opencv 0.22.0

//! # Hierarchical Data Format I/O routines
//!
//! This module provides storage routines for Hierarchical Data Format objects.
//! # Hierarchical Data Format version 5
//!
//! Hierarchical Data Format version 5
//! --------------------------------------------------------
use std::os::raw::{c_char, c_void};
use libc::{ptrdiff_t, size_t};
use crate::{Error, Result, core, sys, types};
use crate::core::{_InputArray, _OutputArray};


/// Open or create hdf5 file
/// ## Parameters
/// * HDF5Filename: specify the HDF5 filename.
///
/// Returns pointer to the hdf5 object class
///
///
/// Note: If hdf5 file does not exist it will be created. Any operations except dscreate() functions on object
/// will be thread safe. Multiple datasets can be created inside single hdf5 file, and can be accessed
/// from same hdf5 object from multiple instances as long read or write operations are done over
/// non-overlapping regions of dataset. Single hdf5 file also can be opened by multiple instances,
/// reads and writes can be instantiated at the same time as long non-overlapping regions are involved. Object
/// is released using close().
///
/// - Example below open and then release the file.
/// ```ignore{.cpp}
/// // open / autocreate hdf5 file
/// cv::Ptr<cv::hdf::HDF5> h5io = cv::hdf::open( "mytest.h5" );
/// // ...
/// // release
/// h5io->close();
/// ```
///
///
/// ![Visualization of 10x10 CV_64FC2 (Hilbert matrix) using HDFView tool](https://docs.opencv.org/3.2.0/hdfview_demo.gif)
///
/// - Text dump (3x3 Hilbert matrix) of hdf5 dataset using **h5dump** tool:
/// ```ignore{.txt}
/// $ h5dump test.h5
/// HDF5 "test.h5" {
/// GROUP "/" {
/// DATASET "hilbert" {
/// DATATYPE  H5T_ARRAY { [2] H5T_IEEE_F64LE }
/// DATASPACE  SIMPLE { ( 3, 3 ) / ( 3, 3 ) }
/// DATA {
/// (0,0): [ 1, -1 ], [ 0.5, -0.5 ], [ 0.333333, -0.333333 ],
/// (1,0): [ 0.5, -0.5 ], [ 0.333333, -0.333333 ], [ 0.25, -0.25 ],
/// (2,0): [ 0.333333, -0.333333 ], [ 0.25, -0.25 ], [ 0.2, -0.2 ]
/// }
/// }
/// }
/// }
/// ```
pub fn open(hdf5_filename: &str) -> Result<types::PtrOfHDF5> {
    string_arg!(mut hdf5_filename);
    unsafe { sys::cv_hdf_open_String(hdf5_filename.as_ptr() as _) }.into_result().map(|ptr| types::PtrOfHDF5 { ptr })
}

// Generating impl for trait cv::hdf::HDF5 (trait)
/// Hierarchical Data Format version 5 interface.
///
/// Notice that module is compiled only when hdf5 is correctly installed.
pub trait HDF5 {
    #[inline(always)] fn as_raw_HDF5(&self) -> *mut c_void;
    /// Close and release hdf5 object.
    fn close(&mut self) -> Result<()> {
        unsafe { sys::cv_hdf_HDF5_close(self.as_raw_HDF5()) }.into_result()
    }
    
    /// 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:
    /// ```ignore{.cpp}
    /// // 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.
    fn grcreate(&mut self, grlabel: &str) -> Result<()> {
        string_arg!(mut grlabel);
        unsafe { sys::cv_hdf_HDF5_grcreate_String(self.as_raw_HDF5(), grlabel.as_ptr() as _) }.into_result()
    }
    
    /// 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.
    fn hlexists(&self, label: &str) -> Result<bool> {
        string_arg!(mut label);
        unsafe { sys::cv_hdf_HDF5_hlexists_const_String(self.as_raw_HDF5(), label.as_ptr() as _) }.into_result()
    }
    
    fn dscreate(&self, rows: i32, cols: i32, _type: i32, dslabel: &str) -> Result<()> {
        string_arg!(mut dslabel);
        unsafe { sys::cv_hdf_HDF5_dscreate_const_int_int_int_String(self.as_raw_HDF5(), rows, cols, _type, dslabel.as_ptr() as _) }.into_result()
    }
    
    fn dscreate_1(&self, rows: i32, cols: i32, _type: i32, dslabel: &str, compresslevel: i32) -> Result<()> {
        string_arg!(mut dslabel);
        unsafe { sys::cv_hdf_HDF5_dscreate_const_int_int_int_String_int(self.as_raw_HDF5(), rows, cols, _type, dslabel.as_ptr() as _, compresslevel) }.into_result()
    }
    
    fn dscreate_2(&self, rows: i32, cols: i32, _type: i32, dslabel: &str, compresslevel: i32, dims_chunks: &types::VectorOfint) -> Result<()> {
        string_arg!(mut dslabel);
        unsafe { sys::cv_hdf_HDF5_dscreate_const_int_int_int_String_int_VectorOfint(self.as_raw_HDF5(), rows, cols, _type, dslabel.as_ptr() as _, compresslevel, dims_chunks.as_raw_VectorOfint()) }.into_result()
    }
    
    /// 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, H5_NONE is default and means no compression.
    /// * 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:
    /// ```ignore{.cpp}
    /// // 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:
    /// ```ignore{.cpp}
    /// // 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:
    /// ```ignore{.cpp}
    /// // 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.
    fn dscreate_3(&self, rows: i32, cols: i32, _type: i32, dslabel: &str, compresslevel: i32, dims_chunks: &i32) -> Result<()> {
        string_arg!(mut dslabel);
        unsafe { sys::cv_hdf_HDF5_dscreate_const_int_int_int_String_int_const_int_X(self.as_raw_HDF5(), rows, cols, _type, dslabel.as_ptr() as _, compresslevel, dims_chunks) }.into_result()
    }
    
    fn dscreate_4(&self, n_dims: i32, sizes: &i32, _type: i32, dslabel: &str) -> Result<()> {
        string_arg!(mut dslabel);
        unsafe { sys::cv_hdf_HDF5_dscreate_const_int_const_int_X_int_String(self.as_raw_HDF5(), n_dims, sizes, _type, dslabel.as_ptr() as _) }.into_result()
    }
    
    fn dscreate_5(&self, n_dims: i32, sizes: &i32, _type: i32, dslabel: &str, compresslevel: i32) -> Result<()> {
        string_arg!(mut dslabel);
        unsafe { sys::cv_hdf_HDF5_dscreate_const_int_const_int_X_int_String_int(self.as_raw_HDF5(), n_dims, sizes, _type, dslabel.as_ptr() as _, compresslevel) }.into_result()
    }
    
    ///
    /// ## C++ default parameters
    /// * compresslevel: HDF5::H5_NONE
    /// * dims_chunks: vector<int>()
    fn dscreate_6(&self, sizes: &types::VectorOfint, _type: i32, dslabel: &str, compresslevel: i32, dims_chunks: &types::VectorOfint) -> Result<()> {
        string_arg!(mut dslabel);
        unsafe { sys::cv_hdf_HDF5_dscreate_const_VectorOfint_int_String_int_VectorOfint(self.as_raw_HDF5(), sizes.as_raw_VectorOfint(), _type, dslabel.as_ptr() as _, compresslevel, dims_chunks.as_raw_VectorOfint()) }.into_result()
    }
    
    /// 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, H5_NONE is default and means no compression.
    /// * 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:
    /// ```ignore{.cpp}
    /// // 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:
    /// ```ignore{.cpp}
    /// // 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:
    /// ```ignore{.cpp}
    /// // 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();
    /// ```
    fn dscreate_7(&self, n_dims: i32, sizes: &i32, _type: i32, dslabel: &str, compresslevel: i32, dims_chunks: &i32) -> Result<()> {
        string_arg!(mut dslabel);
        unsafe { sys::cv_hdf_HDF5_dscreate_const_int_const_int_X_int_String_int_const_int_X(self.as_raw_HDF5(), n_dims, sizes, _type, dslabel.as_ptr() as _, compresslevel, dims_chunks) }.into_result()
    }
    
    /// 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. The H5_GETCHUNKDIMS will
    /// return the dimension of chunk if dataset was created with chunking options otherwise returned vector size
    /// will be zero.
    ///
    /// ## C++ default parameters
    /// * dims_flag: HDF5::H5_GETDIMS
    fn dsgetsize(&self, dslabel: &str, dims_flag: i32) -> Result<types::VectorOfint> {
        string_arg!(mut dslabel);
        unsafe { sys::cv_hdf_HDF5_dsgetsize_const_String_int(self.as_raw_HDF5(), dslabel.as_ptr() as _, dims_flag) }.into_result().map(|ptr| types::VectorOfint { ptr })
    }
    
    /// 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.
    fn dsgettype(&self, dslabel: &str) -> Result<i32> {
        string_arg!(mut dslabel);
        unsafe { sys::cv_hdf_HDF5_dsgettype_const_String(self.as_raw_HDF5(), dslabel.as_ptr() as _) }.into_result()
    }
    
    fn dswrite(&self, array: &dyn core::ToInputArray, dslabel: &str) -> Result<()> {
        input_array_arg!(array);
        string_arg!(mut dslabel);
        unsafe { sys::cv_hdf_HDF5_dswrite_const__InputArray_String(self.as_raw_HDF5(), array.as_raw__InputArray(), dslabel.as_ptr() as _) }.into_result()
    }
    
    fn dswrite_1(&self, array: &dyn core::ToInputArray, dslabel: &str, dims_offset: &i32) -> Result<()> {
        input_array_arg!(array);
        string_arg!(mut dslabel);
        unsafe { sys::cv_hdf_HDF5_dswrite_const__InputArray_String_const_int_X(self.as_raw_HDF5(), array.as_raw__InputArray(), dslabel.as_ptr() as _, dims_offset) }.into_result()
    }
    
    ///
    /// ## C++ default parameters
    /// * dims_counts: vector<int>()
    fn dswrite_2(&self, array: &dyn core::ToInputArray, dslabel: &str, dims_offset: &types::VectorOfint, dims_counts: &types::VectorOfint) -> Result<()> {
        input_array_arg!(array);
        string_arg!(mut dslabel);
        unsafe { sys::cv_hdf_HDF5_dswrite_const__InputArray_String_VectorOfint_VectorOfint(self.as_raw_HDF5(), array.as_raw__InputArray(), dslabel.as_ptr() as _, dims_offset.as_raw_VectorOfint(), dims_counts.as_raw_VectorOfint()) }.into_result()
    }
    
    /// 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:
    /// ```ignore{.cpp}
    /// // 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.
    /// ```ignore{.cpp}
    /// // 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();
    /// ```
    fn dswrite_3(&self, array: &dyn core::ToInputArray, dslabel: &str, dims_offset: &i32, dims_counts: &i32) -> Result<()> {
        input_array_arg!(array);
        string_arg!(mut dslabel);
        unsafe { sys::cv_hdf_HDF5_dswrite_const__InputArray_String_const_int_X_const_int_X(self.as_raw_HDF5(), array.as_raw__InputArray(), dslabel.as_ptr() as _, dims_offset, dims_counts) }.into_result()
    }
    
    fn dsinsert(&self, array: &dyn core::ToInputArray, dslabel: &str) -> Result<()> {
        input_array_arg!(array);
        string_arg!(mut dslabel);
        unsafe { sys::cv_hdf_HDF5_dsinsert_const__InputArray_String(self.as_raw_HDF5(), array.as_raw__InputArray(), dslabel.as_ptr() as _) }.into_result()
    }
    
    fn dsinsert_1(&self, array: &dyn core::ToInputArray, dslabel: &str, dims_offset: &i32) -> Result<()> {
        input_array_arg!(array);
        string_arg!(mut dslabel);
        unsafe { sys::cv_hdf_HDF5_dsinsert_const__InputArray_String_const_int_X(self.as_raw_HDF5(), array.as_raw__InputArray(), dslabel.as_ptr() as _, dims_offset) }.into_result()
    }
    
    ///
    /// ## C++ default parameters
    /// * dims_counts: vector<int>()
    fn dsinsert_2(&self, array: &dyn core::ToInputArray, dslabel: &str, dims_offset: &types::VectorOfint, dims_counts: &types::VectorOfint) -> Result<()> {
        input_array_arg!(array);
        string_arg!(mut dslabel);
        unsafe { sys::cv_hdf_HDF5_dsinsert_const__InputArray_String_VectorOfint_VectorOfint(self.as_raw_HDF5(), array.as_raw__InputArray(), dslabel.as_ptr() as _, dims_offset.as_raw_VectorOfint(), dims_counts.as_raw_VectorOfint()) }.into_result()
    }
    
    /// 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:
    /// ```ignore{.cpp}
    /// // 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();
    /// ```
    fn dsinsert_3(&self, array: &dyn core::ToInputArray, dslabel: &str, dims_offset: &i32, dims_counts: &i32) -> Result<()> {
        input_array_arg!(array);
        string_arg!(mut dslabel);
        unsafe { sys::cv_hdf_HDF5_dsinsert_const__InputArray_String_const_int_X_const_int_X(self.as_raw_HDF5(), array.as_raw__InputArray(), dslabel.as_ptr() as _, dims_offset, dims_counts) }.into_result()
    }
    
    fn dsread(&self, array: &mut dyn core::ToOutputArray, dslabel: &str) -> Result<()> {
        output_array_arg!(array);
        string_arg!(mut dslabel);
        unsafe { sys::cv_hdf_HDF5_dsread_const__OutputArray_String(self.as_raw_HDF5(), array.as_raw__OutputArray(), dslabel.as_ptr() as _) }.into_result()
    }
    
    fn dsread_1(&self, array: &mut dyn core::ToOutputArray, dslabel: &str, dims_offset: &i32) -> Result<()> {
        output_array_arg!(array);
        string_arg!(mut dslabel);
        unsafe { sys::cv_hdf_HDF5_dsread_const__OutputArray_String_const_int_X(self.as_raw_HDF5(), array.as_raw__OutputArray(), dslabel.as_ptr() as _, dims_offset) }.into_result()
    }
    
    ///
    /// ## C++ default parameters
    /// * dims_counts: vector<int>()
    fn dsread_2(&self, array: &mut dyn core::ToOutputArray, dslabel: &str, dims_offset: &types::VectorOfint, dims_counts: &types::VectorOfint) -> Result<()> {
        output_array_arg!(array);
        string_arg!(mut dslabel);
        unsafe { sys::cv_hdf_HDF5_dsread_const__OutputArray_String_VectorOfint_VectorOfint(self.as_raw_HDF5(), array.as_raw__OutputArray(), dslabel.as_ptr() as _, dims_offset.as_raw_VectorOfint(), dims_counts.as_raw_VectorOfint()) }.into_result()
    }
    
    /// 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:
    /// ```ignore{.cpp}
    /// // 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.
    /// ```ignore{.cpp}
    /// // 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();
    /// ```
    fn dsread_3(&self, array: &mut dyn core::ToOutputArray, dslabel: &str, dims_offset: &i32, dims_counts: &i32) -> Result<()> {
        output_array_arg!(array);
        string_arg!(mut dslabel);
        unsafe { sys::cv_hdf_HDF5_dsread_const__OutputArray_String_const_int_X_const_int_X(self.as_raw_HDF5(), array.as_raw__OutputArray(), dslabel.as_ptr() as _, dims_offset, dims_counts) }.into_result()
    }
    
    /// 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.
    ///
    /// ## C++ default parameters
    /// * dims_flag: HDF5::H5_GETDIMS
    fn kpgetsize(&self, kplabel: &str, dims_flag: i32) -> Result<i32> {
        string_arg!(mut kplabel);
        unsafe { sys::cv_hdf_HDF5_kpgetsize_const_String_int(self.as_raw_HDF5(), kplabel.as_ptr() as _, dims_flag) }.into_result()
    }
    
    /// 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 specify 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:
    /// ```ignore{.cpp}
    /// // 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:
    /// ```ignore{.cpp}
    /// // 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" );
    /// ```
    ///
    /// ## C++ default parameters
    /// * compresslevel: H5_NONE
    /// * chunks: H5_NONE
    fn kpcreate(&self, size: i32, kplabel: &str, compresslevel: i32, chunks: i32) -> Result<()> {
        string_arg!(mut kplabel);
        unsafe { sys::cv_hdf_HDF5_kpcreate_const_int_String_int_int(self.as_raw_HDF5(), size, kplabel.as_ptr() as _, compresslevel, chunks) }.into_result()
    }
    
    /// 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:
    /// ```ignore{.cpp}
    /// // 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:
    /// ```ignore{.cpp}
    /// // 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();
    /// ```
    ///
    /// ## C++ default parameters
    /// * offset: H5_NONE
    /// * counts: H5_NONE
    fn kpwrite(&self, keypoints: &types::VectorOfKeyPoint, kplabel: &str, offset: i32, counts: i32) -> Result<()> {
        string_arg!(mut kplabel);
        unsafe { sys::cv_hdf_HDF5_kpwrite_const_VectorOfKeyPoint_String_int_int(self.as_raw_HDF5(), keypoints.as_raw_VectorOfKeyPoint(), kplabel.as_ptr() as _, offset, counts) }.into_result()
    }
    
    /// 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:
    /// ```ignore{.cpp}
    /// // 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();
    /// ```
    ///
    /// ## C++ default parameters
    /// * offset: H5_NONE
    /// * counts: H5_NONE
    fn kpinsert(&self, keypoints: &types::VectorOfKeyPoint, kplabel: &str, offset: i32, counts: i32) -> Result<()> {
        string_arg!(mut kplabel);
        unsafe { sys::cv_hdf_HDF5_kpinsert_const_VectorOfKeyPoint_String_int_int(self.as_raw_HDF5(), keypoints.as_raw_VectorOfKeyPoint(), kplabel.as_ptr() as _, offset, counts) }.into_result()
    }
    
    /// 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:
    /// ```ignore{.cpp}
    /// // 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.
    /// ```ignore{.cpp}
    /// // 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();
    /// ```
    ///
    /// ## C++ default parameters
    /// * offset: H5_NONE
    /// * counts: H5_NONE
    fn kpread(&self, keypoints: &mut types::VectorOfKeyPoint, kplabel: &str, offset: i32, counts: i32) -> Result<()> {
        string_arg!(mut kplabel);
        unsafe { sys::cv_hdf_HDF5_kpread_const_VectorOfKeyPoint_String_int_int(self.as_raw_HDF5(), keypoints.as_raw_VectorOfKeyPoint(), kplabel.as_ptr() as _, offset, counts) }.into_result()
    }
    
}