def select_annotation_boxes(sample_token, lyftd: LyftDataset, box_vis_level: BoxVisibility = BoxVisibility.ALL,
camera_type=['CAM_FRONT', 'CAM_BACK', 'CAM_FRONT_LEFT', 'CAM_FRONT_RIGHT', 'CAM_BACK_RIGHT',
'CAM_BACK_LEFT']) -> (str, str, Box):
"""
Select annotations that is a camera image defined by box_vis_level
:param sample_token:
:param box_vis_level:BoxVisbility.ALL or BoxVisibility.ANY
:param camera_type: a list of camera that the token should be selected from
:return: yield (sample_token,cam_token, Box)
"""
sample_record = lyftd.get('sample', sample_token)
cams = [key for key in sample_record["data"].keys() if "CAM" in key]
cams = [cam for cam in cams if cam in camera_type]
for ann_token in sample_record['anns']:
for cam in cams:
cam_token = sample_record["data"][cam]
_, boxes_in_sensor_coord, cam_intrinsic = lyftd.get_sample_data(
cam_token, box_vis_level=box_vis_level, selected_anntokens=[ann_token]
)
if len(boxes_in_sensor_coord) > 0:
assert len(boxes_in_sensor_coord) == 1
box_in_world_coord = lyftd.get_box(boxes_in_sensor_coord[0].token)
yield sample_token, cam_token, box_in_world_coord
def read_frustum_pointnet_output(ldt: LyftDataset, inference_pickle_file,
token_pickle_file, from_rgb_detection: bool):
with open(inference_pickle_file, 'rb') as fp:
ps_list = pickle.load(fp)
if not from_rgb_detection:
seg_list = pickle.load(fp)
segp_list = pickle.load(fp)
center_list = pickle.load(fp)
heading_cls_list = pickle.load(fp)
heading_res_list = pickle.load(fp)
size_cls_list = pickle.load(fp)
size_res_list = pickle.load(fp)
rot_angle_list = pickle.load(fp)
score_list = pickle.load(fp)
if from_rgb_detection:
onehot_list = pickle.load(fp)
with open(token_pickle_file, 'rb') as fp:
sample_token_list = pickle.load(fp)
annotation_token_list = pickle.load(fp)
camera_data_token_list = pickle.load(fp)
type_list = pickle.load(fp)
ldt.get('sample', sample_token_list[0])
if annotation_token_list:
ldt.get('sample_annotation', annotation_token_list[0])
print("lengh of sample token:", len(sample_token_list))
print("lengh of ps list:", len(ps_list))
assert len(sample_token_list) == len(ps_list)
boxes = []
gt_boxes = []
for data_idx in range(len(ps_list)):
inferred_box = get_box_from_inference(
lyftd=ldt,
heading_cls=heading_cls_list[data_idx],
heading_res=heading_res_list[data_idx],
rot_angle=rot_angle_list[data_idx],
size_cls=size_cls_list[data_idx],
size_res=size_res_list[data_idx],
center_coord=center_list[data_idx],
sample_data_token=camera_data_token_list[data_idx],
score=score_list[data_idx])
inferred_box.name = type_list[data_idx]
boxes.append(inferred_box)
if not from_rgb_detection:
gt_boxes.append(ldt.get_box(annotation_token_list[data_idx]))
return boxes, gt_boxes, sample_token_list
axes_str = ['X', 'Y', 'Z']
display_frame_statistics(pc.points, points=0.5)
lyftdata.render_sample_data(lidar_top['token'], with_anns=False)
## Ego pose
lyftdata.get('ego_pose', cam_front['ego_pose_token'])
## caliberated_sensor
lyftdata.get('calibrated_sensor', cam_front['calibrated_sensor_token'])
## Annotations
my_annotation = lyftdata.get('sample_annotation', my_sample['anns'][0])
my_box = lyftdata.get_box(my_annotation['token'])
lyftdata.render_annotation(my_annotation['token'], margin=10)
## Attributes
my_attribute1 = lyftdata.get('attribute', my_annotation['attribute_tokens'][0])
my_attribute2 = lyftdata.get('attribute', my_annotation['attribute_tokens'][1])
## Instances
my_instance = lyftdata.get('instance', my_annotation['instance_token'])
lyftdata.render_instance(my_instance['token'])
print("First annotated sample of this instance:")
lyftdata.render_annotation(my_instance['first_annotation_token'])
print("Last annotated sample of this instance")
lyftdata.render_annotation(my_instance['last_annotation_token'])
