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► Keras 3 API documentation / Layers API / Preprocessing layers / Image augmentation layers / RandomSharpness layer

RandomSharpness layer

[source]

RandomSharpness class

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

Randomly performs the sharpness operation on given images.

The sharpness operation first performs a blur, then blends between the
original image and the processed image. This operation adjusts the clarity
of the edges in an image, ranging from blurred to enhanced sharpness.

**Note:** This layer is safe to use inside a [`tf.data`](https://www.tensorflow.org/api_docs/python/tf/data) or `grain` pipeline
(independently of which backend you're using).

# Arguments
    factor: A tuple of two floats or a single float.
        `factor` controls the extent to which the image sharpness
        is impacted. `factor=0.0` results in a fully blurred image,
        `factor=0.5` applies no operation (preserving the original image),
        and `factor=1.0` enhances the sharpness beyond the original. Values
        should be between `0.0` and `1.0`. 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. To ensure the value is always the same,
        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

layer = keras.layers.RandomSharpness(value_range=(0, 255))
images = np.random.randint(0, 255, (8, 224, 224, 3), dtype="uint8")

labels = keras.ops.one_hot(
    np.array([0, 1, 2, 0, 1, 2, 0, 1]),
    num_classes=3
)

segmentation_masks = np.random.randint(0, 3, (8, 224, 224, 1), dtype="uint8")

output = layer(
    {
        "images": images,
        "labels": labels,
        "segmentation_masks": segmentation_masks
    },
    training=True
)
RandomSharpness layer
RandomSharpness class