问用YOLOv5和OpenCV加载C++时出错

EN

#include <vector>
#include <string>
#include <algorithm>
#include <sstream>
#include <iterator>
#include <cmath>
#include <opencv2/opencv.hpp>
void load_net(cv::dnn::Net& net, bool is_cuda)
{
    auto result = cv::dnn::readNetFromONNX("yolov5s.onnx");
    if (is_cuda)
    {
        std::cout << "Attempty to use CUDA\n";
        result.setPreferableBackend(cv::dnn::DNN_BACKEND_CUDA);
        result.setPreferableTarget(cv::dnn::DNN_TARGET_CUDA_FP16);
    }
    else
    {
        std::cout << "Running on CPU\n";
        result.setPreferableBackend(cv::dnn::DNN_BACKEND_OPENCV);
        result.setPreferableTarget(cv::dnn::DNN_TARGET_CPU);
    }
    net = result;
}
const std::vector<cv::Scalar> colors = { cv::Scalar(255, 255, 0), cv::Scalar(0, 255, 0), cv::Scalar(0, 255, 255), cv::Scalar(255, 0, 0) };
const float INPUT_WIDTH = 640.0;
const float INPUT_HEIGHT = 640.0;
const float SCORE_THRESHOLD = 0.2;
const float NMS_THRESHOLD = 0.4;
const float CONFIDENCE_THRESHOLD = 0.4;
struct Detection
{
    int class_id;
    float confidence;
    cv::Rect box;
};
cv::Mat format_yolov5(const cv::Mat& source) {
    int col = source.cols;
    int row = source.rows;
    int _max = MAX(col, row);
    cv::Mat result = cv::Mat::zeros(_max, _max, CV_8UC3);
    source.copyTo(result(cv::Rect(0, 0, col, row)));
    return result;
}
void detect(cv::Mat& image, cv::dnn::Net& net, std::vector<Detection>& output, const std::vector<std::string>& className) {
    cv::Mat blob;
    auto input_image = format_yolov5(image);
    cv::dnn::blobFromImage(input_image, blob, 1. / 255., cv::Size(INPUT_WIDTH, INPUT_HEIGHT), cv::Scalar(), true, false);
    net.setInput(blob);
    std::vector<cv::Mat> outputs;
    net.forward(outputs, net.getUnconnectedOutLayersNames());
    float x_factor = input_image.cols / INPUT_WIDTH;
    float y_factor = input_image.rows / INPUT_HEIGHT;
    float* data = (float*)outputs[0].data;
    const int dimensions = 85;
    const int rows = 25200;
    std::vector<int> class_ids;
    std::vector<float> confidences;
    std::vector<cv::Rect> boxes;
    for (int i = 0; i < rows; ++i) {
        float confidence = data[4];
        if (confidence >= CONFIDENCE_THRESHOLD) {
            float* classes_scores = data + 5;
            cv::Mat scores(1, className.size(), CV_32FC1, classes_scores);
            cv::Point class_id;
            double max_class_score;
            minMaxLoc(scores, 0, &max_class_score, 0, &class_id);
            if (max_class_score > SCORE_THRESHOLD) {
                confidences.push_back(confidence);
                class_ids.push_back(class_id.x);
                float x = data[0];
                float y = data[1];
                float w = data[2];
                float h = data[3];
                int left = int((x - 0.5 * w) * x_factor);
                int top = int((y - 0.5 * h) * y_factor);
                int width = int(w * x_factor);
                int height = int(h * y_factor);
                boxes.push_back(cv::Rect(left, top, width, height));
            }
        }
        data += 85;
    }
    std::vector<int> nms_result;
    cv::dnn::NMSBoxes(boxes, confidences, SCORE_THRESHOLD, NMS_THRESHOLD, nms_result);
    for (int i = 0; i < nms_result.size(); i++) {
        int idx = nms_result[i];
        Detection result;
        result.class_id = class_ids[idx];
        result.confidence = confidences[idx];
        result.box = boxes[idx];
        output.push_back(result);
    }
}
int main(int argc, char** argv)
{
    std::vector<std::string> class_list = { "person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "sofa", "pottedplant", "bed", "diningtable", "toilet", "tvmonitor", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush" };
    cv::Mat frame;
    cv::VideoCapture capture(0);
    if (!capture.isOpened())
    {
        std::cerr << "Error opening video file\n";
        return -1;
    }
    bool is_cuda = argc > 1 && strcmp(argv[1], "cuda") == 0;
    cv::dnn::Net net;
    load_net(net, is_cuda);
    auto start = std::chrono::high_resolution_clock::now();
    int frame_count = 0;
    float fps = -1;
    int total_frames = 0;
    while (true)
    {
        capture.read(frame);
        if (frame.empty())
        {
            std::cout << "End of stream\n";
            break;
        }
        std::vector<Detection> output;
        detect(frame, net, output, class_list);
        frame_count++;
        total_frames++;
        int detections = output.size();
        for (int i = 0; i < detections; ++i)
        {
            auto detection = output[i];
            auto box = detection.box;
            auto classId = detection.class_id;
            const auto color = colors[classId % colors.size()];
            cv::rectangle(frame, box, color, 3);
            cv::rectangle(frame, cv::Point(box.x, box.y - 20), cv::Point(box.x + box.width, box.y), color, cv::FILLED);
            cv::putText(frame, class_list[classId].c_str(), cv::Point(box.x, box.y - 5), cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 0, 0));
        }
        if (frame_count >= 30)
        {
            auto end = std::chrono::high_resolution_clock::now();
            fps = frame_count * 1000.0 / std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count();
            frame_count = 0;
            start = std::chrono::high_resolution_clock::now();
        }
        if (fps > 0)
        {
            std::ostringstream fps_label;
            fps_label << std::fixed << std::setprecision(2);
            fps_label << "FPS: " << fps;
            std::string fps_label_str = fps_label.str();
            cv::putText(frame, fps_label_str.c_str(), cv::Point(10, 25), cv::FONT_HERSHEY_SIMPLEX, 1, cv::Scalar(0, 0, 255), 2);
        }
        cv::imshow("output", frame);
        if (cv::waitKey(1) != -1)
        {
            capture.release();
            std::cout << "finished by user\n";
            break;
        }
        char c = (char)cv::waitKey(25);//Allowing 25 milliseconds frame processing time and initiating break condition//
        if (c == 27) { //If 'Esc' is entered break the loop//
            break;
        }
    }
    std::cout << "Total frames: " << total_frames << "\n";
    return 0;
}

OpenCV(4.5.5) Error: Unspecified error (> Node [Range@ai.onnx]:(354) parse error: OpenCV(4.5.5) C:\build\master_winpack-build-win64-vc14\opencv\modules\dnn\src\dnn.cpp:621: error: (-2:Unspecified error) Can't create layer "354" of type "Range" in function 'cv::dnn::dnn4_v20211220::LayerData::getLayerInstance'
> ) in cv::dnn::dnn4_v20211220::ONNXImporter::handleNode, file C:\build\master_winpack-build-win64-vc14\opencv\modules\dnn\src\onnx\onnx_importer.cpp, line 928
OpenCV: terminate handler is called! The last OpenCV error is:
OpenCV(4.5.5) Error: Unspecified error (> Node [Range@ai.onnx]:(354) parse error: OpenCV(4.5.5) C:\build\master_winpack-build-win64-vc14\opencv\modules\dnn\src\dnn.cpp:621: error: (-2:Unspecified error) Can't create layer "354" of type "Range" in function 'cv::dnn::dnn4_v20211220::LayerData::getLayerInstance'
> ) in cv::dnn::dnn4_v20211220::ONNXImporter::handleNode, file C:\build\master_winpack-build-win64-vc14\opencv\modules\dnn\src\onnx\onnx_importer.cpp, line 928