import tensorflow as tf
from tensorflow import keras
from keras_aug.datapoints import image as image_utils
from keras_aug.layers.base.vectorized_base_random_layer import (
VectorizedBaseRandomLayer,
)
[docs]@keras.utils.register_keras_serializable(package="keras_aug")
class AutoContrast(VectorizedBaseRandomLayer):
"""Performs the AutoContrast operation on the input images.
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``.
Args:
value_range (Sequence[int|float]): The range of values the incoming
images will have. This is typically either ``[0, 1]`` or
``[0, 255]`` depending on how your preprocessing pipeline is set up.
References:
- `KerasCV <https://github.com/keras-team/keras-cv>`_
"""
def __init__(self, value_range, **kwargs):
super().__init__(**kwargs)
self.value_range = value_range
def augment_ragged_image(self, image, transformation, **kwargs):
images = tf.expand_dims(image, axis=0)
images = self.augment_images(
images=images, transformations=transformation, **kwargs
)
return tf.squeeze(images, axis=0)
def augment_images(self, images, transformations, **kwargs):
images = image_utils.transform_value_range(
images,
original_range=self.value_range,
target_range=(0, 255),
dtype=self.compute_dtype,
)
lows = tf.reduce_min(images, axis=(1, 2), keepdims=True)
highs = tf.reduce_max(images, axis=(1, 2), keepdims=True)
scales = 255.0 / (highs - lows)
eq_idxs = tf.math.is_inf(scales)
lows = tf.where(eq_idxs, 0.0, lows)
scales = tf.where(eq_idxs, 1.0, scales)
images = tf.clip_by_value((images - lows) * scales, 0, 255)
images = image_utils.transform_value_range(
images,
original_range=(0, 255),
target_range=self.value_range,
dtype=self.compute_dtype,
)
return images
def augment_labels(self, labels, transformations, **kwargs):
return labels
def augment_bounding_boxes(self, bounding_boxes, transformations, **kwargs):
return bounding_boxes
def augment_segmentation_masks(
self, segmentation_masks, transformations, **kwargs
):
return segmentation_masks
def augment_keypoints(self, keypoints, transformations, **kwargs):
return keypoints
def get_config(self):
config = super().get_config()
config.update({"value_range": self.value_range})
return config