| import numpy as np | |
| # Expected values for matrices in subdivide_bezier and others | |
| # Note that in bezier.py this is a list of dicts, | |
| # not a dict of dicts! | |
| SUBDIVISION_MATRICES = { | |
| # For 0-degree Béziers | |
| 0: { | |
| 2: np.array([[1], [1]]), | |
| 3: np.array([[1], [1], [1]]), | |
| 4: np.array([[1], [1], [1], [1]]), | |
| }, | |
| # For linear Béziers | |
| 1: { | |
| 2: np.array( | |
| [ | |
| [2, 0], | |
| [1, 1], | |
| [1, 1], | |
| [0, 2], | |
| ] | |
| ) | |
| / 2, | |
| 3: np.array( | |
| [ | |
| [3, 0], | |
| [2, 1], | |
| [2, 1], | |
| [1, 2], | |
| [1, 2], | |
| [0, 3], | |
| ] | |
| ) | |
| / 3, | |
| 4: np.array( | |
| [ | |
| [4, 0], | |
| [3, 1], | |
| [3, 1], | |
| [2, 2], | |
| [2, 2], | |
| [1, 3], | |
| [1, 3], | |
| [0, 4], | |
| ] | |
| ) | |
| / 4, | |
| }, | |
| # For quadratic Béziers | |
| 2: { | |
| 2: np.array( | |
| [ | |
| [4, 0, 0], | |
| [2, 2, 0], | |
| [1, 2, 1], | |
| [1, 2, 1], | |
| [0, 2, 2], | |
| [0, 0, 4], | |
| ] | |
| ) | |
| / 4, | |
| 3: np.array( | |
| [ | |
| [9, 0, 0], | |
| [6, 3, 0], | |
| [4, 4, 1], | |
| [4, 4, 1], | |
| [2, 5, 2], | |
| [1, 4, 4], | |
| [1, 4, 4], | |
| [0, 3, 6], | |
| [0, 0, 9], | |
| ] | |
| ) | |
| / 9, | |
| 4: np.array( | |
| [ | |
| [16, 0, 0], | |
| [12, 4, 0], | |
| [9, 6, 1], | |
| [9, 6, 1], | |
| [6, 8, 2], | |
| [4, 8, 4], | |
| [4, 8, 4], | |
| [2, 8, 6], | |
| [1, 6, 9], | |
| [1, 6, 9], | |
| [0, 4, 12], | |
| [0, 0, 16], | |
| ] | |
| ) | |
| / 16, | |
| }, | |
| # For cubic Béziers | |
| 3: { | |
| 2: np.array( | |
| [ | |
| [8, 0, 0, 0], | |
| [4, 4, 0, 0], | |
| [2, 4, 2, 0], | |
| [1, 3, 3, 1], | |
| [1, 3, 3, 1], | |
| [0, 2, 4, 2], | |
| [0, 0, 4, 4], | |
| [0, 0, 0, 8], | |
| ] | |
| ) | |
| / 8, | |
| 3: np.array( | |
| [ | |
| [27, 0, 0, 0], | |
| [18, 9, 0, 0], | |
| [12, 12, 3, 0], | |
| [8, 12, 6, 1], | |
| [8, 12, 6, 1], | |
| [4, 12, 9, 2], | |
| [2, 9, 12, 4], | |
| [1, 6, 12, 8], | |
| [1, 6, 12, 8], | |
| [0, 3, 12, 12], | |
| [0, 0, 9, 18], | |
| [0, 0, 0, 27], | |
| ] | |
| ) | |
| / 27, | |
| 4: np.array( | |
| [ | |
| [64, 0, 0, 0], | |
| [48, 16, 0, 0], | |
| [36, 24, 4, 0], | |
| [27, 27, 9, 1], | |
| [27, 27, 9, 1], | |
| [18, 30, 14, 2], | |
| [12, 28, 20, 4], | |
| [8, 24, 24, 8], | |
| [8, 24, 24, 8], | |
| [4, 20, 28, 12], | |
| [2, 14, 30, 18], | |
| [1, 9, 27, 27], | |
| [1, 9, 27, 27], | |
| [0, 4, 24, 36], | |
| [0, 0, 16, 48], | |
| [0, 0, 0, 64], | |
| ] | |
| ) | |
| / 64, | |
| }, | |
| # Test case with a quartic Bézier | |
| # to check if the fallback algorithms work | |
| 4: { | |
| 2: np.array( | |
| [ | |
| [16, 0, 0, 0, 0], | |
| [8, 8, 0, 0, 0], | |
| [4, 8, 4, 0, 0], | |
| [2, 6, 6, 2, 0], | |
| [1, 4, 6, 4, 1], | |
| [1, 4, 6, 4, 1], | |
| [0, 2, 6, 6, 2], | |
| [0, 0, 4, 8, 4], | |
| [0, 0, 0, 8, 8], | |
| [0, 0, 0, 0, 16], | |
| ] | |
| ) | |
| / 16, | |
| }, | |
| } | |