sital
1SP11cs
Basic:
import cv2
import face_recognition
imgElon = face_recognition.load_image_file('ImagesBasic/Elon Musk.jpg')
imgElon = cv2.cvtColor(imgElon,cv2.COLOR_BGR2RGB)
imgTest = face_recognition.load_image_file('ImagesBasic/Bill gates.jpg')
imgTest = cv2.cvtColor(imgTest,cv2.COLOR_BGR2RGB)
faceLoc = face_recognition.face_locations(imgElon)[0]
encodeElon = face_recognition.face_encodings(imgElon)[0]
cv2.rectangle(imgElon,(faceLoc[3],faceLoc[0]),(faceLoc[1],faceLoc[2]),(255,0,255),2)
faceLocTest = face_recognition.face_locations(imgTest)[0]
encodeTest = face_recognition.face_encodings(imgTest)[0]
cv2.rectangle(imgTest,(faceLocTest[3],faceLocTest[0]),(faceLocTest[1],faceLocTest[2]),(255,0,255),2)
results = face_recognition.compare_faces([encodeElon],encodeTest)
faceDis = face_recognition.face_distance([encodeElon],encodeTest)
print(results,faceDis)
cv2.putText(imgTest,f'{results} {round(faceDis[0],2)}',(50,50),cv2.FONT_HERSHEY_COMPLEX,1,(0,0,255),2)
cv2.imshow('Elon Musk',imgElon)
cv2.imshow('Elon Test',imgTest)
cv2.waitKey(0)
import cv2
import numpy as np
import face_recognition
import os
from datetime import datetime
# from PIL import ImageGrab
path = 'ImagesAttendance'
images = []
classNames = []
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])
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'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
encodeListKnown = findEncodings(images)
print('Encoding Complete')
cap = cv2.VideoCapture(0)
while True:
success, img = cap.read()
#img = captureScreen()
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('Webcam',img)
cv2.waitKey(1)
Labeling Unknown faces
1 2 3 4 5 6 7 8 9 | 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) |
To find the unknown faces we will replace
with this
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.
