如何使用Yolov8創建一個基于YOLOv8的駕駛員疲勞駕駛檢測系統

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疲勞駕駛檢測數據集。yolo標簽。標簽類別序號為0，1，2，3。注意編號從0開始計數，共4類。

創建一個基于YOLOv8的駕駛員疲勞駕駛檢測系統，并且帶有PyQt界面，我們可以按照以下步驟進行。請注意，由于YOLOv8在撰寫此回答時并不是一個實際發布的模型版本，我們將基于YOLOv5的流程和假設YOLOv8有類似的API進行說明。請根據實際情況調整代碼以適配YOLOv8的具體實現。

文章及代碼僅供參考。

1. 數據集準備

首先，確保你的數據集已經準備好，并按照YOLO格式標注（即每行代表一個對象，格式為class_id x_center y_center width height，所有值均為相對值）。對于疲勞駕駛檢測，假設我們有4種類別：

• 0: 疲勞的眼睛
• 1: 打哈欠
• 2: 頭部下垂
• 3: 正常狀態

2. 安裝依賴

安裝必要的依賴庫：

pip install torch torchvision torchaudio
git clone https://github.com/ultralytics/yolov5  # 假設YOLOv8有相似的倉庫結構
cd yolov5
pip install -r requirements.txt

3. 創建PyQt界面

創建一個簡單的PyQt界面用于展示檢測結果、選擇圖片或視頻文件以及啟動攝像頭實時檢測。

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  # 假設YOLOv8使用類似接口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')  # 使用適合的預訓練模型或自定義模型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. 模型訓練

如果你需要重新訓練模型，可以參考前面提供的關于YOLOv5訓練的示例代碼進行調整。記得修改data.yaml文件中的類別數量和名稱以匹配你的數據集。

為了創建一個基于YOLOv8的駕駛員疲勞駕駛檢測系統，并且帶有PyQt界面，我們需要詳細說明模型訓練和界面開發的步驟。以下是詳細的代碼和解釋。

1. 數據集準備

確保你的數據集已經準備好，并按照YOLO格式標注（即每行代表一個對象，格式為class_id x_center y_center width height，所有值均為相對值）。對于疲勞駕駛檢測，假設我們有4種類別：

• 0: 疲勞的眼睛
• 1: 打哈欠
• 2: 頭部下垂
• 3: 正常狀態

2. 模型訓練

數據集配置文件 (data.yaml)

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

訓練腳本 (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界面開發

創建一個簡單的PyQt界面用于展示檢測結果、選擇圖片或視頻文件以及啟動攝像頭實時檢測。

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. 運行項目

1. 安裝依賴：

   pip install torch torchvision torchaudio
   git clone https://github.com/ultralytics/yolov5  # 假設YOLOv8有相似的倉庫結構
   cd yolov5
   pip install -r requirements.txt
   

2. 運行訓練腳本：

   python train.py
   

3. 運行主程序：

   python MainProgram.py
   

5. 關鍵代碼解釋

數據集配置文件 (data.yaml)

• train, val, test: 數據集路徑。
• nc: 類別數量。
• names: 類別名稱。

訓練腳本 (train.py)

• YOLO('yolov8n.yaml'): 加載YOLOv8模型。
• model.train(): 開始訓練模型。

主程序 (MainProgram.py)

• YOLO('runs/train/driver_fatigue/weights/best.pt'): 加載訓練好的模型。
• detect_image(), detect_video(), start_camera(): 分別處理圖像、視頻和攝像頭檢測。
• show_image(): 顯示檢測結果。

通過以上步驟，tongxue 你就構建一個完整的駕駛員疲勞駕駛檢測系統，并帶有PyQt界面進行交互。