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Showing content from https://keras.io/api/layers/preprocessing_layers/image_augmentation/random_hue below:

RandomHue layer

[source]

keras.layers.RandomHue(
    factor, value_range=(0, 255), data_format=None, seed=None, **kwargs
)

Randomly adjusts the hue on given images.

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.

Arguments

• factor: A single float or a tuple of two floats. factor controls the extent to which the image hue is impacted. factor=0.0 makes this layer perform a no-op operation, while a value of 1.0 performs the most aggressive contrast adjustment available. If a tuple is used, a factor is sampled between the two values for every image augmented. If a single float is used, a value between 0.0 and the passed float is sampled. In order to ensure the value is always the same, please pass a tuple with two identical floats: (0.5, 0.5).
• value_range: the range of values the incoming images will have. Represented as a two-number tuple written [low, high]. This is typically either [0, 1] or [0, 255] depending on how your preprocessing pipeline is set up.
• seed: Integer. Used to create a random seed.

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])

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