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utils.py
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utils.py
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import os
import math
import torch
import numpy as np
from torch.utils.data import Dataset
import networkx as nx
from tqdm import tqdm
def anorm(p1,p2):
NORM = math.sqrt((p1[0]-p2[0])**2+ (p1[1]-p2[1])**2)
if NORM ==0:
return 0
return 1/(NORM)
def seq_to_graph(seq_,seq_rel,norm_lap_matr = True):
seq_ = seq_.squeeze()
seq_rel = seq_rel.squeeze()
seq_len = seq_.shape[2]
max_nodes = seq_.shape[0]
V = np.zeros((seq_len,max_nodes,2))
A = np.zeros((seq_len,max_nodes,max_nodes))
for s in range(seq_len):
step_ = seq_[:,:,s]
step_rel = seq_rel[:,:,s]
for h in range(len(step_)):
V[s,h,:] = step_rel[h]
A[s,h,h] = 1
for k in range(h+1,len(step_)):
l2_norm = anorm(step_rel[h],step_rel[k])
A[s,h,k] = l2_norm
A[s,k,h] = l2_norm
if norm_lap_matr:
G = nx.from_numpy_matrix(A[s,:,:])
A[s,:,:] = nx.normalized_laplacian_matrix(G).toarray()
return torch.from_numpy(V).type(torch.float),\
torch.from_numpy(A).type(torch.float)
def poly_fit(traj, traj_len, threshold):
"""
Input:
- traj: Numpy array of shape (2, traj_len)
- traj_len: Len of trajectory
- threshold: Minimum error to be considered for non linear traj
Output:
- int: 1 -> Non Linear 0-> Linear
"""
t = np.linspace(0, traj_len - 1, traj_len)
res_x = np.polyfit(t, traj[0, -traj_len:], 2, full=True)[1]
res_y = np.polyfit(t, traj[1, -traj_len:], 2, full=True)[1]
if res_x + res_y >= threshold:
return 1.0
else:
return 0.0
def read_file(_path, delim='\t'):
data = []
if delim == 'tab':
delim = '\t'
elif delim == 'space':
delim = ' '
with open(_path, 'r') as f:
for line in f:
line = line.strip().split(delim)
line = [float(i) for i in line]
data.append(line)
return np.asarray(data)
class TrajectoryDataset(Dataset):
"""Dataloder for the Trajectory datasets"""
def __init__(
self, data_dir, obs_len=8, pred_len=8, skip=1, threshold=0.002,
min_ped=1, delim='\t',norm_lap_matr = True):
"""
Args:
- data_dir: Directory containing dataset files in the format
<frame_id> <ped_id> <x> <y>
- obs_len: Number of time-steps in input trajectories
- pred_len: Number of time-steps in output trajectories
- skip: Number of frames to skip while making the dataset
- threshold: Minimum error to be considered for non linear traj
when using a linear predictor
- min_ped: Minimum number of pedestrians that should be in a seqeunce
- delim: Delimiter in the dataset files
"""
super(TrajectoryDataset, self).__init__()
self.max_peds_in_frame = 0
self.data_dir = data_dir
self.obs_len = obs_len
self.pred_len = pred_len
self.skip = skip
self.seq_len = self.obs_len + self.pred_len
self.delim = delim
self.norm_lap_matr = norm_lap_matr
all_files = os.listdir(self.data_dir)
all_files = [os.path.join(self.data_dir, _path) for _path in all_files]
num_peds_in_seq = []
seq_list = []
seq_list_rel = []
loss_mask_list = []
non_linear_ped = []
for path in all_files:
if 'graph_data.dat' in path:
continue
data = read_file(path, delim)
frames = np.unique(data[:, 0]).tolist()
frame_data = []
for frame in frames:
frame_data.append(data[frame == data[:, 0], :])
num_sequences = int(
math.ceil((len(frames) - self.seq_len + 1) / skip))
for person_idx in range(0, num_sequences * self.skip + 1, skip):
curr_seq_data = np.concatenate(
frame_data[person_idx:person_idx + self.seq_len], axis=0)
peds_in_curr_seq = np.unique(curr_seq_data[:, 1])
self.max_peds_in_frame = max(self.max_peds_in_frame,len(peds_in_curr_seq))
curr_seq_rel = np.zeros((len(peds_in_curr_seq), 2,
self.seq_len))
curr_seq = np.zeros((len(peds_in_curr_seq), 2, self.seq_len))
curr_loss_mask = np.zeros((len(peds_in_curr_seq),
self.seq_len))
num_peds_considered = 0
_non_linear_ped = []
for _, ped_id in enumerate(peds_in_curr_seq):
curr_ped_seq = curr_seq_data[curr_seq_data[:, 1] ==
ped_id, :]
curr_ped_seq = np.around(curr_ped_seq, decimals=4)
pad_front = frames.index(curr_ped_seq[0, 0]) - person_idx
pad_end = frames.index(curr_ped_seq[-1, 0]) - person_idx + 1
if pad_end - pad_front != self.seq_len:
continue
curr_ped_seq = np.transpose(curr_ped_seq[:, 2:])
curr_ped_seq = curr_ped_seq
# Make coordinates relative
rel_curr_ped_seq = np.zeros(curr_ped_seq.shape)
rel_curr_ped_seq[:, 1:] = \
curr_ped_seq[:, 1:] - curr_ped_seq[:, :-1]
_idx = num_peds_considered
curr_seq[_idx, :, pad_front:pad_end] = curr_ped_seq
curr_seq_rel[_idx, :, pad_front:pad_end] = rel_curr_ped_seq
# Linear vs Non-Linear Trajectory
_non_linear_ped.append(
poly_fit(curr_ped_seq, pred_len, threshold))
curr_loss_mask[_idx, pad_front:pad_end] = 1
num_peds_considered += 1
if num_peds_considered > min_ped:
non_linear_ped += _non_linear_ped
num_peds_in_seq.append(num_peds_considered)
loss_mask_list.append(curr_loss_mask[:num_peds_considered])
seq_list.append(curr_seq[:num_peds_considered])
seq_list_rel.append(curr_seq_rel[:num_peds_considered])
self.num_seq = len(seq_list)
seq_list = np.concatenate(seq_list, axis=0)
seq_list_rel = np.concatenate(seq_list_rel, axis=0)
loss_mask_list = np.concatenate(loss_mask_list, axis=0)
non_linear_ped = np.asarray(non_linear_ped)
# Convert numpy -> Torch Tensor
self.obs_traj = torch.from_numpy(
seq_list[:, :, :self.obs_len]).type(torch.float)
self.pred_traj = torch.from_numpy(
seq_list[:, :, self.obs_len:]).type(torch.float)
self.obs_traj_rel = torch.from_numpy(
seq_list_rel[:, :, :self.obs_len]).type(torch.float)
self.pred_traj_rel = torch.from_numpy(
seq_list_rel[:, :, self.obs_len:]).type(torch.float)
self.loss_mask = torch.from_numpy(loss_mask_list).type(torch.float)
self.non_linear_ped = torch.from_numpy(non_linear_ped).type(torch.float)
cum_start_idx = [0] + np.cumsum(num_peds_in_seq).tolist()
self.seq_start_end = [
(start, end)
for start, end in zip(cum_start_idx, cum_start_idx[1:])
]
# Warning: this step is very time-consuming, adapted to save/load once for all
# Convert to Graphs
graph_data_path = os.path.join(self.data_dir, 'graph_data.dat')
if not os.path.exists(graph_data_path):
# process graph data from scratch
self.v_obs = []
self.A_obs = []
self.v_pred = []
self.A_pred = []
print("Processing Data .....")
pbar = tqdm(total=len(self.seq_start_end))
for ss in range(len(self.seq_start_end)):
pbar.update(1)
start, end = self.seq_start_end[ss]
v_,a_ = seq_to_graph(self.obs_traj[start:end,:],self.obs_traj_rel[start:end, :],self.norm_lap_matr)
self.v_obs.append(v_.clone())
self.A_obs.append(a_.clone())
v_,a_=seq_to_graph(self.pred_traj[start:end,:],self.pred_traj_rel[start:end, :],self.norm_lap_matr)
self.v_pred.append(v_.clone())
self.A_pred.append(a_.clone())
pbar.close()
graph_data = {'v_obs': self.v_obs, 'A_obs': self.A_obs, 'v_pred': self.v_pred, 'A_pred': self.A_pred}
torch.save(graph_data, graph_data_path)
else:
graph_data = torch.load(graph_data_path)
self.v_obs, self.A_obs, self.v_pred, self.A_pred = graph_data['v_obs'], graph_data['A_obs'], graph_data['v_pred'], graph_data['A_pred']
print('Loaded pre-processed graph data at {:s}.'.format(graph_data_path))
# prepare safe trajectory mask
self.safe_traj_masks = []
for batch_idx in range(len(self.seq_start_end)):
start, end = self.seq_start_end[batch_idx]
pred_traj_gt = self.pred_traj[start:end, :] # [num_person, 2, 12]
num_person = pred_traj_gt.size(0)
safety_gt = torch.zeros(num_person).bool()
label_tarj_all = pred_traj_gt.permute(0, 2, 1).cpu().numpy() # [num_person, 12, 2]
for person_idx in range(num_person):
label_traj_primary = label_tarj_all[person_idx]
cur_traj_col_free = np.logical_not(compute_col(label_traj_primary, label_tarj_all).max())
safety_gt[person_idx] = True if cur_traj_col_free else False
self.safe_traj_masks.append(safety_gt)
def __len__(self):
return self.num_seq
def __getitem__(self, index):
start, end = self.seq_start_end[index]
if 'train' in self.data_dir:
out = [
self.obs_traj[start:end, :], self.pred_traj[start:end, :],
self.obs_traj_rel[start:end, :], self.pred_traj_rel[start:end, :],
self.non_linear_ped[start:end], self.loss_mask[start:end, :],
self.v_obs[index], self.A_obs[index],
self.v_pred[index], self.A_pred[index], self.safe_traj_masks[index]
]
else:
out = [
self.obs_traj[start:end, :], self.pred_traj[start:end, :],
self.obs_traj_rel[start:end, :], self.pred_traj_rel[start:end, :],
self.non_linear_ped[start:end], self.loss_mask[start:end, :],
self.v_obs[index], self.A_obs[index],
self.v_pred[index], self.A_pred[index]
]
return out
def interpolate_traj(traj, num_interp=4):
'''
Add linearly interpolated points of a trajectory
'''
sz = traj.shape
dense = np.zeros((sz[0], (sz[1] - 1) * (num_interp + 1) + 1, 2))
dense[:, :1, :] = traj[:, :1]
for i in range(num_interp+1):
ratio = (i + 1) / (num_interp + 1)
dense[:, i+1::num_interp+1, :] = traj[:, 0:-1] * (1 - ratio) + traj[:, 1:] * ratio
return dense
def compute_col(predicted_traj, predicted_trajs_all, thres=0.2):
'''
Input:
predicted_trajs: predicted trajectory of the primary agents, [12, 2]
predicted_trajs_all: predicted trajectory of all agents in the scene, [num_person, 12, 2]
'''
ph = predicted_traj.shape[0]
num_interp = 4
assert predicted_trajs_all.shape[0] > 1
dense_all = interpolate_traj(predicted_trajs_all, num_interp)
dense_ego = interpolate_traj(predicted_traj[None, :], num_interp)
distances = np.linalg.norm(dense_all - dense_ego, axis=-1) # [num_person, 12 * num_interp]
mask = distances[:, 0] > 0 # exclude primary agent itself
return (distances[mask].min(axis=0) < thres)