爱情来的太快就像龙卷风，深度学习目标检测直接使用YOLOv8进行训练龙卷风数据集实现可视化集评估训练自己的龙卷风数据集模型

数据集包含1类别

收集整理好的数据带标注520张
包含voc格式数据，转换好的yolo格式数据
如何啊你的数据集已经整理好，并且包含 1 个类别（假设为 “longjuanfeng”），总共有 520 张图像，并且数据集同时提供了 VOC 格式和 YOLO 格式的标注文件。那么咱们可以直接使用 YOLOv8 进行训练。

文章内所有代码仅供参考！

项目概述

我们将使用 YOLOv8 训练一个龙卷风检测模型。以下是详细的步骤：

1. 数据集准备：确保数据集格式正确。
2. 环境部署：安装必要的库。
3. 模型训练：使用 YOLOv8 训练模型。
4. 指标可视化：查看训练过程中的各项指标。
5. PyQt5 界面设计：创建一个简单的 GUI 应用来进行预测。

数据集结构

假设你的数据集已经准备好，并且是以 YOLO 格式存储的。以下是数据集的标准结构：

dataset/ ├── images/ │ ├── train/ │ │ ├── image1.jpg │ │ ├── image2.jpg │ │ └── ... │ └── val/ │ ├── image3.jpg │ ├── image4.jpg │ └── ... ├── labels/ │ ├── train/ │ │ ├── image1.txt │ │ ├── image2.txt │ │ └── ... │ └── val/ │ ├── image3.txt │ ├── image4.txt │ └── ... └── dataset.yaml

dataset.yaml内容如下：

train:./images/trainval:./images/valnc:1names:['tornado']

每个图像对应的标签文件是一个文本文件，每行表示一个边界框，格式为：

<class_id> <x_center> <y_center> <width> <height>

环境部署说明

首先，确保你已经安装了必要的库。以下是详细的环境部署步骤：

安装依赖

# 创建虚拟环境（可选）conda create-ntornado_detection_envpython=3.9conda activate tornado_detection_env# 安装PyTorchpipinstalltorch==1.9torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu111# 安装其他依赖pipinstallopencv-python pyqt5 ultralytics scikit-learn pandas matplotlib seaborn

模型训练权重和指标可视化展示

我们将使用 YOLOv8 进行训练，并在训练过程中记录各种指标，如 F1 曲线、准确率、召回率、损失曲线和混淆矩阵。

训练脚本train_yolov8.py

[<title="Training YOLOv8 on Tornado Detection Dataset">]fromultralyticsimportYOLOimportos# Define pathsdataset_path='path/to/dataset'weights_path='runs/train/exp/weights/best.pt'# Create dataset.yamlyaml_content=f""" train:{os.path.join(dataset_path,'images/train')}val:{os.path.join(dataset_path,'images/val')}nc: 1 names: ['tornado'] """withopen(os.path.join(dataset_path,'dataset.yaml'),'w')asf:f.write(yaml_content)# Train YOLOv8model=YOLO('yolov8n.pt')# Load a pretrained model (recommended for training)results=model.train(data=os.path.join(dataset_path,'dataset.yaml'),epochs=100,imgsz=640,save=True)# Save the best weightsbest_weights_path=os.path.join('runs','train','exp','weights','best.pt')shutil.copy(best_weights_path,weights_path)

请将path/to/dataset替换为实际的数据集路径。

指标可视化展示

我们将编写代码来可视化训练过程中的各项指标，包括 F1 曲线、准确率、召回率、损失曲线和混淆矩阵。

可视化脚本visualize_metrics.py

[<title="Visualizing Training Metrics for YOLOv8">]importosimportjsonimportmatplotlib.pyplotaspltimportnumpyasnpfromsklearn.metricsimportconfusion_matrix,ConfusionMatrixDisplay# Load metricsresults_dir='runs/train/exp'metrics_path=os.path.join(results_dir,'results.json')withopen(metrics_path,'r')asf:results=json.load(f)# Extract metricsloss=[entry['loss']forentryinresultsif'loss'inentry]precision=[entry['metrics/precision(m)']forentryinresultsif'metrics/precision(m)'inentry]recall=[entry['metrics/recall(m)']forentryinresultsif'metrics/recall(m)'inentry]mAP_05=[entry['metrics/mAP50(m)']forentryinresultsif'metrics/mAP50(m)'inentry]# Plot loss curveplt.figure(figsize=(15,5))plt.subplot(1,3,1)plt.plot(loss,label='Loss')plt.xlabel('Epochs')plt.ylabel('Loss')plt.title('Training Loss Curve')plt.legend()# Plot precision and recall curvesplt.subplot(1,3,2)plt.plot(precision,label='Precision')plt.plot(recall,label='Recall')plt.xlabel('Epochs')plt.ylabel('Score')plt.title('Precision and Recall Curves')plt.legend()# Plot mAP@0.5 curveplt.subplot(1,3,3)plt.plot(mAP_05,label='mAP@0.5')plt.xlabel('Epochs')plt.ylabel('mAP@0.5')plt.title('mAP@0.5 Curve')plt.legend()plt.tight_layout()plt.show()# Confusion matrix# Assuming you have predictions and true labels# For demonstration, let's create some dummy datanum_classes=1true_labels=np.random.randint(0,num_classes,size=100)# Random true labelspredictions=np.random.randint(0,num_classes,size=100)# Random predicted labelscm=confusion_matrix(true_labels,predictions,labels=list(range(num_classes)))labels=['tornado']disp=ConfusionMatrixDisplay(confusion_matrix=cm,display_labels=labels)disp.plot(cmap=plt.cm.Blues)plt.title('Confusion Matrix')plt.xticks(rotation=90)plt.yticks(rotation=0)plt.tight_layout()plt.show()

PyQt5设计的界面

我们将使用 PyQt5 设计一个简单的 GUI 界面来进行模型预测。

GUI代码gui_app.py

[<title="PyQt5 GUI for YOLOv8 Tornado Detection">]importsysimportcv2importnumpyasnpfromPyQt5.QtWidgetsimportQApplication,QMainWindow,QLabel,QPushButton,QVBoxLayout,QWidget,QFileDialog,QMessageBox,QProgressBar,QTextEditfromPyQt5.QtGuiimportQImage,QPixmapfromPyQt5.QtCoreimportQTimerfromultralyticsimportYOLOclassMainWindow(QMainWindow):def__init__(self):super().__init__()self.setWindowTitle("Tornado Detection")self.setGeometry(100,100,800,600)self.central_widget=QWidget(self)self.setCentralWidget(self.central_widget)self.layout=QVBoxLayout(self.central_widget)self.label_display=QLabel(self)self.layout.addWidget(self.label_display)self.button_layout=QHBoxLayout()self.pushButton_image=QPushButton("Open Image",self)self.pushButton_image.clicked.connect(self.open_image)self.button_layout.addWidget(self.pushButton_image)self.pushButton_folder=QPushButton("Open Folder",self)self.pushButton_folder.clicked.connect(self.open_folder)self.button_layout.addWidget(self.pushButton_folder)self.pushButton_video=QPushButton("Open Video",self)self.pushButton_video.clicked.connect(self.open_video)self.button_layout.addWidget(self.pushButton_video)self.pushButton_camera=QPushButton("Start Camera",self)self.pushButton_camera.clicked.connect(self.start_camera)self.button_layout.addWidget(self.pushButton_camera)self.pushButton_stop=QPushButton("Stop Camera",self)self.pushButton_stop.clicked.connect(self.stop_camera)self.button_layout.addWidget(self.pushButton_stop)self.layout.addLayout(self.button_layout)self.model=YOLO('runs/train/exp/weights/best.pt')self.cap=Noneself.timer=QTimer()self.timer.timeout.connect(self.process_frame)defload_image(self,file_name):img=cv2.imread(file_name)# BGRassertimgisnotNone,f'Image Not Found{file_name}'returnimgdefprocess_image(self,img):results=self.model(img,stream=True)forresultinresults:boxes=result.boxes.cpu().numpy()forboxinboxes:r=box.xyxy[0].astype(int)cls=int(box.cls[0])conf=box.conf[0]label=f'{self.model.names[cls]}{conf:.2f}'color=(0,255,0)# Greencv2.rectangle(img,r[:2],r[2:],color,2)cv2.putText(img,label,(r[0],r[1]-10),cv2.FONT_HERSHEY_SIMPLEX,0.9,color,2)rgb_image=cv2.cvtColor(img,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(800,600))defopen_image(self):options=QFileDialog.Options()file_name,_=QFileDialog.getOpenFileName(self,"QFileDialog.getOpenFileName()","","Images (*.jpeg *.jpg);;All Files (*)",options=options)iffile_name:img=self.load_image(file_name)self.process_image(img)defopen_folder(self):folder_name=QFileDialog.getExistingDirectory(self,"Select Folder")iffolder_name:forfilenameinos.listdir(folder_name):iffilename.lower().endswith(('.png','.jpg','.jpeg')):file_path=os.path.join(folder_name,filename)img=self.load_image(file_path)self.process_image(img)defopen_video(self):options=QFileDialog.Options()file_name,_=QFileDialog.getOpenFileName(self,"QFileDialog.getOpenFileName()","","Videos (*.mp4 *.avi);;All Files (*)",options=options)iffile_name:self.cap=cv2.VideoCapture(file_name)self.timer.start(30)# Process frame every 30 msdefstart_camera(self):self.cap=cv2.VideoCapture(0)self.timer.start(30)# Process frame every 30 msdefstop_camera(self):ifself.capisnotNone:self.cap.release()self.cap=Noneself.timer.stop()defprocess_frame(self):ifself.capisnotNone:ret,frame=self.cap.read()ifret:self.process_image(frame)else:self.cap.release()self.cap=Noneself.timer.stop()if__name__=="__main__":app=QApplication(sys.argv)window=MainWindow()window.show()sys.exit(app.exec_())

辅助工具文件utils.py

这个文件可以用来存放一些辅助函数，比如保存结果等。

[<title="Utility Functions for Tornado Detection">]importcv2importosdefsave_results(image,detections,output_dir,filename):fordetindetections:r=det['bbox']cls=det['class']conf=det['confidence']label=f'{cls}{conf:.2f}'color=(0,255,0)# Greencv2.rectangle(image,(int(r[0]),int(r[1])),(int(r[2]),int(r[3])),color,2)cv2.putText(image,label,(int(r[0]),int(r[1])-10),cv2.FONT_HERSHEY_SIMPLEX,0.9,color,2)output_path=os.path.join(output_dir,filename)cv2.imwrite(output_path,image)

运行效果展示

假设你已经有了运行效果的图像，可以在README.md中添加这些图像以供参考。

# Tornado Detection System ## Overview This project provides a deep learning-based system for detecting tornados in images. The system can identify tornados in images, folders, videos, and live camera feeds. ## Environment Setup - Software: PyCharm + Anaconda - Environment: Python=3.9, OpenCV-Python, PyQt5, Torch=1.9 ## Features - Detects tornados. - Supports detection on images, folders, videos, and live camera feed. - Batch processing of images. - Real-time display of detected tornados with confidence scores and bounding boxes. - Saving detection results. ## Usage 1. Run the program. 2. Choose an option to detect tornados in images, folders, videos, or via the camera. ## Screenshots ![Example Screenshot](data/screenshots/example_screenshot.png)

总结

通过以上步骤，我们可以构建一个完整的基于 YOLOv8 的龙卷风检测系统，包括数据集准备、环境部署、模型训练、指标可视化展示和 PyQt5 界面设计。以下是所有相关的代码文件：

1. 训练脚本(train_yolov8.py)
2. 指标可视化脚本(visualize_metrics.py)
3. GUI应用代码(gui_app.py)
4. 辅助工具文件(utils.py)
5. 文档(README.md)