run.py

import os
import face_recognition
import numpy as np
from PIL import Image, ImageDraw
import pandas as pd


def predict_and_show(image):
    X = np.load("X.npy")
    Y = np.load("Y.npy")

    attendance = []
    image = face_recognition.load_image_file(image)
    face_bounding_boxes = face_recognition.face_locations(image)
    if len(face_bounding_boxes) == 0:
        print("No face found in the image")
        return
    X_test = face_recognition.face_encodings(image, known_face_locations=face_bounding_boxes)
    for i in range(len(X_test)):
        distances = np.linalg.norm(np.array(X) - np.array(X_test[i]), axis=1)
        min_distance = np.min(distances)
        if min_distance > 0.55:
            print("Unknown")
        else:
            print(Y[np.argmin(distances)], min_distance)
            attendance.append(Y[np.argmin(distances)])
            top, right, bottom, left = face_bounding_boxes[i]
            face_image = image[top:bottom, left:right]
            pil_image = Image.fromarray(face_image)
            draw = ImageDraw.Draw(pil_image)
            draw.text((0, 0), Y[np.argmin(distances)], (255, 255, 255))
            # pil_image.show()

    return attendance

# predict_and_show()

# #ask for input string from user
# print("Enter the name of the image file")
# image_path = input()

# predict_and_show(image_path)
# #save the attendance list to a txt file
# with open("attendance.txt", "w") as file:
#     for i in attendance:
#         file.write(i + "\n")