new_contents: from scipy import ndimage
  import numpy as np
  from scipy.misc import imresize
  import json
  
  from network import YOLO
  
  
  class Predict:
  
      def __init__(self, config):
          self.model_pickle_file = config['network']['model_pickle_file']
  
          self.network = YOLO(config)
          self.network.load_model()
  
          self.image_size = config['image_info']['image_size']
          self.pixel_depth = config['image_info']['pixel_depth']
          self.color_channels = config['image_info']['color_channels']
  
      def predict(self, image_file):
          image = self.get_image_from_file(image_file)
  
          predict = self.network.predict(image)
          return predict
  
      def get_image_from_file(self, image_file):
          image_data = (ndimage.imread(image_file).astype(float) -
                        (self.pixel_depth / 2)) / self.pixel_depth
  
          if len(image_data.shape) != self.color_channels:
              image_data = np.repeat(image_data[:, :, np.newaxis],
                                     self.color_channels, axis=2)
  
          if image_data.shape != (self.image_size, self.image_size,
                                  self.color_channels):
              image = imresize(image_data, size=(self.image_size,
                                                 self.image_size))
          else:
              image = image_data
  
          image = np.expand_dims(image, axis=0)
          return image
  
  
  if __name__ == '__main__':
      with open('./config.json') as config_file:
          config = json.load(config_file)
  
      predict = Predict(config)
  
      path = './dataset/one_not_crop/cvpr10_multiview_pedestrians/test/00036.png'
  
      prediction = predict.predict(path)
      print(prediction)
  
subject: Add prediction to network