Next changeset 1:e9243cad1d67 (2020-09-28) |
Commit message:
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/image_processing/imagej2 commit b08f0e6d1546caaf627b21f8c94044285d5d5b9c-dirty" |
added:
imagej2_adjust_threshold_binary.py imagej2_adjust_threshold_binary_jython_script.py imagej2_analyze_particles_binary.py imagej2_analyze_particles_binary_jython_script.py imagej2_analyze_skeleton.py imagej2_analyze_skeleton_jython_script.py imagej2_base_utils$py.class imagej2_base_utils.py imagej2_base_utils.pyc imagej2_binary_to_edm.py imagej2_binary_to_edm_jython_script.py imagej2_bunwarpj_adapt_transform.py imagej2_bunwarpj_align.py imagej2_bunwarpj_align.xml imagej2_bunwarpj_align_jython_script.py imagej2_bunwarpj_compare_elastic.py imagej2_bunwarpj_compare_elastic_raw.py imagej2_bunwarpj_compare_raw.py imagej2_bunwarpj_compose_elastic.py imagej2_bunwarpj_compose_raw.py imagej2_bunwarpj_compose_raw_elastic.py imagej2_bunwarpj_convert_to_raw.py imagej2_bunwarpj_elastic_transform.py imagej2_bunwarpj_elastic_transform_jython_script.py imagej2_bunwarpj_raw_transform.py imagej2_bunwarpj_raw_transform_jython_script.py imagej2_create_image.py imagej2_create_image_jython_script.py imagej2_enhance_contrast.py imagej2_enhance_contrast_jython_script.py imagej2_find_edges.py imagej2_find_edges_jython_script.py imagej2_find_maxima.py imagej2_find_maxima_jython_script.py imagej2_macros.xml imagej2_make_binary.py imagej2_make_binary_jython_script.py imagej2_math.py imagej2_math_jython_script.py imagej2_noise.py imagej2_noise_jython_script.py imagej2_shadows.py imagej2_shadows_jython_script.py imagej2_sharpen.py imagej2_sharpen_jython_script.py imagej2_skeletonize3d.py imagej2_skeletonize3d_jython_script.py imagej2_smooth.py imagej2_smooth_jython_script.py imagej2_watershed_binary.py imagej2_watershed_binary_jython_script.py jython_utils$py.class jython_utils.py readme.md static/images/bunwarpj_scheme.png test-data/adapted_transformation.txt test-data/add_specified_noise.gif test-data/analyze_particles_masks.gif test-data/analyze_particles_nothing.tabular test-data/analyze_particles_outlines.gif test-data/basic.tabular test-data/blobs.gif test-data/blobs_black_edm.gif test-data/blobs_count.tabular test-data/blobs_direct_transf.txt test-data/blobs_edm.gif test-data/blobs_equalize.gif test-data/blobs_find_edges.gif test-data/blobs_list.tabular test-data/blobs_log.gif test-data/blobs_macro.gif test-data/blobs_min.gif test-data/blobs_multiply.gif test-data/blobs_normalize.gif test-data/blobs_northwest.gif test-data/blobs_saturate.gif test-data/blobs_segmented.gif test-data/blobs_sharpen.gif test-data/blobs_single_points.gif test-data/blobs_smooth.gif test-data/blobs_square.gif test-data/blobs_threshold_default.gif test-data/blobs_threshold_huang_dark.gif test-data/blobs_threshold_ijiso.gif test-data/blobs_tolerance.gif test-data/blobs_watershed_binary.gif test-data/clown.jpg test-data/clown_binary.jpg test-data/composed_raw_elastic_transformation.txt test-data/composed_raw_transformation.txt test-data/create_image1.jpg test-data/despeckle.gif test-data/detailed.tabular test-data/dot_blot.jpg test-data/dot_blot.png test-data/dot_blot.tiff test-data/dotblot.jpg test-data/elastic_trans_registered_source1.png test-data/largest_shortest_path_basic.tabular test-data/mask_ramp.gif test-data/mask_white.png test-data/raw_trans_registered_source1.png test-data/raw_transformation.txt test-data/registered_source1.png test-data/registered_source2.png test-data/registered_target1.png test-data/registered_target2.png test-data/remove_outliers.gif test-data/shortest_branch_all_yes.tabular test-data/shortest_branch_basic.tabular test-data/skeletonized_blobs.gif test-data/skeletonized_clown.jpg test-data/source_elastic_transformation.txt test-data/source_raw_transformation.txt test-data/target_elastic_transformation.txt test-data/target_raw_transformation.txt test-data/warping_index.txt test-data/warping_index1.txt test-data/warping_index2.txt test-data/warping_index_raw.txt |
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diff -r 000000000000 -r ab54024c0a88 imagej2_adjust_threshold_binary.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_adjust_threshold_binary.py Tue Sep 17 17:02:18 2019 -0400 |
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@@ -0,0 +1,63 @@ +#!/usr/bin/env python +import argparse +import os +import shutil +import subprocess +import tempfile +import imagej2_base_utils + +parser = argparse.ArgumentParser() +parser.add_argument( '--input', dest='input', help='Path to the input file' ) +parser.add_argument( '--input_datatype', dest='input_datatype', help='Datatype of the input image' ) +parser.add_argument( '--threshold_min', dest='threshold_min', type=float, help='Minimum threshold value' ) +parser.add_argument( '--threshold_max', dest='threshold_max', type=float, help='Maximum threshold value' ) +parser.add_argument( '--method', dest='method', help='Threshold method' ) +parser.add_argument( '--display', dest='display', help='Display mode' ) +parser.add_argument( '--black_background', dest='black_background', help='Black background' ) +parser.add_argument( '--stack_histogram', dest='stack_histogram', help='Stack histogram' ) +parser.add_argument( '--jython_script', dest='jython_script', help='Path to the Jython script' ) +parser.add_argument( '--output', dest='output', help='Path to the output file' ) +parser.add_argument( '--output_datatype', dest='output_datatype', help='Datatype of the output image' ) +args = parser.parse_args() + +tmp_dir = imagej2_base_utils.get_temp_dir() +# ImageJ expects valid image file extensions, so the Galaxy .dat extension does not +# work for some features. The following creates a symlink with an appropriate file +# extension that points to the Galaxy dataset. This symlink is used by ImageJ. +tmp_input_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.input, args.input_datatype ) +tmp_output_path = imagej2_base_utils.get_temporary_image_path( tmp_dir, args.output_datatype ) +# Define command response buffers. +tmp_out = tempfile.NamedTemporaryFile().name +tmp_stdout = open( tmp_out, 'wb' ) +tmp_err = tempfile.NamedTemporaryFile().name +tmp_stderr = open( tmp_err, 'wb' ) +# Java writes a lot of stuff to stderr, so we'll specify a file for handling actual errors. +error_log = tempfile.NamedTemporaryFile( delete=False ).name +# Build the command line. +cmd = imagej2_base_utils.get_base_command_imagej2( None, jython_script=args.jython_script ) +if cmd is None: + imagej2_base_utils.stop_err( "ImageJ not found!" ) +cmd += ' %s' % error_log +cmd += ' %s' % tmp_input_path +cmd += ' %.3f' % args.threshold_min +cmd += ' %.3f' % args.threshold_max +cmd += ' %s' % args.method +cmd += ' %s' % args.display +cmd += ' %s' % args.black_background +cmd += ' %s' % args.stack_histogram +cmd += ' %s' % tmp_output_path +cmd += ' %s' % args.output_datatype +# Run the command. +proc = subprocess.Popen( args=cmd, stderr=tmp_stderr, stdout=tmp_stdout, shell=True ) +rc = proc.wait() +# Handle execution errors. +if rc != 0: + error_message = imagej2_base_utils.get_stderr_exception( tmp_err, tmp_stderr, tmp_out, tmp_stdout ) + imagej2_base_utils.stop_err( error_message ) +# Handle processing errors. +if os.path.getsize( error_log ) > 0: + error_message = open( error_log, 'r' ).read() + imagej2_base_utils.stop_err( error_message ) +# Save the output image. +shutil.move( tmp_output_path, args.output ) +imagej2_base_utils.cleanup_before_exit( tmp_dir ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_adjust_threshold_binary_jython_script.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_adjust_threshold_binary_jython_script.py Tue Sep 17 17:02:18 2019 -0400 |
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@@ -0,0 +1,49 @@ +import jython_utils +import sys +from ij import IJ + +# Fiji Jython interpreter implements Python 2.5 which does not +# provide support for argparse. +error_log = sys.argv[ -10 ] +input = sys.argv[ -9 ] +threshold_min = float( sys.argv[ -8 ] ) +threshold_max = float( sys.argv[ -7 ] ) +method = sys.argv[ -6 ] +display = sys.argv[ -5 ] +black_background = jython_utils.asbool( sys.argv[ -4 ] ) +# TODO: this is not being used. +stack_histogram = jython_utils.asbool( sys.argv[ -3 ] ) +tmp_output_path = sys.argv[ -2 ] +output_datatype = sys.argv[ -1 ] + +# Open the input image file. +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: + # Convert image to binary if necessary. + if not image_processor_copy.isBinary(): + # Convert the image to binary grayscale. + IJ.run( input_image_plus_copy, "Make Binary","iterations=1 count=1 edm=Overwrite do=Nothing" ) + # Set the options. + if black_background: + method_str = "%s dark" % method + else: + method_str = method + IJ.setAutoThreshold( input_image_plus_copy, method_str ) + if display == "red": + display_mode = "Red" + elif display == "bw": + display_mode = "Black & White" + elif display == "over_under": + display_mode = "Over/Under" + IJ.setThreshold( input_image_plus_copy, threshold_min, threshold_max, display_mode ) + # Run the command. + IJ.run( input_image_plus_copy, "threshold", "" ) + # Save the ImagePlus object as a new image. + IJ.saveAs( input_image_plus_copy, output_datatype, tmp_output_path ) +except Exception, e: + jython_utils.handle_error( error_log, str( e ) ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_analyze_particles_binary.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_analyze_particles_binary.py Tue Sep 17 17:02:18 2019 -0400 |
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@@ -0,0 +1,81 @@ +#!/usr/bin/env python +import argparse +import os +import shutil +import subprocess +import tempfile +import imagej2_base_utils + +parser = argparse.ArgumentParser() +parser.add_argument( '--input', dest='input', help='Path to the input file' ) +parser.add_argument( '--input_datatype', dest='input_datatype', help='Datatype of the input image' ) +parser.add_argument( '--black_background', dest='black_background', help='Black background' ) +parser.add_argument( '--size', dest='size', help='Size (pixel^2)' ) +parser.add_argument( '--circularity_min', dest='circularity_min', type=float, help='Circularity minimum' ) +parser.add_argument( '--circularity_max', dest='circularity_max', type=float, help='Circularity maximum' ) +parser.add_argument( '--show', dest='show', help='Show' ) +parser.add_argument( '--display_results', dest='display_results', help='Display results' ) +parser.add_argument( '--all_results', dest='all_results', help='All results' ) +parser.add_argument( '--exclude_edges', dest='exclude_edges', help='Exclude edges' ) +parser.add_argument( '--include_holes', dest='include_holes', help='Include holes' ) +parser.add_argument( '--jython_script', dest='jython_script', help='Path to the Jython script' ) +parser.add_argument( '--results', dest='results', default=None, help='Path to the output results file' ) +parser.add_argument( '--output', dest='output', default=None, help='Path to the output image file' ) +parser.add_argument( '--output_datatype', dest='output_datatype', default='data', help='Datatype of the output image' ) +args = parser.parse_args() + +tmp_dir = imagej2_base_utils.get_temp_dir() +# ImageJ expects valid image file extensions, so the Galaxy .dat extension does not +# work for some features. The following creates a symlink with an appropriate file +# extension that points to the Galaxy dataset. This symlink is used by ImageJ. +tmp_input_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.input, args.input_datatype ) +if args.output is None: + tmp_output_path = None +else: + tmp_output_path = imagej2_base_utils.get_temporary_image_path( tmp_dir, args.output_datatype ) + +# Define command response buffers. +tmp_out = tempfile.NamedTemporaryFile().name +tmp_stdout = open( tmp_out, 'wb' ) +tmp_err = tempfile.NamedTemporaryFile().name +tmp_stderr = open( tmp_err, 'wb' ) +# Java writes a lot of stuff to stderr, so we'll specify a file for handling actual errors. +error_log = tempfile.NamedTemporaryFile( delete=False ).name + +# Build the command line. +cmd = imagej2_base_utils.get_base_command_imagej2( None, jython_script=args.jython_script ) +if cmd is None: + imagej2_base_utils.stop_err( "ImageJ not found!" ) +cmd += ' %s' % error_log +cmd += ' %s' % tmp_input_path +cmd += ' %s' % args.black_background +cmd += ' %s' % args.size +cmd += ' %.3f' % args.circularity_min +cmd += ' %.3f' % args.circularity_max +cmd += ' %s' % args.show +cmd += ' %s' % args.display_results +cmd += '%s' % imagej2_base_utils.handle_none_type( args.all_results, val_type='str' ) +cmd += ' %s' % args.exclude_edges +cmd += ' %s' % args.include_holes +cmd += '%s' % imagej2_base_utils.handle_none_type( tmp_output_path, val_type='str' ) +cmd += ' %s' % args.output_datatype +cmd += '%s' % imagej2_base_utils.handle_none_type( args.results, val_type='str' ) + +# Run the command. +proc = subprocess.Popen( args=cmd, stderr=tmp_stderr, stdout=tmp_stdout, shell=True ) +rc = proc.wait() + +# Handle execution errors. +if rc != 0: + error_message = imagej2_base_utils.get_stderr_exception( tmp_err, tmp_stderr, tmp_out, tmp_stdout ) + imagej2_base_utils.stop_err( error_message ) + +# Handle processing errors. +if os.path.getsize( error_log ) > 0: + error_message = open( error_log, 'r' ).read() + imagej2_base_utils.stop_err( error_message ) + +if tmp_output_path is not None: + # Save the output image. + shutil.move( tmp_output_path, args.output ) +imagej2_base_utils.cleanup_before_exit( tmp_dir ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_analyze_particles_binary_jython_script.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_analyze_particles_binary_jython_script.py Tue Sep 17 17:02:18 2019 -0400 |
[ |
@@ -0,0 +1,72 @@ +import jython_utils +import sys +from ij import IJ +from ij.plugin.filter import Analyzer + +# Fiji Jython interpreter implements Python 2.5 which does not +# provide support for argparse. +error_log = sys.argv[ -14 ] +input = sys.argv[ -13 ] +black_background = jython_utils.asbool( sys.argv[ -12 ] ) +size = sys.argv[ -11 ] +circularity_min = float( sys.argv[ -10 ] ) +circularity_max = float( sys.argv[ -9 ] ) +show = sys.argv[ -8 ] +display_results = jython_utils.asbool( sys.argv[ -7 ] ) +all_results = jython_utils.asbool( sys.argv[ -6 ] ) +exclude_edges = jython_utils.asbool( sys.argv[ -5 ] ) +include_holes = jython_utils.asbool( sys.argv[ -4 ] ) +tmp_output_path = sys.argv[ -3 ] +output_datatype = sys.argv[ -2 ] +results_path = sys.argv[ -1 ] + +# Open the input image file. +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() +analyzer = Analyzer( input_image_plus_copy ) + +try: + # Set binary options. + options = jython_utils.get_binary_options( black_background=black_background ) + IJ.run( input_image_plus_copy, "Options...", options ) + + # Convert image to binary if necessary. + if not image_processor_copy.isBinary(): + # Convert the image to binary grayscale. + IJ.run( input_image_plus_copy, "Make Binary", "" ) + + # Set the options. + options = [ 'size=%s' % size ] + circularity_str = '%.3f-%.3f' % ( circularity_min, circularity_max ) + options.append( 'circularity=%s' % circularity_str ) + if show.find( '_' ) >= 0: + show_str = '[%s]' % show.replace( '_', ' ' ) + else: + show_str = show + options.append( 'show=%s' % show_str ) + if display_results: + options.append( 'display' ) + if not all_results: + options.append( 'summarize' ) + if exclude_edges: + options.append( 'exclude' ) + if include_holes: + options.append( 'include' ) + # Always run "in_situ". + options.append( 'in_situ' ) + + # Run the command. + IJ.run( input_image_plus_copy, "Analyze Particles...", " ".join( options ) ) + + # Save outputs. + if tmp_output_path not in [ None, 'None' ]: + # Save the ImagePlus object as a new image. + IJ.saveAs( input_image_plus_copy, output_datatype, tmp_output_path ) + if display_results and results_path not in [ None, 'None' ]: + results_table = analyzer.getResultsTable() + results_table.saveAs( results_path ) +except Exception, e: + jython_utils.handle_error( error_log, str( e ) ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_analyze_skeleton.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_analyze_skeleton.py Tue Sep 17 17:02:18 2019 -0400 |
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@@ -0,0 +1,61 @@ +#!/usr/bin/env python +import argparse +import os +import shutil +import subprocess +import tempfile +import imagej2_base_utils + +parser = argparse.ArgumentParser() +parser.add_argument( '--input', dest='input', help='Path to the input file' ) +parser.add_argument( '--input_datatype', dest='input_datatype', help='Datatype of the input image' ) +parser.add_argument( '--black_background', dest='black_background', help='Black background' ) +parser.add_argument( '--prune_cycle_method', dest='prune_cycle_method', default='none', help='Prune cycle method' ) +parser.add_argument( '--prune_ends', dest='prune_ends', default='no', help='Prune ends' ) +parser.add_argument( '--calculate_largest_shortest_path', dest='calculate_largest_shortest_path', default='no', help='Calculate largest shortest path' ) +parser.add_argument( '--show_detailed_info', dest='show_detailed_info', default='no', help='Show detailed info' ) +parser.add_argument( '--jython_script', dest='jython_script', help='Path to the Jython script' ) +parser.add_argument( '--output', dest='output', help='Path to the output file' ) +args = parser.parse_args() + +tmp_dir = imagej2_base_utils.get_temp_dir() +# ImageJ expects valid image file extensions, so the Galaxy .dat extension does not +# work for some features. The following creates a symlink with an appropriate file +# extension that points to the Galaxy dataset. This symlink is used by ImageJ. +tmp_input_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.input, args.input_datatype ) + +# Define command response buffers. +tmp_out = tempfile.NamedTemporaryFile().name +tmp_stdout = open( tmp_out, 'wb' ) +tmp_err = tempfile.NamedTemporaryFile().name +tmp_stderr = open( tmp_err, 'wb' ) +# Java writes a lot of stuff to stderr, so we'll specify a file for handling actual errors. +error_log = tempfile.NamedTemporaryFile( delete=False ).name + +# Build the command line. +cmd = imagej2_base_utils.get_base_command_imagej2( None, jython_script=args.jython_script ) +if cmd is None: + imagej2_base_utils.stop_err( "ImageJ not found!" ) +cmd += ' %s' % error_log +cmd += ' %s' % tmp_input_path +cmd += ' %s' % args.black_background +cmd += ' %s' % args.prune_cycle_method +cmd += ' %s' % args.prune_ends +cmd += ' %s' % args.calculate_largest_shortest_path +cmd += ' %s' % args.show_detailed_info +cmd += ' %s' % args.output + +# Run the command. +proc = subprocess.Popen( args=cmd, stderr=tmp_stderr, stdout=tmp_stdout, shell=True ) +rc = proc.wait() + +# Handle execution errors. +if rc != 0: + error_message = imagej2_base_utils.get_stderr_exception( tmp_err, tmp_stderr, tmp_out, tmp_stdout ) + imagej2_base_utils.stop_err( error_message ) +# Handle processing errors. +if os.path.getsize( error_log ) > 0: + error_message = open( error_log, 'r' ).read() + imagej2_base_utils.stop_err( error_message ) + +imagej2_base_utils.cleanup_before_exit( tmp_dir ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_analyze_skeleton_jython_script.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_analyze_skeleton_jython_script.py Tue Sep 17 17:02:18 2019 -0400 |
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@@ -0,0 +1,147 @@ +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' ] + +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 ): + length = 0 + for edge in graph.getEdges(): + length = length + edge.getLength() + return length + +def get_points( vertex ): + # An array of Point, which has attributes x,y,z. + point = vertex.getPoints()[ 0 ] + return point.x, point.y, point.z + +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 ) + return edges + +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 ) + 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 ] ) + 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 ] ) + else: + outf.write( '\n' ) + if show_detailed_info: + 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 ): + vertex1 = edge.getV1() + 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 ) ) + 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' ) + else: + 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 ] ) +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 ] + +# Open the input image file. +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 ) + + # 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 ) ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_base_utils$py.class |
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Binary file imagej2_base_utils$py.class has changed |
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diff -r 000000000000 -r ab54024c0a88 imagej2_base_utils.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_base_utils.py Tue Sep 17 17:02:18 2019 -0400 |
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@@ -0,0 +1,169 @@ +import os +import shutil +import sys +import tempfile + +BUFF_SIZE = 1048576 + + +def cleanup_before_exit(tmp_dir): + """ + Remove temporary files and directories prior to tool exit. + """ + if tmp_dir and os.path.exists(tmp_dir): + shutil.rmtree(tmp_dir) + + +def get_base_cmd_bunwarpj(jvm_memory): + if jvm_memory in [None, 'None']: + jvm_memory_str = '' + else: + jvm_memory_str = '-Xmx%s' % jvm_memory + # The following bunwarpj_base_cmd string will look something like this: + # "java %s -cp $JAR_DIR/ij-1.49k.jar:$PLUGINS_DIR/bUnwarpJ_-2.6.1.jar \ + # bunwarpj.bUnwarpJ_" % (jvm_memory_str) + # See the bunwarpj.sh script for the fiji 20151222 + # bioconda recipe in github. + bunwarpj_base_cmd = "bunwarpj %s" % jvm_memory_str + return bunwarpj_base_cmd + + +def get_base_command_imagej2(memory_size=None, macro=None, jython_script=None): + imagej2_executable = get_imagej2_executable() + if imagej2_executable is None: + return None + cmd = '%s --ij2 --headless --debug' % imagej2_executable + if memory_size is not None: + memory_size_cmd = ' -DXms=%s -DXmx=%s' % (memory_size, memory_size) + cmd += memory_size_cmd + if macro is not None: + cmd += ' --macro %s' % os.path.abspath(macro) + if jython_script is not None: + cmd += ' --jython %s' % os.path.abspath(jython_script) + return cmd + + +def get_file_extension(image_format): + """ + Return a valid bioformats file extension based on the received + value of image_format(e.g., "gif" is returned as ".gif". + """ + return '.%s' % image_format + + +def get_file_name_without_extension(file_path): + """ + Eliminate the .ext from the received file name, assuming that + the file name consists of only a single '.'. + """ + if os.path.exists(file_path): + path, name = os.path.split(file_path) + name_items = name.split('.') + return name_items[0] + return None + + +def get_imagej2_executable(): + """ + Fiji names the ImageJ executable different names for different + architectures, but our bioconda recipe allows us to do this. + """ + return 'ImageJ' + + +def get_input_image_path(tmp_dir, input_file, image_format): + """ + Bioformats uses file extensions (e.g., .job, .gif, etc) + when reading and writing image files, so the Galaxy dataset + naming convention of setting all file extensions as .dat + must be handled. + """ + image_path = get_temporary_image_path(tmp_dir, image_format) + # Remove the file so we can create a symlink. + os.remove(image_path) + os.symlink(input_file, image_path) + return image_path + + +def get_platform_info_dict(): + '''Return a dict with information about the current platform.''' + platform_dict = {} + sysname, nodename, release, version, machine = os.uname() + platform_dict['os'] = sysname.lower() + platform_dict['architecture'] = machine.lower() + return platform_dict + + +def get_stderr_exception(tmp_err, tmp_stderr, tmp_out, tmp_stdout, include_stdout=False): + tmp_stderr.close() + """ + Return a stderr string of reasonable size. + """ + # Get stderr, allowing for case where it's very large. + tmp_stderr = open(tmp_err, 'rb') + stderr_str = '' + buffsize = BUFF_SIZE + try: + while True: + stderr_str += tmp_stderr.read(buffsize) + if not stderr_str or len(stderr_str) % buffsize != 0: + break + except OverflowError: + pass + tmp_stderr.close() + if include_stdout: + tmp_stdout = open(tmp_out, 'rb') + stdout_str = '' + buffsize = BUFF_SIZE + try: + while True: + stdout_str += tmp_stdout.read(buffsize) + if not stdout_str or len(stdout_str) % buffsize != 0: + break + except OverflowError: + pass + tmp_stdout.close() + if include_stdout: + return 'STDOUT\n%s\n\nSTDERR\n%s\n' % (stdout_str, stderr_str) + return stderr_str + + +def get_temp_dir(prefix='tmp-imagej-', dir=None): + """ + Return a temporary directory. + """ + return tempfile.mkdtemp(prefix=prefix, dir=dir) + + +def get_tempfilename(dir=None, suffix=None): + """ + Return a temporary file name. + """ + fd, name = tempfile.mkstemp(suffix=suffix, dir=dir) + os.close(fd) + return name + + +def get_temporary_image_path(tmp_dir, image_format): + """ + Return the path to a temporary file with a valid image format + file extension that can be used with bioformats. + """ + file_extension = get_file_extension(image_format) + return get_tempfilename(tmp_dir, file_extension) + + +def handle_none_type(val, val_type='float'): + if val is None: + return ' None' + else: + if val_type == 'float': + return ' %.3f' % val + elif val_type == 'int': + return ' %d' % val + return ' %s' % val + + +def stop_err(msg): + sys.stderr.write(msg) + sys.exit(1) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_base_utils.pyc |
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Binary file imagej2_base_utils.pyc has changed |
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diff -r 000000000000 -r ab54024c0a88 imagej2_binary_to_edm.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_binary_to_edm.py Tue Sep 17 17:02:18 2019 -0400 |
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@@ -0,0 +1,65 @@ +#!/usr/bin/env python +import argparse +import os +import shutil +import subprocess +import tempfile +import imagej2_base_utils + +parser = argparse.ArgumentParser() +parser.add_argument( '--input', dest='input', help='Path to the input file' ) +parser.add_argument( '--input_datatype', dest='input_datatype', help='Datatype of the input image' ) +parser.add_argument( '--iterations', dest='iterations', type=int, help='Iterations' ) +parser.add_argument( '--count', dest='count', type=int, help='Count' ) +parser.add_argument( '--black_background', dest='black_background', help='Black background' ) +parser.add_argument( '--pad_edges_when_eroding', dest='pad_edges_when_eroding', help='Pad edges when eroding' ) +parser.add_argument( '--jython_script', dest='jython_script', help='Path to the Jython script' ) +parser.add_argument( '--output', dest='output', help='Path to the output file' ) +parser.add_argument( '--output_datatype', dest='output_datatype', help='Datatype of the output image' ) +args = parser.parse_args() + +tmp_dir = imagej2_base_utils.get_temp_dir() +# ImageJ expects valid image file extensions, so the Galaxy .dat extension does not +# work for some features. The following creates a symlink with an appropriate file +# extension that points to the Galaxy dataset. This symlink is used by ImageJ. +tmp_input_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.input, args.input_datatype ) +tmp_output_path = imagej2_base_utils.get_temporary_image_path( tmp_dir, args.output_datatype ) + +# Define command response buffers. +tmp_out = tempfile.NamedTemporaryFile().name +tmp_stdout = open( tmp_out, 'wb' ) +tmp_err = tempfile.NamedTemporaryFile().name +tmp_stderr = open( tmp_err, 'wb' ) +# Java writes a lot of stuff to stderr, so we'll specify a file for handling actual errors. +error_log = tempfile.NamedTemporaryFile( delete=False ).name + +# Build the command line. +cmd = imagej2_base_utils.get_base_command_imagej2( None, jython_script=args.jython_script ) +if cmd is None: + imagej2_base_utils.stop_err( "ImageJ not found!" ) +cmd += ' %s' % error_log +cmd += ' %s' % tmp_input_path +cmd += ' %d' % args.iterations +cmd += ' %d' % args.count +cmd += ' %s' % args.black_background +cmd += ' %s' % args.pad_edges_when_eroding +cmd += ' %s' % tmp_output_path +cmd += ' %s' % args.output_datatype + +# Run the command. +proc = subprocess.Popen( args=cmd, stderr=tmp_stderr, stdout=tmp_stdout, shell=True ) +rc = proc.wait() + +# Handle execution errors. +if rc != 0: + error_message = imagej2_base_utils.get_stderr_exception( tmp_err, tmp_stderr, tmp_out, tmp_stdout ) + imagej2_base_utils.stop_err( error_message ) + +# Handle processing errors. +if os.path.getsize( error_log ) > 0: + error_message = open( error_log, 'r' ).read() + imagej2_base_utils.stop_err( error_message ) + +# Save the output image. +shutil.move( tmp_output_path, args.output ) +imagej2_base_utils.cleanup_before_exit( tmp_dir ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_binary_to_edm_jython_script.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_binary_to_edm_jython_script.py Tue Sep 17 17:02:18 2019 -0400 |
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@@ -0,0 +1,44 @@ +import jython_utils +import sys +from ij import IJ +from ij import ImagePlus +from ij.plugin.filter import Analyzer +from ij.plugin.filter import EDM + +# Fiji Jython interpreter implements Python 2.5 which does not +# provide support for argparse. +error_log = sys.argv[ -8 ] +input = sys.argv[ -7 ] +iterations = int( sys.argv[ -6 ] ) +count = int( sys.argv[ -5 ] ) +black_background = jython_utils.asbool( sys.argv[ -4 ] ) +pad_edges_when_eroding = jython_utils.asbool( sys.argv[ -3 ] ) +tmp_output_path = sys.argv[ -2 ] +output_datatype = sys.argv[ -1 ] + +# Open the input image file. +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, + iterations=iterations, + count=count, + pad_edges_when_eroding=pad_edges_when_eroding ) + IJ.run( input_image_plus_copy, "Options...", options ) + + # Convert image to binary if necessary. + if not image_processor_copy.isBinary(): + # Convert the image to binary grayscale. + IJ.run( input_image_plus_copy, "Make Binary", "" ) + + # Run the command. + IJ.run( input_image_plus_copy, "Distance Map", "" ) + # Save the ImagePlus object as a new image. + IJ.saveAs( input_image_plus_copy, output_datatype, tmp_output_path ) +except Exception, e: + jython_utils.handle_error( error_log, str( e ) ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_bunwarpj_adapt_transform.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_bunwarpj_adapt_transform.py Tue Sep 17 17:02:18 2019 -0400 |
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@@ -0,0 +1,65 @@ +#!/usr/bin/env python +import argparse +import subprocess +import tempfile +import imagej2_base_utils + +# Parse Command Line. +parser = argparse.ArgumentParser() +parser.add_argument( '--source_image', dest='source_image', help='Source image' ) +parser.add_argument( '--source_image_format', dest='source_image_format', help='Source image format' ) +parser.add_argument( '--target_image', dest='target_image', help='Target image' ) +parser.add_argument( '--target_image_format', dest='target_image_format', help='Target image format' ) +parser.add_argument( '--input_elastic_transformation', dest='input_elastic_transformation', help='Input elastic transformation matrix' ) +parser.add_argument( '--image_size_factor', dest='image_size_factor', type=float, help='Image size factor' ) +parser.add_argument( '--output', dest='output', help='Warping index' ) + +args = parser.parse_args() + +tmp_dir = imagej2_base_utils.get_temp_dir() +source_image_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.source_image, args.source_image_format ) +target_image_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.target_image, args.target_image_format ) +input_elastic_transformation_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.input_elastic_transformation, 'txt' ) + +# Define command response buffers. +tmp_out = tempfile.NamedTemporaryFile().name +tmp_stdout = open( tmp_out, 'wb' ) +tmp_err = tempfile.NamedTemporaryFile().name +tmp_stderr = open( tmp_err, 'wb' ) + +def is_power2( val ): + if val < 0: + return False + if val < 1: + val = 1.0 / val + val = int( val ) + return ( ( val & ( val - 1 ) ) == 0 ) + +# Build the command line to adapt the transformation. +cmd = imagej2_base_utils.get_base_cmd_bunwarpj( None ) +if cmd is None: + imagej2_base_utils.stop_err( "bUnwarpJ not found!" ) +cmd += ' -adapt_transform' + +# Make sure the value of image_size_factor is a power of 2 (positive or negative). +if is_power2( args.image_size_factor ): + image_size_factor = args.image_size_factor +else: + msg = "Image size factor must be a positive or negative power of 2 (0.25, 0.5, 2, 4, 8, etc)." + imagej2_base_utils.stop_err( msg ) + +# Target is sent before source. +cmd += ' %s' % target_image_path +cmd += ' %s' % source_image_path +cmd += ' %s' % input_elastic_transformation_path +cmd += ' %s' % args.output +cmd += ' %2.f' % image_size_factor + +# Adapt the transformation based on the image size factor using bUnwarpJ. +proc = subprocess.Popen( args=cmd, stderr=subprocess.PIPE, stdout=subprocess.PIPE, shell=True ) +rc = proc.wait() +if rc != 0: + error_message = imagej2_base_utils.get_stderr_exception( tmp_err, tmp_stderr, tmp_out, tmp_stdout ) + imagej2_base_utils.stop_err( error_message ) + +imagej2_base_utils.cleanup_before_exit( tmp_dir ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_bunwarpj_align.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_bunwarpj_align.py Tue Sep 17 17:02:18 2019 -0400 |
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b"@@ -0,0 +1,178 @@\n+#!/usr/bin/env python\n+import argparse\n+import os\n+import shutil\n+import subprocess\n+import tempfile\n+import imagej2_base_utils\n+\n+# Parse Command Line.\n+parser = argparse.ArgumentParser()\n+parser.add_argument( '--source_image', dest='source_image', help='Source image' )\n+parser.add_argument( '--source_image_format', dest='source_image_format', help='Source image format' )\n+parser.add_argument( '--source_mask', dest='source_mask', default=None, help='Source mask' )\n+parser.add_argument( '--source_mask_format', dest='source_mask_format', default=None, help='Source mask image format' )\n+parser.add_argument( '--target_image', dest='target_image', help='Target image' )\n+parser.add_argument( '--target_image_format', dest='target_image_format', help='Target image format' )\n+parser.add_argument( '--target_mask', dest='target_mask', default=None, help='Target mask' )\n+parser.add_argument( '--target_mask_format', dest='target_mask_format', default=None, help='Target mask image format' )\n+parser.add_argument( '--min_scale_def', dest='min_scale_def', type=int, help='Initial deformation' )\n+parser.add_argument( '--max_scale_def', dest='max_scale_def', type=int, help='Final deformation' )\n+parser.add_argument( '--max_subsamp_fact', dest='max_subsamp_fact', type=int, help='Image sub-sample factor' )\n+parser.add_argument( '--divergence_weight', dest='divergence_weight', type=float, help='Divergence weight' )\n+parser.add_argument( '--curl_weight', dest='curl_weight', type=float, help='Curl weight' )\n+parser.add_argument( '--image_weight', dest='image_weight', type=float, help='Image weight' )\n+parser.add_argument( '--consistency_weight', dest='consistency_weight', type=float, help='Consistency weight' )\n+parser.add_argument( '--landmarks_weight', dest='landmarks_weight', type=float, help='Landmarks weight' )\n+parser.add_argument( '--landmarks_file', dest='landmarks_file', default=None, help='Landmarks file' )\n+parser.add_argument( '--source_affine_file', dest='source_affine_file', default=None, help='Initial source affine matrix transformation' )\n+parser.add_argument( '--target_affine_file', dest='target_affine_file', default=None, help='Initial target affine matrix transformation' )\n+parser.add_argument( '--mono', dest='mono', default=False, help='Unidirectional registration (source to target)' )\n+parser.add_argument( '--source_trans_out', dest='source_trans_out', default=None, help='Direct source transformation matrix' )\n+parser.add_argument( '--target_trans_out', dest='target_trans_out', default=None, help='Inverse target transformation matrix' )\n+parser.add_argument( '--source_out', help='Output source image' )\n+parser.add_argument( '--source_out_datatype', help='Output registered source image format' )\n+parser.add_argument( '--target_out', default=None, help='Output target image' )\n+parser.add_argument( '--target_out_datatype', default=None, help='Output registered target image format' )\n+parser.add_argument( '--jython_script', dest='jython_script', help='Path to the Jython script' )\n+\n+args = parser.parse_args()\n+\n+if args.source_trans_out is not None and args.target_trans_out is not None:\n+ save_transformation = True\n+else:\n+ save_transformation = False\n+\n+tmp_dir = imagej2_base_utils.get_temp_dir()\n+source_image_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.source_image, args.source_image_format )\n+tmp_source_out_tiff_path = imagej2_base_utils.get_temporary_image_path( tmp_dir, 'tiff' )\n+tmp_source_out_path = imagej2_base_utils.get_temporary_image_path( tmp_dir, args.source_out_datatype )\n+target_image_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.target_image, args.target_image_format )\n+if not args.mono:\n+ tmp_target_out_tiff_path = imagej2_base_utils.get_temporary_image_path( tmp_dir, 'tiff' )\n+ tmp_target_out_path = imagej2_base_utils.get_temporary_image_path( tmp_dir, args.target_out_datatype )\n+if args.source_mask is not None and args.target_mask is not None:\n+ t"..b'max_scale_def\n+cmd += \' %d\' % args.max_subsamp_fact\n+cmd += \' %.1f\' % args.divergence_weight\n+cmd += \' %.1f\' % args.curl_weight\n+cmd += \' %.1f\' % args.image_weight\n+cmd += \' %.1f\' % args.consistency_weight\n+# Source is produced before target.\n+cmd += \' %s\' % tmp_source_out_tiff_path\n+if not args.mono:\n+ cmd += \' %s\' % tmp_target_out_tiff_path\n+if args.landmarks_file is not None:\n+ # We have to create a temporary file with a .txt extension here so that\n+ # bUnwarpJ will not ignore the Galaxy "dataset.dat" file.\n+ tmp_landmarks_file_path = imagej2_base_utils.get_input_image_path( tmp_dir,\n+ args.landmarks_file,\n+ \'txt\' )\n+ cmd += \' -landmarks\'\n+ cmd += \' %.1f\' % args.landmarks_weight\n+ cmd += \' %s\' % tmp_landmarks_file_path\n+if args.source_affine_file is not None and args.target_affine_file is not None:\n+ # Target is sent before source.\n+ cmd += \' -affine\'\n+ cmd += \' %s\' % args.target_affine_file\n+ cmd += \' %s\' % args.source_affine_file\n+if args.mono:\n+ cmd += \' -mono\'\n+if save_transformation:\n+ cmd += \' -save_transformation\'\n+\n+# Align the two images using bUnwarpJ.\n+proc = subprocess.Popen( args=cmd, stderr=tmp_stderr, stdout=tmp_stdout, shell=True )\n+rc = proc.wait()\n+if rc != 0:\n+ error_message = imagej2_base_utils.get_stderr_exception( tmp_err, tmp_stderr, tmp_out, tmp_stdout )\n+ imagej2_base_utils.stop_err( error_message )\n+\n+# bUnwarpJ produces tiff image stacks consisting of 3 slices which can be viewed in ImageJ.\n+# The 3 slices are:: 1) the registered image, 2) the target image and 3) the black/white\n+# warp image. Galaxy supports only single-layered images, so we\'ll convert the images so they\n+# can be viewed in Galaxy.\n+\n+# Define command response buffers.\n+tmp_out = tempfile.NamedTemporaryFile().name\n+tmp_stdout = open( tmp_out, \'wb\' )\n+tmp_err = tempfile.NamedTemporaryFile().name\n+tmp_stderr = open( tmp_err, \'wb\' )\n+\n+# Build the command line to handle the multi-slice tiff images.\n+cmd = imagej2_base_utils.get_base_command_imagej2( None, jython_script=args.jython_script )\n+if cmd is None:\n+ imagej2_base_utils.stop_err( "ImageJ not found!" )\n+if args.mono:\n+ # bUnwarpJ will produce only a registered source image.\n+ cmd += \' %s %s %s %s\' % ( tmp_source_out_tiff_path,\n+ args.source_out_datatype,\n+ tmp_source_out_path,\n+ args.mono )\n+else:\n+ # bUnwarpJ will produce registered source and target images.\n+ cmd += \' %s %s %s %s %s %s %s\' % ( tmp_source_out_tiff_path,\n+ args.source_out_datatype,\n+ tmp_source_out_path,\n+ tmp_target_out_tiff_path,\n+ args.target_out_datatype,\n+ tmp_target_out_path,\n+ args.mono )\n+\n+# Merge the multi-slice tiff layers into an image that can be viewed in Galaxy.\n+proc = subprocess.Popen( args=cmd, stderr=tmp_stderr, stdout=tmp_stdout, shell=True )\n+rc = proc.wait()\n+if rc != 0:\n+ error_message = imagej2_base_utils.get_stderr_exception( tmp_err, tmp_stderr, tmp_out, tmp_stdout )\n+ imagej2_base_utils.stop_err( error_message )\n+\n+# Save the Registered Source Image to the output dataset.\n+shutil.move( tmp_source_out_path, args.source_out )\n+if not args.mono:\n+ # Move the Registered Target Image to the output dataset.\n+ shutil.move( tmp_target_out_path, args.target_out )\n+\n+# If requested, save matrix transformations as additional datasets.\n+if save_transformation:\n+ shutil.move( tmp_source_out_transf_path, args.source_trans_out )\n+ if not args.mono:\n+ shutil.move( tmp_target_out_transf_path, args.target_trans_out )\n+\n+imagej2_base_utils.cleanup_before_exit( tmp_dir )\n' |
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diff -r 000000000000 -r ab54024c0a88 imagej2_bunwarpj_align.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_bunwarpj_align.xml Tue Sep 17 17:02:18 2019 -0400 |
[ |
b'@@ -0,0 +1,247 @@\n+<?xml version=\'1.0\' encoding=\'UTF-8\'?>\n+<tool id="imagej2_bunwarpj_align" name="Align two images" version="@WRAPPER_VERSION@.0">\n+ <description>with bUnwarpJ</description>\n+ <macros>\n+ <import>imagej2_macros.xml</import>\n+ </macros>\n+ <expand macro="fiji_requirements" />\n+ <command>\n+<![CDATA[\n+ python $__tool_directory__/imagej2_bunwarpj_align.py\n+ --target_image "$target_image"\n+ --target_image_format $target_image.ext\n+ --source_image "$source_image"\n+ --source_image_format $source_image.ext\n+ #if $align_with_mask_cond.align_with_mask == \'yes\':\n+ --target_mask "$target_mask"\n+ --target_mask_format $target_mask.ext\n+ --source_mask "$source_mask"\n+ --source_mask_format $source_mask.ext\n+ #end if\n+ --min_scale_def $min_scale_def\n+ --max_scale_def $max_scale_def\n+ --max_subsamp_fact $max_subsamp_fact\n+ --divergence_weight $divergence_weight\n+ --curl_weight $curl_weight\n+ --image_weight $image_weight\n+ --consistency_weight $consistency_weight\n+ #if $advanced_options_cond.advanced_options == \'landmarks\':\n+ --landmarks_weight $advanced_options_cond.landmarks_weight\n+ --landmarks_file "$advanced_options_cond.landmarks_file"\n+ #else if $advanced_options_cond.advanced_options == \'affine\':\n+ --target_affine_file "$advanced_options_cond.target_affine_file"\n+ --source_affine_file "$advanced_options_cond.source_affine_file"\n+ #else if $advanced_options_cond.advanced_options == \'mono\':\n+ --mono "true"\n+ #else if $advanced_options_cond.advanced_options == \'save_transformation\':\n+ --target_trans_out "$target_trans_out"\n+ --source_trans_out "$source_trans_out"\n+ #end if\n+ --source_out "$source_out"\n+ --source_out_datatype $source_out_datatype\n+ #if $advanced_options_cond.advanced_options != \'mono\':\n+ --target_out "$target_out"\n+ --target_out_datatype $target_out_datatype\n+ #end if\n+ --jython_script $__tool_directory__/imagej2_bunwarpj_align_jython_script.py\n+]]>\n+ </command>\n+ <inputs>\n+ <param format="bmp,eps,gif,jpg,pcx,pgm,png,psd,tiff" name="target_image" type="data" label="Target image"/>\n+ <param format="bmp,eps,gif,jpg,pcx,pgm,png,psd,tiff" name="source_image" type="data" label="Source image"/>\n+ <conditional name="align_with_mask_cond">\n+ <param name="align_with_mask" type="select" label="Align with masks?" help="Source and target mask can be any image format.">\n+ <option value="no" selected="True">No</option>\n+ <option value="yes">Yes</option>\n+ </param>\n+ <when value="no" />\n+ <when value="yes">\n+ <param format="bmp,eps,gif,jpg,pcx,pgm,png,psd,tiff" name="target_mask" type="data" label="Target mask"/>\n+ <param format="bmp,eps,gif,jpg,pcx,pgm,png,psd,tiff" name="source_mask" type="data" label="Source mask"/>\n+ </when>\n+ </conditional>\n+ <param name="min_scale_def" type="select" label="Initial deformation">\n+ <option value="0" selected="True">Very Coarse</option>\n+ <option value="1">Coarse</option>\n+ <option value="2">Fine</option>\n+ <option value="3">Very Fine</option>\n+ </param>\n+ <param name="max_scale_def" type="select" label="Final deformation">\n+ <option value="0">Very Coarse</option>\n+ <option value="1">Coarse</option>\n+ <option value="2" selected="True">Fine</option>\n+ <option value="3">Very Fine</option>\n+ </param>\n+ <param name="max_subsamp_fact" type="integer" value="0" min="0" label="Image sub-sample factor" help="Power of 2: [0, 1, 2 .. 7]"/>\n+ <param name="divergence_weight" type="float" value="0.0" label="Weight of the divergence term" help="Value is a floating point number"/>\n+ <param name="curl_weight" type="float" label="Weight of the'..b' <output name="target_out" file="registered_target1.png" compare="sim_size" />\n+ <output name="source_trans_out" file="source_elastic_transformation.txt" />\n+ <output name="target_trans_out" file="target_elastic_transformation.txt" />\n+ </test>\n+ <test>\n+ <!-- Align two images without landmarks but with mask, not saving transformation matrices -->\n+ <param name="target_image" value="dotblot.jpg" />\n+ <param name="source_image" value="blobs.gif" />\n+ <param name="target_mask" value="mask_white.png" />\n+ <param name="source_mask" value="mask_ramp.gif" />\n+ <param name="min_scale_def" value="0" />\n+ <param name="max_scale_def" value="2" />\n+ <param name="max_subsamp_fact" value="1" />\n+ <param name="divergence_weight" value="0.0" />\n+ <param name="curl_weight" value="0.0" />\n+ <param name="image_weight" value="1.0" />\n+ <param name="consistency_weight" value="10.0" />\n+ <param name="target_out_datatype" value="png" />\n+ <param name="source_out_datatype" value="png" />\n+ <output name="source_out" file="registered_source2.png" compare="sim_size" />\n+ <output name="target_out" file="registered_target2.png" compare="sim_size" />\n+ </test>\n+ </tests>\n+ <help>\n+**What it does**\n+\n+<![CDATA[\n+Performs a simultaneous registration of two images, A and B. Image A is elastically deformed\n+in order to look as similar as possible to image B, and, at the same time, the "inverse"\n+transformation (from B to A) is also calculated so a pseudo-invertibility of the final deformation\n+could be guaranteed. RGB Color images will be converted to grayscale during the registration\n+process but the resulting transformations will be applied to the original color images.\n+\n+Two images are produced: the deformed versions of A and B images.\n+\n+.. image:: ./static/images/bunwarpj_scheme.png\n+\n+Both selected images will work simultaneously as source and target, their tags are there only for the sake\n+of clarification. The registration mode is currently restricted to "Fast" ("Mono" has been eliminated for now).\n+The registration mode "Mono" perfoms only unidirectional registration, i.e. from source to target. The\n+registration mode "Fast" involve performing bidirectional registration and affects the stopping criteria\n+internally used by the bUnwarpJ program.\n+\n+Using the **Align with masks** otion, masks are introduced together with the input images. In this mode, the\n+input images is treated as the first slice of the mask pair and the mask is treated as the second slice. In this\n+way, the mask can have any shape.\n+\n+The **Initial deformation** and **Final deformation** options allow you to select the coarsest and finest scale\n+of the spline deformation field. "Very coarse" corresponds to 4 splines (one in each corner of the image). As you\n+increase the deformation level, the number of splines is doubled in each direction (horizontal and vertical).\n+\n+The value of the **Image sub-sample factor** will enable the registration to be calculated using the subsampled\n+versions of the images but the results will be applied to the original ones. The image subsampling parameter can\n+be set from 0 and 7, i.e. the image dimensions can be reduced by a factor of 20 = 1 to 27 = 128. This is very\n+useful when registering large images.\n+\n+The different weights of the goal function control the relative weight of each one of the terms. These weights\n+are not restricted to be between 0 and 1, and they may take any value as long as it is non-negative=2E.\n+\n+If the **Advanced option - Save transformation** option is selected, two files are produced in addition to\n+the registerd source and target images: the direct transformation matrix of A and the inverse transformation\n+matrix of B.\n+]]>\n+\n+ </help>\n+ <expand macro="bunwarpj_citations" />\n+</tool>\n' |
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diff -r 000000000000 -r ab54024c0a88 imagej2_bunwarpj_align_jython_script.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_bunwarpj_align_jython_script.py Tue Sep 17 17:02:18 2019 -0400 |
[ |
@@ -0,0 +1,37 @@ +import sys +import jython_utils +from ij import IJ + +# Fiji Jython interpreter implements Python 2.5 which does not +# provide support for argparse. + +if sys.argv[ -1 ].lower() in [ 'true' ]: + mono = True +else: + mono = False + +if mono: + # bUnwarpJ has been called with the -mono param. + source_tiff_path = sys.argv[ -4 ] + source_datatype = sys.argv[ -3 ] + source_path = sys.argv[ -2 ] +else: + source_tiff_path = sys.argv[ -7 ] + source_datatype = sys.argv[ -6 ] + source_path = sys.argv[ -5 ] + target_tiff_path = sys.argv[ -4 ] + target_datatype = sys.argv[ -3 ] + target_path = sys.argv[ -2 ] + +# Save the Registered Source Image. +registered_source_image = IJ.openImage( source_tiff_path ) +if source_datatype == 'tiff': + registered_source_image = jython_utils.convert_before_saving_as_tiff( registered_source_image ) +IJ.saveAs( registered_source_image, source_datatype, source_path ) + +if not mono: + # Save the Registered Target Image. + registered_target_image = IJ.openImage( target_tiff_path ) + if target_datatype == 'tiff': + registered_target_image = jython_utils.convert_before_saving_as_tiff( registered_target_image ) + IJ.saveAs( registered_target_image, target_datatype, target_path ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_bunwarpj_compare_elastic.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_bunwarpj_compare_elastic.py Tue Sep 17 17:02:18 2019 -0400 |
[ |
@@ -0,0 +1,65 @@ +#!/usr/bin/env python +import argparse +import subprocess +import tempfile +import imagej2_base_utils + +# Parse Command Line. +parser = argparse.ArgumentParser() +parser.add_argument( '--source_image', dest='source_image', help='Source image' ) +parser.add_argument( '--source_image_format', dest='source_image_format', help='Source image format' ) +parser.add_argument( '--target_image', dest='target_image', help='Target image' ) +parser.add_argument( '--target_image_format', dest='target_image_format', help='Target image format' ) +parser.add_argument( '--source_transformation', dest='source_transformation', help='Direct source transformation matrix' ) +parser.add_argument( '--target_transformation', dest='target_transformation', help='Inverse target transformation matrix' ) +parser.add_argument( '--output', dest='output', help='Warping index' ) + +args = parser.parse_args() + +tmp_dir = imagej2_base_utils.get_temp_dir() +source_image_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.source_image, args.source_image_format ) +target_image_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.target_image, args.target_image_format ) +source_transformation_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.source_transformation, 'txt' ) +target_transformation_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.target_transformation, 'txt' ) +# bUnwarpJ produces several lines of output that we need to discard, so +# we'll use a temporary output file from which we'll read only the last line. +tmp_output_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.output, 'txt' ) + +# Define command response buffers. +tmp_out = tempfile.NamedTemporaryFile().name +tmp_stdout = open( tmp_out, 'wb' ) +tmp_err = tempfile.NamedTemporaryFile().name +tmp_stderr = open( tmp_err, 'wb' ) + +# Build the command line to calculate the warping index. +cmd = imagej2_base_utils.get_base_cmd_bunwarpj( None ) +if cmd is None: + imagej2_base_utils.stop_err( "bUnwarpJ not found!" ) +cmd += ' -compare_elastic' +# Target is sent before source. +cmd += ' %s' % target_image_path +cmd += ' %s' % source_image_path +cmd += ' %s' % target_transformation_path +cmd += ' %s' % source_transformation_path +cmd += ' > %s' % tmp_output_path + +# Calculate the warping index of two elastic transformations using bUnwarpJ. +proc = subprocess.Popen( args=cmd, stderr=subprocess.PIPE, stdout=subprocess.PIPE, shell=True ) +rc = proc.wait() +if rc != 0: + error_message = imagej2_base_utils.get_stderr_exception( tmp_err, tmp_stderr, tmp_out, tmp_stdout ) + imagej2_base_utils.stop_err( error_message ) + +# Example contents of tmp_output_path: +# ['Target image : ~/tmpKAYF1P.jpg\n', +# 'Source image : ~/tmpgQX0dy.gif\n', +# 'Target Transformation file : ~/tmpZJC_4B.txt\n', +# 'Source Transformation file : ~/tmphsSojl.txt\n', +# ' Warping index = 14.87777347388348\n'] +results = open( tmp_output_path, 'r' ).readlines() +warp_index = results[ -1 ].split( ' ' )[ -1 ] +outf = open( args.output, 'wb' ) +outf.write( '%s' % warp_index ) +outf.close() + +imagej2_base_utils.cleanup_before_exit( tmp_dir ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_bunwarpj_compare_elastic_raw.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_bunwarpj_compare_elastic_raw.py Tue Sep 17 17:02:18 2019 -0400 |
[ |
@@ -0,0 +1,64 @@ +#!/usr/bin/env python +import argparse +import subprocess +import tempfile +import imagej2_base_utils + +# Parse Command Line. +parser = argparse.ArgumentParser() +parser.add_argument( '--source_image', dest='source_image', help='Source image' ) +parser.add_argument( '--source_image_format', dest='source_image_format', help='Source image format' ) +parser.add_argument( '--target_image', dest='target_image', help='Target image' ) +parser.add_argument( '--target_image_format', dest='target_image_format', help='Target image format' ) +parser.add_argument( '--target_elastic_transformation', dest='target_elastic_transformation', help='Target elastic transformation matrix' ) +parser.add_argument( '--source_raw_transformation', dest='source_raw_transformation', help='Source raw transformation matrix' ) +parser.add_argument( '--output', dest='output', help='Warping index' ) + +args = parser.parse_args() + +tmp_dir = imagej2_base_utils.get_temp_dir() +source_image_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.source_image, args.source_image_format ) +target_image_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.target_image, args.target_image_format ) +target_elastic_transformation_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.target_elastic_transformation, 'txt' ) +source_raw_transformation_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.source_raw_transformation, 'txt' ) +# bUnwarpJ produces several lines of output that we need to discard, so +# we'll use a temporary output file from which we'll read only the last line. +tmp_output_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.output, 'txt' ) + +# Define command response buffers. +tmp_out = tempfile.NamedTemporaryFile().name +tmp_stdout = open( tmp_out, 'wb' ) +tmp_err = tempfile.NamedTemporaryFile().name +tmp_stderr = open( tmp_err, 'wb' ) + +# Build the command line to calculate the warping index. +cmd = imagej2_base_utils.get_base_cmd_bunwarpj( None ) +if cmd is None: + imagej2_base_utils.stop_err( "bUnwarpJ not found!" ) +cmd += ' -compare_elastic_raw' +cmd += ' %s' % target_image_path +cmd += ' %s' % source_image_path +cmd += ' %s' % target_elastic_transformation_path +cmd += ' %s' % source_raw_transformation_path +cmd += ' > %s' % tmp_output_path + +# Calculate the warping index of elastic and raw transformations using bUnwarpJ. +proc = subprocess.Popen( args=cmd, stderr=subprocess.PIPE, stdout=subprocess.PIPE, shell=True ) +rc = proc.wait() +if rc != 0: + error_message = imagej2_base_utils.get_stderr_exception( tmp_err, tmp_stderr, tmp_out, tmp_stdout ) + imagej2_base_utils.stop_err( error_message ) + +# Example contents of tmp_output_path: +# ['Target image : ~/tmpHdt9Cs.jpg\n', +# 'Source image : ~/tmpu6kyfc.gif\n', +# 'Elastic Transformation file : ~/tmp4vZurG.txt\n', +# 'Raw Transformation file : ~/tmp2PNQcT.txt\n', +# ' Warping index = 25.007467512204983\n'] +results = open( tmp_output_path, 'r' ).readlines() +warp_index = results[ -1 ].split( ' ' )[ -1 ] +outf = open( args.output, 'wb' ) +outf.write( '%s' % warp_index ) +outf.close() + +imagej2_base_utils.cleanup_before_exit( tmp_dir ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_bunwarpj_compare_raw.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_bunwarpj_compare_raw.py Tue Sep 17 17:02:18 2019 -0400 |
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@@ -0,0 +1,64 @@ +#!/usr/bin/env python +import argparse +import subprocess +import tempfile +import imagej2_base_utils + +# Parse Command Line. +parser = argparse.ArgumentParser() +parser.add_argument( '--source_image', dest='source_image', help='Source image' ) +parser.add_argument( '--source_image_format', dest='source_image_format', help='Source image format' ) +parser.add_argument( '--target_image', dest='target_image', help='Target image' ) +parser.add_argument( '--target_image_format', dest='target_image_format', help='Target image format' ) +parser.add_argument( '--target_raw_transformation', dest='target_raw_transformation', help='First raw transformation matrix' ) +parser.add_argument( '--source_raw_transformation', dest='source_raw_transformation', help='Second raw transformation matrix' ) +parser.add_argument( '--output', dest='output', help='Warping index' ) + +args = parser.parse_args() + +tmp_dir = imagej2_base_utils.get_temp_dir() +source_image_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.source_image, args.source_image_format ) +target_image_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.target_image, args.target_image_format ) +target_raw_transformation_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.target_raw_transformation, 'txt' ) +source_raw_transformation_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.source_raw_transformation, 'txt' ) +# bUnwarpJ produces several lines of output that we need to discard, so +# we'll use a temporary output file from which we'll read only the last line. +tmp_output_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.output, 'txt' ) + +# Define command response buffers. +tmp_out = tempfile.NamedTemporaryFile().name +tmp_stdout = open( tmp_out, 'wb' ) +tmp_err = tempfile.NamedTemporaryFile().name +tmp_stderr = open( tmp_err, 'wb' ) + +# Build the command line to calculate the warping index. +cmd = imagej2_base_utils.get_base_cmd_bunwarpj( None ) +if cmd is None: + imagej2_base_utils.stop_err( "bUnwarpJ not found!" ) +cmd += ' -compare_raw' +cmd += ' %s' % target_image_path +cmd += ' %s' % source_image_path +cmd += ' %s' % target_raw_transformation_path +cmd += ' %s' % source_raw_transformation_path +cmd += ' > %s' % tmp_output_path + +# Calculate the warping index of two raw transformations using bUnwarpJ. +proc = subprocess.Popen( args=cmd, stderr=subprocess.PIPE, stdout=subprocess.PIPE, shell=True ) +rc = proc.wait() +if rc != 0: + error_message = imagej2_base_utils.get_stderr_exception( tmp_err, tmp_stderr, tmp_out, tmp_stdout ) + imagej2_base_utils.stop_err( error_message ) + +# Example contents of tmp_output_path: +# ['Target image : ~/tmp5WmDku.jpg\n', +# 'Source image : ~/tmps74U40.gif\n', +# 'Target Transformation file : ~/tmpXofC1x.txt\n', +# 'Source Transformation file : ~/tmpFqNYe4.txt\n', +# ' Warping index = 24.111209027033937\n'] +results = open( tmp_output_path, 'r' ).readlines() +warp_index = results[ -1 ].split( ' ' )[ -1 ] +outf = open( args.output, 'wb' ) +outf.write( '%s' % warp_index ) +outf.close() + +imagej2_base_utils.cleanup_before_exit( tmp_dir ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_bunwarpj_compose_elastic.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_bunwarpj_compose_elastic.py Tue Sep 17 17:02:18 2019 -0400 |
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@@ -0,0 +1,50 @@ +#!/usr/bin/env python +import argparse +import subprocess +import tempfile +import imagej2_base_utils + +# Parse Command Line. +parser = argparse.ArgumentParser() +parser.add_argument( '--source_image', dest='source_image', help='Source image' ) +parser.add_argument( '--source_image_format', dest='source_image_format', help='Source image format' ) +parser.add_argument( '--target_image', dest='target_image', help='Target image' ) +parser.add_argument( '--target_image_format', dest='target_image_format', help='Target image format' ) +parser.add_argument( '--source_elastic_transformation', dest='source_elastic_transformation', help='Direct source transformation matrix' ) +parser.add_argument( '--target_elastic_transformation', dest='target_elastic_transformation', help='Inverse target transformation matrix' ) +parser.add_argument( '--output', dest='output', help='Warping index' ) + +args = parser.parse_args() + +tmp_dir = imagej2_base_utils.get_temp_dir() +source_image_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.source_image, args.source_image_format ) +target_image_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.target_image, args.target_image_format ) +source_elastic_transformation_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.source_elastic_transformation, 'txt' ) +target_elastic_transformation_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.target_elastic_transformation, 'txt' ) + +# Define command response buffers. +tmp_out = tempfile.NamedTemporaryFile().name +tmp_stdout = open( tmp_out, 'wb' ) +tmp_err = tempfile.NamedTemporaryFile().name +tmp_stderr = open( tmp_err, 'wb' ) + +# Build the command line to compose the transformations. +cmd = imagej2_base_utils.get_base_cmd_bunwarpj( None ) +if cmd is None: + imagej2_base_utils.stop_err( "bUnwarpJ not found!" ) +cmd += ' -compose_elastic' +# Target is sent before source. +cmd += ' %s' % target_image_path +cmd += ' %s' % source_image_path +cmd += ' %s' % target_elastic_transformation_path +cmd += ' %s' % source_elastic_transformation_path +cmd += ' %s' % args.output + +# Compose the two elastic transformations into a raw transformation using bUnwarpJ. +proc = subprocess.Popen( args=cmd, stderr=subprocess.PIPE, stdout=subprocess.PIPE, shell=True ) +rc = proc.wait() +if rc != 0: + error_message = imagej2_base_utils.get_stderr_exception( tmp_err, tmp_stderr, tmp_out, tmp_stdout ) + imagej2_base_utils.stop_err( error_message ) + +imagej2_base_utils.cleanup_before_exit( tmp_dir ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_bunwarpj_compose_raw.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_bunwarpj_compose_raw.py Tue Sep 17 17:02:18 2019 -0400 |
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@@ -0,0 +1,50 @@ +#!/usr/bin/env python +import argparse +import subprocess +import tempfile +import imagej2_base_utils + +# Parse Command Line. +parser = argparse.ArgumentParser() +parser.add_argument( '--source_image', dest='source_image', help='Source image' ) +parser.add_argument( '--source_image_format', dest='source_image_format', help='Source image format' ) +parser.add_argument( '--target_image', dest='target_image', help='Target image' ) +parser.add_argument( '--target_image_format', dest='target_image_format', help='Target image format' ) +parser.add_argument( '--source_raw_transformation', dest='source_raw_transformation', help='Direct source transformation matrix' ) +parser.add_argument( '--target_raw_transformation', dest='target_raw_transformation', help='Inverse target transformation matrix' ) +parser.add_argument( '--output', dest='output', help='Warping index' ) + +args = parser.parse_args() + +tmp_dir = imagej2_base_utils.get_temp_dir() +source_image_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.source_image, args.source_image_format ) +target_image_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.target_image, args.target_image_format ) +source_raw_transformation_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.source_raw_transformation, 'txt' ) +target_raw_transformation_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.target_raw_transformation, 'txt' ) + +# Define command response buffers. +tmp_out = tempfile.NamedTemporaryFile().name +tmp_stdout = open( tmp_out, 'wb' ) +tmp_err = tempfile.NamedTemporaryFile().name +tmp_stderr = open( tmp_err, 'wb' ) + +# Build the command line to compose the two raw transformations. +cmd = imagej2_base_utils.get_base_cmd_bunwarpj( None ) +if cmd is None: + imagej2_base_utils.stop_err( "bUnwarpJ not found!" ) +cmd += ' -compose_raw' +# Target is sent before source. +cmd += ' %s' % target_image_path +cmd += ' %s' % source_image_path +cmd += ' %s' % target_raw_transformation_path +cmd += ' %s' % source_raw_transformation_path +cmd += ' %s' % args.output + +# Compose the two raw transformations into another raw transformation using bUnwarpJ. +proc = subprocess.Popen( args=cmd, stderr=subprocess.PIPE, stdout=subprocess.PIPE, shell=True ) +rc = proc.wait() +if rc != 0: + error_message = imagej2_base_utils.get_stderr_exception( tmp_err, tmp_stderr, tmp_out, tmp_stdout ) + imagej2_base_utils.stop_err( error_message ) + +imagej2_base_utils.cleanup_before_exit( tmp_dir ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_bunwarpj_compose_raw_elastic.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_bunwarpj_compose_raw_elastic.py Tue Sep 17 17:02:18 2019 -0400 |
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@@ -0,0 +1,50 @@ +#!/usr/bin/env python +import argparse +import subprocess +import tempfile +import imagej2_base_utils + +# Parse Command Line. +parser = argparse.ArgumentParser() +parser.add_argument( '--source_image', dest='source_image', help='Source image' ) +parser.add_argument( '--source_image_format', dest='source_image_format', help='Source image format' ) +parser.add_argument( '--target_image', dest='target_image', help='Target image' ) +parser.add_argument( '--target_image_format', dest='target_image_format', help='Target image format' ) +parser.add_argument( '--source_elastic_transformation', dest='source_elastic_transformation', help='Direct source transformation matrix' ) +parser.add_argument( '--target_raw_transformation', dest='target_raw_transformation', help='Inverse target transformation matrix' ) +parser.add_argument( '--output', dest='output', help='Warping index' ) + +args = parser.parse_args() + +tmp_dir = imagej2_base_utils.get_temp_dir() +source_image_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.source_image, args.source_image_format ) +target_image_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.target_image, args.target_image_format ) +source_elastic_transformation_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.source_elastic_transformation, 'txt' ) +target_raw_transformation_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.target_raw_transformation, 'txt' ) + +# Define command response buffers. +tmp_out = tempfile.NamedTemporaryFile().name +tmp_stdout = open( tmp_out, 'wb' ) +tmp_err = tempfile.NamedTemporaryFile().name +tmp_stderr = open( tmp_err, 'wb' ) + +# Build the command line to compose the raw and elastic transformations. +cmd = imagej2_base_utils.get_base_cmd_bunwarpj( None ) +if cmd is None: + imagej2_base_utils.stop_err( "bUnwarpJ not found!" ) +cmd += ' -compose_raw_elastic' +# Target is sent before source. +cmd += ' %s' % target_image_path +cmd += ' %s' % source_image_path +cmd += ' %s' % target_raw_transformation_path +cmd += ' %s' % source_elastic_transformation_path +cmd += ' %s' % args.output + +# Compose the raw and elastic transformations into another raw transformation using bUnwarpJ. +proc = subprocess.Popen( args=cmd, stderr=subprocess.PIPE, stdout=subprocess.PIPE, shell=True ) +rc = proc.wait() +if rc != 0: + error_message = imagej2_base_utils.get_stderr_exception( tmp_err, tmp_stderr, tmp_out, tmp_stdout ) + imagej2_base_utils.stop_err( error_message ) + +imagej2_base_utils.cleanup_before_exit( tmp_dir ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_bunwarpj_convert_to_raw.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_bunwarpj_convert_to_raw.py Tue Sep 17 17:02:18 2019 -0400 |
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@@ -0,0 +1,47 @@ +#!/usr/bin/env python +import argparse +import subprocess +import tempfile +import imagej2_base_utils + +# Parse Command Line. +parser = argparse.ArgumentParser() +parser.add_argument( '--source_image', dest='source_image', help='Source image' ) +parser.add_argument( '--source_image_format', dest='source_image_format', help='Source image format' ) +parser.add_argument( '--target_image', dest='target_image', help='Target image' ) +parser.add_argument( '--target_image_format', dest='target_image_format', help='Target image format' ) +parser.add_argument( '--elastic_transformation', dest='elastic_transformation', help='Elastic transformation as saved by bUnwarpJ in elastic format' ) +parser.add_argument( '--raw_transformation', dest='raw_transformation', help='Raw transformation' ) + +args = parser.parse_args() + +tmp_dir = imagej2_base_utils.get_temp_dir() +source_image_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.source_image, args.source_image_format ) +target_image_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.target_image, args.target_image_format ) +elastic_transformation_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.elastic_transformation, 'txt' ) + +# Define command response buffers. +tmp_out = tempfile.NamedTemporaryFile().name +tmp_stdout = open( tmp_out, 'wb' ) +tmp_err = tempfile.NamedTemporaryFile().name +tmp_stderr = open( tmp_err, 'wb' ) + +# Build the command line to convert the B-spline (i.e., elastic) transformation to raw. +cmd = imagej2_base_utils.get_base_cmd_bunwarpj( None ) +if cmd is None: + imagej2_base_utils.stop_err( "bUnwarpJ not found!" ) +cmd += ' -convert_to_raw' +# Target is sent before source. +cmd += ' %s' % target_image_path +cmd += ' %s' % source_image_path +cmd += ' %s' % elastic_transformation_path +cmd += ' %s' % args.raw_transformation + +# Convert the elastic transformation to raw using bUnwarpJ. +proc = subprocess.Popen( args=cmd, stderr=tmp_stderr, stdout=tmp_stdout, shell=True ) +rc = proc.wait() +if rc != 0: + error_message = imagej2_base_utils.get_stderr_exception( tmp_err, tmp_stderr, tmp_out, tmp_stdout ) + imagej2_base_utils.stop_err( error_message ) + +imagej2_base_utils.cleanup_before_exit( tmp_dir ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_bunwarpj_elastic_transform.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_bunwarpj_elastic_transform.py Tue Sep 17 17:02:18 2019 -0400 |
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@@ -0,0 +1,73 @@ +#!/usr/bin/env python +import argparse +import shutil +import subprocess +import tempfile +import imagej2_base_utils + +# Parse Command Line. +parser = argparse.ArgumentParser() +parser.add_argument( '--source_image', dest='source_image', help='Source image' ) +parser.add_argument( '--source_image_format', dest='source_image_format', help='Source image format' ) +parser.add_argument( '--target_image', dest='target_image', help='Target image' ) +parser.add_argument( '--target_image_format', dest='target_image_format', help='Target image format' ) +parser.add_argument( '--elastic_transformation', dest='elastic_transformation', help='Elastic transformation as saved by bUnwarpJ in elastic format' ) +parser.add_argument( '--source_out', help='Output source image' ) +parser.add_argument( '--source_out_datatype', help='Output registered source image format' ) +parser.add_argument( '--jython_script', dest='jython_script', help='Path to the Jython script' ) + +args = parser.parse_args() + +tmp_dir = imagej2_base_utils.get_temp_dir() +source_image_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.source_image, args.source_image_format ) +tmp_source_out_tiff_path = imagej2_base_utils.get_temporary_image_path( tmp_dir, 'tiff' ) +tmp_source_out_path = imagej2_base_utils.get_temporary_image_path( tmp_dir, args.source_out_datatype ) +target_image_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.target_image, args.target_image_format ) +elastic_transformation_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.elastic_transformation, 'txt' ) + +# Define command response buffers. +tmp_out = tempfile.NamedTemporaryFile().name +tmp_stdout = open( tmp_out, 'wb' ) +tmp_err = tempfile.NamedTemporaryFile().name +tmp_stderr = open( tmp_err, 'wb' ) + +# Build the command line to apply the transformation. +cmd = imagej2_base_utils.get_base_cmd_bunwarpj( None ) +if cmd is None: + imagej2_base_utils.stop_err( "bUnwarpJ not found!" ) +cmd += ' -elastic_transform' +# Target is sent before source. +cmd += ' %s' % target_image_path +cmd += ' %s' % source_image_path +cmd += ' %s' % elastic_transformation_path +cmd += ' %s' % tmp_source_out_tiff_path + +# Apply the elastic transformation using bUnwarpJ. +proc = subprocess.Popen( args=cmd, stderr=tmp_stderr, stdout=tmp_stdout, shell=True ) +rc = proc.wait() +if rc != 0: + error_message = imagej2_base_utils.get_stderr_exception( tmp_err, tmp_stderr, tmp_out, tmp_stdout ) + imagej2_base_utils.stop_err( error_message ) + +# Convert the registered image to the specified output format. +tmp_out = tempfile.NamedTemporaryFile().name +tmp_stdout = open( tmp_out, 'wb' ) +tmp_err = tempfile.NamedTemporaryFile().name +tmp_stderr = open( tmp_err, 'wb' ) + +cmd = imagej2_base_utils.get_base_command_imagej2( None, jython_script=args.jython_script ) +if cmd is None: + imagej2_base_utils.stop_err( "ImageJ not found!" ) +cmd += ' %s %s %s' % ( tmp_source_out_tiff_path, + args.source_out_datatype, + tmp_source_out_path ) + +proc = subprocess.Popen( args=cmd, stderr=tmp_stderr, stdout=tmp_stdout, shell=True ) +rc = proc.wait() +if rc != 0: + error_message = imagej2_base_utils.get_stderr_exception( tmp_err, tmp_stderr, tmp_out, tmp_stdout ) + imagej2_base_utils.stop_err( error_message ) + +# Save the Registered Source Image to the defined output. +shutil.move( tmp_source_out_path, args.source_out ) +imagej2_base_utils.cleanup_before_exit( tmp_dir ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_bunwarpj_elastic_transform_jython_script.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_bunwarpj_elastic_transform_jython_script.py Tue Sep 17 17:02:18 2019 -0400 |
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@@ -0,0 +1,16 @@ +import sys +import jython_utils +from ij import IJ + +# Fiji Jython interpreter implements Python 2.5 which does not +# provide support for argparse. + +source_tiff_path = sys.argv[ -3 ] +source_datatype = sys.argv[ -2 ] +source_path = sys.argv[ -1 ] + +# Save the Registered Source Image. +registered_source_image = IJ.openImage( source_tiff_path ) +if source_datatype == 'tiff': + registered_source_image = jython_utils.convert_before_saving_as_tiff( registered_source_image ) +IJ.saveAs( registered_source_image, source_datatype, source_path ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_bunwarpj_raw_transform.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_bunwarpj_raw_transform.py Tue Sep 17 17:02:18 2019 -0400 |
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@@ -0,0 +1,73 @@ +#!/usr/bin/env python +import argparse +import shutil +import subprocess +import tempfile +import imagej2_base_utils + +# Parse Command Line. +parser = argparse.ArgumentParser() +parser.add_argument( '--source_image', dest='source_image', help='Source image' ) +parser.add_argument( '--source_image_format', dest='source_image_format', help='Source image format' ) +parser.add_argument( '--target_image', dest='target_image', help='Target image' ) +parser.add_argument( '--target_image_format', dest='target_image_format', help='Target image format' ) +parser.add_argument( '--raw_transformation', dest='raw_transformation', help='Raw transformation as saved by bUnwarpJ' ) +parser.add_argument( '--source_out', help='Output source image' ) +parser.add_argument( '--source_out_datatype', help='Output registered source image format' ) +parser.add_argument( '--jython_script', dest='jython_script', help='Path to the Jython script' ) + +args = parser.parse_args() + +tmp_dir = imagej2_base_utils.get_temp_dir() +source_image_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.source_image, args.source_image_format ) +tmp_source_out_tiff_path = imagej2_base_utils.get_temporary_image_path( tmp_dir, 'tiff' ) +tmp_source_out_path = imagej2_base_utils.get_temporary_image_path( tmp_dir, args.source_out_datatype ) +target_image_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.target_image, args.target_image_format ) +raw_transformation_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.raw_transformation, 'txt' ) + +# Define command response buffers. +tmp_out = tempfile.NamedTemporaryFile().name +tmp_stdout = open( tmp_out, 'wb' ) +tmp_err = tempfile.NamedTemporaryFile().name +tmp_stderr = open( tmp_err, 'wb' ) + +# Build the command line to apply the raw transformation. +cmd = imagej2_base_utils.get_base_cmd_bunwarpj( None ) +if cmd is None: + imagej2_base_utils.stop_err( "bUnwarpJ not found!" ) +cmd += ' -raw_transform' +# Target is sent before source. +cmd += ' %s' % target_image_path +cmd += ' %s' % source_image_path +cmd += ' %s' % raw_transformation_path +cmd += ' %s' % tmp_source_out_tiff_path + +# Apply the raw transformation using bUnwarpJ. +proc = subprocess.Popen( args=cmd, stderr=tmp_stderr, stdout=tmp_stdout, shell=True ) +rc = proc.wait() +if rc != 0: + error_message = imagej2_base_utils.get_stderr_exception( tmp_err, tmp_stderr, tmp_out, tmp_stdout ) + imagej2_base_utils.stop_err( error_message ) + +# Convert the registered image to the specified output format. +tmp_out = tempfile.NamedTemporaryFile().name +tmp_stdout = open( tmp_out, 'wb' ) +tmp_err = tempfile.NamedTemporaryFile().name +tmp_stderr = open( tmp_err, 'wb' ) + +cmd = imagej2_base_utils.get_base_command_imagej2( None, jython_script=args.jython_script ) +if cmd is None: + imagej2_base_utils.stop_err( "ImageJ not found!" ) +cmd += ' %s %s %s' % ( tmp_source_out_tiff_path, + args.source_out_datatype, + tmp_source_out_path ) + +proc = subprocess.Popen( args=cmd, stderr=tmp_stderr, stdout=tmp_stdout, shell=True ) +rc = proc.wait() +if rc != 0: + error_message = imagej2_base_utils.get_stderr_exception( tmp_err, tmp_stderr, tmp_out, tmp_stdout ) + imagej2_base_utils.stop_err( error_message ) + +# Save the Registered Source Image to the defined output. +shutil.move( tmp_source_out_path, args.source_out ) +imagej2_base_utils.cleanup_before_exit( tmp_dir ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_bunwarpj_raw_transform_jython_script.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_bunwarpj_raw_transform_jython_script.py Tue Sep 17 17:02:18 2019 -0400 |
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@@ -0,0 +1,16 @@ +import sys +import jython_utils +from ij import IJ + +# Fiji Jython interpreter implements Python 2.5 which does not +# provide support for argparse. + +source_tiff_path = sys.argv[ -3 ] +source_datatype = sys.argv[ -2 ] +source_path = sys.argv[ -1 ] + +# Save the Registered Source Image. +registered_source_image = IJ.openImage( source_tiff_path ) +if source_datatype == 'tiff': + registered_source_image = jython_utils.convert_before_saving_as_tiff( registered_source_image ) +IJ.saveAs( registered_source_image, source_datatype, source_path ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_create_image.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_create_image.py Tue Sep 17 17:02:18 2019 -0400 |
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@@ -0,0 +1,40 @@ +#!/usr/bin/env python +import argparse +import shutil +import subprocess +import tempfile +import imagej2_base_utils + +if __name__=="__main__": + # Parse Command Line. + parser = argparse.ArgumentParser() + parser.add_argument( '--width', dest='width', type=int, help='Image width in pixels' ) + parser.add_argument( '--height', dest='height', type=int, help='Image height in pixels' ) + parser.add_argument( '--depth', dest='depth', type=int, help='Image depth (specifies the number of stack slices)' ) + parser.add_argument( '--image_type', dest='image_type', help='Image type' ) + parser.add_argument( '--image_title', dest='image_title', default='', help='Image title' ) + parser.add_argument( '--output_datatype', dest='output_datatype', help='Output image format' ) + parser.add_argument( '--jython_script', dest='jython_script', help='Path to the Jython script' ) + parser.add_argument( '--out_fname', help='Path to the output file' ) + args = parser.parse_args() + + tmp_dir = imagej2_base_utils.get_temp_dir() + tmp_image_path = imagej2_base_utils.get_temporary_image_path( tmp_dir, args.output_datatype ) + + # Define command response buffers. + tmp_out = tempfile.NamedTemporaryFile().name + tmp_stdout = open( tmp_out, 'wb' ) + tmp_err = tempfile.NamedTemporaryFile().name + tmp_stderr = open( tmp_err, 'wb' ) + # Build the command line. + cmd = imagej2_base_utils.get_base_command_imagej2( None, jython_script=args.jython_script ) + if cmd is None: + imagej2_base_utils.stop_err( "ImageJ not found!" ) + cmd += ' %s %d %d %d %s %s' % ( args.image_title, args.width, args.height, args.depth, args.image_type, tmp_image_path ) + proc = subprocess.Popen( args=cmd, stderr=tmp_stderr, stdout=tmp_stdout, shell=True ) + rc = proc.wait() + if rc != 0: + error_message = imagej2_base_utils.get_stderr_exception( tmp_err, tmp_stderr, tmp_out, tmp_stdout ) + imagej2_base_utils.stop_err( error_message ) + shutil.move( tmp_image_path, args.out_fname ) + imagej2_base_utils.cleanup_before_exit( tmp_dir ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_create_image_jython_script.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_create_image_jython_script.py Tue Sep 17 17:02:18 2019 -0400 |
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@@ -0,0 +1,14 @@ +import sys +from ij import IJ + +# Fiji Jython interpreter implements Python 2.5 which does not +# provide support for argparse. +title = sys.argv[ -6 ] +width = int( sys.argv[ -5 ] ) +height = int( sys.argv[ -4 ] ) +depth = int( sys.argv[ -3 ] ) +type = sys.argv[ -2 ].replace( '_', ' ' ) +tmp_image_path = sys.argv[ -1 ] + +imp = IJ.newImage( title, type, width, height, depth ) +IJ.save( imp, "%s" % tmp_image_path ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_enhance_contrast.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_enhance_contrast.py Tue Sep 17 17:02:18 2019 -0400 |
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@@ -0,0 +1,63 @@ +#!/usr/bin/env python +import argparse +import os +import shutil +import subprocess +import tempfile +import imagej2_base_utils + +parser = argparse.ArgumentParser() +parser.add_argument( '--input', dest='input', help='Path to the input file' ) +parser.add_argument( '--input_datatype', dest='input_datatype', help='Datatype of the input image' ) +parser.add_argument( '--equalize_histogram', dest='equalize_histogram', help='Equalize_histogram' ) +parser.add_argument( '--saturated_pixels', dest='saturated_pixels', type=float, default=None, help='Saturated pixel pct' ) +parser.add_argument( '--normalize', dest='normalize', help='Normalize' ) +parser.add_argument( '--jython_script', dest='jython_script', help='Path to the Jython script' ) +parser.add_argument( '--output', dest='output', help='Path to the output file' ) +parser.add_argument( '--output_datatype', dest='output_datatype', help='Datatype of the output image' ) +args = parser.parse_args() + +tmp_dir = imagej2_base_utils.get_temp_dir() +# ImageJ expects valid image file extensions, so the Galaxy .dat extension does not +# work for some features. The following creates a symlink with an appropriate file +# extension that points to the Galaxy dataset. This symlink is used by ImageJ. +tmp_input_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.input, args.input_datatype ) +tmp_output_path = imagej2_base_utils.get_temporary_image_path( tmp_dir, args.output_datatype ) + +# Define command response buffers. +tmp_out = tempfile.NamedTemporaryFile().name +tmp_stdout = open( tmp_out, 'wb' ) +tmp_err = tempfile.NamedTemporaryFile().name +tmp_stderr = open( tmp_err, 'wb' ) +# Java writes a lot of stuff to stderr, so we'll specify a file for handling actual errors. +error_log = tempfile.NamedTemporaryFile( delete=False ).name + +# Build the command line. +cmd = imagej2_base_utils.get_base_command_imagej2( None, jython_script=args.jython_script ) +if cmd is None: + imagej2_base_utils.stop_err( "ImageJ not found!" ) +cmd += ' %s' % error_log +cmd += ' %s' % tmp_input_path +cmd += ' %s' % args.equalize_histogram +cmd += imagej2_base_utils.handle_none_type( args.saturated_pixels ) +cmd += ' %s' % args.normalize +cmd += ' %s' % tmp_output_path +cmd += ' %s' % args.output_datatype + +# Run the command. +proc = subprocess.Popen( args=cmd, stderr=tmp_stderr, stdout=tmp_stdout, shell=True ) +rc = proc.wait() + +# Handle execution errors. +if rc != 0: + error_message = imagej2_base_utils.get_stderr_exception( tmp_err, tmp_stderr, tmp_out, tmp_stdout ) + imagej2_base_utils.stop_err( error_message ) + +# Handle processing errors. +if os.path.getsize( error_log ) > 0: + error_message = open( error_log, 'r' ).read() + imagej2_base_utils.stop_err( error_message ) + +# Save the output image. +shutil.move( tmp_output_path, args.output ) +imagej2_base_utils.cleanup_before_exit( tmp_dir ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_enhance_contrast_jython_script.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_enhance_contrast_jython_script.py Tue Sep 17 17:02:18 2019 -0400 |
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@@ -0,0 +1,42 @@ +import jython_utils +import sys +from ij import IJ + +# Fiji Jython interpreter implements Python 2.5 which does not +# provide support for argparse. +error_log = sys.argv[ -7 ] +input = sys.argv[ -6 ] +equalize_histogram = jython_utils.asbool( sys.argv[ -5 ] ) +saturated_pixels = sys.argv[ -4 ] +normalize = jython_utils.asbool( sys.argv[ -3 ] ) +tmp_output_path = sys.argv[ -2 ] +output_datatype = sys.argv[ -1 ] + +# Open the input image file. +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() +bit_depth = image_processor_copy.getBitDepth() + +# Set the options +options = [] +# If equalize_histogram, saturated_pixels and normalize are ignored. +if equalize_histogram: + options.append( 'equalize' ) +else: + if saturated_pixels not in [ None, 'None' ]: + # Fiji allows only a single decimal place for this value. + options.append( 'saturated=%.3f' % float( saturated_pixels ) ) + # Normalization of RGB images is not supported. + if bit_depth != 24 and normalize: + options.append( 'normalize' ) +try: + # Run the command. + options = "%s" % ' '.join( options ) + IJ.run( input_image_plus_copy, "Enhance Contrast...", options ) + # Save the ImagePlus object as a new image. + IJ.saveAs( input_image_plus_copy, output_datatype, tmp_output_path ) +except Exception, e: + jython_utils.handle_error( error_log, str( e ) ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_find_edges.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_find_edges.py Tue Sep 17 17:02:18 2019 -0400 |
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@@ -0,0 +1,57 @@ +#!/usr/bin/env python +import argparse +import os +import shutil +import subprocess +import tempfile +import imagej2_base_utils + +parser = argparse.ArgumentParser() +parser.add_argument( '--input', dest='input', help='Path to the input file' ) +parser.add_argument( '--input_datatype', dest='input_datatype', help='Datatype of the input image' ) +parser.add_argument( '--jython_script', dest='jython_script', help='Path to the Jython script' ) +parser.add_argument( '--output', dest='output', help='Path to the output file' ) +parser.add_argument( '--output_datatype', dest='output_datatype', help='Datatype of the output image' ) +args = parser.parse_args() + +tmp_dir = imagej2_base_utils.get_temp_dir() +# ImageJ expects valid image file extensions, so the Galaxy .dat extension does not +# work for some features. The following creates a symlink with an appropriate file +# extension that points to the Galaxy dataset. This symlink is used by ImageJ. +tmp_input_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.input, args.input_datatype ) +tmp_output_path = imagej2_base_utils.get_temporary_image_path( tmp_dir, args.output_datatype ) + +# Define command response buffers. +tmp_out = tempfile.NamedTemporaryFile().name +tmp_stdout = open( tmp_out, 'wb' ) +tmp_err = tempfile.NamedTemporaryFile().name +tmp_stderr = open( tmp_err, 'wb' ) +# Java writes a lot of stuff to stderr, so we'll specify a file for handling actual errors. +error_log = tempfile.NamedTemporaryFile( delete=False ).name + +# Build the command line. +cmd = imagej2_base_utils.get_base_command_imagej2( None, jython_script=args.jython_script ) +if cmd is None: + imagej2_base_utils.stop_err( "ImageJ not found!" ) +cmd += ' %s' % error_log +cmd += ' %s' % tmp_input_path +cmd += ' %s' % tmp_output_path +cmd += ' %s' % args.output_datatype + +# Run the command. +proc = subprocess.Popen( args=cmd, stderr=tmp_stderr, stdout=tmp_stdout, shell=True ) +rc = proc.wait() + +# Handle execution errors. +if rc != 0: + error_message = imagej2_base_utils.get_stderr_exception( tmp_err, tmp_stderr, tmp_out, tmp_stdout ) + imagej2_base_utils.stop_err( error_message ) + +# Handle processing errors. +if os.path.getsize( error_log ) > 0: + error_message = open( error_log, 'r' ).read() + imagej2_base_utils.stop_err( error_message ) + +# Save the output image. +shutil.move( tmp_output_path, args.output ) +imagej2_base_utils.cleanup_before_exit( tmp_dir ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_find_edges_jython_script.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_find_edges_jython_script.py Tue Sep 17 17:02:18 2019 -0400 |
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@@ -0,0 +1,25 @@ +import jython_utils +import sys +from ij import IJ + +# Fiji Jython interpreter implements Python 2.5 which does not +# provide support for argparse. +error_log = sys.argv[ -4 ] +input = sys.argv[ -3 ] +tmp_output_path = sys.argv[ -2 ] +output_datatype = sys.argv[ -1 ] + +# Open the input image file. +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: + # Run the command. + IJ.run( input_image_plus_copy, "Find Edges", "" ) + # Save the ImagePlus object as a new image. + IJ.saveAs( input_image_plus_copy, output_datatype, tmp_output_path ) +except Exception, e: + jython_utils.handle_error( error_log, str( e ) ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_find_maxima.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_find_maxima.py Tue Sep 17 17:02:18 2019 -0400 |
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@@ -0,0 +1,69 @@ +#!/usr/bin/env python +import argparse +import os +import shutil +import subprocess +import tempfile +import imagej2_base_utils + +parser = argparse.ArgumentParser() +parser.add_argument( '--input', dest='input', help='Path to the input file' ) +parser.add_argument( '--input_datatype', dest='input_datatype', help='Datatype of the input image' ) +parser.add_argument( '--scale_when_converting', dest='scale_when_converting', help='Scale when converting RGB image' ) +parser.add_argument( '--weighted_rgb_conversions', dest='weighted_rgb_conversions', help='Weighted RGB conversions for RGB image' ) +parser.add_argument( '--noise_tolerance', dest='noise_tolerance', type=int, help='Noise tolerance' ) +parser.add_argument( '--output_type', dest='output_type', help='Output type' ) +parser.add_argument( '--exclude_edge_maxima', dest='exclude_edge_maxima', help='Exclude edge maxima' ) +parser.add_argument( '--light_background', dest='light_background', help='Light background' ) +parser.add_argument( '--jython_script', dest='jython_script', help='Path to the Jython script' ) +parser.add_argument( '--output', dest='output', help='Path to the output file' ) +parser.add_argument( '--output_datatype', dest='output_datatype', help='Datatype of the output image' ) +args = parser.parse_args() + +tmp_dir = imagej2_base_utils.get_temp_dir() +# ImageJ expects valid image file extensions, so the Galaxy .dat extension does not +# work for some features. The following creates a symlink with an appropriate file +# extension that points to the Galaxy dataset. This symlink is used by ImageJ. +tmp_input_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.input, args.input_datatype ) +tmp_output_path = imagej2_base_utils.get_temporary_image_path( tmp_dir, args.output_datatype ) + +# Define command response buffers. +tmp_out = tempfile.NamedTemporaryFile().name +tmp_stdout = open( tmp_out, 'wb' ) +tmp_err = tempfile.NamedTemporaryFile().name +tmp_stderr = open( tmp_err, 'wb' ) +# Java writes a lot of stuff to stderr, so we'll specify a file for handling actual errors. +error_log = tempfile.NamedTemporaryFile( delete=False ).name + +# Build the command line. +cmd = imagej2_base_utils.get_base_command_imagej2( None, jython_script=args.jython_script ) +if cmd is None: + imagej2_base_utils.stop_err( "ImageJ not found!" ) +cmd += ' %s' % error_log +cmd += ' %s' % tmp_input_path +cmd += ' %s' % args.scale_when_converting +cmd += ' %s' % args.weighted_rgb_conversions +cmd += ' %d' % args.noise_tolerance +cmd += ' %s' % args.output_type +cmd += ' %s' % args.exclude_edge_maxima +cmd += ' %s' % args.light_background +cmd += ' %s' % tmp_output_path +cmd += ' %s' % args.output_datatype + +# Run the command. +proc = subprocess.Popen( args=cmd, stderr=tmp_stderr, stdout=tmp_stdout, shell=True ) +rc = proc.wait() + +# Handle execution errors. +if rc != 0: + error_message = imagej2_base_utils.get_stderr_exception( tmp_err, tmp_stderr, tmp_out, tmp_stdout ) + imagej2_base_utils.stop_err( error_message ) + +# Handle processing errors. +if os.path.getsize( error_log ) > 0: + error_message = open( error_log, 'r' ).read() + imagej2_base_utils.stop_err( error_message ) + +# Save the output image. +shutil.move( tmp_output_path, args.output ) +imagej2_base_utils.cleanup_before_exit( tmp_dir ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_find_maxima_jython_script.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_find_maxima_jython_script.py Tue Sep 17 17:02:18 2019 -0400 |
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@@ -0,0 +1,94 @@ +import sys +import jython_utils +from ij import ImagePlus, IJ +from ij.plugin.filter import Analyzer, MaximumFinder +from ij.process import ImageProcessor +from jarray import array + +# Fiji Jython interpreter implements Python 2.5 which does not +# provide support for argparse. +error_log = sys.argv[ -10 ] +input = sys.argv[ -9 ] +scale_when_converting = jython_utils.asbool( sys.argv[ -8 ] ) +weighted_rgb_conversions = jython_utils.asbool( sys.argv[ -7 ] ) +noise_tolerance = int( sys.argv[ -6 ] ) +output_type = sys.argv[ -5 ] +exclude_edge_maxima = jython_utils.asbool( sys.argv[ -4 ] ) +light_background = jython_utils.asbool( sys.argv[ -3 ] ) +tmp_output_path = sys.argv[ -2 ] +output_datatype = sys.argv[ -1 ] + +# Open the input image file. +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() +bit_depth = image_processor_copy.getBitDepth() +analyzer = Analyzer( input_image_plus_copy ) + +try: + # Set the conversion options. + options = [] + # The following 2 options are applicable only to RGB images. + if bit_depth == 24: + if scale_when_converting: + option.append( "scale" ) + if weighted_rgb_conversions: + options.append( "weighted" ) + # Perform conversion - must happen even if no options are set. + IJ.run( input_image_plus_copy, "Conversions...", "%s" % " ".join( options ) ) + if output_type in [ 'List', 'Count' ]: + # W're generating a tabular file for the output. + # Set the Find Maxima options. + options = [ 'noise=%d' % noise_tolerance ] + if output_type.find( '_' ) > 0: + output_type_str = 'output=[%s]' % output_type.replace( '_', ' ' ) + else: + output_type_str = 'output=%s' % output_type + options.append( output_type_str ) + if exclude_edge_maxima: + options.append( 'exclude' ) + if light_background: + options.append( 'light' ) + # Run the command. + IJ.run( input_image_plus_copy, "Find Maxima...", "%s" % " ".join( options ) ) + results_table = analyzer.getResultsTable() + results_table.saveAs( tmp_output_path ) + else: + # Find the maxima of an image (does not find minima). + # LIMITATIONS: With output_type=Segmented_Particles + # (watershed segmentation), some segmentation lines + # may be improperly placed if local maxima are suppressed + # by the tolerance. + mf = MaximumFinder() + if output_type == 'Single_Points': + output_type_param = mf.SINGLE_POINTS + elif output_type == 'Maxima_Within_Tolerance': + output_type_param = mf.IN_TOLERANCE + elif output_type == 'Segmented_Particles': + output_type_param = mf.SEGMENTED + elif output_type == 'List': + output_type_param = mf.LIST + elif output_type == 'Count': + output_type_param = mf.COUNT + # Get a new byteProcessor with a normal (uninverted) LUT where + # the marked points are set to 255 (Background 0). Pixels outside + # of the roi of the input image_processor_copy are not set. No + # output image is created for output types POINT_SELECTION, LIST + # and COUNT. In these cases findMaxima returns null. + byte_processor = mf.findMaxima( image_processor_copy, + noise_tolerance, + ImageProcessor.NO_THRESHOLD, + output_type_param, + exclude_edge_maxima, + False ) + # Invert the image or ROI. + byte_processor.invert() + if output_type == 'Segmented_Particles' and not light_background: + # Invert the values in this image's LUT (indexed color model). + byte_processor.invertLut() + image_plus = ImagePlus( "output", byte_processor ) + IJ.saveAs( image_plus, output_datatype, tmp_output_path ) +except Exception, e: + jython_utils.handle_error( error_log, str( e ) ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_macros.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_macros.xml Tue Sep 17 17:02:18 2019 -0400 |
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@@ -0,0 +1,106 @@ +<?xml version='1.0' encoding='UTF-8'?> +<macros> + <token name="@WRAPPER_VERSION@">3.0</token> + <xml name="fiji_requirements"> + <requirements> + <requirement type="package" version="20170530">fiji</requirement> + </requirements> + </xml> + <xml name="stdio"> + <stdio> + <exit_code range="1:"/> + <exit_code range=":-1"/> + <regex match="Error:"/> + <regex match="Exception:"/> + </stdio> + </xml> + <xml name="image_type"> + <param name="image_type" type="select" label="Image type"> + <option value="8-bit_white" selected="True">8-bit white</option> + <option value="8-bit_black">8-bit black</option> + <option value="8-bit_random">8-bit random</option> + <option value="8-bit_ramp">8-bit ramp</option> + <option value="16-bit_white">16-bit white</option> + <option value="16-bit_black">16-bit black</option> + <option value="16-bit_random">16-bit random</option> + <option value="16-bit_ramp">16-bit ramp</option> + <option value="32-bit_white">32-bit white</option> + <option value="32-bit_black">32-bit black</option> + <option value="32-bit_random">32-bit random</option> + <option value="32-bit_ramp">32-bit ramp</option> + <option value="RGB_white">RGB white</option> + <option value="RGB_black">RGB black</option> + <option value="RGB_random">RGB random</option> + <option value="RGB_ramp">RGB ramp</option> + </param> + </xml> + <xml name="make_binary_params"> + <param name="iterations" type="integer" value="1" min="1" max="100" label="Iterations" help="The number of times (1-100) erosion, dilation, opening, and closing are performed."/> + <param name="count" type="integer" value="1" min="1" max="8" label="Count" help="The number of adjacent background pixels necessary (1-8) for erosion or dilation."/> + <param name="black_background" type="select" label="Black background" help="If Yes, the background is black and the foreground is white (no implies the opposite)."> + <option value="no" selected="True">No</option> + <option value="yes">Yes</option> + </param> + <param name="pad_edges_when_eroding" type="select" label="Pad edges when eroding" help="If Yes, eroding does not erode from the edges of the image."> + <option value="no" selected="True">No</option> + <option value="yes">Yes</option> + </param> + </xml> + <xml name="black_background_param"> + <param name="black_background" type="select" label="Black background" help="If Yes, the background is black and the foreground is white (no implies the opposite)."> + <option value="no" selected="True">No</option> + <option value="yes">Yes</option> + </param> + </xml> + <token name="@make_binary_args@"> + --iterations $iterations + --count $count + --black_background $black_background + --pad_edges_when_eroding $pad_edges_when_eroding + </token> + <token name="@requires_binary_input@"> +.. class:: warningmark + +This tool works on binary images, so other image types will automatically be converted to binary +before they are analyzed. This step is performed using the ImageJ2 **Make Binary** command with +the following settings: **Iterations:** 1, **Count:** 1, **Pad edges when eroding:** No. The tool +allows you to choose the **Black background** setting. If these settings are not appropriate, +first manually convert the image to binary using the **Convert to binary (black and white)** +tool, which allows you to change them. + </token> + <xml name="image_datatypes"> + <option value="bmp">bmp</option> + <option value="gif">gif</option> + <option value="jpg">jpg</option> + <option value="png" selected="true">png</option> + <option value="tiff">tiff</option> + </xml> + <xml name="bunwarpj_citations"> + <citations> + <citation type="bibtex"> + @InProceedings(Arganda-Carreras2006, + author = "Ignacio Arganda-Carreras and + Carlos Oscar S{\'a}nchez Sorzano and + Roberto Marabini and + Jos{\'e} Mar\'{\i}a Carazo and + Carlos Ortiz-de-Solorzano and + Jan Kybic", + title = "Consistent and Elastic Registration of Histological Sections Using Vector-Spline Regularization", + publisher = "Springer Berlin / Heidelberg", + booktitle = "Computer Vision Approaches to Medical Image Analysis", + series = "Lecture Notes in Computer Science", + year = "2006", + volume = "4241", + pages = "85-95", + month = "May", + city = "Graz, Austria") + </citation> + <citation type="doi">10.1038/nmeth.2019</citation> + </citations> + </xml> + <xml name="fiji_headless_citations"> + <citations> + <citation type="doi">10.1038/nmeth.2102</citation> + </citations> + </xml> +</macros> |
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diff -r 000000000000 -r ab54024c0a88 imagej2_make_binary.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_make_binary.py Tue Sep 17 17:02:18 2019 -0400 |
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@@ -0,0 +1,59 @@ +#!/usr/bin/env python +import argparse +import os +import shutil +import subprocess +import tempfile +import imagej2_base_utils + +parser = argparse.ArgumentParser() +parser.add_argument( '--input', dest='input', help='Path to the input file' ) +parser.add_argument( '--input_datatype', dest='input_datatype', help='Datatype of the input image' ) +parser.add_argument( '--iterations', dest='iterations', type=int, help='Iterations' ) +parser.add_argument( '--count', dest='count', type=int, help='Count' ) +parser.add_argument( '--black_background', dest='black_background', help='Black background' ) +parser.add_argument( '--pad_edges_when_eroding', dest='pad_edges_when_eroding', help='Pad edges when eroding' ) +parser.add_argument( '--jython_script', dest='jython_script', help='Path to the Jython script' ) +parser.add_argument( '--output', dest='output', help='Path to the output file' ) +parser.add_argument( '--output_datatype', dest='output_datatype', help='Datatype of the output image' ) +args = parser.parse_args() + +tmp_dir = imagej2_base_utils.get_temp_dir() +# ImageJ expects valid image file extensions, so the Galaxy .dat extension does not +# work for some features. The following creates a symlink with an appropriate file +# extension that points to the Galaxy dataset. This symlink is used by ImageJ. +tmp_input_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.input, args.input_datatype ) +tmp_output_path = imagej2_base_utils.get_temporary_image_path( tmp_dir, args.output_datatype ) +# Define command response buffers. +tmp_out = tempfile.NamedTemporaryFile().name +tmp_stdout = open( tmp_out, 'wb' ) +tmp_err = tempfile.NamedTemporaryFile().name +tmp_stderr = open( tmp_err, 'wb' ) +# Java writes a lot of stuff to stderr, so we'll specify a file for handling actual errors. +error_log = tempfile.NamedTemporaryFile( delete=False ).name +# Build the command line. +cmd = imagej2_base_utils.get_base_command_imagej2( None, jython_script=args.jython_script ) +if cmd is None: + imagej2_base_utils.stop_err( "ImageJ not found!" ) +cmd += ' %s' % error_log +cmd += ' %s' % tmp_input_path +cmd += ' %d' % args.iterations +cmd += ' %d' % args.count +cmd += ' %s' % args.black_background +cmd += ' %s' % args.pad_edges_when_eroding +cmd += ' %s' % tmp_output_path +cmd += ' %s' % args.output_datatype +# Run the command. +proc = subprocess.Popen( args=cmd, stderr=tmp_stderr, stdout=tmp_stdout, shell=True ) +rc = proc.wait() +# Handle execution errors. +if rc != 0: + error_message = imagej2_base_utils.get_stderr_exception( tmp_err, tmp_stderr, tmp_out, tmp_stdout ) + imagej2_base_utils.stop_err( error_message ) +# Handle processing errors. +if os.path.getsize( error_log ) > 0: + error_message = open( error_log, 'r' ).read() + imagej2_base_utils.stop_err( error_message ) +# Save the output image. +shutil.move( tmp_output_path, args.output ) +imagej2_base_utils.cleanup_before_exit( tmp_dir ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_make_binary_jython_script.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_make_binary_jython_script.py Tue Sep 17 17:02:18 2019 -0400 |
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@@ -0,0 +1,37 @@ +import jython_utils +import sys +from ij import IJ + +# Fiji Jython interpreter implements Python 2.5 which does not +# provide support for argparse. +error_log = sys.argv[ -8 ] +input = sys.argv[ -7 ] +iterations = int( sys.argv[ -6 ] ) +count = int( sys.argv[ -5 ] ) +black_background = jython_utils.asbool( sys.argv[ -4 ] ) +pad_edges_when_eroding = jython_utils.asbool( sys.argv[ -3 ] ) +tmp_output_path = sys.argv[ -2 ] +output_datatype = sys.argv[ -1 ] + +# Open the input image file. +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, + iterations=iterations, + count=count, + pad_edges_when_eroding=pad_edges_when_eroding ) + IJ.run( input_image_plus_copy, "Options...", options ) + + # Run the command. + IJ.run( input_image_plus_copy, "Make Binary", "" ) + + # Save the ImagePlus object as a new image. + IJ.saveAs( input_image_plus_copy, output_datatype, tmp_output_path ) +except Exception, e: + jython_utils.handle_error( error_log, str( e ) ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_math.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_math.py Tue Sep 17 17:02:18 2019 -0400 |
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@@ -0,0 +1,69 @@ +#!/usr/bin/env python +import argparse +import os +import shutil +import subprocess +import tempfile +import imagej2_base_utils + +parser = argparse.ArgumentParser() +parser.add_argument( '--input', dest='input', help='Path to the input file' ) +parser.add_argument( '--input_datatype', dest='input_datatype', help='Datatype of the input image' ) +parser.add_argument( '--operation', dest='operation', help='Operation' ) +parser.add_argument( '--expression', dest='expression', default=None, help='Expression' ) +parser.add_argument( '--bin_constant', dest='bin_constant', type=int, default=None, help='Constant of type binary integer' ) +parser.add_argument( '--float_constant', dest='float_constant', type=float, default=None, help='Constant of type float' ) +parser.add_argument( '--jython_script', dest='jython_script', help='Path to the Jython script' ) +parser.add_argument( '--output', dest='output', help='Path to the output file' ) +parser.add_argument( '--output_datatype', dest='output_datatype', help='Datatype of the output image' ) +args = parser.parse_args() + +tmp_dir = imagej2_base_utils.get_temp_dir() +# ImageJ expects valid image file extensions, so the Galaxy .dat extension does not +# work for some features. The following creates a symlink with an appropriate file +# extension that points to the Galaxy dataset. This symlink is used by ImageJ. +tmp_input_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.input, args.input_datatype ) +tmp_output_path = imagej2_base_utils.get_temporary_image_path( tmp_dir, args.output_datatype ) + +# Define command response buffers. +tmp_out = tempfile.NamedTemporaryFile().name +tmp_stdout = open( tmp_out, 'wb' ) +tmp_err = tempfile.NamedTemporaryFile().name +tmp_stderr = open( tmp_err, 'wb' ) +# Java writes a lot of stuff to stderr, so we'll specify a file for handling actual errors. +error_log = tempfile.NamedTemporaryFile( delete=False ).name + +# Build the command line. +cmd = imagej2_base_utils.get_base_command_imagej2( None, jython_script=args.jython_script ) +if cmd is None: + imagej2_base_utils.stop_err( "ImageJ not found!" ) +cmd += ' %s' % error_log +cmd += ' %s' % tmp_input_path +cmd += ' %s' % args.operation +# Handle the expression, which must be enclosed in " if not None. +if args.expression in [ None, 'None' ]: + cmd += ' None' +else: + cmd += ' "%s"' % args.expression +cmd += imagej2_base_utils.handle_none_type( args.bin_constant, val_type='int' ) +cmd += imagej2_base_utils.handle_none_type( args.float_constant ) +cmd += ' %s' % tmp_output_path +cmd += ' %s' % args.output_datatype + +# Run the command. +proc = subprocess.Popen( args=cmd, stderr=tmp_stderr, stdout=tmp_stdout, shell=True ) +rc = proc.wait() + +# Handle execution errors. +if rc != 0: + error_message = imagej2_base_utils.get_stderr_exception( tmp_err, tmp_stderr, tmp_out, tmp_stdout ) + imagej2_base_utils.stop_err( error_message ) + +# Handle processing errors. +if os.path.getsize( error_log ) > 0: + error_message = open( error_log, 'r' ).read() + imagej2_base_utils.stop_err( error_message ) + +# Save the output image. +shutil.move( tmp_output_path, args.output ) +imagej2_base_utils.cleanup_before_exit( tmp_dir ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_math_jython_script.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_math_jython_script.py Tue Sep 17 17:02:18 2019 -0400 |
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@@ -0,0 +1,78 @@ +import jython_utils +import sys +from ij import IJ + +# Fiji Jython interpreter implements Python 2.5 which does not +# provide support for argparse. +error_log = sys.argv[ -8 ] +input = sys.argv[ -7 ] +operation = sys.argv[ -6 ] +expression = sys.argv[ -5 ] +if sys.argv[ -4 ] in [ None, 'None' ]: + bin_constant = None +else: + bin_constant = int( sys.argv[ -4 ] ) +if sys.argv[ -3 ] in [ None, 'None' ]: + float_constant = None +else: + float_constant = float( sys.argv[ -3 ] ) +tmp_output_path = sys.argv[ -2 ] +output_datatype = sys.argv[ -1 ] + +# Open the input image file. +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() +bit_depth = image_processor_copy.getBitDepth() + +try: + if operation.find( '_' ) > 0: + # Square_Root. + new_operation = operation.replace( '_', ' ' ) + elif operation in [ 'Square', 'Log', 'Exp', 'Abs', 'Reciprocal' ]: + # Unfortunately some ImageJ commands require a "..." ending + # while others do not. There seems to be no pattern. + new_operation = '%s' % operation + else: + new_operation = '%s...' % operation + + if operation == 'Macro': + # Apply the macro code to the image via a call to it's + # ImageProcessor since this option does not work using + # the IJ.run() method. + new_expression = expression.lstrip( '"' ).rstrip( '"' ) + options = 'code=%s' % new_expression + image_processor_copy.applyMacro( new_expression ) + elif operation == 'Min': + # Min does not work without using the ImageProcessor. + image_processor_copy.min( float_constant ) + elif operation == 'Max': + # Max does not work without using the ImageProcessor. + image_processor_copy.max( float_constant ) + elif operation == 'Abs': + if bit_depth not in [ 16, 32 ]: + # Convert the image to 32-bit. + IJ.run( input_image_plus_copy, "32-bit", "" ) + IJ.run( input_image_plus_copy, new_operation, "" ) + elif operation == 'Reciprocal': + if bit_depth != 32: + # Convert the image to 32 bit. + IJ.run( input_image_plus_copy, "32-bit", "" ) + IJ.run( input_image_plus_copy, new_operation, "" ) + else: + if operation in [ 'AND', 'OR', 'XOR' ]: + # Value is a binary number. + options = 'value=%d' % bin_constant + elif operation in [ 'Log', 'Exp', 'Square', 'Square_Root' ]: + # No constant value. + options = '' + else: + # Value is a floating point number. + options = 'value=%.3f' % float_constant + IJ.run( input_image_plus_copy, "%s" % new_operation, "%s" % options ) + # Save the ImagePlus object as a new image. + IJ.saveAs( input_image_plus_copy, output_datatype, tmp_output_path ) +except Exception, e: + jython_utils.handle_error( error_log, str( e ) ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_noise.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_noise.py Tue Sep 17 17:02:18 2019 -0400 |
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@@ -0,0 +1,84 @@ +#!/usr/bin/env python +import argparse +import os +import shutil +import subprocess +import tempfile +import imagej2_base_utils + +if __name__=="__main__": + # Parse Command Line. + parser = argparse.ArgumentParser() + parser.add_argument( '--input', dest='input', help='Path to the input file' ) + parser.add_argument( '--input_datatype', dest='input_datatype', help='Datatype of the input image' ) + parser.add_argument( '--noise', dest='noise', help='Specified noise to add to or remove from the image' ) + parser.add_argument( '--standard_deviation', dest='standard_deviation', type=float, default=None, help='Standard deviation' ) + parser.add_argument( '--radius', dest='radius', type=float, default=None, help='Radius' ) + parser.add_argument( '--threshold', dest='threshold', type=float, default=None, help='Threshold' ) + parser.add_argument( '--which_outliers', dest='which_outliers', default=None, help='Which outliers' ) + parser.add_argument( '--randomj', dest='randomj', default=None, help='RandomJ' ) + parser.add_argument( '--trials', dest='trials', type=float, default=None, help='Trials' ) + parser.add_argument( '--probability', dest='probability', type=float, default=None, help='Probability' ) + parser.add_argument( '--lammbda', dest='lammbda', type=float, default=None, help='Lambda' ) + parser.add_argument( '--order', dest='order', type=int, default=None, help='Order' ) + parser.add_argument( '--mean', dest='mean', type=float, default=None, help='Mean' ) + parser.add_argument( '--sigma', dest='sigma', type=float, default=None, help='Sigma' ) + parser.add_argument( '--min', dest='min', type=float, default=None, help='Min' ) + parser.add_argument( '--max', dest='max', type=float, default=None, help='Max' ) + parser.add_argument( '--insertion', dest='insertion', default=None, help='Insertion' ) + parser.add_argument( '--jython_script', dest='jython_script', help='Path to the Jython script' ) + parser.add_argument( '--output', dest='output', help='Path to the output file' ) + args = parser.parse_args() + + tmp_dir = imagej2_base_utils.get_temp_dir() + # ImageJ expects valid image file extensions, so the Galaxy .dat extension does not + # work for some features. The following creates a symlink with an appropriate file + # extension that points to the Galaxy dataset. This symlink is used by ImageJ. + tmp_input_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.input, args.input_datatype ) + tmp_output_path = imagej2_base_utils.get_temporary_image_path( tmp_dir, args.input_datatype ) + + # Define command response buffers. + tmp_out = tempfile.NamedTemporaryFile().name + tmp_stdout = open( tmp_out, 'wb' ) + tmp_err = tempfile.NamedTemporaryFile().name + tmp_stderr = open( tmp_err, 'wb' ) + # Java writes a lot of stuff to stderr, so we'll specify a file for handling actual errors. + error_log = tempfile.NamedTemporaryFile( delete=False ).name + # Build the command line. + cmd = imagej2_base_utils.get_base_command_imagej2( None, jython_script=args.jython_script ) + if cmd is None: + imagej2_base_utils.stop_err( "ImageJ not found!" ) + cmd += ' %s' % error_log + cmd += ' %s' % tmp_input_path + cmd += ' %s' % args.input_datatype + cmd += ' %s ' % args.noise + cmd += imagej2_base_utils.handle_none_type( args.standard_deviation ) + cmd += imagej2_base_utils.handle_none_type( args.radius ) + cmd += imagej2_base_utils.handle_none_type( args.threshold ) + cmd += ' %s' % args.which_outliers + cmd += ' %s' % args.randomj + cmd += imagej2_base_utils.handle_none_type( args.trials ) + cmd += imagej2_base_utils.handle_none_type( args.probability ) + cmd += imagej2_base_utils.handle_none_type( args.lammbda ) + cmd += imagej2_base_utils.handle_none_type( args.order, val_type='int' ) + cmd += imagej2_base_utils.handle_none_type( args.mean ) + cmd += imagej2_base_utils.handle_none_type( args.sigma ) + cmd += imagej2_base_utils.handle_none_type( args.min ) + cmd += imagej2_base_utils.handle_none_type( args.max ) + cmd += ' %s' % args.insertion + cmd += ' %s' % tmp_output_path + + proc = subprocess.Popen( args=cmd, stderr=tmp_stderr, stdout=tmp_stdout, shell=True ) + rc = proc.wait() + + # Handle execution errors. + if rc != 0: + error_message = imagej2_base_utils.get_stderr_exception( tmp_err, tmp_stderr, tmp_out, tmp_stdout ) + imagej2_base_utils.stop_err( error_message ) + # Handle processing errors. + if os.path.getsize( error_log ) > 0: + error_message = open( error_log, 'r' ).read() + imagej2_base_utils.stop_err( error_message ) + # Save the output image. + shutil.move( tmp_output_path, args.output ) + imagej2_base_utils.cleanup_before_exit( tmp_dir ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_noise_jython_script.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_noise_jython_script.py Tue Sep 17 17:02:18 2019 -0400 |
[ |
@@ -0,0 +1,84 @@ +import sys +from ij import IJ +from ij import ImagePlus +import jython_utils + +# Fiji Jython interpreter implements Python 2.5 which does not +# provide support for argparse. +error_log = sys.argv[ -19 ] +input = sys.argv[ -18 ] +image_datatype = sys.argv[ -17 ] +noise = sys.argv[ -16 ] +standard_deviation = sys.argv[ -15 ] +radius = sys.argv[ -14 ] +threshold = sys.argv[ -13 ] +which_outliers = sys.argv[ -12 ] +randomj = sys.argv[ -11 ] +trials = sys.argv[ -10 ] +probability = sys.argv[ -9 ] +# Note the spelling - so things don't get confused due to Python lambda function. +lammbda = sys.argv[ -8 ] +order = sys.argv[ -7 ] +mean = sys.argv[ -6 ] +sigma = sys.argv[ -5 ] +min = sys.argv[ -4 ] +max = sys.argv[ -3 ] +insertion = sys.argv[ -2 ] +tmp_output_path = sys.argv[ -1 ] + +error = False + +# Open the input image file. +image_plus = IJ.openImage( input ) +bit_depth = image_plus.getBitDepth() +image_type = image_plus.getType() +# Create an ImagePlus object for the image. +image_plus_copy = image_plus.duplicate() +# Make a copy of the image. +image_processor_copy = image_plus_copy.getProcessor() + +# Perform the analysis on the ImagePlus object. +if noise == 'add_noise': + IJ.run( image_plus_copy, "Add Noise", "" ) +elif noise == 'add_specified_noise': + IJ.run( image_plus_copy, "Add Specified Noise", "standard=&standard_deviation" ) +elif noise == 'salt_and_pepper': + IJ.run( image_plus_copy, "Salt and Pepper", "" ) +elif noise == 'despeckle': + IJ.run( image_plus_copy, "Despeckle", "" ) +elif noise == 'remove_outliers': + IJ.run( image_plus_copy, "Remove Outliers", "radius=&radius threshold=&threshold which=&which_outliers" ) +elif noise == 'remove_nans': + if bit_depth == 32: + IJ.run( image_plus_copy, "Remove NaNs", "" ) + else: + # When Galaxy metadata for images is enhanced to include information like this, + # we'll be able to write tool validators rather than having to stop the job in + # an error state. + msg = "Remove NaNs requires a 32-bit image, the selected image is %d-bit" % bit_depth + jython_utils.handle_error( error_log, msg ) + error = True +elif noise == 'rof_denoise': + if image_type == ImagePlus.GRAY32: + IJ.run( image_plus_copy, "ROF Denoise", "" ) + else: + msg = "ROF Denoise requires an image of type 32-bit grayscale, the selected image is %d-bit" % ( bit_depth ) + jython_utils.handle_error( error_log, msg ) + error = True +elif noise == 'randomj': + if randomj == 'randomj_binomial': + IJ.run( image_plus_copy, "RandomJ Binomial", "trials=&trials probability=&probability insertion=&insertion" ) + elif randomj == 'randomj_exponential': + IJ.run( image_plus_copy, "RandomJ Exponential", "lambda=&lammbda insertion=&insertion" ) + elif randomj == 'randomj_gamma': + IJ.run( image_plus_copy, "RandomJ Gamma", "order=&order insertion=&insertion" ) + elif randomj == 'randomj_gaussian': + IJ.run( image_plus_copy, "RandomJ Gaussian", "mean=&mean sigma=&sigma insertion=&insertion" ) + elif randomj == 'randomj_poisson': + IJ.run( image_plus_copy, "RandomJ Poisson", "mean=&mean insertion=&insertion" ) + elif randomj == 'randomj_uniform': + IJ.run( image_plus_copy, "RandomJ Uniform", "min=&min max=&max insertion=&insertion" ) + +if not error: + # Save the ImagePlus object as a new image. + IJ.saveAs( image_plus_copy, image_datatype, tmp_output_path ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_shadows.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_shadows.py Tue Sep 17 17:02:18 2019 -0400 |
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@@ -0,0 +1,59 @@ +#!/usr/bin/env python +import argparse +import os +import shutil +import subprocess +import tempfile +import imagej2_base_utils + +parser = argparse.ArgumentParser() +parser.add_argument( '--input', dest='input', help='Path to the input file' ) +parser.add_argument( '--input_datatype', dest='input_datatype', help='Datatype of the input image' ) +parser.add_argument( '--direction', dest='direction', help='Direction' ) +parser.add_argument( '--jython_script', dest='jython_script', help='Path to the Jython script' ) +parser.add_argument( '--output', dest='output', help='Path to the output file' ) +parser.add_argument( '--output_datatype', dest='output_datatype', help='Datatype of the output image' ) +args = parser.parse_args() + +tmp_dir = imagej2_base_utils.get_temp_dir() +# ImageJ expects valid image file extensions, so the Galaxy .dat extension does not +# work for some features. The following creates a symlink with an appropriate file +# extension that points to the Galaxy dataset. This symlink is used by ImageJ. +tmp_input_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.input, args.input_datatype ) +tmp_output_path = imagej2_base_utils.get_temporary_image_path( tmp_dir, args.output_datatype ) + +# Define command response buffers. +tmp_out = tempfile.NamedTemporaryFile().name +tmp_stdout = open( tmp_out, 'wb' ) +tmp_err = tempfile.NamedTemporaryFile().name +tmp_stderr = open( tmp_err, 'wb' ) +# Java writes a lot of stuff to stderr, so we'll specify a file for handling actual errors. +error_log = tempfile.NamedTemporaryFile( delete=False ).name + +# Build the command line. +cmd = imagej2_base_utils.get_base_command_imagej2( None, jython_script=args.jython_script ) +if cmd is None: + imagej2_base_utils.stop_err( "ImageJ not found!" ) +cmd += ' %s' % error_log +cmd += ' %s' % tmp_input_path +cmd += ' %s' % args.direction +cmd += ' %s' % tmp_output_path +cmd += ' %s' % args.output_datatype + +# Run the command. +proc = subprocess.Popen( args=cmd, stderr=tmp_stderr, stdout=tmp_stdout, shell=True ) +rc = proc.wait() + +# Handle execution errors. +if rc != 0: + error_message = imagej2_base_utils.get_stderr_exception( tmp_err, tmp_stderr, tmp_out, tmp_stdout ) + imagej2_base_utils.stop_err( error_message ) + +# Handle processing errors. +if os.path.getsize( error_log ) > 0: + error_message = open( error_log, 'r' ).read() + imagej2_base_utils.stop_err( error_message ) + +# Save the output image. +shutil.move( tmp_output_path, args.output ) +imagej2_base_utils.cleanup_before_exit( tmp_dir ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_shadows_jython_script.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_shadows_jython_script.py Tue Sep 17 17:02:18 2019 -0400 |
[ |
@@ -0,0 +1,26 @@ +import jython_utils +import sys +from ij import IJ + +# Fiji Jython interpreter implements Python 2.5 which does not +# provide support for argparse. +error_log = sys.argv[ -5 ] +input = sys.argv[ -4 ] +direction = sys.argv[ -3 ] +tmp_output_path = sys.argv[ -2 ] +output_datatype = sys.argv[ -1 ] + +# Open the input image file. +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: + # Run the command. + IJ.run( input_image_plus_copy, direction, "" ) + # Save the ImagePlus object as a new image. + IJ.saveAs( input_image_plus_copy, output_datatype, tmp_output_path ) +except Exception, e: + jython_utils.handle_error( error_log, str( e ) ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_sharpen.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_sharpen.py Tue Sep 17 17:02:18 2019 -0400 |
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@@ -0,0 +1,57 @@ +#!/usr/bin/env python +import argparse +import os +import shutil +import subprocess +import tempfile +import imagej2_base_utils + +parser = argparse.ArgumentParser() +parser.add_argument( '--input', dest='input', help='Path to the input file' ) +parser.add_argument( '--input_datatype', dest='input_datatype', help='Datatype of the input image' ) +parser.add_argument( '--jython_script', dest='jython_script', help='Path to the Jython script' ) +parser.add_argument( '--output', dest='output', help='Path to the output file' ) +parser.add_argument( '--output_datatype', dest='output_datatype', help='Datatype of the output image' ) +args = parser.parse_args() + +tmp_dir = imagej2_base_utils.get_temp_dir() +# ImageJ expects valid image file extensions, so the Galaxy .dat extension does not +# work for some features. The following creates a symlink with an appropriate file +# extension that points to the Galaxy dataset. This symlink is used by ImageJ. +tmp_input_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.input, args.input_datatype ) +tmp_output_path = imagej2_base_utils.get_temporary_image_path( tmp_dir, args.output_datatype ) + +# Define command response buffers. +tmp_out = tempfile.NamedTemporaryFile().name +tmp_stdout = open( tmp_out, 'wb' ) +tmp_err = tempfile.NamedTemporaryFile().name +tmp_stderr = open( tmp_err, 'wb' ) +# Java writes a lot of stuff to stderr, so we'll specify a file for handling actual errors. +error_log = tempfile.NamedTemporaryFile( delete=False ).name + +# Build the command line. +cmd = imagej2_base_utils.get_base_command_imagej2( None, jython_script=args.jython_script ) +if cmd is None: + imagej2_base_utils.stop_err( "ImageJ not found!" ) +cmd += ' %s' % error_log +cmd += ' %s' % tmp_input_path +cmd += ' %s' % tmp_output_path +cmd += ' %s' % args.output_datatype + +# Run the command. +proc = subprocess.Popen( args=cmd, stderr=tmp_stderr, stdout=tmp_stdout, shell=True ) +rc = proc.wait() + +# Handle execution errors. +if rc != 0: + error_message = imagej2_base_utils.get_stderr_exception( tmp_err, tmp_stderr, tmp_out, tmp_stdout ) + imagej2_base_utils.stop_err( error_message ) + +# Handle processing errors. +if os.path.getsize( error_log ) > 0: + error_message = open( error_log, 'r' ).read() + imagej2_base_utils.stop_err( error_message ) + +# Save the output image. +shutil.move( tmp_output_path, args.output ) +imagej2_base_utils.cleanup_before_exit( tmp_dir ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_sharpen_jython_script.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_sharpen_jython_script.py Tue Sep 17 17:02:18 2019 -0400 |
[ |
@@ -0,0 +1,25 @@ +import jython_utils +import sys +from ij import IJ + +# Fiji Jython interpreter implements Python 2.5 which does not +# provide support for argparse. +error_log = sys.argv[ -4 ] +input = sys.argv[ -3 ] +tmp_output_path = sys.argv[ -2 ] +output_datatype = sys.argv[ -1 ] + +# Open the input image file. +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: + # Run the command. + IJ.run( input_image_plus_copy, "Sharpen", "" ) + # Save the ImagePlus object as a new image. + IJ.saveAs( input_image_plus_copy, output_datatype, tmp_output_path ) +except Exception, e: + jython_utils.handle_error( error_log, str( e ) ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_skeletonize3d.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_skeletonize3d.py Tue Sep 17 17:02:18 2019 -0400 |
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@@ -0,0 +1,53 @@ +#!/usr/bin/env python +import argparse +import os +import shutil +import subprocess +import tempfile +import imagej2_base_utils + +parser = argparse.ArgumentParser() +parser.add_argument( '--input', dest='input', help='Path to the input file' ) +parser.add_argument( '--input_datatype', dest='input_datatype', help='Datatype of the input image' ) +parser.add_argument( '--black_background', dest='black_background', help='Black background' ) +parser.add_argument( '--jython_script', dest='jython_script', help='Path to the Jython script' ) +parser.add_argument( '--output', dest='output', help='Path to the output file' ) +parser.add_argument( '--output_datatype', dest='output_datatype', help='Datatype of the output image' ) +args = parser.parse_args() + +tmp_dir = imagej2_base_utils.get_temp_dir() +# ImageJ expects valid image file extensions, so the Galaxy .dat extension does not +# work for some features. The following creates a symlink with an appropriate file +# extension that points to the Galaxy dataset. This symlink is used by ImageJ. +tmp_input_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.input, args.input_datatype ) +tmp_output_path = imagej2_base_utils.get_temporary_image_path( tmp_dir, args.output_datatype ) +# Define command response buffers. +tmp_out = tempfile.NamedTemporaryFile().name +tmp_stdout = open( tmp_out, 'wb' ) +tmp_err = tempfile.NamedTemporaryFile().name +tmp_stderr = open( tmp_err, 'wb' ) +# Java writes a lot of stuff to stderr, so we'll specify a file for handling actual errors. +error_log = tempfile.NamedTemporaryFile( delete=False ).name +# Build the command line. +cmd = imagej2_base_utils.get_base_command_imagej2( None, jython_script=args.jython_script ) +if cmd is None: + imagej2_base_utils.stop_err( "ImageJ not found!" ) +cmd += ' %s' % error_log +cmd += ' %s' % tmp_input_path +cmd += ' %s' % args.black_background +cmd += ' %s' % tmp_output_path +cmd += ' %s' % args.output_datatype +# Run the command. +proc = subprocess.Popen( args=cmd, stderr=tmp_stderr, stdout=tmp_stdout, shell=True ) +rc = proc.wait() +# Handle execution errors. +if rc != 0: + error_message = imagej2_base_utils.get_stderr_exception( tmp_err, tmp_stderr, tmp_out, tmp_stdout ) + imagej2_base_utils.stop_err( error_message ) +# Handle processing errors. +if os.path.getsize( error_log ) > 0: + error_message = open( error_log, 'r' ).read() + imagej2_base_utils.stop_err( error_message ) +# Save the output image. +shutil.move( tmp_output_path, args.output ) +imagej2_base_utils.cleanup_before_exit( tmp_dir ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_skeletonize3d_jython_script.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_skeletonize3d_jython_script.py Tue Sep 17 17:02:18 2019 -0400 |
[ |
@@ -0,0 +1,36 @@ +import jython_utils +import sys +from ij import IJ + +# Fiji Jython interpreter implements Python 2.5 which does not +# provide support for argparse. +error_log = sys.argv[ -5 ] +input = sys.argv[ -4 ] +black_background = jython_utils.asbool( sys.argv[ -3 ] ) +tmp_output_path = sys.argv[ -2 ] +output_datatype = sys.argv[ -1 ] + +# Open the input image file. +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 ) + + # Convert image to binary if necessary. + if not image_processor_copy.isBinary(): + # Convert the image to binary grayscale. + IJ.run( input_image_plus_copy, "Make Binary", "" ) + + # Run the command. + IJ.run( input_image_plus_copy, "Skeletonize (2D/3D)", "" ) + + # Save the ImagePlus object as a new image. + IJ.saveAs( input_image_plus_copy, output_datatype, tmp_output_path ) +except Exception, e: + jython_utils.handle_error( error_log, str( e ) ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_smooth.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_smooth.py Tue Sep 17 17:02:18 2019 -0400 |
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@@ -0,0 +1,57 @@ +#!/usr/bin/env python +import argparse +import os +import shutil +import subprocess +import tempfile +import imagej2_base_utils + +parser = argparse.ArgumentParser() +parser.add_argument( '--input', dest='input', help='Path to the input file' ) +parser.add_argument( '--input_datatype', dest='input_datatype', help='Datatype of the input image' ) +parser.add_argument( '--jython_script', dest='jython_script', help='Path to the Jython script' ) +parser.add_argument( '--output', dest='output', help='Path to the output file' ) +parser.add_argument( '--output_datatype', dest='output_datatype', help='Datatype of the output image' ) +args = parser.parse_args() + +tmp_dir = imagej2_base_utils.get_temp_dir() +# ImageJ expects valid image file extensions, so the Galaxy .dat extension does not +# work for some features. The following creates a symlink with an appropriate file +# extension that points to the Galaxy dataset. This symlink is used by ImageJ. +tmp_input_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.input, args.input_datatype ) +tmp_output_path = imagej2_base_utils.get_temporary_image_path( tmp_dir, args.output_datatype ) + +# Define command response buffers. +tmp_out = tempfile.NamedTemporaryFile().name +tmp_stdout = open( tmp_out, 'wb' ) +tmp_err = tempfile.NamedTemporaryFile().name +tmp_stderr = open( tmp_err, 'wb' ) +# Java writes a lot of stuff to stderr, so we'll specify a file for handling actual errors. +error_log = tempfile.NamedTemporaryFile( delete=False ).name + +# Build the command line. +cmd = imagej2_base_utils.get_base_command_imagej2( None, jython_script=args.jython_script ) +if cmd is None: + imagej2_base_utils.stop_err( "ImageJ not found!" ) +cmd += ' %s' % error_log +cmd += ' %s' % tmp_input_path +cmd += ' %s' % tmp_output_path +cmd += ' %s' % args.output_datatype + +# Run the command. +proc = subprocess.Popen( args=cmd, stderr=tmp_stderr, stdout=tmp_stdout, shell=True ) +rc = proc.wait() + +# Handle execution errors. +if rc != 0: + error_message = imagej2_base_utils.get_stderr_exception( tmp_err, tmp_stderr, tmp_out, tmp_stdout ) + imagej2_base_utils.stop_err( error_message ) + +# Handle processing errors. +if os.path.getsize( error_log ) > 0: + error_message = open( error_log, 'r' ).read() + imagej2_base_utils.stop_err( error_message ) + +# Save the output image. +shutil.move( tmp_output_path, args.output ) +imagej2_base_utils.cleanup_before_exit( tmp_dir ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_smooth_jython_script.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_smooth_jython_script.py Tue Sep 17 17:02:18 2019 -0400 |
[ |
@@ -0,0 +1,25 @@ +import jython_utils +import sys +from ij import IJ + +# Fiji Jython interpreter implements Python 2.5 which does not +# provide support for argparse. +error_log = sys.argv[ -4 ] +input = sys.argv[ -3 ] +tmp_output_path = sys.argv[ -2 ] +output_datatype = sys.argv[ -1 ] + +# Open the input image file. +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: + # Run the command. + IJ.run( input_image_plus_copy, "Smooth", "" ) + # Save the ImagePlus object as a new image. + IJ.saveAs( input_image_plus_copy, output_datatype, tmp_output_path ) +except Exception, e: + jython_utils.handle_error( error_log, str( e ) ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_watershed_binary.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_watershed_binary.py Tue Sep 17 17:02:18 2019 -0400 |
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@@ -0,0 +1,53 @@ +#!/usr/bin/env python +import argparse +import os +import shutil +import subprocess +import tempfile +import imagej2_base_utils + +parser = argparse.ArgumentParser() +parser.add_argument( '--input', dest='input', help='Path to the input file' ) +parser.add_argument( '--input_datatype', dest='input_datatype', help='Datatype of the input image' ) +parser.add_argument( '--black_background', dest='black_background', help='Black background' ) +parser.add_argument( '--jython_script', dest='jython_script', help='Path to the Jython script' ) +parser.add_argument( '--output', dest='output', help='Path to the output file' ) +parser.add_argument( '--output_datatype', dest='output_datatype', help='Datatype of the output image' ) +args = parser.parse_args() + +tmp_dir = imagej2_base_utils.get_temp_dir() +# ImageJ expects valid image file extensions, so the Galaxy .dat extension does not +# work for some features. The following creates a symlink with an appropriate file +# extension that points to the Galaxy dataset. This symlink is used by ImageJ. +tmp_input_path = imagej2_base_utils.get_input_image_path( tmp_dir, args.input, args.input_datatype ) +tmp_output_path = imagej2_base_utils.get_temporary_image_path( tmp_dir, args.output_datatype ) +# Define command response buffers. +tmp_out = tempfile.NamedTemporaryFile().name +tmp_stdout = open( tmp_out, 'wb' ) +tmp_err = tempfile.NamedTemporaryFile().name +tmp_stderr = open( tmp_err, 'wb' ) +# Java writes a lot of stuff to stderr, so we'll specify a file for handling actual errors. +error_log = tempfile.NamedTemporaryFile( delete=False ).name +# Build the command line. +cmd = imagej2_base_utils.get_base_command_imagej2( None, jython_script=args.jython_script ) +if cmd is None: + imagej2_base_utils.stop_err( "ImageJ not found!" ) +cmd += ' %s' % error_log +cmd += ' %s' % tmp_input_path +cmd += ' %s' % args.black_background +cmd += ' %s' % tmp_output_path +cmd += ' %s' % args.output_datatype +# Run the command. +proc = subprocess.Popen( args=cmd, stderr=tmp_stderr, stdout=tmp_stdout, shell=True ) +rc = proc.wait() +# Handle execution errors. +if rc != 0: + error_message = imagej2_base_utils.get_stderr_exception( tmp_err, tmp_stderr, tmp_out, tmp_stdout ) + imagej2_base_utils.stop_err( error_message ) +# Handle processing errors. +if os.path.getsize( error_log ) > 0: + error_message = open( error_log, 'r' ).read() + imagej2_base_utils.stop_err( error_message ) +# Save the output image. +shutil.move( tmp_output_path, args.output ) +imagej2_base_utils.cleanup_before_exit( tmp_dir ) |
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diff -r 000000000000 -r ab54024c0a88 imagej2_watershed_binary_jython_script.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_watershed_binary_jython_script.py Tue Sep 17 17:02:18 2019 -0400 |
[ |
@@ -0,0 +1,36 @@ +import jython_utils +import sys +from ij import IJ + +# Fiji Jython interpreter implements Python 2.5 which does not +# provide support for argparse. +error_log = sys.argv[ -5 ] +input = sys.argv[ -4 ] +black_background = jython_utils.asbool( sys.argv[ -3 ] ) +tmp_output_path = sys.argv[ -2 ] +output_datatype = sys.argv[ -1 ] + +# Open the input image file. +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 ) + + # Convert image to binary if necessary. + if not image_processor_copy.isBinary(): + # Convert the image to binary grayscale. + IJ.run( input_image_plus_copy, "Make Binary", "" ) + + # Run the command. + IJ.run( input_image_plus_copy, "Watershed", "" ) + + # Save the ImagePlus object as a new image. + IJ.saveAs( input_image_plus_copy, output_datatype, tmp_output_path ) +except Exception, e: + jython_utils.handle_error( error_log, str( e ) ) |
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diff -r 000000000000 -r ab54024c0a88 jython_utils$py.class |
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diff -r 000000000000 -r ab54024c0a88 jython_utils.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/jython_utils.py Tue Sep 17 17:02:18 2019 -0400 |
[ |
@@ -0,0 +1,48 @@ +import imagej2_base_utils +from ij import IJ + +IMAGE_PLUS_IMAGE_TYPE_FIELD_VALUES = { '0':'GRAY8', '1':'GRAY16', '2':'GRAY32', + '3':'COLOR_256', '4':'COLOR_RGB' } + +def asbool( val ): + return str( val ).lower() in [ 'yes', 'true' ] + +def convert_before_saving_as_tiff( image_plus ): + # The bUnwarpJ plug-in produces TIFF image stacks consisting of 3 + # slices which can be viewed in ImageJ. The 3 slices are: 1) the + # registered image, 2) the target image and 3) the black/white warp + # image. When running bUnwarpJ from the command line (as these + # Galaxy wrappers do) the initial call to IJ.openImage() (to open the + # registered source and target images produced by bUnwarpJ) in the + # tool's jython_script.py returns an ImagePlus object with a single + # slice which is the "generally undesired" slice 3 discussed above. + # However, a call to IJ.saveAs() will convert the single-slice TIFF + # into a 3-slice TIFF image stack (as described above) if the selected + # format for saving is TIFF. Galaxy supports only single-layered + # images, so to work around this behavior, we have to convert the + # image to something other than TIFF so that slices are eliminated. + # We can then convert back to TIFF for saving. There might be a way + # to do this without converting twice, but I spent a lot of time looking + # and I have yet to discover it. + tmp_dir = imagej2_base_utils.get_temp_dir() + tmp_out_png_path = imagej2_base_utils.get_temporary_image_path( tmp_dir, 'png' ) + IJ.saveAs( image_plus, 'png', tmp_out_png_path ) + return IJ.openImage( tmp_out_png_path ) + +def get_binary_options( black_background, iterations=1, count=1, pad_edges_when_eroding='no' ): + options = [ 'edm=Overwrite', 'iterations=%d' % iterations, 'count=%d' % count ] + if asbool( pad_edges_when_eroding ): + options.append( 'pad' ) + if asbool( black_background ): + options.append( "black" ) + return " ".join( options ) + +def get_display_image_type( image_type ): + return IMAGE_PLUS_IMAGE_TYPE_FIELD_VALUES.get( str( image_type ), None ) + +def handle_error( error_log, msg ): + # Java writes a lot of stuff to stderr, so the received error_log + # will log actual errors. + elh = open( error_log, 'wb' ) + elh.write( msg ) + elh.close() |
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diff -r 000000000000 -r ab54024c0a88 readme.md --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/readme.md Tue Sep 17 17:02:18 2019 -0400 |
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b'@@ -0,0 +1,120 @@\n+Galaxy wrappers for ImageJ2 tools\n+==================================\n+\n+ImageJ2 is a new version of ImageJ for the next generation of multidimensional image data, with a focus on scientific imaging. Its central goal is to broaden the paradigm of ImageJ beyond the limitations of ImageJ 1.x, to support the next generation of multidimensional scientific imaging.\n+\n+Fiji is an image processing package. It can be described as a "batteries-included" distribution of ImageJ (and ImageJ2), bundling Java, Java3D and a lot of plugins organized into a coherent menu structure. Fiji compares to ImageJ as Ubuntu compares to Linux.\n+\n+More informations is available at:\n+\n+* [http://fiji.sc/ImageJ2](http://fiji.sc/ImageJ2)\n+* [http://fiji.sc/Fiji](http://fiji.sc/Fiji)\n+\n+\n+Installation\n+============\n+\n+Galaxy tool wrappers use specified Fiji Lifeline versions available from [http://fiji.sc/Downloads](http://fiji.sc/Downloads). Galaxy should be able to automatically install this package.\n+\n+The wrappers are available at [https://github.com/bgruening/galaxytools/tree/master/tools/image_processing/imagej2](https://github.com/bgruening/galaxytools/tree/master/tools/image_processing/imagej2).\n+\n+\n+Use Docker\n+==========\n+\n+A docker image that installs Galaxy with these imaging tools is available at [https://github.com/bgruening/galaxy-imaging](https://github.com/bgruening/galaxy-imaging).\n+\n+\n+Using Fiji with Galaxy tools\n+============================\n+\n+Galaxy ImageJ2 tool wrappers generate a command line that calls a Python script, passing it a series of arguments including a Jython script named jython_script.py that resides in the same directory as the tool wrapper. During tool execution, the Python script will call ImageJ2 with the --headless argument to run without the ImageJ2 GUI. The Jython script is also passed to ImageJ2 along with all command line arguments that it expects. ImageJ2 will execute the Jython script, passing the expected arguments. The command line to run ImageJ2 from a Galaxy tool wrapper looks something like this:\n+\n+`ImageJ2 --ij2 --headless --jython ~jython_script.py arg1, arg2, ...`\n+\n+Each tool execution starts the ImageJ2 application within a Java virtual machine (JVM). When ImageJ2 is finished processing the Jython script, the results are either written to a file or returned to the calling Galaxy process. The JVM is shut down, and the Galaxy job terminates. This approach provides the ability to run ImageJ2 tools from Galaxy on any supported HPC environment.\n+\n+Of course, eliminating the ImageJ2 GUI restricts us to wrapping only those ImageJ2 plugins that do not require any GUI components (i.e., the ImageJ2 window manager). Plugins are written by an open community, so not all of them are written in such a way that they can be executed from the command line and produce useful results. For example, some plugins create one or more images that can only be accessed via calls to the ImageJ2 window manager, and running in headless mode eliminates the window manager as well as other GUI components.\n+\n+Those familiar with ImageJ2 will find differences with this general pattern for executing ImageJ2 tools within Galaxy. ImageJ2 accounts for user defined global preferences which are available to tools throughout the session, and an image can be uploaded and run through any number of available tools, saving only the final image. While Galaxy currently does not account for user preferences defined in ImageJ2, enhancements to the Galaxy framework are planned that will accomodate these kinds of settings (e.g., binary image options). Also, since Galaxy initiates a new ImageJ2 session with each tool execution, initial images are uploaded to ImageJ2 and resulting images are saved for each tool execution.\n+\n+The Galaxy ImageJ2 tools currently fall into the following categories. Additional tools will be added at a steady pace.\n+\n+Working with Pixels\n+===================\n+These Galaxy tools wrap the Image'..b'+* **Convert binary image to EDM** - Converts a binary image into a 8-bit grayscale Euclidean Distance Map (EDM). Each foreground (nonzero) pixel in the binary image is assigned a value equal to its distance from the nearest background (zero) pixel.\n+\n+**Interpreting binary Images in ImageJ2**\n+\n+Binary images are thresholded to only two values, typically 0 and 1, but often \xe2\x80\x94 as with ImageJ \xe2\x80\x94 0 and 255, that represent black and white on an 8-bit scale.\n+\n+The interpretation of binary images is not universal. While some software packages will always perform binary operations on 255 values (or 1, or any non-zero value), ImageJ takes into account the foreground and background colors of the binary image.\n+\n+In ImageJ, the **Black background** global preference setting defines not only how new binary images will be created, but also how previously created images are interpreted. This means objects will be inferred on a image-per-image basis. As such, inverting the LUT (i.e., pixels with a value of zero are white and pixels with a value 255 are black) of a binary image without updating the black background option may lead to unexpected results. This issue can currently be avoided by properly selecting the **Black background** option available on all Galaxy binary image tools.\n+\n+BunwarpJ Plugin Tools\n+=====================\n+These Galaxy tools wrap the bUnwarpJ plugin [http://fiji.sc/BUnwarpJ](http://fiji.sc/BUnwarpJ).\n+\n+* **Adapt an elastic transformation** - Adapts an elastic transformation to a new image size by transforming the\n+coefficients of a specified elastic transformation according to a real image factor.\n+* **Align two images** - Performs a simultaneous registration of two images, A and B. Image A is elastically deformed\n+in order to look as similar as possible to image B, and, at the same time, the "inverse"\n+transformation (from B to A) is also calculated so a pseudo-invertibility of the final deformation\n+could be guaranteed. Two images are produced: the deformed versions of A and B images.\n+* **Compare opposite elastic deformations** - Calculates the warping index of two opposite elast transformations, i.e. the average of the geometrical distance between every pixel and its version after applying both transformations (direct and inverse).\n+* **Compare elastic and raw deformation** - Calculates the warping index of an elastic transformation and a raw transformation.\n+* **Compare two raw deformations** - Calculates the warping index of two raw transformations (same direction).\n+* **Compose two elastic transformations** - Composes two elastic transformations into a raw transformation.\n+* **Compose two raw transformations** - Composes two raw transformations into another raw transformation.\n+* **Compose a raw and an elastic transformation** - Composes a raw transformation and an elastic transformation\n+into a raw transformation.\n+* **Convert elastic transformation to raw** - Converts an elastic (i.e., B-spline ) transformation file into a raw transformation file.\n+* **Apply elastic transformation** - Applies an elastic transformation to an image, producing another image which is elastically\n+deformed according to the transformation.\n+* **Apply raw transformation** - Applies a raw transformation to an image, producing another image which is deformed according\n+to the transformation.\n+\n+Other Tools\n+===========\n+* **Create new image** - Creates a new image of a selected type, size, depth and format.\n+* **Convert image format** - Converts the format of an input image file, producing an output image.\n+\n+Licence\n+=======\n+\n+Fiji is released as open source under the GNU General Public License: [http://www.gnu.org/licenses/gpl.html](http://www.gnu.org/licenses/gpl.html)\n+\n+Fiji builds on top of the ImageJ2 core, which is licensed under the permissive BSD 2-Clause license: [http://opensource.org/licenses/BSD-2-Clause](http://opensource.org/licenses/BSD-2-Clause)\n+\n+Plugins and other components have their own licenses.\n+\n' |
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diff -r 000000000000 -r ab54024c0a88 static/images/bunwarpj_scheme.png |
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diff -r 000000000000 -r ab54024c0a88 test-data/adapted_transformation.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/adapted_transformation.txt Tue Sep 17 17:02:18 2019 -0400 |
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diff -r 000000000000 -r ab54024c0a88 test-data/analyze_particles_masks.gif |
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diff -r 000000000000 -r ab54024c0a88 test-data/analyze_particles_nothing.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/analyze_particles_nothing.tabular Tue Sep 17 17:02:18 2019 -0400 |
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diff -r 000000000000 -r ab54024c0a88 test-data/basic.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/basic.tabular Tue Sep 17 17:02:18 2019 -0400 |
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diff -r 000000000000 -r ab54024c0a88 test-data/blobs.gif |
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diff -r 000000000000 -r ab54024c0a88 test-data/blobs_count.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/blobs_count.tabular Tue Sep 17 17:02:18 2019 -0400 |
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diff -r 000000000000 -r ab54024c0a88 test-data/blobs_direct_transf.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/blobs_direct_transf.txt Tue Sep 17 17:02:18 2019 -0400 |
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diff -r 000000000000 -r ab54024c0a88 test-data/blobs_list.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/blobs_list.tabular Tue Sep 17 17:02:18 2019 -0400 |
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diff -r 000000000000 -r ab54024c0a88 test-data/composed_raw_elastic_transformation.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/composed_raw_elastic_transformation.txt Tue Sep 17 17:02:18 2019 -0400 |
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diff -r 000000000000 -r ab54024c0a88 test-data/composed_raw_transformation.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/composed_raw_transformation.txt Tue Sep 17 17:02:18 2019 -0400 |
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diff -r 000000000000 -r ab54024c0a88 test-data/detailed.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/detailed.tabular Tue Sep 17 17:02:18 2019 -0400 |
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diff -r 000000000000 -r ab54024c0a88 test-data/largest_shortest_path_basic.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/largest_shortest_path_basic.tabular Tue Sep 17 17:02:18 2019 -0400 |
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diff -r 000000000000 -r ab54024c0a88 test-data/raw_transformation.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/raw_transformation.txt Tue Sep 17 17:02:18 2019 -0400 |
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@@ -0,0 +1,5 @@ +Width=144 +Height=144 + +X Trans ----------------------------------- + 4.176715938136983 5.028059425766952 5.848163112344083 6.625286081420434 7.346949869450837 7.999993504600507 8.570638908853306 9.044622789513964 9.413506255387968 9.681218421492703 9.854312101993061 9.940614113498254 9.949133680349874 9.889960336823316 9.774150445497835 9.613603978540679 9.420928827203937 9.209297594621251 8.99229520815497 8.783758029488393 8.597605040212112 8.447661785513965 8.347477892926463 8.310139148227263 8.348075306899938 8.472865046334638 8.695039719264653 9.02388784955587 9.467262612735961 10.031394859423866 10.720714562420634 11.537683888429232 12.482635931423559 13.548553680644847 14.713909933407903 15.955437681044362 17.250615997032845 18.57910317041673 19.924588848162042 21.273131961240793 22.61287165006273 23.933978509626925 25.228599373795102 26.49079330268239 27.7164571781934 28.90324005022652 30.050446065539795 31.15892642005994 32.23096128077027 33.27013301056534 34.28119229030748 35.269918863348025 36.24297862912084 37.20777868670669 38.172321680760554 39.14506043617836 40.134753390327724 41.15032074914446 42.20070061327572 43.29470355140843 44.44086325080732 45.64727996412613 46.921452516062644 48.27009365995084 49.69892261951472 51.21242468486518 52.81308002996115 54.5006890705208 56.27295866194047 58.12547164246331 60.05155284704986 62.042126167374654 64.0853963435782 66.16300087019144 68.25096023630147 70.32434810515224 72.35799991286407 74.32708587789452 76.20772360258024 77.97761366869307 79.61667819048832 81.10767966466686 82.43679584207585 83.59412588149885 84.57410381192264 85.37579734320398 86.0030732794589 86.46461509691974 86.77378348443933 86.94831659732299 87.00987319114694 86.98342840026854 86.89653840770765 86.77849631690853 86.65940540750502 86.56678652831073 86.51894680093264 86.52962234401939 86.6091568313555 86.76444343256262 86.99795950541491 87.30767458029689 87.6887773859162 88.1343150744824 88.63573469192004 89.18339795340363 89.76705660855026 90.37627810648544 91.00098259921862 91.63313750582321 92.26649727091988 92.89592945322954 93.51736271382487 94.12771798912087 94.72482575123138 95.30733544608778 95.87462173881887 96.42669102951422 96.9640907767786 97.48782343666693 97.99926625358638 98.50009769694455 98.99223099750193 99.47775497987517 99.95888219552717 100.43790422009906 100.91715387889398 101.39897409567641 101.8856930154123 102.37960502529904 102.88295728576551 103.39794138037603 103.92668969786209 104.47127616862906 105.03372099039578 105.61599899198129 106.2200512999695 106.84779998979062 107.50116542083198 108.1820859751434 108.89253994223978 109.63456932007529 110.41030533674008 111.22199287092927 |
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diff -r 000000000000 -r ab54024c0a88 test-data/registered_source2.png |
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diff -r 000000000000 -r ab54024c0a88 test-data/registered_target1.png |
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diff -r 000000000000 -r ab54024c0a88 test-data/registered_target2.png |
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diff -r 000000000000 -r ab54024c0a88 test-data/remove_outliers.gif |
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diff -r 000000000000 -r ab54024c0a88 test-data/shortest_branch_all_yes.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/shortest_branch_all_yes.tabular Tue Sep 17 17:02:18 2019 -0400 |
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b'@@ -0,0 +1,205 @@\n+# Branches\tJunctions\tEnd-point Voxels\tJunction Voxels\tSlab Voxels\tAverage branch length\tTriple Points\tQuadruple Points\tMaximum Branch Length\tLongest Shortest 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diff -r 000000000000 -r ab54024c0a88 test-data/shortest_branch_basic.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/shortest_branch_basic.tabular Tue Sep 17 17:02:18 2019 -0400 |
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@@ -0,0 +1,205 @@ +# Branches Junctions End-point Voxels Junction Voxels Slab Voxels Average branch length Triple Points Quadruple Points Maximum Branch Length +1 0 2 0 1 2.414 0 0 2.414 +1 0 2 0 0 1.000 0 0 1.000 +0 0 1 0 0 0.000 0 0 0.000 +143 75 40 144 918 9.176 61 9 96.113 +0 0 1 0 0 0.000 0 0 0.000 +0 0 1 0 0 0.000 0 0 0.000 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 0 1.000 0 0 1.000 +0 0 1 0 0 0.000 0 0 0.000 +0 0 1 0 0 0.000 0 0 0.000 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 0 1.000 0 0 1.000 +1 0 2 0 0 1.414 0 0 1.414 +1 0 2 0 3 4.000 0 0 4.000 +1 0 2 0 1 2.000 0 0 2.000 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 3 4.414 0 0 4.414 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 3 5.657 0 0 5.657 +5 2 4 2 97 23.182 2 0 58.385 +0 0 1 0 0 0.000 0 0 0.000 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 4 5.828 0 0 5.828 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 4 5.828 0 0 5.828 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 0 1.000 0 0 1.000 +0 0 1 0 0 0.000 0 0 0.000 +262 117 105 266 957 5.803 65 37 34.142 +96 43 31 104 373 6.437 25 11 34.142 +0 0 1 0 0 0.000 0 0 0.000 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 0 1.000 0 0 1.000 +0 0 1 0 0 0.000 0 0 0.000 +0 0 1 0 0 0.000 0 0 0.000 +22 11 10 24 151 9.834 10 1 20.728 +5 2 4 2 44 12.285 2 0 26.799 +0 0 1 0 0 0.000 0 0 0.000 +0 0 1 0 0 0.000 0 0 0.000 +0 0 1 0 0 0.000 0 0 0.000 +0 0 1 0 0 0.000 0 0 0.000 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 0 1.000 0 0 1.000 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 0 1.414 0 0 1.414 +1 0 2 0 1 2.000 0 0 2.000 +1 0 2 0 0 1.414 0 0 1.414 +1 0 2 0 0 1.000 0 0 1.000 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 0 1.000 0 0 1.000 +1 0 2 0 0 1.000 0 0 1.000 +0 0 1 0 0 0.000 0 0 0.000 +4 1 4 4 6 4.282 0 1 5.650 +0 0 1 0 0 0.000 0 0 0.000 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 2 3.000 0 0 3.000 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 0 1.000 0 0 1.000 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 1 2.414 0 0 2.414 +1 0 2 0 0 1.000 0 0 1.000 +1 0 2 0 0 1.000 0 0 1.000 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 1 2.000 0 0 2.000 +1 0 2 0 1 2.414 0 0 2.414 +0 0 1 0 0 0.000 0 0 0.000 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 0 1.414 0 0 1.414 +0 0 1 0 0 0.000 0 0 0.000 +0 0 1 0 0 0.000 0 0 0.000 +0 0 1 0 0 0.000 0 0 0.000 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 0 1.000 0 0 1.000 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 6 8.657 0 0 8.657 +0 0 1 0 0 0.000 0 0 0.000 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 0 1.000 0 0 1.000 +0 0 1 0 0 0.000 0 0 0.000 +0 0 1 0 0 0.000 0 0 0.000 +0 0 1 0 0 0.000 0 0 0.000 +0 0 1 0 0 0.000 0 0 0.000 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 0 1.000 0 0 1.000 +1 0 2 0 2 3.414 0 0 3.414 +1 0 2 0 1 2.000 0 0 2.000 +1 0 2 0 0 1.000 0 0 1.000 +0 0 1 0 0 0.000 0 0 0.000 +3 1 3 1 8 4.219 1 0 6.414 +0 0 1 0 0 0.000 0 0 0.000 +0 0 1 0 0 0.000 0 0 0.000 +0 0 1 0 0 0.000 0 0 0.000 +3 1 3 1 3 3.162 1 0 4.243 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 1 2.000 0 0 2.000 +0 0 1 0 0 0.000 0 0 0.000 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 0 1.000 0 0 1.000 +1 0 2 0 1 2.000 0 0 2.000 +1 0 2 0 0 1.000 0 0 1.000 +0 0 1 0 0 0.000 0 0 0.000 +0 0 1 0 0 0.000 0 0 0.000 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 0 1.000 0 0 1.000 +1 0 2 0 0 1.000 0 0 1.000 +1 0 2 0 0 1.000 0 0 1.000 +1 0 2 0 0 1.000 0 0 1.000 +1 0 2 0 1 2.414 0 0 2.414 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 0 1.000 0 0 1.000 +1 0 2 0 2 3.000 0 0 3.000 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 0 1.414 0 0 1.414 +1 0 2 0 0 1.000 0 0 1.000 +0 0 1 0 0 0.000 0 0 0.000 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 6 7.414 0 0 7.414 +1 0 2 0 1 2.000 0 0 2.000 +1 0 2 0 0 1.000 0 0 1.000 +0 0 1 0 0 0.000 0 0 0.000 +0 0 1 0 0 0.000 0 0 0.000 +0 0 1 0 0 0.000 0 0 0.000 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 0 1.000 0 0 1.000 +1 0 2 0 11 13.243 0 0 13.243 +1 0 2 0 1 2.000 0 0 2.000 +1 0 2 0 3 4.828 0 0 4.828 +1 0 2 0 0 1.000 0 0 1.000 +1 0 2 0 0 1.000 0 0 1.000 +1 0 2 0 1 2.000 0 0 2.000 +0 0 1 0 0 0.000 0 0 0.000 +0 0 1 0 0 0.000 0 0 0.000 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 0 1.000 0 0 1.000 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 2 3.000 0 0 3.000 +1 0 2 0 0 1.000 0 0 1.000 +1 0 2 0 0 1.000 0 0 1.000 +1 0 2 0 0 1.000 0 0 1.000 +1 0 2 0 1 2.000 0 0 2.000 +3 1 3 3 12 6.495 1 0 7.828 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 0 1.000 0 0 1.000 +1 0 2 0 1 2.414 0 0 2.414 +1 0 2 0 2 3.414 0 0 3.414 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 3 4.414 0 0 4.414 +1 0 2 0 1 2.414 0 0 2.414 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 1 2.414 0 0 2.414 +0 0 1 0 0 0.000 0 0 0.000 +13 6 8 10 55 5.956 6 0 20.899 +0 0 1 0 0 0.000 0 0 0.000 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 0 1.000 0 0 1.000 +1 0 2 0 1 2.000 0 0 2.000 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 0 1.000 0 0 1.000 +1 0 2 0 7 8.828 0 0 8.828 +0 0 1 0 0 0.000 0 0 0.000 +0 0 1 0 0 0.000 0 0 0.000 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 0 1.000 0 0 1.000 +1 0 2 0 0 1.000 0 0 1.000 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 0 1.000 0 0 1.000 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 1 2.000 0 0 2.000 +1 0 2 0 1 2.000 0 0 2.000 +5 1 5 3 15 5.994 0 0 12.243 +1 0 2 0 1 2.414 0 0 2.414 +1 0 2 0 0 1.000 0 0 1.000 +1 0 2 0 2 3.000 0 0 3.000 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 1 2.414 0 0 2.414 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 0 1.000 0 0 1.000 +0 0 1 0 0 0.000 0 0 0.000 +0 0 1 0 0 0.000 0 0 0.000 +0 0 1 0 0 0.000 0 0 0.000 +0 0 1 0 0 0.000 0 0 0.000 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 0 1.000 0 0 1.000 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 5 6.000 0 0 6.000 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 4 6.243 0 0 6.243 +1 0 2 0 8 10.657 0 0 10.657 +1 0 2 0 4 5.000 0 0 5.000 +0 0 1 0 0 0.000 0 0 0.000 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 0 1.000 0 0 1.000 +1 0 2 0 0 1.000 0 0 1.000 +1 0 2 0 0 1.000 0 0 1.000 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 0 1.414 0 0 1.414 +1 0 2 0 0 1.000 0 0 1.000 +1 0 2 0 0 1.000 0 0 1.000 +1 0 2 0 2 3.000 0 0 3.000 +1 0 2 0 0 1.000 0 0 1.000 +1 0 2 0 1 2.000 0 0 2.000 +0 0 1 0 0 0.000 0 0 0.000 +1 0 2 0 1 2.000 0 0 2.000 +1 0 2 0 0 1.000 0 0 1.000 |
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diff -r 000000000000 -r ab54024c0a88 test-data/skeletonized_blobs.gif |
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diff -r 000000000000 -r ab54024c0a88 test-data/skeletonized_clown.jpg |
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diff -r 000000000000 -r ab54024c0a88 test-data/source_elastic_transformation.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/source_elastic_transformation.txt Tue Sep 17 17:02:18 2019 -0400 |
b |
@@ -0,0 +1,19 @@ +Intervals=4 + +X Coeffs ----------------------------------- + -35.61130748114458 -0.17721383962095547 35.27738550739418 70.74023165057304 106.19906568058776 141.68187320104443 177.21663981554897 + -35.64176128119247 -18.613115472047358 38.17042470972303 80.01104484186907 136.77671593587 127.39645957510831 177.1399905793402 + -35.62132146852445 -16.358740968707146 88.9500299845919 64.14925112250332 70.46044758857497 129.40395338071698 177.06456148930593 + -35.58296385817069 -2.6180801775805382 100.0976208671127 125.33720259305008 87.98163606182615 134.90911744170705 177.02823631252767 + -35.55966426516137 -5.939275483027422 112.77890812894067 115.24264668240022 129.2642045183008 151.9582002938364 177.06889881608703 + -35.54485311365049 10.151323307758267 37.19497464942767 75.47427646299542 116.96351939728723 151.63921266991827 177.14722421045656 + -35.531960827792005 -0.07375112224360444 35.35776999600116 70.78294848245052 106.22213029261276 141.6956613819961 177.22388770610888 + +Y Coeffs ----------------------------------- + -35.5 -35.48676484054587 -35.44705936218348 -35.42058904327523 -35.44705936218348 -35.48676484054587 -35.5 + 0.0 0.01985273918119175 7.101095519506047 59.71640630985081 -32.3949456169071 -39.03805348699179 0.0 + 35.5 20.999826791718604 24.400825199903792 -2.48415771328592 32.96275013101309 26.264502624500558 35.5 + 71.0 21.355186591300996 -5.22979035632634 68.8318867576894 6.844809670377878 63.31233622680649 71.00021721525376 + 106.49999999999999 107.29333528999683 145.24976471347117 105.73866993653938 106.50155009418333 121.38028270693044 106.50086886101502 + 142.0 142.0002554624381 162.13354825161514 148.02403664863937 153.73330610830502 154.9675538982438 142.00130329152253 + 177.49999999999997 177.50017030829204 177.50068123316828 177.50123906500616 176.74925831450878 177.50147359981466 177.500868861015 |
b |
diff -r 000000000000 -r ab54024c0a88 test-data/source_raw_transformation.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/source_raw_transformation.txt Tue Sep 17 17:02:18 2019 -0400 |
b |
b'@@ -0,0 +1,294 @@\n+Width=144\n+Height=144\n+\n+X Trans -----------------------------------\n+ -8.356784649818302 -7.197224454983437 -6.007291128456463 -4.78857035976083 -3.542648755368276 -2.27111292175059 -0.9755494653795207 0.34245500727314293 1.6813138897356357 3.0394405755361955 4.415248458203067 5.807150931264452 7.21356138824859 8.632893222683734 10.063559828098088 11.503974598019894 12.952550925977388 14.407702205498786 15.867841830112352 17.331383193346277 18.796739688728827 20.262324709788203 21.726551650052656 23.187833903050404 24.64458486230968 26.09521792135873 27.53814647372579 28.971783912939053 30.39454363252679 31.80483902601722 33.20108348693856 34.58169040881904 35.94507318518693 37.28964520957042 38.613819875497754 39.91601057649718 41.19463931189525 42.44894522166797 43.67977041049533 44.8881387226239 46.07507400230021 47.24160009377076 48.38874084128215 49.51752008908086 50.62896168141348 51.724089462526514 52.803927276666506 53.86949896808 54.92182838101353 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b |
diff -r 000000000000 -r ab54024c0a88 test-data/target_elastic_transformation.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/target_elastic_transformation.txt Tue Sep 17 17:02:18 2019 -0400 |
b |
@@ -0,0 +1,19 @@ +Intervals=4 + +X Coeffs ----------------------------------- + -36.117111387825766 -5.385082876408411 29.45328994432716 69.52713740463037 104.86444345420553 140.38588675017897 176.15810397808303 + -36.2264937297932 32.74325846227494 -23.191917392170573 114.86951887066796 57.81410506673627 93.56163377395188 175.07587514376843 + 0.8001972037270695 12.384298514696155 18.96171978376125 -9.054176385210205 169.21498785601005 94.5062179325982 133.72979056309 + 37.4575728168268 6.705478803782103 16.339916785568008 5.562964841921719 65.90677629039934 122.5348986039493 141.4180288611356 + 5.496315162772514 8.984072369276337 19.160018686442353 -3.7679568417648954 43.56505263435343 117.04632483917443 169.8089818139191 + -30.985076022709965 30.48234563028456 -4.57185947623744 39.9368298981367 73.31325150981691 133.0187666782411 176.1637707773676 + -35.824759556202444 -0.21353961425912088 30.2488566576995 69.96729226623545 105.30466899887463 140.78641175637736 176.45425772333607 + +Y Coeffs ----------------------------------- + -33.457457947943325 -32.95237416015525 -32.61732073835082 -32.48427183266622 -32.58520159323763 -32.90603931346188 -33.43271428673581 + 2.474884239673922 6.153086829496797 2.176031929152565 -46.73739868123545 -6.641482787072329 31.63483537910857 2.439245655742275 + 38.282600013666176 115.10962128034645 99.44890686454225 106.09291532187778 85.86380388606122 120.14511724076014 38.219638319719145 + 76.76990361540554 95.85872248257112 91.75200358207188 96.626066210422 91.19160682525583 78.03096008955086 75.31885003707583 + 109.49947623795049 79.13551534555077 86.11217821438672 82.74394547366322 87.45682442725105 84.10531604274911 107.04136479340134 + 144.8490318765146 131.9043794552622 128.01742994365395 132.9301726951304 130.2019758552892 126.07871666147047 144.55505941286611 + 179.9547514779979 183.11538557329476 195.11844341969527 177.9158356900086 178.83724202056163 180.2225249725144 179.6371932718197 |
b |
diff -r 000000000000 -r ab54024c0a88 test-data/target_raw_transformation.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/target_raw_transformation.txt Tue Sep 17 17:02:18 2019 -0400 |
b |
b'@@ -0,0 +1,294 @@\n+Width=144\n+Height=144\n+\n+X Trans -----------------------------------\n+ -0.9471327820908413 -0.036947364843447794 0.8665129887435837 1.7635773766466396 2.654574326398728 3.539832365532834 4.419680021581961 5.294445822079093 6.164458294557222 7.030045966549346 7.8915373655884675 8.74926101920756 9.603545454939628 10.45471920031767 11.303110782874665 12.149048730143617 12.992861569657515 13.834877828949354 14.675426035552135 15.514834716998834 16.353432400822467 17.191547614556 18.029508885732444 18.867644741884785 19.706283710546025 20.545754319249152 21.38638509552716 22.228504566913045 23.07244126093979 23.9185237051404 24.767080427047865 25.618439954195168 26.472930814115326 27.330881534341312 28.192620642406126 29.058476665842765 29.92877814392578 30.803701529270146 31.683138826385786 32.56694769384605 33.45498579022433 34.34711077409394 35.2431803040283 36.14305203860072 37.04658363638463 37.95363275595335 38.864057055880245 39.77771419473869 40.69446183110206 41.61415762354373 42.536659230637 43.46182431095532 44.389510523072 45.31957552556042 46.25187697699394 47.186272535945946 48.122619860989786 49.06077661069881 50.00060044364642 50.94194901840596 51.88467999355079 52.82865102765428 53.77371977928979 54.71974390703071 55.666581069450366 56.61408892512216 57.562125132619435 58.51054735051555 59.459213237383885 60.407980451797826 61.3567066523307 62.30524949755586 63.253470346980464 64.20128065895713 65.14865007282945 66.09554923482635 67.04194879117674 67.98781938810946 68.93313167185342 69.87785628863749 70.8219638846906 71.76542510624161 72.70821059951939 73.65029101075284 74.59163698617084 75.5322191720023 76.47200821447609 77.41097475982112 78.34908945426622 79.28632294404034 80.22264587537234 81.15802889449108 82.09244264762548 83.02585778100443 83.95824494085682 84.88957477341152 85.81981792489742 86.74894504154342 87.67692676957836 88.6037337552312 89.52933664473078 90.45370608430598 91.37681272018574 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9.762626330955415 10.606956255164164 11.448344763898254 12.287156150379097 13.123754707828105 13.958504729466675 14.791770508516228 15.623916338198146 16.45530651173386 17.28630532234475 18.11727706325224 18.948586027677724 19.780596508842624 20.613672799968334 21.448179194276264 22.284479984987808 23.122939465324393 23.963921928507407 24.807791667758266 25.654912976298355 26.50565014734912 27.36036747'..b'5577995234032 135.419784220157 135.28859216070194 135.16424341640416 135.04657099130387 134.93540653694066 134.83058170485415 134.73192814658395 134.63927751366964 134.55246145765085 134.47131163006716 134.3956596824582 134.32533726636353 134.26017603332275 134.20000763487548 134.14466372256135 134.0939759479199 134.04777596249085 134.00589541781363 133.96816596542797 133.93441925687344 133.90448694368965 133.87820067741615 133.8553921095926 133.8358928917586 133.8195346754537 133.80614911221755 133.79556785358974 133.78762255110988 133.78214485631753 133.77896642075234 133.7779188959539 133.7788339334618 133.78154318481566 133.78587830155507 133.79167093521957 133.7987527373489 133.80695269909975 \n+ 143.57341840948715 143.52409221274127 143.47689934583906 143.43181660411574 143.38881794786036 143.34787733736184 143.3089687329091 143.27206609479126 143.23714338329714 143.20417455871575 143.17313358133606 143.14399441144707 143.11673100933774 143.091317335297 143.06772734961382 143.04593501257722 143.02591428447613 143.00763912559952 142.99108349623637 142.97622135667564 142.96302666720635 142.95147338811736 142.94153547969773 142.93318690223637 142.92640161602228 142.92115358134447 142.9174167584918 142.91516510775332 142.91437258941798 142.91501316377477 142.91706079111262 142.9204894317205 142.92527304588742 142.9313855939023 142.93880103605412 142.9474933326319 142.95743588921954 142.9685331860426 142.98055596858387 142.99325959747262 143.00639943333812 143.01973083680969 143.03300916851657 143.04598978908805 143.0584280591534 143.07007933934196 143.0806989902829 143.0900423726056 143.09786484693927 143.10392177391319 143.1079685141567 143.109760428299 143.10905287696943 143.10560122079724 143.0991608204117 143.08948703644214 143.07633522951772 143.05946076026785 143.03861898932178 143.01356527730871 142.984054984858 142.94984347259887 142.91068610116065 142.8663382311726 142.81655522326398 142.76109243806414 142.69970523620222 142.63214897830764 142.55817902500954 142.47755073693736 142.39001947472025 142.29534059898756 142.1932821314333 142.08390706786426 141.9675725082867 141.844648503743 141.71550510527527 141.58051236392572 141.4400403307365 141.29445905674982 141.1441385930079 140.9894489905529 140.830760300427 140.66844257367245 140.50286586133137 140.334400214446 140.16341568405852 139.99028232121105 139.8153701769459 139.6390493023052 139.4616897483311 139.2836615660659 139.10533480655164 138.9270795208307 138.74926575994508 138.57226357493707 138.39644301684885 138.22217413672263 138.04982698560056 137.87977161452483 137.71237807453767 137.54801641668126 137.38705669199777 137.22986895152937 137.0768232463183 136.92828962740674 136.78463814583685 136.64623885265084 136.51342820465817 136.38625249617593 136.26460775208992 136.14838895080575 136.0374910707289 135.93180909026484 135.83123798781915 135.73567274179732 135.6450083306049 135.55913973264737 135.47796192633024 135.40136989005907 135.32925860223935 135.26152304127663 135.19805818557637 135.1387590135442 135.0835205035855 135.03223763410588 134.9848053835108 134.94111873020586 134.90107265259647 134.86456212908826 134.83148213808664 134.80172765799722 134.77519366722547 134.75177514417692 134.73136706725708 134.71386441487147 134.69916216542566 134.6871552973251 134.6777387889753 134.67080761878185 134.66625676515022 134.6639812064859 134.6638759211945 134.6658331976206 \n' |
b |
diff -r 000000000000 -r ab54024c0a88 test-data/warping_index.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/warping_index.txt Tue Sep 17 17:02:18 2019 -0400 |
b |
@@ -0,0 +1,1 @@ +22.224744070209738 |
b |
diff -r 000000000000 -r ab54024c0a88 test-data/warping_index1.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/warping_index1.txt Tue Sep 17 17:02:18 2019 -0400 |
b |
@@ -0,0 +1,1 @@ +33.6 |
b |
diff -r 000000000000 -r ab54024c0a88 test-data/warping_index2.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/warping_index2.txt Tue Sep 17 17:02:18 2019 -0400 |
b |
@@ -0,0 +1,1 @@ +0.0 |
b |
diff -r 000000000000 -r ab54024c0a88 test-data/warping_index_raw.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/warping_index_raw.txt Tue Sep 17 17:02:18 2019 -0400 |
b |
@@ -0,0 +1,1 @@ +66.4 |