将上面的代码改成 yolov8-seg.pt分割模型适用的

以下是将代码修改为适用于 YOLOv8-Seg 分割模型的示例代码：

import io
import json
import base64
from PIL import Image
import torch
import torchvision.transforms as transforms
from models import YOLOv8Seg

def handler(context, event):
    context.logger.info("Run yolov8-seg model")
    data = event.body
    buf = io.BytesIO(base64.b64decode(data["image"]))
    threshold = float(data.get("threshold", 0.35))

    # 加载模型
    model = YOLOv8Seg("yolov8-seg.pt")
    model.eval()

    # 图像预处理
    transform = transforms.Compose([
        transforms.Resize((512, 512)),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    ])
    image = transform(Image.open(buf)).unsqueeze(0)

    # 模型推理
    with torch.no_grad():
        output = model(image)

    # 处理分割结果
    results = []
    for class_id in range(1, output.shape[1]):
        mask = (output[0, class_id] > threshold).float()
        contours, _ = cv2.findContours(mask.numpy().astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        for contour in contours:
            x, y, w, h = cv2.boundingRect(contour)
            results.append({
                "label": str(class_id),
                "points": [int(x), int(y), int(x + w), int(y + h)],
                "type": "rectangle"
            })

    return context.Response(body=json.dumps(results), headers={},
                            content_type='application/json', status_code=200)

在这个示例中，我们假设您有一个名为 `YOLOv8Seg` 的自定义模型类，可以从 `yolov8-seg.pt` 加载 YOLOv8-Seg 模型。代码中包括模型加载、图像预处理、模型推理和结果处理的步骤。请根据您的实际情况调整代码。