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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import numpy as np | |
| from scipy.special import comb as n_over_k | |
| from mmocr.utils.typing_utils import ArrayLike | |
| def bezier_coefficient(n, t, k): | |
| return t**k * (1 - t)**(n - k) * n_over_k(n, k) | |
| def bezier_coefficients(time, point_num, ratios): | |
| return [[bezier_coefficient(time, ratio, num) for num in range(point_num)] | |
| for ratio in ratios] | |
| def linear_interpolation(point1: np.ndarray, | |
| point2: np.ndarray, | |
| number: int = 2) -> np.ndarray: | |
| t = np.linspace(0, 1, number + 2).reshape(-1, 1) | |
| return point1 + (point2 - point1) * t | |
| def curve2bezier(curve: ArrayLike): | |
| curve = np.array(curve).reshape(-1, 2) | |
| if len(curve) == 2: | |
| return linear_interpolation(curve[0], curve[1]) | |
| diff = curve[1:] - curve[:-1] | |
| distance = np.linalg.norm(diff, axis=-1) | |
| norm_distance = distance / distance.sum() | |
| norm_distance = np.hstack(([0], norm_distance)) | |
| cum_norm_dis = norm_distance.cumsum() | |
| pseudo_inv = np.linalg.pinv(bezier_coefficients(3, 4, cum_norm_dis)) | |
| control_points = pseudo_inv.dot(curve) | |
| return control_points | |
| def bezier2curve(bezier: np.ndarray, num_sample: int = 10): | |
| bezier = np.asarray(bezier) | |
| t = np.linspace(0, 1, num_sample) | |
| return np.array(bezier_coefficients(3, 4, t)).dot(bezier) | |
| def poly2bezier(poly): | |
| poly = np.array(poly).reshape(-1, 2) | |
| points_num = len(poly) | |
| up_curve = poly[:points_num // 2] | |
| down_curve = poly[points_num // 2:] | |
| up_bezier = curve2bezier(up_curve) | |
| down_bezier = curve2bezier(down_curve) | |
| up_bezier[0] = up_curve[0] | |
| up_bezier[-1] = up_curve[-1] | |
| down_bezier[0] = down_curve[0] | |
| down_bezier[-1] = down_curve[-1] | |
| return np.vstack((up_bezier, down_bezier)).flatten().tolist() | |
| def bezier2poly(bezier, num_sample=20): | |
| bezier = bezier.reshape(2, 4, 2) | |
| curve_top = bezier2curve(bezier[0], num_sample) | |
| curve_bottom = bezier2curve(bezier[1], num_sample) | |
| return np.vstack((curve_top, curve_bottom)).flatten().tolist() | |