-
Notifications
You must be signed in to change notification settings - Fork 0
/
make_data.py
92 lines (77 loc) · 3.17 KB
/
make_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
import xml.etree.ElementTree as ET
import os
import re
import yaml
import random
with open("data/custom.yaml", "r")as f:
config = yaml.load(f.read(), Loader=yaml.FullLoader)
classes = config["names"]
train_path = config["train"]
val_path = config["val"]
test_path = config["test"]
def convert(size, box):
dw, dh = 1. / (size[0]), 1. / (size[1])
x, y = (box[0] + box[1]) / 2.0 - 1, (box[2] + box[3]) / 2.0 - 1
w, h = box[1] - box[0], box[3] - box[2]
x, y = x * dw, y * dh
w, h = w * dw, h * dh
return (x, y, w, h)
def convert_annotation(xml_path):
with open(xml_path, "r", encoding='UTF-8') as in_file:
name = xml_path.split('/')[-2]
print(xml_path)
txt_file = re.sub(name, 'labels', re.sub('xml', 'txt', xml_path))
save_path = re.sub(rf'{name}/.+?.xml', 'labels', xml_path)
if not os.path.exists(save_path):
os.makedirs(save_path)
with open(txt_file, "w+", encoding='UTF-8') as out_file:
tree = ET.parse(in_file)
root = tree.getroot()
size = root.find('size')
w = int(size.find('width').text)
h = int(size.find('height').text)
for obj in root.iter('object'):
difficult = 0
if obj.find('difficult') is not None:
difficult = obj.find('difficult').text
cls = obj.find('name').text
if cls not in classes or int(difficult) == 1:
continue
cls_id = classes.index(cls)
xmlbox = obj.find('bndbox')
b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text),
float(xmlbox.find('ymax').text))
bb = convert((w, h), b)
out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
def voc2yolo(path):
xmls = os.listdir(path)
for xml in xmls:
xml_path = os.path.join(path, xml)
if xml.split('.')[-1].lower() == 'xml':
convert_annotation(xml_path)
def write_file(path, datas):
with open(path, 'w') as f:
f.write('\n'.join(datas))
def make_train_val_test(path, train_ratio, val_ratio, test_ratio):
# 只保留jpg格式的文件名称
images = [image_path for image_path in os.listdir(path) if image_path.endswith('jpg')]
images_path = [os.path.join(path, image) for image in images]
random.shuffle(images_path)
num = len(images_path)
# train_data = [:0.8] val_data = [0.8:0.9] test_data = [0.9:]
train_data = images_path[:round(num * train_ratio)]
val_data = images_path[round(num * train_ratio):round(num * (train_ratio + val_ratio))]
test_data = images_path[round(num * (train_ratio + val_ratio)):]
write_file(train_path, train_data)
write_file(val_path, val_data)
write_file(test_path, test_data)
if __name__ == "__main__":
# vol2yolo
annotations_path = 'data/Annotations'
voc2yolo(annotations_path)
# 生成train val test
images_path = 'data/images'
train_ratio = 0.8
val_ratio = 0.1
test_ratio = 0.1
make_train_val_test(images_path, train_ratio, val_ratio, test_ratio)