python opencv人脸识别考勤系统的完整源码

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运行结果如下：

代码如下：

import wximport wx.gridfrom time import localtime,strftimeimport osimport ioimport zlibimport dlib # 人脸识别的库dlibimport numpy as np # 数据处理的库numpyimport cv2 # 图像处理的库OpenCvimport _threadimport threading ID_NEW_REGISTER = 160ID_FINISH_REGISTER = 161 ID_START_PUNCHCARD = 190ID_END_PUNCARD = 191 ID_OPEN_LOGCAT = 283ID_CLOSE_LOGCAT = 284 ID_WORKER_UNAVIABLE = -1 PATH_FACE = 'data/face_img_database/'# face recognition model, the object maps human faces into 128D vectorsfacerec = dlib.face_recognition_model_v1('model/dlib_face_recognition_resnet_model_v1.dat')# Dlib 预测器detector = dlib.get_frontal_face_detector()predictor = dlib.shape_predictor(’model/shape_predictor_68_face_landmarks.dat’) class WAS(wx.Frame): def __init__(self):wx.Frame.__init__(self,parent=None,title='员工考勤系统',size=(920,560)) self.initMenu()self.initInfoText()self.initGallery()self.initDatabase()self.initData() def initData(self):self.name = ''self.id =ID_WORKER_UNAVIABLEself.face_feature = ''self.pic_num = 0self.flag_registed = Falseself.puncard_time = '21:00:00'self.loadDataBase(1) def initMenu(self): menuBar = wx.MenuBar() #生成菜单栏menu_Font = wx.Font()#Font(faceName='consolas',pointsize=20)menu_Font.SetPointSize(14)menu_Font.SetWeight(wx.BOLD) registerMenu = wx.Menu() #生成菜单self.new_register = wx.MenuItem(registerMenu,ID_NEW_REGISTER,'新建录入')self.new_register.SetBitmap(wx.Bitmap('drawable/new_register.png'))self.new_register.SetTextColour('SLATE BLUE')self.new_register.SetFont(menu_Font)registerMenu.Append(self.new_register) self.finish_register = wx.MenuItem(registerMenu,ID_FINISH_REGISTER,'完成录入')self.finish_register.SetBitmap(wx.Bitmap('drawable/finish_register.png'))self.finish_register.SetTextColour('SLATE BLUE')self.finish_register.SetFont(menu_Font)self.finish_register.Enable(False)registerMenu.Append(self.finish_register) puncardMenu = wx.Menu()self.start_punchcard = wx.MenuItem(puncardMenu,ID_START_PUNCHCARD,'开始签到')self.start_punchcard.SetBitmap(wx.Bitmap('drawable/start_punchcard.png'))self.start_punchcard.SetTextColour('SLATE BLUE')self.start_punchcard.SetFont(menu_Font)puncardMenu.Append(self.start_punchcard) self.close_logcat = wx.MenuItem(logcatMenu, ID_CLOSE_LOGCAT, '关闭日志')self.close_logcat.SetBitmap(wx.Bitmap('drawable/close_logcat.png'))self.close_logcat.SetFont(menu_Font)self.close_logcat.SetTextColour('SLATE BLUE')logcatMenu.Append(self.close_logcat) menuBar.Append(registerMenu,'&人脸录入')menuBar.Append(puncardMenu,'&刷脸签到')menuBar.Append(logcatMenu,'&考勤日志')self.SetMenuBar(menuBar) self.Bind(wx.EVT_MENU,self.OnNewRegisterClicked,id=ID_NEW_REGISTER)self.Bind(wx.EVT_MENU,self.OnFinishRegisterClicked,id=ID_FINISH_REGISTER)self.Bind(wx.EVT_MENU,self.OnStartPunchCardClicked,id=ID_START_PUNCHCARD)self.Bind(wx.EVT_MENU,self.OnEndPunchCardClicked,id=ID_END_PUNCARD)self.Bind(wx.EVT_MENU,self.OnOpenLogcatClicked,id=ID_OPEN_LOGCAT)self.Bind(wx.EVT_MENU,self.OnCloseLogcatClicked,id=ID_CLOSE_LOGCAT) pass def OnCloseLogcatClicked(self,event):self.SetSize(920,560) self.initGallery()pass def register_cap(self,event):# 创建 cv2 摄像头对象self.cap = cv2.VideoCapture(0)# cap.set(propId, value)# 设置视频参数，propId设置的视频参数，value设置的参数值# self.cap.set(3, 600)# self.cap.set(4,600)# cap是否初始化成功while self.cap.isOpened(): # cap.read() # 返回两个值： # 一个布尔值true/false，用来判断读取视频是否成功/是否到视频末尾 # 图像对象，图像的三维矩阵 flag, im_rd = self.cap.read() # 每帧数据延时1ms，延时为0读取的是静态帧 kk = cv2.waitKey(1) # 人脸数 dets dets = detector(im_rd, 1) # 检测到人脸 if len(dets) != 0:biggest_face = dets[0]#取占比最大的脸maxArea = 0for det in dets: w = det.right() - det.left() h = det.top()-det.bottom() if w*h > maxArea:biggest_face = detmaxArea = w*h# 绘制矩形框 cv2.rectangle(im_rd, tuple([biggest_face.left(), biggest_face.top()]), tuple([biggest_face.right(), biggest_face.bottom()]), (255, 0, 0), 2)img_height, img_width = im_rd.shape[:2]image1 = cv2.cvtColor(im_rd, cv2.COLOR_BGR2RGB)pic = wx.Bitmap.FromBuffer(img_width, img_height, image1)# 显示图片在panel上self.bmp.SetBitmap(pic) # 获取当前捕获到的图像的所有人脸的特征，存储到 features_cap_arrshape = predictor(im_rd, biggest_face)features_cap = facerec.compute_face_descriptor(im_rd, shape) # 对于某张人脸，遍历所有存储的人脸特征for i,knew_face_feature in enumerate(self.knew_face_feature): # 将某张人脸与存储的所有人脸数据进行比对 compare = return_euclidean_distance(features_cap, knew_face_feature) if compare == 'same': # 找到了相似脸self.infoText.AppendText(self.getDateAndTime()+'工号:'+str(self.knew_id[i]) +' 姓名:'+self.knew_name[i]+' 的人脸数据已存在rn')self.flag_registed = Trueself.OnFinishRegister()_thread.exit() # print(features_known_arr[i][-1])face_height = biggest_face.bottom()-biggest_face.top()face_width = biggest_face.right()- biggest_face.left()im_blank = np.zeros((face_height, face_width, 3), np.uint8)try: for ii in range(face_height):for jj in range(face_width): im_blank[ii][jj] = im_rd[biggest_face.top() + ii]parent=self.bmp,max=100000000,min=ID_WORKER_UNAVIABLE) for knew_id in self.knew_id:if knew_id == self.id: self.id = ID_WORKER_UNAVIABLE wx.MessageBox(message='工号已存在，请重新输入', caption='警告') while self.name == ’’: self.name = wx.GetTextFromUser(message='请输入您的的姓名,用于创建姓名文件夹', caption='温馨提示', default_value='', parent=self.bmp) # 监测是否重名 for exsit_name in (os.listdir(PATH_FACE)):if self.name == exsit_name: wx.MessageBox(message='姓名文件夹已存在，请重新输入', caption='警告') self.name = ’’ breakos.makedirs(PATH_FACE+self.name)_thread.start_new_thread(self.register_cap,(event,))pass def OnFinishRegister(self): self.new_register.Enable(True)self.finish_register.Enable(False)self.cap.release() self.bmp.SetBitmap(wx.Bitmap(self.pic_index))if self.flag_registed == True: dir = PATH_FACE + self.name for file in os.listdir(dir):os.remove(dir+'/'+file)print('已删除已录入人脸的图片', dir+'/'+file) os.rmdir(PATH_FACE + self.name) print('已删除已录入人脸的姓名文件夹', dir) self.initData() returnif self.pic_num>0: pics = os.listdir(PATH_FACE + self.name) feature_list = [] feature_average = [] for i in range(len(pics)):pic_path = PATH_FACE + self.name + '/' + pics[i]print('正在读的人脸图像：', pic_path)img = iio.imread(pic_path)img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)dets = detector(img_gray, 1)if len(dets) != 0: shape = predictor(img_gray, dets[0]) face_descriptor = facerec.compute_face_descriptor(img_gray, shape) feature_list.append(face_descriptor)else: face_descriptor = 0 print('未在照片中识别到人脸') if len(feature_list) > 0:for j in range(128): #防止越界 feature_average.append(0) for i in range(len(feature_list)):feature_average[j] += feature_list[i][j] feature_average[j] = (feature_average[j]) / len(feature_list)self.insertARow([self.id,self.name,feature_average],1)self.infoText.AppendText(self.getDateAndTime()+'工号:'+str(self.id) +' 姓名:'+self.name+' 的人脸数据已成功存入rn') pass else: os.rmdir(PATH_FACE + self.name) print('已删除空文件夹',PATH_FACE + self.name)self.initData() def OnFinishRegisterClicked(self,event):self.OnFinishRegister()pass def OnStartPunchCardClicked(self,event):# cur_hour = datetime.datetime.now().hour# print(cur_hour)# if cur_hour>=8 or cur_hour<6:# wx.MessageBox(message=’’’您错过了今天的签到时间，请明天再来n# 每天的签到时间是:6:00~7:59’’’, caption='警告')# returnself.start_punchcard.Enable(False)self.end_puncard.Enable(True)self.loadDataBase(2)threading.Thread(target=self.punchcard_cap,args=(event,)).start()#_thread.start_new_thread(self.punchcard_cap,(event,))pass def OnEndPunchCardClicked(self,event):self.start_punchcard.Enable(True)self.end_puncard.Enable(False)pass def initGallery(self):self.pic_index = wx.Image('drawable/index.png', wx.BITMAP_TYPE_ANY).Scale(600, 500)self.bmp = wx.StaticBitmap(parent=self, pos=(320,0), bitmap=wx.Bitmap(self.pic_index))pass def getDateAndTime(self):dateandtime = strftime('%Y-%m-%d %H:%M:%S',localtime())return '['+dateandtime+']' #数据库部分 #初始化数据库 def initDatabase(self):conn = sqlite3.connect('inspurer.db') #建立数据库连接cur = conn.cursor() #得到游标对象cur.execute(’’’create table if not exists worker_info(name text not null,id int not null primary key,face_feature array not null)’’’)cur.execute(’’’create table if not exists logcat (datetime text not null, id int not null, name text not null, late text not null)’’’)cur.close()conn.commit()conn.close() def adapt_array(self,arr):out = io.BytesIO()np.save(out, arr)out.seek(0) dataa = out.read()# 压缩数据流return sqlite3.Binary(zlib.compress(dataa, zlib.Z_BEST_COMPRESSION)) def convert_array(self,text):out = io.BytesIO(text)out.seek(0) dataa = out.read()# 解压缩数据流out = io.BytesIO(zlib.decompress(dataa))return np.load(out) def insertARow(self,Row,type):conn = sqlite3.connect('inspurer.db') # 建立数据库连接cur = conn.cursor() # 得到游标对象if type == 1: cur.execute('insert into worker_info (id,name,face_feature) values(?,?,?)', (Row[0],Row[1],self.adapt_array(Row[2]))) print('写人脸数据成功')if type == 2: cur.execute('insert into logcat (id,name,datetime,late) values(?,?,?,?)',(Row[0],Row[1],Row[2],Row[3])) print('写日志成功') passcur.close()conn.commit()conn.close()pass def loadDataBase(self,type): conn = sqlite3.connect('inspurer.db') # 建立数据库连接cur = conn.cursor() # 得到游标对象 if type == 1: self.knew_id = [] self.knew_name = [] self.knew_face_feature = [] cur.execute(’select id,name,face_feature from worker_info’) origin = cur.fetchall() for row in origin:print(row[0])self.knew_id.append(row[0])print(row[1])self.knew_name.append(row[1])print(self.convert_array(row[2]))self.knew_face_feature.append(self.convert_array(row[2]))if type == 2: self.logcat_id = [] self.logcat_name = [] self.logcat_datetime = [] self.logcat_late = [] cur.execute(’select id,name,datetime,late from logcat’) origin = cur.fetchall() for row in origin:print(row[0])self.logcat_id.append(row[0])print(row[1])self.logcat_name.append(row[1])print(row[2])self.logcat_datetime.append(row[2])print(row[3])self.logcat_late.append(row[3])passapp = wx.App()frame = WAS()frame.Show()app.MainLoop()

运行结果如下：

C++学习参考实例：

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https://www.jb51.net/article/180940.htm

C++实现简易五子棋游戏

https://www.jb51.net/article/190548.htm

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https://www.jb51.net/article/207158.htm

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