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diff --git a/python-pymatting.spec b/python-pymatting.spec new file mode 100644 index 0000000..75b880e --- /dev/null +++ b/python-pymatting.spec @@ -0,0 +1,562 @@ +%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 +[](https://opensource.org/licenses/MIT) +[](https://github.com/pymatting/pymatting/actions?query=workflow%3Atests) +[](https://pypi.org/project/PyMatting/) +[](https://joss.theoj.org/papers/9766cab65bfbf07a70c8a835edd3875a) +[](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) + + + +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 + +<a id="1">[1]</a> +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. + +<a id="2">[2]</a> +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. + +<a id="3">[3]</a> +Qifeng Chen, Dingzeyu Li, and Chi-Keung Tang. Knn matting. IEEE transactions on pattern analysis and machine intelligence, 35(9):2175–2188, 2013. + +<a id="4">[4]</a> +Yuanjie Zheng and Chandra Kambhamettu. Learning based digital matting. In 2009 IEEE 12th international conference on computer vision, 889–896. IEEE, 2009. + +<a id="5">[5]</a> +Leo Grady, Thomas Schiwietz, Shmuel Aharon, and Rüdiger Westermann. Random walks for interactive alpha-matting. In Proceedings of VIIP, volume 2005, 423–429. 2005. + +<a id="6">[6]</a> +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 +[](https://opensource.org/licenses/MIT) +[](https://github.com/pymatting/pymatting/actions?query=workflow%3Atests) +[](https://pypi.org/project/PyMatting/) +[](https://joss.theoj.org/papers/9766cab65bfbf07a70c8a835edd3875a) +[](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) + + + +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 + +<a id="1">[1]</a> +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. + +<a id="2">[2]</a> +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. + +<a id="3">[3]</a> +Qifeng Chen, Dingzeyu Li, and Chi-Keung Tang. Knn matting. IEEE transactions on pattern analysis and machine intelligence, 35(9):2175–2188, 2013. + +<a id="4">[4]</a> +Yuanjie Zheng and Chandra Kambhamettu. Learning based digital matting. In 2009 IEEE 12th international conference on computer vision, 889–896. IEEE, 2009. + +<a id="5">[5]</a> +Leo Grady, Thomas Schiwietz, Shmuel Aharon, and Rüdiger Westermann. Random walks for interactive alpha-matting. In Proceedings of VIIP, volume 2005, 423–429. 2005. + +<a id="6">[6]</a> +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 +[](https://opensource.org/licenses/MIT) +[](https://github.com/pymatting/pymatting/actions?query=workflow%3Atests) +[](https://pypi.org/project/PyMatting/) +[](https://joss.theoj.org/papers/9766cab65bfbf07a70c8a835edd3875a) +[](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) + + + +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 + +<a id="1">[1]</a> +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. + +<a id="2">[2]</a> +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. + +<a id="3">[3]</a> +Qifeng Chen, Dingzeyu Li, and Chi-Keung Tang. Knn matting. IEEE transactions on pattern analysis and machine intelligence, 35(9):2175–2188, 2013. + +<a id="4">[4]</a> +Yuanjie Zheng and Chandra Kambhamettu. Learning based digital matting. In 2009 IEEE 12th international conference on computer vision, 889–896. IEEE, 2009. + +<a id="5">[5]</a> +Leo Grady, Thomas Schiwietz, Shmuel Aharon, and Rüdiger Westermann. Random walks for interactive alpha-matting. In Proceedings of VIIP, volume 2005, 423–429. 2005. + +<a id="6">[6]</a> +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 <Python_Bot@openeuler.org> - 1.1.8-1 +- Package Spec generated |