Add CLAHE #21898
Description
CLAHE is a variant of Adaptive histogram equalization (AHE) which takes care of over-amplification of the contrast.
Initial ticket: keras-cv/#359
Initial PR: keras-cv/#420
pseudo code
# https://github.com/tensorflow/models
@tf.function
def equalize(image, mode='grayscale'):
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)
if mode == 'grayscale':
image = scale_channel(image, 0)
return tf.cast(image, tf.float32)
elif mode == 'rgb':
s1 = scale_channel(image, 0)
s2 = scale_channel(image, 1)
s3 = scale_channel(image, 2)
image = tf.stack([s1, s2, s3], -1)
return tf.cast(image, tf.float32)Example:
