163 lines
4.6 KiB
Python
163 lines
4.6 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 __future__ import print_function, division, absolute_import
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from math import hypot
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from fontTools.misc import bezierTools
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def vector(p1, p2):
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"""Return the vector from p1 to p2."""
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return p2[0] - p1[0], p2[1] - p1[1]
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def translate(p, v):
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"""Translate a point by a vector."""
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return p[0] + v[0], p[1] + v[1]
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def scale(v, n):
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"""Scale a vector."""
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return v[0] * n, v[1] * n
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def dist(p1, p2):
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"""Calculate the distance between two points."""
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return hypot(p1[0] - p2[0], p1[1] - p2[1])
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def dot(v1, v2):
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"""Return the dot product of two vectors."""
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return v1[0] * v2[0] + v1[1] * v2[1]
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def lerp(a, b, t):
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"""Linearly interpolate between scalars a and b at time t."""
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return a * (1 - t) + b * t
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def lerp_pt(p1, p2, t):
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"""Linearly interpolate between points p1 and p2 at time t."""
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(x1, y1), (x2, y2) = p1, p2
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return lerp(x1, x2, t), lerp(y1, y2, 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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(x1, y1), (x2, y2), (x3, y3) = p
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return (
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lerp(lerp(x1, x2, t), lerp(x2, x3, t), t),
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lerp(lerp(y1, y2, t), lerp(y2, y3, 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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(x1, y1), (x2, y2), (x3, y3), (x4, y4) = p
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return (
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lerp(lerp(lerp(x1, x2, t), lerp(x2, x3, t), t),
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lerp(lerp(x2, x3, t), lerp(x3, x4, t), t), t),
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lerp(lerp(lerp(y1, y2, t), lerp(y2, y3, t), t),
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lerp(lerp(y2, y3, t), lerp(y3, y4, 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_pt(p[0], p[1], 1.5)
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p2 = lerp_pt(p[3], p[2], 1.5)
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return p[0], lerp_pt(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 = vector(a, b)
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cd = vector(c, d)
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p = -ab[1], ab[0]
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try:
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h = dot(p, vector(c, a)) / dot(p, cd)
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except ZeroDivisionError:
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raise ValueError('Parallel vectors given to calc_intersect.')
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return translate(c, scale(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 = [i / n for i in range(1, n)]
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segments = 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], 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_pt(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, (j / steps + i - 1) / n)
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p2 = quadratic_bezier_at(segment, j / steps)
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error = max(error, dist(p1, 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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