View Source Evision.CUDA.HoughSegmentDetector (Evision v0.1.28)

Link to this section Summary

Types

t()

Type that represents an CUDA.HoughSegmentDetector struct.

Functions

Clears the algorithm state

Variant 1:

Finds line segments in a binary image using the probabilistic Hough transform.

Variant 1:

Finds line segments in a binary image using the probabilistic Hough transform.

Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read

getDefaultName

getMaxLineGap

getMaxLines

getMinLineLength

getRho

getTheta

Reads algorithm parameters from a file storage

Stores algorithm parameters in a file storage

Link to this section Types

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

Type that represents an CUDA.HoughSegmentDetector struct.

  • ref. reference()

    The underlying erlang resource variable.

Link to this section Functions

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

Clears the algorithm state

Positional Arguments
  • self: Evision.CUDA.HoughSegmentDetector.t()

Python prototype (for reference only):

clear() -> None
@spec detect(t(), Evision.Mat.maybe_mat_in()) ::
  Evision.Mat.t() | {:error, String.t()}
@spec detect(t(), Evision.CUDA.GpuMat.t()) ::
  Evision.CUDA.GpuMat.t() | {:error, String.t()}

Variant 1:

Finds line segments in a binary image using the probabilistic Hough transform.

Positional Arguments
  • self: Evision.CUDA.HoughSegmentDetector.t()

  • src: Evision.Mat.t().

    8-bit, single-channel binary source image.

Keyword Arguments
  • stream: Evision.CUDA.Stream.t().

    Stream for the asynchronous version.

Return
  • lines: Evision.Mat.t().

    Output vector of lines. Each line is represented by a 4-element vector \f$(x_1, y_1, x_2, y_2)\f$ , where \f$(x_1,y_1)\f$ and \f$(x_2, y_2)\f$ are the ending points of each detected line segment.

@sa HoughLinesP

Python prototype (for reference only):

detect(src[, lines[, stream]]) -> lines

Variant 2:

Finds line segments in a binary image using the probabilistic Hough transform.

Positional Arguments
  • self: Evision.CUDA.HoughSegmentDetector.t()

  • src: Evision.CUDA.GpuMat.t().

    8-bit, single-channel binary source image.

Keyword Arguments
  • stream: Evision.CUDA.Stream.t().

    Stream for the asynchronous version.

Return
  • lines: Evision.CUDA.GpuMat.t().

    Output vector of lines. Each line is represented by a 4-element vector \f$(x_1, y_1, x_2, y_2)\f$ , where \f$(x_1,y_1)\f$ and \f$(x_2, y_2)\f$ are the ending points of each detected line segment.

@sa HoughLinesP

Python prototype (for reference only):

detect(src[, lines[, stream]]) -> lines
@spec detect(t(), Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) ::
  Evision.Mat.t() | {:error, String.t()}
@spec detect(t(), Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil) ::
  Evision.CUDA.GpuMat.t() | {:error, String.t()}

Variant 1:

Finds line segments in a binary image using the probabilistic Hough transform.

Positional Arguments
  • self: Evision.CUDA.HoughSegmentDetector.t()

  • src: Evision.Mat.t().

    8-bit, single-channel binary source image.

Keyword Arguments
  • stream: Evision.CUDA.Stream.t().

    Stream for the asynchronous version.

Return
  • lines: Evision.Mat.t().

    Output vector of lines. Each line is represented by a 4-element vector \f$(x_1, y_1, x_2, y_2)\f$ , where \f$(x_1,y_1)\f$ and \f$(x_2, y_2)\f$ are the ending points of each detected line segment.

@sa HoughLinesP

Python prototype (for reference only):

detect(src[, lines[, stream]]) -> lines

Variant 2:

Finds line segments in a binary image using the probabilistic Hough transform.

Positional Arguments
  • self: Evision.CUDA.HoughSegmentDetector.t()

  • src: Evision.CUDA.GpuMat.t().

    8-bit, single-channel binary source image.

Keyword Arguments
  • stream: Evision.CUDA.Stream.t().

    Stream for the asynchronous version.

Return
  • lines: Evision.CUDA.GpuMat.t().

    Output vector of lines. Each line is represented by a 4-element vector \f$(x_1, y_1, x_2, y_2)\f$ , where \f$(x_1,y_1)\f$ and \f$(x_2, y_2)\f$ are the ending points of each detected line segment.

@sa HoughLinesP

Python prototype (for reference only):

detect(src[, lines[, stream]]) -> lines
@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.CUDA.HoughSegmentDetector.t()
Return
  • retval: bool

Python prototype (for reference only):

empty() -> retval
@spec getDefaultName(t()) :: binary() | {:error, String.t()}

getDefaultName

Positional Arguments
  • self: Evision.CUDA.HoughSegmentDetector.t()
Return

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 getMaxLineGap(t()) :: integer() | {:error, String.t()}

getMaxLineGap

Positional Arguments
  • self: Evision.CUDA.HoughSegmentDetector.t()
Return
  • retval: int

Python prototype (for reference only):

getMaxLineGap() -> retval
@spec getMaxLines(t()) :: integer() | {:error, String.t()}

getMaxLines

Positional Arguments
  • self: Evision.CUDA.HoughSegmentDetector.t()
Return
  • retval: int

Python prototype (for reference only):

getMaxLines() -> retval
@spec getMinLineLength(t()) :: integer() | {:error, String.t()}

getMinLineLength

Positional Arguments
  • self: Evision.CUDA.HoughSegmentDetector.t()
Return
  • retval: int

Python prototype (for reference only):

getMinLineLength() -> retval
@spec getRho(t()) :: number() | {:error, String.t()}

getRho

Positional Arguments
  • self: Evision.CUDA.HoughSegmentDetector.t()
Return
  • retval: float

Python prototype (for reference only):

getRho() -> retval
@spec getTheta(t()) :: number() | {:error, String.t()}

getTheta

Positional Arguments
  • self: Evision.CUDA.HoughSegmentDetector.t()
Return
  • retval: float

Python prototype (for reference only):

getTheta() -> retval
@spec read(t(), Evision.FileNode.t()) :: t() | {:error, String.t()}

Reads algorithm parameters from a file storage

Positional Arguments
  • self: Evision.CUDA.HoughSegmentDetector.t()
  • fn_: Evision.FileNode.t()

Python prototype (for reference only):

read(fn_) -> None
@spec save(t(), binary()) :: t() | {:error, String.t()}

save

Positional Arguments
  • self: Evision.CUDA.HoughSegmentDetector.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
Link to this function

setMaxLineGap(self, maxLineGap)

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

setMaxLineGap

Positional Arguments
  • self: Evision.CUDA.HoughSegmentDetector.t()
  • maxLineGap: int

Python prototype (for reference only):

setMaxLineGap(maxLineGap) -> None
Link to this function

setMaxLines(self, maxLines)

View Source
@spec setMaxLines(t(), integer()) :: t() | {:error, String.t()}

setMaxLines

Positional Arguments
  • self: Evision.CUDA.HoughSegmentDetector.t()
  • maxLines: int

Python prototype (for reference only):

setMaxLines(maxLines) -> None
Link to this function

setMinLineLength(self, minLineLength)

View Source
@spec setMinLineLength(t(), integer()) :: t() | {:error, String.t()}

setMinLineLength

Positional Arguments
  • self: Evision.CUDA.HoughSegmentDetector.t()
  • minLineLength: int

Python prototype (for reference only):

setMinLineLength(minLineLength) -> None
@spec setRho(t(), number()) :: t() | {:error, String.t()}

setRho

Positional Arguments
  • self: Evision.CUDA.HoughSegmentDetector.t()
  • rho: float

Python prototype (for reference only):

setRho(rho) -> None
@spec setTheta(t(), number()) :: t() | {:error, String.t()}

setTheta

Positional Arguments
  • self: Evision.CUDA.HoughSegmentDetector.t()
  • theta: float

Python prototype (for reference only):

setTheta(theta) -> None
@spec write(t(), Evision.FileStorage.t()) :: t() | {:error, String.t()}

Stores algorithm parameters in a file storage

Positional Arguments
  • self: Evision.CUDA.HoughSegmentDetector.t()
  • fs: Evision.FileStorage.t()

Python prototype (for reference only):

write(fs) -> None
@spec write(t(), Evision.FileStorage.t(), binary()) :: t() | {:error, String.t()}

write

Positional Arguments
  • self: Evision.CUDA.HoughSegmentDetector.t()
  • fs: Evision.FileStorage.t()
  • name: String

Has overloading in C++

Python prototype (for reference only):

write(fs, name) -> None