使用Python图像库（PIL）规格化一组图像的直方图（亮度和对比度）(Normalize histogram (brightness and contrast) of a set of images using Python Image Library (PIL))

 我有一个脚本，它使用Google Maps API下载一系列相同大小的方形卫星图像并生成PDF。 图像需要事先旋转，我已经使用PIL来完成。 
 
 我注意到，由于不同的光照和地形条件，一些图像太亮，另一些太暗，并且由此产生的pdf最终变得有点丑陋，“在现场”的阅读条件不甚理想（这是穷国山地自行车，在那里我想打印特定十字路口的缩略图）。 
 
 （编辑）然后，目标是让所有图像以相似的亮度和对比度结束。 所以，太亮的图像必须变暗，而黑暗的图像必须变亮。 （顺便说一下，我曾经使用过imagemagick auto-gamma autocontrast ，或auto-gamma ， equalize ，或auto-gamma或类似的东西，在医学图像中有趣的结果，但不知道如何在PIL中做这些）。 
 
 转换成灰度图后（我刚才使用了灰度打印机），我已经使用了一些图像校正功能，但结果也不好。 这是我的灰度代码： 
 
#!/usr/bin/python

def myEqualize(im)
    im=im.convert('L')
    contr = ImageEnhance.Contrast(im)
    im = contr.enhance(0.3)
    bright = ImageEnhance.Brightness(im)
    im = bright.enhance(2)
    #im.show()
    return im
 
 此代码对每个图像都独立工作。 我想知道是不是先分析所有图像然后“归一化”它们的视觉特性（对比度，亮度，伽玛等）会更好。 
 
 此外，我认为有必要在图像中进行一些分析（直方图？），以便根据每幅图像应用自定义校正，而不是对所有图像进行相同的校正（尽管任何“增强”功能隐含地考虑初始接头）。 
 
 有没有人有这样的问题和/或知道一个很好的替代方案与彩色图像做到这一点（无灰度）？ 
 
 任何帮助将不胜感激，谢谢阅读！ 

I have a script which uses Google Maps API to download a sequence of equal-sized square satellite images and generates a PDF. The images need to be rotated beforehand, and I already do so using PIL.
 
I noticed that, due to different light and terrain conditions, some images are too bright, others are too dark, and the resulting pdf ends up a bit ugly, with less-than-ideal reading conditions "in the field" (which is backcountry mountain biking, where I want to have a printed thumbnail of specific crossroads).
 
(EDIT) The goal then is to make all images end up with similar apparent brightness and contrast. So, the images that are too bright would have to be darkened, and the dark ones would have to be lightened. (by the way, I once used imagemagick autocontrast, or auto-gamma, or equalize, or autolevel, or something like that, with interesting results in medical images, but don't know how to do any of these in PIL).
 
I already used some image corrections after converting to grayscale (had a grayscale printer a time ago), but the results weren't good, either. Here is my grayscale code:
 
#!/usr/bin/python

def myEqualize(im)
    im=im.convert('L')
    contr = ImageEnhance.Contrast(im)
    im = contr.enhance(0.3)
    bright = ImageEnhance.Brightness(im)
    im = bright.enhance(2)
    #im.show()
    return im
 
This code works independently for each image. I wonder if it would be better to analyze all images first and then "normalize" their visual properties (contrast, brightness, gamma, etc).
 
Also, I think it would be necessary to perform some analysis in the image (histogram?), so as to apply a custom correction depending on each image, and not an equal correction for all of them (although any "enhance" function implicitly considers initial contitions).
 
Does anybody had such problem and/or know a good alternative to do this with the colored images (no grayscale)?
 
Any help will be appreciated, thanks for reading!

原文：https://stackoverflow.com/questions/7116113