可以通过训练一个神经网络模型来使图像达到同一亮度的示例代码

是的，可以通过训练一个神经网络模型来使图像达到同一亮度。以下是使用Python和PyTorch框架的示例代码：

首先，我们需要导入必要的库：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision.datasets import ImageFolder
from torchvision.transforms import transforms

然后，我们定义一个自定义的数据转换函数，该函数将对图像进行预处理（例如，缩放、裁剪、标准化等）：

def preprocess_image(image):
    transform = 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])
    ])
    return transform(image)

接下来，我们定义一个自定义的神经网络模型，该模型将输入图像作为张量，并输出亮度调整后的图像：

class BrightnessAdjustmentModel(nn.Module):
    def __init__(self):
        super(BrightnessAdjustmentModel, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(64, 64, kernel_size=3, padding=1)
        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.conv4 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
        self.conv5 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
        self.conv6 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
        self.conv7 = nn.Conv2d(256, 512, kernel_size=3, padding=1)
        self.conv8 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.relu = nn.ReLU(inplace=True)
        self.dropout = nn.Dropout(0.5)
        self.fc1 = nn.Linear(512 * 7 * 7, 1024)
        self.fc2 = nn.Linear(1024, 512)
        self.fc3 = nn.Linear(512, 256)
        self.fc4 = nn.Linear(256, 3)

    def forward(self, x):
        x = self.conv1(x)
        x = self.relu(x)
        x = self.conv2(x)
        x = self.relu(x)
        x = self.pool(x)
        x = self.conv3(x)
        x = self.relu(x)
        x = self.conv4(x)
        x = self.relu(x)
        x = self.pool(x)
        x = self.conv5(x)
        x = self.relu(x)
        x = self.conv6(x)
        x = self.relu(x)
        x = self.pool(x)
        x = self.conv7(x)
        x = self.relu(x)
        x = self.conv8(x)
        x = self.relu(x)
        x = self.pool(x)
        x = x.view(-1, 512 * 7 * 7)
        x = self.dropout(x)
        x = self.fc1(x)
        x = self.relu(x)
        x = self.dropout(x)
        x = self.fc2(x)
        x = self.relu(x)
        x = self.dropout(x)
        x = self.fc3(x)
        x = self.relu(x)
        x = self.dropout(x)
        x = self.fc4(x)
        return x

接下来，我们加载数据集并定义数据加载器：

train_dataset = ImageFolder('path/to/training/dataset', transform=preprocess_image)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)

val_dataset = ImageFolder('path/to/validation/dataset', transform=preprocess_image)
val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)

然后，我们定义损失函数和优化器：

model = BrightnessAdjustmentModel()
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

接下来，我们定义训练函数和测试函数：

def train(model, criterion, optimizer, train_loader, device):
    model.train()
    running_loss = 0.0
    for i, (inputs, _) in enumerate(train_loader):
        inputs = inputs.to(device)
        outputs = model(inputs)
        loss = criterion(outputs, inputs)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        running_loss += loss.item() * inputs.size(0)
    epoch_loss = running_loss / len(train_loader.dataset)
    return epoch_loss

def test(model, criterion, val_loader, device):
    model.eval()
    running_loss = 0.0
    with torch.no_grad():
        for i, (inputs, _) in enumerate(val_loader):
            inputs = inputs.to(device)
            outputs = model(inputs)
            loss = criterion(outputs, inputs)
            running_loss += loss.item() * inputs.size(0)
    epoch_loss = running_loss / len(val_loader.dataset)
    return epoch_loss

最后，我们训练模型并保存模型：

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)

best_loss = float('inf')
for epoch in range(10):
    train_loss = train(model, criterion, optimizer, train_loader, device)
    val_loss = test(model, criterion, val_loader, device)
    print(f'Epoch {epoch+1}/{10} - Train Loss: {train_loss:.4f} - Val Loss: {val_loss:.4f}')
    if val_loss < best_loss:
        best_loss = val_loss
        torch.save(model.state_dict(), 'brightness_adjustment_model.pth')

这将训练一个神经网络模型，该模型将输入图像作为张量并输出亮度调整后的图像。您可以调整模型的超参数和架构以获得更好的性能。