-
Notifications
You must be signed in to change notification settings - Fork 4
/
Copy pathfinal_pie_dataloder.py
209 lines (164 loc) · 7.82 KB
/
final_pie_dataloder.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
import torch
import torch.utils.data as data
import os
import pickle5 as pk
import matplotlib.pyplot as plt
from pathlib import Path
import numpy as np
from torchvision import transforms as A
from tqdm import tqdm, trange
from pie_data import PIE
class DataSet(data.Dataset):
def __init__(self, path, pie_path, frame, vel, balance=True, bh='all', t23=False, transforms=None, seg_map=False, h3d=True, pcpa=None, forecast=True, last2=False, time_crop=False):
np.random.seed(42)
self.time_crop = time_crop
self.forecast = forecast
self.last2 = last2
self.h3d = h3d # bool if true 3D human key points are avalable otherwise 2D is going to be used
self.bh = bh
self.seg = seg_map
self.t23 = t23
self.pcpa = os.getcwd() / Path(pcpa)
self.transforms = transforms
self.frame = frame
self.vel= vel
self.balance = balance
self.data_set = 'test'
self.maxw_var = 9
self.maxh_var = 6
self.maxd_var = 2
self.input_size = int(32 * 1)
nsamples = [1624, 879, 611]
balance_data = [max(nsamples) / s for s in nsamples]
if bh != 'all':
balance_data[2] = 0
elif t23:
balance_data = [1, (nsamples[0] + nsamples[2])/nsamples[1], 1]
self.data_path = os.getcwd() / Path(path) / 'data'
self.imgs_path = os.getcwd() / Path(path) / 'imgs'
self.data_list = [data_name for data_name in os.listdir(self.data_path)]
self.pie_path = pie_path
imdb = PIE(data_path=self.pie_path)
params = {'data_split_type': 'default',}
self.vid_ids, _ = imdb._get_data_ids(self.data_set, params)
filt_list = lambda x: not 'r' in x.split('.')[0].split('_')[-1]
ped_ids = list(filter(filt_list, self.data_list))
filt_list = lambda x: x.split('_')[0] in self.vid_ids
ped_ids = list(filter(filt_list, self.data_list))
pcpa_ = self.load_3part()
self.models_data = {}
for k_id in pcpa_['ped_id'].keys():
set_n, vid_n, frm, ped_id = k_id.split('-')
pcpa_key = set_n + '_' + vid_n + '_pid_' + ped_id + '_fr_' + frm
if pcpa_key + '.pkl' in ped_ids:
self.models_data[pcpa_key] = [pcpa_['result'][k_id], pcpa_['labels'][k_id]]
list_k = list(self.models_data.keys())
filt_list = lambda x: x.split('.')[0] in list_k
ped_ids = list(filter(filt_list, ped_ids))
self.ped_data = {}
for ped_id in tqdm(ped_ids, desc=f'loading {self.data_set} data in memory'):
ped_path = self.data_path.joinpath(ped_id).as_posix()
loaded_data = self.load_data(ped_path)
img_file = str(self.imgs_path / loaded_data['crop_img'].stem) + '.pkl'
loaded_data['crop_img'] = self.load_data(img_file)
if loaded_data['irr'] == 1 and bh != 'all':
continue
if balance:
if loaded_data['irr'] == 1: # irrelevant
self.repet_data(balance_data[2], loaded_data, ped_id)
elif loaded_data['crossing'] == 0: # no crossing
self.repet_data(balance_data[0], loaded_data, ped_id)
elif loaded_data['crossing'] == 1: # crossing
self.repet_data(balance_data[1], loaded_data, ped_id)
else:
self.ped_data[ped_id.split('.')[0]] = loaded_data
self.ped_ids = list(self.ped_data.keys())
self.data_len = len(self.ped_ids)
def load_3part(self, ):
pcpa = {}
if self.last2:
pcpa['result'] = self.load_data(self.pcpa / f'test_results/pie/pcpa_preds_pie_all_last2.pkl')
pcpa['labels'] = self.load_data(self.pcpa / f'test_results/pie/pcpa_labels_pie_all_last2.pkl')
pcpa['ped_id'] = self.load_data(self.pcpa / f'test_results/pie/pcpa_ped_ids_pie_all_last2.pkl')
else:
pcpa['result'] = self.load_data(self.pcpa / f'test_results/pie/pcpa_preds_pie_all.pkl')
pcpa['labels'] = self.load_data(self.pcpa / f'test_results/pie/pcpa_labels_pie_all.pkl')
pcpa['ped_id'] = self.load_data(self.pcpa / f'test_results/pie/pcpa_ped_ids_pie_all.pkl')
return pcpa
def repet_data(self, n_rep, data, ped_id):
ped_id = ped_id.split('.')[0]
if self.data_set == 'train' or self.data_set == 'val' or self.t23:
prov = n_rep % 1
n_rep = int(n_rep) if prov == 0 else int(n_rep) + np.random.choice(2, 1, p=[1 - prov, prov])[0]
else:
n_rep = int(n_rep)
for i in range(int(n_rep)):
self.ped_data[ped_id + f'-r{i}'] = data
def load_data(self, data_path):
with open(data_path, 'rb') as fid:
database = pk.load(fid, encoding='bytes')
return database
def __len__(self):
return self.data_len
def __getitem__(self, item):
ped_id = self.ped_ids[item]
pcpa_data = self.models_data[ped_id.split('-')[0]]
ped_data = self.ped_data[ped_id]
w, h = ped_data['w'], ped_data['h']
if self.forecast:
ped_data['kps'][-30:] = ped_data['kps_forcast']
kp = ped_data['kps']
else:
kp = ped_data['kps'][:-30]
# key points data augmentation
if self.data_set == 'train':
kp[..., 0] = np.clip(kp[..., 0] + np.random.randint(self.maxw_var, size=kp[..., 0].shape), 0, w)
kp[..., 1] = np.clip(kp[..., 1] + np.random.randint(self.maxh_var, size=kp[..., 1].shape), 0, w)
kp[..., 2] = np.clip(kp[..., 2] + np.random.randint(self.maxd_var, size=kp[..., 2].shape), 0, w)
# normalize key points data
kp[..., 0] /= w
kp[..., 1] /= h
kp[..., 2] /= 80
kp = torch.from_numpy(kp.transpose(2, 0, 1)).float().contiguous()
seg_map = torch.from_numpy(ped_data['crop_img'][:1]).float()
seg_map = (seg_map - 78.26) / 45.12
img = ped_data['crop_img'][1:]
img = self.transforms(img.transpose(1, 2, 0)).contiguous()
if self.seg:
img = torch.cat([seg_map, img], 0)
vel_obd = np.asarray(ped_data['obd_speed']).reshape(1, -1) / 120.0 # normalize
vel_gps = np.asarray(ped_data['gps_speed']).reshape(1, -1) / 120.0 # normalize
vel = torch.from_numpy(np.concatenate([vel_gps, vel_obd], 0)).float().contiguous()
if not self.forecast:
vel = vel[:, :-30]
# 0 for no crosing, 1 for crossing, 2 for irrelevant
if ped_data['irr']:
bh = torch.from_numpy(np.ones(1).reshape([1])) * 2
else:
bh = torch.from_numpy(ped_data['crossing'].reshape([1])).float()
if not self.h3d:
kp = kp[[0, 1, 3], ].clone()
if self.frame and not self.vel:
return kp, bh, img, pcpa_data
elif self.frame and self.vel:
return kp, bh, img, vel, pcpa_data
else:
return kp, bh, pcpa_data
def main():
data_path = './data/PIE'
pie_path = './PIE'
pcpa = './data'
transform = A.Compose([
A.ToTensor(),
A.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
tr_data = DataSet(path=data_path, pie_path=pie_path, balance=True, frame=True, vel=True, seg_map=True, bh='all', t23=False, transforms=transform, pcpa=pcpa, last2=True)
iter_ = trange(len(tr_data))
cx = np.zeros([len(tr_data), 3])
for i in iter_:
x, y, f, v, pcpca = tr_data.__getitem__(i)
cx[i, y.long().item()] = 1
print(f'No Crosing: {cx.sum(0)[0]} Crosing: {cx.sum(0)[1]}, Irrelevant: {cx.sum(0)[2]} ')
print('finish')
if __name__ == "__main__":
main()