code: def infer(self, img):
      if self.model is None:
          # 默认在没有模型得情况下使用推理时直接返回空列表
          return []
      output = self.model.run(img, visual=False)
      res_list = []
      for anchor in output["boxes"]:
          clas_id, bbox, score = int(anchor[0]), anchor[2:], anchor[1]
          if score > self.model.threshold:
              x_min, y_min, x_max, y_max = bbox
              p = (int(x_min), int(y_min))
              size = (int(x_max - x_min), int(y_max - y_min))
              res_list.append((COCO_CLASS_DICT[clas_id], p, size))
      return res_list
  
imports: from .detection_model import Detector
  from util.coco.detlabel import COCO_CLASS_DICT
label_desc: Use image node_id 3 for calling the DetInfer obj's underlying member method code with example usage: obj.infer(img) and returns: res_list, list