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utils.py
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utils.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import numpy as np
import os
import json
import glob
import random
import collections
import math
import time
EPS = 1e-12
def pad(lst_arrays, seq_lengths):
max_length = max(seq_lengths)
padded_arrays = np.array([np.lib.pad(item, (0, max_length - seq_lengths[i]),
'constant', constant_values=0) for i,item in enumerate(lst_arrays)])
return padded_arrays
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 / 50 - 1, a_chan / 110, b_chan / 110]
def deprocess_lab(L_chan, a_chan, b_chan):
with tf.name_scope("deprocess_lab"):
# this is axis=3 instead of axis=2 because we process individual images but deprocess batches
return tf.stack([(L_chan + 1) / 2 * 100, a_chan * 110, b_chan * 110], axis=3)
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 conv(batch_input, out_channels, stride):
with tf.variable_scope("conv"):
in_channels = batch_input.get_shape()[3]
filter = tf.get_variable("filter", [4, 4, in_channels, out_channels], dtype=tf.float32, initializer=tf.random_normal_initializer(0, 0.02))
# [batch, in_height, in_width, in_channels], [filter_width, filter_height, in_channels, out_channels]
# => [batch, out_height, out_width, out_channels]
padded_input = tf.pad(batch_input, [[0, 0], [1, 1], [1, 1], [0, 0]], mode="CONSTANT")
conv = tf.nn.conv2d(padded_input, filter, [1, stride, stride, 1], padding="VALID")
return conv
def lrelu(x, a):
with tf.name_scope("lrelu"):
# adding these together creates the leak part and linear part
# then cancels them out by subtracting/adding an absolute value term
# leak: a*x/2 - a*abs(x)/2
# linear: x/2 + abs(x)/2
# this block looks like it has 2 inputs on the graph unless we do this
x = tf.identity(x)
return (0.5 * (1 + a)) * x + (0.5 * (1 - a)) * tf.abs(x)
def batchnorm(input):
with tf.variable_scope("batchnorm"):
# this block looks like it has 3 inputs on the graph unless we do this
input = tf.identity(input)
channels = input.get_shape()[3]
offset = tf.get_variable("offset", [channels], dtype=tf.float32, initializer=tf.zeros_initializer())
scale = tf.get_variable("scale", [channels], dtype=tf.float32, initializer=tf.random_normal_initializer(1.0, 0.02))
mean, variance = tf.nn.moments(input, axes=[0, 1, 2], keep_dims=False)
variance_epsilon = 1e-5
normalized = tf.nn.batch_normalization(input, mean, variance, offset, scale, variance_epsilon=variance_epsilon)
return normalized
def deconv(batch_input, out_channels):
with tf.variable_scope("deconv"):
batch, in_height, in_width, in_channels = [int(d) for d in batch_input.get_shape()]
filter = tf.get_variable("filter", [4, 4, out_channels, in_channels], dtype=tf.float32, initializer=tf.random_normal_initializer(0, 0.02))
# [batch, in_height, in_width, in_channels], [filter_width, filter_height, out_channels, in_channels]
# => [batch, out_height, out_width, out_channels]
conv = tf.nn.conv2d_transpose(batch_input, filter, [batch, in_height * 2, in_width * 2, out_channels], [1, 2, 2, 1], padding="SAME")
return conv
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))