new_contents: #!/usr/bin/env python3
  import argparse
  from importlib import import_module
  from os.path import splitext, join as pjoin
  
  import cv2
  import numpy as np
  import h5py
  
  import lib
  from lib.models import add_defaults
  
  
  if __name__ == '__main__':
      parser = argparse.ArgumentParser(description='Embed many images.')
      parser.add_argument('--basedir', default='.',
                          help='Path to the folder containing all images.')
      parser.add_argument('--outfile', default='embeddings.h5',
                          help='Name of the output hdf5 file in which to store the embeddings.')
      parser.add_argument('--model', default='lunet2',
                          help='Name of the model to load. Corresponds to module names in lib/models. Or `fake`')
      parser.add_argument('--weights', default='/work/breuers/dukeMTMC/models/lunet2-final.pkl',
                          help='Name of the weights to load for the model (path to .pkl file).')
      parser.add_argument('--scale', default=1.0, type=float,
                          help='Scale factor to scale images before embedding them.')
      parser.add_argument('--t0', type=int)
      parser.add_argument('--t1', type=int)
      args = parser.parse_args()
      print(args)
  
  
      mod = import_module('lib.models.' + args.model)
      net = add_defaults(mod.mknet())
  
      try:
          net.load(args.weights)
      except ValueError:
          print("!!!!!!!THE WEIGHTS YOU LOADED DON'T BELONG TO THE MODEL YOU'RE USING!!!!!!")
          raise
  
      # Shares the weights, just replaces the avg-pooling layer.
      net_hires = mod.hires_shared_twin(net)
      net_hires.evaluate()
  
      if args.t0 is None or args.t1 is None:
          all_files = sane_listdir(args.basedir, sortkey=lambda f: int(splitext(f)[0]))
      else:
          all_files = ['{}.jpg'.format(i) for i in range(args.t0, args.t1+1)]
  
      print("Precompiling network...", end='', flush=True)
      img = lib.imread(pjoin(args.basedir, all_files[0]))
      img = lib.img2df(img, lib.scale_shape(img.shape, args.scale))
      out = net_hires.forward(img[None])
      print(" Done", flush=True)
  
      with h5py.File(args.outfile, 'w') as f_out:
          ds = f_out.create_dataset('embs', shape=(len(all_files),) + out.shape[1:], dtype=out.dtype)
          for i, fname in enumerate(all_files):
              print("\r{} ({}/{})".format(fname, i, len(all_files)), end='', flush=True)
  
              img = lib.imread(pjoin(args.basedir, fname))
              img = lib.img2df(img, lib.scale_shape(img.shape, args.scale))
              ds[i] = net_hires.forward(img[None])
  
              if i % 100 == 0:
                  f_out.flush()
  
      print(" Done")
  
subject: Add script to embed all images.