''''
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"

'''

import cv2
import numpy as np
from PIL import Image #pillow package
import os

# Path for face image database
face_path = 'dataset/Users'
gun_path = 'dataset/Guns'

recognizer = cv2.face.LBPHFaceRecognizer_create()
gun_detector = cv2.CascadeClassifier("cascade.xml");
face_detector = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")

# function to get the images and label data
def getImagesAndLabels(path, x):

    imagePaths = [os.path.join(path,f) for f in os.listdir(path)]
    faceSamples=[]
    ids = []

    if x=='f':
        detector = face_detector
    elif x == 'g':
        detector = gun_detector

    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(face_path, 'f')
recognizer.train(faces, np.array(ids))

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

print ("\n [INFO] Training guns. It will take a few seconds. Wait ...")
faces,ids = getImagesAndLabels(gun_path, 'g')
recognizer.train(faces, np.array(ids))

# Save the model into trainer/trainer.yml
recognizer.write('trainer/guntrainer.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))))
