Python | Face recognition using GUI

In this article, a fairly simple way is mentioned to implement facial recognition system using Python and OpenCV module along with the explanation of the code step by step in the comments.

Before starting we need to install some libraries in order to implement the code. Below you will see the usage of the library along with the code to install it:

  • OpenCV:

    OpenCV (Open Source Computer Vision Library) is an open-source computer vision and machine learning software library. which is built to provide a common infrastructure for machine learning algorithms and computer vision. It has thousands of optimized algorithms which can be used different purposes like detecting and recognizing faces, identifying objects and many more. We need it to take pictures using our webcam and some manipulation needed to be done in the image.



    To install the library you need to install pip in your system after that you can follow the steps in command prompt:
    Step 1: pip install opencv-python
    Step 2: pip install opencv-contrib-python

  • NumPy:
    NumPy is the fundamental package for scientific computing in Python which provides a multidimensional array object other mathematical operations can be performed using this but simply speaking we just need it to convert our images into some form of an array so that we can store the model that has been trained.

    To install the library you can type a simple line of code in your command shell:
    pip install numpy

  • Haar Cascade:

    Haar Cascade is basically a classifier which is used to detect the objects for which it has been trained for, from the source. The result is an XML file which stores the trained result. If said simply the Haar Cascade is trained by superimposing the positive image over a set of negative images. The training requires a high spec system and a good internet connection and thousands of training images that is why it is carried out in the server. For increasing the efficiency of the results they use high-quality images and increase the number of stages for which the classifier is trained. We need haar cascade frontal face recognizer to detect the face from our webcam.
    To download the haar casade files of different objects you can go the below link:
    GitHub: HaarCascades

  • Python GUI (tkinter):

    Tkinter is a simple GUI module used for implementing fairly simple GUI and helps us to interact with code in a simple way. Though for understanding the code its not important for you to know how it works.
    If you wanna learn more about Tkinter click the link below
    Python GUI – tkinter

Code: Python implementing to recognize face using GUI

filter_none

edit
close

play_arrow

link
brightness_4
code

# importing libraries
import tkinter as tk
from tkinter import Message, Text
import cv2
import os
import shutil
import csv
import numpy as np
from PIL import Image, ImageTk
import pandas as pd
import datetime
import time
import tkinter.ttk as ttk
import tkinter.font as font
from pathlib import Path
  
window = tk.Tk() 
window.title("Face_Recogniser")
window.configure(background ='white')
window.grid_rowconfigure(0, weight = 1)
window.grid_columnconfigure(0, weight = 1)
message = tk.Label(
    window, text ="Face-Recognition-System"
    bg ="green", fg = "white", width = 50
    height = 3, font = ('times', 30, 'bold')) 
      
message.place(x = 200, y = 20)
  
lbl = tk.Label(window, text = "No."
width = 20, height = 2, fg ="green"
bg = "white", font = ('times', 15, ' bold ') ) 
lbl.place(x = 400, y = 200)
  
txt = tk.Entry(window, 
width = 20, bg ="white"
fg ="green", font = ('times', 15, ' bold '))
txt.place(x = 700, y = 215)
  
lbl2 = tk.Label(window, text ="Name"
width = 20, fg ="green", bg ="white"
height = 2, font =('times', 15, ' bold ')) 
lbl2.place(x = 400, y = 300)
  
txt2 = tk.Entry(window, width = 20
bg ="white", fg ="green"
font = ('times', 15, ' bold ')  )
txt2.place(x = 700, y = 315)
  
# The function beow is used for checking 
# whether the text below is number or not ?   
def is_number(s):
    try:
        float(s)
        return True
    except ValueError:
        pass
   
    try:
        import unicodedata
        unicodedata.numeric(s)
        return True
    except (TypeError, ValueError):
        pass
   
    return False
# Take Images is a function used for creating
# the sample of the images which is used for 
# training the model. It takes 60 Images of 
# every new user.  
def TakeImages():        
      
    # Both ID and Name is used for recognising the Image
    Id =(txt.get()) 
    name =(txt2.get())
      
    # Checking if the ID is numeric and name is Alphabetical
    if(is_number(Id) and name.isalpha()): 
        # Opening the primary camera if you want to access
        # the secondary camera you can mention the number 
        # as 1 inside the parenthesis
        cam = cv2.VideoCapture(0
        # Specifying the path to haarcascade file
        harcascadePath = "data\haarcascade_frontalface_default.xml" 
        # Creating the classier based on the haarcascade file.
        detector = cv2.CascadeClassifier(harcascadePath) 
        # Initializing the sample number(No. of images) as 0
        sampleNum = 0 
        while(True):
            # Reading the video captures by camera frame by frame
            ret, img = cam.read() 
            # Converting the image into grayscale as most of 
            # the the processing is done in gray scale format
            gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 
              
            # It converts the images in different sizes 
            # (decreases by 1.3 times) and 5 specifies the
            # number of times scaling happens
            faces = detector.detectMultiScale(gray, 1.3, 5)
              
            # For creating a rectangle around the image
            for (x, y, w, h) in faces: 
                # Specifying the coordinates of the image as well
                # as color and thickness of the rectangle.       
                # incrementing sample number for each image
                cv2.rectangle(img, (x, y), (
                    x + w, y + h), (255, 0, 0), 2
                sampleNum = sampleNum + 1
                # saving the captured face in the dataset folder 
                # TrainingImage as the image needs to be trained 
                # are saved in this folder
                cv2.imwrite(
                    "TrainingImage\ "+name +"."+Id +'.'+ str(
                        sampleNum) + ".jpg", gray[y:y + h, x:x + w])
                # display the frame that has been captured 
                # and drawn rectangle around it.
                cv2.imshow('frame', img)
            # wait for 100 miliseconds 
            if cv2.waitKey(100) & 0xFF == ord('q'):
                break
            # break if the sample number is more than 60
            elif sampleNum>60:
                break
        # releasing the resources
        cam.release() 
        # closing all the windows
        cv2.destroyAllWindows() 
        # Displaying message for the user
        res = "Images Saved for ID : " + Id +" Name : "+ name  
        # Creating the entry for the user in a csv file
        row = [Id, name] 
        with open('UserDetails\UserDetails.csv', 'a+') as csvFile:
            writer = csv.writer(csvFile)
            # Entry of the row in csv file
            writer.writerow(row) 
        csvFile.close()
        message.configure(text = res)
    else:
        if(is_number(Id)):
            res = "Enter Alphabetical Name"
            message.configure(text = res)
        if(name.isalpha()):
            res = "Enter Numeric Id"
            message.configure(text = res)
              
# Training the images saved in training image folder    
def TrainImages():
    # Local Binary Pattern Histogram is an Face Recognizer
    # algorithm inside OpenCV module used for training the image dataset
    recognizer = cv2.face.LBPHFaceRecognizer_create()  
    # Specifying the path for HaarCascade file
    harcascadePath = "data\haarcascade_frontalface_default.xml"
    # creating detector for faces
    detector = cv2.CascadeClassifier(harcascadePath)
    # Saving the detected faces in variables 
    faces, Id = getImagesAndLabels("TrainingImage"
    # Saving the trained faces and their respective ID's 
    # in a model named as "trainner.yml".
    recognizer.train(faces, np.array(Id))     
    recognizer.save("TrainingImageLabel\Trainner.yml"
    # Displaying the message
    res = "Image Trained" 
    message.configure(text = res)
  
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)] 
    faces =[]
    # creating empty ID list
    Ids =[]
    # now looping through all the image paths and loading the
    # Ids and the images saved in the folder
    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)        
    return faces, Ids
# For testing phase
def TrackImages():
    recognizer = cv2.face.LBPHFaceRecognizer_create()
    # Reading the trained model
    recognizer.read("TrainingImageLabel\Trainner.yml"
    harcascadePath = "data\haarcascade_frontalface_default.xml"
    faceCascade = cv2.CascadeClassifier(harcascadePath)
    # getting the name from "userdetails.csv"
    df = pd.read_csv("UserDetails\UserDetails.csv")  
    cam = cv2.VideoCapture(0)
    font = cv2.FONT_HERSHEY_SIMPLEX        
    while True:
        ret, im = cam.read()
        gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
        faces = faceCascade.detectMultiScale(gray, 1.2, 5)    
        for(x, y, w, h) in faces:
            cv2.rectangle(im, (x, y), (x + w, y + h), (225, 0, 0), 2)
            Id, conf = recognizer.predict(gray[y:y + h, x:x + w])                                   
            if(conf < 50):
                aa = df.loc[df['Id'] == Id]['Name'].values
                tt = str(Id)+"-"+aa    
            else:
                Id ='Unknown'                
                tt = str(Id)  
            if(conf > 75):
                noOfFile = len(os.listdir("ImagesUnknown"))+1
                cv2.imwrite("ImagesUnknown\Image"+ 
                str(noOfFile) + ".jpg", im[y:y + h, x:x + w])            
            cv2.putText(im, str(tt), (x, y + h), 
            font, 1, (255, 255, 255), 2)        
        cv2.imshow('im', im) 
        if (cv2.waitKey(1)== ord('q')):
            break
    cam.release()
    cv2.destroyAllWindows()
    
    
takeImg = tk.Button(window, text ="Sample"
command = TakeImages, fg ="white", bg ="green"
width = 20, height = 3, activebackground = "Red"
font =('times', 15, ' bold '))
takeImg.place(x = 200, y = 500)
trainImg = tk.Button(window, text ="Training"
command = TrainImages, fg ="white", bg ="green"
width = 20, height = 3, activebackground = "Red"
font =('times', 15, ' bold '))
trainImg.place(x = 500, y = 500)
trackImg = tk.Button(window, text ="Testing"
command = TrackImages, fg ="white", bg ="green"
width = 20, height = 3, activebackground = "Red"
font =('times', 15, ' bold '))
trackImg.place(x = 800, y = 500)
quitWindow = tk.Button(window, text ="Quit"
command = window.destroy, fg ="white", bg ="green"
width = 20, height = 3, activebackground = "Red"
font =('times', 15, ' bold '))
quitWindow.place(x = 1100, y = 500)
  
   
window.mainloop()

chevron_right


Sample Image of the GUI:

Folder Structure For reference :




My Personal Notes arrow_drop_up

Check out this Author's contributed articles.

If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to contribute@geeksforgeeks.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.

Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.