我是SVM的新手。我曾经使用HAAR Cascading进行物体检测。现在我正在尝试实现SVM进行对象检测。我在网上搜索并了解了基础知识。 我想在编写c ++时使用libsvm。我遇到了很多问题。 任何人都可以逐步解释使用它进行物体检测的过程。 顺便说一句,我调查了opencv documentation of svm。但我无法继续做任何事情。
此外,我获得了此代码,用于训练我的SVM并将其保存到xml文件中。 现在我想要一个可以使用这个xml并在测试用例中检测对象的代码。
#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <cv.h>
#include <highgui.h>
#include <cvaux.h>
#include <iostream>
#include <vector>
#include<string.h>
using namespace std;
using namespace cv;
int main ( int argc, char** argv )
{
cout << "OpenCV Training SVM Automatic Number Plate Recognition\n";
cout << "\n";
char* path_Plates;
char* path_NoPlates;
int numPlates;
int numNoPlates;
int imageWidth=150;
int imageHeight=150;
//Check if user specify image to process
if(1)
{
numPlates= 11;
numNoPlates= 90 ;
path_Plates= "/home/kaushik/opencv_work/Manas6/Pics/Positive_Images/";
path_NoPlates= "/home/kaushik/opencv_work/Manas6/Pics/Negative_Images/i";
}else{
cout << "Usage:\n" << argv[0] << " <num Plate Files> <num Non Plate Files> <path to plate folder files> <path to non plate files> \n";
return 0;
}
Mat classes;//(numPlates+numNoPlates, 1, CV_32FC1);
Mat trainingData;//(numPlates+numNoPlates, imageWidth*imageHeight, CV_32FC1 );
Mat trainingImages;
vector<int> trainingLabels;
for(int i=1; i<= numPlates; i++)
{
stringstream ss(stringstream::in | stringstream::out);
ss<<path_Plates<<i<<".jpg";
try{
const char* a = ss.str().c_str();
printf("\n%s\n",a);
Mat img = imread(ss.str(), CV_LOAD_IMAGE_UNCHANGED);
img= img.clone().reshape(1, 1);
//imshow("Window",img);
//cout<<ss.str();
trainingImages.push_back(img);
trainingLabels.push_back(1);
}
catch(Exception e){;}
}
for(int i=0; i< numNoPlates; i++)
{
stringstream ss(stringstream::in | stringstream::out);
ss << path_NoPlates<<i << ".jpg";
try
{
const char* a = ss.str().c_str();
printf("\n%s\n",a);
Mat img=imread(ss.str(), 0);
//imshow("Win",img);
img= img.clone().reshape(1, 1);
trainingImages.push_back(img);
trainingLabels.push_back(0);
//cout<<ss.str();
}
catch(Exception e){;}
}
Mat(trainingImages).copyTo(trainingData);
//trainingData = trainingData.reshape(1,trainingData.rows);
trainingData.convertTo(trainingData, CV_32FC1);
Mat(trainingLabels).copyTo(classes);
FileStorage fs("SVM.xml", FileStorage::WRITE);
fs << "TrainingData" << trainingData;
fs << "classes" << classes;
fs.release();
return 0;
}
非常感谢任何帮助。
另外,我很乐意就如何实现libsvm进行对象检测提出建议。
答案 0 :(得分:1)
这是一个简单的代码,您可以使用xml文件进行测试:
#include "highgui.h"
#include "opencv2/imgproc/imgproc.hpp"
#include "cv.h"
#include <vector>
#include <string.h>
#include <ml.h>
#include <iostream>
#include <io.h>
using namespace cv;
using namespace std;
int main()
{
FileStorage fs;
fs.open("SVM.xml", FileStorage::READ);
Mat trainingData;
Mat classes;
fs["TrainingData"] >> trainingData;
fs["classes"] >> classes;
CvSVMParams SVM_params;
SVM_params.svm_type = CvSVM::C_SVC;
SVM_params.kernel_type = CvSVM::LINEAR; //CvSVM::LINEAR;
SVM_params.degree = 1;
SVM_params.gamma = 1;
SVM_params.coef0 = 0;
SVM_params.C = 1;
SVM_params.nu = 0;
SVM_params.p = 0;
SVM_params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER, 1000, 0.01);
CvSVM svm(trainingData, classes, Mat(), Mat(), SVM_params);
Mat src = imread("D:\\SVM\\samples\\\pos\\10.jpg");
Mat gray;
cvtColor(src, gray, CV_BGR2GRAY);
Mat p = gray.reshape(1, 1);
p.convertTo(p, CV_32FC1);
int response = (int)svm.predict( p );
if(response ==1 )
{
cout<<"this is a object!"<<endl;
cout<<endl;
}
else
{
cout<<"no object detected!"<<endl;
cout<<endl;
}
return 0;
}
顺便说一下,在运行你提供的代码时似乎没有什么问题,结果表明:&#34; opencv错误,图像步骤是错误的cv :: Mat :: reshape&#34;。你见过了吗?以前这种情况?谢谢。