torchmaxflow 0.0.1

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torchmaxflow: Max-flow/Min-cut in PyTorch for 2D images and 3D volumes

Pytorch-based implementation of Max-flow/Min-cut based on the following paper:

• Boykov, Yuri, and Vladimir Kolmogorov. "An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision." IEEE transactions on pattern analysis and machine intelligence 26.9 (2004): 1124-1137.

This repository depends on the code for maxflow from: https://pub.ist.ac.at/~vnk/software/maxflow-v3.04.src.zip, which has been included.

Installation instructions

pip install git+https://github.com/masadcv/torchmaxflow

TODO:

pip install torchmaxflow

Example usage

The following demonstrates a simple example showing torchmaxflow usage:

image = np.asarray(Image.open('data/image2d.png').convert('L'), np.float32)
image = torch.from_numpy(image).unsqueeze(0).unsqueeze(0)

prob = np.asarray(Image.open('data/image2d_prob.png'), np.float32)
prob = torch.from_numpy(prob).unsqueeze(0)

lamda = 20.0
sigma = 10.0

post_proc_label = torchmaxflow.maxflow(image, prob, lamda, sigma)

For more usage examples see:

2D and 3D maxflow and interactive maxflow examples: demo_maxflow.py

References

• SimpleCRF's maxflow implementation: https://github.com/HiLab-git/SimpleCRF
• Yuri Boykov and Vladimir Kolmogorov's maxflow implementation: https://pub.ist.ac.at/~vnk/software/maxflow-v3.04.src.zip

Citation

If you use this code in your research, then please consider citing:

Asad, Muhammad, Lucas Fidon, and Tom Vercauteren. "ECONet: Efficient Convolutional Online Likelihood Network for Scribble-based Interactive Segmentation." arXiv preprint arXiv:2201.04584 (2022).