4.2.2. discorpy.prep.linepattern

Module of pre-processing methods for handling a line-pattern image:

  • Determine the slopes and distances between lines.

  • Extract points belong to a line.

  • Convert a chessboard image to a line-pattern image.

Functions:

locate_subpixel_point(list_point[, option])

Locate the extremum point of a 1D array with subpixel accuracy.

get_local_extrema_points(list_data[, ...])

Get a list of local extremum points from a 1D array.

calc_slope_distance_hor_lines(mat[, ratio, ...])

Calculate the representative distance between horizontal lines and the representative slope of these lines using the ROI around the middle of a line-pattern image.

calc_slope_distance_ver_lines(mat[, ratio, ...])

Calculate the representative distance between vertical lines and the representative slope of these lines using the ROI around the middle of a line-pattern image.

get_tilted_profile(mat, index, angle_deg, ...)

Get the intensity-profile along a tilted line across an image.

get_cross_points_hor_lines(mat, slope_ver, ...)

Get points on horizontal lines of a line-pattern image by intersecting with a list of generated vertical-lines.

get_cross_points_ver_lines(mat, slope_hor, ...)

Get points on vertical lines of a line-pattern image by intersecting with a list of generated horizontal-lines.

convert_chessboard_to_linepattern(mat[, ...])

Convert a chessboard image to a line-pattern image.
discorpy.prep.linepattern.locate_subpixel_point(list_point, option='min')[source]

Locate the extremum point of a 1D array with subpixel accuracy.

Parameters:

  • list_point (array_like) – 1D array.

  • option ({“min”, “max”}) – To locate the minimum point or the maximum point.

Returns:

float – Subpixel position of the extremum point.

discorpy.prep.linepattern.get_local_extrema_points(list_data, option='min', radius=7, sensitive=0.1, denoise=True, norm=True, subpixel=True)[source]

Get a list of local extremum points from a 1D array.

Parameters:

  • list_data (array_like) – 1D array.

  • option ({“min”, “max”}) – To get minimum points or maximum points

  • radius (int) – Search radius. Used to locate extremum points.

  • sensitive (float) – To detect extremum points against random noise. Smaller is more sensitive.

  • denoise (bool, optional) – Applying a smoothing filter if True.

  • norm (bool, optional) – Apply background normalization to the array.

  • subpixel (bool, optional) – Locate points with subpixel accuracy.

Returns:

array_like – 1D array. Positions of local extremum points.

discorpy.prep.linepattern.calc_slope_distance_hor_lines(mat, ratio=0.3, search_range=30.0, radius=9, sensitive=0.1, bgr='bright', denoise=True, norm=True, subpixel=True)[source]

Calculate the representative distance between horizontal lines and the representative slope of these lines using the ROI around the middle of a line-pattern image.

Parameters:

  • mat (array_like) – 2D array.

  • ratio (float) – Used to select the ROI around the middle of an image.

  • search_range (float) – Search range in Degree to determine the slope of lines.

  • radius (int) – Search radius. Used to locate lines.

  • sensitive (float) – To detect lines against random noise. Smaller is more sensitive.

  • bgr ({“bright”, “dark”}) – Specify the brightness of the background against the lines.

  • denoise (bool, optional) – Applying a smoothing filter if True.

  • norm (bool, optional) – Apply background normalization to the array.

  • subpixel (bool, optional) – Locate points with subpixel accuracy.

Returns:

  • slope (float) – Slope of horizontal lines in Radian.

  • distance (float) – Distance between horizontal lines.

discorpy.prep.linepattern.calc_slope_distance_ver_lines(mat, ratio=0.3, search_range=30.0, radius=9, sensitive=0.1, bgr='bright', denoise=True, norm=True, subpixel=True)[source]

Calculate the representative distance between vertical lines and the representative slope of these lines using the ROI around the middle of a line-pattern image.

Parameters:

  • mat (array_like) – 2D array.

  • ratio (float) – Used to select the ROI around the middle of an image.

  • search_range (float) – Search range in Degree to determine the slope of lines.

  • radius (int) – Search radius. Used to locate lines.

  • sensitive (float) – To detect lines against random noise. Smaller is more sensitive.

  • bgr ({“bright”, “dark”}) – Specify the brightness of the background against the lines.

  • denoise (bool, optional) – Applying a smoothing filter if True.

  • subpixel (bool, optional) – Locate points with subpixel accuracy.

Returns:

  • slope (float) – Slope of vertical lines in Radian.

  • distance (float) – Distance between vertical lines.

discorpy.prep.linepattern.get_tilted_profile(mat, index, angle_deg, direction)[source]

Get the intensity-profile along a tilted line across an image. Positive angle is counterclockwise.

Parameters:

  • mat (array_like) – 2D array.

  • index (int) – Index of the line.

  • angle_deg (float) – Tilted angle in Degree.

  • direction ({“horizontal”, “vertical”}) – Direction of line-profile.

Returns:

  • xlist (array_like) – 1D array. x-positions of points on the line.

  • ylist (array_like) – 1D array. y-positions of points on the line.

  • profile (array_like) – 1D array. Intensities of points on the line.

discorpy.prep.linepattern.get_cross_points_hor_lines(mat, slope_ver, dist_ver, ratio=1.0, norm=True, offset=0, bgr='bright', radius=7, sensitive=0.1, denoise=True, subpixel=True)[source]

Get points on horizontal lines of a line-pattern image by intersecting with a list of generated vertical-lines.

Parameters:

  • mat (array_like) – 2D array.

  • slope_ver (float) – Slope in Radian of generated vertical lines.

  • dist_ver (float) – Distance between two adjacent generated lines.

  • ratio (float) – To adjust the distance between generated lines to get more/less lines.

  • norm (bool, optional) – Apply background normalization to the array.

  • offset (int) – Starting index of generated lines.

  • bgr ({“bright”, “dark”}) – Specify the brightness of the background against the lines.

  • radius (int) – Search radius. Used to locate extremum points.

  • sensitive (float) – To detect extremum points against random noise. Smaller is more sensitive.

  • denoise (bool, optional) – Applying a smoothing filter if True.

  • subpixel (bool, optional) – Locate points with subpixel accuracy.

Returns:

array_like – List of (y,x)-coordinates of points.

discorpy.prep.linepattern.get_cross_points_ver_lines(mat, slope_hor, dist_hor, ratio=1.0, norm=True, offset=0, bgr='bright', radius=7, sensitive=0.1, denoise=True, subpixel=True)[source]

Get points on vertical lines of a line-pattern image by intersecting with a list of generated horizontal-lines.

Parameters:

  • mat (array_like) – 2D array.

  • slope_hor (float) – Slope in Radian of generated horizontal lines.

  • dist_hor (float) – Distance between two adjacent generated lines.

  • ratio (float) – To adjust the distance between generated lines to get more/less lines.

  • norm (bool, optional) – Apply background normalization to the array.

  • offset (int) – Starting index of generated lines.

  • bgr ({“bright”, “dark”}) – Specify the brightness of the background against the lines.

  • radius (int) – Search radius. Used to locate extremum points.

  • sensitive (float) – To detect extremum points against random noise. Smaller is more sensitive.

  • denoise (bool, optional) – Applying a smoothing filter if True.

  • subpixel (bool, optional) – Locate points with subpixel accuracy.

Returns:

array_like – List of (y,x)-coordinates of points.

discorpy.prep.linepattern.convert_chessboard_to_linepattern(mat, smooth=False, bgr='bright')[source]

Convert a chessboard image to a line-pattern image.

Parameters:

  • mat (array_like) – 2D array.

  • smooth (bool, optional) – Apply a gaussian smoothing filter if True.

  • bgr ({‘bright’, ‘dark’}) – Select the background of the output image.

Returns:

array_like – Line-pattern image.