public void ExtractFeatures()
{
var featureExtraction = new FeatureExtraction();
featureExtraction.ExtractFeatures(); //Insert
//featureExtraction.ExtractWordBigramFeatures(); //Update
}
//method that runs for every capturing
public void OnCaptured(CaptureResult captureResult)
{
try
{
// Check capture quality and throw an error if bad.
if (!fp.CheckCaptureResult(captureResult))
{
return;
}
else
{
//convert to a valid finger print first
DataResult <Fmd> resultConversion = FeatureExtraction.CreateFmdFromFid(captureResult.Data, Constants.Formats.Fmd.ANSI);
if (resultConversion.ResultCode != Constants.ResultCode.DP_SUCCESS)
{
throw new Exception(resultConversion.ResultCode.ToString());
}
else
{
//if it succesfully convert then display for user to see
// Create bitmap - this part display the captured on screen
foreach (Fid.Fiv fiv in captureResult.Data.Views)
{
SendMessage(Action.SendBitmap, fp.CreateBitmap(fiv.RawImage, fiv.Width, fiv.Height));
}
firstFinger = resultConversion.Data;
}
}
}
catch (Exception ex)
{
// Send error message, then close form
SendMessage(Action.SendMessage, "Error: " + ex.Message);
}
}
//Gathers the required counts for the features and performs feature selection
private FeaturesStatistics selectFeatures(List <Document> dataset, int numberOfFeatures = 30)
{
FeatureExtraction featureExtractor = new FeatureExtraction();
//the FeatureStatistica object contains statistics about all the features found in the documents
FeaturesStatistics statistics = featureExtractor.extractFeatureStatistics(dataset);
//we pass this information to the feature selection algorithm and we get a list with the selected features
Dictionary <String, Double> selectedFeatures = featureExtractor.select(statistics, numberOfFeatures);
Dictionary <String, Dictionary <String, int> > newfeatureCategoryJointCount = new Dictionary <string, Dictionary <string, int> >();
//clip from the stats all the features that are not selected
foreach (var arr in statistics.featureCategoryJointCount)
{
if (selectedFeatures.ContainsKey(arr.Key))
{
newfeatureCategoryJointCount.Add(arr.Key, arr.Value);
}
}
statistics.featureCategoryJointCount = newfeatureCategoryJointCount;
return(statistics);
}
private IEnumerable <Fmd> CaptureAndExtractFmd()
{
while (!objVariables.reset)
{
DataResult <Fmd> resultConversion;
try
{
if (objVariables.count >= 8)
{
MessageBox.Show("El registro no se completó, vuelve a intentarlo.",
"Aviso",
MessageBoxButtons.OK,
MessageBoxIcon.Exclamation,
MessageBoxDefaultButton.Button1);
objVariables.count = 0;
break;
}
Fid fid = null;
if (objVariables.currentReader == null)
{
objVariables.currentReader = objDevice.IndexDevice();
objDevice.InitializeDevice(ref objVariables.currentReader);
}
if (!CaptureFinger(ref fid))
{
//break;
}
if (fid == null)
{
continue;
}
objVariables.count++;
resultConversion = FeatureExtraction.CreateFmdFromFid(fid, Constants.Formats.Fmd.ANSI);
objPrincipal.txtSalida.Dispatcher.BeginInvoke(new Action(delegate()
{
objPrincipal.txtSalida.AppendText("\nLa huella se ha capturado. Captura Numero: " + objVariables.count);
}));
if (resultConversion.ResultCode != Constants.ResultCode.DP_SUCCESS)
{
break;
}
}
catch (Exception)
{
break;
}
yield return(resultConversion.Data);
}
}
public static void ExtractClusteringFeatures()
{
LoggedConsole.WriteLine("feature extraction process...");
var inputDir = @"D:\Mahnoosh\Liz\Least_Bittern\";
var resultDir = Path.Combine(inputDir, "FeatureLearning");
//var trainSetPath = Path.Combine(inputDir, "TrainSet");
var testSetPath = Path.Combine(inputDir, "TestSet\\one_min_recordings");
var configPath = @"D:\Mahnoosh\Liz\Least_Bittern\FeatureLearningConfig.yml";
var centroidsPath = Path.Combine(resultDir, "ClusterCentroids0.csv");
var configFile = configPath.ToFileInfo();
if (configFile == null)
{
throw new FileNotFoundException("No config file argument provided");
}
else if (!configFile.Exists)
{
throw new ArgumentException($"Config file {configFile.FullName} not found");
}
var configuration = ConfigFile.Deserialize <FeatureLearningSettings>(configFile);
List <double[][]> centroids = new List <double[][]>();
centroids.Add(Csv.ReadMatrixFromCsv <double>(centroidsPath.ToFileInfo(), TwoDimensionalArray.None).ToJagged());
FeatureExtraction.UnsupervisedFeatureExtraction(configuration, centroids, testSetPath, resultDir);
LoggedConsole.WriteLine("Done...");
}
/// <summary>
/// Gathers the required counts for the features and performs feature selection
/// on the above counts. It returns a FeatureStats object that is later used
/// for calculating the probabilities of the model.
/// </summary>
/// <param name="dataset"> </param>
/// <returns> </returns>
private FeatureStats selectFeatures(IList <Document> dataset)
{
FeatureExtraction featureExtractor = new FeatureExtraction();
//the FeatureStats object contains statistics about all the features found in the documents
FeatureStats stats = featureExtractor.extractFeatureStats(dataset); //extract the stats of the dataset
//we pass this information to the feature selection algorithm and we get a list with the selected features
IDictionary <string, double?> selectedFeatures = featureExtractor.chisquare(stats, chisquareCriticalValue);
//clip from the stats all the features that are not selected
IEnumerator <KeyValuePair <string, IDictionary <string, int> > > it = stats.featureCategoryJointCount.GetEnumerator();
while (it.MoveNext())
{
string feature = it.Current.Key;
if (selectedFeatures.ContainsKey(feature) == false)
{
//if the feature is not in the selectedFeatures list remove it
it.Current.Value.Remove(feature);
}
}
return(stats);
}
/// <summary>
/// Handler for when a fingerprint is captured.
/// </summary>
/// <param name="captureResult">contains info and data on the fingerprint capture</param>
private void OnCaptured(CaptureResult captureResult)
{
try
{
// Check capture quality and throw an error if bad.
if (!_sender.CheckCaptureResult(captureResult))
{
return;
}
SendMessage(Action.SendMessage, "A finger was captured.");
DataResult <Fmd> resultConversion = FeatureExtraction.CreateFmdFromFid(captureResult.Data, Constants.Formats.Fmd.ANSI);
if (captureResult.ResultCode != Constants.ResultCode.DP_SUCCESS)
{
_sender.Reset = true;
throw new Exception(captureResult.ResultCode.ToString());
}
if (count == 0)
{
rightIndex = resultConversion.Data;
count += 1;
SendMessage(Action.SendMessage, "Now place your right thumb on the reader.");
}
else if (count == 1)
{
rightThumb = resultConversion.Data;
count += 1;
SendMessage(Action.SendMessage, "Now place any finger on the reader.");
}
else if (count == 2)
{
anyFinger = resultConversion.Data;
Fmd[] fmds = new Fmd[2];
fmds[0] = rightIndex;
fmds[1] = rightThumb;
// See the SDK documentation for an explanation on threshold scores.
int thresholdScore = DPFJ_PROBABILITY_ONE * 1 / 100000;
IdentifyResult identifyResult = Comparison.Identify(anyFinger, 0, fmds, thresholdScore, 2);
if (identifyResult.ResultCode != Constants.ResultCode.DP_SUCCESS)
{
_sender.Reset = true;
throw new Exception(identifyResult.ResultCode.ToString());
}
SendMessage(Action.SendMessage, "Identification resulted in the following number of matches: " + identifyResult.Indexes.Length.ToString());
SendMessage(Action.SendMessage, "Place your right index finger on the reader.");
count = 0;
}
}
catch (Exception ex)
{
// Send error message, then close form
SendMessage(Action.SendMessage, "Error: " + ex.Message);
}
}
private void OnCaptured(CaptureResult captureResult)
{
bool existio = false;
try
{
if (!Clases.HuellaDigital.CheckCaptureResult(captureResult))
{
return;
}
////Creamos el bitmap para el PictureBox
//foreach (Fid.Fiv fiv in captureResult.Data.Views)
//{
// SendMessage(Action.SendBitmap, Clases.HuellaDigital.CreateBitmap(fiv.RawImage, fiv.Width, fiv.Height));
//}
//Guardamos la variable de la huella
res = FeatureExtraction.CreateFmdFromFid(captureResult.Data, Constants.Formats.Fmd.ANSI);
if (captureResult.ResultCode != Constants.ResultCode.DP_SUCCESS)
{
Clases.HuellaDigital.Reset = true;
throw new Exception(captureResult.ResultCode.ToString());
}
Fmd[] fmds = new Fmd[2];
foreach (int k in Clases.HuellaDigital.Fmds.Keys)
{
fmds[0] = Clases.HuellaDigital.Fmds[k];
int probabilidad = DPFJ_PROBABILITY_ONE * 1 / 100000;
IdentifyResult ideRes = Comparison.Identify(res.Data, 0, fmds, probabilidad, Clases.HuellaDigital.Fmds.Count + 1);
if (ideRes.ResultCode != Constants.ResultCode.DP_SUCCESS)
{
Clases.HuellaDigital.Reset = true;
throw new Exception(ideRes.ResultCode.ToString());
}
if (ideRes.Indexes.Length > 0)
{
SendMessage(Action.SendOK, k.ToString());
existio = true;
break;
}
}
if (!existio)
{
SendMessage(Action.SendMessage, "La huella no coincide con alguna del registro\n");
}
}
catch (Exception ex)
{
//Mostramos un MessageBox con el mensaje de error
SendMessage(Action.SendMessage, ex.Message);
}
}
protected FeatureSet ExtractFeatures(Sample sample, DataPurpose purpose)
{
var extractor = new FeatureExtraction();
var feedback = CaptureFeedback.None;
var features = new FeatureSet();
extractor.CreateFeatureSet(sample, purpose, ref feedback, ref features);
return((feedback == CaptureFeedback.Good) ? features : null);
}
/// <summary>
/// Handler for when a fingerprint is captured.
/// </summary>
/// <param name="captureResult">contains info and data on the fingerprint capture</param>
private void OnCaptured(CaptureResult captureResult)
{
try
{
// Check capture quality and throw an error if bad.
if (!_sender.CheckCaptureResult(captureResult))
{
return;
}
SendMessage(Action.SendMessage, oHelper.Mensaje2);
DataResult <Fmd> resultConversion = FeatureExtraction.CreateFmdFromFid(captureResult.Data, Constants.Formats.Fmd.ANSI);
if (resultConversion.ResultCode != Constants.ResultCode.DP_SUCCESS)
{
_sender.Reset = true;
throw new Exception(resultConversion.ResultCode.ToString());
}
if (count == 0)
{
firstFinger = resultConversion.Data;
OperadorEncontrado = funciones.CompararHuella(firstFinger, secondFinger);
if (OperadorEncontrado.IdOperador != 0)
{
SendMessage(Action.SendMessage, "Huella coincide con operador " + OperadorEncontrado.Nombre + " y su status es " + OperadorEncontrado.Status);
SendMessage(Action.SendMessage, oHelper.Mensaje1);
}
else
{
SendMessage(Action.SendMessage, oHelper.Mensaje5);
SendMessage(Action.SendMessage, oHelper.Mensaje1);
}
count = 0;
}
}
catch (Exception ex)
{
// Send error message, then close form
SendMessage(Action.SendMessage, oHelper.Error + ex.Message);
}
///si el lector es desconectado con la ventana de verification abierta, mandamos a cerrarla automaticamente
///para llamar al evento Verification_Closed y que se cargue el load del main para que salga el mensajeBox de lector
///desconectado (Reintentar/Salir)
if (captureResult.ResultCode == Constants.ResultCode.DP_DEVICE_FAILURE)
{
Thread t = new Thread(cerrar);
t.Start();
}
}
/// <summary>
/// Handler for when a fingerprint is captured.
/// </summary>
/// <param name="captureResult">contains info and data on the fingerprint capture</param>
private void OnCaptured(CaptureResult captureResult)
{
try
{
// Check capture quality and throw an error if bad.
if (!_sender.CheckCaptureResult(captureResult))
{
return;
}
count++;
DataResult <Fmd> resultConversion = FeatureExtraction.CreateFmdFromFid(captureResult.Data, Constants.Formats.Fmd.ANSI);
SendMessage(Action.SendMessage, "A finger was captured. \r\nCount: " + (count));
if (resultConversion.ResultCode != Constants.ResultCode.DP_SUCCESS)
{
_sender.Reset = true;
throw new Exception(resultConversion.ResultCode.ToString());
}
preenrollmentFmds.Add(resultConversion.Data);
if (count >= 4)
{
DataResult <Fmd> resultEnrollment = DPUruNet.Enrollment.CreateEnrollmentFmd(Constants.Formats.Fmd.ANSI, preenrollmentFmds);
if (resultEnrollment.ResultCode == Constants.ResultCode.DP_SUCCESS)
{
SendMessage(Action.SendMessage, "An enrollment FMD was successfully created.");
SendMessage(Action.SendMessage, "Place a finger on the reader.");
preenrollmentFmds.Clear();
count = 0;
return;
}
else if (resultEnrollment.ResultCode == Constants.ResultCode.DP_ENROLLMENT_INVALID_SET)
{
SendMessage(Action.SendMessage, "Enrollment was unsuccessful. Please try again.");
SendMessage(Action.SendMessage, "Place a finger on the reader.");
preenrollmentFmds.Clear();
count = 0;
return;
}
}
SendMessage(Action.SendMessage, "Now place the same finger on the reader.");
}
catch (Exception ex)
{
// Send error message, then close form
SendMessage(Action.SendMessage, "Error: " + ex.Message);
}
}
public void OnCaptured(CaptureResult captureResult)
{
this.picBoxToCap = picBoxFingerPrint;
this.fingerToCapture = 0;
count++;
lblCount.Invoke(new Action(() => { lblCount.Text = count.ToString(); }));
DataResult <Fmd> resultConversion = FeatureExtraction.CreateFmdFromFid(captureResult.Data, Constants.Formats.Fmd.ANSI);
if (captureResult.ResultCode != Constants.ResultCode.DP_SUCCESS)
{
if (CurrentReader != null)
{
CurrentReader.Dispose();
CurrentReader = null;
}
MessageBox.Show("Error: " + captureResult.ResultCode);
}
if (captureResult.Data != null)
{
foreach (Fid.Fiv fiv in captureResult.Data.Views)
{
picBoxFingerPrint.Image = CreateBitmap(fiv.RawImage, fiv.Width, fiv.Height);
picBoxFingerPrint.SizeMode = PictureBoxSizeMode.Zoom;
}
}
preenrollmentFmds.Add(resultConversion.Data);
if (count >= 4)
{
resultEnrollment = Enrollment.CreateEnrollmentFmd(Constants.Formats.Fmd.ANSI, preenrollmentFmds);
TempFingerPrint = Fmd.SerializeXml(resultConversion.Data);
resultEnrollment = DPUruNet.Enrollment.CreateEnrollmentFmd(Constants.Formats.Fmd.ANSI, preenrollmentFmds);
if (resultEnrollment.ResultCode == Constants.ResultCode.DP_SUCCESS)
{
FileStream fs = new System.IO.FileStream(@"..\..\Pictures\right.bmp", FileMode.Open, FileAccess.Read);
picRigithWrong.Invoke(new Action(() => { picRigithWrong.Image = Image.FromStream(fs); }));
picRigithWrong.SizeMode = PictureBoxSizeMode.Zoom;
//preenrollmentFmds.Clear();
//count = 0;
MessageBox.Show("An enrollment was successfully created.");
return;
}
else if (resultEnrollment.ResultCode == Constants.ResultCode.DP_ENROLLMENT_INVALID_SET)
{
MessageBox.Show("Enrollment was unsuccessful. Please try again.");
preenrollmentFmds.Clear();
count = 0;
return;
}
}
}
static void Main(string[] args)
{
var repositorio = new DigitalRepositorio();
var biometrias = repositorio.RecuperarPagina(1, 8000);
Console.WriteLine($"{biometrias.Count()} biometrias recuperadas...");
foreach (var biometria in biometrias)
{
var teste = FeatureExtraction.CreateFmdFromRaw(biometria.TemplateISO, 1, 1, 1, 1, 1, Constants.Formats.Fmd.ISO);
}
}
public FingerTemplate Extract(FingerImage image)
{
if (image is FingerImageDP)
{
var fingerImage = image as FingerImageDP;
DataResult <Fmd> resultConversion = FeatureExtraction.CreateFmdFromFid(fingerImage.fid, Constants.Formats.Fmd.ANSI);
if (resultConversion.ResultCode == Constants.ResultCode.DP_SUCCESS)
{
return(new TemplateDP(resultConversion.Data));
}
}
return(null);
}
/// <summary>
/// Handler for when a fingerprint is captured.
/// </summary>
/// <param name="captureResult">contains info and data on the fingerprint capture</param>
private void OnCaptured(CaptureResult captureResult)
{
try
{
// Check capture quality and throw an error if bad.
if (!_sender.CheckCaptureResult(captureResult))
{
return;
}
SendMessage(Action.SendMessage, "A finger was captured.");
IDs.Clear();
DataResult <Fmd> resultConversion = FeatureExtraction.CreateFmdFromFid(captureResult.Data, Constants.Formats.Fmd.ANSI);
if (captureResult.ResultCode != Constants.ResultCode.DP_SUCCESS)
{
_sender.Reset = true;
throw new Exception(captureResult.ResultCode.ToString());
}
SendMessage(Action.SendMessage, "Please wait while searching is done in background.");
anyFinger = resultConversion.Data;
Fmd[] fmdsArray = fmds.ToArray();
foreach (Fmd fmd in fmdsArray)
{
CompareResult compareResult = Comparison.Compare(anyFinger, 0, fmd, 0);
if (compareResult.Score < (PROBABILITY_ONE / 100000))
{
IDs.Add(fmdsDic[fmd]);
}
}
SendMessage(Action.SendMessage, IDs.Count.ToString() + " Finger Print(s) Matched.");
SendMessage(Action.SendMessage, "Click the button below to show results or place thumb to search again.");
if (IDs.Count > 0)
{
SendMessage(Action.ShowMatchedEntry, "");
}
else
{
SendMessage(Action.HideMatchedEntry, "");
}
}
catch (Exception ex)
{
Console.WriteLine("Error in Identification: " + ex.Message);
}
}
public void Extract_Tests()
{
var featureExtraction = new FeatureExtraction();
var validValues = "Button Fly,Button-End,D Ring,Double D Ring,Drawstring,Elastic,Flat Solid Buckle,Hook & Eye,J-Clip,Pull On,Round Classic Ring,Self Tie,Snap On,Snaps,Square Classic Ring,Velcro,Zipper";
//Scenario 1: exact match
var source1 = "Double D Ring";
var scenario1 = featureExtraction.Extract(source1, validValues);
Assert.AreEqual("Double D Ring", scenario1);
//Scenario 2: source contains list of valid values
// 2.1 source contains single matching
// 2.2 source contains multiple matching
// 2.2 source contains multiple matching but get only one
// 2.3 source contains multiple matching but get only one
// with assistance of keyword
var source21 = "Versace Collection D Ring woven leather tote bag with golden hardware. Flat top handles with hanging logo medallion, 5.5\" drop. Removable chain shoulder strap, 18.5\" drop. Flap top with logo plaque; snap closure. Interior, cotton lining; one zip and two slip pockets. Metal feet protect bottom of bag. 10.5\"H x 14\"W x 4.5\"D; weighs 13 oz. Imported.";
var scenario21 = featureExtraction.Extract(source21, validValues);
Assert.AreEqual("D Ring", scenario21);
var source221 =
"Versace Collection D Ring woven leather tote bag with golden hardware. Flat top handles with hanging logo medallion, 5.5\" drop. Removable chain shoulder strap, 18.5\" drop. Flap top with logo plaque; snap closure. Interior, cotton lining; one zip and two slip pockets. Metal feet protect bottom of bag. 10.5\"H x 14\"W x 4.5\"D; weighs 13 oz. Imported. Elastic";
var scenario221 = featureExtraction.Extract(source221, validValues,
3, 0);
Assert.AreEqual("D Ring/Elastic", scenario221);
var scenario222 = featureExtraction.Extract(source221, validValues,
3, 1);
Assert.AreEqual("D Ring", scenario222);
//var scenario223 = featureExtraction.Extract(source221, validValues,
// 3, 1, "/", "closure");
//Assert.AreEqual("Snaps", scenario223);
//Scenario 3:
//Scenario 4: similarity checks
// return multiple result
var source4 = "Versace Collection woven D Rin leather tote bag with golden hardware. Flat top handles with hanging logo medallion, 5.5\" drop. Removable chain shoulder strap, 18.5\" drop. Flap top with logo plaque; snap closure. Interior, cotton lining; one zip and two slip pockets. Metal feet protect bottom of bag. 10.5\"H x 14\"W x 4.5\"D; weighs 13 oz. Imported.";
var scenario4 = featureExtraction.Extract(source4, validValues, 1, 0);
var source41 = "Versace Collection woven Elasti leather tote bag with golden hardware. Flat top handles with hanging logo medallion, 5.5\" drop. Removable chain shoulder strap, 18.5\" drop. Flap top with logo plaque; snap closure. Interior, cotton lining; one zip and two slip pockets. Metal feet protect bottom of bag. 10.5\"H x 14\"W x 4.5\"D; weighs 13 oz. Imported.";
var scenario41 = featureExtraction.Extract(source41, validValues, 1, 1);
//var source = "Versace Collection D Ring woven leather tote bag with golden hardware. Flat top handles with hanging logo medallion, 5.5\" drop. Removable chain shoulder strap, 18.5\" drop. Flap top with logo plaque; snap closure. Interior, cotton lining; one zip and two slip pockets. Metal feet protect bottom of bag. 10.5\"H x 14\"W x 4.5\"D; weighs 13 oz. Imported.";
//var s = new FeatureExtraction().Extract(source,validValues,3);
}
protected FeatureSet ExtractFeatures(Sample Sample, DataPurpose Purpose)
{
FeatureExtraction Extractor = new FeatureExtraction();
CaptureFeedback feedback = CaptureFeedback.None;
FeatureSet features = new FeatureSet();
Extractor.CreateFeatureSet(Sample, Purpose, ref feedback, ref features);
if (feedback == CaptureFeedback.Good)
{
return(features);
}
return(null);
}
private async void Identify(object Huella = null)
{
try
{
if (FingerPrintData == null)
{
return;
}
Error = "Verificando huella...";
IniciaAnimacion = true;
Thread.Sleep(5000);
var Service = new BiometricoServiceClient();
var CompareResult = Service.CompararHuellaImputado(new ComparationRequest {
BIOMETRICO = FeatureExtraction.CreateFmdFromFid(FingerPrintData, Constants.Formats.Fmd.ANSI).Data.Bytes, ID_TIPO_BIOMETRICO = DD_Dedo.HasValue ? DD_Dedo.Value : enumTipoBiometrico.INDICE_DERECHO, ID_TIPO_FORMATO = enumTipoFormato.FMTO_DP
});
if (CompareResult.Identify)
{
var result = CompareResult.Result[0];
var NombreLogin = await StaticSourcesViewModel.CargarDatosAsync(() => new cImputado().GetData().Where(w => w.ID_ANIO == result.ID_ANIO && w.ID_CENTRO == result.ID_CENTRO && w.ID_IMPUTADO == result.ID_IMPUTADO).FirstOrDefault());
var nombreComparar = NombreLogin.NOMBRE.Trim() + " " + NombreLogin.PATERNO.Trim() + " " + NombreLogin.MATERNO.Trim();
//cambiar el proceso para que traiga el username y de ahi sacar el resto de los datos.
if (_usuario.NombreCompleto != nombreComparar)
{
Error = "El capturista no pertenece a esta sesion";
}
else
{
Error = "Su huella ha sido autenticada con exito";
OnProgress.Start();
Autenticado = true;
}
}
else
{
Error = "Esta huella no esta registrada en el sistema";
}
IniciaAnimacion = false;
FingerPrintData = null;
}
catch (Exception ex)
{
Error = "Ocurrió un error al verficar la huella";
IniciaAnimacion = false;
}
}
public static FeatureSet ExtractFeatures(Sample sample, DataPurpose datapurpose)
{
FeatureExtraction extractor = new FeatureExtraction();
CaptureFeedback feedback = CaptureFeedback.None;
FeatureSet feature = new FeatureSet();
extractor.CreateFeatureSet(sample, datapurpose, ref feedback, ref feature);
if (feedback == CaptureFeedback.Good)
{
return(feature);
}
else
{
return(null);
}
}
// Use this for initialization
void Start()
{
//EPOC..
buffer = new Buffer();
fe = new FeatureExtraction();
sample = new float[Buffer.CH];
gyroXmean = new float[gyroIndexMax];
gyroYmean = new float[gyroIndexMax];
gyroIndex = 0;
gyroTag = false;
gyroX = gyroY = 0;
rotationX = rotationY = 0;
// create stream info and outlet
//dispText.text = "initial";
ReceivingFromLSL.Start();
RetrivingFromBuffer.Start();
}
void reader_On_Captured(CaptureResult capResult)
{
if (capResult.Quality == Constants.CaptureQuality.DP_QUALITY_GOOD)
{
DataResult <Fmd> fmdResult = FeatureExtraction.CreateFmdFromFid(capResult.Data, Constants.Formats.Fmd.DP_VERIFICATION);
//If successfully extracted fmd then assign fmd
if (fmdResult.ResultCode == Constants.ResultCode.DP_SUCCESS)
{
fmd = fmdResult.Data;
// Get view bytes to create bitmap.
foreach (Fid.Fiv fiv in capResult.Data.Views)
{
UpdateVerifyMessage("Fingerprint Captured", CreateBitmap(fiv.RawImage, fiv.Width, fiv.Height));
break;
}
}
else
{
UpdateVerifyMessage("Could not successfully create a verification FMD", null);
}
//Perform indentification of fmd of captured sample against enrolledFmdList
IdentifyResult vResult = Comparison.Identify(fmd, 0, enrolledFmdList, 21474, 5);
//If number of matches returned by IdentificationResult are greater than 0 then user is authorized
if (vResult.ResultCode == Constants.ResultCode.DP_SUCCESS)
{
if (vResult.Indexes.Length > 0)
{
UpdateVerifyMessage("User Authorized", null);
}
else
{
UpdateVerifyMessage("User Unauthorized", null);
}
}
else
{
UpdateVerifyMessage("Error occured on verfication.", null);
}
}
else
{
UpdateVerifyMessage("Please swipe finger again", null);
}
}
void reader_On_Captured(CaptureResult capResult)
{
if (capResult.Quality == Constants.CaptureQuality.DP_QUALITY_GOOD)
{
count++;
DataResult <Fmd> fmd = FeatureExtraction.CreateFmdFromFid(capResult.Data, Constants.Formats.Fmd.DP_PRE_REGISTRATION);
// Get view bytes to create bitmap.
foreach (Fid.Fiv fiv in capResult.Data.Views)
{
//Ask user to press finger to get fingerprint
UpdateEnrollMessage("Click save button to save fingerprint", HelperFunctions.CreateBitmap(fiv.RawImage, fiv.Width, fiv.Height));
break;
}
//preEnrollmentFmd.Add(fmd.Data);
//enrollmentFmd = DPUruNet.Enrollment.CreateEnrollmentFmd(Constants.Formats.Fmd.DP_REGISTRATION, preEnrollmentFmd);
////if (enrollmentFmd.ResultCode == Constants.ResultCode.DP_SUCCESS)
//{
// if (fingerindex == 0)
// {
// fmd1 = enrollmentFmd;
// fingerindex++;
// count = 0;
// preEnrollmentFmd.Clear();
// UpdateEnrollMessage("Please swipe 2nd finger", null);
// }
// else if (fingerindex == 1)
// {
// fmd2 = enrollmentFmd;
// preEnrollmentFmd.Clear();
// UpdateEnrollMessage("User " + userIDTb.Text.ToString() + " Successfully Enrolled!", null);
// string userid = userIDTb.Text.ToString();
// HelperFunctions.updateFMDUserIDList(fmd1.Data, fmd2.Data, userid);
// if (!CheckIfUserExists())
// {
// SaveEnrolledFmd(userIDTb.Text.ToString(), Fmd.SerializeXml(fmd1.Data), Fmd.SerializeXml(fmd2.Data));
// }
// }
// }
}
}
/// <summary>
/// Handler for when a fingerprint is captured.
/// </summary>
/// <param name="captureResult">contains info and data on the fingerprint capture</param>
private void OnCaptured(CaptureResult captureResult)
{
try
{
// Check capture quality and throw an error if bad.
if (!_sender.CheckCaptureResult(captureResult))
{
return;
}
SendMessage(Action.SendMessage, "A finger was captured.");
DataResult <Fmd> resultConversion = FeatureExtraction.CreateFmdFromFid(captureResult.Data, Constants.Formats.Fmd.ANSI);
if (resultConversion.ResultCode != Constants.ResultCode.DP_SUCCESS)
{
_sender.Reset = true;
throw new Exception(resultConversion.ResultCode.ToString());
}
if (count == 0)
{
firstFinger = resultConversion.Data;
count += 1;
SendMessage(Action.SendMessage, "Now place the same or a different finger on the reader.");
}
else if (count == 1)
{
secondFinger = resultConversion.Data;
CompareResult compareResult = Comparison.Compare(firstFinger, 0, secondFinger, 0);
if (compareResult.ResultCode != Constants.ResultCode.DP_SUCCESS)
{
_sender.Reset = true;
throw new Exception(compareResult.ResultCode.ToString());
}
SendMessage(Action.SendMessage, "Comparison resulted in a dissimilarity score of " + compareResult.Score.ToString() + (compareResult.Score < (PROBABILITY_ONE / 100000) ? " (fingerprints matched)" : " (fingerprints did not match)"));
SendMessage(Action.SendMessage, "Place a finger on the reader.");
count = 0;
}
}
catch (Exception ex)
{
// Send error message, then close form
SendMessage(Action.SendMessage, "Error: " + ex.Message);
}
}
/// <summary>
/// Create FMD from samples
/// </summary>
/// <param name="fid"></param>
/// <returns></returns>
private IEnumerable <Fmd> CreateFMD(string folderPath)
{
//Convert the fingerprint samples to raw byte[]
//Create FMD of the fingerprint samples from Raw
foreach (string file in Directory.EnumerateFiles(folderPath, "*.bmp"))
{
Image img = Image.FromFile(file);
//DataResult<Fmd> data = Importer.ImportFmd(imageToByteArray(img), Constants.Formats.Fmd.ISO, Constants.Formats.Fmd.ISO);
//yield return data.Data;
DataResult <Fmd> fmd = FeatureExtraction.CreateFmdFromRaw(ConvertToRaw(img), 0, 0, img.Width, img.Height,
(int)img.HorizontalResolution, Constants.Formats.Fmd.ISO);
yield return(fmd.Data);
}
}
private IEnumerable <Fmd> CaptureAndExtractFmd()
{
while (!reset)
{
DataResult <Fmd> resultConversion;
try
{
if (count >= 8)
{
SendMessage("Enrollment was unsuccessful. Please try again.");
count = 0;
break;
}
Fid fid = null;
if (!_sender.CaptureFinger(ref fid))
{
break;
}
if (fid == null)
{
continue;
}
count++;
resultConversion = FeatureExtraction.CreateFmdFromFid(fid, Constants.Formats.Fmd.ANSI);
SendMessage("A finger was captured. \r\nCount: " + (count));
if (resultConversion.ResultCode != Constants.ResultCode.DP_SUCCESS)
{
break;
}
}
catch (Exception)
{
break;
}
yield return(resultConversion.Data);
}
}
private async void Identify(object Huella = null)
{
try
{
if (FingerPrintData == null)
{
return;
}
Error = "Iniciando sesión...";
var Service = new BiometricoServiceClient();
var CompareResult = Service.CompararHuellaImputado(new ComparationRequest {
BIOMETRICO = FeatureExtraction.CreateFmdFromFid(FingerPrintData, Constants.Formats.Fmd.ANSI).Data.Bytes, ID_TIPO_BIOMETRICO = DD_Dedo.HasValue ? DD_Dedo.Value : BiometricoServiceReference.enumTipoBiometrico.INDICE_DERECHO, ID_TIPO_FORMATO = enumTipoFormato.FMTO_DP
});
if (CompareResult.Identify)
{
var result = CompareResult.Result[0];
var NombreLogin = await StaticSourcesViewModel.CargarDatosAsync(() => new cImputado().GetData().Where(w => w.ID_ANIO == result.ID_ANIO && w.ID_CENTRO == result.ID_CENTRO && w.ID_IMPUTADO == result.ID_IMPUTADO).FirstOrDefault());
User.NombreCompleto = NombreLogin.NOMBRE.Trim() + " " + NombreLogin.PATERNO.Trim() + " " + NombreLogin.MATERNO.Trim();
//cambiar el proceso para que traiga el username y de ahi sacar el resto de los datos.
if (User.NombreCompleto == "ANGEL EMILIO AREVALO MARQUEZ")
{
User.Username = "******";
}
else
{
User.Username = "******";
}
OnProgress.Start();
}
else
{
Error = "El usuario o la contraseña son incorrectos";
}
FingerPrintData = null;
}
catch
{
Error = "Ocurrió un error al iniciar sesión";
}
}
private static bool RunJob(string jobCode)
{
var result = false;
switch (jobCode)
{
case "FE":
Console.WriteLine(string.Format("[{0}] {1} is starting...", DateTime.Now, _jobList[jobCode]));
var stopWatch = new Stopwatch();
stopWatch.Start();
try
{
var featureExtraction = new FeatureExtraction();
featureExtraction.ExtractFeatures();
stopWatch.Stop();
var ts = stopWatch.Elapsed;
var elapsedTime = string.Format("{0:00}:{1:00}:{2:00}.{3:00}", ts.Hours, ts.Minutes, ts.Seconds, ts.Milliseconds / 10);
Console.WriteLine(string.Format("[{0}] {1} has finished, in {2}.", DateTime.Now, _jobList[jobCode], elapsedTime));
result = true;
}
catch (Exception ex)
{
stopWatch.Stop();
var ts = stopWatch.Elapsed;
var elapsedTime = string.Format("{0:00}:{1:00}:{2:00}.{3:00}", ts.Hours, ts.Minutes, ts.Seconds, ts.Milliseconds / 10);
Console.WriteLine(string.Format("[{0}] {1} has stopped, in {2}, with error: \n{3}", DateTime.Now, _jobList[jobCode], elapsedTime, ex.ToString()));
result = false;
}
Console.WriteLine();
break;
}
return(result);
}
private async Task BroadcastAggregate(Reference <DataAggregate> aggregate, OrchestratorTwin twin)
{
List <Task> messages = new List <Task>();
foreach (Routing item in Enum.GetValues(typeof(Routing)))
{
if (twin.RoutingMode.HasFlag(item))
{
switch (item)
{
case Routing.VisualizeFeature:
messages.Add(Visualization.PublishAsync(new GraphData()
{
CorrelationID = "Feature",
Values = aggregate.Message.Values
}
));
break;
case Routing.FeatureExtraction:
messages.Add(FeatureExtraction.PublishAsync(new DataAggregate()
{
CorrelationID = "Feature",
Values = aggregate.Message.Values
}));
break;
default:
continue;
}
}
}
if (messages.Count > 0)
{
await Task.WhenAll(messages);
}
}
/// <summary>
/// Handler for when a fingerprint is captured.
/// </summary>
/// <param name="captureResult">contains info and data on the fingerprint capture</param>
public void OnCaptured(CaptureResult captureResult)
{
try
{
// Check capture quality and throw an error if bad.
if (!Clases.HuellaDigital.CheckCaptureResult(captureResult))
{
return;
}
// Create bitmap
foreach (Fid.Fiv fiv in captureResult.Data.Views)
{
SendMessage(Action.SendBitmap, Clases.HuellaDigital.CreateBitmap(fiv.RawImage, fiv.Width, fiv.Height));
}
//Guardamos el FMD de la huella
res = FeatureExtraction.CreateFmdFromFid(captureResult.Data, Constants.Formats.Fmd.ANSI);
}
catch (Exception ex)
{
// Send error message, then close form
SendMessage(Action.SendMessage, "Error: " + ex.Message);
}
}
public void Execute(Arguments arguments)
{
LoggedConsole.WriteLine("feature learning process...");
var inputDir = @"D:\Mahnoosh\Liz\Least_Bittern\";
var inputPath = Path.Combine(inputDir, "TrainSet\\one_min_recordings");
var trainSetPath = Path.Combine(inputDir, "TrainSet\\train_data");
// var testSetPath = Path.Combine(inputDir, "TestSet");
var configPath = @"D:\Mahnoosh\Liz\Least_Bittern\FeatureLearningConfig.yml";
var resultDir = Path.Combine(inputDir, "FeatureLearning");
Directory.CreateDirectory(resultDir);
// var outputMelImagePath = Path.Combine(resultDir, "MelScaleSpectrogram.png");
// var outputNormMelImagePath = Path.Combine(resultDir, "NormalizedMelScaleSpectrogram.png");
// var outputNoiseReducedMelImagePath = Path.Combine(resultDir, "NoiseReducedMelSpectrogram.png");
// var outputReSpecImagePath = Path.Combine(resultDir, "ReconstrcutedSpectrogram.png");
// var outputClusterImagePath = Path.Combine(resultDir, "Clusters.bmp");
// +++++++++++++++++++++++++++++++++++++++++++++++++patch sampling from 1-min recordings
var configFile = configPath.ToFileInfo();
if (configFile == null)
{
throw new FileNotFoundException("No config file argument provided");
}
else if (!configFile.Exists)
{
throw new ArgumentException($"Config file {configFile.FullName} not found");
}
var configuration = ConfigFile.Deserialize <FeatureLearningSettings>(configFile);
int patchWidth =
(configuration.MaxFreqBin - configuration.MinFreqBin + 1) / configuration.NumFreqBand;
var clusteringOutputList = FeatureLearning.UnsupervisedFeatureLearning(configuration, inputPath);
List <double[][]> allBandsCentroids = new List <double[][]>();
for (int i = 0; i < clusteringOutputList.Count; i++)
{
var clusteringOutput = clusteringOutputList[i];
// writing centroids to a csv file
// note that Csv.WriteToCsv can't write data types like dictionary<int, double[]> (problems with arrays)
// I converted the dictionary values to a matrix and used the Csv.WriteMatrixToCsv
// it might be a better way to do this
string pathToClusterCsvFile = Path.Combine(resultDir, "ClusterCentroids" + i.ToString() + ".csv");
var clusterCentroids = clusteringOutput.ClusterIdCentroid.Values.ToArray();
Csv.WriteMatrixToCsv(pathToClusterCsvFile.ToFileInfo(), clusterCentroids.ToMatrix());
// sorting clusters based on size and output it to a csv file
Dictionary <int, double> clusterIdSize = clusteringOutput.ClusterIdSize;
int[] sortOrder = KmeansClustering.SortClustersBasedOnSize(clusterIdSize);
// Write cluster ID and size to a CSV file
string pathToClusterSizeCsvFile = Path.Combine(resultDir, "ClusterSize" + i.ToString() + ".csv");
Csv.WriteToCsv(pathToClusterSizeCsvFile.ToFileInfo(), clusterIdSize);
// Draw cluster image directly from clustering output
List <KeyValuePair <int, double[]> > list = clusteringOutput.ClusterIdCentroid.ToList();
double[][] centroids = new double[list.Count][];
for (int j = 0; j < list.Count; j++)
{
centroids[j] = list[j].Value;
}
allBandsCentroids.Add(centroids);
List <double[, ]> allCentroids = new List <double[, ]>();
for (int k = 0; k < centroids.Length; k++)
{
// convert each centroid to a matrix in order of cluster ID
// double[,] cent = PatchSampling.ArrayToMatrixByColumn(centroids[i], patchWidth, patchHeight);
// OR: in order of cluster size
double[,] cent = MatrixTools.ArrayToMatrixByColumn(centroids[sortOrder[k]], patchWidth, configuration.PatchHeight);
// normalize each centroid
double[,] normCent = DataTools.normalise(cent);
// add a row of zero to each centroid
double[,] cent2 = PatchSampling.AddRow(normCent);
allCentroids.Add(cent2);
}
// concatenate all centroids
double[,] mergedCentroidMatrix = PatchSampling.ListOf2DArrayToOne2DArray(allCentroids);
// Draw clusters
var clusterImage = ImageTools.DrawMatrixWithoutNormalisation(mergedCentroidMatrix);
clusterImage.RotateFlip(RotateFlipType.Rotate270FlipNone);
var outputClusteringImage = Path.Combine(resultDir, "ClustersWithGrid" + i.ToString() + ".bmp");
clusterImage.Save(outputClusteringImage);
}
// extracting features
FeatureExtraction.UnsupervisedFeatureExtraction(configuration, allBandsCentroids, trainSetPath, resultDir);
LoggedConsole.WriteLine("Done...");
}