Monday, August 26, 2024

Face Recognition Attendance System Using Python 2024

 

                        INTRODUCTION

K-nearest neighbor definition
As a classification algorithm, kNN assigns a new data point to the majority set within its neighbors. As a regression algorithm, kNN makes a prediction based on the average of the values closest to the query point.


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FULL CODE 😃👇


DATASET.PY

import cv2
import pickle
import numpy as np
import os
video=cv2.VideoCapture(0)
facedetect=cv2.CascadeClassifier('haarcascade_frontalface_default .xml')

faces_data=[]

i=0

name=input("Enter Your Name: ")

while True:
    ret,frame=video.read()
    gray=cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    faces=facedetect.detectMultiScale(gray, 1.3 ,5)
    for (x,y,w,h) in faces:
        crop_img=frame[y:y+h, x:x+w, :]
        resized_img=cv2.resize(crop_img, (50,50))
        if len(faces_data)<=100 and i%10==0:
            faces_data.append(resized_img)
        i=i+1
        cv2.putText(frame, str(len(faces_data)), (50,50), cv2.FONT_HERSHEY_COMPLEX, 1, (50,50,255), 1)
        cv2.rectangle(frame, (x,y), (x+w, y+h), (50,50,255), 1)
    cv2.imshow("Frame",frame)
    k=cv2.waitKey(1)
    if len(faces_data)==50:
        break
video.release()
cv2.destroyAllWindows()

faces_data=np.asarray(faces_data)
faces_data=faces_data.reshape(100, -1)


if 'names.pkl' not in os.listdir('data/'):
    names=[name]*100
    with open('data/names.pkl', 'wb') as f:
        pickle.dump(names, f)
else:
    with open('data/names.pkl', 'rb') as f:
        names=pickle.load(f)
    names=names+[name]*100
    with open('data/names.pkl', 'wb') as f:
        pickle.dump(names, f)

if 'faces_data.pkl' not in os.listdir('data/'):
    with open('data/faces_data.pkl', 'wb') as f:
        pickle.dump(faces_data, f)
else:
    with open('data/faces_data.pkl', 'rb') as f:
        faces=pickle.load(f)
    faces=np.append(faces, faces_data, axis=0)
    with open('data/faces_data.pkl', 'wb') as f:
        pickle.dump(faces, f)

ATTENDANCE.PY  CODE:

from sklearn.neighbors import KNeighborsClassifier
import cv2
import pickle
import numpy as np
import os
import csv
import time
from datetime import datetime


from win32com.client import Dispatch

def speak(str1):
    speak=Dispatch(("SAPI.SpVoice"))
    speak.Speak(str1)

video=cv2.VideoCapture(0)
facedetect=cv2.CascadeClassifier('haarcascade_frontalface_default .xml')

with open('data/names.pkl', 'rb') as w:
    LABELS=pickle.load(w)
with open('data/faces_data.pkl', 'rb') as f:
    FACES=pickle.load(f)

print('Shape of Faces matrix --> ', FACES.shape)

knn=KNeighborsClassifier(n_neighbors=5)
knn.fit(FACES, LABELS)

imgBackground=cv2.imread("background.png")

COL_NAMES = ['NAME', 'TIME']

while True:
    ret,frame=video.read().......
    ...........


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