extract_clusters.h File Reference

#include <pcl/pcl_base.h>
#include "pcl/kdtree/kdtree.h"
#include "pcl/kdtree/tree_types.h"

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Classes
class	pcl::EuclideanClusterExtraction< PointT >
	EuclideanClusterExtraction represents a segmentation class for cluster extraction in an Euclidean sense. More...
Namespaces
namespace	pcl
Functions
bool	pcl::comparePointClusters (const pcl::PointIndices &a, const pcl::PointIndices &b)
	Sort clusters method (for std::sort).
template<typename PointT , typename Normal >
void	pcl::extractEuclideanClusters (const PointCloud< PointT > &cloud, const PointCloud< Normal > &normals, const std::vector< int > &indices, const boost::shared_ptr< KdTree< PointT > > &tree, float tolerance, std::vector< PointIndices > &clusters, double eps_angle, unsigned int min_pts_per_cluster=1, unsigned int max_pts_per_cluster=(std::numeric_limits< int >::max)())
	Decompose a region of space into clusters based on the euclidean distance between points, and the normal angular deviation.
template<typename PointT , typename Normal >
void	pcl::extractEuclideanClusters (const PointCloud< PointT > &cloud, const PointCloud< Normal > &normals, float tolerance, const boost::shared_ptr< KdTree< PointT > > &tree, std::vector< PointIndices > &clusters, double eps_angle, unsigned int min_pts_per_cluster=1, unsigned int max_pts_per_cluster=(std::numeric_limits< int >::max)())
	Decompose a region of space into clusters based on the euclidean distance between points, and the normal angular deviation.
template<typename PointT >
void	pcl::extractEuclideanClusters (const PointCloud< PointT > &cloud, const std::vector< int > &indices, const boost::shared_ptr< KdTree< PointT > > &tree, float tolerance, std::vector< PointIndices > &clusters, unsigned int min_pts_per_cluster=1, unsigned int max_pts_per_cluster=(std::numeric_limits< int >::max)())
	Decompose a region of space into clusters based on the Euclidean distance between points.
template<typename PointT >
void	pcl::extractEuclideanClusters (const PointCloud< PointT > &cloud, const boost::shared_ptr< KdTree< PointT > > &tree, float tolerance, std::vector< PointIndices > &clusters, unsigned int min_pts_per_cluster=1, unsigned int max_pts_per_cluster=(std::numeric_limits< int >::max)())
	Decompose a region of space into clusters based on the Euclidean distance between points.