old_contents: # --------------------------------------------------------
  # Fast R-CNN
  # Copyright (c) 2015 Microsoft
  # Licensed under The MIT License [see LICENSE for details]
  # Written by Ross Girshick
  # --------------------------------------------------------
  
  """Blob helper functions."""
  
  import numpy as np
  import cv2
  from utils.cython_mathutils import cpu_subtract
  
  def im_list_to_blob(ims):
      """Convert a list of images into a network input.
  
      Assumes images are already prepared (means subtracted, BGR order, ...).
      """
      max_shape = np.array([im.shape for im in ims]).max(axis=0)
      num_images = len(ims)
      blob = np.zeros((num_images, max_shape[0], max_shape[1], 3),
                      dtype=np.float32)
      for i in xrange(num_images):
          im = ims[i]
          blob[i, 0:im.shape[0], 0:im.shape[1], :] = im
      # Move channels (axis 3) to axis 1
      # Axis order will become: (batch elem, channel, height, width)
      channel_swap = (0, 3, 1, 2)
      blob = blob.transpose(channel_swap)
      return blob
  
  def prep_im_for_blob(im, pixel_means, target_size, max_size):
      """Mean subtract and scale an image for use in a blob."""
      im = im.astype(np.float32, copy=False)
      pixel_means = pixel_means.astype(np.float32, copy=False)
      # im -= pixel_means
      cpu_subtract(im, pixel_means)
      # finish = time()
      # print "mean subtraction takes: %f ms" % ((finish - start) * 1000.)
      im_shape = im.shape
      im_size_min = np.min(im_shape[0:2])
      im_size_max = np.max(im_shape[0:2])
      im_scale = float(target_size) / float(im_size_min)
      # Prevent the biggest axis from being more than MAX_SIZE
      if np.round(im_scale * im_size_max) > max_size:
          im_scale = float(max_size) / float(im_size_max)
      im = cv2.resize(im, None, None, fx=im_scale, fy=im_scale,
                      interpolation=cv2.INTER_LINEAR)
  
      return im, im_scale
  
new_contents: # --------------------------------------------------------
  # Fast R-CNN
  # Copyright (c) 2015 Microsoft
  # Licensed under The MIT License [see LICENSE for details]
  # Written by Ross Girshick
  # --------------------------------------------------------
  
  """Blob helper functions."""
  
  import numpy as np
  import cv2
  from utils.cython_mathutils import cpu_subtract
  
  def im_list_to_blob(ims):
      """Convert a list of images into a network input.
  
      Assumes images are already prepared (means subtracted, BGR order, ...).
      """
      max_shape = np.array([im.shape for im in ims]).max(axis=0)
      num_images = len(ims)
      blob = np.zeros((num_images, max_shape[0], max_shape[1], 3),
                      dtype=np.float32)
      for i in xrange(num_images):
          im = ims[i]
          blob[i, 0:im.shape[0], 0:im.shape[1], :] = im
      # Move channels (axis 3) to axis 1
      # Axis order will become: (batch elem, channel, height, width)
      channel_swap = (0, 3, 1, 2)
      blob = blob.transpose(channel_swap)
      return blob
  
  def prep_im_for_blob(im, pixel_means, target_size, max_size):
      """Mean subtract and scale an image for use in a blob."""
      im = im.astype(np.float32, copy=False)
      pixel_means = pixel_means.astype(np.float32, copy=False)
      # im -= pixel_means
      cpu_subtract(im, pixel_means)
  
      im_shape = im.shape
      im_size_min = np.min(im_shape[0:2])
      im_size_max = np.max(im_shape[0:2])
      im_scale = float(target_size) / float(im_size_min)
      # Prevent the biggest axis from being more than MAX_SIZE
      if np.round(im_scale * im_size_max) > max_size:
          im_scale = float(max_size) / float(im_size_max)
      im = cv2.resize(im, None, None, fx=im_scale, fy=im_scale,
                      interpolation=cv2.INTER_LINEAR)
  
      return im, im_scale
  
subject: clean code