Detecting bCodes

Lets begin by downloading an example image that shows a lot of bees with a bCode attached to their thorax inside an observation hive. To detect bCodes in this image at 8 equally spaced intensity thresholds between 40 and 110, and to only report a detected bCode if at least 85% of its template (excluding the border) are preserved, run

java -jar bcode_detector.jar min.intensity.threshold=40 max.intensity.threshold=110 intensity.step.size=10<br/> min.template.conservation=0.85 conserve.margin=0 image.filename=2013-07-18-13-57-25-600.jpg<br/> detected.bCodes.filename=detected_bcodes.txt<br/>

This will result in a file named detected_bcodes.txt that contains the detection results. You will notice that this file lists fewer bCodes than can be seen in the image. To improve the detection rate, we highly recommend to sharpen the image before performing bCode detection (e.g. with unsharp masking). If the bCodes are very small, you might also want to magnify the image.

Before examining bCode detection results or processing them with another program, you should probably convert them to a more convenient format (not required for this tutorial):

java -jar converter.jar raw.bCode.file=detected_bcodes.txt human.readable.file=converted_bcode_detections.txt

Converting detection results will produce a file named converted_bcode_detections.txt in the format described here.