File size: 1,947 Bytes
dbce1bc 54652fe dbce1bc 54652fe f55730e 37eaffe 54652fe dbce1bc 54652fe dbce1bc 54652fe dbce1bc |
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 |
# Objects365 https://www.objects365.org labels JSON to YOLO script
# 1. Download Object 365 from the Object 365 website And unpack all images in datasets/object365/images
# 2. Place this file and zhiyuan_objv2_train.json file in datasets/objects365
# 3. Execute this file from datasets/object365 path
# /datasets
# /objects365
# /images
# /labels
from pycocotools.coco import COCO
from utils.general import download, Path
# Make Directories
dir = Path('../datasets/objects365') # dataset directory
for p in 'images', 'labels':
(dir / p).mkdir(parents=True, exist_ok=True)
for q in 'train', 'val':
(dir / p / q).mkdir(parents=True, exist_ok=True)
# Download
url = "https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/train/"
download([url + 'zhiyuan_objv2_train.tar.gz'], dir=dir) # annotations json
download([url + f for f in [f'patch{i}.tar.gz' for i in range(51)]], dir=dir / 'images' / 'train', curl=True, threads=8)
# Labels
coco = COCO(dir / 'zhiyuan_objv2_train.json')
names = [x["name"] for x in coco.loadCats(coco.getCatIds())]
for categoryId, cat in enumerate(names):
catIds = coco.getCatIds(catNms=[cat])
imgIds = coco.getImgIds(catIds=catIds)
for im in coco.loadImgs(imgIds):
width, height = im["width"], im["height"]
path = Path(im["file_name"]) # image filename
try:
with open(dir / 'labels' / 'train' / path.with_suffix('.txt').name, 'a') as file:
annIds = coco.getAnnIds(imgIds=im["id"], catIds=catIds, iscrowd=None)
for a in coco.loadAnns(annIds):
x, y, w, h = a['bbox'] # bounding box in xywh (xy top-left corner)
x, y = x + w / 2, y + h / 2 # xy to center
file.write(f"{categoryId} {x / width:.5f} {y / height:.5f} {w / width:.5f} {h / height:.5f}\n")
except Exception as e:
print(e)
|