Face detection using OpenCV

Smart Face detection using OpenCV. In this system, you can set up your home, office, or school. It mainly use for attendance management syst

Jun 3, 2021

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opencv

smart attendance system

facedetection

Components and supplies

Raspberry Pi 3 Model B

Webcam, Logitech® HD Pro

Battery Tester, 6V / 12V

Webduino Fly + Arduino UNO

Apps and platforms

OpenCV

Project description

Code

python

Here is the project code.

1import cv2
2import numpy as np
3import face_recognition
4import  os
5from datetime import datetime
6
7path = 'ImagesAttendance'
8images =  []
9classNames =[]
10myList = os.listdir(path)
11print(myList)
12for c1 in  myList:
13    curImg = cv2.imread(f'{path}/{c1}')
14    images.append(curImg)
15    classNames.append(os.path.splitext(c1)[0])
16print(classNames)
17
18def findEncodings(images):
19    encodeList = []
20    for img in images:
21        img = cv2.cvtColor(img,  cv2.COLOR_BGR2RGB)
22        encode = face_recognition.face_encodings(img)[0]
23        encodeList.append(encode)
24    return encodeList
25
26def markAttendance(name):
27    with open('Attendance.csv', 'r+') as f:
28        myDataList = f.readlines()
29        nameList =[]
30        for line in  myDataList:
31            entry =  line.split(',')
32            nameList.append(entry[0])
33        if name not  in nameList:
34            now= datetime.now()
35            dtString =now.strftime('%H:%M:%S')
36            f.writelines(f'\
37{name},{dtString}')
38
39encodeListKnown = findEncodings(images)
40print('Encoding  Complete')
41cap = cv2.VideoCapture(0)
42
43while True:
44    success, img =  cap.read()
45    imgS = cv2.resize(img,(0,0), None, 0.25, 0.25)
46    imgS = cv2.cvtColor(imgS,  cv2.COLOR_BGR2RGB)
47
48    facesCurFrame = face_recognition.face_locations(imgS)
49    encodesCurFrame = face_recognition.face_encodings(imgS, facesCurFrame)
50
51    for encodeFace , faceLoc in zip(encodesCurFrame, facesCurFrame):
52        matches  = face_recognition.compare_faces(encodeListKnown, encodeFace)
53        faceDis  = face_recognition.face_distance(encodeListKnown, encodeFace)
54        print(faceDis)
55        matchIndex = np.argmin(faceDis)
56
57        if matches[matchIndex]:
58            name = classNames[matchIndex].upper()
59            print(name)
60            y1, x2, y2, x1 = faceLoc
61            y1, x2, y2, x1 = y1*4, x2*4,  y2*4, x1*4
62            cv2.rectangle(img,(x1, y1), (x2,y2), (0, 255, 0), 2)
63            cv2.rectangle(img,(x1, y2-35), (x2, y2), (0, 255, 0), cv2.FILLED)
64            cv2.putText(img, name, (x1+6, y2-6), cv2.FONT_HERSHEY_COMPLEX, 1, (255,  255, 255), 2)
65            markAttendance(name)
66
67
68    cv2.imshow('Webcame',  img)
69    cv2.waitKey(1)
70
71
72
73
74
75
76
77

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

path = 'ImagesAttendance'
images =  []
classNames =[]
myList = os.listdir(path)
print(myList)
for c1 in  myList:
    curImg = cv2.imread(f'{path}/{c1}')
    images.append(curImg)
    classNames.append(os.path.splitext(c1)[0])
print(classNames)

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.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'\
{name},{dtString}')

encodeListKnown = findEncodings(images)
print('Encoding  Complete')
cap = cv2.VideoCapture(0)

while True:
    success, 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)
        print(faceDis)
        matchIndex = np.argmin(faceDis)

        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)


    cv2.imshow('Webcame',  img)
    cv2.waitKey(1)

Documentation

Excel File

Here is the excel file.

Excel File

Here is the excel file.

Excel File

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