utils.py

# ==============================================================================
# Binaries and/or source for the following packages or projects
# are presented under one or more of the following open source licenses:
# utils.py    The OpenLane-V2 Dataset Authors    Apache License, Version 2.0
#
# Contact wanghuijie@pjlab.org.cn if you have any issue.
#
# Copyright (c) 2023 The OpenLane-v2 Dataset Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
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# See the License for the specific language governing permissions and
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# ==============================================================================

import numpy as np

THICKNESS = 4

COLOR_DEFAULT = (0, 0, 255)
COLOR_DICT = {
    0: COLOR_DEFAULT,
    1: (255, 0, 0),
    2: (0, 255, 0),
    3: (255, 255, 0),
    4: (255, 0, 255),
    5: (0, 128, 128),
    6: (0, 128, 0),
    7: (128, 0, 0),
    8: (128, 0, 128),
    9: (128, 128, 0),
    10: (0, 0, 128),
    11: (64, 64, 64),
    12: (192, 192, 192),
}


def interp_arc(points, t=1000):
    r'''
    Linearly interpolate equally-spaced points along a polyline, either in 2d or 3d.

    Parameters
    ----------
    points : List
        List of shape (N,2) or (N,3), representing 2d or 3d-coordinates.
    t : array_like
        Number of points that will be uniformly interpolated and returned.

    Returns
    -------
    array_like
        Numpy array of shape (N,2) or (N,3)

    Notes
    -----
    Adapted from https://github.com/johnwlambert/argoverse2-api/blob/main/src/av2/geometry/interpolate.py#L120

    '''

    # filter consecutive points with same coordinate
    temp = []
    for point in points:
        point = point.tolist()
        if temp == [] or point != temp[-1]:
            temp.append(point)
    if len(temp) <= 1:
        return None
    points = np.array(temp, dtype=points.dtype)

    assert points.ndim == 2

    # the number of points on the curve itself
    n, _ = points.shape

    # equally spaced in arclength -- the number of points that will be uniformly interpolated
    eq_spaced_points = np.linspace(0, 1, t)

    # Compute the chordal arclength of each segment.
    # Compute differences between each x coord, to get the dx's
    # Do the same to get dy's. Then the hypotenuse length is computed as a norm.
    chordlen = np.linalg.norm(np.diff(points, axis=0), axis=1)  # type: ignore
    # Normalize the arclengths to a unit total
    chordlen = chordlen / np.sum(chordlen)
    # cumulative arclength

    cumarc = np.zeros(len(chordlen) + 1)
    cumarc[1:] = np.cumsum(chordlen)

    # which interval did each point fall in, in terms of eq_spaced_points? (bin index)
    tbins = np.digitize(eq_spaced_points, bins=cumarc).astype(int)  # type: ignore

    # #catch any problems at the ends
    tbins[np.where((tbins <= 0) | (eq_spaced_points <= 0))] = 1  # type: ignore
    tbins[np.where((tbins >= n) | (eq_spaced_points >= 1))] = n - 1

    s = np.divide((eq_spaced_points - cumarc[tbins - 1]), chordlen[tbins - 1])
    anchors = points[tbins - 1, :]
    # broadcast to scale each row of `points` by a different row of s
    offsets = (points[tbins, :] - points[tbins - 1, :]) * s.reshape(-1, 1)
    points_interp = anchors + offsets

    return points_interp


def assign_attribute(annotation):
    topology_lcte = np.array(annotation['topology_lcte'], dtype=bool)
    for i in range(len(annotation['lane_centerline'])):
        annotation['lane_centerline'][i]['attributes'] = \
            set([ts['attribute'] for j, ts in enumerate(annotation['traffic_element']) if topology_lcte[i][j]])
    return annotation


def assign_topology(annotation):
    topology_lcte = np.array(annotation['topology_lcte'], dtype=bool)
    annotation['topology'] = []
    for i in range(topology_lcte.shape[0]):
        for j in range(topology_lcte.shape[1]):
            if topology_lcte[i][j]:
                annotation['topology'].append({
                    'lane_centerline': annotation['lane_centerline'][i]['points'],
                    'traffic_element': annotation['traffic_element'][j]['points'],
                    'attribute': annotation['traffic_element'][j]['attribute'],
                })
    return annotation