. """ from . import convex_hull_image selem = np.zeros((m + 2 * n, m + 2 * n)) selem[0, n] = 1 selem[n, 0] = 1 selem[0, m + n - 1] = 1 selem[m + n - 1, 0] = 1 selem[-1, n] = 1 selem[n, -1] = 1 selem[-1, m + n - 1] = 1 selem[m + n - 1, -1] = 1 selem = convex_hull_image(selem).astype(dtype) return selem def star(a, dtype=np.uint8): """Generates a star shaped structuring element. Start has 8 vertices and is an overlap of square of size `2*a + 1` with its 45 degree rotated version. The slanted sides are 45 or 135 degrees to the horizontal axis. Parameters ---------- a : int Parameter deciding the size of the star structural element. The side of the square array returned is `2*a + 1 + 2*floor(a / 2)`. Other Parameters ---------------- dtype : data-type The data type of the structuring element. Returns ------- selem : ndarray The structuring element where elements of the neighborhood are 1 and 0 otherwise. """ from . import convex_hull_image if a == 1: bfilter = np.zeros((3, 3), dtype) bfilter[:] = 1 return bfilter m = 2 * a + 1 n = a // 2 selem_square = np.zeros((m + 2 * n, m + 2 * n)) selem_square[n: m + n, n: m + n] = 1 c = (m + 2 * n - 1) // 2 selem_rotated = np.zeros((m + 2 * n, m + 2 * n)) selem_rotated[0, c] = selem_rotated[-1, c] = 1 selem_rotated[c, 0] = selem_rotated[c, -1] = 1 selem_rotated = convex_hull_image(selem_rotated).astype(int) selem = selem_square + selem_rotated selem[selem > 0] = 1 return selem.astype(dtype) def _default_selem(ndim): """Generates a cross-shaped structuring element (connectivity=1). This is the default structuring element (selem) if no selem was specified. Parameters ---------- ndim : int Number of dimensions of the image. Returns ------- selem : ndarray The structuring element where elements of the neighborhood are 1 and 0 otherwise. """ return ndi.morphology.generate_binary_structure(ndim, 1) scikit-image-0.13.1/skimage/morphology/setup.py000066400000000000000000000034041316256236300215030ustar00rootroot00000000000000#!/usr/bin/env python import os from skimage._build import cython base_path = os.path.abspath(os.path.dirname(__file__)) def configuration(parent_package='', top_path=None): from numpy.distutils.misc_util import Configuration, get_numpy_include_dirs config = Configuration('morphology', parent_package, top_path) config.add_data_dir('tests') cython(['_watershed.pyx'], working_path=base_path) cython(['_skeletonize_cy.pyx'], working_path=base_path) cython(['_convex_hull.pyx'], working_path=base_path) cython(['_greyreconstruct.pyx'], working_path=base_path) cython(['_skeletonize_3d_cy.pyx.in'], working_path=base_path) config.add_extension('_watershed', sources=['_watershed.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension('_skeletonize_cy', sources=['_skeletonize_cy.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension('_convex_hull', sources=['_convex_hull.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension('_greyreconstruct', sources=['_greyreconstruct.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension('_skeletonize_3d_cy', sources=['_skeletonize_3d_cy.c'], include_dirs=[get_numpy_include_dirs()]) return config if __name__ == '__main__': from numpy.distutils.core import setup setup(maintainer='scikit-image Developers', author='Damian Eads', maintainer_email='scikit-image@python.org', description='Morphology Wrapper', url='https://github.com/scikit-image/scikit-image', license='SciPy License (BSD Style)', **(configuration(top_path='').todict()) ) scikit-image-0.13.1/skimage/morphology/tests/000077500000000000000000000000001316256236300211325ustar00rootroot00000000000000scikit-image-0.13.1/skimage/morphology/tests/__init__.py000066400000000000000000000001771316256236300232500ustar00rootroot00000000000000from ..._shared.testing import setup_test, teardown_test def setup(): setup_test() def teardown(): teardown_test() scikit-image-0.13.1/skimage/morphology/tests/test_binary.py000066400000000000000000000134121316256236300240300ustar00rootroot00000000000000import numpy as np from numpy import testing from skimage import data, color from skimage.util import img_as_bool from skimage.morphology import binary, grey, selem from scipy import ndimage as ndi img = color.rgb2gray(data.astronaut()) bw_img = img > 100 def test_non_square_image(): strel = selem.square(3) binary_res = binary.binary_erosion(bw_img[:100, :200], strel) grey_res = img_as_bool(grey.erosion(bw_img[:100, :200], strel)) testing.assert_array_equal(binary_res, grey_res) def test_binary_erosion(): strel = selem.square(3) binary_res = binary.binary_erosion(bw_img, strel) grey_res = img_as_bool(grey.erosion(bw_img, strel)) testing.assert_array_equal(binary_res, grey_res) def test_binary_dilation(): strel = selem.square(3) binary_res = binary.binary_dilation(bw_img, strel) grey_res = img_as_bool(grey.dilation(bw_img, strel)) testing.assert_array_equal(binary_res, grey_res) def test_binary_closing(): strel = selem.square(3) binary_res = binary.binary_closing(bw_img, strel) grey_res = img_as_bool(grey.closing(bw_img, strel)) testing.assert_array_equal(binary_res, grey_res) def test_binary_opening(): strel = selem.square(3) binary_res = binary.binary_opening(bw_img, strel) grey_res = img_as_bool(grey.opening(bw_img, strel)) testing.assert_array_equal(binary_res, grey_res) def test_selem_overflow(): strel = np.ones((17, 17), dtype=np.uint8) img = np.zeros((20, 20), dtype=bool) img[2:19, 2:19] = True binary_res = binary.binary_erosion(img, strel) grey_res = img_as_bool(grey.erosion(img, strel)) testing.assert_array_equal(binary_res, grey_res) def test_out_argument(): for func in (binary.binary_erosion, binary.binary_dilation): strel = np.ones((3, 3), dtype=np.uint8) img = np.ones((10, 10)) out = np.zeros_like(img) out_saved = out.copy() func(img, strel, out=out) testing.assert_(np.any(out != out_saved)) testing.assert_array_equal(out, func(img, strel)) def test_default_selem(): functions = [binary.binary_erosion, binary.binary_dilation, binary.binary_opening, binary.binary_closing] strel = selem.diamond(radius=1) image = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], np.uint8) for function in functions: im_expected = function(image, strel) im_test = function(image) yield testing.assert_array_equal, im_expected, im_test def test_3d_fallback_default_selem(): # 3x3x3 cube inside a 7x7x7 image: image = np.zeros((7, 7, 7), np.bool) image[2:-2, 2:-2, 2:-2] = 1 opened = binary.binary_opening(image) # expect a "hyper-cross" centered in the 5x5x5: image_expected = np.zeros((7, 7, 7), dtype=bool) image_expected[2:5, 2:5, 2:5] = ndi.generate_binary_structure(3, 1) testing.assert_array_equal(opened, image_expected) def test_3d_fallback_cube_selem(): # 3x3x3 cube inside a 7x7x7 image: image = np.zeros((7, 7, 7), np.bool) image[2:-2, 2:-2, 2:-2] = 1 cube = np.ones((3, 3, 3), dtype=np.uint8) for function in [binary.binary_closing, binary.binary_opening]: new_image = function(image, cube) yield testing.assert_array_equal, new_image, image def test_2d_ndimage_equivalence(): image = np.zeros((9, 9), np.uint16) image[2:-2, 2:-2] = 2**14 image[3:-3, 3:-3] = 2**15 image[4, 4] = 2**16-1 bin_opened = binary.binary_opening(image) bin_closed = binary.binary_closing(image) selem = ndi.generate_binary_structure(2, 1) ndimage_opened = ndi.binary_opening(image, structure=selem) ndimage_closed = ndi.binary_closing(image, structure=selem) testing.assert_array_equal(bin_opened, ndimage_opened) testing.assert_array_equal(bin_closed, ndimage_closed) def test_binary_output_2d(): image = np.zeros((9, 9), np.uint16) image[2:-2, 2:-2] = 2**14 image[3:-3, 3:-3] = 2**15 image[4, 4] = 2**16-1 bin_opened = binary.binary_opening(image) bin_closed = binary.binary_closing(image) int_opened = np.empty_like(image, dtype=np.uint8) int_closed = np.empty_like(image, dtype=np.uint8) binary.binary_opening(image, out=int_opened) binary.binary_closing(image, out=int_closed) testing.assert_equal(bin_opened.dtype, np.bool) testing.assert_equal(bin_closed.dtype, np.bool) testing.assert_equal(int_opened.dtype, np.uint8) testing.assert_equal(int_closed.dtype, np.uint8) def test_binary_output_3d(): image = np.zeros((9, 9, 9), np.uint16) image[2:-2, 2:-2, 2:-2] = 2**14 image[3:-3, 3:-3, 3:-3] = 2**15 image[4, 4, 4] = 2**16-1 bin_opened = binary.binary_opening(image) bin_closed = binary.binary_closing(image) int_opened = np.empty_like(image, dtype=np.uint8) int_closed = np.empty_like(image, dtype=np.uint8) binary.binary_opening(image, out=int_opened) binary.binary_closing(image, out=int_closed) testing.assert_equal(bin_opened.dtype, np.bool) testing.assert_equal(bin_closed.dtype, np.bool) testing.assert_equal(int_opened.dtype, np.uint8) testing.assert_equal(int_closed.dtype, np.uint8) if __name__ == '__main__': testing.run_module_suite() scikit-image-0.13.1/skimage/morphology/tests/test_ccomp.py000066400000000000000000000221061316256236300236450ustar00rootroot00000000000000import numpy as np from numpy.testing import assert_array_equal, run_module_suite from skimage.measure import label import skimage.measure._ccomp as ccomp from skimage._shared._warnings import expected_warnings # Background value BG = 0 class TestConnectedComponents: def setup(self): self.x = np.array([ [0, 0, 3, 2, 1, 9], [0, 1, 1, 9, 2, 9], [0, 0, 1, 9, 9, 9], [3, 1, 1, 5, 3, 0]]) self.labels = np.array([ [0, 0, 1, 2, 3, 4], [0, 5, 5, 4, 2, 4], [0, 0, 5, 4, 4, 4], [6, 5, 5, 7, 8, 0]]) # No background - there is no label 0, instead, labelling starts with 1 # and all labels are incremented by 1. self.labels_nobg = self.labels + 1 # The 0 at lower right corner is isolated, so it should get a new label self.labels_nobg[-1, -1] = 10 # We say that background value is 9 (and bg label is 0) self.labels_bg_9 = self.labels_nobg.copy() self.labels_bg_9[self.x == 9] = 0 # Then, where there was the label 5, we now expect 4 etc. # (we assume that the label of value 9 would normally be 5) self.labels_bg_9[self.labels_bg_9 > 5] -= 1 def test_basic(self): assert_array_equal(label(self.x), self.labels) # Make sure data wasn't modified assert self.x[0, 2] == 3 # Check that everything works if there is no background assert_array_equal(label(self.x, background=99), self.labels_nobg) # Check that everything works if background value != 0 assert_array_equal(label(self.x, background=9), self.labels_bg_9) def test_random(self): x = (np.random.rand(20, 30) * 5).astype(np.int) labels = label(x) n = labels.max() for i in range(n): values = x[labels == i] assert np.all(values == values[0]) def test_diag(self): x = np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]]) assert_array_equal(label(x), x) def test_4_vs_8(self): x = np.array([[0, 1], [1, 0]], dtype=int) assert_array_equal(label(x, 4), [[0, 1], [2, 0]]) assert_array_equal(label(x, 8), [[0, 1], [1, 0]]) def test_background(self): x = np.array([[1, 0, 0], [1, 1, 5], [0, 0, 0]]) assert_array_equal(label(x), [[1, 0, 0], [1, 1, 2], [0, 0, 0]]) assert_array_equal(label(x, background=0), [[1, 0, 0], [1, 1, 2], [0, 0, 0]]) def test_background_two_regions(self): x = np.array([[0, 0, 6], [0, 0, 6], [5, 5, 5]]) res = label(x, background=0) assert_array_equal(res, [[0, 0, 1], [0, 0, 1], [2, 2, 2]]) def test_background_one_region_center(self): x = np.array([[0, 0, 0], [0, 1, 0], [0, 0, 0]]) assert_array_equal(label(x, neighbors=4, background=0), [[0, 0, 0], [0, 1, 0], [0, 0, 0]]) def test_return_num(self): x = np.array([[1, 0, 6], [0, 0, 6], [5, 5, 5]]) assert_array_equal(label(x, return_num=True)[1], 3) assert_array_equal(label(x, background=-1, return_num=True)[1], 4) class TestConnectedComponents3d: def setup(self): self.x = np.zeros((3, 4, 5), int) self.x[0] = np.array([[0, 3, 2, 1, 9], [0, 1, 9, 2, 9], [0, 1, 9, 9, 9], [3, 1, 5, 3, 0]]) self.x[1] = np.array([[3, 3, 2, 1, 9], [0, 3, 9, 2, 1], [0, 3, 3, 1, 1], [3, 1, 3, 3, 0]]) self.x[2] = np.array([[3, 3, 8, 8, 0], [2, 3, 9, 8, 8], [2, 3, 0, 8, 0], [2, 1, 0, 0, 0]]) self.labels = np.zeros((3, 4, 5), int) self.labels[0] = np.array([[0, 1, 2, 3, 4], [0, 5, 4, 2, 4], [0, 5, 4, 4, 4], [1, 5, 6, 1, 0]]) self.labels[1] = np.array([[1, 1, 2, 3, 4], [0, 1, 4, 2, 3], [0, 1, 1, 3, 3], [1, 5, 1, 1, 0]]) self.labels[2] = np.array([[1, 1, 7, 7, 0], [8, 1, 4, 7, 7], [8, 1, 0, 7, 0], [8, 5, 0, 0, 0]]) def test_basic(self): labels = label(self.x) assert_array_equal(labels, self.labels) assert self.x[0, 0, 2] == 2, \ "Data was modified!" def test_random(self): x = (np.random.rand(20, 30) * 5).astype(np.int) labels = label(x) n = labels.max() for i in range(n): values = x[labels == i] assert np.all(values == values[0]) def test_diag(self): x = np.zeros((3, 3, 3), int) x[0, 2, 2] = 1 x[1, 1, 1] = 1 x[2, 0, 0] = 1 assert_array_equal(label(x), x) def test_4_vs_8(self): x = np.zeros((2, 2, 2), int) x[0, 1, 1] = 1 x[1, 0, 0] = 1 label4 = x.copy() label4[1, 0, 0] = 2 assert_array_equal(label(x, 4), label4) assert_array_equal(label(x, 8), x) def test_background(self): x = np.zeros((2, 3, 3), int) x[0] = np.array([[1, 0, 0], [1, 0, 0], [0, 0, 0]]) x[1] = np.array([[0, 0, 0], [0, 1, 5], [0, 0, 0]]) lnb = x.copy() lnb[0] = np.array([[1, 2, 2], [1, 2, 2], [2, 2, 2]]) lnb[1] = np.array([[2, 2, 2], [2, 1, 3], [2, 2, 2]]) lb = x.copy() lb[0] = np.array([[1, BG, BG], [1, BG, BG], [BG, BG, BG]]) lb[1] = np.array([[BG, BG, BG], [BG, 1, 2], [BG, BG, BG]]) assert_array_equal(label(x), lb) assert_array_equal(label(x, background=-1), lnb) def test_background_two_regions(self): x = np.zeros((2, 3, 3), int) x[0] = np.array([[0, 0, 6], [0, 0, 6], [5, 5, 5]]) x[1] = np.array([[6, 6, 0], [5, 0, 0], [0, 0, 0]]) lb = x.copy() lb[0] = np.array([[BG, BG, 1], [BG, BG, 1], [2, 2, 2]]) lb[1] = np.array([[1, 1, BG], [2, BG, BG], [BG, BG, BG]]) res = label(x, background=0) assert_array_equal(res, lb) def test_background_one_region_center(self): x = np.zeros((3, 3, 3), int) x[1, 1, 1] = 1 lb = np.ones_like(x) * BG lb[1, 1, 1] = 1 assert_array_equal(label(x, neighbors=4, background=0), lb) def test_return_num(self): x = np.array([[1, 0, 6], [0, 0, 6], [5, 5, 5]]) assert_array_equal(label(x, return_num=True)[1], 3) assert_array_equal(label(x, background=-1, return_num=True)[1], 4) def test_1D(self): x = np.array((0, 1, 2, 2, 1, 1, 0, 0)) xlen = len(x) y = np.array((0, 1, 2, 2, 3, 3, 0, 0)) reshapes = ((xlen,), (1, xlen), (xlen, 1), (1, xlen, 1), (xlen, 1, 1), (1, 1, xlen)) for reshape in reshapes: x2 = x.reshape(reshape) labelled = label(x2) assert_array_equal(y, labelled.flatten()) def test_nd(self): x = np.ones((1, 2, 3, 4)) np.testing.assert_raises(NotImplementedError, label, x) class TestSupport: def test_reshape(self): shapes_in = ((3, 1, 2), (1, 4, 5), (3, 1, 1), (2, 1), (1,)) for shape in shapes_in: shape = np.array(shape) numones = sum(shape == 1) inp = np.random.random(shape) fixed, swaps = ccomp.reshape_array(inp) shape2 = fixed.shape # now check that all ones are at the beginning for i in range(numones): assert shape2[i] == 1 back = ccomp.undo_reshape_array(fixed, swaps) # check that the undo works as expected assert_array_equal(inp, back) if __name__ == "__main__": run_module_suite() scikit-image-0.13.1/skimage/morphology/tests/test_convex_hull.py000066400000000000000000000122171316256236300250740ustar00rootroot00000000000000import numpy as np from numpy.testing import assert_array_equal, assert_raises from numpy.testing.decorators import skipif from skimage.morphology import convex_hull_image, convex_hull_object from skimage.morphology._convex_hull import possible_hull try: import scipy.spatial scipy_spatial = True except ImportError: scipy_spatial = False @skipif(not scipy_spatial) def test_basic(): image = np.array( [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 1, 0, 1, 0, 0, 0], [0, 0, 1, 0, 0, 0, 1, 0, 0], [0, 1, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=bool) expected = np.array( [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=bool) assert_array_equal(convex_hull_image(image), expected) # Test that an error is raised on passing a 3D image: image3d = np.empty((5, 5, 5)) assert_raises(ValueError, convex_hull_image, image3d) @skipif(not scipy_spatial) def test_qhull_offset_example(): nonzeros = (([1367, 1368, 1368, 1368, 1369, 1369, 1369, 1369, 1369, 1370, 1370, 1370, 1370, 1370, 1370, 1370, 1371, 1371, 1371, 1371, 1371, 1371, 1371, 1371, 1371, 1372, 1372, 1372, 1372, 1372, 1372, 1372, 1372, 1372, 1373, 1373, 1373, 1373, 1373, 1373, 1373, 1373, 1373, 1374, 1374, 1374, 1374, 1374, 1374, 1374, 1375, 1375, 1375, 1375, 1375, 1376, 1376, 1376, 1377]), ([151, 150, 151, 152, 149, 150, 151, 152, 153, 148, 149, 150, 151, 152, 153, 154, 147, 148, 149, 150, 151, 152, 153, 154, 155, 146, 147, 148, 149, 150, 151, 152, 153, 154, 146, 147, 148, 149, 150, 151, 152, 153, 154, 147, 148, 149, 150, 151, 152, 153, 148, 149, 150, 151, 152, 149, 150, 151, 150])) image = np.zeros((1392, 1040), dtype=bool) image[nonzeros] = True expected = image.copy() assert_array_equal(convex_hull_image(image), expected) @skipif(not scipy_spatial) def test_pathological_qhull_example(): image = np.array( [[0, 0, 0, 0, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0, 0]], dtype=bool) expected = np.array( [[0, 0, 0, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 0, 0, 0]], dtype=bool) assert_array_equal(convex_hull_image(image), expected) @skipif(not scipy_spatial) def test_possible_hull(): image = np.array( [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 1, 0, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=np.uint8) expected = np.array([[1, 4], [2, 3], [3, 2], [4, 1], [4, 1], [3, 2], [2, 3], [1, 4], [2, 5], [3, 6], [4, 7], [2, 5], [3, 6], [4, 7], [4, 2], [4, 3], [4, 4], [4, 5], [4, 6]]) ph = possible_hull(image) assert_array_equal(ph, expected) @skipif(not scipy_spatial) def test_object(): image = np.array( [[0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 0, 0, 1, 0, 1], [1, 0, 0, 0, 0, 0, 0, 1, 0], [1, 0, 0, 0, 0, 0, 1, 0, 1], [1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=bool) expected4 = np.array( [[0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 0, 0, 1, 0, 1], [1, 1, 1, 0, 0, 0, 0, 1, 0], [1, 1, 0, 0, 0, 0, 1, 0, 1], [1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=bool) assert_array_equal(convex_hull_object(image, 4), expected4) expected8 = np.array( [[0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 0, 0, 1, 1, 1], [1, 1, 1, 0, 0, 0, 1, 1, 1], [1, 1, 0, 0, 0, 0, 1, 1, 1], [1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=bool) assert_array_equal(convex_hull_object(image, 8), expected8) assert_raises(ValueError, convex_hull_object, image, 7) # Test that an error is raised on passing a 3D image: image3d = np.empty((5, 5, 5)) assert_raises(ValueError, convex_hull_object, image3d) if __name__ == "__main__": np.testing.run_module_suite() scikit-image-0.13.1/skimage/morphology/tests/test_extrema.py000066400000000000000000000333541316256236300242200ustar00rootroot00000000000000import math import unittest import numpy as np from skimage.morphology import extrema from scipy import ndimage as ndi eps = 1e-12 def diff(a, b): a = np.asarray(a, dtype=np.float64) b = np.asarray(b, dtype=np.float64) t = ((a - b)**2).sum() return math.sqrt(t) class TestExtrema(unittest.TestCase): def test_saturated_arithmetic(self): "Adding/subtracting a constant and clipping" # Test for unsigned integer data = np.array([[250, 251, 5, 5], [100, 200, 253, 252], [4, 10, 1, 3]], dtype=np.uint8) # adding the constant img_constant_added = extrema._add_constant_clip(data, 4) expected = np.array([[254, 255, 9, 9], [104, 204, 255, 255], [8, 14, 5, 7]], dtype=np.uint8) error = diff(img_constant_added, expected) assert error < eps img_constant_subtracted = extrema._subtract_constant_clip(data, 4) expected = np.array([[246, 247, 1, 1], [96, 196, 249, 248], [0, 6, 0, 0]], dtype=np.uint8) error = diff(img_constant_subtracted, expected) assert error < eps # Test for signed integer data = np.array([[32767, 32766], [-32768, -32767]], dtype=np.int16) img_constant_added = extrema._add_constant_clip(data, 1) expected = np.array([[32767, 32767], [-32767, -32766]], dtype=np.int16) error = diff(img_constant_added, expected) assert error < eps img_constant_subtracted = extrema._subtract_constant_clip(data, 1) expected = np.array([[32766, 32765], [-32768, -32768]], dtype=np.int16) error = diff(img_constant_subtracted, expected) assert error < eps def test_local_maxima(self): "local maxima for various data types" data = np.array([[10, 11, 13, 14, 14, 15, 14, 14, 13, 11], [11, 13, 15, 16, 16, 16, 16, 16, 15, 13], [13, 15, 40, 40, 18, 18, 18, 60, 60, 15], [14, 16, 40, 40, 19, 19, 19, 60, 60, 16], [14, 16, 18, 19, 19, 19, 19, 19, 18, 16], [15, 16, 18, 19, 19, 20, 19, 19, 18, 16], [14, 16, 18, 19, 19, 19, 19, 19, 18, 16], [14, 16, 80, 80, 19, 19, 19, 100, 100, 16], [13, 15, 80, 80, 18, 18, 18, 100, 100, 15], [11, 13, 15, 16, 16, 16, 16, 16, 15, 13]], dtype=np.uint8) expected_result = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0, 0, 1, 1, 0], [0, 0, 1, 1, 0, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0, 0, 1, 1, 0], [0, 0, 1, 1, 0, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=np.uint8) for dtype in [np.uint8, np.uint64, np.int8, np.int64]: test_data = data.astype(dtype) out = extrema.local_maxima(test_data) error = diff(expected_result, out) assert error < eps assert out.dtype == expected_result.dtype def test_local_minima(self): "local minima for various data types" data = np.array([[10, 11, 13, 14, 14, 15, 14, 14, 13, 11], [11, 13, 15, 16, 16, 16, 16, 16, 15, 13], [13, 15, 40, 40, 18, 18, 18, 60, 60, 15], [14, 16, 40, 40, 19, 19, 19, 60, 60, 16], [14, 16, 18, 19, 19, 19, 19, 19, 18, 16], [15, 16, 18, 19, 19, 20, 19, 19, 18, 16], [14, 16, 18, 19, 19, 19, 19, 19, 18, 16], [14, 16, 80, 80, 19, 19, 19, 100, 100, 16], [13, 15, 80, 80, 18, 18, 18, 100, 100, 15], [11, 13, 15, 16, 16, 16, 16, 16, 15, 13]], dtype=np.uint8) data = 100 - data expected_result = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0, 0, 1, 1, 0], [0, 0, 1, 1, 0, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0, 0, 1, 1, 0], [0, 0, 1, 1, 0, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=np.uint8) for dtype in [np.uint8, np.uint64, np.int8, np.int64]: data = data.astype(dtype) out = extrema.local_minima(data) error = diff(expected_result, out) assert error < eps assert out.dtype == expected_result.dtype def test_h_maxima(self): "h-maxima for various data types" data = np.array([[10, 11, 13, 14, 14, 15, 14, 14, 13, 11], [11, 13, 15, 16, 16, 16, 16, 16, 15, 13], [13, 15, 40, 40, 18, 18, 18, 60, 60, 15], [14, 16, 40, 40, 19, 19, 19, 60, 60, 16], [14, 16, 18, 19, 19, 19, 19, 19, 18, 16], [15, 16, 18, 19, 19, 20, 19, 19, 18, 16], [14, 16, 18, 19, 19, 19, 19, 19, 18, 16], [14, 16, 80, 80, 19, 19, 19, 100, 100, 16], [13, 15, 80, 80, 18, 18, 18, 100, 100, 15], [11, 13, 15, 16, 16, 16, 16, 16, 15, 13]], dtype=np.uint8) expected_result = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0, 0, 1, 1, 0], [0, 0, 1, 1, 0, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=np.uint8) for dtype in [np.uint8, np.uint64, np.int8, np.int64]: data = data.astype(dtype) out = extrema.h_maxima(data, 40) error = diff(expected_result, out) assert error < eps def test_h_minima(self): "h-minima for various data types" data = np.array([[10, 11, 13, 14, 14, 15, 14, 14, 13, 11], [11, 13, 15, 16, 16, 16, 16, 16, 15, 13], [13, 15, 40, 40, 18, 18, 18, 60, 60, 15], [14, 16, 40, 40, 19, 19, 19, 60, 60, 16], [14, 16, 18, 19, 19, 19, 19, 19, 18, 16], [15, 16, 18, 19, 19, 20, 19, 19, 18, 16], [14, 16, 18, 19, 19, 19, 19, 19, 18, 16], [14, 16, 80, 80, 19, 19, 19, 100, 100, 16], [13, 15, 80, 80, 18, 18, 18, 100, 100, 15], [11, 13, 15, 16, 16, 16, 16, 16, 15, 13]], dtype=np.uint8) data = 100 - data expected_result = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0, 0, 1, 1, 0], [0, 0, 1, 1, 0, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=np.uint8) for dtype in [np.uint8, np.uint64, np.int8, np.int64]: data = data.astype(dtype) out = extrema.h_minima(data, 40) error = diff(expected_result, out) assert error < eps assert out.dtype == expected_result.dtype def test_extrema_float(self): "specific tests for float type" data = np.array([[0.10, 0.11, 0.13, 0.14, 0.14, 0.15, 0.14, 0.14, 0.13, 0.11], [0.11, 0.13, 0.15, 0.16, 0.16, 0.16, 0.16, 0.16, 0.15, 0.13], [0.13, 0.15, 0.40, 0.40, 0.18, 0.18, 0.18, 0.60, 0.60, 0.15], [0.14, 0.16, 0.40, 0.40, 0.19, 0.19, 0.19, 0.60, 0.60, 0.16], [0.14, 0.16, 0.18, 0.19, 0.19, 0.19, 0.19, 0.19, 0.18, 0.16], [0.15, 0.182, 0.18, 0.19, 0.204, 0.20, 0.19, 0.19, 0.18, 0.16], [0.14, 0.16, 0.18, 0.19, 0.19, 0.19, 0.19, 0.19, 0.18, 0.16], [0.14, 0.16, 0.80, 0.80, 0.19, 0.19, 0.19, 1.0, 1.0, 0.16], [0.13, 0.15, 0.80, 0.80, 0.18, 0.18, 0.18, 1.0, 1.0, 0.15], [0.11, 0.13, 0.15, 0.16, 0.16, 0.16, 0.16, 0.16, 0.15, 0.13]], dtype=np.float32) inverted_data = 1.0 - data expected_result = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0, 0, 1, 1, 0], [0, 0, 1, 1, 0, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0, 0, 1, 1, 0], [0, 0, 1, 1, 0, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=np.uint8) # test for local maxima with automatic step calculation out = extrema.local_maxima(data) error = diff(expected_result, out) assert error < eps # test for local minima with automatic step calculation out = extrema.local_minima(inverted_data) error = diff(expected_result, out) assert error < eps out = extrema.h_maxima(data, 0.003) expected_result = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0, 0, 1, 1, 0], [0, 0, 1, 1, 0, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0, 0, 1, 1, 0], [0, 0, 1, 1, 0, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=np.uint8) error = diff(expected_result, out) assert error < eps out = extrema.h_minima(inverted_data, 0.003) error = diff(expected_result, out) assert error < eps def test_3d(self): """tests the detection of maxima in 3D.""" img = np.zeros((8, 8, 8), dtype=np.uint8) local_maxima = np.zeros((8, 8, 8), dtype=np.uint8) # first maximum: only one pixel img[1, 1:3, 1:3] = 100 img[2, 2, 2] = 200 img[3, 1:3, 1:3] = 100 local_maxima[2, 2, 2] = 1 # second maximum: three pixels in z-direction img[5:8, 1, 1] = 200 local_maxima[5:8, 1, 1] = 1 # third: two maxima in 0 and 3. img[0, 5:8, 5:8] = 200 img[1, 6, 6] = 100 img[2, 5:7, 5:7] = 200 img[0:3, 5:8, 5:8] += 50 local_maxima[0, 5:8, 5:8] = 1 local_maxima[2, 5:7, 5:7] = 1 # four : one maximum in the corner of the square img[6:8, 6:8, 6:8] = 200 img[7, 7, 7] = 255 local_maxima[7, 7, 7] = 1 se = ndi.generate_binary_structure(3, 1) out = extrema.local_maxima(img, se) error = diff(local_maxima, out) assert error < eps if __name__ == "__main__": np.testing.run_module_suite() scikit-image-0.13.1/skimage/morphology/tests/test_grey.py000066400000000000000000000234721316256236300235210ustar00rootroot00000000000000import os.path import numpy as np from numpy import testing from scipy import ndimage as ndi from skimage import color, data, transform from skimage import img_as_uint, img_as_ubyte, data_dir from skimage.morphology import grey, selem from skimage._shared._warnings import expected_warnings class TestMorphology(): # These expected outputs were generated with skimage v0.12.1 # using: # # from skimage.morphology.tests.test_grey import TestMorphology # import numpy as np # output = TestMorphology()._build_expected_output() # np.savez_compressed('gray_morph_output.npz', **output) def _build_expected_output(self): funcs = (grey.erosion, grey.dilation, grey.opening, grey.closing, grey.white_tophat, grey.black_tophat) selems_2D = (selem.square, selem.diamond, selem.disk, selem.star) with expected_warnings(['Possible precision loss']): image = img_as_ubyte(transform.downscale_local_mean( color.rgb2gray(data.coffee()), (20, 20))) output = {} for n in range(1, 4): for strel in selems_2D: for func in funcs: key = '{0}_{1}_{2}'.format(strel.__name__, n, func.__name__) output[key] = func(image, strel(n)) return output def test_gray_morphology(self): expected = dict(np.load(os.path.join(data_dir, 'gray_morph_output.npz'))) calculated = self._build_expected_output() testing.assert_equal(expected, calculated) class TestEccentricStructuringElements(): def setUp(self): self.black_pixel = 255 * np.ones((4, 4), dtype=np.uint8) self.black_pixel[1, 1] = 0 self.white_pixel = 255 - self.black_pixel self.selems = [selem.square(2), selem.rectangle(2, 2), selem.rectangle(2, 1), selem.rectangle(1, 2)] def test_dilate_erode_symmetry(self): for s in self.selems: c = grey.erosion(self.black_pixel, s) d = grey.dilation(self.white_pixel, s) assert np.all(c == (255 - d)) def test_open_black_pixel(self): for s in self.selems: grey_open = grey.opening(self.black_pixel, s) assert np.all(grey_open == self.black_pixel) def test_close_white_pixel(self): for s in self.selems: grey_close = grey.closing(self.white_pixel, s) assert np.all(grey_close == self.white_pixel) def test_open_white_pixel(self): for s in self.selems: assert np.all(grey.opening(self.white_pixel, s) == 0) def test_close_black_pixel(self): for s in self.selems: assert np.all(grey.closing(self.black_pixel, s) == 255) def test_white_tophat_white_pixel(self): for s in self.selems: tophat = grey.white_tophat(self.white_pixel, s) assert np.all(tophat == self.white_pixel) def test_black_tophat_black_pixel(self): for s in self.selems: tophat = grey.black_tophat(self.black_pixel, s) assert np.all(tophat == (255 - self.black_pixel)) def test_white_tophat_black_pixel(self): for s in self.selems: tophat = grey.white_tophat(self.black_pixel, s) assert np.all(tophat == 0) def test_black_tophat_white_pixel(self): for s in self.selems: tophat = grey.black_tophat(self.white_pixel, s) assert np.all(tophat == 0) def test_default_selem(): functions = [grey.erosion, grey.dilation, grey.opening, grey.closing, grey.white_tophat, grey.black_tophat] strel = selem.diamond(radius=1) image = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], np.uint8) for function in functions: im_expected = function(image, strel) im_test = function(image) yield testing.assert_array_equal, im_expected, im_test def test_3d_fallback_default_selem(): # 3x3x3 cube inside a 7x7x7 image: image = np.zeros((7, 7, 7), np.bool) image[2:-2, 2:-2, 2:-2] = 1 opened = grey.opening(image) # expect a "hyper-cross" centered in the 5x5x5: image_expected = np.zeros((7, 7, 7), dtype=bool) image_expected[2:5, 2:5, 2:5] = ndi.generate_binary_structure(3, 1) testing.assert_array_equal(opened, image_expected) def test_3d_fallback_cube_selem(): # 3x3x3 cube inside a 7x7x7 image: image = np.zeros((7, 7, 7), np.bool) image[2:-2, 2:-2, 2:-2] = 1 cube = np.ones((3, 3, 3), dtype=np.uint8) for function in [grey.closing, grey.opening]: new_image = function(image, cube) yield testing.assert_array_equal, new_image, image def test_3d_fallback_white_tophat(): image = np.zeros((7, 7, 7), dtype=bool) image[2, 2:4, 2:4] = 1 image[3, 2:5, 2:5] = 1 image[4, 3:5, 3:5] = 1 with expected_warnings(['operator.*deprecated|\A\Z']): new_image = grey.white_tophat(image) footprint = ndi.generate_binary_structure(3,1) with expected_warnings(['operator.*deprecated|\A\Z']): image_expected = ndi.white_tophat(image,footprint=footprint) testing.assert_array_equal(new_image, image_expected) def test_3d_fallback_black_tophat(): image = np.ones((7, 7, 7), dtype=bool) image[2, 2:4, 2:4] = 0 image[3, 2:5, 2:5] = 0 image[4, 3:5, 3:5] = 0 with expected_warnings(['operator.*deprecated|\A\Z']): new_image = grey.black_tophat(image) footprint = ndi.generate_binary_structure(3,1) with expected_warnings(['operator.*deprecated|\A\Z']): image_expected = ndi.black_tophat(image,footprint=footprint) testing.assert_array_equal(new_image, image_expected) def test_2d_ndimage_equivalence(): image = np.zeros((9, 9), np.uint8) image[2:-2, 2:-2] = 128 image[3:-3, 3:-3] = 196 image[4, 4] = 255 opened = grey.opening(image) closed = grey.closing(image) selem = ndi.generate_binary_structure(2, 1) ndimage_opened = ndi.grey_opening(image, footprint=selem) ndimage_closed = ndi.grey_closing(image, footprint=selem) testing.assert_array_equal(opened, ndimage_opened) testing.assert_array_equal(closed, ndimage_closed) # float test images im = np.array([[ 0.55, 0.72, 0.6 , 0.54, 0.42], [ 0.65, 0.44, 0.89, 0.96, 0.38], [ 0.79, 0.53, 0.57, 0.93, 0.07], [ 0.09, 0.02, 0.83, 0.78, 0.87], [ 0.98, 0.8 , 0.46, 0.78, 0.12]]) eroded = np.array([[ 0.55, 0.44, 0.54, 0.42, 0.38], [ 0.44, 0.44, 0.44, 0.38, 0.07], [ 0.09, 0.02, 0.53, 0.07, 0.07], [ 0.02, 0.02, 0.02, 0.78, 0.07], [ 0.09, 0.02, 0.46, 0.12, 0.12]]) dilated = np.array([[ 0.72, 0.72, 0.89, 0.96, 0.54], [ 0.79, 0.89, 0.96, 0.96, 0.96], [ 0.79, 0.79, 0.93, 0.96, 0.93], [ 0.98, 0.83, 0.83, 0.93, 0.87], [ 0.98, 0.98, 0.83, 0.78, 0.87]]) opened = np.array([[ 0.55, 0.55, 0.54, 0.54, 0.42], [ 0.55, 0.44, 0.54, 0.44, 0.38], [ 0.44, 0.53, 0.53, 0.78, 0.07], [ 0.09, 0.02, 0.78, 0.78, 0.78], [ 0.09, 0.46, 0.46, 0.78, 0.12]]) closed = np.array([[ 0.72, 0.72, 0.72, 0.54, 0.54], [ 0.72, 0.72, 0.89, 0.96, 0.54], [ 0.79, 0.79, 0.79, 0.93, 0.87], [ 0.79, 0.79, 0.83, 0.78, 0.87], [ 0.98, 0.83, 0.78, 0.78, 0.78]]) def test_float(): np.testing.assert_allclose(grey.erosion(im), eroded) np.testing.assert_allclose(grey.dilation(im), dilated) np.testing.assert_allclose(grey.opening(im), opened) np.testing.assert_allclose(grey.closing(im), closed) def test_uint16(): with expected_warnings(['Possible precision loss']): im16, eroded16, dilated16, opened16, closed16 = ( map(img_as_uint, [im, eroded, dilated, opened, closed])) np.testing.assert_allclose(grey.erosion(im16), eroded16) np.testing.assert_allclose(grey.dilation(im16), dilated16) np.testing.assert_allclose(grey.opening(im16), opened16) np.testing.assert_allclose(grey.closing(im16), closed16) def test_discontiguous_out_array(): image = np.array([[5, 6, 2], [7, 2, 2], [3, 5, 1]], np.uint8) out_array_big = np.zeros((5, 5), np.uint8) out_array = out_array_big[::2, ::2] expected_dilation = np.array([[7, 0, 6, 0, 6], [0, 0, 0, 0, 0], [7, 0, 7, 0, 2], [0, 0, 0, 0, 0], [7, 0, 5, 0, 5]], np.uint8) expected_erosion = np.array([[5, 0, 2, 0, 2], [0, 0, 0, 0, 0], [2, 0, 2, 0, 1], [0, 0, 0, 0, 0], [3, 0, 1, 0, 1]], np.uint8) grey.dilation(image, out=out_array) testing.assert_array_equal(out_array_big, expected_dilation) grey.erosion(image, out=out_array) testing.assert_array_equal(out_array_big, expected_erosion) if __name__ == '__main__': testing.run_module_suite() scikit-image-0.13.1/skimage/morphology/tests/test_misc.py000066400000000000000000000162251316256236300235040ustar00rootroot00000000000000import numpy as np from numpy.testing import (assert_array_equal, assert_equal, assert_raises, assert_warns) from skimage.morphology import remove_small_objects, remove_small_holes from ..._shared._warnings import expected_warnings test_image = np.array([[0, 0, 0, 1, 0], [1, 1, 1, 0, 0], [1, 1, 1, 0, 1]], bool) def test_one_connectivity(): expected = np.array([[0, 0, 0, 0, 0], [1, 1, 1, 0, 0], [1, 1, 1, 0, 0]], bool) observed = remove_small_objects(test_image, min_size=6) assert_array_equal(observed, expected) def test_two_connectivity(): expected = np.array([[0, 0, 0, 1, 0], [1, 1, 1, 0, 0], [1, 1, 1, 0, 0]], bool) observed = remove_small_objects(test_image, min_size=7, connectivity=2) assert_array_equal(observed, expected) def test_in_place(): observed = remove_small_objects(test_image, min_size=6, in_place=True) assert_equal(observed is test_image, True, "remove_small_objects in_place argument failed.") def test_labeled_image(): labeled_image = np.array([[2, 2, 2, 0, 1], [2, 2, 2, 0, 1], [2, 0, 0, 0, 0], [0, 0, 3, 3, 3]], dtype=int) expected = np.array([[2, 2, 2, 0, 0], [2, 2, 2, 0, 0], [2, 0, 0, 0, 0], [0, 0, 3, 3, 3]], dtype=int) observed = remove_small_objects(labeled_image, min_size=3) assert_array_equal(observed, expected) def test_uint_image(): labeled_image = np.array([[2, 2, 2, 0, 1], [2, 2, 2, 0, 1], [2, 0, 0, 0, 0], [0, 0, 3, 3, 3]], dtype=np.uint8) expected = np.array([[2, 2, 2, 0, 0], [2, 2, 2, 0, 0], [2, 0, 0, 0, 0], [0, 0, 3, 3, 3]], dtype=np.uint8) observed = remove_small_objects(labeled_image, min_size=3) assert_array_equal(observed, expected) def test_single_label_warning(): image = np.array([[0, 0, 0, 1, 0], [1, 1, 1, 0, 0], [1, 1, 1, 0, 0]], int) with expected_warnings(['use a boolean array?']): remove_small_objects(image, min_size=6) def test_float_input(): float_test = np.random.rand(5, 5) assert_raises(TypeError, remove_small_objects, float_test) def test_negative_input(): negative_int = np.random.randint(-4, -1, size=(5, 5)) assert_raises(ValueError, remove_small_objects, negative_int) test_holes_image = np.array([[0,0,0,0,0,0,1,0,0,0], [0,1,1,1,1,1,0,0,0,0], [0,1,0,0,1,1,0,0,0,0], [0,1,1,1,0,1,0,0,0,0], [0,1,1,1,1,1,0,0,0,0], [0,0,0,0,0,0,0,1,1,1], [0,0,0,0,0,0,0,1,0,1], [0,0,0,0,0,0,0,1,1,1]], bool) def test_one_connectivity_holes(): expected = np.array([[0,0,0,0,0,0,1,0,0,0], [0,1,1,1,1,1,0,0,0,0], [0,1,1,1,1,1,0,0,0,0], [0,1,1,1,1,1,0,0,0,0], [0,1,1,1,1,1,0,0,0,0], [0,0,0,0,0,0,0,1,1,1], [0,0,0,0,0,0,0,1,1,1], [0,0,0,0,0,0,0,1,1,1]], bool) observed = remove_small_holes(test_holes_image, min_size=3) assert_array_equal(observed, expected) def test_two_connectivity_holes(): expected = np.array([[0,0,0,0,0,0,1,0,0,0], [0,1,1,1,1,1,0,0,0,0], [0,1,0,0,1,1,0,0,0,0], [0,1,1,1,0,1,0,0,0,0], [0,1,1,1,1,1,0,0,0,0], [0,0,0,0,0,0,0,1,1,1], [0,0,0,0,0,0,0,1,1,1], [0,0,0,0,0,0,0,1,1,1]], bool) observed = remove_small_holes(test_holes_image, min_size=3, connectivity=2) assert_array_equal(observed, expected) def test_in_place_holes(): observed = remove_small_holes(test_holes_image, min_size=3, in_place=True) assert_equal(observed is test_holes_image, True, "remove_small_holes in_place argument failed.") def test_labeled_image_holes(): labeled_holes_image = np.array([[0,0,0,0,0,0,1,0,0,0], [0,1,1,1,1,1,0,0,0,0], [0,1,0,0,1,1,0,0,0,0], [0,1,1,1,0,1,0,0,0,0], [0,1,1,1,1,1,0,0,0,0], [0,0,0,0,0,0,0,2,2,2], [0,0,0,0,0,0,0,2,0,2], [0,0,0,0,0,0,0,2,2,2]], dtype=int) expected = np.array([[0,0,0,0,0,0,1,0,0,0], [0,1,1,1,1,1,0,0,0,0], [0,1,1,1,1,1,0,0,0,0], [0,1,1,1,1,1,0,0,0,0], [0,1,1,1,1,1,0,0,0,0], [0,0,0,0,0,0,0,1,1,1], [0,0,0,0,0,0,0,1,1,1], [0,0,0,0,0,0,0,1,1,1]], dtype=bool) observed = remove_small_holes(labeled_holes_image, min_size=3) assert_array_equal(observed, expected) def test_uint_image_holes(): labeled_holes_image = np.array([[0,0,0,0,0,0,1,0,0,0], [0,1,1,1,1,1,0,0,0,0], [0,1,0,0,1,1,0,0,0,0], [0,1,1,1,0,1,0,0,0,0], [0,1,1,1,1,1,0,0,0,0], [0,0,0,0,0,0,0,2,2,2], [0,0,0,0,0,0,0,2,0,2], [0,0,0,0,0,0,0,2,2,2]], dtype=np.uint8) expected = np.array([[0,0,0,0,0,0,1,0,0,0], [0,1,1,1,1,1,0,0,0,0], [0,1,1,1,1,1,0,0,0,0], [0,1,1,1,1,1,0,0,0,0], [0,1,1,1,1,1,0,0,0,0], [0,0,0,0,0,0,0,1,1,1], [0,0,0,0,0,0,0,1,1,1], [0,0,0,0,0,0,0,1,1,1]], dtype=bool) observed = remove_small_holes(labeled_holes_image, min_size=3) assert_array_equal(observed, expected) def test_label_warning_holes(): labeled_holes_image = np.array([[0,0,0,0,0,0,1,0,0,0], [0,1,1,1,1,1,0,0,0,0], [0,1,0,0,1,1,0,0,0,0], [0,1,1,1,0,1,0,0,0,0], [0,1,1,1,1,1,0,0,0,0], [0,0,0,0,0,0,0,2,2,2], [0,0,0,0,0,0,0,2,0,2], [0,0,0,0,0,0,0,2,2,2]], dtype=int) with expected_warnings(['use a boolean array?']): remove_small_holes(labeled_holes_image, min_size=3) def test_float_input_holes(): float_test = np.random.rand(5, 5) assert_raises(TypeError, remove_small_holes, float_test) if __name__ == "__main__": np.testing.run_module_suite() scikit-image-0.13.1/skimage/morphology/tests/test_reconstruction.py000066400000000000000000000072111316256236300256250ustar00rootroot00000000000000""" These tests are originally part of CellProfiler, code licensed under both GPL and BSD licenses. Website: http://www.cellprofiler.org Copyright (c) 2003-2009 Massachusetts Institute of Technology Copyright (c) 2009-2011 Broad Institute All rights reserved. Original author: Lee Kamentsky """ import numpy as np from numpy.testing import (assert_array_almost_equal as assert_close, assert_raises) from skimage.morphology.greyreconstruct import reconstruction def test_zeros(): """Test reconstruction with image and mask of zeros""" assert_close(reconstruction(np.zeros((5, 7)), np.zeros((5, 7))), 0) def test_image_equals_mask(): """Test reconstruction where the image and mask are the same""" assert_close(reconstruction(np.ones((7, 5)), np.ones((7, 5))), 1) def test_image_less_than_mask(): """Test reconstruction where the image is uniform and less than mask""" image = np.ones((5, 5)) mask = np.ones((5, 5)) * 2 assert_close(reconstruction(image, mask), 1) def test_one_image_peak(): """Test reconstruction with one peak pixel""" image = np.ones((5, 5)) image[2, 2] = 2 mask = np.ones((5, 5)) * 3 assert_close(reconstruction(image, mask), 2) def test_two_image_peaks(): """Test reconstruction with two peak pixels isolated by the mask""" image = np.array([[1, 1, 1, 1, 1, 1, 1, 1], [1, 2, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 3, 1], [1, 1, 1, 1, 1, 1, 1, 1]]) mask = np.array([[4, 4, 4, 1, 1, 1, 1, 1], [4, 4, 4, 1, 1, 1, 1, 1], [4, 4, 4, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 4, 4, 4], [1, 1, 1, 1, 1, 4, 4, 4], [1, 1, 1, 1, 1, 4, 4, 4]]) expected = np.array([[2, 2, 2, 1, 1, 1, 1, 1], [2, 2, 2, 1, 1, 1, 1, 1], [2, 2, 2, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 3, 3, 3], [1, 1, 1, 1, 1, 3, 3, 3], [1, 1, 1, 1, 1, 3, 3, 3]]) assert_close(reconstruction(image, mask), expected) def test_zero_image_one_mask(): """Test reconstruction with an image of all zeros and a mask that's not""" result = reconstruction(np.zeros((10, 10)), np.ones((10, 10))) assert_close(result, 0) def test_fill_hole(): """Test reconstruction by erosion, which should fill holes in mask.""" seed = np.array([0, 8, 8, 8, 8, 8, 8, 8, 8, 0]) mask = np.array([0, 3, 6, 2, 1, 1, 1, 4, 2, 0]) result = reconstruction(seed, mask, method='erosion') assert_close(result, np.array([0, 3, 6, 4, 4, 4, 4, 4, 2, 0])) def test_invalid_seed(): seed = np.ones((5, 5)) mask = np.ones((5, 5)) assert_raises(ValueError, reconstruction, seed * 2, mask, method='dilation') assert_raises(ValueError, reconstruction, seed * 0.5, mask, method='erosion') def test_invalid_selem(): seed = np.ones((5, 5)) mask = np.ones((5, 5)) assert_raises(ValueError, reconstruction, seed, mask, selem=np.ones((4, 4))) assert_raises(ValueError, reconstruction, seed, mask, selem=np.ones((3, 4))) reconstruction(seed, mask, selem=np.ones((3, 3))) def test_invalid_method(): seed = np.array([0, 8, 8, 8, 8, 8, 8, 8, 8, 0]) mask = np.array([0, 3, 6, 2, 1, 1, 1, 4, 2, 0]) assert_raises(ValueError, reconstruction, seed, mask, method='foo') if __name__ == '__main__': from numpy import testing testing.run_module_suite() scikit-image-0.13.1/skimage/morphology/tests/test_selem.py000066400000000000000000000147651316256236300236650ustar00rootroot00000000000000""" Tests for Morphological structuring elements (skimage.morphology.selem) Author: Damian Eads """ import os.path import numpy as np from numpy.testing import assert_equal from skimage import data_dir from skimage.morphology import selem class TestSElem(): def test_square_selem(self): """Test square structuring elements""" for k in range(0, 5): actual_mask = selem.square(k) expected_mask = np.ones((k, k), dtype='uint8') assert_equal(expected_mask, actual_mask) def test_rectangle_selem(self): """Test rectangle structuring elements""" for i in range(0, 5): for j in range(0, 5): actual_mask = selem.rectangle(i, j) expected_mask = np.ones((i, j), dtype='uint8') assert_equal(expected_mask, actual_mask) def test_cube_selem(self): """Test cube structuring elements""" for k in range(0, 5): actual_mask = selem.cube(k) expected_mask = np.ones((k, k, k), dtype='uint8') assert_equal(expected_mask, actual_mask) def strel_worker(self, fn, func): matlab_masks = np.load(os.path.join(data_dir, fn)) k = 0 for arrname in sorted(matlab_masks): expected_mask = matlab_masks[arrname] actual_mask = func(k) if expected_mask.shape == (1,): expected_mask = expected_mask[:, np.newaxis] assert_equal(expected_mask, actual_mask) k = k + 1 def strel_worker_3d(self, fn, func): matlab_masks = np.load(os.path.join(data_dir, fn)) k = 0 for arrname in sorted(matlab_masks): expected_mask = matlab_masks[arrname] actual_mask = func(k) if expected_mask.shape == (1,): expected_mask = expected_mask[:, np.newaxis] # Test center slice for each dimension. This gives a good # indication of validity without the need for a 3D reference # mask. c = int(expected_mask.shape[0]/2) assert_equal(expected_mask, actual_mask[c, :, :]) assert_equal(expected_mask, actual_mask[:, c, :]) assert_equal(expected_mask, actual_mask[:, :, c]) k = k + 1 def test_selem_disk(self): """Test disk structuring elements""" self.strel_worker("disk-matlab-output.npz", selem.disk) def test_selem_diamond(self): """Test diamond structuring elements""" self.strel_worker("diamond-matlab-output.npz", selem.diamond) def test_selem_ball(self): """Test ball structuring elements""" self.strel_worker_3d("disk-matlab-output.npz", selem.ball) def test_selem_octahedron(self): """Test octahedron structuring elements""" self.strel_worker_3d("diamond-matlab-output.npz", selem.octahedron) def test_selem_octagon(self): """Test octagon structuring elements""" expected_mask1 = np.array([[0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0]], dtype=np.uint8) actual_mask1 = selem.octagon(5, 3) expected_mask2 = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]], dtype=np.uint8) actual_mask2 = selem.octagon(1, 1) assert_equal(expected_mask1, actual_mask1) assert_equal(expected_mask2, actual_mask2) def test_selem_ellipse(self): """Test ellipse structuring elements""" expected_mask1 = np.array([[0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]], dtype=np.uint8) actual_mask1 = selem.ellipse(5, 3) expected_mask2 = np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1]], dtype=np.uint8) actual_mask2 = selem.ellipse(1, 1) assert_equal(expected_mask1, actual_mask1) assert_equal(expected_mask2, actual_mask2) assert_equal(expected_mask1, selem.ellipse(3, 5).T) assert_equal(expected_mask2, selem.ellipse(1, 1).T) def test_selem_star(self): """Test star structuring elements""" expected_mask1 = np.array([[0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0]], dtype=np.uint8) actual_mask1 = selem.star(4) expected_mask2 = np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1]], dtype=np.uint8) actual_mask2 = selem.star(1) assert_equal(expected_mask1, actual_mask1) assert_equal(expected_mask2, actual_mask2) if __name__ == '__main__': np.testing.run_module_suite() scikit-image-0.13.1/skimage/morphology/tests/test_skeletonize.py000066400000000000000000000206251316256236300251040ustar00rootroot00000000000000import numpy as np from skimage.morphology import skeletonize, medial_axis, thin from skimage.morphology._skeletonize import (_generate_thin_luts, G123_LUT, G123P_LUT) import numpy.testing from skimage import draw from scipy.ndimage import correlate from skimage.io import imread from skimage import data_dir import os.path class TestSkeletonize(): def test_skeletonize_no_foreground(self): im = np.zeros((5, 5)) result = skeletonize(im) numpy.testing.assert_array_equal(result, np.zeros((5, 5))) def test_skeletonize_wrong_dim1(self): im = np.zeros((5)) numpy.testing.assert_raises(ValueError, skeletonize, im) def test_skeletonize_wrong_dim2(self): im = np.zeros((5, 5, 5)) numpy.testing.assert_raises(ValueError, skeletonize, im) def test_skeletonize_not_binary(self): im = np.zeros((5, 5)) im[0, 0] = 1 im[0, 1] = 2 numpy.testing.assert_raises(ValueError, skeletonize, im) def test_skeletonize_unexpected_value(self): im = np.zeros((5, 5)) im[0, 0] = 2 numpy.testing.assert_raises(ValueError, skeletonize, im) def test_skeletonize_all_foreground(self): im = np.ones((3, 4)) skeletonize(im) def test_skeletonize_single_point(self): im = np.zeros((5, 5), np.uint8) im[3, 3] = 1 result = skeletonize(im) numpy.testing.assert_array_equal(result, im) def test_skeletonize_already_thinned(self): im = np.zeros((5, 5), np.uint8) im[3, 1:-1] = 1 im[2, -1] = 1 im[4, 0] = 1 result = skeletonize(im) numpy.testing.assert_array_equal(result, im) def test_skeletonize_output(self): im = imread(os.path.join(data_dir, "bw_text.png"), as_grey=True) # make black the foreground im = (im == 0) result = skeletonize(im) expected = np.load(os.path.join(data_dir, "bw_text_skeleton.npy")) numpy.testing.assert_array_equal(result, expected) def test_skeletonize_num_neighbours(self): # an empty image image = np.zeros((300, 300)) # foreground object 1 image[10:-10, 10:100] = 1 image[-100:-10, 10:-10] = 1 image[10:-10, -100:-10] = 1 # foreground object 2 rs, cs = draw.line(250, 150, 10, 280) for i in range(10): image[rs + i, cs] = 1 rs, cs = draw.line(10, 150, 250, 280) for i in range(20): image[rs + i, cs] = 1 # foreground object 3 ir, ic = np.indices(image.shape) circle1 = (ic - 135)**2 + (ir - 150)**2 < 30**2 circle2 = (ic - 135)**2 + (ir - 150)**2 < 20**2 image[circle1] = 1 image[circle2] = 0 result = skeletonize(image) # there should never be a 2x2 block of foreground pixels in a skeleton mask = np.array([[1, 1], [1, 1]], np.uint8) blocks = correlate(result, mask, mode='constant') assert not numpy.any(blocks == 4) def test_lut_fix(self): im = np.zeros((6, 6), np.uint8) im[1, 2] = 1 im[2, 2] = 1 im[2, 3] = 1 im[3, 3] = 1 im[3, 4] = 1 im[4, 4] = 1 im[4, 5] = 1 result = skeletonize(im) expected = np.array([[0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0], [0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0]], dtype=np.uint8) assert np.all(result == expected) class TestThin(): @property def input_image(self): """image to test thinning with""" ii = np.array([[0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0], [0, 1, 0, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0]], dtype=np.uint8) return ii def test_zeros(self): assert np.all(thin(np.zeros((10, 10))) == False) def test_iter_1(self): result = thin(self.input_image, 1).astype(np.uint8) expected = np.array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0], [0, 1, 0, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]], dtype=np.uint8) numpy.testing.assert_array_equal(result, expected) def test_noiter(self): result = thin(self.input_image).astype(np.uint8) expected = np.array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0], [0, 1, 0, 1, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]], dtype=np.uint8) numpy.testing.assert_array_equal(result, expected) def test_baddim(self): for ii in [np.zeros((3)), np.zeros((3, 3, 3))]: numpy.testing.assert_raises(ValueError, thin, ii) def test_lut_generation(self): g123, g123p = _generate_thin_luts() numpy.testing.assert_array_equal(g123, G123_LUT) numpy.testing.assert_array_equal(g123p, G123P_LUT) class TestMedialAxis(): def test_00_00_zeros(self): '''Test skeletonize on an array of all zeros''' result = medial_axis(np.zeros((10, 10), bool)) assert np.all(result == False) def test_00_01_zeros_masked(self): '''Test skeletonize on an array that is completely masked''' result = medial_axis(np.zeros((10, 10), bool), np.zeros((10, 10), bool)) assert np.all(result == False) def test_01_01_rectangle(self): '''Test skeletonize on a rectangle''' image = np.zeros((9, 15), bool) image[1:-1, 1:-1] = True # # The result should be four diagonals from the # corners, meeting in a horizontal line # expected = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], bool) result = medial_axis(image) assert np.all(result == expected) result, distance = medial_axis(image, return_distance=True) assert distance.max() == 4 def test_01_02_hole(self): '''Test skeletonize on a rectangle with a hole in the middle''' image = np.zeros((9, 15), bool) image[1:-1, 1:-1] = True image[4, 4:-4] = False expected = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], bool) result = medial_axis(image) assert np.all(result == expected) def test_narrow_image(self): """Test skeletonize on a 1-pixel thin strip""" image = np.zeros((1, 5), bool) image[:, 1:-1] = True result = medial_axis(image) assert np.all(result == image) if __name__ == '__main__': np.testing.run_module_suite() scikit-image-0.13.1/skimage/morphology/tests/test_skeletonize_3d.py000066400000000000000000000145541316256236300254760ustar00rootroot00000000000000from __future__ import division, print_function, absolute_import import os import warnings import numpy as np from numpy.testing import (assert_equal, run_module_suite, assert_raises, assert_) import scipy.ndimage as ndi import skimage from skimage import io, draw, data_dir from skimage.data import binary_blobs from skimage.util import img_as_ubyte from skimage.morphology import skeletonize_3d # basic behavior tests (mostly copied over from 2D skeletonize) def test_skeletonize_wrong_dim(): im = np.zeros(5, dtype=np.uint8) assert_raises(ValueError, skeletonize_3d, im) im = np.zeros((5, 5, 5, 5), dtype=np.uint8) assert_raises(ValueError, skeletonize_3d, im) def test_skeletonize_1D(): # a corner case of an image of a shape(1, N) im = np.ones((5, 1), dtype=np.uint8) res = skeletonize_3d(im) assert_equal(res, im) def test_skeletonize_no_foreground(): im = np.zeros((5, 5), dtype=np.uint8) result = skeletonize_3d(im) assert_equal(result, im) def test_skeletonize_all_foreground(): im = np.ones((3, 4), dtype=np.uint8) assert_equal(skeletonize_3d(im), np.array([[0, 0, 0, 0], [1, 1, 1, 1], [0, 0, 0, 0]], dtype=np.uint8)) def test_skeletonize_single_point(): im = np.zeros((5, 5), dtype=np.uint8) im[3, 3] = 1 result = skeletonize_3d(im) assert_equal(result, im) def test_skeletonize_already_thinned(): im = np.zeros((5, 5), dtype=np.uint8) im[3, 1:-1] = 1 im[2, -1] = 1 im[4, 0] = 1 result = skeletonize_3d(im) assert_equal(result, im) def test_dtype_conv(): # check that the operation does the right thing with floats etc # also check non-contiguous input img = np.random.random((16, 16))[::2, ::2] img[img < 0.5] = 0 orig = img.copy() with warnings.catch_warnings(): # UserWarning for possible precision loss, expected warnings.simplefilter('ignore', UserWarning) res = skeletonize_3d(img) assert_equal(res.dtype, np.uint8) assert_equal(img, orig) # operation does not clobber the original assert_equal(res.max(), img_as_ubyte(img).max()) # the intensity range is preserved def test_input(): # check that the input is not clobbered # for 2D and 3D images of varying dtypes imgs = [np.ones((8, 8), dtype=float), np.ones((4, 8, 8), dtype=float), np.ones((8, 8), dtype=np.uint8), np.ones((4, 8, 8), dtype=np.uint8), np.ones((8, 8), dtype=bool), np.ones((4, 8, 8), dtype=bool)] for img in imgs: yield check_input, img def check_input(img): orig = img.copy() with warnings.catch_warnings(): # UserWarning for possible precision loss, expected warnings.simplefilter('ignore', UserWarning) res = skeletonize_3d(img) assert_equal(img, orig) def test_skeletonize_num_neighbours(): # an empty image image = np.zeros((300, 300)) # foreground object 1 image[10:-10, 10:100] = 1 image[-100:-10, 10:-10] = 1 image[10:-10, -100:-10] = 1 # foreground object 2 rs, cs = draw.line(250, 150, 10, 280) for i in range(10): image[rs + i, cs] = 1 rs, cs = draw.line(10, 150, 250, 280) for i in range(20): image[rs + i, cs] = 1 # foreground object 3 ir, ic = np.indices(image.shape) circle1 = (ic - 135)**2 + (ir - 150)**2 < 30**2 circle2 = (ic - 135)**2 + (ir - 150)**2 < 20**2 image[circle1] = 1 image[circle2] = 0 result = skeletonize_3d(image) # there should never be a 2x2 block of foreground pixels in a skeleton mask = np.array([[1, 1], [1, 1]], np.uint8) blocks = ndi.correlate(result, mask, mode='constant') assert_(not np.any(blocks == 4)) def test_two_hole_image(): # test a simple 2D image against FIJI img_o = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0], [0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0], [0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0], [0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0], [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=np.uint8) img_f = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=np.uint8) res = skeletonize_3d(img_o) assert_equal(res, img_f) def test_3d_vs_fiji(): # generate an image with blobs and compate its skeleton to # the skeleton generated by FIJI img = binary_blobs(32, 0.05, n_dim=3, seed=1234) img = img[:-2, ...] img = img.astype(np.uint8)*255 img_s = skeletonize_3d(img) img_f = io.imread(os.path.join(data_dir, "_blobs_3d_fiji_skeleton.tif")) assert_equal(img_s, img_f) if __name__ == '__main__': run_module_suite() scikit-image-0.13.1/skimage/morphology/tests/test_watershed.py000066400000000000000000000570761316256236300245500ustar00rootroot00000000000000"""test_watershed.py - tests the watershed function Originally part of CellProfiler, code licensed under both GPL and BSD licenses. Website: http://www.cellprofiler.org Copyright (c) 2003-2009 Massachusetts Institute of Technology Copyright (c) 2009-2011 Broad Institute All rights reserved. Original author: Lee Kamentsky """ #Portions of this test were taken from scipy's watershed test in test_ndimage.py # # Copyright (C) 2003-2005 Peter J. Verveer # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions # are met: # # 1. Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # 2. Redistributions in binary form must reproduce the above # copyright notice, this list of conditions and the following # disclaimer in the documentation and/or other materials provided # with the distribution. # # 3. The name of the author may not be used to endorse or promote # products derived from this software without specific prior # written permission. # # THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS # OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY # DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE # GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, # WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING # NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. import math import unittest import numpy as np from scipy import ndimage as ndi from skimage.morphology.watershed import watershed from skimage.measure import label eps = 1e-12 def diff(a, b): if not isinstance(a, np.ndarray): a = np.asarray(a) if not isinstance(b, np.ndarray): b = np.asarray(b) if (0 in a.shape) and (0 in b.shape): return 0.0 b[a == 0] = 0 if (a.dtype in [np.complex64, np.complex128] or b.dtype in [np.complex64, np.complex128]): a = np.asarray(a, np.complex128) b = np.asarray(b, np.complex128) t = ((a.real - b.real)**2).sum() + ((a.imag - b.imag)**2).sum() else: a = np.asarray(a) a = a.astype(np.float64) b = np.asarray(b) b = b.astype(np.float64) t = ((a - b)**2).sum() return math.sqrt(t) class TestWatershed(unittest.TestCase): eight = np.ones((3, 3), bool) def test_watershed01(self): "watershed 1" data = np.array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0], [0, 1, 0, 0, 0, 1, 0], [0, 1, 0, 0, 0, 1, 0], [0, 1, 0, 0, 0, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]], np.uint8) markers = np.array([[ -1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 1, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0]], np.int8) out = watershed(data, markers, self.eight) expected = np.array([[-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, 1, 1, 1, 1, 1, -1], [-1, 1, 1, 1, 1, 1, -1], [-1, 1, 1, 1, 1, 1, -1], [-1, 1, 1, 1, 1, 1, -1], [-1, 1, 1, 1, 1, 1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]]) error = diff(expected, out) assert error < eps def test_watershed02(self): "watershed 2" data = np.array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0], [0, 1, 0, 0, 0, 1, 0], [0, 1, 0, 0, 0, 1, 0], [0, 1, 0, 0, 0, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]], np.uint8) markers = np.array([[-1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]], np.int8) out = watershed(data, markers) error = diff([[-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, 1, 1, 1, -1, -1], [-1, 1, 1, 1, 1, 1, -1], [-1, 1, 1, 1, 1, 1, -1], [-1, 1, 1, 1, 1, 1, -1], [-1, -1, 1, 1, 1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], out) self.assertTrue(error < eps) def test_watershed03(self): "watershed 3" data = np.array([[0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0], [0, 1, 0, 1, 0, 1, 0], [0, 1, 0, 1, 0, 1, 0], [0, 1, 0, 1, 0, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]], np.uint8) markers = np.array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, -1]], np.int8) out = watershed(data, markers) error = diff([[-1, -1, -1, -1, -1, -1, -1], [-1, 0, 2, 0, 3, 0, -1], [-1, 2, 2, 0, 3, 3, -1], [-1, 2, 2, 0, 3, 3, -1], [-1, 2, 2, 0, 3, 3, -1], [-1, 0, 2, 0, 3, 0, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], out) self.assertTrue(error < eps) def test_watershed04(self): "watershed 4" data = np.array([[0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0], [0, 1, 0, 1, 0, 1, 0], [0, 1, 0, 1, 0, 1, 0], [0, 1, 0, 1, 0, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]], np.uint8) markers = np.array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, -1]], np.int8) out = watershed(data, markers, self.eight) error = diff([[-1, -1, -1, -1, -1, -1, -1], [-1, 2, 2, 0, 3, 3, -1], [-1, 2, 2, 0, 3, 3, -1], [-1, 2, 2, 0, 3, 3, -1], [-1, 2, 2, 0, 3, 3, -1], [-1, 2, 2, 0, 3, 3, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], out) self.assertTrue(error < eps) def test_watershed05(self): "watershed 5" data = np.array([[0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0], [0, 1, 0, 1, 0, 1, 0], [0, 1, 0, 1, 0, 1, 0], [0, 1, 0, 1, 0, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]], np.uint8) markers = np.array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 0, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, -1]], np.int8) out = watershed(data, markers, self.eight) error = diff([[-1, -1, -1, -1, -1, -1, -1], [-1, 3, 3, 0, 2, 2, -1], [-1, 3, 3, 0, 2, 2, -1], [-1, 3, 3, 0, 2, 2, -1], [-1, 3, 3, 0, 2, 2, -1], [-1, 3, 3, 0, 2, 2, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], out) self.assertTrue(error < eps) def test_watershed06(self): "watershed 6" data = np.array([[0, 1, 0, 0, 0, 1, 0], [0, 1, 0, 0, 0, 1, 0], [0, 1, 0, 0, 0, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]], np.uint8) markers = np.array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [-1, 0, 0, 0, 0, 0, 0]], np.int8) out = watershed(data, markers, self.eight) error = diff([[-1, 1, 1, 1, 1, 1, -1], [-1, 1, 1, 1, 1, 1, -1], [-1, 1, 1, 1, 1, 1, -1], [-1, 1, 1, 1, 1, 1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], out) self.assertTrue(error < eps) def test_watershed07(self): "A regression test of a competitive case that failed" data = np.array([[255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255], [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255], [255, 255, 255, 255, 255, 204, 204, 204, 204, 204, 204, 255, 255, 255, 255, 255], [255, 255, 255, 204, 204, 183, 153, 153, 153, 153, 183, 204, 204, 255, 255, 255], [255, 255, 204, 183, 153, 141, 111, 103, 103, 111, 141, 153, 183, 204, 255, 255], [255, 255, 204, 153, 111, 94, 72, 52, 52, 72, 94, 111, 153, 204, 255, 255], [255, 255, 204, 153, 111, 72, 39, 1, 1, 39, 72, 111, 153, 204, 255, 255], [255, 255, 204, 183, 141, 111, 72, 39, 39, 72, 111, 141, 183, 204, 255, 255], [255, 255, 255, 204, 183, 141, 111, 72, 72, 111, 141, 183, 204, 255, 255, 255], [255, 255, 255, 255, 204, 183, 141, 94, 94, 141, 183, 204, 255, 255, 255, 255], [255, 255, 255, 255, 255, 204, 153, 103, 103, 153, 204, 255, 255, 255, 255, 255], [255, 255, 255, 255, 204, 183, 141, 94, 94, 141, 183, 204, 255, 255, 255, 255], [255, 255, 255, 204, 183, 141, 111, 72, 72, 111, 141, 183, 204, 255, 255, 255], [255, 255, 204, 183, 141, 111, 72, 39, 39, 72, 111, 141, 183, 204, 255, 255], [255, 255, 204, 153, 111, 72, 39, 1, 1, 39, 72, 111, 153, 204, 255, 255], [255, 255, 204, 153, 111, 94, 72, 52, 52, 72, 94, 111, 153, 204, 255, 255], [255, 255, 204, 183, 153, 141, 111, 103, 103, 111, 141, 153, 183, 204, 255, 255], [255, 255, 255, 204, 204, 183, 153, 153, 153, 153, 183, 204, 204, 255, 255, 255], [255, 255, 255, 255, 255, 204, 204, 204, 204, 204, 204, 255, 255, 255, 255, 255], [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255], [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255]]) mask = (data != 255) markers = np.zeros(data.shape, int) markers[6, 7] = 1 markers[14, 7] = 2 out = watershed(data, markers, self.eight, mask=mask) # # The two objects should be the same size, except possibly for the # border region # size1 = np.sum(out == 1) size2 = np.sum(out == 2) self.assertTrue(abs(size1 - size2) <= 6) def test_watershed08(self): "The border pixels + an edge are all the same value" data = np.array([[255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255], [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255], [255, 255, 255, 255, 255, 204, 204, 204, 204, 204, 204, 255, 255, 255, 255, 255], [255, 255, 255, 204, 204, 183, 153, 153, 153, 153, 183, 204, 204, 255, 255, 255], [255, 255, 204, 183, 153, 141, 111, 103, 103, 111, 141, 153, 183, 204, 255, 255], [255, 255, 204, 153, 111, 94, 72, 52, 52, 72, 94, 111, 153, 204, 255, 255], [255, 255, 204, 153, 111, 72, 39, 1, 1, 39, 72, 111, 153, 204, 255, 255], [255, 255, 204, 183, 141, 111, 72, 39, 39, 72, 111, 141, 183, 204, 255, 255], [255, 255, 255, 204, 183, 141, 111, 72, 72, 111, 141, 183, 204, 255, 255, 255], [255, 255, 255, 255, 204, 183, 141, 94, 94, 141, 183, 204, 255, 255, 255, 255], [255, 255, 255, 255, 255, 204, 153, 141, 141, 153, 204, 255, 255, 255, 255, 255], [255, 255, 255, 255, 204, 183, 141, 94, 94, 141, 183, 204, 255, 255, 255, 255], [255, 255, 255, 204, 183, 141, 111, 72, 72, 111, 141, 183, 204, 255, 255, 255], [255, 255, 204, 183, 141, 111, 72, 39, 39, 72, 111, 141, 183, 204, 255, 255], [255, 255, 204, 153, 111, 72, 39, 1, 1, 39, 72, 111, 153, 204, 255, 255], [255, 255, 204, 153, 111, 94, 72, 52, 52, 72, 94, 111, 153, 204, 255, 255], [255, 255, 204, 183, 153, 141, 111, 103, 103, 111, 141, 153, 183, 204, 255, 255], [255, 255, 255, 204, 204, 183, 153, 153, 153, 153, 183, 204, 204, 255, 255, 255], [255, 255, 255, 255, 255, 204, 204, 204, 204, 204, 204, 255, 255, 255, 255, 255], [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255], [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255]]) mask = (data != 255) markers = np.zeros(data.shape, int) markers[6, 7] = 1 markers[14, 7] = 2 out = watershed(data, markers, self.eight, mask=mask) # # The two objects should be the same size, except possibly for the # border region # size1 = np.sum(out == 1) size2 = np.sum(out == 2) self.assertTrue(abs(size1 - size2) <= 6) def test_watershed09(self): """Test on an image of reasonable size This is here both for timing (does it take forever?) and to ensure that the memory constraints are reasonable """ image = np.zeros((1000, 1000)) coords = np.random.uniform(0, 1000, (100, 2)).astype(int) markers = np.zeros((1000, 1000), int) idx = 1 for x, y in coords: image[x, y] = 1 markers[x, y] = idx idx += 1 image = ndi.gaussian_filter(image, 4) watershed(image, markers, self.eight) ndi.watershed_ift(image.astype(np.uint16), markers, self.eight) def test_watershed10(self): "watershed 10" data = np.array([[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]], np.uint8) markers = np.array([[1, 0, 0, 2], [0, 0, 0, 0], [0, 0, 0, 0], [3, 0, 0, 4]], np.int8) out = watershed(data, markers, self.eight) error = diff([[1, 1, 2, 2], [1, 1, 2, 2], [3, 3, 4, 4], [3, 3, 4, 4]], out) self.assertTrue(error < eps) def test_watershed11(self): '''Make sure that all points on this plateau are assigned to closest seed''' # https://github.com/scikit-image/scikit-image/issues/803 # # Make sure that no point in a level image is farther away # from its seed than any other # image = np.zeros((21, 21)) markers = np.zeros((21, 21), int) markers[5, 5] = 1 markers[5, 10] = 2 markers[10, 5] = 3 markers[10, 10] = 4 structure = np.array([[False, True, False], [True, True, True], [False, True, False]]) out = watershed(image, markers, structure) i, j = np.mgrid[0:21, 0:21] d = np.dstack( [np.sqrt((i.astype(float)-i0)**2, (j.astype(float)-j0)**2) for i0, j0 in ((5, 5), (5, 10), (10, 5), (10, 10))]) dmin = np.min(d, 2) self.assertTrue(np.all(d[i, j, out[i, j]-1] == dmin)) def test_watershed12(self): "The watershed line" data = np.array([[203, 255, 203, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153], [203, 255, 203, 153, 153, 153, 102, 102, 102, 102, 102, 102, 153, 153, 153, 153], [203, 255, 203, 203, 153, 153, 102, 102, 77, 0, 102, 102, 153, 153, 203, 203], [203, 255, 255, 203, 153, 153, 153, 102, 102, 102, 102, 153, 153, 203, 203, 255], [203, 203, 255, 203, 203, 203, 153, 153, 153, 153, 153, 153, 203, 203, 255, 255], [153, 203, 255, 255, 255, 203, 203, 203, 203, 203, 203, 203, 203, 255, 255, 203], [153, 203, 203, 203, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 203, 203], [153, 153, 153, 203, 203, 203, 203, 203, 255, 203, 203, 203, 203, 203, 203, 153], [102, 102, 153, 153, 153, 153, 203, 203, 255, 203, 203, 255, 203, 153, 153, 153], [102, 102, 102, 102, 102, 153, 203, 255, 255, 203, 203, 203, 203, 153, 102, 153], [102, 51, 51, 102, 102, 153, 203, 255, 203, 203, 153, 153, 153, 153, 102, 153], [ 77, 51, 51, 102, 153, 153, 203, 255, 203, 203, 203, 153, 102, 102, 102, 153], [ 77, 0, 51, 102, 153, 203, 203, 255, 203, 255, 203, 153, 102, 51, 102, 153], [ 77, 0, 51, 102, 153, 203, 255, 255, 203, 203, 203, 153, 102, 0, 102, 153], [102, 0, 51, 102, 153, 203, 255, 203, 203, 153, 153, 153, 102, 102, 102, 153], [102, 102, 102, 102, 153, 203, 255, 203, 153, 153, 153, 153, 153, 153, 153, 153]]) markerbin = (data==0) marker = label(markerbin) ws = watershed(data, marker, connectivity=2, watershed_line=True) for lab, area in zip(range(4), [34,74,74,74]): self.assertTrue(np.sum(ws == lab) == area) def test_compact_watershed(): image = np.zeros((5, 6)) image[:, 3:] = 1 seeds = np.zeros((5, 6), dtype=int) seeds[2, 0] = 1 seeds[2, 3] = 2 compact = watershed(image, seeds, compactness=0.01) expected = np.array([[1, 1, 1, 2, 2, 2], [1, 1, 1, 2, 2, 2], [1, 1, 1, 2, 2, 2], [1, 1, 1, 2, 2, 2], [1, 1, 1, 2, 2, 2]], dtype=int) np.testing.assert_equal(compact, expected) normal = watershed(image, seeds) expected = np.ones(image.shape, dtype=int) expected[2, 3:] = 2 np.testing.assert_equal(normal, expected) def test_numeric_seed_watershed(): """Test that passing just the number of seeds to watershed works.""" image = np.zeros((5, 6)) image[:, 3:] = 1 compact = watershed(image, 2, compactness=0.01) expected = np.array([[1, 1, 1, 1, 2, 2], [1, 1, 1, 1, 2, 2], [1, 1, 1, 1, 2, 2], [1, 1, 1, 1, 2, 2], [1, 1, 1, 1, 2, 2]], dtype=np.int32) np.testing.assert_equal(compact, expected) if __name__ == "__main__": np.testing.run_module_suite() scikit-image-0.13.1/skimage/morphology/watershed.py000066400000000000000000000251331316256236300223340ustar00rootroot00000000000000"""watershed.py - watershed algorithm This module implements a watershed algorithm that apportions pixels into marked basins. The algorithm uses a priority queue to hold the pixels with the metric for the priority queue being pixel value, then the time of entry into the queue - this settles ties in favor of the closest marker. Some ideas taken from Soille, "Automated Basin Delineation from Digital Elevation Models Using Mathematical Morphology", Signal Processing 20 (1990) 171-182. The most important insight in the paper is that entry time onto the queue solves two problems: a pixel should be assigned to the neighbor with the largest gradient or, if there is no gradient, pixels on a plateau should be split between markers on opposite sides. Originally part of CellProfiler, code licensed under both GPL and BSD licenses. Website: http://www.cellprofiler.org Copyright (c) 2003-2009 Massachusetts Institute of Technology Copyright (c) 2009-2011 Broad Institute All rights reserved. Original author: Lee Kamentsky """ import numpy as np from scipy import ndimage as ndi from . import _watershed from ..util import crop, regular_seeds def _validate_inputs(image, markers, mask): """Ensure that all inputs to watershed have matching shapes and types. Parameters ---------- image : array The input image. markers : int or array of int The marker image. mask : array, or None A boolean mask, True where we want to compute the watershed. Returns ------- image, markers, mask : arrays The validated and formatted arrays. Image will have dtype float64, markers int32, and mask int8. If ``None`` was given for the mask, it is a volume of all 1s. Raises ------ ValueError If the shapes of the given arrays don't match. """ if not isinstance(markers, (np.ndarray, list, tuple)): # not array-like, assume int markers = regular_seeds(image.shape, markers) elif markers.shape != image.shape: raise ValueError("`markers` (shape {}) must have same shape " "as `image` (shape {})".format(markers.shape, image.shape)) if mask is not None and mask.shape != image.shape: raise ValueError("`mask` must have same shape as `image`") if mask is None: # Use a complete `True` mask if none is provided mask = np.ones(image.shape, bool) return (image.astype(np.float64), markers.astype(np.int32), mask.astype(np.int8)) def _validate_connectivity(image_dim, connectivity, offset): """Convert any valid connectivity to a structuring element and offset. Parameters ---------- image_dim : int The number of dimensions of the input image. connectivity : int, array, or None The neighborhood connectivity. An integer is interpreted as in ``scipy.ndimage.generate_binary_structure``, as the maximum number of orthogonal steps to reach a neighbor. An array is directly interpreted as a structuring element and its shape is validated against the input image shape. ``None`` is interpreted as a connectivity of 1. offset : tuple of int, or None The coordinates of the center of the structuring element. Returns ------- c_connectivity : array of bool The structuring element corresponding to the input `connectivity`. offset : array of int The offset corresponding to the center of the structuring element. Raises ------ ValueError: If the image dimension and the connectivity or offset dimensions don't match. """ if connectivity is None: connectivity = 1 if np.isscalar(connectivity): c_connectivity = ndi.generate_binary_structure(image_dim, connectivity) else: c_connectivity = np.array(connectivity, bool) if c_connectivity.ndim != image_dim: raise ValueError("Connectivity dimension must be same as image") if offset is None: if any([x % 2 == 0 for x in c_connectivity.shape]): raise ValueError("Connectivity array must have an unambiguous " "center") offset = np.array(c_connectivity.shape) // 2 return c_connectivity, offset def _compute_neighbors(image, structure, offset): """Compute neighborhood as an array of linear offsets into the image. These are sorted according to Euclidean distance from the center (given by `offset`), ensuring that immediate neighbors are visited first. """ structure[tuple(offset)] = 0 # ignore the center; it's not a neighbor locations = np.transpose(np.nonzero(structure)) sqdistances = np.sum((locations - offset)**2, axis=1) neighborhood = (np.ravel_multi_index(locations.T, image.shape) - np.ravel_multi_index(offset, image.shape)).astype(np.int32) sorted_neighborhood = neighborhood[np.argsort(sqdistances)] return sorted_neighborhood def watershed(image, markers, connectivity=1, offset=None, mask=None, compactness=0, watershed_line=False): """Find watershed basins in `image` flooded from given `markers`. Parameters ---------- image: ndarray (2-D, 3-D, ...) of integers Data array where the lowest value points are labeled first. markers: int, or ndarray of int, same shape as `image` The desired number of markers, or an array marking the basins with the values to be assigned in the label matrix. Zero means not a marker. connectivity: ndarray, optional An array with the same number of dimensions as `image` whose non-zero elements indicate neighbors for connection. Following the scipy convention, default is a one-connected array of the dimension of the image. offset: array_like of shape image.ndim, optional offset of the connectivity (one offset per dimension) mask: ndarray of bools or 0s and 1s, optional Array of same shape as `image`. Only points at which mask == True will be labeled. compactness : float, optional Use compact watershed [3]_ with given compactness parameter. Higher values result in more regularly-shaped watershed basins. watershed_line : bool, optional If watershed_line is True, a one-pixel wide line separates the regions obtained by the watershed algorithm. The line has the label 0. Returns ------- out: ndarray A labeled matrix of the same type and shape as markers See also -------- skimage.segmentation.random_walker: random walker segmentation A segmentation algorithm based on anisotropic diffusion, usually slower than the watershed but with good results on noisy data and boundaries with holes. Notes ----- This function implements a watershed algorithm [1]_ [2]_ that apportions pixels into marked basins. The algorithm uses a priority queue to hold the pixels with the metric for the priority queue being pixel value, then the time of entry into the queue - this settles ties in favor of the closest marker. Some ideas taken from Soille, "Automated Basin Delineation from Digital Elevation Models Using Mathematical Morphology", Signal Processing 20 (1990) 171-182 The most important insight in the paper is that entry time onto the queue solves two problems: a pixel should be assigned to the neighbor with the largest gradient or, if there is no gradient, pixels on a plateau should be split between markers on opposite sides. This implementation converts all arguments to specific, lowest common denominator types, then passes these to a C algorithm. Markers can be determined manually, or automatically using for example the local minima of the gradient of the image, or the local maxima of the distance function to the background for separating overlapping objects (see example). References ---------- .. [1] http://en.wikipedia.org/wiki/Watershed_%28image_processing%29 .. [2] http://cmm.ensmp.fr/~beucher/wtshed.html .. [3] Peer Neubert & Peter Protzel (2014). Compact Watershed and Preemptive SLIC: On Improving Trade-offs of Superpixel Segmentation Algorithms. ICPR 2014, pp 996-1001. DOI:10.1109/ICPR.2014.181 https://www.tu-chemnitz.de/etit/proaut/forschung/rsrc/cws_pSLIC_ICPR.pdf Examples -------- The watershed algorithm is useful to separate overlapping objects. We first generate an initial image with two overlapping circles: >>> x, y = np.indices((80, 80)) >>> x1, y1, x2, y2 = 28, 28, 44, 52 >>> r1, r2 = 16, 20 >>> mask_circle1 = (x - x1)**2 + (y - y1)**2 < r1**2 >>> mask_circle2 = (x - x2)**2 + (y - y2)**2 < r2**2 >>> image = np.logical_or(mask_circle1, mask_circle2) Next, we want to separate the two circles. We generate markers at the maxima of the distance to the background: >>> from scipy import ndimage as ndi >>> distance = ndi.distance_transform_edt(image) >>> from skimage.feature import peak_local_max >>> local_maxi = peak_local_max(distance, labels=image, ... footprint=np.ones((3, 3)), ... indices=False) >>> markers = ndi.label(local_maxi)[0] Finally, we run the watershed on the image and markers: >>> labels = watershed(-distance, markers, mask=image) The algorithm works also for 3-D images, and can be used for example to separate overlapping spheres. """ image, markers, mask = _validate_inputs(image, markers, mask) connectivity, offset = _validate_connectivity(image.ndim, connectivity, offset) # pad the image, markers, and mask so that we can use the mask to # keep from running off the edges pad_width = [(p, p) for p in offset] image = np.pad(image, pad_width, mode='constant') mask = np.pad(mask, pad_width, mode='constant').ravel() output = np.pad(markers, pad_width, mode='constant') flat_neighborhood = _compute_neighbors(image, connectivity, offset) marker_locations = np.flatnonzero(output).astype(np.int32) image_strides = np.array(image.strides, dtype=np.int32) // image.itemsize _watershed.watershed_raveled(image.ravel(), marker_locations, flat_neighborhood, mask, image_strides, compactness, output.ravel(), watershed_line) output = crop(output, pad_width, copy=True) if watershed_line: min_val = output.min() output[output == min_val] = 0 return output scikit-image-0.13.1/skimage/novice/000077500000000000000000000000001316256236300170545ustar00rootroot00000000000000scikit-image-0.13.1/skimage/novice/__init__.py000066400000000000000000000045361316256236300211750ustar00rootroot00000000000000""" skimage.novice ============== A special Python image submodule for beginners. Description ----------- ``skimage.novice`` provides a simple image manipulation interface for beginners. It allows for easy loading, manipulating, and saving of image files. This module is primarily intended for teaching and differs significantly from the normal, array-oriented image functions used by scikit-image. .. note:: This module uses the Cartesian coordinate system, where the origin is at the lower-left corner instead of the upper-right and the order is x, y instead of row, column. Examples -------- We can create a Picture object open opening an image file: >>> from skimage import novice >>> from skimage import data >>> picture = novice.open(data.data_dir + '/chelsea.png') We can display pictures (after running this command, close the window to access the prompt again): >>> picture.show() # doctest: +SKIP Pictures know their format: >>> picture.format 'png' ... and where they came from: >>> picture.path.endswith('chelsea.png') True ... and their size: >>> picture.size (451, 300) >>> picture.width 451 As a reminder, we can preview the picture with our earlier command: >>> picture.show() # doctest: +SKIP Changing `size` resizes the picture. >>> picture.size = (45, 30) We can preview the changes we made to the picture with the ``compare`` command: >>> picture.compare() # doctest: +SKIP You can iterate over pixels, which have RGB values between 0 and 255, and know their location in the picture. >>> for pixel in picture: ... if (pixel.red > 128) and (pixel.x < picture.width): ... pixel.red = pixel.red / 2 Pictures know if they've been modified from the original file >>> picture.modified True >>> print(picture.path) None Pictures can be indexed like arrays >>> picture[0:20, 0:20] = (0, 0, 0) Saving the picture updates the path attribute, format, and modified state. >>> picture.save('save-demo.jpg') >>> picture.path.endswith('save-demo.jpg') True >>> picture.format 'jpeg' >>> picture.modified False An image can also be restored to its original state after modification: >>> picture[0:20, 0:20] = (0, 0, 0) >>> picture.compare() # doctest: +SKIP >>> picture.reset() >>> picture.compare() # doctest: +SKIP """ from ._novice import Picture, open, colors, color_dict __all__ = ['Picture', 'open', 'colors', 'color_dict'] scikit-image-0.13.1/skimage/novice/_novice.py000066400000000000000000000351141316256236300210540ustar00rootroot00000000000000import os import imghdr from collections import namedtuple import numpy as np from .. import io, img_as_ubyte from ..transform import resize from ..color import color_dict from ..io.util import file_or_url_context, is_url from ..io.collection import ImageCollection import six from six.moves.urllib import request urlopen = request.urlopen # Convert colors from `skimage.color` to uint8 and allow access through # dict or a named tuple. color_dict = dict((name, tuple(int(255 * c + 0.5) for c in rgb)) for name, rgb in six.iteritems(color_dict)) colors = namedtuple('colors', color_dict.keys())(**color_dict) def open(path): """Return Picture object from the given image path.""" return Picture(path) def _verify_picture_index(index): """Raise error if picture index is not a 2D index/slice.""" if not (isinstance(index, tuple) and len(index) == 2): raise IndexError("Expected 2D index but got {0!r}".format(index)) if all(isinstance(i, int) for i in index): return index # In case we need to fix the array index, convert tuple to list. index = list(index) for i, dim_slice in enumerate(index): # If either index is a slice, ensure index object returns 2D array. if isinstance(dim_slice, int): index[i] = dim_slice = slice(dim_slice, dim_slice + 1) return tuple(index) def rgb_transpose(array): """Return RGB array with first 2 axes transposed.""" return np.transpose(array, (1, 0, 2)) def array_to_xy_origin(image): """Return view of image transformed from array to Cartesian origin.""" return rgb_transpose(image[::-1]) def xy_to_array_origin(image): """Return view of image transformed from Cartesian to array origin.""" return rgb_transpose(image[:, ::-1]) class Pixel(object): """A single pixel in a Picture. Attributes ---------- pic : Picture The Picture object that this pixel references. array : array_like Byte array with raw image data (RGB). x : int Horizontal coordinate of this pixel (left = 0). y : int Vertical coordinate of this pixel (bottom = 0). rgb : tuple RGB tuple with red, green, and blue components (0-255) alpha : int Transparency component (0-255), 255 (opaque) by default """ def __init__(self, pic, array, x, y, rgb, alpha=255): self._picture = pic self._x = x self._y = y self._red = self._validate(rgb[0]) self._green = self._validate(rgb[1]) self._blue = self._validate(rgb[2]) self._alpha = self._validate(alpha) @property def x(self): """Horizontal location of this pixel in the parent image(left = 0).""" return self._x @property def y(self): """Vertical location of this pixel in the parent image (bottom = 0).""" return self._y @property def red(self): """The red component of the pixel (0-255).""" return self._red @red.setter def red(self, value): self._red = self._validate(value) self._setpixel() @property def green(self): """The green component of the pixel (0-255).""" return self._green @green.setter def green(self, value): self._green = self._validate(value) self._setpixel() @property def blue(self): """The blue component of the pixel (0-255).""" return self._blue @blue.setter def blue(self, value): self._blue = self._validate(value) self._setpixel() @property def alpha(self): """The transparency component of the pixel (0-255).""" return self._alpha @alpha.setter def alpha(self, value): self._alpha = self._validate(value) self._setpixel() @property def rgb(self): """The RGB color components of the pixel (3 values 0-255).""" return (self.red, self.green, self.blue) @rgb.setter def rgb(self, value): if len(value) == 4: self.rgba = value else: self._red, self._green, self._blue \ = (self._validate(v) for v in value) self._alpha = 255 self._setpixel() @property def rgba(self): """The RGB color and transparency components of the pixel (4 values 0-255). """ return (self.red, self.green, self.blue, self.alpha) @rgba.setter def rgba(self, value): self._red, self._green, self._blue, self._alpha \ = (self._validate(v) for v in value) self._setpixel() def _validate(self, value): """Verifies that the pixel value is in [0, 255].""" try: value = int(value) if (value < 0) or (value > 255): raise ValueError() except ValueError: msg = "Expected an integer between 0 and 255, but got {0} instead!" raise ValueError(msg.format(value)) return value def _setpixel(self): # RGB + alpha self._picture.xy_array[self._x, self._y] = self.rgba self._picture._array_modified() def __eq__(self, other): if isinstance(other, Pixel): return self.rgba == other.rgba def __repr__(self): args = self.red, self.green, self.blue, self.alpha return "Pixel(red={0}, green={1}, blue={2}, alpha={3})".format(*args) class Picture(object): """A 2-D picture made up of pixels. Attributes ---------- path : str Path to an image file to load / URL of an image array : array Raw RGB or RGBA image data [0-255], with origin at top-left. xy_array : array Raw RGB or RGBA image data [0-255], with origin at bottom-left. Examples -------- Load an image from a file: >>> from skimage import novice >>> from skimage import data >>> picture = novice.open(data.data_dir + '/chelsea.png') Load an image from a URL (the URL must start with ``http(s)://`` or ``ftp(s)://``): >>> picture = novice.open('http://scikit-image.org/_static/img/logo.png') Create a blank 100 pixel wide, 200 pixel tall white image: >>> pic = Picture.from_size((100, 200), color=(255, 255, 255)) Use numpy to make an RGB byte array (shape is height x width x 3): >>> import numpy as np >>> data = np.zeros(shape=(200, 100, 3), dtype=np.uint8) >>> data[:, :, 0] = 255 # Set red component to maximum >>> pic = Picture(array=data) Get the bottom-left pixel: >>> pic[0, 0] Pixel(red=255, green=0, blue=0, alpha=255) Get the top row of the picture: >>> pic[:, pic.height-1] Picture(100 x 1) Set the bottom-left pixel to black: >>> pic[0, 0] = (0, 0, 0) Set the top row to red: >>> pic[:, pic.height-1] = (255, 0, 0) """ def __init__(self, path=None, array=None, xy_array=None): self._modified = False self._path = None self._format = None n_args = len([a for a in [path, array, xy_array] if a is not None]) if n_args != 1: msg = "Must provide a single keyword arg (path, array, xy_array)." ValueError(msg) elif path is not None: if not is_url(path): path = os.path.abspath(path) self._path = path with file_or_url_context(path) as context: self.array = img_as_ubyte(io.imread(context)) self._format = imghdr.what(context) elif array is not None: self.array = array elif xy_array is not None: self.xy_array = xy_array # Force RGBA internally (use max alpha) if self.array.shape[-1] == 3: self.array = np.insert(self.array, 3, values=255, axis=2) @staticmethod def from_size(size, color='black'): """Return a Picture of the specified size and a uniform color. Parameters ---------- size : tuple Width and height of the picture in pixels. color : tuple or str RGB or RGBA tuple with the fill color for the picture [0-255] or a valid key in `color_dict`. """ if isinstance(color, six.string_types): color = color_dict[color] rgb_size = tuple(size) + (len(color),) color = np.array(color, dtype=np.uint8) array = np.ones(rgb_size, dtype=np.uint8) * color # Force RGBA internally (use max alpha) if array.shape[-1] == 3: array = np.insert(array, 3, values=255, axis=2) return Picture(array=array) @property def array(self): """Image data stored as numpy array.""" return self._array @array.setter def array(self, array): self._array = array.astype(np.uint8) self._xy_array = array_to_xy_origin(self._array) self._array_backup = self._array.copy() @property def xy_array(self): """Image data stored as numpy array with origin at the bottom-left.""" return self._xy_array @xy_array.setter def xy_array(self, array): self._xy_array = array self._array = xy_to_array_origin(array) def save(self, path): """Saves the picture to the given path. Parameters ---------- path : str Path (with file extension) where the picture is saved. """ io.imsave(path, self.array) self._modified = False self._path = os.path.abspath(path) self._format = imghdr.what(path) def reset(self): """Reset image to its original state, removing modifications. """ self.array = self._array_backup @property def path(self): """The path to the picture.""" return self._path @property def modified(self): """True if the picture has changed.""" return self._modified def _array_modified(self): self._modified = True self._path = None @property def format(self): """The image format of the picture.""" return self._format @property def size(self): """The size (width, height) of the picture.""" return self.xy_array.shape[:2] @size.setter def size(self, value): # Don't resize if no change in size if (value[0] != self.width) or (value[1] != self.height): # skimage dimensions are flipped: y, x new_size = (int(value[1]), int(value[0])) new_array = resize(self.array, new_size, order=0, preserve_range=True) self.array = new_array.astype(np.uint8) self._array_modified() @property def width(self): """The width of the picture.""" return self.size[0] @width.setter def width(self, value): self.size = (value, self.height) @property def height(self): """The height of the picture.""" return self.size[1] @height.setter def height(self, value): self.size = (self.width, value) def show(self): """Display the image.""" io.imshow(self.array) io.show() def compare(self): """Compare the image to its unmodified version.""" images = [self._array_backup, self.array] ic = ImageCollection([0, 1], load_func=lambda x: images[x]) io.imshow_collection(images) io.show() def _makepixel(self, x, y): """Create a Pixel object for a given x, y location.""" rgb = self.xy_array[x, y] return Pixel(self, self.array, x, y, rgb) def _get_channel(self, channel): """Return a specific dimension out of the raw image data slice.""" return self._array[:, :, channel] def _set_channel(self, channel, value): """Set a specific dimension in the raw image data slice.""" self._array[:, :, channel] = value @property def red(self): """The red component of the pixel (0-255).""" return self._get_channel(0).ravel() @red.setter def red(self, value): self._set_channel(0, value) @property def green(self): """The green component of the pixel (0-255).""" return self._get_channel(1).ravel() @green.setter def green(self, value): self._set_channel(1, value) @property def blue(self): """The blue component of the pixel (0-255).""" return self._get_channel(2).ravel() @blue.setter def blue(self, value): self._set_channel(2, value) @property def alpha(self): """The transparency component of the pixel (0-255).""" return self._get_channel(3).ravel() @alpha.setter def alpha(self, value): self._set_channel(3, value) @property def rgb(self): """The RGB color components of the pixel (3 values 0-255).""" return self.xy_array[:, :, :3] @rgb.setter def rgb(self, value): self.xy_array[:, :, :3] = value @property def rgba(self): """The RGBA color components of the pixel (4 values 0-255).""" return self.xy_array @rgba.setter def rgba(self, value): self.xy_array[:] = value def __iter__(self): """Iterates over all pixels in the image.""" for x in range(self.width): for y in range(self.height): yield self._makepixel(x, y) def __getitem__(self, xy_index): """Return `Picture`s for slices and `Pixel`s for indexes.""" xy_index = _verify_picture_index(xy_index) if all(isinstance(index, int) for index in xy_index): return self._makepixel(*xy_index) else: return Picture(xy_array=self.xy_array[xy_index]) def __setitem__(self, xy_index, value): xy_index = _verify_picture_index(xy_index) if isinstance(value, tuple): self[xy_index].rgb = value elif isinstance(value, Picture): self.xy_array[xy_index] = value.xy_array else: raise TypeError("Invalid value type") self._array_modified() def __eq__(self, other): if not isinstance(other, Picture): raise NotImplementedError() return np.all(self.array == other.array) def __repr__(self): return "Picture({0} x {1})".format(*self.size) def _repr_png_(self): return self._repr_image_format('png') def _repr_jpeg_(self): return self._repr_image_format('jpeg') def _repr_image_format(self, format_str): str_buffer = six.BytesIO() io.imsave(str_buffer, self.array, format_str=format_str) return_str = str_buffer.getvalue() str_buffer.close() return return_str if __name__ == '__main__': import doctest doctest.testmod() scikit-image-0.13.1/skimage/novice/tests/000077500000000000000000000000001316256236300202165ustar00rootroot00000000000000scikit-image-0.13.1/skimage/novice/tests/__init__.py000066400000000000000000000001771316256236300223340ustar00rootroot00000000000000from ..._shared.testing import setup_test, teardown_test def setup(): setup_test() def teardown(): teardown_test() scikit-image-0.13.1/skimage/novice/tests/test_novice.py000066400000000000000000000202421316256236300231120ustar00rootroot00000000000000import os import tempfile import numpy as np from nose.tools import assert_true from numpy.testing import assert_equal, raises, assert_allclose from skimage import novice from skimage.novice._novice import (array_to_xy_origin, xy_to_array_origin, rgb_transpose) from skimage import data_dir from skimage._shared.utils import all_warnings IMAGE_PATH = os.path.join(data_dir, "chelsea.png") SMALL_IMAGE_PATH = os.path.join(data_dir, "block.png") def _array_2d_to_RGBA(array): return np.tile(array[:, :, np.newaxis], (1, 1, 4)) def test_xy_to_array_origin(): h, w = 3, 5 array = np.arange(h * w).reshape(h, w, 1) out = xy_to_array_origin(array_to_xy_origin(array.copy())) assert np.allclose(out, array) def test_pic_info(): pic = novice.open(IMAGE_PATH) assert_equal(pic.format, "png") assert_equal(pic.path, os.path.abspath(IMAGE_PATH)) assert_equal(pic.size, (451, 300)) assert_equal(pic.width, 451) assert_equal(pic.height, 300) assert not pic.modified def test_pixel_iteration(): pic = novice.open(SMALL_IMAGE_PATH) num_pixels = sum(1 for p in pic) assert_equal(num_pixels, pic.width * pic.height) def test_modify(): pic = novice.open(SMALL_IMAGE_PATH) assert_equal(pic.modified, False) for p in pic: if p.x < (pic.width / 2): p.red /= 2 p.green /= 2 p.blue /= 2 for p in pic: if p.x < (pic.width / 2): assert p.red <= 128 assert p.green <= 128 assert p.blue <= 128 s = pic.size with all_warnings(): # precision loss pic.size = (pic.width / 2, pic.height / 2) assert_equal(pic.size, (int(s[0] / 2), int(s[1] / 2))) assert pic.modified assert pic.path is None def test_pixel_rgb(): pic = novice.Picture.from_size((3, 3), color=(10, 10, 10)) pixel = pic[0, 0] pixel.rgb = np.arange(3) assert_equal(pixel.rgb, np.arange(3)) for i, channel in enumerate((pixel.red, pixel.green, pixel.blue)): assert_equal(channel, i) pixel.red = 3 pixel.green = 4 pixel.blue = 5 assert_equal(pixel.rgb, np.arange(3) + 3) for i, channel in enumerate((pixel.red, pixel.green, pixel.blue)): assert_equal(channel, i + 3) pixel.rgb = np.arange(4) assert_equal(pixel.rgb, np.arange(3)) assert pic.array.dtype == np.uint8 def test_pixel_rgba(): pic = novice.Picture.from_size((3, 3), color=(10, 10, 10)) pixel = pic[0, 0] pixel.rgba = np.arange(4) assert_equal(pixel.rgba, np.arange(4)) for i, channel in enumerate((pixel.red, pixel.green, pixel.blue, pixel.alpha)): assert_equal(channel, i) pixel.red = 3 pixel.green = 4 pixel.blue = 5 pixel.alpha = 6 assert_equal(pixel.rgba, np.arange(4) + 3) for i, channel in enumerate((pixel.red, pixel.green, pixel.blue, pixel.alpha)): assert_equal(channel, i + 3) def test_pixel_rgb_float(): pixel = novice.Picture.from_size((1, 1))[0, 0] pixel.rgb = (1.1, 1.1, 1.1) assert_equal(pixel.rgb, (1, 1, 1)) def test_pixel_rgba_float(): pixel = novice.Picture.from_size((1, 1))[0, 0] pixel.rgba = (1.1, 1.1, 1.1, 1.1) assert_equal(pixel.rgba, (1, 1, 1, 1)) def test_modified_on_set(): pic = novice.Picture(SMALL_IMAGE_PATH) pic[0, 0] = (1, 1, 1) assert pic.modified assert pic.path is None def test_modified_on_set_pixel(): data = np.zeros(shape=(10, 5, 3), dtype=np.uint8) pic = novice.Picture(array=data) pixel = pic[0, 0] pixel.green = 1 assert pic.modified def test_reset(): pic = novice.Picture(SMALL_IMAGE_PATH) v = pic[0, 0] pic[0, 0] = (1, 1, 1) pic.reset() assert_true(pic[0, 0] == v) def test_update_on_save(): pic = novice.Picture(array=np.zeros((3, 3, 3))) pic[0, 0] = (255, 255, 255) # prevent attempting to save low-contrast image with all_warnings(): # precision loss pic.size = (6, 6) assert pic.modified assert pic.path is None fd, filename = tempfile.mkstemp(suffix=".jpg") os.close(fd) try: pic.save(filename) assert not pic.modified assert_equal(pic.path, os.path.abspath(filename)) assert_equal(pic.format, "jpeg") finally: os.unlink(filename) def test_indexing(): array = 128 * np.ones((10, 10, 3), dtype=np.uint8) pic = novice.Picture(array=array) pic[0:5, 0:5] = (0, 0, 0) for p in pic: if (p.x < 5) and (p.y < 5): assert_equal(p.rgb, (0, 0, 0)) assert_equal(p.red, 0) assert_equal(p.green, 0) assert_equal(p.blue, 0) pic[:5, :5] = (255, 255, 255) for p in pic: if (p.x < 5) and (p.y < 5): assert_equal(p.rgb, (255, 255, 255)) assert_equal(p.red, 255) assert_equal(p.green, 255) assert_equal(p.blue, 255) pic[5:pic.width, 5:pic.height] = (255, 0, 255) for p in pic: if (p.x >= 5) and (p.y >= 5): assert_equal(p.rgb, (255, 0, 255)) assert_equal(p.red, 255) assert_equal(p.green, 0) assert_equal(p.blue, 255) pic[5:, 5:] = (0, 0, 255) for p in pic: if (p.x >= 5) and (p.y >= 5): assert_equal(p.rgb, (0, 0, 255)) assert_equal(p.red, 0) assert_equal(p.green, 0) assert_equal(p.blue, 255) def test_picture_slice(): array = _array_2d_to_RGBA(np.arange(0, 10)[np.newaxis, :]) pic = novice.Picture(array=array) x_slice = slice(3, 8) subpic = pic[:, x_slice] assert_allclose(subpic.array, array[x_slice, :]) def test_move_slice(): h, w = 3, 12 array = _array_2d_to_RGBA(np.linspace(0, 255, h * w).reshape(h, w)) array = array.astype(np.uint8) pic = novice.Picture(array=array) pic_orig = novice.Picture(array=array.copy()) # Move left cut of image to the right side. cut = 5 rest = pic.width - cut temp = pic[:cut, :] temp.array = temp.array.copy() pic[:rest, :] = pic[cut:, :] pic[rest:, :] = temp assert pic[rest:, :] == pic_orig[:cut, :] assert pic[:rest, :] == pic_orig[cut:, :] def test_negative_index(): n = 10 array = _array_2d_to_RGBA(np.arange(0, n)[np.newaxis, :]) # Test both x and y indices. pic = novice.Picture(array=array) assert pic[-1, 0] == pic[n - 1, 0] pic = novice.Picture(array=rgb_transpose(array)) assert pic[0, -1] == pic[0, n - 1] def test_negative_slice(): n = 10 array = _array_2d_to_RGBA(np.arange(0, n)[np.newaxis, :]) # Test both x and y slices. pic = novice.Picture(array=array) assert pic[-3:, 0] == pic[n - 3:, 0] pic = novice.Picture(array=rgb_transpose(array)) assert pic[0, -3:] == pic[0, n - 3:] def test_getitem_with_step(): h, w = 5, 5 array = _array_2d_to_RGBA(np.linspace(0, 255, h * w).reshape(h, w)) pic = novice.Picture(array=array) sliced_pic = pic[::2, ::2] assert sliced_pic == novice.Picture(array=array[::2, ::2]) @raises(IndexError) def test_1d_getitem_raises(): pic = novice.Picture.from_size((1, 1)) pic[1] @raises(IndexError) def test_3d_getitem_raises(): pic = novice.Picture.from_size((1, 1)) pic[1, 2, 3] @raises(IndexError) def test_1d_setitem_raises(): pic = novice.Picture.from_size((1, 1)) pic[1] = 0 @raises(IndexError) def test_3d_setitem_raises(): pic = novice.Picture.from_size((1, 1)) pic[1, 2, 3] = 0 @raises(IndexError) def test_out_of_bounds_indexing(): pic = novice.open(SMALL_IMAGE_PATH) pic[pic.width, pic.height] @raises(ValueError) def test_pixel_rgb_raises(): pixel = novice.Picture.from_size((1, 1))[0, 0] pixel.rgb = (-1, -1, -1) @raises(ValueError) def test_pixel_red_raises(): pixel = novice.Picture.from_size((1, 1))[0, 0] pixel.red = 256 @raises(ValueError) def test_pixel_green_raises(): pixel = novice.Picture.from_size((1, 1))[0, 0] pixel.green = 256 @raises(ValueError) def test_pixel_blue_raises(): pixel = novice.Picture.from_size((1, 1))[0, 0] pixel.blue = 256 @raises(ValueError) def test_pixel_alpha_raises(): pixel = novice.Picture.from_size((1, 1))[0, 0] pixel.alpha = 256 if __name__ == '__main__': np.testing.run_module_suite() scikit-image-0.13.1/skimage/restoration/000077500000000000000000000000001316256236300201425ustar00rootroot00000000000000scikit-image-0.13.1/skimage/restoration/__init__.py000066400000000000000000000017201316256236300222530ustar00rootroot00000000000000# -*- coding: utf-8 -*- """Image restoration module. """ from .deconvolution import wiener, unsupervised_wiener, richardson_lucy from .unwrap import unwrap_phase from ._denoise import denoise_tv_chambolle, denoise_tv_bregman, \ denoise_bilateral, denoise_wavelet, estimate_sigma from .non_local_means import denoise_nl_means from .inpaint import inpaint_biharmonic from .._shared.utils import copy_func, deprecated nl_means_denoising = copy_func(denoise_nl_means, name='nl_means_denoising') nl_means_denoising = deprecated('skimage.restoration.denoise_nl_means')(nl_means_denoising) __all__ = ['wiener', 'unsupervised_wiener', 'richardson_lucy', 'unwrap_phase', 'denoise_tv_bregman', 'denoise_tv_chambolle', 'denoise_bilateral', 'denoise_wavelet', 'denoise_nl_means', 'nl_means_denoising', 'inpaint_biharmonic'] del copy_func, deprecated scikit-image-0.13.1/skimage/restoration/_denoise.py000066400000000000000000000614061316256236300223100ustar00rootroot00000000000000# coding: utf-8 import scipy.stats import numpy as np from math import ceil from .. import img_as_float from ..restoration._denoise_cy import _denoise_bilateral, _denoise_tv_bregman from .._shared.utils import skimage_deprecation, warn import pywt import skimage.color as color import numbers def denoise_bilateral(image, win_size=None, sigma_color=None, sigma_spatial=1, bins=10000, mode='constant', cval=0, multichannel=None, sigma_range=None): """Denoise image using bilateral filter. This is an edge-preserving, denoising filter. It averages pixels based on their spatial closeness and radiometric similarity [1]_. Spatial closeness is measured by the Gaussian function of the Euclidean distance between two pixels and a certain standard deviation (`sigma_spatial`). Radiometric similarity is measured by the Gaussian function of the Euclidean distance between two color values and a certain standard deviation (`sigma_color`). Parameters ---------- image : ndarray, shape (M, N[, 3]) Input image, 2D grayscale or RGB. win_size : int Window size for filtering. If win_size is not specified, it is calculated as ``max(5, 2 * ceil(3 * sigma_spatial) + 1)``. sigma_color : float Standard deviation for grayvalue/color distance (radiometric similarity). A larger value results in averaging of pixels with larger radiometric differences. Note, that the image will be converted using the `img_as_float` function and thus the standard deviation is in respect to the range ``[0, 1]``. If the value is ``None`` the standard deviation of the ``image`` will be used. sigma_spatial : float Standard deviation for range distance. A larger value results in averaging of pixels with larger spatial differences. bins : int Number of discrete values for Gaussian weights of color filtering. A larger value results in improved accuracy. mode : {'constant', 'edge', 'symmetric', 'reflect', 'wrap'} How to handle values outside the image borders. See `numpy.pad` for detail. cval : string Used in conjunction with mode 'constant', the value outside the image boundaries. multichannel : bool Whether the last axis of the image is to be interpreted as multiple channels or another spatial dimension. Returns ------- denoised : ndarray Denoised image. References ---------- .. [1] http://users.soe.ucsc.edu/~manduchi/Papers/ICCV98.pdf Examples -------- >>> from skimage import data, img_as_float >>> astro = img_as_float(data.astronaut()) >>> astro = astro[220:300, 220:320] >>> noisy = astro + 0.6 * astro.std() * np.random.random(astro.shape) >>> noisy = np.clip(noisy, 0, 1) >>> denoised = denoise_bilateral(noisy, sigma_color=0.05, sigma_spatial=15) """ if multichannel is None: warn('denoise_bilateral will default to multichannel=False in v0.15') multichannel = True if multichannel: if image.ndim != 3: if image.ndim == 2: raise ValueError("Use ``multichannel=False`` for 2D grayscale " "images. The last axis of the input image " "must be multiple color channels not another " "spatial dimension.") else: raise ValueError("Bilateral filter is only implemented for " "2D grayscale images (image.ndim == 2) and " "2D multichannel (image.ndim == 3) images, " "but the input image has {0} dimensions. " "".format(image.ndim)) elif image.shape[2] not in (3, 4): if image.shape[2] > 4: msg = ("The last axis of the input image is interpreted as " "channels. Input image with shape {0} has {1} channels " "in last axis. ``denoise_bilateral`` is implemented " "for 2D grayscale and color images only") warn(msg.format(image.shape, image.shape[2])) else: msg = "Input image must be grayscale, RGB, or RGBA; " \ "but has shape {0}." warn(msg.format(image.shape)) else: if image.ndim > 2: raise ValueError("Bilateral filter is not implemented for " "grayscale images of 3 or more dimensions, " "but input image has {0} dimension. Use " "``multichannel=True`` for 2-D RGB " "images.".format(image.shape)) if sigma_range is not None: warn('`sigma_range` has been deprecated in favor of ' '`sigma_color`. The `sigma_range` keyword argument ' 'will be removed in v0.14', skimage_deprecation) # If sigma_range is provided, assign it to sigma_color sigma_color = sigma_range if win_size is None: win_size = max(5, 2 * int(ceil(3 * sigma_spatial)) + 1) return _denoise_bilateral(image, win_size, sigma_color, sigma_spatial, bins, mode, cval) def denoise_tv_bregman(image, weight, max_iter=100, eps=1e-3, isotropic=True): """Perform total-variation denoising using split-Bregman optimization. Total-variation denoising (also know as total-variation regularization) tries to find an image with less total-variation under the constraint of being similar to the input image, which is controlled by the regularization parameter ([1]_, [2]_, [3]_, [4]_). Parameters ---------- image : ndarray Input data to be denoised (converted using img_as_float`). weight : float Denoising weight. The smaller the `weight`, the more denoising (at the expense of less similarity to the `input`). The regularization parameter `lambda` is chosen as `2 * weight`. eps : float, optional Relative difference of the value of the cost function that determines the stop criterion. The algorithm stops when:: SUM((u(n) - u(n-1))**2) < eps max_iter : int, optional Maximal number of iterations used for the optimization. isotropic : boolean, optional Switch between isotropic and anisotropic TV denoising. Returns ------- u : ndarray Denoised image. References ---------- .. [1] http://en.wikipedia.org/wiki/Total_variation_denoising .. [2] Tom Goldstein and Stanley Osher, "The Split Bregman Method For L1 Regularized Problems", ftp://ftp.math.ucla.edu/pub/camreport/cam08-29.pdf .. [3] Pascal Getreuer, "Rudin–Osher–Fatemi Total Variation Denoising using Split Bregman" in Image Processing On Line on 2012–05–19, http://www.ipol.im/pub/art/2012/g-tvd/article_lr.pdf .. [4] http://www.math.ucsb.edu/~cgarcia/UGProjects/BregmanAlgorithms_JacquelineBush.pdf """ return _denoise_tv_bregman(image, weight, max_iter, eps, isotropic) def _denoise_tv_chambolle_nd(im, weight=0.1, eps=2.e-4, n_iter_max=200): """Perform total-variation denoising on n-dimensional images. Parameters ---------- im : ndarray n-D input data to be denoised. weight : float, optional Denoising weight. The greater `weight`, the more denoising (at the expense of fidelity to `input`). eps : float, optional Relative difference of the value of the cost function that determines the stop criterion. The algorithm stops when: (E_(n-1) - E_n) < eps * E_0 n_iter_max : int, optional Maximal number of iterations used for the optimization. Returns ------- out : ndarray Denoised array of floats. Notes ----- Rudin, Osher and Fatemi algorithm. """ ndim = im.ndim p = np.zeros((im.ndim, ) + im.shape, dtype=im.dtype) g = np.zeros_like(p) d = np.zeros_like(im) i = 0 while i < n_iter_max: if i > 0: # d will be the (negative) divergence of p d = -p.sum(0) slices_d = [slice(None), ] * ndim slices_p = [slice(None), ] * (ndim + 1) for ax in range(ndim): slices_d[ax] = slice(1, None) slices_p[ax+1] = slice(0, -1) slices_p[0] = ax d[slices_d] += p[slices_p] slices_d[ax] = slice(None) slices_p[ax+1] = slice(None) out = im + d else: out = im E = (d ** 2).sum() # g stores the gradients of out along each axis # e.g. g[0] is the first order finite difference along axis 0 slices_g = [slice(None), ] * (ndim + 1) for ax in range(ndim): slices_g[ax+1] = slice(0, -1) slices_g[0] = ax g[slices_g] = np.diff(out, axis=ax) slices_g[ax+1] = slice(None) norm = np.sqrt((g ** 2).sum(axis=0))[np.newaxis, ...] E += weight * norm.sum() tau = 1. / (2.*ndim) norm *= tau / weight norm += 1. p -= tau * g p /= norm E /= float(im.size) if i == 0: E_init = E E_previous = E else: if np.abs(E_previous - E) < eps * E_init: break else: E_previous = E i += 1 return out def denoise_tv_chambolle(im, weight=0.1, eps=2.e-4, n_iter_max=200, multichannel=False): """Perform total-variation denoising on n-dimensional images. Parameters ---------- im : ndarray of ints, uints or floats Input data to be denoised. `im` can be of any numeric type, but it is cast into an ndarray of floats for the computation of the denoised image. weight : float, optional Denoising weight. The greater `weight`, the more denoising (at the expense of fidelity to `input`). eps : float, optional Relative difference of the value of the cost function that determines the stop criterion. The algorithm stops when: (E_(n-1) - E_n) < eps * E_0 n_iter_max : int, optional Maximal number of iterations used for the optimization. multichannel : bool, optional Apply total-variation denoising separately for each channel. This option should be true for color images, otherwise the denoising is also applied in the channels dimension. Returns ------- out : ndarray Denoised image. Notes ----- Make sure to set the multichannel parameter appropriately for color images. The principle of total variation denoising is explained in http://en.wikipedia.org/wiki/Total_variation_denoising The principle of total variation denoising is to minimize the total variation of the image, which can be roughly described as the integral of the norm of the image gradient. Total variation denoising tends to produce "cartoon-like" images, that is, piecewise-constant images. This code is an implementation of the algorithm of Rudin, Fatemi and Osher that was proposed by Chambolle in [1]_. References ---------- .. [1] A. Chambolle, An algorithm for total variation minimization and applications, Journal of Mathematical Imaging and Vision, Springer, 2004, 20, 89-97. Examples -------- 2D example on astronaut image: >>> from skimage import color, data >>> img = color.rgb2gray(data.astronaut())[:50, :50] >>> img += 0.5 * img.std() * np.random.randn(*img.shape) >>> denoised_img = denoise_tv_chambolle(img, weight=60) 3D example on synthetic data: >>> x, y, z = np.ogrid[0:20, 0:20, 0:20] >>> mask = (x - 22)**2 + (y - 20)**2 + (z - 17)**2 < 8**2 >>> mask = mask.astype(np.float) >>> mask += 0.2*np.random.randn(*mask.shape) >>> res = denoise_tv_chambolle(mask, weight=100) """ im_type = im.dtype if not im_type.kind == 'f': im = img_as_float(im) if multichannel: out = np.zeros_like(im) for c in range(im.shape[-1]): out[..., c] = _denoise_tv_chambolle_nd(im[..., c], weight, eps, n_iter_max) else: out = _denoise_tv_chambolle_nd(im, weight, eps, n_iter_max) return out def _bayes_thresh(details, var): """BayesShrink threshold for a zero-mean details coeff array.""" # Equivalent to: dvar = np.var(details) for 0-mean details array dvar = np.mean(details*details) eps = np.finfo(details.dtype).eps thresh = var / np.sqrt(max(dvar - var, eps)) return thresh def _sigma_est_dwt(detail_coeffs, distribution='Gaussian'): """Calculate the robust median estimator of the noise standard deviation. Parameters ---------- detail_coeffs : ndarray The detail coefficients corresponding to the discrete wavelet transform of an image. distribution : str The underlying noise distribution. Returns ------- sigma : float The estimated noise standard deviation (see section 4.2 of [1]_). References ---------- .. [1] D. L. Donoho and I. M. Johnstone. "Ideal spatial adaptation by wavelet shrinkage." Biometrika 81.3 (1994): 425-455. DOI:10.1093/biomet/81.3.425 """ # Consider regions with detail coefficients exactly zero to be masked out detail_coeffs = detail_coeffs[np.nonzero(detail_coeffs)] if distribution.lower() == 'gaussian': # 75th quantile of the underlying, symmetric noise distribution denom = scipy.stats.norm.ppf(0.75) sigma = np.median(np.abs(detail_coeffs)) / denom else: raise ValueError("Only Gaussian noise estimation is currently " "supported") return sigma def _wavelet_threshold(img, wavelet, threshold=None, sigma=None, mode='soft', wavelet_levels=None): """Perform wavelet thresholding. Parameters ---------- img : ndarray (2d or 3d) of ints, uints or floats Input data to be denoised. `img` can be of any numeric type, but it is cast into an ndarray of floats for the computation of the denoised image. wavelet : string The type of wavelet to perform. Can be any of the options pywt.wavelist outputs. For example, this may be any of ``{db1, db2, db3, db4, haar}``. sigma : float, optional The standard deviation of the noise. The noise is estimated when sigma is None (the default) by the method in [2]_. threshold : float, optional The thresholding value. All wavelet coefficients less than this value are set to 0. The default value (None) uses the BayesShrink method found in [1]_ to remove noise. mode : {'soft', 'hard'}, optional An optional argument to choose the type of denoising performed. It noted that choosing soft thresholding given additive noise finds the best approximation of the original image. wavelet_levels : int or None, optional The number of wavelet decomposition levels to use. The default is three less than the maximum number of possible decomposition levels (see Notes below). Returns ------- out : ndarray Denoised image. References ---------- .. [1] Chang, S. Grace, Bin Yu, and Martin Vetterli. "Adaptive wavelet thresholding for image denoising and compression." Image Processing, IEEE Transactions on 9.9 (2000): 1532-1546. DOI: 10.1109/83.862633 .. [2] D. L. Donoho and I. M. Johnstone. "Ideal spatial adaptation by wavelet shrinkage." Biometrika 81.3 (1994): 425-455. DOI: 10.1093/biomet/81.3.425 """ wavelet = pywt.Wavelet(wavelet) # original_extent is used to workaround PyWavelets issue #80 # odd-sized input results in an image with 1 extra sample after waverecn original_extent = [slice(s) for s in img.shape] # Determine the number of wavelet decomposition levels if wavelet_levels is None: # Determine the maximum number of possible levels for img dlen = wavelet.dec_len wavelet_levels = np.min( [pywt.dwt_max_level(s, dlen) for s in img.shape]) # Skip coarsest wavelet scales (see Notes in docstring). wavelet_levels = max(wavelet_levels - 3, 1) coeffs = pywt.wavedecn(img, wavelet=wavelet, level=wavelet_levels) # Detail coefficients at each decomposition level dcoeffs = coeffs[1:] if sigma is None: # Estimate the noise via the method in [2]_ detail_coeffs = dcoeffs[-1]['d' * img.ndim] sigma = _sigma_est_dwt(detail_coeffs, distribution='Gaussian') if threshold is None: # The BayesShrink thresholds from [1]_ in docstring var = sigma**2 threshold = [{key: _bayes_thresh(level[key], var) for key in level} for level in dcoeffs] if np.isscalar(threshold): # A single threshold for all coefficient arrays denoised_detail = [{key: pywt.threshold(level[key], value=threshold, mode=mode) for key in level} for level in dcoeffs] else: # Dict of unique threshold coefficients for each detail coeff. array denoised_detail = [{key: pywt.threshold(level[key], value=thresh[key], mode=mode) for key in level} for thresh, level in zip(threshold, dcoeffs)] denoised_coeffs = [coeffs[0]] + denoised_detail return pywt.waverecn(denoised_coeffs, wavelet)[original_extent] def denoise_wavelet(img, sigma=None, wavelet='db1', mode='soft', wavelet_levels=None, multichannel=False, convert2ycbcr=False): """Perform wavelet denoising on an image. Parameters ---------- img : ndarray ([M[, N[, ...P]][, C]) of ints, uints or floats Input data to be denoised. `img` can be of any numeric type, but it is cast into an ndarray of floats for the computation of the denoised image. sigma : float or list, optional The noise standard deviation used when computing the threshold adaptively as described in [1]_ for each color channel. When None (default), the noise standard deviation is estimated via the method in [2]_. wavelet : string, optional The type of wavelet to perform and can be any of the options ``pywt.wavelist`` outputs. The default is `'db1'`. For example, ``wavelet`` can be any of ``{'db2', 'haar', 'sym9'}`` and many more. mode : {'soft', 'hard'}, optional An optional argument to choose the type of denoising performed. It noted that choosing soft thresholding given additive noise finds the best approximation of the original image. wavelet_levels : int or None, optional The number of wavelet decomposition levels to use. The default is three less than the maximum number of possible decomposition levels. multichannel : bool, optional Apply wavelet denoising separately for each channel (where channels correspond to the final axis of the array). convert2ycbcr : bool, optional If True and multichannel True, do the wavelet denoising in the YCbCr colorspace instead of the RGB color space. This typically results in better performance for RGB images. Returns ------- out : ndarray Denoised image. Notes ----- The wavelet domain is a sparse representation of the image, and can be thought of similarly to the frequency domain of the Fourier transform. Sparse representations have most values zero or near-zero and truly random noise is (usually) represented by many small values in the wavelet domain. Setting all values below some threshold to 0 reduces the noise in the image, but larger thresholds also decrease the detail present in the image. If the input is 3D, this function performs wavelet denoising on each color plane separately. The output image is clipped between either [-1, 1] and [0, 1] depending on the input image range. When YCbCr conversion is done, every color channel is scaled between 0 and 1, and `sigma` values are applied to these scaled color channels. References ---------- .. [1] Chang, S. Grace, Bin Yu, and Martin Vetterli. "Adaptive wavelet thresholding for image denoising and compression." Image Processing, IEEE Transactions on 9.9 (2000): 1532-1546. DOI: 10.1109/83.862633 .. [2] D. L. Donoho and I. M. Johnstone. "Ideal spatial adaptation by wavelet shrinkage." Biometrika 81.3 (1994): 425-455. DOI: 10.1093/biomet/81.3.425 Examples -------- >>> from skimage import color, data >>> img = img_as_float(data.astronaut()) >>> img = color.rgb2gray(img) >>> img += 0.1 * np.random.randn(*img.shape) >>> img = np.clip(img, 0, 1) >>> denoised_img = denoise_wavelet(img, sigma=0.1) """ img = img_as_float(img) if multichannel: if isinstance(sigma, numbers.Number) or sigma is None: sigma = [sigma] * img.shape[-1] if multichannel: if convert2ycbcr: out = color.rgb2ycbcr(img) for i in range(3): # renormalizing this color channel to live in [0, 1] min, max = out[..., i].min(), out[..., i].max() channel = out[..., i] - min channel /= max - min out[..., i] = denoise_wavelet(channel, sigma=sigma[i], wavelet=wavelet, mode=mode, wavelet_levels=wavelet_levels) out[..., i] = out[..., i] * (max - min) out[..., i] += min out = color.ycbcr2rgb(out) else: out = np.empty_like(img) for c in range(img.shape[-1]): out[..., c] = _wavelet_threshold(img[..., c], wavelet=wavelet, mode=mode, sigma=sigma[c], wavelet_levels=wavelet_levels) else: out = _wavelet_threshold(img, wavelet=wavelet, mode=mode, sigma=sigma, wavelet_levels=wavelet_levels) clip_range = (-1, 1) if img.min() < 0 else (0, 1) return np.clip(out, *clip_range) def estimate_sigma(im, average_sigmas=False, multichannel=False): """ Robust wavelet-based estimator of the (Gaussian) noise standard deviation. Parameters ---------- im : ndarray Image for which to estimate the noise standard deviation. average_sigmas : bool, optional If true, average the channel estimates of `sigma`. Otherwise return a list of sigmas corresponding to each channel. multichannel : bool Estimate sigma separately for each channel. Returns ------- sigma : float or list Estimated noise standard deviation(s). If `multichannel` is True and `average_sigmas` is False, a separate noise estimate for each channel is returned. Otherwise, the average of the individual channel estimates is returned. Notes ----- This function assumes the noise follows a Gaussian distribution. The estimation algorithm is based on the median absolute deviation of the wavelet detail coefficients as described in section 4.2 of [1]_. References ---------- .. [1] D. L. Donoho and I. M. Johnstone. "Ideal spatial adaptation by wavelet shrinkage." Biometrika 81.3 (1994): 425-455. DOI:10.1093/biomet/81.3.425 Examples -------- >>> import skimage.data >>> from skimage import img_as_float >>> img = img_as_float(skimage.data.camera()) >>> sigma = 0.1 >>> img = img + sigma * np.random.standard_normal(img.shape) >>> sigma_hat = estimate_sigma(img, multichannel=False) """ if multichannel: nchannels = im.shape[-1] sigmas = [estimate_sigma( im[..., c], multichannel=False) for c in range(nchannels)] if average_sigmas: sigmas = np.mean(sigmas) return sigmas elif im.shape[-1] <= 4: msg = ("image is size {0} on the last axis, but multichannel is " "False. If this is a color image, please set multichannel " "to True for proper noise estimation.") warn(msg.format(im.shape[-1])) coeffs = pywt.dwtn(im, wavelet='db2') detail_coeffs = coeffs['d' * im.ndim] return _sigma_est_dwt(detail_coeffs, distribution='Gaussian') scikit-image-0.13.1/skimage/restoration/_denoise_cy.pyx000066400000000000000000000211451316256236300231670ustar00rootroot00000000000000#cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False #cython: wraparound=False cimport numpy as cnp import numpy as np from libc.math cimport exp, fabs, sqrt from libc.float cimport DBL_MAX from .._shared.interpolation cimport get_pixel3d from ..util import img_as_float cdef inline double _gaussian_weight(double sigma, double value): return exp(-0.5 * (value / sigma)**2) cdef double[:] _compute_color_lut(Py_ssize_t bins, double sigma, double max_value): cdef: double[:] color_lut = np.empty(bins, dtype=np.double) Py_ssize_t b for b in range(bins): color_lut[b] = _gaussian_weight(sigma, b * max_value / bins) return color_lut cdef double[:] _compute_range_lut(Py_ssize_t win_size, double sigma): cdef: double[:] range_lut = np.empty(win_size**2, dtype=np.double) Py_ssize_t kr, kc Py_ssize_t window_ext = (win_size - 1) / 2 double dist for kr in range(win_size): for kc in range(win_size): dist = sqrt((kr - window_ext)**2 + (kc - window_ext)**2) range_lut[kr * win_size + kc] = _gaussian_weight(sigma, dist) return range_lut cdef inline Py_ssize_t Py_ssize_t_min(Py_ssize_t value1, Py_ssize_t value2): if value1 < value2: return value1 else: return value2 def _denoise_bilateral(image, Py_ssize_t win_size, sigma_range, double sigma_spatial, Py_ssize_t bins, mode, double cval): cdef: double min_value, max_value min_value = image.min() max_value = image.max() if min_value == max_value: return image # if image.max() is 0, then dist_scale can have an unverified value # and color_lut[(dist * dist_scale)] may cause a segmentation fault # so we verify we have a positive image and that the max is not 0.0. if min_value < 0.0: raise ValueError("Image must contain only positive values") if max_value == 0.0: raise ValueError("The maximum value found in the image was 0.") image = np.atleast_3d(img_as_float(image)) cdef: Py_ssize_t rows = image.shape[0] Py_ssize_t cols = image.shape[1] Py_ssize_t dims = image.shape[2] Py_ssize_t window_ext = (win_size - 1) / 2 Py_ssize_t max_color_lut_bin = bins - 1 double[:, :, ::1] cimage double[:, :, ::1] out double[:] color_lut double[:] range_lut Py_ssize_t r, c, d, wr, wc, kr, kc, rr, cc, pixel_addr, color_lut_bin double value, weight, dist, total_weight, csigma_range, color_weight, \ range_weight double dist_scale double[:] values double[:] centres double[:] total_values if sigma_range is None: csigma_range = image.std() else: csigma_range = sigma_range if mode not in ('constant', 'wrap', 'symmetric', 'reflect', 'edge'): raise ValueError("Invalid mode specified. Please use `constant`, " "`edge`, `wrap`, `symmetric` or `reflect`.") cdef char cmode = ord(mode[0].upper()) cimage = np.ascontiguousarray(image) out = np.zeros((rows, cols, dims), dtype=np.double) color_lut = _compute_color_lut(bins, csigma_range, max_value) range_lut = _compute_range_lut(win_size, sigma_spatial) dist_scale = bins / dims / max_value values = np.empty(dims, dtype=np.double) centres = np.empty(dims, dtype=np.double) total_values = np.empty(dims, dtype=np.double) for r in range(rows): for c in range(cols): total_weight = 0 for d in range(dims): total_values[d] = 0 centres[d] = cimage[r, c, d] for wr in range(-window_ext, window_ext + 1): rr = wr + r kr = wr + window_ext for wc in range(-window_ext, window_ext + 1): cc = wc + c kc = wc + window_ext # save pixel values for all dims and compute euclidian # distance between centre stack and current position dist = 0 for d in range(dims): value = get_pixel3d(&cimage[0, 0, 0], rows, cols, dims, rr, cc, d, cmode, cval) values[d] = value dist += (centres[d] - value)**2 dist = sqrt(dist) range_weight = range_lut[kr * win_size + kc] color_lut_bin = Py_ssize_t_min( (dist * dist_scale), max_color_lut_bin) color_weight = color_lut[color_lut_bin] weight = range_weight * color_weight for d in range(dims): total_values[d] += values[d] * weight total_weight += weight for d in range(dims): out[r, c, d] = total_values[d] / total_weight return np.squeeze(np.asarray(out)) def _denoise_tv_bregman(image, double weight, int max_iter, double eps, char isotropic): image = np.atleast_3d(img_as_float(image)) cdef: Py_ssize_t rows = image.shape[0] Py_ssize_t cols = image.shape[1] Py_ssize_t dims = image.shape[2] Py_ssize_t rows2 = rows + 2 Py_ssize_t cols2 = cols + 2 Py_ssize_t r, c, k Py_ssize_t total = rows * cols * dims shape_ext = (rows2, cols2, dims) u = np.zeros(shape_ext, dtype=np.double) cdef: double[:, :, ::1] cimage = np.ascontiguousarray(image) double[:, :, ::1] cu = u double[:, :, ::1] dx = np.zeros(shape_ext, dtype=np.double) double[:, :, ::1] dy = np.zeros(shape_ext, dtype=np.double) double[:, :, ::1] bx = np.zeros(shape_ext, dtype=np.double) double[:, :, ::1] by = np.zeros(shape_ext, dtype=np.double) double ux, uy, uprev, unew, bxx, byy, dxx, dyy, s int i = 0 double lam = 2 * weight double rmse = DBL_MAX double norm = (weight + 4 * lam) u[1:-1, 1:-1] = image # reflect image u[0, 1:-1] = image[1, :] u[1:-1, 0] = image[:, 1] u[-1, 1:-1] = image[-2, :] u[1:-1, -1] = image[:, -2] while i < max_iter and rmse > eps: rmse = 0 for k in range(dims): for r in range(1, rows + 1): for c in range(1, cols + 1): uprev = cu[r, c, k] # forward derivatives ux = cu[r, c + 1, k] - uprev uy = cu[r + 1, c, k] - uprev # Gauss-Seidel method unew = ( lam * ( + cu[r + 1, c, k] + cu[r - 1, c, k] + cu[r, c + 1, k] + cu[r, c - 1, k] + dx[r, c - 1, k] - dx[r, c, k] + dy[r - 1, c, k] - dy[r, c, k] - bx[r, c - 1, k] + bx[r, c, k] - by[r - 1, c, k] + by[r, c, k] ) + weight * cimage[r - 1, c - 1, k] ) / norm cu[r, c, k] = unew # update root mean square error rmse += (unew - uprev)**2 bxx = bx[r, c, k] byy = by[r, c, k] # d_subproblem after reference [4] if isotropic: s = sqrt((ux + bxx)**2 + (uy + byy)**2) dxx = s * lam * (ux + bxx) / (s * lam + 1) dyy = s * lam * (uy + byy) / (s * lam + 1) else: s = ux + bxx if s > 1 / lam: dxx = s - 1/lam elif s < -1 / lam: dxx = s + 1 / lam else: dxx = 0 s = uy + byy if s > 1 / lam: dyy = s - 1 / lam elif s < -1 / lam: dyy = s + 1 / lam else: dyy = 0 dx[r, c, k] = dxx dy[r, c, k] = dyy bx[r, c, k] += ux - dxx by[r, c, k] += uy - dyy rmse = sqrt(rmse / total) i += 1 return np.squeeze(np.asarray(u[1:-1, 1:-1])) scikit-image-0.13.1/skimage/restoration/_nl_means_denoising.pyx000066400000000000000000000714641316256236300247120ustar00rootroot00000000000000import numpy as np cimport numpy as np cimport cython from libc.math cimport exp ctypedef np.float32_t IMGDTYPE cdef float DISTANCE_CUTOFF = 5. @cython.boundscheck(False) cdef inline float patch_distance_2d(IMGDTYPE [:, :] p1, IMGDTYPE [:, :] p2, IMGDTYPE [:, ::] w, int s): """ Compute a Gaussian distance between two image patches. Parameters ---------- p1 : 2-D array_like First patch. p2 : 2-D array_like Second patch. w : 2-D array_like Array of weigths for the different pixels of the patches. s : int Linear size of the patches. Returns ------- distance : float Gaussian distance between the two patches Notes ----- The returned distance is given by .. math:: \exp( -w (p1 - p2)^2) """ cdef int i, j cdef int center = s / 2 # Check if central pixel is too different in the 2 patches cdef float tmp_diff = p1[center, center] - p2[center, center] cdef float init = w[center, center] * tmp_diff * tmp_diff if init > 1: return 0. cdef float distance = 0 for i in range(s): # exp of large negative numbers will be 0, so we'd better stop if distance > DISTANCE_CUTOFF: return 0. for j in range(s): tmp_diff = p1[i, j] - p2[i, j] distance += (w[i, j] * tmp_diff * tmp_diff) distance = exp(-distance) return distance @cython.boundscheck(False) cdef inline float patch_distance_2drgb(IMGDTYPE [:, :, :] p1, IMGDTYPE [:, :, :] p2, IMGDTYPE [:, ::] w, int s): """ Compute a Gaussian distance between two image patches. Parameters ---------- p1 : 3-D array_like First patch, 2D image with last dimension corresponding to channels. p2 : 3-D array_like Second patch, 2D image with last dimension corresponding to channels. w : 2-D array_like Array of weights for the different pixels of the patches. s : int Linear size of the patches. Returns ------- distance : float Gaussian distance between the two patches Notes ----- The returned distance is given by .. math:: \exp( -w (p1 - p2)^2) """ cdef int i, j cdef int center = s / 2 cdef int color cdef float tmp_diff = 0 cdef float distance = 0 for i in range(s): # exp of large negative numbers will be 0, so we'd better stop if distance > DISTANCE_CUTOFF: return 0. for j in range(s): for color in range(3): tmp_diff = p1[i, j, color] - p2[i, j, color] distance += w[i, j] * tmp_diff * tmp_diff distance = exp(-distance) return distance @cython.boundscheck(False) cdef inline float patch_distance_3d(IMGDTYPE [:, :, :] p1, IMGDTYPE [:, :, :] p2, IMGDTYPE [:, :, ::] w, int s): """ Compute a Gaussian distance between two image patches. Parameters ---------- p1 : 3-D array_like First patch. p2 : 3-D array_like Second patch. w : 3-D array_like Array of weights for the different pixels of the patches. s : int Linear size of the patches. Returns ------- distance : float Gaussian distance between the two patches Notes ----- The returned distance is given by .. math:: \exp( -w (p1 - p2)^2) """ cdef int i, j, k cdef float distance = 0 cdef float tmp_diff for i in range(s): # exp of large negative numbers will be 0, so we'd better stop if distance > DISTANCE_CUTOFF: return 0. for j in range(s): for k in range(s): tmp_diff = p1[i, j, k] - p2[i, j, k] distance += w[i, j, k] * tmp_diff * tmp_diff distance = exp(-distance) return distance @cython.cdivision(True) @cython.boundscheck(False) def _nl_means_denoising_2d(image, int s=7, int d=13, float h=0.1): """ Perform non-local means denoising on 2-D RGB image Parameters ---------- image : ndarray Input RGB image to be denoised s : int, optional Size of patches used for denoising d : int, optional Maximal distance in pixels where to search patches used for denoising h : float, optional Cut-off distance (in gray levels). The higher h, the more permissive one is in accepting patches. Returns ------- result : ndarray Denoised image, of same shape as input image. """ if s % 2 == 0: s += 1 # odd value for symmetric patch cdef int n_row, n_col, n_ch n_row, n_col, n_ch = image.shape cdef int offset = s / 2 cdef int row, col, i, j, color cdef int row_start, row_end, col_start, col_end cdef int row_start_i, row_end_i, col_start_j, col_end_j cdef IMGDTYPE [::1] new_values = np.zeros(n_ch).astype(np.float32) cdef IMGDTYPE [:, :, ::1] padded = np.ascontiguousarray(np.pad(image, ((offset, offset), (offset, offset), (0, 0)), mode='reflect').astype(np.float32)) cdef IMGDTYPE [:, :, ::1] result = padded.copy() cdef float A = ((s - 1.) / 4.) cdef float new_value cdef float weight_sum, weight xg_row, xg_col = np.mgrid[-offset:offset + 1, -offset:offset + 1] cdef IMGDTYPE [:, ::1] w = np.ascontiguousarray(np.exp( -(xg_row ** 2 + xg_col ** 2) / (2 * A ** 2)). astype(np.float32)) cdef float distance w = 1. / (n_ch * np.sum(w) * h ** 2) * w # Coordinates of central pixel # Iterate over rows, taking padding into account for row in range(offset, n_row + offset): row_start = row - offset row_end = row + offset + 1 # Iterate over columns, taking padding into account for col in range(offset, n_col + offset): # Initialize per-channel bins for color in range(n_ch): new_values[color] = 0 # Reset weights for each local region weight_sum = 0 col_start = col - offset col_end = col + offset + 1 # Iterate over local 2d patch for each pixel # First rows for i in range(max(-d, offset - row), min(d + 1, n_row + offset - row)): row_start_i = row_start + i row_end_i = row_end + i # Local patch columns for j in range(max(-d, offset - col), min(d + 1, n_col + offset - col)): col_start_j = col_start + j col_end_j = col_end + j # Shortcut for grayscale, else assume RGB if n_ch == 1: weight = patch_distance_2d( padded[row_start:row_end, col_start:col_end, 0], padded[row_start_i:row_end_i, col_start_j:col_end_j, 0], w, s) else: weight = patch_distance_2drgb( padded[row_start:row_end, col_start:col_end, :], padded[row_start_i:row_end_i, col_start_j:col_end_j, :], w, s) # Collect results in weight sum weight_sum += weight # Apply to each channel multiplicatively for color in range(n_ch): new_values[color] += weight * padded[row + i, col + j, color] # Normalize the result for color in range(n_ch): result[row, col, color] = new_values[color] / weight_sum # Return cropped result, undoing padding return result[offset:-offset, offset:-offset] @cython.cdivision(True) @cython.boundscheck(False) def _nl_means_denoising_3d(image, int s=7, int d=13, float h=0.1): """ Perform non-local means denoising on 3-D array Parameters ---------- image : ndarray Input data to be denoised. s : int, optional Size of patches used for denoising. d : int, optional Maximal distance in pixels where to search patches used for denoising. h : float, optional Cut-off distance (in gray levels). Returns ------- result : ndarray Denoised image, of same shape as input image. """ if s % 2 == 0: s += 1 # odd value for symmetric patch cdef int n_pln, n_row, n_col n_pln, n_row, n_col = image.shape cdef int offset = s / 2 # padd the image so that boundaries are denoised as well cdef IMGDTYPE [:, :, ::1] padded = np.ascontiguousarray(np.pad( image.astype(np.float32), offset, mode='reflect')) cdef IMGDTYPE [:, :, ::1] result = padded.copy() cdef float A = ((s - 1.) / 4.) cdef float new_value cdef float weight_sum, weight xg_pln, xg_row, xg_col = np.mgrid[-offset: offset + 1, -offset: offset + 1, -offset: offset + 1] cdef IMGDTYPE [:, :, ::1] w = np.ascontiguousarray(np.exp( -(xg_pln ** 2 + xg_row ** 2 + xg_col ** 2) / (2 * A ** 2)).astype(np.float32)) cdef float distance cdef int pln, row, col, i, j, k cdef int pln_start, pln_end, row_start, row_end, col_start, col_end cdef int pln_start_i, pln_end_i, row_start_j, row_end_j, \ col_start_k, col_end_k w = 1. / (np.sum(w) * h ** 2) * w # Coordinates of central pixel # Iterate over planes, taking padding into account for pln in range(offset, n_pln + offset): pln_start = pln - offset pln_end = pln + offset + 1 # Iterate over rows, taking padding into account for row in range(offset, n_row + offset): row_start = row - offset row_end = row + offset + 1 # Iterate over columns, taking padding into account for col in range(offset, n_col + offset): col_start = col - offset col_end = col + offset + 1 new_value = 0 weight_sum = 0 # Iterate over local 3d patch for each pixel # First planes for i in range(max(-d, offset - pln), min(d + 1, n_pln + offset - pln)): pln_start_i = pln_start + i pln_end_i = pln_end + i # Rows for j in range(max(-d, offset - row), min(d + 1, n_row + offset - row)): row_start_j = row_start + j row_end_j = row_end + j # Columns for k in range(max(-d, offset - col), min(d + 1, n_col + offset - col)): col_start_k = col_start + k col_end_k = col_end + k weight = patch_distance_3d( padded[pln_start:pln_end, row_start:row_end, col_start:col_end], padded[pln_start_i:pln_end_i, row_start_j:row_end_j, col_start_k:col_end_k], w, s) # Collect results in weight sum weight_sum += weight new_value += weight * padded[pln + i, row + j, col + k] # Normalize the result result[pln, row, col] = new_value / weight_sum # Return cropped result, undoing padding return result[offset:-offset, offset:-offset, offset:-offset] #-------------- Accelerated algorithm of Froment 2015 ------------------ @cython.cdivision(True) @cython.boundscheck(False) cdef inline float _integral_to_distance_2d(IMGDTYPE [:, ::] integral, int row, int col, int offset, float h2s2): """ References ---------- J. Darbon, A. Cunha, T.F. Chan, S. Osher, and G.J. Jensen, Fast nonlocal filtering applied to electron cryomicroscopy, in 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2008, pp. 1331-1334. Jacques Froment. Parameter-Free Fast Pixelwise Non-Local Means Denoising. Image Processing On Line, 2014, vol. 4, p. 300-326. Used in _fast_nl_means_denoising_2d """ cdef float distance distance = integral[row + offset, col + offset] + \ integral[row - offset, col - offset] - \ integral[row - offset, col + offset] - \ integral[row + offset, col - offset] distance /= h2s2 return distance @cython.cdivision(True) @cython.boundscheck(False) cdef inline float _integral_to_distance_3d(IMGDTYPE [:, :, ::] integral, int pln, int row, int col, int offset, float s_cube_h_square): """ References ---------- J. Darbon, A. Cunha, T.F. Chan, S. Osher, and G.J. Jensen, Fast nonlocal filtering applied to electron cryomicroscopy, in 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2008, pp. 1331-1334. Jacques Froment. Parameter-Free Fast Pixelwise Non-Local Means Denoising. Image Processing On Line, 2014, vol. 4, p. 300-326. Used in _fast_nl_means_denoising_3d """ cdef float distance distance = (integral[pln + offset, row + offset, col + offset] - integral[pln - offset, row - offset, col - offset] + integral[pln - offset, row - offset, col + offset] + integral[pln - offset, row + offset, col - offset] + integral[pln + offset, row - offset, col - offset] - integral[pln - offset, row + offset, col + offset] - integral[pln + offset, row - offset, col + offset] - integral[pln + offset, row + offset, col - offset]) distance /= s_cube_h_square return distance @cython.cdivision(True) @cython.boundscheck(False) cdef inline _integral_image_2d(IMGDTYPE [:, :, ::] padded, IMGDTYPE [:, ::] integral, int t_row, int t_col, int n_row, int n_col, int n_ch): """ Computes the integral of the squared difference between an image ``padded`` and the same image shifted by ``(t_row, t_col)``. Parameters ---------- padded : ndarray of shape (n_row, n_col, n_ch) Image of interest. integral : ndarray Output of the function. The array is filled with integral values. ``integral`` should have the same shape as ``padded``. t_row : int Shift along the row axis. t_col : int Shift along the column axis. n_row : int n_col : int n_ch : int Notes ----- The integral computation could be performed using ``transform.integral_image``, but this helper function saves memory by avoiding copies of ``padded``. """ cdef int row, col cdef float distance for row in range(max(1, -t_row), min(n_row, n_row - t_row)): for col in range(max(1, -t_col), min(n_col, n_col - t_col)): if n_ch == 1: distance = (padded[row, col, 0] - padded[row + t_row, col + t_col, 0])**2 else: distance = ((padded[row, col, 0] - padded[row + t_row, col + t_col, 0])**2 + (padded[row, col, 1] - padded[row + t_row, col + t_col, 1])**2 + (padded[row, col, 2] - padded[row + t_row, col + t_col, 2])**2) integral[row, col] = distance + \ integral[row - 1, col] + \ integral[row, col - 1] - \ integral[row - 1, col - 1] @cython.cdivision(True) @cython.boundscheck(False) cdef inline _integral_image_3d(IMGDTYPE [:, :, ::] padded, IMGDTYPE [:, :, ::] integral, int t_pln, int t_row, int t_col, int n_pln, int n_row, int n_col): """ Computes the integral of the squared difference between an image ``padded`` and the same image shifted by ``(t_pln, t_row, t_col)``. Parameters ---------- padded : ndarray of shape (n_pln, n_row, n_col) Image of interest. integral : ndarray Output of the function. The array is filled with integral values. ``integral`` should have the same shape as ``padded``. t_pln : int Shift along the plane axis. t_row : int Shift along the row axis. t_col : int Shift along the column axis. n_pln : int n_row : int n_col : int Notes ----- The integral computation could be performed using ``transform.integral_image``, but this helper function saves memory by avoiding copies of ``padded``. """ cdef int pln, row, col cdef float distance for pln in range(max(1, -t_pln), min(n_pln, n_pln - t_pln)): for row in range(max(1, -t_row), min(n_row, n_row - t_row)): for col in range(max(1, -t_col), min(n_col, n_col - t_col)): integral[pln, row, col] = \ ((padded[pln, row, col] - padded[pln + t_pln, row + t_row, col + t_col])**2 + integral[pln - 1, row, col] + integral[pln, row - 1, col] + integral[pln, row, col - 1] + integral[pln - 1, row - 1, col - 1] - integral[pln - 1, row - 1, col] - integral[pln, row - 1, col - 1] - integral[pln - 1, row, col - 1]) @cython.cdivision(True) @cython.boundscheck(False) def _fast_nl_means_denoising_2d(image, int s=7, int d=13, float h=0.1): """ Perform fast non-local means denoising on 2-D array, with the outer loop on patch shifts in order to reduce the number of operations. Parameters ---------- image : ndarray 2-D input data to be denoised, grayscale or RGB. s : int, optional Size of patches used for denoising. d : int, optional Maximal distance in pixels where to search patches used for denoising. h : float, optional Cut-off distance (in gray levels). The higher h, the more permissive one is in accepting patches. Returns ------- result : ndarray Denoised image, of same shape as input image. References ---------- J. Darbon, A. Cunha, T.F. Chan, S. Osher, and G.J. Jensen, Fast nonlocal filtering applied to electron cryomicroscopy, in 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2008, pp. 1331-1334. Jacques Froment. Parameter-Free Fast Pixelwise Non-Local Means Denoising. Image Processing On Line, 2014, vol. 4, p. 300-326. """ if s % 2 == 0: s += 1 # odd value for symmetric patch cdef int offset = s / 2 # Image padding: we need to account for patch size, possible shift, # + 1 for the boundary effects in finite differences cdef int pad_size = offset + d + 1 cdef IMGDTYPE [:, :, ::1] padded = np.ascontiguousarray(np.pad(image, ((pad_size, pad_size), (pad_size, pad_size), (0, 0)), mode='reflect').astype(np.float32)) cdef IMGDTYPE [:, :, ::1] result = np.zeros_like(padded) cdef IMGDTYPE [:, ::1] weights = np.zeros_like(padded[..., 0], order='C') cdef IMGDTYPE [:, ::1] integral = np.zeros_like(padded[..., 0], order='C') cdef int n_row, n_col, n_ch, t_row, t_col, row, col cdef float weight, distance cdef float alpha cdef float h2 = h ** 2. cdef float s2 = s ** 2. n_row, n_col, n_ch = image.shape cdef float h2s2 = n_ch * h2 * s2 n_row += 2 * pad_size n_col += 2 * pad_size # Outer loops on patch shifts # With t2 >= 0, reference patch is always on the left of test patch # Iterate over shifts along the row axis for t_row in range(-d, d + 1): # Iterate over shifts along the column axis for t_col in range(0, d + 1): # alpha is to account for patches on the same column # distance is computed twice in this case if t_col == 0 and t_row is not 0: alpha = 0.5 else: alpha = 1. # Compute integral image of the squared difference between # padded and the same image shifted by (t_row, t_col) integral = np.zeros_like(padded[..., 0], order='C') _integral_image_2d(padded, integral, t_row, t_col, n_row, n_col, n_ch) # Inner loops on pixel coordinates # Iterate over rows, taking offset and shift into account for row in range(max(offset, offset - t_row), min(n_row - offset, n_row - offset - t_row)): # Iterate over columns, taking offset and shift into account for col in range(max(offset, offset - t_col), min(n_col - offset, n_col - offset - t_col)): # Compute squared distance between shifted patches distance = _integral_to_distance_2d(integral, row, col, offset, h2s2) # exp of large negative numbers is close to zero if distance > DISTANCE_CUTOFF: continue weight = alpha * exp(-distance) # Accumulate weights corresponding to different shifts weights[row, col] += weight weights[row + t_row, col + t_col] += weight # Iterate over channels for ch in range(n_ch): result[row, col, ch] += weight * \ padded[row + t_row, col + t_col, ch] result[row + t_row, col + t_col, ch] += \ weight * padded[row, col, ch] # Normalize pixel values using sum of weights of contributing patches for row in range(offset, n_row - offset): for col in range(offset, n_col - offset): for channel in range(n_ch): # No risk of division by zero, since the contribution # of a null shift is strictly positive result[row, col, channel] /= weights[row, col] # Return cropped result, undoing padding return result[pad_size:-pad_size, pad_size:-pad_size] @cython.cdivision(True) @cython.boundscheck(False) def _fast_nl_means_denoising_3d(image, int s=5, int d=7, float h=0.1): """ Perform fast non-local means denoising on 3-D array, with the outer loop on patch shifts in order to reduce the number of operations. Parameters ---------- image : ndarray 3-D input data to be denoised. s : int, optional Size of patches used for denoising. d : int, optional Maximal distance in pixels where to search patches used for denoising. h : float, optional cut-off distance (in gray levels). The higher h, the more permissive one is in accepting patches. Returns ------- result : ndarray Denoised image, of same shape as input image. References ---------- J. Darbon, A. Cunha, T.F. Chan, S. Osher, and G.J. Jensen, Fast nonlocal filtering applied to electron cryomicroscopy, in 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2008, pp. 1331-1334. Jacques Froment. Parameter-Free Fast Pixelwise Non-Local Means Denoising. Image Processing On Line, 2014, vol. 4, p. 300-326. """ if s % 2 == 0: s += 1 # odd value for symmetric patch cdef int offset = s / 2 # Image padding: we need to account for patch size, possible shift, # + 1 for the boundary effects in finite differences cdef int pad_size = offset + d + 1 cdef IMGDTYPE [:, :, ::1] padded = np.ascontiguousarray(np.pad(image, pad_size, mode='reflect').astype(np.float32)) cdef IMGDTYPE [:, :, ::1] result = np.zeros_like(padded) cdef IMGDTYPE [:, :, ::1] weights = np.zeros_like(padded) cdef IMGDTYPE [:, :, ::1] integral = np.zeros_like(padded) cdef int n_pln, n_row, n_col, t_pln, t_row, t_col, \ pln, row, col cdef int pln_dist_min, pln_dist_max, row_dist_min, row_dist_max, \ col_dist_min, col_dist_max cdef float weight, distance cdef float alpha cdef float h_square = h ** 2. cdef float s_cube = s ** 3. cdef float s_cube_h_square = h_square * s_cube n_pln, n_row, n_col = image.shape n_pln += 2 * pad_size n_row += 2 * pad_size n_col += 2 * pad_size # Outer loops on patch shifts # With t2 >= 0, reference patch is always on the left of test patch # Iterate over shifts along the plane axis for t_pln in range(-d, d + 1): pln_dist_min = max(offset, offset - t_pln) pln_dist_max = min(n_pln - offset, n_pln - offset - t_pln) # Iterate over shifts along the row axis for t_row in range(-d, d + 1): row_dist_min = max(offset, offset - t_row) row_dist_max = min(n_row - offset, n_row - offset - t_row) # Iterate over shifts along the column axis for t_col in range(0, d + 1): col_dist_min = max(offset, offset - t_col) col_dist_max = min(n_col - offset, n_col - offset - t_col) # alpha is to account for patches on the same column # distance is computed twice in this case if t_col == 0 and (t_pln is not 0 or t_row is not 0): alpha = 0.5 else: alpha = 1. # Compute integral image of the squared difference between # padded and the same image shifted by (t_pln, t_row, t_col) integral = np.zeros_like(padded) _integral_image_3d(padded, integral, t_pln, t_row, t_col, n_pln, n_row, n_col) # Inner loops on pixel coordinates # Iterate over planes, taking offset and shift into account for pln in range(pln_dist_min, pln_dist_max): # Iterate over rows, taking offset and shift into account for row in range(row_dist_min, row_dist_max): # Iterate over columns for col in range(col_dist_min, col_dist_max): # Compute squared distance between shifted patches distance = _integral_to_distance_3d(integral, pln, row, col, offset, s_cube_h_square) # exp of large negative numbers is close to zero if distance > DISTANCE_CUTOFF: continue weight = alpha * exp(-distance) # Accumulate weights for the different shifts weights[pln, row, col] += weight weights[pln + t_pln, row + t_row, col + t_col] += weight result[pln, row, col] += weight * \ padded[pln + t_pln, row + t_row, col + t_col] result[pln + t_pln, row + t_row, col + t_col] += weight * \ padded[pln, row, col] # Normalize pixel values using sum of weights of contributing patches for pln in range(offset, n_pln - offset): for row in range(offset, n_row - offset): for col in range(offset, n_col - offset): # No risk of division by zero, since the contribution # of a null shift is strictly positive result[pln, row, col] /= weights[pln, row, col] # Return cropped result, undoing padding return result[pad_size:-pad_size, pad_size:-pad_size, pad_size:-pad_size] scikit-image-0.13.1/skimage/restoration/_unwrap_1d.pyx000066400000000000000000000011551316256236300227450ustar00rootroot00000000000000#cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False #cython: wraparound=False from libc.math cimport M_PI def unwrap_1d(double[::1] image, double[::1] unwrapped_image): '''Phase unwrapping using the naive approach.''' cdef: Py_ssize_t i double difference long periods = 0 unwrapped_image[0] = image[0] for i in range(1, image.shape[0]): difference = image[i] - image[i - 1] if difference > M_PI: periods -= 1 elif difference < -M_PI: periods += 1 unwrapped_image[i] = image[i] + 2 * M_PI * periods scikit-image-0.13.1/skimage/restoration/_unwrap_2d.pyx000066400000000000000000000013171316256236300227460ustar00rootroot00000000000000cdef extern from *: void unwrap2D(double* wrapped_image, double* unwrapped_image, unsigned char* input_mask, int image_width, int image_height, int wrap_around_x, int wrap_around_y, char use_seed, unsigned int seed) def unwrap_2d(double[:, ::1] image, unsigned char[:, ::1] mask, double[:, ::1] unwrapped_image, wrap_around, seed): unwrap2D(&image[0, 0], &unwrapped_image[0, 0], &mask[0, 0], image.shape[1], image.shape[0], wrap_around[1], wrap_around[0], seed is None, 0 if seed is None else seed) scikit-image-0.13.1/skimage/restoration/_unwrap_3d.pyx000066400000000000000000000014541316256236300227510ustar00rootroot00000000000000cdef extern from *: void unwrap3D(double* wrapped_volume, double* unwrapped_volume, unsigned char* input_mask, int image_width, int image_height, int volume_depth, int wrap_around_x, int wrap_around_y, int wrap_around_z, char use_seed, unsigned int seed) def unwrap_3d(double[:, :, ::1] image, unsigned char[:, :, ::1] mask, double[:, :, ::1] unwrapped_image, wrap_around, seed): unwrap3D(&image[0, 0, 0], &unwrapped_image[0, 0, 0], &mask[0, 0, 0], image.shape[2], image.shape[1], image.shape[0], #TODO: check!!! wrap_around[2], wrap_around[1], wrap_around[0], seed is None, 0 if seed is None else seed) scikit-image-0.13.1/skimage/restoration/deconvolution.py000066400000000000000000000352721316256236300234150ustar00rootroot00000000000000# -*- coding: utf-8 -*- # deconvolution.py --- Image deconvolution """Implementations restoration functions""" from __future__ import division import numpy as np import numpy.random as npr from scipy.signal import fftconvolve, convolve from . import uft __keywords__ = "restoration, image, deconvolution" def wiener(image, psf, balance, reg=None, is_real=True, clip=True): """Wiener-Hunt deconvolution Return the deconvolution with a Wiener-Hunt approach (i.e. with Fourier diagonalisation). Parameters ---------- image : (M, N) ndarray Input degraded image psf : ndarray Point Spread Function. This is assumed to be the impulse response (input image space) if the data-type is real, or the transfer function (Fourier space) if the data-type is complex. There is no constraints on the shape of the impulse response. The transfer function must be of shape `(M, N)` if `is_real is True`, `(M, N // 2 + 1)` otherwise (see `np.fft.rfftn`). balance : float The regularisation parameter value that tunes the balance between the data adequacy that improve frequency restoration and the prior adequacy that reduce frequency restoration (to avoid noise artifacts). reg : ndarray, optional The regularisation operator. The Laplacian by default. It can be an impulse response or a transfer function, as for the psf. Shape constraint is the same as for the `psf` parameter. is_real : boolean, optional True by default. Specify if ``psf`` and ``reg`` are provided with hermitian hypothesis, that is only half of the frequency plane is provided (due to the redundancy of Fourier transform of real signal). It's apply only if ``psf`` and/or ``reg`` are provided as transfer function. For the hermitian property see ``uft`` module or ``np.fft.rfftn``. clip : boolean, optional True by default. If True, pixel values of the result above 1 or under -1 are thresholded for skimage pipeline compatibility. Returns ------- im_deconv : (M, N) ndarray The deconvolved image. Examples -------- >>> from skimage import color, data, restoration >>> img = color.rgb2gray(data.astronaut()) >>> from scipy.signal import convolve2d >>> psf = np.ones((5, 5)) / 25 >>> img = convolve2d(img, psf, 'same') >>> img += 0.1 * img.std() * np.random.standard_normal(img.shape) >>> deconvolved_img = restoration.wiener(img, psf, 1100) Notes ----- This function applies the Wiener filter to a noisy and degraded image by an impulse response (or PSF). If the data model is .. math:: y = Hx + n where :math:`n` is noise, :math:`H` the PSF and :math:`x` the unknown original image, the Wiener filter is .. math:: \hat x = F^\dag (|\Lambda_H|^2 + \lambda |\Lambda_D|^2) \Lambda_H^\dag F y where :math:`F` and :math:`F^\dag` are the Fourier and inverse Fourier transfroms respectively, :math:`\Lambda_H` the transfer function (or the Fourier transfrom of the PSF, see [Hunt] below) and :math:`\Lambda_D` the filter to penalize the restored image frequencies (Laplacian by default, that is penalization of high frequency). The parameter :math:`\lambda` tunes the balance between the data (that tends to increase high frequency, even those coming from noise), and the regularization. These methods are then specific to a prior model. Consequently, the application or the true image nature must corresponds to the prior model. By default, the prior model (Laplacian) introduce image smoothness or pixel correlation. It can also be interpreted as high-frequency penalization to compensate the instability of the solution with respect to the data (sometimes called noise amplification or "explosive" solution). Finally, the use of Fourier space implies a circulant property of :math:`H`, see [Hunt]. References ---------- .. [1] François Orieux, Jean-François Giovannelli, and Thomas Rodet, "Bayesian estimation of regularization and point spread function parameters for Wiener-Hunt deconvolution", J. Opt. Soc. Am. A 27, 1593-1607 (2010) http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-27-7-1593 http://research.orieux.fr/files/papers/OGR-JOSA10.pdf .. [2] B. R. Hunt "A matrix theory proof of the discrete convolution theorem", IEEE Trans. on Audio and Electroacoustics, vol. au-19, no. 4, pp. 285-288, dec. 1971 """ if reg is None: reg, _ = uft.laplacian(image.ndim, image.shape, is_real=is_real) if not np.iscomplexobj(reg): reg = uft.ir2tf(reg, image.shape, is_real=is_real) if psf.shape != reg.shape: trans_func = uft.ir2tf(psf, image.shape, is_real=is_real) else: trans_func = psf wiener_filter = np.conj(trans_func) / (np.abs(trans_func) ** 2 + balance * np.abs(reg) ** 2) if is_real: deconv = uft.uirfft2(wiener_filter * uft.urfft2(image), shape=image.shape) else: deconv = uft.uifft2(wiener_filter * uft.ufft2(image)) if clip: deconv[deconv > 1] = 1 deconv[deconv < -1] = -1 return deconv def unsupervised_wiener(image, psf, reg=None, user_params=None, is_real=True, clip=True): """Unsupervised Wiener-Hunt deconvolution. Return the deconvolution with a Wiener-Hunt approach, where the hyperparameters are automatically estimated. The algorithm is a stochastic iterative process (Gibbs sampler) described in the reference below. See also ``wiener`` function. Parameters ---------- image : (M, N) ndarray The input degraded image. psf : ndarray The impulse response (input image's space) or the transfer function (Fourier space). Both are accepted. The transfer function is automatically recognized as being complex (``np.iscomplexobj(psf)``). reg : ndarray, optional The regularisation operator. The Laplacian by default. It can be an impulse response or a transfer function, as for the psf. user_params : dict Dictionary of parameters for the Gibbs sampler. See below. clip : boolean, optional True by default. If true, pixel values of the result above 1 or under -1 are thresholded for skimage pipeline compatibility. Returns ------- x_postmean : (M, N) ndarray The deconvolved image (the posterior mean). chains : dict The keys ``noise`` and ``prior`` contain the chain list of noise and prior precision respectively. Other parameters ---------------- The keys of ``user_params`` are: threshold : float The stopping criterion: the norm of the difference between to successive approximated solution (empirical mean of object samples, see Notes section). 1e-4 by default. burnin : int The number of sample to ignore to start computation of the mean. 100 by default. min_iter : int The minimum number of iterations. 30 by default. max_iter : int The maximum number of iterations if ``threshold`` is not satisfied. 150 by default. callback : callable (None by default) A user provided callable to which is passed, if the function exists, the current image sample for whatever purpose. The user can store the sample, or compute other moments than the mean. It has no influence on the algorithm execution and is only for inspection. Examples -------- >>> from skimage import color, data, restoration >>> img = color.rgb2gray(data.astronaut()) >>> from scipy.signal import convolve2d >>> psf = np.ones((5, 5)) / 25 >>> img = convolve2d(img, psf, 'same') >>> img += 0.1 * img.std() * np.random.standard_normal(img.shape) >>> deconvolved_img = restoration.unsupervised_wiener(img, psf) Notes ----- The estimated image is design as the posterior mean of a probability law (from a Bayesian analysis). The mean is defined as a sum over all the possible images weighted by their respective probability. Given the size of the problem, the exact sum is not tractable. This algorithm use of MCMC to draw image under the posterior law. The practical idea is to only draw highly probable images since they have the biggest contribution to the mean. At the opposite, the less probable images are drawn less often since their contribution is low. Finally the empirical mean of these samples give us an estimation of the mean, and an exact computation with an infinite sample set. References ---------- .. [1] François Orieux, Jean-François Giovannelli, and Thomas Rodet, "Bayesian estimation of regularization and point spread function parameters for Wiener-Hunt deconvolution", J. Opt. Soc. Am. A 27, 1593-1607 (2010) http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-27-7-1593 http://research.orieux.fr/files/papers/OGR-JOSA10.pdf """ params = {'threshold': 1e-4, 'max_iter': 200, 'min_iter': 30, 'burnin': 15, 'callback': None} params.update(user_params or {}) if reg is None: reg, _ = uft.laplacian(image.ndim, image.shape, is_real=is_real) if not np.iscomplexobj(reg): reg = uft.ir2tf(reg, image.shape, is_real=is_real) if psf.shape != reg.shape: trans_fct = uft.ir2tf(psf, image.shape, is_real=is_real) else: trans_fct = psf # The mean of the object x_postmean = np.zeros(trans_fct.shape) # The previous computed mean in the iterative loop prev_x_postmean = np.zeros(trans_fct.shape) # Difference between two successive mean delta = np.NAN # Initial state of the chain gn_chain, gx_chain = [1], [1] # The correlation of the object in Fourier space (if size is big, # this can reduce computation time in the loop) areg2 = np.abs(reg) ** 2 atf2 = np.abs(trans_fct) ** 2 # The Fourier transfrom may change the image.size attribut, so we # store it. if is_real: data_spectrum = uft.urfft2(image.astype(np.float)) else: data_spectrum = uft.ufft2(image.astype(np.float)) # Gibbs sampling for iteration in range(params['max_iter']): # Sample of Eq. 27 p(circX^k | gn^k-1, gx^k-1, y). # weighting (correlation in direct space) precision = gn_chain[-1] * atf2 + gx_chain[-1] * areg2 # Eq. 29 excursion = np.sqrt(0.5) / np.sqrt(precision) * ( np.random.standard_normal(data_spectrum.shape) + 1j * np.random.standard_normal(data_spectrum.shape)) # mean Eq. 30 (RLS for fixed gn, gamma0 and gamma1 ...) wiener_filter = gn_chain[-1] * np.conj(trans_fct) / precision # sample of X in Fourier space x_sample = wiener_filter * data_spectrum + excursion if params['callback']: params['callback'](x_sample) # sample of Eq. 31 p(gn | x^k, gx^k, y) gn_chain.append(npr.gamma(image.size / 2, 2 / uft.image_quad_norm(data_spectrum - x_sample * trans_fct))) # sample of Eq. 31 p(gx | x^k, gn^k-1, y) gx_chain.append(npr.gamma((image.size - 1) / 2, 2 / uft.image_quad_norm(x_sample * reg))) # current empirical average if iteration > params['burnin']: x_postmean = prev_x_postmean + x_sample if iteration > (params['burnin'] + 1): current = x_postmean / (iteration - params['burnin']) previous = prev_x_postmean / (iteration - params['burnin'] - 1) delta = np.sum(np.abs(current - previous)) / \ np.sum(np.abs(x_postmean)) / (iteration - params['burnin']) prev_x_postmean = x_postmean # stop of the algorithm if (iteration > params['min_iter']) and (delta < params['threshold']): break # Empirical average \approx POSTMEAN Eq. 44 x_postmean = x_postmean / (iteration - params['burnin']) if is_real: x_postmean = uft.uirfft2(x_postmean, shape=image.shape) else: x_postmean = uft.uifft2(x_postmean) if clip: x_postmean[x_postmean > 1] = 1 x_postmean[x_postmean < -1] = -1 return (x_postmean, {'noise': gn_chain, 'prior': gx_chain}) def richardson_lucy(image, psf, iterations=50, clip=True): """Richardson-Lucy deconvolution. Parameters ---------- image : ndarray Input degraded image (can be N dimensional). psf : ndarray The point spread function. iterations : int Number of iterations. This parameter plays the role of regularisation. clip : boolean, optional True by default. If true, pixel value of the result above 1 or under -1 are thresholded for skimage pipeline compatibility. Returns ------- im_deconv : ndarray The deconvolved image. Examples -------- >>> from skimage import color, data, restoration >>> camera = color.rgb2gray(data.camera()) >>> from scipy.signal import convolve2d >>> psf = np.ones((5, 5)) / 25 >>> camera = convolve2d(camera, psf, 'same') >>> camera += 0.1 * camera.std() * np.random.standard_normal(camera.shape) >>> deconvolved = restoration.richardson_lucy(camera, psf, 5) References ---------- .. [1] http://en.wikipedia.org/wiki/Richardson%E2%80%93Lucy_deconvolution """ # compute the times for direct convolution and the fft method. The fft is of # complexity O(N log(N)) for each dimension and the direct method does # straight arithmetic (and is O(n*k) to add n elements k times) direct_time = np.prod(image.shape + psf.shape) fft_time = np.sum([n*np.log(n) for n in image.shape + psf.shape]) # see whether the fourier transform convolution method or the direct # convolution method is faster (discussed in scikit-image PR #1792) time_ratio = 40.032 * fft_time / direct_time if time_ratio <= 1 or len(image.shape) > 2: convolve_method = fftconvolve else: convolve_method = convolve image = image.astype(np.float) psf = psf.astype(np.float) im_deconv = 0.5 * np.ones(image.shape) psf_mirror = psf[::-1, ::-1] for _ in range(iterations): relative_blur = image / convolve_method(im_deconv, psf, 'same') im_deconv *= convolve_method(relative_blur, psf_mirror, 'same') if clip: im_deconv[im_deconv > 1] = 1 im_deconv[im_deconv < -1] = -1 return im_deconv scikit-image-0.13.1/skimage/restoration/inpaint.py000066400000000000000000000115401316256236300221570ustar00rootroot00000000000000from __future__ import division import numpy as np from scipy import sparse from scipy.sparse.linalg import spsolve import scipy.ndimage as ndi from scipy.ndimage.filters import laplace import skimage from skimage.measure import label def _get_neighborhood(nd_idx, radius, nd_shape): bounds_lo = (nd_idx - radius).clip(min=0) bounds_hi = (nd_idx + radius + 1).clip(max=nd_shape) return bounds_lo, bounds_hi def _inpaint_biharmonic_single_channel(img, mask, out, limits): # Initialize sparse matrices matrix_unknown = sparse.lil_matrix((np.sum(mask), out.size)) matrix_known = sparse.lil_matrix((np.sum(mask), out.size)) # Find indexes of masked points in flatten array mask_i = np.ravel_multi_index(np.where(mask), mask.shape) # Find masked points and prepare them to be easily enumerate over mask_pts = np.array(np.where(mask)).T # Iterate over masked points for mask_pt_n, mask_pt_idx in enumerate(mask_pts): # Get bounded neighborhood of selected radius b_lo, b_hi = _get_neighborhood(mask_pt_idx, 2, out.shape) # Create biharmonic coefficients ndarray neigh_coef = np.zeros(b_hi - b_lo) neigh_coef[tuple(mask_pt_idx - b_lo)] = 1 neigh_coef = laplace(laplace(neigh_coef)) # Iterate over masked point's neighborhood it_inner = np.nditer(neigh_coef, flags=['multi_index']) for coef in it_inner: if coef == 0: continue tmp_pt_idx = np.add(b_lo, it_inner.multi_index) tmp_pt_i = np.ravel_multi_index(tmp_pt_idx, mask.shape) if mask[tuple(tmp_pt_idx)]: matrix_unknown[mask_pt_n, tmp_pt_i] = coef else: matrix_known[mask_pt_n, tmp_pt_i] = coef # Prepare diagonal matrix flat_diag_image = sparse.dia_matrix((out.flatten(), np.array([0])), shape=(out.size, out.size)) # Calculate right hand side as a sum of known matrix's columns matrix_known = matrix_known.tocsr() rhs = -(matrix_known * flat_diag_image).sum(axis=1) # Solve linear system for masked points matrix_unknown = matrix_unknown[:, mask_i] matrix_unknown = sparse.csr_matrix(matrix_unknown) result = spsolve(matrix_unknown, rhs) # Handle enormous values result = np.clip(result, *limits) result = result.ravel() # Substitute masked points with inpainted versions for mask_pt_n, mask_pt_idx in enumerate(mask_pts): out[tuple(mask_pt_idx)] = result[mask_pt_n] return out def inpaint_biharmonic(img, mask, multichannel=False): """Inpaint masked points in image with biharmonic equations. Parameters ---------- img : (M[, N[, ..., P]][, C]) ndarray Input image. mask : (M[, N[, ..., P]]) ndarray Array of pixels to be inpainted. Have to be the same shape as one of the 'img' channels. Unknown pixels have to be represented with 1, known pixels - with 0. multichannel : boolean, optional If True, the last `img` dimension is considered as a color channel, otherwise as spatial. Returns ------- out : (M[, N[, ..., P]][, C]) ndarray Input image with masked pixels inpainted. References ---------- .. [1] N.S.Hoang, S.B.Damelin, "On surface completion and image inpainting by biharmonic functions: numerical aspects" Examples -------- >>> img = np.tile(np.square(np.linspace(0, 1, 5)), (5, 1)) >>> mask = np.zeros_like(img) >>> mask[2, 2:] = 1 >>> mask[1, 3:] = 1 >>> mask[0, 4:] = 1 >>> out = inpaint_biharmonic(img, mask) """ if img.ndim < 1: raise ValueError('Input array has to be at least 1D') img_baseshape = img.shape[:-1] if multichannel else img.shape if img_baseshape != mask.shape: raise ValueError('Input arrays have to be the same shape') if np.ma.isMaskedArray(img): raise TypeError('Masked arrays are not supported') img = skimage.img_as_float(img) mask = mask.astype(np.bool) # Split inpainting mask into independent regions kernel = ndi.morphology.generate_binary_structure(mask.ndim, 1) mask_dilated = ndi.morphology.binary_dilation(mask, structure=kernel) mask_labeled, num_labels = label(mask_dilated, return_num=True) mask_labeled *= mask if not multichannel: img = img[..., np.newaxis] out = np.copy(img) for idx_channel in range(img.shape[-1]): known_points = img[..., idx_channel][~mask] limits = (np.min(known_points), np.max(known_points)) for idx_region in range(1, num_labels+1): mask_region = mask_labeled == idx_region _inpaint_biharmonic_single_channel( img[..., idx_channel], mask_region, out[..., idx_channel], limits) if not multichannel: out = out[..., 0] return out scikit-image-0.13.1/skimage/restoration/non_local_means.py000066400000000000000000000127221316256236300236470ustar00rootroot00000000000000import numpy as np from .._shared.utils import skimage_deprecation, warn from ._nl_means_denoising import ( _nl_means_denoising_2d, _nl_means_denoising_3d, _fast_nl_means_denoising_2d, _fast_nl_means_denoising_3d) def denoise_nl_means(image, patch_size=7, patch_distance=11, h=0.1, multichannel=None, fast_mode=True): """ Perform non-local means denoising on 2-D or 3-D grayscale images, and 2-D RGB images. Parameters ---------- image : 2D or 3D ndarray Input image to be denoised, which can be 2D or 3D, and grayscale or RGB (for 2D images only, see ``multichannel`` parameter). patch_size : int, optional Size of patches used for denoising. patch_distance : int, optional Maximal distance in pixels where to search patches used for denoising. h : float, optional Cut-off distance (in gray levels). The higher h, the more permissive one is in accepting patches. A higher h results in a smoother image, at the expense of blurring features. For a Gaussian noise of standard deviation sigma, a rule of thumb is to choose the value of h to be sigma of slightly less. multichannel : bool, optional Whether the last axis of the image is to be interpreted as multiple channels or another spatial dimension. Set to ``False`` for 3-D images. fast_mode : bool, optional If True (default value), a fast version of the non-local means algorithm is used. If False, the original version of non-local means is used. See the Notes section for more details about the algorithms. Returns ------- result : ndarray Denoised image, of same shape as `image`. Notes ----- The non-local means algorithm is well suited for denoising images with specific textures. The principle of the algorithm is to average the value of a given pixel with values of other pixels in a limited neighbourhood, provided that the *patches* centered on the other pixels are similar enough to the patch centered on the pixel of interest. In the original version of the algorithm [1]_, corresponding to ``fast=False``, the computational complexity is image.size * patch_size ** image.ndim * patch_distance ** image.ndim Hence, changing the size of patches or their maximal distance has a strong effect on computing times, especially for 3-D images. However, the default behavior corresponds to ``fast_mode=True``, for which another version of non-local means [2]_ is used, corresponding to a complexity of image.size * patch_distance ** image.ndim The computing time depends only weakly on the patch size, thanks to the computation of the integral of patches distances for a given shift, that reduces the number of operations [1]_. Therefore, this algorithm executes faster than the classic algorith (``fast_mode=False``), at the expense of using twice as much memory. This implementation has been proven to be more efficient compared to other alternatives, see e.g. [3]_. Compared to the classic algorithm, all pixels of a patch contribute to the distance to another patch with the same weight, no matter their distance to the center of the patch. This coarser computation of the distance can result in a slightly poorer denoising performance. Moreover, for small images (images with a linear size that is only a few times the patch size), the classic algorithm can be faster due to boundary effects. The image is padded using the `reflect` mode of `skimage.util.pad` before denoising. References ---------- .. [1] Buades, A., Coll, B., & Morel, J. M. (2005, June). A non-local algorithm for image denoising. In CVPR 2005, Vol. 2, pp. 60-65, IEEE. .. [2] J. Darbon, A. Cunha, T.F. Chan, S. Osher, and G.J. Jensen, Fast nonlocal filtering applied to electron cryomicroscopy, in 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2008, pp. 1331-1334. .. [3] Jacques Froment. Parameter-Free Fast Pixelwise Non-Local Means Denoising. Image Processing On Line, 2014, vol. 4, p. 300-326. Examples -------- >>> a = np.zeros((40, 40)) >>> a[10:-10, 10:-10] = 1. >>> a += 0.3 * np.random.randn(*a.shape) >>> denoised_a = denoise_nl_means(a, 7, 5, 0.1) """ if multichannel is None: warn('denoise_nl_means will default to multichannel=False in v0.15') multichannel = True if image.ndim == 2: image = image[..., np.newaxis] multichannel = True if image.ndim != 3: raise NotImplementedError("Non-local means denoising is only \ implemented for 2D grayscale and RGB images or 3-D grayscale images.") if multichannel: # 2-D images if fast_mode: return np.squeeze(np.array(_fast_nl_means_denoising_2d(image, patch_size, patch_distance, h))) else: return np.squeeze(np.array(_nl_means_denoising_2d(image, patch_size, patch_distance, h))) else: # 3-D grayscale if fast_mode: return np.array(_fast_nl_means_denoising_3d(image, s=patch_size, d=patch_distance, h=h)) else: return np.array(_nl_means_denoising_3d(image, patch_size, patch_distance, h)) scikit-image-0.13.1/skimage/restoration/setup.py000066400000000000000000000035711316256236300216620ustar00rootroot00000000000000#!/usr/bin/env python import os from skimage._build import cython base_path = os.path.abspath(os.path.dirname(__file__)) def configuration(parent_package='', top_path=None): from numpy.distutils.misc_util import Configuration, get_numpy_include_dirs config = Configuration('restoration', parent_package, top_path) config.add_data_dir('tests') cython(['_unwrap_1d.pyx'], working_path=base_path) cython(['_unwrap_2d.pyx'], working_path=base_path) cython(['_unwrap_3d.pyx'], working_path=base_path) cython(['_denoise_cy.pyx'], working_path=base_path) cython(['_nl_means_denoising.pyx'], working_path=base_path) config.add_extension('_unwrap_1d', sources=['_unwrap_1d.c'], include_dirs=[get_numpy_include_dirs()]) unwrap_sources_2d = ['_unwrap_2d.c', 'unwrap_2d_ljmu.c'] config.add_extension('_unwrap_2d', sources=unwrap_sources_2d, include_dirs=[get_numpy_include_dirs()]) unwrap_sources_3d = ['_unwrap_3d.c', 'unwrap_3d_ljmu.c'] config.add_extension('_unwrap_3d', sources=unwrap_sources_3d, include_dirs=[get_numpy_include_dirs()]) config.add_extension('_denoise_cy', sources=['_denoise_cy.c'], include_dirs=[get_numpy_include_dirs(), '../_shared']) config.add_extension('_nl_means_denoising', sources=['_nl_means_denoising.c'], include_dirs=[get_numpy_include_dirs()]) return config if __name__ == '__main__': from numpy.distutils.core import setup setup(maintainer='scikit-image Developers', author='scikit-image Developers', maintainer_email='scikit-image@python.org', description='Restoration', url='https://github.com/scikit-image/scikit-image', license='SciPy License (BSD Style)', **(configuration(top_path='').todict()) ) scikit-image-0.13.1/skimage/restoration/tests/000077500000000000000000000000001316256236300213045ustar00rootroot00000000000000scikit-image-0.13.1/skimage/restoration/tests/__init__.py000066400000000000000000000001771316256236300234220ustar00rootroot00000000000000from ..._shared.testing import setup_test, teardown_test def setup(): setup_test() def teardown(): teardown_test() scikit-image-0.13.1/skimage/restoration/tests/camera_rl.npy000066400000000000000000100001201316256236300237530ustar00rootroot00000000000000NUMPYF{'descr': '?Z?b :]:?Zےg??IEfܹ?I#~ظ?+ ?p?*z ?*q?wR}? l o?_B?a˴?NQ?Xh?_7"?W,YA?Q󥍹??_`w~?"CxŹ?c}Fb{?^YW?M@䲺?L;?l?j?t ?HOya?" ?l_.?ؑxỾ?z?9Qm?P~ع?h'k?-Cݓ?v ?v?Gɮ~?(?UT!]?sC?jaW?5,? :?mϪ??W˳?SJxT?58ʼU?Ck@9?KJ!?m? _ȼ?)L۱? ~Ż?%Jݦ?ҞC?$?tU H?c*?[0aIͽ?~N0?yQ!?atX?nsmҼ?:?(̦=?'6??8? nȼ?%̻?Q!?-BٵM? ?#=7 '?\&V@M$?#=D?)Ֆ굼?ؾ>L??O_=G?1z?#\{Qf?l?'iX¼?W?&0#?̱?8+Dμ?㌼?xA?6'ż?jꀁ?|J?hb|9?L'?Oc?$' ?yjI?WHT?#s''?`Jӽ?W)?>Si?/?Ary? 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XlCq?Œ_?[UK(?l͞?T?QpO?N~y?scikit-image-0.13.1/skimage/restoration/tests/test_denoise.py000066400000000000000000000503071316256236300243500ustar00rootroot00000000000000import numpy as np from numpy.testing import (run_module_suite, assert_raises, assert_equal, assert_almost_equal, assert_warns, assert_) from skimage import restoration, data, color, img_as_float, measure from skimage._shared._warnings import expected_warnings from skimage.measure import compare_psnr from skimage.restoration._denoise import _wavelet_threshold import pywt np.random.seed(1234) astro = img_as_float(data.astronaut()[:128, :128]) astro_gray = color.rgb2gray(astro) checkerboard_gray = img_as_float(data.checkerboard()) checkerboard = color.gray2rgb(checkerboard_gray) def test_denoise_tv_chambolle_2d(): # astronaut image img = astro_gray.copy() # add noise to astronaut img += 0.5 * img.std() * np.random.rand(*img.shape) # clip noise so that it does not exceed allowed range for float images. img = np.clip(img, 0, 1) # denoise denoised_astro = restoration.denoise_tv_chambolle(img, weight=0.1) # which dtype? assert_(denoised_astro.dtype in [np.float, np.float32, np.float64]) from scipy import ndimage as ndi grad = ndi.morphological_gradient(img, size=((3, 3))) grad_denoised = ndi.morphological_gradient(denoised_astro, size=((3, 3))) # test if the total variation has decreased assert_(grad_denoised.dtype == np.float) assert_(np.sqrt((grad_denoised**2).sum()) < np.sqrt((grad**2).sum())) def test_denoise_tv_chambolle_multichannel(): denoised0 = restoration.denoise_tv_chambolle(astro[..., 0], weight=0.1) denoised = restoration.denoise_tv_chambolle(astro, weight=0.1, multichannel=True) assert_equal(denoised[..., 0], denoised0) # tile astronaut subset to generate 3D+channels data astro3 = np.tile(astro[:64, :64, np.newaxis, :], [1, 1, 2, 1]) # modify along tiled dimension to give non-zero gradient on 3rd axis astro3[:, :, 0, :] = 2*astro3[:, :, 0, :] denoised0 = restoration.denoise_tv_chambolle(astro3[..., 0], weight=0.1) denoised = restoration.denoise_tv_chambolle(astro3, weight=0.1, multichannel=True) assert_equal(denoised[..., 0], denoised0) def test_denoise_tv_chambolle_float_result_range(): # astronaut image img = astro_gray int_astro = np.multiply(img, 255).astype(np.uint8) assert_(np.max(int_astro) > 1) denoised_int_astro = restoration.denoise_tv_chambolle(int_astro, weight=0.1) # test if the value range of output float data is within [0.0:1.0] assert_(denoised_int_astro.dtype == np.float) assert_(np.max(denoised_int_astro) <= 1.0) assert_(np.min(denoised_int_astro) >= 0.0) def test_denoise_tv_chambolle_3d(): """Apply the TV denoising algorithm on a 3D image representing a sphere.""" x, y, z = np.ogrid[0:40, 0:40, 0:40] mask = (x - 22)**2 + (y - 20)**2 + (z - 17)**2 < 8**2 mask = 100 * mask.astype(np.float) mask += 60 mask += 20 * np.random.rand(*mask.shape) mask[mask < 0] = 0 mask[mask > 255] = 255 res = restoration.denoise_tv_chambolle(mask.astype(np.uint8), weight=0.1) assert_(res.dtype == np.float) assert_(res.std() * 255 < mask.std()) def test_denoise_tv_chambolle_1d(): """Apply the TV denoising algorithm on a 1D sinusoid.""" x = 125 + 100*np.sin(np.linspace(0, 8*np.pi, 1000)) x += 20 * np.random.rand(x.size) x = np.clip(x, 0, 255) res = restoration.denoise_tv_chambolle(x.astype(np.uint8), weight=0.1) assert_(res.dtype == np.float) assert_(res.std() * 255 < x.std()) def test_denoise_tv_chambolle_4d(): """ TV denoising for a 4D input.""" im = 255 * np.random.rand(8, 8, 8, 8) res = restoration.denoise_tv_chambolle(im.astype(np.uint8), weight=0.1) assert_(res.dtype == np.float) assert_(res.std() * 255 < im.std()) def test_denoise_tv_chambolle_weighting(): # make sure a specified weight gives consistent results regardless of # the number of input image dimensions rstate = np.random.RandomState(1234) img2d = astro_gray.copy() img2d += 0.15 * rstate.standard_normal(img2d.shape) img2d = np.clip(img2d, 0, 1) # generate 4D image by tiling img4d = np.tile(img2d[..., None, None], (1, 1, 2, 2)) w = 0.2 denoised_2d = restoration.denoise_tv_chambolle(img2d, weight=w) denoised_4d = restoration.denoise_tv_chambolle(img4d, weight=w) assert_(measure.compare_ssim(denoised_2d, denoised_4d[:, :, 0, 0]) > 0.99) def test_denoise_tv_bregman_2d(): img = checkerboard_gray.copy() # add some random noise img += 0.5 * img.std() * np.random.rand(*img.shape) img = np.clip(img, 0, 1) out1 = restoration.denoise_tv_bregman(img, weight=10) out2 = restoration.denoise_tv_bregman(img, weight=5) # make sure noise is reduced in the checkerboard cells assert_(img[30:45, 5:15].std() > out1[30:45, 5:15].std()) assert_(out1[30:45, 5:15].std() > out2[30:45, 5:15].std()) def test_denoise_tv_bregman_float_result_range(): # astronaut image img = astro_gray.copy() int_astro = np.multiply(img, 255).astype(np.uint8) assert_(np.max(int_astro) > 1) denoised_int_astro = restoration.denoise_tv_bregman(int_astro, weight=60.0) # test if the value range of output float data is within [0.0:1.0] assert_(denoised_int_astro.dtype == np.float) assert_(np.max(denoised_int_astro) <= 1.0) assert_(np.min(denoised_int_astro) >= 0.0) def test_denoise_tv_bregman_3d(): img = checkerboard.copy() # add some random noise img += 0.5 * img.std() * np.random.rand(*img.shape) img = np.clip(img, 0, 1) out1 = restoration.denoise_tv_bregman(img, weight=10) out2 = restoration.denoise_tv_bregman(img, weight=5) # make sure noise is reduced in the checkerboard cells assert_(img[30:45, 5:15].std() > out1[30:45, 5:15].std()) assert_(out1[30:45, 5:15].std() > out2[30:45, 5:15].std()) def test_denoise_bilateral_2d(): img = checkerboard_gray.copy()[:50, :50] # add some random noise img += 0.5 * img.std() * np.random.rand(*img.shape) img = np.clip(img, 0, 1) out1 = restoration.denoise_bilateral(img, sigma_color=0.1, sigma_spatial=10, multichannel=False) out2 = restoration.denoise_bilateral(img, sigma_color=0.2, sigma_spatial=20, multichannel=False) # make sure noise is reduced in the checkerboard cells assert_(img[30:45, 5:15].std() > out1[30:45, 5:15].std()) assert_(out1[30:45, 5:15].std() > out2[30:45, 5:15].std()) def test_denoise_bilateral_zeros(): img = np.zeros((10, 10)) assert_equal(img, restoration.denoise_bilateral(img, multichannel=False)) def test_denoise_bilateral_constant(): img = np.ones((10, 10)) * 5 assert_equal(img, restoration.denoise_bilateral(img, multichannel=False)) def test_denoise_bilateral_color(): img = checkerboard.copy()[:50, :50] # add some random noise img += 0.5 * img.std() * np.random.rand(*img.shape) img = np.clip(img, 0, 1) out1 = restoration.denoise_bilateral(img, sigma_color=0.1, sigma_spatial=10, multichannel=True) out2 = restoration.denoise_bilateral(img, sigma_color=0.2, sigma_spatial=20, multichannel=True) # make sure noise is reduced in the checkerboard cells assert_(img[30:45, 5:15].std() > out1[30:45, 5:15].std()) assert_(out1[30:45, 5:15].std() > out2[30:45, 5:15].std()) def test_denoise_bilateral_3d_grayscale(): img = np.ones((50, 50, 3)) assert_raises(ValueError, restoration.denoise_bilateral, img, multichannel=False) def test_denoise_bilateral_3d_multichannel(): img = np.ones((50, 50, 50)) with expected_warnings(["grayscale"]): result = restoration.denoise_bilateral(img, multichannel=True) assert_equal(result, img) def test_denoise_bilateral_multidimensional(): img = np.ones((10, 10, 10, 10)) assert_raises(ValueError, restoration.denoise_bilateral, img, multichannel=False) assert_raises(ValueError, restoration.denoise_bilateral, img, multichannel=True) def test_denoise_bilateral_nan(): img = np.full((50, 50), np.NaN) out = restoration.denoise_bilateral(img, multichannel=False) assert_equal(img, out) def test_denoise_sigma_range(): img = checkerboard_gray.copy()[:50, :50] # add some random noise img += 0.5 * img.std() * np.random.rand(*img.shape) img = np.clip(img, 0, 1) out1 = restoration.denoise_bilateral(img, sigma_color=0.1, sigma_spatial=10, multichannel=False) with expected_warnings( '`sigma_range` has been deprecated in favor of `sigma_color`. ' 'The `sigma_range` keyword argument will be removed in v0.14'): out2 = restoration.denoise_bilateral(img, sigma_range=0.1, sigma_spatial=10, multichannel=False) assert_equal(out1, out2) def test_denoise_sigma_range_and_sigma_color(): img = checkerboard_gray.copy()[:50, :50] # add some random noise img += 0.5 * img.std() * np.random.rand(*img.shape) img = np.clip(img, 0, 1) out1 = restoration.denoise_bilateral(img, sigma_color=0.1, sigma_spatial=10, multichannel=False) with expected_warnings( '`sigma_range` has been deprecated in favor of `sigma_color`. ' 'The `sigma_range` keyword argument will be removed in v0.14'): out2 = restoration.denoise_bilateral(img, sigma_color=0.2, sigma_range=0.1, sigma_spatial=10, multichannel=False) assert_equal(out1, out2) def test_nl_means_denoising_2d(): img = np.zeros((40, 40)) img[10:-10, 10:-10] = 1. img += 0.3*np.random.randn(*img.shape) denoised = restoration.denoise_nl_means(img, 7, 5, 0.2, fast_mode=True, multichannel=True) # make sure noise is reduced assert_(img.std() > denoised.std()) denoised = restoration.denoise_nl_means(img, 7, 5, 0.2, fast_mode=False, multichannel=True) # make sure noise is reduced assert_(img.std() > denoised.std()) def test_denoise_nl_means_2drgb(): # reduce image size because nl means is very slow img = np.copy(astro[:50, :50]) # add some random noise img += 0.5 * img.std() * np.random.random(img.shape) img = np.clip(img, 0, 1) denoised = restoration.denoise_nl_means(img, 7, 9, 0.3, fast_mode=True, multichannel=True) # make sure noise is reduced assert_(img.std() > denoised.std()) denoised = restoration.denoise_nl_means(img, 7, 9, 0.3, fast_mode=False, multichannel=True) # make sure noise is reduced assert_(img.std() > denoised.std()) def test_denoise_nl_means_3d(): img = np.zeros((20, 20, 10)) img[5:-5, 5:-5, 3:-3] = 1. img += 0.3*np.random.randn(*img.shape) denoised = restoration.denoise_nl_means(img, 5, 4, 0.2, fast_mode=True, multichannel=False) # make sure noise is reduced assert_(img.std() > denoised.std()) denoised = restoration.denoise_nl_means(img, 5, 4, 0.2, fast_mode=False, multichannel=False) # make sure noise is reduced assert_(img.std() > denoised.std()) def test_denoise_nl_means_multichannel(): img = np.zeros((21, 20, 10)) img[10, 9:11, 2:-2] = 1. img += 0.3*np.random.randn(*img.shape) denoised_wrong_multichannel = restoration.denoise_nl_means( img, 5, 4, 0.1, fast_mode=True, multichannel=True) denoised_ok_multichannel = restoration.denoise_nl_means( img, 5, 4, 0.1, fast_mode=True, multichannel=False) snr_wrong = 10 * np.log10(1. / ((denoised_wrong_multichannel - img)**2).mean()) snr_ok = 10 * np.log10(1. / ((denoised_ok_multichannel - img)**2).mean()) assert_(snr_ok > snr_wrong) def test_denoise_nl_means_wrong_dimension(): img = np.zeros((5, 5, 5, 5)) assert_raises(NotImplementedError, restoration.denoise_nl_means, img, multichannel=True) def test_no_denoising_for_small_h(): img = np.zeros((40, 40)) img[10:-10, 10:-10] = 1. img += 0.3*np.random.randn(*img.shape) # very small h should result in no averaging with other patches denoised = restoration.denoise_nl_means(img, 7, 5, 0.01, fast_mode=True, multichannel=True) assert_(np.allclose(denoised, img)) denoised = restoration.denoise_nl_means(img, 7, 5, 0.01, fast_mode=False, multichannel=True) assert_(np.allclose(denoised, img)) def test_wavelet_denoising(): rstate = np.random.RandomState(1234) # version with one odd-sized dimension astro_gray_odd = astro_gray[:, :-1] astro_odd = astro[:, :-1] for img, multichannel, convert2ycbcr in [(astro_gray, False, False), (astro_gray_odd, False, False), (astro_odd, True, False), (astro_odd, True, True)]: sigma = 0.1 noisy = img + sigma * rstate.randn(*(img.shape)) noisy = np.clip(noisy, 0, 1) # Verify that SNR is improved when true sigma is used denoised = restoration.denoise_wavelet(noisy, sigma=sigma, multichannel=multichannel, convert2ycbcr=convert2ycbcr) psnr_noisy = compare_psnr(img, noisy) psnr_denoised = compare_psnr(img, denoised) assert_(psnr_denoised > psnr_noisy) # Verify that SNR is improved with internally estimated sigma denoised = restoration.denoise_wavelet(noisy, multichannel=multichannel, convert2ycbcr=convert2ycbcr) psnr_noisy = compare_psnr(img, noisy) psnr_denoised = compare_psnr(img, denoised) assert_(psnr_denoised > psnr_noisy) # SNR is improved less with 1 wavelet level than with the default. denoised_1 = restoration.denoise_wavelet(noisy, multichannel=multichannel, wavelet_levels=1, convert2ycbcr=convert2ycbcr) psnr_denoised_1 = compare_psnr(img, denoised_1) assert_(psnr_denoised > psnr_denoised_1) assert_(psnr_denoised_1 > psnr_noisy) # Test changing noise_std (higher threshold, so less energy in signal) res1 = restoration.denoise_wavelet(noisy, sigma=2*sigma, multichannel=multichannel) res2 = restoration.denoise_wavelet(noisy, sigma=sigma, multichannel=multichannel) assert_(np.sum(res1**2) <= np.sum(res2**2)) def test_wavelet_threshold(): rstate = np.random.RandomState(1234) img = astro_gray sigma = 0.1 noisy = img + sigma * rstate.randn(*(img.shape)) noisy = np.clip(noisy, 0, 1) # employ a single, uniform threshold instead of BayesShrink sigmas denoised = _wavelet_threshold(noisy, wavelet='db1', threshold=sigma) psnr_noisy = compare_psnr(img, noisy) psnr_denoised = compare_psnr(img, denoised) assert_(psnr_denoised > psnr_noisy) def test_wavelet_denoising_nd(): rstate = np.random.RandomState(1234) for ndim in range(1, 5): # Generate a very simple test image img = 0.2*np.ones((16, )*ndim) img[[slice(5, 13), ] * ndim] = 0.8 sigma = 0.1 noisy = img + sigma * rstate.randn(*(img.shape)) noisy = np.clip(noisy, 0, 1) # Verify that SNR is improved with internally estimated sigma denoised = restoration.denoise_wavelet(noisy) psnr_noisy = compare_psnr(img, noisy) psnr_denoised = compare_psnr(img, denoised) assert_(psnr_denoised > psnr_noisy) def test_wavelet_denoising_levels(): rstate = np.random.RandomState(1234) ndim = 2 N = 256 wavelet = 'db1' # Generate a very simple test image img = 0.2*np.ones((N, )*ndim) img[[slice(5, 13), ] * ndim] = 0.8 sigma = 0.1 noisy = img + sigma * rstate.randn(*(img.shape)) noisy = np.clip(noisy, 0, 1) denoised = restoration.denoise_wavelet(noisy, wavelet=wavelet) denoised_1 = restoration.denoise_wavelet(noisy, wavelet=wavelet, wavelet_levels=1) psnr_noisy = compare_psnr(img, noisy) psnr_denoised = compare_psnr(img, denoised) psnr_denoised_1 = compare_psnr(img, denoised_1) # multi-level case should outperform single level case assert_(psnr_denoised > psnr_denoised_1 > psnr_noisy) # invalid number of wavelet levels results in a ValueError max_level = pywt.dwt_max_level(np.min(img.shape), pywt.Wavelet(wavelet).dec_len) assert_raises(ValueError, restoration.denoise_wavelet, noisy, wavelet=wavelet, wavelet_levels=max_level+1) assert_raises(ValueError, restoration.denoise_wavelet, noisy, wavelet=wavelet, wavelet_levels=-1) def test_estimate_sigma_gray(): rstate = np.random.RandomState(1234) # astronaut image img = astro_gray.copy() sigma = 0.1 # add noise to astronaut img += sigma * rstate.standard_normal(img.shape) sigma_est = restoration.estimate_sigma(img, multichannel=False) assert_almost_equal(sigma, sigma_est, decimal=2) def test_estimate_sigma_masked_image(): # Verify computation on an image with a large, noise-free border. # (zero regions will be masked out by _sigma_est_dwt to avoid returning # sigma = 0) rstate = np.random.RandomState(1234) # uniform image img = np.zeros((128, 128)) center_roi = [slice(32, 96), slice(32, 96)] img[center_roi] = 0.8 sigma = 0.1 img[center_roi] = sigma * rstate.standard_normal(img[center_roi].shape) sigma_est = restoration.estimate_sigma(img, multichannel=False) assert_almost_equal(sigma, sigma_est, decimal=1) def test_estimate_sigma_color(): rstate = np.random.RandomState(1234) # astronaut image img = astro.copy() sigma = 0.1 # add noise to astronaut img += sigma * rstate.standard_normal(img.shape) sigma_est = restoration.estimate_sigma(img, multichannel=True, average_sigmas=True) assert_almost_equal(sigma, sigma_est, decimal=2) sigma_list = restoration.estimate_sigma(img, multichannel=True, average_sigmas=False) assert_equal(len(sigma_list), img.shape[-1]) assert_almost_equal(sigma_list[0], sigma_est, decimal=2) # default multichannel=False should raise a warning about last axis size assert_warns(UserWarning, restoration.estimate_sigma, img) def test_wavelet_denoising_args(): """ Some of the functions inside wavelet denoising throw an error the wrong arguments are passed. This protects against that and verifies that all arguments can be passed. """ img = astro noisy = img.copy() + 0.1 * np.random.randn(*(img.shape)) for convert2ycbcr in [True, False]: for multichannel in [True, False]: for sigma in [0.1, [0.1, 0.1, 0.1], None]: if (not multichannel and not convert2ycbcr) or \ (isinstance(sigma, list) and not multichannel): continue restoration.denoise_wavelet(noisy, sigma=sigma, convert2ycbcr=convert2ycbcr, multichannel=multichannel) def test_multichannel_warnings(): img = data.astronaut() assert_warns(UserWarning, restoration.denoise_bilateral, img) assert_warns(UserWarning, restoration.denoise_nl_means, img) if __name__ == "__main__": run_module_suite() scikit-image-0.13.1/skimage/restoration/tests/test_inpaint.py000066400000000000000000000044741316256236300243700ustar00rootroot00000000000000from __future__ import print_function, division import numpy as np from numpy.testing import (run_module_suite, assert_allclose, assert_raises) from skimage.restoration import inpaint def test_inpaint_biharmonic_2d(): img = np.tile(np.square(np.linspace(0, 1, 5)), (5, 1)) mask = np.zeros_like(img) mask[2, 2:] = 1 mask[1, 3:] = 1 mask[0, 4:] = 1 img[np.where(mask)] = 0 out = inpaint.inpaint_biharmonic(img, mask) ref = np.array( [[0., 0.0625, 0.25000000, 0.5625000, 0.73925058], [0., 0.0625, 0.25000000, 0.5478048, 0.76557821], [0., 0.0625, 0.25842878, 0.5623079, 0.85927796], [0., 0.0625, 0.25000000, 0.5625000, 1.00000000], [0., 0.0625, 0.25000000, 0.5625000, 1.00000000]] ) assert_allclose(ref, out) def test_inpaint_biharmonic_3d(): img = np.tile(np.square(np.linspace(0, 1, 5)), (5, 1)) img = np.dstack((img, img.T)) mask = np.zeros_like(img) mask[2, 2:, :] = 1 mask[1, 3:, :] = 1 mask[0, 4:, :] = 1 img[np.where(mask)] = 0 out = inpaint.inpaint_biharmonic(img, mask) ref = np.dstack(( np.array( [[0.0000, 0.0625, 0.25000000, 0.56250000, 0.53752796], [0.0000, 0.0625, 0.25000000, 0.44443780, 0.53762210], [0.0000, 0.0625, 0.23693666, 0.46621112, 0.68615592], [0.0000, 0.0625, 0.25000000, 0.56250000, 1.00000000], [0.0000, 0.0625, 0.25000000, 0.56250000, 1.00000000]]), np.array( [[0.0000, 0.0000, 0.00000000, 0.00000000, 0.19621902], [0.0625, 0.0625, 0.06250000, 0.17470756, 0.30140091], [0.2500, 0.2500, 0.27241289, 0.35155440, 0.43068654], [0.5625, 0.5625, 0.56250000, 0.56250000, 0.56250000], [1.0000, 1.0000, 1.00000000, 1.00000000, 1.00000000]]) )) assert_allclose(ref, out) def test_invalid_input(): img, mask = np.zeros([]), np.zeros([]) assert_raises(ValueError, inpaint.inpaint_biharmonic, img, mask) img, mask = np.zeros((2, 2)), np.zeros((4, 1)) assert_raises(ValueError, inpaint.inpaint_biharmonic, img, mask) img = np.ma.array(np.zeros((2, 2)), mask=[[0, 0], [0, 0]]) mask = np.zeros((2, 2)) assert_raises(TypeError, inpaint.inpaint_biharmonic, img, mask) if __name__ == '__main__': run_module_suite() scikit-image-0.13.1/skimage/restoration/tests/test_restoration.py000066400000000000000000000065251316256236300252760ustar00rootroot00000000000000from os.path import abspath, dirname, join as pjoin import numpy as np from scipy.signal import convolve2d from scipy import ndimage as ndi import skimage from skimage.data import camera from skimage import restoration from skimage.restoration import uft test_img = skimage.img_as_float(camera()) def test_wiener(): psf = np.ones((5, 5)) / 25 data = convolve2d(test_img, psf, 'same') np.random.seed(0) data += 0.1 * data.std() * np.random.standard_normal(data.shape) deconvolved = restoration.wiener(data, psf, 0.05) path = pjoin(dirname(abspath(__file__)), 'camera_wiener.npy') np.testing.assert_allclose(deconvolved, np.load(path), rtol=1e-3) _, laplacian = uft.laplacian(2, data.shape) otf = uft.ir2tf(psf, data.shape, is_real=False) deconvolved = restoration.wiener(data, otf, 0.05, reg=laplacian, is_real=False) np.testing.assert_allclose(np.real(deconvolved), np.load(path), rtol=1e-3) def test_unsupervised_wiener(): psf = np.ones((5, 5)) / 25 data = convolve2d(test_img, psf, 'same') np.random.seed(0) data += 0.1 * data.std() * np.random.standard_normal(data.shape) deconvolved, _ = restoration.unsupervised_wiener(data, psf) path = pjoin(dirname(abspath(__file__)), 'camera_unsup.npy') np.testing.assert_allclose(deconvolved, np.load(path), rtol=1e-3) _, laplacian = uft.laplacian(2, data.shape) otf = uft.ir2tf(psf, data.shape, is_real=False) np.random.seed(0) deconvolved = restoration.unsupervised_wiener( data, otf, reg=laplacian, is_real=False, user_params={"callback": lambda x: None})[0] path = pjoin(dirname(abspath(__file__)), 'camera_unsup2.npy') np.testing.assert_allclose(np.real(deconvolved), np.load(path), rtol=1e-3) def test_image_shape(): """Test that shape of output image in deconvolution is same as input. This addresses issue #1172. """ point = np.zeros((5, 5), np.float) point[2, 2] = 1. psf = ndi.gaussian_filter(point, sigma=1.) # image shape: (45, 45), as reported in #1172 image = skimage.img_as_float(camera()[110:155, 225:270]) # just the face image_conv = ndi.convolve(image, psf) deconv_sup = restoration.wiener(image_conv, psf, 1) deconv_un = restoration.unsupervised_wiener(image_conv, psf)[0] # test the shape np.testing.assert_equal(image.shape, deconv_sup.shape) np.testing.assert_equal(image.shape, deconv_un.shape) # test the reconstruction error sup_relative_error = np.abs(deconv_sup - image) / image un_relative_error = np.abs(deconv_un - image) / image np.testing.assert_array_less(np.median(sup_relative_error), 0.1) np.testing.assert_array_less(np.median(un_relative_error), 0.1) def test_richardson_lucy(): psf = np.ones((5, 5)) / 25 data = convolve2d(test_img, psf, 'same') np.random.seed(0) data += 0.1 * data.std() * np.random.standard_normal(data.shape) deconvolved = restoration.richardson_lucy(data, psf, 5) path = pjoin(dirname(abspath(__file__)), 'camera_rl.npy') np.testing.assert_allclose(deconvolved, np.load(path), rtol=1e-3) if __name__ == '__main__': from numpy import testing testing.run_module_suite() scikit-image-0.13.1/skimage/restoration/tests/test_unwrap.py000066400000000000000000000173711316256236300242420ustar00rootroot00000000000000from __future__ import print_function, division import numpy as np from numpy.testing import (run_module_suite, assert_array_almost_equal_nulp, assert_almost_equal, assert_array_equal, assert_raises, assert_) import warnings from skimage.restoration import unwrap_phase from skimage._shared._warnings import expected_warnings def assert_phase_almost_equal(a, b, *args, **kwargs): """An assert_almost_equal insensitive to phase shifts of n*2*pi.""" shift = 2 * np.pi * np.round((b.mean() - a.mean()) / (2 * np.pi)) with warnings.catch_warnings(): warnings.simplefilter("ignore") print('assert_phase_allclose, abs', np.max(np.abs(a - (b - shift)))) print('assert_phase_allclose, rel', np.max(np.abs((a - (b - shift)) / a))) if np.ma.isMaskedArray(a): assert_(np.ma.isMaskedArray(b)) assert_array_equal(a.mask, b.mask) au = np.asarray(a) bu = np.asarray(b) with warnings.catch_warnings(): warnings.simplefilter("ignore") print('assert_phase_allclose, no mask, abs', np.max(np.abs(au - (bu - shift)))) print('assert_phase_allclose, no mask, rel', np.max(np.abs((au - (bu - shift)) / au))) assert_array_almost_equal_nulp(a + shift, b, *args, **kwargs) def check_unwrap(image, mask=None): image_wrapped = np.angle(np.exp(1j * image)) if mask is not None: print('Testing a masked image') image = np.ma.array(image, mask=mask) image_wrapped = np.ma.array(image_wrapped, mask=mask) image_unwrapped = unwrap_phase(image_wrapped, seed=0) assert_phase_almost_equal(image_unwrapped, image) def test_unwrap_1d(): image = np.linspace(0, 10 * np.pi, 100) check_unwrap(image) # Masked arrays are not allowed in 1D assert_raises(ValueError, check_unwrap, image, True) # wrap_around is not allowed in 1D assert_raises(ValueError, unwrap_phase, image, True, seed=0) def test_unwrap_2d(): x, y = np.ogrid[:8, :16] image = 2 * np.pi * (x * 0.2 + y * 0.1) yield check_unwrap, image mask = np.zeros(image.shape, dtype=np.bool) mask[4:6, 4:8] = True yield check_unwrap, image, mask def test_unwrap_3d(): x, y, z = np.ogrid[:8, :12, :16] image = 2 * np.pi * (x * 0.2 + y * 0.1 + z * 0.05) yield check_unwrap, image mask = np.zeros(image.shape, dtype=np.bool) mask[4:6, 4:6, 1:3] = True yield check_unwrap, image, mask def check_wrap_around(ndim, axis): # create a ramp, but with the last pixel along axis equalling the first elements = 100 ramp = np.linspace(0, 12 * np.pi, elements) ramp[-1] = ramp[0] image = ramp.reshape(tuple([elements if n == axis else 1 for n in range(ndim)])) image_wrapped = np.angle(np.exp(1j * image)) index_first = tuple([0] * ndim) index_last = tuple([-1 if n == axis else 0 for n in range(ndim)]) # unwrap the image without wrap around with warnings.catch_warnings(): # We do not want warnings about length 1 dimensions warnings.simplefilter("ignore") image_unwrap_no_wrap_around = unwrap_phase(image_wrapped, seed=0) print('endpoints without wrap_around:', image_unwrap_no_wrap_around[index_first], image_unwrap_no_wrap_around[index_last]) # without wrap around, the endpoints of the image should differ assert_(abs(image_unwrap_no_wrap_around[index_first] - image_unwrap_no_wrap_around[index_last]) > np.pi) # unwrap the image with wrap around wrap_around = [n == axis for n in range(ndim)] with warnings.catch_warnings(): # We do not want warnings about length 1 dimensions warnings.simplefilter("ignore") image_unwrap_wrap_around = unwrap_phase(image_wrapped, wrap_around, seed=0) print('endpoints with wrap_around:', image_unwrap_wrap_around[index_first], image_unwrap_wrap_around[index_last]) # with wrap around, the endpoints of the image should be equal assert_almost_equal(image_unwrap_wrap_around[index_first], image_unwrap_wrap_around[index_last]) def test_wrap_around(): for ndim in (2, 3): for axis in range(ndim): yield check_wrap_around, ndim, axis def test_mask(): length = 100 ramps = [np.linspace(0, 4 * np.pi, length), np.linspace(0, 8 * np.pi, length), np.linspace(0, 6 * np.pi, length)] image = np.vstack(ramps) mask_1d = np.ones((length,), dtype=np.bool) mask_1d[0] = mask_1d[-1] = False for i in range(len(ramps)): # mask all ramps but the i'th one mask = np.zeros(image.shape, dtype=np.bool) mask |= mask_1d.reshape(1, -1) mask[i, :] = False # unmask i'th ramp image_wrapped = np.ma.array(np.angle(np.exp(1j * image)), mask=mask) image_unwrapped = unwrap_phase(image_wrapped) image_unwrapped -= image_unwrapped[0, 0] # remove phase shift # The end of the unwrapped array should have value equal to the # endpoint of the unmasked ramp assert_array_almost_equal_nulp(image_unwrapped[:, -1], image[i, -1]) assert_(np.ma.isMaskedArray(image_unwrapped)) # Same tests, but forcing use of the 3D unwrapper by reshaping with expected_warnings(['length 1 dimension']): shape = (1,) + image_wrapped.shape image_wrapped_3d = image_wrapped.reshape(shape) image_unwrapped_3d = unwrap_phase(image_wrapped_3d) # remove phase shift image_unwrapped_3d -= image_unwrapped_3d[0, 0, 0] assert_array_almost_equal_nulp(image_unwrapped_3d[:, :, -1], image[i, -1]) def test_invalid_input(): assert_raises(ValueError, unwrap_phase, np.zeros([])) assert_raises(ValueError, unwrap_phase, np.zeros((1, 1, 1, 1))) assert_raises(ValueError, unwrap_phase, np.zeros((1, 1)), 3 * [False]) assert_raises(ValueError, unwrap_phase, np.zeros((1, 1)), 'False') def test_unwrap_3d_middle_wrap_around(): # Segmentation fault in 3D unwrap phase with middle dimension connected # GitHub issue #1171 image = np.zeros((20, 30, 40), dtype=np.float32) unwrap = unwrap_phase(image, wrap_around=[False, True, False]) assert_(np.all(unwrap == 0)) def test_unwrap_2d_compressed_mask(): # ValueError when image is masked array with a compressed mask (no masked # elments). GitHub issue #1346 image = np.ma.zeros((10, 10)) unwrap = unwrap_phase(image) assert_(np.all(unwrap == 0)) def test_unwrap_2d_all_masked(): # Segmentation fault when image is masked array with a all elements masked # GitHub issue #1347 # all elements masked image = np.ma.zeros((10, 10)) image[:] = np.ma.masked unwrap = unwrap_phase(image) assert_(np.ma.isMaskedArray(unwrap)) assert_(np.all(unwrap.mask)) # 1 unmasked element, still zero edges image = np.ma.zeros((10, 10)) image[:] = np.ma.masked image[0, 0] = 0 unwrap = unwrap_phase(image) assert_(np.ma.isMaskedArray(unwrap)) assert_(np.sum(unwrap.mask) == 99) # all but one masked assert_(unwrap[0, 0] == 0) def test_unwrap_3d_all_masked(): # all elements masked image = np.ma.zeros((10, 10, 10)) image[:] = np.ma.masked unwrap = unwrap_phase(image) assert_(np.ma.isMaskedArray(unwrap)) assert_(np.all(unwrap.mask)) # 1 unmasked element, still zero edges image = np.ma.zeros((10, 10, 10)) image[:] = np.ma.masked image[0, 0, 0] = 0 unwrap = unwrap_phase(image) assert_(np.ma.isMaskedArray(unwrap)) assert_(np.sum(unwrap.mask) == 999) # all but one masked assert_(unwrap[0, 0, 0] == 0) if __name__ == "__main__": run_module_suite() scikit-image-0.13.1/skimage/restoration/uft.py000066400000000000000000000304231316256236300213140ustar00rootroot00000000000000# -*- coding: utf-8 -*- # uft.py --- Unitary fourier transform """Function of unitary fourier transform and utilities This module implements the unitary fourier transform, also known as the ortho-normal transform. It is especially useful for convolution [1], as it respects the Parseval equality. The value of the null frequency is equal to .. math:: \frac{1}{\sqrt{n}} \sum_i x_i so the Fourier transform has the same energy as the original image (see ``image_quad_norm`` function). The transform is applied from the last axis for performance (assuming a C-order array input). References ---------- .. [1] B. R. Hunt "A matrix theory proof of the discrete convolution theorem", IEEE Trans. on Audio and Electroacoustics, vol. au-19, no. 4, pp. 285-288, dec. 1971 """ from __future__ import division, print_function import numpy as np __keywords__ = "fft, Fourier Transform, orthonormal, unitary" def ufftn(inarray, dim=None): """N-dimensional unitary Fourier transform. Parameters ---------- inarray : ndarray The array to transform. dim : int, optional The last axis along which to compute the transform. All axes by default. Returns ------- outarray : ndarray (same shape than inarray) The unitary N-D Fourier transform of ``inarray``. Examples -------- >>> input = np.ones((3, 3, 3)) >>> output = ufftn(input) >>> np.allclose(np.sum(input) / np.sqrt(input.size), output[0, 0, 0]) True >>> output.shape (3, 3, 3) """ if dim is None: dim = inarray.ndim outarray = np.fft.fftn(inarray, axes=range(-dim, 0)) return outarray / np.sqrt(np.prod(inarray.shape[-dim:])) def uifftn(inarray, dim=None): """N-dimensional unitary inverse Fourier transform. Parameters ---------- inarray : ndarray The array to transform. dim : int, optional The last axis along which to compute the transform. All axes by default. Returns ------- outarray : ndarray (same shape than inarray) The unitary inverse N-D Fourier transform of ``inarray``. Examples -------- >>> input = np.ones((3, 3, 3)) >>> output = uifftn(input) >>> np.allclose(np.sum(input) / np.sqrt(input.size), output[0, 0, 0]) True >>> output.shape (3, 3, 3) """ if dim is None: dim = inarray.ndim outarray = np.fft.ifftn(inarray, axes=range(-dim, 0)) return outarray * np.sqrt(np.prod(inarray.shape[-dim:])) def urfftn(inarray, dim=None): """N-dimensional real unitary Fourier transform. This transform considers the Hermitian property of the transform on real-valued input. Parameters ---------- inarray : ndarray, shape (M, N, ..., P) The array to transform. dim : int, optional The last axis along which to compute the transform. All axes by default. Returns ------- outarray : ndarray, shape (M, N, ..., P / 2 + 1) The unitary N-D real Fourier transform of ``inarray``. Notes ----- The ``urfft`` functions assume an input array of real values. Consequently, the output has a Hermitian property and redundant values are not computed or returned. Examples -------- >>> input = np.ones((5, 5, 5)) >>> output = urfftn(input) >>> np.allclose(np.sum(input) / np.sqrt(input.size), output[0, 0, 0]) True >>> output.shape (5, 5, 3) """ if dim is None: dim = inarray.ndim outarray = np.fft.rfftn(inarray, axes=range(-dim, 0)) return outarray / np.sqrt(np.prod(inarray.shape[-dim:])) def uirfftn(inarray, dim=None, shape=None): """N-dimensional inverse real unitary Fourier transform. This transform considers the Hermitian property of the transform from complex to real input. Parameters ---------- inarray : ndarray The array to transform. dim : int, optional The last axis along which to compute the transform. All axes by default. shape : tuple of int, optional The shape of the output. The shape of ``rfft`` is ambiguous in case of odd-valued input shape. In this case, this parameter should be provided. See ``np.fft.irfftn``. Returns ------- outarray : ndarray The unitary N-D inverse real Fourier transform of ``inarray``. Notes ----- The ``uirfft`` function assumes that the output array is real-valued. Consequently, the input is assumed to have a Hermitian property and redundant values are implicit. Examples -------- >>> input = np.ones((5, 5, 5)) >>> output = uirfftn(urfftn(input), shape=input.shape) >>> np.allclose(input, output) True >>> output.shape (5, 5, 5) """ if dim is None: dim = inarray.ndim outarray = np.fft.irfftn(inarray, shape, axes=range(-dim, 0)) return outarray * np.sqrt(np.prod(outarray.shape[-dim:])) def ufft2(inarray): """2-dimensional unitary Fourier transform. Compute the Fourier transform on the last 2 axes. Parameters ---------- inarray : ndarray The array to transform. Returns ------- outarray : ndarray (same shape as inarray) The unitary 2-D Fourier transform of ``inarray``. See Also -------- uifft2, ufftn, urfftn Examples -------- >>> input = np.ones((10, 128, 128)) >>> output = ufft2(input) >>> np.allclose(np.sum(input[1, ...]) / np.sqrt(input[1, ...].size), ... output[1, 0, 0]) True >>> output.shape (10, 128, 128) """ return ufftn(inarray, 2) def uifft2(inarray): """2-dimensional inverse unitary Fourier transform. Compute the inverse Fourier transform on the last 2 axes. Parameters ---------- inarray : ndarray The array to transform. Returns ------- outarray : ndarray (same shape as inarray) The unitary 2-D inverse Fourier transform of ``inarray``. See Also -------- uifft2, uifftn, uirfftn Examples -------- >>> input = np.ones((10, 128, 128)) >>> output = uifft2(input) >>> np.allclose(np.sum(input[1, ...]) / np.sqrt(input[1, ...].size), ... output[0, 0, 0]) True >>> output.shape (10, 128, 128) """ return uifftn(inarray, 2) def urfft2(inarray): """2-dimensional real unitary Fourier transform Compute the real Fourier transform on the last 2 axes. This transform considers the Hermitian property of the transform from complex to real-valued input. Parameters ---------- inarray : ndarray, shape (M, N, ..., P) The array to transform. Returns ------- outarray : ndarray, shape (M, N, ..., 2 * (P - 1)) The unitary 2-D real Fourier transform of ``inarray``. See Also -------- ufft2, ufftn, urfftn Examples -------- >>> input = np.ones((10, 128, 128)) >>> output = urfft2(input) >>> np.allclose(np.sum(input[1,...]) / np.sqrt(input[1,...].size), ... output[1, 0, 0]) True >>> output.shape (10, 128, 65) """ return urfftn(inarray, 2) def uirfft2(inarray, shape=None): """2-dimensional inverse real unitary Fourier transform. Compute the real inverse Fourier transform on the last 2 axes. This transform considers the Hermitian property of the transform from complex to real-valued input. Parameters ---------- inarray : ndarray, shape (M, N, ..., P) The array to transform. Returns ------- outarray : ndarray, shape (M, N, ..., 2 * (P - 1)) The unitary 2-D inverse real Fourier transform of ``inarray``. See Also -------- urfft2, uifftn, uirfftn Examples -------- >>> input = np.ones((10, 128, 128)) >>> output = uirfftn(urfftn(input), shape=input.shape) >>> np.allclose(input, output) True >>> output.shape (10, 128, 128) """ return uirfftn(inarray, 2, shape=shape) def image_quad_norm(inarray): """Return the quadratic norm of images in Fourier space. This function detects whether the input image satisfies the Hermitian property. Parameters ---------- inarray : ndarray Input image. The image data should reside in the final two axes. Returns ------- norm : float The quadratic norm of ``inarray``. Examples -------- >>> input = np.ones((5, 5)) >>> image_quad_norm(ufft2(input)) == np.sum(np.abs(input)**2) True >>> image_quad_norm(ufft2(input)) == image_quad_norm(urfft2(input)) True """ # If there is a Hermitian symmetry if inarray.shape[-1] != inarray.shape[-2]: return (2 * np.sum(np.sum(np.abs(inarray) ** 2, axis=-1), axis=-1) - np.sum(np.abs(inarray[..., 0]) ** 2, axis=-1)) else: return np.sum(np.sum(np.abs(inarray) ** 2, axis=-1), axis=-1) def ir2tf(imp_resp, shape, dim=None, is_real=True): """Compute the transfer function of an impulse response (IR). This function makes the necessary correct zero-padding, zero convention, correct fft2, etc... to compute the transfer function of IR. To use with unitary Fourier transform for the signal (ufftn or equivalent). Parameters ---------- imp_resp : ndarray The impulse responses. shape : tuple of int A tuple of integer corresponding to the target shape of the transfer function. dim : int, optional The last axis along which to compute the transform. All axes by default. is_real : boolean (optional, default True) If True, imp_resp is supposed real and the Hermitian property is used with rfftn Fourier transform. Returns ------- y : complex ndarray The transfer function of shape ``shape``. See Also -------- ufftn, uifftn, urfftn, uirfftn Examples -------- >>> np.all(np.array([[4, 0], [0, 0]]) == ir2tf(np.ones((2, 2)), (2, 2))) True >>> ir2tf(np.ones((2, 2)), (512, 512)).shape == (512, 257) True >>> ir2tf(np.ones((2, 2)), (512, 512), is_real=False).shape == (512, 512) True Notes ----- The input array can be composed of multiple-dimensional IR with an arbitrary number of IR. The individual IR must be accessed through the first axes. The last ``dim`` axes contain the space definition. """ if not dim: dim = imp_resp.ndim # Zero padding and fill irpadded = np.zeros(shape) irpadded[tuple([slice(0, s) for s in imp_resp.shape])] = imp_resp # Roll for zero convention of the fft to avoid the phase # problem. Work with odd and even size. for axis, axis_size in enumerate(imp_resp.shape): if axis >= imp_resp.ndim - dim: irpadded = np.roll(irpadded, shift=-int(np.floor(axis_size / 2)), axis=axis) if is_real: return np.fft.rfftn(irpadded, axes=range(-dim, 0)) else: return np.fft.fftn(irpadded, axes=range(-dim, 0)) def laplacian(ndim, shape, is_real=True): """Return the transfer function of the Laplacian. Laplacian is the second order difference, on row and column. Parameters ---------- ndim : int The dimension of the Laplacian. shape : tuple, shape The support on which to compute the transfer function is_real : boolean (optional, default True) If True, imp_resp is assumed to be real-valued and the Hermitian property is used with rfftn Fourier transform to return the transfer function. Returns ------- tf : array_like, complex The transfer function. impr : array_like, real The Laplacian. Examples -------- >>> tf, ir = laplacian(2, (32, 32)) >>> np.all(ir == np.array([[0, -1, 0], [-1, 4, -1], [0, -1, 0]])) True >>> np.all(tf == ir2tf(ir, (32, 32))) True """ impr = np.zeros([3] * ndim) for dim in range(ndim): idx = tuple([slice(1, 2)] * dim + [slice(None)] + [slice(1, 2)] * (ndim - dim - 1)) impr[idx] = np.array([-1.0, 0.0, -1.0]).reshape([-1 if i == dim else 1 for i in range(ndim)]) impr[([slice(1, 2)] * ndim)] = 2.0 * ndim return ir2tf(impr, shape, is_real=is_real), impr scikit-image-0.13.1/skimage/restoration/unwrap.py000066400000000000000000000111471316256236300220340ustar00rootroot00000000000000import numpy as np from six import string_types from .._shared.utils import warn from ._unwrap_1d import unwrap_1d from ._unwrap_2d import unwrap_2d from ._unwrap_3d import unwrap_3d def unwrap_phase(image, wrap_around=False, seed=None): '''Recover the original from a wrapped phase image. From an image wrapped to lie in the interval [-pi, pi), recover the original, unwrapped image. Parameters ---------- image : 1D, 2D or 3D ndarray of floats, optionally a masked array The values should be in the range [-pi, pi). If a masked array is provided, the masked entries will not be changed, and their values will not be used to guide the unwrapping of neighboring, unmasked values. Masked 1D arrays are not allowed, and will raise a `ValueError`. wrap_around : bool or sequence of bool, optional When an element of the sequence is `True`, the unwrapping process will regard the edges along the corresponding axis of the image to be connected and use this connectivity to guide the phase unwrapping process. If only a single boolean is given, it will apply to all axes. Wrap around is not supported for 1D arrays. seed : int, optional Unwrapping 2D or 3D images uses random initialization. This sets the seed of the PRNG to achieve deterministic behavior. Returns ------- image_unwrapped : array_like, double Unwrapped image of the same shape as the input. If the input `image` was a masked array, the mask will be preserved. Raises ------ ValueError If called with a masked 1D array or called with a 1D array and ``wrap_around=True``. Examples -------- >>> c0, c1 = np.ogrid[-1:1:128j, -1:1:128j] >>> image = 12 * np.pi * np.exp(-(c0**2 + c1**2)) >>> image_wrapped = np.angle(np.exp(1j * image)) >>> image_unwrapped = unwrap_phase(image_wrapped) >>> np.std(image_unwrapped - image) < 1e-6 # A constant offset is normal True References ---------- .. [1] Miguel Arevallilo Herraez, David R. Burton, Michael J. Lalor, and Munther A. Gdeisat, "Fast two-dimensional phase-unwrapping algorithm based on sorting by reliability following a noncontinuous path", Journal Applied Optics, Vol. 41, No. 35 (2002) 7437, .. [2] Abdul-Rahman, H., Gdeisat, M., Burton, D., & Lalor, M., "Fast three-dimensional phase-unwrapping algorithm based on sorting by reliability following a non-continuous path. In W. Osten, C. Gorecki, & E. L. Novak (Eds.), Optical Metrology (2005) 32--40, International Society for Optics and Photonics. ''' if image.ndim not in (1, 2, 3): raise ValueError('Image must be 1, 2, or 3 dimensional') if isinstance(wrap_around, bool): wrap_around = [wrap_around] * image.ndim elif (hasattr(wrap_around, '__getitem__') and not isinstance(wrap_around, string_types)): if len(wrap_around) != image.ndim: raise ValueError('Length of `wrap_around` must equal the ' 'dimensionality of image') wrap_around = [bool(wa) for wa in wrap_around] else: raise ValueError('`wrap_around` must be a bool or a sequence with ' 'length equal to the dimensionality of image') if image.ndim == 1: if np.ma.isMaskedArray(image): raise ValueError('1D masked images cannot be unwrapped') if wrap_around[0]: raise ValueError('`wrap_around` is not supported for 1D images') if image.ndim in (2, 3) and 1 in image.shape: warn('Image has a length 1 dimension. Consider using an ' 'array of lower dimensionality to use a more efficient ' 'algorithm') if np.ma.isMaskedArray(image): mask = np.require(np.ma.getmaskarray(image), np.uint8, ['C']) else: mask = np.zeros_like(image, dtype=np.uint8, order='C') image_not_masked = np.asarray( np.ma.getdata(image), dtype=np.double, order='C') image_unwrapped = np.empty_like(image, dtype=np.double, order='C', subok=False) if image.ndim == 1: unwrap_1d(image_not_masked, image_unwrapped) elif image.ndim == 2: unwrap_2d(image_not_masked, mask, image_unwrapped, wrap_around, seed) elif image.ndim == 3: unwrap_3d(image_not_masked, mask, image_unwrapped, wrap_around, seed) if np.ma.isMaskedArray(image): return np.ma.array(image_unwrapped, mask=mask) else: return image_unwrapped scikit-image-0.13.1/skimage/restoration/unwrap_2d_ljmu.c000066400000000000000000000632641316256236300232510ustar00rootroot00000000000000// 2D phase unwrapping, modified for inclusion in scipy by Gregor Thalhammer // Original file name: Miguel_2D_unwrapper_with_mask_and_wrap_around_option.c // This program was written by Munther Gdeisat and Miguel Arevallilo Herraez to // program the two-dimensional unwrapper // entitled "Fast two-dimensional phase-unwrapping algorithm based on sorting by // reliability following a noncontinuous path" // by Miguel Arevallilo Herraez, David R. Burton, Michael J. Lalor, and Munther // A. Gdeisat // published in the Journal Applied Optics, Vol. 41, No. 35, pp. 7437, 2002. // This program was written by Munther Gdeisat, Liverpool John Moores // University, United Kingdom. // Date 26th August 2007 // The wrapped phase map is assumed to be of floating point data type. The // resultant unwrapped phase map is also of floating point type. // The mask is of byte data type. // When the mask is 255 this means that the pixel is valid // When the mask is 0 this means that the pixel is invalid (noisy or corrupted // pixel) // This program takes into consideration the image wrap around problem // encountered in MRI imaging. #include #include #include #include #include #ifndef M_PI #define M_PI 3.1415926535897932384626433832795 #endif #define PI M_PI #define TWOPI (2 * M_PI) // TODO: remove global variables // TODO: make thresholds independent #define NOMASK 0 #define MASK 1 typedef struct { double mod; int x_connectivity; int y_connectivity; int no_of_edges; } params_t; // PIXELM information struct PIXELM { int increment; // No. of 2*pi to add to the pixel to unwrap it int number_of_pixels_in_group; // No. of pixel in the pixel group double value; // value of the pixel double reliability; unsigned char input_mask; // 0 pixel is masked. NOMASK pixel is not masked unsigned char extended_mask; // 0 pixel is masked. NOMASK pixel is not masked int group; // group No. int new_group; struct PIXELM *head; // pointer to the first pixel in the group in the linked // list struct PIXELM *last; // pointer to the last pixel in the group struct PIXELM *next; // pointer to the next pixel in the group }; typedef struct PIXELM PIXELM; // the EDGE is the line that connects two pixels. // if we have S pixels, then we have S horizontal edges and S vertical edges struct EDGE { double reliab; // reliabilty of the edge and it depends on the two pixels PIXELM *pointer_1; // pointer to the first pixel PIXELM *pointer_2; // pointer to the second pixel int increment; // No. of 2*pi to add to one of the pixels to // unwrap it with respect to the second }; typedef struct EDGE EDGE; //---------------start quicker_sort algorithm -------------------------------- #define swap(x, y) \ { \ EDGE t; \ t = x; \ x = y; \ y = t; \ } #define order(x, y) \ if (x.reliab > y.reliab) swap(x, y) #define o2(x, y) order(x, y) #define o3(x, y, z) \ o2(x, y); \ o2(x, z); \ o2(y, z) typedef enum { yes, no } yes_no; yes_no find_pivot(EDGE *left, EDGE *right, double *pivot_ptr) { EDGE a, b, c, *p; a = *left; b = *(left + (right - left) / 2); c = *right; o3(a, b, c); if (a.reliab < b.reliab) { *pivot_ptr = b.reliab; return yes; } if (b.reliab < c.reliab) { *pivot_ptr = c.reliab; return yes; } for (p = left + 1; p <= right; ++p) { if (p->reliab != left->reliab) { *pivot_ptr = (p->reliab < left->reliab) ? left->reliab : p->reliab; return yes; } } return no; } EDGE *partition(EDGE *left, EDGE *right, double pivot) { while (left <= right) { while (left->reliab < pivot) ++left; while (right->reliab >= pivot) --right; if (left < right) { swap(*left, *right); ++left; --right; } } return left; } void quicker_sort(EDGE *left, EDGE *right) { EDGE *p; double pivot; if (find_pivot(left, right, &pivot) == yes) { p = partition(left, right, pivot); quicker_sort(left, p - 1); quicker_sort(p, right); } } //--------------end quicker_sort algorithm ----------------------------------- //--------------------start initialize pixels ---------------------------------- // initialize pixels. See the explination of the pixel class above. // initially every pixel is assumed to belong to a group consisting of only // itself void initialisePIXELs(double *wrapped_image, unsigned char *input_mask, unsigned char *extended_mask, PIXELM *pixel, int image_width, int image_height, char use_seed, unsigned int seed) { PIXELM *pixel_pointer = pixel; double *wrapped_image_pointer = wrapped_image; unsigned char *input_mask_pointer = input_mask; unsigned char *extended_mask_pointer = extended_mask; int i, j; if (use_seed) { srand(seed); } for (i = 0; i < image_height; i++) { for (j = 0; j < image_width; j++) { pixel_pointer->increment = 0; pixel_pointer->number_of_pixels_in_group = 1; pixel_pointer->value = *wrapped_image_pointer; pixel_pointer->reliability = rand(); pixel_pointer->input_mask = *input_mask_pointer; pixel_pointer->extended_mask = *extended_mask_pointer; pixel_pointer->head = pixel_pointer; pixel_pointer->last = pixel_pointer; pixel_pointer->next = NULL; pixel_pointer->new_group = 0; pixel_pointer->group = -1; pixel_pointer++; wrapped_image_pointer++; input_mask_pointer++; extended_mask_pointer++; } } } //-------------------end initialize pixels ----------- // gamma function in the paper double wrap(double pixel_value) { double wrapped_pixel_value; if (pixel_value > PI) wrapped_pixel_value = pixel_value - TWOPI; else if (pixel_value < -PI) wrapped_pixel_value = pixel_value + TWOPI; else wrapped_pixel_value = pixel_value; return wrapped_pixel_value; } // pixelL_value is the left pixel, pixelR_value is the right pixel int find_wrap(double pixelL_value, double pixelR_value) { double difference; int wrap_value; difference = pixelL_value - pixelR_value; if (difference > PI) wrap_value = -1; else if (difference < -PI) wrap_value = 1; else wrap_value = 0; return wrap_value; } void extend_mask(unsigned char *input_mask, unsigned char *extended_mask, int image_width, int image_height, params_t *params) { int i, j; int image_width_plus_one = image_width + 1; int image_width_minus_one = image_width - 1; unsigned char *IMP = input_mask + image_width + 1; // input mask pointer unsigned char *EMP = extended_mask + image_width + 1; // extended mask // pointer // extend the mask for the image except borders for (i = 1; i < image_height - 1; ++i) { for (j = 1; j < image_width - 1; ++j) { if ((*IMP) == NOMASK && (*(IMP + 1) == NOMASK) && (*(IMP - 1) == NOMASK) && (*(IMP + image_width) == NOMASK) && (*(IMP - image_width) == NOMASK) && (*(IMP - image_width_minus_one) == NOMASK) && (*(IMP - image_width_plus_one) == NOMASK) && (*(IMP + image_width_minus_one) == NOMASK) && (*(IMP + image_width_plus_one) == NOMASK)) { *EMP = NOMASK; } ++EMP; ++IMP; } EMP += 2; IMP += 2; } if (params->x_connectivity == 1) { // extend the mask for the right border of the image IMP = input_mask + 2 * image_width - 1; EMP = extended_mask + 2 * image_width - 1; for (i = 1; i < image_height - 1; ++i) { if ((*IMP) == NOMASK && (*(IMP - 1) == NOMASK) && (*(IMP + 1) == NOMASK) && (*(IMP + image_width) == NOMASK) && (*(IMP - image_width) == NOMASK) && (*(IMP - image_width - 1) == NOMASK) && (*(IMP - image_width + 1) == NOMASK) && (*(IMP + image_width - 1) == NOMASK) && (*(IMP - 2 * image_width + 1) == NOMASK)) { *EMP = NOMASK; } EMP += image_width; IMP += image_width; } // extend the mask for the left border of the image IMP = input_mask + image_width; EMP = extended_mask + image_width; for (i = 1; i < image_height - 1; ++i) { if ((*IMP) == NOMASK && (*(IMP - 1) == NOMASK) && (*(IMP + 1) == NOMASK) && (*(IMP + image_width) == NOMASK) && (*(IMP - image_width) == NOMASK) && (*(IMP - image_width + 1) == NOMASK) && (*(IMP + image_width + 1) == NOMASK) && (*(IMP + image_width - 1) == NOMASK) && (*(IMP + 2 * image_width - 1) == NOMASK)) { *EMP = NOMASK; } EMP += image_width; IMP += image_width; } } if (params->y_connectivity == 1) { // extend the mask for the top border of the image IMP = input_mask + 1; EMP = extended_mask + 1; for (i = 1; i < image_width - 1; ++i) { if ((*IMP) == NOMASK && (*(IMP - 1) == NOMASK) && (*(IMP + 1) == NOMASK) && (*(IMP + image_width) == NOMASK) && (*(IMP + image_width * (image_height - 1)) == NOMASK) && (*(IMP + image_width + 1) == NOMASK) && (*(IMP + image_width - 1) == NOMASK) && (*(IMP + image_width * (image_height - 1) - 1) == NOMASK) && (*(IMP + image_width * (image_height - 1) + 1) == NOMASK)) { *EMP = NOMASK; } EMP++; IMP++; } // extend the mask for the bottom border of the image IMP = input_mask + image_width * (image_height - 1) + 1; EMP = extended_mask + image_width * (image_height - 1) + 1; for (i = 1; i < image_width - 1; ++i) { if ((*IMP) == NOMASK && (*(IMP - 1) == NOMASK) && (*(IMP + 1) == NOMASK) && (*(IMP - image_width) == NOMASK) && (*(IMP - image_width - 1) == NOMASK) && (*(IMP - image_width + 1) == NOMASK) && (*(IMP - image_width * (image_height - 1)) == NOMASK) && (*(IMP - image_width * (image_height - 1) - 1) == NOMASK) && (*(IMP - image_width * (image_height - 1) + 1) == NOMASK)) { *EMP = NOMASK; } EMP++; IMP++; } } } void calculate_reliability(double *wrappedImage, PIXELM *pixel, int image_width, int image_height, params_t *params) { int image_width_plus_one = image_width + 1; int image_width_minus_one = image_width - 1; PIXELM *pixel_pointer = pixel + image_width_plus_one; double *WIP = wrappedImage + image_width_plus_one; // WIP is the wrapped image pointer double H, V, D1, D2; int i, j; for (i = 1; i < image_height - 1; ++i) { for (j = 1; j < image_width - 1; ++j) { if (pixel_pointer->extended_mask == NOMASK) { H = wrap(*(WIP - 1) - *WIP) - wrap(*WIP - *(WIP + 1)); V = wrap(*(WIP - image_width) - *WIP) - wrap(*WIP - *(WIP + image_width)); D1 = wrap(*(WIP - image_width_plus_one) - *WIP) - wrap(*WIP - *(WIP + image_width_plus_one)); D2 = wrap(*(WIP - image_width_minus_one) - *WIP) - wrap(*WIP - *(WIP + image_width_minus_one)); pixel_pointer->reliability = H * H + V * V + D1 * D1 + D2 * D2; } pixel_pointer++; WIP++; } pixel_pointer += 2; WIP += 2; } if (params->x_connectivity == 1) { // calculating the reliability for the left border of the image PIXELM *pixel_pointer = pixel + image_width; double *WIP = wrappedImage + image_width; for (i = 1; i < image_height - 1; ++i) { if (pixel_pointer->extended_mask == NOMASK) { H = wrap(*(WIP + image_width - 1) - *WIP) - wrap(*WIP - *(WIP + 1)); V = wrap(*(WIP - image_width) - *WIP) - wrap(*WIP - *(WIP + image_width)); D1 = wrap(*(WIP - 1) - *WIP) - wrap(*WIP - *(WIP + image_width_plus_one)); D2 = wrap(*(WIP - image_width_minus_one) - *WIP) - wrap(*WIP - *(WIP + 2 * image_width - 1)); pixel_pointer->reliability = H * H + V * V + D1 * D1 + D2 * D2; } pixel_pointer += image_width; WIP += image_width; } // calculating the reliability for the right border of the image pixel_pointer = pixel + 2 * image_width - 1; WIP = wrappedImage + 2 * image_width - 1; for (i = 1; i < image_height - 1; ++i) { if (pixel_pointer->extended_mask == NOMASK) { H = wrap(*(WIP - 1) - *WIP) - wrap(*WIP - *(WIP - image_width_minus_one)); V = wrap(*(WIP - image_width) - *WIP) - wrap(*WIP - *(WIP + image_width)); D1 = wrap(*(WIP - image_width_plus_one) - *WIP) - wrap(*WIP - *(WIP + 1)); D2 = wrap(*(WIP - 2 * image_width - 1) - *WIP) - wrap(*WIP - *(WIP + image_width_minus_one)); pixel_pointer->reliability = H * H + V * V + D1 * D1 + D2 * D2; } pixel_pointer += image_width; WIP += image_width; } } if (params->y_connectivity == 1) { // calculating the reliability for the top border of the image PIXELM *pixel_pointer = pixel + 1; double *WIP = wrappedImage + 1; for (i = 1; i < image_width - 1; ++i) { if (pixel_pointer->extended_mask == NOMASK) { H = wrap(*(WIP - 1) - *WIP) - wrap(*WIP - *(WIP + 1)); V = wrap(*(WIP + image_width * (image_height - 1)) - *WIP) - wrap(*WIP - *(WIP + image_width)); D1 = wrap(*(WIP + image_width * (image_height - 1) - 1) - *WIP) - wrap(*WIP - *(WIP + image_width_plus_one)); D2 = wrap(*(WIP + image_width * (image_height - 1) + 1) - *WIP) - wrap(*WIP - *(WIP + image_width_minus_one)); pixel_pointer->reliability = H * H + V * V + D1 * D1 + D2 * D2; } pixel_pointer++; WIP++; } // calculating the reliability for the bottom border of the image pixel_pointer = pixel + (image_height - 1) * image_width + 1; WIP = wrappedImage + (image_height - 1) * image_width + 1; for (i = 1; i < image_width - 1; ++i) { if (pixel_pointer->extended_mask == NOMASK) { H = wrap(*(WIP - 1) - *WIP) - wrap(*WIP - *(WIP + 1)); V = wrap(*(WIP - image_width) - *WIP) - wrap(*WIP - *(WIP - (image_height - 1) * (image_width))); D1 = wrap(*(WIP - image_width_plus_one) - *WIP) - wrap(*WIP - *(WIP - (image_height - 1) * (image_width) + 1)); D2 = wrap(*(WIP - image_width_minus_one) - *WIP) - wrap(*WIP - *(WIP - (image_height - 1) * (image_width) - 1)); pixel_pointer->reliability = H * H + V * V + D1 * D1 + D2 * D2; } pixel_pointer++; WIP++; } } } // calculate the reliability of the horizontal edges of the image // it is calculated by adding the reliability of pixel and the relibility of // its right-hand neighbour // edge is calculated between a pixel and its next neighbour void horizontalEDGEs(PIXELM *pixel, EDGE *edge, int image_width, int image_height, params_t *params) { int i, j; EDGE *edge_pointer = edge; PIXELM *pixel_pointer = pixel; int no_of_edges = params->no_of_edges; for (i = 0; i < image_height; i++) { for (j = 0; j < image_width - 1; j++) { if (pixel_pointer->input_mask == NOMASK && (pixel_pointer + 1)->input_mask == NOMASK) { edge_pointer->pointer_1 = pixel_pointer; edge_pointer->pointer_2 = (pixel_pointer + 1); edge_pointer->reliab = pixel_pointer->reliability + (pixel_pointer + 1)->reliability; edge_pointer->increment = find_wrap(pixel_pointer->value, (pixel_pointer + 1)->value); edge_pointer++; no_of_edges++; } pixel_pointer++; } pixel_pointer++; } // construct edges at the right border of the image if (params->x_connectivity == 1) { pixel_pointer = pixel + image_width - 1; for (i = 0; i < image_height; i++) { if (pixel_pointer->input_mask == NOMASK && (pixel_pointer - image_width + 1)->input_mask == NOMASK) { edge_pointer->pointer_1 = pixel_pointer; edge_pointer->pointer_2 = (pixel_pointer - image_width + 1); edge_pointer->reliab = pixel_pointer->reliability + (pixel_pointer - image_width + 1)->reliability; edge_pointer->increment = find_wrap( pixel_pointer->value, (pixel_pointer - image_width + 1)->value); edge_pointer++; no_of_edges++; } pixel_pointer += image_width; } } params->no_of_edges = no_of_edges; } // calculate the reliability of the vertical edges of the image // it is calculated by adding the reliability of pixel and the relibility of // its lower neighbour in the image. void verticalEDGEs(PIXELM *pixel, EDGE *edge, int image_width, int image_height, params_t *params) { int i, j; int no_of_edges = params->no_of_edges; PIXELM *pixel_pointer = pixel; EDGE *edge_pointer = edge + no_of_edges; for (i = 0; i < image_height - 1; i++) { for (j = 0; j < image_width; j++) { if (pixel_pointer->input_mask == NOMASK && (pixel_pointer + image_width)->input_mask == NOMASK) { edge_pointer->pointer_1 = pixel_pointer; edge_pointer->pointer_2 = (pixel_pointer + image_width); edge_pointer->reliab = pixel_pointer->reliability + (pixel_pointer + image_width)->reliability; edge_pointer->increment = find_wrap( pixel_pointer->value, (pixel_pointer + image_width)->value); edge_pointer++; no_of_edges++; } pixel_pointer++; } // j loop } // i loop // construct edges that connect at the bottom border of the image if (params->y_connectivity == 1) { pixel_pointer = pixel + image_width * (image_height - 1); for (i = 0; i < image_width; i++) { if (pixel_pointer->input_mask == NOMASK && (pixel_pointer - image_width * (image_height - 1))->input_mask == NOMASK) { edge_pointer->pointer_1 = pixel_pointer; edge_pointer->pointer_2 = (pixel_pointer - image_width * (image_height - 1)); edge_pointer->reliab = pixel_pointer->reliability + (pixel_pointer - image_width * (image_height - 1))->reliability; edge_pointer->increment = find_wrap( pixel_pointer->value, (pixel_pointer - image_width * (image_height - 1))->value); edge_pointer++; no_of_edges++; } pixel_pointer++; } } params->no_of_edges = no_of_edges; } // gather the pixels of the image into groups void gatherPIXELs(EDGE *edge, params_t *params) { int k; PIXELM *PIXEL1; PIXELM *PIXEL2; PIXELM *group1; PIXELM *group2; EDGE *pointer_edge = edge; int incremento; for (k = 0; k < params->no_of_edges; k++) { PIXEL1 = pointer_edge->pointer_1; PIXEL2 = pointer_edge->pointer_2; // PIXELM 1 and PIXELM 2 belong to different groups // initially each pixel is a group by it self and one pixel can construct a // group // no else or else if to this if if (PIXEL2->head != PIXEL1->head) { // PIXELM 2 is alone in its group // merge this pixel with PIXELM 1 group and find the number of 2 pi to add // to or subtract to unwrap it if ((PIXEL2->next == NULL) && (PIXEL2->head == PIXEL2)) { PIXEL1->head->last->next = PIXEL2; PIXEL1->head->last = PIXEL2; (PIXEL1->head->number_of_pixels_in_group)++; PIXEL2->head = PIXEL1->head; PIXEL2->increment = PIXEL1->increment - pointer_edge->increment; } // PIXELM 1 is alone in its group // merge this pixel with PIXELM 2 group and find the number of 2 pi to add // to or subtract to unwrap it else if ((PIXEL1->next == NULL) && (PIXEL1->head == PIXEL1)) { PIXEL2->head->last->next = PIXEL1; PIXEL2->head->last = PIXEL1; (PIXEL2->head->number_of_pixels_in_group)++; PIXEL1->head = PIXEL2->head; PIXEL1->increment = PIXEL2->increment + pointer_edge->increment; } // PIXELM 1 and PIXELM 2 both have groups else { group1 = PIXEL1->head; group2 = PIXEL2->head; // if the no. of pixels in PIXELM 1 group is larger than the // no. of pixels in PIXELM 2 group. Merge PIXELM 2 group to // PIXELM 1 group and find the number of wraps between PIXELM 2 // group and PIXELM 1 group to unwrap PIXELM 2 group with respect // to PIXELM 1 group. the no. of wraps will be added to PIXELM 2 // group in the future if (group1->number_of_pixels_in_group > group2->number_of_pixels_in_group) { // merge PIXELM 2 with PIXELM 1 group group1->last->next = group2; group1->last = group2->last; group1->number_of_pixels_in_group = group1->number_of_pixels_in_group + group2->number_of_pixels_in_group; incremento = PIXEL1->increment - pointer_edge->increment - PIXEL2->increment; // merge the other pixels in PIXELM 2 group to PIXELM 1 group while (group2 != NULL) { group2->head = group1; group2->increment += incremento; group2 = group2->next; } } // if the no. of pixels in PIXELM 2 group is larger than the // no. of pixels in PIXELM 1 group. Merge PIXELM 1 group to // PIXELM 2 group and find the number of wraps between PIXELM 2 // group and PIXELM 1 group to unwrap PIXELM 1 group with respect // to PIXELM 2 group. the no. of wraps will be added to PIXELM 1 // group in the future else { // merge PIXELM 1 with PIXELM 2 group group2->last->next = group1; group2->last = group1->last; group2->number_of_pixels_in_group = group2->number_of_pixels_in_group + group1->number_of_pixels_in_group; incremento = PIXEL2->increment + pointer_edge->increment - PIXEL1->increment; // merge the other pixels in PIXELM 2 group to PIXELM 1 group while (group1 != NULL) { group1->head = group2; group1->increment += incremento; group1 = group1->next; } // while } // else } // else } // if pointer_edge++; } } // unwrap the image void unwrapImage(PIXELM *pixel, int image_width, int image_height) { int i; int image_size = image_width * image_height; PIXELM *pixel_pointer = pixel; for (i = 0; i < image_size; i++) { pixel_pointer->value += TWOPI * (double)(pixel_pointer->increment); pixel_pointer++; } } // set the masked pixels (mask = 0) to the minimum of the unwrapper phase void maskImage(PIXELM *pixel, unsigned char *input_mask, int image_width, int image_height) { int image_width_plus_one = image_width + 1; int image_height_plus_one = image_height + 1; int image_width_minus_one = image_width - 1; int image_height_minus_one = image_height - 1; PIXELM *pointer_pixel = pixel; unsigned char *IMP = input_mask; // input mask pointer double min = DBL_MAX; int i; int image_size = image_width * image_height; // find the minimum of the unwrapped phase for (i = 0; i < image_size; i++) { if ((pointer_pixel->value < min) && (*IMP == NOMASK)) min = pointer_pixel->value; pointer_pixel++; IMP++; } pointer_pixel = pixel; IMP = input_mask; // set the masked pixels to minimum for (i = 0; i < image_size; i++) { if ((*IMP) == MASK) { pointer_pixel->value = min; } pointer_pixel++; IMP++; } } // the input to this unwrapper is an array that contains the wrapped // phase map. copy the image on the buffer passed to this unwrapper to // over-write the unwrapped phase map on the buffer of the wrapped // phase map. void returnImage(PIXELM *pixel, double *unwrapped_image, int image_width, int image_height) { int i; int image_size = image_width * image_height; double *unwrapped_image_pointer = unwrapped_image; PIXELM *pixel_pointer = pixel; for (i = 0; i < image_size; i++) { *unwrapped_image_pointer = pixel_pointer->value; pixel_pointer++; unwrapped_image_pointer++; } } // the main function of the unwrapper void unwrap2D(double *wrapped_image, double *UnwrappedImage, unsigned char *input_mask, int image_width, int image_height, int wrap_around_x, int wrap_around_y, char use_seed, unsigned int seed) { params_t params = {TWOPI, wrap_around_x, wrap_around_y, 0}; unsigned char *extended_mask; PIXELM *pixel; EDGE *edge; int image_size = image_height * image_width; int No_of_Edges_initially = 2 * image_width * image_height; extended_mask = (unsigned char *)calloc(image_size, sizeof(unsigned char)); pixel = (PIXELM *)calloc(image_size, sizeof(PIXELM)); edge = (EDGE *)calloc(No_of_Edges_initially, sizeof(EDGE)); extend_mask(input_mask, extended_mask, image_width, image_height, ¶ms); initialisePIXELs(wrapped_image, input_mask, extended_mask, pixel, image_width, image_height, use_seed, seed); calculate_reliability(wrapped_image, pixel, image_width, image_height, ¶ms); horizontalEDGEs(pixel, edge, image_width, image_height, ¶ms); verticalEDGEs(pixel, edge, image_width, image_height, ¶ms); if (params.no_of_edges != 0) { // sort the EDGEs depending on their reiability. The PIXELs with higher // relibility (small value) first quicker_sort(edge, edge + params.no_of_edges - 1); } // gather PIXELs into groups gatherPIXELs(edge, ¶ms); unwrapImage(pixel, image_width, image_height); maskImage(pixel, input_mask, image_width, image_height); // copy the image from PIXELM structure to the unwrapped phase array // passed to this function // TODO: replace by (cython?) function to directly write into numpy array ? returnImage(pixel, UnwrappedImage, image_width, image_height); free(edge); free(pixel); free(extended_mask); } scikit-image-0.13.1/skimage/restoration/unwrap_3d_ljmu.c000066400000000000000000001355431316256236300232520ustar00rootroot00000000000000// 3D phase unwrapping, modified for inclusion in scipy by Gregor Thalhammer // Original file name: Hussein_3D_unwrapper_with_mask_and_wrap_around_option.c // This program was written by Hussein Abdul-Rahman and Munther Gdeisat to // program the three-dimensional phase unwrapper // entitled "Fast three-dimensional phase-unwrapping algorithm based on sorting // by // reliability following a noncontinuous path" // by Hussein Abdul-Rahman, Munther A. Gdeisat, David R. Burton, and Michael J. // Lalor, // published in the Proceedings of SPIE - // The International Society for Optical Engineering, Vol. 5856, No. 1, 2005, // pp. 32-40 // This program was written by Munther Gdeisat, Liverpool John Moores // University, United Kingdom. // Date 31st August 2007 // The wrapped phase volume is assumed to be of floating point data type. The // resultant unwrapped phase volume is also of floating point type. // Read the data from the file frame by frame // The mask is of byte data type. // When the mask is 255 this means that the voxel is valid // When the mask is 0 this means that the voxel is invalid (noisy or corrupted // voxel) // This program takes into consideration the image wrap around problem // encountered in MRI imaging. #include #include #include #include #include #ifndef M_PI #define M_PI 3.1415926535897932384626433832795 #endif #define PI M_PI #define TWOPI (2 * M_PI) #define NOMASK 0 #define MASK 1 typedef struct { double mod; int x_connectivity; int y_connectivity; int z_connectivity; int no_of_edges; } params_t; // VOXELM information struct VOXELM { int increment; // No. of 2*pi to add to the voxel to unwrap it int number_of_voxels_in_group; // No. of voxel in the voxel group double value; // value of the voxel double reliability; unsigned char input_mask; // MASK voxel is masked. NOMASK voxel is not masked unsigned char extended_mask; // MASK voxel is masked. NOMASK voxel is not // masked int group; // group No. int new_group; struct VOXELM *head; // pointer to the first voxel in the group in the linked // list struct VOXELM *last; // pointer to the last voxel in the group struct VOXELM *next; // pointer to the next voxel in the group }; typedef struct VOXELM VOXELM; // the EDGE is the line that connects two voxels. // if we have S voxels, then we have S horizontal edges and S vertical edges struct EDGE { double reliab; // reliabilty of the edge and it depends on the two voxels VOXELM *pointer_1; // pointer to the first voxel VOXELM *pointer_2; // pointer to the second voxel int increment; // No. of 2*pi to add to one of the // voxels to unwrap it with respect to // the second }; typedef struct EDGE EDGE; //---------------start quicker_sort algorithm -------------------------------- #define swap(x, y) \ { \ EDGE t; \ t = x; \ x = y; \ y = t; \ } #define order(x, y) \ if (x.reliab > y.reliab) swap(x, y) #define o2(x, y) order(x, y) #define o3(x, y, z) \ o2(x, y); \ o2(x, z); \ o2(y, z) typedef enum { yes, no } yes_no; yes_no find_pivot(EDGE *left, EDGE *right, double *pivot_ptr) { EDGE a, b, c, *p; a = *left; b = *(left + (right - left) / 2); c = *right; o3(a, b, c); if (a.reliab < b.reliab) { *pivot_ptr = b.reliab; return yes; } if (b.reliab < c.reliab) { *pivot_ptr = c.reliab; return yes; } for (p = left + 1; p <= right; ++p) { if (p->reliab != left->reliab) { *pivot_ptr = (p->reliab < left->reliab) ? left->reliab : p->reliab; return yes; } } return no; } EDGE *partition(EDGE *left, EDGE *right, double pivot) { while (left <= right) { while (left->reliab < pivot) ++left; while (right->reliab >= pivot) --right; if (left < right) { swap(*left, *right); ++left; --right; } } return left; } void quicker_sort(EDGE *left, EDGE *right) { EDGE *p; double pivot; if (find_pivot(left, right, &pivot) == yes) { p = partition(left, right, pivot); quicker_sort(left, p - 1); quicker_sort(p, right); } } //--------------end quicker_sort algorithm ----------------------------------- //--------------------start initialize voxels ---------------------------------- // initiale voxels. See the explanation of the voxel class above. // initially every voxel is assumed to belong to a group consisting of only // itself void initialiseVOXELs(double *WrappedVolume, unsigned char *input_mask, unsigned char *extended_mask, VOXELM *voxel, int volume_width, int volume_height, int volume_depth, char use_seed, unsigned int seed) { VOXELM *voxel_pointer = voxel; double *wrapped_volume_pointer = WrappedVolume; unsigned char *input_mask_pointer = input_mask; unsigned char *extended_mask_pointer = extended_mask; int n, i, j; if (use_seed) { srand(seed); } for (n = 0; n < volume_depth; n++) { for (i = 0; i < volume_height; i++) { for (j = 0; j < volume_width; j++) { voxel_pointer->increment = 0; voxel_pointer->number_of_voxels_in_group = 1; voxel_pointer->value = *wrapped_volume_pointer; voxel_pointer->reliability = rand(); voxel_pointer->input_mask = *input_mask_pointer; voxel_pointer->extended_mask = *extended_mask_pointer; voxel_pointer->head = voxel_pointer; voxel_pointer->last = voxel_pointer; voxel_pointer->next = NULL; voxel_pointer->new_group = 0; voxel_pointer->group = -1; voxel_pointer++; wrapped_volume_pointer++; input_mask_pointer++; extended_mask_pointer++; } } } } //-------------------end initialize voxels ----------- // gamma function in the paper double wrap(double voxel_value) { double wrapped_voxel_value; if (voxel_value > PI) wrapped_voxel_value = voxel_value - TWOPI; else if (voxel_value < -PI) wrapped_voxel_value = voxel_value + TWOPI; else wrapped_voxel_value = voxel_value; return wrapped_voxel_value; } // voxelL_value is the left voxel, voxelR_value is the right voxel int find_wrap(double voxelL_value, double voxelR_value) { double difference; int wrap_value; difference = voxelL_value - voxelR_value; if (difference > PI) wrap_value = -1; else if (difference < -PI) wrap_value = 1; else wrap_value = 0; return wrap_value; } void extend_mask(unsigned char *input_mask, unsigned char *extended_mask, int volume_width, int volume_height, int volume_depth, params_t *params) { int n, i, j; int vw = volume_width, vh = volume_height, vd = volume_depth; int fs = volume_width * volume_height; // frame size int frame_size = volume_width * volume_height; int volume_size = volume_width * volume_height * volume_depth; // volume size int vs = volume_size; unsigned char *IMP = input_mask + frame_size + volume_width + 1; // input mask pointer unsigned char *EMP = extended_mask + frame_size + volume_width + 1; // extended mask pointer // extend the mask for the volume except borders for (n = 1; n < volume_depth - 1; n++) { for (i = 1; i < volume_height - 1; i++) { for (j = 1; j < volume_width - 1; j++) { if ((*IMP) == NOMASK && (*(IMP - 1) == NOMASK) && (*(IMP + 1) == NOMASK) && (*(IMP + vw) == NOMASK) && (*(IMP + vw - 1) == NOMASK) && (*(IMP + vw + 1) == NOMASK) && (*(IMP - vw) == NOMASK) && (*(IMP - vw - 1) == NOMASK) && (*(IMP - vw + 1) == NOMASK) && (*(IMP + fs) == NOMASK) && (*(IMP + fs - 1) == NOMASK) && (*(IMP + fs + 1) == NOMASK) && (*(IMP + fs - vw) == NOMASK) && (*(IMP + fs - vw - 1) == NOMASK) && (*(IMP + fs - vw + 1) == NOMASK) && (*(IMP + fs + vw) == NOMASK) && (*(IMP + fs + vw - 1) == NOMASK) && (*(IMP + fs + vw + 1) == NOMASK) && (*(IMP - fs) == NOMASK) && (*(IMP - fs - 1) == NOMASK) && (*(IMP - fs + 1) == NOMASK) && (*(IMP - fs - vw) == NOMASK) && (*(IMP - fs - vw - 1) == NOMASK) && (*(IMP - fs - vw + 1) == NOMASK) && (*(IMP - fs + vw) == NOMASK) && (*(IMP - fs + vw - 1) == NOMASK) && (*(IMP - fs + vw + 1) == NOMASK)) { *EMP = NOMASK; } ++EMP; ++IMP; } EMP += 2; IMP += 2; } EMP += 2 * volume_width; IMP += 2 * volume_width; } if (params->x_connectivity == 1) { // extend the mask to the front side of the phase volume IMP = input_mask + frame_size + volume_width; // input mask pointer EMP = extended_mask + frame_size + volume_width; // extended mask pointer for (n = 1; n < volume_depth - 1; n++) { for (i = 1; i < volume_height - 1; i++) { if ((*IMP) == NOMASK && (*(IMP + vw - 1) == NOMASK) && (*(IMP + 1) == NOMASK) && (*(IMP - vw) == NOMASK) && (*(IMP + vw) == NOMASK) && (*(IMP - fs) == NOMASK) && (*(IMP + fs) == NOMASK) && (*(IMP - 1) == NOMASK) && (*(IMP + vw + 1) == NOMASK) && (*(IMP - vw + 1) == NOMASK) && (*(IMP + 2 * vw - 1) == NOMASK) && (*(IMP - fs - 1) == NOMASK) && (*(IMP + fs + vw + 1) == NOMASK) && (*(IMP - fs - vw) == NOMASK) && (*(IMP + fs + vw) == NOMASK) && (*(IMP - fs - vw + 1) == NOMASK) && (*(IMP + fs + 2 * vw - 1) == NOMASK) && (*(IMP - fs + vw - 1) == NOMASK) && (*(IMP + fs + 1) == NOMASK) && (*(IMP - fs + 1) == NOMASK) && (*(IMP + fs + vw - 1) == NOMASK) && (*(IMP - fs + 2 * vw - 1) == NOMASK) && (*(IMP + fs - vw + 1) == NOMASK) && (*(IMP - fs + vw) == NOMASK) && (*(IMP + fs - vw) == NOMASK) && (*(IMP - fs + vw + 1) == NOMASK) && (*(IMP + fs - 1) == NOMASK)) { *EMP = NOMASK; } EMP += vw; IMP += vw; } EMP += 2 * vw; IMP += 2 * vw; } // extend the mask to the rear side of the phase volume IMP = input_mask + frame_size + 2 * volume_width - 1; // input mask pointer EMP = extended_mask + frame_size + 2 * volume_width - 1; // extended mask pointer for (n = 1; n < volume_depth - 1; n++) { for (i = 1; i < volume_height - 1; i++) { if ((*IMP) == NOMASK && (*(IMP - vw + 1) == NOMASK) && (*(IMP - 1) == NOMASK) && (*(IMP - vw) == NOMASK) && (*(IMP + vw) == NOMASK) && (*(IMP - fs) == NOMASK) && (*(IMP + fs) == NOMASK) && (*(IMP - vw - 1) == NOMASK) && (*(IMP + 1) == NOMASK) && (*(IMP + vw - 1) == NOMASK) && (*(IMP - 2 * vw + 1) == NOMASK) && (*(IMP - fs - vw - 1) == NOMASK) && (*(IMP + fs + 1) == NOMASK) && (*(IMP - fs - 2 * vw + 1) == NOMASK) && (*(IMP + fs + vw - 1) == NOMASK) && (*(IMP - fs - 1) == NOMASK) && (*(IMP + fs - vw + 1) == NOMASK) && (*(IMP - fs - vw + 1) == NOMASK) && (*(IMP + fs - 1) == NOMASK) && (*(IMP - fs - vw) == NOMASK) && (*(IMP + fs + vw) == NOMASK) && (*(IMP - fs + vw - 1) == NOMASK) && (*(IMP + fs - 2 * vw + 1) == NOMASK) && (*(IMP - fs + vw) == NOMASK) && (*(IMP + fs - vw) == NOMASK) && (*(IMP - fs + 1) == NOMASK) && (*(IMP + fs - vw - 1) == NOMASK)) { *EMP = NOMASK; } EMP += vw; IMP += vw; } EMP += 2 * vw; IMP += 2 * vw; } } if (params->y_connectivity == 1) { // extend the mask to the left side of the phase volume IMP = input_mask + frame_size + 1; EMP = extended_mask + frame_size + 1; for (n = 1; n < volume_depth - 1; n++) { for (j = 1; j < volume_width - 1; j++) { if ((*IMP) == NOMASK && (*(IMP - 1) == NOMASK) && (*(IMP + 1) == NOMASK) && (*(IMP + fs - vw) == NOMASK) && (*(IMP + vw) == NOMASK) && (*(IMP - fs) == NOMASK) && (*(IMP + fs) == NOMASK) && (*(IMP + fs - vw - 1) == NOMASK) && (*(IMP + vw + 1) == NOMASK) && (*(IMP + fs - vw + 1) == NOMASK) && (*(IMP + vw - 1) == NOMASK) && (*(IMP - vw - 1) == NOMASK) && (*(IMP + fs + vw + 1) == NOMASK) && (*(IMP - vw) == NOMASK) && (*(IMP + fs + vw) == NOMASK) && (*(IMP - vw + 1) == NOMASK) && (*(IMP + fs + vw - 1) == NOMASK) && (*(IMP - fs - 1) == NOMASK) && (*(IMP + fs + 1) == NOMASK) && (*(IMP - fs + 1) == NOMASK) && (*(IMP + fs - 1) == NOMASK) && (*(IMP - fs + vw - 1) == NOMASK) && (*(IMP + 2 * fs - vw + 1) == NOMASK) && (*(IMP - fs + vw) == NOMASK) && (*(IMP + 2 * fs - vw) == NOMASK) && (*(IMP - fs + vw + 1) == NOMASK) && (*(IMP + 2 * fs - vw - 1) == NOMASK)) { *EMP = NOMASK; } EMP++; IMP++; } EMP += fs - vw + 2; IMP += fs - vw + 2; } // extend the mask to the right side of the phase volume IMP = input_mask + 2 * frame_size - volume_width + 1; EMP = extended_mask + 2 * frame_size - volume_width + 1; for (n = 1; n < volume_depth - 1; n++) { for (j = 1; j < volume_width - 1; j++) { if ((*IMP) == NOMASK && (*(IMP + 1) == NOMASK) && (*(IMP - 1) == NOMASK) && (*(IMP - vw) == NOMASK) && (*(IMP - fs + vw) == NOMASK) && (*(IMP - fs) == NOMASK) && (*(IMP + fs) == NOMASK) && (*(IMP - vw - 1) == NOMASK) && (*(IMP - fs + vw + 1) == NOMASK) && (*(IMP - vw + 1) == NOMASK) && (*(IMP - fs + vw - 1) == NOMASK) && (*(IMP - fs - vw - 1) == NOMASK) && (*(IMP + vw + 1) == NOMASK) && (*(IMP - fs - vw + 1) == NOMASK) && (*(IMP + vw - 1) == NOMASK) && (*(IMP - fs - vw) == NOMASK) && (*(IMP + vw) == NOMASK) && (*(IMP - fs - 1) == NOMASK) && (*(IMP + fs + 1) == NOMASK) && (*(IMP - fs + 1) == NOMASK) && (*(IMP + fs - 1) == NOMASK) && (*(IMP - 2 * fs + vw - 1) == NOMASK) && (*(IMP + fs - vw + 1) == NOMASK) && (*(IMP - 2 * fs + vw) == NOMASK) && (*(IMP + fs - vw) == NOMASK) && (*(IMP - 2 * fs + vw + 1) == NOMASK) && (*(IMP + fs - vw - 1) == NOMASK)) { *EMP = NOMASK; } EMP++; IMP++; } EMP += fs - vw + 2; IMP += fs - vw + 2; } } if (params->z_connectivity == 1) { // extend the mask to the bottom side of the phase volume IMP = input_mask + volume_width + 1; EMP = extended_mask + volume_width + 1; for (i = 1; i < volume_height - 1; ++i) { for (j = 1; j < volume_width - 1; ++j) { if ((*IMP) == NOMASK && (*(IMP - 1) == NOMASK) && (*(IMP + 1) == NOMASK) && (*(IMP - vw) == NOMASK) && (*(IMP + vw) == NOMASK) && (*(IMP + fs) == NOMASK) && (*(IMP + vs - fs) == NOMASK) && (*(IMP - vw - 1) == NOMASK) && (*(IMP + vw + 1) == NOMASK) && (*(IMP - vw + 1) == NOMASK) && (*(IMP + vw - 1) == NOMASK) && (*(IMP + vs - fs - vw - 1) == NOMASK) && (*(IMP + fs + vw + 1) == NOMASK) && (*(IMP + vs - fs - vw) == NOMASK) && (*(IMP + fs + vw) == NOMASK) && (*(IMP + vs - fs - vw + 1) == NOMASK) && (*(IMP + fs + vw - 1) == NOMASK) && (*(IMP + vs - fs - 1) == NOMASK) && (*(IMP + fs + 1) == NOMASK) && (*(IMP + vs - fs + 1) == NOMASK) && (*(IMP + fs - 1) == NOMASK) && (*(IMP + vs - fs + vw - 1) == NOMASK) && (*(IMP + fs - vw + 1) == NOMASK) && (*(IMP + vs - fs + vw) == NOMASK) && (*(IMP + fs - vw) == NOMASK) && (*(IMP + vs - fs + vw + 1) == NOMASK) && (*(IMP + fs - vw - 1) == NOMASK)) { *EMP = NOMASK; } EMP++; IMP++; } EMP += 2; IMP += 2; } // extend the mask to the top side of the phase volume IMP = input_mask + volume_size - frame_size + volume_width + 1; EMP = extended_mask + volume_size - frame_size + volume_width + 1; for (i = 1; i < volume_height - 1; ++i) { for (j = 1; j < volume_width - 1; ++j) { if ((*IMP) == NOMASK && (*(IMP + 1) == NOMASK) && (*(IMP - 1) == NOMASK) && (*(IMP - vw) == NOMASK) && (*(IMP - fs + vw) == NOMASK) && (*(IMP - fs) == NOMASK) && (*(IMP - vs + fs) == NOMASK) && (*(IMP - vw - 1) == NOMASK) && (*(IMP + vw + 1) == NOMASK) && (*(IMP - vw + 1) == NOMASK) && (*(IMP + vw - 1) == NOMASK) && (*(IMP - fs - vw - 1) == NOMASK) && (*(IMP - vs + fs + vw + 1) == NOMASK) && (*(IMP - fs - vw + 1) == NOMASK) && (*(IMP - vs + fs + vw - 1) == NOMASK) && (*(IMP - fs - vw) == NOMASK) && (*(IMP - vs + fs + vw) == NOMASK) && (*(IMP - fs - 1) == NOMASK) && (*(IMP - vs + fs + 1) == NOMASK) && (*(IMP - fs + 1) == NOMASK) && (*(IMP - vs + fs - 1) == NOMASK) && (*(IMP - fs + vw - 1) == NOMASK) && (*(IMP - vs + fs - vw + 1) == NOMASK) && (*(IMP - fs + vw) == NOMASK) && (*(IMP - vs + fs - vw) == NOMASK) && (*(IMP - fs + vw + 1) == NOMASK) && (*(IMP - vs + fs - vw - 1) == NOMASK)) { *EMP = NOMASK; } EMP++; IMP++; } EMP += 2; IMP += 2; } } } void calculate_reliability(double *wrappedVolume, VOXELM *voxel, int volume_width, int volume_height, int volume_depth, params_t *params) { int frame_size = volume_width * volume_height; int volume_size = volume_width * volume_height * volume_depth; VOXELM *voxel_pointer; double H, V, N, D1, D2, D3, D4, D5, D6, D7, D8, D9, D10; double *WVP; int n, i, j; WVP = wrappedVolume + frame_size + volume_width + 1; voxel_pointer = voxel + frame_size + volume_width + 1; for (n = 1; n < volume_depth - 1; n++) { for (i = 1; i < volume_height - 1; i++) { for (j = 1; j < volume_width - 1; j++) { if (voxel_pointer->extended_mask == NOMASK) { H = wrap(*(WVP - 1) - *WVP) - wrap(*WVP - *(WVP + 1)); V = wrap(*(WVP - volume_width) - *WVP) - wrap(*WVP - *(WVP + volume_width)); N = wrap(*(WVP - frame_size) - *WVP) - wrap(*WVP - *(WVP + frame_size)); D1 = wrap(*(WVP - volume_width - 1) - *WVP) - wrap(*WVP - *(WVP + volume_width + 1)); D2 = wrap(*(WVP - volume_width + 1) - *WVP) - wrap(*WVP - *(WVP + volume_width - 1)); D3 = wrap(*(WVP - frame_size - volume_width - 1) - *WVP) - wrap(*WVP - *(WVP + frame_size + volume_width + 1)); D4 = wrap(*(WVP - frame_size - volume_width) - *WVP) - wrap(*WVP - *(WVP + frame_size + volume_width)); D5 = wrap(*(WVP - frame_size - volume_width + 1) - *WVP) - wrap(*WVP - *(WVP + frame_size + volume_width - 1)); D6 = wrap(*(WVP - frame_size - 1) - *WVP) - wrap(*WVP - *(WVP + frame_size + 1)); D7 = wrap(*(WVP - frame_size + 1) - *WVP) - wrap(*WVP - *(WVP + frame_size - 1)); D8 = wrap(*(WVP - frame_size + volume_width - 1) - *WVP) - wrap(*WVP - *(WVP + frame_size - volume_width + 1)); D9 = wrap(*(WVP - frame_size + volume_width) - *WVP) - wrap(*WVP - *(WVP + frame_size - volume_width)); D10 = wrap(*(WVP - frame_size + volume_width + 1) - *WVP) - wrap(*WVP - *(WVP + frame_size - volume_width - 1)); voxel_pointer->reliability = H * H + V * V + N * N + D1 * D1 + D2 * D2 + D3 * D3 + D4 * D4 + D5 * D5 + D6 * D6 + D7 * D7 + D8 * D8 + D9 * D9 + D10 * D10; } voxel_pointer++; WVP++; } voxel_pointer += 2; WVP += 2; } voxel_pointer += 2 * volume_width; WVP += 2 * volume_width; } if (params->x_connectivity == 1) { // calculating reliability for the front side of the phase volume...add // volume_width WVP = wrappedVolume + frame_size + volume_width; voxel_pointer = voxel + frame_size + volume_width; for (n = 1; n < volume_depth - 1; ++n) { for (i = 1; i < volume_height - 1; ++i) { if (voxel_pointer->extended_mask == NOMASK) { H = wrap(*(WVP + volume_width - 1) - *WVP) - wrap(*WVP - *(WVP + 1)); V = wrap(*(WVP - volume_width) - *WVP) - wrap(*WVP - *(WVP + volume_width)); N = wrap(*(WVP - frame_size) - *WVP) - wrap(*WVP - *(WVP + frame_size)); D1 = wrap(*(WVP - 1) - *WVP) - wrap(*WVP - *(WVP + volume_width + 1)); D2 = wrap(*(WVP - volume_width + 1) - *WVP) - wrap(*WVP - *(WVP + 2 * volume_width - 1)); D3 = wrap(*(WVP - frame_size - 1) - *WVP) - wrap(*WVP - *(WVP + frame_size + volume_width + 1)); D4 = wrap(*(WVP - frame_size - volume_width) - *WVP) - wrap(*WVP - *(WVP + frame_size + volume_width)); D5 = wrap(*(WVP - frame_size - volume_width + 1) - *WVP) - wrap(*WVP - *(WVP + frame_size + 2 * volume_width - 1)); D6 = wrap(*(WVP - frame_size + volume_width - 1) - *WVP) - wrap(*WVP - *(WVP + frame_size + 1)); D7 = wrap(*(WVP - frame_size + 1) - *WVP) - wrap(*WVP - *(WVP + frame_size + volume_width - 1)); D8 = wrap(*(WVP - frame_size + 2 * volume_width - 1) - *WVP) - wrap(*WVP - *(WVP + frame_size - volume_width + 1)); D9 = wrap(*(WVP - frame_size + volume_width) - *WVP) - wrap(*WVP - *(WVP + frame_size - volume_width)); D10 = wrap(*(WVP - frame_size + volume_width + 1) - *WVP) - wrap(*WVP - *(WVP + frame_size - 1)); voxel_pointer->reliability = H * H + V * V + N * N + D1 * D1 + D2 * D2 + D3 * D3 + D4 * D4 + D5 * D5 + D6 * D6 + D7 * D7 + D8 * D8 + D9 * D9 + D10 * D10; } voxel_pointer += volume_width; WVP += volume_width; } voxel_pointer += 2 * volume_width; WVP += 2 * volume_width; } // calculating reliability for the rear side of the phase volume..... // subtract volume_width WVP = wrappedVolume + frame_size + 2 * volume_width - 1; voxel_pointer = voxel + frame_size + 2 * volume_width - 1; for (n = 1; n < volume_depth - 1; ++n) { for (i = 1; i < volume_height - 1; ++i) { if (voxel_pointer->extended_mask == NOMASK) { H = wrap(*(WVP - volume_width + 1) - *WVP) - wrap(*WVP - *(WVP - 1)); V = wrap(*(WVP - volume_width) - *WVP) - wrap(*WVP - *(WVP + volume_width)); N = wrap(*(WVP - frame_size) - *WVP) - wrap(*WVP - *(WVP + frame_size)); D1 = wrap(*(WVP - volume_width - 1) - *WVP) - wrap(*WVP - *(WVP + 1)); D2 = wrap(*(WVP + volume_width - 1) - *WVP) - wrap(*WVP - *(WVP - 2 * volume_width + 1)); D3 = wrap(*(WVP - frame_size - volume_width - 1) - *WVP) - wrap(*WVP - *(WVP + frame_size + 1)); D4 = wrap(*(WVP - frame_size - 2 * volume_width + 1) - *WVP) - wrap(*WVP - *(WVP + frame_size + volume_width - 1)); D5 = wrap(*(WVP - frame_size - 1) - *WVP) - wrap(*WVP - *(WVP + frame_size - volume_width + 1)); D6 = wrap(*(WVP - frame_size - volume_width + 1) - *WVP) - wrap(*WVP - *(WVP + frame_size - 1)); D7 = wrap(*(WVP - frame_size - volume_width) - *WVP) - wrap(*WVP - *(WVP + frame_size + volume_width)); D8 = wrap(*(WVP - frame_size + volume_width - 1) - *WVP) - wrap(*WVP - *(WVP + frame_size - 2 * volume_width + 1)); D9 = wrap(*(WVP - frame_size + volume_width) - *WVP) - wrap(*WVP - *(WVP + frame_size - volume_width)); D10 = wrap(*(WVP - frame_size + 1) - *WVP) - wrap(*WVP - *(WVP + frame_size - volume_width - 1)); voxel_pointer->reliability = H * H + V * V + N * N + D1 * D1 + D2 * D2 + D3 * D3 + D4 * D4 + D5 * D5 + D6 * D6 + D7 * D7 + D8 * D8 + D9 * D9 + D10 * D10; } voxel_pointer += volume_width; WVP += volume_width; } voxel_pointer += 2 * volume_width; WVP += 2 * volume_width; } } if (params->y_connectivity == 1) { // calculating reliability for the left side of the phase volume...add // frame_size WVP = wrappedVolume + frame_size + 1; voxel_pointer = voxel + frame_size + 1; for (n = 1; n < volume_depth - 1; ++n) { for (j = 1; j < volume_width - 1; ++j) { if (voxel_pointer->extended_mask == NOMASK) { H = wrap(*(WVP - 1) - *WVP) - wrap(*WVP - *(WVP + 1)); V = wrap(*(WVP + frame_size - volume_width) - *WVP) - wrap(*WVP - *(WVP + volume_width)); N = wrap(*(WVP - frame_size) - *WVP) - wrap(*WVP - *(WVP + frame_size)); D1 = wrap(*(WVP + frame_size - volume_width - 1) - *WVP) - wrap(*WVP - *(WVP + volume_width + 1)); D2 = wrap(*(WVP + frame_size - volume_width + 1) - *WVP) - wrap(*WVP - *(WVP + volume_width - 1)); D3 = wrap(*(WVP - volume_width - 1) - *WVP) - wrap(*WVP - *(WVP + frame_size + volume_width + 1)); D4 = wrap(*(WVP - volume_width) - *WVP) - wrap(*WVP - *(WVP + frame_size + volume_width)); D5 = wrap(*(WVP - volume_width + 1) - *WVP) - wrap(*WVP - *(WVP + frame_size + volume_width - 1)); D6 = wrap(*(WVP - frame_size - 1) - *WVP) - wrap(*WVP - *(WVP + frame_size + 1)); D7 = wrap(*(WVP - frame_size + 1) - *WVP) - wrap(*WVP - *(WVP + frame_size - 1)); D8 = wrap(*(WVP - frame_size + volume_width - 1) - *WVP) - wrap(*WVP - *(WVP + 2 * frame_size - volume_width + 1)); D9 = wrap(*(WVP - frame_size + volume_width) - *WVP) - wrap(*WVP - *(WVP + 2 * frame_size - volume_width)); D10 = wrap(*(WVP - frame_size + volume_width + 1) - *WVP) - wrap(*WVP - *(WVP + 2 * frame_size - volume_width - 1)); voxel_pointer->reliability = H * H + V * V + N * N + D1 * D1 + D2 * D2 + D3 * D3 + D4 * D4 + D5 * D5 + D6 * D6 + D7 * D7 + D8 * D8 + D9 * D9 + D10 * D10; } voxel_pointer++; WVP++; } voxel_pointer += frame_size - volume_width + 2; WVP += frame_size - volume_width + 2; } // calculating reliability for the right side of the phase volume...subtract // frame_size WVP = wrappedVolume + 2 * frame_size - volume_width + 1; voxel_pointer = voxel + 2 * frame_size - volume_width + 1; for (n = 1; n < volume_depth - 1; ++n) { for (j = 1; j < volume_width - 1; ++j) { if (voxel_pointer->extended_mask == NOMASK) { H = wrap(*(WVP + 1) - *WVP) - wrap(*WVP - *(WVP - 1)); V = wrap(*(WVP - volume_width) - *WVP) - wrap(*WVP - *(WVP - frame_size + volume_width)); N = wrap(*(WVP - frame_size) - *WVP) - wrap(*WVP - *(WVP + frame_size)); D1 = wrap(*(WVP - volume_width - 1) - *WVP) - wrap(*WVP - *(WVP - frame_size + volume_width + 1)); D2 = wrap(*(WVP - volume_width + 1) - *WVP) - wrap(*WVP - *(WVP - frame_size + volume_width - 1)); D3 = wrap(*(WVP - frame_size - volume_width - 1) - *WVP) - wrap(*WVP - *(WVP + volume_width + 1)); D4 = wrap(*(WVP - frame_size - volume_width + 1) - *WVP) - wrap(*WVP - *(WVP + volume_width - 1)); D5 = wrap(*(WVP - frame_size - volume_width) - *WVP) - wrap(*WVP - *(WVP + volume_width)); D6 = wrap(*(WVP - frame_size - 1) - *WVP) - wrap(*WVP - *(WVP + frame_size + 1)); D7 = wrap(*(WVP - frame_size + 1) - *WVP) - wrap(*WVP - *(WVP + frame_size - 1)); D8 = wrap(*(WVP - 2 * frame_size + volume_width - 1) - *WVP) - wrap(*WVP - *(WVP + frame_size - volume_width + 1)); D9 = wrap(*(WVP - 2 * frame_size + volume_width) - *WVP) - wrap(*WVP - *(WVP + frame_size - volume_width)); D10 = wrap(*(WVP - 2 * frame_size + volume_width + 1) - *WVP) - wrap(*WVP - *(WVP + frame_size - volume_width - 1)); voxel_pointer->reliability = H * H + V * V + N * N + D1 * D1 + D2 * D2 + D3 * D3 + D4 * D4 + D5 * D5 + D6 * D6 + D7 * D7 + D8 * D8 + D9 * D9 + D10 * D10; } voxel_pointer++; WVP++; } voxel_pointer += frame_size - volume_width + 2; WVP += frame_size - volume_width + 2; } } if (params->z_connectivity == 1) { // calculating reliability for the bottom side of the phase volume...add // volume_size WVP = wrappedVolume + volume_width + 1; voxel_pointer = voxel + volume_width + 1; for (i = 1; i < volume_height - 1; ++i) { for (j = 1; j < volume_width - 1; ++j) { if (voxel_pointer->extended_mask == NOMASK) { H = wrap(*(WVP - 1) - *WVP) - wrap(*WVP - *(WVP + 1)); V = wrap(*(WVP - volume_width) - *WVP) - wrap(*WVP - *(WVP + volume_width)); N = wrap(*(WVP + frame_size) - *WVP) - wrap(*WVP - *(WVP + volume_size - frame_size)); D1 = wrap(*(WVP - volume_width - 1) - *WVP) - wrap(*WVP - *(WVP + volume_width + 1)); D2 = wrap(*(WVP - volume_width + 1) - *WVP) - wrap(*WVP - *(WVP + volume_width - 1)); D3 = wrap(*(WVP + volume_size - frame_size - volume_width - 1) - *WVP) - wrap(*WVP - *(WVP + frame_size + volume_width + 1)); D4 = wrap(*(WVP + volume_size - frame_size - volume_width) - *WVP) - wrap(*WVP - *(WVP + frame_size + volume_width)); D5 = wrap(*(WVP + volume_size - frame_size - volume_width + 1) - *WVP) - wrap(*WVP - *(WVP + frame_size + volume_width - 1)); D6 = wrap(*(WVP + volume_size - frame_size - 1) - *WVP) - wrap(*WVP - *(WVP + frame_size + 1)); D7 = wrap(*(WVP + volume_size - frame_size + 1) - *WVP) - wrap(*WVP - *(WVP + frame_size - 1)); D8 = wrap(*(WVP + volume_size - frame_size + volume_width - 1) - *WVP) - wrap(*WVP - *(WVP + frame_size - volume_width + 1)); D9 = wrap(*(WVP + volume_size - frame_size + volume_width) - *WVP) - wrap(*WVP - *(WVP + frame_size - volume_width)); D10 = wrap(*(WVP + volume_size - frame_size + volume_width + 1) - *WVP) - wrap(*WVP - *(WVP + frame_size - volume_width - 1)); voxel_pointer->reliability = H * H + V * V + N * N + D1 * D1 + D2 * D2 + D3 * D3 + D4 * D4 + D5 * D5 + D6 * D6 + D7 * D7 + D8 * D8 + D9 * D9 + D10 * D10; } voxel_pointer++; WVP++; } voxel_pointer += 2; WVP += 2; } // calculating reliability for the top side of the phase volume...subtract // volume_size WVP = wrappedVolume + volume_size - frame_size + volume_width + 1; voxel_pointer = voxel + volume_size - frame_size + volume_width + 1; for (i = 1; i < volume_height - 1; ++i) { for (j = 1; j < volume_width - 1; ++j) { if (voxel_pointer->extended_mask == NOMASK) { H = wrap(*(WVP + 1) - *WVP) - wrap(*WVP - *(WVP - 1)); V = wrap(*(WVP - volume_width) - *WVP) - wrap(*WVP - *(WVP + volume_width)); N = wrap(*(WVP - frame_size) - *WVP) - wrap(*WVP - *(WVP - volume_size + frame_size)); D1 = wrap(*(WVP - volume_width - 1) - *WVP) - wrap(*WVP - *(WVP + volume_width + 1)); D2 = wrap(*(WVP - volume_width + 1) - *WVP) - wrap(*WVP - *(WVP + volume_width - 1)); D3 = wrap(*(WVP - frame_size - volume_width - 1) - *WVP) - wrap(*WVP - *(WVP - volume_size + frame_size + volume_width + 1)); D4 = wrap(*(WVP - frame_size - volume_width + 1) - *WVP) - wrap(*WVP - *(WVP - volume_size + frame_size + volume_width - 1)); D5 = wrap(*(WVP - frame_size - volume_width) - *WVP) - wrap(*WVP - *(WVP - volume_size + frame_size + volume_width)); D6 = wrap(*(WVP - frame_size - 1) - *WVP) - wrap(*WVP - *(WVP - volume_size + frame_size + 1)); D7 = wrap(*(WVP - frame_size + 1) - *WVP) - wrap(*WVP - *(WVP - volume_size + frame_size - 1)); D8 = wrap(*(WVP - frame_size + volume_width - 1) - *WVP) - wrap(*WVP - *(WVP - volume_size + frame_size - volume_width + 1)); D9 = wrap(*(WVP - frame_size + volume_width) - *WVP) - wrap(*WVP - *(WVP - volume_size + frame_size - volume_width)); D10 = wrap(*(WVP - frame_size + volume_width + 1) - *WVP) - wrap(*WVP - *(WVP - volume_size + frame_size - volume_width - 1)); voxel_pointer->reliability = H * H + V * V + N * N + D1 * D1 + D2 * D2 + D3 * D3 + D4 * D4 + D5 * D5 + D6 * D6 + D7 * D7 + D8 * D8 + D9 * D9 + D10 * D10; } voxel_pointer++; WVP++; } voxel_pointer += 2; WVP += 2; } } } // calculate the reliability of the horizontal edges of the volume. it // is calculated by adding the reliability of voxel and the relibility // of its right neighbour. edge is calculated between a voxel and its // next neighbour void horizontalEDGEs(VOXELM *voxel, EDGE *edge, int volume_width, int volume_height, int volume_depth, params_t *params) { int n, i, j; EDGE *edge_pointer = edge; VOXELM *voxel_pointer = voxel; int no_of_edges = params->no_of_edges; for (n = 0; n < volume_depth; n++) { for (i = 0; i < volume_height; i++) { for (j = 0; j < volume_width - 1; j++) { if (voxel_pointer->input_mask == NOMASK && (voxel_pointer + 1)->input_mask == NOMASK) { edge_pointer->pointer_1 = voxel_pointer; edge_pointer->pointer_2 = (voxel_pointer + 1); edge_pointer->reliab = voxel_pointer->reliability + (voxel_pointer + 1)->reliability; edge_pointer->increment = find_wrap(voxel_pointer->value, (voxel_pointer + 1)->value); edge_pointer++; no_of_edges++; } voxel_pointer++; } voxel_pointer++; } } if (params->x_connectivity == 1) { voxel_pointer = voxel + volume_width - 1; for (n = 0; n < volume_depth; n++) { for (i = 0; i < volume_height; i++) { if (voxel_pointer->input_mask == NOMASK && (voxel_pointer - volume_width + 1)->input_mask == NOMASK) { edge_pointer->pointer_1 = voxel_pointer; edge_pointer->pointer_2 = (voxel_pointer - volume_width + 1); edge_pointer->reliab = voxel_pointer->reliability + (voxel_pointer - volume_width + 1)->reliability; edge_pointer->increment = find_wrap( voxel_pointer->value, (voxel_pointer - volume_width + 1)->value); edge_pointer++; no_of_edges++; } voxel_pointer += volume_width; } } } params->no_of_edges = no_of_edges; } void verticalEDGEs(VOXELM *voxel, EDGE *edge, int volume_width, int volume_height, int volume_depth, params_t *params) { int n, i, j; int no_of_edges = params->no_of_edges; VOXELM *voxel_pointer = voxel; EDGE *edge_pointer = edge + no_of_edges; int frame_size = volume_width * volume_height; int next_voxel = frame_size - volume_width; for (n = 0; n < volume_depth; n++) { for (i = 0; i < volume_height - 1; i++) { for (j = 0; j < volume_width; j++) { if (voxel_pointer->input_mask == NOMASK && (voxel_pointer + volume_width)->input_mask == NOMASK) { edge_pointer->pointer_1 = voxel_pointer; edge_pointer->pointer_2 = (voxel_pointer + volume_width); edge_pointer->reliab = voxel_pointer->reliability + (voxel_pointer + volume_width)->reliability; edge_pointer->increment = find_wrap( voxel_pointer->value, (voxel_pointer + volume_width)->value); edge_pointer++; no_of_edges++; } voxel_pointer++; } } voxel_pointer += volume_width; } if (params->y_connectivity == 1) { voxel_pointer = voxel + frame_size - volume_width; for (n = 0; n < volume_depth; n++) { for (i = 0; i < volume_width; i++) { if (voxel_pointer->input_mask == NOMASK && (voxel_pointer - next_voxel)->input_mask == NOMASK) { edge_pointer->pointer_1 = voxel_pointer; edge_pointer->pointer_2 = (voxel_pointer - next_voxel); edge_pointer->reliab = voxel_pointer->reliability + (voxel_pointer - next_voxel)->reliability; edge_pointer->increment = find_wrap( voxel_pointer->value, (voxel_pointer - next_voxel)->value); edge_pointer++; no_of_edges++; } voxel_pointer++; } voxel_pointer += next_voxel; } } params->no_of_edges = no_of_edges; } void normalEDGEs(VOXELM *voxel, EDGE *edge, int volume_width, int volume_height, int volume_depth, params_t *params) { int n, i, j; int no_of_edges = params->no_of_edges; int frame_size = volume_width * volume_height; int volume_size = volume_width * volume_height * volume_depth; VOXELM *voxel_pointer = voxel; EDGE *edge_pointer = edge + no_of_edges; int next_voxel = volume_size - frame_size; for (n = 0; n < volume_depth - 1; n++) { for (i = 0; i < volume_height; i++) { for (j = 0; j < volume_width; j++) { if (voxel_pointer->input_mask == NOMASK && (voxel_pointer + frame_size)->input_mask == NOMASK) { edge_pointer->pointer_1 = voxel_pointer; edge_pointer->pointer_2 = (voxel_pointer + frame_size); edge_pointer->reliab = voxel_pointer->reliability + (voxel_pointer + frame_size)->reliability; edge_pointer->increment = find_wrap( voxel_pointer->value, (voxel_pointer + frame_size)->value); edge_pointer++; no_of_edges++; } voxel_pointer++; } } } if (params->z_connectivity == 1) { voxel_pointer = voxel + next_voxel; for (i = 0; i < volume_height; i++) { for (j = 0; j < volume_width; j++) { if (voxel_pointer->input_mask == NOMASK && (voxel_pointer - next_voxel)->input_mask == NOMASK) { edge_pointer->pointer_1 = voxel_pointer; edge_pointer->pointer_2 = (voxel_pointer - next_voxel); edge_pointer->reliab = voxel_pointer->reliability + (voxel_pointer - next_voxel)->reliability; edge_pointer->increment = find_wrap( voxel_pointer->value, (voxel_pointer - next_voxel)->value); edge_pointer++; no_of_edges++; } voxel_pointer++; } } } params->no_of_edges = no_of_edges; } // gather the voxels of the volume into groups void gatherVOXELs(EDGE *edge, params_t *params) { int k; VOXELM *VOXEL1; VOXELM *VOXEL2; VOXELM *group1; VOXELM *group2; EDGE *pointer_edge = edge; int incremento; for (k = 0; k < params->no_of_edges; k++) { VOXEL1 = pointer_edge->pointer_1; VOXEL2 = pointer_edge->pointer_2; // VOXELM 1 and VOXELM 2 belong to different groups // initially each voxel is in a group by itself and one voxel can construct // a group // no else or else if to this if if (VOXEL2->head != VOXEL1->head) { // VOXELM 2 is alone in its group // merge this voxel with VOXELM 1 group and find the number of 2 pi to add // to or subtract to unwrap it if ((VOXEL2->next == NULL) && (VOXEL2->head == VOXEL2)) { VOXEL1->head->last->next = VOXEL2; VOXEL1->head->last = VOXEL2; (VOXEL1->head->number_of_voxels_in_group)++; VOXEL2->head = VOXEL1->head; VOXEL2->increment = VOXEL1->increment - pointer_edge->increment; } // VOXELM 1 is alone in its group // merge this voxel with VOXELM 2 group and find the number of 2 pi to add // to or subtract to unwrap it else if ((VOXEL1->next == NULL) && (VOXEL1->head == VOXEL1)) { VOXEL2->head->last->next = VOXEL1; VOXEL2->head->last = VOXEL1; (VOXEL2->head->number_of_voxels_in_group)++; VOXEL1->head = VOXEL2->head; VOXEL1->increment = VOXEL2->increment + pointer_edge->increment; } // VOXELM 1 and VOXELM 2 both have groups else { group1 = VOXEL1->head; group2 = VOXEL2->head; // if the no. of voxels in VOXELM 1 group is larger than the no. of // voxels // in VOXELM 2 group. Merge VOXELM 2 group to VOXELM 1 group // and find the number of wraps between VOXELM 2 group and VOXELM 1 // group // to unwrap VOXELM 2 group with respect to VOXELM 1 group. // the no. of wraps will be added to VOXELM 2 grop in the future if (group1->number_of_voxels_in_group > group2->number_of_voxels_in_group) { // merge VOXELM 2 with VOXELM 1 group group1->last->next = group2; group1->last = group2->last; group1->number_of_voxels_in_group = group1->number_of_voxels_in_group + group2->number_of_voxels_in_group; incremento = VOXEL1->increment - pointer_edge->increment - VOXEL2->increment; // merge the other voxels in VOXELM 2 group to VOXELM 1 group while (group2 != NULL) { group2->head = group1; group2->increment += incremento; group2 = group2->next; } } // if the no. of voxels in VOXELM 2 group is larger than the no. of // voxels // in VOXELM 1 group. Merge VOXELM 1 group to VOXELM 2 group // and find the number of wraps between VOXELM 2 group and VOXELM 1 // group // to unwrap VOXELM 1 group with respect to VOXELM 2 group. // the no. of wraps will be added to VOXELM 1 grop in the future else { // merge VOXELM 1 with VOXELM 2 group group2->last->next = group1; group2->last = group1->last; group2->number_of_voxels_in_group = group2->number_of_voxels_in_group + group1->number_of_voxels_in_group; incremento = VOXEL2->increment + pointer_edge->increment - VOXEL1->increment; // merge the other voxels in VOXELM 2 group to VOXELM 1 group while (group1 != NULL) { group1->head = group2; group1->increment += incremento; group1 = group1->next; } // while } // else } // else } // if pointer_edge++; } } // unwrap the volume void unwrapVolume(VOXELM *voxel, int volume_width, int volume_height, int volume_depth) { int i; int volume_size = volume_width * volume_height * volume_depth; VOXELM *voxel_pointer = voxel; for (i = 0; i < volume_size; i++) { voxel_pointer->value += TWOPI * (double)(voxel_pointer->increment); voxel_pointer++; } } // set the masked voxels (mask = 0) to the minimum of the unwrapper phase void maskVolume(VOXELM *voxel, unsigned char *input_mask, int volume_width, int volume_height, int volume_depth) { int volume_width_plus_one = volume_width + 1; int volume_height_plus_one = volume_height + 1; int volume_width_minus_one = volume_width - 1; int volume_height_minus_one = volume_height - 1; VOXELM *pointer_voxel = voxel; unsigned char *IMP = input_mask; // input mask pointer double min = DBL_MAX; int i, j; int volume_size = volume_width * volume_height * volume_depth; // find the minimum of the unwrapped phase for (i = 0; i < volume_size; i++) { if ((pointer_voxel->value < min) && (*IMP == NOMASK)) min = pointer_voxel->value; pointer_voxel++; IMP++; } pointer_voxel = voxel; IMP = input_mask; // set the masked voxels to minimum for (i = 0; i < volume_size; i++) { if ((*IMP) == MASK) { pointer_voxel->value = min; } pointer_voxel++; IMP++; } } // the input to this unwrapper is an array that contains the wrapped // phase map. copy the volume on the buffer passed to this unwrapper // to over-write the unwrapped phase map on the buffer of the wrapped // phase map. void returnVolume(VOXELM *voxel, double *unwrappedVolume, int volume_width, int volume_height, int volume_depth) { int i; int volume_size = volume_width * volume_height * volume_depth; double *unwrappedVolume_pointer = unwrappedVolume; VOXELM *voxel_pointer = voxel; for (i = 0; i < volume_size; i++) { *unwrappedVolume_pointer = voxel_pointer->value; voxel_pointer++; unwrappedVolume_pointer++; } } // the main function of the unwrapper void unwrap3D(double *wrapped_volume, double *unwrapped_volume, unsigned char *input_mask, int volume_width, int volume_height, int volume_depth, int wrap_around_x, int wrap_around_y, int wrap_around_z, char use_seed, unsigned int seed) { params_t params = {TWOPI, wrap_around_x, wrap_around_y, wrap_around_z, 0}; unsigned char *extended_mask; VOXELM *voxel; EDGE *edge; int volume_size = volume_height * volume_width * volume_depth; int No_of_Edges_initially = 3 * volume_width * volume_height * volume_depth; extended_mask = (unsigned char *)calloc(volume_size, sizeof(unsigned char)); voxel = (VOXELM *)calloc(volume_size, sizeof(VOXELM)); edge = (EDGE *)calloc(No_of_Edges_initially, sizeof(EDGE)); ; extend_mask(input_mask, extended_mask, volume_width, volume_height, volume_depth, ¶ms); initialiseVOXELs(wrapped_volume, input_mask, extended_mask, voxel, volume_width, volume_height, volume_depth, use_seed, seed); calculate_reliability(wrapped_volume, voxel, volume_width, volume_height, volume_depth, ¶ms); horizontalEDGEs(voxel, edge, volume_width, volume_height, volume_depth, ¶ms); verticalEDGEs(voxel, edge, volume_width, volume_height, volume_depth, ¶ms); normalEDGEs(voxel, edge, volume_width, volume_height, volume_depth, ¶ms); if (params.no_of_edges != 0) { // sort the EDGEs depending on their reiability. The VOXELs with higher // relibility (small value) first quicker_sort(edge, edge + params.no_of_edges - 1); } // gather VOXELs into groups gatherVOXELs(edge, ¶ms); unwrapVolume(voxel, volume_width, volume_height, volume_depth); maskVolume(voxel, input_mask, volume_width, volume_height, volume_depth); // copy the volume from VOXELM structure to the unwrapped phase array passed // to this function returnVolume(voxel, unwrapped_volume, volume_width, volume_height, volume_depth); free(edge); free(voxel); free(extended_mask); } scikit-image-0.13.1/skimage/scripts/000077500000000000000000000000001316256236300172605ustar00rootroot00000000000000scikit-image-0.13.1/skimage/scripts/__init__.py000066400000000000000000000000001316256236300213570ustar00rootroot00000000000000scikit-image-0.13.1/skimage/scripts/skivi000077500000000000000000000001511316256236300203300ustar00rootroot00000000000000#!/usr/bin/env python if __name__ == "__main__": from skimage.scripts import skivi skivi.main() scikit-image-0.13.1/skimage/scripts/skivi.py000066400000000000000000000004031316256236300207540ustar00rootroot00000000000000"""skimage viewer""" def main(): import skimage.io as io import sys if len(sys.argv) != 2: print("Usage: skivi ") sys.exit(-1) io.use_plugin('qt') io.imshow(io.imread(sys.argv[1]), fancy=True) io.show() scikit-image-0.13.1/skimage/segmentation/000077500000000000000000000000001316256236300202665ustar00rootroot00000000000000scikit-image-0.13.1/skimage/segmentation/__init__.py000066400000000000000000000013701316256236300224000ustar00rootroot00000000000000from .random_walker_segmentation import random_walker from .active_contour_model import active_contour from ._felzenszwalb import felzenszwalb from .slic_superpixels import slic from ._quickshift import quickshift from .boundaries import find_boundaries, mark_boundaries from ._clear_border import clear_border from ._join import join_segmentations, relabel_from_one, relabel_sequential from ..morphology import watershed __all__ = ['random_walker', 'active_contour', 'felzenszwalb', 'slic', 'quickshift', 'find_boundaries', 'mark_boundaries', 'clear_border', 'join_segmentations', 'relabel_from_one', 'relabel_sequential', 'watershed'] scikit-image-0.13.1/skimage/segmentation/_clear_border.py000066400000000000000000000051321316256236300234230ustar00rootroot00000000000000import numpy as np from ..measure import label def clear_border(labels, buffer_size=0, bgval=0, in_place=False): """Clear objects connected to the label image border. Parameters ---------- labels : (M[, N[, ..., P]]) array of int or bool Imaging data labels. buffer_size : int, optional The width of the border examined. By default, only objects that touch the outside of the image are removed. bgval : float or int, optional Cleared objects are set to this value. in_place : bool, optional Whether or not to manipulate the labels array in-place. Returns ------- out : (M[, N[, ..., P]]) array Imaging data labels with cleared borders Examples -------- >>> import numpy as np >>> from skimage.segmentation import clear_border >>> labels = np.array([[0, 0, 0, 0, 0, 0, 0, 1, 0], ... [0, 0, 0, 0, 1, 0, 0, 0, 0], ... [1, 0, 0, 1, 0, 1, 0, 0, 0], ... [0, 0, 1, 1, 1, 1, 1, 0, 0], ... [0, 1, 1, 1, 1, 1, 1, 1, 0], ... [0, 0, 0, 0, 0, 0, 0, 0, 0]]) >>> clear_border(labels) array([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 1, 0, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]]) """ image = labels if any( ( buffer_size >= s for s in image.shape)): raise ValueError("buffer size may not be greater than image size") # create borders with buffer_size borders = np.zeros_like(image, dtype=np.bool_) ext = buffer_size + 1 slstart = slice(ext) slend = slice(-ext, None) slices = [slice(s) for s in image.shape] for d in range(image.ndim): slicedim = list(slices) slicedim[d] = slstart borders[slicedim] = True slicedim[d] = slend borders[slicedim] = True # Re-label, in case we are dealing with a binary image # and to get consistent labeling labels = label(image, background=0) number = np.max(labels) + 1 # determine all objects that are connected to borders borders_indices = np.unique(labels[borders]) indices = np.arange(number + 1) # mask all label indices that are connected to borders label_mask = np.in1d(indices, borders_indices) # create mask for pixels to clear mask = label_mask[labels.ravel()].reshape(labels.shape) if not in_place: image = image.copy() # clear border pixels image[mask] = bgval return image scikit-image-0.13.1/skimage/segmentation/_felzenszwalb.py000066400000000000000000000043161316256236300235110ustar00rootroot00000000000000import numpy as np from ._felzenszwalb_cy import _felzenszwalb_cython def felzenszwalb(image, scale=1, sigma=0.8, min_size=20, multichannel=True): """Computes Felsenszwalb's efficient graph based image segmentation. Produces an oversegmentation of a multichannel (i.e. RGB) image using a fast, minimum spanning tree based clustering on the image grid. The parameter ``scale`` sets an observation level. Higher scale means less and larger segments. ``sigma`` is the diameter of a Gaussian kernel, used for smoothing the image prior to segmentation. The number of produced segments as well as their size can only be controlled indirectly through ``scale``. Segment size within an image can vary greatly depending on local contrast. For RGB images, the algorithm uses the euclidean distance between pixels in color space. Parameters ---------- image : (width, height, 3) or (width, height) ndarray Input image. scale : float Free parameter. Higher means larger clusters. sigma : float Width of Gaussian kernel used in preprocessing. min_size : int Minimum component size. Enforced using postprocessing. multichannel : bool, optional (default: True) Whether the last axis of the image is to be interpreted as multiple channels. A value of False, for a 3D image, is not currently supported. Returns ------- segment_mask : (width, height) ndarray Integer mask indicating segment labels. References ---------- .. [1] Efficient graph-based image segmentation, Felzenszwalb, P.F. and Huttenlocher, D.P. International Journal of Computer Vision, 2004 Examples -------- >>> from skimage.segmentation import felzenszwalb >>> from skimage.data import coffee >>> img = coffee() >>> segments = felzenszwalb(img, scale=3.0, sigma=0.95, min_size=5) """ if not multichannel and image.ndim > 2: raise ValueError("This algorithm works only on single or " "multi-channel 2d images. ") image = np.atleast_3d(image) return _felzenszwalb_cython(image, scale=scale, sigma=sigma, min_size=min_size) scikit-image-0.13.1/skimage/segmentation/_felzenszwalb_cy.pyx000066400000000000000000000131101316256236300243640ustar00rootroot00000000000000#cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False #cython: wraparound=False import numpy as np from scipy import ndimage as ndi cimport cython cimport numpy as cnp from ..measure._ccomp cimport find_root, join_trees from ..util import img_as_float from .._shared.utils import warn def _felzenszwalb_cython(image, double scale=1, sigma=0.8, Py_ssize_t min_size=20): """Felzenszwalb's efficient graph based segmentation for single or multiple channels. Produces an oversegmentation of a single or multi-channel image using a fast, minimum spanning tree based clustering on the image grid. The number of produced segments as well as their size can only be controlled indirectly through ``scale``. Segment size within an image can vary greatly depending on local contrast. Parameters ---------- image : (N, M, C) ndarray Input image. scale : float, optional (default 1) Sets the obervation level. Higher means larger clusters. sigma : float, optional (default 0.8) Width of Gaussian smoothing kernel used in preprocessing. Larger sigma gives smother segment boundaries. min_size : int, optional (default 20) Minimum component size. Enforced using postprocessing. Returns ------- segment_mask : (N, M) ndarray Integer mask indicating segment labels. """ if image.shape[2] > 3: warn(RuntimeWarning("Got image with third dimension of %s. This image " "will be interpreted as a multichannel 2d image, " "which may not be intended." % str(image.shape[2]))) image = img_as_float(image) # rescale scale to behave like in reference implementation scale = float(scale) / 255. image = ndi.gaussian_filter(image, sigma=[sigma, sigma, 0]) # compute edge weights in 8 connectivity: down_cost = np.sqrt(np.sum((image[1:, :, :] - image[:-1, :, :]) *(image[1:, :, :] - image[:-1, :, :]), axis=-1)) right_cost = np.sqrt(np.sum((image[:, 1:, :] - image[:, :-1, :]) *(image[:, 1:, :] - image[:, :-1, :]), axis=-1)) dright_cost = np.sqrt(np.sum((image[1:, 1:, :] - image[:-1, :-1, :]) *(image[1:, 1:, :] - image[:-1, :-1, :]), axis=-1)) uright_cost = np.sqrt(np.sum((image[1:, :-1, :] - image[:-1, 1:, :]) *(image[1:, :-1, :] - image[:-1, 1:, :]), axis=-1)) cdef cnp.ndarray[cnp.float_t, ndim=1] costs = np.hstack([ right_cost.ravel(), down_cost.ravel(), dright_cost.ravel(), uright_cost.ravel()]).astype(np.float) # compute edges between pixels: height, width = image.shape[:2] cdef cnp.ndarray[cnp.intp_t, ndim=2] segments \ = np.arange(width * height, dtype=np.intp).reshape(height, width) down_edges = np.c_[segments[1:, :].ravel(), segments[:-1, :].ravel()] right_edges = np.c_[segments[:, 1:].ravel(), segments[:, :-1].ravel()] dright_edges = np.c_[segments[1:, 1:].ravel(), segments[:-1, :-1].ravel()] uright_edges = np.c_[segments[:-1, 1:].ravel(), segments[1:, :-1].ravel()] cdef cnp.ndarray[cnp.intp_t, ndim=2] edges \ = np.vstack([right_edges, down_edges, dright_edges, uright_edges]) # initialize data structures for segment size # and inner cost, then start greedy iteration over edges. edge_queue = np.argsort(costs) edges = np.ascontiguousarray(edges[edge_queue]) costs = np.ascontiguousarray(costs[edge_queue]) cdef cnp.intp_t *segments_p = segments.data cdef cnp.intp_t *edges_p = edges.data cdef cnp.float_t *costs_p = costs.data cdef cnp.ndarray[cnp.intp_t, ndim=1] segment_size \ = np.ones(width * height, dtype=np.intp) # inner cost of segments cdef cnp.ndarray[cnp.float_t, ndim=1] cint = np.zeros(width * height) cdef cnp.intp_t seg0, seg1, seg_new, e cdef float cost, inner_cost0, inner_cost1 cdef Py_ssize_t num_costs = costs.size with nogil: # set costs_p back one. we increase it before we use it # since we might continue before that. costs_p -= 1 for e in range(num_costs): seg0 = find_root(segments_p, edges_p[0]) seg1 = find_root(segments_p, edges_p[1]) edges_p += 2 costs_p += 1 if seg0 == seg1: continue inner_cost0 = cint[seg0] + scale / segment_size[seg0] inner_cost1 = cint[seg1] + scale / segment_size[seg1] if costs_p[0] < min(inner_cost0, inner_cost1): # update size and cost join_trees(segments_p, seg0, seg1) seg_new = find_root(segments_p, seg0) segment_size[seg_new] = segment_size[seg0] + segment_size[seg1] cint[seg_new] = costs_p[0] # postprocessing to remove small segments edges_p = edges.data for e in range(num_costs): seg0 = find_root(segments_p, edges_p[0]) seg1 = find_root(segments_p, edges_p[1]) edges_p += 2 if seg0 == seg1: continue if segment_size[seg0] < min_size or segment_size[seg1] < min_size: join_trees(segments_p, seg0, seg1) seg_new = find_root(segments_p, seg0) segment_size[seg_new] = segment_size[seg0] + segment_size[seg1] # unravel the union find tree flat = segments.ravel() old = np.zeros_like(flat) while (old != flat).any(): old = flat flat = flat[flat] flat = np.unique(flat, return_inverse=True)[1] return flat.reshape((height, width)) scikit-image-0.13.1/skimage/segmentation/_join.py000066400000000000000000000114261316256236300217420ustar00rootroot00000000000000import numpy as np from .._shared.utils import deprecated def join_segmentations(s1, s2): """Return the join of the two input segmentations. The join J of S1 and S2 is defined as the segmentation in which two voxels are in the same segment if and only if they are in the same segment in *both* S1 and S2. Parameters ---------- s1, s2 : numpy arrays s1 and s2 are label fields of the same shape. Returns ------- j : numpy array The join segmentation of s1 and s2. Examples -------- >>> from skimage.segmentation import join_segmentations >>> s1 = np.array([[0, 0, 1, 1], ... [0, 2, 1, 1], ... [2, 2, 2, 1]]) >>> s2 = np.array([[0, 1, 1, 0], ... [0, 1, 1, 0], ... [0, 1, 1, 1]]) >>> join_segmentations(s1, s2) array([[0, 1, 3, 2], [0, 5, 3, 2], [4, 5, 5, 3]]) """ if s1.shape != s2.shape: raise ValueError("Cannot join segmentations of different shape. " + "s1.shape: %s, s2.shape: %s" % (s1.shape, s2.shape)) s1 = relabel_sequential(s1)[0] s2 = relabel_sequential(s2)[0] j = (s2.max() + 1) * s1 + s2 j = relabel_sequential(j)[0] return j @deprecated('relabel_sequential') def relabel_from_one(label_field): """Convert labels in an arbitrary label field to {1, ... number_of_labels}. This function is deprecated, see ``relabel_sequential`` for more. """ return relabel_sequential(label_field, offset=1) def relabel_sequential(label_field, offset=1): """Relabel arbitrary labels to {`offset`, ... `offset` + number_of_labels}. This function also returns the forward map (mapping the original labels to the reduced labels) and the inverse map (mapping the reduced labels back to the original ones). Parameters ---------- label_field : numpy array of int, arbitrary shape An array of labels. offset : int, optional The return labels will start at `offset`, which should be strictly positive. Returns ------- relabeled : numpy array of int, same shape as `label_field` The input label field with labels mapped to {offset, ..., number_of_labels + offset - 1}. forward_map : numpy array of int, shape ``(label_field.max() + 1,)`` The map from the original label space to the returned label space. Can be used to re-apply the same mapping. See examples for usage. inverse_map : 1D numpy array of int, of length offset + number of labels The map from the new label space to the original space. This can be used to reconstruct the original label field from the relabeled one. Notes ----- The label 0 is assumed to denote the background and is never remapped. The forward map can be extremely big for some inputs, since its length is given by the maximum of the label field. However, in most situations, ``label_field.max()`` is much smaller than ``label_field.size``, and in these cases the forward map is guaranteed to be smaller than either the input or output images. Examples -------- >>> from skimage.segmentation import relabel_sequential >>> label_field = np.array([1, 1, 5, 5, 8, 99, 42]) >>> relab, fw, inv = relabel_sequential(label_field) >>> relab array([1, 1, 2, 2, 3, 5, 4]) >>> fw array([0, 1, 0, 0, 0, 2, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5]) >>> inv array([ 0, 1, 5, 8, 42, 99]) >>> (fw[label_field] == relab).all() True >>> (inv[relab] == label_field).all() True >>> relab, fw, inv = relabel_sequential(label_field, offset=5) >>> relab array([5, 5, 6, 6, 7, 9, 8]) """ m = label_field.max() if not np.issubdtype(label_field.dtype, np.int): new_type = np.min_scalar_type(int(m)) label_field = label_field.astype(new_type) m = m.astype(new_type) # Ensures m is an integer labels = np.unique(label_field) labels0 = labels[labels != 0] if m == len(labels0): # nothing to do, already 1...n labels return label_field, labels, labels forward_map = np.zeros(m + 1, int) forward_map[labels0] = np.arange(offset, offset + len(labels0)) if not (labels == 0).any(): labels = np.concatenate(([0], labels)) inverse_map = np.zeros(offset - 1 + len(labels), dtype=np.intp) inverse_map[(offset - 1):] = labels relabeled = forward_map[label_field] return relabeled, forward_map, inverse_map scikit-image-0.13.1/skimage/segmentation/_quickshift.py000066400000000000000000000047701316256236300231610ustar00rootroot00000000000000import numpy as np import scipy.ndimage as ndi from ..util import img_as_float from ..color import rgb2lab from ._quickshift_cy import _quickshift_cython def quickshift(image, ratio=1.0, kernel_size=5, max_dist=10, return_tree=False, sigma=0, convert2lab=True, random_seed=42): """Segments image using quickshift clustering in Color-(x,y) space. Produces an oversegmentation of the image using the quickshift mode-seeking algorithm. Parameters ---------- image : (width, height, channels) ndarray Input image. ratio : float, optional, between 0 and 1 Balances color-space proximity and image-space proximity. Higher values give more weight to color-space. kernel_size : float, optional Width of Gaussian kernel used in smoothing the sample density. Higher means fewer clusters. max_dist : float, optional Cut-off point for data distances. Higher means fewer clusters. return_tree : bool, optional Whether to return the full segmentation hierarchy tree and distances. sigma : float, optional Width for Gaussian smoothing as preprocessing. Zero means no smoothing. convert2lab : bool, optional Whether the input should be converted to Lab colorspace prior to segmentation. For this purpose, the input is assumed to be RGB. random_seed : int, optional Random seed used for breaking ties. Returns ------- segment_mask : (width, height) ndarray Integer mask indicating segment labels. Notes ----- The authors advocate to convert the image to Lab color space prior to segmentation, though this is not strictly necessary. For this to work, the image must be given in RGB format. References ---------- .. [1] Quick shift and kernel methods for mode seeking, Vedaldi, A. and Soatto, S. European Conference on Computer Vision, 2008 """ image = img_as_float(np.atleast_3d(image)) if convert2lab: if image.shape[2] != 3: ValueError("Only RGB images can be converted to Lab space.") image = rgb2lab(image) if kernel_size < 1: raise ValueError("`kernel_size` should be >= 1.") image = ndi.gaussian_filter(image, [sigma, sigma, 0]) image = np.ascontiguousarray(image * ratio) segment_mask = _quickshift_cython( image, kernel_size=kernel_size, max_dist=max_dist, return_tree=return_tree, random_seed=random_seed) return segment_mask scikit-image-0.13.1/skimage/segmentation/_quickshift_cy.pyx000066400000000000000000000122551316256236300240410ustar00rootroot00000000000000#cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False #cython: wraparound=False import numpy as np cimport numpy as cnp from libc.math cimport exp, sqrt, ceil from libc.float cimport DBL_MAX def _quickshift_cython(double[:, :, ::1] image, double kernel_size, double max_dist, bint return_tree, int random_seed): """Segments image using quickshift clustering in Color-(x,y) space. Produces an oversegmentation of the image using the quickshift mode-seeking algorithm. Parameters ---------- image : (width, height, channels) ndarray Input image. kernel_size : float Width of Gaussian kernel used in smoothing the sample density. Higher means fewer clusters. max_dist : float Cut-off point for data distances. Higher means fewer clusters. return_tree : bool Whether to return the full segmentation hierarchy tree and distances. random_seed : int Random seed used for breaking ties. Returns ------- segment_mask : (width, height) ndarray Integer mask indicating segment labels. """ random_state = np.random.RandomState(random_seed) # TODO join orphaned roots? # Some nodes might not have a point of higher density within the # search window. We could do a global search over these in the end. # Reference implementation doesn't do that, though, and it only has # an effect for very high max_dist. # window size for neighboring pixels to consider cdef double inv_kernel_size_sqr = -0.5 / kernel_size**2 cdef int kernel_width = ceil(3 * kernel_size) cdef Py_ssize_t height = image.shape[0] cdef Py_ssize_t width = image.shape[1] cdef Py_ssize_t channels = image.shape[2] cdef double[:, ::1] densities = np.zeros((height, width), dtype=np.double) cdef double current_density, closest, dist cdef Py_ssize_t r, c, r_, c_, channel, r_min, r_max, c_min, c_max cdef double* current_pixel_ptr # compute densities with nogil: current_pixel_ptr = &image[0, 0, 0] for r in range(height): r_min = max(r - kernel_width, 0) r_max = min(r + kernel_width + 1, height) for c in range(width): c_min = max(c - kernel_width, 0) c_max = min(c + kernel_width + 1, width) for r_ in range(r_min, r_max): for c_ in range(c_min, c_max): dist = 0 for channel in range(channels): dist += (current_pixel_ptr[channel] - image[r_, c_, channel])**2 dist += (r - r_)**2 + (c - c_)**2 densities[r, c] += exp(dist * inv_kernel_size_sqr) current_pixel_ptr += channels # this will break ties that otherwise would give us headache densities += random_state.normal(scale=0.00001, size=(height, width)) # default parent to self cdef Py_ssize_t[:, ::1] parent = \ np.arange(width * height, dtype=np.intp).reshape(height, width) cdef double[:, ::1] dist_parent = np.zeros((height, width), dtype=np.double) # find nearest node with higher density with nogil: current_pixel_ptr = &image[0, 0, 0] for r in range(height): r_min = max(r - kernel_width, 0) r_max = min(r + kernel_width + 1, height) for c in range(width): current_density = densities[r, c] closest = DBL_MAX c_min = max(c - kernel_width, 0) c_max = min(c + kernel_width + 1, width) for r_ in range(r_min, r_max): for c_ in range(c_min, c_max): if densities[r_, c_] > current_density: dist = 0 # We compute the distances twice since otherwise # we get crazy memory overhead # (width * height * windowsize**2) for channel in range(channels): dist += (current_pixel_ptr[channel] - image[r_, c_, channel])**2 dist += (r - r_)**2 + (c - c_)**2 if dist < closest: closest = dist parent[r, c] = r_ * width + c_ dist_parent[r, c] = sqrt(closest) current_pixel_ptr += channels dist_parent_flat = np.array(dist_parent).ravel() parent_flat = np.array(parent).ravel() # remove parents with distance > max_dist too_far = dist_parent_flat > max_dist parent_flat[too_far] = np.arange(width * height)[too_far] old = np.zeros_like(parent_flat) # flatten forest (mark each pixel with root of corresponding tree) while (old != parent_flat).any(): old = parent_flat parent_flat = parent_flat[parent_flat] parent_flat = np.unique(parent_flat, return_inverse=True)[1] parent_flat = parent_flat.reshape(height, width) if return_tree: return parent_flat, parent, dist_parent return parent_flat scikit-image-0.13.1/skimage/segmentation/_slic.pyx000066400000000000000000000270421316256236300221260ustar00rootroot00000000000000#cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False #cython: wraparound=False from libc.float cimport DBL_MAX from cpython cimport bool import numpy as np cimport numpy as cnp from ..util import regular_grid def _slic_cython(double[:, :, :, ::1] image_zyx, double[:, ::1] segments, float step, Py_ssize_t max_iter, double[::1] spacing, bint slic_zero): """Helper function for SLIC segmentation. Parameters ---------- image_zyx : 4D array of double, shape (Z, Y, X, C) The input image. segments : 2D array of double, shape (N, 3 + C) The initial centroids obtained by SLIC as [Z, Y, X, C...]. step : double The size of the step between two seeds in voxels. max_iter : int The maximum number of k-means iterations. spacing : 1D array of double, shape (3,) The voxel spacing along each image dimension. This parameter controls the weights of the distances along z, y, and x during k-means clustering. slic_zero : bool True to run SLIC-zero, False to run original SLIC. Returns ------- nearest_segments : 3D array of int, shape (Z, Y, X) The label field/superpixels found by SLIC. Notes ----- The image is considered to be in (z, y, x) order, which can be surprising. More commonly, the order (x, y, z) is used. However, in 3D image analysis, 'z' is usually the "special" dimension, with, for example, a different effective resolution than the other two axes. Therefore, x and y are often processed together, or viewed as a cut-plane through the volume. So, if the order was (x, y, z) and we wanted to look at the 5th cut plane, we would write:: my_z_plane = img3d[:, :, 5] but, assuming a C-contiguous array, this would grab a discontiguous slice of memory, which is bad for performance. In contrast, if we see the image as (z, y, x) ordered, we would do:: my_z_plane = img3d[5] and get back a contiguous block of memory. This is better both for performance and for readability. """ # initialize on grid cdef Py_ssize_t depth, height, width depth = image_zyx.shape[0] height = image_zyx.shape[1] width = image_zyx.shape[2] cdef Py_ssize_t n_segments = segments.shape[0] # number of features [X, Y, Z, ...] cdef Py_ssize_t n_features = segments.shape[1] # approximate grid size for desired n_segments cdef Py_ssize_t step_z, step_y, step_x slices = regular_grid((depth, height, width), n_segments) step_z, step_y, step_x = [int(s.step if s.step is not None else 1) for s in slices] cdef Py_ssize_t[:, :, ::1] nearest_segments \ = np.empty((depth, height, width), dtype=np.intp) cdef double[:, :, ::1] distance \ = np.empty((depth, height, width), dtype=np.double) cdef Py_ssize_t[::1] n_segment_elems = np.zeros(n_segments, dtype=np.intp) cdef Py_ssize_t i, c, k, x, y, z, x_min, x_max, y_min, y_max, z_min, z_max cdef char change cdef double dist_center, cx, cy, cz, dy, dz cdef double sz, sy, sx sz = spacing[0] sy = spacing[1] sx = spacing[2] # The colors are scaled before being passed to _slic_cython so # max_color_sq can be initialised as all ones cdef double[::1] max_dist_color = np.ones(n_segments, dtype=np.double) cdef double dist_color # The reference implementation (Achanta et al.) calls this invxywt cdef double spatial_weight = float(1) / (step ** 2) with nogil: for i in range(max_iter): change = 0 distance[:, :, :] = DBL_MAX # assign pixels to segments for k in range(n_segments): # segment coordinate centers cz = segments[k, 0] cy = segments[k, 1] cx = segments[k, 2] # compute windows z_min = max(cz - 2 * step_z, 0) z_max = min(cz + 2 * step_z + 1, depth) y_min = max(cy - 2 * step_y, 0) y_max = min(cy + 2 * step_y + 1, height) x_min = max(cx - 2 * step_x, 0) x_max = min(cx + 2 * step_x + 1, width) for z in range(z_min, z_max): dz = (sz * (cz - z)) ** 2 for y in range(y_min, y_max): dy = (sy * (cy - y)) ** 2 for x in range(x_min, x_max): dist_center = (dz + dy + (sx * (cx - x)) ** 2) * spatial_weight dist_color = 0 for c in range(3, n_features): dist_color += (image_zyx[z, y, x, c - 3] - segments[k, c]) ** 2 if slic_zero: dist_center += dist_color / max_dist_color[k] else: dist_center += dist_color if distance[z, y, x] > dist_center: nearest_segments[z, y, x] = k distance[z, y, x] = dist_center change = 1 # stop if no pixel changed its segment if change == 0: break # recompute segment centers # sum features for all segments n_segment_elems[:] = 0 segments[:, :] = 0 for z in range(depth): for y in range(height): for x in range(width): k = nearest_segments[z, y, x] n_segment_elems[k] += 1 segments[k, 0] += z segments[k, 1] += y segments[k, 2] += x for c in range(3, n_features): segments[k, c] += image_zyx[z, y, x, c - 3] # divide by number of elements per segment to obtain mean for k in range(n_segments): for c in range(n_features): segments[k, c] /= n_segment_elems[k] # If in SLICO mode, update the color distance maxima if slic_zero: for z in range(depth): for y in range(height): for x in range(width): k = nearest_segments[z, y, x] dist_color = 0 for c in range(3, n_features): dist_color += (image_zyx[z, y, x, c - 3] - segments[k, c]) ** 2 # The reference implementation seems to only change # the color if it increases from previous iteration if max_dist_color[k] < dist_color: max_dist_color[k] = dist_color return np.asarray(nearest_segments) def _enforce_label_connectivity_cython(Py_ssize_t[:, :, ::1] segments, Py_ssize_t n_segments, Py_ssize_t min_size, Py_ssize_t max_size): """ Helper function to remove small disconnected regions from the labels Parameters ---------- segments : 3D array of int, shape (Z, Y, X) The label field/superpixels found by SLIC. n_segments: int Number of specified segments min_size: int Minimum size of the segment max_size: int Maximum size of the segment. This is done for performance reasons, to pre-allocate a sufficiently large array for the breadth first search Returns ------- connected_segments : 3D array of int, shape (Z, Y, X) A label field with connected labels starting at label=1 """ # get image dimensions cdef Py_ssize_t depth, height, width depth = segments.shape[0] height = segments.shape[1] width = segments.shape[2] # neighborhood arrays cdef Py_ssize_t[::1] ddx = np.array((1, -1, 0, 0, 0, 0), dtype=np.intp) cdef Py_ssize_t[::1] ddy = np.array((0, 0, 1, -1, 0, 0), dtype=np.intp) cdef Py_ssize_t[::1] ddz = np.array((0, 0, 0, 0, 1, -1), dtype=np.intp) # new object with connected segments initialized to -1 cdef Py_ssize_t[:, :, ::1] connected_segments \ = -1 * np.ones_like(segments, dtype=np.intp) cdef Py_ssize_t current_new_label = 0 cdef Py_ssize_t label = 0 # variables for the breadth first search cdef Py_ssize_t current_segment_size = 1 cdef Py_ssize_t bfs_visited = 0 cdef Py_ssize_t adjacent cdef Py_ssize_t zz, yy, xx cdef Py_ssize_t[:, ::1] coord_list = np.zeros((max_size, 3), dtype=np.intp) with nogil: for z in range(depth): for y in range(height): for x in range(width): if connected_segments[z, y, x] >= 0: continue # find the component size adjacent = 0 label = segments[z, y, x] connected_segments[z, y, x] = current_new_label current_segment_size = 1 bfs_visited = 0 coord_list[bfs_visited, 0] = z coord_list[bfs_visited, 1] = y coord_list[bfs_visited, 2] = x #perform a breadth first search to find # the size of the connected component while bfs_visited < current_segment_size < max_size: for i in range(6): zz = coord_list[bfs_visited, 0] + ddz[i] yy = coord_list[bfs_visited, 1] + ddy[i] xx = coord_list[bfs_visited, 2] + ddx[i] if (0 <= xx < width and 0 <= yy < height and 0 <= zz < depth): if (segments[zz, yy, xx] == label and connected_segments[zz, yy, xx] == -1): connected_segments[zz, yy, xx] = \ current_new_label coord_list[current_segment_size, 0] = zz coord_list[current_segment_size, 1] = yy coord_list[current_segment_size, 2] = xx current_segment_size += 1 if current_segment_size >= max_size: break elif (connected_segments[zz, yy, xx] >= 0 and connected_segments[zz, yy, xx] != current_new_label): adjacent = connected_segments[zz, yy, xx] bfs_visited += 1 # change to an adjacent one, like in the original paper if current_segment_size < min_size: for i in range(current_segment_size): connected_segments[coord_list[i, 0], coord_list[i, 1], coord_list[i, 2]] = adjacent else: current_new_label += 1 return np.asarray(connected_segments) scikit-image-0.13.1/skimage/segmentation/active_contour_model.py000066400000000000000000000203071316256236300250460ustar00rootroot00000000000000import numpy as np from skimage import img_as_float import scipy import scipy.linalg from scipy.interpolate import RectBivariateSpline, interp2d from skimage.filters import sobel def active_contour(image, snake, alpha=0.01, beta=0.1, w_line=0, w_edge=1, gamma=0.01, bc='periodic', max_px_move=1.0, max_iterations=2500, convergence=0.1): """Active contour model. Active contours by fitting snakes to features of images. Supports single and multichannel 2D images. Snakes can be periodic (for segmentation) or have fixed and/or free ends. The output snake has the same length as the input boundary. As the number of points is constant, make sure that the initial snake has enough points to capture the details of the final contour. Parameters ---------- image : (N, M) or (N, M, 3) ndarray Input image. snake : (N, 2) ndarray Initialisation coordinates of snake. For periodic snakes, it should not include duplicate endpoints. alpha : float, optional Snake length shape parameter. Higher values makes snake contract faster. beta : float, optional Snake smoothness shape parameter. Higher values makes snake smoother. w_line : float, optional Controls attraction to brightness. Use negative values to attract to dark regions. w_edge : float, optional Controls attraction to edges. Use negative values to repel snake from edges. gamma : float, optional Explicit time stepping parameter. bc : {'periodic', 'free', 'fixed'}, optional Boundary conditions for worm. 'periodic' attaches the two ends of the snake, 'fixed' holds the end-points in place, and'free' allows free movement of the ends. 'fixed' and 'free' can be combined by parsing 'fixed-free', 'free-fixed'. Parsing 'fixed-fixed' or 'free-free' yields same behaviour as 'fixed' and 'free', respectively. max_px_move : float, optional Maximum pixel distance to move per iteration. max_iterations : int, optional Maximum iterations to optimize snake shape. convergence: float, optional Convergence criteria. Returns ------- snake : (N, 2) ndarray Optimised snake, same shape as input parameter. References ---------- .. [1] Kass, M.; Witkin, A.; Terzopoulos, D. "Snakes: Active contour models". International Journal of Computer Vision 1 (4): 321 (1988). Examples -------- >>> from skimage.draw import circle_perimeter >>> from skimage.filters import gaussian Create and smooth image: >>> img = np.zeros((100, 100)) >>> rr, cc = circle_perimeter(35, 45, 25) >>> img[rr, cc] = 1 >>> img = gaussian(img, 2) Initiliaze spline: >>> s = np.linspace(0, 2*np.pi,100) >>> init = 50*np.array([np.cos(s), np.sin(s)]).T+50 Fit spline to image: >>> snake = active_contour(img, init, w_edge=0, w_line=1) #doctest: +SKIP >>> dist = np.sqrt((45-snake[:, 0])**2 +(35-snake[:, 1])**2) #doctest: +SKIP >>> int(np.mean(dist)) #doctest: +SKIP 25 """ split_version = scipy.__version__.split('.') if not(split_version[-1].isdigit()): split_version.pop() scipy_version = list(map(int, split_version)) new_scipy = scipy_version[0] > 0 or \ (scipy_version[0] == 0 and scipy_version[1] >= 14) if not new_scipy: raise NotImplementedError('You are using an old version of scipy. ' 'Active contours is implemented for scipy versions ' '0.14.0 and above.') max_iterations = int(max_iterations) if max_iterations <= 0: raise ValueError("max_iterations should be >0.") convergence_order = 10 valid_bcs = ['periodic', 'free', 'fixed', 'free-fixed', 'fixed-free', 'fixed-fixed', 'free-free'] if bc not in valid_bcs: raise ValueError("Invalid boundary condition.\n" + "Should be one of: "+", ".join(valid_bcs)+'.') img = img_as_float(image) RGB = img.ndim == 3 # Find edges using sobel: if w_edge != 0: if RGB: edge = [sobel(img[:, :, 0]), sobel(img[:, :, 1]), sobel(img[:, :, 2])] else: edge = [sobel(img)] for i in range(3 if RGB else 1): edge[i][0, :] = edge[i][1, :] edge[i][-1, :] = edge[i][-2, :] edge[i][:, 0] = edge[i][:, 1] edge[i][:, -1] = edge[i][:, -2] else: edge = [0] # Superimpose intensity and edge images: if RGB: img = w_line*np.sum(img, axis=2) \ + w_edge*sum(edge) else: img = w_line*img + w_edge*edge[0] # Interpolate for smoothness: if new_scipy: intp = RectBivariateSpline(np.arange(img.shape[1]), np.arange(img.shape[0]), img.T, kx=2, ky=2, s=0) else: intp = np.vectorize(interp2d(np.arange(img.shape[1]), np.arange(img.shape[0]), img, kind='cubic', copy=False, bounds_error=False, fill_value=0)) x, y = snake[:, 0].astype(np.float), snake[:, 1].astype(np.float) xsave = np.empty((convergence_order, len(x))) ysave = np.empty((convergence_order, len(x))) # Build snake shape matrix for Euler equation n = len(x) a = np.roll(np.eye(n), -1, axis=0) + \ np.roll(np.eye(n), -1, axis=1) - \ 2*np.eye(n) # second order derivative, central difference b = np.roll(np.eye(n), -2, axis=0) + \ np.roll(np.eye(n), -2, axis=1) - \ 4*np.roll(np.eye(n), -1, axis=0) - \ 4*np.roll(np.eye(n), -1, axis=1) + \ 6*np.eye(n) # fourth order derivative, central difference A = -alpha*a + beta*b # Impose boundary conditions different from periodic: sfixed = False if bc.startswith('fixed'): A[0, :] = 0 A[1, :] = 0 A[1, :3] = [1, -2, 1] sfixed = True efixed = False if bc.endswith('fixed'): A[-1, :] = 0 A[-2, :] = 0 A[-2, -3:] = [1, -2, 1] efixed = True sfree = False if bc.startswith('free'): A[0, :] = 0 A[0, :3] = [1, -2, 1] A[1, :] = 0 A[1, :4] = [-1, 3, -3, 1] sfree = True efree = False if bc.endswith('free'): A[-1, :] = 0 A[-1, -3:] = [1, -2, 1] A[-2, :] = 0 A[-2, -4:] = [-1, 3, -3, 1] efree = True # Only one inversion is needed for implicit spline energy minimization: inv = scipy.linalg.inv(A+gamma*np.eye(n)) # Explicit time stepping for image energy minimization: for i in range(max_iterations): if new_scipy: fx = intp(x, y, dx=1, grid=False) fy = intp(x, y, dy=1, grid=False) else: fx = intp(x, y, dx=1) fy = intp(x, y, dy=1) if sfixed: fx[0] = 0 fy[0] = 0 if efixed: fx[-1] = 0 fy[-1] = 0 if sfree: fx[0] *= 2 fy[0] *= 2 if efree: fx[-1] *= 2 fy[-1] *= 2 xn = np.dot(inv, gamma*x + fx) yn = np.dot(inv, gamma*y + fy) # Movements are capped to max_px_move per iteration: dx = max_px_move*np.tanh(xn-x) dy = max_px_move*np.tanh(yn-y) if sfixed: dx[0] = 0 dy[0] = 0 if efixed: dx[-1] = 0 dy[-1] = 0 x += dx y += dy # Convergence criteria needs to compare to a number of previous # configurations since oscillations can occur. j = i % (convergence_order+1) if j < convergence_order: xsave[j, :] = x ysave[j, :] = y else: dist = np.min(np.max(np.abs(xsave-x[None, :]) + np.abs(ysave-y[None, :]), 1)) if dist < convergence: break return np.array([x, y]).T scikit-image-0.13.1/skimage/segmentation/boundaries.py000066400000000000000000000233751316256236300230050ustar00rootroot00000000000000from __future__ import division import numpy as np from scipy import ndimage as ndi from ..morphology import dilation, erosion, square from ..util import img_as_float, view_as_windows from ..color import gray2rgb def _find_boundaries_subpixel(label_img): """See ``find_boundaries(..., mode='subpixel')``. Notes ----- This function puts in an empty row and column between each *actual* row and column of the image, for a corresponding shape of $2s - 1$ for every image dimension of size $s$. These "interstitial" rows and columns are filled as ``True`` if they separate two labels in `label_img`, ``False`` otherwise. I used ``view_as_windows`` to get the neighborhood of each pixel. Then I check whether there are two labels or more in that neighborhood. """ ndim = label_img.ndim max_label = np.iinfo(label_img.dtype).max label_img_expanded = np.zeros([(2 * s - 1) for s in label_img.shape], label_img.dtype) pixels = [slice(None, None, 2)] * ndim label_img_expanded[pixels] = label_img edges = np.ones(label_img_expanded.shape, dtype=bool) edges[pixels] = False label_img_expanded[edges] = max_label windows = view_as_windows(np.pad(label_img_expanded, 1, mode='constant', constant_values=0), (3,) * ndim) boundaries = np.zeros_like(edges) for index in np.ndindex(label_img_expanded.shape): if edges[index]: values = np.unique(windows[index].ravel()) if len(values) > 2: # single value and max_label boundaries[index] = True return boundaries def find_boundaries(label_img, connectivity=1, mode='thick', background=0): """Return bool array where boundaries between labeled regions are True. Parameters ---------- label_img : array of int or bool An array in which different regions are labeled with either different integers or boolean values. connectivity: int in {1, ..., `label_img.ndim`}, optional A pixel is considered a boundary pixel if any of its neighbors has a different label. `connectivity` controls which pixels are considered neighbors. A connectivity of 1 (default) means pixels sharing an edge (in 2D) or a face (in 3D) will be considered neighbors. A connectivity of `label_img.ndim` means pixels sharing a corner will be considered neighbors. mode: string in {'thick', 'inner', 'outer', 'subpixel'} How to mark the boundaries: - thick: any pixel not completely surrounded by pixels of the same label (defined by `connectivity`) is marked as a boundary. This results in boundaries that are 2 pixels thick. - inner: outline the pixels *just inside* of objects, leaving background pixels untouched. - outer: outline pixels in the background around object boundaries. When two objects touch, their boundary is also marked. - subpixel: return a doubled image, with pixels *between* the original pixels marked as boundary where appropriate. background: int, optional For modes 'inner' and 'outer', a definition of a background label is required. See `mode` for descriptions of these two. Returns ------- boundaries : array of bool, same shape as `label_img` A bool image where ``True`` represents a boundary pixel. For `mode` equal to 'subpixel', ``boundaries.shape[i]`` is equal to ``2 * label_img.shape[i] - 1`` for all ``i`` (a pixel is inserted in between all other pairs of pixels). Examples -------- >>> labels = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], ... [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], ... [0, 0, 0, 0, 0, 5, 5, 5, 0, 0], ... [0, 0, 1, 1, 1, 5, 5, 5, 0, 0], ... [0, 0, 1, 1, 1, 5, 5, 5, 0, 0], ... [0, 0, 1, 1, 1, 5, 5, 5, 0, 0], ... [0, 0, 0, 0, 0, 5, 5, 5, 0, 0], ... [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], ... [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=np.uint8) >>> find_boundaries(labels, mode='thick').astype(np.uint8) array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 0, 1, 1, 0], [0, 1, 1, 0, 1, 1, 0, 1, 1, 0], [0, 1, 1, 1, 1, 1, 0, 1, 1, 0], [0, 0, 1, 1, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8) >>> find_boundaries(labels, mode='inner').astype(np.uint8) array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 1, 0, 0], [0, 0, 1, 0, 1, 1, 0, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 1, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8) >>> find_boundaries(labels, mode='outer').astype(np.uint8) array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0, 1, 0], [0, 1, 0, 0, 1, 1, 0, 0, 1, 0], [0, 1, 0, 0, 1, 1, 0, 0, 1, 0], [0, 1, 0, 0, 1, 1, 0, 0, 1, 0], [0, 0, 1, 1, 1, 1, 0, 0, 1, 0], [0, 0, 0, 0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8) >>> labels_small = labels[::2, ::3] >>> labels_small array([[0, 0, 0, 0], [0, 0, 5, 0], [0, 1, 5, 0], [0, 0, 5, 0], [0, 0, 0, 0]], dtype=uint8) >>> find_boundaries(labels_small, mode='subpixel').astype(np.uint8) array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0], [0, 0, 0, 1, 0, 1, 0], [0, 1, 1, 1, 0, 1, 0], [0, 1, 0, 1, 0, 1, 0], [0, 1, 1, 1, 0, 1, 0], [0, 0, 0, 1, 0, 1, 0], [0, 0, 0, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0]], dtype=uint8) >>> bool_image = np.array([[False, False, False, False, False], ... [False, False, False, False, False], ... [False, False, True, True, True], ... [False, False, True, True, True], ... [False, False, True, True, True]], dtype=np.bool) >>> find_boundaries(bool_image) array([[False, False, False, False, False], [False, False, True, True, True], [False, True, True, True, True], [False, True, True, False, False], [False, True, True, False, False]], dtype=bool) """ if label_img.dtype == 'bool': label_img = label_img.astype(np.uint8) ndim = label_img.ndim selem = ndi.generate_binary_structure(ndim, connectivity) if mode != 'subpixel': boundaries = dilation(label_img, selem) != erosion(label_img, selem) if mode == 'inner': foreground_image = (label_img != background) boundaries &= foreground_image elif mode == 'outer': max_label = np.iinfo(label_img.dtype).max background_image = (label_img == background) selem = ndi.generate_binary_structure(ndim, ndim) inverted_background = np.array(label_img, copy=True) inverted_background[background_image] = max_label adjacent_objects = ((dilation(label_img, selem) != erosion(inverted_background, selem)) & ~background_image) boundaries &= (background_image | adjacent_objects) return boundaries else: boundaries = _find_boundaries_subpixel(label_img) return boundaries def mark_boundaries(image, label_img, color=(1, 1, 0), outline_color=None, mode='outer', background_label=0): """Return image with boundaries between labeled regions highlighted. Parameters ---------- image : (M, N[, 3]) array Grayscale or RGB image. label_img : (M, N) array of int Label array where regions are marked by different integer values. color : length-3 sequence, optional RGB color of boundaries in the output image. outline_color : length-3 sequence, optional RGB color surrounding boundaries in the output image. If None, no outline is drawn. mode : string in {'thick', 'inner', 'outer', 'subpixel'}, optional The mode for finding boundaries. background_label : int, optional Which label to consider background (this is only useful for modes ``inner`` and ``outer``). Returns ------- marked : (M, N, 3) array of float An image in which the boundaries between labels are superimposed on the original image. See Also -------- find_boundaries """ marked = img_as_float(image, force_copy=True) if marked.ndim == 2: marked = gray2rgb(marked) if mode == 'subpixel': # Here, we want to interpose an extra line of pixels between # each original line - except for the last axis which holds # the RGB information. ``ndi.zoom`` then performs the (cubic) # interpolation, filling in the values of the interposed pixels marked = ndi.zoom(marked, [2 - 1/s for s in marked.shape[:-1]] + [1], mode='reflect') boundaries = find_boundaries(label_img, mode=mode, background=background_label) if outline_color is not None: outlines = dilation(boundaries, square(3)) marked[outlines] = outline_color marked[boundaries] = color return marked scikit-image-0.13.1/skimage/segmentation/random_walker_segmentation.py000066400000000000000000000500641316256236300262470ustar00rootroot00000000000000""" Random walker segmentation algorithm from *Random walks for image segmentation*, Leo Grady, IEEE Trans Pattern Anal Mach Intell. 2006 Nov;28(11):1768-83. Installing pyamg and using the 'cg_mg' mode of random_walker improves significantly the performance. """ import numpy as np from scipy import sparse, ndimage as ndi from .._shared.utils import warn # executive summary for next code block: try to import umfpack from # scipy, but make sure not to raise a fuss if it fails since it's only # needed to speed up a few cases. # See discussions at: # https://groups.google.com/d/msg/scikit-image/FrM5IGP6wh4/1hp-FtVZmfcJ # http://stackoverflow.com/questions/13977970/ignore-exceptions-printed-to-stderr-in-del/13977992?noredirect=1#comment28386412_13977992 try: from scipy.sparse.linalg.dsolve import umfpack old_del = umfpack.UmfpackContext.__del__ def new_del(self): try: old_del(self) except AttributeError: pass umfpack.UmfpackContext.__del__ = new_del UmfpackContext = umfpack.UmfpackContext() except: UmfpackContext = None try: from pyamg import ruge_stuben_solver amg_loaded = True except ImportError: amg_loaded = False from scipy.sparse.linalg import cg from ..util import img_as_float from ..filters import rank_order #-----------Laplacian-------------------- def _make_graph_edges_3d(n_x, n_y, n_z): """Returns a list of edges for a 3D image. Parameters ---------- n_x: integer The size of the grid in the x direction. n_y: integer The size of the grid in the y direction n_z: integer The size of the grid in the z direction Returns ------- edges : (2, N) ndarray with the total number of edges:: N = n_x * n_y * (nz - 1) + n_x * (n_y - 1) * nz + (n_x - 1) * n_y * nz Graph edges with each column describing a node-id pair. """ vertices = np.arange(n_x * n_y * n_z).reshape((n_x, n_y, n_z)) edges_deep = np.vstack((vertices[:, :, :-1].ravel(), vertices[:, :, 1:].ravel())) edges_right = np.vstack((vertices[:, :-1].ravel(), vertices[:, 1:].ravel())) edges_down = np.vstack((vertices[:-1].ravel(), vertices[1:].ravel())) edges = np.hstack((edges_deep, edges_right, edges_down)) return edges def _compute_weights_3d(data, spacing, beta=130, eps=1.e-6, multichannel=False): # Weight calculation is main difference in multispectral version # Original gradient**2 replaced with sum of gradients ** 2 gradients = 0 for channel in range(0, data.shape[-1]): gradients += _compute_gradients_3d(data[..., channel], spacing) ** 2 # All channels considered together in this standard deviation beta /= 10 * data.std() if multichannel: # New final term in beta to give == results in trivial case where # multiple identical spectra are passed. beta /= np.sqrt(data.shape[-1]) gradients *= beta weights = np.exp(- gradients) weights += eps return weights def _compute_gradients_3d(data, spacing): gr_deep = np.abs(data[:, :, :-1] - data[:, :, 1:]).ravel() / spacing[2] gr_right = np.abs(data[:, :-1] - data[:, 1:]).ravel() / spacing[1] gr_down = np.abs(data[:-1] - data[1:]).ravel() / spacing[0] return np.r_[gr_deep, gr_right, gr_down] def _make_laplacian_sparse(edges, weights): """ Sparse implementation """ pixel_nb = edges.max() + 1 diag = np.arange(pixel_nb) i_indices = np.hstack((edges[0], edges[1])) j_indices = np.hstack((edges[1], edges[0])) data = np.hstack((-weights, -weights)) lap = sparse.coo_matrix((data, (i_indices, j_indices)), shape=(pixel_nb, pixel_nb)) connect = - np.ravel(lap.sum(axis=1)) lap = sparse.coo_matrix( (np.hstack((data, connect)), (np.hstack((i_indices, diag)), np.hstack((j_indices, diag)))), shape=(pixel_nb, pixel_nb)) return lap.tocsr() def _clean_labels_ar(X, labels, copy=False): X = X.astype(labels.dtype) if copy: labels = np.copy(labels) labels = np.ravel(labels) labels[labels == 0] = X return labels def _buildAB(lap_sparse, labels): """ Build the matrix A and rhs B of the linear system to solve. A and B are two block of the laplacian of the image graph. """ labels = labels[labels >= 0] indices = np.arange(labels.size) unlabeled_indices = indices[labels == 0] seeds_indices = indices[labels > 0] # The following two lines take most of the time in this function B = lap_sparse[unlabeled_indices][:, seeds_indices] lap_sparse = lap_sparse[unlabeled_indices][:, unlabeled_indices] nlabels = labels.max() rhs = [] for lab in range(1, nlabels + 1): mask = (labels[seeds_indices] == lab) fs = sparse.csr_matrix(mask) fs = fs.transpose() rhs.append(B * fs) return lap_sparse, rhs def _mask_edges_weights(edges, weights, mask): """ Remove edges of the graph connected to masked nodes, as well as corresponding weights of the edges. """ mask0 = np.hstack((mask[:, :, :-1].ravel(), mask[:, :-1].ravel(), mask[:-1].ravel())) mask1 = np.hstack((mask[:, :, 1:].ravel(), mask[:, 1:].ravel(), mask[1:].ravel())) ind_mask = np.logical_and(mask0, mask1) edges, weights = edges[:, ind_mask], weights[ind_mask] max_node_index = edges.max() # Reassign edges labels to 0, 1, ... edges_number - 1 order = np.searchsorted(np.unique(edges.ravel()), np.arange(max_node_index + 1)) edges = order[edges.astype(np.int64)] return edges, weights def _build_laplacian(data, spacing, mask=None, beta=50, multichannel=False): l_x, l_y, l_z = tuple(data.shape[i] for i in range(3)) edges = _make_graph_edges_3d(l_x, l_y, l_z) weights = _compute_weights_3d(data, spacing, beta=beta, eps=1.e-10, multichannel=multichannel) if mask is not None: edges, weights = _mask_edges_weights(edges, weights, mask) lap = _make_laplacian_sparse(edges, weights) del edges, weights return lap #----------- Random walker algorithm -------------------------------- def random_walker(data, labels, beta=130, mode='bf', tol=1.e-3, copy=True, multichannel=False, return_full_prob=False, spacing=None): """Random walker algorithm for segmentation from markers. Random walker algorithm is implemented for gray-level or multichannel images. Parameters ---------- data : array_like Image to be segmented in phases. Gray-level `data` can be two- or three-dimensional; multichannel data can be three- or four- dimensional (multichannel=True) with the highest dimension denoting channels. Data spacing is assumed isotropic unless the `spacing` keyword argument is used. labels : array of ints, of same shape as `data` without channels dimension Array of seed markers labeled with different positive integers for different phases. Zero-labeled pixels are unlabeled pixels. Negative labels correspond to inactive pixels that are not taken into account (they are removed from the graph). If labels are not consecutive integers, the labels array will be transformed so that labels are consecutive. In the multichannel case, `labels` should have the same shape as a single channel of `data`, i.e. without the final dimension denoting channels. beta : float Penalization coefficient for the random walker motion (the greater `beta`, the more difficult the diffusion). mode : string, available options {'cg_mg', 'cg', 'bf'} Mode for solving the linear system in the random walker algorithm. If no preference given, automatically attempt to use the fastest option available ('cg_mg' from pyamg >> 'cg' with UMFPACK > 'bf'). - 'bf' (brute force): an LU factorization of the Laplacian is computed. This is fast for small images (<1024x1024), but very slow and memory-intensive for large images (e.g., 3-D volumes). - 'cg' (conjugate gradient): the linear system is solved iteratively using the Conjugate Gradient method from scipy.sparse.linalg. This is less memory-consuming than the brute force method for large images, but it is quite slow. - 'cg_mg' (conjugate gradient with multigrid preconditioner): a preconditioner is computed using a multigrid solver, then the solution is computed with the Conjugate Gradient method. This mode requires that the pyamg module (http://pyamg.org/) is installed. For images of size > 512x512, this is the recommended (fastest) mode. tol : float tolerance to achieve when solving the linear system, in cg' and 'cg_mg' modes. copy : bool If copy is False, the `labels` array will be overwritten with the result of the segmentation. Use copy=False if you want to save on memory. multichannel : bool, default False If True, input data is parsed as multichannel data (see 'data' above for proper input format in this case) return_full_prob : bool, default False If True, the probability that a pixel belongs to each of the labels will be returned, instead of only the most likely label. spacing : iterable of floats Spacing between voxels in each spatial dimension. If `None`, then the spacing between pixels/voxels in each dimension is assumed 1. Returns ------- output : ndarray * If `return_full_prob` is False, array of ints of same shape as `data`, in which each pixel has been labeled according to the marker that reached the pixel first by anisotropic diffusion. * If `return_full_prob` is True, array of floats of shape `(nlabels, data.shape)`. `output[label_nb, i, j]` is the probability that label `label_nb` reaches the pixel `(i, j)` first. See also -------- skimage.morphology.watershed: watershed segmentation A segmentation algorithm based on mathematical morphology and "flooding" of regions from markers. Notes ----- Multichannel inputs are scaled with all channel data combined. Ensure all channels are separately normalized prior to running this algorithm. The `spacing` argument is specifically for anisotropic datasets, where data points are spaced differently in one or more spatial dimensions. Anisotropic data is commonly encountered in medical imaging. The algorithm was first proposed in *Random walks for image segmentation*, Leo Grady, IEEE Trans Pattern Anal Mach Intell. 2006 Nov;28(11):1768-83. The algorithm solves the diffusion equation at infinite times for sources placed on markers of each phase in turn. A pixel is labeled with the phase that has the greatest probability to diffuse first to the pixel. The diffusion equation is solved by minimizing x.T L x for each phase, where L is the Laplacian of the weighted graph of the image, and x is the probability that a marker of the given phase arrives first at a pixel by diffusion (x=1 on markers of the phase, x=0 on the other markers, and the other coefficients are looked for). Each pixel is attributed the label for which it has a maximal value of x. The Laplacian L of the image is defined as: - L_ii = d_i, the number of neighbors of pixel i (the degree of i) - L_ij = -w_ij if i and j are adjacent pixels The weight w_ij is a decreasing function of the norm of the local gradient. This ensures that diffusion is easier between pixels of similar values. When the Laplacian is decomposed into blocks of marked and unmarked pixels:: L = M B.T B A with first indices corresponding to marked pixels, and then to unmarked pixels, minimizing x.T L x for one phase amount to solving:: A x = - B x_m where x_m = 1 on markers of the given phase, and 0 on other markers. This linear system is solved in the algorithm using a direct method for small images, and an iterative method for larger images. Examples -------- >>> np.random.seed(0) >>> a = np.zeros((10, 10)) + 0.2 * np.random.rand(10, 10) >>> a[5:8, 5:8] += 1 >>> b = np.zeros_like(a) >>> b[3, 3] = 1 # Marker for first phase >>> b[6, 6] = 2 # Marker for second phase >>> random_walker(a, b) array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 2, 2, 2, 1, 1], [1, 1, 1, 1, 1, 2, 2, 2, 1, 1], [1, 1, 1, 1, 1, 2, 2, 2, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], dtype=int32) """ # Parse input data if mode is None: if amg_loaded: mode = 'cg_mg' elif UmfpackContext is not None: mode = 'cg' else: mode = 'bf' elif mode not in ('cg_mg', 'cg', 'bf'): raise ValueError("{mode} is not a valid mode. Valid modes are 'cg_mg'," " 'cg' and 'bf'".format(mode=mode)) if UmfpackContext is None and mode == 'cg': warn('"cg" mode will be used, but it may be slower than ' '"bf" because SciPy was built without UMFPACK. Consider' ' rebuilding SciPy with UMFPACK; this will greatly ' 'accelerate the conjugate gradient ("cg") solver. ' 'You may also install pyamg and run the random_walker ' 'function in "cg_mg" mode (see docstring).') if (labels != 0).all(): warn('Random walker only segments unlabeled areas, where ' 'labels == 0. No zero valued areas in labels were ' 'found. Returning provided labels.') if return_full_prob: # Find and iterate over valid labels unique_labels = np.unique(labels) unique_labels = unique_labels[unique_labels > 0] out_labels = np.empty(labels.shape + (len(unique_labels),), dtype=np.bool) for n, i in enumerate(unique_labels): out_labels[..., n] = (labels == i) else: out_labels = labels return out_labels # This algorithm expects 4-D arrays of floats, where the first three # dimensions are spatial and the final denotes channels. 2-D images have # a singleton placeholder dimension added for the third spatial dimension, # and single channel images likewise have a singleton added for channels. # The following block ensures valid input and coerces it to the correct # form. if not multichannel: if data.ndim < 2 or data.ndim > 3: raise ValueError('For non-multichannel input, data must be of ' 'dimension 2 or 3.') dims = data.shape # To reshape final labeled result data = np.atleast_3d(img_as_float(data))[..., np.newaxis] else: if data.ndim < 3: raise ValueError('For multichannel input, data must have 3 or 4 ' 'dimensions.') dims = data[..., 0].shape # To reshape final labeled result data = img_as_float(data) if data.ndim == 3: # 2D multispectral, needs singleton in 3rd axis data = data[:, :, np.newaxis, :] # Spacing kwarg checks if spacing is None: spacing = np.asarray((1.,) * 3) elif len(spacing) == len(dims): if len(spacing) == 2: # Need a dummy spacing for singleton 3rd dim spacing = np.r_[spacing, 1.] else: # Convert to array spacing = np.asarray(spacing) else: raise ValueError('Input argument `spacing` incorrect, should be an ' 'iterable with one number per spatial dimension.') if copy: labels = np.copy(labels) label_values = np.unique(labels) # Reorder label values to have consecutive integers (no gaps) if np.any(np.diff(label_values) != 1): mask = labels >= 0 labels[mask] = rank_order(labels[mask])[0].astype(labels.dtype) labels = labels.astype(np.int32) # If the array has pruned zones, be sure that no isolated pixels # exist between pruned zones (they could not be determined) if np.any(labels < 0): filled = ndi.binary_propagation(labels > 0, mask=labels >= 0) labels[np.logical_and(np.logical_not(filled), labels == 0)] = -1 del filled labels = np.atleast_3d(labels) if np.any(labels < 0): lap_sparse = _build_laplacian(data, spacing, mask=labels >= 0, beta=beta, multichannel=multichannel) else: lap_sparse = _build_laplacian(data, spacing, beta=beta, multichannel=multichannel) lap_sparse, B = _buildAB(lap_sparse, labels) # We solve the linear system # lap_sparse X = B # where X[i, j] is the probability that a marker of label i arrives # first at pixel j by anisotropic diffusion. if mode == 'cg': X = _solve_cg(lap_sparse, B, tol=tol, return_full_prob=return_full_prob) if mode == 'cg_mg': if not amg_loaded: warn("""pyamg (http://pyamg.org/)) is needed to use this mode, but is not installed. The 'cg' mode will be used instead.""") X = _solve_cg(lap_sparse, B, tol=tol, return_full_prob=return_full_prob) else: X = _solve_cg_mg(lap_sparse, B, tol=tol, return_full_prob=return_full_prob) if mode == 'bf': X = _solve_bf(lap_sparse, B, return_full_prob=return_full_prob) # Clean up results if return_full_prob: labels = labels.astype(np.float) X = np.array([_clean_labels_ar(Xline, labels, copy=True).reshape(dims) for Xline in X]) for i in range(1, int(labels.max()) + 1): mask_i = np.squeeze(labels == i) X[:, mask_i] = 0 X[i - 1, mask_i] = 1 else: X = _clean_labels_ar(X + 1, labels).reshape(dims) return X def _solve_bf(lap_sparse, B, return_full_prob=False): """ solves lap_sparse X_i = B_i for each phase i. An LU decomposition of lap_sparse is computed first. For each pixel, the label i corresponding to the maximal X_i is returned. """ lap_sparse = lap_sparse.tocsc() solver = sparse.linalg.factorized(lap_sparse.astype(np.double)) X = np.array([solver(np.array((-B[i]).todense()).ravel()) for i in range(len(B))]) if not return_full_prob: X = np.argmax(X, axis=0) return X def _solve_cg(lap_sparse, B, tol, return_full_prob=False): """ solves lap_sparse X_i = B_i for each phase i, using the conjugate gradient method. For each pixel, the label i corresponding to the maximal X_i is returned. """ lap_sparse = lap_sparse.tocsc() X = [] for i in range(len(B)): x0 = cg(lap_sparse, -B[i].todense(), tol=tol)[0] X.append(x0) if not return_full_prob: X = np.array(X) X = np.argmax(X, axis=0) return X def _solve_cg_mg(lap_sparse, B, tol, return_full_prob=False): """ solves lap_sparse X_i = B_i for each phase i, using the conjugate gradient method with a multigrid preconditioner (ruge-stuben from pyamg). For each pixel, the label i corresponding to the maximal X_i is returned. """ X = [] ml = ruge_stuben_solver(lap_sparse) M = ml.aspreconditioner(cycle='V') for i in range(len(B)): x0 = cg(lap_sparse, -B[i].todense(), tol=tol, M=M, maxiter=30)[0] X.append(x0) if not return_full_prob: X = np.array(X) X = np.argmax(X, axis=0) return X scikit-image-0.13.1/skimage/segmentation/setup.py000066400000000000000000000024421316256236300220020ustar00rootroot00000000000000#!/usr/bin/env python import os from skimage._build import cython base_path = os.path.abspath(os.path.dirname(__file__)) def configuration(parent_package='', top_path=None): from numpy.distutils.misc_util import Configuration, get_numpy_include_dirs config = Configuration('segmentation', parent_package, top_path) cython(['_felzenszwalb_cy.pyx'], working_path=base_path) config.add_extension('_felzenszwalb_cy', sources=['_felzenszwalb_cy.c'], include_dirs=[get_numpy_include_dirs()]) cython(['_quickshift_cy.pyx'], working_path=base_path) config.add_extension('_quickshift_cy', sources=['_quickshift_cy.c'], include_dirs=[get_numpy_include_dirs()]) cython(['_slic.pyx'], working_path=base_path) config.add_extension('_slic', sources=['_slic.c'], include_dirs=[get_numpy_include_dirs()]) return config if __name__ == '__main__': from numpy.distutils.core import setup setup(maintainer='scikit-image Developers', maintainer_email='scikit-image@python.org', description='Segmentation Algorithms', url='https://github.com/scikit-image/scikit-image', license='SciPy License (BSD Style)', **(configuration(top_path='').todict()) ) scikit-image-0.13.1/skimage/segmentation/slic_superpixels.py000066400000000000000000000167331316256236300242470ustar00rootroot00000000000000# coding=utf-8 import collections as coll import numpy as np from scipy import ndimage as ndi from ..util import img_as_float, regular_grid from ..segmentation._slic import (_slic_cython, _enforce_label_connectivity_cython) from ..color import rgb2lab def slic(image, n_segments=100, compactness=10., max_iter=10, sigma=0, spacing=None, multichannel=True, convert2lab=None, enforce_connectivity=True, min_size_factor=0.5, max_size_factor=3, slic_zero=False): """Segments image using k-means clustering in Color-(x,y,z) space. Parameters ---------- image : 2D, 3D or 4D ndarray Input image, which can be 2D or 3D, and grayscale or multichannel (see `multichannel` parameter). n_segments : int, optional The (approximate) number of labels in the segmented output image. compactness : float, optional Balances color proximity and space proximity. Higher values give more weight to space proximity, making superpixel shapes more square/cubic. In SLICO mode, this is the initial compactness. This parameter depends strongly on image contrast and on the shapes of objects in the image. We recommend exploring possible values on a log scale, e.g., 0.01, 0.1, 1, 10, 100, before refining around a chosen value. max_iter : int, optional Maximum number of iterations of k-means. sigma : float or (3,) array-like of floats, optional Width of Gaussian smoothing kernel for pre-processing for each dimension of the image. The same sigma is applied to each dimension in case of a scalar value. Zero means no smoothing. Note, that `sigma` is automatically scaled if it is scalar and a manual voxel spacing is provided (see Notes section). spacing : (3,) array-like of floats, optional The voxel spacing along each image dimension. By default, `slic` assumes uniform spacing (same voxel resolution along z, y and x). This parameter controls the weights of the distances along z, y, and x during k-means clustering. multichannel : bool, optional Whether the last axis of the image is to be interpreted as multiple channels or another spatial dimension. convert2lab : bool, optional Whether the input should be converted to Lab colorspace prior to segmentation. The input image *must* be RGB. Highly recommended. This option defaults to ``True`` when ``multichannel=True`` *and* ``image.shape[-1] == 3``. enforce_connectivity: bool, optional Whether the generated segments are connected or not min_size_factor: float, optional Proportion of the minimum segment size to be removed with respect to the supposed segment size ```depth*width*height/n_segments``` max_size_factor: float, optional Proportion of the maximum connected segment size. A value of 3 works in most of the cases. slic_zero: bool, optional Run SLIC-zero, the zero-parameter mode of SLIC. [2]_ Returns ------- labels : 2D or 3D array Integer mask indicating segment labels. Raises ------ ValueError If ``convert2lab`` is set to ``True`` but the last array dimension is not of length 3. Notes ----- * If `sigma > 0`, the image is smoothed using a Gaussian kernel prior to segmentation. * If `sigma` is scalar and `spacing` is provided, the kernel width is divided along each dimension by the spacing. For example, if ``sigma=1`` and ``spacing=[5, 1, 1]``, the effective `sigma` is ``[0.2, 1, 1]``. This ensures sensible smoothing for anisotropic images. * The image is rescaled to be in [0, 1] prior to processing. * Images of shape (M, N, 3) are interpreted as 2D RGB images by default. To interpret them as 3D with the last dimension having length 3, use `multichannel=False`. References ---------- .. [1] Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Süsstrunk, SLIC Superpixels Compared to State-of-the-art Superpixel Methods, TPAMI, May 2012. .. [2] http://ivrg.epfl.ch/research/superpixels#SLICO Examples -------- >>> from skimage.segmentation import slic >>> from skimage.data import astronaut >>> img = astronaut() >>> segments = slic(img, n_segments=100, compactness=10) Increasing the compactness parameter yields more square regions: >>> segments = slic(img, n_segments=100, compactness=20) """ image = img_as_float(image) is_2d = False if image.ndim == 2: # 2D grayscale image image = image[np.newaxis, ..., np.newaxis] is_2d = True elif image.ndim == 3 and multichannel: # Make 2D multichannel image 3D with depth = 1 image = image[np.newaxis, ...] is_2d = True elif image.ndim == 3 and not multichannel: # Add channel as single last dimension image = image[..., np.newaxis] if spacing is None: spacing = np.ones(3) elif isinstance(spacing, (list, tuple)): spacing = np.array(spacing, dtype=np.double) if not isinstance(sigma, coll.Iterable): sigma = np.array([sigma, sigma, sigma], dtype=np.double) sigma /= spacing.astype(np.double) elif isinstance(sigma, (list, tuple)): sigma = np.array(sigma, dtype=np.double) if (sigma > 0).any(): # add zero smoothing for multichannel dimension sigma = list(sigma) + [0] image = ndi.gaussian_filter(image, sigma) if multichannel and (convert2lab or convert2lab is None): if image.shape[-1] != 3 and convert2lab: raise ValueError("Lab colorspace conversion requires a RGB image.") elif image.shape[-1] == 3: image = rgb2lab(image) depth, height, width = image.shape[:3] # initialize cluster centroids for desired number of segments grid_z, grid_y, grid_x = np.mgrid[:depth, :height, :width] slices = regular_grid(image.shape[:3], n_segments) step_z, step_y, step_x = [int(s.step if s.step is not None else 1) for s in slices] segments_z = grid_z[slices] segments_y = grid_y[slices] segments_x = grid_x[slices] segments_color = np.zeros(segments_z.shape + (image.shape[3],)) segments = np.concatenate([segments_z[..., np.newaxis], segments_y[..., np.newaxis], segments_x[..., np.newaxis], segments_color], axis=-1).reshape(-1, 3 + image.shape[3]) segments = np.ascontiguousarray(segments) # we do the scaling of ratio in the same way as in the SLIC paper # so the values have the same meaning step = float(max((step_z, step_y, step_x))) ratio = 1.0 / compactness image = np.ascontiguousarray(image * ratio) labels = _slic_cython(image, segments, step, max_iter, spacing, slic_zero) if enforce_connectivity: segment_size = depth * height * width / n_segments min_size = int(min_size_factor * segment_size) max_size = int(max_size_factor * segment_size) labels = _enforce_label_connectivity_cython(labels, n_segments, min_size, max_size) if is_2d: labels = labels[0] return labels scikit-image-0.13.1/skimage/segmentation/tests/000077500000000000000000000000001316256236300214305ustar00rootroot00000000000000scikit-image-0.13.1/skimage/segmentation/tests/__init__.py000066400000000000000000000001771316256236300235460ustar00rootroot00000000000000from ..._shared.testing import setup_test, teardown_test def setup(): setup_test() def teardown(): teardown_test() scikit-image-0.13.1/skimage/segmentation/tests/test_active_contour_model.py000066400000000000000000000104011316256236300272410ustar00rootroot00000000000000import numpy as np from skimage import data from skimage.color import rgb2gray from skimage.filters import gaussian from skimage.segmentation import active_contour from numpy.testing import assert_equal, assert_allclose, assert_raises def test_periodic_reference(): img = data.astronaut() img = rgb2gray(img) s = np.linspace(0, 2*np.pi, 400) x = 220 + 100*np.cos(s) y = 100 + 100*np.sin(s) init = np.array([x, y]).T snake = active_contour(gaussian(img, 3), init, alpha=0.015, beta=10, w_line=0, w_edge=1, gamma=0.001) refx = [299, 298, 298, 298, 298, 297, 297, 296, 296, 295] refy = [98, 99, 100, 101, 102, 103, 104, 105, 106, 108] assert_equal(np.array(snake[:10, 0], dtype=np.int32), refx) assert_equal(np.array(snake[:10, 1], dtype=np.int32), refy) def test_fixed_reference(): img = data.text() x = np.linspace(5, 424, 100) y = np.linspace(136, 50, 100) init = np.array([x, y]).T snake = active_contour(gaussian(img, 1), init, bc='fixed', alpha=0.1, beta=1.0, w_line=-5, w_edge=0, gamma=0.1) refx = [5, 9, 13, 17, 21, 25, 30, 34, 38, 42] refy = [136, 135, 134, 133, 132, 131, 129, 128, 127, 125] assert_equal(np.array(snake[:10, 0], dtype=np.int32), refx) assert_equal(np.array(snake[:10, 1], dtype=np.int32), refy) def test_free_reference(): img = data.text() x = np.linspace(5, 424, 100) y = np.linspace(70, 40, 100) init = np.array([x, y]).T snake = active_contour(gaussian(img, 3), init, bc='free', alpha=0.1, beta=1.0, w_line=-5, w_edge=0, gamma=0.1) refx = [10, 13, 16, 19, 23, 26, 29, 32, 36, 39] refy = [76, 76, 75, 74, 73, 72, 71, 70, 69, 69] assert_equal(np.array(snake[:10, 0], dtype=np.int32), refx) assert_equal(np.array(snake[:10, 1], dtype=np.int32), refy) def test_RGB(): img = gaussian(data.text(), 1) imgR = np.zeros((img.shape[0], img.shape[1], 3)) imgG = np.zeros((img.shape[0], img.shape[1], 3)) imgRGB = np.zeros((img.shape[0], img.shape[1], 3)) imgR[:, :, 0] = img imgG[:, :, 1] = img imgRGB[:, :, :] = img[:, :, None] x = np.linspace(5, 424, 100) y = np.linspace(136, 50, 100) init = np.array([x, y]).T snake = active_contour(imgR, init, bc='fixed', alpha=0.1, beta=1.0, w_line=-5, w_edge=0, gamma=0.1) refx = [5, 9, 13, 17, 21, 25, 30, 34, 38, 42] refy = [136, 135, 134, 133, 132, 131, 129, 128, 127, 125] assert_equal(np.array(snake[:10, 0], dtype=np.int32), refx) assert_equal(np.array(snake[:10, 1], dtype=np.int32), refy) snake = active_contour(imgG, init, bc='fixed', alpha=0.1, beta=1.0, w_line=-5, w_edge=0, gamma=0.1) assert_equal(np.array(snake[:10, 0], dtype=np.int32), refx) assert_equal(np.array(snake[:10, 1], dtype=np.int32), refy) snake = active_contour(imgRGB, init, bc='fixed', alpha=0.1, beta=1.0, w_line=-5/3., w_edge=0, gamma=0.1) assert_equal(np.array(snake[:10, 0], dtype=np.int32), refx) assert_equal(np.array(snake[:10, 1], dtype=np.int32), refy) def test_end_points(): img = data.astronaut() img = rgb2gray(img) s = np.linspace(0, 2*np.pi, 400) x = 220 + 100*np.cos(s) y = 100 + 100*np.sin(s) init = np.array([x, y]).T snake = active_contour(gaussian(img, 3), init, bc='periodic', alpha=0.015, beta=10, w_line=0, w_edge=1, gamma=0.001, max_iterations=100) assert np.sum(np.abs(snake[0, :]-snake[-1, :])) < 2 snake = active_contour(gaussian(img, 3), init, bc='free', alpha=0.015, beta=10, w_line=0, w_edge=1, gamma=0.001, max_iterations=100) assert np.sum(np.abs(snake[0, :]-snake[-1, :])) > 2 snake = active_contour(gaussian(img, 3), init, bc='fixed', alpha=0.015, beta=10, w_line=0, w_edge=1, gamma=0.001, max_iterations=100) assert_allclose(snake[0, :], [x[0], y[0]], atol=1e-5) def test_bad_input(): img = np.zeros((10, 10)) x = np.linspace(5, 424, 100) y = np.linspace(136, 50, 100) init = np.array([x, y]).T assert_raises(ValueError, active_contour, img, init, bc='wrong') assert_raises(ValueError, active_contour, img, init, max_iterations=-15) if __name__ == "__main__": np.testing.run_module_suite() scikit-image-0.13.1/skimage/segmentation/tests/test_boundaries.py000066400000000000000000000114371316256236300252020ustar00rootroot00000000000000import numpy as np from numpy.testing import assert_array_equal, assert_allclose from skimage.segmentation import find_boundaries, mark_boundaries white = (1, 1, 1) def test_find_boundaries(): image = np.zeros((10, 10), dtype=np.uint8) image[2:7, 2:7] = 1 ref = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0, 0], [0, 1, 1, 1, 1, 1, 1, 1, 0, 0], [0, 1, 1, 0, 0, 0, 1, 1, 0, 0], [0, 1, 1, 0, 0, 0, 1, 1, 0, 0], [0, 1, 1, 0, 0, 0, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]) result = find_boundaries(image) assert_array_equal(result, ref) def test_find_boundaries_bool(): image = np.zeros((5, 5), dtype=np.bool) image[2:5, 2:5] = True ref = np.array([[False, False, False, False, False], [False, False, True, True, True], [False, True, True, True, True], [False, True, True, False, False], [False, True, True, False, False]], dtype=np.bool) result = find_boundaries(image) assert_array_equal(result, ref) def test_mark_boundaries(): image = np.zeros((10, 10)) label_image = np.zeros((10, 10), dtype=np.uint8) label_image[2:7, 2:7] = 1 ref = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0, 0], [0, 1, 1, 1, 1, 1, 1, 1, 0, 0], [0, 1, 1, 0, 0, 0, 1, 1, 0, 0], [0, 1, 1, 0, 0, 0, 1, 1, 0, 0], [0, 1, 1, 0, 0, 0, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]) marked = mark_boundaries(image, label_image, color=white, mode='thick') result = np.mean(marked, axis=-1) assert_array_equal(result, ref) ref = np.array([[0, 2, 2, 2, 2, 2, 2, 2, 0, 0], [2, 2, 1, 1, 1, 1, 1, 2, 2, 0], [2, 1, 1, 1, 1, 1, 1, 1, 2, 0], [2, 1, 1, 2, 2, 2, 1, 1, 2, 0], [2, 1, 1, 2, 0, 2, 1, 1, 2, 0], [2, 1, 1, 2, 2, 2, 1, 1, 2, 0], [2, 1, 1, 1, 1, 1, 1, 1, 2, 0], [2, 2, 1, 1, 1, 1, 1, 2, 2, 0], [0, 2, 2, 2, 2, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]) marked = mark_boundaries(image, label_image, color=white, outline_color=(2, 2, 2), mode='thick') result = np.mean(marked, axis=-1) assert_array_equal(result, ref) def test_mark_boundaries_bool(): image = np.zeros((10, 10), dtype=np.bool) label_image = np.zeros((10, 10), dtype=np.uint8) label_image[2:7, 2:7] = 1 ref = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0, 0], [0, 1, 1, 1, 1, 1, 1, 1, 0, 0], [0, 1, 1, 0, 0, 0, 1, 1, 0, 0], [0, 1, 1, 0, 0, 0, 1, 1, 0, 0], [0, 1, 1, 0, 0, 0, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]) marked = mark_boundaries(image, label_image, color=white, mode='thick') result = np.mean(marked, axis=-1) assert_array_equal(result, ref) def test_mark_boundaries_subpixel(): labels = np.array([[0, 0, 0, 0], [0, 0, 5, 0], [0, 1, 5, 0], [0, 0, 5, 0], [0, 0, 0, 0]], dtype=np.uint8) np.random.seed(0) image = np.round(np.random.rand(*labels.shape), 2) marked = mark_boundaries(image, labels, color=white, mode='subpixel') marked_proj = np.round(np.mean(marked, axis=-1), 2) ref_result = np.array( [[ 0.55, 0.63, 0.72, 0.69, 0.6 , 0.55, 0.54], [ 0.45, 0.58, 0.72, 1. , 1. , 1. , 0.69], [ 0.42, 0.54, 0.65, 1. , 0.44, 1. , 0.89], [ 0.69, 1. , 1. , 1. , 0.69, 1. , 0.83], [ 0.96, 1. , 0.38, 1. , 0.79, 1. , 0.53], [ 0.89, 1. , 1. , 1. , 0.38, 1. , 0.16], [ 0.57, 0.78, 0.93, 1. , 0.07, 1. , 0.09], [ 0.2 , 0.52, 0.92, 1. , 1. , 1. , 0.54], [ 0.02, 0.35, 0.83, 0.9 , 0.78, 0.81, 0.87]]) assert_allclose(marked_proj, ref_result, atol=0.01) if __name__ == "__main__": np.testing.run_module_suite() scikit-image-0.13.1/skimage/segmentation/tests/test_clear_border.py000066400000000000000000000103571316256236300254720ustar00rootroot00000000000000import numpy as np from numpy.testing import assert_array_equal, assert_ from skimage.segmentation import clear_border def test_clear_border(): image = np.array( [[0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [1, 0, 0, 1, 0, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]]) # test default case result = clear_border(image.copy()) ref = image.copy() ref[2, 0] = 0 ref[0, -2] = 0 assert_array_equal(result, ref) # test buffer result = clear_border(image.copy(), 1) assert_array_equal(result, np.zeros(result.shape)) # test background value result = clear_border(image.copy(), buffer_size=1, bgval=2) assert_array_equal(result, 2 * np.ones_like(image)) def test_clear_border_3d(): image = np.array([ [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [1, 0, 0, 0]], [[0, 0, 0, 0], [0, 1, 1, 0], [0, 0, 1, 0], [0, 0, 0, 0]], [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], ]) # test default case result = clear_border(image.copy()) ref = image.copy() ref[0, 3, 0] = 0 assert_array_equal(result, ref) # test buffer result = clear_border(image.copy(), 1) assert_array_equal(result, np.zeros(result.shape)) # test background value result = clear_border(image.copy(), buffer_size=1, bgval=2) assert_array_equal(result, 2 * np.ones_like(image)) def test_clear_border_non_binary(): image = np.array([[1, 2, 3, 1, 2], [3, 3, 5, 4, 2], [3, 4, 5, 4, 2], [3, 3, 2, 1, 2]]) result = clear_border(image) expected = np.array([[0, 0, 0, 0, 0], [0, 0, 5, 4, 0], [0, 4, 5, 4, 0], [0, 0, 0, 0, 0]]) assert_array_equal(result, expected) assert_(not np.all(image == result)) def test_clear_border_non_binary_3d(): image3d = np.array( [[[1, 2, 3, 1, 2], [3, 3, 3, 4, 2], [3, 4, 3, 4, 2], [3, 3, 2, 1, 2]], [[1, 2, 3, 1, 2], [3, 3, 5, 4, 2], [3, 4, 5, 4, 2], [3, 3, 2, 1, 2]], [[1, 2, 3, 1, 2], [3, 3, 3, 4, 2], [3, 4, 3, 4, 2], [3, 3, 2, 1, 2]], ]) result = clear_border(image3d) expected = np.array( [[[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]], [[0, 0, 0, 0, 0], [0, 0, 5, 0, 0], [0, 0, 5, 0, 0], [0, 0, 0, 0, 0]], [[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]], ]) assert_array_equal(result, expected) assert_(not np.all(image3d == result)) def test_clear_border_non_binary_inplace(): image = np.array([[1, 2, 3, 1, 2], [3, 3, 5, 4, 2], [3, 4, 5, 4, 2], [3, 3, 2, 1, 2]]) result = clear_border(image, in_place=True) expected = np.array([[0, 0, 0, 0, 0], [0, 0, 5, 4, 0], [0, 4, 5, 4, 0], [0, 0, 0, 0, 0]]) assert_array_equal(result, expected) assert_array_equal(image, result) def test_clear_border_non_binary_inplace_3d(): image3d = np.array( [[[1, 2, 3, 1, 2], [3, 3, 3, 4, 2], [3, 4, 3, 4, 2], [3, 3, 2, 1, 2]], [[1, 2, 3, 1, 2], [3, 3, 5, 4, 2], [3, 4, 5, 4, 2], [3, 3, 2, 1, 2]], [[1, 2, 3, 1, 2], [3, 3, 3, 4, 2], [3, 4, 3, 4, 2], [3, 3, 2, 1, 2]], ]) result = clear_border(image3d, in_place=True) expected = np.array( [[[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]], [[0, 0, 0, 0, 0], [0, 0, 5, 0, 0], [0, 0, 5, 0, 0], [0, 0, 0, 0, 0]], [[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]], ]) assert_array_equal(result, expected) assert_array_equal(image3d, result) if __name__ == "__main__": np.testing.run_module_suite() scikit-image-0.13.1/skimage/segmentation/tests/test_felzenszwalb.py000066400000000000000000000053471316256236300255600ustar00rootroot00000000000000import numpy as np from numpy.testing import (assert_equal, assert_array_equal, assert_raises, assert_warns, assert_no_warnings) from skimage._shared.testing import assert_greater, test_parallel from skimage.segmentation import felzenszwalb from skimage import data @test_parallel() def test_grey(): # very weak tests. img = np.zeros((20, 21)) img[:10, 10:] = 0.2 img[10:, :10] = 0.4 img[10:, 10:] = 0.6 seg = felzenszwalb(img, sigma=0) # we expect 4 segments: assert_equal(len(np.unique(seg)), 4) # that mostly respect the 4 regions: for i in range(4): hist = np.histogram(img[seg == i], bins=[0, 0.1, 0.3, 0.5, 1])[0] assert_greater(hist[i], 40) def test_minsize(): # single-channel: img = data.coins()[20:168,0:128] for min_size in np.arange(10, 100, 10): segments = felzenszwalb(img, min_size=min_size, sigma=3) counts = np.bincount(segments.ravel()) # actually want to test greater or equal. assert_greater(counts.min() + 1, min_size) # multi-channel: coffee = data.coffee()[::4, ::4] for min_size in np.arange(10, 100, 10): segments = felzenszwalb(coffee, min_size=min_size, sigma=3) counts = np.bincount(segments.ravel()) # actually want to test greater or equal. assert_greater(counts.min() + 1, min_size) def test_3D(): grey_img = np.zeros((10, 10)) rgb_img = np.zeros((10, 10, 3)) three_d_img = np.zeros((10, 10, 10)) with assert_no_warnings(): felzenszwalb(grey_img, multichannel=True) felzenszwalb(grey_img, multichannel=False) felzenszwalb(rgb_img, multichannel=True) with assert_warns(RuntimeWarning): felzenszwalb(three_d_img, multichannel=True) with assert_raises(ValueError): felzenszwalb(rgb_img, multichannel=False) felzenszwalb(three_d_img, multichannel=False) def test_color(): # very weak tests. img = np.zeros((20, 21, 3)) img[:10, :10, 0] = 1 img[10:, :10, 1] = 1 img[10:, 10:, 2] = 1 seg = felzenszwalb(img, sigma=0) # we expect 4 segments: assert_equal(len(np.unique(seg)), 4) assert_array_equal(seg[:10, :10], 0) assert_array_equal(seg[10:, :10], 2) assert_array_equal(seg[:10, 10:], 1) assert_array_equal(seg[10:, 10:], 3) def test_merging(): # test region merging in the post-processing step img = np.array([[0, 0.3], [0.7, 1]]) # With scale=0, only the post-processing is performed. seg = felzenszwalb(img, scale=0, sigma=0, min_size=2) # we expect 2 segments: assert_equal(len(np.unique(seg)), 2) assert_array_equal(seg[0, :], 0) assert_array_equal(seg[1, :], 1) if __name__ == '__main__': from numpy import testing testing.run_module_suite() scikit-image-0.13.1/skimage/segmentation/tests/test_join.py000066400000000000000000000056471316256236300240140ustar00rootroot00000000000000import numpy as np from numpy.testing import assert_array_equal, assert_raises from skimage.segmentation import join_segmentations, relabel_sequential def test_join_segmentations(): s1 = np.array([[0, 0, 1, 1], [0, 2, 1, 1], [2, 2, 2, 1]]) s2 = np.array([[0, 1, 1, 0], [0, 1, 1, 0], [0, 1, 1, 1]]) # test correct join # NOTE: technically, equality to j_ref is not required, only that there # is a one-to-one mapping between j and j_ref. I don't know of an easy way # to check this (i.e. not as error-prone as the function being tested) j = join_segmentations(s1, s2) j_ref = np.array([[0, 1, 3, 2], [0, 5, 3, 2], [4, 5, 5, 3]]) assert_array_equal(j, j_ref) # test correct exception when arrays are different shapes s3 = np.array([[0, 0, 1, 1], [0, 2, 2, 1]]) assert_raises(ValueError, join_segmentations, s1, s3) def test_relabel_sequential_offset1(): ar = np.array([1, 1, 5, 5, 8, 99, 42]) ar_relab, fw, inv = relabel_sequential(ar) ar_relab_ref = np.array([1, 1, 2, 2, 3, 5, 4]) assert_array_equal(ar_relab, ar_relab_ref) fw_ref = np.zeros(100, int) fw_ref[1] = 1; fw_ref[5] = 2; fw_ref[8] = 3; fw_ref[42] = 4; fw_ref[99] = 5 assert_array_equal(fw, fw_ref) inv_ref = np.array([0, 1, 5, 8, 42, 99]) assert_array_equal(inv, inv_ref) def test_relabel_sequential_offset5(): ar = np.array([1, 1, 5, 5, 8, 99, 42]) ar_relab, fw, inv = relabel_sequential(ar, offset=5) ar_relab_ref = np.array([5, 5, 6, 6, 7, 9, 8]) assert_array_equal(ar_relab, ar_relab_ref) fw_ref = np.zeros(100, int) fw_ref[1] = 5; fw_ref[5] = 6; fw_ref[8] = 7; fw_ref[42] = 8; fw_ref[99] = 9 assert_array_equal(fw, fw_ref) inv_ref = np.array([0, 0, 0, 0, 0, 1, 5, 8, 42, 99]) assert_array_equal(inv, inv_ref) def test_relabel_sequential_offset5_with0(): ar = np.array([1, 1, 5, 5, 8, 99, 42, 0]) ar_relab, fw, inv = relabel_sequential(ar, offset=5) ar_relab_ref = np.array([5, 5, 6, 6, 7, 9, 8, 0]) assert_array_equal(ar_relab, ar_relab_ref) fw_ref = np.zeros(100, int) fw_ref[1] = 5; fw_ref[5] = 6; fw_ref[8] = 7; fw_ref[42] = 8; fw_ref[99] = 9 assert_array_equal(fw, fw_ref) inv_ref = np.array([0, 0, 0, 0, 0, 1, 5, 8, 42, 99]) assert_array_equal(inv, inv_ref) def test_relabel_sequential_dtype(): ar = np.array([1, 1, 5, 5, 8, 99, 42, 0], dtype=float) ar_relab, fw, inv = relabel_sequential(ar, offset=5) ar_relab_ref = np.array([5, 5, 6, 6, 7, 9, 8, 0]) assert_array_equal(ar_relab, ar_relab_ref) fw_ref = np.zeros(100, int) fw_ref[1] = 5; fw_ref[5] = 6; fw_ref[8] = 7; fw_ref[42] = 8; fw_ref[99] = 9 assert_array_equal(fw, fw_ref) inv_ref = np.array([0, 0, 0, 0, 0, 1, 5, 8, 42, 99]) assert_array_equal(inv, inv_ref) if __name__ == "__main__": np.testing.run_module_suite() scikit-image-0.13.1/skimage/segmentation/tests/test_quickshift.py000066400000000000000000000033521316256236300252160ustar00rootroot00000000000000import numpy as np from numpy.testing import assert_equal, assert_array_equal from nose.tools import assert_true from skimage._shared.testing import assert_greater, test_parallel from skimage.segmentation import quickshift @test_parallel() def test_grey(): rnd = np.random.RandomState(0) img = np.zeros((20, 21)) img[:10, 10:] = 0.2 img[10:, :10] = 0.4 img[10:, 10:] = 0.6 img += 0.1 * rnd.normal(size=img.shape) seg = quickshift(img, kernel_size=2, max_dist=3, random_seed=0, convert2lab=False, sigma=0) # we expect 4 segments: assert_equal(len(np.unique(seg)), 4) # that mostly respect the 4 regions: for i in range(4): hist = np.histogram(img[seg == i], bins=[0, 0.1, 0.3, 0.5, 1])[0] assert_greater(hist[i], 20) def test_color(): rnd = np.random.RandomState(0) img = np.zeros((20, 21, 3)) img[:10, :10, 0] = 1 img[10:, :10, 1] = 1 img[10:, 10:, 2] = 1 img += 0.01 * rnd.normal(size=img.shape) img[img > 1] = 1 img[img < 0] = 0 seg = quickshift(img, random_seed=0, max_dist=30, kernel_size=10, sigma=0) # we expect 4 segments: assert_equal(len(np.unique(seg)), 4) assert_array_equal(seg[:10, :10], 1) assert_array_equal(seg[10:, :10], 2) assert_array_equal(seg[:10, 10:], 0) assert_array_equal(seg[10:, 10:], 3) seg2 = quickshift(img, kernel_size=1, max_dist=2, random_seed=0, convert2lab=False, sigma=0) # very oversegmented: assert_equal(len(np.unique(seg2)), 7) # still don't cross lines assert_true((seg2[9, :] != seg2[10, :]).all()) assert_true((seg2[:, 9] != seg2[:, 10]).all()) if __name__ == '__main__': from numpy import testing testing.run_module_suite() scikit-image-0.13.1/skimage/segmentation/tests/test_random_walker.py000066400000000000000000000305201316256236300256660ustar00rootroot00000000000000import numpy as np from skimage.segmentation import random_walker from skimage.transform import resize from skimage._shared._warnings import expected_warnings # older versions of scipy raise a warning with new NumPy because they use # numpy.rank() instead of arr.ndim or numpy.linalg.matrix_rank. SCIPY_EXPECTED = 'numpy.linalg.matrix_rank|\A\Z' PYAMG_EXPECTED_WARNING = 'pyamg|\A\Z' PYAMG_SCIPY_EXPECTED = SCIPY_EXPECTED + '|' + PYAMG_EXPECTED_WARNING def make_2d_syntheticdata(lx, ly=None): if ly is None: ly = lx np.random.seed(1234) data = np.zeros((lx, ly)) + 0.1 * np.random.randn(lx, ly) small_l = int(lx // 5) data[lx // 2 - small_l:lx // 2 + small_l, ly // 2 - small_l:ly // 2 + small_l] = 1 data[lx // 2 - small_l + 1:lx // 2 + small_l - 1, ly // 2 - small_l + 1:ly // 2 + small_l - 1] = ( 0.1 * np.random.randn(2 * small_l - 2, 2 * small_l - 2)) data[lx // 2 - small_l, ly // 2 - small_l // 8:ly // 2 + small_l // 8] = 0 seeds = np.zeros_like(data) seeds[lx // 5, ly // 5] = 1 seeds[lx // 2 + small_l // 4, ly // 2 - small_l // 4] = 2 return data, seeds def make_3d_syntheticdata(lx, ly=None, lz=None): if ly is None: ly = lx if lz is None: lz = lx np.random.seed(1234) data = np.zeros((lx, ly, lz)) + 0.1 * np.random.randn(lx, ly, lz) small_l = int(lx // 5) data[lx // 2 - small_l:lx // 2 + small_l, ly // 2 - small_l:ly // 2 + small_l, lz // 2 - small_l:lz // 2 + small_l] = 1 data[lx // 2 - small_l + 1:lx // 2 + small_l - 1, ly // 2 - small_l + 1:ly // 2 + small_l - 1, lz // 2 - small_l + 1:lz // 2 + small_l - 1] = 0 # make a hole hole_size = np.max([1, small_l // 8]) data[lx // 2 - small_l, ly // 2 - hole_size:ly // 2 + hole_size, lz // 2 - hole_size:lz // 2 + hole_size] = 0 seeds = np.zeros_like(data) seeds[lx // 5, ly // 5, lz // 5] = 1 seeds[lx // 2 + small_l // 4, ly // 2 - small_l // 4, lz // 2 - small_l // 4] = 2 return data, seeds def test_2d_bf(): lx = 70 ly = 100 data, labels = make_2d_syntheticdata(lx, ly) labels_bf = random_walker(data, labels, beta=90, mode='bf') assert (labels_bf[25:45, 40:60] == 2).all() assert data.shape == labels.shape full_prob_bf = random_walker(data, labels, beta=90, mode='bf', return_full_prob=True) assert (full_prob_bf[1, 25:45, 40:60] >= full_prob_bf[0, 25:45, 40:60]).all() assert data.shape == labels.shape # Now test with more than two labels labels[55, 80] = 3 full_prob_bf = random_walker(data, labels, beta=90, mode='bf', return_full_prob=True) assert (full_prob_bf[1, 25:45, 40:60] >= full_prob_bf[0, 25:45, 40:60]).all() assert len(full_prob_bf) == 3 assert data.shape == labels.shape def test_2d_cg(): lx = 70 ly = 100 data, labels = make_2d_syntheticdata(lx, ly) with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): labels_cg = random_walker(data, labels, beta=90, mode='cg') assert (labels_cg[25:45, 40:60] == 2).all() assert data.shape == labels.shape with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): full_prob = random_walker(data, labels, beta=90, mode='cg', return_full_prob=True) assert (full_prob[1, 25:45, 40:60] >= full_prob[0, 25:45, 40:60]).all() assert data.shape == labels.shape return data, labels_cg def test_2d_cg_mg(): lx = 70 ly = 100 data, labels = make_2d_syntheticdata(lx, ly) expected = 'scipy.sparse.sparsetools|%s' % PYAMG_SCIPY_EXPECTED with expected_warnings([expected]): labels_cg_mg = random_walker(data, labels, beta=90, mode='cg_mg') assert (labels_cg_mg[25:45, 40:60] == 2).all() assert data.shape == labels.shape with expected_warnings([expected]): full_prob = random_walker(data, labels, beta=90, mode='cg_mg', return_full_prob=True) assert (full_prob[1, 25:45, 40:60] >= full_prob[0, 25:45, 40:60]).all() assert data.shape == labels.shape return data, labels_cg_mg def test_types(): lx = 70 ly = 100 data, labels = make_2d_syntheticdata(lx, ly) data = 255 * (data - data.min()) // (data.max() - data.min()) data = data.astype(np.uint8) with expected_warnings([PYAMG_SCIPY_EXPECTED]): labels_cg_mg = random_walker(data, labels, beta=90, mode='cg_mg') assert (labels_cg_mg[25:45, 40:60] == 2).all() assert data.shape == labels.shape return data, labels_cg_mg def test_reorder_labels(): lx = 70 ly = 100 data, labels = make_2d_syntheticdata(lx, ly) labels[labels == 2] = 4 labels_bf = random_walker(data, labels, beta=90, mode='bf') assert (labels_bf[25:45, 40:60] == 2).all() assert data.shape == labels.shape return data, labels_bf def test_2d_inactive(): lx = 70 ly = 100 data, labels = make_2d_syntheticdata(lx, ly) labels[10:20, 10:20] = -1 labels[46:50, 33:38] = -2 labels = random_walker(data, labels, beta=90) assert (labels.reshape((lx, ly))[25:45, 40:60] == 2).all() assert data.shape == labels.shape return data, labels def test_3d(): n = 30 lx, ly, lz = n, n, n data, labels = make_3d_syntheticdata(lx, ly, lz) with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): labels = random_walker(data, labels, mode='cg') assert (labels.reshape(data.shape)[13:17, 13:17, 13:17] == 2).all() assert data.shape == labels.shape return data, labels def test_3d_inactive(): n = 30 lx, ly, lz = n, n, n data, labels = make_3d_syntheticdata(lx, ly, lz) old_labels = np.copy(labels) labels[5:25, 26:29, 26:29] = -1 after_labels = np.copy(labels) with expected_warnings(['"cg" mode|CObject type' + '|' + SCIPY_EXPECTED]): labels = random_walker(data, labels, mode='cg') assert (labels.reshape(data.shape)[13:17, 13:17, 13:17] == 2).all() assert data.shape == labels.shape return data, labels, old_labels, after_labels def test_multispectral_2d(): lx, ly = 70, 100 data, labels = make_2d_syntheticdata(lx, ly) data = data[..., np.newaxis].repeat(2, axis=-1) # Expect identical output with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): multi_labels = random_walker(data, labels, mode='cg', multichannel=True) assert data[..., 0].shape == labels.shape with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): single_labels = random_walker(data[..., 0], labels, mode='cg') assert (multi_labels.reshape(labels.shape)[25:45, 40:60] == 2).all() assert data[..., 0].shape == labels.shape return data, multi_labels, single_labels, labels def test_multispectral_3d(): n = 30 lx, ly, lz = n, n, n data, labels = make_3d_syntheticdata(lx, ly, lz) data = data[..., np.newaxis].repeat(2, axis=-1) # Expect identical output with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): multi_labels = random_walker(data, labels, mode='cg', multichannel=True) assert data[..., 0].shape == labels.shape with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): single_labels = random_walker(data[..., 0], labels, mode='cg') assert (multi_labels.reshape(labels.shape)[13:17, 13:17, 13:17] == 2).all() assert (single_labels.reshape(labels.shape)[13:17, 13:17, 13:17] == 2).all() assert data[..., 0].shape == labels.shape return data, multi_labels, single_labels, labels def test_spacing_0(): n = 30 lx, ly, lz = n, n, n data, _ = make_3d_syntheticdata(lx, ly, lz) # Rescale `data` along Z axis data_aniso = np.zeros((n, n, n // 2)) for i, yz in enumerate(data): data_aniso[i, :, :] = resize(yz, (n, n // 2)) # Generate new labels small_l = int(lx // 5) labels_aniso = np.zeros_like(data_aniso) labels_aniso[lx // 5, ly // 5, lz // 5] = 1 labels_aniso[lx // 2 + small_l // 4, ly // 2 - small_l // 4, lz // 4 - small_l // 8] = 2 # Test with `spacing` kwarg with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): labels_aniso = random_walker(data_aniso, labels_aniso, mode='cg', spacing=(1., 1., 0.5)) assert (labels_aniso[13:17, 13:17, 7:9] == 2).all() def test_spacing_1(): n = 30 lx, ly, lz = n, n, n data, _ = make_3d_syntheticdata(lx, ly, lz) # Rescale `data` along Y axis # `resize` is not yet 3D capable, so this must be done by looping in 2D. data_aniso = np.zeros((n, n * 2, n)) for i, yz in enumerate(data): data_aniso[i, :, :] = resize(yz, (n * 2, n)) # Generate new labels small_l = int(lx // 5) labels_aniso = np.zeros_like(data_aniso) labels_aniso[lx // 5, ly // 5, lz // 5] = 1 labels_aniso[lx // 2 + small_l // 4, ly - small_l // 2, lz // 2 - small_l // 4] = 2 # Test with `spacing` kwarg # First, anisotropic along Y with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): labels_aniso = random_walker(data_aniso, labels_aniso, mode='cg', spacing=(1., 2., 1.)) assert (labels_aniso[13:17, 26:34, 13:17] == 2).all() # Rescale `data` along X axis # `resize` is not yet 3D capable, so this must be done by looping in 2D. data_aniso = np.zeros((n, n * 2, n)) for i in range(data.shape[1]): data_aniso[i, :, :] = resize(data[:, 1, :], (n * 2, n)) # Generate new labels small_l = int(lx // 5) labels_aniso2 = np.zeros_like(data_aniso) labels_aniso2[lx // 5, ly // 5, lz // 5] = 1 labels_aniso2[lx - small_l // 2, ly // 2 + small_l // 4, lz // 2 - small_l // 4] = 2 # Anisotropic along X with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): labels_aniso2 = random_walker(data_aniso, labels_aniso2, mode='cg', spacing=(2., 1., 1.)) assert (labels_aniso2[26:34, 13:17, 13:17] == 2).all() def test_trivial_cases(): # When all voxels are labeled img = np.ones((10, 10)) labels = np.ones((10, 10)) with expected_warnings(["Returning provided labels"]): pass_through = random_walker(img, labels) np.testing.assert_array_equal(pass_through, labels) # When all voxels are labeled AND return_full_prob is True labels[:, :5] = 3 expected = np.concatenate(((labels == 1)[..., np.newaxis], (labels == 3)[..., np.newaxis]), axis=2) with expected_warnings(["Returning provided labels"]): test = random_walker(img, labels, return_full_prob=True) np.testing.assert_array_equal(test, expected) def test_length2_spacing(): # If this passes without raising an exception (warnings OK), the new # spacing code is working properly. np.random.seed(42) img = np.ones((10, 10)) + 0.2 * np.random.normal(size=(10, 10)) labels = np.zeros((10, 10), dtype=np.uint8) labels[2, 4] = 1 labels[6, 8] = 4 random_walker(img, labels, spacing=(1., 2.)) def test_bad_inputs(): # Too few dimensions img = np.ones(10) labels = np.arange(10) np.testing.assert_raises(ValueError, random_walker, img, labels) np.testing.assert_raises(ValueError, random_walker, img, labels, multichannel=True) # Too many dimensions np.random.seed(42) img = np.random.normal(size=(3, 3, 3, 3, 3)) labels = np.arange(3 ** 5).reshape(img.shape) np.testing.assert_raises(ValueError, random_walker, img, labels) np.testing.assert_raises(ValueError, random_walker, img, labels, multichannel=True) # Spacing incorrect length img = np.random.normal(size=(10, 10)) labels = np.zeros((10, 10)) labels[2, 4] = 2 labels[6, 8] = 5 np.testing.assert_raises(ValueError, random_walker, img, labels, spacing=(1,)) # Invalid mode img = np.random.normal(size=(10, 10)) labels = np.zeros((10, 10)) np.testing.assert_raises(ValueError, random_walker, img, labels, mode='bad') if __name__ == '__main__': np.testing.run_module_suite() scikit-image-0.13.1/skimage/segmentation/tests/test_slic.py000066400000000000000000000157571316256236300240120ustar00rootroot00000000000000import itertools as it import numpy as np from numpy.testing import assert_equal, assert_raises from skimage.segmentation import slic from skimage._shared.testing import test_parallel @test_parallel() def test_color_2d(): rnd = np.random.RandomState(0) img = np.zeros((20, 21, 3)) img[:10, :10, 0] = 1 img[10:, :10, 1] = 1 img[10:, 10:, 2] = 1 img += 0.01 * rnd.normal(size=img.shape) img[img > 1] = 1 img[img < 0] = 0 seg = slic(img, n_segments=4, sigma=0, enforce_connectivity=False) # we expect 4 segments assert_equal(len(np.unique(seg)), 4) assert_equal(seg.shape, img.shape[:-1]) assert_equal(seg[:10, :10], 0) assert_equal(seg[10:, :10], 2) assert_equal(seg[:10, 10:], 1) assert_equal(seg[10:, 10:], 3) def test_multichannel_2d(): rnd = np.random.RandomState(0) img = np.zeros((20, 20, 8)) img[:10, :10, 0:2] = 1 img[:10, 10:, 2:4] = 1 img[10:, :10, 4:6] = 1 img[10:, 10:, 6:8] = 1 img += 0.01 * rnd.normal(size=img.shape) img = np.clip(img, 0, 1, out=img) seg = slic(img, n_segments=4, enforce_connectivity=False) # we expect 4 segments assert_equal(len(np.unique(seg)), 4) assert_equal(seg.shape, img.shape[:-1]) assert_equal(seg[:10, :10], 0) assert_equal(seg[10:, :10], 2) assert_equal(seg[:10, 10:], 1) assert_equal(seg[10:, 10:], 3) def test_gray_2d(): rnd = np.random.RandomState(0) img = np.zeros((20, 21)) img[:10, :10] = 0.33 img[10:, :10] = 0.67 img[10:, 10:] = 1.00 img += 0.0033 * rnd.normal(size=img.shape) img[img > 1] = 1 img[img < 0] = 0 seg = slic(img, sigma=0, n_segments=4, compactness=1, multichannel=False, convert2lab=False) assert_equal(len(np.unique(seg)), 4) assert_equal(seg.shape, img.shape) assert_equal(seg[:10, :10], 0) assert_equal(seg[10:, :10], 2) assert_equal(seg[:10, 10:], 1) assert_equal(seg[10:, 10:], 3) def test_color_3d(): rnd = np.random.RandomState(0) img = np.zeros((20, 21, 22, 3)) slices = [] for dim_size in img.shape[:-1]: midpoint = dim_size // 2 slices.append((slice(None, midpoint), slice(midpoint, None))) slices = list(it.product(*slices)) colors = list(it.product(*(([0, 1],) * 3))) for s, c in zip(slices, colors): img[s] = c img += 0.01 * rnd.normal(size=img.shape) img[img > 1] = 1 img[img < 0] = 0 seg = slic(img, sigma=0, n_segments=8) assert_equal(len(np.unique(seg)), 8) for s, c in zip(slices, range(8)): assert_equal(seg[s], c) def test_gray_3d(): rnd = np.random.RandomState(0) img = np.zeros((20, 21, 22)) slices = [] for dim_size in img.shape: midpoint = dim_size // 2 slices.append((slice(None, midpoint), slice(midpoint, None))) slices = list(it.product(*slices)) shades = np.arange(0, 1.000001, 1.0 / 7) for s, sh in zip(slices, shades): img[s] = sh img += 0.001 * rnd.normal(size=img.shape) img[img > 1] = 1 img[img < 0] = 0 seg = slic(img, sigma=0, n_segments=8, compactness=1, multichannel=False, convert2lab=False) assert_equal(len(np.unique(seg)), 8) for s, c in zip(slices, range(8)): assert_equal(seg[s], c) def test_list_sigma(): rnd = np.random.RandomState(0) img = np.array([[1, 1, 1, 0, 0, 0], [0, 0, 0, 1, 1, 1]], np.float) img += 0.1 * rnd.normal(size=img.shape) result_sigma = np.array([[0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1]], np.int) seg_sigma = slic(img, n_segments=2, sigma=[1, 50, 1], multichannel=False) assert_equal(seg_sigma, result_sigma) def test_spacing(): rnd = np.random.RandomState(0) img = np.array([[1, 1, 1, 0, 0], [1, 1, 0, 0, 0]], np.float) result_non_spaced = np.array([[0, 0, 0, 1, 1], [0, 0, 1, 1, 1]], np.int) result_spaced = np.array([[0, 0, 0, 0, 0], [1, 1, 1, 1, 1]], np.int) img += 0.1 * rnd.normal(size=img.shape) seg_non_spaced = slic(img, n_segments=2, sigma=0, multichannel=False, compactness=1.0) seg_spaced = slic(img, n_segments=2, sigma=0, spacing=[1, 500, 1], compactness=1.0, multichannel=False) assert_equal(seg_non_spaced, result_non_spaced) assert_equal(seg_spaced, result_spaced) def test_invalid_lab_conversion(): img = np.array([[1, 1, 1, 0, 0], [1, 1, 0, 0, 0]], np.float) + 1 assert_raises(ValueError, slic, img, multichannel=True, convert2lab=True) def test_enforce_connectivity(): img = np.array([[0, 0, 0, 1, 1, 1], [1, 0, 0, 1, 1, 0], [0, 0, 0, 1, 1, 0]], np.float) segments_connected = slic(img, 2, compactness=0.0001, enforce_connectivity=True, convert2lab=False) segments_disconnected = slic(img, 2, compactness=0.0001, enforce_connectivity=False, convert2lab=False) # Make sure nothing fatal occurs (e.g. buffer overflow) at low values of # max_size_factor segments_connected_low_max = slic(img, 2, compactness=0.0001, enforce_connectivity=True, convert2lab=False, max_size_factor=0.8) result_connected = np.array([[0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1]], np.float) result_disconnected = np.array([[0, 0, 0, 1, 1, 1], [1, 0, 0, 1, 1, 0], [0, 0, 0, 1, 1, 0]], np.float) assert_equal(segments_connected, result_connected) assert_equal(segments_disconnected, result_disconnected) assert_equal(segments_connected_low_max, result_connected) def test_slic_zero(): # Same as test_color_2d but with slic_zero=True rnd = np.random.RandomState(0) img = np.zeros((20, 21, 3)) img[:10, :10, 0] = 1 img[10:, :10, 1] = 1 img[10:, 10:, 2] = 1 img += 0.01 * rnd.normal(size=img.shape) img[img > 1] = 1 img[img < 0] = 0 seg = slic(img, n_segments=4, sigma=0, slic_zero=True) # we expect 4 segments assert_equal(len(np.unique(seg)), 4) assert_equal(seg.shape, img.shape[:-1]) assert_equal(seg[:10, :10], 0) assert_equal(seg[10:, :10], 2) assert_equal(seg[:10, 10:], 1) assert_equal(seg[10:, 10:], 3) def test_more_segments_than_pixels(): rnd = np.random.RandomState(0) img = np.zeros((20, 21)) img[:10, :10] = 0.33 img[10:, :10] = 0.67 img[10:, 10:] = 1.00 img += 0.0033 * rnd.normal(size=img.shape) img[img > 1] = 1 img[img < 0] = 0 seg = slic(img, sigma=0, n_segments=500, compactness=1, multichannel=False, convert2lab=False) assert np.all(seg.ravel() == np.arange(seg.size)) if __name__ == '__main__': from numpy import testing testing.run_module_suite() scikit-image-0.13.1/skimage/setup.py000066400000000000000000000027141316256236300173070ustar00rootroot00000000000000import os def configuration(parent_package='', top_path=None): from numpy.distutils.misc_util import Configuration config = Configuration('skimage', parent_package, top_path) config.add_subpackage('_shared') config.add_subpackage('color') config.add_subpackage('data') config.add_subpackage('draw') config.add_subpackage('exposure') config.add_subpackage('feature') config.add_subpackage('restoration') config.add_subpackage('filters') config.add_subpackage('future') config.add_subpackage('graph') config.add_subpackage('io') config.add_subpackage('measure') config.add_subpackage('morphology') config.add_subpackage('transform') config.add_subpackage('util') config.add_subpackage('segmentation') config.add_subpackage('external') def add_test_directories(arg, dirname, fnames): if dirname.split(os.path.sep)[-1] == 'tests': config.add_data_dir(dirname) # Add test directories from os.path import isdir, dirname, join rel_isdir = lambda d: isdir(join(curpath, d)) curpath = join(dirname(__file__), './') subdirs = [join(d, 'tests') for d in os.listdir(curpath) if rel_isdir(d)] subdirs = [d for d in subdirs if rel_isdir(d)] for test_dir in subdirs: config.add_data_dir(test_dir) return config if __name__ == "__main__": from numpy.distutils.core import setup config = configuration(top_path='').todict() setup(**config) scikit-image-0.13.1/skimage/transform/000077500000000000000000000000001316256236300176045ustar00rootroot00000000000000scikit-image-0.13.1/skimage/transform/__init__.py000066400000000000000000000040431316256236300217160ustar00rootroot00000000000000from .hough_transform import (hough_line, hough_line_peaks, probabilistic_hough_line, hough_circle, hough_circle_peaks, hough_ellipse) from .radon_transform import (radon, iradon, iradon_sart, order_angles_golden_ratio) from .finite_radon_transform import frt2, ifrt2 from .integral import integral_image, integrate from ._geometric import (estimate_transform, matrix_transform, EuclideanTransform, SimilarityTransform, AffineTransform, ProjectiveTransform, FundamentalMatrixTransform, EssentialMatrixTransform, PolynomialTransform, PiecewiseAffineTransform) from ._warps import (swirl, resize, rotate, rescale, downscale_local_mean, warp, warp_coords) from .pyramids import (pyramid_reduce, pyramid_expand, pyramid_gaussian, pyramid_laplacian) from .seam_carving import seam_carve __all__ = ['hough_circle', 'hough_ellipse', 'hough_line', 'probabilistic_hough_line', 'hough_circle_peaks', 'hough_line_peaks', 'radon', 'iradon', 'iradon_sart', 'order_angles_golden_ratio', 'frt2', 'ifrt2', 'integral_image', 'integrate', 'warp', 'warp_coords', 'estimate_transform', 'matrix_transform', 'EuclideanTransform', 'SimilarityTransform', 'AffineTransform', 'ProjectiveTransform', 'EssentialMatrixTransform', 'FundamentalMatrixTransform', 'PolynomialTransform', 'PiecewiseAffineTransform', 'swirl', 'resize', 'rotate', 'rescale', 'downscale_local_mean', 'pyramid_reduce', 'pyramid_expand', 'pyramid_gaussian', 'pyramid_laplacian', 'seam_carve'] scikit-image-0.13.1/skimage/transform/_geometric.py000066400000000000000000001221031316256236300222720ustar00rootroot00000000000000from __future__ import division import math import numpy as np from scipy import spatial from .._shared.utils import get_bound_method_class, safe_as_int def _to_ndimage_mode(mode): """Convert from `numpy.pad` mode name to the corresponding ndimage mode.""" mode_translation_dict = dict(edge='nearest', symmetric='reflect', reflect='mirror') if mode in mode_translation_dict: mode = mode_translation_dict[mode] return mode def _center_and_normalize_points(points): """Center and normalize image points. The points are transformed in a two-step procedure that is expressed as a transformation matrix. The matrix of the resulting points is usually better conditioned than the matrix of the original points. Center the image points, such that the new coordinate system has its origin at the centroid of the image points. Normalize the image points, such that the mean distance from the points to the origin of the coordinate system is sqrt(2). Parameters ---------- points : (N, 2) array The coordinates of the image points. Returns ------- matrix : (3, 3) array The transformation matrix to obtain the new points. new_points : (N, 2) array The transformed image points. References ---------- .. [1] Hartley, Richard I. "In defense of the eight-point algorithm." Pattern Analysis and Machine Intelligence, IEEE Transactions on 19.6 (1997): 580-593. """ centroid = np.mean(points, axis=0) rms = math.sqrt(np.sum((points - centroid) ** 2) / points.shape[0]) norm_factor = math.sqrt(2) / rms matrix = np.array([[norm_factor, 0, -norm_factor * centroid[0]], [0, norm_factor, -norm_factor * centroid[1]], [0, 0, 1]]) pointsh = np.row_stack([points.T, np.ones((points.shape[0]),)]) new_pointsh = np.dot(matrix, pointsh).T new_points = new_pointsh[:, :2] new_points[:, 0] /= new_pointsh[:, 2] new_points[:, 1] /= new_pointsh[:, 2] return matrix, new_points def _umeyama(src, dst, estimate_scale): """Estimate N-D similarity transformation with or without scaling. Parameters ---------- src : (M, N) array Source coordinates. dst : (M, N) array Destination coordinates. estimate_scale : bool Whether to estimate scaling factor. Returns ------- T : (N + 1, N + 1) The homogeneous similarity transformation matrix. The matrix contains NaN values only if the problem is not well-conditioned. References ---------- .. [1] "Least-squares estimation of transformation parameters between two point patterns", Shinji Umeyama, PAMI 1991, DOI: 10.1109/34.88573 """ num = src.shape[0] dim = src.shape[1] # Compute mean of src and dst. src_mean = src.mean(axis=0) dst_mean = dst.mean(axis=0) # Subtract mean from src and dst. src_demean = src - src_mean dst_demean = dst - dst_mean # Eq. (38). A = np.dot(dst_demean.T, src_demean) / num # Eq. (39). d = np.ones((dim,), dtype=np.double) if np.linalg.det(A) < 0: d[dim - 1] = -1 T = np.eye(dim + 1, dtype=np.double) U, S, V = np.linalg.svd(A) # Eq. (40) and (43). rank = np.linalg.matrix_rank(A) if rank == 0: return np.nan * T elif rank == dim - 1: if np.linalg.det(U) * np.linalg.det(V) > 0: T[:dim, :dim] = np.dot(U, V) else: s = d[dim - 1] d[dim - 1] = -1 T[:dim, :dim] = np.dot(U, np.dot(np.diag(d), V)) d[dim - 1] = s else: T[:dim, :dim] = np.dot(U, np.dot(np.diag(d), V)) if estimate_scale: # Eq. (41) and (42). scale = 1.0 / src_demean.var(axis=0).sum() * np.dot(S, d) else: scale = 1.0 T[:dim, dim] = dst_mean - scale * np.dot(T[:dim, :dim], src_mean.T) T[:dim, :dim] *= scale return T class GeometricTransform(object): """Base class for geometric transformations. """ def __call__(self, coords): """Apply forward transformation. Parameters ---------- coords : (N, 2) array Source coordinates. Returns ------- coords : (N, 2) array Destination coordinates. """ raise NotImplementedError() def inverse(self, coords): """Apply inverse transformation. Parameters ---------- coords : (N, 2) array Destination coordinates. Returns ------- coords : (N, 2) array Source coordinates. """ raise NotImplementedError() def residuals(self, src, dst): """Determine residuals of transformed destination coordinates. For each transformed source coordinate the euclidean distance to the respective destination coordinate is determined. Parameters ---------- src : (N, 2) array Source coordinates. dst : (N, 2) array Destination coordinates. Returns ------- residuals : (N, ) array Residual for coordinate. """ return np.sqrt(np.sum((self(src) - dst)**2, axis=1)) def __add__(self, other): """Combine this transformation with another. """ raise NotImplementedError() class FundamentalMatrixTransform(GeometricTransform): """Fundamental matrix transformation. The fundamental matrix relates corresponding points between a pair of uncalibrated images. The matrix transforms homogeneous image points in one image to epipolar lines in the other image. The fundamental matrix is only defined for a pair of moving images. In the case of pure rotation or planar scenes, the homography describes the geometric relation between two images (`ProjectiveTransform`). If the intrinsic calibration of the images is known, the essential matrix describes the metric relation between the two images (`EssentialMatrixTransform`). References ---------- .. [1] Hartley, Richard, and Andrew Zisserman. Multiple view geometry in computer vision. Cambridge university press, 2003. Parameters ---------- matrix : (3, 3) array, optional Fundamental matrix. Attributes ---------- params : (3, 3) array Fundamental matrix. """ def __init__(self, matrix=None): if matrix is None: # default to an identity transform matrix = np.eye(3) if matrix.shape != (3, 3): raise ValueError("Invalid shape of transformation matrix") self.params = matrix def __call__(self, coords): """Apply forward transformation. Parameters ---------- coords : (N, 2) array Source coordinates. Returns ------- coords : (N, 3) array Epipolar lines in the destination image. """ coords_homogeneous = np.column_stack([coords, np.ones(coords.shape[0])]) return np.dot(coords_homogeneous, self.params.T) def inverse(self, coords): """Apply inverse transformation. Parameters ---------- coords : (N, 2) array Destination coordinates. Returns ------- coords : (N, 3) array Epipolar lines in the source image. """ coords_homogeneous = np.column_stack([coords, np.ones(coords.shape[0])]) return np.dot(coords_homogeneous, self.params) def _setup_constraint_matrix(self, src, dst): """Setup and solve the homogeneous epipolar constraint matrix:: dst' * F * src = 0. Parameters ---------- src : (N, 2) array Source coordinates. dst : (N, 2) array Destination coordinates. Returns ------- F_normalized : (3, 3) array The normalized solution to the homogeneous system. If the system is not well-conditioned, this matrix contains NaNs. src_matrix : (3, 3) array The transformation matrix to obtain the normalized source coordinates. dst_matrix : (3, 3) array The transformation matrix to obtain the normalized destination coordinates. """ assert src.shape == dst.shape assert src.shape[0] >= 8 # Center and normalize image points for better numerical stability. try: src_matrix, src = _center_and_normalize_points(src) dst_matrix, dst = _center_and_normalize_points(dst) except ZeroDivisionError: self.params = np.full((3, 3), np.nan) return 3 * [np.full((3, 3), np.nan)] # Setup homogeneous linear equation as dst' * F * src = 0. A = np.ones((src.shape[0], 9)) A[:, :2] = src A[:, :3] *= dst[:, 0, np.newaxis] A[:, 3:5] = src A[:, 3:6] *= dst[:, 1, np.newaxis] A[:, 6:8] = src # Solve for the nullspace of the constraint matrix. _, _, V = np.linalg.svd(A) F_normalized = V[-1, :].reshape(3, 3) return F_normalized, src_matrix, dst_matrix def estimate(self, src, dst): """Estimate fundamental matrix using 8-point algorithm. The 8-point algorithm requires at least 8 corresponding point pairs for a well-conditioned solution, otherwise the over-determined solution is estimated. Parameters ---------- src : (N, 2) array Source coordinates. dst : (N, 2) array Destination coordinates. Returns ------- success : bool True, if model estimation succeeds. """ F_normalized, src_matrix, dst_matrix = \ self._setup_constraint_matrix(src, dst) # Enforcing the internal constraint that two singular values must be # non-zero and one must be zero. U, S, V = np.linalg.svd(F_normalized) S[2] = 0 F = np.dot(U, np.dot(np.diag(S), V)) self.params = np.dot(dst_matrix.T, np.dot(F, src_matrix)) return True def residuals(self, src, dst): """Compute the Sampson distance. The Sampson distance is the first approximation to the geometric error. Parameters ---------- src : (N, 2) array Source coordinates. dst : (N, 2) array Destination coordinates. Returns ------- residuals : (N, ) array Sampson distance. """ src_homogeneous = np.column_stack([src, np.ones(src.shape[0])]) dst_homogeneous = np.column_stack([dst, np.ones(dst.shape[0])]) F_src = np.dot(self.params, src_homogeneous.T) Ft_dst = np.dot(self.params.T, dst_homogeneous.T) dst_F_src = np.sum(dst_homogeneous * F_src.T, axis=1) return np.abs(dst_F_src) / np.sqrt(F_src[0] ** 2 + F_src[1] ** 2 + Ft_dst[0] ** 2 + Ft_dst[1] ** 2) class EssentialMatrixTransform(FundamentalMatrixTransform): """Essential matrix transformation. The essential matrix relates corresponding points between a pair of calibrated images. The matrix transforms normalized, homogeneous image points in one image to epipolar lines in the other image. The essential matrix is only defined for a pair of moving images capturing a non-planar scene. In the case of pure rotation or planar scenes, the homography describes the geometric relation between two images (`ProjectiveTransform`). If the intrinsic calibration of the images is unknown, the fundamental matrix describes the projective relation between the two images (`FundamentalMatrixTransform`). References ---------- .. [1] Hartley, Richard, and Andrew Zisserman. Multiple view geometry in computer vision. Cambridge university press, 2003. Parameters ---------- rotation : (3, 3) array, optional Rotation matrix of the relative camera motion. translation : (3, 1) array, optional Translation vector of the relative camera motion. The vector must have unit length. matrix : (3, 3) array, optional Essential matrix. Attributes ---------- params : (3, 3) array Essential matrix. """ def __init__(self, rotation=None, translation=None, matrix=None): if rotation is not None: if translation is None: raise ValueError("Both rotation and translation required") if rotation.shape != (3, 3): raise ValueError("Invalid shape of rotation matrix") if abs(np.linalg.det(rotation) - 1) > 1e-6: raise ValueError("Rotation matrix must have unit determinant") if translation.size != 3: raise ValueError("Invalid shape of translation vector") if abs(np.linalg.norm(translation) - 1) > 1e-6: raise ValueError("Translation vector must have unit length") # Matrix representation of the cross product for t. t_x = np.array([0, -translation[2], translation[1], translation[2], 0, -translation[0], -translation[1], translation[0], 0]).reshape(3, 3) self.params = np.dot(t_x, rotation) elif matrix is not None: if matrix.shape != (3, 3): raise ValueError("Invalid shape of transformation matrix") self.params = matrix else: # default to an identity transform self.params = np.eye(3) def estimate(self, src, dst): """Estimate essential matrix using 8-point algorithm. The 8-point algorithm requires at least 8 corresponding point pairs for a well-conditioned solution, otherwise the over-determined solution is estimated. Parameters ---------- src : (N, 2) array Source coordinates. dst : (N, 2) array Destination coordinates. Returns ------- success : bool True, if model estimation succeeds. """ E_normalized, src_matrix, dst_matrix = \ self._setup_constraint_matrix(src, dst) # Enforcing the internal constraint that two singular values must be # equal and one must be zero. U, S, V = np.linalg.svd(E_normalized) S[0] = (S[0] + S[1]) / 2.0 S[1] = S[0] S[2] = 0 E = np.dot(U, np.dot(np.diag(S), V)) self.params = np.dot(dst_matrix.T, np.dot(E, src_matrix)) return True class ProjectiveTransform(GeometricTransform): """Projective transformation. Apply a projective transformation (homography) on coordinates. For each homogeneous coordinate :math:`\mathbf{x} = [x, y, 1]^T`, its target position is calculated by multiplying with the given matrix, :math:`H`, to give :math:`H \mathbf{x}`:: [[a0 a1 a2] [b0 b1 b2] [c0 c1 1 ]]. E.g., to rotate by theta degrees clockwise, the matrix should be:: [[cos(theta) -sin(theta) 0] [sin(theta) cos(theta) 0] [0 0 1]] or, to translate x by 10 and y by 20:: [[1 0 10] [0 1 20] [0 0 1 ]]. Parameters ---------- matrix : (3, 3) array, optional Homogeneous transformation matrix. Attributes ---------- params : (3, 3) array Homogeneous transformation matrix. """ _coeffs = range(8) def __init__(self, matrix=None): if matrix is None: # default to an identity transform matrix = np.eye(3) if matrix.shape != (3, 3): raise ValueError("invalid shape of transformation matrix") self.params = matrix @property def _inv_matrix(self): return np.linalg.inv(self.params) def _apply_mat(self, coords, matrix): coords = np.array(coords, copy=False, ndmin=2) x, y = np.transpose(coords) src = np.vstack((x, y, np.ones_like(x))) dst = np.dot(src.transpose(), matrix.transpose()) # rescale to homogeneous coordinates dst[:, 0] /= dst[:, 2] dst[:, 1] /= dst[:, 2] return dst[:, :2] def __call__(self, coords): """Apply forward transformation. Parameters ---------- coords : (N, 2) array Source coordinates. Returns ------- coords : (N, 2) array Destination coordinates. """ return self._apply_mat(coords, self.params) def inverse(self, coords): """Apply inverse transformation. Parameters ---------- coords : (N, 2) array Destination coordinates. Returns ------- coords : (N, 2) array Source coordinates. """ return self._apply_mat(coords, self._inv_matrix) def estimate(self, src, dst): """Estimate the transformation from a set of corresponding points. You can determine the over-, well- and under-determined parameters with the total least-squares method. Number of source and destination coordinates must match. The transformation is defined as:: X = (a0*x + a1*y + a2) / (c0*x + c1*y + 1) Y = (b0*x + b1*y + b2) / (c0*x + c1*y + 1) These equations can be transformed to the following form:: 0 = a0*x + a1*y + a2 - c0*x*X - c1*y*X - X 0 = b0*x + b1*y + b2 - c0*x*Y - c1*y*Y - Y which exist for each set of corresponding points, so we have a set of N * 2 equations. The coefficients appear linearly so we can write A x = 0, where:: A = [[x y 1 0 0 0 -x*X -y*X -X] [0 0 0 x y 1 -x*Y -y*Y -Y] ... ... ] x.T = [a0 a1 a2 b0 b1 b2 c0 c1 c3] In case of total least-squares the solution of this homogeneous system of equations is the right singular vector of A which corresponds to the smallest singular value normed by the coefficient c3. In case of the affine transformation the coefficients c0 and c1 are 0. Thus the system of equations is:: A = [[x y 1 0 0 0 -X] [0 0 0 x y 1 -Y] ... ... ] x.T = [a0 a1 a2 b0 b1 b2 c3] Parameters ---------- src : (N, 2) array Source coordinates. dst : (N, 2) array Destination coordinates. Returns ------- success : bool True, if model estimation succeeds. """ try: src_matrix, src = _center_and_normalize_points(src) dst_matrix, dst = _center_and_normalize_points(dst) except ZeroDivisionError: self.params = np.nan * np.empty((3, 3)) return False xs = src[:, 0] ys = src[:, 1] xd = dst[:, 0] yd = dst[:, 1] rows = src.shape[0] # params: a0, a1, a2, b0, b1, b2, c0, c1 A = np.zeros((rows * 2, 9)) A[:rows, 0] = xs A[:rows, 1] = ys A[:rows, 2] = 1 A[:rows, 6] = - xd * xs A[:rows, 7] = - xd * ys A[rows:, 3] = xs A[rows:, 4] = ys A[rows:, 5] = 1 A[rows:, 6] = - yd * xs A[rows:, 7] = - yd * ys A[:rows, 8] = xd A[rows:, 8] = yd # Select relevant columns, depending on params A = A[:, list(self._coeffs) + [8]] _, _, V = np.linalg.svd(A) H = np.zeros((3, 3)) # solution is right singular vector that corresponds to smallest # singular value H.flat[list(self._coeffs) + [8]] = - V[-1, :-1] / V[-1, -1] H[2, 2] = 1 # De-center and de-normalize H = np.dot(np.linalg.inv(dst_matrix), np.dot(H, src_matrix)) self.params = H return True def __add__(self, other): """Combine this transformation with another. """ if isinstance(other, ProjectiveTransform): # combination of the same types result in a transformation of this # type again, otherwise use general projective transformation if type(self) == type(other): tform = self.__class__ else: tform = ProjectiveTransform return tform(other.params.dot(self.params)) elif (hasattr(other, '__name__') and other.__name__ == 'inverse' and hasattr(get_bound_method_class(other), '_inv_matrix')): return ProjectiveTransform(self._inv_matrix.dot(self.params)) else: raise TypeError("Cannot combine transformations of differing " "types.") class AffineTransform(ProjectiveTransform): """2D affine transformation of the form: X = a0*x + a1*y + a2 = = sx*x*cos(rotation) - sy*y*sin(rotation + shear) + a2 Y = b0*x + b1*y + b2 = = sx*x*sin(rotation) + sy*y*cos(rotation + shear) + b2 where ``sx`` and ``sy`` are scale factors in the x and y directions, and the homogeneous transformation matrix is:: [[a0 a1 a2] [b0 b1 b2] [0 0 1]] Parameters ---------- matrix : (3, 3) array, optional Homogeneous transformation matrix. scale : (sx, sy) as array, list or tuple, optional Scale factors. rotation : float, optional Rotation angle in counter-clockwise direction as radians. shear : float, optional Shear angle in counter-clockwise direction as radians. translation : (tx, ty) as array, list or tuple, optional Translation parameters. Attributes ---------- params : (3, 3) array Homogeneous transformation matrix. """ _coeffs = range(6) def __init__(self, matrix=None, scale=None, rotation=None, shear=None, translation=None): params = any(param is not None for param in (scale, rotation, shear, translation)) if params and matrix is not None: raise ValueError("You cannot specify the transformation matrix and" " the implicit parameters at the same time.") elif matrix is not None: if matrix.shape != (3, 3): raise ValueError("Invalid shape of transformation matrix.") self.params = matrix elif params: if scale is None: scale = (1, 1) if rotation is None: rotation = 0 if shear is None: shear = 0 if translation is None: translation = (0, 0) sx, sy = scale self.params = np.array([ [sx * math.cos(rotation), -sy * math.sin(rotation + shear), 0], [sx * math.sin(rotation), sy * math.cos(rotation + shear), 0], [ 0, 0, 1] ]) self.params[0:2, 2] = translation else: # default to an identity transform self.params = np.eye(3) @property def scale(self): sx = math.sqrt(self.params[0, 0] ** 2 + self.params[1, 0] ** 2) sy = math.sqrt(self.params[0, 1] ** 2 + self.params[1, 1] ** 2) return sx, sy @property def rotation(self): return math.atan2(self.params[1, 0], self.params[0, 0]) @property def shear(self): beta = math.atan2(- self.params[0, 1], self.params[1, 1]) return beta - self.rotation @property def translation(self): return self.params[0:2, 2] class PiecewiseAffineTransform(GeometricTransform): """2D piecewise affine transformation. Control points are used to define the mapping. The transform is based on a Delaunay triangulation of the points to form a mesh. Each triangle is used to find a local affine transform. Attributes ---------- affines : list of AffineTransform objects Affine transformations for each triangle in the mesh. inverse_affines : list of AffineTransform objects Inverse affine transformations for each triangle in the mesh. """ def __init__(self): self._tesselation = None self._inverse_tesselation = None self.affines = None self.inverse_affines = None def estimate(self, src, dst): """Estimate the transformation from a set of corresponding points. Number of source and destination coordinates must match. Parameters ---------- src : (N, 2) array Source coordinates. dst : (N, 2) array Destination coordinates. Returns ------- success : bool True, if model estimation succeeds. """ # forward piecewise affine # triangulate input positions into mesh self._tesselation = spatial.Delaunay(src) # find affine mapping from source positions to destination self.affines = [] for tri in self._tesselation.vertices: affine = AffineTransform() affine.estimate(src[tri, :], dst[tri, :]) self.affines.append(affine) # inverse piecewise affine # triangulate input positions into mesh self._inverse_tesselation = spatial.Delaunay(dst) # find affine mapping from source positions to destination self.inverse_affines = [] for tri in self._inverse_tesselation.vertices: affine = AffineTransform() affine.estimate(dst[tri, :], src[tri, :]) self.inverse_affines.append(affine) return True def __call__(self, coords): """Apply forward transformation. Coordinates outside of the mesh will be set to `- 1`. Parameters ---------- coords : (N, 2) array Source coordinates. Returns ------- coords : (N, 2) array Transformed coordinates. """ out = np.empty_like(coords, np.double) # determine triangle index for each coordinate simplex = self._tesselation.find_simplex(coords) # coordinates outside of mesh out[simplex == -1, :] = -1 for index in range(len(self._tesselation.vertices)): # affine transform for triangle affine = self.affines[index] # all coordinates within triangle index_mask = simplex == index out[index_mask, :] = affine(coords[index_mask, :]) return out def inverse(self, coords): """Apply inverse transformation. Coordinates outside of the mesh will be set to `- 1`. Parameters ---------- coords : (N, 2) array Source coordinates. Returns ------- coords : (N, 2) array Transformed coordinates. """ out = np.empty_like(coords, np.double) # determine triangle index for each coordinate simplex = self._inverse_tesselation.find_simplex(coords) # coordinates outside of mesh out[simplex == -1, :] = -1 for index in range(len(self._inverse_tesselation.vertices)): # affine transform for triangle affine = self.inverse_affines[index] # all coordinates within triangle index_mask = simplex == index out[index_mask, :] = affine(coords[index_mask, :]) return out class EuclideanTransform(ProjectiveTransform): """2D Euclidean transformation of the form: X = a0 * x - b0 * y + a1 = = x * cos(rotation) - y * sin(rotation) + a1 Y = b0 * x + a0 * y + b1 = = x * sin(rotation) + y * cos(rotation) + b1 where the homogeneous transformation matrix is:: [[a0 b0 a1] [b0 a0 b1] [0 0 1]] The Euclidean transformation is a rigid transformation with rotation and translation parameters. The similarity transformation extends the Euclidean transformation with a single scaling factor. Parameters ---------- matrix : (3, 3) array, optional Homogeneous transformation matrix. rotation : float, optional Rotation angle in counter-clockwise direction as radians. translation : (tx, ty) as array, list or tuple, optional x, y translation parameters. Attributes ---------- params : (3, 3) array Homogeneous transformation matrix. """ def __init__(self, matrix=None, rotation=None, translation=None): params = any(param is not None for param in (rotation, translation)) if params and matrix is not None: raise ValueError("You cannot specify the transformation matrix and" " the implicit parameters at the same time.") elif matrix is not None: if matrix.shape != (3, 3): raise ValueError("Invalid shape of transformation matrix.") self.params = matrix elif params: if rotation is None: rotation = 0 if translation is None: translation = (0, 0) self.params = np.array([ [math.cos(rotation), - math.sin(rotation), 0], [math.sin(rotation), math.cos(rotation), 0], [ 0, 0, 1] ]) self.params[0:2, 2] = translation else: # default to an identity transform self.params = np.eye(3) def estimate(self, src, dst): """Estimate the transformation from a set of corresponding points. You can determine the over-, well- and under-determined parameters with the total least-squares method. Number of source and destination coordinates must match. Parameters ---------- src : (N, 2) array Source coordinates. dst : (N, 2) array Destination coordinates. Returns ------- success : bool True, if model estimation succeeds. """ self.params = _umeyama(src, dst, False) return True @property def rotation(self): return math.atan2(self.params[1, 0], self.params[1, 1]) @property def translation(self): return self.params[0:2, 2] class SimilarityTransform(EuclideanTransform): """2D similarity transformation of the form: X = a0 * x - b0 * y + a1 = = s * x * cos(rotation) - s * y * sin(rotation) + a1 Y = b0 * x + a0 * y + b1 = = s * x * sin(rotation) + s * y * cos(rotation) + b1 where ``s`` is a scale factor and the homogeneous transformation matrix is:: [[a0 b0 a1] [b0 a0 b1] [0 0 1]] The similarity transformation extends the Euclidean transformation with a single scaling factor in addition to the rotation and translation parameters. Parameters ---------- matrix : (3, 3) array, optional Homogeneous transformation matrix. scale : float, optional Scale factor. rotation : float, optional Rotation angle in counter-clockwise direction as radians. translation : (tx, ty) as array, list or tuple, optional x, y translation parameters. Attributes ---------- params : (3, 3) array Homogeneous transformation matrix. """ def __init__(self, matrix=None, scale=None, rotation=None, translation=None): params = any(param is not None for param in (scale, rotation, translation)) if params and matrix is not None: raise ValueError("You cannot specify the transformation matrix and" " the implicit parameters at the same time.") elif matrix is not None: if matrix.shape != (3, 3): raise ValueError("Invalid shape of transformation matrix.") self.params = matrix elif params: if scale is None: scale = 1 if rotation is None: rotation = 0 if translation is None: translation = (0, 0) self.params = np.array([ [math.cos(rotation), - math.sin(rotation), 0], [math.sin(rotation), math.cos(rotation), 0], [ 0, 0, 1] ]) self.params[0:2, 0:2] *= scale self.params[0:2, 2] = translation else: # default to an identity transform self.params = np.eye(3) def estimate(self, src, dst): """Estimate the transformation from a set of corresponding points. You can determine the over-, well- and under-determined parameters with the total least-squares method. Number of source and destination coordinates must match. Parameters ---------- src : (N, 2) array Source coordinates. dst : (N, 2) array Destination coordinates. Returns ------- success : bool True, if model estimation succeeds. """ self.params = _umeyama(src, dst, True) return True @property def scale(self): if abs(math.cos(self.rotation)) < np.spacing(1): # sin(self.rotation) == 1 scale = self.params[1, 0] else: scale = self.params[0, 0] / math.cos(self.rotation) return scale class PolynomialTransform(GeometricTransform): """2D polynomial transformation of the form: X = sum[j=0:order]( sum[i=0:j]( a_ji * x**(j - i) * y**i )) Y = sum[j=0:order]( sum[i=0:j]( b_ji * x**(j - i) * y**i )) Parameters ---------- params : (2, N) array, optional Polynomial coefficients where `N * 2 = (order + 1) * (order + 2)`. So, a_ji is defined in `params[0, :]` and b_ji in `params[1, :]`. Attributes ---------- params : (2, N) array Polynomial coefficients where `N * 2 = (order + 1) * (order + 2)`. So, a_ji is defined in `params[0, :]` and b_ji in `params[1, :]`. """ def __init__(self, params=None): if params is None: # default to transformation which preserves original coordinates params = np.array([[0, 1, 0], [0, 0, 1]]) if params.shape[0] != 2: raise ValueError("invalid shape of transformation parameters") self.params = params def estimate(self, src, dst, order=2): """Estimate the transformation from a set of corresponding points. You can determine the over-, well- and under-determined parameters with the total least-squares method. Number of source and destination coordinates must match. The transformation is defined as:: X = sum[j=0:order]( sum[i=0:j]( a_ji * x**(j - i) * y**i )) Y = sum[j=0:order]( sum[i=0:j]( b_ji * x**(j - i) * y**i )) These equations can be transformed to the following form:: 0 = sum[j=0:order]( sum[i=0:j]( a_ji * x**(j - i) * y**i )) - X 0 = sum[j=0:order]( sum[i=0:j]( b_ji * x**(j - i) * y**i )) - Y which exist for each set of corresponding points, so we have a set of N * 2 equations. The coefficients appear linearly so we can write A x = 0, where:: A = [[1 x y x**2 x*y y**2 ... 0 ... 0 -X] [0 ... 0 1 x y x**2 x*y y**2 -Y] ... ... ] x.T = [a00 a10 a11 a20 a21 a22 ... ann b00 b10 b11 b20 b21 b22 ... bnn c3] In case of total least-squares the solution of this homogeneous system of equations is the right singular vector of A which corresponds to the smallest singular value normed by the coefficient c3. Parameters ---------- src : (N, 2) array Source coordinates. dst : (N, 2) array Destination coordinates. order : int, optional Polynomial order (number of coefficients is order + 1). Returns ------- success : bool True, if model estimation succeeds. """ xs = src[:, 0] ys = src[:, 1] xd = dst[:, 0] yd = dst[:, 1] rows = src.shape[0] # number of unknown polynomial coefficients order = safe_as_int(order) u = (order + 1) * (order + 2) A = np.zeros((rows * 2, u + 1)) pidx = 0 for j in range(order + 1): for i in range(j + 1): A[:rows, pidx] = xs ** (j - i) * ys ** i A[rows:, pidx + u // 2] = xs ** (j - i) * ys ** i pidx += 1 A[:rows, -1] = xd A[rows:, -1] = yd _, _, V = np.linalg.svd(A) # solution is right singular vector that corresponds to smallest # singular value params = - V[-1, :-1] / V[-1, -1] self.params = params.reshape((2, u // 2)) return True def __call__(self, coords): """Apply forward transformation. Parameters ---------- coords : (N, 2) array source coordinates Returns ------- coords : (N, 2) array Transformed coordinates. """ x = coords[:, 0] y = coords[:, 1] u = len(self.params.ravel()) # number of coefficients -> u = (order + 1) * (order + 2) order = int((- 3 + math.sqrt(9 - 4 * (2 - u))) / 2) dst = np.zeros(coords.shape) pidx = 0 for j in range(order + 1): for i in range(j + 1): dst[:, 0] += self.params[0, pidx] * x ** (j - i) * y ** i dst[:, 1] += self.params[1, pidx] * x ** (j - i) * y ** i pidx += 1 return dst def inverse(self, coords): raise Exception( 'There is no explicit way to do the inverse polynomial ' 'transformation. Instead, estimate the inverse transformation ' 'parameters by exchanging source and destination coordinates,' 'then apply the forward transformation.') TRANSFORMS = { 'euclidean': EuclideanTransform, 'similarity': SimilarityTransform, 'affine': AffineTransform, 'piecewise-affine': PiecewiseAffineTransform, 'projective': ProjectiveTransform, 'fundamental': FundamentalMatrixTransform, 'essential': EssentialMatrixTransform, 'polynomial': PolynomialTransform, } def estimate_transform(ttype, src, dst, **kwargs): """Estimate 2D geometric transformation parameters. You can determine the over-, well- and under-determined parameters with the total least-squares method. Number of source and destination coordinates must match. Parameters ---------- ttype : {'euclidean', similarity', 'affine', 'piecewise-affine', \ 'projective', 'polynomial'} Type of transform. kwargs : array or int Function parameters (src, dst, n, angle):: NAME / TTYPE FUNCTION PARAMETERS 'euclidean' `src, `dst` 'similarity' `src, `dst` 'affine' `src, `dst` 'piecewise-affine' `src, `dst` 'projective' `src, `dst` 'polynomial' `src, `dst`, `order` (polynomial order, default order is 2) Also see examples below. Returns ------- tform : :class:`GeometricTransform` Transform object containing the transformation parameters and providing access to forward and inverse transformation functions. Examples -------- >>> import numpy as np >>> from skimage import transform as tf >>> # estimate transformation parameters >>> src = np.array([0, 0, 10, 10]).reshape((2, 2)) >>> dst = np.array([12, 14, 1, -20]).reshape((2, 2)) >>> tform = tf.estimate_transform('similarity', src, dst) >>> np.allclose(tform.inverse(tform(src)), src) True >>> # warp image using the estimated transformation >>> from skimage import data >>> image = data.camera() >>> warp(image, inverse_map=tform.inverse) # doctest: +SKIP >>> # create transformation with explicit parameters >>> tform2 = tf.SimilarityTransform(scale=1.1, rotation=1, ... translation=(10, 20)) >>> # unite transformations, applied in order from left to right >>> tform3 = tform + tform2 >>> np.allclose(tform3(src), tform2(tform(src))) True """ ttype = ttype.lower() if ttype not in TRANSFORMS: raise ValueError('the transformation type \'%s\' is not' 'implemented' % ttype) tform = TRANSFORMS[ttype]() tform.estimate(src, dst, **kwargs) return tform def matrix_transform(coords, matrix): """Apply 2D matrix transform. Parameters ---------- coords : (N, 2) array x, y coordinates to transform matrix : (3, 3) array Homogeneous transformation matrix. Returns ------- coords : (N, 2) array Transformed coordinates. """ return ProjectiveTransform(matrix)(coords) scikit-image-0.13.1/skimage/transform/_hough_transform.pyx000066400000000000000000000420451316256236300237170ustar00rootroot00000000000000#cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False #cython: wraparound=False import numpy as np cimport numpy as cnp cimport cython from libc.math cimport abs, fabs, sqrt, ceil, atan2, M_PI from libc.stdlib cimport rand from ..draw import circle_perimeter from .._shared.interpolation cimport round def _hough_circle(cnp.ndarray img, cnp.ndarray[ndim=1, dtype=cnp.intp_t] radius, char normalize=True, char full_output=False): """Perform a circular Hough transform. Parameters ---------- img : (M, N) ndarray Input image with nonzero values representing edges. radius : ndarray Radii at which to compute the Hough transform. normalize : boolean, optional (default True) Normalize the accumulator with the number of pixels used to draw the radius. full_output : boolean, optional (default False) Extend the output size by twice the largest radius in order to detect centers outside the input picture. Returns ------- H : 3D ndarray (radius index, (M + 2R, N + 2R) ndarray) Hough transform accumulator for each radius. R designates the larger radius if full_output is True. Otherwise, R = 0. """ if img.ndim != 2: raise ValueError('The input image must be 2D.') cdef Py_ssize_t xmax = img.shape[0] cdef Py_ssize_t ymax = img.shape[1] # compute the nonzero indexes cdef cnp.ndarray[ndim=1, dtype=cnp.intp_t] x, y x, y = np.nonzero(img) cdef Py_ssize_t num_pixels = x.size cdef Py_ssize_t offset = 0 if full_output: # Offset the image offset = radius.max() x = x + offset y = y + offset cdef Py_ssize_t i, p, c, num_circle_pixels, tx, ty cdef double incr cdef cnp.ndarray[ndim=1, dtype=cnp.intp_t] circle_x, circle_y cdef cnp.ndarray[ndim=3, dtype=cnp.double_t] acc = \ np.zeros((radius.size, img.shape[0] + 2 * offset, img.shape[1] + 2 * offset), dtype=np.double) for i, rad in enumerate(radius): # Store in memory the circle of given radius # centered at (0,0) circle_x, circle_y = circle_perimeter(0, 0, rad) num_circle_pixels = circle_x.size with nogil: if normalize: incr = 1.0 / num_circle_pixels else: incr = 1 # For each non zero pixel for p in range(num_pixels): # Plug the circle at (px, py), # its coordinates are (tx, ty) for c in range(num_circle_pixels): tx = circle_x[c] + x[p] ty = circle_y[c] + y[p] if offset: acc[i, tx, ty] += incr elif 0 <= tx < xmax and 0 <= ty < ymax: acc[i, tx, ty] += incr return acc def _hough_ellipse(cnp.ndarray img, int threshold=4, double accuracy=1, int min_size=4, max_size=None): """Perform an elliptical Hough transform. Parameters ---------- img : (M, N) ndarray Input image with nonzero values representing edges. threshold: int, optional (default 4) Accumulator threshold value. accuracy : double, optional (default 1) Bin size on the minor axis used in the accumulator. min_size : int, optional (default 4) Minimal major axis length. max_size : int, optional Maximal minor axis length. (default None) If None, the value is set to the half of the smaller image dimension. Returns ------- result : ndarray with fields [(accumulator, y0, x0, a, b, orientation)] Where ``(yc, xc)`` is the center, ``(a, b)`` the major and minor axes, respectively. The `orientation` value follows `skimage.draw.ellipse_perimeter` convention. Examples -------- >>> img = np.zeros((25, 25), dtype=np.uint8) >>> rr, cc = ellipse_perimeter(10, 10, 6, 8) >>> img[cc, rr] = 1 >>> result = hough_ellipse(img, threshold=8) [(10, 10.0, 8.0, 6.0, 0.0, 10.0)] Notes ----- The accuracy must be chosen to produce a peak in the accumulator distribution. In other words, a flat accumulator distribution with low values may be caused by a too low bin size. References ---------- .. [1] Xie, Yonghong, and Qiang Ji. "A new efficient ellipse detection method." Pattern Recognition, 2002. Proceedings. 16th International Conference on. Vol. 2. IEEE, 2002 """ if img.ndim != 2: raise ValueError('The input image must be 2D.') cdef Py_ssize_t[:, ::1] pixels = np.row_stack(np.nonzero(img)) cdef Py_ssize_t num_pixels = pixels.shape[1] cdef list acc = list() cdef list results = list() cdef double bin_size = accuracy ** 2 cdef int max_b_squared if max_size is None: if img.shape[0] < img.shape[1]: max_b_squared = np.round(0.5 * img.shape[0]) ** 2 else: max_b_squared = np.round(0.5 * img.shape[1]) ** 2 else: max_b_squared = max_size**2 cdef Py_ssize_t p1, p2, p3, p1x, p1y, p2x, p2y, p3x, p3y cdef double xc, yc, a, b, d, k cdef double cos_tau_squared, b_squared, f_squared, orientation for p1 in range(num_pixels): p1x = pixels[1, p1] p1y = pixels[0, p1] for p2 in range(p1): p2x = pixels[1, p2] p2y = pixels[0, p2] # Candidate: center (xc, yc) and main axis a a = 0.5 * sqrt((p1x - p2x)**2 + (p1y - p2y)**2) if a > 0.5 * min_size: xc = 0.5 * (p1x + p2x) yc = 0.5 * (p1y + p2y) for p3 in range(num_pixels): p3x = pixels[1, p3] p3y = pixels[0, p3] d = sqrt((p3x - xc)**2 + (p3y - yc)**2) if d > min_size: f_squared = (p3x - p1x)**2 + (p3y - p1y)**2 cos_tau_squared = ((a**2 + d**2 - f_squared) / (2 * a * d))**2 # Consider b2 > 0 and avoid division by zero k = a**2 - d**2 * cos_tau_squared if k > 0 and cos_tau_squared < 1: b_squared = a**2 * d**2 * (1 - cos_tau_squared) / k # b2 range is limited to avoid histogram memory # overflow if b_squared <= max_b_squared: acc.append(b_squared) if len(acc) > 0: bins = np.arange(0, np.max(acc) + bin_size, bin_size) hist, bin_edges = np.histogram(acc, bins=bins) hist_max = np.max(hist) if hist_max > threshold: orientation = atan2(p1x - p2x, p1y - p2y) b = sqrt(bin_edges[hist.argmax()]) # to keep ellipse_perimeter() convention if orientation != 0: orientation = M_PI - orientation # When orientation is not in [-pi:pi] # it would mean in ellipse_perimeter() # that a < b. But we keep a > b. if orientation > M_PI: orientation = orientation - M_PI / 2. a, b = b, a results.append((hist_max, # Accumulator yc, xc, a, b, orientation)) acc = [] return np.array(results, dtype=[('accumulator', np.intp), ('yc', np.double), ('xc', np.double), ('a', np.double), ('b', np.double), ('orientation', np.double)]) def _hough_line(cnp.ndarray img, cnp.ndarray[ndim=1, dtype=cnp.double_t] theta): """Perform a straight line Hough transform. Parameters ---------- img : (M, N) ndarray Input image with nonzero values representing edges. theta : 1D ndarray of double Angles at which to compute the transform, in radians. Returns ------- H : 2-D ndarray of uint64 Hough transform accumulator. theta : ndarray Angles at which the transform was computed, in radians. distances : ndarray Distance values. Notes ----- The origin is the top left corner of the original image. X and Y axis are horizontal and vertical edges respectively. The distance is the minimal algebraic distance from the origin to the detected line. Examples -------- Generate a test image: >>> img = np.zeros((100, 150), dtype=bool) >>> img[30, :] = 1 >>> img[:, 65] = 1 >>> img[35:45, 35:50] = 1 >>> for i in range(90): ... img[i, i] = 1 >>> img += np.random.random(img.shape) > 0.95 Apply the Hough transform: >>> out, angles, d = hough_line(img) .. plot:: hough_tf.py """ # Compute the array of angles and their sine and cosine cdef cnp.ndarray[ndim=1, dtype=cnp.double_t] ctheta cdef cnp.ndarray[ndim=1, dtype=cnp.double_t] stheta ctheta = np.cos(theta) stheta = np.sin(theta) # compute the bins and allocate the accumulator array cdef cnp.ndarray[ndim=2, dtype=cnp.uint64_t] accum cdef cnp.ndarray[ndim=1, dtype=cnp.double_t] bins cdef Py_ssize_t max_distance, offset max_distance = 2 * ceil(sqrt(img.shape[0] * img.shape[0] + img.shape[1] * img.shape[1])) accum = np.zeros((max_distance, theta.shape[0]), dtype=np.uint64) bins = np.linspace(-max_distance / 2.0, max_distance / 2.0, max_distance) offset = max_distance / 2 # compute the nonzero indexes cdef cnp.ndarray[ndim=1, dtype=cnp.npy_intp] x_idxs, y_idxs y_idxs, x_idxs = np.nonzero(img) # finally, run the transform cdef Py_ssize_t nidxs, nthetas, i, j, x, y, accum_idx with nogil: nidxs = y_idxs.shape[0] # x and y are the same shape nthetas = theta.shape[0] for i in range(nidxs): x = x_idxs[i] y = y_idxs[i] for j in range(nthetas): accum_idx = round((ctheta[j] * x + stheta[j] * y)) + offset accum[accum_idx, j] += 1 return accum, theta, bins def _probabilistic_hough_line(cnp.ndarray img, int threshold, int line_length, int line_gap, cnp.ndarray[ndim=1, dtype=cnp.double_t] theta): """Return lines from a progressive probabilistic line Hough transform. Parameters ---------- img : (M, N) ndarray Input image with nonzero values representing edges. threshold : int Threshold line_length : int Minimum accepted length of detected lines. Increase the parameter to extract longer lines. line_gap : int Maximum gap between pixels to still form a line. Increase the parameter to merge broken lines more aggresively. theta : 1D ndarray, dtype=double Angles at which to compute the transform, in radians. Returns ------- lines : list List of lines identified, lines in format ((x0, y0), (x1, y0)), indicating line start and end. References ---------- .. [1] C. Galamhos, J. Matas and J. Kittler, "Progressive probabilistic Hough transform for line detection", in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1999. """ cdef Py_ssize_t height = img.shape[0] cdef Py_ssize_t width = img.shape[1] # compute the bins and allocate the accumulator array cdef cnp.ndarray[ndim=2, dtype=cnp.uint8_t] mask = \ np.zeros((height, width), dtype=np.uint8) cdef cnp.int32_t[:, ::1] line_end = np.zeros((2, 2), dtype=np.int32) cdef Py_ssize_t max_distance, offset, num_indexes, index cdef double a, b cdef Py_ssize_t nidxs, i, j, x, y, px, py, accum_idx cdef int value, max_value, max_theta cdef int shift = 16 # maximum line number cutoff cdef Py_ssize_t lines_max = 2 ** 15 cdef Py_ssize_t xflag, x0, y0, dx0, dy0, dx, dy, gap, x1, y1, \ good_line, count cdef list lines = list() max_distance = 2 * ceil((sqrt(img.shape[0] * img.shape[0] + img.shape[1] * img.shape[1]))) cdef cnp.int64_t[:, ::1] accum = \ np.zeros((max_distance, theta.shape[0]), dtype=np.int64) offset = max_distance / 2 nthetas = theta.shape[0] # compute sine and cosine of angles cdef cnp.double_t[::1] ctheta = np.cos(theta) cdef cnp.double_t[::1] stheta = np.sin(theta) # find the nonzero indexes y_idxs, x_idxs = np.nonzero(img) cdef list points = list(zip(x_idxs, y_idxs)) # mask all non-zero indexes mask[y_idxs, x_idxs] = 1 while 1: # quit if no remaining points count = len(points) if count == 0: break # select random non-zero point index = rand() % count x = points[index][0] y = points[index][1] del points[index] # if previously eliminated, skip if not mask[y, x]: continue value = 0 max_value = threshold - 1 max_theta = -1 # apply hough transform on point for j in range(nthetas): accum_idx = round((ctheta[j] * x + stheta[j] * y)) + offset accum[accum_idx, j] += 1 value = accum[accum_idx, j] if value > max_value: max_value = value max_theta = j if max_value < threshold: continue # from the random point walk in opposite directions and find line # beginning and end a = -stheta[max_theta] b = ctheta[max_theta] x0 = x y0 = y # calculate gradient of walks using fixed point math xflag = fabs(a) > fabs(b) if xflag: if a > 0: dx0 = 1 else: dx0 = -1 dy0 = round(b * (1 << shift) / fabs(a)) y0 = (y0 << shift) + (1 << (shift - 1)) else: if b > 0: dy0 = 1 else: dy0 = -1 dx0 = round(a * (1 << shift) / fabs(b)) x0 = (x0 << shift) + (1 << (shift - 1)) # pass 1: walk the line, merging lines less than specified gap length for k in range(2): gap = 0 px = x0 py = y0 dx = dx0 dy = dy0 if k > 0: dx = -dx dy = -dy while 1: if xflag: x1 = px y1 = py >> shift else: x1 = px >> shift y1 = py # check when line exits image boundary if x1 < 0 or x1 >= width or y1 < 0 or y1 >= height: break gap += 1 # if non-zero point found, continue the line if mask[y1, x1]: gap = 0 line_end[k, 1] = y1 line_end[k, 0] = x1 # if gap to this point was too large, end the line elif gap > line_gap: break px += dx py += dy # confirm line length is sufficient good_line = abs(line_end[1, 1] - line_end[0, 1]) >= line_length or \ abs(line_end[1, 0] - line_end[0, 0]) >= line_length # pass 2: walk the line again and reset accumulator and mask for k in range(2): px = x0 py = y0 dx = dx0 dy = dy0 if k > 0: dx = -dx dy = -dy while 1: if xflag: x1 = px y1 = py >> shift else: x1 = px >> shift y1 = py # if non-zero point found, continue the line if mask[y1, x1]: if good_line: accum_idx = round((ctheta[j] * x1 \ + stheta[j] * y1)) + offset accum[accum_idx, max_theta] -= 1 mask[y1, x1] = 0 # exit when the point is the line end if x1 == line_end[k, 0] and y1 == line_end[k, 1]: break px += dx py += dy # add line to the result if good_line: lines.append(((line_end[0, 0], line_end[0, 1]), (line_end[1, 0], line_end[1, 1]))) if len(lines) > lines_max: return lines return lines scikit-image-0.13.1/skimage/transform/_radon_transform.pyx000066400000000000000000000200171316256236300237030ustar00rootroot00000000000000#cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False #cython: wraparound=False import numpy as np cimport numpy as cnp cimport cython from libc.math cimport cos, sin, floor, ceil, sqrt, abs, M_PI cpdef bilinear_ray_sum(cnp.double_t[:, :] image, cnp.double_t theta, cnp.double_t ray_position): """ Compute the projection of an image along a ray. Parameters ---------- image : 2D array, dtype=float Image to project. theta : float Angle of the projection ray_position : float Position of the ray within the projection Returns ------- projected_value : float Ray sum along the projection norm_of_weights : A measure of how long the ray's path through the reconstruction circle was """ theta = theta / 180. * M_PI cdef cnp.double_t radius = image.shape[0] // 2 - 1 cdef cnp.double_t projection_center = image.shape[0] // 2 cdef cnp.double_t rotation_center = image.shape[0] // 2 # (s, t) is the (x, y) system rotated by theta cdef cnp.double_t t = ray_position - projection_center # s0 is the half-length of the ray's path in the reconstruction circle cdef cnp.double_t s0 s0 = sqrt(radius**2 - t**2) if radius**2 >= t**2 else 0. cdef Py_ssize_t Ns = 2 * (ceil(2 * s0)) # number of steps # along the ray cdef cnp.double_t ray_sum = 0. cdef cnp.double_t weight_norm = 0. cdef cnp.double_t ds, dx, dy, x0, y0, x, y, di, dj, cdef cnp.double_t index_i, index_j, weight cdef Py_ssize_t k, i, j with nogil: if Ns > 0: # step length between samples ds = 2 * s0 / Ns dx = -ds * cos(theta) dy = -ds * sin(theta) # point of entry of the ray into the reconstruction circle x0 = s0 * cos(theta) - t * sin(theta) y0 = s0 * sin(theta) + t * cos(theta) for k in range(Ns + 1): x = x0 + k * dx y = y0 + k * dy index_i = x + rotation_center index_j = y + rotation_center i = floor(index_i) j = floor(index_j) di = index_i - floor(index_i) dj = index_j - floor(index_j) # Use linear interpolation between values # Where values fall outside the array, assume zero if i > 0 and j > 0: weight = (1. - di) * (1. - dj) * ds ray_sum += weight * image[i, j] weight_norm += weight**2 if i > 0 and j < image.shape[1] - 1: weight = (1. - di) * dj * ds ray_sum += weight * image[i, j+1] weight_norm += weight**2 if i < image.shape[0] - 1 and j > 0: weight = di * (1 - dj) * ds ray_sum += weight * image[i+1, j] weight_norm += weight**2 if i < image.shape[0] - 1 and j < image.shape[1] - 1: weight = di * dj * ds ray_sum += weight * image[i+1, j+1] weight_norm += weight**2 return ray_sum, weight_norm cpdef bilinear_ray_update(cnp.double_t[:, :] image, cnp.double_t[:, :] image_update, cnp.double_t theta, cnp.double_t ray_position, cnp.double_t projected_value): """Compute the update along a ray using bilinear interpolation. Parameters ---------- image : 2D array, dtype=float Current reconstruction estimate. image_update : 2D array, dtype=float Array of same shape as ``image``. Updates will be added to this array. theta : float Angle of the projection. ray_position : float Position of the ray within the projection. projected_value : float Projected value (from the sinogram). Returns ------- deviation : Deviation before updating the image. """ cdef cnp.double_t ray_sum, weight_norm, deviation ray_sum, weight_norm = bilinear_ray_sum(image, theta, ray_position) if weight_norm > 0.: deviation = -(ray_sum - projected_value) / weight_norm else: deviation = 0. theta = theta / 180. * M_PI cdef cnp.double_t radius = image.shape[0] // 2 - 1 cdef cnp.double_t projection_center = image.shape[0] // 2 cdef cnp.double_t rotation_center = image.shape[0] // 2 # (s, t) is the (x, y) system rotated by theta cdef cnp.double_t t = ray_position - projection_center # s0 is the half-length of the ray's path in the reconstruction circle cdef cnp.double_t s0 s0 = sqrt(radius*radius - t*t) if radius**2 >= t**2 else 0. cdef Py_ssize_t Ns = 2 * (ceil(2 * s0)) # beta for equiripple Hamming window cdef cnp.double_t hamming_beta = 0.46164 cdef cnp.double_t ds, dx, dy, x0, y0, x, y, di, dj, index_i, index_j cdef cnp.double_t hamming_window cdef Py_ssize_t k, i, j with nogil: if Ns > 0: # Step length between samples ds = 2 * s0 / Ns dx = -ds * cos(theta) dy = -ds * sin(theta) # Point of entry of the ray into the reconstruction circle x0 = s0 * cos(theta) - t * sin(theta) y0 = s0 * sin(theta) + t * cos(theta) for k in range(Ns + 1): x = x0 + k * dx y = y0 + k * dy index_i = x + rotation_center index_j = y + rotation_center i = floor(index_i) j = floor(index_j) di = index_i - floor(index_i) dj = index_j - floor(index_j) hamming_window = ((1 - hamming_beta) - hamming_beta * cos(2 * M_PI * k / (Ns - 1))) if i > 0 and j > 0: image_update[i, j] += (deviation * (1. - di) * (1. - dj) * ds * hamming_window) if i > 0 and j < image.shape[1] - 1: image_update[i, j+1] += (deviation * (1. - di) * dj * ds * hamming_window) if i < image.shape[0] - 1 and j > 0: image_update[i+1, j] += (deviation * di * (1 - dj) * ds * hamming_window) if i < image.shape[0] - 1 and j < image.shape[1] - 1: image_update[i+1, j+1] += (deviation * di * dj * ds * hamming_window) return deviation @cython.boundscheck(True) def sart_projection_update(cnp.double_t[:, :] image not None, cnp.double_t theta, cnp.double_t[:] projection not None, cnp.double_t projection_shift=0.): """ Compute update to a reconstruction estimate from a single projection using bilinear interpolation. Parameters ---------- image : 2D array, dtype=float Current reconstruction estimate theta : float Angle of the projection projection : 1D array, dtype=float Projected values, taken from the sinogram projection_shift : float Shift the position of the projection by this many pixels before using it to compute an update to the reconstruction estimate Returns ------- image_update : 2D array, dtype=float Array of same shape as ``image`` containing updates that should be added to ``image`` to improve the reconstruction estimate """ cdef cnp.ndarray[cnp.double_t, ndim=2] image_update = np.zeros_like(image) cdef cnp.double_t ray_position cdef Py_ssize_t i for i in range(projection.shape[0]): ray_position = i + projection_shift bilinear_ray_update(image, image_update, theta, ray_position, projection[i]) return image_update scikit-image-0.13.1/skimage/transform/_seam_carving.pyx000066400000000000000000000201111316256236300231360ustar00rootroot00000000000000# cython: cdivision=True # cython: boundscheck=False # cython: nonecheck=False # cython: wraparound=False import numpy as np cimport numpy as cnp cdef cnp.double_t DBL_MAX = np.finfo(np.double).max cdef void _preprocess_image(cnp.double_t[:, :, ::1] energy_img, cnp.double_t[:, ::1] cumulative_img, cnp.int8_t[:, ::1] track_img, Py_ssize_t cols) nogil: """ For each row, compute the lowest seam value for all its columns. This function updates `cumulative_img` such that `cumulative_img[r, c]` is the total energy of the lowest energy seam ending at `(r, c)`. Parameters ---------- energy_img : (M, N, 1) ndarray Cost array representing the expense to remove each pixel. Seam carving tries to avoid pixels with high costs. cumulative_img : (M, N) ndarray The array to be updated inplace with the total cost of lowest energy seams. track_img : (M, N) ndarray For each pixel, `track_img` stores the relative column offset in the previous row which has the lowest value in `cumulative_img`. This helps in in re-tracing the minimum cost seam. cols : int Number of columns to process. """ cdef Py_ssize_t r, c, offset, c_idx cdef Py_ssize_t rows = energy_img.shape[0] cdef cnp.double_t min_cost = DBL_MAX cdef Py_ssize_t colsm1 = cols - 1 cdef Py_ssize_t rm1 for c in range(cols): cumulative_img[0, c] = energy_img[0, c, 0] for r in range(1, rows): rm1 = r - 1 for c in range(cols): min_cost = DBL_MAX for offset in range(-1, 2): c_idx = c + offset if (c_idx > colsm1) or (c_idx < 0): continue if cumulative_img[rm1, c_idx] < min_cost: min_cost = cumulative_img[rm1, c_idx] track_img[r, c] = offset cumulative_img[r, c] = min_cost + energy_img[r, c, 0] cdef bint _mark_seam(cnp.int8_t[:, ::1] track_img, Py_ssize_t start_index, cnp.uint8_t[:, ::1] seam_map, Py_ssize_t[::1] seam_buffer) nogil: """ Re-trace the optimal seam from a given column in the last row. This function tries to re-track an optimal seam from `start_index` and tries to mark it in `seam_map`. If this seam intersects with any existing seam in `seam_map` the function returns `0` without marking anything. Else it marks the seam in `seam_map` and returns `1`. track_img : (M, N) ndarray The array of relative column indices as updated by `_preprocess_image`. start_index : int The column number of the bottom most row from where to start re-tracing the seam. seam_map : (M, N) ndarray The array used to mark seams. If a pixel is marked as as seam it is set to `1`, else `0`. seam_buffer : (M,) ndarray Buffer used to store the column indices of the seam currently being checked. This is preallocated to save time. Returns ------- success : int `1` if seam was marked, `0` is seam intersects and was not marked. """ cdef Py_ssize_t rows = track_img.shape[0] cdef Py_ssize_t[::1] current_seam_indices = seam_buffer cdef Py_ssize_t row, col cdef cnp.int8_t offset cdef Py_ssize_t seams current_seam_indices[rows - 1] = start_index for row in range(rows - 2, -1, -1): col = current_seam_indices[row + 1] offset = track_img[row, col] col = col + offset current_seam_indices[row] = col if seam_map[row, col]: return 0 for row in range(rows): col = current_seam_indices[row] seam_map[row, col] = 1 return 1 cdef void _remove_seam(cnp.double_t[:, :, ::1] img, cnp.uint8_t[:, ::1] seam_map, Py_ssize_t cols) nogil: """ Remove marked seams from an image. Parameters ---------- img : (M, N, P) ndarray Input image whose vertical seams are to be removed. seam_map : (M, N) ndarray Array with seams to be removed marked by non-zero entries. cols : int The number of columns to process. """ cdef Py_ssize_t rows = img.shape[0] cdef Py_ssize_t channels = img.shape[2] cdef Py_ssize_t r, c, ch, shift cdef Py_ssize_t c_shift for r in range(rows): shift = 0 for c in range(cols): shift += seam_map[r, c] c_shift = c + shift for ch in range(channels): img[r, c, ch] = img[r, c_shift, ch] def _seam_carve_v(img, energy_map, iters, border): """ Carve vertical seams off an image. Carves out vertical seams from an image while using the given energy map to decide the importance of each pixel.[1]_ Parameters ---------- img : (M, N) or (M, N, 3) ndarray Input image whose vertical seams are to be removed. energy_map : (M, N) ndarray Cost array denoting importance of each pixel. The algorithm will try to retain high valued pixels. iters : int Number of vertical seams to be removed. border : int, optional The number of pixels in the right, left and bottom end of the image to be excluded from being considered for a seam. This is important as certain filters just ignore image boundaries and set them to `0`. By default border is set to `1`. Returns ------- image : (M, N - iters, 3) ndarray of float The cropped image with the vertical seams removed. References ---------- .. [1] Shai Avidan and Ariel Shamir "Seam Carving for Content-Aware Image Resizing" http://www.cs.jhu.edu/~misha/ReadingSeminar/Papers/Avidan07.pdf """ # This reference has been kept to be used for the `np.argsort` call last_row_obj = np.zeros(img.shape[1], dtype=np.float) cdef cnp.double_t[::1] last_row = last_row_obj cdef Py_ssize_t[::1] sorted_indices cdef cnp.uint8_t[:, ::1] seam_map = np.zeros(img.shape[0:2], dtype=np.uint8) cdef Py_ssize_t cols = img.shape[1] cdef Py_ssize_t rows = img.shape[0] cdef Py_ssize_t seams_left = iters cdef Py_ssize_t seams_removed cdef Py_ssize_t seam_idx cdef Py_ssize_t[::1] seam_buffer = np.zeros(rows, dtype=np.intp) cdef cnp.double_t[:, :, ::1] image = img cdef cnp.int8_t[:, ::1] track_img = np.zeros(img.shape[0:2], dtype=np.int8) cdef cnp.double_t[:, ::1] cumulative_img = np.zeros(img.shape[0:2], dtype=np.float) cdef cnp.double_t[:, :, ::1] energy_img energy_map[:, 0:border] = DBL_MAX energy_map[:, cols-border:cols] = DBL_MAX # Filters often let the boundary be `0`. If all the entries in the last # row of `energy_img` are equal, the minimum value in the penultimate row # of `cumulative_img` will result in 3 minimum values in its last row. # Hence, two successive removals will always intersect as the 3 least seams # will share the same pixels except they will differ in the last row. energy_map[rows-border:rows, :] = energy_map[rows-2*border:rows-border, :] energy_map = np.ascontiguousarray(energy_map[:, :, np.newaxis]) energy_img = energy_map _preprocess_image(energy_img, cumulative_img, track_img, cols) last_row[...] = cumulative_img[rows - 1, :] sorted_indices = np.argsort(last_row_obj) seam_idx = 0 while seams_left > 0: if _mark_seam(track_img, sorted_indices[seam_idx], seam_map, seam_buffer): seams_left -= 1 cols -= 1 seam_idx += 1 else: seam_idx = 0 _remove_seam(image, seam_map, cols) _remove_seam(energy_img, seam_map, cols) seam_map[...] = 0 _preprocess_image(energy_img, cumulative_img, track_img, cols) last_row[:cols] = cumulative_img[rows - 1, :cols] sorted_indices = np.argsort(last_row_obj) _remove_seam(image, seam_map, cols) return img[:, 0:cols] scikit-image-0.13.1/skimage/transform/_warps.py000066400000000000000000000727711316256236300214670ustar00rootroot00000000000000import six import numpy as np from scipy import ndimage as ndi from ._geometric import (SimilarityTransform, AffineTransform, ProjectiveTransform, _to_ndimage_mode) from ._warps_cy import _warp_fast from ..measure import block_reduce from ..util import img_as_float from .._shared.utils import get_bound_method_class, safe_as_int, warn, convert_to_float HOMOGRAPHY_TRANSFORMS = ( SimilarityTransform, AffineTransform, ProjectiveTransform ) def resize(image, output_shape, order=1, mode=None, cval=0, clip=True, preserve_range=False): """Resize image to match a certain size. Performs interpolation to up-size or down-size images. For down-sampling N-dimensional images by applying a function or the arithmetic mean, see `skimage.measure.block_reduce` and `skimage.transform.downscale_local_mean`, respectively. Parameters ---------- image : ndarray Input image. output_shape : tuple or ndarray Size of the generated output image `(rows, cols[, dim])`. If `dim` is not provided, the number of channels is preserved. In case the number of input channels does not equal the number of output channels a 3-dimensional interpolation is applied. Returns ------- resized : ndarray Resized version of the input. Other parameters ---------------- order : int, optional The order of the spline interpolation, default is 1. The order has to be in the range 0-5. See `skimage.transform.warp` for detail. mode : {'constant', 'edge', 'symmetric', 'reflect', 'wrap'}, optional Points outside the boundaries of the input are filled according to the given mode. Modes match the behaviour of `numpy.pad`. The default mode is 'constant'. cval : float, optional Used in conjunction with mode 'constant', the value outside the image boundaries. clip : bool, optional Whether to clip the output to the range of values of the input image. This is enabled by default, since higher order interpolation may produce values outside the given input range. preserve_range : bool, optional Whether to keep the original range of values. Otherwise, the input image is converted according to the conventions of `img_as_float`. Notes ----- Modes 'reflect' and 'symmetric' are similar, but differ in whether the edge pixels are duplicated during the reflection. As an example, if an array has values [0, 1, 2] and was padded to the right by four values using symmetric, the result would be [0, 1, 2, 2, 1, 0, 0], while for reflect it would be [0, 1, 2, 1, 0, 1, 2]. Examples -------- >>> from skimage import data >>> from skimage.transform import resize >>> image = data.camera() >>> resize(image, (100, 100), mode='reflect').shape (100, 100) """ if mode is None: mode = 'constant' warn("The default mode, 'constant', will be changed to 'reflect' in " "skimage 0.15.") rows, cols = output_shape[0], output_shape[1] orig_rows, orig_cols = image.shape[0], image.shape[1] row_scale = float(orig_rows) / rows col_scale = float(orig_cols) / cols # 3-dimensional interpolation if len(output_shape) == 3 and (image.ndim == 2 or output_shape[2] != image.shape[2]): ndi_mode = _to_ndimage_mode(mode) dim = output_shape[2] if image.ndim == 2: image = image[:, :, np.newaxis] orig_dim = image.shape[2] dim_scale = float(orig_dim) / dim map_rows, map_cols, map_dims = np.mgrid[:rows, :cols, :dim] map_rows = row_scale * (map_rows + 0.5) - 0.5 map_cols = col_scale * (map_cols + 0.5) - 0.5 map_dims = dim_scale * (map_dims + 0.5) - 0.5 coord_map = np.array([map_rows, map_cols, map_dims]) image = convert_to_float(image, preserve_range) out = ndi.map_coordinates(image, coord_map, order=order, mode=ndi_mode, cval=cval) _clip_warp_output(image, out, order, mode, cval, clip) else: # 2-dimensional interpolation if rows == 1 and cols == 1: tform = AffineTransform(translation=(orig_cols / 2.0 - 0.5, orig_rows / 2.0 - 0.5)) else: # 3 control points necessary to estimate exact AffineTransform src_corners = np.array([[1, 1], [1, rows], [cols, rows]]) - 1 dst_corners = np.zeros(src_corners.shape, dtype=np.double) # take into account that 0th pixel is at position (0.5, 0.5) dst_corners[:, 0] = col_scale * (src_corners[:, 0] + 0.5) - 0.5 dst_corners[:, 1] = row_scale * (src_corners[:, 1] + 0.5) - 0.5 tform = AffineTransform() tform.estimate(src_corners, dst_corners) out = warp(image, tform, output_shape=output_shape, order=order, mode=mode, cval=cval, clip=clip, preserve_range=preserve_range) return out def rescale(image, scale, order=1, mode=None, cval=0, clip=True, preserve_range=False): """Scale image by a certain factor. Performs interpolation to upscale or down-scale images. For down-sampling N-dimensional images with integer factors by applying a function or the arithmetic mean, see `skimage.measure.block_reduce` and `skimage.transform.downscale_local_mean`, respectively. Parameters ---------- image : ndarray Input image. scale : {float, tuple of floats} Scale factors. Separate scale factors can be defined as `(row_scale, col_scale)`. Returns ------- scaled : ndarray Scaled version of the input. Other parameters ---------------- order : int, optional The order of the spline interpolation, default is 1. The order has to be in the range 0-5. See `skimage.transform.warp` for detail. mode : {'constant', 'edge', 'symmetric', 'reflect', 'wrap'}, optional Points outside the boundaries of the input are filled according to the given mode. Modes match the behaviour of `numpy.pad`. The default mode is 'constant'. cval : float, optional Used in conjunction with mode 'constant', the value outside the image boundaries. clip : bool, optional Whether to clip the output to the range of values of the input image. This is enabled by default, since higher order interpolation may produce values outside the given input range. preserve_range : bool, optional Whether to keep the original range of values. Otherwise, the input image is converted according to the conventions of `img_as_float`. Examples -------- >>> from skimage import data >>> from skimage.transform import rescale >>> image = data.camera() >>> rescale(image, 0.1, mode='reflect').shape (51, 51) >>> rescale(image, 0.5, mode='reflect').shape (256, 256) """ try: row_scale, col_scale = scale except TypeError: row_scale = col_scale = scale orig_rows, orig_cols = image.shape[0], image.shape[1] rows = np.round(row_scale * orig_rows) cols = np.round(col_scale * orig_cols) output_shape = (rows, cols) return resize(image, output_shape, order=order, mode=mode, cval=cval, clip=clip, preserve_range=preserve_range) def rotate(image, angle, resize=False, center=None, order=1, mode='constant', cval=0, clip=True, preserve_range=False): """Rotate image by a certain angle around its center. Parameters ---------- image : ndarray Input image. angle : float Rotation angle in degrees in counter-clockwise direction. resize : bool, optional Determine whether the shape of the output image will be automatically calculated, so the complete rotated image exactly fits. Default is False. center : iterable of length 2 The rotation center. If ``center=None``, the image is rotated around its center, i.e. ``center=(rows / 2 - 0.5, cols / 2 - 0.5)``. Returns ------- rotated : ndarray Rotated version of the input. Other parameters ---------------- order : int, optional The order of the spline interpolation, default is 1. The order has to be in the range 0-5. See `skimage.transform.warp` for detail. mode : {'constant', 'edge', 'symmetric', 'reflect', 'wrap'}, optional Points outside the boundaries of the input are filled according to the given mode. Modes match the behaviour of `numpy.pad`. cval : float, optional Used in conjunction with mode 'constant', the value outside the image boundaries. clip : bool, optional Whether to clip the output to the range of values of the input image. This is enabled by default, since higher order interpolation may produce values outside the given input range. preserve_range : bool, optional Whether to keep the original range of values. Otherwise, the input image is converted according to the conventions of `img_as_float`. Examples -------- >>> from skimage import data >>> from skimage.transform import rotate >>> image = data.camera() >>> rotate(image, 2).shape (512, 512) >>> rotate(image, 2, resize=True).shape (530, 530) >>> rotate(image, 90, resize=True).shape (512, 512) """ rows, cols = image.shape[0], image.shape[1] # rotation around center if center is None: center = np.array((cols, rows)) / 2. - 0.5 else: center = np.asarray(center) tform1 = SimilarityTransform(translation=center) tform2 = SimilarityTransform(rotation=np.deg2rad(angle)) tform3 = SimilarityTransform(translation=-center) tform = tform3 + tform2 + tform1 output_shape = None if resize: # determine shape of output image corners = np.array([ [0, 0], [0, rows - 1], [cols - 1, rows - 1], [cols - 1, 0] ]) corners = tform.inverse(corners) minc = corners[:, 0].min() minr = corners[:, 1].min() maxc = corners[:, 0].max() maxr = corners[:, 1].max() out_rows = maxr - minr + 1 out_cols = maxc - minc + 1 output_shape = np.ceil((out_rows, out_cols)) # fit output image in new shape translation = (minc, minr) tform4 = SimilarityTransform(translation=translation) tform = tform4 + tform return warp(image, tform, output_shape=output_shape, order=order, mode=mode, cval=cval, clip=clip, preserve_range=preserve_range) def downscale_local_mean(image, factors, cval=0, clip=True): """Down-sample N-dimensional image by local averaging. The image is padded with `cval` if it is not perfectly divisible by the integer factors. In contrast to the 2-D interpolation in `skimage.transform.resize` and `skimage.transform.rescale` this function may be applied to N-dimensional images and calculates the local mean of elements in each block of size `factors` in the input image. Parameters ---------- image : ndarray N-dimensional input image. factors : array_like Array containing down-sampling integer factor along each axis. cval : float, optional Constant padding value if image is not perfectly divisible by the integer factors. Returns ------- image : ndarray Down-sampled image with same number of dimensions as input image. Examples -------- >>> a = np.arange(15).reshape(3, 5) >>> a array([[ 0, 1, 2, 3, 4], [ 5, 6, 7, 8, 9], [10, 11, 12, 13, 14]]) >>> downscale_local_mean(a, (2, 3)) array([[ 3.5, 4. ], [ 5.5, 4.5]]) """ return block_reduce(image, factors, np.mean, cval) def _swirl_mapping(xy, center, rotation, strength, radius): x, y = xy.T x0, y0 = center rho = np.sqrt((x - x0) ** 2 + (y - y0) ** 2) # Ensure that the transformation decays to approximately 1/1000-th # within the specified radius. radius = radius / 5 * np.log(2) theta = rotation + strength * \ np.exp(-rho / radius) + \ np.arctan2(y - y0, x - x0) xy[..., 0] = x0 + rho * np.cos(theta) xy[..., 1] = y0 + rho * np.sin(theta) return xy def swirl(image, center=None, strength=1, radius=100, rotation=0, output_shape=None, order=1, mode=None, cval=0, clip=True, preserve_range=False): """Perform a swirl transformation. Parameters ---------- image : ndarray Input image. center : (row, column) tuple or (2,) ndarray, optional Center coordinate of transformation. strength : float, optional The amount of swirling applied. radius : float, optional The extent of the swirl in pixels. The effect dies out rapidly beyond `radius`. rotation : float, optional Additional rotation applied to the image. Returns ------- swirled : ndarray Swirled version of the input. Other parameters ---------------- output_shape : tuple (rows, cols), optional Shape of the output image generated. By default the shape of the input image is preserved. order : int, optional The order of the spline interpolation, default is 1. The order has to be in the range 0-5. See `skimage.transform.warp` for detail. mode : {'constant', 'edge', 'symmetric', 'reflect', 'wrap'}, optional Points outside the boundaries of the input are filled according to the given mode, with 'constant' used as the default. Modes match the behaviour of `numpy.pad`. cval : float, optional Used in conjunction with mode 'constant', the value outside the image boundaries. clip : bool, optional Whether to clip the output to the range of values of the input image. This is enabled by default, since higher order interpolation may produce values outside the given input range. preserve_range : bool, optional Whether to keep the original range of values. Otherwise, the input image is converted according to the conventions of `img_as_float`. """ if mode is None: warn('The default of `mode` in `skimage.transform.swirl` ' 'will change to `reflect` in version 0.15.') mode = 'constant' if center is None: center = np.array(image.shape)[:2] / 2 warp_args = {'center': center, 'rotation': rotation, 'strength': strength, 'radius': radius} return warp(image, _swirl_mapping, map_args=warp_args, output_shape=output_shape, order=order, mode=mode, cval=cval, clip=clip, preserve_range=preserve_range) def _stackcopy(a, b): """Copy b into each color layer of a, such that:: a[:,:,0] = a[:,:,1] = ... = b Parameters ---------- a : (M, N) or (M, N, P) ndarray Target array. b : (M, N) Source array. Notes ----- Color images are stored as an ``(M, N, 3)`` or ``(M, N, 4)`` arrays. """ if a.ndim == 3: a[:] = b[:, :, np.newaxis] else: a[:] = b def warp_coords(coord_map, shape, dtype=np.float64): """Build the source coordinates for the output of a 2-D image warp. Parameters ---------- coord_map : callable like GeometricTransform.inverse Return input coordinates for given output coordinates. Coordinates are in the shape (P, 2), where P is the number of coordinates and each element is a ``(row, col)`` pair. shape : tuple Shape of output image ``(rows, cols[, bands])``. dtype : np.dtype or string dtype for return value (sane choices: float32 or float64). Returns ------- coords : (ndim, rows, cols[, bands]) array of dtype `dtype` Coordinates for `scipy.ndimage.map_coordinates`, that will yield an image of shape (orows, ocols, bands) by drawing from source points according to the `coord_transform_fn`. Notes ----- This is a lower-level routine that produces the source coordinates for 2-D images used by `warp()`. It is provided separately from `warp` to give additional flexibility to users who would like, for example, to re-use a particular coordinate mapping, to use specific dtypes at various points along the the image-warping process, or to implement different post-processing logic than `warp` performs after the call to `ndi.map_coordinates`. Examples -------- Produce a coordinate map that shifts an image up and to the right: >>> from skimage import data >>> from scipy.ndimage import map_coordinates >>> >>> def shift_up10_left20(xy): ... return xy - np.array([-20, 10])[None, :] >>> >>> image = data.astronaut().astype(np.float32) >>> coords = warp_coords(shift_up10_left20, image.shape) >>> warped_image = map_coordinates(image, coords) """ shape = safe_as_int(shape) rows, cols = shape[0], shape[1] coords_shape = [len(shape), rows, cols] if len(shape) == 3: coords_shape.append(shape[2]) coords = np.empty(coords_shape, dtype=dtype) # Reshape grid coordinates into a (P, 2) array of (row, col) pairs tf_coords = np.indices((cols, rows), dtype=dtype).reshape(2, -1).T # Map each (row, col) pair to the source image according to # the user-provided mapping tf_coords = coord_map(tf_coords) # Reshape back to a (2, M, N) coordinate grid tf_coords = tf_coords.T.reshape((-1, cols, rows)).swapaxes(1, 2) # Place the y-coordinate mapping _stackcopy(coords[1, ...], tf_coords[0, ...]) # Place the x-coordinate mapping _stackcopy(coords[0, ...], tf_coords[1, ...]) if len(shape) == 3: coords[2, ...] = range(shape[2]) return coords def _convert_warp_input(image, preserve_range): """Convert input image to double image with the appropriate range.""" if preserve_range: image = image.astype(np.double) else: image = img_as_float(image) return image def _clip_warp_output(input_image, output_image, order, mode, cval, clip): """Clip output image to range of values of input image. Note that this function modifies the values of `output_image` in-place and it is only modified if ``clip=True``. Parameters ---------- input_image : ndarray Input image. output_image : ndarray Output image, which is modified in-place. Other parameters ---------------- order : int, optional The order of the spline interpolation, default is 1. The order has to be in the range 0-5. See `skimage.transform.warp` for detail. mode : {'constant', 'edge', 'symmetric', 'reflect', 'wrap'}, optional Points outside the boundaries of the input are filled according to the given mode. Modes match the behaviour of `numpy.pad`. cval : float, optional Used in conjunction with mode 'constant', the value outside the image boundaries. clip : bool, optional Whether to clip the output to the range of values of the input image. This is enabled by default, since higher order interpolation may produce values outside the given input range. """ if clip and order != 0: min_val = input_image.min() max_val = input_image.max() preserve_cval = mode == 'constant' and not \ (min_val <= cval <= max_val) if preserve_cval: cval_mask = output_image == cval np.clip(output_image, min_val, max_val, out=output_image) if preserve_cval: output_image[cval_mask] = cval def warp(image, inverse_map, map_args={}, output_shape=None, order=1, mode='constant', cval=0., clip=True, preserve_range=False): """Warp an image according to a given coordinate transformation. Parameters ---------- image : ndarray Input image. inverse_map : transformation object, callable ``cr = f(cr, **kwargs)``, or ndarray Inverse coordinate map, which transforms coordinates in the output images into their corresponding coordinates in the input image. There are a number of different options to define this map, depending on the dimensionality of the input image. A 2-D image can have 2 dimensions for gray-scale images, or 3 dimensions with color information. - For 2-D images, you can directly pass a transformation object, e.g. `skimage.transform.SimilarityTransform`, or its inverse. - For 2-D images, you can pass a ``(3, 3)`` homogeneous transformation matrix, e.g. `skimage.transform.SimilarityTransform.params`. - For 2-D images, a function that transforms a ``(M, 2)`` array of ``(col, row)`` coordinates in the output image to their corresponding coordinates in the input image. Extra parameters to the function can be specified through `map_args`. - For N-D images, you can directly pass an array of coordinates. The first dimension specifies the coordinates in the input image, while the subsequent dimensions determine the position in the output image. E.g. in case of 2-D images, you need to pass an array of shape ``(2, rows, cols)``, where `rows` and `cols` determine the shape of the output image, and the first dimension contains the ``(row, col)`` coordinate in the input image. See `scipy.ndimage.map_coordinates` for further documentation. Note, that a ``(3, 3)`` matrix is interpreted as a homogeneous transformation matrix, so you cannot interpolate values from a 3-D input, if the output is of shape ``(3,)``. See example section for usage. map_args : dict, optional Keyword arguments passed to `inverse_map`. output_shape : tuple (rows, cols), optional Shape of the output image generated. By default the shape of the input image is preserved. Note that, even for multi-band images, only rows and columns need to be specified. order : int, optional The order of interpolation. The order has to be in the range 0-5: - 0: Nearest-neighbor - 1: Bi-linear (default) - 2: Bi-quadratic - 3: Bi-cubic - 4: Bi-quartic - 5: Bi-quintic mode : {'constant', 'edge', 'symmetric', 'reflect', 'wrap'}, optional Points outside the boundaries of the input are filled according to the given mode. Modes match the behaviour of `numpy.pad`. cval : float, optional Used in conjunction with mode 'constant', the value outside the image boundaries. clip : bool, optional Whether to clip the output to the range of values of the input image. This is enabled by default, since higher order interpolation may produce values outside the given input range. preserve_range : bool, optional Whether to keep the original range of values. Otherwise, the input image is converted according to the conventions of `img_as_float`. Returns ------- warped : double ndarray The warped input image. Notes ----- - The input image is converted to a `double` image. - In case of a `SimilarityTransform`, `AffineTransform` and `ProjectiveTransform` and `order` in [0, 3] this function uses the underlying transformation matrix to warp the image with a much faster routine. Examples -------- >>> from skimage.transform import warp >>> from skimage import data >>> image = data.camera() The following image warps are all equal but differ substantially in execution time. The image is shifted to the bottom. Use a geometric transform to warp an image (fast): >>> from skimage.transform import SimilarityTransform >>> tform = SimilarityTransform(translation=(0, -10)) >>> warped = warp(image, tform) Use a callable (slow): >>> def shift_down(xy): ... xy[:, 1] -= 10 ... return xy >>> warped = warp(image, shift_down) Use a transformation matrix to warp an image (fast): >>> matrix = np.array([[1, 0, 0], [0, 1, -10], [0, 0, 1]]) >>> warped = warp(image, matrix) >>> from skimage.transform import ProjectiveTransform >>> warped = warp(image, ProjectiveTransform(matrix=matrix)) You can also use the inverse of a geometric transformation (fast): >>> warped = warp(image, tform.inverse) For N-D images you can pass a coordinate array, that specifies the coordinates in the input image for every element in the output image. E.g. if you want to rescale a 3-D cube, you can do: >>> cube_shape = np.array([30, 30, 30]) >>> cube = np.random.rand(*cube_shape) Setup the coordinate array, that defines the scaling: >>> scale = 0.1 >>> output_shape = (scale * cube_shape).astype(int) >>> coords0, coords1, coords2 = np.mgrid[:output_shape[0], ... :output_shape[1], :output_shape[2]] >>> coords = np.array([coords0, coords1, coords2]) Assume that the cube contains spatial data, where the first array element center is at coordinate (0.5, 0.5, 0.5) in real space, i.e. we have to account for this extra offset when scaling the image: >>> coords = (coords + 0.5) / scale - 0.5 >>> warped = warp(cube, coords) """ image = _convert_warp_input(image, preserve_range) input_shape = np.array(image.shape) if output_shape is None: output_shape = input_shape else: output_shape = safe_as_int(output_shape) warped = None if order == 2: # When fixing this issue, make sure to fix the branches further # below in this function warn("Bi-quadratic interpolation behavior has changed due " "to a bug in the implementation of scikit-image. " "The new version now serves as a wrapper " "around SciPy's interpolation functions, which itself " "is not verified to be a correct implementation. Until " "skimage's implementation is fixed, we recommend " "to use bi-linear or bi-cubic interpolation instead.") if order in (0, 1, 3) and not map_args: # use fast Cython version for specific interpolation orders and input matrix = None if isinstance(inverse_map, np.ndarray) and inverse_map.shape == (3, 3): # inverse_map is a transformation matrix as numpy array matrix = inverse_map elif isinstance(inverse_map, HOMOGRAPHY_TRANSFORMS): # inverse_map is a homography matrix = inverse_map.params elif (hasattr(inverse_map, '__name__') and inverse_map.__name__ == 'inverse' and get_bound_method_class(inverse_map) \ in HOMOGRAPHY_TRANSFORMS): # inverse_map is the inverse of a homography matrix = np.linalg.inv(six.get_method_self(inverse_map).params) if matrix is not None: matrix = matrix.astype(np.double) if image.ndim == 2: warped = _warp_fast(image, matrix, output_shape=output_shape, order=order, mode=mode, cval=cval) elif image.ndim == 3: dims = [] for dim in range(image.shape[2]): dims.append(_warp_fast(image[..., dim], matrix, output_shape=output_shape, order=order, mode=mode, cval=cval)) warped = np.dstack(dims) if warped is None: # use ndi.map_coordinates if (isinstance(inverse_map, np.ndarray) and inverse_map.shape == (3, 3)): # inverse_map is a transformation matrix as numpy array, # this is only used for order >= 4. inverse_map = ProjectiveTransform(matrix=inverse_map) if isinstance(inverse_map, np.ndarray): # inverse_map is directly given as coordinates coords = inverse_map else: # inverse_map is given as function, that transforms (N, 2) # destination coordinates to their corresponding source # coordinates. This is only supported for 2(+1)-D images. if image.ndim < 2 or image.ndim > 3: raise ValueError("Only 2-D images (grayscale or color) are " "supported, when providing a callable " "`inverse_map`.") def coord_map(*args): return inverse_map(*args, **map_args) if len(input_shape) == 3 and len(output_shape) == 2: # Input image is 2D and has color channel, but output_shape is # given for 2-D images. Automatically add the color channel # dimensionality. output_shape = (output_shape[0], output_shape[1], input_shape[2]) coords = warp_coords(coord_map, output_shape) # Pre-filtering not necessary for order 0, 1 interpolation prefilter = order > 1 ndi_mode = _to_ndimage_mode(mode) warped = ndi.map_coordinates(image, coords, prefilter=prefilter, mode=ndi_mode, order=order, cval=cval) _clip_warp_output(image, warped, order, mode, cval, clip) return warped scikit-image-0.13.1/skimage/transform/_warps_cy.pyx000066400000000000000000000105031316256236300223330ustar00rootroot00000000000000#cython: cdivision=True #cython: boundscheck=False #cython: nonecheck=False #cython: wraparound=False import numpy as np cimport numpy as cnp from .._shared.interpolation cimport (nearest_neighbour_interpolation, bilinear_interpolation, biquadratic_interpolation, bicubic_interpolation) cdef inline void _matrix_transform(double x, double y, double* H, double *x_, double *y_) nogil: """Apply a homography to a coordinate. Parameters ---------- x, y : double Input coordinate. H : (3,3) *double Transformation matrix. x_, y_ : *double Output coordinate. """ cdef double xx, yy, zz xx = H[0] * x + H[1] * y + H[2] yy = H[3] * x + H[4] * y + H[5] zz = H[6] * x + H[7] * y + H[8] x_[0] = xx / zz y_[0] = yy / zz def _warp_fast(cnp.ndarray image, cnp.ndarray H, output_shape=None, int order=1, mode='constant', double cval=0): """Projective transformation (homography). Perform a projective transformation (homography) of a floating point image, using interpolation. For each pixel, given its homogeneous coordinate :math:`\mathbf{x} = [x, y, 1]^T`, its target position is calculated by multiplying with the given matrix, :math:`H`, to give :math:`H \mathbf{x}`. E.g., to rotate by theta degrees clockwise, the matrix should be:: [[cos(theta) -sin(theta) 0] [sin(theta) cos(theta) 0] [0 0 1]] or, to translate x by 10 and y by 20:: [[1 0 10] [0 1 20] [0 0 1 ]]. Parameters ---------- image : 2-D array Input image. H : array of shape ``(3, 3)`` Transformation matrix H that defines the homography. output_shape : tuple (rows, cols), optional Shape of the output image generated (default None). order : {0, 1, 2, 3}, optional Order of interpolation:: * 0: Nearest-neighbor * 1: Bi-linear (default) * 2: Bi-quadratic * 3: Bi-cubic mode : {'constant', 'edge', 'symmetric', 'reflect', 'wrap'}, optional Points outside the boundaries of the input are filled according to the given mode. Modes match the behaviour of `numpy.pad`. cval : string, optional (default 0) Used in conjunction with mode 'C' (constant), the value outside the image boundaries. Notes ----- Modes 'reflect' and 'symmetric' are similar, but differ in whether the edge pixels are duplicated during the reflection. As an example, if an array has values [0, 1, 2] and was padded to the right by four values using symmetric, the result would be [0, 1, 2, 2, 1, 0, 0], while for reflect it would be [0, 1, 2, 1, 0, 1, 2]. """ cdef double[:, ::1] img = np.ascontiguousarray(image, dtype=np.double) cdef double[:, ::1] M = np.ascontiguousarray(H) if mode not in ('constant', 'wrap', 'symmetric', 'reflect', 'edge'): raise ValueError("Invalid mode specified. Please use `constant`, " "`edge`, `wrap`, `reflect` or `symmetric`.") cdef char mode_c = ord(mode[0].upper()) cdef Py_ssize_t out_r, out_c if output_shape is None: out_r = int(img.shape[0]) out_c = int(img.shape[1]) else: out_r = int(output_shape[0]) out_c = int(output_shape[1]) cdef double[:, ::1] out = np.zeros((out_r, out_c), dtype=np.double) cdef Py_ssize_t tfr, tfc cdef double r, c cdef Py_ssize_t rows = img.shape[0] cdef Py_ssize_t cols = img.shape[1] cdef double (*interp_func)(double*, Py_ssize_t, Py_ssize_t, double, double, char, double) nogil if order == 0: interp_func = nearest_neighbour_interpolation elif order == 1: interp_func = bilinear_interpolation elif order == 2: interp_func = biquadratic_interpolation elif order == 3: interp_func = bicubic_interpolation with nogil: for tfr in range(out_r): for tfc in range(out_c): _matrix_transform(tfc, tfr, &M[0, 0], &c, &r) out[tfr, tfc] = interp_func(&img[0, 0], rows, cols, r, c, mode_c, cval) return np.asarray(out) scikit-image-0.13.1/skimage/transform/finite_radon_transform.py000066400000000000000000000062341316256236300247170ustar00rootroot00000000000000""" :author: Gary Ruben, 2009 :license: modified BSD """ __all__ = ["frt2", "ifrt2"] import numpy as np from numpy import roll, newaxis def frt2(a): """Compute the 2-dimensional finite radon transform (FRT) for an n x n integer array. Parameters ---------- a : array_like A 2-D square n x n integer array. Returns ------- FRT : 2-D ndarray Finite Radon Transform array of (n+1) x n integer coefficients. See Also -------- ifrt2 : The two-dimensional inverse FRT. Notes ----- The FRT has a unique inverse if and only if n is prime. [FRT] The idea for this algorithm is due to Vlad Negnevitski. Examples -------- Generate a test image: Use a prime number for the array dimensions >>> SIZE = 59 >>> img = np.tri(SIZE, dtype=np.int32) Apply the Finite Radon Transform: >>> f = frt2(img) References ---------- .. [FRT] A. Kingston and I. Svalbe, "Projective transforms on periodic discrete image arrays," in P. Hawkes (Ed), Advances in Imaging and Electron Physics, 139 (2006) """ if a.ndim != 2 or a.shape[0] != a.shape[1]: raise ValueError("Input must be a square, 2-D array") ai = a.copy() n = ai.shape[0] f = np.empty((n + 1, n), np.uint32) f[0] = ai.sum(axis=0) for m in range(1, n): # Roll the pth row of ai left by p places for row in range(1, n): ai[row] = roll(ai[row], -row) f[m] = ai.sum(axis=0) f[n] = ai.sum(axis=1) return f def ifrt2(a): """Compute the 2-dimensional inverse finite radon transform (iFRT) for an (n+1) x n integer array. Parameters ---------- a : array_like A 2-D (n+1) row x n column integer array. Returns ------- iFRT : 2-D n x n ndarray Inverse Finite Radon Transform array of n x n integer coefficients. See Also -------- frt2 : The two-dimensional FRT Notes ----- The FRT has a unique inverse if and only if n is prime. See [1]_ for an overview. The idea for this algorithm is due to Vlad Negnevitski. Examples -------- >>> SIZE = 59 >>> img = np.tri(SIZE, dtype=np.int32) Apply the Finite Radon Transform: >>> f = frt2(img) Apply the Inverse Finite Radon Transform to recover the input >>> fi = ifrt2(f) Check that it's identical to the original >>> assert len(np.nonzero(img-fi)[0]) == 0 References ---------- .. [1] A. Kingston and I. Svalbe, "Projective transforms on periodic discrete image arrays," in P. Hawkes (Ed), Advances in Imaging and Electron Physics, 139 (2006) """ if a.ndim != 2 or a.shape[0] != a.shape[1] + 1: raise ValueError("Input must be an (n+1) row x n column, 2-D array") ai = a.copy()[:-1] n = ai.shape[1] f = np.empty((n, n), np.uint32) f[0] = ai.sum(axis=0) for m in range(1, n): # Rolls the pth row of ai right by p places. for row in range(1, ai.shape[0]): ai[row] = roll(ai[row], row) f[m] = ai.sum(axis=0) f += a[-1][newaxis].T f = (f - ai[0].sum()) / n return f scikit-image-0.13.1/skimage/transform/hough_transform.py000066400000000000000000000275411316256236300233740ustar00rootroot00000000000000import numpy as np from scipy import ndimage from .. import measure from ._hough_transform import (_hough_circle, _hough_ellipse, _hough_line, _probabilistic_hough_line as _prob_hough_line) def hough_line_peaks(hspace, angles, dists, min_distance=9, min_angle=10, threshold=None, num_peaks=np.inf): """Return peaks in a straight line Hough transform. Identifies most prominent lines separated by a certain angle and distance in a Hough transform. Non-maximum suppression with different sizes is applied separately in the first (distances) and second (angles) dimension of the Hough space to identify peaks. Parameters ---------- hspace : (N, M) array Hough space returned by the `hough_line` function. angles : (M,) array Angles returned by the `hough_line` function. Assumed to be continuous. (`angles[-1] - angles[0] == PI`). dists : (N, ) array Distances returned by the `hough_line` function. min_distance : int, optional Minimum distance separating lines (maximum filter size for first dimension of hough space). min_angle : int, optional Minimum angle separating lines (maximum filter size for second dimension of hough space). threshold : float, optional Minimum intensity of peaks. Default is `0.5 * max(hspace)`. num_peaks : int, optional Maximum number of peaks. When the number of peaks exceeds `num_peaks`, return `num_peaks` coordinates based on peak intensity. Returns ------- accum, angles, dists : tuple of array Peak values in Hough space, angles and distances. Examples -------- >>> from skimage.transform import hough_line, hough_line_peaks >>> from skimage.draw import line >>> img = np.zeros((15, 15), dtype=np.bool_) >>> rr, cc = line(0, 0, 14, 14) >>> img[rr, cc] = 1 >>> rr, cc = line(0, 14, 14, 0) >>> img[cc, rr] = 1 >>> hspace, angles, dists = hough_line(img) >>> hspace, angles, dists = hough_line_peaks(hspace, angles, dists) >>> len(angles) 2 """ from skimage.feature.peak import _prominent_peaks h, a, d = _prominent_peaks(hspace, min_xdistance=min_angle, min_ydistance=min_distance, threshold=threshold, num_peaks=num_peaks) return (h, angles[a], dists[d]) def hough_circle(image, radius, normalize=True, full_output=False): """Perform a circular Hough transform. Parameters ---------- image : (M, N) ndarray Input image with nonzero values representing edges. radius : scalar or sequence of scalars Radii at which to compute the Hough transform. Floats are converted to integers. normalize : boolean, optional (default True) Normalize the accumulator with the number of pixels used to draw the radius. full_output : boolean, optional (default False) Extend the output size by twice the largest radius in order to detect centers outside the input picture. Returns ------- H : 3D ndarray (radius index, (M + 2R, N + 2R) ndarray) Hough transform accumulator for each radius. R designates the larger radius if full_output is True. Otherwise, R = 0. Examples -------- >>> from skimage.transform import hough_circle >>> from skimage.draw import circle_perimeter >>> img = np.zeros((100, 100), dtype=np.bool_) >>> rr, cc = circle_perimeter(25, 35, 23) >>> img[rr, cc] = 1 >>> try_radii = np.arange(5, 50) >>> res = hough_circle(img, try_radii) >>> ridx, r, c = np.unravel_index(np.argmax(res), res.shape) >>> r, c, try_radii[ridx] (25, 35, 23) """ radius = np.atleast_1d(np.asarray(radius)) return _hough_circle(image, radius.astype(np.intp), normalize=normalize, full_output=full_output) def hough_ellipse(img, threshold=4, accuracy=1, min_size=4, max_size=None): """Perform an elliptical Hough transform. Parameters ---------- img : (M, N) ndarray Input image with nonzero values representing edges. threshold: int, optional Accumulator threshold value. accuracy : double, optional Bin size on the minor axis used in the accumulator. min_size : int, optional Minimal major axis length. max_size : int, optional Maximal minor axis length. If None, the value is set to the half of the smaller image dimension. Returns ------- result : ndarray with fields [(accumulator, y0, x0, a, b, orientation)] Where ``(yc, xc)`` is the center, ``(a, b)`` the major and minor axes, respectively. The `orientation` value follows `skimage.draw.ellipse_perimeter` convention. Examples -------- >>> from skimage.transform import hough_ellipse >>> from skimage.draw import ellipse_perimeter >>> img = np.zeros((25, 25), dtype=np.uint8) >>> rr, cc = ellipse_perimeter(10, 10, 6, 8) >>> img[cc, rr] = 1 >>> result = hough_ellipse(img, threshold=8) >>> result.tolist() [(10, 10.0, 10.0, 8.0, 6.0, 0.0)] Notes ----- The accuracy must be chosen to produce a peak in the accumulator distribution. In other words, a flat accumulator distribution with low values may be caused by a too low bin size. References ---------- .. [1] Xie, Yonghong, and Qiang Ji. "A new efficient ellipse detection method." Pattern Recognition, 2002. Proceedings. 16th International Conference on. Vol. 2. IEEE, 2002 """ return _hough_ellipse(img, threshold=threshold, accuracy=accuracy, min_size=min_size, max_size=max_size) def hough_line(img, theta=None): """Perform a straight line Hough transform. Parameters ---------- img : (M, N) ndarray Input image with nonzero values representing edges. theta : 1D ndarray of double, optional Angles at which to compute the transform, in radians. Defaults to a vector of 180 angles evenly spaced from -pi/2 to pi/2. Returns ------- hspace : 2-D ndarray of uint64 Hough transform accumulator. angles : ndarray Angles at which the transform is computed, in radians. distances : ndarray Distance values. Notes ----- The origin is the top left corner of the original image. X and Y axis are horizontal and vertical edges respectively. The distance is the minimal algebraic distance from the origin to the detected line. The angle accuracy can be improved by decreasing the step size in the `theta` array. Examples -------- Generate a test image: >>> img = np.zeros((100, 150), dtype=bool) >>> img[30, :] = 1 >>> img[:, 65] = 1 >>> img[35:45, 35:50] = 1 >>> for i in range(90): ... img[i, i] = 1 >>> img += np.random.random(img.shape) > 0.95 Apply the Hough transform: >>> out, angles, d = hough_line(img) .. plot:: hough_tf.py """ if img.ndim != 2: raise ValueError('The input image `img` must be 2D.') if theta is None: # These values are approximations of pi/2 theta = np.linspace(-np.pi / 2, np.pi / 2, 180) return _hough_line(img, theta=theta) def probabilistic_hough_line(img, threshold=10, line_length=50, line_gap=10, theta=None): """Return lines from a progressive probabilistic line Hough transform. Parameters ---------- img : (M, N) ndarray Input image with nonzero values representing edges. threshold : int, optional Threshold line_length : int, optional Minimum accepted length of detected lines. Increase the parameter to extract longer lines. line_gap : int, optional Maximum gap between pixels to still form a line. Increase the parameter to merge broken lines more aggresively. theta : 1D ndarray, dtype=double, optional Angles at which to compute the transform, in radians. If None, use a range from -pi/2 to pi/2. Returns ------- lines : list List of lines identified, lines in format ((x0, y0), (x1, y1)), indicating line start and end. References ---------- .. [1] C. Galamhos, J. Matas and J. Kittler, "Progressive probabilistic Hough transform for line detection", in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1999. """ if img.ndim != 2: raise ValueError('The input image `img` must be 2D.') if theta is None: theta = np.pi / 2 - np.arange(180) / 180.0 * np.pi return _prob_hough_line(img, threshold=threshold, line_length=line_length, line_gap=line_gap, theta=theta) def hough_circle_peaks(hspaces, radii, min_xdistance=1, min_ydistance=1, threshold=None, num_peaks=np.inf, total_num_peaks=np.inf, normalize=False): """Return peaks in a circle Hough transform. Identifies most prominent circles separated by certain distances in a Hough space. Non-maximum suppression with different sizes is applied separately in the first and second dimension of the Hough space to identify peaks. Parameters ---------- hspaces : (N, M) array Hough spaces returned by the `hough_circle` function. radii : (M,) array Radii corresponding to Hough spaces. min_xdistance : int, optional Minimum distance separating centers in the x dimension. min_ydistance : int, optional Minimum distance separating centers in the y dimension. threshold : float, optional Minimum intensity of peaks in each Hough space. Default is `0.5 * max(hspace)`. num_peaks : int, optional Maximum number of peaks in each Hough space. When the number of peaks exceeds `num_peaks`, only `num_peaks` coordinates based on peak intensity are considered for the corresponding radius. total_num_peaks : int, optional Maximum number of peaks. When the number of peaks exceeds `num_peaks`, return `num_peaks` coordinates based on peak intensity. normalize : bool, optional If True, normalize the accumulator by the radius to sort the prominent peaks. Returns ------- accum, cx, cy, rad : tuple of array Peak values in Hough space, x and y center coordinates and radii. Examples -------- >>> from skimage import transform as tf >>> from skimage import draw >>> img = np.zeros((120, 100), dtype=int) >>> radius, x_0, y_0 = (20, 99, 50) >>> y, x = draw.circle_perimeter(y_0, x_0, radius) >>> img[x, y] = 1 >>> hspaces = tf.hough_circle(img, radius) >>> accum, cx, cy, rad = hough_circle_peaks(hspaces, [radius,]) """ from skimage.feature.peak import _prominent_peaks r = [] cx = [] cy = [] accum = [] for rad, hp in zip(radii, hspaces): h_p, x_p, y_p = _prominent_peaks(hp, min_xdistance=min_xdistance, min_ydistance=min_ydistance, threshold=threshold, num_peaks=num_peaks) r.extend((rad,)*len(h_p)) cx.extend(x_p) cy.extend(y_p) accum.extend(h_p) r = np.array(r) cx = np.array(cx) cy = np.array(cy) accum = np.array(accum) if normalize: s = np.argsort(accum / r) else: s = np.argsort(accum) if total_num_peaks != np.inf: tnp = total_num_peaks return (accum[s][::-1][:tnp], cx[s][::-1][:tnp], cy[s][::-1][:tnp], r[s][::-1][:tnp]) return (accum[s][::-1], cx[s][::-1], cy[s][::-1], r[s][::-1]) scikit-image-0.13.1/skimage/transform/integral.py000066400000000000000000000123221316256236300217630ustar00rootroot00000000000000import numpy as np import collections from .._shared.utils import warn def integral_image(img): """Integral image / summed area table. The integral image contains the sum of all elements above and to the left of it, i.e.: .. math:: S[m, n] = \sum_{i \leq m} \sum_{j \leq n} X[i, j] Parameters ---------- img : ndarray Input image. Returns ------- S : ndarray Integral image/summed area table of same shape as input image. References ---------- .. [1] F.C. Crow, "Summed-area tables for texture mapping," ACM SIGGRAPH Computer Graphics, vol. 18, 1984, pp. 207-212. """ S = img for i in range(img.ndim): S = S.cumsum(axis=i) return S def integrate(ii, start, end, *args): """Use an integral image to integrate over a given window. Parameters ---------- ii : ndarray Integral image. start : List of tuples, each tuple of length equal to dimension of `ii` Coordinates of top left corner of window(s). Each tuple in the list contains the starting row, col, ... index i.e `[(row_win1, col_win1, ...), (row_win2, col_win2,...), ...]`. end : List of tuples, each tuple of length equal to dimension of `ii` Coordinates of bottom right corner of window(s). Each tuple in the list containing the end row, col, ... index i.e `[(row_win1, col_win1, ...), (row_win2, col_win2, ...), ...]`. args: optional For backward compatibility with versions prior to 0.12. The earlier function signature was `integrate(ii, r0, c0, r1, c1)`, where `r0`, `c0` are int(lists) specifying start coordinates of window(s) to be integrated and `r1`, `c1` the end coordinates. Returns ------- S : scalar or ndarray Integral (sum) over the given window(s). Examples -------- >>> arr = np.ones((5, 6), dtype=np.float) >>> ii = integral_image(arr) >>> integrate(ii, (1, 0), (1, 2)) # sum from (1, 0) to (1, 2) array([ 3.]) >>> integrate(ii, [(3, 3)], [(4, 5)]) # sum from (3, 3) to (4, 5) array([ 6.]) >>> # sum from (1, 0) to (1, 2) and from (3, 3) to (4, 5) >>> integrate(ii, [(1, 0), (3, 3)], [(1, 2), (4, 5)]) array([ 3., 6.]) """ rows = 1 # handle input from new input format if len(args) == 0: start = np.atleast_2d(np.array(start)) end = np.atleast_2d(np.array(end)) rows = start.shape[0] # handle deprecated input format else: warn("The syntax 'integrate(ii, r0, c0, r1, c1)' is " "deprecated, and will be phased out in release 0.14. " "The new syntax is " "'integrate(ii, (r0, c0), (r1, c1))'.") if isinstance(start, collections.Iterable): rows = len(start) args = (start, end) + args start = np.array(args[:int(len(args)/2)]).T end = np.array(args[int(len(args)/2):]).T total_shape = ii.shape total_shape = np.tile(total_shape, [rows, 1]) # convert negative indices into equivalent positive indices start_negatives = start < 0 end_negatives = end < 0 start = (start + total_shape) * start_negatives + \ start * ~(start_negatives) end = (end + total_shape) * end_negatives + \ end * ~(end_negatives) if np.any((end - start) < 0): raise IndexError('end coordinates must be greater or equal to start') # bit_perm is the total number of terms in the expression # of S. For example, in the case of a 4x4 2D image # sum of image from (1,1) to (2,2) is given by # S = + ii[2, 2] # - ii[0, 2] - ii[2, 0] # + ii[0, 0] # The total terms = 4 = 2 ** 2(dims) S = np.zeros(rows) bit_perm = 2 ** ii.ndim width = len(bin(bit_perm - 1)[2:]) # Sum of a (hyper)cube, from an integral image is computed using # values at the corners of the cube. The corners of cube are # selected using binary numbers as described in the following example. # In a 3D cube there are 8 corners. The corners are selected using # binary numbers 000 to 111. Each number is called a permutation, where # perm(000) means, select end corner where none of the coordinates # is replaced, i.e ii[end_row, end_col, end_depth]. Similarly, perm(001) # means replace last coordinate by start - 1, i.e # ii[end_row, end_col, start_depth - 1], and so on. # Sign of even permutations is positive, while those of odd is negative. # If 'start_coord - 1' is -ve it is labeled bad and not considered in # the final sum. for i in range(bit_perm): # for all permutations # boolean permutation array eg [True, False] for '10' binary = bin(i)[2:].zfill(width) bool_mask = [bit == '1' for bit in binary] sign = (-1)**sum(bool_mask) # determine sign of permutation bad = [np.any(((start[r] - 1) * bool_mask) < 0) for r in range(rows)] # find out bad start rows corner_points = (end * (np.invert(bool_mask))) + \ ((start - 1) * bool_mask) # find corner for each row S += [sign * ii[tuple(corner_points[r])] if(not bad[r]) else 0 for r in range(rows)] # add only good rows return S scikit-image-0.13.1/skimage/transform/pyramids.py000066400000000000000000000232761316256236300220200ustar00rootroot00000000000000import math import numpy as np from scipy import ndimage as ndi from ..transform import resize from ..util import img_as_float def _smooth(image, sigma, mode, cval): """Return image with each channel smoothed by the Gaussian filter.""" smoothed = np.empty(image.shape, dtype=np.double) # apply Gaussian filter to all dimensions independently if image.ndim == 3: for dim in range(image.shape[2]): ndi.gaussian_filter(image[..., dim], sigma, output=smoothed[..., dim], mode=mode, cval=cval) else: ndi.gaussian_filter(image, sigma, output=smoothed, mode=mode, cval=cval) return smoothed def _check_factor(factor): if factor <= 1: raise ValueError('scale factor must be greater than 1') def pyramid_reduce(image, downscale=2, sigma=None, order=1, mode='reflect', cval=0): """Smooth and then downsample image. Parameters ---------- image : array Input image. downscale : float, optional Downscale factor. sigma : float, optional Sigma for Gaussian filter. Default is `2 * downscale / 6.0` which corresponds to a filter mask twice the size of the scale factor that covers more than 99% of the Gaussian distribution. order : int, optional Order of splines used in interpolation of downsampling. See `skimage.transform.warp` for detail. mode : {'reflect', 'constant', 'edge', 'symmetric', 'wrap'}, optional The mode parameter determines how the array borders are handled, where cval is the value when mode is equal to 'constant'. cval : float, optional Value to fill past edges of input if mode is 'constant'. Returns ------- out : array Smoothed and downsampled float image. References ---------- .. [1] http://web.mit.edu/persci/people/adelson/pub_pdfs/pyramid83.pdf """ _check_factor(downscale) image = img_as_float(image) rows = image.shape[0] cols = image.shape[1] out_rows = math.ceil(rows / float(downscale)) out_cols = math.ceil(cols / float(downscale)) if sigma is None: # automatically determine sigma which covers > 99% of distribution sigma = 2 * downscale / 6.0 smoothed = _smooth(image, sigma, mode, cval) out = resize(smoothed, (out_rows, out_cols), order=order, mode=mode, cval=cval) return out def pyramid_expand(image, upscale=2, sigma=None, order=1, mode='reflect', cval=0): """Upsample and then smooth image. Parameters ---------- image : array Input image. upscale : float, optional Upscale factor. sigma : float, optional Sigma for Gaussian filter. Default is `2 * upscale / 6.0` which corresponds to a filter mask twice the size of the scale factor that covers more than 99% of the Gaussian distribution. order : int, optional Order of splines used in interpolation of upsampling. See `skimage.transform.warp` for detail. mode : {'reflect', 'constant', 'edge', 'symmetric', 'wrap'}, optional The mode parameter determines how the array borders are handled, where cval is the value when mode is equal to 'constant'. cval : float, optional Value to fill past edges of input if mode is 'constant'. Returns ------- out : array Upsampled and smoothed float image. References ---------- .. [1] http://web.mit.edu/persci/people/adelson/pub_pdfs/pyramid83.pdf """ _check_factor(upscale) image = img_as_float(image) rows = image.shape[0] cols = image.shape[1] out_rows = math.ceil(upscale * rows) out_cols = math.ceil(upscale * cols) if sigma is None: # automatically determine sigma which covers > 99% of distribution sigma = 2 * upscale / 6.0 resized = resize(image, (out_rows, out_cols), order=order, mode=mode, cval=cval) out = _smooth(resized, sigma, mode, cval) return out def pyramid_gaussian(image, max_layer=-1, downscale=2, sigma=None, order=1, mode='reflect', cval=0): """Yield images of the Gaussian pyramid formed by the input image. Recursively applies the `pyramid_reduce` function to the image, and yields the downscaled images. Note that the first image of the pyramid will be the original, unscaled image. The total number of images is `max_layer + 1`. In case all layers are computed, the last image is either a one-pixel image or the image where the reduction does not change its shape. Parameters ---------- image : array Input image. max_layer : int Number of layers for the pyramid. 0th layer is the original image. Default is -1 which builds all possible layers. downscale : float, optional Downscale factor. sigma : float, optional Sigma for Gaussian filter. Default is `2 * downscale / 6.0` which corresponds to a filter mask twice the size of the scale factor that covers more than 99% of the Gaussian distribution. order : int, optional Order of splines used in interpolation of downsampling. See `skimage.transform.warp` for detail. mode : {'reflect', 'constant', 'edge', 'symmetric', 'wrap'}, optional The mode parameter determines how the array borders are handled, where cval is the value when mode is equal to 'constant'. cval : float, optional Value to fill past edges of input if mode is 'constant'. Returns ------- pyramid : generator Generator yielding pyramid layers as float images. References ---------- .. [1] http://web.mit.edu/persci/people/adelson/pub_pdfs/pyramid83.pdf """ _check_factor(downscale) # cast to float for consistent data type in pyramid image = img_as_float(image) layer = 0 rows = image.shape[0] cols = image.shape[1] prev_layer_image = image yield image # build downsampled images until max_layer is reached or downscale process # does not change image size while layer != max_layer: layer += 1 layer_image = pyramid_reduce(prev_layer_image, downscale, sigma, order, mode, cval) prev_rows = rows prev_cols = cols prev_layer_image = layer_image rows = layer_image.shape[0] cols = layer_image.shape[1] # no change to previous pyramid layer if prev_rows == rows and prev_cols == cols: break yield layer_image def pyramid_laplacian(image, max_layer=-1, downscale=2, sigma=None, order=1, mode='reflect', cval=0): """Yield images of the laplacian pyramid formed by the input image. Each layer contains the difference between the downsampled and the downsampled, smoothed image:: layer = resize(prev_layer) - smooth(resize(prev_layer)) Note that the first image of the pyramid will be the difference between the original, unscaled image and its smoothed version. The total number of images is `max_layer + 1`. In case all layers are computed, the last image is either a one-pixel image or the image where the reduction does not change its shape. Parameters ---------- image : array Input image. max_layer : int Number of layers for the pyramid. 0th layer is the original image. Default is -1 which builds all possible layers. downscale : float, optional Downscale factor. sigma : float, optional Sigma for Gaussian filter. Default is `2 * downscale / 6.0` which corresponds to a filter mask twice the size of the scale factor that covers more than 99% of the Gaussian distribution. order : int, optional Order of splines used in interpolation of downsampling. See `skimage.transform.warp` for detail. mode : {'reflect', 'constant', 'edge', 'symmetric', 'wrap'}, optional The mode parameter determines how the array borders are handled, where cval is the value when mode is equal to 'constant'. cval : float, optional Value to fill past edges of input if mode is 'constant'. Returns ------- pyramid : generator Generator yielding pyramid layers as float images. References ---------- .. [1] http://web.mit.edu/persci/people/adelson/pub_pdfs/pyramid83.pdf .. [2] http://sepwww.stanford.edu/data/media/public/sep/morgan/texturematch/paper_html/node3.html """ _check_factor(downscale) # cast to float for consistent data type in pyramid image = img_as_float(image) if sigma is None: # automatically determine sigma which covers > 99% of distribution sigma = 2 * downscale / 6.0 layer = 0 rows = image.shape[0] cols = image.shape[1] smoothed_image = _smooth(image, sigma, mode, cval) yield image - smoothed_image # build downsampled images until max_layer is reached or downscale process # does not change image size while layer != max_layer: layer += 1 out_rows = math.ceil(rows / float(downscale)) out_cols = math.ceil(cols / float(downscale)) resized_image = resize(smoothed_image, (out_rows, out_cols), order=order, mode=mode, cval=cval) smoothed_image = _smooth(resized_image, sigma, mode, cval) prev_rows = rows prev_cols = cols rows = resized_image.shape[0] cols = resized_image.shape[1] # no change to previous pyramid layer if prev_rows == rows and prev_cols == cols: break yield resized_image - smoothed_image scikit-image-0.13.1/skimage/transform/radon_transform.py000066400000000000000000000444611316256236300233650ustar00rootroot00000000000000# -*- coding: utf-8 -*- from __future__ import division import numpy as np from scipy.fftpack import fft, ifft, fftfreq from scipy.interpolate import interp1d from ._warps_cy import _warp_fast from ._radon_transform import sart_projection_update from warnings import warn __all__ = ['radon', 'order_angles_golden_ratio', 'iradon', 'iradon_sart'] def radon(image, theta=None, circle=None): """ Calculates the radon transform of an image given specified projection angles. Parameters ---------- image : array_like, dtype=float Input image. The rotation axis will be located in the pixel with indices ``(image.shape[0] // 2, image.shape[1] // 2)``. theta : array_like, dtype=float, optional (default np.arange(180)) Projection angles (in degrees). circle : boolean, optional Assume image is zero outside the inscribed circle, making the width of each projection (the first dimension of the sinogram) equal to ``min(image.shape)``. The default behavior (None) is equivalent to False. Returns ------- radon_image : ndarray Radon transform (sinogram). The tomography rotation axis will lie at the pixel index ``radon_image.shape[0] // 2`` along the 0th dimension of ``radon_image``. References ---------- .. [1] AC Kak, M Slaney, "Principles of Computerized Tomographic Imaging", IEEE Press 1988. .. [2] B.R. Ramesh, N. Srinivasa, K. Rajgopal, "An Algorithm for Computing the Discrete Radon Transform With Some Applications", Proceedings of the Fourth IEEE Region 10 International Conference, TENCON '89, 1989 Notes ----- Based on code of Justin K. Romberg (http://www.clear.rice.edu/elec431/projects96/DSP/bpanalysis.html) """ if image.ndim != 2: raise ValueError('The input image must be 2-D') if theta is None: theta = np.arange(180) if circle is None: warn('The default of `circle` in `skimage.transform.radon` ' 'will change to `True` in version 0.15.') circle = False if circle: radius = min(image.shape) // 2 c0, c1 = np.ogrid[0:image.shape[0], 0:image.shape[1]] reconstruction_circle = ((c0 - image.shape[0] // 2) ** 2 + (c1 - image.shape[1] // 2) ** 2) reconstruction_circle = reconstruction_circle <= radius ** 2 if not np.all(reconstruction_circle | (image == 0)): warn('Radon transform: image must be zero outside the ' 'reconstruction circle') # Crop image to make it square slices = [] for d in (0, 1): if image.shape[d] > min(image.shape): excess = image.shape[d] - min(image.shape) slices.append(slice(int(np.ceil(excess / 2)), int(np.ceil(excess / 2) + min(image.shape)))) else: slices.append(slice(None)) slices = tuple(slices) padded_image = image[slices] else: diagonal = np.sqrt(2) * max(image.shape) pad = [int(np.ceil(diagonal - s)) for s in image.shape] new_center = [(s + p) // 2 for s, p in zip(image.shape, pad)] old_center = [s // 2 for s in image.shape] pad_before = [nc - oc for oc, nc in zip(old_center, new_center)] pad_width = [(pb, p - pb) for pb, p in zip(pad_before, pad)] padded_image = np.pad(image, pad_width, mode='constant', constant_values=0) # padded_image is always square assert padded_image.shape[0] == padded_image.shape[1] radon_image = np.zeros((padded_image.shape[0], len(theta))) center = padded_image.shape[0] // 2 shift0 = np.array([[1, 0, -center], [0, 1, -center], [0, 0, 1]]) shift1 = np.array([[1, 0, center], [0, 1, center], [0, 0, 1]]) def build_rotation(theta): T = np.deg2rad(theta) R = np.array([[np.cos(T), np.sin(T), 0], [-np.sin(T), np.cos(T), 0], [0, 0, 1]]) return shift1.dot(R).dot(shift0) for i in range(len(theta)): rotated = _warp_fast(padded_image, build_rotation(theta[i])) radon_image[:, i] = rotated.sum(0) return radon_image def _sinogram_circle_to_square(sinogram): diagonal = int(np.ceil(np.sqrt(2) * sinogram.shape[0])) pad = diagonal - sinogram.shape[0] old_center = sinogram.shape[0] // 2 new_center = diagonal // 2 pad_before = new_center - old_center pad_width = ((pad_before, pad - pad_before), (0, 0)) return np.pad(sinogram, pad_width, mode='constant', constant_values=0) def iradon(radon_image, theta=None, output_size=None, filter="ramp", interpolation="linear", circle=None): """ Inverse radon transform. Reconstruct an image from the radon transform, using the filtered back projection algorithm. Parameters ---------- radon_image : array_like, dtype=float Image containing radon transform (sinogram). Each column of the image corresponds to a projection along a different angle. The tomography rotation axis should lie at the pixel index ``radon_image.shape[0] // 2`` along the 0th dimension of ``radon_image``. theta : array_like, dtype=float, optional Reconstruction angles (in degrees). Default: m angles evenly spaced between 0 and 180 (if the shape of `radon_image` is (N, M)). output_size : int Number of rows and columns in the reconstruction. filter : str, optional (default ramp) Filter used in frequency domain filtering. Ramp filter used by default. Filters available: ramp, shepp-logan, cosine, hamming, hann. Assign None to use no filter. interpolation : str, optional (default 'linear') Interpolation method used in reconstruction. Methods available: 'linear', 'nearest', and 'cubic' ('cubic' is slow). circle : boolean, optional Assume the reconstructed image is zero outside the inscribed circle. Also changes the default output_size to match the behaviour of ``radon`` called with ``circle=True``. The default behavior (None) is equivalent to False. Returns ------- reconstructed : ndarray Reconstructed image. The rotation axis will be located in the pixel with indices ``(reconstructed.shape[0] // 2, reconstructed.shape[1] // 2)``. References ---------- .. [1] AC Kak, M Slaney, "Principles of Computerized Tomographic Imaging", IEEE Press 1988. .. [2] B.R. Ramesh, N. Srinivasa, K. Rajgopal, "An Algorithm for Computing the Discrete Radon Transform With Some Applications", Proceedings of the Fourth IEEE Region 10 International Conference, TENCON '89, 1989 Notes ----- It applies the Fourier slice theorem to reconstruct an image by multiplying the frequency domain of the filter with the FFT of the projection data. This algorithm is called filtered back projection. """ if radon_image.ndim != 2: raise ValueError('The input image must be 2-D') if theta is None: m, n = radon_image.shape theta = np.linspace(0, 180, n, endpoint=False) else: theta = np.asarray(theta) if len(theta) != radon_image.shape[1]: raise ValueError("The given ``theta`` does not match the number of " "projections in ``radon_image``.") interpolation_types = ('linear', 'nearest', 'cubic') if interpolation not in interpolation_types: raise ValueError("Unknown interpolation: %s" % interpolation) if not output_size: # If output size not specified, estimate from input radon image if circle: output_size = radon_image.shape[0] else: output_size = int(np.floor(np.sqrt((radon_image.shape[0]) ** 2 / 2.0))) if circle is None: warn('The default of `circle` in `skimage.transform.iradon` ' 'will change to `True` in version 0.15.') circle = False if circle: radon_image = _sinogram_circle_to_square(radon_image) th = (np.pi / 180.0) * theta # resize image to next power of two (but no less than 64) for # Fourier analysis; speeds up Fourier and lessens artifacts projection_size_padded = \ max(64, int(2 ** np.ceil(np.log2(2 * radon_image.shape[0])))) pad_width = ((0, projection_size_padded - radon_image.shape[0]), (0, 0)) img = np.pad(radon_image, pad_width, mode='constant', constant_values=0) # Construct the Fourier filter f = fftfreq(projection_size_padded).reshape(-1, 1) # digital frequency omega = 2 * np.pi * f # angular frequency fourier_filter = 2 * np.abs(f) # ramp filter if filter == "ramp": pass elif filter == "shepp-logan": # Start from first element to avoid divide by zero fourier_filter[1:] = fourier_filter[1:] * np.sin(omega[1:]) / omega[1:] elif filter == "cosine": fourier_filter *= np.cos(omega) elif filter == "hamming": fourier_filter *= (0.54 + 0.46 * np.cos(omega / 2)) elif filter == "hann": fourier_filter *= (1 + np.cos(omega / 2)) / 2 elif filter is None: fourier_filter[:] = 1 else: raise ValueError("Unknown filter: %s" % filter) # Apply filter in Fourier domain projection = fft(img, axis=0) * fourier_filter radon_filtered = np.real(ifft(projection, axis=0)) # Resize filtered image back to original size radon_filtered = radon_filtered[:radon_image.shape[0], :] reconstructed = np.zeros((output_size, output_size)) # Determine the center of the projections (= center of sinogram) mid_index = radon_image.shape[0] // 2 [X, Y] = np.mgrid[0:output_size, 0:output_size] xpr = X - int(output_size) // 2 ypr = Y - int(output_size) // 2 # Reconstruct image by interpolation for i in range(len(theta)): t = ypr * np.cos(th[i]) - xpr * np.sin(th[i]) x = np.arange(radon_filtered.shape[0]) - mid_index if interpolation == 'linear': backprojected = np.interp(t, x, radon_filtered[:, i], left=0, right=0) else: interpolant = interp1d(x, radon_filtered[:, i], kind=interpolation, bounds_error=False, fill_value=0) backprojected = interpolant(t) reconstructed += backprojected if circle: radius = output_size // 2 reconstruction_circle = (xpr ** 2 + ypr ** 2) <= radius ** 2 reconstructed[~reconstruction_circle] = 0. return reconstructed * np.pi / (2 * len(th)) def order_angles_golden_ratio(theta): """ Order angles to reduce the amount of correlated information in subsequent projections. Parameters ---------- theta : 1D array of floats Projection angles in degrees. Duplicate angles are not allowed. Returns ------- indices_generator : generator yielding unsigned integers The returned generator yields indices into ``theta`` such that ``theta[indices]`` gives the approximate golden ratio ordering of the projections. In total, ``len(theta)`` indices are yielded. All non-negative integers < ``len(theta)`` are yielded exactly once. Notes ----- The method used here is that of the golden ratio introduced by T. Kohler. References ---------- .. [1] Kohler, T. "A projection access scheme for iterative reconstruction based on the golden section." Nuclear Science Symposium Conference Record, 2004 IEEE. Vol. 6. IEEE, 2004. .. [2] Winkelmann, Stefanie, et al. "An optimal radial profile order based on the Golden Ratio for time-resolved MRI." Medical Imaging, IEEE Transactions on 26.1 (2007): 68-76. """ interval = 180 def angle_distance(a, b): difference = a - b return min(abs(difference % interval), abs(difference % -interval)) remaining = list(np.argsort(theta)) # indices into theta # yield an arbitrary angle to start things off index = remaining.pop(0) angle = theta[index] yield index # determine subsequent angles using the golden ratio method angle_increment = interval * (1 - (np.sqrt(5) - 1) / 2) while remaining: angle = (angle + angle_increment) % interval insert_point = np.searchsorted(theta[remaining], angle) index_below = insert_point - 1 index_above = 0 if insert_point == len(remaining) else insert_point distance_below = angle_distance(angle, theta[remaining[index_below]]) distance_above = angle_distance(angle, theta[remaining[index_above]]) if distance_below < distance_above: yield remaining.pop(index_below) else: yield remaining.pop(index_above) def iradon_sart(radon_image, theta=None, image=None, projection_shifts=None, clip=None, relaxation=0.15): """ Inverse radon transform Reconstruct an image from the radon transform, using a single iteration of the Simultaneous Algebraic Reconstruction Technique (SART) algorithm. Parameters ---------- radon_image : 2D array, dtype=float Image containing radon transform (sinogram). Each column of the image corresponds to a projection along a different angle. The tomography rotation axis should lie at the pixel index ``radon_image.shape[0] // 2`` along the 0th dimension of ``radon_image``. theta : 1D array, dtype=float, optional Reconstruction angles (in degrees). Default: m angles evenly spaced between 0 and 180 (if the shape of `radon_image` is (N, M)). image : 2D array, dtype=float, optional Image containing an initial reconstruction estimate. Shape of this array should be ``(radon_image.shape[0], radon_image.shape[0])``. The default is an array of zeros. projection_shifts : 1D array, dtype=float Shift the projections contained in ``radon_image`` (the sinogram) by this many pixels before reconstructing the image. The i'th value defines the shift of the i'th column of ``radon_image``. clip : length-2 sequence of floats Force all values in the reconstructed tomogram to lie in the range ``[clip[0], clip[1]]`` relaxation : float Relaxation parameter for the update step. A higher value can improve the convergence rate, but one runs the risk of instabilities. Values close to or higher than 1 are not recommended. Returns ------- reconstructed : ndarray Reconstructed image. The rotation axis will be located in the pixel with indices ``(reconstructed.shape[0] // 2, reconstructed.shape[1] // 2)``. Notes ----- Algebraic Reconstruction Techniques are based on formulating the tomography reconstruction problem as a set of linear equations. Along each ray, the projected value is the sum of all the values of the cross section along the ray. A typical feature of SART (and a few other variants of algebraic techniques) is that it samples the cross section at equidistant points along the ray, using linear interpolation between the pixel values of the cross section. The resulting set of linear equations are then solved using a slightly modified Kaczmarz method. When using SART, a single iteration is usually sufficient to obtain a good reconstruction. Further iterations will tend to enhance high-frequency information, but will also often increase the noise. References ---------- .. [1] AC Kak, M Slaney, "Principles of Computerized Tomographic Imaging", IEEE Press 1988. .. [2] AH Andersen, AC Kak, "Simultaneous algebraic reconstruction technique (SART): a superior implementation of the ART algorithm", Ultrasonic Imaging 6 pp 81--94 (1984) .. [3] S Kaczmarz, "Angenäherte auflösung von systemen linearer gleichungen", Bulletin International de l’Academie Polonaise des Sciences et des Lettres 35 pp 355--357 (1937) .. [4] Kohler, T. "A projection access scheme for iterative reconstruction based on the golden section." Nuclear Science Symposium Conference Record, 2004 IEEE. Vol. 6. IEEE, 2004. .. [5] Kaczmarz' method, Wikipedia, http://en.wikipedia.org/wiki/Kaczmarz_method """ if radon_image.ndim != 2: raise ValueError('radon_image must be two dimensional') reconstructed_shape = (radon_image.shape[0], radon_image.shape[0]) if theta is None: theta = np.linspace(0, 180, radon_image.shape[1], endpoint=False) elif theta.shape != (radon_image.shape[1],): raise ValueError('Shape of theta (%s) does not match the ' 'number of projections (%d)' % (projection_shifts.shape, radon_image.shape[1])) if image is None: image = np.zeros(reconstructed_shape, dtype=np.float) elif image.shape != reconstructed_shape: raise ValueError('Shape of image (%s) does not match first dimension ' 'of radon_image (%s)' % (image.shape, reconstructed_shape)) if projection_shifts is None: projection_shifts = np.zeros((radon_image.shape[1],), dtype=np.float) elif projection_shifts.shape != (radon_image.shape[1],): raise ValueError('Shape of projection_shifts (%s) does not match the ' 'number of projections (%d)' % (projection_shifts.shape, radon_image.shape[1])) if clip is not None: if len(clip) != 2: raise ValueError('clip must be a length-2 sequence') clip = (float(clip[0]), float(clip[1])) relaxation = float(relaxation) for angle_index in order_angles_golden_ratio(theta): image_update = sart_projection_update(image, theta[angle_index], radon_image[:, angle_index], projection_shifts[angle_index]) image += relaxation * image_update if clip is not None: image = np.clip(image, clip[0], clip[1]) return image scikit-image-0.13.1/skimage/transform/seam_carving.py000066400000000000000000000044701316256236300226210ustar00rootroot00000000000000from ._seam_carving import _seam_carve_v from .. import util from .._shared import utils import numpy as np def seam_carve(img, energy_map, mode, num, border=1, force_copy=True): """ Carve vertical or horizontal seams off an image. Carves out vertical/horizontal seams from an image while using the given energy map to decide the importance of each pixel. Parameters ---------- image : (M, N) or (M, N, 3) ndarray Input image whose seams are to be removed. energy_map : (M, N) ndarray The array to decide the importance of each pixel. The higher the value corresponding to a pixel, the more the algorithm will try to keep it in the image. mode : str {'horizontal', 'vertical'} Indicates whether seams are to be removed vertically or horizontally. Removing seams horizontally will decrease the height whereas removing vertically will decrease the width. num : int Number of seams are to be removed. border : int, optional The number of pixels in the right, left and bottom end of the image to be excluded from being considered for a seam. This is important as certain filters just ignore image boundaries and set them to `0`. By default border is set to `1`. force_copy : bool, optional If set, the `image` and `energy_map` are copied before being used by the method which modifies it in place. Set this to `False` if the original image and the energy map are no longer needed after this opetration. Returns ------- out : ndarray The cropped image with the seams removed. References ---------- .. [1] Shai Avidan and Ariel Shamir "Seam Carving for Content-Aware Image Resizing" http://www.cs.jhu.edu/~misha/ReadingSeminar/Papers/Avidan07.pdf """ utils.assert_nD(img, (2, 3)) image = util.img_as_float(img, force_copy) energy_map = util.img_as_float(energy_map, force_copy) if image.ndim == 2: image = image[..., np.newaxis] if mode == 'horizontal': image = np.transpose(image, (1, 0, 2)) image = np.ascontiguousarray(image) out = _seam_carve_v(image, energy_map, num, border) if mode == 'horizontal': out = np.transpose(out, (1, 0, 2)) return np.squeeze(out) scikit-image-0.13.1/skimage/transform/setup.py000066400000000000000000000031371316256236300213220ustar00rootroot00000000000000#!/usr/bin/env python import os from skimage._build import cython base_path = os.path.abspath(os.path.dirname(__file__)) def configuration(parent_package='', top_path=None): from numpy.distutils.misc_util import Configuration, get_numpy_include_dirs config = Configuration('transform', parent_package, top_path) config.add_data_dir('tests') cython(['_hough_transform.pyx'], working_path=base_path) cython(['_warps_cy.pyx'], working_path=base_path) cython(['_radon_transform.pyx'], working_path=base_path) cython(['_seam_carving.pyx'], working_path=base_path) config.add_extension('_hough_transform', sources=['_hough_transform.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension('_warps_cy', sources=['_warps_cy.c'], include_dirs=[get_numpy_include_dirs(), '../_shared']) config.add_extension('_radon_transform', sources=['_radon_transform.c'], include_dirs=[get_numpy_include_dirs()]) config.add_extension('_seam_carving', sources=['_seam_carving.c'], include_dirs=[get_numpy_include_dirs()]) return config if __name__ == '__main__': from numpy.distutils.core import setup setup(maintainer='scikit-image Developers', author='scikit-image Developers', maintainer_email='scikit-image@python.org', description='Transforms', url='https://github.com/scikit-image/scikit-image', license='SciPy License (BSD Style)', **(configuration(top_path='').todict()) ) scikit-image-0.13.1/skimage/transform/tests/000077500000000000000000000000001316256236300207465ustar00rootroot00000000000000scikit-image-0.13.1/skimage/transform/tests/__init__.py000066400000000000000000000001771316256236300230640ustar00rootroot00000000000000from ..._shared.testing import setup_test, teardown_test def setup(): setup_test() def teardown(): teardown_test() scikit-image-0.13.1/skimage/transform/tests/test_finite_radon_transform.py000066400000000000000000000007561316256236300271230ustar00rootroot00000000000000import numpy as np from skimage.transform import frt2, ifrt2 def test_frt(): SIZE = 59 try: import sympy.ntheory as sn assert sn.isprime(SIZE) == True except ImportError: pass # Generate a test image L = np.tri(SIZE, dtype=np.int32) + np.tri(SIZE, dtype=np.int32)[::-1] f = frt2(L) fi = ifrt2(f) assert len(np.nonzero(L - fi)[0]) == 0 if __name__ == '__main__': from numpy.testing import run_module_suite run_module_suite() scikit-image-0.13.1/skimage/transform/tests/test_geometric.py000066400000000000000000000363561316256236300243520ustar00rootroot00000000000000import numpy as np from numpy.testing import (assert_equal, assert_almost_equal, assert_raises) from skimage.transform._geometric import GeometricTransform from skimage.transform import (estimate_transform, matrix_transform, EuclideanTransform, SimilarityTransform, AffineTransform, FundamentalMatrixTransform, EssentialMatrixTransform, ProjectiveTransform, PolynomialTransform, PiecewiseAffineTransform) from skimage._shared._warnings import expected_warnings SRC = np.array([ [-12.3705, -10.5075], [-10.7865, 15.4305], [8.6985, 10.8675], [11.4975, -9.5715], [7.8435, 7.4835], [-5.3325, 6.5025], [6.7905, -6.3765], [-6.1695, -0.8235], ]) DST = np.array([ [0, 0], [0, 5800], [4900, 5800], [4900, 0], [4479, 4580], [1176, 3660], [3754, 790], [1024, 1931], ]) def test_estimate_transform(): for tform in ('euclidean', 'similarity', 'affine', 'projective', 'polynomial'): estimate_transform(tform, SRC[:2, :], DST[:2, :]) assert_raises(ValueError, estimate_transform, 'foobar', SRC[:2, :], DST[:2, :]) def test_matrix_transform(): tform = AffineTransform(scale=(0.1, 0.5), rotation=2) assert_equal(tform(SRC), matrix_transform(SRC, tform.params)) def test_euclidean_estimation(): # exact solution tform = estimate_transform('euclidean', SRC[:2, :], SRC[:2, :] + 10) assert_almost_equal(tform(SRC[:2, :]), SRC[:2, :] + 10) assert_almost_equal(tform.params[0, 0], tform.params[1, 1]) assert_almost_equal(tform.params[0, 1], - tform.params[1, 0]) # over-determined tform2 = estimate_transform('euclidean', SRC, DST) assert_almost_equal(tform2.inverse(tform2(SRC)), SRC) assert_almost_equal(tform2.params[0, 0], tform2.params[1, 1]) assert_almost_equal(tform2.params[0, 1], - tform2.params[1, 0]) # via estimate method tform3 = EuclideanTransform() tform3.estimate(SRC, DST) assert_almost_equal(tform3.params, tform2.params) def test_euclidean_init(): # init with implicit parameters rotation = 1 translation = (1, 1) tform = EuclideanTransform(rotation=rotation, translation=translation) assert_almost_equal(tform.rotation, rotation) assert_almost_equal(tform.translation, translation) # init with transformation matrix tform2 = EuclideanTransform(tform.params) assert_almost_equal(tform2.rotation, rotation) assert_almost_equal(tform2.translation, translation) # test special case for scale if rotation=0 rotation = 0 translation = (1, 1) tform = EuclideanTransform(rotation=rotation, translation=translation) assert_almost_equal(tform.rotation, rotation) assert_almost_equal(tform.translation, translation) # test special case for scale if rotation=90deg rotation = np.pi / 2 translation = (1, 1) tform = EuclideanTransform(rotation=rotation, translation=translation) assert_almost_equal(tform.rotation, rotation) assert_almost_equal(tform.translation, translation) def test_similarity_estimation(): # exact solution tform = estimate_transform('similarity', SRC[:2, :], DST[:2, :]) assert_almost_equal(tform(SRC[:2, :]), DST[:2, :]) assert_almost_equal(tform.params[0, 0], tform.params[1, 1]) assert_almost_equal(tform.params[0, 1], - tform.params[1, 0]) # over-determined tform2 = estimate_transform('similarity', SRC, DST) assert_almost_equal(tform2.inverse(tform2(SRC)), SRC) assert_almost_equal(tform2.params[0, 0], tform2.params[1, 1]) assert_almost_equal(tform2.params[0, 1], - tform2.params[1, 0]) # via estimate method tform3 = SimilarityTransform() tform3.estimate(SRC, DST) assert_almost_equal(tform3.params, tform2.params) def test_similarity_init(): # init with implicit parameters scale = 0.1 rotation = 1 translation = (1, 1) tform = SimilarityTransform(scale=scale, rotation=rotation, translation=translation) assert_almost_equal(tform.scale, scale) assert_almost_equal(tform.rotation, rotation) assert_almost_equal(tform.translation, translation) # init with transformation matrix tform2 = SimilarityTransform(tform.params) assert_almost_equal(tform2.scale, scale) assert_almost_equal(tform2.rotation, rotation) assert_almost_equal(tform2.translation, translation) # test special case for scale if rotation=0 scale = 0.1 rotation = 0 translation = (1, 1) tform = SimilarityTransform(scale=scale, rotation=rotation, translation=translation) assert_almost_equal(tform.scale, scale) assert_almost_equal(tform.rotation, rotation) assert_almost_equal(tform.translation, translation) # test special case for scale if rotation=90deg scale = 0.1 rotation = np.pi / 2 translation = (1, 1) tform = SimilarityTransform(scale=scale, rotation=rotation, translation=translation) assert_almost_equal(tform.scale, scale) assert_almost_equal(tform.rotation, rotation) assert_almost_equal(tform.translation, translation) def test_affine_estimation(): # exact solution tform = estimate_transform('affine', SRC[:3, :], DST[:3, :]) assert_almost_equal(tform(SRC[:3, :]), DST[:3, :]) # over-determined tform2 = estimate_transform('affine', SRC, DST) assert_almost_equal(tform2.inverse(tform2(SRC)), SRC) # via estimate method tform3 = AffineTransform() tform3.estimate(SRC, DST) assert_almost_equal(tform3.params, tform2.params) def test_affine_init(): # init with implicit parameters scale = (0.1, 0.13) rotation = 1 shear = 0.1 translation = (1, 1) tform = AffineTransform(scale=scale, rotation=rotation, shear=shear, translation=translation) assert_almost_equal(tform.scale, scale) assert_almost_equal(tform.rotation, rotation) assert_almost_equal(tform.shear, shear) assert_almost_equal(tform.translation, translation) # init with transformation matrix tform2 = AffineTransform(tform.params) assert_almost_equal(tform2.scale, scale) assert_almost_equal(tform2.rotation, rotation) assert_almost_equal(tform2.shear, shear) assert_almost_equal(tform2.translation, translation) def test_piecewise_affine(): tform = PiecewiseAffineTransform() tform.estimate(SRC, DST) # make sure each single affine transform is exactly estimated assert_almost_equal(tform(SRC), DST) assert_almost_equal(tform.inverse(DST), SRC) def test_fundamental_matrix_estimation(): src = np.array([1.839035, 1.924743, 0.543582, 0.375221, 0.473240, 0.142522, 0.964910, 0.598376, 0.102388, 0.140092, 15.994343, 9.622164, 0.285901, 0.430055, 0.091150, 0.254594]).reshape(-1, 2) dst = np.array([1.002114, 1.129644, 1.521742, 1.846002, 1.084332, 0.275134, 0.293328, 0.588992, 0.839509, 0.087290, 1.779735, 1.116857, 0.878616, 0.602447, 0.642616, 1.028681]).reshape(-1, 2) tform = estimate_transform('fundamental', src, dst) # Reference values obtained using COLMAP SfM library. tform_ref = np.array([[-0.217859, 0.419282, -0.0343075], [-0.0717941, 0.0451643, 0.0216073], [0.248062, -0.429478, 0.0221019]]) assert_almost_equal(tform.params, tform_ref, 6) def test_fundamental_matrix_residuals(): essential_matrix_tform = EssentialMatrixTransform( rotation=np.eye(3), translation=np.array([1, 0, 0])) tform = FundamentalMatrixTransform() tform.params = essential_matrix_tform.params src = np.array([[0, 0], [0, 0], [0, 0]]) dst = np.array([[2, 0], [2, 1], [2, 2]]) assert_almost_equal(tform.residuals(src, dst)**2, [0, 0.5, 2]) def test_fundamental_matrix_forward(): essential_matrix_tform = EssentialMatrixTransform( rotation=np.eye(3), translation=np.array([1, 0, 0])) tform = FundamentalMatrixTransform() tform.params = essential_matrix_tform.params src = np.array([[0, 0], [0, 1], [1, 1]]) assert_almost_equal(tform(src), [[0, -1, 0], [0, -1, 1], [0, -1, 1]]) def test_fundamental_matrix_inverse(): essential_matrix_tform = EssentialMatrixTransform( rotation=np.eye(3), translation=np.array([1, 0, 0])) tform = FundamentalMatrixTransform() tform.params = essential_matrix_tform.params src = np.array([[0, 0], [0, 1], [1, 1]]) assert_almost_equal(tform.inverse(src), [[0, 1, 0], [0, 1, -1], [0, 1, -1]]) def test_essential_matrix_init(): tform = EssentialMatrixTransform(rotation=np.eye(3), translation=np.array([0, 0, 1])) assert_equal(tform.params, np.array([0, -1, 0, 1, 0, 0, 0, 0, 0]).reshape(3, 3)) def test_essential_matrix_estimation(): src = np.array([1.839035, 1.924743, 0.543582, 0.375221, 0.473240, 0.142522, 0.964910, 0.598376, 0.102388, 0.140092, 15.994343, 9.622164, 0.285901, 0.430055, 0.091150, 0.254594]).reshape(-1, 2) dst = np.array([1.002114, 1.129644, 1.521742, 1.846002, 1.084332, 0.275134, 0.293328, 0.588992, 0.839509, 0.087290, 1.779735, 1.116857, 0.878616, 0.602447, 0.642616, 1.028681]).reshape(-1, 2) tform = estimate_transform('essential', src, dst) # Reference values obtained using COLMAP SfM library. tform_ref = np.array([[-0.0811666, 0.255449, -0.0478999], [-0.192392, -0.0531675, 0.119547], [0.177784, -0.22008, -0.015203]]) assert_almost_equal(tform.params, tform_ref, 6) def test_essential_matrix_forward(): tform = EssentialMatrixTransform(rotation=np.eye(3), translation=np.array([1, 0, 0])) src = np.array([[0, 0], [0, 1], [1, 1]]) assert_almost_equal(tform(src), [[0, -1, 0], [0, -1, 1], [0, -1, 1]]) def test_essential_matrix_inverse(): tform = EssentialMatrixTransform(rotation=np.eye(3), translation=np.array([1, 0, 0])) src = np.array([[0, 0], [0, 1], [1, 1]]) assert_almost_equal(tform.inverse(src), [[0, 1, 0], [0, 1, -1], [0, 1, -1]]) def test_essential_matrix_residuals(): tform = EssentialMatrixTransform(rotation=np.eye(3), translation=np.array([1, 0, 0])) src = np.array([[0, 0], [0, 0], [0, 0]]) dst = np.array([[2, 0], [2, 1], [2, 2]]) assert_almost_equal(tform.residuals(src, dst)**2, [0, 0.5, 2]) def test_projective_estimation(): # exact solution tform = estimate_transform('projective', SRC[:4, :], DST[:4, :]) assert_almost_equal(tform(SRC[:4, :]), DST[:4, :]) # over-determined tform2 = estimate_transform('projective', SRC, DST) assert_almost_equal(tform2.inverse(tform2(SRC)), SRC) # via estimate method tform3 = ProjectiveTransform() tform3.estimate(SRC, DST) assert_almost_equal(tform3.params, tform2.params) def test_projective_init(): tform = estimate_transform('projective', SRC, DST) # init with transformation matrix tform2 = ProjectiveTransform(tform.params) assert_almost_equal(tform2.params, tform.params) def test_polynomial_estimation(): # over-determined tform = estimate_transform('polynomial', SRC, DST, order=10) assert_almost_equal(tform(SRC), DST, 6) # via estimate method tform2 = PolynomialTransform() tform2.estimate(SRC, DST, order=10) assert_almost_equal(tform2.params, tform.params) def test_polynomial_init(): tform = estimate_transform('polynomial', SRC, DST, order=10) # init with transformation parameters tform2 = PolynomialTransform(tform.params) assert_almost_equal(tform2.params, tform.params) def test_polynomial_default_order(): tform = estimate_transform('polynomial', SRC, DST) tform2 = estimate_transform('polynomial', SRC, DST, order=2) assert_almost_equal(tform2.params, tform.params) def test_polynomial_inverse(): assert_raises(Exception, PolynomialTransform().inverse, 0) def test_union(): tform1 = SimilarityTransform(scale=0.1, rotation=0.3) tform2 = SimilarityTransform(scale=0.1, rotation=0.9) tform3 = SimilarityTransform(scale=0.1 ** 2, rotation=0.3 + 0.9) tform = tform1 + tform2 assert_almost_equal(tform.params, tform3.params) tform1 = AffineTransform(scale=(0.1, 0.1), rotation=0.3) tform2 = SimilarityTransform(scale=0.1, rotation=0.9) tform3 = SimilarityTransform(scale=0.1 ** 2, rotation=0.3 + 0.9) tform = tform1 + tform2 assert_almost_equal(tform.params, tform3.params) assert tform.__class__ == ProjectiveTransform tform = AffineTransform(scale=(0.1, 0.1), rotation=0.3) assert_almost_equal((tform + tform.inverse).params, np.eye(3)) def test_union_differing_types(): tform1 = SimilarityTransform() tform2 = PolynomialTransform() assert_raises(TypeError, tform1.__add__, tform2) def test_geometric_tform(): tform = GeometricTransform() assert_raises(NotImplementedError, tform, 0) assert_raises(NotImplementedError, tform.inverse, 0) assert_raises(NotImplementedError, tform.__add__, 0) def test_invalid_input(): assert_raises(ValueError, ProjectiveTransform, np.zeros((2, 3))) assert_raises(ValueError, AffineTransform, np.zeros((2, 3))) assert_raises(ValueError, SimilarityTransform, np.zeros((2, 3))) assert_raises(ValueError, EuclideanTransform, np.zeros((2, 3))) assert_raises(ValueError, AffineTransform, matrix=np.zeros((2, 3)), scale=1) assert_raises(ValueError, SimilarityTransform, matrix=np.zeros((2, 3)), scale=1) assert_raises(ValueError, EuclideanTransform, matrix=np.zeros((2, 3)), translation=(0, 0)) assert_raises(ValueError, PolynomialTransform, np.zeros((3, 3))) assert_raises(ValueError, FundamentalMatrixTransform, matrix=np.zeros((3, 2))) assert_raises(ValueError, EssentialMatrixTransform, matrix=np.zeros((3, 2))) assert_raises(ValueError, EssentialMatrixTransform, rotation=np.zeros((3, 2))) assert_raises(ValueError, EssentialMatrixTransform, rotation=np.zeros((3, 3))) assert_raises(ValueError, EssentialMatrixTransform, rotation=np.eye(3)) assert_raises(ValueError, EssentialMatrixTransform, rotation=np.eye(3), translation=np.zeros((2,))) assert_raises(ValueError, EssentialMatrixTransform, rotation=np.eye(3), translation=np.zeros((2,))) assert_raises(ValueError, EssentialMatrixTransform, rotation=np.eye(3), translation=np.zeros((3,))) def test_degenerate(): src = dst = np.zeros((10, 2)) tform = SimilarityTransform() tform.estimate(src, dst) assert np.all(np.isnan(tform.params)) tform = AffineTransform() tform.estimate(src, dst) assert np.all(np.isnan(tform.params)) tform = ProjectiveTransform() tform.estimate(src, dst) assert np.all(np.isnan(tform.params)) if __name__ == "__main__": from numpy.testing import run_module_suite run_module_suite() scikit-image-0.13.1/skimage/transform/tests/test_hough_transform.py000066400000000000000000000337651316256236300256020ustar00rootroot00000000000000import numpy as np from numpy.testing import assert_almost_equal, assert_equal, assert_raises import skimage.transform as tf from skimage.draw import line, circle_perimeter, ellipse_perimeter from skimage._shared._warnings import expected_warnings from skimage._shared.testing import test_parallel @test_parallel() def test_hough_line(): # Generate a test image img = np.zeros((100, 150), dtype=int) rr, cc = line(60, 130, 80, 10) img[rr, cc] = 1 out, angles, d = tf.hough_line(img) y, x = np.where(out == out.max()) dist = d[y[0]] theta = angles[x[0]] assert_almost_equal(dist, 80.723, 1) assert_almost_equal(theta, 1.41, 1) def test_hough_line_angles(): img = np.zeros((10, 10)) img[0, 0] = 1 out, angles, d = tf.hough_line(img, np.linspace(0, 360, 10)) assert_equal(len(angles), 10) def test_hough_line_bad_input(): img = np.zeros(100) img[10] = 1 # Expected error, img must be 2D assert_raises(ValueError, tf.hough_line, img, np.linspace(0, 360, 10)) def test_probabilistic_hough(): # Generate a test image img = np.zeros((100, 100), dtype=int) for i in range(25, 75): img[100 - i, i] = 100 img[i, i] = 100 # decrease default theta sampling because similar orientations may confuse # as mentioned in article of Galambos et al theta = np.linspace(0, np.pi, 45) lines = tf.probabilistic_hough_line(img, threshold=10, line_length=10, line_gap=1, theta=theta) # sort the lines according to the x-axis sorted_lines = [] for line in lines: line = list(line) line.sort(key=lambda x: x[0]) sorted_lines.append(line) assert([(25, 75), (74, 26)] in sorted_lines) assert([(25, 25), (74, 74)] in sorted_lines) # Execute with default theta tf.probabilistic_hough_line(img, line_length=10, line_gap=3) def test_probabilistic_hough_bad_input(): img = np.zeros(100) img[10] = 1 # Expected error, img must be 2D assert_raises(ValueError, tf.probabilistic_hough_line, img) def test_hough_line_peaks(): img = np.zeros((100, 150), dtype=int) rr, cc = line(60, 130, 80, 10) img[rr, cc] = 1 out, angles, d = tf.hough_line(img) out, theta, dist = tf.hough_line_peaks(out, angles, d) assert_equal(len(dist), 1) assert_almost_equal(dist[0], 80.723, 1) assert_almost_equal(theta[0], 1.41, 1) def test_hough_line_peaks_ordered(): # Regression test per PR #1421 testim = np.zeros((256, 64), dtype=np.bool) testim[50:100, 20] = True testim[85:200, 25] = True testim[15:35, 50] = True testim[1:-1, 58] = True hough_space, angles, dists = tf.hough_line(testim) hspace, _, _ = tf.hough_line_peaks(hough_space, angles, dists) assert hspace[0] > hspace[1] def test_hough_line_peaks_dist(): img = np.zeros((100, 100), dtype=np.bool_) img[:, 30] = True img[:, 40] = True hspace, angles, dists = tf.hough_line(img) assert len(tf.hough_line_peaks(hspace, angles, dists, min_distance=5)[0]) == 2 assert len(tf.hough_line_peaks(hspace, angles, dists, min_distance=15)[0]) == 1 def test_hough_line_peaks_angle(): check_hough_line_peaks_angle() def check_hough_line_peaks_angle(): img = np.zeros((100, 100), dtype=np.bool_) img[:, 0] = True img[0, :] = True hspace, angles, dists = tf.hough_line(img) assert len(tf.hough_line_peaks(hspace, angles, dists, min_angle=45)[0]) == 2 assert len(tf.hough_line_peaks(hspace, angles, dists, min_angle=90)[0]) == 1 theta = np.linspace(0, np.pi, 100) hspace, angles, dists = tf.hough_line(img, theta) assert len(tf.hough_line_peaks(hspace, angles, dists, min_angle=45)[0]) == 2 assert len(tf.hough_line_peaks(hspace, angles, dists, min_angle=90)[0]) == 1 theta = np.linspace(np.pi / 3, 4. / 3 * np.pi, 100) hspace, angles, dists = tf.hough_line(img, theta) assert len(tf.hough_line_peaks(hspace, angles, dists, min_angle=45)[0]) == 2 assert len(tf.hough_line_peaks(hspace, angles, dists, min_angle=90)[0]) == 1 def test_hough_line_peaks_num(): img = np.zeros((100, 100), dtype=np.bool_) img[:, 30] = True img[:, 40] = True hspace, angles, dists = tf.hough_line(img) assert len(tf.hough_line_peaks(hspace, angles, dists, min_distance=0, min_angle=0, num_peaks=1)[0]) == 1 @test_parallel() def test_hough_circle(): # Prepare picture img = np.zeros((120, 100), dtype=int) radius = 20 x_0, y_0 = (99, 50) y, x = circle_perimeter(y_0, x_0, radius) img[x, y] = 1 out1 = tf.hough_circle(img, radius) out2 = tf.hough_circle(img, [radius]) assert_equal(out1, out2) out = tf.hough_circle(img, np.array([radius], dtype=np.intp)) assert_equal(out, out1) x, y = np.where(out[0] == out[0].max()) assert_equal(x[0], x_0) assert_equal(y[0], y_0) def test_hough_circle_extended(): # Prepare picture # The circle center is outside the image img = np.zeros((100, 100), dtype=int) radius = 20 x_0, y_0 = (-5, 50) y, x = circle_perimeter(y_0, x_0, radius) img[x[np.where(x > 0)], y[np.where(x > 0)]] = 1 out = tf.hough_circle(img, np.array([radius], dtype=np.intp), full_output=True) x, y = np.where(out[0] == out[0].max()) # Offset for x_0, y_0 assert_equal(x[0], x_0 + radius) assert_equal(y[0], y_0 + radius) def test_hough_circle_peaks(): x_0, y_0, rad_0 = (99, 50, 20) img = np.zeros((120, 100), dtype=int) y, x = circle_perimeter(y_0, x_0, rad_0) img[x, y] = 1 x_1, y_1, rad_1 = (49, 60, 30) y, x = circle_perimeter(y_1, x_1, rad_1) img[x, y] = 1 radii = [rad_0, rad_1] hspaces = tf.hough_circle(img, radii) out = tf.hough_circle_peaks(hspaces, radii, min_xdistance=1, min_ydistance=1, threshold=None, num_peaks=np.inf, total_num_peaks=np.inf) s = np.argsort(out[3]) # sort by radii assert_equal(out[1][s], np.array([y_0, y_1])) assert_equal(out[2][s], np.array([x_0, x_1])) assert_equal(out[3][s], np.array([rad_0, rad_1])) def test_hough_circle_peaks_total_peak(): img = np.zeros((120, 100), dtype=int) x_0, y_0, rad_0 = (99, 50, 20) y, x = circle_perimeter(y_0, x_0, rad_0) img[x, y] = 1 x_1, y_1, rad_1 = (49, 60, 30) y, x = circle_perimeter(y_1, x_1, rad_1) img[x, y] = 1 radii = [rad_0, rad_1] hspaces = tf.hough_circle(img, radii) out = tf.hough_circle_peaks(hspaces, radii, min_xdistance=1, min_ydistance=1, threshold=None, num_peaks=np.inf, total_num_peaks=1) assert_equal(out[1][0], np.array([y_1,])) assert_equal(out[2][0], np.array([x_1,])) assert_equal(out[3][0], np.array([rad_1,])) def test_hough_ellipse_zero_angle(): img = np.zeros((25, 25), dtype=int) rx = 6 ry = 8 x0 = 12 y0 = 15 angle = 0 rr, cc = ellipse_perimeter(y0, x0, ry, rx) img[rr, cc] = 1 result = tf.hough_ellipse(img, threshold=9) best = result[-1] assert_equal(best[1], y0) assert_equal(best[2], x0) assert_almost_equal(best[3], ry, decimal=1) assert_almost_equal(best[4], rx, decimal=1) assert_equal(best[5], angle) # Check if I re-draw the ellipse, points are the same! # ie check API compatibility between hough_ellipse and ellipse_perimeter rr2, cc2 = ellipse_perimeter(y0, x0, int(best[3]), int(best[4]), orientation=best[5]) assert_equal(rr, rr2) assert_equal(cc, cc2) def test_hough_ellipse_non_zero_posangle1(): # ry > rx, angle in [0:pi/2] img = np.zeros((30, 24), dtype=int) rx = 6 ry = 12 x0 = 10 y0 = 15 angle = np.pi / 1.35 rr, cc = ellipse_perimeter(y0, x0, ry, rx, orientation=angle) img[rr, cc] = 1 result = tf.hough_ellipse(img, threshold=15, accuracy=3) result.sort(order='accumulator') best = result[-1] assert_almost_equal(best[1] / 100., y0 / 100., decimal=1) assert_almost_equal(best[2] / 100., x0 / 100., decimal=1) assert_almost_equal(best[3] / 10., ry / 10., decimal=1) assert_almost_equal(best[4] / 100., rx / 100., decimal=1) assert_almost_equal(best[5], angle, decimal=1) # Check if I re-draw the ellipse, points are the same! # ie check API compatibility between hough_ellipse and ellipse_perimeter rr2, cc2 = ellipse_perimeter(y0, x0, int(best[3]), int(best[4]), orientation=best[5]) assert_equal(rr, rr2) assert_equal(cc, cc2) def test_hough_ellipse_non_zero_posangle2(): # ry < rx, angle in [0:pi/2] img = np.zeros((30, 24), dtype=int) rx = 12 ry = 6 x0 = 10 y0 = 15 angle = np.pi / 1.35 rr, cc = ellipse_perimeter(y0, x0, ry, rx, orientation=angle) img[rr, cc] = 1 result = tf.hough_ellipse(img, threshold=15, accuracy=3) result.sort(order='accumulator') best = result[-1] assert_almost_equal(best[1] / 100., y0 / 100., decimal=1) assert_almost_equal(best[2] / 100., x0 / 100., decimal=1) assert_almost_equal(best[3] / 10., ry / 10., decimal=1) assert_almost_equal(best[4] / 100., rx / 100., decimal=1) assert_almost_equal(best[5], angle, decimal=1) # Check if I re-draw the ellipse, points are the same! # ie check API compatibility between hough_ellipse and ellipse_perimeter rr2, cc2 = ellipse_perimeter(y0, x0, int(best[3]), int(best[4]), orientation=best[5]) assert_equal(rr, rr2) assert_equal(cc, cc2) def test_hough_ellipse_non_zero_posangle3(): # ry < rx, angle in [pi/2:pi] img = np.zeros((30, 24), dtype=int) rx = 12 ry = 6 x0 = 10 y0 = 15 angle = np.pi / 1.35 + np.pi / 2. rr, cc = ellipse_perimeter(y0, x0, ry, rx, orientation=angle) img[rr, cc] = 1 result = tf.hough_ellipse(img, threshold=15, accuracy=3) result.sort(order='accumulator') best = result[-1] # Check if I re-draw the ellipse, points are the same! # ie check API compatibility between hough_ellipse and ellipse_perimeter rr2, cc2 = ellipse_perimeter(y0, x0, int(best[3]), int(best[4]), orientation=best[5]) assert_equal(rr, rr2) assert_equal(cc, cc2) def test_hough_ellipse_non_zero_posangle4(): # ry < rx, angle in [pi:3pi/4] img = np.zeros((30, 24), dtype=int) rx = 12 ry = 6 x0 = 10 y0 = 15 angle = np.pi / 1.35 + np.pi rr, cc = ellipse_perimeter(y0, x0, ry, rx, orientation=angle) img[rr, cc] = 1 result = tf.hough_ellipse(img, threshold=15, accuracy=3) result.sort(order='accumulator') best = result[-1] # Check if I re-draw the ellipse, points are the same! # ie check API compatibility between hough_ellipse and ellipse_perimeter rr2, cc2 = ellipse_perimeter(y0, x0, int(best[3]), int(best[4]), orientation=best[5]) assert_equal(rr, rr2) assert_equal(cc, cc2) def test_hough_ellipse_non_zero_negangle1(): # ry > rx, angle in [0:-pi/2] img = np.zeros((30, 24), dtype=int) rx = 6 ry = 12 x0 = 10 y0 = 15 angle = - np.pi / 1.35 rr, cc = ellipse_perimeter(y0, x0, ry, rx, orientation=angle) img[rr, cc] = 1 result = tf.hough_ellipse(img, threshold=15, accuracy=3) result.sort(order='accumulator') best = result[-1] # Check if I re-draw the ellipse, points are the same! # ie check API compatibility between hough_ellipse and ellipse_perimeter rr2, cc2 = ellipse_perimeter(y0, x0, int(best[3]), int(best[4]), orientation=best[5]) assert_equal(rr, rr2) assert_equal(cc, cc2) def test_hough_ellipse_non_zero_negangle2(): # ry < rx, angle in [0:-pi/2] img = np.zeros((30, 24), dtype=int) rx = 12 ry = 6 x0 = 10 y0 = 15 angle = - np.pi / 1.35 rr, cc = ellipse_perimeter(y0, x0, ry, rx, orientation=angle) img[rr, cc] = 1 result = tf.hough_ellipse(img, threshold=15, accuracy=3) result.sort(order='accumulator') best = result[-1] # Check if I re-draw the ellipse, points are the same! # ie check API compatibility between hough_ellipse and ellipse_perimeter rr2, cc2 = ellipse_perimeter(y0, x0, int(best[3]), int(best[4]), orientation=best[5]) assert_equal(rr, rr2) assert_equal(cc, cc2) def test_hough_ellipse_non_zero_negangle3(): # ry < rx, angle in [-pi/2:-pi] img = np.zeros((30, 24), dtype=int) rx = 12 ry = 6 x0 = 10 y0 = 15 angle = - np.pi / 1.35 - np.pi / 2. rr, cc = ellipse_perimeter(y0, x0, ry, rx, orientation=angle) img[rr, cc] = 1 result = tf.hough_ellipse(img, threshold=15, accuracy=3) result.sort(order='accumulator') best = result[-1] # Check if I re-draw the ellipse, points are the same! # ie check API compatibility between hough_ellipse and ellipse_perimeter rr2, cc2 = ellipse_perimeter(y0, x0, int(best[3]), int(best[4]), orientation=best[5]) assert_equal(rr, rr2) assert_equal(cc, cc2) def test_hough_ellipse_non_zero_negangle4(): # ry < rx, angle in [-pi:-3pi/4] img = np.zeros((30, 24), dtype=int) rx = 12 ry = 6 x0 = 10 y0 = 15 angle = - np.pi / 1.35 - np.pi rr, cc = ellipse_perimeter(y0, x0, ry, rx, orientation=angle) img[rr, cc] = 1 result = tf.hough_ellipse(img, threshold=15, accuracy=3) result.sort(order='accumulator') best = result[-1] # Check if I re-draw the ellipse, points are the same! # ie check API compatibility between hough_ellipse and ellipse_perimeter rr2, cc2 = ellipse_perimeter(y0, x0, int(best[3]), int(best[4]), orientation=best[5]) assert_equal(rr, rr2) assert_equal(cc, cc2) if __name__ == "__main__": np.testing.run_module_suite() scikit-image-0.13.1/skimage/transform/tests/test_integral.py000066400000000000000000000032511316256236300241650ustar00rootroot00000000000000import numpy as np from numpy.testing import assert_equal from skimage.transform import integral_image, integrate np.random.seed(0) x = (np.random.rand(50, 50) * 255).astype(np.uint8) s = integral_image(x) def test_validity(): y = np.arange(12).reshape((4, 3)) y = (np.random.rand(50, 50) * 255).astype(np.uint8) assert_equal(integral_image(y)[-1, -1], y.sum()) def test_basic(): assert_equal(x[12:24, 10:20].sum(), integrate(s, (12, 10), (23, 19))) assert_equal(x[:20, :20].sum(), integrate(s, (0, 0), (19, 19))) assert_equal(x[:20, 10:20].sum(), integrate(s, (0, 10), (19, 19))) assert_equal(x[10:20, :20].sum(), integrate(s, (10, 0), (19, 19))) def test_single(): assert_equal(x[0, 0], integrate(s, (0, 0), (0, 0))) assert_equal(x[10, 10], integrate(s, (10, 10), (10, 10))) def test_vectorized_integrate(): r0 = np.array([12, 0, 0, 10, 0, 10, 30]) c0 = np.array([10, 0, 10, 0, 0, 10, 31]) r1 = np.array([23, 19, 19, 19, 0, 10, 49]) c1 = np.array([19, 19, 19, 19, 0, 10, 49]) expected = np.array([x[12:24, 10:20].sum(), x[:20, :20].sum(), x[:20, 10:20].sum(), x[10:20, :20].sum(), x[0,0], x[10, 10], x[30:, 31:].sum()]) start_pts = [(r0[i], c0[i]) for i in range(len(r0))] end_pts = [(r1[i], c1[i]) for i in range(len(r0))] assert_equal(expected, integrate(s, r0, c0, r1, c1)) # test deprecated assert_equal(expected, integrate(s, start_pts, end_pts)) if __name__ == '__main__': from numpy.testing import run_module_suite run_module_suite() scikit-image-0.13.1/skimage/transform/tests/test_pyramids.py000066400000000000000000000043761316256236300242210ustar00rootroot00000000000000from numpy.testing import assert_array_equal, assert_raises, run_module_suite from skimage import data from skimage.transform import pyramids image = data.astronaut() image_gray = image[..., 0] def test_pyramid_reduce_rgb(): rows, cols, dim = image.shape out = pyramids.pyramid_reduce(image, downscale=2) assert_array_equal(out.shape, (rows / 2, cols / 2, dim)) def test_pyramid_reduce_gray(): rows, cols = image_gray.shape out = pyramids.pyramid_reduce(image_gray, downscale=2) assert_array_equal(out.shape, (rows / 2, cols / 2)) def test_pyramid_expand_rgb(): rows, cols, dim = image.shape out = pyramids.pyramid_expand(image, upscale=2) assert_array_equal(out.shape, (rows * 2, cols * 2, dim)) def test_pyramid_expand_gray(): rows, cols = image_gray.shape out = pyramids.pyramid_expand(image_gray, upscale=2) assert_array_equal(out.shape, (rows * 2, cols * 2)) def test_build_gaussian_pyramid_rgb(): rows, cols, dim = image.shape pyramid = pyramids.pyramid_gaussian(image, downscale=2) for layer, out in enumerate(pyramid): layer_shape = (rows / 2 ** layer, cols / 2 ** layer, dim) assert_array_equal(out.shape, layer_shape) def test_build_gaussian_pyramid_gray(): rows, cols = image_gray.shape pyramid = pyramids.pyramid_gaussian(image_gray, downscale=2) for layer, out in enumerate(pyramid): layer_shape = (rows / 2 ** layer, cols / 2 ** layer) assert_array_equal(out.shape, layer_shape) def test_build_laplacian_pyramid_rgb(): rows, cols, dim = image.shape pyramid = pyramids.pyramid_laplacian(image, downscale=2) for layer, out in enumerate(pyramid): layer_shape = (rows / 2 ** layer, cols / 2 ** layer, dim) assert_array_equal(out.shape, layer_shape) def test_build_laplacian_pyramid_gray(): rows, cols = image_gray.shape pyramid = pyramids.pyramid_laplacian(image_gray, downscale=2) for layer, out in enumerate(pyramid): layer_shape = (rows / 2 ** layer, cols / 2 ** layer) assert_array_equal(out.shape, layer_shape) def test_check_factor(): assert_raises(ValueError, pyramids._check_factor, 0.99) assert_raises(ValueError, pyramids._check_factor, - 2) if __name__ == "__main__": run_module_suite() scikit-image-0.13.1/skimage/transform/tests/test_radon_transform.py000066400000000000000000000343651316256236300255700ustar00rootroot00000000000000from __future__ import print_function, division import numpy as np from numpy.testing import assert_raises import itertools import os.path from skimage.transform import radon, iradon, iradon_sart, rescale from skimage.io import imread from skimage import data_dir from skimage._shared.testing import test_parallel from skimage._shared._warnings import expected_warnings PHANTOM = imread(os.path.join(data_dir, "phantom.png"), as_grey=True)[::2, ::2] PHANTOM = rescale(PHANTOM, 0.5, order=1, mode='reflect') def _debug_plot(original, result, sinogram=None): from matplotlib import pyplot as plt imkwargs = dict(cmap='gray', interpolation='nearest') if sinogram is None: plt.figure(figsize=(15, 6)) sp = 130 else: plt.figure(figsize=(11, 11)) sp = 221 plt.subplot(sp + 0) plt.imshow(sinogram, aspect='auto', **imkwargs) plt.subplot(sp + 1) plt.imshow(original, **imkwargs) plt.subplot(sp + 2) plt.imshow(result, vmin=original.min(), vmax=original.max(), **imkwargs) plt.subplot(sp + 3) plt.imshow(result - original, **imkwargs) plt.colorbar() plt.show() def _rescale_intensity(x): x = x.astype(float) x -= x.min() x /= x.max() return x def check_radon_center(shape, circle): # Create a test image with only a single non-zero pixel at the origin image = np.zeros(shape, dtype=np.float) image[(shape[0] // 2, shape[1] // 2)] = 1. # Calculate the sinogram theta = np.linspace(0., 180., max(shape), endpoint=False) sinogram = radon(image, theta=theta, circle=circle) # The sinogram should be a straight, horizontal line sinogram_max = np.argmax(sinogram, axis=0) print(sinogram_max) assert np.std(sinogram_max) < 1e-6 def test_radon_center(): shapes = [(16, 16), (17, 17)] circles = [False, True] for shape, circle in itertools.product(shapes, circles): yield check_radon_center, shape, circle rectangular_shapes = [(32, 16), (33, 17)] for shape in rectangular_shapes: yield check_radon_center, shape, False def check_iradon_center(size, theta, circle): debug = False # Create a test sinogram corresponding to a single projection # with a single non-zero pixel at the rotation center if circle: sinogram = np.zeros((size, 1), dtype=np.float) sinogram[size // 2, 0] = 1. else: diagonal = int(np.ceil(np.sqrt(2) * size)) sinogram = np.zeros((diagonal, 1), dtype=np.float) sinogram[sinogram.shape[0] // 2, 0] = 1. maxpoint = np.unravel_index(np.argmax(sinogram), sinogram.shape) print('shape of generated sinogram', sinogram.shape) print('maximum in generated sinogram', maxpoint) # Compare reconstructions for theta=angle and theta=angle + 180; # these should be exactly equal reconstruction = iradon(sinogram, theta=[theta], circle=circle) reconstruction_opposite = iradon(sinogram, theta=[theta + 180], circle=circle) print('rms deviance:', np.sqrt(np.mean((reconstruction_opposite - reconstruction)**2))) if debug: import matplotlib.pyplot as plt imkwargs = dict(cmap='gray', interpolation='nearest') plt.figure() plt.subplot(221) plt.imshow(sinogram, **imkwargs) plt.subplot(222) plt.imshow(reconstruction_opposite - reconstruction, **imkwargs) plt.subplot(223) plt.imshow(reconstruction, **imkwargs) plt.subplot(224) plt.imshow(reconstruction_opposite, **imkwargs) plt.show() assert np.allclose(reconstruction, reconstruction_opposite) def test_iradon_center(): sizes = [16, 17] thetas = [0, 90] circles = [False, True] for size, theta, circle in itertools.product(sizes, thetas, circles): yield check_iradon_center, size, theta, circle def check_radon_iradon(interpolation_type, filter_type): debug = False image = PHANTOM reconstructed = iradon(radon(image, circle=False), filter=filter_type, interpolation=interpolation_type) delta = np.mean(np.abs(image - reconstructed)) print('\n\tmean error:', delta) if debug: _debug_plot(image, reconstructed) if filter_type in ('ramp', 'shepp-logan'): if interpolation_type == 'nearest': allowed_delta = 0.03 else: allowed_delta = 0.025 else: allowed_delta = 0.05 assert delta < allowed_delta def test_radon_iradon(): filter_types = ["ramp", "shepp-logan", "cosine", "hamming", "hann"] interpolation_types = ['linear', 'nearest'] for interpolation_type, filter_type in \ itertools.product(interpolation_types, filter_types): yield check_radon_iradon, interpolation_type, filter_type # cubic interpolation is slow; only run one test for it yield check_radon_iradon, 'cubic', 'shepp-logan' def test_iradon_angles(): """ Test with different number of projections """ size = 100 # Synthetic data image = np.tri(size) + np.tri(size)[::-1] # Large number of projections: a good quality is expected nb_angles = 200 theta = np.linspace(0, 180, nb_angles, endpoint=False) radon_image_200 = radon(image, theta=theta, circle=False) reconstructed = iradon(radon_image_200, circle=False) delta_200 = np.mean(abs(_rescale_intensity(image) - _rescale_intensity(reconstructed))) assert delta_200 < 0.03 # Lower number of projections nb_angles = 80 radon_image_80 = radon(image, theta=theta, circle=False) # Test whether the sum of all projections is approximately the same s = radon_image_80.sum(axis=0) assert np.allclose(s, s[0], rtol=0.01) reconstructed = iradon(radon_image_80, circle=False) delta_80 = np.mean(abs(image / np.max(image) - reconstructed / np.max(reconstructed))) # Loss of quality when the number of projections is reduced assert delta_80 > delta_200 def check_radon_iradon_minimal(shape, slices): debug = False theta = np.arange(180) image = np.zeros(shape, dtype=np.float) image[slices] = 1. sinogram = radon(image, theta, circle=False) reconstructed = iradon(sinogram, theta, circle=False) print('\n\tMaximum deviation:', np.max(np.abs(image - reconstructed))) if debug: _debug_plot(image, reconstructed, sinogram) if image.sum() == 1: assert (np.unravel_index(np.argmax(reconstructed), image.shape) == np.unravel_index(np.argmax(image), image.shape)) def test_radon_iradon_minimal(): shapes = [(3, 3), (4, 4), (5, 5)] for shape in shapes: c0, c1 = shape[0] // 2, shape[1] // 2 coordinates = itertools.product((c0 - 1, c0, c0 + 1), (c1 - 1, c1, c1 + 1)) for coordinate in coordinates: yield check_radon_iradon_minimal, shape, coordinate def test_reconstruct_with_wrong_angles(): a = np.zeros((3, 3)) p = radon(a, theta=[0, 1, 2], circle=False) iradon(p, theta=[0, 1, 2], circle=False) assert_raises(ValueError, iradon, p, theta=[0, 1, 2, 3]) def _random_circle(shape): # Synthetic random data, zero outside reconstruction circle np.random.seed(98312871) image = np.random.rand(*shape) c0, c1 = np.ogrid[0:shape[0], 0:shape[1]] r = np.sqrt((c0 - shape[0] // 2)**2 + (c1 - shape[1] // 2)**2) radius = min(shape) // 2 image[r > radius] = 0. return image def test_radon_circle(): a = np.ones((10, 10)) with expected_warnings(['reconstruction circle']): radon(a, circle=True) # Synthetic data, circular symmetry shape = (61, 79) c0, c1 = np.ogrid[0:shape[0], 0:shape[1]] r = np.sqrt((c0 - shape[0] // 2)**2 + (c1 - shape[1] // 2)**2) radius = min(shape) // 2 image = np.clip(radius - r, 0, np.inf) image = _rescale_intensity(image) angles = np.linspace(0, 180, min(shape), endpoint=False) sinogram = radon(image, theta=angles, circle=True) assert np.all(sinogram.std(axis=1) < 1e-2) # Synthetic data, random image = _random_circle(shape) sinogram = radon(image, theta=angles, circle=True) mass = sinogram.sum(axis=0) average_mass = mass.mean() relative_error = np.abs(mass - average_mass) / average_mass print(relative_error.max(), relative_error.mean()) assert np.all(relative_error < 3.2e-3) def check_sinogram_circle_to_square(size): from skimage.transform.radon_transform import _sinogram_circle_to_square image = _random_circle((size, size)) theta = np.linspace(0., 180., size, False) sinogram_circle = radon(image, theta, circle=True) argmax_shape = lambda a: np.unravel_index(np.argmax(a), a.shape) print('\n\targmax of circle:', argmax_shape(sinogram_circle)) sinogram_square = radon(image, theta, circle=False) print('\targmax of square:', argmax_shape(sinogram_square)) sinogram_circle_to_square = _sinogram_circle_to_square(sinogram_circle) print('\targmax of circle to square:', argmax_shape(sinogram_circle_to_square)) error = abs(sinogram_square - sinogram_circle_to_square) print(np.mean(error), np.max(error)) assert (argmax_shape(sinogram_square) == argmax_shape(sinogram_circle_to_square)) def test_sinogram_circle_to_square(): for size in (50, 51): yield check_sinogram_circle_to_square, size def check_radon_iradon_circle(interpolation, shape, output_size): # Forward and inverse radon on synthetic data image = _random_circle(shape) radius = min(shape) // 2 sinogram_rectangle = radon(image, circle=False) reconstruction_rectangle = iradon(sinogram_rectangle, output_size=output_size, interpolation=interpolation, circle=False) sinogram_circle = radon(image, circle=True) reconstruction_circle = iradon(sinogram_circle, output_size=output_size, interpolation=interpolation, circle=True) # Crop rectangular reconstruction to match circle=True reconstruction width = reconstruction_circle.shape[0] excess = int(np.ceil((reconstruction_rectangle.shape[0] - width) / 2)) s = np.s_[excess:width + excess, excess:width + excess] reconstruction_rectangle = reconstruction_rectangle[s] # Find the reconstruction circle, set reconstruction to zero outside c0, c1 = np.ogrid[0:width, 0:width] r = np.sqrt((c0 - width // 2)**2 + (c1 - width // 2)**2) reconstruction_rectangle[r > radius] = 0. print(reconstruction_circle.shape) print(reconstruction_rectangle.shape) np.allclose(reconstruction_rectangle, reconstruction_circle) def test_radon_iradon_circle(): shape = (61, 79) interpolations = ('nearest', 'linear') output_sizes = (None, min(shape), max(shape), 97) for interpolation, output_size in itertools.product(interpolations, output_sizes): yield check_radon_iradon_circle, interpolation, shape, output_size def test_order_angles_golden_ratio(): from skimage.transform.radon_transform import order_angles_golden_ratio np.random.seed(1231) lengths = [1, 4, 10, 180] for l in lengths: theta_ordered = np.linspace(0, 180, l, endpoint=False) theta_random = np.random.uniform(0, 180, l) for theta in (theta_random, theta_ordered): indices = [x for x in order_angles_golden_ratio(theta)] # no duplicate indices allowed assert len(indices) == len(set(indices)) @test_parallel() def test_iradon_sart(): debug = False image = rescale(PHANTOM, 0.8, mode='reflect') theta_ordered = np.linspace(0., 180., image.shape[0], endpoint=False) theta_missing_wedge = np.linspace(0., 150., image.shape[0], endpoint=True) for theta, error_factor in ((theta_ordered, 1.), (theta_missing_wedge, 2.)): sinogram = radon(image, theta, circle=True) reconstructed = iradon_sart(sinogram, theta) if debug: from matplotlib import pyplot as plt plt.figure() plt.subplot(221) plt.imshow(image, interpolation='nearest') plt.subplot(222) plt.imshow(sinogram, interpolation='nearest') plt.subplot(223) plt.imshow(reconstructed, interpolation='nearest') plt.subplot(224) plt.imshow(reconstructed - image, interpolation='nearest') plt.show() delta = np.mean(np.abs(reconstructed - image)) print('delta (1 iteration) =', delta) assert delta < 0.02 * error_factor reconstructed = iradon_sart(sinogram, theta, reconstructed) delta = np.mean(np.abs(reconstructed - image)) print('delta (2 iterations) =', delta) assert delta < 0.014 * error_factor reconstructed = iradon_sart(sinogram, theta, clip=(0, 1)) delta = np.mean(np.abs(reconstructed - image)) print('delta (1 iteration, clip) =', delta) assert delta < 0.018 * error_factor np.random.seed(1239867) shifts = np.random.uniform(-3, 3, sinogram.shape[1]) x = np.arange(sinogram.shape[0]) sinogram_shifted = np.vstack(np.interp(x + shifts[i], x, sinogram[:, i]) for i in range(sinogram.shape[1])).T reconstructed = iradon_sart(sinogram_shifted, theta, projection_shifts=shifts) if debug: from matplotlib import pyplot as plt plt.figure() plt.subplot(221) plt.imshow(image, interpolation='nearest') plt.subplot(222) plt.imshow(sinogram_shifted, interpolation='nearest') plt.subplot(223) plt.imshow(reconstructed, interpolation='nearest') plt.subplot(224) plt.imshow(reconstructed - image, interpolation='nearest') plt.show() delta = np.mean(np.abs(reconstructed - image)) print('delta (1 iteration, shifted sinogram) =', delta) assert delta < 0.022 * error_factor if __name__ == "__main__": from numpy.testing import run_module_suite run_module_suite() scikit-image-0.13.1/skimage/transform/tests/test_seam_carving.py000066400000000000000000000011521316256236300250140ustar00rootroot00000000000000from skimage import transform import numpy as np from numpy import testing def test_seam_carving(): img = np.array([[0, 0, 1, 0, 0], [0, 0, 1, 0, 0], [0, 0, 1, 0, 0], [0, 1, 0, 0, 0], [1, 0, 0, 0, 0]], dtype=np.float) energy = 1 - img out = transform.seam_carve(img, energy, 'vertical', 1, border=0) testing.assert_allclose(out, 0) img = img.T out = transform.seam_carve(img, energy, 'horizontal', 1, border=0) testing.assert_allclose(out, 0) if __name__ == '__main__': np.testing.run_module_suite() scikit-image-0.13.1/skimage/transform/tests/test_warps.py000066400000000000000000000237031316256236300235200ustar00rootroot00000000000000from numpy.testing import (assert_almost_equal, run_module_suite, assert_equal, assert_raises) import numpy as np from scipy.ndimage import map_coordinates from skimage.transform._warps import _stackcopy from skimage.transform import (warp, warp_coords, rotate, resize, rescale, AffineTransform, ProjectiveTransform, SimilarityTransform, downscale_local_mean) from skimage import transform as tf, data, img_as_float from skimage.color import rgb2gray from skimage._shared._warnings import expected_warnings from skimage._shared.testing import test_parallel np.random.seed(0) def test_stackcopy(): layers = 4 x = np.empty((3, 3, layers)) y = np.eye(3, 3) _stackcopy(x, y) for i in range(layers): assert_almost_equal(x[..., i], y) def test_warp_tform(): x = np.zeros((5, 5), dtype=np.double) x[2, 2] = 1 theta = - np.pi / 2 tform = SimilarityTransform(scale=1, rotation=theta, translation=(0, 4)) x90 = warp(x, tform, order=1) assert_almost_equal(x90, np.rot90(x)) x90 = warp(x, tform.inverse, order=1) assert_almost_equal(x90, np.rot90(x)) def test_warp_callable(): x = np.zeros((5, 5), dtype=np.double) x[2, 2] = 1 refx = np.zeros((5, 5), dtype=np.double) refx[1, 1] = 1 shift = lambda xy: xy + 1 outx = warp(x, shift, order=1) assert_almost_equal(outx, refx) @test_parallel() def test_warp_matrix(): x = np.zeros((5, 5), dtype=np.double) x[2, 2] = 1 refx = np.zeros((5, 5), dtype=np.double) refx[1, 1] = 1 matrix = np.array([[1, 0, 1], [0, 1, 1], [0, 0, 1]]) # _warp_fast outx = warp(x, matrix, order=1) assert_almost_equal(outx, refx) # check for ndimage.map_coordinates outx = warp(x, matrix, order=5) def test_warp_nd(): for dim in range(2, 8): shape = dim * (5,) x = np.zeros(shape, dtype=np.double) x_c = dim * (2,) x[x_c] = 1 refx = np.zeros(shape, dtype=np.double) refx_c = dim * (1,) refx[refx_c] = 1 coord_grid = dim * (slice(0, 5, 1),) coords = np.array(np.mgrid[coord_grid]) + 1 outx = warp(x, coords, order=0, cval=0) assert_almost_equal(outx, refx) def test_warp_clip(): x = np.zeros((5, 5), dtype=np.double) x[2, 2] = 1 with expected_warnings(['The default mode']): outx = rescale(x, 3, order=3, clip=False) assert outx.min() < 0 with expected_warnings(['The default mode']): outx = rescale(x, 3, order=3, clip=True) assert_almost_equal(outx.min(), 0) assert_almost_equal(outx.max(), 1) def test_homography(): x = np.zeros((5, 5), dtype=np.double) x[1, 1] = 1 theta = -np.pi / 2 M = np.array([[np.cos(theta), - np.sin(theta), 0], [np.sin(theta), np.cos(theta), 4], [0, 0, 1]]) x90 = warp(x, inverse_map=ProjectiveTransform(M).inverse, order=1) assert_almost_equal(x90, np.rot90(x)) def test_rotate(): x = np.zeros((5, 5), dtype=np.double) x[1, 1] = 1 x90 = rotate(x, 90) assert_almost_equal(x90, np.rot90(x)) def test_rotate_resize(): x = np.zeros((10, 10), dtype=np.double) x45 = rotate(x, 45, resize=False) assert x45.shape == (10, 10) x45 = rotate(x, 45, resize=True) # new dimension should be d = sqrt(2 * (10/2)^2) assert x45.shape == (14, 14) def test_rotate_center(): x = np.zeros((10, 10), dtype=np.double) x[4, 4] = 1 refx = np.zeros((10, 10), dtype=np.double) refx[2, 5] = 1 x20 = rotate(x, 20, order=0, center=(0, 0)) assert_almost_equal(x20, refx) x0 = rotate(x20, -20, order=0, center=(0, 0)) assert_almost_equal(x0, x) def test_rotate_resize_center(): x = np.zeros((10, 10), dtype=np.double) x[0, 0] = 1 ref_x45 = np.zeros((14, 14), dtype=np.double) ref_x45[6, 0] = 1 ref_x45[7, 0] = 1 x45 = rotate(x, 45, resize=True, center=(3, 3), order=0) # new dimension should be d = sqrt(2 * (10/2)^2) assert x45.shape == (14, 14) assert_equal(x45, ref_x45) def test_rescale(): # same scale factor x = np.zeros((5, 5), dtype=np.double) x[1, 1] = 1 with expected_warnings(['The default mode']): scaled = rescale(x, 2, order=0) ref = np.zeros((10, 10)) ref[2:4, 2:4] = 1 assert_almost_equal(scaled, ref) # different scale factors x = np.zeros((5, 5), dtype=np.double) x[1, 1] = 1 with expected_warnings(['The default mode']): scaled = rescale(x, (2, 1), order=0) ref = np.zeros((10, 5)) ref[2:4, 1] = 1 assert_almost_equal(scaled, ref) def test_resize2d(): x = np.zeros((5, 5), dtype=np.double) x[1, 1] = 1 with expected_warnings(['The default mode']): resized = resize(x, (10, 10), order=0) ref = np.zeros((10, 10)) ref[2:4, 2:4] = 1 assert_almost_equal(resized, ref) def test_resize3d_keep(): # keep 3rd dimension x = np.zeros((5, 5, 3), dtype=np.double) x[1, 1, :] = 1 with expected_warnings(['The default mode']): resized = resize(x, (10, 10), order=0) ref = np.zeros((10, 10, 3)) ref[2:4, 2:4, :] = 1 assert_almost_equal(resized, ref) with expected_warnings(['The default mode']): resized = resize(x, (10, 10, 3), order=0) assert_almost_equal(resized, ref) def test_resize3d_resize(): # resize 3rd dimension x = np.zeros((5, 5, 3), dtype=np.double) x[1, 1, :] = 1 with expected_warnings(['The default mode']): resized = resize(x, (10, 10, 1), order=0) ref = np.zeros((10, 10, 1)) ref[2:4, 2:4] = 1 assert_almost_equal(resized, ref) def test_resize3d_2din_3dout(): # 3D output with 2D input x = np.zeros((5, 5), dtype=np.double) x[1, 1] = 1 with expected_warnings(['The default mode']): resized = resize(x, (10, 10, 1), order=0) ref = np.zeros((10, 10, 1)) ref[2:4, 2:4] = 1 assert_almost_equal(resized, ref) def test_resize3d_bilinear(): # bilinear 3rd dimension x = np.zeros((5, 5, 2), dtype=np.double) x[1, 1, 0] = 0 x[1, 1, 1] = 1 resized = resize(x, (10, 10, 1), order=1, mode='constant') ref = np.zeros((10, 10, 1)) ref[1:5, 1:5, :] = 0.03125 ref[1:5, 2:4, :] = 0.09375 ref[2:4, 1:5, :] = 0.09375 ref[2:4, 2:4, :] = 0.28125 assert_almost_equal(resized, ref) def test_swirl(): image = img_as_float(data.checkerboard()) swirl_params = {'radius': 80, 'rotation': 0, 'order': 2, 'mode': 'reflect'} with expected_warnings(['Bi-quadratic.*bug']): swirled = tf.swirl(image, strength=10, **swirl_params) unswirled = tf.swirl(swirled, strength=-10, **swirl_params) assert np.mean(np.abs(image - unswirled)) < 0.01 swirl_params.pop('mode') with expected_warnings(['Bi-quadratic.*bug', 'default']): swirled = tf.swirl(image, strength=10, **swirl_params) unswirled = tf.swirl(swirled, strength=-10, **swirl_params) assert np.mean(np.abs(image[1:-1,1:-1] - unswirled[1:-1,1:-1])) < 0.01 def test_const_cval_out_of_range(): img = np.random.randn(100, 100) cval = - 10 warped = warp(img, AffineTransform(translation=(10, 10)), cval=cval) assert np.sum(warped == cval) == (2 * 100 * 10 - 10 * 10) def test_warp_identity(): img = img_as_float(rgb2gray(data.astronaut())) assert len(img.shape) == 2 assert np.allclose(img, warp(img, AffineTransform(rotation=0))) assert not np.allclose(img, warp(img, AffineTransform(rotation=0.1))) rgb_img = np.transpose(np.asarray([img, np.zeros_like(img), img]), (1, 2, 0)) warped_rgb_img = warp(rgb_img, AffineTransform(rotation=0.1)) assert np.allclose(rgb_img, warp(rgb_img, AffineTransform(rotation=0))) assert not np.allclose(rgb_img, warped_rgb_img) # assert no cross-talk between bands assert np.all(0 == warped_rgb_img[:, :, 1]) def test_warp_coords_example(): image = data.astronaut().astype(np.float32) assert 3 == image.shape[2] tform = SimilarityTransform(translation=(0, -10)) coords = warp_coords(tform, (30, 30, 3)) map_coordinates(image[:, :, 0], coords[:2]) def test_downscale_local_mean(): image1 = np.arange(4 * 6).reshape(4, 6) out1 = downscale_local_mean(image1, (2, 3)) expected1 = np.array([[ 4., 7.], [ 16., 19.]]) assert_equal(expected1, out1) image2 = np.arange(5 * 8).reshape(5, 8) out2 = downscale_local_mean(image2, (4, 5)) expected2 = np.array([[ 14. , 10.8], [ 8.5, 5.7]]) assert_equal(expected2, out2) def test_invalid(): assert_raises(ValueError, warp, np.ones((4, 3, 3, 3)), SimilarityTransform()) def test_inverse(): tform = SimilarityTransform(scale=0.5, rotation=0.1) inverse_tform = SimilarityTransform(matrix=np.linalg.inv(tform.params)) image = np.arange(10 * 10).reshape(10, 10).astype(np.double) assert_equal(warp(image, inverse_tform), warp(image, tform.inverse)) def test_slow_warp_nonint_oshape(): image = np.random.rand(5, 5) assert_raises(ValueError, warp, image, lambda xy: xy, output_shape=(13.1, 19.5)) warp(image, lambda xy: xy, output_shape=(13.0001, 19.9999)) def test_keep_range(): image = np.linspace(0, 2, 25).reshape(5, 5) with expected_warnings(['The default mode']): out = rescale(image, 2, preserve_range=False, clip=True, order=0) assert out.min() == 0 assert out.max() == 2 with expected_warnings(['The default mode']): out = rescale(image, 2, preserve_range=True, clip=True, order=0) assert out.min() == 0 assert out.max() == 2 with expected_warnings(['The default mode']): out = rescale(image.astype(np.uint8), 2, preserve_range=False, clip=True, order=0) assert out.min() == 0 assert out.max() == 2 / 255.0 if __name__ == "__main__": run_module_suite() scikit-image-0.13.1/skimage/util/000077500000000000000000000000001316256236300165465ustar00rootroot00000000000000scikit-image-0.13.1/skimage/util/__init__.py000066400000000000000000000016261316256236300206640ustar00rootroot00000000000000from .dtype import (img_as_float, img_as_int, img_as_uint, img_as_ubyte, img_as_bool, dtype_limits) from .shape import view_as_blocks, view_as_windows from .noise import random_noise from .apply_parallel import apply_parallel from .arraycrop import crop from ._regular_grid import regular_grid, regular_seeds from .unique import unique_rows from ._invert import invert from .._shared.utils import copy_func from numpy import pad as numpy_pad pad = copy_func(numpy_pad, name='pad') __all__ = ['img_as_float', 'img_as_int', 'img_as_uint', 'img_as_ubyte', 'img_as_bool', 'dtype_limits', 'view_as_blocks', 'view_as_windows', 'pad', 'crop', 'random_noise', 'regular_grid', 'regular_seeds', 'apply_parallel', 'invert', 'unique_rows'] scikit-image-0.13.1/skimage/util/_invert.py000066400000000000000000000013261316256236300205700ustar00rootroot00000000000000import numpy as np from .dtype import dtype_limits def invert(image): """Invert an image. Substract the image to the maximum value allowed by the dtype maximum. Parameters ---------- image : ndarray The input image. Returns ------- invert : ndarray Inverted image. Examples -------- >>> img = np.array([[100, 0, 200], ... [0, 50, 0], ... [30, 0, 255]], np.uint8) >>> invert(img) array([[155, 255, 55], [255, 205, 255], [225, 255, 0]], dtype=uint8) """ if image.dtype == 'bool': return ~image else: return dtype_limits(image, clip_negative=False)[1] - image scikit-image-0.13.1/skimage/util/_regular_grid.py000066400000000000000000000064721316256236300217360ustar00rootroot00000000000000import numpy as np def regular_grid(ar_shape, n_points): """Find `n_points` regularly spaced along `ar_shape`. The returned points (as slices) should be as close to cubically-spaced as possible. Essentially, the points are spaced by the Nth root of the input array size, where N is the number of dimensions. However, if an array dimension cannot fit a full step size, it is "discarded", and the computation is done for only the remaining dimensions. Parameters ---------- ar_shape : array-like of ints The shape of the space embedding the grid. ``len(ar_shape)`` is the number of dimensions. n_points : int The (approximate) number of points to embed in the space. Returns ------- slices : list of slice objects A slice along each dimension of `ar_shape`, such that the intersection of all the slices give the coordinates of regularly spaced points. Examples -------- >>> ar = np.zeros((20, 40)) >>> g = regular_grid(ar.shape, 8) >>> g [slice(5, None, 10), slice(5, None, 10)] >>> ar[g] = 1 >>> ar.sum() 8.0 >>> ar = np.zeros((20, 40)) >>> g = regular_grid(ar.shape, 32) >>> g [slice(2, None, 5), slice(2, None, 5)] >>> ar[g] = 1 >>> ar.sum() 32.0 >>> ar = np.zeros((3, 20, 40)) >>> g = regular_grid(ar.shape, 8) >>> g [slice(1, None, 3), slice(5, None, 10), slice(5, None, 10)] >>> ar[g] = 1 >>> ar.sum() 8.0 """ ar_shape = np.asanyarray(ar_shape) ndim = len(ar_shape) unsort_dim_idxs = np.argsort(np.argsort(ar_shape)) sorted_dims = np.sort(ar_shape) space_size = float(np.prod(ar_shape)) if space_size <= n_points: return [slice(None)] * ndim stepsizes = (space_size / n_points) ** (1.0 / ndim) * np.ones(ndim) if (sorted_dims < stepsizes).any(): for dim in range(ndim): stepsizes[dim] = sorted_dims[dim] space_size = float(np.prod(sorted_dims[dim + 1:])) stepsizes[dim + 1:] = ((space_size / n_points) ** (1.0 / (ndim - dim - 1))) if (sorted_dims >= stepsizes).all(): break starts = (stepsizes // 2).astype(int) stepsizes = np.round(stepsizes).astype(int) slices = [slice(start, None, step) for start, step in zip(starts, stepsizes)] slices = [slices[i] for i in unsort_dim_idxs] return slices def regular_seeds(ar_shape, n_points, dtype=int): """Return an image with ~`n_points` regularly-spaced nonzero pixels. Parameters ---------- ar_shape : tuple of int The shape of the desired output image. n_points : int The desired number of nonzero points. dtype : numpy data type, optional The desired data type of the output. Returns ------- seed_img : array of int or bool The desired image. Examples -------- >>> regular_seeds((5, 5), 4) array([[0, 0, 0, 0, 0], [0, 1, 0, 2, 0], [0, 0, 0, 0, 0], [0, 3, 0, 4, 0], [0, 0, 0, 0, 0]]) """ grid = regular_grid(ar_shape, n_points) seed_img = np.zeros(ar_shape, dtype=dtype) seed_img[grid] = 1 + np.reshape(np.arange(seed_img[grid].size), seed_img[grid].shape) return seed_img scikit-image-0.13.1/skimage/util/apply_parallel.py000066400000000000000000000075571316256236300221370ustar00rootroot00000000000000from math import ceil from multiprocessing import cpu_count __all__ = ['apply_parallel'] try: import dask.array as da dask_available = True except ImportError: dask_available = False def _get_chunks(shape, ncpu): """Split the array into equal sized chunks based on the number of available processors. The last chunk in each dimension absorbs the remainder array elements if the number of CPUs does not divide evenly into the number of array elements. Examples -------- >>> _get_chunks((4, 4), 4) ((2, 2), (2, 2)) >>> _get_chunks((4, 4), 2) ((2, 2), (4,)) >>> _get_chunks((5, 5), 2) ((2, 3), (5,)) >>> _get_chunks((2, 4), 2) ((1, 1), (4,)) """ chunks = [] nchunks_per_dim = int(ceil(ncpu ** (1./len(shape)))) used_chunks = 1 for i in shape: if used_chunks < ncpu: regular_chunk = i // nchunks_per_dim remainder_chunk = regular_chunk + (i % nchunks_per_dim) if regular_chunk == 0: chunk_lens = (remainder_chunk,) else: chunk_lens = ((regular_chunk,) * (nchunks_per_dim - 1) + (remainder_chunk,)) else: chunk_lens = (i,) chunks.append(chunk_lens) used_chunks *= nchunks_per_dim return tuple(chunks) def apply_parallel(function, array, chunks=None, depth=0, mode=None, extra_arguments=(), extra_keywords={}): """Map a function in parallel across an array. Split an array into possibly overlapping chunks of a given depth and boundary type, call the given function in parallel on the chunks, combine the chunks and return the resulting array. Parameters ---------- function : function Function to be mapped which takes an array as an argument. array : numpy array Array which the function will be applied to. chunks : int, tuple, or tuple of tuples, optional A single integer is interpreted as the length of one side of a square chunk that should be tiled across the array. One tuple of length ``array.ndim`` represents the shape of a chunk, and it is tiled across the array. A list of tuples of length ``ndim``, where each sub-tuple is a sequence of chunk sizes along the corresponding dimension. If None, the array is broken up into chunks based on the number of available cpus. More information about chunks is in the documentation `here `_. depth : int, optional Integer equal to the depth of the added boundary cells. Defaults to zero. mode : {'reflect', 'symmetric', 'periodic', 'wrap', 'nearest', 'edge'}, optional type of external boundary padding. extra_arguments : tuple, optional Tuple of arguments to be passed to the function. extra_keywords : dictionary, optional Dictionary of keyword arguments to be passed to the function. Notes ----- Numpy edge modes 'symmetric', 'wrap', and 'edge' are converted to the equivalent `dask` boundary modes 'reflect', 'periodic' and 'nearest', respectively. """ if not dask_available: raise RuntimeError("Could not import 'dask'. Please install " "using 'pip install dask'") if chunks is None: shape = array.shape try: ncpu = cpu_count() except NotImplementedError: ncpu = 4 chunks = _get_chunks(shape, ncpu) if mode == 'wrap': mode = 'periodic' elif mode == 'symmetric': mode = 'reflect' elif mode == 'edge': mode = 'nearest' def wrapped_func(arr): return function(arr, *extra_arguments, **extra_keywords) darr = da.from_array(array, chunks=chunks) return darr.map_overlap(wrapped_func, depth, boundary=mode).compute() scikit-image-0.13.1/skimage/util/arraycrop.py000066400000000000000000000155431316256236300211320ustar00rootroot00000000000000""" The arraycrop module contains functions to crop values from the edges of an n-dimensional array. """ from __future__ import division, absolute_import, print_function import numpy as np from numpy.lib.arraypad import _validate_lengths __all__ = ['crop'] # The below functions are retained in comments in case the NumPy architecture # changes and we need copies of these helper functions for `crop`. # These are identical to functions in numpy.lib.arraypad.py as of NumPy v1.11 # def _normalize_shape(ndarray, shape, cast_to_int=True): # """ # Private function which does some checks and normalizes the possibly # much simpler representations of 'pad_width', 'stat_length', # 'constant_values', 'end_values'. # # Parameters # ---------- # narray : ndarray # Input ndarray # shape : {sequence, array_like, float, int}, optional # The width of padding (pad_width), the number of elements on the # edge of the narray used for statistics (stat_length), the constant # value(s) to use when filling padded regions (constant_values), or the # endpoint target(s) for linear ramps (end_values). # ((before_1, after_1), ... (before_N, after_N)) unique number of # elements for each axis where `N` is rank of `narray`. # ((before, after),) yields same before and after constants for each # axis. # (constant,) or val is a shortcut for before = after = constant for # all axes. # cast_to_int : bool, optional # Controls if values in ``shape`` will be rounded and cast to int # before being returned. # # Returns # ------- # normalized_shape : tuple of tuples # val => ((val, val), (val, val), ...) # [[val1, val2], [val3, val4], ...] => ((val1, val2), (val3, val4), ...) # ((val1, val2), (val3, val4), ...) => no change # [[val1, val2], ] => ((val1, val2), (val1, val2), ...) # ((val1, val2), ) => ((val1, val2), (val1, val2), ...) # [[val , ], ] => ((val, val), (val, val), ...) # ((val , ), ) => ((val, val), (val, val), ...) # # """ # ndims = ndarray.ndim # # # Shortcut shape=None # if shape is None: # return ((None, None), ) * ndims # # # Convert any input `info` to a NumPy array # arr = np.asarray(shape) # # # Switch based on what input looks like # if arr.ndim <= 1: # if arr.shape == () or arr.shape == (1,): # # Single scalar input # # Create new array of ones, multiply by the scalar # arr = np.ones((ndims, 2), dtype=ndarray.dtype) * arr # elif arr.shape == (2,): # # Apply padding (before, after) each axis # # Create new axis 0, repeat along it for every axis # arr = arr[np.newaxis, :].repeat(ndims, axis=0) # else: # fmt = "Unable to create correctly shaped tuple from %s" # raise ValueError(fmt % (shape,)) # # elif arr.ndim == 2: # if arr.shape[1] == 1 and arr.shape[0] == ndims: # # Padded before and after by the same amount # arr = arr.repeat(2, axis=1) # elif arr.shape[0] == ndims: # # Input correctly formatted, pass it on as `arr` # arr = shape # else: # fmt = "Unable to create correctly shaped tuple from %s" # raise ValueError(fmt % (shape,)) # # else: # fmt = "Unable to create correctly shaped tuple from %s" # raise ValueError(fmt % (shape,)) # # # Cast if necessary # if cast_to_int is True: # arr = np.round(arr).astype(int) # # # Convert list of lists to tuple of tuples # return tuple(tuple(axis) for axis in arr.tolist()) # def _validate_lengths(narray, number_elements): # """ # Private function which does some checks and reformats pad_width and # stat_length using _normalize_shape. # # Parameters # ---------- # narray : ndarray # Input ndarray # number_elements : {sequence, int}, optional # The width of padding (pad_width) or the number of elements on the edge # of the narray used for statistics (stat_length). # ((before_1, after_1), ... (before_N, after_N)) unique number of # elements for each axis. # ((before, after),) yields same before and after constants for each # axis. # (constant,) or int is a shortcut for before = after = constant for all # axes. # # Returns # ------- # _validate_lengths : tuple of tuples # int => ((int, int), (int, int), ...) # [[int1, int2], [int3, int4], ...] => ((int1, int2), (int3, int4), ...) # ((int1, int2), (int3, int4), ...) => no change # [[int1, int2], ] => ((int1, int2), (int1, int2), ...) # ((int1, int2), ) => ((int1, int2), (int1, int2), ...) # [[int , ], ] => ((int, int), (int, int), ...) # ((int , ), ) => ((int, int), (int, int), ...) # # """ # normshp = _normalize_shape(narray, number_elements) # for i in normshp: # chk = [1 if x is None else x for x in i] # chk = [1 if x >= 0 else -1 for x in chk] # if (chk[0] < 0) or (chk[1] < 0): # fmt = "%s cannot contain negative values." # raise ValueError(fmt % (number_elements,)) # return normshp def crop(ar, crop_width, copy=False, order='K'): """Crop array `ar` by `crop_width` along each dimension. Parameters ---------- ar : array-like of rank N Input array. crop_width : {sequence, int} Number of values to remove from the edges of each axis. ``((before_1, after_1),`` ... ``(before_N, after_N))`` specifies unique crop widths at the start and end of each axis. ``((before, after),)`` specifies a fixed start and end crop for every axis. ``(n,)`` or ``n`` for integer ``n`` is a shortcut for before = after = ``n`` for all axes. copy : bool, optional If `True`, ensure the returned array is a contiguous copy. Normally, a crop operation will return a discontiguous view of the underlying input array. order : {'C', 'F', 'A', 'K'}, optional If ``copy==True``, control the memory layout of the copy. See ``np.copy``. Returns ------- cropped : array The cropped array. If ``copy=False`` (default), this is a sliced view of the input array. """ ar = np.array(ar, copy=False) crops = _validate_lengths(ar, crop_width) slices = [slice(a, ar.shape[i] - b) for i, (a, b) in enumerate(crops)] if copy: cropped = np.array(ar[slices], order=order, copy=True) else: cropped = ar[slices] return cropped scikit-image-0.13.1/skimage/util/colormap.py000066400000000000000000000577251316256236300207540ustar00rootroot00000000000000from matplotlib.colors import LinearSegmentedColormap from matplotlib import cm viridis_data = [[ 0.26700401, 0.00487433, 0.32941519], [ 0.26851048, 0.00960483, 0.33542652], [ 0.26994384, 0.01462494, 0.34137895], [ 0.27130489, 0.01994186, 0.34726862], [ 0.27259384, 0.02556309, 0.35309303], [ 0.27380934, 0.03149748, 0.35885256], [ 0.27495242, 0.03775181, 0.36454323], [ 0.27602238, 0.04416723, 0.37016418], [ 0.2770184 , 0.05034437, 0.37571452], [ 0.27794143, 0.05632444, 0.38119074], [ 0.27879067, 0.06214536, 0.38659204], [ 0.2795655 , 0.06783587, 0.39191723], [ 0.28026658, 0.07341724, 0.39716349], [ 0.28089358, 0.07890703, 0.40232944], [ 0.28144581, 0.0843197 , 0.40741404], [ 0.28192358, 0.08966622, 0.41241521], [ 0.28232739, 0.09495545, 0.41733086], [ 0.28265633, 0.10019576, 0.42216032], [ 0.28291049, 0.10539345, 0.42690202], [ 0.28309095, 0.11055307, 0.43155375], [ 0.28319704, 0.11567966, 0.43611482], [ 0.28322882, 0.12077701, 0.44058404], [ 0.28318684, 0.12584799, 0.44496 ], [ 0.283072 , 0.13089477, 0.44924127], [ 0.28288389, 0.13592005, 0.45342734], [ 0.28262297, 0.14092556, 0.45751726], [ 0.28229037, 0.14591233, 0.46150995], [ 0.28188676, 0.15088147, 0.46540474], [ 0.28141228, 0.15583425, 0.46920128], [ 0.28086773, 0.16077132, 0.47289909], [ 0.28025468, 0.16569272, 0.47649762], [ 0.27957399, 0.17059884, 0.47999675], [ 0.27882618, 0.1754902 , 0.48339654], [ 0.27801236, 0.18036684, 0.48669702], [ 0.27713437, 0.18522836, 0.48989831], [ 0.27619376, 0.19007447, 0.49300074], [ 0.27519116, 0.1949054 , 0.49600488], [ 0.27412802, 0.19972086, 0.49891131], [ 0.27300596, 0.20452049, 0.50172076], [ 0.27182812, 0.20930306, 0.50443413], [ 0.27059473, 0.21406899, 0.50705243], [ 0.26930756, 0.21881782, 0.50957678], [ 0.26796846, 0.22354911, 0.5120084 ], [ 0.26657984, 0.2282621 , 0.5143487 ], [ 0.2651445 , 0.23295593, 0.5165993 ], [ 0.2636632 , 0.23763078, 0.51876163], [ 0.26213801, 0.24228619, 0.52083736], [ 0.26057103, 0.2469217 , 0.52282822], [ 0.25896451, 0.25153685, 0.52473609], [ 0.25732244, 0.2561304 , 0.52656332], [ 0.25564519, 0.26070284, 0.52831152], [ 0.25393498, 0.26525384, 0.52998273], [ 0.25219404, 0.26978306, 0.53157905], [ 0.25042462, 0.27429024, 0.53310261], [ 0.24862899, 0.27877509, 0.53455561], [ 0.2468114 , 0.28323662, 0.53594093], [ 0.24497208, 0.28767547, 0.53726018], [ 0.24311324, 0.29209154, 0.53851561], [ 0.24123708, 0.29648471, 0.53970946], [ 0.23934575, 0.30085494, 0.54084398], [ 0.23744138, 0.30520222, 0.5419214 ], [ 0.23552606, 0.30952657, 0.54294396], [ 0.23360277, 0.31382773, 0.54391424], [ 0.2316735 , 0.3181058 , 0.54483444], [ 0.22973926, 0.32236127, 0.54570633], [ 0.22780192, 0.32659432, 0.546532 ], [ 0.2258633 , 0.33080515, 0.54731353], [ 0.22392515, 0.334994 , 0.54805291], [ 0.22198915, 0.33916114, 0.54875211], [ 0.22005691, 0.34330688, 0.54941304], [ 0.21812995, 0.34743154, 0.55003755], [ 0.21620971, 0.35153548, 0.55062743], [ 0.21429757, 0.35561907, 0.5511844 ], [ 0.21239477, 0.35968273, 0.55171011], [ 0.2105031 , 0.36372671, 0.55220646], [ 0.20862342, 0.36775151, 0.55267486], [ 0.20675628, 0.37175775, 0.55311653], [ 0.20490257, 0.37574589, 0.55353282], [ 0.20306309, 0.37971644, 0.55392505], [ 0.20123854, 0.38366989, 0.55429441], [ 0.1994295 , 0.38760678, 0.55464205], [ 0.1976365 , 0.39152762, 0.55496905], [ 0.19585993, 0.39543297, 0.55527637], [ 0.19410009, 0.39932336, 0.55556494], [ 0.19235719, 0.40319934, 0.55583559], [ 0.19063135, 0.40706148, 0.55608907], [ 0.18892259, 0.41091033, 0.55632606], [ 0.18723083, 0.41474645, 0.55654717], [ 0.18555593, 0.4185704 , 0.55675292], [ 0.18389763, 0.42238275, 0.55694377], [ 0.18225561, 0.42618405, 0.5571201 ], [ 0.18062949, 0.42997486, 0.55728221], [ 0.17901879, 0.43375572, 0.55743035], [ 0.17742298, 0.4375272 , 0.55756466], [ 0.17584148, 0.44128981, 0.55768526], [ 0.17427363, 0.4450441 , 0.55779216], [ 0.17271876, 0.4487906 , 0.55788532], [ 0.17117615, 0.4525298 , 0.55796464], [ 0.16964573, 0.45626209, 0.55803034], [ 0.16812641, 0.45998802, 0.55808199], [ 0.1666171 , 0.46370813, 0.55811913], [ 0.16511703, 0.4674229 , 0.55814141], [ 0.16362543, 0.47113278, 0.55814842], [ 0.16214155, 0.47483821, 0.55813967], [ 0.16066467, 0.47853961, 0.55811466], [ 0.15919413, 0.4822374 , 0.5580728 ], [ 0.15772933, 0.48593197, 0.55801347], [ 0.15626973, 0.4896237 , 0.557936 ], [ 0.15481488, 0.49331293, 0.55783967], [ 0.15336445, 0.49700003, 0.55772371], [ 0.1519182 , 0.50068529, 0.55758733], [ 0.15047605, 0.50436904, 0.55742968], [ 0.14903918, 0.50805136, 0.5572505 ], [ 0.14760731, 0.51173263, 0.55704861], [ 0.14618026, 0.51541316, 0.55682271], [ 0.14475863, 0.51909319, 0.55657181], [ 0.14334327, 0.52277292, 0.55629491], [ 0.14193527, 0.52645254, 0.55599097], [ 0.14053599, 0.53013219, 0.55565893], [ 0.13914708, 0.53381201, 0.55529773], [ 0.13777048, 0.53749213, 0.55490625], [ 0.1364085 , 0.54117264, 0.55448339], [ 0.13506561, 0.54485335, 0.55402906], [ 0.13374299, 0.54853458, 0.55354108], [ 0.13244401, 0.55221637, 0.55301828], [ 0.13117249, 0.55589872, 0.55245948], [ 0.1299327 , 0.55958162, 0.55186354], [ 0.12872938, 0.56326503, 0.55122927], [ 0.12756771, 0.56694891, 0.55055551], [ 0.12645338, 0.57063316, 0.5498411 ], [ 0.12539383, 0.57431754, 0.54908564], [ 0.12439474, 0.57800205, 0.5482874 ], [ 0.12346281, 0.58168661, 0.54744498], [ 0.12260562, 0.58537105, 0.54655722], [ 0.12183122, 0.58905521, 0.54562298], [ 0.12114807, 0.59273889, 0.54464114], [ 0.12056501, 0.59642187, 0.54361058], [ 0.12009154, 0.60010387, 0.54253043], [ 0.11973756, 0.60378459, 0.54139999], [ 0.11951163, 0.60746388, 0.54021751], [ 0.11942341, 0.61114146, 0.53898192], [ 0.11948255, 0.61481702, 0.53769219], [ 0.11969858, 0.61849025, 0.53634733], [ 0.12008079, 0.62216081, 0.53494633], [ 0.12063824, 0.62582833, 0.53348834], [ 0.12137972, 0.62949242, 0.53197275], [ 0.12231244, 0.63315277, 0.53039808], [ 0.12344358, 0.63680899, 0.52876343], [ 0.12477953, 0.64046069, 0.52706792], [ 0.12632581, 0.64410744, 0.52531069], [ 0.12808703, 0.64774881, 0.52349092], [ 0.13006688, 0.65138436, 0.52160791], [ 0.13226797, 0.65501363, 0.51966086], [ 0.13469183, 0.65863619, 0.5176488 ], [ 0.13733921, 0.66225157, 0.51557101], [ 0.14020991, 0.66585927, 0.5134268 ], [ 0.14330291, 0.66945881, 0.51121549], [ 0.1466164 , 0.67304968, 0.50893644], [ 0.15014782, 0.67663139, 0.5065889 ], [ 0.15389405, 0.68020343, 0.50417217], [ 0.15785146, 0.68376525, 0.50168574], [ 0.16201598, 0.68731632, 0.49912906], [ 0.1663832 , 0.69085611, 0.49650163], [ 0.1709484 , 0.69438405, 0.49380294], [ 0.17570671, 0.6978996 , 0.49103252], [ 0.18065314, 0.70140222, 0.48818938], [ 0.18578266, 0.70489133, 0.48527326], [ 0.19109018, 0.70836635, 0.48228395], [ 0.19657063, 0.71182668, 0.47922108], [ 0.20221902, 0.71527175, 0.47608431], [ 0.20803045, 0.71870095, 0.4728733 ], [ 0.21400015, 0.72211371, 0.46958774], [ 0.22012381, 0.72550945, 0.46622638], [ 0.2263969 , 0.72888753, 0.46278934], [ 0.23281498, 0.73224735, 0.45927675], [ 0.2393739 , 0.73558828, 0.45568838], [ 0.24606968, 0.73890972, 0.45202405], [ 0.25289851, 0.74221104, 0.44828355], [ 0.25985676, 0.74549162, 0.44446673], [ 0.26694127, 0.74875084, 0.44057284], [ 0.27414922, 0.75198807, 0.4366009 ], [ 0.28147681, 0.75520266, 0.43255207], [ 0.28892102, 0.75839399, 0.42842626], [ 0.29647899, 0.76156142, 0.42422341], [ 0.30414796, 0.76470433, 0.41994346], [ 0.31192534, 0.76782207, 0.41558638], [ 0.3198086 , 0.77091403, 0.41115215], [ 0.3277958 , 0.77397953, 0.40664011], [ 0.33588539, 0.7770179 , 0.40204917], [ 0.34407411, 0.78002855, 0.39738103], [ 0.35235985, 0.78301086, 0.39263579], [ 0.36074053, 0.78596419, 0.38781353], [ 0.3692142 , 0.78888793, 0.38291438], [ 0.37777892, 0.79178146, 0.3779385 ], [ 0.38643282, 0.79464415, 0.37288606], [ 0.39517408, 0.79747541, 0.36775726], [ 0.40400101, 0.80027461, 0.36255223], [ 0.4129135 , 0.80304099, 0.35726893], [ 0.42190813, 0.80577412, 0.35191009], [ 0.43098317, 0.80847343, 0.34647607], [ 0.44013691, 0.81113836, 0.3409673 ], [ 0.44936763, 0.81376835, 0.33538426], [ 0.45867362, 0.81636288, 0.32972749], [ 0.46805314, 0.81892143, 0.32399761], [ 0.47750446, 0.82144351, 0.31819529], [ 0.4870258 , 0.82392862, 0.31232133], [ 0.49661536, 0.82637633, 0.30637661], [ 0.5062713 , 0.82878621, 0.30036211], [ 0.51599182, 0.83115784, 0.29427888], [ 0.52577622, 0.83349064, 0.2881265 ], [ 0.5356211 , 0.83578452, 0.28190832], [ 0.5455244 , 0.83803918, 0.27562602], [ 0.55548397, 0.84025437, 0.26928147], [ 0.5654976 , 0.8424299 , 0.26287683], [ 0.57556297, 0.84456561, 0.25641457], [ 0.58567772, 0.84666139, 0.24989748], [ 0.59583934, 0.84871722, 0.24332878], [ 0.60604528, 0.8507331 , 0.23671214], [ 0.61629283, 0.85270912, 0.23005179], [ 0.62657923, 0.85464543, 0.22335258], [ 0.63690157, 0.85654226, 0.21662012], [ 0.64725685, 0.85839991, 0.20986086], [ 0.65764197, 0.86021878, 0.20308229], [ 0.66805369, 0.86199932, 0.19629307], [ 0.67848868, 0.86374211, 0.18950326], [ 0.68894351, 0.86544779, 0.18272455], [ 0.69941463, 0.86711711, 0.17597055], [ 0.70989842, 0.86875092, 0.16925712], [ 0.72039115, 0.87035015, 0.16260273], [ 0.73088902, 0.87191584, 0.15602894], [ 0.74138803, 0.87344918, 0.14956101], [ 0.75188414, 0.87495143, 0.14322828], [ 0.76237342, 0.87642392, 0.13706449], [ 0.77285183, 0.87786808, 0.13110864], [ 0.78331535, 0.87928545, 0.12540538], [ 0.79375994, 0.88067763, 0.12000532], [ 0.80418159, 0.88204632, 0.11496505], [ 0.81457634, 0.88339329, 0.11034678], [ 0.82494028, 0.88472036, 0.10621724], [ 0.83526959, 0.88602943, 0.1026459 ], [ 0.84556056, 0.88732243, 0.09970219], [ 0.8558096 , 0.88860134, 0.09745186], [ 0.86601325, 0.88986815, 0.09595277], [ 0.87616824, 0.89112487, 0.09525046], [ 0.88627146, 0.89237353, 0.09537439], [ 0.89632002, 0.89361614, 0.09633538], [ 0.90631121, 0.89485467, 0.09812496], [ 0.91624212, 0.89609127, 0.1007168 ], [ 0.92610579, 0.89732977, 0.10407067], [ 0.93590444, 0.8985704 , 0.10813094], [ 0.94563626, 0.899815 , 0.11283773], [ 0.95529972, 0.90106534, 0.11812832], [ 0.96489353, 0.90232311, 0.12394051], [ 0.97441665, 0.90358991, 0.13021494], [ 0.98386829, 0.90486726, 0.13689671], [ 0.99324789, 0.90615657, 0.1439362 ]] magma_data = [[0.001462, 0.000466, 0.013866], [0.002258, 0.001295, 0.018331], [0.003279, 0.002305, 0.023708], [0.004512, 0.003490, 0.029965], [0.005950, 0.004843, 0.037130], [0.007588, 0.006356, 0.044973], [0.009426, 0.008022, 0.052844], [0.011465, 0.009828, 0.060750], [0.013708, 0.011771, 0.068667], [0.016156, 0.013840, 0.076603], [0.018815, 0.016026, 0.084584], [0.021692, 0.018320, 0.092610], [0.024792, 0.020715, 0.100676], [0.028123, 0.023201, 0.108787], [0.031696, 0.025765, 0.116965], [0.035520, 0.028397, 0.125209], [0.039608, 0.031090, 0.133515], [0.043830, 0.033830, 0.141886], [0.048062, 0.036607, 0.150327], [0.052320, 0.039407, 0.158841], [0.056615, 0.042160, 0.167446], [0.060949, 0.044794, 0.176129], [0.065330, 0.047318, 0.184892], [0.069764, 0.049726, 0.193735], [0.074257, 0.052017, 0.202660], [0.078815, 0.054184, 0.211667], [0.083446, 0.056225, 0.220755], [0.088155, 0.058133, 0.229922], [0.092949, 0.059904, 0.239164], [0.097833, 0.061531, 0.248477], [0.102815, 0.063010, 0.257854], [0.107899, 0.064335, 0.267289], [0.113094, 0.065492, 0.276784], [0.118405, 0.066479, 0.286321], [0.123833, 0.067295, 0.295879], [0.129380, 0.067935, 0.305443], [0.135053, 0.068391, 0.315000], [0.140858, 0.068654, 0.324538], [0.146785, 0.068738, 0.334011], [0.152839, 0.068637, 0.343404], [0.159018, 0.068354, 0.352688], [0.165308, 0.067911, 0.361816], [0.171713, 0.067305, 0.370771], [0.178212, 0.066576, 0.379497], [0.184801, 0.065732, 0.387973], [0.191460, 0.064818, 0.396152], [0.198177, 0.063862, 0.404009], [0.204935, 0.062907, 0.411514], [0.211718, 0.061992, 0.418647], [0.218512, 0.061158, 0.425392], [0.225302, 0.060445, 0.431742], [0.232077, 0.059889, 0.437695], [0.238826, 0.059517, 0.443256], [0.245543, 0.059352, 0.448436], [0.252220, 0.059415, 0.453248], [0.258857, 0.059706, 0.457710], [0.265447, 0.060237, 0.461840], [0.271994, 0.060994, 0.465660], [0.278493, 0.061978, 0.469190], [0.284951, 0.063168, 0.472451], [0.291366, 0.064553, 0.475462], [0.297740, 0.066117, 0.478243], [0.304081, 0.067835, 0.480812], [0.310382, 0.069702, 0.483186], [0.316654, 0.071690, 0.485380], [0.322899, 0.073782, 0.487408], [0.329114, 0.075972, 0.489287], [0.335308, 0.078236, 0.491024], [0.341482, 0.080564, 0.492631], [0.347636, 0.082946, 0.494121], [0.353773, 0.085373, 0.495501], [0.359898, 0.087831, 0.496778], [0.366012, 0.090314, 0.497960], [0.372116, 0.092816, 0.499053], [0.378211, 0.095332, 0.500067], [0.384299, 0.097855, 0.501002], [0.390384, 0.100379, 0.501864], [0.396467, 0.102902, 0.502658], [0.402548, 0.105420, 0.503386], [0.408629, 0.107930, 0.504052], [0.414709, 0.110431, 0.504662], [0.420791, 0.112920, 0.505215], [0.426877, 0.115395, 0.505714], [0.432967, 0.117855, 0.506160], [0.439062, 0.120298, 0.506555], [0.445163, 0.122724, 0.506901], [0.451271, 0.125132, 0.507198], [0.457386, 0.127522, 0.507448], [0.463508, 0.129893, 0.507652], [0.469640, 0.132245, 0.507809], [0.475780, 0.134577, 0.507921], [0.481929, 0.136891, 0.507989], [0.488088, 0.139186, 0.508011], [0.494258, 0.141462, 0.507988], [0.500438, 0.143719, 0.507920], [0.506629, 0.145958, 0.507806], [0.512831, 0.148179, 0.507648], [0.519045, 0.150383, 0.507443], [0.525270, 0.152569, 0.507192], [0.531507, 0.154739, 0.506895], [0.537755, 0.156894, 0.506551], [0.544015, 0.159033, 0.506159], [0.550287, 0.161158, 0.505719], [0.556571, 0.163269, 0.505230], [0.562866, 0.165368, 0.504692], [0.569172, 0.167454, 0.504105], [0.575490, 0.169530, 0.503466], [0.581819, 0.171596, 0.502777], [0.588158, 0.173652, 0.502035], [0.594508, 0.175701, 0.501241], [0.600868, 0.177743, 0.500394], [0.607238, 0.179779, 0.499492], [0.613617, 0.181811, 0.498536], [0.620005, 0.183840, 0.497524], [0.626401, 0.185867, 0.496456], [0.632805, 0.187893, 0.495332], [0.639216, 0.189921, 0.494150], [0.645633, 0.191952, 0.492910], [0.652056, 0.193986, 0.491611], [0.658483, 0.196027, 0.490253], [0.664915, 0.198075, 0.488836], [0.671349, 0.200133, 0.487358], [0.677786, 0.202203, 0.485819], [0.684224, 0.204286, 0.484219], [0.690661, 0.206384, 0.482558], [0.697098, 0.208501, 0.480835], [0.703532, 0.210638, 0.479049], [0.709962, 0.212797, 0.477201], [0.716387, 0.214982, 0.475290], [0.722805, 0.217194, 0.473316], [0.729216, 0.219437, 0.471279], [0.735616, 0.221713, 0.469180], [0.742004, 0.224025, 0.467018], [0.748378, 0.226377, 0.464794], [0.754737, 0.228772, 0.462509], [0.761077, 0.231214, 0.460162], [0.767398, 0.233705, 0.457755], [0.773695, 0.236249, 0.455289], [0.779968, 0.238851, 0.452765], [0.786212, 0.241514, 0.450184], [0.792427, 0.244242, 0.447543], [0.798608, 0.247040, 0.444848], [0.804752, 0.249911, 0.442102], [0.810855, 0.252861, 0.439305], [0.816914, 0.255895, 0.436461], [0.822926, 0.259016, 0.433573], [0.828886, 0.262229, 0.430644], [0.834791, 0.265540, 0.427671], [0.840636, 0.268953, 0.424666], [0.846416, 0.272473, 0.421631], [0.852126, 0.276106, 0.418573], [0.857763, 0.279857, 0.415496], [0.863320, 0.283729, 0.412403], [0.868793, 0.287728, 0.409303], [0.874176, 0.291859, 0.406205], [0.879464, 0.296125, 0.403118], [0.884651, 0.300530, 0.400047], [0.889731, 0.305079, 0.397002], [0.894700, 0.309773, 0.393995], [0.899552, 0.314616, 0.391037], [0.904281, 0.319610, 0.388137], [0.908884, 0.324755, 0.385308], [0.913354, 0.330052, 0.382563], [0.917689, 0.335500, 0.379915], [0.921884, 0.341098, 0.377376], [0.925937, 0.346844, 0.374959], [0.929845, 0.352734, 0.372677], [0.933606, 0.358764, 0.370541], [0.937221, 0.364929, 0.368567], [0.940687, 0.371224, 0.366762], [0.944006, 0.377643, 0.365136], [0.947180, 0.384178, 0.363701], [0.950210, 0.390820, 0.362468], [0.953099, 0.397563, 0.361438], [0.955849, 0.404400, 0.360619], [0.958464, 0.411324, 0.360014], [0.960949, 0.418323, 0.359630], [0.963310, 0.425390, 0.359469], [0.965549, 0.432519, 0.359529], [0.967671, 0.439703, 0.359810], [0.969680, 0.446936, 0.360311], [0.971582, 0.454210, 0.361030], [0.973381, 0.461520, 0.361965], [0.975082, 0.468861, 0.363111], [0.976690, 0.476226, 0.364466], [0.978210, 0.483612, 0.366025], [0.979645, 0.491014, 0.367783], [0.981000, 0.498428, 0.369734], [0.982279, 0.505851, 0.371874], [0.983485, 0.513280, 0.374198], [0.984622, 0.520713, 0.376698], [0.985693, 0.528148, 0.379371], [0.986700, 0.535582, 0.382210], [0.987646, 0.543015, 0.385210], [0.988533, 0.550446, 0.388365], [0.989363, 0.557873, 0.391671], [0.990138, 0.565296, 0.395122], [0.990871, 0.572706, 0.398714], [0.991558, 0.580107, 0.402441], [0.992196, 0.587502, 0.406299], [0.992785, 0.594891, 0.410283], [0.993326, 0.602275, 0.414390], [0.993834, 0.609644, 0.418613], [0.994309, 0.616999, 0.422950], [0.994738, 0.624350, 0.427397], [0.995122, 0.631696, 0.431951], [0.995480, 0.639027, 0.436607], [0.995810, 0.646344, 0.441361], [0.996096, 0.653659, 0.446213], [0.996341, 0.660969, 0.451160], [0.996580, 0.668256, 0.456192], [0.996775, 0.675541, 0.461314], [0.996925, 0.682828, 0.466526], [0.997077, 0.690088, 0.471811], [0.997186, 0.697349, 0.477182], [0.997254, 0.704611, 0.482635], [0.997325, 0.711848, 0.488154], [0.997351, 0.719089, 0.493755], [0.997351, 0.726324, 0.499428], [0.997341, 0.733545, 0.505167], [0.997285, 0.740772, 0.510983], [0.997228, 0.747981, 0.516859], [0.997138, 0.755190, 0.522806], [0.997019, 0.762398, 0.528821], [0.996898, 0.769591, 0.534892], [0.996727, 0.776795, 0.541039], [0.996571, 0.783977, 0.547233], [0.996369, 0.791167, 0.553499], [0.996162, 0.798348, 0.559820], [0.995932, 0.805527, 0.566202], [0.995680, 0.812706, 0.572645], [0.995424, 0.819875, 0.579140], [0.995131, 0.827052, 0.585701], [0.994851, 0.834213, 0.592307], [0.994524, 0.841387, 0.598983], [0.994222, 0.848540, 0.605696], [0.993866, 0.855711, 0.612482], [0.993545, 0.862859, 0.619299], [0.993170, 0.870024, 0.626189], [0.992831, 0.877168, 0.633109], [0.992440, 0.884330, 0.640099], [0.992089, 0.891470, 0.647116], [0.991688, 0.898627, 0.654202], [0.991332, 0.905763, 0.661309], [0.990930, 0.912915, 0.668481], [0.990570, 0.920049, 0.675675], [0.990175, 0.927196, 0.682926], [0.989815, 0.934329, 0.690198], [0.989434, 0.941470, 0.697519], [0.989077, 0.948604, 0.704863], [0.988717, 0.955742, 0.712242], [0.988367, 0.962878, 0.719649], [0.988033, 0.970012, 0.727077], [0.987691, 0.977154, 0.734536], [0.987387, 0.984288, 0.742002], [0.987053, 0.991438, 0.749504]] def register_cmap_safe(name, data): "Register a colormap if not already registered." try: return cm.get_cmap(name) except ValueError: cmap = LinearSegmentedColormap.from_list(name, data) cm.register_cmap(name, cmap) return cmap viridis = register_cmap_safe('viridis', viridis_data) magma = register_cmap_safe('magma', magma_data) scikit-image-0.13.1/skimage/util/dtype.py000066400000000000000000000334641316256236300202570ustar00rootroot00000000000000from __future__ import division import numpy as np from warnings import warn __all__ = ['img_as_float', 'img_as_int', 'img_as_uint', 'img_as_ubyte', 'img_as_bool', 'dtype_limits'] dtype_range = {np.bool_: (False, True), np.bool8: (False, True), np.uint8: (0, 255), np.uint16: (0, 65535), np.uint32: (0, 2**32 - 1), np.uint64: (0, 2**64 - 1), np.int8: (-128, 127), np.int16: (-32768, 32767), np.int32: (-2**31, 2**31 - 1), np.int64: (-2**63, 2**63 - 1), np.float16: (-1, 1), np.float32: (-1, 1), np.float64: (-1, 1)} _supported_types = (np.bool_, np.bool8, np.uint8, np.uint16, np.uint32, np.uint64, np.int8, np.int16, np.int32, np.int64, np.float16, np.float32, np.float64) def dtype_limits(image, clip_negative=None): """Return intensity limits, i.e. (min, max) tuple, of the image's dtype. Parameters ---------- image : ndarray Input image. clip_negative : bool, optional If True, clip the negative range (i.e. return 0 for min intensity) even if the image dtype allows negative values. The default behavior (None) is equivalent to True. Returns ------- imin, imax : tuple Lower and upper intensity limits. """ if clip_negative is None: clip_negative = True warn('The default of `clip_negative` in `skimage.util.dtype_limits` ' 'will change to `False` in version 0.15.') imin, imax = dtype_range[image.dtype.type] if clip_negative: imin = 0 return imin, imax def convert(image, dtype, force_copy=False, uniform=False): """ Convert an image to the requested data-type. Warnings are issued in case of precision loss, or when negative values are clipped during conversion to unsigned integer types (sign loss). Floating point values are expected to be normalized and will be clipped to the range [0.0, 1.0] or [-1.0, 1.0] when converting to unsigned or signed integers respectively. Numbers are not shifted to the negative side when converting from unsigned to signed integer types. Negative values will be clipped when converting to unsigned integers. Parameters ---------- image : ndarray Input image. dtype : dtype Target data-type. force_copy : bool, optional Force a copy of the data, irrespective of its current dtype. uniform : bool, optional Uniformly quantize the floating point range to the integer range. By default (uniform=False) floating point values are scaled and rounded to the nearest integers, which minimizes back and forth conversion errors. References ---------- .. [1] DirectX data conversion rules. http://msdn.microsoft.com/en-us/library/windows/desktop/dd607323%28v=vs.85%29.aspx .. [2] Data Conversions. In "OpenGL ES 2.0 Specification v2.0.25", pp 7-8. Khronos Group, 2010. .. [3] Proper treatment of pixels as integers. A.W. Paeth. In "Graphics Gems I", pp 249-256. Morgan Kaufmann, 1990. .. [4] Dirty Pixels. J. Blinn. In "Jim Blinn's corner: Dirty Pixels", pp 47-57. Morgan Kaufmann, 1998. """ image = np.asarray(image) dtypeobj_in = image.dtype dtypeobj_out = np.dtype(dtype) dtype_in = dtypeobj_in.type dtype_out = dtypeobj_out.type kind_in = dtypeobj_in.kind kind_out = dtypeobj_out.kind itemsize_in = dtypeobj_in.itemsize itemsize_out = dtypeobj_out.itemsize if dtype_in == dtype_out: if force_copy: image = image.copy() return image if not (dtype_in in _supported_types and dtype_out in _supported_types): raise ValueError("Can not convert from {} to {}." .format(dtypeobj_in, dtypeobj_out)) def sign_loss(): warn("Possible sign loss when converting negative image of type " "{} to positive image of type {}." .format(dtypeobj_in, dtypeobj_out)) def prec_loss(): warn("Possible precision loss when converting from {} to {}" .format(dtypeobj_in, dtypeobj_out)) def _dtype_itemsize(itemsize, *dtypes): # Return first of `dtypes` with itemsize greater than `itemsize` return next(dt for dt in dtypes if np.dtype(dt).itemsize >= itemsize) def _dtype_bits(kind, bits, itemsize=1): # Return dtype of `kind` that can store a `bits` wide unsigned int def compare(x, y, kind='u'): if kind == 'u': return x <= y else: return x < y s = next(i for i in (itemsize, ) + (2, 4, 8) if compare(bits, i * 8, kind=kind)) return np.dtype(kind + str(s)) def _scale(a, n, m, copy=True): """Scale an array of unsigned/positive integers from `n` to `m` bits. Numbers can be represented exactly only if `m` is a multiple of `n`. Parameters ---------- a : ndarray Input image array. n : int Number of bits currently used to encode the values in `a`. m : int Desired number of bits to encode the values in `out`. copy : bool, optional If True, allocates and returns new array. Otherwise, modifies `a` in place. Returns ------- out : array Output image array. Has the same kind as `a`. """ kind = a.dtype.kind if n > m and a.max() < 2 ** m: mnew = int(np.ceil(m / 2) * 2) if mnew > m: dtype = "int{}".format(mnew) else: dtype = "uint{}".format(mnew) n = int(np.ceil(n / 2) * 2) warn("Downcasting {} to {} without scaling because max " "value {} fits in {}".format(a.dtype, dtype, a.max(), dtype)) return a.astype(_dtype_bits(kind, m)) elif n == m: return a.copy() if copy else a elif n > m: # downscale with precision loss prec_loss() if copy: b = np.empty(a.shape, _dtype_bits(kind, m)) np.floor_divide(a, 2**(n - m), out=b, dtype=a.dtype, casting='unsafe') return b else: a //= 2**(n - m) return a elif m % n == 0: # exact upscale to a multiple of `n` bits if copy: b = np.empty(a.shape, _dtype_bits(kind, m)) np.multiply(a, (2**m - 1) // (2**n - 1), out=b, dtype=b.dtype) return b else: a = a.astype(_dtype_bits(kind, m, a.dtype.itemsize), copy=False) a *= (2**m - 1) // (2**n - 1) return a else: # upscale to a multiple of `n` bits, # then downscale with precision loss prec_loss() o = (m // n + 1) * n if copy: b = np.empty(a.shape, _dtype_bits(kind, o)) np.multiply(a, (2**o - 1) // (2**n - 1), out=b, dtype=b.dtype) b //= 2**(o - m) return b else: a = a.astype(_dtype_bits(kind, o, a.dtype.itemsize), copy=False) a *= (2**o - 1) // (2**n - 1) a //= 2**(o - m) return a if kind_in in 'ui': imin_in = np.iinfo(dtype_in).min imax_in = np.iinfo(dtype_in).max if kind_out in 'ui': imin_out = np.iinfo(dtype_out).min imax_out = np.iinfo(dtype_out).max # any -> binary if kind_out == 'b': if kind_in in "fi": sign_loss() prec_loss() return image > dtype_in(dtype_range[dtype_in][1] / 2) # binary -> any if kind_in == 'b': result = image.astype(dtype_out) if kind_out != 'f': result *= dtype_out(dtype_range[dtype_out][1]) return result # float -> any if kind_in == 'f': if np.min(image) < -1.0 or np.max(image) > 1.0: raise ValueError("Images of type float must be between -1 and 1.") if kind_out == 'f': # float -> float if itemsize_in > itemsize_out: prec_loss() return image.astype(dtype_out) # floating point -> integer prec_loss() # use float type that can represent output integer type image = image.astype(_dtype_itemsize(itemsize_out, dtype_in, np.float32, np.float64)) if not uniform: if kind_out == 'u': image *= imax_out else: image *= imax_out - imin_out image -= 1.0 image /= 2.0 np.rint(image, out=image) np.clip(image, imin_out, imax_out, out=image) elif kind_out == 'u': image *= imax_out + 1 np.clip(image, 0, imax_out, out=image) else: image *= (imax_out - imin_out + 1.0) / 2.0 np.floor(image, out=image) np.clip(image, imin_out, imax_out, out=image) return image.astype(dtype_out) # signed/unsigned int -> float if kind_out == 'f': if itemsize_in >= itemsize_out: prec_loss() # use float type that can exactly represent input integers image = image.astype(_dtype_itemsize(itemsize_in, dtype_out, np.float32, np.float64)) if kind_in == 'u': image /= imax_in # DirectX uses this conversion also for signed ints # if imin_in: # np.maximum(image, -1.0, out=image) else: image *= 2.0 image += 1.0 image /= imax_in - imin_in return image.astype(dtype_out) # unsigned int -> signed/unsigned int if kind_in == 'u': if kind_out == 'i': # unsigned int -> signed int image = _scale(image, 8 * itemsize_in, 8 * itemsize_out - 1) return image.view(dtype_out) else: # unsigned int -> unsigned int return _scale(image, 8 * itemsize_in, 8 * itemsize_out) # signed int -> unsigned int if kind_out == 'u': sign_loss() image = _scale(image, 8 * itemsize_in - 1, 8 * itemsize_out) result = np.empty(image.shape, dtype_out) np.maximum(image, 0, out=result, dtype=image.dtype, casting='unsafe') return result # signed int -> signed int if itemsize_in > itemsize_out: return _scale(image, 8 * itemsize_in - 1, 8 * itemsize_out - 1) image = image.astype(_dtype_bits('i', itemsize_out * 8)) image -= imin_in image = _scale(image, 8 * itemsize_in, 8 * itemsize_out, copy=False) image += imin_out return image.astype(dtype_out) def img_as_float(image, force_copy=False): """Convert an image to double-precision (64-bit) floating point format. Parameters ---------- image : ndarray Input image. force_copy : bool, optional Force a copy of the data, irrespective of its current dtype. Returns ------- out : ndarray of float64 Output image. Notes ----- The range of a floating point image is [0.0, 1.0] or [-1.0, 1.0] when converting from unsigned or signed datatypes, respectively. If the input image has a float type, intensity values are not modified and can be outside the ranges [0.0, 1.0] or [-1.0, 1.0]. """ return convert(image, np.float64, force_copy) def img_as_uint(image, force_copy=False): """Convert an image to 16-bit unsigned integer format. Parameters ---------- image : ndarray Input image. force_copy : bool, optional Force a copy of the data, irrespective of its current dtype. Returns ------- out : ndarray of uint16 Output image. Notes ----- Negative input values will be clipped. Positive values are scaled between 0 and 65535. """ return convert(image, np.uint16, force_copy) def img_as_int(image, force_copy=False): """Convert an image to 16-bit signed integer format. Parameters ---------- image : ndarray Input image. force_copy : bool, optional Force a copy of the data, irrespective of its current dtype. Returns ------- out : ndarray of uint16 Output image. Notes ----- The values are scaled between -32768 and 32767. If the input data-type is positive-only (e.g., uint8), then the output image will still only have positive values. """ return convert(image, np.int16, force_copy) def img_as_ubyte(image, force_copy=False): """Convert an image to 8-bit unsigned integer format. Parameters ---------- image : ndarray Input image. force_copy : bool, optional Force a copy of the data, irrespective of its current dtype. Returns ------- out : ndarray of ubyte (uint8) Output image. Notes ----- Negative input values will be clipped. Positive values are scaled between 0 and 255. """ return convert(image, np.uint8, force_copy) def img_as_bool(image, force_copy=False): """Convert an image to boolean format. Parameters ---------- image : ndarray Input image. force_copy : bool, optional Force a copy of the data, irrespective of its current dtype. Returns ------- out : ndarray of bool (`bool_`) Output image. Notes ----- The upper half of the input dtype's positive range is True, and the lower half is False. All negative values (if present) are False. """ return convert(image, np.bool_, force_copy) scikit-image-0.13.1/skimage/util/montage.py000066400000000000000000000062031316256236300205530ustar00rootroot00000000000000__all__ = ['montage2d'] import numpy as np from .. import exposure EPSILON = 1e-6 def montage2d(arr_in, fill='mean', rescale_intensity=False, grid_shape=None): """Create a 2-dimensional 'montage' from a 3-dimensional input array representing an ensemble of equally shaped 2-dimensional images. For example, ``montage2d(arr_in, fill)`` with the following `arr_in` +---+---+---+ | 1 | 2 | 3 | +---+---+---+ will return: +---+---+ | 1 | 2 | +---+---+ | 3 | * | +---+---+ Where the '*' patch will be determined by the `fill` parameter. Parameters ---------- arr_in: ndarray, shape=[n_images, height, width] 3-dimensional input array representing an ensemble of n_images of equal shape (i.e. [height, width]). fill: float or 'mean', optional How to fill the 2-dimensional output array when sqrt(n_images) is not an integer. If 'mean' is chosen, then fill = arr_in.mean(). rescale_intensity: bool, optional Whether to rescale the intensity of each image to [0, 1]. grid_shape: tuple, optional The desired grid shape for the montage (tiles_y, tiles_x). The default aspect ratio is square. Returns ------- arr_out: ndarray, shape=[alpha * height, alpha * width] Output array where 'alpha' has been determined automatically to fit (at least) the `n_images` in `arr_in`. Examples -------- >>> import numpy as np >>> from skimage.util.montage import montage2d >>> arr_in = np.arange(3 * 2 * 2).reshape(3, 2, 2) >>> arr_in # doctest: +NORMALIZE_WHITESPACE array([[[ 0, 1], [ 2, 3]], [[ 4, 5], [ 6, 7]], [[ 8, 9], [10, 11]]]) >>> arr_out = montage2d(arr_in) >>> arr_out.shape (4, 4) >>> arr_out array([[ 0. , 1. , 4. , 5. ], [ 2. , 3. , 6. , 7. ], [ 8. , 9. , 5.5, 5.5], [ 10. , 11. , 5.5, 5.5]]) >>> arr_in.mean() 5.5 >>> arr_out_nonsquare = montage2d(arr_in, grid_shape=(1, 3)) >>> arr_out_nonsquare array([[ 0., 1., 4., 5., 8., 9.], [ 2., 3., 6., 7., 10., 11.]]) >>> arr_out_nonsquare.shape (2, 6) """ assert arr_in.ndim == 3 n_images, height, width = arr_in.shape arr_in = arr_in.copy() # -- rescale intensity if necessary if rescale_intensity: for i in range(n_images): arr_in[i] = exposure.rescale_intensity(arr_in[i]) # -- determine alpha if grid_shape: alpha_y, alpha_x = grid_shape else: alpha_y = alpha_x = int(np.ceil(np.sqrt(n_images))) # -- fill missing patches if fill == 'mean': fill = arr_in.mean() n_missing = int((alpha_y * alpha_x) - n_images) missing = np.ones((n_missing, height, width), dtype=arr_in.dtype) * fill arr_out = np.vstack((arr_in, missing)) # -- reshape to 2d montage, step by step arr_out = arr_out.reshape(alpha_y, alpha_x, height, width) arr_out = arr_out.swapaxes(1, 2) arr_out = arr_out.reshape(alpha_y * height, alpha_x * width) return arr_out scikit-image-0.13.1/skimage/util/noise.py000066400000000000000000000166361316256236300202510ustar00rootroot00000000000000import numpy as np from .dtype import img_as_float __all__ = ['random_noise'] def random_noise(image, mode='gaussian', seed=None, clip=True, **kwargs): """ Function to add random noise of various types to a floating-point image. Parameters ---------- image : ndarray Input image data. Will be converted to float. mode : str One of the following strings, selecting the type of noise to add: - 'gaussian' Gaussian-distributed additive noise. - 'localvar' Gaussian-distributed additive noise, with specified local variance at each point of `image` - 'poisson' Poisson-distributed noise generated from the data. - 'salt' Replaces random pixels with 1. - 'pepper' Replaces random pixels with 0 (for unsigned images) or -1 (for signed images). - 's&p' Replaces random pixels with either 1 or `low_val`, where `low_val` is 0 for unsigned images or -1 for signed images. - 'speckle' Multiplicative noise using out = image + n*image, where n is uniform noise with specified mean & variance. seed : int If provided, this will set the random seed before generating noise, for valid pseudo-random comparisons. clip : bool If True (default), the output will be clipped after noise applied for modes `'speckle'`, `'poisson'`, and `'gaussian'`. This is needed to maintain the proper image data range. If False, clipping is not applied, and the output may extend beyond the range [-1, 1]. mean : float Mean of random distribution. Used in 'gaussian' and 'speckle'. Default : 0. var : float Variance of random distribution. Used in 'gaussian' and 'speckle'. Note: variance = (standard deviation) ** 2. Default : 0.01 local_vars : ndarray Array of positive floats, same shape as `image`, defining the local variance at every image point. Used in 'localvar'. amount : float Proportion of image pixels to replace with noise on range [0, 1]. Used in 'salt', 'pepper', and 'salt & pepper'. Default : 0.05 salt_vs_pepper : float Proportion of salt vs. pepper noise for 's&p' on range [0, 1]. Higher values represent more salt. Default : 0.5 (equal amounts) Returns ------- out : ndarray Output floating-point image data on range [0, 1] or [-1, 1] if the input `image` was unsigned or signed, respectively. Notes ----- Speckle, Poisson, Localvar, and Gaussian noise may generate noise outside the valid image range. The default is to clip (not alias) these values, but they may be preserved by setting `clip=False`. Note that in this case the output may contain values outside the ranges [0, 1] or [-1, 1]. Use this option with care. Because of the prevalence of exclusively positive floating-point images in intermediate calculations, it is not possible to intuit if an input is signed based on dtype alone. Instead, negative values are explicity searched for. Only if found does this function assume signed input. Unexpected results only occur in rare, poorly exposes cases (e.g. if all values are above 50 percent gray in a signed `image`). In this event, manually scaling the input to the positive domain will solve the problem. The Poisson distribution is only defined for positive integers. To apply this noise type, the number of unique values in the image is found and the next round power of two is used to scale up the floating-point result, after which it is scaled back down to the floating-point image range. To generate Poisson noise against a signed image, the signed image is temporarily converted to an unsigned image in the floating point domain, Poisson noise is generated, then it is returned to the original range. """ mode = mode.lower() # Detect if a signed image was input if image.min() < 0: low_clip = -1. else: low_clip = 0. image = img_as_float(image) if seed is not None: np.random.seed(seed=seed) allowedtypes = { 'gaussian': 'gaussian_values', 'localvar': 'localvar_values', 'poisson': 'poisson_values', 'salt': 'sp_values', 'pepper': 'sp_values', 's&p': 's&p_values', 'speckle': 'gaussian_values'} kwdefaults = { 'mean': 0., 'var': 0.01, 'amount': 0.05, 'salt_vs_pepper': 0.5, 'local_vars': np.zeros_like(image) + 0.01} allowedkwargs = { 'gaussian_values': ['mean', 'var'], 'localvar_values': ['local_vars'], 'sp_values': ['amount'], 's&p_values': ['amount', 'salt_vs_pepper'], 'poisson_values': []} for key in kwargs: if key not in allowedkwargs[allowedtypes[mode]]: raise ValueError('%s keyword not in allowed keywords %s' % (key, allowedkwargs[allowedtypes[mode]])) # Set kwarg defaults for kw in allowedkwargs[allowedtypes[mode]]: kwargs.setdefault(kw, kwdefaults[kw]) if mode == 'gaussian': noise = np.random.normal(kwargs['mean'], kwargs['var'] ** 0.5, image.shape) out = image + noise elif mode == 'localvar': # Ensure local variance input is correct if (kwargs['local_vars'] <= 0).any(): raise ValueError('All values of `local_vars` must be > 0.') # Safe shortcut usage broadcasts kwargs['local_vars'] as a ufunc out = image + np.random.normal(0, kwargs['local_vars'] ** 0.5) elif mode == 'poisson': # Determine unique values in image & calculate the next power of two vals = len(np.unique(image)) vals = 2 ** np.ceil(np.log2(vals)) # Ensure image is exclusively positive if low_clip == -1.: old_max = image.max() image = (image + 1.) / (old_max + 1.) # Generating noise for each unique value in image. out = np.random.poisson(image * vals) / float(vals) # Return image to original range if input was signed if low_clip == -1.: out = out * (old_max + 1.) - 1. elif mode == 'salt': # Re-call function with mode='s&p' and p=1 (all salt noise) out = random_noise(image, mode='s&p', seed=seed, amount=kwargs['amount'], salt_vs_pepper=1.) elif mode == 'pepper': # Re-call function with mode='s&p' and p=1 (all pepper noise) out = random_noise(image, mode='s&p', seed=seed, amount=kwargs['amount'], salt_vs_pepper=0.) elif mode == 's&p': out = image.copy() p = kwargs['amount'] q = kwargs['salt_vs_pepper'] flipped = np.random.choice([True, False], size=image.shape, p=[p, 1 - p]) salted = np.random.choice([True, False], size=image.shape, p=[q, 1 - q]) peppered = ~salted out[flipped & salted] = 1 out[flipped & peppered] = low_clip elif mode == 'speckle': noise = np.random.normal(kwargs['mean'], kwargs['var'] ** 0.5, image.shape) out = image + image * noise # Clip back to original range, if necessary if clip: out = np.clip(out, low_clip, 1.0) return out scikit-image-0.13.1/skimage/util/shape.py000066400000000000000000000203571316256236300202270ustar00rootroot00000000000000from __future__ import division import numbers import numpy as np from numpy.lib.stride_tricks import as_strided from warnings import warn __all__ = ['view_as_blocks', 'view_as_windows'] def view_as_blocks(arr_in, block_shape): """Block view of the input n-dimensional array (using re-striding). Blocks are non-overlapping views of the input array. Parameters ---------- arr_in : ndarray N-d input array. block_shape : tuple The shape of the block. Each dimension must divide evenly into the corresponding dimensions of `arr_in`. Returns ------- arr_out : ndarray Block view of the input array. If `arr_in` is non-contiguous, a copy is made. Examples -------- >>> import numpy as np >>> from skimage.util.shape import view_as_blocks >>> A = np.arange(4*4).reshape(4,4) >>> A array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11], [12, 13, 14, 15]]) >>> B = view_as_blocks(A, block_shape=(2, 2)) >>> B[0, 0] array([[0, 1], [4, 5]]) >>> B[0, 1] array([[2, 3], [6, 7]]) >>> B[1, 0, 1, 1] 13 >>> A = np.arange(4*4*6).reshape(4,4,6) >>> A # doctest: +NORMALIZE_WHITESPACE array([[[ 0, 1, 2, 3, 4, 5], [ 6, 7, 8, 9, 10, 11], [12, 13, 14, 15, 16, 17], [18, 19, 20, 21, 22, 23]], [[24, 25, 26, 27, 28, 29], [30, 31, 32, 33, 34, 35], [36, 37, 38, 39, 40, 41], [42, 43, 44, 45, 46, 47]], [[48, 49, 50, 51, 52, 53], [54, 55, 56, 57, 58, 59], [60, 61, 62, 63, 64, 65], [66, 67, 68, 69, 70, 71]], [[72, 73, 74, 75, 76, 77], [78, 79, 80, 81, 82, 83], [84, 85, 86, 87, 88, 89], [90, 91, 92, 93, 94, 95]]]) >>> B = view_as_blocks(A, block_shape=(1, 2, 2)) >>> B.shape (4, 2, 3, 1, 2, 2) >>> B[2:, 0, 2] # doctest: +NORMALIZE_WHITESPACE array([[[[52, 53], [58, 59]]], [[[76, 77], [82, 83]]]]) """ if not isinstance(block_shape, tuple): raise TypeError('block needs to be a tuple') block_shape = np.array(block_shape) if (block_shape <= 0).any(): raise ValueError("'block_shape' elements must be strictly positive") if block_shape.size != arr_in.ndim: raise ValueError("'block_shape' must have the same length " "as 'arr_in.shape'") arr_shape = np.array(arr_in.shape) if (arr_shape % block_shape).sum() != 0: raise ValueError("'block_shape' is not compatible with 'arr_in'") # -- restride the array to build the block view if not arr_in.flags.contiguous: warn(RuntimeWarning("Cannot provide views on a non-contiguous input " "array without copying.")) arr_in = np.ascontiguousarray(arr_in) new_shape = tuple(arr_shape // block_shape) + tuple(block_shape) new_strides = tuple(arr_in.strides * block_shape) + arr_in.strides arr_out = as_strided(arr_in, shape=new_shape, strides=new_strides) return arr_out def view_as_windows(arr_in, window_shape, step=1): """Rolling window view of the input n-dimensional array. Windows are overlapping views of the input array, with adjacent windows shifted by a single row or column (or an index of a higher dimension). Parameters ---------- arr_in : ndarray N-d input array. window_shape : integer or tuple of length arr_in.ndim Defines the shape of the elementary n-dimensional orthotope (better know as hyperrectangle [1]_) of the rolling window view. If an integer is given, the shape will be a hypercube of sidelength given by its value. step : integer or tuple of length arr_in.ndim Indicates step size at which extraction shall be performed. If integer is given, then the step is uniform in all dimensions. Returns ------- arr_out : ndarray (rolling) window view of the input array. If `arr_in` is non-contiguous, a copy is made. Notes ----- One should be very careful with rolling views when it comes to memory usage. Indeed, although a 'view' has the same memory footprint as its base array, the actual array that emerges when this 'view' is used in a computation is generally a (much) larger array than the original, especially for 2-dimensional arrays and above. For example, let us consider a 3 dimensional array of size (100, 100, 100) of ``float64``. This array takes about 8*100**3 Bytes for storage which is just 8 MB. If one decides to build a rolling view on this array with a window of (3, 3, 3) the hypothetical size of the rolling view (if one was to reshape the view for example) would be 8*(100-3+1)**3*3**3 which is about 203 MB! The scaling becomes even worse as the dimension of the input array becomes larger. References ---------- .. [1] http://en.wikipedia.org/wiki/Hyperrectangle Examples -------- >>> import numpy as np >>> from skimage.util.shape import view_as_windows >>> A = np.arange(4*4).reshape(4,4) >>> A array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11], [12, 13, 14, 15]]) >>> window_shape = (2, 2) >>> B = view_as_windows(A, window_shape) >>> B[0, 0] array([[0, 1], [4, 5]]) >>> B[0, 1] array([[1, 2], [5, 6]]) >>> A = np.arange(10) >>> A array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) >>> window_shape = (3,) >>> B = view_as_windows(A, window_shape) >>> B.shape (8, 3) >>> B array([[0, 1, 2], [1, 2, 3], [2, 3, 4], [3, 4, 5], [4, 5, 6], [5, 6, 7], [6, 7, 8], [7, 8, 9]]) >>> A = np.arange(5*4).reshape(5, 4) >>> A array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11], [12, 13, 14, 15], [16, 17, 18, 19]]) >>> window_shape = (4, 3) >>> B = view_as_windows(A, window_shape) >>> B.shape (2, 2, 4, 3) >>> B # doctest: +NORMALIZE_WHITESPACE array([[[[ 0, 1, 2], [ 4, 5, 6], [ 8, 9, 10], [12, 13, 14]], [[ 1, 2, 3], [ 5, 6, 7], [ 9, 10, 11], [13, 14, 15]]], [[[ 4, 5, 6], [ 8, 9, 10], [12, 13, 14], [16, 17, 18]], [[ 5, 6, 7], [ 9, 10, 11], [13, 14, 15], [17, 18, 19]]]]) """ # -- basic checks on arguments if not isinstance(arr_in, np.ndarray): raise TypeError("`arr_in` must be a numpy ndarray") ndim = arr_in.ndim if isinstance(window_shape, numbers.Number): window_shape = (window_shape,) * ndim if not (len(window_shape) == ndim): raise ValueError("`window_shape` is incompatible with `arr_in.shape`") if isinstance(step, numbers.Number): if step < 1: raise ValueError("`step` must be >= 1") step = (step,) * ndim if len(step) != ndim: raise ValueError("`step` is incompatible with `arr_in.shape`") arr_shape = np.array(arr_in.shape) window_shape = np.array(window_shape, dtype=arr_shape.dtype) if ((arr_shape - window_shape) < 0).any(): raise ValueError("`window_shape` is too large") if ((window_shape - 1) < 0).any(): raise ValueError("`window_shape` is too small") # -- build rolling window view if not arr_in.flags.contiguous: warn(RuntimeWarning("Cannot provide views on a non-contiguous input " "array without copying.")) arr_in = np.ascontiguousarray(arr_in) slices = [slice(None, None, st) for st in step] window_strides = np.array(arr_in.strides) indexing_strides = arr_in[slices].strides win_indices_shape = (((np.array(arr_in.shape) - np.array(window_shape)) // np.array(step)) + 1) new_shape = tuple(list(win_indices_shape) + list(window_shape)) strides = tuple(list(indexing_strides) + list(window_strides)) arr_out = as_strided(arr_in, shape=new_shape, strides=strides) return arr_out scikit-image-0.13.1/skimage/util/tests/000077500000000000000000000000001316256236300177105ustar00rootroot00000000000000scikit-image-0.13.1/skimage/util/tests/__init__.py000066400000000000000000000001771316256236300220260ustar00rootroot00000000000000from ..._shared.testing import setup_test, teardown_test def setup(): setup_test() def teardown(): teardown_test() scikit-image-0.13.1/skimage/util/tests/test_apply_parallel.py000066400000000000000000000036571316256236300243350ustar00rootroot00000000000000from __future__ import absolute_import import numpy as np from numpy.testing import assert_array_almost_equal from numpy.testing.decorators import skipif from skimage.filters import threshold_local, gaussian from skimage.util.apply_parallel import apply_parallel, dask_available @skipif(not dask_available) def test_apply_parallel(): # data a = np.arange(144).reshape(12, 12).astype(float) # apply the filter expected1 = threshold_local(a, 3) result1 = apply_parallel(threshold_local, a, chunks=(6, 6), depth=5, extra_arguments=(3,), extra_keywords={'mode': 'reflect'}) assert_array_almost_equal(result1, expected1) def wrapped_gauss(arr): return gaussian(arr, 1, mode='reflect') expected2 = gaussian(a, 1, mode='reflect') result2 = apply_parallel(wrapped_gauss, a, chunks=(6, 6), depth=5) assert_array_almost_equal(result2, expected2) @skipif(not dask_available) def test_no_chunks(): a = np.ones(1 * 4 * 8 * 9).reshape(1, 4, 8, 9) def add_42(arr): return arr + 42 expected = add_42(a) result = apply_parallel(add_42, a) assert_array_almost_equal(result, expected) @skipif(not dask_available) def test_apply_parallel_wrap(): def wrapped(arr): return gaussian(arr, 1, mode='wrap') a = np.arange(144).reshape(12, 12).astype(float) expected = gaussian(a, 1, mode='wrap') result = apply_parallel(wrapped, a, chunks=(6, 6), depth=5, mode='wrap') assert_array_almost_equal(result, expected) @skipif(not dask_available) def test_apply_parallel_nearest(): def wrapped(arr): return gaussian(arr, 1, mode='nearest') a = np.arange(144).reshape(12, 12).astype(float) expected = gaussian(a, 1, mode='nearest') result = apply_parallel(wrapped, a, chunks=(6, 6), depth={0: 5, 1: 5}, mode='nearest') assert_array_almost_equal(result, expected) scikit-image-0.13.1/skimage/util/tests/test_arraycrop.py000066400000000000000000000025311316256236300233240ustar00rootroot00000000000000"""Tests for array cropping.""" from __future__ import division, absolute_import, print_function import numpy as np from numpy.testing import (assert_array_equal, assert_equal) from skimage.util import crop def test_multi_crop(): arr = np.arange(45).reshape(9, 5) out = crop(arr, ((1, 2), (2, 1))) assert_array_equal(out[0], [7, 8]) assert_array_equal(out[-1], [32, 33]) assert_equal(out.shape, (6, 2)) def test_pair_crop(): arr = np.arange(45).reshape(9, 5) out = crop(arr, (1, 2)) assert_array_equal(out[0], [6, 7]) assert_array_equal(out[-1], [31, 32]) assert_equal(out.shape, (6, 2)) def test_int_crop(): arr = np.arange(45).reshape(9, 5) out = crop(arr, 1) assert_array_equal(out[0], [6, 7, 8]) assert_array_equal(out[-1], [36, 37, 38]) assert_equal(out.shape, (7, 3)) def test_copy_crop(): arr = np.arange(45).reshape(9, 5) out0 = crop(arr, 1, copy=True) assert out0.flags.c_contiguous out0[0, 0] = 100 assert not np.any(arr == 100) assert not np.may_share_memory(arr, out0) out1 = crop(arr, 1) out1[0, 0] = 100 assert arr[1, 1] == 100 assert np.may_share_memory(arr, out1) def test_zero_crop(): arr = np.arange(45).reshape(9, 5) out = crop(arr, 0) assert out.shape == (9, 5) if __name__ == "__main__": np.testing.run_module_suite() scikit-image-0.13.1/skimage/util/tests/test_arraypad.py000066400000000000000000001216511316256236300231320ustar00rootroot00000000000000"""Tests for the array padding functions. """ from __future__ import division, absolute_import, print_function import numpy as np from numpy.testing import (assert_array_equal, assert_raises, assert_allclose, TestCase) from skimage.util import pad class TestConditionalShortcuts(TestCase): def test_zero_padding_shortcuts(self): test = np.arange(120).reshape(4, 5, 6) pad_amt = [(0, 0) for axis in test.shape] modes = ['constant', 'edge', 'linear_ramp', 'maximum', 'mean', 'median', 'minimum', 'reflect', 'symmetric', 'wrap', ] for mode in modes: assert_array_equal(test, pad(test, pad_amt, mode=mode)) def test_shallow_statistic_range(self): test = np.arange(120).reshape(4, 5, 6) pad_amt = [(1, 1) for axis in test.shape] modes = ['maximum', 'mean', 'median', 'minimum', ] for mode in modes: assert_array_equal(pad(test, pad_amt, mode='edge'), pad(test, pad_amt, mode=mode, stat_length=1)) def test_clip_statistic_range(self): test = np.arange(30).reshape(5, 6) pad_amt = [(3, 3) for axis in test.shape] modes = ['maximum', 'mean', 'median', 'minimum', ] for mode in modes: assert_array_equal(pad(test, pad_amt, mode=mode), pad(test, pad_amt, mode=mode, stat_length=30)) class TestStatistic(TestCase): def test_check_mean_stat_length(self): a = np.arange(100).astype('f') a = pad(a, ((25, 20), ), 'mean', stat_length=((2, 3), )) b = np.array( [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55., 56., 57., 58., 59., 60., 61., 62., 63., 64., 65., 66., 67., 68., 69., 70., 71., 72., 73., 74., 75., 76., 77., 78., 79., 80., 81., 82., 83., 84., 85., 86., 87., 88., 89., 90., 91., 92., 93., 94., 95., 96., 97., 98., 99., 98., 98., 98., 98., 98., 98., 98., 98., 98., 98., 98., 98., 98., 98., 98., 98., 98., 98., 98., 98. ]) assert_array_equal(a, b) def test_check_maximum_1(self): a = np.arange(100) a = pad(a, (25, 20), 'maximum') b = np.array( [99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99] ) assert_array_equal(a, b) def test_check_maximum_2(self): a = np.arange(100) + 1 a = pad(a, (25, 20), 'maximum') b = np.array( [100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100] ) assert_array_equal(a, b) def test_check_maximum_stat_length(self): a = np.arange(100) + 1 a = pad(a, (25, 20), 'maximum', stat_length=10) b = np.array( [10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100] ) assert_array_equal(a, b) def test_check_minimum_1(self): a = np.arange(100) a = pad(a, (25, 20), 'minimum') b = np.array( [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] ) assert_array_equal(a, b) def test_check_minimum_2(self): a = np.arange(100) + 2 a = pad(a, (25, 20), 'minimum') b = np.array( [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2] ) assert_array_equal(a, b) def test_check_minimum_stat_length(self): a = np.arange(100) + 1 a = pad(a, (25, 20), 'minimum', stat_length=10) b = np.array( [ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91] ) assert_array_equal(a, b) def test_check_median(self): a = np.arange(100).astype('f') a = pad(a, (25, 20), 'median') b = np.array( [49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55., 56., 57., 58., 59., 60., 61., 62., 63., 64., 65., 66., 67., 68., 69., 70., 71., 72., 73., 74., 75., 76., 77., 78., 79., 80., 81., 82., 83., 84., 85., 86., 87., 88., 89., 90., 91., 92., 93., 94., 95., 96., 97., 98., 99., 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5] ) assert_array_equal(a, b) def test_check_median_01(self): a = np.array([[3, 1, 4], [4, 5, 9], [9, 8, 2]]) a = pad(a, 1, 'median') b = np.array( [[4, 4, 5, 4, 4], [3, 3, 1, 4, 3], [5, 4, 5, 9, 5], [8, 9, 8, 2, 8], [4, 4, 5, 4, 4]] ) assert_array_equal(a, b) def test_check_median_02(self): a = np.array([[3, 1, 4], [4, 5, 9], [9, 8, 2]]) a = pad(a.T, 1, 'median').T b = np.array( [[5, 4, 5, 4, 5], [3, 3, 1, 4, 3], [5, 4, 5, 9, 5], [8, 9, 8, 2, 8], [5, 4, 5, 4, 5]] ) assert_array_equal(a, b) def test_check_median_stat_length(self): a = np.arange(100).astype('f') a[1] = 2. a[97] = 96. a = pad(a, (25, 20), 'median', stat_length=(3, 5)) b = np.array( [ 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 0., 2., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55., 56., 57., 58., 59., 60., 61., 62., 63., 64., 65., 66., 67., 68., 69., 70., 71., 72., 73., 74., 75., 76., 77., 78., 79., 80., 81., 82., 83., 84., 85., 86., 87., 88., 89., 90., 91., 92., 93., 94., 95., 96., 96., 98., 99., 96., 96., 96., 96., 96., 96., 96., 96., 96., 96., 96., 96., 96., 96., 96., 96., 96., 96., 96., 96.] ) assert_array_equal(a, b) def test_check_mean_shape_one(self): a = [[4, 5, 6]] a = pad(a, (5, 7), 'mean', stat_length=2) b = np.array( [[4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6], [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6], [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6], [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6], [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6], [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6], [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6], [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6], [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6], [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6], [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6], [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6], [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6]] ) assert_array_equal(a, b) def test_check_mean_2(self): a = np.arange(100).astype('f') a = pad(a, (25, 20), 'mean') b = np.array( [49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55., 56., 57., 58., 59., 60., 61., 62., 63., 64., 65., 66., 67., 68., 69., 70., 71., 72., 73., 74., 75., 76., 77., 78., 79., 80., 81., 82., 83., 84., 85., 86., 87., 88., 89., 90., 91., 92., 93., 94., 95., 96., 97., 98., 99., 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5] ) assert_array_equal(a, b) class TestConstant(TestCase): def test_check_constant(self): a = np.arange(100) a = pad(a, (25, 20), 'constant', constant_values=(10, 20)) b = np.array( [10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20] ) assert_array_equal(a, b) def test_check_constant_zeros(self): a = np.arange(100) a = pad(a, (25, 20), 'constant') b = np.array( [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] ) assert_array_equal(a, b) def test_check_constant_float(self): # If input array is int, but constant_values are float, the dtype of # the array to be padded is kept arr = np.arange(30).reshape(5, 6) test = pad(arr, (1, 2), mode='constant', constant_values=1.1) expected = np.array( [[ 1, 1, 1, 1, 1, 1, 1, 1, 1], [ 1, 0, 1, 2, 3, 4, 5, 1, 1], [ 1, 6, 7, 8, 9, 10, 11, 1, 1], [ 1, 12, 13, 14, 15, 16, 17, 1, 1], [ 1, 18, 19, 20, 21, 22, 23, 1, 1], [ 1, 24, 25, 26, 27, 28, 29, 1, 1], [ 1, 1, 1, 1, 1, 1, 1, 1, 1], [ 1, 1, 1, 1, 1, 1, 1, 1, 1]] ) assert_allclose(test, expected) def test_check_constant_float2(self): # If input array is float, and constant_values are float, the dtype of # the array to be padded is kept - here retaining the float constants arr = np.arange(30).reshape(5, 6) arr_float = arr.astype(np.float64) test = pad(arr_float, ((1, 2), (1, 2)), mode='constant', constant_values=1.1) expected = np.array( [[ 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1], [ 1.1, 0. , 1. , 2. , 3. , 4. , 5. , 1.1, 1.1], [ 1.1, 6. , 7. , 8. , 9. , 10. , 11. , 1.1, 1.1], [ 1.1, 12. , 13. , 14. , 15. , 16. , 17. , 1.1, 1.1], [ 1.1, 18. , 19. , 20. , 21. , 22. , 23. , 1.1, 1.1], [ 1.1, 24. , 25. , 26. , 27. , 28. , 29. , 1.1, 1.1], [ 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1], [ 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1]] ) assert_allclose(test, expected) def test_check_constant_float3(self): a = np.arange(100, dtype=float) a = pad(a, (25, 20), 'constant', constant_values=(-1.1, -1.2)) b = np.array( [-1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2] ) assert_allclose(a, b) def test_check_constant_odd_pad_amount(self): arr = np.arange(30).reshape(5, 6) test = pad(arr, ((1,), (2,)), mode='constant', constant_values=3) expected = np.array( [[ 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [ 3, 3, 0, 1, 2, 3, 4, 5, 3, 3], [ 3, 3, 6, 7, 8, 9, 10, 11, 3, 3], [ 3, 3, 12, 13, 14, 15, 16, 17, 3, 3], [ 3, 3, 18, 19, 20, 21, 22, 23, 3, 3], [ 3, 3, 24, 25, 26, 27, 28, 29, 3, 3], [ 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]] ) assert_allclose(test, expected) class TestLinearRamp(TestCase): def test_check_simple(self): a = np.arange(100).astype('f') a = pad(a, (25, 20), 'linear_ramp', end_values=(4, 5)) b = np.array( [4.00, 3.84, 3.68, 3.52, 3.36, 3.20, 3.04, 2.88, 2.72, 2.56, 2.40, 2.24, 2.08, 1.92, 1.76, 1.60, 1.44, 1.28, 1.12, 0.96, 0.80, 0.64, 0.48, 0.32, 0.16, 0.00, 1.00, 2.00, 3.00, 4.00, 5.00, 6.00, 7.00, 8.00, 9.00, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0, 60.0, 61.0, 62.0, 63.0, 64.0, 65.0, 66.0, 67.0, 68.0, 69.0, 70.0, 71.0, 72.0, 73.0, 74.0, 75.0, 76.0, 77.0, 78.0, 79.0, 80.0, 81.0, 82.0, 83.0, 84.0, 85.0, 86.0, 87.0, 88.0, 89.0, 90.0, 91.0, 92.0, 93.0, 94.0, 95.0, 96.0, 97.0, 98.0, 99.0, 94.3, 89.6, 84.9, 80.2, 75.5, 70.8, 66.1, 61.4, 56.7, 52.0, 47.3, 42.6, 37.9, 33.2, 28.5, 23.8, 19.1, 14.4, 9.7, 5.] ) assert_allclose(a, b, rtol=1e-5, atol=1e-5) def test_check_2d(self): arr = np.arange(20).reshape(4, 5).astype(np.float64) test = pad(arr, (2, 2), mode='linear_ramp', end_values=(0, 0)) expected = np.array( [[0., 0., 0., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0.5, 1., 1.5, 2., 1., 0.], [0., 0., 0., 1., 2., 3., 4., 2., 0.], [0., 2.5, 5., 6., 7., 8., 9., 4.5, 0.], [0., 5., 10., 11., 12., 13., 14., 7., 0.], [0., 7.5, 15., 16., 17., 18., 19., 9.5, 0.], [0., 3.75, 7.5, 8., 8.5, 9., 9.5, 4.75, 0.], [0., 0., 0., 0., 0., 0., 0., 0., 0.]]) assert_allclose(test, expected) class TestReflect(TestCase): def test_check_simple(self): a = np.arange(100) a = pad(a, (25, 20), 'reflect') b = np.array( [25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 98, 97, 96, 95, 94, 93, 92, 91, 90, 89, 88, 87, 86, 85, 84, 83, 82, 81, 80, 79] ) assert_array_equal(a, b) def test_check_odd_method(self): a = np.arange(100) a = pad(a, (25, 20), 'reflect', reflect_type='odd') b = np.array( [-25, -24, -23, -22, -21, -20, -19, -18, -17, -16, -15, -14, -13, -12, -11, -10, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119] ) assert_array_equal(a, b) def test_check_large_pad(self): a = [[4, 5, 6], [6, 7, 8]] a = pad(a, (5, 7), 'reflect') b = np.array( [[7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7], [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5], [7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7], [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5], [7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7], [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5], [7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7], [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5], [7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7], [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5], [7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7], [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5], [7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7], [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5]] ) assert_array_equal(a, b) def test_check_shape(self): a = [[4, 5, 6]] a = pad(a, (5, 7), 'reflect') b = np.array( [[5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5], [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5], [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5], [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5], [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5], [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5], [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5], [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5], [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5], [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5], [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5], [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5], [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5]] ) assert_array_equal(a, b) def test_check_01(self): a = pad([1, 2, 3], 2, 'reflect') b = np.array([3, 2, 1, 2, 3, 2, 1]) assert_array_equal(a, b) def test_check_02(self): a = pad([1, 2, 3], 3, 'reflect') b = np.array([2, 3, 2, 1, 2, 3, 2, 1, 2]) assert_array_equal(a, b) def test_check_03(self): a = pad([1, 2, 3], 4, 'reflect') b = np.array([1, 2, 3, 2, 1, 2, 3, 2, 1, 2, 3]) assert_array_equal(a, b) class TestSymmetric(TestCase): def test_check_simple(self): a = np.arange(100) a = pad(a, (25, 20), 'symmetric') b = np.array( [24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 99, 98, 97, 96, 95, 94, 93, 92, 91, 90, 89, 88, 87, 86, 85, 84, 83, 82, 81, 80] ) assert_array_equal(a, b) def test_check_odd_method(self): a = np.arange(100) a = pad(a, (25, 20), 'symmetric', reflect_type='odd') b = np.array( [-24, -23, -22, -21, -20, -19, -18, -17, -16, -15, -14, -13, -12, -11, -10, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118] ) assert_array_equal(a, b) def test_check_large_pad(self): a = [[4, 5, 6], [6, 7, 8]] a = pad(a, (5, 7), 'symmetric') b = np.array( [[5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6], [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6], [7, 8, 8, 7, 6, 6, 7, 8, 8, 7, 6, 6, 7, 8, 8], [7, 8, 8, 7, 6, 6, 7, 8, 8, 7, 6, 6, 7, 8, 8], [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6], [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6], [7, 8, 8, 7, 6, 6, 7, 8, 8, 7, 6, 6, 7, 8, 8], [7, 8, 8, 7, 6, 6, 7, 8, 8, 7, 6, 6, 7, 8, 8], [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6], [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6], [7, 8, 8, 7, 6, 6, 7, 8, 8, 7, 6, 6, 7, 8, 8], [7, 8, 8, 7, 6, 6, 7, 8, 8, 7, 6, 6, 7, 8, 8], [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6], [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6]] ) assert_array_equal(a, b) def test_check_large_pad_odd(self): a = [[4, 5, 6], [6, 7, 8]] a = pad(a, (5, 7), 'symmetric', reflect_type='odd') b = np.array( [[-3, -2, -2, -1, 0, 0, 1, 2, 2, 3, 4, 4, 5, 6, 6], [-3, -2, -2, -1, 0, 0, 1, 2, 2, 3, 4, 4, 5, 6, 6], [-1, 0, 0, 1, 2, 2, 3, 4, 4, 5, 6, 6, 7, 8, 8], [-1, 0, 0, 1, 2, 2, 3, 4, 4, 5, 6, 6, 7, 8, 8], [ 1, 2, 2, 3, 4, 4, 5, 6, 6, 7, 8, 8, 9, 10, 10], [ 1, 2, 2, 3, 4, 4, 5, 6, 6, 7, 8, 8, 9, 10, 10], [ 3, 4, 4, 5, 6, 6, 7, 8, 8, 9, 10, 10, 11, 12, 12], [ 3, 4, 4, 5, 6, 6, 7, 8, 8, 9, 10, 10, 11, 12, 12], [ 5, 6, 6, 7, 8, 8, 9, 10, 10, 11, 12, 12, 13, 14, 14], [ 5, 6, 6, 7, 8, 8, 9, 10, 10, 11, 12, 12, 13, 14, 14], [ 7, 8, 8, 9, 10, 10, 11, 12, 12, 13, 14, 14, 15, 16, 16], [ 7, 8, 8, 9, 10, 10, 11, 12, 12, 13, 14, 14, 15, 16, 16], [ 9, 10, 10, 11, 12, 12, 13, 14, 14, 15, 16, 16, 17, 18, 18], [ 9, 10, 10, 11, 12, 12, 13, 14, 14, 15, 16, 16, 17, 18, 18]] ) assert_array_equal(a, b) def test_check_shape(self): a = [[4, 5, 6]] a = pad(a, (5, 7), 'symmetric') b = np.array( [[5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6], [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6], [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6], [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6], [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6], [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6], [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6], [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6], [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6], [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6], [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6], [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6], [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6]] ) assert_array_equal(a, b) def test_check_01(self): a = pad([1, 2, 3], 2, 'symmetric') b = np.array([2, 1, 1, 2, 3, 3, 2]) assert_array_equal(a, b) def test_check_02(self): a = pad([1, 2, 3], 3, 'symmetric') b = np.array([3, 2, 1, 1, 2, 3, 3, 2, 1]) assert_array_equal(a, b) def test_check_03(self): a = pad([1, 2, 3], 6, 'symmetric') b = np.array([1, 2, 3, 3, 2, 1, 1, 2, 3, 3, 2, 1, 1, 2, 3]) assert_array_equal(a, b) class TestWrap(TestCase): def test_check_simple(self): a = np.arange(100) a = pad(a, (25, 20), 'wrap') b = np.array( [75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19] ) assert_array_equal(a, b) def test_check_large_pad(self): a = np.arange(12) a = np.reshape(a, (3, 4)) a = pad(a, (10, 12), 'wrap') b = np.array( [[10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11], [2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3], [6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7], [10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11], [2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3], [6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7], [10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11], [2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3], [6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7], [10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11], [2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3], [6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7], [10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11], [2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3], [6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7], [10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11], [2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3], [6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7], [10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11], [2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3], [6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7], [10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11], [2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3], [6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7], [10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11]] ) assert_array_equal(a, b) def test_check_01(self): a = pad([1, 2, 3], 3, 'wrap') b = np.array([1, 2, 3, 1, 2, 3, 1, 2, 3]) assert_array_equal(a, b) def test_check_02(self): a = pad([1, 2, 3], 4, 'wrap') b = np.array([3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1]) assert_array_equal(a, b) class TestStatLen(TestCase): def test_check_simple(self): a = np.arange(30) a = np.reshape(a, (6, 5)) a = pad(a, ((2, 3), (3, 2)), mode='mean', stat_length=(3,)) b = np.array( [[6, 6, 6, 5, 6, 7, 8, 9, 8, 8], [6, 6, 6, 5, 6, 7, 8, 9, 8, 8], [1, 1, 1, 0, 1, 2, 3, 4, 3, 3], [6, 6, 6, 5, 6, 7, 8, 9, 8, 8], [11, 11, 11, 10, 11, 12, 13, 14, 13, 13], [16, 16, 16, 15, 16, 17, 18, 19, 18, 18], [21, 21, 21, 20, 21, 22, 23, 24, 23, 23], [26, 26, 26, 25, 26, 27, 28, 29, 28, 28], [21, 21, 21, 20, 21, 22, 23, 24, 23, 23], [21, 21, 21, 20, 21, 22, 23, 24, 23, 23], [21, 21, 21, 20, 21, 22, 23, 24, 23, 23]] ) assert_array_equal(a, b) class TestEdge(TestCase): def test_check_simple(self): a = np.arange(12) a = np.reshape(a, (4, 3)) a = pad(a, ((2, 3), (3, 2)), 'edge') b = np.array( [[0, 0, 0, 0, 1, 2, 2, 2], [0, 0, 0, 0, 1, 2, 2, 2], [0, 0, 0, 0, 1, 2, 2, 2], [3, 3, 3, 3, 4, 5, 5, 5], [6, 6, 6, 6, 7, 8, 8, 8], [9, 9, 9, 9, 10, 11, 11, 11], [9, 9, 9, 9, 10, 11, 11, 11], [9, 9, 9, 9, 10, 11, 11, 11], [9, 9, 9, 9, 10, 11, 11, 11]] ) assert_array_equal(a, b) class TestZeroPadWidth(TestCase): def test_zero_pad_width(self): arr = np.arange(30) arr = np.reshape(arr, (6, 5)) for pad_width in (0, (0, 0), ((0, 0), (0, 0))): assert_array_equal(arr, pad(arr, pad_width, mode='constant')) class TestLegacyVectorFunction(TestCase): def test_legacy_vector_functionality(self): def _padwithtens(vector, pad_width, iaxis, kwargs): vector[:pad_width[0]] = 10 vector[-pad_width[1]:] = 10 return vector a = np.arange(6).reshape(2, 3) a = pad(a, 2, _padwithtens) b = np.array( [[10, 10, 10, 10, 10, 10, 10], [10, 10, 10, 10, 10, 10, 10], [10, 10, 0, 1, 2, 10, 10], [10, 10, 3, 4, 5, 10, 10], [10, 10, 10, 10, 10, 10, 10], [10, 10, 10, 10, 10, 10, 10]] ) assert_array_equal(a, b) class TestNdarrayPadWidth(TestCase): def test_check_simple(self): a = np.arange(12) a = np.reshape(a, (4, 3)) a = pad(a, np.array(((2, 3), (3, 2))), 'edge') b = np.array( [[0, 0, 0, 0, 1, 2, 2, 2], [0, 0, 0, 0, 1, 2, 2, 2], [0, 0, 0, 0, 1, 2, 2, 2], [3, 3, 3, 3, 4, 5, 5, 5], [6, 6, 6, 6, 7, 8, 8, 8], [9, 9, 9, 9, 10, 11, 11, 11], [9, 9, 9, 9, 10, 11, 11, 11], [9, 9, 9, 9, 10, 11, 11, 11], [9, 9, 9, 9, 10, 11, 11, 11]] ) assert_array_equal(a, b) class ValueError1(TestCase): def test_check_simple(self): arr = np.arange(30) arr = np.reshape(arr, (6, 5)) kwargs = dict(mode='mean', stat_length=(3, )) assert_raises(ValueError, pad, arr, ((2, 3), (3, 2), (4, 5)), **kwargs) def test_check_negative_stat_length(self): arr = np.arange(30) arr = np.reshape(arr, (6, 5)) kwargs = dict(mode='mean', stat_length=(-3, )) assert_raises(ValueError, pad, arr, ((2, 3), (3, 2)), **kwargs) def test_check_negative_pad_width(self): arr = np.arange(30) arr = np.reshape(arr, (6, 5)) kwargs = dict(mode='mean', stat_length=(3, )) assert_raises(ValueError, pad, arr, ((-2, 3), (3, 2)), **kwargs) class ValueError2(TestCase): def test_check_negative_pad_amount(self): arr = np.arange(30) arr = np.reshape(arr, (6, 5)) kwargs = dict(mode='mean', stat_length=(3, )) assert_raises(ValueError, pad, arr, ((-2, 3), (3, 2)), **kwargs) class ValueError3(TestCase): def test_check_kwarg_not_allowed(self): arr = np.arange(30).reshape(5, 6) assert_raises(ValueError, pad, arr, 4, mode='mean', reflect_type='odd') def test_mode_not_set(self): arr = np.arange(30).reshape(5, 6) assert_raises((ValueError, TypeError), pad, arr, 4) def test_malformed_pad_amount(self): arr = np.arange(30).reshape(5, 6) assert_raises(ValueError, pad, arr, (4, 5, 6, 7), mode='constant') def test_malformed_pad_amount2(self): arr = np.arange(30).reshape(5, 6) assert_raises(ValueError, pad, arr, ((3, 4, 5), (0, 1, 2)), mode='constant') def test_pad_too_many_axes(self): arr = np.arange(30).reshape(5, 6) # Attempt to pad using a 3D array equivalent bad_shape = (((3,), (4,), (5,)), ((0,), (1,), (2,))) assert_raises(ValueError, pad, arr, bad_shape, mode='constant') class TypeError1(TestCase): def test_float(self): arr = np.arange(30) assert_raises(TypeError, pad, arr, ((-2.1, 3), (3, 2))) assert_raises(TypeError, pad, arr, np.array(((-2.1, 3), (3, 2)))) def test_str(self): arr = np.arange(30) assert_raises(TypeError, pad, arr, 'foo') assert_raises(TypeError, pad, arr, np.array('foo')) def test_object(self): class FooBar(object): pass arr = np.arange(30) assert_raises(TypeError, pad, arr, FooBar()) def test_complex(self): arr = np.arange(30) assert_raises(TypeError, pad, arr, complex(1, -1)) assert_raises(TypeError, pad, arr, np.array(complex(1, -1))) def test_check_wrong_pad_amount(self): arr = np.arange(30) arr = np.reshape(arr, (6, 5)) kwargs = dict(mode='mean', stat_length=(3, )) assert_raises(TypeError, pad, arr, ((2, 3, 4), (3, 2)), **kwargs) if __name__ == "__main__": np.testing.run_module_suite() scikit-image-0.13.1/skimage/util/tests/test_dtype.py000066400000000000000000000067571316256236300224650ustar00rootroot00000000000000import numpy as np from numpy.testing import assert_equal, assert_raises from skimage import img_as_int, img_as_float, \ img_as_uint, img_as_ubyte from skimage.util.dtype import convert from skimage._shared._warnings import expected_warnings dtype_range = {np.uint8: (0, 255), np.uint16: (0, 65535), np.int8: (-128, 127), np.int16: (-32768, 32767), np.float32: (-1.0, 1.0), np.float64: (-1.0, 1.0)} def _verify_range(msg, x, vmin, vmax, dtype): assert_equal(x[0], vmin) assert_equal(x[-1], vmax) assert x.dtype == dtype def test_range(): for dtype in dtype_range: imin, imax = dtype_range[dtype] x = np.linspace(imin, imax, 10).astype(dtype) for (f, dt) in [(img_as_int, np.int16), (img_as_float, np.float64), (img_as_uint, np.uint16), (img_as_ubyte, np.ubyte)]: with expected_warnings(['precision loss|sign loss|\A\Z']): y = f(x) omin, omax = dtype_range[dt] if imin == 0 or omin == 0: omin = 0 imin = 0 yield (_verify_range, "From %s to %s" % (np.dtype(dtype), np.dtype(dt)), y, omin, omax, np.dtype(dt)) def test_range_extra_dtypes(): """Test code paths that are not skipped by `test_range`""" # Add non-standard data types that are allowed by the `convert` function. dtype_range_extra = dtype_range.copy() dtype_range_extra.update({np.int32: (-2147483648, 2147483647), np.uint32: (0, 4294967295)}) dtype_pairs = [(np.uint8, np.uint32), (np.int8, np.uint32), (np.int8, np.int32), (np.int32, np.int8), (np.float64, np.float32), (np.int32, np.float32)] for dtype_in, dt in dtype_pairs: imin, imax = dtype_range_extra[dtype_in] x = np.linspace(imin, imax, 10).astype(dtype_in) with expected_warnings(['precision loss|sign loss|\A\Z']): y = convert(x, dt) omin, omax = dtype_range_extra[dt] yield (_verify_range, "From %s to %s" % (np.dtype(dtype_in), np.dtype(dt)), y, omin, omax, np.dtype(dt)) def test_downcast(): x = np.arange(10).astype(np.uint64) with expected_warnings('Downcasting'): y = img_as_int(x) assert np.allclose(y, x.astype(np.int16)) assert y.dtype == np.int16, y.dtype def test_float_out_of_range(): too_high = np.array([2], dtype=np.float32) assert_raises(ValueError, img_as_int, too_high) too_low = np.array([-2], dtype=np.float32) assert_raises(ValueError, img_as_int, too_low) def test_copy(): x = np.array([1], dtype=np.float64) y = img_as_float(x) z = img_as_float(x, force_copy=True) assert y is x assert z is not x def test_bool(): img_ = np.zeros((10, 10), np.bool_) img8 = np.zeros((10, 10), np.bool8) img_[1, 1] = True img8[1, 1] = True for (func, dt) in [(img_as_int, np.int16), (img_as_float, np.float64), (img_as_uint, np.uint16), (img_as_ubyte, np.ubyte)]: converted_ = func(img_) assert np.sum(converted_) == dtype_range[dt][1] converted8 = func(img8) assert np.sum(converted8) == dtype_range[dt][1] if __name__ == '__main__': np.testing.run_module_suite() scikit-image-0.13.1/skimage/util/tests/test_invert.py000066400000000000000000000030641316256236300226330ustar00rootroot00000000000000import numpy as np from numpy.testing import assert_array_equal from skimage import dtype_limits from skimage.util import invert def test_invert_bool(): dtype = 'bool' image = np.zeros((3, 3), dtype=dtype) upper_dtype_limit = dtype_limits(image, clip_negative=False)[1] image[1, :] = upper_dtype_limit expected = np.zeros((3, 3), dtype=dtype) + upper_dtype_limit expected[1, :] = 0 result = invert(image) assert_array_equal(expected, result) def test_invert_uint8(): dtype = 'uint8' image = np.zeros((3, 3), dtype=dtype) upper_dtype_limit = dtype_limits(image, clip_negative=False)[1] image[1, :] = upper_dtype_limit expected = np.zeros((3, 3), dtype=dtype) + upper_dtype_limit expected[1, :] = 0 result = invert(image) assert_array_equal(expected, result) def test_invert_int8(): dtype = 'int8' image = np.zeros((3, 3), dtype=dtype) upper_dtype_limit = dtype_limits(image, clip_negative=False)[1] image[1, :] = upper_dtype_limit expected = np.zeros((3, 3), dtype=dtype) + upper_dtype_limit expected[1, :] = 0 result = invert(image) assert_array_equal(expected, result) def test_invert_float64(): dtype = 'float64' image = np.zeros((3, 3), dtype=dtype) upper_dtype_limit = dtype_limits(image, clip_negative=False)[1] image[1, :] = upper_dtype_limit expected = np.zeros((3, 3), dtype=dtype) + upper_dtype_limit expected[1, :] = 0 result = invert(image) assert_array_equal(expected, result) if __name__ == '__main__': np.testing.run_module_suite() scikit-image-0.13.1/skimage/util/tests/test_montage.py000066400000000000000000000054371316256236300227640ustar00rootroot00000000000000from nose.tools import assert_equal, raises from numpy.testing import assert_array_equal import numpy as np from skimage.util.montage import montage2d def test_simple(): n_images = 3 height, width = 2, 3, arr_in = np.arange(n_images * height * width) arr_in = arr_in.reshape(n_images, height, width) arr_out = montage2d(arr_in) gt = np.array( [[ 0. , 1. , 2. , 6. , 7. , 8. ], [ 3. , 4. , 5. , 9. , 10. , 11. ], [ 12. , 13. , 14. , 8.5, 8.5, 8.5], [ 15. , 16. , 17. , 8.5, 8.5, 8.5]] ) assert_array_equal(arr_out, gt) def test_fill(): n_images = 3 height, width = 2, 3, arr_in = np.arange(n_images * height * width) arr_in = arr_in.reshape(n_images, height, width) arr_out = montage2d(arr_in, fill=0) gt = np.array( [[ 0. , 1. , 2. , 6. , 7. , 8. ], [ 3. , 4. , 5. , 9. , 10. , 11. ], [ 12. , 13. , 14. , 0. , 0. , 0. ], [ 15. , 16. , 17. , 0. , 0. , 0. ]] ) assert_array_equal(arr_out, gt) def test_shape(): n_images = 15 height, width = 11, 7 arr_in = np.arange(n_images * height * width) arr_in = arr_in.reshape(n_images, height, width) alpha = int(np.ceil(np.sqrt(n_images))) arr_out = montage2d(arr_in) assert_equal(arr_out.shape, (alpha * height, alpha * width)) def test_grid_shape(): n_images = 6 height, width = 2, 2 arr_in = np.arange(n_images * height * width, dtype=np.float32) arr_in = arr_in.reshape(n_images, height, width) arr_out = montage2d(arr_in, grid_shape=(3,2)) correct_arr_out = np.array( [[ 0., 1., 4., 5.], [ 2., 3., 6., 7.], [ 8., 9., 12., 13.], [ 10., 11., 14., 15.], [ 16., 17., 20., 21.], [ 18., 19., 22., 23.]] ) assert_array_equal(arr_out, correct_arr_out) def test_rescale_intensity(): n_images = 4 height, width = 3, 3 arr_in = np.arange(n_images * height * width, dtype=np.float32) arr_in = arr_in.reshape(n_images, height, width) arr_out = montage2d(arr_in, rescale_intensity=True) gt = np.array( [[ 0. , 0.125, 0.25 , 0. , 0.125, 0.25 ], [ 0.375, 0.5 , 0.625, 0.375, 0.5 , 0.625], [ 0.75 , 0.875, 1. , 0.75 , 0.875, 1. ], [ 0. , 0.125, 0.25 , 0. , 0.125, 0.25 ], [ 0.375, 0.5 , 0.625, 0.375, 0.5 , 0.625], [ 0.75 , 0.875, 1. , 0.75 , 0.875, 1. ]] ) assert_equal(arr_out.min(), 0.0) assert_equal(arr_out.max(), 1.0) assert_array_equal(arr_out, gt) @raises(AssertionError) def test_error_ndim(): arr_error = np.random.randn(1, 2, 3, 4) montage2d(arr_error) if __name__ == '__main__': np.testing.run_module_suite() scikit-image-0.13.1/skimage/util/tests/test_random_noise.py000066400000000000000000000175051316256236300240060ustar00rootroot00000000000000from numpy.testing import assert_array_equal, assert_allclose, assert_raises import numpy as np from skimage.data import camera from skimage.util import random_noise, img_as_float def test_set_seed(): seed = 42 cam = camera() test = random_noise(cam, seed=seed) assert_array_equal(test, random_noise(cam, seed=seed)) def test_salt(): seed = 42 cam = img_as_float(camera()) cam_noisy = random_noise(cam, seed=seed, mode='salt', amount=0.15) saltmask = cam != cam_noisy # Ensure all changes are to 1.0 assert_allclose(cam_noisy[saltmask], np.ones(saltmask.sum())) # Ensure approximately correct amount of noise was added proportion = float(saltmask.sum()) / (cam.shape[0] * cam.shape[1]) assert 0.11 < proportion <= 0.15 def test_salt_p1(): image = np.random.rand(2, 3) noisy = random_noise(image, mode='salt', amount=1) assert_array_equal(noisy, [[1, 1, 1], [1, 1, 1]]) def test_singleton_dim(): """Ensure images where size of a given dimension is 1 work correctly.""" image = np.random.rand(1, 20) noisy = random_noise(image, mode='salt', amount=0.1, seed=42) assert np.sum(noisy == 1) == 2 def test_pepper(): seed = 42 cam = img_as_float(camera()) data_signed = cam * 2. - 1. # Same image, on range [-1, 1] cam_noisy = random_noise(cam, seed=seed, mode='pepper', amount=0.15) peppermask = cam != cam_noisy # Ensure all changes are to 1.0 assert_allclose(cam_noisy[peppermask], np.zeros(peppermask.sum())) # Ensure approximately correct amount of noise was added proportion = float(peppermask.sum()) / (cam.shape[0] * cam.shape[1]) assert 0.11 < proportion <= 0.15 # Check to make sure pepper gets added properly to signed images orig_zeros = (data_signed == -1).sum() cam_noisy_signed = random_noise(data_signed, seed=seed, mode='pepper', amount=.15) proportion = (float((cam_noisy_signed == -1).sum() - orig_zeros) / (cam.shape[0] * cam.shape[1])) assert 0.11 < proportion <= 0.15 def test_salt_and_pepper(): seed = 42 cam = img_as_float(camera()) cam_noisy = random_noise(cam, seed=seed, mode='s&p', amount=0.15, salt_vs_pepper=0.25) saltmask = np.logical_and(cam != cam_noisy, cam_noisy == 1.) peppermask = np.logical_and(cam != cam_noisy, cam_noisy == 0.) # Ensure all changes are to 0. or 1. assert_allclose(cam_noisy[saltmask], np.ones(saltmask.sum())) assert_allclose(cam_noisy[peppermask], np.zeros(peppermask.sum())) # Ensure approximately correct amount of noise was added proportion = float( saltmask.sum() + peppermask.sum()) / (cam.shape[0] * cam.shape[1]) assert 0.11 < proportion <= 0.18 # Verify the relative amount of salt vs. pepper is close to expected assert 0.18 < saltmask.sum() / float(peppermask.sum()) < 0.33 def test_gaussian(): seed = 42 data = np.zeros((128, 128)) + 0.5 data_gaussian = random_noise(data, seed=seed, var=0.01) assert 0.008 < data_gaussian.var() < 0.012 data_gaussian = random_noise(data, seed=seed, mean=0.3, var=0.015) assert 0.28 < data_gaussian.mean() - 0.5 < 0.32 assert 0.012 < data_gaussian.var() < 0.018 def test_localvar(): seed = 42 data = np.zeros((128, 128)) + 0.5 local_vars = np.zeros((128, 128)) + 0.001 local_vars[:64, 64:] = 0.1 local_vars[64:, :64] = 0.25 local_vars[64:, 64:] = 0.45 data_gaussian = random_noise(data, mode='localvar', seed=seed, local_vars=local_vars, clip=False) assert 0. < data_gaussian[:64, :64].var() < 0.002 assert 0.095 < data_gaussian[:64, 64:].var() < 0.105 assert 0.245 < data_gaussian[64:, :64].var() < 0.255 assert 0.445 < data_gaussian[64:, 64:].var() < 0.455 # Ensure local variance bounds checking works properly bad_local_vars = np.zeros_like(data) assert_raises(ValueError, random_noise, data, mode='localvar', seed=seed, local_vars=bad_local_vars) bad_local_vars += 0.1 bad_local_vars[0, 0] = -1 assert_raises(ValueError, random_noise, data, mode='localvar', seed=seed, local_vars=bad_local_vars) def test_speckle(): seed = 42 data = np.zeros((128, 128)) + 0.1 np.random.seed(seed=seed) noise = np.random.normal(0.1, 0.02 ** 0.5, (128, 128)) expected = np.clip(data + data * noise, 0, 1) data_speckle = random_noise(data, mode='speckle', seed=seed, mean=0.1, var=0.02) assert_allclose(expected, data_speckle) def test_poisson(): seed = 42 data = camera() # 512x512 grayscale uint8 cam_noisy = random_noise(data, mode='poisson', seed=seed) cam_noisy2 = random_noise(data, mode='poisson', seed=seed, clip=False) np.random.seed(seed=seed) expected = np.random.poisson(img_as_float(data) * 256) / 256. assert_allclose(cam_noisy, np.clip(expected, 0., 1.)) assert_allclose(cam_noisy2, expected) def test_clip_poisson(): seed = 42 data = camera() # 512x512 grayscale uint8 data_signed = img_as_float(data) * 2. - 1. # Same image, on range [-1, 1] # Signed and unsigned, clipped cam_poisson = random_noise(data, mode='poisson', seed=seed, clip=True) cam_poisson2 = random_noise(data_signed, mode='poisson', seed=seed, clip=True) assert (cam_poisson.max() == 1.) and (cam_poisson.min() == 0.) assert (cam_poisson2.max() == 1.) and (cam_poisson2.min() == -1.) # Signed and unsigned, unclipped cam_poisson = random_noise(data, mode='poisson', seed=seed, clip=False) cam_poisson2 = random_noise(data_signed, mode='poisson', seed=seed, clip=False) assert (cam_poisson.max() > 1.15) and (cam_poisson.min() == 0.) assert (cam_poisson2.max() > 1.3) and (cam_poisson2.min() == -1.) def test_clip_gaussian(): seed = 42 data = camera() # 512x512 grayscale uint8 data_signed = img_as_float(data) * 2. - 1. # Same image, on range [-1, 1] # Signed and unsigned, clipped cam_gauss = random_noise(data, mode='gaussian', seed=seed, clip=True) cam_gauss2 = random_noise(data_signed, mode='gaussian', seed=seed, clip=True) assert (cam_gauss.max() == 1.) and (cam_gauss.min() == 0.) assert (cam_gauss2.max() == 1.) and (cam_gauss2.min() == -1.) # Signed and unsigned, unclipped cam_gauss = random_noise(data, mode='gaussian', seed=seed, clip=False) cam_gauss2 = random_noise(data_signed, mode='gaussian', seed=seed, clip=False) assert (cam_gauss.max() > 1.22) and (cam_gauss.min() < -0.36) assert (cam_gauss2.max() > 1.219) and (cam_gauss2.min() < -1.337) def test_clip_speckle(): seed = 42 data = camera() # 512x512 grayscale uint8 data_signed = img_as_float(data) * 2. - 1. # Same image, on range [-1, 1] # Signed and unsigned, clipped cam_speckle = random_noise(data, mode='speckle', seed=seed, clip=True) cam_speckle2 = random_noise(data_signed, mode='speckle', seed=seed, clip=True) assert (cam_speckle.max() == 1.) and (cam_speckle.min() == 0.) assert (cam_speckle2.max() == 1.) and (cam_speckle2.min() == -1.) # Signed and unsigned, unclipped cam_speckle = random_noise(data, mode='speckle', seed=seed, clip=False) cam_speckle2 = random_noise(data_signed, mode='speckle', seed=seed, clip=False) assert (cam_speckle.max() > 1.219) and (cam_speckle.min() == 0.) assert (cam_speckle2.max() > 1.219) and (cam_speckle2.min() < -1.306) def test_bad_mode(): data = np.zeros((64, 64)) assert_raises(KeyError, random_noise, data, 'perlin') if __name__ == '__main__': np.testing.run_module_suite() scikit-image-0.13.1/skimage/util/tests/test_regular_grid.py000066400000000000000000000020201316256236300237610ustar00rootroot00000000000000import numpy as np from numpy.testing import assert_equal from skimage.util import regular_grid def test_regular_grid_full(): ar = np.zeros((2, 2)) g = regular_grid(ar, 25) assert_equal(g, [slice(None, None, None), slice(None, None, None)]) ar[g] = 1 assert_equal(ar.size, ar.sum()) def test_regular_grid_2d_8(): ar = np.zeros((20, 40)) g = regular_grid(ar.shape, 8) assert_equal(g, [slice(5.0, None, 10.0), slice(5.0, None, 10.0)]) ar[g] = 1 assert_equal(ar.sum(), 8) def test_regular_grid_2d_32(): ar = np.zeros((20, 40)) g = regular_grid(ar.shape, 32) assert_equal(g, [slice(2.0, None, 5.0), slice(2.0, None, 5.0)]) ar[g] = 1 assert_equal(ar.sum(), 32) def test_regular_grid_3d_8(): ar = np.zeros((3, 20, 40)) g = regular_grid(ar.shape, 8) assert_equal(g, [slice(1.0, None, 3.0), slice(5.0, None, 10.0), slice(5.0, None, 10.0)]) ar[g] = 1 assert_equal(ar.sum(), 8) if __name__ == '__main__': np.testing.run_module_suite() scikit-image-0.13.1/skimage/util/tests/test_shape.py000066400000000000000000000116551316256236300224310ustar00rootroot00000000000000import numpy as np from nose.tools import raises from numpy.testing import assert_equal, assert_warns from skimage.util.shape import view_as_blocks, view_as_windows from skimage._shared._warnings import expected_warnings @raises(TypeError) def test_view_as_blocks_block_not_a_tuple(): A = np.arange(10) view_as_blocks(A, [5]) @raises(ValueError) def test_view_as_blocks_negative_shape(): A = np.arange(10) view_as_blocks(A, (-2,)) @raises(ValueError) def test_view_as_blocks_block_too_large(): A = np.arange(10) view_as_blocks(A, (11,)) @raises(ValueError) def test_view_as_blocks_wrong_block_dimension(): A = np.arange(10) view_as_blocks(A, (2, 2)) @raises(ValueError) def test_view_as_blocks_1D_array_wrong_block_shape(): A = np.arange(10) view_as_blocks(A, (3,)) def test_view_as_blocks_1D_array(): A = np.arange(10) B = view_as_blocks(A, (5,)) assert_equal(B, np.array([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]])) def test_view_as_blocks_2D_array(): A = np.arange(4 * 4).reshape(4, 4) B = view_as_blocks(A, (2, 2)) assert_equal(B[0, 1], np.array([[2, 3], [6, 7]])) assert_equal(B[1, 0, 1, 1], 13) def test_view_as_blocks_3D_array(): A = np.arange(4 * 4 * 6).reshape(4, 4, 6) B = view_as_blocks(A, (1, 2, 2)) assert_equal(B.shape, (4, 2, 3, 1, 2, 2)) assert_equal(B[2:, 0, 2], np.array([[[[52, 53], [58, 59]]], [[[76, 77], [82, 83]]]])) @raises(TypeError) def test_view_as_windows_input_not_array(): A = [1, 2, 3, 4, 5] view_as_windows(A, (2,)) @raises(ValueError) def test_view_as_windows_wrong_window_dimension(): A = np.arange(10) view_as_windows(A, (2, 2)) @raises(ValueError) def test_view_as_windows_negative_window_length(): A = np.arange(10) view_as_windows(A, (-1,)) @raises(ValueError) def test_view_as_windows_window_too_large(): A = np.arange(10) view_as_windows(A, (11,)) @raises(ValueError) def test_view_as_windows_step_below_one(): A = np.arange(10) view_as_windows(A, (11,), step=0.9) def test_view_as_windows_1D(): A = np.arange(10) window_shape = (3,) B = view_as_windows(A, window_shape) assert_equal(B, np.array([[0, 1, 2], [1, 2, 3], [2, 3, 4], [3, 4, 5], [4, 5, 6], [5, 6, 7], [6, 7, 8], [7, 8, 9]])) def test_view_as_windows_2D(): A = np.arange(5 * 4).reshape(5, 4) window_shape = (4, 3) B = view_as_windows(A, window_shape) assert_equal(B.shape, (2, 2, 4, 3)) assert_equal(B, np.array([[[[0, 1, 2], [4, 5, 6], [8, 9, 10], [12, 13, 14]], [[1, 2, 3], [5, 6, 7], [9, 10, 11], [13, 14, 15]]], [[[4, 5, 6], [8, 9, 10], [12, 13, 14], [16, 17, 18]], [[5, 6, 7], [9, 10, 11], [13, 14, 15], [17, 18, 19]]]])) def test_view_as_windows_with_skip(): A = np.arange(20).reshape((5, 4)) B = view_as_windows(A, 2, step=2) assert_equal(B, [[[[0, 1], [4, 5]], [[2, 3], [6, 7]]], [[[8, 9], [12, 13]], [[10, 11], [14, 15]]]]) C = view_as_windows(A, 2, step=4) assert_equal(C.shape, (1, 1, 2, 2)) def test_views_non_contiguous(): A = np.arange(16).reshape((4, 4)) A = A[::2, :] assert_warns(RuntimeWarning, view_as_blocks, A, (2, 2)) assert_warns(RuntimeWarning, view_as_windows, A, (2, 2)) def test_view_as_windows_step_tuple(): A = np.arange(24).reshape((6, 4)) B = view_as_windows(A, (3, 2), step=3) assert B.shape == (2, 1, 3, 2) assert B.size != A.size C = view_as_windows(A, (3, 2), step=(3, 2)) assert C.shape == (2, 2, 3, 2) assert C.size == A.size assert_equal(C, [[[[0, 1], [4, 5], [8, 9]], [[2, 3], [6, 7], [10, 11]]], [[[12, 13], [16, 17], [20, 21]], [[14, 15], [18, 19], [22, 23]]]]) if __name__ == '__main__': np.testing.run_module_suite() scikit-image-0.13.1/skimage/util/tests/test_unique_rows.py000066400000000000000000000021471316256236300237050ustar00rootroot00000000000000import numpy as np from numpy.testing import assert_equal, assert_raises from skimage.util import unique_rows def test_discontiguous_array(): ar = np.array([[1, 0, 1], [0, 1, 0], [1, 0, 1]], np.uint8) ar = ar[::2] ar_out = unique_rows(ar) desired_ar_out = np.array([[1, 0, 1]], np.uint8) assert_equal(ar_out, desired_ar_out) def test_uint8_array(): ar = np.array([[1, 0, 1], [0, 1, 0], [1, 0, 1]], np.uint8) ar_out = unique_rows(ar) desired_ar_out = np.array([[0, 1, 0], [1, 0, 1]], np.uint8) assert_equal(ar_out, desired_ar_out) def test_float_array(): ar = np.array([[1.1, 0.0, 1.1], [0.0, 1.1, 0.0], [1.1, 0.0, 1.1]], np.float) ar_out = unique_rows(ar) desired_ar_out = np.array([[0.0, 1.1, 0.0], [1.1, 0.0, 1.1]], np.float) assert_equal(ar_out, desired_ar_out) def test_1d_array(): ar = np.array([1, 0, 1, 1], np.uint8) assert_raises(ValueError, unique_rows, ar) def test_3d_array(): ar = np.arange(8).reshape((2, 2, 2)) assert_raises(ValueError, unique_rows, ar) if __name__ == '__main__': np.testing.run_module_suite() scikit-image-0.13.1/skimage/util/unique.py000066400000000000000000000027421316256236300204330ustar00rootroot00000000000000import numpy as np def unique_rows(ar): """Remove repeated rows from a 2D array. In particular, if given an array of coordinates of shape (Npoints, Ndim), it will remove repeated points. Parameters ---------- ar : 2-D ndarray The input array. Returns ------- ar_out : 2-D ndarray A copy of the input array with repeated rows removed. Raises ------ ValueError : if `ar` is not two-dimensional. Notes ----- The function will generate a copy of `ar` if it is not C-contiguous, which will negatively affect performance for large input arrays. Examples -------- >>> ar = np.array([[1, 0, 1], ... [0, 1, 0], ... [1, 0, 1]], np.uint8) >>> unique_rows(ar) array([[0, 1, 0], [1, 0, 1]], dtype=uint8) """ if ar.ndim != 2: raise ValueError("unique_rows() only makes sense for 2D arrays, " "got %dd" % ar.ndim) # the view in the next line only works if the array is C-contiguous ar = np.ascontiguousarray(ar) # np.unique() finds identical items in a raveled array. To make it # see each row as a single item, we create a view of each row as a # byte string of length itemsize times number of columns in `ar` ar_row_view = ar.view('|S%d' % (ar.itemsize * ar.shape[1])) _, unique_row_indices = np.unique(ar_row_view, return_index=True) ar_out = ar[unique_row_indices] return ar_out scikit-image-0.13.1/skimage/viewer/000077500000000000000000000000001316256236300170725ustar00rootroot00000000000000scikit-image-0.13.1/skimage/viewer/__init__.py000066400000000000000000000002321316256236300212000ustar00rootroot00000000000000from .._shared.utils import warn from .viewers import ImageViewer, CollectionViewer from .qt import has_qt if not has_qt: warn('Viewer requires Qt') scikit-image-0.13.1/skimage/viewer/canvastools/000077500000000000000000000000001316256236300214265ustar00rootroot00000000000000scikit-image-0.13.1/skimage/viewer/canvastools/__init__.py000066400000000000000000000001631316256236300235370ustar00rootroot00000000000000from .linetool import LineTool, ThickLineTool from .recttool import RectangleTool from .painttool import PaintTool scikit-image-0.13.1/skimage/viewer/canvastools/base.py000066400000000000000000000074451316256236300227240ustar00rootroot00000000000000import numpy as np from matplotlib import lines __all__ = ['CanvasToolBase', 'ToolHandles'] def _pass(*args): pass class CanvasToolBase(object): """Base canvas tool for matplotlib axes. Parameters ---------- manager : Viewer or PlotPlugin. Skimage viewer or plot plugin object. on_move : function Function called whenever a control handle is moved. This function must accept the end points of line as the only argument. on_release : function Function called whenever the control handle is released. on_enter : function Function called whenever the "enter" key is pressed. """ def __init__(self, manager, on_move=None, on_enter=None, on_release=None, useblit=True, ax=None): self.manager = manager self.ax = manager.ax self.artists = [] self.active = True self.callback_on_move = _pass if on_move is None else on_move self.callback_on_enter = _pass if on_enter is None else on_enter self.callback_on_release = _pass if on_release is None else on_release def ignore(self, event): """Return True if event should be ignored. This method (or a version of it) should be called at the beginning of any event callback. """ return not self.active def hit_test(self, event): return False def redraw(self): self.manager.redraw() def set_visible(self, val): for artist in self.artists: artist.set_visible(val) def on_key_press(self, event): if event.key == 'enter': self.callback_on_enter(self.geometry) self.set_visible(False) self.manager.redraw() def on_mouse_press(self, event): pass def on_mouse_release(self, event): pass def on_move(self, event): pass def on_scroll(self, event): pass def remove(self): self.manager.remove_tool(self) @property def geometry(self): """Geometry information that gets passed to callback functions.""" return None class ToolHandles(object): """Control handles for canvas tools. Parameters ---------- ax : :class:`matplotlib.axes.Axes` Matplotlib axes where tool handles are displayed. x, y : 1D arrays Coordinates of control handles. marker : str Shape of marker used to display handle. See `matplotlib.pyplot.plot`. marker_props : dict Additional marker properties. See :class:`matplotlib.lines.Line2D`. """ def __init__(self, ax, x, y, marker='o', marker_props=None): self.ax = ax props = dict(marker=marker, markersize=7, mfc='w', ls='none', alpha=0.5, visible=False) props.update(marker_props if marker_props is not None else {}) self._markers = lines.Line2D(x, y, animated=True, **props) self.ax.add_line(self._markers) self.artist = self._markers @property def x(self): return self._markers.get_xdata() @property def y(self): return self._markers.get_ydata() def set_data(self, pts, y=None): """Set x and y positions of handles""" if y is not None: x = pts pts = np.array([x, y]) self._markers.set_data(pts) def set_visible(self, val): self._markers.set_visible(val) def set_animated(self, val): self._markers.set_animated(val) def closest(self, x, y): """Return index and pixel distance to closest index.""" pts = np.transpose((self.x, self.y)) # Transform data coordinates to pixel coordinates. pts = self.ax.transData.transform(pts) diff = pts - ((x, y)) dist = np.sqrt(np.sum(diff**2, axis=1)) return np.argmin(dist), np.min(dist) scikit-image-0.13.1/skimage/viewer/canvastools/linetool.py000066400000000000000000000153771316256236300236420ustar00rootroot00000000000000import numpy as np from matplotlib import lines from ...viewer.canvastools.base import CanvasToolBase, ToolHandles __all__ = ['LineTool', 'ThickLineTool'] class LineTool(CanvasToolBase): """Widget for line selection in a plot. Parameters ---------- manager : Viewer or PlotPlugin. Skimage viewer or plot plugin object. on_move : function Function called whenever a control handle is moved. This function must accept the end points of line as the only argument. on_release : function Function called whenever the control handle is released. on_enter : function Function called whenever the "enter" key is pressed. maxdist : float Maximum pixel distance allowed when selecting control handle. line_props : dict Properties for :class:`matplotlib.lines.Line2D`. handle_props : dict Marker properties for the handles (also see :class:`matplotlib.lines.Line2D`). Attributes ---------- end_points : 2D array End points of line ((x1, y1), (x2, y2)). """ def __init__(self, manager, on_move=None, on_release=None, on_enter=None, maxdist=10, line_props=None, handle_props=None, **kwargs): super(LineTool, self).__init__(manager, on_move=on_move, on_enter=on_enter, on_release=on_release, **kwargs) props = dict(color='r', linewidth=1, alpha=0.4, solid_capstyle='butt') props.update(line_props if line_props is not None else {}) self.linewidth = props['linewidth'] self.maxdist = maxdist self._active_pt = None x = (0, 0) y = (0, 0) self._end_pts = np.transpose([x, y]) self._line = lines.Line2D(x, y, visible=False, animated=True, **props) self.ax.add_line(self._line) self._handles = ToolHandles(self.ax, x, y, marker_props=handle_props) self._handles.set_visible(False) self.artists = [self._line, self._handles.artist] if on_enter is None: def on_enter(pts): x, y = np.transpose(pts) print("length = %0.2f" % np.sqrt(np.diff(x)**2 + np.diff(y)**2)) self.callback_on_enter = on_enter self.manager.add_tool(self) @property def end_points(self): return self._end_pts.astype(int) @end_points.setter def end_points(self, pts): self._end_pts = np.asarray(pts) self._line.set_data(np.transpose(pts)) self._handles.set_data(np.transpose(pts)) self._line.set_linewidth(self.linewidth) self.set_visible(True) self.redraw() def hit_test(self, event): if event.button != 1 or not self.ax.in_axes(event): return False idx, px_dist = self._handles.closest(event.x, event.y) if px_dist < self.maxdist: self._active_pt = idx return True else: self._active_pt = None return False def on_mouse_press(self, event): self.set_visible(True) if self._active_pt is None: self._active_pt = 0 x, y = event.xdata, event.ydata self._end_pts = np.array([[x, y], [x, y]]) def on_mouse_release(self, event): if event.button != 1: return self._active_pt = None self.callback_on_release(self.geometry) self.redraw() def on_move(self, event): if event.button != 1 or self._active_pt is None: return if not self.ax.in_axes(event): return self.update(event.xdata, event.ydata) self.callback_on_move(self.geometry) def update(self, x=None, y=None): if x is not None: self._end_pts[self._active_pt, :] = x, y self.end_points = self._end_pts @property def geometry(self): return self.end_points class ThickLineTool(LineTool): """Widget for line selection in a plot. The thickness of the line can be varied using the mouse scroll wheel, or with the '+' and '-' keys. Parameters ---------- manager : Viewer or PlotPlugin. Skimage viewer or plot plugin object. on_move : function Function called whenever a control handle is moved. This function must accept the end points of line as the only argument. on_release : function Function called whenever the control handle is released. on_enter : function Function called whenever the "enter" key is pressed. on_change : function Function called whenever the line thickness is changed. maxdist : float Maximum pixel distance allowed when selecting control handle. line_props : dict Properties for :class:`matplotlib.lines.Line2D`. handle_props : dict Marker properties for the handles (also see :class:`matplotlib.lines.Line2D`). Attributes ---------- end_points : 2D array End points of line ((x1, y1), (x2, y2)). """ def __init__(self, manager, on_move=None, on_enter=None, on_release=None, on_change=None, maxdist=10, line_props=None, handle_props=None): super(ThickLineTool, self).__init__(manager, on_move=on_move, on_enter=on_enter, on_release=on_release, maxdist=maxdist, line_props=line_props, handle_props=handle_props) if on_change is None: def on_change(*args): pass self.callback_on_change = on_change def on_scroll(self, event): if not event.inaxes: return if event.button == 'up': self._thicken_scan_line() elif event.button == 'down': self._shrink_scan_line() def on_key_press(self, event): if event.key == '+': self._thicken_scan_line() elif event.key == '-': self._shrink_scan_line() def _thicken_scan_line(self): self.linewidth += 1 self.update() self.callback_on_change(self.geometry) def _shrink_scan_line(self): if self.linewidth > 1: self.linewidth -= 1 self.update() self.callback_on_change(self.geometry) if __name__ == '__main__': # pragma: no cover from ... import data from ...viewer import ImageViewer image = data.camera() viewer = ImageViewer(image) h, w = image.shape line_tool = ThickLineTool(viewer) line_tool.end_points = ([w/3, h/2], [2*w/3, h/2]) viewer.show() scikit-image-0.13.1/skimage/viewer/canvastools/painttool.py000066400000000000000000000155461316256236300240240ustar00rootroot00000000000000import numpy as np import matplotlib.pyplot as plt import matplotlib.colors as mcolors LABELS_CMAP = mcolors.ListedColormap(['white', 'red', 'dodgerblue', 'gold', 'greenyellow', 'blueviolet']) from ...viewer.canvastools.base import CanvasToolBase __all__ = ['PaintTool'] class PaintTool(CanvasToolBase): """Widget for painting on top of a plot. Parameters ---------- manager : Viewer or PlotPlugin. Skimage viewer or plot plugin object. overlay_shape : shape tuple 2D shape tuple used to initialize overlay image. alpha : float (between [0, 1]) Opacity of overlay on_move : function Function called whenever a control handle is moved. This function must accept the end points of line as the only argument. on_release : function Function called whenever the control handle is released. on_enter : function Function called whenever the "enter" key is pressed. rect_props : dict Properties for :class:`matplotlib.patches.Rectangle`. This class redefines defaults in :class:`matplotlib.widgets.RectangleSelector`. Attributes ---------- overlay : array Overlay of painted labels displayed on top of image. label : int Current paint color. Examples ---------- >>> from skimage.data import camera >>> import matplotlib.pyplot as plt >>> from skimage.viewer.canvastools import PaintTool >>> import numpy as np >>> img = camera() #doctest: +SKIP >>> ax = plt.subplot(111) #doctest: +SKIP >>> plt.imshow(img, cmap=plt.cm.gray) #doctest: +SKIP >>> p = PaintTool(ax,np.shape(img[:-1]),10,0.2) #doctest: +SKIP >>> plt.show() #doctest: +SKIP >>> mask = p.overlay #doctest: +SKIP >>> plt.imshow(mask,cmap=plt.cm.gray) #doctest: +SKIP >>> plt.show() #doctest: +SKIP """ def __init__(self, manager, overlay_shape, radius=5, alpha=0.3, on_move=None, on_release=None, on_enter=None, rect_props=None): super(PaintTool, self).__init__(manager, on_move=on_move, on_enter=on_enter, on_release=on_release) props = dict(edgecolor='r', facecolor='0.7', alpha=0.5, animated=True) props.update(rect_props if rect_props is not None else {}) self.alpha = alpha self.cmap = LABELS_CMAP self._overlay_plot = None self.shape = overlay_shape self._cursor = plt.Rectangle((0, 0), 0, 0, **props) self._cursor.set_visible(False) self.ax.add_patch(self._cursor) # `label` and `radius` can only be set after initializing `_cursor` self.label = 1 self.radius = radius # Note that the order is important: Redraw cursor *after* overlay self.artists = [self._overlay_plot, self._cursor] self.manager.add_tool(self) @property def label(self): return self._label @label.setter def label(self, value): if value >= self.cmap.N: raise ValueError('Maximum label value = %s' % len(self.cmap - 1)) self._label = value self._cursor.set_edgecolor(self.cmap(value)) @property def radius(self): return self._radius @radius.setter def radius(self, r): self._radius = r self._width = 2 * r + 1 self._cursor.set_width(self._width) self._cursor.set_height(self._width) self.window = CenteredWindow(r, self._shape) @property def overlay(self): return self._overlay @overlay.setter def overlay(self, image): self._overlay = image if image is None: self.ax.images.remove(self._overlay_plot) self._overlay_plot = None elif self._overlay_plot is None: props = dict(cmap=self.cmap, alpha=self.alpha, norm=mcolors.NoNorm(), animated=True) self._overlay_plot = self.ax.imshow(image, **props) else: self._overlay_plot.set_data(image) self.redraw() @property def shape(self): return self._shape @shape.setter def shape(self, shape): self._shape = shape if not self._overlay_plot is None: self._overlay_plot.set_extent((-0.5, shape[1] + 0.5, shape[0] + 0.5, -0.5)) self.radius = self._radius self.overlay = np.zeros(shape, dtype='uint8') def on_key_press(self, event): if event.key == 'enter': self.callback_on_enter(self.geometry) self.redraw() def on_mouse_press(self, event): if event.button != 1 or not self.ax.in_axes(event): return self.update_cursor(event.xdata, event.ydata) self.update_overlay(event.xdata, event.ydata) def on_mouse_release(self, event): if event.button != 1: return self.callback_on_release(self.geometry) def on_move(self, event): if not self.ax.in_axes(event): self._cursor.set_visible(False) self.redraw() # make sure cursor is not visible return self._cursor.set_visible(True) self.update_cursor(event.xdata, event.ydata) if event.button != 1: self.redraw() # update cursor position return self.update_overlay(event.xdata, event.ydata) self.callback_on_move(self.geometry) def update_overlay(self, x, y): overlay = self.overlay overlay[self.window.at(y, x)] = self.label # Note that overlay calls `redraw` self.overlay = overlay def update_cursor(self, x, y): x = x - self.radius - 1 y = y - self.radius - 1 self._cursor.set_xy((x, y)) @property def geometry(self): return self.overlay class CenteredWindow(object): """Window that create slices numpy arrays over 2D windows. Examples -------- >>> a = np.arange(16).reshape(4, 4) >>> w = CenteredWindow(1, a.shape) >>> a[w.at(1, 1)] array([[ 0, 1, 2], [ 4, 5, 6], [ 8, 9, 10]]) >>> a[w.at(0, 0)] array([[0, 1], [4, 5]]) >>> a[w.at(4, 3)] array([[14, 15]]) """ def __init__(self, radius, array_shape): self.radius = radius self.array_shape = array_shape def at(self, row, col): h, w = self.array_shape r = self.radius xmin = max(0, col - r) xmax = min(w, col + r + 1) ymin = max(0, row - r) ymax = min(h, row + r + 1) return [slice(ymin, ymax), slice(xmin, xmax)] if __name__ == '__main__': # pragma: no cover np.testing.rundocs() from ... import data from ...viewer import ImageViewer image = data.camera() viewer = ImageViewer(image) paint_tool = PaintTool(viewer, image.shape) viewer.show() scikit-image-0.13.1/skimage/viewer/canvastools/recttool.py000066400000000000000000000212661316256236300236420ustar00rootroot00000000000000from matplotlib.widgets import RectangleSelector from ...viewer.canvastools.base import CanvasToolBase from ...viewer.canvastools.base import ToolHandles __all__ = ['RectangleTool'] class RectangleTool(CanvasToolBase, RectangleSelector): """Widget for selecting a rectangular region in a plot. After making the desired selection, press "Enter" to accept the selection and call the `on_enter` callback function. Parameters ---------- manager : Viewer or PlotPlugin. Skimage viewer or plot plugin object. on_move : function Function called whenever a control handle is moved. This function must accept the rectangle extents as the only argument. on_release : function Function called whenever the control handle is released. on_enter : function Function called whenever the "enter" key is pressed. maxdist : float Maximum pixel distance allowed when selecting control handle. rect_props : dict Properties for :class:`matplotlib.patches.Rectangle`. This class redefines defaults in :class:`matplotlib.widgets.RectangleSelector`. Attributes ---------- extents : tuple Rectangle extents: (xmin, xmax, ymin, ymax). Examples ---------- >>> from skimage import data >>> from skimage.viewer import ImageViewer >>> from skimage.viewer.canvastools import RectangleTool >>> from skimage.draw import line >>> from skimage.draw import set_color >>> viewer = ImageViewer(data.coffee()) # doctest: +SKIP >>> def print_the_rect(extents): ... global viewer ... im = viewer.image ... coord = np.int64(extents) ... [rr1, cc1] = line(coord[2],coord[0],coord[2],coord[1]) ... [rr2, cc2] = line(coord[2],coord[1],coord[3],coord[1]) ... [rr3, cc3] = line(coord[3],coord[1],coord[3],coord[0]) ... [rr4, cc4] = line(coord[3],coord[0],coord[2],coord[0]) ... set_color(im, (rr1, cc1), [255, 255, 0]) ... set_color(im, (rr2, cc2), [0, 255, 255]) ... set_color(im, (rr3, cc3), [255, 0, 255]) ... set_color(im, (rr4, cc4), [0, 0, 0]) ... viewer.image=im >>> rect_tool = RectangleTool(viewer, on_enter=print_the_rect) # doctest: +SKIP >>> viewer.show() # doctest: +SKIP """ def __init__(self, manager, on_move=None, on_release=None, on_enter=None, maxdist=10, rect_props=None): self._rect = None props = dict(edgecolor=None, facecolor='r', alpha=0.15) props.update(rect_props if rect_props is not None else {}) if props['edgecolor'] is None: props['edgecolor'] = props['facecolor'] RectangleSelector.__init__(self, manager.ax, lambda *args: None, rectprops=props) CanvasToolBase.__init__(self, manager, on_move=on_move, on_enter=on_enter, on_release=on_release) # Events are handled by the viewer try: self.disconnect_events() except AttributeError: # disconnect the events manually (hack for older mpl versions) [self.canvas.mpl_disconnect(i) for i in range(10)] # Alias rectangle attribute, which is initialized in RectangleSelector. self._rect = self.to_draw self._rect.set_animated(True) self.maxdist = maxdist self.active_handle = None self._extents_on_press = None if on_enter is None: def on_enter(extents): print("(xmin=%.3g, xmax=%.3g, ymin=%.3g, ymax=%.3g)" % extents) self.callback_on_enter = on_enter props = dict(mec=props['edgecolor']) self._corner_order = ['NW', 'NE', 'SE', 'SW'] xc, yc = self.corners self._corner_handles = ToolHandles(self.ax, xc, yc, marker_props=props) self._edge_order = ['W', 'N', 'E', 'S'] xe, ye = self.edge_centers self._edge_handles = ToolHandles(self.ax, xe, ye, marker='s', marker_props=props) self.artists = [self._rect, self._corner_handles.artist, self._edge_handles.artist] self.manager.add_tool(self) @property def _rect_bbox(self): if not self._rect: return 0, 0, 0, 0 x0 = self._rect.get_x() y0 = self._rect.get_y() width = self._rect.get_width() height = self._rect.get_height() return x0, y0, width, height @property def corners(self): """Corners of rectangle from lower left, moving clockwise.""" x0, y0, width, height = self._rect_bbox xc = x0, x0 + width, x0 + width, x0 yc = y0, y0, y0 + height, y0 + height return xc, yc @property def edge_centers(self): """Midpoint of rectangle edges from left, moving clockwise.""" x0, y0, width, height = self._rect_bbox w = width / 2. h = height / 2. xe = x0, x0 + w, x0 + width, x0 + w ye = y0 + h, y0, y0 + h, y0 + height return xe, ye @property def extents(self): """Return (xmin, xmax, ymin, ymax).""" x0, y0, width, height = self._rect_bbox xmin, xmax = sorted([x0, x0 + width]) ymin, ymax = sorted([y0, y0 + height]) return xmin, xmax, ymin, ymax @extents.setter def extents(self, extents): x1, x2, y1, y2 = extents xmin, xmax = sorted([x1, x2]) ymin, ymax = sorted([y1, y2]) # Update displayed rectangle self._rect.set_x(xmin) self._rect.set_y(ymin) self._rect.set_width(xmax - xmin) self._rect.set_height(ymax - ymin) # Update displayed handles self._corner_handles.set_data(*self.corners) self._edge_handles.set_data(*self.edge_centers) self.set_visible(True) self.redraw() def on_mouse_release(self, event): if event.button != 1: return if not self.ax.in_axes(event): self.eventpress = None return RectangleSelector.release(self, event) self._extents_on_press = None # Undo hiding of rectangle and redraw. self.set_visible(True) self.redraw() self.callback_on_release(self.geometry) def on_mouse_press(self, event): if event.button != 1 or not self.ax.in_axes(event): return self._set_active_handle(event) if self.active_handle is None: # Clear previous rectangle before drawing new rectangle. self.set_visible(False) self.redraw() self.set_visible(True) RectangleSelector.press(self, event) def _set_active_handle(self, event): """Set active handle based on the location of the mouse event""" # Note: event.xdata/ydata in data coordinates, event.x/y in pixels c_idx, c_dist = self._corner_handles.closest(event.x, event.y) e_idx, e_dist = self._edge_handles.closest(event.x, event.y) # Set active handle as closest handle, if mouse click is close enough. if c_dist > self.maxdist and e_dist > self.maxdist: self.active_handle = None return elif c_dist < e_dist: self.active_handle = self._corner_order[c_idx] else: self.active_handle = self._edge_order[e_idx] # Save coordinates of rectangle at the start of handle movement. x1, x2, y1, y2 = self.extents # Switch variables so that only x2 and/or y2 are updated on move. if self.active_handle in ['W', 'SW', 'NW']: x1, x2 = x2, event.xdata if self.active_handle in ['N', 'NW', 'NE']: y1, y2 = y2, event.ydata self._extents_on_press = x1, x2, y1, y2 def on_move(self, event): if self.eventpress is None or not self.ax.in_axes(event): return if self.active_handle is None: # New rectangle x1 = self.eventpress.xdata y1 = self.eventpress.ydata x2, y2 = event.xdata, event.ydata else: x1, x2, y1, y2 = self._extents_on_press if self.active_handle in ['E', 'W'] + self._corner_order: x2 = event.xdata if self.active_handle in ['N', 'S'] + self._corner_order: y2 = event.ydata self.extents = (x1, x2, y1, y2) self.callback_on_move(self.geometry) @property def geometry(self): return self.extents if __name__ == '__main__': # pragma: no cover from ...viewer import ImageViewer from ... import data viewer = ImageViewer(data.camera()) rect_tool = RectangleTool(viewer) viewer.show() print("Final selection:") rect_tool.callback_on_enter(rect_tool.extents) scikit-image-0.13.1/skimage/viewer/plugins/000077500000000000000000000000001316256236300205535ustar00rootroot00000000000000scikit-image-0.13.1/skimage/viewer/plugins/__init__.py000066400000000000000000000004571316256236300226720ustar00rootroot00000000000000from .base import Plugin from .canny import CannyPlugin from .color_histogram import ColorHistogram from .crop import Crop from .labelplugin import LabelPainter from .lineprofile import LineProfile from .measure import Measure from .overlayplugin import OverlayPlugin from .plotplugin import PlotPlugin scikit-image-0.13.1/skimage/viewer/plugins/base.py000066400000000000000000000223001316256236300220340ustar00rootroot00000000000000""" Base class for Plugins that interact with ImageViewer. """ from warnings import warn import numpy as np from ..qt import QtWidgets, QtCore, Signal from ..utils import RequiredAttr, init_qtapp class Plugin(QtWidgets.QDialog): """Base class for plugins that interact with an ImageViewer. A plugin connects an image filter (or another function) to an image viewer. Note that a Plugin is initialized *without* an image viewer and attached in a later step. See example below for details. Parameters ---------- image_viewer : ImageViewer Window containing image used in measurement/manipulation. image_filter : function Function that gets called to update image in image viewer. This value can be `None` if, for example, you have a plugin that extracts information from an image and doesn't manipulate it. Alternatively, this function can be defined as a method in a Plugin subclass. height, width : int Size of plugin window in pixels. Note that Qt will automatically resize a window to fit components. So if you're adding rows of components, you can leave `height = 0` and just let Qt determine the final height. useblit : bool If True, use blitting to speed up animation. Only available on some Matplotlib backends. If None, set to True when using Agg backend. This only has an effect if you draw on top of an image viewer. Attributes ---------- image_viewer : ImageViewer Window containing image used in measurement. name : str Name of plugin. This is displayed as the window title. artist : list List of Matplotlib artists and canvastools. Any artists created by the plugin should be added to this list so that it gets cleaned up on close. Examples -------- >>> from skimage.viewer import ImageViewer >>> from skimage.viewer.widgets import Slider >>> from skimage import data >>> >>> plugin = Plugin(image_filter=lambda img, ... threshold: img > threshold) # doctest: +SKIP >>> plugin += Slider('threshold', 0, 255) # doctest: +SKIP >>> >>> image = data.coins() >>> viewer = ImageViewer(image) # doctest: +SKIP >>> viewer += plugin # doctest: +SKIP >>> thresholded = viewer.show()[0][0] # doctest: +SKIP The plugin will automatically delegate parameters to `image_filter` based on its parameter type, i.e., `ptype` (widgets for required arguments must be added in the order they appear in the function). The image attached to the viewer is **automatically passed as the first argument** to the filter function. #TODO: Add flag so image is not passed to filter function by default. `ptype = 'kwarg'` is the default for most widgets so it's unnecessary here. """ name = 'Plugin' image_viewer = RequiredAttr("%s is not attached to ImageViewer" % name) # Signals used when viewers are linked to the Plugin output. image_changed = Signal(np.ndarray) _started = Signal(int) def __init__(self, image_filter=None, height=0, width=400, useblit=True, dock='bottom'): init_qtapp() super(Plugin, self).__init__() self.dock = dock self.image_viewer = None # If subclass defines `image_filter` method ignore input. if not hasattr(self, 'image_filter'): self.image_filter = image_filter elif image_filter is not None: warn("If the Plugin class defines an `image_filter` method, " "then the `image_filter` argument is ignored.") self.setWindowTitle(self.name) self.layout = QtWidgets.QGridLayout(self) self.resize(width, height) self.row = 0 self.arguments = [] self.keyword_arguments = {} self.useblit = useblit self.cids = [] self.artists = [] def attach(self, image_viewer): """Attach the plugin to an ImageViewer. Note that the ImageViewer will automatically call this method when the plugin is added to the ImageViewer. For example:: viewer += Plugin(...) Also note that `attach` automatically calls the filter function so that the image matches the filtered value specified by attached widgets. """ self.setParent(image_viewer) self.setWindowFlags(QtCore.Qt.Dialog) self.image_viewer = image_viewer self.image_viewer.plugins.append(self) #TODO: Always passing image as first argument may be bad assumption. self.arguments = [self.image_viewer.original_image] # Call filter so that filtered image matches widget values self.filter_image() def add_widget(self, widget): """Add widget to plugin. Alternatively, Plugin's `__add__` method is overloaded to add widgets:: plugin += Widget(...) Widgets can adjust required or optional arguments of filter function or parameters for the plugin. This is specified by the Widget's `ptype`. """ if widget.ptype == 'kwarg': name = widget.name.replace(' ', '_') self.keyword_arguments[name] = widget widget.callback = self.filter_image elif widget.ptype == 'arg': self.arguments.append(widget) widget.callback = self.filter_image elif widget.ptype == 'plugin': widget.callback = self.update_plugin widget.plugin = self self.layout.addWidget(widget, self.row, 0) self.row += 1 def __add__(self, widget): self.add_widget(widget) return self def filter_image(self, *widget_arg): """Call `image_filter` with widget args and kwargs Note: `display_filtered_image` is automatically called. """ # `widget_arg` is passed by the active widget but is unused since all # filter arguments are pulled directly from attached the widgets. if self.image_filter is None: return arguments = [self._get_value(a) for a in self.arguments] kwargs = dict([(name, self._get_value(a)) for name, a in self.keyword_arguments.items()]) filtered = self.image_filter(*arguments, **kwargs) self.display_filtered_image(filtered) self.image_changed.emit(filtered) def _get_value(self, param): # If param is a widget, return its `val` attribute. return param if not hasattr(param, 'val') else param.val def _update_original_image(self, image): """Update the original image argument passed to the filter function. This method is called by the viewer when the original image is updated. """ self.arguments[0] = image self._on_new_image(image) self.filter_image() def _on_new_image(self, image): """Override this method to update your plugin for new images.""" pass @property def filtered_image(self): """Return filtered image.""" return self.image_viewer.image def display_filtered_image(self, image): """Display the filtered image on image viewer. If you don't want to simply replace the displayed image with the filtered image (e.g., you want to display a transparent overlay), you can override this method. """ self.image_viewer.image = image def update_plugin(self, name, value): """Update keyword parameters of the plugin itself. These parameters will typically be implemented as class properties so that they update the image or some other component. """ setattr(self, name, value) def show(self, main_window=True): """Show plugin.""" super(Plugin, self).show() self.activateWindow() self.raise_() # Emit signal with x-hint so new windows can be displayed w/o overlap. size = self.frameGeometry() x_hint = size.x() + size.width() self._started.emit(x_hint) def closeEvent(self, event): """On close disconnect all artists and events from ImageViewer. Note that artists must be appended to `self.artists`. """ self.clean_up() self.close() def clean_up(self): self.remove_image_artists() if self in self.image_viewer.plugins: self.image_viewer.plugins.remove(self) self.image_viewer.reset_image() self.image_viewer.redraw() def remove_image_artists(self): """Remove artists that are connected to the image viewer.""" for a in self.artists: a.remove() def output(self): """Return the plugin's representation and data. Returns ------- image : array, same shape as ``self.image_viewer.image``, or None The filtered image. data : None Any data associated with the plugin. Notes ----- Derived classes should override this method to return a tuple containing an *overlay* of the same shape of the image, and a *data* object. Either of these is optional: return ``None`` if you don't want to return a value. """ return (self.image_viewer.image, None) scikit-image-0.13.1/skimage/viewer/plugins/canny.py000066400000000000000000000023641316256236300222420ustar00rootroot00000000000000import numpy as np import skimage from ...feature import canny from .overlayplugin import OverlayPlugin from ..widgets import Slider, ComboBox class CannyPlugin(OverlayPlugin): """Canny filter plugin to show edges of an image.""" name = 'Canny Filter' def __init__(self, *args, **kwargs): super(CannyPlugin, self).__init__(image_filter=canny, **kwargs) def attach(self, image_viewer): image = image_viewer.image imin, imax = skimage.dtype_limits(image, clip_negative=False) itype = 'float' if np.issubdtype(image.dtype, float) else 'int' self.add_widget(Slider('sigma', 0, 5, update_on='release')) self.add_widget(Slider('low threshold', imin, imax, value_type=itype, update_on='release')) self.add_widget(Slider('high threshold', imin, imax, value_type=itype, update_on='release')) self.add_widget(ComboBox('color', self.color_names, ptype='plugin')) # Call parent method at end b/c it calls `filter_image`, which needs # the values specified by the widgets. Alternatively, move call to # parent method to beginning and add a call to `self.filter_image()` super(CannyPlugin,self).attach(image_viewer) scikit-image-0.13.1/skimage/viewer/plugins/color_histogram.py000066400000000000000000000063071316256236300243260ustar00rootroot00000000000000import numpy as np import matplotlib.pyplot as plt from ... import color, exposure from .plotplugin import PlotPlugin from ..canvastools import RectangleTool class ColorHistogram(PlotPlugin): name = 'Color Histogram' def __init__(self, max_pct=0.99, **kwargs): super(ColorHistogram, self).__init__(height=400, **kwargs) self.max_pct = max_pct print(self.help()) def attach(self, image_viewer): super(ColorHistogram, self).attach(image_viewer) self.rect_tool = RectangleTool(self, on_release=self.ab_selected) self._on_new_image(image_viewer.image) def _on_new_image(self, image): self.lab_image = color.rgb2lab(image) # Calculate color histogram in the Lab colorspace: L, a, b = self.lab_image.T left, right = -100, 100 ab_extents = [left, right, right, left] self.mask = np.ones(L.shape, bool) bins = np.arange(left, right) hist, x_edges, y_edges = np.histogram2d(a.flatten(), b.flatten(), bins, normed=True) self.data = {'bins': bins, 'hist': hist, 'edges': (x_edges, y_edges), 'extents': (left, right, left, right)} # Clip bin heights that dominate a-b histogram max_val = pct_total_area(hist, percentile=self.max_pct) hist = exposure.rescale_intensity(hist, in_range=(0, max_val)) self.ax.imshow(hist, extent=ab_extents, cmap=plt.cm.gray) self.ax.set_title('Color Histogram') self.ax.set_xlabel('b') self.ax.set_ylabel('a') def help(self): helpstr = ("Color Histogram tool:", "Select region of a-b colorspace to highlight on image.") return '\n'.join(helpstr) def ab_selected(self, extents): x0, x1, y0, y1 = extents self.data['extents'] = extents lab_masked = self.lab_image.copy() L, a, b = lab_masked.T self.mask = ((a > y0) & (a < y1)) & ((b > x0) & (b < x1)) lab_masked[..., 1:][~self.mask.T] = 0 self.image_viewer.image = color.lab2rgb(lab_masked) def output(self): """Return the image mask and the histogram data. Returns ------- mask : array of bool, same shape as image The selected pixels. data : dict The data describing the histogram and the selected region. The dictionary contains: - 'bins' : array of float The bin boundaries for both `a` and `b` channels. - 'hist' : 2D array of float The normalized histogram. - 'edges' : tuple of array of float The bin edges along each dimension - 'extents' : tuple of float The left and right and top and bottom of the selected region. """ return (self.mask, self.data) def pct_total_area(image, percentile=0.80): """Return threshold value based on percentage of total area. The specified percent of pixels less than the given intensity threshold. """ idx = int((image.size - 1) * percentile) sorted_pixels = np.sort(image.flat) return sorted_pixels[idx] scikit-image-0.13.1/skimage/viewer/plugins/crop.py000066400000000000000000000025431316256236300220740ustar00rootroot00000000000000from .base import Plugin from ..canvastools import RectangleTool from ...viewer.widgets import SaveButtons, Button __all__ = ['Crop'] class Crop(Plugin): name = 'Crop' def __init__(self, maxdist=10, **kwargs): super(Crop, self).__init__(**kwargs) self.maxdist = maxdist self.add_widget(SaveButtons()) print(self.help()) def attach(self, image_viewer): super(Crop, self).attach(image_viewer) self.rect_tool = RectangleTool(image_viewer, maxdist=self.maxdist, on_enter=self.crop) self.artists.append(self.rect_tool) self.reset_button = Button('Reset', self.reset) self.add_widget(self.reset_button) def help(self): helpstr = ("Crop tool", "Select rectangular region and press enter to crop.") return '\n'.join(helpstr) def crop(self, extents): xmin, xmax, ymin, ymax = extents if xmin == xmax or ymin == ymax: return image = self.image_viewer.image[ymin:ymax+1, xmin:xmax+1] self.image_viewer.image = image self.image_viewer.ax.relim() def reset(self): self.rect_tool.extents = -10, -10, -10, -10 self.image_viewer.image = self.image_viewer.original_image self.image_viewer.ax.relim() scikit-image-0.13.1/skimage/viewer/plugins/labelplugin.py000066400000000000000000000035241316256236300234270ustar00rootroot00000000000000import numpy as np from .base import Plugin from ..widgets import ComboBox, Slider from ..canvastools import PaintTool __all__ = ['LabelPainter'] rad2deg = 180 / np.pi class LabelPainter(Plugin): name = 'LabelPainter' def __init__(self, max_radius=20, **kwargs): super(LabelPainter, self).__init__(**kwargs) # These widgets adjust plugin properties instead of an image filter. self._radius_widget = Slider('radius', low=1, high=max_radius, value=5, value_type='int', ptype='plugin') labels = [str(i) for i in range(6)] labels[0] = 'Erase' self._label_widget = ComboBox('label', labels, ptype='plugin') self.add_widget(self._radius_widget) self.add_widget(self._label_widget) print(self.help()) def help(self): helpstr = ("Label painter", "Hold left-mouse button and paint on canvas.") return '\n'.join(helpstr) def attach(self, image_viewer): super(LabelPainter, self).attach(image_viewer) image = image_viewer.original_image self.paint_tool = PaintTool(image_viewer, image.shape, on_enter=self.on_enter) self.paint_tool.radius = self.radius self.paint_tool.label = self._label_widget.index = 1 self.artists.append(self.paint_tool) def _on_new_image(self, image): """Update plugin for new images.""" self.paint_tool.shape = image.shape def on_enter(self, overlay): pass @property def radius(self): return self._radius_widget.val @radius.setter def radius(self, val): self.paint_tool.radius = val @property def label(self): return self._label_widget.val @label.setter def label(self, val): self.paint_tool.label = val scikit-image-0.13.1/skimage/viewer/plugins/lineprofile.py000066400000000000000000000135221316256236300234400ustar00rootroot00000000000000from __future__ import division import numpy as np from ...util.dtype import dtype_range from ... import draw, measure from .plotplugin import PlotPlugin from ..canvastools import ThickLineTool __all__ = ['LineProfile'] class LineProfile(PlotPlugin): """Plugin to compute interpolated intensity under a scan line on an image. See PlotPlugin and Plugin classes for additional details. Parameters ---------- maxdist : float Maximum pixel distance allowed when selecting end point of scan line. limits : tuple or {None, 'image', 'dtype'} (minimum, maximum) intensity limits for plotted profile. The following special values are defined: None : rescale based on min/max intensity along selected scan line. 'image' : fixed scale based on min/max intensity in image. 'dtype' : fixed scale based on min/max intensity of image dtype. """ name = 'Line Profile' def __init__(self, maxdist=10, epsilon='deprecated', limits='image', **kwargs): super(LineProfile, self).__init__(**kwargs) self.maxdist = maxdist self._limit_type = limits print(self.help()) def attach(self, image_viewer): super(LineProfile, self).attach(image_viewer) image = image_viewer.original_image if self._limit_type == 'image': self.limits = (np.min(image), np.max(image)) elif self._limit_type == 'dtype': self.limits = dtype_range[image.dtype.type] elif self._limit_type is None or len(self._limit_type) == 2: self.limits = self._limit_type else: raise ValueError("Unrecognized `limits`: %s" % self._limit_type) if not self._limit_type is None: self.ax.set_ylim(self.limits) h, w = image.shape[0:2] x = [w / 3, 2 * w / 3] y = [h / 2] * 2 self.line_tool = ThickLineTool(self.image_viewer, maxdist=self.maxdist, on_move=self.line_changed, on_change=self.line_changed) self.line_tool.end_points = np.transpose([x, y]) scan_data = measure.profile_line(image, *self.line_tool.end_points[:, ::-1]) self.scan_data = scan_data if scan_data.ndim == 1: scan_data = scan_data[:, np.newaxis] self.reset_axes(scan_data) self._autoscale_view() def help(self): helpstr = ("Line profile tool", "+ and - keys or mouse scroll changes width of scan line.", "Select and drag ends of the scan line to adjust it.") return '\n'.join(helpstr) def get_profiles(self): """Return intensity profile of the selected line. Returns ------- end_points: (2, 2) array The positions ((x1, y1), (x2, y2)) of the line ends. profile: list of 1d arrays Profile of intensity values. Length 1 (grayscale) or 3 (rgb). """ self._update_data() profiles = [data.get_ydata() for data in self.profile] return self.line_tool.end_points, profiles def _autoscale_view(self): if self.limits is None: self.ax.autoscale_view(tight=True) else: self.ax.autoscale_view(scaley=False, tight=True) def line_changed(self, end_points): x, y = np.transpose(end_points) self.line_tool.end_points = end_points self._update_data() self.ax.relim() self._autoscale_view() self.redraw() def _update_data(self): scan = measure.profile_line(self.image_viewer.image, *self.line_tool.end_points[:, ::-1], linewidth=self.line_tool.linewidth) self.scan_data = scan if scan.ndim == 1: scan = scan[:, np.newaxis] if scan.shape[1] != len(self.profile): self.reset_axes(scan) for i in range(len(scan[0])): self.profile[i].set_xdata(np.arange(scan.shape[0])) self.profile[i].set_ydata(scan[:, i]) def reset_axes(self, scan_data): # Clear lines out for line in self.ax.lines: self.ax.lines = [] if scan_data.shape[1] == 1: self.profile = self.ax.plot(scan_data, 'k-') else: self.profile = self.ax.plot(scan_data[:, 0], 'r-', scan_data[:, 1], 'g-', scan_data[:, 2], 'b-') def output(self): """Return the drawn line and the resulting scan. Returns ------- line_image : (M, N) uint8 array, same shape as image An array of 0s with the scanned line set to 255. If the linewidth of the line tool is greater than 1, sets the values within the profiled polygon to 128. scan : (P,) or (P, 3) array of int or float The line scan values across the image. """ end_points = self.line_tool.end_points line_image = np.zeros(self.image_viewer.image.shape[:2], np.uint8) width = self.line_tool.linewidth if width > 1: rp, cp = measure.profile._line_profile_coordinates( *end_points[:, ::-1], linewidth=width) # the points are aliased, so create a polygon using the corners yp = np.rint(rp[[0, 0, -1, -1],[0, -1, -1, 0]]).astype(int) xp = np.rint(cp[[0, 0, -1, -1],[0, -1, -1, 0]]).astype(int) rp, cp = draw.polygon(yp, xp, line_image.shape) line_image[rp, cp] = 128 (x1, y1), (x2, y2) = end_points.astype(int) rr, cc = draw.line(y1, x1, y2, x2) line_image[rr, cc] = 255 return line_image, self.scan_data scikit-image-0.13.1/skimage/viewer/plugins/measure.py000066400000000000000000000024541316256236300225730ustar00rootroot00000000000000# -*- coding: utf-8 -*- import numpy as np from .base import Plugin from ..widgets import Text from ..canvastools import LineTool __all__ = ['Measure'] rad2deg = 180 / np.pi class Measure(Plugin): name = 'Measure' def __init__(self, maxdist=10, **kwargs): super(Measure, self).__init__(**kwargs) self.maxdist = maxdist self._length = Text('Length:') self._angle = Text('Angle:') self.add_widget(self._length) self.add_widget(self._angle) print(self.help()) def attach(self, image_viewer): super(Measure, self).attach(image_viewer) image = image_viewer.original_image h, w = image.shape self.line_tool = LineTool(self.image_viewer, maxdist=self.maxdist, on_move=self.line_changed) self.artists.append(self.line_tool) def help(self): helpstr = ("Measure tool", "Select line to measure distance and angle.") return '\n'.join(helpstr) def line_changed(self, end_points): x, y = np.transpose(end_points) dx = np.diff(x)[0] dy = np.diff(y)[0] self._length.text = '%.1f' % np.hypot(dx, dy) self._angle.text = '%.1f°' % (180 - np.arctan2(dy, dx) * rad2deg) scikit-image-0.13.1/skimage/viewer/plugins/overlayplugin.py000066400000000000000000000070371316256236300240340ustar00rootroot00000000000000from warnings import warn from ...util.dtype import dtype_range from .base import Plugin from ..utils import ClearColormap, update_axes_image import six from ..._shared.version_requirements import is_installed __all__ = ['OverlayPlugin'] class OverlayPlugin(Plugin): """Plugin for ImageViewer that displays an overlay on top of main image. The base Plugin class displays the filtered image directly on the viewer. OverlayPlugin will instead overlay an image with a transparent colormap. See base Plugin class for additional details. Attributes ---------- overlay : array Overlay displayed on top of image. This overlay defaults to a color map with alpha values varying linearly from 0 to 1. color : int Color of overlay. """ colors = {'red': (1, 0, 0), 'yellow': (1, 1, 0), 'green': (0, 1, 0), 'cyan': (0, 1, 1)} def __init__(self, **kwargs): if not is_installed('matplotlib', '>=1.2'): msg = "Matplotlib >= 1.2 required for OverlayPlugin." warn(RuntimeWarning(msg)) super(OverlayPlugin, self).__init__(**kwargs) self._overlay_plot = None self._overlay = None self.cmap = None self.color_names = sorted(list(self.colors.keys())) def attach(self, image_viewer): super(OverlayPlugin, self).attach(image_viewer) #TODO: `color` doesn't update GUI widget when set manually. self.color = 0 @property def overlay(self): return self._overlay @overlay.setter def overlay(self, image): self._overlay = image ax = self.image_viewer.ax if image is None: ax.images.remove(self._overlay_plot) self._overlay_plot = None elif self._overlay_plot is None: vmin, vmax = dtype_range[image.dtype.type] self._overlay_plot = ax.imshow(image, cmap=self.cmap, vmin=vmin, vmax=vmax) else: update_axes_image(self._overlay_plot, image) if self.image_viewer.useblit: self.image_viewer._blit_manager.background = None self.image_viewer.redraw() @property def color(self): return self._color @color.setter def color(self, index): # Update colormap whenever color is changed. if isinstance(index, six.string_types) and \ index not in self.color_names: raise ValueError("%s not defined in OverlayPlugin.colors" % index) else: name = self.color_names[index] self._color = name rgb = self.colors[name] self.cmap = ClearColormap(rgb) if self._overlay_plot is not None: self._overlay_plot.set_cmap(self.cmap) self.image_viewer.redraw() @property def filtered_image(self): """Return filtered image. This "filtered image" is used when saving from the plugin. """ return self.overlay def display_filtered_image(self, image): """Display filtered image as an overlay on top of image in viewer.""" self.overlay = image def closeEvent(self, event): # clear overlay from ImageViewer on close self.overlay = None super(OverlayPlugin, self).closeEvent(event) def output(self): """Return the overlaid image. Returns ------- overlay : array, same shape as image The overlay currently displayed. data : None """ return (self.overlay, None) scikit-image-0.13.1/skimage/viewer/plugins/plotplugin.py000066400000000000000000000045451316256236300233320ustar00rootroot00000000000000import numpy as np from ..qt import QtGui from ..utils import new_plot from ..utils.canvas import BlitManager, EventManager from .base import Plugin __all__ = ['PlotPlugin'] class PlotPlugin(Plugin): """Plugin for ImageViewer that contains a plot canvas. Base class for plugins that contain a Matplotlib plot canvas, which can, for example, display an image histogram. See base Plugin class for additional details. """ def __init__(self, image_filter=None, height=150, width=400, **kwargs): super(PlotPlugin, self).__init__(image_filter=image_filter, height=height, width=width, **kwargs) self._height = height self._width = width self._blit_manager = None self._tools = [] self._event_manager = None def attach(self, image_viewer): super(PlotPlugin, self).attach(image_viewer) # Add plot for displaying intensity profile. self.add_plot() if image_viewer.useblit: self._blit_manager = BlitManager(self.ax) self._event_manager = EventManager(self.ax) def redraw(self): """Redraw plot.""" self.canvas.draw_idle() def add_plot(self): self.fig, self.ax = new_plot() self.fig.set_figwidth(self._width / float(self.fig.dpi)) self.fig.set_figheight(self._height / float(self.fig.dpi)) self.canvas = self.fig.canvas #TODO: Converted color is slightly different than Qt background. qpalette = QtGui.QPalette() qcolor = qpalette.color(QtGui.QPalette.Window) bgcolor = qcolor.toRgb().value() if np.isscalar(bgcolor): bgcolor = str(bgcolor / 255.) self.fig.patch.set_facecolor(bgcolor) self.layout.addWidget(self.canvas, self.row, 0) def _update_original_image(self, image): super(PlotPlugin, self)._update_original_image(image) self.redraw() def add_tool(self, tool): if self._blit_manager: self._blit_manager.add_artists(tool.artists) self._tools.append(tool) self._event_manager.attach(tool) def remove_tool(self, tool): if tool not in self._tools: return if self._blit_manager: self._blit_manager.remove_artists(tool.artists) self._tools.remove(tool) self._event_manager.detach(tool) scikit-image-0.13.1/skimage/viewer/qt.py000066400000000000000000000024011316256236300200650ustar00rootroot00000000000000_qt_version = None has_qt = True try: from matplotlib.backends.qt_compat import QtGui, QtCore, QtWidgets, QT_RC_MAJOR_VERSION as _qt_version except ImportError: try: from matplotlib.backends.qt4_compat import QtGui, QtCore QtWidgets = QtGui _qt_version = 4 except ImportError: # Mock objects class QtGui_cls(object): QMainWindow = object QDialog = object QWidget = object class QtCore_cls(object): class Qt(object): TopDockWidgetArea = None BottomDockWidgetArea = None LeftDockWidgetArea = None RightDockWidgetArea = None def Signal(self, *args, **kwargs): pass QtGui = QtWidgets = QtGui_cls() QtCore = QtCore_cls() has_qt = False if _qt_version == 5: from matplotlib.backends.backend_qt5 import FigureManagerQT from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg elif _qt_version == 4: from matplotlib.backends.backend_qt4 import FigureManagerQT from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg else: FigureManagerQT = object FigureCanvasQTAgg = object Qt = QtCore.Qt Signal = QtCore.Signal scikit-image-0.13.1/skimage/viewer/tests/000077500000000000000000000000001316256236300202345ustar00rootroot00000000000000scikit-image-0.13.1/skimage/viewer/tests/__init__.py000066400000000000000000000001771316256236300223520ustar00rootroot00000000000000from ..._shared.testing import setup_test, teardown_test def setup(): setup_test() def teardown(): teardown_test() scikit-image-0.13.1/skimage/viewer/tests/test_plugins.py000066400000000000000000000125511316256236300233320ustar00rootroot00000000000000# -*- coding: utf-8 -*- import numpy as np import skimage import skimage.data as data from skimage.filters.rank import median from skimage.morphology import disk from skimage.viewer import ImageViewer, has_qt from skimage.viewer.plugins.base import Plugin from skimage.viewer.widgets import Slider from skimage.viewer.plugins import ( LineProfile, Measure, CannyPlugin, LabelPainter, Crop, ColorHistogram, PlotPlugin) from numpy.testing import assert_equal, assert_allclose, assert_almost_equal from numpy.testing.decorators import skipif from skimage._shared._warnings import expected_warnings def setup_line_profile(image, limits='image'): viewer = ImageViewer(skimage.img_as_float(image)) plugin = LineProfile(limits=limits) viewer += plugin return plugin @skipif(not has_qt) def test_line_profile(): """ Test a line profile using an ndim=2 image""" plugin = setup_line_profile(data.camera()) line_image, scan_data = plugin.output() for inp in [line_image.nonzero()[0].size, line_image.sum() / line_image.max(), scan_data.size]: assert_equal(inp, 172) assert_equal(line_image.shape, (512, 512)) assert_allclose(scan_data.max(), 0.9176, rtol=1e-3) assert_allclose(scan_data.mean(), 0.2812, rtol=1e-3) @skipif(not has_qt) def test_line_profile_rgb(): """ Test a line profile using an ndim=3 image""" plugin = setup_line_profile(data.chelsea(), limits=None) for i in range(6): plugin.line_tool._thicken_scan_line() line_image, scan_data = plugin.output() assert_equal(line_image[line_image == 128].size, 750) assert_equal(line_image[line_image == 255].size, 151) assert_equal(line_image.shape, (300, 451)) assert_equal(scan_data.shape, (151, 3)) assert_allclose(scan_data.max(), 0.772, rtol=1e-3) assert_allclose(scan_data.mean(), 0.4359, rtol=1e-3) @skipif(not has_qt) def test_line_profile_dynamic(): """Test a line profile updating after an image transform""" image = data.coins()[:-50, :] # shave some off to make the line lower image = skimage.img_as_float(image) viewer = ImageViewer(image) lp = LineProfile(limits='dtype') viewer += lp line = lp.get_profiles()[-1][0] assert line.size == 129 assert_almost_equal(np.std(viewer.image), 0.208, 3) assert_almost_equal(np.std(line), 0.229, 3) assert_almost_equal(np.max(line) - np.min(line), 0.725, 1) with expected_warnings(['precision loss']): viewer.image = skimage.img_as_float(median(image, selem=disk(radius=3))) line = lp.get_profiles()[-1][0] assert_almost_equal(np.std(viewer.image), 0.198, 3) assert_almost_equal(np.std(line), 0.220, 3) assert_almost_equal(np.max(line) - np.min(line), 0.639, 1) @skipif(not has_qt) def test_measure(): image = data.camera() viewer = ImageViewer(image) m = Measure() viewer += m m.line_changed([(0, 0), (10, 10)]) assert_equal(str(m._length.text), '14.1') assert_equal(str(m._angle.text[:5]), '135.0') @skipif(not has_qt) def test_canny(): image = data.camera() viewer = ImageViewer(image) c = CannyPlugin() viewer += c canny_edges = viewer.show(False) viewer.close() edges = canny_edges[0][0] assert edges.sum() == 2852 @skipif(not has_qt) def test_label_painter(): image = data.camera() moon = data.moon() viewer = ImageViewer(image) lp = LabelPainter() viewer += lp assert_equal(lp.radius, 5) lp.label = 1 assert_equal(str(lp.label), '1') lp.label = 2 assert_equal(str(lp.paint_tool.label), '2') assert_equal(lp.paint_tool.radius, 5) lp._on_new_image(moon) assert_equal(lp.paint_tool.shape, moon.shape) @skipif(not has_qt) def test_crop(): image = data.camera() viewer = ImageViewer(image) c = Crop() viewer += c c.crop((0, 100, 0, 100)) assert_equal(viewer.image.shape, (101, 101)) @skipif(not has_qt) def test_color_histogram(): image = skimage.img_as_float(data.load('color.png')) viewer = ImageViewer(image) ch = ColorHistogram(dock='right') viewer += ch assert_almost_equal(viewer.image.std(), 0.352, 3), ch.ab_selected((0, 100, 0, 100)), assert_almost_equal(viewer.image.std(), 0.325, 3) @skipif(not has_qt) def test_plot_plugin(): viewer = ImageViewer(data.moon()) plugin = PlotPlugin(image_filter=lambda x: x) viewer += plugin assert_equal(viewer.image, data.moon()) plugin._update_original_image(data.coins()) assert_equal(viewer.image, data.coins()) viewer.close() @skipif(not has_qt) def test_plugin(): img = skimage.img_as_float(data.moon()) viewer = ImageViewer(img) def median_filter(img, radius=3): with expected_warnings(['precision loss']): return median(img, selem=disk(radius=radius)) plugin = Plugin(image_filter=median_filter) viewer += plugin plugin += Slider('radius', 1, 5) assert_almost_equal(np.std(viewer.image), 12.556, 3) plugin.filter_image() assert_almost_equal(np.std(viewer.image), 12.931, 3) plugin.show() plugin.close() plugin.clean_up() img, _ = plugin.output() assert_equal(img, viewer.image) scikit-image-0.13.1/skimage/viewer/tests/test_tools.py000066400000000000000000000130621316256236300230070ustar00rootroot00000000000000from collections import namedtuple import numpy as np from numpy.testing import assert_equal from numpy.testing.decorators import skipif from skimage import data from skimage.viewer import ImageViewer, has_qt from skimage.viewer.canvastools import ( LineTool, ThickLineTool, RectangleTool, PaintTool) from skimage.viewer.canvastools.base import CanvasToolBase try: from matplotlib.testing.decorators import cleanup except ImportError: def cleanup(func): return func def get_end_points(image): h, w = image.shape[0:2] x = [w / 3, 2 * w / 3] y = [h / 2] * 2 return np.transpose([x, y]) def do_event(viewer, etype, button=1, xdata=0, ydata=0, key=None): """ *name* the event name *canvas* the FigureCanvas instance generating the event *guiEvent* the GUI event that triggered the matplotlib event *x* x position - pixels from left of canvas *y* y position - pixels from bottom of canvas *inaxes* the :class:`~matplotlib.axes.Axes` instance if mouse is over axes *xdata* x coord of mouse in data coords *ydata* y coord of mouse in data coords *button* button pressed None, 1, 2, 3, 'up', 'down' (up and down are used for scroll events) *key* the key depressed when the mouse event triggered (see :class:`KeyEvent`) *step* number of scroll steps (positive for 'up', negative for 'down') """ ax = viewer.ax event = namedtuple('Event', ('name canvas guiEvent x y inaxes xdata ydata ' 'button key step')) event.button = button event.x, event.y = ax.transData.transform((xdata, ydata)) event.xdata, event.ydata = xdata, ydata event.inaxes = ax event.canvas = ax.figure.canvas event.key = key event.step = 1 event.guiEvent = None event.name = 'Custom' func = getattr(viewer._event_manager, 'on_%s' % etype) func(event) @cleanup @skipif(not has_qt) def test_line_tool(): img = data.camera() viewer = ImageViewer(img) tool = LineTool(viewer, maxdist=10, line_props=dict(linewidth=3), handle_props=dict(markersize=5)) tool.end_points = get_end_points(img) assert_equal(tool.end_points, np.array([[170, 256], [341, 256]])) # grab a handle and move it do_event(viewer, 'mouse_press', xdata=170, ydata=256) do_event(viewer, 'move', xdata=180, ydata=260) do_event(viewer, 'mouse_release') assert_equal(tool.geometry, np.array([[180, 260], [341, 256]])) # create a new line do_event(viewer, 'mouse_press', xdata=10, ydata=10) do_event(viewer, 'move', xdata=100, ydata=100) do_event(viewer, 'mouse_release') assert_equal(tool.geometry, np.array([[100, 100], [10, 10]])) @cleanup @skipif(not has_qt) def test_thick_line_tool(): img = data.camera() viewer = ImageViewer(img) tool = ThickLineTool(viewer, maxdist=10, line_props=dict(color='red'), handle_props=dict(markersize=5)) tool.end_points = get_end_points(img) do_event(viewer, 'scroll', button='up') assert_equal(tool.linewidth, 2) do_event(viewer, 'scroll', button='down') assert_equal(tool.linewidth, 1) do_event(viewer, 'key_press', key='+') assert_equal(tool.linewidth, 2) do_event(viewer, 'key_press', key='-') assert_equal(tool.linewidth, 1) @cleanup @skipif(not has_qt) def test_rect_tool(): img = data.camera() viewer = ImageViewer(img) tool = RectangleTool(viewer, maxdist=10) tool.extents = (100, 150, 100, 150) assert_equal(tool.corners, ((100, 150, 150, 100), (100, 100, 150, 150))) assert_equal(tool.extents, (100, 150, 100, 150)) assert_equal(tool.edge_centers, ((100, 125.0, 150, 125.0), (125.0, 100, 125.0, 150))) assert_equal(tool.geometry, (100, 150, 100, 150)) # grab a corner and move it do_event(viewer, 'mouse_press', xdata=100, ydata=100) do_event(viewer, 'move', xdata=120, ydata=120) do_event(viewer, 'mouse_release') #assert_equal(tool.geometry, [120, 150, 120, 150]) # create a new line do_event(viewer, 'mouse_press', xdata=10, ydata=10) do_event(viewer, 'move', xdata=100, ydata=100) do_event(viewer, 'mouse_release') assert_equal(tool.geometry, [10, 100, 10, 100]) @cleanup @skipif(not has_qt) def test_paint_tool(): img = data.moon() viewer = ImageViewer(img) tool = PaintTool(viewer, img.shape) tool.radius = 10 assert_equal(tool.radius, 10) tool.label = 2 assert_equal(tool.label, 2) assert_equal(tool.shape, img.shape) do_event(viewer, 'mouse_press', xdata=100, ydata=100) do_event(viewer, 'move', xdata=110, ydata=110) do_event(viewer, 'mouse_release') assert_equal(tool.overlay[tool.overlay == 2].size, 761) tool.label = 5 do_event(viewer, 'mouse_press', xdata=20, ydata=20) do_event(viewer, 'move', xdata=40, ydata=40) do_event(viewer, 'mouse_release') assert_equal(tool.overlay[tool.overlay == 5].size, 881) assert_equal(tool.overlay[tool.overlay == 2].size, 761) do_event(viewer, 'key_press', key='enter') tool.overlay = tool.overlay * 0 assert_equal(tool.overlay.sum(), 0) @cleanup @skipif(not has_qt) def test_base_tool(): img = data.moon() viewer = ImageViewer(img) tool = CanvasToolBase(viewer) tool.set_visible(False) tool.set_visible(True) do_event(viewer, 'key_press', key='enter') tool.redraw() tool.remove() tool = CanvasToolBase(viewer, useblit=False) tool.redraw() scikit-image-0.13.1/skimage/viewer/tests/test_utils.py000066400000000000000000000020011316256236300227760ustar00rootroot00000000000000# -*- coding: utf-8 -*- from skimage.viewer import utils from skimage.viewer.utils import dialogs from skimage.viewer.qt import QtCore, QtGui, has_qt from numpy.testing.decorators import skipif @skipif(not has_qt) def test_event_loop(): utils.init_qtapp() timer = QtCore.QTimer() timer.singleShot(10, QtGui.QApplication.quit) utils.start_qtapp() @skipif(not has_qt) def test_format_filename(): fname = dialogs._format_filename(('apple', 2)) assert fname == 'apple' fname = dialogs._format_filename('') assert fname is None @skipif(not has_qt) def test_open_file_dialog(): utils.init_qtapp() timer = QtCore.QTimer() timer.singleShot(100, lambda: QtGui.QApplication.quit()) filename = dialogs.open_file_dialog() assert filename is None @skipif(not has_qt) def test_save_file_dialog(): utils.init_qtapp() timer = QtCore.QTimer() timer.singleShot(100, lambda: QtGui.QApplication.quit()) filename = dialogs.save_file_dialog() assert filename is None scikit-image-0.13.1/skimage/viewer/tests/test_viewer.py000066400000000000000000000041651316256236300231540ustar00rootroot00000000000000 from skimage import data from skimage.viewer.qt import QtGui, QtCore, has_qt from skimage.viewer import ImageViewer, CollectionViewer from skimage.viewer.plugins import OverlayPlugin from skimage.transform import pyramid_gaussian from skimage.filters import sobel from numpy.testing import assert_equal from numpy.testing.decorators import skipif from skimage._shared.version_requirements import is_installed from skimage._shared._warnings import expected_warnings @skipif(not has_qt) def test_viewer(): astro = data.astronaut() coins = data.coins() view = ImageViewer(astro) import tempfile _, filename = tempfile.mkstemp(suffix='.png') view.show(False) view.close() view.save_to_file(filename) view.open_file(filename) assert_equal(view.image, astro) view.image = coins assert_equal(view.image, coins), view.save_to_file(filename), view.open_file(filename), view.reset_image(), assert_equal(view.image, coins) def make_key_event(key): return QtGui.QKeyEvent(QtCore.QEvent.KeyPress, key, QtCore.Qt.NoModifier) @skipif(not has_qt) def test_collection_viewer(): img = data.astronaut() img_collection = tuple(pyramid_gaussian(img)) view = CollectionViewer(img_collection) make_key_event(48) view.update_index('', 2), assert_equal(view.image, img_collection[2]) view.keyPressEvent(make_key_event(53)) assert_equal(view.image, img_collection[5]) view._format_coord(10, 10) @skipif(not has_qt) @skipif(not is_installed('matplotlib', '>=1.2')) def test_viewer_with_overlay(): img = data.coins() ov = OverlayPlugin(image_filter=sobel) viewer = ImageViewer(img) viewer += ov import tempfile _, filename = tempfile.mkstemp(suffix='.png') ov.color = 3 assert_equal(ov.color, 'yellow') with expected_warnings(['precision loss']): viewer.save_to_file(filename) ov.display_filtered_image(img) assert_equal(ov.overlay, img) ov.overlay = None assert_equal(ov.overlay, None) ov.overlay = img assert_equal(ov.overlay, img) assert_equal(ov.filtered_image, img) scikit-image-0.13.1/skimage/viewer/tests/test_widgets.py000066400000000000000000000064331316256236300233210ustar00rootroot00000000000000 import os from skimage import data, img_as_float, io, img_as_uint from skimage.viewer import ImageViewer from skimage.viewer.qt import QtGui, QtCore, has_qt from skimage.viewer.widgets import ( Slider, OKCancelButtons, SaveButtons, ComboBox, CheckBox, Text) from skimage.viewer.plugins.base import Plugin from numpy.testing import assert_almost_equal, assert_equal from numpy.testing.decorators import skipif from skimage._shared._warnings import expected_warnings def get_image_viewer(): image = data.coins() viewer = ImageViewer(img_as_float(image)) viewer += Plugin() return viewer @skipif(not has_qt) def test_check_box(): viewer = get_image_viewer() cb = CheckBox('hello', value=True, alignment='left') viewer.plugins[0] += cb assert_equal(cb.val, True) cb.val = False assert_equal(cb.val, False) cb.val = 1 assert_equal(cb.val, True) cb.val = 0 assert_equal(cb.val, False) @skipif(not has_qt) def test_combo_box(): viewer = get_image_viewer() cb = ComboBox('hello', ('a', 'b', 'c')) viewer.plugins[0] += cb assert_equal(str(cb.val), 'a') assert_equal(cb.index, 0) cb.index = 2 assert_equal(str(cb.val), 'c'), assert_equal(cb.index, 2) @skipif(not has_qt) def test_text_widget(): viewer = get_image_viewer() txt = Text('hello', 'hello, world!') viewer.plugins[0] += txt assert_equal(str(txt.text), 'hello, world!') txt.text = 'goodbye, world!' assert_equal(str(txt.text), 'goodbye, world!') @skipif(not has_qt) def test_slider_int(): viewer = get_image_viewer() sld = Slider('radius', 2, 10, value_type='int') viewer.plugins[0] += sld assert_equal(sld.val, 4) sld.val = 6 assert_equal(sld.val, 6) sld.editbox.setText('5') sld._on_editbox_changed() assert_equal(sld.val, 5) @skipif(not has_qt) def test_slider_float(): viewer = get_image_viewer() sld = Slider('alpha', 2.1, 3.1, value=2.1, value_type='float', orientation='vertical', update_on='move') viewer.plugins[0] += sld assert_equal(sld.val, 2.1) sld.val = 2.5 assert_almost_equal(sld.val, 2.5, 2) sld.editbox.setText('0.1') sld._on_editbox_changed() assert_almost_equal(sld.val, 2.5, 2) @skipif(not has_qt) def test_save_buttons(): viewer = get_image_viewer() sv = SaveButtons() viewer.plugins[0] += sv import tempfile fid, filename = tempfile.mkstemp(suffix='.png') os.close(fid) timer = QtCore.QTimer() timer.singleShot(100, QtGui.QApplication.quit) # exercise the button clicks sv.save_stack.click() sv.save_file.click() # call the save functions directly sv.save_to_stack() with expected_warnings(['precision loss']): sv.save_to_file(filename) img = data.imread(filename) with expected_warnings(['precision loss']): assert_almost_equal(img, img_as_uint(viewer.image)) img = io.pop() assert_almost_equal(img, viewer.image) os.remove(filename) @skipif(not has_qt) def test_ok_buttons(): viewer = get_image_viewer() ok = OKCancelButtons() viewer.plugins[0] += ok ok.update_original_image(), ok.close_plugin() scikit-image-0.13.1/skimage/viewer/utils/000077500000000000000000000000001316256236300202325ustar00rootroot00000000000000scikit-image-0.13.1/skimage/viewer/utils/__init__.py000066400000000000000000000000241316256236300223370ustar00rootroot00000000000000from .core import * scikit-image-0.13.1/skimage/viewer/utils/canvas.py000066400000000000000000000060641316256236300220650ustar00rootroot00000000000000 class BlitManager(object): """Object that manages blits on an axes""" def __init__(self, ax): self.ax = ax self.canvas = ax.figure.canvas self.canvas.mpl_connect('draw_event', self.on_draw_event) self.ax = ax self.background = None self.artists = [] def add_artists(self, artists): self.artists.extend(artists) self.redraw() def remove_artists(self, artists): for artist in artists: self.artists.remove(artist) def on_draw_event(self, event=None): self.background = self.canvas.copy_from_bbox(self.ax.bbox) self.draw_artists() def redraw(self): if self.background is not None: self.canvas.restore_region(self.background) self.draw_artists() self.canvas.blit(self.ax.bbox) else: self.canvas.draw_idle() def draw_artists(self): for artist in self.artists: self.ax.draw_artist(artist) class EventManager(object): """Object that manages events on a canvas""" def __init__(self, ax): self.canvas = ax.figure.canvas self.connect_event('button_press_event', self.on_mouse_press) self.connect_event('key_press_event', self.on_key_press) self.connect_event('button_release_event', self.on_mouse_release) self.connect_event('motion_notify_event', self.on_move) self.connect_event('scroll_event', self.on_scroll) self.tools = [] self.active_tool = None def connect_event(self, name, handler): self.canvas.mpl_connect(name, handler) def attach(self, tool): self.tools.append(tool) self.active_tool = tool def detach(self, tool): self.tools.remove(tool) if self.tools: self.active_tool = self.tools[-1] else: self.active_tool = None def on_mouse_press(self, event): for tool in self.tools: if not tool.ignore(event) and tool.hit_test(event): self.active_tool = tool break if self.active_tool and not self.active_tool.ignore(event): self.active_tool.on_mouse_press(event) return for tool in reversed(self.tools): if not tool.ignore(event): self.active_tool = tool tool.on_mouse_press(event) return def on_key_press(self, event): tool = self._get_tool(event) if tool is not None: tool.on_key_press(event) def _get_tool(self, event): if not self.tools or self.active_tool.ignore(event): return None return self.active_tool def on_mouse_release(self, event): tool = self._get_tool(event) if tool is not None: tool.on_mouse_release(event) def on_move(self, event): tool = self._get_tool(event) if tool is not None: tool.on_move(event) def on_scroll(self, event): tool = self._get_tool(event) if tool is not None: tool.on_scroll(event) scikit-image-0.13.1/skimage/viewer/utils/core.py000066400000000000000000000146331316256236300215430ustar00rootroot00000000000000import numpy as np from ..qt import QtWidgets, has_qt, FigureManagerQT, FigureCanvasQTAgg from ..._shared.utils import warn import matplotlib as mpl from matplotlib.figure import Figure from matplotlib import _pylab_helpers from matplotlib.colors import LinearSegmentedColormap if has_qt and 'agg' not in mpl.get_backend().lower(): warn("Recommended matplotlib backend is `Agg` for full " "skimage.viewer functionality.") __all__ = ['init_qtapp', 'start_qtapp', 'RequiredAttr', 'figimage', 'LinearColormap', 'ClearColormap', 'FigureCanvas', 'new_plot', 'update_axes_image'] QApp = None def init_qtapp(): """Initialize QAppliction. The QApplication needs to be initialized before creating any QWidgets """ global QApp QApp = QtWidgets.QApplication.instance() if QApp is None: QApp = QtWidgets.QApplication([]) return QApp def is_event_loop_running(app=None): """Return True if event loop is running.""" if app is None: app = init_qtapp() if hasattr(app, '_in_event_loop'): return app._in_event_loop else: return False def start_qtapp(app=None): """Start Qt mainloop""" if app is None: app = init_qtapp() if not is_event_loop_running(app): app._in_event_loop = True app.exec_() app._in_event_loop = False else: app._in_event_loop = True class RequiredAttr(object): """A class attribute that must be set before use.""" instances = dict() def __init__(self, init_val=None): self.instances[self, None] = init_val def __get__(self, obj, objtype): value = self.instances[self, obj] if value is None: raise AttributeError('Required attribute not set') return value def __set__(self, obj, value): self.instances[self, obj] = value class LinearColormap(LinearSegmentedColormap): """LinearSegmentedColormap in which color varies smoothly. This class is a simplification of LinearSegmentedColormap, which doesn't support jumps in color intensities. Parameters ---------- name : str Name of colormap. segmented_data : dict Dictionary of 'red', 'green', 'blue', and (optionally) 'alpha' values. Each color key contains a list of `x`, `y` tuples. `x` must increase monotonically from 0 to 1 and corresponds to input values for a mappable object (e.g. an image). `y` corresponds to the color intensity. """ def __init__(self, name, segmented_data, **kwargs): segmented_data = dict((key, [(x, y, y) for x, y in value]) for key, value in segmented_data.items()) LinearSegmentedColormap.__init__(self, name, segmented_data, **kwargs) class ClearColormap(LinearColormap): """Color map that varies linearly from alpha = 0 to 1 """ def __init__(self, rgb, max_alpha=1, name='clear_color'): r, g, b = rgb cg_speq = {'blue': [(0.0, b), (1.0, b)], 'green': [(0.0, g), (1.0, g)], 'red': [(0.0, r), (1.0, r)], 'alpha': [(0.0, 0.0), (1.0, max_alpha)]} LinearColormap.__init__(self, name, cg_speq) class FigureCanvas(FigureCanvasQTAgg): """Canvas for displaying images.""" def __init__(self, figure, **kwargs): self.fig = figure FigureCanvasQTAgg.__init__(self, self.fig) FigureCanvasQTAgg.setSizePolicy(self, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Expanding) FigureCanvasQTAgg.updateGeometry(self) def resizeEvent(self, event): FigureCanvasQTAgg.resizeEvent(self, event) # Call to `resize_event` missing in FigureManagerQT. # See https://github.com/matplotlib/matplotlib/pull/1585 self.resize_event() def new_canvas(*args, **kwargs): """Return a new figure canvas.""" allnums = _pylab_helpers.Gcf.figs.keys() num = max(allnums) + 1 if allnums else 1 FigureClass = kwargs.pop('FigureClass', Figure) figure = FigureClass(*args, **kwargs) canvas = FigureCanvas(figure) fig_manager = FigureManagerQT(canvas, num) return fig_manager.canvas def new_plot(parent=None, subplot_kw=None, **fig_kw): """Return new figure and axes. Parameters ---------- parent : QtWidget Qt widget that displays the plot objects. If None, you must manually call ``canvas.setParent`` and pass the parent widget. subplot_kw : dict Keyword arguments passed ``matplotlib.figure.Figure.add_subplot``. fig_kw : dict Keyword arguments passed ``matplotlib.figure.Figure``. """ if subplot_kw is None: subplot_kw = {} canvas = new_canvas(**fig_kw) canvas.setParent(parent) fig = canvas.figure ax = fig.add_subplot(1, 1, 1, **subplot_kw) return fig, ax def figimage(image, scale=1, dpi=None, **kwargs): """Return figure and axes with figure tightly surrounding image. Unlike pyplot.figimage, this actually plots onto an axes object, which fills the figure. Plotting the image onto an axes allows for subsequent overlays of axes artists. Parameters ---------- image : array image to plot scale : float If scale is 1, the figure and axes have the same dimension as the image. Smaller values of `scale` will shrink the figure. dpi : int Dots per inch for figure. If None, use the default rcParam. """ dpi = dpi if dpi is not None else mpl.rcParams['figure.dpi'] kwargs.setdefault('interpolation', 'nearest') kwargs.setdefault('cmap', 'gray') h, w, d = np.atleast_3d(image).shape figsize = np.array((w, h), dtype=float) / dpi * scale fig, ax = new_plot(figsize=figsize, dpi=dpi) fig.subplots_adjust(left=0, bottom=0, right=1, top=1) ax.set_axis_off() ax.imshow(image, **kwargs) ax.figure.canvas.draw() return fig, ax def update_axes_image(image_axes, image): """Update the image displayed by an image plot. This sets the image plot's array and updates its shape appropriately Parameters ---------- image_axes : `matplotlib.image.AxesImage` Image axes to update. image : array Image array. """ image_axes.set_array(image) # Adjust size if new image shape doesn't match the original h, w = image.shape[:2] image_axes.set_extent((0, w, h, 0)) scikit-image-0.13.1/skimage/viewer/utils/dialogs.py000066400000000000000000000016341316256236300222320ustar00rootroot00000000000000import os from ..qt import QtGui __all__ = ['open_file_dialog', 'save_file_dialog'] def _format_filename(filename): if isinstance(filename, tuple): # Handle discrepancy between PyQt4 and PySide APIs. filename = filename[0] if len(filename) == 0: return None return str(filename) def open_file_dialog(): """Return user-selected file path.""" filename = QtGui.QFileDialog.getOpenFileName() filename = _format_filename(filename) return filename def save_file_dialog(default_format='png'): """Return user-selected file path.""" filename = QtGui.QFileDialog.getSaveFileName() filename = _format_filename(filename) if filename is None: return None #TODO: io plugins should assign default image formats basename, ext = os.path.splitext(filename) if not ext: filename = '%s.%s' % (filename, default_format) return filename scikit-image-0.13.1/skimage/viewer/viewers/000077500000000000000000000000001316256236300205565ustar00rootroot00000000000000scikit-image-0.13.1/skimage/viewer/viewers/__init__.py000066400000000000000000000000601316256236300226630ustar00rootroot00000000000000from .core import ImageViewer, CollectionViewer scikit-image-0.13.1/skimage/viewer/viewers/core.py000066400000000000000000000317211316256236300220640ustar00rootroot00000000000000""" ImageViewer class for viewing and interacting with images. """ import numpy as np from ... import io, img_as_float from ...util.dtype import dtype_range from ...exposure import rescale_intensity from ..qt import QtWidgets, Qt, Signal from ..widgets import Slider from ..utils import (dialogs, init_qtapp, figimage, start_qtapp, update_axes_image) from ..utils.canvas import BlitManager, EventManager from ..plugins.base import Plugin __all__ = ['ImageViewer', 'CollectionViewer'] def mpl_image_to_rgba(mpl_image): """Return RGB image from the given matplotlib image object. Each image in a matplotlib figure has its own colormap and normalization function. Return RGBA (RGB + alpha channel) image with float dtype. Parameters ---------- mpl_image : matplotlib.image.AxesImage object The image being converted. Returns ------- img : array of float, shape (M, N, 4) An image of float values in [0, 1]. """ image = mpl_image.get_array() if image.ndim == 2: input_range = (mpl_image.norm.vmin, mpl_image.norm.vmax) image = rescale_intensity(image, in_range=input_range) # cmap complains on bool arrays image = mpl_image.cmap(img_as_float(image)) elif image.ndim == 3 and image.shape[2] == 3: # add alpha channel if it's missing image = np.dstack((image, np.ones_like(image))) return img_as_float(image) class ImageViewer(QtWidgets.QMainWindow): """Viewer for displaying images. This viewer is a simple container object that holds a Matplotlib axes for showing images. `ImageViewer` doesn't subclass the Matplotlib axes (or figure) because of the high probability of name collisions. Subclasses and plugins will likely extend the `update_image` method to add custom overlays or filter the displayed image. Parameters ---------- image : array Image being viewed. Attributes ---------- canvas, fig, ax : Matplotlib canvas, figure, and axes Matplotlib canvas, figure, and axes used to display image. image : array Image being viewed. Setting this value will update the displayed frame. original_image : array Plugins typically operate on (but don't change) the *original* image. plugins : list List of attached plugins. Examples -------- >>> from skimage import data >>> image = data.coins() >>> viewer = ImageViewer(image) # doctest: +SKIP >>> viewer.show() # doctest: +SKIP """ dock_areas = {'top': Qt.TopDockWidgetArea, 'bottom': Qt.BottomDockWidgetArea, 'left': Qt.LeftDockWidgetArea, 'right': Qt.RightDockWidgetArea} # Signal that the original image has been changed original_image_changed = Signal(np.ndarray) def __init__(self, image, useblit=True): # Start main loop init_qtapp() super(ImageViewer, self).__init__() #TODO: Add ImageViewer to skimage.io window manager self.setAttribute(Qt.WA_DeleteOnClose) self.setWindowTitle("Image Viewer") self.file_menu = QtWidgets.QMenu('&File', self) self.file_menu.addAction('Open file', self.open_file, Qt.CTRL + Qt.Key_O) self.file_menu.addAction('Save to file', self.save_to_file, Qt.CTRL + Qt.Key_S) self.file_menu.addAction('Quit', self.close, Qt.CTRL + Qt.Key_Q) self.menuBar().addMenu(self.file_menu) self.main_widget = QtWidgets.QWidget() self.setCentralWidget(self.main_widget) if isinstance(image, Plugin): plugin = image image = plugin.filtered_image plugin.image_changed.connect(self._update_original_image) # When plugin is started, start plugin._started.connect(self._show) self.fig, self.ax = figimage(image) self.canvas = self.fig.canvas self.canvas.setParent(self) self.ax.autoscale(enable=False) self._tools = [] self.useblit = useblit if useblit: self._blit_manager = BlitManager(self.ax) self._event_manager = EventManager(self.ax) self._image_plot = self.ax.images[0] self._update_original_image(image) self.plugins = [] self.layout = QtWidgets.QVBoxLayout(self.main_widget) self.layout.addWidget(self.canvas) status_bar = self.statusBar() self.status_message = status_bar.showMessage sb_size = status_bar.sizeHint() cs_size = self.canvas.sizeHint() self.resize(cs_size.width(), cs_size.height() + sb_size.height()) self.connect_event('motion_notify_event', self._update_status_bar) def __add__(self, plugin): """Add plugin to ImageViewer""" plugin.attach(self) self.original_image_changed.connect(plugin._update_original_image) if plugin.dock: location = self.dock_areas[plugin.dock] dock_location = Qt.DockWidgetArea(location) dock = QtWidgets.QDockWidget() dock.setWidget(plugin) dock.setWindowTitle(plugin.name) self.addDockWidget(dock_location, dock) horiz = (self.dock_areas['left'], self.dock_areas['right']) dimension = 'width' if location in horiz else 'height' self._add_widget_size(plugin, dimension=dimension) return self def _add_widget_size(self, widget, dimension='width'): widget_size = widget.sizeHint() viewer_size = self.frameGeometry() dx = dy = 0 if dimension == 'width': dx = widget_size.width() elif dimension == 'height': dy = widget_size.height() w = viewer_size.width() h = viewer_size.height() self.resize(w + dx, h + dy) def open_file(self, filename=None): """Open image file and display in viewer.""" if filename is None: filename = dialogs.open_file_dialog() if filename is None: return image = io.imread(filename) self._update_original_image(image) def update_image(self, image): """Update displayed image. This method can be overridden or extended in subclasses and plugins to react to image changes. """ self._update_original_image(image) def _update_original_image(self, image): self.original_image = image # update saved image self.image = image.copy() # update displayed image self.original_image_changed.emit(image) def save_to_file(self, filename=None): """Save current image to file. The current behavior is not ideal: It saves the image displayed on screen, so all images will be converted to RGB, and the image size is not preserved (resizing the viewer window will alter the size of the saved image). """ if filename is None: filename = dialogs.save_file_dialog() if filename is None: return if len(self.ax.images) == 1: io.imsave(filename, self.image) else: underlay = mpl_image_to_rgba(self.ax.images[0]) overlay = mpl_image_to_rgba(self.ax.images[1]) alpha = overlay[:, :, 3] # alpha can be set by channel of array or by a scalar value. # Prefer the alpha channel, but fall back to scalar value. if np.all(alpha == 1): alpha = np.ones_like(alpha) * self.ax.images[1].get_alpha() alpha = alpha[:, :, np.newaxis] composite = (overlay[:, :, :3] * alpha + underlay[:, :, :3] * (1 - alpha)) io.imsave(filename, composite) def closeEvent(self, event): self.close() def _show(self, x=0): self.move(x, 0) for p in self.plugins: p.show() super(ImageViewer, self).show() self.activateWindow() self.raise_() def show(self, main_window=True): """Show ImageViewer and attached plugins. This behaves much like `matplotlib.pyplot.show` and `QWidget.show`. """ self._show() if main_window: start_qtapp() return [p.output() for p in self.plugins] def redraw(self): if self.useblit: self._blit_manager.redraw() else: self.canvas.draw_idle() @property def image(self): return self._img @image.setter def image(self, image): self._img = image update_axes_image(self._image_plot, image) # update display (otherwise image doesn't fill the canvas) h, w = image.shape[:2] self.ax.set_xlim(0, w) self.ax.set_ylim(h, 0) # update color range clim = dtype_range[image.dtype.type] if clim[0] < 0 and image.min() >= 0: clim = (0, clim[1]) self._image_plot.set_clim(clim) if self.useblit: self._blit_manager.background = None self.redraw() def reset_image(self): self.image = self.original_image.copy() def connect_event(self, event, callback): """Connect callback function to matplotlib event and return id.""" cid = self.canvas.mpl_connect(event, callback) return cid def disconnect_event(self, callback_id): """Disconnect callback by its id (returned by `connect_event`).""" self.canvas.mpl_disconnect(callback_id) def _update_status_bar(self, event): if event.inaxes and event.inaxes.get_navigate(): self.status_message(self._format_coord(event.xdata, event.ydata)) else: self.status_message('') def add_tool(self, tool): if self.useblit: self._blit_manager.add_artists(tool.artists) self._tools.append(tool) self._event_manager.attach(tool) def remove_tool(self, tool): if tool not in self._tools: return if self.useblit: self._blit_manager.remove_artists(tool.artists) self._tools.remove(tool) self._event_manager.detach(tool) def _format_coord(self, x, y): # callback function to format coordinate display in status bar x = int(x + 0.5) y = int(y + 0.5) try: return "%4s @ [%4s, %4s]" % (self.image[y, x], x, y) except IndexError: return "" class CollectionViewer(ImageViewer): """Viewer for displaying image collections. Select the displayed frame of the image collection using the slider or with the following keyboard shortcuts: left/right arrows Previous/next image in collection. number keys, 0--9 0% to 90% of collection. For example, "5" goes to the image in the middle (i.e. 50%) of the collection. home/end keys First/last image in collection. Parameters ---------- image_collection : list of images List of images to be displayed. update_on : {'move' | 'release'} Control whether image is updated on slide or release of the image slider. Using 'on_release' will give smoother behavior when displaying large images or when writing a plugin/subclass that requires heavy computation. """ def __init__(self, image_collection, update_on='move', **kwargs): self.image_collection = image_collection self.index = 0 self.num_images = len(self.image_collection) first_image = image_collection[0] super(CollectionViewer, self).__init__(first_image) slider_kws = dict(value=0, low=0, high=self.num_images - 1) slider_kws['update_on'] = update_on slider_kws['callback'] = self.update_index slider_kws['value_type'] = 'int' self.slider = Slider('frame', **slider_kws) self.layout.addWidget(self.slider) #TODO: Adjust height to accomodate slider; the following doesn't work # s_size = self.slider.sizeHint() # cs_size = self.canvas.sizeHint() # self.resize(cs_size.width(), cs_size.height() + s_size.height()) def update_index(self, name, index): """Select image on display using index into image collection.""" index = int(round(index)) if index == self.index: return # clip index value to collection limits index = max(index, 0) index = min(index, self.num_images - 1) self.index = index self.slider.val = index self.update_image(self.image_collection[index]) def keyPressEvent(self, event): if type(event) == QtWidgets.QKeyEvent: key = event.key() # Number keys (code: 0 = key 48, 9 = key 57) move to deciles if 48 <= key < 58: index = 0.1 * int(key - 48) * self.num_images self.update_index('', index) event.accept() else: event.ignore() else: event.ignore() scikit-image-0.13.1/skimage/viewer/widgets/000077500000000000000000000000001316256236300205405ustar00rootroot00000000000000scikit-image-0.13.1/skimage/viewer/widgets/__init__.py000066400000000000000000000012001316256236300226420ustar00rootroot00000000000000""" Widgets for interacting with ImageViewer. These widgets should be added to a Plugin subclass using its `add_widget` method or calling:: plugin += Widget(...) on a Plugin instance. The Plugin will delegate action based on the widget's parameter type specified by its `ptype` attribute, which can be:: 'arg' : positional argument passed to Plugin's `filter_image` method. 'kwarg' : keyword argument passed to Plugin's `filter_image` method. 'plugin' : attribute of Plugin. You'll probably need to add a class property of the same name that updates the display. """ from .core import * from .history import * scikit-image-0.13.1/skimage/viewer/widgets/core.py000066400000000000000000000245241316256236300220510ustar00rootroot00000000000000from ..qt import QtWidgets, QtCore, Qt, QtGui from ..utils import RequiredAttr __all__ = ['BaseWidget', 'Slider', 'ComboBox', 'CheckBox', 'Text', 'Button'] class BaseWidget(QtWidgets.QWidget): plugin = RequiredAttr("Widget is not attached to a Plugin.") def __init__(self, name, ptype=None, callback=None): super(BaseWidget, self).__init__() self.name = name self.ptype = ptype self.callback = callback self.plugin = None @property def val(self): msg = "Subclass of BaseWidget requires `val` property" raise NotImplementedError(msg) def _value_changed(self, value): self.callback(self.name, value) class Text(BaseWidget): def __init__(self, name=None, text=''): super(Text, self).__init__(name) self._label = QtWidgets.QLabel() self.text = text self.layout = QtWidgets.QHBoxLayout(self) if name is not None: name_label = QtWidgets.QLabel() name_label.setText(name) self.layout.addWidget(name_label) self.layout.addWidget(self._label) @property def text(self): return self._label.text() @text.setter def text(self, text_str): self._label.setText(text_str) class Slider(BaseWidget): """Slider widget for adjusting numeric parameters. Parameters ---------- name : str Name of slider parameter. If this parameter is passed as a keyword argument, it must match the name of that keyword argument (spaces are replaced with underscores). In addition, this name is displayed as the name of the slider. low, high : float Range of slider values. value : float Default slider value. If None, use midpoint between `low` and `high`. value_type : {'float' | 'int'}, optional Numeric type of slider value. ptype : {'kwarg' | 'arg' | 'plugin'}, optional Parameter type. callback : callable f(widget_name, value), optional Callback function called in response to slider changes. *Note:* This function is typically set (overridden) when the widget is added to a plugin. orientation : {'horizontal' | 'vertical'}, optional Slider orientation. update_on : {'release' | 'move'}, optional Control when callback function is called: on slider move or release. """ def __init__(self, name, low=0.0, high=1.0, value=None, value_type='float', ptype='kwarg', callback=None, max_edit_width=60, orientation='horizontal', update_on='release'): super(Slider, self).__init__(name, ptype, callback) if value is None: value = (high - low) / 2. # Set widget orientation #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ if orientation == 'vertical': self.slider = QtWidgets.QSlider(Qt.Vertical) alignment = QtCore.Qt.AlignHCenter align_text = QtCore.Qt.AlignHCenter align_value = QtCore.Qt.AlignHCenter self.layout = QtWidgets.QVBoxLayout(self) elif orientation == 'horizontal': self.slider = QtWidgets.QSlider(Qt.Horizontal) alignment = QtCore.Qt.AlignVCenter align_text = QtCore.Qt.AlignLeft align_value = QtCore.Qt.AlignRight self.layout = QtWidgets.QHBoxLayout(self) else: msg = "Unexpected value %s for 'orientation'" raise ValueError(msg % orientation) #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Set slider behavior for float and int values. #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ if value_type == 'float': # divide slider into 1000 discrete values slider_max = 1000 self._scale = float(high - low) / slider_max self.slider.setRange(0, slider_max) self.value_fmt = '%2.2f' elif value_type == 'int': self.slider.setRange(low, high) self.value_fmt = '%d' else: msg = "Expected `value_type` to be 'float' or 'int'; received: %s" raise ValueError(msg % value_type) #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ self.value_type = value_type self._low = low self._high = high # Update slider position to default value self.val = value if update_on == 'move': self.slider.valueChanged.connect(self._on_slider_changed) elif update_on == 'release': self.slider.sliderReleased.connect(self._on_slider_changed) else: raise ValueError("Unexpected value %s for 'update_on'" % update_on) self.slider.setFocusPolicy(QtCore.Qt.StrongFocus) self.name_label = QtWidgets.QLabel() self.name_label.setText(self.name) self.name_label.setAlignment(align_text) self.editbox = QtWidgets.QLineEdit() self.editbox.setMaximumWidth(max_edit_width) self.editbox.setText(self.value_fmt % self.val) self.editbox.setAlignment(align_value) self.editbox.editingFinished.connect(self._on_editbox_changed) self.layout.addWidget(self.name_label) self.layout.addWidget(self.slider) self.layout.addWidget(self.editbox) def _on_slider_changed(self): """Call callback function with slider's name and value as parameters""" value = self.val self.editbox.setText(str(value)[:4]) self.callback(self.name, value) def _on_editbox_changed(self): """Validate input and set slider value""" try: value = float(self.editbox.text()) except ValueError: self._bad_editbox_input() return if not self._low <= value <= self._high: self._bad_editbox_input() return self.val = value self._good_editbox_input() self.callback(self.name, value) def _good_editbox_input(self): self.editbox.setStyleSheet("background-color: rgb(255, 255, 255)") def _bad_editbox_input(self): self.editbox.setStyleSheet("background-color: rgb(255, 200, 200)") @property def val(self): value = self.slider.value() if self.value_type == 'float': value = value * self._scale + self._low return value @val.setter def val(self, value): if self.value_type == 'float': value = (value - self._low) / self._scale self.slider.setValue(value) class ComboBox(BaseWidget): """ComboBox widget for selecting among a list of choices. Parameters ---------- name : str Name of ComboBox parameter. If this parameter is passed as a keyword argument, it must match the name of that keyword argument (spaces are replaced with underscores). In addition, this name is displayed as the name of the ComboBox. items: list of str Allowed parameter values. ptype : {'arg' | 'kwarg' | 'plugin'}, optional Parameter type. callback : callable f(widget_name, value), optional Callback function called in response to combobox changes. *Note:* This function is typically set (overridden) when the widget is added to a plugin. """ def __init__(self, name, items, ptype='kwarg', callback=None): super(ComboBox, self).__init__(name, ptype, callback) self.name_label = QtWidgets.QLabel() self.name_label.setText(self.name) self.name_label.setAlignment(QtCore.Qt.AlignLeft) self._combo_box = QtWidgets.QComboBox() self._combo_box.addItems(list(items)) self.layout = QtWidgets.QHBoxLayout(self) self.layout.addWidget(self.name_label) self.layout.addWidget(self._combo_box) self._combo_box.currentIndexChanged.connect(self._value_changed) @property def val(self): return self._combo_box.currentText() @property def index(self): return self._combo_box.currentIndex() @index.setter def index(self, i): self._combo_box.setCurrentIndex(i) class CheckBox(BaseWidget): """CheckBox widget Parameters ---------- name : str Name of CheckBox parameter. If this parameter is passed as a keyword argument, it must match the name of that keyword argument (spaces are replaced with underscores). In addition, this name is displayed as the name of the CheckBox. value: {False, True}, optional Initial state of the CheckBox. alignment: {'center','left','right'}, optional Checkbox alignment ptype : {'arg' | 'kwarg' | 'plugin'}, optional Parameter type callback : callable f(widget_name, value), optional Callback function called in response to checkbox changes. *Note:* This function is typically set (overridden) when the widget is added to a plugin. """ def __init__(self, name, value=False, alignment='center', ptype='kwarg', callback=None): super(CheckBox, self).__init__(name, ptype, callback) self._check_box = QtWidgets.QCheckBox() self._check_box.setChecked(value) self._check_box.setText(self.name) self.layout = QtWidgets.QHBoxLayout(self) if alignment == 'center': self.layout.setAlignment(QtCore.Qt.AlignCenter) elif alignment == 'left': self.layout.setAlignment(QtCore.Qt.AlignLeft) elif alignment == 'right': self.layout.setAlignment(QtCore.Qt.AlignRight) else: raise ValueError("Unexpected value %s for 'alignment'" % alignment) self.layout.addWidget(self._check_box) self._check_box.stateChanged.connect(self._value_changed) @property def val(self): return self._check_box.isChecked() @val.setter def val(self, i): self._check_box.setChecked(i) class Button(BaseWidget): """Button which calls callback upon click. Parameters ---------- name : str Name of button. callback : callable f() Function to call when button is clicked. """ def __init__(self, name, callback): super(Button, self).__init__(self) self._button = QtGui.QPushButton(name) self._button.clicked.connect(callback) self.layout = QtGui.QHBoxLayout(self) self.layout.addWidget(self._button) scikit-image-0.13.1/skimage/viewer/widgets/history.py000066400000000000000000000064121316256236300226160ustar00rootroot00000000000000from textwrap import dedent from ..qt import QtGui, QtCore import numpy as np import skimage from ... import io, img_as_ubyte from .core import BaseWidget from ..utils import dialogs __all__ = ['OKCancelButtons', 'SaveButtons'] class OKCancelButtons(BaseWidget): """Buttons that close the parent plugin. OK will replace the original image with the current (filtered) image. Cancel will just close the plugin. """ def __init__(self, button_width=80): name = 'OK/Cancel' super(OKCancelButtons, self).__init__(name) self.ok = QtGui.QPushButton('OK') self.ok.clicked.connect(self.update_original_image) self.ok.setMaximumWidth(button_width) self.ok.setFocusPolicy(QtCore.Qt.NoFocus) self.cancel = QtGui.QPushButton('Cancel') self.cancel.clicked.connect(self.close_plugin) self.cancel.setMaximumWidth(button_width) self.cancel.setFocusPolicy(QtCore.Qt.NoFocus) self.layout = QtGui.QHBoxLayout(self) self.layout.addStretch() self.layout.addWidget(self.cancel) self.layout.addWidget(self.ok) def update_original_image(self): image = self.plugin.image_viewer.image self.plugin.image_viewer.original_image = image self.plugin.close() def close_plugin(self): # Image viewer will restore original image on close. self.plugin.close() class SaveButtons(BaseWidget): """Buttons to save image to io.stack or to a file.""" def __init__(self, name='Save to:', default_format='png'): super(SaveButtons, self).__init__(name) self.default_format = default_format self.name_label = QtGui.QLabel() self.name_label.setText(name) self.save_file = QtGui.QPushButton('File') self.save_file.clicked.connect(self.save_to_file) self.save_file.setFocusPolicy(QtCore.Qt.NoFocus) self.save_stack = QtGui.QPushButton('Stack') self.save_stack.clicked.connect(self.save_to_stack) self.save_stack.setFocusPolicy(QtCore.Qt.NoFocus) self.layout = QtGui.QHBoxLayout(self) self.layout.addWidget(self.name_label) self.layout.addWidget(self.save_stack) self.layout.addWidget(self.save_file) def save_to_stack(self): image = self.plugin.filtered_image.copy() io.push(image) msg = dedent('''\ The image has been pushed to the io stack. Use io.pop() to retrieve the most recently pushed image. NOTE: The io stack only works in interactive sessions.''') notify(msg) def save_to_file(self, filename=None): if not filename: filename = dialogs.save_file_dialog() if not filename: return image = self.plugin.filtered_image if image.dtype == np.bool: #TODO: This check/conversion should probably be in `imsave`. image = img_as_ubyte(image) io.imsave(filename, image) def notify(msg): msglabel = QtGui.QLabel(msg) dialog = QtGui.QDialog() ok = QtGui.QPushButton('OK', dialog) ok.clicked.connect(dialog.accept) ok.setDefault(True) dialog.layout = QtGui.QGridLayout(dialog) dialog.layout.addWidget(msglabel, 0, 0, 1, 3) dialog.layout.addWidget(ok, 1, 1) dialog.exec_() scikit-image-0.13.1/tools/000077500000000000000000000000001316256236300153115ustar00rootroot00000000000000scikit-image-0.13.1/tools/appveyor/000077500000000000000000000000001316256236300171565ustar00rootroot00000000000000scikit-image-0.13.1/tools/appveyor/install.ps1000066400000000000000000000151401316256236300212520ustar00rootroot00000000000000# Sample script to install Python and pip under Windows # Authors: Olivier Grisel, Jonathan Helmus and Kyle Kastner # License: CC0 1.0 Universal: http://creativecommons.org/publicdomain/zero/1.0/ $MINICONDA_URL = "http://repo.continuum.io/miniconda/" $BASE_URL = "https://www.python.org/ftp/python/" $GET_PIP_URL = "https://bootstrap.pypa.io/get-pip.py" $GET_PIP_PATH = "C:\get-pip.py" function DownloadPython ($python_version, $platform_suffix) { $webclient = New-Object System.Net.WebClient $filename = "python-" + $python_version + $platform_suffix + ".msi" $url = $BASE_URL + $python_version + "/" + $filename $basedir = $pwd.Path + "\" $filepath = $basedir + $filename if (Test-Path $filename) { Write-Host "Reusing" $filepath return $filepath } # Download and retry up to 3 times in case of network transient errors. Write-Host "Downloading" $filename "from" $url $retry_attempts = 2 for($i=0; $i -lt $retry_attempts; $i++){ try { $webclient.DownloadFile($url, $filepath) break } Catch [Exception]{ Start-Sleep 1 } } if (Test-Path $filepath) { Write-Host "File saved at" $filepath } else { # Retry once to get the error message if any at the last try $webclient.DownloadFile($url, $filepath) } return $filepath } function InstallPython ($python_version, $architecture, $python_home) { Write-Host "Installing Python" $python_version "for" $architecture "bit architecture to" $python_home if (Test-Path $python_home) { Write-Host $python_home "already exists, skipping." return $false } if ($architecture -eq "32") { $platform_suffix = "" } else { $platform_suffix = ".amd64" } $msipath = DownloadPython $python_version $platform_suffix Write-Host "Installing" $msipath "to" $python_home $install_log = $python_home + ".log" $install_args = "/qn /log $install_log /i $msipath TARGETDIR=$python_home" $uninstall_args = "/qn /x $msipath" RunCommand "msiexec.exe" $install_args if (-not(Test-Path $python_home)) { Write-Host "Python seems to be installed else-where, reinstalling." RunCommand "msiexec.exe" $uninstall_args RunCommand "msiexec.exe" $install_args } if (Test-Path $python_home) { Write-Host "Python $python_version ($architecture) installation complete" } else { Write-Host "Failed to install Python in $python_home" Get-Content -Path $install_log Exit 1 } } function RunCommand ($command, $command_args) { Write-Host $command $command_args Start-Process -FilePath $command -ArgumentList $command_args -Wait -Passthru } function InstallPip ($python_home) { $pip_path = $python_home + "\Scripts\pip.exe" $python_path = $python_home + "\python.exe" if (-not(Test-Path $pip_path)) { Write-Host "Installing pip..." $webclient = New-Object System.Net.WebClient $webclient.DownloadFile($GET_PIP_URL, $GET_PIP_PATH) Write-Host "Executing:" $python_path $GET_PIP_PATH Start-Process -FilePath "$python_path" -ArgumentList "$GET_PIP_PATH" -Wait -Passthru } else { Write-Host "pip already installed." } } function DownloadMiniconda ($python_version, $platform_suffix) { $webclient = New-Object System.Net.WebClient if ($python_version -eq "3.4") { $filename = "Miniconda3-3.5.5-Windows-" + $platform_suffix + ".exe" } else { $filename = "Miniconda-3.5.5-Windows-" + $platform_suffix + ".exe" } $url = $MINICONDA_URL + $filename $basedir = $pwd.Path + "\" $filepath = $basedir + $filename if (Test-Path $filename) { Write-Host "Reusing" $filepath return $filepath } # Download and retry up to 3 times in case of network transient errors. Write-Host "Downloading" $filename "from" $url $retry_attempts = 2 for($i=0; $i -lt $retry_attempts; $i++){ try { $webclient.DownloadFile($url, $filepath) break } Catch [Exception]{ Start-Sleep 1 } } if (Test-Path $filepath) { Write-Host "File saved at" $filepath } else { # Retry once to get the error message if any at the last try $webclient.DownloadFile($url, $filepath) } return $filepath } function InstallMiniconda ($python_version, $architecture, $python_home) { Write-Host "Installing Python" $python_version "for" $architecture "bit architecture to" $python_home if (Test-Path $python_home) { Write-Host $python_home "already exists, skipping." return $false } if ($architecture -eq "32") { $platform_suffix = "x86" } else { $platform_suffix = "x86_64" } $filepath = DownloadMiniconda $python_version $platform_suffix Write-Host "Installing" $filepath "to" $python_home $install_log = $python_home + ".log" $args = "/S /D=$python_home" Write-Host $filepath $args Start-Process -FilePath $filepath -ArgumentList $args -Wait -Passthru if (Test-Path $python_home) { Write-Host "Python $python_version ($architecture) installation complete" } else { Write-Host "Failed to install Python in $python_home" Get-Content -Path $install_log Exit 1 } } function InstallMinicondaPip ($python_home) { $pip_path = $python_home + "\Scripts\pip.exe" $conda_path = $python_home + "\Scripts\conda.exe" if (-not(Test-Path $pip_path)) { Write-Host "Installing pip..." $args = "install --yes pip" Write-Host $conda_path $args Start-Process -FilePath "$conda_path" -ArgumentList $args -Wait -Passthru } else { Write-Host "pip already installed." } } function UpdateConda ($python_home) { $conda_path = $python_home + "\Scripts\conda.exe" Write-Host "Updating conda..." $args = "update --yes conda" Write-Host $conda_path $args Start-Process -FilePath "$conda_path" -ArgumentList $args -Wait -Passthru } function InstallCondaPackages ($python_home, $spec) { $conda_path = $python_home + "\Scripts\conda.exe" $args = "install --yes " + $spec Write-Host ("conda " + $args) Start-Process -FilePath "$conda_path" -ArgumentList $args -Wait -Passthru } function main () { #InstallPython $env:PYTHON_VERSION $env:PYTHON_ARCH $env:PYTHON #InstallPip $env:PYTHON InstallMiniconda $env:PYTHON_VERSION $env:PYTHON_ARCH $env:PYTHON InstallMinicondaPip $env:PYTHON UpdateConda $env:PYTHON InstallCondaPackages $env:PYTHON "numpy scipy cython wheel nose msvc_runtime numpydoc" } main scikit-image-0.13.1/tools/appveyor/run_with_env.cmd000066400000000000000000000033721316256236300223570ustar00rootroot00000000000000:: To build extensions for 64 bit Python 3, we need to configure environment :: variables to use the MSVC 2010 C++ compilers from GRMSDKX_EN_DVD.iso of: :: MS Windows SDK for Windows 7 and .NET Framework 4 (SDK v7.1) :: :: To build extensions for 64 bit Python 2, we need to configure environment :: variables to use the MSVC 2008 C++ compilers from GRMSDKX_EN_DVD.iso of: :: MS Windows SDK for Windows 7 and .NET Framework 3.5 (SDK v7.0) :: :: 32 bit builds do not require specific environment configurations. :: :: Note: this script needs to be run with the /E:ON and /V:ON flags for the :: cmd interpreter, at least for (SDK v7.0) :: :: More details at: :: https://github.com/cython/cython/wiki/64BitCythonExtensionsOnWindows :: http://stackoverflow.com/a/13751649/163740 :: :: Author: Olivier Grisel :: License: BSD 3 clause @ECHO OFF SET COMMAND_TO_RUN=%* SET WIN_SDK_ROOT=C:\Program Files\Microsoft SDKs\Windows SET MAJOR_PYTHON_VERSION="%PYTHON_VERSION:~0,1%" IF %MAJOR_PYTHON_VERSION% == "2" ( SET WINDOWS_SDK_VERSION="v7.0" ) ELSE IF %MAJOR_PYTHON_VERSION% == "3" ( SET WINDOWS_SDK_VERSION="v7.1" ) ELSE ( ECHO Unsupported Python version: "%MAJOR_PYTHON_VERSION%" EXIT 1 ) IF "%PYTHON_ARCH%"=="64" ( ECHO Configuring Windows SDK %WINDOWS_SDK_VERSION% for Python %MAJOR_PYTHON_VERSION% on a 64 bit architecture SET DISTUTILS_USE_SDK=1 SET MSSdk=1 "%WIN_SDK_ROOT%\%WINDOWS_SDK_VERSION%\Setup\WindowsSdkVer.exe" -q -version:%WINDOWS_SDK_VERSION% "%WIN_SDK_ROOT%\%WINDOWS_SDK_VERSION%\Bin\SetEnv.cmd" /x64 /release ECHO Executing: %COMMAND_TO_RUN% call %COMMAND_TO_RUN% || EXIT 1 ) ELSE ( ECHO Using default MSVC build environment for 32 bit architecture ECHO Executing: %COMMAND_TO_RUN% call %COMMAND_TO_RUN% || EXIT 1 ) scikit-image-0.13.1/tools/build_versions.py000077500000000000000000000005731316256236300207220ustar00rootroot00000000000000#!/usr/bin/env python from __future__ import print_function import numpy as np import scipy as sp import matplotlib as mpl import six from PIL import Image import Cython import networkx for m in (np, sp, mpl, six, Image, networkx, Cython): if m is Image: version = m.VERSION else: version = m.__version__ print(m.__name__.rjust(10), ' ', version) scikit-image-0.13.1/tools/check_bento_build.py000077500000000000000000000062741316256236300213220ustar00rootroot00000000000000#!/usr/bin/env python """ Check that Cython extensions in setup.py files match those in bento.info. """ import os import re import sys RE_CYTHON = re.compile("config.add_extension\(\s*['\"]([\S]+)['\"]") BENTO_TEMPLATE = """ Extension: {module_path} Sources: {dir_path}.pyx""" def each_setup_in_pkg(top_dir): """Yield path and file object for each setup.py file""" for dir_path, dir_names, filenames in os.walk(top_dir): for fname in filenames: if fname == 'setup.py': with open(os.path.join(dir_path, 'setup.py')) as f: yield dir_path, f def each_cy_in_setup(top_dir): """Yield path for each cython extension package's setup file.""" for dir_path, f in each_setup_in_pkg(top_dir): text = f.read() match = RE_CYTHON.findall(text) if match: for cy_file in match: # if cython files in different directory than setup.py if '.' in cy_file: parts = cy_file.split('.') cy_file = parts[-1] # Don't overwrite dir_path for subsequent iterations. path = os.path.join(dir_path, *parts[:-1]) else: path = dir_path full_path = os.path.join(path, cy_file) yield full_path def each_cy_in_bento(bento_file='bento.info'): """Yield path for each cython extension in bento info file.""" with open(bento_file) as f: for line in f: line = line.strip() if line.startswith('Extension:'): path = line.lstrip('Extension:').strip() yield path def remove_common_extensions(cy_bento, cy_setup): # normalize so that cy_setup and cy_bento have the same separator cy_setup = set(ext.replace('/', '.') for ext in cy_setup) cy_setup_diff = cy_setup.difference(cy_bento) cy_setup_diff = set(ext.replace('.', '/') for ext in cy_setup_diff) cy_bento_diff = cy_bento.difference(cy_setup) return cy_bento_diff, cy_setup_diff def print_results(cy_bento, cy_setup): def info(text): print('') print(text) print('-' * len(text)) if not (cy_bento or cy_setup): print("bento.info and setup.py files match.") if cy_bento: info("Extensions found in 'bento.info' but not in any 'setup.py:") print('\n'.join(cy_bento)) if cy_setup: info("Extensions found in a 'setup.py' but not in any 'bento.info:") print('\n'.join(cy_setup)) info("Consider adding the following to the 'bento.info' Library:") for dir_path in cy_setup: module_path = dir_path.replace('/', '.') print(BENTO_TEMPLATE.format(module_path=module_path, dir_path=dir_path)) if __name__ == '__main__': # All cython extensions defined in 'setup.py' files. cy_setup = set(each_cy_in_setup('skimage')) # All cython extensions defined 'bento.info' file. cy_bento = set(each_cy_in_bento()) cy_bento, cy_setup = remove_common_extensions(cy_bento, cy_setup) print_results(cy_bento, cy_setup) if cy_setup or cy_bento: sys.exit(1) scikit-image-0.13.1/tools/check_sdist.py000077500000000000000000000032371316256236300201560ustar00rootroot00000000000000#!/usr/bin/env python from __future__ import print_function import os import subprocess import sys base_dir = os.path.join(os.path.dirname(__file__), '..') os.chdir(base_dir) p = subprocess.Popen("python setup.py sdist".split(), stdout=subprocess.PIPE) out, err = p.communicate() data = out.decode('utf-8').split('\n') data = [l for l in data if l.startswith('hard linking')] data = [l.replace('hard linking ', '') for l in data] data = ['./' + l.split(' ->')[0] for l in data] ignore_exts = ['.pyc', '.so', '.o', '#', '~', '.gitignore'] ignore_dirs = ['./dist', './tools', './doc', './viewer_examples', './downloads', './scikit_image.egg-info'] ignore_files = ['./TODO.md', './README.md', './MANIFEST', './.gitignore', './.travis.yml', './.gitmodules', './.mailmap', './.coveragerc', './appveyor.yml', './tools/check_bento_build.py', './skimage/filters/rank/README.rst'] missing = [] for root, dirs, files in os.walk('./'): for d in ignore_dirs: if root.startswith(d): break else: if root.startswith('./.'): continue for fn in files: for ext in ignore_exts: if fn.endswith(ext): break else: fn = os.path.join(root, fn) if not (fn in data or fn in ignore_files): missing.append(fn) if missing: print('Missing from source distribution:\n') for m in missing: print(' ', m) print('\nPlease update MANIFEST.in') sys.exit(1) else: print('All expected source files accounted for in sdist') scikit-image-0.13.1/tools/deploy_docs.sh000066400000000000000000000015201316256236300201470ustar00rootroot00000000000000#!/bin/bash if [[ $TRAVIS_PULL_REQUEST == false && $TRAVIS_BRANCH == "master" && $TRAVIS_PYTHON_VERSION == 3.4 ]] then # See https://help.github.com/articles/creating-an-access-token-for-command-line-use/ for how to generate a token # See http://docs.travis-ci.com/user/encryption-keys/ for how to generate # a secure variable on Travis echo "-- pushing docs --" ( git config --global user.email "travis@travis-ci.com" git config --global user.name "Travis Bot" git clone --quiet --branch=gh-pages https://${GH_REF} doc_build cd doc_build git rm -r dev cp -r ../doc/build/html dev git add dev git commit -m "Deployed to GitHub Pages" git push --force --quiet "https://${GH_TOKEN}@${GH_REF}" gh-pages > /dev/null 2>&1 ) else echo "-- will only push docs from master --" fi scikit-image-0.13.1/tools/header.py000077500000000000000000000004531316256236300171200ustar00rootroot00000000000000#!/usr/bin/env python from __future__ import print_function import sys screen_width = 50 print('*' * screen_width) if len(sys.argv) > 1: header = ' '.join(sys.argv[1:]) header = header.replace('.', ' ') print('*', header.center(screen_width - 4), '*') print('*' * screen_width) scikit-image-0.13.1/tools/mailmap.py000077500000000000000000000032251316256236300173100ustar00rootroot00000000000000#!/usr/bin/env python # Requires package 'editdistance' # A mailmap file is used (by GitHub and other tools) to associate multiple # commit emails with one user. This helps to count number of commits, # contributors, etc. import subprocess import shlex import numpy as np from collections import defaultdict from editdistance import eval as dist threshold = 5 def call(cmd): return subprocess.check_output(shlex.split(cmd), universal_newlines=True).split('\n') def _clean_email(email): if not '@' in email: return name, domain = email.split('@') name = name.split('+', 1)[0] return '{}@{}'.format(name, domain).lower() call("rm -f .mailmap") authors = call("git log --format='%aN::%aE'") names, emails = [], [] for (name, email) in (author.split('::') for author in authors if author.strip()): if email not in emails: names.append(name) emails.append(email) N = len(names) D = np.zeros((N, N)) + np.infty for i in range(1, N): for j in range(i): D[i, j] = dist(names[i], names[j]) for i in range(N): dupes, = np.where(D[:, i] < threshold) for j in dupes: names[j] = names[i] mailmap = defaultdict(set) for (name, email) in zip(names, emails): email = _clean_email(email) if email: mailmap[name].add(email) for key, value in list(mailmap.items()): if len(value) < 2 or (len(key.split()) < 2): mailmap.pop(key) entries = [] for name, emails in mailmap.items(): entries.append([name]) entries[-1].extend(['<{}>'.format(email) for email in emails]) entries = sorted(entries, key=lambda x: x[0].split()[-1]) for entry in entries: print(' '.join(entry)) scikit-image-0.13.1/tools/rm_pyx_c_file.sh000077500000000000000000000002361316256236300204700ustar00rootroot00000000000000#!/bin/bash # Accepts a .pyx file path and deletes an associated .c file, if present. filename="${1%.*}".c if [ -e "$filename" ]; then rm -f "$filename"; fi scikit-image-0.13.1/tools/travis_before_install.sh000077500000000000000000000055671316256236300222450ustar00rootroot00000000000000#!/usr/bin/env bash set -ex export COVERALLS_REPO_TOKEN=7LdFN9232ZbSY3oaXHbQIzLazrSf6w2pQ export PIP_DEFAULT_TIMEOUT=60 # This URL is for any extra wheels that are not available on pypi. As of 14 # Jan 2017, the major packages such as numpy and matplotlib are up for all # platforms. The URL points to a Rackspace CDN belonging to the scikit-learn # team. Please contact Olivier Grisel or Matthew Brett if you need # permissions for this folder. EXTRA_WHEELS="https://5cf40426d9f06eb7461d-6fe47d9331aba7cd62fc36c7196769e4.ssl.cf2.rackcdn.com" WHEELHOUSE="--find-links=$EXTRA_WHEELS" if [[ "$TRAVIS_OS_NAME" != "osx" ]]; then sh -e /etc/init.d/xvfb start # This one is for wheels we can only build on the travis precise container. # As of 14 Jan 2017, this is only pyside. Also on Rackspace, see above. # To build new wheels for this container, consider using: # https://github.com/matthew-brett/travis-wheel-builder . The wheels from # that building repo upload to the container "travis-wheels" available at # https://8167b5c3a2af93a0a9fb-13c6eee0d707a05fa610c311eec04c66.ssl.cf2.rackcdn.com # You then need to transfer them to the container pointed to by the URL # below (called "precise-wheels" on the Rackspace interface). PRECISE_WHEELS="https://7d8d0debcc2964ae0517-cec8b1780d3c0de237cc726d565607b4.ssl.cf2.rackcdn.com" WHEELHOUSE="--find-links=$PRECISE_WHEELS $WHEELHOUSE" fi export WHEELHOUSE export DISPLAY=:99.0 export PYTHONWARNINGS="d,all:::skimage" export TEST_ARGS="--exe --ignore-files=^_test -v --with-doctest \ --ignore-files=^setup.py$" WHEELBINARIES="matplotlib scipy pillow cython" retry () { # https://gist.github.com/fungusakafungus/1026804 local retry_max=3 local count=$retry_max while [ $count -gt 0 ]; do "$@" && break count=$(($count - 1)) sleep 1 done [ $count -eq 0 ] && { echo "Retry failed [$retry_max]: $@" >&2 return 1 } return 0 } # add build dependencies echo "cython>=0.23.4" >> requirements.txt echo "numpydoc>=0.6" >> requirements.txt if [[ $MINIMUM_REQUIREMENTS == 1 ]]; then sed -i 's/>=/==/g' requirements.txt fi # create new empty venv virtualenv -p python ~/venv source ~/venv/bin/activate python -m pip install --upgrade pip pip install --retries 3 -q wheel flake8 codecov nose # install numpy from PyPI instead of our wheelhouse pip install --retries 3 -q wheel numpy # install wheels for requirement in $WHEELBINARIES; do WHEELS="$WHEELS $(grep $requirement requirements.txt)" done pip install --retries 3 -q $PIP_FLAGS $WHEELHOUSE $WHEELS pip install --retries 3 -q $PIP_FLAGS -r requirements.txt # Show what's installed pip list section () { echo -en "travis_fold:start:$1\r" tools/header.py $1 } section_end () { echo -en "travis_fold:end:$1\r" } export -f section export -f section_end export -f retry set +ex scikit-image-0.13.1/tools/travis_notes.txt000066400000000000000000000020011316256236300205630ustar00rootroot00000000000000 - Use http://yaml-online-parser.appspot.com/ to make sure the yaml file is valid. http://lint.travis-ci.org/ is recommended elsewhere but does not give helpful error reports. - Make sure all of your "-" lines start on the same column - Use bash scripts for `before_install` and `script` or any part that has conditional statements - Make sure they are "executable" (chmod +x) - Use the following header: ``` #!/usr/bin/env bash set -ex ``` - Use the `retry` bash function from `before_install.sh` before a command to have it try 3 times before failing. - Use `pip install --retries N` for retrying package downloads. - Use the `section` function to start a folded section of the script. Section names must have underscores or dots instead of spaces and must be accompanied by a corresponding `section_end` call. - Feel free to cancel a build rather than waiting for it to go to completion if you have made a change to that branch. - A VM with 64bit Ubuntu 12.04 is a huge help for debugging. scikit-image-0.13.1/tools/travis_osx_install.sh000077500000000000000000000006571316256236300216070ustar00rootroot00000000000000#!/bin/bash brew update brew install ccache brew tap caskroom/cask brew cask install basictex export PATH="$PATH:/Library/TeX/texbin" sudo tlmgr update --self sudo tlmgr install ucs dvipng anyfontsize git clone https://github.com/MacPython/terryfy.git ~/terryfy source ~/terryfy/travis_tools.sh get_python_environment macpython $TRAVIS_PYTHON_VERSION ~/macpython_venv source ~/macpython_venv/bin/activate pip install virtualenv scikit-image-0.13.1/tools/travis_script.sh000077500000000000000000000055301316256236300205470ustar00rootroot00000000000000#!/usr/bin/env bash set -ex PY=$TRAVIS_PYTHON_VERSION # Matplotlib settings - do not show figures during doc examples if [[ $MINIMUM_REQUIREMENTS == 1 || $TRAVIS_OS_NAME == "osx" ]]; then MPL_DIR=$HOME/.matplotlib else MPL_DIR=$HOME/.config/matplotlib fi mkdir -p $MPL_DIR touch $MPL_DIR/matplotlibrc if [[ $TRAVIS_OS_NAME == "osx" ]]; then echo 'backend : Template' > $MPL_DIR/matplotlibrc fi section "Test.with.min.requirements" nosetests $TEST_ARGS skimage section_end "Test.with.min.requirements" section "Build.docs" if [[ $NO_SPHINX != 1 ]]; then export SPHINXCACHE=$HOME/.cache/sphinx; make html fi section_end "Build.docs" section "Flake8.test" flake8 --exit-zero --exclude=test_*,six.py skimage doc/examples viewer_examples section_end "Flake8.test" section "Install.optional.dependencies" # Install most of the optional packages if [[ $OPTIONAL_DEPS == 1 ]]; then pip install --retries 3 -q -r ./optional_requirements.txt $WHEELHOUSE fi # Install Qt and then update the Matplotlib settings if [[ $WITH_QT == 1 ]]; then # http://stackoverflow.com/a/9716100 LIBS=( PyQt4 sip.so ) VAR=( $(which -a python$PY) ) GET_PYTHON_LIB_CMD="from distutils.sysconfig import get_python_lib; print (get_python_lib())" LIB_VIRTUALENV_PATH=$(python -c "$GET_PYTHON_LIB_CMD") LIB_SYSTEM_PATH=$(${VAR[-1]} -c "$GET_PYTHON_LIB_CMD") for LIB in ${LIBS[@]} do ln -sf $LIB_SYSTEM_PATH/$LIB $LIB_VIRTUALENV_PATH/$LIB done elif [ "$WITH_PYSIDE" == 1 ] && [ -e ~/venv/bin/pyside_postinstall.py ]; then python ~/venv/bin/pyside_postinstall.py -install fi if [[ $WITH_PYAMG == 1 ]]; then pip install --retries 3 -q pyamg fi # Show what's installed pip list section_end "Install.optional.dependencies" section "Run.doc.examples" echo 'backend : Template' > $MPL_DIR/matplotlibrc for f in doc/examples/*/*.py; do python "$f" if [ $? -ne 0 ]; then exit 1 fi done section_end "Run.doc.examples" section "Run.doc.applications" for f in doc/examples/xx_applications/*.py; do python "$f" if [ $? -ne 0 ]; then exit 1 fi done # Now configure Matplotlib to use Qt4 if [[ $WITH_QT == 1 ]]; then MPL_QT_API=PyQt4 export QT_API=pyqt elif [[ $WITH_PYSIDE == 1 ]]; then MPL_QT_API=PySide export QT_API=pyside fi if [[ $WITH_QT == 1 || $WITH_PYSIDE == 1 ]]; then echo 'backend: Qt4Agg' > $MPL_DIR/matplotlibrc echo 'backend.qt4 : '$MPL_QT_API >> $MPL_DIR/matplotlibrc fi section_end "Run.doc.applications" section "Test.with.optional.dependencies" # run tests again with optional dependencies to get more coverage if [[ $OPTIONAL_DEPS == 1 ]]; then TEST_ARGS="$TEST_ARGS --with-cov --cover-package skimage" fi nosetests $TEST_ARGS section_end "Test.with.optional.dependencies" section "Prepare.release" doc/release/contribs.py HEAD~10 section_end "Prepare.release" scikit-image-0.13.1/tools/upload_wheels.sh000077500000000000000000000012161316256236300205030ustar00rootroot00000000000000#!/bin/bash # Script to download / check and upload scikit_image wheels for release if [ "`which twine`" == "" ]; then echo "twine not on path; need to pip install twine?" exit 1 fi if [ "`which wheel-uploader`" == "" ]; then echo "wheel-uploader not on path; see https://github.com/MacPython/terryfy" exit 1 fi SK_VERSION=`git describe --tags` if [ "${SK_VERSION:0:1}" != 'v' ]; then echo "scikit image version $SK_VERSION does not start with 'v'" exit 1 fi echo "Trying download / upload of version ${SK_VERSION:1}" wheel-uploader -v scikit_image "${SK_VERSION:1}" wheel-uploader -v scikit_image -t manylinux1 "${SK_VERSION:1}" scikit-image-0.13.1/viewer_examples/000077500000000000000000000000001316256236300173505ustar00rootroot00000000000000scikit-image-0.13.1/viewer_examples/plugins/000077500000000000000000000000001316256236300210315ustar00rootroot00000000000000scikit-image-0.13.1/viewer_examples/plugins/canny.py000066400000000000000000000003431316256236300225130ustar00rootroot00000000000000from skimage import data from skimage.viewer import ImageViewer from skimage.viewer.plugins.canny import CannyPlugin image = data.camera() viewer = ImageViewer(image) viewer += CannyPlugin() canny_edges = viewer.show()[0][0] scikit-image-0.13.1/viewer_examples/plugins/canny_simple.py000066400000000000000000000014401316256236300240630ustar00rootroot00000000000000from skimage import data from skimage.feature import canny from skimage.viewer import ImageViewer from skimage.viewer.widgets import Slider from skimage.viewer.widgets.history import SaveButtons from skimage.viewer.plugins.overlayplugin import OverlayPlugin image = data.camera() # You can create a UI for a filter just by passing a filter function... plugin = OverlayPlugin(image_filter=canny) # ... and adding widgets to adjust parameter values. plugin += Slider('sigma', 0, 5) plugin += Slider('low threshold', 0, 255) plugin += Slider('high threshold', 0, 255) # ... and we can also add buttons to save the overlay: plugin += SaveButtons(name='Save overlay to:') # Finally, attach the plugin to an image viewer. viewer = ImageViewer(image) viewer += plugin canny_edges = viewer.show()[0][0] scikit-image-0.13.1/viewer_examples/plugins/collection_overlay.py000066400000000000000000000007671316256236300253110ustar00rootroot00000000000000""" ============================================== ``CollectionViewer`` with an ``OverlayPlugin`` ============================================== Demo of a CollectionViewer for viewing collections of images with an overlay plugin. """ from skimage import data from skimage.viewer import CollectionViewer from skimage.viewer.plugins.canny import CannyPlugin img_collection = [data.camera(), data.coins(), data.text()] viewer = CollectionViewer(img_collection) viewer += CannyPlugin() viewer.show() scikit-image-0.13.1/viewer_examples/plugins/collection_plugin.py000066400000000000000000000015461316256236300251220ustar00rootroot00000000000000""" ================================== ``CollectionViewer`` with a plugin ================================== Demo of a CollectionViewer for viewing collections of images with the `autolevel` rank filter connected as a plugin. """ from skimage import data from skimage.filters import rank from skimage.morphology import disk from skimage.viewer import CollectionViewer from skimage.viewer.widgets import Slider from skimage.viewer.plugins.base import Plugin # Wrap autolevel function to make the disk size a filter argument. def autolevel(image, disk_size): return rank.autolevel(image, disk(disk_size)) img_collection = [data.camera(), data.coins(), data.text()] plugin = Plugin(image_filter=autolevel) plugin += Slider('disk_size', 2, 8, value_type='int') plugin.name = "Autolevel" viewer = CollectionViewer(img_collection) viewer += plugin viewer.show() scikit-image-0.13.1/viewer_examples/plugins/color_histogram.py000066400000000000000000000003641316256236300246010ustar00rootroot00000000000000from skimage.viewer import ImageViewer from skimage.viewer.plugins.color_histogram import ColorHistogram from skimage import data image = data.load('color.png') viewer = ImageViewer(image) viewer += ColorHistogram(dock='right') viewer.show() scikit-image-0.13.1/viewer_examples/plugins/croptool.py000066400000000000000000000003001316256236300232350ustar00rootroot00000000000000from skimage import data from skimage.viewer import ImageViewer from skimage.viewer.plugins.crop import Crop image = data.camera() viewer = ImageViewer(image) viewer += Crop() viewer.show() scikit-image-0.13.1/viewer_examples/plugins/lineprofile.py000066400000000000000000000003501316256236300237110ustar00rootroot00000000000000from skimage import data from skimage.viewer import ImageViewer from skimage.viewer.plugins.lineprofile import LineProfile image = data.camera() viewer = ImageViewer(image) viewer += LineProfile() line, profile = viewer.show()[0] scikit-image-0.13.1/viewer_examples/plugins/lineprofile_rgb.py000066400000000000000000000003561316256236300245510ustar00rootroot00000000000000from skimage import data from skimage.viewer import ImageViewer from skimage.viewer.plugins.lineprofile import LineProfile image = data.chelsea() viewer = ImageViewer(image) viewer += LineProfile() line, rgb_profiles = viewer.show()[0] scikit-image-0.13.1/viewer_examples/plugins/measure.py000066400000000000000000000003111316256236300230370ustar00rootroot00000000000000from skimage import data from skimage.viewer import ImageViewer from skimage.viewer.plugins.measure import Measure image = data.camera() viewer = ImageViewer(image) viewer += Measure() viewer.show() scikit-image-0.13.1/viewer_examples/plugins/median_filter.py000066400000000000000000000010731316256236300242060ustar00rootroot00000000000000from skimage import data from skimage.filters.rank import median from skimage.morphology import disk from skimage.viewer import ImageViewer from skimage.viewer.widgets import Slider, OKCancelButtons, SaveButtons from skimage.viewer.plugins.base import Plugin def median_filter(image, radius): return median(image, selem=disk(radius)) image = data.coins() viewer = ImageViewer(image) plugin = Plugin(image_filter=median_filter) plugin += Slider('radius', 2, 10, value_type='int') plugin += SaveButtons() plugin += OKCancelButtons() viewer += plugin viewer.show() scikit-image-0.13.1/viewer_examples/plugins/probabilistic_hough.py000066400000000000000000000026661316256236300254350ustar00rootroot00000000000000import numpy as np from skimage import data from skimage import draw from skimage.transform import probabilistic_hough_line from skimage.viewer import ImageViewer from skimage.viewer.widgets import Slider from skimage.viewer.plugins.overlayplugin import OverlayPlugin from skimage.viewer.plugins.canny import CannyPlugin def line_image(shape, lines): image = np.zeros(shape, dtype=bool) for end_points in lines: # hough lines returns (x, y) points, draw.line wants (row, columns) end_points = np.asarray(end_points)[:, ::-1] image[draw.line(*np.ravel(end_points))] = 1 return image def hough_lines(image, *args, **kwargs): # Set threshold to 0.5 since we're working with a binary image (from canny) lines = probabilistic_hough_line(image, threshold=0.5, *args, **kwargs) image = line_image(image.shape, lines) return image image = data.camera() canny_viewer = ImageViewer(image) canny_plugin = CannyPlugin() canny_viewer += canny_plugin hough_plugin = OverlayPlugin(image_filter=hough_lines) hough_plugin.name = 'Hough Lines' hough_plugin += Slider('line length', 0, 100) hough_plugin += Slider('line gap', 0, 20) # Passing a plugin to a viewer connects the output of the plugin to the viewer. hough_viewer = ImageViewer(canny_plugin) hough_viewer += hough_plugin # Show viewers displays both viewers since `hough_viewer` is connected to # `canny_viewer` through `canny_plugin` canny_viewer.show() scikit-image-0.13.1/viewer_examples/plugins/tv_denoise.py000066400000000000000000000015211316256236300235410ustar00rootroot00000000000000from skimage import data from skimage.restoration import denoise_tv_chambolle from skimage.util import img_as_float from numpy import random, clip from skimage.viewer import ImageViewer from skimage.viewer.widgets import (Slider, CheckBox, OKCancelButtons, SaveButtons) from skimage.viewer.plugins.base import Plugin image = img_as_float(data.chelsea()) sigma = 30/255. image = image + random.normal(loc=0, scale=sigma, size=image.shape) image = clip(image, 0, 1) viewer = ImageViewer(image) plugin = Plugin(image_filter=denoise_tv_chambolle) plugin += Slider('weight', 0.01, 5, value=0.3, value_type='float') plugin += Slider('n_iter_max', 1, 100, value=20, value_type='int') plugin += CheckBox('multichannel', value=True) plugin += SaveButtons() plugin += OKCancelButtons() viewer += plugin viewer.show() scikit-image-0.13.1/viewer_examples/plugins/watershed_demo.py000066400000000000000000000023751316256236300244040ustar00rootroot00000000000000import matplotlib.pyplot as plt from skimage import data from skimage import filters from skimage import morphology from skimage.viewer import ImageViewer from skimage.viewer.widgets import history from skimage.viewer.plugins.labelplugin import LabelPainter class OKCancelButtons(history.OKCancelButtons): def update_original_image(self): # OKCancelButtons updates the original image with the filtered image # by default. Override this method to update the overlay. self.plugin._show_watershed() self.plugin.close() class WatershedPlugin(LabelPainter): def help(self): helpstr = ("Watershed plugin", "----------------", "Use mouse to paint each region with a different label.", "Press OK to display segmented image.") return '\n'.join(helpstr) def _show_watershed(self): viewer = self.image_viewer edge_image = filter.sobel(viewer.image) labels = morphology.watershed(edge_image, self.paint_tool.overlay) viewer.ax.imshow(labels, cmap=plt.cm.jet, alpha=0.5) viewer.redraw() image = data.coins() plugin = WatershedPlugin() plugin += OKCancelButtons() viewer = ImageViewer(image) viewer += plugin viewer.show() scikit-image-0.13.1/viewer_examples/viewers/000077500000000000000000000000001316256236300210345ustar00rootroot00000000000000scikit-image-0.13.1/viewer_examples/viewers/collection_viewer.py000066400000000000000000000014401316256236300251210ustar00rootroot00000000000000""" ===================== CollectionViewer demo ===================== Demo of CollectionViewer for viewing collections of images. This demo uses the different layers of the gaussian pyramid as image collection. You can scroll through images with the slider, or you can interact with the viewer using your keyboard: left/right arrows Previous/next image in collection. number keys, 0--9 0% to 90% of collection. For example, "5" goes to the image in the middle (i.e. 50%) of the collection. home/end keys First/last image in collection. """ from skimage import data from skimage.viewer import CollectionViewer from skimage.transform import pyramid_gaussian img = data.astronaut() img_collection = tuple(pyramid_gaussian(img)) view = CollectionViewer(img_collection) view.show() scikit-image-0.13.1/viewer_examples/viewers/image_viewer.py000066400000000000000000000002021316256236300240430ustar00rootroot00000000000000from skimage import data from skimage.viewer import ImageViewer image = data.camera() viewer = ImageViewer(image) viewer.show()

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