code: def filter_low_thresh(
      boxes,
      scores,
      classes,
      category_index,
      thresh,
      box_to_display_str_map,
      box_to_color_map,
  ):
      for i in range(boxes.shape[0]):
          if scores[i] > thresh:
              box = tuple(boxes[i].tolist())
              if classes[i] in six.viewkeys(category_index):
                  class_name = category_index[classes[i]]["name"]
              else:
                  class_name = "N/A"
              display_str = str(class_name)
              display_str = "{}: {}%".format(
                  display_str, int(100 * scores[i])
              )
              box_to_display_str_map[box].append(display_str)
              box_to_color_map[box] = STANDARD_COLORS[
                  classes[i] % len(STANDARD_COLORS)
              ]
          else:
              break
  
file_path: deep-learning-for-image-processing/others_project/readPbFile/using_function.py
imports: import collections
  import six
  import PIL.Image
  import PIL.ImageDraw
  import PIL.ImageFont
  import numpy
  import tensorflow
  import cv2
label_desc: Use image node_id 1 for calling a global function with example usage: filter_low_thresh(boxes, scores, classes, category_index, thresh, box_to_display_str_map, box_to_color_map) without return types