Chengfang Lu
7c43b44c57
Huaxu Sentinel Active Safety Platform with embedded algorithm code, Docker Compose setup, and vendored dataset scaffolds for clone-and-run. Co-authored-by: Cursor <cursoragent@cursor.com>
51 lines
1.7 KiB
Python
51 lines
1.7 KiB
Python
from scipy.interpolate import InterpolatedUnivariateSpline
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import numpy as np
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class Lane:
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def __init__(self, points=None, invalid_value=-2., metadata=None):
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super(Lane, self).__init__()
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self.curr_iter = 0
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self.points = points
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self.invalid_value = invalid_value
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self.function = InterpolatedUnivariateSpline(points[:, 1],
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points[:, 0],
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k=min(3,
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len(points) - 1))
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self.min_y = points[:, 1].min() - 0.01
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self.max_y = points[:, 1].max() + 0.01
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self.metadata = metadata or {}
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def __repr__(self):
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return '[Lane]\n' + str(self.points) + '\n[/Lane]'
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def __call__(self, lane_ys):
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lane_xs = self.function(lane_ys)
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lane_xs[(lane_ys < self.min_y) |
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(lane_ys > self.max_y)] = self.invalid_value
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return lane_xs
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def to_array(self, cfg):
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sample_y = cfg.sample_y
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img_w, img_h = cfg.ori_img_w, cfg.ori_img_h
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ys = np.array(sample_y) / float(img_h)
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xs = self(ys)
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valid_mask = (xs >= 0) & (xs < 1)
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lane_xs = xs[valid_mask] * img_w
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lane_ys = ys[valid_mask] * img_h
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lane = np.concatenate((lane_xs.reshape(-1, 1), lane_ys.reshape(-1, 1)),
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axis=1)
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return lane
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def __iter__(self):
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return self
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def __next__(self):
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if self.curr_iter < len(self.points):
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self.curr_iter += 1
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return self.points[self.curr_iter - 1]
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self.curr_iter = 0
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raise StopIteration
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