text: #!/usr/bin/env python
  # coding: utf-8
  
  # ## Load necessary modules
  # import keras
   
  from keras.models import Model
  from keras.layers import Concatenate
  import sys
  sys.path.insert(0, '../')
  
  
  # import keras_retinanet
  from keras_retinanet import models
  from keras_retinanet.utils.image import read_image_bgr, preprocess_image, resize_image
  from keras_retinanet.utils.visualization import draw_box, draw_caption
  from keras_retinanet.utils.colors import label_color
  from keras_retinanet.utils.gpu import setup_gpu
  
  # import miscellaneous modules
  import matplotlib.pyplot as plt
  import cv2
  import os
  import numpy as np
  import time
  
  # set tf backend to allow memory to grow, instead of claiming everything
  import tensorflow as tf
  
  # use this to change which GPU to use
  gpu = 0
  
  # set the modified tf session as backend in keras
  setup_gpu(gpu)
  
  
  # ## Load RetinaNet model
  
  # In[ ]:
  
  
  # adjust this to point to your downloaded/trained model
  # models can be downloaded here: https://github.com/fizyr/keras-retinanet/releases
  model_path = os.path.join('..', 'snapshots', 'resnet50_coco_best_v2.1.0.h5')
  
  # load retinanet model
  model = models.load_model(model_path, backbone_name='resnet50')
  
  # get classification and regression layers and build new model 
  #model1 = Model(model.input, outputs=[model.layers[-6].output, model.layers[-2].output])
  
  # if the model is not converted to an inference model, use the line below
  # see: https://github.com/fizyr/keras-retinanet#converting-a-training-model-to-inference-model
  #model2 = models.convert_model(model1)
  
  #print(model2.summary())
  
  # load label to names mapping for visualization purposes
  labels_to_names = {0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus', 6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant', 11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat', 16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear', 22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag', 27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard', 32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove', 36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle', 40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl', 46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli', 51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake', 56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table', 61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard', 67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink', 72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors', 77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush'}
  
  
  # ## Run detection on example
  
  # In[ ]:
  
  
  # load image
  image = read_image_bgr('000000008021.jpg')
  
  # copy to draw on
  draw = image.copy()
  draw = cv2.cvtColor(draw, cv2.COLOR_BGR2RGB)
  
  # preprocess image for network
  image = preprocess_image(image)
  image, scale = resize_image(image)
  
  # process image
  start = time.time()
  #boxes, scores, labels = model2.predict_on_batch(np.expand_dims(image, axis=0))
  boxes, scores, labels = model.predict_on_batch(np.expand_dims(image, axis=0))
  print(boxes.shape, scores.shape, labels.shape)
  print("processing time: ", time.time() - start)
  
  # correct for image scale
  boxes /= scale
  
  # visualize detections
  for box, score, label in zip(boxes[0], scores[0], labels[0]):
      # scores are sorted so we can break
      if score < 0.5:
          break
          
      color = label_color(label)
      
      b = box.astype(int)
      draw_box(draw, b, color=color)
      
      caption = "{} {:.3f}".format(labels_to_names[label], score)
      draw_caption(draw, b, caption)
      
  plt.figure(figsize=(15, 15))
  plt.axis('off')
  plt.imshow(draw)
  plt.show()
  
  
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