Add ops.image.scale_and_translate.

keras/api/_tf_keras/keras/ops/image/__init__.py

@@ -16,3 +16,4 @@
 from keras.src.ops.image import resize as resize
 from keras.src.ops.image import rgb_to_grayscale as rgb_to_grayscale
 from keras.src.ops.image import rgb_to_hsv as rgb_to_hsv
+from keras.src.ops.image import scale_and_translate as scale_and_translate

keras/api/ops/image/__init__.py

@@ -16,3 +16,4 @@
 from keras.src.ops.image import resize as resize
 from keras.src.ops.image import rgb_to_grayscale as rgb_to_grayscale
 from keras.src.ops.image import rgb_to_hsv as rgb_to_hsv
+from keras.src.ops.image import scale_and_translate as scale_and_translate

keras/src/backend/jax/image.py

@@ -14,6 +14,34 @@
     "lanczos5",
     "bicubic",
 )
+AFFINE_TRANSFORM_INTERPOLATIONS = {  # map to order
+    "nearest": 0,
+    "bilinear": 1,
+}
+AFFINE_TRANSFORM_FILL_MODES = {
+    "constant",
+    "nearest",
+    "wrap",
+    "mirror",
+    "reflect",
+}
+MAP_COORDINATES_FILL_MODES = {
+    "constant",
+    "nearest",
+    "wrap",
+    "mirror",
+    "reflect",
+}
+SCALE_AND_TRANSLATE_METHODS = {
+    "linear",
+    "bilinear",
+    "trilinear",
+    "cubic",
+    "bicubic",
+    "tricubic",
+    "lanczos3",
+    "lanczos5",
+}
 
 
 def rgb_to_grayscale(images, data_format=None):
@@ -372,19 +400,6 @@ def resize(
     )
 
 
-AFFINE_TRANSFORM_INTERPOLATIONS = {  # map to order
-    "nearest": 0,
-    "bilinear": 1,
-}
-AFFINE_TRANSFORM_FILL_MODES = {
-    "constant",
-    "nearest",
-    "wrap",
-    "mirror",
-    "reflect",
-}
-
-
 def affine_transform(
     images,
     transform,
@@ -483,15 +498,6 @@ def affine_transform(
     return affined
 
 
-MAP_COORDINATES_FILL_MODES = {
-    "constant",
-    "nearest",
-    "wrap",
-    "mirror",
-    "reflect",
-}
-
-
 def perspective_transform(
     images,
     start_points,
@@ -545,7 +551,7 @@ def perspective_transform(
     if data_format == "channels_first":
         images = jnp.transpose(images, (0, 2, 3, 1))
 
-    batch_size, height, width, channels = images.shape
+    _, height, width, _ = images.shape
     transforms = compute_homography_matrix(
         jnp.asarray(start_points, dtype="float32"),
         jnp.asarray(end_points, dtype="float32"),
@@ -859,3 +865,31 @@ def elastic_transform(
     transformed_images = transformed_images.astype(input_dtype)
 
     return transformed_images
+
+
+def scale_and_translate(
+    images,
+    output_shape,
+    scale,
+    translation,
+    spatial_dims,
+    method,
+    antialias=True,
+):
+    if method not in SCALE_AND_TRANSLATE_METHODS:
+        raise ValueError(
+            "Invalid value for argument `method`. Expected of one "
+            f"{SCALE_AND_TRANSLATE_METHODS}. Received: method={method}"
+        )
+    images = convert_to_tensor(images)
+    scale = convert_to_tensor(scale)
+    translation = convert_to_tensor(translation)
+    return jax.image.scale_and_translate(
+        images,
+        output_shape,
+        spatial_dims,
+        scale,
+        translation,
+        method,
+        antialias,
+    )

keras/src/backend/numpy/image.py

@@ -13,6 +13,34 @@
     "lanczos5",
     "bicubic",
 )
+AFFINE_TRANSFORM_INTERPOLATIONS = {  # map to order
+    "nearest": 0,
+    "bilinear": 1,
+}
+AFFINE_TRANSFORM_FILL_MODES = {
+    "constant",
+    "nearest",
+    "wrap",
+    "mirror",
+    "reflect",
+}
+MAP_COORDINATES_FILL_MODES = {
+    "constant",
+    "nearest",
+    "wrap",
+    "mirror",
+    "reflect",
+}
+SCALE_AND_TRANSLATE_METHODS = {
+    "linear",
+    "bilinear",
+    "trilinear",
+    "cubic",
+    "bicubic",
+    "tricubic",
+    "lanczos3",
+    "lanczos5",
+}
 
 
 def rgb_to_grayscale(images, data_format=None):
@@ -367,7 +395,7 @@ def resize(
     return _resize(images, size, method=interpolation, antialias=antialias)
 
 
-def compute_weight_mat(
+def _compute_weight_mat(
     input_size, output_size, scale, translation, kernel, antialias
 ):
     dtype = np.result_type(scale, translation)
@@ -410,32 +438,11 @@ def compute_weight_mat(
 
 
 def _resize(image, shape, method, antialias):
-    def _fill_triangle_kernel(x):
-        return np.maximum(0, 1 - np.abs(x))
-
-    def _fill_keys_cubic_kernel(x):
-        out = ((1.5 * x - 2.5) * x) * x + 1.0
-        out = np.where(x >= 1.0, ((-0.5 * x + 2.5) * x - 4.0) * x + 2.0, out)
-        return np.where(x >= 2.0, 0.0, out)
-
-    def _fill_lanczos_kernel(radius, x):
-        y = radius * np.sin(np.pi * x) * np.sin(np.pi * x / radius)
-        out = np.where(
-            x > 1e-3, np.divide(y, np.where(x != 0, np.pi**2 * x**2, 1)), 1
-        )
-        return np.where(x > radius, 0.0, out)
-
     if method == "nearest":
         return _resize_nearest(image, shape)
-    elif method == "bilinear":
-        kernel = _fill_triangle_kernel
-    elif method == "lanczos3":
-        kernel = lambda x: _fill_lanczos_kernel(3.0, x)
-    elif method == "lanczos5":
-        kernel = lambda x: _fill_lanczos_kernel(5.0, x)
-    elif method == "bicubic":
-        kernel = _fill_keys_cubic_kernel
     else:
+        kernel = _kernels.get(method, None)
+    if kernel is None:
         raise ValueError("Unknown resize method")
 
     spatial_dims = tuple(
@@ -473,6 +480,34 @@ def _resize_nearest(x, output_shape):
     return x
 
 
+def _fill_triangle_kernel(x):
+    return np.maximum(0, 1 - np.abs(x))
+
+
+def _fill_keys_cubic_kernel(x):
+    out = ((1.5 * x - 2.5) * x) * x + 1.0
+    out = np.where(x >= 1.0, ((-0.5 * x + 2.5) * x - 4.0) * x + 2.0, out)
+    return np.where(x >= 2.0, 0.0, out)
+
+
+def _fill_lanczos_kernel(radius, x):
+    y = radius * np.sin(np.pi * x) * np.sin(np.pi * x / radius)
+    out = np.where(
+        x > 1e-3, np.divide(y, np.where(x != 0, np.pi**2 * x**2, 1)), 1
+    )
+    return np.where(x > radius, 0.0, out)
+
+
+_kernels = {
+    "linear": _fill_triangle_kernel,
+    "bilinear": _fill_triangle_kernel,  # For `resize`.
+    "cubic": _fill_keys_cubic_kernel,
+    "bicubic": _fill_keys_cubic_kernel,  # For `resize`.
+    "lanczos3": lambda x: _fill_lanczos_kernel(3.0, x),
+    "lanczos5": lambda x: _fill_lanczos_kernel(5.0, x),
+}
+
+
 def _scale_and_translate(
     x, output_shape, spatial_dims, scale, translation, kernel, antialias
 ):
@@ -492,9 +527,9 @@ def _scale_and_translate(
         d = d % x.ndim
         m, n = input_shape[d], output_shape[d]
 
-        w = compute_weight_mat(
+        w = _compute_weight_mat(
             m, n, scale[i], translation[i], kernel, antialias
-        ).astype(np.float32)
+        ).astype(output.dtype)
         output = np.tensordot(output, w, axes=(d, 0))
         output = np.moveaxis(output, -1, d)
 
@@ -504,19 +539,6 @@ def _scale_and_translate(
     return output
 
 
-AFFINE_TRANSFORM_INTERPOLATIONS = {  # map to order
-    "nearest": 0,
-    "bilinear": 1,
-}
-AFFINE_TRANSFORM_FILL_MODES = {
-    "constant",
-    "nearest",
-    "wrap",
-    "mirror",
-    "reflect",
-}
-
-
 def affine_transform(
     images,
     transform,
@@ -877,15 +899,6 @@ def compute_homography_matrix(start_points, end_points):
     return homography_matrix
 
 
-MAP_COORDINATES_FILL_MODES = {
-    "constant",
-    "nearest",
-    "wrap",
-    "mirror",
-    "reflect",
-}
-
-
 def map_coordinates(
     inputs, coordinates, order, fill_mode="constant", fill_value=0.0
 ):
@@ -1135,3 +1148,40 @@ def elastic_transform(
     transformed_images = transformed_images.astype(input_dtype)
 
     return transformed_images
+
+
+def scale_and_translate(
+    images,
+    output_shape,
+    scale,
+    translation,
+    spatial_dims,
+    method,
+    antialias=True,
+):
+    if method not in SCALE_AND_TRANSLATE_METHODS:
+        raise ValueError(
+            "Invalid value for argument `method`. Expected of one "
+            f"{SCALE_AND_TRANSLATE_METHODS}. Received: method={method}"
+        )
+    if method in ("linear", "bilinear", "trilinear", "triangle"):
+        method = "linear"
+    elif method in ("cubic", "bicubic", "tricubic"):
+        method = "cubic"
+
+    images = convert_to_tensor(images)
+    scale = convert_to_tensor(scale)
+    translation = convert_to_tensor(translation)
+    kernel = _kernels[method]
+    dtype = backend.result_type(scale.dtype, translation.dtype)
+    scale = scale.astype(dtype)
+    translation = translation.astype(dtype)
+    return _scale_and_translate(
+        images,
+        output_shape,
+        spatial_dims,
+        scale,
+        translation,
+        kernel,
+        antialias,
+    )

keras/src/backend/openvino/image.py

@@ -31,9 +31,59 @@ def affine_transform(
     )
 
 
+def perspective_transform(
+    images,
+    start_points,
+    end_points,
+    interpolation="bilinear",
+    fill_value=0,
+    data_format=None,
+):
+    raise NotImplementedError(
+        "`perspective_transform` is not supported with openvino backend"
+    )
+
+
 def map_coordinates(
     inputs, coordinates, order, fill_mode="constant", fill_value=0
 ):
     raise NotImplementedError(
         "`map_coordinates` is not supported with openvino backend"
     )
+
+
+def gaussian_blur(
+    images, kernel_size=(3, 3), sigma=(1.0, 1.0), data_format=None
+):
+    raise NotImplementedError(
+        "`gaussian_blur` is not supported with openvino backend"
+    )
+
+
+def elastic_transform(
+    images,
+    alpha=20.0,
+    sigma=5.0,
+    interpolation="bilinear",
+    fill_mode="reflect",
+    fill_value=0.0,
+    seed=None,
+    data_format=None,
+):
+    raise NotImplementedError(
+        "`elastic_transform` is not supported with openvino backend"
+    )
+
+
+def scale_and_translate(
+    images,
+    output_shape,
+    scale,
+    translation,
+    spatial_dims,
+    method,
+    antialias=True,
+):
+    raise NotImplementedError(
+        "`scale_and_translate` is not supported with openvino backend"
+    )