face_recognizer.py

import cv2
import numpy as np
import pickle

def recognize_faces():
    print("Starting face recognition...")
    recognizer = cv2.face.LBPHFaceRecognizer_create()
    recognizer.read(r"FaceRecognition\trainer\trainer.yml")

    with open(r"FaceRecognition\trainer\labels.pickle", 'rb') as f:
        labels_dict = pickle.load(f)
        labels_dict = {v: k for k, v in labels_dict.items()}  # Invert the dictionary

    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")

    font = cv2.FONT_HERSHEY_SIMPLEX
    cam = cv2.VideoCapture(0)
    cam.set(3, 640)  # set video width
    cam.set(4, 480)  # set video height

    minW = 0.1 * cam.get(3)
    minH = 0.1 * cam.get(4)

    while True:
        ret, img = cam.read()
        if not ret:
            print("Failed to capture image")
            break

        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

        faces = face_cascade.detectMultiScale(
            gray,
            scaleFactor=1.2,
            minNeighbors=5,
            minSize=(int(minW), int(minH)),
        )

        for (x, y, w, h) in faces:
            cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
            id_, confidence = recognizer.predict(gray[y:y + h, x:x + w])

            if confidence < 100:
                name = labels_dict.get(id_, "You are Weird")
                confidence_text = f"  {round(100 - confidence)}%"
            else:
                name = "You are Weird"
                confidence_text = f"  {round(100 - confidence)}%"

            cv2.putText(img, str(name), (x + 5, y - 5), font, 1, (255, 255, 255), 2)
            cv2.putText(img, str(confidence_text), (x + 5, y + h - 5), font, 1, (255, 255, 0), 1)

        cv2.imshow('camera', img)
        k = cv2.waitKey(10) & 0xFF  # Press 'ESC' for exiting video
        if k == 27:
            break

    cam.release()
    cv2.destroyAllWindows()
    print("Face recognition finished.")