ยป Keras API reference / KerasCV / Layers / Preprocessing layers / RandomChannelShift layer

RandomChannelShift layer

[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 setup.
  • 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 sampled 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.

Usage:

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