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()
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)
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()
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
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.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)
self.combinator = Rasterizer()
self.show_agent = True
def __len__(self):
return len(self.nus.sample)
def get_ego_pose(self, idx):
sample_data_lidar = self.nus.get('sample_data', self.nus.sample[idx]['data']['LIDAR_TOP'])
ego_pose = self.nus.get('ego_pose', sample_data_lidar['ego_pose_token'])
return ego_pose['translation'], ego_pose['rotation']
def get_trajectory(self, idx):
scene_id = idx
sample = self.nus.sample[idx]
sample_token = sample['token']
# 1. calculate ego pose
ego_pose_xy, ego_pose_rotation = self.get_ego_pose(idx)
# 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
# global
agent_mask, xy_global = self.agent_layer.generate_mask(
ego_pose_xy, ego_pose_rotation, sample_token, self.seconds, show_agent=self.show_agent)
# local
xy_local = []
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).tolist())
xy_local.append(pose_xy)
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).tolist())
map_img = self.combinator.combine([map_img, agent_mask])
scene_data = {
'ego_pose_xy': ego_pose_xy,
'ego_pose_rotation': ego_pose_rotation,
'map_masks': map_masks,
'map_img': map_img,
'driving_lines': lanes,
'agent_mask': agent_mask,
'xy_global': xy_global,
'xy_local': xy_local,
'scene_id': scene_id
}
return scene_data
def render_sample(self, idx):
sample = self.nus.sample[idx]
sample_token = sample['token']
self.nus.render_sample(sample_token)
def render_scene(self, idx):
sample = self.nus.sample[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 __getitem__(self, 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']
# 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.agent_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)
lane_tokens = list(lanes.keys())
num_lanes = len(lane_tokens)
lanes_disc = [np.array(lanes[token])[:, :2] for token in lane_tokens]
lanes_arc = []
for seq in lanes_disc:
seq_len = len(seq)
lane_middle = seq[seq_len // 2]
opt_middle = (seq[0] + seq[-1]) / 2
lane_h = np.linalg.norm(lane_middle - opt_middle)
lane_w = np.linalg.norm(seq[-1] - seq[0])
curve = lane_h / 2 + lane_w**2 / (8 * lane_h)
lanes_arc.append(curve)
lanes_arc = np.array(lanes_arc)
# 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)
# 경로 곡률
agents_arc = []
for seq in [past_xy_global, future_xy_global]:
seq_len = len(seq)
if seq_len < 2:
continue
path_middle = seq[seq_len // 2]
opt_middle = (seq[0] + seq[-1]) / 2
path_h = np.linalg.norm(path_middle - opt_middle)
path_w = np.linalg.norm(seq[-1] - seq[0])
if path_h == 0:
path_h = 0.001
if path_w == 0:
path_w = 0.001
curve = path_h / 2 + (path_w * path_w) / (8 * path_h)
agents_arc.append(curve)
agents_arc = np.array(agents_arc)
# 로컬 주행경로
xy_local = convert_global_coords_to_local(
np.array([xy_global[:2]]), ego_pose_xy, ego_pose_rotation)
if len(past_xy_global) < 1:
past_xy_global = np.append(past_xy_global, [0., 0.])
if len(future_xy_global) < 1:
future_xy_global = np.append(future_xy_global, [0., 0.])
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,
'curvature': agents_arc
}
agents.append(agent_states)
return map_masks, agent_mask, agents, idx