face_recognition.py

import cv2
import numpy as np
import os 

recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read('trainer/trainer.yml')

faceCascade = cv2.CascadeClassifier('cascades/haarcascade_frontalface_default.xml')

font = cv2.FONT_HERSHEY_SIMPLEX

id = 0

names = ['None'] 

cam = cv2.VideoCapture(0)
cam.set(3, 640) # set video widht
cam.set(4, 480) # set video height

# Define min window size to be recognized as a face
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 = 1.2,
        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)
        id, confidence = recognizer.predict(gray[y:y+h,x:x+w])

        print("id:{} => conf: {}".format(id, confidence))

        # Check if confidence is less them 100 ==> "0" is perfect match 
        if confidence < 100 and confidence > 50:
            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-10), font, 0.5, (0,255,0), 2)
        cv2.putText(img, "confidence: " + str(confidence), (x+5, y + 20), font, 0.5, (255,255,0), 1)  
    
    cv2.imshow('camera',img) 

    k = cv2.waitKey(10) & 0xff # Press 'ESC' for exiting video
    if k == 27:
        break

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