def equalize(image):
  """Implements Equalize function from PIL using TF ops."""
  def scale_channel(im, c):
    """Scale the data in the channel to implement equalize."""
    im = tf.cast(im[:, :, c], tf.int32)
    # Compute the histogram of the image channel.
    histo = tf.histogram_fixed_width(im, [0, 255], nbins=256)

    # For the purposes of computing the step, filter out the nonzeros.
    nonzero = tf.where(tf.not_equal(histo, 0))
    nonzero_histo = tf.reshape(tf.gather(histo, nonzero), [-1])
    step = (tf.reduce_sum(nonzero_histo) - nonzero_histo[-1]) // 255

    def build_lut(histo, step):
      # Compute the cumulative sum, shifting by step // 2
      # and then normalization by step.
      lut = (tf.cumsum(histo) + (step // 2)) // step
      # Shift lut, prepending with 0.
      lut = tf.concat([[0], lut[:-1]], 0)
      # Clip the counts to be in range.  This is done
      # in the C code for image.point.
      return tf.clip_by_value(lut, 0, 255)

    # If step is zero, return the original image.  Otherwise, build
    # lut from the full histogram and step and then index from it.
    result = tf.cond(tf.equal(step, 0),
                     lambda: im,
                     lambda: tf.gather(build_lut(histo, step), im))

    return tf.cast(result, tf.uint8)

  # Assumes RGB for now.  Scales each channel independently
  # and then stacks the result.
  s1 = scale_channel(image, 0)
  s2 = scale_channel(image, 1)
  s3 = scale_channel(image, 2)
  image = tf.stack([s1, s2, s3], 2)
  return image
Implements Equalize function from PIL using TF ops.
inference_api/src/main/object_detection/utils/autoaugment_utils.py