Metadata-Version: 2.1
Name: PyMatting
Version: 1.1.4
Summary: Python package for alpha matting.
Home-page: https://pymatting.github.io
Author: The PyMatting Developers
Author-email: pymatting@gmail.com
License: MIT
Project-URL: Source, https://github.com/pymatting/pymatting
Keywords: alpha matting
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3
Description-Content-Type: text/markdown
Requires-Dist: numpy (>=1.16.0)
Requires-Dist: pillow (>=5.2.0)
Requires-Dist: numba (!=0.49.0)
Requires-Dist: scipy (>=1.1.0)

# PyMatting: A Python Library for Alpha Matting
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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"
```

The last line is necessary to rebuild the ahead-of-time compiled module. Without it, the module will be rebuilt on first import, but the old module will already be loaded at that point, which might cause compatibility issues. Simply re-running the code should usually fix it.

## 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/).


