101 lines
3.4 KiB
YAML
101 lines
3.4 KiB
YAML
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# YOLOv5 🚀 by Ultralytics, 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: python train.py --data VOC.yaml
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# parent
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# ├── yolov5
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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 utils.general import download, 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', delete=False, 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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