face recognition on raspberry pi create LBP - python-2.7

Question

I just finished a work program to faces recognition using python on ubuntu system
But when you want to move the work to "Raspberry pi" gives this error
this is full error :
AttributeError: 'module' object has no attribute 'createLBPHFaceRecognizer'
What is the solution
Thank you
import cv2
import sys
import cv
import glob
import numpy as np
import os
labeltest=[]
Images=[]
Len=0
model = cv2.createLBPHFaceRecognizer(1,8,8,8,70.0)
Labels=[]
textsay=""
# *********** Read *****************\\
def read():
arr={}
with open("csv.ext") as f:
for line in f:
arr=line.split("%",2)
labeltest.append(arr[1])
Images.append(cv2.imread(arr[0],cv2.IMREAD_GRAYSCALE))
label=range(0,len(labeltest))
for i in range(0,len(labeltest)):
label[i]=int(labeltest[i])
print (label)
model.train(np.asarray(Images),np.asarray(label))
model.save("mezo.xml")
model.load("mezo.xml")
# //*********** Read *****************
def writetofile(key):
fo = open("csv.ext", "a+")
fo.write(key)
fo.write("\n")
def searchName(key):
lines=tuple(open("Names.txt","r"))
for i in range(0,len(lines)):
test=lines[i].split("\n")
print test[0]
if str(key.lower())==str(test[0].lower()):
return i
return -1
def readName():
lines=tuple(open("Names.txt","r"))
for i in range(0,len(lines)):
Labels.append(lines[i])
print Labels
def AddName(key):
fo = open("Names.txt", "a+")
fo.write(key)
fo.write("\n")
readName()
# *********** Add *****************\\
def Add(faces,gray):
count=Len+100
for (x, y, w, h) in faces:
filename = "/home/mohammad/Desktop/traning/%03d"%count +".pgm"
f=gray[y:y+h,x:x+w]
f=cv2.resize(f,(92,112),interpolation=cv2.INTER_LANCZOS4)
newName=raw_input("Enter the Name : ")
index=searchName(newName)
if index==-1:
index=len(Labels)
AddName(newName)
filenameIn = filename+"%"+str(index)
writetofile(filenameIn)
cv2.imwrite(filename,f)
count+=1
read()
# //*********** Add *****************
path={}
path=glob.glob("/home/mohammad/Desktop/traning/*.pgm")
Len=len(path)-1
cascPath = "haarcascade_frontalface_default.xml"
faceCascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
count=0
video_capture = cv2.VideoCapture(0)
read();
readName()
while True:
# Capture frame-by-frame
ret, frame = video_capture.read()
cv2.waitKey(10)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale(
frame,
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30),
flags=cv2.cv.CV_HAAR_SCALE_IMAGE
)
# Draw a rectangle around the faces
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
f=gray[y:y+h,x:x+w]
f=cv2.resize(f,(92,112),interpolation=cv2.INTER_LANCZOS4)
cv2.imwrite("11.pgm",f)
label, confidence = model.predict(f)
print"Threshold : ", model.getDouble("threshold")
if label>-1:
if Labels[label] != textsay:
cmd = 'espeak "{0}" 2>/dev/null'.format(Labels[label])
os.system(cmd)
textsay=Labels[label]
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(frame,Labels[label],(x,y-10), font, 1.0,(255,255,255))
print "\n"+str(Labels[label])+" | "+str(confidence)
# Display the resulting frame
cv2.imshow('Video', frame)
k=cv2.waitKey(5)& 0xFF
if k==97 :
Add(faces,gray)
if k==27:
exit()