tf.keras.preprocessing.image.smart_resize

Resize images to a target size without aspect ratio distortion.

tf.keras.preprocessing.image.smart_resize(
    x, size, interpolation='bilinear'
)

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 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).

x Input image (as a tensor or NumPy array). Must be in format (height, width, channels).
size Tuple of (height, width) integer. Target size.
interpolation String, interpolation to use for resizing. Defaults to 'bilinear'. Supports bilinear, nearest, bicubic, area, lanczos3, lanczos5, gaussian, mitchellcubic.

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.