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by 7373737373
2402 days ago
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For a little project of mine [0], I want to generate a set of uniformly distributed RGB values that then average to another RGB value which is known in advance. Would this be possible using the same approach? Edit: Yes, it seems to, instead of generating and summing over RGB, it would be over all the R, G and B values separately. Edit2: Hmmm, if the number of RGB values is large, the combinatorics involved seem to make it computationally infeasible... [0] https://qewasd.com/ |
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I haven't looked too much into the general case. You could consider a tuple (R1, R2, ..., Rn) and compute the probability distributions one at a time for R1, R2, etc. I don't want to get sucked into the exact details of how to do that, but I think it would involve some head-wrecking combinatorics in a loop. Then you could do the same for G and B.
Like you implied, this would be slow if you did it 1000s of times. That's why I pre-computed the R probability distribution in my code, which unfortunately would not be applicable to the general case because there would be too many possible distributions (exponential) to pre-compute. To generate 1 set of RGBs, however, I think it would be fine.
Alternatively, you could try a non-combinatorics interpretation. The folks on Reddit (thread linked in the article) suggested a geometric interpretation of the problem, which might work better.
There's also the approach I suggested at the end of my article: compute the number of possible tuples that average to A (one-off combinatorics), generate an index k between 1 and this number, then pass k to a magic function f that spits out the kth tuple. I'm just not sure what f would look like, hehe. It could be a recursive function. Not sure how to write f without having to do the same probability / combinatorics as before. Or looping through all possible tuples (yikes).