test.py

import face_recognition as fc
import os
import numpy as np
import cv2
from datetime import datetime

path = 'Photos'
photos = []
NameClass = []
ImgList = os.listdir(path)

for cl in ImgList:
    curImg = cv2.imread(f'{path}/{cl}')
    photos.append(curImg)
    NameClass.append(os.path.splitext(cl)[0])


def EncodingsFind(images):
    ListEncodes = []
    for img in photos:
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        encode = fc.face_encodings(img)[0]
        ListEncodes.append(encode)
    return ListEncodes


def Attendance(name):
    with open('AttendanceRecord.csv', 'r+') as f:
        DataList = f.readlines()
        nameList = []
        for line in DataList:
            gap = line.split(',')
            nameList.append(gap[0])
        if name not in nameList:
            time = datetime.now()
            dtString = time.strftime('%H:%M:%S')
            f.writelines(f'\n{name},{dtString}')


ListEncodesKnown = EncodingsFind(photos)

capture = cv2.VideoCapture(0)

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

    facesFrame = fc.face_locations(imgS)
    encodesCurFrame = fc.face_encodings(imgS, facesFrame)

    for encodeFace, faceLoc in zip(encodesCurFrame, facesFrame):
        match = fc.compare_faces(ListEncodesKnown, encodeFace)
        Distance = fc.face_distance(ListEncodesKnown, encodeFace)

        matchIndex = np.argmin(Distance)

        if match[matchIndex]:
            name = NameClass[matchIndex].upper()

            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)

            Attendance(name)

    cv2.imshow('webcam', img)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break