#include <dlib/opencv.h> #include <opencv2/opencv.hpp> #include <dlib/image_processing/frontal_face_detector.h>//cv2+dlib库自带frontal_face_detector(人脸征检测器)
#include <dlib/image_processing/render_face_detections.h> #include <dlib/image_processing.h> #include <dlib/gui_widgets.h> #include<windows.h> #include<string> #include<cmath> #include <dlib/gui_widgets.h> #include <dlib/clustering.h> #include <dlib/string.h> #include <dlib/dnn.h> #include <dlib/image_io.h> #include <dlib/image_processing/frontal_face_detector.h> using namespace dlib; using namespace std; //下面是神经网络需要用到的东西 CNN网络 template <template <int, template<typename>class, int, typename> class block, int N, template<typename>class BN, typename SUBNET> using residual = add_prev1<block<N, BN, 1, tag1<SUBNET>>>; template <template <int, template<typename>class, int, typename> class block, int N, template<typename>class BN, typename SUBNET> using residual_down = add_prev2<avg_pool<2, 2, 2, 2, skip1<tag2<block<N, BN, 2, tag1<SUBNET>>>>>>; template <int N, template <typename> class BN, int stride, typename SUBNET> using block = BN<con<N, 3, 3, 1, 1, relu<BN<con<N, 3, 3, stride, stride, SUBNET>>>>>; template <int N, typename SUBNET> using ares = relu<residual<block, N, affine, SUBNET>>; template <int N, typename SUBNET> using ares_down = relu<residual_down<block, N, affine, SUBNET>>; template <typename SUBNET> using alevel0 = ares_down<256, SUBNET>; template <typename SUBNET> using alevel1 = ares<256, ares<256, ares_down<256, SUBNET>>>; template <typename SUBNET> using alevel2 = ares<128, ares<128, ares_down<128, SUBNET>>>; template <typename SUBNET> using alevel3 = ares<64, ares<64, ares<64, ares_down<64, SUBNET>>>>; template <typename SUBNET> using alevel4 = ares<32, ares<32, ares<32, SUBNET>>>; using anet_type = loss_metric<fc_no_bias<128, avg_pool_everything< alevel0< alevel1< alevel2< alevel3< alevel4< max_pool<3, 3, 2, 2, relu<affine<con<32, 7, 7, 2, 2, input_rgb_image_sized<150> >>>>>>>>>>>>; // ---------------------------------------------------------------------------------------- int main() { try { cv::VideoCapture cap(0);//调用摄像头 if (!cap.isOpened()) { cerr << "unable to connect to camera" << endl;//cerr跟cout差不多 但是常用来输出错误信息 return 1; } frontal_face_detector detector = get_frontal_face_detector(); //姿势判断 shape_predictor sp; deserialize("D:/code/vs_dlib/library/shape_predictor_68_face_landmarks.dat") >> sp; // dnn神经网络的应用 anet_type net; deserialize("D:/code/vs_dlib/library/dlib_face_recognition_resnet_model_v1.dat") >> net; int key;//用来处理键盘事件 string name;//用来 保存截屏图片的名字 while (cv::waitKey(30) != 27){ //等于27 的时候是指按下Esc键的时候 key = cv::waitKey(10); //----------------------------------------------------------------------------------------------------------------------------------------------------------- - 摄像头图片 //用mat来存储图像数据 cv::Mat temp; cap >> temp;//将数据传给cap cv_image<bgr_pixel> cimg(temp);//转换成opencv的iamge形式 std::vector<rectangle> faceshh = detector(cimg); // Find the pose of each face. std::vector<full_object_detection> shapes; for (unsigned long i = 0; i < faceshh.size(); ++i) shapes.push_back(sp(cimg, faceshh[i])); std::vector<matrix<rgb_pixel>> faces; //对摄像头图片进行缩放 for (auto face : detector(cimg)) { auto shape = sp(cimg, face); matrix<rgb_pixel> face_chip; //对 shape特征值 以及人脸进行缩放 extract_image_chip(cimg, get_face_chip_details(shape, 150, 0.25), face_chip); faces.push_back(move(face_chip)); } //--------------------------------------------------------------------------------------------------------------------------------------------------- //文件夹中的图片 matrix<rgb_pixel> xxr; load_image(xxr, "D:\code\vs_dlib\face_Recognize\x64\Release\faces\xxr.jpg"); std::vector<matrix<rgb_pixel>> xxr_face; for (auto face : detector(xxr)) { auto shape_xxr = sp(xxr, face); matrix<rgb_pixel> face_chip; //对 shape特征值 以及人脸进行缩放 extract_image_chip(xxr, get_face_chip_details(shape_xxr, 150, 0.25), face_chip); xxr_face.push_back(move(face_chip)); } //----------------------------------------------------------------------------------------------------------------------------------------------------------- //键盘事件的处理 switch (key) { case 's': //执行截图事件 cv::imwrite("D:\code\vs_dlib\face_Recognize\x64\Release\faces\" + name + ".jpg",temp); break; case 'i': //执行输入姓名事件 while (key != '!') { name = name +(char) key; } break; } //--------------------------------------------------------------------------------------------------------------------------------------------------------------- //人脸特征的存储 std::vector<matrix<float, 0, 1>> face_descriptors = net(faces); std::vector<matrix<float, 0, 1>> facexxr_descriptors = net(xxr_face); //人脸识别的判断 for (size_t i = 0; i < face_descriptors.size(); ++i) { for (size_t j = 0; j < facexxr_descriptors.size(); j++){ if (length(face_descriptors[i] - facexxr_descriptors[j]) < 0.6) { //说明这两个人脸是相似的 circle(temp, cvPoint(shapes[i].part(30).x(), shapes[i].part(30).y()), 100, cv::Scalar(0, 0, 255), 1); putText(temp,"xxr",cvPoint(shapes[i].part(30).x(), shapes[i].part(30).y() + 100), CV_FONT_HERSHEY_SIMPLEX, 1, (187, 255, 255), 4.8); }else { circle(temp, cvPoint(shapes[i].part(30).x(), shapes[i].part(30).y()), 100, cv::Scalar(255, 0, 0), 1); putText(temp,"Unknown",cvPoint(shapes[i].part(30).x(), shapes[i].part(30).y() + 100), CV_FONT_HERSHEY_SIMPLEX, 1, (187, 255, 255), 4.8); } } } imshow("Dlib特征点", temp); } } catch (std::exception& e) { cout << e.what() << endl; } }
好像是添加了MFC界面的代码：
// camera_mfc_test1Dlg.cpp : 实现文件 // #include "stdafx.h" #include <opencv2/opencv.hpp> #include "CvvImage.h" #include <dlib/opencv.h> #include <opencv2/opencv.hpp> #include <dlib/image_processing/frontal_face_detector.h> #include <dlib/image_processing/render_face_detections.h> #include <dlib/image_processing.h> #include <dlib/gui_widgets.h> #include<windows.h> #include<string> #include<cmath> #include <dlib/gui_widgets.h> #include <dlib/clustering.h> #include <dlib/string.h> #include <dlib/dnn.h> #include <dlib/image_io.h> #include <dlib/image_processing/frontal_face_detector.h> #include "camera_mfc_test1.h" #include "camera_mfc_test1Dlg.h" #include "afxdialogex.h" using namespace std; using namespace dlib; using namespace cv; #ifdef _DEBUG #define new DEBUG_NEW #endif #define WM_UPDATE_STATIC (WM_USER + 100) CvCapture* m_Video; VideoCapture Capture; Mat frame; //定义Mat变量，用来存储每一帧 IplImage frame2;//opencv里面的对图像处理的 像素 IplImage *framecv; IplImage* m_grabframe; CRect rect; CDC *pDC;//CDC中各种绘图函数 HDC hDC;// CWnd *pwnd; //放图片的名字 std::vector<cv::String> files; //int ImgNum = 0; bool threadFlag = FALSE;//线程的开关 CWinThread *m_pThread; CString str_name; //下面是神经网络需要用到的东西 CNN网络 template <template <int, template<typename>class, int, typename> class block, int N, template<typename>class BN, typename SUBNET> using residual = add_prev1<block<N, BN, 1, tag1<SUBNET>>>; template <template <int, template<typename>class, int, typename> class block, int N, template<typename>class BN, typename SUBNET> using residual_down = add_prev2<avg_pool<2, 2, 2, 2, skip1<tag2<block<N, BN, 2, tag1<SUBNET>>>>>>; template <int N, template <typename> class BN, int stride, typename SUBNET> using block = BN<con<N, 3, 3, 1, 1, relu<BN<con<N, 3, 3, stride, stride, SUBNET>>>>>; template <int N, typename SUBNET> using ares = relu<residual<block, N, affine, SUBNET>>; template <int N, typename SUBNET> using ares_down = relu<residual_down<block, N, affine, SUBNET>>; template <typename SUBNET> using alevel0 = ares_down<256, SUBNET>; template <typename SUBNET> using alevel1 = ares<256, ares<256, ares_down<256, SUBNET>>>; template <typename SUBNET> using alevel2 = ares<128, ares<128, ares_down<128, SUBNET>>>; template <typename SUBNET> using alevel3 = ares<64, ares<64, ares<64, ares_down<64, SUBNET>>>>; template <typename SUBNET> using alevel4 = ares<32, ares<32, ares<32, SUBNET>>>; using anet_type = loss_metric<fc_no_bias<128, avg_pool_everything< alevel0< alevel1< alevel2< alevel3< alevel4< max_pool<3, 3, 2, 2, relu<affine<con<32, 7, 7, 2, 2, input_rgb_image_sized<150> >>>>>>>>>>>>; // 用于应用程序“关于”菜单项的 CAboutDlg 对话框 class CAboutDlg : public CDialogEx { public: CAboutDlg(); // 对话框数据 #ifdef AFX_DESIGN_TIME enum { IDD = IDD_ABOUTBOX }; #endif protected: virtual void DoDataExchange(CDataExchange* pDX); // DDX/DDV 支持 // 实现 protected: DECLARE_MESSAGE_MAP() }; CAboutDlg::CAboutDlg() : CDialogEx(IDD_ABOUTBOX) { } void CAboutDlg::DoDataExchange(CDataExchange* pDX) { CDialogEx::DoDataExchange(pDX); } BEGIN_MESSAGE_MAP(CAboutDlg, CDialogEx) END_MESSAGE_MAP() // Ccamera_mfc_test1Dlg 对话框 Ccamera_mfc_test1Dlg::Ccamera_mfc_test1Dlg(CWnd* pParent /*=NULL*/) : CDialogEx(IDD_CAMERA_MFC_TEST1_DIALOG, pParent) { m_hIcon = AfxGetApp()->LoadIcon(IDR_MAINFRAME); } void Ccamera_mfc_test1Dlg::DoDataExchange(CDataExchange* pDX) { CDialogEx::DoDataExchange(pDX); } BEGIN_MESSAGE_MAP(Ccamera_mfc_test1Dlg, CDialogEx) ON_WM_SYSCOMMAND() ON_WM_PAINT() ON_WM_QUERYDRAGICON() ON_MESSAGE(WM_UPDATE_STATIC, &Ccamera_mfc_test1Dlg::OnUpdateStatic) ON_BN_CLICKED(open, &Ccamera_mfc_test1Dlg::On_OpenCamera) ON_WM_TIMER() ON_BN_CLICKED(close, &Ccamera_mfc_test1Dlg::On_CloseCamera) ON_BN_CLICKED(get_pic, &Ccamera_mfc_test1Dlg::On_CaptureImage) END_MESSAGE_MAP() // Ccamera_mfc_test1Dlg 消息处理程序 BOOL Ccamera_mfc_test1Dlg::OnInitDialog() { CDialogEx::OnInitDialog(); // 将“关于...”菜单项添加到系统菜单中。 // IDM_ABOUTBOX 必须在系统命令范围内。 ASSERT((IDM_ABOUTBOX & 0xFFF0) == IDM_ABOUTBOX); ASSERT(IDM_ABOUTBOX < 0xF000); CMenu* pSysMenu = GetSystemMenu(FALSE); if (pSysMenu != NULL) { BOOL bNameValid; CString strAboutMenu; bNameValid = strAboutMenu.LoadString(IDS_ABOUTBOX); ASSERT(bNameValid); if (!strAboutMenu.IsEmpty()) { pSysMenu->AppendMenu(MF_SEPARATOR); pSysMenu->AppendMenu(MF_STRING, IDM_ABOUTBOX, strAboutMenu); } } // 设置此对话框的图标。 当应用程序主窗口不是对话框时，框架将自动 // 执行此操作 SetIcon(m_hIcon, TRUE); // 设置大图标 SetIcon(m_hIcon, FALSE); // 设置小图标 // TODO: 在此添加额外的初始化代码 return TRUE; // 除非将焦点设置到控件，否则返回 TRUE } void Ccamera_mfc_test1Dlg::OnSysCommand(UINT nID, LPARAM lParam) { if ((nID & 0xFFF0) == IDM_ABOUTBOX) { CAboutDlg dlgAbout; dlgAbout.DoModal(); } else { CDialogEx::OnSysCommand(nID, lParam); } } // 如果向对话框添加最小化按钮，则需要下面的代码 // 来绘制该图标。 对于使用文档/视图模型的 MFC 应用程序， // 这将由框架自动完成。 void Ccamera_mfc_test1Dlg::OnPaint() { if (IsIconic()) { CPaintDC dc(this); // 用于绘制的设备上下文 SendMessage(WM_ICONERASEBKGND, reinterpret_cast<WPARAM>(dc.GetSafeHdc()), 0); // 使图标在工作区矩形中居中 int cxIcon = GetSystemMetrics(SM_CXICON); int cyIcon = GetSystemMetrics(SM_CYICON); CRect rect; GetClientRect(&rect); int x = (rect.Width() - cxIcon + 1) / 2; int y = (rect.Height() - cyIcon + 1) / 2; // 绘制图标 dc.DrawIcon(x, y, m_hIcon); } else { CDialogEx::OnPaint(); } } //当用户拖动最小化窗口时系统调用此函数取得光标 //显示。 HCURSOR Ccamera_mfc_test1Dlg::OnQueryDragIcon() { return static_cast<HCURSOR>(m_hIcon); } //多线程实现 视频的播放 LRESULT Ccamera_mfc_test1Dlg::OnUpdateStatic(WPARAM wParam, LPARAM lParam) { //GetDlgItem(IDC_STATIC)->SetWindowText(L"Hello Linux"); pDC = GetDlgItem(IDC_PIC_STATIC)->GetDC();//GetDlgItem(IDC_PIC_STATIC)意思为获取显示控件的句柄（句柄就是指针），获取显示控件的DC GetDlgItem(IDC_PIC_STATIC)->GetClientRect(&rect); hDC = pDC->GetSafeHdc();//获取显示控件的句柄 CvvImage m_CvvImage; m_CvvImage.CopyOf(framecv, 1); //复制该帧图像 m_CvvImage.DrawToHDC(hDC, &rect); //显示到设备的矩形框内 cvWaitKey(25); return 0; } UINT Ccamera_mfc_test1Dlg::ThreadFunction(LPVOID pParam) { Ccamera_mfc_test1Dlg *pDlg = (Ccamera_mfc_test1Dlg *)pParam; cv::VideoCapture Capture(0);//调用摄像头 frontal_face_detector detector = get_frontal_face_detector(); //姿势判断 shape_predictor sp; deserialize("D:/code/vs_dlib/library/shape_predictor_68_face_landmarks.dat") >> sp; // dnn神经网络的应用 anet_type net; deserialize("D:/code/vs_dlib/library/dlib_face_recognition_resnet_model_v1.dat") >> net; if (!Capture.isOpened()) { pDlg->MessageBox(_T("无法连接摄像头！！！")); return 0; } while (Capture.isOpened()) { Capture >> frame;//读取当前帧方法一 cv_image<bgr_pixel> cimg(frame);//转换成opencv的iamge形式 std::vector<dlib::rectangle> faceshh = detector(cimg); // Find the pose of each face. std::vector<full_object_detection> shapes; for (unsigned long i = 0; i < faceshh.size(); ++i) shapes.push_back(sp(cimg, faceshh[i])); std::vector<matrix<rgb_pixel>> faces; //对摄像头图片进行缩放 for (auto face : detector(cimg)) { auto shape = sp(cimg, face); matrix<rgb_pixel> face_chip; //对 shape特征值 以及人脸进行缩放 extract_image_chip(cimg, get_face_chip_details(shape, 150, 0.25), face_chip); faces.push_back(move(face_chip)); } //--------------------------------------------------------------------------------------------------------------------------------------------------- //文件夹中的图片 matrix<rgb_pixel> xxr; Mat img; string files_path = "C:/Users/xxr/Pictures/Saved Pictures/*.jpg"; glob( files_path, files, false); size_t count = files.size(); for (int i = 0; i < count; i++) { img.push_back(imread(files[i])); array2d< bgr_pixel> arrimg(img.rows, img.cols); dlib::assign_image(xxr, cv_image<rgb_pixel>(img)); } //load_image(xxr, "D:\code\vs_dlib\face_Recognize\x64\Release\faces\xxr.jpg"); std::vector<matrix<rgb_pixel>> xxr_face; for (auto face : detector(xxr)) { auto shape_xxr = sp(xxr, face); matrix<rgb_pixel> face_chip; //对 shape特征值 以及人脸进行缩放 extract_image_chip(xxr, get_face_chip_details(shape_xxr, 150, 0.25), face_chip); xxr_face.push_back(move(face_chip)); } //人脸特征的存储 std::vector<matrix<float, 0, 1>> face_descriptors = net(faces); std::vector<matrix<float, 0, 1>> facexxr_descriptors = net(xxr_face); for (size_t i = 0; i < face_descriptors.size(); ++i) {//人脸识别的判断 bool flag = false; for (size_t j = 0; j < facexxr_descriptors.size(); j++){ if (length(face_descriptors[i] - facexxr_descriptors[j]) < 0.6) { //说明这两个人脸是相似的 circle(frame, cvPoint(shapes[i].part(30).x(), shapes[i].part(30).y()), 100, cv::Scalar(0, 0, 255), 1); std::string hh = files[j-1]; putText(frame,hh.erase(0, 37) ,cvPoint(shapes[i].part(30).x(), shapes[i].part(30).y() + 100), CV_FONT_HERSHEY_SIMPLEX, 1, (187, 255, 255), 4.8); flag = true; } } if (!flag) { circle(frame, cvPoint(shapes[i].part(30).x(), shapes[i].part(30).y()), 100, cv::Scalar(255, 0, 0), 1); putText(frame,"Unknown",cvPoint(shapes[i].part(30).x(), shapes[i].part(30).y() + 100), CV_FONT_HERSHEY_SIMPLEX, 1, (187, 255, 255), 4.8); } } frame2 = frame; framecv = cvCloneImage(&frame2); ::PostMessage(pDlg->m_hWnd, WM_UPDATE_STATIC, 0, 0); //imshow("Dlib特征点", temp); } return 0; } void Ccamera_mfc_test1Dlg::On_OpenCamera() { // MessageBox(_T("dakai摄像头！！！")); m_pThread = AfxBeginThread((AFX_THREADPROC)ThreadFunction, this); } void Ccamera_mfc_test1Dlg::On_CloseCamera() { // TODO: 在此添加控件通知处理程序代码 // TODO: Add your command handler code here if (!Capture.isOpened()) { MessageBox(_T("没有打开摄像头！！！")); return; } Capture.release(); pDC = GetDlgItem(IDC_PIC_STATIC)->GetDC();//GetDlgItem(IDC_PIC_STATIC)意思为获取显示控件的句柄（句柄就是指针），获取显示控件的DC GetDlgItem(IDC_PIC_STATIC)->GetClientRect(&rect); hDC = pDC->GetSafeHdc();//获取显示控件的句柄 framecv = cvLoadImage("C:\Users\xxr\Pictures\Saved Pictures\hh.jpg"); //图片读取路径可以自己设定 CvvImage m_CvvImage; m_CvvImage.CopyOf(framecv, 1); //复制该帧图像 m_CvvImage.DrawToHDC(hDC, &rect); //显示到设备的矩形框内 ReleaseDC(pDC); } void Ccamera_mfc_test1Dlg::On_CaptureImage() { CEdit *edit_name; edit_name=(CEdit*)GetDlgItem(edit_namehh); edit_name-> GetWindowText(str_name); // TODO: 在此添加控件通知处理程序代码 m_grabframe = framecv; if (m_grabframe == 0) { MessageBox(_T("摄像头已关闭，无法捕捉图像！！！")); return; } CString ImagePath=TEXT("C:\Users\xxr\Pictures\Saved Pictures\"); //CString ImagePath = _T("D:\Documents\Visual Studio 2013\Projects\标定图片\"); if (!PathIsDirectory(ImagePath)) { CreateDirectory(ImagePath, 0);//不存在则创建 MessageBox(_T("标定图片文件夹已创建！！！")); return; } char ImagesName[100]; //ImgNum = ImgNum + 1; std::string str( CW2A( str_name.GetString() ) ); sprintf_s(ImagesName, "%s%s%s", "C:\Users\xxr\Pictures\Saved Pictures\", str, ".jpg"); IplImage * m_snap = cvCreateImage(cvGetSize(m_grabframe), m_grabframe->depth, m_grabframe->nChannels); cvCopy(m_grabframe, m_snap, NULL); cvSaveImage(ImagesName, m_snap); //把图像写入指定文件夹的文件中去 //以下代码是完成图像的显示过程 pDC = GetDlgItem(IDC_PIC1_STATIC)->GetDC();//GetDlgItem(IDC_PIC_STATIC)意思为获取显示控件的句柄（句柄就是指针），获取显示控件的DC GetDlgItem(IDC_PIC1_STATIC)->GetClientRect(&rect); hDC = pDC->GetSafeHdc();//获取显示控件的句柄 CvvImage m_CvvImage; m_CvvImage.CopyOf(m_snap, 1); //复制该帧图像 m_CvvImage.DrawToHDC(hDC, &rect); //显示到设备环境的矩形框内 }
连接数据库
#include <dlib/opencv.h> #include <opencv2/opencv.hpp> #include <dlib/image_processing/frontal_face_detector.h> #include <dlib/image_processing/render_face_detections.h> #include <dlib/image_processing.h> #include <dlib/gui_widgets.h> #include<windows.h> #include<string> #include<cmath> #include <dlib/gui_widgets.h> #include <dlib/clustering.h> #include <dlib/string.h> #include <dlib/dnn.h> #include <dlib/image_io.h> #include <dlib/image_processing/frontal_face_detector.h> #include <winsock.h> #include <iostream> #include <string> #include <mysql.h> //单步执行，不想单步执行就注释掉 #define STEPBYSTEP using namespace dlib; using namespace std; //下面是神经网络需要用到的东西 CNN网络 template <template <int, template<typename>class, int, typename> class block, int N, template<typename>class BN, typename SUBNET> using residual = add_prev1<block<N, BN, 1, tag1<SUBNET>>>; template <template <int, template<typename>class, int, typename> class block, int N, template<typename>class BN, typename SUBNET> using residual_down = add_prev2<avg_pool<2, 2, 2, 2, skip1<tag2<block<N, BN, 2, tag1<SUBNET>>>>>>; template <int N, template <typename> class BN, int stride, typename SUBNET> using block = BN<con<N, 3, 3, 1, 1, relu<BN<con<N, 3, 3, stride, stride, SUBNET>>>>>; template <int N, typename SUBNET> using ares = relu<residual<block, N, affine, SUBNET>>; template <int N, typename SUBNET> using ares_down = relu<residual_down<block, N, affine, SUBNET>>; template <typename SUBNET> using alevel0 = ares_down<256, SUBNET>; template <typename SUBNET> using alevel1 = ares<256, ares<256, ares_down<256, SUBNET>>>; template <typename SUBNET> using alevel2 = ares<128, ares<128, ares_down<128, SUBNET>>>; template <typename SUBNET> using alevel3 = ares<64, ares<64, ares<64, ares_down<64, SUBNET>>>>; template <typename SUBNET> using alevel4 = ares<32, ares<32, ares<32, SUBNET>>>; using anet_type = loss_metric<fc_no_bias<128, avg_pool_everything< alevel0< alevel1< alevel2< alevel3< alevel4< max_pool<3, 3, 2, 2, relu<affine<con<32, 7, 7, 2, 2, input_rgb_image_sized<150> >>>>>>>>>>>>; // ---------------------------------------------------------------------------------------- int main() { try { //必备的一个数据结构 MYSQL mydata; //初始化数据库 if (0 == mysql_library_init(0, NULL, NULL)) { cout << "mysql_library_init() succeed" << endl; } else { cout << "mysql_library_init() failed" << endl; return -1; } //初始化数据结构 if (NULL != mysql_init(&mydata)) { cout << "mysql_init() succeed" << endl; } else { cout << "mysql_init() failed" << endl; return -1; } //在连接数据库之前，设置额外的连接选项 //可以设置的选项很多，这里设置字符集，否则无法处理中文 if (0 == mysql_options(&mydata, MYSQL_SET_CHARSET_NAME, "gbk")) { cout << "mysql_options() succeed" << endl; } else { cout << "mysql_options() failed" << endl; return -1; } //连接数据库 if (NULL != mysql_real_connect(&mydata, "localhost", "root", "19990209", "test",3306, NULL, 0)) //这里的地址，用户名，密码，端口可以根据自己本地的情况更改 { cout << "mysql_real_connect() succeed" << endl; } else { cout << "mysql_real_connect() failed" << endl; return -1; } //sql字符串 string sqlstr; //创建一个表 sqlstr = "CREATE TABLE IF NOT EXISTS face_dect"; sqlstr += "("; sqlstr += "user_id INT UNSIGNED NOT NULL AUTO_INCREMENT PRIMARY KEY COMMENT 'Unique User ID',"; sqlstr += "user_name VARCHAR(100) CHARACTER SET gb2312 COLLATE gb2312_chinese_ci NULL COMMENT 'Name Of User',"; sqlstr += "user_fece VARCHAR(60000) "; sqlstr += ");"; if (0 == mysql_query(&mydata, sqlstr.c_str())) { cout << "mysql_query() create table succeed" << endl; } else { cout << "mysql_query() create table failed" << endl; mysql_close(&mydata); return -1; } cv::VideoCapture cap(0);//调用摄像头 if (!cap.isOpened()) { cerr << "unable to connect to camera" << endl;//cerr跟cout差不多 但是常用来输出错误信息 return 1; } frontal_face_detector detector = get_frontal_face_detector(); //姿势判断 shape_predictor sp; deserialize("D:/code/vs_dlib/library/shape_predictor_68_face_landmarks.dat") >> sp; // dnn神经网络的应用 anet_type net; deserialize("D:/code/vs_dlib/library/dlib_face_recognition_resnet_model_v1.dat") >> net; int key;//用来处理键盘事件 string name;//用来 保存截屏图片的名字 while (cv::waitKey(30) != 27){ //等于27 的时候是指按下Esc键的时候 key = cv::waitKey(10); //----------------------------------------------------------------------------------------------------------------------------------------------------------- - 摄像头图片 //用mat来存储图像数据 cv::Mat temp; cap >> temp;//将数据传给cap cv_image<bgr_pixel> cimg(temp);//转换成opencv的iamge形式 std::vector<rectangle> faceshh = detector(cimg); // Find the pose of each face. std::vector<full_object_detection> shapes; for (unsigned long i = 0; i < faceshh.size(); ++i) shapes.push_back(sp(cimg, faceshh[i])); std::vector<matrix<rgb_pixel>> faces; //对摄像头图片进行缩放 for (auto face : detector(cimg)) { auto shape = sp(cimg, face); matrix<rgb_pixel> face_chip; //对 shape特征值 以及人脸进行缩放 extract_image_chip(cimg, get_face_chip_details(shape, 150, 0.25), face_chip); faces.push_back(move(face_chip)); } //--------------------------------------------------------------------------------------------------------------------------------------------------- //文件夹中的图片 matrix<rgb_pixel> xxr; load_image(xxr, "D:\code\vs_dlib\face_Recognize\x64\Release\faces\xxr.jpg"); std::vector<matrix<rgb_pixel>> xxr_face; for (auto face : detector(xxr)) { auto shape_xxr = sp(xxr, face); matrix<rgb_pixel> face_chip; //对 shape特征值 以及人脸进行缩放 extract_image_chip(xxr, get_face_chip_details(shape_xxr, 150, 0.25), face_chip); xxr_face.push_back(move(face_chip)); } //----------------------------------------------------------------------------------------------------------------------------------------------------------- //键盘事件的处理 switch (key) { case 's': //执行截图事件 cv::imwrite("D:\code\vs_dlib\face_Recognize\x64\Release\faces\" + name + ".jpg",temp); break; case 'i': //执行输入姓名事件 while (key != '!') { name = name +(char) key; } break; } //--------------------------------------------------------------------------------------------------------------------------------------------------------------- //人脸特征的存储 std::vector<matrix<float, 0, 1>> face_descriptors = net(faces); std::vector<matrix<float, 0, 1>> facexxr_descriptors = net(xxr_face); string str; bool flag = true; if (true) { for (int i = 0; i < facexxr_descriptors.size(); i++) { for (long r = 0; r < facexxr_descriptors[i].nr(); r++) { for (long c = 0; c < facexxr_descriptors[i].nc(); ++c) { cout << "第"<<i<<"个"<< "第" << r << "行" << "第" << c << "列"; cout << facexxr_descriptors[i](r, c)<<endl; str = facexxr_descriptors[i](r, c) ; str = str + "&"; } } //向表中插入数据 sqlstr = "INSERT INTO face_dect(user_name,user_face) VALUES('xxr',str);"; if (0 == mysql_query(&mydata, sqlstr.c_str())) { cout << "mysql_query() insert data succeed" << endl; } else { cout << "mysql_query() insert data failed" << endl; mysql_close(&mydata); return -1; } } flag = false; } // printf(face_descriptors); //人脸识别的判断 for (size_t i = 0; i < face_descriptors.size(); ++i) { for (size_t j = 0; j < facexxr_descriptors.size(); j++){ if (length(face_descriptors[i] - facexxr_descriptors[j]) < 0.6) { //说明这两个人脸是相似的 circle(temp, cvPoint(shapes[i].part(30).x(), shapes[i].part(30).y()), 100, cv::Scalar(0, 0, 255), 1); putText(temp,"xxr",cvPoint(shapes[i].part(30).x(), shapes[i].part(30).y() + 100), CV_FONT_HERSHEY_SIMPLEX, 1, (187, 255, 255), 4.8); }else { circle(temp, cvPoint(shapes[i].part(30).x(), shapes[i].part(30).y()), 100, cv::Scalar(255, 0, 0), 1); putText(temp,"Unknown",cvPoint(shapes[i].part(30).x(), shapes[i].part(30).y() + 100), CV_FONT_HERSHEY_SIMPLEX, 1, (187, 255, 255), 4.8); } } } imshow("Dlib特征点", temp); } } catch (std::exception& e) { cout << e.what() << endl; } }