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train.py
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train.py
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from ntpath import join
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
import pyttsx3
engine = pyttsx3.init()
voices = engine.getProperty('voices')
engine.setProperty('voice', voices[1].id) # 1 is for female voice and 0 is for male voice
def speak_va(transcribed_query):
engine.say(transcribed_query)
engine.runAndWait()
class Train:
def __init__(self, root):
self.root=root
self.root.geometry("1530x798+8+8")
self.root.title("Face Recogniton System")
title_lbl=Label(self.root, text="TRAIN DATA SET", font=("times new roman",35,"bold"),bg="blue",fg="white")
title_lbl.place(x=0, y=0,width=1530, height=45)
img_top=Image.open(r"C:\Users\Akansha Jain\Desktop\Desktop\project\project\images\main background.jpg")
img_top=img_top.resize((1530,798),Image.ANTIALIAS)
self.photoimg_top=ImageTk.PhotoImage(img_top)
f_lbl=Label(self.root,image=self.photoimg_top)
f_lbl.place(x=0,y=55,width=1530 ,height=798)
#button
b1_1=Button(self.root, text="TRAIN DATA",command=self.train_classifier,cursor="hand2",font=("times new roman",30,"bold"),bg="blue",fg="white")
b1_1.place(x=450, y=380,width=600,height=60)
img_bottom=Image.open(r"C:\Users\Akansha Jain\Desktop\Desktop\project\project\images\training.jpg")
img_bottom=img_bottom.resize((350,360),Image.ANTIALIAS)
self.photoimage_bottom=ImageTk.PhotoImage(img_bottom)
f_lbl=Label(self.root,image=self.photoimage_bottom)
f_lbl.place(x=620,y=85,width=250 ,height=250)
def train_classifier(self):
data_dir = (r"C:\Users\Akansha Jain\Desktop\Desktop\project\project\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')
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
clf=cv2.face.LBPHFaceRecognizer_create()
clf.train(faces,ids)
clf.write("classifier.xml")
cv2.destroyAllWindows()
speak_va("Training datasets completed successfully!")
messagebox.showinfo("Result","Training datasets completed successfully!",parent=self.root)
self.root.destroy()
if __name__ == "__main__":
root=Tk()
obj=Train(root)
root.mainloop()