import face_recognition import cv2 import numpy as np from datetime import datetime import csv import os

Initialize webcam

video_capture = cv2.VideoCapture(0, cv2.CAP_DSHOW) # Specifying backend for Windows

Load images and encode faces

tesla_image = face_recognition.load_image_file(r"C:\Users\peetl\OneDrive\students1\tesla.jpg") tesla_encoding = face_recognition.face_encodings(tesla_image)[0]

selva_image = face_recognition.load_image_file(r"C:\Users\peetl\OneDrive\students1\selva.jpg") selva_encoding = face_recognition.face_encodings(selva_image)[0]

siva_image = face_recognition.load_image_file(r"C:\Users\peetl\OneDrive\students1\siva.jpg") siva_encoding = face_recognition.face_encodings(siva_image)[0]

eswar_image = face_recognition.load_image_file(r"C:\Users\peetl\OneDrive\students1\eswar.jpg") eswar_encoding = face_recognition.face_encodings(eswar_image)[0]

revanth_image = face_recognition.load_image_file(r"C:\Users\peetl\OneDrive\students1\revanth.jpg") revanth_encoding = face_recognition.face_encodings(revanth_image)[0]

ashok_image = face_recognition.load_image_file(r"C:\Users\peetl\OneDrive\students1\ashok.jpg") ashok_encoding = face_recognition.face_encodings(ashok_image)[0]

Create arrays of known face encodings and their names

known_face_encodings = [tesla_encoding, eswar_encoding, revanth_encoding, ashok_encoding] known_face_names = ["Tesla", "Eswar", "Revanth", "Ashok"]

Initialize variables

face_locations = [] face_encodings = []

Get the current date and time for file naming

now = datetime.now() current_date = now.strftime("%Y-%m-%d") csv_file = f"{current_date}.csv"

Check if the CSV file exists, and if not, create it with headers

if not os.path.exists(csv_file): with open(csv_file, mode='w', newline='') as file: writer = csv.writer(file) writer.writerow(["Name", "Entry Time"]) # Add headers if new file

Create a set to track recorded faces to avoid multiple entries for the same person

logged_faces = set()

while True: # Capture frame from webcam ret, frame = video_capture.read() if not ret: print("Failed to grab frame") break

# Resize frame to 1/4 size for faster processing
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)

# Convert the image from BGR color (OpenCV) to RGB color (face_recognition)
rgb_small_frame = cv2.cvtColor(small_frame, cv2.COLOR_BGR2RGB)

# Find all the face locations and face encodings in the current frame
face_locations = face_recognition.face_locations(rgb_small_frame)
face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)

for face_encoding in face_encodings:
    # Check if the face matches any known faces
    matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
    face_distance = face_recognition.face_distance(known_face_encodings, face_encoding)
    best_match_index = np.argmin(face_distance)

    if matches[best_match_index]:
        name = known_face_names[best_match_index]

        # Only log the entry if the person hasn't been recorded already
        if name not in logged_faces:
            logged_faces.add(name)

            # Get the current time
            current_time = datetime.now().strftime("%H:%M:%S")

            # Write the name and time to the CSV file
            with open(csv_file, mode='a', newline='') as file:
                writer = csv.writer(file)
                writer.writerow([name, current_time])

            # Display the name on the webcam feed
            font = cv2.FONT_HERSHEY_SIMPLEX
            bottomLeftCornerOfText = (10, 100)
            fontScale = 1.5
            fontColor = (255, 0, 0)  # Blue color in BGR
            thickness = 3
            lineType = 2

            cv2.putText(frame, f"{name} Present", bottomLeftCornerOfText, font, fontScale, fontColor, thickness, lineType)

# Display the resulting frame
cv2.imshow("camera", frame)

# Break the loop if 'q' is pressed
if cv2.waitKey(1) & 0xFF == ord('q'):
    break

Release the webcam and close windows

video_capture.release()