基于lab的黑白图片彩色化代码

这是一个基于PyTorch和LAB色彩空间的黑白图片彩色化代码：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
from torchvision import datasets, transforms
from PIL import Image
import numpy as np

# 定义转换器，将PIL图像转换为张量，并将图像归一化到[-1,1]之间
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])

# 定义数据集
train_data = datasets.ImageFolder('path/to/folder', transform=transform)

# 创建数据加载器
train_loader = torch.utils.data.DataLoader(train_data, batch_size=64, shuffle=True)

# 定义网络结构
class ColorNet(nn.Module):
    def __init__(self):
        super(ColorNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 64, kernel_size=3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1)
        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
        self.conv4 = nn.Conv2d(128, 128, kernel_size=3, stride=2, padding=1)
        self.conv5 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
        self.conv6 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
        self.conv7 = nn.Conv2d(256, 256, kernel_size=3, stride=2, padding=1)
        self.conv8 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
        self.conv9 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.conv10 = nn.Conv2d(512, 512, kernel_size=3, stride=2, padding=1)
        self.conv11 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.conv12 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.conv13 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.conv14 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.conv15 = nn.Conv2d(512, 512, kernel_size=3, stride=2, padding=1)
        self.fc1 = nn.Linear(512 * 7 * 7, 4096)
        self.fc2 = nn.Linear(4096, 4096)
        self.fc3 = nn.Linear(4096, 1000)
        self.fc4 = nn.Linear(1000, 256 * 8 * 8)
        self.deconv1 = nn.ConvTranspose2d(256, 128, kernel_size=4, stride=2, padding=1)
        self.deconv2 = nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1)
        self.deconv3 = nn.ConvTranspose2d(64, 32, kernel_size=4, stride=2, padding=1)
        self.deconv4 = nn.ConvTranspose2d(32, 2, kernel_size=4, stride=2, padding=1)

    def forward(self, x):
        x = nn.functional.relu(self.conv1(x))
        x = nn.functional.relu(self.conv2(x))
        x = nn.functional.relu(self.conv3(x))
        x = nn.functional.relu(self.conv4(x))
        x = nn.functional.relu(self.conv5(x))
        x = nn.functional.relu(self.conv6(x))
        x = nn.functional.relu(self.conv7(x))
        x = nn.functional.relu(self.conv8(x))
        x = nn.functional.relu(self.conv9(x))
        x = nn.functional.relu(self.conv10(x))
        x = nn.functional.relu(self.conv11(x))
        x = nn.functional.relu(self.conv12(x))
        x = nn.functional.relu(self.conv13(x))
        x = nn.functional.relu(self.conv14(x))
        x = nn.functional.relu(self.conv15(x))
        x = x.view(-1, 512 * 7 * 7)
        x = nn.functional.relu(self.fc1(x))
        x = nn.functional.dropout(x, p=0.5)
        x = nn.functional.relu(self.fc2(x))
        x = nn.functional.dropout(x, p=0.5)
        x = nn.functional.relu(self.fc3(x))
        x = nn.functional.dropout(x, p=0.5)
        x = nn.functional.relu(self.fc4(x))
        x = x.view(-1, 256, 8, 8)
        x = nn.functional.relu(self.deconv1(x))
        x = nn.functional.relu(self.deconv2(x))
        x = nn.functional.relu(self.deconv3(x))
        x = nn.functional.tanh(self.deconv4(x))
        return x

# 定义损失函数和优化器
net = ColorNet()
criterion = nn.MSELoss()
optimizer = optim.Adam(net.parameters(), lr=0.001)

# 训练网络
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(train_loader):
        inputs, _ = data
        lab = np.zeros((inputs.shape[0], 3, 64, 64))
        for j in range(inputs.shape[0]):
            img = inputs[j].numpy().transpose((1, 2, 0))
            lab[j] = np.array(Image.fromarray((img + 1) / 2 * 255).convert('LAB'))[..., 1:]
            lab[j] /= 128
        inputs = torch.from_numpy(lab[:, np.newaxis, :, :]).float()
        inputs, _ = Variable(inputs), Variable(inputs)
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, inputs)
        loss.backward()
        optimizer.step()
        running_loss += loss.data[0]
        if i % 10 == 9:
            print('[%d, %5d] loss: %.6f' % (epoch + 1, i + 1, running_loss / 10))
            running_loss = 0.0

print('Finished Training')

# 保存模型
torch.save(net.state_dict(), 'path/to/save/model.pth')

在上面的代码中，我们定义了一个名为`ColorNet`的卷积神经网络，它接受L通道的灰度图像，并输出AB通道的彩色图像。我们使用MSE损失函数和Adam优化器来训练网络。

在训练过程中，我们将输入图像转换为LAB色彩空间，并将L通道作为网络的输入。在每个训练迭代中，我们计算网络的输出与输入之间的MSE损失，并使用反向传播来更新网络的权重。最终，我们保存了训练好的模型。

在使用训练好的模型进行彩色化时，我们可以将灰度图像转换为LAB色彩空间，并将L通道输入到网络中。网络输出的AB通道可以与原始L通道合并为彩色图像。