OpenCv Trainer

import cv2, os
import numpy as np
from PIL import Image

# Create data analizer for facerecognition
recognizer = cv2.face.LBPHFaceRecognizer_create()
# Assign the facerecognition classifier to use
detector = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
# Assign the path where dataset are stored
path = 'C:/Users/PC/PycharmProjects/pythonProject1/DataSet'


# Create an data structure
def getImagesID(path):
    # get the path of all the files in the folder
    imagePaths = [os.path.join(path, f) for f in os.listdir(path)]
    # create empty face list
    faceSamples = []
    # create empty ID list
    Ids = []
    # now looping through all the image paths and loading the Ids and the images
    for imagePath in imagePaths:
        # ignore if the file does not have jpg extension :
        print(imagePath)
        if os.path.split(imagePath)[-1].split(".")[-1] != 'jpg':
            continue
        # loading the image and converting it to gray scale
        faceImage = Image.open(imagePath).convert('L')

        # Now we are converting the PIL image into numpy array
        faceNp = np.array(faceImage, 'uint8')
        print(faceNp)
        # getting the Id from the image
        Id = os.path.split(imagePath)[-1].split(".")[1]
        print(Id)
        # extract the face from the training image sample
        faces = detector.detectMultiScale(faceNp)
        # Add face i,age in the array to faceSamples
        faceSamples.append(faceNp)
        print(faceSamples)
        # Add label Id to Ids array
        Ids.append(Id)
        # print the Id for checking
        # display the extracted face image on the screen
        cv2.imshow("Training", faceNp)
        # wait for user key press
        cv2.waitKey(10)
    # exit the loop and return array Ids and faceSamples
    return Ids, faceSamples


# Assign the content of getImagesID data structure to Ids and
Ids, faceSamples = getImagesID(path)
for i in range(0, len(Ids)):
    Ids[i] = int(Ids[i])
# Analize and record the data from facesample and Ids
print(Ids)
recognizer.train(faceSamples, np.array(Ids))
# Save the process data in the training data file
recognizer.save('C:/Users/PC/PycharmProjects/pythonProject1/trainner.yml')
# Close all opened windows cv2.destroyAllWindows