OpenCV  4.6.0-dev
Open Source Computer Vision
Public Types | Public Member Functions | List of all members

Hierarchical Data Format version 5 interface. More...

#include <opencv2/hdf/hdf5.hpp>

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 (int *value, const String &atlabel)=0
 
virtual void atread (double *value, const String &atlabel)=0
 
virtual void atread (String *value, const String &atlabel)=0
 
virtual void atread (OutputArray value, const String &atlabel)=0
 
virtual void atwrite (const int value, const String &atlabel)=0
 
virtual void atwrite (const double 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. More...
 
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 vector< int > &dims_chunks) 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. More...
 
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 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 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. More...
 
virtual vector< int > dsgetsize (const String &dslabel, int dims_flag=HDF5::H5_GETDIMS) const =0
 Fetch dataset sizes. More...
 
virtual int dsgettype (const String &dslabel) const =0
 Fetch dataset type. More...
 
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 vector< int > &dims_offset, const vector< int > &dims_counts=vector< int >()) 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. More...
 
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 vector< int > &dims_offset, const vector< int > &dims_counts=vector< int >()) 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. More...
 
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 vector< int > &dims_offset, const vector< int > &dims_counts=vector< int >()) 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. More...
 
virtual void grcreate (const String &grlabel)=0
 Create a group. More...
 
virtual bool hlexists (const String &label) const =0
 Check if label exists or not. More...
 
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. More...
 
virtual int kpgetsize (const String &kplabel, int dims_flag=HDF5::H5_GETDIMS) const =0
 Fetch keypoint dataset size. More...
 
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. More...
 
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. More...
 
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. More...
 

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
atlabelthe 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
atlabelthe 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 ( int *  value,
const String atlabel 
)
pure virtual

Read an attribute from the root group.

Parameters
valueaddress where the attribute is read into
atlabelattribute 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() [2/4]

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() [3/4]

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.

◆ atread() [4/4]

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

Read an attribute from the root group.

Parameters
valueattribute value. Currently, only n-d continuous multi-channel arrays are supported.
atlabelattribute name.
Note
The attribute MUST exist, otherwise CV_Error() is called. Use atexists() to check if it exists beforehand.
See also
atexists, atdelete, atwrite

◆ atwrite() [1/4]

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

Write an attribute inside the root group.

Parameters
valueattribute value.
atlabelattribute 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() [2/4]

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() [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
valueattribute value. Currently, only n-d continuous multi-channel arrays are supported.
atlabelattribute 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  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() [2/8]

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() [3/8]

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() [4/8]

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
rowsdeclare amount of rows
colsdeclare amount of columns
typetype to be used, e.g, CV_8UC3, CV_32FC1 and etc.
dslabelspecify the hdf5 dataset label. Existing dataset label will cause an error.
compresslevelspecify 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_chunkseach 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.
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() [5/8]

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

◆ dscreate() [6/8]

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() [7/8]

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

◆ dscreate() [8/8]

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_dimsdeclare number of dimensions
sizesarray containing sizes for each dimensions
typetype to be used, e.g., CV_8UC3, CV_32FC1, etc.
dslabelspecify the hdf5 dataset label. Existing dataset label will cause an error.
compresslevelspecify 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_chunkseach 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.

◆ dsgetsize()

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

Fetch dataset sizes.

Parameters
dslabelspecify the hdf5 dataset label to be measured.
dims_flagwill 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
dslabelspecify 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 vector< int > &  dims_offset,
const vector< int > &  dims_counts = vector< int >() 
) const
pure virtual

◆ dsinsert() [4/4]

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
Arrayspecify Mat data array to be written.
dslabelspecify the target hdf5 dataset label.
dims_offseteach array member specify the offset location over dataset's each dimensions from where InputArray will be (over)written into dataset.
dims_countseach 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();

◆ 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 vector< int > &  dims_offset,
const vector< int > &  dims_counts = vector< int >() 
) const
pure virtual

◆ dsread() [4/4]

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
ArrayMat container where data reads will be returned.
dslabelspecify the source hdf5 dataset label.
dims_offseteach array member specify the offset location over each dimensions from where dataset starts to read into OutputArray.
dims_countseach 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
    // 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
    int offset[2] = { 1, 2 };
    int counts[2] = { 3, 5 };
    // read hibert dataset
    h5io->read( H, "hilbert", offset, counts );
    // release
    h5io->close();

◆ 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 vector< int > &  dims_offset,
const vector< int > &  dims_counts = vector< int >() 
) const
pure virtual

◆ dswrite() [4/4]

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
Arrayspecify Mat data array to be written.
dslabelspecify the target hdf5 dataset label.
dims_offseteach array member specify the offset location over dataset's each dimensions from where InputArray will be (over)written into dataset.
dims_countseach 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();

◆ grcreate()

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

Create a group.

Parameters
grlabelspecify 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

    create_groups.png
    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
labelspecify 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
sizedeclare fixed number of KeyPoints
kplabelspecify the hdf5 dataset label, any existing dataset with the same label will be overwritten.
compresslevelspecify the compression level 0-9 to be used, H5_NONE is default and means no compression.
chunkseach 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
kplabelspecify the hdf5 dataset label to be measured.
dims_flagwill 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
keypointsspecify keypoints data list to be written.
kplabelspecify the target hdf5 dataset label.
offsetspecify the offset location on dataset from where keypoints will be (over)written into dataset.
countsspecify 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
keypointsvector<KeyPoint> container where data reads will be returned.
kplabelspecify the source hdf5 dataset label.
offsetspecify the offset location over dataset from where read starts.
countsspecify 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
keypointsspecify keypoints data list to be written.
kplabelspecify the target hdf5 dataset label.
offsetspecify the offset location on dataset from where keypoints will be (over)written into dataset.
countsspecify 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: