Point Cloud Library (PCL) 1.12.0
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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
48 * \note the tree has to be created as a spatial locator on \a cloud
49 * \param[in] tolerance the spatial cluster tolerance as a measure in L2 Euclidean space
50 * \param[out] labeled_clusters the resultant clusters containing point indices (as a
51 * vector of PointIndices)
52 * \param[in] min_pts_per_cluster minimum number of points that a cluster may contain
53 * (default: 1)
54 * \param[in] max_pts_per_cluster maximum number of points that a cluster may contain
55 * (default: max int)
56 * \param[in] max_label
57 * \ingroup segmentation
58 */
59template <typename PointT>
60PCL_DEPRECATED(1, 14, "Use of max_label is deprecated")
62 const PointCloud<PointT>& cloud,
63 const typename search::Search<PointT>::Ptr& tree,
64 float tolerance,
65 std::vector<std::vector<PointIndices>>& labeled_clusters,
66 unsigned int min_pts_per_cluster,
67 unsigned int max_pts_per_cluster,
68 unsigned int max_label);
69
70/** \brief Decompose a region of space into clusters based on the Euclidean distance
71 * between points
72 * \param[in] cloud the point cloud message
73 * \param[in] tree the spatial locator (e.g., kd-tree) used for nearest neighbors
74 * searching \note the tree has to be created as a spatial locator on \a cloud
75 * \param[in] tolerance the spatial cluster tolerance as a measure in L2 Euclidean space
76 * \param[out] labeled_clusters the resultant clusters containing point indices
77 * (as a vector of PointIndices)
78 * \param[in] min_pts_per_cluster minimum number of points that a cluster may contain
79 * (default: 1)
80 * \param[in] max_pts_per_cluster maximum number of points that a cluster may contain
81 * (default: max int)
82 * \ingroup segmentation
83 */
84template <typename PointT>
85void
87 const PointCloud<PointT>& cloud,
88 const typename search::Search<PointT>::Ptr& tree,
89 float tolerance,
90 std::vector<std::vector<PointIndices>>& labeled_clusters,
91 unsigned int min_pts_per_cluster = 1,
92 unsigned int max_pts_per_cluster = std::numeric_limits<unsigned int>::max());
93
94//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
95//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
96//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
97/** \brief @b LabeledEuclideanClusterExtraction represents a segmentation class for
98 * cluster extraction in an Euclidean sense, with label info. \author Koen Buys
99 * \ingroup segmentation
100 */
101template <typename PointT>
104
105public:
109
111 using KdTreePtr = typename KdTree::Ptr;
112
115
116 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
117 /** \brief Empty constructor. */
119 : tree_()
120 , cluster_tolerance_(0)
121 , min_pts_per_cluster_(1)
122 , max_pts_per_cluster_(std::numeric_limits<int>::max())
123 , max_label_(std::numeric_limits<int>::max()){};
124
125 /** \brief Provide a pointer to the search object.
126 * \param[in] tree a pointer to the spatial search object.
127 */
128 inline void
130 {
131 tree_ = tree;
132 }
133
134 /** \brief Get a pointer to the search method used. */
135 inline KdTreePtr
137 {
138 return (tree_);
139 }
140
141 /** \brief Set the spatial cluster tolerance as a measure in the L2 Euclidean space
142 * \param[in] tolerance the spatial cluster tolerance as a measure in the L2 Euclidean
143 * space
144 */
145 inline void
147 {
148 cluster_tolerance_ = tolerance;
149 }
150
151 /** \brief Get the spatial cluster tolerance as a measure in the L2 Euclidean space.
152 */
153 inline double
155 {
156 return (cluster_tolerance_);
157 }
158
159 /** \brief Set the minimum number of points that a cluster needs to contain in order
160 * to be considered valid. \param[in] min_cluster_size the minimum cluster size
161 */
162 inline void
163 setMinClusterSize(int min_cluster_size)
164 {
165 min_pts_per_cluster_ = min_cluster_size;
166 }
167
168 /** \brief Get the minimum number of points that a cluster needs to contain in order
169 * to be considered valid. */
170 inline int
172 {
173 return (min_pts_per_cluster_);
174 }
175
176 /** \brief Set the maximum number of points that a cluster needs to contain in order
177 * to be considered valid. \param[in] max_cluster_size the maximum cluster size
178 */
179 inline void
180 setMaxClusterSize(int max_cluster_size)
181 {
182 max_pts_per_cluster_ = max_cluster_size;
183 }
184
185 /** \brief Get the maximum number of points that a cluster needs to contain in order
186 * to be considered valid. */
187 inline int
189 {
190 return (max_pts_per_cluster_);
191 }
192
193 /** \brief Set the maximum number of labels in the cloud.
194 * \param[in] max_label the maximum
195 */
196 PCL_DEPRECATED(1, 14, "Max label is being deprecated")
197 inline void
198 setMaxLabels(unsigned int max_label)
199 {
200 max_label_ = max_label;
201 }
202
203 /** \brief Get the maximum number of labels */
204 PCL_DEPRECATED(1, 14, "Max label is being deprecated")
205 inline unsigned int
206 getMaxLabels() const
207 {
208 return (max_label_);
209 }
210
211 /** \brief Cluster extraction in a PointCloud given by <setInputCloud (), setIndices
212 * ()> \param[out] labeled_clusters the resultant point clusters
213 */
214 void
215 extract(std::vector<std::vector<PointIndices>>& labeled_clusters);
216
217protected:
218 // Members derived from the base class
219 using BasePCLBase::deinitCompute;
220 using BasePCLBase::indices_;
221 using BasePCLBase::initCompute;
222 using BasePCLBase::input_;
223
224 /** \brief A pointer to the spatial search object. */
226
227 /** \brief The spatial cluster tolerance as a measure in the L2 Euclidean space. */
229
230 /** \brief The minimum number of points that a cluster needs to contain in order to be
231 * considered valid (default = 1). */
233
234 /** \brief The maximum number of points that a cluster needs to contain in order to be
235 * considered valid (default = MAXINT). */
237
238 /** \brief The maximum number of labels we can find in this pointcloud (default =
239 * MAXINT)*/
240 unsigned int max_label_;
241
242 /** \brief Class getName method. */
243 virtual std::string
245 {
246 return ("LabeledEuclideanClusterExtraction");
247 }
248};
249
250/** \brief Sort clusters method (for std::sort).
251 * \ingroup segmentation
252 */
253inline bool
255{
256 return (a.indices.size() < b.indices.size());
257}
258} // namespace pcl
259
260#ifdef PCL_NO_PRECOMPILE
261#include <pcl/segmentation/impl/extract_labeled_clusters.hpp>
262#endif
Iterator class for point clouds with or without given indices.
std::size_t size() const
Size of the range the iterator is going through.
shared_ptr< KdTree< PointT > > Ptr
Definition kdtree.h:68
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.
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.
void setSearchMethod(const KdTreePtr &tree)
Provide a pointer to the search object.
PCL base class.
Definition pcl_base.h:70
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
bool compareLabeledPointClusters(const pcl::PointIndices &a, const pcl::PointIndices &b)
Sort clusters method (for std::sort).
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, unsigned int max_pts_per_cluster, unsigned int max_label)
Decompose a region of space into clusters based on the Euclidean distance between points.
#define PCL_DEPRECATED(Major, Minor, Message)
macro for compatibility across compilers and help remove old deprecated items for the Major....
Definition pcl_macros.h:156
shared_ptr< ::pcl::PointIndices > Ptr
shared_ptr< const ::pcl::PointIndices > ConstPtr
A point structure representing Euclidean xyz coordinates, and the RGB color.