text: import argparse
  import cv2
  
  from PIL import Image
  import numpy as np
  import matplotlib.pyplot as plt
  
  import utils.config as config_utils
  import utils.gpu as gpu
  import model.unet as unet
  
  
  parser = argparse.ArgumentParser(
      description='Detect classes (drone dataset) using trained model')
  parser.add_argument('path',
                      help='path of the picture')
  args = parser.parse_args()
  
  try:
      data = np.array(Image.open(args.path))
  except IOError:
      print('Error: File not found.')
      exit(1)
  
  config = config_utils.get_config()
  gpu.configure(config.force_cpu, config.gpu_mem_limit)
  
  data = cv2.resize(data, config.resolution,
                    interpolation=cv2.INTER_LINEAR)
  
  model = unet.get_model(data.shape, n_filters=config.n_filters, n_classes=len(
      config.classes))
  model.load_weights(config.output_path)
  
  prediction = model.predict(np.array([data])/255)
  prediction = np.squeeze(prediction, axis=0)
  
  out = np.zeros(prediction.shape[0:2])
  
  out = np.argmax(prediction, axis=2)
  out = np.ma.masked_where(out == 0, out)
  
  plt.imshow(data)
  plt.imshow(out, 'jet', alpha=0.7)
  plt.show()
  