创建一个基于YOLOv8+PyQt界面的驾驶员疲劳驾驶检测系统 实现对驾驶员疲劳状态的打哈欠检测，头部下垂 疲劳眼睛检测识别

如何使用Yolov8创建一个基于YOLOv8的驾驶员疲劳驾驶检测系统

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疲劳驾驶检测数据集。yolo标签。标签类别序号为0，1，2，3。注意编号从0开始计数，共4类。

创建一个基于YOLOv8的驾驶员疲劳驾驶检测系统，并且带有PyQt界面，我们可以按照以下步骤进行。请注意，由于YOLOv8在撰写此回答时并不是一个实际发布的模型版本，我们将基于YOLOv5的流程和假设YOLOv8有类似的API进行说明。请根据实际情况调整代码以适配YOLOv8的具体实现。

文章及代码仅供参考。

1. 数据集准备

首先，确保你的数据集已经准备好，并按照YOLO格式标注（即每行代表一个对象，格式为class_id x_center y_center width height，所有值均为相对值）。对于疲劳驾驶检测，假设我们有4种类别：

• 0: 疲劳的眼睛
• 1: 打哈欠
• 2: 头部下垂
• 3: 正常状态

2. 安装依赖

安装必要的依赖库：

pip install torch torchvision torchaudio
git clone https://github.com/ultralytics/yolov5  # 假设YOLOv8有相似的仓库结构
cd yolov5
pip install -r requirements.txt

3. 创建PyQt界面

创建一个简单的PyQt界面用于展示检测结果、选择图片或视频文件以及启动摄像头实时检测。

import sys
from PyQt5.QtWidgets import QApplication, QMainWindow, QLabel, QVBoxLayout, QWidget, QPushButton, QFileDialog
from PyQt5.QtGui import QImage, QPixmap
from PyQt5.QtCore import QTimer
import cv2
import torch
from ultralytics import YOLO  # 假设YOLOv8使用类似接口class MainWindow(QMainWindow):def __init__(self):super().__init__()self.setWindowTitle("Driver Fatigue Detection")self.setGeometry(100, 100, 640, 480)self.initUI()self.model = YOLO('yolov8n.pt')  # 使用适合的预训练模型或自定义模型def initUI(self):self.image_label = QLabel(self)self.layout = QVBoxLayout()self.layout.addWidget(self.image_label)self.detect_image_button = QPushButton("Detect Image", self)self.detect_image_button.clicked.connect(self.detect_image)self.layout.addWidget(self.detect_image_button)self.detect_video_button = QPushButton("Detect Video", self)self.detect_video_button.clicked.connect(self.detect_video)self.layout.addWidget(self.detect_video_button)self.camera_button = QPushButton("Camera", self)self.camera_button.clicked.connect(self.start_camera)self.layout.addWidget(self.camera_button)central_widget = QWidget()central_widget.setLayout(self.layout)self.setCentralWidget(central_widget)def detect_image(self):file_path, _ = QFileDialog.getOpenFileName(self, "Select Image", "", "Images (*.png *.xpm *.jpg *.bmp)")if file_path:img = cv2.imread(file_path)results = self.model(img)annotated_img = results[0].plot()self.show_image(annotated_img)def detect_video(self):file_path, _ = QFileDialog.getOpenFileName(self, "Select Video", "", "Videos (*.mp4 *.avi *.mov)")if file_path:cap = cv2.VideoCapture(file_path)while True:ret, frame = cap.read()if not ret: breakresults = self.model(frame)annotated_frame = results[0].plot()self.show_image(annotated_frame)if cv2.waitKey(1) & 0xFF == ord('q'): breakcap.release()def start_camera(self):self.cap = cv2.VideoCapture(0)self.timer = QTimer(self)self.timer.timeout.connect(self.update_frame)self.timer.start(30)def update_frame(self):ret, frame = self.cap.read()if ret:results = self.model(frame)annotated_frame = results[0].plot()self.show_image(annotated_frame)def show_image(self, img):h, w, ch = img.shapebytes_per_line = ch * wqimg = QImage(img.data, w, h, bytes_per_line, QImage.Format_BGR888)pixmap = QPixmap.fromImage(qimg)self.image_label.setPixmap(pixmap)if __name__ == "__main__":app = QApplication(sys.argv)window = MainWindow()window.show()sys.exit(app.exec_())

4. 模型训练

如果你需要重新训练模型，可以参考前面提供的关于YOLOv5训练的示例代码进行调整。记得修改data.yaml文件中的类别数量和名称以匹配你的数据集。

为了创建一个基于YOLOv8的驾驶员疲劳驾驶检测系统，并且带有PyQt界面，我们需要详细说明模型训练和界面开发的步骤。以下是详细的代码和解释。

1. 数据集准备

确保你的数据集已经准备好，并按照YOLO格式标注（即每行代表一个对象，格式为class_id x_center y_center width height，所有值均为相对值）。对于疲劳驾驶检测，假设我们有4种类别：

• 0: 疲劳的眼睛
• 1: 打哈欠
• 2: 头部下垂
• 3: 正常状态

2. 模型训练

数据集配置文件 (data.yaml)

train: ./images/train
val: ./images/val
test: ./images/testnc: 4  # number of classes
names: ['open_eye', 'yawn', 'head_down', 'normal']

训练脚本 (train.py)

import torch
from ultralytics import YOLO# Load the model
model = YOLO('yolov8n.yaml')  # or yolov8s, yolov8x, custom# Train the model
results = model.train(data='path/to/data.yaml',epochs=100,imgsz=640,batch=16,name='driver_fatigue'
)

3. PyQt界面开发

创建一个简单的PyQt界面用于展示检测结果、选择图片或视频文件以及启动摄像头实时检测。

主程序 (MainProgram.py)

import sys
from PyQt5.QtWidgets import QApplication, QMainWindow, QLabel, QVBoxLayout, QWidget, QPushButton, QFileDialog
from PyQt5.QtGui import QImage, QPixmap
from PyQt5.QtCore import QTimer
import cv2
import torch
from ultralytics import YOLOclass MainWindow(QMainWindow):def __init__(self):super().__init__()self.setWindowTitle("Driver Fatigue Detection")self.setGeometry(100, 100, 640, 480)self.initUI()self.model = YOLO('runs/train/driver_fatigue/weights/best.pt')def initUI(self):self.image_label = QLabel(self)self.layout = QVBoxLayout()self.layout.addWidget(self.image_label)self.detect_image_button = QPushButton("Detect Image", self)self.detect_image_button.clicked.connect(self.detect_image)self.layout.addWidget(self.detect_image_button)self.detect_video_button = QPushButton("Detect Video", self)self.detect_video_button.clicked.connect(self.detect_video)self.layout.addWidget(self.detect_video_button)self.camera_button = QPushButton("Camera", self)self.camera_button.clicked.connect(self.start_camera)self.layout.addWidget(self.camera_button)central_widget = QWidget()central_widget.setLayout(self.layout)self.setCentralWidget(central_widget)def detect_image(self):file_path, _ = QFileDialog.getOpenFileName(self, "Select Image", "", "Images (*.png *.xpm *.jpg *.bmp)")if file_path:img = cv2.imread(file_path)results = self.model(img)annotated_img = results[0].plot()self.show_image(annotated_img)def detect_video(self):file_path, _ = QFileDialog.getOpenFileName(self, "Select Video", "", "Videos (*.mp4 *.avi *.mov)")if file_path:cap = cv2.VideoCapture(file_path)while True:ret, frame = cap.read()if not ret: breakresults = self.model(frame)annotated_frame = results[0].plot()self.show_image(annotated_frame)if cv2.waitKey(1) & 0xFF == ord('q'): breakcap.release()def start_camera(self):self.cap = cv2.VideoCapture(0)self.timer = QTimer(self)self.timer.timeout.connect(self.update_frame)self.timer.start(30)def update_frame(self):ret, frame = self.cap.read()if ret:results = self.model(frame)annotated_frame = results[0].plot()self.show_image(annotated_frame)def show_image(self, img):h, w, ch = img.shapebytes_per_line = ch * wqimg = QImage(img.data, w, h, bytes_per_line, QImage.Format_BGR888)pixmap = QPixmap.fromImage(qimg)self.image_label.setPixmap(pixmap)if __name__ == "__main__":app = QApplication(sys.argv)window = MainWindow()window.show()sys.exit(app.exec_())

4. 运行项目

1. 安装依赖：

   pip install torch torchvision torchaudio
   git clone https://github.com/ultralytics/yolov5  # 假设YOLOv8有相似的仓库结构
   cd yolov5
   pip install -r requirements.txt
   

2. 运行训练脚本：

   python train.py
   

3. 运行主程序：

   python MainProgram.py
   

5. 关键代码解释

数据集配置文件 (data.yaml)

• train, val, test: 数据集路径。
• nc: 类别数量。
• names: 类别名称。

训练脚本 (train.py)

• YOLO('yolov8n.yaml'): 加载YOLOv8模型。
• model.train(): 开始训练模型。

主程序 (MainProgram.py)

• YOLO('runs/train/driver_fatigue/weights/best.pt'): 加载训练好的模型。
• detect_image(), detect_video(), start_camera(): 分别处理图像、视频和摄像头检测。
• show_image(): 显示检测结果。

通过以上步骤，tongxue 你就构建一个完整的驾驶员疲劳驾驶检测系统，并带有PyQt界面进行交互。