本代码可以帮你自动剪切掉图片的边缘空白区域，如果你的图片有大片空白区域（只要是同一颜色形成一定的面积就认为是空白区域），下面的python代码可以帮你自动切除，如果是透明图像，会自动剪切大片的透明部分。
本代码需要PIL模块

import Image, ImageChops     def autoCrop(image,backgroundColor=None):      '''Intelligent automatic image cropping.         This functions removes the usless "white" space around an image.                   If the image has an alpha (tranparency) channel, it will be used         to choose what to crop.                   Otherwise, this function will try to find the most popular color         on the edges of the image and consider this color "whitespace".         (You can override this color with the backgroundColor parameter)             Input:              image (a PIL Image object): The image to crop.              backgroundColor (3 integers tuple): eg. (0,0,255)                   The color to consider "background to crop".                   If the image is transparent, this parameters will be ignored.                   If the image is not transparent and this parameter is not                   provided, it will be automatically calculated.            Output:              a PIL Image object : The cropped image.      '''             def mostPopularEdgeColor(image):          ''' Compute who's the most popular color on the edges of an image.              (left,right,top,bottom)                             Input:                  image: a PIL Image object                             Ouput:                  The most popular color (A tuple of integers (R,G,B))          '''          im = image          if im.mode != 'RGB':              im = image.convert("RGB")                     # Get pixels from the edges of the image:          width,height = im.size          left   = im.crop((0,1,1,height-1))          right  = im.crop((width-1,1,width,height-1))          top    = im.crop((0,0,width,1))          bottom = im.crop((0,height-1,width,height))          pixels = left.tostring() + right.tostring() + top.tostring() + bottom.tostring()             # Compute who's the most popular RGB triplet          counts = {}          for i in range(0,len(pixels),3):              RGB = pixels[i]+pixels[i+1]+pixels[i+2]              if RGB in counts:                  counts[RGB] += 1              else:                  counts[RGB] = 1                        # Get the colour which is the most popular:                 mostPopularColor = sorted([(count,rgba) for (rgba,count) in counts.items()],reverse=True)[0][1]          return ord(mostPopularColor[0]),ord(mostPopularColor[1]),ord(mostPopularColor[2])             bbox = None             # If the image has an alpha (tranparency) layer, we use it to crop the image.      # Otherwise, we look at the pixels around the image (top, left, bottom and right)      # and use the most used color as the color to crop.             # --- For transparent images -----------------------------------------------      if 'A' in image.getbands(): # If the image has a transparency layer, use it.          # This works for all modes which have transparency layer          bbox = image.split()[list(image.getbands()).index('A')].getbbox()      # --- For non-transparent images -------------------------------------------      elif image.mode=='RGB':          if not backgroundColor:              backgroundColor = mostPopularEdgeColor(image)          # Crop a non-transparent image.          # .getbbox() always crops the black color.          # So we need to substract the "background" color from our image.          bg = Image.new("RGB", image.size, backgroundColor)          diff = ImageChops.difference(image, bg)  # Substract background color from image          bbox = diff.getbbox()  # Try to find the real bounding box of the image.      else:          raise NotImplementedError, "Sorry, this function is not implemented yet for images in mode '%s'." % image.mode                 if bbox:          image = image.crop(bbox)                 return image           #范例：裁剪透明图片:  im = Image.open('myTransparentImage.png')  cropped = autoCrop(im)  cropped.show()     #范例：裁剪非透明图片  im = Image.open('myImage.png')  cropped = autoCrop(im)  cropped.show()