opencv3/C++ PHash算法图像检索详解

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我是靠谱客的博主魁梧热狗，最近开发中收集的这篇文章主要介绍opencv3/C++ PHash算法图像检索详解，觉得挺不错的，现在分享给大家，希望可以做个参考。

概述

PHash算法即感知哈希算法/Perceptual Hash algorithm，计算基于低频的均值哈希．对每张图像生成一个指纹字符串，通过对该字符串比较可以判断图像间的相似度．

PHash算法原理

将图像转为灰度图，然后将图片大小调整为32*32像素并通过DCT变换，取左上角的8*8像素区域。然后计算这64个像素的灰度值的均值。将每个像素的灰度值与均值对比，大于均值记为1，小于均值记为0，得到64位哈希值。

PHash算法实现

将图片转为灰度值

将图片尺寸缩小为32*32

resize(src, src, Size(32, 32));

DCT变换

 Mat srcDCT; 
 dct(src, srcDCT);

计算DCT左上角8*8像素区域均值，求hash值

 double sum = 0;
 for (int i = 0; i < 8; i++)
  for (int j = 0; j < 8; j++)
   sum += srcDCT.at<float>(i,j);

 double average = sum/64;
 Mat phashcode= Mat::zeros(Size(8, 8), CV_8U);
 for (int i = 0; i < 8; i++)
  for (int j = 0; j < 8; j++)
   phashcode.at<char>(i,j) = srcDCT.at<float>(i,j) > average ? 1:0;

hash值匹配

  int d = 0;
  for (int n = 0; n < srchash.size[1]; n++)
   if (srchash.at<uchar>(0,n) != dsthash.at<uchar>(0,n)) d++;

即，计算两幅图哈希值之间的汉明距离，汉明距离越大，两图片越不相似。

OpenCV实现

如图在下图中对比各个图像与图person.jpg的汉明距离，以此衡量两图之间的额相似度。

#include <iostream> 
#include <stdio.h>
#include <fstream>
#include <io.h>
#include <string>
#include <opencv2opencv.hpp> 
#include <opencv2corecore.hpp>
#include <opencv2coremat.hpp>
using namespace std; 
using namespace cv; 
int fingerprint(Mat src, Mat* hash);

int main()
{
 Mat src = imread("E:\image\image\image\person.jpg", 0); 
 if(src.empty())
 {
  cout << "the image is not exist" << endl; 
  return -1;
 }
 Mat srchash, dsthash;
 fingerprint(src, &srchash);
 for(int i = 1; i <= 8; i++)
 { 
  string path0 = "E:\image\image\image\person";
  string number; 
  stringstream ss; 
  ss << i; 
  ss >> number; 
  string path = "E:\image\image\image\person" + number +".jpg"; 
  Mat dst = imread(path, 0); 
  if(dst.empty())
  {
   cout << "the image is not exist" << endl; 
   return -1;
  }
  fingerprint(dst, &dsthash);
  int d = 0;
  for (int n = 0; n < srchash.size[1]; n++)
   if (srchash.at<uchar>(0,n) != dsthash.at<uchar>(0,n)) d++; 

  cout <<"person" << i <<" distance= " <<d<<"n"; 
 }

 system("pause");
 return 0;
}


int fingerprint(Mat src, Mat* hash)
{
 resize(src, src, Size(32, 32));
 src.convertTo(src, CV_32F);
 Mat srcDCT; 
 dct(src, srcDCT);
 srcDCT = abs(srcDCT);
 double sum = 0;
 for (int i = 0; i < 8; i++)
  for (int j = 0; j < 8; j++)
   sum += srcDCT.at<float>(i,j);

 double average = sum/64;
 Mat phashcode= Mat::zeros(Size(8, 8), CV_8U);
 for (int i = 0; i < 8; i++)
  for (int j = 0; j < 8; j++)
   phashcode.at<char>(i,j) = srcDCT.at<float>(i,j) > average ? 1:0;

 *hash = phashcode.reshape(0,1).clone();
 return 0;
}

输出汉明距离：