RandomChannelShift layer
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]. Iffactoris a single value, it will interpret as equivalent to the tuple(0.0, factor). Thefactorwill 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)