View Source Evision.Ft (Evision v0.2.9)

Summary

Types

t()

Type that represents an Ft struct.

Enumerator

Types

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

Type that represents an Ft struct.

  • ref. reference()

    The underlying erlang resource variable.

Functions

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createKernel1(named_args)

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@spec createKernel1(Keyword.t()) :: any() | {:error, String.t()}
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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.

    Basic function used in axis x.

  • b: Evision.Mat.

    Basic function used in axis y.

  • chn: integer().

    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.

    Basic function used in axis x.

  • b: Evision.Mat.

    Basic function used in axis y.

  • chn: integer().

    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(named_args)

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

    Function type could be one of the following:

    • LINEAR Linear basic function.
  • radius: integer().

    Radius of the basic function.

  • chn: integer().

    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: integer().

    Function type could be one of the following:

    • LINEAR Linear basic function.
  • radius: integer().

    Radius of the basic function.

  • chn: integer().

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

Image filtering

Positional Arguments
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
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
@spec inpaint(Keyword.t()) :: any() | {:error, String.t()}
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inpaint(image, mask, radius, function, algorithm)

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Image inpainting

Positional Arguments
  • image: Evision.Mat.

    Input image.

  • mask: Evision.Mat.

    Mask used for unwanted area marking.

  • radius: integer().

    Radius of the basic function.

  • function: integer().

    Function type could be one of the following:

    • ft::LINEAR Linear basic function.
  • algorithm: integer().

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

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@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.

    Input image.

  • mask: Evision.Mat.

    Mask used for unwanted area marking.

  • radius: integer().

    Radius of the basic function.

  • function: integer().

    Function type could be one of the following:

    • ft::LINEAR Linear basic function.
  • algorithm: integer().

    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