faces.py

import numpy as np
import cv2
import pickle

recognizer = cv2.face.LBPHFaceRecognizer_create()
face_cascade = cv2.CascadeClassifier('cascades/data/haarcascade_frontalface_alt2.xml')
eye_cascade = cv2.CascadeClassifier('cascades/data/haarcascade_eye.xml')
recognizer.read("trainer.yml")

labels = {}
with open("labels.pkl",'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>=4 and conf<=85:
            print(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 = "7.png"
        cv2.imwrite(img_item, roi_gray)

        color = (255, 0, 0)
        stroke = 2
        width = x + w
        height = y + h
        cv2.rectangle(frame, (x, y), (width, height), color, stroke)
        eyes = eye_cascade.detectMultiScale(roi_gray)
        for(ex, ey, ew, eh) in eyes:
            cv2.rectangle(roi_color, (ex,ey),(ex+ew, ey+eh),(0,255,0),2)



    cv2.imshow('frame',frame)
    if cv2.waitKey(20) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()