Data extractor class for extracting data from nuScenes dataset
Download and set up nuScenes.
Specify nuScenes python-sdk path in main.py and nusc_data_extractor.py, then run:
>>> python main.py
Select the desired scene with
scene_name = 'scene-0001'
de_nusc.set_scene(scene_name)
For iterating through the time steps of the scene use
de_nusc.advance()
It is also possible to get a list with all the scenes using
scene_list = de_nusc.get_scene_list()
Extract data of the lidar sensor for current time step:
lidar_points = de_nusc.get_lidar_pointcloud()
Extract data of one radar sensor for current time step:
radar_channel = 'RADAR_FRONT'
radar_points, radar_velocities = de_nusc.get_radar_pointcloud(radar_channel)
Extract data of all radar sensors for current time step:
radar_points, radar_velocities = de_nusc.get_all_radar_pointclouds()
Extract raw image of one camera sensor for current time step:
camera_channel = 'CAM_FRONT'
camera_image = de_nusc.get_camera_image(camera_channel)
Get annotations from the current scene using
bboxes, class_ID, velocity=de_nusc.get_annotations(1) #1 if we want 2D bboxes or 2 if we want 3D bboxes
Get annotations for all possible annotations. If a sensor channel (e.g. 'RADAR_FRONT') is specified, just the annotations seen by the sensor are given.
de_nusc.get_annotations()
de_nusc.get_annotations('CAM_FRONT')
de_nusc.get_annotations('RADAR_FRONT')
It is possible to save the current LiDAR point cloud into an image of a fixed maximum axis dimmension as well as the labels of that image using:
#Save the image with the LiDAR points, the maximum axis value and the name used to save the file. The labels will also be saved
de_nusc.save_figure_and_labels("LIDAR_TOP", 40, "image0000")
Use the code "labelme2coco" from: https://github.com/Tony607/labelme2coco/blob/master/labelme2coco.py to get COCO structure for the labels.