计算机视觉在AI人工智能领域的前沿应用

    import torch
    import torch.nn as nn
    
    class SimpleCNN(nn.Module):
    def __init__(self):
        super(SimpleCNN, self).__init__()
        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1)
        self.relu = nn.ReLU()
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
        self.fc = nn.Linear(32 * 56 * 56, 10)  # 假设输入为224x224
    
    def forward(self, x):
        x = self.conv1(x)
        x = self.relu(x)
        x = self.pool(x)
        x = self.conv2(x)
        x = self.relu(x)
        x = self.pool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-16/4FJWvNxUQmjl5Zo9LiSfarRkzPdt.png)

    class YOLOv5(nn.Module):
    def __init__(self, nc=80, anchors=None):
        super().__init__()
        self.backbone = CSPDarknet()
        self.neck = PANet()
        self.head = YOLOHead(nc, anchors)
    
    def forward(self, x):
        x = self.backbone(x)
        x = self.neck(x)
        return self.head(x)
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-16/FIb3B7eH2ZwhVGLTNnoX4DAkM0i9.png)

    class VisionTransformer(nn.Module):
    def __init__(self, image_size=224, patch_size=16, num_classes=1000):
        super().__init__()
        num_patches = (image_size // patch_size) *
        self.patch_embed = PatchEmbed(image_size, patch_size)
        self.cls_token = nn.Parameter(torch.zeros(1, 1, 768))
        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, 768))
        self.blocks = nn.ModuleList([
            TransformerBlock(768) for _ in range(12)
        ])
        self.head = nn.Linear(768, num_classes)
    
    def forward(self, x):
        x = self.patch_embed(x)
        cls_tokens = self.cls_token.expand(x.shape[0], -1, -1)
        x = torch.cat((cls_tokens, x), dim=1)
        x = x + self.pos_embed
        for blk in self.blocks:
            x = blk(x)
        x = x[:, 0]
        return self.head(x)
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-16/mc7zlQoS3uL1wtJFDZykgVsr20qN.png)

    # 创建conda环境
    conda create -n cv_demo python=3.8
    conda activate cv_demo
    
    # 安装核心库
    pip install torch torchvision torchaudio
    pip install opencv-python matplotlib numpy
    pip install albumentations pytorch-lightning
    
    
    bash

    import cv2
    import torch
    from models.experimental import attempt_load
    from utils.general import non_max_suppression
    
    class YOLOv5Detector:
    def __init__(self, weights_path, device='cuda'):
        self.device = device
        self.model = attempt_load(weights_path, map_location=device)
        self.names = self.model.module.names if hasattr(self.model, 'module') else self.model.names
    
    def detect(self, img, conf_thres=0.5, iou_thres=0.45):
        # 预处理
        img = self.preprocess(img)
    
        # 推理
        with torch.no_grad():
            pred = self.model(img)[0]
    
        # 后处理
        pred = non_max_suppression(pred, conf_thres, iou_thres)
    
        return pred
    
    def preprocess(self, img):
        # 实现图像预处理逻辑
        pass
    
    def visualize(self, img, detections):
        # 实现可视化逻辑
        pass
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-16/iybraOhNGUHMmRpTBZDl2ex6s5jX.png)

    import torch.nn as nn
    
    class DoubleConv(nn.Module):
    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.double_conv = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True)
        )
    
    def forward(self, x):
        return self.double_conv(x)
    
    class UNet(nn.Module):
    def __init__(self, n_channels, n_classes):
        super().__init__()
        # 实现完整的U-Net架构
        pass
    
    def forward(self, x):
        # 实现前向传播逻辑
        pass
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-16/VUrLgMQcmDTBpPYyNXvj3zHK7nSi.png)