class_docstr: Display and save evaluation results.
  
  Args:
      show (bool): Whether to show the image. Default: True.
      wait_time (float): Value of waitKey param. Default: 0.
      score_thr (float): Minimum score of bboxes to be shown.
         Default: 0.
      runner (:obj:`Runner`): The runner of the visualization process.
class_docstr_tok: ["Display","and","save","evaluation","results",".","Args",":","show","(","bool",")",":","Whether","to","show","the","image",".","Default",":","True",".","wait_time","(","float",")",":","Value","of","waitKey","param",".","Default",":","0.","score_thr","(","float",")",":","Minimum","score","of","bboxes","to","be","shown",".","Default",":","0.","runner","(",":","obj",":","`","Runner","`",")",":","The","runner","of","the","visualization","process","."]
code: def __init__(self, show=False, wait_time=0, score_thr=0, runner=None):
      self.show = show
      self.wait_time = wait_time
      self.score_thr = score_thr
      self.visualizer = DetLocalVisualizer()
      self.runner = runner
      self.evaluator = runner.test_evaluator
  
imports: import argparse
  import os.path
  from multiprocessing import Pool
  import mmcv
  import numpy
  from mmengine.config import Config, DictAction
  from mmengine.fileio import load
  from mmengine.registry import init_default_scope
  from mmengine.runner import Runner
  from mmengine.structures import InstanceData, PixelData
  from mmengine.utils import ProgressBar, check_file_exist, mkdir_or_exist
  from mmdet.datasets import get_loading_pipeline
  from mmdet.evaluation import eval_map
  from mmdet.registry import DATASETS, RUNNERS
  from mmdet.structures import DetDataSample
  from mmdet.utils import replace_cfg_vals, update_data_root
  from mmdet.visualization import DetLocalVisualizer
label_desc: Use image node_id 1 to create a new ResultVisualizer object  with example: obj = ResultVisualizer(show, wait_time, score_thr, runner)