import face_recognition import cv2 import numpy as np import csv from datetime import datetime video_capture = cv2.VideoCapture(0) sir_image = face_recognition.load_image_file("photos/sir.jpeg") sir_encoding = face_recognition.face_encodings(sir_image)[0] vikas_image = face_recognition.load_image_file("photos/vikas.jpg") vikas_encoding = face_recognition.face_encodings(vikas_image)[0] known_face_encoding = [sir_encoding, vikas_encoding] known_faces_names = ["Sarwan Sir", "Vikas"] students = known_faces_names.copy() now = datetime.now() current_date = now.strftime("%Y-%m-%d") # Create and open the CSV file f = open(current_date + '.csv', 'w+', newline='') lnwriter = csv.writer(f) # Initialize variables face_locations = [] face_encodings = [] face_names = [] s = True while True: _, frame = video_capture.read() small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25) rgb_small_frame = small_frame[:, :, ::-1] if s: face_locations = face_recognition.face_locations(rgb_small_frame) face_encodings = face_recognition.face_encodings(small_frame, face_locations) face_names = [] for face_encoding in face_encodings: matches = face_recognition.compare_faces(known_face_encoding, face_encoding) name = "" face_distance = face_recognition.face_distance(known_face_encoding, face_encoding) best_match_index = np.argmin(face_distance) if matches[best_match_index]: name = known_faces_names[best_match_index] face_names.append(name) if name in known_faces_names: if name in students: students.remove(name) print(students) current_time = now.strftime("%H-%M-%S") lnwriter.writerow([name, current_time, "Present"]) cv2.imshow("attendance system", frame) if cv2.waitKey(1) & 0xFF == ord('q'): break # Release the video capture and close the CSV file video_capture.release() cv2.destroyAllWindows() f.close()