MRI脊椎分割数据集 脊椎分割识别数据集 脊椎分割项目解决方法如何 标签分别2460张

MRI 脊椎分割数据集/脊椎分割项目解决

包含脊椎分割数据集:
原图，标签分别2460张

代码仅供参考

MRI 脊椎分割数据集/脊椎分割项目解决
包含脊椎分割数据集:
原图，标签分别2460张

完整的基于YOLOv5的MRI脊椎分割项目的实现。我们将涵盖以下内容：

1. 项目结构
2. 依赖项安装
3. 数据准备
4. 模型训练
5. 评估模型
6. 推理界面

完整项目结构

spine_segmentation/ ├── main.py ├── train.py ├── evaluate.py ├── infer.py ├── ui_files/ │ ├── infer_ui.ui │ ├── infer_ui.qrc │ └── infer_ui_rc.py ├── datasets/ │ ├── spine/ │ │ ├── images/ │ │ ├── labels/ │ │ ├── train.txt │ │ └── val.txt ├── best_spine.pt ├── requirements.txt └── data.yaml

文件内容

requirements.txt

opencv-python==4.5.3.56 torch==1.9.0+cu111 PyQt5==5.15.4 labelme shutil matplotlib scikit-image numpy pandas

data.yaml

train:./datasets/spine/images/trainval:./datasets/spine/images/valnc:1names:['vertebra']

train.py

importtorchfromyolov5importtrain# 设置随机种子以保证可重复性torch.manual_seed(42)# 定义数据集路径dataset_config='data.yaml'# 训练模型results=train.run(imgsz=640,batch=16,epochs=50,data=dataset_config,weights='yolov5s.pt',name='spine',project='runs/train')# 打印训练结果print(results)

evaluate.py

fromyolov5importval# 初始化YOLOv5模型model_path='runs/train/spine/weights/best.pt'# 评估模型results=val.run(data='data.yaml',weights=model_path,imgsz=640,task='val')# 打印评估结果print(results)

infer.py

importsysimportcv2importnumpyasnpfromPyQt5.QtWidgetsimportQApplication,QMainWindow,QFileDialog,QMessageBox,QLabel,QPushButton,QVBoxLayout,QWidget,QProgressBarfromPyQt5.QtGuiimportQImage,QPixmapfromPyQt5.QtCoreimportQTimerimporttorchfrompathlibimportPathfromyolov5.utils.generalimportnon_max_suppression,scale_coordsfromyolov5.models.experimentalimportattempt_loadfromyolov5.utils.torch_utilsimportselect_deviceclassMainWindow(QMainWindow):def__init__(self):super(MainWindow,self).__init__()self.setWindowTitle("MRI 脊椎分割")self.setGeometry(100,100,800,600)# 初始化YOLOv5模型self.device=select_device('')self.model=attempt_load('runs/train/spine/weights/best.pt',map_location=self.device)self.stride=int(self.model.stride.max())# model strideself.imgsz=640# 创建界面元素self.label_display=QLabel(self)self.label_display.setAlignment(Qt.AlignCenter)self.button_select_image=QPushButton("选择图片",self)self.button_select_folder=QPushButton("选择文件夹",self)self.button_select_video=QPushButton("选择视频",self)self.button_start_camera=QPushButton("开始摄像头",self)self.button_stop_camera=QPushButton("停止摄像头",self)self.progress_bar=QProgressBar(self)self.progress_bar.setVisible(False)layout=QVBoxLayout()layout.addWidget(self.label_display)layout.addWidget(self.button_select_image)layout.addWidget(self.button_select_folder)layout.addWidget(self.button_select_video)layout.addWidget(self.button_start_camera)layout.addWidget(self.button_stop_camera)layout.addWidget(self.progress_bar)container=QWidget()container.setLayout(layout)self.setCentralWidget(container)self.button_select_image.clicked.connect(self.select_image)self.button_select_folder.clicked.connect(self.select_folder)self.button_select_video.clicked.connect(self.select_video)self.button_start_camera.clicked.connect(self.start_camera)self.button_stop_camera.clicked.connect(self.stop_camera)self.timer=QTimer()self.timer.timeout.connect(self.update_frame)self.cap=Noneself.results=[]defload_image(self,image_path):frame=cv2.imread(image_path,cv2.IMREAD_GRAYSCALE)frame=cv2.cvtColor(frame,cv2.COLOR_GRAY2BGR)results=self.detect(frame)annotated_frame=self.draw_annotations(frame,results)returnannotated_framedefdetect(self,img0):img=letterbox(img0,new_shape=self.imgsz,stride=self.stride)[0]img=img[:,:,::-1].transpose(2,0,1)# BGR to RGB, to 3x416x416img=np.ascontiguousarray(img)img=torch.from_numpy(img).to(self.device)img=img.float()# uint8 to fp16/32img/=255.0# 0 - 255 to 0.0 - 1.0ifimg.ndimension()==3:img=img.unsqueeze(0)pred=self.model(img,augment=False)[0]pred=non_max_suppression(pred,0.25,0.45,classes=None,agnostic=False)returnpreddefdraw_annotations(self,frame,results):fordetinresults:iflen(det):det[:,:4]=scale_coords(frame.shape[2:],det[:,:4],frame.shape).round()for*xyxy,conf,clsinreversed(det):label=f'{self.model.names[int(cls)]}{conf:.2f}'plot_one_box(xyxy,frame,label=label,color=(0,255,0),line_thickness=3)returnframedefdisplay_image(self,frame):rgb_image=cv2.cvtColor(frame,cv2.COLOR_BGR2RGB)h,w,ch=rgb_image.shape bytes_per_line=ch*w qt_image=QImage(rgb_image.data,w,h,bytes_per_line,QImage.Format_RGB888)pixmap=QPixmap.fromImage(qt_image)self.label_display.setPixmap(pixmap.scaled(self.label_display.width(),self.label_display.height()))defselect_image(self):options=QFileDialog.Options()file_path,_=QFileDialog.getOpenFileName(self,"选择图片","","图片 (*.jpg *.jpeg *.png *.tif);;所有文件 (*)",options=options)iffile_path:annotated_frame=self.load_image(file_path)self.display_image(annotated_frame)self.results.append((file_path,annotated_frame))defselect_folder(self):folder_path=QFileDialog.getExistingDirectory(self,"选择文件夹")iffolder_path:files=[os.path.join(folder_path,f)forfinos.listdir(folder_path)iff.endswith(('.jpg','.jpeg','.png','.tif'))]total_files=len(files)self.progress_bar.setMaximum(total_files)self.progress_bar.setValue(0)self.progress_bar.setVisible(True)fori,file_pathinenumerate(files):annotated_frame=self.load_image(file_path)self.display_image(annotated_frame)self.results.append((file_path,annotated_frame))self.progress_bar.setValue(i+1)self.progress_bar.setVisible(False)defselect_video(self):options=QFileDialog.Options()file_path,_=QFileDialog.getOpenFileName(self,"选择视频","","视频 (*.mp4 *.avi);;所有文件 (*)",options=options)iffile_path:self.process_video(file_path)defprocess_video(self,video_path):self.cap=cv2.VideoCapture(video_path)whileself.cap.isOpened():ret,frame=self.cap.read()ifnotret:breakresults=self.detect(frame)annotated_frame=self.draw_annotations(frame,results)self.display_image(annotated_frame)self.results.append((video_path,annotated_frame))ifcv2.waitKey(1)&0xFF==ord('q'):breakself.cap.release()defstart_camera(self):self.cap=cv2.VideoCapture(0)self.timer.start(30)defstop_camera(self):self.timer.stop()ifself.capisnotNone:self.cap.release()self.label_display.clear()defupdate_frame(self):ret,frame=self.cap.read()ifnotret:returnresults=self.detect(frame)annotated_frame=self.draw_annotations(frame,results)self.display_image(annotated_frame)self.results.append(('camera',annotated_frame))defletterbox(img,new_shape=(640,640),color=(114,114,114),auto=True,scaleFill=False,scaleup=True,stride=32):shape=img.shape[:2]# current shape [height, width]r=min(new_shape[0]/shape[0],new_shape[1]/shape[1])ifnotscaleup:# only scale down, do not scale up (for better test mAP)r=min(r,1.0)ratio=r,r# width, height ratiosnew_unpad=int(round(shape[1]*r)),int(round(shape[0]*r))dw,dh=new_shape[1]-new_unpad[0],new_shape[0]-new_unpad[1]# wh paddingifauto:# minimum rectangledw,dh=np.mod(dw,stride),np.mod(dh,stride)# wh paddingelifscaleFill:# stretchdw,dh=0.0,0.0new_unpad=(new_shape[1],new_shape[0])ratio=new_shape[1]/shape[1],new_shape[0]/shape[0]# width, height ratiosdw/=2# divide padding into 2 sidesdh/=2ifshape[::-1]!=new_unpad:# resizeimg=cv2.resize(img,new_unpad,interpolation=cv2.INTER_LINEAR)top,bottom=int(round(dh-0.1)),int(round(dh+0.1))left,right=int(round(dw-0.1)),int(round(dw+0.1))img=cv2.copyMakeBorder(img,top,bottom,left,right,cv2.BORDER_CONSTANT,value=color)# add borderreturnimg,ratio,(dw,dh)defplot_one_box(x,img,color=None,label=None,line_thickness=None):tl=line_thicknessorround(0.002*(img.shape[0]+img.shape[1])/2)+1# line/font thicknesscolor=coloror[random.randint(0,255)for_inrange(3)]c1,c2=(int(x[0]),int(x[1])),(int(x[2]),int(x[3]))cv2.rectangle(img,c1,c2,color,thickness=tl,lineType=cv2.LINE_AA)iflabel:tf=max(tl-1,1)# font thicknesst_size=cv2.getTextSize(label,0,fontScale=tl/3,thickness=tf)[0]c2=c1[0]+t_size[0],c1[1]-t_size[1]-3cv2.rectangle(img,c1,c2,color,-1,cv2.LINE_AA)# filledcv2.putText(img,label,(c1[0],c1[1]-2),0,tl/3,[225,255,255],thickness=tf,lineType=cv2.LINE_AA)if__name__=="__main__":app=QApplication(sys.argv)window=MainWindow()window.show()sys.exit(app.exec_())

运行步骤总结

1. 克隆项目仓库（如果有的话）：

   gitclone https://github.com/yourusername/spine_segmentation.gitcdspine_segmentation

2. 安装依赖项：

   conda create--namespine_envpython=3.8conda activate spine_env pipinstall-rrequirements.txt

3. 下载YOLOv5代码：

   gitclone https://github.com/ultralytics/yolov5.gitcdyolov5 pipinstall-rrequirements.txtcd..

4. 准备数据集：

   • 将你的MRI脊椎图像放入datasets/spine/images目录。
   • 将对应的标注文件（假设为YOLO格式的TXT文件）放入datasets/spine/labels目录。
   • 使用脚本划分数据集为训练集和验证集，并生成train.txt和val.txt文件。

5. 训练模型：

   python train.py

6. 评估模型：

   python evaluate.py

7. 运行推理界面：

   python infer.py

操作界面

• 选择图片进行检测：点击“选择图片”按钮，选择一张图片进行检测。
• 选择文件夹批量检测：点击“选择文件夹”按钮，选择一个包含多张图片的文件夹进行批量检测。
• 选择视频进行检测：点击“选择视频”按钮，选择一个视频文件进行检测。
• 摄像头检测：点击“开始摄像头”按钮，使用摄像头进行实时检测；点击“停止摄像头”按钮停止检测。

详细解释

requirements.txt

列出项目所需的所有Python包及其版本。

data.yaml

配置数据集路径和类别信息，用于YOLOv5模型训练。

train.py

加载预训练的YOLOv5s模型并使用自定义数据集进行训练。训练完成后打印训练结果。

evaluate.py

加载训练好的YOLOv5模型并对验证集进行评估，打印评估结果。

infer.py

创建一个GUI应用程序，支持选择图片、文件夹、视频或使用摄像头进行实时检测，并显示检测结果。