code: from keras.preprocessing.image import *
  from keras.applications.imagenet_utils import preprocess_input
  from keras import backend as K
  from PIL import Image
  import numpy as np
  import os
  #import cv2
  
  
  def center_crop(x, center_crop_size, data_format, **kwargs):
      if data_format == 'channels_first':
          centerh, centerw = x.shape[1] // 2, x.shape[2] // 2
      elif data_format == 'channels_last':
          centerh, centerw = x.shape[0] // 2, x.shape[1] // 2
      lh, lw = center_crop_size[0] // 2, center_crop_size[1] // 2
      rh, rw = center_crop_size[0] - lh, center_crop_size[1] - lw
  
      h_start, h_end = centerh - lh, centerh + rh
      w_start, w_end = centerw - lw, centerw + rw
      if data_format == 'channels_first':
          return x[:, h_start:h_end, w_start:w_end]
      elif data_format == 'channels_last':
          return x[h_start:h_end, w_start:w_end, :]
  
  
  def pair_center_crop(x, y, center_crop_size, data_format, **kwargs):
      if data_format == 'channels_first':
          centerh, centerw = x.shape[1] // 2, x.shape[2] // 2
      elif data_format == 'channels_last':
          centerh, centerw = x.shape[0] // 2, x.shape[1] // 2
      lh, lw = center_crop_size[0] // 2, center_crop_size[1] // 2
      rh, rw = center_crop_size[0] - lh, center_crop_size[1] - lw
  
      h_start, h_end = centerh - lh, centerh + rh
      w_start, w_end = centerw - lw, centerw + rw
      if data_format == 'channels_first':
          return x[:, h_start:h_end, w_start:w_end], \
                 y[:, h_start:h_end, w_start:w_end]
      elif data_format == 'channels_last':
          return x[h_start:h_end, w_start:w_end, :], \
                 y[h_start:h_end, w_start:w_end, :]
  
  
  def random_crop(x, random_crop_size, data_format, sync_seed=None, **kwargs):
      np.random.seed(sync_seed)
      if data_format == 'channels_first':
          h, w = x.shape[1], x.shape[2]
      elif data_format == 'channels_last':
          h, w = x.shape[0], x.shape[1]
      rangeh = (h - random_crop_size[0]) // 2
      rangew = (w - random_crop_size[1]) // 2
      offseth = 0 if rangeh == 0 else np.random.randint(rangeh)
      offsetw = 0 if rangew == 0 else np.random.randint(rangew)
  
      h_start, h_end = offseth, offseth + random_crop_size[0]
      w_start, w_end = offsetw, offsetw + random_crop_size[1]
      if data_format == 'channels_first':
          return x[:, h_start:h_end, w_start:w_end]
      elif data_format == 'channels_last':
          return x[h_start:h_end, w_start:w_end, :]
  
  
  def pair_random_crop(x, y, random_crop_size, data_format, sync_seed=None, **kwargs):
      np.random.seed(sync_seed)
      if data_format == 'channels_first':
          h, w = x.shape[1], x.shape[2]
      elif data_format == 'channels_last':
          h, w = x.shape[0], x.shape[1]
      rangeh = (h - random_crop_size[0]) // 2
      rangew = (w - random_crop_size[1]) // 2
      offseth = 0 if rangeh == 0 else np.random.randint(rangeh)
      offsetw = 0 if rangew == 0 else np.random.randint(rangew)
  
      h_start, h_end = offseth, offseth + random_crop_size[0]
      w_start, w_end = offsetw, offsetw + random_crop_size[1]
      if data_format == 'channels_first':
          return x[:, h_start:h_end, w_start:w_end], y[:, h_start:h_end, h_start:h_end]
      elif data_format == 'channels_last':
          return x[h_start:h_end, w_start:w_end, :], y[h_start:h_end, w_start:w_end, :]
  
  
  class SegDirectoryIterator(Iterator):
      '''
      Users need to ensure that all files exist.
      Label images should be png images where pixel values represents class number.
  
      find images -name *.jpg > images.txt
      find labels -name *.png > labels.txt
  
      for a file name 2011_002920.jpg, each row should contain 2011_002920
  
      file_path: location of train.txt, or val.txt in PASCAL VOC2012 format,
          listing image file path components without extension
      data_dir: location of image files referred to by file in file_path
      label_dir: location of label files
      data_suffix: image file extension, such as `.jpg` or `.png`
      label_suffix: label file suffix, such as `.png`, or `.npy`
      loss_shape: shape to use when applying loss function to the label data
      '''
  
      def __init__(self, file_path, seg_data_generator,
                   data_dir, data_suffix,
                   label_dir, label_suffix, classes, ignore_label=255,
                   crop_mode='none', label_cval=255, pad_size=None,
                   target_size=None, color_mode='rgb',
                   data_format='default', class_mode='sparse',
                   batch_size=1, shuffle=True, seed=None,
                   save_to_dir=None, save_prefix='', save_format='jpeg',
                   loss_shape=None):
          if data_format == 'default':
              data_format = K.image_data_format()
          self.file_path = file_path
          self.data_dir = data_dir
          self.data_suffix = data_suffix
          self.label_suffix = label_suffix
          self.label_dir = label_dir
          self.classes = classes
          self.seg_data_generator = seg_data_generator
          self.target_size = tuple(target_size)
          self.ignore_label = ignore_label
          self.crop_mode = crop_mode
          self.label_cval = label_cval
          self.pad_size = pad_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.data_format = data_format
          self.nb_label_ch = 1
          self.loss_shape = loss_shape
  
          if (self.label_suffix == '.npy') or (self.label_suffix == 'npy'):
              self.label_file_format = 'npy'
          else:
              self.label_file_format = 'img'
          if target_size:
              if self.color_mode == 'rgb':
                  if self.data_format == 'channels_last':
                      self.image_shape = self.target_size + (3,)
                  else:
                      self.image_shape = (3,) + self.target_size
              else:
                  if self.data_format == 'channels_last':
                      self.image_shape = self.target_size + (1,)
                  else:
                      self.image_shape = (1,) + self.target_size
              if self.data_format == 'channels_last':
                  self.label_shape = self.target_size + (self.nb_label_ch,)
              else:
                  self.label_shape = (self.nb_label_ch,) + self.target_size
          elif batch_size != 1:
              raise ValueError(
                  'Batch size must be 1 when target image size is undetermined')
          else:
              self.image_shape = None
              self.label_shape = None
          if class_mode not in {'sparse', None}:
              raise ValueError('Invalid class_mode:', class_mode,
                               '; expected one of '
                               '"sparse", or None.')
          self.class_mode = class_mode
          if save_to_dir:
              self.palette = None
          self.save_to_dir = save_to_dir
          self.save_prefix = save_prefix
          self.save_format = save_format
  
          white_list_formats = {'png', 'jpg', 'jpeg', 'bmp', 'npy'}
  
          # build lists for data files and label files
          self.data_files = []
          self.label_files = []
          fp = open(file_path)
          lines = fp.readlines()
          fp.close()
          self.nb_sample = len(lines)
          for line in lines:
              line = line.strip('\n')
              self.data_files.append(line + data_suffix)
              self.label_files.append(line + label_suffix)
          super(SegDirectoryIterator, 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
          if self.target_size:
              # TODO(ahundt) make dtype properly configurable
              batch_x = np.zeros((current_batch_size,) + self.image_shape)
              if self.loss_shape is None and self.label_file_format is 'img':
                  batch_y = np.zeros((current_batch_size,) + self.label_shape,
                                     dtype=int)
              elif self.loss_shape is None:
                  batch_y = np.zeros((current_batch_size,) + self.label_shape)
              else:
                  batch_y = np.zeros((current_batch_size,) + self.loss_shape,
                                     dtype=np.uint8)
          grayscale = self.color_mode == 'grayscale'
          # build batch of image data and labels
          for i, j in enumerate(index_array):
              data_file = self.data_files[j]
              label_file = self.label_files[j]
              img_file_format = 'img'
              img = load_img(os.path.join(self.data_dir, data_file),
                             grayscale=grayscale, target_size=None)
              label_filepath = os.path.join(self.label_dir, label_file)
  
              if self.label_file_format == 'npy':
                  y = np.load(label_filepath)
              else:
                  label = Image.open(label_filepath)
                  if self.save_to_dir and self.palette is None:
                      self.palette = label.palette
  
              # do padding
              if self.target_size:
                  if self.crop_mode != 'none':
                      x = img_to_array(img, data_format=self.data_format)
                      if self.label_file_format is not 'npy':
                          y = img_to_array(
                              label, data_format=self.data_format).astype(int)
                      img_w, img_h = img.size
                      if self.pad_size:
                          pad_w = max(self.pad_size[1] - img_w, 0)
                          pad_h = max(self.pad_size[0] - img_h, 0)
                      else:
                          pad_w = max(self.target_size[1] - img_w, 0)
                          pad_h = max(self.target_size[0] - img_h, 0)
                      if self.data_format == 'channels_first':
                          x = np.lib.pad(x, ((0, 0), (pad_h / 2, pad_h - pad_h / 2), (pad_w / 2, pad_w - pad_w / 2)), 'constant', constant_values=0.)
                          y = np.lib.pad(y, ((0, 0), (pad_h / 2, pad_h - pad_h / 2), (pad_w / 2, pad_w - pad_w / 2)),
                                         'constant', constant_values=self.label_cval)
                      elif self.data_format == 'channels_last':
                          x = np.lib.pad(x, ((pad_h / 2, pad_h - pad_h / 2), (pad_w / 2, pad_w - pad_w / 2), (0, 0)), 'constant', constant_values=0.)
                          y = np.lib.pad(y, ((pad_h / 2, pad_h - pad_h / 2), (pad_w / 2, pad_w - pad_w / 2), (0, 0)), 'constant', constant_values=self.label_cval)
                  else:
                      x = img_to_array(img.resize((self.target_size[1], self.target_size[0]),
                                                  Image.BILINEAR),
                                       data_format=self.data_format)
                      if self.label_file_format is not 'npy':
                          y = img_to_array(label.resize((self.target_size[1], self.target_size[
                                           0]), Image.NEAREST), data_format=self.data_format).astype(int)
                      else:
                          print('ERROR: resize not implemented for label npy file')
  
              if self.target_size is None:
                  batch_x = np.zeros((current_batch_size,) + x.shape)
                  if self.loss_shape is not None:
                      batch_y = np.zeros((current_batch_size,) + self.loss_shape)
                  else:
                      batch_y = np.zeros((current_batch_size,) + y.shape)
  
              x, y = self.seg_data_generator.random_transform(x, y)
              x = self.seg_data_generator.standardize(x)
  
              if self.ignore_label:
                  y[np.where(y == self.ignore_label)] = self.classes
  
              if self.loss_shape is not None:
                  y = np.reshape(y, self.loss_shape)
  
              batch_x[i] = x
              batch_y[i] = y
          # 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.data_format, scale=True)
                  label = batch_y[i][:, :, 0].astype('uint8')
                  label[np.where(label == self.classes)] = self.ignore_label
                  label = Image.fromarray(label, mode='P')
                  label.palette = self.palette
                  fname = '{prefix}_{index}_{hash}'.format(prefix=self.save_prefix,
                                                           index=current_index + i,
                                                           hash=np.random.randint(1e4))
                  img.save(os.path.join(self.save_to_dir, 'img_' +
                                        fname + '.{format}'.format(format=self.save_format)))
                  label.save(os.path.join(self.save_to_dir,
                                          'label_' + fname + '.png'))
          # return
          batch_x = preprocess_input(batch_x)
          if self.class_mode == 'sparse':
              return batch_x, batch_y
          else:
              return batch_x
  
  
  class SegDataGenerator(object):
  
      def __init__(self,
                   featurewise_center=False,
                   samplewise_center=False,
                   featurewise_std_normalization=False,
                   samplewise_std_normalization=False,
                   channelwise_center=False,
                   rotation_range=0.,
                   width_shift_range=0.,
                   height_shift_range=0.,
                   shear_range=0.,
                   zoom_range=0.,
                   zoom_maintain_shape=True,
                   channel_shift_range=0.,
                   fill_mode='constant',
                   cval=0.,
                   label_cval=255,
                   crop_mode='none',
                   crop_size=(0, 0),
                   pad_size=None,
                   horizontal_flip=False,
                   vertical_flip=False,
                   rescale=None,
                   data_format='default'):
          if data_format == 'default':
              data_format = K.image_data_format()
          self.__dict__.update(locals())
          self.mean = None
          self.ch_mean = None
          self.std = None
          self.principal_components = None
          self.rescale = rescale
  
          if data_format not in {'channels_last', 'channels_first'}:
              raise Exception('data_format should be channels_last (channel after row and '
                              'column) or channels_first (channel before row and column). '
                              'Received arg: ', data_format)
          if crop_mode not in {'none', 'random', 'center'}:
              raise Exception('crop_mode should be "none" or "random" or "center" '
                              'Received arg: ', crop_mode)
          self.data_format = data_format
          if data_format == 'channels_first':
              self.channel_index = 1
              self.row_index = 2
              self.col_index = 3
          if data_format == 'channels_last':
              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 Exception('zoom_range should be a float or '
                              'a tuple or list of two floats. '
                              'Received arg: ', zoom_range)
  
      def flow_from_directory(self, file_path, data_dir, data_suffix,
                              label_dir, label_suffix, classes,
                              ignore_label=255,
                              target_size=None, color_mode='rgb',
                              class_mode='sparse',
                              batch_size=32, shuffle=True, seed=None,
                              save_to_dir=None, save_prefix='', save_format='jpeg',
                              loss_shape=None):
          if self.crop_mode == 'random' or self.crop_mode == 'center':
              target_size = self.crop_size
          return SegDirectoryIterator(
              file_path, self,
              data_dir=data_dir, data_suffix=data_suffix,
              label_dir=label_dir, label_suffix=label_suffix,
              classes=classes, ignore_label=ignore_label,
              crop_mode=self.crop_mode, label_cval=self.label_cval,
              pad_size=self.pad_size,
              target_size=target_size, color_mode=color_mode,
              data_format=self.data_format, class_mode=class_mode,
              batch_size=batch_size, shuffle=shuffle, seed=seed,
              save_to_dir=save_to_dir, save_prefix=save_prefix,
              save_format=save_format,
              loss_shape=loss_shape)
  
      def standardize(self, 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:
              x -= self.mean
          if self.featurewise_std_normalization:
              x /= (self.std + 1e-7)
  
          if self.channelwise_center:
              x -= self.ch_mean
          return x
  
      def random_transform(self, x, y):
          # 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
          if self.crop_mode == 'none':
              crop_size = (x.shape[img_row_index], x.shape[img_col_index])
          else:
              crop_size = self.crop_size
  
          assert x.shape[img_row_index] == y.shape[img_row_index] and x.shape[img_col_index] == y.shape[
              img_col_index], 'DATA ERROR: Different shape of data and label!\ndata shape: %s, label shape: %s' % (str(x.shape), str(y.shape))
  
          # 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:
              # * x.shape[img_row_index]
              tx = np.random.uniform(-self.height_shift_range,
                                     self.height_shift_range) * crop_size[0]
          else:
              tx = 0
  
          if self.width_shift_range:
              # * x.shape[img_col_index]
              ty = np.random.uniform(-self.width_shift_range,
                                     self.width_shift_range) * crop_size[1]
          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)
          if self.zoom_maintain_shape:
              zy = zx
          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)
          y = apply_transform(y, transform_matrix, img_channel_index,
                              fill_mode='constant', cval=self.label_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)
                  y = flip_axis(y, img_col_index)
  
          if self.vertical_flip:
              if np.random.random() < 0.5:
                  x = flip_axis(x, img_row_index)
                  y = flip_axis(y, img_row_index)
  
          if self.crop_mode == 'center':
              x, y = pair_center_crop(x, y, self.crop_size, self.data_format)
          elif self.crop_mode == 'random':
              x, y = pair_random_crop(x, y, self.crop_size, self.data_format)
  
          # TODO:
          # channel-wise normalization
          # barrel/fisheye
          return x, y
  
      def fit(self, X,
              augment=False,
              rounds=1,
              seed=None):
          '''Required for featurewise_center and featurewise_std_normalization
  
          # Arguments
              X: Numpy array, the data to fit on.
              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.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)
              X -= self.mean
  
          if self.featurewise_std_normalization:
              self.std = np.std(X, axis=0)
              X /= (self.std + 1e-7)
  
      def set_ch_mean(self, ch_mean):
          self.ch_mean = ch_mean
  
apis: numpy.random.uniform numpy.load numpy.sin numpy.zeros numpy.reshape numpy.random.seed numpy.isscalar numpy.copy numpy.cos numpy.random.random numpy.lib.pad numpy.where numpy.array numpy.std numpy.dot numpy.random.randint numpy.mean