ranjanritesh's profile - activity

platform: Ubuntu 12.04 LTS 64bit

OpenCV version: 2.4.2

I am trying to make a program that captures face (say user 1) using haarcascade.I then run the crop the face are of image and add it to a vector<mat>.I then add diff faces(say user 2) using the same method to the same vector. But I add different labels (say 0 and 1) for the 2 different cases. After some kind of keyboard interrupt,I train the FaceRecognizer using train() with both the vector (Mat and int). From the next frame onwards I take the face area and try to predict the label.

The code is compiling and running fine.But the outputs are a little irritating. It always outputs the same label( on more inspection I found, the label that I "push_back" for the very first frame) and the confidence is always 0.

What I am basically trying to achieve is a kind of online learning FaceRecognizer. But Since the predicted labels are not correct,I assume I have done something wrong. Is online learning even possible with PCA/LDA/LBPH?? I have tried using the same model as well as saving and opening with another model. Below is my code.Am I doing something wrong??Any help will be much appreciated!!! Thanks

    using namespace cv;
    using namespace std;
    vector< Rect_<int> > faces;

    vector<cv::Mat> learnt_face;
    vector<int> learnt_label;

    bool pred = false;
    bool pos_ex = false;

    int main(int argc,char* argv[])
    {
        cv::CascadeClassifier haar_cascade;

        Ptr<cv::FaceRecognizer> model = cv::createEigenFaceRecognizer(0,140.0);
        Ptr<cv::FaceRecognizer> model0 = cv::createEigenFaceRecognizer(0,140.0);
        string fn_haar = string("haarcascade_frontalface_alt.xml");
        haar_cascade.load(fn_haar);

        VideoCapture cap(0);

        Mat img,gray_img,crop_face,crop_face_res;
        for(;;)
        {
            cap>>img;
            cv::cvtColor(img,gray_img,CV_RGB2GRAY);

            haar_cascade.detectMultiScale(gray_img, faces); 

                Rect face_i = faces[0];                 //Take only one face at a time * For debugging Purposes

                //Crop and Resize
                crop_face = gray_img(face_i);
                cv::resize(crop_face, crop_face_res, Size(100,100), 1.0, 1.0, INTER_CUBIC);

                if((!crop_face.empty()) && pred == false && pos_ex == true ) //If cropped,not predicting and learning positive images
                {
                    cout<<"Learning"<<endl;

                    learnt_face.push_back(crop_face_res);
                    learnt_label.push_back(0);

                    if(learnt_face.size() >= 100)
                    {
                        learnt_face.erase( learnt_face.begin());
                        learnt_label.erase( learnt_label.begin());
                    }
                    rectangle(img, face_i, CV_RGB(0, 255,0), 1);    //Green Faces label 0.

                    model->train(learnt_face,learnt_label);

                }
                //If cropped,not predicting and learning negetive images
                else if((!crop_face.empty()) && pred == false && pos_ex == false )
                {
                    cout<<"Learning"<<endl;
                    learnt_face.push_back(crop_face_res);
                    learnt_label.push_back(1);

                    if(learnt_face.size() >= 100)
                    {
                        learnt_face.erase( learnt_face.begin());
                        learnt_label.erase( learnt_label.begin());
                    }
                    rectangle(img, face_i, CV_RGB(255, 0,0), 1); //Red faces label 1.

                    model->train(learnt_face,learnt_label);

                }
                //If cropped and predicting 
                else if((!crop_face.empty()) && pred == true)
                {

                        //model->save("model.xml");
                        //model0->load("model.xml");

                    cout<<"Predicting"<<endl;
                    int prediction = -1;
                    double predicted_confidence = 0.0;
                    cout<<model->getDouble("threshold")<<endl;
                    model->predict(crop_face_res,prediction,predicted_confidence);
                    rectangle(img, face_i, CV_RGB(0, 0,255), 1);

                    string box_text = format("Prediction = %d  Confidence = %f", prediction,predicted_confidence);

                    int pos_x = std::max(face_i.tl().x - 10, 0);
                    int pos_y ...