爱情来的太快就像龙卷风

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！

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：

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 1234567891011121314151617181920

dataset.yaml 内容如下：

train: ./images/train val: ./images/val nc: 1 names: ['tornado'] 12345

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> 1 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：

# 创建虚拟环境（可选） conda create -n tornado_detection_env python=3.9 conda activate tornado_detection_env # 安装PyTorch pip install torch==1.9 torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu111 # 安装其他依赖 pip install opencv-python pyqt5 ultralytics scikit-learn pandas matplotlib seaborn 123456789 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。

[<title="Training YOLOv8 on Tornado Detection Dataset">] from ultralytics import YOLO import os # Define paths dataset_path = 'path/to/dataset' weights_path = 'runs/train/exp/weights/best.pt' # Create dataset.yaml yaml_content = f""" train: {os.path.join(dataset_path, 'images/train')} val: {os.path.join(dataset_path, 'images/val')} nc: 1 names: ['tornado'] """ with open(os.path.join(dataset_path, 'dataset.yaml'), 'w') as f: f.write(yaml_content) # Train YOLOv8 model = 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 weights best_weights_path = os.path.join('runs', 'train', 'exp', 'weights', 'best.pt') shutil.copy(best_weights_path, weights_path) 123456789101112131415161718192021222324252627

请将 path/to/dataset 替换为实际的数据集路径。

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。

[<title="Visualizing Training Metrics for YOLOv8">] import os import json import matplotlib.pyplot as plt import numpy as np from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay # Load metrics results_dir = 'runs/train/exp' metrics_path = os.path.join(results_dir, 'results.json') with open(metrics_path, 'r') as f: results = json.load(f) # Extract metrics loss = [entry['loss'] for entry in results if 'loss' in entry] precision = [entry['metrics/precision(m)'] for entry in results if 'metrics/precision(m)' in entry] recall = [entry['metrics/recall(m)'] for entry in results if 'metrics/recall(m)' in entry] mAP_05 = [entry['metrics/mAP50(m)'] for entry in results if 'metrics/mAP50(m)' in entry] # Plot loss curve plt.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 curves plt.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 curve plt.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 data num_classes = 1 true_labels = np.random.randint(0, num_classes, size=100) # Random true labels predictions = np.random.randint(0, num_classes, size=100) # Random predicted labels cm = 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() 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566 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。

[<title="PyQt5 GUI for YOLOv8 Tornado Detection">] import sys import cv2 import numpy as np from PyQt5.QtWidgets import QApplication, QMainWindow, QLabel, QPushButton, QVBoxLayout, QWidget, QFileDialog, QMessageBox, QProgressBar, QTextEdit from PyQt5.QtGui import QImage, QPixmap from PyQt5.QtCore import QTimer from ultralytics import YOLO class MainWindow(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 = None self.timer = QTimer() self.timer.timeout.connect(self.process_frame) def load_image(self, file_name): img = cv2.imread(file_name) # BGR assert img is not None, f'Image Not Found {file_name}' return img def process_image(self, img): results = self.model(img, stream=True) for result in results: boxes = result.boxes.cpu().numpy() for box in boxes: 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) # Green cv2.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)) def open_image(self): options = QFileDialog.Options() file_name, _ = QFileDialog.getOpenFileName(self, "QFileDialog.getOpenFileName()", "", "Images (*.jpeg *.jpg);;All Files (*)", options=options) if file_name: img = self.load_image(file_name) self.process_image(img) def open_folder(self): folder_name = QFileDialog.getExistingDirectory(self, "Select Folder") if folder_name: for filename in os.listdir(folder_name): if filename.lower().endswith(('.png', '.jpg', '.jpeg')): file_path = os.path.join(folder_name, filename) img = self.load_image(file_path) self.process_image(img) def open_video(self): options = QFileDialog.Options() file_name, _ = QFileDialog.getOpenFileName(self, "QFileDialog.getOpenFileName()", "", "Videos (*.mp4 *.avi);;All Files (*)", options=options) if file_name: self.cap = cv2.VideoCapture(file_name) self.timer.start(30) # Process frame every 30 ms def start_camera(self): self.cap = cv2.VideoCapture(0) self.timer.start(30) # Process frame every 30 ms def stop_camera(self): if self.cap is not None: self.cap.release() self.cap = None self.timer.stop() def process_frame(self): if self.cap is not None: ret, frame = self.cap.read() if ret: self.process_image(frame) else: self.cap.release() self.cap = None self.timer.stop() if __name__ == "__main__": app = QApplication(sys.argv) window = MainWindow() window.show() sys.exit(app.exec_()) 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126 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">] import cv2 import os def save_results(image, detections, output_dir, filename): for det in detections: r = det['bbox'] cls = det['class'] conf = det['confidence'] label = f'{cls} {conf:.2f}' color = (0, 255, 0) # Green cv2.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) 12345678910111213141516 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  12345678910111213141516171819202122 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：

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