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comparison slice_image.py @ 2:f312d414f234 draft

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planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/slice_image/ commit 2286a6c9da88596349ed9d967c51541409c0a7bf
author imgteam
date Mon, 13 Nov 2023 22:12:22 +0000
parents 8856a7c85e4c
children 1faa7e3c94ff
comparison
equal deleted inserted replaced
1:8856a7c85e4c 2:f312d414f234
1 import argparse 1 import argparse
2 import sys 2 import os.path
3 import random
3 import warnings 4 import warnings
5
4 import numpy as np 6 import numpy as np
5 import random 7 import skimage.feature
6 import os.path
7 import skimage.io 8 import skimage.io
8 import skimage.util 9 import skimage.util
9 import skimage.feature 10
10 from scipy.stats import entropy as scipy_entropy 11
11 12 def slice_image(input_file, out_folder, label=None, label_out_folder=None, window_size=64,
12 def slice_image(input_file, out_folder, label=None, label_out_folder=None, window_size=64,
13 stride=1, bg_thresh=1, limit_slices=False, n_thresh=5000, seed=None): 13 stride=1, bg_thresh=1, limit_slices=False, n_thresh=5000, seed=None):
14 #TODO NOT Implemented:process labels 14 # TODO NOT Implemented:process labels
15 # --> label and label_out_folder useless so far 15 # --> label and label_out_folder useless so far
16 16
17 # primarily for testing purposes: 17 # primarily for testing purposes:
18 if seed is not None: 18 if seed is not None:
19 random.seed(seed) 19 random.seed(seed)
20 20
21 img_raw = skimage.io.imread(input_file) 21 img_raw = skimage.io.imread(input_file)
22 if len(img_raw.shape) == 2: 22 if len(img_raw.shape) == 2:
23 img_raw = np.expand_dims(img_raw, 3) 23 img_raw = np.expand_dims(img_raw, 3)
24 24
25 with warnings.catch_warnings(): # ignore FutureWarning 25 with warnings.catch_warnings(): # ignore FutureWarning
26 warnings.simplefilter("ignore") 26 warnings.simplefilter("ignore")
27 patches_raw = skimage.util.view_as_windows(img_raw, (window_size, window_size, img_raw.shape[2]), step=stride) 27 patches_raw = skimage.util.view_as_windows(img_raw, (window_size, window_size, img_raw.shape[2]), step=stride)
28 patches_raw = patches_raw.reshape([-1, window_size, window_size, img_raw.shape[2]]) 28 patches_raw = patches_raw.reshape([-1, window_size, window_size, img_raw.shape[2]])
29 29
30 new_path = os.path.join(out_folder, "%d.tiff") 30 new_path = os.path.join(out_folder, "%d.tiff")
31 31
32 #samples for thresholding the amount of slices 32 # samples for thresholding the amount of slices
33 sample = random.sample(range(patches_raw.shape[0]), n_thresh) 33 sample = random.sample(range(patches_raw.shape[0]), n_thresh)
34 34
35 for i in range(0, patches_raw.shape[0]): 35 for i in range(0, patches_raw.shape[0]):
36 # TODO improve 36 # TODO improve
37 sum_image = np.sum(patches_raw[i], 2)/img_raw.shape[2] 37 sum_image = np.sum(patches_raw[i], 2) / img_raw.shape[2]
38 total_entr = np.var(sum_image.reshape([-1]))
39 38
40 if bg_thresh > 0: 39 if bg_thresh > 0:
41 sum_image = skimage.util.img_as_uint(sum_image) 40 sum_image = skimage.util.img_as_uint(sum_image)
42 g = skimage.feature.greycomatrix(sum_image, [1,2], [0, np.pi/2], nnormed=True, symmetric=True) 41 g = skimage.feature.greycomatrix(sum_image, [1, 2], [0, np.pi / 2], nnormed=True, symmetric=True)
43 hom = np.var(skimage.feature.greycoprops(g, prop='homogeneity')) 42 hom = np.var(skimage.feature.greycoprops(g, prop='homogeneity'))
44 if hom > bg_thresh: #0.0005 43 if hom > bg_thresh: # 0.0005
45 continue 44 continue
46 45
47 if limit_slices == True: 46 if limit_slices:
48 if i in sample: 47 if i in sample:
49 res = skimage.util.img_as_uint(patches_raw[i]) #Attention: precision loss 48 res = skimage.util.img_as_uint(patches_raw[i]) # Attention: precision loss
50 skimage.io.imsave(new_path % i, res, plugin='tifffile') 49 skimage.io.imsave(new_path % i, res, plugin='tifffile')
51 else: 50 else:
52 res = skimage.util.img_as_uint(patches_raw[i]) #Attention: precision loss 51 res = skimage.util.img_as_uint(patches_raw[i]) # Attention: precision loss
53 skimage.io.imsave(new_path % i, res, plugin='tifffile') 52 skimage.io.imsave(new_path % i, res, plugin='tifffile')
54 53
55 54
56 if __name__ == "__main__": 55 if __name__ == "__main__":
57 parser = argparse.ArgumentParser() 56 parser = argparse.ArgumentParser()
58 parser.add_argument('input_file', type=argparse.FileType('r'), help='input file') 57 parser.add_argument('input_file', type=argparse.FileType('r'), help='input file')
59 parser.add_argument('out_folder', help='out folder') 58 parser.add_argument('out_folder', help='out folder')
67 parser.add_argument('--seed', dest='seed', type=int, default=None, help='seed for random choice of limited slices') 66 parser.add_argument('--seed', dest='seed', type=int, default=None, help='seed for random choice of limited slices')
68 args = parser.parse_args() 67 args = parser.parse_args()
69 68
70 slice_image(args.input_file.name, args.out_folder, 69 slice_image(args.input_file.name, args.out_folder,
71 label=args.label_file, label_out_folder=args.label_out_folder, 70 label=args.label_file, label_out_folder=args.label_out_folder,
72 stride=args.stride, window_size=args.window_size, bg_thresh=args.bg_thresh, 71 stride=args.stride, window_size=args.window_size, bg_thresh=args.bg_thresh,
73 limit_slices=args.limit_slices, n_thresh=args.n_thresh, seed=args.seed) 72 limit_slices=args.limit_slices, n_thresh=args.n_thresh, seed=args.seed)