class_docstr: Args:
      min_subgraph_size (int): number of tensorRT graphs.
      use_gpu (bool): whether use gpu
      threshold (float): threshold to reserve the result for output.
class_docstr_tok: ["Args",":","min_subgraph_size","(","int",")",":","number","of","tensorRT","graphs",".","use_gpu","(","bool",")",":","whether","use","gpu","threshold","(","float",")",":","threshold","to","reserve","the","result","for","output","."]
code: def postprocess(
      self,
      np_boxes: np.ndarray,
      np_masks: np.ndarray,
      threshold: float = 0.5,
  ):
      # postprocess output of predictor
      results = {}
      expect_boxes = (np_boxes[:, 1] > threshold) & (
          np_boxes[:, 0] > -1
      )
      np_boxes = np_boxes[expect_boxes, :]
      for box in np_boxes:
          print(
              "class_id:{:d}, confidence:{:.4f},"
              "left_top:[{:.2f},{:.2f}],"
              " right_bottom:[{:.2f},{:.2f}]".format(
                  int(box[0]), box[1], box[2], box[3], box[4], box[5]
              )
          )
      results["boxes"] = np_boxes
      if np_masks is not None:
          np_masks = np_masks[expect_boxes, :, :, :]
          results["masks"] = np_masks
      return results
  
imports: import base64
  import os
  import time
  from functools import reduce
  from typing import Union
  import numpy
  import solov2.data_feed
  import solov2.processor
  from paddlehub.module.module import moduleinfo
  from paddlehub.module.module import serving
label_desc: Use image node_id 3 for calling the Detector obj's underlying member method code with example usage: obj.postprocess(np_boxes, np_masks, threshold) and returns: results