Mercurial > repos > imgteam > colorize_labels

comparison colorize_labels.py @ 0:6f09b367cd9a draft default tip

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planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/colorize_labels commit 4344f548f365dba87c20188d6e3c2df8630d2313
author imgteam
date Tue, 24 Sep 2024 17:30:11 +0000
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-1:000000000000 0:6f09b367cd9a
1 import argparse
2
3 import giatools.io
4 import matplotlib.pyplot as plt
5 import networkx as nx
6 import numpy as np
7 import scipy.ndimage as ndi
8 import skimage.io
9 import skimage.util
10
11
12 def color_hex_to_rgb_tuple(hex):
13 if hex.startswith('#'):
14 hex = hex[1:]
15 return (
16 int(hex[0:2], 16),
17 int(hex[2:4], 16),
18 int(hex[4:6], 16),
19 )
20
21
22 def build_label_adjacency_graph(im, radius, bg_label):
23 G = nx.Graph()
24 for label in np.unique(im):
25
26 if label == bg_label:
27 continue
28
29 G.add_node(label)
30
31 cc = (im == label)
32 neighborhood = (ndi.distance_transform_edt(~cc) <= radius)
33 adjacent_labels = np.unique(im[neighborhood])
34
35 for adjacent_label in adjacent_labels:
36
37 if adjacent_label == bg_label or adjacent_label <= label:
38 continue
39
40 G.add_edge(label, adjacent_label)
41 G.add_edge(adjacent_label, label)
42
43 return G
44
45
46 def get_n_unique_mpl_colors(n, colormap='jet', cyclic=False):
47 """
48 Yields `n` unique colors from the given `colormap`.
49
50 Set `cyclic` to `True` if the `colormap` is cyclic.
51 """
52 cmap = plt.get_cmap(colormap)
53 m = n if cyclic else n - 1
54 for i in range(n):
55 yield np.multiply(255, cmap(i / m))
56
57
58 if __name__ == '__main__':
59
60 parser = argparse.ArgumentParser()
61 parser.add_argument('input', type=str)
62 parser.add_argument('--bg_label', type=int)
63 parser.add_argument('--bg_color', type=str)
64 parser.add_argument('--radius', type=int)
65 parser.add_argument('--output', type=str)
66 args = parser.parse_args()
67
68 # Load image and normalize
69 im = giatools.io.imread(args.input)
70 im = np.squeeze(im)
71 assert im.ndim == 2
72
73 # Build adjacency graph of the labels
74 G = build_label_adjacency_graph(im, args.radius, args.bg_label)
75
76 # Apply greedy coloring
77 graph_coloring = nx.greedy_color(G)
78 unique_colors = frozenset(graph_coloring.values())
79
80 # Assign colors to nodes based on the greedy coloring
81 graph_color_to_mpl_color = dict(zip(unique_colors, get_n_unique_mpl_colors(len(unique_colors))))
82 node_colors = [graph_color_to_mpl_color[graph_coloring[n]] for n in G.nodes()]
83
84 # Render result
85 bg_color_rgb = color_hex_to_rgb_tuple(args.bg_color)
86 result = np.dstack([np.full(im.shape, bg_color_rgb[ch], np.uint8) for ch in range(3)])
87 for label, label_color in zip(G.nodes(), node_colors):
88
89 cc = (im == label)
90 for ch in range(3):
91 result[:, :, ch][cc] = label_color[ch]
92
93 # Write result image
94 skimage.io.imsave(args.output, result)