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

Empty constructor for python

Return

• retval: Evision.DNNSuperRes.DNNSuperResImpl.t()

Python prototype (for reference only):

create() -> retval

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

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

Returns the scale factor of the model:

Positional Arguments

• self: Evision.DNNSuperRes.DNNSuperResImpl.t()

Return

• retval: String

@return Current algorithm.

Python prototype (for reference only):

getAlgorithm() -> retval

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

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

Returns the scale factor of the model:

Positional Arguments

• self: Evision.DNNSuperRes.DNNSuperResImpl.t()

Return

• retval: integer()

@return Current scale factor.

Python prototype (for reference only):

getScale() -> retval

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

Read the model from the given path

Positional Arguments

• self: Evision.DNNSuperRes.DNNSuperResImpl.t()

• path: String.

  Path to the model file.

Python prototype (for reference only):

readModel(path) -> None

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

Set desired model

Positional Arguments

• self: Evision.DNNSuperRes.DNNSuperResImpl.t()

• algo: String.

  String containing one of the desired models:

  • edsr
  • espcn
  • fsrcnn
  • lapsrn

• scale: integer().

  Integer specifying the upscale factor

Python prototype (for reference only):

setModel(algo, scale) -> None

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

Set computation backend

Positional Arguments

• self: Evision.DNNSuperRes.DNNSuperResImpl.t()
• backendId: integer()

Python prototype (for reference only):

setPreferableBackend(backendId) -> None

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

Set computation target

Positional Arguments

• self: Evision.DNNSuperRes.DNNSuperResImpl.t()
• targetId: integer()

Python prototype (for reference only):

setPreferableTarget(targetId) -> None

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

Upsample via neural network

Positional Arguments

• self: Evision.DNNSuperRes.DNNSuperResImpl.t()

• img: Evision.Mat.

  Image to upscale

Return

• result: Evision.Mat.t().

  Destination upscaled image

Python prototype (for reference only):

upsample(img[, result]) -> result

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

Upsample via neural network

Positional Arguments

• self: Evision.DNNSuperRes.DNNSuperResImpl.t()

• img: Evision.Mat.

  Image to upscale

Return

• result: Evision.Mat.t().

  Destination upscaled image

Python prototype (for reference only):

upsample(img[, result]) -> result

@spec upsampleMultioutput(
  t(),
  Evision.Mat.maybe_mat_in(),
  [Evision.Mat.maybe_mat_in()],
  [integer()],
  [
    binary()
  ]
) :: t() | {:error, String.t()}

Upsample via neural network of multiple outputs

Positional Arguments

• self: Evision.DNNSuperRes.DNNSuperResImpl.t()

• img: Evision.Mat.

  Image to upscale

• imgs_new: [Evision.Mat].

  Destination upscaled images

• scale_factors: [integer()].

  Scaling factors of the output nodes

• node_names: [String].

  Names of the output nodes in the neural network

Python prototype (for reference only):

upsampleMultioutput(img, imgs_new, scale_factors, node_names) -> None