sudo ./test_cpp_example./test_cpp_example: symbol lookup error: ./test_cpp_example: undefined symbol: _ZN2cv8cvtColorERKNS_11_InputArrayERKNS_12_OutputArrayEii

sudo ./test_cpp_exampleOpenCV version : 4.10.0Major version : 4Minor version : 10Subminor version : 0rows is:8400dim is:84rows is:8400dim is:84rows is:8400dim is:84rows is:8400

Platform: Jetson Orin NanoOpenCV Version: 4.10.0 (installed manually and also using (https://github.com/mdegans/nano_build_opencv/tree/master))YOLOv9 Integration: Works correctly with other OpenCV functions.Behavior with OpenCV 4.9.0: cv::cvtColor runs without issue, but YOLOv9 does not work correctly.

#include <iostream>#include <opencv2/core/core.hpp>#include <opencv2/imgproc.hpp>#include <opencv2/highgui.hpp>#include <opencv2/dnn.hpp>#include <float.h>#include <stdio.h>#include <vector>#include <opencv2/opencv.hpp>using namespace cv;using namespace std;using namespace dnn;std::vector<std::string> class_names{"person", "bicycle", "car", "motorcycle", "airplane", "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", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"};void porcess_with_one_image(cv::dnn::Net &net,cv::Mat& frame,float modelinput_height, float modelinput_width){    float modelScoreThreshold=0.2;    float modelNMSThreshold=0.3;    //image preprocess    int w = frame.cols;    int h = frame.rows;    int _max = std::max(h, w);    cv::Mat image = cv::Mat::zeros(cv::Size(_max, _max), CV_8UC3);    cv::Rect roi(0, 0, w, h);    frame.copyTo(image(roi));    float x_factor = image.cols / modelinput_width;    float y_factor = image.rows / modelinput_height;    // inference using model    cv::Mat blob;    cv::dnn::blobFromImage(image,blob,1 / 255.0, cv::Size(modelinput_height, modelinput_width), cv::Scalar(), true, false);    net.setInput(blob);    std::vector<cv::Mat> outputs;    net.forward(outputs, net.getUnconnectedOutLayersNames());    // postprocess the image     // yolov8 has an output of shape (batchSize, 84,  8400) ( box[x,y,w,h]+Num classes )    int rows = outputs[0].size[2]; //8400    int dimensions =outputs[0].size[1]; //84    std::cout<<"rows is:"<<rows<<std::endl;    std::cout<<"dim is:"<<dimensions<<std::endl;    //(1,84,8400)-->(84,8400)    outputs[0] = outputs[0].reshape(1, dimensions);    //(84,8400) --> (8400,84)    cv::transpose(outputs[0], outputs[0]);    //get the first data pointer;finaaly will get all 8400 bounding box    float *data = (float *)outputs[0].data;    // the storage to save the results    std::vector<int> class_ids;    std::vector<float> confidences;    std::vector<cv::Rect> boxes;    //rows stand for the total number of bounding box    for (int i = 0; i < rows; ++i)    {                //skip the x,y,w,h         float *classes_scores = data+4;        cv::Mat scores(1, class_names.size(), CV_32FC1, classes_scores);        cv::Point class_id;        double maxClassScore;        minMaxLoc(scores, 0, &maxClassScore, 0, &class_id);        if (maxClassScore > modelScoreThreshold)        {            confidences.push_back(maxClassScore);            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));        }                        //pointer to next 84  group        data += dimensions;            }    // NMS    std::vector<int> indexes;    cv::dnn::NMSBoxes(boxes, confidences, modelScoreThreshold, modelNMSThreshold, indexes);    for (size_t i = 0; i < indexes.size(); i++) {        int index = indexes[i];        int idx = class_ids[index];        cv::rectangle(frame, boxes[index], (0,0,255), 2, 8);        cv::rectangle(frame, cv::Point(boxes[index].tl().x, boxes[index].tl().y - 20),            cv::Point(boxes[index].br().x, boxes[index].tl().y), cv::Scalar(255, 255, 255), -1);        cv::putText(frame, class_names[idx], cv::Point(boxes[index].tl().x, boxes[index].tl().y - 10), cv::FONT_HERSHEY_SIMPLEX, .5, cv::Scalar(0, 0, 0));    }}// void detect_video(std::string& onnxpath,float modelinput_height, float modelinput_width,std::string video_name)void detect_video(cv::dnn::Net &net,float modelinput_height, float modelinput_width,std::string video_name){        cv::VideoCapture capture(video_name);    cv::Mat frame;    while (true)     {        bool ret = capture.read(frame);        if (frame.empty()) {            break;        }        int64 start = cv::getTickCount();        porcess_with_one_image(net,frame,modelinput_height, modelinput_width);        float t = (cv::getTickCount() - start) / static_cast<float>(cv::getTickFrequency());        putText(frame, cv::format("FPS: %.2f", 1.0 / t), cv::Point(20, 40), cv::FONT_HERSHEY_PLAIN, 2.0, cv::Scalar(255, 0, 0), 2, 8);        cv::imshow("OpenCV4.10 inference FOR YOLOV9", frame);        char c = cv::waitKey(1);        if (c == 27)         {            break;        }    }}int main(){    std::cout << "OpenCV version : " << CV_VERSION << std::endl;    std::cout << "Major version : " << CV_MAJOR_VERSION << std::endl;    std::cout << "Minor version : " << CV_MINOR_VERSION << std::endl;    std::cout << "Subminor version : " << CV_SUBMINOR_VERSION << std::endl;    std::string onnxpath = "yolov9s_simplified.onnx";    auto net = cv::dnn::readNetFromONNX(onnxpath);    net.setPreferableBackend(cv::dnn::DNN_BACKEND_CUDA);    net.setPreferableTarget(cv::dnn::DNN_TARGET_CUDA);    cv::Mat FrameRGBc0;    string name_frame_c0 = "frame_60_c0.jpg";    FrameRGBc0 = cv::imread(name_frame_c0, cv::IMREAD_COLOR);    //Convert to gray scale:    cv::Mat framec0_gray;    // cv::cvtColor(FrameRGBc0, framec0_gray, cv::COLOR_RGB2GRAY);    // cv::imshow("OpenCV4.10 inference FOR YOLOV9", framec1_gray);    // char c = cv::waitKey(0);    detect_video(net,640.0,640.0,"40030-424911975_tiny.mp4");    return 0;}