62 lines
2.7 KiB
YAML
62 lines
2.7 KiB
YAML
# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset
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# Train command: python train.py --data VisDrone.yaml
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# Default dataset location is next to YOLOv5:
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# /parent_folder
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# /VisDrone
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# /yolov5
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# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
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train: ../VisDrone/VisDrone2019-DET-train/images # 6471 images
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val: ../VisDrone/VisDrone2019-DET-val/images # 548 images
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test: ../VisDrone/VisDrone2019-DET-test-dev/images # 1610 images
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# number of classes
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nc: 10
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# class names
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names: [ 'pedestrian', 'people', 'bicycle', 'car', 'van', 'truck', 'tricycle', 'awning-tricycle', 'bus', 'motor' ]
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# download command/URL (optional) --------------------------------------------------------------------------------------
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download: |
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from utils.general import download, os, Path
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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(os.sep + 'annotations' + os.sep, 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('../VisDrone') # dataset directory
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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)
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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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