identify.py

import numpy as np
import cv2
import pickle
#	This File was just to test the the recognizer
#Face cascade used for face detector
face_cascade = cv2.CascadeClassifier('cascades/data/haarcascade_frontalface_alt2.xml')
#This is to used for Face Recognizer
recognizer = cv2.face.LBPHFaceRecognizer_create()
#this reads the recognizer model
recognizer.read("trainner.yml")
#this creates a dictionary
labels = {"person_name":1}
#We deserialize the to the labels
with open("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] #(cord1-height, cord2-height)
		roi_color = gray[y:y+h,x:x+w]
		
		
		id_,conf =recognizer.predict(roi_gray)
		if conf>=0 and conf<=40:
			#print(id_)
			#print (labels[id_])
			font = cv2.FONT_HERSHEY_SIMPLEX
			name = labels[id_]+"  "+ str(int(100-conf))+"%"
			color = (255 ,255,255)
			stroke =2
			cv2.putText(frame,name ,(x,y),font,1,color,stroke,cv2.LINE_AA)
			#img_item=name+".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()