#coding=utf8
import cv2
import os
import time
recognizer = cv2.face.LBPHFaceRecognizer_create()

recognizer.read('face_trainer/trainer.yml')

cascadePath = "haarcascade_frontalface_default.xml"

faceCascade = cv2.CascadeClassifier(cascadePath)

font = cv2.FONT_HERSHEY_SIMPLEX

alarm = 0

idnum = 0


names = ['Derek', 'Nemo']


cam = cv2.VideoCapture(0)

minW = 0.1*cam.get(3)

minH = 0.1*cam.get(4)


while True:

    ret, img = cam.read()

    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)


    faces = faceCascade.detectMultiScale(

        gray,

        scaleFactor=1.2,

        minNeighbors=5,

        minSize=(int(minW), int(minH))

    )


    for (x, y, w, h) in faces:

        cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 2)

        idnum, confidence = recognizer.predict(gray[y:y+h, x:x+w])


        if confidence < 100:

            idnum = names[idnum]

            confidence = "{0}%".format(round(100 - confidence))

        else:

            idnum = "unknown"
            #time.sleep(5)
            times = 0
            times = times+1
           if times = 3:
             break
            #alarm = 1
            confidence = "{0}%".format(round(100 - confidence))
            

        cv2.putText(img, str(idnum), (x+5, y-5), font, 1, (0, 0, 255), 1)

        cv2.putText(img, str(confidence), (x+5, y+h-5), font, 1, (0, 0, 0), 1)


    cv2.imshow('camera', img)

    k = cv2.waitKey(10)

    if k == 27 or alarm == 1:

        cv2.imwrite('face.jpg',img)
        break

os.system("python /home/pi/python2.7_guest/main.py")

os.system("rm face.jpg")

cam.release()
cv2.destroyAllWindows()