Face Recognition.py

import cv2
import numpy as np

# Initialize OpenCV's pre-trained Haar Cascade classifier for face detection
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

# Initialize variables
face_id = 0
face_data = {}

def add_new_face(face_image):
    global face_id
    if face_id is None:
        face_id = 0
    # Increment face_id for new face
    face_id += 1
    # Store face in the database (for now, just store the face image)
    face_data[face_id] = face_image
    return face_id

def recognize_face(face_image):
    # Placeholder function for face recognition
    # In a real implementation, you would compare face_image with existing face data
    # and return the ID if recognized, or None if not recognized.
    return None

# Initialize the video capture
cap = cv2.VideoCapture(0)

while True:
    # Capture frame-by-frame
    ret, frame = cap.read()

    # Convert 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=1.3, minNeighbors=5, minSize=(30, 30))

    # Draw rectangles around the faces and identify
    for (x, y, w, h) in faces:
        face_image = gray[y:y+h, x:x+w]  # Extract the face ROI
        
        # Try to recognize the face
        face_id = recognize_face(face_image)
        
        if face_id is None:
            # If face is not recognized, add it as a new face
            face_id = add_new_face(face_image)
        
        # Draw a rectangle around the face
        cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
        cv2.putText(frame, f'Face ID: {face_id}', (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 0, 0), 2)

    # Display the resulting frame
    cv2.imshow('Face Recognition System', frame)

    # Exit the loop if 'q' is pressed
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Release the capture and destroy all windows
cap.release()
cv2.destroyAllWindows()