python图像风格迁移
时间: 2024-05-31 21:06:27 浏览: 39
Python图像风格迁移是一种将一张图片的风格迁移到另一张图片上的技术,通过计算两张图片中的内容和风格之间的差异,生成一张新的图片,使其既保留了原始图片的内容,同时又具有迁移图片的风格。这种技术可以用于艺术创作,视频处理,虚拟现实等领域。
在Python中,实现图像风格迁移的方法有很多,其中最流行的是基于深度学习的方法。这些方法基于卷积神经网络(CNN)和反向传播算法,通过训练一个神经网络来计算两张图片之间的风格和内容的差异,并生成新的合成图片。
具体实现过程包括以下几步:
1. 读取原始图片和目标图片,并将它们转换成神经网络能够处理的格式
2. 加载预训练的卷积神经网络模型
3. 定义损失函数,计算原始图片与目标图片之间的内容差异和风格差异
4. 使用反向传播算法来更新输入图片以最小化损失函数
5. 重复步骤4多次,直到生成合成图片
相关问题
图像风格迁移python
图像风格迁移是一种将一张图片的内容与另一张图片的风格进行融合的技术。在Python中,可以使用深度学习框架TensorFlow或PyTorch来实现图像风格迁移。其中,常用的算法包括基于VGG网络的方法和基于残差网络的方法。
基于VGG网络的方法主要是通过将待处理的图片输入到预训练好的VGG网络中,提取出不同层次的特征表示,然后通过最小化内容图片与目标风格图片在这些特征层次上的差异来实现风格迁移。
基于残差网络的方法则是通过构建一个深度残差网络,将待处理的图片输入到网络中,通过最小化内容图片与目标风格图片在网络输出上的差异来实现风格迁移。
以下是一个基于PyTorch实现图像风格迁移的示例代码:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
import torchvision.models as models
from PIL import Image
# 定义图像预处理函数
preprocess = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
# 加载预训练的VGG19网络
vgg = models.vgg19(pretrained=True).features
# 将VGG网络转换为只包含特定层次的子网络
class VGG(nn.Module):
def __init__(self):
super(VGG, self).__init__()
self.layers = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(64, 64, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(64, 128, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(128, 128, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(128, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(256, 512, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2)
)
def forward(self, x):
features = []
for layer in self.layers:
x = layer(x)
if isinstance(layer, nn.ReLU):
features.append(x)
return features
# 定义Gram矩阵计算函数
def gram_matrix(input):
batch_size, channel, height, width = input.size()
features = input.view(batch_size * channel, height * width)
gram = torch.mm(features, features.t())
return gram
# 加载内容图片和目标风格图片
content_image = Image.open('content.jpg')
style_image = Image.open('style.jpg')
# 对内容图片和目标风格图片进行预处理
content_tensor = preprocess(content_image)
style_tensor = preprocess(style_image)
# 将内容图片和目标风格图片输入到VGG网络中,提取出不同层次的特征表示
vgg = VGG().cuda()
content_features = vgg(content_tensor.unsqueeze(0).cuda())
style_features = vgg(style_tensor.unsqueeze(0).cuda())
# 初始化生成的图片
generated_tensor = torch.randn(content_tensor.size()).cuda()
# 定义优化器和损失函数
optimizer = optim.Adam([generated_tensor.requires_grad_()], lr=0.01)
mse_loss = nn.MSELoss()
# 迭代优化生成的图片,使其同时具有内容图片的内容和目标风格图片的风格
for i in range(500):
optimizer.zero_grad()
generated_features = vgg(generated_tensor.unsqueeze(0))
content_loss = mse_loss(generated_features[2], content_features[2])
style_loss = 0
for j in range(len(generated_features)):
generated_gram = gram_matrix(generated_features[j])
style_gram = gram_matrix(style_features[j])
style_loss += mse_loss(generated_gram, style_gram)
total_loss = content_loss + 100 * style_loss
total_loss.backward()
optimizer.step()
if i % 50 == 0:
print('Iteration %d, Total loss: %.4f, Content loss: %.4f, Style loss: %.4f' % (i, total_loss.item(), content_loss.item(), style_loss.item()))
# 将生成的图片保存到本地
generated_image = generated_tensor.cpu().detach().squeeze(0)
generated_image = generated_image.permute(1, 2, 0).numpy()
generated_image = generated_image * [0.229, 0.224, 0.225] + [0.485, 0.456, 0.406]
generated_image = (generated_image * 255).clip(0, 255).astype('uint8')
Image.fromarray(generated_image).save('generated.jpg')
```
图像风格迁移python代码
以下是一个基于PyTorch实现的图像风格迁移的Python代码示例:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
import torchvision.models as models
from PIL import Image
import matplotlib.pyplot as plt
# 定义图像处理函数
loader = transforms.Compose([
transforms.Resize((512, 512)), # 调整图像大小
transforms.ToTensor() # 转换为张量
])
unloader = transforms.ToPILImage() # 将张量转换为图像
# 定义图像加载函数
def image_loader(image_name):
image = Image.open(image_name)
image = loader(image).unsqueeze(0)
return image.to(device, torch.float)
# 定义模型
class ContentLoss(nn.Module):
def __init__(self, target):
super(ContentLoss, self).__init__()
self.target = target.detach()
def forward(self, input):
self.loss = nn.functional.mse_loss(input, self.target)
return input
class StyleLoss(nn.Module):
def __init__(self, target_feature):
super(StyleLoss, self).__init__()
self.target = gram_matrix(target_feature).detach()
def forward(self, input):
G = gram_matrix(input)
self.loss = nn.functional.mse_loss(G, self.target)
return input
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)
class Normalization(nn.Module):
def __init__(self, mean, std):
super(Normalization, self).__init__()
self.mean = torch.tensor(mean).view(-1, 1, 1)
self.std = torch.tensor(std).view(-1, 1, 1)
def forward(self, img):
return (img - self.mean) / self.std
# 定义模型
class StyleTransferModel(nn.Module):
def __init__(self, content_img, style_img, cnn=models.vgg19(pretrained=True).features.to(device).eval(),
content_layers=['conv_4'], style_layers=['conv_1', 'conv_2', 'conv_3', 'conv_4', 'conv_5']):
super(StyleTransferModel, self).__init__()
self.content_layers = content_layers
self.style_layers = style_layers
self.content_losses = []
self.style_losses = []
self.model = nn.Sequential(Normalization([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]))
i = 0
for layer in cnn.children():
if isinstance(layer, nn.Conv2d):
i += 1
name = 'conv_{}'.format(i)
elif isinstance(layer, nn.ReLU):
name = 'relu_{}'.format(i)
layer = nn.ReLU(inplace=False)
elif isinstance(layer, nn.MaxPool2d):
name = 'pool_{}'.format(i)
elif isinstance(layer, nn.BatchNorm2d):
name = 'bn_{}'.format(i)
else:
raise RuntimeError('Unrecognized layer: {}'.format(layer.__class__.__name__))
self.model.add_module(name, layer)
if name in content_layers:
target = self.model(content_img).detach()
content_loss = ContentLoss(target)
self.model.add_module("content_loss_{}".format(i), content_loss)
self.content_losses.append(content_loss)
if name in style_layers:
target_feature = self.model(style_img).detach()
style_loss = StyleLoss(target_feature)
self.model.add_module("style_loss_{}".format(i), style_loss)
self.style_losses.append(style_loss)
for i in range(len(self.model) - 1, -1, -1):
if isinstance(self.model[i], ContentLoss) or isinstance(self.model[i], StyleLoss):
break
self.model = self.model[:(i + 1)]
def forward(self, x):
return self.model(x)
# 定义图像风格迁移函数
def style_transfer(content_img_path, style_img_path, num_steps=300,
style_weight=100000, content_weight=1):
content_img = image_loader(content_img_path)
style_img = image_loader(style_img_path)
input_img = content_img.clone()
optimizer = optim.LBFGS([input_img.requires_grad_()])
model = StyleTransferModel(content_img, style_img).to(device)
print('Start style transfer...')
run = [0]
while run[0] <= num_steps:
def closure():
input_img.data.clamp_(0, 1)
optimizer.zero_grad()
model(input_img)
style_score = 0
content_score = 0
for sl in model.style_losses:
style_score += sl.loss
for cl in model.content_losses:
content_score += cl.loss
style_score *= style_weight
content_score *= content_weight
loss = style_score + content_score
loss.backward()
run[0] += 1
if run[0] % 50 == 0:
print("run {}:".format(run))
print('Style Loss : {:4f} Content Loss: {:4f}'.format(
style_score.item(), content_score.item()))
print()
return style_score + content_score
optimizer.step(closure)
input_img.data.clamp_(0, 1)
return input_img
# 定义图像显示函数
def imshow(tensor, title=None):
image = tensor.cpu().clone()
image = image.squeeze(0)
image = unloader(image)
plt.imshow(image)
if title is not None:
plt.title(title)
plt.pause(0.001)
# 设置设备
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# 运行图像风格迁移
output = style_transfer('content.jpg', 'style.jpg')
# 显示结果
plt.figure()
imshow(output, title='Output Image')
# 保存结果
output_img = unloader(output.cpu().clone().squeeze(0))
output_img.save('output.jpg')
```
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