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reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["alpha-matting","foreground","image-processing","python3"],"created_at":"2024-07-30T19:00:44.432Z","updated_at":"2026-01-26T11:08:11.079Z","avatar_url":"https://github.com/pymatting.png","language":"Python","funding_links":[],"categories":["Uncategorized","Image Processing","资源列表","Python","Tools"],"sub_categories":["Uncategorized","图像处理"],"readme":"# PyMatting: A Python Library for Alpha Matting\n[![License: MIT](https://img.shields.io/github/license/pymatting/pymatting?color=brightgreen)](https://opensource.org/licenses/MIT)\n[![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)\n[![PyPI](https://img.shields.io/pypi/v/pymatting)](https://pypi.org/project/PyMatting/)\n[![JOSS](https://joss.theoj.org/papers/9766cab65bfbf07a70c8a835edd3875a/status.svg)](https://joss.theoj.org/papers/9766cab65bfbf07a70c8a835edd3875a)\n[![Gitter](https://img.shields.io/gitter/room/pymatting/pymatting)](https://gitter.im/pymatting/community?utm_source=badge\u0026utm_medium=badge\u0026utm_campaign=pr-badge)\n\nWe introduce the PyMatting package for Python which implements various methods to solve the alpha matting problem.\n\n- **Website and Documentation:** [https://pymatting.github.io/](https://pymatting.github.io)\n- **Benchmarks:**  [https://pymatting.github.io/benchmarks.html](https://pymatting.github.io/benchmarks.html)\n\n![Lemur](https://github.com/pymatting/pymatting/raw/master/data/lemur/lemur_at_the_beach.png)\n\nGiven 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).\n\nPyMatting provides:\n- Alpha matting implementations for:\n  - Closed Form Alpha Matting [[1]](#1)\n  - Large Kernel Matting [[2]](#2)\n  - KNN Matting [[3]](#3)\n  - Learning Based Digital Matting [[4]](#4)\n  - Random Walk Matting [[5]](#5)\n  - Shared Sampling Matting [[6]](#6)\n- Foreground estimation implementations for:\n  - Closed Form Foreground Estimation [[1]](#1)\n  - Fast Multi-Level Foreground Estimation (CPU, CUDA and OpenCL) [[7]](#7)\n- Fast multithreaded KNN search\n- Preconditioners to accelerate the convergence rate of conjugate gradient descent:\n  - The *incomplete thresholded Cholesky decomposition* (*Incomplete* is part of the name. The implementation is quite complete.)\n  - The V-Cycle Geometric Multigrid preconditioner\n- Readable code leveraging [NumPy](https://numpy.org/), [SciPy](https://scipy.org/) and [Numba](http://numba.pydata.org/)\n\n## Getting Started\n\n### Requirements\n\nMinimal requirements\n* numpy\u003e=1.16.0\n* pillow\u003e=5.2.0\n* numba\u003e=0.47.0\n* scipy\u003e=1.1.0\n\nAdditional requirements for GPU support\n* cupy-cuda90\u003e=6.5.0 or similar\n* pyopencl\u003e=2019.1.2\n\nRequirements to run the tests\n* pytest\u003e=5.3.4\n\n### Installation with PyPI\n\n```bash\npip3 install pymatting\n```\n\n### Installation from Source\n\n```bash\ngit clone https://github.com/pymatting/pymatting\ncd pymatting\npip3 install .\n```\n\n## Example\n\n```python\n# First import will take a minute due to compilation\nfrom pymatting import cutout\n\ncutout(\n    # input image path\n    \"data/lemur/lemur.png\",\n    # input trimap path\n    \"data/lemur/lemur_trimap.png\",\n    # output cutout path\n    \"lemur_cutout.png\")\n```\n\n[More advanced examples](https://pymatting.github.io/examples.html)\n\n## Trimap Construction\n\nAll implemented methods rely on trimaps which roughly classify the image into foreground, background and unknown regions.\nTrimaps are expected to be `numpy.ndarrays` of type `np.float64`  having the same shape as the input image with only one color-channel.\nTrimap values of 0.0 denote pixels which are 100% background.\nSimilarly, trimap values of 1.0 denote pixels which are 100% foreground.\nAll other values indicate unknown pixels which will be estimated by the algorithm.\n\n\n## Testing\n\nRun the tests from the main directory:\n```\npip3 install -r .[test]\npytest\n```\n\nCurrently 89% of the code is covered by tests.\n\n## Upgrade\n\n```bash\npip3 install --upgrade pymatting\npython3 -c \"import pymatting\"\n```\n\n## GPU-Support\n\nThere is\n[CuPy](https://pymatting.github.io/foreground.html#estimate_foreground_ml_cupy)\nand\n[PyOpenCL](https://pymatting.github.io/foreground.html#estimate_foreground_ml_pyopencl)\nsupport for foreground estimation.\n\n```\npip3 install -r .[gpu]\npytest tests/test_foreground.py\n```\n\nYou still need to install proper drivers for GPU support separately.\n\nIf you have installed drivers, but CuPy can not find `libnvrtc.so.12`, you might have link to CUDA libraries manually:\n\n```\nsudo apt install plocate\n# might resolve to something like /home/user/myenv/lib/python3.12/site-packages/nvidia/cuda_nvrtc/lib/\nexport LD_LIBRARY_PATH=\"$(dirname $(locate libnvrtc.so.12)):$LD_LIBRARY_PATH\"\n```\n\nFor PyOpenCL, see [docs](https://documen.tician.de/pyopencl/misc.html).  \u003c!-- codespell:ignore documen --\u003e\nYou can select computing device by setting `PYOPENCL_CTX` environment variable.\n\nThere currently is no GPU support for alpha estimation, only for foreground estimation.\n\n## Bug Reports, Questions and Pull-Requests\n\nPlease, see [our community guidelines](https://github.com/pymatting/pymatting/blob/master/CONTRIBUTING.md).\n\n## Authors\n\n- **Thomas Germer**\n- **Tobias Uelwer**\n- **Stefan Conrad**\n- **Stefan Harmeling**\n\nSee also the list of [contributors](https://github.com/pymatting/pymatting/contributors) who participated in this project.\n\n## Projects using PyMatting\n\n* [Rembg](https://github.com/danielgatis/rembg) - an excellent tool for removing image backgrounds.\n* [PaddleSeg](https://github.com/PaddlePaddle/PaddleSeg) - a library for a wide range of image segmentation tasks.\n* [chaiNNer](https://github.com/chaiNNer-org/chaiNNer) - a node-based image processing GUI.\n* [LSA-Matting](https://github.com/kfeng123/LSA-Matting) - improving deep image matting via local smoothness assumption.\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details\n\n## Citing\n\nIf you found PyMatting to be useful for your work, please consider citing our [paper](https://doi.org/10.21105/joss.02481):\n\n```\n@article{Germer2020,\n  doi = {10.21105/joss.02481},\n  url = {https://doi.org/10.21105/joss.02481},\n  year = {2020},\n  publisher = {The Open Journal},\n  volume = {5},\n  number = {54},\n  pages = {2481},\n  author = {Thomas Germer and Tobias Uelwer and Stefan Conrad and Stefan Harmeling},\n  title = {PyMatting: A Python Library for Alpha Matting},\n  journal = {Journal of Open Source Software}\n}\n```\n\n## References\n\n\u003ca id=\"1\"\u003e[1]\u003c/a\u003e\nAnat 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.\n\n\u003ca id=\"2\"\u003e[2]\u003c/a\u003e\nKaiming 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.\n\n\u003ca id=\"3\"\u003e[3]\u003c/a\u003e\nQifeng Chen, Dingzeyu Li, and Chi-Keung Tang. Knn matting. IEEE transactions on pattern analysis and machine intelligence, 35(9):2175–2188, 2013.\n\n\u003ca id=\"4\"\u003e[4]\u003c/a\u003e\nYuanjie Zheng and Chandra Kambhamettu. Learning based digital matting. In 2009 IEEE 12th international conference on computer vision, 889–896. IEEE, 2009.\n\n\u003ca id=\"5\"\u003e[5]\u003c/a\u003e\nLeo Grady, Thomas Schiwietz, Shmuel Aharon, and Rüdiger Westermann. Random walks for interactive alpha-matting. In Proceedings of VIIP, volume 2005, 423–429. 2005.\n\n\u003ca id=\"6\"\u003e[6]\u003c/a\u003e\nEduardo 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.\n\n\u003ca id=\"7\"\u003e[7]\u003c/a\u003e\nGermer, T., Uelwer, T., Conrad, S., \u0026 Harmeling, S. (2020). Fast Multi-Level Foreground Estimation. arXiv preprint arXiv:2006.14970.\n\nLemur 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/).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpymatting%2Fpymatting","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpymatting%2Fpymatting","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpymatting%2Fpymatting/lists"}