深度有趣 | 04 图像风格迁移

时间 2019-11-18

标签深度有趣图像风格迁移繁體版

原文原文链接

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块儿，从而生成一些有意思的图片python

如下是将一些艺术做品的风格，迁移到一张内容图以后的效果git

咱们使用TensorFlow和Keras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CNNgithub

准备

安装包网络

pip install numpy scipy tensorflow keras

再准备一些风格图片，和一张内容图片dom

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，在内容上应当尽量接近内容图，在风格上应当尽量接近风格图ide

所以须要定义内容损失函数和风格损失函数，通过加权后做为总的损失函数函数

实现步骤以下布局

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
通过多轮迭代，获得优化后的图片

内容损失函数

两张图片在内容上类似，不能仅仅靠简单的纯像素比较学习

CNN具备抽象和理解图像的能力，所以能够考虑将各个卷积层的输出做为图像的内容优化

以VGG19为例，其中包括了多个卷积层、池化层，以及最后的全链接层

这里咱们使用conv4_2的输出做为图像的内容表示，定义内容损失函数以下

$$ L_{content}(\vec{p},\vec{x},l)=\frac{1}{2}\sum_{i,j} {(F_{ij}^{l}-P_{ij}^{l})^2} $$

风格损失函数

风格是一个很难说清楚的概念，多是笔触、纹理、结构、布局、用色等等

这里咱们使用卷积层各个特征图之间的互相关做为图像的风格，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
每一个特征图都是对上一层输出的一种理解，能够类比成64我的对同一幅画的不一样理解
这些人可能分别偏好印象派、现代主义、超现实主义、表现主义等不一样风格
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
当图像是另外一种风格时，可能这一部分人不喜欢，但那一部分人很欣赏
64我的之间理解的差别，能够用特征图的互相关表示，这里使用Gram矩阵计算互相关
不一样的风格会致使差别化的互相关结果

Gram矩阵的计算以下，若是有64个特征图，那么Gram矩阵的大小即是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

$$ G_{ij}^l=\sum_k{F_{ik}^l F_{jk}^l} $$

风格损失函数定义以下，对多个卷积层的风格表示差别进行加权

$$ E_l=\frac{1}{4N_l^2 M_l^2}\sum_{i,j} {(G_{ij}^l-A_{ij}^l)^2} $$

$$ L_{style}(\vec{a},\vec{x})=\sum_{l=0}^{L}{\omega_l E_l} $$

这里咱们使用conv1_1、conv2_1、conv3_1、conv4_1、conv5_1五个卷积层，进行风格损失函数的计算，不一样的权重会致使不一样的迁移效果

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，不一样的权重会致使不一样的迁移效果

$$ L_{total}(\vec{p},\vec{a},\vec{x})=\alpha L_{content}(\vec{p},\vec{x})+\beta L_{style}(\vec{a},\vec{x}) $$

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

https://github.com/fchollet/keras/blob/master/examples/neural_style_transfer.py

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

Keras相对TensorFlow封装更高，因此实现已有的模块更方便，但须要造轮子时较麻烦
增长了全变差正则，以生成的图像做为参数
使用conv5_2计算内容损失
将内容图做为一开始的结果，即不使用随机产生的图片

代码使用方法以下

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：https://arxiv.org/abs/1508.06576
TensorFlow Implementation of "A Neural Algorithm of Artistic Style"：http://www.chioka.in/tensorflow-implementation-neural-algorithm-of-artistic-style
图像风格迁移简史：https://zhuanlan.zhihu.com/p/26746283
【啄米平常】图像风格转移：https://zhuanlan.zhihu.com/p/23479658

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