C++ opencv-3.4.1 调用darkNet

// Brief Sample of using OpenCV dnn module in real time with device capture, video and image.
#include <opencv2/dnn.hpp>
#include <opencv2/dnn/shape_utils.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
#include <fstream>
#include <iostream>
#include <algorithm>
#include <cstdlib>

using namespace std;
using namespace cv;
using namespace cv::dnn;

int main()
{

    String modelConfiguration = "yolo.cfg";
    String modelBinary = "yolo.weights";
    dnn::Net net = readNetFromDarknet(modelConfiguration, modelBinary);

    if (net.empty())
    {
        printf("Could not load net...\n");
        return -1;
    }

    // 类别信息
    vector<string> classNamesVec;
    ifstream classNamesFile("coco.names");
    if (classNamesFile.is_open())
    {
        string className = "";
        while (std::getline(classNamesFile, className))
            classNamesVec.push_back(className);
    }


    // 加载图像
    Mat frame = imread("2.jpg");
    Mat inputBlob = blobFromImage(frame, 1 / 255.F, Size(416, 416), Scalar(), true, false);
    net.setInput(inputBlob, "data");

    // 检测
    Mat detectionMat = net.forward("detection_out");
    vector<double> layersTimings;
    double freq = getTickFrequency() / 1000;
    double time = net.getPerfProfile(layersTimings) / freq;
    ostringstream ss;
    ss << "detection time: " << time << " ms";
    putText(frame, ss.str(), Point(20, 20), 0, 0.5, Scalar(0, 0, 255));

    // 输出结果
    float confidenceThreshold = 0.2;
    for (int i = 0; i < detectionMat.rows; i++)
    {
        const int probability_index = 5;
        const int probability_size = detectionMat.cols - probability_index;
        float *prob_array_ptr = &detectionMat.at<float>(i, probability_index);
        size_t objectClass = max_element(prob_array_ptr, prob_array_ptr + probability_size) - prob_array_ptr;
        float confidence = detectionMat.at<float>(i, (int)objectClass + probability_index);
        cout <<  " confidence : "<< confidence << endl;
        if (confidence > confidenceThreshold)
        {
            float x = detectionMat.at<float>(i, 0);
            float y = detectionMat.at<float>(i, 1);
            float width = detectionMat.at<float>(i, 2);
            float height = detectionMat.at<float>(i, 3);
            int xLeftBottom = static_cast<int>((x - width / 2) * frame.cols);
            int yLeftBottom = static_cast<int>((y - height / 2) * frame.rows);
            int xRightTop = static_cast<int>((x + width / 2) * frame.cols);
            int yRightTop = static_cast<int>((y + height / 2) * frame.rows);
            Rect object(xLeftBottom, yLeftBottom,
                xRightTop - xLeftBottom,
                yRightTop - yLeftBottom);
            rectangle(frame, object, Scalar(0, 0, 255), 2, 8);
            if (objectClass < classNamesVec.size())
            {
                ss.str("");
                ss << confidence;
                String conf(ss.str());
                String label = String(classNamesVec[objectClass]) + ": " + conf;
                int baseLine = 0;
                Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
                rectangle(frame, Rect(Point(xLeftBottom, yLeftBottom),
                    Size(labelSize.width, labelSize.height + baseLine)),
                    Scalar(255, 255, 255), CV_FILLED);
                putText(frame, label, Point(xLeftBottom, yLeftBottom + labelSize.height),
                    FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 0, 0));
            }
        }
    }
    imshow("YOLO-Detections", frame);
    waitKey(0);

    return 0;
}

参考：
OpenCV DNN之YOLO实时对象检测
gloomyfish1998