faceTrainer.py

import cv2
import numpy as np
from PIL import Image
import os
from paths import getHaarcascadePath
# Path for face image database

def faceTrainer():
    path = 'dataset'
    recognizer = cv2.face.LBPHFaceRecognizer_create()
    detector = cv2.CascadeClassifier(getHaarcascadePath());
    # function to get the images and label data
    def getImagesAndLabels(path):
        listDir = os.listdir(path)
        imagePaths = []
        for x in listDir:
            paths = [os.path.join(path+"/"+x,f) for f in os.listdir(path+"/"+x)]
            for y in paths:
                imagePaths.append(y)
        faceSamples=[]
        ids = []
        for imagePath in imagePaths:
            PIL_img = Image.open(imagePath).convert('L') # convert it to grayscale
            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
    print ("\n [INFO] Training faces. It will take a few seconds. Wait ...")
    faces,ids = getImagesAndLabels(path)
    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))))