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Approximating Data with a Multi-Segment Cubic Bézier Curve: Incorporating Distance and Curvature Constraints
Problem:
The author seeks an algorithm for approximating given geo data using a multi-segment cubic Bézier curve with two constraints:
Solution:
The author discovered a solution involving the conversion of a B-Spline that approximates the data in a least squares sense to a multi-segment Bézier curve using the FITPACK library and the Python binding from scipy. The B-Spline representation offers advantages in smoothness control and providing a way to specify the desired smoothness of the approximation.
Algorithm (Simplified):
Implementation:
<code class="python">import matplotlib.pyplot as plt import numpy as np from scipy import interpolate # Read data into lists x and y tck, u = interpolate.splprep([x, y], s=3) # Generate B-Spline with smoothness parameter s=3 # Convert B-Spline to Bézier curve bezier_curves = b_spline_to_bezier_series(tck) # Evaluate and plot the Bézier curve unew = np.arange(0, 1.01, 0.01) out = interpolate.splev(unew, tck) plt.figure() plt.plot(x, y, out[0], out[1]) plt.show()</code>
By adjusting the smoothness parameter s, the user can find a curve that satisfies the desired distance and curvature constraints. The provided b_spline_to_bezier_series function converts the B-Spline back into a multi-segment cubic Bézier curve for further analysis or manipulation.
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