Namespaces
	anyimpl

	lsh

Classes
struct	Accumulator

struct	Accumulator< char >

struct	Accumulator< int >

struct	Accumulator< short >

struct	Accumulator< unsigned char >

struct	Accumulator< unsigned int >

struct	Accumulator< unsigned short >

struct	any

class	AutotunedIndex

struct	AutotunedIndexParams

struct	BranchStruct

struct	ChiSquareDistance

class	CompositeIndex

struct	CompositeIndexParams

class	CreatorNotFound

struct	Datatype

struct	Datatype< char >

struct	Datatype< double >

struct	Datatype< float >

struct	Datatype< int >

struct	Datatype< short >

struct	Datatype< unsigned char >

struct	Datatype< unsigned int >

struct	Datatype< unsigned short >

class	DynamicBitset

class	False

class	FLANNException

struct	Hamming

struct	Hamming2

struct	HammingLUT

struct	HammingLUT2

class	Heap

struct	HellingerDistance

class	HierarchicalClusteringIndex

struct	HierarchicalClusteringIndexParams

struct	HistIntersectionDistance

class	Index

struct	index_creator

struct	index_creator< False, False, Distance >

struct	index_creator< False, VectorSpace, Distance >

struct	IndexHeader

class	KDTreeIndex

struct	KDTreeIndexParams

class	KDTreeSingleIndex

struct	KDTreeSingleIndexParams

struct	KL_Divergence

class	KMeansIndex

struct	KMeansIndexParams

class	KNNRadiusUniqueResultSet

class	KNNResultSet

class	KNNSimpleResultSet

class	KNNUniqueResultSet

struct	L1

struct	L2

struct	L2_Simple

class	LinearIndex

struct	LinearIndexParams

class	Logger

class	LshIndex

struct	LshIndexParams

class	Matrix

struct	MaxDistance

struct	MinkowskiDistance

class	NNIndex

class	ObjectFactory

class	PooledAllocator

class	RadiusResultSet

class	RadiusUniqueResultSet

class	ResultSet

struct	SavedIndexParams

struct	SearchParams

struct	simpleDistance

struct	simpleDistance< ChiSquareDistance< ElementType >, ElementType >

struct	simpleDistance< HellingerDistance< ElementType >, ElementType >

struct	simpleDistance< L2< ElementType >, ElementType >

struct	simpleDistance< L2_Simple< ElementType >, ElementType >

struct	simpleDistance< MinkowskiDistance< ElementType >, ElementType >

struct	squareDistance

struct	squareDistance< ChiSquareDistance< ElementType >, ElementType >

struct	squareDistance< HellingerDistance< ElementType >, ElementType >

struct	squareDistance< L2< ElementType >, ElementType >

struct	squareDistance< L2_Simple< ElementType >, ElementType >

struct	squareDistance< MinkowskiDistance< ElementType >, ElementType >

class	StartStopTimer

class	True

class	UniqueRandom

class	UniqueResultSet

class	UntypedMatrix

struct	ZeroIterator

Typedefs
typedef std::map< std::string, any >	IndexParams

Enumerations
enum	flann_algorithm_t { FLANN_INDEX_LINEAR = 0, FLANN_INDEX_KDTREE = 1, FLANN_INDEX_KMEANS = 2, FLANN_INDEX_COMPOSITE = 3, FLANN_INDEX_KDTREE_SINGLE = 4, FLANN_INDEX_HIERARCHICAL = 5, FLANN_INDEX_LSH = 6, FLANN_INDEX_SAVED = 254, FLANN_INDEX_AUTOTUNED = 255, LINEAR = 0, KDTREE = 1, KMEANS = 2, COMPOSITE = 3, KDTREE_SINGLE = 4, SAVED = 254, AUTOTUNED = 255 }

enum	flann_centers_init_t { FLANN_CENTERS_RANDOM = 0, FLANN_CENTERS_GONZALES = 1, FLANN_CENTERS_KMEANSPP = 2, CENTERS_RANDOM = 0, CENTERS_GONZALES = 1, CENTERS_KMEANSPP = 2 }

enum	flann_log_level_t { FLANN_LOG_NONE = 0, FLANN_LOG_FATAL = 1, FLANN_LOG_ERROR = 2, FLANN_LOG_WARN = 3, FLANN_LOG_INFO = 4 }

enum	flann_distance_t { FLANN_DIST_EUCLIDEAN = 1, FLANN_DIST_L2 = 1, FLANN_DIST_MANHATTAN = 2, FLANN_DIST_L1 = 2, FLANN_DIST_MINKOWSKI = 3, FLANN_DIST_MAX = 4, FLANN_DIST_HIST_INTERSECT = 5, FLANN_DIST_HELLINGER = 6, FLANN_DIST_CHI_SQUARE = 7, FLANN_DIST_CS = 7, FLANN_DIST_KULLBACK_LEIBLER = 8, FLANN_DIST_KL = 8, FLANN_DIST_HAMMING = 9, EUCLIDEAN = 1, MANHATTAN = 2, MINKOWSKI = 3, MAX_DIST = 4, HIST_INTERSECT = 5, HELLINGER = 6, CS = 7, KL = 8, KULLBACK_LEIBLER = 8 }

enum	flann_datatype_t { FLANN_INT8 = 0, FLANN_INT16 = 1, FLANN_INT32 = 2, FLANN_INT64 = 3, FLANN_UINT8 = 4, FLANN_UINT16 = 5, FLANN_UINT32 = 6, FLANN_UINT64 = 7, FLANN_FLOAT32 = 8, FLANN_FLOAT64 = 9 }

enum	{ FLANN_CHECKS_UNLIMITED = -1, FLANN_CHECKS_AUTOTUNED = -2 }

Functions
flann_distance_t	flann_distance_type ()

FLANN_DEPRECATED void	set_distance_type (flann_distance_t distance_type, int order)

void	log_verbosity (int level)

template<typename Distance >
NNIndex< Distance > *	load_saved_index (const Matrix< typename Distance::ElementType > &dataset, const std::string &filename, Distance distance)

template<typename Distance >
int	hierarchicalClustering (const Matrix< typename Distance::ElementType > &points, Matrix< typename Distance::ResultType > &centers, const KMeansIndexParams &params, Distance d=Distance())

template<typename T >
void	save_to_file (const cvflann::Matrix< T > &dataset, const std::string &filename, const std::string &name)

template<typename T >
void	load_from_file (cvflann::Matrix< T > &dataset, const std::string &filename, const std::string &name)

template<typename T >
T	abs (T x)

template<>
int	abs< int > (int x)

template<>
float	abs< float > (float x)

template<>
double	abs< double > (double x)

template<typename Distance >
Distance::ResultType	ensureSquareDistance (typename Distance::ResultType dist)

template<typename Distance >
Distance::ResultType	ensureSimpleDistance (typename Distance::ResultType dist)

template<typename T >
T *	allocate (size_t count=1)

template<typename Distance >
void	find_nearest (const Matrix< typename Distance::ElementType > &dataset, typename Distance::ElementType query, int matches, int nn, int skip=0, Distance distance=Distance())

template<typename Distance >
void	compute_ground_truth (const Matrix< typename Distance::ElementType > &dataset, const Matrix< typename Distance::ElementType > &testset, Matrix< int > &matches, int skip=0, Distance d=Distance())

template<typename Distance >
void	save_header (FILE *stream, const NNIndex< Distance > &index)

IndexHeader	load_header (FILE *stream)

template<typename T >
void	save_value (FILE *stream, const T &value, size_t count=1)

template<typename T >
void	save_value (FILE *stream, const cvflann::Matrix< T > &value)

template<typename T >
void	save_value (FILE *stream, const std::vector< T > &value)

template<typename T >
void	load_value (FILE *stream, T &value, size_t count=1)

template<typename T >
void	load_value (FILE *stream, cvflann::Matrix< T > &value)

template<typename T >
void	load_value (FILE *stream, std::vector< T > &value)

template<typename T >
T	get_param (const IndexParams &params, std::string name, const T &default_value)

template<typename T >
T	get_param (const IndexParams &params, std::string name)

void	print_params (const IndexParams &params, std::ostream &stream)

void	print_params (const IndexParams &params)

int	countCorrectMatches (int neighbors, int groundTruth, int n)

template<typename Distance >
Distance::ResultType	computeDistanceRaport (const Matrix< typename Distance::ElementType > &inputData, typename Distance::ElementType target, int neighbors, int *groundTruth, int veclen, int n, const Distance &distance)

template<typename Distance >
float	search_with_ground_truth (NNIndex< Distance > &index, const Matrix< typename Distance::ElementType > &inputData, const Matrix< typename Distance::ElementType > &testData, const Matrix< int > &matches, int nn, int checks, float &time, typename Distance::ResultType &dist, const Distance &distance, int skipMatches)

template<typename Distance >
float	test_index_checks (NNIndex< Distance > &index, const Matrix< typename Distance::ElementType > &inputData, const Matrix< typename Distance::ElementType > &testData, const Matrix< int > &matches, int checks, float &precision, const Distance &distance, int nn=1, int skipMatches=0)

template<typename Distance >
float	test_index_precision (NNIndex< Distance > &index, const Matrix< typename Distance::ElementType > &inputData, const Matrix< typename Distance::ElementType > &testData, const Matrix< int > &matches, float precision, int &checks, const Distance &distance, int nn=1, int skipMatches=0)

template<typename Distance >
void	test_index_precisions (NNIndex< Distance > &index, const Matrix< typename Distance::ElementType > &inputData, const Matrix< typename Distance::ElementType > &testData, const Matrix< int > &matches, float *precisions, int precisions_length, const Distance &distance, int nn=1, int skipMatches=0, float maxTime=0)

void	dummyfunc ()

template<typename T >
Matrix< T >	random_sample (Matrix< T > &srcMatrix, long size, bool remove=false)

template<typename T >
Matrix< T >	random_sample (const Matrix< T > &srcMatrix, size_t size)

void	seed_random (unsigned int seed)

double	rand_double (double high=1.0, double low=0)

int	rand_int (int high=RAND_MAX, int low=0)

template<typename Distance >
NNIndex< Distance > *	create_index_by_type (const Matrix< typename Distance::ElementType > &dataset, const IndexParams &params, const Distance &distance)

std::ostream &	operator<< (std::ostream &out, const any &any_val)

template<typename T >
void	addValue (int pos, float val, float vals, T point, T *points, int n)

template<typename T , typename F >
float	optimizeSimplexDownhill (T points, int n, F func, float vals=NULL)

Variables
const size_t	WORDSIZE =16

const size_t	BLOCKSIZE =8192

Typedef Documentation

§ IndexParams

typedef std::map<std::string, any> cvflann::IndexParams

Enumeration Type Documentation

§ anonymous enum

anonymous enum

Enumerator
FLANN_CHECKS_UNLIMITED
FLANN_CHECKS_AUTOTUNED

§ flann_algorithm_t

enum cvflann::flann_algorithm_t

Enumerator
FLANN_INDEX_LINEAR
FLANN_INDEX_KDTREE
FLANN_INDEX_KMEANS
FLANN_INDEX_COMPOSITE
FLANN_INDEX_KDTREE_SINGLE
FLANN_INDEX_HIERARCHICAL
FLANN_INDEX_LSH
FLANN_INDEX_SAVED
FLANN_INDEX_AUTOTUNED
LINEAR
KDTREE
KMEANS
COMPOSITE
KDTREE_SINGLE
SAVED
AUTOTUNED

§ flann_centers_init_t

enum cvflann::flann_centers_init_t

Enumerator
FLANN_CENTERS_RANDOM
FLANN_CENTERS_GONZALES
FLANN_CENTERS_KMEANSPP
CENTERS_RANDOM
CENTERS_GONZALES
CENTERS_KMEANSPP

§ flann_datatype_t

enum cvflann::flann_datatype_t

Enumerator
FLANN_INT8
FLANN_INT16
FLANN_INT32
FLANN_INT64
FLANN_UINT8
FLANN_UINT16
FLANN_UINT32
FLANN_UINT64
FLANN_FLOAT32
FLANN_FLOAT64

§ flann_distance_t

enum cvflann::flann_distance_t

Enumerator
FLANN_DIST_EUCLIDEAN
FLANN_DIST_L2
FLANN_DIST_MANHATTAN
FLANN_DIST_L1
FLANN_DIST_MINKOWSKI
FLANN_DIST_MAX
FLANN_DIST_HIST_INTERSECT
FLANN_DIST_HELLINGER
FLANN_DIST_CHI_SQUARE
FLANN_DIST_CS
FLANN_DIST_KULLBACK_LEIBLER
FLANN_DIST_KL
FLANN_DIST_HAMMING
EUCLIDEAN
MANHATTAN
MINKOWSKI
MAX_DIST
HIST_INTERSECT
HELLINGER
CS
KL
KULLBACK_LEIBLER

§ flann_log_level_t

enum cvflann::flann_log_level_t

Enumerator
FLANN_LOG_NONE
FLANN_LOG_FATAL
FLANN_LOG_ERROR
FLANN_LOG_WARN
FLANN_LOG_INFO

Function Documentation

§ abs()

template<typename T >

T cvflann::abs ( T x )

inline

§ abs< double >()

template<>

double cvflann::abs< double > ( double x )

inline

§ abs< float >()

template<>

float cvflann::abs< float > ( float x )

inline

§ abs< int >()

template<>

int cvflann::abs< int > ( int x )

inline

§ addValue()

template<typename T >

void cvflann::addValue	(	int	pos,
		float	val,
		float *	vals,
		T *	point,
		T *	points,
		int	n
	)

Adds val to array vals (and point to array points) and keeping the arrays sorted by vals.

§ allocate()

template<typename T >

T* cvflann::allocate ( size_t count = 1 )

Allocates (using C's malloc) a generic type T.

Params: count = number of instances to allocate. Returns: pointer (of type T*) to memory buffer

§ compute_ground_truth()

template<typename Distance >

void cvflann::compute_ground_truth	(	const Matrix< typename Distance::ElementType > &	dataset,
		const Matrix< typename Distance::ElementType > &	testset,
		Matrix< int > &	matches,
		int	skip = `0`,
		Distance	d = `Distance()`
	)

§ computeDistanceRaport()

template<typename Distance >

Distance::ResultType cvflann::computeDistanceRaport	(	const Matrix< typename Distance::ElementType > &	inputData,
		typename Distance::ElementType *	target,
		int *	neighbors,
		int *	groundTruth,
		int	veclen,
		int	n,
		const Distance &	distance
	)

§ countCorrectMatches()

int cvflann::countCorrectMatches	(	int *	neighbors,
		int *	groundTruth,
		int	n
	)

inline

§ create_index_by_type()

template<typename Distance >

NNIndex< Distance > * cvflann::create_index_by_type	(	const Matrix< typename Distance::ElementType > &	dataset,
		const IndexParams &	params,
		const Distance &	distance
	)

§ dummyfunc()

void cvflann::dummyfunc ( )

§ ensureSimpleDistance()

template<typename Distance >

Distance::ResultType cvflann::ensureSimpleDistance ( typename Distance::ResultType dist )

§ ensureSquareDistance()

template<typename Distance >

Distance::ResultType cvflann::ensureSquareDistance ( typename Distance::ResultType dist )

§ find_nearest()

template<typename Distance >

void cvflann::find_nearest	(	const Matrix< typename Distance::ElementType > &	dataset,
		typename Distance::ElementType *	query,
		int *	matches,
		int	nn,
		int	skip = `0`,
		Distance	distance = `Distance()`
	)

§ flann_distance_type()

flann_distance_t cvflann::flann_distance_type ( )

§ get_param() [1/2]

template<typename T >

T cvflann::get_param	(	const IndexParams &	params,
		std::string	name,
		const T &	default_value
	)

§ get_param() [2/2]

template<typename T >

T cvflann::get_param	(	const IndexParams &	params,
		std::string	name
	)

§ hierarchicalClustering()

template<typename Distance >

int cvflann::hierarchicalClustering	(	const Matrix< typename Distance::ElementType > &	points,
		Matrix< typename Distance::ResultType > &	centers,
		const KMeansIndexParams &	params,
		Distance	d = `Distance()`
	)

Performs a hierarchical clustering of the points passed as argument and then takes a cut in the the clustering tree to return a flat clustering.

Parameters

[in]	points	Points to be clustered
	centers	The computed cluster centres. Matrix should be preallocated and centers.rows is the number of clusters requested.
	params	Clustering parameters (The same as for cvflann::KMeansIndex)
	d	Distance to be used for clustering (eg: cvflann::L2)

Returns: number of clusters computed (can be different than clusters.rows and is the highest number of the form (branching-1)*K+1 smaller than clusters.rows).

§ load_from_file()

template<typename T >

void cvflann::load_from_file	(	cvflann::Matrix< T > &	dataset,
		const std::string &	filename,
		const std::string &	name
	)

§ load_header()

IndexHeader cvflann::load_header ( FILE * stream )

inline

Parameters

stream - Stream to load from

Returns: Index header

§ load_saved_index()

template<typename Distance >

NNIndex<Distance>* cvflann::load_saved_index	(	const Matrix< typename Distance::ElementType > &	dataset,
		const std::string &	filename,
		Distance	distance
	)

§ load_value() [1/3]

template<typename T >

void cvflann::load_value	(	FILE *	stream,
		T &	value,
		size_t	count = `1`
	)

§ load_value() [2/3]

template<typename T >

void cvflann::load_value	(	FILE *	stream,
		cvflann::Matrix< T > &	value
	)

§ load_value() [3/3]

template<typename T >

void cvflann::load_value	(	FILE *	stream,
		std::vector< T > &	value
	)

§ log_verbosity()

void cvflann::log_verbosity ( int level )

inline

Sets the log level used for all flann functions

Parameters

level Verbosity level

§ operator<<()

std::ostream& cvflann::operator<<	(	std::ostream &	out,
		const any &	any_val
	)

inline

§ optimizeSimplexDownhill()

template<typename T , typename F >

float cvflann::optimizeSimplexDownhill	(	T *	points,
		int	n,
		F	func,
		float *	vals = `NULL`
	)

Simplex downhill optimization function. Preconditions: points is a 2D mattrix of size (n+1) x n func is the cost function taking n an array of n params and returning float vals is the cost function in the n+1 simplex points, if NULL it will be computed

Postcondition: returns optimum value and points[0..n] are the optimum parameters

§ print_params() [1/2]

void cvflann::print_params	(	const IndexParams &	params,
		std::ostream &	stream
	)

inline

§ print_params() [2/2]

void cvflann::print_params ( const IndexParams & params )

inline

§ rand_double()

double cvflann::rand_double	(	double	high = `1.0`,
		double	low = `0`
	)

inline

Generates a random double value.

Parameters

high	Upper limit
low	Lower limit

Returns: Random double value

§ rand_int()

int cvflann::rand_int	(	int	high = `RAND_MAX`,
		int	low = `0`
	)

inline

Generates a random integer value.

Parameters

high	Upper limit
low	Lower limit

Returns: Random integer value

§ random_sample() [1/2]

template<typename T >

Matrix<T> cvflann::random_sample	(	Matrix< T > &	srcMatrix,
		long	size,
		bool	remove = `false`
	)

§ random_sample() [2/2]

template<typename T >

Matrix<T> cvflann::random_sample	(	const Matrix< T > &	srcMatrix,
		size_t	size
	)

§ save_header()

template<typename Distance >

void cvflann::save_header	(	FILE *	stream,
		const NNIndex< Distance > &	index
	)

Saves index header to stream

Parameters

stream	- Stream to save to
index	- The index to save

§ save_to_file()

template<typename T >

void cvflann::save_to_file	(	const cvflann::Matrix< T > &	dataset,
		const std::string &	filename,
		const std::string &	name
	)

§ save_value() [1/3]

template<typename T >

void cvflann::save_value	(	FILE *	stream,
		const T &	value,
		size_t	count = `1`
	)

§ save_value() [2/3]

template<typename T >

void cvflann::save_value	(	FILE *	stream,
		const cvflann::Matrix< T > &	value
	)

§ save_value() [3/3]

template<typename T >

void cvflann::save_value	(	FILE *	stream,
		const std::vector< T > &	value
	)

§ search_with_ground_truth()

template<typename Distance >

float cvflann::search_with_ground_truth	(	NNIndex< Distance > &	index,
		const Matrix< typename Distance::ElementType > &	inputData,
		const Matrix< typename Distance::ElementType > &	testData,
		const Matrix< int > &	matches,
		int	nn,
		int	checks,
		float &	time,
		typename Distance::ResultType &	dist,
		const Distance &	distance,
		int	skipMatches
	)

§ seed_random()

void cvflann::seed_random ( unsigned int seed )

inline

Seeds the random number generator

Parameters

seed	Random seed

§ set_distance_type()

FLANN_DEPRECATED void cvflann::set_distance_type	(	flann_distance_t	distance_type,
		int	order
	)

§ test_index_checks()

template<typename Distance >

float cvflann::test_index_checks	(	NNIndex< Distance > &	index,
		const Matrix< typename Distance::ElementType > &	inputData,
		const Matrix< typename Distance::ElementType > &	testData,
		const Matrix< int > &	matches,
		int	checks,
		float &	precision,
		const Distance &	distance,
		int	nn = `1`,
		int	skipMatches = `0`
	)

§ test_index_precision()

template<typename Distance >

float cvflann::test_index_precision	(	NNIndex< Distance > &	index,
		const Matrix< typename Distance::ElementType > &	inputData,
		const Matrix< typename Distance::ElementType > &	testData,
		const Matrix< int > &	matches,
		float	precision,
		int &	checks,
		const Distance &	distance,
		int	nn = `1`,
		int	skipMatches = `0`
	)

§ test_index_precisions()

template<typename Distance >

void cvflann::test_index_precisions	(	NNIndex< Distance > &	index,
		const Matrix< typename Distance::ElementType > &	inputData,
		const Matrix< typename Distance::ElementType > &	testData,
		const Matrix< int > &	matches,
		float *	precisions,
		int	precisions_length,
		const Distance &	distance,
		int	nn = `1`,
		int	skipMatches = `0`,
		float	maxTime = `0`
	)

Variable Documentation

§ BLOCKSIZE

const size_t cvflann::BLOCKSIZE =8192

§ WORDSIZE

const size_t cvflann::WORDSIZE =16

Pooled storage allocator

The following routines allow for the efficient allocation of storage in small chunks from a specified pool. Rather than allowing each structure to be freed individually, an entire pool of storage is freed at once. This method has two advantages over just using malloc() and free(). First, it is far more efficient for allocating small objects, as there is no overhead for remembering all the information needed to free each object or consolidating fragmented memory. Second, the decision about how long to keep an object is made at the time of allocation, and there is no need to track down all the objects to free them.

Namespaces

Classes

Typedefs

Enumerations

Functions

Variables

Typedef Documentation

§ IndexParams

Enumeration Type Documentation

§ anonymous enum

§ flann_algorithm_t

§ flann_centers_init_t

§ flann_datatype_t

§ flann_distance_t

§ flann_log_level_t

Function Documentation

§ abs()

§ abs< double >()

§ abs< float >()

§ abs< int >()

§ addValue()

§ allocate()

§ compute_ground_truth()

§ computeDistanceRaport()

§ countCorrectMatches()

§ create_index_by_type()

§ dummyfunc()

§ ensureSimpleDistance()

§ ensureSquareDistance()

§ find_nearest()

§ flann_distance_type()

§ get_param() [1/2]

§ get_param() [2/2]

§ hierarchicalClustering()

§ load_from_file()

§ load_header()

§ load_saved_index()

§ load_value() [1/3]

§ load_value() [2/3]

§ load_value() [3/3]

§ log_verbosity()

§ operator<<()

§ optimizeSimplexDownhill()

§ print_params() [1/2]

§ print_params() [2/2]

§ rand_double()

§ rand_int()

§ random_sample() [1/2]

§ random_sample() [2/2]

§ save_header()

§ save_to_file()

§ save_value() [1/3]

§ save_value() [2/3]

§ save_value() [3/3]

§ search_with_ground_truth()

§ seed_random()

§ set_distance_type()

§ test_index_checks()

§ test_index_precision()

§ test_index_precisions()

Variable Documentation

§ BLOCKSIZE

§ WORDSIZE