tf.keras.layers.RandomShear

tf.keras.layers.RandomShear(
    x_factor=0.0,
    y_factor=0.0,
    interpolation='bilinear',
    fill_mode='reflect',
    fill_value=0.0,
    data_format=None,
    seed=None,
    **kwargs
)

This layer shears the input images along the x-axis and/or y-axis by a
randomly selected factor within the specified range. The shear
transformation is applied to each image independently in a batch. Empty
regions created during the transformation are filled according to the
`fill_mode` and `fill_value` parameters.

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:
    x_factor: A tuple of two floats. For each augmented image, a value
        is sampled from the provided range. If a float is passed, the
        range is interpreted as `(0, x_factor)`. Values represent a
        percentage of the image to shear over. For example, 0.3 shears
        pixels up to 30% of the way across the image. All provided values
        should be positive.
    y_factor: A tuple of two floats. For each augmented image, a value
        is sampled from the provided range. If a float is passed, the
        range is interpreted as `(0, y_factor)`. Values represent a
        percentage of the image to shear over. For example, 0.3 shears
        pixels up to 30% of the way across the image. All provided values
        should be positive.
    interpolation: Interpolation mode. Supported values: `"nearest"`,
        `"bilinear"`.
    fill_mode: Points outside the boundaries of the input are filled
        according to the given mode. Available methods are `"constant"`,
        `"nearest"`, `"wrap"` and `"reflect"`. Defaults to `"constant"`.

        - `"reflect"`: `(d c b a | a b c d | d c b a)`
            The input is extended by reflecting about the edge of the
            last pixel.
        - `"constant"`: `(k k k k | a b c d | k k k k)`
            The input is extended by filling all values beyond the edge
            with the same constant value `k` specified by `fill_value`.
        - `"wrap"`: `(a b c d | a b c d | a b c d)`
            The input is extended by wrapping around to the opposite edge.
        - `"nearest"`: `(a a a a | a b c d | d d d d)`
            The input is extended by the nearest pixel.
        Note that when using torch backend, `"reflect"` is redirected to
        `"mirror"` `(c d c b | a b c d | c b a b)` because torch does
        not support `"reflect"`.
        Note that torch backend does not support `"wrap"`.
    fill_value: A float representing the value to be filled outside the
        boundaries when `fill_mode="constant"`.
    seed: Integer. Used to create a random seed.

Example:

layer = keras.layers.RandomShear(bounding_box_format="xyxy")
images = np.random.randint(0, 255, (4, 224, 224, 3), dtype="uint8")

bounding_boxes = {
    "boxes": np.array([
        [[10, 20, 100, 150], [50, 60, 200, 250]],
        [[15, 25, 110, 160], [55, 65, 210, 260]],
        [[20, 30, 120, 170], [60, 70, 220, 270]],
        [[25, 35, 130, 180], [65, 75, 230, 280]],
    ], dtype="float32"),
    "labels": np.array([[0, 1], [1, 2], [2, 3], [0, 3]], dtype="int32")
}

labels = keras.ops.one_hot(
    np.array([0, 1, 2, 3]),
    num_classes=4
)

segmentation_masks = np.random.randint(0, 3, (4, 224, 224, 1), dtype="uint8")

output = layer(
    {
        "images": images,
        "bounding_boxes": bounding_boxes,
        "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
)

get_shifted_bbox(
    bounding_boxes, w_shift_factor, h_shift_factor
)

get_transformed_x_y(
    x, y, transform
)

transform_bounding_boxes(
    bounding_boxes, transformation, training=True
)

transform_images(
    images, transformation, 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
)