VGG19和Tensorflow实现图像风格转换
这是一般的图像风格转换，每生成一张图片都要训练一次网络
import tensorflow as tf
import numpy as np
import scipy.io
import scipy.misc
import os
import time
os.chdir('E:\\电子书\\04 图像风格迁移\\04 图像风格迁移')
#定义时间函数
def the_current_time():
print(time.strftime('%Y-%m-%d%H:%M:%S',time.localtime(int(time.time()))))
#定义全局参数
CONTENT_IMG='cat.jpg'
STYLE_IMG='er.jpg'
OUTPUT_DIR='zijijijij/'
if not os.path.exists(OUTPUT_DIR): #输出文件夹
os.mkdir(OUTPUT_DIR)
IMAGE_H=600
IMAGE_W=800
COLOR_C=3
NOISE_RATIO=0.7
BETA=5
ALPHA=100
VGG_MODEL= 'imagenet-vgg-verydeep-19.mat' #载入模块
MEAN_VALUES = np.array([123.68, 116.779, 103.939]).reshape((1, 1, 1, 3))
#图片归一化
def load_vgg_model(path):
vgg=scipy.io.loadmat(path)
vgg_layer=vgg['layers']
def _weights(layer,expected_layer_name):
W=vgg_layer[0][layer][0][0][2][0][0]
b=vgg_layer[0][layer][0][0][2][0][1]
layer_name=vgg_layer[0][layer][0][0][0][0]
assert layer_name==expected_layer_name
return W,b
def _conv2d_relu(prev_layer,layer,layer_name):
W,b=_weights(layer,layer_name)
W=tf.constant(W)
b=tf.constant(np.reshape(b,(b.size)))
return tf.nn.relu(tf.nn.conv2d(prev_layer, \
filter=W, strides=[1, 1, 1, 1], padding='SAME') + b)
def _avgpool(prev_layer):
return tf.nn.avg_pool(prev_layer, ksize=[1, 2, 2, 1], \
strides=[1, 2, 2, 1], padding='SAME')
graph={}
graph['input'] = tf.Variable(np.zeros((1, IMAGE_H, IMAGE_W, COLOR_C)), dtype='float32')
graph['conv1_1'] = _conv2d_relu(graph['input'], 0, 'conv1_1')
graph['conv1_2'] = _conv2d_relu(graph['conv1_1'], 2, 'conv1_2')
graph['avgpool1'] = _avgpool(graph['conv1_2'])
graph['conv2_1'] = _conv2d_relu(graph['avgpool1'], 5, 'conv2_1')
graph['conv2_2'] = _conv2d_relu(graph['conv2_1'], 7, 'conv2_2')
graph['avgpool2'] = _avgpool(graph['conv2_2'])
graph['conv3_1'] = _conv2d_relu(graph['avgpool2'], 10, 'conv3_1')
graph['conv3_2'] = _conv2d_relu(graph['conv3_1'], 12, 'conv3_2')
graph['conv3_3'] = _conv2d_relu(graph['conv3_2'], 14, 'conv3_3')
graph['conv3_4'] = _conv2d_relu(graph['conv3_3'], 16, 'conv3_4')
graph['avgpool3'] = _avgpool(graph['conv3_4'])
graph['conv4_1'] = _conv2d_relu(graph['avgpool3'], 19, 'conv4_1')
graph['conv4_2'] = _conv2d_relu(graph['conv4_1'], 21, 'conv4_2')
graph['conv4_3'] = _conv2d_relu(graph['conv4_2'], 23, 'conv4_3')
graph['conv4_4'] = _conv2d_relu(graph['conv4_3'], 25, 'conv4_4')
graph['avgpool4'] = _avgpool(graph['conv4_4'])
graph['conv5_1'] = _conv2d_relu(graph['avgpool4'], 28, 'conv5_1')
graph['conv5_2'] = _conv2d_relu(graph['conv5_1'], 30, 'conv5_2')
graph['conv5_3'] = _conv2d_relu(graph['conv5_2'], 32, 'conv5_3')
graph['conv5_4'] = _conv2d_relu(graph['conv5_3'], 34, 'conv5_4')
graph['avgpool5'] = _avgpool(graph['conv5_4'])
return graph
def load_image(path):#加载图片
image=scipy.misc.imread(path)
image=scipy.misc.imresize(image,(IMAGE_H,IMAGE_W))#更改大小
image=np.reshape(image,((1,)+image.shape))
image=image-MEAN_VALUES
return image
def save_image(path,image):#保存图片
image=image+MEAN_VALUES
image=image[0]
image=np.clip(image,0,225).astype('uint8')
scipy.misc.imsave(path,image)
def content_loss_func(sess,model):
def _content_loss(p,x):
N=p.shape[3] #通道数
M=p.shape[1]*p.shape[2] #长和宽乘积
return (1/(4*M*N))*tf.reduce_sum(tf.pow(x-p,2))
return _content_loss(sess.run(model['conv4_2']),model['conv4_2'])
STYLE_LAYERS=[('conv1_1',0.5),('conv2_1',1.0),('conv3_1',1.5),('conv4_1',3.0),('conv5_1',4.0)]
def style_loss_func(sess,model):
def _gram_matrix(F,N,M):
Ft=tf.reshape(F,(M,N))
return tf.matmul(tf.transpose(Ft),Ft)#內积
def _style_loss(a,x):
N=a.shape[3]
M=a.shape[1]*a.shape[2]
A=_gram_matrix(a,N,M)
B=_gram_matrix(x,N,M)
return (1/(4*N**2*M**2))*tf.reduce_sum(tf.pow(B-A,2))
return sum([_style_loss(sess.run(model[layer_name]), model[layer_name]) * w \
for layer_name, w in STYLE_LAYERS])
def generate_noise_image(content_image,noise_ratio=NOISE_RATIO):
noise_image=np.random.uniform(-20,20,(1,IMAGE_H,IMAGE_W,COLOR_C)).astype('float32')
#生成的噪音图像
input_image=noise_image*noise_ratio+(1-noise_ratio)*content_image
return input_image
the_current_time()
with tf.Session() as sess:
content_image=load_image(CONTENT_IMG) #载入内容图片，进行处理
style_image=load_image(STYLE_IMG) #载入风格图片
model=load_vgg_model(VGG_MODEL) #载入模型
input_image=generate_noise_image(content_image)#生成噪音图片
sess.run(tf.global_variables_initializer()) #全局变量初始化
sess.run(model['input'].assign(content_image))
content_loss=content_loss_func(sess,model)
sess.run(model['input'].assign(style_image))
style_loss=style_loss_func(sess,model)
total_loss=BETA*content_loss+ALPHA*style_loss
optimizer=tf.train.AdamOptimizer(2.0)
train=optimizer.minimize(total_loss)
sess.run(tf.global_variables_initializer())
sess.run(model['input'].assign(input_image))
ITERATIONS = 20
for i in range(ITERATIONS):
sess.run(train)
if i%2==0:
output_image=sess.run(model['input'])
the_current_time()
print('Iteration %d' % i)
print('Cost: ', sess.run(total_loss))
save_image(os.path.join(OUTPUT_DIR, 'output_%d.jpg' % i), output_image)