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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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25
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11 #set input = $input_type_cond.input
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23
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12 #if str($input_type_cond.input_type) == 'image':
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13 #set input_filename = $input.file_name
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14 #set full_name = $input.name
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18
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15 && ln -s $input_filename input_dir/$full_name
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24
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16 && darknet detect cfg/yolo.cfg yolo.weights 'input_dir/$full_name' -thresh $thresh > $output_shape_confidence
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17 && mv ./predictions.png $output_shape
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23
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18 #else:
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19 #for $i in $input:
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20 #set input_filename = $i.file_name
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21 #set full_name = $i.name
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22 #set head = $full_name.split('.')[0]
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23 #set output_filename_png = '%s_predictions.png' % $head
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24 #set output_filename_shape_confidence = '%s_shape_detection_confidence.tabular' % $head
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25 && ln -s $input_filename input_dir/$full_name
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26 && darknet detect cfg/yolo.cfg yolo.weights 'input_dir/$full_name' -thresh $thresh > output_shape_confidence_dir/$output_filename_shape_confidence
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27 && mv ./predictions.png output_png_dir/$output_filename_png
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28 #end for
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29 #end if
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0
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30 ]]></command>
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31 <inputs>
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23
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32 <conditional name="input_type_cond">
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33 <param name="input_type" type="select" label="Select">
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34 <option value="image" selected="true">a single image</option>
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35 <option value="images">a collection of images</option>
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36 </param>
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37 <when value="image">
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38 <param name="input" format="jpg" type="data" label="Image"/>
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39 </when>
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40 <when value="images">
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41 <param name="input" format="jpg" type="data_collection" collection_type="list" label="Collection of image files"/>
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42 </when>
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43 </conditional>
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44 <param name="thresh" type="float" value="0.25" label="Object detection threshold"/>
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24
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45 <param name="output_shape_confidence_log" type="select" display="radio" label="Output shape detection confidence levels?">
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19
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46 <option value="yes" selected="true">Yes</option>
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47 <option value="no">No</option>
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48 </param>
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0
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49 </inputs>
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50 <outputs>
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23
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51 <data name="output_shape_confidence" format="tabular" label="${tool.name} (shape detection confidence) on ${on_string}">
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24
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52 <filter>input_type_cond['input_type'] == 'image' and output_shape_confidence_log == 'yes'</filter>
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23
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53 </data>
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54 <data name="output_shape" format="png" label="${tool.name} (shapes) on ${on_string}">
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55 <filter>input_type_cond['input_type'] == 'image'</filter>
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56 </data>
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57 <collection name="output_shape_confidences" type="list" label="${tool.name} (shape detection confidence) on ${on_string}">
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19
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58 <discover_datasets pattern="__name__" directory="output_shape_confidence_dir" format="tabular" />
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24
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59 <filter>input_type_cond['input_type'] == 'images' and output_shape_confidence_log == 'yes'</filter>
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8
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60 </collection>
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23
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61 <collection name="output_shapes" type="list" label="${tool.name} (shapes) on ${on_string}">
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21
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62 <discover_datasets pattern="__name__" directory="output_png_dir" format="png" />
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23
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63 <filter>input_type_cond['input_type'] == 'images'</filter>
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21
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64 </collection>
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0
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65 </outputs>
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66 <tests>
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67 <test>
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68 </test>
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69 </tests>
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70 <help>
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71 **What it does**
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72
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73 You only look once (YOLO) is a state-of-the-art, real-time object detection system.
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74
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75 -----
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76
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77 **Options**
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78
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79 </help>
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80 <citations>
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81 <citation type="bibtex">
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82 @misc{darknet13,
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83 author = {Joseph Redmon},
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84 title = {Darknet: Open Source Neural Networks in C},
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85 url = {http://pjreddie.com/darknet/},
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86 year = {2013--2016}}
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87 </citation>
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88 <citation type="bibtex">
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89 @article{redmon2016yolo9000,
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90 title={YOLO9000: Better, Faster, Stronger},
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91 author={Redmon, Joseph and Farhadi, Ali},
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92 journal={arXiv preprint arXiv:1612.08242},
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93 year={2016}}
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94 </citation>
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95 </citations>
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96 </tool>
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