I've spent my day trying to understand Gideons code to create histograms of new levels. I've finally managed to find out how to create trainingdata, how to train the histogram and how to output the histogram as a .hist file. I can now experiments with different environments to see if the omnicam is effictive there.
I haven't done any real testing but the first result. The first picture shows the original image of the office. The 2nd picture shows how when it is normalized, it loses a lot of detail which is making this environment hard to classify. The 3rd picture(don't look at the yellow scanline) shows the initial result, it classifies the wall as free space... I have then tried to put the probability threshold higher, at 0.38 i've gotten the 4th picture. It now classifies the floor but it loses a big part of the floor, which is not what we found. 0.37 would give the 3rd picture again. It might be that the trainingsdata isn't good, i haven't spend much time researching if its good. It can also be that the environment is somewhat the same color, which seems like a good answer. I will need to run some more tests to figure it out.
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