def add_center_dist(nusc: NuScenes, eval_boxes: EvalBoxes):
"""
Adds the cylindrical (xy) center distance from ego vehicle to each box.
:param nusc: The NuScenes instance.
:param eval_boxes: A set of boxes, either GT or predictions.
:return: eval_boxes augmented with center distances.
"""
for sample_token in eval_boxes.sample_tokens:
sample_rec = nusc.get('sample', sample_token)
sd_record = nusc.get('sample_data', sample_rec['data']['LIDAR_TOP'])
pose_record = nusc.get('ego_pose', sd_record['ego_pose_token'])
for box in eval_boxes[sample_token]:
# Both boxes and ego pose are given in global coord system, so distance can be calculated directly.
# Note that the z component of the ego pose is 0.
ego_translation = (box.translation[0] -
pose_record['translation'][0],
box.translation[1] -
pose_record['translation'][1],
box.translation[2] -
pose_record['translation'][2])
if isinstance(box, DetectionBox) or isinstance(box, TrackingBox):
box.ego_translation = ego_translation
else:
raise NotImplementedError
return eval_boxes
def test_load_pointclouds(self):
"""
Loads up lidar and radar pointclouds.
"""
assert 'NUSCENES' in os.environ, 'Set NUSCENES env. variable to enable tests.'
dataroot = os.environ['NUSCENES']
nusc = NuScenes(version='v1.0-mini', dataroot=dataroot, verbose=False)
sample_rec = nusc.sample[0]
lidar_name = nusc.get('sample_data',
sample_rec['data']['LIDAR_TOP'])['filename']
radar_name = nusc.get('sample_data',
sample_rec['data']['RADAR_FRONT'])['filename']
lidar_path = os.path.join(dataroot, lidar_name)
radar_path = os.path.join(dataroot, radar_name)
pc1 = LidarPointCloud.from_file(lidar_path)
pc2 = RadarPointCloud.from_file(radar_path)
pc3, _ = LidarPointCloud.from_file_multisweep(nusc,
sample_rec,
'LIDAR_TOP',
'LIDAR_TOP',
nsweeps=2)
pc4, _ = RadarPointCloud.from_file_multisweep(nusc,
sample_rec,
'RADAR_FRONT',
'RADAR_FRONT',
nsweeps=2)
# Check for valid dimensions.
assert pc1.points.shape[0] == pc3.points.shape[
0] == 4, 'Error: Invalid dimension for lidar pointcloud!'
assert pc2.points.shape[0] == pc4.points.shape[
0] == 18, 'Error: Invalid dimension for radar pointcloud!'
assert pc1.points.dtype == pc3.points.dtype, 'Error: Invalid dtype for lidar pointcloud!'
assert pc2.points.dtype == pc4.points.dtype, 'Error: Invalid dtype for radar pointcloud!'
def get_pcl():
# v1.0-trainval
# nusc = Nuscenes(version='v1.0-trainval', dataroot='/home/fengjia/data/sets/nuscenes', verbose=True)
nusc = NuScenes(version='v1.0-trainval',
dataroot='/home/fengjia/data/sets/nuscenes',
verbose=True)
f = open(r'annotations_list.txt', 'w')
count = 0
for scene in nusc.scene:
sample_token = scene['first_sample_token']
my_sample = nusc.get('sample', sample_token)
while sample_token != '':
my_sample = nusc.get('sample', sample_token)
for i in range(len(my_sample['anns'])):
my_annotation_token = my_sample['anns'][i]
my_annotation_metadata = nusc.get('sample_annotation',
my_annotation_token)
my_sample_token = my_annotation_metadata['sample_token']
my_sample_temp = nusc.get('sample', my_sample_token)
sample_data_cam = nusc.get('sample_data',
my_sample_temp['data']['CAM_FRONT'])
s = sample_data_cam['token']
s += '_'
s += my_annotation_metadata['token']
s += '\n'
f.write(s)
count += 1
sample_token = my_sample['next']
f.close()
print(count)
def parse_frame(
data: NuScenes,
scene_name: str,
frame_index: int,
cam_token: str,
boxes: Optional[List[Box]] = None,
) -> Tuple[Frame, Optional[str]]:
"""Parse a single camera frame."""
cam_data = data.get("sample_data", cam_token)
ego_pose_cam = data.get("ego_pose", cam_data["ego_pose_token"])
cam_filepath = cam_data["filename"]
img_wh = (cam_data["width"], cam_data["height"])
calibration_cam = data.get("calibrated_sensor",
cam_data["calibrated_sensor_token"])
labels: Optional[List[Label]] = None
if boxes is not None:
labels = parse_labels(data, boxes, ego_pose_cam, calibration_cam,
img_wh)
frame = Frame(
name=os.path.basename(cam_filepath),
videoName=scene_name,
frameIndex=frame_index,
url=cam_filepath,
timestamp=cam_data["timestamp"],
extrinsics=get_extrinsics(ego_pose_cam, calibration_cam),
intrinsics=calibration_to_intrinsics(calibration_cam),
size=ImageSize(width=img_wh[0], height=img_wh[1]),
labels=labels,
)
next_token: Optional[str] = None
if (cam_data["next"] != ""
and not data.get("sample_data", cam_data["next"])["is_key_frame"]):
next_token = cam_data["next"]
return frame, next_token
def load_data(filepath: str, version: str) -> Tuple[NuScenes, pd.DataFrame]:
"""Load nuscenes data and extract meta-information into dataframe."""
data = NuScenes(version=version, dataroot=filepath, verbose=True)
records = [(data.get("sample",
record["first_sample_token"])["timestamp"], record)
for record in data.scene]
entries = []
for start_time, record in sorted(records):
start_time = (
data.get("sample", record["first_sample_token"])["timestamp"] /
1000000)
token = record["token"]
name = record["name"]
date = datetime.utcfromtimestamp(start_time)
host = "-".join(record["name"].split("-")[:2])
first_sample_token = record["first_sample_token"]
entries.append((host, name, date, token, first_sample_token))
dataframe = pd.DataFrame(
entries,
columns=[
"host",
"scene_name",
"date",
"scene_token",
"first_sample_token",
],
)
return data, dataframe
def pred_to_world(nusc: NuScenes,
pointsensor_token: str,
bbox_3d,
pointsensor_channel: str = 'LIDAR_TOP'):
"""
Given an annotation token (3d detection in world coordinate frame) and pointsensor sample_data token,
transform the label from world-coordinate frame to LiDAR.
:param nusc: NuScenes instance.
:param pointsensor_token: Lidar/radar sample_data token.
:param bbox_3d: box object with the predicted 3D bbox info.
:param pointsensor_channel: Laser channel name, e.g. 'LIDAR_TOP'.
:return box mapped in the world coordinate frame.
"""
# Point LiDAR sample
point_data = nusc.get('sample_data',
pointsensor_token) # Sample LiDAR info
# From LiDAR to ego
cs_rec = nusc.get('calibrated_sensor',
point_data['calibrated_sensor_token'])
# Transformation metadata from ego to world coordinate frame
pose_rec = nusc.get('ego_pose', point_data['ego_pose_token'])
# Map tp ego-vehicle coordinate frame
bbox_3d.rotate(Quaternion(cs_rec['rotation']))
bbox_3d.translate(np.array(cs_rec['translation']))
# Map from ego-vehicle to world coordinate frame
bbox_3d.rotate(Quaternion(pose_rec['rotation']))
bbox_3d.translate(np.array(pose_rec['translation']))
return bbox_3d
def get_camera_data(nusc: NuScenes,
annotation_token: str,
box_vis_level: BoxVisibility = BoxVisibility.ANY):
"""
Given an annotation token (3d detection in world coordinate frame) this method
returns the camera in which the annotation is located. If the box is splitted
between 2 cameras, it brings the first one found.
:param nusc: NuScenes instance.
:param annotation_token: Annotation token.
:param box_vis_level: If sample_data is an image, this sets required visibility for boxes.
:return camera channel.
"""
#Get sample annotation
ann_record = nusc.get('sample_annotation', annotation_token)
sample_record = nusc.get('sample', ann_record['sample_token'])
boxes, cam = [], []
#Stores every camera
cams = [key for key in sample_record['data'].keys() if 'CAM' in key]
#Try with every camera a match for the annotation
for cam in cams:
_, boxes, _ = nusc.get_sample_data(
sample_record['data'][cam],
box_vis_level=box_vis_level,
selected_anntokens=[annotation_token])
if len(boxes) > 0:
break # Breaks if find an image that matches
assert len(boxes) < 2, "Found multiple annotations. Something is wrong!"
return cam
def filter_eval_boxes(nusc: NuScenes,
eval_boxes: EvalBoxes,
max_dist: Dict[str, float],
verbose: bool = False) -> EvalBoxes:
"""
Applies filtering to boxes. Distance, bike-racks and points per box.
:param nusc: An instance of the NuScenes class.
:param eval_boxes: An instance of the EvalBoxes class.
:param max_dist: Maps the detection name to the eval distance threshold for that class.
:param verbose: Whether to print to stdout.
"""
# Accumulators for number of filtered boxes.
total, dist_filter, point_filter, bike_rack_filter = 0, 0, 0, 0
for ind, sample_token in enumerate(eval_boxes.sample_tokens):
# Filter on distance first
total += len(eval_boxes[sample_token])
eval_boxes.boxes[sample_token] = [box for box in eval_boxes[sample_token] if
box.ego_dist < max_dist[box.detection_name]]
dist_filter += len(eval_boxes[sample_token])
# Then remove boxes with zero points in them. Eval boxes have -1 points by default.
eval_boxes.boxes[sample_token] = [box for box in eval_boxes[sample_token] if not box.num_pts == 0]
point_filter += len(eval_boxes[sample_token])
# Perform bike-rack filtering
sample_anns = nusc.get('sample', sample_token)['anns']
bikerack_recs = [nusc.get('sample_annotation', ann) for ann in sample_anns if
nusc.get('sample_annotation', ann)['category_name'] == 'static_object.bicycle_rack']
bikerack_boxes = [Box(rec['translation'], rec['size'], Quaternion(rec['rotation'])) for rec in bikerack_recs]
filtered_boxes = []
for box in eval_boxes[sample_token]:
if box.detection_name in ['bicycle', 'motorcycle']:
in_a_bikerack = False
for bikerack_box in bikerack_boxes:
if np.sum(points_in_box(bikerack_box, np.expand_dims(np.array(box.translation), axis=1))) > 0:
in_a_bikerack = True
if not in_a_bikerack:
filtered_boxes.append(box)
else:
filtered_boxes.append(box)
eval_boxes.boxes[sample_token] = filtered_boxes
bike_rack_filter += len(eval_boxes.boxes[sample_token])
if verbose:
print("=> Original number of boxes: %d" % total)
print("=> After distance based filtering: %d" % dist_filter)
print("=> After LIDAR points based filtering: %d" % point_filter)
print("=> After bike rack filtering: %d" % bike_rack_filter)
return eval_boxes
def get_sample_ground_plane(root_path, version):
nusc = NuScenes(version=version, dataroot=root_path, verbose=True)
rets = {}
for sample in tqdm(nusc.sample):
chan = "LIDAR_TOP"
sd_token = sample["data"][chan]
sd_rec = nusc.get("sample_data", sd_token)
lidar_path, _, _ = get_sample_data(nusc, sd_token)
points = read_file(lidar_path)
points = np.concatenate((points[:, :3], np.ones((points.shape[0], 1))),
axis=1)
plane, inliers, outliers = fit_plane_LSE_RANSAC(
points, return_outlier_list=True)
xx = points[:, 0]
yy = points[:, 1]
zz = (-plane[0] * xx - plane[1] * yy - plane[3]) / plane[2]
rets.update({sd_token: {
"plane": plane,
"height": zz,
}})
with open(nusc.root_path / "infos_trainval_ground_plane.pkl", "wb") as f:
pickle.dump(rets, f)
def seg_concat():
nusc = NuScenes(
version='v1.0-trainval',
dataroot=
'/mrtstorage/users/kpeng/nuscene_pcdet/data/nuscenes/v1.0-trainval/',
verbose=True)
for my_sample in nusc.sample:
sample_data_token = my_sample['data']['LIDAR_TOP']
ori_filename = nusc.get('sample_data', sample_data_token)['filename']
#print(ori_filename)
anno_data = torch.from_numpy(
np.float32(
np.fromfile(
"/mrtstorage/users/kpeng/nu_lidar_seg/processed_anno/" +
sample_data_token + "_lidarseg.bin",
dtype=np.uint8,
count=-1)))
ori_data = np.fromfile(file_path + ori_filename,
dtype=np.float32,
count=-1)
ori_data = torch.from_numpy(ori_data.reshape(-1, 5))
des_data = torch.cat([ori_data, anno_data.unsqueeze(1)],
dim=-1).flatten()
des_data = des_data.numpy().tobytes()
binfile = open(save_path + ori_filename, 'wb+')
binfile.write(des_data)
binfile.close()
"""
def parse_labels(
data: NuScenes,
boxes: List[Box],
ego_pose: DictStrAny,
calib_sensor: DictStrAny,
img_size: Optional[Tuple[int, int]] = None,
) -> Optional[List[Label]]:
"""Parse NuScenes labels into sensor frame."""
if len(boxes):
labels = []
# transform into the sensor coord system
transform_boxes(boxes, ego_pose, calib_sensor)
intrinsic_matrix: NDArrayF64 = np.array(
calib_sensor["camera_intrinsic"], dtype=np.float64)
for box in boxes:
box_class = category_to_detection_name(box.name)
in_image = True
if img_size is not None:
in_image = box_in_image(box, intrinsic_matrix, img_size)
if in_image and box_class is not None:
xyz = tuple(box.center.tolist())
w, l, h = box.wlh
roty = quaternion_to_yaw(box.orientation)
box2d = None
if img_size is not None:
# Project 3d box to 2d.
corners = box.corners()
corner_coords = (view_points(corners, intrinsic_matrix,
True).T[:, :2].tolist())
# Keep only corners that fall within the image, transform
box2d = xyxy_to_box2d(*post_process_coords(corner_coords))
instance_data = data.get("sample_annotation", box.token)
# Attributes can be retrieved via instance_data and also the
# category is more fine-grained than box_class.
# This information could be stored in attributes if needed in
# the future
label = Label(
id=instance_data["instance_token"],
category=box_class,
box2d=box2d,
box3d=Box3D(
location=xyz,
dimension=(h, w, l),
orientation=(0, roty, 0),
alpha=rotation_y_to_alpha(roty, xyz), # type: ignore
),
)
labels.append(label)
return labels
return None
def what_are_the_objects_types_used_in_prediction_tasks(
DATAROOT='./data/sets/nuscenes', dataset_version='v1.0-mini'):
nuscenes = NuScenes(dataset_version, DATAROOT)
train_agents = get_prediction_challenge_split("train", dataroot=DATAROOT)
val_agents = get_prediction_challenge_split("val", dataroot=DATAROOT)
#
# agents = get_prediction_challenge_split("train", dataroot=DATAROOT)
agents = np.concatenate((train_agents, val_agents), axis=0)
categories = {}
token_to_name = {}
for current_sample in agents:
instance_token, sample_token = current_sample.split("_")
category_token = nuscenes.get("instance",
instance_token)["category_token"]
category_name = nuscenes.get("category", category_token)["name"]
categories[category_name] = category_token
token_to_name[category_token] = category_name
print(categories.items())
print(token_to_name.items())
def target_to_cam(nusc: NuScenes,
pointsensor_token: str,
annotation_token: str,
pointsensor_channel: str = 'LIDAR_TOP'):
"""
Given an annotation token (3d detection in world coordinate frame) and pointsensor sample_data token,
transform the label from world-coordinate frame to LiDAR.
:param nusc: NuScenes instance.
:param pointsensor_token: Lidar/radar sample_data token.
:param annotation_token: Camera sample_annotation token.
:param pointsensor_channel: Laser channel name, e.g. 'LIDAR_TOP'.
:return box with the labels for the 3d detection task in the LiDAR channel frame.
"""
# Point LiDAR sample
point_data = nusc.get('sample_data',
pointsensor_token) # Sample LiDAR info
# From LiDAR to ego
cs_rec = nusc.get('calibrated_sensor',
point_data['calibrated_sensor_token'])
# Transformation metadata from ego to world coordinate frame
pose_rec = nusc.get('ego_pose', point_data['ego_pose_token'])
# Obtain the annotation from the token
annotation_metadata = nusc.get('sample_annotation', annotation_token)
# Obtain box parameters
box = nusc.get_box(annotation_metadata['token'])
# Move them to the ego-pose frame.
box.translate(-np.array(pose_rec['translation']))
box.rotate(Quaternion(pose_rec['rotation']).inverse)
# Move them to the calibrated sensor frame.
box.translate(-np.array(cs_rec['translation']))
box.rotate(Quaternion(cs_rec['rotation']).inverse)
return box
def get_samples_in_eval_set(nusc: NuScenes, eval_set: str) -> List[str]:
"""
Gets all the sample tokens from the split that are relevant to the eval set.
:param nusc: A NuScenes object.
:param eval_set: The dataset split to evaluate on, e.g. train, val or test.
:return: A list of sample tokens.
"""
# Create a dict to map from scene name to scene token for quick lookup later on.
scene_name2tok = dict()
for rec in nusc.scene:
scene_name2tok[rec['name']] = rec['token']
# Get scenes splits from nuScenes.
scenes_splits = create_splits_scenes(verbose=False)
# Collect sample tokens for each scene.
samples = []
for scene in scenes_splits[eval_set]:
scene_record = nusc.get('scene', scene_name2tok[scene])
total_num_samples = scene_record['nbr_samples']
first_sample_token = scene_record['first_sample_token']
last_sample_token = scene_record['last_sample_token']
sample_token = first_sample_token
i = 0
while sample_token != '':
sample_record = nusc.get('sample', sample_token)
samples.append(sample_record['token'])
if sample_token == last_sample_token:
sample_token = ''
else:
sample_token = sample_record['next']
i += 1
assert total_num_samples == i, 'Error: There were supposed to be {} keyframes, ' \
'but only {} keyframes were processed'.format(total_num_samples, i)
return samples
def get_pcl():
nusc = NuScenes(version='v1.0-mini', dataroot='/data/sets/nuscenes', verbose=True)
explorer = NuScenesExplorer(nusc)
imglist = []
count = 0
for scene in nusc.scene:
token = scene['first_sample_token']
while token != '':
count += 1
sample = nusc.get('sample',token)
sample_data_cam = nusc.get('sample_data', sample['data']['CAM_FRONT'])
sample_data_pcl = nusc.get('sample_data', sample['data']['LIDAR_TOP'])
# get CAM_FRONT datapath
data_path, boxes, camera_intrinsic = explorer.nusc.get_sample_data(sample_data_cam['token'], box_vis_level = BoxVisibility.ANY)
imglist += [data_path]
# get LiDAR point cloud
sample_rec = explorer.nusc.get('sample', sample_data_pcl['sample_token'])
chan = sample_data_pcl['channel']
ref_chan = 'LIDAR_TOP'
pc, times = LidarPointCloud.from_file_multisweep(nusc, sample_rec, chan, ref_chan)
radius = np.sqrt((pc.points[0])**2 + (pc.points[1])**2 + (pc.points[2])**2)
pc = pc.points.transpose()
pc = pc[np.where(radius<20)]
print(pc)
display(pc.transpose(), count)
token = sample['next']
if count == 2:
break
if count == 2:
break
plt.show()
print(len(imglist))
def add_center_dist(nusc: NuScenes, eval_boxes: EvalBoxes):
"""
Adds the cylindrical (xy) center distance from ego vehicle to each box.
:param nusc: The NuScenes instance.
:param eval_boxes: A set of boxes, either GT or predictions.
:return: eval_boxes augmented with center distances.
"""
for sample_token in eval_boxes.sample_tokens:
sample_rec = nusc.get('sample', sample_token)
sd_record = nusc.get('sample_data', sample_rec['data']['LIDAR_TOP'])
pose_record = nusc.get('ego_pose', sd_record['ego_pose_token'])
cs_record = nusc.get('calibrated_sensor',
sd_record['calibrated_sensor_token'])
lidar2ego_rotation = cs_record['rotation']
lidar2ego_translation = cs_record['translation']
ego2global_rotation = pose_record['rotation']
ego2global_translation = pose_record['translation']
for box in eval_boxes[sample_token]:
# Both boxes and ego pose are given in global coord system, so distance can be calculated directly.
# Note that the z component of the ego pose is 0.
center_ego = np.array(
box.translation) - np.array(ego2global_translation)
center_ego_tmp = np.dot(
Quaternion(ego2global_rotation).inverse.rotation_matrix,
center_ego)
center_lidar_tmp = center_ego_tmp - np.array(lidar2ego_translation)
center_lidar = np.dot(
Quaternion(lidar2ego_rotation).inverse.rotation_matrix,
center_lidar_tmp)
if isinstance(box, DetectionBox) or isinstance(box, TrackingBox):
box.ego_translation = tuple(center_ego)
box.lidar_translation = tuple(center_lidar)
else:
raise NotImplementedError
return eval_boxes
def export_videos(nusc: NuScenes, out_dir: str):
""" Export videos of the images displayed in the images. """
# Load NuScenes class
scene_tokens = [s['token'] for s in nusc.scene]
# Create output directory
if not os.path.isdir(out_dir):
os.makedirs(out_dir)
# Write videos to disk
for scene_token in scene_tokens:
scene = nusc.get('scene', scene_token)
print('Writing scene %s' % scene['name'])
out_path = os.path.join(out_dir, scene['name']) + '.avi'
if not os.path.exists(out_path):
nusc.render_scene(scene['token'], out_path=out_path)
def _mock_submission(nusc: NuScenes,
split: str,
add_errors: bool = False) -> Dict[str, dict]:
"""
Creates "reasonable" submission (results and metadata) by looping through the mini-val set, adding 1 GT
prediction per sample. Predictions will be permuted randomly along all axes.
:param nusc: NuScenes instance.
:param split: Dataset split to use.
:param add_errors: Whether to use GT or add errors to it.
"""
def random_class(category_name: str, _add_errors: bool = False) -> Optional[str]:
# Alter 10% of the valid labels.
class_names = sorted(TRACKING_NAMES)
tmp = category_to_tracking_name(category_name)
if tmp is None:
return None
else:
if not _add_errors or np.random.rand() < .9:
return tmp
else:
return class_names[np.random.randint(0, len(class_names) - 1)]
def random_id(instance_token: str, _add_errors: bool = False) -> str:
# Alter 10% of the valid ids to be a random string, which hopefully corresponds to a new track.
if not _add_errors or np.random.rand() < .9:
_tracking_id = instance_token + '_pred'
else:
_tracking_id = str(np.random.randint(0, sys.maxsize))
return _tracking_id
mock_meta = {
'use_camera': False,
'use_lidar': True,
'use_radar': False,
'use_map': False,
'use_external': False,
}
mock_results = {}
# Get all samples in the current evaluation split.
splits = create_splits_scenes()
val_samples = []
for sample in nusc.sample:
if nusc.get('scene', sample['scene_token'])['name'] in splits[split]:
val_samples.append(sample)
# Prepare results.
instance_to_score = dict()
for sample in tqdm(val_samples, leave=False):
sample_res = []
for ann_token in sample['anns']:
ann = nusc.get('sample_annotation', ann_token)
translation = np.array(ann['translation'])
size = np.array(ann['size'])
rotation = np.array(ann['rotation'])
velocity = nusc.box_velocity(ann_token)[:2]
tracking_id = random_id(ann['instance_token'], _add_errors=add_errors)
tracking_name = random_class(ann['category_name'], _add_errors=add_errors)
# Skip annotations for classes not part of the detection challenge.
if tracking_name is None:
continue
# Skip annotations with 0 lidar/radar points.
num_pts = ann['num_lidar_pts'] + ann['num_radar_pts']
if num_pts == 0:
continue
# If we randomly assign a score in [0, 1] to each box and later average over the boxes in the track,
# the average score will be around 0.5 and we will have 0 predictions above that.
# Therefore we assign the same scores to each box in a track.
if ann['instance_token'] not in instance_to_score:
instance_to_score[ann['instance_token']] = random.random()
tracking_score = instance_to_score[ann['instance_token']]
tracking_score = np.clip(tracking_score + random.random() * 0.3, 0, 1)
if add_errors:
translation += 4 * (np.random.rand(3) - 0.5)
size *= (np.random.rand(3) + 0.5)
rotation += (np.random.rand(4) - 0.5) * .1
velocity *= np.random.rand(3)[:2] + 0.5
sample_res.append({
'sample_token': sample['token'],
'translation': list(translation),
'size': list(size),
'rotation': list(rotation),
'velocity': list(velocity),
'tracking_id': tracking_id,
'tracking_name': tracking_name,
'tracking_score': tracking_score
})
mock_results[sample['token']] = sample_res
mock_submission = {
'meta': mock_meta,
'results': mock_results
}
return mock_submission
class NusLoaderQ10(Dataset):
def __init__(self, root='/datasets/nuscene/v1.0-mini', sampling_time=3, layer_names=None, colors=None):
if layer_names is None:
layer_names = ['drivable_area', 'road_segment', 'road_block',
'lane', 'ped_crossing', 'walkway', 'stop_line',
'carpark_area', 'road_divider', 'lane_divider']
if colors is None:
colors = [(255, 255, 255), (255, 255, 255), (255, 255, 255),
(255, 255, 255), (255, 255, 255), (255, 255, 255), (255, 255, 255),
(255, 255, 255), (255, 255, 255), (255, 255, 255),]
self.root = root
self.nus = NuScenes('v1.0-mini', dataroot=self.root)
self.scenes = self.nus.scene
self.samples = self.nus.sample
self.layer_names = layer_names
self.colors = colors
self.helper = PredictHelper(self.nus)
self.seconds = sampling_time
def __len__(self):
return len(self.scenes)
def __getitem__(self, idx):
# 1. past_agents_traj : (Num obv agents in batch_i X 20 X 2)
# 2. past_agents_traj_len : (Num obv agents in batch_i, )
# 3. future_agents_traj : (Num pred agents in batch_i X 20 X 2)
# 4. future_agents_traj_len : (Num pred agents in batch_i, )
# 5. future_agent_masks : (Num obv agents in batch_i)
# 6. decode_rel_pos: (Num pred agents in batch_i X 2)
# 7. decode_start_pos: (Num pred agents in batch_i X 2)
# 8. map_image : (3 X 224 X 224)
# 9. scene ID: (string)
sample = self.samples[idx]
sample_token = sample['token']
# 1. calculate ego pose
sample_data_lidar = self.nus.get('sample_data', sample['data']['LIDAR_TOP'])
ego_pose = self.nus.get('ego_pose', sample_data_lidar['ego_pose_token'])
ego_pose_xy = ego_pose['translation']
ego_pose_rotation = ego_pose['rotation']
# 타임스탬프
timestamp = ego_pose['timestamp']
# 2. Generate Map & Agent Masks
scene = self.nus.get('scene', sample['scene_token'])
log = self.nus.get('log', scene['log_token'])
location = log['location']
nus_map = NuScenesMap(dataroot=self.root, map_name=location)
static_layer = StaticLayerRasterizer(self.helper, layer_names=self.layer_names, colors=self.colors)
agent_layer = AgentBoxesWithFadedHistory(self.helper, seconds_of_history=self.seconds)
map_masks, lanes, map_img = static_layer.generate_mask(ego_pose_xy, ego_pose_rotation, sample_token)
agent_mask = agent_layer.generate_mask(ego_pose_xy, ego_pose_rotation, sample_token)
# 3. Generate Agent Trajectory
annotation_tokens = sample['anns']
num_agent = len(annotation_tokens)
agents = []
for ans_token in annotation_tokens:
agent_states = []
agent = self.nus.get('sample_annotation', ans_token)
instance_token = agent['instance_token']
# 에이전트 주행경로
xy_global = agent['translation']
past_xy_global = self.helper.get_past_for_agent(
instance_token, sample_token, seconds=self.seconds, in_agent_frame=False)
future_xy_global = self.helper.get_future_for_agent(
instance_token, sample_token, seconds=self.seconds, in_agent_frame=False)
# 로컬 주행경로
xy_local = convert_global_coords_to_local(np.array([xy_global]), ego_pose_xy, ego_pose_rotation)
past_xy_local = convert_global_coords_to_local(past_xy_global, ego_pose_xy, ego_pose_rotation)
future_xy_local = convert_global_coords_to_local(future_xy_global, ego_pose_xy, ego_pose_rotation)
# 에이전트 주행상태
rot = agent['rotation']
vel = self.helper.get_velocity_for_agent(instance_token, sample_token)
accel = self.helper.get_acceleration_for_agent(instance_token, sample_token)
yaw_rate = self.helper.get_heading_change_rate_for_agent(instance_token, sample_token)
agent_states = {'present_pos': xy_global, 'past_pos': past_xy_global, 'future_pos': future_xy_global,
'rot': rot, 'vel': vel, 'accel': accel, 'yaw_rate': yaw_rate,
'present_local_xy': xy_local, 'past_local_xy': past_xy_local,
'future_local_xy': future_xy_local}
agents.append(agent_states)
return map_masks, agents
def _mock_submission(nusc: NuScenes, split: str) -> Dict[str, dict]:
"""
Creates "reasonable" submission (results and metadata) by looping through the mini-val set, adding 1 GT
prediction per sample. Predictions will be permuted randomly along all axes.
"""
def random_class(category_name: str) -> str:
# Alter 10% of the valid labels.
class_names = sorted(DETECTION_NAMES)
tmp = category_to_detection_name(category_name)
if tmp is not None and np.random.rand() < .9:
return tmp
else:
return class_names[np.random.randint(0, len(class_names) - 1)]
def random_attr(name: str) -> str:
"""
This is the most straight-forward way to generate a random attribute.
Not currently used b/c we want the test fixture to be back-wards compatible.
"""
# Get relevant attributes.
rel_attributes = detection_name_to_rel_attributes(name)
if len(rel_attributes) == 0:
# Empty string for classes without attributes.
return ''
else:
# Pick a random attribute otherwise.
return rel_attributes[np.random.randint(
0, len(rel_attributes))]
mock_meta = {
'use_camera': False,
'use_lidar': True,
'use_radar': False,
'use_map': False,
'use_external': False,
}
mock_results = {}
splits = create_splits_scenes()
val_samples = []
for sample in nusc.sample:
if nusc.get('scene',
sample['scene_token'])['name'] in splits[split]:
val_samples.append(sample)
for sample in tqdm(val_samples, leave=False):
sample_res = []
for ann_token in sample['anns']:
ann = nusc.get('sample_annotation', ann_token)
detection_name = random_class(ann['category_name'])
sample_res.append({
'sample_token':
sample['token'],
'translation':
list(
np.array(ann['translation']) + 5 *
(np.random.rand(3) - 0.5)),
'size':
list(
np.array(ann['size']) * 2 * (np.random.rand(3) + 0.5)),
'rotation':
list(
np.array(ann['rotation']) +
((np.random.rand(4) - 0.5) * .1)),
'velocity':
list(
nusc.box_velocity(ann_token)[:2] *
(np.random.rand(3)[:2] + 0.5)),
'detection_name':
detection_name,
'detection_score':
random.random(),
'attribute_name':
random_attr(detection_name)
})
mock_results[sample['token']] = sample_res
mock_submission = {'meta': mock_meta, 'results': mock_results}
return mock_submission
def __init__(self, batch_size, num_classes, training=True, normalize=None):
self._num_classes = num_classes
# we make the height of image consistent to trim_height, trim_width
self.trim_height = cfg.TRAIN.TRIM_HEIGHT
self.trim_width = cfg.TRAIN.TRIM_WIDTH
self.max_num_box = cfg.MAX_NUM_GT_BOXES
self.training = training
self.normalize = normalize
self.batch_size = batch_size
self.classes = ('__background__', 'pedestrian', 'barrier',
'trafficcone', 'bicycle', 'bus', 'car', 'construction',
'motorcycle', 'trailer', 'truck')
self.max_num_box = 50
# Checks if pickle file of dataset already exists. If it doesn't exist, creates the file
if os.path.exists('lib/roi_data_layer/roidb_CAMFRONT.pkl'):
print("Reading roidb..")
pickle_in = open("lib/roi_data_layer/roidb_CAMFRONT.pkl", "rb")
self.roidb = pickle.load(pickle_in)
trainsize = 20000
if training == True:
self.roidb = self.roidb[:trainsize]
print("roidb size: " + str(len(self.roidb)))
else:
self.roidb = self.roidb[trainsize:]
else:
nusc_path = '/data/sets/nuscenes'
nusc = NuScenes(version='v1.0-trainval',
dataroot=nusc_path,
verbose=True)
file_dir = os.path.dirname(os.path.abspath(__file__))
roots = file_dir.split('/')[:-2]
root_dir = ""
for folder in roots:
if folder != "":
root_dir = root_dir + "/" + folder
PATH = root_dir + '/data/CAMFRONT.txt'
with open(PATH) as f:
image_token = [x.strip() for x in f.readlines()]
roidb = []
# Loads information on images and ground truth boxes and saves it as pickle file for faster loading
print("Loading roidb...")
for i in range(len(image_token)):
im_token = image_token[i]
sample_data = nusc.get('sample_data', im_token)
image_name = sample_data['filename']
image_path = nusc_path + '/' + image_name
data_path, boxes, camera_intrinsic = nusc.get_sample_data(
im_token, box_vis_level=BoxVisibility.ALL)
# Only accepts boxes with above level 3 or 4 visibility and classes with more than 1000 instances
gt_boxes = []
gt_cls = []
for box in boxes:
visibility_token = nusc.get('sample_annotation',
box.token)['visibility_token']
vis_level = int(
nusc.get('visibility', visibility_token)['token'])
if (vis_level == 3) or (vis_level == 4):
visible = True
else:
visible = False
if visible == True:
if box.name.split('.')[0] == 'vehicle':
if box.name.split('.')[1] != 'emergency':
name = box.name.split('.')[1]
else:
name = ''
elif box.name.split('.')[0] == 'human':
name = 'pedestrian'
elif box.name.split('.')[0] == 'movable_object':
if box.name.split(
'.')[1] != 'debris' and box.name.split(
'.')[1] != 'pushable_pullable':
name = box.name.split('.')[1]
else:
name = ''
else:
name = ''
if name != '':
corners = view_points(box.corners(),
view=camera_intrinsic,
normalize=True)[:2, :]
box = np.zeros(4)
box[0] = np.min(corners[0])
box[1] = np.min(corners[1])
box[2] = np.max(corners[0])
box[3] = np.max(corners[1])
gt_boxes = gt_boxes + [box]
gt_cls = gt_cls + [self.classes.index(name)]
# Only accepts images with at least one object
if len(gt_boxes) >= 2:
image = {}
image['image'] = image_path
image['width'] = 1600
image['height'] = 900
image['boxes'] = np.asarray(gt_boxes)
image['gt_classes'] = np.asarray(gt_cls)
roidb = roidb + [image]
print(len(roidb))
print("Saving roidb")
pickle_out = open("lib/roi_data_layer/roidb_CAMFRONT.pkl", "wb")
pickle.dump(roidb, pickle_out)
pickle_out.close()
self.roidb = roidb
trainsize = int(len(self.roidb) * 0.8)
if training == True:
self.roidb = self.roidb[:trainsize]
else:
self.roidb = self.roidb[trainsize:]
nusc = NuScenes(version='v1.0-trainval', dataroot=data_path, verbose=True)
explorer = NuScenesExplorer(nusc)
PATH = data_path + '/CAMFRONT.txt'
with open(PATH) as f:
image_token = [x.strip() for x in f.readlines()]
annotations = []
counter = 0
max_v = 0
min_v = 999999
# pdb.set_trace()
for im_token in image_token:
sample_data = nusc.get('sample_data', im_token)
image_name = sample_data['filename']
img = cv2.imread('/home/fengjia/data/sets/nuscenes/' + image_name)
im = np.array(img)
sample = nusc.get('sample', sample_data['sample_token'])
lidar_token = sample['data']['LIDAR_TOP']
# get ground truth boxes
_, boxes, img_camera_intrinsic = nusc.get_sample_data(im_token, box_vis_level=BoxVisibility.ALL)
for box in boxes:
visibility_token = nusc.get('sample_annotation', box.token)['visibility_token']
vis_level = int(nusc.get('visibility', visibility_token)['token'])
if (vis_level != 3) and (vis_level != 4):
def load_merge_from_pkl(nusc: NuScenes,
pkl_path: str,
box_cls,
verbose: bool = False) -> EvalBoxes:
# Init.
if box_cls == DetectionBox:
attribute_map = {a['token']: a['name'] for a in nusc.attribute}
if verbose:
print('Loading annotations for {} split from nuScenes version: {}'.
format(pkl_path, nusc.version))
import mmcv
infos = mmcv.load(pkl_path)['infos']
samples = []
for info in infos:
samples.append(nusc.get('sample', info['token']))
all_annotations = EvalBoxes()
# Load annotations and filter predictions and annotations.
merge_map = dict(car='vehicle',
truck='vehicle',
bus='vehicle',
trailer='vehicle',
construction_vehicle='vehicle',
pedestrian='pedestrian',
motorcycle='bike',
bicycle='bike',
traffic_cone='traffic_boundary',
barrier='traffic_boundary')
for sample in tqdm.tqdm(samples, leave=verbose):
sample_token = sample['token']
cam_token = sample['data']['CAM_FRONT']
_, boxes_cam, _ = nusc.get_sample_data(cam_token)
sample_annotation_tokens = [box.token for box in boxes_cam]
# sample = nusc.get('sample', sample_token)
# sample_annotation_tokens = sample['anns']
sample_boxes = []
for sample_annotation_token in sample_annotation_tokens:
sample_annotation = nusc.get('sample_annotation',
sample_annotation_token)
if box_cls == DetectionBox:
# Get label name in detection task and filter unused labels.
detection_name = category_to_detection_name(
sample_annotation['category_name'])
if detection_name is None:
continue
detection_name = merge_map[detection_name]
# Get attribute_name.
attr_tokens = sample_annotation['attribute_tokens']
attr_count = len(attr_tokens)
if attr_count == 0:
attribute_name = ''
elif attr_count == 1:
attribute_name = attribute_map[attr_tokens[0]]
else:
raise Exception(
'Error: GT annotations must not have more than one attribute!'
)
sample_boxes.append(
box_cls(
sample_token=sample_token,
translation=sample_annotation['translation'],
size=sample_annotation['size'],
rotation=sample_annotation['rotation'],
velocity=nusc.box_velocity(
sample_annotation['token'])[:2],
num_pts=sample_annotation['num_lidar_pts'] +
sample_annotation['num_radar_pts'],
detection_name=detection_name,
detection_score=-1.0, # GT samples do not have a score.
attribute_name=attribute_name))
else:
raise NotImplementedError('Error: Invalid box_cls %s!' %
box_cls)
all_annotations.add_boxes(sample_token, sample_boxes)
if verbose:
print("Loaded ground truth annotations for {} samples.".format(
len(all_annotations.sample_tokens)))
return all_annotations
class NusLoaderQ10(Dataset):
def __init__(self, root='/datasets/nuscene/v1.0-mini', sampling_time=3, agent_time=0, layer_names=None,
colors=None, resolution: float = 0.1, # meters / pixel
meters_ahead: float = 25, meters_behind: float = 25,
meters_left: float = 25, meters_right: float = 25, version='v1.0-mini'):
if layer_names is None:
layer_names = ['drivable_area', 'road_segment', 'road_block',
'lane', 'ped_crossing', 'walkway', 'stop_line',
'carpark_area', 'road_divider', 'lane_divider']
if colors is None:
colors = [(255, 255, 255), (255, 255, 255), (255, 255, 255),
(255, 255, 255), (255, 255, 255), (255, 255, 255), (255, 255, 255),
(255, 255, 255), (255, 255, 255), (255, 255, 255), ]
self.root = root
self.nus = NuScenes(version, dataroot=self.root)
self.scenes = self.nus.scene
self.samples = self.nus.sample
self.layer_names = layer_names
self.colors = colors
self.helper = PredictHelper(self.nus)
self.seconds = sampling_time
self.agent_seconds = agent_time
self.static_layer = StaticLayerRasterizer(self.helper, layer_names=self.layer_names, colors=self.colors,
resolution=resolution, meters_ahead=meters_ahead,
meters_behind=meters_behind,
meters_left=meters_left, meters_right=meters_right)
self.agent_layer = AgentBoxesWithFadedHistory(self.helper, seconds_of_history=self.agent_seconds,
resolution=resolution, meters_ahead=meters_ahead,
meters_behind=meters_behind,
meters_left=meters_left, meters_right=meters_right)
def __len__(self):
return len(self.samples)
def __getitem__(self, idx):
scene_id = idx
sample = self.samples[idx]
sample_token = sample['token']
# 1. calculate ego pose
sample_data_lidar = self.nus.get('sample_data', sample['data']['LIDAR_TOP'])
ego_pose = self.nus.get('ego_pose', sample_data_lidar['ego_pose_token'])
ego_pose_xy = ego_pose['translation']
ego_pose_rotation = ego_pose['rotation']
# 2. Generate map mask
map_masks, lanes, map_img = self.static_layer.generate_mask(ego_pose_xy, ego_pose_rotation, sample_token)
# 3. Generate Agent Trajectory
agent_mask, xy_global = self.agent_layer.generate_mask(
ego_pose_xy, ego_pose_rotation, sample_token, self.seconds, show_agent=False)
xy_local = []
# past, future trajectory
for path_global in xy_global[:2]:
pose_xy = []
for path_global_i in path_global:
if len(path_global_i) == 0:
pose_xy.append(path_global_i)
else:
pose_xy.append(convert_global_coords_to_local(path_global_i, ego_pose_xy, ego_pose_rotation))
xy_local.append(pose_xy)
# current pose
if len(xy_global[2]) == 0:
xy_local.append(xy_global[2])
else:
xy_local.append(convert_global_coords_to_local(xy_global[2], ego_pose_xy, ego_pose_rotation))
# 4. Generate Virtual Agent Trajectory
lane_tokens = list(lanes.keys())
lanes_disc = [np.array(lanes[token])[:, :2] for token in lane_tokens]
virtual_mask, virtual_xy = self.agent_layer.generate_virtual_mask(
ego_pose_xy, ego_pose_rotation, lanes_disc, sample_token, show_agent=False,
past_trj_len=4, future_trj_len=6, min_dist=6)
virtual_xy_local = []
# past, future trajectory
for path_global in virtual_xy[:2]:
pose_xy = []
for path_global_i in path_global:
if len(path_global_i) == 0:
pose_xy.append(path_global_i)
else:
pose_xy.append(convert_global_coords_to_local(path_global_i, ego_pose_xy, ego_pose_rotation))
virtual_xy_local.append(pose_xy)
# current pose
if len(virtual_xy[2]) == 0:
virtual_xy_local.append(virtual_xy[2])
else:
virtual_xy_local.append(convert_global_coords_to_local(virtual_xy[2], ego_pose_xy, ego_pose_rotation))
return map_masks, map_img, agent_mask, xy_local, virtual_mask, virtual_xy_local, scene_id
def render_sample(self, idx):
sample = self.samples[idx]
sample_token = sample['token']
self.nus.render_sample(sample_token)
def render_scene(self, idx):
sample = self.samples[idx]
sample_token = sample['token']
scene = self.nus.get('scene', sample['scene_token'])
log = self.nus.get('log', scene['log_token'])
location = log['location']
nus_map = NuScenesMap(dataroot=self.root, map_name=location)
layer_names = ['road_segment', 'lane', 'ped_crossing', 'walkway', 'stop_line', 'carpark_area']
camera_channel = 'CAM_FRONT'
nus_map.render_map_in_image(self.nus, sample_token, layer_names=layer_names, camera_channel=camera_channel)
def render_map(self, idx, combined=True):
sample = self.samples[idx]
sample_token = sample['token']
sample_data_lidar = self.nus.get('sample_data', sample['data']['LIDAR_TOP'])
ego_pose = self.nus.get('ego_pose', sample_data_lidar['ego_pose_token'])
ego_pose_xy = ego_pose['translation']
ego_pose_rotation = ego_pose['rotation']
timestamp = ego_pose['timestamp']
# 2. Generate Map & Agent Masks
scene = self.nus.get('scene', sample['scene_token'])
log = self.nus.get('log', scene['log_token'])
location = log['location']
nus_map = NuScenesMap(dataroot=self.root, map_name=location)
static_layer = StaticLayerRasterizer(self.helper, layer_names=self.layer_names, colors=self.colors)
agent_layer = AgentBoxesWithFadedHistory(self.helper, seconds_of_history=self.seconds)
map_masks, lanes, map_img = static_layer.generate_mask(ego_pose_xy, ego_pose_rotation, sample_token)
agent_mask = agent_layer.generate_mask(ego_pose_xy, ego_pose_rotation, sample_token)
if combined:
plt.subplot(1, 2, 1)
plt.title("combined map")
plt.imshow(map_img)
plt.subplot(1, 2, 2)
plt.title("agent")
plt.imshow(agent_mask)
plt.show()
else:
num_labels = len(self.layer_names)
num_rows = num_labels // 3
fig, ax = plt.subplots(num_rows, 3, figsize=(10, 3 * num_rows))
for row in range(num_rows):
for col in range(3):
num = 3 * row + col
if num == num_labels - 1:
break
ax[row][col].set_title(self.layer_names[num])
ax[row][col].imshow(map_masks[num])
plt.show()
coloring = coloring[mask]
pdb.set_trace()
return points, coloring, im
PATH = '/data/sets/nuscenes/train_mini.txt'
with open(PATH) as f:
image_token = [x.strip() for x in f.readlines()]
#model = PointNetCls(k=16)
#model.cuda()
#model.load_state_dict(torch.load('/home/julia/pointnet.pytorch/utils/cls/cls_model_22.pth'))
#model.eval()
im_token = image_token[0]
sample_data = nusc.get('sample_data', im_token)
sample_token = sample_data['sample_token']
sample =nusc.get('sample', sample_token)
image_name = sample_data['filename']
img = imread('/data/sets/nuscenes/' + image_name)
# load in the LiDAR data for the sample
sample_data_pcl = nusc.get('sample_data', sample['data']['LIDAR_TOP'])
sample_rec = explorer.nusc.get('sample', sample_data_pcl['sample_token'])
# get LiDAR point cloud
chan = sample_data_pcl['channel']
ref_chan = 'LIDAR_TOP'
pc, times = LidarPointCloud.from_file_multisweep(nusc, sample_rec, chan, ref_chan)
points= pc.points[:3,:]
def track_nuscenes(data_root: str,
detection_file: str,
save_dir: str,
eval_set: str = 'val',
covariance_id: int = 0,
match_distance: str = 'iou',
match_threshold: float = 0.1,
match_algorithm: str = 'h',
use_angular_velocity: bool = False):
'''
submission {
"meta": {
"use_camera": <bool> -- Whether this submission uses camera data as an input.
"use_lidar": <bool> -- Whether this submission uses lidar data as an input.
"use_radar": <bool> -- Whether this submission uses radar data as an input.
"use_map": <bool> -- Whether this submission uses map data as an input.
"use_external": <bool> -- Whether this submission uses external data as an input.
},
"results": {
sample_token <str>: List[sample_result] -- Maps each sample_token to a list of sample_results.
}
}
'''
if 'train' in eval_set:
version = 'v1.0-trainval'
elif 'val' in eval_set:
version = 'v1.0-trainval'
elif 'mini' in eval_set:
version = 'v1.0-mini'
elif 'test' in eval_set:
version = 'v1.0-test'
else:
version = eval_set
print("WARNING: Unknown subset version: '{}'".format(version))
nusc = NuScenes(version=version, dataroot=data_root, verbose=True)
mkdir_if_missing(save_dir)
output_path = os.path.join(save_dir, 'probabilistic_tracking_results.json')
results = {}
total_time = 0.0
total_frames = 0
with open(detection_file) as f:
data = json.load(f)
assert 'results' in data, 'Error: No field `results` in result file. Please note that the result format changed.' \
'See https://www.nuscenes.org/object-detection for more information.'
all_results = EvalBoxes.deserialize(data['results'], DetectionBox)
meta = data['meta']
print('meta: ', meta)
print("Loaded results from {}. Found detections for {} samples.".format(
detection_file, len(all_results.sample_tokens)))
processed_scene_tokens = set()
for sample_token_idx in tqdm(range(len(all_results.sample_tokens))):
sample_token = all_results.sample_tokens[sample_token_idx]
scene_token = nusc.get('sample', sample_token)['scene_token']
if scene_token in processed_scene_tokens:
continue
first_sample_token = nusc.get('scene',
scene_token)['first_sample_token']
current_sample_token = first_sample_token
mot_trackers = {
tracking_name: AB3DMOT(covariance_id,
tracking_name=tracking_name,
use_angular_velocity=use_angular_velocity,
tracking_nuscenes=True)
for tracking_name in NUSCENES_TRACKING_NAMES
}
while current_sample_token != '':
results[current_sample_token] = []
dets = {
tracking_name: []
for tracking_name in NUSCENES_TRACKING_NAMES
}
info = {
tracking_name: []
for tracking_name in NUSCENES_TRACKING_NAMES
}
for box in all_results.boxes[current_sample_token]:
if box.detection_name not in NUSCENES_TRACKING_NAMES:
continue
q = Quaternion(box.rotation)
angle = q.angle if q.axis[2] > 0 else -q.angle
#print('box.rotation, angle, axis: ', box.rotation, q.angle, q.axis)
#print('box.rotation, angle, axis: ', q.angle, q.axis)
#[h, w, l, x, y, z, rot_y]
detection = np.array([
box.size[2], box.size[0], box.size[1], box.translation[0],
box.translation[1], box.translation[2], angle
])
#print('detection: ', detection)
information = np.array([box.detection_score])
dets[box.detection_name].append(detection)
info[box.detection_name].append(information)
dets_all = {
tracking_name: {
'dets': np.array(dets[tracking_name]),
'info': np.array(info[tracking_name])
}
for tracking_name in NUSCENES_TRACKING_NAMES
}
total_frames += 1
start_time = time.time()
for tracking_name in NUSCENES_TRACKING_NAMES:
if dets_all[tracking_name]['dets'].shape[0] > 0:
trackers = mot_trackers[tracking_name].update(
dets_all[tracking_name], match_distance,
match_threshold, match_algorithm, scene_token)
# (N, 9)
# (h, w, l, x, y, z, rot_y), tracking_id, tracking_score
# print('trackers: ', trackers)
for i in range(trackers.shape[0]):
sample_result = format_sample_result(
current_sample_token, tracking_name, trackers[i])
results[current_sample_token].append(sample_result)
cycle_time = time.time() - start_time
total_time += cycle_time
# get next frame and continue the while loop
current_sample_token = nusc.get('sample',
current_sample_token)['next']
# left while loop and mark this scene as processed
processed_scene_tokens.add(scene_token)
# finished tracking all scenes, write output data
output_data = {'meta': meta, 'results': results}
with open(output_path, 'w') as outfile:
json.dump(output_data, outfile)
print("Total Tracking took: %.3f for %d frames or %.1f FPS" %
(total_time, total_frames, total_frames / total_time))
def load_gt(nusc: NuScenes,
eval_split: str,
box_cls,
verbose: bool = False) -> EvalBoxes:
"""
Loads ground truth boxes from DB.
:param nusc: A NuScenes instance.
:param eval_split: The evaluation split for which we load GT boxes.
:param box_cls: Type of box to load, e.g. DetectionBox or TrackingBox.
:param verbose: Whether to print messages to stdout.
:return: The GT boxes.
"""
# Init.
if box_cls == DetectionBox:
attribute_map = {a['token']: a['name'] for a in nusc.attribute}
if verbose:
print('Loading annotations for {} split from nuScenes version: {}'.
format(eval_split, nusc.version))
# Read out all sample_tokens in DB.
sample_tokens_all = [s['token'] for s in nusc.sample]
assert len(sample_tokens_all) > 0, "Error: Database has no samples!"
# Only keep samples from this split.
splits = create_splits_scenes()
# Check compatibility of split with nusc_version.
version = nusc.version
if eval_split in {'train', 'val', 'train_detect', 'train_track'}:
assert version.endswith('trainval'), \
'Error: Requested split {} which is not compatible with NuScenes version {}'.format(eval_split, version)
elif eval_split in {'mini_train', 'mini_val'}:
assert version.endswith('mini'), \
'Error: Requested split {} which is not compatible with NuScenes version {}'.format(eval_split, version)
elif eval_split == 'test':
assert version.endswith('test'), \
'Error: Requested split {} which is not compatible with NuScenes version {}'.format(eval_split, version)
else:
raise ValueError(
'Error: Requested split {} which this function cannot map to the correct NuScenes version.'
.format(eval_split))
if eval_split == 'test':
# Check that you aren't trying to cheat :).
assert len(nusc.sample_annotation) > 0, \
'Error: You are trying to evaluate on the test set but you do not have the annotations!'
sample_tokens = []
for sample_token in sample_tokens_all:
scene_token = nusc.get('sample', sample_token)['scene_token']
scene_record = nusc.get('scene', scene_token)
if scene_record['name'] in splits[eval_split]:
sample_tokens.append(sample_token)
all_annotations = EvalBoxes()
# Load annotations and filter predictions and annotations.
tracking_id_set = set()
for sample_token in tqdm.tqdm(sample_tokens, leave=verbose):
sample = nusc.get('sample', sample_token)
sample_annotation_tokens = sample['anns']
sample_boxes = []
for sample_annotation_token in sample_annotation_tokens:
sample_annotation = nusc.get('sample_annotation',
sample_annotation_token)
if box_cls == DetectionBox:
# Get label name in detection task and filter unused labels.
detection_name = category_to_detection_name(
sample_annotation['category_name'])
if detection_name is None:
continue
# Get attribute_name.
attr_tokens = sample_annotation['attribute_tokens']
attr_count = len(attr_tokens)
if attr_count == 0:
attribute_name = ''
elif attr_count == 1:
attribute_name = attribute_map[attr_tokens[0]]
else:
raise Exception(
'Error: GT annotations must not have more than one attribute!'
)
sample_boxes.append(
box_cls(
sample_token=sample_token,
translation=sample_annotation['translation'],
size=sample_annotation['size'],
rotation=sample_annotation['rotation'],
velocity=nusc.box_velocity(
sample_annotation['token'])[:2],
num_pts=sample_annotation['num_lidar_pts'] +
sample_annotation['num_radar_pts'],
detection_name=detection_name,
detection_score=-1.0, # GT samples do not have a score.
attribute_name=attribute_name))
elif box_cls == TrackingBox:
# Use nuScenes token as tracking id.
tracking_id = sample_annotation['instance_token']
tracking_id_set.add(tracking_id)
# Get label name in detection task and filter unused labels.
tracking_name = category_to_tracking_name(
sample_annotation['category_name'])
if tracking_name is None:
continue
sample_boxes.append(
box_cls(
sample_token=sample_token,
translation=sample_annotation['translation'],
size=sample_annotation['size'],
rotation=sample_annotation['rotation'],
velocity=nusc.box_velocity(
sample_annotation['token'])[:2],
num_pts=sample_annotation['num_lidar_pts'] +
sample_annotation['num_radar_pts'],
tracking_id=tracking_id,
tracking_name=tracking_name,
tracking_score=-1.0 # GT samples do not have a score.
))
else:
raise NotImplementedError('Error: Invalid box_cls %s!' %
box_cls)
all_annotations.add_boxes(sample_token, sample_boxes)
if verbose:
print("Loaded ground truth annotations for {} samples.".format(
len(all_annotations.sample_tokens)))
return all_annotations
def pointcloud_color_from_image(
nusc: NuScenes, pointsensor_token: str,
camera_token: str) -> Tuple[np.array, np.array]:
"""
Given a point sensor (lidar/radar) token and camera sample_data token, load pointcloud and map it to the image
plane, then retrieve the colors of the closest image pixels.
:param nusc: NuScenes instance.
:param pointsensor_token: Lidar/radar sample_data token.
:param camera_token: Camera sample data token.
:return (coloring <np.float: 3, n>, mask <np.bool: m>). Returns the colors for n points that reproject into the
image out of m total points. The mask indicates which points are selected.
"""
cam = nusc.get('sample_data', camera_token)
pointsensor = nusc.get('sample_data', pointsensor_token)
pc = LidarPointCloud.from_file(
osp.join(nusc.dataroot, pointsensor['filename']))
im = Image.open(osp.join(nusc.dataroot, cam['filename']))
# Points live in the point sensor frame. So they need to be transformed via global to the image plane.
# First step: transform the pointcloud to the ego vehicle frame for the timestamp of the sweep.
cs_record = nusc.get('calibrated_sensor',
pointsensor['calibrated_sensor_token'])
pc.rotate(Quaternion(cs_record['rotation']).rotation_matrix)
pc.translate(np.array(cs_record['translation']))
# Second step: transform to the global frame.
poserecord = nusc.get('ego_pose', pointsensor['ego_pose_token'])
pc.rotate(Quaternion(poserecord['rotation']).rotation_matrix)
pc.translate(np.array(poserecord['translation']))
# Third step: transform into the ego vehicle frame for the timestamp of the image.
poserecord = nusc.get('ego_pose', cam['ego_pose_token'])
pc.translate(-np.array(poserecord['translation']))
pc.rotate(Quaternion(poserecord['rotation']).rotation_matrix.T)
# Fourth step: transform into the camera.
cs_record = nusc.get('calibrated_sensor', cam['calibrated_sensor_token'])
pc.translate(-np.array(cs_record['translation']))
pc.rotate(Quaternion(cs_record['rotation']).rotation_matrix.T)
# Fifth step: actually take a "picture" of the point cloud.
# Grab the depths (camera frame z axis points away from the camera).
depths = pc.points[2, :]
# Take the actual picture (matrix multiplication with camera-matrix + renormalization).
points = view_points(pc.points[:3, :],
np.array(cs_record['camera_intrinsic']),
normalize=True)
# Remove points that are either outside or behind the camera. Leave a margin of 1 pixel for aesthetic reasons.
mask = np.ones(depths.shape[0], dtype=bool)
mask = np.logical_and(mask, depths > 0)
mask = np.logical_and(mask, points[0, :] > 1)
mask = np.logical_and(mask, points[0, :] < im.size[0] - 1)
mask = np.logical_and(mask, points[1, :] > 1)
mask = np.logical_and(mask, points[1, :] < im.size[1] - 1)
points = points[:, mask]
# Pick the colors of the points
im_data = np.array(im)
coloring = np.zeros(points.shape)
for i, p in enumerate(points.transpose()):
point = p[:2].round().astype(np.int32)
coloring[:, i] = im_data[point[1], point[0], :]
return coloring, mask
class NusTrajectoryExtractor:
def __init__(
self,
root='/datasets/nuscene/v1.0-mini',
sampling_time=3,
agent_time=0,
layer_names=None,
colors=None,
resolution: float = 0.1, # meters / pixel
meters_ahead: float = 32,
meters_behind: float = 32,
meters_left: float = 32,
meters_right: float = 32,
version='v1.0-mini'):
self.layer_names = [
'drivable_area', 'road_segment', 'road_block', 'lane',
'ped_crossing', 'walkway', 'stop_line', 'carpark_area',
'road_divider', 'lane_divider'
] if layer_names is None else layer_names
self.colors = [
(255, 255, 255),
(255, 255, 255),
(255, 255, 255),
(255, 255, 255),
(255, 255, 255),
(255, 255, 255),
(255, 255, 255),
(255, 255, 255),
(255, 255, 255),
(255, 255, 255),
] if colors is None else colors
self.root = root
self.nus = NuScenes(version, dataroot=self.root)
self.helper = PredictHelper(self.nus)
self.sampling_time = sampling_time
self.agent_seconds = agent_time
self.scene_size = (64, 64)
self.past_len = 4
self.future_len = 6
self.static_layer = StaticLayerRasterizer(self.helper,
layer_names=self.layer_names,
colors=self.colors,
resolution=resolution,
meters_ahead=meters_ahead,
meters_behind=meters_behind,
meters_left=meters_left,
meters_right=meters_right)
self.agent_layer = AgentBoxesWithFadedHistory(
self.helper,
seconds_of_history=self.agent_seconds,
resolution=resolution,
meters_ahead=meters_ahead,
meters_behind=meters_behind,
meters_left=meters_left,
meters_right=meters_right)
self.combinator = Rasterizer()
self.show_agent = True
self.resized_ratio = 0.5 # resize ratio of image for visualizing
self.drivable_area_idx = self.layer_names.index('drivable_area')
self.road_divider_idx = self.layer_names.index('road_divider')
self.lane_divider_idx = self.layer_names.index('lane_divider')
def __len__(self):
return len(self.nus.sample)
def get_annotation(self, instance_token, sample_token):
annotation = self.helper.get_sample_annotation(
instance_token, sample_token) # agent annotation
# ego pose
ego_pose_xy = annotation['translation']
ego_pose_rotation = annotation['rotation']
# agent attributes
agent_attributes = {
'category_name': annotation['category_name'],
'translation': annotation['translation'],
'size': annotation['size'],
'rotation': annotation['rotation'],
'visibility': int(annotation['visibility_token']
) # 1 ~ 4 (0~40%, 40~60%, 60~80%, 80~100%)
}
# agent trajectory annotation
agent_mask, total_agents_annotation, agent_annotation = \
self.agent_layer.generate_mask_with_path(
instance_token, sample_token, seconds=self.sampling_time, vehicle_only=True)
'''
total_agents_annotation:
'instance_tokens', category_names',
'translations', 'pasts', 'futures', 'velocities', 'accelerations', 'yaw_rates'
'''
agent_trajectories = {
'agent_mask': agent_mask,
'total_agents_annotation': total_agents_annotation,
'agent_annotation': agent_annotation
}
# map attributes
map_masks, lanes, map_img, map_img_with_lanes = \
self.static_layer.generate_mask(ego_pose_xy, ego_pose_rotation, sample_token)
map_attributes = {
'map_masks': map_masks,
'lanes': lanes,
'map_img': map_img,
'map_img_with_lanes': map_img_with_lanes,
'map_img_with_agents':
self.combinator.combine([map_img, agent_mask])
}
scene_data = {
'instance_token': instance_token,
'sample_token': sample_token,
'annotation': annotation,
'ego_pose_xy': ego_pose_xy,
'ego_pose_rotation': ego_pose_rotation,
'agent_attributes': agent_attributes,
'agent_trajectories': agent_trajectories,
'map_attributes': map_attributes,
}
return scene_data
def get_cmu_annotation(self, instance_tokens, sample_token):
scene_data = self.get_annotation(instance_tokens[0], sample_token)
ego_pose_xy = scene_data['ego_pose_xy']
ego_pose_rotation = scene_data['ego_pose_rotation']
agents_annotation = scene_data['agent_trajectories'][
'total_agents_annotation']
agents_past = [
convert_global_coords_to_local(path_global_i, ego_pose_xy,
ego_pose_rotation).tolist()
if len(path_global_i) != 0 else []
for path_global_i in agents_annotation['pasts']
]
agents_future = [
convert_global_coords_to_local(path_global_i, ego_pose_xy,
ego_pose_rotation).tolist()
if len(path_global_i) != 0 else []
for path_global_i in agents_annotation['futures']
]
agents_translation = \
convert_global_coords_to_local(agents_annotation['translations'], ego_pose_xy, ego_pose_rotation).tolist() \
if len(agents_annotation['translations']) != 0 else []
drivable_area = scene_data['map_attributes']['map_masks'][
self.drivable_area_idx]
road_divider = scene_data['map_attributes']['map_masks'][
self.road_divider_idx]
lane_divider = scene_data['map_attributes']['map_masks'][
self.lane_divider_idx]
drivable_area = cv2.cvtColor(
cv2.resize(drivable_area, self.scene_size), cv2.COLOR_BGR2GRAY)
road_divider = cv2.cvtColor(cv2.resize(road_divider, self.scene_size),
cv2.COLOR_BGR2GRAY)
lane_divider = cv2.cvtColor(cv2.resize(lane_divider, self.scene_size),
cv2.COLOR_BGR2GRAY)
# distance_map, prior = self.generateDistanceMaskFromColorMap(
# drivable_area, image_size=self.scene_size, prior_size=self.scene_size)
map_image_show = cv2.resize(
scene_data['map_attributes']['map_img_with_agents'],
dsize=(0, 0),
fx=self.resized_ratio,
fy=self.resized_ratio,
interpolation=cv2.INTER_LINEAR)
num_agents = len(agents_past)
agents_mask = [
tk in instance_tokens
for tk in agents_annotation['instance_tokens']
] # agents to decode
future_agent_masks = []
past_agents_traj = []
future_agents_traj = []
past_agents_traj_len = []
future_agents_traj_len = []
decode_start_vel = []
decode_start_pos = []
for idx in range(num_agents):
past = agents_past[idx][-self.past_len +
1:] + [agents_translation[idx]]
future = agents_future[idx][:self.future_len]
if len(past) != self.past_len:
continue
elif not (abs(past[-1][0]) <= self.scene_size[1] // 2) or not (abs(
past[-1][1]) <= self.scene_size[0] // 2):
continue
if len(future) != self.future_len:
agents_mask[idx] = False
future_agent_masks.append(agents_mask[idx])
past_agents_traj.append(past)
past_agents_traj_len.append(len(past))
future_agents_traj.append(future)
future_agents_traj_len.append(len(future))
decode_start_pos.append(past[-1])
decode_start_vel.append([
(past[-1][0] - past[-2][0]) / self.sampling_time,
(past[-1][1] - past[-2][1]) / self.sampling_time
])
episode = [
past_agents_traj, past_agents_traj_len, future_agents_traj,
future_agents_traj_len, future_agent_masks, decode_start_vel,
decode_start_pos
]
episode_img = [drivable_area, road_divider, lane_divider]
return {
'episode': episode,
'episode_img': episode_img,
'img_show': map_image_show
}
def save_cmu_dataset(self, save_dir, partition='all'):
from nuscenes.eval.prediction.splits import get_prediction_challenge_split
split_types = ['mini_train', 'mini_val', 'train', 'train_val', 'val']
if partition == 'mini':
split_types = ['mini_train', 'mini_val']
if not os.path.isdir(save_dir):
os.mkdir(save_dir)
for split in tqdm(split_types, desc='split dataset'):
partition_tokens = get_prediction_challenge_split(
split, dataroot=self.root)
tokens_dict = {}
for token in partition_tokens:
instance_token, sample_token = token.split('_')
try:
tokens_dict[sample_token].append(instance_token)
except KeyError:
tokens_dict[sample_token] = [instance_token]
with open('{}/{}.tokens'.format(save_dir, split), 'wb') as f:
pickle.dump(tokens_dict, f, pickle.HIGHEST_PROTOCOL)
for sample_tk, instance_tks in tqdm(tokens_dict.items(),
desc=split,
total=len(tokens_dict)):
sample_dir = os.path.join(save_dir, sample_tk)
if not os.path.isdir(sample_dir):
os.mkdir(sample_dir)
scene_data = self.get_cmu_annotation(
instance_tks, sample_tk)
with open('{}/map.bin'.format(sample_dir), 'wb') as f:
pickle.dump(scene_data['episode_img'], f,
pickle.HIGHEST_PROTOCOL)
with open('{}/viz.bin'.format(sample_dir), 'wb') as f:
pickle.dump(scene_data['img_show'], f,
pickle.HIGHEST_PROTOCOL)
with open('{}/episode.bin'.format(sample_dir), 'wb') as f:
pickle.dump(scene_data['episode'], f,
pickle.HIGHEST_PROTOCOL)
with open('{}/instance_tks.bin'.format(sample_dir),
'wb') as f:
pickle.dump(instance_tks, f, pickle.HIGHEST_PROTOCOL)
def render_sample(self, sample_token):
self.nus.render_sample(sample_token)
def render_scene(self, sample_token):
sample = self.nus.get('sample', sample_token)
scene = self.nus.get('scene', sample['scene_token'])
log = self.nus.get('log', scene['log_token'])
location = log['location']
nus_map = NuScenesMap(dataroot=self.root, map_name=location)
layer_names = [
'road_segment', 'lane', 'ped_crossing', 'walkway', 'stop_line',
'carpark_area'
]
camera_channel = 'CAM_FRONT'
nus_map.render_map_in_image(self.nus,
sample_token,
layer_names=layer_names,
camera_channel=camera_channel)
def export_scene_pointcloud(nusc: NuScenes,
out_path: str,
scene_token: str,
channel: str = 'LIDAR_TOP',
min_dist: float = 3.0,
max_dist: float = 30.0,
verbose: bool = True) -> None:
"""
Export fused point clouds of a scene to a Wavefront OBJ file.
This pointcloud can be viewed in your favorite 3D rendering tool, e.g. Meshlab or Maya.
:param nusc: NuScenes instance.
:param out_path: Output path to write the pointcloud to.
:param scene_token: Unique identifier of scene to render.
:param channel: Channel to render.
:param min_dist: Minimum distance to ego vehicle below which points are dropped.
:param max_dist: Maximum distance to ego vehicle above which points are dropped.
:param verbose: Whether to print messages to stdout.
"""
# Check inputs.
valid_channels = [
'LIDAR_TOP', 'RADAR_FRONT', 'RADAR_FRONT_RIGHT', 'RADAR_FRONT_LEFT',
'RADAR_BACK_LEFT', 'RADAR_BACK_RIGHT'
]
camera_channels = [
'CAM_FRONT_LEFT', 'CAM_FRONT', 'CAM_FRONT_RIGHT', 'CAM_BACK_LEFT',
'CAM_BACK', 'CAM_BACK_RIGHT'
]
assert channel in valid_channels, 'Input channel {} not valid.'.format(
channel)
# Get records from DB.
scene_rec = nusc.get('scene', scene_token)
start_sample_rec = nusc.get('sample', scene_rec['first_sample_token'])
sd_rec = nusc.get('sample_data', start_sample_rec['data'][channel])
# Make list of frames
cur_sd_rec = sd_rec
sd_tokens = []
while cur_sd_rec['next'] != '':
cur_sd_rec = nusc.get('sample_data', cur_sd_rec['next'])
sd_tokens.append(cur_sd_rec['token'])
# Write pointcloud.
with open(out_path, 'w') as f:
f.write("OBJ File:\n")
for sd_token in tqdm(sd_tokens):
if verbose:
print('Processing {}'.format(sd_rec['filename']))
sc_rec = nusc.get('sample_data', sd_token)
sample_rec = nusc.get('sample', sc_rec['sample_token'])
lidar_token = sd_rec['token']
lidar_rec = nusc.get('sample_data', lidar_token)
pc = LidarPointCloud.from_file(
osp.join(nusc.dataroot, lidar_rec['filename']))
# Get point cloud colors.
coloring = np.ones((3, pc.points.shape[1])) * -1
for channel in camera_channels:
camera_token = sample_rec['data'][channel]
cam_coloring, cam_mask = pointcloud_color_from_image(
nusc, lidar_token, camera_token)
coloring[:, cam_mask] = cam_coloring
# Points live in their own reference frame. So they need to be transformed via global to the image plane.
# First step: transform the point cloud to the ego vehicle frame for the timestamp of the sweep.
cs_record = nusc.get('calibrated_sensor',
lidar_rec['calibrated_sensor_token'])
pc.rotate(Quaternion(cs_record['rotation']).rotation_matrix)
pc.translate(np.array(cs_record['translation']))
# Optional Filter by distance to remove the ego vehicle.
dists_origin = np.sqrt(np.sum(pc.points[:3, :]**2, axis=0))
keep = np.logical_and(min_dist <= dists_origin,
dists_origin <= max_dist)
pc.points = pc.points[:, keep]
coloring = coloring[:, keep]
if verbose:
print('Distance filter: Keeping %d of %d points...' %
(keep.sum(), len(keep)))
# Second step: transform to the global frame.
poserecord = nusc.get('ego_pose', lidar_rec['ego_pose_token'])
pc.rotate(Quaternion(poserecord['rotation']).rotation_matrix)
pc.translate(np.array(poserecord['translation']))
# Write points to file
for (v, c) in zip(pc.points.transpose(), coloring.transpose()):
if (c == -1).any():
# Ignore points without a color.
pass
else:
f.write(
"v {v[0]:.8f} {v[1]:.8f} {v[2]:.8f} {c[0]:.4f} {c[1]:.4f} {c[2]:.4f}\n"
.format(v=v, c=c / 255.0))
if not sd_rec['next'] == "":
sd_rec = nusc.get('sample_data', sd_rec['next'])
