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运行结果如下：
代码如下：
import wx
import wx.grid
from time import localtime,strftime
import os
import io
import zlib
import dlib # 人脸识别的库dlib
import numpy as np # 数据处理的库numpy
import cv2 # 图像处理的库OpenCv
import _thread
import threading
ID_NEW_REGISTER = 160
ID_FINISH_REGISTER = 161
ID_START_PUNCHCARD = 190
ID_END_PUNCARD = 191
ID_OPEN_LOGCAT = 283
ID_CLOSE_LOGCAT = 284
ID_WORKER_UNAVIABLE = -1
PATH_FACE = "data/face_img_database/"
# face recognition model, the object maps human faces into 128D vectors
facerec = 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_UNAVIABLE
self.face_feature = ""
self.pic_num = 0
self.flag_registed = False
self.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_cahttp://www.cppcns.comp(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 = 0
for det in dets:
w = det.right() - det.left()
h = det.top()-det.bottom()
if w*h > maxArea:
biggest_face = det
maxArea = 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_arr
shape = 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])
www.cppcns.com +" 姓名:"+self.knew_name[i]+" 的人脸数据已存在\r\n")
self.flag_registed = True
self.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 = ''
break
os.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()
return
if 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\r\n")
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")
# return
self.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 nuhttp://www.cppcns.comll)''')
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.decowww.cppcns.commpress(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 http://www.cppcns.com ("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")
pass
cur.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])
pass
app = wx.App()
frame = WAS()
frame.Show()
app.MainLoop()
运行结果如下：
C++ 学习参考实例 ：
使用C++ MFC编写一个简单的五子棋游戏程序
https://www.jb51.net/article/180940.htm
C++实现简易五子棋游戏
https://www.jb51.net/article/190548.htm
c++ 基于opencv 识别、定位二维码
https://www.jb51.net/article/207158.htm
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