AttendanceProjects.py

#Importing required libraries
import cv2
import numpy as np
import face_recognition
import os
from datetime import datetime

#defining path of input images

path = 'ImagesAttendance'
# Initialize some variables
images = []                  # LIST CONTAINING ALL THE IMAGES
classNames = []              # LIST CONTAINING ALL THE CORRESPONDING CLASS Names
myList = os.listdir(path)
print(myList)
for cl in myList:
    curImg = cv2.imread(f'{path}/{cl}')
    images.append(curImg)
    classNames.append(os.path.splitext(cl)[0])              #used to print only name (Elon Musk) not full class names(Elon Musk.jpg)
print(classNames)

#we will first convert it into RGB and then find its encoding using the face_encodings() function.
#Then we will append each encoding to our list.
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

#markAttendance is a function that requires only one input which is the name of the user.
# We can use the datetime class in the date time package to get the current time.

def markAttendance(name):
    with open('Attendance.csv', '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.writelines(f'\n{name},{dtString}')


#### FOR CAPTURING SCREEN RATHER THAN WEBCAM
# def captureScreen(bbox=(300,300,690+300,530+300)):
#     capScr = np.array(ImageGrab.grab(bbox))
#     capScr = cv2.cvtColor(capScr, cv2.COLOR_RGB2BGR)
#     return capScr


 #calling findEncodings function with the images list as the input arguments
encodeListKnown = findEncodings(images)
print('Encoding Complete')



def loopfunction():
     #a video capture object cap defined so that we can grab frames from the webcam.
    cap = cv2.VideoCapture(0)
    
    while True:
        success, img = cap.read()
        # img = captureScreen()
        
        # Resize frame of video to 1/4 size for faster face recognition processing
        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)
        
        
        #we can match the current face encodings to our known faces encoding list to find the matches

        for encodeFace, faceLoc in zip(encodesCurFrame, facesCurFrame):
            matches = face_recognition.compare_faces(encodeListKnown, encodeFace)
            faceDis = face_recognition.face_distance(encodeListKnown, encodeFace)
            # print(faceDis)
            
            #finding minimum face distance
            matchIndex = np.argmin(faceDis)
            
            #we can label unknown faces with the help of using below if-else structure.

            if faceDis[matchIndex] < 0.50:
                name = classNames[matchIndex].upper()
                markAttendance(name)
            else:
                name = 'Unknown'
            print(name)
            y1, x2, y2, x1 = faceLoc
             # Scale back up face locations since the frame we detected in was scaled to 1/4 size
            y1, x2, y2, x1 = y1 * 4, x2 * 4, y2 * 4, x1 * 4
              # Draw a box around the face
            cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
            # Draw a label with a name below the face
            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)

        cv2.imshow('Webcam', img)
        # cv2.waitKey(1)

        # Hit 'q' on the keyboard to quit!
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

    # Release handle to the webcam
    cap.release()
    cv2.destroyAllWindows()