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tf.keras.preprocessing.image.DirectoryIterator

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Iterator capable of reading images from a directory on disk.

Inherits From: Iterator

tf.keras.preprocessing.image.DirectoryIterator(
    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
)
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.
Attributes
filepaths List of absolute paths to image files
labels Class labels of every observation
sample_weight

Methods

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.

__getitem__

__getitem__(
    idx
)

__iter__

__iter__()

__len__

__len__()

Class Variables

  • allowed_class_modes
  • white_list_formats

© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/keras/preprocessing/image/DirectoryIterator