Attendance.py

import cv2
import numpy as np
import face_recognition
import os
import datetime
import pyrebase

config = {
    "apiKey": "AIzaSyBttgLVbtWWdtRhos39BzbqvQZDIJaIe5U",
    "authDomain": "mark-it-ec28b.firebaseapp.com",
    "projectId": "mark-it-ec28b",
    "storageBucket": "mark-it-ec28b.appspot.com",
    "messagingSenderId": "187768173767",
    "appId": "1:187768173767:web:e9ba36b17e9112fc6cfae2",
    "measurementId": "G-42HYDHB4Q1",
    "databaseURL": "gs://mark-it-ec28b.appspot.com"
}

firebase = pyrebase.initialize_app(config)
storage = firebase.storage()

path_on_cloud = "Attendance/Attendance.csv"
path_local = "Attendance.csv"

path = 'Image Database'
images = []
classNames = []
myList = os.listdir(path)
print(myList)

for cls in myList:
    curImg = cv2.imread(f'{path}/{cls}')
    images.append(curImg)
    classNames.append(os.path.splitext(cls)[0])

print(classNames)


def findEncodings(images):
    encodeList = []
    for img in images:
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        encode = face_recognition.face_encodings(img)[0]
        encodeList.append(encode)
    return encodeList


def markAttendance(name):

    with open('Attendance.csv', 'r+') as f:
        myDataList = f.readlines()
        nameList = []
        for line in myDataList:
            entry = line.split(',')
            nameList.append(entry[0])

        if name not in nameList:
            now = datetime.datetime.now()
            dtString = now.strftime('%H:%M:%S')
            f.writelines(f'\n{name}, {dtString}')
            # upload csv to firebase
            storage.child(path_on_cloud).put(path_local)


encodeListKnown = findEncodings(images)
print('Encoding Complete')

cap = cv2.VideoCapture(0)

while True:
    success, img = cap.read()
    imgS = cv2.resize(img, (0, 0), None, 0.25, 0.25)
    imgS = cv2.cvtColor(imgS, cv2.COLOR_BGR2RGB)

    facesCurFrame = face_recognition.face_locations(imgS)
    encodesCurFrame = face_recognition.face_encodings(imgS, facesCurFrame)

    for encodeFace, faceLoc in zip(encodesCurFrame, facesCurFrame):
        matches = face_recognition.compare_faces(encodeListKnown, encodeFace)
        faceDis = face_recognition.face_distance(encodeListKnown, encodeFace)
        # print(faceDis)
        matchIndex = np.argmin(faceDis)

        if matches[matchIndex]:
            name = classNames[matchIndex].upper()
            # print(name)
            y1, x2, y2, x1 = faceLoc
            y1, x2, y2, x1 = y1 * 4, x2 * 4, y2 * 4, x1 * 4
            cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
            cv2.rectangle(img, (x1, y2 - 35), (x2, y2), (0, 255, 0), cv2.FILLED)
            cv2.putText(img, name, (x1 + 6, y2 - 6), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2)
            markAttendance(name)
            storage.child(path_on_cloud).put(path_local)

    cv2.imshow('Webcam', img)
    cv2.waitKey(1)