159 lines
4.8 KiB
Python
159 lines
4.8 KiB
Python
# Copyright 2015 Google Inc. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from math import hypot
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from fontTools.misc import bezierTools
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class Point:
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"""An arithmetic-compatible 2D vector.
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We use this because arithmetic with RoboFab's RPoint is prohibitively slow.
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"""
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def __init__(self, p):
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self.p = map(float, p)
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def __getitem__(self, key):
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return self.p[key]
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def __add__(self, other):
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return Point([a + b for a, b in zip(self.p, other.p)])
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def __sub__(self, other):
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return Point([a - b for a, b in zip(self.p, other.p)])
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def __mul__(self, n):
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return Point([a * n for a in self.p])
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def dist(self, other):
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"""Calculate the distance between two points."""
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return hypot(self[0] - other[0], self[1] - other[1])
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def dot(self, other):
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"""Return the dot product of two points."""
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return sum(a * b for a, b in zip(self.p, other.p))
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def lerp(p1, p2, t):
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"""Linearly interpolate between p1 and p2 at time t."""
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return p1 * (1 - t) + p2 * t
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def quadratic_bezier_at(p, t):
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"""Return the point on a quadratic bezier curve at time t."""
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return Point([
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lerp(lerp(p[0][0], p[1][0], t), lerp(p[1][0], p[2][0], t), t),
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lerp(lerp(p[0][1], p[1][1], t), lerp(p[1][1], p[2][1], t), t)])
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def cubic_bezier_at(p, t):
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"""Return the point on a cubic bezier curve at time t."""
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return Point([
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lerp(lerp(lerp(p[0][0], p[1][0], t), lerp(p[1][0], p[2][0], t), t),
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lerp(lerp(p[1][0], p[2][0], t), lerp(p[2][0], p[3][0], t), t), t),
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lerp(lerp(lerp(p[0][1], p[1][1], t), lerp(p[1][1], p[2][1], t), t),
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lerp(lerp(p[1][1], p[2][1], t), lerp(p[2][1], p[3][1], t), t), t)])
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def cubic_approx(p, t):
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"""Approximate a cubic bezier curve with a quadratic one."""
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p1 = lerp(p[0], p[1], 1.5)
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p2 = lerp(p[3], p[2], 1.5)
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return [p[0], lerp(p1, p2, t), p[3]]
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def calc_intersect(p):
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"""Calculate the intersection of ab and cd, given [a, b, c, d]."""
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a, b, c, d = p
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ab = b - a
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cd = d - c
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p = Point([-ab[1], ab[0]])
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try:
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h = p.dot(a - c) / p.dot(cd)
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except ZeroDivisionError:
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raise ValueError('Parallel vectors given to calc_intersect.')
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return c + cd * h
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def cubic_approx_spline(p, n):
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"""Approximate a cubic bezier curve with a spline of n quadratics.
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Returns None if n is 1 and the cubic's control vectors are parallel, since
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no quadratic exists with this cubic's tangents.
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"""
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if n == 1:
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try:
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p1 = calc_intersect(p)
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except ValueError:
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return None
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return p[0], p1, p[3]
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spline = [p[0]]
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ts = [(float(i) / n) for i in range(1, n)]
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segments = [
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map(Point, segment)
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for segment in bezierTools.splitCubicAtT(p[0], p[1], p[2], p[3], *ts)]
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for i in range(len(segments)):
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segment = cubic_approx(segments[i], float(i) / (n - 1))
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spline.append(segment[1])
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spline.append(p[3])
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return spline
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def curve_spline_dist(bezier, spline):
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"""Max distance between a bezier and quadratic spline at sampled ts."""
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TOTAL_STEPS = 20
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error = 0
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n = len(spline) - 2
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steps = TOTAL_STEPS / n
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for i in range(1, n + 1):
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segment = [
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spline[0] if i == 1 else segment[2],
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spline[i],
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spline[i + 1] if i == n else lerp(spline[i], spline[i + 1], 0.5)]
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for j in range(steps):
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p1 = cubic_bezier_at(bezier, (float(j) / steps + i - 1) / n)
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p2 = quadratic_bezier_at(segment, float(j) / steps)
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error = max(error, p1.dist(p2))
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return error
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def curve_to_quadratic(p, max_err, max_n):
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"""Return a quadratic spline approximating this cubic bezier."""
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for n in range(1, max_n + 1):
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spline = cubic_approx_spline(p, n)
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if spline and curve_spline_dist(p, spline) <= max_err:
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break
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return spline
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def curves_to_quadratic(curves, max_errors, max_n):
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"""Return quadratic splines approximating these cubic beziers."""
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for n in range(1, max_n + 1):
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splines = [cubic_approx_spline(c, n) for c in curves]
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if (all(splines) and
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all(curve_spline_dist(c, s) < max_err
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for c, s, max_err in zip(curves, splines, max_errors))):
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break
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return splines
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