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Re: Vision Target Detections makes cRIO code slow
It is possible to use the cRio for image processing successfully, but it takes a bit of adjusting.
1) Minimize the resolution to only what is necessary for good target detection.
320X240 seems to work well.
2) Use compression to reduce image size, but not too much as to over reduce image quality.
30%-50% has worked well
3) Choose the lowest possible frame rate that allows good robot response.
15 FPS for the camera and limiting the vision processing loop to once every 100ms has worked well and is a good starting point.
As mentioned earlier, the cRio isn't the most powerful vision processor, but it is sufficient if you are willing to make a few compromises.
If compromise isn't an option, vision processing on your DS is your quickest solution because you already have the hardware and code to set it up.
The alternatives to DS processing is an on-board co-processor like the BeagleBone, O-Droid, PCDuino and RPi.
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CalGames 2009 Autonomous Champion Award winner
Sacramento 2010 Creativity in Design winner, Sacramento 2010 Quarter finalist
2011 Sacramento Finalist, 2011 Madtown Engineering Inspiration Award.
2012 Sacramento Semi-Finals, 2012 Sacramento Innovation in Control Award, 2012 SVR Judges Award.
2012 CalGames Autonomous Challenge Award winner ($$$).
2014 2X Rockwell Automation: Innovation in Control Award (CVR and SAC). Curie Division Gracious Professionalism Award.
2014 Capital City Classic Winner AND Runner Up. Madtown Throwdown: Runner up.
2015 Innovation in Control Award, Sacramento.
2016 Chezy Champs Finalist, 2016 MTTD Finalist
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