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#!/usr/bin/env python3
import face_recognition
import cv2
import os
import numpy as np
import subprocess
import time
import argparse
import tempfile
import logging
import pwd
# Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
# Function to load known faces
def load_known_faces(known_faces_dir):
known_faces = []
known_names = []
for filename in os.listdir(known_faces_dir):
image_path = os.path.join(known_faces_dir, filename)
image = face_recognition.load_image_file(image_path)
encodings = face_recognition.face_encodings(image)
if encodings:
known_faces.append(encodings[0])
known_names.append(os.path.splitext(filename)[0])
else:
logger.warning(f"No face found in {filename}, deleting file.")
logger.info(f"Loaded {len(known_faces)} known face(s) from {known_faces_dir}")
return known_faces, known_names
# Function to capture an image
def capture_image():
video_capture = cv2.VideoCapture(0)
if not video_capture.isOpened():
logger.error("Could not open webcam")
return None
ret, frame = video_capture.read()
video_capture.release()
if not ret:
logger.error("Failed to capture image")
return None
return frame
# Function to save image with an incremented filename in a temporary directory
def save_image_with_increment(tmp_dir, base_filename, image):
base, ext = os.path.splitext(base_filename)
filename = os.path.join(tmp_dir, base + ext)
i = 0
while os.path.exists(filename):
filename = os.path.join(tmp_dir, f"{base}_{i}{ext}")
i += 1
cv2.imwrite(filename, image)
logger.info(f"Image saved as '{filename}'")
return filename
# Function to filter background and crop face
def filter_background(image, padding=40):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
face_cascade_path = '/usr/share/opencv4/haarcascades/haarcascade_frontalface_default.xml' # Adjust path as needed
face_cascade = cv2.CascadeClassifier(face_cascade_path)
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
if len(faces) == 0:
logger.warning("No face detected")
return None, None
mask = np.zeros_like(image)
for (x, y, w, h) in faces:
x_start = max(x - padding, 0)
y_start = max(y - padding, 0)
x_end = min(x + w + padding, image.shape[1])
y_end = min(y + h + padding, image.shape[0])
mask[y_start:y_end, x_start:x_end] = image[y_start:y_end, x_start:x_end]
result = cv2.bitwise_and(image, mask)
for (x, y, w, h) in faces:
x_start = max(x - padding, 0)
y_start = max(y - padding, 0)
x_end = min(x + w + padding, image.shape[1])
y_end = min(y + h + padding, image.shape[0])
cropped_result = result[y_start:y_end, x_start:x_end]
break
return cropped_result, (y, x + w, y + h, x) # Return the correct face bounding box format
# Function to compare faces
def compare_faces(face_encodings):
if len(face_encodings) < 2:
logger.warning("Not enough faces to compare")
return True
for i in range(1, len(face_encodings)):
match = face_recognition.compare_faces([face_encodings[0]], face_encodings[i])[0]
if not match:
return False
return True
# Function to recognize face from the webcam
def recognize_face(known_faces, tmp_dir):
image = capture_image()
if image is None:
logger.error("Failed to capture image from webcam")
return None, None
face_locations_original = face_recognition.face_locations(image)
if len(face_locations_original) == 0:
logger.warning("No face detected in the original captured image, trying again")
return None, None
save_image_with_increment(tmp_dir, 'captured_image.jpg', image)
filtered_image, face_box = filter_background(image)
if filtered_image is None:
logger.warning("No face detected in captured image, trying again")
return None, None
save_image_with_increment(tmp_dir, 'filtered_image.jpg', filtered_image)
face_encodings = face_recognition.face_encodings(image, face_locations_original)
logger.info(f"Detected {len(face_encodings)} face encoding(s) in the original image")
return face_encodings, face_box
# Function to lock the screen
def lock_screen(session_id):
try:
result = subprocess.run(["loginctl", "lock-session", session_id], check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
logger.info("Screen locked successfully")
except subprocess.CalledProcessError as e:
logger.error(f"Failed to lock the screen: {e}")
logger.error(f"Error output: {e.stderr.decode().strip()}")
# Function to get the current session ID
def get_session_id():
try:
user = os.getenv('USER') or pwd.getpwuid(os.getuid()).pw_name
result = subprocess.run(["loginctl", "show-user", user, "--value", "-p", "Display"], check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
session_id = result.stdout.decode().strip()
return session_id
except subprocess.CalledProcessError as e:
logger.error(f"Failed to get session ID: {e}")
return None
# Function to check if the session is active and unlocked
def is_session_active(session_id):
try:
result = subprocess.run(["loginctl", "show-session", session_id, "-p", "Active", "-p", "LockedHint"], check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
output = result.stdout.decode().strip().split("\n")
status = {line.split("=")[0]: line.split("=")[1] for line in output}
return status.get("Active") == "yes" and status.get("LockedHint") == "no"
except subprocess.CalledProcessError as e:
logger.error(f"Failed to check session state: {e}")
return False
# Main function
def main(known_faces_dir, check_interval, retries):
known_faces, known_names = load_known_faces(known_faces_dir)
if not known_faces:
logger.error("No known faces loaded. Exiting.")
return
session_id = get_session_id()
if not session_id:
logger.error("Failed to retrieve session ID. Exiting.")
return
with tempfile.TemporaryDirectory(prefix="face_recognition_") as tmp_dir:
while True:
if is_session_active(session_id):
unrecognized_count = 0
for _ in range(retries):
face_encodings, face_box = recognize_face(known_faces, tmp_dir)
if face_encodings is None:
continue
recognized = False
for face_encoding in face_encodings:
matches = face_recognition.compare_faces(known_faces, face_encoding)
face_distances = face_recognition.face_distance(known_faces, face_encoding)
best_match_index = np.argmin(face_distances)
if matches[best_match_index]:
recognized = True
logger.info("Recognized face")
break
if not recognized:
unrecognized_count += 1
logger.warning("Unrecognized face, trying again")
if unrecognized_count >= retries:
logger.warning("Unrecognized face detected, locking screen.")
lock_screen(session_id)
else:
logger.info("Session not active or locked, waiting...")
time.sleep(check_interval)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Capture, filter, compare, and recognize faces to lock the screen if an unrecognized face is detected.")
parser.add_argument('--known_faces_dir', type=str, default="/usr/local/share/known_faces", help='Directory containing known faces')
parser.add_argument('--check_interval', type=int, default=60, help='Interval in seconds to check for unrecognized faces')
parser.add_argument('--retries', type=int, default=2, help='Number of retries to confirm unrecognized face')
args = parser.parse_args()
main(args.known_faces_dir, args.check_interval, args.retries)
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