Python+OpenCV实现的人脸识别签到考勤系统.zip

# 前言 本项目为IOT实验室人员签到考勤设计，系统实现功能： **1.人员人脸识别并完成签到/签退 2.考勤时间计算 3.保存考勤数据为CSV格式（Excel表格）** *PS：本系统2D人脸识别，节约了繁琐的人脸识别训练部分，简洁快捷* **该项目为测试版，正式版会加入更多的功能，持续更新中.....** *测试版项目地址我会放到结尾* ## 项目效果图 **系统初始化登陆界面**  **主界面展示图：**  **签到功能展示**   **签退功能展示**  **后台签到数据记录**  **是否签到/退判断**  # 项目需要的环境 *核心环境：* **OpenCV-Python 4.5.5.64** **face_recognition 1.30 face_recognition_model 0.3.0 dlib 19.23.1** *UI窗体界面:* **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** ## 编译器 **Pycham 2021.1.3**  ``` **Python版本 3.9.12** ```  **Anaconda**  ### 辅助开发QT-designer   #### 项目配置  # 代码部分 ## 核心代码 **MainWindow.py** *UI文件加载：* ```python 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_ = None ``` *摄像头调用：* ```python 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** *获取当前系统时间* ```python 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 = None ``` *签到时间计算* ```python 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) ``` *人脸识别部分* ```python # 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 frame ``` *签到数据保存与判断* ```python # 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)