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####################################################################################################
#
# Patro - A Python library to make patterns for fashion design
# Copyright (C) 2017 Fabrice Salvaire
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
####################################################################################################
"""Module to implement Bézier curve.
"""
# C0 = continuous
# G1 = geometric continuity
# Tangents point to the same direction
# C1 = parametric continuity
# Tangents are the same, implies G1
# C2 = curvature continuity
# Tangents and their derivatives are the same
####################################################################################################
from Patro.Common.Math.Root import quadratic_root, cubic_root, fifth_root
from .Interpolation import interpolate_two_points
from .Primitive import Primitive3P, Primitive4P, PrimitiveNP, Primitive2DMixin
from .Transformation import AffineTransformation
from .Vector import Vector2D
####################################################################################################
# Fixme:
# max distance to the chord for linear approximation
# fitting
####################################################################################################
class BezierMixin2D(Primitive2DMixin):
"""Mixin to implements 2D Bezier Curve."""
LineInterpolationPrecision = 0.05
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##############################################
def interpolated_length(self, dt=None):
# Length of the curve obtained via line interpolation
if dt is None:
dt = self.LineInterpolationPrecision / (self.end_point - self.start_point).magnitude
length = 0
t = 0
while t < 1:
t0 = t
t = min(t + dt, 1)
length += (self.point_at_t(t) - self.point_at_t(t0)).magnitude
return length
##############################################
def length_at_t(self, t, cache=False):
"""Compute the length of the curve at *t*."""
if cache: # lookup cache
if not hasattr(self, '_length_cache'):
self._length_cache = {}
length = self._length_cache.get(t, None)
if length is not None:
return length
length = self.split_at_t(t).length
if cache: # save
self._length_cache[t] = length
return length
##############################################
def t_at_length(self, length, precision=1e-6):
"""Compute t for the given length. Length must lie in [0, curve length] range]. """
if length < 0:
raise ValueError('Negative length')
if length == 0:
return 0
curve_length = self.length # Fixme: cache ?
if (curve_length - length) <= precision:
return 1
if length > curve_length:
raise ValueError('Out of length')
# Search t for length using dichotomy
# convergence rate :
# 10 iterations corresponds to curve length / 1024
# 16 / 65536
# start range
inf = 0
sup = 1
while True:
middle = (sup + inf) / 2
length_at_middle = self.length_at_t(middle, cache=True) # Fixme: out of memory, use LRU ???
# exit condition
if abs(length_at_middle - length) <= precision:
return middle
elif length_at_middle < length:
inf = middle
else: # length < length_at_middle
sup = middle
##############################################
def split_at_two_t(self, t1, t2):
if t1 == t2:
return self.point_at_t(t1)
if t2 < t1:
# Fixme: raise ?
t1, t2 = t2, t1
# curve = self
# if t1 > 0:
curve = self.split_at_t(t1)[1] # right
if t2 < 1:
# Interpolate the parameter at t2 in the new curve
t = (t2 - t1) / (1 - t1)
curve = curve.split_at_t(t)[0] # left
return curve
##############################################
def _map_to_line(self, line):
transformation = AffineTransformation.Rotation(-line.v.orientation)
# Fixme: use __vector_cls__
transformation *= AffineTransformation.Translation(Vector2D(0, -line.p.y))
# Fixme: better API ?
return self.clone().transform(transformation)
##############################################
def non_parametric_curve(self, line):
"""Return the non-parametric Bezier curve D(ti, di(t)) where di(t) is the distance of the curve from
the baseline of the fat-line, ti is equally spaced in [0, 1].
"""
ts = np.arange(0, 1, 1/(self.number_of_points-1))
distances = [line.distance_to_line(p) for p in self.points]
points = [Vector2D(t, d) for t, f in zip(ts, distances)]
return self.__class__(*points)
##############################################
def distance_to_point(self, point):
p = self.closest_point(point)
if p is not None:
return (point - p).magnitude
else:
return None
####################################################################################################
class QuadraticBezier2D(BezierMixin2D, Primitive3P):
"""Class to implements 2D Quadratic Bezier Curve."""
# Q(t) = Transformation * Control * Basis * T(t)
#
# / P1x P2x P3x \ / 1 -2 1 \ / 1 \
# Q(t) = Tr | P1y P2x P3x | | 0 2 -2 | | t |
# \ 1 1 1 / \ 0 0 1 / \ t**2 /
#
# Q(t) = P0 * (1 - 2*t + t**2) +
# P1 * ( 2*t - t**2) +
# P2 * t**2
BASIS = np.array((
(1, -2, 1),
(0, 2, -2),
(0, 0, 1),
))
INVERSE_BASIS = np.array((
(-2, 1, -2),
(-1, -3, 1),
(-1, -1, -2),
))
##############################################
def __init__(self, p0, p1, p2):
##############################################
def __repr__(self):
return self.__class__.__name__ + '({0._p0}, {0._p1}, {0._p2})'.format(self)
##############################################
@property
def length(self):
# Algorithm:
#
# http://www.gamedev.net/topic/551455-length-of-a-generalized-quadratic-bezier-curve-in-3d
# Dave Eberly Posted October 25, 2009
#
# The quadratic Bezier is
# (x(t),y(t)) = (1-t)^2*(x0,y0) + 2*t*(1-t)*(x1,y1) + t^2*(x2,y2)
#
# The derivative is
# (x'(t),y'(t)) = -2*(1-t)*(x0,y0) + (2-4*t)*(x1,y1) + 2*t*(x2,y2)
#
# The length of the curve for 0 <= t <= 1 is
# Integral[0,1] sqrt((x'(t))^2 + (y'(t))^2) dt
# The integrand is of the form sqrt(c*t^2 + b*t + a)
#
# You have three separate cases: c = 0, c > 0, or c < 0.
# * The case c = 0 is easy.
# * For the case c > 0, an antiderivative is
# (2*c*t+b)*sqrt(c*t^2+b*t+a)/(4*c) + (0.5*k)*log(2*sqrt(c*(c*t^2+b*t+a)) + 2*c*t + b)/sqrt(c)
# where k = 4*c/q with q = 4*a*c - b*b.
# * For the case c < 0, an antiderivative is
# (2*c*t+b)*sqrt(c*t^2+b*t+a)/(4*c) - (0.5*k)*arcsin((2*c*t+b)/sqrt(-q))/sqrt(-c)
A0 = self._p1 - self._p0
A1 = self._p0 - self._p1 * 2 + self._p2
c = 4 * A1.dot(A1)
b = 8 * A0.dot(A1)
a = 4 * A0.dot(A0)
q = 4 * a * c - b * b
two_cb = 2 * c + b
sum_cba = c + b + a
m0 = 0.25 / c
m1 = q / (8 * c**1.5)
return (m0 * (two_cb * sqrt(sum_cba) - b * sqrt(a)) +
m1 * (log(2 * sqrt(c * sum_cba) + two_cb) - log(2 * sqrt(c * a) + b)))
else:
##############################################
def point_at_t(self, t):
# if 0 < t or 1 < t:
# raise ValueError()
u = 1 - t
return self._p0 * u**2 + self._p1 * 2 * t * u + self._p2 * t**2
##############################################
def split_at_t(self, t):
if t <= 0:
return None, self
elif t >= 1:
return self, None
else:
p01 = interpolate_two_points(self._p0, self._p1, t)
p12 = interpolate_two_points(self._p1, self._p2, t)
p = interpolate_two_points(p01, p12, t) # p = p012
# p = self.point_at_t(t)
return (QuadraticBezier2D(self._p0, p01, p), QuadraticBezier2D(p, p12, self._p2))
@property
def tangent0(self):
return (self._p1 - self._p0).normalise()
##############################################
@property
def tangent1(self):
return (self._p2 - self._p1).normalise()
##############################################
@property
def normal0(self):
return self.tangent0.normal()
##############################################
@property
def tangent1(self):
return self.tangent1.normal()
##############################################
def tangent_at(self, t):
u = 1 - t
return (self._p1 - self._p0) * u + (self._p2 - self._p1) * t
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##############################################
def intersect_line(self, line):
"""Find the intersections of the curve with a line."""
# Algorithm:
# Apply a transformation to the curve that maps the line onto the X-axis.
# Then we only need to test the Y-values for a zero.
# t, p0, p1, p2, p3 = symbols('t p0 p1 p2 p3')
# u = 1 - t
# B = p0 * u**2 + p1 * 2*t*u + p2 * t**2
# collect(expand(B), t)
# solveset(B, t)
curve = self._map_to_line(line)
p0 = curve.p0.y
p1 = curve.p1.y
p2 = curve.p2.y
return quadratic_root(
p2 - 2*p1 + p0, # t**2
2*(p1 - p0), # t
p0,
)
### a = p0 - 2*p1 + p2 # t**2
### # b = 2*(-p0 + p1) # t
### b = -p0 + p1 # was / 2 !!!
### c = p0
###
### # discriminant = b**2 - 4*a*c
### # discriminant = 4 * (p1**2 - p0*p2)
### discriminant = p1**2 - p0*p2 # was / 4 !!!
###
### if discriminant < 0:
### return None
### elif discriminant == 0:
### return -b / a # dropped 2
### else:
### # dropped 2
### y1 = (-b - sqrt(discriminant)) / a
### y2 = (-b + sqrt(discriminant)) / a
### return y1, y2
##############################################
def fat_line(self):
line = Line2D.from_two_points(self._p0, self._p3)
d1 = line.distance_to_line(self._p1)
d_min = min(0, d1 / 2)
d_max = max(0, d1 / 2)
return (line, d_min, d_max)
##############################################
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def closest_point(self, point):
# Reference:
# https://hal.archives-ouvertes.fr/inria-00518379/document
# Improved Algebraic Algorithm On Point Projection For Bézier Curves
# Xiao-Diao Chen, Yin Zhou, Zhenyu Shu, Hua Su, Jean-Claude Paul
# Condition:
# (P - B(t)) . B'(t) = 0 where t in [0,1]
#
# P. B'(t) - B(t). B'(t) = 0
# A = P1 - P0
# B = P2 - P1 - A
# M = P0 - P
# Q(t) = P0*(1-t)**2 + P1*2*t*(1-t) + P2*t**2
# Q'(t) = -2*P0*(1 - t) + 2*P1*(1 - 2*t) + 2*P2*t
# = 2*(A + B*t)
# P0, P1, P2, P, t = symbols('P0 P1 P2 P t')
# Q = P0 * (1-t)**2 + P1 * 2*t*(1-t) + P2 * t**2
# Qp = simplify(Q.diff(t))
# collect(expand((P*Qp - Q*Qp)/-2), t)
# (P0**2 - 4*P0*P1 + 2*P0*P2 + 4*P1**2 - 4*P1*P2 + P2**2) * t**3
# (-3*P0**2 + 9*P0*P1 - 3*P0*P2 - 6*P1**2 + 3*P1*P2) * t**2
# (-P*P0 + 2*P*P1 - P*P2 + 3*P0**2 - 6*P0*P1 + P0*P2 + 2*P1**2) * t
# P*P0 - P*P1 - P0**2 + P0*P1
# factorisation
# (P0 - 2*P1 + P2)**2 * t**3
# 3*(P1 - P0)*(P0 - 2*P1 + P2) * t**2
# ...
# (P0 - P)*(P1 - P0)
# B**2 * t**3
# 3*A*B * t**2
# (2*A**2 + M*B) * t
# M*A
A = self._p1 - self._p0
B = self._p2 - self._p1 - A
M = self._p0 - point
roots = cubic_root(
B.magnitude_square,
3*A.dot(B),
2*A.magnitude_square + M.dot(B),
M.dot(A),
)
t = [root for root in roots if 0 <= root <= 1]
if not t:
return None
elif len(t) > 1:
raise NameError("Found more than on root")
else:
return self.point_at_t(t)
####################################################################################################
_Sqrt3 = sqrt(3)
_Div18Sqrt3 = 18 / _Sqrt3
_OneThird = 1 / 3
_Sqrt3Div36 = _Sqrt3 / 36
class CubicBezier2D(BezierMixin2D, Primitive4P):
"""Class to implements 2D Cubic Bezier Curve."""
InterpolationPrecision = 0.001
# Q(t) = Transformation * Control * Basis * T(t)
#
# / P1x P2x P3x P4x \ / 1 -3 3 -1 \ / 1 \
# Q(t) = Tr | P1y P2x P3x P4x | | 0 3 -6 3 | | t |
# | 0 0 0 0 | | 0 0 3 -3 | | t**2 |
# \ 1 1 1 1 / \ 0 0 0 1 / \ t**3 /
BASIS = np.array((
(1, -3, 3, -1),
(0, 3, -6, 3),
(0, 0, 3, -3),
(0, 0, 0, 1),
))
INVERSE_BASIS = np.array((
(1, 1, 1, 1),
(0, 1/3, 2/3, 1),
(0, 0, 1/3, 1),
(0, 0, 0, 1),
))
#######################################
def __init__(self, p0, p1, p2, p3):
##############################################
def __repr__(self):
return self.__class__.__name__ + '({0._p0}, {0._p1}, {0._p2}, {0._p3})'.format(self)
##############################################
from .Spline import CubicUniformSpline2D
basis = np.dot(self.BASIS, CubicUniformSpline2D.INVERSE_BASIS)
points = np.dot(self.geometry_matrix, basis).transpose()
return CubicUniformSpline2D(*points)
##############################################
@property
def length(self):
return self.adaptive_length_approximation()
##############################################
def point_at_t(self, t):
# if 0 < t or 1 < t:
# raise ValueError()
return (self._p0 +
(self._p1 - self._p0) * 3 * t +
(self._p2 - self._p1*2 + self._p0) * 3 * t**2 +
(self._p3 - self._p2*3 + self._p1*3 - self._p0) * t**3)
# interpolate = point_at_t
##############################################
def _q_point(self):
"""Return the control point for mid-point quadratic approximation"""
return (self._p2*3 - self._p3 + self._p1*3 - self._p0) / 4
##############################################
def mid_point_quadratic_approximation(self):
"""Return the mid-point quadratic approximation"""
p1 = self._q_point()
return QuadraticBezier2D(self._p0, p1, self._p3)
##############################################
def split_at_t(self, t):
p01 = interpolate_two_points(self._p0, self._p1, t)
p12 = interpolate_two_points(self._p1, self._p2, t)
p23 = interpolate_two_points(self._p2, self._p3, t)
p012 = interpolate_two_points(p01, p12, t)
p123 = interpolate_two_points(p12, p23, t)
p = interpolate_two_points(p012, p123, t) # p = p0123
# p = self.point_at_t(t)
return (CubicBezier2D(self._p0, p01, p012, p), CubicBezier2D(p, p123, p23, self._p3))
##############################################
def _d01(self):
"""Return the distance between 0 and 1 quadratic aproximations"""
return (self._p3 - self._p2 * 3 + self._p1 * 3 - self._p0).magnitude / 2
##############################################
def _t_max(self):
"""Return the split point for adaptive quadratic approximation"""
return (_Div18Sqrt3 * self.InterpolationPrecision / self._d01())**_OneThird
##############################################
def q_length(self):
"""Return the length of the mid-point quadratic approximation"""
return self.mid_point_quadratic_approximation().length
##############################################
def adaptive_length_approximation(self):
"""Return the length of the adaptive quadratic approximation"""
segments = []
segment = self
t_max = segment._t_max()
while t_max < 1:
split = segment.split_at_t(t_max)
segments.append(split[0])
segment = split[1]
t_max = segment._t_max()
segments.append(segment)
return sum([segment.q_length() for segment in segments])
##############################################
@property
def tangent1(self):
return (self._p3 - self._p2).normalise()
##############################################
def tangent_at(self, t):
u = 1 - t
return (self._p1 - self._p0) * u**2 + (self._p2 - self._p1) * 2 * t * u + (self._p3 - self._p2) * t**2
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##############################################
def intersect_line(self, line):
"""Find the intersections of the curve with a line."""
# Algorithm: same as for quadratic
# t, p0, p1, p2, p3, p4 = symbols('t p0 p1 p2 p3 p4')
# u = 1 - t
# B = p0 * u**3 +
# p1 * 3 * u**2 * t +
# p2 * 3 * u * t**2 +
# p3 * t**3
# B = p0 +
# (p1 - p0) * 3 * t +
# (p2 - p1 * 2 + p0) * 3 * t**2 +
# (p3 - p2 * 3 + p1 * 3 - p0) * t**3
# solveset(B, t)
curve = self._map_to_line(line)
p0 = curve.p0.y
p1 = curve.p1.y
p2 = curve.p2.y
p3 = curve.p3.y
return cubic_root(
p3 - 3*p2 + 3*p1 - p0,
3 * (p2 - p1 * 2 + p0),
3 * (p1 - p0),
p0,
)
##############################################
def fat_line(self):
line = Line2D.from_two_points(self._p0, self._p3)
d1 = line.distance_to_line(self._p1)
d2 = line.distance_to_line(self._p2)
if d1*d2 > 0:
factor = 3 / 4
else:
factor = 4 / 9
d_min = factor * min(0, d1, d2)
d_max = factor * max(0, d1, d2)
return (line, d_min, d_max)
##############################################
def _clipping_convex_hull(self):
line_03 = Line2D(self._p0, self._p3)
d1 = line_03.distance_to_line(self._p1)
d2 = line_03.distance_to_line(self._p2)
# Check if p1 and p2 are on the same side of the line [p0, p3]
if d1 * d2 < 0:
# p1 and p2 lie on different sides of [p0, p3].
# The hull is a quadrilateral and line [p0, p3] is not part of the hull.
# The top part includes p1, we will reverse it later if that is not the case.
hull = [
[self._p0, self._p1, self._p3], # top part
[self._p0, self._p2, self._p3] # bottom part
]
flip = d1 < 0
else:
# p1 and p2 lie on the same sides of [p0, p3]. The hull can be a triangle or a
# quadrilateral and line [p0, p3] is part of the hull. Check if the hull is a triangle
# or a quadrilateral. Also, if at least one of the distances for p1 or p2, from line
# [p0, p3] is zero then hull must at most have 3 vertices.
# Fixme: check cross product
y0, y1, y2, y3 = [p.y for p in self.points]
if abs(d1) < abs(d2):
pmax = p2;
# apex is y0 in this case, and the other apex point is y3
# vector yapex -> yapex2 or base vector which is already part of the hull
# V30xV10 * V10xV20
cross_product = ((y1 - y0) - (y3 - y0)/3) * (2*(y1 - y0) - (y2 - y0)) /3
else:
pmax = p1;
# apex is y3 and the other apex point is y0
# vector yapex -> yapex2 or base vector which is already part of the hull
# V32xV30 * V32xV31
cross_product = ((y3 - y2) - (y3 - y0)/3) * (2*(y3 - y2) - (y3 + y1)) /3
# Compare cross products of these vectors to determine if the point is in the triangle
# [p3, pmax, p0], or if it is a quadrilateral.
has_null_distance = d1 == 0 or d2 == 0 # Fixme: don't need to compute cross_product
if cross_product < 0 or has_null_distance:
# hull is a triangle
hull = [
[self._p0, pmax, self._p3], # top part is a triangle
[self._p0, self._p3], # bottom part is just an edge
]
else:
hull = [
[self._p0, self._p1, self._p2, self._p3], # top part is a quadrilateral
[self._p0, self._p3], # bottom part is just an edge
]
flip = d1 < 0 if d1 else d2 < 0
if flip:
hull.reverse()
return hull
##############################################
@staticmethod
def _clip_convex_hull(hull_top, hull_bottom, d_min, d_max) :
# Top /----
# / ---/
# / /
# d_max -------------------*---
# / / t_max
# t_min / /
# d_min -------*---------------
# / /
# / ----/ Bottom
# p0 /----
if (hull_top[0].y < d_min):
# Left of hull is below d_min,
# walk through the hull until it enters the region between d_min and d_max
return self._clip_convex_hull_part(hull_top, True, d_min);
elif (hull_bottom[0].y > d_max) :
# Left of hull is above d_max,
# walk through the hull until it enters the region between d_min and d_max
return self._clip_convex_hull_part(hull_bottom, False, d_max);
else :
# Left of hull is between d_min and d_max, no clipping possible
return hull_top[0].x; # Fixme: 0 ???
##############################################
@staticmethod
def _clip_convex_hull_part(part, top, threshold) :
"""Clip the bottom or top part of the convex hull.
*part* is a list of points, *top* is a boolean flag to indicate if it corresponds to the top
part, *threshold* is d_min if top part else d_max.
"""
# Walk on the edges
px = part[0].x;
py = part[0].y;
for i in range(1, len(part)):
qx = part[i].x;
qy = part[i].y;
if (qy >= threshold if top else qy <= threshold):
# compute a linear interpolation
# threshold = s * (t - px) + py
# t = (threshold - py) / s + px
return px + (threshold - py) * (qx - px) / (qy - py);
px = qx;
py = qy;
return None; # no intersection
##############################################
@staticmethod
def _instersect_curve(
curve1, curve2,
t_min=0, t_max=1,
u_min=0, u_max=1,
old_delta_t=1,
reverse=False, # flag to indicate that 1 <-> 2 when we store locations
recursion=0, # number of recursions
recursion_limit=32,
t_limit=0.8,
locations=[],
) :
# Code inspired from
# https://github.com/paperjs/paper.js/blob/master/src/path/Curve.js
# http://nbviewer.jupyter.org/gist/hkrish/0a128f21a5b9e5a7a914 The Bezier Clipping Algorithm
# https://gist.github.com/hkrish/5ef0f2da7f9882341ee5 hkrish/bezclip_manual.py
# Note:
# see https://github.com/paperjs/paper.js/issues/565
# It was determined that more than 20 recursions are needed sometimes, depending on the
# delta_t threshold values further below when determining which curve converges the
# least. He also recommended a threshold of 0.5 instead of the initial 0.8
if recursion > recursion_limit:
return
tolerance = 1e-5
epsilon = 1e-10
# t_min_new = 0.
# t_max_new = 0.
# delta_t = 0.
# NOTE: the recursion threshold of 4 is needed to prevent this issue from occurring:
# https://github.com/paperjs/paper.js/issues/571
# when two curves share an end point
if curve1.p0.x == curve1.p3.x and u_max - u_min <= epsilon and recursion > 4:
# The fat-line of curve1 has converged to a point, the clipping is not reliable.
# Return the value we have even though we will miss the precision.
t_max_new = t_min_new = (t_max + t_min) / 2
delta_t = 0
else :
# Compute the fat-line for curve1:
# a baseline and two offsets which completely encloses the curve
fatline, d_min, d_max = curve1.fat_line()
# Calculate a non-parametric bezier curve D(ti, di(t)) where di(t) is the distance of curve2 from
# the baseline, ti is equally spaced in [0, 1]
non_parametric_curve = curve2.non_parametric_curve(fatline)
# Get the top and bottom parts of the convex-hull
top, bottom = non_parametric_curve._clip_convex_hull()
# Clip the convex-hull with d_min and d_max
t_min_clip = self.clip_convex_hull(top, bottom, d_min, d_max);
top.reverse()
bottom.reverse()
t_max_clip = clipConvexHull(top, bottom, d_min, d_max);
# No intersections if one of the t values is None
if t_min_clip is None or t_max_clip is None:
return
# Clip curve2 with the fat-line for curve1
curve2 = curve2.split_at_two_t(t_min_clip, t_max_clip)
delta_t = t_max_clip - t_min_clip
# t_min and t_max are within the range [0, 1]
# We need to project it to the original parameter range
t_min_new = t_max * t_min_clip + t_min * (1 - t_min_clip)
t_max_new = t_max * t_max_clip + t_min * (1 - t_max_clip)
delta_t_new = t_max_new - t_min_new
delta_u = u_max - u_min
# Check if we need to subdivide the curves
if old_delta_t > t_limit and delta_t > t_limit:
# Subdivide the curve which has converged the least.
args = (delta_t, not reverse, recursion+1, recursion_limit, t_limit, locations)
if delta_u < delta_t_new: # curve2 < curve1
parts = curve1.split_at_t(0.5)
t = t_min_new + delta_t_new / 2
self._intersect_curve(curve2, parts[0], u_min, u_max, t_min_new, t, *args)
self._intersect_curve(curve2, parts[1], u_min, u_max, t, t_max_new, *args)
else :
parts = curve2.split_at_t(0.5)
t = u_min + delta_u / 2
self._intersect_curve(parts[0], curve1, u_min, t, t_min_new, t_max_new, *args)
self._intersect_curve(parts[1], curve1, t, u_max, t_min_new, t_max_new, *args)
elif max(delta_u, delta_t_new) < tolerance:
# We have isolated the intersection with sufficient precision
t1 = t_min_new + delta_t_new / 2
t2 = u_min + delta_u / 2
if reverse:
t1, t2 = t2, t1
p1 = curve1.point_at_t(t1)
p2 = curve2.point_at_t(t2)
locations.append([t1, point1, t2, point2])
else:
args = (delta_t, not reverse, recursion+1, recursion_limit, t_limit)
self._intersect_curve(curve2, curve1, locations, u_min, u_max, t_min_new, t_max_new, *args)
##############################################
def is_flat_enough(self, flatness):
"""Determines if a curve is sufficiently flat, meaning it appears as a straight line and has
curve-time that is enough linear, as specified by the given *flatness* parameter.
*flatness* is the maximum error allowed for the straight line to deviate from the curve.
"""
# Reference:
# Kaspar Fischer and Roger Willcocks http://hcklbrrfnn.files.wordpress.com/2012/08/bez.pdf
# PostScript Language Reference. Addison- Wesley, third edition, 1999
# We define the flatness of the curve as the argmax of the distance from the curve to the
# line passing by the start and stop point.
#
# flatness = argmax(d(t)) for t in [0, 1] where d(t) = | B(t) - L(t) |
#
# L = (1-t)*P0 + t*P1
#
# Let
# u = 3*P1 - 2*P0 - P3
# v = 3*P2 - P0 - 2*P3
#
# d(t) = (1-t)**2 * t * (3*P1 - 2*P0 - P3) + (1-t) * t**2 * (3*P2 - P0 - 2*P3)
# = (1-t)**2 * t * u + (1-t) * t**2 * v
#
# d(t)**2 = (1 - t)**2 * t**2 * (((1 - t)*ux + t*vx)**2 + ((1 - t)*uy + t*vy)**2
#
# argmax((1 - t)**2 * t**2) = 1/16
# argmax((1 - t)*a + t*b) = argmax(a, b)
#
# flatness**2 = argmax(d(t)**2) <= 1/16 * (argmax(ux**2, vx**2) + argmax(uy**2, vy**2))
#
# argmax(ux**2, vx**2) + argmax(uy**2, vy**2) is thus an upper bound of 16 * flatness**2
# x0, y0 = list(self._p0)
# x1, y1 = list(self._p1)
# x2, y2 = list(self._p2)
# x3, y3 = list(self._p3)
# ux = 3*x1 - 2*x0 - x3
# uy = 3*y1 - 2*y0 - y3
# vx = 3*x2 - 2*x3 - x0
# vy = 3*y2 - 2*y3 - y0
u = 3*P1 - 2*P0 - P3
v = 3*P2 - 2*P3 - P0
return max(u.x**2, v.x**2) + max(u.y**2, v.y**2) <= 16 * flatness**2
##############################################
@property
def area(self):
"""Compute the area delimited by the curve and the segment across the start and stop point."""
# Reference: http://objectmix.com/graphics/133553-area-closed-bezier-curve.html BUT DEAD LINK
# Proof using divergence theorem ???
# Fixme: any proof !
x0, y0 = list(self._p0)
x1, y1 = list(self._p1)
x2, y2 = list(self._p2)
x3, y3 = list(self._p3)
return (3 * ((y3 - y0) * (x1 + x2) - (x3 - x0) * (y1 + y2)
+ y1 * (x0 - x2) - x1 * (y0 - y2)
+ y3 * (x2 + x0 / 3) - x3 * (y2 + y0 / 3)) / 20)
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
##############################################
def closest_point(self, point):
# Q(t) = (P3 - 3*P2 + 3*P1 - P0) * t**3 +
# 3*(P2 - 2*P1 + P0) * t**2 +
# 3*(P1 - P0) * t +
# P0
# n = P3 - 3*P2 + 3*P1 - P0
# r = 3*(P2 - 2*P1 + P0
# s = 3*(P1 - P0)
# v = P0
# Q(t) = n*t**3 + r*t**2 + s*t + v
# Q'(t) = 3*n*t**2 + 2*r*t + s
# P0, P1, P2, P3, P, t = symbols('P0 P1 P2 P3 P t')
# n, r, s, v = symbols('n r s v')
# Q = n*t**3 + r*t**2 + s*t + v
# Qp = simplify(Q.diff(t))
# collect(expand((P*Qp - Q*Qp)), t)
# -3*n**2 * t**5
# -5*n*r * t**4
# -2*(2*n*s + r**2) * t**3
# 3*(P*n - n*v - r*s) * t**2
# (2*P*r - 2*r*v - s**2) * t
# P*s - s*v
n = self._p3 - self._p2*3 + self._p1*3 - self._p0
r = (self._p2 - self._p1*2 + self._p0)*3
s = (self._p1 - self._p0)*3
v = self._p0
roots = fifth_root(
-3 * n.magnitude_square,
-5 * n.dot(r),
-2 * (2*n.dot(s) + r.magnitude_square),
3 * (point.dot(n) - n.dot(v) - r.dot(s)),
2*point.dot(r) - 2*r.dot(v) - s.magnitude_square,
point.dot(s) - s.dot(v),
)
# Fixme: to func
t = [root for root in roots if 0 <= root <= 1]
if not t:
return None
elif len(t) > 1:
raise NameError("Found more than on root")
else: