use tf-ops for smart_resize

keras_cv/backend/tf_ops.py

@@ -52,67 +52,7 @@
 def smart_resize(x, size, interpolation="bilinear"):
     """Resize images to a target size without aspect ratio distortion.
 
-    Warning: `tf.keras.preprocessing.image.smart_resize` is not recommended for
-    new code. Prefer `tf.keras.layers.Resizing`, which provides the same
-    functionality as a preprocessing layer and adds `tf.RaggedTensor` support.
-    See the [preprocessing layer guide](
-    https://www.tensorflow.org/guide/tf_keras/preprocessing_layers)
-    for an overview of preprocessing layers.
-
-    TensorFlow image datasets typically yield images that have each a different
-    size. However, these images need to be batched before they can be
-    processed by TF-Keras layers. To be batched, images need to share the same
-    height and width.
-
-    You could simply do:
-
-    ```python
-    size = (200, 200)
-    ds = ds.map(lambda img: tf.image.resize(img, size))
-    ```
-
-    However, if you do this, you distort the aspect ratio of your images, since
-    in general they do not all have the same aspect ratio as `size`. This is
-    fine in many cases, but not always (e.g. for GANs this can be a problem).
-
-    Note that passing the argument `preserve_aspect_ratio=True` to `resize`
-    will preserve the aspect ratio, but at the cost of no longer respecting the
-    provided target size. Because `tf.image.resize` doesn't crop images,
-    your output images will still have different sizes.
-
-    This calls for:
-
-    ```python
-    size = (200, 200)
-    ds = ds.map(lambda img: smart_resize(img, size))
-    ```
-
-    Your output images will actually be `(200, 200)`, and will not be distorted.
-    Instead, the parts of the image that do not fit within the target size
-    get cropped out.
-
-    The resizing process is:
-
-    1. Take the largest centered crop of the image that has the same aspect
-    ratio as the target size. For instance, if `size=(200, 200)` and the input
-    image has size `(340, 500)`, we take a crop of `(340, 340)` centered along
-    the width.
-    2. Resize the cropped image to the target size. In the example above,
-    we resize the `(340, 340)` crop to `(200, 200)`.
-
-    Args:
-      x: Input image or batch of images (as a tensor or NumPy array). Must be in
-        format `(height, width, channels)` or `(batch_size, height, width,
-        channels)`.
-      size: Tuple of `(height, width)` integer. Target size.
-      interpolation: String, interpolation to use for resizing. Supports
-        `bilinear`, `nearest`, `bicubic`, `area`, `lanczos3`, `lanczos5`,
-        `gaussian`, `mitchellcubic`. Defaults to `'bilinear'`.
-
-    Returns:
-      Array with shape `(size[0], size[1], channels)`. If the input image was a
-      NumPy array, the output is a NumPy array, and if it was a TF tensor,
-      the output is a TF tensor.
+    Copied from `tf_keras` for Keras 3 and for use in `tf.data` pipeline.
     """
     if len(size) != 2:
         raise ValueError(