#include <nanoflann.hpp>

Inherits nanoflann::KDTreeBaseClass< KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, -1, uint32_t >, Distance, DatasetAdaptor, -1, uint32_t >.

Public Types
using	Base

using	Offset = typename Base::Offset

using	Size = typename Base::Size

using	Dimension = typename Base::Dimension

using	ElementType = typename Base::ElementType

using	DistanceType = typename Base::DistanceType

using	IndexType = typename Base::IndexType

using	Node = typename Base::Node

using	NodePtr = Node*

using	Interval = typename Base::Interval

using	BoundingBox = typename Base::BoundingBox

using	distance_vector_t = typename Base::distance_vector_t

Public Types inherited from nanoflann::KDTreeBaseClass< KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, -1, uint32_t >, Distance, DatasetAdaptor, -1, uint32_t >
using	ElementType

using	DistanceType

using	IndexType

using	Offset

using	Size

using	Dimension

using	NodePtr

using	NodeConstPtr

using	BoundingBox

using	distance_vector_t

Public Member Functions
	KDTreeSingleIndexAdaptor (const KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, index_t > &)=delete

template<class... Args>
	KDTreeSingleIndexAdaptor (const Dimension dimensionality, const DatasetAdaptor &inputData, const KDTreeSingleIndexAdaptorParams &params, Args &&... args)

	KDTreeSingleIndexAdaptor (const Dimension dimensionality, const DatasetAdaptor &inputData, const KDTreeSingleIndexAdaptorParams &params={})

void	buildIndex ()

void	init_vind ()

void	computeBoundingBox (BoundingBox &bbox)

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

void	saveIndex (std::ostream &stream) const

void	loadIndex (std::istream &stream)

Query methods
template<typename RESULTSET>
bool	findNeighbors (RESULTSET &result, const ElementType *vec, const SearchParameters &searchParams={}) const

Size	knnSearch (const ElementType query_point, const Size num_closest, IndexType out_indices, DistanceType *out_distances) const

Size	radiusSearch (const ElementType *query_point, const DistanceType &radius, std::vector< ResultItem< IndexType, DistanceType > > &IndicesDists, const SearchParameters &searchParams={}) const

template<class SEARCH_CALLBACK>
Size	radiusSearchCustomCallback (const ElementType *query_point, SEARCH_CALLBACK &resultSet, const SearchParameters &searchParams={}) const

Size	rknnSearch (const ElementType query_point, const Size num_closest, IndexType out_indices, DistanceType *out_distances, const DistanceType &radius) const

Public Member Functions inherited from nanoflann::KDTreeBaseClass< KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, -1, uint32_t >, Distance, DatasetAdaptor, -1, uint32_t >
void	freeIndex (KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, -1, uint32_t > &obj)

Size	size (const KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, -1, uint32_t > &obj) const

Size	veclen (const KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, -1, uint32_t > &obj) const

ElementType	dataset_get (const KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, -1, uint32_t > &obj, IndexType element, Dimension component) const
	Helper accessor to the dataset points:

Size	usedMemory (const KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, -1, uint32_t > &obj) const

void	computeMinMax (const KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, -1, uint32_t > &obj, Offset ind, Size count, Dimension element, ElementType &min_elem, ElementType &max_elem) const

NodePtr	divideTree (KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, -1, uint32_t > &obj, const Offset left, const Offset right, BoundingBox &bbox)

NodePtr	divideTreeConcurrent (KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, -1, uint32_t > &obj, const Offset left, const Offset right, BoundingBox &bbox, std::atomic< unsigned int > &thread_count, std::mutex &mutex)

void	middleSplit_ (const KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, -1, uint32_t > &obj, const Offset ind, const Size count, Offset &index, Dimension &cutfeat, DistanceType &cutval, const BoundingBox &bbox)

void	planeSplit (const KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, -1, uint32_t > &obj, const Offset ind, const Size count, const Dimension cutfeat, const DistanceType &cutval, Offset &lim1, Offset &lim2)

DistanceType	computeInitialDistances (const KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, -1, uint32_t > &obj, const ElementType *vec, distance_vector_t &dists) const

void	saveIndex (const KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, -1, uint32_t > &obj, std::ostream &stream) const

void	loadIndex (KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, -1, uint32_t > &obj, std::istream &stream)

Data Fields
const DatasetAdaptor &	dataset_

const KDTreeSingleIndexAdaptorParams	indexParams

Distance	distance_

Data Fields inherited from nanoflann::KDTreeBaseClass< KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, -1, uint32_t >, Distance, DatasetAdaptor, -1, uint32_t >
std::vector< IndexType >	vAcc_

NodePtr	root_node_

Size	leaf_max_size_

Size	n_thread_build_
	Number of thread for concurrent tree build.

Size	size_
	Number of current points in the dataset.

Size	size_at_index_build_
	Number of points in the dataset when the index was built.

Dimension	dim_
	Dimensionality of each data point.

BoundingBox	root_bbox_

PooledAllocator	pool_

Private Member Functions
void	init (const Dimension dimensionality, const KDTreeSingleIndexAdaptorParams &params)

Additional Inherited Members
Static Public Member Functions inherited from nanoflann::KDTreeBaseClass< KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, -1, uint32_t >, Distance, DatasetAdaptor, -1, uint32_t >
static void	save_tree (const KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, -1, uint32_t > &obj, std::ostream &stream, const NodeConstPtr tree)

static void	load_tree (KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, -1, uint32_t > &obj, std::istream &stream, NodePtr &tree)

Detailed Description

template<typename Distance, class DatasetAdaptor, int32_t DIM = -1, typename index_t = uint32_t>
class nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, index_t >

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
  size_t kdtree_get_point_count() const { ... }
 
 
  // Must return the dim'th component of the idx'th point in the class:
  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, which must be ensured to have a lifetime equal or longer than the instance of this class.
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 1545 of file nanoflann.hpp.

Member Typedef Documentation

◆ Base

template<typename Distance, class DatasetAdaptor, int32_t DIM = -1, typename index_t = uint32_t>

using nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, index_t >::Base

Initial value:

 typename nanoflann::KDTreeBaseClass<
        nanoflann::KDTreeSingleIndexAdaptor<
            Distance, DatasetAdaptor, DIM, index_t>,
        Distance, DatasetAdaptor, DIM, index_t>

Definition at line 1563 of file nanoflann.hpp.

◆ BoundingBox

template<typename Distance, class DatasetAdaptor, int32_t DIM = -1, typename index_t = uint32_t>

using nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, index_t >::BoundingBox = typename Base::BoundingBox

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

Definition at line 1583 of file nanoflann.hpp.

◆ Dimension

template<typename Distance, class DatasetAdaptor, int32_t DIM = -1, typename index_t = uint32_t>

using nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, index_t >::Dimension = typename Base::Dimension

Definition at line 1570 of file nanoflann.hpp.

◆ distance_vector_t

template<typename Distance, class DatasetAdaptor, int32_t DIM = -1, typename index_t = uint32_t>

using nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, index_t >::distance_vector_t = typename Base::distance_vector_t

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

Definition at line 1587 of file nanoflann.hpp.

◆ DistanceType

template<typename Distance, class DatasetAdaptor, int32_t DIM = -1, typename index_t = uint32_t>

using nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, index_t >::DistanceType = typename Base::DistanceType

Definition at line 1573 of file nanoflann.hpp.

◆ ElementType

template<typename Distance, class DatasetAdaptor, int32_t DIM = -1, typename index_t = uint32_t>

using nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, index_t >::ElementType = typename Base::ElementType

Definition at line 1572 of file nanoflann.hpp.

◆ IndexType

template<typename Distance, class DatasetAdaptor, int32_t DIM = -1, typename index_t = uint32_t>

using nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, index_t >::IndexType = typename Base::IndexType

Definition at line 1574 of file nanoflann.hpp.

◆ Interval

template<typename Distance, class DatasetAdaptor, int32_t DIM = -1, typename index_t = uint32_t>

using nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, index_t >::Interval = typename Base::Interval

Definition at line 1579 of file nanoflann.hpp.

◆ Node

template<typename Distance, class DatasetAdaptor, int32_t DIM = -1, typename index_t = uint32_t>

using nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, index_t >::Node = typename Base::Node

Definition at line 1576 of file nanoflann.hpp.

◆ NodePtr

template<typename Distance, class DatasetAdaptor, int32_t DIM = -1, typename index_t = uint32_t>

using nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, index_t >::NodePtr = Node*

Definition at line 1577 of file nanoflann.hpp.

◆ Offset

template<typename Distance, class DatasetAdaptor, int32_t DIM = -1, typename index_t = uint32_t>

using nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, index_t >::Offset = typename Base::Offset

Definition at line 1568 of file nanoflann.hpp.

◆ Size

template<typename Distance, class DatasetAdaptor, int32_t DIM = -1, typename index_t = uint32_t>

using nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, index_t >::Size = typename Base::Size

Definition at line 1569 of file nanoflann.hpp.

Constructor & Destructor Documentation

◆ KDTreeSingleIndexAdaptor() [1/3]

template<typename Distance, class DatasetAdaptor, int32_t DIM = -1, typename index_t = uint32_t>

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

explicitdelete

Deleted copy constructor

◆ KDTreeSingleIndexAdaptor() [2/3]

template<typename Distance, class DatasetAdaptor, int32_t DIM = -1, typename index_t = uint32_t>

template<class... Args>

nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, index_t >::KDTreeSingleIndexAdaptor	(	const Dimension	dimensionality,
		const DatasetAdaptor &	inputData,
		const KDTreeSingleIndexAdaptorParams &	params,
		Args &&...	args )

inlineexplicit

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. Its lifetime must be equal or longer than that of the instance of this class.
params	Basically, the maximum leaf node size

Note that there is a variable number of optional additional parameters which will be forwarded to the metric class constructor. Refer to example examples/pointcloud_custom_metric.cpp for a use case.

Definition at line 1610 of file nanoflann.hpp.

◆ KDTreeSingleIndexAdaptor() [3/3]

template<typename Distance, class DatasetAdaptor, int32_t DIM = -1, typename index_t = uint32_t>

nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, index_t >::KDTreeSingleIndexAdaptor	(	const Dimension	dimensionality,
		const DatasetAdaptor &	inputData,
		const KDTreeSingleIndexAdaptorParams &	params = {} )

inlineexplicit

Definition at line 1620 of file nanoflann.hpp.

Member Function Documentation

◆ buildIndex()

template<typename Distance, class DatasetAdaptor, int32_t DIM = -1, typename index_t = uint32_t>

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

inline

Builds the index

Definition at line 1660 of file nanoflann.hpp.

◆ computeBoundingBox()

template<typename Distance, class DatasetAdaptor, int32_t DIM = -1, typename index_t = uint32_t>

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

inline

Definition at line 1845 of file nanoflann.hpp.

◆ findNeighbors()

template<typename Distance, class DatasetAdaptor, int32_t DIM = -1, typename index_t = uint32_t>

template<typename RESULTSET>

bool nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, index_t >::findNeighbors	(	RESULTSET &	result,
		const ElementType *	vec,
		const SearchParameters &	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

Note: If L2 norms are used, all returned distances are actually squared distances.

Definition at line 1708 of file nanoflann.hpp.

◆ init()

template<typename Distance, class DatasetAdaptor, int32_t DIM = -1, typename index_t = uint32_t>

void nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, index_t >::init	(	const Dimension	dimensionality,
		const KDTreeSingleIndexAdaptorParams &	params )

inlineprivate

Definition at line 1629 of file nanoflann.hpp.

◆ init_vind()

template<typename Distance, class DatasetAdaptor, int32_t DIM = -1, typename index_t = uint32_t>

void nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, index_t >::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 1836 of file nanoflann.hpp.

◆ knnSearch()

template<typename Distance, class DatasetAdaptor, int32_t DIM = -1, typename index_t = uint32_t>

Size nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, index_t >::knnSearch	(	const ElementType *	query_point,
		const Size	num_closest,
		IndexType *	out_indices,
		DistanceType *	out_distances ) const

inline

Find the "num_closest" nearest neighbors to the query_point[0:dim-1]. Their indices and distances are stored in the provided pointers to array/vector.

See also: radiusSearch, findNeighbors

Returns: Number N of valid points in the result set.

Note: If L2 norms are used, all returned distances are actually squared distances.; Only the first N entries in out_indices and out_distances will be valid. Return is less than num_closest only if the number of elements in the tree is less than num_closest.

Definition at line 1748 of file nanoflann.hpp.

◆ loadIndex()

template<typename Distance, class DatasetAdaptor, int32_t DIM = -1, typename index_t = uint32_t>

void nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, index_t >::loadIndex ( std::istream & 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 1978 of file nanoflann.hpp.

◆ radiusSearch()

template<typename Distance, class DatasetAdaptor, int32_t DIM = -1, typename index_t = uint32_t>

Size nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, index_t >::radiusSearch	(	const ElementType *	query_point,
		const DistanceType &	radius,
		std::vector< ResultItem< IndexType, DistanceType > > &	IndicesDists,
		const SearchParameters &	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() )

Note: If L2 norms are used, search radius and all returned distances are actually squared distances.

Definition at line 1777 of file nanoflann.hpp.

◆ radiusSearchCustomCallback()

template<typename Distance, class DatasetAdaptor, int32_t DIM = -1, typename index_t = uint32_t>

template<class SEARCH_CALLBACK>

Size nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, index_t >::radiusSearchCustomCallback	(	const ElementType *	query_point,
		SEARCH_CALLBACK &	resultSet,
		const SearchParameters &	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 1795 of file nanoflann.hpp.

◆ rknnSearch()

template<typename Distance, class DatasetAdaptor, int32_t DIM = -1, typename index_t = uint32_t>

Size nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, index_t >::rknnSearch	(	const ElementType *	query_point,
		const Size	num_closest,
		IndexType *	out_indices,
		DistanceType *	out_distances,
		const DistanceType &	radius ) const

inline

Find the first N 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.

See also: radiusSearch, findNeighbors

Returns: Number N of valid points in the result set.

Note: If L2 norms are used, all returned distances are actually squared distances.; Only the first N entries in out_indices and out_distances will be valid. Return is less than num_closest only if the number of elements in the tree is less than num_closest.

Definition at line 1819 of file nanoflann.hpp.

◆ saveIndex()

template<typename Distance, class DatasetAdaptor, int32_t DIM = -1, typename index_t = uint32_t>

void nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, index_t >::saveIndex ( std::ostream & stream ) const

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 1968 of file nanoflann.hpp.

◆ searchLevel()

template<typename Distance, class DatasetAdaptor, int32_t DIM = -1, typename index_t = uint32_t>

template<class RESULTSET>

bool nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, index_t >::searchLevel	(	RESULTSET &	result_set,
		const ElementType *	vec,
		const NodePtr	node,
		DistanceType	mindist,
		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 1885 of file nanoflann.hpp.

Field Documentation

◆ dataset_

template<typename Distance, class DatasetAdaptor, int32_t DIM = -1, typename index_t = uint32_t>

const DatasetAdaptor& nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, index_t >::dataset_

The data source used by this index

Definition at line 1557 of file nanoflann.hpp.

◆ distance_

template<typename Distance, class DatasetAdaptor, int32_t DIM = -1, typename index_t = uint32_t>

Distance nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, index_t >::distance_

Definition at line 1561 of file nanoflann.hpp.

◆ indexParams

template<typename Distance, class DatasetAdaptor, int32_t DIM = -1, typename index_t = uint32_t>

const KDTreeSingleIndexAdaptorParams nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, index_t >::indexParams

Definition at line 1559 of file nanoflann.hpp.

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

nanoflann.hpp

Public Types

Public Member Functions

Data Fields

Private Member Functions

Additional Inherited Members

Detailed Description

Member Typedef Documentation

◆ Base

◆ BoundingBox

◆ Dimension

◆ distance_vector_t

◆ DistanceType

◆ ElementType

◆ IndexType

◆ Interval

◆ Node

◆ NodePtr

◆ Offset

◆ Size

Constructor & Destructor Documentation

◆ KDTreeSingleIndexAdaptor() [1/3]

◆ KDTreeSingleIndexAdaptor() [2/3]

◆ KDTreeSingleIndexAdaptor() [3/3]

Member Function Documentation

◆ buildIndex()

◆ computeBoundingBox()

◆ findNeighbors()

◆ init()

◆ init_vind()

◆ knnSearch()

◆ loadIndex()

◆ radiusSearch()

◆ radiusSearchCustomCallback()

◆ rknnSearch()

◆ saveIndex()

◆ searchLevel()

Field Documentation

◆ dataset_

◆ distance_

◆ indexParams