tfm.vision.preprocess_ops.normalize_image

Normalizes the image to zero mean and unit variance.

tfm.vision.preprocess_ops.normalize_image(
    image: tf.Tensor,
    offset: Sequence[float] = tfm.vision.preprocess_ops.MEAN_NORM,
    scale: Sequence[float] = tfm.vision.preprocess_ops.STDDEV_NORM
) -> tf.Tensor

This function normalizes the input image by subtracting the offset and dividing by the scale.

Important Note about Input Types and Normalization:

  • Integer Images: If the input image is an integer type (e.g., uint8), the provided offset and scale values should be already normalized to the range [0, 1]. This is because the function converts integer images to float32 with values in the range [0, 1] before the normalization happens.

  • Float Images: If the input image is a float type (e.g., float32), the offset and scale values should be in the same range as the image data.

    • If the image has values in [0, 1], the offset and scale should also be in [0, 1].
    • If the image has values in [0, 255], the offset and scale should also be in [0, 255].

image A tf.Tensor in either: (1) float dtype with values in range [0, 1) or [0, 255], or (2) int type with values in range [0, 255].
offset A tuple of mean values to be subtracted from the image.
scale A tuple of normalization factors.

A normalized image tensor.