tf.keras.layers.RandomSharpness

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

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 <a href="../../../tf/data"><code>tf.data</code></a> or `grain` pipeline
(independently of which backend you're using).

Args:
    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
)

convert_weight(
    weight
)

@classmethod
from_config(
    config
)

if "dtype" in config and isinstance(config["dtype"], dict):
    policy = dtype_policies.deserialize(config["dtype"])

get_random_transformation(
    data, training=True, seed=None
)

transform_bounding_boxes(
    bounding_boxes, transformation, training=True
)

transform_images(
    images, transformation=None, training=True
)

transform_labels(
    labels, transformation, training=True
)

transform_segmentation_masks(
    segmentation_masks, transformation, training=True
)

transform_single_bounding_box(
    bounding_box, transformation, training=True
)

transform_single_image(
    image, transformation, training=True
)

transform_single_label(
    label, transformation, training=True
)

transform_single_segmentation_mask(
    segmentation_mask, transformation, training=True
)