Face_recognition_software/train.py at master · Agam00/Face_recognition_software · GitHub

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from tkinter import*
from tkinter import ttk
from PIL import Image,ImageTk
from tkinter import messagebox
import mysql.connector
import cv2
import os
import numpy as np

class Train:
    def __init__(self,root):
        self.root=root
        self.root.geometry("1366x768+0+0")
        self.root.title("Face Recognition System")
#==========title lable===========

        title_lbl=Label(self.root,text="TRAIN DATA SET",font=("times new roman",32,"bold"),bg="white",fg="red")
        title_lbl.place(x=0,y=0,width=1360,height=35)
#==========top image=============

        img_top = Image.open(r"img\facialrecognition.png")
        img_top = img_top.resize((1360,300), Image.ANTIALIAS)
        self.photoimg_top = ImageTk.PhotoImage(img_top)

        f_lbl = Label(self.root, image=self.photoimg_top)
        f_lbl.place(x=0, y=40, width=1360, height=300)
#===========train button=========

        b1_1 = Button(self.root,text="TRAIN DATA",command=self.train_classifier,cursor="hand2",font=("times new roman",30,"bold"),bg="red",fg="white")
        b1_1.place(x=0, y=340, width=1360, height=35)
#===========bottom image=========

        img_bottom = Image.open(r"img\photos.jpg")
        img_bottom = img_bottom.resize((1360,300), Image.ANTIALIAS)
        self.photoimg_bottom = ImageTk.PhotoImage(img_bottom)

        f_lbl = Label(self.root, image=self.photoimg_bottom)
        f_lbl.place(x=0, y=400, width=1360, height=300)

    def train_classifier(self):
        data_dir=("data")
        path=[os.path.join(data_dir,file) for file in os.listdir(data_dir)]

        faces=[]
        ids=[]

        for image in path:
            img=Image.open(image).convert('L')   #Gray scale image
            imageNp=np.array(img,'uint8') #in array uint8 is a data type in array
            id=int(os.path.split(image)[1].split('.')[1])

            faces.append(imageNp)
            ids.append(id)
            cv2.imshow("Training",imageNp)
            cv2.waitKey(1)==13
        ids=np.array(ids)

        #=======Train the classifier and save=========
        recognizer = cv2.face.LBPHFaceRecognizer_create()
        recognizer.train(faces,ids)
        recognizer.write("classifier.xml")
        cv2.destroyAllWindows()
        messagebox.showinfo("Result","face training completed",parent=self.root)


if __name__ == "__main__":
    root=Tk()
    obj=Train(root)
    root.mainloop()