script.py

import cv2
import numpy as np
import face_recognition
import os

path = 'img'
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 find_encodings(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 = find_encodings(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)
    img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)

    facesCurFrame = face_recognition.face_locations(img)
    encodesCurFrame = face_recognition.face_encodings(img,facesCurFrame)

    for encodeFaces,faceLoc in zip(encodesCurFrame,facesCurFrame):
        matches = face_recognition.compare_faces(encodeListKnown,encodeFaces)
        faceDis = face_recognition.face_distance(encodeListKnown,encodeFaces)
        matchIndex = np.argmin(faceDis)

        if matches[matchIndex]:
            name = classNames[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)
    cv2.imshow('webcam',img)
    cv2.waitKey(4)