@carloalbertobarbano

Just merged here https://github.com/EIDOSLAB/torchstain/blob/main/torchstain/torch/normalizers/multitarget.py should work with torch :)

@carloalbertobarbano Unfortunately, there seems to be a bug making it challenging for me to use it. I opened a PR to make it working, at least similarly to the other methods. See PR #64.

Should be fixed in the development branch in commit 7c2a95f you should be able to install it with

pip3 install git+https://github.com/EIDOSLAB/torchstain.git@development

Example usage:

target = cv2.resize(cv2.cvtColor(cv2.imread(os.path.join(curr_file_path, "../data/target.png")), cv2.COLOR_BGR2RGB), (size, size))
to_transform = cv2.resize(cv2.cvtColor(cv2.imread(os.path.join(curr_file_path, "../data/source.png")), cv2.COLOR_BGR2RGB), (size, size))

# setup preprocessing and preprocess image to be normalized
T = transforms.Compose([
    transforms.ToTensor(),
    transforms.Lambda(lambda x: x * 255)
])
target = T(target)
t_to_transform = T(to_transform)

multi_normalizer = torchstain.normalizers.MultiMacenkoNormalizer(backend="torch", norm_mode="avg-post")
multi_normalizer.fit([target, target, target])

result_multi, _, _ = multi_normalizer.normalize(I=t_to_transform, stains=True)

of course instead of [target, target, target] you are supposed to open multiple reference images

@carloalbertobarbano Oh, this fit([target, target, target]) was new to me.

Why don't we just do .fit(target) and move this special case handling inside the specific MultiMacenkoNormalizer.fit()?

I argue that it should be as easy as possible for the user to use this method, and that it should follow the same API, as much as possible, for all normalizers.

Because it is supposed to work with multiple different reference images, so .fit should take an array

Because it is supposed to work with multiple different reference images, so .fit should take an array

Aaah, makes sense. Thanks for the clarification. But then after this is merged, I will make a PR to extend this to the different backends, and add the appropriate tests.

@carloalbertobarbano Created PR #66 to add support for the missing NumPy and TensorFlow backends :]

Seems to work! At least new tests pass, indicating that we reach the same result as for PyTorch backend.