#include <opencv2/flann/kdtree_single_index.h>

Inheritance diagram for cvflann::KDTreeSingleIndex< Distance >:

Public Types
typedef Distance::ResultType	DistanceType

typedef Distance::ElementType	ElementType

Public Member Functions
	KDTreeSingleIndex (const Matrix< ElementType > &inputData, const IndexParams &params=KDTreeSingleIndexParams(), Distance d=Distance())

	KDTreeSingleIndex (const KDTreeSingleIndex &)

	~KDTreeSingleIndex ()

void	buildIndex () CV_OVERRIDE

void	findNeighbors (ResultSet< DistanceType > &result, const ElementType *vec, const SearchParams &searchParams) CV_OVERRIDE

IndexParams	getParameters () const CV_OVERRIDE

flann_algorithm_t	getType () const CV_OVERRIDE

void	knnSearch (const Matrix< ElementType > &queries, Matrix< int > &indices, Matrix< DistanceType > &dists, int knn, const SearchParams &params) CV_OVERRIDE
	Perform k-nearest neighbor search.

void	loadIndex (FILE *stream) CV_OVERRIDE
	Loads the index from a stream.

KDTreeSingleIndex &	operator= (const KDTreeSingleIndex &)

void	saveIndex (FILE *stream) CV_OVERRIDE
	Saves the index to a stream.

size_t	size () const CV_OVERRIDE

int	usedMemory () const CV_OVERRIDE

size_t	veclen () const CV_OVERRIDE

Public Member Functions inherited from cvflann::NNIndex< Distance >
virtual	~NNIndex ()

virtual int	radiusSearch (const Matrix< ElementType > &query, Matrix< int > &indices, Matrix< DistanceType > &dists, float radius, const SearchParams &params)
	Perform radius search.

Detailed Description

template<typename Distance>
class cvflann::KDTreeSingleIndex< Distance >

Randomized kd-tree index

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

Member Typedef Documentation

template<typename Distance>

typedef Distance::ResultType cvflann::KDTreeSingleIndex< Distance >::DistanceType

template<typename Distance>

typedef Distance::ElementType cvflann::KDTreeSingleIndex< Distance >::ElementType

Constructor & Destructor Documentation

template<typename Distance>

cvflann::KDTreeSingleIndex< Distance >::KDTreeSingleIndex	(	const Matrix< ElementType > &	inputData,
		const IndexParams &	params = `KDTreeSingleIndexParams()`,
		Distance	d = `Distance()`
	)

inline

KDTree constructor

Params: inputData = dataset with the input features params = parameters passed to the kdtree algorithm

template<typename Distance>

cvflann::KDTreeSingleIndex< Distance >::KDTreeSingleIndex ( const KDTreeSingleIndex< Distance > & )

template<typename Distance>

cvflann::KDTreeSingleIndex< Distance >::~KDTreeSingleIndex ( )

inline

Standard destructor

Member Function Documentation

template<typename Distance>

void cvflann::KDTreeSingleIndex< Distance >::buildIndex ( )

inlinevirtual

Builds the index

Implements cvflann::NNIndex< Distance >.

template<typename Distance>

void cvflann::KDTreeSingleIndex< Distance >::findNeighbors	(	ResultSet< DistanceType > &	result,
		const ElementType *	vec,
		const SearchParams &	searchParams
	)

inlinevirtual

Find set of nearest neighbors to vec. 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 maxCheck = the maximum number of restarts (in a best-bin-first manner)

Implements cvflann::NNIndex< Distance >.

template<typename Distance>

IndexParams cvflann::KDTreeSingleIndex< Distance >::getParameters ( ) const

inlinevirtual

Returns: The index parameters

Implements cvflann::NNIndex< Distance >.

template<typename Distance>

flann_algorithm_t cvflann::KDTreeSingleIndex< Distance >::getType ( ) const

inlinevirtual

Returns: The index type (kdtree, kmeans,...)

Implements cvflann::NNIndex< Distance >.

template<typename Distance>

void cvflann::KDTreeSingleIndex< Distance >::knnSearch	(	const Matrix< ElementType > &	queries,
		Matrix< int > &	indices,
		Matrix< DistanceType > &	dists,
		int	knn,
		const SearchParams &	params
	)

inlinevirtual

Perform k-nearest neighbor search.

Parameters

[in]	queries	The query points for which to find the nearest neighbors
[out]	indices	The indices of the nearest neighbors found
[out]	dists	Distances to the nearest neighbors found
[in]	knn	Number of nearest neighbors to return
[in]	params	Search parameters