<filename>recognize.py
#import packages
import cv2
import numpy as np
import os
import sqlite3

#call function of recognizer
recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read('C:/Users/ACER/Desktop/PROJECT ALL RESOURCE/PROJECT ALL RESOURCE/Face recognition/trainer/trainer.yml')
cascadePath = "C:/Users/ACER/Desktop/PROJECT ALL RESOURCE/PROJECT ALL RESOURCE/Face recognition/HaarCascade/haarcascade_frontalface_default.xml"
faceCascade = cv2.CascadeClassifier(cascadePath)
connect = sqlite3.connect("C:/Users/ACER/Desktop/PROJECT ALL RESOURCE/PROJECT ALL RESOURCE/Face recognition/sqlite3/Studentdb.db")
font = cv2.FONT_HERSHEY_SIMPLEX
cursor1 = connect.cursor()
cursor2 = connect.cursor()
cursor3 = connect.cursor()
cursor1.execute("UPDATE Student SET STATUS='ABSENT' WHERE STATUS='PRESENT' ")
connect.commit()
print(cursor1.rowcount, "RECORD INSERTED.")
cursor2.execute( "UPDATE Student  SET DATE=strftime('%d/%m/%y','now')")
connect.commit()
print(cursor2.rowcount, "RECORD INSERTED.")
print("Database UPDATED")

#initiate id counter
id = 0
# names related to ids: example ==> Vignesh: id=1,  etc and so on
names = ['None']
cmd = "SELECT FULLNAME from Student ORDER BY ID"
cursor = connect.execute(cmd)
rows=cursor.fetchall()
for row in rows:
    names.append('{0}'.format(row[0]))
#We Initialize and start realtime video capture
cam = cv2.VideoCapture(0)
cam.set(3, 640) # set video width
cam.set(4, 480) # set video height

# We Define min window size to be recognized as a face
minW = 0.1*cam.get(3)
minH = 0.1*cam.get(4)

#Actual Program for face 
while True:

    ret, img =cam.read()
    img = cv2.flip(img,1) # We Flip vertically
    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
    faces = faceCascade.detectMultiScale( 
        gray,
        scaleFactor = 1.2,
        minNeighbors = 5,
        minSize = (int(minW), int(minH)), )
    for(x,y,w,h) in faces:
        cv2.rectangle(img, (x,y), (x+w,y+h), (0,255,0), 2)
        id, confidence = recognizer.predict(gray[y:y+h,x:x+w])
        # Checking if confidence is less then 100 ==> "0" is perfect match 
        if (confidence < 100):
            id = names[id]
            confidence = "  {0}%".format(round(100 - confidence))
            sql = "Update Student SET STATUS='PRESENT' where FULLNAME='"+id+"'"
            cursor3.execute(sql)
            connect.commit()
        else:
            id = "unknown"
            confidence = "  {0}%".format(round(100 - confidence))
        cv2.putText(img, str(id), (x+5,y-5), font, 1, (255,255,255), 2)
        cv2.putText(img, str(confidence), (x+5,y+h-5), font, 1, (255,255,0), 1)  
    cv2.imshow('camera',img) 
    k = cv2.waitKey(10) & 0xff # Pressing 'ESC' for exiting video
    if k == 27:
        break
            
# Doing  a bit of cleanup
print("\n [INFO] Exiting Program and cleanup stuff")
cam.release()
cv2.destroyAllWindows()

<filename>recognize.py
#import packages
import cv2
import numpy as np
import os
import sqlite3

#call function of recognizer
recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read('C:/Users/ACER/Desktop/PROJECT ALL RESOURCE/PROJECT ALL RESOURCE/Face recognition/trainer/trainer.yml')
cascadePath = "C:/Users/ACER/Desktop/PROJECT ALL RESOURCE/PROJECT ALL RESOURCE/Face recognition/HaarCascade/haarcascade_frontalface_default.xml"
faceCascade = cv2.CascadeClassifier(cascadePath)
connect = sqlite3.connect("C:/Users/ACER/Desktop/PROJECT ALL RESOURCE/PROJECT ALL RESOURCE/Face recognition/sqlite3/Studentdb.db")
font = cv2.FONT_HERSHEY_SIMPLEX
cursor1 = connect.cursor()
cursor2 = connect.cursor()
cursor3 = connect.cursor()
cursor1.execute("UPDATE Student SET STATUS='ABSENT' WHERE STATUS='PRESENT' ")
connect.commit()
print(cursor1.rowcount, "RECORD INSERTED.")
cursor2.execute( "UPDATE Student  SET DATE=strftime('%d/%m/%y','now')")
connect.commit()
print(cursor2.rowcount, "RECORD INSERTED.")
print("Database UPDATED")

#initiate id counter
id = 0
# names related to ids: example ==> Vignesh: id=1,  etc and so on
names = ['None']
cmd = "SELECT FULLNAME from Student ORDER BY ID"
cursor = connect.execute(cmd)
rows=cursor.fetchall()
for row in rows:
    names.append('{0}'.format(row[0]))
#We Initialize and start realtime video capture
cam = cv2.VideoCapture(0)
cam.set(3, 640) # set video width
cam.set(4, 480) # set video height

# We Define min window size to be recognized as a face
minW = 0.1*cam.get(3)
minH = 0.1*cam.get(4)

#Actual Program for face 
while True:

    ret, img =cam.read()
    img = cv2.flip(img,1) # We Flip vertically
    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
    faces = faceCascade.detectMultiScale( 
        gray,
        scaleFactor = 1.2,
        minNeighbors = 5,
        minSize = (int(minW), int(minH)), )
    for(x,y,w,h) in faces:
        cv2.rectangle(img, (x,y), (x+w,y+h), (0,255,0), 2)
        id, confidence = recognizer.predict(gray[y:y+h,x:x+w])
        # Checking if confidence is less then 100 ==> "0" is perfect match 
        if (confidence < 100):
            id = names[id]
            confidence = "  {0}%".format(round(100 - confidence))
            sql = "Update Student SET STATUS='PRESENT' where FULLNAME='"+id+"'"
            cursor3.execute(sql)
            connect.commit()
        else:
            id = "unknown"
            confidence = "  {0}%".format(round(100 - confidence))
        cv2.putText(img, str(id), (x+5,y-5), font, 1, (255,255,255), 2)
        cv2.putText(img, str(confidence), (x+5,y+h-5), font, 1, (255,255,0), 1)  
    cv2.imshow('camera',img) 
    k = cv2.waitKey(10) & 0xff # Pressing 'ESC' for exiting video
    if k == 27:
        break
            
# Doing  a bit of cleanup
print("\n [INFO] Exiting Program and cleanup stuff")
cam.release()
cv2.destroyAllWindows()

#import packages #call function of recognizer #initiate id counter # names related to ids: example ==> Vignesh: id=1,  etc and so on #We Initialize and start realtime video capture # set video width # set video height # We Define min window size to be recognized as a face #Actual Program for face # We Flip vertically # Checking if confidence is less then 100 ==> "0" is perfect match # Pressing 'ESC' for exiting video # Doing  a bit of cleanup