Detecting trophallaxis

To prepare for detecting trophallaxis in the example image, you need to index the bCode detection results that we obtained in the previous tutorial. You can do this by running

java -jar indexer.jar file=2013-07-18-13-57-25-600.txt

This will create an index file named 2013-07-18-13-57-25-600.idx.

We can now leverage the bCode detection results to predict which bees 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=2013-07-18-13-57-25-600.txt \
trophallaxis.file=trophallaxis_contacts.txt

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