三维重构终版
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linknet/main.py
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273
linknet/main.py
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import os
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import random
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import cv2
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.optim as optim
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from torch.utils.data import Dataset, DataLoader
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from torchvision import models
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from sklearn.model_selection import train_test_split
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import matplotlib.pyplot as plt
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import time
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# --- 1. 配置参数 ---
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class Config:
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IMAGE_DIR = "data_up/images"
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MASK_DIR = "data_up/masks_line1"
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IMAGE_SIZE = 256 # 将所有图片缩放到 256x256
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BATCH_SIZE = 4
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EPOCHS = 50 # 训练轮数
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LEARNING_RATE = 1e-4
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TEST_SPLIT = 0.1 # 20% 的数据用作验证集
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# --- 2. 数据集加载和预处理 ---
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class WeldSeamDataset(Dataset):
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def __init__(self, image_paths, mask_paths, size):
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self.image_paths = image_paths
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self.mask_paths = mask_paths
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self.size = size
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def __len__(self):
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return len(self.image_paths)
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def __getitem__(self, idx):
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# 读取图像
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img = cv2.imread(self.image_paths[idx], cv2.IMREAD_GRAYSCALE)
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img = cv2.resize(img, (self.size, self.size))
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img = img / 255.0 # 归一化到 [0, 1]
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img = np.expand_dims(img, axis=0) # 增加通道维度 (H, W) -> (C, H, W)
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img_tensor = torch.from_numpy(img).float()
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# 读取掩码
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mask = cv2.imread(self.mask_paths[idx], cv2.IMREAD_GRAYSCALE)
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mask = cv2.resize(mask, (self.size, self.size))
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mask = mask / 255.0 # 归一化到 {0, 1}
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mask[mask > 0.5] = 1.0
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mask[mask <= 0.5] = 0.0
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mask = np.expand_dims(mask, axis=0)
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mask_tensor = torch.from_numpy(mask).float()
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return img_tensor, mask_tensor
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# --- 3. LinkNet模型定义 ---
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class DecoderBlock(nn.Module):
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def __init__(self, in_channels, out_channels):
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super().__init__()
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self.block = nn.Sequential(
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nn.Conv2d(in_channels, in_channels // 4, kernel_size=1),
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nn.ReLU(inplace=True),
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nn.ConvTranspose2d(in_channels // 4, in_channels // 4, kernel_size=2, stride=2),
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nn.ReLU(inplace=True),
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nn.Conv2d(in_channels // 4, out_channels, kernel_size=1),
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nn.ReLU(inplace=True)
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)
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def forward(self, x):
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return self.block(x)
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class LinkNet(nn.Module):
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def __init__(self, num_classes=1):
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super().__init__()
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# 使用预训练的ResNet18作为编码器
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resnet = models.resnet18(weights=models.ResNet18_Weights.DEFAULT)
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self.firstconv = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)
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self.firstbn = resnet.bn1
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self.firstrelu = resnet.relu
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self.firstmaxpool = resnet.maxpool
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# 编码器层
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self.encoder1 = resnet.layer1
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self.encoder2 = resnet.layer2
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self.encoder3 = resnet.layer3
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self.encoder4 = resnet.layer4
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# 解码器层
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self.decoder4 = DecoderBlock(512, 256)
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self.decoder3 = DecoderBlock(256, 128)
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self.decoder2 = DecoderBlock(128, 64)
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self.decoder1 = DecoderBlock(64, 64)
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# 最终输出层
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self.final_deconv = nn.ConvTranspose2d(64, 32, kernel_size=2, stride=2)
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self.final_relu = nn.ReLU(inplace=True)
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self.final_conv = nn.Conv2d(32, num_classes, kernel_size=1)
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def forward(self, x):
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# 编码器
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x = self.firstconv(x)
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x = self.firstbn(x)
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x = self.firstrelu(x)
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x = self.firstmaxpool(x) # -> 64x64
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e1 = self.encoder1(x) # -> 64x64
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e2 = self.encoder2(e1) # -> 32x32
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e3 = self.encoder3(e2) # -> 16x16
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e4 = self.encoder4(e3) # -> 8x8
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# 解码器
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d4 = self.decoder4(e4) + e3 # -> 16x16
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d3 = self.decoder3(d4) + e2 # -> 32x32
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d2 = self.decoder2(d3) + e1 # -> 64x64
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d1 = self.decoder1(d2) # -> 128x128
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f = self.final_deconv(d1) # -> 256x256
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f = self.final_relu(f)
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f = self.final_conv(f)
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return torch.sigmoid(f) # 使用Sigmoid输出概率图
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# --- 4. 损失函数 (Dice Loss + BCE Loss) ---
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def dice_loss(pred, target, smooth=1.):
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pred = pred.contiguous()
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target = target.contiguous()
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intersection = (pred * target).sum(dim=2).sum(dim=2)
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loss = (1 - ((2. * intersection + smooth) / (pred.sum(dim=2).sum(dim=2) + target.sum(dim=2).sum(dim=2) + smooth)))
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return loss.mean()
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def bce_dice_loss(pred, target):
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bce = nn.BCELoss()(pred, target)
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dice = dice_loss(pred, target)
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return bce + dice
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# --- 5. 训练和评估 ---
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def train_model(model, train_loader, val_loader, criterion, optimizer, num_epochs):
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model.to(device)
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print(f"Training on {device}")
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best_val_loss = float('inf')
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for epoch in range(num_epochs):
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start_time = time.time()
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model.train()
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running_loss = 0.0
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for images, masks in train_loader:
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images = images.to(device)
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masks = masks.to(device)
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optimizer.zero_grad()
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outputs = model(images)
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loss = criterion(outputs, masks)
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loss.backward()
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optimizer.step()
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running_loss += loss.item() * images.size(0)
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epoch_loss = running_loss / len(train_loader.dataset)
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# 验证
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model.eval()
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val_loss = 0.0
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with torch.no_grad():
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for images, masks in val_loader:
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images = images.to(device)
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masks = masks.to(device)
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outputs = model(images)
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loss = criterion(outputs, masks)
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val_loss += loss.item() * images.size(0)
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val_loss /= len(val_loader.dataset)
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duration = time.time() - start_time
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print(f"Epoch {epoch + 1}/{num_epochs}.. "
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f"Train Loss: {epoch_loss:.4f}.. "
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f"Val Loss: {val_loss:.4f}.. "
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f"Time: {duration:.2f}s")
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# 保存最佳模型
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if val_loss < best_val_loss:
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best_val_loss = val_loss
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torch.save(model.state_dict(), 'best_linknet_model.pth')
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print("Model saved!")
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print("Training complete.")
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def predict_and_visualize(model, image_path, model_path, size):
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model.load_state_dict(torch.load(model_path, map_location=device))
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model.to(device)
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model.eval()
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# 加载和预处理单张图片
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img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
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original_size = (img.shape[1], img.shape[0]) # (width, height)
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img_resized = cv2.resize(img, (size, size))
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img_normalized = img_resized / 255.0
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img_tensor = torch.from_numpy(np.expand_dims(np.expand_dims(img_normalized, axis=0), axis=0)).float()
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img_tensor = img_tensor.to(device)
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with torch.no_grad():
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output = model(img_tensor)
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# 后处理
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pred_mask = output.cpu().numpy()[0, 0] # 从 (B, C, H, W) -> (H, W)
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pred_mask = (pred_mask > 0.5).astype(np.uint8) * 255 # 二值化
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pred_mask = cv2.resize(pred_mask, original_size) # 恢复到原始尺寸
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# 可视化
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plt.figure(figsize=(12, 6))
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plt.subplot(1, 3, 1)
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plt.title("Original Image")
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plt.imshow(cv2.cvtColor(img, cv2.COLOR_GRAY2RGB))
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plt.subplot(1, 3, 2)
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plt.title("Predicted Mask")
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plt.imshow(pred_mask, cmap='gray')
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# 将掩码叠加到原图
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overlay = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
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overlay[pred_mask == 255] = [255, 0, 0] # 红色
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plt.subplot(1, 3, 3)
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plt.title("Overlay")
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plt.imshow(overlay)
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plt.show()
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# --- 6. 主程序入口 ---
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if __name__ == '__main__':
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cfg = Config()
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# 准备数据集
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image_files = sorted([os.path.join(cfg.IMAGE_DIR, f) for f in os.listdir(cfg.IMAGE_DIR)])
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mask_files = sorted([os.path.join(cfg.MASK_DIR, f) for f in os.listdir(cfg.MASK_DIR)])
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# 划分训练集和验证集
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train_imgs, val_imgs, train_masks, val_masks = train_test_split(
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image_files, mask_files, test_size=cfg.TEST_SPLIT, random_state=42
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)
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train_dataset = WeldSeamDataset(train_imgs, train_masks, cfg.IMAGE_SIZE)
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val_dataset = WeldSeamDataset(val_imgs, val_masks, cfg.IMAGE_SIZE)
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train_loader = DataLoader(train_dataset, batch_size=cfg.BATCH_SIZE, shuffle=True)
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val_loader = DataLoader(val_dataset, batch_size=cfg.BATCH_SIZE, shuffle=False)
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# 初始化模型、损失函数和优化器
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model = LinkNet(num_classes=1)
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criterion = bce_dice_loss
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optimizer = optim.Adam(model.parameters(), lr=cfg.LEARNING_RATE)
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# --- 训练模型 ---
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# 如果你想开始训练，取消下面这行的注释
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train_model(model, train_loader, val_loader, criterion, optimizer, cfg.EPOCHS)
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# --- 使用训练好的模型进行预测 ---
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# 训练完成后，使用这个函数来测试
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# 确保 'best_linknet_bottom_model_line1.pth' 文件存在
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# print("\n--- Running Prediction ---")
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# # 随机选择一张验证集图片进行测试
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# test_image_path = random.choice(val_imgs)
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# print(f"Predicting on image: {test_image_path}")
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# predict_and_visualize(model, test_image_path, 'best_linknet_up_model_line1.pth', cfg.IMAGE_SIZE)
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