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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");
        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;
}

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;
}