text: import os, sys
  
  import tensorflow as tf
  import pickle
  from collections import defaultdict
  flag_dict = defaultdict(list)
  #os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
  #os.environ['CUDA_VISIBLE_DEVICES'] = '0'
  from glob import glob
  # change this as you see fit
  import sys
  input_folder = sys.argv[1] #'/home/brian/tensorflow/models/research/object_detection/m18/m18/'
  output_pickle = sys.argv[2] #'flag_m18_2'
  for name in glob(input_folder+'/*'):
      image_path = name#sys.argv[1]
      print (name)
      # Read in the image_data
      image_data = tf.compat.v1.gfile.FastGFile(image_path, 'rb').read()
  
      # Loads label file, strips off carriage return
      label_lines = [line.rstrip() for line 
                         in tf.io.gfile.GFile("results/retrained_labels2.txt")]
  
      # Unpersists graph from file
      with tf.compat.v1.gfile.FastGFile("src/retrained_graph2.pb", 'rb') as f:
          graph_def = tf.compat.v1.GraphDef()
          graph_def.ParseFromString(f.read())
          tf.import_graph_def(graph_def, name='')
  
      with tf.compat.v1.Session() as sess:
          # Feed the image_data as input to the graph and get first prediction
          softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')
          
          predictions = sess.run(softmax_tensor, \
                   {'DecodeJpeg/contents:0': image_data})
          
          # Sort to show labels of first prediction in order of confidence
          top_k = predictions[0].argsort()[-len(predictions[0]):][::-1]
          
          for node_id in top_k:
              human_string = label_lines[node_id]
              score = predictions[0][node_id]
              #print('%s (score = %.5f)' % (human_string, score))
              flag_dict[name].append((human_string, score))
  with open(output_pickle+'.pickle', 'wb') as f:
      pickle.dump(flag_dict,f,protocol=pickle.HIGHEST_PROTOCOL)