Python 基于哈希和直方图的视频图像处理

 import cv2

    
 import numpy as np
    
 import matplotlib.pyplot as plt
    
 import os
    
 from PIL import Image
    
 os.chdir('视频所在位置')

 def aHash(img):

    
     plt.imshow(img)
    
     plt.axis('off')
    
     plt.show()
    
     img=cv2.resize(img,(8,8))
    
     plt.imshow(img)
    
     plt.axis('off')
    
     plt.show()
    
     
    
     gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
    
     s=0
    
     hash_str=''
    
     # 遍历累加求像素和
    
     for i in range(8):
    
     for j in range(8):
    
         s=s+gray[i,j]
    
     #求平均灰度
    
     avg=s/64
    
     # 灰度大于平均值为1相反为0生成图片的hash值
    
     for i in range(8):
    
     for j in range(8):
    
         if gray[i,j]>avg:
    
             hash_str=hash_str+'1'
    
         else:
    
             hash_str=hash_str+'0'
    
     return hash_str

 def classify_hist_with_split(image1,image2,size=(256,256)):

    
     image1=cv2.resize(image1,size)
    
     image2=cv2.resize(image2,size)
    
     plt.imshow(image1)
    
     plt.show()
    
     plt.axis('off')
    
     
    
     plt.imshow(image2)
    
     plt.show()
    
     plt.axis('off')
    
     
    
     sub_image1=cv2.split(image1)
    
     sub_image2=cv2.split(image2)
    
     sub_data=0
    
     
    
     for im1,im2 in zip(sub_image1,sub_image2):
    
     sub_data+=calculate(im1,im2)
    
     sub_data=sub_data/3
    
     return sub_data

 def calculate(image1,image2):

    
     hist1=cv2.calcHist([image1],[0],None,[256],[0.0,255.0])
    
     hist2=cv2.calcHist([image2],[0],None,[256],[0.0,255.0])
    
     plt.plot(hist1,color='r')
    
     plt.plot(hist2,color='g')
    
     
    
     # 计算直方图的重合度
    
     degree=0
    
     for i in range(len(hist1)):
    
     if hist1[i]!=hist2[i]:
    
         degree=degree+(1-abs(hist1[i]-hist2[i])/max(hist1[i],hist2[i]))
    
     else:
    
         degree=degree+1
    
         #统计相似
    
     degree=degree/len(hist1)
    
     return degree

 def cmpHash(hash1,hash2):

    
     n=0
    
     print(hash1)
    
     print(hash2)
    
     # hash长度不同则返回-1代表传参出错
    
     if len(hash1)!=len(hash2):
    
     return -1
    
     for i in range(len(hash1)):
    
     if hash1[i]!=hash2[i]:
    
         n=n+1
    
     return n

 img1=cv2.imread('PythonAds2021/.pic/image0.jpg')

    
 img2=cv2.imread('PythonAds2021/.pic/image1.jpg')
    
  
    
 hash1=aHash(img1)
    
 hash2=aHash(img2)
    
 n=cmpHash(hash1,hash2)
    
 print('均值哈希算法相似度：',n)
    
  
    
 n=classify_hist_with_split(img1,img2)
    
 print('三直方图算法相似度：',n)

 for i in range(549):

    
     img1=cv2.imread('.pic/image{}.jpg'.format(i))
    
     img2=cv2.imread('.pic/image{}.jpg'.format(i+1))
    
     hash1=aHash(img1)
    
     hash2=aHash(img2)
    
     n=cmpHash(hash1,hash2)
    
     if (n>32):
    
     print('均值哈希算法相似度：',n/64)
    
     cv2.imwrite('.shot/image{}.jpg'.format(i+1),img2)