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rgb_lab_formulation.py
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'''
Functions for
RGB to LAB color-space conversion
LAB to RGB color-space conversion
Use the test_.py for testing
--Md Jahidul Islam ([email protected])
MASSIVE help from:
------------------
https://github.com/affinelayer/pix2pix-tensorflow/blob/master/pix2pix.py
https://github.com/torch/image/blob/9f65c30167b2048ecbe8b7befdc6b2d6d12baee9/generic/image.c
https://github.com/cameronfabbri/Colorizing-Images-Using-Adversarial-Networks
'''
from __future__ import division
from __future__ import absolute_import
import tensorflow as tf
import math
import random
import glob
import fnmatch
def preprocess(image):
with tf.name_scope('preprocess'):
# [0, 1] => [-1, 1]
return image * 2 - 1
def deprocess(image):
with tf.name_scope('deprocess'):
# [-1, 1] => [0, 1]
return (image + 1) / 2
def preprocess_lab(lab):
with tf.name_scope('preprocess_lab'):
L_chan, a_chan, b_chan = tf.unstack(lab, axis=2)
# L_chan: black and white with input range [0, 100]
# a_chan/b_chan: color channels with input range ~[-110, 110], not exact
# [0, 100] => [-1, 1], ~[-110, 110] => [-1, 1]
return [L_chan / 100, a_chan / 220 + 0.5, b_chan / 220 + 0.5]
def deprocess_lab(L_chan, a_chan, b_chan):
with tf.name_scope('deprocess_lab'):
#TODO This is axis=3 instead of axis=2 when deprocessing batch of images
# ( we process individual images but deprocess batches)
#return tf.stack([(L_chan + 1) / 2 * 100, a_chan * 110, b_chan * 110], axis=3)
return tf.stack([(L_chan + 1) / 2 * 100, a_chan * 110, b_chan * 110], axis=2)
def augment(image, brightness):
# (a, b) color channels, combine with L channel and convert to rgb
a_chan, b_chan = tf.unstack(image, axis=3)
L_chan = tf.squeeze(brightness, axis=3)
lab = deprocess_lab(L_chan, a_chan, b_chan)
rgb = lab_to_rgb(lab)
return rgb
def check_image(image):
assertion = tf.assert_equal(tf.shape(image)[-1], 3, message='image must have 3 color channels')
with tf.control_dependencies([assertion]):
image = tf.identity(image)
if image.get_shape().ndims not in (3, 4):
raise ValueError('image must be either 3 or 4 dimensions')
# make the last dimension 3 so that you can unstack the colors
shape = list(image.get_shape())
shape[-1] = 3
image.set_shape(shape)
return image
# based on https://github.com/torch/image/blob/9f65c30167b2048ecbe8b7befdc6b2d6d12baee9/generic/image.c
def rgb_to_lab(srgb):
with tf.name_scope('rgb_to_lab'):
srgb = check_image(srgb)
srgb_pixels = tf.reshape(srgb, [-1, 3])
with tf.name_scope('srgb_to_xyz'):
linear_mask = tf.cast(srgb_pixels <= 0.04045, dtype=tf.float32)
exponential_mask = tf.cast(srgb_pixels > 0.04045, dtype=tf.float32)
rgb_pixels = (srgb_pixels / 12.92 * linear_mask) + (((srgb_pixels + 0.055) / 1.055) ** 2.4) * exponential_mask
rgb_to_xyz = tf.constant([
# X Y Z
[0.412453, 0.212671, 0.019334], # R
[0.357580, 0.715160, 0.119193], # G
[0.180423, 0.072169, 0.950227], # B
])
xyz_pixels = tf.matmul(rgb_pixels, rgb_to_xyz)
# https://en.wikipedia.org/wiki/Lab_color_space#CIELAB-CIEXYZ_conversions
with tf.name_scope('xyz_to_cielab'):
# convert to fx = f(X/Xn), fy = f(Y/Yn), fz = f(Z/Zn)
# normalize for D65 white point
xyz_normalized_pixels = tf.multiply(xyz_pixels, [1/0.950456, 1.0, 1/1.088754])
epsilon = 6/29
linear_mask = tf.cast(xyz_normalized_pixels <= (epsilon**3), dtype=tf.float32)
exponential_mask = tf.cast(xyz_normalized_pixels > (epsilon**3), dtype=tf.float32)
fxfyfz_pixels = (xyz_normalized_pixels / (3 * epsilon**2) + 4/29) * linear_mask + (xyz_normalized_pixels ** (1/3)) * exponential_mask
# convert to lab
fxfyfz_to_lab = tf.constant([
# l a b
[ 0.0, 500.0, 0.0], # fx
[116.0, -500.0, 200.0], # fy
[ 0.0, 0.0, -200.0], # fz
])
lab_pixels = tf.matmul(fxfyfz_pixels, fxfyfz_to_lab) + tf.constant([-16.0, 0.0, 0.0])
return tf.reshape(lab_pixels, tf.shape(srgb))
def lab_to_rgb(lab):
with tf.name_scope('lab_to_rgb'):
lab = check_image(lab)
lab_pixels = tf.reshape(lab, [-1, 3])
# https://en.wikipedia.org/wiki/Lab_color_space#CIELAB-CIEXYZ_conversions
with tf.name_scope('cielab_to_xyz'):
# convert to fxfyfz
lab_to_fxfyfz = tf.constant([
# fx fy fz
[1/116.0, 1/116.0, 1/116.0], # l
[1/500.0, 0.0, 0.0], # a
[ 0.0, 0.0, -1/200.0], # b
])
fxfyfz_pixels = tf.matmul(lab_pixels + tf.constant([16.0, 0.0, 0.0]), lab_to_fxfyfz)
# convert to xyz
epsilon = 6/29
linear_mask = tf.cast(fxfyfz_pixels <= epsilon, dtype=tf.float32)
exponential_mask = tf.cast(fxfyfz_pixels > epsilon, dtype=tf.float32)
xyz_pixels = (3 * epsilon**2 * (fxfyfz_pixels - 4/29)) * linear_mask + (fxfyfz_pixels ** 3) * exponential_mask
# denormalize for D65 white point
xyz_pixels = tf.multiply(xyz_pixels, [0.950456, 1.0, 1.088754])
with tf.name_scope('xyz_to_srgb'):
xyz_to_rgb = tf.constant([
# r g b
[ 3.2404542, -0.9692660, 0.0556434], # x
[-1.5371385, 1.8760108, -0.2040259], # y
[-0.4985314, 0.0415560, 1.0572252], # z
])
rgb_pixels = tf.matmul(xyz_pixels, xyz_to_rgb)
# avoid a slightly negative number messing up the conversion
rgb_pixels = tf.clip_by_value(rgb_pixels, 0.0, 1.0)
linear_mask = tf.cast(rgb_pixels <= 0.0031308, dtype=tf.float32)
exponential_mask = tf.cast(rgb_pixels > 0.0031308, dtype=tf.float32)
srgb_pixels = (rgb_pixels * 12.92 * linear_mask) + ((rgb_pixels ** (1/2.4) * 1.055) - 0.055) * exponential_mask
return tf.reshape(srgb_pixels, tf.shape(lab))