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backup_code.py
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backup_code.py
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import sys
import face_recognition
import cv2
import numpy as np
import os
import csv
from datetime import datetime
path = 'Images_test'
images=[] # list of all images
classNames=[] # names of images
myList = os.listdir(path)
for img in myList:
currentImg = cv2.imread(f'{path}/{img}') # used to load images
images.append(currentImg) #appends current image to images list
classNames.append(os.path.splitext(img)[0]) # used to remove .jpg and append just the name to classNames list
def findEncodings(images): # function to find encodings from image
encodeList=[]
for img in images:
img=cv2.cvtColor(img,cv2.COLOR_BGR2RGB) # converts image to RGB
encodeImg = face_recognition.face_encodings(img)[0] # finds encodings of image
encodeList.append(encodeImg) # appends the encodings to list
return encodeList
def markAttendance(name): # function to mark attendance in .csv file
with open('Attendance.csv','r+') as f: # opens attendance.csv file
myDataList = f.readlines()
nameList = []
for line in myDataList:
entry = line.split(',') # splits the entry based on comma
nameList.append(entry[0]) # appends the name to nameList
if name not in nameList:
now = datetime.now()
dateString = now.strftime('%H:%M:%S') # formats the dateString into HH:MM:SS format
f.writelines(f'\n{name},{dateString}')
encodeListKnown=findEncodings(images) # calls the findEncodings function
print('ENCODING COMPLETE.')
video_capture = cv2.VideoCapture(1)
while True:
success,img = video_capture.read()
imgS = cv2.resize(img,(0,0),None,0.25,0.25) # scales image to 0.25 of its original size
imgS = cv2.cvtColor(imgS,cv2.COLOR_BGR2RGB)
facesCurrFrame = face_recognition.face_locations(imgS) # finds locations of faces in current frame
encodesCurrFrame = face_recognition.face_encodings(imgS,facesCurrFrame) # finds encodings of faces in current frame
for encodeFace, faceLoc in zip(encodesCurrFrame,facesCurrFrame): # grabs one face location from facesCurrFrame and then it will grab the encoding from encodesCurrFrame
matches = face_recognition.compare_faces(encodeListKnown,encodeFace) # compares faces from encodeListKnown and encodeFace
faceDis = face_recognition.face_distance(encodeListKnown,encodeFace) # gives us the face distance in the form of a list - lowest distance is best match
matchIndex = np.argmin(faceDis) # gives us the minimum value
if matches[matchIndex]:
name = classNames[matchIndex].upper()
# code to draw a rectangle around the face and display the name of the person
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_SMALL,1,(255,255,255),2)
markAttendance(name) # calls the function to mark attendance'''
cv2.imshow('Webcam',img)
key = cv2.waitKey(1)
if key == 27 or key == 113: # terminates the program if 'esc' or 'q' is pressed
break
cv2.destroyAllWindows