【原】基于四叉树的计算机艺术

该程序以输入图像为目标。输入图像被分成四个象限。根据输入图像中的颜色为每个象限分配一个平均颜色。误差最大的象限被分成四个子象限以细化图像。这个过程重复N次。

https://github.com/fogleman/Quads

from PIL import Image, ImageDrawfrom collections import Counterimport heapqimport sys
MODE_RECTANGLE = 1MODE_ELLIPSE = 2MODE_ROUNDED_RECTANGLE = 3
MODE = MODE_RECTANGLEITERATIONS = 1024LEAF_SIZE = 4PADDING = 1FILL_COLOR = (0, 0, 0)SAVE_FRAMES = FalseERROR_RATE = 0.5AREA_POWER = 0.25OUTPUT_SCALE = 1
def weighted_average(hist): total = sum(hist) value = sum(i * x for i, x in enumerate(hist)) / total error = sum(x * (value - i) ** 2 for i, x in enumerate(hist)) / total error = error ** 0.5 return value, error
def color_from_histogram(hist): r, re = weighted_average(hist[:256]) g, ge = weighted_average(hist[256:512]) b, be = weighted_average(hist[512:768]) e = re * 0.2989 + ge * 0.5870 + be * 0.1140 return (r, g, b), e
def rounded_rectangle(draw, box, radius, color): l, t, r, b = box d = radius * 2 draw.ellipse((l, t, l + d, t + d), color) draw.ellipse((r - d, t, r, t + d), color) draw.ellipse((l, b - d, l + d, b), color) draw.ellipse((r - d, b - d, r, b), color) d = radius draw.rectangle((l, t + d, r, b - d), color) draw.rectangle((l + d, t, r - d, b), color)
class Quad(object): def __init__(self, model, box, depth): self.model = model self.box = box self.depth = depth hist = self.model.im.crop(self.box).histogram() self.color, self.error = color_from_histogram(hist) self.leaf = self.is_leaf() self.area = self.compute_area() self.children = [] def is_leaf(self): l, t, r, b = self.box return int(r - l <= LEAF_SIZE or b - t <= LEAF_SIZE) def compute_area(self): l, t, r, b = self.box return (r - l) * (b - t) def split(self): l, t, r, b = self.box lr = l + (r - l) / 2 tb = t + (b - t) / 2 depth = self.depth + 1 tl = Quad(self.model, (l, t, lr, tb), depth) tr = Quad(self.model, (lr, t, r, tb), depth) bl = Quad(self.model, (l, tb, lr, b), depth) br = Quad(self.model, (lr, tb, r, b), depth) self.children = (tl, tr, bl, br) return self.children def get_leaf_nodes(self, max_depth=None): if not self.children: return [self] if max_depth is not None and self.depth >= max_depth: return [self] result = [] for child in self.children: result.extend(child.get_leaf_nodes(max_depth)) return result
class Model(object): def __init__(self, path): self.im = Image.open(path).convert('RGB') self.width, self.height = self.im.size self.heap = [] self.root = Quad(self, (0, 0, self.width, self.height), 0) self.error_sum = self.root.error * self.root.area self.push(self.root) @property def quads(self): return [x[-1] for x in self.heap] def average_error(self): return self.error_sum / (self.width * self.height) def push(self, quad): score = -quad.error * (quad.area ** AREA_POWER) heapq.heappush(self.heap, (quad.leaf, score, quad)) def pop(self): return heapq.heappop(self.heap)[-1] def split(self): quad = self.pop() self.error_sum -= quad.error * quad.area children = quad.split() for child in children: self.push(child) self.error_sum += child.error * child.area def render(self, path, max_depth=None): m = OUTPUT_SCALE dx, dy = (PADDING, PADDING) im = Image.new('RGB', (self.width * m + dx, self.height * m + dy)) draw = ImageDraw.Draw(im) draw.rectangle((0, 0, self.width * m, self.height * m), FILL_COLOR) for quad in self.root.get_leaf_nodes(max_depth): l, t, r, b = quad.box box = (l * m + dx, t * m + dy, r * m - 1, b * m - 1) if MODE == MODE_ELLIPSE: draw.ellipse(box, quad.color) elif MODE == MODE_ROUNDED_RECTANGLE: radius = m * min((r - l), (b - t)) / 4 rounded_rectangle(draw, box, radius, quad.color) else: draw.rectangle(box, quad.color) del draw im.save(path, 'PNG')
def main(): args = sys.argv[1:] if len(args) != 1: print 'Usage: python main.py input_image' return model = Model(args[0]) previous = None for i in range(ITERATIONS): error = model.average_error() if previous is None or previous - error > ERROR_RATE: print i, error if SAVE_FRAMES: model.render('frames/%06d.png' % i) previous = error model.split() model.render('output.png') print '-' * 32 depth = Counter(x.depth for x in model.quads) for key in sorted(depth): value = depth[key] n = 4 ** key pct = 100.0 * value / n print '%3d %8d %8d %8.2f%%' % (key, n, value, pct) print '-' * 32 print ' %8d %8.2f%%' % (len(model.quads), 100) # for max_depth in range(max(depth.keys()) + 1): # model.render('out%d.png' % max_depth, max_depth)
if __name__ == '__main__':    main()

代码解读明日附上。