Python dHash算法使用说明
缩小图片:缩小到9*8,这样它就有72个像素点。
转换成灰度图。
计算差异值:dHash算法在相邻像素之间工作,因此每行9个像素之间产生8个不同的差异,总共8行,产生64个差异值。
获取指纹:如果左像素比右像素亮,记录为1,否则为0。
最后对比两张图片的指纹,获得汉明距离。
Python dHash算法使用实例
# -*- coding: utf-8 -*- # 利用python实现多种方法来实现图像识别 import cv2 import numpy as np from matplotlib import pyplot as plt # 最简单的以灰度直方图作为相似比较的实现 def classify_gray_hist(image1,image2,size = (256,256)): # 先计算直方图 # 几个参数必须用方括号括起来 # 这里直接用灰度图计算直方图,所以是使用第一个通道, # 也可以进行通道分离后,得到多个通道的直方图 # bins 取为16 image1 = cv2.resize(image1,size) image2 = cv2.resize(image2,size) hist1 = cv2.calcHist([image1],[0],None,[256],[0.0,255.0]) hist2 = cv2.calcHist([image2],[0],None,[256],[0.0,255.0]) # 可以比较下直方图 plt.plot(range(256),hist1,'r') plt.plot(range(256),hist2,'b') plt.show() # 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 degree = degree/len(hist1) return degree # 计算单通道的直方图的相似值 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]) # 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 degree = degree/len(hist1) return degree # 通过得到每个通道的直方图来计算相似度 def classify_hist_with_split(image1,image2,size = (256,256)): # 将图像resize后,分离为三个通道,再计算每个通道的相似值 image1 = cv2.resize(image1,size) image2 = cv2.resize(image2,size) 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 # 平均哈希算法计算 def classify_aHash(image1,image2): image1 = cv2.resize(image1,(8,8)) image2 = cv2.resize(image2,(8,8)) gray1 = cv2.cvtColor(image1,cv2.COLOR_BGR2GRAY) gray2 = cv2.cvtColor(image2,cv2.COLOR_BGR2GRAY) hash1 = getHash(gray1) hash2 = getHash(gray2) return Hamming_distance(hash1,hash2) def classify_pHash(image1,image2): image1 = cv2.resize(image1,(32,32)) image2 = cv2.resize(image2,(32,32)) gray1 = cv2.cvtColor(image1,cv2.COLOR_BGR2GRAY) gray2 = cv2.cvtColor(image2,cv2.COLOR_BGR2GRAY) # 将灰度图转为浮点型,再进行dct变换 dct1 = cv2.dct(np.float32(gray1)) dct2 = cv2.dct(np.float32(gray2)) # 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 dct1_roi = dct1[0:8,0:8] dct2_roi = dct2[0:8,0:8] hash1 = getHash(dct1_roi) hash2 = getHash(dct2_roi) return Hamming_distance(hash1,hash2) # 输入灰度图,返回hash def getHash(image): 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 # 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__': img1 = cv2.imread('10.jpg') cv2.imshow('img1',img1) img2 = cv2.imread('11.jpg') cv2.imshow('img2',img2) degree = classify_gray_hist(img1,img2) #degree = classify_hist_with_split(img1,img2) #degree = classify_aHash(img1,img2) #degree = classify_pHash(img1,img2) print degree cv2.waitKey(0)
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