Closed sungjinp11 closed 6 years ago
If uou are using python3 use pip3 instead of using pip for: pip3 install opencv-contrib-python.
then you can simply do: recognizer = cv2.face.LBPHFaceRecognizer_create()
amazingily worked
i long solved it but thanks buddy.
On Sun, Jun 14, 2020 at 8:14 PM rahul06101997 notifications@github.com wrote:
i am getting error recoginizer = cv2.face.createLBPHFaceRecognizer() AttributeError: 'module' object has no attribute 'face' first pip install opencv-contrib-python and try like this
recoginizer = cv2.face.LBPHFaceRecognizer_create()
and for training data (if get any error for recoginizer.load(....yml)) use recoginizer.read(....yml)
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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 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')