def _kinematics_from_tokens(helper: PredictHelper, instance: str,
sample: str) -> KinematicsData:
"""
Returns the 2D position, velocity and acceleration vectors from the given track records,
along with the speed, yaw rate, (scalar) acceleration (magnitude), and heading.
:param helper: Instance of PredictHelper.
:instance: Token of instance.
:sample: Token of sample.
:return: KinematicsData.
"""
annotation = helper.get_sample_annotation(instance, sample)
x, y, _ = annotation['translation']
yaw = quaternion_yaw(Quaternion(annotation['rotation']))
velocity = helper.get_velocity_for_agent(instance, sample)
acceleration = helper.get_acceleration_for_agent(instance, sample)
yaw_rate = helper.get_heading_change_rate_for_agent(instance, sample)
if np.isnan(velocity):
velocity = 0.0
if np.isnan(acceleration):
acceleration = 0.0
if np.isnan(yaw_rate):
yaw_rate = 0.0
hx, hy = np.cos(yaw), np.sin(yaw)
vx, vy = velocity * hx, velocity * hy
ax, ay = acceleration * hx, acceleration * hy
return x, y, vx, vy, ax, ay, velocity, yaw_rate, acceleration, yaw
def test_get_sample_annotation(self, ):
mock_annotation = {
'token': '1',
'instance_token': 'instance_1',
'sample_token': 'sample_1'
}
mock_sample = {'token': 'sample_1', 'timestamp': 0}
nusc = MockNuScenes([mock_annotation], [mock_sample])
helper = PredictHelper(nusc)
self.assertDictEqual(
mock_annotation,
helper.get_sample_annotation('instance_1', 'sample_1'))
class NuScenesFormatTransformer:
def __init__(self,
DATAROOT='./data/sets/nuscenes',
dataset_version='v1.0-mini'):
self.DATAROOT = DATAROOT
self.dataset_version = dataset_version
self.nuscenes = NuScenes(dataset_version, dataroot=self.DATAROOT)
self.helper = PredictHelper(self.nuscenes)
# ['vehicle.car', 'vehicle.truck', 'vehicle.bus.rigid', 'vehicle.bus.bendy', 'vehicle.construction']
self.category_token_to_id = {
"fd69059b62a3469fbaef25340c0eab7f": 1, # 'vehicle.car'
"6021b5187b924d64be64a702e5570edf": 1, # 'vehicle.truck'
"fedb11688db84088883945752e480c2c": 2, # 'vehicle.bus.rigid'
"003edbfb9ca849ee8a7496e9af3025d4": 2, # 'vehicle.bus.bendy'
"5b3cd6f2bca64b83aa3d0008df87d0e4": 3, # 'vehicle.construction'
"7b2ff083a64e4d53809ae5d9be563504": 1
} # vehicle.emergency.police
def get_new_format(self,
samples_agents,
format_for_model,
out_file=("./transformer_format.txt"),
num_seconds=None):
# for current_sample in samples_agents:
# instance_token, sample_token = current_sample.split("_")
# traj = self.helper.get_future_for_agent(instance_token, sample_token, 6, True)
# past_traj = self.helper.get_past_for_agent(instance_token, sample_token, 6, True)
#
# if len(past_traj) + len(traj) + 1 < 20:
# print(len(past_traj) + len(traj) + 1)
#
# exit()
####################
# Sample Token (frame) to a sequential id
# for each sample (agent_frame), get the scene it belongs to, and then get the first sample (frame)
# loop on all samples from the first sample till the end
# set to dictionary the sequential id for each sample
splitting_format = '\t'
if format_for_model.value == FORMAT_FOR_MODEL.TRAFFIC_PREDICT.value:
splitting_format = " "
instance_token_to_id_dict = {}
sample_token_to_id_dict = {}
scene_token_dict = {}
sample_id = 0
instance_id = 0
for current_sample in samples_agents:
instance_token, sample_token = current_sample.split("_")
scene_token = self.nuscenes.get('sample',
sample_token)["scene_token"]
if scene_token in scene_token_dict:
continue
# get the first sample in this sequence
scene_token_dict[scene_token] = True
first_sample_token = self.nuscenes.get(
"scene", scene_token)["first_sample_token"]
current_sample = self.nuscenes.get('sample', first_sample_token)
while True:
if current_sample['token'] not in sample_token_to_id_dict:
sample_token_to_id_dict[
current_sample['token']] = sample_id
sample_id += 1
else:
print("should not happen?")
instances_in_sample = self.helper.get_annotations_for_sample(
current_sample['token'])
for sample_instance in instances_in_sample:
if sample_instance[
'instance_token'] not in instance_token_to_id_dict:
instance_token_to_id_dict[
sample_instance['instance_token']] = instance_id
instance_id += 1
if current_sample['next'] == "":
break
current_sample = self.nuscenes.get('sample',
current_sample['next'])
#############
# Converting to the transformer network format
# frame_id, agent_id, pos_x, pos_y
# todo:
# loop on all the agents, if agent not taken:
# 1- add it to takens agents (do not retake the agent)
# 2- get the number of appearance of this agent
# 3- skip this agent if the number is less than 10s (4 + 6)
# 4- get the middle agent's token
# 5- get the past and future agent's locations relative to its location
samples_new_format = []
taken_instances = {}
ds_size = 0
for current_sample in samples_agents:
instance_token, sample_token = current_sample.split("_")
instance_id, sample_id = instance_token_to_id_dict[
instance_token], sample_token_to_id_dict[sample_token]
if instance_id in taken_instances:
continue
taken_instances[instance_id] = True
trajectory = self.get_trajectory_around_sample(
instance_token, sample_token)
trajectory_full_instances = self.get_trajectory_around_sample(
instance_token, sample_token, just_xy=False)
# traj_samples_token = [instance['sample_token'] for instance in trajectory_full_instances]
if len(trajectory) < 20:
print("length is less than 20 samples, trajectory length is: ",
len(trajectory))
continue
ds_size += 1
if num_seconds is not None:
start, end = len(trajectory) // 2 - 9, len(
trajectory) // 2 + 11,
starting_frame = (start + end) // 2
middle_sample_token = trajectory_full_instances[
starting_frame]["sample_token"]
trajectory = self.get_trajectory_around_sample(
instance_token,
middle_sample_token,
just_xy=True,
num_seconds=num_seconds,
in_agent_frame=True)
trajectory_full_instances = self.get_trajectory_around_sample(
instance_token,
middle_sample_token,
just_xy=False,
num_seconds=num_seconds,
in_agent_frame=True)
# traj_samples_token = [instance['sample_token'] for instance in trajectory_full_instances]
# get_trajectory at this position
for i in range(trajectory.shape[0]):
traj_sample, sample_token = trajectory[
i], trajectory_full_instances[i]["sample_token"]
sample_id = sample_token_to_id_dict[sample_token]
if format_for_model.value == FORMAT_FOR_MODEL.TRANSFORMER_NET.value:
yaw = quaternion_yaw(
Quaternion(trajectory_full_instances[i]["rotation"]))
# samples_new_format.append(str(sample_id) + splitting_format + str(instance_id)\
# + splitting_format + str(traj_sample[0]) + splitting_format \
# + str(traj_sample[1]) + splitting_format + str(yaw) + "\n")
x, y, z = trajectory_full_instances[i]["translation"]
w, l, h = trajectory_full_instances[i]["size"]
samples_new_format.append(
str(sample_id) + splitting_format +
str(instance_id
) #+ splitting_format + str(object_type)\
+ splitting_format + str(x) + splitting_format +
str(y) #+ splitting_format + str(z)
# + splitting_format + str(l) + splitting_format + str(w) + splitting_format + str(h)
+ splitting_format + str(yaw) + "\n")
elif format_for_model.value == FORMAT_FOR_MODEL.TRAFFIC_PREDICT.value:
# raise Exception("not implemented yet")
category_token = self.nuscenes.get(
"instance", instance_token)["category_token"]
object_type = self.category_token_to_id[category_token]
# frame_id, object_id, object_type,
# position_x, position_y, position_z,
# object_length, object_width, object_height,
# heading
x, y, z = trajectory_full_instances[i]["translation"]
w, l, h = trajectory_full_instances[i]["size"]
# yaw = angle_of_rotation(quaternion_yaw(Quaternion(trajectory_full_instances[i]["rotation"])))
yaw = quaternion_yaw(
Quaternion(trajectory_full_instances[i]["rotation"]))
samples_new_format.append(str(sample_id) + splitting_format + str(instance_id) + splitting_format + str(object_type)\
+ splitting_format + str(x) + splitting_format + str(y) + splitting_format + str(z) + splitting_format
+ splitting_format + str(l) + splitting_format + str(w) + splitting_format + str(h) + splitting_format
+ str(yaw) + "\n")
# annotations = helper.get_annotations_for_sample(sample_token)
# for ann in annotations:
# # q = ann['rotation']
# # yaw = math.atan2(2.0 * (q[3] * q[0] + q[1] * q[2]), - 1.0 + 2.0 * (q[0] * q[0] + q[1] * q[1]))*180/math.pi
# # if yaw < 0:
# # yaw += 360
# # selected_sample_data = [sample_id, instance_id] + ann['translation'] + [yaw] + ann['size']
# selected_sample_data = str(sample_id) + " " + str(instance_token_to_id_dict[ann['instance_token']])\
# + " " + str(ann['translation'][0]) + " " + str(ann['translation'][2]) + "\n"
# samples_new_format.append(selected_sample_data)
# no need for sorting as it occurs in the TransformationNet data loader
# left it for similarity
samples_new_format.sort(
key=lambda x: int(x.split(splitting_format)[0]))
with open(out_file, 'w') as fw:
fw.writelines(samples_new_format)
print(out_file + "size " + str(ds_size))
def run(self, format_for_model):
if self.dataset_version.find("mini") != -1:
train_agents = get_prediction_challenge_split(
"mini_train", dataroot=self.DATAROOT)
val_agents = get_prediction_challenge_split("mini_val",
dataroot=self.DATAROOT)
else:
train_agents = get_prediction_challenge_split(
"train", dataroot=self.DATAROOT)
train_agents.extend(
get_prediction_challenge_split("train_val",
dataroot=self.DATAROOT))
val_agents = get_prediction_challenge_split("val",
dataroot=self.DATAROOT)
self.get_new_format(
train_agents, format_for_model,
"/home/bassel/PycharmProjects/Trajectory-Transformer/datasets/nuscenes/bkup/transformer_train_"
+ self.dataset_version + ".txt")
self.get_new_format(
val_agents, format_for_model,
"/home/bassel/PycharmProjects/Trajectory-Transformer/datasets/nuscenes/bkup/transformer_val_"
+ self.dataset_version + ".txt")
# shutil.copy("/home/bassel/PycharmProjects/Trajectory-Transformer/datasets/nuscenes/val/transformer_val_"+self.dataset_version+".txt",
# "/home/bassel/PycharmProjects/Trajectory-Transformer/datasets/nuscenes/test/transformer_val_"+self.dataset_version+".txt")
def get_trajectory_around_sample(self,
instance_token,
sample_token,
just_xy=True,
num_seconds=1000,
in_agent_frame=False):
future_samples = self.helper.get_future_for_agent(
instance_token, sample_token, num_seconds, in_agent_frame, just_xy)
past_samples = self.helper.get_past_for_agent(instance_token,
sample_token,
num_seconds,
in_agent_frame,
just_xy)[::-1]
current_sample = self.helper.get_sample_annotation(
instance_token, sample_token)
if num_seconds == 5:
if len(past_samples) > 9:
past_samples = past_samples[0:9]
if len(future_samples) > 10:
future_samples = future_samples[0:10]
if just_xy:
current_sample = current_sample["translation"][:2]
trajectory = np.append(past_samples,
np.append([current_sample],
future_samples,
axis=0),
axis=0)
else:
trajectory = np.append(past_samples,
np.append([current_sample],
future_samples,
axis=0),
axis=0)
return trajectory
class NuScenesFormatTransformer:
def __init__(self,
DATAROOT='./data/sets/nuscenes',
dataset_version='v1.0-mini'):
self.DATAROOT = DATAROOT
self.dataset_version = dataset_version
self.nuscenes = NuScenes(dataset_version, dataroot=self.DATAROOT)
self.helper = PredictHelper(self.nuscenes)
def get_format_mha_jam(self,
samples_agents,
out_file="./transformer_format.txt"):
instance_token_to_id_dict = {}
sample_token_to_id_dict = {}
scene_token_dict = {}
sample_id = 0
instance_id = 0
for current_sample in tqdm(samples_agents):
instance_token, sample_token = current_sample.split("_")
scene_token = self.nuscenes.get('sample',
sample_token)["scene_token"]
if scene_token in scene_token_dict:
continue
# get the first sample in this sequence
scene_token_dict[scene_token] = True
first_sample_token = self.nuscenes.get(
"scene", scene_token)["first_sample_token"]
current_sample = self.nuscenes.get('sample', first_sample_token)
while True:
if current_sample['token'] not in sample_token_to_id_dict:
sample_token_to_id_dict[
current_sample['token']] = sample_id
sample_token_to_id_dict[sample_id] = current_sample[
'token']
sample_id += 1
else:
print("should not happen?")
instances_in_sample = self.helper.get_annotations_for_sample(
current_sample['token'])
for sample_instance in instances_in_sample:
if sample_instance[
'instance_token'] not in instance_token_to_id_dict:
instance_token_to_id_dict[
sample_instance['instance_token']] = instance_id
instance_token_to_id_dict[
instance_id] = sample_instance['instance_token']
instance_id += 1
if current_sample['next'] == "":
break
current_sample = self.nuscenes.get('sample',
current_sample['next'])
mode = "train" if out_file.find("_train") != -1 else "val"
mini = "mini" if out_file.find("mini") != -1 else "main"
with open(
"dicts_sample_and_instances_id2token_" + mode + "_" + mini +
".json", 'w') as fw:
json.dump([instance_token_to_id_dict, sample_token_to_id_dict], fw)
#############
# Converting to the transformer network format
# frame_id, agent_id, pos_x, pos_y
# todo:
# loop on all the agents, if agent not taken:
# 1- add it to takens agents (do not retake the agent)
# 2- get the number of appearance of this agent
# 3- skip this agent if the number is less than 10s (4 + 6)
# 4- get the middle agent's token
# 5- get the past and future agent's locations relative to its location
samples_new_format = []
taken_instances = {}
ds_size = 0
# max_past_traj_len = -1
for current_sample in samples_agents:
instance_token, sample_token = current_sample.split("_")
instance_id = instance_token_to_id_dict[instance_token]
if instance_id in taken_instances:
continue
taken_instances[instance_id] = True
# trajectory_full_instances = self.get_trajectory_around_sample(instance_token, sample_token,
# just_xy=False)
# //////////////////////
future_samples = self.helper.get_future_for_agent(
instance_token, sample_token, 6, True, False)
past_samples = self.helper.get_past_for_agent(
instance_token, sample_token, 1000, True,
False)[:MAX_TRAJ_LEN - 1][::-1]
current_sample = self.helper.get_sample_annotation(
instance_token, sample_token)
assert len(past_samples) >= 1
assert len(future_samples) == 12
# assert len(past_samples) < 7
# if len(past_samples) > max_past_traj_len:
# max_past_traj_len = len(past_samples)
# past_samples = np.append(past_samples, [current_sample], axis=0)
ds_size += 1
# get_trajectory at this position
center_pos = len(past_samples)
future_samples_local = self.helper.get_future_for_agent(
instance_token, sample_token, 6, True, True)
past_samples_local = self.helper.get_past_for_agent(
instance_token, sample_token, 1000, True,
True)[:MAX_TRAJ_LEN - 1][::-1]
# current_sample = self.helper.get_sample_annotation(instance_token, sample_token)
assert len(future_samples_local) == 12
# if len(past_samples) > 7:
# past_samples = past_samples[len(past_samples)-7:]
# past_samples_local = past_samples_local[past_samples_local.shape[0]-7:]
trajectory = np.append(past_samples_local,
np.append([[0, 0]],
future_samples_local,
axis=0),
axis=0)
past_samples = [
sample_token_to_id_dict[p['sample_token']]
for p in past_samples
]
future_samples = [
sample_token_to_id_dict[p['sample_token']]
for p in future_samples
]
trajectory_tokens = np.append(
past_samples,
np.append([sample_token_to_id_dict[sample_token]],
future_samples,
axis=0),
axis=0)
trajectory_ = np.zeros((trajectory.shape[0], 6))
trajectory_[:, 0] = trajectory_tokens[:]
trajectory_[:, 1:3] = trajectory
trajectory = trajectory_
len_future_samples = len(future_samples)
del trajectory_, trajectory_tokens, past_samples, future_samples, past_samples_local, future_samples_local
curr_sample = self.helper.get_past_for_agent(
instance_token, sample_token, 1000, False,
False)[:MAX_TRAJ_LEN][-1]
for i in range(trajectory.shape[0]):
# instance_id, sample_id, x, y, velocity, acc, yaw
velocity = self.helper.get_velocity_for_agent(
instance_token, curr_sample["sample_token"])
acceleration = self.helper.get_acceleration_for_agent(
instance_token, curr_sample["sample_token"])
heading_change_rate = self.helper.get_heading_change_rate_for_agent(
instance_token, curr_sample["sample_token"])
if math.isnan(velocity):
velocity = 0
if math.isnan(acceleration):
acceleration = 0
if math.isnan(heading_change_rate):
heading_change_rate = 0
# need to check paper for relative velocity? same for acc and yaw
trajectory[i][3:] = [
velocity, acceleration, heading_change_rate
]
# if curr_sample['next'] == '':
# import pdb
# pdb.set_trace()
# No need to get next sample token in case this is last element in the series
# prevents bug
if i < trajectory.shape[0] - 1:
next_sample_token = self.nuscenes.get(
'sample_annotation',
curr_sample['next'])['sample_token']
curr_sample = self.helper.get_sample_annotation(
instance_token, next_sample_token)
s = str(instance_id) + ","
# assert (MAX_TRAJ_LEN+len_future_samples) >= trajectory.shape[0]
repeat = (MAX_TRAJ_LEN + len_future_samples) - trajectory.shape[0]
leading_arr = np.array(
repeat * [-1, -64, -64, -64, -64, -64]).reshape((repeat, 6))
trajectory = np.append(leading_arr, trajectory, axis=0)
# print("Built In!")
# self.nuim.render_trajectory(sample_token, rotation_yaw=0, center_key_pose=True)
# print("Bassel's!")
# visualize_traffic(trajectory[(trajectory != [-1, -64, -64, -64, -64, -64]).all(axis=1), 1:3].copy())
for i in range(trajectory.shape[0]):
sample_id, x, y, velocity, acceleration, heading_change_rate = trajectory[
i]
s += str(sample_id) + "," + str(x) + "," + str(y) + "," + str(velocity) + "," \
+ str(acceleration) + "," + str(heading_change_rate)
if i != trajectory.shape[0] - 1:
s += ","
else:
s += "\n"
samples_new_format.append(s)
# print("max past trajectory len:",max_past_traj_len)
# samples_new_format.sort(key=lambda x: int(x.split(",")[0]))
with open(out_file, 'w') as fw:
fw.writelines(samples_new_format)
print(out_file + "size " + str(ds_size))
def get_format_mha_jam_context(self, states_filepath, out_file):
with open(states_filepath) as fr:
agents_states = fr.readlines()
# format
# agent_id, 20x(frame_id, x, y, v, a, yaw_rate)]
agents_states = [[float(x.rstrip()) for x in s.split(',')]
for s in agents_states]
mode = "train" if out_file.find("_train") != -1 else "val"
mini = "mini" if out_file.find("mini") != -1 else "main"
with open("dicts_sample_and_instances_id2token_" + mode + "_" + mini +
".json") as fr:
instance_dict_id_token, sample_dict_id_token = json.load(fr)
# Get Context for each sample in states
context = []
agent_ind = 0
for agent in tqdm(agents_states):
instance_token = instance_dict_id_token[str(int(agent[0]))]
mid_frame_id = int(agent[1 + 6 * (MAX_TRAJ_LEN - 1)])
sample_token = sample_dict_id_token[str(mid_frame_id)]
frame_annotations = self.helper.get_annotations_for_sample(
sample_token)
surroundings_agents_coords = []
surroundings_agents_instance_token = []
for ann in frame_annotations:
if ann['category_name'].find("vehicle") == -1:
continue
if ann['instance_token'] == instance_token:
agent_ann = ann
else:
surroundings_agents_coords.append(ann["translation"][:2])
surroundings_agents_instance_token.append(
ann["instance_token"])
if len(surroundings_agents_coords) != 0:
surroundings_agents_coords = convert_global_coords_to_local(
surroundings_agents_coords, agent_ann["translation"],
agent_ann["rotation"])
# for i in range(len(surroundings_agents_coords)):
# if surroundings_agents_coords[i][0] < -25 or surroundings_agents_coords[i][0] > 25 \
# or surroundings_agents_coords[i][1] < -10 or surroundings_agents_coords[i][1] > 40:
# surroundings_agents_coords[i] = None
# surroundings_agents_instance_token[i] = None
total_area_side = 50
cell_size = 1.5625
map_side_size = int(total_area_side // cell_size)
map = [[[-64, -64, -64, -64, -64] for i in range(MAX_TRAJ_LEN)]
for j in range(map_side_size * map_side_size)]
for n in range(len(surroundings_agents_coords)):
# if np.isnan(surroundings_agents_coords[n][0]): # ---> surroundings_agents_coords[n] is None
# continue
# search for the agent location in the map
# agent_found = False
# for i in range(map_side_size):
# for j in range(map_side_size):
# # if agent found in the cell
# if surroundings_agents_coords[n][0] >= (j * cell_size) - 25\
# and surroundings_agents_coords[n][0] < (j * cell_size) - 25 + cell_size \
# and surroundings_agents_coords[n][1] < 40 - (i * cell_size) \
# and surroundings_agents_coords[n][1] > 40 - (i * cell_size + cell_size):
# found_i, found_j = i, j
# break
# get the agent location in the map!
alpha_y = (surroundings_agents_coords[n][1] - (-10)) / (40 -
(-10))
i = (map_side_size - 1) - int(alpha_y * map_side_size + 0)
alpha_x = (surroundings_agents_coords[n][0] - (-25)) / (25 -
(-25))
j = int(alpha_x * map_side_size + 0)
# Confirmation the 2 methods yield the same results
# if not(found_i == i and found_j == j):
# raise Exception("Calculations error")
# prevent out of bound cases (which shall never happen if none is set for out of bound (line 240)
if not (i >= 0 and i < map_side_size and j >= 0
and j < map_side_size):
# raise Exception("Calculations error")
continue
pos = i * map_side_size + j
past_trajectory = self.get_current_past_trajectory(
surroundings_agents_instance_token[n],
sample_token,
num_seconds=1000)[:MAX_TRAJ_LEN]
assert len(past_trajectory) <= MAX_TRAJ_LEN
retrieved_trajectory_len = len(past_trajectory)
if map[pos][-1][0] != -64:
skip_traj = False
# Save the trajectory with greater length
for ind, map_pos in enumerate(map[pos]):
if map_pos[0] != 64:
if MAX_TRAJ_LEN - ind > retrieved_trajectory_len:
skip_traj = True
if skip_traj:
agent_found = True
break
else:
# print("new longer agent trajectory in cell")
pass
past_trajectory = convert_global_coords_to_local(
past_trajectory, agent_ann["translation"],
agent_ann["rotation"])
if retrieved_trajectory_len != MAX_TRAJ_LEN:
past_trajectory = np.concatenate([
np.array([[-64, -64]
for _ in range(MAX_TRAJ_LEN -
past_trajectory.shape[0])]),
past_trajectory
],
axis=0)
neighbour_agent_features = []
skip_traj = False
for k in range(0, MAX_TRAJ_LEN):
if retrieved_trajectory_len > k:
if k == 0:
sample_token_i = sample_dict_id_token[str(
mid_frame_id)]
else:
sample_token_i = self.helper.get_sample_annotation(
surroundings_agents_instance_token[n],
sample_token_i)["prev"]
sample_token_i = self.nuscenes.get(
'sample_annotation',
sample_token_i)['sample_token']
try:
velocity = self.helper.get_velocity_for_agent(
surroundings_agents_instance_token[n],
sample_token_i)
except:
skip_traj = True
# print("error")
break
acceleration = self.helper.get_acceleration_for_agent(
surroundings_agents_instance_token[n],
sample_token_i)
heading_change_rate = self.helper.get_heading_change_rate_for_agent(
surroundings_agents_instance_token[n],
sample_token_i)
if math.isnan(velocity):
velocity = 0
if math.isnan(acceleration):
acceleration = 0
if math.isnan(heading_change_rate):
heading_change_rate = 0
neighbour_agent_features.append(
[velocity, acceleration, heading_change_rate])
else:
neighbour_agent_features.append([-64, -64, -64])
if skip_traj:
print("skipping agent because it has missing data")
agent_found = True
break
past_trajectory = np.concatenate(
[past_trajectory, neighbour_agent_features], axis=1)
map[pos] = past_trajectory.tolist()
# agent_found = True
# break
# if agent_found:
# break
map = np.array(map).astype(np.float16)
if VISUALIZE_DATA:
visualize_traffic_neighbours(map,
map_side_size * map_side_size)
# context.append(map)
if not os.path.exists(os.path.dirname(out_file)):
os.makedirs(os.path.dirname(out_file))
np.save(out_file.replace("_.txt", "__" + str(agent_ind) + ".txt"),
map)
agent_ind += 1
# with open(out_file, 'ab') as fw:
# pickle.dump(map, fw)
# continue
# fw.write(map)
def get_current_past_trajectory(self,
instance_token,
sample_token,
num_seconds,
just_xy=True,
in_agent_frame=False):
past_samples = self.helper.get_past_for_agent(
instance_token, sample_token, num_seconds, in_agent_frame,
just_xy)[::-1] #[0:7][::-1]
current_sample = self.helper.get_sample_annotation(
instance_token, sample_token)
if just_xy:
current_sample = current_sample["translation"][:2]
if past_samples.shape[0] == 0:
trajectory = np.array([current_sample])
else:
trajectory = np.append(past_samples, [current_sample], axis=0)
else:
trajectory = np.append(past_samples, [current_sample], axis=0)
return trajectory
def get_format_mha_jam_maps(self, states_filepath, out_file):
with open(states_filepath) as fr:
agents_states = fr.readlines()
# format
# agen t_id, 20x(frame_id, x, y, v, a, yaw_rate)]
agents_states = [[float(x.rstrip()) for x in s.split(',')]
for s in agents_states]
mode = "train" if out_file.find("_train") != -1 else "val"
mini = "mini" if out_file.find("mini") != -1 else "main"
with open("dicts_sample_and_instances_id2token_" + mode + "_" + mini +
".json") as fr:
instance_dict_id_token, sample_dict_id_token = json.load(fr)
# Get map for each sample in states
agent_ind = 0
static_layer_rasterizer = StaticLayerRasterizer(self.helper)
agent_rasterizer = AgentBoxesWithFadedHistory(self.helper,
seconds_of_history=1)
mtp_input_representation = InputRepresentation(static_layer_rasterizer,
agent_rasterizer,
Rasterizer())
if not os.path.exists(os.path.dirname(out_file)):
os.makedirs(os.path.dirname(out_file))
for agent in tqdm(agents_states):
instance_token = instance_dict_id_token[str(int(agent[0]))]
mid_frame_id = int(agent[1 + 6 * (MAX_TRAJ_LEN)])
sample_token = sample_dict_id_token[str(mid_frame_id)]
img = mtp_input_representation.make_input_representation(
instance_token, sample_token)
# img = cv2.resize(img, (1024, 1024))
cv2.imwrite(
out_file.replace("_.jpg", "__" + str(agent_ind) + ".jpg"), img)
agent_ind += 1
def run(self, out_dir):
if self.dataset_version.find("mini") != -1:
train_agents = get_prediction_challenge_split(
"mini_train", dataroot=self.DATAROOT)
val_agents = get_prediction_challenge_split("mini_val",
dataroot=self.DATAROOT)
else:
train_agents = get_prediction_challenge_split(
"train", dataroot=self.DATAROOT)
train_agents.extend(
get_prediction_challenge_split("train_val",
dataroot=self.DATAROOT))
val_agents = get_prediction_challenge_split("val",
dataroot=self.DATAROOT)
## Statistics
# mx =-1
# for in train_agents:
# instance_token, sample_token = current_sample.split("_")
# past_samples_local = self.helper.get_past_for_agent(instance_token, sample_token, 100, True, True)[::-1]
# if len(past_samples_local) > mx:
# mx = len(past_samples_local)
# print("max length of the past sequences for trainval is:",mx)
# for instance_token, sample_token in train_agents:
# past_samples_local = self.helper.get_past_for_agent(instance_token, sample_token, 100, True, True)[::-1]
# if len(past_samples_local) > mx:
# mx = len(past_samples_local)
# print("max length of the past sequence for val is:",mx)
# return
self.get_format_mha_jam(
train_agents,
os.path.join(out_dir,
"states_train_" + self.dataset_version + ".txt"))
self.get_format_mha_jam_context(
os.path.join(out_dir,
"states_train_" + self.dataset_version + ".txt"),
os.path.join(out_dir, "context_train_" + self.dataset_version,
"context_train_.txt"))
self.get_format_mha_jam_maps(
os.path.join(out_dir,
"states_train_" + self.dataset_version + ".txt"),
os.path.join(out_dir, "maps_train_" + self.dataset_version,
"maps_train_.jpg"))
# 25
self.get_format_mha_jam(
val_agents,
os.path.join(out_dir,
"states_val_" + self.dataset_version + ".txt"))
self.get_format_mha_jam_context(
os.path.join(out_dir,
"states_val_" + self.dataset_version + ".txt"),
os.path.join(out_dir, "context_val_" + self.dataset_version,
"context_val_.txt"))
self.get_format_mha_jam_maps(
os.path.join(out_dir,
"states_val_" + self.dataset_version + ".txt"),
os.path.join(out_dir, "maps_val_" + self.dataset_version,
"maps_val_.jpg"))
class nuScenesdata(Dataset):
def __init__(self, set_name="mini_train"):
#assert statements
set_paths = [
'train', 'val', 'test', 'mini_train', 'mini_val', 'train_detect',
'train_track'
]
assert set_name in set_paths, "Incorrect set_name"
#Initialize data and Prediction Helper classes
self.data_path = DATA_PATH
self.nusc = NuScenes(version=DATA_VERSION,
dataroot=self.data_path,
verbose=True)
self.helper = PredictHelper(self.nusc)
#get all the scenes
self.scenes = create_splits_scenes()
#get all the scenes in the trainset
self.set_name = set_name
self.trainset = self.scenes[
self.set_name] #List of scenes as part of training set
self.prediction_scenes = json.load(
open(self.data_path + "maps/prediction_scenes.json", "r")
) #Dictionary containing list of instance and sample tokens for each scene
print("Number of samples in train set: %d" % (len(self.trainset)))
def __len__(self):
return len(self.trainset
) #return length of labels or input should be the same
def __getitem__(self, test_idx):
#get the scene
scene = self.trainset[test_idx]
#get all the tokens in the scene
#List of scene tokens in the given scene where each item comprises of an instance token and a sample token seperated by underscore
scene_tokens = self.prediction_scenes[scene]
#Return if fewer than 2 tokens in this scene
if len(scene_tokens) < 2:
print("Not enough agents in the scene")
return []
#get the tokens in the scene: we will be using the instance tokens as that is the agent in the scene
tokens = [scene_tok.split("_") for scene_tok in scene_tokens]
#List of instance tokens and sample tokens
instance_tokens, sample_tokens = list(list(zip(*tokens))[0]), list(
list(zip(*tokens))[1])
assert len(instance_tokens) == len(
sample_tokens), "Instance and Sample tokens count does not match"
'''
1. Convert list of sample and instance tokens into an ordered dict where sample tokens are the keys
2. Iterate over all combinations (of length TRAJECOTRY_TIME_INTERVAL) of consecutive samples
3. Form a list of data points where each data point has TRAJECOTRY_TIME_INTERVAL sample tokens where
each sample token has data for all instance tokens identified in step 2
4. Create 3 numy arrays each for coordinates, heading_change_rate and map with appropriate shapes
5. Iterate: per sample per instance and fill in numpy arrays with respective data
6. Form a dict containing the 3 numpyarrays and return it
'''
ordered_tokens = OrderedDict(zip(sample_tokens, instance_tokens))
print("Printing Ordered_tokens: ", ordered_tokens)
return []
#Dictionary containing count for number of samples per token
token_count = Counter(instance_tokens)
#used to find n agents with highest number of sample_tokens
minCount = sorted(list(token_count.values()),
reverse=True)[NUM_AGENTS - 1]
#Convert isntance and sample tokens to dict format
instance_sample_tokens = {}
for instance_token, sample_token in zip(instance_tokens,
sample_tokens):
if token_count[instance_token] >= minCount:
try:
instance_sample_tokens[instance_token].append(sample_token)
except:
instance_sample_tokens[instance_token] = [sample_token]
# print("Instance:samples ===============================================================================")
# print(instance_sample_tokens)
if len(list(instance_sample_tokens.keys())) != NUM_AGENTS:
print()
# print("Instance_sample_tokens: \n", instance_sample_tokens)
'''
Format:
{coordinates: [[coord_at_t0, coord_at_t1, coord_at_t2, ..., coord_at_tTAJECTORY_TIME_INTERVAL],...numDatapointsInScene ],
heading_change_rate; [[h_at_t0, h_at_t1, h_at_t2, ..., h_at_tTAJECTORY_TIME_INTERVAL], ...numDatapointaInScene]
}
'''
#Initialize map rasterizers
static_layer_rasterizer = StaticLayerRasterizer(self.helper)
agent_rasterizer = AgentBoxesWithFadedHistory(self.helper,
seconds_of_history=2.5)
mtp_input_representation = InputRepresentation(static_layer_rasterizer,
agent_rasterizer,
Rasterizer())
#Initialize Output data
output_data = {
"coordinates":
np.zeros((len(instance_sample_tokens.keys()), 1)),
"heading_change_rate":
np.zeros((len(instance_sample_tokens.keys()), 1)),
"map": [0] * len(instance_sample_tokens.keys())
}
for t, instance_token in enumerate(instance_sample_tokens.keys()):
instance_coordinates = np.zeros((int(
len(instance_sample_tokens[instance_token]) /
TRAJECTORY_TIME_INTERVAL), TRAJECTORY_TIME_INTERVAL, 3))
instance_heading_change_rate = np.zeros((int(
len(instance_sample_tokens[instance_token]) /
TRAJECTORY_TIME_INTERVAL), TRAJECTORY_TIME_INTERVAL))
print("Shape of instance_coordinates: ",
instance_coordinates.shape)
idx = 0 #0 --> numData points for this instance (dimension 1)
num = 0 #0 --> TRAJECTORY_TIME_INTERVAL (dimension 2)
for sample_token in (instance_sample_tokens[instance_token]):
# print(idx, " ", num)
# print(self.nusc.get('sample', sample_token)["timestamp"])
#how to get the annotation for the instance in the sample
annotation = self.helper.get_sample_annotation(
instance_token, sample_token)
instance_coordinates[idx][num] = annotation["translation"]
#get the heading change rate of the agent
heading_change_rate = self.helper.get_heading_change_rate_for_agent(
instance_token, sample_token)
instance_heading_change_rate[idx][num] = heading_change_rate
num = num + 1
#reached the number of records per sample
if num == TRAJECTORY_TIME_INTERVAL:
idx = idx + 1
num = 0
if idx == instance_coordinates.shape[0]:
break
img = mtp_input_representation.make_input_representation(
instance_token, sample_token)
# cv2.imshow("map",img)
output_data["map"][t] = (img)
# plt.imsave('test'+str(test_idx)+str(t)+'.jpg',img)
output_data["coordinates"][t] = instance_coordinates
output_data["heading_change_rate"][
t] = instance_heading_change_rate
# test = pd.DataFrame(output_data,columns=["coordinates", "heading_change_rate", "map"])
# test.to_csv('test'+str(test_idx)+'.csv')
print("Printing Output data")
print((output_data["coordinates"]))
print(len(output_data["heading_change_rate"]))
print(len(output_data["coordinates"]))
return output_data
