如何使用 opencv 加载 darknet yolo 预训练模型？

opencv 版本 > 3.4 以上

constexpr const char *image_path = "darknet.jpg";//待检测图片

constexpr const char *darknet_cfg = "darknet.cfg";//网络文件

constexpr const char *darknet_weights = "darknet.weights";//训练模型

const std::vector<std::string> class_labels = {"darknet","yolo"};//类标签

void darknetDetection(const std::string &path,const std::string &darknet_cfg,const std::string &darknet_weights,std::vector<std::string> class_labels,float confidenceThreshold)

{

    // 加载模型

    cv::dnn::Net net = cv::dnn::readNetFromDarknet(darknet_cfg,darknet_weights);

    // 加载标签集

    std::vector<std::string> classLabels = class_labels;

    // 读取待检测图片

    cv::Mat img = cv::imread(path);

    cv::Mat blob = cv::dnn::blobFromImage(img,1.0/255.0,{416,416},0.00392,true);

    net.setInput(blob,"data");

    // 检测

    cv::Mat detectionMat = net.forward("detection_out");// 6 845 1 W x H x C

    // 获取网络每层的用时并获取总用时

    std::vector<double> layersTimings;

    double freq = cv::getTickFrequency() / 1000;

    double time = net.getPerfProfile(layersTimings) / freq;

    std::ostringstream ss;

    ss << "detection time: " << time << " ms";

    // 绘制总用时至原始图片

    cv::putText(img, ss.str(), cv::Point(20, 20), 0, 0.5, cv::Scalar(0, 0, 255));

    // 遍历所有结果集

    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 = std::max_element(prob_array_ptr, prob_array_ptr + probability_size) - prob_array_ptr;

        float confidence = detectionMat.at<float>(i, (int)objectClass + probability_index);

        // 比较置信度并绘制满足条件的置信度

        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) * img.cols);

            int yLeftBottom = static_cast<int>((y - height / 2) * img.rows);

            int xRightTop = static_cast<int>((x + width / 2) * img.cols);

            int yRightTop = static_cast<int>((y + height / 2) * img.rows);

            cv::Rect object(xLeftBottom, yLeftBottom,xRightTop - xLeftBottom,yRightTop - yLeftBottom);//x y w h

            cv::rectangle(img, object, cv::Scalar(0, 0, 255), 2, 8);

            // 判断类id是否符合标签范围并绘制该标签，也就是矩阵的下标索引

            if (objectClass < classLabels.size())

            {

                cv::String label = cv::String(classLabels[objectClass]) + ": " + std::to_string(confidence);

                int baseLine = 0;

                cv::Size labelSize = cv::getTextSize(label,cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);

                cv::rectangle(img, cv::Rect(cv::Point(xLeftBottom, yLeftBottom),cv::Size(labelSize.width, labelSize.height + baseLine)),cv::Scalar(255, 255, 255), cv::FILLED);

                cv::putText(img, label, cv::Point(xLeftBottom, yLeftBottom + labelSize.height),cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 0, 0));

            }

        }

    }

    // 显示图片

    cv::imshow("Darknet",img);

    cv::waitKey(0);

}