Mercurial > repos > imgteam > imagej2_bunwarpj_elastic_transform
comparison imagej2_analyze_skeleton_jython_script.py @ 2:55bf135d6b0c draft
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/image_processing/imagej2 commit 57a0433defa3cbc37ab34fbb0ebcfaeb680db8d5
author | imgteam |
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date | Sun, 05 Nov 2023 14:26:15 +0000 |
parents | 39b2bc251b2f |
children | f72f8e6787cd |
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1:39b2bc251b2f | 2:55bf135d6b0c |
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2 import sys | 2 import sys |
3 | 3 |
4 from ij import IJ | 4 from ij import IJ |
5 from sc.fiji.analyzeSkeleton import AnalyzeSkeleton_ | 5 from sc.fiji.analyzeSkeleton import AnalyzeSkeleton_ |
6 | 6 |
7 BASIC_NAMES = ['Branches', 'Junctions', 'End-point Voxels', | 7 BASIC_NAMES = [ |
8 'Junction Voxels', 'Slab Voxels', 'Average branch length', | 8 "Branches", |
9 'Triple Points', 'Quadruple Points', 'Maximum Branch Length'] | 9 "Junctions", |
10 DETAIL_NAMES = ['Skeleton ID', 'Branch length', 'V1 x', 'V1 y', 'V1 z', 'V2 x', | 10 "End-point Voxels", |
11 'V2 y', 'V2 z', 'Euclidean distance'] | 11 "Junction Voxels", |
12 OPTIONS = ['edm=Overwrite', 'iterations=1', 'count=1'] | 12 "Slab Voxels", |
13 "Average branch length", | |
14 "Triple Points", | |
15 "Quadruple Points", | |
16 "Maximum Branch Length", | |
17 ] | |
18 DETAIL_NAMES = [ | |
19 "Skeleton ID", | |
20 "Branch length", | |
21 "V1 x", | |
22 "V1 y", | |
23 "V1 z", | |
24 "V2 x", | |
25 "V2 y", | |
26 "V2 z", | |
27 "Euclidean distance", | |
28 ] | |
29 OPTIONS = ["edm=Overwrite", "iterations=1", "count=1"] | |
13 | 30 |
14 | 31 |
15 def get_euclidean_distance(vertex1, vertex2): | 32 def get_euclidean_distance(vertex1, vertex2): |
16 x1, y1, z1 = get_points(vertex1) | 33 x1, y1, z1 = get_points(vertex1) |
17 x2, y2, z2 = get_points(vertex2) | 34 x2, y2, z2 = get_points(vertex2) |
18 return math.sqrt(math.pow((x2 - x1), 2) + math.pow((y2 - y1), 2) + math.pow((z2 - z1), 2)) | 35 return math.sqrt( |
36 math.pow((x2 - x1), 2) + math.pow((y2 - y1), 2) + math.pow((z2 - z1), 2) | |
37 ) | |
19 | 38 |
20 | 39 |
21 def get_graph_length(graph): | 40 def get_graph_length(graph): |
22 length = 0 | 41 length = 0 |
23 for edge in graph.getEdges(): | 42 for edge in graph.getEdges(): |
44 # Sort graphs from longest to shortest. | 63 # Sort graphs from longest to shortest. |
45 graphs = sorted(graphs, key=lambda g: get_graph_length(g), reverse=True) | 64 graphs = sorted(graphs, key=lambda g: get_graph_length(g), reverse=True) |
46 return graphs | 65 return graphs |
47 | 66 |
48 | 67 |
49 def save(result, output, show_detailed_info, calculate_largest_shortest_path, sep='\t'): | 68 def save(result, output, show_detailed_info, calculate_largest_shortest_path, sep="\t"): |
50 num_trees = int(result.getNumOfTrees()) | 69 num_trees = int(result.getNumOfTrees()) |
51 outf = open(output, 'wb') | 70 outf = open(output, "wb") |
52 outf.write('# %s\n' % sep.join(BASIC_NAMES)) | 71 outf.write("# %s\n" % sep.join(BASIC_NAMES)) |
53 for index in range(num_trees): | 72 for index in range(num_trees): |
54 outf.write('%d%s' % (result.getBranches()[index], sep)) | 73 outf.write("%d%s" % (result.getBranches()[index], sep)) |
55 outf.write('%d%s' % (result.getJunctions()[index], sep)) | 74 outf.write("%d%s" % (result.getJunctions()[index], sep)) |
56 outf.write('%d%s' % (result.getEndPoints()[index], sep)) | 75 outf.write("%d%s" % (result.getEndPoints()[index], sep)) |
57 outf.write('%d%s' % (result.getJunctionVoxels()[index], sep)) | 76 outf.write("%d%s" % (result.getJunctionVoxels()[index], sep)) |
58 outf.write('%d%s' % (result.getSlabs()[index], sep)) | 77 outf.write("%d%s" % (result.getSlabs()[index], sep)) |
59 outf.write('%.3f%s' % (result.getAverageBranchLength()[index], sep)) | 78 outf.write("%.3f%s" % (result.getAverageBranchLength()[index], sep)) |
60 outf.write('%d%s' % (result.getTriples()[index], sep)) | 79 outf.write("%d%s" % (result.getTriples()[index], sep)) |
61 outf.write('%d%s' % (result.getQuadruples()[index], sep)) | 80 outf.write("%d%s" % (result.getQuadruples()[index], sep)) |
62 outf.write('%.3f' % result.getMaximumBranchLength()[index]) | 81 outf.write("%.3f" % result.getMaximumBranchLength()[index]) |
63 if calculate_largest_shortest_path: | 82 if calculate_largest_shortest_path: |
64 outf.write('%s%.3f%s' % (sep, result.shortestPathList.get(index), sep)) | 83 outf.write("%s%.3f%s" % (sep, result.shortestPathList.get(index), sep)) |
65 outf.write('%d%s' % (result.spStartPosition[index][0], sep)) | 84 outf.write("%d%s" % (result.spStartPosition[index][0], sep)) |
66 outf.write('%d%s' % (result.spStartPosition[index][1], sep)) | 85 outf.write("%d%s" % (result.spStartPosition[index][1], sep)) |
67 outf.write('%d\n' % result.spStartPosition[index][2]) | 86 outf.write("%d\n" % result.spStartPosition[index][2]) |
68 else: | 87 else: |
69 outf.write('\n') | 88 outf.write("\n") |
70 if show_detailed_info: | 89 if show_detailed_info: |
71 outf.write('# %s\n' % sep.join(DETAIL_NAMES)) | 90 outf.write("# %s\n" % sep.join(DETAIL_NAMES)) |
72 # The following index is a placeholder for the skeleton ID. | 91 # The following index is a placeholder for the skeleton ID. |
73 # The terms "graph" and "skeleton" refer to the same thing. | 92 # The terms "graph" and "skeleton" refer to the same thing. |
74 # Also, the SkeletonResult.java code states that the | 93 # Also, the SkeletonResult.java code states that the |
75 # private Graph[] graph object is an array of graphs (one | 94 # private Graph[] graph object is an array of graphs (one |
76 # per tree). | 95 # per tree). |
78 for edge in get_sorted_edge_lengths(graph): | 97 for edge in get_sorted_edge_lengths(graph): |
79 vertex1 = edge.getV1() | 98 vertex1 = edge.getV1() |
80 x1, y1, z1 = get_points(vertex1) | 99 x1, y1, z1 = get_points(vertex1) |
81 vertex2 = edge.getV2() | 100 vertex2 = edge.getV2() |
82 x2, y2, z2 = get_points(vertex2) | 101 x2, y2, z2 = get_points(vertex2) |
83 outf.write('%d%s' % (index + 1, sep)) | 102 outf.write("%d%s" % (index + 1, sep)) |
84 outf.write('%.3f%s' % (edge.getLength(), sep)) | 103 outf.write("%.3f%s" % (edge.getLength(), sep)) |
85 outf.write('%d%s' % (x1, sep)) | 104 outf.write("%d%s" % (x1, sep)) |
86 outf.write('%d%s' % (y1, sep)) | 105 outf.write("%d%s" % (y1, sep)) |
87 outf.write('%d%s' % (z1, sep)) | 106 outf.write("%d%s" % (z1, sep)) |
88 outf.write('%d%s' % (x2, sep)) | 107 outf.write("%d%s" % (x2, sep)) |
89 outf.write('%d%s' % (y2, sep)) | 108 outf.write("%d%s" % (y2, sep)) |
90 outf.write('%d%s' % (z2, sep)) | 109 outf.write("%d%s" % (z2, sep)) |
91 outf.write('%.3f' % get_euclidean_distance(vertex1, vertex2)) | 110 outf.write("%.3f" % get_euclidean_distance(vertex1, vertex2)) |
92 if calculate_largest_shortest_path: | 111 if calculate_largest_shortest_path: |
93 # Keep number of separated items the same for each line. | 112 # Keep number of separated items the same for each line. |
94 outf.write('%s %s' % (sep, sep)) | 113 outf.write("%s %s" % (sep, sep)) |
95 outf.write(' %s' % sep) | 114 outf.write(" %s" % sep) |
96 outf.write(' %s' % sep) | 115 outf.write(" %s" % sep) |
97 outf.write(' \n') | 116 outf.write(" \n") |
98 else: | 117 else: |
99 outf.write('\n') | 118 outf.write("\n") |
100 outf.close() | 119 outf.close() |
101 | 120 |
102 | 121 |
103 # Fiji Jython interpreter implements Python 2.5 which does not | 122 # Fiji Jython interpreter implements Python 2.5 which does not |
104 # provide support for argparse. | 123 # provide support for argparse. |
107 black_background = sys.argv[-6] == "yes" | 126 black_background = sys.argv[-6] == "yes" |
108 prune_cycle_method = sys.argv[-5] | 127 prune_cycle_method = sys.argv[-5] |
109 prune_ends = sys.argv[-4] == "yes" | 128 prune_ends = sys.argv[-4] == "yes" |
110 calculate_largest_shortest_path = sys.argv[-3] == "yes" | 129 calculate_largest_shortest_path = sys.argv[-3] == "yes" |
111 if calculate_largest_shortest_path: | 130 if calculate_largest_shortest_path: |
112 BASIC_NAMES.extend(['Longest Shortest Path', 'spx', 'spy', 'spz']) | 131 BASIC_NAMES.extend(["Longest Shortest Path", "spx", "spy", "spz"]) |
113 DETAIL_NAMES.extend([' ', ' ', ' ', ' ']) | 132 DETAIL_NAMES.extend([" ", " ", " ", " "]) |
114 show_detailed_info = sys.argv[-2] == "yes" | 133 show_detailed_info = sys.argv[-2] == "yes" |
115 output = sys.argv[-1] | 134 output = sys.argv[-1] |
116 | 135 |
117 # Open the input image file. | 136 # Open the input image file. |
118 input_image_plus = IJ.openImage(input) | 137 input_image_plus = IJ.openImage(input) |
133 IJ.run(input_image_plus_copy, "Make Binary", "") | 152 IJ.run(input_image_plus_copy, "Make Binary", "") |
134 | 153 |
135 # Run AnalyzeSkeleton | 154 # Run AnalyzeSkeleton |
136 analyze_skeleton = AnalyzeSkeleton_() | 155 analyze_skeleton = AnalyzeSkeleton_() |
137 analyze_skeleton.setup("", input_image_plus_copy) | 156 analyze_skeleton.setup("", input_image_plus_copy) |
138 if prune_cycle_method == 'none': | 157 if prune_cycle_method == "none": |
139 prune_index = analyze_skeleton.NONE | 158 prune_index = analyze_skeleton.NONE |
140 elif prune_cycle_method == 'shortest_branch': | 159 elif prune_cycle_method == "shortest_branch": |
141 prune_index = analyze_skeleton.SHORTEST_BRANCH | 160 prune_index = analyze_skeleton.SHORTEST_BRANCH |
142 elif prune_cycle_method == 'lowest_intensity_voxel': | 161 elif prune_cycle_method == "lowest_intensity_voxel": |
143 prune_index = analyze_skeleton.LOWEST_INTENSITY_VOXEL | 162 prune_index = analyze_skeleton.LOWEST_INTENSITY_VOXEL |
144 elif prune_cycle_method == 'lowest_intensity_branch': | 163 elif prune_cycle_method == "lowest_intensity_branch": |
145 prune_index = analyze_skeleton.LOWEST_INTENSITY_BRANCH | 164 prune_index = analyze_skeleton.LOWEST_INTENSITY_BRANCH |
146 result = analyze_skeleton.run(prune_index, prune_ends, calculate_largest_shortest_path, input_image_plus_copy, True, True) | 165 result = analyze_skeleton.run( |
166 prune_index, | |
167 prune_ends, | |
168 calculate_largest_shortest_path, | |
169 input_image_plus_copy, | |
170 True, | |
171 True, | |
172 ) | |
147 # Save the results. | 173 # Save the results. |
148 save(result, output, show_detailed_info, calculate_largest_shortest_path) | 174 save(result, output, show_detailed_info, calculate_largest_shortest_path) |