Chengfang Lu
e72bc061c5
Major changes: - New frontend (platform/web/): Vite + React 18 + TypeScript + Tailwind - 4-module navigation: 数据送标 / 模型管理 / 车队管理 / 系统管理 - Data catalog with charts (DMS/ADAS/Lane 3-tab view) - Quality review workflow (标注质检): Good/Fine/Bad scoring with auto-advance - Audit enhancements: batch operations, rejection categories, Feishu notifications - Operation audit log (操作日志) - World model simulation studio (仿真工坊) - Dataset version management with snapshots and diff - ADAS 7-class dataset integration (138K images organized + compressed) - User management with Feishu integration and pagination - CRUD/search/filter on all pages, card layout redesign - PIL-optimized image overlay rendering - Auto-snapshot on build, in_review workflow stage - Removed embedded algorithm code (now in workspace)
103 lines
3.7 KiB
YAML
103 lines
3.7 KiB
YAML
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC by University of Oxford
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# Documentation: https://docs.ultralytics.com/datasets/detect/voc/
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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: 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 pathlib import Path
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from ultralytics.utils.downloads import download
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from ultralytics.utils import ASSETS_URL, TQDM
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def convert_label(path, lb_path, year, image_id):
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"""Converts XML annotations from VOC format to YOLO format by extracting bounding boxes and class IDs."""
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def convert_box(size, box):
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dw, dh = 1.0 / size[0], 1.0 / 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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with open(path / f"VOC{year}/Annotations/{image_id}.xml") as in_file, open(lb_path, "w", encoding="utf-8") as out_file:
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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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urls = [
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f"{ASSETS_URL}/VOCtrainval_06-Nov-2007.zip", # 446MB, 5012 images
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f"{ASSETS_URL}/VOCtest_06-Nov-2007.zip", # 438MB, 4953 images
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f"{ASSETS_URL}/VOCtrainval_11-May-2012.zip", # 1.95GB, 17126 images
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]
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download(urls, dir=dir / "images", threads=3, exist_ok=True) # download and unzip over existing (required)
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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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