View Source Evision.CUDA.HostMem (Evision v0.1.14)
Link to this section Summary
Types
Type that represents an Evision.CUDA.HostMem
struct.
Functions
Return
- retval:
int
Python prototype (for reference):
Positional Arguments
- rows:
int
- cols:
int
- type:
int
Python prototype (for reference):
Return
- retval:
int
Python prototype (for reference):
Return
- retval:
size_t
Python prototype (for reference):
Return
- retval:
size_t
Python prototype (for reference):
Return
- retval:
bool
Python prototype (for reference):
Keyword Arguments
- alloc_type:
HostMem_AllocType
.
Return
- self:
Evision.CUDA.HostMem
Python prototype (for reference):
Variant 1:
Positional Arguments
- arr:
Evision.Mat
Keyword Arguments
- alloc_type:
HostMem_AllocType
.
Return
- self:
Evision.CUDA.HostMem
Python prototype (for reference):
Variant 1:
Positional Arguments
- size:
Size
- type:
int
Keyword Arguments
- alloc_type:
HostMem_AllocType
.
Return
- self:
Evision.CUDA.HostMem
Python prototype (for reference):
Variant 1:
Positional Arguments
- rows:
int
- cols:
int
- type:
int
Keyword Arguments
- alloc_type:
HostMem_AllocType
.
Return
- self:
Evision.CUDA.HostMem
Python prototype (for reference):
Positional Arguments
- rows:
int
- cols:
int
- type:
int
Keyword Arguments
- alloc_type:
HostMem_AllocType
.
Return
- self:
Evision.CUDA.HostMem
Python prototype (for reference):
Maps CPU memory to GPU address space and creates the cuda::GpuMat header without reference counting for it.
Positional Arguments
- cn:
int
Keyword Arguments
- rows:
int
.
Return
- retval:
Evision.CUDA.HostMem
Python prototype (for reference):
Positional Arguments
- cn:
int
Keyword Arguments
- rows:
int
.
Return
- retval:
Evision.CUDA.HostMem
Python prototype (for reference):
Return
- retval:
Size
Python prototype (for reference):
Return
- retval:
size_t
Python prototype (for reference):
Return
- retval:
int
Python prototype (for reference):
Link to this section Types
@type t() :: %Evision.CUDA.HostMem{ref: reference()}
Type that represents an Evision.CUDA.HostMem
struct.
ref.
reference()
The underlying erlang resource variable.
Link to this section Functions
Return
- retval:
int
Python prototype (for reference):
channels() -> retval
Return
- retval:
Evision.CUDA.HostMem
Python prototype (for reference):
clone() -> retval
Positional Arguments
- rows:
int
- cols:
int
- type:
int
Python prototype (for reference):
create(rows, cols, type) -> None
@spec createMatHeader(t()) :: Evision.Mat.t() | {:error, String.t()}
Return
- retval:
Evision.Mat
Python prototype (for reference):
createMatHeader() -> retval
Return
- retval:
int
Python prototype (for reference):
depth() -> retval
Return
- retval:
size_t
Python prototype (for reference):
elemSize1() -> retval
Return
- retval:
size_t
Python prototype (for reference):
elemSize() -> retval
Return
- retval:
bool
Python prototype (for reference):
empty() -> retval
Keyword Arguments
- alloc_type:
HostMem_AllocType
.
Return
- self:
Evision.CUDA.HostMem
Python prototype (for reference):
HostMem([, alloc_type]) -> <cuda_HostMem object>
@spec hostMem([{atom(), term()}, ...] | nil) :: t() | {:error, String.t()}
@spec hostMem(Evision.Mat.maybe_mat_in()) :: t() | {:error, String.t()}
@spec hostMem(Evision.CUDA.GpuMat.t()) :: t() | {:error, String.t()}
Variant 1:
Positional Arguments
- arr:
Evision.Mat
Keyword Arguments
- alloc_type:
HostMem_AllocType
.
Return
- self:
Evision.CUDA.HostMem
Python prototype (for reference):
HostMem(arr[, alloc_type]) -> <cuda_HostMem object>
Variant 2:
Positional Arguments
- arr:
Evision.CUDA.GpuMat
Keyword Arguments
- alloc_type:
HostMem_AllocType
.
Return
- self:
Evision.CUDA.HostMem
Python prototype (for reference):
HostMem(arr[, alloc_type]) -> <cuda_HostMem object>
Variant 3:
Keyword Arguments
- alloc_type:
HostMem_AllocType
.
Return
- self:
Evision.CUDA.HostMem
Python prototype (for reference):
HostMem([, alloc_type]) -> <cuda_HostMem object>
@spec hostMem(Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) :: t() | {:error, String.t()}
@spec hostMem(Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil) :: t() | {:error, String.t()}
@spec hostMem( {number(), number()}, integer() ) :: t() | {:error, String.t()}
Variant 1:
Positional Arguments
- size:
Size
- type:
int
Keyword Arguments
- alloc_type:
HostMem_AllocType
.
Return
- self:
Evision.CUDA.HostMem
Python prototype (for reference):
HostMem(size, type[, alloc_type]) -> <cuda_HostMem object>
Variant 2:
Positional Arguments
- arr:
Evision.Mat
Keyword Arguments
- alloc_type:
HostMem_AllocType
.
Return
- self:
Evision.CUDA.HostMem
Python prototype (for reference):
HostMem(arr[, alloc_type]) -> <cuda_HostMem object>
Variant 3:
Positional Arguments
- arr:
Evision.CUDA.GpuMat
Keyword Arguments
- alloc_type:
HostMem_AllocType
.
Return
- self:
Evision.CUDA.HostMem
Python prototype (for reference):
HostMem(arr[, alloc_type]) -> <cuda_HostMem object>
@spec hostMem({number(), number()}, integer(), [{atom(), term()}, ...] | nil) :: t() | {:error, String.t()}
@spec hostMem(integer(), integer(), integer()) :: t() | {:error, String.t()}
Variant 1:
Positional Arguments
- rows:
int
- cols:
int
- type:
int
Keyword Arguments
- alloc_type:
HostMem_AllocType
.
Return
- self:
Evision.CUDA.HostMem
Python prototype (for reference):
HostMem(rows, cols, type[, alloc_type]) -> <cuda_HostMem object>
Variant 2:
Positional Arguments
- size:
Size
- type:
int
Keyword Arguments
- alloc_type:
HostMem_AllocType
.
Return
- self:
Evision.CUDA.HostMem
Python prototype (for reference):
HostMem(size, type[, alloc_type]) -> <cuda_HostMem object>
@spec hostMem(integer(), integer(), integer(), [{atom(), term()}, ...] | nil) :: t() | {:error, String.t()}
Positional Arguments
- rows:
int
- cols:
int
- type:
int
Keyword Arguments
- alloc_type:
HostMem_AllocType
.
Return
- self:
Evision.CUDA.HostMem
Python prototype (for reference):
HostMem(rows, cols, type[, alloc_type]) -> <cuda_HostMem object>
Maps CPU memory to GPU address space and creates the cuda::GpuMat header without reference counting for it.
Return
- retval:
bool
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.
Python prototype (for reference):
isContinuous() -> retval
Positional Arguments
- cn:
int
Keyword Arguments
- rows:
int
.
Return
- retval:
Evision.CUDA.HostMem
Python prototype (for reference):
reshape(cn[, rows]) -> retval
Positional Arguments
- cn:
int
Keyword Arguments
- rows:
int
.
Return
- retval:
Evision.CUDA.HostMem
Python prototype (for reference):
reshape(cn[, rows]) -> retval
Return
- retval:
Size
Python prototype (for reference):
size() -> retval
Return
- retval:
size_t
Python prototype (for reference):
step1() -> retval
Positional Arguments
Python prototype (for reference):
swap(b) -> None
Return
- retval:
int
Python prototype (for reference):
type() -> retval