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Indeed. That's pretty similar to the ideas I had -- "Eg. grab a random image, process the image with a filter to reduce it to a just points of a particular color or hue; or maybe use a Conway's Life type process to manipulate the pixels until groups of similar hues the reduce to single points; or a dozen other ideas; and then use those points as my dataset." -- triggered by roboticus' post. However, it turns out to be rather more difficult than I imagined. I thought of two ways to tackle this approach:
To generate the weight maps, I pick a few random points and pick a random weight for those points. Then I grade those high points out to the edges of the area in the x-axis. Then I grade those values to the strips of values created by the other points, or the edges in the y-axis. Drawn in grey scale, this produces weight maps like these: img img img, which I'm rather pleased with. Once weight-maps like these have been vectorised and then used to pick a 1000 weight-random pixels, the results look like these:img img img. The results are everything I could have hoped for; though the currently implementation leaves a lot to be desired - especially the slowness of the vectorisation when higher weight range is used. I'll probably have to move that process and the grading process into C to make this usable. If you can see improvements to either the grading process -- which currently occasionally produces really bizarre effects for reasons I haven't tracked down -- or ways of speeding up the vectorisation without dropping into C, I'd be very interested to hear them. The current code: <Reveal this spoiler or all in this thread>
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In reply to Re^4: Randomly biased, random numbers. (A working solution)
by BrowserUk
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