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

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

Clears the algorithm state

Positional Arguments

• self: Evision.LineSegmentDetector.t()

Python prototype (for reference only):

clear() -> None

@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: integer()

• image: Evision.Mat.t().

  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 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: integer()

• image: Evision.Mat.t().

  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.t().

  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.t().

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

• prec: Evision.Mat.t().

  Vector of precisions with which the lines are found.

• nfa: Evision.Mat.t().

  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.

Python prototype (for reference only):

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

@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.t().

  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.t().

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

• prec: Evision.Mat.t().

  Vector of precisions with which the lines are found.

• nfa: Evision.Mat.t().

  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.

Python prototype (for reference only):

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

@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.t().

  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(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.LineSegmentDetector.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.LineSegmentDetector.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.LineSegmentDetector.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.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()) :: t() | {:error, String.t()}

Stores algorithm parameters in a file storage

Positional Arguments

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

Has overloading in C++

Python prototype (for reference only):

write(fs, name) -> None