yolorecognizer.cpp

#include "yolorecognizer.h"

vector<string> classes;

YoloRecognizer::YoloRecognizer(QString fileName){
    _fileName = fileName;
};

void YoloRecognizer::analyzeVideo(){
    // Load names of classes
    string classesFile = "coco.names";
    ifstream ifs(classesFile.c_str());
    string line;

    // Load the network
    Net net = readNetFromDarknet(modelConfiguration, modelWeights);
    net.setPreferableBackend(DNN_BACKEND_OPENCV);
    net.setPreferableTarget(DNN_TARGET_CPU);

    QMediaPlayer* player = new QMediaPlayer;
    QVideoWidget* vw = new QVideoWidget;

    player->setVideoOutput(vw);
    player->setMedia(QUrl::fromUserInput(_fileName));
    vw->setGeometry(100,100,300,400);
    vw->show();

    player->play();

    // Open a video file or an image file or a camera stream.
        string str, outputFile;
        VideoCapture cap;
        VideoWriter video;
        Mat frame, blob;

        try {

            outputFile = "yolo_out_cpp.avi";
                // Open the video file
                str = _fileName.toUtf8().constData();
                ifstream ifile(str);
                if (!ifile) throw("error");
                cap.open(str);
                str.replace(str.end()-4, str.end(), "_yolo_out_cpp.avi");
                outputFile = str;
            }catch(...) {
            cout << "Could not open the input image/video stream" << endl;
        }

        // Get the video writer initialized to save the output video
            video.open(outputFile, VideoWriter::fourcc('M','J','P','G'), 28, Size(cap.get(CAP_PROP_FRAME_WIDTH), cap.get(CAP_PROP_FRAME_HEIGHT)));


        // Create a window
        static const string kWinName = "Deep learning object detection in OpenCV";
        namedWindow(kWinName, WINDOW_NORMAL);

        // Process frames.
        while (waitKey(1) < 0)
        {
            // get frame from the video
            cap >> frame;

            // Stop the program if reached end of video
            if (frame.empty()) {
                cout << "Done processing !!!" << endl;
                cout << "Output file is stored as " << outputFile << endl;
                waitKey(3000);
                break;
            }
            // Create a 4D blob from a frame.
            blobFromImage(frame, blob, 1/255.0, Size(INPUT_IMAGE_WIDTH, INPUT_IMAGE_HEIGHT), Scalar(0,0,0), true, false);

            //Sets the input to the network
            net.setInput(blob);

            // Runs the forward pass to get output of the output layers
            vector<Mat> outs;
            net.forward(outs, getOutputsNames(net));

            // Remove the bounding boxes with low confidence
            postprocess(frame, outs);

            // Put efficiency information. The function getPerfProfile returns the overall time for inference(t) and the timings for each of the layers(in layersTimes)
            vector<double> layersTimes;
            double freq = getTickFrequency() / 1000;
            double t = net.getPerfProfile(layersTimes) / freq;
            string label = format("Inference time for a frame : %.2f ms", t);
            putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 0, 255));

            // Write the frame with the detection boxes
            Mat detectedFrame;
            frame.convertTo(detectedFrame, CV_8U);

            video.write(detectedFrame);

            imshow(kWinName, frame);

        }

        cap.release();
        video.release();
}

// Remove the bounding boxes with low confidence using non-maxima suppression
void YoloRecognizer::postprocess(Mat& frame, const vector<Mat>& outs)
{
    vector<int> classIds;
    vector<float> confidences;
    vector<Rect> boxes;

    for (size_t i = 0; i < outs.size(); ++i)
    {
        float* data = (float*)outs[i].data;
        for (int j = 0; j < outs[i].rows; ++j, data += outs[i].cols)
        {
            Mat scores = outs[i].row(j).colRange(5, outs[i].cols);
            Point classIdPoint;
            double confidence;
            // Get the value and location of the maximum score
            minMaxLoc(scores, 0, &confidence, 0, &classIdPoint);
            if (confidence > CONFIDENT_THRESHOLD)
            {
                int centerX = (int)(data[0] * frame.cols);
                int centerY = (int)(data[1] * frame.rows);
                int width = (int)(data[2] * frame.cols);
                int height = (int)(data[3] * frame.rows);
                int left = centerX - width / 2;
                int top = centerY - height / 2;

                classIds.push_back(classIdPoint.x);
                confidences.push_back((float)confidence);
                boxes.push_back(Rect(left, top, width, height));
            }
        }
    }

    // Perform non maximum suppression to eliminate redundant overlapping boxes with
    // lower confidences
    vector<int> indices;
    NMSBoxes(boxes, confidences, CONFIDENT_THRESHOLD, NON_MAXIMUM_SUPPRESSION, indices);
    for (size_t i = 0; i < indices.size(); ++i)
    {
        int idx = indices[i];
        Rect box = boxes[idx];
        drawPred(classIds[idx], confidences[idx], box.x, box.y,
                 box.x + box.width, box.y + box.height, frame);
    }
}

// Draw the predicted bounding box
void YoloRecognizer::drawPred(int classId, float conf, int left, int top, int right, int bottom, Mat& frame)
{
    //Draw a rectangle displaying the bounding box
    rectangle(frame, Point(left, top), Point(right, bottom), Scalar(255, 178, 50), 3);

    //Get the label for the class name and its confidence
    string label = format("%.2f", conf);
    if (!classes.empty())
    {
        CV_Assert(classId < (int)classes.size());
        label = classes[classId] + ":" + label;
    }

    //Display the label at the top of the bounding box
    int baseLine;
    Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
    top = max(top, labelSize.height);
    rectangle(frame, Point(left, top - round(1.5*labelSize.height)), Point(left + round(1.5*labelSize.width), top + baseLine), Scalar(255, 255, 255), FILLED);
    putText(frame, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 0.75, Scalar(0,0,0),1);
}

// Get the names of the output layers
vector<String> YoloRecognizer::getOutputsNames(const Net& net)
{
    static vector<String> names;
    if (names.empty())
    {
        //Get the indices of the output layers, i.e. the layers with unconnected outputs
        vector<int> outLayers = net.getUnconnectedOutLayers();

        //get the names of all the layers in the network
        vector<String> layersNames = net.getLayerNames();

        // Get the names of the output layers in names
        names.resize(outLayers.size());
        for (size_t i = 0; i < outLayers.size(); ++i)
        names[i] = layersNames[outLayers[i] - 1];
    }
    return names;
}