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Poisson Blending with adapted gradients

This library is a simple demonstration of using Optimal Transport Mapping Estimation [1] in a context of seamless copy between images. See [1] for details over the method

Installation

The Library has been tested on Linux and MacOSX. Among other classical dependencies, it requires the installation of POT, the Python Optimal Transport library (https://github.com/rflamary/POT)

  • Numpy (>=1.11)
  • Scipy (>=0.17)
  • Matplotlib (>=1.5)
  • Pyamg (>=3.1)
  • POT (>=1.0)

If you want to execute the video demo, then you also need to have OpenCV for python installed.

Examples

One notebook is provided as example of use:

The video demo is available in the test_video.py file.

References

[1] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for discrete optimal transport", Neural Information Processing Systems (NIPS), 2016.

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