nuscene 数据集

nuscenes数据集官网：
https://nuscenes.org/

from nuscenes.nuscenes import NuScenes
nusc = NuScenes(version='v1.0-mini', dataroot='/media/algo/data_1/project_others/0000paper/lss/nuScenes/mini/', verbose=True)

nusc.list_scenes()

my_scene = nusc.scene[0]
print("\n=====>>>my_scene:")
print(my_scene)


first_sample_token = my_scene["first_sample_token"]
print("\n=====>>>first_sample_token:")
print(first_sample_token)

my_sample = nusc.get("sample", first_sample_token)
print("\n=====>>>my_sample:")
print(my_sample)

print("\n=====>>>my_sample[data]:")
print(my_sample['data'])

sensor_radar = 'RADAR_FRONT'
radar_front_data = nusc.get('sample_data', my_sample['data'][sensor_radar])
print("\n=====>>>radar_front_data:")
print(radar_front_data)

nusc.render_sample_data(radar_front_data['token'])

my_annotation_token = my_sample['anns'][18]
my_annotation_metadata = nusc.get("sample_annotation", my_annotation_token)
print("\n=====>>>my_annotation_metadata:")
print(my_annotation_metadata)

# nusc.render_annotation(my_annotation_metadata['token']) #不知道为啥不显示？ 加一些参数图片保存在本地
nusc.render_annotation(my_annotation_metadata['token'], extra_info=True, out_path="./22")

#
# nusc.render_annotation(my_annotation_token, extra_info=True, out_path="./11")

# my_instance = nusc.instance[0]
# # print(my_instance)
#
# instance_token = my_instance['token']
# nusc.render_instance(instance_token)

打印：

======
Loading NuScenes tables for version v1.0-mini...
23 category,
8 attribute,
4 visibility,
911 instance,
12 sensor,
120 calibrated_sensor,
31206 ego_pose,
8 log,
10 scene,
404 sample,
31206 sample_data,
18538 sample_annotation,
4 map,
Done loading in 0.226 seconds.
======
Reverse indexing ...
Done reverse indexing in 0.1 seconds.
======
scene-0061, Parked truck, construction, intersectio... [18-07-24 03:28:47]   19s, singapore-onenorth, #anns:4622
scene-0103, Many peds right, wait for turning car, ... [18-08-01 19:26:43]   19s, boston-seaport, #anns:2046
scene-0655, Parking lot, parked cars, jaywalker, be... [18-08-27 15:51:32]   20s, boston-seaport, #anns:2332
scene-0553, Wait at intersection, bicycle, large tr... [18-08-28 20:48:16]   20s, boston-seaport, #anns:1950
scene-0757, Arrive at busy intersection, bus, wait ... [18-08-30 19:25:08]   20s, boston-seaport, #anns:592
scene-0796, Scooter, peds on sidewalk, bus, cars, t... [18-10-02 02:52:24]   20s, singapore-queensto, #anns:708
scene-0916, Parking lot, bicycle rack, parked bicyc... [18-10-08 07:37:13]   20s, singapore-queensto, #anns:2387
scene-1077, Night, big street, bus stop, high speed... [18-11-21 11:39:27]   20s, singapore-hollandv, #anns:890
scene-1094, Night, after rain, many peds, PMD, ped ... [18-11-21 11:47:27]   19s, singapore-hollandv, #anns:1762
scene-1100, Night, peds in sidewalk, peds cross cro... [18-11-21 11:49:47]   19s, singapore-hollandv, #anns:935

=====>>>my_scene:
{'token': 'cc8c0bf57f984915a77078b10eb33198', 'log_token': '7e25a2c8ea1f41c5b0da1e69ecfa71a2', 'nbr_samples': 39, 'first_sample_token': 'ca9a282c9e77460f8360f564131a8af5', 'last_sample_token': 'ed5fc18c31904f96a8f0dbb99ff069c0', 'name': 'scene-0061', 'description': 'Parked truck, construction, intersection, turn left, following a van'}

=====>>>first_sample_token:
ca9a282c9e77460f8360f564131a8af5

=====>>>my_sample:
{'token': 'ca9a282c9e77460f8360f564131a8af5', 'timestamp': 1532402927647951, 'prev': '', 'next': '39586f9d59004284a7114a68825e8eec', 'scene_token': 'cc8c0bf57f984915a77078b10eb33198', 'data': {'RADAR_FRONT': '37091c75b9704e0daa829ba56dfa0906', 'RADAR_FRONT_LEFT': '11946c1461d14016a322916157da3c7d', 'RADAR_FRONT_RIGHT': '491209956ee3435a9ec173dad3aaf58b', 'RADAR_BACK_LEFT': '312aa38d0e3e4f01b3124c523e6f9776', 'RADAR_BACK_RIGHT': '07b30d5eb6104e79be58eadf94382bc1', 'LIDAR_TOP': '9d9bf11fb0e144c8b446d54a8a00184f', 'CAM_FRONT': 'e3d495d4ac534d54b321f50006683844', 'CAM_FRONT_RIGHT': 'aac7867ebf4f446395d29fbd60b63b3b', 'CAM_BACK_RIGHT': '79dbb4460a6b40f49f9c150cb118247e', 'CAM_BACK': '03bea5763f0f4722933508d5999c5fd8', 'CAM_BACK_LEFT': '43893a033f9c46d4a51b5e08a67a1eb7', 'CAM_FRONT_LEFT': 'fe5422747a7d4268a4b07fc396707b23'}, 'anns': ['ef63a697930c4b20a6b9791f423351da', '6b89da9bf1f84fd6a5fbe1c3b236f809', '924ee6ac1fed440a9d9e3720aac635a0', '91e3608f55174a319246f361690906ba', 'cd051723ed9c40f692b9266359f547af', '36d52dfedd764b27863375543c965376', '70af124fceeb433ea73a79537e4bea9e', '63b89fe17f3e41ecbe28337e0e35db8e', 'e4a3582721c34f528e3367f0bda9485d', 'fcb2332977ed4203aa4b7e04a538e309', 'a0cac1c12246451684116067ae2611f6', '02248ff567e3497c957c369dc9a1bd5c', '9db977e264964c2887db1e37113cddaa', 'ca9c5dd6cf374aa980fdd81022f016fd', '179b8b54ee74425893387ebc09ee133d', '5b990ac640bf498ca7fd55eaf85d3e12', '16140fbf143d4e26a4a7613cbd3aa0e8', '54939f11a73d4398b14aeef500bf0c23', '83d881a6b3d94ef3a3bc3b585cc514f8', '74986f1604f047b6925d409915265bf7', 'e86330c5538c4858b8d3ffe874556cc5', 'a7bd5bb89e27455bbb3dba89a576b6a1', 'fbd9d8c939b24f0eb6496243a41e8c41', '198023a1fb5343a5b6fad033ab8b7057', 'ffeafb90ecd5429cba23d0be9a5b54ee', 'cc636a58e27e446cbdd030c14f3718fd', '076a7e3ec6244d3b84e7df5ebcbac637', '0603fbaef1234c6c86424b163d2e3141', 'd76bd5dcc62f4c57b9cece1c7bcfabc5', '5acb6c71bcd64aa188804411b28c4c8f', '49b74a5f193c4759b203123b58ca176d', '77519174b48f4853a895f58bb8f98661', 'c5e9455e98bb42c0af7d1990db1df0c9', 'fcc5b4b5c4724179ab24962a39ca6d65', '791d1ca7e228433fa50b01778c32449a', '316d20eb238c43ef9ee195642dd6e3fe', 'cda0a9085607438c9b1ea87f4360dd64', 'e865152aaa194f22b97ad0078c012b21', '7962506dbc24423aa540a5e4c7083dad', '29cca6a580924b72a90b9dd6e7710d3e', 'a6f7d4bb60374f868144c5ba4431bf4c', 'f1ae3f713ba946069fa084a6b8626fbf', 'd7af8ede316546f68d4ab4f3dbf03f88', '91cb8f15ed4444e99470d43515e50c1d', 'bc638d33e89848f58c0b3ccf3900c8bb', '26fb370c13f844de9d1830f6176ebab6', '7e66fdf908d84237943c833e6c1b317a', '67c5dbb3ddcc4aff8ec5140930723c37', 'eaf2532c820740ae905bb7ed78fb1037', '3e2d17fa9aa5484d9cabc1dfca532193', 'de6bd5ffbed24aa59c8891f8d9c32c44', '9d51d699f635478fbbcd82a70396dd62', 'b7cbc6d0e80e4dfda7164871ece6cb71', '563a3f547bd64a2f9969278c5ef447fd', 'df8917888b81424f8c0670939e61d885', 'bb3ef5ced8854640910132b11b597348', 'a522ce1d7f6545d7955779f25d01783b', '1fafb2468af5481ca9967407af219c32', '05de82bdb8484623906bb9d97ae87542', 'bfedb0d85e164b7697d1e72dd971fb72', 'ca0f85b4f0d44beb9b7ff87b1ab37ff5', 'bca4bbfdef3d4de980842f28be80b3ca', 'a834fb0389a8453c810c3330e3503e16', '6c804cb7d78943b195045082c5c2d7fa', 'adf1594def9e4722b952fea33b307937', '49f76277d07541c5a584aa14c9d28754', '15a3b4d60b514db5a3468e2aef72a90c', '18cc2837f2b9457c80af0761a0b83ccc', '2bfcc693ae9946daba1d9f2724478fd4']}

=====>>>my_sample[data]:
{'RADAR_FRONT': '37091c75b9704e0daa829ba56dfa0906', 'RADAR_FRONT_LEFT': '11946c1461d14016a322916157da3c7d', 'RADAR_FRONT_RIGHT': '491209956ee3435a9ec173dad3aaf58b', 'RADAR_BACK_LEFT': '312aa38d0e3e4f01b3124c523e6f9776', 'RADAR_BACK_RIGHT': '07b30d5eb6104e79be58eadf94382bc1', 'LIDAR_TOP': '9d9bf11fb0e144c8b446d54a8a00184f', 'CAM_FRONT': 'e3d495d4ac534d54b321f50006683844', 'CAM_FRONT_RIGHT': 'aac7867ebf4f446395d29fbd60b63b3b', 'CAM_BACK_RIGHT': '79dbb4460a6b40f49f9c150cb118247e', 'CAM_BACK': '03bea5763f0f4722933508d5999c5fd8', 'CAM_BACK_LEFT': '43893a033f9c46d4a51b5e08a67a1eb7', 'CAM_FRONT_LEFT': 'fe5422747a7d4268a4b07fc396707b23'}

=====>>>radar_front_data:
{'token': '37091c75b9704e0daa829ba56dfa0906', 'sample_token': 'ca9a282c9e77460f8360f564131a8af5', 'ego_pose_token': '37091c75b9704e0daa829ba56dfa0906', 'calibrated_sensor_token': 'f4d2a6c281f34a7eb8bb033d82321f79', 'timestamp': 1532402927664178, 'fileformat': 'pcd', 'is_key_frame': True, 'height': 0, 'width': 0, 'filename': 'samples/RADAR_FRONT/n015-2018-07-24-11-22-45+0800__RADAR_FRONT__1532402927664178.pcd', 'prev': '', 'next': 'f0b8593e08594a3eb1152c138b312813', 'sensor_modality': 'radar', 'channel': 'RADAR_FRONT'}

=====>>>my_annotation_metadata:
{'token': '83d881a6b3d94ef3a3bc3b585cc514f8', 'sample_token': 'ca9a282c9e77460f8360f564131a8af5', 'instance_token': 'e91afa15647c4c4994f19aeb302c7179', 'visibility_token': '4', 'attribute_tokens': ['58aa28b1c2a54dc88e169808c07331e3'], 'translation': [409.989, 1164.099, 1.623], 'size': [2.877, 10.201, 3.595], 'rotation': [-0.5828819500503033, 0.0, 0.0, 0.812556848660791], 'prev': '', 'next': 'f3721bdfd7ee4fd2a4f94874286df471', 'num_lidar_pts': 495, 'num_radar_pts': 13, 'category_name': 'vehicle.truck'}

Process finished with exit code 0