templeblock / pymatting

A Python library for alpha matting

PyMatting: A Python Library for Alpha Matting

We introduce the PyMatting package for Python which implements various methods to solve the alpha matting problem.

• Website and Documentation: https://pymatting.github.io/
• Benchmarks: 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]
  • Large Kernel Matting [2]
  • KNN Matting [3]
  • Learning Based Digital Matting [4]
  • Random Walk Matting [5]
• Foreground estimation implementations for:
  • Closed Form Foreground Estimation [1]
  • Multilevel Foreground Estimation (CPU, CUDA and OpenCL)
• 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, SciPy and Numba

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

pip3 install pymatting

Installation from Source

git clone https://github.com/pymatting/pymatting
cd pymatting
pip3 install .

Example

from pymatting import cutout

cutout(
    # input image path
    "data/lemur.png",
    # input trimap path
    "data/lemur_trimap.png",
    # output cutout path
    "lemur_cutout.png")

More advanced examples

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.

Authors

• Thomas Germer
• Tobias Uelwer
• Stefan Conrad
• Stefan Harmeling

See also the list of contributors who participated in this project.

License

This project is licensed under the MIT License - see the LICENSE.md file for details

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.