Point Cloud Library (PCL) 1.15.0
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extract_labeled_clusters.h
1/*
2 * Software License Agreement (BSD License)
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4 * Copyright (c) 2011, Willow Garage, Inc.
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35
36#pragma once
37
38#include <pcl/search/search.h>
39#include <pcl/pcl_base.h>
40
41namespace pcl {
42//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
43/** \brief Decompose a region of space into clusters based on the Euclidean distance
44 * between points
45 * \param[in] cloud the point cloud message
46 * \param[in] tree the spatial locator (e.g., kd-tree) used for nearest neighbors
47 * searching \note the tree has to be created as a spatial locator on \a cloud
48 * \param[in] tolerance the spatial cluster tolerance as a measure in L2 Euclidean space
49 * \param[out] labeled_clusters the resultant clusters containing point indices
50 * (as a vector of PointIndices)
51 * \param[in] min_pts_per_cluster minimum number of points that a cluster may contain
52 * (default: 1)
53 * \param[in] max_pts_per_cluster maximum number of points that a cluster may contain
54 * (default: max int)
55 * \ingroup segmentation
56 */
57template <typename PointT>
58void
60 const PointCloud<PointT>& cloud,
61 const typename search::Search<PointT>::Ptr& tree,
62 float tolerance,
63 std::vector<std::vector<PointIndices>>& labeled_clusters,
64 unsigned int min_pts_per_cluster = 1,
65 unsigned int max_pts_per_cluster = std::numeric_limits<unsigned int>::max());
66
67//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
68//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
69//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
70/** \brief @b LabeledEuclideanClusterExtraction represents a segmentation class for
71 * cluster extraction in an Euclidean sense, with label info. \author Koen Buys
72 * \ingroup segmentation
73 */
74template <typename PointT>
77
78public:
82
84 using KdTreePtr = typename KdTree::Ptr;
85
88
89 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
90 /** \brief Empty constructor. */
92
93 /** \brief Provide a pointer to the search object.
94 * \param[in] tree a pointer to the spatial search object.
95 */
96 inline void
98 {
99 tree_ = tree;
100 }
101
102 /** \brief Get a pointer to the search method used. */
103 inline KdTreePtr
105 {
106 return (tree_);
107 }
108
109 /** \brief Set the spatial cluster tolerance as a measure in the L2 Euclidean space
110 * \param[in] tolerance the spatial cluster tolerance as a measure in the L2 Euclidean
111 * space
112 */
113 inline void
114 setClusterTolerance(double tolerance)
115 {
116 cluster_tolerance_ = tolerance;
117 }
118
119 /** \brief Get the spatial cluster tolerance as a measure in the L2 Euclidean space.
120 */
121 inline double
123 {
124 return (cluster_tolerance_);
125 }
126
127 /** \brief Set the minimum number of points that a cluster needs to contain in order
128 * to be considered valid. \param[in] min_cluster_size the minimum cluster size
129 */
130 inline void
131 setMinClusterSize(int min_cluster_size)
132 {
133 min_pts_per_cluster_ = min_cluster_size;
134 }
135
136 /** \brief Get the minimum number of points that a cluster needs to contain in order
137 * to be considered valid. */
138 inline int
140 {
141 return (min_pts_per_cluster_);
142 }
143
144 /** \brief Set the maximum number of points that a cluster needs to contain in order
145 * to be considered valid. \param[in] max_cluster_size the maximum cluster size
146 */
147 inline void
148 setMaxClusterSize(int max_cluster_size)
149 {
150 max_pts_per_cluster_ = max_cluster_size;
151 }
152
153 /** \brief Get the maximum number of points that a cluster needs to contain in order
154 * to be considered valid. */
155 inline int
157 {
158 return (max_pts_per_cluster_);
159 }
160
161 /** \brief Cluster extraction in a PointCloud given by <setInputCloud (), setIndices
162 * ()> \param[out] labeled_clusters the resultant point clusters
163 */
164 void
165 extract(std::vector<std::vector<PointIndices>>& labeled_clusters);
166
167protected:
168 // Members derived from the base class
173
174 /** \brief A pointer to the spatial search object. */
175 KdTreePtr tree_{nullptr};
176
177 /** \brief The spatial cluster tolerance as a measure in the L2 Euclidean space. */
179
180 /** \brief The minimum number of points that a cluster needs to contain in order to be
181 * considered valid (default = 1). */
183
184 /** \brief The maximum number of points that a cluster needs to contain in order to be
185 * considered valid (default = MAXINT). */
186 int max_pts_per_cluster_{std::numeric_limits<int>::max()};
187
188 /** \brief The maximum number of labels we can find in this pointcloud (default =
189 * MAXINT)*/
190 PCL_DEPRECATED(1, 18, "this member variable is unused")
191 unsigned int max_label_{std::numeric_limits<unsigned int>::max()};
192
193 /** \brief Class getName method. */
194 virtual std::string
196 {
197 return ("LabeledEuclideanClusterExtraction");
198 }
199};
200
201/** \brief Sort clusters method (for std::sort).
202 * \ingroup segmentation
203 */
204inline bool
206{
207 return (a.indices.size() < b.indices.size());
208}
209} // namespace pcl
210
211#ifdef PCL_NO_PRECOMPILE
212#include <pcl/segmentation/impl/extract_labeled_clusters.hpp>
213#endif
LabeledEuclideanClusterExtraction represents a segmentation class for cluster extraction in an Euclid...
unsigned int max_label_
The maximum number of labels we can find in this pointcloud (default = MAXINT)
void setClusterTolerance(double tolerance)
Set the spatial cluster tolerance as a measure in the L2 Euclidean space.
KdTreePtr tree_
A pointer to the spatial search object.
double cluster_tolerance_
The spatial cluster tolerance as a measure in the L2 Euclidean space.
typename PointCloud::ConstPtr PointCloudConstPtr
void setMinClusterSize(int min_cluster_size)
Set the minimum number of points that a cluster needs to contain in order to be considered valid.
void extract(std::vector< std::vector< PointIndices > > &labeled_clusters)
Cluster extraction in a PointCloud given by <setInputCloud (), setIndices ()>
int getMaxClusterSize() const
Get the maximum number of points that a cluster needs to contain in order to be considered valid.
int min_pts_per_cluster_
The minimum number of points that a cluster needs to contain in order to be considered valid (default...
double getClusterTolerance() const
Get the spatial cluster tolerance as a measure in the L2 Euclidean space.
virtual std::string getClassName() const
Class getName method.
KdTreePtr getSearchMethod() const
Get a pointer to the search method used.
int getMinClusterSize() const
Get the minimum number of points that a cluster needs to contain in order to be considered valid.
int max_pts_per_cluster_
The maximum number of points that a cluster needs to contain in order to be considered valid (default...
void setMaxClusterSize(int max_cluster_size)
Set the maximum number of points that a cluster needs to contain in order to be considered valid.
LabeledEuclideanClusterExtraction()=default
Empty constructor.
void setSearchMethod(const KdTreePtr &tree)
Provide a pointer to the search object.
PCL base class.
Definition pcl_base.h:70
PointCloudConstPtr input_
The input point cloud dataset.
Definition pcl_base.h:147
IndicesPtr indices_
A pointer to the vector of point indices to use.
Definition pcl_base.h:150
bool initCompute()
This method should get called before starting the actual computation.
Definition pcl_base.hpp:138
bool deinitCompute()
This method should get called after finishing the actual computation.
Definition pcl_base.hpp:175
PointCloud represents the base class in PCL for storing collections of 3D points.
shared_ptr< PointCloud< PointT > > Ptr
shared_ptr< const PointCloud< PointT > > ConstPtr
Generic search class.
Definition search.h:75
shared_ptr< pcl::search::Search< PointT > > Ptr
Definition search.h:81
void extractLabeledEuclideanClusters(const PointCloud< PointT > &cloud, const typename search::Search< PointT >::Ptr &tree, float tolerance, std::vector< std::vector< PointIndices > > &labeled_clusters, unsigned int min_pts_per_cluster=1, unsigned int max_pts_per_cluster=std::numeric_limits< unsigned int >::max())
Decompose a region of space into clusters based on the Euclidean distance between points.
bool compareLabeledPointClusters(const pcl::PointIndices &a, const pcl::PointIndices &b)
Sort clusters method (for std::sort).
#define PCL_DEPRECATED(Major, Minor, Message)
macro for compatibility across compilers and help remove old deprecated items for the Major....
Definition pcl_macros.h:158
shared_ptr< ::pcl::PointIndices > Ptr
shared_ptr< const ::pcl::PointIndices > ConstPtr