# Copyright 2022 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import tensorflow as tf

from keras_cv.layers.preprocessing.base_image_augmentation_layer import (
    BaseImageAugmentationLayer,
)
from keras_cv.utils import preprocessing


@tf.keras.utils.register_keras_serializable(package="keras_cv")
class RandomSharpness(BaseImageAugmentationLayer):
    """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.

    References:
        - [PIL](https://pillow.readthedocs.io/en/stable/reference/ImageEnhance.html)

    Args:
        factor: A tuple of two floats, a single float or `keras_cv.FactorSampler`.
            `factor` controls the extent to which the image sharpness is impacted.
            `factor=0.0` makes this layer perform a no-op operation, while a value of
            1.0 uses the sharpened result entirely.  Values between 0 and 1 result in
            linear interpolation between the original image and the sharpened image.
            Values should be between `0.0` and `1.0`.  If a tuple is used, a `factor` is
            sampled between the two values for every image augmented.  If a single float
            is used, a value between `0.0` and the passed float is sampled.  In order to
            ensure the value is always the same, please pass a tuple with two identical
            floats: `(0.5, 0.5)`.
        value_range: the 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 setup.
    """

    def __init__(
        self,
        factor,
        value_range,
        seed=None,
        **kwargs,
    ):
        super().__init__(seed=seed, **kwargs)
        self.value_range = value_range
        self.factor = preprocessing.parse_factor(factor)
        self.seed = seed

    def get_random_transformation(self, **kwargs):
        return self.factor()

    def augment_image(self, image, transformation=None, **kwargs):
        image = preprocessing.transform_value_range(
            image, original_range=self.value_range, target_range=(0, 255)
        )
        original_image = image

        # Make image 4D for conv operation.
        image = tf.expand_dims(image, axis=0)

        # [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=tf.float32, shape=[3, 3, 1, 1]
            )
            / 13.0
        )

        # Tile across channel dimension.
        channels = tf.shape(image)[-1]
        kernel = tf.tile(kernel, [1, 1, channels, 1])
        strides = [1, 1, 1, 1]

        smoothed_image = tf.nn.depthwise_conv2d(
            image, kernel, strides, padding="VALID", dilations=[1, 1]
        )
        smoothed_image = tf.clip_by_value(smoothed_image, 0.0, 255.0)
        smoothed_image = tf.squeeze(smoothed_image, axis=0)

        # For the borders of the resulting image, fill in the values of the
        # original image.
        mask = tf.ones_like(smoothed_image)
        padded_mask = tf.pad(mask, [[1, 1], [1, 1], [0, 0]])
        padded_smoothed_image = tf.pad(smoothed_image, [[1, 1], [1, 1], [0, 0]])

        result = tf.where(
            tf.equal(padded_mask, 1), padded_smoothed_image, original_image
        )
        # Blend the final result.
        result = preprocessing.blend(original_image, result, transformation)
        result = preprocessing.transform_value_range(
            result, original_range=(0, 255), target_range=self.value_range
        )
        return result

    def augment_bounding_boxes(self, bounding_boxes, transformation, **kwargs):
        return bounding_boxes

    def augment_label(self, label, transformation=None, **kwargs):
        return label

    def augment_segmentation_mask(self, segmentation_mask, transformation, **kwargs):
        return segmentation_mask

    def get_config(self):
        config = super().get_config()
        config.update(
            {"factor": self.factor, "value_range": self.value_range, "seed": self.seed}
        )
        return config