<tool id="ip_segmetrics" name="SegMetrics" version="1.4.0-1" profile="20.05">
   <description>image segmentation and object detection performance measures</description>
   <requirements> 
        <requirement type="package" version="1.4">segmetrics</requirement>
        <requirement type="package" version="0.23.4">pandas</requirement>
   </requirements>
   <command detect_errors="aggressive">
   <![CDATA[
   python '$__tool_directory__/run-segmetrics.py'
   '$input_seg'
   '$input_gt'
   ./results.tsv
   $unzip
   $is_seg_unique
   $is_gt_unique
   #for $m in $measures
       #set $kwargs = ''
       #set $suffix = ''
       #set $is_distance = False
       #if str($m.measure_type.measure_type_selector) == 'ISBIScore':
           #set $kwargs = 'min_ref_size=' + str($m.measure_type.min_ref_size) + ', '
       #elif str($m.measure_type.measure_type_selector) == 'Hausdorff':
           #set $kwargs = 'mode="' + str($m.measure_type.mode) + '", quantile=' + str($m.measure_type.quantile) + ', '
           #set $is_distance = True
       #elif str($m.measure_type.measure_type_selector) == 'NSD':
           #set $is_distance = True
       #end if
       #if $is_distance:
           #if $m.measure_type.object_based:
               #set $suffix = '.object_based()'
           #end if
       #end if
       #if str($m.measure_type.aggregation) != '':
           #set $kwargs = 'aggregation="' + str($m.measure_type.aggregation) + '", ' + $kwargs
       #end if
       'sm.${m.measure_type.measure_type_selector}(${kwargs})${suffix}'
   #end for
   ]]>
   </command>
   <inputs>

        <param name="input_seg" type="data" format="tiff,png,zip" label="Segmented images" />
        <param name="input_gt"  type="data" format="tiff,png,zip" label="Ground truth images" />

        <param name="unzip" type="boolean" checked="false" truevalue="-unzip" falsevalue="" label="Unzip" />

        <param name="is_seg_unique" type="boolean" checked="false" truevalue="-seg_unique" falsevalue="" label="Segmentation is uniquely labeled" />
        <param name="is_gt_unique"  type="boolean" checked="false" truevalue="-gt_unique"  falsevalue="" label="Ground truth is uniquely labeled" />

        <repeat name="measures" title="Performance measure(s)" min="1">
            <conditional name="measure_type">
                <param name="measure_type_selector" type="select" label="Type">
                    <option value="Dice">Region-based / Dice</option>
                    <option value="ISBIScore">Region-based / SEG</option>
                    <option value="JaccardCoefficient">Region-based / Jaccard Coefficient</option>
                    <option value="JaccardIndex">Region-based / Jaccard Index</option>
                    <option value="RandIndex">Region-based / Rand Index</option>
                    <option value="AdjustedRandIndex">Region-based / Adjusted Rand Index</option>
                    <option value="Hausdorff">Contour-based / Hausdorff Distance</option>
                    <option value="NSD">Contour-based / Normalized Sum of Distances</option>
                    <option value="FalseSplit">Detection-based / Count Falsely Split Objects</option>
                    <option value="FalseMerge">Detection-based / Count Falsely Merged Objects</option>
                    <option value="FalsePositive">Detection-based / Count Spurious Objects</option>
                    <option value="FalseNegative">Detection-based / Count Missing Objects</option>
                </param>
                <when value="Dice">
                    <param name="aggregation" type="hidden" value="" />
                </when>
                <when value="ISBIScore">
                    <param name="aggregation" type="hidden" value="" />
                    <param name="min_ref_size" type="integer" value="1" label="Minimum size of ground truth objects (in pixels)" help="Ground truth objects smaller than this value (in pixels) are skipped. It is reasonable to set this value to 2 so that objects of a single pixel in size are skipped, since such objects obviously correspond to misannotations which distort the performance evaluation. However, for compatibility to the official implementation, the value is set to 1 by default so all ground truth objects are included."/>
                </when>
                <when value="JaccardCoefficient">
                    <param name="aggregation" type="hidden" value="" />
                </when>
                <when value="JaccardIndex">
                    <param name="aggregation" type="hidden" value="" />
                </when>
                <when value="RandIndex">
                    <param name="aggregation" type="hidden" value="" />
                </when>
                <when value="AdjustedRandIndex">
                    <param name="aggregation" type="hidden" value="" />
                </when>
                <when value="Hausdorff">
                    <param name="aggregation" type="hidden" value="" />
                    <param name="mode" type="select" label="Direction">
                        <option value="a2e">Actual (segmentation result) to expected (ground truth)</option>
                        <option value="e2a">Expected (ground truth) to actual (segmentation result)</option>
                        <option value="sym" selected="true">Symmetric (maximum of the two)</option>
                    </param>
                    <param name="quantile" type="float" min="0" max="1" value="1" label="Quantile" help="Must be between 0 and 1. If set to 1, then the implementaiton corresponds to the Hausdorff distance described by Bamford (2003). Any other value corresponds to the quantile method described by Rucklidge (1997)." />
                    <param name="object_based" type="boolean" label="Object-based" help="Object correspondances between the segmented and the ground truth objects are established on a many-to-many basis, so that the resulting distances are minimal." />
                </when>
                <when value="NSD">
                    <param name="aggregation" type="hidden" value="" />
                    <param name="object_based" type="boolean" label="Object-based" help="Object correspondances between the segmented and the ground truth objects are established on a many-to-many basis, so that the resulting distances are minimal." />
                </when>
                <when value="FalseSplit">
                    <param name="aggregation" type="select" label="Aggregation">
                        <option value="mean" selected="true">Mean per Image</option>
                        <option value="sum">Sum over all images</option>
                        <option value="obj-mean">Proportion w.r.t. ground truth objects</option>
                    </param>
                </when>
                <when value="FalseMerge">
                    <param name="aggregation" type="select" label="Aggregation">
                        <option value="mean" selected="true">Mean per Image</option>
                        <option value="sum">Sum over all images</option>
                        <option value="obj-mean">Proportion w.r.t. ground truth objects</option>
                    </param>
                </when>
                <when value="FalsePositive">
                    <param name="aggregation" type="select" label="Aggregation">
                        <option value="mean" selected="true">Mean per Image</option>
                        <option value="sum">Sum over all images</option>
                        <option value="obj-mean">Proportion w.r.t. ground truth objects</option>
                    </param>
                </when>
                <when value="FalseNegative">
                    <param name="aggregation" type="select" label="Aggregation">
                        <option value="mean" selected="true">Mean per Image</option>
                        <option value="sum">Sum over all images</option>
                        <option value="obj-mean">Proportion w.r.t. ground truth objects</option>
                    </param>
                </when>
            </conditional>
        </repeat>
    </inputs>
    <outputs>
       <data format="tsv" name="results" from_work_dir="results.tsv" />
    </outputs>
    <tests>
        <test>
            <param name="input_seg" value="input1.png"/>
            <param name="input_gt"  value="input2.png"/>
            <output name="results" value="results1.tsv" ftype="tsv" compare="diff"/>
            <param name="is_seg_unique" value="true"/>
            <param name="is_gt_unique"  value="true"/>
            <repeat name="measures">
                <conditional name="measure_type">
                    <param name="measure_type_selector" value="Dice" />
                </conditional>
            </repeat>
            <repeat name="measures">
                <conditional name="measure_type">
                    <param name="measure_type_selector" value="ISBIScore" />
                </conditional>
            </repeat>
            <repeat name="measures">
                <conditional name="measure_type">
                    <param name="measure_type_selector" value="JaccardCoefficient" />
                </conditional>
            </repeat>
            <repeat name="measures">
                <conditional name="measure_type">
                    <param name="measure_type_selector" value="JaccardIndex" />
                </conditional>
            </repeat>
            <repeat name="measures">
                <conditional name="measure_type">
                    <param name="measure_type_selector" value="RandIndex" />
                </conditional>
            </repeat>
            <repeat name="measures">
                <conditional name="measure_type">
                    <param name="measure_type_selector" value="AdjustedRandIndex" />
                </conditional>
            </repeat>
            <repeat name="measures">
                <conditional name="measure_type">
                    <param name="measure_type_selector" value="Hausdorff" />
                </conditional>
            </repeat>
            <repeat name="measures">
                <conditional name="measure_type">
                    <param name="measure_type_selector" value="Hausdorff" />
                    <param name="quantile" value="0.9" />
                </conditional>
            </repeat>
            <repeat name="measures">
                <conditional name="measure_type">
                    <param name="measure_type_selector" value="Hausdorff" />
                    <param name="object_based" value="true" />
                </conditional>
            </repeat>
            <repeat name="measures">
                <conditional name="measure_type">
                    <param name="measure_type_selector" value="NSD" />
                </conditional>
            </repeat>
            <repeat name="measures">
                <conditional name="measure_type">
                    <param name="measure_type_selector" value="NSD" />
                    <param name="object_based" value="true" />
                </conditional>
            </repeat>
            <repeat name="measures">
                <conditional name="measure_type">
                    <param name="measure_type_selector" value="FalseSplit" />
                </conditional>
            </repeat>
            <repeat name="measures">
                <conditional name="measure_type">
                    <param name="measure_type_selector" value="FalseMerge" />
                </conditional>
            </repeat>
            <repeat name="measures">
                <conditional name="measure_type">
                    <param name="measure_type_selector" value="FalsePositive" />
                </conditional>
            </repeat>
            <repeat name="measures">
                <conditional name="measure_type">
                    <param name="measure_type_selector" value="FalseNegative" />
                </conditional>
            </repeat>
        </test>
        <test>
            <param name="input_seg" value="input1.zip"/>
            <param name="input_gt"  value="input2.zip"/>
            <output name="results" value="results2.tsv" ftype="tsv" compare="diff"/>
            <param name="is_seg_unique" value="true"/>
            <param name="is_gt_unique"  value="true"/>
            <param name="unzip" value="true"/>
            <repeat name="measures">
                <conditional name="measure_type">
                    <param name="measure_type_selector" value="Dice" />
                </conditional>
            </repeat>
            <repeat name="measures">
                <conditional name="measure_type">
                    <param name="measure_type_selector" value="ISBIScore" />
                </conditional>
            </repeat>
            <repeat name="measures">
                <conditional name="measure_type">
                    <param name="measure_type_selector" value="JaccardCoefficient" />
                </conditional>
            </repeat>
            <repeat name="measures">
                <conditional name="measure_type">
                    <param name="measure_type_selector" value="JaccardIndex" />
                </conditional>
            </repeat>
            <repeat name="measures">
                <conditional name="measure_type">
                    <param name="measure_type_selector" value="RandIndex" />
                </conditional>
            </repeat>
            <repeat name="measures">
                <conditional name="measure_type">
                    <param name="measure_type_selector" value="AdjustedRandIndex" />
                </conditional>
            </repeat>
            <repeat name="measures">
                <conditional name="measure_type">
                    <param name="measure_type_selector" value="Hausdorff" />
                </conditional>
            </repeat>
            <repeat name="measures">
                <conditional name="measure_type">
                    <param name="measure_type_selector" value="Hausdorff" />
                    <param name="quantile" value="0.9" />
                </conditional>
            </repeat>
            <repeat name="measures">
                <conditional name="measure_type">
                    <param name="measure_type_selector" value="Hausdorff" />
                    <param name="object_based" value="true" />
                </conditional>
            </repeat>
            <repeat name="measures">
                <conditional name="measure_type">
                    <param name="measure_type_selector" value="NSD" />
                </conditional>
            </repeat>
            <repeat name="measures">
                <conditional name="measure_type">
                    <param name="measure_type_selector" value="NSD" />
                    <param name="object_based" value="true" />
                </conditional>
            </repeat>
            <repeat name="measures">
                <conditional name="measure_type">
                    <param name="measure_type_selector" value="FalseSplit" />
                </conditional>
            </repeat>
            <repeat name="measures">
                <conditional name="measure_type">
                    <param name="measure_type_selector" value="FalseMerge" />
                </conditional>
            </repeat>
            <repeat name="measures">
                <conditional name="measure_type">
                    <param name="measure_type_selector" value="FalsePositive" />
                </conditional>
            </repeat>
            <repeat name="measures">
                <conditional name="measure_type">
                    <param name="measure_type_selector" value="FalseNegative" />
                </conditional>
            </repeat>
        </test>
    </tests>
    <help>
        This tool permits the computation of image segmentation and object detection performance measures for 2-D image data.

        You can either use a pair of individual input images (a segmented and a ground truth image), or a pair of ZIP archives which contain the segmented and the correspondiong ground truth images. When using a pair of individual images, remember to turn off the "Unzip" option. When using ZIP archives, turn it on instead and make sure that the directory structure is the same for the segmented and the ground truth images. Correspondences are estbalished based on the file names, which should be *identical* for a pair of corresponding images.

        If all objects within your segmented images are *uniquely* labeled, you can turn on the "Segmentation is uniquely labeled" switch to speed up the computations. Leave it off otherwise, or if you are unsure. The same accounts for the "Ground truth is uniquely labeled" switch and your ground turth image data.
    </help>
    <citations>
        <citation type="doi">10.1093/bioinformatics/btu080</citation>
        <citation type="doi">10.1109/ISBI.2009.5193098</citation>
        <citation type="doi">10.1109/ICIP.2003.1246871</citation>
        <citation type="doi">10.1023/A:1007975324482</citation>
    </citations>
</tool>