opencv人脸识别c++代码yolo模型

以下是使用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>

using namespace cv;
using namespace dnn;

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

    // 加载类别标签
    std::vector<String> classNames;
    String classesFile = "coco.names";
    std::ifstream ifs(classesFile.c_str());
    std::string line;
    while (std::getline(ifs, line))
    {
        classNames.push_back(line);
    }

    // 加载图像
    Mat image = imread("input.jpg");

    // 创建blob
    Mat blob;
    double scalefactor = 1 / 255.0;
    Size size = Size(416, 416);
    Scalar mean = Scalar(0, 0, 0);
    bool swapRB = true;
    bool crop = false;
    blobFromImage(image, blob, scalefactor, size, mean, swapRB, crop);

    // 设置输入层
    net.setInput(blob);

    // 执行前向传播
    std::vector<Mat> outs;
    std::vector<String> outNames = net.getUnconnectedOutLayersNames();
    net.forward(outs, outNames);

    // 解析输出层
    std::vector<int> classIds;
    std::vector<float> confidences;
    std::vector<Rect> boxes;

    for (size_t i = 0; i < outs.size(); ++i)
    {
        // 获取检测结果
        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)
            {
                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));
            }
        }
    }

    // 非最大抑制
    std::vector<int> indices;
    NMSBoxes(boxes, confidences, 0.5, 0.4, indices);

    // 绘制检测结果
    for (size_t i = 0; i < indices.size(); ++i)
    {
        int idx = indices[i];
        Rect box = boxes[idx];

        // 绘制边界框
        rectangle(image, box, Scalar(0, 0, 255), 2);

        // 绘制类别标签和置信度
        String label = classNames[classIds[idx]] + ": " + std::to_string(confidences[idx]);
        int baseLine;
        Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
        int top = std::max(box.y, labelSize.height);
        rectangle(image, Point(box.x, top - labelSize.height),
                  Point(box.x + labelSize.width, top + baseLine), Scalar(255, 255, 255), FILLED);
        putText(image, label, Point(box.x, top), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 0, 0));
    }

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

    return 0;
}

请确保正确配置YOLO模型文件（`.cfg`和`.weights`文件）以及类别标签文件（`.names`文件）。此代码将加载一张名为`input.jpg`的图像，并在图像中检测和标记人脸。