def scale_channel(image):
    """Scale the 2D image using the autocontrast rule."""
    # A possibly cheaper version can be done using cumsum/unique_with_counts
    # over the histogram values, rather than iterating over the entire image.
    # to compute mins and maxes.
    lo = tf.to_float(tf.reduce_min(image))
    hi = tf.to_float(tf.reduce_max(image))

    # Scale the image, making the lowest value 0 and the highest value 255.
    def scale_values(im):
      scale = 255.0 / (hi - lo)
      offset = -lo * scale
      im = tf.to_float(im) * scale + offset
      im = tf.clip_by_value(im, 0.0, 255.0)
      return tf.cast(im, tf.uint8)

    result = tf.cond(hi > lo, lambda: scale_values(image), lambda: image)
    return result
Scale the 2D image using the autocontrast rule.
inference_api/src/main/object_detection/utils/autoaugment_utils.py