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David Rotermund 2023-07-13 15:30:48 +02:00 committed by GitHub
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@ -39,3 +39,26 @@ Not all packages are necessary (probably these are enougth: torch torchaudio tor
We used a RTX 3090 as test GPU. We used a RTX 3090 as test GPU.
For installing torch under Windows see here: https://pytorch.org/get-started/locally/ For installing torch under Windows see here: https://pytorch.org/get-started/locally/
# Processing pipeline:
## run_svd.py
- convert avi file to npy file
- load npy file into RAM (np.ndarray: input)
- copy to the GPU (input -> torch.Tensor: data)
- do some pre-processing for helping to find landmarks in the movie [needs improvement!]
- select a reference image
- calculate translation changes between the reference image and the frames of the movie
- copy to the GPU (input -> torch.Tensor: data)
- apply spatial shift to compensate for movement between reference image and the frames
- calculate a SVD over the whole movie. Calculate whitening matrices and co from it.
- whiten the movie and average over the spatial dimensions -> data_svd
- copy to the GPU (input -> torch.Tensor: data)
- calculate scaling factor between data_svd and data for all the individual pixels.
- scale data_svd (-> to_remove) and remove it from data (data -= to_remove)
- the movie is downsamples in time
- torchaudio.functional.resample from 30fps to 3 fps
- bandpass filter 0.1 - 1.0 Hz
- SVD Denosing
- torch.nn.AvgPool2d
- save as ..._decorrelated.npy