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",})

这段代码是一个函数`handler`，它接受一个`context`和一个`event`作为参数。在初始化上下文时，加载了一个YOLOV8模型，并将其存储在`context.user_data.model`中。然后，从`event`中获取图像数据，并对其进行处理。通过调用模型的`__call__`方法，传入图像和阈值参数进行预测。然后，从预测结果中提取标签、置信度、掩码等信息，并将其添加到结果列表中。最后，返回结果列表。

你有关于这段代码的具体问题吗？