faces_train.py

import os
import cv2 as cv
import numpy as np

people = ['anna', 'rte', 'sydney']
DIR = r'C:\Users\adaek\Desktop\OPENCVcourse\Photos\faces'

haar_cascade = cv.CascadeClassifier('FACEDETECTION/haar_face.xml')
features = []
labels = []

def create_train():
    for person in people:
        path = os.path.join(DIR, person)
        label = people.index(person)

        for img in os.listdir(path):
            img_path = os.path.join(path, img)

            img_array = cv.imread(img_path)
            gray = cv.cvtColor(img_array, cv.COLOR_BGR2GRAY)

            faces_rect = haar_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=15)

            for(x,y,w,h) in faces_rect:
                facesroi = gray[y:y+h, x:x+w]
                features.append(facesroi)
                labels.append(label)
create_train()
print('Traning Done')
features = np.array(features, dtype='object')
labels = np.array(labels)

face_recognizer = cv.face.LBPHFaceRecognizer_create()

print(f'Length of the features = {len(features)}')
print(f'Length of the labels = {len(labels)}')
# Train recognizer on features list and labels list
face_recognizer.train(features, labels)

face_recognizer.save('face_trained.yml')
np.save('features.npy', features)
np.save('labels.npy', labels)