View Source Evision.Ft (Evision v0.1.28)

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Types

t()

Type that represents an Ft struct.

Functions

Creates kernel from basic functions.

Creates kernel from basic functions.

Creates kernel from general functions.

Creates kernel from general functions.

Image filtering

Image filtering

Link to this section Types

@type t() :: %Evision.Ft{ref: reference()}

Type that represents an Ft struct.

  • ref. reference()

    The underlying erlang resource variable.

Link to this section Functions

Link to this function

createKernel1(a, b, chn)

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@spec createKernel1(Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), integer()) ::
  Evision.Mat.t() | {:error, String.t()}

Creates kernel from basic functions.

Positional Arguments
  • a: Evision.Mat.t().

    Basic function used in axis x.

  • b: Evision.Mat.t().

    Basic function used in axis y.

  • chn: int.

    Number of kernel channels.

Return
  • kernel: Evision.Mat.t().

    Final 32-bit kernel derived from A and B.

The function creates kernel usable for latter fuzzy image processing.

Python prototype (for reference only):

createKernel1(A, B, chn[, kernel]) -> kernel
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createKernel1(a, b, chn, opts)

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@spec createKernel1(
  Evision.Mat.maybe_mat_in(),
  Evision.Mat.maybe_mat_in(),
  integer(),
  [{atom(), term()}, ...] | nil
) :: Evision.Mat.t() | {:error, String.t()}

Creates kernel from basic functions.

Positional Arguments
  • a: Evision.Mat.t().

    Basic function used in axis x.

  • b: Evision.Mat.t().

    Basic function used in axis y.

  • chn: int.

    Number of kernel channels.

Return
  • kernel: Evision.Mat.t().

    Final 32-bit kernel derived from A and B.

The function creates kernel usable for latter fuzzy image processing.

Python prototype (for reference only):

createKernel1(A, B, chn[, kernel]) -> kernel
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createKernel(function, radius, chn)

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@spec createKernel(integer(), integer(), integer()) ::
  Evision.Mat.t() | {:error, String.t()}

Creates kernel from general functions.

Positional Arguments
  • function: int.

    Function type could be one of the following:

    • LINEAR Linear basic function.
  • radius: int.

    Radius of the basic function.

  • chn: int.

    Number of kernel channels.

Return
  • kernel: Evision.Mat.t().

    Final 32-bit kernel.

The function creates kernel from predefined functions.

Python prototype (for reference only):

createKernel(function, radius, chn[, kernel]) -> kernel
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createKernel(function, radius, chn, opts)

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

Creates kernel from general functions.

Positional Arguments
  • function: int.

    Function type could be one of the following:

    • LINEAR Linear basic function.
  • radius: int.

    Radius of the basic function.

  • chn: int.

    Number of kernel channels.

Return
  • kernel: Evision.Mat.t().

    Final 32-bit kernel.

The function creates kernel from predefined functions.

Python prototype (for reference only):

createKernel(function, radius, chn[, kernel]) -> kernel

Image filtering

Positional Arguments
  • image: Evision.Mat.t().

    Input image.

  • kernel: Evision.Mat.t().

    Final 32-bit kernel.

Return
  • output: Evision.Mat.t().

    Output 32-bit image.

Filtering of the input image by means of F-transform.

Python prototype (for reference only):

filter(image, kernel[, output]) -> output
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filter(image, kernel, opts)

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

Image filtering

Positional Arguments
  • image: Evision.Mat.t().

    Input image.

  • kernel: Evision.Mat.t().

    Final 32-bit kernel.

Return
  • output: Evision.Mat.t().

    Output 32-bit image.

Filtering of the input image by means of F-transform.

Python prototype (for reference only):

filter(image, kernel[, output]) -> output
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inpaint(image, mask, radius, function, algorithm)

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Image inpainting

Positional Arguments
  • image: Evision.Mat.t().

    Input image.

  • mask: Evision.Mat.t().

    Mask used for unwanted area marking.

  • radius: int.

    Radius of the basic function.

  • function: int.

    Function type could be one of the following:

    • ft::LINEAR Linear basic function.
  • algorithm: int.

    Algorithm could be one of the following:

    • ft::ONE_STEP One step algorithm.
    • ft::MULTI_STEP This algorithm automaticaly increases radius of the basic function.
    • ft::ITERATIVE Iterative algorithm running in more steps using partial computations.
Return
  • output: Evision.Mat.t().

    Output 32-bit image.

This function provides inpainting technique based on the fuzzy mathematic. Note: The algorithms are described in paper @cite Perf:rec.

Python prototype (for reference only):

inpaint(image, mask, radius, function, algorithm[, output]) -> output
Link to this function

inpaint(image, mask, radius, function, algorithm, opts)

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

Image inpainting

Positional Arguments
  • image: Evision.Mat.t().

    Input image.

  • mask: Evision.Mat.t().

    Mask used for unwanted area marking.

  • radius: int.

    Radius of the basic function.

  • function: int.

    Function type could be one of the following:

    • ft::LINEAR Linear basic function.
  • algorithm: int.

    Algorithm could be one of the following:

    • ft::ONE_STEP One step algorithm.
    • ft::MULTI_STEP This algorithm automaticaly increases radius of the basic function.
    • ft::ITERATIVE Iterative algorithm running in more steps using partial computations.
Return
  • output: Evision.Mat.t().

    Output 32-bit image.

This function provides inpainting technique based on the fuzzy mathematic. Note: The algorithms are described in paper @cite Perf:rec.

Python prototype (for reference only):

inpaint(image, mask, radius, function, algorithm[, output]) -> output