Face Attendance

 

sital

1SP11cs

Let try it with another testing image. This time we will test it with an image of Bill Gates. As we can see the result is False and the distance is much higher than before indicating a bad match.

Attendance Project

Now using the methods we have seen above, we will develop an attendance system where the user is automatically logged when they are detected in the camera. We will store the name along with the time when they appeared.

Importing Images

As we have imported before we can use the same face_recognition.load_image_file() function to import our images. But when we have multiple images, importing them individually can become messy. Therefore we will write a script to import all images in a given folder at once. For this we will need the os library so we will import that first. We will store all the images in one list and their names in another.

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import face_recognition import cv2 import numpy as np import os

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path = 'ImagesAttendance' images = []     # LIST CONTAINING ALL THE IMAGES className = []    # LIST CONTAINING ALL THE CORRESPONDING CLASS Names myList = os.listdir(path) print("Total Classes Detected:",len(myList)) for x,cl in enumerate(myList):         curImg = cv2.imread(f'{path}/{cl}')         images.append(curImg)         className.append(os.path.splitext(cl)[0])

Compute Encodings

Now that we have a list of images we can iterate through those and create a corresponding encoded list for known faces. To do this we will create a function. As earlier 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.

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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

Now we can simply call this function with the images list as the input arguments.

1 2	encodeListKnown = findEncodings(images) print('Encodings Complete')

The While loop

The while loop is created to run the webcam. But before the while loop we have to create a video capture object so that we can grab frames from the webcam.

1	cap = cv2.VideoCapture(0)

Webcam Image

First we will read the image from the webcam and then resize it to quarter the size. This is done to increase the speed of the system. Even though the image being used is 1/4 th of the original, we will still use the original size while displaying. Next we will convert it to RGB.

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while True:     success, img = cap.read()     imgS = cv2.resize(img, (0, 0), fx=0.25, fy=0.25)     imgS = cv2.cvtColor(imgS, cv2.COLOR_BGR2RGB)

Webcam Encodings

Once we have the webcam frame we will find all the faces in our image. The face_locations function is used for this purpose. Later we will find the face_encodings as well.

1 2	facesCurFrame = face_recognition.face_locations(imgS) encodesCurFrame = face_recognition.face_encodings(imgS, facesCurFrame)

Find Matches

Now we can match the current face encodings to our known faces encoding list to find the matches. We will also compute the distance. This is done to find the best match in case more than one face is detected at a time.

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for encodeFace,faceLoc in zip(encodesCurFrame,facesCurFrame):     matches = face_recognition.compare_faces(encodeListKnown, encodeFace)     faceDis = face_recognition.face_distance(encodeListKnown, encodeFace)

Once we have the list of face distances we can find the minimum one, as this would be the best match.

1	matchIndex = np.argmin(faceDis)

Now based on the index value we can determine the name of the person and display it on the original Image.

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if matches[matchIndex]:     name = className[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_DUPLEX, 1.0, (255, 255, 255), 1)

Marking Attendance

Lastly we are going to add the automated attendance code. We will start by writing a function that requires only one input which is the name of the user. First we open our Attendance file which is in csv format. Then we read all the lines and iterate through each line using a for loop. Next we can split using comma ‘,’. This will allow us to get the first element which is the name of the user. If the user in the camera already has an entry in the file then nothing will happen. On the other hand if the user is new then the name of the user along with the current time stamp will be stored. We can use the datetime class in the date time package to get the current time.

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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  line:             now = datetime.now()             dt_string = now.strftime("%H:%M:%S")             f.writelines(f'n{name},{dt_string}')

Comma Separated Values Storied in Attendance File

Labeling Unknown faces as well

To find the unknown faces we will replace

Python

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if matches[matchIndex]:     name = classNames[matchIndex].upper()     #print(name)     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)

with this

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if faceDis[matchIndex]< 0.50:     name = classNames[matchIndex].upper()     markAttendance(name) else: name = 'Unknown' #print(name) 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)

All this does is to check if the distance to our min face is less than 0.5 or not. If its not then this means the person is unknown so we change the name to unknown and don’t mark the attendance.

https://www.computervision.zone/lessons/face-attendance-video-lesson/