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。