Hierarchical Data Format version 5 interface. More...

#include "hdf5.hpp"

Public Types
enum	{ H5_UNLIMITED = -1, H5_NONE = -1, H5_GETDIMS = 100, H5_GETMAXDIMS = 101 }

Public Member Functions
virtual	~HDF5 ()

virtual void	close ()=0
	Close and release hdf5 object. More...

virtual void	dscreate (const int rows, const int cols, const int type, String dslabel, const int compresslevel=HDF5::H5_NONE, const vector< int > &dims_chunks=vector< int >()) const =0

virtual void	dscreate (const int rows, const int cols, const int type, String dslabel, const int compresslevel=HDF5::H5_NONE, const int *dims_chunks=NULL) const =0
	Create and allocate storage for two dimensional single or multi channel dataset. More...

virtual void	dscreate (const vector< int > &sizes, const int type, String dslabel, const int compresslevel=HDF5::H5_NONE, const vector< int > &dims_chunks=vector< int >()) const =0

virtual void	dscreate (const int n_dims, const int sizes, const int type, String dslabel, const int compresslevel=HDF5::H5_NONE, const int dims_chunks=NULL) const =0
	Create and allocate storage for n-dimensional dataset, single or mutichannel type. More...

virtual vector< int >	dsgetsize (String dslabel, int dims_flag=HDF5::H5_GETDIMS) const =0
	Fetch dataset sizes. More...

virtual int	dsgettype (String dslabel) const =0
	Fetch dataset type. More...

virtual void	dsinsert (InputArray Array, String dslabel, const vector< int > &dims_offset=vector< int >(), const vector< int > &dims_counts=vector< int >()) const =0

virtual void	dsinsert (InputArray Array, String dslabel, const int dims_offset=NULL, const int dims_counts=NULL) const =0
	Insert or overwrite a Mat object into specified dataset and autoexpand dataset size if unlimited property allows. More...

virtual void	dsread (OutputArray Array, String dslabel, const vector< int > &dims_offset=vector< int >(), const vector< int > &dims_counts=vector< int >()) const =0

virtual void	dsread (OutputArray Array, String dslabel, const int dims_offset=NULL, const int dims_counts=NULL) const =0
	Read specific dataset from hdf5 file into Mat object. More...

virtual void	dswrite (InputArray Array, String dslabel, const vector< int > &dims_offset=vector< int >(), const vector< int > &dims_counts=vector< int >()) const =0

virtual void	dswrite (InputArray Array, String dslabel, const int dims_offset=NULL, const int dims_counts=NULL) const =0
	Write or overwrite a Mat object into specified dataset of hdf5 file. More...

virtual void	grcreate (String grlabel)=0
	Create a group. More...

virtual bool	hlexists (String label) const =0
	Check if label exists or not. More...

virtual void	kpcreate (const int size, String kplabel, const int compresslevel=H5_NONE, const int chunks=H5_NONE) const =0
	Create and allocate special storage for cv::KeyPoint dataset. More...

virtual int	kpgetsize (String kplabel, int dims_flag=HDF5::H5_GETDIMS) const =0
	Fetch keypoint dataset size. More...

virtual void	kpinsert (const vector< KeyPoint > keypoints, String kplabel, const int offset=H5_NONE, const int counts=H5_NONE) const =0
	Insert or overwrite list of KeyPoint into specified dataset and autoexpand dataset size if unlimited property allows. More...

virtual void	kpread (vector< KeyPoint > &keypoints, String kplabel, const int offset=H5_NONE, const int counts=H5_NONE) const =0
	Read specific keypoint dataset from hdf5 file into vector<KeyPoint> object. More...

virtual void	kpwrite (const vector< KeyPoint > keypoints, String kplabel, const int offset=H5_NONE, const int counts=H5_NONE) const =0
	Write or overwrite list of KeyPoint into specified dataset of hdf5 file. More...

Detailed Description

Hierarchical Data Format version 5 interface.

Notice that module is compiled only when hdf5 is correctly installed.

Constructor & Destructor Documentation

virtual cv::hdf::HDF5::~HDF5 ( )

inlinevirtual

Member Function Documentation

virtual void cv::hdf::HDF5::close ( )

pure virtual

Close and release hdf5 object.

virtual void cv::hdf::HDF5::dscreate	(	const int	rows,
		const int	cols,
		const int	type,
		String	dslabel,
		const int	compresslevel = `HDF5::H5_NONE`,
		const vector< int > &	dims_chunks = `vector< int >()`
	)		const

pure virtual

virtual void cv::hdf::HDF5::dscreate	(	const int	rows,
		const int	cols,
		const int	type,
		String	dslabel,
		const int	compresslevel = `HDF5::H5_NONE`,
		const int *	dims_chunks = `NULL`
	)		const

pure virtual

Create and allocate storage for two dimensional single or multi channel dataset.

Parameters

rows	declare amount of rows
cols	declare amount of cols
type	type to be used
dslabel	specify the hdf5 dataset label, any existing dataset with the same label will be overwritten.
compresslevel	specify the compression level 0-9 to be used, by default H5_NONE means none at all.
dims_chunks	each array member specify chunking sizes to be used for block i/o, by default NULL means none at all.

Note: If the dataset already exists an exception will be thrown.

Existence of the dataset can be checked using hlexists(), see in this example:
// open / autocreate hdf5 file

cv::Ptr<cv::hdf::HDF5> h5io = cv::hdf::open( "mytest.h5" );

// create space for 100x50 CV_64FC2 matrix

if ( ! h5io->hlexists( "hilbert" ) )

h5io->dscreate( 100, 50, CV_64FC2, "hilbert" );

else

printf("DS already created, skipping\n" );

// release

h5io->close();

Note: Activating compression requires internal chunking. Chunking can significantly improve access speed booth at read or write time especially for windowed access logic that shifts offset inside dataset. If no custom chunking is specified default one will be invoked by the size of whole dataset as single big chunk of data.

See example of level 9 compression using internal default chunking:
// open / autocreate hdf5 file

cv::Ptr<cv::hdf::HDF5> h5io = cv::hdf::open( "mytest.h5" );

// create level 9 compressed space for CV_64FC2 matrix

if ( ! h5io->hlexists( "hilbert", 9 ) )

h5io->dscreate( 100, 50, CV_64FC2, "hilbert", 9 );

else

printf("DS already created, skipping\n" );

// release

h5io->close();

Note: A value of H5_UNLIMITED for rows or cols or booth means unlimited data on the specified dimension, thus is possible to expand anytime such dataset on row, col or booth directions. Presence of H5_UNLIMITED on any dimension require to define custom chunking. No default chunking will be defined in unlimited scenario since default size on that dimension will be zero, and will grow once dataset is written. Writing into dataset that have H5_UNLIMITED on some of its dimension requires dsinsert() that allow growth on unlimited dimension instead of dswrite() that allows to write only in predefined data space.

Example below shows no compression but unlimited dimension on cols using 100x100 internal chunking:
// open / autocreate hdf5 file

cv::Ptr<cv::hdf::HDF5> h5io = cv::hdf::open( "mytest.h5" );

// create level 9 compressed space for CV_64FC2 matrix

int chunks[2] = { 100, 100 };

h5io->dscreate( 100, cv::hdf::HDF5::H5_UNLIMITED, CV_64FC2, "hilbert", cv::hdf::HDF5::H5_NONE, chunks );

// release

h5io->close();

Note: It is not thread safe, it must be called only once at dataset creation otherwise exception will occur. Multiple datasets inside single hdf5 file is allowed.

virtual void cv::hdf::HDF5::dscreate	(	const vector< int > &	sizes,
		const int	type,
		String	dslabel,
		const int	compresslevel = `HDF5::H5_NONE`,
		const vector< int > &	dims_chunks = `vector< int >()`
	)		const

pure virtual

virtual void cv::hdf::HDF5::dscreate	(	const int	n_dims,
		const int *	sizes,
		const int	type,
		String	dslabel,
		const int	compresslevel = `HDF5::H5_NONE`,
		const int *	dims_chunks = `NULL`
	)		const

pure virtual

Create and allocate storage for n-dimensional dataset, single or mutichannel type.

Parameters

n_dims	declare number of dimensions
sizes	array containing sizes for each dimensions
type	type to be used
dslabel	specify the hdf5 dataset label, any existing dataset with the same label will be overwritten.
compresslevel	specify the compression level 0-9 to be used, by default H5_NONE means none at all.
dims_chunks	each array member specify chunking sizes to be used for block i/o, by default NULL means none at all.

Note: If the dataset already exists an exception will be thrown. Existence of the dataset can be checked using hlexists().

See example below that creates a 6 dimensional storage space:
// open / autocreate hdf5 file

cv::Ptr<cv::hdf::HDF5> h5io = cv::hdf::open( "mytest.h5" );

// create space for 6 dimensional CV_64FC2 matrix

if ( ! h5io->hlexists( "nddata" ) )

int n_dims = 5;

int dsdims[n_dims] = { 100, 100, 20, 10, 5, 5 };

h5io->dscreate( n_dims, sizes, CV_64FC2, "nddata" );

else

printf("DS already created, skipping\n" );

// release

h5io->close();

Note: Activating compression requires internal chunking. Chunking can significantly improve access speed booth at read or write time especially for windowed access logic that shifts offset inside dataset. If no custom chunking is specified default one will be invoked by the size of whole dataset as single big chunk of data.

See example of level 0 compression (shallow) using chunking against first dimension, thus storage will consists by 100 chunks of data:
// open / autocreate hdf5 file

cv::Ptr<cv::hdf::HDF5> h5io = cv::hdf::open( "mytest.h5" );

// create space for 6 dimensional CV_64FC2 matrix

if ( ! h5io->hlexists( "nddata" ) )

int n_dims = 5;

int dsdims[n_dims] = { 100, 100, 20, 10, 5, 5 };

int chunks[n_dims] = { 1, 100, 20, 10, 5, 5 };

h5io->dscreate( n_dims, dsdims, CV_64FC2, "nddata", 0, chunks );

else

printf("DS already created, skipping\n" );

// release

h5io->close();

Note: A value of H5_UNLIMITED inside the sizes array means unlimited data on that dimension, thus is possible to expand anytime such dataset on those unlimited directions. Presence of H5_UNLIMITED on any dimension require** to define custom chunking. No default chunking will be defined in unlimited scenario since default size on that dimension will be zero, and will grow once dataset is written. Writing into dataset that have H5_UNLIMITED on some of its dimension requires dsinsert() instead of dswrite() that allow growth on unlimited dimension instead of dswrite() that allows to write only in predefined data space.

Example below shows a 3 dimensional dataset using no compression with all unlimited sizes and one unit chunking:
// open / autocreate hdf5 file

cv::Ptr<cv::hdf::HDF5> h5io = cv::hdf::open( "mytest.h5" );

int n_dims = 3;

int chunks[n_dims] = { 1, 1, 1 };

int dsdims[n_dims] = { cv::hdf::HDF5::H5_UNLIMITED, cv::hdf::HDF5::H5_UNLIMITED, cv::hdf::HDF5::H5_UNLIMITED };

h5io->dscreate( n_dims, dsdims, CV_64FC2, "nddata", cv::hdf::HDF5::H5_NONE, chunks );

// release

h5io->close();

virtual vector<int> cv::hdf::HDF5::dsgetsize	(	String	dslabel,
		int	dims_flag = `HDF5::H5_GETDIMS`
	)		const

pure virtual

Fetch dataset sizes.

Parameters

dslabel	specify the hdf5 dataset label to be measured.
dims_flag	will fetch dataset dimensions on H5_GETDIMS, and dataset maximum dimensions on H5_GETMAXDIMS.

Returns vector object containing sizes of dataset on each dimensions.

Note: Resulting vector size will match the amount of dataset dimensions. By default H5_GETDIMS will return actual dataset dimensions. Using H5_GETMAXDIM flag will get maximum allowed dimension which normally match actual dataset dimension but can hold H5_UNLIMITED value if dataset was prepared in unlimited mode on some of its dimension. It can be useful to check existing dataset dimensions before overwrite it as whole or subset. Trying to write with oversized source data into dataset target will thrown exception.

virtual int cv::hdf::HDF5::dsgettype ( String dslabel ) const

pure virtual

Fetch dataset type.

Parameters

dslabel specify the hdf5 dataset label to be checked.

Returns the stored matrix type. This is an identifier compatible with the CvMat type system, like e.g. CV_16SC5 (16-bit signed 5-channel array), and so on.

Note: Result can be parsed with CV_MAT_CN() to obtain amount of channels and CV_MAT_DEPTH() to obtain native cvdata type. It is thread safe.

virtual void cv::hdf::HDF5::dsinsert	(	InputArray	Array,
		String	dslabel,
		const vector< int > &	dims_offset = `vector< int >()`,
		const vector< int > &	dims_counts = `vector< int >()`
	)		const

pure virtual

virtual void cv::hdf::HDF5::dsinsert	(	InputArray	Array,
		String	dslabel,
		const int *	dims_offset = `NULL`,
		const int *	dims_counts = `NULL`
	)		const

pure virtual

Insert or overwrite a Mat object into specified dataset and autoexpand dataset size if unlimited property allows.

Parameters

Array	specify Mat data array to be written.
dslabel	specify the target hdf5 dataset label.
dims_offset	each array member specify the offset location over dataset's each dimensions from where InputArray will be (over)written into dataset.
dims_counts	each array member specify the amount of data over dataset's each dimensions from InputArray that will be written into dataset.

Writes Mat object into targeted dataset and autoexpand dataset dimension if allowed.

Note: Unlike dswrite(), datasets are not created automatically. Only Mat is supported and it must to be continuous. If dsinsert() happen over outer regions of dataset dimensions and on that dimension of dataset is in unlimited mode then dataset is expanded, otherwise exception is thrown. To create datasets with unlimited property on specific or more dimensions see dscreate() and the optional H5_UNLIMITED flag at creation time. It is not thread safe over same dataset but multiple datasets can be merged inside single hdf5 file.

Example below creates unlimited rows x 100 cols and expand rows 5 times with dsinsert() using single 100x100 CV_64FC2 over the dataset. Final size will have 5x100 rows and 100 cols, reflecting H matrix five times over row's span. Chunks size is 100x100 just optimized against the H matrix size having compression disabled. If routine is called multiple times dataset will be just overwritten:
// dual channel hilbert matrix

cv::Mat H(50, 100, CV_64FC2);

for(int i = 0; i < H.rows; i++)

for(int j = 0; j < H.cols; j++)

{

H.at<cv::Vec2d>(i,j)[0] = 1./(i+j+1);

H.at<cv::Vec2d>(i,j)[1] = -1./(i+j+1);

count++;

}

// open / autocreate hdf5 file

cv::Ptr<cv::hdf::HDF5> h5io = cv::hdf::open( "mytest.h5" );

// optimise dataset by chunks

int chunks[2] = { 100, 100 };

// create Unlimited x 100 CV_64FC2 space

h5io->dscreate( cv::hdf::HDF5::H5_UNLIMITED, 100, CV_64FC2, "hilbert", cv::hdf::HDF5::H5_NONE, chunks );

// write into first half

int offset[2] = { 0, 0 };

for ( int t = 0; t < 5; t++ )

{

offset[0] += 100 * t;

h5io->dsinsert( H, "hilbert", offset );

}

// release

h5io->close();

virtual void cv::hdf::HDF5::dsread	(	OutputArray	Array,
		String	dslabel,
		const vector< int > &	dims_offset = `vector< int >()`,
		const vector< int > &	dims_counts = `vector< int >()`
	)		const

pure virtual

virtual void cv::hdf::HDF5::dsread	(	OutputArray	Array,
		String	dslabel,
		const int *	dims_offset = `NULL`,
		const int *	dims_counts = `NULL`
	)		const

pure virtual

Read specific dataset from hdf5 file into Mat object.

Parameters

Array	Mat container where data reads will be returned.
dslabel	specify the source hdf5 dataset label.
dims_offset	each array member specify the offset location over each dimensions from where dataset starts to read into OutputArray.
dims_counts	each array member specify the amount over dataset's each dimensions of dataset to read into OutputArray.

Reads out Mat object reflecting the stored dataset.

Note: If hdf5 file does not exist an exception will be thrown. Use hlexists() to check dataset presence. It is thread safe.

Example below reads a dataset:
// open hdf5 file

cv::Ptr<cv::hdf::HDF5> h5io = cv::hdf::open( "mytest.h5" );

// blank Mat container

cv::Mat H;

// read hibert dataset

h5io->read( H, "hilbert" );

// release

h5io->close();
Example below perform read of 3x5 submatrix from second row and third element.
// open hdf5 file

cv::Ptr<cv::hdf::HDF5> h5io = cv::hdf::open( "mytest.h5" );

// blank Mat container

cv::Mat H;

int offset[2] = { 1, 2 };

int counts[2] = { 3, 5 };

// read hibert dataset

h5io->read( H, "hilbert", offset, counts );

// release

h5io->close();

virtual void cv::hdf::HDF5::dswrite	(	InputArray	Array,
		String	dslabel,
		const vector< int > &	dims_offset = `vector< int >()`,
		const vector< int > &	dims_counts = `vector< int >()`
	)		const

pure virtual

virtual void cv::hdf::HDF5::dswrite	(	InputArray	Array,
		String	dslabel,
		const int *	dims_offset = `NULL`,
		const int *	dims_counts = `NULL`
	)		const

pure virtual

Write or overwrite a Mat object into specified dataset of hdf5 file.

Parameters

Array	specify Mat data array to be written.
dslabel	specify the target hdf5 dataset label.
dims_offset	each array member specify the offset location over dataset's each dimensions from where InputArray will be (over)written into dataset.
dims_counts	each array member specify the amount of data over dataset's each dimensions from InputArray that will be written into dataset.

Writes Mat object into targeted dataset.

Note: If dataset is not created and does not exist it will be created automatically. Only Mat is supported and it must to be continuous. It is thread safe but it is recommended that writes to happen over separate non overlapping regions. Multiple datasets can be written inside single hdf5 file.

Example below writes a 100x100 CV_64FC2 matrix into a dataset. No dataset precreation required. If routine is called multiple times dataset will be just overwritten:
// dual channel hilbert matrix

cv::Mat H(100, 100, CV_64FC2);

for(int i = 0; i < H.rows; i++)

for(int j = 0; j < H.cols; j++)

{

H.at<cv::Vec2d>(i,j)[0] = 1./(i+j+1);

H.at<cv::Vec2d>(i,j)[1] = -1./(i+j+1);

count++;

}

// open / autocreate hdf5 file

cv::Ptr<cv::hdf::HDF5> h5io = cv::hdf::open( "mytest.h5" );

// write / overwrite dataset

h5io->dswrite( H, "hilbert" );

// release

h5io->close();
Example below writes a smaller 50x100 matrix into 100x100 compressed space optimised by two 50x100 chunks. Matrix is written twice into first half (0->50) and second half (50->100) of data space using offset.
// dual channel hilbert matrix

cv::Mat H(50, 100, CV_64FC2);

for(int i = 0; i < H.rows; i++)

for(int j = 0; j < H.cols; j++)

{

H.at<cv::Vec2d>(i,j)[0] = 1./(i+j+1);

H.at<cv::Vec2d>(i,j)[1] = -1./(i+j+1);

count++;

}

// open / autocreate hdf5 file

cv::Ptr<cv::hdf::HDF5> h5io = cv::hdf::open( "mytest.h5" );

// optimise dataset by two chunks

int chunks[2] = { 50, 100 };

// create 100x100 CV_64FC2 compressed space

h5io->dscreate( 100, 100, CV_64FC2, "hilbert", 9, chunks );

// write into first half

int offset1[2] = { 0, 0 };

h5io->dswrite( H, "hilbert", offset1 );

// write into second half

int offset2[2] = { 50, 0 };

h5io->dswrite( H, "hilbert", offset2 );

// release

h5io->close();

virtual void cv::hdf::HDF5::grcreate ( String grlabel )

pure virtual

Create a group.

Parameters

grlabel specify the hdf5 group label.

Create a hdf5 group.

Note: Groups are useful for better organise multiple datasets. It is possible to create subgroups within any group. Existence of a particular group can be checked using hlexists(). In case of subgroups label would be e.g: 'Group1/SubGroup1' where SubGroup1 is within the root group Group1.

In this example Group1 will have one subgrup labeled SubGroup1:
// open / autocreate hdf5 file

cv::Ptr<cv::hdf::HDF5> h5io = cv::hdf::open( "mytest.h5" );

// create Group1 if does not exists

if ( ! h5io->hlexists( "Group1" ) )

h5io->grcreate( "Group1" );

else

printf("Group1 already created, skipping\n" );

// create SubGroup1 if does not exists

if ( ! h5io->hlexists( "Group1/SubGroup1" ) )

h5io->grcreate( "Group1/SubGroup1" );

else

printf("SubGroup1 already created, skipping\n" );

// release

h5io->close();

Note: When a dataset is created with dscreate() or kpcreate() it can be created right within a group by specifying full path within the label, in our example would be: 'Group1/SubGroup1/MyDataSet'. It is not thread safe.

virtual bool cv::hdf::HDF5::hlexists ( String label ) const

pure virtual

Check if label exists or not.

Parameters

label specify the hdf5 dataset label.

Returns true if dataset exists, and false if does not.

Note: Checks if dataset, group or other object type (hdf5 link) exists under the label name. It is thread safe.

virtual void cv::hdf::HDF5::kpcreate	(	const int	size,
		String	kplabel,
		const int	compresslevel = `H5_NONE`,
		const int	chunks = `H5_NONE`
	)		const

pure virtual

Create and allocate special storage for cv::KeyPoint dataset.

Parameters

size	declare fixed number of KeyPoints
kplabel	specify the hdf5 dataset label, any existing dataset with the same label will be overwritten.
compresslevel	specify the compression level 0-9 to be used, by default H5_NONE means none at all.
chunks	each array member specify chunking sizes to be used for block i/o, by default H5_NONE means none at all.

Note: If the dataset already exists an exception will be thrown. Existence of the dataset can be checked using hlexists().

See example below that creates space for 100 keypoints in the dataset:
// open hdf5 file

cv::Ptr<cv::hdf::HDF5> h5io = cv::hdf::open( "mytest.h5" );

if ( ! h5io->hlexists( "keypoints" ) )

h5io->kpcreate( 100, "keypoints" );

else

printf("DS already created, skipping\n" );

Note: A value of H5_UNLIMITED for size means unlimited keypoints, thus is possible to expand anytime such dataset by adding or inserting. Presence of H5_UNLIMITED require to define custom chunking. No default chunking will be defined in unlimited scenario since default size on that dimension will be zero, and will grow once dataset is written. Writing into dataset that have H5_UNLIMITED on some of its dimension requires kpinsert() that allow growth on unlimited dimension instead of kpwrite() that allows to write only in predefined data space.

See example below that creates unlimited space for keypoints chunking size of 100 but no compression:
// open hdf5 file

cv::Ptr<cv::hdf::HDF5> h5io = cv::hdf::open( "mytest.h5" );

if ( ! h5io->hlexists( "keypoints" ) )

h5io->kpcreate( cv::hdf::HDF5::H5_UNLIMITED, "keypoints", cv::hdf::HDF5::H5_NONE, 100 );

else

printf("DS already created, skipping\n" );

virtual int cv::hdf::HDF5::kpgetsize	(	String	kplabel,
		int	dims_flag = `HDF5::H5_GETDIMS`
	)		const

pure virtual

Fetch keypoint dataset size.

Parameters

kplabel	specify the hdf5 dataset label to be measured.
dims_flag	will fetch dataset dimensions on H5_GETDIMS, and dataset maximum dimensions on H5_GETMAXDIMS.

Returns size of keypoints dataset.

Note: Resulting size will match the amount of keypoints. By default H5_GETDIMS will return actual dataset dimension. Using H5_GETMAXDIM flag will get maximum allowed dimension which normally match actual dataset dimension but can hold H5_UNLIMITED value if dataset was prepared in unlimited mode. It can be useful to check existing dataset dimension before overwrite it as whole or subset. Trying to write with oversized source data into dataset target will thrown exception.

virtual void cv::hdf::HDF5::kpinsert	(	const vector< KeyPoint >	keypoints,
		String	kplabel,
		const int	offset = `H5_NONE`,
		const int	counts = `H5_NONE`
	)		const

pure virtual

Insert or overwrite list of KeyPoint into specified dataset and autoexpand dataset size if unlimited property allows.

Parameters

keypoints	specify keypoints data list to be written.
kplabel	specify the target hdf5 dataset label.
offset	specify the offset location on dataset from where keypoints will be (over)written into dataset.
counts	specify the amount of keypoints that will be written into dataset.

Writes vector<KeyPoint> object into targeted dataset and autoexpand dataset dimension if allowed.

Note: Unlike kpwrite(), datasets are not created automatically. If dsinsert() happen over outer region of dataset and dataset has been created in unlimited mode then dataset is expanded, otherwise exception is thrown. To create datasets with unlimited property see kpcreate() and the optional H5_UNLIMITED flag at creation time. It is not thread safe over same dataset but multiple datasets can be merged inside single hdf5 file.

Example below creates unlimited space for keypoints storage, and inserts a list of 10 keypoints ten times into that space. Final dataset will have 100 keypoints. Chunks size is 10 just optimized against list of keypoints. If routine is called multiple times dataset will be just overwritten:
// generate 10 dummy keypoints

std::vector<cv::KeyPoint> keypoints;

for(int i = 0; i < 10; i++)

keypoints.push_back( cv::KeyPoint(i, -i, 1, -1, 0, 0, -1) );

// open / autocreate hdf5 file

cv::Ptr<cv::hdf::HDF5> h5io = cv::hdf::open( "mytest.h5" );

// create unlimited size space with chunk size of 10

h5io->kpcreate( cv::hdf::HDF5::H5_UNLIMITED, "keypoints", -1, 10 );

// insert 10 times same 10 keypoints

for(int i = 0; i < 10; i++)

h5io->kpinsert( keypoints, "keypoints", i * 10 );

// release

h5io->close();

virtual void cv::hdf::HDF5::kpread	(	vector< KeyPoint > &	keypoints,
		String	kplabel,
		const int	offset = `H5_NONE`,
		const int	counts = `H5_NONE`
	)		const

pure virtual

Read specific keypoint dataset from hdf5 file into vector<KeyPoint> object.

Parameters

keypoints	vector<KeyPoint> container where data reads will be returned.
kplabel	specify the source hdf5 dataset label.
offset	specify the offset location over dataset from where read starts.
counts	specify the amount of keypoints from dataset to read.

Reads out vector<KeyPoint> object reflecting the stored dataset.

Note: If hdf5 file does not exist an exception will be thrown. Use hlexists() to check dataset presence. It is thread safe.

Example below reads a dataset containing keypoints starting with second entry:
// open hdf5 file

cv::Ptr<cv::hdf::HDF5> h5io = cv::hdf::open( "mytest.h5" );

// blank KeyPoint container

std::vector<cv::KeyPoint> keypoints;

// read keypoints starting second one

h5io->kpread( keypoints, "keypoints", 1 );

// release

h5io->close();
Example below perform read of 3 keypoints from second entry.
// open hdf5 file

cv::Ptr<cv::hdf::HDF5> h5io = cv::hdf::open( "mytest.h5" );

// blank KeyPoint container

std::vector<cv::KeyPoint> keypoints;

// read three keypoints starting second one

h5io->kpread( keypoints, "keypoints", 1, 3 );

// release

h5io->close();

virtual void cv::hdf::HDF5::kpwrite	(	const vector< KeyPoint >	keypoints,
		String	kplabel,
		const int	offset = `H5_NONE`,
		const int	counts = `H5_NONE`
	)		const

pure virtual

Write or overwrite list of KeyPoint into specified dataset of hdf5 file.

Parameters

keypoints	specify keypoints data list to be written.
kplabel	specify the target hdf5 dataset label.
offset	specify the offset location on dataset from where keypoints will be (over)written into dataset.
counts	specify the amount of keypoints that will be written into dataset.

Writes vector<KeyPoint> object into targeted dataset.

Note: If dataset is not created and does not exist it will be created automatically. It is thread safe but it is recommended that writes to happen over separate non overlapping regions. Multiple datasets can be written inside single hdf5 file.

Example below writes a 100 keypoints into a dataset. No dataset precreation required. If routine is called multiple times dataset will be just overwritten:
// generate 100 dummy keypoints

std::vector<cv::KeyPoint> keypoints;

for(int i = 0; i < 100; i++)

keypoints.push_back( cv::KeyPoint(i, -i, 1, -1, 0, 0, -1) );

// open / autocreate hdf5 file

cv::Ptr<cv::hdf::HDF5> h5io = cv::hdf::open( "mytest.h5" );

// write / overwrite dataset

h5io->kpwrite( keypoints, "keypoints" );

// release

h5io->close();
Example below uses smaller set of 50 keypoints and writes into compressed space of 100 keypoints optimised by 10 chunks. Same keypoint set is written three times, first into first half (0->50) and at second half (50->75) then into remaining slots (75->99) of data space using offset and count parameters to settle the window for write access.If routine is called multiple times dataset will be just overwritten:
// generate 50 dummy keypoints

std::vector<cv::KeyPoint> keypoints;

for(int i = 0; i < 50; i++)

keypoints.push_back( cv::KeyPoint(i, -i, 1, -1, 0, 0, -1) );

// open / autocreate hdf5 file

cv::Ptr<cv::hdf::HDF5> h5io = cv::hdf::open( "mytest.h5" );

// create maximum compressed space of size 100 with chunk size 10

h5io->kpcreate( 100, "keypoints", 9, 10 );

// write into first half

h5io->kpwrite( keypoints, "keypoints", 0 );

// write first 25 keypoints into second half

h5io->kpwrite( keypoints, "keypoints", 50, 25 );

// write first 25 keypoints into remained space of second half

h5io->kpwrite( keypoints, "keypoints", 75, 25 );

// release

h5io->close();

The documentation for this class was generated from the following file:

/build/master-contrib_docs-mac/opencv_contrib/modules/hdf/include/opencv2/hdf/hdf5.hpp

Public Types

Public Member Functions

Detailed Description

Constructor & Destructor Documentation

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