Working on my #bot to explore escape-time fractals, rendered with distance estimator colouring using my #et project. It's a #bash script that calls out to #ghc #haskell for calculator functionality, plus image fitness function in custom #C code (using #openmp for #parallel processing).
Flatness of #directionality #histogram seems to be a good #metric to add into the #fitness function for exploring #fractals algorithmically, because stretched/skewed images will have strong directionality peaks, while more #isotropic regions will be flatter.
I implemented it using 5x5 #Sobel filters as suggested on the #ImageJ website. Nothing fancy (like Earth Mover's Distance, which I haven't figured out for circular arrays yet) for the histogram comparison, just Euclidean vector distance.
The bot only renders high resolution versions of the most interesting (according to its algorithmic fitness functions) images.
I chose the 4 most interesting (according to my own personal preference) of 8 of those high resolution images.
It takes about a minute (wall-clock time, powerful CPU) to decide whether to render high resolution, and about 3 minutes to render (all of this depends on fractal iteration formula, which is randomly generated, and location specifics).