text: #!/usr/bin/env python2
  #
  # Copyright 2015-2016 Carnegie Mellon University
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import sys
  sys.path.append(".")
  
  import argparse
  import numpy as np
  import os
  import random
  
  import cv2
  
  import openface
  from openface.alignment import NaiveDlib
  from openface.data import iterImgs
  
  fileDir = os.path.dirname(os.path.realpath(__file__))
  modelDir = os.path.join(fileDir, '..', 'models')
  dlibModelDir = os.path.join(modelDir, 'dlib')
  openfaceModelDir = os.path.join(modelDir, 'openface')
  
  parser = argparse.ArgumentParser()
  parser.add_argument('imgDir', type=str, help="Input image directory.")
  parser.add_argument('--numImages', type=int, default=1000)
  parser.add_argument('--model', type=str, help="TODO",
                      default=os.path.join(openfaceModelDir, 'nn4.small2.v1.t7'))
  parser.add_argument('--dlibFacePredictor', type=str, help="Path to dlib's face predictor.",
                      default=os.path.join(dlibModelDir, "shape_predictor_68_face_landmarks.dat"))
  parser.add_argument('--outputFile', type=str,
                      help="Output file, stored in numpy serialized format.",
                      default="./unknown.npy")
  parser.add_argument('--imgDim', type=int, help="Default image size.",
                      default=96)
  args = parser.parse_args()
  
  align = NaiveDlib(args.dlibFacePredictor)
  net = openface.TorchWrap(args.model, imgDim=args.imgDim, cuda=False)
  
  
  def getRep(imgPath):
      bgrImg = cv2.imread(imgPath)
      if bgrImg is None:
          return None
      rgbImg = cv2.cvtColor(bgrImg, cv2.COLOR_BGR2RGB)
  
      bb = align.getLargestFaceBoundingBox(rgbImg)
      if bb is None:
          return None
  
      alignedFace = align.alignImg("affine", args.imgDim, rgbImg, bb)
      if alignedFace is None:
          return None
  
      rep = net.forwardImage(alignedFace)
      return rep
  
  if __name__ == '__main__':
      allImgs = list(iterImgs(args.imgDir))
      imgObjs = random.sample(allImgs, args.numImages)
  
      reps = []
      for imgObj in imgObjs:
          rep = getRep(imgObj.path)
          reps.append(rep)
  
      np.save(args.outputFile, np.row_stack(reps))
  