training (1).py

import cv2, os
import numpy as np

from PIL import Image

recognizer = cv2.face.LBPHFaceRecognizer_create()

detector = cv2.CascadeClassifier("haarcascade_frontalface_default.xml");

path = 'datasets'

def getImagesAndLabels(path):

    imagePaths = [os.path.join(path,f) for f in os.listdir(path)] 
    
    faceSamples=[]
    
    ids = []

    for imagePath in imagePaths:

        PIL_img = Image.open(imagePath).convert('L')

        img_numpy = np.array(PIL_img,'uint8')

        id = int(os.path.split(imagePath)[-1].split(".")[1])

        faces = detector.detectMultiScale(img_numpy)

        for (x,y,w,h) in faces:

            faceSamples.append(img_numpy[y:y+h,x:x+w])
            ids.append(id)

    return faceSamples,ids

faces,ids = getImagesAndLabels('datasets')
recognizer.train(faces, np.array(ids))
recognizer.write('trainer/trainer.yml')