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基于动态光影融合的缺陷实时检测和材质量化方法，并且整合EventPS、VMNer和EvDiG

article 2025/3/31 19:23:15

要完成基于动态光影融合的缺陷实时检测和材质量化方法，并且整合EventPS、VMNer和EvDiG，是一个复杂且综合性的任务。以下是一个大致的实现步骤和代码示例，不过要完整完成论文和所有实验还需要大量的细化和调整。

整体思路

数据加载与预处理：加载图像数据，进行必要的预处理，如归一化、裁剪等。
模型整合：将EventPS、VMNer和EvDiG模型整合到一个统一的框架中。
动态光影融合：实现动态光影融合算法，用于增强图像特征。
缺陷检测与材质量化：使用整合后的模型进行缺陷检测和材质量化。
消融实验：进行消融实验，评估不同组件的贡献。

代码示例

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset
import numpy as np
import cv2

# 假设的数据集类
class CustomDataset(Dataset):
    def __init__(self, data, labels):
        self.data = data
        self.labels = labels

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        sample = self.data[idx]
        label = self.labels[idx]
        return sample, label

# 简单的动态光影融合函数示例
def dynamic_light_fusion(image):
    # 这里可以实现更复杂的光影融合算法
    blurred = cv2.GaussianBlur(image, (5, 5), 0)
    fused = cv2.addWeighted(image, 1.5, blurred, -0.5, 0)
    return fused

# 假设的EventPS、VMNer和EvDiG模型类
class EventPS(nn.Module):
    def __init__(self):
        super(EventPS, self).__init__()
        # 定义模型结构
        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1)
        self.relu = nn.ReLU()

    def forward(self, x):
        x = self.conv1(x)
        x = self.relu(x)
        return x

class VMNer(nn.Module):
    def __init__(self):
        super(VMNer, self).__init__()
        # 定义模型结构
        self.conv1 = nn.Conv2d(16, 32, kernel_size=3, padding=1)
        self.relu = nn.ReLU()

    def forward(self, x):
        x = self.conv1(x)
        x = self.relu(x)
        return x

class EvDiG(nn.Module):
    def __init__(self):
        super(EvDiG, self).__init__()
        # 定义模型结构
        self.fc1 = nn.Linear(32 * 10 * 10, 1)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        x = x.view(-1, 32 * 10 * 10)
        x = self.fc1(x)
        x = self.sigmoid(x)
        return x

# 整合模型
class IntegratedModel(nn.Module):
    def __init__(self):
        super(IntegratedModel, self).__init__()
        self.eventps = EventPS()
        self.vmner = VMNer()
        self.evdig = EvDiG()

    def forward(self, x):
        x = self.eventps(x)
        x = self.vmner(x)
        x = self.evdig(x)
        return x

# 训练函数
def train_model(model, train_loader, criterion, optimizer, epochs):
    model.train()
    for epoch in range(epochs):
        running_loss = 0.0
        for i, (inputs, labels) in enumerate(train_loader):
            optimizer.zero_grad()
            outputs = model(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()
            running_loss += loss.item()
        print(f'Epoch {epoch + 1}, Loss: {running_loss / len(train_loader)}')

# 主函数
if __name__ == "__main__":
    # 生成一些示例数据
    data = np.random.rand(100, 3, 10, 10).astype(np.float32)
    labels = np.random.randint(0, 2, 100).astype(np.float32)

    # 数据预处理
    preprocessed_data = []
    for img in data:
        img = np.transpose(img, (1, 2, 0))
        img = dynamic_light_fusion(img)
        img = np.transpose(img, (2, 0, 1))
        preprocessed_data.append(img)
    preprocessed_data = np.array(preprocessed_data)

    # 创建数据集和数据加载器
    dataset = CustomDataset(torch.from_numpy(preprocessed_data), torch.from_numpy(labels).unsqueeze(1))
    train_loader = DataLoader(dataset, batch_size=10, shuffle=True)

    # 初始化模型、损失函数和优化器
    model = IntegratedModel()
    criterion = nn.BCELoss()
    optimizer = optim.Adam(model.parameters(), lr=0.001)

    # 训练模型
    train_model(model, train_loader, criterion, optimizer, epochs=10)

    # 消融实验示例
    # 移除EventPS模块
    class AblatedModel1(nn.Module):
        def __init__(self):
            super(AblatedModel1, self).__init__()
            self.vmner = VMNer()
            self.evdig = EvDiG()

        def forward(self, x):
            x = self.vmner(x)
            x = self.evdig(x)
            return x

    ablated_model1 = AblatedModel1()
    optimizer_ablated1 = optim.Adam(ablated_model1.parameters(), lr=0.001)
    train_model(ablated_model1, train_loader, criterion, optimizer_ablated1, epochs=10)

    # 可以继续进行其他消融实验，如移除VMNer或EvDiG模块