from PIL import Image import math import numpy import random random.seed(10) a = 2000 br = 200 grad = [] for i in range(int(a/br)+1): row = [] for j in range(int(a/br)+1): row.append([random.randint(-1,1),random.randint(-1,1)]) grad.append(row) def wt(a,b,w): return (b - a) * ((w * (w * 6.0 - 15.0) + 10.0) * w * w * w) + a #return (b - a) * w + a #return (b - a) * (3.0 - w * 2.0) * w * w + a def graddotproduct(x,y,dx,dy): return( ( grad[y][x][0] * dx) + (grad[y][x][1] * dy) ) def perlin(x,y,numpix): x0 = int(math.floor(x)) x1 = x0 + 1 y0 = int(math.floor(y)) y1 = y0 + 1 sx0 = x - x0 sx1 = x - x1 sy0 = y - y0 sy1 = y - y1 d1 = [sx0,sy0] d2 = [sx1,sy0] d3 = [sx0,sy1] d4 = [sx1,sy1] dot1 = graddotproduct(x0,y0,sx0,sy0) dot2 = graddotproduct(x1,y0,sx1,sy0) dot3 = graddotproduct(x0,y1,sx0,sy1) dot4 = graddotproduct(x1,y1,sx1,sy1) ix0 = wt(dot1,dot2,sx0) ix1 = wt(dot3,dot4,sx0) avrg = wt(ix0,ix1,sy0) return avrg img = Image.new( 'RGB', (a,a), "black") pixels = img.load() for x in range(a): for y in range(a): perll = perlin(x/br,y/br,a) pixels[x,y] = (int((perll+1)*128),int((perll+1)*128),int((perll+1)*128)) img.show() img.save("vector.png") I made this by following a Wiki article about Perlin noise that implemented it in C. The creator of the wiki article implemented their own random number generation, but I didn't understand it well enough to replicate it in Python, so I resorted to using Python's 'Random' module. The Wiki article: https://en.wikipedia.org/wiki/Perlin_noise The code usually takes around 10-15 seconds to produce the image. Which is quite slow compared to noise generators used by other applications such as Blender or Unity. Continue reading...
