Detecting trophallaxis

To prepare for detecting trophallaxis, you need to index the bCode detection results, which we obtained in the previous tutorial. If your operating system uses a single character to signify the end of a line of text (e.g. Linux), you can do this by running

java -jar indexer.jar eol.byte.count=1 file=detected_bcodes.txt

This will create an index file named detected_bCodes.idx. Indexing makes the most sense if bCode detection results of multiple images are stored in the same file, but it is also necessary for files containing the detection result of a single image.

We can now leverage the bCode detection results to predict which bees in the example image from the previous tutorial are engaged in trophallaxis

java -jar trophallaxis_detector.jar distance.label.head=60 geometry.min.distance=61 geometry.max.distance=123 geometry.max.angle.sum=90 leveling.threshold=140 mean.head.pixel.intensity=25 vision.min.distance=85 vision.max.distance=117 vision.max.angle.sum=90 thresholding.method=Bernsen thresholding.radius=5 contrast.threshold=30 max.path.thickness=12 max.thick.segment.length=20 image.filename=2013-07-18-13-57-25-600.jpg filtered.data.file=detected_bcodes.txt trophallaxis.file=trophallaxis_contacts.txt

This will result in a file named trophallaxis_contacts.txt that describes all detected instances of trophallaxis (how the parameter values for this command line were obtained is described in the Supplementary Methods of this paper).