pcell.py

import numpy as np
import cv2

import pickle

recognizer = cv2.face.LBPHFaceRecognizer_create()
face_cascade = cv2.CascadeClassifier('data/haarcascade_frontalface_alt2.xml')
recognizer.read("trainner.yml")


labels={"person": 1}
with open("labels.pickle", 'rb') as f:
    o_labels= pickle.load(f)
    labels ={v:k for k,v in o_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=1.3, 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>=75 and conf<=85:
         print(id_)
         print(labels[id_])
         cv2.putText(frame, "Identified", (250, 450), cv2.FONT_HERSHEY_COMPLEX, 1, (0,255,0), 2)
         font=cv2.FONT_HERSHEY_SIMPLEX
         name= labels[id_]
         color=(0,150,150)
         stroke=2
         cv2.putText(frame, name, (x,y), font, 1, color, stroke, cv2.LINE_AA)
     else:
         cv2.putText(frame, "Unidentified", (250, 450), cv2.FONT_HERSHEY_COMPLEX, 1, (0,0,255), 2)
    
     #img_item= "my-image.png"
     #cv2.imwrite(img_item, roi_gray)
     color=(255,0,0)
     stroke=2
     end_cord_x=x+w
     end_cord_y=y+h
     cv2.rectangle(frame, (x,y), (end_cord_x,end_cord_y), color, stroke)




 cv2.imshow('frame',frame)
 if cv2.waitKey(20) & 0xff == ord('q'):
     break
    
cap.release()
cv2.destroyAllWindows()