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74 lines
2.9 KiB
74 lines
2.9 KiB
# Ultralytics YOLO 🚀, GPL-3.0 license
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# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University
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# Example usage: yolo train data=VisDrone.yaml
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# parent
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# ├── ultralytics
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# └── datasets
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# └── VisDrone ← downloads here (2.3 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/VisDrone # dataset root dir
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train: VisDrone2019-DET-train/images # train images (relative to 'path') 6471 images
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val: VisDrone2019-DET-val/images # val images (relative to 'path') 548 images
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test: VisDrone2019-DET-test-dev/images # test images (optional) 1610 images
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# Classes
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names:
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0: pedestrian
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1: people
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2: bicycle
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3: car
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4: van
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5: truck
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6: tricycle
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7: awning-tricycle
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8: bus
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9: motor
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# Download script/URL (optional) ---------------------------------------------------------------------------------------
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download: |
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import os
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from pathlib import Path
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from ultralytics.yolo.utils.downloads import download
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def visdrone2yolo(dir):
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from PIL import Image
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from tqdm import tqdm
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def convert_box(size, box):
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# Convert VisDrone box to YOLO xywh box
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dw = 1. / size[0]
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dh = 1. / size[1]
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return (box[0] + box[2] / 2) * dw, (box[1] + box[3] / 2) * dh, box[2] * dw, box[3] * dh
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(dir / 'labels').mkdir(parents=True, exist_ok=True) # make labels directory
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pbar = tqdm((dir / 'annotations').glob('*.txt'), desc=f'Converting {dir}')
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for f in pbar:
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img_size = Image.open((dir / 'images' / f.name).with_suffix('.jpg')).size
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lines = []
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with open(f, 'r') as file: # read annotation.txt
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for row in [x.split(',') for x in file.read().strip().splitlines()]:
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if row[4] == '0': # VisDrone 'ignored regions' class 0
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continue
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cls = int(row[5]) - 1
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box = convert_box(img_size, tuple(map(int, row[:4])))
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lines.append(f"{cls} {' '.join(f'{x:.6f}' for x in box)}\n")
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with open(str(f).replace(f'{os.sep}annotations{os.sep}', f'{os.sep}labels{os.sep}'), 'w') as fl:
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fl.writelines(lines) # write label.txt
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# Download
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dir = Path(yaml['path']) # dataset root dir
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urls = ['https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-train.zip',
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'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-val.zip',
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'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-dev.zip',
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'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-challenge.zip']
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download(urls, dir=dir, curl=True, threads=4)
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# Convert
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for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev':
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visdrone2yolo(dir / d) # convert VisDrone annotations to YOLO labels
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