Opencv neural network, incorrect prediction

Question

I am trying to create a neural network in C ++ with OpenCV. The goal is to recognize road signs. I created the network this way, but it predicts poorly because it returns strange results:

Samples of images from the training selection are as follows:

Can anyone help?

  trainNN() {
    char* templates_directory[] = {
        "speed50ver1\\", 
        "speed60ver1\\", 
        "speed70ver1\\",
        "speed80ver1\\"
    };

    int const numFilesChars[]={ 213, 100, 385, 163};

    char const strCharacters[] = { '5', '6', '7', '8' };

    Mat trainingData; 
    Mat trainingLabels(0, 0, CV_32S);

    int const numCharacters = 4;  

    // load images from directory
    for (int i = 0; i != numCharacters; ++i) {
        int numFiles = numFilesChars[i];

        DIR *dir;
        struct dirent *ent;

        char* s1 = templates_directory[i];

        if ((dir = opendir (s1)) != NULL) {
            Size size(80, 80);

            while ((ent = readdir (dir)) != NULL) {
                string s = s1;
                s.append(ent->d_name);

                if(s.substr(s.find_last_of(".") + 1) == "jpg") {
                    Mat img = imread(s,0);
                    Mat img_mat;
                    resize(img, img_mat, size);
                    Mat new_img = img_mat.reshape(1, 1);
                    trainingData.push_back(new_img);
                    trainingLabels.push_back(i);

                }

            }
            int b = 0;
            closedir (dir);
        } else {
            /* could not open directory */
            perror ("");
        }
    }

    trainingData.convertTo(trainingData, CV_32FC1);

    Mat trainClasses(trainingData.rows, numCharacters, CV_32FC1);
    for( int i = 0; i !=  trainClasses.rows; ++i){
        int const labels = *trainingLabels.ptr<int>(i);
        auto train_ptr = trainClasses.ptr<float>(i);
        for(int k = 0; k != trainClasses.cols; ++k){
            *train_ptr = k != labels ? 0 : 1;
            ++train_ptr;
        }
    }

    int layers_d[] = { trainingData.cols, 10,  numCharacters};
    Mat layers(1, 3, CV_32SC1, layers_d);
    ann.create(layers, CvANN_MLP::SIGMOID_SYM, 1, 1);

    CvANN_MLP_TrainParams params = CvANN_MLP_TrainParams(
        // terminate the training after either 1000
        // iterations or a very small change in the
        // network wieghts below the specified value
        cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 1000, 0.000001),

        // use backpropogation for training
        CvANN_MLP_TrainParams::BACKPROP,

        // co-efficents for backpropogation training
        // (refer to manual)
        0.1,
        0.1);

    int iterations = ann.train(trainingData, trainClasses, cv::Mat(), cv::Mat(), params);

    CvFileStorage* storage = cvOpenFileStorage( "neural_network_2.xml", 0, CV_STORAGE_WRITE );
    ann.write(storage,"digit_recognition");
    cvReleaseFileStorage(&storage);

}  


void analysis(char* file, bool a) {
    //trainNN(a);
    read_nn();


    // load image
    Mat img = imread(file,  0);

    Size my_size(80,80);
    resize(img, img, my_size);

    Mat r_img = img.reshape(1,1);

    r_img.convertTo(r_img, CV_32FC1);   

    Mat classOut(1,4,CV_32FC1); 

    ann.predict(r_img, classOut);

    double min1, max1;
    cv::Point min_loc, max_loc;
    minMaxLoc(classOut, &min1, &max1, &min_loc, &max_loc);
    int x = max_loc.x;


    //create windows
    namedWindow("Original Image", CV_WINDOW_AUTOSIZE);
    imshow("Original Image", img);

    waitKey(0); //wait for key press

    img.release();
    rr.release();

    destroyAllWindows(); //destroy all open windows
}

strange results: answer 3 for this entry (because I have only 4 classes - speed limit 50, 60, 70, 80). This is true for a speed limit sign of 80.

But for the rest of the inputs, the results are incorrect. They are the same for signs 50, 60, 70. max1 = min1 = 1.02631 ... (as in the first picture) This is strange.

Answer 1

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, , - read_nn(). , - : ann.load("neural_network_2.xml");

, , , . , . () , ANN ? , ANN ?

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Answer 2

NN OpenCV , , , , :

" cvANN_MLP : cvANN_MLP:: SIGMOID_SYM, [-1,1] [0,1].

, :

*train_ptr = k != labels ? 0 : 1;

, :

*train_ptr = k != labels ? -1 : 1;

, .