使用Keras中的VGG16模型进行图像风格迁移

图像风格迁移是一种将一个图像的风格应用到另一个图像中的技术。它通过优化一个损失函数来实现，在保持目标图像的内容的同时，将源图像的风格应用到目标图像上。VGG16是一种常用的卷积神经网络模型，可以用来提取图像的特征。在Keras中，我们可以使用预训练的VGG16模型来进行图像风格迁移。

首先我们需要导入必要的库和模块：

import numpy as np
from keras import backend as K
from keras.applications import vgg16
from keras.models import Model
from keras.preprocessing.image import load_img, img_to_array
from keras.applications.vgg16 import preprocess_input
from scipy.optimize import fmin_l_bfgs_b
import matplotlib.pyplot as plt

接下来，我们定义一些辅助函数。

def preprocess_image(image_path):
    img = load_img(image_path, target_size=(img_height, img_width))
    img = img_to_array(img)
    img = np.expand_dims(img, axis=0)
    img = preprocess_input(img)
    return img

def deprocess_image(x):
    if K.image_data_format() == 'channels_first':
        x = x.reshape((3, img_height, img_width))
        x = x.transpose((1, 2, 0))
    else:
        x = x.reshape((img_height, img_width, 3))
    x[:, :, 0] += 103.939
    x[:, :, 1] += 116.779
    x[:, :, 2] += 123.68
    x = x[:, :, ::-1]
    x = np.clip(x, 0, 255).astype('uint8')
    return x

接下来，我们加载预训练的VGG16模型，并选择一些层作为风格特征和内容特征的提取器。

base_model = vgg16.VGG16(weights='imagenet', include_top=False, input_shape=(img_height, img_width, 3))

# 在这里选择一些中间层作为风格特征提取器。
style_layers = ['block1_conv1', 'block2_conv1', 'block3_conv1', 'block4_conv1', 'block5_conv1']

# 选择一个中间层作为内容特征提取器。
content_layer = 'block4_conv2'

# 创建一个新的模型，只包含需要的层。
model = Model(inputs=base_model.input, outputs=[base_model.get_layer(layer).output for layer in style_layers + [content_layer]])

接下来，我们定义计算风格损失、内容损失和总损失的函数。

def style_loss(style, combination):
    style = K.batch_flatten(K.permute_dimensions(style, (2, 0, 1)))
    combination = K.batch_flatten(K.permute_dimensions(combination, (2, 0, 1)))
    style = K.dot(style, K.transpose(style))
    combination = K.dot(combination, K.transpose(combination))
    loss = K.sum(K.square(style - combination)) / (4.0 * (channels ** 2) * (img_height * img_width) ** 2)
    return loss

def content_loss(base, combination):
    loss = K.sum(K.square(combination - base))
    return loss

def total_variation_loss(x):
    a = K.square(x[:, :img_height - 1, :img_width - 1, :] - x[:, 1:, :img_width - 1, :])
    b = K.square(x[:, :img_height - 1, :img_width - 1, :] - x[:, :img_height - 1, 1:, :])
    loss = K.sum(K.pow(a + b, 1.25))
    return loss

def compute_loss(combination_image, base_image, style_reference_image):
    input_tensor = K.concatenate([base_image, style_reference_image, combination_image], axis=0)
    features = model(input_tensor)
    loss = K.variable(0.0)
    layer_features = features[0:len(style_layers)]
    weight_per_layer = 1.0 / len(layer_features)
    for target_features, combination_features in zip(layer_features, features[len(style_layers):]):
        loss += weight_per_layer * style_loss(target_features[0], combination_features[0])
    loss += content_weight * content_loss(features[len(style_layers)][0], combination_image)
    loss += total_variation_weight * total_variation_loss(combination_image)
    return loss

最后，我们定义一个函数来实现图像风格迁移：

def style_transfer(base_image_path, style_reference_image_path, iterations=10):
    base_image = K.variable(preprocess_image(base_image_path))
    style_reference_image = K.variable(preprocess_image(style_reference_image_path))
    combination_image = K.placeholder((1, img_height, img_width, 3))
    input_tensor = K.concatenate([base_image, style_reference_image, combination_image], axis=0)
    loss = compute_loss(combination_image, base_image, style_reference_image)
    grads = K.gradients(loss, combination_image)[0]
    fetch_loss_and_grads = K.function([combination_image], [loss, grads])

    def eval_loss_and_grads(x):
        x = x.reshape((1, img_height, img_width, 3))
        outs = fetch_loss_and_grads([x])
        loss_value = outs[0]
        grad_values = outs[1].flatten().astype('float64')
        return loss_value, grad_values

    class Evaluator:
        def __init__(self):
            self.loss_value = None
            self.grad_values = None

        def loss(self, x):
            assert self.loss_value is None
            loss_value, grad_values = eval_loss_and_grads(x)
            self.loss_value = loss_value
            self.grad_values = grad_values
            return self.loss_value

        def grads(self, x):
            assert self.loss_value is not None
            grad_values = np.copy(self.grad_values)
            self.loss_value = None
            self.grad_values = None
            return grad_values

    evaluator = Evaluator()

    x = preprocess_image(base_image_path)
    x = x.flatten()

    for i in range(iterations):
        print('Start of iteration', i+1)
        x, min_val, info = fmin_l_bfgs_b(evaluator.loss, x, fprime=evaluator.grads, maxfun=20)
        print('Current loss value:', min_val)

        img = deprocess_image(x.copy())
        fname = 'styled_image_iter' + str(i+1) + '.png'
        save_img(fname, img)

        # 保存中间结果以观察
        if i % 5 == 0:
            plt.imshow(img)
            plt.show()

    return img

现在，我们可以调用函数来实现图像风格迁移。

style_transfer('path_to_base_image', 'path_to_style_reference_image', iterations=10)

在这个例子中，我们将通过10次迭代将基础图像的风格迁移到风格参考图像上。您可以根据需要自行调整迭代次数。

通过这个示例，您可以使用Keras中的VGG16模型进行图像风格迁移。这是一个强大的工具，可以帮助您创造出独特和富有创意的图像。