Exemplo n.º 1
0
    bool updateModule()
    {
        if (ImageOf<PixelRgb> *iImg=iPort.read())
        {
            LockGuard lg(mutex);
            ImageOf<PixelRgb> &oImg=oPort.prepare();
            oImg=*iImg;

            cv::Mat frame=cv::cvarrToMat((IplImage*)oImg.getIplImage());
            cv::Rect2d result;
            
            if (state==init)
            {
                tracker=cv::Tracker::create("BOOSTING");
                tracker->init(frame,initRect);
                result=initRect;
                state=run;
            }
            else if (state==run)
                tracker->update(frame,result);

            if (state!=idle)
                cv::rectangle(frame,
                              cv::Point((int)result.x,(int)result.y),
                              cv::Point((int)(result.x+result.width),(int)(result.y+result.height)),
                              cv::Scalar(0,255,0),2);

            oPort.write();
        }

        return true;
    }
Exemplo n.º 2
0
void FisherFaceRecognizer::predict(cv::InputArray _src, cv::Ptr<cv::face::PredictCollector> collector) const
{
    qCWarning(DIGIKAM_FACESENGINE_LOG) << "Predicting face image using fisherfaces";

    if (m_projections.empty())
    {
        // throw error if no data (or simply return -1?)
        String error_message = "This Fisherfaces model is not computed yet. Did you call the train method?";
        CV_Error(CV_StsBadArg, error_message);
    }

    Mat src = _src.getMat();//254*254

    //make sure the size of input image is the same as training image
    if (m_src.size() >= 1 && (src.rows != m_src[0].rows || src.cols != m_src[0].cols))
    {
        //resize(src, src, Size(m_src[0].rows, m_src[0].cols), (0, 0), (0, 0), INTER_LINEAR);
        resize(src, src, Size(m_src[0].rows, m_src[0].cols));
    }

    collector->init(0);//here need to confirm

    Mat q = LDA::subspaceProject(m_eigenvectors, m_mean, src.reshape(1, 1));

    //find nearest neighbor
    for (size_t sampleIdx = 0 ; sampleIdx < m_projections.size() ; sampleIdx++)
    {
        double dist = norm(m_projections[sampleIdx], q, NORM_L2);
        int label   = m_labels.at<int>((int) sampleIdx);

        if (!collector->collect(label, dist))
        {
            return;
        }
    }
}