main.py

import face_recognition
import cv2
import numpy as np

'''Load Train and Test Image of Elon Musk'''
imgElon = face_recognition.load_image_file(r'ImagesBasic/elon_train.jpg')
imgElon = cv2.cvtColor(imgElon,cv2.COLOR_BGR2RGB)
imgTest = face_recognition.load_image_file(r'ImagesBasic/elon_test.jpg')
imgTest = cv2.cvtColor(imgTest,cv2.COLOR_BGR2RGB)

'''Drawing Bounding Box on Train Image'''
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) # top, right, bottom, left

'''Drawing Bounding Box on Test Image'''
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)

'''Find encodings between Train and Test Image'''
results = face_recognition.compare_faces([encodeElon], encodeTest)
faceDis = face_recognition.face_distance([encodeElon], encodeTest)
print(results)
print(faceDis) #The lower the distance - the better the match
cv2.putText(imgTest,f'{results} {round(faceDis[0],2)} ',(50,50),cv2.FONT_HERSHEY_COMPLEX,1,(255,0,255),3)

#Show Test and /Train /image
cv2.imshow('Elon Musk', imgElon)
cv2.imshow('Elon Test', imgTest)
cv2.waitKey(0)