Create README.md

README.md

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+---
+license: cc-by-nc-nd-4.0
+tags:
+- Autonomous Driving
+- Computer Vision
+---
+# Dataset Tutorial
+
+### The MARS dataset follows the same structure as the NuScenes Dataset.
+
+Multitraversal: each location is saved as one NuScenes object, and each traversal is one scene.
+
+Multiagent: the whole set is a NuScenes object, and each multi-agent encounter is one scene.
+
+<br/>
+
+## Initialization
+First, install `nuscenes-devkit` following NuScenes's repo tutorial, [Devkit setup section](https://github.com/nutonomy/nuscenes-devkit?tab=readme-ov-file#devkit-setup). The easiest way is install via pip:
+```
+pip install nuscenes-devkit
+```
+
+## Usage:
+Import NuScenes devkit:
+```
+from nuscenes.nuscenes import NuScenes
+```
+
+Multitraversal example: loading data of location 10:
+```
+# The "version" variable is the name of the folder holding all .json metadata tables.
+location = 10
+mars_10 = NuScenes(version='v1.0', dataroot=f'/MARS_multitraversal/{location}', verbose=True)
+```
+
+Multiagent example: loading data for the full set:
+```
+mars_multiagent = NuScenes(version='v1.0', dataroot=f'/MARS_multiagent', verbose=True)
+```
+
+<br/>
+
+## Scene
+To see all scenes in one set (one location of the Multitraversal set, or the whole Multiagent set):
+```
+print(nusc.scene)
+```
+Output:
+```
+[{'token': '97hitl8ya1335v8zkixvsj3q69tgx801', 'nbr_samples': 611, 'first_sample_token': 'udrq868482482o88p9r2n8b86li7cfxx', 'last_sample_token': '7s5ogk8m9id7apixkqoh3rep0s9113xu', 'name': '2023_10_04_scene_3_maisy', 'intersection': 10, 'err_max': 20068.00981996727},
+{'token': 'o858jv3a464383gk9mm8at71ai994d3n', 'nbr_samples': 542, 'first_sample_token': '933ho5988jo3hu848b54749x10gd7u14', 'last_sample_token': 'os54se39x1px2ve12x3r1b87e0d7l1gn', 'name': '2023_10_04_scene_4_maisy', 'intersection': 10, 'err_max': 23959.357933579337},
+{'token': 'xv2jkx6m0o3t044bazyz9nwbe5d5i7yy', 'nbr_samples': 702, 'first_sample_token': '8rqb40c919d6n5cd553c3j01v178k28m', 'last_sample_token': 'skr79z433oyi6jljr4nx7ft8c42549nn', 'name': '2023_10_04_scene_6_mike', 'intersection': 10, 'err_max': 27593.048433048432},
+{'token': '48e90c7dx401j97391g6549zmljbg0hk', 'nbr_samples': 702, 'first_sample_token': 'ui8631xb2in5la133319c5301wvx1fib', 'last_sample_token': 'xrns1rpma4p00hf39305ckol3p91x59w', 'name': '2023_10_04_scene_9_mike', 'intersection': 10, 'err_max': 24777.237891737892},
+...
+]
+
+```
+
+The scenes can then be retrieved by indexing:
+```
+num_of_scenes = len(nusc.scene)
+my_scene = nusc.scene[0]    # scene at index 0, which is the first scene of this location
+print(first_scene)
+```
+Output:
+```
+{'token': '97hitl8ya1335v8zkixvsj3q69tgx801',
+'nbr_samples': 611,
+'first_sample_token': 'udrq868482482o88p9r2n8b86li7cfxx',
+'last_sample_token': '7s5ogk8m9id7apixkqoh3rep0s9113xu',
+'name': '2023_10_04_scene_3_maisy',
+'intersection': 10,
+'err_max': 20068.00981996727}
+```
+- `nbr_samples`: number of samples (frames) of this scene.
+- `name`: name of the scene, including its date and name of the vehicle it is from (in this example, the data is from Oct. 4th 2023, vehicle maisy).
+- `intersection`: location index. 
+- `err_max`: maximum time difference (in millisecond) between camera images of a same frame in this scene.
+
+<br/>
+
+## Sample
+Get the first sample (frame) of one scene:
+```
+first_sample_token = my_scene['first_sample_token']    # get sample token
+my_sample = nusc.get('sample', first_sample_token)    # get sample metadata
+print(my_sample)
+```
+
+Output:
+```
+{'token': 'udrq868482482o88p9r2n8b86li7cfxx',
+'timestamp': 1696454482883182,
+'prev': '',
+'next': 'v15b2l4iaq1x0abxr45jn6bi08j72i01',
+'scene_token': '97hitl8ya1335v8zkixvsj3q69tgx801',
+'data': {
+  'CAM_FRONT_CENTER': 'q9e0pgk3wiot983g4ha8178zrnr37m50',
+  'CAM_FRONT_LEFT': 'c13nf903o913k30rrz33b0jq4f0z7y2d',
+  'CAM_FRONT_RIGHT': '67ydh75sam2dtk67r8m3bk07ba0lz3ib',
+  'CAM_BACK_CENTER': '1n09qfm9vw65xpohjqgji2g58459gfuq',
+  'CAM_SIDE_LEFT': '14up588181925s8bqe3pe44d60316ey0',
+  'CAM_SIDE_RIGHT': 'x95k7rvhmxkndcj8mc2821c1cs8d46y5',
+  'LIDAR_FRONT_CENTER': '13y90okaf208cqqy1v54z87cpv88k2qy',
+  'IMU_TOP': 'to711a9v6yltyvxn5653cth9w2o493z4'
+},
+'anns': []}
+```
+- `prev`: token of the previous sample.
+- `next`': token of the next sample.
+- `data`: dict of data tokens of this sample's sensor data.
+- `anns`: empty as we do not have annotation data at this moment.
+
+<br/>
+
+## Sample Data
+Our sensor names are different from NuScenes' sensor names. It is important that you use the correct name when querying sensor data. Our sensor names are:
+```
+['CAM_FRONT_CENTER',
+'CAM_FRONT_LEFT',
+'CAM_FRONT_RIGHT',
+'CAM_BACK_CENTER',
+'CAM_SIDE_LEFT',
+'CAM_SIDE_RIGHT',
+'LIDAR_FRONT_CENTER',
+'IMU_TOP']
+```
+
+---
+### Camera Data
+```
+sensor = 'CAM_FRONT_CENTER'
+sample_data_token = my_sample['data'][sensor]
+FC_data = nusc.get('sample_data', sample_data_token)
+print(FC_data)
+```
+Output: 
+```
+{'token': 'q9e0pgk3wiot983g4ha8178zrnr37m50',
+'sample_token': 'udrq868482482o88p9r2n8b86li7cfxx',
+'ego_pose_token': 'q9e0pgk3wiot983g4ha8178zrnr37m50',
+'calibrated_sensor_token': 'r5491t78vlex3qii8gyh3vjp0avkrj47',
+'timestamp': 1696454482897062,
+'fileformat': 'jpg',
+'is_key_frame': True,
+'height': 464,
+'width': 720,
+'filename': 'sweeps/CAM_FRONT_CENTER/1696454482897062.jpg',
+'prev': '',
+'next': '33r4265w297khyvqe033sl2r6m5iylcr',
+'sensor_modality': 'camera',
+'channel': 'CAM_FRONT_CENTER'}
+```
+- `ego_pose_token`: token of vehicle ego pose at the time of this sample.
+- `calibrated_sensor_token`: token of sensor calibration information (e.g. distortion coefficient, camera intrinsics, sensor pose & location relative to vehicle, etc.).
+- `is_key_frame`: disregard; all images have been marked as key frame in our dataset.
+- `height`: image height in pixel
+- `width`: image width in pixel
+- `filename`: image directory relative to the dataset's root folder
+- `prev`: previous data token for this sensor
+- `next`: next data token for this sensor
+
+All image data are already undistorted. You may now load the image in any ways you'd like. Here's an example using cv2:
+```
+data_path, boxes, camera_intrinsic = nusc.get_sample_data(sample_data_token)
+img = cv2.imread(data_path)
+cv2.imshow('fc_img', img)
+cv2.waitKey()
+```
+
+Output: 
+```
+('{$dataset_root}/MARS_multitraversal/10/sweeps/CAM_FRONT_CENTER/1696454482897062.jpg',
+[],
+array([[661.094568 ,   0.       , 370.6625195],
+       [  0.       , 657.7004865, 209.509716 ],
+       [  0.       ,   0.       ,   1.       ]]))
+```
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/66651bd4e4be2069a695e5a1/piuFfzzsBrzW4LKgKHxAJ.png)
+
+---
+### LiDAR Data
+
+Impoirt data calss "LidarPointCloud" from NuScenes devkit for convenient lidar pcd loading and manipulation.
+
+The `.bcd.bin` LiDAR data in our dataset has 5 dimensions: [ x || y || z || intensity || ring ].
+
+The 5-dimensional data array is in `pcd.points`. Below is an example of visualizing the pcd with Open3d interactive visualizer.
+
+
+```
+from nuscenes.utils.data_classes import LidarPointCloud
+
+sensor = 'LIDAR_FRONT_CENTER'
+sample_data_token = my_sample['data'][sensor]
+lidar_data = nusc.get('sample_data', sample_data_token)
+
+data_path, boxes, _ = nusc.get_sample_data(my_sample['data'][sensor])
+
+pcd = LidarPointCloud.from_file(data_path)
+print(pcd.points)
+pts = pcd.points[:3].T
+
+# open3d visualizer
+vis1 = o3d.visualization.Visualizer()
+vis1.create_window(
+    window_name='pcd viewer',
+    width=256 * 4,
+    height=256 * 4,
+    left=480,
+    top=270)
+vis1.get_render_option().background_color = [0, 0, 0]
+vis1.get_render_option().point_size = 1
+vis1.get_render_option().show_coordinate_frame = True
+
+o3d_pcd = o3d.geometry.PointCloud()
+o3d_pcd.points = o3d.utility.Vector3dVector(pts)
+
+vis1.add_geometry(o3d_pcd)
+while True:
+    vis1.update_geometry(o3d_pcd)
+    vis1.poll_events()
+    vis1.update_renderer()
+    time.sleep(0.005)
+```
+
+Output: 
+```
+5-d lidar data: 
+[[ 3.7755847e+00  5.0539265e+00  5.4277039e+00 ...  3.1050100e+00
+   3.4012783e+00  3.7089713e+00]
+ [-6.3800979e+00 -7.9569578e+00 -7.9752398e+00 ... -7.9960880e+00
+  -7.9981585e+00 -8.0107889e+00]
+ [-1.5409404e+00 -3.2752687e-01  5.7313687e-01 ...  5.5921113e-01
+  -7.5427920e-01  6.6252775e-02]
+ [ 9.0000000e+00  1.6000000e+01  1.4000000e+01 ...  1.1000000e+01
+   1.8000000e+01  1.6000000e+01]
+ [ 4.0000000e+00  5.3000000e+01  1.0200000e+02 ...  1.0500000e+02
+   2.6000000e+01  7.5000000e+01]]
+```
+
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/66651bd4e4be2069a695e5a1/gxyTJM7Y45AWE9k54Q9ur.png)
+
+
+---
+### IMU Data
+IMU data in our dataset is saved as json files.
+```
+sensor = 'IMU_TOP'
+sample_data_token = my_sample['data'][sensor]
+lidar_data = nusc.get('sample_data', sample_data_token)
+
+data_path, boxes, _ = nusc.get_sample_data(my_sample['data'][sensor])
+
+imu_data = json.load(open(data_path))
+print(imu_data)
+```
+
+Output:
+```
+{'utime': 1696454482879084,
+'lat': 42.28098291158676,
+'lon': -83.74725341796875,
+'elev': 259.40500593185425,
+'vel': [0.19750464521348476, -4.99952995654127e-27, -0.00017731071625348704],
+'avel': [-0.0007668623868539726, -0.0006575787383553688, 0.0007131154834496556],
+'acc': [-0.28270150907337666, -0.03748669268679805, 9.785771369934082]}
+```
+- `lat`: GPS latitude.
+- `lon`: GPS longitude.
+- `elev`: GPS elevation.
+- `vel`: vehicle instant velocity [x, y, z] in m/s.
+- `avel`: vehicle instant angular velocity [x, y, z] in rad/s.
+- `acc`: vehicle instant acceleration [x, y, z] in m/s^2.
+
+---
+### Vehicle and Sensor Pose
+Poses are represented as one rotation matrix and one translation matrix.
+- rotation: quaternion [w, x, y, z]
+- translation: [x, y, z] in meters
+
+Sensor-to-vehicle poses may differ for different vehicles. But for each vehicle, its sensor poses should remain unchanged across all scenes & samples.
+
+Vehicle ego pose can be quaried from sensor data. It should be the same for all sensors in the same sample.
+
+```
+# get the vehicle ego pose at the time of this FC_data
+vehicle_pose_fc = nusc.get('ego_pose', FC_data['ego_pose_token'])
+print("vehicle pose: \n", vehicle_pose_fc, "\n")
+
+# get the vehicle ego pose at the time of this lidar_data, should be the same as that queried from FC_data as they are from the same sample.
+vehicle_pose = nusc.get('ego_pose', lidar_data['ego_pose_token'])
+print("vehicle pose: \n", vehicle_pose, "\n")
+
+# get camera pose relative to vehicle at the time of this sample
+fc_pose = nusc.get('calibrated_sensor', FC_data['calibrated_sensor_token'])
+print("CAM_FRONT_CENTER pose: \n", fc_pose, "\n")
+
+# get lidar pose relative to vehicle at the time of this sample
+lidar_pose = nusc.get('calibrated_sensor', lidar_data['calibrated_sensor_token'])
+print("CAM_FRONT_CENTER pose: \n", lidar_pose)
+```
+
+Output: 
+```
+vehicle pose: 
+ {'token': 'q9e0pgk3wiot983g4ha8178zrnr37m50',
+'timestamp': 1696454482883182,
+'rotation': [-0.7174290249840286, 0.0, -0.0, -0.6966316057361065],
+'translation': [-146.83352790433003, -21.327001411798392, 0.0]} 
+
+vehicle pose: 
+ {'token': '13y90okaf208cqqy1v54z87cpv88k2qy',
+'timestamp': 1696454482883182,
+'rotation': [-0.7174290249840286, 0.0, -0.0, -0.6966316057361065],
+'translation': [-146.83352790433003, -21.327001411798392, 0.0]} 
+
+CAM_FRONT_CENTER pose: 
+ {'token': 'r5491t78vlex3qii8gyh3vjp0avkrj47',
+'sensor_token': '1gk062vf442xsn86xo152qw92596k8b9',
+'translation': [2.24715, 0.0, 1.4725],
+'rotation': [0.49834929780875276, -0.4844970241435727, 0.5050790448056688, -0.5116695901338464],
+'camera_intrinsic': [[661.094568, 0.0, 370.6625195], [0.0, 657.7004865, 209.509716], [0.0, 0.0, 1.0]],
+'distortion_coefficient': [0.122235, -1.055498, 2.795589, -2.639154]} 
+
+CAM_FRONT_CENTER pose: 
+ {'token': '6f367iy1b5c97e8gu614n63jg1f5os19',
+'sensor_token': 'myfmnd47g91ijn0a7481eymfk253iwy9',
+'translation': [2.12778, 0.0, 1.57],
+'rotation': [0.9997984797097376, 0.009068089160690487, 0.006271772522201215, -0.016776012592418482]}
+
+```
+
+<br/>
+
+## LiDAR-Image projection
+- Use NuScenes devkit's `render_pointcloud_in_image()` method.
+- The first variable is a sample token.
+- Use `camera_channel` to specify the camera name you'd like to project the poiint cloud onto.
+```
+nusc.render_pointcloud_in_image(my_sample['token'],
+                                pointsensor_channel='LIDAR_FRONT_CENTER',
+                                camera_channel='CAM_FRONT_CENTER',
+                                render_intensity=False,
+                                show_lidarseg=False)
+```
+
+Output: 
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/66651bd4e4be2069a695e5a1/KV715ekDEgLt3CysI4R9S.png)