python3+opencv 使用灰度直方图来判断图片的亮暗操作

 import cv2; import numpy as np; import matplotlib.pyplot as plt; import os; import sys; def func(img,pic_path,pic): #把图片转换为灰度图 gray_img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY); #获取灰度图矩阵的行数和列数 r,c = gray_img.shape[:2]; dark_sum=0;	#偏暗的像素 初始化为0个 dark_prop=0;	#偏暗像素所占比例初始化为0 piexs_sum=r*c;	#整个弧度图的像素个数为r*c #遍历灰度图的所有像素 for row in gray_img: for colum in row: if colum=0.75:	#人为设置的超参数:表示若偏暗像素所占比例超过0.78,则这张图被认为整体环境黑暗的图片 print(pic_path+" is dark!"); cv2.imwrite("../DarkPicDir/"+pic,img);#把被认为黑暗的图片保存 else: print(pic_path+" is bright!") #hist(pic_path); #若要查看图片的灰度值分布情况,可以这个注释解除 #用于显示图片的灰度直方图 def hist(pic_path): img=cv2.imread(pic_path,0); hist = cv2.calcHist([img],[0],None,[256],[0,256]) plt.subplot(121) plt.imshow(img,'gray') plt.xticks([]) plt.yticks([]) plt.title("Original") plt.subplot(122) plt.hist(img.ravel(),256,[0,256]) plt.show() #读取给定目录的所有图片 def readAllPictures(pics_path): if not os.path.exists(pics_path): print("路径错误，路径不存在！") return; allPics = []; pics = os.listdir(pics_path); for pic in pics: pic_path = os.path.join(pics_path,pic); if os.path.isfile(pic_path): allPics.append(pic_path); img=cv2.imread(pic_path); func(img,pic_path,pic); return allPics; #创建用于存放黑暗图片的目录 def createDarkDir(): DarkDirPath = "../DarkPicDir"; isExists = os.path.exists(DarkDirPath); if not isExists: os.makedirs(DarkDirPath); print("dark pics dir is created successfully!"); return True; else: return False; if __name__ =='__main__': pics_path = sys.argv[1];#获取所给图片目录 createDarkDir(); allPics=readAllPictures(pics_path);