import json import base64 from PIL import Image import io import cv2 import numpy as np from ultralytics import YOLO import supervision as sv def init_context(context): context.logger.info("Init context... 0%") model_path = "yolov8m-seg.pt" # YOLOV8模型放在nuclio目录下构建 model = YOLO(model_path) # Read the DL model context.user_data.model = model context.logger.info("Init context...100%") 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",}) return context.Response(body=json.dumps(results), headers={}, content_type='application/json', status_code=200)不用supervision 包 用别的方式解析

如果你不想使用 `supervision` 包，可以使用其他方式解析 `masks`。

以下是修改后的代码示例，使用 OpenCV 库中的 `cv2.findContours()` 函数和 `cv2.drawContours 函数来解析 `masks` 并结果列表：

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

def init_context(context):
    context.logger.info("Init context...  0%")

    model_path = "yolov8m-seg.pt"  #  YOLOV8模型放在nuclio目录下构建

    model = YOLO(model_path)

    # Read the DL model
    context.user_data.model = model

    context.logger.info("Init context...100%")


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 = yolo_results.pred

    results = []

    for detection in detections:
        class_id = int(detection[5])
        score = detection[4]
        mask = detection[6]

        # 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[class_id],
                "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，并且使用了 `YOLO` 类来获取预测结果。

我修改了 `handler` 函数中的代码，将 `detections` 对象中的每个检测结果进行遍历。对于每个检测结果，我提取了类别ID、置信度和掩码。然后，使用 `cv2.findContours()` 函数将掩码转换为轮廓，并使用 `cv2.drawContours()` 函数绘制轮廓。

最后，我将每个检测结果的类别、置信度和轮廓点添加到结果列表中。

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。