1 - 引言

相信大家都使用过一种滤镜，可以把一张照片转换成不同风格的照片，如下图所示：
在这里插入图片描述
那么我们就来利用TensorFlow来实现以下这个算法，这个算法出自Gatys的A Neural Algorithm of Artistic Style论文，十分有趣，让我们来详细的介绍一下这个算法吧

2 - 利用VGG提取特征

总得来说，就是利用一个训练好的卷积神经网络 VGG-19，这个网络在ImageNet 上已经训练过了。

给定一张风格图像和一张普通图像，风格图像经过VGG-19 的时候在每个卷积层会得到很多 feature maps, 这些feature maps 组成一个集合，同样的，普通图像通过 VGG-19 的时候也会得到很多 feature maps，这些feature maps 组成一个集合，然后生成一张随机噪声图像 , 随机噪声图像通过VGG-19 的时候也会生成很多feature maps，这些 feature maps 构成集合和分别对应集合和 , 最终的优化函数是希望调整让随机噪声图像最后看起来既保持普通图像的内容, 又有一定的风格图像的风格。
在这里插入图片描述

3 - 神经风格转换

总得来说，就是利用一个训练好的卷积神经网络 VGG-19，这个网络在ImageNet 上已经训练过了。

所以我们先要构建这些损失函数：

构建内容损失函数 $j_{content}(C,G)$
构建风格损失函数 $J_{style}(S,G)$
把它们放在一起构造总代价函数 $J(G)=\alpha J_{content}(C,G)+\beta J_{style}(S,G)$

3.1 - 计算内容损失

根据内容损失函数
$J_{content}(C,G)=\frac{1}{4*n_h*n_w*n_c}\sum_{所有条目}(a^{(C)}-a^{(G)})^2$

3.2 - 计算风格损失

根据风格损失函数
$J_{style}(S,G)=\frac{1}{4*n_c^2*(n_h*n_w)^2}\sum_{i=1}^{n_c}\sum_{j=1}^{n_c}(G_{ij}^(S)-G_{ij}^(G))^2$

3.3 - 总体成本优化公式

最后我们要创建一个最小化风格的内容成本函数
$J(G)= \alpha J_{content}(C,G)+\beta J_{style}(S,G)$

4 - 使用TensorFlow实现算法

代码运行前先确保下载好了VGG-19模型

import os
import sys
import numpy as np
import scipy.io
import scipy.misc
import tensorflow as tf
 
# Output folder for the images.
OUTPUT_DIR = 'output/'
# Style image to use.
STYLE_IMAGE = '/images/ocean.jpg'
# Content image to use.
CONTENT_IMAGE = '/images/Taipei101.jpg'
# Image dimensions constants.
IMAGE_WIDTH = 800
IMAGE_HEIGHT = 600
COLOR_CHANNELS = 3
 
###############################################################################
# Algorithm constants
###############################################################################
# 设置随机噪声图像与内容图像的比率
NOISE_RATIO = 0.6
# 设置迭代次数
ITERATIONS = 1000
# 设置内容图像与风格图像的权重
alpha = 1
beta = 500
# 加载VGG-19 MODEL及设定均值
VGG_Model = 'Downloads/imagenet-vgg-verydeep-19.mat'
MEAN_VALUES = np.array([123.68, 116.779, 103.939]).reshape((1, 1, 1, 3))
# 设置需要用到的卷积层
CONTENT_LAYERS = [('conv4_2', 1.)]
STYLE_LAYERS = [('conv1_1', 0.2), ('conv2_1', 0.2), ('conv3_1', 0.2), ('conv4_1', 0.2), ('conv5_1', 0.2)]
 
# 生成随机噪声图，与content图以一定比率融合
def generate_noise_image(content_image, noise_ratio = NOISE_RATIO):
    """
    Returns a noise image intermixed with the content image at a certain ratio.
    """
    noise_image = np.random.uniform(
            -20, 20,
            (1, IMAGE_HEIGHT, IMAGE_WIDTH, COLOR_CHANNELS)).astype('float32')
    # White noise image from the content representation. Take a weighted average
    # of the values
    img = noise_image * noise_ratio + content_image * (1 - noise_ratio)
    return img
 
def load_image(path):
    image = scipy.misc.imread(path)
    # Resize the image for convnet input, there is no change but just
    # add an extra dimension.
    image = np.reshape(image, ((1,) + image.shape))
    # Input to the VGG net expects the mean to be subtracted.
    image = image - MEAN_VALUES
    return image
 
def save_image(path, image):
    # Output should add back the mean.
    image = image + MEAN_VALUES
    # Get rid of the first useless dimension, what remains is the image.
    image = image[0]
    image = np.clip(image, 0, 255).astype('uint8')
    scipy.misc.imsave(path, image)
 
 
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][2][0][0]
    weights = tf.constant(weights)
    bias = vgg_layers[i][0][0][2][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_HEIGHT, IMAGE_WIDTH, 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 content_layer_loss(p, x):
 
    M = p.shape[1] * p.shape[2]
    N = p.shape[3]
    loss = (1. / (2 * N * M)) * tf.reduce_sum(tf.pow((x - p), 2))
    return loss
 
 
def content_loss_func(sess, net):
 
    layers = CONTENT_LAYERS
    total_content_loss = 0.0
    for layer_name, weight in layers:
        p = sess.run(net[layer_name])
        x = net[layer_name]
        total_content_loss += content_layer_loss(p, x)*weight
 
    total_content_loss /= float(len(layers))
    return total_content_loss
 
 
def gram_matrix(x, area, depth):
 
    x1 = tf.reshape(x, (area, depth))
    g = tf.matmul(tf.transpose(x1), x1)
    return g
 
def style_layer_loss(a, x):
 
    M = a.shape[1] * a.shape[2]
    N = a.shape[3]
    A = gram_matrix(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 style_loss_func(sess, net):
 
    layers = STYLE_LAYERS
    total_style_loss = 0.0
    for layer_name, weight in layers:
        a = sess.run(net[layer_name])
        x = net[layer_name]
        total_style_loss += style_layer_loss(a, x) * weight
    total_style_loss /= float(len(layers))
    return total_style_loss
 
 
def main():
    net = build_vgg19(VGG_Model)
    sess = tf.Session()
    sess.run(tf.initialize_all_variables())
 
    content_img = load_image(CONTENT_IMAGE)
    style_img = load_image(STYLE_IMAGE)
 
    sess.run([net['input'].assign(content_img)])
    cost_content = content_loss_func(sess, net)
 
    sess.run([net['input'].assign(style_img)])
    cost_style = style_loss_func(sess, net)
 
    total_loss = alpha * cost_content + beta * cost_style
    optimizer = tf.train.AdamOptimizer(2.0)
 
    init_img = generate_noise_image(content_img)
 
    train_op = optimizer.minimize(total_loss)
    sess.run(tf.initialize_all_variables())
    sess.run(net['input'].assign(init_img))
 
    for it in range(ITERATIONS):
        sess.run(train_op)
        if it % 100 == 0:
            # Print every 100 iteration.
            mixed_image = sess.run(net['input'])
            print('Iteration %d' % (it))
            print('sum : ', sess.run(tf.reduce_sum(mixed_image)))
            print('cost: ', sess.run(total_loss))
 
            if not os.path.exists(OUTPUT_DIR):
                os.mkdir(OUTPUT_DIR)
 
            filename = 'output/%d.png' % (it)
            save_image(filename, mixed_image)
 
if __name__ == '__main__':
    main()

5 - 实验结果

将晴空下的卢浮宫设为待融合的原始图片（很好看有木有）：
在这里插入图片描述

与之融合的图片，山水画（也很美有木有）：
在这里插入图片描述

那么我们利用图像风格迁移融合的效果是什么呢？
在这里插入图片描述
是不是很酷呢？你们也可以换成其他图片来试一试效果。

机器学习笔记（二十二）：TensorFlow实战十四（图像风格迁移）