卷积神经网络做图像风格迁移的项目代码笔记

代码参考自：https://github.com/ckmarkoh/neuralart_tensorflow

算法来源 CVPR 2016 的文章 “Image Style Transfer Using Convolutional Neural Networks”。

主要是利用一个已经在ImageNet 上训练好的卷积神经网络 VGG-19。

再重新用VGG-19网络训练变量 随机噪声图像x（实际上它在该网络里是输入变量），算是一种巧妙的迁移学习。

算法框架如下：

算法代码如下：

'''
## NeuralArt
Implementation of [A Neural Algorithm of Artistic Style](http://arxiv.org/abs/1508.06576) by Tensorflow.

## Requirements
 - [Tensorflow](http://www.tensorflow.org/)
 - [VGG 19 model](https://drive.google.com/file/d/0B8QJdgMvQDrVU2cyZjFKU1RrLUU/view?usp=sharing)

'''

import tensorflow as tf
import numpy as np
import scipy.io
import scipy.misc
import os

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'
# 随机噪声与内容图像的比例
INI_NOISE_RATIO = 0.7
# 内容图像和风格图像的权重
CONTENT_STRENGTH = 1
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(n_type, n_in, n_wb=None):
  if n_type == 'conv':
    return tf.nn.relu(tf.nn.conv2d(n_in, n_wb[0], strides=[1, 1, 1, 1], padding='SAME')+ n_wb[1])
  elif n_type == 'pool':
    return tf.nn.avg_pool(n_in, 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

# (普通图像p的feature maps集合P, 随机噪声图像x的feature maps集合F)
def build_content_loss(p, x):  
  print(type(p),np.shape(p))  # net['conv4_2']--  (1, 75, 100, 512)
  print(type(x),np.shape(x))  # net['conv4_2']--  (1, 75, 100, 512)
  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

# (风格图像a的feature maps集合A, 随机噪声图像x的feature maps集合G)
def build_style_loss(a, x):

  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
  
  M = a.shape[1]*a.shape[2]
  N = a.shape[3]
  G = _gram_matrix(x, M, N)
  A = _gram_matrix_val(a, M, N)
  loss = (1./(4 * N**2 * M**2)) * tf.reduce_sum(tf.pow((G - A),2))
  return loss

# 读写图像的函数定义,使用scipy.misc库
def read_image(path):
  image = scipy.misc.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.global_variables_initializer())

  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)

  ### 注意这里写法：----------------------------------------------------------------

  ## build_content_loss()的第一个参数由content_img获得;第二个参数由噪声图像x计算而得,x在迭代中更新
  sess.run([net['input'].assign(content_img)])
  cost_content = sum(map(lambda lay: lay[1]*build_content_loss(sess.run(net[lay[0]]), net[lay[0]]), CONTENT_LAYERS))

  ## build_style_loss()的第一个参数由style_img获得;STYLE_LAYERS包含5层,权重自定义,这里lay[1]都是1
  sess.run([net['input'].assign(style_img)])
  cost_style = sum(map(lambda lay: lay[1]*build_style_loss(sess.run(net[lay[0]]), net[lay[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.global_variables_initializer())
  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):
    # net['input']是一个tf变量由于tf.Variable()从而每次迭代更新;而vgg19中的weights和bias都是tf常量不会被更新
    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)
  

if __name__ == '__main__':
  main()

运行效果图：

2000次迭代后已经变化不大了

最终效果图（5000次迭代结果）

content image：

style image：

mixed image：