我想在ROS平台上使用YOLOv4的OpenCV DNN模块来检测一个对象，但是它不起作用。不过我是在没有ROS平台的情况下用yolov4运行opencv dnn模块的。相同的代码片段不能在ROS平台上运行。

CMakeList.txt：（我链接了OpenCV）

cmake_minimum_required(VERSION 3.0.2)
project(test_opencv)
  
set(CMAKE_CXX_STANDARD_REQUIRED 17)
add_compile_options(--std=c++17 -g3)
set(OpenCV_DIR "/home/usernamepc/opencv_build/opencv/build")

find_package(OpenCV 4.4.0 REQUIRED)

我的代码在这里。这段代码在没有ROS的情况下有效，但在有ROS的情况下无效。

int main(int argc, char **argv){

ros::init(argc, argv, "test_opencv");

int inpWidth = 608;
int inpHeight = 608;
int count = 0;
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;

vector<string> class_names;
{
    ifstream class_file("/home/USERNAMEPC/people_detection_ws/src/test_opencv/input/data/obj_license.names");

    if (!class_file)
    {
        cerr << "failed to open classes.txt\n";
        return 0;
    }

    string line;
    while (getline(class_file, line))
    {
        class_names.push_back(line);
        cout << "Data: " << line << endl;
    }
}
string video = "/home/USERNAMEPC/people_detection_ws/src/test_opencv/input/plate/1.mp4";
VideoCapture cap(video); 
auto net = readNetFromDarknet("/home/USERNAMEPC/people_detection_ws/src/test_opencv/input/cfg/yolov4_obj_license.cfg",
                              "/home/USERNAMEPC/people_detection_ws/src/test_opencv/input/custom_license.weights");

auto output_names = net.getUnconnectedOutLayersNames();
double inference_fps = 0;
double total_fps = 0;

Mat frame, blob;
vector<Mat> detections;
while (waitKey(1) < 1)
{

    cap >> frame;

    if (frame.empty())
    {
        waitKey();
        break;
    }
    auto total_start = chrono::steady_clock::now();

    imshow("frame", frame);
    waitKey(1);
    cv::dnn::blobFromImage(frame, blob, 0.00392, Size(inpWidth, inpHeight), Scalar(), true, false, CV_32F);

    net.setInput(blob);
    if (blob.empty())
    {
        std::cout << "blob is empty" << std::endl;
        return -1;
    }

    auto dnn_start = chrono::steady_clock::now();
    cout << "6 " << endl;

    net.forward(detections, output_names);

    cout << "7  " << endl;

    auto dnn_end = chrono::steady_clock::now();

    vector<int> indices[NUM_CLASSES];
    vector<Rect> boxes[NUM_CLASSES];
    vector<float> scores[NUM_CLASSES];

    for (auto &output : detections)
    {
        const auto num_boxes = output.rows;
        for (int i = 0; i < num_boxes; i++)
        {

            auto x = output.at<float>(i, 0) * frame.cols;
            auto y = output.at<float>(i, 1) * frame.rows;
            auto width = output.at<float>(i, 2) * frame.cols;
            auto height = output.at<float>(i, 3) * frame.rows;
            Rect rect(x - width / 2, y - height / 2, width, height);

            for (int c = 0; c < NUM_CLASSES; c++)
            {
                auto confidence = *output.ptr<float>(i, 5 + c);
                if (confidence >= CONFIDENCE_THRESHOLD)
                {
                    boxes[c].push_back(rect);
                    scores[c].push_back(confidence);
                    detected_control = true;
                }
            }
        }
    }

    for (int c = 0; c < NUM_CLASSES; c++)
    {
        NMSBoxes(boxes[c], scores[c], 0.0, NMS_THRESHOLD, indices[c]);
    }

    for (int c = 0; c < NUM_CLASSES; c++)
    {
        for (size_t i = 0; i < indices[c].size(); ++i)
        {

            const auto color = colors[c % NUM_COLORS];
            auto idx = indices[c][i];
            auto &rect = boxes[c][idx];
            rectangle(frame, Point(rect.x, rect.y), Point(rect.x + rect.width, rect.y + rect.height), color, 3);
            ostringstream label_ss;
            label_ss << class_names[c] << ": " << fixed << setprecision(2) << scores[c][idx];
            auto label = label_ss.str();

            int baseline;
            auto label_bg_sz = getTextSize(label.c_str(), FONT_HERSHEY_COMPLEX_SMALL, 1, 1, &baseline);
            rectangle(frame, Point(rect.x, rect.y - label_bg_sz.height - baseline - 10), Point(rect.x + label_bg_sz.width, rect.y), color, FILLED);
            putText(frame, label.c_str(), Point(rect.x, rect.y - baseline - 5), FONT_HERSHEY_COMPLEX_SMALL, 1, Scalar(0, 0, 0));
        }
    }

    auto total_end = chrono::steady_clock::now();
    inference_fps = 1000.0 / chrono::duration_cast<chrono::milliseconds>(dnn_end - dnn_start).count();
    total_fps = 1000.0 / chrono::duration_cast<chrono::milliseconds>(total_end - total_start).count();
    ostringstream stats_ss;
    stats_ss << fixed << setprecision(2);
    stats_ss << "Inference FPS: " << inference_fps << ", Total FPS: " << total_fps;
    auto stats = stats_ss.str();

    int baseline;
    auto stats_bg_sz = getTextSize(stats.c_str(), FONT_HERSHEY_COMPLEX_SMALL, 1, 1, &baseline);
    rectangle(frame, Point(0, 0), Point(stats_bg_sz.width, stats_bg_sz.height + 10), Scalar(0, 0, 0), FILLED);
    putText(frame, stats.c_str(), Point(0, stats_bg_sz.height + 5), FONT_HERSHEY_COMPLEX_SMALL, 1, Scalar(255, 255, 255));
    // namedWindow("output");
    count++;
}

cout << "Inference FPS: " << inference_fps << ", Total FPS: " << total_fps << endl;

ros::spin();

return 0; }

当代码转到步骤时，我收到一个错误

net.forward（检测，输出名称）；

错误如下:

OpenCV错误:断言失败(dims

当我使用GDB调试器时，我得到如下所示的错误输出:

__GI_raise（sig）中的（gdb）bt#0 0x00007ffff61b5e87=sig@entry=6）在/sysdeps/unix/sysv/linux/rese.c:51

#1 0x00007ffff61b77f1在__GI_abort（）在堕胎. c： 79

#2 0x00007ffff680c957 in（），位于/usr/lib/x86_64-linux-gnu/libstdc.so.6

#3 0x00007ffff6812ae6 in （） at /usr/lib/x86_64-linux-gnu/libstdc .so.6

# 4 0x 00007 ffff 6812 b21 in()at/usr/lib/x86 _ 64-Linux-GNU/libstdc . so . 6

# 5 0x 00007 ffff 6812d 54 in()at/usr/lib/x86 _ 64-Linux-GNU/libstdc . so . 6

cv::error中的# 6 0x 00007 ffff 77 c 38 a 2(cv::异常常量

# 7 0x 00007 ffff 77c 39 BF in cv::error(int，cv::String const

位于/usr/lib/x86 _ 64-Linux-GNU/libopencv _ core . so . 3.2的cv::Mat::shape(int，int) const()中的#8 0x00007ffff7734b1c

# 9 0x 00007 ffff 6 efb 344 in cv::dnn::ConvolutionLayerImpl::finalize(cv::_ input array const

#10 0x00007ffff6eb12d7 in cv：:d nn：:d nn4_v20200609：：Layer：：finalize（std：：vector

#11 0x00007ff6ed1ff3，位于cv:：dnn:：dnv4_v20200609：：Net:：Impl:：allocateLayer（int，std:：map

cv::dnn::dnn 4 _ v 20200609::Net::Impl::allocate layers中的# 12 0x 00007 ffff 6 ed 3 ff 2(STD::vector

cv::dnn::dnn 4 _ v 20200609::Net::Impl::setup Net中的# 13 0x 00007 ffff 6 ed 7675(STD::vector

#14 0x00007ffff6ed8ac3在cv::dnn::dnn4_v20200609::::forward（cv::_OutputArray常量

# 15 0x 0000555555561 c0e in main(int，char**) (argc=1，argv = 0x 7 fffffffd 918)at/home/username PC/people _ detection _ ws/src/test _ opencv/src/test _ opencv . CPP:147

我如何解决这个问题？