Dynamic Face Recognition based entry and exit system

import face_recognition
import cv2
from pygame import mixer
import time
from datetime import datetime
from face_reg import known_face_encodings
from face_reg import known_face_names
from boltiot import Bolt
import pyodbc
import itertools


#Bolt iot
api_key = " "  # Enter your Bolt API Key
device_id  = " " # Enter your Bolt  device ID
mybolt = Bolt(api_key, device_id)


# Database Connectivity
conn = pyodbc.connect('Driver={ODBC Driver 17 for SQL Server};'   # Enter your driver, server and 
                      'Server=DESKTOP-TDBKSHJ\SQLEXPRESS;'        # and database name
                      'Database=face_rec;'
                      'Trusted_Connection=yes;')
cursor = conn.cursor()

# '0' for primary camera and '1' for secondary camera
video_capture = cv2.VideoCapture(0)

# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
seen_faces = []
process_this_frame = True

while True:
    # Grab a single frame of video
    ret, frame = video_capture.read()

    # Resizing frame of video to 1/4 size for faster face recognition processing
    small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)

    # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
    rgb_small_frame = small_frame[:, :, ::-1]

    # Only process every other frame of video to save time
    if process_this_frame:

        # Find all the faces and face encodings in the current frame of video
        face_locations = face_recognition.face_locations(rgb_small_frame)
        face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)

        face_names = []
        for face_encoding in face_encodings:
            # See if the face is a match for the known face(s)
            matches = face_recognition.compare_faces(known_face_encodings, face_encoding,0.5)
            name = "Unknown"

            if True in matches:
                first_match_index = matches.index(True)
                name = known_face_names[first_match_index]

                # Get the indices of seen faces from the database and store it in the seen_faces list
                cursor.execute('select seen_face_index from dbo.seen_face')
                result = cursor.fetchall()
                seen_faces = list(itertools.chain(*result))

                # The condition for entry is either the seen_faces list should be empty or the matched index should
                # not be in the seen_faces list
                if len(seen_faces) == 0 or first_match_index not in seen_faces:
                   mixer.init()
                   mixer.music.load('D:\Projects\MP\pypi\_'+ str(first_match_index) + '.mp3')
                   # Play the welcome audio
                   mixer.music.play()

                   # insert the matched index into the column of seen face indices
                   cursor.execute('insert into face_rec.dbo.seen_face(seen_face_index) values(?)',
                                  (first_match_index))
                   conn.commit()

                   # Get the current date and time
                   now = datetime.now()
                   dt_string = now.date()
                   tm_string = now.time()

                   # insert the time and date to the 'entry_time' table in the data base
                   cursor.execute('insert into face_rec.dbo.entry_time(emp_id,indate,intime) values(?,?,?)',(first_match_index,dt_string,tm_string))
                   conn.commit()

                   #  Door Control
                   response = mybolt.digitalWrite('0', 'HIGH')
                   # number of seconds the door should be left open
                   time.sleep(2)
                   response = mybolt.digitalWrite('0', 'LOW')

            # alert the security
            if(name=="Unknown"):
                response = mybolt.digitalWrite('1','HIGH')

            # add the name to the face_names list
            face_names.append(name)


    process_this_frame = not process_this_frame

    # Display the results
    for (top, right, bottom, left), name in zip(face_locations, face_names):
        # Scale back up face locations since the frame we detected in was scaled to 1/4 size
        top *= 4
        right *= 4
        bottom *= 4
        left *= 4

        # Draw a box around the face
        cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)

        # Draw a label with a name below the face
        cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
        font = cv2.FONT_HERSHEY_DUPLEX
        cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)

    # Display the resulting image
    cv2.imshow('Video', frame)

    # Hit 'q' on the keyboard to quit!
    if cv2.waitKey(1) & 0xFF == ord('q'):
       break

# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()

import face_recognition
import cv2
import time
from datetime import datetime
from face_reg import known_face_encodings
from face_reg import known_face_names
from boltiot import Bolt
import pyodbc
import itertools

# Bolt iot
api_key = " " # Enter your API Key
device_id = " " # Enter your device ID
mybolt = Bolt(api_key, device_id)

# Database Connectivity
conn = pyodbc.connect('Driver={ODBC Driver 17 for SQL Server};' # Enter your Driver, server and
                      'Server=DESKTOP-TDBKSHJ\SQLEXPRESS;'      # database name
                      'Database=face_rec;'
                      'Trusted_Connection=yes;')
cursor = conn.cursor()

# '0' for primary camera and '1' for secondary camera
video_capture = cv2.VideoCapture(1)

# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
seen_faces = []

process_this_frame = True

while True:
    # Grab a single frame of video
    ret, frame = video_capture.read()

    # Resizing frame of video to 1/4 size for faster face recognition processing
    small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)

    # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
    rgb_small_frame = small_frame[:, :, ::-1]

    # Only process every other frame of video to save time
    if process_this_frame:

        # Find all the faces and face encodings in the current frame of video
        face_locations = face_recognition.face_locations(rgb_small_frame)
        face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)

        face_names = []
        for face_encoding in face_encodings:
            # See if the face is a match for the known face(s)
            matches = face_recognition.compare_faces(known_face_encodings, face_encoding, 0.5)
            name = "Unknown"

            if True in matches:
                first_match_index = matches.index(True)
                name = known_face_names[first_match_index]

                # Get the indices of seen faces from the database and store it in the seen_faces list
                cursor.execute('select seen_face_index from dbo.seen_face')
                result = cursor.fetchall()
                seen_faces = list(itertools.chain(*result))

                # person is allowed in the exit gate only if the seen_faces list is not empty or matched index
                # is in the seen faces list
                if len(seen_faces) != 0 or first_match_index in seen_faces:

                    # remove the matched index from the 'seen_face' table in the database
                    cursor.execute('delete from dbo.seen_face where seen_face_index=?',
                                   (first_match_index))
                    conn.commit()
                    print(seen_faces)

                    # Get the current date and time
                    now = datetime.now()
                    dt_string = now.date()
                    tm_string = now.time()
                    cursor.execute('insert into face_rec.dbo.exit_time(emp_id,out_date,out_time) values(?,?,?)',
                                   (first_match_index, dt_string, tm_string))
                    conn.commit()

                    #  Door Control
                    response = mybolt.digitalWrite('0', 'HIGH')
                    # number of seconds the door should be left open
                    time.sleep(2)
                    response = mybolt.digitalWrite('0', 'LOW')


            # alert security
            if (name == "Unknown"):
                response = mybolt.digitalWrite('1', 'HIGH')

            face_names.append(name)


    process_this_frame = not process_this_frame

    # Display the results
    for (top, right, bottom, left), name in zip(face_locations, face_names):
        # Scale back up face locations since the frame we detected in was scaled to 1/4 size
        top *= 4
        right *= 4
        bottom *= 4
        left *= 4

        # Draw a box around the face
        cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)

        # Draw a label with a name below the face
        cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
        font = cv2.FONT_HERSHEY_DUPLEX
        cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)

    # Display the resulting image
    cv2.imshow('Video', frame)

    # Hit 'q' on the keyboard to quit!
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()

<!DOCTYPE html>
<html>
    <head>
        <title>Bolt IoT Platform</title>
        <script type="text/javascript" src="https://cloud.boltiot.com/static/js/boltCommands.js"></script>
        <script>
        setKey('{{ApiKey}}','{{Name}}');
        </script>
    </head>
    <body>
        <center>
            <h1>Door Control</h1>
        <button onclick="digitalWrite(0, 'HIGH');">Open</button>
        <button onclick="digitalWrite(0, 'LOW');">Close</button>
        </center>
        <br>
        <hr>
         <center>
            <h1>Buzzer Control</h1>
        <button onclick="digitalWrite(1, 'HIGH');">ON</button>
        <button onclick="digitalWrite(1, 'LOW');">OFF</button>
        </center>     
    </body>
</html>