face_train.py

import cv2 as cv
import os 
import numpy as np 

people=[]
for i in os.listdir(r'E:\Things I Did\opencv\Faces\train'):
    people.append(i)
DIR=r'E:\Things I Did\opencv\Faces\train'


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

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

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

            img_array=cv.imread(img_path)
            gray=cv.cvtColor(img_array, cv.COLOR_BGR2GRAY)
            
            faces_rect=haar_cascade.detectMultiScale(gray,scaleFactor= 1.1,minNeighbors=4)
            for x,y,w,h in faces_rect:
                faces_roi=gray[y:y+h,x:x+w]
                features.append(faces_roi)
                labels.append(label)

create_train()

print("Training done_____________")
# print("Length of features", len(features))
# print("Length of labels ", len(labels))
features=np.array(features, dtype=object)
labels =np.array(labels)


face_recognizer=cv.face.LBPHFaceRecognizer_create()

#Training the recognizer on the features and their corresponding labels list 
face_recognizer.train(features, labels)

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