| Previous changeset 1:0b8f8406ee16 (2020-04-09) Next changeset 3:a98608f3abc5 (2020-05-11) |
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Commit message:
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools commit 1907942bef43b20edfdbd1d1b5eb1cac3602848b" |
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modified:
cellprofiler.xml macros.xml |
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added:
cp_common_functions.py image_math.py test-data/common_image_math.cppipe test-data/image_math_1.cppipe test-data/image_math_2.cppipe test-data/image_math_3.cppipe |
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| diff -r 0b8f8406ee16 -r 78a16d8c8d5e cellprofiler.xml --- a/cellprofiler.xml Thu Apr 09 08:14:24 2020 -0400 +++ b/cellprofiler.xml Thu Apr 16 05:40:08 2020 -0400 |
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| @@ -1,5 +1,5 @@ -<tool id="cp_cellprofiler" name="cellProfiler" version="@CP_VERSION@"> - <description>runs a cellProfiler pipeline assembled by all the cellProfiler tools upstream in the workflow</description> +<tool id="cp_cellprofiler" name="CellProfiler" version="@CP_VERSION@"> + <description>run a CellProfiler pipeline</description> <macros> <import>macros.xml</import> <xml name="test_assert_content" token_n="291"> @@ -31,11 +31,11 @@ <inputs> <param name="pipeline" type="data" format="txt" label="Pipeline file" help="A ready-to-run .cppipe file. It can be created with various CellProfiler modules." /> <param name="input_images" type="data" multiple="true" format="@FORMATS@" label="Images" /> - <param name="detailed_output" type="boolean" label="Detailed logging file?" help="If set, a detailed cellprofiler log file will be generated." /> + <param name="detailed_output" type="boolean" label="Detailed logging file?" help="If set, a detailed CellProfiler log file will be generated." /> </inputs> <outputs> - <collection name="pipeline_output" type="list" label="Cellprofiler pipeline output files"> + <collection name="pipeline_output" type="list" label="CellProfiler pipeline output files"> <discover_datasets pattern="__designation_and_ext__" visible="false" directory="output"/> </collection> <data format="txt" name="logs" from_work_dir="cp.log" label="CellProfiler log"> @@ -112,13 +112,13 @@ </tests> <help> - This tool runs a cellProfiler @CP_VERSION@ pipeline file. + This tool runs a CellProfiler @CP_VERSION@ pipeline file. *** Parameters *** images: multi-select image files - pipeline: cellProfiler pipeline file(.cppipe) file. + pipeline: CellProfiler pipeline file(.cppipe) file. *** Note *** |
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| diff -r 0b8f8406ee16 -r 78a16d8c8d5e cp_common_functions.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/cp_common_functions.py Thu Apr 16 05:40:08 2020 -0400 |
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| @@ -0,0 +1,61 @@ +INDENTATION = " " +LINE_NUM_MODULES = 4 + +def get_json_value(json_input, keys_path): + """Returns the value specified in keys_path (using dot notation) or an empty string if the key doesn't exist""" + if not isinstance(json_input, dict): + return "" + keys = keys_path.split(".") + try: + value = json_input[keys[0]] + for key in keys[1:]: + value = value[key] + return value + except KeyError: + return "" + + +def concat_conditional(a, b): + if a == "" or b == "": + return "" + else: + return f"{a}_{b}" + + +def get_total_number_of_modules(pipeline_lines): + """Gets the number of modules from the header of the previous pipeline""" + number_of_modules = pipeline_lines[LINE_NUM_MODULES].strip().split(':')[1] + return int(number_of_modules) + + +def get_pipeline_lines(input_pipeline): + """Returns a list with the lines in the .cppipe file""" + with open(input_pipeline) as f: + lines = f.readlines() + return lines + + +def update_module_count(pipeline_lines, count): + """Updates the number of modules in the .cppipe header""" + module_count_entry = pipeline_lines[LINE_NUM_MODULES].strip().split(':')[0] + pipeline_lines[4] = f"{module_count_entry}:{count}\n" + return pipeline_lines + + +def write_pipeline(filename, lines_pipeline): + """Writes the lines composing the pipeline into a file""" + with open(filename, "w") as f: + f.writelines(lines_pipeline) + + +def build_header(module_name, module_number): + """Creates the first line of a module given the name and module number""" + result = "|".join([f"{module_name}:[module_num:{module_number}", + "svn_version:\\'Unknown\\'", + "variable_revision_number:4", + "show_window:False", + "notes:\\x5B\\x5D", + "batch_state:array(\\x5B\\x5D, dtype=uint8)", + "enabled:True", + "wants_pause:False]\n"]) + return result |
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| diff -r 0b8f8406ee16 -r 78a16d8c8d5e image_math.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/image_math.py Thu Apr 16 05:40:08 2020 -0400 |
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| @@ -0,0 +1,129 @@ +#!/usr/bin/env python + +import json +import sys +import os +import argparse +from cp_common_functions import * + +MODULE_NAME = "ImageMath" +OUTPUT_FILENAME = "output.cppipe" + +operator_map = { + "add": "Add", + "subtract": "Subtract", + "multiply": "Multiply", + "divide": "Divide", + "average": "Average", + "minimum": "Minimum", + "maximum": "Maximum", + "invert": "Invert", + "log_2": "Log transform (base 2)", + "log_legacy": "Log transform (legacy)", + "and": "And", + "or": "Or", + "not": "Not", + "equals": "Equals" +} + + +def build_main_block(input_params): + """Creates the main block of the CP pipeline with the parameters that don't depend on conditional choices""" + operation = operator_map[get_json_value( + input_params, 'operation.operation')] + result = INDENTATION.join([f"{INDENTATION}Operation:{operation}\n", + f"Raise the power of the result by:{get_json_value(input_params,'operation.op_results.raise_the_power_of_the_result_by')}\n", + f"Multiply the result by:{get_json_value(input_params,'operation.op_results.multiply_the_result_by')}\n", + f"Add to result:{get_json_value(input_params,'operation.op_results.add_to_result')}\n", + f"Set values less than 0 equal to 0?:{get_json_value(input_params,'operation.op_results.set_values_less_than_0_equal_to_0')}\n", + f"Set values greater than 1 equal to 1?:{get_json_value(input_params,'operation.op_results.set_values_greater_than_1_equal_to_1')}\n", + f"Ignore the image masks?:{get_json_value(input_params,'ignore_the_image_masks')}\n", + f"Name the output image:{get_json_value(input_params,'name_output_image')}" + ]) + return result + + +def build_variable_block(inputs_galaxy): + result = "" + first_image_block = build_first_image_block( + get_json_value(inputs_galaxy, 'operation.first_image')) + result += f"\n{first_image_block}" + second_image_block = build_second_image_block( + get_json_value(inputs_galaxy, 'operation.second_image')) + result += f"\n{second_image_block}" + return result + + +def build_first_image_block(input_params): + """Creates the block of parameters for the first operator in operations""" + + value_select = get_json_value( + input_params, 'image_or_measurement_first.image_or_measurement_first') + image_name = get_json_value( + input_params, 'image_or_measurement_first.select_the_first_image') + value_multiply = get_json_value( + input_params, 'multiply_the_first_image_by') + category = get_json_value( + input_params, 'image_or_measurement_first.category_first.category_first') + measurement = get_json_value( + input_params, 'image_or_measurement_first.category_first.measurement_first') + + result = INDENTATION.join( + [f"{INDENTATION}Image or measurement?:{value_select}\n", + f"Select the first image:{image_name}\n", + f"Multiply the first image by:{value_multiply}\n", + f"Measurement:{concat_conditional(category, measurement)}" + ]) + return result + + +def build_second_image_block(input_params): + """Creates the block of parameters for the second operator in binary operations""" + + value_select = get_json_value( + input_params, 'image_or_measurement_second.image_or_measurement_second') + image_name = get_json_value( + input_params, 'image_or_measurement_second.select_the_second_image') + value_multiply = get_json_value( + input_params, 'multiply_the_second_image_by') + category = get_json_value( + input_params, 'image_or_measurement_second.category_second.category_second') + measurement = get_json_value( + input_params, 'image_or_measurement_second.category_second.measurement_second') + + result = INDENTATION.join( + [f"{INDENTATION}Image or measurement?:{value_select}\n", + f"Select the second image:{image_name}\n", + f"Multiply the second image by:{value_multiply}\n", + f"Measurement:{concat_conditional(category, measurement)}" + ]) + return result + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + '-p', '--pipeline', + help='CellProfiler pipeline' + ) + parser.add_argument( + '-i', '--inputs', + help='JSON inputs from Galaxy' + ) + args = parser.parse_args() + + pipeline_lines = get_pipeline_lines(args.pipeline) + inputs_galaxy = json.load(open(args.inputs, "r")) + + current_module_num = get_total_number_of_modules(pipeline_lines) + current_module_num += 1 + pipeline_lines = update_module_count(pipeline_lines, current_module_num) + + header_block = build_header(MODULE_NAME, current_module_num) + main_block = build_main_block(inputs_galaxy) + variable_block = build_variable_block(inputs_galaxy) + + module_pipeline = f"\n{header_block}{main_block}{variable_block}\n" + pipeline_lines.append(module_pipeline) + + write_pipeline(OUTPUT_FILENAME, pipeline_lines) |
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| diff -r 0b8f8406ee16 -r 78a16d8c8d5e macros.xml --- a/macros.xml Thu Apr 09 08:14:24 2020 -0400 +++ b/macros.xml Thu Apr 16 05:40:08 2020 -0400 |
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| @@ -1,73 +1,105 @@ <macros> <token name="@CP_VERSION@">3.1.9</token> - <token name="@PY_VERSION@">2.7.16</token> + <token name="@PY_VERSION@">3.7</token> <token name="@FORMATS@">jpg,png,tiff,bmp,gif,pcx,ppm,psd,pbm,pgm,eps</token> + <token name="@SPACES@">" "</token> + <!-- four spaces needed for CP pipeline file --> + <token name="@COMMON_HELP@"> + .. class:: infomark - <token name="@SPACES@">" "</token> <!-- four spaces needed for CP pipeline file --> + **Input** + + Existing CellProfiler pipeline file *(.cppipe)* or generated by linking CellProfiler tools. + + .. class:: infomark + + **Output** + + The input CellProfiler pipeline file *(.cppipe)* in addition to the settings of this module. + + .. class:: warningmark + + **IMPORTANT** + + The first tool in a CellProfiler workflow has to be **Starting modules** and the last one **CellProfiler**. You can also execute the entire pipeline with the final CellProfiler tool, in which you feed in the images you want to process as well. + </token> + <xml name="output_pipeline_macro"> - <data name="out_file" from_work_dir="output" format="txt" label="Output pipeline" /> + <data name="out_file" from_work_dir="output" format="txt"/> </xml> + <xml name="input_pipeline_macro"> - <param name="input_pipeline" type="data" format="data" label="Pipeline to select"/> + <param name="input_pipeline" type="data" format="data" label="Select the input CellProfiler pipeline"/> </xml> + <xml name="cp_requirements"> <requirements> <requirement type="package" version="@CP_VERSION@">cellprofiler</requirement> </requirements> </xml> + <xml name="py_requirements"> <requirements> <requirement type="package" version="@PY_VERSION@">python</requirement> </requirements> </xml> + <xml name="citations"> <citations> <citation type="bibtex"> - @misc{galaxytoolscellprofiler, - author = {Sun, Yi}, - year = {2020}, - title = {Cellprofiler Galaxy tools}, - publisher = {Github}, - journal = {Github repository}, - url = {https://github.com/bgruening/galaxytools/tools/cellrprofiler}, - } + @article{McQuin_2018, + title = {CellProfiler 3.0: Next-generation image processing for biology}, + author = {McQuin C, Goodman A, Chernyshev V, Kamentsky L, Cimini BA, Karhohs KW, Doan M, Ding L, Rafelski SM, Thirstrup D, Wiegraebe W, Singh S, Becker T, Caicedo JC, Carpenter AE}, + year = {2018}, + volume = {16(7):e2005970}, + DOI = {10.1371/journal.pbio.2005970}, + journal = {PLoS Biol.}, + url = {https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.2005970}, + } </citation> </citations> </xml> + <xml name="cmd_modules"> - <command detect_errors="aggressive"><![CDATA[ - python '$script_file' '$inputs' '$input_pipeline' - ]]></command> + <command detect_errors="aggressive"> + <![CDATA[ + python '$script_file' '$inputs' '$input_pipeline' + ]]> + </command> </xml> + <xml name="text_validator"> <validator type="regex" message="Only numbers, letters, hyphen, underscore and spaces are allowed">^[A-Za-z0-9 _-]*$</validator> </xml> + <xml name="test_input_pipeline_param"> <param name="input_pipeline" value="common.txt" /> </xml> + <xml name="test_out_file" token_file="common.txt"> <output name="out_file" ftype="txt" file="@FILE@" lines_diff="0"/> </xml> - + + <xml name="help" token_module="common"> - <help> - This tool appends the inputs of the @MODULE@ module to an existing pipeline file (.cppipe) + <help> + This tool appends the inputs of the @MODULE@ module to an existing pipeline file (.cppipe) - Input: existing pipeline file + Input: existing pipeline file - Output: new pipeline file + Output: new pipeline file - Combine this tool with "Common" tool and "CellProfiler" tool together to run the current module alone. - </help> - </xml> + Combine this tool with "Common" tool and "CellProfiler" tool together to run the current module alone. + </help> + </xml> </macros> |
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| diff -r 0b8f8406ee16 -r 78a16d8c8d5e test-data/common_image_math.cppipe --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/common_image_math.cppipe Thu Apr 16 05:40:08 2020 -0400 |
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| @@ -0,0 +1,94 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:319 +GitHash: +ModuleCount:6 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Images only + Select the rule criteria:and (extension does isimage) (directory doesnot startwith ".") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:1 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression to extract from file name:(?P<field1>.*)_(?P<field2>[a-zA-Z0-9]+)_(?P<field3>[a-zA-Z0-9]+)_(?P<field4>[a-zA-Z0-9]+) + Regular expression to extract from folder name:(?P<field1>.*) + Extract metadata from:All images + Select the filtering criteria:and (file does contain "") + Metadata file location: + Match file and image metadata:[] + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:8|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:[] + Image set matching method:Order + Set intensity range from:Image metadata + Assignments count:1 + Single images count:0 + Maximum intensity:255.0 + Process as 3D?:No + Relative pixel spacing in X:1.0 + Relative pixel spacing in Y:1.0 + Relative pixel spacing in Z:1.0 + Select the rule criteria:and (file does startwith "im") + Name to assign these images:DNA + Name to assign these objects:Cell + Select the image type:Grayscale image + Set intensity range from:Image metadata + Select the image type:Grayscale image + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:Yes + grouping metadata count:1 + Metadata category:Screen + +IdentifyPrimaryObjects:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:13|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DNA + Name the primary objects to be identified:Nucleus + Typical diameter of objects, in pixel units (Min,Max):15,200 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:Yes + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Use advanced settings?:No + Threshold setting version:10 + Threshold strategy:Global + Thresholding method:Minimum cross entropy + Threshold smoothing scale:1.3488 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Two classes + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:50 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + Thresholding method:Otsu + +ConvertObjectsToImage:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:1|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input objects:Nucleus + Name the output image:CellImage + Select the color format:Binary (black & white) + Select the colormap:Default |
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| diff -r 0b8f8406ee16 -r 78a16d8c8d5e test-data/image_math_1.cppipe --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/image_math_1.cppipe Thu Apr 16 05:40:08 2020 -0400 |
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| @@ -0,0 +1,112 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:319 +GitHash: +ModuleCount:7 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Images only + Select the rule criteria:and (extension does isimage) (directory doesnot startwith ".") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:1 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression to extract from file name:(?P<field1>.*)_(?P<field2>[a-zA-Z0-9]+)_(?P<field3>[a-zA-Z0-9]+)_(?P<field4>[a-zA-Z0-9]+) + Regular expression to extract from folder name:(?P<field1>.*) + Extract metadata from:All images + Select the filtering criteria:and (file does contain "") + Metadata file location: + Match file and image metadata:[] + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:8|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:[] + Image set matching method:Order + Set intensity range from:Image metadata + Assignments count:1 + Single images count:0 + Maximum intensity:255.0 + Process as 3D?:No + Relative pixel spacing in X:1.0 + Relative pixel spacing in Y:1.0 + Relative pixel spacing in Z:1.0 + Select the rule criteria:and (file does startwith "im") + Name to assign these images:DNA + Name to assign these objects:Cell + Select the image type:Grayscale image + Set intensity range from:Image metadata + Select the image type:Grayscale image + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:Yes + grouping metadata count:1 + Metadata category:Screen + +IdentifyPrimaryObjects:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:13|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DNA + Name the primary objects to be identified:Nucleus + Typical diameter of objects, in pixel units (Min,Max):15,200 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:Yes + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Use advanced settings?:No + Threshold setting version:10 + Threshold strategy:Global + Thresholding method:Minimum cross entropy + Threshold smoothing scale:1.3488 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Two classes + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:50 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + Thresholding method:Otsu + +ConvertObjectsToImage:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:1|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input objects:Nucleus + Name the output image:CellImage + Select the color format:Binary (black & white) + Select the colormap:Default + +ImageMath:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Operation:Add + Raise the power of the result by:1.0 + Multiply the result by:1.0 + Add to result:0.0 + Set values less than 0 equal to 0?:Yes + Set values greater than 1 equal to 1?:Yes + Ignore the image masks?:No + Name the output image:ImageAfterMath + Image or measurement?:Image + Select the first image:DNA + Multiply the first image by:1.0 + Measurement: + Image or measurement?:Image + Select the second image:CellImage + Multiply the second image by:2.0 + Measurement: |
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| diff -r 0b8f8406ee16 -r 78a16d8c8d5e test-data/image_math_2.cppipe --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/image_math_2.cppipe Thu Apr 16 05:40:08 2020 -0400 |
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| @@ -0,0 +1,112 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:319 +GitHash: +ModuleCount:7 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Images only + Select the rule criteria:and (extension does isimage) (directory doesnot startwith ".") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:1 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression to extract from file name:(?P<field1>.*)_(?P<field2>[a-zA-Z0-9]+)_(?P<field3>[a-zA-Z0-9]+)_(?P<field4>[a-zA-Z0-9]+) + Regular expression to extract from folder name:(?P<field1>.*) + Extract metadata from:All images + Select the filtering criteria:and (file does contain "") + Metadata file location: + Match file and image metadata:[] + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:8|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:[] + Image set matching method:Order + Set intensity range from:Image metadata + Assignments count:1 + Single images count:0 + Maximum intensity:255.0 + Process as 3D?:No + Relative pixel spacing in X:1.0 + Relative pixel spacing in Y:1.0 + Relative pixel spacing in Z:1.0 + Select the rule criteria:and (file does startwith "im") + Name to assign these images:DNA + Name to assign these objects:Cell + Select the image type:Grayscale image + Set intensity range from:Image metadata + Select the image type:Grayscale image + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:Yes + grouping metadata count:1 + Metadata category:Screen + +IdentifyPrimaryObjects:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:13|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DNA + Name the primary objects to be identified:Nucleus + Typical diameter of objects, in pixel units (Min,Max):15,200 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:Yes + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Use advanced settings?:No + Threshold setting version:10 + Threshold strategy:Global + Thresholding method:Minimum cross entropy + Threshold smoothing scale:1.3488 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Two classes + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:50 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + Thresholding method:Otsu + +ConvertObjectsToImage:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:1|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input objects:Nucleus + Name the output image:CellImage + Select the color format:Binary (black & white) + Select the colormap:Default + +ImageMath:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Operation:Subtract + Raise the power of the result by:1.0 + Multiply the result by:1.0 + Add to result:0.0 + Set values less than 0 equal to 0?:Yes + Set values greater than 1 equal to 1?:No + Ignore the image masks?:No + Name the output image:ImageAfterMath + Image or measurement?:Image + Select the first image:DNA + Multiply the first image by:1.0 + Measurement: + Image or measurement?:Measurement + Select the second image: + Multiply the second image by:5.0 + Measurement:FileName_DNA |
| b |
| diff -r 0b8f8406ee16 -r 78a16d8c8d5e test-data/image_math_3.cppipe --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/image_math_3.cppipe Thu Apr 16 05:40:08 2020 -0400 |
| [ |
| @@ -0,0 +1,112 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:319 +GitHash: +ModuleCount:7 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Images only + Select the rule criteria:and (extension does isimage) (directory doesnot startwith ".") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:1 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression to extract from file name:(?P<field1>.*)_(?P<field2>[a-zA-Z0-9]+)_(?P<field3>[a-zA-Z0-9]+)_(?P<field4>[a-zA-Z0-9]+) + Regular expression to extract from folder name:(?P<field1>.*) + Extract metadata from:All images + Select the filtering criteria:and (file does contain "") + Metadata file location: + Match file and image metadata:[] + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:8|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:[] + Image set matching method:Order + Set intensity range from:Image metadata + Assignments count:1 + Single images count:0 + Maximum intensity:255.0 + Process as 3D?:No + Relative pixel spacing in X:1.0 + Relative pixel spacing in Y:1.0 + Relative pixel spacing in Z:1.0 + Select the rule criteria:and (file does startwith "im") + Name to assign these images:DNA + Name to assign these objects:Cell + Select the image type:Grayscale image + Set intensity range from:Image metadata + Select the image type:Grayscale image + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:Yes + grouping metadata count:1 + Metadata category:Screen + +IdentifyPrimaryObjects:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:13|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DNA + Name the primary objects to be identified:Nucleus + Typical diameter of objects, in pixel units (Min,Max):15,200 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:Yes + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Use advanced settings?:No + Threshold setting version:10 + Threshold strategy:Global + Thresholding method:Minimum cross entropy + Threshold smoothing scale:1.3488 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Two classes + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:50 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + Thresholding method:Otsu + +ConvertObjectsToImage:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:1|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input objects:Nucleus + Name the output image:CellImage + Select the color format:Binary (black & white) + Select the colormap:Default + +ImageMath:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Operation:Not + Raise the power of the result by: + Multiply the result by: + Add to result: + Set values less than 0 equal to 0?: + Set values greater than 1 equal to 1?: + Ignore the image masks?:No + Name the output image:ImageAfterMath + Image or measurement?:Image + Select the first image:DNA + Multiply the first image by: + Measurement: + Image or measurement?: + Select the second image: + Multiply the second image by: + Measurement: |