def __init__(self, directory, image_data_generator, target_size=(256, 256), color_mode='rgb', classes=None, class_mode='categorical', batch_size=32, shuffle=True, seed=None, data_format=None, save_to_dir=None, save_prefix='', save_format='png', follow_links=False, subset=None, interpolation='nearest'):
    if (data_format is None):
        data_format = K.image_data_format()
    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.data_format = data_format
    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)
    elif (self.data_format == 'channels_last'):
        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', 'input', None}):
        raise ValueError('Invalid class_mode:', class_mode, '; expected one of "categorical", "binary", "sparse", "input" or None.')
    self.class_mode = class_mode
    self.save_to_dir = save_to_dir
    self.save_prefix = save_prefix
    self.save_format = save_format
    self.interpolation = interpolation
    if (subset is not None):
        validation_split = self.image_data_generator._validation_split
        if (subset == 'validation'):
            split = (0, validation_split)
        elif (subset == 'training'):
            split = (validation_split, 1)
        else:
            raise ValueError('Invalid subset name: ', subset, '; expected "training" or "validation"')
    else:
        split = None
    self.subset = subset
    white_list_formats = {'png', 'jpg', 'jpeg', 'bmp', 'ppm', 'tif', 'tiff'}
    self.samples = 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.num_classes = len(classes)
    self.class_indices = dict(zip(classes, range(len(classes))))
    pool = multiprocessing.pool.ThreadPool()
    function_partial = partial(_count_valid_files_in_directory, white_list_formats=white_list_formats, follow_links=follow_links, split=split)
    self.samples = sum(pool.map(function_partial, (os.path.join(directory, subdir) for subdir in classes)))
    print(('Found %d images belonging to %d classes.' % (self.samples, self.num_classes)))
    results = []
    self.filenames = []
    self.classes = np.zeros((self.samples,), dtype='int32')
    i = 0
    for dirpath in (os.path.join(directory, subdir) for subdir in classes):
        results.append(pool.apply_async(_list_valid_filenames_in_directory, (dirpath, white_list_formats, split, self.class_indices, follow_links)))
    for res in results:
        (classes, filenames) = res.get()
        self.classes[i:(i + len(classes))] = classes
        self.filenames += filenames
        i += len(classes)
    pool.close()
    pool.join()
    super(DirectoryIterator, self).__init__(self.samples, batch_size, shuffle, seed)
def __init__(self, directory, image_data_generator, target_size=(256, 256), color_mode='rgb', classes=None, class_mode='categorical', batch_size=32, shuffle=True, seed=None, data_format=None, save_to_dir=None, save_prefix=, save_format='png', follow_links=False, subset=None, interpolation='nearest'):
    if (data_format is None):
        data_format = K.image_data_format()
    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.data_format = data_format
    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)
    elif (self.data_format == 'channels_last'):
        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', 'input', None}):
        raise ValueError('Invalid class_mode:', class_mode, '; expected one of "categorical", "binary", "sparse", "input" or None.')
    self.class_mode = class_mode
    self.save_to_dir = save_to_dir
    self.save_prefix = save_prefix
    self.save_format = save_format
    self.interpolation = interpolation
    if (subset is not None):
        validation_split = self.image_data_generator._validation_split
        if (subset == 'validation'):
            split = (0, validation_split)
        elif (subset == 'training'):
            split = (validation_split, 1)
        else:
            raise ValueError('Invalid subset name: ', subset, '; expected "training" or "validation"')
    else:
        split = None
    self.subset = subset
    white_list_formats = {'png', 'jpg', 'jpeg', 'bmp', 'ppm', 'tif', 'tiff'}
    self.samples = 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.num_classes = len(classes)
    self.class_indices = dict(zip(classes, range(len(classes))))
    pool = multiprocessing.pool.ThreadPool()
    function_partial = partial(_count_valid_files_in_directory, white_list_formats=white_list_formats, follow_links=follow_links, split=split)
    self.samples = sum(pool.map(function_partial, (os.path.join(directory, subdir) for subdir in classes)))
    print(('Found %d images belonging to %d classes.' % (self.samples, self.num_classes)))
    results = []
    self.filenames = []
    self.classes = np.zeros((self.samples,), dtype='int32')
    i = 0
    for dirpath in (os.path.join(directory, subdir) for subdir in classes):
        results.append(pool.apply_async(_list_valid_filenames_in_directory, (dirpath, white_list_formats, split, self.class_indices, follow_links)))
    for res in results:
        (classes, filenames) = res.get()
        self.classes[i:(i + len(classes))] = classes
        self.filenames += filenames
        i += len(classes)
    pool.close()
    pool.join()
    super(DirectoryIterator, self).__init__(self.samples, batch_size, shuffle, seed)