init commit

2025-11-08 19:15:39 +01:00
parent ecffcb08e8
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--- a/ultralytics/cfg/datasets/VOC.yaml
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+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC by University of Oxford
+# Documentation: # Documentation: https://docs.ultralytics.com/datasets/detect/voc/
+# Example usage: yolo train data=VOC.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── VOC ← downloads here (2.8 GB)
+
+# 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, ..]
+path: VOC
+train: # train images (relative to 'path') 16551 images
+  - images/train2012
+  - images/train2007
+  - images/val2012
+  - images/val2007
+val: # val images (relative to 'path') 4952 images
+  - images/test2007
+test: # test images (optional)
+  - images/test2007
+
+# Classes
+names:
+  0: aeroplane
+  1: bicycle
+  2: bird
+  3: boat
+  4: bottle
+  5: bus
+  6: car
+  7: cat
+  8: chair
+  9: cow
+  10: diningtable
+  11: dog
+  12: horse
+  13: motorbike
+  14: person
+  15: pottedplant
+  16: sheep
+  17: sofa
+  18: train
+  19: tvmonitor
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+  import xml.etree.ElementTree as ET
+  from pathlib import Path
+
+  from ultralytics.utils.downloads import download
+  from ultralytics.utils import TQDM
+
+  def convert_label(path, lb_path, year, image_id):
+      """Converts XML annotations from VOC format to YOLO format by extracting bounding boxes and class IDs."""
+
+      def convert_box(size, box):
+          dw, dh = 1.0 / size[0], 1.0 / size[1]
+          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]
+          return x * dw, y * dh, w * dw, h * dh
+
+      in_file = open(path / f"VOC{year}/Annotations/{image_id}.xml")
+      out_file = open(lb_path, "w")
+      tree = ET.parse(in_file)
+      root = tree.getroot()
+      size = root.find("size")
+      w = int(size.find("width").text)
+      h = int(size.find("height").text)
+
+      names = list(yaml["names"].values())  # names list
+      for obj in root.iter("object"):
+          cls = obj.find("name").text
+          if cls in names and int(obj.find("difficult").text) != 1:
+              xmlbox = obj.find("bndbox")
+              bb = convert_box((w, h), [float(xmlbox.find(x).text) for x in ("xmin", "xmax", "ymin", "ymax")])
+              cls_id = names.index(cls)  # class id
+              out_file.write(" ".join(str(a) for a in (cls_id, *bb)) + "\n")
+
+
+  # Download
+  dir = Path(yaml["path"])  # dataset root dir
+  url = "https://github.com/ultralytics/assets/releases/download/v0.0.0/"
+  urls = [
+      f"{url}VOCtrainval_06-Nov-2007.zip",  # 446MB, 5012 images
+      f"{url}VOCtest_06-Nov-2007.zip",  # 438MB, 4953 images
+      f"{url}VOCtrainval_11-May-2012.zip",  # 1.95GB, 17126 images
+  ]
+  download(urls, dir=dir / "images", threads=3, exist_ok=True)  # download and unzip over existing (required)
+
+  # Convert
+  path = dir / "images/VOCdevkit"
+  for year, image_set in ("2012", "train"), ("2012", "val"), ("2007", "train"), ("2007", "val"), ("2007", "test"):
+      imgs_path = dir / "images" / f"{image_set}{year}"
+      lbs_path = dir / "labels" / f"{image_set}{year}"
+      imgs_path.mkdir(exist_ok=True, parents=True)
+      lbs_path.mkdir(exist_ok=True, parents=True)
+
+      with open(path / f"VOC{year}/ImageSets/Main/{image_set}.txt") as f:
+          image_ids = f.read().strip().split()
+      for id in TQDM(image_ids, desc=f"{image_set}{year}"):
+          f = path / f"VOC{year}/JPEGImages/{id}.jpg"  # old img path
+          lb_path = (lbs_path / f.name).with_suffix(".txt")  # new label path
+          f.rename(imgs_path / f.name)  # move image
+          convert_label(path, lb_path, year, id)  # convert labels to YOLO format