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73 lines
2.7 KiB
73 lines
2.7 KiB
# Ultralytics YOLO 🚀, GPL-3.0 license |
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# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ by Argo AI |
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# Example usage: yolo train data=Argoverse.yaml |
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# parent |
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# ├── ultralytics |
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# └── datasets |
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# └── Argoverse ← downloads here (31.3 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/Argoverse # dataset root dir |
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train: Argoverse-1.1/images/train/ # train images (relative to 'path') 39384 images |
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val: Argoverse-1.1/images/val/ # val images (relative to 'path') 15062 images |
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test: Argoverse-1.1/images/test/ # test images (optional) https://eval.ai/web/challenges/challenge-page/800/overview |
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# Classes |
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names: |
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0: person |
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1: bicycle |
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2: car |
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3: motorcycle |
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4: bus |
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5: truck |
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6: traffic_light |
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7: stop_sign |
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# Download script/URL (optional) --------------------------------------------------------------------------------------- |
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download: | |
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import json |
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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 argoverse2yolo(set): |
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labels = {} |
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a = json.load(open(set, "rb")) |
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for annot in tqdm(a['annotations'], desc=f"Converting {set} to YOLOv5 format..."): |
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img_id = annot['image_id'] |
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img_name = a['images'][img_id]['name'] |
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img_label_name = f'{img_name[:-3]}txt' |
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cls = annot['category_id'] # instance class id |
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x_center, y_center, width, height = annot['bbox'] |
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x_center = (x_center + width / 2) / 1920.0 # offset and scale |
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y_center = (y_center + height / 2) / 1200.0 # offset and scale |
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width /= 1920.0 # scale |
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height /= 1200.0 # scale |
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img_dir = set.parents[2] / 'Argoverse-1.1' / 'labels' / a['seq_dirs'][a['images'][annot['image_id']]['sid']] |
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if not img_dir.exists(): |
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img_dir.mkdir(parents=True, exist_ok=True) |
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k = str(img_dir / img_label_name) |
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if k not in labels: |
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labels[k] = [] |
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labels[k].append(f"{cls} {x_center} {y_center} {width} {height}\n") |
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for k in labels: |
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with open(k, "w") as f: |
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f.writelines(labels[k]) |
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# Download |
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dir = Path(yaml['path']) # dataset root dir |
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urls = ['https://argoverse-hd.s3.us-east-2.amazonaws.com/Argoverse-HD-Full.zip'] |
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download(urls, dir=dir) |
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# Convert |
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annotations_dir = 'Argoverse-HD/annotations/' |
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(dir / 'Argoverse-1.1' / 'tracking').rename(dir / 'Argoverse-1.1' / 'images') # rename 'tracking' to 'images' |
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for d in "train.json", "val.json": |
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argoverse2yolo(dir / annotations_dir / d) # convert VisDrone annotations to YOLO labels
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