import numpy as np
import cv2
import pickle
face_cascade = cv2.CascadeClassifier('cascad/data/haarcascade_frontalface_alt2.xml')
eye_cascade = cv2.CascadeClassifier('cascades/data/haarcascade_eye.xml')
smile_cascade = cv2.CascadeClassifier('cascades/data/haarcascade_smile.xml')
recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read("face-trainner.yml")

labels = {"person_name": 1}                         
with open("pickles/face-labels.pickle", 'rb') as f:
	og_labels = pickle.load(f)
	labels = {v:k for k,v in og_labels.items()}
cap = cv2.VideoCapture(0)

while True:
    ret,frame=cap.read()
    gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.5,minNeighbors=5)
    for (x,y,w,h) in faces:
    	print(x,y,w,h)
    	roi_gray = gray[y:y+h,x:x+w] 
    	roi_color = frame[y:y+h,x:x+w]
    	id_, conf = recognizer.predict(roi_gray)
    	if conf>=45: #and conf <= 85:
    		#print(5: #id_)
    		#print(labels[id_])
    		font = cv2.FONT_HERSHEY_SIMPLEX
    		name = labels[id_]
    		color = (255, 255, 255)
    		stroke = 2
    		cv2.putText(frame, name, (x,y), font, 1, color, stroke, cv2.LINE_AA)

    	#img_item = 'my-imagew.png'
    	#cv2.imwrite(img_item,roi_gray)
    	color = (255,0,0) #bgr color of rec
    	stroke = 2 #thiknes of rec 
    	end_cord_x=x+w
    	end_cord_y=y+h
    	cv2.rectangle(frame,(x,y),(end_cord_x,end_cord_y),color,stroke) #calling rec
    cv2.imshow('frame',frame)
    if cv2.waitKey(20) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()
