def handle_seq(self, x_seq, lookup_seq, PedsList_seq, angle):
# add noise and rotate a trajectory
vectorized_x_seq, first_values_dict = vectorize_seq(
x_seq, PedsList_seq, lookup_seq)
modified_x_seq = vectorized_x_seq.clone()
mean = torch.FloatTensor([self.noise_mean, self.noise_mean])
stddev = torch.FloatTensor([self.noise_std, self.noise_std])
origin = (0, 0)
for ind, frame in enumerate(vectorized_x_seq):
for ped in PedsList_seq[ind]:
selected_point = frame[lookup_seq[ped], :]
# rotate a frame point
rotated_point = rotate(origin, selected_point,
math.radians(angle))
noise = torch.normal(mean, stddev).clone()
# add random noise
modified_x_seq[ind, lookup_seq[ped],
0] = rotated_point[0] + noise[0]
modified_x_seq[ind, lookup_seq[ped],
1] = rotated_point[1] + noise[1]
# modified_x_seq[ind, lookup_seq[ped], :] = torch.cat(rotate(origin, first_values_dict[ped], math.radians(angle))) + modified_x_seq[ind, lookup_seq[ped], :]
# roatate first frame value as well and add it back to get absoute coordinates
modified_x_seq[ind, lookup_seq[ped], 0] = (rotate(
origin, first_values_dict[ped], math.radians(
angle)))[0] + modified_x_seq[ind, lookup_seq[ped], 0]
modified_x_seq[ind, lookup_seq[ped], 1] = (rotate(
origin, first_values_dict[ped], math.radians(
angle)))[1] + modified_x_seq[ind, lookup_seq[ped], 1]
return modified_x_seq
def vectorize_traj(traj, nodesPresent, look_up):
#wrap up for vectorizing traj
traj, _ = vectorize_seq(Variable(torch.FloatTensor(traj)), nodesPresent, look_up)
traj = np.array([seq.data.numpy() for seq in traj])
return traj
