AttributeError: 'module' object has no attribute 'face'

edit

I got an error when running opencv in Python on raspberry pi.

I tried to find and apply it to fix the error, but it did not work out. I also confirmed that the module "face" is in the file opencv_contrib-3.3.0. I do not know why for some reason.

error 1

Traceback (most recent call last):
File "training.py", line 13, in 
recognizer = cv2.face.createLBPHFaceRecognizer()
AttributeError: 'module' object has no attribute 'face'

error 2

Traceback (most recent call last):
File "training.py", line 13, in 
help(cv2.face)
AttributeError: 'module' object has no attribute 'face'

error3

Traceback (most recent call last):
File "training.py", line 13, in 
help(cv2.face.createLBPHFaceRecognizer)
AttributeError: 'module' object has no attribute 'face'

python : 3.5.3 opencv-3.3.0 opencv_contrib-3.3.0

source code

# Import OpenCV2 for image processing
# Import os for file path
import cv2, os

# Import numpy for matrix calculation
import numpy as np

# Import Python Image Library (PIL)
from PIL import Image

# Create Local Binary Patterns Histograms for face recognization
recognizer = cv2.face.createLBPHFaceRecognizer()

# Using prebuilt frontal face training model, for face detection
detector = cv2.CascadeClassifier("haarcascade_frontalface_default.xml");

# Create method to get the images and label data
def getImagesAndLabels(path):

    # Get all file path
    imagePaths = [os.path.join(path,f) for f in os.listdir(path)] 

    # Initialize empty face sample
    faceSamples=[]

    # Initialize empty id
    ids = []

    # Loop all the file path
    for imagePath in imagePaths:

        # Get the image and convert it to grayscale
        PIL_img = Image.open(imagePath).convert('L')

        # PIL image to numpy array
        img_numpy = np.array(PIL_img,'uint8')

        # Get the image id
        id = int(os.path.split(imagePath)[-1].split(".")[1])
        print(id)

        # Get the face from the training images
        faces = detector.detectMultiScale(img_numpy)

        # Loop for each face, append to their respective ID
        for (x,y,w,h) in faces:

            # Add the image to face samples
            faceSamples.append(img_numpy[y:y+h,x:x+w])

            # Add the ID to IDs
            ids.append(id)

    # Pass the face array and IDs array
    return faceSamples,ids

# Get the faces and IDs
faces,ids = getImagesAndLabels('dataset')

# Train the model using the faces and IDs
recognizer.train(faces, np.array(ids))

# Save the model into trainer.yml
recognizer.save('trainer/trainer.yml')