face_utils.py

import cv2
import numpy as np

def recognize_face(image):
    try:
        # Convert image to grayscale
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        # Load OpenCV's pre-trained Haar Cascade for face detection
        face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
        if face_cascade.empty():
            raise Exception("Failed to load Haar Cascade classifier")
        # Detect faces in the image
        faces = face_cascade.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=5)
        if len(faces) > 0:
            return "Customer"  # Face detected, assume it's a customer
        else:
            return "Unknown"  # No face detected
    except Exception as e:
        print(f"Error in face recognition: {e}")
        return "Error reading face"