Iterator capable of reading images from a directory on disk.
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
image_data_generator: Instance of
ImageDataGeneratorto use for random transformations and normalization.
target_size: tuple of integers, dimensions to resize input images to.
color_mode: One of
"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
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_format: Format to use for saving sample images (if
subset: Subset of data (
"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.
__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' )
__next__( *args, **kwargs )
For python 2.x.
The next batch.