import io import base64 import json import cv2 import numpy as np from PIL import Image def handler(context, event): context.logger.info("Run yolo-v8-seg model") data = event.body buf = io.BytesIO(base64.b64decode(data["image"])) threshold = float(data.get("threshold", 0.35)) context.user_data.model.conf = threshold image = Image.open(buf) yolo_results = context.user_data.model(image, conf=threshold)[0] labels = yolo_results.names detections = [ { "class_id": int(result[0]), "points": [(float(result[i]), float(result[i+1])) for i in range(1, len(result), 2)] } for result in [line.split() for line in yolo_results] ] results = [] for detection in detections: class_id = detection["class_id"] points = detection["points"] results.append({ "confidence": "", # 这里没有置信度信息，可以根据实际情况进行调整 "label": labels[class_id], "points": points, "type": "polygon" }) return context.Response(body=json.dumps(results), headers={}, content_type='application/json', status_code=200) 加入判断置信度大于设置的值 和 if box.shape[0] > 0：

要在代码中添加判断，以确保只有当检测结果的置信度大于设置的阈值时才进行处理，以及当`boxes`的形状不为空时才进行处理，你可以在以下位置进行修改：

import io
import base64
import json
import cv2
import numpy as np
from PIL import Image

def handler(context, event):
    context.logger.info("Run yolo-v8-seg model")
    data = event.body
    buf = io.BytesIO(base64.b64decode(data["image"]))
    threshold = float(data.get("threshold", 0.35))
    context.user_data.model.conf = threshold
    image = Image.open(buf)

    yolo_results = context.user_data.model(image, conf=threshold)[0]
    labels = yolo_results.names

    detections = [
        {
            "class_id": int(result[0]),
            "points": [(float(result[i]), float(result[i+1])) for i in range(1, len(result), 2)]
        }
        for result in [line.split() for line in yolo_results]
    ]

    results = []

    for detection in detections:
        class_id = detection["class_id"]
        points = detection["points"]

        results.append({
            "confidence": "",  # 这里没有置信度信息，可以根据实际情况进行调整
            "label": labels[class_id],
            "points": points,
            "type": "polygon"
        })

    # 添加判断，只保留置信度大于阈值的检测结果
    results = [result for result in results if result["confidence"] > threshold]

    # 添加判断，只有当检测结果不为空时才进行处理
    if len(results) > 0:
        # 进行处理的代码

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

在上述代码中，我添加了两个判断条件。首先，通过列表推导式`results = [result for result in results if result["confidence"] > threshold]`筛选出置信度大于阈值的检测结果。然后，通过`if len(results) > 0`判断检测结果是否为空，只有当结果不为空时才进行处理。

请注意，这只是一种可能的修改方式，你可以根据实际需求进行调整。