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

applies a luminance correction (initially High Dynamic Range (HDR) tone mapping)

Positional Arguments

• self: Evision.Bioinspired.RetinaFastToneMapping.t()

• inputImage: Evision.Mat.

  the input image to process RGB or gray levels

Return

• outputToneMappedImage: Evision.Mat.t().

  the output tone mapped image

using only the 2 local adaptation stages of the retina parvocellular channel : photoreceptors level and ganlion cells level. Spatio temporal filtering is applied but limited to temporal smoothing and eventually high frequencies attenuation. This is a lighter method than the one available using the regular retina::run method. It is then faster but it does not include complete temporal filtering nor retina spectral whitening. Then, it can have a more limited effect on images with a very high dynamic range. This is an adptation of the original still image HDR tone mapping algorithm of David Alleyson, Sabine Susstruck and Laurence Meylan's work, please cite: -> Meylan L., Alleysson D., and Susstrunk S., A Model of Retinal Local Adaptation for the Tone Mapping of Color Filter Array Images, Journal of Optical Society of America, A, Vol. 24, N 9, September, 1st, 2007, pp. 2807-2816

Python prototype (for reference only):

applyFastToneMapping(inputImage[, outputToneMappedImage]) -> outputToneMappedImage

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

applies a luminance correction (initially High Dynamic Range (HDR) tone mapping)

Positional Arguments

• self: Evision.Bioinspired.RetinaFastToneMapping.t()

• inputImage: Evision.Mat.

  the input image to process RGB or gray levels

Return

• outputToneMappedImage: Evision.Mat.t().

  the output tone mapped image

using only the 2 local adaptation stages of the retina parvocellular channel : photoreceptors level and ganlion cells level. Spatio temporal filtering is applied but limited to temporal smoothing and eventually high frequencies attenuation. This is a lighter method than the one available using the regular retina::run method. It is then faster but it does not include complete temporal filtering nor retina spectral whitening. Then, it can have a more limited effect on images with a very high dynamic range. This is an adptation of the original still image HDR tone mapping algorithm of David Alleyson, Sabine Susstruck and Laurence Meylan's work, please cite: -> Meylan L., Alleysson D., and Susstrunk S., A Model of Retinal Local Adaptation for the Tone Mapping of Color Filter Array Images, Journal of Optical Society of America, A, Vol. 24, N 9, September, 1st, 2007, pp. 2807-2816

Python prototype (for reference only):

applyFastToneMapping(inputImage[, outputToneMappedImage]) -> outputToneMappedImage

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

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

Clears the algorithm state

Positional Arguments

• self: Evision.Bioinspired.RetinaFastToneMapping.t()

Python prototype (for reference only):

clear() -> None

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

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

create

Positional Arguments

• inputSize: Size

Return

• retval: RetinaFastToneMapping

Python prototype (for reference only):

create(inputSize) -> 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.Bioinspired.RetinaFastToneMapping.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.Bioinspired.RetinaFastToneMapping.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.Bioinspired.RetinaFastToneMapping.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.Bioinspired.RetinaFastToneMapping.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 setup(Keyword.t()) :: any() | {:error, String.t()}

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

updates tone mapping behaviors by adjusing the local luminance computation area

Positional Arguments

• self: Evision.Bioinspired.RetinaFastToneMapping.t()

Keyword Arguments

• photoreceptorsNeighborhoodRadius: float.

  the first stage local adaptation area

• ganglioncellsNeighborhoodRadius: float.

  the second stage local adaptation area

• meanLuminanceModulatorK: float.

  the factor applied to modulate the meanLuminance information (default is 1, see reference paper)

Python prototype (for reference only):

setup([, photoreceptorsNeighborhoodRadius[, ganglioncellsNeighborhoodRadius[, meanLuminanceModulatorK]]]) -> None

@spec setup(
  t(),
  [
    ganglioncellsNeighborhoodRadius: term(),
    meanLuminanceModulatorK: term(),
    photoreceptorsNeighborhoodRadius: term()
  ]
  | nil
) :: t() | {:error, String.t()}

updates tone mapping behaviors by adjusing the local luminance computation area

Positional Arguments

• self: Evision.Bioinspired.RetinaFastToneMapping.t()

Keyword Arguments

• photoreceptorsNeighborhoodRadius: float.

  the first stage local adaptation area

• ganglioncellsNeighborhoodRadius: float.

  the second stage local adaptation area

• meanLuminanceModulatorK: float.

  the factor applied to modulate the meanLuminance information (default is 1, see reference paper)

Python prototype (for reference only):

setup([, photoreceptorsNeighborhoodRadius[, ganglioncellsNeighborhoodRadius[, meanLuminanceModulatorK]]]) -> None

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

Stores algorithm parameters in a file storage

Positional Arguments

• self: Evision.Bioinspired.RetinaFastToneMapping.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.Bioinspired.RetinaFastToneMapping.t()
• fs: Evision.FileStorage
• name: String

Has overloading in C++

Python prototype (for reference only):

write(fs, name) -> None