Mercurial > repos > imgteam > color_deconvolution

comparison color_deconvolution.py @ 2:387414aa6496 draft default tip

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/color_deconvolution/ commit f546b3cd5cbd3a8613cd517975c7ad1d1f83514e
author imgteam
date Thu, 06 Mar 2025 18:12:13 +0000
parents 29110ca1b63a
children
comparison
equal deleted inserted replaced
1:29110ca1b63a 2:387414aa6496
1 import argparse 1 import argparse
2 import sys 2 import sys
3 import warnings 3 import warnings
4
5 import giatools.io
4 import numpy as np 6 import numpy as np
7 import skimage.color
5 import skimage.io 8 import skimage.io
6 import skimage.color
7 import skimage.util 9 import skimage.util
8 from sklearn.decomposition import PCA, NMF, FastICA, FactorAnalysis 10 import tifffile
11 from sklearn.decomposition import FactorAnalysis, FastICA, NMF, PCA
12
13 # Stain separation matrix for H&E color deconvolution, extracted from ImageJ/FIJI
14 rgb_from_he = np.array([
15 [0.64431860, 0.7166757, 0.26688856],
16 [0.09283128, 0.9545457, 0.28324000],
17 [0.63595444, 0.0010000, 0.77172660],
18 ])
9 19
10 convOptions = { 20 convOptions = {
11 'hed2rgb' : lambda img_raw: skimage.color.hed2rgb(img_raw), 21 # General color space conversion operations
12 'hsv2rgb' : lambda img_raw: skimage.color.hsv2rgb(img_raw), 22 'hed2rgb': lambda img_raw: skimage.color.hed2rgb(img_raw),
13 'lab2lch' : lambda img_raw: skimage.color.lab2lch(img_raw), 23 'hsv2rgb': lambda img_raw: skimage.color.hsv2rgb(img_raw),
14 'lab2rgb' : lambda img_raw: skimage.color.lab2rgb(img_raw), 24 'lab2lch': lambda img_raw: skimage.color.lab2lch(img_raw),
15 'lab2xyz' : lambda img_raw: skimage.color.lab2xyz(img_raw), 25 'lab2rgb': lambda img_raw: skimage.color.lab2rgb(img_raw),
16 'lch2lab' : lambda img_raw: skimage.color.lch2lab(img_raw), 26 'lab2xyz': lambda img_raw: skimage.color.lab2xyz(img_raw),
17 'luv2rgb' : lambda img_raw: skimage.color.luv2rgb(img_raw), 27 'lch2lab': lambda img_raw: skimage.color.lch2lab(img_raw),
18 'luv2xyz' : lambda img_raw: skimage.color.luv2xyz(img_raw), 28 'luv2rgb': lambda img_raw: skimage.color.luv2rgb(img_raw),
19 'rgb2hed' : lambda img_raw: skimage.color.rgb2hed(img_raw), 29 'luv2xyz': lambda img_raw: skimage.color.luv2xyz(img_raw),
20 'rgb2hsv' : lambda img_raw: skimage.color.rgb2hsv(img_raw), 30 'rgb2hed': lambda img_raw: skimage.color.rgb2hed(img_raw),
21 'rgb2lab' : lambda img_raw: skimage.color.rgb2lab(img_raw), 31 'rgb2hsv': lambda img_raw: skimage.color.rgb2hsv(img_raw),
22 'rgb2luv' : lambda img_raw: skimage.color.rgb2luv(img_raw), 32 'rgb2lab': lambda img_raw: skimage.color.rgb2lab(img_raw),
23 'rgb2rgbcie' : lambda img_raw: skimage.color.rgb2rgbcie(img_raw), 33 'rgb2luv': lambda img_raw: skimage.color.rgb2luv(img_raw),
24 'rgb2xyz' : lambda img_raw: skimage.color.rgb2xyz(img_raw), 34 'rgb2rgbcie': lambda img_raw: skimage.color.rgb2rgbcie(img_raw),
25 #'rgb2ycbcr' : lambda img_raw: skimage.color.rgb2ycbcr(img_raw), 35 'rgb2xyz': lambda img_raw: skimage.color.rgb2xyz(img_raw),
26 #'rgb2yiq' : lambda img_raw: skimage.color.rgb2yiq(img_raw), 36 'rgbcie2rgb': lambda img_raw: skimage.color.rgbcie2rgb(img_raw),
27 #'rgb2ypbpr' : lambda img_raw: skimage.color.rgb2ypbpr(img_raw), 37 'xyz2lab': lambda img_raw: skimage.color.xyz2lab(img_raw),
28 #'rgb2yuv' : lambda img_raw: skimage.color.rgb2yuv(img_raw), 38 'xyz2luv': lambda img_raw: skimage.color.xyz2luv(img_raw),
29 #'rgba2rgb' : lambda img_raw: skimage.color.rgba2rgb(img_raw), 39 'xyz2rgb': lambda img_raw: skimage.color.xyz2rgb(img_raw),
30 'rgbcie2rgb' : lambda img_raw: skimage.color.rgbcie2rgb(img_raw), 40
31 'xyz2lab' : lambda img_raw: skimage.color.xyz2lab(img_raw), 41 # Color deconvolution operations
32 'xyz2luv' : lambda img_raw: skimage.color.xyz2luv(img_raw), 42 'hed_from_rgb': lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.hed_from_rgb),
33 'xyz2rgb' : lambda img_raw: skimage.color.xyz2rgb(img_raw), 43 'hdx_from_rgb': lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.hdx_from_rgb),
34 #'ycbcr2rgb' : lambda img_raw: skimage.color.ycbcr2rgb(img_raw), 44 'fgx_from_rgb': lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.fgx_from_rgb),
35 #'yiq2rgb' : lambda img_raw: skimage.color.yiq2rgb(img_raw), 45 'bex_from_rgb': lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.bex_from_rgb),
36 #'ypbpr2rgb' : lambda img_raw: skimage.color.ypbpr2rgb(img_raw), 46 'rbd_from_rgb': lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.rbd_from_rgb),
37 #'yuv2rgb' : lambda img_raw: skimage.color.yuv2rgb(img_raw), 47 'gdx_from_rgb': lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.gdx_from_rgb),
38 48 'hax_from_rgb': lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.hax_from_rgb),
39 'rgb_from_hed' : lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_hed), 49 'bro_from_rgb': lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.bro_from_rgb),
40 'rgb_from_hdx' : lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_hdx), 50 'bpx_from_rgb': lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.bpx_from_rgb),
41 'rgb_from_fgx' : lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_fgx), 51 'ahx_from_rgb': lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.ahx_from_rgb),
42 'rgb_from_bex' : lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_bex), 52 'hpx_from_rgb': lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.hpx_from_rgb),
43 'rgb_from_rbd' : lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_rbd), 53
44 'rgb_from_gdx' : lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_gdx), 54 # Recomposition operations (reverse color deconvolution)
45 'rgb_from_hax' : lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_hax), 55 'rgb_from_hed': lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_hed),
46 'rgb_from_bro' : lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_bro), 56 'rgb_from_hdx': lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_hdx),
47 'rgb_from_bpx' : lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_bpx), 57 'rgb_from_fgx': lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_fgx),
48 'rgb_from_ahx' : lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_ahx), 58 'rgb_from_bex': lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_bex),
49 'rgb_from_hpx' : lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_hpx), 59 'rgb_from_rbd': lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_rbd),
50 60 'rgb_from_gdx': lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_gdx),
51 'hed_from_rgb' : lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.hed_from_rgb), 61 'rgb_from_hax': lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_hax),
52 'hdx_from_rgb' : lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.hdx_from_rgb), 62 'rgb_from_bro': lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_bro),
53 'fgx_from_rgb' : lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.fgx_from_rgb), 63 'rgb_from_bpx': lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_bpx),
54 'bex_from_rgb' : lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.bex_from_rgb), 64 'rgb_from_ahx': lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_ahx),
55 'rbd_from_rgb' : lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.rbd_from_rgb), 65 'rgb_from_hpx': lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_hpx),
56 'gdx_from_rgb' : lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.gdx_from_rgb), 66
57 'hax_from_rgb' : lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.hax_from_rgb), 67 # Custom color deconvolution and recomposition operations
58 'bro_from_rgb' : lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.bro_from_rgb), 68 'rgb_from_he': lambda img_raw: skimage.color.combine_stains(img_raw, rgb_from_he),
59 'bpx_from_rgb' : lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.bpx_from_rgb), 69 'he_from_rgb': lambda img_raw: skimage.color.separate_stains(img_raw, np.linalg.inv(rgb_from_he)),
60 'ahx_from_rgb' : lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.ahx_from_rgb), 70
61 'hpx_from_rgb' : lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.hpx_from_rgb), 71 # Unsupervised machine learning-based operations
62 72 'pca': lambda img_raw: np.reshape(PCA(n_components=3).fit_transform(np.reshape(img_raw, [-1, img_raw.shape[2]])),
63 'pca' : lambda img_raw: np.reshape(PCA(n_components=3).fit_transform(np.reshape(img_raw, [-1, img_raw.shape[2]])), 73 [img_raw.shape[0], img_raw.shape[1], -1]),
64 [img_raw.shape[0],img_raw.shape[1],-1]), 74 'nmf': lambda img_raw: np.reshape(NMF(n_components=3, init='nndsvda').fit_transform(np.reshape(img_raw, [-1, img_raw.shape[2]])),
65 'nmf' : lambda img_raw: np.reshape(NMF(n_components=3, init='nndsvda').fit_transform(np.reshape(img_raw, [-1, img_raw.shape[2]])), 75 [img_raw.shape[0], img_raw.shape[1], -1]),
66 [img_raw.shape[0],img_raw.shape[1],-1]), 76 'ica': lambda img_raw: np.reshape(FastICA(n_components=3).fit_transform(np.reshape(img_raw, [-1, img_raw.shape[2]])),
67 'ica' : lambda img_raw: np.reshape(FastICA(n_components=3).fit_transform(np.reshape(img_raw, [-1, img_raw.shape[2]])), 77 [img_raw.shape[0], img_raw.shape[1], -1]),
68 [img_raw.shape[0],img_raw.shape[1],-1]), 78 'fa': lambda img_raw: np.reshape(FactorAnalysis(n_components=3).fit_transform(np.reshape(img_raw, [-1, img_raw.shape[2]])),
69 'fa' : lambda img_raw: np.reshape(FactorAnalysis(n_components=3).fit_transform(np.reshape(img_raw, [-1, img_raw.shape[2]])), 79 [img_raw.shape[0], img_raw.shape[1], -1])
70 [img_raw.shape[0],img_raw.shape[1],-1])
71 } 80 }
72 81
73 parser = argparse.ArgumentParser() 82 parser = argparse.ArgumentParser()
74 parser.add_argument('input_file', type=argparse.FileType('r'), default=sys.stdin, help='input file') 83 parser.add_argument('input_file', type=argparse.FileType('r'), default=sys.stdin, help='input file')
75 parser.add_argument('out_file', type=argparse.FileType('w'), default=sys.stdin, help='out file (TIFF)') 84 parser.add_argument('out_file', type=argparse.FileType('w'), default=sys.stdin, help='out file (TIFF)')
76 parser.add_argument('conv_type', choices=convOptions.keys(), help='conversion type') 85 parser.add_argument('conv_type', choices=convOptions.keys(), help='conversion type')
77 args = parser.parse_args() 86 parser.add_argument('--isolate_channel', type=int, help='set all other channels to zero (1-3)', default=0)
87 args = parser.parse_args()
78 88
79 img_in = skimage.io.imread(args.input_file.name)[:,:,0:3] 89 # Read and normalize the input image as TZYXC
80 res = convOptions[args.conv_type](img_in) 90 img_in = giatools.io.imread(args.input_file.name)
81 res[res<-1]=-1 91
82 res[res>1]=1 92 # Verify input image
93 assert img_in.shape[0] == 1, f'Image must have 1 frame (it has {img_in.shape[0]} frames)'
94 assert img_in.shape[1] == 1, f'Image must have 1 slice (it has {img_in.shape[1]} slices)'
95 assert img_in.shape[4] == 3, f'Image must have 3 channels (it has {img_in.shape[4]} channels)'
96
97 # Normalize the image from TZYXC to YXC
98 img_in = img_in.squeeze()
99 assert img_in.ndim == 3
100
101 # Apply channel isolation
102 if args.isolate_channel:
103 for ch in range(3):
104 if ch + 1 != args.isolate_channel:
105 img_in[:, :, ch] = 0
106
107 result = convOptions[args.conv_type](img_in)
108
109 # It is sufficient to store 32bit floating point data, the precision loss is tolerable
110 if result.dtype == np.float64:
111 result = result.astype(np.float32)
83 112
84 with warnings.catch_warnings(): 113 with warnings.catch_warnings():
85 warnings.simplefilter("ignore") 114 warnings.simplefilter('ignore')
86 res = skimage.util.img_as_uint(res) #Attention: precision loss 115 tifffile.imwrite(args.out_file.name, result)
87 skimage.io.imsave(args.out_file.name, res, plugin='tifffile')