1.背景介绍
随着人工智能技术的快速发展，图像处理的技术也日新月异。风格迁移（Style Transfer）是一种将颜色和内容从图像A转移到图像B的技术，从而生成一张新的艺术图像的过程。这项技术可以用于艺术创作、图像修复、图像增强等领域。
2.方法介绍
2.1 神经风格迁移
神经风格迁移是由Gatys等人在2015年提出的，它是一种基于卷积神经网络的方法，可以实现图像的风格迁移。这种方法通过训练一个卷积神经网络来提取风格特征和内容特征，再通过调整输入图像的像素值来生成一张新的图像。
具体的操作步骤如下：
选取一个样式图像和一张目标图像
import torch
import torchvision
from torchvision import transforms
from PIL import Image
# 声明一些变量
imsize = 512
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# 加载图像
loader = transforms.Compose([
transforms.Resize(imsize),
transforms.ToTensor()])
def image_loader(image_name):
image = Image.open(image_name)
image = loader(image).unsqueeze(0)
return image.to(device, torch.float)
style_img = image_loader("./style.jpg")
content_img = image_loader("./content.jpg")
使用预训练的卷积神经网络提取特征
import torchvision.models as models
cnn = models.vgg19(pretrained=True).features.to(device).eval()
def get_features(image, model, layers=None):
# 定义用于提取特征的模型，layers参数是要提取特征的网络层的名称
if layers is None:
layers = {'0': 'conv1_1',
'5': 'conv2_1',
'10': 'conv3_1',
'19': 'conv4_1',
'28': 'conv5_1'}
features = {}
x = image
for name, layer in model._modules.items():
x = layer(x)
if name in layers:
features[layers[name]] = x
return features
style_features = get_features(style_img, cnn)
content_features = get_features(content_img, cnn)
计算样式损失和内容损失
def gram_matrix(input):
a, b, c, d = input.size()
features = input.view(a * b, c * d)
G = torch.mm(features, features.t())
return G.div(a * b * c * d)
style_grams = {layer: gram_matrix(style_features[layer]) for layer in style_features}
target = content_img.clone().requires_grad_(True).to(device)
style_weight = 1000
content_weight = 1
def get_loss(style_features, content_features, target, style_weight, content_weight):
style_loss = 0
content_loss = 0
for layer in content_features:
content_loss += torch.mean((target - content_features[layer])**2)
for layer in style_features:
target_feature = target.detach()
target_gram = gram_matrix(target_feature[layer])
_, d, h, w = target_feature[layer].shape
style_gram = style_grams[layer]
layer_style_loss = style_weight * torch.mean((target_gram - style_gram)**2)
style_loss += layer_style_loss / (d * h * w)
total_loss = content_weight * content_loss + style_loss
return total_loss
optimizer = torch.optim.Adam([target], lr=0.01)
steps = 2000
for step in range(steps):
optimizer.zero_grad()
loss = get_loss(style_features, content_features, target, style_weight, content_weight)
loss.backward()
optimizer.step()
if step % 100 == 0:
print("Step [{}/{}], Content Loss: {:.4f}, Style Loss: {:.4f}".format(step, steps, content_weight * content_loss.item(), style_weight * style_loss.item()))
生成新的图像
import matplotlib.pyplot as plt
# 在生成新图像之前，需要对图像像素值进行剪裁
def clip_image_values(image):
return torch.Tensor.clamp_(image, 0, 1)
target = clip_image_values(target)
plt.imshow(target.squeeze().cpu().detach().numpy())
2.2 Fast Style Transfer
Fast Style Transfer是由Johnson等人在2016年提出的一种基于卷积神经网络的快速风格迁移方法。相比于神经风格迁移，Fast Style Transfer的迁移速度快，可以实时生成图像，而且得到的结果更加良好。
由于Fast Style Transfer的过程较为复杂，这里就不再赘述了。感兴趣的读者可以参考原始论文A Neural Algorithm of Artistic Style进行了解。
3.代码实现
本文将使用PyTorch和torchvision库实现神经风格迁移。
3.1 环境搭建
在使用本代码前，您需要先安装以下软件：
PyTorch
torchvision
PIL
matplotlib
3.2 代码实现
将以下代码保存为一个Python文件（如style_transfer.py），然后运行该文件即可生成迁移后的图像。
import torch
import torchvision
from torchvision import transforms
from PIL import Image
# 声明一些变量
imsize = 512
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# 加载图像
loader = transforms.Compose([
transforms.Resize(imsize),
transforms.ToTensor()])
def image_loader(image_name):
image = Image.open(image_name)
image = loader(image).unsqueeze(0)
return image.to(device, torch.float)
# 定义转换函数
unloader = transforms.ToPILImage()
def tensor_to_image(tensor):
image = tensor.cpu().clone()
image = image.squeeze(0)
image = unloader(image)
return image
# 定义卷积神经网络
class VGG(nn.Module):
def __init__(self):
super(VGG, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
)
self.classifier = nn.Sequential(
nn.Linear(512 * 7 * 7, 4096),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(4096, 4096),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(4096, 1000),
)
def forward(self, x):
x = self.features(x)
x = x.view(x.size(0), -1)
x = self.classifier(x)
return x
# 加载预训练模型
vgg = VGG().to(device)
vgg.load_state_dict(torch.load('./vgg19-dcbb9e9d.pth'))
# 声明需要迁移的风格和内容图像
style_img = image_loader("./style.jpg")
content_img = image_loader("./content.jpg")
# 计算风格和内容图像的特征
def get_features(image, model):
layers = {'0': 'conv1_1',
'5': 'conv2_1',
'10': 'conv3_1',
'19': 'conv4_1',
'28': 'conv5_1'}
features = {}
x = image
for name, layer in model.features._modules.items():
x = layer(x)
if name in layers:
features[layers[name]] = x
return features
style_features = get_features(style_img, vgg)
content_features = get_features(content_img, vgg)
# 定义目标图像，即要生成的风格迁移图像
target = content_img.clone().requires_grad_(True).to(device)
# 定义生成新图像的函数
def style_transfer(style_features, content_features, target, device, steps=2000, show_every=100, style_weight=1000000, content_weight=1, temperature=0.6):
optimizer = torch.optim.LBFGS([target.requires_grad_()])
def closure():
target.data.clamp_(0, 1)
optimizer.zero_grad()
style_loss = 0
content_loss = 0
for layer in content_features:
content_loss += torch.mean((target - content_features[layer])**2)
for layer in style_features:
target_feature = target.detach()
target_gram = gram_matrix(target_feature[layer])
_, d, h, w = target_feature[layer].shape
style_gram = style_grams[layer]
layer_style_loss = style_weight * torch.mean((target_gram - style_gram)**2)
style_loss += layer_style_loss / (d * h * w)
total_loss = content_weight * content_loss + style_loss
total_loss.backward()
return total_loss
for step in range(steps):
optimizer.step(closure)
if step % show_every == 0:
print("Step [{}/{}], Content Loss: {:.4f}, Style Loss: {:.4f}".format(step, steps, content_loss.item(), style_loss.item()))
image = tensor_to_image(target.squeeze().cpu())
image.save("./output/%d.png" % step)
return target
# 进行风格迁移
generated = style_transfer(style_features, content_features, target, device, temperature=0.6)
# 显示最终生成的图像
plt.imshow(generated.squeeze().cpu().detach().numpy())
plt.show()
4.总结
本文介绍了神经风格迁移的原理和实现方法，并提供了一份参考代码。通过这篇文章，读者可以学会如何使用PyTorch和torchvision对图像进行风格迁移，进而将其应用到图像处理的相关工作中。