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#include 
   
    #include 
    
     #include 
     
      using namespace std;using namespace cv;using namespace cv::ml;int main(){	//文件路径的前缀	string PrePath = "C:/Users/GuSheng/Desktop/photo/pedestrians128x64/";	//文件名字的前缀	string PreFileName1 = "per0000";	string PreFileName2 = "per000";	ostringstream is;	vector
      
        filename;//保存文件的名字	filename.clear();	//文件的个数为20	int total = 20;	for (int i = 1; i <= total;++i)	{		is.str("");//这句话是必须的		is << i;		if (i<=9)		{			filename.push_back(PrePath + PreFileName1 + is.str() + ".ppm");		}		else		{			filename.push_back(PrePath + PreFileName2 + is.str() + ".ppm");		}			}	//开始进行HOG检测	//影像的大小为64*128 块的大小为16*16 块的步长为8 cell的大小为8*8 bin的大小为9	//则一副影像的特征向量为 7*15*4*9=3780	Mat DataMat = Mat::zeros(total, 3780, CV_32FC1);	Mat labelMat = Mat::zeros(total, 1, CV_32SC1);	for (size_t i = 0; i < filename.size();++i)	{		vector
       
         descriptor;		descriptor.clear();		Ptr
        
          hog = new HOGDescriptor(Size(64, 128), Size(16, 16), Size(8, 8), Size(8, 8), 9); Mat img = imread(filename[i], IMREAD_GRAYSCALE); if (img.empty()) { continue; } hog->compute(img, descriptor, Size(1, 1)); //填充样本数据 for (size_t j = 0; j < descriptor.size();++j) { DataMat.at
         
          (i, j) = descriptor[j]; } labelMat.at
          
           (i, 0) = i+1; } //开始进行训练 Ptr
           
             svm = SVM::create(); svm->setType(SVM::C_SVC); svm->setKernel(SVM::LINEAR); svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, 100, 1e-06)); //开始进行训练 svm->train(DataMat, ROW_SAMPLE, labelMat); svm->save("SVM_people.xml"); string testName = "C:/Users/GuSheng/Desktop/photo/pedestrians128x64/per00012.ppm"; Mat testImg = imread(testName, IMREAD_GRAYSCALE); if (testImg.empty()) { return -1; } Ptr
            
              hog = new HOGDescriptor(Size(64, 128), Size(16, 16), Size(8, 8), Size(8, 8), 9); vector
             
               descriptor; hog->compute(testImg, descriptor); int ret = svm->predict(descriptor); cout << ret << endl; return 0;}
             
            
           
          
         
        
       
      
     
    
   

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