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 = sv.Detections.from_yolov8(yolo_results) detections = detections[detections.confidence > threshold] masks = detections.xy conf = detections.confidence class_ids = detections.class_id results = [] if masks.shape[0] > 0: for label, score, mask in zip(class_ids, conf, masks): # 将mask转换为轮廓 contours, _ = cv2.findContours(mask.astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) for contour in contours: points = [] for point in contour: x = point[0][0] y = point[0][1] points.append([x, y]) results.append({ "confidence": str(score), "label": labels.get(label, "unknown"), "points": points, "type": "polygon",})不用这个包了 解析mask掩码

如果你不再使用 `sv.Detections` 包，而是希望解析 `mask` 掩码，可以使用 OpenCV 库来完成。以下是修改后的代码示例，用于解析 `mask` 掩码并生成结果列表：

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 = sv.Detections.from_yolov8(yolo_results)

    detections = detections[detections.confidence > threshold]
    masks = detections.mask.astype(np.uint8)
    conf = detections.confidence
    class_ids = detections.class_id

    results = []

    if masks.shape[0] > 0:
        for label, score, mask in zip(class_ids, conf, masks):
            # 将mask转换为轮廓
            contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

            for contour in contours:
                points = []
                for point in contour:
                    x = point[0][0]
                    y = point[0][1]
                    points.append([x, y])

                results.append({
                    "confidence": str(score),
                    "label": labels.get(label, "unknown"),
                    "points": points,
                    "type": "polygon"})

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

在这个修改后的代码中，我假设你已经正确导入了所需的库和模块，并且 `sv.Detections` 包已被移除。我将 `mask` 数组转换为了 `np.uint8` 类型，并使用 OpenCV 的 `findContours()` 函数来找到轮廓。然后，通过遍历轮廓并保存每个轮廓的点，生成了结果列表。

请注意，这只是一个示例代码，可能需要根据你的实际需求进行调整和优化。

希望这能帮助到你！如果还有其他问题，请随时提问。