Method ParseRFDETR

src/Detector/OCVDNNDetector.cpp:884–940

\brief OCVDNNDetector::ParseRFDETR \param crop \param detections \param tmpRegions

/// \param tmpRegions
///
void OCVDNNDetector::ParseRFDETR(const cv::Rect& crop, std::vector<cv::Mat>& detections, regions_t& tmpRegions)
{
    int rows = detections[0].size[1];
    int dimensionsDets = detections[0].size[2];
    int dimensionsLabels = detections[1].size[2];

    //0: name: input, size : 1x3x560x560
    //1: name: dets, size : 1x300x4
    //2: name: labels, size : 1x300x91

    float* dets = (float*)detections[0].data;
    float* labels = (float*)detections[1].data;

    float x_factor = crop.width / static_cast<float>(m_inWidth);
    float y_factor = crop.height / static_cast<float>(m_inHeight);

    auto L2Conf = [](float v)
    {
        return 1.f / (1.f + std::exp(-v));
    };

    for (int i = 0; i < rows; ++i)
    {
        float maxClassScore = L2Conf(labels[0]);
        size_t classId = 0;
        for (size_t cli = 1; cli < static_cast<size_t>(dimensionsLabels); ++cli)
        {
            auto conf = L2Conf(labels[cli]);
            if (maxClassScore < conf)
            {
                maxClassScore = conf;
                classId = cli;
            }
        }
        if (classId > 0)
            --classId;

        if (maxClassScore > m_confidenceThreshold)
        {
            float x = dets[0];
            float y = dets[1];
            float w = dets[2];
            float h = dets[3];

            int left = cvRound((x - 0.5f * w) * x_factor);
            int top = cvRound((y - 0.5f * h) * y_factor);

            int width = cvRound(w * x_factor);
            int height = cvRound(h * y_factor);

            if (m_classesWhiteList.empty() || m_classesWhiteList.find(T2T(classId)) != std::end(m_classesWhiteList))
                tmpRegions.emplace_back(cv::Rect(left + crop.x, top + crop.y, width, height), T2T(classId), static_cast<float>(maxClassScore));
        }
        dets += dimensionsDets;
        labels += dimensionsLabels;
    }
}