@spec clear(Keyword.t()) :: any() | {:error, String.t()}

@spec clear(t()) :: t() | {:error, String.t()}

Clears the algorithm state

Positional Arguments

• self: Evision.CUDA.Convolution.t()

Python prototype (for reference only):

clear() -> None

@spec convolve(t(), Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in()) ::
  Evision.Mat.t() | {:error, String.t()}

@spec convolve(t(), Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t()) ::
  Evision.CUDA.GpuMat.t() | {:error, String.t()}

Variant 1:

Computes a convolution (or cross-correlation) of two images.

Positional Arguments

• self: Evision.CUDA.Convolution.t()

• image: Evision.Mat.

  Source image. Only CV_32FC1 images are supported for now.

• templ: Evision.Mat.

  Template image. The size is not greater than the image size. The type is the same as image .

Keyword Arguments

• ccorr: bool.

  Flags to evaluate cross-correlation instead of convolution.

• stream: Evision.CUDA.Stream.t().

  Stream for the asynchronous version.

Return

• result: Evision.Mat.t().

  Result image. If image is W x H and templ is w x h, then result must be W-w+1 x H-h+1.

Python prototype (for reference only):

convolve(image, templ[, result[, ccorr[, stream]]]) -> result

Variant 2:

Computes a convolution (or cross-correlation) of two images.

Positional Arguments

• self: Evision.CUDA.Convolution.t()

• image: Evision.CUDA.GpuMat.t().

  Source image. Only CV_32FC1 images are supported for now.

• templ: Evision.CUDA.GpuMat.t().

  Template image. The size is not greater than the image size. The type is the same as image .

Keyword Arguments

• ccorr: bool.

  Flags to evaluate cross-correlation instead of convolution.

• stream: Evision.CUDA.Stream.t().

  Stream for the asynchronous version.

Return

• result: Evision.CUDA.GpuMat.t().

  Result image. If image is W x H and templ is w x h, then result must be W-w+1 x H-h+1.

Python prototype (for reference only):

convolve(image, templ[, result[, ccorr[, stream]]]) -> result

@spec convolve(
  t(),
  Evision.Mat.maybe_mat_in(),
  Evision.Mat.maybe_mat_in(),
  [ccorr: term(), stream: term()] | nil
) :: Evision.Mat.t() | {:error, String.t()}

@spec convolve(
  t(),
  Evision.CUDA.GpuMat.t(),
  Evision.CUDA.GpuMat.t(),
  [ccorr: term(), stream: term()] | nil
) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}

Variant 1:

Computes a convolution (or cross-correlation) of two images.

Positional Arguments

• self: Evision.CUDA.Convolution.t()

• image: Evision.Mat.

  Source image. Only CV_32FC1 images are supported for now.

• templ: Evision.Mat.

  Template image. The size is not greater than the image size. The type is the same as image .

Keyword Arguments

• ccorr: bool.

  Flags to evaluate cross-correlation instead of convolution.

• stream: Evision.CUDA.Stream.t().

  Stream for the asynchronous version.

Return

• result: Evision.Mat.t().

  Result image. If image is W x H and templ is w x h, then result must be W-w+1 x H-h+1.

Python prototype (for reference only):

convolve(image, templ[, result[, ccorr[, stream]]]) -> result

Variant 2:

Computes a convolution (or cross-correlation) of two images.

Positional Arguments

• self: Evision.CUDA.Convolution.t()

• image: Evision.CUDA.GpuMat.t().

  Source image. Only CV_32FC1 images are supported for now.

• templ: Evision.CUDA.GpuMat.t().

  Template image. The size is not greater than the image size. The type is the same as image .

Keyword Arguments

• ccorr: bool.

  Flags to evaluate cross-correlation instead of convolution.

• stream: Evision.CUDA.Stream.t().

  Stream for the asynchronous version.

Return

• result: Evision.CUDA.GpuMat.t().

  Result image. If image is W x H and templ is w x h, then result must be W-w+1 x H-h+1.

Python prototype (for reference only):

convolve(image, templ[, result[, ccorr[, stream]]]) -> result

@spec empty(Keyword.t()) :: any() | {:error, String.t()}

@spec empty(t()) :: boolean() | {:error, String.t()}

Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read

Positional Arguments

• self: Evision.CUDA.Convolution.t()

Return

• retval: bool

Python prototype (for reference only):

empty() -> retval

@spec getDefaultName(Keyword.t()) :: any() | {:error, String.t()}

@spec getDefaultName(t()) :: binary() | {:error, String.t()}

getDefaultName

Positional Arguments

• self: Evision.CUDA.Convolution.t()

Return

• retval: String

Returns the algorithm string identifier. This string is used as top level xml/yml node tag when the object is saved to a file or string.

Python prototype (for reference only):

getDefaultName() -> retval

@spec read(t(), Evision.FileNode.t()) :: t() | {:error, String.t()}

Reads algorithm parameters from a file storage

Positional Arguments

• self: Evision.CUDA.Convolution.t()
• func: Evision.FileNode

Python prototype (for reference only):

read(fn) -> None

@spec save(t(), binary()) :: t() | {:error, String.t()}

save

Positional Arguments

• self: Evision.CUDA.Convolution.t()
• filename: String

Saves the algorithm to a file. In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).

Python prototype (for reference only):

save(filename) -> None

@spec write(t(), Evision.FileStorage.t()) :: t() | {:error, String.t()}

Stores algorithm parameters in a file storage

Positional Arguments

• self: Evision.CUDA.Convolution.t()
• fs: Evision.FileStorage

Python prototype (for reference only):

write(fs) -> None

@spec write(t(), Evision.FileStorage.t(), binary()) :: t() | {:error, String.t()}

write

Positional Arguments

• self: Evision.CUDA.Convolution.t()
• fs: Evision.FileStorage
• name: String

Has overloading in C++

Python prototype (for reference only):

write(fs, name) -> None