Data Structures

Classes
class	cv::cuda::BufferPool
	BufferPool for use with CUDA streams. More...
class	cv::cuda::Event
struct	cv::cuda::EventAccessor
	Class that enables getting cudaEvent_t from cuda::Event. More...
struct	cv::cuda::GpuData
class	cv::cuda::GpuMat
	Base storage class for GPU memory with reference counting. More...
class	cv::cuda::GpuMatND
class	cv::cuda::HostMem
	Class with reference counting wrapping special memory type allocation functions from CUDA. More...
class	cv::cuda::Stream
	This class encapsulates a queue of asynchronous calls. More...
struct	cv::cuda::StreamAccessor
	Class that enables getting cudaStream_t from cuda::Stream. More...

Functions
void	cv::cuda::createContinuous (int rows, int cols, int type, OutputArray arr)
	Creates a continuous matrix. More...
void	cv::cuda::ensureSizeIsEnough (int rows, int cols, int type, OutputArray arr)
	Ensures that the size of a matrix is big enough and the matrix has a proper type. More...
void	cv::cuda::registerPageLocked (Mat &m)
	Page-locks the memory of matrix and maps it for the device(s). More...
void	cv::cuda::setBufferPoolConfig (int deviceId, size_t stackSize, int stackCount)
void	cv::cuda::setBufferPoolUsage (bool on)
	BufferPool management (must be called before Stream creation) More...
void	cv::cuda::unregisterPageLocked (Mat &m)
	Unmaps the memory of matrix and makes it pageable again. More...

Detailed Description

Function Documentation

createContinuous()

void cv::cuda::createContinuous	(	int	rows,
		int	cols,
		int	type,
		OutputArray	arr
	)

Python:
	cv.cuda.createContinuous(	rows, cols, type[, arr]	) ->	arr

#include <opencv2/core/cuda.hpp>

Creates a continuous matrix.

Parameters

rows	Row count.
cols	Column count.
type	Type of the matrix.
arr	Destination matrix. This parameter changes only if it has a proper type and area ( \(\texttt{rows} \times \texttt{cols}\) ).

Matrix is called continuous if its elements are stored continuously, that is, without gaps at the end of each row.

ensureSizeIsEnough()

void cv::cuda::ensureSizeIsEnough	(	int	rows,
		int	cols,
		int	type,
		OutputArray	arr
	)

Python:
	cv.cuda.ensureSizeIsEnough(	rows, cols, type[, arr]	) ->	arr

#include <opencv2/core/cuda.hpp>

Ensures that the size of a matrix is big enough and the matrix has a proper type.

Parameters

rows	Minimum desired number of rows.
cols	Minimum desired number of columns.
type	Desired matrix type.
arr	Destination matrix.

The function does not reallocate memory if the matrix has proper attributes already.

registerPageLocked()

void cv::cuda::registerPageLocked

(

Mat &

m

)

Python:
	cv.cuda.registerPageLocked(	m	) ->	None

#include <opencv2/core/cuda.hpp>

Page-locks the memory of matrix and maps it for the device(s).

Parameters

m	Input matrix.

setBufferPoolConfig()

void cv::cuda::setBufferPoolConfig	(	int	deviceId,
		size_t	stackSize,
		int	stackCount
	)

Python:
	cv.cuda.setBufferPoolConfig(	deviceId, stackSize, stackCount	) ->	None

#include <opencv2/core/cuda.hpp>

setBufferPoolUsage()

void cv::cuda::setBufferPoolUsage

(

bool

on

)

Python:
	cv.cuda.setBufferPoolUsage(	on	) ->	None

#include <opencv2/core/cuda.hpp>

BufferPool management (must be called before Stream creation)

unregisterPageLocked()

void cv::cuda::unregisterPageLocked

(

Mat &

m

)

Python:
	cv.cuda.unregisterPageLocked(	m	) ->	None

#include <opencv2/core/cuda.hpp>

Unmaps the memory of matrix and makes it pageable again.

Parameters

m	Input matrix.