文章目录
一、nuscense 数据集
我们的数据集由以下基本构建块组成：
log- 数据提取自的日志信息。scene- 汽车行驶的 20 秒片段。sample- 特定时间戳的场景注释快照。sample_data- 从特定传感器收集的数据。ego_pose- 特定时间戳的自我车辆姿势。sensor- 特定传感器类型。calibrated sensor- 特定传感器在特定车辆上校准的定义。instance- 我们观察到的所有对象实例的枚举。category- 对象类别的分类（例如车辆、人类）。attribute- 实例的属性，在类别保持不变的情况下可以更改。visibility- 从 6 个不同相机收集的所有图像中可见像素的分数。sample_annotation- 我们感兴趣的对象的注释实例。map- 从自上而下的视图存储为二进制语义掩码的地图数据。
二、观察数据集
1.scene
nuScenes 是一个大型数据库，其中包含 1000 个场景 的带注释样本，每个场景大约 20 秒。让我们来看看加载的数据库中的场景。
nusc.list_scenes()
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
让我们看一下场景元数据。
my_scene = nusc.scene[0]
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'}
2.sample
在场景中，我们每半秒（2 Hz）注释一次数据。
我们将“样本”定义为给定时间戳的场景注释关键帧。关键帧是来自所有传感器的数据时间戳应非常接近其指向的样本时间戳的帧。
现在，让我们看看这个场景中的第一个注释样本。
first_sample_token = my_scene['first_sample_token']
my_sample = nusc.get('sample', first_sample_token)
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']}
一个有用的方法是“list_sample()”，它列出与“sample”相关的所有相关的“sample_data”关键帧和“sample_annotation”，我们将在后续部分详细讨论。
nusc.list_sample(my_sample['token'])
Sample: ca9a282c9e77460f8360f564131a8af5
sample_data_token: 37091c75b9704e0daa829ba56dfa0906, mod: radar, channel: RADAR_FRONT
sample_data_token: 11946c1461d14016a322916157da3c7d, mod: radar, channel: RADAR_FRONT_LEFT
sample_data_token: 491209956ee3435a9ec173dad3aaf58b, mod: radar, channel: RADAR_FRONT_RIGHT
sample_data_token: 312aa38d0e3e4f01b3124c523e6f9776, mod: radar, channel: RADAR_BACK_LEFT
sample_data_token: 07b30d5eb6104e79be58eadf94382bc1, mod: radar, channel: RADAR_BACK_RIGHT
sample_data_token: 9d9bf11fb0e144c8b446d54a8a00184f, mod: lidar, channel: LIDAR_TOP
sample_data_token: e3d495d4ac534d54b321f50006683844, mod: camera, channel: CAM_FRONT
sample_data_token: aac7867ebf4f446395d29fbd60b63b3b, mod: camera, channel: CAM_FRONT_RIGHT
sample_data_token: 79dbb4460a6b40f49f9c150cb118247e, mod: camera, channel: CAM_BACK_RIGHT
sample_data_token: 03bea5763f0f4722933508d5999c5fd8, mod: camera, channel: CAM_BACK
sample_data_token: 43893a033f9c46d4a51b5e08a67a1eb7, mod: camera, channel: CAM_BACK_LEFT
sample_data_token: fe5422747a7d4268a4b07fc396707b23, mod: camera, channel: CAM_FRONT_LEFT
sample_annotation_token: ef63a697930c4b20a6b9791f423351da, category: human.pedestrian.adult
sample_annotation_token: 6b89da9bf1f84fd6a5fbe1c3b236f809, category: human.pedestrian.adult
sample_annotation_token: 924ee6ac1fed440a9d9e3720aac635a0, category: vehicle.car
sample_annotation_token: 91e3608f55174a319246f361690906ba, category: human.pedestrian.adult
sample_annotation_token: cd051723ed9c40f692b9266359f547af, category: movable_object.trafficcone
sample_annotation_token: 36d52dfedd764b27863375543c965376, category: vehicle.bicycle
sample_annotation_token: 70af124fceeb433ea73a79537e4bea9e, category: human.pedestrian.adult
sample_annotation_token: 63b89fe17f3e41ecbe28337e0e35db8e, category: vehicle.car
sample_annotation_token: e4a3582721c34f528e3367f0bda9485d, category: human.pedestrian.adult
sample_annotation_token: fcb2332977ed4203aa4b7e04a538e309, category: movable_object.barrier
sample_annotation_token: a0cac1c12246451684116067ae2611f6, category: movable_object.barrier
sample_annotation_token: 02248ff567e3497c957c369dc9a1bd5c, category: human.pedestrian.adult
sample_annotation_token: 9db977e264964c2887db1e37113cddaa, category: human.pedestrian.adult
sample_annotation_token: ca9c5dd6cf374aa980fdd81022f016fd, category: human.pedestrian.adult
sample_annotation_token: 179b8b54ee74425893387ebc09ee133d, category: human.pedestrian.adult
sample_annotation_token: 5b990ac640bf498ca7fd55eaf85d3e12, category: movable_object.barrier
sample_annotation_token: 16140fbf143d4e26a4a7613cbd3aa0e8, category: vehicle.car
sample_annotation_token: 54939f11a73d4398b14aeef500bf0c23, category: human.pedestrian.adult
sample_annotation_token: 83d881a6b3d94ef3a3bc3b585cc514f8, category: vehicle.truck
sample_annotation_token: 74986f1604f047b6925d409915265bf7, category: vehicle.car
sample_annotation_token: e86330c5538c4858b8d3ffe874556cc5, category: human.pedestrian.adult
sample_annotation_token: a7bd5bb89e27455bbb3dba89a576b6a1, category: movable_object.barrier
sample_annotation_token: fbd9d8c939b24f0eb6496243a41e8c41, category: movable_object.barrier
sample_annotation_token: 198023a1fb5343a5b6fad033ab8b7057, category: movable_object.barrier
sample_annotation_token: ffeafb90ecd5429cba23d0be9a5b54ee, category: movable_object.trafficcone
sample_annotation_token: cc636a58e27e446cbdd030c14f3718fd, category: movable_object.barrier
sample_annotation_token: 076a7e3ec6244d3b84e7df5ebcbac637, category: vehicle.bus.rigid
sample_annotation_token: 0603fbaef1234c6c86424b163d2e3141, category: human.pedestrian.adult
sample_annotation_token: d76bd5dcc62f4c57b9cece1c7bcfabc5, category: human.pedestrian.adult
sample_annotation_token: 5acb6c71bcd64aa188804411b28c4c8f, category: movable_object.barrier
sample_annotation_token: 49b74a5f193c4759b203123b58ca176d, category: human.pedestrian.adult
sample_annotation_token: 77519174b48f4853a895f58bb8f98661, category: human.pedestrian.adult
sample_annotation_token: c5e9455e98bb42c0af7d1990db1df0c9, category: movable_object.barrier
sample_annotation_token: fcc5b4b5c4724179ab24962a39ca6d65, category: human.pedestrian.adult
sample_annotation_token: 791d1ca7e228433fa50b01778c32449a, category: human.pedestrian.adult
sample_annotation_token: 316d20eb238c43ef9ee195642dd6e3fe, category: movable_object.barrier
sample_annotation_token: cda0a9085607438c9b1ea87f4360dd64, category: vehicle.car
sample_annotation_token: e865152aaa194f22b97ad0078c012b21, category: movable_object.barrier
sample_annotation_token: 7962506dbc24423aa540a5e4c7083dad, category: movable_object.barrier
sample_annotation_token: 29cca6a580924b72a90b9dd6e7710d3e, category: human.pedestrian.adult
sample_annotation_token: a6f7d4bb60374f868144c5ba4431bf4c, category: vehicle.car
sample_annotation_token: f1ae3f713ba946069fa084a6b8626fbf, category: movable_object.barrier
sample_annotation_token: d7af8ede316546f68d4ab4f3dbf03f88, category: movable_object.barrier
sample_annotation_token: 91cb8f15ed4444e99470d43515e50c1d, category: vehicle.construction
sample_annotation_token: bc638d33e89848f58c0b3ccf3900c8bb, category: movable_object.barrier
sample_annotation_token: 26fb370c13f844de9d1830f6176ebab6, category: vehicle.car
sample_annotation_token: 7e66fdf908d84237943c833e6c1b317a, category: human.pedestrian.adult
sample_annotation_token: 67c5dbb3ddcc4aff8ec5140930723c37, category: human.pedestrian.adult
sample_annotation_token: eaf2532c820740ae905bb7ed78fb1037, category: human.pedestrian.adult
sample_annotation_token: 3e2d17fa9aa5484d9cabc1dfca532193, category: movable_object.trafficcone
sample_annotation_token: de6bd5ffbed24aa59c8891f8d9c32c44, category: human.pedestrian.adult
sample_annotation_token: 9d51d699f635478fbbcd82a70396dd62, category: human.pedestrian.adult
sample_annotation_token: b7cbc6d0e80e4dfda7164871ece6cb71, category: vehicle.truck
sample_annotation_token: 563a3f547bd64a2f9969278c5ef447fd, category: human.pedestrian.adult
sample_annotation_token: df8917888b81424f8c0670939e61d885, category: human.pedestrian.adult
sample_annotation_token: bb3ef5ced8854640910132b11b597348, category: human.pedestrian.adult
sample_annotation_token: a522ce1d7f6545d7955779f25d01783b, category: human.pedestrian.adult
sample_annotation_token: 1fafb2468af5481ca9967407af219c32, category: human.pedestrian.adult
sample_annotation_token: 05de82bdb8484623906bb9d97ae87542, category: human.pedestrian.adult
sample_annotation_token: bfedb0d85e164b7697d1e72dd971fb72, category: movable_object.pushable_pullable
sample_annotation_token: ca0f85b4f0d44beb9b7ff87b1ab37ff5, category: movable_object.barrier
sample_annotation_token: bca4bbfdef3d4de980842f28be80b3ca, category: movable_object.barrier
sample_annotation_token: a834fb0389a8453c810c3330e3503e16, category: human.pedestrian.adult
sample_annotation_token: 6c804cb7d78943b195045082c5c2d7fa, category: movable_object.barrier
sample_annotation_token: adf1594def9e4722b952fea33b307937, category: movable_object.barrier
sample_annotation_token: 49f76277d07541c5a584aa14c9d28754, category: vehicle.car
sample_annotation_token: 15a3b4d60b514db5a3468e2aef72a90c, category: movable_object.barrier
sample_annotation_token: 18cc2837f2b9457c80af0761a0b83ccc, category: movable_object.barrier
sample_annotation_token: 2bfcc693ae9946daba1d9f2724478fd4, category: movable_object.barrier
3. sample_data
nuScenes 数据集包含从完整传感器套件收集的数据。因此，对于场景的每个快照，都提供了从这些传感器收集的一系列数据的引用。
提供了一个“数据”键来访问这些数据：
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'}
请注意，键指的是构成传感器套件的不同传感器。让我们看一下从“CAM_FRONT”获取的“sample_data”的元数据。
sensor = 'CAM_FRONT'
cam_front_data = nusc.get('sample_data', my_sample['data'][sensor])
cam_front_data
{'token': 'e3d495d4ac534d54b321f50006683844',
'sample_token': 'ca9a282c9e77460f8360f564131a8af5',
'ego_pose_token': 'e3d495d4ac534d54b321f50006683844',
'calibrated_sensor_token': '1d31c729b073425e8e0202c5c6e66ee1',
'timestamp': 1532402927612460,
'fileformat': 'jpg',
'is_key_frame': True,
'height': 900,
'width': 1600,
'filename': 'samples/CAM_FRONT/n015-2018-07-24-11-22-45+0800__CAM_FRONT__1532402927612460.jpg',
'prev': '',
'next': '68e8e98cf7b0487baa139df808641db7',
'sensor_modality': 'camera',
'channel': 'CAM_FRONT'}
我们还可以在特定传感器上渲染sample_data。
nusc.render_sample_data(cam_front_data[‘token’])
4. sample_annotation
sample_annotation 指的是任何 定义样本中可见物体位置的边界框。所有位置数据均相对于全局坐标系给出。让我们从上面的 sample 中查看一个例子。
my_annotation_token = my_sample['anns'][18]
my_annotation_metadata = nusc.get('sample_annotation', my_annotation_token)
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'}
我们还可以渲染注释以便更仔细地观察。
nusc.render_annotation(my_annotation_token)
5. instance
对象实例是需要由 AV 检测或跟踪的实例（例如特定车辆、行人）。让我们检查一下实例元数据
my_instance = nusc.instance[599]
my_instance
{'token': '9cba9cd8af85487fb010652c90d845b5',
'category_token': 'fedb11688db84088883945752e480c2c',
'nbr_annotations': 16,
'first_annotation_token': '77afa772cb4a4e5c8a5a53f2019bdba0',
'last_annotation_token': '6fed6d902e5e487abb7444f62e1a2341'}
实例记录记录了它的第一个和最后一个注释标记。让我们渲染它们
6. category
“类别”是注释的对象分配。让我们看看数据库中的类别表。该表包含不同对象类别的分类法，还列出了子类别（用句点划定）。
nusc.list_categories()
Category stats for split v1.0-mini:
human.pedestrian.adult n= 4765, width= 0.68±0.11, len= 0.73±0.17, height= 1.76±0.12, lw_aspect= 1.08±0.23
人类.行人.成人 n= 4765，宽度= 0.68±0.11，长度= 0.73±0.17，高度= 1.76±0.12，长宽比= 1.08±0.23
human.pedestrian.child n= 46, width= 0.46±0.08, len= 0.45±0.09, height= 1.37±0.06, lw_aspect= 0.97±0.05
human.pedestrian.constructi n= 193, width= 0.69±0.07, len= 0.74±0.12, height= 1.78±0.05, lw_aspect= 1.07±0.16
human.pedestrian.personal_m n= 25, width= 0.83±0.00, len= 1.28±0.00, height= 1.87±0.00, lw_aspect= 1.55±0.00
human.pedestrian.police_off n= 11, width= 0.59±0.00, len= 0.47±0.00, height= 1.81±0.00, lw_aspect= 0.80±0.00
movable_object.barrier n= 2323, width= 2.32±0.49, len= 0.61±0.11, height= 1.06±0.10, lw_aspect= 0.28±0.09
movable_object.debris n= 13, width= 0.43±0.00, len= 1.43±0.00, height= 0.46±0.00, lw_aspect= 3.35±0.00
movable_object.pushable_pul n= 82, width= 0.51±0.06, len= 0.79±0.10, height= 1.04±0.20, lw_aspect= 1.55±0.18
movable_object.trafficcone n= 1378, width= 0.47±0.14, len= 0.45±0.07, height= 0.78±0.13, lw_aspect= 0.99±0.12
static_object.bicycle_rack n= 54, width= 2.67±1.46, len=10.09±6.19, height= 1.40±0.00, lw_aspect= 5.97±4.02
vehicle.bicycle n= 243, width= 0.64±0.12, len= 1.82±0.14, height= 1.39±0.34, lw_aspect= 2.94±0.41
vehicle.bus.bendy n= 57, width= 2.83±0.09, len= 9.23±0.33, height= 3.32±0.07, lw_aspect= 3.27±0.22
vehicle.bus.rigid n= 353, width= 2.95±0.26, len=11.46±1.79, height= 3.80±0.62, lw_aspect= 3.88±0.57
vehicle.car n= 7619, width= 1.92±0.16, len= 4.62±0.36, height= 1.69±0.21, lw_aspect= 2.41±0.18
vehicle.construction n= 196, width= 2.58±0.35, len= 5.57±1.57, height= 2.38±0.33, lw_aspect= 2.18±0.62
vehicle.motorcycle n= 471, width= 0.68±0.21, len= 1.95±0.38, height= 1.47±0.20, lw_aspect= 3.00±0.62
vehicle.trailer n= 60, width= 2.28±0.08, len=10.14±5.69, height= 3.71±0.27, lw_aspect= 4.37±2.41
vehicle.truck n= 649, width= 2.35±0.34, len= 6.50±1.56, height= 2.62±0.68, lw_aspect= 2.75±0.37
类别记录包含该特定类别的名称和描述。
nusc.category[9]
{'token': 'dfd26f200ade4d24b540184e16050022',
'name': 'vehicle.motorcycle',
'description': 'Gasoline or electric powered 2-wheeled vehicle designed to move rapidly (at the speed of standard cars) on the road surface. This category includes all motorcycles, vespas and scooters.'}
{'token': 'dfd26f200ade4d24b540184e16050022',
'name': 'vehicle.motorcycle',
'description': '汽油或电动两轮车，设计用于在路面上快速行驶（速度与标准汽车相当）。此类别包括所有摩托车、vespa 和踏板车。'}
7. attribute
属性是实例的属性，在场景的不同部分可能会发生变化，但类别保持不变。这里我们列出了提供的属性以及与特定属性相关的注释数量。
nusc.list_attributes()
The history saving thread hit an unexpected error (OperationalError('attempt to write a readonly database')).History will not be written to the database.
cycle.with_rider: 305
cycle.without_rider: 434
pedestrian.moving: 3875
pedestrian.sitting_lying_down: 111
pedestrian.standing: 1029
vehicle.moving: 2715
vehicle.parked: 4674
vehicle.stopped: 1545
Let's take a look at an example how an attribute may change over one scene
my_instance = nusc.instance[27]
first_token = my_instance['first_annotation_token']
last_token = my_instance['last_annotation_token']
nbr_samples = my_instance['nbr_annotations']
current_token = first_token
i = 0
found_change = False
while current_token != last_token:
current_ann = nusc.get('sample_annotation', current_token)
current_attr = nusc.get('attribute', current_ann['attribute_tokens'][0])['name']
if i == 0:
pass
elif current_attr != last_attr:
print("Changed from `{}` to `{}` at timestamp {} out of {} annotated timestamps".format(last_attr, current_attr, i, nbr_samples))
found_change = True
next_token = current_ann['next']
current_token = next_token
last_attr = current_attr
i += 1
Changed from `pedestrian.moving` to `pedestrian.standing` at timestamp 21 out of 39 annotated timestamps
在 39 个带注释的时间戳中的第 21 个时间戳处，从“pedestrian.moving”更改为“pedestrian.standing”
##8. visibility
“可见性”定义为在 6 个摄像头馈送中可见的特定注释的像素分数，分为 4 个箱。
nusc.visibility
[{'description': 'visibility of whole object is between 0 and 40%',
'token': '1',
'level': 'v0-40'},
{'description': 'visibility of whole object is between 40 and 60%',
'token': '2',
'level': 'v40-60'},
{'description': 'visibility of whole object is between 60 and 80%',
'token': '3',
'level': 'v60-80'},
{'description': 'visibility of whole object is between 80 and 100%',
'token': '4',
'level': 'v80-100'}]
anntoken = 'a7d0722bce164f88adf03ada491ea0ba'
visibility_token = nusc.get('sample_annotation', anntoken)['visibility_token']
print("Visibility: {}".format(nusc.get('visibility', visibility_token)))
nusc.render_annotation(anntoken)
Visibility: {'description': 'visibility of whole object is between 80 and 100%', 'token': '4', 'level': 'v80-100'}
9.sensor
The nuScenes dataset consists of data collected from our full sensor suite which consists of:
- 1 x LIDAR,
- 5 x RADAR,
- 6 x cameras,
nusc.sensor
[{'token': '725903f5b62f56118f4094b46a4470d8',
'channel': 'CAM_FRONT',
'modality': 'camera'},
{'token': 'ce89d4f3050b5892b33b3d328c5e82a3',
'channel': 'CAM_BACK',
'modality': 'camera'},
{'token': 'a89643a5de885c6486df2232dc954da2',
'channel': 'CAM_BACK_LEFT',
'modality': 'camera'},
{'token': 'ec4b5d41840a509984f7ec36419d4c09',
'channel': 'CAM_FRONT_LEFT',
'modality': 'camera'},
{'token': '2f7ad058f1ac5557bf321c7543758f43',
'channel': 'CAM_FRONT_RIGHT',
'modality': 'camera'},
{'token': 'ca7dba2ec9f95951bbe67246f7f2c3f7',
'channel': 'CAM_BACK_RIGHT',
'modality': 'camera'},
{'token': 'dc8b396651c05aedbb9cdaae573bb567',
'channel': 'LIDAR_TOP',
'modality': 'lidar'},
{'token': '47fcd48f71d75e0da5c8c1704a9bfe0a',
'channel': 'RADAR_FRONT',
'modality': 'radar'},
{'token': '232a6c4dc628532e81de1c57120876e9',
'channel': 'RADAR_FRONT_RIGHT',
'modality': 'radar'},
{'token': '1f69f87a4e175e5ba1d03e2e6d9bcd27',
'channel': 'RADAR_FRONT_LEFT',
'modality': 'radar'},
{'token': 'df2d5b8be7be55cca33c8c92384f2266',
'channel': 'RADAR_BACK_LEFT',
'modality': 'radar'},
{'token': '5c29dee2f70b528a817110173c2e71b9',
'channel': 'RADAR_BACK_RIGHT',
'modality': 'radar'}]
12. log
13.map
Map information is stored as binary semantic masks from a top-down view. Let’s check the number of maps and metadata of a map.
从上到下看，地图信息以二进制语义掩码的形式存储。让我们来看看地图的数量和地图的元数据。
print("There are {} maps masks in the loaded dataset".format(len(nusc.map)))
nusc.map[0]
{'category': 'semantic_prior',
'token': '53992ee3023e5494b90c316c183be829',
'filename': 'maps/53992ee3023e5494b90c316c183be829.png',
'log_tokens': ['0986cb758b1d43fdaa051ab23d45582b',
'1c9b302455ff44a9a290c372b31aa3ce',
'e60234ec7c324789ac7c8441a5e49731',
'46123a03f41e4657adc82ed9ddbe0ba2',
'a5bb7f9dd1884f1ea0de299caefe7ef4',
'bc41a49366734ebf978d6a71981537dc',
'f8699afb7a2247e38549e4d250b4581b',
'd0450edaed4a46f898403f45fa9e5f0d',
'f38ef5a1e9c941aabb2155768670b92a',
'7e25a2c8ea1f41c5b0da1e69ecfa71a2',
'ddc03471df3e4c9bb9663629a4097743',
'31e9939f05c1485b88a8f68ad2cf9fa4',
'783683d957054175bda1b326453a13f4',
'343d984344e440c7952d1e403b572b2a',
'92af2609d31445e5a71b2d895376fed6',
'47620afea3c443f6a761e885273cb531',
'd31dc715d1c34b99bd5afb0e3aea26ed',
'34d0574ea8f340179c82162c6ac069bc',
'd7fd2bb9696d43af901326664e42340b',
'b5622d4dcb0d4549b813b3ffb96fbdc9',
'da04ae0b72024818a6219d8dd138ea4b',
'6b6513e6c8384cec88775cae30b78c0e',
'eda311bda86f4e54857b0554639d6426',
'cfe71bf0b5c54aed8f56d4feca9a7f59',
'ee155e99938a4c2698fed50fc5b5d16a',
'700b800c787842ba83493d9b2775234a'],
'mask': <nuscenes.utils.map_mask.MapMask at 0x7fbf8c230a60>}
完毕~，祝顺利