comparison yolo.xml @ 21:916208f6745d draft

Uploaded

20:c834e636fd66

cp -R /home/greg/_conda/envs/__darknet@1.0/bin/* .
#for $i in $input:
    #set input_filename = $i.file_name
    #set full_name = $i.name
    #set head = $full_name.split('.')[0]
    #set output_filename_png = '%s_predictions.png' % $head
    #set output_filename_shape_confidence = '%s_shape_detection_confidence.tabular' % $head
    && ln -s $input_filename input_dir/$full_name
    && darknet detect cfg/yolo.cfg yolo.weights 'input_dir/$full_name' -thresh $thresh > output_shape_confidence_dir/$output_filename_shape_confidence
    && mv ./predictions.png output_png_dir/$output_filename_png
#end for
    ]]></command>
    <inputs>
        <param name="input" format="jpg" type="data_collection" collection_type="list" label="Collection of image files" />
        <param name="thresh" type="float" value="0.25" label="Object detection threshold" />

            <option value="yes" selected="true">Yes</option>
            <option value="no">No</option>
        </param>
    </inputs>
    <outputs>
        <collection name="output_shape" type="list" label="${tool.name} (shapes) on ${on_string}">
            <discover_datasets pattern="__name__" directory="output_png_dir" format="png" />
        </collection>
        <collection name="output_shape_confidence" type="list" label="${tool.name} (shape detection confidence) on ${on_string}">
            <discover_datasets pattern="__name__" directory="output_shape_confidence_dir" format="tabular" />
            <filter>output_shape_confidence == 'yes'</filter>
        </collection>
    </outputs>
    <tests>
        <test>
        </test>

21:916208f6745d

cp -R /home/greg/_conda/envs/__darknet@1.0/bin/* .
#for $i in $input:
    #set input_filename = $i.file_name
    #set full_name = $i.name
    #set head = $full_name.split('.')[0]
    #set output_filename_image = '%s_predictions.png' % $head
    #set output_filename_shape_confidence = '%s_shape_detection_confidence.tabular' % $head
    && ln -s $input_filename input_dir/$full_name
    && darknet detect cfg/yolo.cfg yolo.weights 'input_dir/$full_name' -thresh $thresh -f jpg > output_shape_confidence_dir/$output_filename_shape_confidence
    && mv ./predictions.jpg output_png_dir/$output_filename_image
#end for
    ]]></command>
    <inputs>
        <param name="input" format="jpg" type="data_collection" collection_type="list" label="Collection of image files" />
        <param name="thresh" type="float" value="0.25" label="Object detection threshold" />

            <option value="yes" selected="true">Yes</option>
            <option value="no">No</option>
        </param>
    </inputs>
    <outputs>
        <collection name="output_shape_confidence" type="list" label="${tool.name} (shape detection confidence) on ${on_string}">
            <discover_datasets pattern="__name__" directory="output_shape_confidence_dir" format="tabular" />
            <filter>output_shape_confidence == 'yes'</filter>
        </collection>
        <collection name="output_shape" type="list" label="${tool.name} (shapes) on ${on_string}">
            <discover_datasets pattern="__name__" directory="output_png_dir" format="png" />
        </collection>
    </outputs>
    <tests>
        <test>
        </test>