code: def bbox_area_sampling(bboxes, labels, scores, target_size, min_size):
      new_bboxes = []
      new_labels = []
      new_scores = []
      for i, bbox in enumerate(bboxes):
          w = float((bbox[2] - bbox[0]) * target_size)
          h = float((bbox[3] - bbox[1]) * target_size)
          if w * h < float(min_size * min_size):
              continue
          else:
              new_bboxes.append(bbox)
              new_labels.append(labels[i])
              if scores is not None and scores.size != 0:
                  new_scores.append(scores[i])
      bboxes = np.array(new_bboxes)
      labels = np.array(new_labels)
      scores = np.array(new_scores)
      return bboxes, labels, scores
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  import random
  import math
  import cv2
label_desc: Use image node_id 6 for calling a global function with example usage: bbox_area_sampling(bboxes, labels, scores, target_size, min_size) and returns: bboxes,  labels,  scores