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.

ref: https://imagej.net/Directionality#Local_gradient_orientation