training_model.py

import cv2
import os
import numpy as np    #numpy for matrix calculations
from PIL import Image

def assure_path_exists(path):           #same as in face_dataset.py
    dir = os.path.dirname(path)
    if not os.path.exists(dir):
        os.makedirs(dir)

recognizer = cv2.face.LBPHFaceRecognizer_create()       # Create Local Binary Patterns Histograms for face recognization

detector = cv2.CascadeClassifier("C:\\Users\\Riyansika\\Desktop\\Lock-Unlock-Laptop-PC-Screen-Using-Face-Recognition-master\\haarcascade_frontalface_default.xml")

def getImagesAndLabels(path):       #method to get the images and label data

    imagePaths = [os.path.join(path,f) for f in os.listdir(path)]      # Get all file path

    faceSamples=[]         # create empty face sample list

    ids = []               # create empty id list

    for imagePath in imagePaths:     # Loop for all the file path

        PIL_img = Image.open(imagePath).convert('L')    # Get the image and convert it to grayscale

        img_numpy = np.array(PIL_img,'uint8')       # PIL image to numpy array

        id = int(os.path.split(imagePath)[-1].split(".")[1])      # Get the image id

        faces = detector.detectMultiScale(img_numpy)  # Get the face from the training images

        for (x,y,w,h) in faces:   # Loop for each face, append to their respective ID

            faceSamples.append(img_numpy[y:y+h,x:x+w])        # Add the image to face samples

            ids.append(id)   # Add the ID to IDs

    return faceSamples,ids  

faces,ids = getImagesAndLabels('C:\\Users\\Riyansika\\Desktop\\Lock-Unlock-Laptop-PC-Screen-Using-Face-Recognition-master\\dataset')  # Get the faces and IDs

recognizer.train(faces, np.array(ids)) # Train the model using the faces and IDs

assure_path_exists('C:\\Users\\Riyansika\\Desktop\\Lock-Unlock-Laptop-PC-Screen-Using-Face-Recognition-master\\trainer\\')    # Save the model into trainer.yml
recognizer.save('C:\\Users\\Riyansika\\Desktop\\Lock-Unlock-Laptop-PC-Screen-Using-Face-Recognition-master\\trainer\\trainer.yml')