►
Keras 3 API documentation /
Layers API /
Preprocessing layers /
Image augmentation layers /
Solarization layer
Solarization layer
Solarization class
keras.layers.Solarization(
addition_factor=0.0, threshold_factor=0.0, value_range=(0, 255), seed=None, **kwargs
)
Applies (max_value - pixel + min_value) for each pixel in the image.
When created without threshold parameter, the layer performs solarization
to all values. When created with specified threshold the layer only
augments pixels that are above the threshold value.
Arguments
- addition_factor: (Optional) A tuple of two floats or a single float,
between 0 and 1.
For each augmented image a value is
sampled from the provided range. If a float is passed, the range is
interpreted as
(0, addition_factor). If specified, this value (times the value range of input images, e.g. 255), is added to each pixel before solarization and thresholding. Defaults to 0.0. - threshold_factor: (Optional) A tuple of two floats or a single float.
For each augmented image a value is
sampled from the provided range. If a float is passed, the range is
interpreted as
(0, threshold_factor). If specified, only pixel values above this threshold will be solarized. - value_range: a tuple or a list of two elements. The first value
represents the lower bound for values in input images, the second
represents the upper bound. Images passed to the layer should have
values within
value_range. Typical values to pass are(0, 255)(RGB image) or(0., 1.)(scaled image). - seed: Integer. Used to create a random seed.
- **kwargs: Base layer keyword arguments, such as
nameanddtype.
Example
(images, labels), _ = keras.datasets.cifar10.load_data()
print(images[0, 0, 0])
# [59 62 63]
# Note that images are Tensor with values in the range [0, 255]
solarization = Solarization(value_range=(0, 255))
images = solarization(images)
print(images[0, 0, 0])
# [196, 193, 192]