face_train.py

"""Image Training"""
# pylint:disable=no-member

import os
import cv2 as cv
import numpy as np

print(cv.__version__)
if hasattr(cv, 'face'):
    print("cv.face module is available!")
else:
    print("cv.face module is not available.")


people = ['Ben Afflek', 'Elton John', 'Jerry Seinfield', 'Madonna', 'Mindy Kaling']
DIR = r'./Faces/train'

haar_cascade = cv.CascadeClassifier('haar_face.xml')

features = []
labels = []

def create_train():
    """Function printing python version."""
    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)
            if img_array is None:
                continue
            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(f'Length of the features = {len(features)}')
print(f'Length of the labels = {len(labels)}')

features = np.array(features, dtype='object')
labels = np.array(labels)

face_recognizer = cv.face.LBPHFaceRecognizer_create()

# Train the Recognizer on the features list and the labels list
face_recognizer.train(features,labels)

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