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1 <tool id="yolo" name="YOLO" version="1.0">
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2 <description>real-time object detection</description>
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3 <requirements>
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4 <requirement type="package" version="1.0">darknet</requirement>
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5 </requirements>
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6 <command detect_errors="exit_code"><![CDATA[
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19
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7 mkdir output_shape_confidence_dir &&
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9
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8 mkdir input_dir &&
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19
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9 mkdir output_png_dir &&
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18
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10 cp -R /home/greg/_conda/envs/__darknet@1.0/bin/* .
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13
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11 #for $i in $input:
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14
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12 #set input_filename = $i.file_name
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15
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13 #set full_name = $i.name
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14 #set head = $full_name.split('.')[0]
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21
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15 #set output_filename_image = '%s_predictions.png' % $head
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19
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16 #set output_filename_shape_confidence = '%s_shape_detection_confidence.tabular' % $head
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18
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17 && ln -s $input_filename input_dir/$full_name
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21
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18 && darknet detect cfg/yolo.cfg yolo.weights 'input_dir/$full_name' -thresh $thresh -f jpg > output_shape_confidence_dir/$output_filename_shape_confidence
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19 && mv ./predictions.jpg output_png_dir/$output_filename_image
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9
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20 #end for
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0
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21 ]]></command>
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22 <inputs>
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8
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23 <param name="input" format="jpg" type="data_collection" collection_type="list" label="Collection of image files" />
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6
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24 <param name="thresh" type="float" value="0.25" label="Object detection threshold" />
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19
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25 <param name="output_shape_confidence" type="select" display="radio" label="Output shape detection confidence levels?">
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26 <option value="yes" selected="true">Yes</option>
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27 <option value="no">No</option>
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28 </param>
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0
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29 </inputs>
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30 <outputs>
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19
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31 <collection name="output_shape_confidence" type="list" label="${tool.name} (shape detection confidence) on ${on_string}">
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32 <discover_datasets pattern="__name__" directory="output_shape_confidence_dir" format="tabular" />
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33 <filter>output_shape_confidence == 'yes'</filter>
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8
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34 </collection>
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21
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35 <collection name="output_shape" type="list" label="${tool.name} (shapes) on ${on_string}">
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36 <discover_datasets pattern="__name__" directory="output_png_dir" format="png" />
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37 </collection>
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0
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38 </outputs>
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39 <tests>
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40 <test>
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41 </test>
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42 </tests>
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43 <help>
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44 **What it does**
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45
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46 You only look once (YOLO) is a state-of-the-art, real-time object detection system.
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47
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48 -----
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49
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50 **Options**
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51
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52 </help>
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53 <citations>
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54 <citation type="bibtex">
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55 @misc{darknet13,
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56 author = {Joseph Redmon},
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57 title = {Darknet: Open Source Neural Networks in C},
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58 url = {http://pjreddie.com/darknet/},
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59 year = {2013--2016}}
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60 </citation>
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61 <citation type="bibtex">
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62 @article{redmon2016yolo9000,
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63 title={YOLO9000: Better, Faster, Stronger},
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64 author={Redmon, Joseph and Farhadi, Ali},
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65 journal={arXiv preprint arXiv:1612.08242},
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66 year={2016}}
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67 </citation>
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68 </citations>
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69 </tool>
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