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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license | |
# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC by University of Oxford | |
# Example usage: python train.py --data VOC.yaml | |
# parent | |
# ├── yolov5 | |
# └── datasets | |
# └── VOC ← downloads here (2.8 GB) | |
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] | |
path: ../datasets/VOC | |
train: # train images (relative to 'path') 16551 images | |
- images/train2012 | |
- images/train2007 | |
- images/val2012 | |
- images/val2007 | |
val: # val images (relative to 'path') 4952 images | |
- images/test2007 | |
test: # test images (optional) | |
- images/test2007 | |
# Classes | |
names: | |
0: aeroplane | |
1: bicycle | |
2: bird | |
3: boat | |
4: bottle | |
5: bus | |
6: car | |
7: cat | |
8: chair | |
9: cow | |
10: diningtable | |
11: dog | |
12: horse | |
13: motorbike | |
14: person | |
15: pottedplant | |
16: sheep | |
17: sofa | |
18: train | |
19: tvmonitor | |
# Download script/URL (optional) --------------------------------------------------------------------------------------- | |
download: | | |
import xml.etree.ElementTree as ET | |
from tqdm import tqdm | |
from utils.general import download, Path | |
def convert_label(path, lb_path, year, image_id): | |
def convert_box(size, box): | |
dw, dh = 1. / size[0], 1. / size[1] | |
x, y, w, h = (box[0] + box[1]) / 2.0 - 1, (box[2] + box[3]) / 2.0 - 1, box[1] - box[0], box[3] - box[2] | |
return x * dw, y * dh, w * dw, h * dh | |
in_file = open(path / f'VOC{year}/Annotations/{image_id}.xml') | |
out_file = open(lb_path, 'w') | |
tree = ET.parse(in_file) | |
root = tree.getroot() | |
size = root.find('size') | |
w = int(size.find('width').text) | |
h = int(size.find('height').text) | |
names = list(yaml['names'].values()) # names list | |
for obj in root.iter('object'): | |
cls = obj.find('name').text | |
if cls in names and int(obj.find('difficult').text) != 1: | |
xmlbox = obj.find('bndbox') | |
bb = convert_box((w, h), [float(xmlbox.find(x).text) for x in ('xmin', 'xmax', 'ymin', 'ymax')]) | |
cls_id = names.index(cls) # class id | |
out_file.write(" ".join([str(a) for a in (cls_id, *bb)]) + '\n') | |
# Download | |
dir = Path(yaml['path']) # dataset root dir | |
url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/' | |
urls = [f'{url}VOCtrainval_06-Nov-2007.zip', # 446MB, 5012 images | |
f'{url}VOCtest_06-Nov-2007.zip', # 438MB, 4953 images | |
f'{url}VOCtrainval_11-May-2012.zip'] # 1.95GB, 17126 images | |
download(urls, dir=dir / 'images', delete=False, curl=True, threads=3) | |
# Convert | |
path = dir / 'images/VOCdevkit' | |
for year, image_set in ('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test'): | |
imgs_path = dir / 'images' / f'{image_set}{year}' | |
lbs_path = dir / 'labels' / f'{image_set}{year}' | |
imgs_path.mkdir(exist_ok=True, parents=True) | |
lbs_path.mkdir(exist_ok=True, parents=True) | |
with open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt') as f: | |
image_ids = f.read().strip().split() | |
for id in tqdm(image_ids, desc=f'{image_set}{year}'): | |
f = path / f'VOC{year}/JPEGImages/{id}.jpg' # old img path | |
lb_path = (lbs_path / f.name).with_suffix('.txt') # new label path | |
f.rename(imgs_path / f.name) # move image | |
convert_label(path, lb_path, year, id) # convert labels to YOLO format | |