Render the future ego_pose for CAM_FRONT

[arvindks89]

Given an instance, want to visualize the ego pose on CAM_FRONT image for the next 12 time steps:

from nuscenes.nuscenes import NuScenes
nusc = NuScenes(version='v1.0-trainval', dataroot='/data/nuscenes/', verbose=True)

sample_token = "3950bd41f74548429c0f7700ff3d8269"
sd_rec = nusc.get('sample', sample_token)
lidar_top_data_start = nusc.get('sample_data', sd_rec['data']['CAM_FRONT'])
ego_pose_start = nusc.get('ego_pose', lidar_top_data_start['ego_pose_token'])

sdc_fut_traj =[]
for _ in range(12):
    next_annotation_token = sd_rec['next']
    if next_annotation_token=='':
        break
    sd_rec = nusc.get('sample', next_annotation_token)
    lidar_top_data_next = nusc.get('sample_data', sd_rec['data']['CAM_FRONT'])
    ego_pose_next = nusc.get('ego_pose', lidar_top_data_next['ego_pose_token'])
    sdc_fut_traj.append(ego_pose_next['translation'][:2])

Now i plot the future trajectory on the image:

x= [i[0] for i in sdc_fut_traj]
y= [i[1] for i in sdc_fut_traj]
from PIL import Image
import matplotlib.pyplot as plt

file_path = lidar_top_data_start["filename"]
img = Image.open("/mnt/ai_testing/02_datasets/UniAD/data/nuscenes/" + file_path)
plt.imshow(img)
plt.scatter(y,x)
plt.show()

But that makes no meaning, the trajectory is not meaningful