cv2. 02_face_training

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

# Path for face image database
path = './face_dataset/0'
recognizer = cv2.face.LBPHFaceRecognizer_create()

cascPath = './data/'
detector = cv2.CascadeClassifier(cascPath + 'haarcascade_frontalface_alt.xml')
#function to get the images and label data

def getImagesAndLabels(path):

imagePaths = [os.path.join(path,f) for f in os.listdir(path)[1:]]

faceSamples = []

ids = []
for imagePath in imagePaths:

PIL_img = Image.open(imagePath).convert('L') #convert it to grayscale
        img_numpy = np.array(PIL_img,dtype='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))))