Add random_sharpness processing layer (#20697)

* Add random_sharpness.py

* Update random_sharpness

* Add test cases

* Fix failed test case

Author: shashaka

keras/api/_tf_keras/keras/layers/__init__.py

@@ -185,6 +185,9 @@
 from keras.src.layers.preprocessing.image_preprocessing.random_saturation import (
     RandomSaturation,
 )
+from keras.src.layers.preprocessing.image_preprocessing.random_sharpness import (
+    RandomSharpness,
+)
 from keras.src.layers.preprocessing.image_preprocessing.random_translation import (
     RandomTranslation,
 )

keras/api/layers/__init__.py

@@ -185,6 +185,9 @@
 from keras.src.layers.preprocessing.image_preprocessing.random_saturation import (
     RandomSaturation,
 )
+from keras.src.layers.preprocessing.image_preprocessing.random_sharpness import (
+    RandomSharpness,
+)
 from keras.src.layers.preprocessing.image_preprocessing.random_translation import (
     RandomTranslation,
 )

keras/src/layers/__init__.py

@@ -129,6 +129,9 @@
 from keras.src.layers.preprocessing.image_preprocessing.random_saturation import (
     RandomSaturation,
 )
+from keras.src.layers.preprocessing.image_preprocessing.random_sharpness import (
+    RandomSharpness,
+)
 from keras.src.layers.preprocessing.image_preprocessing.random_translation import (
     RandomTranslation,
 )

keras/src/layers/preprocessing/image_preprocessing/random_sharpness.py

@@ -0,0 +1,168 @@
+from keras.src.api_export import keras_export
+from keras.src.layers.preprocessing.image_preprocessing.base_image_preprocessing_layer import (  # noqa: E501
+    BaseImagePreprocessingLayer,
+)
+from keras.src.random import SeedGenerator
+
+
+@keras_export("keras.layers.RandomSharpness")
+class RandomSharpness(BaseImagePreprocessingLayer):
+    """Randomly performs the sharpness operation on given images.
+
+    The sharpness operation first performs a blur, then blends between the
+    original image and the processed image. This operation adjusts the clarity
+    of the edges in an image, ranging from blurred to enhanced sharpness.
+
+    Args:
+        factor: A tuple of two floats or a single float.
+            `factor` controls the extent to which the image sharpness
+            is impacted. `factor=0.0` results in a fully blurred image,
+            `factor=0.5` applies no operation (preserving the original image),
+            and `factor=1.0` enhances the sharpness beyond the original. 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. To ensure the value is always the same,
+            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 set up.
+        seed: Integer. Used to create a random seed.
+    """
+
+    _USE_BASE_FACTOR = False
+    _FACTOR_BOUNDS = (0, 1)
+
+    _VALUE_RANGE_VALIDATION_ERROR = (
+        "The `value_range` argument should be a list of two numbers. "
+    )
+
+    def __init__(
+        self,
+        factor,
+        value_range=(0, 255),
+        data_format=None,
+        seed=None,
+        **kwargs,
+    ):
+        super().__init__(data_format=data_format, **kwargs)
+        self._set_factor(factor)
+        self._set_value_range(value_range)
+        self.seed = seed
+        self.generator = SeedGenerator(seed)
+
+    def _set_value_range(self, value_range):
+        if not isinstance(value_range, (tuple, list)):
+            raise ValueError(
+                self._VALUE_RANGE_VALIDATION_ERROR
+                + f"Received: value_range={value_range}"
+            )
+        if len(value_range) != 2:
+            raise ValueError(
+                self._VALUE_RANGE_VALIDATION_ERROR
+                + f"Received: value_range={value_range}"
+            )
+        self.value_range = sorted(value_range)
+
+    def get_random_transformation(self, data, training=True, seed=None):
+        if isinstance(data, dict):
+            images = data["images"]
+        else:
+            images = data
+        images_shape = self.backend.shape(images)
+        rank = len(images_shape)
+        if rank == 3:
+            batch_size = 1
+        elif rank == 4:
+            batch_size = images_shape[0]
+        else:
+            raise ValueError(
+                "Expected the input image to be rank 3 or 4. Received: "
+                f"inputs.shape={images_shape}"
+            )
+
+        if seed is None:
+            seed = self._get_seed_generator(self.backend._backend)
+
+        factor = self.backend.random.uniform(
+            (batch_size,),
+            minval=self.factor[0],
+            maxval=self.factor[1],
+            seed=seed,
+        )
+        return {"factor": factor}
+
+    def transform_images(self, images, transformation=None, training=True):
+        images = self.backend.cast(images, self.compute_dtype)
+        if training:
+            if self.data_format == "channels_first":
+                images = self.backend.numpy.swapaxes(images, -3, -1)
+
+            sharpness_factor = self.backend.cast(
+                transformation["factor"] * 2, dtype=self.compute_dtype
+            )
+            sharpness_factor = self.backend.numpy.reshape(
+                sharpness_factor, (-1, 1, 1, 1)
+            )
+
+            num_channels = self.backend.shape(images)[-1]
+
+            a, b = 1.0 / 13.0, 5.0 / 13.0
+            kernel = self.backend.convert_to_tensor(
+                [[a, a, a], [a, b, a], [a, a, a]], dtype=self.compute_dtype
+            )
+            kernel = self.backend.numpy.reshape(kernel, (3, 3, 1, 1))
+            kernel = self.backend.numpy.tile(kernel, [1, 1, num_channels, 1])
+            kernel = self.backend.cast(kernel, self.compute_dtype)
+
+            smoothed_image = self.backend.nn.depthwise_conv(
+                images,
+                kernel,
+                strides=1,
+                padding="same",
+                data_format="channels_last",
+            )
+
+            smoothed_image = self.backend.cast(
+                smoothed_image, dtype=self.compute_dtype
+            )
+            images = images + (1.0 - sharpness_factor) * (
+                smoothed_image - images
+            )
+
+            images = self.backend.numpy.clip(
+                images, self.value_range[0], self.value_range[1]
+            )
+
+            if self.data_format == "channels_first":
+                images = self.backend.numpy.swapaxes(images, -3, -1)
+
+        return images
+
+    def transform_labels(self, labels, transformation, training=True):
+        return labels
+
+    def transform_segmentation_masks(
+        self, segmentation_masks, transformation, training=True
+    ):
+        return segmentation_masks
+
+    def transform_bounding_boxes(
+        self, bounding_boxes, transformation, training=True
+    ):
+        return bounding_boxes
+
+    def get_config(self):
+        config = super().get_config()
+        config.update(
+            {
+                "factor": self.factor,
+                "value_range": self.value_range,
+                "seed": self.seed,
+            }
+        )
+        return config
+
+    def compute_output_shape(self, input_shape):
+        return input_shape

keras/src/layers/preprocessing/image_preprocessing/random_sharpness_test.py

@@ -0,0 +1,65 @@
+import numpy as np
+import pytest
+from tensorflow import data as tf_data
+
+import keras
+from keras.src import backend
+from keras.src import layers
+from keras.src import testing
+
+
+class RandomSharpnessTest(testing.TestCase):
+    @pytest.mark.requires_trainable_backend
+    def test_layer(self):
+        self.run_layer_test(
+            layers.RandomSharpness,
+            init_kwargs={
+                "factor": 0.75,
+                "seed": 1,
+            },
+            input_shape=(8, 3, 4, 3),
+            supports_masking=False,
+            expected_output_shape=(8, 3, 4, 3),
+        )
+
+    def test_random_sharpness_value_range(self):
+        image = keras.random.uniform(shape=(3, 3, 3), minval=0, maxval=1)
+
+        layer = layers.RandomSharpness(0.2)
+        adjusted_image = layer(image)
+
+        self.assertTrue(keras.ops.numpy.all(adjusted_image >= 0))
+        self.assertTrue(keras.ops.numpy.all(adjusted_image <= 1))
+
+    def test_random_sharpness_no_op(self):
+        data_format = backend.config.image_data_format()
+        if data_format == "channels_last":
+            inputs = np.random.random((2, 8, 8, 3))
+        else:
+            inputs = np.random.random((2, 3, 8, 8))
+
+        layer = layers.RandomSharpness((0.5, 0.5))
+        output = layer(inputs, training=False)
+        self.assertAllClose(inputs, output, atol=1e-3, rtol=1e-5)
+
+    def test_random_sharpness_randomness(self):
+        image = keras.random.uniform(shape=(3, 3, 3), minval=0, maxval=1)[:5]
+
+        layer = layers.RandomSharpness(0.2)
+        adjusted_images = layer(image)
+
+        self.assertNotAllClose(adjusted_images, image)
+
+    def test_tf_data_compatibility(self):
+        data_format = backend.config.image_data_format()
+        if data_format == "channels_last":
+            input_data = np.random.random((2, 8, 8, 3))
+        else:
+            input_data = np.random.random((2, 3, 8, 8))
+        layer = layers.RandomSharpness(
+            factor=0.5, data_format=data_format, seed=1337
+        )
+
+        ds = tf_data.Dataset.from_tensor_slices(input_data).batch(2).map(layer)
+        for output in ds.take(1):
+            output.numpy()