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,
)
from keras_aug.utils import augmentation as augmentation_utils
[docs]@keras.utils.register_keras_serializable(package="keras_aug")
class RandomSolarize(VectorizedBaseRandomLayer):
"""Randomly applies ``(max_value - pixel + min_value)`` for each pixel in
the input images.
When created without ``threshold_factor`` parameter, the layer performs
solarization to all values. When created with specified ``threshold_factor``
the layer only augments pixels that are above the ``threshold_factor``
value.
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.
threshold_factor (float|Sequence[float]|keras_aug.FactorSampler):
The range of the threshold factor. Only the pixel values above the
threshold will be solarized. When represented as a single float,
the factor will be picked between ``[0, upper]``. ``255``
means no thresholding.
addition_factor (float|Sequence[float]|keras_aug.FactorSampler, optional):
The range of the addition factor that is added to each pixel before
solarization and thresholding. When represented as a single float,
the factor will be picked between ``[0, upper]``. ``0`` means no
addition. Defaults to ``0``.
seed (int|float, optional): The random seed. Defaults to
``None``.
References:
- `AutoAugment <https://arxiv.org/abs/1805.09501>`_
- `RandAugment <https://arxiv.org/abs/1909.13719>`_
- `KerasCV <https://github.com/keras-team/keras-cv>`_
""" # noqa: E501
def __init__(
self,
value_range,
threshold_factor,
addition_factor=0,
seed=None,
**kwargs,
):
super().__init__(seed=seed, **kwargs)
self.value_range = value_range
if isinstance(threshold_factor, (int, float)):
threshold_factor = (0, threshold_factor)
self.threshold_factor = augmentation_utils.parse_factor(
threshold_factor,
max_value=255,
seed=seed,
param_name="threshold_factor",
)
if isinstance(addition_factor, (int, float)):
addition_factor = (0, addition_factor)
self.addition_factor = augmentation_utils.parse_factor(
addition_factor,
max_value=255,
seed=seed,
param_name="addition_factor",
)
self.seed = seed
def get_random_transformation_batch(self, batch_size, **kwargs):
return {
"additions": self.addition_factor(
shape=(batch_size, 1, 1, 1), dtype=self.compute_dtype
),
"thresholds": self.threshold_factor(
shape=(batch_size, 1, 1, 1), dtype=self.compute_dtype
),
}
def augment_ragged_image(self, image, transformation, **kwargs):
image = tf.expand_dims(image, axis=0)
transformation = augmentation_utils.expand_dict_dims(
transformation, axis=0
)
image = self.augment_images(
images=image, transformations=transformation, **kwargs
)
return tf.squeeze(image, axis=0)
def augment_images(self, images, transformations, **kwargs):
thresholds = transformations["thresholds"]
additions = transformations["additions"]
images = image_utils.transform_value_range(
images,
original_range=self.value_range,
target_range=(0, 255),
dtype=self.compute_dtype,
)
images = images + additions
images = tf.clip_by_value(images, 0, 255)
images = tf.where(images < thresholds, images, 255 - images)
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,
"threshold_factor": self.threshold_factor,
"addition_factor": self.addition_factor,
"seed": self.seed,
}
)
return config