Point Cloud Library (PCL)  1.11.1-dev
extract_clusters.h
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39 
40 #pragma once
41 
42 #include <pcl/console/print.h> // for PCL_ERROR
43 #include <pcl/pcl_base.h>
44 
45 #include <pcl/search/search.h> // for Search
46 #include <pcl/search/kdtree.h> // for KdTree
47 
48 namespace pcl
49 {
50  //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
51  /** \brief Decompose a region of space into clusters based on the Euclidean distance between points
52  * \param cloud the point cloud message
53  * \param tree the spatial locator (e.g., kd-tree) used for nearest neighbors searching
54  * \note the tree has to be created as a spatial locator on \a cloud
55  * \param tolerance the spatial cluster tolerance as a measure in L2 Euclidean space
56  * \param clusters the resultant clusters containing point indices (as a vector of PointIndices)
57  * \param min_pts_per_cluster minimum number of points that a cluster may contain (default: 1)
58  * \param max_pts_per_cluster maximum number of points that a cluster may contain (default: max int)
59  * \ingroup segmentation
60  */
61  template <typename PointT> void
63  const PointCloud<PointT> &cloud, const typename search::Search<PointT>::Ptr &tree,
64  float tolerance, std::vector<PointIndices> &clusters,
65  unsigned int min_pts_per_cluster = 1, unsigned int max_pts_per_cluster = (std::numeric_limits<int>::max) ());
66 
67  //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
68  /** \brief Decompose a region of space into clusters based on the Euclidean distance between points
69  * \param cloud the point cloud message
70  * \param indices a list of point indices to use from \a cloud
71  * \param tree the spatial locator (e.g., kd-tree) used for nearest neighbors searching
72  * \note the tree has to be created as a spatial locator on \a cloud and \a indices
73  * \param tolerance the spatial cluster tolerance as a measure in L2 Euclidean space
74  * \param clusters the resultant clusters containing point indices (as a vector of PointIndices)
75  * \param min_pts_per_cluster minimum number of points that a cluster may contain (default: 1)
76  * \param max_pts_per_cluster maximum number of points that a cluster may contain (default: max int)
77  * \ingroup segmentation
78  */
79  template <typename PointT> void
81  const PointCloud<PointT> &cloud, const Indices &indices,
82  const typename search::Search<PointT>::Ptr &tree, float tolerance, std::vector<PointIndices> &clusters,
83  unsigned int min_pts_per_cluster = 1, unsigned int max_pts_per_cluster = (std::numeric_limits<int>::max) ());
84 
85  //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
86  /** \brief Decompose a region of space into clusters based on the euclidean distance between points, and the normal
87  * angular deviation between points. Each point added to the cluster is origin to another radius search. Each point
88  * within radius range will be compared to the origin in respect to normal angle and euclidean distance. If both
89  * are under their respective threshold the point will be added to the cluster. Generally speaking the cluster
90  * algorithm will not stop on smooth surfaces but on surfaces with sharp edges.
91  * \param cloud the point cloud message
92  * \param normals the point cloud message containing normal information
93  * \param tree the spatial locator (e.g., kd-tree) used for nearest neighbors searching
94  * \note the tree has to be created as a spatial locator on \a cloud
95  * \param tolerance the spatial cluster tolerance as a measure in the L2 Euclidean space
96  * \param clusters the resultant clusters containing point indices (as a vector of PointIndices)
97  * \param eps_angle the maximum allowed difference between normals in radians for cluster/region growing
98  * \param min_pts_per_cluster minimum number of points that a cluster may contain (default: 1)
99  * \param max_pts_per_cluster maximum number of points that a cluster may contain (default: max int)
100  * \ingroup segmentation
101  */
102  template <typename PointT, typename Normal> void
104  const PointCloud<PointT> &cloud, const PointCloud<Normal> &normals,
105  float tolerance, const typename KdTree<PointT>::Ptr &tree,
106  std::vector<PointIndices> &clusters, double eps_angle,
107  unsigned int min_pts_per_cluster = 1,
108  unsigned int max_pts_per_cluster = (std::numeric_limits<int>::max) ())
109  {
110  if (tree->getInputCloud ()->size () != cloud.size ())
111  {
112  PCL_ERROR("[pcl::extractEuclideanClusters] Tree built for a different point "
113  "cloud dataset (%zu) than the input cloud (%zu)!\n",
114  static_cast<std::size_t>(tree->getInputCloud()->size()),
115  static_cast<std::size_t>(cloud.size()));
116  return;
117  }
118  if (cloud.size () != normals.size ())
119  {
120  PCL_ERROR("[pcl::extractEuclideanClusters] Number of points in the input point "
121  "cloud (%zu) different than normals (%zu)!\n",
122  static_cast<std::size_t>(cloud.size()),
123  static_cast<std::size_t>(normals.size()));
124  return;
125  }
126  const double cos_eps_angle = std::cos (eps_angle); // compute this once instead of acos many times (faster)
127 
128  // Create a bool vector of processed point indices, and initialize it to false
129  std::vector<bool> processed (cloud.size (), false);
130 
131  Indices nn_indices;
132  std::vector<float> nn_distances;
133  // Process all points in the indices vector
134  for (std::size_t i = 0; i < cloud.size (); ++i)
135  {
136  if (processed[i])
137  continue;
138 
139  Indices seed_queue;
140  int sq_idx = 0;
141  seed_queue.push_back (static_cast<index_t> (i));
142 
143  processed[i] = true;
144 
145  while (sq_idx < static_cast<int> (seed_queue.size ()))
146  {
147  // Search for sq_idx
148  if (!tree->radiusSearch (seed_queue[sq_idx], tolerance, nn_indices, nn_distances))
149  {
150  sq_idx++;
151  continue;
152  }
153 
154  for (std::size_t j = 1; j < nn_indices.size (); ++j) // nn_indices[0] should be sq_idx
155  {
156  if (processed[nn_indices[j]]) // Has this point been processed before ?
157  continue;
158 
159  //processed[nn_indices[j]] = true;
160  // [-1;1]
161  double dot_p = normals[seed_queue[sq_idx]].normal[0] * normals[nn_indices[j]].normal[0] +
162  normals[seed_queue[sq_idx]].normal[1] * normals[nn_indices[j]].normal[1] +
163  normals[seed_queue[sq_idx]].normal[2] * normals[nn_indices[j]].normal[2];
164  if ( std::abs (dot_p) > cos_eps_angle )
165  {
166  processed[nn_indices[j]] = true;
167  seed_queue.push_back (nn_indices[j]);
168  }
169  }
170 
171  sq_idx++;
172  }
173 
174  // If this queue is satisfactory, add to the clusters
175  if (seed_queue.size () >= min_pts_per_cluster && seed_queue.size () <= max_pts_per_cluster)
176  {
178  r.indices.resize (seed_queue.size ());
179  for (std::size_t j = 0; j < seed_queue.size (); ++j)
180  r.indices[j] = seed_queue[j];
181 
182  // These two lines should not be needed: (can anyone confirm?) -FF
183  std::sort (r.indices.begin (), r.indices.end ());
184  r.indices.erase (std::unique (r.indices.begin (), r.indices.end ()), r.indices.end ());
185 
186  r.header = cloud.header;
187  clusters.push_back (r); // We could avoid a copy by working directly in the vector
188  }
189  else
190  {
191  PCL_DEBUG("[pcl::extractEuclideanClusters] This cluster has %zu points, which is not between %u and %u points, so it is not a final cluster\n",
192  seed_queue.size (), min_pts_per_cluster, max_pts_per_cluster);
193  }
194  }
195  }
196 
197 
198  //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
199  /** \brief Decompose a region of space into clusters based on the euclidean distance between points, and the normal
200  * angular deviation between points. Each point added to the cluster is origin to another radius search. Each point
201  * within radius range will be compared to the origin in respect to normal angle and euclidean distance. If both
202  * are under their respective threshold the point will be added to the cluster. Generally speaking the cluster
203  * algorithm will not stop on smooth surfaces but on surfaces with sharp edges.
204  * \param cloud the point cloud message
205  * \param normals the point cloud message containing normal information
206  * \param indices a list of point indices to use from \a cloud
207  * \param tree the spatial locator (e.g., kd-tree) used for nearest neighbors searching
208  * \note the tree has to be created as a spatial locator on \a cloud
209  * \param tolerance the spatial cluster tolerance as a measure in the L2 Euclidean space
210  * \param clusters the resultant clusters containing point indices (as PointIndices)
211  * \param eps_angle the maximum allowed difference between normals in radians for cluster/region growing
212  * \param min_pts_per_cluster minimum number of points that a cluster may contain (default: 1)
213  * \param max_pts_per_cluster maximum number of points that a cluster may contain (default: max int)
214  * \ingroup segmentation
215  */
216  template <typename PointT, typename Normal>
218  const PointCloud<PointT> &cloud, const PointCloud<Normal> &normals,
219  const Indices &indices, const typename KdTree<PointT>::Ptr &tree,
220  float tolerance, std::vector<PointIndices> &clusters, double eps_angle,
221  unsigned int min_pts_per_cluster = 1,
222  unsigned int max_pts_per_cluster = (std::numeric_limits<int>::max) ())
223  {
224  // \note If the tree was created over <cloud, indices>, we guarantee a 1-1 mapping between what the tree returns
225  //and indices[i]
226  if (tree->getInputCloud()->size() != cloud.size()) {
227  PCL_ERROR("[pcl::extractEuclideanClusters] Tree built for a different point "
228  "cloud dataset (%zu) than the input cloud (%zu)!\n",
229  static_cast<std::size_t>(tree->getInputCloud()->size()),
230  static_cast<std::size_t>(cloud.size()));
231  return;
232  }
233  if (tree->getIndices()->size() != indices.size()) {
234  PCL_ERROR("[pcl::extractEuclideanClusters] Tree built for a different set of "
235  "indices (%zu) than the input set (%zu)!\n",
236  static_cast<std::size_t>(tree->getIndices()->size()),
237  indices.size());
238  return;
239  }
240  if (cloud.size() != normals.size()) {
241  PCL_ERROR("[pcl::extractEuclideanClusters] Number of points in the input point "
242  "cloud (%zu) different than normals (%zu)!\n",
243  static_cast<std::size_t>(cloud.size()),
244  static_cast<std::size_t>(normals.size()));
245  return;
246  }
247  const double cos_eps_angle = std::cos (eps_angle); // compute this once instead of acos many times (faster)
248  // Create a bool vector of processed point indices, and initialize it to false
249  std::vector<bool> processed (cloud.size (), false);
250 
251  Indices nn_indices;
252  std::vector<float> nn_distances;
253  // Process all points in the indices vector
254  for (std::size_t i = 0; i < indices.size (); ++i)
255  {
256  if (processed[indices[i]])
257  continue;
258 
259  Indices seed_queue;
260  int sq_idx = 0;
261  seed_queue.push_back (indices[i]);
262 
263  processed[indices[i]] = true;
264 
265  while (sq_idx < static_cast<int> (seed_queue.size ()))
266  {
267  // Search for sq_idx
268  if (!tree->radiusSearch (cloud[seed_queue[sq_idx]], tolerance, nn_indices, nn_distances))
269  {
270  sq_idx++;
271  continue;
272  }
273 
274  for (std::size_t j = 1; j < nn_indices.size (); ++j) // nn_indices[0] should be sq_idx
275  {
276  if (processed[nn_indices[j]]) // Has this point been processed before ?
277  continue;
278 
279  //processed[nn_indices[j]] = true;
280  // [-1;1]
281  double dot_p = normals[seed_queue[sq_idx]].normal[0] * normals[nn_indices[j]].normal[0] +
282  normals[seed_queue[sq_idx]].normal[1] * normals[nn_indices[j]].normal[1] +
283  normals[seed_queue[sq_idx]].normal[2] * normals[nn_indices[j]].normal[2];
284  if ( std::abs (dot_p) > cos_eps_angle )
285  {
286  processed[nn_indices[j]] = true;
287  seed_queue.push_back (nn_indices[j]);
288  }
289  }
290 
291  sq_idx++;
292  }
293 
294  // If this queue is satisfactory, add to the clusters
295  if (seed_queue.size () >= min_pts_per_cluster && seed_queue.size () <= max_pts_per_cluster)
296  {
298  r.indices.resize (seed_queue.size ());
299  for (std::size_t j = 0; j < seed_queue.size (); ++j)
300  r.indices[j] = seed_queue[j];
301 
302  // These two lines should not be needed: (can anyone confirm?) -FF
303  std::sort (r.indices.begin (), r.indices.end ());
304  r.indices.erase (std::unique (r.indices.begin (), r.indices.end ()), r.indices.end ());
305 
306  r.header = cloud.header;
307  clusters.push_back (r);
308  }
309  else
310  {
311  PCL_DEBUG("[pcl::extractEuclideanClusters] This cluster has %zu points, which is not between %u and %u points, so it is not a final cluster\n",
312  seed_queue.size (), min_pts_per_cluster, max_pts_per_cluster);
313  }
314  }
315  }
316 
317  //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
318  //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
319  //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
320  /** \brief @b EuclideanClusterExtraction represents a segmentation class for cluster extraction in an Euclidean sense.
321  * \author Radu Bogdan Rusu
322  * \ingroup segmentation
323  */
324  template <typename PointT>
325  class EuclideanClusterExtraction: public PCLBase<PointT>
326  {
328 
329  public:
331  using PointCloudPtr = typename PointCloud::Ptr;
333 
335  using KdTreePtr = typename KdTree::Ptr;
336 
339 
340  //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
341  /** \brief Empty constructor. */
343  cluster_tolerance_ (0),
345  max_pts_per_cluster_ (std::numeric_limits<pcl::uindex_t>::max ())
346  {};
347 
348  /** \brief Provide a pointer to the search object.
349  * \param[in] tree a pointer to the spatial search object.
350  */
351  inline void
352  setSearchMethod (const KdTreePtr &tree)
353  {
354  tree_ = tree;
355  }
356 
357  /** \brief Get a pointer to the search method used.
358  * @todo fix this for a generic search tree
359  */
360  inline KdTreePtr
361  getSearchMethod () const
362  {
363  return (tree_);
364  }
365 
366  /** \brief Set the spatial cluster tolerance as a measure in the L2 Euclidean space
367  * \param[in] tolerance the spatial cluster tolerance as a measure in the L2 Euclidean space
368  */
369  inline void
370  setClusterTolerance (double tolerance)
371  {
372  cluster_tolerance_ = tolerance;
373  }
374 
375  /** \brief Get the spatial cluster tolerance as a measure in the L2 Euclidean space. */
376  inline double
378  {
379  return (cluster_tolerance_);
380  }
381 
382  /** \brief Set the minimum number of points that a cluster needs to contain in order to be considered valid.
383  * \param[in] min_cluster_size the minimum cluster size
384  */
385  inline void
386  setMinClusterSize (pcl::uindex_t min_cluster_size)
387  {
388  min_pts_per_cluster_ = min_cluster_size;
389  }
390 
391  /** \brief Get the minimum number of points that a cluster needs to contain in order to be considered valid. */
392  inline pcl::uindex_t
394  {
395  return (min_pts_per_cluster_);
396  }
397 
398  /** \brief Set the maximum number of points that a cluster needs to contain in order to be considered valid.
399  * \param[in] max_cluster_size the maximum cluster size
400  */
401  inline void
402  setMaxClusterSize (pcl::uindex_t max_cluster_size)
403  {
404  max_pts_per_cluster_ = max_cluster_size;
405  }
406 
407  /** \brief Get the maximum number of points that a cluster needs to contain in order to be considered valid. */
408  inline pcl::uindex_t
410  {
411  return (max_pts_per_cluster_);
412  }
413 
414  /** \brief Cluster extraction in a PointCloud given by <setInputCloud (), setIndices ()>
415  * \param[out] clusters the resultant point clusters
416  */
417  void
418  extract (std::vector<PointIndices> &clusters);
419 
420  protected:
421  // Members derived from the base class
422  using BasePCLBase::input_;
423  using BasePCLBase::indices_;
426 
427  /** \brief A pointer to the spatial search object. */
429 
430  /** \brief The spatial cluster tolerance as a measure in the L2 Euclidean space. */
432 
433  /** \brief The minimum number of points that a cluster needs to contain in order to be considered valid (default = 1). */
435 
436  /** \brief The maximum number of points that a cluster needs to contain in order to be considered valid (default = MAXINT). */
438 
439  /** \brief Class getName method. */
440  virtual std::string getClassName () const { return ("EuclideanClusterExtraction"); }
441 
442  };
443 
444  /** \brief Sort clusters method (for std::sort).
445  * \ingroup segmentation
446  */
447  inline bool
449  {
450  return (a.indices.size () < b.indices.size ());
451  }
452 }
453 
454 #ifdef PCL_NO_PRECOMPILE
455 #include <pcl/segmentation/impl/extract_clusters.hpp>
456 #endif
pcl::search::Search
Generic search class.
Definition: search.h:74
pcl
Definition: convolution.h:46
pcl::EuclideanClusterExtraction::setClusterTolerance
void setClusterTolerance(double tolerance)
Set the spatial cluster tolerance as a measure in the L2 Euclidean space.
Definition: extract_clusters.h:370
pcl::EuclideanClusterExtraction::extract
void extract(std::vector< PointIndices > &clusters)
Cluster extraction in a PointCloud given by <setInputCloud (), setIndices ()>
Definition: extract_clusters.hpp:228
pcl::PCLBase::PointCloudConstPtr
typename PointCloud::ConstPtr PointCloudConstPtr
Definition: pcl_base.h:74
pcl::KdTree
KdTree represents the base spatial locator class for kd-tree implementations.
Definition: kdtree.h:54
pcl::PCLBase::input_
PointCloudConstPtr input_
The input point cloud dataset.
Definition: pcl_base.h:147
pcl::PCLBase::PointCloudPtr
typename PointCloud::Ptr PointCloudPtr
Definition: pcl_base.h:73
pcl::EuclideanClusterExtraction::setMinClusterSize
void setMinClusterSize(pcl::uindex_t min_cluster_size)
Set the minimum number of points that a cluster needs to contain in order to be considered valid.
Definition: extract_clusters.h:386
pcl::PointIndices::indices
Indices indices
Definition: PointIndices.h:21
pcl::PointIndices::header
::pcl::PCLHeader header
Definition: PointIndices.h:19
pcl::EuclideanClusterExtraction::setMaxClusterSize
void setMaxClusterSize(pcl::uindex_t max_cluster_size)
Set the maximum number of points that a cluster needs to contain in order to be considered valid.
Definition: extract_clusters.h:402
pcl::PCLBase
PCL base class.
Definition: pcl_base.h:69
pcl::PCLBase::PointIndicesConstPtr
PointIndices::ConstPtr PointIndicesConstPtr
Definition: pcl_base.h:77
pcl::PointCloud
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition: distances.h:55
pcl::index_t
detail::int_type_t< detail::index_type_size, detail::index_type_signed > index_t
Type used for an index in PCL.
Definition: types.h:110
pcl::EuclideanClusterExtraction::KdTreePtr
typename KdTree::Ptr KdTreePtr
Definition: extract_clusters.h:335
pcl::EuclideanClusterExtraction::setSearchMethod
void setSearchMethod(const KdTreePtr &tree)
Provide a pointer to the search object.
Definition: extract_clusters.h:352
pcl::EuclideanClusterExtraction::EuclideanClusterExtraction
EuclideanClusterExtraction()
Empty constructor.
Definition: extract_clusters.h:342
pcl::KdTree::radiusSearch
virtual int radiusSearch(const PointT &p_q, double radius, Indices &k_indices, std::vector< float > &k_sqr_distances, unsigned int max_nn=0) const =0
Search for all the nearest neighbors of the query point in a given radius.
pcl::EuclideanClusterExtraction
EuclideanClusterExtraction represents a segmentation class for cluster extraction in an Euclidean sen...
Definition: extract_clusters.h:325
pcl::PointIndices::ConstPtr
shared_ptr< const ::pcl::PointIndices > ConstPtr
Definition: PointIndices.h:14
pcl::KdTree::getIndices
IndicesConstPtr getIndices() const
Get a pointer to the vector of indices used.
Definition: kdtree.h:93
pcl::KdTree::getInputCloud
PointCloudConstPtr getInputCloud() const
Get a pointer to the input point cloud dataset.
Definition: kdtree.h:100
pcl::search::Search::Ptr
shared_ptr< pcl::search::Search< PointT > > Ptr
Definition: search.h:81
pcl::EuclideanClusterExtraction::getSearchMethod
KdTreePtr getSearchMethod() const
Get a pointer to the search method used.
Definition: extract_clusters.h:361
pcl::PointIndices
Definition: PointIndices.h:11
pcl::PointCloud::header
pcl::PCLHeader header
The point cloud header.
Definition: point_cloud.h:386
pcl::EuclideanClusterExtraction::max_pts_per_cluster_
pcl::uindex_t max_pts_per_cluster_
The maximum number of points that a cluster needs to contain in order to be considered valid (default...
Definition: extract_clusters.h:437
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:131
pcl::PointIndices::Ptr
shared_ptr< ::pcl::PointIndices > Ptr
Definition: PointIndices.h:13
pcl::PointCloud::size
std::size_t size() const
Definition: point_cloud.h:437
pcl::EuclideanClusterExtraction::getMinClusterSize
pcl::uindex_t getMinClusterSize() const
Get the minimum number of points that a cluster needs to contain in order to be considered valid.
Definition: extract_clusters.h:393
pcl::PointCloud::Ptr
shared_ptr< PointCloud< PointT > > Ptr
Definition: point_cloud.h:407
pcl::PCLBase::deinitCompute
bool deinitCompute()
This method should get called after finishing the actual computation.
Definition: pcl_base.hpp:174
pcl::PCLBase::indices_
IndicesPtr indices_
A pointer to the vector of point indices to use.
Definition: pcl_base.h:150
pcl::EuclideanClusterExtraction::tree_
KdTreePtr tree_
A pointer to the spatial search object.
Definition: extract_clusters.h:428
pcl::PointCloud::ConstPtr
shared_ptr< const PointCloud< PointT > > ConstPtr
Definition: point_cloud.h:408
pcl::EuclideanClusterExtraction::min_pts_per_cluster_
pcl::uindex_t min_pts_per_cluster_
The minimum number of points that a cluster needs to contain in order to be considered valid (default...
Definition: extract_clusters.h:434
pcl::EuclideanClusterExtraction::getClusterTolerance
double getClusterTolerance() const
Get the spatial cluster tolerance as a measure in the L2 Euclidean space.
Definition: extract_clusters.h:377
pcl::PCLBase::PointIndicesPtr
PointIndices::Ptr PointIndicesPtr
Definition: pcl_base.h:76
pcl::comparePointClusters
bool comparePointClusters(const pcl::PointIndices &a, const pcl::PointIndices &b)
Sort clusters method (for std::sort).
Definition: extract_clusters.h:448
pcl::EuclideanClusterExtraction::cluster_tolerance_
double cluster_tolerance_
The spatial cluster tolerance as a measure in the L2 Euclidean space.
Definition: extract_clusters.h:431
pcl::EuclideanClusterExtraction::getMaxClusterSize
pcl::uindex_t getMaxClusterSize() const
Get the maximum number of points that a cluster needs to contain in order to be considered valid.
Definition: extract_clusters.h:409
pcl::PCLBase::initCompute
bool initCompute()
This method should get called before starting the actual computation.
Definition: pcl_base.hpp:138
pcl::PointCloud::push_back
void push_back(const PointT &pt)
Insert a new point in the cloud, at the end of the container.
Definition: point_cloud.h:646
pcl::extractEuclideanClusters
void extractEuclideanClusters(const PointCloud< PointT > &cloud, const typename search::Search< PointT >::Ptr &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.
Definition: extract_clusters.hpp:46
pcl::uindex_t
detail::int_type_t< detail::index_type_size, false > uindex_t
Type used for an unsigned index in PCL.
Definition: types.h:118
pcl::EuclideanClusterExtraction::getClassName
virtual std::string getClassName() const
Class getName method.
Definition: extract_clusters.h:440