基于Transformer和小波变化的图像风格迁移代码

以下是基于Transformer和小波变换的图像风格迁移的参考代码，仅供参考：

import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import cv2
from scipy import linalg

# 定义小波变换矩阵
def get_wavelet_matrix(size):
    w = np.zeros((size, size))
    h = size // 2
    w[:h, :h] = np.eye(h)
    w[h:, :h] = np.eye(h) * -1
    w[:h, h:] = np.fliplr(np.eye(h) * -1)
    w[h:, h:] = np.eye(h)
    return w

# 定义小波变换函数
def wavelet_transform(img):
    # 将图像转为灰度图
    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    # 获取小波变换矩阵
    wavelet = get_wavelet_matrix(img.shape[0])
    # 小波变换
    wavelet_transform = np.dot(wavelet, img)
    wavelet_transform = np.dot(wavelet_transform, wavelet.T)
    # 将小波系数转为浮点数
    wavelet_transform = wavelet_transform.astype(np.float32)
    return wavelet_transform

# 定义反小波变换函数
def inverse_wavelet_transform(wavelet_transform):
    # 获取小波变换矩阵
    wavelet = get_wavelet_matrix(wavelet_transform.shape[0])
    # 反小波变换
    inverse_wavelet_transform = np.dot(wavelet.T, wavelet_transform)
    inverse_wavelet_transform = np.dot(inverse_wavelet_transform, wavelet)
    # 将反小波系数转为整数
    inverse_wavelet_transform = inverse_wavelet_transform.astype(np.uint8)
    return inverse_wavelet_transform

# 定义Transformer模型
class Transformer(nn.Module):
    def __init__(self, in_channels, out_channels, n_heads, n_layers):
        super(Transformer, self).__init__()
        self.heads = nn.ModuleList([nn.MultiheadAttention(embed_dim=in_channels, num_heads=n_heads) for _ in range(n_layers)])
        self.norm1 = nn.ModuleList([nn.LayerNorm(in_channels) for _ in range(n_layers)])
        self.norm2 = nn.ModuleList([nn.LayerNorm(in_channels) for _ in range(n_layers)])
        self.mlp = nn.ModuleList([nn.Sequential(
            nn.Linear(in_channels, in_channels * 4),
            nn.GELU(),
            nn.Linear(in_channels * 4, in_channels)
        ) for _ in range(n_layers)])
        self.out = nn.Linear(in_channels, out_channels)

    def forward(self, x):
        for i in range(len(self.heads)):
            residual = x
            x = self.norm1[i](x)
            x, _ = self.heads[i](x, x, x)
            x += residual
            residual = x
            x = self.norm2[i](x)
            x = self.mlp[i](x)
            x += residual
        x = self.out(x)
        return x

# 定义图像风格迁移函数
def style_transfer(content_image, style_image, alpha, n_heads, n_layers):
    # 将图像转为小波系数
    content_wavelet = wavelet_transform(content_image)
    style_wavelet = wavelet_transform(style_image)
    # 对小波系数进行Transformer变换
    content_wavelet = np.expand_dims(content_wavelet, axis=0)
    content_wavelet = torch.from_numpy(content_wavelet).unsqueeze(0)
    content_wavelet = content_wavelet.permute(0, 2, 1).float()
    style_wavelet = np.expand_dims(style_wavelet, axis=0)
    style_wavelet = torch.from_numpy(style_wavelet).unsqueeze(0)
    style_wavelet = style_wavelet.permute(0, 2, 1).float()
    transformer = Transformer(in_channels=content_wavelet.shape[-1], out_channels=style_wavelet.shape[-1], n_heads=n_heads, n_layers=n_layers)
    optimizer = torch.optim.Adam(transformer.parameters(), lr=0.001)
    criterion = nn.MSELoss()
    for i in range(2000):
        optimizer.zero_grad()
        output_wavelet = transformer(content_wavelet)
        loss = criterion(output_wavelet, style_wavelet)
        loss.backward()
        optimizer.step()
        if i % 100 == 0:
            print('Iteration %d, Loss=%.4f' % (i, loss.item()))
    output_wavelet = output_wavelet.permute(0, 2, 1).detach().numpy()[0]
    # 将小波系数转为图像
    output_image = inverse_wavelet_transform(output_wavelet)
    # 将风格迁移结果与原始图像进行混合
    output_image = cv2.addWeighted(content_image, alpha, output_image, 1-alpha, 0)
    return output_image

# 测试代码
content_image = cv2.imread('content.jpg')
style_image = cv2.imread('style.jpg')
output_image = style_transfer(content_image, style_image, alpha=0.5, n_heads=8, n_layers=4)
cv2.imwrite('output.jpg', output_image)

注意：以上代码仅供参考，可能需要根据您的具体需求进行修改和优化。