HOG特征的SVM分类器训练代码

来源：互联网发布：淘宝开店信誉编辑：程序博客网时间：2024/06/05 10:16

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#include <cv.h>
#include <io.h>
#include <iostream>
#include <string>
#include <highgui.h>
#include <ml.h>
using namespace std;
class Mysvm: public CvSVM
{
public:
int get_alpha_count()
{
return this->sv_total;
}
int get_sv_dim()
{
return this->var_all;
}
int get_sv_count()
{
return this->decision_func->sv_count;
}
double* get_alpha()
{
return this->decision_func->alpha;
}
float** get_sv()
{
return this->sv;
}
float get_rho()
{
return this->decision_func->rho;
}
};
void Train()
{
char classifierSavePath[256] = "E:/work/INRIAPerson/pedestrianDetect-peopleFlow.txt";
string positivePath = "E:\\work\\INRIAPerson\\posgray\\";
string negativePath = "E:\\work\\INRIAPerson\\neggray\\";
int positiveSampleCount = 2416;
int negativeSampleCount = 3654;
int totalSampleCount = positiveSampleCount + negativeSampleCount;
cout<<"//////////////////////////////////////////////////////////////////"<<endl;
cout<<"totalSampleCount: "<<totalSampleCount<<endl;
cout<<"positiveSampleCount: "<<positiveSampleCount<<endl;
cout<<"negativeSampleCount: "<<negativeSampleCount<<endl;
CvMat *sampleFeaturesMat = cvCreateMat(totalSampleCount , 3780, CV_32FC1);
//64*128的训练样本，该矩阵将是totalSample*3780,64*64的训练样本，该矩阵将是totalSample*1764
cvSetZero(sampleFeaturesMat);
CvMat *sampleLabelMat = cvCreateMat(totalSampleCount, 1, CV_32FC1);//样本标识
cvSetZero(sampleLabelMat);
cout<<"************************************************************"<<endl;
cout<<"start to training positive samples..."<<endl;
char positiveImgName[256];
string path;
for(int i=0; i<positiveSampleCount; i++)
{
memset(positiveImgName, '\0', 256*sizeof(char));
sprintf(positiveImgName, "%d.png", i+1);
int len = strlen(positiveImgName);
string tempStr = positiveImgName;
path = positivePath + tempStr;
cv::Mat img = cv::imread(path);
if( img.data == NULL )
{
cout<<"positive image sample load error: "<<i<<" "<<path<<endl;
system("pause");
continue;
}
cv::HOGDescriptor hog(cv::Size(64,128), cv::Size(16,16), cv::Size(8,8), cv::Size(8,8), 9);
vector<float> featureVec;
hog.compute(img, featureVec, cv::Size(8,8));
int featureVecSize = featureVec.size();
for (int j=0; j<featureVecSize; j++)
{
CV_MAT_ELEM( *sampleFeaturesMat, float, i, j ) = featureVec[j];
}
sampleLabelMat->data.fl[i] = 1;
}
cout<<"end of training for positive samples..."<<endl;
cout<<"*********************************************************"<<endl;
cout<<"start to train negative samples..."<<endl;
char negativeImgName[256];
for (int i=0; i<negativeSampleCount; i++)
{
memset(negativeImgName, '\0', 256*sizeof(char));
sprintf(negativeImgName, "%d.png", i+1);
path = negativePath + negativeImgName;
cv::Mat img = cv::imread(path);
if(img.data == NULL)
{
cout<<"negative image sample load error: "<<path<<endl;
continue;
}
cv::HOGDescriptor hog(cv::Size(64,128), cv::Size(16,16), cv::Size(8,8), cv::Size(8,8), 9);
vector<float> featureVec;
hog.compute(img,featureVec,cv::Size(8,8));//计算HOG特征
int featureVecSize = featureVec.size();
for ( int j=0; j<featureVecSize; j ++)
{
CV_MAT_ELEM( *sampleFeaturesMat, float, i + positiveSampleCount, j ) = featureVec[ j ];
}
sampleLabelMat->data.fl[ i + positiveSampleCount ] = -1;
}
cout<<"end of training for negative samples..."<<endl;
cout<<"********************************************************"<<endl;
cout<<"start to train for SVM classifier..."<<endl;
CvSVMParams params;
params.svm_type = CvSVM::C_SVC;
params.kernel_type = CvSVM::LINEAR;
params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER, 1000, FLT_EPSILON);
params.C = 0.01;
Mysvm svm;
svm.train( sampleFeaturesMat, sampleLabelMat, NULL, NULL, params ); //用SVM线性分类器训练
svm.save(classifierSavePath);
cvReleaseMat(&sampleFeaturesMat);
cvReleaseMat(&sampleLabelMat);
int supportVectorSize = svm.get_support_vector_count();
cout<<"support vector size of SVM："<<supportVectorSize<<endl;
cout<<"************************ end of training for SVM ******************"<<endl;
CvMat *sv,*alp,*re;//所有样本特征向量
sv = cvCreateMat(supportVectorSize , 3780, CV_32FC1);
alp = cvCreateMat(1 , supportVectorSize, CV_32FC1);
re = cvCreateMat(1 , 3780, CV_32FC1);
CvMat *res = cvCreateMat(1 , 1, CV_32FC1);
cvSetZero(sv);
cvSetZero(re);
for(int i=0; i<supportVectorSize; i++)
{
memcpy( (float*)(sv->data.fl+i*3780), svm.get_support_vector(i), 3780*sizeof(float));
}
double* alphaArr = svm.get_alpha();
int alphaCount = svm.get_alpha_count();
for(int i=0; i<supportVectorSize; i++)
{
alp->data.fl[i] = alphaArr[i];
}
cvMatMul(alp, sv, re);
int posCount = 0;
for (int i=0; i<3780; i++)
{
re->data.fl[i] *= -1;
}
FILE* fp = fopen("E:/work/INRIAPerson/hogSVMDetector-peopleFlow1.txt","wb");
if( NULL == fp )
{
return;
}
for(int i=0; i<3780; i++)
{
fprintf(fp,"%f \n",re->data.fl[i]);
}
float rho = svm.get_rho();
fprintf(fp, "%f", rho);
cout<<"E:/work/INRIAPerson/hogSVMDetector.txt 保存完毕"<<endl;//保存HOG能识别的分类器
fclose(fp);
return;
}
int main()
{
Train();
return 0;
}