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(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(i,j) = srcDCT.at(i,j) > average ? 1:0;

hash值匹配

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

cout <<"person" << i <<" distance= " <

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(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(i,j) = srcDCT.at(i,j) > average ? 1:0; 

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

输出汉明距离：

可以看出若将阈值设置为20则可将后三张其他图片筛选掉。