training.py

#Project done by Jishnu J,Jahnavi and Harshitha Reddy
#For more info visit jishnu.co


import cv2, os

import numpy as np

from PIL import Image

import os

def assure_path_exists(path):
    dir = os.path.dirname(path)
    if not os.path.exists(dir):
        os.makedirs(dir)

recognizer = cv2.face.LBPHFaceRecognizer_create()

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

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('dataset')

recognizer.train(faces, np.array(ids))

assure_path_exists('trainer/')
recognizer.save('trainer/trainer.yml')