1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160
// Copyright 2022 The Kurbo Authors
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// https://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//! Computation of offset curves of cubic Béziers, based on a curve fitting
//! approach.
//!
//! See the [Parallel curves of cubic Béziers] blog post for a discussion of how
//! this algorithm works and what kind of results can be expected. In general, it
//! is expected to perform much better than most published algorithms. The number
//! of curve segments needed to attain a given accuracy scales as O(n^6) with
//! accuracy.
//!
//! In general, to compute the offset curve (also known as parallel curve) of
//! a cubic Bézier segment, create a [`CubicOffset`] struct with the curve
//! segment and offset, then use [`fit_to_bezpath`] or [`fit_to_bezpath_opt`]
//! depending on how much time to spend optimizing the resulting path.
//!
//! [`fit_to_bezpath`]: crate::fit_to_bezpath
//! [`fit_to_bezpath_opt`]: crate::fit_to_bezpath_opt
//! [Parallel curves of cubic Béziers]: https://raphlinus.github.io/curves/2022/09/09/parallel-beziers.html
use core::ops::Range;
#[cfg(not(feature = "std"))]
use crate::common::FloatFuncs;
use crate::{
common::solve_itp, CubicBez, CurveFitSample, ParamCurve, ParamCurveDeriv, ParamCurveFit, Point,
QuadBez, Vec2,
};
/// The offset curve of a cubic Bézier.
///
/// This is a representation of the offset curve of a cubic Bézier segment, for
/// purposes of curve fitting.
///
/// See the [module-level documentation] for a bit more discussion of the approach,
/// and how this struct is to be used.
///
/// [module-level documentation]: crate::offset
pub struct CubicOffset {
/// Source curve.
c: CubicBez,
/// Derivative of source curve.
q: QuadBez,
/// Offset.
d: f64,
// c0 + c1 t + c2 t^2 is the cross product of second and first
// derivatives of the underlying cubic, multiplied by offset (for
// computing cusp).
c0: f64,
c1: f64,
c2: f64,
}
impl CubicOffset {
/// Create a new curve from Bézier segment and offset.
pub fn new(c: CubicBez, d: f64) -> Self {
let q = c.deriv();
let d0 = q.p0.to_vec2();
let d1 = 2.0 * (q.p1 - q.p0);
let d2 = q.p0.to_vec2() - 2.0 * q.p1.to_vec2() + q.p2.to_vec2();
CubicOffset {
c,
q,
d,
c0: d * d1.cross(d0),
c1: d * 2.0 * d2.cross(d0),
c2: d * d2.cross(d1),
}
}
fn eval_offset(&self, t: f64) -> Vec2 {
let dp = self.q.eval(t).to_vec2();
let norm = Vec2::new(-dp.y, dp.x);
// TODO: deal with hypot = 0
norm * self.d / dp.hypot()
}
fn eval(&self, t: f64) -> Point {
// Point on source curve.
self.c.eval(t) + self.eval_offset(t)
}
/// Evaluate derivative of curve.
fn eval_deriv(&self, t: f64) -> Vec2 {
self.cusp_sign(t) * self.q.eval(t).to_vec2()
}
// Compute a function which has a zero-crossing at cusps, and is
// positive at low curvatures on the source curve.
fn cusp_sign(&self, t: f64) -> f64 {
let ds2 = self.q.eval(t).to_vec2().hypot2();
((self.c2 * t + self.c1) * t + self.c0) / (ds2 * ds2.sqrt()) + 1.0
}
}
impl ParamCurveFit for CubicOffset {
fn sample_pt_tangent(&self, t: f64, sign: f64) -> CurveFitSample {
let p = self.eval(t);
const CUSP_EPS: f64 = 1e-8;
let mut cusp = self.cusp_sign(t);
if cusp.abs() < CUSP_EPS {
// This is a numerical derivative, which is probably good enough
// for all practical purposes, but an analytical derivative would
// be more elegant.
//
// Also, we're not dealing with second or higher order cusps.
cusp = sign * (self.cusp_sign(t + CUSP_EPS) - self.cusp_sign(t - CUSP_EPS));
}
let tangent = self.q.eval(t).to_vec2() * cusp.signum();
CurveFitSample { p, tangent }
}
fn sample_pt_deriv(&self, t: f64) -> (Point, Vec2) {
(self.eval(t), self.eval_deriv(t))
}
fn break_cusp(&self, range: Range<f64>) -> Option<f64> {
const CUSP_EPS: f64 = 1e-8;
// When an endpoint is on (or very near) a cusp, move just far enough
// away from the cusp that we're confident we have the right sign.
let break_cusp_help = |mut x, mut d| {
let mut cusp = self.cusp_sign(x);
while cusp.abs() < CUSP_EPS && d < 1.0 {
x += d;
let old_cusp = cusp;
cusp = self.cusp_sign(x);
if cusp.abs() > old_cusp.abs() {
break;
}
d *= 2.0;
}
(x, cusp)
};
let (a, cusp0) = break_cusp_help(range.start, 1e-12);
let (b, cusp1) = break_cusp_help(range.end, -1e-12);
if a >= b || cusp0 * cusp1 >= 0.0 {
// Discussion point: maybe we should search for double cusps in the interior
// of the range.
return None;
}
let s = cusp1.signum();
let f = |t| s * self.cusp_sign(t);
let k1 = 0.2 / (b - a);
const ITP_EPS: f64 = 1e-12;
let x = solve_itp(f, a, b, ITP_EPS, 1, k1, s * cusp0, s * cusp1);
Some(x)
}
}