#!/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,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,b)
return dominant_color
def main():
img = Image.open("meitu.jpg")
print '#%02x%02x%02x' % 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,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,but still give them a low
# weight.
score = (saturation + 0.1) * count
if score > max_score:
max_score = score
dominant_color.append((r,b))
return dominant_color
def main():
img = Image.open("meitu.jpg")
colors = get_dominant_color(img)
for item in colors:
print '#%02x%02x%02x' % item
if __name__ == '__main__':
main()
