nanoflann
C++ header-only ANN library
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nanoflann::KDTreeSingleIndexDynamicAdaptor_< Distance, DatasetAdaptor, DIM, IndexType > Class Template Reference

#include <nanoflann.hpp>

Inheritance diagram for nanoflann::KDTreeSingleIndexDynamicAdaptor_< Distance, DatasetAdaptor, DIM, IndexType >:
nanoflann::KDTreeBaseClass< KDTreeSingleIndexDynamicAdaptor_< Distance, DatasetAdaptor, DIM, IndexType >, Distance, DatasetAdaptor, DIM, IndexType >

Public Types

typedef Distance::ElementType ElementType
 
typedef Distance::DistanceType DistanceType
 
typedef
nanoflann::KDTreeBaseClass
< nanoflann::KDTreeSingleIndexDynamicAdaptor_
< Distance, DatasetAdaptor,
DIM, IndexType >, Distance,
DatasetAdaptor, DIM, IndexType >
::Node 
Node
 
typedef NodeNodePtr
 
typedef
nanoflann::KDTreeBaseClass
< nanoflann::KDTreeSingleIndexDynamicAdaptor_
< Distance, DatasetAdaptor,
DIM, IndexType >, Distance,
DatasetAdaptor, DIM, IndexType >
::Interval 
Interval
 
typedef
nanoflann::KDTreeBaseClass
< nanoflann::KDTreeSingleIndexDynamicAdaptor_
< Distance, DatasetAdaptor,
DIM, IndexType >, Distance,
DatasetAdaptor, DIM, IndexType >
::BoundingBox 
BoundingBox
 
typedef
nanoflann::KDTreeBaseClass
< nanoflann::KDTreeSingleIndexDynamicAdaptor_
< Distance, DatasetAdaptor,
DIM, IndexType >, Distance,
DatasetAdaptor, DIM, IndexType >
::distance_vector_t 
distance_vector_t
 

Public Member Functions

 KDTreeSingleIndexDynamicAdaptor_ (const int dimensionality, DatasetAdaptor &inputData, std::vector< int > &treeIndex_, const KDTreeSingleIndexAdaptorParams &params=KDTreeSingleIndexAdaptorParams())
 
KDTreeSingleIndexDynamicAdaptor_ operator= (const KDTreeSingleIndexDynamicAdaptor_ &rhs)
 
 ~KDTreeSingleIndexDynamicAdaptor_ ()
 
void buildIndex ()
 
ElementType dataset_get (size_t idx, int component) const
 Helper accessor to the dataset points:
 
void save_tree (FILE *stream, NodePtr tree)
 
void load_tree (FILE *stream, NodePtr &tree)
 
void computeBoundingBox (BoundingBox &bbox)
 
template<class RESULTSET >
void searchLevel (RESULTSET &result_set, const ElementType *vec, const NodePtr node, DistanceType mindistsq, distance_vector_t &dists, const float epsError) const
 
void saveIndex (FILE *stream)
 
void loadIndex (FILE *stream)
 
Query methods
template<typename RESULTSET >
bool findNeighbors (RESULTSET &result, const ElementType *vec, const SearchParams &searchParams) const
 
size_t knnSearch (const ElementType *query_point, const size_t num_closest, IndexType *out_indices, DistanceType *out_distances_sq, const int=10) const
 
size_t radiusSearch (const ElementType *query_point, const DistanceType &radius, std::vector< std::pair< IndexType, DistanceType > > &IndicesDists, const SearchParams &searchParams) const
 
template<class SEARCH_CALLBACK >
size_t radiusSearchCustomCallback (const ElementType *query_point, SEARCH_CALLBACK &resultSet, const SearchParams &searchParams=SearchParams()) const
 
- Public Member Functions inherited from nanoflann::KDTreeBaseClass< KDTreeSingleIndexDynamicAdaptor_< Distance, DatasetAdaptor, DIM, IndexType >, Distance, DatasetAdaptor, DIM, IndexType >
void freeIndex (KDTreeSingleIndexDynamicAdaptor_< Distance, DatasetAdaptor, DIM, IndexType > &obj)
 

Public Attributes

std::vector< IndexType > vind
 
size_t m_leaf_max_size
 
DatasetAdaptor & dataset
 The source of our data. More...
 
KDTreeSingleIndexAdaptorParams index_params
 
std::vector< int > & treeIndex
 
size_t m_size
 Number of current points in the dataset.
 
size_t m_size_at_index_build
 Number of points in the dataset when the index was built.
 
int dim
 Dimensionality of each data point.
 
NodePtr root_node
 
BoundingBox root_bbox
 
PooledAllocator pool
 
Distance distance
 

Additional Inherited Members

- Protected Types inherited from nanoflann::KDTreeBaseClass< KDTreeSingleIndexDynamicAdaptor_< Distance, DatasetAdaptor, DIM, IndexType >, Distance, DatasetAdaptor, DIM, IndexType >
typedef Distance::ElementType ElementType
 
typedef Distance::DistanceType DistanceType
 
typedef Node * NodePtr
 
typedef
array_or_vector_selector< DIM,
Interval >::container_t 
BoundingBox
 
typedef
array_or_vector_selector< DIM,
DistanceType >::container_t 
distance_vector_t
 
- Protected Member Functions inherited from nanoflann::KDTreeBaseClass< KDTreeSingleIndexDynamicAdaptor_< Distance, DatasetAdaptor, DIM, IndexType >, Distance, DatasetAdaptor, DIM, IndexType >
size_t size (const KDTreeSingleIndexDynamicAdaptor_< Distance, DatasetAdaptor, DIM, IndexType > &obj) const
 
size_t veclen (const KDTreeSingleIndexDynamicAdaptor_< Distance, DatasetAdaptor, DIM, IndexType > &obj)
 
size_t usedMemory (KDTreeSingleIndexDynamicAdaptor_< Distance, DatasetAdaptor, DIM, IndexType > &obj)
 
void computeMinMax (const KDTreeSingleIndexDynamicAdaptor_< Distance, DatasetAdaptor, DIM, IndexType > &obj, IndexType *ind, IndexType count, int element, ElementType &min_elem, ElementType &max_elem)
 
NodePtr divideTree (KDTreeSingleIndexDynamicAdaptor_< Distance, DatasetAdaptor, DIM, IndexType > &obj, const IndexType left, const IndexType right, BoundingBox &bbox)
 
void middleSplit_ (KDTreeSingleIndexDynamicAdaptor_< Distance, DatasetAdaptor, DIM, IndexType > &obj, IndexType *ind, IndexType count, IndexType &index, int &cutfeat, DistanceType &cutval, const BoundingBox &bbox)
 
void planeSplit (KDTreeSingleIndexDynamicAdaptor_< Distance, DatasetAdaptor, DIM, IndexType > &obj, IndexType *ind, const IndexType count, int cutfeat, DistanceType &cutval, IndexType &lim1, IndexType &lim2)
 
DistanceType computeInitialDistances (const KDTreeSingleIndexDynamicAdaptor_< Distance, DatasetAdaptor, DIM, IndexType > &obj, const ElementType *vec, distance_vector_t &dists) const
 

Detailed Description

template<typename Distance, class DatasetAdaptor, int DIM = -1, typename IndexType = size_t>
class nanoflann::KDTreeSingleIndexDynamicAdaptor_< Distance, DatasetAdaptor, DIM, IndexType >

kd-tree dynamic index

Contains the k-d trees and other information for indexing a set of points for nearest-neighbor matching.

The class "DatasetAdaptor" must provide the following interface (can be non-virtual, inlined methods):

// Must return the number of data poins
inline size_t kdtree_get_point_count() const { ... }
// Must return the dim'th component of the idx'th point in the class:
inline T kdtree_get_pt(const size_t idx, int dim) const { ... }
// Optional bounding-box computation: return false to default to a standard bbox computation loop.
// Return true if the BBOX was already computed by the class and returned in "bb" so it can be avoided to redo it again.
// Look at bb.size() to find out the expected dimensionality (e.g. 2 or 3 for point clouds)
template <class BBOX>
bool kdtree_get_bbox(BBOX &bb) const
{
bb[0].low = ...; bb[0].high = ...; // 0th dimension limits
bb[1].low = ...; bb[1].high = ...; // 1st dimension limits
...
return true;
}
Template Parameters
DatasetAdaptorThe user-provided adaptor (see comments above).
DistanceThe distance metric to use: nanoflann::metric_L1, nanoflann::metric_L2, nanoflann::metric_L2_Simple, etc.
DIMDimensionality of data points (e.g. 3 for 3D points)
IndexTypeWill be typically size_t or int

Member Typedef Documentation

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>
typedef nanoflann::KDTreeBaseClass<nanoflann::KDTreeSingleIndexDynamicAdaptor_<Distance, DatasetAdaptor, DIM, IndexType>, Distance, DatasetAdaptor, DIM, IndexType>::BoundingBox nanoflann::KDTreeSingleIndexDynamicAdaptor_< Distance, DatasetAdaptor, DIM, IndexType >::BoundingBox

Define "BoundingBox" as a fixed-size or variable-size container depending on "DIM"

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>
typedef nanoflann::KDTreeBaseClass<nanoflann::KDTreeSingleIndexDynamicAdaptor_<Distance, DatasetAdaptor, DIM, IndexType>, Distance, DatasetAdaptor, DIM, IndexType>::distance_vector_t nanoflann::KDTreeSingleIndexDynamicAdaptor_< Distance, DatasetAdaptor, DIM, IndexType >::distance_vector_t

Define "distance_vector_t" as a fixed-size or variable-size container depending on "DIM"

Constructor & Destructor Documentation

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>
nanoflann::KDTreeSingleIndexDynamicAdaptor_< Distance, DatasetAdaptor, DIM, IndexType >::KDTreeSingleIndexDynamicAdaptor_ ( const int  dimensionality,
DatasetAdaptor &  inputData,
std::vector< int > &  treeIndex_,
const KDTreeSingleIndexAdaptorParams params = KDTreeSingleIndexAdaptorParams() 
)
inline

KDTree constructor

Refer to docs in README.md or online in https://github.com/jlblancoc/nanoflann

The KD-Tree point dimension (the length of each point in the datase, e.g. 3 for 3D points) is determined by means of:

  • The DIM template parameter if >0 (highest priority)
  • Otherwise, the dimensionality parameter of this constructor.
Parameters
inputDataDataset with the input features
paramsBasically, the maximum leaf node size
template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>
nanoflann::KDTreeSingleIndexDynamicAdaptor_< Distance, DatasetAdaptor, DIM, IndexType >::~KDTreeSingleIndexDynamicAdaptor_ ( )
inline

Standard destructor

Member Function Documentation

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>
void nanoflann::KDTreeSingleIndexDynamicAdaptor_< Distance, DatasetAdaptor, DIM, IndexType >::buildIndex ( )
inline

Builds the index

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>
template<typename RESULTSET >
bool nanoflann::KDTreeSingleIndexDynamicAdaptor_< Distance, DatasetAdaptor, DIM, IndexType >::findNeighbors ( RESULTSET &  result,
const ElementType *  vec,
const SearchParams searchParams 
) const
inline

Find set of nearest neighbors to vec[0:dim-1]. Their indices are stored inside the result object.

Params: result = the result object in which the indices of the nearest-neighbors are stored vec = the vector for which to search the nearest neighbors

Template Parameters
RESULTSETShould be any ResultSet<DistanceType>
Returns
True if the requested neighbors could be found.
See Also
knnSearch, radiusSearch
template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>
size_t nanoflann::KDTreeSingleIndexDynamicAdaptor_< Distance, DatasetAdaptor, DIM, IndexType >::knnSearch ( const ElementType *  query_point,
const size_t  num_closest,
IndexType *  out_indices,
DistanceType *  out_distances_sq,
const int  = 10 
) const
inline

Find the "num_closest" nearest neighbors to the query_point[0:dim-1]. Their indices are stored inside the result object.

See Also
radiusSearch, findNeighbors
Note
nChecks_IGNORED is ignored but kept for compatibility with the original FLANN interface.
Returns
Number N of valid points in the result set. Only the first N entries in out_indices and out_distances_sq will be valid. Return may be less than num_closest only if the number of elements in the tree is less than num_closest.
template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>
void nanoflann::KDTreeSingleIndexDynamicAdaptor_< Distance, DatasetAdaptor, DIM, IndexType >::loadIndex ( FILE *  stream)
inline

Loads a previous index from a binary file. IMPORTANT NOTE: The set of data points is NOT stored in the file, so the index object must be constructed associated to the same source of data points used while building the index. See the example: examples/saveload_example.cpp

See Also
loadIndex
template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>
KDTreeSingleIndexDynamicAdaptor_ nanoflann::KDTreeSingleIndexDynamicAdaptor_< Distance, DatasetAdaptor, DIM, IndexType >::operator= ( const KDTreeSingleIndexDynamicAdaptor_< Distance, DatasetAdaptor, DIM, IndexType > &  rhs)
inline

Assignment operator definiton

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>
size_t nanoflann::KDTreeSingleIndexDynamicAdaptor_< Distance, DatasetAdaptor, DIM, IndexType >::radiusSearch ( const ElementType *  query_point,
const DistanceType &  radius,
std::vector< std::pair< IndexType, DistanceType > > &  IndicesDists,
const SearchParams searchParams 
) const
inline

Find all the neighbors to query_point[0:dim-1] within a maximum radius. The output is given as a vector of pairs, of which the first element is a point index and the second the corresponding distance. Previous contents of IndicesDists are cleared.

If searchParams.sorted==true, the output list is sorted by ascending distances.

For a better performance, it is advisable to do a .reserve() on the vector if you have any wild guess about the number of expected matches.

See Also
knnSearch, findNeighbors, radiusSearchCustomCallback
Returns
The number of points within the given radius (i.e. indices.size() or dists.size() )
template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>
template<class SEARCH_CALLBACK >
size_t nanoflann::KDTreeSingleIndexDynamicAdaptor_< Distance, DatasetAdaptor, DIM, IndexType >::radiusSearchCustomCallback ( const ElementType *  query_point,
SEARCH_CALLBACK &  resultSet,
const SearchParams searchParams = SearchParams() 
) const
inline

Just like radiusSearch() but with a custom callback class for each point found in the radius of the query. See the source of RadiusResultSet<> as a start point for your own classes.

See Also
radiusSearch
template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>
void nanoflann::KDTreeSingleIndexDynamicAdaptor_< Distance, DatasetAdaptor, DIM, IndexType >::saveIndex ( FILE *  stream)
inline

Stores the index in a binary file. IMPORTANT NOTE: The set of data points is NOT stored in the file, so when loading the index object it must be constructed associated to the same source of data points used while building it. See the example: examples/saveload_example.cpp

See Also
loadIndex
template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>
template<class RESULTSET >
void nanoflann::KDTreeSingleIndexDynamicAdaptor_< Distance, DatasetAdaptor, DIM, IndexType >::searchLevel ( RESULTSET &  result_set,
const ElementType *  vec,
const NodePtr  node,
DistanceType  mindistsq,
distance_vector_t dists,
const float  epsError 
) const
inline

Performs an exact search in the tree starting from a node.

Template Parameters
RESULTSETShould be any ResultSet<DistanceType>

Member Data Documentation

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>
DatasetAdaptor& nanoflann::KDTreeSingleIndexDynamicAdaptor_< Distance, DatasetAdaptor, DIM, IndexType >::dataset

The source of our data.

The dataset used by this index

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>
PooledAllocator nanoflann::KDTreeSingleIndexDynamicAdaptor_< Distance, DatasetAdaptor, DIM, IndexType >::pool

Pooled memory allocator.

Using a pooled memory allocator is more efficient than allocating memory directly when there is a large number small of memory allocations.

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>
NodePtr nanoflann::KDTreeSingleIndexDynamicAdaptor_< Distance, DatasetAdaptor, DIM, IndexType >::root_node

The KD-tree used to find neighbours

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>
std::vector<IndexType> nanoflann::KDTreeSingleIndexDynamicAdaptor_< Distance, DatasetAdaptor, DIM, IndexType >::vind

Array of indices to vectors in the dataset.


The documentation for this class was generated from the following file: