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wavelet-texture-synthesis

Code for the paper: "Generalized Rectifier Wavelet Covariance Model For texture Synthesis" (Brochard, Zhang, Mallat, ICLR 2022) https://openreview.net/pdf?id=ziRLU3Y2PN_.

Requirements:

  • Pytorch (version >=1.8.0)
  • Kymatio (pip install kymatio) to create the Morlet wavelet filters.
  • Numpy, scipy, scikit-image, Pillow, tqdm, matplotlib

Create the wavelet filters by running python build-filters.py

To generate a synthesis:

  • Grayscale images: python synthesis/gray.py
  • Color images: python synthesis/color.py

Specify the input image with the argument --image (e.g. python synthesis/color.py --image honeycomb). The argument --save stores the synthesis in the 'results' folder, in .npy format. Other arguments, to specify the model, can be found in 'synthesis/gray.py' and 'synthesis/color.py'.

To generate a synthesis using PS model:

  • Grayscale images: ps/demo_gray.m
  • Color images: ps/demo_color.m

p.s. You need download the original Matlab code (http://www.cns.nyu.edu/~lcv/texture/)

To generate a synthesis using RF model:

  • Grayscale images: rf/do_synthesis0.sh
  • Color images: rf/do_synthesis01.sh

p.s. You need install the following packages using conda:

  • theano, version 1.0.4
  • matplotlib, scikit-image
  • Lasagne-0.2.dev1, install via pip:
 pip3  install --upgrade https://github.com/Lasagne/Lasagne/archive/master.zip # Lasagne-0.2.dev1
  • To use GPU, configure ~/.theanorc
[global]
floatX = float32
device = cuda

License

Releasing under an Apache-2.0 license.