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# PyMatting: A Python Library for Alpha Matting |
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[![License: MIT](https://img.shields.io/github/license/pymatting/pymatting?color=brightgreen)](https://opensource.org/licenses/MIT) |
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[![CI](https://img.shields.io/github/actions/workflow/status/pymatting/pymatting/.github/workflows/tests.yml?branch=master)](https://github.com/pymatting/pymatting/actions?query=workflow%3Atests) |
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[![PyPI](https://img.shields.io/pypi/v/pymatting)](https://pypi.org/project/PyMatting/) |
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[![JOSS](https://joss.theoj.org/papers/9766cab65bfbf07a70c8a835edd3875a/status.svg)](https://joss.theoj.org/papers/9766cab65bfbf07a70c8a835edd3875a) |
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[![Gitter](https://img.shields.io/gitter/room/pymatting/pymatting)](https://gitter.im/pymatting/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge) |
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We introduce the PyMatting package for Python which implements various methods to solve the alpha matting problem. |
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- **Website and Documentation:** [https://pymatting.github.io/](https://pymatting.github.io) |
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- **Benchmarks:** [https://pymatting.github.io/benchmarks.html](https://pymatting.github.io/benchmarks.html) |
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![Lemur](https://github.com/pymatting/pymatting/raw/master/data/lemur/lemur_at_the_beach.png) |
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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). |
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PyMatting provides: |
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- Alpha matting implementations for: |
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- Closed Form Alpha Matting [[1]](#1) |
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- Large Kernel Matting [[2]](#2) |
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- KNN Matting [[3]](#3) |
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- Learning Based Digital Matting [[4]](#4) |
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- Random Walk Matting [[5]](#5) |
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- Shared Sampling Matting [[6]](#6) |
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- Foreground estimation implementations for: |
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- Closed Form Foreground Estimation [[1]](#1) |
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- Fast Multi-Level Foreground Estimation (CPU, CUDA and OpenCL) [[7]](#7) |
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- Fast multithreaded KNN search |
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- Preconditioners to accelerate the convergence rate of conjugate gradient descent: |
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- The *incomplete thresholded Cholesky decomposition* (*Incomplete* is part of the name. The implementation is quite complete.) |
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- The V-Cycle Geometric Multigrid preconditioner |
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- Readable code leveraging [NumPy](https://numpy.org/), [SciPy](https://scipy.org/) and [Numba](http://numba.pydata.org/) |
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## Getting Started |
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### Requirements |
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Minimal requirements |
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* numpy>=1.16.0 |
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* pillow>=5.2.0 |
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* numba>=0.47.0 |
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* scipy>=1.1.0 |
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Additional requirements for GPU support |
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* cupy-cuda90>=6.5.0 or similar |
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* pyopencl>=2019.1.2 |
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Requirements to run the tests |
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* pytest>=5.3.4 |
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### Installation with PyPI |
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```bash |
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pip3 install pymatting |
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``` |
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### Installation from Source |
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```bash |
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git clone https://github.com/pymatting/pymatting |
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cd pymatting |
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pip3 install . |
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``` |
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## Example |
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```python |
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from pymatting import cutout |
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cutout( |
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# input image path |
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"data/lemur/lemur.png", |
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# input trimap path |
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"data/lemur/lemur_trimap.png", |
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# output cutout path |
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"lemur_cutout.png") |
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``` |
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[More advanced examples](https://pymatting.github.io/examples.html) |
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## Trimap Construction |
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All implemented methods rely on trimaps which roughly classify the image into foreground, background and unknown regions. |
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Trimaps are expected to be `numpy.ndarrays` of type `np.float64` having the same shape as the input image with only one color-channel. |
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Trimap values of 0.0 denote pixels which are 100% background. |
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Similarly, trimap values of 1.0 denote pixels which are 100% foreground. |
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All other values indicate unknown pixels which will be estimated by the algorithm. |
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## Testing |
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Run the tests from the main directory: |
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``` |
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python3 tests/download_images.py |
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pip3 install -r requirements_tests.txt |
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pytest |
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``` |
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Currently 89% of the code is covered by tests. |
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## Upgrade |
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```bash |
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pip3 install --upgrade pymatting |
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python3 -c "import pymatting" |
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``` |
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## Bug Reports, Questions and Pull-Requests |
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Please, see [our community guidelines](https://github.com/pymatting/pymatting/blob/master/CONTRIBUTING.md). |
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## Authors |
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- **Thomas Germer** |
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- **Tobias Uelwer** |
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- **Stefan Conrad** |
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- **Stefan Harmeling** |
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See also the list of [contributors](https://github.com/pymatting/pymatting/contributors) who participated in this project. |
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## Projects using PyMatting |
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* [Rembg](https://github.com/danielgatis/rembg) - an excellent tool for removing image backgrounds. |
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* [PaddleSeg](https://github.com/PaddlePaddle/PaddleSeg) - a library for a wide range of image segmentation tasks. |
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* [chaiNNer](https://github.com/chaiNNer-org/chaiNNer) - a node-based image processing GUI. |
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* [LSA-Matting](https://github.com/kfeng123/LSA-Matting) - improving deep image matting via local smoothness assumption. |
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## License |
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This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details |
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## Citing |
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If you found PyMatting to be useful for your work, please consider citing our [paper](https://doi.org/10.21105/joss.02481): |
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``` |
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@article{Germer2020, |
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doi = {10.21105/joss.02481}, |
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url = {https://doi.org/10.21105/joss.02481}, |
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year = {2020}, |
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publisher = {The Open Journal}, |
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volume = {5}, |
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number = {54}, |
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pages = {2481}, |
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author = {Thomas Germer and Tobias Uelwer and Stefan Conrad and Stefan Harmeling}, |
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title = {PyMatting: A Python Library for Alpha Matting}, |
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journal = {Journal of Open Source Software} |
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} |
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``` |
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## References |
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<a id="1">[1]</a> |
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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. |
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<a id="2">[2]</a> |
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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. |
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<a id="3">[3]</a> |
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Qifeng Chen, Dingzeyu Li, and Chi-Keung Tang. Knn matting. IEEE transactions on pattern analysis and machine intelligence, 35(9):2175–2188, 2013. |
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<a id="4">[4]</a> |
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Yuanjie Zheng and Chandra Kambhamettu. Learning based digital matting. In 2009 IEEE 12th international conference on computer vision, 889–896. IEEE, 2009. |
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<a id="5">[5]</a> |
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Leo Grady, Thomas Schiwietz, Shmuel Aharon, and Rüdiger Westermann. Random walks for interactive alpha-matting. In Proceedings of VIIP, volume 2005, 423–429. 2005. |
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<a id="6">[6]</a> |
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Eduardo S. L. Gastal and Manuel M. Oliveira. "Shared Sampling for Real-Time Alpha Matting". Computer Graphics Forum. Volume 29 (2010), Number 2, Proceedings of Eurographics 2010, pp. 575-584. |
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<a id="7">[7]</a> |
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Germer, T., Uelwer, T., Conrad, S., & Harmeling, S. (2020). Fast Multi-Level Foreground Estimation. arXiv preprint arXiv:2006.14970. |
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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/). |
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