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 RandomSharpness(VectorizedBaseRandomLayer):
"""Randomly performs the sharpness operation on given images.
The sharpness operation first performs a blur operation, then blends between
the original image and the blurred image. This operation makes the edges of
an image less sharp than they were in the original image.
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.
factor (float|Sequence[float]|keras_aug.FactorSampler): The range of the
sharpness factor. When represented as a single float, the factor
will be picked between ``[1.0 - lower, 1.0 + upper]``. ``1.0`` will
give the original image. ``0.0`` makes the image to be blurred.
``2.0`` will enhance the sharpness by a factor of 2.
seed (int|float, optional): The random seed. Defaults to ``None``.
References:
- `PIL/ImageEnhance <https://pillow.readthedocs.io/en/stable/reference/ImageEnhance.html>`_
- `KerasCV <https://github.com/keras-team/keras-cv>`_
- `Tensorflow Model augment <https://github.com/tensorflow/models/blob/v2.12.0/official/vision/ops/augment.py>`_
""" # noqa: E501
def __init__(
self,
value_range,
factor,
seed=None,
**kwargs,
):
super().__init__(seed=seed, **kwargs)
self.value_range = value_range
self.factor = augmentation_utils.parse_factor(
factor, max_value=None, center_value=1, seed=seed
)
self.seed = seed
def get_random_transformation_batch(self, batch_size, **kwargs):
return self.factor(
shape=(batch_size, 1, 1, 1), dtype=self.compute_dtype
)
def augment_ragged_image(self, image, transformation, **kwargs):
images = tf.expand_dims(image, axis=0)
new_transformation = tf.expand_dims(transformation, axis=0)
output = self.augment_images(images, new_transformation)
return tf.squeeze(output, 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,
)
original_images = images
# [1 1 1]
# [1 5 1]
# [1 1 1]
# all divided by 13 is the default 3x3 gaussian smoothing kernel.
# Correlating or Convolving with this filter is equivalent to performing
# a gaussian blur.
kernel = (
tf.constant(
[[1, 1, 1], [1, 5, 1], [1, 1, 1]],
dtype=self.compute_dtype,
shape=[3, 3, 1, 1],
)
/ 13.0
)
# Tile across channel dimension.
channels = tf.shape(images)[-1]
kernel = tf.tile(kernel, [1, 1, channels, 1])
strides = [1, 1, 1, 1]
smoothed_image = tf.nn.depthwise_conv2d(
images, kernel, strides, padding="VALID", dilations=[1, 1]
)
smoothed_image = tf.clip_by_value(smoothed_image, 0.0, 255.0)
# For the borders of the resulting image, fill in the values of the
# original image.
mask = tf.ones_like(smoothed_image)
mask = tf.pad(mask, [[0, 0], [1, 1], [1, 1], [0, 0]])
smoothed_image = tf.pad(
smoothed_image, [[0, 0], [1, 1], [1, 1], [0, 0]]
)
images = tf.where(tf.equal(mask, 1), smoothed_image, original_images)
# Blend the final result.
images = image_utils.blend(
images, original_images, transformations, (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,
"factor": self.factor,
"seed": self.seed,
}
)
return config