python实现风格迁移的代码

以下是使用TensorFlow实现风格迁移的Python代码：

import numpy as np
import tensorflow as tf
import scipy.misc
import argparse
import os
import time
import sys

# 定义参数
parser = argparse.ArgumentParser()
parser.add_argument('--content', type=str, default='content.jpg',
                    help='Content image')
parser.add_argument('--style', type=str, default='style.jpg',
                    help='Style image')
parser.add_argument('--output', type=str, default='output.jpg',
                    help='Output image')
parser.add_argument('--iterations', type=int, default=1000,
                    help='Number of iterations')
parser.add_argument('--content_weight', type=float, default=0.1,
                    help='Content weight')
parser.add_argument('--style_weight', type=float, default=1.0,
                    help='Style weight')
parser.add_argument('--learning_rate', type=float, default=10,
                    help='Learning rate')
args = parser.parse_args()

# 加载图像
def load_image(path):
    image = scipy.misc.imread(path).astype(np.float)
    image = np.expand_dims(image, axis=0)
    image = vgg19.preprocess(image)
    return image

# 保存图像
def save_image(path, image):
    image = image.reshape((image.shape[1], image.shape[2], 3))
    image = vgg19.deprocess(image)
    scipy.misc.imsave(path, image)

# 定义VGG19模型
class VGG19:
    def __init__(self, input_image):
        self.input_image = input_image
        self.mean_pixel = np.array([123.68, 116.779, 103.939]).reshape((1,1,1,3))
        self.layers = [
            'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1',
            'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2',
            'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3',
            'relu3_3', 'conv3_4', 'relu3_4', 'pool3',
            'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3',
            'relu4_3', 'conv4_4', 'relu4_4', 'pool4',
            'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3',
            'relu5_3', 'conv5_4', 'relu5_4'
        ]
        self.weights = self.load_weights()

    # 加载VGG19模型的权重
    def load_weights(self):
        weights = np.load('vgg19_weights.npy', encoding='latin1').item()
        return weights

    # 预处理图像
    def preprocess(self, image):
        return image - self.mean_pixel

    # 反向预处理图像
    def deprocess(self, image):
        return image + self.mean_pixel

    # 定义卷积层
    def conv_layer(self, input, weights, bias):
        conv = tf.nn.conv2d(input, tf.constant(weights), strides=(1,1,1,1), padding='SAME')
        return tf.nn.bias_add(conv, bias)

    # 定义池化层
    def pool_layer(self, input):
        return tf.nn.max_pool(input, ksize=(1,2,2,1), strides=(1,2,2,1), padding='SAME')

    # 定义前向传播
    def forward(self):
        net = {}
        current = self.input_image
        for i, name in enumerate(self.layers):
            kind = name[:4]
            if kind == 'conv':
                kernels = self.weights[name][0][0]
                bias = self.weights[name][0][1]
                current = self.conv_layer(current, kernels, bias)
            elif kind == 'relu':
                current = tf.nn.relu(current)
            elif kind == 'pool':
                current = self.pool_layer(current)
            net[name] = current
        return net

# 定义内容损失
def content_loss(content, combination):
    return tf.reduce_sum(tf.square(combination - content))

# 定义风格损失
def style_loss(style, combination):
    S = gram_matrix(style)
    C = gram_matrix(combination)
    channels = 3
    size = style.shape[1] * style.shape[2]
    return tf.reduce_sum(tf.square(S - C)) / (4.0 * (channels ** 2) * (size ** 2))

# 定义Gram矩阵
def gram_matrix(input_tensor):
    channels = int(input_tensor.shape[-1])
    a = tf.reshape(input_tensor, [-1, channels])
    n = tf.shape(a)[0]
    gram = tf.matmul(a, a, transpose_a=True)
    return gram / tf.cast(n, tf.float32)

# 定义总损失
def total_loss(content_image, style_image, combination_image):
    with tf.variable_scope('loss') as scope:
        content_layers = ['conv4_2']
        style_layers = ['conv1_1', 'conv2_1', 'conv3_1', 'conv4_1', 'conv5_1']
        vgg = VGG19(combination_image)
        net = vgg.forward()
        content_features = {}
        style_features = {}
        for layer in content_layers:
            content_features[layer] = net[layer]
        for layer in style_layers:
            style_features[layer] = net[layer]
        loss = 0
        # 计算内容损失
        for layer in content_layers:
            content_feature = content_features[layer]
            combination_feature = net[layer]
            loss += args.content_weight * content_loss(content_feature, combination_feature)
        # 计算风格损失
        for layer in style_layers:
            style_feature = style_features[layer]
            combination_feature = net[layer]
            sl = style_loss(style_feature, combination_feature)
            loss += (args.style_weight / len(style_layers)) * sl
        # 计算总变差损失
        loss += args.tv_weight * tf.image.total_variation(combination_image)
    return loss

# 定义训练函数
def train(content_image, style_image, output_image):
    content_shape = content_image.shape
    style_shape = style_image.shape
    with tf.Graph().as_default():
        # 定义输入
        content = tf.placeholder(tf.float32, shape=content_shape, name='content')
        style = tf.placeholder(tf.float32, shape=style_shape, name='style')
        combination = tf.Variable(tf.random_normal(content_shape), name='combination')
        # 定义损失函数
        loss = total_loss(content, style, combination)
        # 定义优化器
        optimizer = tf.train.AdamOptimizer(args.learning_rate).minimize(loss)
        # 开始训练
        with tf.Session() as sess:
            sess.run(tf.global_variables_initializer())
            sess.run(tf.assign(combination, content))
            for i in range(args.iterations):
                sess.run(optimizer, feed_dict={content: content_image, style: style_image})
                if i % 100 == 0:
                    print('Iteration %d' % i)
                    output_image = sess.run(combination, feed_dict={content: content_image, style: style_image})
                    save_image(args.output, output_image)
            output_image = sess.run(combination, feed_dict={content: content_image, style: style_image})
            save_image(args.output, output_image)

# 加载图像
content_image = load_image(args.content)
style_image = load_image(args.style)

# 进行风格迁移
train(content_image, style_image, args.output)