Public Member Functions | Protected Types | Private Attributes
pcl::ConditionalEuclideanClustering< PointT > Class Template Reference

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

#include <conditional_euclidean_clustering.h>

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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_clustersSet 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_clustersThe resultant clusters that contain less than min_cluster_size points
[out]large_clustersThe 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]clustersThe 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_toleranceThe 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:

  • PointT The first point of the point pair
  • PointT The second point of the point pair
  • float The squared distance between the points

The output argument is a boolean, returning true will merge the second point into the cluster of the first point.

Parameters:
[in]condition_functionThe condition function that needs to hold for clustering

Definition at line 125 of file conditional_euclidean_clustering.h.

template<typename PointT>
void pcl::ConditionalEuclideanClustering< PointT >::setMaxClusterSize ( int  max_cluster_size) [inline]

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

Parameters:
[in]max_cluster_sizeThe maximum cluster size (default = unlimited)

Definition at line 168 of file conditional_euclidean_clustering.h.

template<typename PointT>
void pcl::ConditionalEuclideanClustering< PointT >::setMinClusterSize ( int  min_cluster_size) [inline]

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

Parameters:
[in]min_cluster_sizeThe minimum cluster size (default = 1)

Definition at line 152 of file conditional_euclidean_clustering.h.


Member Data Documentation

template<typename PointT>
float pcl::ConditionalEuclideanClustering< PointT >::cluster_tolerance_ [private]

The distance to scan for cluster candidates (default = 0.0)

Definition at line 217 of file conditional_euclidean_clustering.h.

template<typename PointT>
bool(* pcl::ConditionalEuclideanClustering< PointT >::condition_function_)(const PointT &, const PointT &, float) [private]

The condition function that needs to hold for clustering.

Definition at line 214 of file conditional_euclidean_clustering.h.

template<typename PointT>
bool pcl::ConditionalEuclideanClustering< PointT >::extract_removed_clusters_ [private]

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 226 of file conditional_euclidean_clustering.h.

The resultant clusters that contain more than max_cluster_size points.

Definition at line 232 of file conditional_euclidean_clustering.h.

template<typename PointT>
int pcl::ConditionalEuclideanClustering< PointT >::max_cluster_size_ [private]

The maximum cluster size (default = unlimited)

Definition at line 223 of file conditional_euclidean_clustering.h.

template<typename PointT>
int pcl::ConditionalEuclideanClustering< PointT >::min_cluster_size_ [private]

The minimum cluster size (default = 1)

Definition at line 220 of file conditional_euclidean_clustering.h.

template<typename PointT>
SearcherPtr pcl::ConditionalEuclideanClustering< PointT >::searcher_ [private]

A pointer to the spatial search object.

Definition at line 211 of file conditional_euclidean_clustering.h.

The resultant clusters that contain less than min_cluster_size points.

Definition at line 229 of file conditional_euclidean_clustering.h.


The documentation for this class was generated from the following files:


pcl
Author(s): Open Perception
autogenerated on Wed Aug 26 2015 15:39:16