Region growing segmentation

region_growing_tutorial.pcd 사용

In this tutorial we will learn how to use the region growing algorithm implemented in the pcl::RegionGrowing class.

목적 : smoothness를 기준으로 비슷한것 묶음 The purpose of the said algorithm is to merge the points that are close enough in terms of the smoothness constraint.

Thereby, the output of this algorithm is the set of clusters, were each cluster is a set of points that are considered to be a part of the same smooth surface.

원리 : 포인트 Normal간의 각도(angle)비교 The work of this algorithm is based on the comparison of the angles between the points normals.

Theoretical Primer

First of all it sorts the points by their curvature value.

It needs to be done because the region begins its growth from the point that has the minimum curvature value.

The reason for this is that the point with the minimum curvature is located in the flat area (growth from the flattest area allows to reduce the total number of segments).

So we have the sorted cloud. Until there are unlabeled points in the cloud, algorithm picks up the point with minimum curvature value and starts the growth of the region.

This process occurs as follows:

Code

#include <iostream>
#include <vector>
#include <pcl/point_types.h>
#include <pcl/io/pcd_io.h>
#include <pcl/search/search.h>
#include <pcl/search/kdtree.h>
#include <pcl/features/normal_3d.h>
#include <pcl/visualization/cloud_viewer.h>
#include <pcl/filters/passthrough.h>
#include <pcl/segmentation/region_growing.h>

int
main (int argc, char** argv)
{
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
  if ( pcl::io::loadPCDFile <pcl::PointXYZ> ("region_growing_tutorial.pcd", *cloud) == -1)
  {
    std::cout << "Cloud reading failed." << std::endl;
    return (-1);
  }

  pcl::search::Search<pcl::PointXYZ>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZ>);
  pcl::PointCloud <pcl::Normal>::Ptr normals (new pcl::PointCloud <pcl::Normal>);
  pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> normal_estimator;
  normal_estimator.setSearchMethod (tree);
  normal_estimator.setInputCloud (cloud);
  normal_estimator.setKSearch (50);
  normal_estimator.compute (*normals);

  pcl::IndicesPtr indices (new std::vector <int>);
  pcl::PassThrough<pcl::PointXYZ> pass;
  pass.setInputCloud (cloud);
  pass.setFilterFieldName ("z");
  pass.setFilterLimits (0.0, 1.0);
  pass.filter (*indices);

  pcl::RegionGrowing<pcl::PointXYZ, pcl::Normal> reg;
  reg.setMinClusterSize (50);
  reg.setMaxClusterSize (1000000);
  reg.setSearchMethod (tree);
  reg.setNumberOfNeighbours (30);
  reg.setInputCloud (cloud);
  //reg.setIndices (indices);
  reg.setInputNormals (normals);
  reg.setSmoothnessThreshold (3.0 / 180.0 * M_PI);
  reg.setCurvatureThreshold (1.0);

  std::vector <pcl::PointIndices> clusters;
  reg.extract (clusters);

  std::cout << "Number of clusters is equal to " << clusters.size () << std::endl;
  std::cout << "First cluster has " << clusters[0].indices.size () << " points." << endl;
  std::cout << "These are the indices of the points of the initial" <<
    std::endl << "cloud that belong to the first cluster:" << std::endl;
  int counter = 0;
  while (counter < clusters[0].indices.size ())
  {
    std::cout << clusters[0].indices[counter] << ", ";
    counter++;
    if (counter % 10 == 0)
      std::cout << std::endl;
  }
  std::cout << std::endl;

  pcl::PointCloud <pcl::PointXYZRGB>::Ptr colored_cloud = reg.getColoredCloud ();
  pcl::visualization::CloudViewer viewer ("Cluster viewer");
  viewer.showCloud(colored_cloud);
  while (!viewer.wasStopped ())
  {
  }

  return (0);
}