View Source Evision.LineSegmentDetector (Evision v0.1.21)

Link to this section Summary

Types

t()

Type that represents an Evision.LineSegmentDetector struct.

Functions

Clears the algorithm state

Draws two groups of lines in blue and red, counting the non overlapping (mismatching) pixels.

Draws two groups of lines in blue and red, counting the non overlapping (mismatching) pixels.

Finds lines in the input image.

Finds lines in the input image.

Draws the line segments on a given image.

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

getDefaultName

Reads algorithm parameters from a file storage

simplified API for language bindings

simplified API for language bindings

Link to this section Types

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

Type that represents an Evision.LineSegmentDetector struct.

  • ref. reference()

    The underlying erlang resource variable.

Link to this section Functions

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

Clears the algorithm state

Positional Arguments
  • self: Evision.LineSegmentDetector.t()

Python prototype (for reference only):

clear() -> None
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compareSegments(self, size, lines1, lines2)

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

Draws two groups of lines in blue and red, counting the non overlapping (mismatching) pixels.

Positional Arguments
  • self: Evision.LineSegmentDetector.t()

  • size: Size.

    The size of the image, where lines1 and lines2 were found.

  • lines1: Evision.Mat.

    The first group of lines that needs to be drawn. It is visualized in blue color.

  • lines2: Evision.Mat.

    The second group of lines. They visualized in red color.

Return
  • retval: int

  • image: Evision.Mat.

    Optional image, where the lines will be drawn. The image should be color(3-channel) in order for lines1 and lines2 to be drawn in the above mentioned colors.

Python prototype (for reference only):

compareSegments(size, lines1, lines2[, image]) -> retval, image
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compareSegments(self, size, lines1, lines2, opts)

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

Draws two groups of lines in blue and red, counting the non overlapping (mismatching) pixels.

Positional Arguments
  • self: Evision.LineSegmentDetector.t()

  • size: Size.

    The size of the image, where lines1 and lines2 were found.

  • lines1: Evision.Mat.

    The first group of lines that needs to be drawn. It is visualized in blue color.

  • lines2: Evision.Mat.

    The second group of lines. They visualized in red color.

Return
  • retval: int

  • image: Evision.Mat.

    Optional image, where the lines will be drawn. The image should be color(3-channel) in order for lines1 and lines2 to be drawn in the above mentioned colors.

Python prototype (for reference only):

compareSegments(size, lines1, lines2[, image]) -> retval, image
@spec detect(t(), Evision.Mat.maybe_mat_in()) ::
  {Evision.Mat.t(), Evision.Mat.t(), Evision.Mat.t(), Evision.Mat.t()}
  | {:error, String.t()}

Finds lines in the input image.

Positional Arguments
  • self: Evision.LineSegmentDetector.t()

  • image: Evision.Mat.

    A grayscale (CV_8UC1) input image. If only a roi needs to be selected, use: lsd_ptr-\>detect(image(roi), lines, ...); lines += Scalar(roi.x, roi.y, roi.x, roi.y);

Return
  • lines: Evision.Mat.

    A vector of Vec4f elements specifying the beginning and ending point of a line. Where Vec4f is (x1, y1, x2, y2), point 1 is the start, point 2 - end. Returned lines are strictly oriented depending on the gradient.

  • width: Evision.Mat.

    Vector of widths of the regions, where the lines are found. E.g. Width of line.

  • prec: Evision.Mat.

    Vector of precisions with which the lines are found.

  • nfa: Evision.Mat.

    Vector containing number of false alarms in the line region, with precision of 10%. The bigger the value, logarithmically better the detection.

    • -1 corresponds to 10 mean false alarms
    • 0 corresponds to 1 mean false alarm
    • 1 corresponds to 0.1 mean false alarms This vector will be calculated only when the objects type is #LSD_REFINE_ADV.

This is the output of the default parameters of the algorithm on the above shown image. image

Python prototype (for reference only):

detect(image[, lines[, width[, prec[, nfa]]]]) -> lines, width, prec, nfa
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detect(self, image, opts)

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

Finds lines in the input image.

Positional Arguments
  • self: Evision.LineSegmentDetector.t()

  • image: Evision.Mat.

    A grayscale (CV_8UC1) input image. If only a roi needs to be selected, use: lsd_ptr-\>detect(image(roi), lines, ...); lines += Scalar(roi.x, roi.y, roi.x, roi.y);

Return
  • lines: Evision.Mat.

    A vector of Vec4f elements specifying the beginning and ending point of a line. Where Vec4f is (x1, y1, x2, y2), point 1 is the start, point 2 - end. Returned lines are strictly oriented depending on the gradient.

  • width: Evision.Mat.

    Vector of widths of the regions, where the lines are found. E.g. Width of line.

  • prec: Evision.Mat.

    Vector of precisions with which the lines are found.

  • nfa: Evision.Mat.

    Vector containing number of false alarms in the line region, with precision of 10%. The bigger the value, logarithmically better the detection.

    • -1 corresponds to 10 mean false alarms
    • 0 corresponds to 1 mean false alarm
    • 1 corresponds to 0.1 mean false alarms This vector will be calculated only when the objects type is #LSD_REFINE_ADV.

This is the output of the default parameters of the algorithm on the above shown image. image

Python prototype (for reference only):

detect(image[, lines[, width[, prec[, nfa]]]]) -> lines, width, prec, nfa
Link to this function

drawSegments(self, image, lines)

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

Draws the line segments on a given image.

Positional Arguments
  • self: Evision.LineSegmentDetector.t()

  • lines: Evision.Mat.

    A vector of the lines that needed to be drawn.

Return
  • image: Evision.Mat.

    The image, where the lines will be drawn. Should be bigger or equal to the image, where the lines were found.

Python prototype (for reference only):

drawSegments(image, lines) -> image
@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.LineSegmentDetector.t()
Return
  • retval: bool

Python prototype (for reference only):

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

getDefaultName

Positional Arguments
  • self: Evision.LineSegmentDetector.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 read(t(), Evision.FileNode.t()) :: :ok | {:error, String.t()}

Reads algorithm parameters from a file storage

Positional Arguments

Python prototype (for reference only):

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

save

Positional Arguments
  • self: Evision.LineSegmentDetector.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 write(t(), Evision.FileStorage.t()) :: :ok | {:error, String.t()}

simplified API for language bindings

Positional Arguments
Keyword Arguments

Has overloading in C++

Python prototype (for reference only):

write(fs[, name]) -> None
@spec write(t(), Evision.FileStorage.t(), [{atom(), term()}, ...] | nil) ::
  :ok | {:error, String.t()}

simplified API for language bindings

Positional Arguments
Keyword Arguments

Has overloading in C++

Python prototype (for reference only):

write(fs[, name]) -> None