Tools for tissue image stain normalization and augmentation in Python 3.
pip install staintools- Install SPAMS. This is a dependency to staintools and is technically available on PyPI (see here). However, personally I have had some issues with the PyPI install and would instead recommend using conda (see here).
Original images:
Stain normalized images:
# Read data target = staintools.read_image("./data/my_target_image.png") to_transform = staintools.read_image("./data/my_image_to_transform.png") # Standardize brightness (optional, can improve the tissue mask calculation) target = staintools.LuminosityStandardizer.standardize(target) to_transform = staintools.LuminosityStandardizer.standardize(to_transform) # Stain normalize normalizer = staintools.StainNormalizer(method='vahadane') normalizer.fit(target) transformed = normalizer.transform(to_transform)
# Read data to_augment = staintools.read_image("./data/my_image_to_augment.png") # Standardize brightness (optional, can improve the tissue mask calculation) to_augment = staintools.LuminosityStandardizer.standardize(to_augment) # Stain augment augmentor = staintools.StainAugmentor(method='vahadane', sigma1=0.2, sigma2=0.2) augmentor.fit(to_augment) augmented_images = [] for _ in range(100): augmented_image = augmentor.pop() augmented_images.append(augmented_image)For more examples see files inside of the examples directory.