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', 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.
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
|
|
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