Function main

examples/features/example_difference_of_normals.cpp:31–230

using SearchPtr = pcl::search::Search<PointT>::Ptr;

int main (int argc, char *argv[])
{
  ///The smallest scale to use in the DoN filter.
  constexpr double scale1 = 0.2;

  ///The largest scale to use in the DoN filter.
  constexpr double scale2 = 2.0;

  ///The minimum DoN magnitude to threshold by
  constexpr double threshold = 0.25;

  ///segment scene into clusters with given distance tolerance using euclidean clustering
  double segradius = 0.2;

  //voxelization factor of pointcloud to use in approximation of normals
  bool approx = false;
  constexpr double decimation = 100;

  if(argc < 3){
    std::cerr << "Expected 2 arguments: inputfile outputfile" << std::endl;
    return 0;
  }

  ///The file to read from.
  std::string infile = argv[1];

  ///The file to output to.
  std::string outfile = argv[2];

  // Load cloud in blob format
  pcl::PCLPointCloud2 blob;
  pcl::io::loadPCDFile (infile, blob);

  pcl::PointCloud<PointT>::Ptr cloud (new pcl::PointCloud<PointT>);
        std::cout << "Loading point cloud...";
        pcl::fromPCLPointCloud2 (blob, *cloud);
        std::cout << "done." << std::endl;

  SearchPtr tree;

  if (cloud->isOrganized ())
  {
    tree.reset (new pcl::search::OrganizedNeighbor<PointT> ());
  }
  else
  {
      tree.reset (new pcl::search::KdTree<PointT> (false));
  }

  tree->setInputCloud (cloud);

  PointCloud<PointT>::Ptr small_cloud_downsampled;
  PointCloud<PointT>::Ptr large_cloud_downsampled;

  // If we are using approximation
  if(approx){
    std::cout << "Downsampling point cloud for approximation" << std::endl;