[apples000]

We ended up using a convex hull then looked at rectangles in 2013(and had some success, but we never used it), but in 2012, we had a harder time picking out the targets without spending time calibrating the camera. So, we turned on our green light ring, captured an image, turned off the green ring, captured an image, and compared. This gave us a great view of the targets because we only allowed items that turned green to show up in the final image. Then, we told the camera to ignore anything that was above or below the actual targets, or too far in the wrong direction. The point was we would only search for targets where we thought they could actually be. Finally we acquired HSL ranges for the targets based on the average of the regions where the camera thought it saw a target, and used the same convex hull/rectangle scoring/pid loop to do the final alignment. This worked well, but it took an additional two seconds for the two images to be captured and our laptop to do all the math.