tf.keras.preprocessing.image.DirectoryIterator

Iterator capable of reading images from a directory on disk.

Inherits From: Iterator, Sequence

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',
    keep_aspect_ratio=False,
    dtype=None
)

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.
keep_aspect_ratio Boolean, whether to resize images to a target size without aspect ratio distortion. The image is cropped in the center with target aspect ratio before resizing.
dtype Dtype to use for generated arrays.

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

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on_epoch_end()

Method called at the end of every epoch.

reset

View source

reset()

set_processing_attrs

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

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

Args
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.
keep_aspect_ratio Boolean, whether to resize images to a target size without aspect ratio distortion. The image is cropped in the center with target aspect ratio before resizing.

__getitem__

View source

__getitem__(
    idx
)

Gets batch at position index.

Args
index position of the batch in the Sequence.

Returns
A batch

__iter__

View source

__iter__()

Create a generator that iterate over the Sequence.

__len__

View source

__len__()

Number of batch in the Sequence.

Returns
The number of batches in the Sequence.

allowed_class_modes 

{
 'binary',
 'categorical',
 'input',
 'sparse',
 None
}

white_list_formats ('png', 'jpg', 'jpeg', 'bmp', 'ppm', 'tif', 'tiff')