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main.py
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main.py
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import tensorflow as tf
import numpy as np
import scipy.io
import scipy.misc
import os
import imageio
IMAGE_W = 800
IMAGE_H = 600
CONTENT_IMG = './images/Taipei101.jpg'
STYLE_IMG = './images/StarryNight.jpg'
OUTOUT_DIR = './results'
OUTPUT_IMG = 'results.png'
VGG_MODEL = 'imagenet-vgg-verydeep-19.mat'
INI_NOISE_RATIO = 0.7
STYLE_STRENGTH = 500
ITERATION = 5000
CONTENT_LAYERS =[('conv4_2',1.)]
STYLE_LAYERS=[('conv1_1',1.),('conv2_1',1.),('conv3_1',1.),('conv4_1',1.),('conv5_1',1.)]
MEAN_VALUES = np.array([123, 117, 104]).reshape((1,1,1,3))
def build_net(ntype, nin, nwb=None):
if ntype == 'conv':
return tf.nn.relu(tf.nn.conv2d(nin, nwb[0], strides=[1, 1, 1, 1], padding='SAME')+ nwb[1])
elif ntype == 'pool':
return tf.nn.avg_pool(nin, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME')
def get_weight_bias(vgg_layers, i,):
weights = vgg_layers[i][0][0][0][0][0]
weights = tf.constant(weights)
bias = vgg_layers[i][0][0][0][0][1]
bias = tf.constant(np.reshape(bias, (bias.size)))
return weights, bias
def build_vgg19(path):
net = {}
vgg_rawnet = scipy.io.loadmat(path)
vgg_layers = vgg_rawnet['layers'][0]
net['input'] = tf.Variable(np.zeros((1, IMAGE_H, IMAGE_W, 3)).astype('float32'))
net['conv1_1'] = build_net('conv',net['input'],get_weight_bias(vgg_layers,0))
net['conv1_2'] = build_net('conv',net['conv1_1'],get_weight_bias(vgg_layers,2))
net['pool1'] = build_net('pool',net['conv1_2'])
net['conv2_1'] = build_net('conv',net['pool1'],get_weight_bias(vgg_layers,5))
net['conv2_2'] = build_net('conv',net['conv2_1'],get_weight_bias(vgg_layers,7))
net['pool2'] = build_net('pool',net['conv2_2'])
net['conv3_1'] = build_net('conv',net['pool2'],get_weight_bias(vgg_layers,10))
net['conv3_2'] = build_net('conv',net['conv3_1'],get_weight_bias(vgg_layers,12))
net['conv3_3'] = build_net('conv',net['conv3_2'],get_weight_bias(vgg_layers,14))
net['conv3_4'] = build_net('conv',net['conv3_3'],get_weight_bias(vgg_layers,16))
net['pool3'] = build_net('pool',net['conv3_4'])
net['conv4_1'] = build_net('conv',net['pool3'],get_weight_bias(vgg_layers,19))
net['conv4_2'] = build_net('conv',net['conv4_1'],get_weight_bias(vgg_layers,21))
net['conv4_3'] = build_net('conv',net['conv4_2'],get_weight_bias(vgg_layers,23))
net['conv4_4'] = build_net('conv',net['conv4_3'],get_weight_bias(vgg_layers,25))
net['pool4'] = build_net('pool',net['conv4_4'])
net['conv5_1'] = build_net('conv',net['pool4'],get_weight_bias(vgg_layers,28))
net['conv5_2'] = build_net('conv',net['conv5_1'],get_weight_bias(vgg_layers,30))
net['conv5_3'] = build_net('conv',net['conv5_2'],get_weight_bias(vgg_layers,32))
net['conv5_4'] = build_net('conv',net['conv5_3'],get_weight_bias(vgg_layers,34))
net['pool5'] = build_net('pool',net['conv5_4'])
return net
def build_content_loss(p, x):
M = p.shape[1]*p.shape[2]
N = p.shape[3]
loss = (1./(2* N**0.5 * M**0.5 )) * tf.reduce_sum(tf.pow((x - p),2))
return loss
def gram_matrix(x, area, depth):
x1 = tf.reshape(x,(area,depth))
g = tf.matmul(tf.transpose(x1), x1)
return g
def gram_matrix_val(x, area, depth):
x1 = x.reshape(area,depth)
g = np.dot(x1.T, x1)
return g
def build_style_loss(a, x):
M = a.shape[1]*a.shape[2]
N = a.shape[3]
A = gram_matrix_val(a, M, N )
G = gram_matrix(x, M, N )
loss = (1./(4 * N**2 * M**2)) * tf.reduce_sum(tf.pow((G - A),2))
return loss
def read_image(path):
image = imageio.imread(path)
image = scipy.misc.imresize(image,(IMAGE_H,IMAGE_W))
image = image[np.newaxis,:,:,:]
image = image - MEAN_VALUES
return image
def write_image(path, image):
image = image + MEAN_VALUES
image = image[0]
image = np.clip(image, 0, 255).astype('uint8')
scipy.misc.imsave(path, image)
def main():
net = build_vgg19(VGG_MODEL)
sess = tf.Session()
sess.run(tf.initialize_all_variables())
noise_img = np.random.uniform(-20, 20, (1, IMAGE_H, IMAGE_W, 3)).astype('float32')
content_img = read_image(CONTENT_IMG)
style_img = read_image(STYLE_IMG)
sess.run([net['input'].assign(content_img)])
cost_content = sum(map(lambda l,: l[1]*build_content_loss(sess.run(net[l[0]]) , net[l[0]])
, CONTENT_LAYERS))
sess.run([net['input'].assign(style_img)])
cost_style = sum(map(lambda l: l[1]*build_style_loss(sess.run(net[l[0]]) , net[l[0]])
, STYLE_LAYERS))
cost_total = cost_content + STYLE_STRENGTH * cost_style
optimizer = tf.train.AdamOptimizer(2.0)
train = optimizer.minimize(cost_total)
sess.run(tf.initialize_all_variables())
sess.run(net['input'].assign( INI_NOISE_RATIO* noise_img + (1.-INI_NOISE_RATIO) * content_img))
if not os.path.exists(OUTOUT_DIR):
os.mkdir(OUTOUT_DIR)
for i in range(ITERATION):
sess.run(train)
if i%100 ==0:
result_img = sess.run(net['input'])
print sess.run(cost_total)
write_image(os.path.join(OUTOUT_DIR,'%s.png'%(str(i).zfill(4))),result_img)
write_image(os.path.join(OUTOUT_DIR,OUTPUT_IMG),result_img)
if __name__ == '__main__':
main()