3.3

将 COCO 转换为YOLO 格式

用于将 COCO JSON 注释转换为适当的YOLO 格式。用于对象检测（边界框）数据集、 use_segments 和 use_keypoints 都应 False

from ultralytics.data.converter import convert_coco

convert_coco(  
    "../datasets/coco/annotations/",
    use_segments=False,
    use_keypoints=False,
    cls91to80=True,
)

有关 convert_coco 功能、 访问参考页面

获取边框尺寸

from ultralytics.utils.plotting import Annotator
from ultralytics import YOLO
import cv2

model = YOLO('yolo11n.pt')  # Load pretrain or fine-tune model

# Process the image
source = cv2.imread('path/to/image.jpg')
results = model(source)

# Extract results
annotator = Annotator(source, example=model.names)

for box in results[0].boxes.xyxy.cpu():
    width, height, area = annotator.get_bbox_dimension(box)
    print("Bounding Box Width {}, Height {}, Area {}".format(
        width.item(), height.item(), area.item()))

将边框转换为线段

现有的 x y w h 边框数据，使用 yolo_bbox2segment 功能。图像和注释文件需要这样组织：

data
|__ images
    ├─ 001.jpg
    ├─ 002.jpg
    ├─ ..
    └─ NNN.jpg
|__ labels
    ├─ 001.txt
    ├─ 002.txt
    ├─ ..
    └─ NNN.txt

from ultralytics.data.converter import yolo_bbox2segment

yolo_bbox2segment(  
    im_dir="path/to/images",
    save_dir=None,  # saved to "labels-segment" in images directory
    sam_model="sam_b.pt",
)

访问 yolo_bbox2segment 参考页 了解有关该功能的更多信息。

将线段转换为边框

如果您的数据集使用了 分割数据集格式 您可以轻松地将其转换为上下（或水平）边界框 (x y w h 格式）。

import numpy as np

from ultralytics.utils.ops import segments2boxes

segments = np.array(
    [
        [805, 392, 797, 400, ..., 808, 714, 808, 392],
        [115, 398, 113, 400, ..., 150, 400, 149, 298],
        [267, 412, 265, 413, ..., 300, 413, 299, 412],
    ]
)

segments2boxes([s.reshape(-1, 2) for s in segments])
# >>> array([[ 741.66, 631.12, 133.31, 479.25],
#           [ 146.81, 649.69, 185.62, 502.88],
#           [ 281.81, 636.19, 118.12, 448.88]],
#           dtype=float32) # xywh bounding boxes