<tool id="yolo" name="YOLO" version="1.0">
    <description>real-time object detection</description>
    <requirements>
        <requirement type="package" version="1.0">darknet</requirement>
    </requirements>
    <command detect_errors="exit_code"><![CDATA[
mkdir input_dir &&
mkdir output_dir &&
cp -R /home/greg/_conda/envs/__darknet@1.0/bin/* . &&
#for $i in $input:
    #set input_filename = $i.file_name
    #set name = $i.name
    #set output_filename = "%s_predictions.png" % $name
    ln -s $input_filename input_dir/$name &&
    darknet detect cfg/yolo.cfg yolo.weights 'input_dir/$name' -thresh $thresh 2> /dev/null &&
    mv ./predictions.png output_dir/$output_filename
#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" />
    </inputs>
    <outputs>
        <collection name="output" type="list">
            <discover_datasets pattern="__name__" directory="output_dir" format="png" />
        </collection>
    </outputs>
    <tests>
        <test>
        </test>
    </tests>
    <help>
**What it does**

You only look once (YOLO) is a state-of-the-art, real-time object detection system.

-----

**Options**

    </help>
    <citations>
        <citation type="bibtex">
            @misc{darknet13,
            author = {Joseph Redmon},
            title = {Darknet: Open Source Neural Networks in C},
            url = {http://pjreddie.com/darknet/},
            year = {2013--2016}}
        </citation>
        <citation type="bibtex">
            @article{redmon2016yolo9000,
            title={YOLO9000: Better, Faster, Stronger},
            author={Redmon, Joseph and Farhadi, Ali},
            journal={arXiv preprint arXiv:1612.08242},
            year={2016}}
        </citation>
    </citations>
</tool>