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planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/color_deconvolution/ commit f546b3cd5cbd3a8613cd517975c7ad1d1f83514e
author | imgteam |
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date | Thu, 06 Mar 2025 18:12:27 +0000 |
parents | 612aa1478fe1 |
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import argparse import sys import warnings import giatools.io import numpy as np import skimage.color import skimage.io import skimage.util import tifffile from sklearn.decomposition import FactorAnalysis, FastICA, NMF, PCA # Stain separation matrix for H&E color deconvolution, extracted from ImageJ/FIJI rgb_from_he = np.array([ [0.64431860, 0.7166757, 0.26688856], [0.09283128, 0.9545457, 0.28324000], [0.63595444, 0.0010000, 0.77172660], ]) convOptions = { # General color space conversion operations 'hed2rgb': lambda img_raw: skimage.color.hed2rgb(img_raw), 'hsv2rgb': lambda img_raw: skimage.color.hsv2rgb(img_raw), 'lab2lch': lambda img_raw: skimage.color.lab2lch(img_raw), 'lab2rgb': lambda img_raw: skimage.color.lab2rgb(img_raw), 'lab2xyz': lambda img_raw: skimage.color.lab2xyz(img_raw), 'lch2lab': lambda img_raw: skimage.color.lch2lab(img_raw), 'luv2rgb': lambda img_raw: skimage.color.luv2rgb(img_raw), 'luv2xyz': lambda img_raw: skimage.color.luv2xyz(img_raw), 'rgb2hed': lambda img_raw: skimage.color.rgb2hed(img_raw), 'rgb2hsv': lambda img_raw: skimage.color.rgb2hsv(img_raw), 'rgb2lab': lambda img_raw: skimage.color.rgb2lab(img_raw), 'rgb2luv': lambda img_raw: skimage.color.rgb2luv(img_raw), 'rgb2rgbcie': lambda img_raw: skimage.color.rgb2rgbcie(img_raw), 'rgb2xyz': lambda img_raw: skimage.color.rgb2xyz(img_raw), 'rgbcie2rgb': lambda img_raw: skimage.color.rgbcie2rgb(img_raw), 'xyz2lab': lambda img_raw: skimage.color.xyz2lab(img_raw), 'xyz2luv': lambda img_raw: skimage.color.xyz2luv(img_raw), 'xyz2rgb': lambda img_raw: skimage.color.xyz2rgb(img_raw), # Color deconvolution operations 'hed_from_rgb': lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.hed_from_rgb), 'hdx_from_rgb': lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.hdx_from_rgb), 'fgx_from_rgb': lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.fgx_from_rgb), 'bex_from_rgb': lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.bex_from_rgb), 'rbd_from_rgb': lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.rbd_from_rgb), 'gdx_from_rgb': lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.gdx_from_rgb), 'hax_from_rgb': lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.hax_from_rgb), 'bro_from_rgb': lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.bro_from_rgb), 'bpx_from_rgb': lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.bpx_from_rgb), 'ahx_from_rgb': lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.ahx_from_rgb), 'hpx_from_rgb': lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.hpx_from_rgb), # Recomposition operations (reverse color deconvolution) 'rgb_from_hed': lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_hed), 'rgb_from_hdx': lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_hdx), 'rgb_from_fgx': lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_fgx), 'rgb_from_bex': lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_bex), 'rgb_from_rbd': lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_rbd), 'rgb_from_gdx': lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_gdx), 'rgb_from_hax': lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_hax), 'rgb_from_bro': lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_bro), 'rgb_from_bpx': lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_bpx), 'rgb_from_ahx': lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_ahx), 'rgb_from_hpx': lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_hpx), # Custom color deconvolution and recomposition operations 'rgb_from_he': lambda img_raw: skimage.color.combine_stains(img_raw, rgb_from_he), 'he_from_rgb': lambda img_raw: skimage.color.separate_stains(img_raw, np.linalg.inv(rgb_from_he)), # Unsupervised machine learning-based operations 'pca': lambda img_raw: np.reshape(PCA(n_components=3).fit_transform(np.reshape(img_raw, [-1, img_raw.shape[2]])), [img_raw.shape[0], img_raw.shape[1], -1]), 'nmf': lambda img_raw: np.reshape(NMF(n_components=3, init='nndsvda').fit_transform(np.reshape(img_raw, [-1, img_raw.shape[2]])), [img_raw.shape[0], img_raw.shape[1], -1]), 'ica': lambda img_raw: np.reshape(FastICA(n_components=3).fit_transform(np.reshape(img_raw, [-1, img_raw.shape[2]])), [img_raw.shape[0], img_raw.shape[1], -1]), 'fa': lambda img_raw: np.reshape(FactorAnalysis(n_components=3).fit_transform(np.reshape(img_raw, [-1, img_raw.shape[2]])), [img_raw.shape[0], img_raw.shape[1], -1]) } parser = argparse.ArgumentParser() parser.add_argument('input_file', type=argparse.FileType('r'), default=sys.stdin, help='input file') parser.add_argument('out_file', type=argparse.FileType('w'), default=sys.stdin, help='out file (TIFF)') parser.add_argument('conv_type', choices=convOptions.keys(), help='conversion type') parser.add_argument('--isolate_channel', type=int, help='set all other channels to zero (1-3)', default=0) args = parser.parse_args() # Read and normalize the input image as TZYXC img_in = giatools.io.imread(args.input_file.name) # Verify input image assert img_in.shape[0] == 1, f'Image must have 1 frame (it has {img_in.shape[0]} frames)' assert img_in.shape[1] == 1, f'Image must have 1 slice (it has {img_in.shape[1]} slices)' assert img_in.shape[4] == 3, f'Image must have 3 channels (it has {img_in.shape[4]} channels)' # Normalize the image from TZYXC to YXC img_in = img_in.squeeze() assert img_in.ndim == 3 # Apply channel isolation if args.isolate_channel: for ch in range(3): if ch + 1 != args.isolate_channel: img_in[:, :, ch] = 0 result = convOptions[args.conv_type](img_in) # It is sufficient to store 32bit floating point data, the precision loss is tolerable if result.dtype == np.float64: result = result.astype(np.float32) with warnings.catch_warnings(): warnings.simplefilter('ignore') tifffile.imwrite(args.out_file.name, result)