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