face.py

import numpy as np
import face_recognition as fr
import cv2

video_capture = cv2.VideoCapture(0)

bertas_image = fr.load_image_file("Images/bertas.jpg")
bertas_face_encoding = fr.face_encodings(bertas_image)[0]

known_face_encodings = [bertas_face_encoding]
known_face_names = ["bertas"]

while True:
    ret, frame = video_capture.read()

    rgb_frame = frame[:, :, ::-1]

    face_locations = fr.face_locations(rgb_frame)
    face_encodings = fr.face_encodings(rgb_frame, face_locations)

    for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):

        matches = fr.compare_faces(known_face_encodings, face_encoding)

        name = "unknown"
        face_distances = fr.face_distance(known_face_encodings, face_encoding)
        best_match_index = np.argmin(face_distances)
        if matches[best_match_index]:
            name = known_face_names[best_match_index]

        cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)

        cv2.rectangle(frame, (left, bottom - 35),
                      (right, bottom), (0, 0, 255), cv2.FILLED)
        font = cv2.FONT_HERSHEY_SIMPLEX
        cv2.putText(frame, name, (left + 6, bottom - 6),
                    font, 1.0, (255, 255, 255), 1)

    cv2.imshow('webcam', frame)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

video_capture.release()
cv2.destroyAllWindows()


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')