cv::cuda::GpuMat Class Reference

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

#include <opencv2/core/cuda.hpp>

Collaboration diagram for cv::cuda::GpuMat:

Classes
class	Allocator

Public Member Functions
	GpuMat (const GpuMat &m)
	copy constructor
	GpuMat (const GpuMat &m, Range rowRange, Range colRange)
	creates a GpuMat header for a part of the bigger matrix
	GpuMat (const GpuMat &m, Rect roi)
	GpuMat (GpuMat::Allocator *allocator=GpuMat::defaultAllocator())
	default constructor
	GpuMat (InputArray arr, GpuMat::Allocator *allocator=GpuMat::defaultAllocator())
	builds GpuMat from host memory (Blocking call)
	GpuMat (int rows, int cols, int type, GpuMat::Allocator *allocator=GpuMat::defaultAllocator())
	constructs GpuMat of the specified size and type
	GpuMat (int rows, int cols, int type, Scalar s, GpuMat::Allocator *allocator=GpuMat::defaultAllocator())
	constructs GpuMat and fills it with the specified value _s
	GpuMat (int rows, int cols, int type, void *data, size_t step=Mat::AUTO_STEP)
	constructor for GpuMat headers pointing to user-allocated data
	GpuMat (Size size, int type, GpuMat::Allocator *allocator=GpuMat::defaultAllocator())
	GpuMat (Size size, int type, Scalar s, GpuMat::Allocator *allocator=GpuMat::defaultAllocator())
	GpuMat (Size size, int type, void *data, size_t step=Mat::AUTO_STEP)
	~GpuMat ()
	destructor - calls release()
GpuMat &	adjustROI (int dtop, int dbottom, int dleft, int dright)
	moves/resizes the current GpuMat ROI inside the parent GpuMat
void	assignTo (GpuMat &m, int type=-1) const
int	channels () const
	returns number of channels
GpuMat	clone () const
	returns deep copy of the GpuMat, i.e. the data is copied
GpuMat	col (int x) const
	returns a new GpuMat header for the specified column
GpuMat	colRange (int startcol, int endcol) const
	... for the specified column span
GpuMat	colRange (Range r) const
void	convertTo (GpuMat &dst, int rtype, double alpha, double beta, Stream &stream) const
	bindings overload which converts GpuMat to another datatype with scaling (Non-Blocking call)
void	convertTo (GpuMat &dst, int rtype, double alpha=1.0, double beta=0.0) const
	bindings overload which converts GpuMat to another datatype with scaling(Blocking call)
void	convertTo (GpuMat &dst, int rtype, Stream &stream) const
	bindings overload which converts GpuMat to another datatype (Non-Blocking call)
void	convertTo (OutputArray dst, int rtype) const
	converts GpuMat to another datatype (Blocking call)
void	convertTo (OutputArray dst, int rtype, double alpha, double beta, Stream &stream) const
	converts GpuMat to another datatype with scaling (Non-Blocking call)
void	convertTo (OutputArray dst, int rtype, double alpha, double beta=0.0) const
	converts GpuMat to another datatype with scaling (Blocking call)
void	convertTo (OutputArray dst, int rtype, double alpha, Stream &stream) const
	converts GpuMat to another datatype with scaling (Non-Blocking call)
void	convertTo (OutputArray dst, int rtype, Stream &stream) const
	converts GpuMat to another datatype (Non-Blocking call)
void	copyTo (GpuMat &dst) const
	bindings overload which copies the GpuMat content to device memory (Blocking call)
void	copyTo (GpuMat &dst, GpuMat &mask) const
	bindings overload which copies those GpuMat elements to "m" that are marked with non-zero mask elements (Blocking call)
void	copyTo (GpuMat &dst, GpuMat &mask, Stream &stream) const
	bindings overload which copies those GpuMat elements to "m" that are marked with non-zero mask elements (Non-Blocking call)
void	copyTo (GpuMat &dst, Stream &stream) const
	bindings overload which copies the GpuMat content to device memory (Non-Blocking call)
void	copyTo (OutputArray dst) const
	copies the GpuMat content to device memory (Blocking call)
void	copyTo (OutputArray dst, InputArray mask) const
	copies those GpuMat elements to "m" that are marked with non-zero mask elements (Blocking call)
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)
void	copyTo (OutputArray dst, Stream &stream) const
	copies the GpuMat content to device memory (Non-Blocking call)
void	create (int rows, int cols, int type)
	allocates new GpuMat data unless the GpuMat already has specified size and type
void	create (Size size, int type)
void *	cudaPtr () const
int	depth () const
	returns element type
void	download (OutputArray dst) const
	Performs data download from GpuMat (Blocking call)
void	download (OutputArray dst, Stream &stream) const
	Performs data download from GpuMat (Non-Blocking call)
size_t	elemSize () const
	returns element size in bytes
size_t	elemSize1 () const
	returns the size of element channel in bytes
bool	empty () const
	returns true if GpuMat data is NULL
bool	isContinuous () const
void	locateROI (Size &wholeSize, Point &ofs) const
	locates GpuMat header within a parent GpuMat
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.)
GpuMat	operator() (Rect roi) const
GpuMat &	operator= (const GpuMat &m)
	assignment operators
uchar *	ptr (int y=0)
	returns pointer to y-th row
template<typename _Tp >
_Tp *	ptr (int y=0)
	template version of the above method
const uchar *	ptr (int y=0) const
template<typename _Tp >
const _Tp *	ptr (int y=0) const
void	release ()
	decreases reference counter, deallocate the data when reference counter reaches 0
GpuMat	reshape (int cn, int rows=0) const
GpuMat	row (int y) const
	returns a new GpuMat header for the specified row
GpuMat	rowRange (int startrow, int endrow) const
	... for the specified row span
GpuMat	rowRange (Range r) const
GpuMat &	setTo (Scalar s)
	sets some of the GpuMat elements to s (Blocking call)
GpuMat &	setTo (Scalar s, InputArray mask)
	sets some of the GpuMat elements to s, according to the mask (Blocking call)
GpuMat &	setTo (Scalar s, InputArray mask, Stream &stream)
	sets some of the GpuMat elements to s, according to the mask (Non-Blocking call)
GpuMat &	setTo (Scalar s, Stream &stream)
	sets some of the GpuMat elements to s (Non-Blocking call)
Size	size () const
	returns GpuMat size : width == number of columns, height == number of rows
size_t	step1 () const
	returns step/elemSize1()
void	swap (GpuMat &mat)
	swaps with other smart pointer
int	type () const
	returns element type
void	updateContinuityFlag ()
	internal use method: updates the continuity flag
void	upload (InputArray arr)
	Performs data upload to GpuMat (Blocking call)
void	upload (InputArray arr, Stream &stream)
	Performs data upload to GpuMat (Non-Blocking call)

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

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

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:
	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(	m, rowRange, colRange	) ->	<cuda_GpuMat object>
	cv.cuda.GpuMat(	m, roi	) ->	<cuda_GpuMat object>
	cv.cuda.GpuMat(	arr[, allocator]	) ->	<cuda_GpuMat object>

default constructor

GpuMat() [2/11]

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

Python:
	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(	m, rowRange, colRange	) ->	<cuda_GpuMat object>
	cv.cuda.GpuMat(	m, roi	) ->	<cuda_GpuMat object>
	cv.cuda.GpuMat(	arr[, allocator]	) ->	<cuda_GpuMat object>

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:
	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(	m, rowRange, colRange	) ->	<cuda_GpuMat object>
	cv.cuda.GpuMat(	m, roi	) ->	<cuda_GpuMat object>
	cv.cuda.GpuMat(	arr[, allocator]	) ->	<cuda_GpuMat object>

GpuMat() [4/11]

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

Python:
	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(	m, rowRange, colRange	) ->	<cuda_GpuMat object>
	cv.cuda.GpuMat(	m, roi	) ->	<cuda_GpuMat object>
	cv.cuda.GpuMat(	arr[, allocator]	) ->	<cuda_GpuMat object>

constructs 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:
	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(	m, rowRange, colRange	) ->	<cuda_GpuMat object>
	cv.cuda.GpuMat(	m, roi	) ->	<cuda_GpuMat object>
	cv.cuda.GpuMat(	arr[, allocator]	) ->	<cuda_GpuMat object>

GpuMat() [6/11]

cv::cuda::GpuMat::GpuMat

(

const GpuMat &

m

)

Python:
	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(	m, rowRange, colRange	) ->	<cuda_GpuMat object>
	cv.cuda.GpuMat(	m, roi	) ->	<cuda_GpuMat object>
	cv.cuda.GpuMat(	arr[, allocator]	) ->	<cuda_GpuMat object>

copy constructor

GpuMat() [7/11]

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

Python:
	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(	m, rowRange, colRange	) ->	<cuda_GpuMat object>
	cv.cuda.GpuMat(	m, roi	) ->	<cuda_GpuMat object>
	cv.cuda.GpuMat(	arr[, allocator]	) ->	<cuda_GpuMat object>

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:
	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(	m, rowRange, colRange	) ->	<cuda_GpuMat object>
	cv.cuda.GpuMat(	m, roi	) ->	<cuda_GpuMat object>
	cv.cuda.GpuMat(	arr[, allocator]	) ->	<cuda_GpuMat object>

GpuMat() [9/11]

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

Python:
	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(	m, rowRange, colRange	) ->	<cuda_GpuMat object>
	cv.cuda.GpuMat(	m, roi	) ->	<cuda_GpuMat object>
	cv.cuda.GpuMat(	arr[, allocator]	) ->	<cuda_GpuMat object>

creates a GpuMat header for a part of the bigger matrix

GpuMat() [10/11]

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

Python:
	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(	m, rowRange, colRange	) ->	<cuda_GpuMat object>
	cv.cuda.GpuMat(	m, roi	) ->	<cuda_GpuMat object>
	cv.cuda.GpuMat(	arr[, allocator]	) ->	<cuda_GpuMat object>

GpuMat() [11/11]

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

explicit

Python:
	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(	m, rowRange, colRange	) ->	<cuda_GpuMat object>
	cv.cuda.GpuMat(	m, roi	) ->	<cuda_GpuMat object>
	cv.cuda.GpuMat(	arr[, allocator]	) ->	<cuda_GpuMat object>

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:
	cv.cuda.GpuMat.adjustROI(	dtop, dbottom, dleft, dright	) ->	retval

moves/resizes the current GpuMat ROI inside the parent GpuMat

assignTo()

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

Python:
	cv.cuda.GpuMat.assignTo(	m[, type]	) ->	None

channels()

int cv::cuda::GpuMat::channels

(

)

const

Python:
	cv.cuda.GpuMat.channels(		) ->	retval

returns number of channels

clone()

GpuMat cv::cuda::GpuMat::clone

(

)

const

Python:
	cv.cuda.GpuMat.clone(		) ->	retval

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

col()

GpuMat cv::cuda::GpuMat::col

(

int

x

)

const

Python:
	cv.cuda.GpuMat.col(	x	) ->	retval

returns a new GpuMat header for the specified column

colRange() [1/2]

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

Python:
	cv.cuda.GpuMat.colRange(	startcol, endcol	) ->	retval
	cv.cuda.GpuMat.colRange(	r	) ->	retval

... for the specified column span

colRange() [2/2]

GpuMat cv::cuda::GpuMat::colRange

(

Range

r

)

const

Python:
	cv.cuda.GpuMat.colRange(	startcol, endcol	) ->	retval
	cv.cuda.GpuMat.colRange(	r	) ->	retval

convertTo() [1/8]

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

inline

Python:
	cv.cuda.GpuMat.convertTo(	rtype, stream[, dst]	) ->	dst
	cv.cuda.GpuMat.convertTo(	rtype[, dst[, alpha[, beta]]]	) ->	dst
	cv.cuda.GpuMat.convertTo(	rtype, alpha, beta, stream[, dst]	) ->	dst

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

convertTo() [2/8]

void cv::cuda::GpuMat::convertTo ( GpuMat &  dst, int  rtype, double  alpha = 1.0, double  beta = 0.0  ) const

inline

Python:
	cv.cuda.GpuMat.convertTo(	rtype, stream[, dst]	) ->	dst
	cv.cuda.GpuMat.convertTo(	rtype[, dst[, alpha[, beta]]]	) ->	dst
	cv.cuda.GpuMat.convertTo(	rtype, alpha, beta, stream[, dst]	) ->	dst

bindings overload which converts GpuMat to another datatype with scaling(Blocking call)

convertTo() [3/8]

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

inline

Python:
	cv.cuda.GpuMat.convertTo(	rtype, stream[, dst]	) ->	dst
	cv.cuda.GpuMat.convertTo(	rtype[, dst[, alpha[, beta]]]	) ->	dst
	cv.cuda.GpuMat.convertTo(	rtype, alpha, beta, stream[, dst]	) ->	dst

bindings overload which converts GpuMat to another datatype (Non-Blocking call)

convertTo() [4/8]

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

Python:
	cv.cuda.GpuMat.convertTo(	rtype, stream[, dst]	) ->	dst
	cv.cuda.GpuMat.convertTo(	rtype[, dst[, alpha[, beta]]]	) ->	dst
	cv.cuda.GpuMat.convertTo(	rtype, alpha, beta, stream[, dst]	) ->	dst

converts GpuMat to another datatype (Blocking call)

convertTo() [5/8]

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

Python:
	cv.cuda.GpuMat.convertTo(	rtype, stream[, dst]	) ->	dst
	cv.cuda.GpuMat.convertTo(	rtype[, dst[, alpha[, beta]]]	) ->	dst
	cv.cuda.GpuMat.convertTo(	rtype, alpha, beta, stream[, dst]	) ->	dst

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

convertTo() [6/8]

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

Python:
	cv.cuda.GpuMat.convertTo(	rtype, stream[, dst]	) ->	dst
	cv.cuda.GpuMat.convertTo(	rtype[, dst[, alpha[, beta]]]	) ->	dst
	cv.cuda.GpuMat.convertTo(	rtype, alpha, beta, stream[, dst]	) ->	dst

converts GpuMat to another datatype with scaling (Blocking call)

convertTo() [7/8]

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

Python:
	cv.cuda.GpuMat.convertTo(	rtype, stream[, dst]	) ->	dst
	cv.cuda.GpuMat.convertTo(	rtype[, dst[, alpha[, beta]]]	) ->	dst
	cv.cuda.GpuMat.convertTo(	rtype, alpha, beta, stream[, dst]	) ->	dst

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

convertTo() [8/8]

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

Python:
	cv.cuda.GpuMat.convertTo(	rtype, stream[, dst]	) ->	dst
	cv.cuda.GpuMat.convertTo(	rtype[, dst[, alpha[, beta]]]	) ->	dst
	cv.cuda.GpuMat.convertTo(	rtype, alpha, beta, stream[, dst]	) ->	dst

converts GpuMat to another datatype (Non-Blocking call)

copyTo() [1/8]

void cv::cuda::GpuMat::copyTo ( GpuMat &  dst ) const

inline

Python:
	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]	) ->	dst

bindings overload which copies the GpuMat content to device memory (Blocking call)

Here is the call graph for this function:

copyTo() [2/8]

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

inline

Python:
	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]	) ->	dst

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

Here is the call graph for this function:

copyTo() [3/8]

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

inline

Python:
	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]	) ->	dst

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

Here is the call graph for this function:

copyTo() [4/8]

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

inline

Python:
	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]	) ->	dst

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

Here is the call graph for this function:

copyTo() [5/8]

void cv::cuda::GpuMat::copyTo

(

OutputArray

dst

)

const

Python:
	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]	) ->	dst

copies the GpuMat content to device memory (Blocking call)

copyTo() [6/8]

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

Python:
	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]	) ->	dst

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

copyTo() [7/8]

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

Python:
	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]	) ->	dst

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

copyTo() [8/8]

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

Python:
	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]	) ->	dst

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

create() [1/2]

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

Python:
	cv.cuda.GpuMat.create(	rows, cols, type	) ->	None
	cv.cuda.GpuMat.create(	size, type	) ->	None

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:
	cv.cuda.GpuMat.create(	rows, cols, type	) ->	None
	cv.cuda.GpuMat.create(	size, type	) ->	None

cudaPtr()

void * cv::cuda::GpuMat::cudaPtr

(

)

const

Python:
	cv.cuda.GpuMat.cudaPtr(		) ->	retval

defaultAllocator()

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

static

Python:
	cv.cuda.GpuMat.defaultAllocator(		) ->	retval
	cv.cuda.GpuMat_defaultAllocator(		) ->	retval

default allocator

depth()

int cv::cuda::GpuMat::depth

(

)

const

Python:
	cv.cuda.GpuMat.depth(		) ->	retval

returns element type

download() [1/2]

void cv::cuda::GpuMat::download

(

OutputArray

dst

)

const

Python:
	cv.cuda.GpuMat.download(	[, dst]	) ->	dst
	cv.cuda.GpuMat.download(	stream[, dst]	) ->	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:
	cv.cuda.GpuMat.download(	[, dst]	) ->	dst
	cv.cuda.GpuMat.download(	stream[, dst]	) ->	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:
	cv.cuda.GpuMat.elemSize(		) ->	retval

returns element size in bytes

elemSize1()

size_t cv::cuda::GpuMat::elemSize1

(

)

const

Python:
	cv.cuda.GpuMat.elemSize1(		) ->	retval

returns the size of element channel in bytes

empty()

bool cv::cuda::GpuMat::empty

(

)

const

Python:
	cv.cuda.GpuMat.empty(		) ->	retval

returns true if GpuMat data is NULL

isContinuous()

bool cv::cuda::GpuMat::isContinuous

(

)

const

Python:
	cv.cuda.GpuMat.isContinuous(		) ->	retval

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:
	cv.cuda.GpuMat.locateROI(	wholeSize, ofs	) ->	None

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]

template<typename _Tp >

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

(

int

y = 0

)

template version of the above method

ptr() [3/4]

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

(

int

y = 0

)

const

ptr() [4/4]

template<typename _Tp >

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

(

int

y = 0

)

const

release()

void cv::cuda::GpuMat::release

(

)

Python:
	cv.cuda.GpuMat.release(		) ->	None

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

reshape()

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

Python:
	cv.cuda.GpuMat.reshape(	cn[, rows]	) ->	retval

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:
	cv.cuda.GpuMat.row(	y	) ->	retval

returns a new GpuMat header for the specified row

rowRange() [1/2]

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

Python:
	cv.cuda.GpuMat.rowRange(	startrow, endrow	) ->	retval
	cv.cuda.GpuMat.rowRange(	r	) ->	retval

... for the specified row span

rowRange() [2/2]

GpuMat cv::cuda::GpuMat::rowRange

(

Range

r

)

const

Python:
	cv.cuda.GpuMat.rowRange(	startrow, endrow	) ->	retval
	cv.cuda.GpuMat.rowRange(	r	) ->	retval

setDefaultAllocator()

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

static

Python:
	cv.cuda.GpuMat.setDefaultAllocator(	allocator	) ->	None
	cv.cuda.GpuMat_setDefaultAllocator(	allocator	) ->	None

setTo() [1/4]

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

(

Scalar

s

)

Python:
	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	) ->	retval

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

setTo() [2/4]

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

Python:
	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	) ->	retval

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

setTo() [3/4]

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

Python:
	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	) ->	retval

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

setTo() [4/4]

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

Python:
	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	) ->	retval

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

size()

Size cv::cuda::GpuMat::size

(

)

const

Python:
	cv.cuda.GpuMat.size(		) ->	retval

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

step1()

size_t cv::cuda::GpuMat::step1

(

)

const

Python:
	cv.cuda.GpuMat.step1(		) ->	retval

returns step/elemSize1()

swap()

void cv::cuda::GpuMat::swap

(

GpuMat &

mat

)

Python:
	cv.cuda.GpuMat.swap(	mat	) ->	None

swaps with other smart pointer

type()

int cv::cuda::GpuMat::type

(

)

const

Python:
	cv.cuda.GpuMat.type(		) ->	retval

returns element type

updateContinuityFlag()

void cv::cuda::GpuMat::updateContinuityFlag

(

)

Python:
	cv.cuda.GpuMat.updateContinuityFlag(		) ->	None

internal use method: updates the continuity flag

upload() [1/2]

void cv::cuda::GpuMat::upload

(

InputArray

arr

)

Python:
	cv.cuda.GpuMat.upload(	arr	) ->	None
	cv.cuda.GpuMat.upload(	arr, stream	) ->	None

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:
	cv.cuda.GpuMat.upload(	arr	) ->	None
	cv.cuda.GpuMat.upload(	arr, stream	) ->	None

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:

• opencv2/core/cuda.hpp