Object Detection using SVM

edit

I was following this answer, which explains step by step the coding for svm. But I am having a runtime error in the predict function, here is the code I came up with

#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>
#include<opencv2/ml.hpp>
#include <iostream>
#include <vector>
using namespace std;
using namespace cv;
using namespace cv::ml;

int main ( int argc, char** argv )
{
    cout << "Manas\n";
    char* filename;
    Mat input_image;
    VideoCapture capture(0);
    FileStorage fs;
    fs.open("SVM.xml", FileStorage::READ);
    Mat SVM_TrainingData;
    Mat SVM_Classes;
    fs["TrainingData"] >> SVM_TrainingData;
    fs["classes"] >> SVM_Classes;
    Ptr<SVM> SVM_params = SVM::create();
    SVM_params->setType(SVM::C_SVC);
    SVM_params->setKernel(SVM::LINEAR);
    SVM_params->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, 100, 0.01));
    Ptr<TrainData> td = TrainData::create(SVM_TrainingData, ROW_SAMPLE, SVM_Classes);
    SVM_params->train(td);
    while (true)
    {
        capture.read(input_image);
        resize(input_image, input_image, Size(200, 200));

        Mat img_gray;
        cvtColor(input_image, img_gray, CV_BGR2GRAY);
        blur(img_gray, img_gray, Size(5,5));
        Mat p= img_gray.reshape(1, img_gray.channels()*img_gray.size().area());
        p.convertTo(p, CV_32F);

        int response = (int)SVM_params->predict( p );
        if(response==1) cout<<"Detected";
        imshow("Plate Detected", input_image);

        int c = waitKey(10);
        if (c == 27)
            break;
    }
    return 0;
}

I am sure I am missing something, but i can't figure out what.Please suggest what else shall i include.

Update

Here is the code that I am using for training my images

#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= 12;
        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(),CV_LOAD_IMAGE_UNCHANGED);
            //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 ...