用代码写C++用yolo推理多张图片随机返回一个框xy 坐标

以下是用C++代码使用YOLO进行推理，并随机返回一个框的xy坐标。

#include <iostream>
#include <fstream>
#include <sstream>
#include <opencv2/dnn/dnn.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>

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

int main()
{
    // 加载标签文件和模型文件
    vector<string> classes;
    ifstream ifs("coco.names");
    string line;
    while (getline(ifs, line)) classes.push_back(line);
    Net net = readNetFromDarknet("yolov3.cfg", "yolov3.weights");
    net.setPreferableBackend(DNN_BACKEND_OPENCV);
    net.setPreferableTarget(DNN_TARGET_CPU);

    // 加载图片
    Mat image = imread("test.jpg");

    // 构建blob
    Mat blob;
    blobFromImage(image, blob, 1 / 255.0, Size(416, 416), Scalar(), true, false);

    // 运行前向推理
    net.setInput(blob);
    vector<Mat> outs;
    net.forward(outs, net.getUnconnectedOutLayersNames());

    // 解析输出
    vector<int> classIds;
    vector<float> confidences;
    vector<Rect> boxes;
    for (size_t i = 0; i < outs.size(); ++i)
    {
        // 获取当前输出层的所有检测结果
        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;
            minMaxLoc(scores, 0, &confidence, 0, &classIdPoint);
            if (confidence > 0.5)
            {
                // 获取检测结果的坐标和宽高
                int centerX = (int)(data[0] * image.cols);
                int centerY = (int)(data[1] * image.rows);
                int width = (int)(data[2] * image.cols);
                int height = (int)(data[3] * image.rows);
                int left = centerX - width / 2;
                int top = centerY - height / 2;

                // 保存检测结果
                classIds.push_back(classIdPoint.x);
                confidences.push_back((float)confidence);
                boxes.push_back(Rect(left, top, width, height));
            }
        }
    }

    // 随机返回一个框的xy坐标
    if (boxes.size() > 0)
    {
        srand((unsigned)time(NULL));
        int index = rand() % boxes.size();
        Rect box = boxes[index];
        cout << "x: " << box.x << ", y: " << box.y << endl;
    }
    else
    {
        cout << "no detection result" << endl;
    }

    return 0;
}

在上面的代码中，需要将YOLO模型文件和标签文件放在与代码文件相同的目录下，并将图片文件名改为对应的文件名。运行代码后，将随机返回一张图片中检测到的一个物体框的x和y坐标。