cvflann Namespace Reference

Namespaces
namespace	anyimpl
namespace	mpi
namespace	lsh
Classes
struct	index_creator
struct	index_creator< False, VectorSpace, Distance >
struct	index_creator< False, False, Distance >
class	PooledAllocator
struct	any
struct	AutotunedIndexParams
class	AutotunedIndex
struct	CompositeIndexParams
class	CompositeIndex
struct	Accumulator
struct	Accumulator< unsigned char >
struct	Accumulator< unsigned short >
struct	Accumulator< unsigned int >
struct	Accumulator< char >
struct	Accumulator< short >
struct	Accumulator< int >
class	True
class	False
struct	L2_Simple
struct	L2
struct	L1
struct	MinkowskiDistance
struct	MaxDistance
struct	HammingLUT
struct	HammingLUT2
struct	Hamming
struct	Hamming2
struct	HistIntersectionDistance
struct	HellingerDistance
struct	ChiSquareDistance
struct	KL_Divergence
struct	ZeroIterator
class	DynamicBitset
struct	SavedIndexParams
class	Index
class	FLANNException
class	Heap
struct	HierarchicalClusteringIndexParams
class	HierarchicalClusteringIndex
struct	KDTreeIndexParams
class	KDTreeIndex
struct	KDTreeSingleIndexParams
class	KDTreeSingleIndex
struct	KMeansIndexParams
class	KMeansIndex
struct	LinearIndexParams
class	LinearIndex
class	Logger
struct	LshIndexParams
class	LshIndex
class	Matrix
class	UntypedMatrix
class	NNIndex
class	CreatorNotFound
class	ObjectFactory
struct	SearchParams
class	UniqueRandom
struct	BranchStruct
class	ResultSet
class	KNNSimpleResultSet
class	KNNResultSet
class	RadiusResultSet
class	UniqueResultSet
class	KNNUniqueResultSet
class	RadiusUniqueResultSet
class	KNNRadiusUniqueResultSet
struct	Datatype
struct	Datatype< char >
struct	Datatype< short >
struct	Datatype< int >
struct	Datatype< unsigned char >
struct	Datatype< unsigned short >
struct	Datatype< unsigned int >
struct	Datatype< float >
struct	Datatype< double >
struct	IndexHeader
class	StartStopTimer
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
template<typename Distance >
NNIndex< Distance > *	create_index_by_type (const Matrix< typename Distance::ElementType > &dataset, const IndexParams &params, const Distance &distance)
template<typename T >
T *	allocate (size_t count=1)
std::ostream &	operator<< (std::ostream &out, const any &any_val)
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)
	__declspec (dllexport) void dummyfunc()
CV_EXPORTS flann_distance_t	flann_distance_type ()
FLANN_DEPRECATED CV_EXPORTS 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 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 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)
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)
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)
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 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)
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 >
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

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

---

Enumeration Type Documentation

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
CENTERS_RANDOM
CENTERS_GONZALES
CENTERS_KMEANSPP

enum cvflann::flann_log_level_t

Enumerator:

FLANN_LOG_NONE
FLANN_LOG_FATAL
FLANN_LOG_ERROR
FLANN_LOG_WARN
FLANN_LOG_INFO

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_datatype_t

Enumerator:

FLANN_INT8
FLANN_INT16
FLANN_INT32
FLANN_INT64
FLANN_UINT8
FLANN_UINT16
FLANN_UINT32
FLANN_UINT64
FLANN_FLOAT32
FLANN_FLOAT64

anonymous enum

Enumerator:

FLANN_CHECKS_UNLIMITED
FLANN_CHECKS_AUTOTUNED

---

Function Documentation

template<typename Distance >

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

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

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

template<typename T >

T cvflann::abs

(

T

x

)

template<>

int cvflann::abs< int >

(

int

x

)

template<>

float cvflann::abs< float >

(

float

x

)

template<>

double cvflann::abs< double >

(

double

x

)

cvflann::__declspec

(

dllexport

)

CV_EXPORTS flann_distance_t cvflann::flann_distance_type

(

)

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

void cvflann::log_verbosity

(

int

level

)

Sets the log level used for all flann functions

Parameters:

level

Verbosity level

template<typename Distance >

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

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).

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()
	)

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 T >

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

template<typename T >

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

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

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
	)

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
	)

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
	)

template<typename T >

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

template<typename T >

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

void cvflann::print_params

(

const IndexParams &

params

)

void cvflann::seed_random

(

unsigned int

seed

)

Seeds the random number generator

Parameters:

seed	Random seed

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

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
	)

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

IndexHeader cvflann::load_header

(

FILE *

stream

)

Parameters:

stream

- Stream to load from

Returns:

Index header

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 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
	)

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 , 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

---

Variable Documentation

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.

const size_t cvflann::BLOCKSIZE = 8192