text: from __future__ import print_function
  
  import matplotlib.pyplot as plt
  # import miscellaneous modules
  import numpy as np
  
  import cv2
  from engine.object_detection_branch.retina_net.keras_retinanet import models
  from keras.utils.data_utils import get_file
  
  from engine.object_detection_branch.retina_net.keras_retinanet.utils.visualization import draw_box, draw_caption
  from engine.object_detection_branch.retina_net.keras_retinanet.utils.colors import label_color
  
  from engine.object_detection_branch.retina_net.keras_retinanet.utils.image import read_image_bgr, preprocess_image, resize_image
  
  MODEL_PATH = 'https://github.com/GKalliatakis/Keras-EMOTIC-resources/releases/download/v1.0.2/resnet50_coco_best_v2.1.0.h5'
  
  
  def RetinaNet_single_img_detection(img_path,
                                     imshow = False):
  
      # load the downloaded/trained model
      model_path = get_file('resnet50_coco_best_v2.h5',
                            MODEL_PATH,
                            cache_subdir='EMOTIC/object_detectors')
  
      # load RetinaNet model
      model = models.load_model(model_path, backbone_name='resnet50')
  
      # load label to names mapping
      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'}
  
      # load image
      image = read_image_bgr(img_path)
  
  
      if imshow:
          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
      boxes, scores, labels = model.predict_on_batch(np.expand_dims(image, axis=0))
  
      # correct for image scale
      boxes /= scale
  
  
      persons_counter = 0
      # run a for loop to define the number of detected persons
      for box, score, label in zip(boxes[0], scores[0], labels[0]):
          # scores are sorted so we can break
          if score < 0.5:
              break
  
          # decode predicted labels
          decoded_label = "{}".format(labels_to_names[label])
  
          if decoded_label == 'person':
              # print('[INFO] `person` was detected')
              persons_counter += 1
  
              # b = box.astype(int)
  
          # TODO must handle the cases where no `person` object class was detected
          # else:
          #     print ('[INFO] No `person` was detected')
  
  
      final_array = np.empty([persons_counter, 4])
      counter = 0
  
      for box, score, label in zip(boxes[0], scores[0], labels[0]):
  
          if counter > persons_counter:
              break
  
          # scores are sorted so we can break
          if score < 0.5:
              break
  
          # decode predicted labels
          decoded_label = "{}".format(labels_to_names[label])
  
          if decoded_label == 'person':
  
              b = box.astype(int)
              final_array[counter][0] = b[0]
              final_array[counter][1] = b[1]
              final_array[counter][2] = b[2]
              final_array[counter][3] = b[3]
  
              counter += 1
  
              if imshow:
                  color = label_color(label)
                  draw_box(draw, b, color=color)
                  caption = "{} {:.3f}".format(labels_to_names[label], score)
                  draw_caption(draw, b, caption)
  
  
  
      if imshow:
          plt.figure(figsize=(20, 12))
          plt.axis('off')
          plt.imshow(draw)
          plt.show()
  
  
      return final_array, persons_counter
  
  
  