发布于 2014-10-10 11:17:38 | 361 次阅读 | 评论: 0 | 来源: 网友投递
Python编程语言
Python 是一种面向对象、解释型计算机程序设计语言，由Guido van Rossum于1989年底发明，第一个公开发行版发行于1991年。Python语法简洁而清晰，具有丰富和强大的类库。它常被昵称为胶水语言，它能够把用其他语言制作的各种模块（尤其是C/C++）很轻松地联结在一起。
本文是二个python实现的可以用来获取 图片主色调的脚本代码,感兴趣的同学参考下.
一幅图片，想通过程序判断获得其主要色调，应该怎么样处理？本文通过python实现判断、获取一张图片的主色调方法,需要的朋友可以参考下
python判断图片主色调，单个颜色：
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判断一张图片的主色调，多个颜色：
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()