This file is part of advanced rangenet_lib.

RangeNet_ws

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// opencv stuff #include <opencv2/core/core.hpp> #include <opencv2/highgui/highgui.hpp> #include <opencv2/imgproc/imgproc.hpp> #include <opencv2/viz.hpp>

// c++ stuff #include #include // for setfill #include #include #include

// net stuff #include <selector.hpp> namespace cl = rangenet::segmentation;

// boost #include <boost/program_options.hpp> namespace po = boost::program_options; #include <boost/filesystem.hpp> namespace fs = boost::filesystem;

typedef std::tuple< u_char, u_char, u_char> color;

int main(int argc, const char *argv[]) { // define options std::string scan; std::string path; std::string backend = "tensorrt"; // 如果verbose为true，则程序会输出更多的运行过程信息，如果为false，则只输出最基本的信息。 bool verbose = false; std::ostringstream scanStream;

std::string kitti_num = "10"; // Replace "01" with the actual value you want std::string base_path = "/home/fairlee/dataset/KITTI/sequences_kitti_00_21/" + kitti_num + "/velodyne/"; std::string file_extension = ".bin";

// Calculate the number of files in the directory int N = std::distance(fs::directory_iterator(base_path), fs::directory_iterator{});

for(int file_num = 1000; file_num < N; ++file_num) {

// cout<<"正在处理-----"<<file_num<<"/"<< N <<"数据"<<endl;

std::cout << std::left << "正在处理 " <<kitti_num<< " 数据集中的第： "<< file_num << " / " << N << " 帧数据" << std::endl;

// Parse options try { po::options_description desc{"Options"}; desc.add_options()("help,h", "Help screen")( "scan,s", po::valuestd::string(&scan), "LiDAR scan to infer. No Default")( "path,p", po::valuestd::string(), "Directory to get the inference model from. No default")( "verbose,v", po::bool_switch(), "Verbose mode. Calculates profile (time to run)");

po::variables_map vm;
po::store(parse_command_line(argc, argv, desc), vm);
po::notify(vm);

std::cout << std::setfill('=') << std::setw(80) << "" << std::endl;

if (vm.count("help")) {
  std::cout << desc << std::endl;
  return 0;
}

std::ostringstream scanStream; scanStream << base_path << std::setfill('0') << std::setw(6) << file_num << file_extension; scan = scanStream.str();

// make defaults count, parameter check, and print
  path = "/home/fairlee/darknet53/";

if (vm.count("verbose")) {
  verbose = vm["verbose"].as<bool>();
  std::cout << "verbose: " << verbose << std::endl;
} else {
  std::cout << "verbose: " << verbose << ". Using default!" << std::endl;
}

std::cout << std::setfill('=') << std::setw(80) << "" << std::endl;

} catch (const po::error &ex) { std::cerr << ex.what() << std::endl; return 1; }

// create a network std::unique_ptrcl::Net net = cl::make_net(path, backend);

// set verbosity net->verbosity(verbose);

// predict each image std::cout << std::setfill('=') << std::setw(80) << "" << std::endl; std::cout << "Predicting image: " << scan << std::endl;

// Open a scan std::ifstream in(scan.c_str(), std::ios::binary); if (!in.is_open()) { std::cerr << "Could not open the scan!" << std::endl; return 1; }

in.seekg(0, std::ios::end); uint32_t num_points = in.tellg() / (4 * sizeof(float)); in.seekg(0, std::ios::beg);

std::vector values(4 * num_points); in.read((char*)&values[0], 4 * num_points * sizeof(float));

// predict std::vector<std::vector> semantic_scan = net->infer(values, num_points);

// get point cloud std::vectorcv::Vec3f points = net->getPoints(values, num_points);

// get color mask std::vectorcv::Vec3b color_mask = net->getLabels(semantic_scan, num_points);

// Create output filename std::ostringstream outfileNameStream; outfileNameStream << "/home/fairlee/dataset/KITTI/sequences_kitti_00_21/" << kitti_num << "/RangeNet_point/" << std::setfill('0') << std::setw(6) << file_num << ".txt"; std::string outfileName = outfileNameStream.str();

// Create an ofstream object
std::ofstream outfile(outfileName);

if (!outfile) {
    std::cerr << "Unable to open output file: " << outfileName << std::endl;
    return 1;
}

// Iterate through each point and corresponding color
for (size_t i = 0; i < points.size(); ++i) {
    // Write the point coordinates and color to the file
    outfile << points[i][0] << " " << points[i][1] << " " << points[i][2];
    outfile << " " << static_cast<int>(color_mask[i][0]) << " " << static_cast<int>(color_mask[i][1]) << " " << static_cast<int>(color_mask[i][2]) << "\n";
}

// Close the file
outfile.close();

// print the output if (verbose) { cv::viz::Viz3d window("semantic scan"); cv::viz::WCloud cloudWidget(points, color_mask); while (!window.wasStopped()) { window.showWidget("cloud", cloudWidget); window.spinOnce(30, true); } } std::cout << std::setfill('=') << std::setw(80) << "" << std::endl; std::cout << "Example finished! "<< std::endl; } return 0; }

RobotsRuning/RangeNet_ws

RangeNet_ws

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