cv::cuda::GpuMat Class Reference

Base storage class for GPU memory with reference counting. More...

#include "cuda.hpp"

Inheritance diagram for cv::cuda::GpuMat:

Classes
class	Allocator

Public Member Functions
	GpuMat (GpuMat::Allocator *allocator=GpuMat::defaultAllocator())
	default constructor More...
	GpuMat (int rows, int cols, int type, GpuMat::Allocator *allocator=GpuMat::defaultAllocator())
	constructs GpuMat of the specified size and type More...
	GpuMat (Size size, int type, GpuMat::Allocator *allocator=GpuMat::defaultAllocator())
	GpuMat (int rows, int cols, int type, Scalar s, GpuMat::Allocator *allocator=GpuMat::defaultAllocator())
	constucts GpuMat and fills it with the specified value _s More...
	GpuMat (Size size, int type, Scalar s, GpuMat::Allocator *allocator=GpuMat::defaultAllocator())
	GpuMat (const GpuMat &m)
	copy constructor More...
	GpuMat (int rows, int cols, int type, void *data, size_t step=Mat::AUTO_STEP)
	constructor for GpuMat headers pointing to user-allocated data More...
	GpuMat (Size size, int type, void *data, size_t step=Mat::AUTO_STEP)
	GpuMat (const GpuMat &m, Range rowRange, Range colRange)
	creates a GpuMat header for a part of the bigger matrix More...
	GpuMat (const GpuMat &m, Rect roi)
	GpuMat (InputArray arr, GpuMat::Allocator *allocator=GpuMat::defaultAllocator())
	builds GpuMat from host memory (Blocking call) More...
	~GpuMat ()
	destructor - calls release() More...
GpuMat &	adjustROI (int dtop, int dbottom, int dleft, int dright)
	moves/resizes the current GpuMat ROI inside the parent GpuMat More...
void	assignTo (GpuMat &m, int type=-1) const
int	channels () const
	returns number of channels More...
GpuMat	clone () const
	returns deep copy of the GpuMat, i.e. the data is copied More...
GpuMat	col (int x) const
	returns a new GpuMat header for the specified column More...
GpuMat	colRange (int startcol, int endcol) const
	... for the specified column span More...
GpuMat	colRange (Range r) const
void	convertTo (OutputArray dst, int rtype) const
	converts GpuMat to another datatype (Blocking call) More...
void	convertTo (OutputArray dst, int rtype, Stream &stream) const
	converts GpuMat to another datatype (Non-Blocking call) More...
void	convertTo (OutputArray dst, int rtype, double alpha, double beta=0.0) const
	converts GpuMat to another datatype with scaling (Blocking call) More...
void	convertTo (OutputArray dst, int rtype, double alpha, Stream &stream) const
	converts GpuMat to another datatype with scaling (Non-Blocking call) More...
void	convertTo (OutputArray dst, int rtype, double alpha, double beta, Stream &stream) const
	converts GpuMat to another datatype with scaling (Non-Blocking call) More...
void	copyTo (OutputArray dst) const
	copies the GpuMat content to device memory (Blocking call) More...
void	copyTo (OutputArray dst, Stream &stream) const
	copies the GpuMat content to device memory (Non-Blocking call) More...
void	copyTo (OutputArray dst, InputArray mask) const
	copies those GpuMat elements to "m" that are marked with non-zero mask elements (Blocking call) More...
void	copyTo (OutputArray dst, InputArray mask, Stream &stream) const
	copies those GpuMat elements to "m" that are marked with non-zero mask elements (Non-Blocking call) More...
void	create (int rows, int cols, int type)
	allocates new GpuMat data unless the GpuMat already has specified size and type More...
void	create (Size size, int type)
int	depth () const
	returns element type More...
void	download (OutputArray dst) const
	Performs data download from GpuMat (Blocking call) More...
void	download (OutputArray dst, Stream &stream) const
	Performs data download from GpuMat (Non-Blocking call) More...
size_t	elemSize () const
	returns element size in bytes More...
size_t	elemSize1 () const
	returns the size of element channel in bytes More...
bool	empty () const
	returns true if GpuMat data is NULL More...
bool	isContinuous () const
void	locateROI (Size &wholeSize, Point &ofs) const
	locates GpuMat header within a parent GpuMat More...
template<typename _Tp >
	operator PtrStep< _Tp > () const
template<typename _Tp >
	operator PtrStepSz< _Tp > () const
GpuMat	operator() (Range rowRange, Range colRange) const
	extracts a rectangular sub-GpuMat (this is a generalized form of row, rowRange etc.) More...
GpuMat	operator() (Rect roi) const
GpuMat &	operator= (const GpuMat &m)
	assignment operators More...
uchar *	ptr (int y=0)
	returns pointer to y-th row More...
const uchar *	ptr (int y=0) const
template<typename _Tp >
_Tp *	ptr (int y=0)
	template version of the above method More...
template<typename _Tp >
const _Tp *	ptr (int y=0) const
void	release ()
	decreases reference counter, deallocate the data when reference counter reaches 0 More...
GpuMat	reshape (int cn, int rows=0) const
GpuMat	row (int y) const
	returns a new GpuMat header for the specified row More...
GpuMat	rowRange (int startrow, int endrow) const
	... for the specified row span More...
GpuMat	rowRange (Range r) const
GpuMat &	setTo (Scalar s)
	sets some of the GpuMat elements to s (Blocking call) More...
GpuMat &	setTo (Scalar s, Stream &stream)
	sets some of the GpuMat elements to s (Non-Blocking call) More...
GpuMat &	setTo (Scalar s, InputArray mask)
	sets some of the GpuMat elements to s, according to the mask (Blocking call) More...
GpuMat &	setTo (Scalar s, InputArray mask, Stream &stream)
	sets some of the GpuMat elements to s, according to the mask (Non-Blocking call) More...
Size	size () const
	returns GpuMat size : width == number of columns, height == number of rows More...
size_t	step1 () const
	returns step/elemSize1() More...
void	swap (GpuMat &mat)
	swaps with other smart pointer More...
int	type () const
	returns element type More...
void	updateContinuityFlag ()
	internal use method: updates the continuity flag More...
void	upload (InputArray arr)
	Performs data upload to GpuMat (Blocking call) More...
void	upload (InputArray arr, Stream &stream)
	Performs data upload to GpuMat (Non-Blocking call) More...

Static Public Member Functions
static GpuMat::Allocator *	defaultAllocator ()
	default allocator More...
static void	setDefaultAllocator (GpuMat::Allocator *allocator)

Public Attributes
Allocator *	allocator
	allocator More...
int	cols
uchar *	data
	pointer to the data More...
const uchar *	dataend
uchar *	datastart
	helper fields used in locateROI and adjustROI More...
int	flags
int *	refcount
int	rows
	the number of rows and columns More...
size_t	step
	a distance between successive rows in bytes; includes the gap if any More...

Detailed Description

Base storage class for GPU memory with reference counting.

Its interface matches the Mat interface with the following limitations:

• no arbitrary dimensions support (only 2D)
• no functions that return references to their data (because references on GPU are not valid for CPU)
• no expression templates technique support

Beware that the latter limitation may lead to overloaded matrix operators that cause memory allocations. The GpuMat class is convertible to cuda::PtrStepSz and cuda::PtrStep so it can be passed directly to the kernel.

Note

In contrast with Mat, in most cases GpuMat::isContinuous() == false . This means that rows are aligned to a size depending on the hardware. Single-row GpuMat is always a continuous matrix.

You are not recommended to leave static or global GpuMat variables allocated, that is, to rely on its destructor. The destruction order of such variables and CUDA context is undefined. GPU memory release function returns error if the CUDA context has been destroyed before.

Some member functions are described as a "Blocking Call" while some are described as a "Non-Blocking Call". Blocking functions are synchronous to host. It is guaranteed that the GPU operation is finished when the function returns. However, non-blocking functions are asynchronous to host. Those functions may return even if the GPU operation is not finished.

Compared to their blocking counterpart, non-blocking functions accept Stream as an additional argument. If a non-default stream is passed, the GPU operation may overlap with operations in other streams.

See also

Mat

Constructor & Destructor Documentation

GpuMat() [1/11]

cv::cuda::GpuMat::GpuMat ( GpuMat::Allocator *  allocator = GpuMat::defaultAllocator() )

explicit

Python:
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	rows, cols, type[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	size, type[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	rows, cols, type, s[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	size, type, s[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	m	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	rows, cols, type, data[, step]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	size, type, data[, step]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	m, rowRange, colRange	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	m, roi	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	arr[, allocator]	)

default constructor

GpuMat() [2/11]

cv::cuda::GpuMat::GpuMat	(	int	rows,
		int	cols,
		int	type,
		GpuMat::Allocator *	allocator = GpuMat::defaultAllocator()
	)

Python:
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	rows, cols, type[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	size, type[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	rows, cols, type, s[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	size, type, s[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	m	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	rows, cols, type, data[, step]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	size, type, data[, step]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	m, rowRange, colRange	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	m, roi	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	arr[, allocator]	)

constructs GpuMat of the specified size and type

GpuMat() [3/11]

cv::cuda::GpuMat::GpuMat	(	Size	size,
		int	type,
		GpuMat::Allocator *	allocator = GpuMat::defaultAllocator()
	)

Python:
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	rows, cols, type[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	size, type[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	rows, cols, type, s[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	size, type, s[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	m	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	rows, cols, type, data[, step]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	size, type, data[, step]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	m, rowRange, colRange	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	m, roi	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	arr[, allocator]	)

GpuMat() [4/11]

cv::cuda::GpuMat::GpuMat	(	int	rows,
		int	cols,
		int	type,
		Scalar	s,
		GpuMat::Allocator *	allocator = GpuMat::defaultAllocator()
	)

Python:
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	rows, cols, type[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	size, type[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	rows, cols, type, s[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	size, type, s[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	m	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	rows, cols, type, data[, step]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	size, type, data[, step]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	m, rowRange, colRange	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	m, roi	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	arr[, allocator]	)

constucts GpuMat and fills it with the specified value _s

GpuMat() [5/11]

cv::cuda::GpuMat::GpuMat	(	Size	size,
		int	type,
		Scalar	s,
		GpuMat::Allocator *	allocator = GpuMat::defaultAllocator()
	)

Python:
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	rows, cols, type[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	size, type[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	rows, cols, type, s[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	size, type, s[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	m	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	rows, cols, type, data[, step]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	size, type, data[, step]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	m, rowRange, colRange	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	m, roi	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	arr[, allocator]	)

GpuMat() [6/11]

cv::cuda::GpuMat::GpuMat

(

const GpuMat &

m

)

Python:
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	rows, cols, type[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	size, type[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	rows, cols, type, s[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	size, type, s[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	m	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	rows, cols, type, data[, step]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	size, type, data[, step]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	m, rowRange, colRange	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	m, roi	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	arr[, allocator]	)

copy constructor

GpuMat() [7/11]

cv::cuda::GpuMat::GpuMat	(	int	rows,
		int	cols,
		int	type,
		void *	data,
		size_t	step = Mat::AUTO_STEP
	)

Python:
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	rows, cols, type[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	size, type[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	rows, cols, type, s[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	size, type, s[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	m	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	rows, cols, type, data[, step]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	size, type, data[, step]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	m, rowRange, colRange	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	m, roi	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	arr[, allocator]	)

constructor for GpuMat headers pointing to user-allocated data

GpuMat() [8/11]

cv::cuda::GpuMat::GpuMat	(	Size	size,
		int	type,
		void *	data,
		size_t	step = Mat::AUTO_STEP
	)

Python:
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	rows, cols, type[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	size, type[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	rows, cols, type, s[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	size, type, s[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	m	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	rows, cols, type, data[, step]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	size, type, data[, step]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	m, rowRange, colRange	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	m, roi	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	arr[, allocator]	)

GpuMat() [9/11]

cv::cuda::GpuMat::GpuMat	(	const GpuMat &	m,
		Range	rowRange,
		Range	colRange
	)

Python:
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	rows, cols, type[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	size, type[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	rows, cols, type, s[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	size, type, s[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	m	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	rows, cols, type, data[, step]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	size, type, data[, step]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	m, rowRange, colRange	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	m, roi	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	arr[, allocator]	)

creates a GpuMat header for a part of the bigger matrix

GpuMat() [10/11]

cv::cuda::GpuMat::GpuMat	(	const GpuMat &	m,
		Rect	roi
	)

Python:
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	rows, cols, type[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	size, type[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	rows, cols, type, s[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	size, type, s[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	m	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	rows, cols, type, data[, step]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	size, type, data[, step]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	m, rowRange, colRange	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	m, roi	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	arr[, allocator]	)

GpuMat() [11/11]

cv::cuda::GpuMat::GpuMat ( InputArray  arr, GpuMat::Allocator *  allocator = GpuMat::defaultAllocator()  )

explicit

Python:
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	rows, cols, type[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	size, type[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	rows, cols, type, s[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	size, type, s[, allocator]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	m	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	rows, cols, type, data[, step]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	size, type, data[, step]	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	m, rowRange, colRange	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	m, roi	)
	<cuda_GpuMat object>	=	cv.cuda_GpuMat(	arr[, allocator]	)

builds GpuMat from host memory (Blocking call)

~GpuMat()

cv::cuda::GpuMat::~GpuMat

(

)

destructor - calls release()

Member Function Documentation

adjustROI()

GpuMat& cv::cuda::GpuMat::adjustROI	(	int	dtop,
		int	dbottom,
		int	dleft,
		int	dright
	)

Python:
	retval	=	cv.cuda_GpuMat.adjustROI(	dtop, dbottom, dleft, dright	)

moves/resizes the current GpuMat ROI inside the parent GpuMat

assignTo()

void cv::cuda::GpuMat::assignTo	(	GpuMat &	m,
		int	type = -1
	)		const

Python:
	None	=	cv.cuda_GpuMat.assignTo(	m[, type]	)

channels()

int cv::cuda::GpuMat::channels

(

)

const

Python:
	retval	=	cv.cuda_GpuMat.channels(		)

returns number of channels

clone()

GpuMat cv::cuda::GpuMat::clone

(

)

const

Python:
	retval	=	cv.cuda_GpuMat.clone(		)

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

col()

GpuMat cv::cuda::GpuMat::col

(

int

x

)

const

Python:
	retval	=	cv.cuda_GpuMat.col(	x	)

returns a new GpuMat header for the specified column

colRange() [1/2]

GpuMat cv::cuda::GpuMat::colRange	(	int	startcol,
		int	endcol
	)		const

Python:
	retval	=	cv.cuda_GpuMat.colRange(	startcol, endcol	)
	retval	=	cv.cuda_GpuMat.colRange(	r	)

... for the specified column span

colRange() [2/2]

GpuMat cv::cuda::GpuMat::colRange

(

Range

r

)

const

Python:
	retval	=	cv.cuda_GpuMat.colRange(	startcol, endcol	)
	retval	=	cv.cuda_GpuMat.colRange(	r	)

convertTo() [1/5]

void cv::cuda::GpuMat::convertTo	(	OutputArray	dst,
		int	rtype
	)		const

Python:
	dst	=	cv.cuda_GpuMat.convertTo(	rtype[, dst]	)
	dst	=	cv.cuda_GpuMat.convertTo(	rtype, stream[, dst]	)
	dst	=	cv.cuda_GpuMat.convertTo(	rtype, alpha[, dst[, beta]]	)
	dst	=	cv.cuda_GpuMat.convertTo(	rtype, alpha, stream[, dst]	)
	dst	=	cv.cuda_GpuMat.convertTo(	rtype, alpha, beta, stream[, dst]	)

converts GpuMat to another datatype (Blocking call)

convertTo() [2/5]

void cv::cuda::GpuMat::convertTo	(	OutputArray	dst,
		int	rtype,
		Stream &	stream
	)		const

Python:
	dst	=	cv.cuda_GpuMat.convertTo(	rtype[, dst]	)
	dst	=	cv.cuda_GpuMat.convertTo(	rtype, stream[, dst]	)
	dst	=	cv.cuda_GpuMat.convertTo(	rtype, alpha[, dst[, beta]]	)
	dst	=	cv.cuda_GpuMat.convertTo(	rtype, alpha, stream[, dst]	)
	dst	=	cv.cuda_GpuMat.convertTo(	rtype, alpha, beta, stream[, dst]	)

converts GpuMat to another datatype (Non-Blocking call)

convertTo() [3/5]

void cv::cuda::GpuMat::convertTo	(	OutputArray	dst,
		int	rtype,
		double	alpha,
		double	beta = 0.0
	)		const

Python:
	dst	=	cv.cuda_GpuMat.convertTo(	rtype[, dst]	)
	dst	=	cv.cuda_GpuMat.convertTo(	rtype, stream[, dst]	)
	dst	=	cv.cuda_GpuMat.convertTo(	rtype, alpha[, dst[, beta]]	)
	dst	=	cv.cuda_GpuMat.convertTo(	rtype, alpha, stream[, dst]	)
	dst	=	cv.cuda_GpuMat.convertTo(	rtype, alpha, beta, stream[, dst]	)

converts GpuMat to another datatype with scaling (Blocking call)

convertTo() [4/5]

void cv::cuda::GpuMat::convertTo	(	OutputArray	dst,
		int	rtype,
		double	alpha,
		Stream &	stream
	)		const

Python:
	dst	=	cv.cuda_GpuMat.convertTo(	rtype[, dst]	)
	dst	=	cv.cuda_GpuMat.convertTo(	rtype, stream[, dst]	)
	dst	=	cv.cuda_GpuMat.convertTo(	rtype, alpha[, dst[, beta]]	)
	dst	=	cv.cuda_GpuMat.convertTo(	rtype, alpha, stream[, dst]	)
	dst	=	cv.cuda_GpuMat.convertTo(	rtype, alpha, beta, stream[, dst]	)

converts GpuMat to another datatype with scaling (Non-Blocking call)

convertTo() [5/5]

void cv::cuda::GpuMat::convertTo	(	OutputArray	dst,
		int	rtype,
		double	alpha,
		double	beta,
		Stream &	stream
	)		const

Python:
	dst	=	cv.cuda_GpuMat.convertTo(	rtype[, dst]	)
	dst	=	cv.cuda_GpuMat.convertTo(	rtype, stream[, dst]	)
	dst	=	cv.cuda_GpuMat.convertTo(	rtype, alpha[, dst[, beta]]	)
	dst	=	cv.cuda_GpuMat.convertTo(	rtype, alpha, stream[, dst]	)
	dst	=	cv.cuda_GpuMat.convertTo(	rtype, alpha, beta, stream[, dst]	)

converts GpuMat to another datatype with scaling (Non-Blocking call)

copyTo() [1/4]

void cv::cuda::GpuMat::copyTo

(

OutputArray

dst

)

const

Python:
	dst	=	cv.cuda_GpuMat.copyTo(	[, dst]	)
	dst	=	cv.cuda_GpuMat.copyTo(	stream[, dst]	)
	dst	=	cv.cuda_GpuMat.copyTo(	mask[, dst]	)
	dst	=	cv.cuda_GpuMat.copyTo(	mask, stream[, dst]	)

copies the GpuMat content to device memory (Blocking call)

copyTo() [2/4]

void cv::cuda::GpuMat::copyTo	(	OutputArray	dst,
		Stream &	stream
	)		const

Python:
	dst	=	cv.cuda_GpuMat.copyTo(	[, dst]	)
	dst	=	cv.cuda_GpuMat.copyTo(	stream[, dst]	)
	dst	=	cv.cuda_GpuMat.copyTo(	mask[, dst]	)
	dst	=	cv.cuda_GpuMat.copyTo(	mask, stream[, dst]	)

copies the GpuMat content to device memory (Non-Blocking call)

copyTo() [3/4]

void cv::cuda::GpuMat::copyTo	(	OutputArray	dst,
		InputArray	mask
	)		const

Python:
	dst	=	cv.cuda_GpuMat.copyTo(	[, dst]	)
	dst	=	cv.cuda_GpuMat.copyTo(	stream[, dst]	)
	dst	=	cv.cuda_GpuMat.copyTo(	mask[, dst]	)
	dst	=	cv.cuda_GpuMat.copyTo(	mask, stream[, dst]	)

copies those GpuMat elements to "m" that are marked with non-zero mask elements (Blocking call)

copyTo() [4/4]

void cv::cuda::GpuMat::copyTo	(	OutputArray	dst,
		InputArray	mask,
		Stream &	stream
	)		const

Python:
	dst	=	cv.cuda_GpuMat.copyTo(	[, dst]	)
	dst	=	cv.cuda_GpuMat.copyTo(	stream[, dst]	)
	dst	=	cv.cuda_GpuMat.copyTo(	mask[, dst]	)
	dst	=	cv.cuda_GpuMat.copyTo(	mask, stream[, dst]	)

copies those GpuMat elements to "m" that are marked with non-zero mask elements (Non-Blocking call)

create() [1/2]

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

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

allocates new GpuMat data unless the GpuMat already has specified size and type

create() [2/2]

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

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

defaultAllocator()

static GpuMat::Allocator* cv::cuda::GpuMat::defaultAllocator ( )

static

Python:
	retval	=	cv.cuda.GpuMat_defaultAllocator(		)

default allocator

depth()

int cv::cuda::GpuMat::depth

(

)

const

Python:
	retval	=	cv.cuda_GpuMat.depth(		)

returns element type

download() [1/2]

void cv::cuda::GpuMat::download

(

OutputArray

dst

)

const

Python:
	dst	=	cv.cuda_GpuMat.download(	[, dst]	)
	dst	=	cv.cuda_GpuMat.download(	stream[, dst]	)

Performs data download from GpuMat (Blocking call)

This function copies data from device memory to host memory. As being a blocking call, it is guaranteed that the copy operation is finished when this function returns.

download() [2/2]

void cv::cuda::GpuMat::download	(	OutputArray	dst,
		Stream &	stream
	)		const

Python:
	dst	=	cv.cuda_GpuMat.download(	[, dst]	)
	dst	=	cv.cuda_GpuMat.download(	stream[, dst]	)

Performs data download from GpuMat (Non-Blocking call)

This function copies data from device memory to host memory. As being a non-blocking call, this function may return even if the copy operation is not finished.

The copy operation may be overlapped with operations in other non-default streams if stream is not the default stream and dst is HostMem allocated with HostMem::PAGE_LOCKED option.

elemSize()

size_t cv::cuda::GpuMat::elemSize

(

)

const

Python:
	retval	=	cv.cuda_GpuMat.elemSize(		)

returns element size in bytes

elemSize1()

size_t cv::cuda::GpuMat::elemSize1

(

)

const

Python:
	retval	=	cv.cuda_GpuMat.elemSize1(		)

returns the size of element channel in bytes

empty()

bool cv::cuda::GpuMat::empty

(

)

const

Python:
	retval	=	cv.cuda_GpuMat.empty(		)

returns true if GpuMat data is NULL

isContinuous()

bool cv::cuda::GpuMat::isContinuous

(

)

const

Python:
	retval	=	cv.cuda_GpuMat.isContinuous(		)

returns true iff the GpuMat data is continuous (i.e. when there are no gaps between successive rows)

locateROI()

void cv::cuda::GpuMat::locateROI	(	Size &	wholeSize,
		Point &	ofs
	)		const

Python:
	None	=	cv.cuda_GpuMat.locateROI(	wholeSize, ofs	)

locates GpuMat header within a parent GpuMat

operator PtrStep< _Tp >()

template<typename _Tp >

cv::cuda::GpuMat::operator PtrStep< _Tp >

(

)

const

operator PtrStepSz< _Tp >()

template<typename _Tp >

cv::cuda::GpuMat::operator PtrStepSz< _Tp >

(

)

const

operator()() [1/2]

GpuMat cv::cuda::GpuMat::operator()	(	Range	rowRange,
		Range	colRange
	)		const

extracts a rectangular sub-GpuMat (this is a generalized form of row, rowRange etc.)

operator()() [2/2]

GpuMat cv::cuda::GpuMat::operator()

(

Rect

roi

)

const

operator=()

GpuMat& cv::cuda::GpuMat::operator=

(

const GpuMat &

m

)

assignment operators

ptr() [1/4]

uchar* cv::cuda::GpuMat::ptr

(

int

y = 0

)

returns pointer to y-th row

ptr() [2/4]

const uchar* cv::cuda::GpuMat::ptr

(

int

y = 0

)

const

ptr() [3/4]

template<typename _Tp >

_Tp* cv::cuda::GpuMat::ptr

(

int

y = 0

)

template version of the above method

ptr() [4/4]

template<typename _Tp >

const _Tp* cv::cuda::GpuMat::ptr

(

int

y = 0

)

const

release()

void cv::cuda::GpuMat::release

(

)

decreases reference counter, deallocate the data when reference counter reaches 0

reshape()

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

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

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

row()

GpuMat cv::cuda::GpuMat::row

(

int

y

)

const

Python:
	retval	=	cv.cuda_GpuMat.row(	y	)

returns a new GpuMat header for the specified row

rowRange() [1/2]

GpuMat cv::cuda::GpuMat::rowRange	(	int	startrow,
		int	endrow
	)		const

Python:
	retval	=	cv.cuda_GpuMat.rowRange(	startrow, endrow	)
	retval	=	cv.cuda_GpuMat.rowRange(	r	)

... for the specified row span

rowRange() [2/2]

GpuMat cv::cuda::GpuMat::rowRange

(

Range

r

)

const

Python:
	retval	=	cv.cuda_GpuMat.rowRange(	startrow, endrow	)
	retval	=	cv.cuda_GpuMat.rowRange(	r	)

setDefaultAllocator()

static void cv::cuda::GpuMat::setDefaultAllocator ( GpuMat::Allocator *  allocator )

static

Python:
	None	=	cv.cuda.GpuMat_setDefaultAllocator(	allocator	)

setTo() [1/4]

GpuMat& cv::cuda::GpuMat::setTo

(

Scalar

s

)

Python:
	retval	=	cv.cuda_GpuMat.setTo(	s	)
	retval	=	cv.cuda_GpuMat.setTo(	s, stream	)
	retval	=	cv.cuda_GpuMat.setTo(	s, mask	)
	retval	=	cv.cuda_GpuMat.setTo(	s, mask, stream	)

sets some of the GpuMat elements to s (Blocking call)

setTo() [2/4]

GpuMat& cv::cuda::GpuMat::setTo	(	Scalar	s,
		Stream &	stream
	)

Python:
	retval	=	cv.cuda_GpuMat.setTo(	s	)
	retval	=	cv.cuda_GpuMat.setTo(	s, stream	)
	retval	=	cv.cuda_GpuMat.setTo(	s, mask	)
	retval	=	cv.cuda_GpuMat.setTo(	s, mask, stream	)

sets some of the GpuMat elements to s (Non-Blocking call)

setTo() [3/4]

GpuMat& cv::cuda::GpuMat::setTo	(	Scalar	s,
		InputArray	mask
	)

Python:
	retval	=	cv.cuda_GpuMat.setTo(	s	)
	retval	=	cv.cuda_GpuMat.setTo(	s, stream	)
	retval	=	cv.cuda_GpuMat.setTo(	s, mask	)
	retval	=	cv.cuda_GpuMat.setTo(	s, mask, stream	)

sets some of the GpuMat elements to s, according to the mask (Blocking call)

setTo() [4/4]

GpuMat& cv::cuda::GpuMat::setTo	(	Scalar	s,
		InputArray	mask,
		Stream &	stream
	)

Python:
	retval	=	cv.cuda_GpuMat.setTo(	s	)
	retval	=	cv.cuda_GpuMat.setTo(	s, stream	)
	retval	=	cv.cuda_GpuMat.setTo(	s, mask	)
	retval	=	cv.cuda_GpuMat.setTo(	s, mask, stream	)

sets some of the GpuMat elements to s, according to the mask (Non-Blocking call)

size()

Size cv::cuda::GpuMat::size

(

)

const

Python:
	retval	=	cv.cuda_GpuMat.size(		)

returns GpuMat size : width == number of columns, height == number of rows

step1()

size_t cv::cuda::GpuMat::step1

(

)

const

Python:
	retval	=	cv.cuda_GpuMat.step1(		)

returns step/elemSize1()

swap()

void cv::cuda::GpuMat::swap

(

GpuMat &

mat

)

Python:
	None	=	cv.cuda_GpuMat.swap(	mat	)

swaps with other smart pointer

type()

int cv::cuda::GpuMat::type

(

)

const

Python:
	retval	=	cv.cuda_GpuMat.type(		)

returns element type

updateContinuityFlag()

void cv::cuda::GpuMat::updateContinuityFlag

(

)

Python:
	None	=	cv.cuda_GpuMat.updateContinuityFlag(		)

internal use method: updates the continuity flag

upload() [1/2]

void cv::cuda::GpuMat::upload

(

InputArray

arr

)

Python:
	None	=	cv.cuda_GpuMat.upload(	arr	)
	None	=	cv.cuda_GpuMat.upload(	arr, stream	)

Performs data upload to GpuMat (Blocking call)

This function copies data from host memory to device memory. As being a blocking call, it is guaranteed that the copy operation is finished when this function returns.

upload() [2/2]

void cv::cuda::GpuMat::upload	(	InputArray	arr,
		Stream &	stream
	)

Python:
	None	=	cv.cuda_GpuMat.upload(	arr	)
	None	=	cv.cuda_GpuMat.upload(	arr, stream	)

Performs data upload to GpuMat (Non-Blocking call)

This function copies data from host memory to device memory. As being a non-blocking call, this function may return even if the copy operation is not finished.

The copy operation may be overlapped with operations in other non-default streams if stream is not the default stream and dst is HostMem allocated with HostMem::PAGE_LOCKED option.

Member Data Documentation

allocator

Allocator* cv::cuda::GpuMat::allocator

allocator

cols

int cv::cuda::GpuMat::cols

data

uchar* cv::cuda::GpuMat::data

pointer to the data

dataend

const uchar* cv::cuda::GpuMat::dataend

datastart

uchar* cv::cuda::GpuMat::datastart

helper fields used in locateROI and adjustROI

flags

int cv::cuda::GpuMat::flags

includes several bit-fields:

• the magic signature
• continuity flag
• depth
• number of channels

refcount

int* cv::cuda::GpuMat::refcount

pointer to the reference counter; when GpuMat points to user-allocated data, the pointer is NULL

rows

int cv::cuda::GpuMat::rows

the number of rows and columns

step

size_t cv::cuda::GpuMat::step

a distance between successive rows in bytes; includes the gap if any

---

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

• core/include/opencv2/core/cuda.hpp