-
Notifications
You must be signed in to change notification settings - Fork 24
/
prepare_all.py
238 lines (202 loc) · 9.73 KB
/
prepare_all.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
# -*- encoding: utf-8 -*-
'''
@File : prepare_data.py
@Contact : whut.hexin@foxmail.com
@License : (C)Copyright 2017-2020, HeXin
@Modify Time @Author @Version @Desciption
------------ ------- -------- -----------
2019/10/28 16:11 xin 1.0 None
'''
import os
import shutil
import pandas as pd
import numpy as np
import random
import glob
from dataset.data import read_image
from skimage.io import imsave, imread
from PIL import ImageFile, Image
from tqdm import tqdm
def process_dataset(txt_label, root_path, save_path,camera_start_id=0):
os.makedirs(save_path,exist_ok=True)
with open(txt_label, 'r') as f:
lines = f.readlines()
with tqdm(total = len(lines)) as pbar:
for i, line in enumerate(lines):
data = line.split(" ")
image_name = data[0].split("/")[1]
pid = data[1].strip("\n")
if not os.path.exists(os.path.join(save_path, pid)):
os.mkdir(os.path.join(save_path, pid))
new_filename = pid+"_c"+str(i+camera_start_id)+".png"
shutil.copy(os.path.join(root_path, image_name), os.path.join(os.path.join(save_path, pid), new_filename))
pbar.update(1)
def dataset_analyse(root_path):
pids = os.listdir(root_path)
counts = list()
for pid in pids:
imgs = os.listdir(os.path.join(root_path, pid))
counts.append(len(imgs))
columns = [u'pid', u'count']
save_df = pd.DataFrame({u'pid': pids, u'count': counts},
columns=columns)
save_df.to_csv('dataset_analyse.csv')
def split_dataset(root_path, train_path, query_path, gallery):
pids = os.listdir(root_path)
for pid in pids:
imgs = os.listdir(os.path.join(root_path, pid))
for img in imgs:
shutil.copy(os.path.join(os.path.join(root_path, pid), img), os.path.join(train_path, img1))
def mk_pseudo_data(root_path, txt_label, csv_data, save_path):
query_dic = dict()
with open(txt_label, 'r') as f:
lines = f.readlines()
for i, line in enumerate(lines):
data = line.split(" ")
image_name = data[0].split("/")[1]
pid = data[1].strip("\n")
query_dic[image_name] = pid
pseudo_csv_data = pd.read_csv(csv_data)
for query_file, gallery_file in zip(pseudo_csv_data['q_imgs'], pseudo_csv_data['g_imgs']):
pid = query_dic[query_file]
# if not os.path.exists(os.path.join(save_path, pid)):
# os.mkdir(os.path.join(save_path, pid))
new_query_filename = pid + "_c" + query_file
shutil.copy(os.path.join(root_path, query_file), os.path.join(save_path, new_query_filename))
new_gallery_filename = pid + "_c" + gallery_file
shutil.copy(os.path.join(root_path, gallery_file), os.path.join(save_path, new_gallery_filename))
if __name__ == "__main__":
root_dir = '/data/Dataset/PReID/'
pre_dir = root_dir
rep_dir = root_dir+'dataset2/'
save_dir = root_dir+'all_dataset/'
os.makedirs(save_dir,exist_ok=True)
if 0:
print('==> copy rep to pid dataset')
process_dataset(rep_dir+'fix_train_list.txt', rep_dir+'train/', save_dir+'pid_dataset/')
camera_start_id = len(glob.glob(rep_dir+'train/*.png'))
print('==> copy pre to pid dataset from camera id:',camera_start_id)
process_dataset(pre_dir+'fix_train_list.txt', pre_dir+'train_set/', save_dir+'pid_dataset/',camera_start_id=camera_start_id)
# fix seed
kfold = 1
# load pre and rep id seperately
pre_train = pd.read_csv(pre_dir+'fix_train_list.txt',sep=' ',header=None,names=['filename','identity'])
rep_train = pd.read_csv(rep_dir+'fix_train_list.txt',sep=' ',header=None,names=['filename','identity'])
pre_train['filename'] = pre_train['filename'].apply(lambda x: x.split('/')[1])
rep_train['filename'] = rep_train['filename'].apply(lambda x: x.split('/')[1])
root_path = save_dir+'pid_dataset/'
# deal with rep data only
pre_pids = pre_train['identity'].astype(str).tolist()
pre_pids = list(set(pre_pids))
pre_pids = sorted(pre_pids)
rep_pids = rep_train['identity'].astype(str).tolist()
rep_pids = list(set(rep_pids))
rep_pids = sorted(rep_pids)
all_pids = pre_pids+rep_pids
if 0:
# trainVal
print('==> copy to rep trainVal')
trainVal_path = save_dir + 'rep_trainVal/'
os.makedirs(trainVal_path,exist_ok=True)
with tqdm(total = len(rep_pids)) as pbar:
for pid in rep_pids:
imgs = os.listdir(os.path.join(root_path, pid))
for img in imgs:
shutil.copy(os.path.join(os.path.join(root_path, pid), img), os.path.join(trainVal_path, img))
pbar.update(1)
# trainVal
print('==> copy to all trainVal')
trainVal_path = save_dir + 'all_trainVal/'
os.makedirs(trainVal_path,exist_ok=True)
with tqdm(total = len(all_pids)) as pbar:
for pid in all_pids:
imgs = os.listdir(os.path.join(root_path, pid))
for img in imgs:
shutil.copy(os.path.join(os.path.join(root_path, pid), img), os.path.join(trainVal_path, img))
pbar.update(1)
if 1:
np.random.seed(kfold)
# split train and val(from rep only)
np.random.shuffle(rep_pids)
train_rep_pids = rep_pids[:int(len(rep_pids)*0.85)]
val_rep_pids = rep_pids[int(len(rep_pids)*0.85):]
train_all_pids = pre_pids + train_rep_pids
# # query gallery
# print('==> copy to rep query and gallery')
# for q_fold in range(5):
# query_path = save_dir+'rep_f{}_query_{}/'.format(kfold,q_fold)
# gallery_path = save_dir + 'rep_f{}_gallery_{}/'.format(kfold,q_fold)
# os.makedirs(query_path,exist_ok=True)
# os.makedirs(gallery_path,exist_ok=True)
# with tqdm(total = len(val_rep_pids)) as pbar:
# img_id = 0
# for pid in val_rep_pids:
# imgs = os.listdir(os.path.join(root_path, pid))
# imgs = sorted(imgs)
# np.random.shuffle(imgs)
# for img in imgs:
# img_id+=1
# if (img_id+q_fold) % 5 == 0:
# shutil.copy(os.path.join(os.path.join(root_path, pid), img), os.path.join(query_path, img))
# else:
# shutil.copy(os.path.join(os.path.join(root_path, pid), img), os.path.join(gallery_path, img))
# pbar.update(1)
# query gallery
print('==> copy to rep query and gallery')
query_path = save_dir+'rep_f{}_query/'.format(kfold)
gallery_path = save_dir + 'rep_f{}_gallery/'.format(kfold)
os.makedirs(query_path,exist_ok=True)
os.makedirs(gallery_path,exist_ok=True)
with tqdm(total = len(val_rep_pids)) as pbar:
img_id = 0
for pid in val_rep_pids:
imgs = os.listdir(os.path.join(root_path, pid))
imgs = sorted(imgs)
np.random.shuffle(imgs)
for img in imgs:
img_id+=1
if img_id % 5 == 0:
shutil.copy(os.path.join(os.path.join(root_path, pid), img), os.path.join(query_path, img))
else:
shutil.copy(os.path.join(os.path.join(root_path, pid), img), os.path.join(gallery_path, img))
pbar.update(1)
print('==> copy to rep train')
train_path = save_dir + 'rep_f{}_train/'.format(kfold)
train2_path = save_dir + 'rep_f{}_train2/'.format(kfold)
train4_path = save_dir + 'rep_f{}_train4/'.format(kfold)
os.makedirs(train_path,exist_ok=True)
os.makedirs(train2_path,exist_ok=True)
os.makedirs(train4_path,exist_ok=True)
with tqdm(total = len(train_rep_pids)) as pbar:
for pid in train_rep_pids:
imgs = os.listdir(os.path.join(root_path, pid))
if 1:
for img in imgs:
shutil.copy(os.path.join(os.path.join(root_path, pid), img), os.path.join(train_path, img))
if len(imgs)>=2:
for img in imgs:
shutil.copy(os.path.join(os.path.join(root_path, pid), img), os.path.join(train2_path, img))
if len(imgs)>=4:
for img in imgs:
shutil.copy(os.path.join(os.path.join(root_path, pid), img), os.path.join(train4_path, img))
pbar.update(1)
print('==> copy to all train')
train_path = save_dir + 'all_f{}_train/'.format(kfold)
train2_path = save_dir + 'all_f{}_train2/'.format(kfold)
train4_path = save_dir + 'all_f{}_train4/'.format(kfold)
os.makedirs(train_path,exist_ok=True)
os.makedirs(train2_path,exist_ok=True)
os.makedirs(train4_path,exist_ok=True)
with tqdm(total = len(train_all_pids)) as pbar:
for pid in train_all_pids:
imgs = os.listdir(os.path.join(root_path, pid))
if 1:
for img in imgs:
shutil.copy(os.path.join(os.path.join(root_path, pid), img), os.path.join(train_path, img))
if len(imgs)>=2:
for img in imgs:
shutil.copy(os.path.join(os.path.join(root_path, pid), img), os.path.join(train2_path, img))
if len(imgs)>=4:
for img in imgs:
shutil.copy(os.path.join(os.path.join(root_path, pid), img), os.path.join(train4_path, img))
pbar.update(1)