我就废话不多说了，大家还是直接看代码吧！

import PIL.Imageimport numpyimport osimport shutildef 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:\Users\Howsome\Desktop\tst" dir1 = r"C:\Users\Howsome\Desktop\AB" 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 npimport matplotlib.pyplot as pltimport cv2import timeimg = cv2.imread('lena.jpg',0)# 以遍历每个像素取反为例# 方法1t1 = 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2t3 = 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3t5 = 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-pixelt6 = 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。