将上面的代码改成 yolov8-seg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 torchvision import models

def handler(context, event):
    context.logger.info("Run yolov8-seg segmentation model")
    data = event.body
    buf = io.BytesIO(base64.b64decode(data["image"]))
    threshold = float(data.get("threshold", 0.35))
    
    # 加载模型
    model = models.segmentation.fcn_resnet50(pretrained=True)
    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)['out']
    output_probs = torch.softmax(output, dim=1)[0]
    output_predictions = torch.argmax(output_probs, dim=0).detach().cpu().numpy()
    
    # 处理分割结果
    results = []
    for class_id in range(1, output_probs.shape[0]):
        mask = (output_predictions == class_id).astype(int)
        contours, _ = cv2.findContours(mask.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)

此示例中使用了 PyTorch 和 torchvision 库，并使用预训练的 FCN-ResNet50 模型进行分割任务。代码包括模型加载、图像预处理、模型推理和结果处理的步骤。请注意，具体的修改可能需要根据您使用的模型和环境进行调整。