Python Sentry using Open CV

Libraries

pip install opencv-python
pip install winsound
pip install plyer
pip install pygame

Sentry code

Make sure to replace C:\Users\User\Downloads\Example.mp3 with the path to the mp3 file you want to play. (or use beep)

import cv2
import winsound
from plyer import notification
import time
import pygame

# Load the pre-trained face detection model
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

# Capture video from the default camera (camera index 0)
cap = cv2.VideoCapture(0)
def beep():
    # Show a system notification
    notification.notify(
        title="Face Detected",
        message="A face has been detected for more than 1 second.",
        timeout=10
    )

    # Sound an alert
    x = 15
    while x > 0:
        winsound.Beep(1500, 50)  # Beep at 1500 Hz for 50 ms 15 *
        time.sleep(.1)
        x -= 1
def play_sound():
    # Show a system notification
    notification.notify(
        title="Face Detected",
        message="A face has been detected for more than 1 second.",
        timeout=10
    )

    # Initialize pygame mixer
    pygame.mixer.init()

    # Load the MP3 file
    try:
        pygame.mixer.music.load(r"C:\Users\User\Downloads\Example.mp3") # Replace this with the correct path to your .mp3 file
    except pygame.error as _ :
        print(f"Error loading sound file: {_} ")
        return

    # Play the MP3 file
    pygame.mixer.music.play()



alert_choice = input("Beeping(1) or Music(2): ")

if alert_choice == "1":
    alert = beep
elif alert_choice == "2":
    alert = play_sound
else:
    print("Invalid choice. Using default beep alert.")
    alert = beep

face_detected_time = 0
face_detected = False


while True:
    ret, frame = cap.read()
    # Convert the frame to grayscale for face detection
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    # Detect faces in the grayscale frame
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=12, minSize=(30, 30)) # Change the minNeighbors value up for lower sensitivity and a lower value for more face sensitivity

    if len(faces) > 0:
        if not face_detected:
            face_detected = True
            face_detected_time = time.time()
        else:
            current_time = time.time()
            if current_time - face_detected_time >= 1:
                alert()  # Call the selected alert function
                face_detected = False

    # Draw Red rectangles around detected faces
    for (x, y, w, h) in faces:
        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255), 5)

    # Display the frame with detected faces
    cv2.imshow('Face Detection', frame)

    # Press 'q' to exit
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break


# Release resources
cap.release()
cv2.destroyAllWindows()

Face or Brightest spot

My original script which simply looks for faces or just the brightest spot in the frame

import cv2

# Load the pre-trained face detection model
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

# Capture video from the default camera (camera index 0)
cap = cv2.VideoCapture(0)

# Prompt the user to choose between face detection or finding the brightest spot
choice = input("Enter 'f' for face detection, 'b' for finding the brightest spot, or nothing for a blank camera : ")

while True:
    # Read a frame from the video capture
    ret, frame = cap.read()

    # Convert the frame to grayscale
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    if choice == 'f':
        # Detect faces in the grayscale frame
        faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=12, minSize=(30, 30))  # Change the minNeighbors value up for lower sensitivity and a lower value for more face sensitivity
        # Draw Red rectangles around detected faces
        for (x, y, w, h) in faces:
            cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

    elif choice == 'b':
        # Find the brightest spot in the frame
        (minVal, maxVal, minLoc, maxLoc) = cv2.minMaxLoc(gray)
        cv2.circle(frame, maxLoc, 30, (0, 0, 255), 2)

    # Display the processed frame
    cv2.imshow('Video', frame)

    # Press 'q' to exit
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Release resources
cap.release()
cv2.destroyAllWindows()