Mercurial > repos > imgteam > mahotas_features
changeset 0:7f3d9a1a0447 draft
planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tools/mahotas-features/ commit c3f4b766f03770f094fda6bda0a5882c0ebd4581
| author | imgteam |
|---|---|
| date | Sat, 09 Feb 2019 14:37:07 -0500 |
| parents | |
| children | 0c76d029dbe3 |
| files | mahotas_features.xml test-data/features.tsv test-data/galaxyIcon_noText.png |
| diffstat | 3 files changed, 58 insertions(+), 0 deletions(-) [+] |
line wrap: on
line diff
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/mahotas_features.xml Sat Feb 09 14:37:07 2019 -0500 @@ -0,0 +1,56 @@ +<tool id="ip_mahotas_features" name="Mahotas-features" version="0.7"> + <description>Compute features using mahotas</description> + <requirements> + <requirement type="package" version="1.4.3">mahotas</requirement> + <requirement type="package" version="4.0.0">pillow</requirement> + <requirement type="package" version="1.12">numpy</requirement> + </requirements> + <command detect_errors="aggressive"><![CDATA[mahotas-features.py +#set files = '" "'.join( [ str( $file ) for $file in $inputs ] ) +"${files}" + +--output $output +--convert-to-bw $convertbw +$haralick +$lbp +--clobber +#if $lbpradius and $lbpradius is not None: +--lbp-radius $lbpradius +#end if + +#if $lbppoints and $lbppoints is not None: +--lbp-points $lbppoints +#end if]]></command> + <inputs> + <param name="inputs" type="data" format="tiff,png,jpg,bmp" multiple="True" label="Image files"/> + <param name="convertbw" type="select" label="Convert color images to greyscale"> + <option value="no" selected="True">no</option> + <option value="max">max projection</option> + <option value="yes">yes</option> + </param> + <param checked="true" help="(--haralick)" label="Compute Haralick features" name="haralick" type="boolean" truevalue="--haralick" falsevalue=""/> + <param checked="true" help="(--lbp)" label="Compute LBP (linear binary patterns) features" name="lbp" type="boolean" truevalue="--lbp" falsevalue=""/> + <param help="(--lbp-radius)" label="Radius to use for LBP features" name="lbpradius" optional="true" type="integer" value="8"/> + <param help="(--lbp-points)" label="Nr of points to use for LBP features" name="lbppoints" optional="true" type="integer" value="6"/> + </inputs> + <outputs> + <data format="tsv" hidden="false" name="output"/> + </outputs> + <tests> + <test> + <param name="inputs" value="galaxyIcon_noText.png"/> + <param name="haralick" value="--haralick" /> + <param name="lbp" value="--lbp" /> + <param name="convertbw" value="max" /> + <output name="output" file="features.tsv" ftype="tsv" lines_diff="2"/> + </test> + </tests> + <help> + **What it does** + + Mahotas is a computer vision and image processing library for Python. This tools computes features using mahotas. + </help> + <citations> + <citation type="doi">10.5334/jors.ac</citation> + </citations> +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/features.tsv Sat Feb 09 14:37:07 2019 -0500 @@ -0,0 +1,2 @@ + mean:Angular Second Moment mean:Contrast mean:Correlation mean:Sum of Squares: Variance mean:Inverse Difference Moment mean:Sum Average mean:Sum Variance mean:Sum Entropy mean:Entropy mean:Difference Variance mean:Difference Entropy mean:Information Measure of Correlation 1 mean:Information Measure of Correlation 2 ptp:Angular Second Moment ptp:Contrast ptp:Correlation ptp:Sum of Squares: Variance ptp:Inverse Difference Moment ptp:Sum Average ptp:Sum Variance ptp:Sum Entropy ptp:Entropy ptp:Difference Variance ptp:Difference Entropy ptp:Information Measure of Correlation 1 ptp:Information Measure of Correlation 2 lbp_r8_p6_0 lbp_r8_p6_1 lbp_r8_p6_2 lbp_r8_p6_3 lbp_r8_p6_4 lbp_r8_p6_5 lbp_r8_p6_6 lbp_r8_p6_7 lbp_r8_p6_8 lbp_r8_p6_9 lbp_r8_p6_10 lbp_r8_p6_11 lbp_r8_p6_12 lbp_r8_p6_13 +galaxyIcon_noText.png 0.282375896829 5971.829075 0.716963415834 10558.0340615 0.807748866113 372.165112179 36260.3071712 2.52066662394 2.73555338992 0.00252122763136 1.36304068933 -0.552303235256 0.932070076794 0.0388473044444 3822.8812 0.181903435512 331.650449629 0.122129460944 4.75788461538 3935.03677629 0.318315119007 0.437719347337 0.000763750451389 0.683142134762 0.226647260141 0.0547750277851 386.0 79.0 106.0 5.0 47.0 0.0 0.0 0.0 27.0 0.0 0.0 0.0 0.0 0.0