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import cv2
import numpy as np
import os 

recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read('trainer/trainer.yml')
cascadePath = "haarcascade_frontalface_default.xml"
faceCascade = cv2.CascadeClassifier(cascadePath);

font = cv2.FONT_HERSHEY_SIMPLEX

id = 0

names = ['None', 'User', 'Paula', 'Ilza', 'Z', 'W'] 

cam = cv2.VideoCapture(0)

minW = 0.1*cam.get(3)
minH = 0.1*cam.get(4)

while True:

ret, img = cam.read()
img = cv2.flip(img, 1)

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

faces = faceCascade.detectMultiScale( 
    gray,
    scaleFactor = 1.1,
    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)

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

    # Check if confidence is less them 100 ==> "0" is perfect match 
    if (confidence < 100):
        id = names[id]
        confidence = "  {0}%".format(round(100 - confidence))
    else:
        id = "unknown"
        confidence = "  {0}%".format(round(100 - confidence))

    cv2.putText(img, str(id), (x+5,y-5), font, 1, (255,255,255), 2)
    cv2.putText(img, str(confidence), (x+5,y+h-5), font, 1, (255,255,0), 1)  

cv2.imshow('camera',img) 

k = cv2.waitKey(0) & 0xff # Press 'ESC' for exiting video
if k == 27:
    break

print("\n [INFO] Exiting Program and cleanup stuff")
cam.release()
cv2.destroyAllWindows()