text: """An example of how to use your own dataset to train a classifier that recognizes people.
  """
  # MIT License
  #
  # Copyright (c) 2016 David Sandberg
  #
  # Permission is hereby granted, free of charge, to any person obtaining a copy
  # of this software and associated documentation files (the "Software"), to deal
  # in the Software without restriction, including without limitation the rights
  # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
  # copies of the Software, and to permit persons to whom the Software is
  # furnished to do so, subject to the following conditions:
  #
  # The above copyright notice and this permission notice shall be included in all
  # copies or substantial portions of the Software.
  #
  # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  # SOFTWARE.
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import tensorflow as tf
  import numpy as np
  import argparse
  import facenet
  import os
  import sys
  import math
  import pickle
  import json
  import time
  from sklearn.svm import SVC
  
  def main(args):
  
      with tf.Graph().as_default():
  
          with tf.Session() as sess:
  
              np.random.seed(seed=args.seed)
  
              if args.use_split_dataset:
                  dataset_tmp = facenet.get_dataset(args.data_dir)
                  train_set, test_set = split_dataset(dataset_tmp, args.min_nrof_images_per_class, args.nrof_train_images_per_class)
                  if (args.mode=='TRAIN'):
                      dataset = train_set
                  elif (args.mode=='CLASSIFY'):
                      dataset = test_set
              else:
                  dataset = facenet.get_dataset(args.data_dir)
  
              # Check that there are at least one training image per class
              for cls in dataset:
                  assert(len(cls.image_paths)>0, 'There must be at least one image for each class in the dataset')
  
  
              paths, labels = facenet.get_image_paths_and_labels(dataset)
  
              print('Number of classes: %d' % len(dataset))
              print('Number of images: %d' % len(paths))
  
              # Load the model
              print('Loading feature extraction model')
              facenet.load_model(args.model)
  
              # Get input and output tensors
              images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
              embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
              phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
              embedding_size = embeddings.get_shape()[1]
  
              # Run forward pass to calculate embeddings
              print('Calculating features for images')
              nrof_images = len(paths)
              nrof_batches_per_epoch = int(math.ceil(1.0*nrof_images / args.batch_size))
              emb_array = np.zeros((nrof_images, embedding_size))
              for i in range(nrof_batches_per_epoch):
                  start_index = i*args.batch_size
                  end_index = min((i+1)*args.batch_size, nrof_images)
                  paths_batch = paths[start_index:end_index]
                  images = facenet.load_data(paths_batch, False, False, args.image_size)
                  type(images)
                  feed_dict = { images_placeholder:images, phase_train_placeholder:False }
                  emb_array[start_index:end_index,:] = sess.run(embeddings, feed_dict=feed_dict)
  
              classifier_filename_exp = os.path.expanduser(args.classifier_filename)
              classifier_index_exp = '/root/Facenet-Server/data/classifier/index.json'
              classifier_name = parse_filename(classifier_filename_exp)
  
  
  
              if (args.mode=='TRAIN'):
                  # Train classifier
                  print('Training classifier')
                  model = SVC(kernel='linear', probability=True)
                  model.fit(emb_array, labels)
  
                  # Create a list of class names
                  class_names = [ cls.name.replace('_', ' ') for cls in dataset]
  
                  # Saving classifier model
                  with open(classifier_filename_exp, 'wb') as outfile:
                      pickle.dump((model, class_names), outfile)
  
                  # Create a json object with classifier name and time created
                  classifier_json = {'name': classifier_name, 'date': str(time.ctime())}
                  append_to_json(classifier_json, classifier_index_exp)
  
                  print('Saved classifier model to file "%s"' % classifier_filename_exp)
                  print('Wrote classifier information to file "%s"' % classifier_index_exp)
  
              elif (args.mode=='CLASSIFY'):
                  # Classify images
                  print('Testing classifier')
                  with open(classifier_filename_exp, 'rb') as infile:
                      (model, class_names) = pickle.load(infile)
  
                  print('Loaded classifier model from file "%s"' % classifier_filename_exp)
  
                  predictions = model.predict_proba(emb_array)
                  best_class_indices = np.argmax(predictions, axis=1)
                  best_class_probabilities = predictions[np.arange(len(best_class_indices)), best_class_indices]
  
                  for i in range(len(best_class_indices)):
                      print('%4d  %s: %.3f' % (i, class_names[best_class_indices[i]], best_class_probabilities[i]))
  
                  accuracy = np.mean(np.equal(best_class_indices, labels))
                  print('Accuracy: %.3f' % accuracy)
  
                  for i in range(len(best_class_indices)):
                      print ('Path: %s, Label: %s' % (paths[i], class_names[best_class_indices[i]]))
                      image_name = parse_filename(paths[i])
                      result_json = {'filename': image_name, 'path': get_public_path(paths[i]), 'name': class_names[best_class_indices[i]]}
                      append_to_json(result_json, '/root/Facenet-Server/data/result.json')
  
  def split_dataset(dataset, min_nrof_images_per_class, nrof_train_images_per_class):
      train_set = []
      test_set = []
      for cls in dataset:
          paths = cls.image_paths
          # Remove classes with less than min_nrof_images_per_class
          if len(paths)>=min_nrof_images_per_class:
              np.random.shuffle(paths)
              train_set.append(facenet.ImageClass(cls.name, paths[:nrof_train_images_per_class]))
              test_set.append(facenet.ImageClass(cls.name, paths[nrof_train_images_per_class:]))
      return train_set, test_set
  
  
  def parse_arguments(argv):
      parser = argparse.ArgumentParser()
  
      parser.add_argument('mode', type=str, choices=['TRAIN', 'CLASSIFY'],
          help='Indicates if a new classifier should be trained or a classification ' +
          'model should be used for classification', default='CLASSIFY')
      parser.add_argument('data_dir', type=str,
          help='Path to the data directory containing aligned LFW face patches.')
      parser.add_argument('model', type=str,
          help='Could be either a directory containing the meta_file and ckpt_file or a model protobuf (.pb) file')
      parser.add_argument('classifier_filename',
          help='Classifier model file name as a pickle (.pkl) file. ' +
          'For training this is the output and for classification this is an input.')
      parser.add_argument('--use_split_dataset',
          help='Indicates that the dataset specified by data_dir should be split into a training and test set. ' +
          'Otherwise a separate test set can be specified using the test_data_dir option.', action='store_true')
      parser.add_argument('--test_data_dir', type=str,
          help='Path to the test data directory containing aligned images used for testing.')
      parser.add_argument('--batch_size', type=int,
          help='Number of images to process in a batch.', default=90)
      parser.add_argument('--image_size', type=int,
          help='Image size (height, width) in pixels.', default=160)
      parser.add_argument('--seed', type=int,
          help='Random seed.', default=666)
      parser.add_argument('--min_nrof_images_per_class', type=int,
          help='Only include classes with at least this number of images in the dataset', default=20)
      parser.add_argument('--nrof_train_images_per_class', type=int,
          help='Use this number of images from each class for training and the rest for testing', default=10)
  
      return parser.parse_args(argv)
  
  def append_to_json(_dict,path):
      with open(path, 'a+') as f:
          # Go to the end of file
          f.seek(0,2)
          # Check if file is empty
          if f.tell() == 0 :
              # If empty, write an array
              json.dump([_dict], f, indent=4, separators=(',', ': '))
          else :
              f.seek(-1,2)
              f.truncate()           # Remove the last character, open the array
              f.write(',')         # Write the separator
              json.dump(_dict, f, indent=4, separators=(',', ': '))     # Dump the dictionary
              f.write(']')           # Close the array
  
  def parse_filename(filepath):
      # Parse the file path string to get the classifier name
      for i in range(len(filepath)-1 ,0, -1):
          if filepath[i] == '/':
              filename = filepath[i+1:]
              return filename
  
  def get_public_path(filepath):
      count = 0
      for i in range(len(filepath)-1 ,0, -1):
          if filepath[i] == '/':
              count = count + 1
              if count == 2:
                  #public_path = 'http://localhost:8081' + filepath[i:]
                  public_path = 'http://54.237.168.141:8081' + filepath[i:]
                  return public_path
  
  if __name__ == '__main__':
      main(parse_arguments(sys.argv[1:]))
  