02 | 图像风格迁移

简介

图像风格迁移是指,将一幅内容图的内容,和一幅或多幅风格图的风格融合在一起,从而生成一些有意思的图片

以下是将一些艺术作品的风格,迁移到一张内容图之后的效果

我们使用TensorFlowKeras分别来实现图像风格迁移,主要用到深度学习中的卷积神经网络,即CNN

准备

安装包

pip install numpy scipy tensorflow keras

再准备一些风格图片,和一张内容图片

原理

为了将风格图的风格和内容图的内容进行融合,所生成的图片,在内容上应当尽可能接近内容图,在风格上应当尽可能接近风格图

因此需要定义内容损失函数风格损失函数,经过加权后作为总的损失函数

实现步骤如下

  • 随机产生一张图片
  • 在每轮迭代中,根据总的损失函数,调整图片的像素值
  • 经过多轮迭代,得到优化后的图片

内容损失函数

两张图片在内容上相似,不能仅仅靠简单的纯像素比较

CNN具有抽象和理解图像的能力,因此可以考虑将各个卷积层的输出作为图像的内容

VGG19为例,其中包括了多个卷积层、池化层,以及最后的全连接层

这里我们使用conv4_2的输出作为图像的内容表示,定义内容损失函数如下

风格损失函数

风格是一个很难说清楚的概念,可能是笔触、纹理、结构、布局、用色等等

这里我们使用卷积层各个特征图之间的互相关作为图像的风格,以conv1_1为例

  • 共包含64个特征图即feature map,或者说图像的深度、通道的个数
  • 每个特征图都是对上一层输出的一种理解,可以类比成64个人对同一幅画的不同理解
  • 这些人可能分别偏好印象派、现代主义、超现实主义、表现主义等不同风格
  • 当图像是某一种风格时,可能这一部分人很欣赏,但那一部分人不喜欢
  • 当图像是另一种风格时,可能这一部分人不喜欢,但那一部分人很欣赏
  • 64个人之间理解的差异,可以用特征图的互相关表示,这里使用Gram矩阵计算互相关
  • 不同的风格会导致差异化的互相关结果

Gram矩阵的计算如下,如果有64个特征图,那么Gram矩阵的大小便是64*64,第i行第j列的值表示第i个特征图和第j个特征图之间的互相关,用内积计算

风格损失函数定义如下,对多个卷积层的风格表示差异进行加权

这里我们使用conv1_1conv2_1conv3_1conv4_1conv5_1五个卷积层,进行风格损失函数的计算,不同的权重会导致不同的迁移效果

总的损失函数

总的损失函数即内容损失函数和风格损失函数的加权,不同的权重会导致不同的迁移效果

TensorFlow实现

加载库


# -*- coding: utf-8 -*-

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

def the_current_time():
    print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(int(time.time()))))

定义一些变量


CONTENT_IMG = 'content.jpg'
STYLE_IMG = 'style5.jpg'
OUTPUT_DIR = 'neural_style_transfer_tensorflow/'

if not os.path.exists(OUTPUT_DIR):
    os.mkdir(OUTPUT_DIR)

IMAGE_W = 800
IMAGE_H = 600
COLOR_C = 3

NOISE_RATIO = 0.7
BETA = 5
ALPHA = 100
VGG_MODEL = 'imagenet-vgg-verydeep-19.mat'
MEAN_VALUES = np.array([123.68, 116.779, 103.939]).reshape((1, 1, 1, 3))

加载VGG19模型


def load_vgg_model(path):
    '''
    Details of the VGG19 model:
    - 0 is conv1_1 (3, 3, 3, 64)
    - 1 is relu
    - 2 is conv1_2 (3, 3, 64, 64)
    - 3 is relu    
    - 4 is maxpool
    - 5 is conv2_1 (3, 3, 64, 128)
    - 6 is relu
    - 7 is conv2_2 (3, 3, 128, 128)
    - 8 is relu
    - 9 is maxpool
    - 10 is conv3_1 (3, 3, 128, 256)
    - 11 is relu
    - 12 is conv3_2 (3, 3, 256, 256)
    - 13 is relu
    - 14 is conv3_3 (3, 3, 256, 256)
    - 15 is relu
    - 16 is conv3_4 (3, 3, 256, 256)
    - 17 is relu
    - 18 is maxpool
    - 19 is conv4_1 (3, 3, 256, 512)
    - 20 is relu
    - 21 is conv4_2 (3, 3, 512, 512)
    - 22 is relu
    - 23 is conv4_3 (3, 3, 512, 512)
    - 24 is relu
    - 25 is conv4_4 (3, 3, 512, 512)
    - 26 is relu
    - 27 is maxpool
    - 28 is conv5_1 (3, 3, 512, 512)
    - 29 is relu
    - 30 is conv5_2 (3, 3, 512, 512)
    - 31 is relu
    - 32 is conv5_3 (3, 3, 512, 512)
    - 33 is relu
    - 34 is conv5_4 (3, 3, 512, 512)
    - 35 is relu
    - 36 is maxpool
    - 37 is fullyconnected (7, 7, 512, 4096)
    - 38 is relu
    - 39 is fullyconnected (1, 1, 4096, 4096)
    - 40 is relu
    - 41 is fullyconnected (1, 1, 4096, 1000)
    - 42 is softmax
    '''
    vgg = scipy.io.loadmat(path)
    vgg_layers = vgg['layers']

    def _weights(layer, expected_layer_name):
        W = vgg_layers[0][layer][0][0][2][0][0]
        b = vgg_layers[0][layer][0][0][2][0][1]
        layer_name = vgg_layers[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

内容损失函数


def content_loss_func(sess, model):
    def _content_loss(p, x):
        N = p.shape[3]
        M = p.shape[1] * p.shape[2]
        return (1 / (4 * N * M)) * tf.reduce_sum(tf.pow(x - p, 2))
    return _content_loss(sess.run(model['conv4_2']), model['conv4_2'])

风格损失函数


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)

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

    return sum([_style_loss(sess.run(model[layer_name]), model[layer_name]) * w for layer_name, w in STYLE_LAYERS])

随机产生一张初始图片


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')
    input_image = noise_image * noise_ratio + content_image * (1 - noise_ratio)
    return input_image

加载图片


def load_image(path):
    image = scipy.misc.imread(path)
    image = scipy.misc.imresize(image, (IMAGE_H, IMAGE_W))
    image = np.reshape(image, ((1, ) + image.shape))
    image = image - MEAN_VALUES
    return image

保存图片


def save_image(path, image):
    image = image + MEAN_VALUES
    image = image[0]
    image = np.clip(image, 0, 255).astype('uint8')
    scipy.misc.imsave(path, image)

调用以上函数并训练模型


the_current_time()

with tf.Session() as sess:
    content_image = load_image(CONTENT_IMG)
    style_image = load_image(STYLE_IMG)
    model = load_vgg_model(VGG_MODEL)

    input_image = generate_noise_image(content_image)
    sess.run(tf.global_variables_initializer())

    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 = 2000
    for i in range(ITERATIONS):
        sess.run(train)
        if i % 100 == 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)

在GPU上跑,花了5分钟左右,2000轮迭代后是这个样子

对比原图

Keras实现

Keras官方提供了图像风格迁移的例子

github.com/fchollet/ke…

代码里引入了一个total variation loss,翻译为全变差正则,据说可以让生成的图像更平滑

  • Keras相对TensorFlow封装更高,所以实现已有的模块更方便,但需要造轮子时较麻烦
  • 增加了全变差正则,以生成的图像作为参数
  • 使用conv5_2计算内容损失
  • 将内容图作为一开始的结果,即不使用随机产生的图片

代码使用方法如下

python neural_style_transfer.py path_to_your_base_image.jpg path_to_your_reference.jpg prefix_for_results

  • --iter:迭代次数,默认为10
  • --content_weight:内容损失权重,默认为0.025
  • --style_weight:风格损失权重,默认为1.0
  • --tv_weight:全变差正则权重,默认为1.0

新建文件夹neural_style_transfer_keras

python main_keras.py content.jpg style5.jpg neural_style_transfer_keras/output

生成的图片长这样,10次迭代,花了1分钟左右

参考

A Neural Algorithm of Artistic Style

TensorFlow Implementation of "A Neural Algorithm of Artistic Style"

图像风格迁移简史

【啄米日常】图像风格转移

results matching ""

    No results matching ""