text: 
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  #----------------------------------------------------
  # MIT License
  #
  # Copyright (c) 2017 Rakshak Talwar
  #
  # 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.
  #----------------------------------------------------
  
  
  import tensorflow as tf
  import numpy as np
  import argparse
  import facenet
  import os
  import sys
  import math
  import pickle
  from sklearn.svm import SVC
  from scipy import misc
  import align.detect_face
  
  def main(args):
    
      images, cout_per_image, nrof_samples = load_and_align_data(args.image_files,args.image_size, args.margin, args.gpu_memory_fraction)
      with tf.Graph().as_default():
  
         with tf.Session() as sess:
        
              # Load the 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")
  
              # Run forward pass to calculate embeddings
                  feed_dict = { images_placeholder: images , phase_train_placeholder:False}
                  emb = sess.run(embeddings, feed_dict=feed_dict)
                  classifier_filename_exp = os.path.expanduser(args.classifier_filename)
                  with open(classifier_filename_exp, 'rb') as infile:
                      (model, class_names) = pickle.load(infile)
                  print('Loaded classifier model from file "%s"\n' % classifier_filename_exp)
                  predictions = model.predict_proba(emb)
                  best_class_indices = np.argmax(predictions, axis=1)
                  best_class_probabilities = predictions[np.arange(len(best_class_indices)), best_class_indices]
                  k=0     
  	    #print predictions       
                  for i in range(nrof_samples):
                      print("\npeople in image %s :" %(args.image_files[i]))
                      for j in range(cout_per_image[i]):
                          print('%s: %.3f' % (class_names[best_class_indices[k]], best_class_probabilities[k]))
                          k+=1
                      
  def load_and_align_data(image_paths, image_size, margin, gpu_memory_fraction):
  
      minsize = 20 # minimum size of face
      threshold = [ 0.6, 0.7, 0.7 ]  # three steps's threshold
      factor = 0.709 # scale factor
      
      print('Creating networks and loading parameters')
      with tf.Graph().as_default():
          gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_memory_fraction)
          sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
          with sess.as_default():
              pnet, rnet, onet = align.detect_face.create_mtcnn(sess, None)
    
      nrof_samples = len(image_paths)
      img_list = [] 
      count_per_image = []
      for i in xrange(nrof_samples):
          img = misc.imread(os.path.expanduser(image_paths[i]))
          img_size = np.asarray(img.shape)[0:2]
          bounding_boxes, _ = align.detect_face.detect_face(img, minsize, pnet, rnet, onet, threshold, factor)
          count_per_image.append(len(bounding_boxes))
          for j in range(len(bounding_boxes)):	
                  det = np.squeeze(bounding_boxes[j,0:4])
                  bb = np.zeros(4, dtype=np.int32)
                  bb[0] = np.maximum(det[0]-margin/2, 0)
                  bb[1] = np.maximum(det[1]-margin/2, 0)
                  bb[2] = np.minimum(det[2]+margin/2, img_size[1])
                  bb[3] = np.minimum(det[3]+margin/2, img_size[0])
                  cropped = img[bb[1]:bb[3],bb[0]:bb[2],:]
                  aligned = misc.imresize(cropped, (image_size, image_size), interp='bilinear')
                  prewhitened = facenet.prewhiten(aligned)
                  img_list.append(prewhitened)		
      images = np.stack(img_list)
      return images, count_per_image, nrof_samples
  
  def parse_arguments(argv):
      parser = argparse.ArgumentParser()
      parser.add_argument('image_files', type=str, nargs='+', help='Path(s) of the image(s)')
      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('--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('--margin', type=int,
          help='Margin for the crop around the bounding box (height, width) in pixels.', default=44)
      parser.add_argument('--gpu_memory_fraction', type=float,
          help='Upper bound on the amount of GPU memory that will be used by the process.', default=1.0)
      return parser.parse_args(argv)
  
  if __name__ == '__main__':
      main(parse_arguments(sys.argv[1:]))
  
  