recognizer = cv2.face.LBPHFaceRecognizer_create()

recognizer.read('trainer.yml')

cascadePath = "Cascades/haarcascade_frontalface_default.xml"

faceCascade = cv2.CascadeClassifier(cascadePath)

font = cv2.FONT_HERSHEY_SIMPLEX

# Initialize and start realtime video capture

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)

img_counter=0

while True:

ret, img =cam.read()

img = cv2.flip(img, 1) # Flip vertically

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])

# Check if confidence is less them 100 ==> "0" is perfect match

if (confidence < 70): #matched with recognised faces

if id - 1>= len(names):

id = names[-1]

else:

id = names[id - 1]

confidence = " {0}%".format(round(100 - confidence))

else:

id = "Unrecognised"

confidence = " {0}%".format(round(100 - confidence))

global tempid

tempid = id

cv2.putText(img, str(id), (x+5,y-5), font, 1, (255,255,255), 2)

cv2.putText(img, str(confidence), (x+5,y+h-5), font, 1, (255,255,0), 1)

if(tempid=="Unrecognised"):

global count

if(count < 1):

print(tempid)

img_name = "unknownPerson{}.jpg".format(img_counter)

cv2.imwrite(img_name, img)

img_counter+=1

app.send_email(img_name)

count=count+1

ret, buffer = cv2.imencode('.jpg', img)

img = buffer.tobytes()

yield (b'--frame\r\n'

b'Content-Type: image/jpeg\r\n\r\n' + img + b'\r\n') # concat frame one by one and show result

k = cv2.waitKey(40) & 0xff # Press 'ESC' for exiting video

if k == 27:

break

# Do a bit of cleanup

print("\n [INFO] Exiting Program and cleaning up stuff")

cam.release()

cv2.destroyAllWindows()