Mercurial > repos > iuc > imagej2_find_maxima
changeset 0:802c21d935e7 draft
planemo upload commit 378c3ecfbaed0330faf197811d379431b0f9e92c-dirty
| author | iuc |
|---|---|
| date | Sun, 11 Oct 2015 13:29:38 -0400 |
| parents | |
| children | f00fd3aa6472 |
| files | imagej2_base_utils.py imagej2_find_maxima.py imagej2_find_maxima.xml imagej2_macros.xml jython_script.py jython_utils.py test-data/blobs.gif test-data/blobs_count.tabular test-data/blobs_list.tabular test-data/blobs_segmented.gif test-data/blobs_single_points.gif test-data/blobs_tolerance.gif tool_dependencies.xml |
| diffstat | 13 files changed, 742 insertions(+), 0 deletions(-) [+] |
line wrap: on
line diff
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_base_utils.py Sun Oct 11 13:29:38 2015 -0400 @@ -0,0 +1,164 @@ +import os +import shutil +import sys +import tempfile + +FIJI_JAR_DIR = os.environ.get( 'FIJI_JAR_DIR', None ) +FIJI_OSX_JAVA3D_DIR = os.environ.get( 'FIJI_OSX_JAVA3D_DIR', None ) +FIJI_PLUGIN_DIR = os.environ.get( 'FIJI_PLUGIN_DIR', None ) +FIJI_ROOT_DIR = os.environ.get( 'FIJI_ROOT_DIR', None ) + +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 FIJI_JAR_DIR is not None and FIJI_PLUGIN_DIR is not None: + if jvm_memory in [ None, 'None' ]: + jvm_memory_str = '' + else: + jvm_memory_str = '-Xmx%s' % jvm_memory + bunwarpj_base_cmd = "java %s -cp %s/ij-1.49k.jar:%s/bUnwarpJ_-2.6.1.jar bunwarpj.bUnwarpJ_" % \ + ( jvm_memory_str, FIJI_JAR_DIR, FIJI_PLUGIN_DIR ) + return bunwarpj_base_cmd + return None + +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, so figure out which name we need. + """ + platform_dict = get_platform_info_dict() + if platform_dict.get( 'architecture', None ) in [ 'x86_64' ]: + if platform_dict.get( 'os', None ) in [ 'darwin' ]: + return 'ImageJ-macosx' + if platform_dict.get( 'os', None ) in [ 'linux' ]: + return 'ImageJ-linux64' + return None + +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 )
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_find_maxima.py Sun Oct 11 13:29:38 2015 -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( '--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 )
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_find_maxima.xml Sun Oct 11 13:29:38 2015 -0400 @@ -0,0 +1,125 @@ +<?xml version='1.0' encoding='UTF-8'?> +<tool id="imagej2_find_maxima" name="Find maxima" version="@WRAPPER_VERSION@.0"> + <description></description> + <macros> + <import>imagej2_macros.xml</import> + </macros> + <expand macro="fiji_requirements" /> + <command> +<![CDATA[ + python $__tool_directory__/imagej2_find_maxima.py + --input "$input" + --input_datatype $input.ext + --scale_when_converting $scale_when_converting + --weighted_rgb_conversions $weighted_rgb_conversions + --noise_tolerance $noise_tolerance + --output_type $output_type + --exclude_edge_maxima $exclude_edge_maxima + --light_background $light_background + --jython_script $__tool_directory__/jython_script.py + #if str( $output_type ) == "List" or str( $output_type ) == "Count": + --output "$results" + #else: + --output "$output" + #end if + --output_datatype $output.ext +]]> + </command> + <inputs> + <param format="bmp,eps,gif,jpg,pcx,pgm,png,psd,tiff" name="input" type="data" label="Select image"/> + <param name="noise_tolerance" type="integer" value="10" min="0" label="Noise tolerance" help="Maxima are ignored if they do not stand out from the surroundings by more than this value."/> + <param name="output_type" type="select" label="Output type"> + <option value="Single_Points" selected="True">Single Points</option> + <option value="Maxima_Within_Tolerance">Maxima Within Tolerance</option> + <option value="Segmented_Particles">Segmented Particles</option> + <option value="List">List</option> + <option value="Count">Count</option> + </param> + <param name="exclude_edge_maxima" type="select" label="Exclude edge maxima?"> + <option value="no" selected="True">No</option> + <option value="yes">Yes</option> + </param> + <param name="light_background" type="select" label="Light background?"> + <option value="no" selected="True">No</option> + <option value="yes">Yes</option> + </param> + <param name="scale_when_converting" type="select" label="Scale when converting?" help="Applies only to 24-bit (RGB) images, ignored for other types."> + <option value="yes" selected="True">Yes</option> + <option value="no">No</option> + </param> + <param name="weighted_rgb_conversions" type="select" label="Weighted RGB conversions?" help="Applies only to 24-bit (RGB) images, ignored for other types."> + <option value="no" selected="True">No</option> + <option value="yes">Yes</option> + </param> + </inputs> + <outputs> + <data name="output" format_source="input" label="${tool.name} on ${on_string}: ${output_type}"> + <filter>output_type != "List" and output_type != "Count"</filter> + </data> + <data name="results" format="tabular" label="${tool.name} on ${on_string}: ${output_type}"> + <filter>output_type == "List" or output_type == "Count"</filter> + </data> + </outputs> + <tests> + <test> + <param name="input" value="blobs.gif" /> + <output name="output" file="blobs_single_points.gif" compare="sim_size" /> + </test> + <test> + <param name="input" value="blobs.gif" /> + <param name="noise_tolerance" value="13" /> + <param name="output_type" value="Maxima_Within_Tolerance" /> + <output name="output" file="blobs_tolerance.gif" compare="sim_size" /> + </test> + <test> + <param name="input" value="blobs.gif" /> + <param name="noise_tolerance" value="16" /> + <param name="output_type" value="Segmented_Particles" /> + <param name="exclude_edge_maxima" value="yes" /> + <output name="output" file="blobs_segmented.gif" compare="sim_size" /> + </test> + <test> + <param name="input" value="blobs.gif" /> + <param name="output_type" value="List" /> + <output name="output" file="blobs_list.tabular" /> + </test> + <test> + <param name="input" value="blobs.gif" /> + <param name="output_type" value="Count" /> + <output name="output" file="blobs_count.tabular" /> + </test> + </tests> + <help> + +**What it does** + +Determines the local maxima in an image and creates a binary (mask-like) image of the same size with the +maxima (or one segmented particle per maximum) marked. For RGB images, maxima of luminance are selected +with the luminance defined as the weighted or unweighted average of the colors depending on the **Scale +when converting** and **Weighted RGB conversions** settings. + +- **Scale when converting** (RGB images only) - If "yes", the tool will scale from min--max to 0--255 when converting from 16-bit or 32-bit to 8-bit or to scale from min--max to 0--65535 when converting from 32-bit to 16-bit. + +- **Weighted RGB conversions** (RGB images only) - If "yes", the formula gray = 0.299 × red + 0.587 × green + 0.114 × blue is used to convert RGB images to grayscale. If "no", the formula gray = (red + green + blue) ⁄ 3 is used. + +- **Noise Tolerance** - Maxima are ignored if they do not stand out from the surroundings by more than this value (calibrated units for calibrated images). In other words, a threshold is set at the maximum value minus noise tolerance and the contiguous area around the maximum above the threshold is analyzed. For accepting a maximum, this area must not contain any point with a value higher than the maximum. Only one maximum within this area is accepted. + +- **Output Type** can be: + + - **Single Points** - Produces an image with one single point per maximum. + + - **Maxima Within Tolerance** - Produces an image with all points within the "Noise Tolerance" for each maximum. + + - **Segmented Particles** - Assumes that each maximum belongs to a particle and segments the image by a watershed algorithm applied to the values of the image. + + - **List** - Produces the list x, y of maxima in the image. + + - **Count** - Produces the number of maxima in the image. + +- **Exclude Edge Maxima** - Excludes maxima if the area within the noise tolerance surrounding a maximum touches the edge of the image (edge of the selection does not matter). + +- **Light Background** - Select "yes" if the image background is brighter than the objects you want to find. + + </help> + <expand macro="fiji_headless_citations" /> +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/imagej2_macros.xml Sun Oct 11 13:29:38 2015 -0400 @@ -0,0 +1,121 @@ +<?xml version='1.0' encoding='UTF-8'?> +<macros> + <token name="@WRAPPER_VERSION@">1.0</token> + <xml name="fiji_requirements"> + <requirements> + <requirement type="package" version="20141125">fiji</requirement> + </requirements> + </xml> + <xml name="python_bioformats_requirements"> + <requirements> + <requirement type="package" version="20141125">fiji</requirement> + <requirement type="package" version="1.0.11">javabridge</requirement> + <requirement type="package" version="1.9">numpy</requirement> + <requirement type="package" version="1.0.4">python_bioformats</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> + <xml name="bioformats_fiji_javabridge_citations"> + <citations> + <citation type="doi">10.1038/nmeth.2102</citation> + <citation type="doi">10.1038/nmeth.2019</citation> + <citation type="doi">10.1083/jcb.201004104</citation> + </citations> + </xml> +</macros>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/jython_script.py Sun Oct 11 13:29:38 2015 -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 ) )
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/jython_utils.py Sun Oct 11 13:29:38 2015 -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()
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/blobs_count.tabular Sun Oct 11 13:29:38 2015 -0400 @@ -0,0 +1,2 @@ + Count +1 112
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/blobs_list.tabular Sun Oct 11 13:29:38 2015 -0400 @@ -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
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tool_dependencies.xml Sun Oct 11 13:29:38 2015 -0400 @@ -0,0 +1,6 @@ +<?xml version="1.0"?> +<tool_dependency> + <package name="fiji" version="20141125"> + <repository changeset_revision="9dbe89105a25" name="package_fiji_20141125" owner="iuc" toolshed="https://testtoolshed.g2.bx.psu.edu" /> + </package> +</tool_dependency>