cvflann Namespace Reference

Namespaces
namespace	anyimpl
namespace	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
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< cv::String, any >	IndexParams

Enumerations
enum	{   FLANN_CHECKS_UNLIMITED = -1,   FLANN_CHECKS_AUTOTUNED = -2 }
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,   FLANN_CENTERS_GROUPWISE = 3,   CENTERS_RANDOM = 0,   CENTERS_GONZALES = 1,   CENTERS_KMEANSPP = 2 }
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_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_log_level_t {   FLANN_LOG_NONE = 0,   FLANN_LOG_FATAL = 1,   FLANN_LOG_ERROR = 2,   FLANN_LOG_WARN = 3,   FLANN_LOG_INFO = 4 }

Functions
template<typename T >
T	abs (T x)
template<>
double	abs< double > (double x)
template<>
float	abs< float > (float x)
template<typename T >
void	addValue (int pos, float val, float *vals, T *point, T *points, int n)
template<typename T >
T *	allocate (size_t count=1)
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 >
Distance::ResultType	computeDistanceRaport (const Matrix< typename Distance::ElementType > &inputData, typename Distance::ElementType *target, int *neighbors, int *groundTruth, int veclen, int n, const Distance &distance)
int	countCorrectMatches (int *neighbors, int *groundTruth, int n)
template<typename Distance >
NNIndex< Distance > *	create_index_by_type (const Matrix< typename Distance::ElementType > &dataset, const IndexParams &params, const Distance &distance)
void	dummyfunc ()
template<typename Distance >
Distance::ResultType	ensureSimpleDistance (typename Distance::ResultType dist)
template<typename Distance >
Distance::ResultType	ensureSquareDistance (typename Distance::ResultType dist)
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())
flann_distance_t	flann_distance_type ()
template<typename T >
T	get_param (const IndexParams &params, cv::String name, const T &default_value)
template<typename T >
T	get_param (const IndexParams &params, cv::String name)
template<typename Distance >
int	hierarchicalClustering (const Matrix< typename Distance::ElementType > &points, Matrix< typename Distance::ResultType > &centers, const KMeansIndexParams &params, Distance d=Distance())
IndexHeader	load_header (FILE *stream)
template<typename Distance >
NNIndex< Distance > *	load_saved_index (const Matrix< typename Distance::ElementType > &dataset, const cv::String &filename, Distance distance)
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)
void	log_verbosity (int level)
std::ostream &	operator<< (std::ostream &out, const any &any_val)
template<typename T , typename F >
float	optimizeSimplexDownhill (T *points, int n, F func, float *vals=NULL)
void	print_params (const IndexParams &params, std::ostream &stream)
void	print_params (const IndexParams &params)
int	rand ()
double	rand_double (double high=1.0, double low=0)
int	rand_int (int high=RAND_MAX, int low=0)
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)
template<typename Distance >
void	save_header (FILE *stream, const NNIndex< Distance > &index)
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 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)
void	seed_random (unsigned int seed)
void	set_distance_type (flann_distance_t distance_type, int order)
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)

Variables
const size_t	BLOCKSIZE =8192
const size_t	WORDSIZE =16

Typedef Documentation

typedef std::map<cv::String, any> cvflann::IndexParams

Enumeration Type Documentation

anonymous enum

Enumerator
FLANN_CHECKS_UNLIMITED
FLANN_CHECKS_AUTOTUNED

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

enum cvflann::flann_centers_init_t

Enumerator
FLANN_CENTERS_RANDOM
FLANN_CENTERS_GONZALES
FLANN_CENTERS_KMEANSPP
FLANN_CENTERS_GROUPWISE
CENTERS_RANDOM
CENTERS_GONZALES
CENTERS_KMEANSPP

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

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

enum cvflann::flann_log_level_t

Enumerator
FLANN_LOG_NONE
FLANN_LOG_FATAL
FLANN_LOG_ERROR
FLANN_LOG_WARN
FLANN_LOG_INFO

Function Documentation

template<typename T >

T cvflann::abs ( T  x )

inline

template<>

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

inline

template<>

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

inline

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.

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

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()
	)

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
	)

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

inline

template<typename Distance >

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

void cvflann::dummyfunc ( )

inline

template<typename Distance >

Distance::ResultType cvflann::ensureSimpleDistance

(

typename Distance::ResultType

dist

)

template<typename Distance >

Distance::ResultType cvflann::ensureSquareDistance

(

typename Distance::ResultType

dist

)

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_t cvflann::flann_distance_type

(

)

template<typename T >

T cvflann::get_param	(	const IndexParams &	params,
		cv::String	name,
		const T &	default_value
	)

template<typename T >

T cvflann::get_param	(	const IndexParams &	params,
		cv::String	name
	)

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).

IndexHeader cvflann::load_header ( FILE *  stream )

inline

Parameters

stream

- Stream to load from

Returns

Index header

template<typename Distance >

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

template<typename T >

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

template<typename T >

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

template<typename T >

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

void cvflann::log_verbosity ( int  level )

inline

Sets the log level used for all flann functions

Parameters

level

Verbosity level

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

inline

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

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

inline

void cvflann::print_params ( const IndexParams &  params )

inline

int cvflann::rand ( )

inline

Examples:

samples/cpp/connected_components.cpp, samples/cpp/train_HOG.cpp, and samples/dnn/segmentation.cpp.

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

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

template<typename T >

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

template<typename T >

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

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

template<typename T >

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

template<typename T >

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

template<typename T >

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

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
	)

void cvflann::seed_random ( unsigned int  seed )

inline

Seeds the random number generator

Parameters

seed	Random seed

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

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
	)

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
	)

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

const size_t cvflann::BLOCKSIZE =8192

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.