c++实现人脸、性别及年龄检测

tuple>> getFaceBox(Net net, Mat& frame, double conf_threshold)
{
    Mat frameOpenCVDNN = frame.clone();
    int frameHeight = frameOpenCVDNN.rows;
    int frameWidth = frameOpenCVDNN.cols;
    double inScaleFactor = 1.0;
    Size size = Size(300, 300);
    // std::vector meanVal = {104, 117, 123};
    Scalar meanVal = Scalar(104, 117, 123);

    cv::Mat inputBlob;
    inputBlob = cv::dnn::blobFromImage(frameOpenCVDNN, inScaleFactor, size, meanVal, true, false);

    net.setInput(inputBlob, "data");
    cv::Mat detection = net.forward("detection_out");

    cv::Mat detectionMat(detection.size[2], detection.size[3], CV_32F, detection.ptr());

    vector> bboxes;

    for (int i = 0; i < detectionMat.rows; i++) {
        float confidence = detectionMat.at(i, 2);

        if (confidence > conf_threshold) {
            int x1 = static_cast(detectionMat.at(i, 3) * frameWidth);
            int y1 = static_cast(detectionMat.at(i, 4) * frameHeight);
            int x2 = static_cast(detectionMat.at(i, 5) * frameWidth);
            int y2 = static_cast(detectionMat.at(i, 6) * frameHeight);
            vector box = { x1, y1, x2, y2 };
            bboxes.push_back(box);
            cv::rectangle(frameOpenCVDNN, cv::Point(x1, y1), cv::Point(x2, y2), cv::Scalar(0, 0, 255));
        }
    }

    return make_tuple(frameOpenCVDNN, bboxes);
}

int main()
{
    clock_t startTime, endTime;
    
    cv::Mat frame;
    int padding = 20;
    Scalar MODEL_MEAN_VALUES = Scalar(78.4263377603, 87.7689143744, 114.895847746);
    vector ageList = { "(0-2)","(4-6)","(8-12)","(15-20)","(25-32)","(38-43)","(48-53)","(60-100)" };
    vector genderList = { "Male","Female" };

    std::string netRootPath = "D://";
    std::string imagepath = "D://sunsuyuan//project//data//doublefusion//results//FittingSmpl//color.png";
    //frame = cv::imread(netRootPath + "sample3.jpg", 1);
    frame = cv::imread(netRootPath+"3.png", 1);
   // frame = cv::imread(imagepath, 1);
    cv::cvtColor(frame, frame, CV_BGRA2BGR);
    if (frame.empty()) {
        fprintf(stderr, "cv::imread %s failed\n", netRootPath + "sample3.jpg");
        return -1;
    }

    // Load Network
    
    Net ageNet = readNet(netRootPath+"age_net.caffemodel", netRootPath+"age_deploy.prototxt");
    Net genderNet = readNet(netRootPath+"gender_net.caffemodel", netRootPath+"gender_deploy.prototxt");
    Net faceNet = readNet(netRootPath+"opencv_face_detector_uint8.pb", netRootPath+"opencv_face_detector.pbtxt");

    vector> bboxes;
    Mat frameFace;
    tie(frameFace, bboxes) = getFaceBox(faceNet, frame, 0.7);

    if (bboxes.size() == 0) {
        cout << "No face detected, checking next frame." << endl;
    }
    else {
        startTime = clock();
        for (auto it = begin(bboxes); it != end(bboxes); ++it) {
            Rect rec(it->at(0) - padding, it->at(1) - padding, it->at(2) - it->at(0) + 2 * padding, it->at(3) - it->at(1) + 2 * padding);
            Mat face = frame(rec); // take the ROI of box on the frame

            Mat blob = blobFromImage(face, 1, Size(227, 227), MODEL_MEAN_VALUES, false);

            genderNet.setInput(blob);
            vector genderPreds = genderNet.forward();
            // find max element index (distance function does the argmax() work in C++)
            int max_index_gender = std::distance(genderPreds.begin(), max_element(genderPreds.begin(), genderPreds.end()));
            string gender = genderList[max_index_gender];


            ageNet.setInput(blob);
            vector agePreds = ageNet.forward();
            // finding maximum indicd in the age_preds vector
            int max_indice_age = std::distance(agePreds.begin(), max_element(agePreds.begin(), agePreds.end()));
            string age = ageList[max_indice_age];

            cout << "Gender: " << gender << "  Age: " << age << endl;

            string label = gender + ", " + age; // label
            cv::putText(frameFace, label, Point(it->at(0), it->at(1) - 15), cv::FONT_HERSHEY_SIMPLEX, 0.7, Scalar(0, 255, 255));
        }
        endTime = clock();//计时结束
        std::cout << "The run time is: " << (double)1000*(endTime - startTime) / CLOCKS_PER_SEC << "ms" <