old_contents: import os
  import time
  import re
  import glob
  import caffe
  import sys
  import numpy as np
  import itertools as itr
  from skimage import color
  import autocolorize
  import argparse
  from autocolorize import image
  
  
  def checker_main(inner):
      parser = argparse.ArgumentParser()
      parser.add_argument('out_dir', type=str, help='Output directory')
      parser.add_argument('-d', '--dataset', default='sun')
      parser.add_argument('-o', '--offset', type=int, default=0)
      parser.add_argument('-n', '--count', type=int, default=10000)
      parser.add_argument('-gto', '--ground-truth-output')
      parser.add_argument('-v', '--verbose', action='store_true')
      parser.add_argument('-s', '--seed', type=int)
      parser.add_argument('-g', '--gpu', type=int)
      parser.add_argument('-p', '--param', type=str)
      args = parser.parse_args()
  
      start = time.time()
  
      if args.gpu is not None:
          caffe.set_device(args.gpu)
      caffe.set_mode_gpu()
  
      classifier = autocolorize.load_classifier('bare.prototxt')
      img_list = autocolorize.load_image_list(args.dataset, offset=args.offset, count=args.count, seed=args.seed)
  
      for img_i, img_fn in enumerate(img_list):
          if time.time() - start > 3.75 * 3600:
              sys.exit(0)
  
          name = os.path.splitext(os.path.basename(img_fn))[0]
          output_fn = os.path.join(args.out_dir, name + '.png')
  
          if os.path.exists(output_fn):
              continue
  
          raw_img = image.load(img_fn)#[..., :3][..., ::-1]
          if raw_img.ndim == 3:
              raw_img = raw_img[..., :3][..., ::-1]
          else:
              raw_img = np.tile(raw_img[..., np.newaxis], 3)
  
          orig_img = raw_img.copy()
          print('Original image size:', raw_img.shape)
  
          rgb = inner(classifier, raw_img,
                      param=args.param, name=output_fn)
  
          # Correct the lightness
          #rgb = autocolorize.match_lightness(rgb, orig_img)
          rgb = rgb.clip(0, 1)
          image.save(output_fn, rgb)
  
          if args.ground_truth_output:
              orig_rgb = orig_img[..., ::-1]
              image.save(os.path.join(args.ground_truth_output, name + '.png'), orig_rgb)
  
new_contents: import os
  import time
  import re
  import glob
  import caffe
  import sys
  import numpy as np
  import itertools as itr
  from skimage import color
  import autocolorize
  import argparse
  from autocolorize import image
  
  
  def checker_main(inner):
      parser = argparse.ArgumentParser()
      parser.add_argument('out_dir', type=str, help='Output directory')
      parser.add_argument('-d', '--dataset', default='sun')
      parser.add_argument('-o', '--offset', type=int, default=0)
      parser.add_argument('-n', '--count', type=int, default=10000)
      parser.add_argument('-gto', '--ground-truth-output')
      parser.add_argument('-v', '--verbose', action='store_true')
      parser.add_argument('-s', '--seed', type=int)
      parser.add_argument('-g', '--gpu', type=int)
      parser.add_argument('-p', '--param', type=str)
      args = parser.parse_args()
  
      start = time.time()
  
      if args.gpu is not None:
          caffe.set_device(args.gpu)
      caffe.set_mode_gpu()
  
      classifier = autocolorize.load_classifier('bare.prototxt')
      img_list = autocolorize.load_image_list(args.dataset, offset=args.offset, count=args.count, seed=args.seed)
  
      for img_i, img_fn in enumerate(img_list):
          if time.time() - start > 3.75 * 3600:
              sys.exit(0)
  
          name = os.path.splitext(os.path.basename(img_fn))[0]
          output_fn = os.path.join(args.out_dir, name + '.png')
  
          if os.path.exists(output_fn):
              continue
  
          raw_img = image.load(img_fn)#[..., :3][..., ::-1]
          if raw_img.ndim == 3:
              raw_img = raw_img[..., :3][..., ::-1]
          else:
              raw_img = np.tile(raw_img[..., np.newaxis], 3)
  
          orig_img = raw_img.copy()
          print('Original image size:', raw_img.shape)
  
          rgb = inner(classifier, raw_img,
                      param=args.param, name=output_fn)
  
          # Correct the lightness
          rgb = autocolorize.match_lightness(rgb, orig_img)
          rgb = rgb.clip(0, 1)
          image.save(output_fn, rgb)
  
          if args.ground_truth_output:
              orig_rgb = orig_img[..., ::-1]
              image.save(os.path.join(args.ground_truth_output, name + '.png'), orig_rgb)
  
subject: Add back lightness fix