C#人脸识别

转自：https://www.cnblogs.com/view12138/p/ViewFaceCore.html#4615868

.NET 人脸识别库 ViewFaceCore

这是基于 SeetaFace6 人脸识别开发的 .NET 平台下的人脸识别库
这是一个使用超简单的人脸识别库
这是一个基于 .NET Standard 2.0 开发的库
这个库已经发布到 NuGet ，你可以一键集成到你的项目
此项目可以免费商业使用

⭐、开源

开源协议：Apache-2.0
GitHub地址： ViewFaceCore
十分感谢您的小星星

一、示例

示例项目地址：WinForm 摄像头人脸检测
示例项目效果：

二、使用

一分钟在你的项目里集成人脸识别

1. 创建你的 .NET 应用

.NET Standard >= 2.0
.NET Core >= 2.0
.NET Framework >= 4.6.1^2

2. 使用 Nuget 安装 ViewFaceCore

• Author : View
• Version >= 0.1.1

此 Nuget 包会自动添加依赖的 C++ 库，以及最精简的识别模型。
如果需要其它场景的识别模型，请下载 SeetaFace6 模型文件。

3. 在项目中编写你的代码

• 按照 说明 自己编写
• 或者参考以下代码

简单的调用示例

 1 static void Main()2         {3             ViewFace viewFace = new ViewFace((str) => { Debug.WriteLine(str); }); // 初始化人脸识别类，并设置 日志回调函数4             viewFace.DetectorSetting = new DetectorSetting() { FaceSize = 20, MaxWidth = 2000, MaxHeight = 2000, Threshold = 0.5 };5 6             // 系统默认使用的轻量级识别模型。如果对精度有要求，请切换到 Normal 模式；并下载需要模型文件 放入生成目录的 model 文件夹中7             viewFace.FaceType = FaceType.Normal;8             // 系统默认使用5个人脸关键点。//不建议改动，除非是使用口罩模型。9             viewFace.MarkType = MarkType.Light;
10 
11             #region 识别老照片
12             float[] oldEigenValues;
13             Bitmap oldImg = (Bitmap)Image.FromFile(@"C:\Users\yangw\OneDrive\图片\Camera Roll\IMG_20181103_142707.jpg"/*老图片路径*/); // 从文件中加载照片 // 或者视频帧等
14             var oldFaces = viewFace.FaceDetector(oldImg); // 检测图片中包含的人脸信息。(置信度、位置、大小)
15             if (oldFaces.Length > 0) //识别到人脸
16             {
17                 { // 打印人脸信息
18                     Console.WriteLine($"识别到的人脸数量：{oldFaces.Length} 。人脸信息：\n");
19                     Console.WriteLine($"序号\t人脸置信度\t位置X\t位置Y\t宽度\t高度");
20                     for (int i = 0; i < oldFaces.Length; i++)
21                     {
22                         Console.WriteLine($"{i + 1}\t{oldFaces[i].Score}\t{oldFaces[i].Location.X}\t{oldFaces[i].Location.Y}\t{oldFaces[i].Location.Width}\t{oldFaces[i].Location.Height}");
23                     }
24                     Console.WriteLine();
25                 }
26                 var oldPoints = viewFace.FaceMark(oldImg, oldFaces[0]); // 获取 第一个人脸 的识别关键点。(人脸识别的关键点数据)
27                 oldEigenValues = viewFace.Extract(oldImg, oldPoints); // 获取 指定的关键点 的特征值。
28             }
29             else { oldEigenValues = new float[0]; /*未识别到人脸*/ }
30             #endregion
31 
32             #region 识别新照片
33             float[] newEigenValues;
34             Bitmap newImg = (Bitmap)Image.FromFile(@"C:\Users\yangw\OneDrive\图片\Camera Roll\IMG_20181129_224339.jpg"/*新图片路径*/); // 从文件中加载照片 // 或者视频帧等
35             var newFaces = viewFace.FaceDetector(newImg); // 检测图片中包含的人脸信息。(置信度、位置、大小)
36             if (newFaces.Length > 0) //识别到人脸
37             {
38                 { // 打印人脸信息
39                     Console.WriteLine($"识别到的人脸数量：{newFaces.Length} 。人脸信息：\n");
40                     Console.WriteLine($"序号\t人脸置信度\t位置X\t位置Y\t宽度\t高度");
41                     for (int i = 0; i < newFaces.Length; i++)
42                     {
43                         Console.WriteLine($"{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]); // 获取 第一个人脸 的识别关键点。(人脸识别的关键点数据)
48                 newEigenValues = viewFace.Extract(newImg, newPoints); // 获取 指定的关键点 的特征值。
49             }
50             else { newEigenValues = new float[0]; /*未识别到人脸*/ }
51             #endregion
52 
53             try
54             {
55                 float similarity = viewFace.Similarity(oldEigenValues, newEigenValues); // 对比两张照片上的数据，确认是否是同一个人。
56                 Console.WriteLine($"阈值 = {Face.Threshold[viewFace.FaceType]}\t相似度 = {similarity}");
57                 Console.WriteLine($"是否是同一个人：{viewFace.IsSelf(similarity)}");
58             }
59             catch (Exception e)
60             { Console.WriteLine(e); }
61 
62             Console.ReadKey();
63         }

三、说明

命名空间：ViewFaceCore.Sharp : 人脸识别类所在的命名空间

• 属性说明：

• 方法说明：

 1 using System.Drawing;2 using ViewFaceCore.Sharp;3 using ViewFaceCore.Sharp.Model;4 5 // 识别 bitmap 中的人脸，并返回人脸的信息。6 FaceInfo[] FaceDetector(Bitmap);7 8 // 识别 bitmap 中指定的人脸信息 info 的关键点坐标。9 FaceMarkPoint[] FaceMark(Bitmap, FaceInfo);
10 
11 // 提取人脸特征值。
12 float[] Extract(Bitmap, FaceMarkPoint[]);
13 
14 // 计算特征值相似度。
15 float Similarity(float[], float[]);
16 
17 // 判断相似度是否为同一个人。
18 bool IsSelf(float);

四、实现

此项目受到了 SeetaFaceEngine.NET 项目的启发

这个项目本质上来说还是调用了 SeetaFace 的 C++ 类库来实现的人脸识别功能。针对本人遇到过的相关的类库的使用都不太方便，而且使用的 SeetaFace 的版本较老，故萌生了自己重新开发的想法。

本项目在开发完成之后为了方便调用，采用了 Nuget 包的形式，将所有需要的依赖以及最小识别模型一起打包。在使用时非常简单，只需要 nuget 安装，编写代码，运行即可，不需要多余的操作。

首先查看 SeetaFace ，已经更新到了v3(v6即v3)(上面前辈的项目是基于v1开发的)，最新版本暂时没有开源，但是可以免费商用。然后是根据以前的经验和 SeetaFace6 文档的指导，以及前辈的项目，做了以下操作。

1.对SeetaFace6 的接口进行了 C++ 形式的封装。

目前主要实现了 人脸检测，关键点提取，特征值提取，特征值对比几个人脸识别中的基础接口。有了这几个接口，可以完整的实现一套人脸识别和验证的流程。

• c++封装的接口代码如下：

  1 #include "seeta/FaceDetector.h"2 #include "seeta/FaceLandmarker.h"3 #include "seeta/FaceRecognizer.h"4 5 #include 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/"; // 模型所在路径14 LogCallBack logger = NULL; // 日志回调函数15 16 // 打印日志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 // 注册日志回调函数25 View_Api void V_SetLogFunction(LogCallBack writeLog)26 {27     logger = writeLog;28     WriteMessage(__FUNCDNAME__, "Successed.");29 }30 31 // 设置人脸模型目录32 View_Api void V_SetModelPath(const char* path)33 {34     modelPath = path;35     WriteMessage(__FUNCDNAME__, "Model.Path:" + modelPath);36 }37 // 获取人脸模型目录38 View_Api bool V_GetModelPath(char** path)39 {40     try41     {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 // 人脸检测结果57 static SeetaFaceInfoArray detectorInfos;58 // 人脸数量检测器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); // 此方法已经是人脸检测的全过程，故计时器显示为 人脸识别方法87         return infos.size;88     }89     catch (const std::exception& e)90     {91         WriteError(__FUNCDNAME__, e);92         return -1;93     }94 }95 // 人脸检测器96 View_Api bool V_Detector(float* score, int* x, int* y, int* width, int* height)97 {98     try99     {
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 = NULL;
112         detectorInfos.size = NULL;
113 
114         //WriteRunTime(__FUNCDNAME__, start); // 此方法只是将 人脸数量检测器 获取到的数据赋值传递，并不耗时。故不显示此方法的调用时间
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 // 人脸关键点数量
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 // 人脸关键点
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 _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 // 获取人脸特征值长度
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 // 提取人脸特征值
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 _features(new float[v_faceRecognizer->GetExtractFeatureSize()], std::default_delete());
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 // 人脸特征值相似度计算
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 // 释放资源
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 }

2.采用 C# 对上诉接口进行了导入。

因为C++的项目测CPU架构区分x86和x64，所以C# 层也需要区分架构封装

using System.Runtime.InteropServices;
using System.Text;
using ViewFaceCore.Sharp.Model;

namespace ViewFaceCore.Plus
{
///

class ViewFacePlus64
{const string LibraryPath = @"FaceLibraries\x64\ViewFace.dll";/// <summary>/// 设置日志回调函数(用于日志打印)/// </summary>/// <param name="writeLog"></param>[DllImport(LibraryPath, EntryPoint = "V_SetLogFunction", CallingConvention = CallingConvention.Cdecl)]public static extern void SetLogFunction(LogCallBack writeLog);/// <summary>/// 设置人脸模型的目录/// </summary>/// <param name="path"></param>[DllImport(LibraryPath, EntryPoint = "V_SetModelPath", CallingConvention = CallingConvention.Cdecl)]private extern static void SetModelPath(byte[] path);/// <summary>/// 设置人脸模型的目录/// </summary>/// <param name="path"></param>public static void SetModelPath(string path) => SetModelPath(Encoding.UTF8.GetBytes(path));/// <summary>/// 释放使用的资源/// </summary>[DllImport(LibraryPath, EntryPoint = "V_Dispose", CallingConvention = CallingConvention.Cdecl)]public extern static void ViewDispose();/// <summary>/// 获取人脸模型的目录/// </summary>/// <param name="path"></param>[DllImport(LibraryPath, EntryPoint = "V_GetModelPath", CallingConvention = CallingConvention.Cdecl)]private extern static bool GetModelPathEx(ref string path);/// <summary>/// 获取人脸模型的目录/// </summary>public static string GetModelPath() { string path = string.Empty; GetModelPathEx(ref path); return path; }/// <summary>/// 人脸检测器检测到的人脸数量/// </summary>/// <param name="imgData"></param>/// <param name="width"></param>/// <param name="height"></param>/// <param name="channels"></param>/// <param name="faceSize">最小人脸是人脸检测器常用的一个概念，默认值为20，单位像素。/// <para>最小人脸和检测器性能息息相关。主要方面是速度，使用建议上，我们建议在应用范围内，这个值设定的越大越好。SeetaFace采用的是BindingBox Regresion的方式训练的检测器。如果最小人脸参数设置为80的话，从检测能力上，可以将原图缩小的原来的1/4，这样从计算复杂度上，能够比最小人脸设置为20时，提速到16倍。</para>/// </param>/// <param name="threshold">检测器阈值默认值是0.9，合理范围为[0, 1]。这个值一般不进行调整，除了用来处理一些极端情况。这个值设置的越小，漏检的概率越小，同时误检的概率会提高</param>/// <param name="maxWidth">可检测的图像最大宽度。默认值2000。</param>/// <param name="maxHeight">可检测的图像最大高度。默认值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>/// 人脸检测器/// <para>调用此方法前必须先调用 <see cref="DetectorSize(byte[], int, int, int, double, double, double, double, int)"/></para>/// </summary>/// <param name="score">人脸置信度集合</param>/// <param name="x">人脸位置集合</param>/// <param name="y">人脸位置集合</param>/// <param name="width">人脸大小集合</param>/// <param name="height">人脸大小集合</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>/// 人脸关键点数量/// </summary>/// <returns></returns>[DllImport(LibraryPath, EntryPoint = "V_FaceMarkSize", CallingConvention = CallingConvention.Cdecl)]public extern static int FaceMarkSize(int type = 0);/// <summary>/// 人脸关键点/// </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>/// 计算相似度/// </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>/// 设置日志回调函数(用于日志打印)/// </summary>/// <param name="writeLog"></param>[DllImport(LibraryPath, EntryPoint = "V_SetLogFunction", CallingConvention = CallingConvention.Cdecl)]public static extern void SetLogFunction(LogCallBack writeLog);/// <summary>/// 设置人脸模型的目录/// </summary>/// <param name="path"></param>[DllImport(LibraryPath, EntryPoint = "V_SetModelPath", CallingConvention = CallingConvention.Cdecl)]private extern static void SetModelPath(byte[] path);/// <summary>/// 设置人脸模型的目录/// </summary>/// <param name="path"></param>public static void SetModelPath(string path) => SetModelPath(Encoding.UTF8.GetBytes(path));/// <summary>/// 释放使用的资源/// </summary>[DllImport(LibraryPath, EntryPoint = "V_Dispose", CallingConvention = CallingConvention.Cdecl)]public extern static void ViewDispose();/// <summary>/// 获取人脸模型的目录/// </summary>/// <param name="path"></param>[DllImport(LibraryPath, EntryPoint = "V_GetModelPath", CallingConvention = CallingConvention.Cdecl)]private extern static bool GetModelPathEx(ref string path);/// <summary>/// 获取人脸模型的目录/// </summary>public static string GetModelPath() { string path = string.Empty; GetModelPathEx(ref path); return path; }/// <summary>/// 人脸检测器检测到的人脸数量/// </summary>/// <param name="imgData"></param>/// <param name="width"></param>/// <param name="height"></param>/// <param name="channels"></param>/// <param name="faceSize">最小人脸是人脸检测器常用的一个概念，默认值为20，单位像素。/// <para>最小人脸和检测器性能息息相关。主要方面是速度，使用建议上，我们建议在应用范围内，这个值设定的越大越好。SeetaFace采用的是BindingBox Regresion的方式训练的检测器。如果最小人脸参数设置为80的话，从检测能力上，可以将原图缩小的原来的1/4，这样从计算复杂度上，能够比最小人脸设置为20时，提速到16倍。</para>/// </param>/// <param name="threshold">检测器阈值默认值是0.9，合理范围为[0, 1]。这个值一般不进行调整，除了用来处理一些极端情况。这个值设置的越小，漏检的概率越小，同时误检的概率会提高</param>/// <param name="maxWidth">可检测的图像最大宽度。默认值2000。</param>/// <param name="maxHeight">可检测的图像最大高度。默认值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>/// 人脸检测器/// <para>调用此方法前必须先调用 <see cref="DetectorSize(byte[], int, int, int, double, double, double, double, int)"/></para>/// </summary>/// <param name="score">人脸置信度集合</param>/// <param name="x">人脸位置集合</param>/// <param name="y">人脸位置集合</param>/// <param name="width">人脸大小集合</param>/// <param name="height">人脸大小集合</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>/// 人脸关键点数量/// </summary>/// <returns></returns>[DllImport(LibraryPath, EntryPoint = "V_FaceMarkSize", CallingConvention = CallingConvention.Cdecl)]public extern static int FaceMarkSize(int type = 0);/// <summary>/// 人脸关键点/// </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>/// 计算相似度/// </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);
}

}

3.采用 C# 的面向对象的封装

因为C#的项目默认都是 AnyCPU，所以为了简化调用，在这一层封装的时候增加了架构判断，当在你的项目中引用的时候，不用做任何修改。

且因为C++的C#导入方法在和原生的C#写法略有差异，且数据的转换和传递比较麻烦，所以类库中对外隐藏了 C# 导入层。并使用大家都更熟悉的C#的面向对象的方式进行进一步的封装和简化。

  1     /// 
2     /// 人脸识别类3     /// 
4     public class ViewFace5     {6         bool Platform64 { get; set; } = false;7         // 需要模型：8 9         // ctor10         /// 
11         /// 使用默认的模型目录初始化人脸识别类12         /// 
13         public ViewFace() : this("./model/") { }14         /// 
15         /// 使用指定的模型目录初始化人脸识别类16         /// 
17         /// 模型目录18         public ViewFace(string modelPath)19         {20             Platform64 = IntPtr.Size == 8;21             if (Platform64)22             { ViewFacePlus64.SetModelPath(modelPath); }23             else24             { ViewFacePlus32.SetModelPath(modelPath); }25         }26         /// 
27         /// 使用指定的日志回调函数初始化人脸识别类28         /// 
29         /// 日志回调函数30         public ViewFace(LogCallBack action) : this("./model/", action) { }31         /// 
32         /// 使用指定的模型目录、日志回调函数初始化人脸识别类33         /// 
34         /// 模型目录35         /// 日志回调函数36         public ViewFace(string modelPath, LogCallBack action) : this(modelPath)37         {38             if (Platform64)39             { ViewFacePlus64.SetLogFunction(action); }40             else41             { ViewFacePlus32.SetLogFunction(action); }42         }43 44         // public property45         /// 
46         /// 获取或设置模型路径47         /// 
48         public string ModelPath49         {50             get51             {52                 if (Platform64)53                 { return ViewFacePlus64.GetModelPath(); }54                 else55                 { return ViewFacePlus32.GetModelPath(); }56             }57             set58             {59                 if (Platform64)60                 { ViewFacePlus64.SetModelPath(value); }61                 else62                 { ViewFacePlus32.SetModelPath(value); }63             }64         }65         /// 
66         /// 获取或设置人脸类型67         /// 68         /// 此属性可影响到以下方法：
69         /// • 
70         /// • 
71         /// • 
72         /// 73         /// 
74         public FaceType FaceType { get; set; } = FaceType.Light;75         /// 
76         /// 获取或设置人脸关键点类型77         /// 78         /// 此属性可影响到以下方法：
79         /// • 
80         /// 81         /// 
82         public MarkType MarkType { get; set; } = MarkType.Light;83         /// 
84         /// 获取或设置人脸检测器设置85         /// 
86         public DetectorSetting DetectorSetting { get; set; } = new DetectorSetting();87 88 89         // public method90         /// 
91         /// 识别  中的人脸，并返回人脸的信息。92         /// 93         /// 当      时， 需要模型：
94         /// 当    时， 需要模型：
95         /// 96         /// 
97         /// 包含人脸的图片98         /// 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 infos = new List();
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         /// 

125         /// 识别  中指定的人脸信息  的关键点坐标。
126         /// 
127         /// 当    时， 需要模型：

128         /// 当    时， 需要模型：

129         /// 当    时， 需要模型：

130         /// 
131         /// 
132         /// 包含人脸的图片
133         /// 指定的人脸信息
134         /// 
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 points = new List();
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("人脸关键点获取失败"); }
159         }
160 
161         /// 

162         /// 提取人脸特征值。
163         /// 
164         /// 当    时， 需要模型：

165         /// 当    时， 需要模型：

166         /// 当    时， 需要模型：

167         /// 
168         /// 
169         /// 
170         /// 
171         /// 
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         /// 

189         /// 计算特征值相似度。
190         /// 只能计算相同  计算出的特征值
191         /// 
192         /// 当    时， 需要模型：

193         /// 当    时， 需要模型：

194         /// 当    时， 需要模型：

195         /// 
196         /// 
197         /// 
198         /// 
199         /// 
200         /// 
201         /// 
202         public float Similarity(float[] leftFeatures, float[] rightFeatures)
203         {
204             if (leftFeatures.Length == 0 || rightFeatures.Length == 0)
205                 throw new ArgumentNullException("参数不能为空", nameof(leftFeatures));
206             if (leftFeatures.Length != rightFeatures.Length)
207                 throw new ArgumentException("两个参数长度不一致");
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         /// 

217         /// 判断相似度是否为同一个人。
218         /// 
219         /// 相似度
220         /// 
221         public bool IsSelf(float similarity) => similarity > Face.Threshold[FaceType];
222 
223         /// 

224         /// 释放资源
225         /// 
226         ~ViewFace()
227         {
228             if (Platform64)
229             { ViewFacePlus64.ViewDispose(); }
230             else
231             { ViewFacePlus32.ViewDispose(); }
232         }
233     }

五、也许…

• 此项目还未实现 SeetaFace6 中的许多特性，也许：

　　　　想起 GitHub 密码，持续更新…
　　　　删除代码仓库跑路…

• 如果在使用过程中遇到问题，你也许可以：

　　　　在 GitHub 报告Bug…
　　　　向我 发送邮件

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