Solarization layer

[source]

Solarization class

keras_cv.layers.Solarization(
    value_range, addition_factor=0.0, threshold_factor=0.0, 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

Reference

• AutoAugment: Learning Augmentation Policies from Data
• RandAugment

Arguments

• value_range: a tuple or a list of two elements. The first value represents the lower bound for values in passed images, the second represents the upper bound. Images passed to the layer should have values within value_range.
• addition_factor: (Optional) A tuple of two floats, a single float or a keras_cv.FactorSampler. 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 is added to each pixel before solarization and thresholding. The addition value should be scaled according to the value range (0, 255), defaults to 0.0.
• threshold_factor: (Optional) A tuple of two floats, a single float or a keras_cv.FactorSampler. 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.
• seed: Integer. Used to create a random seed.

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]

Call arguments

• images: Tensor of type int or float, with pixels in range [0, 255] and shape [batch, height, width, channels] or [height, width, channels].