Point Cloud Library (PCL)  1.11.0-dev
search.h
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38 
39 #pragma once
40 
41 #include <pcl/pcl_base.h> // for IndicesConstPtr
42 #include <pcl/point_cloud.h>
43 #include <pcl/for_each_type.h>
44 #include <pcl/common/concatenate.h>
45 #include <pcl/common/copy_point.h>
46 
47 namespace pcl
48 {
49  namespace search
50  {
51  /** \brief Generic search class. All search wrappers must inherit from this.
52  *
53  * Each search method must implement 2 different types of search:
54  * - \b nearestKSearch - search for K-nearest neighbors.
55  * - \b radiusSearch - search for all nearest neighbors in a sphere of a given radius
56  *
57  * The input to each search method can be given in 3 different ways:
58  * - as a query point
59  * - as a (cloud, index) pair
60  * - as an index
61  *
62  * For the latter option, it is assumed that the user specified the input
63  * via a \ref setInputCloud () method first.
64  *
65  * \note In case of an error, all methods are supposed to return 0 as the number of neighbors found.
66  *
67  * \note libpcl_search deals with three-dimensional search problems. For higher
68  * level dimensional search, please refer to the libpcl_kdtree module.
69  *
70  * \author Radu B. Rusu
71  * \ingroup search
72  */
73  template<typename PointT>
74  class Search
75  {
76  public:
78  using PointCloudPtr = typename PointCloud::Ptr;
80 
81  using Ptr = shared_ptr<pcl::search::Search<PointT> >;
82  using ConstPtr = shared_ptr<const pcl::search::Search<PointT> >;
83 
86 
87  /** Constructor. */
88  Search (const std::string& name = "", bool sorted = false);
89 
90  /** Destructor. */
91  virtual
93  {
94  }
95 
96  /** \brief Returns the search method name
97  */
98  virtual const std::string&
99  getName () const;
100 
101  /** \brief sets whether the results should be sorted (ascending in the distance) or not
102  * \param[in] sorted should be true if the results should be sorted by the distance in ascending order.
103  * Otherwise the results may be returned in any order.
104  */
105  virtual void
106  setSortedResults (bool sorted);
107 
108  /** \brief Gets whether the results should be sorted (ascending in the distance) or not
109  * Otherwise the results may be returned in any order.
110  */
111  virtual bool
112  getSortedResults ();
113 
114 
115  /** \brief Pass the input dataset that the search will be performed on.
116  * \param[in] cloud a const pointer to the PointCloud data
117  * \param[in] indices the point indices subset that is to be used from the cloud
118  */
119  virtual void
120  setInputCloud (const PointCloudConstPtr& cloud,
121  const IndicesConstPtr &indices = IndicesConstPtr ());
122 
123  /** \brief Get a pointer to the input point cloud dataset. */
124  virtual PointCloudConstPtr
125  getInputCloud () const
126  {
127  return (input_);
128  }
129 
130  /** \brief Get a pointer to the vector of indices used. */
131  virtual IndicesConstPtr
132  getIndices () const
133  {
134  return (indices_);
135  }
136 
137  /** \brief Search for the k-nearest neighbors for the given query point.
138  * \param[in] point the given query point
139  * \param[in] k the number of neighbors to search for
140  * \param[out] k_indices the resultant indices of the neighboring points (must be resized to \a k a priori!)
141  * \param[out] k_sqr_distances the resultant squared distances to the neighboring points (must be resized to \a k
142  * a priori!)
143  * \return number of neighbors found
144  */
145  virtual int
146  nearestKSearch (const PointT &point, int k, Indices &k_indices,
147  std::vector<float> &k_sqr_distances) const = 0;
148 
149  /** \brief Search for k-nearest neighbors for the given query point.
150  * This method accepts a different template parameter for the point type.
151  * \param[in] point the given query point
152  * \param[in] k the number of neighbors to search for
153  * \param[out] k_indices the resultant indices of the neighboring points (must be resized to \a k a priori!)
154  * \param[out] k_sqr_distances the resultant squared distances to the neighboring points (must be resized to \a k
155  * a priori!)
156  * \return number of neighbors found
157  */
158  template <typename PointTDiff> inline int
159  nearestKSearchT (const PointTDiff &point, int k,
160  Indices &k_indices, std::vector<float> &k_sqr_distances) const
161  {
162  PointT p;
163  copyPoint (point, p);
164  return (nearestKSearch (p, k, k_indices, k_sqr_distances));
165  }
166 
167  /** \brief Search for k-nearest neighbors for the given query point.
168  *
169  * \attention This method does not do any bounds checking for the input index
170  * (i.e., index >= cloud.points.size () || index < 0), and assumes valid (i.e., finite) data.
171  *
172  * \param[in] cloud the point cloud data
173  * \param[in] index a \a valid index in \a cloud representing a \a valid (i.e., finite) query point
174  * \param[in] k the number of neighbors to search for
175  * \param[out] k_indices the resultant indices of the neighboring points (must be resized to \a k a priori!)
176  * \param[out] k_sqr_distances the resultant squared distances to the neighboring points (must be resized to \a k
177  * a priori!)
178  *
179  * \return number of neighbors found
180  *
181  * \exception asserts in debug mode if the index is not between 0 and the maximum number of points
182  */
183  virtual int
184  nearestKSearch (const PointCloud &cloud, index_t index, int k,
185  Indices &k_indices,
186  std::vector<float> &k_sqr_distances) const;
187 
188  /** \brief Search for k-nearest neighbors for the given query point (zero-copy).
189  *
190  * \attention This method does not do any bounds checking for the input index
191  * (i.e., index >= cloud.points.size () || index < 0), and assumes valid (i.e., finite) data.
192  *
193  * \param[in] index a \a valid index representing a \a valid query point in the dataset given
194  * by \a setInputCloud. If indices were given in setInputCloud, index will be the position in
195  * the indices vector.
196  *
197  * \param[in] k the number of neighbors to search for
198  * \param[out] k_indices the resultant indices of the neighboring points (must be resized to \a k a priori!)
199  * \param[out] k_sqr_distances the resultant squared distances to the neighboring points (must be resized to \a k
200  * a priori!)
201  * \return number of neighbors found
202  *
203  * \exception asserts in debug mode if the index is not between 0 and the maximum number of points
204  */
205  virtual int
206  nearestKSearch (index_t index, int k,
207  Indices &k_indices,
208  std::vector<float> &k_sqr_distances) const;
209 
210  /** \brief Search for the k-nearest neighbors for the given query point.
211  * \param[in] cloud the point cloud data
212  * \param[in] indices a vector of point cloud indices to query for nearest neighbors
213  * \param[in] k the number of neighbors to search for
214  * \param[out] k_indices the resultant indices of the neighboring points, k_indices[i] corresponds to the neighbors of the query point i
215  * \param[out] k_sqr_distances the resultant squared distances to the neighboring points, k_sqr_distances[i] corresponds to the neighbors of the query point i
216  */
217  virtual void
218  nearestKSearch (const PointCloud& cloud, const Indices& indices,
219  int k, std::vector<Indices>& k_indices,
220  std::vector< std::vector<float> >& k_sqr_distances) const;
221 
222  /** \brief Search for the k-nearest neighbors for the given query point. Use this method if the query points are of a different type than the points in the data set (e.g. PointXYZRGBA instead of PointXYZ).
223  * \param[in] cloud the point cloud data
224  * \param[in] indices a vector of point cloud indices to query for nearest neighbors
225  * \param[in] k the number of neighbors to search for
226  * \param[out] k_indices the resultant indices of the neighboring points, k_indices[i] corresponds to the neighbors of the query point i
227  * \param[out] k_sqr_distances the resultant squared distances to the neighboring points, k_sqr_distances[i] corresponds to the neighbors of the query point i
228  * \note This method copies the input point cloud of type PointTDiff to a temporary cloud of type PointT and performs the batch search on the new cloud. You should prefer the single-point search if you don't use a search algorithm that accelerates batch NN search.
229  */
230  template <typename PointTDiff> void
231  nearestKSearchT (const pcl::PointCloud<PointTDiff> &cloud, const Indices& indices, int k, std::vector<Indices> &k_indices,
232  std::vector< std::vector<float> > &k_sqr_distances) const
233  {
234  // Copy all the data fields from the input cloud to the output one
235  using FieldListInT = typename pcl::traits::fieldList<PointT>::type;
236  using FieldListOutT = typename pcl::traits::fieldList<PointTDiff>::type;
237  using FieldList = typename pcl::intersect<FieldListInT, FieldListOutT>::type;
238 
240  if (indices.empty ())
241  {
242  pc.resize (cloud.size());
243  for (std::size_t i = 0; i < cloud.size(); i++)
244  {
245  pcl::for_each_type <FieldList> (pcl::NdConcatenateFunctor <PointTDiff, PointT> (
246  cloud[i], pc[i]));
247  }
248  nearestKSearch (pc,Indices(),k,k_indices,k_sqr_distances);
249  }
250  else
251  {
252  pc.resize (indices.size());
253  for (std::size_t i = 0; i < indices.size(); i++)
254  {
255  pcl::for_each_type <FieldList> (pcl::NdConcatenateFunctor <PointTDiff, PointT> (
256  cloud[indices[i]], pc[i]));
257  }
258  nearestKSearch (pc,Indices(),k,k_indices,k_sqr_distances);
259  }
260  }
261 
262  /** \brief Search for all the nearest neighbors of the query point in a given radius.
263  * \param[in] point the given query point
264  * \param[in] radius the radius of the sphere bounding all of p_q's neighbors
265  * \param[out] k_indices the resultant indices of the neighboring points
266  * \param[out] k_sqr_distances the resultant squared distances to the neighboring points
267  * \param[in] max_nn if given, bounds the maximum returned neighbors to this value. If \a max_nn is set to
268  * 0 or to a number higher than the number of points in the input cloud, all neighbors in \a radius will be
269  * returned.
270  * \return number of neighbors found in radius
271  */
272  virtual int
273  radiusSearch (const PointT& point, double radius, Indices& k_indices,
274  std::vector<float>& k_sqr_distances, unsigned int max_nn = 0) const = 0;
275 
276  /** \brief Search for all the nearest neighbors of the query point in a given radius.
277  * \param[in] point the given query point
278  * \param[in] radius the radius of the sphere bounding all of p_q's neighbors
279  * \param[out] k_indices the resultant indices of the neighboring points
280  * \param[out] k_sqr_distances the resultant squared distances to the neighboring points
281  * \param[in] max_nn if given, bounds the maximum returned neighbors to this value. If \a max_nn is set to
282  * 0 or to a number higher than the number of points in the input cloud, all neighbors in \a radius will be
283  * returned.
284  * \return number of neighbors found in radius
285  */
286  template <typename PointTDiff> inline int
287  radiusSearchT (const PointTDiff &point, double radius, Indices &k_indices,
288  std::vector<float> &k_sqr_distances, unsigned int max_nn = 0) const
289  {
290  PointT p;
291  copyPoint (point, p);
292  return (radiusSearch (p, radius, k_indices, k_sqr_distances, max_nn));
293  }
294 
295  /** \brief Search for all the nearest neighbors of the query point in a given radius.
296  *
297  * \attention This method does not do any bounds checking for the input index
298  * (i.e., index >= cloud.points.size () || index < 0), and assumes valid (i.e., finite) data.
299  *
300  * \param[in] cloud the point cloud data
301  * \param[in] index a \a valid index in \a cloud representing a \a valid (i.e., finite) query point
302  * \param[in] radius the radius of the sphere bounding all of p_q's neighbors
303  * \param[out] k_indices the resultant indices of the neighboring points
304  * \param[out] k_sqr_distances the resultant squared distances to the neighboring points
305  * \param[in] max_nn if given, bounds the maximum returned neighbors to this value. If \a max_nn is set to
306  * 0 or to a number higher than the number of points in the input cloud, all neighbors in \a radius will be
307  * returned.
308  * \return number of neighbors found in radius
309  *
310  * \exception asserts in debug mode if the index is not between 0 and the maximum number of points
311  */
312  virtual int
313  radiusSearch (const PointCloud &cloud, index_t index, double radius,
314  Indices &k_indices, std::vector<float> &k_sqr_distances,
315  unsigned int max_nn = 0) const;
316 
317  /** \brief Search for all the nearest neighbors of the query point in a given radius (zero-copy).
318  *
319  * \attention This method does not do any bounds checking for the input index
320  * (i.e., index >= cloud.points.size () || index < 0), and assumes valid (i.e., finite) data.
321  *
322  * \param[in] index a \a valid index representing a \a valid query point in the dataset given
323  * by \a setInputCloud. If indices were given in setInputCloud, index will be the position in
324  * the indices vector.
325  *
326  * \param[in] radius the radius of the sphere bounding all of p_q's neighbors
327  * \param[out] k_indices the resultant indices of the neighboring points
328  * \param[out] k_sqr_distances the resultant squared distances to the neighboring points
329  * \param[in] max_nn if given, bounds the maximum returned neighbors to this value. If \a max_nn is set to
330  * 0 or to a number higher than the number of points in the input cloud, all neighbors in \a radius will be
331  * returned.
332  * \return number of neighbors found in radius
333  *
334  * \exception asserts in debug mode if the index is not between 0 and the maximum number of points
335  */
336  virtual int
337  radiusSearch (index_t index, double radius, Indices &k_indices,
338  std::vector<float> &k_sqr_distances, unsigned int max_nn = 0) const;
339 
340  /** \brief Search for all the nearest neighbors of the query point in a given radius.
341  * \param[in] cloud the point cloud data
342  * \param[in] indices the indices in \a cloud. If indices is empty, neighbors will be searched for all points.
343  * \param[in] radius the radius of the sphere bounding all of p_q's neighbors
344  * \param[out] k_indices the resultant indices of the neighboring points, k_indices[i] corresponds to the neighbors of the query point i
345  * \param[out] k_sqr_distances the resultant squared distances to the neighboring points, k_sqr_distances[i] corresponds to the neighbors of the query point i
346  * \param[in] max_nn if given, bounds the maximum returned neighbors to this value. If \a max_nn is set to
347  * 0 or to a number higher than the number of points in the input cloud, all neighbors in \a radius will be
348  * returned.
349  */
350  virtual void
351  radiusSearch (const PointCloud& cloud,
352  const Indices& indices,
353  double radius,
354  std::vector<Indices>& k_indices,
355  std::vector< std::vector<float> > &k_sqr_distances,
356  unsigned int max_nn = 0) const;
357 
358  /** \brief Search for all the nearest neighbors of the query points in a given radius.
359  * \param[in] cloud the point cloud data
360  * \param[in] indices a vector of point cloud indices to query for nearest neighbors
361  * \param[in] radius the radius of the sphere bounding all of p_q's neighbors
362  * \param[out] k_indices the resultant indices of the neighboring points, k_indices[i] corresponds to the neighbors of the query point i
363  * \param[out] k_sqr_distances the resultant squared distances to the neighboring points, k_sqr_distances[i] corresponds to the neighbors of the query point i
364  * \param[in] max_nn if given, bounds the maximum returned neighbors to this value. If \a max_nn is set to
365  * 0 or to a number higher than the number of points in the input cloud, all neighbors in \a radius will be
366  * returned.
367  * \note This method copies the input point cloud of type PointTDiff to a temporary cloud of type PointT and performs the batch search on the new cloud. You should prefer the single-point search if you don't use a search algorithm that accelerates batch NN search.
368  */
369  template <typename PointTDiff> void
371  const Indices& indices,
372  double radius,
373  std::vector<Indices> &k_indices,
374  std::vector< std::vector<float> > &k_sqr_distances,
375  unsigned int max_nn = 0) const
376  {
377  // Copy all the data fields from the input cloud to the output one
378  using FieldListInT = typename pcl::traits::fieldList<PointT>::type;
379  using FieldListOutT = typename pcl::traits::fieldList<PointTDiff>::type;
380  using FieldList = typename pcl::intersect<FieldListInT, FieldListOutT>::type;
381 
383  if (indices.empty ())
384  {
385  pc.resize (cloud.size ());
386  for (std::size_t i = 0; i < cloud.size (); ++i)
387  pcl::for_each_type <FieldList> (pcl::NdConcatenateFunctor <PointTDiff, PointT> (cloud[i], pc[i]));
388  radiusSearch (pc, Indices (), radius, k_indices, k_sqr_distances, max_nn);
389  }
390  else
391  {
392  pc.resize (indices.size ());
393  for (std::size_t i = 0; i < indices.size (); ++i)
394  pcl::for_each_type <FieldList> (pcl::NdConcatenateFunctor <PointTDiff, PointT> (cloud[indices[i]], pc[i]));
395  radiusSearch (pc, Indices(), radius, k_indices, k_sqr_distances, max_nn);
396  }
397  }
398 
399  protected:
400  void
401  sortResults (Indices& indices, std::vector<float>& distances) const;
402 
406  std::string name_;
407 
408  private:
409  struct Compare
410  {
411  Compare (const std::vector<float>& distances)
412  : distances_ (distances)
413  {
414  }
415 
416  bool
417  operator () (index_t first, index_t second) const
418  {
419  return (distances_ [first] < distances_[second]);
420  }
421 
422  const std::vector<float>& distances_;
423  };
424  }; // class Search
425  } // namespace search
426 } // namespace pcl
427 
428 #ifdef PCL_NO_PRECOMPILE
429 #include <pcl/search/impl/search.hpp>
430 #endif
pcl::search::Search< PointXYZRGB >::IndicesPtr
pcl::IndicesPtr IndicesPtr
Definition: search.h:84
pcl::search::Search
Generic search class.
Definition: search.h:74
pcl
Definition: convolution.h:46
pcl::search::Search< PointXYZRGB >::ConstPtr
shared_ptr< const pcl::search::Search< PointXYZRGB > > ConstPtr
Definition: search.h:82
pcl::IndicesPtr
shared_ptr< Indices > IndicesPtr
Definition: pcl_base.h:61
pcl::search::Search::setSortedResults
virtual void setSortedResults(bool sorted)
sets whether the results should be sorted (ascending in the distance) or not
Definition: search.hpp:61
pcl::search::Search::getInputCloud
virtual PointCloudConstPtr getInputCloud() const
Get a pointer to the input point cloud dataset.
Definition: search.h:125
pcl::search::Search< PointXYZRGB >::IndicesConstPtr
pcl::IndicesConstPtr IndicesConstPtr
Definition: search.h:85
pcl::PointCloud::resize
void resize(std::size_t n)
Resize the cloud.
Definition: point_cloud.h:466
pcl::search::Search::sorted_results_
bool sorted_results_
Definition: search.h:405
pcl::search::Search::getName
virtual const std::string & getName() const
Returns the search method name.
Definition: search.hpp:54
pcl::search::Search::radiusSearch
virtual int radiusSearch(const PointT &point, 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::search::Search::name_
std::string name_
Definition: search.h:406
pcl::search::Search::nearestKSearchT
int nearestKSearchT(const PointTDiff &point, int k, Indices &k_indices, std::vector< float > &k_sqr_distances) const
Search for k-nearest neighbors for the given query point.
Definition: search.h:159
pcl::IndicesConstPtr
shared_ptr< const Indices > IndicesConstPtr
Definition: pcl_base.h:62
pcl::search::Search::getSortedResults
virtual bool getSortedResults()
Gets whether the results should be sorted (ascending in the distance) or not Otherwise the results ma...
Definition: search.hpp:68
pcl::search::Search::nearestKSearch
virtual int nearestKSearch(const PointT &point, int k, Indices &k_indices, std::vector< float > &k_sqr_distances) const =0
Search for the k-nearest neighbors for the given query point.
pcl::search::Search::radiusSearchT
int radiusSearchT(const PointTDiff &point, double radius, Indices &k_indices, std::vector< float > &k_sqr_distances, unsigned int max_nn=0) const
Search for all the nearest neighbors of the query point in a given radius.
Definition: search.h:287
pcl::intersect::type
typename boost::mpl::remove_if< Sequence1, boost::mpl::not_< boost::mpl::contains< Sequence2, boost::mpl::_1 > > >::type type
Definition: for_each_type.h:105
pcl::NdConcatenateFunctor
Helper functor structure for concatenate.
Definition: concatenate.h:51
pcl::PointCloud
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition: projection_matrix.h:53
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:136
pcl::PointXYZRGB
A point structure representing Euclidean xyz coordinates, and the RGB color.
Definition: point_types.hpp:628
pcl::search::Search< PointXYZRGB >::PointCloudConstPtr
typename PointCloud::ConstPtr PointCloudConstPtr
Definition: search.h:79
pcl::search::Search::radiusSearchT
void radiusSearchT(const pcl::PointCloud< PointTDiff > &cloud, const Indices &indices, double radius, std::vector< Indices > &k_indices, std::vector< std::vector< float > > &k_sqr_distances, unsigned int max_nn=0) const
Search for all the nearest neighbors of the query points in a given radius.
Definition: search.h:370
pcl::copyPoint
void copyPoint(const PointInT &point_in, PointOutT &point_out)
Copy the fields of a source point into a target point.
Definition: copy_point.hpp:137
pcl::search::Search::input_
PointCloudConstPtr input_
Definition: search.h:403
pcl::search::Search::getIndices
virtual IndicesConstPtr getIndices() const
Get a pointer to the vector of indices used.
Definition: search.h:132
pcl::search::Search< PointXYZRGB >::Ptr
shared_ptr< pcl::search::Search< PointXYZRGB > > Ptr
Definition: search.h:81
pcl::search::Search::Search
Search(const std::string &name="", bool sorted=false)
Constructor.
Definition: search.hpp:45
pcl::search::Search::nearestKSearchT
void nearestKSearchT(const pcl::PointCloud< PointTDiff > &cloud, const Indices &indices, int k, std::vector< Indices > &k_indices, std::vector< std::vector< float > > &k_sqr_distances) const
Search for the k-nearest neighbors for the given query point.
Definition: search.h:231
pcl::search::Search::sortResults
void sortResults(Indices &indices, std::vector< float > &distances) const
Definition: search.hpp:188
pcl::search::Search::~Search
virtual ~Search()
Destructor.
Definition: search.h:92
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:149
pcl::PointCloud::size
std::size_t size() const
Definition: point_cloud.h:459
pcl::PointCloud::Ptr
shared_ptr< PointCloud< PointT > > Ptr
Definition: point_cloud.h:429
pcl::search::Search::setInputCloud
virtual void setInputCloud(const PointCloudConstPtr &cloud, const IndicesConstPtr &indices=IndicesConstPtr())
Pass the input dataset that the search will be performed on.
Definition: search.hpp:75
pcl::search::Search::indices_
IndicesConstPtr indices_
Definition: search.h:404
pcl::PointCloud::ConstPtr
shared_ptr< const PointCloud< PointT > > ConstPtr
Definition: point_cloud.h:430
pcl::search::Search< PointXYZRGB >::PointCloudPtr
typename PointCloud::Ptr PointCloudPtr
Definition: search.h:78