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

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

Clears the algorithm state

Positional Arguments

• self: Evision.XImgProc.AdaptiveManifoldFilter.t()

Python prototype (for reference only):

clear() -> None

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

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

collectGarbage

Positional Arguments

• self: Evision.XImgProc.AdaptiveManifoldFilter.t()

Python prototype (for reference only):

collectGarbage() -> None

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

create

Return

• retval: Evision.XImgProc.AdaptiveManifoldFilter.t()

Python prototype (for reference only):

create() -> retval

@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.XImgProc.AdaptiveManifoldFilter.t()

Return

• retval: bool

Python prototype (for reference only):

empty() -> retval

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

Apply high-dimensional filtering using adaptive manifolds.

Positional Arguments

• self: Evision.XImgProc.AdaptiveManifoldFilter.t()

• src: Evision.Mat.

  filtering image with any numbers of channels.

Keyword Arguments

• joint: Evision.Mat.

  optional joint (also called as guided) image with any numbers of channels.

Return

• dst: Evision.Mat.t().

  output image.

Python prototype (for reference only):

filter(src[, dst[, joint]]) -> dst

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

Apply high-dimensional filtering using adaptive manifolds.

Positional Arguments

• self: Evision.XImgProc.AdaptiveManifoldFilter.t()

• src: Evision.Mat.

  filtering image with any numbers of channels.

Keyword Arguments

• joint: Evision.Mat.

  optional joint (also called as guided) image with any numbers of channels.

Return

• dst: Evision.Mat.t().

  output image.

Python prototype (for reference only):

filter(src[, dst[, joint]]) -> dst

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

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

getDefaultName

Positional Arguments

• self: Evision.XImgProc.AdaptiveManifoldFilter.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.XImgProc.AdaptiveManifoldFilter.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.XImgProc.AdaptiveManifoldFilter.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.XImgProc.AdaptiveManifoldFilter.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.XImgProc.AdaptiveManifoldFilter.t()
• fs: Evision.FileStorage
• name: String

Has overloading in C++

Python prototype (for reference only):

write(fs, name) -> None