Opencv运用训练好的SVM分类器进行行人检测（效果不理想

#include <opencv\highgui.h>

#include<opencv\cxcore.h>

#include <fstream>  

#include<opencv2\core.hpp>

#include<iostream>

#include <cstdio>

#include <string>

#include <sstream>

#include "opencv2/opencv.hpp"  

#include "opencv2/video/background_segm.hpp"  

#include <iostream>    

#include <string>     

#include <opencv2/objdetect/objdetect.hpp>  

#include <opencv2/ml/ml.hpp>  

#include <opencv2/opencv.hpp>    

using namespace cv;

using namespace cv::ml;

using namespace std;

class MySVM : public  ml::SVM

{

public:
//获得SVM的决策函数中的alpha数组
double get_svm_rho()
{
return this->getDecisionFunction(0, svm_alpha, svm_svidx);
}

//获得SVM的决策函数中的rho参数,即偏移量

vector<float> svm_alpha;
vector<float> svm_svidx;
float  svm_rho;

};

int main()

{
namedWindow("src", 0);
//检测窗口(64,128),块尺寸(16,16),块步长(8,8),cell尺寸(8,8),直方图bin个数9
//HOGDescriptor hog(Size(64, 128), Size(16, 16), Size(8, 8), Size(8, 8), 9);//HOG检测器，用来计算HOG描述子的
int DescriptorDim;//HOG描述子的维数，由图片大小、检测窗口大小、块大小、细胞单元中直方图bin个数决定
 //Ptr svm = ml::SVM::create();
Ptr<ml::SVM>svm = ml::SVM::load<ml::SVM>("D://SVM_HOG_11_23.xml");
DescriptorDim = svm->getVarCount();//特征向量的维数，即HOG描述子的维数
Mat supportVector = svm->getSupportVectors();//支持向量的个数
int supportVectorNum = supportVector.rows;
cout << "支持向量个数：" << supportVectorNum << endl;
//-------------------------------------------------
vector<float> svm_alpha;
vector<float> svm_svidx;
float  svm_rho;

svm_rho = svm->getDecisionFunction(0, svm_alpha, svm_svidx);
//-------------------------------------------------
Mat alphaMat = Mat::zeros(1, supportVectorNum, CV_32FC1);//alpha向量，长度等于支持向量个数
Mat supportVectorMat = Mat::zeros(supportVectorNum, DescriptorDim, CV_32FC1);//支持向量矩阵
Mat resultMat = Mat::zeros(1, DescriptorDim, CV_32FC1);//alpha向量乘以支持向量矩阵的结果
supportVectorMat = supportVector;
////将alpha向量的数据复制到alphaMat中
//double * pAlphaData = svm.get_alpha_vector();//返回SVM的决策函数中的alpha向量
for (int i = 0; i < supportVectorNum; i++)
{
alphaMat.at<float>(0, i) = svm_alpha[i];
}

//计算-(alphaMat * supportVectorMat),结果放到resultMat中
//gemm(alphaMat, supportVectorMat, -1, 0, 1, resultMat);//不知道为什么加负号？
resultMat = -1 * alphaMat * supportVectorMat;

//得到最终的setSVMDetector(const vector& detector)参数中可用的检测子
vector<float> myDetector;
//将resultMat中的数据复制到数组myDetector中
for (int i = 0; i < DescriptorDim; i++)
{
myDetector.push_back(resultMat.at<float>(0, i));
}
//最后添加偏移量rho，得到检测子
myDetector.push_back(svm_rho);
cout << "检测子维数：" << myDetector.size() << endl;
//设置HOGDescriptor的检测子
HOGDescriptor myHOG;

//myHOG.setSVMDetector(HOGDescriptor::getDefaultPeopleDetector());
myHOG.setSVMDetector(myDetector);
//myHOG.setSVMDetector(HOGDescriptor::getDefaultPeopleDetector());

/**************读入图片进行HOG行人检测******************/
//Mat src = imread("00000.jpg");
//Mat src = imread("2007_000423.jpg");
Size s1(128, 128);
Size s2(64, 64);
myHOG.winSize = s1;
myHOG.blockSize = s1;
myHOG.blockStride = s1;
myHOG.cellSize = s2;
myHOG.nbins = 9;

Mat frame;

while (true)
{

Mat src = imread("D://ppp01.jpg");

vector<Rect> found, found_filtered;//矩形框数组
  //cout << "进行多尺度HOG人体检测" << endl;
myHOG.detectMultiScale(src, found, 0, Size(32, 32), Size(32, 32), 1.05, 2);//对图片进行多尺度行人检测
  //cout << "找到的矩形框个数：" << found.size() << endl;

  //找出所有没有嵌套的矩形框r,并放入found_filtered中,如果有嵌套的话,则取外面最大的那个矩形框放入found_filtered中
for (int i = 0; i < found.size(); i++)
{
Rect r = found[i];
int j = 0;
for (; j < found.size(); j++)
if (j != i && (r & found[j]) == r)
break;
if (j == found.size())
found_filtered.push_back(r);
}

//画矩形框，因为hog检测出的矩形框比实际人体框要稍微大些,所以这里需要做一些调整
for (int i = 0; i < found_filtered.size(); i++)
{
Rect r = found_filtered[i];
r.x += cvRound(r.width*0.1);
r.width = cvRound(r.width*0.8);
r.y += cvRound(r.height*0.07);
r.height = cvRound(r.height*0.8);
rectangle(src, r.tl(), r.br(), Scalar(255, 255, 255), 3);
}

imshow("src", src);
waitKey(0);//注意：imshow之后必须加waitKey，否则无法显示图像

}

}