tf.keras.layers.AutoContrast

See Stable See Nightly

Performs the auto-contrast operation on an image.

Inherits From: Layer, Operation

tf.keras.layers.AutoContrast(
    value_range=(0, 255), **kwargs
)

Auto contrast stretches the values of an image across the entire available value_range. This makes differences between pixels more obvious. An example of this is if an image only has values [0, 1] out of the range [0, 255], auto contrast will change the 1 values to be 255.

This layer is active at both training and inference time.

Note: This layer is safe to use inside a tf.data or grain pipeline (independently of which backend you're using).

Args
value_range	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. Defaults to (0, 255).

Attributes
input	Retrieves the input tensor(s) of a symbolic operation. Only returns the tensor(s) corresponding to the first time the operation was called.
output	Retrieves the output tensor(s) of a layer. Only returns the tensor(s) corresponding to the first time the operation was called.

Methods

convert_weight

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convert_weight(
    weight
)

Convert the weight if it is from the a different backend.

from_config

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@classmethod
from_config(
    config
)

Creates an operation from its config.

This method is the reverse of get_config, capable of instantiating the same operation from the config dictionary.

Note: If you override this method, you might receive a serialized dtype config, which is a dict. You can deserialize it as follows:

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

Args
config	A Python dictionary, typically the output of get_config.

Returns
An operation instance.

get_random_transformation

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

transform_bounding_boxes

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transform_bounding_boxes(
    bounding_boxes, transformation, training=True
)

transform_images

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

transform_labels

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transform_labels(
    labels, transformation, training=True
)

transform_segmentation_masks

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transform_segmentation_masks(
    segmentation_masks, transformation, training=True
)

transform_single_bounding_box

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transform_single_bounding_box(
    bounding_box, transformation, training=True
)

transform_single_image

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transform_single_image(
    image, transformation, training=True
)

transform_single_label

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transform_single_label(
    label, transformation, training=True
)

transform_single_segmentation_mask

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transform_single_segmentation_mask(
    segmentation_mask, transformation, training=True
)