face_recognition_detection.py

import cv2
import numpy as np
import time
import os
from face_detection import *
from buzzer import alertor, stopAlertor

global thres
thres = 0

##############################################################################
#face_detection.py PART
def detect_faces(img,train=0):
    face_clf = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_clf.detectMultiScale(gray, 1.3, 5)

    array_face = []
    rect = []
    for (x, y, w, h) in faces:
        fc = gray[y:y+h, x:x+w]
        rect.append((x,y,w,h))
        array_face.append(fc)

    if train: 
        if len(array_face) != 0: return array_face[0],rect[0]
        return [],[]
    return array_face,rect

#face_recognition.py PART
def label_reading(filename):
	s = filename.split('.jpg')[0]
	return ''.join([i for i in s if not i.isdigit()])

names = [label_reading(name) for name in os.listdir('images')]

class Face_recog:

	def __init__(self):
		self.face_recognizer = cv2.face.LBPHFaceRecognizer_create()
		self.path = 'images/'

	def train(self):
		images = os.listdir(self.path)
		faces = []
		labels = []
		
		for image in images:
			img = cv2.imread(self.path+image)

			labels.append(names.index(label_reading(image)))
			img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
			faces.append(img)
			#print(img.shape)
		faces = np.array(faces)
		
		self.face_recognizer.train(faces, np.array(labels))

	def recognition(self,img):
		face, rect = detect_faces(img)
		global thres
		for i in range(0, len(face)) :
			label,confidence = self.face_recognizer.predict(face[i])
			#print(confidence)
			
			label_text = names[label]
			if confidence > 65:
				thres += 1
				label_text = 'Unknown'
				print(thres)
			(x, y, w, h) = rect[i]
			cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0))
			cv2.putText(img, label_text, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.5, (0, 255, 0), 2)
			if label_text == 'Unknown':
				if thres > 10:
					alertor()
			else:
				thres = 0
				stopAlertor()
		return img