tf.keras.layers.RandomRotation

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

This layer will apply random rotations to each image, filling empty space
according to `fill_mode`.

By default, random rotations are only applied during training.
At inference time, the layer does nothing. If you need to apply random
rotations at inference time, pass `training=True` when calling the layer.

Input pixel values can be of any range (e.g. `[0., 1.)` or `[0, 255]`) and
of integer or floating point dtype.
By default, the layer will output floats.

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).

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

Args:
    factor: a float represented as fraction of 2 Pi, or a tuple of size 2
        representing lower and upper bound for rotating clockwise and
        counter-clockwise. A positive values means rotating
        counter clock-wise,
        while a negative value means clock-wise.
        When represented as a single
        float, this value is used for both the upper and lower bound.
        For instance, `factor=(-0.2, 0.3)`
        results in an output rotation by a random
        amount in the range `[-20% * 360, 30% * 360]`.
        `factor=0.2` results in an
        output rotating by a random amount
        in the range `[-20% * 360, 20% * 360]`.
    fill_mode: Points outside the boundaries of the input are filled
        according to the given mode
        (one of `{"constant", "reflect", "wrap", "nearest"}`).

        - *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 = 0.
        - *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.
    interpolation: Interpolation mode. Supported values: `"nearest"`,
        `"bilinear"`.
    seed: Integer. Used to create a random seed.
    fill_value: a float represents the value to be filled outside
        the boundaries when `fill_mode="constant"`.
    data_format: string, either `"channels_last"` or `"channels_first"`.
        The ordering of the dimensions in the inputs. `"channels_last"`
        corresponds to inputs with shape `(batch, height, width, channels)`
        while `"channels_first"` corresponds to inputs with shape
        `(batch, channels, height, width)`. It defaults to the
        `image_data_format` value found in your Keras config file at
        `~/.keras/keras.json`. If you never set it, then it will be
        `"channels_last"`.

Example:

layer = keras.layers.RandomRotation(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
)

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
)