Voronoi from Picture
Days ago I’ve seen this challenge on Code Golf. It ask for a Voronoi diagram that resemble the original picture as close as possible.
It hooked me on. And other’s results are great! Like a fine Divisionism/Pointillism movement. So I give it a try myself also.
import random from PIL import Image from PIL.ImageFilter import FIND_EDGES, GaussianBlur, SHARPEN def coordinate(width, height): yield from ((x, y) for y in range(height) for x in range(width)) def normalized(choices): lower = min(weight for _, weight in choices) upper = max(weight for _, weight in choices) norm = lambda weight: (weight-lower) + (upper-lower)//4 return [((x, y), norm(weight)) for (x, y), weight in choices] def random_by_weight(choices): rand_val = random.uniform(0, sum(weight for _, weight in choices)) index = 0 count = 0 while count < rand_val: count += choices[index] index += 1 return choices.pop(index-1) def init_centroids(image, cells): width, height = image.size edge_img = image.filter(FIND_EDGES).filter(GaussianBlur).filter(SHARPEN) weight = lambda x, y: 256 - max(edge_img.getpixel((x, y))) choices = [((x, y), weight(x, y)) for x, y in coordinate(width, height)] return [random_by_weight(normalized(choices)) for _ in range(cells)] def init_rgbs(image, centroids): rgb_im = image.convert('RGB') return [rgb_im.getpixel((x, y)) for x, y in centroids] def simulate_voronoi(image_path, cells=25, scale=None): image = Image.open(image_path) if scale is not None: image.thumbnail((scale, scale), Image.ANTIALIAS) centroids = init_centroids(image, cells) rgbs = init_rgbs(image, centroids) return image.size, list(zip(centroids, rgbs))
Here are my results (at 500 Voronoi sites).
Can’t say that I can compete with these. Especially consider that I have no background knowledge in image processing at all. Still, it’s a fun experience that let me try PIL for the first time.