665 lines
26 KiB
Python
665 lines
26 KiB
Python
import mmcv
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import numpy as np
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import os
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from collections import OrderedDict
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from nuscenes.nuscenes import NuScenes
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from nuscenes.utils.geometry_utils import view_points
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from os import path as osp
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from pyquaternion import Quaternion
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from shapely.geometry import MultiPoint, box
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from typing import List, Tuple, Union
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from mmdet3d.core.bbox.box_np_ops import points_cam2img
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from mmdet3d.datasets import NuScenesDataset
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nus_categories = ('car', 'truck', 'trailer', 'bus', 'construction_vehicle',
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'bicycle', 'motorcycle', 'pedestrian', 'traffic_cone',
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'barrier')
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nus_attributes = ('cycle.with_rider', 'cycle.without_rider',
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'pedestrian.moving', 'pedestrian.standing',
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'pedestrian.sitting_lying_down', 'vehicle.moving',
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'vehicle.parked', 'vehicle.stopped', 'None')
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def create_nuscenes_infos(root_path,
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info_prefix,
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version='v1.0-trainval',
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max_sweeps=10,
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max_radar_sweeps=10):
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"""Create info file of nuscene dataset.
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Given the raw data, generate its related info file in pkl format.
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Args:
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root_path (str): Path of the data root.
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info_prefix (str): Prefix of the info file to be generated.
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version (str): Version of the data.
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Default: 'v1.0-trainval'
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max_sweeps (int): Max number of sweeps.
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Default: 10
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max_radar_sweeps (int): Max number of radar sweeps.
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Default: 10
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"""
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from nuscenes.nuscenes import NuScenes
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nusc = NuScenes(version=version, dataroot=root_path, verbose=True)
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from nuscenes.utils import splits
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available_vers = ['v1.0-trainval', 'v1.0-test', 'v1.0-mini']
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assert version in available_vers
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if version == 'v1.0-trainval':
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train_scenes = splits.train
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val_scenes = splits.val
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elif version == 'v1.0-test':
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train_scenes = splits.test
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val_scenes = []
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elif version == 'v1.0-mini':
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train_scenes = splits.mini_train
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val_scenes = splits.mini_val
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else:
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raise ValueError('unknown')
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# filter existing scenes.
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available_scenes = get_available_scenes(nusc)
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available_scene_names = [s['name'] for s in available_scenes]
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train_scenes = list(
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filter(lambda x: x in available_scene_names, train_scenes))
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val_scenes = list(filter(lambda x: x in available_scene_names, val_scenes))
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train_scenes = set([
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available_scenes[available_scene_names.index(s)]['token']
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for s in train_scenes
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])
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val_scenes = set([
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available_scenes[available_scene_names.index(s)]['token']
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for s in val_scenes
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])
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test = 'test' in version
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if test:
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print('test scene: {}'.format(len(train_scenes)))
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else:
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print('train scene: {}, val scene: {}'.format(
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len(train_scenes), len(val_scenes)))
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train_nusc_infos, val_nusc_infos = _fill_trainval_infos(
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nusc, train_scenes, val_scenes, test, max_sweeps=max_sweeps, max_radar_sweeps=max_radar_sweeps)
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metadata = dict(version=version)
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if test:
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print('test sample: {}'.format(len(train_nusc_infos)))
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data = dict(infos=train_nusc_infos, metadata=metadata)
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info_path = osp.join(root_path,
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'{}_infos_test_radar.pkl'.format(info_prefix))
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mmcv.dump(data, info_path)
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else:
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print(info_prefix)
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print('train sample: {}, val sample: {}'.format(
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len(train_nusc_infos), len(val_nusc_infos)))
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data = dict(infos=train_nusc_infos, metadata=metadata)
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info_path = osp.join(info_prefix,
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'{}_infos_train.pkl'.format(info_prefix))
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mmcv.dump(data, info_path)
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data['infos'] = val_nusc_infos
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info_val_path = osp.join(info_prefix,
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'{}_infos_val.pkl'.format(info_prefix))
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mmcv.dump(data, info_val_path)
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def get_available_scenes(nusc):
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"""Get available scenes from the input nuscenes class.
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Given the raw data, get the information of available scenes for
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further info generation.
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Args:
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nusc (class): Dataset class in the nuScenes dataset.
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Returns:
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available_scenes (list[dict]): List of basic information for the
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available scenes.
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"""
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available_scenes = []
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print('total scene num: {}'.format(len(nusc.scene)))
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for scene in nusc.scene:
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scene_token = scene['token']
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scene_rec = nusc.get('scene', scene_token)
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sample_rec = nusc.get('sample', scene_rec['first_sample_token'])
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sd_rec = nusc.get('sample_data', sample_rec['data']['LIDAR_TOP'])
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has_more_frames = True
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scene_not_exist = False
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while has_more_frames:
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lidar_path, boxes, _ = nusc.get_sample_data(sd_rec['token'])
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lidar_path = str(lidar_path)
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if os.getcwd() in lidar_path:
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# path from lyftdataset is absolute path
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lidar_path = lidar_path.split(f'{os.getcwd()}/')[-1]
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# relative path
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if not mmcv.is_filepath(lidar_path):
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scene_not_exist = True
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break
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else:
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break
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if scene_not_exist:
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continue
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available_scenes.append(scene)
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print('exist scene num: {}'.format(len(available_scenes)))
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return available_scenes
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def _fill_trainval_infos(nusc,
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train_scenes,
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val_scenes,
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test=False,
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max_sweeps=10,
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max_radar_sweeps=10):
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"""Generate the train/val infos from the raw data.
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Args:
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nusc (:obj:`NuScenes`): Dataset class in the nuScenes dataset.
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train_scenes (list[str]): Basic information of training scenes.
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val_scenes (list[str]): Basic information of validation scenes.
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test (bool): Whether use the test mode. In the test mode, no
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annotations can be accessed. Default: False.
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max_sweeps (int): Max number of sweeps. Default: 10.
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max_radar_sweeps (int): Max number of radar sweeps. Default: 10.
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Returns:
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tuple[list[dict]]: Information of training set and validation set
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that will be saved to the info file.
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"""
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train_nusc_infos = []
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val_nusc_infos = []
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token2idx = {}
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i_ = 0
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for sample in mmcv.track_iter_progress(nusc.sample):
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# i_ += 1
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# if i_ > 6:
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# break
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lidar_token = sample['data']['LIDAR_TOP']
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sd_rec = nusc.get('sample_data', sample['data']['LIDAR_TOP'])
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cs_record = nusc.get('calibrated_sensor',
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sd_rec['calibrated_sensor_token'])
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pose_record = nusc.get('ego_pose', sd_rec['ego_pose_token'])
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lidar_path, boxes, _ = nusc.get_sample_data(lidar_token)
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mmcv.check_file_exist(lidar_path)
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info = {
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'lidar_path': lidar_path,
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'token': sample['token'],
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'sweeps': [],
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'cams': dict(),
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'radars': dict(),
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'lidar2ego_translation': cs_record['translation'],
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'lidar2ego_rotation': cs_record['rotation'],
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'ego2global_translation': pose_record['translation'],
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'ego2global_rotation': pose_record['rotation'],
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'timestamp': sample['timestamp'],
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'prev_token': sample['prev']
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}
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l2e_r = info['lidar2ego_rotation']
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l2e_t = info['lidar2ego_translation']
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e2g_r = info['ego2global_rotation']
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e2g_t = info['ego2global_translation']
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l2e_r_mat = Quaternion(l2e_r).rotation_matrix
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e2g_r_mat = Quaternion(e2g_r).rotation_matrix
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# obtain 6 image's information per frame
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camera_types = [
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'CAM_FRONT',
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'CAM_FRONT_RIGHT',
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'CAM_FRONT_LEFT',
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'CAM_BACK',
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'CAM_BACK_LEFT',
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'CAM_BACK_RIGHT',
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]
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for cam in camera_types:
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cam_token = sample['data'][cam]
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cam_path, _, cam_intrinsic = nusc.get_sample_data(cam_token)
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cam_info = obtain_sensor2top(nusc, cam_token, l2e_t, l2e_r_mat,
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e2g_t, e2g_r_mat, cam)
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cam_info.update(cam_intrinsic=cam_intrinsic)
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info['cams'].update({cam: cam_info})
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radar_names = ['RADAR_FRONT', 'RADAR_FRONT_LEFT', 'RADAR_FRONT_RIGHT', 'RADAR_BACK_LEFT', 'RADAR_BACK_RIGHT']
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for radar_name in radar_names:
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radar_token = sample['data'][radar_name]
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radar_rec = nusc.get('sample_data', radar_token)
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sweeps = []
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while len(sweeps) < max_radar_sweeps:
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if not radar_rec['prev'] == '':
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radar_path, _, radar_intrin = nusc.get_sample_data(radar_token)
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radar_info = obtain_sensor2top(nusc, radar_token, l2e_t, l2e_r_mat,
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e2g_t, e2g_r_mat, radar_name)
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sweeps.append(radar_info)
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radar_token = radar_rec['prev']
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radar_rec = nusc.get('sample_data', radar_token)
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else:
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radar_path, _, radar_intrin = nusc.get_sample_data(radar_token)
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radar_info = obtain_sensor2top(nusc, radar_token, l2e_t, l2e_r_mat,
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e2g_t, e2g_r_mat, radar_name)
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sweeps.append(radar_info)
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info['radars'].update({radar_name: sweeps})
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# obtain sweeps for a single key-frame
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sd_rec = nusc.get('sample_data', sample['data']['LIDAR_TOP'])
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sweeps = []
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while len(sweeps) < max_sweeps:
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if not sd_rec['prev'] == '':
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sweep = obtain_sensor2top(nusc, sd_rec['prev'], l2e_t,
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l2e_r_mat, e2g_t, e2g_r_mat, 'lidar')
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sweeps.append(sweep)
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sd_rec = nusc.get('sample_data', sd_rec['prev'])
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else:
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break
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info['sweeps'] = sweeps
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# obtain annotation
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if not test:
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annotations = [
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nusc.get('sample_annotation', token)
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for token in sample['anns']
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]
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locs = np.array([b.center for b in boxes]).reshape(-1, 3)
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dims = np.array([b.wlh for b in boxes]).reshape(-1, 3)
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rots = np.array([b.orientation.yaw_pitch_roll[0]
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for b in boxes]).reshape(-1, 1)
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velocity = np.array(
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[nusc.box_velocity(token)[:2] for token in sample['anns']])
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valid_flag = np.array(
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[(anno['num_lidar_pts'] + anno['num_radar_pts']) > 0
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for anno in annotations],
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dtype=bool).reshape(-1)
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# convert velo from global to lidar
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for i in range(len(boxes)):
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velo = np.array([*velocity[i], 0.0])
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velo = velo @ np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(
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l2e_r_mat).T
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velocity[i] = velo[:2]
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names = [b.name for b in boxes]
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for i in range(len(names)):
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if names[i] in NuScenesDataset.NameMapping:
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names[i] = NuScenesDataset.NameMapping[names[i]]
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names = np.array(names)
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# we need to convert rot to SECOND format.
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gt_boxes = np.concatenate([locs, dims, -rots - np.pi / 2], axis=1)
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assert len(gt_boxes) == len(
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annotations), f'{len(gt_boxes)}, {len(annotations)}'
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info['gt_boxes'] = gt_boxes
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info['gt_names'] = names
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info['gt_velocity'] = velocity.reshape(-1, 2)
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info['num_lidar_pts'] = np.array(
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[a['num_lidar_pts'] for a in annotations])
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info['num_radar_pts'] = np.array(
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[a['num_radar_pts'] for a in annotations])
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info['valid_flag'] = valid_flag
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if sample['scene_token'] in train_scenes:
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train_nusc_infos.append(info)
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token2idx[info['token']] = ('train', len(train_nusc_infos) - 1)
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else:
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val_nusc_infos.append(info)
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token2idx[info['token']] = ('val', len(val_nusc_infos) - 1)
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for info in train_nusc_infos:
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prev_token = info['prev_token']
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if prev_token == '':
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info['prev'] = -1
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else:
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prev_set, prev_idx = token2idx[prev_token]
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assert prev_set == 'train'
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info['prev'] = prev_idx
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for info in val_nusc_infos:
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prev_token = info['prev_token']
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if prev_token == '':
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info['prev'] = -1
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else:
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prev_set, prev_idx = token2idx[prev_token]
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assert prev_set == 'val'
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info['prev'] = prev_idx
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return train_nusc_infos, val_nusc_infos
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def obtain_sensor2top(nusc,
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sensor_token,
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l2e_t,
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l2e_r_mat,
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e2g_t,
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e2g_r_mat,
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sensor_type='lidar'):
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"""Obtain the info with RT matric from general sensor to Top LiDAR.
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Args:
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nusc (class): Dataset class in the nuScenes dataset.
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sensor_token (str): Sample data token corresponding to the
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specific sensor type.
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l2e_t (np.ndarray): Translation from lidar to ego in shape (1, 3).
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l2e_r_mat (np.ndarray): Rotation matrix from lidar to ego
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in shape (3, 3).
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e2g_t (np.ndarray): Translation from ego to global in shape (1, 3).
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e2g_r_mat (np.ndarray): Rotation matrix from ego to global
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in shape (3, 3).
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sensor_type (str): Sensor to calibrate. Default: 'lidar'.
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Returns:
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sweep (dict): Sweep information after transformation.
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"""
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sd_rec = nusc.get('sample_data', sensor_token)
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cs_record = nusc.get('calibrated_sensor',
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sd_rec['calibrated_sensor_token'])
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pose_record = nusc.get('ego_pose', sd_rec['ego_pose_token'])
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data_path = str(nusc.get_sample_data_path(sd_rec['token']))
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if os.getcwd() in data_path: # path from lyftdataset is absolute path
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data_path = data_path.split(f'{os.getcwd()}/')[-1] # relative path
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sweep = {
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'data_path': data_path,
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'type': sensor_type,
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'sample_data_token': sd_rec['token'],
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'sensor2ego_translation': cs_record['translation'],
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'sensor2ego_rotation': cs_record['rotation'],
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'ego2global_translation': pose_record['translation'],
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'ego2global_rotation': pose_record['rotation'],
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'timestamp': sd_rec['timestamp']
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}
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l2e_r_s = sweep['sensor2ego_rotation']
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l2e_t_s = sweep['sensor2ego_translation']
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e2g_r_s = sweep['ego2global_rotation']
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e2g_t_s = sweep['ego2global_translation']
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# obtain the RT from sensor to Top LiDAR
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# sweep->ego->global->ego'->lidar
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l2e_r_s_mat = Quaternion(l2e_r_s).rotation_matrix
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e2g_r_s_mat = Quaternion(e2g_r_s).rotation_matrix
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R = (l2e_r_s_mat.T @ e2g_r_s_mat.T) @ (
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np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(l2e_r_mat).T)
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T = (l2e_t_s @ e2g_r_s_mat.T + e2g_t_s) @ (
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np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(l2e_r_mat).T)
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T -= e2g_t @ (np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(l2e_r_mat).T
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) + l2e_t @ np.linalg.inv(l2e_r_mat).T
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sweep['sensor2lidar_rotation'] = R.T # points @ R.T + T
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sweep['sensor2lidar_translation'] = T
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return sweep
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def export_2d_annotation(root_path, info_path, version, mono3d=True):
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"""Export 2d annotation from the info file and raw data.
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Args:
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root_path (str): Root path of the raw data.
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info_path (str): Path of the info file.
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version (str): Dataset version.
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mono3d (bool): Whether to export mono3d annotation. Default: True.
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"""
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# get bbox annotations for camera
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camera_types = [
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'CAM_FRONT',
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'CAM_FRONT_RIGHT',
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'CAM_FRONT_LEFT',
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'CAM_BACK',
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'CAM_BACK_LEFT',
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'CAM_BACK_RIGHT',
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]
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nusc_infos = mmcv.load(info_path)['infos']
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nusc = NuScenes(version=version, dataroot=root_path, verbose=True)
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# info_2d_list = []
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cat2Ids = [
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dict(id=nus_categories.index(cat_name), name=cat_name)
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for cat_name in nus_categories
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]
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coco_ann_id = 0
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coco_2d_dict = dict(annotations=[], images=[], categories=cat2Ids)
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for info in mmcv.track_iter_progress(nusc_infos):
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for cam in camera_types:
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cam_info = info['cams'][cam]
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coco_infos = get_2d_boxes(
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nusc,
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cam_info['sample_data_token'],
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visibilities=['', '1', '2', '3', '4'],
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mono3d=mono3d)
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(height, width, _) = mmcv.imread(cam_info['data_path']).shape
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coco_2d_dict['images'].append(
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dict(
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file_name=cam_info['data_path'].split('data/nuscenes/')
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[-1],
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id=cam_info['sample_data_token'],
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token=info['token'],
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cam2ego_rotation=cam_info['sensor2ego_rotation'],
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cam2ego_translation=cam_info['sensor2ego_translation'],
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ego2global_rotation=info['ego2global_rotation'],
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ego2global_translation=info['ego2global_translation'],
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cam_intrinsic=cam_info['cam_intrinsic'],
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width=width,
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height=height))
|
|
for coco_info in coco_infos:
|
|
if coco_info is None:
|
|
continue
|
|
# add an empty key for coco format
|
|
coco_info['segmentation'] = []
|
|
coco_info['id'] = coco_ann_id
|
|
coco_2d_dict['annotations'].append(coco_info)
|
|
coco_ann_id += 1
|
|
if mono3d:
|
|
json_prefix = f'{info_path[:-4]}_mono3d'
|
|
else:
|
|
json_prefix = f'{info_path[:-4]}'
|
|
mmcv.dump(coco_2d_dict, f'{json_prefix}.coco.json')
|
|
|
|
|
|
def get_2d_boxes(nusc,
|
|
sample_data_token: str,
|
|
visibilities: List[str],
|
|
mono3d=True):
|
|
"""Get the 2D annotation records for a given `sample_data_token`.
|
|
Args:
|
|
sample_data_token (str): Sample data token belonging to a camera \
|
|
keyframe.
|
|
visibilities (list[str]): Visibility filter.
|
|
mono3d (bool): Whether to get boxes with mono3d annotation.
|
|
Return:
|
|
list[dict]: List of 2D annotation record that belongs to the input
|
|
`sample_data_token`.
|
|
"""
|
|
|
|
# Get the sample data and the sample corresponding to that sample data.
|
|
sd_rec = nusc.get('sample_data', sample_data_token)
|
|
|
|
assert sd_rec[
|
|
'sensor_modality'] == 'camera', 'Error: get_2d_boxes only works' \
|
|
' for camera sample_data!'
|
|
if not sd_rec['is_key_frame']:
|
|
raise ValueError(
|
|
'The 2D re-projections are available only for keyframes.')
|
|
|
|
s_rec = nusc.get('sample', sd_rec['sample_token'])
|
|
|
|
# Get the calibrated sensor and ego pose
|
|
# record to get the transformation matrices.
|
|
cs_rec = nusc.get('calibrated_sensor', sd_rec['calibrated_sensor_token'])
|
|
pose_rec = nusc.get('ego_pose', sd_rec['ego_pose_token'])
|
|
camera_intrinsic = np.array(cs_rec['camera_intrinsic'])
|
|
|
|
# Get all the annotation with the specified visibilties.
|
|
ann_recs = [
|
|
nusc.get('sample_annotation', token) for token in s_rec['anns']
|
|
]
|
|
ann_recs = [
|
|
ann_rec for ann_rec in ann_recs
|
|
if (ann_rec['visibility_token'] in visibilities)
|
|
]
|
|
|
|
repro_recs = []
|
|
|
|
for ann_rec in ann_recs:
|
|
# Augment sample_annotation with token information.
|
|
ann_rec['sample_annotation_token'] = ann_rec['token']
|
|
ann_rec['sample_data_token'] = sample_data_token
|
|
|
|
# Get the box in global coordinates.
|
|
box = nusc.get_box(ann_rec['token'])
|
|
|
|
# Move them to the ego-pose frame.
|
|
box.translate(-np.array(pose_rec['translation']))
|
|
box.rotate(Quaternion(pose_rec['rotation']).inverse)
|
|
|
|
# Move them to the calibrated sensor frame.
|
|
box.translate(-np.array(cs_rec['translation']))
|
|
box.rotate(Quaternion(cs_rec['rotation']).inverse)
|
|
|
|
# Filter out the corners that are not in front of the calibrated
|
|
# sensor.
|
|
corners_3d = box.corners()
|
|
in_front = np.argwhere(corners_3d[2, :] > 0).flatten()
|
|
corners_3d = corners_3d[:, in_front]
|
|
|
|
# Project 3d box to 2d.
|
|
corner_coords = view_points(corners_3d, camera_intrinsic,
|
|
True).T[:, :2].tolist()
|
|
|
|
# Keep only corners that fall within the image.
|
|
final_coords = post_process_coords(corner_coords)
|
|
|
|
# Skip if the convex hull of the re-projected corners
|
|
# does not intersect the image canvas.
|
|
if final_coords is None:
|
|
continue
|
|
else:
|
|
min_x, min_y, max_x, max_y = final_coords
|
|
|
|
# Generate dictionary record to be included in the .json file.
|
|
repro_rec = generate_record(ann_rec, min_x, min_y, max_x, max_y,
|
|
sample_data_token, sd_rec['filename'])
|
|
|
|
# If mono3d=True, add 3D annotations in camera coordinates
|
|
if mono3d and (repro_rec is not None):
|
|
loc = box.center.tolist()
|
|
dim = box.wlh.tolist()
|
|
rot = [box.orientation.yaw_pitch_roll[0]]
|
|
|
|
global_velo2d = nusc.box_velocity(box.token)[:2]
|
|
global_velo3d = np.array([*global_velo2d, 0.0])
|
|
e2g_r_mat = Quaternion(pose_rec['rotation']).rotation_matrix
|
|
c2e_r_mat = Quaternion(cs_rec['rotation']).rotation_matrix
|
|
cam_velo3d = global_velo3d @ np.linalg.inv(
|
|
e2g_r_mat).T @ np.linalg.inv(c2e_r_mat).T
|
|
velo = cam_velo3d[0::2].tolist()
|
|
|
|
repro_rec['bbox_cam3d'] = loc + dim + rot
|
|
repro_rec['velo_cam3d'] = velo
|
|
|
|
center3d = np.array(loc).reshape([1, 3])
|
|
center2d = points_cam2img(
|
|
center3d, camera_intrinsic, with_depth=True)
|
|
repro_rec['center2d'] = center2d.squeeze().tolist()
|
|
# normalized center2D + depth
|
|
# if samples with depth < 0 will be removed
|
|
if repro_rec['center2d'][2] <= 0:
|
|
continue
|
|
|
|
ann_token = nusc.get('sample_annotation',
|
|
box.token)['attribute_tokens']
|
|
if len(ann_token) == 0:
|
|
attr_name = 'None'
|
|
else:
|
|
attr_name = nusc.get('attribute', ann_token[0])['name']
|
|
attr_id = nus_attributes.index(attr_name)
|
|
repro_rec['attribute_name'] = attr_name
|
|
repro_rec['attribute_id'] = attr_id
|
|
|
|
repro_recs.append(repro_rec)
|
|
|
|
return repro_recs
|
|
|
|
|
|
def post_process_coords(
|
|
corner_coords: List, imsize: Tuple[int, int] = (1600, 900)
|
|
) -> Union[Tuple[float, float, float, float], None]:
|
|
"""Get the intersection of the convex hull of the reprojected bbox corners
|
|
and the image canvas, return None if no intersection.
|
|
Args:
|
|
corner_coords (list[int]): Corner coordinates of reprojected
|
|
bounding box.
|
|
imsize (tuple[int]): Size of the image canvas.
|
|
Return:
|
|
tuple [float]: Intersection of the convex hull of the 2D box
|
|
corners and the image canvas.
|
|
"""
|
|
polygon_from_2d_box = MultiPoint(corner_coords).convex_hull
|
|
img_canvas = box(0, 0, imsize[0], imsize[1])
|
|
|
|
if polygon_from_2d_box.intersects(img_canvas):
|
|
img_intersection = polygon_from_2d_box.intersection(img_canvas)
|
|
intersection_coords = np.array(
|
|
[coord for coord in img_intersection.exterior.coords])
|
|
|
|
min_x = min(intersection_coords[:, 0])
|
|
min_y = min(intersection_coords[:, 1])
|
|
max_x = max(intersection_coords[:, 0])
|
|
max_y = max(intersection_coords[:, 1])
|
|
|
|
return min_x, min_y, max_x, max_y
|
|
else:
|
|
return None
|
|
|
|
|
|
def generate_record(ann_rec: dict, x1: float, y1: float, x2: float, y2: float,
|
|
sample_data_token: str, filename: str) -> OrderedDict:
|
|
"""Generate one 2D annotation record given various informations on top of
|
|
the 2D bounding box coordinates.
|
|
Args:
|
|
ann_rec (dict): Original 3d annotation record.
|
|
x1 (float): Minimum value of the x coordinate.
|
|
y1 (float): Minimum value of the y coordinate.
|
|
x2 (float): Maximum value of the x coordinate.
|
|
y2 (float): Maximum value of the y coordinate.
|
|
sample_data_token (str): Sample data token.
|
|
filename (str):The corresponding image file where the annotation
|
|
is present.
|
|
Returns:
|
|
dict: A sample 2D annotation record.
|
|
- file_name (str): flie name
|
|
- image_id (str): sample data token
|
|
- area (float): 2d box area
|
|
- category_name (str): category name
|
|
- category_id (int): category id
|
|
- bbox (list[float]): left x, top y, dx, dy of 2d box
|
|
- iscrowd (int): whether the area is crowd
|
|
"""
|
|
repro_rec = OrderedDict()
|
|
repro_rec['sample_data_token'] = sample_data_token
|
|
coco_rec = dict()
|
|
|
|
relevant_keys = [
|
|
'attribute_tokens',
|
|
'category_name',
|
|
'instance_token',
|
|
'next',
|
|
'num_lidar_pts',
|
|
'num_radar_pts',
|
|
'prev',
|
|
'sample_annotation_token',
|
|
'sample_data_token',
|
|
'visibility_token',
|
|
]
|
|
|
|
for key, value in ann_rec.items():
|
|
if key in relevant_keys:
|
|
repro_rec[key] = value
|
|
|
|
repro_rec['bbox_corners'] = [x1, y1, x2, y2]
|
|
repro_rec['filename'] = filename
|
|
|
|
coco_rec['file_name'] = filename
|
|
coco_rec['image_id'] = sample_data_token
|
|
coco_rec['area'] = (y2 - y1) * (x2 - x1)
|
|
|
|
if repro_rec['category_name'] not in NuScenesDataset.NameMapping:
|
|
return None
|
|
cat_name = NuScenesDataset.NameMapping[repro_rec['category_name']]
|
|
coco_rec['category_name'] = cat_name
|
|
coco_rec['category_id'] = nus_categories.index(cat_name)
|
|
coco_rec['bbox'] = [x1, y1, x2 - x1, y2 - y1]
|
|
coco_rec['iscrowd'] = 0
|
|
|
|
return coco_rec
|
|
|
|
|
|
if __name__ == '__main__':
|
|
create_nuscenes_infos('data/nuscenes/', 'radar_nuscenes_5sweeps') |