C++ Net::getPerfProfile方法代码示例

    void expectNoFallbacks(Net& net)
    {
        // Check if all the layers are supported with current backend and target.
        // Some layers might be fused so their timings equal to zero.
        std::vector<double> timings;
        net.getPerfProfile(timings);
        std::vector<String> names = net.getLayerNames();
        CV_Assert(names.size() == timings.size());

        for (int i = 0; i < names.size(); ++i)
        {
            Ptr<dnn::Layer> l = net.getLayer(net.getLayerId(names[i]));
            bool fused = !timings[i];
            if ((!l->supportBackend(backend) || l->preferableTarget != target) && !fused)
                CV_Error(Error::StsNotImplemented, "Layer [" + l->name + "] of type [" +
                         l->type + "] is expected to has backend implementation");
        }
    }

int main(int argc, char** argv)
{
    CommandLineParser parser(argc, argv, keys);
    parser.about("Use this script to run object detection deep learning networks using OpenCV.");
    if (argc == 1 || parser.has("help"))
    {
        parser.printMessage();
        return 0;
    }

    confThreshold = parser.get<float>("thr");
    float scale = parser.get<float>("scale");
    Scalar mean = parser.get<Scalar>("mean");
    bool swapRB = parser.get<bool>("rgb");
    int inpWidth = parser.get<int>("width");
    int inpHeight = parser.get<int>("height");

    // Open file with classes names.
    if (parser.has("classes"))
    {
        std::string file = parser.get<String>("classes");
        std::ifstream ifs(file.c_str());
        if (!ifs.is_open())
            CV_Error(Error::StsError, "File " + file + " not found");
        std::string line;
        while (std::getline(ifs, line))
        {
            classes.push_back(line);
        }
    }

    // Load a model.
    CV_Assert(parser.has("model"));
    Net net = readNet(parser.get<String>("model"), parser.get<String>("config"), parser.get<String>("framework"));
    net.setPreferableBackend(parser.get<int>("backend"));
    net.setPreferableTarget(parser.get<int>("target"));

    // Create a window
    static const std::string kWinName = "Deep learning object detection in OpenCV";
    namedWindow(kWinName, WINDOW_NORMAL);
    int initialConf = (int)(confThreshold * 100);
    createTrackbar("Confidence threshold, %", kWinName, &initialConf, 99, callback);

    // Open a video file or an image file or a camera stream.
    VideoCapture cap;
    if (parser.has("input"))
        cap.open(parser.get<String>("input"));
    else
        cap.open(0);

    // Process frames.
    Mat frame, blob;
    while (waitKey(1) < 0)
    {
        cap >> frame;
        if (frame.empty())
        {
            waitKey();
            break;
        }

        // Create a 4D blob from a frame.
        Size inpSize(inpWidth > 0 ? inpWidth : frame.cols,
                     inpHeight > 0 ? inpHeight : frame.rows);
        blobFromImage(frame, blob, scale, inpSize, mean, swapRB, false);

        // Run a model.
        net.setInput(blob);
        if (net.getLayer(0)->outputNameToIndex("im_info") != -1)  // Faster-RCNN or R-FCN
        {
            resize(frame, frame, inpSize);
            Mat imInfo = (Mat_<float>(1, 3) << inpSize.height, inpSize.width, 1.6f);
            net.setInput(imInfo, "im_info");
        }
        std::vector<Mat> outs;
        net.forward(outs, getOutputsNames(net));

        postprocess(frame, outs, net);

        // Put efficiency information.
        std::vector<double> layersTimes;
        double freq = getTickFrequency() / 1000;
        double t = net.getPerfProfile(layersTimes) / freq;
        std::string label = format("Inference time: %.2f ms", t);
        putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));

        imshow(kWinName, frame);
    }
    return 0;
}