Point Cloud Library (PCL)  1.12.1-dev
kdtree_flann.hpp
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38 
39 #ifndef PCL_KDTREE_KDTREE_IMPL_FLANN_H_
40 #define PCL_KDTREE_KDTREE_IMPL_FLANN_H_
41 
42 #include <flann/flann.hpp>
43 
44 #include <pcl/kdtree/kdtree_flann.h>
45 #include <pcl/console/print.h>
46 
47 ///////////////////////////////////////////////////////////////////////////////////////////
48 template <typename PointT, typename Dist>
50  : pcl::KdTree<PointT> (sorted)
51  , flann_index_ ()
52  , identity_mapping_ (false)
53  , dim_ (0), total_nr_points_ (0)
54  , param_k_ (::flann::SearchParams (-1 , epsilon_))
55  , param_radius_ (::flann::SearchParams (-1, epsilon_, sorted))
56 {
57  if (!std::is_same<std::size_t, pcl::index_t>::value) {
58  const auto message = "FLANN is not optimized for current index type. Will incur "
59  "extra allocations and copy\n";
60  if (std::is_same<int, pcl::index_t>::value) {
61  PCL_DEBUG(message); // since this has been the default behavior till PCL 1.12
62  }
63  else {
64  PCL_WARN(message);
65  }
66  }
67 }
68 
69 ///////////////////////////////////////////////////////////////////////////////////////////
70 template <typename PointT, typename Dist>
72  : pcl::KdTree<PointT> (false)
73  , flann_index_ ()
74  , identity_mapping_ (false)
75  , dim_ (0), total_nr_points_ (0)
76  , param_k_ (::flann::SearchParams (-1 , epsilon_))
77  , param_radius_ (::flann::SearchParams (-1, epsilon_, false))
78 {
79  *this = k;
80 }
81 
82 ///////////////////////////////////////////////////////////////////////////////////////////
83 template <typename PointT, typename Dist> void
85 {
86  epsilon_ = eps;
87  param_k_ = ::flann::SearchParams (-1 , epsilon_);
88  param_radius_ = ::flann::SearchParams (-1 , epsilon_, sorted_);
89 }
90 
91 ///////////////////////////////////////////////////////////////////////////////////////////
92 template <typename PointT, typename Dist> void
94 {
95  sorted_ = sorted;
96  param_k_ = ::flann::SearchParams (-1, epsilon_);
97  param_radius_ = ::flann::SearchParams (-1, epsilon_, sorted_);
98 }
99 
100 ///////////////////////////////////////////////////////////////////////////////////////////
101 template <typename PointT, typename Dist> void
103 {
104  cleanup (); // Perform an automatic cleanup of structures
105 
106  epsilon_ = 0.0f; // default error bound value
107  dim_ = point_representation_->getNumberOfDimensions (); // Number of dimensions - default is 3 = xyz
108 
109  input_ = cloud;
110  indices_ = indices;
111 
112  // Allocate enough data
113  if (!input_)
114  {
115  PCL_ERROR ("[pcl::KdTreeFLANN::setInputCloud] Invalid input!\n");
116  return;
117  }
118  if (indices != nullptr)
119  {
120  convertCloudToArray (*input_, *indices_);
121  }
122  else
123  {
124  convertCloudToArray (*input_);
125  }
126  total_nr_points_ = static_cast<uindex_t> (index_mapping_.size ());
127  if (total_nr_points_ == 0)
128  {
129  PCL_ERROR ("[pcl::KdTreeFLANN::setInputCloud] Cannot create a KDTree with an empty input cloud!\n");
130  return;
131  }
132 
133  flann_index_.reset (new FLANNIndex (::flann::Matrix<float> (cloud_.get (),
134  index_mapping_.size (),
135  dim_),
136  ::flann::KDTreeSingleIndexParams (15))); // max 15 points/leaf
137  flann_index_->buildIndex ();
138 }
139 
140 ///////////////////////////////////////////////////////////////////////////////////////////
141 namespace pcl {
142 namespace detail {
143 // Replace using constexpr in C++17
144 template <class IndexT,
145  class A,
146  class B,
147  class C,
148  class D,
149  class F,
150  CompatWithFlann<IndexT> = true>
151 int
152 knn_search(A& index, B& query, C& k_indices, D& dists, unsigned int k, F& params)
153 {
154  // Wrap k_indices vector (no data allocation)
155  ::flann::Matrix<index_t> k_indices_mat(&k_indices[0], 1, k);
156  return index.knnSearch(query, k_indices_mat, dists, k, params);
157 }
158 
159 template <class IndexT,
160  class A,
161  class B,
162  class C,
163  class D,
164  class F,
165  NotCompatWithFlann<IndexT> = true>
166 int
167 knn_search(A& index, B& query, C& k_indices, D& dists, unsigned int k, F& params)
168 {
169  std::vector<std::size_t> indices(k);
170  k_indices.resize(k);
171  // Wrap indices vector (no data allocation)
172  ::flann::Matrix<std::size_t> indices_mat(&indices[0], 1, k);
173  auto ret = index.knnSearch(query, indices_mat, dists, k, params);
174  // cast appropriately
175  std::transform(indices.cbegin(),
176  indices.cend(),
177  k_indices.begin(),
178  [](const auto& x) { return static_cast<pcl::index_t>(x); });
179  return ret;
180 }
181 
182 template <class IndexT, class A, class B, class F, CompatWithFlann<IndexT> = true>
183 int
184 knn_search(A& index,
185  B& query,
186  std::vector<Indices>& k_indices,
187  std::vector<std::vector<float>>& dists,
188  unsigned int k,
189  F& params)
190 {
191  return index.knnSearch(query, k_indices, dists, k, params);
192 }
193 
194 template <class IndexT, class A, class B, class F, NotCompatWithFlann<IndexT> = true>
195 int
196 knn_search(A& index,
197  B& query,
198  std::vector<Indices>& k_indices,
199  std::vector<std::vector<float>>& dists,
200  unsigned int k,
201  F& params)
202 {
203  std::vector<std::vector<std::size_t>> indices;
204  // flann will resize accordingly
205  auto ret = index.knnSearch(query, indices, dists, k, params);
206 
207  k_indices.resize(indices.size());
208  {
209  auto it = indices.cbegin();
210  auto jt = k_indices.begin();
211  for (; it != indices.cend(); ++it, ++jt) {
212  jt->resize(it->size());
213  std::copy(it->cbegin(), it->cend(), jt->begin());
214  }
215  }
216  return ret;
217 }
218 } // namespace detail
219 template <class FlannIndex,
220  class Query,
221  class Indices,
222  class Distances,
223  class SearchParams>
224 int
225 knn_search(const FlannIndex& index,
226  const Query& query,
227  Indices& indices,
228  Distances& dists,
229  unsigned int k,
230  const SearchParams& params)
231 {
232  return detail::knn_search<pcl::index_t>(index, query, indices, dists, k, params);
233 }
234 } // namespace pcl
235 
236 template <typename PointT, typename Dist> int
238  Indices &k_indices,
239  std::vector<float> &k_distances) const
240 {
241  assert (point_representation_->isValid (point) && "Invalid (NaN, Inf) point coordinates given to nearestKSearch!");
242 
243  if (k > total_nr_points_)
244  k = total_nr_points_;
245 
246  k_indices.resize (k);
247  k_distances.resize (k);
248 
249  if (k==0)
250  return 0;
251 
252  std::vector<float> query (dim_);
253  point_representation_->vectorize (static_cast<PointT> (point), query);
254 
255  // Wrap the k_distances vector (no data copy)
256  ::flann::Matrix<float> k_distances_mat (&k_distances[0], 1, k);
257 
258  knn_search(*flann_index_,
259  ::flann::Matrix<float>(&query[0], 1, dim_),
260  k_indices,
261  k_distances_mat,
262  k,
263  param_k_);
264 
265  // Do mapping to original point cloud
266  if (!identity_mapping_)
267  {
268  for (std::size_t i = 0; i < static_cast<std::size_t> (k); ++i)
269  {
270  auto& neighbor_index = k_indices[i];
271  neighbor_index = index_mapping_[neighbor_index];
272  }
273  }
274 
275  return (k);
276 }
277 
278 ///////////////////////////////////////////////////////////////////////////////////////////
279 namespace pcl {
280 namespace detail {
281 // Replace using constexpr in C++17
282 template <class IndexT,
283  class A,
284  class B,
285  class C,
286  class D,
287  class F,
288  CompatWithFlann<IndexT> = true>
289 int
290 radius_search(A& index, B& query, C& k_indices, D& dists, float radius, F& params)
291 {
292  std::vector<pcl::Indices> indices(1);
293  int neighbors_in_radius = index.radiusSearch(query, indices, dists, radius, params);
294  k_indices = std::move(indices[0]);
295  return neighbors_in_radius;
296 }
297 
298 template <class IndexT,
299  class A,
300  class B,
301  class C,
302  class D,
303  class F,
304  NotCompatWithFlann<IndexT> = true>
305 int
306 radius_search(A& index, B& query, C& k_indices, D& dists, float radius, F& params)
307 {
308  std::vector<std::vector<std::size_t>> indices(1);
309  int neighbors_in_radius = index.radiusSearch(query, indices, dists, radius, params);
310  k_indices.resize(indices[0].size());
311  // cast appropriately
312  std::transform(indices[0].cbegin(),
313  indices[0].cend(),
314  k_indices.begin(),
315  [](const auto& x) { return static_cast<pcl::index_t>(x); });
316  return neighbors_in_radius;
317 }
318 
319 template <class IndexT, class A, class B, class F, CompatWithFlann<IndexT> = true>
320 int
321 radius_search(A& index,
322  B& query,
323  std::vector<Indices>& k_indices,
324  std::vector<std::vector<float>>& dists,
325  float radius,
326  F& params)
327 {
328  return index.radiusSearch(query, k_indices, dists, radius, params);
329 }
330 
331 template <class IndexT, class A, class B, class F, NotCompatWithFlann<IndexT> = true>
332 int
333 radius_search(A& index,
334  B& query,
335  std::vector<Indices>& k_indices,
336  std::vector<std::vector<float>>& dists,
337  float radius,
338  F& params)
339 {
340  std::vector<std::vector<std::size_t>> indices;
341  // flann will resize accordingly
342  auto ret = index.radiusSearch(query, indices, dists, radius, params);
343 
344  k_indices.resize(indices.size());
345  {
346  auto it = indices.cbegin();
347  auto jt = k_indices.begin();
348  for (; it != indices.cend(); ++it, ++jt) {
349  jt->resize(it->size());
350  std::copy(it->cbegin(), it->cend(), jt->begin());
351  }
352  }
353  return ret;
354 }
355 } // namespace detail
356 template <class FlannIndex,
357  class Query,
358  class Indices,
359  class Distances,
360  class SearchParams>
361 int
362 radius_search(const FlannIndex& index,
363  const Query& query,
364  Indices& indices,
365  Distances& dists,
366  float radius,
367  const SearchParams& params)
368 {
369  return detail::radius_search<pcl::index_t>(
370  index, query, indices, dists, radius, params);
371 }
372 } // namespace pcl
373 
374 template <typename PointT, typename Dist> int
375 pcl::KdTreeFLANN<PointT, Dist>::radiusSearch (const PointT &point, double radius, Indices &k_indices,
376  std::vector<float> &k_sqr_dists, unsigned int max_nn) const
377 {
378  assert (point_representation_->isValid (point) && "Invalid (NaN, Inf) point coordinates given to radiusSearch!");
379 
380  std::vector<float> query (dim_);
381  point_representation_->vectorize (static_cast<PointT> (point), query);
382 
383  // Has max_nn been set properly?
384  if (max_nn == 0 || max_nn > total_nr_points_)
385  max_nn = total_nr_points_;
386 
387  std::vector<std::vector<float> > dists(1);
388 
389  ::flann::SearchParams params (param_radius_);
390  if (max_nn == total_nr_points_)
391  params.max_neighbors = -1; // return all neighbors in radius
392  else
393  params.max_neighbors = max_nn;
394 
395  auto query_mat = ::flann::Matrix<float>(&query[0], 1, dim_);
396  int neighbors_in_radius = radius_search(*flann_index_,
397  query_mat,
398  k_indices,
399  dists,
400  static_cast<float>(radius * radius),
401  params);
402 
403  k_sqr_dists = dists[0];
404 
405  // Do mapping to original point cloud
406  if (!identity_mapping_)
407  {
408  for (int i = 0; i < neighbors_in_radius; ++i)
409  {
410  auto& neighbor_index = k_indices[i];
411  neighbor_index = index_mapping_[neighbor_index];
412  }
413  }
414 
415  return (neighbors_in_radius);
416 }
417 
418 ///////////////////////////////////////////////////////////////////////////////////////////
419 template <typename PointT, typename Dist> void
421 {
422  // Data array cleanup
423  index_mapping_.clear ();
424 
425  if (indices_)
426  indices_.reset ();
427 }
428 
429 ///////////////////////////////////////////////////////////////////////////////////////////
430 template <typename PointT, typename Dist> void
432 {
433  // No point in doing anything if the array is empty
434  if (cloud.empty ())
435  {
436  cloud_.reset ();
437  return;
438  }
439 
440  const auto original_no_of_points = cloud.size ();
441 
442  cloud_.reset (new float[original_no_of_points * dim_], std::default_delete<float[]> ());
443  float* cloud_ptr = cloud_.get ();
444  index_mapping_.reserve (original_no_of_points);
445  identity_mapping_ = true;
446 
447  for (std::size_t cloud_index = 0; cloud_index < original_no_of_points; ++cloud_index)
448  {
449  // Check if the point is invalid
450  if (!point_representation_->isValid (cloud[cloud_index]))
451  {
452  identity_mapping_ = false;
453  continue;
454  }
455 
456  index_mapping_.push_back (cloud_index);
457 
458  point_representation_->vectorize (cloud[cloud_index], cloud_ptr);
459  cloud_ptr += dim_;
460  }
461 }
462 
463 ///////////////////////////////////////////////////////////////////////////////////////////
464 template <typename PointT, typename Dist> void
466 {
467  // No point in doing anything if the array is empty
468  if (cloud.empty ())
469  {
470  cloud_.reset ();
471  return;
472  }
473 
474  int original_no_of_points = static_cast<int> (indices.size ());
475 
476  cloud_.reset (new float[original_no_of_points * dim_], std::default_delete<float[]> ());
477  float* cloud_ptr = cloud_.get ();
478  index_mapping_.reserve (original_no_of_points);
479  // its a subcloud -> false
480  // true only identity:
481  // - indices size equals cloud size
482  // - indices only contain values between 0 and cloud.size - 1
483  // - no index is multiple times in the list
484  // => index is complete
485  // But we can not guarantee that => identity_mapping_ = false
486  identity_mapping_ = false;
487 
488  for (const auto &index : indices)
489  {
490  // Check if the point is invalid
491  if (!point_representation_->isValid (cloud[index]))
492  continue;
493 
494  // map from 0 - N -> indices [0] - indices [N]
495  index_mapping_.push_back (index); // If the returned index should be for the indices vector
496 
497  point_representation_->vectorize (cloud[index], cloud_ptr);
498  cloud_ptr += dim_;
499  }
500 }
501 
502 #define PCL_INSTANTIATE_KdTreeFLANN(T) template class PCL_EXPORTS pcl::KdTreeFLANN<T>;
503 
504 #endif //#ifndef _PCL_KDTREE_KDTREE_IMPL_FLANN_H_
505 
KdTreeFLANN is a generic type of 3D spatial locator using kD-tree structures.
Definition: kdtree_flann.h:132
void setEpsilon(float eps) override
Set the search epsilon precision (error bound) for nearest neighbors searches.
int nearestKSearch(const PointT &point, unsigned int k, Indices &k_indices, std::vector< float > &k_sqr_distances) const override
Search for k-nearest neighbors for the given query point.
int radiusSearch(const PointT &point, double radius, Indices &k_indices, std::vector< float > &k_sqr_distances, unsigned int max_nn=0) const override
Search for all the nearest neighbors of the query point in a given radius.
void setSortedResults(bool sorted)
KdTreeFLANN(bool sorted=true)
Default Constructor for KdTreeFLANN.
void setInputCloud(const PointCloudConstPtr &cloud, const IndicesConstPtr &indices=IndicesConstPtr()) override
Provide a pointer to the input dataset.
KdTree represents the base spatial locator class for kd-tree implementations.
Definition: kdtree.h:55
shared_ptr< const Indices > IndicesConstPtr
Definition: kdtree.h:58
typename PointCloud::ConstPtr PointCloudConstPtr
Definition: kdtree.h:62
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition: point_cloud.h:173
bool empty() const
Definition: point_cloud.h:446
std::size_t size() const
Definition: point_cloud.h:443
@ B
Definition: norms.h:54
int knn_search(A &index, B &query, C &k_indices, D &dists, unsigned int k, F &params)
int radius_search(A &index, B &query, C &k_indices, D &dists, float radius, F &params)
detail::int_type_t< detail::index_type_size, false > uindex_t
Type used for an unsigned index in PCL.
Definition: types.h:120
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:133
int radius_search(const FlannIndex &index, const Query &query, Indices &indices, Distances &dists, float radius, const SearchParams &params)
Comaptibility template function to allow use of various types of indices with FLANN.
int knn_search(const FlannIndex &index, const Query &query, Indices &indices, Distances &dists, unsigned int k, const SearchParams &params)
Comaptibility template function to allow use of various types of indices with FLANN.
A point structure representing Euclidean xyz coordinates, and the RGB color.