我通过PIL和numpy处理的图片现在是一个numpy array，我希望它存回png格式，于是查到了scipy.misc.toimage可以做到，但是这里面有一些需要注意的地方。

直接上我的code（包含处理过程，image_pooling()的最后一句是存图）:

#-*-coding:utf-8-*-
import os
import random
import shutil
from PIL import Image
import numpy as np
from collections import Counter
from scipy import misc

def image_pooling( ori_dir='gtFine/train/', goal_dir='gtFine_pool/train/' ):
    sub_dirs_ori = [f1 for f1 in os.listdir(ori_dir) if os.path.isdir(os.path.abspath(os.path.join(ori_dir,f1)))]
    # print sub_dirs_ori
    # img_index = 0
#    train_files = []
#    assert len(sub_dirs_data) == len(sub_dirs_labels)

    for sub_dir in sub_dirs_ori:
        ori_complete_dir = os.path.join(ori_dir, sub_dir)
        goal_complete_dir = os.path.join(goal_dir, sub_dir)
        os.mkdir(goal_complete_dir)

        for f in os.listdir(ori_complete_dir):
            ori_path = os.path.abspath(os.path.join(ori_complete_dir, f))
            print "original_path is ", ori_path
            goal_path = os.path.abspath(os.path.join(goal_complete_dir, f))
            print "goal_path is ", goal_path
            if os.path.isfile(ori_path) and 'labelIds' in f:
                # shutil.copyfile(ori_path, goal_path) 
                img = np.array(Image.open(ori_path))
                print img.shape
                img_goal = np.zeros((img.shape[0]//8, img.shape[1]//8))
                pool_h = img.shape[0]//8
                pool_w = img.shape[1]//8
                for i in range(pool_h):
                    for j in range(pool_w):
                        img_goal[i,j] = int( Counter((img[8*i:8*(i+1),8*j:8*(j+1)].flatten())).most_common(1)[0][0] )
                # print img_goal
                misc.toimage(img_goal, cmin=0, cmax=255).save(goal_path)               

if __name__ == '__main__':
    image_pooling()

需要注意的是看下misc.toimage文档说明和source code，如果我们不给cmin和cmax参数的话，保存的时候会把图像按照矩阵中的最大最小值在0-255区间进行normalization。这不是我想要的结果，所以要给出cmin和cmax，来确保不会这样做。看misc.toimage源码：

def toimage(arr, high=255, low=0, cmin=None, cmax=None, pal=None,
                                     mode=None, channel_axis=None):

........................

data = (data*1.0 - cmin)*(high - low)/(cmax - cmin) + low
.........................

可以看到如果默认high=255, low=0的话，那cmax和cmin也需要分别等于255和０，这一步的处理才不会影响我们把numpy矩阵完好的存成png图像。