Training.py

''''
Training Multiple Faces stored on a DataBase:
	==> Each face should have a unique numeric integer ID as 1, 2, 3, etc                       
	==> LBPH computed model will be saved on trainer/ directory. (if it does not exist, pls create one)
	==> for using PIL, install pillow library with "pip install pillow"

Based on original code by Anirban Kar: https://github.com/thecodacus/Face-Recognition    

Developed by Marcelo Rovai - MJRoBot.org @ 21Feb18   

'''

import cv2
import numpy as np
from PIL import Image
import os

# Path for face image database
pathData = 'dataset'

recognizer = cv2.face.LBPHFaceRecognizer_create()
detector = cv2.CascadeClassifier("haarcascade_frontalface_default.xml");

# function to get the images and label data
def getImagesAndLabels(main_path):
    faceSamples = []
    ids = []
    folder_index = 0

    for user_folder in os.listdir(main_path):
        user_folder_path = os.path.join(main_path, user_folder)
        if os.path.isdir(user_folder_path):  
            for image_name in os.listdir(user_folder_path):
                image_path = os.path.join(user_folder_path, image_name)
                if os.path.isfile(image_path):  
                    PIL_img = Image.open(image_path).convert('L')  
                    img_numpy = np.array(PIL_img, 'uint8')
                    id = folder_index 
                    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)
            folder_index += 1

    return faceSamples, ids
print ("\n [INFO] Training faces. It will take a few seconds. Wait ...")
faces,ids = getImagesAndLabels(pathData)
recognizer.train(faces, np.array(ids))

# Save the model into trainer/trainer.yml
recognizer.write('trainer/trainer.yml') # recognizer.save() worked on Mac, but not on Pi

# Print the numer of faces trained and end program
print("\n [INFO] {0} faces trained. Exiting Program".format(len(np.unique(ids))))