我就废话不多说了，大家还是直接看代码吧！

import PIL.Image

import numpy

import os

import shutil

def sum_right(path):

img = PIL.Image.open(path)

array = numpy.array(img)

num = array.sum(axis=0)

print(type(num))

res_left = 0

res_right = 0

for i in range(256,512):

res_right += num[i]

print(res_right)

if __name__ == "__main__":

dir2 = r"C:UsersHowsomeDesktopst"

dir1 = r"C:UsersHowsomeDesktopAB"

names = os.listdir(dir1)

n = len(names)

print("文件数量",n)

res = 0

average_5 = 25565356

average_25 = 26409377

average_5_right = 10006019

#average_tmp = (average_25+average_5)//2

count = 0

#show(os.path.join(dir1, "uni4F6C.png"))

for i in range(n):

#取图片

img = PIL.Image.open(os.path.join(dir1,names[i]))

file = os.path.join(dir1,names[i])

rmfile = os.path.join(dir2,names[i])

array = numpy.array(img)

num = array.sum(axis=0)

res_right = 0

for i in range(256, 512):

res_right += num[i]

average_5_right += res_right/n

if res_right > average_5_right:

shutil.copyfile(file, rmfile)

os.remove(file)

count += 1

print(average_5_right)

print(count)

补充知识：python遍历灰度图像像素方法总结

啥也不说了，看代码吧！

import numpy as np

import matplotlib.pyplot as plt

import cv2

import time

img = cv2.imread("lena.jpg",0)

# 以遍历每个像素取反为例

# 方法1

t1 = time.time()

img1 = np.copy(img)

rows,cols = img1.shape[:2]

for row in range(rows):

for col in range(cols):

img[row,col] = 255 - img[row,col]

t2 = time.time()

print("方法1所需时间：",t2-t1)

# 方法2

t3 = time.time()

img2 = np.copy(img)

rows,cols = img2.shape[:2]

img2 = img2.reshape(rows*cols)

# print(img2)

for i in range(rows*cols):

img2[i] = 255-img2[i]

img2 = img2.reshape(rows,cols)

# print(img2)

t4 = time.time()

print("方法2所需时间：",t4-t3)

# 方法3

t5 = time.time()

img3 = np.copy(img)

# 使用多维迭代生成器

for (x,y), pixel in np.ndenumerate(img3):

img3[x,y] = 255-pixel

t6 = time.time()

print("方法3所需时间：",t6-t5)

测试结果：

方法1所需时间： 0.14431977272033691

方法2所需时间： 0.13863205909729004

方法3所需时间： 0.24196243286132812

以上这篇用python按照图像灰度值统计并筛选图片的操作(PIL,shutil,os)就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持云海天教程。

原文链接：https://blog.csdn.net/shenlanjizou/article/details/100321653