problem with facedetection model from dnn module

edit

here's some working code. indeed, you have to parse the prediction output in the same way, as it is with other ssd object detection models:

• you can also use a "minified" uint8 tf model (smaller load size)
• the network was trained on 300x300 input, however 128x96 seems to be the best compromise between speed & accuracy
• Scalar(104, 177, 123, 0) is the mean value of the train dataset. (we usually subtract that from the image, to get "mean-free" input, it's a common normalization technique)

#include "opencv2/opencv.hpp"
#include "opencv2/dnn.hpp"

using namespace cv;
using namespace std;

void drawPred(int classId, float conf, int left, int top, int right, int bottom, Mat& frame)
{
    rectangle(frame, Point(left, top), Point(right, bottom), Scalar(0, 255, 0));

    std::string label = format("%.2f %d", conf, classId);

    int baseLine;
    Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);

    top = max(top, labelSize.height);
    rectangle(frame, Point(left, top - labelSize.height),
              Point(left + labelSize.width, top + baseLine), Scalar::all(255), FILLED);
    putText(frame, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 0.5, Scalar());
}

int main(int argc, char** argv)
{
    float confThreshold = 0.25;

    //dnn::Net net = dnn::readNetFromCaffe("c:/data/mdl/face_detector.prototxt", "c:/data/mdl/res10_300x300_ssd_iter_140000_fp16.caffemodel");
    dnn::Net net = dnn::readNetFromTensorflow("c:/data/mdl/opencv_face_detector_uint8.pb","c:/data/mdl/opencv_face_detector.pbtxt");

    VideoCapture cap(0);
    static bool once=false;
    while(1) {
        Mat f;
        cap.read(f);

        Mat blob = dnn::blobFromImage(f, 1, Size(128,96), Scalar(104, 177, 123, 0), false, false);
        net.setInput(blob);
        Mat res = net.forward("detection_out");

        // just for debugging; --  print out the network layers (once)
        if (!once) {
            dnn::MatShape ms1 { blob.size[0], blob.size[1] , blob.size[2], blob.size[3] };
            vector<String> lnames = net.getLayerNames();
            for (size_t i=1; i<lnames.size(); i++) { // skip __NetInputLayer__
                Ptr<dnn::Layer> lyr = net.getLayer((unsigned)i);
                vector<dnn::MatShape> in,out;
                net.getLayerShapes(ms1,i,in,out);
                cerr << format("%-38s %-13s", lyr->name.c_str(), lyr->type.c_str());
                for (auto j:in) cerr << "i" << Mat(j).t() << "\t";
                for (auto j:out) cerr << "o" << Mat(j).t() << "\t";
                cerr << endl;
            }
            once = true;
        }


        Mat faces(res.size[2],res.size[3], CV_32F, res.ptr<float>());
        //cout << res.size << " " << faces.size() << endl;
        //cout << faces << endl;
        for (int i=0; i<faces.rows; i++)
        {
            float *data = faces.ptr<float>(i);
            float confidence = data[2];
            if (confidence > confThreshold)
            {
                int left = (int)(data[3] * f.cols);
                int top = (int)(data[4] * f.rows);
                int right = (int)(data[5] * f.cols);
                int bottom = (int)(data[6] * f.rows);
                int classId = (int)(data[1]) - 1;  // Skip 0th background class id.
                drawPred(classId, confidence, left, top, right, bottom, f);
                cout << classId<< " " << confidence<< " " << left<< " " << top<< " " << right<< " " << bottom<< endl;
            }
        }
        imshow("F",f);
        int k = waitKey(19);
        if (k>0) break;

    }
    return 0;
}