python判斷、獲取一張圖片主色調(diào)的2個實例

更新時間：2014年04月10日 14:51:19 作者：

一幅圖片，想通過程序判斷獲得其主要色調(diào)，應該怎么樣處理？本文通過python實現(xiàn)判斷、獲取一張圖片的主色調(diào)方法,需要的朋友可以參考下

python判斷圖片主色調(diào)，單個顏色：

復制代碼代碼如下:

#!/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 '#%02x%02x%02x' % get_dominant_color(img)

if __name__ == '__main__':
main()

python判斷一張圖片的主色調(diào)，多個顏色：

復制代碼代碼如下:

#!/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 '#%02x%02x%02x' % item

if __name__ == '__main__':
main()

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