forked from SharpAI/DeepCamera
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathexport_csv.py
More file actions
91 lines (71 loc) · 2.69 KB
/
Copy pathexport_csv.py
File metadata and controls
91 lines (71 loc) · 2.69 KB
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
# coding=utf-8
"""
从数据库中导出url并下载，生成csv
"""
import csv, os, urllib
from upload_api import TrainSet
headers = ['path', 'embed', 'is_or_isnot', 'person_id', 'device_id', 'face_id', 'group_id']
# dataset = TrainSet.query.all() # 查询所有行，是一个list
# dataset_is = TrainSet.query.filter_by(is_or_isnot=True).all()
# dataset_isnot = TrainSet.query.filter_by(is_or_isnot=False).all()
BASE_FOLDER = os.path.join(os.getenv('RUNTIME_BASEDIR',os.path.abspath(os.path.dirname(__file__))), 'dataset')
if not os.path.exists(BASE_FOLDER):
os.makedirs(BASE_FOLDER)
"""
- [data]
- [(device_id)_(face_id)]
- (device_id)_(face_id)_(0001).jpg
...
- []
- []
"""
def download_img(img_url, decice_id, face_id, i):
u = urllib.urlopen(img_url)
foldername = '{}_{}'.format(decice_id, face_id)
folder_path = os.path.join(BASE_FOLDER, foldername)
if not os.path.exists(folder_path):
os.makedirs(folder_path)
filename = foldername + str(i) + '.jpg'
local_path = os.path.join(folder_path, filename)
if not os.path.isfile(local_path):
with open(local_path, 'wb') as f:
f.write(u.read())
return os.path.join(foldername, filename)
# all export
def all_trainset(dataset):
rows = [(download_img(d.url, d.device_id, d.face_id, i),
d.embed, d.is_or_isnot, d.person_id,
d.device_id, d.face_id, d.group_id)
for i, d in enumerate(dataset)]
csv_path = os.path.join(BASE_FOLDER, 'all_dataset.csv')
with open(csv_path, 'w') as f:
f_csv = csv.writer(f)
f_csv.writerow(headers)
f_csv.writerows(rows)
print 'export to csv OK!'
# is
def is_trainset(dataset):
rows = [(download_img(d.url, d.device_id, d.face_id, i),
d.embed, d.is_or_isnot, d.person_id,
d.device_id, d.face_id, d.group_id)
for i, d in enumerate(dataset) if d.is_or_isnot == True]
csv_path = os.path.join(BASE_FOLDER, 'is_dataset.csv')
with open(csv_path, 'w') as f:
f_csv = csv.writer(f)
f_csv.writerow(headers)
f_csv.writerows(rows)
print 'export to csv OK!'
# isnot
def isnot_trainset(dataset):
rows = [(download_img(d.url, d.device_id, d.face_id, i),
d.embed, d.is_or_isnot, d.person_id,
d.device_id, d.face_id, d.group_id)
for i, d in enumerate(dataset) if d.is_or_isnot == False]
csv_path = os.path.join(BASE_FOLDER, 'isnot_dataset.csv')
with open(csv_path, 'w') as f:
f_csv = csv.writer(f)
f_csv.writerow(headers)
f_csv.writerows(rows)
print 'export to csv OK!'
if __name__ == '__main__':
all_trainset(dataset=TrainSet.query.all())