-
Notifications
You must be signed in to change notification settings - Fork 31
/
process_data.py
290 lines (218 loc) · 10.4 KB
/
process_data.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
import sys
import os
import numpy as np
import pandas as pd
import dill
import pickle
from environment import Environment, Scene, Node, derivative_of
desired_max_time = 100
pred_indices = [2, 3]
state_dim = 6
frame_diff = 10
desired_frame_diff = 1
dt = 0.4
standardization = {
'PEDESTRIAN': {
'position': {
'x': {'mean': 0, 'std': 1},
'y': {'mean': 0, 'std': 1}
},
'velocity': {
'x': {'mean': 0, 'std': 2},
'y': {'mean': 0, 'std': 2}
},
'acceleration': {
'x': {'mean': 0, 'std': 1},
'y': {'mean': 0, 'std': 1}
}
}
}
def maybe_makedirs(path_to_create):
"""This function will create a directory, unless it exists already,
at which point the function will return.
The exception handling is necessary as it prevents a race condition
from occurring.
Inputs:
path_to_create - A string path to a directory you'd like created.
"""
try:
os.makedirs(path_to_create)
except OSError:
if not os.path.isdir(path_to_create):
raise
def augment_scene(scene, angle):
def rotate_pc(pc, alpha):
M = np.array([[np.cos(alpha), -np.sin(alpha)],
[np.sin(alpha), np.cos(alpha)]])
return M @ pc
data_columns = pd.MultiIndex.from_product([['position', 'velocity', 'acceleration'], ['x', 'y']])
scene_aug = Scene(timesteps=scene.timesteps, dt=scene.dt, name=scene.name)
alpha = angle * np.pi / 180
for node in scene.nodes:
x = node.data.position.x.copy()
y = node.data.position.y.copy()
x, y = rotate_pc(np.array([x, y]), alpha)
vx = derivative_of(x, scene.dt)
vy = derivative_of(y, scene.dt)
ax = derivative_of(vx, scene.dt)
ay = derivative_of(vy, scene.dt)
data_dict = {('position', 'x'): x,
('position', 'y'): y,
('velocity', 'x'): vx,
('velocity', 'y'): vy,
('acceleration', 'x'): ax,
('acceleration', 'y'): ay}
node_data = pd.DataFrame(data_dict, columns=data_columns)
node = Node(node_type=node.type, node_id=node.id, data=node_data, first_timestep=node.first_timestep)
scene_aug.nodes.append(node)
return scene_aug
def augment(scene):
scene_aug = np.random.choice(scene.augmented)
scene_aug.temporal_scene_graph = scene.temporal_scene_graph
return scene_aug
nl = 0
l = 0
data_folder_name = 'processed_data_noise'
maybe_makedirs(data_folder_name)
data_columns = pd.MultiIndex.from_product([['position', 'velocity', 'acceleration'], ['x', 'y']])
# Process ETH-UCY
for desired_source in ['eth', 'hotel', 'univ', 'zara1', 'zara2']:
for data_class in ['train', 'val', 'test']:
env = Environment(node_type_list=['PEDESTRIAN'], standardization=standardization)
attention_radius = dict()
attention_radius[(env.NodeType.PEDESTRIAN, env.NodeType.PEDESTRIAN)] = 3.0
env.attention_radius = attention_radius
scenes = []
data_dict_path = os.path.join(data_folder_name, '_'.join([desired_source, data_class]) + '.pkl')
for subdir, dirs, files in os.walk(os.path.join('raw_data', desired_source, data_class)):
for file in files:
if file.endswith('.txt'):
input_data_dict = dict()
full_data_path = os.path.join(subdir, file)
print('At', full_data_path)
data = pd.read_csv(full_data_path, sep='\t', index_col=False, header=None)
data.columns = ['frame_id', 'track_id', 'pos_x', 'pos_y']
data['frame_id'] = pd.to_numeric(data['frame_id'], downcast='integer')
data['track_id'] = pd.to_numeric(data['track_id'], downcast='integer')
data['frame_id'] = data['frame_id'] // 10
data['frame_id'] -= data['frame_id'].min()
data['node_type'] = 'PEDESTRIAN'
data['node_id'] = data['track_id'].astype(str)
data.sort_values('frame_id', inplace=True)
if desired_source == "eth" and data_class == "test":
data['pos_x'] = data['pos_x'] * 0.6
data['pos_y'] = data['pos_y'] * 0.6
# if data_class == "train":
# #data_gauss = data.copy(deep=True)
# data['pos_x'] = data['pos_x'] + 2 * np.random.normal(0,1)
# data['pos_y'] = data['pos_y'] + 2 * np.random.normal(0,1)
#data = pd.concat([data, data_gauss])
data['pos_x'] = data['pos_x'] - data['pos_x'].mean()
data['pos_y'] = data['pos_y'] - data['pos_y'].mean()
max_timesteps = data['frame_id'].max()
scene = Scene(timesteps=max_timesteps+1, dt=dt, name=desired_source + "_" + data_class, aug_func=augment if data_class == 'train' else None)
for node_id in pd.unique(data['node_id']):
node_df = data[data['node_id'] == node_id]
node_values = node_df[['pos_x', 'pos_y']].values
if node_values.shape[0] < 2:
continue
new_first_idx = node_df['frame_id'].iloc[0]
x = node_values[:, 0]
y = node_values[:, 1]
vx = derivative_of(x, scene.dt)
vy = derivative_of(y, scene.dt)
ax = derivative_of(vx, scene.dt)
ay = derivative_of(vy, scene.dt)
data_dict = {('position', 'x'): x,
('position', 'y'): y,
('velocity', 'x'): vx,
('velocity', 'y'): vy,
('acceleration', 'x'): ax,
('acceleration', 'y'): ay}
node_data = pd.DataFrame(data_dict, columns=data_columns)
node = Node(node_type=env.NodeType.PEDESTRIAN, node_id=node_id, data=node_data)
node.first_timestep = new_first_idx
scene.nodes.append(node)
if data_class == 'train':
scene.augmented = list()
angles = np.arange(0, 360, 15) if data_class == 'train' else [0]
for angle in angles:
scene.augmented.append(augment_scene(scene, angle))
print(scene)
scenes.append(scene)
print(f'Processed {len(scenes):.2f} scene for data class {data_class}')
env.scenes = scenes
if len(scenes) > 0:
with open(data_dict_path, 'wb') as f:
dill.dump(env, f, protocol=dill.HIGHEST_PROTOCOL)
exit()
# Process Stanford Drone. Data obtained from Y-Net github repo
data_columns = pd.MultiIndex.from_product([['position', 'velocity', 'acceleration'], ['x', 'y']])
for data_class in ["train", "test"]:
raw_path = "raw_data/stanford"
out_path = "processed_data"
data_path = os.path.join(raw_path, f"{data_class}_trajnet.pkl")
print(f"Processing SDD {data_class}")
data_out_path = os.path.join(out_path, f"sdd_{data_class}.pkl")
df = pickle.load(open(data_path, "rb"))
env = Environment(node_type_list=['PEDESTRIAN'], standardization=standardization)
attention_radius = dict()
attention_radius[(env.NodeType.PEDESTRIAN, env.NodeType.PEDESTRIAN)] = 3.0
env.attention_radius = attention_radius
scenes = []
group = df.groupby("sceneId")
for scene, data in group:
data['frame'] = pd.to_numeric(data['frame'], downcast='integer')
data['trackId'] = pd.to_numeric(data['trackId'], downcast='integer')
data['frame'] = data['frame'] // 12
data['frame'] -= data['frame'].min()
data['node_type'] = 'PEDESTRIAN'
data['node_id'] = data['trackId'].astype(str)
# apply data scale as same as PECnet
data['x'] = data['x']/50
data['y'] = data['y']/50
# Mean Position
data['x'] = data['x'] - data['x'].mean()
data['y'] = data['y'] - data['y'].mean()
max_timesteps = data['frame'].max()
if len(data) > 0:
scene = Scene(timesteps=max_timesteps+1, dt=dt, name="sdd_" + data_class, aug_func=augment if data_class == 'train' else None)
n=0
for node_id in pd.unique(data['node_id']):
node_df = data[data['node_id'] == node_id]
if len(node_df) > 1:
assert np.all(np.diff(node_df['frame']) == 1)
if not np.all(np.diff(node_df['frame']) == 1):
pdb.set_trace()
node_values = node_df[['x', 'y']].values
if node_values.shape[0] < 2:
continue
new_first_idx = node_df['frame'].iloc[0]
x = node_values[:, 0]
y = node_values[:, 1]
vx = derivative_of(x, scene.dt)
vy = derivative_of(y, scene.dt)
ax = derivative_of(vx, scene.dt)
ay = derivative_of(vy, scene.dt)
data_dict = {('position', 'x'): x,
('position', 'y'): y,
('velocity', 'x'): vx,
('velocity', 'y'): vy,
('acceleration', 'x'): ax,
('acceleration', 'y'): ay}
node_data = pd.DataFrame(data_dict, columns=data_columns)
node = Node(node_type=env.NodeType.PEDESTRIAN, node_id=node_id, data=node_data)
node.first_timestep = new_first_idx
scene.nodes.append(node)
if data_class == 'train':
scene.augmented = list()
angles = np.arange(0, 360, 15) if data_class == 'train' else [0]
for angle in angles:
scene.augmented.append(augment_scene(scene, angle))
print(scene)
scenes.append(scene)
env.scenes = scenes
if len(scenes) > 0:
with open(data_out_path, 'wb') as f:
#pdb.set_trace()
dill.dump(env, f, protocol=dill.HIGHEST_PROTOCOL)