[next ViewFaceCore 0.2]
.NET 人臉識別庫 ViewFaceCore
這是基于 SeetaFace6 人臉識別開發的 .NET 平臺下的人臉識別庫
這是一個使用超簡單的人臉識別庫
這是一個基于 .NET Standard 2.0 開發的庫
這個庫已經發布到 NuGet ，你可以一鍵集成到你的專案
此專案可以免費商業使用
?、開源
開源協議：Apache-2.0
GitHub地址： ViewFaceCore
十分感謝您的小星星
一、示例
示例專案地址：WinForm 攝像頭人臉檢測
示例專案效果：
二、使用
一分鐘在你的專案里集成人臉識別
1. 創建你的 .NET 應用
.NET Standard >= 2.0
.NET Core >= 2.0
.NET Framework >= 4.6.1^2
2. 使用 Nuget 安裝 ViewFaceCore
- Author : View
- Version >= 0.1.1
此 Nuget 包會自動添加依賴的 C++ 庫，以及最精簡的識別模型，
如果需要其它場景的識別模型，請下載 SeetaFace6 模型檔案，
3. 在專案中撰寫你的代碼
- 按照 說明 自己撰寫
- 或者參考以下代碼
簡單的呼叫示例
1 static void Main() 2 { 3 ViewFace viewFace = new ViewFace((str) => { Debug.WriteLine(str); }); // 初始化人臉識別類，并設定 日志回呼函式 4 viewFace.DetectorSetting = new DetectorSetting() { FaceSize = 20, MaxWidth = 2000, MaxHeight = 2000, Threshold = 0.5 }; 5 6 // 系統默認使用的輕量級識別模型，如果對精度有要求，請切換到 Normal 模式；并下載需要模型檔案 放入生成目錄的 model 檔案夾中 7 viewFace.FaceType = FaceType.Normal; 8 // 系統默認使用5個人臉關鍵點，//不建議改動，除非是使用口罩模型， 9 viewFace.MarkType = MarkType.Light; 10 11 #region 識別老照片 12 float[] oldEigenValues; 13 Bitmap oldImg = (Bitmap)Image.FromFile(@"C:\Users\yangw\OneDrive\圖片\Camera Roll\IMG_20181103_142707.jpg"/*老圖片路徑*/); // 從檔案中加載照片 // 或者視頻幀等 14 var oldFaces = viewFace.FaceDetector(oldImg); // 檢測圖片中包含的人臉資訊，(置信度、位置、大小) 15 if (oldFaces.Length > 0) //識別到人臉 16 { 17 { // 列印人臉資訊 18 Console.WriteLine($"識別到的人臉數量：{oldFaces.Length} ，人臉資訊：\n"); 19 Console.WriteLine($"序號\t人臉置信度\t位置X\t位置Y\t寬度\t高度"); 20 for (int i = 0; i < oldFaces.Length; i++) 21 { 22 Console.WriteLine($"{i + 1}\t{oldFaces[i].Score}\t{oldFaces[i].Location.X}\t{oldFaces[i].Location.Y}\t{oldFaces[i].Location.Width}\t{oldFaces[i].Location.Height}"); 23 } 24 Console.WriteLine(); 25 } 26 var oldPoints = viewFace.FaceMark(oldImg, oldFaces[0]); // 獲取 第一個人臉 的識別關鍵點，(人臉識別的關鍵點資料) 27 oldEigenValues = viewFace.Extract(oldImg, oldPoints); // 獲取 指定的關鍵點 的特征值， 28 } 29 else { oldEigenValues = new float[0]; /*未識別到人臉*/ } 30 #endregion 31 32 #region 識別新照片 33 float[] newEigenValues; 34 Bitmap newImg = (Bitmap)Image.FromFile(@"C:\Users\yangw\OneDrive\圖片\Camera Roll\IMG_20181129_224339.jpg"/*新圖片路徑*/); // 從檔案中加載照片 // 或者視頻幀等 35 var newFaces = viewFace.FaceDetector(newImg); // 檢測圖片中包含的人臉資訊，(置信度、位置、大小) 36 if (newFaces.Length > 0) //識別到人臉 37 { 38 { // 列印人臉資訊 39 Console.WriteLine($"識別到的人臉數量：{newFaces.Length} ，人臉資訊：\n"); 40 Console.WriteLine($"序號\t人臉置信度\t位置X\t位置Y\t寬度\t高度"); 41 for (int i = 0; i < newFaces.Length; i++) 42 { 43 Console.WriteLine($"{i + 1}\t{newFaces[i].Score}\t{newFaces[i].Location.X}\t{newFaces[i].Location.Y}\t{newFaces[i].Location.Width}\t{newFaces[i].Location.Height}"); 44 } 45 Console.WriteLine(); 46 } 47 var newPoints = viewFace.FaceMark(newImg, newFaces[0]); // 獲取 第一個人臉 的識別關鍵點，(人臉識別的關鍵點資料) 48 newEigenValues = viewFace.Extract(newImg, newPoints); // 獲取 指定的關鍵點 的特征值， 49 } 50 else { newEigenValues = new float[0]; /*未識別到人臉*/ } 51 #endregion 52 53 try 54 { 55 float similarity = viewFace.Similarity(oldEigenValues, newEigenValues); // 對比兩張照片上的資料，確認是否是同一個人， 56 Console.WriteLine($"閾值 = {Face.Threshold[viewFace.FaceType]}\t相似度 = {similarity}"); 57 Console.WriteLine($"是否是同一個人：{viewFace.IsSelf(similarity)}"); 58 } 59 catch (Exception e) 60 { Console.WriteLine(e); } 61 62 Console.ReadKey(); 63 }ViewFaceCore 使用示例
三、說明
命名空間：ViewFaceCore.Sharp : 人臉識別類所在的命名空間
- 屬性說明：
- 方法說明：
| 屬性名稱 | 型別 | 說明 | 默認值 |
| ModelPath | string | 獲取或設定模型路徑 [ 如非必要，請勿修改 ] | ./model/ |
| FaceType | FaceType | 獲取或設定人臉型別 | FaceType.Light |
| MarkType | MarkType | 獲取或設定人臉關鍵點型別 | MarkType.Light |
| DetectorSetting | DetectorSetting | 獲取或設定人臉檢測器設定 | new DetectorSetting() |
1 using System.Drawing; 2 using ViewFaceCore.Sharp; 3 using ViewFaceCore.Sharp.Model; 4 5 // 識別 bitmap 中的人臉，并回傳人臉的資訊， 6 FaceInfo[] FaceDetector(Bitmap); 7 8 // 識別 bitmap 中指定的人臉資訊 info 的關鍵點坐標， 9 FaceMarkPoint[] FaceMark(Bitmap, FaceInfo); 10 11 // 提取人臉特征值， 12 float[] Extract(Bitmap, FaceMarkPoint[]); 13 14 // 計算特征值相似度， 15 float Similarity(float[], float[]); 16 17 // 判斷相似度是否為同一個人， 18 bool IsSelf(float);
四、實作
此專案受到了 SeetaFaceEngine.NET 專案的啟發
這個專案本質上來說還是呼叫了 SeetaFace 的 C++ 類別庫來實作的人臉識別功能，針對本人遇到過的相關的類別庫的使用都不太方便，而且使用的 SeetaFace 的版本較老，故萌生了自己重新開發的想法，
本專案在開發完成之后為了方便呼叫，采用了 Nuget 包的形式，將所有需要的依賴以及最小識別模型一起打包，在使用時非常簡單，只需要 nuget 安裝，撰寫代碼，運行即可，不需要多余的操作，
首先查看 SeetaFace ，已經更新到了v3(v6即v3)(上面前輩的專案是基于v1開發的)，最新版本暫時沒有開源，但是可以免費商用，然后是根據以前的經驗和 SeetaFace6 檔案的指導，以及前輩的專案，做了以下操作，
1.對SeetaFace6 的介面進行了 C++ 形式的封裝，
目前主要實作了 人臉檢測，關鍵點提取，特征值提取，特征值對比幾個人臉識別中的基礎介面，有了這幾個介面，可以完整的實作一套人臉識別和驗證的流程，
- c++封裝的介面代碼如下：
1 #include "seeta/FaceDetector.h" 2 #include "seeta/FaceLandmarker.h" 3 #include "seeta/FaceRecognizer.h" 4 5 #include <time.h> 6 7 #define View_Api extern "C" __declspec(dllexport) 8 9 using namespace std; 10 11 typedef void(_stdcall* LogCallBack)(const char* logText); 12 13 string modelPath = "./model/"; // 模型所在路徑 14 LogCallBack logger = NULL; // 日志回呼函式 15 16 // 列印日志 17 void WriteLog(string str) { if (logger != NULL) { logger(str.c_str()); } } 18 19 void WriteMessage(string fanctionName, string message) { WriteLog(fanctionName + "\t Message:" + message); } 20 void WriteModelName(string fanctionName, string modelName) { WriteLog(fanctionName + "\t Model.Name:" + modelName); } 21 void WriteRunTime(string fanctionName, int start) { WriteLog(fanctionName + "\t Run.Time:" + to_string(clock() - start) + " ms"); } 22 void WriteError(string fanctionName, const std::exception& e) { WriteLog(fanctionName + "\t Error:" + e.what()); } 23 24 // 注冊日志回呼函式 25 View_Api void V_SetLogFunction(LogCallBack writeLog) 26 { 27 logger = writeLog; 28 WriteMessage(__FUNCDNAME__, "Successed."); 29 } 30 31 // 設定人臉模型目錄 32 View_Api void V_SetModelPath(const char* path) 33 { 34 modelPath = path; 35 WriteMessage(__FUNCDNAME__, "Model.Path:" + modelPath); 36 } 37 // 獲取人臉模型目錄 38 View_Api bool V_GetModelPath(char** path) 39 { 40 try 41 { 42 #pragma warning(disable:4996) 43 strcpy(*path, modelPath.c_str()); 44 45 return true; 46 } 47 catch (const std::exception& e) 48 { 49 WriteError(__FUNCDNAME__, e); 50 return false; 51 } 52 } 53 54 seeta::FaceDetector* v_faceDetector = NULL; 55 56 // 人臉檢測結果 57 static SeetaFaceInfoArray detectorInfos; 58 // 人臉數量檢測器 59 View_Api int V_DetectorSize(unsigned char* imgData, int width, int height, int channels, double faceSize = 20, double threshold = 0.9, double maxWidth = 2000, double maxHeight = 2000, int type = 0) 60 { 61 try { 62 clock_t start = clock(); 63 64 SeetaImageData img = { width, height, channels, imgData }; 65 if (v_faceDetector == NULL) { 66 seeta::ModelSetting setting; 67 setting.set_device(SEETA_DEVICE_CPU); 68 string modelName = "face_detector.csta"; 69 switch (type) 70 { 71 case 1: modelName = "mask_detector.csta"; break; 72 } 73 setting.append(modelPath + modelName); 74 WriteModelName(__FUNCDNAME__, modelName); 75 v_faceDetector = new seeta::FaceDetector(setting); 76 } 77 78 if (faceSize != 20) { v_faceDetector->set(seeta::FaceDetector::Property::PROPERTY_MIN_FACE_SIZE, faceSize); } 79 if (threshold != 0.9) { v_faceDetector->set(seeta::FaceDetector::Property::PROPERTY_THRESHOLD, threshold); } 80 if (maxWidth != 2000) { v_faceDetector->set(seeta::FaceDetector::Property::PROPERTY_MAX_IMAGE_WIDTH, maxWidth); } 81 if (maxHeight != 2000) { v_faceDetector->set(seeta::FaceDetector::Property::PROPERTY_MAX_IMAGE_HEIGHT, maxHeight); } 82 83 auto infos = v_faceDetector->detect(img); 84 detectorInfos = infos; 85 86 WriteRunTime("V_Detector", start); // 此方法已經是人臉檢測的全程序，故計時器顯示為 人臉識別方法 87 return infos.size; 88 } 89 catch (const std::exception& e) 90 { 91 WriteError(__FUNCDNAME__, e); 92 return -1; 93 } 94 } 95 // 人臉檢測器 96 View_Api bool V_Detector(float* score, int* x, int* y, int* width, int* height) 97 { 98 try 99 { 100 //clock_t start = clock(); 101 102 for (int i = 0; i < detectorInfos.size; i++, detectorInfos.data++) 103 { 104 *score = detectorInfos.data->score; 105 *x = detectorInfos.data->pos.x; 106 *y = detectorInfos.data->pos.y; 107 *width = detectorInfos.data->pos.width; 108 *height = detectorInfos.data->pos.height; 109 score++, x++, y++, width++, height++; 110 } 111 detectorInfos.data =https://www.cnblogs.com/view12138/p/ NULL; 112 detectorInfos.size = NULL; 113 114 //WriteRunTime(__FUNCDNAME__, start); // 此方法只是將 人臉數量檢測器 獲取到的資料賦值傳遞，并不耗時，故不顯示此方法的呼叫時間 115 return true; 116 } 117 catch (const std::exception& e) 118 { 119 WriteError(__FUNCDNAME__, e); 120 return false; 121 } 122 } 123 124 125 seeta::FaceLandmarker* v_faceLandmarker = NULL; 126 // 人臉關鍵點數量 127 View_Api int V_FaceMarkSize(int type = 0) 128 { 129 try 130 { 131 clock_t start = clock(); 132 133 if (v_faceLandmarker == NULL) { 134 seeta::ModelSetting setting; 135 setting.set_device(SEETA_DEVICE_CPU); 136 string modelName = "face_landmarker_pts68.csta"; 137 switch (type) 138 { 139 case 1: modelName = "face_landmarker_mask_pts5.csta"; break; 140 case 2: modelName = "face_landmarker_pts5.csta"; break; 141 } 142 setting.append(modelPath + modelName); 143 WriteModelName(__FUNCDNAME__, modelName); 144 v_faceLandmarker = new seeta::FaceLandmarker(setting); 145 } 146 int size = v_faceLandmarker->number(); 147 148 WriteRunTime(__FUNCDNAME__, start); 149 return size; 150 } 151 catch (const std::exception& e) 152 { 153 WriteError(__FUNCDNAME__, e); 154 return -1; 155 } 156 } 157 // 人臉關鍵點 158 View_Api bool V_FaceMark(unsigned char* imgData, int width, int height, int channels, int x, int y, int fWidth, int fHeight, double* pointX, double* pointY, int type = 0) 159 { 160 try 161 { 162 clock_t start = clock(); 163 164 SeetaImageData img = { width, height, channels, imgData }; 165 SeetaRect face = { x, y, fWidth, fHeight }; 166 if (v_faceLandmarker == NULL) { 167 seeta::ModelSetting setting; 168 setting.set_device(SEETA_DEVICE_CPU); 169 string modelName = "face_landmarker_pts68.csta"; 170 switch (type) 171 { 172 case 1: modelName = "face_landmarker_mask_pts5.csta"; break; 173 case 2: modelName = "face_landmarker_pts5.csta"; break; 174 } 175 setting.append(modelPath + modelName); 176 WriteModelName(__FUNCDNAME__, modelName); 177 v_faceLandmarker = new seeta::FaceLandmarker(setting); 178 } 179 std::vector<SeetaPointF> _points = v_faceLandmarker->mark(img, face); 180 181 if (!_points.empty()) { 182 for (auto iter = _points.begin(); iter != _points.end(); iter++) 183 { 184 *pointX = (*iter).x; 185 *pointY = (*iter).y; 186 pointX++; 187 pointY++; 188 } 189 190 WriteRunTime(__FUNCDNAME__, start); 191 return true; 192 } 193 else { return false; } 194 } 195 catch (const std::exception& e) 196 { 197 WriteError(__FUNCDNAME__, e); 198 return false; 199 } 200 } 201 202 seeta::FaceRecognizer* v_faceRecognizer = NULL; 203 // 獲取人臉特征值長度 204 View_Api int V_ExtractSize(int type = 0) 205 { 206 try 207 { 208 clock_t start = clock(); 209 210 if (v_faceRecognizer == NULL) { 211 seeta::ModelSetting setting; 212 setting.set_id(0); 213 setting.set_device(SEETA_DEVICE_CPU); 214 string modelName = "face_recognizer.csta"; 215 switch (type) 216 { 217 case 1: modelName = "face_recognizer_mask.csta"; break; 218 case 2: modelName = "face_recognizer_light.csta"; break; 219 } 220 setting.append(modelPath + modelName); 221 WriteModelName(__FUNCDNAME__, modelName); 222 v_faceRecognizer = new seeta::FaceRecognizer(setting); 223 } 224 int length = v_faceRecognizer->GetExtractFeatureSize(); 225 226 WriteRunTime(__FUNCDNAME__, start); 227 return length; 228 } 229 catch (const std::exception& e) 230 { 231 WriteError(__FUNCDNAME__, e); 232 return -1; 233 } 234 } 235 // 提取人臉特征值 236 View_Api bool V_Extract(unsigned char* imgData, int width, int height, int channels, SeetaPointF* points, float* features, int type = 0) 237 { 238 try 239 { 240 clock_t start = clock(); 241 242 SeetaImageData img = { width, height, channels, imgData }; 243 if (v_faceRecognizer == NULL) { 244 seeta::ModelSetting setting; 245 setting.set_id(0); 246 setting.set_device(SEETA_DEVICE_CPU); 247 string modelName = "face_recognizer.csta"; 248 switch (type) 249 { 250 case 1: modelName = "face_recognizer_mask.csta"; break; 251 case 2: modelName = "face_recognizer_light.csta"; break; 252 } 253 setting.append(modelPath + modelName); 254 WriteModelName(__FUNCDNAME__, modelName); 255 v_faceRecognizer = new seeta::FaceRecognizer(setting); 256 } 257 int length = v_faceRecognizer->GetExtractFeatureSize(); 258 std::shared_ptr<float> _features(new float[v_faceRecognizer->GetExtractFeatureSize()], std::default_delete<float[]>()); 259 v_faceRecognizer->Extract(img, points, _features.get()); 260 261 for (int i = 0; i < length; i++) 262 { 263 *features = _features.get()[i]; 264 features++; 265 } 266 267 WriteRunTime(__FUNCDNAME__, start); 268 return true; 269 270 } 271 catch (const std::exception& e) 272 { 273 WriteError(__FUNCDNAME__, e); 274 return false; 275 } 276 } 277 // 人臉特征值相似度計算 278 View_Api float V_CalculateSimilarity(float* leftFeatures, float* rightFeatures, int type = 0) 279 { 280 try 281 { 282 clock_t start = clock(); 283 284 if (v_faceRecognizer == NULL) { 285 seeta::ModelSetting setting; 286 setting.set_id(0); 287 setting.set_device(SEETA_DEVICE_CPU); 288 string modelName = "face_recognizer.csta"; 289 switch (type) 290 { 291 case 1: modelName = "face_recognizer_mask.csta"; break; 292 case 2: modelName = "face_recognizer_light.csta"; break; 293 } 294 setting.append(modelPath + modelName); 295 WriteModelName(__FUNCDNAME__, modelName); 296 v_faceRecognizer = new seeta::FaceRecognizer(setting); 297 } 298 299 auto similarity = v_faceRecognizer->CalculateSimilarity(leftFeatures, rightFeatures); 300 WriteMessage(__FUNCDNAME__, "Similarity = " + to_string(similarity)); 301 WriteRunTime(__FUNCDNAME__, start); 302 return similarity; 303 } 304 catch (const std::exception& e) 305 { 306 WriteError(__FUNCDNAME__, e); 307 return -1; 308 } 309 } 310 311 // 釋放資源 312 View_Api void V_Dispose() 313 { 314 if (v_faceDetector != NULL) delete v_faceDetector; 315 if (v_faceLandmarker != NULL) delete v_faceLandmarker; 316 if (v_faceRecognizer != NULL) delete v_faceRecognizer; 317 }C++ 封裝層
2.采用 C# 對上訴介面進行了匯入，
因為C++的專案測CPU架構區分x86和x64，所以C# 層也需要區分架構封裝
using System.Runtime.InteropServices; using System.Text; using ViewFaceCore.Sharp.Model; namespace ViewFaceCore.Plus { /// <summary> /// 日志回呼函式 /// </summary> /// <param name="logText"></param> public delegate void LogCallBack(string logText); class ViewFacePlus64 { const string LibraryPath = @"FaceLibraries\x64\ViewFace.dll"; /// <summary> /// 設定日志回呼函式(用于日志列印) /// </summary> /// <param name="writeLog"></param> [DllImport(LibraryPath, EntryPoint = "V_SetLogFunction", CallingConvention = CallingConvention.Cdecl)] public static extern void SetLogFunction(LogCallBack writeLog); /// <summary> /// 設定人臉模型的目錄 /// </summary> /// <param name="path"></param> [DllImport(LibraryPath, EntryPoint = "V_SetModelPath", CallingConvention = CallingConvention.Cdecl)] private extern static void SetModelPath(byte[] path); /// <summary> /// 設定人臉模型的目錄 /// </summary> /// <param name="path"></param> public static void SetModelPath(string path) => SetModelPath(Encoding.UTF8.GetBytes(path)); /// <summary> /// 釋放使用的資源 /// </summary> [DllImport(LibraryPath, EntryPoint = "V_Dispose", CallingConvention = CallingConvention.Cdecl)] public extern static void ViewDispose(); /// <summary> /// 獲取人臉模型的目錄 /// </summary> /// <param name="path"></param> [DllImport(LibraryPath, EntryPoint = "V_GetModelPath", CallingConvention = CallingConvention.Cdecl)] private extern static bool GetModelPathEx(ref string path); /// <summary> /// 獲取人臉模型的目錄 /// </summary> public static string GetModelPath() { string path = string.Empty; GetModelPathEx(ref path); return path; } /// <summary> /// 人臉檢測器檢測到的人臉數量 /// </summary> /// <param name="imgData"></param> /// <param name="width"></param> /// <param name="height"></param> /// <param name="channels"></param> /// <param name="faceSize">最小人臉是人臉檢測器常用的一個概念，默認值為20，單位像素， /// <para>最小人臉和檢測器性能息息相關，主要方面是速度，使用建議上，我們建議在應用范圍內，這個值設定的越大越好，SeetaFace采用的是BindingBox Regresion的方式訓練的檢測器，如果最小人臉引數設定為80的話，從檢測能力上，可以將原圖縮小的原來的1/4，這樣從計算復雜度上，能夠比最小人臉設定為20時，提速到16倍，</para> /// </param> /// <param name="threshold">檢測器閾值默認值是0.9，合理范圍為[0, 1]，這個值一般不進行調整，除了用來處理一些極端情況，這個值設定的越小，漏檢的概率越小，同時誤檢的概率會提高</param> /// <param name="maxWidth">可檢測的影像最大寬度，默認值2000，</param> /// <param name="maxHeight">可檢測的影像最大高度，默認值2000，</param> /// <param name="type"></param> /// <returns></returns> [DllImport(LibraryPath, EntryPoint = "V_DetectorSize", CallingConvention = CallingConvention.Cdecl)] public extern static int DetectorSize(byte[] imgData, int width, int height, int channels, double faceSize = 20, double threshold = 0.9, double maxWidth = 2000, double maxHeight = 2000, int type = 0); /// <summary> /// 人臉檢測器 /// <para>呼叫此方法前必須先呼叫 <see cref="DetectorSize(byte[], int, int, int, double, double, double, double, int)"/></para> /// </summary> /// <param name="score">人臉置信度集合</param> /// <param name="x">人臉位置集合</param> /// <param name="y">人臉位置集合</param> /// <param name="width">人臉大小集合</param> /// <param name="height">人臉大小集合</param> /// <returns></returns> [DllImport(LibraryPath, EntryPoint = "V_Detector", CallingConvention = CallingConvention.Cdecl)] public extern static bool Detector(float[] score, int[] x, int[] y, int[] width, int[] height); /// <summary> /// 人臉關鍵點數量 /// </summary> /// <returns></returns> [DllImport(LibraryPath, EntryPoint = "V_FaceMarkSize", CallingConvention = CallingConvention.Cdecl)] public extern static int FaceMarkSize(int type = 0); /// <summary> /// 人臉關鍵點 /// </summary> /// <param name="imgData"></param> /// <param name="width"></param> /// <param name="height"></param> /// <param name="channels"></param> /// <param name="x"></param> /// <param name="y"></param> /// <param name="fWidth"></param> /// <param name="fHeight"></param> /// <param name="pointX"></param> /// <param name="pointY"></param> /// <param name="type"></param> /// <returns></returns> [DllImport(LibraryPath, EntryPoint = "V_FaceMark", CallingConvention = CallingConvention.Cdecl)] public extern static bool FaceMark(byte[] imgData, int width, int height, int channels, int x, int y, int fWidth, int fHeight, double[] pointX, double[] pointY, int type = 0); /// <summary> /// 提取特征值 /// </summary> /// <param name="imgData"></param> /// <param name="width"></param> /// <param name="height"></param> /// <param name="channels"></param> /// <param name="points"></param> /// <param name="features"></param> /// <param name="type"></param> /// <returns></returns> [DllImport(LibraryPath, EntryPoint = "V_Extract", CallingConvention = CallingConvention.Cdecl)] public extern static bool Extract(byte[] imgData, int width, int height, int channels, FaceMarkPoint[] points, float[] features, int type = 0); /// <summary> /// 特征值大小 /// </summary> /// <returns></returns> [DllImport(LibraryPath, EntryPoint = "V_ExtractSize", CallingConvention = CallingConvention.Cdecl)] public extern static int ExtractSize(int type = 0); /// <summary> /// 計算相似度 /// </summary> /// <param name="leftFeatures"></param> /// <param name="rightFeatures"></param> /// <param name="type"></param> /// <returns></returns> [DllImport(LibraryPath, EntryPoint = "V_CalculateSimilarity", CallingConvention = CallingConvention.Cdecl)] public extern static float Similarity(float[] leftFeatures, float[] rightFeatures, int type = 0); } class ViewFacePlus32 { const string LibraryPath = @"FaceLibraries\x86\ViewFace.dll"; /// <summary> /// 設定日志回呼函式(用于日志列印) /// </summary> /// <param name="writeLog"></param> [DllImport(LibraryPath, EntryPoint = "V_SetLogFunction", CallingConvention = CallingConvention.Cdecl)] public static extern void SetLogFunction(LogCallBack writeLog); /// <summary> /// 設定人臉模型的目錄 /// </summary> /// <param name="path"></param> [DllImport(LibraryPath, EntryPoint = "V_SetModelPath", CallingConvention = CallingConvention.Cdecl)] private extern static void SetModelPath(byte[] path); /// <summary> /// 設定人臉模型的目錄 /// </summary> /// <param name="path"></param> public static void SetModelPath(string path) => SetModelPath(Encoding.UTF8.GetBytes(path)); /// <summary> /// 釋放使用的資源 /// </summary> [DllImport(LibraryPath, EntryPoint = "V_Dispose", CallingConvention = CallingConvention.Cdecl)] public extern static void ViewDispose(); /// <summary> /// 獲取人臉模型的目錄 /// </summary> /// <param name="path"></param> [DllImport(LibraryPath, EntryPoint = "V_GetModelPath", CallingConvention = CallingConvention.Cdecl)] private extern static bool GetModelPathEx(ref string path); /// <summary> /// 獲取人臉模型的目錄 /// </summary> public static string GetModelPath() { string path = string.Empty; GetModelPathEx(ref path); return path; } /// <summary> /// 人臉檢測器檢測到的人臉數量 /// </summary> /// <param name="imgData"></param> /// <param name="width"></param> /// <param name="height"></param> /// <param name="channels"></param> /// <param name="faceSize">最小人臉是人臉檢測器常用的一個概念，默認值為20，單位像素， /// <para>最小人臉和檢測器性能息息相關，主要方面是速度，使用建議上，我們建議在應用范圍內，這個值設定的越大越好，SeetaFace采用的是BindingBox Regresion的方式訓練的檢測器，如果最小人臉引數設定為80的話，從檢測能力上，可以將原圖縮小的原來的1/4，這樣從計算復雜度上，能夠比最小人臉設定為20時，提速到16倍，</para> /// </param> /// <param name="threshold">檢測器閾值默認值是0.9，合理范圍為[0, 1]，這個值一般不進行調整，除了用來處理一些極端情況，這個值設定的越小，漏檢的概率越小，同時誤檢的概率會提高</param> /// <param name="maxWidth">可檢測的影像最大寬度，默認值2000，</param> /// <param name="maxHeight">可檢測的影像最大高度，默認值2000，</param> /// <param name="type"></param> /// <returns></returns> [DllImport(LibraryPath, EntryPoint = "V_DetectorSize", CallingConvention = CallingConvention.Cdecl)] public extern static int DetectorSize(byte[] imgData, int width, int height, int channels, double faceSize = 20, double threshold = 0.9, double maxWidth = 2000, double maxHeight = 2000, int type = 0); /// <summary> /// 人臉檢測器 /// <para>呼叫此方法前必須先呼叫 <see cref="DetectorSize(byte[], int, int, int, double, double, double, double, int)"/></para> /// </summary> /// <param name="score">人臉置信度集合</param> /// <param name="x">人臉位置集合</param> /// <param name="y">人臉位置集合</param> /// <param name="width">人臉大小集合</param> /// <param name="height">人臉大小集合</param> /// <returns></returns> [DllImport(LibraryPath, EntryPoint = "V_Detector", CallingConvention = CallingConvention.Cdecl)] public extern static bool Detector(float[] score, int[] x, int[] y, int[] width, int[] height); /// <summary> /// 人臉關鍵點數量 /// </summary> /// <returns></returns> [DllImport(LibraryPath, EntryPoint = "V_FaceMarkSize", CallingConvention = CallingConvention.Cdecl)] public extern static int FaceMarkSize(int type = 0); /// <summary> /// 人臉關鍵點 /// </summary> /// <param name="imgData"></param> /// <param name="width"></param> /// <param name="height"></param> /// <param name="channels"></param> /// <param name="x"></param> /// <param name="y"></param> /// <param name="fWidth"></param> /// <param name="fHeight"></param> /// <param name="pointX"></param> /// <param name="pointY"></param> /// <param name="type"></param> /// <returns></returns> [DllImport(LibraryPath, EntryPoint = "V_FaceMark", CallingConvention = CallingConvention.Cdecl)] public extern static bool FaceMark(byte[] imgData, int width, int height, int channels, int x, int y, int fWidth, int fHeight, double[] pointX, double[] pointY, int type = 0); /// <summary> /// 提取特征值 /// </summary> /// <param name="imgData"></param> /// <param name="width"></param> /// <param name="height"></param> /// <param name="channels"></param> /// <param name="points"></param> /// <param name="features"></param> /// <param name="type"></param> /// <returns></returns> [DllImport(LibraryPath, EntryPoint = "V_Extract", CallingConvention = CallingConvention.Cdecl)] public extern static bool Extract(byte[] imgData, int width, int height, int channels, FaceMarkPoint[] points, float[] features, int type = 0); /// <summary> /// 特征值大小 /// </summary> /// <returns></returns> [DllImport(LibraryPath, EntryPoint = "V_ExtractSize", CallingConvention = CallingConvention.Cdecl)] public extern static int ExtractSize(int type = 0); /// <summary> /// 計算相似度 /// </summary> /// <param name="leftFeatures"></param> /// <param name="rightFeatures"></param> /// <param name="type"></param> /// <returns></returns> [DllImport(LibraryPath, EntryPoint = "V_CalculateSimilarity", CallingConvention = CallingConvention.Cdecl)] public extern static float Similarity(float[] leftFeatures, float[] rightFeatures, int type = 0); } }C# 匯入層
3.采用 C# 的面向物件的封裝
因為C#的專案默認都是 AnyCPU，所以為了簡化呼叫，在這一層封裝的時候增加了架構判斷，當在你的專案中參考的時候，不用做任何修改，
且因為C++的C#匯入方法在和原生的C#寫法略有差異，且資料的轉換和傳遞比較麻煩，所以類別庫中對外隱藏了 C# 匯入層，并使用大家都更熟悉的C#的面向物件的方式進行進一步的封裝和簡化，
1 /// <summary> 2 /// 人臉識別類 3 /// </summary> 4 public class ViewFace 5 { 6 bool Platform64 { get; set; } = false; 7 // <para>需要模型：<see langword=""/></para> 8 9 // ctor 10 /// <summary> 11 /// 使用默認的模型目錄初始化人臉識別類 12 /// </summary> 13 public ViewFace() : this("./model/") { } 14 /// <summary> 15 /// 使用指定的模型目錄初始化人臉識別類 16 /// </summary> 17 /// <param name="modelPath">模型目錄</param> 18 public ViewFace(string modelPath) 19 { 20 Platform64 = IntPtr.Size == 8; 21 if (Platform64) 22 { ViewFacePlus64.SetModelPath(modelPath); } 23 else 24 { ViewFacePlus32.SetModelPath(modelPath); } 25 } 26 /// <summary> 27 /// 使用指定的日志回呼函式初始化人臉識別類 28 /// </summary> 29 /// <param name="action">日志回呼函式</param> 30 public ViewFace(LogCallBack action) : this("./model/", action) { } 31 /// <summary> 32 /// 使用指定的模型目錄、日志回呼函式初始化人臉識別類 33 /// </summary> 34 /// <param name="modelPath">模型目錄</param> 35 /// <param name="action">日志回呼函式</param> 36 public ViewFace(string modelPath, LogCallBack action) : this(modelPath) 37 { 38 if (Platform64) 39 { ViewFacePlus64.SetLogFunction(action); } 40 else 41 { ViewFacePlus32.SetLogFunction(action); } 42 } 43 44 // public property 45 /// <summary> 46 /// 獲取或設定模型路徑 47 /// </summary> 48 public string ModelPath 49 { 50 get 51 { 52 if (Platform64) 53 { return ViewFacePlus64.GetModelPath(); } 54 else 55 { return ViewFacePlus32.GetModelPath(); } 56 } 57 set 58 { 59 if (Platform64) 60 { ViewFacePlus64.SetModelPath(value); } 61 else 62 { ViewFacePlus32.SetModelPath(value); } 63 } 64 } 65 /// <summary> 66 /// 獲取或設定人臉型別 67 /// <para> 68 /// <listheader>此屬性可影響到以下方法：</listheader><br /> 69 /// • <c><see cref="FaceDetector(Bitmap)"/></c><br /> 70 /// • <c><see cref="Extract(Bitmap, FaceMarkPoint[])"/></c><br /> 71 /// • <c><see cref="Similarity(float[], float[])"/></c><br /> 72 /// </para> 73 /// </summary> 74 public FaceType FaceType { get; set; } = FaceType.Light; 75 /// <summary> 76 /// 獲取或設定人臉關鍵點型別 77 /// <para> 78 /// <listheader>此屬性可影響到以下方法：</listheader><br /> 79 /// • <c><see cref="FaceMark(Bitmap, FaceInfo)"/></c><br /> 80 /// </para> 81 /// </summary> 82 public MarkType MarkType { get; set; } = MarkType.Light; 83 /// <summary> 84 /// 獲取或設定人臉檢測器設定 85 /// </summary> 86 public DetectorSetting DetectorSetting { get; set; } = new DetectorSetting(); 87 88 89 // public method 90 /// <summary> 91 /// 識別 <paramref name="bitmap"/> 中的人臉，并回傳人臉的資訊， 92 /// <para> 93 /// 當 <c><see cref="FaceType"/> <see langword="="/> <see cref="FaceType.Normal"/> <see langword="||"/> <see cref="FaceType.Light"/></c> 時， 需要模型：<see langword="face_detector.csta"/><br/> 94 /// 當 <c><see cref="FaceType"/> <see langword="="/> <see cref="FaceType.Mask"/></c> 時， 需要模型：<see langword="mask_detector.csta"/><br/> 95 /// </para> 96 /// </summary> 97 /// <param name="bitmap">包含人臉的圖片</param> 98 /// <returns></returns> 99 public FaceInfo[] FaceDetector(Bitmap bitmap) 100 { 101 byte[] bgr = ImageSet.Get24BGRFromBitmap(bitmap, out int width, out int height, out int channels); 102 int size; 103 if (Platform64) 104 { size = ViewFacePlus64.DetectorSize(bgr, width, height, channels, DetectorSetting.FaceSize, DetectorSetting.Threshold, DetectorSetting.MaxWidth, DetectorSetting.MaxHeight, (int)FaceType); } 105 else 106 { size = ViewFacePlus32.DetectorSize(bgr, width, height, channels, DetectorSetting.FaceSize, DetectorSetting.Threshold, DetectorSetting.MaxWidth, DetectorSetting.MaxHeight, (int)FaceType); } 107 float[] _socre = new float[size]; 108 int[] _x = new int[size]; 109 int[] _y = new int[size]; 110 int[] _width = new int[size]; 111 int[] _height = new int[size]; 112 if (Platform64) 113 { _ = ViewFacePlus64.Detector(_socre, _x, _y, _width, _height); } 114 else 115 { _ = ViewFacePlus32.Detector(_socre, _x, _y, _width, _height); } 116 List<FaceInfo> infos = new List<FaceInfo>(); 117 for (int i = 0; i < size; i++) 118 { 119 infos.Add(new FaceInfo() { Score = _socre[i], Location = new FaceRect() { X = _x[i], Y = _y[i], Width = _width[i], Height = _height[i] } }); 120 } 121 return infos.ToArray(); 122 } 123 124 /// <summary> 125 /// 識別 <paramref name="bitmap"/> 中指定的人臉資訊 <paramref name="info"/> 的關鍵點坐標， 126 /// <para> 127 /// 當 <see cref="FaceType"/> <see langword="="/> <see cref="FaceType.Normal"/> 時， 需要模型：<see langword="face_landmarker_pts68.csta"/><br/> 128 /// 當 <see cref="FaceType"/> <see langword="="/> <see cref="FaceType.Mask"/> 時， 需要模型：<see langword="face_landmarker_mask_pts5.csta"/><br/> 129 /// 當 <see cref="FaceType"/> <see langword="="/> <see cref="FaceType.Light"/> 時， 需要模型：<see langword="face_landmarker_pts5.csta"/><br/> 130 /// </para> 131 /// </summary> 132 /// <param name="bitmap">包含人臉的圖片</param> 133 /// <param name="info">指定的人臉資訊</param> 134 /// <returns></returns> 135 public FaceMarkPoint[] FaceMark(Bitmap bitmap, FaceInfo info) 136 { 137 byte[] bgr = ImageSet.Get24BGRFromBitmap(bitmap, out int width, out int height, out int channels); 138 int size; 139 if (Platform64) 140 { size = ViewFacePlus64.FaceMarkSize((int)MarkType); } 141 else 142 { size = ViewFacePlus32.FaceMarkSize((int)MarkType); } 143 double[] _pointX = new double[size]; 144 double[] _pointY = new double[size]; 145 bool val; 146 if (Platform64) 147 { val = ViewFacePlus64.FaceMark(bgr, width, height, channels, info.Location.X, info.Location.Y, info.Location.Width, info.Location.Height, _pointX, _pointY, (int)MarkType); } 148 else 149 { val = ViewFacePlus32.FaceMark(bgr, width, height, channels, info.Location.X, info.Location.Y, info.Location.Width, info.Location.Height, _pointX, _pointY, (int)MarkType); } 150 if (val) 151 { 152 List<FaceMarkPoint> points = new List<FaceMarkPoint>(); 153 for (int i = 0; i < size; i++) 154 { points.Add(new FaceMarkPoint() { X = _pointX[i], Y = _pointY[i] }); } 155 return points.ToArray(); 156 } 157 else 158 { throw new Exception("人臉關鍵點獲取失敗"); } 159 } 160 161 /// <summary> 162 /// 提取人臉特征值， 163 /// <para> 164 /// 當 <see cref="FaceType"/> <see langword="="/> <see cref="FaceType.Normal"/> 時， 需要模型：<see langword="face_recognizer.csta"/><br/> 165 /// 當 <see cref="FaceType"/> <see langword="="/> <see cref="FaceType.Mask"/> 時， 需要模型：<see langword="face_recognizer_mask.csta"/><br/> 166 /// 當 <see cref="FaceType"/> <see langword="="/> <see cref="FaceType.Light"/> 時， 需要模型：<see langword="face_recognizer_light.csta"/><br/> 167 /// </para> 168 /// </summary> 169 /// <param name="bitmap"></param> 170 /// <param name="points"></param> 171 /// <returns></returns> 172 public float[] Extract(Bitmap bitmap, FaceMarkPoint[] points) 173 { 174 byte[] bgr = ImageSet.Get24BGRFromBitmap(bitmap, out int width, out int height, out int channels); 175 float[] features; 176 if (Platform64) 177 { features = new float[ViewFacePlus64.ExtractSize((int)FaceType)]; } 178 else 179 { features = new float[ViewFacePlus32.ExtractSize((int)FaceType)]; } 180 181 if (Platform64) 182 { ViewFacePlus64.Extract(bgr, width, height, channels, points, features, (int)FaceType); } 183 else 184 { ViewFacePlus32.Extract(bgr, width, height, channels, points, features, (int)FaceType); } 185 return features; 186 } 187 188 /// <summary> 189 /// 計算特征值相似度， 190 /// <para>只能計算相同 <see cref="FaceType"/> 計算出的特征值</para> 191 /// <para> 192 /// 當 <see cref="FaceType"/> <see langword="="/> <see cref="FaceType.Normal"/> 時， 需要模型：<see langword="face_recognizer.csta"/><br/> 193 /// 當 <see cref="FaceType"/> <see langword="="/> <see cref="FaceType.Mask"/> 時， 需要模型：<see langword="face_recognizer_mask.csta"/><br/> 194 /// 當 <see cref="FaceType"/> <see langword="="/> <see cref="FaceType.Light"/> 時， 需要模型：<see langword="face_recognizer_light.csta"/><br/> 195 /// </para> 196 /// </summary> 197 /// <exception cref="ArgumentException"/> 198 /// <exception cref="ArgumentNullException"/> 199 /// <param name="leftFeatures"></param> 200 /// <param name="rightFeatures"></param> 201 /// <returns></returns> 202 public float Similarity(float[] leftFeatures, float[] rightFeatures) 203 { 204 if (leftFeatures.Length == 0 || rightFeatures.Length == 0) 205 throw new ArgumentNullException("引數不能為空", nameof(leftFeatures)); 206 if (leftFeatures.Length != rightFeatures.Length) 207 throw new ArgumentException("兩個引數長度不一致"); 208 209 210 if (Platform64) 211 { return ViewFacePlus64.Similarity(leftFeatures, rightFeatures, (int)FaceType); } 212 else 213 { return ViewFacePlus32.Similarity(leftFeatures, rightFeatures, (int)FaceType); } 214 } 215 216 /// <summary> 217 /// 判斷相似度是否為同一個人， 218 /// </summary> 219 /// <param name="similarity">相似度</param> 220 /// <returns></returns> 221 public bool IsSelf(float similarity) => similarity > Face.Threshold[FaceType]; 222 223 /// <summary> 224 /// 釋放資源 225 /// </summary> 226 ~ViewFace() 227 { 228 if (Platform64) 229 { ViewFacePlus64.ViewDispose(); } 230 else 231 { ViewFacePlus32.ViewDispose(); } 232 } 233 }C# 面向物件層
五、也許…
- 此專案還未實作 SeetaFace6 中的許多特性，也許：
想起 GitHub 密碼，持續更新…
洗掉代碼倉庫跑路…
- 如果在使用程序中遇到問題，你也許可以：
在 GitHub 報告Bug…
向我 發送郵件
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標籤：.NET Core