Function main

CVTNet_libtorch/ws/fast_cvtnet.cpp:127–214

int main()
{

    torch::DeviceType device_type;
    device_type = torch::kCUDA;
    torch::Device device(device_type);
    std::cout<<"cuda support:"<< (torch::cuda::is_available()?"ture":"false")<<std::endl;
    torch::jit::script::Module module = torch::jit::load("../../CVTNet.pt");
    module.to(torch::kCUDA);
    module.eval();

    std::string lidar_file = "../1.pcd";
    std::vector<float> lidar_data = read_lidar_data(lidar_file);
    pcl::PointCloud<pcl::PointXYZI>::Ptr cloud0(new pcl::PointCloud<pcl::PointXYZI>);

    for (std::size_t i = 0; i < lidar_data.size(); i += 1)
    {
        pcl::PointXYZI point;
        point.x = lidar_data[i];
        point.y = lidar_data[i + 1];
        point.z = lidar_data[i + 2];
        point.intensity = lidar_data[i + 3];
        cloud0->points.push_back(point);
    }

    int width = 900;
    int height = 32;
    std::vector<float> range_thresh = {0.0, 15.0, 30.0, 45.0, 60.0};
    std::vector<float> height_thresh = {-4.0, 0.0, 4.0, 8.0, 12.0};
    int interval = range_thresh.size();
    int len_arr = width*height;
    int len_arr_all = 2*interval*width*height;
    float ri_bev_image[len_arr_all];

    for(int th=0; th<range_thresh.size(); th++)
    {
        float range_image[len_arr];
        float farer_bound = range_thresh[th+1];
        float nearer_bound = range_thresh[th];
        if (th == range_thresh.size()-1)
        {
            gen_range_image(range_image, cloud0, 15, -16, 32, 900, 80, range_thresh[int(interval-1)], range_thresh[0]);
        }
        else
        {
            gen_range_image(range_image, cloud0, 15, -16, 32, 900, 80, farer_bound, nearer_bound);
        }
        for(int x=0; x<width; x++)
        {
            for(int y=0; y<height; y++)
            {
                int idx_in_all = int(th*width*height + y*width + x);
                ri_bev_image[idx_in_all] = range_image[y*width + x];
            }
        }
    }

    for(int th=0; th<height_thresh.size(); th++)