| Next changeset 1:29a4d422f32a (2020-09-28) |
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Commit message:
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/image_processing/imagej2 commit b08f0e6d1546caaf627b21f8c94044285d5d5b9c-dirty" |
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added:
imagej2_adjust_threshold_binary.py imagej2_adjust_threshold_binary.xml 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$py.class imagej2_base_utils.py imagej2_base_utils.pyc imagej2_binary_to_edm.py imagej2_binary_to_edm_jython_script.py imagej2_bunwarpj_adapt_transform.py imagej2_bunwarpj_align.py imagej2_bunwarpj_align_jython_script.py imagej2_bunwarpj_compare_elastic.py imagej2_bunwarpj_compare_elastic_raw.py imagej2_bunwarpj_compare_raw.py imagej2_bunwarpj_compose_elastic.py imagej2_bunwarpj_compose_raw.py imagej2_bunwarpj_compose_raw_elastic.py imagej2_bunwarpj_convert_to_raw.py imagej2_bunwarpj_elastic_transform.py imagej2_bunwarpj_elastic_transform_jython_script.py imagej2_bunwarpj_raw_transform.py imagej2_bunwarpj_raw_transform_jython_script.py imagej2_create_image.py imagej2_create_image_jython_script.py imagej2_enhance_contrast.py imagej2_enhance_contrast_jython_script.py imagej2_find_edges.py imagej2_find_edges_jython_script.py imagej2_find_maxima.py imagej2_find_maxima_jython_script.py imagej2_macros.xml imagej2_make_binary.py imagej2_make_binary_jython_script.py imagej2_math.py imagej2_math_jython_script.py imagej2_noise.py imagej2_noise_jython_script.py imagej2_shadows.py imagej2_shadows_jython_script.py imagej2_sharpen.py imagej2_sharpen_jython_script.py imagej2_skeletonize3d.py imagej2_skeletonize3d_jython_script.py imagej2_smooth.py imagej2_smooth_jython_script.py imagej2_watershed_binary.py imagej2_watershed_binary_jython_script.py jython_utils$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 f1ba33cd9edf imagej2_adjust_threshold_binary.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_adjust_threshold_binary.py Tue Sep 17 17:09:25 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 f1ba33cd9edf imagej2_adjust_threshold_binary.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_adjust_threshold_binary.xml Tue Sep 17 17:09:25 2019 -0400 |
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| @@ -0,0 +1,117 @@ +<?xml version='1.0' encoding='UTF-8'?> +<tool id="imagej2_adjust_threshold_binary" name="Adjust threshold" version="@WRAPPER_VERSION@.0"> + <description>of binary image</description> + <macros> + <import>imagej2_macros.xml</import> + </macros> + <expand macro="fiji_requirements" /> + <command> +<![CDATA[ + python $__tool_directory__/imagej2_adjust_threshold_binary.py + --input "$input" + --input_datatype $input.ext + --threshold_min $threshold_min + --threshold_max $threshold_max + --method $method + --display $display + --black_background $black_background + --stack_histogram $stack_histogram + --jython_script $__tool_directory__/imagej2_adjust_threshold_binary_jython_script.py + --output_datatype $output.ext + --output "$output" +]]> + </command> + <inputs> + <param format="bmp,eps,gif,jpg,pcx,pgm,png,psd,tiff" name="input" type="data" label="Select image"/> + <param name="threshold_min" type="float" value="0" min="0" max="255" label="Minimum threshold value" /> + <param name="threshold_max" type="float" value="0" min="0" max="255" label="Maximum threshold value" /> + <param name="method" type="select" label="Method" help="The Default method is the modified IsoData algorithm."> + <option value="Default" selected="True">Default</option> + <option value="Huang">Huang</option> + <option value="Intermodes">Intermodes</option> + <option value="IsoData">IsoData</option> + <option value="IJ_IsoData">IJ_IsoData</option> + <option value="Li">Li</option> + <option value="MaxEntropy">MaxEntropy</option> + <option value="Mean">Mean</option> + <option value="MinError">MinError</option> + <option value="Minimum">Minimum</option> + <option value="Moments">Moments</option> + <option value="Otsu">Otsu</option> + <option value="RenyiEntropy">RenyiEntropy</option> + <option value="Shanbhag">Shanbhag</option> + <option value="Triangle">Triangle</option> + <option value="Yen">Yen</option> + </param> + <param name="display" type="select" label="Display"> + <option value="red" selected="True">Red</option> + <option value="bw">Black and White</option> + <option value="over_under">Over/Under</option> + </param> + <param name="black_background" type="select" label="Black background" help="Select yes if features are lighter than the background."> + <option value="no" selected="True">No</option> + <option value="yes">Yes</option> + </param> + <param name="stack_histogram" type="select" label="Stack histogram" help="Select yes to first compute the histogram of the whole stack (or hyperstack) and then compute the threshold based on that histogram."> + <option value="no" selected="True">No</option> + <option value="yes">Yes</option> + </param> + </inputs> + <outputs> + <data name="output" format_source="input" label="${tool.name} on ${on_string}"/> + </outputs> + <tests> + <test> + <param name="input" value="blobs.gif" /> + <param name="output_datatype" value="gif" /> + <param name="threshold_min" value="0.0" /> + <param name="threshold_max" value="129.0" /> + <param name="method" value="Default" /> + <param name="display" value="red" /> + <param name="black_background" value="no" /> + <param name="stack_histogram" value="no" /> + <output name="output" file="blobs_threshold_default.gif" compare="sim_size" /> + </test> + <test> + <param name="input" value="blobs.gif" /> + <param name="output_datatype" value="gif" /> + <param name="threshold_min" value="118.0" /> + <param name="threshold_max" value="255.0" /> + <param name="method" value="IJ_IsoData" /> + <param name="display" value="over_under" /> + <param name="black_background" value="no" /> + <param name="stack_histogram" value="no" /> + <output name="output" file="blobs_threshold_ijiso.gif" compare="sim_size" /> + </test> + <test> + <param name="input" value="blobs.gif" /> + <param name="output_datatype" value="gif" /> + <param name="threshold_min" value="72.0" /> + <param name="threshold_max" value="255.0" /> + <param name="method" value="Huang" /> + <param name="display" value="bw" /> + <param name="black_background" value="yes" /> + <param name="stack_histogram" value="no" /> + <output name="output" file="blobs_threshold_huang_dark.gif" compare="sim_size" /> + </test> + </tests> + <help> + +@requires_binary_input@ + +**What it does** + +<![CDATA[ +Sets lower and upper threshold values, segmenting grayscale images into features of interest and background + +- **Minimum threshold value** - Adjusts the minimum threshold value. +- **Maximum threshold value** - Adjusts the maximum threshold value. +- **Method** - Allows any of the 16 different automatic thresholding methods to be selected. These are global thresholding methods that typically cannot deal with unevenly illuminated images (such as in brightfield microscopy)." +- **Display** - Selects one of three display mode: **Red** displays the thresholded values in red, **Black and White** features are displayed in black and background in white, **Over/Under** displays pixels below the lower threshold value in blue, thresholded pixels in grayscale, and pixels above the upper threshold value in green. +- **Black background** - Select **yes** when features are lighter than the background. +- **Stack histogram** Select **yes** to first compute the histogram of the whole stack (or hyperstack) and then compute the threshold based on that histogram. As such, all slices are binarized using the single computed value. If unchecked, the threshold of each slice is computed separately. +]]> + + </help> + <expand macro="fiji_headless_citations" /> +</tool> |
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| diff -r 000000000000 -r f1ba33cd9edf 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 17:09:25 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 f1ba33cd9edf imagej2_analyze_particles_binary.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_analyze_particles_binary.py Tue Sep 17 17:09:25 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 f1ba33cd9edf 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 17:09:25 2019 -0400 |
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| @@ -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 f1ba33cd9edf imagej2_analyze_skeleton.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_analyze_skeleton.py Tue Sep 17 17:09:25 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 f1ba33cd9edf 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 17:09:25 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 f1ba33cd9edf 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 f1ba33cd9edf imagej2_base_utils.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_base_utils.py Tue Sep 17 17:09:25 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 f1ba33cd9edf imagej2_base_utils.pyc |
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| Binary file imagej2_base_utils.pyc has changed |
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| diff -r 000000000000 -r f1ba33cd9edf imagej2_binary_to_edm.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_binary_to_edm.py Tue Sep 17 17:09:25 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 f1ba33cd9edf 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 17:09:25 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 f1ba33cd9edf imagej2_bunwarpj_adapt_transform.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_bunwarpj_adapt_transform.py Tue Sep 17 17:09:25 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 f1ba33cd9edf imagej2_bunwarpj_align.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_bunwarpj_align.py Tue Sep 17 17:09:25 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' |
| b |
| diff -r 000000000000 -r f1ba33cd9edf 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 17:09:25 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 ) |
| b |
| diff -r 000000000000 -r f1ba33cd9edf imagej2_bunwarpj_compare_elastic.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_bunwarpj_compare_elastic.py Tue Sep 17 17:09:25 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 ) |
| b |
| diff -r 000000000000 -r f1ba33cd9edf 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 17:09:25 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 ) |
| b |
| diff -r 000000000000 -r f1ba33cd9edf imagej2_bunwarpj_compare_raw.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_bunwarpj_compare_raw.py Tue Sep 17 17:09:25 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_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 f1ba33cd9edf imagej2_bunwarpj_compose_elastic.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_bunwarpj_compose_elastic.py Tue Sep 17 17:09:25 2019 -0400 |
| b |
| @@ -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 ) |
| b |
| diff -r 000000000000 -r f1ba33cd9edf imagej2_bunwarpj_compose_raw.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_bunwarpj_compose_raw.py Tue Sep 17 17:09:25 2019 -0400 |
| b |
| @@ -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 f1ba33cd9edf 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 17:09:25 2019 -0400 |
| b |
| @@ -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 f1ba33cd9edf 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 17:09:25 2019 -0400 |
| b |
| @@ -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 ) |
| b |
| diff -r 000000000000 -r f1ba33cd9edf imagej2_bunwarpj_elastic_transform.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_bunwarpj_elastic_transform.py Tue Sep 17 17:09:25 2019 -0400 |
| b |
| @@ -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 ) |
| b |
| diff -r 000000000000 -r f1ba33cd9edf 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 17:09:25 2019 -0400 |
| [ |
| @@ -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 ) |
| b |
| diff -r 000000000000 -r f1ba33cd9edf imagej2_bunwarpj_raw_transform.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_bunwarpj_raw_transform.py Tue Sep 17 17:09:25 2019 -0400 |
| b |
| @@ -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 ) |
| b |
| diff -r 000000000000 -r f1ba33cd9edf 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 17:09:25 2019 -0400 |
| [ |
| @@ -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 ) |
| b |
| diff -r 000000000000 -r f1ba33cd9edf imagej2_create_image.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_create_image.py Tue Sep 17 17:09:25 2019 -0400 |
| b |
| @@ -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 ) |
| b |
| diff -r 000000000000 -r f1ba33cd9edf 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 17:09:25 2019 -0400 |
| [ |
| @@ -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 ) |
| b |
| diff -r 000000000000 -r f1ba33cd9edf imagej2_enhance_contrast.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_enhance_contrast.py Tue Sep 17 17:09:25 2019 -0400 |
| b |
| @@ -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 f1ba33cd9edf 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 17:09:25 2019 -0400 |
| [ |
| @@ -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 f1ba33cd9edf imagej2_find_edges.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_find_edges.py Tue Sep 17 17:09:25 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 f1ba33cd9edf 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 17:09:25 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, "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 f1ba33cd9edf imagej2_find_maxima.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_find_maxima.py Tue Sep 17 17:09:25 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 f1ba33cd9edf 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 17:09:25 2019 -0400 |
| [ |
| @@ -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 f1ba33cd9edf imagej2_macros.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_macros.xml Tue Sep 17 17:09:25 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 f1ba33cd9edf imagej2_make_binary.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_make_binary.py Tue Sep 17 17:09:25 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 f1ba33cd9edf 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 17:09:25 2019 -0400 |
| [ |
| @@ -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 f1ba33cd9edf imagej2_math.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_math.py Tue Sep 17 17:09:25 2019 -0400 |
| [ |
| @@ -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 f1ba33cd9edf imagej2_math_jython_script.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_math_jython_script.py Tue Sep 17 17:09:25 2019 -0400 |
| [ |
| @@ -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 f1ba33cd9edf imagej2_noise.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_noise.py Tue Sep 17 17:09:25 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 f1ba33cd9edf imagej2_noise_jython_script.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_noise_jython_script.py Tue Sep 17 17:09:25 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 f1ba33cd9edf imagej2_shadows.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_shadows.py Tue Sep 17 17:09:25 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 f1ba33cd9edf imagej2_shadows_jython_script.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_shadows_jython_script.py Tue Sep 17 17:09:25 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 f1ba33cd9edf imagej2_sharpen.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_sharpen.py Tue Sep 17 17:09:25 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 f1ba33cd9edf imagej2_sharpen_jython_script.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_sharpen_jython_script.py Tue Sep 17 17:09:25 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 f1ba33cd9edf imagej2_skeletonize3d.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_skeletonize3d.py Tue Sep 17 17:09:25 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 f1ba33cd9edf imagej2_skeletonize3d_jython_script.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_skeletonize3d_jython_script.py Tue Sep 17 17:09:25 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 f1ba33cd9edf imagej2_smooth.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_smooth.py Tue Sep 17 17:09:25 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 f1ba33cd9edf imagej2_smooth_jython_script.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_smooth_jython_script.py Tue Sep 17 17:09:25 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 f1ba33cd9edf imagej2_watershed_binary.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_watershed_binary.py Tue Sep 17 17:09:25 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 f1ba33cd9edf 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 17:09:25 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 f1ba33cd9edf jython_utils$py.class |
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| diff -r 000000000000 -r f1ba33cd9edf jython_utils.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/jython_utils.py Tue Sep 17 17:09:25 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 f1ba33cd9edf readme.md --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/readme.md Tue Sep 17 17:09:25 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 f1ba33cd9edf test-data/adapted_transformation.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/adapted_transformation.txt Tue Sep 17 17:09:25 2019 -0400 |
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| @@ -0,0 +1,19 @@ +Intervals=4 + +X Coeffs ----------------------------------- + -71.22261496228916 -0.35442767924191093 70.55477101478836 141.48046330114607 212.39813136117553 283.36374640208885 354.43327963109795 + -71.28352256238495 -37.226230944094716 76.34084941944606 160.02208968373813 273.55343187174 254.79291915021662 354.2799811586804 + -71.2426429370489 -32.71748193741429 177.9000599691838 128.29850224500663 140.92089517714993 258.80790676143397 354.12912297861186 + -71.16592771634139 -5.2361603551610765 200.1952417342254 250.67440518610016 175.9632721236523 269.8182348834141 354.05647262505533 + -71.11932853032275 -11.878550966054844 225.55781625788134 230.48529336480044 258.5284090366016 303.9164005876728 354.13779763217406 + -71.08970622730098 20.302646615516533 74.38994929885534 150.94855292599084 233.92703879457446 303.27842533983653 354.2944484209131 + -71.06392165558401 -0.1475022444872089 70.71553999200232 141.56589696490104 212.44426058522552 283.3913227639922 354.44777541221777 + +Y Coeffs ----------------------------------- + -71.0 -70.97352968109173 -70.89411872436696 -70.84117808655046 -70.89411872436696 -70.97352968109173 -71.0 + 0.0 0.0397054783623835 14.202191039012094 119.43281261970162 -64.7898912338142 -78.07610697398358 0.0 + 71.0 41.99965358343721 48.801650399807585 -4.96831542657184 65.92550026202618 52.529005249001116 71.0 + 142.0 42.71037318260199 -10.45958071265268 137.6637735153788 13.689619340755756 126.62467245361297 142.0004344305075 + 212.99999999999997 214.58667057999367 290.49952942694233 211.47733987307876 213.00310018836666 242.7605654138609 213.00173772203004 + 284.0 284.0005109248762 324.2670965032303 296.04807329727873 307.46661221661003 309.9351077964876 284.00260658304506 + 354.99999999999994 355.0003406165841 355.00136246633656 355.0024781300123 353.49851662901756 355.0029471996293 355.00173772203 |
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| diff -r 000000000000 -r f1ba33cd9edf 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 17:09:25 2019 -0400 |
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| @@ -0,0 +1,66 @@ + Area Mean Min Max +1 136 255 255 255 +2 60 255 255 255 +3 206 255 255 255 +4 139 255 255 255 +5 152 255 255 255 +6 86 255 255 255 +7 72 255 255 255 +8 25 255 255 255 +9 85 255 255 255 +10 9 255 255 255 +11 157 255 255 255 +12 207 255 255 255 +13 29 255 255 255 +14 73 255 255 255 +15 143 255 255 255 +16 125 255 255 255 +17 159 255 255 255 +18 133 255 255 255 +19 85 255 255 255 +20 109 255 255 255 +21 51 255 255 255 +22 133 255 255 255 +23 133 255 255 255 +24 81 255 255 255 +25 162 255 255 255 +26 88 255 255 255 +27 212 255 255 255 +28 55 255 255 255 +29 116 255 255 255 +30 172 255 255 255 +31 103 255 255 255 +32 4 255 255 255 +33 60 255 255 255 +34 198 255 255 255 +35 187 255 255 255 +36 7 255 255 255 +37 85 255 255 255 +38 80 255 255 255 +39 75 255 255 255 +40 103 255 255 255 +41 151 255 255 255 +42 52 255 255 255 +43 122 255 255 255 +44 129 255 255 255 +45 77 255 255 255 +46 171 255 255 255 +47 117 255 255 255 +48 207 255 255 255 +49 119 255 255 255 +50 181 255 255 255 +51 22 255 255 255 +52 49 255 255 255 +53 150 255 255 255 +54 191 255 255 255 +55 170 255 255 255 +56 64 255 255 255 +57 174 255 255 255 +58 270 255 255 255 +59 87 255 255 255 +60 69 255 255 255 +61 1 255 255 255 +62 29 255 255 255 +63 25 255 255 255 +64 16 255 255 255 +65 15 255 255 255 |
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| diff -r 000000000000 -r f1ba33cd9edf test-data/analyze_particles_outlines.gif |
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| diff -r 000000000000 -r f1ba33cd9edf test-data/basic.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/basic.tabular Tue Sep 17 17:09:25 2019 -0400 |
| b |
| @@ -0,0 +1,2 @@ +# Branches Junctions End-point Voxels Junction Voxels Slab Voxels Average branch length Triple Points Quadruple Points Maximum Branch Length +96 60 7 120 1246 17.344 56 3 70.882 |
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| diff -r 000000000000 -r f1ba33cd9edf test-data/blobs.gif |
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| diff -r 000000000000 -r f1ba33cd9edf test-data/blobs_count.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/blobs_count.tabular Tue Sep 17 17:09:25 2019 -0400 |
| b |
| @@ -0,0 +1,2 @@ + Count +1 112 |
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| diff -r 000000000000 -r f1ba33cd9edf 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 17:09:25 2019 -0400 |
| b |
| @@ -0,0 +1,19 @@ +Intervals=4 + +X Coeffs ----------------------------------- + -34.14981674286569 3.144052189417975 44.74398427283767 72.62900018057132 111.3348086331012 134.27021754923854 171.74048996962574 + -46.475609806523806 -28.37507243951631 71.19906566193379 30.10778479539863 122.71885776990422 109.9563576074076 171.94005579124322 + -57.04494430952696 1.8032931596380026 61.404945193416715 42.75919945626539 148.2715738833391 126.39195563069309 116.8758739961032 + -26.50696765072751 24.133275156317662 45.18779137671111 49.91727740928712 130.5425749032711 160.35055773949284 186.2385413131219 + 30.36695633747302 -3.333376652604397 35.957597759623795 86.8060703274396 102.5208634329241 126.298277744805 243.1342175649626 + -2.831201175463878 -4.836159041803193 36.263197544298954 77.65977608215381 98.47306066697166 149.98143182373533 193.72941653859635 + -33.88117649278133 7.9003473752729985 41.603347919804314 72.11109321021485 111.05849721622616 148.16049042863358 181.51669289966162 + +Y Coeffs ----------------------------------- + -32.99874935645494 -10.853014366833959 -18.11337422707787 120.45933796140201 -11.717505555260894 -42.65980408275417 -41.34878020779432 + 11.306632136922623 42.01572254879719 -18.137465736571315 41.67904406737918 -9.059457409112 -63.14804168936847 -7.646807909694754 + 20.638424092275454 35.302620259132304 1.8587715711200654 2.065183621887666 13.47064662534885 8.966817348422527 65.74329336525717 + 79.92027086396175 117.61262084713007 78.2409336472003 102.3526144171297 97.29273111510625 48.80095761073018 89.32772899111102 + 121.0699654326738 114.38154300759474 23.57251043213103 101.87328690674049 115.94282218472065 106.18526585145909 111.14979545782822 + 140.58687592247674 130.54971240393465 177.05271414686374 150.48052118800214 150.41722526235608 156.3116913517668 146.21075369002716 + 175.51191703543347 174.9228152249439 173.31675176966468 181.87538254503764 170.81399893021742 186.14994867024973 185.85560874061068 |
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| diff -r 000000000000 -r f1ba33cd9edf test-data/blobs_edm.gif |
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| diff -r 000000000000 -r f1ba33cd9edf test-data/blobs_find_edges.gif |
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| diff -r 000000000000 -r f1ba33cd9edf test-data/blobs_list.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/blobs_list.tabular Tue Sep 17 17:09:25 2019 -0400 |
| b |
| @@ -0,0 +1,113 @@ + X Y +1 95 8 +2 107 24 +3 10 25 +4 34 16 +5 2 20 +6 118 11 +7 19 10 +8 132 0 +9 124 143 +10 130 139 +11 142 126 +12 140 108 +13 125 99 +14 133 80 +15 30 65 +16 46 52 +17 42 41 +18 116 34 +19 24 36 +20 136 33 +21 50 29 +22 86 29 +23 125 23 +24 143 23 +25 71 10 +26 39 8 +27 105 5 +28 5 3 +29 23 0 +30 2 0 +31 114 141 +32 31 140 +33 112 136 +34 20 133 +35 125 122 +36 28 116 +37 110 109 +38 54 105 +39 15 101 +40 142 95 +41 96 93 +42 4 88 +43 112 91 +44 86 91 +45 58 90 +46 42 90 +47 76 77 +48 102 84 +49 44 81 +50 29 75 +51 41 73 +52 57 73 +53 0 72 +54 118 66 +55 44 68 +56 16 60 +57 67 64 +58 125 63 +59 85 63 +60 108 62 +61 88 49 +62 122 47 +63 97 48 +64 64 43 +65 143 47 +66 28 44 +67 85 46 +68 1 44 +69 14 42 +70 127 40 +71 63 36 +72 93 28 +73 60 28 +74 23 26 +75 73 23 +76 62 24 +77 142 18 +78 49 15 +79 77 3 +80 101 1 +81 95 1 +82 95 140 +83 83 138 +84 69 139 +85 68 126 +86 6 133 +87 70 135 +88 52 135 +89 90 124 +90 88 116 +91 1 114 +92 51 112 +93 8 113 +94 83 112 +95 62 109 +96 31 105 +97 81 99 +98 33 99 +99 31 92 +100 59 85 +101 51 70 +102 79 57 +103 109 54 +104 112 50 +105 104 48 +106 12 48 +107 94 64 +108 43 24 +109 98 22 +110 67 78 +111 143 7 +112 143 0 |
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| diff -r 000000000000 -r f1ba33cd9edf test-data/blobs_northwest.gif |
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| diff -r 000000000000 -r f1ba33cd9edf test-data/clown.jpg |
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| diff -r 000000000000 -r f1ba33cd9edf 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 17:09:25 2019 -0400 |
| b |
| @@ -0,0 +1,5 @@ +Width=144 +Height=144 + +X Trans ----------------------------------- + -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 6.722616523231703 7.982832965851492 9.241901922762699 10.500157904929564 11.755439850906992 13.007881998138114 14.257818796273467 15.503597042436024 16.7441831444607 17.979203521782882 19.209729191621175 20.434924351265607 21.653927817871562 22.868018271403987 24.077212618583225 25.28220858222662 26.482822213238034 27.68108269563495 28.876976885081163 30.071735843481154 31.267312088467172 32.461884574486334 33.6540253250871 34.84451070010306 36.032159052340404 37.21731193936262 38.40098887470948 39.58514301848773 40.7697991777691 41.95621659878733 43.14336916337468 44.332762755666245 45.52368427263207 46.71616063628192 47.9102587132078 49.10447011578231 50.29945996745416 51.49280946178481 52.68452392595436 53.87243480117253 55.0558589967881 56.23259866253449 57.40168688627666 58.560586048253406 59.70814582308569 60.842020356312204 61.96028478681958 63.06129999594824 64.14282865164152 65.20294849643733 66.23977708520655 67.251514074432 68.2364787499741 69.19379132689076 70.12368273237351 71.02688237947935 71.90443109666018 72.75759402782955 73.58797464576547 74.39754123580246 75.18880500856693 75.96371546546276 76.7252296396663 77.47583769656181 78.21957975177521 78.95905016293707 79.69712587468834 80.4370485162662 81.18210364892593 81.93429837927215 82.69597230924478 83.4691472921311 84.25544962565074 85.05614376573314 85.87215879473054 86.70392313153135 87.55146827026859 88.41445157658053 89.29218897542948 90.18369572122522 91.08773317913175 102.68235537543303 103.7042131782845 104.71196201385267 105.70449758967617 106.68071561329351 107.63951179224333 108.57978183406415 109.50042144629455 110.40032633647319 111.2783922121385 112.13351478082917 112.96458975008372 113.77051282744074 114.55017972043879 115.30267699164074 116.02873972512184 116.72995672023089 117.40792272643284 118.0642324931926 118.70048076997512 119.31826230624539 119.91917185146836 120.50480415510899 121.07675396663221 121.63661603550297 122.18598511118623 122.72645594314696 123.2596232808501 123.78708187376054 124.31042647134338 124.83125182306345 125.35115267838574 125.87172378677525 126.39455989769684 126.92125576061551 127.45340612499622 127.99260574030393 128.54044935600356 129.0985317215601 129.66844758643848 130.25179170010364 130.85015881202057 131.4651436716542 132.0983410284695 132.75134563193137 133.42575223150482 134.12315557665482 134.84515041684625 135.5933315015441 136.36929118355562 |
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| diff -r 000000000000 -r f1ba33cd9edf 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 17:09:25 2019 -0400 |
| b |
| @@ -0,0 +1,5 @@ +Width=144 +Height=144 + +X Trans ----------------------------------- + -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 6.722616523231703 7.982832965851492 9.241901922762699 10.500157904929564 11.755439850906992 13.007881998138114 14.257818796273467 15.503597042436024 16.7441831444607 17.979203521782882 19.209729191621175 20.434924351265607 21.653927817871562 22.868018271403987 24.077212618583225 25.28220858222662 26.482822213238034 27.68108269563495 28.876976885081163 30.071735843481154 31.267312088467172 32.461884574486334 33.6540253250871 34.84451070010306 36.032159052340404 37.21731193936262 38.40098887470948 39.58514301848773 40.7697991777691 41.95621659878733 43.14336916337468 44.332762755666245 45.52368427263207 46.71616063628192 47.9102587132078 49.10447011578231 50.29945996745416 51.49280946178481 52.68452392595436 53.87243480117253 55.0558589967881 56.23259866253449 57.40168688627666 58.560586048253406 59.70814582308569 60.842020356312204 61.96028478681958 63.06129999594824 64.14282865164152 65.20294849643733 66.23977708520655 67.251514074432 68.2364787499741 69.19379132689076 70.12368273237351 71.02688237947935 71.90443109666018 72.75759402782955 73.58797464576547 74.39754123580246 75.18880500856693 75.96371546546276 76.7252296396663 77.47583769656181 78.21957975177521 78.95905016293707 79.69712587468834 80.4370485162662 81.18210364892593 81.93429837927215 82.69597230924478 83.4691472921311 84.25544962565074 85.05614376573314 85.87215879473054 86.70392313153135 87.55146827026859 88.41445157658053 89.29218897542948 90.18369572122522 91.08773317913175 102.68235537543303 103.7042131782845 104.71196201385267 105.70449758967617 106.68071561329351 107.63951179224333 108.57978183406415 109.50042144629455 110.40032633647319 111.2783922121385 112.13351478082917 112.96458975008372 113.77051282744074 114.55017972043879 115.30267699164074 116.02873972512184 116.72995672023089 117.40792272643284 118.0642324931926 118.70048076997512 119.31826230624539 119.91917185146836 120.50480415510899 121.07675396663221 121.63661603550297 122.18598511118623 122.72645594314696 123.2596232808501 123.78708187376054 124.31042647134338 124.83125182306345 125.35115267838574 125.87172378677525 126.39455989769684 126.92125576061551 127.45340612499622 127.99260574030393 128.54044935600356 129.0985317215601 129.66844758643848 130.25179170010364 130.85015881202057 131.4651436716542 132.0983410284695 132.75134563193137 133.42575223150482 134.12315557665482 134.84515041684625 135.5933315015441 136.36929118355562 |
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| diff -r 000000000000 -r f1ba33cd9edf test-data/detailed.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/detailed.tabular Tue Sep 17 17:09:25 2019 -0400 |
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| @@ -0,0 +1,2 @@ +# Branches Junctions End-point Voxels Junction Voxels Slab Voxels Average branch length Triple Points Quadruple Points Maximum Branch Length +96 60 7 120 1246 17.344 56 3 70.882 |
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| diff -r 000000000000 -r f1ba33cd9edf 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 17:09:25 2019 -0400 |
| b |
| @@ -0,0 +1,2 @@ +# Branches Junctions End-point Voxels Junction Voxels Slab Voxels Average branch length Triple Points Quadruple Points Maximum Branch Length Longest Shortest Path spx spy spz +96 60 7 120 1246 17.344 56 3 70.882 207.380 135 137 0 |
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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 f1ba33cd9edf 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 17:09:25 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 f1ba33cd9edf 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 17:09:25 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 f1ba33cd9edf test-data/skeletonized_blobs.gif |
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| Binary file test-data/skeletonized_blobs.gif has changed |
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| diff -r 000000000000 -r f1ba33cd9edf test-data/skeletonized_clown.jpg |
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| diff -r 000000000000 -r f1ba33cd9edf 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 17:09:25 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 f1ba33cd9edf 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 17:09:25 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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155.37419580384014 155.21191791934865 155.03413535208156 154.84206364849632 154.63691835505023 154.41991501820058 154.1922691844048 153.95519640012026 153.70991221180424 153.4576321659141 153.19957180890725 152.936946687241 152.67097234737275 152.40286433575972 152.1338381988594 151.86510948312906 151.5978937350261 151.33340650100786 151.07286332753165 150.81747976105487 150.56847134803485 150.32705363492894 150.09444216819452 149.87185249428887 149.66050015966943 149.46160071079348 149.27636969411842 149.10598788905725 148.95084272664363 148.81052683715328 148.68459856794385 148.57261626637296 148.47413827979818 148.38872295557715 148.31592864106744 148.25531368362664 148.20643643061246 148.1688552293824 148.14212842729404 148.12581437170508 148.11947140997316 148.12265788945575 148.13493215751055 148.15585256149515 148.1849774487671 148.2218651666841 148.26607406260374 148.31716248388352 148.37468877788112 148.4382112919542 148.50728837346028 148.581478369757 148.66033962820197 148.74343049615283 148.83030932096707 148.9205344500024 149.0136642306164 149.10925701016671 149.2068711360109 149.30606495550654 149.4063968160113 149.50742506488274 149.6087080494785 149.7098135053973 149.81038919811107 149.91012475333372 150.008709995401 150.1058347486486 150.20118883741236 150.29446208602795 150.38534431883116 150.47352536015774 150.55869503434337 150.6405431657239 150.71875957863497 150.7930340974124 150.86305654639193 150.9285167499093 150.98910453230025 151.0445097179005 151.09442213104586 151.13853159607203 151.17652793731477 151.20810097910982 151.23294054579296 151.25073646169986 151.26117855116632 151.26395663852813 151.258760548121 151.24528010428062 151.2232051313428 151.19222545364326 151.15203089551784 151.10231128130212 151.04275643533197 150.9730561819431 150.89290034547125 150.80197875025215 150.6999782464026 \n' |
| b |
| diff -r 000000000000 -r f1ba33cd9edf 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 17:09:25 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 f1ba33cd9edf 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 17:09:25 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 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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 f1ba33cd9edf test-data/warping_index.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/warping_index.txt Tue Sep 17 17:09:25 2019 -0400 |
| b |
| @@ -0,0 +1,1 @@ +22.224744070209738 |
| b |
| diff -r 000000000000 -r f1ba33cd9edf test-data/warping_index1.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/warping_index1.txt Tue Sep 17 17:09:25 2019 -0400 |
| b |
| @@ -0,0 +1,1 @@ +33.6 |
| b |
| diff -r 000000000000 -r f1ba33cd9edf test-data/warping_index2.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/warping_index2.txt Tue Sep 17 17:09:25 2019 -0400 |
| b |
| @@ -0,0 +1,1 @@ +0.0 |
| b |
| diff -r 000000000000 -r f1ba33cd9edf 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 17:09:25 2019 -0400 |
| b |
| @@ -0,0 +1,1 @@ +66.4 |
