Face_Recognition_Excel.py

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


path = r'/Images' # Path to your dataset of images
images = []  # List containing all the images
className = []  # List containing all the corresponding class names
myList = os.listdir(path)
print("Total Classes Detected:", len(myList))

# Load images and their names
for cl in myList:
    curImg = cv2.imread(f'{path}/{cl}')
    images.append(curImg)
    className.append(os.path.splitext(cl)[0])

# Function to find encodings of images
def findEncodings(images):
    encodeList = []
    for img in images:
        try:
            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
            encode = face_recognition.face_encodings(img)[0]
            encodeList.append(encode)
        except IndexError:
            print("Warning: No face detected in one of the images. Skipping...")
    return encodeList

# Function to mark attendance
def markAttendance(name):
    if not os.path.isfile('Attendance.csv'):  # Check if the file exists
        with open('Attendance.csv', 'w') as f:  # Create the file and add a header
            f.write("Name,Time\n")
    with open('Attendance.csv', 'r+') as f:
        existing_names = {line.split(',')[0] for line in f.readlines()}  # Get existing names
        if name not in existing_names:
            now = datetime.now()
            dt_string = now.strftime("%H:%M:%S")
            f.writelines(f'\n{name},{dt_string}')
            print(f"Attendance marked for {name} at {dt_string}")

# Generate face encodings for the known images
encodeListKnown = findEncodings(images)
print('Encodings Complete')

# Start the webcam
cap = cv2.VideoCapture(0, cv2.CAP_DSHOW)

while True:
    success, img = cap.read()
    imgS = cv2.resize(img, (0, 0), fx=0.25, fy=0.25)  # Reduce size for faster processing
    imgS = cv2.cvtColor(imgS, cv2.COLOR_BGR2RGB)

    # Detect faces and find encodings in the current frame
    facesCurFrame = face_recognition.face_locations(imgS)
    encodesCurFrame = face_recognition.face_encodings(imgS, facesCurFrame)

    # Compare faces in the current frame with known faces
    for encodeFace, faceLoc in zip(encodesCurFrame, facesCurFrame):
        matches = face_recognition.compare_faces(encodeListKnown, encodeFace)
        faceDis = face_recognition.face_distance(encodeListKnown, encodeFace)
        matchIndex = np.argmin(faceDis)

        if matches[matchIndex] and faceDis[matchIndex] < 0.50:
            name = className[matchIndex].upper()
            markAttendance(name)
        else:
            name = 'Unknown'

        # Draw a rectangle and label on the detected face
        y1, x2, y2, x1 = faceLoc
        y1, x2, y2, x1 = y1 * 4, x2 * 4, y2 * 4, x1 * 4  # Scale back to the original size
        cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
        cv2.rectangle(img, (x1, y2 - 35), (x2, y2), (0, 255, 0), cv2.FILLED)
        cv2.putText(img, name, (x1 + 6, y2 - 6), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2)

    # Show the webcam feed
    cv2.imshow('Webcam', img)

    # Exit condition: press 'q' to quit
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Release the webcam and close windows
cap.release()
cv2.destroyAllWindows()