#include <nanoflann.hpp>

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

Public Types
typedef Distance::ElementType	ElementType

typedef Distance::DistanceType	DistanceType

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

typedef Node *	NodePtr

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

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

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

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

	~KDTreeSingleIndexAdaptor ()

void	buildIndex ()

void	init_vind ()

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 >
bool	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< KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >, Distance, DatasetAdaptor, DIM, IndexType >
void	freeIndex (KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType > &obj)

Public Attributes
std::vector< IndexType >	vind

size_t	m_leaf_max_size

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

const KDTreeSingleIndexAdaptorParams	index_params

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< KDTreeSingleIndexAdaptor< 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< KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >, Distance, DatasetAdaptor, DIM, IndexType >
size_t	size (const KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType > &obj) const

size_t	veclen (const KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType > &obj)

size_t	usedMemory (KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType > &obj)

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

NodePtr	divideTree (KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType > &obj, const IndexType left, const IndexType right, BoundingBox &bbox)

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

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

DistanceType	computeInitialDistances (const KDTreeSingleIndexAdaptor< 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::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, 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

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

Member Typedef Documentation

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>::BoundingBox nanoflann::KDTreeSingleIndexAdaptor< 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::KDTreeSingleIndexAdaptor<Distance, DatasetAdaptor, DIM, IndexType>, Distance, DatasetAdaptor, DIM, IndexType>::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"

Constructor & Destructor Documentation

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

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

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

inline

Standard destructor

Member Function Documentation

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

void nanoflann::KDTreeSingleIndexAdaptor< 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::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

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.

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.

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

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() )

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

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

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

Member Data Documentation

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

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

PooledAllocator nanoflann::KDTreeSingleIndexAdaptor< 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::KDTreeSingleIndexAdaptor< 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::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::vind

Array of indices to vectors in the dataset.

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

include/nanoflann.hpp

Public Types

Public Member Functions

Public Attributes

Additional Inherited Members

Detailed Description

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

Member Typedef Documentation

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation

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