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|
Randomly adjusts the hue on given images.
Inherits From: Layer, Operation
tf.keras.layers.RandomHue(
factor, value_range=(0, 255), data_format=None, seed=None, **kwargs
)
This layer will randomly increase/reduce the hue for the input RGB images.
The image hue is adjusted by converting the image(s) to HSV and rotating the hue channel (H) by delta. The image is then converted back to RGB.
Example:
(images, labels), _ = keras.datasets.cifar10.load_data()
random_hue = keras.layers.RandomHue(factor=0.5, value_range=[0, 1])
images = keras.ops.cast(images, "float32")
augmented_images_batch = random_hue(images[:8])
Methods
convert_weight
convert_weight(
weight
)
Convert the weight if it is from the a different backend.
from_config
@classmethodfrom_config( config )
Creates an operation from its config.
This method is the reverse of get_config, capable of instantiating the
same operation from the config dictionary.
if "dtype" in config and isinstance(config["dtype"], dict):
policy = dtype_policies.deserialize(config["dtype"])
| Args | |
|---|---|
config
|
A Python dictionary, typically the output of get_config.
|
| Returns | |
|---|---|
| An operation instance. |
get_random_transformation
get_random_transformation(
data, training=True, seed=None
)
transform_bounding_boxes
transform_bounding_boxes(
bounding_boxes, transformation, training=True
)
transform_images
transform_images(
images, transformation=None, training=True
)
transform_labels
transform_labels(
labels, transformation, training=True
)
transform_segmentation_masks
transform_segmentation_masks(
segmentation_masks, transformation, training=True
)
transform_single_bounding_box
transform_single_bounding_box(
bounding_box, transformation, training=True
)
transform_single_image
transform_single_image(
image, transformation, training=True
)
transform_single_label
transform_single_label(
label, transformation, training=True
)
transform_single_segmentation_mask
transform_single_segmentation_mask(
segmentation_mask, transformation, training=True
)
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