相似图片搜索原理二(phash—c++实现)

标签：图像检索   感知hash   

前段时间介绍过相似图片搜索原理一(ahash) http://blog.csdn.net/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.csdn.net/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.csdn.net/luoweifu/article/details/8220992包括java代码实现

相似图片搜索原理二(phash—c++实现)

标签：图像检索   感知hash   

原文地址：http://blog.csdn.net/lu597203933/article/details/45798293