old_contents: # USAGE
  # python demo_guided_optional.py --base-model $CAFFE_ROOT/models/bvlc_googlenet \
  #	--image initial_images/clouds.jpg \
  #	--guide-image initial_images/seed_images/starry_night.jpg \
  #	--output examples/output/seeded/clouds_and_starry_night.jpg
  
  # import the necessary packages
  from batcountry import BatCountry
  from PIL import Image
  import numpy as np
  import argparse,time
  
  # construct the argument parser and parse the arguments
  ap = argparse.ArgumentParser()
  ap.add_argument("-b", "--base-model", required=True, help="base model path")
  ap.add_argument("-l", "--layer", type=str, help="layer of CNN to use")  #TODO add support choose layer
  ap.add_argument("-i", "--image", required=True, help="path to base image")
  ap.add_argument("-g", "--guide-image", required=False, help="path to guide image")
  ap.add_argument("-o", "--output", required=False, help="path to output image")
  ap.add_argument("-t", "--test", required=False, help="not used right now")
  args = ap.parse_args()
  if args.output == None:
    args.output = "/data/output/"+ str(int(time.time())) + ".jpg"
  
  # we can't stop here...
  bc = BatCountry(args.base_model)
  if args.guide_image == None:
    if args.layer == None:
      args.layer = "conv2/3x3"
    image = bc.dream(np.float32(Image.open(args.image)), end=args.layer)
  else:
    if args.layer == None:
      args.layer = "inception_4c/output"
    features = bc.prepare_guide(Image.open(args.guide_image), end=args.layer)
    image = bc.dream(np.float32(Image.open(args.image)), end=args.layer,
            iter_n=20, objective_fn=BatCountry.guided_objective,
            objective_features=features,)
  bc.cleanup()
  
  # write the output image to file
  result = Image.fromarray(np.uint8(image))
  result.save(args.output)
  print args.output
  
new_contents: # USAGE
  # python demo_guided_optional.py --base-model $CAFFE_ROOT/models/bvlc_googlenet \
  #	--image initial_images/clouds.jpg \
  #	--guide-image initial_images/seed_images/starry_night.jpg \
  #	--output examples/output/seeded/clouds_and_starry_night.jpg
  
  # import the necessary packages
  from batcountry import BatCountry
  from PIL import Image
  import numpy as np
  import argparse,time
  
  # construct the argument parser and parse the arguments
  ap = argparse.ArgumentParser()
  ap.add_argument("-b", "--base-model", required=True, help="base model path")
  ap.add_argument("-l", "--layer", type=str, help="layer of CNN to use")  #TODO add support choose layer
  ap.add_argument("-i", "--image", required=True, help="path to base image")
  ap.add_argument("-g", "--guide-image", required=False, help="path to guide image")
  ap.add_argument("-o", "--output", required=False, help="path to output image")
  ap.add_argument("-t", "--test", required=False, help="not used right now")
  ap.add_argument("-p", "--patch-model", required=False, help="path to patch model")
  args = ap.parse_args()
  if args.output == None:
    args.output = "/data/output/"+ str(int(time.time())) + ".jpg"
  
  # we can't stop here...
  bc = BatCountry(args.base_model, patch_model = args.patch_model)
  if args.guide_image == None:
    if args.layer == None:
      args.layer = "conv2/3x3"
    image = bc.dream(np.float32(Image.open(args.image)), end=args.layer)
  else:
    if args.layer == None:
      args.layer = "inception_4c/output"
    features = bc.prepare_guide(Image.open(args.guide_image), end=args.layer)
    image = bc.dream(np.float32(Image.open(args.image)), end=args.layer,
            iter_n=20, objective_fn=BatCountry.guided_objective,
            objective_features=features,)
  bc.cleanup()
  
  # write the output image to file
  result = Image.fromarray(np.uint8(image))
  result.save(args.output)
  print args.output
  
subject: add patch_model to args