ArcFac_C#_DEMO开发

public delegate bool MatchHandler(string userid, string studyid, string photoid, string photopath);

MatchHandler handler = new MatchHandler(MatchUserFace.GetAndMatchImage);
string Identification = string.Format("USERID:{0} STUDYID:{1} PHID:{2}", userid, studyid, photoid);
IAsyncResult result = handler.BeginInvoke(userid, studyid, photoid, pathstr, RecognizeEngine, DetectEngine, new AsyncCallback(CallbackFunc), Identification);

static void CallbackFunc(IAsyncResult result)
{
MatchHandler handler = (MatchHandler)((AsyncResult)result).AsyncDelegate;
bool match = handler.EndInvoke(result);
string strmatch = string.Empty;
if (match)
{
strmatch = " 比对结果:OK";
}
else
{

strmatch = " 比对结果:NO";
}
Console.WriteLine(result.AsyncState + strmatch);
GC.Collect();
}

string appId = "4yHjnxK94FCK6L7HaJieWawSLubnANXXXXX";
string sdkFDKey = "7S6Xp4mtroLnjTt7qDYnd2dqHXXXXX";
string sdkFRKey = "7S6Xp4mtroLnjTt7qDYnd2dxSgXXXXX";
int retCode = AFDFunction.AFD_FSDK_InitialFaceEngine(appId, sdkFDKey, pMem, detectSize, ref DetectEngine, 5, nScale, nMaxFaceNum);
int retCode2 = AFRFunction.AFR_FSDK_InitialEngine(appId, sdkFRKey, pMemRecongnize, detectSize, ref RecognizeEngine);

MatchHandler handler = new MatchHandler(MatchUserFace.GetAndMatchImage);
string Identification = string.Format("USERID:{0} STUDYID:{1} PHID:{2}", userid, studyid, photoid);
IAsyncResult result = handler.BeginInvoke(userid, studyid, photoid, pathstr, RecognizeEngine, DetectEngine, new AsyncCallback(CallbackFunc), Identification);

private static byte[] detectAndExtractFeature(Image imageParam, out Image facerect,
IntPtr RecognizeEngine, IntPtr DetectEngine)
{
byte[] feature = null; facerect = null;

try
{
int width = 0; int height = 0; int pitch = 0;
Bitmap bitmap = new Bitmap(imageParam);
byte[] imageData = getBGR(bitmap, ref width, ref height, ref pitch);
IntPtr imageDataPtr = Marshal.AllocHGlobal(imageData.Length);
Marshal.Copy(imageData, 0, imageDataPtr, imageData.Length);

ASVLOFFSCREEN offInput = new ASVLOFFSCREEN();
offInput.u32PixelArrayFormat = 513;
offInput.ppu8Plane = new IntPtr[4];
offInput.ppu8Plane[0] = imageDataPtr;
offInput.i32Width = width;
offInput.i32Height = height;
offInput.pi32Pitch = new int[4];
offInput.pi32Pitch[0] = pitch;
AFD_FSDK_FACERES faceRes = new AFD_FSDK_FACERES();
IntPtr offInputPtr = Marshal.AllocHGlobal(Marshal.SizeOf(offInput));
Marshal.StructureToPtr(offInput, offInputPtr, false);
IntPtr faceResPtr = Marshal.AllocHGlobal(Marshal.SizeOf(faceRes));

//人脸检测
int detectResult = AFDFunction.AFD_FSDK_StillImageFaceDetection(DetectEngine, offInputPtr, ref faceResPtr);
if (detectResult == 0)
{
try
{
object obj = Marshal.PtrToStructure(faceResPtr, typeof(AFD_FSDK_FACERES));
faceRes = (AFD_FSDK_FACERES)obj;
for (int i = 0; i  0)
{
try
{
AFR_FSDK_FaceInput faceResult = new AFR_FSDK_FaceInput();
int orient = (int)Marshal.PtrToStructure(faceRes.lfaceOrient, typeof(int));
faceResult.lOrient = orient;
faceResult.rcFace = new MRECT();
MRECT rect = (MRECT)Marshal.PtrToStructure(faceRes.rcFace, typeof(MRECT));
faceResult.rcFace = rect;
IntPtr faceResultPtr = Marshal.AllocHGlobal(Marshal.SizeOf(faceResult));
Marshal.StructureToPtr(faceResult, faceResultPtr, false);

AFR_FSDK_FaceModel localFaceModels = new AFR_FSDK_FaceModel();
IntPtr localFaceModelsPtr = Marshal.AllocHGlobal(Marshal.SizeOf(localFaceModels));
int extractResult = AFRFunction.AFR_FSDK_ExtractFRFeature(RecognizeEngine, offInputPtr, faceResultPtr, localFaceModelsPtr);
if (extractResult == 0)
{
Marshal.FreeHGlobal(faceResultPtr);
Marshal.FreeHGlobal(offInputPtr);

object objFeature = Marshal.PtrToStructure(localFaceModelsPtr, typeof(AFR_FSDK_FaceModel));

Marshal.FreeHGlobal(localFaceModelsPtr);

localFaceModels = (AFR_FSDK_FaceModel)objFeature;
feature = new byte[localFaceModels.lFeatureSize];
Marshal.Copy(localFaceModels.pbFeature, feature, 0, localFaceModels.lFeatureSize);

localFaceModels = new AFR_FSDK_FaceModel();
}
}
catch (Exception ex)
{

LogNetWriter.Error("提取特征时出错:" + ex.Message);
}

 

}

bitmap.Dispose();
imageData = null;
Marshal.FreeHGlobal(imageDataPtr);
//Marshal.FreeHGlobal(faceResPtr);
offInput = new ASVLOFFSCREEN();
faceRes = new AFD_FSDK_FACERES();
}
catch (Exception ex)
{
LogNetWriter.Error("识别人脸并提取人脸特征出错:" + ex.Message);
}
return feature;
}
```
当然了，比对的时候也作了一些修改，就是当比对完了以后，就做了指针释放；

```
Marshal.FreeHGlobal(firstFeaturePtr);
Marshal.FreeHGlobal(secondFeaturePtr);
Marshal.FreeHGlobal(firstPtr);
Marshal.FreeHGlobal(secondPtr);
```
经过这一次修改后，再发布到服务器，哟...不错哦..运行的时间久了...但还是会卦，而且那个w3wp.exe还是会不断的拉内存；这个版本的运行时间可以达到4小左右了；我就想总得有个解决办法吧；再次致电虹软，再次反映这个问题，虹软这边给我的建议就是不要去进行多线程，我想想也对，要把逻辑简单化，我就把识别核心打包成一个EXE.然后在Program里调用这个EXE.意思就是每当我有需要识别的图片，我就调一个EXE.然后EXE处理完以后，就自我释放了...
于是我改了第三版：

```
//这里就是一条线程在做处理
string strmatch = string.Empty;
ControlExeClass _ControlExeClass = new Model.ControlExeClass();
//这个方法是调一个EXE,EXE的内容是:ControlExeClass.cs;
//做的任务就是把图片进行人脸检测,人脸特征提取,人脸识别;
bool bo = _ControlExeClass.ControlExe(userid, studyid, photoid, pathstr);
if (bo)
{
iCheck_OK++;
label5.Text = iCheck_OK.ToString();
strmatch = " 比对结果:OK";
}
else
{

strmatch = " 比对结果:NO";
}
string dates = " 比对时间:" + DateTime.Now.ToString("yyyy-MM-dd HH:mm:ss");

textBox1.Text += " USERID:" + userid + " STUDYID:" + studyid + " PHID:" + photoid + strmatch + dates + Environment.NewLine;

private static void CycleData()
{
while (true)
{
if (_DoWork)
{
break;
}
else
{
QueryDataFile("U");
Thread.Sleep(1500);
}

Thread.Sleep(2000);
}

}