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tf.keras.preprocessing.image.DirectoryIterator

TensorFlow 2.0 version View source on GitHub

Class DirectoryIterator

Iterator capable of reading images from a directory on disk.

Inherits From: Iterator

Aliases:

  • Class tf.compat.v1.keras.preprocessing.image.DirectoryIterator
  • Class tf.compat.v2.keras.preprocessing.image.DirectoryIterator

Arguments:

  • directory: Path to the directory to read images from. Each subdirectory in this directory will be considered to contain images from one class, or alternatively you could specify class subdirectories via the classes argument.
  • image_data_generator: Instance of ImageDataGenerator to use for random transformations and normalization.
  • target_size: tuple of integers, dimensions to resize input images to.
  • color_mode: One of "rgb", "rgba", "grayscale". Color mode to read images.
  • classes: Optional list of strings, names of subdirectories containing images from each class (e.g. ["dogs", "cats"]). It will be computed automatically if not set.
  • class_mode: Mode for yielding the targets: "binary": binary targets (if there are only two classes), "categorical": categorical targets, "sparse": integer targets, "input": targets are images identical to input images (mainly used to work with autoencoders), None: no targets get yielded (only input images are yielded).
  • batch_size: Integer, size of a batch.
  • shuffle: Boolean, whether to shuffle the data between epochs.
  • seed: Random seed for data shuffling.
  • data_format: String, one of channels_first, channels_last.
  • save_to_dir: Optional directory where to save the pictures being yielded, in a viewable format. This is useful for visualizing the random transformations being applied, for debugging purposes.
  • save_prefix: String prefix to use for saving sample images (if save_to_dir is set).
  • save_format: Format to use for saving sample images (if save_to_dir is set).
  • subset: Subset of data ("training" or "validation") if validation_split is set in ImageDataGenerator.
  • interpolation: Interpolation method used to resample the image if the target size is different from that of the loaded image. Supported methods are "nearest", "bilinear", and "bicubic". If PIL version 1.1.3 or newer is installed, "lanczos" is also supported. If PIL version 3.4.0 or newer is installed, "box" and "hamming" are also supported. By default, "nearest" is used.
  • dtype: Dtype to use for generated arrays.

__init__

View source

__init__(
    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',
    dtype=None
)

Properties

filepaths

labels

sample_weight

Methods

__getitem__

__getitem__(idx)

__iter__

__iter__()

__len__

__len__()

next

next()

For python 2.x.

Returns

The next batch.

on_epoch_end

on_epoch_end()

reset

reset()

set_processing_attrs

set_processing_attrs(
    image_data_generator,
    target_size,
    color_mode,
    data_format,
    save_to_dir,
    save_prefix,
    save_format,
    subset,
    interpolation
)

Sets attributes to use later for processing files into a batch.

Arguments

image_data_generator: Instance of `ImageDataGenerator`
    to use for random transformations and normalization.
target_size: tuple of integers, dimensions to resize input images to.
color_mode: One of `"rgb"`, `"rgba"`, `"grayscale"`.
    Color mode to read images.
data_format: String, one of `channels_first`, `channels_last`.
save_to_dir: Optional directory where to save the pictures
    being yielded, in a viewable format. This is useful
    for visualizing the random transformations being
    applied, for debugging purposes.
save_prefix: String prefix to use for saving sample
    images (if `save_to_dir` is set).
save_format: Format to use for saving sample images
    (if `save_to_dir` is set).
subset: Subset of data (`"training"` or `"validation"`) if
    validation_split is set in ImageDataGenerator.
interpolation: Interpolation method used to resample the image if the
    target size is different from that of the loaded image.
    Supported methods are "nearest", "bilinear", and "bicubic".
    If PIL version 1.1.3 or newer is installed, "lanczos" is also
    supported. If PIL version 3.4.0 or newer is installed, "box" and
    "hamming" are also supported. By default, "nearest" is used.

Class Members

  • allowed_class_modes
  • white_list_formats