%global _empty_manifest_terminate_build 0 Name: python-PyMatting Version: 1.1.8 Release: 1 Summary: Python package for alpha matting. License: MIT URL: https://pymatting.github.io Source0: https://mirrors.nju.edu.cn/pypi/web/packages/6b/2c/5aac0e60d4485503c9ba6d6690d9722414305e5beadd6252d98ed64a89e0/PyMatting-1.1.8.tar.gz BuildArch: noarch Requires: python3-numpy Requires: python3-pillow Requires: python3-numba Requires: python3-scipy %description # PyMatting: A Python Library for Alpha Matting [![License: MIT](https://img.shields.io/github/license/pymatting/pymatting?color=brightgreen)](https://opensource.org/licenses/MIT) [![CI](https://img.shields.io/github/workflow/status/pymatting/pymatting/tests?label=tests)](https://github.com/pymatting/pymatting/actions?query=workflow%3Atests) [![PyPI](https://img.shields.io/pypi/v/pymatting)](https://pypi.org/project/PyMatting/) [![JOSS](https://joss.theoj.org/papers/9766cab65bfbf07a70c8a835edd3875a/status.svg)](https://joss.theoj.org/papers/9766cab65bfbf07a70c8a835edd3875a) [![Gitter](https://img.shields.io/gitter/room/pymatting/pymatting)](https://gitter.im/pymatting/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge) We introduce the PyMatting package for Python which implements various methods to solve the alpha matting problem. - **Website and Documentation:** [https://pymatting.github.io/](https://pymatting.github.io) - **Benchmarks:** [https://pymatting.github.io/benchmark.html](https://pymatting.github.io/benchmark.html) ![Lemur](https://github.com/pymatting/pymatting/raw/master/data/lemur/lemur_at_the_beach.png) Given an input image and a hand-drawn trimap (top row), alpha matting estimates the alpha channel of a foreground object which can then be composed onto a different background (bottom row). PyMatting provides: - Alpha matting implementations for: - Closed Form Alpha Matting [[1]](#1) - Large Kernel Matting [[2]](#2) - KNN Matting [[3]](#3) - Learning Based Digital Matting [[4]](#4) - Random Walk Matting [[5]](#5) - Foreground estimation implementations for: - Closed Form Foreground Estimation [[1]](#1) - Fast Multi-Level Foreground Estimation (CPU, CUDA and OpenCL) [[6]](#6) - Fast multithreaded KNN search - Preconditioners to accelerate the convergence rate of conjugate gradient descent: - The *incomplete thresholded Cholesky decomposition* (*Incomplete* is part of the name. The implementation is quite complete.) - The V-Cycle Geometric Multigrid preconditioner - Readable code leveraging [NumPy](https://numpy.org/), [SciPy](https://www.scipy.org/scipylib/index.html) and [Numba](http://numba.pydata.org/) ## Getting Started ### Requirements Minimal requiremens * numpy>=1.16.0 * pillow>=5.2.0 * numba>=0.47.0 * scipy>=1.1.0 Additional requirements for GPU support * cupy-cuda90>=6.5.0 or similar * pyopencl>=2019.1.2 Requirements to run the tests * pytest>=5.3.4 ### Installation with PyPI ```bash pip3 install pymatting ``` ### Installation from Source ```bash git clone https://github.com/pymatting/pymatting cd pymatting pip3 install . ``` ## Example ```python from pymatting import cutout cutout( # input image path "data/lemur/lemur.png", # input trimap path "data/lemur/lemur_trimap.png", # output cutout path "lemur_cutout.png") ``` [More advanced examples](https://pymatting.github.io/examples.html) ## Trimap Construction All implemented methods rely on trimaps which roughly classify the image into foreground, background and unknown reagions. Trimaps are expected to be `numpy.ndarrays` of type `np.float64` having the same shape as the input image with only one color-channel. Trimap values of 0.0 denote pixels which are 100% background. Similarly, trimap values of 1.0 denote pixels which are 100% foreground. All other values indicate unknown pixels which will be estimated by the algorithm. ## Testing Run the tests from the main directory: ``` python3 tests/download_images.py pip3 install -r requirements_tests.txt pytest ``` Currently 89% of the code is covered by tests. ## Upgrade ```bash pip3 install --upgrade pymatting python3 -c "import pymatting" ``` ## Bug Reports, Questions and Pull-Requests Please, see [our community guidelines](https://github.com/pymatting/pymatting/blob/master/CONTRIBUTING.md). ## Authors - **Thomas Germer** - **Tobias Uelwer** - **Stefan Conrad** - **Stefan Harmeling** See also the list of [contributors](https://github.com/pymatting/pymatting/contributors) who participated in this project. ## License This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details ## Citing If you found PyMatting to be useful for your work, please consider citing our [paper](https://doi.org/10.21105/joss.02481): ``` @article{Germer2020, doi = {10.21105/joss.02481}, url = {https://doi.org/10.21105/joss.02481}, year = {2020}, publisher = {The Open Journal}, volume = {5}, number = {54}, pages = {2481}, author = {Thomas Germer and Tobias Uelwer and Stefan Conrad and Stefan Harmeling}, title = {PyMatting: A Python Library for Alpha Matting}, journal = {Journal of Open Source Software} } ``` ## References [1] Anat Levin, Dani Lischinski, and Yair Weiss. A closed-form solution to natural image matting. IEEE transactions on pattern analysis and machine intelligence, 30(2):228–242, 2007. [2] Kaiming He, Jian Sun, and Xiaoou Tang. Fast matting using large kernel matting laplacian matrices. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2165–2172. IEEE, 2010. [3] Qifeng Chen, Dingzeyu Li, and Chi-Keung Tang. Knn matting. IEEE transactions on pattern analysis and machine intelligence, 35(9):2175–2188, 2013. [4] Yuanjie Zheng and Chandra Kambhamettu. Learning based digital matting. In 2009 IEEE 12th international conference on computer vision, 889–896. IEEE, 2009. [5] Leo Grady, Thomas Schiwietz, Shmuel Aharon, and Rüdiger Westermann. Random walks for interactive alpha-matting. In Proceedings of VIIP, volume 2005, 423–429. 2005. [6] Germer, T., Uelwer, T., Conrad, S., & Harmeling, S. (2020). Fast Multi-Level Foreground Estimation. arXiv preprint arXiv:2006.14970. Lemur image by Mathias Appel from https://www.flickr.com/photos/mathiasappel/25419442300/ licensed under [CC0 1.0 Universal (CC0 1.0) Public Domain License](https://creativecommons.org/publicdomain/zero/1.0/). %package -n python3-PyMatting Summary: Python package for alpha matting. Provides: python-PyMatting BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-PyMatting # PyMatting: A Python Library for Alpha Matting [![License: MIT](https://img.shields.io/github/license/pymatting/pymatting?color=brightgreen)](https://opensource.org/licenses/MIT) [![CI](https://img.shields.io/github/workflow/status/pymatting/pymatting/tests?label=tests)](https://github.com/pymatting/pymatting/actions?query=workflow%3Atests) [![PyPI](https://img.shields.io/pypi/v/pymatting)](https://pypi.org/project/PyMatting/) [![JOSS](https://joss.theoj.org/papers/9766cab65bfbf07a70c8a835edd3875a/status.svg)](https://joss.theoj.org/papers/9766cab65bfbf07a70c8a835edd3875a) [![Gitter](https://img.shields.io/gitter/room/pymatting/pymatting)](https://gitter.im/pymatting/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge) We introduce the PyMatting package for Python which implements various methods to solve the alpha matting problem. - **Website and Documentation:** [https://pymatting.github.io/](https://pymatting.github.io) - **Benchmarks:** [https://pymatting.github.io/benchmark.html](https://pymatting.github.io/benchmark.html) ![Lemur](https://github.com/pymatting/pymatting/raw/master/data/lemur/lemur_at_the_beach.png) Given an input image and a hand-drawn trimap (top row), alpha matting estimates the alpha channel of a foreground object which can then be composed onto a different background (bottom row). PyMatting provides: - Alpha matting implementations for: - Closed Form Alpha Matting [[1]](#1) - Large Kernel Matting [[2]](#2) - KNN Matting [[3]](#3) - Learning Based Digital Matting [[4]](#4) - Random Walk Matting [[5]](#5) - Foreground estimation implementations for: - Closed Form Foreground Estimation [[1]](#1) - Fast Multi-Level Foreground Estimation (CPU, CUDA and OpenCL) [[6]](#6) - Fast multithreaded KNN search - Preconditioners to accelerate the convergence rate of conjugate gradient descent: - The *incomplete thresholded Cholesky decomposition* (*Incomplete* is part of the name. The implementation is quite complete.) - The V-Cycle Geometric Multigrid preconditioner - Readable code leveraging [NumPy](https://numpy.org/), [SciPy](https://www.scipy.org/scipylib/index.html) and [Numba](http://numba.pydata.org/) ## Getting Started ### Requirements Minimal requiremens * numpy>=1.16.0 * pillow>=5.2.0 * numba>=0.47.0 * scipy>=1.1.0 Additional requirements for GPU support * cupy-cuda90>=6.5.0 or similar * pyopencl>=2019.1.2 Requirements to run the tests * pytest>=5.3.4 ### Installation with PyPI ```bash pip3 install pymatting ``` ### Installation from Source ```bash git clone https://github.com/pymatting/pymatting cd pymatting pip3 install . ``` ## Example ```python from pymatting import cutout cutout( # input image path "data/lemur/lemur.png", # input trimap path "data/lemur/lemur_trimap.png", # output cutout path "lemur_cutout.png") ``` [More advanced examples](https://pymatting.github.io/examples.html) ## Trimap Construction All implemented methods rely on trimaps which roughly classify the image into foreground, background and unknown reagions. Trimaps are expected to be `numpy.ndarrays` of type `np.float64` having the same shape as the input image with only one color-channel. Trimap values of 0.0 denote pixels which are 100% background. Similarly, trimap values of 1.0 denote pixels which are 100% foreground. All other values indicate unknown pixels which will be estimated by the algorithm. ## Testing Run the tests from the main directory: ``` python3 tests/download_images.py pip3 install -r requirements_tests.txt pytest ``` Currently 89% of the code is covered by tests. ## Upgrade ```bash pip3 install --upgrade pymatting python3 -c "import pymatting" ``` ## Bug Reports, Questions and Pull-Requests Please, see [our community guidelines](https://github.com/pymatting/pymatting/blob/master/CONTRIBUTING.md). ## Authors - **Thomas Germer** - **Tobias Uelwer** - **Stefan Conrad** - **Stefan Harmeling** See also the list of [contributors](https://github.com/pymatting/pymatting/contributors) who participated in this project. ## License This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details ## Citing If you found PyMatting to be useful for your work, please consider citing our [paper](https://doi.org/10.21105/joss.02481): ``` @article{Germer2020, doi = {10.21105/joss.02481}, url = {https://doi.org/10.21105/joss.02481}, year = {2020}, publisher = {The Open Journal}, volume = {5}, number = {54}, pages = {2481}, author = {Thomas Germer and Tobias Uelwer and Stefan Conrad and Stefan Harmeling}, title = {PyMatting: A Python Library for Alpha Matting}, journal = {Journal of Open Source Software} } ``` ## References [1] Anat Levin, Dani Lischinski, and Yair Weiss. A closed-form solution to natural image matting. IEEE transactions on pattern analysis and machine intelligence, 30(2):228–242, 2007. [2] Kaiming He, Jian Sun, and Xiaoou Tang. Fast matting using large kernel matting laplacian matrices. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2165–2172. IEEE, 2010. [3] Qifeng Chen, Dingzeyu Li, and Chi-Keung Tang. Knn matting. IEEE transactions on pattern analysis and machine intelligence, 35(9):2175–2188, 2013. [4] Yuanjie Zheng and Chandra Kambhamettu. Learning based digital matting. In 2009 IEEE 12th international conference on computer vision, 889–896. IEEE, 2009. [5] Leo Grady, Thomas Schiwietz, Shmuel Aharon, and Rüdiger Westermann. Random walks for interactive alpha-matting. In Proceedings of VIIP, volume 2005, 423–429. 2005. [6] Germer, T., Uelwer, T., Conrad, S., & Harmeling, S. (2020). Fast Multi-Level Foreground Estimation. arXiv preprint arXiv:2006.14970. Lemur image by Mathias Appel from https://www.flickr.com/photos/mathiasappel/25419442300/ licensed under [CC0 1.0 Universal (CC0 1.0) Public Domain License](https://creativecommons.org/publicdomain/zero/1.0/). %package help Summary: Development documents and examples for PyMatting Provides: python3-PyMatting-doc %description help # PyMatting: A Python Library for Alpha Matting [![License: MIT](https://img.shields.io/github/license/pymatting/pymatting?color=brightgreen)](https://opensource.org/licenses/MIT) [![CI](https://img.shields.io/github/workflow/status/pymatting/pymatting/tests?label=tests)](https://github.com/pymatting/pymatting/actions?query=workflow%3Atests) [![PyPI](https://img.shields.io/pypi/v/pymatting)](https://pypi.org/project/PyMatting/) [![JOSS](https://joss.theoj.org/papers/9766cab65bfbf07a70c8a835edd3875a/status.svg)](https://joss.theoj.org/papers/9766cab65bfbf07a70c8a835edd3875a) [![Gitter](https://img.shields.io/gitter/room/pymatting/pymatting)](https://gitter.im/pymatting/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge) We introduce the PyMatting package for Python which implements various methods to solve the alpha matting problem. - **Website and Documentation:** [https://pymatting.github.io/](https://pymatting.github.io) - **Benchmarks:** [https://pymatting.github.io/benchmark.html](https://pymatting.github.io/benchmark.html) ![Lemur](https://github.com/pymatting/pymatting/raw/master/data/lemur/lemur_at_the_beach.png) Given an input image and a hand-drawn trimap (top row), alpha matting estimates the alpha channel of a foreground object which can then be composed onto a different background (bottom row). PyMatting provides: - Alpha matting implementations for: - Closed Form Alpha Matting [[1]](#1) - Large Kernel Matting [[2]](#2) - KNN Matting [[3]](#3) - Learning Based Digital Matting [[4]](#4) - Random Walk Matting [[5]](#5) - Foreground estimation implementations for: - Closed Form Foreground Estimation [[1]](#1) - Fast Multi-Level Foreground Estimation (CPU, CUDA and OpenCL) [[6]](#6) - Fast multithreaded KNN search - Preconditioners to accelerate the convergence rate of conjugate gradient descent: - The *incomplete thresholded Cholesky decomposition* (*Incomplete* is part of the name. The implementation is quite complete.) - The V-Cycle Geometric Multigrid preconditioner - Readable code leveraging [NumPy](https://numpy.org/), [SciPy](https://www.scipy.org/scipylib/index.html) and [Numba](http://numba.pydata.org/) ## Getting Started ### Requirements Minimal requiremens * numpy>=1.16.0 * pillow>=5.2.0 * numba>=0.47.0 * scipy>=1.1.0 Additional requirements for GPU support * cupy-cuda90>=6.5.0 or similar * pyopencl>=2019.1.2 Requirements to run the tests * pytest>=5.3.4 ### Installation with PyPI ```bash pip3 install pymatting ``` ### Installation from Source ```bash git clone https://github.com/pymatting/pymatting cd pymatting pip3 install . ``` ## Example ```python from pymatting import cutout cutout( # input image path "data/lemur/lemur.png", # input trimap path "data/lemur/lemur_trimap.png", # output cutout path "lemur_cutout.png") ``` [More advanced examples](https://pymatting.github.io/examples.html) ## Trimap Construction All implemented methods rely on trimaps which roughly classify the image into foreground, background and unknown reagions. Trimaps are expected to be `numpy.ndarrays` of type `np.float64` having the same shape as the input image with only one color-channel. Trimap values of 0.0 denote pixels which are 100% background. Similarly, trimap values of 1.0 denote pixels which are 100% foreground. All other values indicate unknown pixels which will be estimated by the algorithm. ## Testing Run the tests from the main directory: ``` python3 tests/download_images.py pip3 install -r requirements_tests.txt pytest ``` Currently 89% of the code is covered by tests. ## Upgrade ```bash pip3 install --upgrade pymatting python3 -c "import pymatting" ``` ## Bug Reports, Questions and Pull-Requests Please, see [our community guidelines](https://github.com/pymatting/pymatting/blob/master/CONTRIBUTING.md). ## Authors - **Thomas Germer** - **Tobias Uelwer** - **Stefan Conrad** - **Stefan Harmeling** See also the list of [contributors](https://github.com/pymatting/pymatting/contributors) who participated in this project. ## License This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details ## Citing If you found PyMatting to be useful for your work, please consider citing our [paper](https://doi.org/10.21105/joss.02481): ``` @article{Germer2020, doi = {10.21105/joss.02481}, url = {https://doi.org/10.21105/joss.02481}, year = {2020}, publisher = {The Open Journal}, volume = {5}, number = {54}, pages = {2481}, author = {Thomas Germer and Tobias Uelwer and Stefan Conrad and Stefan Harmeling}, title = {PyMatting: A Python Library for Alpha Matting}, journal = {Journal of Open Source Software} } ``` ## References [1] Anat Levin, Dani Lischinski, and Yair Weiss. A closed-form solution to natural image matting. IEEE transactions on pattern analysis and machine intelligence, 30(2):228–242, 2007. [2] Kaiming He, Jian Sun, and Xiaoou Tang. Fast matting using large kernel matting laplacian matrices. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2165–2172. IEEE, 2010. [3] Qifeng Chen, Dingzeyu Li, and Chi-Keung Tang. Knn matting. IEEE transactions on pattern analysis and machine intelligence, 35(9):2175–2188, 2013. [4] Yuanjie Zheng and Chandra Kambhamettu. Learning based digital matting. In 2009 IEEE 12th international conference on computer vision, 889–896. IEEE, 2009. [5] Leo Grady, Thomas Schiwietz, Shmuel Aharon, and Rüdiger Westermann. Random walks for interactive alpha-matting. In Proceedings of VIIP, volume 2005, 423–429. 2005. [6] Germer, T., Uelwer, T., Conrad, S., & Harmeling, S. (2020). Fast Multi-Level Foreground Estimation. arXiv preprint arXiv:2006.14970. Lemur image by Mathias Appel from https://www.flickr.com/photos/mathiasappel/25419442300/ licensed under [CC0 1.0 Universal (CC0 1.0) Public Domain License](https://creativecommons.org/publicdomain/zero/1.0/). %prep %autosetup -n PyMatting-1.1.8 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-PyMatting -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Fri May 05 2023 Python_Bot - 1.1.8-1 - Package Spec generated