Mercurial > repos > bgruening > cp_measure_granularity
view measure_granularity.xml @ 5:ac5bea85a391 draft
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools commit 7d7a519c3a2cc612d38695b335d0f6c75a099de3"
author | bgruening |
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date | Fri, 26 Feb 2021 14:13:40 +0000 |
parents | 382413f8ec1d |
children | 2564f461de62 |
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<tool id="cp_measure_granularity" name="MeasureGranularity" version="@CP_VERSION@+galaxy@VERSION_SUFFIX@"> <description>output spectra of size measurements of the textures</description> <macros> <import>macros.xml</import> <token name="@VERSION_SUFFIX@">0</token> </macros> <expand macro="py_requirements"/> <expand macro="cmd_modules" /> <configfiles> <inputs name="inputs" /> <configfile name="script_file"> import json import sys import os FOURSPACES=@SPACES@ input_json_path = sys.argv[1] input_pipeline= sys.argv[2] params = json.load(open(input_json_path, "r")) def writemg(): _str = "\nMeasureGranularity:[module_num:%d|svn_version:\\'Unknown\\'|variable_revision_number:3|show_window:False|notes:\\x5B\\'PARAMS\\x3A\\', \\'- Radius\\', \'- Range\\'\\x5D|batch_state:array(\\x5B\\x5D, dtype=uint8)|enabled:True|wants_pause:False]\n" % new_count image_count = len(params['rpt_image']) _str += FOURSPACES + "Image count:%d\n" % image_count for img in params['rpt_image']: obj_count = len(img['rpt_object']) _str += FOURSPACES + "Object count:%d\n" % obj_count _str += FOURSPACES + "Select an image to measure:%s\n" % img['image_to_measure'] _str += FOURSPACES + "Subsampling factor for granularity measurements:%s\n" % img['subsampling_factor_granularity'] _str += FOURSPACES + "Subsampling factor for background reduction:%.2f\n" % img['subsampling_factor_background_reduction'] _str += FOURSPACES + "Radius of structuring element:%d\n" % img['radius'] _str += FOURSPACES + "Range of the granular spectrum:%d\n" % img['range_granular_spectrum'] for obj in img['rpt_object']: _str += FOURSPACES + "Select objects to meausre:%s\n" % obj['object_to_measure'] return _str with open(input_pipeline) as fin: lines = fin.readlines() k, v = lines[4].strip().split(':') module_count = int(v) new_count = module_count + 1 lines[4] = k + ":%d\n" % new_count with open("output.cppipe", "w") as f: f.writelines(lines) f.write(writemg()) f.close() </configfile> </configfiles> <inputs> <expand macro="input_pipeline_param" /> <repeat name="rpt_image" title="new image" min="1"> <param name="image_to_measure" type="text" label="Enter the name of a greyscale image to measure"> <expand macro="text_validator" /> </param> <param name="subsampling_factor_granularity" type="float" value="0.25" label="Subsampling factor for granularity measurements" help="A factor lower than 1 if the textures of interest are larger than a few pixels. A factor greater than 1 might increase the accuracy but also require more processing time. Images are typically of higher resolution than is required for granularity measurements, so the default value is 0.25."/> <param name="subsampling_factor_background_reduction" type="float" value="0.25" min="0.125" max="0.25" label="Subsampling factor for background reduction" help="A small factor should be used if the structures of interest are large." /> <param name="radius" type="integer" value="10" label="Radius of structuring element" help="This radius should correspond to the radius of the textures of interest after subsampling." /> <param name="range_granular_spectrum" type="integer" value="16" label="Range of the granular spectrum" help="You may need a trial run to see which granular spectrum range yields informative measurements. Start by using a wide spectrum and narrow it down to the informative range to save time." /> <repeat name="rpt_object" title="new object"> <param name="object_to_measure" type="text" label="Enter the name of the object whose granularity will be measured" help="You can select objects from prior modules that identify objects, such as IdentifyPrimaryObjects. If you only want to measure the granularity for the image overall, you can remove all objects."> <expand macro="text_validator" /> </param> </repeat> </repeat> </inputs> <outputs> <expand macro="output_pipeline_param" /> </outputs> <tests> <test> <expand macro="test_input_pipeline_param" /> <repeat name="rpt_image"> <param name="image_to_measure" value="DNA" /> <param name="subsampling_factor_granularity" value="0.25" /> <param name="subsampling_factor_background_reduction" value="0.25" /> <param name="radius" value="10" /> <param name="range_granular_spectrum" value="16" /> </repeat> <expand macro="test_out_file" file="measure_granularity.cppipe" /> </test> </tests> <help> <![CDATA[ .. class:: infomark **What it does** Image granularity is a texture measurement that tries to fit a series of structure elements of increasing size into the texture of the image and outputs a spectrum of measures based on how well they fit. Granularity is measured as described by Ilya Ravkin (in the paper *Bit representation techniques and image processing*). The size of the starting structure element as well as the range of the spectrum is given as input. @COMMON_HELP@ ]]> </help> <expand macro="citations" /> </tool>