TransUNet遥感河流分割 河流分割数据集 基于TransUNet的遥感图像 河流智能分割系统 gui界面

TransUNet遥感河流分割项目 pytorch模型

实现原理：

*项目使用数据集，有训练样本共82个，测试样本8个，原图是三通道彩色图像，label是二值化(0与255)掩模图像

代码运行环境
pytorch1.10.0 cpu or gpu都可
python3.8，可用pip install -r命令安装

基于TransUNet的遥感河流分割项目，从环境搭建、数据准备、模型训练到GUI界面开发逐步进行。

代码示例

一、环境搭建

1. 安装依赖项

确保你的系统已安装Python 3.8，并使用requirements.txt文件安装必要的依赖项。

pipinstall-rrequirements.txt

2. 确认PyTorch版本

确保你安装了正确的PyTorch版本（1.10.0）。

python-c"import torch; print(torch.__version__)"

二、数据准备

假设你已经有82个训练样本和8个测试样本的数据集，并且这些图像已经按照标准格式组织。

数据集结构

确保数据集按如下结构组织：

river_segmentation_dataset/ ├── images/ │ ├── train/ │ └── test/ └── labels/ ├── train/ └── test/

数据预处理

编写一个简单的脚本来加载和预处理数据。

importosimportcv2importnumpyasnpfromtorch.utils.dataimportDataset,DataLoaderfromtorchvisionimporttransformsclassRiverDataset(Dataset):def__init__(self,image_dir,label_dir,transform=None):self.image_dir=image_dir self.label_dir=label_dir self.transform=transform self.images=os.listdir(image_dir)def__len__(self):returnlen(self.images)def__getitem__(self,idx):img_path=os.path.join(self.image_dir,self.images[idx])label_path=os.path.join(self.label_dir,self.images[idx].replace('.jpg','.png'))image=cv2.imread(img_path)label=cv2.imread(label_path,cv2.IMREAD_GRAYSCALE)label=(label>0).astype(np.float32)# Convert to binary maskifself.transform:image=self.transform(image)label=self.transform(label)returnimage,label# Define transformationstransform=transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean=[0.485,0.456,0.406],std=[0.229,0.224,0.225])])# Create datasets and dataloaderstrain_dataset=RiverDataset('river_segmentation_dataset/images/train','river_segmentation_dataset/labels/train',transform=transform)test_dataset=RiverDataset('river_segmentation_dataset/images/test','river_segmentation_dataset/labels/test',transform=transform)train_loader=DataLoader(train_dataset,batch_size=4,shuffle=True)test_loader=DataLoader(test_dataset,batch_size=4,shuffle=False)

三、模型训练

TransUNet模型定义

importtorchimporttorch.nnasnnimporttorch.nn.functionalasFclassTransUNet(nn.Module):def__init__(self):super(TransUNet,self).__init__()# Define your TransUNet architecture herepassdefforward(self,x):# Implement the forward passpass# Initialize the modelmodel=TransUNet()iftorch.cuda.is_available():model=model.cuda()# Loss function and optimizercriterion=nn.BCEWithLogitsLoss()optimizer=torch.optim.Adam(model.parameters(),lr=0.001)# Training loopnum_epochs=100forepochinrange(num_epochs):fori,(images,labels)inenumerate(train_loader):iftorch.cuda.is_available():images=images.cuda()labels=labels.cuda()outputs=model(images)loss=criterion(outputs,labels.unsqueeze(1))optimizer.zero_grad()loss.backward()optimizer.step()if(i+1)%10==0:print(f'Epoch [{epoch+1}/{num_epochs}], Step [{i+1}/{len(train_loader)}], Loss:{loss.item():.4f}')

四、构建GUI界面

使用PyQt5创建GUI界面

importsysfromPyQt5.QtWidgetsimportQApplication,QMainWindow,QLabel,QPushButton,QFileDialog,QVBoxLayout,QWidgetfromPyQt5.QtGuiimportQPixmapimportcv2importnumpyasnpimporttorchclassMainWindow(QMainWindow):def__init__(self):super().__init__()self.setWindowTitle("Remote Sensing Image River Segmentation")self.setGeometry(100,100,800,600)self.central_widget=QWidget()self.setCentralWidget(self.central_widget)self.layout=QVBoxLayout(self.central_widget)self.upload_button=QPushButton("Upload Image",self)self.upload_button.clicked.connect(self.upload_image)self.layout.addWidget(self.upload_button)self.image_label=QLabel(self)self.layout.addWidget(self.image_label)self.segment_button=QPushButton("Perform Segmentation",self)self.segment_button.clicked.connect(self.perform_segmentation)self.layout.addWidget(self.segment_button)self.result_label=QLabel(self)self.layout.addWidget(self.result_label)self.model=torch.load('path_to_your_trained_model.pth')# Load your trained modeldefupload_image(self):fname,_=QFileDialog.getOpenFileName(self,'Open file','',"Image files (*.jpg *.png)")iffname:self.image_path=fname pixmap=QPixmap(fname)self.image_label.setPixmap(pixmap)defperform_segmentation(self):image=cv2.imread(self.image_path)image=cv2.resize(image,(256,256))# Resize to match input size of your modelimage=image/255.0# Normalizeimage=np.transpose(image,(2,0,1))# Change order from HWC to CHWimage=torch.tensor(image,dtype=torch.float32).unsqueeze(0)iftorch.cuda.is_available():image=image.cuda()self.model=self.model.cuda()withtorch.no_grad():output=self.model(image)output=torch.sigmoid(output)output=output.cpu().numpy()[0][0]# Get the first channel of the first batch itemoutput=(output>0.5).astype(np.uint8)*255# Threshold and convert to binary maskresult=cv2.cvtColor(output,cv2.COLOR_GRAY2BGR)result=cv2.addWeighted(cv2.imread(self.image_path),0.7,result,0.3,0)# Overlay on original imageheight,width,channel=result.shape bytes_per_line=3*width q_img=QImage(result.data,width,height,bytes_per_line,QImage.Format_RGB888).rgbSwapped()self.result_label.setPixmap(QPixmap.fromImage(q_img))if__name__=="__main__":app=QApplication(sys.argv)window=MainWindow()window.show()sys.exit(app.exec_())

运行项目

保存上述代码为gui.py，然后运行它：

python gui.py

启动一个窗口应用程序，允许用户上传遥感图像并进行河流分割。记得在实际部署前对模型进行充分的测试与调优。