'''
CODE DESCRIPTION:
This Python script is used to train a face recognition model using Local Binary Patterns Histograms (LBPH) in OpenCV.
It reads face images stored in a directory (./Myfaces/user/), converts them into grayscale, labels them, and then trains the face recognition model.
The trained model can later be used for face recognition tasks, such as identifying or verifying users.
'''
import cv2
import numpy as np
from os import listdir
from os.path import isfile, join
# Get the training data we previously made
data_path = './Myfaces/user/'
onlyfiles = [f for f in listdir(data_path) if isfile(join(data_path, f))]
# Create arrays for training data and labels
Training_Data, Labels = [], []
# Open training images in our datapath
# Create a numpy array for training data
for i, files in enumerate(onlyfiles):
image_path = data_path + onlyfiles[i]
images = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
Training_Data.append(np.asarray(images, dtype=np.uint8))
Labels.append(i)
# Create a numpy array for both training data and labels
Labels = np.asarray(Labels, dtype=np.int32)
# Initialize facial recognizer
model = cv2.face.LBPHFaceRecognizer_create()
# NOTE: For OpenCV 3.0 use cv2.face.createLBPHFaceRecognizer()
# Let's train our model
model.train(np.asarray(Training_Data), np.asarray(Labels))
print("Model trained sucessefully")