#include <opencv2/flann/kdtree_index.h>

Inheritance diagram for cvflann::KDTreeIndex< Distance >:

Public Types
typedef Distance::ResultType	DistanceType

typedef Distance::ElementType	ElementType

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

	KDTreeIndex (const KDTreeIndex &)

	~KDTreeIndex ()

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

KDTreeIndex &	operator= (const KDTreeIndex &)

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

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::KDTreeIndex< 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::KDTreeIndex< Distance >::DistanceType

template<typename Distance>

typedef Distance::ElementType cvflann::KDTreeIndex< Distance >::ElementType

Constructor & Destructor Documentation

template<typename Distance>

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

inline

KDTree constructor

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

template<typename Distance>

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

template<typename Distance>

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

inline

Standard destructor

Member Function Documentation

template<typename Distance>

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

inlinevirtual

Builds the index

Implements cvflann::NNIndex< Distance >.

template<typename Distance>

void cvflann::KDTreeIndex< 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::KDTreeIndex< Distance >::getParameters ( ) const

inlinevirtual