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Reparameterize a curve by its arc-length.
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"""Numerically reparameterize a curve by its arc-length. | |
""" | |
from typing import Optional | |
import numpy as np | |
import scipy | |
import scipy.interpolate | |
def reparameterize(X: np.ndarray, num_steps: Optional[int] = None) \ | |
-> np.ndarray: | |
"""Reparameterize a closed curve by its arc-length. | |
Parameters | |
---------- | |
X: np.ndarray | |
An array of shape (num_points, 2) containing the points of the curve. | |
num_steps: int or None | |
Number of steps for the reparameterized curve. If set to None, the | |
resulting curve contains the same number of points as the original | |
curve. | |
Returns | |
------- | |
Y: np.ndarray | |
An array containing the points of the reparameterized curve. | |
""" | |
def interpolator(x, y): | |
return scipy.interpolate.PchipInterpolator(x, y, extrapolate=True) | |
n = X.shape[0] | |
t = np.linspace(0, 1, n) | |
x1, x2 = interpolator(t, X[:, 0]), interpolator(t, X[:, 1]) | |
dx1, dx2 = x1.derivative(), x2.derivative() | |
s = np.cumsum(np.sqrt(dx1(t)**2 + dx2(t)**2)) | |
tau = interpolator(s, t) | |
ss = np.linspace(s[0], s[-1], num_steps or n) | |
return np.c_[x1(tau(ss)), x2(tau(ss))] |
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