RandomChannelShift layer

• Original Link : https://keras.io/api/keras_cv/layers/augmentation/random_channel_shift/
• Last Checked at : 2024-11-25

source

RandomChannelShift class

keras_cv.layers.RandomChannelShift(
    value_range, factor, channels=3, seed=None, **kwargs
)

Randomly shift values for each channel of the input image(s).

The input images should have values in the [0-255] or [0-1] range.

Input shape

3D (unbatched) or 4D (batched) tensor with shape: (..., height, width, channels), in channels_last format.

Output shape

3D (unbatched) or 4D (batched) tensor with shape: (..., height, width, channels), in channels_last format.

Arguments

• 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.
• factor: A scalar value, or tuple/list of two floating values in the range [0.0, 1.0]. If factor is a single value, it will interpret as equivalent to the tuple (0.0, factor). The factor will sample between its range for every image to augment.
• channels: integer, the number of channels to shift, defaults to 3 which corresponds to an RGB shift. In some cases, there may ber more or less channels.
• seed: Integer. Used to create a random seed.

Example

(images, labels), _ = keras.datasets.cifar10.load_data()
rgb_shift = keras_cv.layers.RandomChannelShift(value_range=(0, 255),
    factor=0.5)
augmented_images = rgb_shift(images)