AttendanceProject.py

import cv2
import numpy as np
import face_recognition
import os

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

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)


#faceLoc = face_recognition.face_locations(imgElon)[0]
#encodeElon = face_recognition.face_encodings(imgElon)[0]
#cv2.rectangle(imgElon,(faceLoc[3],faceLoc[0]),(faceLoc[1],faceLoc[2]),(255,0,255),2)

#faceLoc = face_recognition.face_locations(imgTest)[0]
#encodeTest = face_recognition.face_encodings(imgTest)[0]
#cv2.rectangle(imgTest,(faceLoc[3],faceLoc[0]),(faceLoc[1],faceLoc[2]),(255,0,255),2)

#results = face_recognition.compare_faces([encodeElon],encodeTest)
#faceDis = face_recognition.face_distance([encodeElon],encodeTest)