sample_rec = nusc.get('sample', scene_rec['first_sample_token'])
f_rad_rec = nusc.get('sample_data', sample_rec['data']['RADAR_FRONT'])
f_cam_rec = nusc.get('sample_data', sample_rec['data']['CAM_FRONT'])
has_more_data = True
while has_more_data:
bboxes = []
filtered_anns = []
ax1 = fig.add_subplot(1, 2, 1)
ax2 = fig.add_subplot(1, 2, 2)
camera_token = f_cam_rec['token']
radar_token = f_rad_rec['token']
## FRONT CAM + RADAR
impath, boxes, camera_intrinsic = nusc.get_select_sample_data(
camera_token, min_ann_vis_level=1)
points, coloring, image = nusc.explorer.map_pointcloud_to_image(
radar_token, camera_token)
points[2, :] = coloring
## -----------------------------------------------------------------
## Do the processing here
# Plot the 3D boxes and create 2D COCO bboxes
for box in boxes:
coco_cat, coco_supercat = nuscene_cat_to_coco(box.name)
if coco_cat is None:
continue
c = np.array(nusc.explorer.get_color(box.name)) / 255.0
box.render(ax1,
view=camera_intrinsic,
bl_rad_rec = nusc.get('sample_data',
sample_rec['data']['RADAR_BACK_LEFT'])
has_more_data = True
while has_more_data:
rnd = random.uniform(0, 1)
anns_f = []
anns_b = []
f_camera_token = f_cam_rec['token']
b_camera_token = b_cam_rec['token']
f_radar_token = f_rad_rec['token']
br_radar_token = br_rad_rec['token']
bl_radar_token = bl_rad_rec['token']
## FRONT CAM + RADAR
impath_f, boxes_f, camera_intrinsic_f = nusc.get_select_sample_data(
f_camera_token, min_ann_vis_level=1)
points_f, coloring_f, image_f = nusc.explorer.map_pointcloud_to_image(
f_radar_token, f_camera_token)
points_f[2, :] = coloring_f
## Back CAM + RADARs
impath_b, boxes_b, camera_intrinsic_b = nusc.get_select_sample_data(
b_camera_token, min_ann_vis_level=1)
points_br, coloring_br, image_b = nusc.explorer.map_pointcloud_to_image(
br_radar_token, b_camera_token)
points_br[2, :] = coloring_br
points_bl, coloring_bl, _ = nusc.explorer.map_pointcloud_to_image(
bl_radar_token, b_camera_token)
points_bl[2, :] = coloring_bl
## Concatenate the two back Radar detections