This is an implementation of the Complex Steerable Pyramid described in Portilla and Simoncelli (IJCV, 2000), forked from tomrunia/PyTorchSteerablePyramid
The complex steerable pyramid expects a batch of images of shape ([N,C,H,W] for Pytorch and [N, W, H, C] for Tensorflow) with current support only for grayscale images (C=1). It returns a list structure containing the low-pass, high-pass and intermediate levels of the pyramid for each image in the batch (as torch.Tensor and tf.Tensor). Computing the steerable pyramid is significantly faster on the GPU as can be observed from the runtime benchmark below.
Please check the scripts in examples/
test_tf_numpy.py and test_tf_torch.py in tests/
Performing parallel the CSP decomposition on the GPU results in a significant speed-up. Increasing the batch size will give faster runtimes. The plot below shows a comprison between the numpy, torch and tensorflow implementations as function of the batch size N and input signal length. These results were obtained on a powerful Linux desktop with NVIDIA GTX1080Ti GPU.
Clone and install:
https://github.com/wtomin/SteerablePyramid_Torch_TF_Numpy.git
cd SteerablePyramid_Torch_TF_Numpy
pip install -r requirements.txt
python setup.py install- Python 2.7 or 3.6 (other versions might also work)
- Numpy (developed with 1.15.4)
- PyTorch >= 0.4.0 (developed with 1.0.0)
- Tensorflow >= 2.1.0
- J. Portilla and E.P. Simoncelli, Complex Steerable Pyramid (IJCV, 2000)
- The Steerable Pyramid
- Official implementation: matPyrTools
- perceptual repository by Dzung Nguyen
MIT License
Copyright (c) 2018 Didan Deng (dengdidanwtomin@gmail.com)
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