Add random_color_degeneration processing layer (#20679)

* Add random_color_degeneration processing layer

* Fix mistypo

* Correct failed test case

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

@@ -155,6 +155,9 @@
 from keras.src.layers.preprocessing.image_preprocessing.random_brightness import (
     RandomBrightness,
 )
+from keras.src.layers.preprocessing.image_preprocessing.random_color_degeneration import (
+    RandomColorDegeneration,
+)
 from keras.src.layers.preprocessing.image_preprocessing.random_color_jitter import (
     RandomColorJitter,
 )

keras/api/layers/__init__.py

@@ -155,6 +155,9 @@
 from keras.src.layers.preprocessing.image_preprocessing.random_brightness import (
     RandomBrightness,
 )
+from keras.src.layers.preprocessing.image_preprocessing.random_color_degeneration import (
+    RandomColorDegeneration,
+)
 from keras.src.layers.preprocessing.image_preprocessing.random_color_jitter import (
     RandomColorJitter,
 )

keras/src/layers/__init__.py

@@ -99,6 +99,9 @@
 from keras.src.layers.preprocessing.image_preprocessing.random_brightness import (
     RandomBrightness,
 )
+from keras.src.layers.preprocessing.image_preprocessing.random_color_degeneration import (
+    RandomColorDegeneration,
+)
 from keras.src.layers.preprocessing.image_preprocessing.random_color_jitter import (
     RandomColorJitter,
 )

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

@@ -0,0 +1,132 @@
+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.RandomColorDegeneration")
+class RandomColorDegeneration(BaseImagePreprocessingLayer):
+    """Randomly performs the color degeneration operation on given images.
+
+    The sharpness operation first converts an image to gray scale, then back to
+    color. It then takes a weighted average between original image and the
+    degenerated image. This makes colors appear more dull.
+
+    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` makes this layer perform a
+            no-op operation, while a value of 1.0 uses the degenerated 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)`.
+        seed: Integer. Used to create a random seed.
+    """
+
+    _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, 1, 1, 1),
+            minval=self.factor[0],
+            maxval=self.factor[1],
+            seed=seed,
+        )
+        factor = factor
+        return {"factor": factor}
+
+    def transform_images(self, images, transformation=None, training=True):
+        if training:
+            images = self.backend.cast(images, self.compute_dtype)
+            factor = self.backend.cast(
+                transformation["factor"], self.compute_dtype
+            )
+            degenerates = self.backend.image.rgb_to_grayscale(
+                images, data_format=self.data_format
+            )
+            images = images + factor * (degenerates - images)
+            images = self.backend.numpy.clip(
+                images, self.value_range[0], self.value_range[1]
+            )
+            images = self.backend.cast(images, self.compute_dtype)
+        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_color_degeneration_test.py

@@ -0,0 +1,77 @@
+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 RandomColorDegenerationTest(testing.TestCase):
+    @pytest.mark.requires_trainable_backend
+    def test_layer(self):
+        self.run_layer_test(
+            layers.RandomColorDegeneration,
+            init_kwargs={
+                "factor": 0.75,
+                "value_range": (0, 1),
+                "seed": 1,
+            },
+            input_shape=(8, 3, 4, 3),
+            supports_masking=False,
+            expected_output_shape=(8, 3, 4, 3),
+        )
+
+    def test_random_color_degeneration_value_range(self):
+        image = keras.random.uniform(shape=(3, 3, 3), minval=0, maxval=1)
+
+        layer = layers.RandomColorDegeneration(0.2, value_range=(0, 1))
+        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_color_degeneration_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.RandomColorDegeneration((0.5, 0.5))
+        output = layer(inputs, training=False)
+        self.assertAllClose(inputs, output, atol=1e-3, rtol=1e-5)
+
+    def test_random_color_degeneration_factor_zero(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.RandomColorDegeneration(factor=(0.0, 0.0))
+        result = layer(inputs)
+
+        self.assertAllClose(inputs, result, atol=1e-3, rtol=1e-5)
+
+    def test_random_color_degeneration_randomness(self):
+        image = keras.random.uniform(shape=(3, 3, 3), minval=0, maxval=1)[:5]
+
+        layer = layers.RandomColorDegeneration(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.RandomColorDegeneration(
+            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()