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import cv2
import face_recognition
import os
import numpy as np
from datetime import datetime
import pickle
path = './autoattend/photos'
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(images)
for img in images:
print(len(face_recognition.face_encodings(img)))
def findEncodings(images):
encodeList = []
for img in images:
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
encoded_face = face_recognition.face_encodings(img)[0]
encodeList.append(encoded_face)
return encodeList
encoded_face_train = findEncodings(images)
def markAttendance(name):
with open('./autoattend/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()
time = now.strftime('%I:%M:%S:%p')
date = now.strftime('%d-%B-%Y')
f.writelines(f'n{name}, {time}, {date}')
# take pictures from webcam
cap = cv2.VideoCapture(1)
while True:
success, img = cap.read()
imgS = cv2.resize(img, (0,0), None, 0.25,0.25)
imgS = cv2.cvtColor(imgS, cv2.COLOR_BGR2RGB)
faces_in_frame = face_recognition.face_locations(imgS)
encoded_faces = face_recognition.face_encodings(imgS, faces_in_frame)
for encode_face, faceloc in zip(encoded_faces,faces_in_frame):
matches = face_recognition.compare_faces(encoded_face_train, encode_face)
faceDist = face_recognition.face_distance(encoded_face_train, encode_face)
matchIndex = np.argmin(faceDist)
print(matchIndex)
if matches[matchIndex]:
name = classNames[matchIndex].upper().lower()
y1,x2,y2,x1 = faceloc
# since we scaled down by 4 times
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,y2-5), cv2.FONT_HERSHEY_COMPLEX,1,(255,255,255),2)
markAttendance(name)
cv2.imshow('webcam', img)
if cv2.waitKey(1) & 0xFF == ord('q'):
break |