均值哈希 感知哈希 差值哈希 用JAVA怎么实现？

[michael_my]

import cv2 import numpy as np #感知哈希算法 def pHash(image): image = cv2.resize(image,(32,32), interpolation=cv2.INTER_CUBIC) image = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY) # cv2.imshow('image', image) # cv2.waitKey(0) # cv2.destroyAllWindows() # 将灰度图转为浮点型，再进行dct变换 dct = cv2.dct(np.float32(image)) # print(dct) # 取左上角的8*8，这些代表图片的最低频率 # 这个操作等价于c++中利用opencv实现的掩码操作 # 在python中进行掩码操作，可以直接这样取出图像矩阵的某一部分 dct_roi = dct[0:8,0:8] avreage = np.mean(dct_roi) hash = [] for i in range(dct_roi.shape[0]): for j in range(dct_roi.shape[1]): if dct_roi[i,j] > avreage: hash.append(1) else: hash.append(0) return hash #均值哈希算法 def aHash(image): #缩放为8*8 image=cv2.resize(image,(8,8),interpolation=cv2.INTER_CUBIC) #转换为灰度图 image=cv2.cvtColor(image,cv2.COLOR_BGR2GRAY) avreage = np.mean(image) hash = [] for i in range(image.shape[0]): for j in range(image.shape[1]): if image[i,j] > avreage: hash.append(1) else: hash.append(0) return hash #差值感知算法 def dHash(image): #缩放9*8 image=cv2.resize(image,(9,8),interpolation=cv2.INTER_CUBIC) #转换灰度图 image=cv2.cvtColor(image,cv2.COLOR_BGR2GRAY) # print(image.shape) hash=[] #每行前一个像素大于后一个像素为1，相反为0，生成哈希 for i in range(8): for j in range(8): if image[i,j]>image[i,j+1]: hash.append(1) else: hash.append(0) return hash #计算汉明距离 def Hamming_distance(hash1,hash2): num = 0 for index in range(len(hash1)): if hash1[index] != hash2[index]: num += 1 return num if __name__ == "__main__": image_file1 = './data/cartoon1.jpg' image_file2 = './data/cartoon3.jpg' img1 = cv2.imread(image_file1) img2 = cv2.imread(image_file2) hash1 = pHash(img1) hash2 = pHash(img2) dist = Hamming_distance(hash1, hash2) #将距离转化为相似度 similarity = 1 - dist * 1.0 / 64 print(dist) print(similarity)