[SOLVED] Face recognition using Euclidean distance

I have a project doing face recognition with Python. I want to put Euclidean distance in my code, for knowing the distance between real time video and my data set (image).
I am confused because it's real time. Many projects just explain about Euclidean distance between Image "X" and Image "Y", for example. Can anybody help me understand how to do this for real time video?
This is the code :
import sys
import os
impo rt numpy as np
from face_recognition_system.videocamera import VideoCamera
from face_recognition_system.detectors import FaceDetector
import face_recognition_system.operations as op
import cv2
from cv2 import __version__

def get_images(frame, faces_coord, shape):

if shape == "rectangle":
    faces_img = op.cut_face_rectangle(frame, faces_coord)
    frame = op.draw_face_rectangle(frame, faces_coord)
elif shape == "ellipse":
    faces_img = op.cut_face_ellipse(frame, faces_coord)
    frame = op.draw_face_ellipse(frame, faces_coord)
faces_img = op.normalize_intensity(faces_img)
faces_img = op.resize(faces_img)
return (frame, faces_img)

def add_person(people_folder, shape):
    person_name = raw_input('What is the name of the new person: ').lower()
folder = people_folder + person_name
if not os.path.exists(folder):
    raw_input("I will now take 20 pictures. Press ENTER when ready.")
    os.mkdir(folder)
    video = VideoCamera()
    detector = FaceDetector('face_recognition_system/frontal_face.xml')
    counter = 1
    timer = 0
    cv2.namedWindow('Video Feed', cv2.WINDOW_AUTOSIZE)
    cv2.namedWindow('Saved Face', cv2.WINDOW_NORMAL)
    while counter < 21:
        frame = video.get_frame()
        face_coord = detector.detect(frame)
        if len(face_coord):
            frame, face_img = get_images(frame, face_coord, shape)
            # save a face every second, we start from an offset '5' because
            # the first frame of the camera gets very high intensity
            # readings.
            if timer % 100 == 5:
                cv2.imwrite(folder + '/' + str(counter) + '.jpg',
                            face_img[0])
                print 'Images Saved:' + str(counter)
                counter += 1
                cv2.imshow('Saved Face', face_img[0])

        cv2.imshow('Video Feed', frame)
        cv2.waitKey(50)
        timer += 5
else:
    print "This name already exists."
    sys.exit()

def recognize_people(people_folder, shape):
try:
    people = [person for person in os.listdir(people_folder)]
except:
    print "Have you added at least one person to the system?"
    sys.exit()
print "This are the people in the Recognition System:"
for person in people:
    print "-" + person

print 30 * '-'
print "   POSSIBLE RECOGNIZERS TO USE"
print 30 * '-'
print "1. EigenFaces"
print "2. FisherFaces"
print "3. LBPHFaces"
print 30 * '-'

choice = check_choice()

detector = FaceDetector('face_recognition_system/frontal_face.xml')
if choice == 1:
    recognizer = cv2.face.createEigenFaceRecognizer()
    threshold = 4000
elif choice == 2:
    recognizer = cv2.face.createFisherFaceRecognizer()
    threshold = 300
elif choice == 3:
    recognizer = cv2.face.createLBPHFaceRecognizer()
    threshold = 105
images = []
labels = []
labels_people = {}
for i, person in enumerate(people):
    labels_people[i] = person
    for image in os.listdir(people_folder + person):
        images.append(cv2.imread(people_folder + person + '/' + image, 0))
        labels.append(i)
try:
    recognizer.train(images, np.array(labels))
except:
    print "\nOpenCV Error: Do you have at least two people in the database?\n"
    sys.exit()

video = VideoCamera()
while True:
    frame = video.get_frame()
    faces_coord = detector.detect(frame, False)
    if len(faces_coord):
        frame, faces_img = get_images(frame, faces_coord, shape)
        for i, face_img in enumerate(faces_img):
            if __version__ == "3.1.0":
                collector = cv2.face.MinDistancePredictCollector()
                recognizer.predict(face_img, collector)
                conf = collector.getDist()
                pred = collector.getLabel()
            else:
                pred, conf = recognizer.predict(face_img)
            print "Prediction: " + str(pred)
            print 'Confidence: ' + str(round(conf))
            print 'Threshold: ' + str(threshold)
            if conf < threshold:
                cv2.putText(frame, labels_people[pred].capitalize(),
                            (faces_coord[i][0], faces_coord[i][1] - 2),
                            cv2.FONT_HERSHEY_PLAIN, 1.7, (206, 0, 209), 2,
                            cv2.LINE_AA)
            else:
                cv2.putText(frame, "Unknown",
                            (faces_coord[i][0], faces_coord[i][1]),
                            cv2.FONT_HERSHEY_PLAIN, 1.7, (206, 0, 209), 2,
                            cv2.LINE_AA)

    cv2.putText(frame, "ESC to exit", (5, frame.shape[0] - 5),
                cv2.FONT_HERSHEY_PLAIN, 1.2, (206, 0, 209), 2, cv2.LINE_AA)
    cv2.imshow('Video', frame)
    if cv2.waitKey(100) & 0xFF == 27:
        sys.exit()

def check_choice():
""" Check if choice is good
"""
is_valid = 0
while not is_valid:
    try:
        choice = int(raw_input('Enter your choice [1-3] : '))
        if choice in [1, 2, 3]:
            is_valid = 1
        else:
            print "'%d' is not an option.\n" % choice
    except ValueError, error:
        print "%s is not an option.\n" % str(error).split(": ")[1]
return choice

if __name__ == '__main__':
print 30 * '-'
print "   POSSIBLE ACTIONS"
print 30 * '-'
print "1. Add person to the recognizer system"
print "2. Start recognizer"
print "3. Exit"
print 30 * '-'

CHOICE = check_choice()

PEOPLE_FOLDER = "face_recognition_system/people/"
SHAPE = "ellipse"

if CHOICE == 1:
    if not os.path.exists(PEOPLE_FOLDER):
        os.makedirs(PEOPLE_FOLDER)
    add_person(PEOPLE_FOLDER, SHAPE)
elif CHOICE == 2:
    recognize_people(PEOPLE_FOLDER, SHAPE)
elif CHOICE == 3:
sys.exit()