-
Notifications
You must be signed in to change notification settings - Fork 122
/
coco2yolo.py
132 lines (114 loc) · 4.82 KB
/
coco2yolo.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
"""
2021/1/24
COCO 格式的数据集转化为 YOLO 格式的数据集，源代码采取遍历方式，太慢，
这里改进了一下时间复杂度，从O(nm)改为O(n+m)，但是牺牲了一些内存占用
--json_path 输入的json文件路径
--save_path 保存的文件夹名字，默认为当前目录下的labels。
"""
import os
import json
from tqdm import tqdm
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--json_path', default='./instances_val2017.json',type=str, help="input: coco format(json)")
parser.add_argument('--save_path', default='./labels', type=str, help="specify where to save the output dir of labels")
arg = parser.parse_args()
def convert(size, box):
dw = 1. / (size[0])
dh = 1. / (size[1])
x = box[0] + box[2] / 2.0
y = box[1] + box[3] / 2.0
w = box[2]
h = box[3]
x = x * dw
w = w * dw
y = y * dh
h = h * dh
return (x, y, w, h)
if __name__ == '__main__':
json_file = arg.json_path # COCO Object Instance 类型的标注
ana_txt_save_path = arg.save_path # 保存的路径
data = json.load(open(json_file, 'r'))
if not os.path.exists(ana_txt_save_path):
os.makedirs(ana_txt_save_path)
id_map = {} # coco数据集的id不连续！重新映射一下再输出！
for i, category in enumerate(data['categories']):
id_map[category['id']] = i
# 通过事先建表来降低时间复杂度
max_id = 0
for img in data['images']:
max_id = max(max_id, img['id'])
# 注意这里不能写作 [[]]*(max_id+1)，否则列表内的空列表共享地址
img_ann_dict = [[] for i in range(max_id+1)]
for i, ann in enumerate(data['annotations']):
img_ann_dict[ann['image_id']].append(i)
for img in tqdm(data['images']):
filename = img["file_name"]
img_width = img["width"]
img_height = img["height"]
img_id = img["id"]
head, tail = os.path.splitext(filename)
ana_txt_name = head + ".txt" # 对应的txt名字，与jpg一致
f_txt = open(os.path.join(ana_txt_save_path, ana_txt_name), 'w')
'''for ann in data['annotations']:
if ann['image_id'] == img_id:
box = convert((img_width, img_height), ann["bbox"])
f_txt.write("%s %s %s %s %s\n" % (id_map[ann["category_id"]], box[0], box[1], box[2], box[3]))'''
# 这里可以直接查表而无需重复遍历
for ann_id in img_ann_dict[img_id]:
ann = data['annotations'][ann_id]
box = convert((img_width, img_height), ann["bbox"])
f_txt.write("%s %s %s %s %s\n" % (id_map[ann["category_id"]], box[0], box[1], box[2], box[3]))
f_txt.close()
# 旧版，很慢hhh
# """
# COCO 格式的数据集转化为 YOLO 格式的数据集
# --json_path 输入的json文件路径
# --save_path 保存的文件夹名字，默认为当前目录下的labels。
# """
# import os
# import json
# from tqdm import tqdm
# import argparse
# parser = argparse.ArgumentParser()
# parser.add_argument('--json_path', default='./instances_val2017.json',type=str, help="input: coco format(json)")
# parser.add_argument('--save_path', default='./labels', type=str, help="specify where to save the output dir of labels")
# arg = parser.parse_args()
# def convert(size, box):
# dw = 1. / (size[0])
# dh = 1. / (size[1])
# x = box[0] + box[2] / 2.0
# y = box[1] + box[3] / 2.0
# w = box[2]
# h = box[3]
# x = x * dw
# w = w * dw
# y = y * dh
# h = h * dh
# return (x, y, w, h)
# if __name__ == '__main__':
# json_file = arg.json_path # COCO Object Instance 类型的标注
# ana_txt_save_path = arg.save_path # 保存的路径
# data = json.load(open(json_file, 'r'))
# if not os.path.exists(ana_txt_save_path):
# os.makedirs(ana_txt_save_path)
# id_map = {} # coco数据集的id不连续！重新映射一下再输出！
# with open(os.path.join(ana_txt_save_path, 'classes.txt'), 'w') as f:
# # 写入classes.txt
# for i, category in enumerate(data['categories']):
# f.write(f"{category['name']}\n")
# id_map[category['id']] = i
# # print(id_map)
# for img in tqdm(data['images']):
# filename = img["file_name"]
# img_width = img["width"]
# img_height = img["height"]
# img_id = img["id"]
# head, tail = os.path.splitext(filename)
# ana_txt_name = head + ".txt" # 对应的txt名字，与jpg一致
# f_txt = open(os.path.join(ana_txt_save_path, ana_txt_name), 'w')
# for ann in data['annotations']:
# if ann['image_id'] == img_id:
# box = convert((img_width, img_height), ann["bbox"])
# f_txt.write("%s %s %s %s %s\n" % (id_map[ann["category_id"]], box[0], box[1], box[2], box[3]))
# f_txt.close()