opencv3/C++ PHash算法圖像檢索詳解

來(lái)源：本站原創(chuàng)|時(shí)間：2020-01-10|欄目：C語(yǔ)言|點(diǎn)擊：次

PHash算法即感知哈希算法/Perceptual Hash algorithm，計(jì)算基于低頻的均值哈希．對(duì)每張圖像生成一個(gè)指紋字符串，通過對(duì)該字符串比較可以判斷圖像間的相似度．

PHash算法原理

將圖像轉(zhuǎn)為灰度圖，然后將圖片大小調(diào)整為32*32像素并通過DCT變換，取左上角的8*8像素區(qū)域。然后計(jì)算這64個(gè)像素的灰度值的均值。將每個(gè)像素的灰度值與均值對(duì)比，大于均值記為1，小于均值記為0，得到64位哈希值。

PHash算法實(shí)現(xiàn)

將圖片轉(zhuǎn)為灰度值

將圖片尺寸縮小為32*32

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

DCT變換

 Mat srcDCT; 
 dct(src, srcDCT);

計(jì)算DCT左上角8*8像素區(qū)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，計(jì)算兩幅圖哈希值之間的漢明距離，漢明距離越大，兩圖片越不相似。

OpenCV實(shí)現(xiàn)

如圖在下圖中對(duì)比各個(gè)圖像與圖person.jpg的漢明距離，以此衡量?jī)蓤D之間的額相似度。

#include <iostream> 
#include <stdio.h>
#include <fstream>
#include <io.h>
#include <string>
#include <opencv2\opencv.hpp> 
#include <opencv2\core\core.hpp>
#include <opencv2\core\mat.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;
}

輸出漢明距離：

可以看出若將閾值設(shè)置為20則可將后三張其他圖片篩選掉。

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