TensorFlow练习14: 使用深度学习给黑白图像上色

网上有很多使用PS给黑白老照片上色的教程，下图是P上颜色的爱因斯坦：

TensorFlow练习14: 使用深度学习给黑白图像上色 TensorFlow练习14: 使用深度学习给黑白图像上色

于是，有大牛使用深度学习制作了一个自动给黑白图像上色的模型，非常非常的牛叉。

本帖就使用前文的妹子图训练这个模型，看看效果。

由于原作者使用的TensorFlow版本太旧，不能直接运行，我做了一点修改。

训练模型

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import tensorflow as tf  # 0.12
import numpy as np
import os
import glob
import sys
from matplotlib import pyplot as plt
 
# 训练文件列表
filenames = glob.glob("./girl/*.jpg")
 
# VGG-16是图像分类模型: https://github.com/ry/tensorflow-vgg16
# 网盘下载: https://pan.baidu.com/s/1slJBoMp
with open("vgg16-20160129.tfmodel", mode='rb') as f:
    fileContent = f.read()
graph_def = tf.GraphDef()
graph_def.ParseFromString(fileContent)
 
if not os.path.exists('summary'):
os.mkdir('summary')
 
def rgb2yuv(rgb):
"""
Convert RGB image into YUV https://en.wikipedia.org/wiki/YUV
"""
rgb2yuv_filter = tf.constant([[[[0.299, -0.169, 0.499],
                                    [0.587, -0.331, -0.418],
                                    [0.114, 0.499, -0.0813]]]])
rgb2yuv_bias = tf.constant([0., 0.5, 0.5])
temp = tf.nn.conv2d(rgb, rgb2yuv_filter, [1, 1, 1, 1], 'SAME')
temp = tf.nn.bias_add(temp, rgb2yuv_bias)
return temp
 
def yuv2rgb(yuv):
"""
Convert YUV image into RGB https://en.wikipedia.org/wiki/YUV
"""
yuv = tf.mul(yuv, 255)
yuv2rgb_filter = tf.constant([[[[1., 1., 1.],
                                    [0., -0.34413999, 1.77199996],
                                    [1.40199995, -0.71414, 0.]]]])
yuv2rgb_bias = tf.constant([-179.45599365, 135.45983887, -226.81599426])
temp = tf.nn.conv2d(yuv, yuv2rgb_filter, [1, 1, 1, 1], 'SAME')
temp = tf.nn.bias_add(temp, yuv2rgb_bias)
temp = tf.maximum(temp, tf.zeros(temp.get_shape(), dtype=tf.float32))
temp = tf.minimum(temp, tf.mul(tf.ones(temp.get_shape(), dtype=tf.float32), 255))
temp = tf.div(temp, 255)
return temp
 
def concat_images(imga, imgb):
"""
Combines two color image ndarrays side-by-side.
"""
ha, wa = imga.shape[:2]
hb, wb = imgb.shape[:2]
max_height = np.max([ha, hb])
total_width = wa + wb
new_img = np.zeros(shape=(max_height, total_width, 3), dtype=np.float32)
new_img[:ha, :wa] = imga
new_img[:hb, wa:wa + wb] = imgb
return new_img
 
class ConvolutionalBatchNormalizer(object):
"""
Helper class that groups the normalization logic and variables.        .                              
"""
def __init__(self, depth, epsilon, ewma_trainer, scale_after_norm):
self.mean = tf.Variable(tf.constant(0.0, shape=[depth]), trainable=False)
self.variance = tf.Variable(tf.constant(1.0, shape=[depth]), trainable=False)
self.beta = tf.Variable(tf.constant(0.0, shape=[depth]))
self.gamma = tf.Variable(tf.constant(1.0, shape=[depth]))
self.ewma_trainer = ewma_trainer
self.epsilon = epsilon
self.scale_after_norm = scale_after_norm
 
def get_assigner(self):
"""Returns an EWMA apply op that must be invoked after optimization."""
return self.ewma_trainer.apply([self.mean, self.variance])
 
def normalize(self, x, train=True):
"""Returns a batch-normalized version of x."""
if train is not None:
mean, variance = tf.nn.moments(x, [0, 1, 2])
assign_mean = self.mean.assign(mean)
assign_variance = self.variance.assign(variance)
with tf.control_dependencies([assign_mean, assign_variance]):
return tf.nn.batch_norm_with_global_normalization(x, mean, variance, self.beta, self.gamma, self.epsilon, self.scale_after_norm)
else:
mean = self.ewma_trainer.average(self.mean)
variance = self.ewma_trainer.average(self.variance)
local_beta = tf.identity(self.beta)
local_gamma = tf.identity(self.gamma)
return tf.nn.batch_norm_with_global_normalization(x, mean, variance, local_beta, local_gamma, self.epsilon, self.scale_after_norm)
 
def read_my_file_format(filename_queue, randomize=False):
reader = tf.WholeFileReader()
key, file = reader.read(filename_queue)
uint8image = tf.image.decode_jpeg(file, channels=3)
uint8image = tf.random_crop(uint8image, (224, 224, 3))
if randomize:
uint8image = tf.image.random_flip_left_right(uint8image)
uint8image = tf.image.random_flip_up_down(uint8image, seed=None)
float_image = tf.div(tf.cast(uint8image, tf.float32), 255)
return float_image
 
def input_pipeline(filenames, batch_size, num_epochs=None):
filename_queue = tf.train.string_input_producer(filenames, num_epochs=num_epochs, shuffle=False)
example = read_my_file_format(filename_queue, randomize=False)
min_after_dequeue = 100
capacity = min_after_dequeue + 3 * batch_size
example_batch = tf.train.shuffle_batch([example], batch_size=batch_size, capacity=capacity, min_after_dequeue=min_after_dequeue)
return example_batch
 
 
batch_size = 1
num_epochs = 1e+9
colorimage = input_pipeline(filenames, batch_size, num_epochs=num_epochs)
 
grayscale = tf.image.rgb_to_grayscale(colorimage)
grayscale_rgb = tf.image.grayscale_to_rgb(grayscale)
grayscale_yuv = rgb2yuv(grayscale_rgb)
grayscale = tf.concat(3, [grayscale, grayscale, grayscale])
 
tf.import_graph_def(graph_def, input_map={"images": grayscale})
graph = tf.get_default_graph()
 
 
phase_train = tf.placeholder(tf.bool, name='phase_train')
# 定义神经网络
def color_net():
"""
Network architecture http://tinyclouds.org/colorize/residual_encoder.png
"""
with tf.variable_scope('vgg'):
conv1_2 = graph.get_tensor_by_name("import/conv1_2/Relu:0")
conv2_2 = graph.get_tensor_by_name("import/conv2_2/Relu:0")
conv3_3 = graph.get_tensor_by_name("import/conv3_3/Relu:0")
conv4_3 = graph.get_tensor_by_name("import/conv4_3/Relu:0")
 
# Store layers weight
weights = {
# 1x1 conv, 512 inputs, 256 outputs
'wc1': tf.Variable(tf.truncated_normal([1, 1, 512, 256], stddev=0.01)),
# 3x3 conv, 512 inputs, 128 outputs
'wc2': tf.Variable(tf.truncated_normal([3, 3, 256, 128], stddev=0.01)),
# 3x3 conv, 256 inputs, 64 outputs
'wc3': tf.Variable(tf.truncated_normal([3, 3, 128, 64], stddev=0.01)),
# 3x3 conv, 128 inputs, 3 outputs
'wc4': tf.Variable(tf.truncated_normal([3, 3, 64, 3], stddev=0.01)),
# 3x3 conv, 6 inputs, 3 outputs
'wc5': tf.Variable(tf.truncated_normal([3, 3, 3, 3], stddev=0.01)),
# 3x3 conv, 3 inputs, 2 outputs
'wc6': tf.Variable(tf.truncated_normal([3, 3, 3, 2], stddev=0.01)),
}
 
def batch_norm(x, depth, phase_train):
with tf.variable_scope('batchnorm'):
ewma = tf.train.ExponentialMovingAverage(decay=0.9999)
bn = ConvolutionalBatchNormalizer(depth, 0.001, ewma, True)
update_assignments = bn.get_assigner()
x = bn.normalize(x, train=phase_train)
return x
 
def conv2d(_X, w, sigmoid=False, bn=False):
with tf.variable_scope('conv2d'):
_X = tf.nn.conv2d(_X, w, [1, 1, 1, 1], 'SAME')
if bn:
_X = batch_norm(_X, w.get_shape()[3], phase_train)
if sigmoid:
return tf.sigmoid(_X)
else:
_X = tf.nn.relu(_X)
return tf.maximum(0.01 * _X, _X)
 
with tf.variable_scope('color_net'):
# Bx28x28x512 -> batch norm -> 1x1 conv = Bx28x28x256
conv1 = tf.nn.relu(tf.nn.conv2d(batch_norm(conv4_3, 512, phase_train), weights['wc1'], [1, 1, 1, 1], 'SAME'))
# upscale to 56x56x256
conv1 = tf.image.resize_bilinear(conv1, (56, 56))
conv1 = tf.add(conv1, batch_norm(conv3_3, 256, phase_train))
 
# Bx56x56x256-> 3x3 conv = Bx56x56x128
conv2 = conv2d(conv1, weights['wc2'], sigmoid=False, bn=True)
# upscale to 112x112x128
conv2 = tf.image.resize_bilinear(conv2, (112, 112))
conv2 = tf.add(conv2, batch_norm(conv2_2, 128, phase_train))
 
# Bx112x112x128 -> 3x3 conv = Bx112x112x64
conv3 = conv2d(conv2, weights['wc3'], sigmoid=False, bn=True)
# upscale to Bx224x224x64
conv3 = tf.image.resize_bilinear(conv3, (224, 224))
conv3 = tf.add(conv3, batch_norm(conv1_2, 64, phase_train))
 
# Bx224x224x64 -> 3x3 conv = Bx224x224x3
conv4 = conv2d(conv3, weights['wc4'], sigmoid=False, bn=True)
conv4 = tf.add(conv4, batch_norm(grayscale, 3, phase_train))
 
# Bx224x224x3 -> 3x3 conv = Bx224x224x3
conv5 = conv2d(conv4, weights['wc5'], sigmoid=False, bn=True)
# Bx224x224x3 -> 3x3 conv = Bx224x224x2
conv6 = conv2d(conv5, weights['wc6'], sigmoid=True, bn=True)
 
return conv6
 
uv = tf.placeholder(tf.uint8, name='uv')
# 训练
def train_color_net():
pred = color_net()
pred_yuv = tf.concat(3, [tf.split(3, 3, grayscale_yuv)[0], pred])
pred_rgb = yuv2rgb(pred_yuv)
 
colorimage_yuv = rgb2yuv(colorimage)
loss = tf.square(tf.sub(pred, tf.concat(3, [tf.split(3, 3, colorimage_yuv)[1], tf.split(3, 3, colorimage_yuv)[2]])))
 
if uv == 1:
loss = tf.split(3, 2, loss)[0]
elif uv == 2:
loss = tf.split(3, 2, loss)[1]
else:
loss = (tf.split(3, 2, loss)[0] + tf.split(3, 2, loss)[1]) / 2
 
global_step = tf.Variable(0, name='global_step', trainable=False)
if phase_train is not None:
optimizer = tf.train.GradientDescentOptimizer(0.0001)
opt = optimizer.minimize(loss, global_step=global_step, gate_gradients=optimizer.GATE_NONE)
 
# Saver.
saver = tf.train.Saver()
sess = tf.Session()
# Initialize the variables.
sess.run(tf.group(tf.global_variables_initializer(), tf.local_variables_initializer()))
 
# Start input enqueue threads.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
 
try:
while not coord.should_stop():
# Run training steps
training_opt = sess.run(opt, feed_dict={phase_train: True, uv: 1})
training_opt = sess.run(opt, feed_dict={phase_train: True, uv: 2})
 
step = sess.run(global_step)
 
if step % 1 == 0:
pred_, pred_rgb_, colorimage_, grayscale_rgb_, cost = sess.run([pred, pred_rgb, colorimage, grayscale_rgb, loss], feed_dict={phase_train: False, uv: 3})
print({"step": step, "cost": np.mean(cost)})
if step % 1000 == 0:
summary_image = concat_images(grayscale_rgb_[0], pred_rgb_[0])
summary_image = concat_images(summary_image, colorimage_[0])
plt.imsave("summary/" + str(step) + "_0", summary_image)
 
if step % 100000 == 99998:
save_path = saver.save(sess, "color_net_model.ckpt")
print("Model saved in file: %s" % save_path)
 
except tf.errors.OutOfRangeError:
print('Done training -- epoch limit reached')
finally:
# When done, ask the threads to stop.
coord.request_stop()
 
# Wait for threads to finish.
coord.join(threads)
sess.close()
 
train_color_net()

TensorFlow练习14: 使用深度学习给黑白图像上色
左: 黑白图像;   中: 上色之后;   右: 原图

后续: 写一个简单的命令行工具使用训练的模型

测试用的老照片一张：

old_pic
你知道她是谁吗？

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