#include <nanoflann.hpp>

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

Public Types
typedef nanoflann::KDTreeBaseClass< nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >, Distance, DatasetAdaptor, DIM, IndexType >	BaseClassRef

typedef BaseClassRef::BoundingBox	BoundingBox

typedef BaseClassRef::distance_vector_t	distance_vector_t

typedef BaseClassRef::DistanceType	DistanceType

typedef BaseClassRef::ElementType	ElementType

typedef BaseClassRef::Interval	Interval

typedef BaseClassRef::Node	Node

typedef Node *	NodePtr

Public Types inherited from nanoflann::KDTreeBaseClass< KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, -1, size_t >, Distance, DatasetAdaptor, -1, size_t >
typedef array_or_vector_selector< DIM, Interval >::container_t	BoundingBox

typedef array_or_vector_selector< DIM, DistanceType >::container_t	distance_vector_t

typedef Distance::DistanceType	DistanceType

typedef Distance::ElementType	ElementType

typedef Node *	NodePtr

Public Member Functions
void	buildIndex ()

void	computeBoundingBox (BoundingBox &bbox)

void	init_vind ()

	KDTreeSingleIndexAdaptor (const int dimensionality, const DatasetAdaptor &inputData, const KDTreeSingleIndexAdaptorParams &params=KDTreeSingleIndexAdaptorParams())

	KDTreeSingleIndexAdaptor (const KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType > &)=delete

void	loadIndex (FILE *stream)

void	saveIndex (FILE *stream)

template<class RESULTSET >
bool	searchLevel (RESULTSET &result_set, const ElementType *vec, const NodePtr node, DistanceType mindistsq, distance_vector_t &dists, const float epsError) const

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< KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, -1, size_t >, Distance, DatasetAdaptor, -1, size_t >
DistanceType	computeInitialDistances (const KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, -1, size_t > &obj, const ElementType *vec, distance_vector_t &dists) const

void	computeMinMax (const KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, -1, size_t > &obj, size_t *ind, size_t count, int element, ElementType &min_elem, ElementType &max_elem)

ElementType	dataset_get (const KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, -1, size_t > &obj, size_t idx, int component) const
	Helper accessor to the dataset points: More...

NodePtr	divideTree (KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, -1, size_t > &obj, const size_t left, const size_t right, BoundingBox &bbox)

void	freeIndex (KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, -1, size_t > &obj)

void	load_tree (KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, -1, size_t > &obj, FILE *stream, NodePtr &tree)

void	loadIndex_ (KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, -1, size_t > &obj, FILE *stream)

void	middleSplit_ (KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, -1, size_t > &obj, size_t *ind, size_t count, size_t &index, int &cutfeat, DistanceType &cutval, const BoundingBox &bbox)

void	planeSplit (KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, -1, size_t > &obj, size_t *ind, const size_t count, int cutfeat, DistanceType &cutval, size_t &lim1, size_t &lim2)

void	save_tree (KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, -1, size_t > &obj, FILE *stream, NodePtr tree)

void	saveIndex_ (KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, -1, size_t > &obj, FILE *stream)

size_t	size (const KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, -1, size_t > &obj) const

size_t	usedMemory (KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, -1, size_t > &obj)

size_t	veclen (const KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, -1, size_t > &obj)

Public Attributes
const DatasetAdaptor &	dataset
	The source of our data. More...

Distance	distance

const KDTreeSingleIndexAdaptorParams	index_params

Public Attributes inherited from nanoflann::KDTreeBaseClass< KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, -1, size_t >, Distance, DatasetAdaptor, -1, size_t >
int	dim
	Dimensionality of each data point. More...

size_t	m_leaf_max_size

size_t	m_size
	Number of current points in the dataset. More...

size_t	m_size_at_index_build

PooledAllocator	pool

BoundingBox	root_bbox

NodePtr	root_node

std::vector< size_t >	vind

Detailed Description

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

kd-tree static 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, const size_t 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

DatasetAdaptor	The user-provided adaptor (see comments above).
Distance	The distance metric to use: nanoflann::metric_L1, nanoflann::metric_L2, nanoflann::metric_L2_Simple, etc.
DIM	Dimensionality of data points (e.g. 3 for 3D points)
IndexType	Will be typically size_t or int

Definition at line 1116 of file nanoflann.hpp.

Member Typedef Documentation

◆ BaseClassRef

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

typedef nanoflann::KDTreeBaseClass< nanoflann::KDTreeSingleIndexAdaptor<Distance, DatasetAdaptor, DIM, IndexType>, Distance, DatasetAdaptor, DIM, IndexType> nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::BaseClassRef

Definition at line 1139 of file nanoflann.hpp.

◆ BoundingBox

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

typedef BaseClassRef::BoundingBox nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::BoundingBox

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

Definition at line 1150 of file nanoflann.hpp.

◆ distance_vector_t

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

typedef BaseClassRef::distance_vector_t nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::distance_vector_t

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

Definition at line 1154 of file nanoflann.hpp.

◆ DistanceType

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

typedef BaseClassRef::DistanceType nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::DistanceType

Definition at line 1142 of file nanoflann.hpp.

◆ ElementType

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

typedef BaseClassRef::ElementType nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::ElementType

Definition at line 1141 of file nanoflann.hpp.

◆ Interval

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

typedef BaseClassRef::Interval nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::Interval

Definition at line 1147 of file nanoflann.hpp.

◆ Node

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

typedef BaseClassRef::Node nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::Node

Definition at line 1144 of file nanoflann.hpp.

◆ NodePtr

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

typedef Node* nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::NodePtr

Definition at line 1145 of file nanoflann.hpp.

Constructor & Destructor Documentation

◆ KDTreeSingleIndexAdaptor() [1/2]

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

nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::KDTreeSingleIndexAdaptor ( const KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType > & )

delete

Deleted copy constructor

◆ KDTreeSingleIndexAdaptor() [2/2]

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

nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::KDTreeSingleIndexAdaptor	(	const int	dimensionality,
		const DatasetAdaptor &	inputData,
		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

inputData	Dataset with the input features
params	Basically, the maximum leaf node size

Definition at line 1170 of file nanoflann.hpp.

Member Function Documentation

◆ buildIndex()

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

void nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::buildIndex ( )

inline

Builds the index

Definition at line 1190 of file nanoflann.hpp.

◆ computeBoundingBox()

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

void nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::computeBoundingBox ( BoundingBox & bbox )

inline

Definition at line 1316 of file nanoflann.hpp.

◆ findNeighbors()

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

template<typename RESULTSET >

bool nanoflann::KDTreeSingleIndexAdaptor< 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

RESULTSET Should be any ResultSet<DistanceType>

Returns: True if the requested neighbors could be found.

See also: knnSearch, radiusSearch

Definition at line 1221 of file nanoflann.hpp.

◆ init_vind()

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

void nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::init_vind ( )

inline

Make sure the auxiliary list vind has the same size than the current dataset, and re-generate if size has changed.

Definition at line 1307 of file nanoflann.hpp.

◆ knnSearch()

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

size_t nanoflann::KDTreeSingleIndexAdaptor< 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.

Definition at line 1252 of file nanoflann.hpp.

◆ loadIndex()

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

void nanoflann::KDTreeSingleIndexAdaptor< 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

Definition at line 1424 of file nanoflann.hpp.

◆ radiusSearch()

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

size_t nanoflann::KDTreeSingleIndexAdaptor< 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() )

Definition at line 1278 of file nanoflann.hpp.

◆ radiusSearchCustomCallback()

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

template<class SEARCH_CALLBACK >

size_t nanoflann::KDTreeSingleIndexAdaptor< 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

Definition at line 1295 of file nanoflann.hpp.

◆ saveIndex()

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

void nanoflann::KDTreeSingleIndexAdaptor< 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

Definition at line 1417 of file nanoflann.hpp.

◆ searchLevel()

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

template<class RESULTSET >

bool nanoflann::KDTreeSingleIndexAdaptor< 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

RESULTSET Should be any ResultSet<DistanceType>

Returns: true if the search should be continued, false if the results are sufficient

Definition at line 1346 of file nanoflann.hpp.

Member Data Documentation

◆ dataset

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

const DatasetAdaptor& nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::dataset

The source of our data.

The dataset used by this index

Definition at line 1129 of file nanoflann.hpp.

◆ distance

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

Distance nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::distance

Definition at line 1133 of file nanoflann.hpp.

◆ index_params

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

const KDTreeSingleIndexAdaptorParams nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::index_params

Definition at line 1131 of file nanoflann.hpp.

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

nanoflann.hpp

Public Types

Public Member Functions

Public Attributes

Detailed Description

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

Member Typedef Documentation

◆ BaseClassRef

◆ BoundingBox

◆ distance_vector_t

◆ DistanceType

◆ ElementType

◆ Interval

◆ Node

◆ NodePtr

Constructor & Destructor Documentation

◆ KDTreeSingleIndexAdaptor() [1/2]

◆ KDTreeSingleIndexAdaptor() [2/2]

Member Function Documentation

◆ buildIndex()

◆ computeBoundingBox()

◆ findNeighbors()

◆ init_vind()

◆ knnSearch()

◆ loadIndex()

◆ radiusSearch()

◆ radiusSearchCustomCallback()

◆ saveIndex()

◆ searchLevel()

Member Data Documentation

◆ dataset

◆ distance

◆ index_params

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