cv::cuda::HostMem 类参考

带有引用计数的类，包装了来自 CUDA 的特殊内存类型分配函数。更多…

#include <opencv2/core/cuda.hpp>

cv::cuda::HostMem 的协作图

公共类型
枚举	AllocType {   PAGE_LOCKED = 1 ,   SHARED = 2 ,   WRITE_COMBINED = 4 }

公共成员函数
	HostMem (const HostMem &m)
	HostMem (HostMem::AllocType alloc_type=HostMem::AllocType::PAGE_LOCKED)
	HostMem (InputArray arr, HostMem::AllocType alloc_type=HostMem::AllocType::PAGE_LOCKED)
	从主机内存创建，并复制数据
	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 ()
int	channels () const
HostMem	clone () const
	返回矩阵的深拷贝，即数据被复制
void	create (int rows, int cols, int type)
	除非矩阵已经具有指定的尺寸和类型，否则分配新的矩阵数据。
void	create (Size size, int type)
GpuMat	createGpuMatHeader () const
	将 CPU 内存映射到 GPU 地址空间，并创建 cuda::GpuMat 头文件，但不为其进行引用计数。
Mat	createMatHeader () const
	返回具有禁用引用计数的矩阵头文件，用于 HostMem 数据。
int	depth () const
size_t	elemSize () const
size_t	elemSize1 () const
bool	empty () const
bool	isContinuous () const
HostMem &	operator= (const HostMem &m)
void	release ()
	递减引用计数，并在需要时释放内存。
HostMem	reshape (int cn, int rows=0) const
Size	size () const
size_t	step1 () const
void	swap (HostMem &b)
	与其他智能指针交换
int	type () const

公共静态成员函数
static MatAllocator *	getAllocator (HostMem::AllocType alloc_type=HostMem::AllocType::PAGE_LOCKED)

公共属性
AllocType	alloc_type
int	cols
uchar *	data
const uchar *	dataend
uchar *	datastart
int	flags
int *	refcount
int	rows
size_t	step

详细描述

带有引用计数的类，包装了来自 CUDA 的特殊内存类型分配函数。

它的接口也类似于 Mat，但具有额外的内存类型参数。

• PAGE_LOCKED 设置页面锁定内存类型，通常用于从 GPU 上传/下载数据的高速异步操作。
• SHARED 指定零拷贝内存分配，如果支持，则可以将主机内存映射到 GPU 地址空间。
• WRITE_COMBINED 设置不被 CPU 缓存的写组合缓冲区。此类缓冲区用于在 GPU 只读取数据时向 GPU 提供数据。其优点是更好的 CPU 缓存利用率。

注意

此类内存类型的分配大小通常有限制。有关更多详细信息，请参阅 _CUDA 2.2 固定内存 API_ 文档或 _CUDA C 编程指南_。

成员枚举文档

AllocType

枚举 cv::cuda::HostMem::AllocType

枚举器
PAGE_LOCKED
共享
写入合并

构造函数和析构函数文档

HostMem() [1/5]

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

显式

Python
	cv.cuda.HostMem(	[, alloc_type]	) ->	<cuda_HostMem 对象>
	cv.cuda.HostMem(	rows, cols, type[, alloc_type]	) ->	<cuda_HostMem 对象>
	cv.cuda.HostMem(	size, type[, alloc_type]	) ->	<cuda_HostMem 对象>
	cv.cuda.HostMem(	arr[, alloc_type]	) ->	<cuda_HostMem 对象>

HostMem() [2/5]

cv::cuda::HostMem::HostMem

(

const HostMem &

m

)

Python
	cv.cuda.HostMem(	[, alloc_type]	) ->	<cuda_HostMem 对象>
	cv.cuda.HostMem(	rows, cols, type[, alloc_type]	) ->	<cuda_HostMem 对象>
	cv.cuda.HostMem(	size, type[, alloc_type]	) ->	<cuda_HostMem 对象>
	cv.cuda.HostMem(	arr[, alloc_type]	) ->	<cuda_HostMem 对象>

HostMem() [3/5]

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

Python
	cv.cuda.HostMem(	[, alloc_type]	) ->	<cuda_HostMem 对象>
	cv.cuda.HostMem(	rows, cols, type[, alloc_type]	) ->	<cuda_HostMem 对象>
	cv.cuda.HostMem(	size, type[, alloc_type]	) ->	<cuda_HostMem 对象>
	cv.cuda.HostMem(	arr[, alloc_type]	) ->	<cuda_HostMem 对象>

HostMem() [4/5]

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

Python
	cv.cuda.HostMem(	[, alloc_type]	) ->	<cuda_HostMem 对象>
	cv.cuda.HostMem(	rows, cols, type[, alloc_type]	) ->	<cuda_HostMem 对象>
	cv.cuda.HostMem(	size, type[, alloc_type]	) ->	<cuda_HostMem 对象>
	cv.cuda.HostMem(	arr[, alloc_type]	) ->	<cuda_HostMem 对象>

HostMem() [5/5]

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

显式

Python
	cv.cuda.HostMem(	[, alloc_type]	) ->	<cuda_HostMem 对象>
	cv.cuda.HostMem(	rows, cols, type[, alloc_type]	) ->	<cuda_HostMem 对象>
	cv.cuda.HostMem(	size, type[, alloc_type]	) ->	<cuda_HostMem 对象>
	cv.cuda.HostMem(	arr[, alloc_type]	) ->	<cuda_HostMem 对象>

从主机内存创建，并复制数据

~HostMem()

cv::cuda::HostMem::~HostMem

(

)

成员函数文档

channels()

int cv::cuda::HostMem::channels

(

)

const

Python
	cv.cuda.HostMem.channels(		) ->	retval

clone()

HostMem cv::cuda::HostMem::clone

(

)

const

Python
	cv.cuda.HostMem.clone(		) ->	retval

返回矩阵的深拷贝，即数据被复制

create() [1/2]

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

Python
	cv.cuda.HostMem.create(	rows, cols, type	) ->	None

除非矩阵已经具有指定的尺寸和类型，否则分配新的矩阵数据。

create() [2/2]

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

Python
	cv.cuda.HostMem.create(	rows, cols, type	) ->	None

createGpuMatHeader()

GpuMat cv::cuda::HostMem::createGpuMatHeader

(

)

const

将 CPU 内存映射到 GPU 地址空间，并创建 cuda::GpuMat 头文件，但不为其进行引用计数。

只有在使用SHARED标志分配内存并且硬件支持的情况下才能执行此操作。笔记本电脑通常共享视频和CPU内存，因此可以映射地址空间，从而避免额外的复制。

createMatHeader()

Mat cv::cuda::HostMem::createMatHeader

(

)

const

Python
	cv.cuda.HostMem.createMatHeader(		) ->	retval

返回具有禁用引用计数的矩阵头文件，用于 HostMem 数据。

depth()

int cv::cuda::HostMem::depth

(

)

const

Python
	cv.cuda.HostMem.depth(		) ->	retval

elemSize()

size_t cv::cuda::HostMem::elemSize

(

)

const

Python
	cv.cuda.HostMem.elemSize(		) ->	retval

elemSize1()

size_t cv::cuda::HostMem::elemSize1

(

)

const

Python
	cv.cuda.HostMem.elemSize1(		) ->	retval

empty()

bool cv::cuda::HostMem::empty

(

)

const

Python
	cv.cuda.HostMem.empty(		) ->	retval

getAllocator()

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

static

isContinuous()

bool cv::cuda::HostMem::isContinuous

(

)

const

Python
	cv.cuda.HostMem.isContinuous(		) ->	retval

operator=()

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

(

const HostMem &

m

)

release()

void cv::cuda::HostMem::release

(

)

递减引用计数，并在需要时释放内存。

reshape()

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

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

为相同数据创建具有不同通道数和/或不同行数的替代HostMem 头。

size()

Size cv::cuda::HostMem::size

(

)

const

Python
	cv.cuda.HostMem.size(		) ->	retval

step1()

size_t cv::cuda::HostMem::step1

(

)

const

Python
	cv.cuda.HostMem.step1(		) ->	retval

swap()

void cv::cuda::HostMem::swap

(

HostMem &

b

)

Python
	cv.cuda.HostMem.swap(	b	) ->	None

与其他智能指针交换

type()

int cv::cuda::HostMem::type

(

)

const

Python
	cv.cuda.HostMem.type(		) ->	retval

成员数据文档

alloc_type

AllocType cv::cuda::HostMem::alloc_type

cols

int cv::cuda::HostMem::cols

data

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

dataend

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

datastart

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

flags

int cv::cuda::HostMem::flags

refcount

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

rows

int cv::cuda::HostMem::rows

step

size_t cv::cuda::HostMem::step

---

此类的文档是从以下文件生成的：

• opencv2/core/cuda.hpp