cv::cuda::HostMem Class Reference

Class with reference counting wrapping special memory type allocation functions from CUDA. More...

#include <opencv2/core/cuda.hpp>

Public Types
enum	AllocType {   PAGE_LOCKED = 1,   SHARED = 2,   WRITE_COMBINED = 4 }

Public Member Functions
	HostMem (HostMem::AllocType alloc_type=HostMem::AllocType::PAGE_LOCKED)
	HostMem (const HostMem &m)
	HostMem (int rows, int cols, int type, HostMem::AllocType alloc_type=HostMem::AllocType::PAGE_LOCKED)
	HostMem (Size size, int type, HostMem::AllocType alloc_type=HostMem::AllocType::PAGE_LOCKED)
	HostMem (InputArray arr, HostMem::AllocType alloc_type=HostMem::AllocType::PAGE_LOCKED)
	creates from host memory with coping data
	~HostMem ()
int	channels () const
HostMem	clone () const
	returns deep copy of the matrix, i.e. the data is copied
void	create (int rows, int cols, int type)
	allocates new matrix data unless the matrix already has specified size and type.
void	create (Size size, int type)
GpuMat	createGpuMatHeader () const
	Maps CPU memory to GPU address space and creates the cuda::GpuMat header without reference counting for it.
Mat	createMatHeader () const
	returns matrix header with disabled reference counting for HostMem data.
int	depth () const
size_t	elemSize () const
size_t	elemSize1 () const
bool	empty () const
bool	isContinuous () const
HostMem &	operator= (const HostMem &m)
void	release ()
	decrements reference counter and released memory if needed.
HostMem	reshape (int cn, int rows=0) const
Size	size () const
size_t	step1 () const
void	swap (HostMem &b)
	swaps with other smart pointer
int	type () const

Static Public Member Functions
static MatAllocator *	getAllocator (HostMem::AllocType alloc_type=HostMem::AllocType::PAGE_LOCKED)

Public Attributes
AllocType	alloc_type
int	cols
uchar *	data
const uchar *	dataend
uchar *	datastart
int	flags
int *	refcount
int	rows
size_t	step

Detailed Description

Class with reference counting wrapping special memory type allocation functions from CUDA.

Its interface is also Mat-like but with additional memory type parameters.

• PAGE_LOCKED sets a page locked memory type used commonly for fast and asynchronous uploading/downloading data from/to GPU.
• SHARED specifies a zero copy memory allocation that enables mapping the host memory to GPU address space, if supported.
• WRITE_COMBINED sets the write combined buffer that is not cached by CPU. Such buffers are used to supply GPU with data when GPU only reads it. The advantage is a better CPU cache utilization.

Note

Allocation size of such memory types is usually limited. For more details, see CUDA 2.2 Pinned Memory APIs document or CUDA C Programming Guide.

Member Enumeration Documentation

enum cv::cuda::HostMem::AllocType

Enumerator
PAGE_LOCKED
SHARED
WRITE_COMBINED

Constructor & Destructor Documentation

cv::cuda::HostMem::HostMem ( HostMem::AllocType  alloc_type = HostMem::AllocType::PAGE_LOCKED )

explicit

Python:
	<cuda_HostMem object>	=	cv.cuda_HostMem(	[, alloc_type]	)
	<cuda_HostMem object>	=	cv.cuda_HostMem(	rows, cols, type[, alloc_type]	)
	<cuda_HostMem object>	=	cv.cuda_HostMem(	size, type[, alloc_type]	)
	<cuda_HostMem object>	=	cv.cuda_HostMem(	arr[, alloc_type]	)

cv::cuda::HostMem::HostMem

(

const HostMem &

m

)

Python:
	<cuda_HostMem object>	=	cv.cuda_HostMem(	[, alloc_type]	)
	<cuda_HostMem object>	=	cv.cuda_HostMem(	rows, cols, type[, alloc_type]	)
	<cuda_HostMem object>	=	cv.cuda_HostMem(	size, type[, alloc_type]	)
	<cuda_HostMem object>	=	cv.cuda_HostMem(	arr[, alloc_type]	)

cv::cuda::HostMem::HostMem	(	int	rows,
		int	cols,
		int	type,
		HostMem::AllocType	alloc_type = HostMem::AllocType::PAGE_LOCKED
	)

Python:
	<cuda_HostMem object>	=	cv.cuda_HostMem(	[, alloc_type]	)
	<cuda_HostMem object>	=	cv.cuda_HostMem(	rows, cols, type[, alloc_type]	)
	<cuda_HostMem object>	=	cv.cuda_HostMem(	size, type[, alloc_type]	)
	<cuda_HostMem object>	=	cv.cuda_HostMem(	arr[, alloc_type]	)

cv::cuda::HostMem::HostMem	(	Size	size,
		int	type,
		HostMem::AllocType	alloc_type = HostMem::AllocType::PAGE_LOCKED
	)

Python:
	<cuda_HostMem object>	=	cv.cuda_HostMem(	[, alloc_type]	)
	<cuda_HostMem object>	=	cv.cuda_HostMem(	rows, cols, type[, alloc_type]	)
	<cuda_HostMem object>	=	cv.cuda_HostMem(	size, type[, alloc_type]	)
	<cuda_HostMem object>	=	cv.cuda_HostMem(	arr[, alloc_type]	)

cv::cuda::HostMem::HostMem ( InputArray  arr, HostMem::AllocType  alloc_type = HostMem::AllocType::PAGE_LOCKED  )

explicit

Python:
	<cuda_HostMem object>	=	cv.cuda_HostMem(	[, alloc_type]	)
	<cuda_HostMem object>	=	cv.cuda_HostMem(	rows, cols, type[, alloc_type]	)
	<cuda_HostMem object>	=	cv.cuda_HostMem(	size, type[, alloc_type]	)
	<cuda_HostMem object>	=	cv.cuda_HostMem(	arr[, alloc_type]	)

creates from host memory with coping data

cv::cuda::HostMem::~HostMem

(

)

Member Function Documentation

int cv::cuda::HostMem::channels

(

)

const

Python:
	retval	=	cv.cuda_HostMem.channels(		)

HostMem cv::cuda::HostMem::clone

(

)

const

Python:
	retval	=	cv.cuda_HostMem.clone(		)

returns deep copy of the matrix, i.e. the data is copied

void cv::cuda::HostMem::create	(	int	rows,
		int	cols,
		int	type
	)

Python:
	None	=	cv.cuda_HostMem.create(	rows, cols, type	)

allocates new matrix data unless the matrix already has specified size and type.

void cv::cuda::HostMem::create	(	Size	size,
		int	type
	)

Python:
	None	=	cv.cuda_HostMem.create(	rows, cols, type	)

GpuMat cv::cuda::HostMem::createGpuMatHeader

(

)

const

Maps CPU memory to GPU address space and creates the cuda::GpuMat header without reference counting for it.

This can be done only if memory was allocated with the SHARED flag and if it is supported by the hardware. Laptops often share video and CPU memory, so address spaces can be mapped, which eliminates an extra copy.

Mat cv::cuda::HostMem::createMatHeader

(

)

const

Python:
	retval	=	cv.cuda_HostMem.createMatHeader(		)

returns matrix header with disabled reference counting for HostMem data.

int cv::cuda::HostMem::depth

(

)

const

Python:
	retval	=	cv.cuda_HostMem.depth(		)

size_t cv::cuda::HostMem::elemSize

(

)

const

Python:
	retval	=	cv.cuda_HostMem.elemSize(		)

size_t cv::cuda::HostMem::elemSize1

(

)

const

Python:
	retval	=	cv.cuda_HostMem.elemSize1(		)

bool cv::cuda::HostMem::empty

(

)

const

Python:
	retval	=	cv.cuda_HostMem.empty(		)

static MatAllocator* cv::cuda::HostMem::getAllocator ( HostMem::AllocType  alloc_type = HostMem::AllocType::PAGE_LOCKED )

static

bool cv::cuda::HostMem::isContinuous

(

)

const

Python:
	retval	=	cv.cuda_HostMem.isContinuous(		)

HostMem& cv::cuda::HostMem::operator=

(

const HostMem &

m

)

void cv::cuda::HostMem::release

(

)

decrements reference counter and released memory if needed.

HostMem cv::cuda::HostMem::reshape	(	int	cn,
		int	rows = 0
	)		const

Python:
	retval	=	cv.cuda_HostMem.reshape(	cn[, rows]	)

creates alternative HostMem header for the same data, with different number of channels and/or different number of rows

Size cv::cuda::HostMem::size

(

)

const

Python:
	retval	=	cv.cuda_HostMem.size(		)

size_t cv::cuda::HostMem::step1

(

)

const

Python:
	retval	=	cv.cuda_HostMem.step1(		)

void cv::cuda::HostMem::swap

(

HostMem &

b

)

Python:
	None	=	cv.cuda_HostMem.swap(	b	)

swaps with other smart pointer

int cv::cuda::HostMem::type

(

)

const

Python:
	retval	=	cv.cuda_HostMem.type(		)

Member Data Documentation

AllocType cv::cuda::HostMem::alloc_type

int cv::cuda::HostMem::cols

uchar* cv::cuda::HostMem::data

const uchar* cv::cuda::HostMem::dataend

uchar* cv::cuda::HostMem::datastart

int cv::cuda::HostMem::flags

int* cv::cuda::HostMem::refcount

int cv::cuda::HostMem::rows

size_t cv::cuda::HostMem::step

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

• modules/core/include/opencv2/core/cuda.hpp