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| import cv2 | |
| import numpy as np | |
| import os | |
| import pandas as pd | |
| import tkinter as tk | |
| from tkinter import messagebox | |
| from PIL import Image, ImageTk | |
| atnd = [] | |
| # Train the face recognition model using the collected dataset | |
| def train_model(): | |
| data_path = 'data' | |
| face_classifier = cv2.CascadeClassifier("haarcascade_frontalface_default.xml") | |
| training_data = [] | |
| labels = [] | |
| for root, dirs, files in os.walk(data_path): | |
| for file in files: | |
| if file.endswith('jpg'): | |
| path = os.path.join(root, file) | |
| label = int(path.split('.')[1]) | |
| image = cv2.imread(path) | |
| gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) | |
| face = face_classifier.detectMultiScale(gray, 1.3, 5) | |
| if face is not (): | |
| for (x, y, w, h) in face: | |
| cropped_face = gray[y:y + h, x:x + w] | |
| training_data.append(cropped_face) | |
| labels.append(label) | |
| labels = np.array(labels) | |
| model = cv2.face.LBPHFaceRecognizer_create() | |
| model.train(training_data, labels) | |
| return model | |
| # Implement the student attention monitoring system | |
| def monitor_attention(): | |
| global atnd | |
| atnd = [] | |
| face_classifier = cv2.CascadeClassifier("haarcascade_frontalface_default.xml") | |
| eye_classifier = cv2.CascadeClassifier("haarcascade_eye.xml") | |
| model = train_model() | |
| cap = cv2.VideoCapture(0) | |
| while True: | |
| ret, frame = cap.read() | |
| gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) | |
| faces = face_classifier.detectMultiScale(gray, 1.3, 5) | |
| if faces is not (): | |
| for (x, y, w, h) in faces: | |
| cropped_face = gray[y:y + h, x:x + w] | |
| label, confidence = model.predict(cropped_face) | |
| if confidence < 100: | |
| eyes = eye_classifier.detectMultiScale(cropped_face) | |
| if eyes is not (): | |
| for (ex, ey, ew, eh) in eyes: | |
| atnd.append(1) | |
| else: | |
| atnd.append(0) | |
| cv2.imshow('Student Attention', frame) | |
| if cv2.waitKey(1) == 13: | |
| break | |
| cap.release() | |
| cv2.destroyAllWindows() | |
| save_attendance() | |
| def save_attendance(): | |
| df = pd.DataFrame(atnd, columns=['Attention']) | |
| df.to_csv('./attention.csv') | |
| messagebox.showinfo("Attendance Saved", "Attendance data has been saved successfully.") | |
| def start_monitoring(): | |
| monitor_attention() | |
| def stop_monitoring(): | |
| messagebox.showinfo("Monitoring Stopped", "Monitoring has been stopped.") | |
| def main(): | |
| root = tk.Tk() | |
| root.title("Student Attentiveness") | |
| video_frame = tk.Label(root) | |
| video_frame.pack() | |
| start_button = tk.Button(root, text="Start Monitoring", command=start_monitoring) | |
| start_button.pack() | |
| stop_button = tk.Button(root, text="Stop Monitoring", command=stop_monitoring) | |
| stop_button.pack() | |
| root.mainloop() | |
| if __name__ == "__main__": | |
| main() | |