|---| | [ View source on GitHub](https://github.com/keras-team/keras/tree/v2.15.0/keras/utils/image_utils.py#L66-L188) | Resize images to a target size without aspect ratio distortion. tf.keras.preprocessing.image.smart_resize( x, size, interpolation='bilinear' ) > [!WARNING] > **Warning:** [`tf.keras.preprocessing.image.smart_resize`](https://www.tensorflow.org/versions/r2.15/api_docs/python/tf/keras/preprocessing/image/smart_resize) is not recommended for new code. Prefer [`tf.keras.layers.Resizing`](https://www.tensorflow.org/versions/r2.15/api_docs/python/tf/keras/layers/Resizing), which provides the same functionality as a preprocessing layer and adds [`tf.RaggedTensor`](https://www.tensorflow.org/versions/r2.15/api_docs/python/tf/RaggedTensor) support. See the [preprocessing layer guide](https://www.tensorflow.org/guide/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 Keras layers. To be batched, images need to share the same height and width. #### You could simply do: 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`](https://www.tensorflow.org/versions/r2.15/api_docs/python/tf/image/resize) doesn't crop images, your output images will still have different sizes. #### This calls for: 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. ||