Main_Authorization.py

import face_recognition
import glob
import cv2
import datetime

known_face_encodings = []


def known_pics_prepare(your_pics_dir, debugging):
    # Load Known Faces
    path_list = glob.glob(your_pics_dir + "\*")
    known_face_imgs = []
    for path in path_list:
        img = face_recognition.load_image_file(path)
        known_face_imgs.append(img)
    if debugging:
        print("Loaded Known Images:", path_list)
    if(len(path_list) == 0): return -2

    # face detection
    known_face_locs = []
    for img in known_face_imgs:
        loc = face_recognition.face_locations(img, model="hog")
        if len(loc) != 1:
            if debugging:
                print("Known Face Analyzation Error")
            return -2
        known_face_locs.append(loc)

    # face recognition
    global known_face_encodings
    for img, loc in zip(known_face_imgs, known_face_locs):
        (encoding,) = face_recognition.face_encodings(img, loc)
        known_face_encodings.append(encoding)
    
    return 0


def camera_capture(capture_pics_dir):
    camera_image = (
        capture_pics_dir
        + "\\"
        + datetime.datetime.now().strftime("%Y%m%d%H%M%S")
        + ".jpg"
    )
    # Open Camera
    cap = cv2.VideoCapture(0)
    # Capture Pic
    ret, frame = cap.read()
    # Save Pic
    cv2.imwrite(camera_image, frame)
    # Close Camera
    cap.release()
    return camera_image


def authorization(your_pics_dir, capture_pics_dir, rigidity, threshold, debugging):
    

    # Load Captured Target Face
    target_image = camera_capture(capture_pics_dir)
    face_img_to_check = face_recognition.load_image_file(target_image)

    # face detection
    face_loc_to_check = face_recognition.face_locations(face_img_to_check, model="hog")
    if len(face_loc_to_check) != 1:
        if debugging:
            print("Target Face Analyzation Error")
        return -2, -1, -1, -1, -1

    # face recognition
    (face_encoding_to_check,) = face_recognition.face_encodings(
        face_img_to_check, face_loc_to_check
    )

    #check difficulty
    global known_face_encodings
    dists = face_recognition.face_distance(
        known_face_encodings, face_encoding_to_check
    )  # check similarity level
    if debugging:
        print(len(dists), "\n", dists)  # 3 [True, False, False]?

    true_count = 0
    ave_threshold = 0
    min_threshold = 1
    max_threshold = 0

    for x in dists:
        if x < threshold:
            true_count += 1
        ave_threshold += x
        if x < min_threshold:
            min_threshold = x
        if x > max_threshold:
            max_threshold = x

    if debugging:
        print("rigidity:", rigidity, "%\n", "threshold:", threshold)
    last_rigidity = true_count / len(dists)
    if last_rigidity >= rigidity * 0.01:
        return (
            0,
            last_rigidity * 100,
            ave_threshold / len(dists),
            min_threshold,
            max_threshold,
        )
    else:
        return (
            1,
            last_rigidity * 100,
            ave_threshold / len(dists),
            min_threshold,
            max_threshold,
        )