old_contents: import os, sys, re
  import numpy as np
  import skimage.io
  import skimage.transform
  from sklearn.preprocessing import LabelBinarizer
  from keras.applications import InceptionV3
  from keras.applications import imagenet_utils
  from keras.models import Model
  from keras.layers import Input, Dense, Flatten, Dropout, Reshape
  
  # Command line arguments: image in-path, feature out-path, .extension for output
  argv = sys.argv
  img_path, out_path, ext = argv[1], argv[2], argv[3]
  
  def extract_features():
  
      # Load dataset (any image-based dataset)
      matches = [(re.match(r'^(([a-zA-Z]+)\d+\.png)', fname), path) 
  	    for path, dirs, files in os.walk(''+str(img_path)) for fname in files]
      patches = [skimage.transform.resize( # resize image to (256, 256) TODO argv patch size?
  	    skimage.io.imread(os.path.join(path, match.group(1))), # open each image
      	    (256, 256)) for match, path in matches if match]
  	
      # change color channel order and shift mean color for ImageNet		
      patches = imagenet_utils.preprocess_input(np.array(patches)) 
      labels = [match.group(2) for match, path in matches if match]
      labels = LabelBinarizer().fit_transform(list(labels)) # one-hot encoding
      print('patches', patches[0].shape, len(patches), 'labels', len(labels))
  
      # Construct model (using ImageNet weights)
      inceptionV3 = InceptionV3(weights = "imagenet", include_top = False, 
          input_shape = patches[0].shape)
  
      x = inceptionV3.output
      x = Flatten()(x)
      x = Dense(1024, activation="relu")(x)
      x = Dropout(0.5)(x)
      x = Dense(1024, activation="relu")(x)
      extractor = Model(inputs=[inceptionV3.input],outputs=[x])
      features = extractor.predict(x=patches, batch_size=2)
      return features
  # this is a comment
  features = extract_features()
  # print("\n[INFO] Output array shape:", features.shape) # Uncomment to verify output shape
  np.savetxt("" + str(out_path) + "." + str(ext), features, fmt='%f')
  
new_contents: import os, sys, re
  import numpy as np
  import skimage.io
  import skimage.transform
  from sklearn.preprocessing import LabelBinarizer
  from keras.applications import InceptionV3
  from keras.applications import imagenet_utils
  from keras.models import Model
  from keras.layers import Input, Dense, Flatten, Dropout, Reshape
  
  # Command line arguments: image in-path, feature out-path, .extension for output
  argv = sys.argv
  img_path, out_path, ext = argv[1], argv[2], argv[3]
  
  def extract_features():
  
      # Load dataset (any image-based dataset)
      matches = [(re.match(r'^(([a-zA-Z]+)\d+\.png)', fname), path) 
  	    for path, dirs, files in os.walk(''+str(img_path)) for fname in files]
      patches = [skimage.transform.resize( # resize image to (256, 256) TODO argv patch size?
  	    skimage.io.imread(os.path.join(path, match.group(1))), # open each image
      	    (256, 256)) for match, path in matches if match]
  	
      # change color channel order and shift mean color for ImageNet		
      patches = imagenet_utils.preprocess_input(np.array(patches)) 
      labels = [match.group(2) for match, path in matches if match]
      labels = LabelBinarizer().fit_transform(list(labels)) # one-hot encoding
      print('patches', patches[0].shape, len(patches), 'labels', len(labels))
  
      # Construct model (using ImageNet weights)
      inceptionV3 = InceptionV3(weights = "imagenet", include_top = False, 
          input_shape = patches[0].shape)
  
      x = inceptionV3.output
      x = Flatten()(x)
      x = Dense(1024, activation="relu")(x)
      x = Dropout(0.5)(x)
      x = Dense(1024, activation="relu")(x)
      extractor = Model(inputs=[inceptionV3.input],outputs=[x])
      features = extractor.predict(x=patches, batch_size=2)
      return features
  
  features = extract_features()
  # print("\n[INFO] Output array shape:", features.shape) # Uncomment to verify output shape
  np.savetxt("" + str(out_path) + "." + str(ext), features, fmt='%f')
  
subject: revert no change