1.1下載sdk運行
百度離線人臉識別sdk的使用
1.2配置環境
添加至項目，可以拖動復制或者以類庫形式添加
face-resource此文件夾 放到根目錄上一層
激活文件與所有dll引用放到根目錄
嫌麻煩可以將sdk的根目錄所有文件直接拖動至項目的根目錄
1.3 使用
百度離線人臉識別sdk只支持Release方式運行，所以項目的配置也改為此方式（不然會提示baiduapi…….一系列dll找不到）
sdk使用OpenCvSharp3-AnyCPU的引用
static class Program { /// <summary> /// 應用程序的主入口點。 /// </summary> [UnmanagedFunctionPointer(CallingConvention.Cdecl)] delegate void FaceCallback(IntPtr bytes, int size, String res); // sdk初始化 [DllImport("BaiduFaceApi.dll", EntryPoint = "sdk_init", CharSet = CharSet.Ansi , CallingConvention = CallingConvention.Cdecl)] private static extern int sdk_init(bool id_card); // 是否授權 [DllImport("BaiduFaceApi.dll", EntryPoint = "is_auth", CharSet = CharSet.Ansi , CallingConvention = CallingConvention.Cdecl)] private static extern bool is_auth(); // 獲取設備指紋 [DllImport("BaiduFaceApi.dll", EntryPoint = "get_device_id", CharSet = CharSet.Ansi , CallingConvention = CallingConvention.Cdecl)] private static extern IntPtr get_device_id(); // sdk銷毀 [DllImport("BaiduFaceApi.dll", EntryPoint = "sdk_destroy", CharSet = CharSet.Ansi , CallingConvention = CallingConvention.Cdecl)] private static extern void sdk_destroy(); // 測試獲取設備指紋device_id public static void test_get_device_id() { IntPtr ptr = get_device_id(); string buf = Marshal.PtrToStringAnsi(ptr); Console.WriteLine("device id is:" + buf); } [STAThread] static void Main() { bool id = false; int n = sdk_init(id); if (n != 0) { Console.WriteLine("auth result is {0:D}", n); Console.ReadLine(); } // 測試是否授權 bool authed = is_auth(); Console.WriteLine("authed res is:" + authed); test_get_device_id(); Application.EnableVisualStyles(); Application.SetCompatibleTextRenderingDefault(false); Application.Run(new Form1()); } }
使用時將Program類修改，不然無法使用人臉檢測接口 錯誤報告為參數非法回傳 -1
1.3.1 人臉圖片檢測方法
[DllImport("BaiduFaceApi.dll", EntryPoint = "track", CharSet = CharSet.Ansi , CallingConvention = CallingConvention.Cdecl)] private static extern IntPtr track(string file_name, int max_track_num); public void test_track() { // 傳入圖片文件絕對路徑 string file_name = "d:\\kehu2.jpg"; int max_track_num = 1; // 設置最多檢測人數（多人臉檢測），默認為1，最多可設為10 IntPtr ptr = track(file_name, max_track_num); string buf = Marshal.PtrToStringAnsi(ptr); Console.WriteLine("track res is:" + buf); }
人臉檢測接口，此方法需要在d盤根目錄放一張名為“kehu2.jpg”的圖片來檢測是否含有人臉。
1.3.2 人臉攝像頭實時檢測方法
public void usb_csharp_track_face(int dev) { using (var window = new Window("face")) using (VideoCapture cap = VideoCapture.FromCamera(dev)) { if (!cap.IsOpened()) { Console.WriteLine("open camera error"); return; } // Frame image buffer Mat image = new Mat(); // When the movie playback reaches end, Mat.data becomes NULL. while (true) { RotatedRect box; cap.Read(image); // same as cvQueryFrame if (!image.Empty()) { int ilen = 2;//傳入的人臉數 TrackFaceInfo[] track_info = new TrackFaceInfo[ilen]; for (int i = 0; i < ilen; i++) { track_info[i] = new TrackFaceInfo(); track_info[i].landmarks = new int[144]; track_info[i].headPose = new float[3]; track_info[i].face_id = 0; track_info[i].score = 0; } int sizeTrack = Marshal.SizeOf(typeof(TrackFaceInfo)); IntPtr ptT = Marshal.AllocHGlobal(sizeTrack* ilen); //Marshal.Copy(ptTrack, 0, ptT, ilen); // FaceInfo[] face_info = new FaceInfo[ilen]; // face_info = new FaceInfo(0.0F,0.0F,0.0F,0.0F,0.0F); //Cv2.ImWrite("usb_track_Cv2.jpg", image); /* trackMat * 傳入參數: maxTrackObjNum:檢測到的最大人臉數，傳入外部分配人臉數，需要分配對應的內存大小。 * 傳出檢測到的最大人臉數 * 返回值: 傳入的人臉數 和 檢測到的人臉數 中的最小值,實際返回的人臉。 ****/ int faceSize = ilen;//返回人臉數 分配人臉數和檢測到人臉數的最小值 int curSize = ilen;//當前人臉數 輸入分配的人臉數，輸出實際檢測到的人臉數 faceSize = track_mat(ptT, image.CvPtr, ref curSize); for (int index = 0; index < faceSize; index++) { IntPtr ptr = new IntPtr(); if( 8 == IntPtr.Size) { ptr = (IntPtr)(ptT.ToInt64() + sizeTrack * index); } else if(4 == IntPtr.Size) { ptr = (IntPtr)(ptT.ToInt32() + sizeTrack * index); } track_info[index] = (TrackFaceInfo)Marshal.PtrToStructure(ptr, typeof(TrackFaceInfo)); //face_info[index] = (FaceInfo)Marshal.PtrToStructure(info_ptr, typeof(FaceInfo)); Console.WriteLine("in Liveness::usb_track face_id is {0}:",track_info[index].face_id); Console.WriteLine("in Liveness::usb_track landmarks is:"); for (int k = 0; k < 1; k ++) { Console.WriteLine("{0},{1},{2},{3},{4},{5},{6},{7},{8},{9},", track_info[index].landmarks[k], track_info[index].landmarks[k+1], track_info[index].landmarks[k+2], track_info[index].landmarks[k + 3], track_info[index].landmarks[k+4], track_info[index].landmarks[k + 5], track_info[index].landmarks[k+6], track_info[index].landmarks[k + 7], track_info[index].landmarks[k+8], track_info[index].landmarks[k + 9] ); } for (int k = 0; k < track_info[index].headPose.Length; k++) { Console.WriteLine("in Liveness::usb_track angle is:{0:f}", track_info[index].headPose[k]); } Console.WriteLine("in Liveness::usb_track score is:{0:f}", track_info[index].score); // 角度 Console.WriteLine("in Liveness::usb_track mAngle is:{0:f}", track_info[index].box.mAngle); // 人臉寬度 Console.WriteLine("in Liveness::usb_track mWidth is:{0:f}", track_info[index].box.mWidth); // 中心點X,Y坐標 Console.WriteLine("in Liveness::usb_track mCenter_x is:{0:f}", track_info[index].box.mCenter_x); Console.WriteLine("in Liveness::usb_track mCenter_y is:{0:f}", track_info[index].box.mCenter_y); 畫人臉框 box = bounding_box(track_info[index].landmarks, track_info[index].landmarks.Length); draw_rotated_box(ref image, ref box, new Scalar(0, 255, 0)); // 實時檢測人臉屬性和質量可能會視頻卡頓，若一定必要可考慮跳幀檢測 // 獲取人臉屬性（通過傳入人臉信息） //IntPtr ptrAttr = FaceAttr.face_attr_by_face(image.CvPtr, ref track_info[index]); //string buf = Marshal.PtrToStringAnsi(ptrAttr); //Console.WriteLine("attr res is:" + buf); 獲取人臉質量（通過傳入人臉信息） //IntPtr ptrQua = FaceQuality.face_quality_by_face(image.CvPtr, ref track_info[index]); //buf = Marshal.PtrToStringAnsi(ptrQua); //Console.WriteLine("quality res is:" + buf); 實時檢測人臉特征值可能會視頻卡頓，若一定必要可考慮跳幀檢測 //float[] feature = new float[512]; //IntPtr ptrfea = new IntPtr(); //int count = FaceCompare.get_face_feature_by_face(image.CvPtr, ref track_info[index], ref ptrfea); 返回值為512表示取到了特征值 //if(ptrfea == IntPtr.Zero) //{ // Console.WriteLine("Get feature failed!"); // continue; //} //if (count == 512) //{ // for (int i = 0; i < count; i++) // { // IntPtr floptr = new IntPtr(); // if ( 8 == IntPtr.Size) // { // floptr = (IntPtr)(ptrfea.ToInt64() + i * count * Marshal.SizeOf(typeof(float))); // } // else if( 4 == IntPtr.Size) // { // floptr = (IntPtr)(ptrfea.ToInt32() + i * count * Marshal.SizeOf(typeof(float))); // } // feature[i] = (float)Marshal.PtrToStructure(floptr, typeof(float)); // Console.WriteLine("feature {0} is: {1:e8}", i, feature[i]); // } // Console.WriteLine("feature count is:{0}", count); //} } Marshal.FreeHGlobal(ptT); window.ShowImage(image); Cv2.WaitKey(1); Console.WriteLine("mat not empty"); } else { Console.WriteLine("mat is empty"); } } image.Release(); } }
以上為原sdk方法；
只可檢測人臉 不可活體檢測；
static VideoCapture camera1 = null; static VideoCapture camera2 = null; public bool usb_csharp_track_face(int dev) { string buf = ""; int faceSize = 0; int faceNum = 2;//傳入的人臉數 int face_size = faceNum;//當前傳入人臉數，傳出人臉數 //VideoCapture cap = VideoCapture.FromCamera(dev); //Mat image = new Mat(); long ir_time = 0; // 序號0為電腦識別的usb攝像頭編號，本demo中0為ir紅外攝像頭 // 不同攝像頭和電腦識別可能有區別 // 編號一般從0-10 */ int device = select_usb_device_id(); camera1 = VideoCapture.FromCamera(device); if (!camera1.IsOpened()) { Console.WriteLine("camera1 open error"); return false; } camera1.Set(CaptureProperty.FrameWidth, 1440); camera1.Set(CaptureProperty.FrameHeight, 1080); camera2 = VideoCapture.FromCamera(device + 1); if (!camera2.IsOpened()) { Console.WriteLine("camera2 open error"); return false; } RotatedRect box; Mat frame1 = new Mat(); Mat frame2 = new Mat(); Mat rgb_mat = new Mat(); Mat ir_mat = new Mat(); //var window_ir = new Window("ir_face"); //var window_rgb = new Window("rgb_face"); if (!camera1.IsOpened()) { return false; } #region 嘗試 while (true) { //Thread.Sleep(1000); camera1.Read(frame1); camera2.Read(frame2); if (!frame1.Empty() && !frame2.Empty()) { int ilen = 2;//傳入的人臉數 TrackFaceInfo[] track_info = new TrackFaceInfo[ilen]; for (int i = 0; i < ilen; i++) { track_info[i] = new TrackFaceInfo(); track_info[i].landmarks = new int[144]; track_info[i].headPose = new float[3]; track_info[i].face_id = 0; track_info[i].score = 0; } int sizeTrack = Marshal.SizeOf(typeof(TrackFaceInfo)); IntPtr ptT = Marshal.AllocHGlobal(sizeTrack * ilen); #region 原 if (frame1.Size(0) > frame2.Size(0)) { rgb_mat = frame1; ir_mat = frame2; } else { rgb_mat = frame2; ir_mat = frame1; } #endregion float rgb_score = 0; float ir_score = 0; //Marshal.Copy(ptTrack, 0, ptT, ilen); //FaceInfo[] face_info = new FaceInfo[ilen]; //face_info = new FaceInfo(0.0F,0.0F,0.0F,0.0F,0.0F); //Cv2.ImWrite("usb_track_Cv2.jpg", image); /* trackMat * 傳入參數: maxTrackObjNum:檢測到的最大人臉數，傳入外部分配人臉數，需要分配對應的內存大小。 * 傳出檢測到的最大人臉數 * 返回值: 傳入的人臉數 和 檢測到的人臉數 中的最小值,實際返回的人臉。 ****/ faceSize = ilen;//返回人臉數 分配人臉數和檢測到人臉數的最小值 int curSize = ilen;//當前人臉數 輸入分配的人臉數，輸出實際檢測到的人臉數 faceSize = track_mat(ptT, frame1.CvPtr, ref curSize); IntPtr ptr1 = rgb_ir_liveness_check_faceinfo(rgb_mat.CvPtr, ir_mat.CvPtr, ref rgb_score, ref ir_score, ref faceSize, ref ir_time, ptT); curSize = ilen; faceSize = track_mat(ptT, frame1.CvPtr, ref curSize); //Console.WriteLine(rgb_score.ToString()); //Console.WriteLine(ir_score.ToString()); //textBox1.Text = faceSize.ToString(); for (int index = 0; index < faceSize; index++) { IntPtr ptr = new IntPtr(); if (8 == IntPtr.Size) { ptr = (IntPtr)(ptT.ToInt64() + sizeTrack * index); } else if (4 == IntPtr.Size) { ptr = (IntPtr)(ptT.ToInt32() + sizeTrack * index); } track_info[index] = (TrackFaceInfo)Marshal.PtrToStructure(ptr, typeof(TrackFaceInfo)); IntPtr ptrQua = FaceQuality.face_quality_by_face(frame1.CvPtr, ref track_info[index]); buf = Marshal.PtrToStringAnsi(ptrQua); } textBox2.Text = buf.ToString(); Marshal.FreeHGlobal(ptT); //window.ShowImage(image); pictureBox1.Image = frame1.ToBitmap(); Cv2.WaitKey(1); #region 測試 if (rgb_score != 0 && ir_score != 0) { if (faceSize >= 1) { JObject jObject = (JObject)JsonConvert.DeserializeObject(buf); var json = (JObject)JsonConvert.DeserializeObject(jObject["data"].ToString()); string result = json["result"].ToString(); // textBox1.Text = json["result"].ToString(); var json1 = (JObject)JsonConvert.DeserializeObject(result); if (double.Parse(json1["bluriness"].ToString()) <= 0.00001 && double.Parse(json1["occl_r_eye"].ToString()) < 0.1 && double.Parse(json1["occl_l_eye"].ToString()) < 0.1 && double.Parse(json1["occl_mouth"].ToString()) <= 0 && double.Parse(json1["occl_nose"].ToString()) < 0.11 && double.Parse(json1["occl_r_contour"].ToString()) < 0.1 && double.Parse(json1["occl_chin"].ToString()) <= 0 && double.Parse(json1["occl_l_contour"].ToString()) < 0.1) { string fileName = DateTime.Now.ToString("yyyyMMdd") + ".jpg"; string path = AppDomain.CurrentDomain.BaseDirectory; string picture = path + @"img" + "\\" + fileName; frame1.SaveImage(picture); falnem = picture; if (Sdistinguish() != "0") { Class1 c = new Class1(); Thread.Sleep(2000); dtTo = new TimeSpan(0, 0, 2); camera2.Release(); camera2.Release(); thread.Abort(); return true; } else { Thread.Sleep(2000); dtTo = new TimeSpan(0, 0, 2); camera2.Release(); camera2.Release(); thread.Abort(); //msg2 msg2 = new msg2(); //msg2.Show(); return true; this.Close(); } Thread.Sleep(2000); thread.Abort(); //msg2 msg2 = new msg2(); //msg2.Show(); this.Close(); //window.Close(); } } } #endregion //return faceSize; } else { Console.WriteLine("mat is empty"); } } #endregion frame1.Release(); return false; //usb_csharp_track_face(1); }
以上為我修改后的；
private string distinguish() { dz = "1"; //string fileName = DateTime.Now.ToString("yyyyMMddHH") + ".jpg"; string fileName = falnem; //string fileName = "" + ".jpg"; //SavePicture(fileName); FaceCompare faceCompare = new FaceCompare(); JObject jObject = (JObject)JsonConvert.DeserializeObject(faceCompare.test_identify(fileName, "group", "")); // JObject jObject = (JObject)JsonConvert.DeserializeObject(faceCompare.test_identify("d:\\4.jpg", "test_group", "u1")); var json = (JObject)JsonConvert.DeserializeObject(jObject["data"].ToString()); var json1 = (JObject)JsonConvert.DeserializeObject(json["result"].ToString()); var json1 = (JArray)json["result"].ToString(); textBox1.Text = "對比值：" + json1["score"].ToString() +"/r用戶："+json1["user_id"].ToString() ; textBox1.Text= faceCompare.test_identify("d:\\4.jpg", "test_group", "test_user"); string result = json["result"].ToString().Substring(1, json["result"].ToString().Length - 2); // textBox1.Text = json["result"].ToString(); var json1 = (JObject)JsonConvert.DeserializeObject(result); //textBox2.Text = "對比值：" + json1["score"].ToString() + "\r\n用戶：" + json1["user_id"].ToString(); string user = json1["user_id"].ToString(); string lockes = "0"; #region 查詢數據庫 using (var db = new ces(dbPath)) { //List<TestFaceDB> testFaces = db.testFaces.Where(x => x.User == json1["user_id"].ToString()).ToList(); var testFaces = db.testFaces.Where(x => x.User == user).ToList(); //string testFaces = db.testFaces.Select(*).Where(x => x.User == json1["user_id"].ToString()).ToString(); //this.Text = $"{DateTime.Now}, 查到{books.Count}條記錄"; lockes = testFaces[0].Locks; } int lo = int.Parse(lockes); if (int.Parse(lockes) > 100) { int d = 0; while (lo > 100) { lo -= 100; d++; } int z = 1; for (int i = 0; i < d - 1; i++) { z = z * 2; }; dz = "0" + z.ToString(); } #endregion double score = double.Parse(json1["score"].ToString()); lockes = lo.ToString(); if (score > 80) { if (int.Parse(lockes) < 10) lockes = "0" + lockes; return lockes; } else { return lockes; } } //1：N比較 public /*void*/string test_identify(string str, string usr_grp, string usr_id) { string file1 = str;//"d:\\6.jpg"; string user_group = usr_grp;//"test_group"; string user_id = usr_id;//"test_user"; IntPtr ptr = identify(file1, user_group, user_id); string buf = Marshal.PtrToStringAnsi(ptr); //Console.WriteLine("identify res is:" + buf); return buf; }
distinguish（）方法為識別后在數據庫中尋找匹配人臉數據；
將活體實時檢測與人臉實時檢測合並為一個方法，關閉了window顯示窗口，修改為pictureBox控件 通過線程調用（不然死循環會崩潰）；關閉攝像頭為 Release();方法
數據均為json數據 ；
具體數據參數見百度文檔
根據需求修改判斷條件是否符合項目中的人臉標准；
識別流程為
啟動線程->打開攝像頭->檢測到人臉->將人臉圖片保存->用圖片傳入調用人臉對比方法;
1.3.3 人臉注冊方法
string user_id = User.Text; string group_id = "group"; string file_name = falnem; string lockse = group.Text; string user_info = info.Text; string zc = faceManager.test_user_add(user_id, group_id, file_name, user_info); JObject jObjectzc = (JObject)JsonConvert.DeserializeObject(zc); if(jObjectzc["msg"].ToString()== "success") { List<TestFaceDB> testFaceDBs = new List<TestFaceDB>() { new TestFaceDB() {User = user_id,Locks = lockse,Phone = user_info} }; textBox1.Text = dbPath; using (var db = new ces(dbPath)) { int count = db.InsertAll(testFaceDBs); //this.Text = $"{DateTime.Now}, 插入{count}條記錄"; } msg msg = new msg(); msg.ShowDialog(); Thread.Sleep(5000); dtTo = new TimeSpan(0, 0, 2); camera1.Release(); camera2.Release(); return true; } Thread.Sleep(3000);
同上的方法 先檢測攝像頭中是否使活體人臉並存圖片文件，保存后調用人臉注冊
public /*void*/ string test_user_add(string uid,string gid,string fil,string uin) { //人臉注冊 //string user_id = "test_user"; //string group_id = "test_group"; //string file_name = "d:\\2.jpg"; //string user_info = "user_info"; string user_id = uid; string group_id = gid; string file_name = fil; string user_info = uin; IntPtr ptr = user_add(user_id, group_id, file_name, user_info); string buf = Marshal.PtrToStringAnsi(ptr); return buf; }
這個我修改添加了參數；
因sdk帶的SQLite數據庫字段比較少，並且我也沒有花心思想辦法去修改它創建的數據庫，所以創建個新的和sdk中數據庫以一個字段相連，就當擴展數據庫了；
在注冊的時候順帶存新數據庫一份就可以，要什么字段看自己需求；
當查找數據庫中數據的時候查到就取那個字段，然后去新數據庫中查找對應的其他字段。
1.4 打包
打包簡單方法直接將根目錄數據放到一個文件夾然后添加到Application Folder文件夾，然后在同目錄將face-resource放進去就可以了，在安裝的時候不能安裝到系統盤 不然接口會出問題；不知道什么原因
安裝到一個電腦的時候需要使用百度離線識別的sdk序列號激活不然不可使用；因為百度離線人臉識別sdk 是通過電腦指紋判斷是否激活，如果遇到LicenseTool.exe打不開彈出缺少系統dll組件請去網絡下載，或許因為電腦系統為盜版系統組件未安裝齊
也可以在百度下載激活的壓縮文件；
在添加輸出的時候將sdk作為本地化資源輸出，項目作為主輸出，輸出類型為Release；
「碼農_半只龍蝦」 也是我 ————————————————
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原csdn鏈接：https://blog.csdn.net/weixin_44986330/article/details/106297549