cv::hdf::HDF5 Class Reference

Hierarchical Data Format version 5 interface. More...

#include <opencv2/hdf/hdf5.hpp>

Collaboration diagram for cv::hdf::HDF5:

Public Types
enum	{   H5_UNLIMITED = -1 ,   H5_NONE = -1 ,   H5_GETDIMS = 100 ,   H5_GETMAXDIMS = 101 ,   H5_GETCHUNKDIMS = 102 }

Public Member Functions
virtual	~HDF5 ()
virtual void	atdelete (const String &atlabel)=0
virtual bool	atexists (const String &atlabel) const =0
virtual void	atread (double *value, const String &atlabel)=0
virtual void	atread (int *value, const String &atlabel)=0
virtual void	atread (OutputArray value, const String &atlabel)=0
virtual void	atread (String *value, const String &atlabel)=0
virtual void	atwrite (const double value, const String &atlabel)=0
virtual void	atwrite (const int value, const String &atlabel)=0
virtual void	atwrite (const String &value, const String &atlabel)=0
virtual void	atwrite (InputArray value, const String &atlabel)=0
virtual void	close ()=0
	Close and release hdf5 object.
virtual void	dscreate (const int n_dims, const int *sizes, const int type, const String &dslabel) const =0
virtual void	dscreate (const int n_dims, const int *sizes, const int type, const String &dslabel, const int compresslevel) const =0
virtual void	dscreate (const int n_dims, const int *sizes, const int type, const String &dslabel, const int compresslevel, const int *dims_chunks) const =0
	Create and allocate storage for n-dimensional dataset, single or multichannel type.
virtual void	dscreate (const int rows, const int cols, const int type, const String &dslabel) const =0
virtual void	dscreate (const int rows, const int cols, const int type, const String &dslabel, const int compresslevel) const =0
virtual void	dscreate (const int rows, const int cols, const int type, const String &dslabel, const int compresslevel, const int *dims_chunks) const =0
	Create and allocate storage for two dimensional single or multi channel dataset.
virtual void	dscreate (const int rows, const int cols, const int type, const String &dslabel, const int compresslevel, const vector< int > &dims_chunks) const =0
virtual void	dscreate (const vector< int > &sizes, const int type, const String &dslabel, const int compresslevel=HDF5::H5_NONE, const vector< int > &dims_chunks=vector< int >()) const =0
virtual vector< int >	dsgetsize (const String &dslabel, int dims_flag=HDF5::H5_GETDIMS) const =0
	Fetch dataset sizes.
virtual int	dsgettype (const String &dslabel) const =0
	Fetch dataset type.
virtual void	dsinsert (InputArray Array, const String &dslabel) const =0
virtual void	dsinsert (InputArray Array, const String &dslabel, const int *dims_offset) const =0
virtual void	dsinsert (InputArray Array, const String &dslabel, const int *dims_offset, const int *dims_counts) const =0
	Insert or overwrite a Mat object into specified dataset and auto expand dataset size if unlimited property allows.
virtual void	dsinsert (InputArray Array, const String &dslabel, const vector< int > &dims_offset, const vector< int > &dims_counts=vector< int >()) const =0
virtual void	dsread (OutputArray Array, const String &dslabel) const =0
virtual void	dsread (OutputArray Array, const String &dslabel, const int *dims_offset) const =0
virtual void	dsread (OutputArray Array, const String &dslabel, const int *dims_offset, const int *dims_counts) const =0
	Read specific dataset from hdf5 file into Mat object.
virtual void	dsread (OutputArray Array, const String &dslabel, const vector< int > &dims_offset, const vector< int > &dims_counts=vector< int >()) const =0
virtual void	dswrite (InputArray Array, const String &dslabel) const =0
virtual void	dswrite (InputArray Array, const String &dslabel, const int *dims_offset) const =0
virtual void	dswrite (InputArray Array, const String &dslabel, const int *dims_offset, const int *dims_counts) const =0
	Write or overwrite a Mat object into specified dataset of hdf5 file.
virtual void	dswrite (InputArray Array, const String &dslabel, const vector< int > &dims_offset, const vector< int > &dims_counts=vector< int >()) const =0
virtual void	grcreate (const String &grlabel)=0
	Create a group.
virtual bool	hlexists (const String &label) const =0
	Check if label exists or not.
virtual void	kpcreate (const int size, const String &kplabel, const int compresslevel=H5_NONE, const int chunks=H5_NONE) const =0
	Create and allocate special storage for cv::KeyPoint dataset.
virtual int	kpgetsize (const String &kplabel, int dims_flag=HDF5::H5_GETDIMS) const =0
	Fetch keypoint dataset size.
virtual void	kpinsert (const vector< KeyPoint > keypoints, const 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.
virtual void	kpread (vector< KeyPoint > &keypoints, const 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.
virtual void	kpwrite (const vector< KeyPoint > keypoints, const 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.

Detailed Description

Hierarchical Data Format version 5 interface.

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

Member Enumeration Documentation

anonymous enum

anonymous enum

Enumerator
H5_UNLIMITED	The dimension size is unlimited,. See also dscreate()
H5_NONE	No compression,. See also dscreate()
H5_GETDIMS	Get the dimension information of a dataset. See also dsgetsize()
H5_GETMAXDIMS	Get the maximum dimension information of a dataset. See also dsgetsize()
H5_GETCHUNKDIMS	Get the chunk sizes of a dataset. See also dsgetsize()

Constructor & Destructor Documentation

~HDF5()

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

inlinevirtual

Member Function Documentation

atdelete()

virtual void cv::hdf::HDF5::atdelete ( const String & atlabel )

pure virtual

Delete an attribute from the root group.

Parameters

atlabel

the attribute to be deleted.

Note

CV_Error() is called if the given attribute does not exist. Use atexists() to check whether it exists or not beforehand.

See also

atexists, atwrite, atread

atexists()

virtual bool cv::hdf::HDF5::atexists ( const String & atlabel ) const

pure virtual

Check whether a given attribute exits or not in the root group.

Parameters

atlabel

the attribute name to be checked.

Returns

true if the attribute exists, false otherwise.

See also

atdelete, atwrite, atread

atread() [1/4]

virtual void cv::hdf::HDF5::atread ( double * value, const String & atlabel )

pure virtual

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

atread() [2/4]

virtual void cv::hdf::HDF5::atread ( int * value, const String & atlabel )

pure virtual

Read an attribute from the root group.

Parameters

value	address where the attribute is read into
atlabel	attribute name

The following example demonstrates how to read an attribute of type cv::String:

    String expected_attr_str;
    h5io->atread(&expected_attr_str, attr_str_name);

Note

The attribute MUST exist, otherwise CV_Error() is called. Use atexists() to check if it exists beforehand.

See also

atexists, atdelete, atwrite

atread() [3/4]

virtual void cv::hdf::HDF5::atread ( OutputArray value, const String & atlabel )

pure virtual

Read an attribute from the root group.

Parameters

value	attribute value. Currently, only n-d continuous multi-channel arrays are supported.
atlabel	attribute name.

Note

The attribute MUST exist, otherwise CV_Error() is called. Use atexists() to check if it exists beforehand.

See also

atexists, atdelete, atwrite

atread() [4/4]

virtual void cv::hdf::HDF5::atread ( String * value, const String & atlabel )

pure virtual

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

atwrite() [1/4]

virtual void cv::hdf::HDF5::atwrite ( const double value, const String & atlabel )

pure virtual

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

atwrite() [2/4]

virtual void cv::hdf::HDF5::atwrite ( const int value, const String & atlabel )

pure virtual

Write an attribute inside the root group.

Parameters

value	attribute value.
atlabel	attribute name.

The following example demonstrates how to write an attribute of type cv::String:

    String attr_str_name = "string attribute";
    String attr_str = "Hello HDF5 from OpenCV!";
    if (!h5io->atexists(attr_str_name))
        h5io->atwrite(attr_str, attr_str_name);

Note

CV_Error() is called if the given attribute already exists. Use atexists() to check whether it exists or not beforehand. And use atdelete() to delete it if it already exists.

See also

atexists, atdelete, atread

atwrite() [3/4]

virtual void cv::hdf::HDF5::atwrite ( const String & value, const String & atlabel )

pure virtual

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

atwrite() [4/4]

virtual void cv::hdf::HDF5::atwrite ( InputArray value, const String & atlabel )

pure virtual

Write an attribute into the root group.

Parameters

value	attribute value. Currently, only n-d continuous multi-channel arrays are supported.
atlabel	attribute name.

Note

CV_Error() is called if the given attribute already exists. Use atexists() to check whether it exists or not beforehand. And use atdelete() to delete it if it already exists.

See also

atexists, atdelete, atread.

close()

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

pure virtual

Close and release hdf5 object.

dscreate() [1/8]

virtual void cv::hdf::HDF5::dscreate ( const int n_dims, const int * sizes, const int type, const String & dslabel ) const

pure virtual

dscreate() [2/8]

virtual void cv::hdf::HDF5::dscreate ( const int n_dims, const int * sizes, const int type, const String & dslabel, const int compresslevel ) const

pure virtual

dscreate() [3/8]

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

pure virtual

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

Parameters

n_dims	declare number of dimensions
sizes	array containing sizes for each dimensions
type	type to be used, e.g., CV_8UC3, CV_32FC1, etc.
dslabel	specify the hdf5 dataset label. Existing dataset label will cause an error.
compresslevel	specify the compression level 0-9 to be used, H5_NONE is the default value and means no compression. The value 0 also means no compression. A value 9 indicating the best compression ration. Note that a higher compression level indicates a higher computational cost. It relies on GNU gzip for compression.
dims_chunks	each array member specifies 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 both at read and write time, especially for windowed access logic that shifts offset inside dataset. If no custom chunking is specified, the 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 the first dimension, thus storage will consists of 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 it is possible to expand anytime such dataset on those unlimited directions. Presence of H5_UNLIMITED on any dimension requires** to define custom chunking. No default chunking will be defined in unlimited scenario since the default size on that dimension will be zero, and will grow once dataset is written. Writing into dataset that has H5_UNLIMITED on some of its dimension requires dsinsert() instead of dswrite() that allows 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();

dscreate() [4/8]

virtual void cv::hdf::HDF5::dscreate ( const int rows, const int cols, const int type, const String & dslabel ) const

pure virtual

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

dscreate() [5/8]

virtual void cv::hdf::HDF5::dscreate ( const int rows, const int cols, const int type, const String & dslabel, const int compresslevel ) const

pure virtual

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

dscreate() [6/8]

virtual void cv::hdf::HDF5::dscreate ( const int rows, const int cols, const int type, const String & dslabel, const int compresslevel, const int * dims_chunks ) 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 columns
type	type to be used, e.g, CV_8UC3, CV_32FC1 and etc.
dslabel	specify the hdf5 dataset label. Existing dataset label will cause an error.
compresslevel	specify the compression level 0-9 to be used, H5_NONE is the default value and means no compression. The value 0 also means no compression. A value 9 indicating the best compression ration. Note that a higher compression level indicates a higher computational cost. It relies on GNU gzip for compression.
dims_chunks	each array member specifies the chunking size 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 (CV_Error() is called).

• 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 both at read and write time, especially for windowed access logic that shifts offset inside dataset. If no custom chunking is specified, the default one will be invoked by the size of the whole dataset as a 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 both means unlimited data on the specified dimension, thus, it is possible to expand anytime such a dataset on row, col or on both directions. Presence of H5_UNLIMITED on any dimension requires to define custom chunking. No default chunking will be defined in the unlimited scenario since default size on that dimension will be zero, and will grow once dataset is written. Writing into a dataset that has H5_UNLIMITED on some of its dimensions requires dsinsert() that allows growth on unlimited dimensions, 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 an exception will occur. Multiple datasets inside a single hdf5 file are allowed.

dscreate() [7/8]

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

pure virtual

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

dscreate() [8/8]

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

pure virtual

dsgetsize()

virtual vector< int > cv::hdf::HDF5::dsgetsize ( const 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, dataset maximum dimensions on H5_GETMAXDIMS, and chunk sizes on H5_GETCHUNKDIMS.

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. The H5_GETCHUNKDIMS will return the dimension of chunk if dataset was created with chunking options otherwise returned vector size will be zero.

dsgettype()

virtual int cv::hdf::HDF5::dsgettype ( const 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.

dsinsert() [1/4]

virtual void cv::hdf::HDF5::dsinsert ( InputArray Array, const String & dslabel ) const

pure virtual

dsinsert() [2/4]

virtual void cv::hdf::HDF5::dsinsert ( InputArray Array, const String & dslabel, const int * dims_offset ) const

pure virtual

dsinsert() [3/4]

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

pure virtual

Insert or overwrite a Mat object into specified dataset and auto expand 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 be continuous. If dsinsert() happens 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 a single hdf5 file.

• Example below creates unlimited rows x 100 cols and expands 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();

dsinsert() [4/4]

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

pure virtual

dsread() [1/4]

virtual void cv::hdf::HDF5::dsread ( OutputArray Array, const String & dslabel ) const

pure virtual

dsread() [2/4]

virtual void cv::hdf::HDF5::dsread ( OutputArray Array, const String & dslabel, const int * dims_offset ) const

pure virtual

dsread() [3/4]

virtual void cv::hdf::HDF5::dsread ( OutputArray Array, const String & dslabel, const int * dims_offset, const int * dims_counts ) 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();

dsread() [4/4]

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

pure virtual

dswrite() [1/4]

virtual void cv::hdf::HDF5::dswrite ( InputArray Array, const String & dslabel ) const

pure virtual

dswrite() [2/4]

virtual void cv::hdf::HDF5::dswrite ( InputArray Array, const String & dslabel, const int * dims_offset ) const

pure virtual

dswrite() [3/4]

virtual void cv::hdf::HDF5::dswrite ( InputArray Array, const String & dslabel, const int * dims_offset, const int * dims_counts ) 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 specifies 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 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 a single hdf5 file.

• Example below writes a 100x100 CV_64FC2 matrix into a dataset. No dataset pre-creation 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();

dswrite() [4/4]

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

pure virtual

grcreate()

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

pure virtual

Create a group.

Parameters

grlabel

specify the hdf5 group label.

Create a hdf5 group with default properties. The group is closed automatically after creation.

Note

Groups are useful for better organising 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, a label would be e.g: 'Group1/SubGroup1' where SubGroup1 is within the root group Group1. Before creating a subgroup, its parent group MUST be created.

• In this example, Group1 will have one subgroup called SubGroup1:

   
      Ptr<hdf::HDF5> h5io = hdf::open("mytest.h5");
   
      // "/" means the root group, which is always present
      if (!h5io->hlexists("/Group1"))
         h5io->grcreate("/Group1");
      else
         std::cout << "/Group1 has already been created, skip it.\n";
   
      // Note that Group1 has been created above, otherwise exception will occur
      if (!h5io->hlexists("/Group1/SubGroup1"))
         h5io->grcreate("/Group1/SubGroup1");
      else
         std::cout << "/Group1/SubGroup1 has already been created, skip it.\n";
   
      h5io->close();
   

  The corresponding result visualized using the HDFView tool is

Visualization of groups using the HDFView tool

Note

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

hlexists()

virtual bool cv::hdf::HDF5::hlexists ( const 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 otherwise.

Note

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

kpcreate()

virtual void cv::hdf::HDF5::kpcreate ( const int size, const 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, H5_NONE is default and means no compression.
chunks	each array member specifies chunking sizes to be used for block I/O, H5_NONE is default and means no compression.

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" );

kpgetsize()

virtual int cv::hdf::HDF5::kpgetsize ( const 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. The H5_GETCHUNKDIMS will return the dimension of chunk if dataset was created with chunking options otherwise returned vector size will be zero.

kpinsert()

virtual void cv::hdf::HDF5::kpinsert ( const vector< KeyPoint > keypoints, const 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();

kpread()

virtual void cv::hdf::HDF5::kpread ( vector< KeyPoint > & keypoints, const 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();

kpwrite()

virtual void cv::hdf::HDF5::kpwrite ( const vector< KeyPoint > keypoints, const 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();

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

• opencv2/hdf/hdf5.hpp