使用Python的PIL模块来进行图片对比

WBOY
Release: 2016-06-10 15:06:02
Original
1179 people have browsed it

在使用google或者baidu搜图的时候会发现有一个图片颜色选项,感觉非常有意思,有人可能会想这肯定是人为的去划分的,呵呵,有这种可能,但是估计人会累死, 开个玩笑,当然是通过机器识别的,海量的图片只有机器识别才能做到。
那用python能不能实现这种功能呢?答案是:能

利用python的PIL模块的强大的图像处理功能就可以做到,下面上代码:

import colorsys def get_dominant_color(image): #颜色模式转换,以便输出rgb颜色值 image = image.convert('RGBA') #生成缩略图,减少计算量,减小cpu压力 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]): # 跳过纯黑色 if a == 0: continue saturation = colorsys.rgb_to_hsv(r / 255.0, g / 255.0, b / 255.0)[1] y = min(abs(r * 2104 + g * 4130 + b * 802 + 4096 + 131072) >> 13, 235) y = (y - 16.0) / (235 - 16) # 忽略高亮色 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
Copy after login


如何使用:

from PIL import Image print get_dominant_color(Image.open('logo.jpg'))
Copy after login

这样就会返回一个rgb颜色,但是这个值是很精确的范围,那我们如何实现百度图片那样的色域呢??
其实方法很简单,r/g/b都是0-255的值,我们只要把这三个值分别划分相等的区间,然后组合,取近似值。例如:划分为0-127,和128-255,然后自由组 合,可以出现八种组合,然后从中挑出比较有代表性的颜色即可。
当然我只是举一个例子,你也可以划分的更细,那样显示的颜色就会更准确~~大家赶快试试吧

PS:通过pil生成缩略图的简单代码

如果是单纯地生成缩略图,我们可以通过pil很简单地办到,这段代码会强行将图片大小修改成250x156:

from PIL import Image img = Image.open('sharejs.jpg') img = img.resize((250, 156), Image.ANTIALIAS) img.save('sharejs_small.jpg')
Copy after login

Related labels:
source:php.cn
Statement of this Website
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn
Latest Downloads
More>
Web Effects
Website Source Code
Website Materials
Front End Template
About us Disclaimer Sitemap
php.cn:Public welfare online PHP training,Help PHP learners grow quickly!