Mercurial > repos > imgteam > imagej2_find_maxima
diff imagej2_analyze_skeleton_jython_script.py @ 1:e2622ebb5ce4 draft
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/image_processing/imagej2 commit 2afb24f3c81d625312186750a714d702363012b5"
author | imgteam |
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date | Mon, 28 Sep 2020 16:49:43 +0000 |
parents | 29b39f5b0e69 |
children | 6a07f71806bb |
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--- a/imagej2_analyze_skeleton_jython_script.py Tue Sep 17 17:01:44 2019 -0400 +++ b/imagej2_analyze_skeleton_jython_script.py Mon Sep 28 16:49:43 2020 +0000 @@ -1,147 +1,148 @@ -import jython_utils import math import sys + from ij import IJ from sc.fiji.analyzeSkeleton import AnalyzeSkeleton_ -BASIC_NAMES = [ 'Branches', 'Junctions', 'End-point Voxels', - 'Junction Voxels', 'Slab Voxels', 'Average branch length', - 'Triple Points', 'Quadruple Points', 'Maximum Branch Length' ] -DETAIL_NAMES = [ 'Skeleton ID', 'Branch length', 'V1 x', 'V1 y', 'V1 z', 'V2 x', - 'V2 y', 'V2 z', 'Euclidean distance' ] +BASIC_NAMES = ['Branches', 'Junctions', 'End-point Voxels', + 'Junction Voxels', 'Slab Voxels', 'Average branch length', + 'Triple Points', 'Quadruple Points', 'Maximum Branch Length'] +DETAIL_NAMES = ['Skeleton ID', 'Branch length', 'V1 x', 'V1 y', 'V1 z', 'V2 x', + 'V2 y', 'V2 z', 'Euclidean distance'] +OPTIONS = ['edm=Overwrite', 'iterations=1', 'count=1'] + -def get_euclidean_distance( vertex1, vertex2 ): - x1, y1, z1 = get_points( vertex1 ) - x2, y2, z2 = get_points( vertex2 ) - return math.sqrt( math.pow( ( x2 - x1 ), 2 ) + - math.pow( ( y2 - y1 ), 2 ) + - math.pow( ( z2 - z1 ), 2 ) ) +def get_euclidean_distance(vertex1, vertex2): + x1, y1, z1 = get_points(vertex1) + x2, y2, z2 = get_points(vertex2) + return math.sqrt(math.pow((x2 - x1), 2) + math.pow((y2 - y1), 2) + math.pow((z2 - z1), 2)) -def get_graph_length( graph ): + +def get_graph_length(graph): length = 0 for edge in graph.getEdges(): length = length + edge.getLength() return length -def get_points( vertex ): + +def get_points(vertex): # An array of Point, which has attributes x,y,z. - point = vertex.getPoints()[ 0 ] + point = vertex.getPoints()[0] return point.x, point.y, point.z - -def get_sorted_edge_lengths( graph ): + + +def get_sorted_edge_lengths(graph): # Return graph edges sorted from longest to shortest. edges = graph.getEdges() - edges = sorted( edges, key=lambda edge: edge.getLength(), reverse=True ) + edges = sorted(edges, key=lambda edge: edge.getLength(), reverse=True) return edges -def get_sorted_graph_lengths( result ): + +def get_sorted_graph_lengths(result): # Get the separate graphs (skeletons). graphs = result.getGraph() # Sort graphs from longest to shortest. - graphs = sorted( graphs, key=lambda g: get_graph_length( g ), reverse=True ) + graphs = sorted(graphs, key=lambda g: get_graph_length(g), reverse=True) return graphs -def save( result, output, show_detailed_info, calculate_largest_shortest_path, sep='\t' ): - num_trees = int( result.getNumOfTrees() ) - outf = open( output, 'wb' ) - outf.write( '# %s\n' % sep.join( BASIC_NAMES ) ) - for index in range( num_trees ): - outf.write( '%d%s' % ( result.getBranches()[ index ], sep ) ) - outf.write( '%d%s' % ( result.getJunctions()[ index ], sep ) ) - outf.write( '%d%s' % ( result.getEndPoints()[ index ], sep ) ) - outf.write( '%d%s' % ( result.getJunctionVoxels()[ index ], sep ) ) - outf.write( '%d%s' % ( result.getSlabs()[ index ], sep ) ) - outf.write( '%.3f%s' % ( result.getAverageBranchLength()[ index ], sep ) ) - outf.write( '%d%s' % ( result.getTriples()[ index ], sep ) ) - outf.write( '%d%s' % ( result.getQuadruples()[ index ], sep ) ) - outf.write( '%.3f' % result.getMaximumBranchLength()[ index ] ) + +def save(result, output, show_detailed_info, calculate_largest_shortest_path, sep='\t'): + num_trees = int(result.getNumOfTrees()) + outf = open(output, 'wb') + outf.write('# %s\n' % sep.join(BASIC_NAMES)) + for index in range(num_trees): + outf.write('%d%s' % (result.getBranches()[index], sep)) + outf.write('%d%s' % (result.getJunctions()[index], sep)) + outf.write('%d%s' % (result.getEndPoints()[index], sep)) + outf.write('%d%s' % (result.getJunctionVoxels()[index], sep)) + outf.write('%d%s' % (result.getSlabs()[index], sep)) + outf.write('%.3f%s' % (result.getAverageBranchLength()[index], sep)) + outf.write('%d%s' % (result.getTriples()[index], sep)) + outf.write('%d%s' % (result.getQuadruples()[index], sep)) + outf.write('%.3f' % result.getMaximumBranchLength()[index]) if calculate_largest_shortest_path: - outf.write( '%s%.3f%s' % ( sep, result.shortestPathList.get( index ), sep ) ) - outf.write( '%d%s' % ( result.spStartPosition[ index ][ 0 ], sep ) ) - outf.write( '%d%s' % ( result.spStartPosition[ index ][ 1 ], sep ) ) - outf.write( '%d\n' % result.spStartPosition[ index ][ 2 ] ) + outf.write('%s%.3f%s' % (sep, result.shortestPathList.get(index), sep)) + outf.write('%d%s' % (result.spStartPosition[index][0], sep)) + outf.write('%d%s' % (result.spStartPosition[index][1], sep)) + outf.write('%d\n' % result.spStartPosition[index][2]) else: - outf.write( '\n' ) + outf.write('\n') if show_detailed_info: - outf.write( '# %s\n' % sep.join( DETAIL_NAMES ) ) + outf.write('# %s\n' % sep.join(DETAIL_NAMES)) # The following index is a placeholder for the skeleton ID. # The terms "graph" and "skeleton" refer to the same thing. # Also, the SkeletonResult.java code states that the # private Graph[] graph object is an array of graphs (one # per tree). - for index, graph in enumerate( get_sorted_graph_lengths( result ) ): - for edge in get_sorted_edge_lengths( graph ): + for index, graph in enumerate(get_sorted_graph_lengths(result)): + for edge in get_sorted_edge_lengths(graph): vertex1 = edge.getV1() - x1, y1, z1 = get_points( vertex1 ) + x1, y1, z1 = get_points(vertex1) vertex2 = edge.getV2() - x2, y2, z2 = get_points( vertex2 ) - outf.write( '%d%s' % ( index+1, sep ) ) - outf.write( '%.3f%s' % ( edge.getLength(), sep ) ) - outf.write( '%d%s' % ( x1, sep ) ) - outf.write( '%d%s' % ( y1, sep ) ) - outf.write( '%d%s' % ( z1, sep ) ) - outf.write( '%d%s' % ( x2, sep ) ) - outf.write( '%d%s' % ( y2, sep ) ) - outf.write( '%d%s' % ( z2, sep ) ) - outf.write( '%.3f' % get_euclidean_distance( vertex1, vertex2 ) ) + x2, y2, z2 = get_points(vertex2) + outf.write('%d%s' % (index + 1, sep)) + outf.write('%.3f%s' % (edge.getLength(), sep)) + outf.write('%d%s' % (x1, sep)) + outf.write('%d%s' % (y1, sep)) + outf.write('%d%s' % (z1, sep)) + outf.write('%d%s' % (x2, sep)) + outf.write('%d%s' % (y2, sep)) + outf.write('%d%s' % (z2, sep)) + outf.write('%.3f' % get_euclidean_distance(vertex1, vertex2)) if calculate_largest_shortest_path: # Keep number of separated items the same for each line. - outf.write( '%s %s' % ( sep, sep ) ) - outf.write( ' %s' % sep ) - outf.write( ' %s' % sep ) - outf.write( ' \n' ) + outf.write('%s %s' % (sep, sep)) + outf.write(' %s' % sep) + outf.write(' %s' % sep) + outf.write(' \n') else: - outf.write( '\n' ) + outf.write('\n') outf.close() + # Fiji Jython interpreter implements Python 2.5 which does not # provide support for argparse. -error_log = sys.argv[ -8 ] -input = sys.argv[ -7 ] -black_background = jython_utils.asbool( sys.argv[ -6 ] ) -prune_cycle_method = sys.argv[ -5 ] -prune_ends = jython_utils.asbool( sys.argv[ -4 ] ) -calculate_largest_shortest_path = jython_utils.asbool( sys.argv[ -3 ] ) +error_log = sys.argv[-8] +input = sys.argv[-7] +black_background = sys.argv[-6] == "yes" +prune_cycle_method = sys.argv[-5] +prune_ends = sys.argv[-4] == "yes" +calculate_largest_shortest_path = sys.argv[-3] == "yes" if calculate_largest_shortest_path: - BASIC_NAMES.extend( [ 'Longest Shortest Path', 'spx', 'spy', 'spz' ] ) - DETAIL_NAMES.extend( [ ' ', ' ', ' ', ' ' ] ) -show_detailed_info = jython_utils.asbool( sys.argv[ -2 ] ) -output = sys.argv[ -1 ] + BASIC_NAMES.extend(['Longest Shortest Path', 'spx', 'spy', 'spz']) + DETAIL_NAMES.extend([' ', ' ', ' ', ' ']) +show_detailed_info = sys.argv[-2] == "yes" +output = sys.argv[-1] # Open the input image file. -input_image_plus = IJ.openImage( input ) +input_image_plus = IJ.openImage(input) # Create a copy of the image. input_image_plus_copy = input_image_plus.duplicate() image_processor_copy = input_image_plus_copy.getProcessor() -try: - # Set binary options. - options = jython_utils.get_binary_options( black_background=black_background ) - IJ.run( input_image_plus_copy, "Options...", options ) +# Set binary options. +options_list = OPTIONS +if black_background: + options_list.append("black") +options = " ".join(options_list) +IJ.run(input_image_plus_copy, "Options...", options) - # Convert image to binary if necessary. - if not image_processor_copy.isBinary(): - IJ.run( input_image_plus_copy, "Make Binary", "" ) +# Convert image to binary if necessary. +if not image_processor_copy.isBinary(): + IJ.run(input_image_plus_copy, "Make Binary", "") - # Run AnalyzeSkeleton - analyze_skeleton = AnalyzeSkeleton_() - analyze_skeleton.setup( "", input_image_plus_copy ) - if prune_cycle_method == 'none': - prune_index = analyze_skeleton.NONE - elif prune_cycle_method == 'shortest_branch': - prune_index = analyze_skeleton.SHORTEST_BRANCH - elif prune_cycle_method == 'lowest_intensity_voxel': - prune_index = analyze_skeleton.LOWEST_INTENSITY_VOXEL - elif prune_cycle_method == 'lowest_intensity_branch': - prune_index = analyze_skeleton.LOWEST_INTENSITY_BRANCH - result = analyze_skeleton.run( prune_index, - prune_ends, - calculate_largest_shortest_path, - input_image_plus_copy, - True, - True ) - # Save the results. - save( result, output, show_detailed_info, calculate_largest_shortest_path ) -except Exception, e: - jython_utils.handle_error( error_log, str( e ) ) +# Run AnalyzeSkeleton +analyze_skeleton = AnalyzeSkeleton_() +analyze_skeleton.setup("", input_image_plus_copy) +if prune_cycle_method == 'none': + prune_index = analyze_skeleton.NONE +elif prune_cycle_method == 'shortest_branch': + prune_index = analyze_skeleton.SHORTEST_BRANCH +elif prune_cycle_method == 'lowest_intensity_voxel': + prune_index = analyze_skeleton.LOWEST_INTENSITY_VOXEL +elif prune_cycle_method == 'lowest_intensity_branch': + prune_index = analyze_skeleton.LOWEST_INTENSITY_BRANCH +result = analyze_skeleton.run(prune_index, prune_ends, calculate_largest_shortest_path, input_image_plus_copy, True, True) +# Save the results. +save(result, output, show_detailed_info, calculate_largest_shortest_path)