#!/usr/bin/env python # -*- coding: utf-8 -*- import colorsys from PIL import Image import optparse def get_dominant_color(image): """ Find a PIL image's dominant color, returning an (r, g, b) tuple. """ image = image.convert('RGBA') # Shrink the image, so we don't spend too long analysing color # frequencies. We're not interpolating so should be quick. image.thumbnail((200, 200)) max_score = None dominant_color = None for count, (r, g, b, a) in image.getcolors(image.size[0] * image.size[1]): # Skip 100% transparent pixels if a == 0: continue # Get color saturation, 0-1 saturation = colorsys.rgb_to_hsv(r / 255.0, g / 255.0, b / 255.0)[1] # Calculate luminance - integer YUV conversion from # http://en.wikipedia.org/wiki/YUV y = min(abs(r * 2104 + g * 4130 + b * 802 + 4096 + 131072) >> 13, 235) # Rescale luminance from 16-235 to 0-1 y = (y - 16.0) / (235 - 16) # Ignore the brightest colors if y > 0.9: continue # Calculate the score, preferring highly saturated colors. # Add 0.1 to the saturation so we don't completely ignore grayscale # colors by multiplying the count by zero, but still give them a low # weight. score = (saturation + 0.1) * count if score > max_score: max_score = score dominant_color = (r, g, b) return dominant_color def main(): img = Image.open("meitu.jpg") print '#xxx' % get_dominant_color(img) if __name__ == '__main__': main()

#!/usr/bin/env python # -*- coding: utf-8 -*- import colorsys from PIL import Image import optparse def get_dominant_color(image): """ Find a PIL image's dominant color, returning an (r, g, b) tuple. """ image = image.convert('RGBA') # Shrink the image, so we don't spend too long analysing color # frequencies. We're not interpolating so should be quick. ## image.thumbnail((200, 200)) max_score = 1 dominant_color = [] for count, (r, g, b, a) in image.getcolors(image.size[0] * image.size[1]): # Skip 100% transparent pixels if a == 0: continue # Get color saturation, 0-1 saturation = colorsys.rgb_to_hsv(r / 255.0, g / 255.0, b / 255.0)[1] # Calculate luminance - integer YUV conversion from # http://en.wikipedia.org/wiki/YUV y = min(abs(r * 2104 + g * 4130 + b * 802 + 4096 + 131072) >> 13, 235) # Rescale luminance from 16-235 to 0-1 y = (y - 16.0) / (235 - 16) # Ignore the brightest colors if y > 0.9: continue # Calculate the score, preferring highly saturated colors. # Add 0.1 to the saturation so we don't completely ignore grayscale # colors by multiplying the count by zero, but still give them a low # weight. score = (saturation + 0.1) * count if score > max_score: max_score = score dominant_color.append((r, g, b)) return dominant_color def main(): img = Image.open("meitu.jpg") colors = get_dominant_color(img) for item in colors: print '#xxx' % item if __name__ == '__main__': main()