Face_Recog.py

#Program to Detect the Face and Recognise the Person based on the data from face-trainner.yml

import cv2 #For Image processing 
import numpy as np #For converting Images to Numerical array 
import os #To handle directories 
from PIL import Image #Pillow lib for handling images 

labels = ["apurva", "Elon Musk"] 

face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
recognizer = cv2.face.createLBPHFaceRecognizer()
recognizer.load("face-trainner.yml")

cap = cv2.VideoCapture(0) #Get vidoe feed from the Camera

while(True):

    ret, img = cap.read() # Break video into frames 
    gray  = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) #convert Video frame to Greyscale
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.5, minNeighbors=5) #Recog. faces
    for (x, y, w, h) in faces:
    	roi_gray = gray[y:y+h, x:x+w] #Convert Face to greyscale 

    	id_, conf = recognizer.predict(roi_gray) #recognize the Face
    
    	if conf>=80:
    		font = cv2.FONT_HERSHEY_SIMPLEX #Font style for the name 
    		name = labels[id_] #Get the name from the List using ID number 
    		cv2.putText(img, name, (x,y), font, 1, (0,0,255), 2)
    	
    	cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2)

    cv2.imshow('Preview',img) #Display the Video
    if cv2.waitKey(20) & 0xFF == ord('q'):
    	break

# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()