Comments (2)
I made a PR which adds reinhard support with numpy backend: #25
Regarding the other backends, it would be beneficial to use tensorflow-io
, as it supports both rgb2lab
and lab2rgb
, which was annoying to implement from scratch in numpy:
https://www.tensorflow.org/io/api_docs/python/tfio/experimental/color/rgb_to_lab
In pytorch there exists a library that have the same color conversion methods:
https://github.com/richzhang/colorization-pytorch/blob/66a1cb2e5258f7c8f374f582acc8b1ef99c13c27/util/util.py#L172
We could either use these libraries directly, or make our own implementations, but this will take time, but I could make an attempt. Will make a separate PR for that, as it will likely be a lot of work.
The reinhard transform itself is quite straight forward to implement.
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New PR which adds torch backend support: #27
Now all backends should be supported after it has been merged. Hence, closing this issue.
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Related Issues (20)
- Support for Vahadane normalization HOT 2
- Multiple backends support HOT 7
- Update runtimes HOT 2
- Conda compatibility?
- Update About and tags HOT 1
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- Support for Modified Reinhard HOT 5
- Support for Jax HOT 24
- Support for torch.linalg.lstsq HOT 3
- Crash when calling MacenkoNormalizer.fit with tensorflow backend HOT 12
- Whole Slide Images HOT 7
- Add stain augmentation alternative HOT 1
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- No GPU acceleration in torch backend
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