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

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
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？

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

相关文章

TensorFlow练习1: 对评论进行分类
TensorFlow练习2: 对评论进行分类
TensorFlow练习3: RNN, Recurrent Neural Networks
TensorFlow练习6: 基于WiFi指纹的室内定位（autoencoder）...
TensorFlow练习7: 基于RNN生成古诗词

《TensorFlow练习14: 使用深度学习给黑白图像上色》有2个想法

发表评论

电子邮件地址不会被公开。 必填项已用*标注