opencv人脸识别c++代码yolo模型

当您使用OpenCV进行人脸识别时，可以结合使用YOLO模型来检测人脸。下面是一个使用OpenCV和YOLO模型进行人脸识别的C++代码示例：

#include <opencv2/opencv.hpp>
#include <opencv2/dnn.hpp>
#include <iostream>

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

int main()
{
    // 加载YOLO模型
    String modelWeights = "path/to/your/yolov3.weights";
    String modelConfiguration = "path/to/your/yolov3.cfg";
    Net net = readNetFromDarknet(modelConfiguration, modelWeights);

    // 加载图像
    Mat image = imread("path/to/your/image.jpg");
    if (image.empty())
    {
        cout << "Could not open or find the image!" << endl;
        return -1;
    }

    // 创建一个4D blob，并将图像传递给网络
    Mat blob;
    double scalefactor = 1.0 / 255.0;
    Size size = Size(416, 416);
    Scalar mean = Scalar(0, 0, 0);
    bool swapRB = true;
    bool crop = false;
    dnn::blobFromImage(image, blob, scalefactor, size, mean, swapRB, crop);

    // 设置输入blob
    net.setInput(blob);

    // 运行前向传播
    vector<Mat> outs;
    net.forward(outs, getOutputsNames(net));

    // 处理网络输出
    float confThreshold = 0.5;
    vector<int> classIds;
    vector<float> confidences;
    vector<Rect> boxes;
    for (size_t i = 0; i < outs.size(); ++i)
    {
        // 提取每个输出层的检测结果
        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 > confThreshold)
            {
                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;

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

    // 应用非极大值抑制来删除重叠边界框
    float nmsThreshold = 0.4;
    vector<int> indices;
    NMSBoxes(boxes, confidences, confThreshold, nmsThreshold, indices);

    // 绘制人脸检测结果
    for (size_t i = 0; i < indices.size(); ++i)
    {
        int idx = indices[i];
        Rect box = boxes[idx];
        rectangle(image, box, Scalar(0, 0, 255), 2);
    }

    // 显示结果
    imshow("Face Detection", image);
    waitKey(0);

    return 0;
}

请确保您已正确安装OpenCV和YOLO模型，并将代码中的路径替换为您自己的模型和图像路径。这段代码将使用YOLO模型检测人脸，并在图像上绘制检测到的人脸边界框。