前段时间介绍过相似图片搜索原理一(ahash) http://blog.youkuaiyun.com/lu597203933/article/details/45101859，它是基于内容检索最简单的一种；这里介绍它的增强版本感知哈希算法(perceptual hash, phash)。它主要也是用缩略图搜原图并能达到较好点的效果.
理论部分：
理论部分主要包括以下几个步骤：
<1> 图像缩放—将图像缩放到32*32大小
<2>灰度化—对32*32大小的图像进行灰度化
<3>离散余弦变换(DCT)—对32*32大小图像进行DCT
<4>计算均值—用32*32大小图片前面8*8大小图片处理并计算这64个像素的均值
<4>得到8*8图像的phash—8*8的像素值中大于均值的则用1表示，小于的用0表示，这样就得到一个64位二进制码作为该图像的phash值。
<5>计算两幅图像ahash值的汉明距离，距离越小，表明两幅图像越相似；距离越大，表明两幅图像距离越大。
这样做能够避免伽马校正或者颜色直方图调整带来的影响。
更详细的理论可以参看：
1：http://www.hackerfactor.com/blog/index.php?/archives/432-Looks-Like-It.html
2：http://blog.youkuaiyun.com/luoweifu/article/details/8220992包括java代码实现
下面我给出自己的c++代码实现：
<1>图像灰度化与缩放
Mat img = imread("E:\\algorithmZack\\ImageSearch\\image\\person.jpg", 1);
if(!img.data){
cout << "the image is not exist" << endl;
return 0;
}
int size = 32; // 图片缩放后大小
resize(img, img, Size(size,size)); // 缩放到32*32
cvtColor(img, img, COLOR_BGR2GRAY); // 灰度化<2>DCT变换
/*
功能：获取DCT系数
n：矩阵大小
quotient: 系数
quotientT: 系数转置
*/
void coefficient(const int &n, double **quotient, double **quotientT){
double sqr = 1.0/sqrt(n+0.0);
for(int i = 0; i < n; i++){
quotient[0][i] = sqr;
quotientT[i][0] = sqr;
}
for(int i = 1; i < n; i++){
for(int j = 0; j < n; j++){
quotient[i][j] = sqrt(2.0/n)*cos(i*(j+0.5)*PI/n); // 由公式得到
quotientT[j][i] = quotient[i][j];
}
}
}
/*
功能：两矩阵相乘
A和B：源输入矩阵
result：输出矩阵
*/
void matrixMultiply(double **A, double **B, int n, double **result){
double t = 0;
for(int i = 0; i < n; i++){
for(int j = 0; j < n; j++){
t = 0;
for(int k = 0; k < n; k++)
t += A[i][k]*B[k][j];
result[i][j] = t;
}
}
}
void DCT(Mat_<uchar> image, const int &n, double **iMatrix){
for(int i = 0; i < n; i++){
for(int j = 0; j < n; j++){
iMatrix[i][j] = (double)image(i,j);
}
}
// 为系数分配空间
double **quotient = new double*[n];
double **quotientT = new double*[n];
double **tmp = new double*[n];
for(int i = 0; i < n; i++){
quotient[i] = new double[n];
quotientT[i] = new double[n];
tmp[i] = new double[n];
}
// 计算系数矩阵
coefficient(n, quotient, quotientT);
matrixMultiply(quotient, iMatrix, n, tmp); // 由公式成绩结果
matrixMultiply(tmp, quotientT, n, iMatrix);
for(int i = 0; i < n; i++){
delete []tmp[i];
delete []quotient[i];
delete []quotientT[i];
}
delete []tmp;
delete []quotient;
delete []quotientT;
}<3>计算均值
// 计算8*8图像的平均灰度
float calcAverage(double **iMatrix, const int &size){
float sum = 0;
for(int i = 0 ; i < size; i++){
for(int j = 0; j < size; j++){
sum += iMatrix[i][j];
}
}
return sum/(size*size);
}<4>计算汉明距离
/* 计算hash值
image:8*8的灰度图像
size: 图像大小 8*8
phash:存放64位hash值
averagePix: 灰度值的平均值
*/
void fingerPrint(double **iMatrix, const int &size, bitset<hashLength> &phash, const float &averagePix){
for(int i = 0; i < size; i++){
int pos = i * size;
for(int j = 0; j < size; j++){
phash[pos+j] = iMatrix[i][j] >= averagePix ? 1:0;
}
}
}完整源代码：
#include <iostream>
#include <bitset>
#include <string>
#include <iomanip>
#include <cmath>
#include <opencv2\highgui\highgui.hpp>
#include <opencv2\imgproc\imgproc.hpp>
#include <opencv2\core\core.hpp>
using namespace std;
using namespace cv;
#define PI 3.1415926
#define hashLength 64
/*
功能：获取DCT系数
n：矩阵大小
quotient: 系数
quotientT: 系数转置
*/
void coefficient(const int &n, double **quotient, double **quotientT){
double sqr = 1.0/sqrt(n+0.0);
for(int i = 0; i < n; i++){
quotient[0][i] = sqr;
quotientT[i][0] = sqr;
}
for(int i = 1; i < n; i++){
for(int j = 0; j < n; j++){
quotient[i][j] = sqrt(2.0/n)*cos(i*(j+0.5)*PI/n); // 由公式得到
quotientT[j][i] = quotient[i][j];
}
}
}
/*
功能：两矩阵相乘
A和B：源输入矩阵
result：输出矩阵
*/
void matrixMultiply(double **A, double **B, int n, double **result){
double t = 0;
for(int i = 0; i < n; i++){
for(int j = 0; j < n; j++){
t = 0;
for(int k = 0; k < n; k++)
t += A[i][k]*B[k][j];
result[i][j] = t;
}
}
}
void DCT(Mat_<uchar> image, const int &n, double **iMatrix){
for(int i = 0; i < n; i++){
for(int j = 0; j < n; j++){
iMatrix[i][j] = (double)image(i,j);
}
}
// 为系数分配空间
double **quotient = new double*[n];
double **quotientT = new double*[n];
double **tmp = new double*[n];
for(int i = 0; i < n; i++){
quotient[i] = new double[n];
quotientT[i] = new double[n];
tmp[i] = new double[n];
}
// 计算系数矩阵
coefficient(n, quotient, quotientT);
matrixMultiply(quotient, iMatrix, n, tmp); // 由公式成绩结果
matrixMultiply(tmp, quotientT, n, iMatrix);
for(int i = 0; i < n; i++){
delete []tmp[i];
delete []quotient[i];
delete []quotientT[i];
}
delete []tmp;
delete []quotient;
delete []quotientT;
}
// 计算8*8图像的平均灰度
float calcAverage(double **iMatrix, const int &size){
float sum = 0;
for(int i = 0 ; i < size; i++){
for(int j = 0; j < size; j++){
sum += iMatrix[i][j];
}
}
return sum/(size*size);
}
/* 计算hash值
image:8*8的灰度图像
size: 图像大小 8*8
phash:存放64位hash值
averagePix: 灰度值的平均值
*/
void fingerPrint(double **iMatrix, const int &size, bitset<hashLength> &phash, const float &averagePix){
for(int i = 0; i < size; i++){
int pos = i * size;
for(int j = 0; j < size; j++){
phash[pos+j] = iMatrix[i][j] >= averagePix ? 1:0;
}
}
}
/*计算汉明距离*/
int hammingDistance(const bitset<hashLength> &query, const bitset<hashLength> &target){
int distance = 0;
for(int i = 0; i < hashLength; i++){
distance += (query[i] == target[i] ? 0 : 1);
}
return distance;
}
string bitTohex(const bitset<hashLength> &target){
string str;
for(int i = 0; i < hashLength; i=i+4){
int sum = 0;
string s;
sum += target[i] + (target[i+1]<<1) + (target[i+2]<<2) + (target[i+3]<<3);
stringstream ss;
ss << hex <<sum; // 以十六进制保存
ss >> s;
str += s;
}
return str;
}
int main(){
Mat img = imread("E:\\algorithmZack\\ImageSearch\\image\\person.jpg", 1);
if(!img.data){
cout << "the image is not exist" << endl;
return 0;
}
int size = 32; // 图片缩放后大小
resize(img, img, Size(size,size)); // 缩放到32*32
cvtColor(img, img, COLOR_BGR2GRAY); // 灰度化
double **iMatrix = new double*[size];
for(int i = 0; i < size; i++)
iMatrix[i] = new double[size];
DCT(img, size, iMatrix); // 离散余弦变换
float averagePix = calcAverage(iMatrix, 8);
cout << averagePix << endl;
bitset<hashLength> phash;
fingerPrint(iMatrix, 8, phash, averagePix);
//cout << phash << endl;
string str = bitTohex(phash);
cout << str << endl;
/*namedWindow("img");
imshow("img", img);
waitKey(0);*/
string img_dir = "E:\\algorithmZack\\ImageSearch\\image\\";
for(int i = 1; i <= 11; i++){
string pos;
stringstream ss;
ss << i;
ss >> pos;
string img_name = img_dir + "person" + pos +".jpg";
Mat target = imread(img_name, 1);
if(!target.data){
cout << "the target image" << img_name << " is not exist" << endl;
continue;
}
resize(target, target, Size(size,size));
cvtColor(target, target, COLOR_BGR2GRAY);
DCT(target, size, iMatrix);
float averagePix2 = calcAverage(iMatrix, 8);
bitset<hashLength> phash2;
fingerPrint(iMatrix, 8, phash2, averagePix2);
//cout << averagePix2 << endl;
int distance = hammingDistance(phash, phash2); // 计算汉明距离
cout <<"【" << i <<"-" << distance << "】 ";
}
cout << endl;
for(int i = 0; i < size; i++)
delete []iMatrix[i];
delete []iMatrix;
return 0;
}
测试图片为：
结果为：
其中【i-j】， i代表personi， j代表personi与person的汉明距离。并由结果可见phash对于图片的旋转肯定是无能为力的。
说明：完整的工程文件等着几篇常规图像检索方法写完后再上传，请关注！
参考文献：
1：http://www.hackerfactor.com/blog/index.php?/archives/432-Looks-Like-It.html英文原始资料
2：http://blog.youkuaiyun.com/luoweifu/article/details/8220992包括java代码实现
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