107 lines
3.7 KiB
YAML
107 lines
3.7 KiB
YAML
# 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 tqdm import tqdm
|
|
|
|
from ultralytics.utils.downloads import download
|
|
|
|
|
|
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", curl=True, 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
|