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import cv2
import numpy as np
import pandas as pd
from datetime import datetime
import face_recognition

# Set the camera index to 0 for the built-in camera
camera_index = 0
cap = cv2.VideoCapture(camera_index)

attendance_file = 'Attendance.csv'

# Check if Attendance.csv exists
if not cv2.os.path.isfile(attendance_file):
    # Create a new DataFrame and save it if the file does not exist
    df = pd.DataFrame(list())
    df.to_csv(attendance_file, index=False)
else:
    print("Attendance.csv exists.")

images = []
classNames = []
myList = cv2.os.listdir('image_folder')
print(myList)

# for cl in myList:
#     curImg = cv2.imread(f'image_folder/{cl}')
#     images.append(curImg)
#     classNames.append(cv2.os.path.splitext(cl)[0])
# print(classNames)

for cl in myList:
    image_path = f'image_folder/{cl}'
    print(f"Loading image: {image_path}")
    curImg = cv2.imread(image_path)

    if curImg is None:
        print(f"Error loading image: {image_path}")
        continue

    images.append(curImg)
    classNames.append(cv2.os.path.splitext(cl)[0])


def findEncodings(images):
    encodeList = []
    for img in images:
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        encode = face_recognition.face_encodings(img)[0]
        encodeList.append(encode)
    return encodeList


def markAttendance(name):
    with open(attendance_file, 'r+') as f:
        myDataList = f.readlines()
        nameList = []
        for line in myDataList:
            entry = line.split(',')
            nameList.append(entry[0])
        if name not in nameList:
            now = datetime.now()
            dtString = now.strftime('%H:%M:%S')
            f.write(f'\n{name},{dtString}')


encodeListKnown = findEncodings(images)
print('Encoding Complete')

while True:
    ret, img = cap.read()
    imgS = cv2.resize(img, (0, 0), None, 0.25, 0.25)
    imgS = cv2.cvtColor(imgS, cv2.COLOR_BGR2RGB)

    facesCurFrame = face_recognition.face_locations(imgS)
    encodesCurFrame = face_recognition.face_encodings(imgS, facesCurFrame)

    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]:
            name = classNames[matchIndex].upper()
            y1, x2, y2, x1 = faceLoc
            y1, x2, y2, x1 = y1 * 4, x2 * 4, y2 * 4, x1 * 4
            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)
            markAttendance(name)

    cv2.imshow('Webcam', img)
    key = cv2.waitKey(1)
    if key == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()