image_processing.py 文件源码

def load_letter(folder, min_num_images):
  """Load the data for a single letter label."""

  image_files = os.listdir(folder)
  dataset = np.ndarray(shape=(len(image_files), image_size, image_size),
                         dtype=np.float32)
  print(folder)

  num_images = 0
  for image_index, image in enumerate(image_files):
    image_file = os.path.join(folder, image)
    try:
      image_data = (ndimage.imread(image_file).astype(float) -      # normalize data
                    pixel_depth / 2) / pixel_depth
      if image_data.shape != (image_size, image_size):
        raise Exception('Unexpected image shape: %s' % str(image_data.shape))
      dataset[num_images, :, :] = image_data
      num_images = num_images + 1
    except IOError as e:
      print('Could not read:', image_file, ':', e, '- it\'s ok, skipping.') # skip unreadable files

  dataset = dataset[0:num_images, :, :]
  if num_images < min_num_images:                                   # check if a given min. no. of images
    raise Exception('Many fewer images than expected: %d < %d' %    # has been loaded
                    (num_images, min_num_images))

  print('Full dataset tensor:', dataset.shape)
  print('Mean:', np.mean(dataset))
  print('Standard deviation:', np.std(dataset))
  return dataset


# function to store the normalized tensors obtained from the load_letter function in
# .pickle files for later use