tf.keras.preprocessing.image.DirectoryIterator

Iterator capable of reading images from a directory on disk.

Inherits From: Iterator, Sequence

View aliases

Compat aliases for migration

tf.compat.v1.keras.preprocessing.image.DirectoryIterator

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`

View source

next()

For python 2.x.

Returns
The next batch.

`on_epoch_end`

View source

on_epoch_end()

`reset`

View source

reset()

`set_processing_attrs`

View source

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`

View source

__getitem__(
    idx
)

`iter`

View source

__iter__()

`len`

View source

__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/r2.4/api_docs/python/tf/keras/preprocessing/image/DirectoryIterator