paper: Team 341 Vision System Code

[JamesTerm]

Originally Posted by Jared341

Easy, it's because I am not the only person who writes software for our team! Java is what is taught to our AP CS students, and is a lot friendlier to our students (in that it is a lot harder to accidentally shoot yourself in the foot).

Ah ok.

Originally Posted by Jared341

But if this is the part of the code that engenders the most discussion, then I'm a bit disappointed

Don't be disappointed... this discussion has taught/reminded us something that we rarely use in c++, and this discussion indirectly helped my co-worker fix a bug today. I do know how you feel though as there is a *lot* of effort that goes into this! I had most-all my vision code written as well, and unfortunately it is all going straight into the bit bucket, as we could not get the deliverables to make it work in time. I do want to look your code over in more detail and post what I did as well, and hopefully at that time the discussion will have more meat in it as I do want some closure in the work that I have done thus far.

I will reveal one piece now with this video:
http://www.termstech.com/files/RR_LockingDemo2.mp4

When I first saw the original video, it screamed high saturation levels of red and blue on the alliance colors, and this turns out to be true. The advantage is that there is a larger line to track at a higher point as I could use particle detection alone. The goal then was to interpret the line to perspective and use that to determine my location on the field. From the location I had everything I needed as I then go to an array table error correction grid with linear interpolation from one point to the next. (The grid among other tweaks are written in LUA more on that later too).

more to come...

There is one question that I would like to throw out there now though... Does anyone at all work with UYVY color space (a.k.a YPbPr). We work with this natively at NewTek, and it would be nice to see who else does.

[Bryscus]

So after attending the Einstein Weekend debugging session this past weekend and chatting with some of the teams about their various OpenCV implementing vision systems, I just HAD to check out Daisy's code (another suggestion from Brad Miller).

So honestly, I have little experience with Java but figured what the heck since it's so close to C++. After following some of the Getting Start Guides and playing with a couple of projects, I downloaded Daisy's code and set to work running main() and passing the example image paths to the code as arguments. This seems to work well and two windows pop up showing the "Raw" and "Result" images. What baffles me is that I get this as output as well:

"Target not found
Processing took 24.22 milliseconds
(41.29 frames per second)
Waiting for ENTER to continue to next image or exit..."

and for the "Result" image I get a vertical green line more or less in the middle of the picture. I ran the program a couple of times with different images and similar results? Can someone tell this C guy what the heck he's doing wrong? Is there something I'm missing? If you guys (Daisy) were using a different color ring light or something, could you provide some sample images that work? Thanks in advance!

- Bryce

P.S. I'm running this on an OLD POS computer running XP and a Pentium D processor. I'll have to run it at home on something with a little bit of muscle and check performance.

[Jared Russell]

The vertical green line is simply an alignment aid that is burned in to each image - it is not an indication that you have successfully detected the target. If the vision system is picking up the vision targets, you should see blue rectangles and dots indicating the outline and center of the targets, respectively.

Which images are you testing with? The supplied images should work with the code "as is". If you are using your own images, are you using a green LED ring? If you are using a different color LED ring, you will need to alter the color threshold values in the code. Note that regardless of LED ring color, adjusting the camera to have a very short exposure time (so that the images are quite dark) increases the SNR of the retroreflections, and makes tracking both more robust and much quicker.