c代码调用YOLO模型_yolo vc运用

 
#include <fstream>
 
#include <sstream>
 
#include <iostream>
 
#include <opencv2/dnn.hpp>
 
#include <opencv2/imgproc.hpp>
 
#include <opencv2/highgui.hpp>
 
#include<vector>
 
 
 
 
 
using namespace std;
 
using namespace cv;
 
using namespace dnn;
 
 
 
vector<string> classes;
 
 
 
vector<String> getOutputsNames(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;
 
}
 
void 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("%.5f", 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);
 
}
 
void postprocess(Mat& frame, const vector<Mat>& outs, float confThreshold, float nmsThreshold)
 
{
 
    vector<int> classIds;
 
    vector<float> confidences;
 
    vector<Rect> boxes;
 
 
 
    for (size_t i = 0; i < outs.size(); ++i)
 
    {
 
        // Scan through all the bounding boxes output from the network and keep only the
 
        // ones with high confidence scores. Assign the box's class label as the class
        // with the highest score for the box.
        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 > confThreshold)
            {
                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, confThreshold, nmsThreshold, 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);
    }
}
 
int main()
{
    string names_file = "/home/xxp/darknet/data/voc-mask.names";
    String model_def = "/home/xxp/darknet/cfg/yolov3-tiny-mask3.cfg";
    String weights = "/home/xxp/darknet/backup/yolov3-tiny-mask3_best.weights";
 
    int in_w, in_h;
    double thresh = 0.5;
    double nms_thresh = 0.25;
    in_w = in_h = 608;
 
    string img_path = "/home/xxp/darknet/testfiles/120.jpg";
 
    //read names
 
    ifstream ifs(names_file.c_str());
    string line;
    while (getline(ifs, line)) classes.push_back(line);
 
    //init model
    Net net = readNetFromDarknet(model_def, weights);
    net.setPreferableBackend(DNN_BACKEND_OPENCV);
    net.setPreferableTarget(DNN_TARGET_CPU);
 
    //read image and forward
    VideoCapture capture(0);// VideoCapture:OENCV中新增的类，捕获视频并显示出来
    while (1)
    {
    Mat frame, blob;
    capture >> frame;
 
 
    blobFromImage(frame, blob, 1 / 255.0, Size(in_w, in_h), Scalar(), true, false);
 
    vector<Mat> mat_blob;
    imagesFromBlob(blob, mat_blob);
 
    //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));
 
    postprocess(frame, outs, thresh, nms_thresh);
 
    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));
 
    imshow("res", frame);
 
    waitKey(10);
    }
    return 0;
}