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:
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 cascade 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
Python3
# 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 below 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 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 milliseconds 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 "trainer.yml". recognizer.train(faces, np.array( Id )) recognizer.save( "TrainingImageLabel\Trainer.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\Trainer.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() |
Sample Image of the GUI:
Folder Structure For reference :