本次是在一格长条上面训练"通过"关键词,我只准备了100多张样本,然后用起来的效果不是很好,没有耐心搞好多样本,不知道是不是样本量过少,还是其他问题,先把能跑的代码粘这里:
注意一开始运行是报错的,因为pro里面木有添加相关库 pro如下:
OPENCV_ROOT_PATH = /home/yhl/software
INCLUDEPATH += $${OPENCV_ROOT_PATH}/include \
$${OPENCV_ROOT_PATH}/include/opencv \
$${OPENCV_ROOT_PATH}/include/opencv2
LIBS += -L$${OPENCV_ROOT_PATH}/lib
LIBS += -lopencv_core \
-lopencv_highgui \
-lopencv_imgproc \
-lopencv_imgcodecs \
-lopencv_videoio \
-lopencv_ml
LIBS += -L/usr/local/libs \
-lopencv_objdetect
源代码如下:
#include <iostream>
#include <fstream>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/objdetect/objdetect.hpp>
#include <opencv2/ml/ml.hpp>
using namespace std;
using namespace cv;
using namespace cv::ml;
#define PosSamNO 100
#define NegSamNO 51
#define TRAIN 0
#define CENTRAL_CROP true
#define HardExampleNO 0
void svm_hog_detect()
{
//HOG检测器,用来计算HOG描述子的
//检测窗口(48,48),块尺寸(16,16),块步长(8,8),cell尺寸(8,8),直方图bin个数9
// cv::HOGDescriptor hog(cv::Size(48, 48), cv::Size(16, 16), cv::Size(8, 8), cv::Size(8, 8), 9);
HOGDescriptor hog(Size(64,32),Size(8,8),Size(4,4),Size(2,2),9);
//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
int DescriptorDim;
//从XML文件读取训练好的SVM模型
cv::Ptr<cv::ml::SVM> svm = cv::ml::SVM::load("SVM_HOG.xml");
if (svm->empty())
{
std::cout << "load svm detector failed!!!" << std::endl;
return;
}
//特征向量的维数,即HOG描述子的维数
DescriptorDim = svm->getVarCount();
//获取svecsmat,元素类型为float
cv::Mat svecsmat = svm->getSupportVectors();
//特征向量维数
int svdim = svm->getVarCount();
int numofsv = svecsmat.rows;
//alphamat和svindex必须初始化,否则getDecisionFunction()函数会报错
cv::Mat alphamat = cv::Mat::zeros(numofsv, svdim, CV_32F);
cv::Mat svindex = cv::Mat::zeros(1, numofsv, CV_64F);
cv::Mat Result;
double rho = svm->getDecisionFunction(0, alphamat, svindex);
//将alphamat元素的数据类型重新转成CV_32F
alphamat.convertTo(alphamat, CV_32F);
Result = -1 * alphamat * svecsmat;
std::vector<float> vec;
for (int i = 0; i < svdim; ++i)
{
vec.push_back(Result.at<float>(0, i));
}
vec.push_back(rho);
//saving HOGDetectorForOpenCV.txt
std::ofstream fout("HOGDetectorForOpenCV.txt");
for (int i = 0; i < vec.size(); ++i)
{
fout << vec[i] << std::endl;
}
hog.setSVMDetector(vec);
fstream infile("/media/d_2/everyday/0402/m_aim_part.txt");
string str;
while(infile>>str)
{
Mat frame=imread(str);
std::vector<cv::Rect> found, found_1, found_filtered;
//对图片进行多尺度检测
hog.detectMultiScale(frame, found, 0, cv::Size(2, 2), cv::Size(8,8), 1.01, 2); //hog.detectMultiScale(frame, found, 0, cv::Size(4, 4), cv::Size(4,4), 1.2, 2);
for (int i = 0; i<found.size(); i++)
{
if (found[i].x > 0 && found[i].y > 0 && (found[i].x + found[i].width)< frame.cols
&& (found[i].y + found[i].height)< frame.rows)
found_1.push_back(found[i]);
}
//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_1.size(); i++)
{
cv::Rect r = found_1[i];
int j = 0;
for (; j < found_1.size(); j++)
if (j != i && (r & found_1[j]) == found_1[j])
break;
if (j == found_1.size())
found_filtered.push_back(r);
}
//画矩形框,因为hog检测出的矩形框比实际目标框要稍微大些,所以这里需要做一些调整,可根据实际情况调整
for (int i = 0; i<found_filtered.size(); i++)
{
cv::Rect r = found_filtered[i];
//将检测矩形框缩小后绘制,根据实际情况调整
r.x += cvRound(r.width*0.1);
r.width = cvRound(r.width*0.8);
r.y += cvRound(r.height*0.1);
r.height = cvRound(r.height*0.8);
}
for (int i = 0; i<found_filtered.size(); i++)
{
cv::Rect r = found_filtered[i];
cv::rectangle(frame, r.tl(), r.br(), cv::Scalar(0, 0, 255), 2);
}
cv::imshow("detect result", frame);
waitKey(0);
}
//cv::resize(frame, frame, cv::Size(width / 2, height / 2));
//目标矩形框数组
return;
}
int main()
{
HOGDescriptor hog(Size(64,32),Size(8,8),Size(4,4),Size(2,2),9);
int DescriptorDim;
// MySVM svm;
cv::Ptr<cv::ml::SVM> svm = cv::ml::SVM::create();
svm->setType(cv::ml::SVM::Types::C_SVC);
svm->setKernel(cv::ml::SVM::KernelTypes::LINEAR);
svm->setTermCriteria(cv::TermCriteria(cv::TermCriteria::MAX_ITER, 10000000, 1e-6));
if(TRAIN)
{
string ImgName;
ifstream finPos("/media/d_2/everyday/0402/sample_pos/pos.txt");
//ifstream finPos("/media/d_2/everyday/0402/sample_neg/neg_list.txt");
ifstream finNeg("/media/d_2/everyday/0402/sample_neg/neg_list.txt");
Mat sampleFeatureMat;
Mat sampleLabelMat;
for(int num=0; num<PosSamNO && getline(finPos,ImgName); num++)
{
cout<<"process:::"<<ImgName<<endl;
Mat src = imread(ImgName);
// if(CENTRAL_CROP)
// src = src(Rect(16,16,64,128));
resize(src,src,Size(64,32));
vector<float> descriptors;
hog.compute(src,descriptors,Size(4,4));
//
cout<<"dim=:"<<descriptors.size()<<endl;
if( 0 == num )
{
DescriptorDim = descriptors.size();
sampleFeatureMat = Mat::zeros(PosSamNO+NegSamNO+HardExampleNO, DescriptorDim, CV_32FC1);
sampleLabelMat = Mat::zeros(PosSamNO+NegSamNO+HardExampleNO, 1, CV_32SC1);
}
for(int i=0; i<DescriptorDim; i++)
sampleFeatureMat.at<float>(num,i) = descriptors[i];
sampleLabelMat.at<float>(num,0) = 1;
}
for(int num=0; num<NegSamNO && getline(finNeg,ImgName); num++)
{
cout<<"process:::"<<ImgName<<endl;
Mat src = imread(ImgName);
resize(src,src,Size(64,32));
vector<float> descriptors;
hog.compute(src,descriptors,Size(4,4));
//cout<<"deal:"<<descriptors.size()<<endl;
for(int i=0; i<DescriptorDim; i++)
sampleFeatureMat.at<float>(num+PosSamNO,i) = descriptors[i];
sampleLabelMat.at<float>(num+PosSamNO,0) = -1;
}
if(HardExampleNO > 0)
{
ifstream finHardExample("HardExample_2400PosINRIA_12000NegList.txt");
for(int num=0; num<HardExampleNO && getline(finHardExample,ImgName); num++)
{
cout<<"deal:"<<ImgName<<endl;
ImgName = "D:\\DataSet\\HardExample_2400PosINRIA_12000Neg\\" + ImgName;
Mat src = imread(ImgName);
//resize(src,img,Size(64,128));
vector<float> descriptors;
hog.compute(src,descriptors,Size(8,8));
//cout<<"deal:"<<descriptors.size()<<endl;
for(int i=0; i<DescriptorDim; i++)
sampleFeatureMat.at<float>(num+PosSamNO+NegSamNO,i) = descriptors[i];
sampleLabelMat.at<float>(num+PosSamNO+NegSamNO,0) = -1;
}
}
//训练SVM分类器
//迭代终止条件,当迭代满1000次或误差小于FLT_EPSILON时停止迭代
std::cout << "开始训练SVM分类器" << std::endl;
cv::Ptr<cv::ml::TrainData> td = cv::ml::TrainData::create(sampleFeatureMat, cv::ml::SampleTypes::ROW_SAMPLE, sampleLabelMat);
//训练分类器
svm->train(td);
std::cout << "训练完成" << std::endl;
//将训练好的SVM模型保存为xml文件
svm->save("SVM_HOG.xml");
}else
{
svm_hog_detect();
}
return true;
}