@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

@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.

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.

  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.

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.

  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