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0
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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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9
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7 mkdir input_dir &&
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8 mkdir output_dir &&
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8
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9 cp -R /home/greg/_conda/envs/__darknet@1.0/bin/* . &&
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13
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10 #for $i in $input:
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14
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11 #set input_filename = $i.file_name
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15
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12 #set full_name = $i.name
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13 #set head = $full_name.split('.')[0]
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14 #set output_filename = "%s_predictions.png" % $head
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15 ln -s $input_filename input_dir/$full_name &&
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16 darknet detect cfg/yolo.cfg yolo.weights 'input_dir/$full_name' -thresh $thresh 2> /dev/null &&
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16
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17 mv ./predictions.png output_dir/$output_filename &&
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9
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18 #end for
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0
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19 ]]></command>
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20 <inputs>
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8
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21 <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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22 <param name="thresh" type="float" value="0.25" label="Object detection threshold" />
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0
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23 </inputs>
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24 <outputs>
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8
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25 <collection name="output" type="list">
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9
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26 <discover_datasets pattern="__name__" directory="output_dir" format="png" />
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8
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27 </collection>
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0
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28 </outputs>
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29 <tests>
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30 <test>
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31 </test>
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32 </tests>
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33 <help>
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34 **What it does**
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35
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36 You only look once (YOLO) is a state-of-the-art, real-time object detection system.
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37
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38 -----
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39
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40 **Options**
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41
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42 </help>
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43 <citations>
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44 <citation type="bibtex">
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45 @misc{darknet13,
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46 author = {Joseph Redmon},
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47 title = {Darknet: Open Source Neural Networks in C},
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48 url = {http://pjreddie.com/darknet/},
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49 year = {2013--2016}}
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50 </citation>
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51 <citation type="bibtex">
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52 @article{redmon2016yolo9000,
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53 title={YOLO9000: Better, Faster, Stronger},
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54 author={Redmon, Joseph and Farhadi, Ali},
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55 journal={arXiv preprint arXiv:1612.08242},
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56 year={2016}}
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57 </citation>
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58 </citations>
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59 </tool>
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