Basics.py

import cv2
import numpy as np
import face_recognition

imgElon = face_recognition.load_image_file('ImagesAttendance/Elon Musk.jpg')
imgElon = cv2.cvtColor(imgElon,cv2.COLOR_BGR2RGB)
imgTest = face_recognition.load_image_file('ImagesBasic/Elon Test.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)

# we will find out distances between both the embeddings and apply linear svm to check if they both are same or not
results = face_recognition.compare_faces([encodeElon],encodeTest)
faceDis = face_recognition.face_distance([encodeElon],encodeTest)
# lower the distance, better the match
print(results,faceDis)
cv2.putText(imgTest,f'{results} {round(faceDis[0],2)}',(50,50),cv2.FONT_HERSHEY_PLAIN,1,(0,0,255),2)

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