text: '''Fairly basic set of tools for real-time data augmentation on image data.
  Can easily be extended to include new transformations,
  new preprocessing methods, etc...
  '''
  from __future__ import absolute_import
  from __future__ import print_function
  
  import numpy as np
  import re
  from scipy import linalg
  import scipy.ndimage as ndi
  from six.moves import range
  import os
  import threading
  import warnings
  
  from .. import backend as K
  
  
  def random_rotation(x, rg, row_index=1, col_index=2, channel_index=0,
                      fill_mode='nearest', cval=0.):
      theta = np.pi / 180 * np.random.uniform(-rg, rg)
      rotation_matrix = np.array([[np.cos(theta), -np.sin(theta), 0],
                                  [np.sin(theta), np.cos(theta), 0],
                                  [0, 0, 1]])
  
      h, w = x.shape[row_index], x.shape[col_index]
      transform_matrix = transform_matrix_offset_center(rotation_matrix, h, w)
      x = apply_transform(x, transform_matrix, channel_index, fill_mode, cval)
      return x
  
  
  def random_shift(x, wrg, hrg, row_index=1, col_index=2, channel_index=0,
                   fill_mode='nearest', cval=0.):
      h, w = x.shape[row_index], x.shape[col_index]
      tx = np.random.uniform(-hrg, hrg) * h
      ty = np.random.uniform(-wrg, wrg) * w
      translation_matrix = np.array([[1, 0, tx],
                                     [0, 1, ty],
                                     [0, 0, 1]])
  
      transform_matrix = translation_matrix  # no need to do offset
      x = apply_transform(x, transform_matrix, channel_index, fill_mode, cval)
      return x
  
  
  def random_shear(x, intensity, row_index=1, col_index=2, channel_index=0,
                   fill_mode='nearest', cval=0.):
      shear = np.random.uniform(-intensity, intensity)
      shear_matrix = np.array([[1, -np.sin(shear), 0],
                               [0, np.cos(shear), 0],
                               [0, 0, 1]])
  
      h, w = x.shape[row_index], x.shape[col_index]
      transform_matrix = transform_matrix_offset_center(shear_matrix, h, w)
      x = apply_transform(x, transform_matrix, channel_index, fill_mode, cval)
      return x
  
  
  def random_zoom(x, zoom_range, row_index=1, col_index=2, channel_index=0,
                  fill_mode='nearest', cval=0.):
      if len(zoom_range) != 2:
          raise ValueError('zoom_range should be a tuple or list of two floats. '
                           'Received arg: ', zoom_range)
  
      if zoom_range[0] == 1 and zoom_range[1] == 1:
          zx, zy = 1, 1
      else:
          zx, zy = np.random.uniform(zoom_range[0], zoom_range[1], 2)
      zoom_matrix = np.array([[zx, 0, 0],
                              [0, zy, 0],
                              [0, 0, 1]])
  
      h, w = x.shape[row_index], x.shape[col_index]
      transform_matrix = transform_matrix_offset_center(zoom_matrix, h, w)
      x = apply_transform(x, transform_matrix, channel_index, fill_mode, cval)
      return x
  
  
  def random_barrel_transform(x, intensity):
      # TODO
      pass
  
  
  def random_channel_shift(x, intensity, channel_index=0):
      x = np.rollaxis(x, channel_index, 0)
      min_x, max_x = np.min(x), np.max(x)
      channel_images = [np.clip(x_channel + np.random.uniform(-intensity, intensity), min_x, max_x)
                        for x_channel in x]
      x = np.stack(channel_images, axis=0)
      x = np.rollaxis(x, 0, channel_index+1)
      return x
  
  
  def transform_matrix_offset_center(matrix, x, y):
      o_x = float(x) / 2 + 0.5
      o_y = float(y) / 2 + 0.5
      offset_matrix = np.array([[1, 0, o_x], [0, 1, o_y], [0, 0, 1]])
      reset_matrix = np.array([[1, 0, -o_x], [0, 1, -o_y], [0, 0, 1]])
      transform_matrix = np.dot(np.dot(offset_matrix, matrix), reset_matrix)
      return transform_matrix
  
  
  def apply_transform(x, transform_matrix, channel_index=0, fill_mode='nearest', cval=0.):
      x = np.rollaxis(x, channel_index, 0)
      final_affine_matrix = transform_matrix[:2, :2]
      final_offset = transform_matrix[:2, 2]
      channel_images = [ndi.interpolation.affine_transform(x_channel, final_affine_matrix,
                        final_offset, order=0, mode=fill_mode, cval=cval) for x_channel in x]
      x = np.stack(channel_images, axis=0)
      x = np.rollaxis(x, 0, channel_index+1)
      return x
  
  
  def flip_axis(x, axis):
      x = np.asarray(x).swapaxes(axis, 0)
      x = x[::-1, ...]
      x = x.swapaxes(0, axis)
      return x
  
  
  def array_to_img(x, dim_ordering='default', scale=True):
      from PIL import Image
      x = np.asarray(x)
      if x.ndim != 3:
          raise ValueError('Expected image array to have rank 3 (single image). '
                           'Got array with shape:', x.shape)
  
      if dim_ordering == 'default':
          dim_ordering = K.image_dim_ordering()
      if dim_ordering not in {'th', 'tf'}:
          raise ValueError('Invalid dim_ordering:', dim_ordering)
  
      # Original Numpy array x has format (height, width, channel)
      # or (channel, height, width)
      # but target PIL image has format (width, height, channel)
      if dim_ordering == 'th':
          x = x.transpose(1, 2, 0)
      if scale:
          x += max(-np.min(x), 0)
          x_max = np.max(x)
          if x_max != 0:
              x /= x_max
          x *= 255
      if x.shape[2] == 3:
          # RGB
          return Image.fromarray(x.astype('uint8'), 'RGB')
      elif x.shape[2] == 1:
          # grayscale
          return Image.fromarray(x[:, :, 0].astype('uint8'), 'L')
      else:
          raise ValueError('Unsupported channel number: ', x.shape[2])
  
  
  def img_to_array(img, dim_ordering='default'):
      if dim_ordering == 'default':
          dim_ordering = K.image_dim_ordering()
      if dim_ordering not in {'th', 'tf'}:
          raise ValueError('Unknown dim_ordering: ', dim_ordering)
      # Numpy array x has format (height, width, channel)
      # or (channel, height, width)
      # but original PIL image has format (width, height, channel)
      x = np.asarray(img, dtype='float32')
      if len(x.shape) == 3:
          if dim_ordering == 'th':
              x = x.transpose(2, 0, 1)
      elif len(x.shape) == 2:
          if dim_ordering == 'th':
              x = x.reshape((1, x.shape[0], x.shape[1]))
          else:
              x = x.reshape((x.shape[0], x.shape[1], 1))
      else:
          raise ValueError('Unsupported image shape: ', x.shape)
      return x
  
  
  def load_img(path, grayscale=False, target_size=None):
      '''Load an image into PIL format.
  
      # Arguments
          path: path to image file
          grayscale: boolean
          target_size: None (default to original size)
              or (img_height, img_width)
      '''
      from PIL import Image
      img = Image.open(path)
      if grayscale:
          img = img.convert('L')
      else:  # Ensure 3 channel even when loaded image is grayscale
          img = img.convert('RGB')
      if target_size:
          img = img.resize((target_size[1], target_size[0]))
      return img
  
  
  def list_pictures(directory, ext='jpg|jpeg|bmp|png'):
      return [os.path.join(root, f)
              for root, dirs, files in os.walk(directory) for f in files
              if re.match('([\w]+\.(?:' + ext + '))', f)]
  
  
  class ImageDataGenerator(object):
      '''Generate minibatches with
      real-time data augmentation.
  
      # Arguments
          featurewise_center: set input mean to 0 over the dataset.
          samplewise_center: set each sample mean to 0.
          featurewise_std_normalization: divide inputs by std of the dataset.
          samplewise_std_normalization: divide each input by its std.
          zca_whitening: apply ZCA whitening.
          rotation_range: degrees (0 to 180).
          width_shift_range: fraction of total width.
          height_shift_range: fraction of total height.
          shear_range: shear intensity (shear angle in radians).
          zoom_range: amount of zoom. if scalar z, zoom will be randomly picked
              in the range [1-z, 1+z]. A sequence of two can be passed instead
              to select this range.
          channel_shift_range: shift range for each channels.
          fill_mode: points outside the boundaries are filled according to the
              given mode ('constant', 'nearest', 'reflect' or 'wrap'). Default
              is 'nearest'.
          cval: value used for points outside the boundaries when fill_mode is
              'constant'. Default is 0.
          horizontal_flip: whether to randomly flip images horizontally.
          vertical_flip: whether to randomly flip images vertically.
          rescale: rescaling factor. If None or 0, no rescaling is applied,
              otherwise we multiply the data by the value provided
              (before applying any other transformation).
          preprocessing_function: function that will be implied on each input.
              The function will run before any other modification on it.
              The function should take one argument: one image (Numpy tensor with rank 3),
              and should output a Numpy tensor with the same shape.
          dim_ordering: 'th' or 'tf'. In 'th' mode, the channels dimension
              (the depth) is at index 1, in 'tf' mode it is at index 3.
              It defaults to the `image_dim_ordering` value found in your
              Keras config file at `~/.keras/keras.json`.
              If you never set it, then it will be "th".
      '''
      def __init__(self,
                   featurewise_center=False,
                   samplewise_center=False,
                   featurewise_std_normalization=False,
                   samplewise_std_normalization=False,
                   zca_whitening=False,
                   rotation_range=0.,
                   width_shift_range=0.,
                   height_shift_range=0.,
                   shear_range=0.,
                   zoom_range=0.,
                   channel_shift_range=0.,
                   fill_mode='nearest',
                   cval=0.,
                   horizontal_flip=False,
                   vertical_flip=False,
                   rescale=None,
                   preprocessing_function=None,
                   dim_ordering='default'):
          if dim_ordering == 'default':
              dim_ordering = K.image_dim_ordering()
          self.__dict__.update(locals())
          self.mean = None
          self.std = None
          self.principal_components = None
          self.rescale = rescale
          self.preprocessing_function = preprocessing_function
  
          if dim_ordering not in {'tf', 'th'}:
              raise ValueError('dim_ordering should be "tf" (channel after row and '
                               'column) or "th" (channel before row and column). '
                               'Received arg: ', dim_ordering)
          self.dim_ordering = dim_ordering
          if dim_ordering == 'th':
              self.channel_index = 1
              self.row_index = 2
              self.col_index = 3
          if dim_ordering == 'tf':
              self.channel_index = 3
              self.row_index = 1
              self.col_index = 2
  
          if np.isscalar(zoom_range):
              self.zoom_range = [1 - zoom_range, 1 + zoom_range]
          elif len(zoom_range) == 2:
              self.zoom_range = [zoom_range[0], zoom_range[1]]
          else:
              raise ValueError('zoom_range should be a float or '
                               'a tuple or list of two floats. '
                               'Received arg: ', zoom_range)
  
      def flow(self, X, y=None, batch_size=32, shuffle=True, seed=None,
               save_to_dir=None, save_prefix='', save_format='jpeg'):
          return NumpyArrayIterator(
              X, y, self,
              batch_size=batch_size, shuffle=shuffle, seed=seed,
              dim_ordering=self.dim_ordering,
              save_to_dir=save_to_dir, save_prefix=save_prefix, save_format=save_format)
  
      def flow_from_directory(self, directory,
                              target_size=(256, 256), color_mode='rgb',
                              classes=None, class_mode='categorical',
                              batch_size=32, shuffle=True, seed=None,
                              save_to_dir=None, save_prefix='', save_format='jpeg',
                              follow_links=False):
          return DirectoryIterator(
              directory, self,
              target_size=target_size, color_mode=color_mode,
              classes=classes, class_mode=class_mode,
              dim_ordering=self.dim_ordering,
              batch_size=batch_size, shuffle=shuffle, seed=seed,
              save_to_dir=save_to_dir, save_prefix=save_prefix, save_format=save_format,
              follow_links=follow_links)
  
      def standardize(self, x):
          if self.preprocessing_function:
              x = self.preprocessing_function(x)
          if self.rescale:
              x *= self.rescale
          # x is a single image, so it doesn't have image number at index 0
          img_channel_index = self.channel_index - 1
          if self.samplewise_center:
              x -= np.mean(x, axis=img_channel_index, keepdims=True)
          if self.samplewise_std_normalization:
              x /= (np.std(x, axis=img_channel_index, keepdims=True) + 1e-7)
  
          if self.featurewise_center:
              if self.mean is not None:
                  x -= self.mean
              else:
                  warnings.warn('This ImageDataGenerator specifies '
                                '`featurewise_center`, but it hasn\'t'
                                'been fit on any training data. Fit it '
                                'first by calling `.fit(numpy_data)`.')
          if self.featurewise_std_normalization:
              if self.std is not None:
                  x /= (self.std + 1e-7)
              else:
                  warnings.warn('This ImageDataGenerator specifies '
                                '`featurewise_std_normalization`, but it hasn\'t'
                                'been fit on any training data. Fit it '
                                'first by calling `.fit(numpy_data)`.')
          if self.zca_whitening:
              if self.principal_components is not None:
                  flatx = np.reshape(x, (x.size))
                  whitex = np.dot(flatx, self.principal_components)
                  x = np.reshape(whitex, (x.shape[0], x.shape[1], x.shape[2]))
              else:
                  warnings.warn('This ImageDataGenerator specifies '
                                '`zca_whitening`, but it hasn\'t'
                                'been fit on any training data. Fit it '
                                'first by calling `.fit(numpy_data)`.')
          return x
  
      def random_transform(self, x):
          # x is a single image, so it doesn't have image number at index 0
          img_row_index = self.row_index - 1
          img_col_index = self.col_index - 1
          img_channel_index = self.channel_index - 1
  
          # use composition of homographies to generate final transform that needs to be applied
          if self.rotation_range:
              theta = np.pi / 180 * np.random.uniform(-self.rotation_range, self.rotation_range)
          else:
              theta = 0
          rotation_matrix = np.array([[np.cos(theta), -np.sin(theta), 0],
                                      [np.sin(theta), np.cos(theta), 0],
                                      [0, 0, 1]])
          if self.height_shift_range:
              tx = np.random.uniform(-self.height_shift_range, self.height_shift_range) * x.shape[img_row_index]
          else:
              tx = 0
  
          if self.width_shift_range:
              ty = np.random.uniform(-self.width_shift_range, self.width_shift_range) * x.shape[img_col_index]
          else:
              ty = 0
  
          translation_matrix = np.array([[1, 0, tx],
                                         [0, 1, ty],
                                         [0, 0, 1]])
          if self.shear_range:
              shear = np.random.uniform(-self.shear_range, self.shear_range)
          else:
              shear = 0
          shear_matrix = np.array([[1, -np.sin(shear), 0],
                                   [0, np.cos(shear), 0],
                                   [0, 0, 1]])
  
          if self.zoom_range[0] == 1 and self.zoom_range[1] == 1:
              zx, zy = 1, 1
          else:
              zx, zy = np.random.uniform(self.zoom_range[0], self.zoom_range[1], 2)
          zoom_matrix = np.array([[zx, 0, 0],
                                  [0, zy, 0],
                                  [0, 0, 1]])
  
          transform_matrix = np.dot(np.dot(np.dot(rotation_matrix, translation_matrix), shear_matrix), zoom_matrix)
  
          h, w = x.shape[img_row_index], x.shape[img_col_index]
          transform_matrix = transform_matrix_offset_center(transform_matrix, h, w)
          x = apply_transform(x, transform_matrix, img_channel_index,
                              fill_mode=self.fill_mode, cval=self.cval)
          if self.channel_shift_range != 0:
              x = random_channel_shift(x, self.channel_shift_range, img_channel_index)
  
          if self.horizontal_flip:
              if np.random.random() < 0.5:
                  x = flip_axis(x, img_col_index)
  
          if self.vertical_flip:
              if np.random.random() < 0.5:
                  x = flip_axis(x, img_row_index)
  
          # TODO:
          # channel-wise normalization
          # barrel/fisheye
          return x
  
      def fit(self, X,
              augment=False,
              rounds=1,
              seed=None):
          '''Required for featurewise_center, featurewise_std_normalization
          and zca_whitening.
  
          # Arguments
              X: Numpy array, the data to fit on. Should have rank 4.
                  In case of grayscale data,
                  the channels axis should have value 1, and in case
                  of RGB data, it should have value 3.
              augment: Whether to fit on randomly augmented samples
              rounds: If `augment`,
                  how many augmentation passes to do over the data
              seed: random seed.
          '''
          X = np.asarray(X)
          if X.ndim != 4:
              raise ValueError('Input to `.fit()` should have rank 4. '
                               'Got array with shape: ' + str(X.shape))
          if X.shape[self.channel_index] not in {1, 3, 4}:
              raise ValueError(
                  'Expected input to be images (as Numpy array) '
                  'following the dimension ordering convention "' + self.dim_ordering + '" '
                  '(channels on axis ' + str(self.channel_index) + '), i.e. expected '
                  'either 1, 3 or 4 channels on axis ' + str(self.channel_index) + '. '
                  'However, it was passed an array with shape ' + str(X.shape) +
                  ' (' + str(X.shape[self.channel_index]) + ' channels).')
  
          if seed is not None:
              np.random.seed(seed)
  
          X = np.copy(X)
          if augment:
              aX = np.zeros(tuple([rounds * X.shape[0]] + list(X.shape)[1:]))
              for r in range(rounds):
                  for i in range(X.shape[0]):
                      aX[i + r * X.shape[0]] = self.random_transform(X[i])
              X = aX
  
          if self.featurewise_center:
              self.mean = np.mean(X, axis=(0, self.row_index, self.col_index))
              broadcast_shape = [1, 1, 1]
              broadcast_shape[self.channel_index - 1] = X.shape[self.channel_index]
              self.mean = np.reshape(self.mean, broadcast_shape)
              X -= self.mean
  
          if self.featurewise_std_normalization:
              self.std = np.std(X, axis=(0, self.row_index, self.col_index))
              broadcast_shape = [1, 1, 1]
              broadcast_shape[self.channel_index - 1] = X.shape[self.channel_index]
              self.std = np.reshape(self.std, broadcast_shape)
              X /= (self.std + K.epsilon())
  
          if self.zca_whitening:
              flatX = np.reshape(X, (X.shape[0], X.shape[1] * X.shape[2] * X.shape[3]))
              sigma = np.dot(flatX.T, flatX) / flatX.shape[0]
              U, S, V = linalg.svd(sigma)
              self.principal_components = np.dot(np.dot(U, np.diag(1. / np.sqrt(S + 10e-7))), U.T)
  
  
  class Iterator(object):
  
      def __init__(self, N, batch_size, shuffle, seed):
          self.N = N
          self.batch_size = batch_size
          self.shuffle = shuffle
          self.batch_index = 0
          self.total_batches_seen = 0
          self.lock = threading.Lock()
          self.index_generator = self._flow_index(N, batch_size, shuffle, seed)
  
      def reset(self):
          self.batch_index = 0
  
      def _flow_index(self, N, batch_size=32, shuffle=False, seed=None):
          # ensure self.batch_index is 0
          self.reset()
          while 1:
              if seed is not None:
                  np.random.seed(seed + self.total_batches_seen)
              if self.batch_index == 0:
                  index_array = np.arange(N)
                  if shuffle:
                      index_array = np.random.permutation(N)
  
              current_index = (self.batch_index * batch_size) % N
              if N >= current_index + batch_size:
                  current_batch_size = batch_size
                  self.batch_index += 1
              else:
                  current_batch_size = N - current_index
                  self.batch_index = 0
              self.total_batches_seen += 1
              yield (index_array[current_index: current_index + current_batch_size],
                     current_index, current_batch_size)
  
      def __iter__(self):
          # needed if we want to do something like:
          # for x, y in data_gen.flow(...):
          return self
  
      def __next__(self, *args, **kwargs):
          return self.next(*args, **kwargs)
  
  
  class NumpyArrayIterator(Iterator):
  
      def __init__(self, X, y, image_data_generator,
                   batch_size=32, shuffle=False, seed=None,
                   dim_ordering='default',
                   save_to_dir=None, save_prefix='', save_format='jpeg'):
          if y is not None and len(X) != len(y):
              raise ValueError('X (images tensor) and y (labels) '
                               'should have the same length. '
                               'Found: X.shape = %s, y.shape = %s' % (np.asarray(X).shape, np.asarray(y).shape))
          if dim_ordering == 'default':
              dim_ordering = K.image_dim_ordering()
          self.X = np.asarray(X)
          if self.X.ndim != 4:
              raise ValueError('Input data in `NumpyArrayIterator` '
                               'should have rank 4. You passed an array '
                               'with shape', self.X.shape)
          channels_axis = 3 if dim_ordering == 'tf' else 1
          if self.X.shape[channels_axis] not in {1, 3, 4}:
              raise ValueError('NumpyArrayIterator is set to use the '
                               'dimension ordering convention "' + dim_ordering + '" '
                               '(channels on axis ' + str(channels_axis) + '), i.e. expected '
                               'either 1, 3 or 4 channels on axis ' + str(channels_axis) + '. '
                               'However, it was passed an array with shape ' + str(self.X.shape) +
                               ' (' + str(self.X.shape[channels_axis]) + ' channels).')
          if y is not None:
              self.y = np.asarray(y)
          else:
              self.y = None
          self.image_data_generator = image_data_generator
          self.dim_ordering = dim_ordering
          self.save_to_dir = save_to_dir
          self.save_prefix = save_prefix
          self.save_format = save_format
          super(NumpyArrayIterator, self).__init__(X.shape[0], batch_size, shuffle, seed)
  
      def next(self):
          # for python 2.x.
          # Keeps under lock only the mechanism which advances
          # the indexing of each batch
          # see http://anandology.com/blog/using-iterators-and-generators/
          with self.lock:
              index_array, current_index, current_batch_size = next(self.index_generator)
          # The transformation of images is not under thread lock so it can be done in parallel
          batch_x = np.zeros(tuple([current_batch_size] + list(self.X.shape)[1:]))
          for i, j in enumerate(index_array):
              x = self.X[j]
              x = self.image_data_generator.random_transform(x.astype('float32'))
              x = self.image_data_generator.standardize(x)
              batch_x[i] = x
          if self.save_to_dir:
              for i in range(current_batch_size):
                  img = array_to_img(batch_x[i], self.dim_ordering, scale=True)
                  fname = '{prefix}_{index}_{hash}.{format}'.format(prefix=self.save_prefix,
                                                                    index=current_index + i,
                                                                    hash=np.random.randint(1e4),
                                                                    format=self.save_format)
                  img.save(os.path.join(self.save_to_dir, fname))
          if self.y is None:
              return batch_x
          batch_y = self.y[index_array]
          return batch_x, batch_y
  
  
  class DirectoryIterator(Iterator):
  
      def __init__(self, directory, image_data_generator,
                   target_size=(256, 256), color_mode='rgb',
                   dim_ordering='default',
                   classes=None, class_mode='categorical',
                   batch_size=32, shuffle=True, seed=None,
                   save_to_dir=None, save_prefix='', save_format='jpeg',
                   follow_links=False):
          if dim_ordering == 'default':
              dim_ordering = K.image_dim_ordering()
          self.directory = directory
          self.image_data_generator = image_data_generator
          self.target_size = tuple(target_size)
          if color_mode not in {'rgb', 'grayscale'}:
              raise ValueError('Invalid color mode:', color_mode,
                               '; expected "rgb" or "grayscale".')
          self.color_mode = color_mode
          self.dim_ordering = dim_ordering
          if self.color_mode == 'rgb':
              if self.dim_ordering == 'tf':
                  self.image_shape = self.target_size + (3,)
              else:
                  self.image_shape = (3,) + self.target_size
          else:
              if self.dim_ordering == 'tf':
                  self.image_shape = self.target_size + (1,)
              else:
                  self.image_shape = (1,) + self.target_size
          self.classes = classes
          if class_mode not in {'categorical', 'binary', 'sparse', None}:
              raise ValueError('Invalid class_mode:', class_mode,
                               '; expected one of "categorical", '
                               '"binary", "sparse", or None.')
          self.class_mode = class_mode
          self.save_to_dir = save_to_dir
          self.save_prefix = save_prefix
          self.save_format = save_format
  
          white_list_formats = {'png', 'jpg', 'jpeg', 'bmp'}
  
          # first, count the number of samples and classes
          self.nb_sample = 0
  
          if not classes:
              classes = []
              for subdir in sorted(os.listdir(directory)):
                  if os.path.isdir(os.path.join(directory, subdir)):
                      classes.append(subdir)
          self.nb_class = len(classes)
          self.class_indices = dict(zip(classes, range(len(classes))))
  
          def _recursive_list(subpath):
              return sorted(os.walk(subpath, followlinks=follow_links), key=lambda tpl: tpl[0])
  
          for subdir in classes:
              subpath = os.path.join(directory, subdir)
              for root, dirs, files in _recursive_list(subpath):
                  for fname in files:
                      is_valid = False
                      for extension in white_list_formats:
                          if fname.lower().endswith('.' + extension):
                              is_valid = True
                              break
                      if is_valid:
                          self.nb_sample += 1
          print('Found %d images belonging to %d classes.' % (self.nb_sample, self.nb_class))
  
          # second, build an index of the images in the different class subfolders
          self.filenames = []
          self.classes = np.zeros((self.nb_sample,), dtype='int32')
          i = 0
          for subdir in classes:
              subpath = os.path.join(directory, subdir)
              for root, dirs, files in _recursive_list(subpath):
                  for fname in files:
                      is_valid = False
                      for extension in white_list_formats:
                          if fname.lower().endswith('.' + extension):
                              is_valid = True
                              break
                      if is_valid:
                          self.classes[i] = self.class_indices[subdir]
                          i += 1
                          # add filename relative to directory
                          absolute_path = os.path.join(root, fname)
                          self.filenames.append(os.path.relpath(absolute_path, directory))
          super(DirectoryIterator, self).__init__(self.nb_sample, batch_size, shuffle, seed)
  
      def next(self):
          with self.lock:
              index_array, current_index, current_batch_size = next(self.index_generator)
          # The transformation of images is not under thread lock so it can be done in parallel
          batch_x = np.zeros((current_batch_size,) + self.image_shape)
          grayscale = self.color_mode == 'grayscale'
          # build batch of image data
          for i, j in enumerate(index_array):
              fname = self.filenames[j]
              img = load_img(os.path.join(self.directory, fname),
                             grayscale=grayscale,
                             target_size=self.target_size)
              x = img_to_array(img, dim_ordering=self.dim_ordering)
              x = self.image_data_generator.random_transform(x)
              x = self.image_data_generator.standardize(x)
              batch_x[i] = x
          # optionally save augmented images to disk for debugging purposes
          if self.save_to_dir:
              for i in range(current_batch_size):
                  img = array_to_img(batch_x[i], self.dim_ordering, scale=True)
                  fname = '{prefix}_{index}_{hash}.{format}'.format(prefix=self.save_prefix,
                                                                    index=current_index + i,
                                                                    hash=np.random.randint(1e4),
                                                                    format=self.save_format)
                  img.save(os.path.join(self.save_to_dir, fname))
          # build batch of labels
          if self.class_mode == 'sparse':
              batch_y = self.classes[index_array]
          elif self.class_mode == 'binary':
              batch_y = self.classes[index_array].astype('float32')
          elif self.class_mode == 'categorical':
              batch_y = np.zeros((len(batch_x), self.nb_class), dtype='float32')
              for i, label in enumerate(self.classes[index_array]):
                  batch_y[i, label] = 1.
          else:
              return batch_x
          return batch_x, batch_y
  