1.Basic

import cv2
import numpy as np
import face_recognition

imgElon = face_recognition.load_image_file('PICTURE/Elon Mask.jpg')
imgElon = cv2.cvtColor(imgElon, cv2.COLOR_BGR2RGB)
imgTest = face_recognition.load_image_file('PICTURE/Ma yun.jpg')
imgTest = cv2.cvtColor(imgTest, cv2.COLOR_BGR2RGB)

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)

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

results = face_recognition.compare_faces([encodeElon], encodeTest)
faceDis = face_recognition.face_distance([encodeElon], encodeTest)
print(results, faceDis)
cv2.putText(imgTest, f'{results}{round(faceDis[0], 2)}', (50, 50), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 0, 255), 2)

cv2.imshow('Elon Mask', imgElon)
cv2.imshow('Elon Test', imgTest)
cv2.waitKey(0)

2.Attendance Project

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

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

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.now()
            dtString = now.strftime('%H:%M:%S')
            f.writelines(f'\n{name}, {dtString}')

encodeListKnown = findEncodings(imges)
# print(len(encodeListKnown))
print('Encoding Complete')

cap.cv2.Videoapture(0)

while True:
    sucess, 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, y2-55), (x2, y2), (0, 255, 0), cv2.FILLED)
            cv2.putText(img, name, (x1+6, y2-6), cv2.FONT_HERSHEY_COMLEX, 1, (255, 255 ,255), 2)
            markAttendance(name)


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

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