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Train_Image.py
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Train_Image.py
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import os
import cv2
import numpy as np
from PIL import Image
from threading import Thread
import PySimpleGUI as sg
# -------------- image labesl ------------------------
def getImagesAndLabels(path):
# get the path of all the files in the folder
imagePaths = [os.path.join(path, f) for f in os.listdir(path)]
value = len(imagePaths)
# create empth face list
faces = []
# create empty ID list
Ids = []
# now looping through all the image paths and loading the Ids and the images
i = 1
for imagePath in imagePaths:
# loading the image and converting it to gray scale
pilImage = Image.open(imagePath).convert('L')
# Now we are converting the PIL image into numpy array
imageNp = np.array(pilImage, 'uint8')
# getting the Id from the image
Id = int(os.path.split(imagePath)[-1].split(".")[1])
# extract the face from the training image sample
faces.append(imageNp)
Ids.append(Id)
sg.one_line_progress_meter('Image Training Model', i, value, 'key', 'Training Time Left: ',orientation="h")
i+=1
return faces, Ids
# ----------- train images function ---------------
def TrainImages():
recognizer = cv2.face_LBPHFaceRecognizer.create()
detector = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
faces, Id = getImagesAndLabels("TrainingImage")
target = recognizer.train(faces, np.array(Id))
recognizer.save("TrainingImageLabel"+os.sep+"Trainner.yml")
sg.popup_auto_close('All Images Trained')