Trainning SVM using HOG feature [closed]

edit

I am trying to train SVM to predict the traffic sign, but I always get the same predict result for difference input image.

Can anyone explain why for me?

Here is my code:

vector < Mat > storeInput;
vector < int > labels;


//load input image
char FullFileName[10000];
char FirstFileName[10000] = "./Prohibit_Blue/prohibit_blue";
int fileNum = 4;
for (int i = 0; i< fileNum; ++i)
{
    //pos
    sprintf_s(FullFileName, "%s_%01d.jpg", FirstFileName, i + 1);
    printf("%s\n", FullFileName);

    //read image file  
    Mat img, img_gray, dst, feature;
    img = imread(FullFileName);

    //resizing  
    resize(img, img, Size(64, 64));

    //convert to grayscale
    cvtColor(img, img_gray, CV_BGR2GRAY);

    storeInput.push_back(img_gray);
    labels.push_back(i);
    imshow("prohibit_blue", img_gray);

    waitKey(5);
}


//compute HOG
cout << "Start hog...";
Size size = Size(64, 64);
Size block_size = Size(size.width / 4, size.height / 4);
Size block_stride = Size(size.width / 8, size.height / 8);
Size cell_size = block_stride;
int num_bins = 9;
HOGDescriptor hog(size, block_size, block_stride, cell_size, num_bins);
Mat inputHOG;
vector< Point > location;
vector< float > descriptors;

for (int i = 0; i < storeInput.size(); i++)
{
    inputHOG = storeInput[i];
    hog.compute(inputHOG, descriptors, Size(0, 0), Size(0, 0), location);
    //cout << "descriptors size: " << descriptors.size() << endl;

    gradient_lst.push_back(Mat(descriptors).clone());

}
cout << "...[done]" << endl;


//trainning with SVM
Mat trainData;

//--Convert data before trainning
const int rows = (int)train_samples.size();
const int cols = (int)std::max(train_samples[0].cols, train_samples[0].rows);
cv::Mat tmp(1, cols, CV_32FC1); //< used for transposition if needed
trainData = cv::Mat(rows, cols, CV_32FC1);
vector< Mat >::const_iterator itr = train_samples.begin();
vector< Mat >::const_iterator end = train_samples.end();
for (int i = 0; itr != end; ++itr, ++i)
{
    if (itr->cols == 1)
    {
        transpose(*(itr), tmp);
        tmp.copyTo(trainData.row(i));
    }
    else if (itr->rows == 1)
    {
        itr->copyTo(trainData.row(i));
    }
}


cout << "Start training...";

Ptr<SVM> svm = SVM::create();
svm->setType(SVM::C_SVC);                                           // classify more than 2 class
svm->setKernel(SVM::LINEAR);                                       
svm->setC(10);
svm->setTermCriteria(cvTermCriteria(CV_TERMCRIT_ITER + CV_TERMCRIT_EPS, 1000, 1e-6));

svm->train(trainData, ROW_SAMPLE, Mat(labels));

cout << "...[done]" << endl;
svm->save("./genfiles/trainSVM_Recog_blue.xml");
cout << "Save XML ..... done!!!" << endl;


//test image

Mat testImg = imread("./Prohibit_Blue/prohibit_blue_1.jpg");
//resizing
resize(img, img, Size(64, 64));
//gray
cvtColor(img, img_gray, COLOR_BGR2GRAY);

//Extract HogFeature
Size size = Size(64, 64);
Size block_size = Size(size.width / 4, size.height / 4);
Size block_stride = Size(size.width / 8, size.height / 8);
Size cell_size = block_stride;
int num_bins = 9;
HOGDescriptor hog(size, block_size, block_stride, cell_size, num_bins);
vector< float> descriptorsValues;
vector< Point> locations;

hog.compute(img_gray, descriptorsValues, Size(0, 0), Size(0, 0), locations);

//vector to Mat
Mat fm = Mat(descriptorsValues);

//Classification data
Ptr<SVM> svm2 = Algorithm::load<SVM>("./genfiles/trainSVM_Recog_blue.xml");
std::cout << "Model Loaded" << std::endl;

Mat image1d(1, fm.rows, CV_32FC1);
imshow("gradient 1", image1d);

float result = svm2->predict(image1d);
cout << "Predict value: " << result << endl;