import cv2 import numpy as np import os import datetime import csv # Load face cascade face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') # Ensure OpenCV contrib package is installed for face recognition try: recognizer = cv2.face.LBPHFaceRecognizer_create() except AttributeError: print("Error: OpenCV face module not found. Install opencv-contrib-python with:") print("pip install opencv-contrib-python") exit() MODEL_FILE = "face_recognizer.yml" # Load model only once if available if os.path.exists(MODEL_FILE): recognizer.read(MODEL_FILE) else: print("Error: Model file not found. Train the model first.") exit() # Create LBPH face recognizer recognizer = cv2.face.LBPHFaceRecognizer_create() # Directory where face data is stored FACE_DIR = "registered_faces" LOG_FILE = "attendance_log.csv" MODEL_FILE = "face_recognizer.yml" USER_ID_FILE = "last_user_id.txt" def save_last_user_id(user_id): """Saves the last registered user ID.""" with open(USER_ID_FILE, "w") as file: file.write(str(user_id)) def get_last_user_id(): """Retrieves the last registered user ID.""" if os.path.exists(USER_ID_FILE): with open(USER_ID_FILE, "r") as file: return file.read().strip() return None def count_checkins_today(): """Counts the total check-ins for the current day.""" today = datetime.datetime.now().strftime("%Y-%m-%d") checkin_count = 0 if os.path.exists(LOG_FILE): with open(LOG_FILE, "r") as file: reader = csv.reader(file) next(reader, None) # Skip header for row in reader: if row[0].startswith(today) and row[2] == "Checkin": checkin_count += 1 return checkin_count def collect_training_data(user_id): """Captures face images for training.""" if not user_id.isdigit(): print("Error: User ID must be a number.") return user_folder = os.path.join(FACE_DIR, user_id) os.makedirs(user_folder, exist_ok=True) cap = cv2.VideoCapture(0) count = 0 while count < 20: # Capture 20 images per user ret, frame = cap.read() if not ret: break gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(50, 50)) for (x, y, w, h) in faces: face_img = gray[y:y + h, x:x + w] cv2.imwrite(os.path.join(user_folder, f"{count}.jpg"), face_img) count += 1 cv2.imshow("Face Registration", frame) if cv2.waitKey(1) & 0xFF == ord('q'): break cap.release() cv2.destroyAllWindows() save_last_user_id(user_id) print(f"Training data collected for user {user_id}.") def train_face_recognizer(): """Trains LBPH face recognizer with collected face images.""" faces = [] labels = [] for user_id in os.listdir(FACE_DIR): if not user_id.isdigit(): continue # Skip invalid folder names user_folder = os.path.join(FACE_DIR, user_id) for filename in os.listdir(user_folder): img_path = os.path.join(user_folder, filename) img = cv2.imread(img_path, 0) if img is None: continue # Skip unreadable files faces.append(img) labels.append(int(user_id)) if not faces: print("No training data found.") return recognizer.train(faces, np.array(labels)) recognizer.save(MODEL_FILE) print("Training complete.") def recognize_user(): """Recognizes a user using the trained model and displays total check-ins.""" if not os.path.exists(MODEL_FILE): print("Error: Model file not found. Train the model first.") return recognizer = cv2.face.LBPHFaceRecognizer_create() recognizer.read(MODEL_FILE) face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml") cap = cv2.VideoCapture(0) while True: ret, frame = cap.read() if not ret: break gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(50, 50)) for (x, y, w, h) in faces: face_img = gray[y:y + h, x:x + w] user_id, confidence = recognizer.predict(face_img) if confidence < 70: text = f"EMP #: {user_id}" color = (0, 255, 0) log_attendance(user_id) else: text = "NEED TO REGISTER" color = (0, 0, 255) cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2) cv2.putText(frame, text, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, color, 2, cv2.LINE_AA) # Get today's check-in count total_checkins = count_checkins_today() checkin_text = f"Total Check-ins Today: {total_checkins}" developer_text = f"Develop by Alejo" # Overlay total check-ins on preview cv2.putText(frame, checkin_text, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 255, 255), 2, cv2.LINE_AA) # Get the height of the frame height, width, _ = frame.shape # Position the developer text at the bottom (10 pixels from the bottom) cv2.putText(frame, developer_text, (10, height - 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 255, 255), 2, cv2.LINE_AA) # Resize the frame to a desired size frame = cv2.resize(frame, (1200, 900)) # Resize to 800x600 cv2.imshow("Face Recognition", frame) if cv2.waitKey(1) & 0xFF == ord('q'): break cap.release() cv2.destroyAllWindows() def log_attendance(user_id): """Logs attendance for recognized users in CSV format.""" now = datetime.datetime.now() today = now.strftime("%Y-%m-%d") timestamp = now.strftime("%Y-%m-%d %H:%M:%S") log_data = [] # Ensure CSV file exists file_exists = os.path.exists(LOG_FILE) if not file_exists: with open(LOG_FILE, "w", newline="") as file: writer = csv.writer(file) writer.writerow(["Timestamp", "User ID", "Attendance"]) # Write header # Read existing logs with open(LOG_FILE, "r") as file: reader = csv.reader(file) next(reader, None) # Skip header log_data = list(reader) # Filter records for today's date and this user user_logs_today = [log for log in log_data if log[1] == str(user_id) and log[0].startswith(today)] if not user_logs_today: # First log of the day - CHECKIN log_data.append([timestamp, user_id, "Checkin"]) print(f"✅ Check-in logged for User {user_id}") elif len(user_logs_today) == 1 and user_logs_today[0][2] == "Checkin": # Second log of the day - CHECKOUT log_data.append([timestamp, user_id, "Checkout"]) print(f"✅ Checkout logged for User {user_id}") else: #print(f"⚠️ User {user_id} already has both Check-in and Check-out for today. No changes made.") return # Write the updated log data back to the CSV with open(LOG_FILE, "w", newline="") as file: writer = csv.writer(file) writer.writerow(["Timestamp", "User ID", "Attendance"]) # Re-write header writer.writerows(log_data) print("✅ Attendance log updated successfully.") if __name__ == "__main__": last_user_id = get_last_user_id() while True: choice = input("1: Register User\n2: Train Model\n3: Recognize Face\n4: Exit\nChoose an option: ") if choice == "1": user_id = input("Enter user ID for registration: ") collect_training_data(user_id) elif choice == "2": train_face_recognizer() elif choice == "3": print(f"Last recognized user: {last_user_id}" if last_user_id else "No user recognized yet.") recognize_user() elif choice == "4": break else: print("Invalid choice. Try again.")