This project is designed for IOT laboratory personnel check-in and attendance, and the system implements the following functions: **1. Personnel face recognition and complete check-in/check-out 2. Calculation of attendance time 3. Save the attendance data in CSV format (Excel) **
PS: This system 2D face recognition, saving the tedious face recognition training part, simple and fast
This project is a beta version, the official version will add more features, continue to update..... I'll put the beta project address at the end
System initialization login interface
OpenCV-Python 4.5.5.64 face_recognition 1.30 face_recognition_model 0.3.0 dlib 19.23.1
PyQt5 5.15.4 pyqt5-plugins 5.15.4.2.2 PyQt5-Qt5 5.15.2 PyQt5-sip 12.10.1 pyqt5-tools 5.15.4.3.2
**PythonVersion 3.9.12**
MainWindow.py UI File：
class Ui_Dialog(QDialog):
def __init__(self):
super(Ui_Dialog, self).__init__()
loadUi("mainwindow.ui", self) #加载QTUI文件
self.runButton.clicked.connect(self.runSlot)
self._new_window = None
self.Videocapture_ = NoneCamera：
def refreshAll(self):
print("当前调用人俩检测摄像头编号（0为笔记本内置摄像头，1为USB外置摄像头）：")
self.Videocapture_ = "0"OutWindow.py request Time
class Ui_OutputDialog(QDialog):
def __init__(self):
super(Ui_OutputDialog, self).__init__()
loadUi("./outputwindow.ui", self) #加载输出窗体UI
#datetime 时间模块
now = QDate.currentDate()
current_date = now.toString('ddd dd MMMM yyyy') #时间格式
current_time = datetime.datetime.now().strftime("%I:%M %p")
self.Date_Label.setText(current_date)
self.Time_Label.setText(current_time)
self.image = NoneCheck in time
def ElapseList(self,name):
with open('Attendance.csv', "r") as csv_file:
csv_reader = csv.reader(csv_file, delimiter=',')
line_count = 2
Time1 = datetime.datetime.now()
Time2 = datetime.datetime.now()
for row in csv_reader:
for field in row:
if field in row:
if field == 'Clock In':
if row[0] == name:
Time1 = (datetime.datetime.strptime(row[1], '%y/%m/%d %H:%M:%S'))
self.TimeList1.append(Time1)
if field == 'Clock Out':
if row[0] == name:
Time2 = (datetime.datetime.strptime(row[1], '%y/%m/%d %H:%M:%S'))
self.TimeList2.append(Time2)FaceRecognition
# 人脸识别部分
faces_cur_frame = face_recognition.face_locations(frame)
encodes_cur_frame = face_recognition.face_encodings(frame, faces_cur_frame)
for encodeFace, faceLoc in zip(encodes_cur_frame, faces_cur_frame):
match = face_recognition.compare_faces(encode_list_known, encodeFace, tolerance=0.50)
face_dis = face_recognition.face_distance(encode_list_known, encodeFace)
name = "unknown" #未知人脸识别为unknown
best_match_index = np.argmin(face_dis)
if match[best_match_index]:
name = class_names[best_match_index].upper()
y1, x2, y2, x1 = faceLoc
cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
cv2.rectangle(frame, (x1, y2 - 20), (x2, y2), (0, 255, 0), cv2.FILLED)
cv2.putText(frame, name, (x1 + 6, y2 - 6), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 1)
mark_attendance(name)
return frameCheckData
# csv表格保存数据
def mark_attendance(name):
"""
:param name: 人脸识别部分
:return:
"""
if self.ClockInButton.isChecked():
self.ClockInButton.setEnabled(False)
with open('Attendance.csv', 'a') as f:
if (name != 'unknown'): #签到判断：是否为已经识别人脸
buttonReply = QMessageBox.question(self, '欢迎 ' + name, '开始签到' ,
QMessageBox.Yes | QMessageBox.No, QMessageBox.No)
if buttonReply == QMessageBox.Yes:
date_time_string = datetime.datetime.now().strftime("%y/%m/%d %H:%M:%S")
f.writelines(f'\n{name},{date_time_string},Clock In')
self.ClockInButton.setChecked(False)
self.NameLabel.setText(name)
self.StatusLabel.setText('签到')
self.HoursLabel.setText('开始签到计时中')
self.MinLabel.setText('')
self.Time1 = datetime.datetime.now()
self.ClockInButton.setEnabled(True)
else:
print('签到操作失败')
self.ClockInButton.setEnabled(True)
elif self.ClockOutButton.isChecked():
self.ClockOutButton.setEnabled(False)
with open('Attendance.csv', 'a') as f:
if (name != 'unknown'):
buttonReply = QMessageBox.question(self, '嗨呀 ' + name, '确认签退?',
QMessageBox.Yes | QMessageBox.No, QMessageBox.No)
if buttonReply == QMessageBox.Yes:
date_time_string = datetime.datetime.now().strftime("%y/%m/%d %H:%M:%S")
f.writelines(f'\n{name},{date_time_string},Clock Out')
self.ClockOutButton.setChecked(False)
self.NameLabel.setText(name)
self.StatusLabel.setText('签退')
self.Time2 = datetime.datetime.now()
self.ElapseList(name)
self.TimeList2.append(datetime.datetime.now())
CheckInTime = self.TimeList1[-1]
CheckOutTime = self.TimeList2[-1]
self.ElapseHours = (CheckOutTime - CheckInTime)
self.MinLabel.setText("{:.0f}".format(abs(self.ElapseHours.total_seconds() / 60)%60) + 'm')
self.HoursLabel.setText("{:.0f}".format(abs(self.ElapseHours.total_seconds() / 60**2)) + 'h')
self.ClockOutButton.setEnabled(True)
else:
print('签退操作失败')
self.ClockOutButton.setEnabled(True)
Because there is no face training model in this system, the system misrecognition rate is high and the security is low The system optimization is poor, the camera captures a low number of frames (8-9), the background occupies a high, and the CPU utilization is high Data is saved in CSV format, which has low security