ConditionalEuclideanClustering performs segmentation based on Euclidean distance and a user-defined clustering condition. More...

#include <conditional_euclidean_clustering.h>

Inheritance diagram for pcl::ConditionalEuclideanClustering< PointT >:

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List of all members.

Public Member Functions
	ConditionalEuclideanClustering (bool extract_removed_clusters=false)
	Constructor.
float	getClusterTolerance ()
	Get the spatial tolerance for new cluster candidates.
int	getMaxClusterSize ()
	Get the maximum number of points that a cluster needs to contain in order to be considered valid.
int	getMinClusterSize ()
	Get the minimum number of points that a cluster needs to contain in order to be considered valid.
void	getRemovedClusters (IndicesClustersPtr &small_clusters, IndicesClustersPtr &large_clusters)
	Get the clusters that are invalidated due to size constraints.
void	segment (IndicesClusters &clusters)
	Segment the input into separate clusters.
void	setClusterTolerance (float cluster_tolerance)
	Set the spatial tolerance for new cluster candidates.
void	setConditionFunction (bool(*condition_function)(const PointT &, const PointT &, float))
	Set the condition that needs to hold for neighboring points to be considered part of the same cluster.
void	setMaxClusterSize (int max_cluster_size)
	Set the maximum number of points that a cluster needs to contain in order to be considered valid.
void	setMinClusterSize (int min_cluster_size)
	Set the minimum number of points that a cluster needs to contain in order to be considered valid.
Protected Types
typedef pcl::search::Search < PointT >::Ptr	SearcherPtr
Private Attributes
float	cluster_tolerance_
	The distance to scan for cluster candidates (default = 0.0)
bool(*	condition_function_ )(const PointT &, const PointT &, float)
	The condition function that needs to hold for clustering.
bool	extract_removed_clusters_
	Set to true if you want to be able to extract the clusters that are too large or too small (default = false)
pcl::IndicesClustersPtr	large_clusters_
	The resultant clusters that contain more than max_cluster_size points.
int	max_cluster_size_
	The maximum cluster size (default = unlimited)
int	min_cluster_size_
	The minimum cluster size (default = 1)
SearcherPtr	searcher_
	A pointer to the spatial search object.
pcl::IndicesClustersPtr	small_clusters_
	The resultant clusters that contain less than min_cluster_size points.

Detailed Description

template<typename PointT>
class pcl::ConditionalEuclideanClustering< PointT >

ConditionalEuclideanClustering performs segmentation based on Euclidean distance and a user-defined clustering condition.

The condition that need to hold is currently passed using a function pointer. For more information check the documentation of setConditionFunction() or the usage example below:

 bool
 enforceIntensitySimilarity (const pcl::PointXYZI& point_a, const pcl::PointXYZI& point_b, float squared_distance)
 {
   if (fabs (point_a.intensity - point_b.intensity) < 0.1f)
     return (true);
   else
     return (false);
 }
 // ...
 // Somewhere down to the main code
 // ...
 pcl::ConditionalEuclideanClustering<pcl::PointXYZI> cec (true);
 cec.setInputCloud (cloud_in);
 cec.setConditionFunction (&enforceIntensitySimilarity);
 // Points within this distance from one another are going to need to validate the enforceIntensitySimilarity function to be part of the same cluster:
 cec.setClusterTolerance (0.09f);
 // Size constraints for the clusters:
 cec.setMinClusterSize (5);
 cec.setMaxClusterSize (30);
 // The resulting clusters (an array of pointindices):
 cec.segment (*clusters);
 // The clusters that are too small or too large in size can also be extracted separately:
 cec.getRemovedClusters (small_clusters, large_clusters);

Author:: Frits Florentinus

Definition at line 81 of file conditional_euclidean_clustering.h.

Member Typedef Documentation

template<typename PointT>

typedef pcl::search::Search<PointT>::Ptr pcl::ConditionalEuclideanClustering< PointT >::SearcherPtr [protected]

Definition at line 84 of file conditional_euclidean_clustering.h.

Constructor & Destructor Documentation

template<typename PointT>

pcl::ConditionalEuclideanClustering< PointT >::ConditionalEuclideanClustering ( bool extract_removed_clusters = false ) [inline]

Constructor.

Parameters:

[in] extract_removed_clusters Set to true if you want to be able to extract the clusters that are too large or too small (default = false)

Definition at line 95 of file conditional_euclidean_clustering.h.

Member Function Documentation

template<typename PointT>

float pcl::ConditionalEuclideanClustering< PointT >::getClusterTolerance ( ) [inline]

Get the spatial tolerance for new cluster candidates.

Definition at line 143 of file conditional_euclidean_clustering.h.

template<typename PointT>

int pcl::ConditionalEuclideanClustering< PointT >::getMaxClusterSize ( ) [inline]

Get the maximum number of points that a cluster needs to contain in order to be considered valid.

Definition at line 175 of file conditional_euclidean_clustering.h.

template<typename PointT>

int pcl::ConditionalEuclideanClustering< PointT >::getMinClusterSize ( ) [inline]

Get the minimum number of points that a cluster needs to contain in order to be considered valid.

Definition at line 159 of file conditional_euclidean_clustering.h.

template<typename PointT>

void pcl::ConditionalEuclideanClustering< PointT >::getRemovedClusters	(	IndicesClustersPtr &	small_clusters,
		IndicesClustersPtr &	large_clusters
	)		`[inline]`

Get the clusters that are invalidated due to size constraints.

Note:: The constructor of this class needs to be initialized with true, and the segment method needs to have been called prior to using this method.

Parameters:

[out]	small_clusters	The resultant clusters that contain less than min_cluster_size points
[out]	large_clusters	The resultant clusters that contain more than max_cluster_size points

Definition at line 198 of file conditional_euclidean_clustering.h.

template<typename PointT >

void pcl::ConditionalEuclideanClustering< PointT >::segment ( pcl::IndicesClusters & clusters )

Segment the input into separate clusters.

The input can be set using setInputCloud() and setIndices().
The size constraints for the resulting clusters can be set using setMinClusterSize() and setMaxClusterSize().
The region growing parameters can be set using setConditionFunction() and setClusterTolerance().

Parameters:

[out] clusters The resultant set of indices, indexing the points of the input cloud that correspond to the clusters

Definition at line 43 of file conditional_euclidean_clustering.hpp.

template<typename PointT>

void pcl::ConditionalEuclideanClustering< PointT >::setClusterTolerance ( float cluster_tolerance ) [inline]

Set the spatial tolerance for new cluster candidates.

Any two points within this distance from one another will need to evaluate a certain condition in order to be made part of the same cluster. The condition can be set using setConditionFunction().

Parameters:

[in] cluster_tolerance The distance to scan for cluster candidates (default = 0.0)

Definition at line 136 of file conditional_euclidean_clustering.h.

template<typename PointT>

void pcl::ConditionalEuclideanClustering< PointT >::setConditionFunction ( bool(*)(const PointT &, const PointT &, float) condition_function ) [inline]

Set the condition that needs to hold for neighboring points to be considered part of the same cluster.

Any two points within a certain distance from one another will need to evaluate this condition in order to be made part of the same cluster. The distance can be set using setClusterTolerance().
Note that for a point to be part of a cluster, the condition only needs to hold for at least 1 point pair. To clarify, the following statement is false: Any two points within a cluster always evaluate this condition function to true.

The input arguments of the condition function are: