diff options
Diffstat (limited to 'lib/utils')
| -rw-r--r-- | lib/utils/smoothing.py | 58 |
1 files changed, 18 insertions, 40 deletions
diff --git a/lib/utils/smoothing.py b/lib/utils/smoothing.py index 0300f6f1..9c16bb0f 100644 --- a/lib/utils/smoothing.py +++ b/lib/utils/smoothing.py @@ -1,8 +1,7 @@ import numpy as np -from scipy.interpolate import splprep, splev +from ..stitches.running_stitch import stitch_curve_evenly from .geometry import Point, coordinate_list_to_point_list -from ..stitches.running_stitch import even_running_stitch def _remove_duplicate_coordinates(coords_array): @@ -22,7 +21,7 @@ def _remove_duplicate_coordinates(coords_array): return coords_array[keepers] -def smooth_path(path, smoothness=1.0): +def smooth_path(path, smoothness=1.0, iterations=5): """Smooth a path of coordinates. Arguments: @@ -33,12 +32,9 @@ def smooth_path(path, smoothness=1.0): Returns: A list of Points. """ - from ..debug.debug import debug - + points = coordinate_list_to_point_list(path) if smoothness == 0: - # s of exactly zero seems to indicate a default level of smoothing - # in splprep, so we'll just exit instead. - return path + return points # Smoothing seems to look nicer if the line segments in the path are mostly # similar in length. If we have some especially long segments, then the @@ -48,35 +44,17 @@ def smooth_path(path, smoothness=1.0): # # Fortunately, we can convert the path to segments that are mostly the same # length by using the running stitch algorithm. - path = even_running_stitch(coordinate_list_to_point_list(path), 5 * smoothness, smoothness / 2) - - # splprep blows up on duplicated consecutive points with "Invalid inputs" - coords = _remove_duplicate_coordinates(np.array(path)) - num_points = len(coords) - - if num_points <= 3: - # splprep throws an error unless num_points > k - return path - - # s is explained in this issue: https://github.com/scipy/scipy/issues/11916 - # the smoothness parameter limits how much the smoothed path can deviate - # from the original path. The standard deviation of the distance between - # the smoothed path and the original path is equal to the smoothness. - # In practical terms, if smoothness is 1mm, then the smoothed path can be - # up to 1mm away from the original path. - s = num_points * (smoothness ** 2) - - # .T transposes the array (for some reason splprep expects - # [[x1, x2, ...], [y1, y2, ...]] - tck, fp, ier, msg = splprep(coords.T, s=s, k=3, nest=-1, full_output=1) - if ier > 0: - debug.log(f"error {ier} smoothing path: {msg}") - return path - - # Evaluate the spline curve at many points along its length to produce the - # smoothed point list. 2 * num_points seems to be a good number, but it - # does produce a lot of points. - smoothed_x_values, smoothed_y_values = splev(np.linspace(0, 1, int(num_points * 2)), tck[0]) - coords = np.array([smoothed_x_values, smoothed_y_values]).T - - return [Point(x, y) for x, y in coords] + points = stitch_curve_evenly(points, smoothness * 5, smoothness * 2) + points = np.array(points) + for _ in range(iterations): + ll = points.repeat(2, axis=0) + r = np.empty_like(ll) + if len(r) == 0: + continue + r[0] = ll[0] + r[2::2] = ll[1:-1:2] + r[1:-1:2] = ll[2::2] + r[-1] = ll[-1] + points = ll * 0.75 + r * 0.25 + + return [Point(*coord) for coord in points] |