public User TestFromFeature(Guid frsId, MatrixString matrixString)
{
var db = new FrcContext();
var frs = db.FaceRecognitionSystems.Where(x => x.FaceRecognitionSystemId == frsId).FirstOrDefault();
if (frs == null)
{
throw new Exception("Face recognition system is not exist");
}
User result;
switch (frs.Type)
{
case "LDA":
result = recognizeLDAbyFeature(frs.TypeSystemId, db, frsId, matrixString);
break;
default:
throw new NotImplementedException();
}
db.Dispose();
return(result);
}
public MatrixString[] GetFrsParemeter(Guid frsId)
{
var db = new FrcContext();
var frs = db.FaceRecognitionSystems.Where(x => x.FaceRecognitionSystemId == frsId).FirstOrDefault();
if (frs == null)
{
throw new Exception("Face recognition system is not exist");
}
MatrixString[] result;
switch (frs.Type)
{
case "LDA":
result = paramLDA(frs.TypeSystemId, db, frsId);
break;
default:
throw new NotImplementedException();
}
db.Dispose();
return(result);
}
private User recognizeLDAbyFeature(Guid ldaId, FrcContext db, Guid frsId, MatrixString matrixString)
{
var systemEtalonCount = 1;
// Копируется в testLDA нужно отрефакторить в более лучшем стиле.
// var ldaEntity = db.LDAs.Where(x => x.LDAId == ldaId).FirstOrDefault();
// if (ldaEntity == null)
// {
// throw new Exception("LDA entity is not exist");
// }
// var averageMatrixString = db.MatrixStrings.Where(x => x.MatrixStringId == ldaEntity.AverageImageMatrixId).FirstOrDefault();
// var leftMatrixString = db.MatrixStrings.Where(x => x.MatrixStringId == ldaEntity.LeftMatrixId).FirstOrDefault();
// var rightMatrixString = db.MatrixStrings.Where(x => x.MatrixStringId == ldaEntity.RightMatrixId).FirstOrDefault();
// var averageMatrix = MatrixHelper.MatrixString2Matrix(averageMatrixString);
// var leftMatrix = MatrixHelper.MatrixString2Matrix(leftMatrixString);
// var rightMatrix = MatrixHelper.MatrixString2Matrix(rightMatrixString);
// var ba = Convert.FromBase64String(imageByteArray);
// var imageMatrix = DenseMatrix.OfArray(ImageHelper.ImageByteArray2pixelArray(ba));
// var featureMatrix = leftMatrix * (imageMatrix - averageMatrix) * rightMatrix;
var featureMatrix = MatrixHelper.MatrixString2Matrix(matrixString);
var etalonList = db.Etalons.Where(e => e.FaceRecognitionSystemId == frsId).ToList();
var etalonForUserTempList = etalonList.GroupBy(x => x.UserId).SelectMany(x => x.Take(systemEtalonCount)).ToList();
var userId = CompareToEtalonList(featureMatrix, etalonForUserTempList, db);
return(db.Users.Where(x => x.UserId == userId).FirstOrDefault());
}
public User TestFromImage(Guid frsId, string imageByteArray, int systemEtalonCount)
{
var db = new FrcContext();
var frs = db.FaceRecognitionSystems.Where(x => x.FaceRecognitionSystemId == frsId).FirstOrDefault();
if (frs == null)
{
throw new Exception("Face recognition system is not exist");
}
User result;
switch (frs.Type)
{
case "LDA":
result = recognizeLDA(frs.TypeSystemId, db, frsId, imageByteArray, systemEtalonCount);
break;
default:
throw new NotImplementedException();
}
db.Dispose();
return(result);
}
private void testLDA(Guid ldaId, FrcContext db, Guid frsId)
{
// копируется в FacerecognitionRegistrator нужно вынести в отдельный файл.
var ldaEntity = db.LDAs.Where(x => x.LDAId == ldaId).FirstOrDefault();
if (ldaEntity == null)
{
throw new Exception("LDA entity is not exist");
}
var averageMatrixString = db.MatrixStrings.Where(x => x.MatrixStringId == ldaEntity.AverageImageMatrixId).FirstOrDefault();
var leftMatrixString = db.MatrixStrings.Where(x => x.MatrixStringId == ldaEntity.LeftMatrixId).FirstOrDefault();
var rightMatrixString = db.MatrixStrings.Where(x => x.MatrixStringId == ldaEntity.RightMatrixId).FirstOrDefault();
var averageMatrix = MatrixHelper.MatrixString2Matrix(averageMatrixString);
var leftMatrix = MatrixHelper.MatrixString2Matrix(leftMatrixString);
var rightMatrix = MatrixHelper.MatrixString2Matrix(rightMatrixString);
var testDatabaseUserList = db.DatabaseTestUsers.Where(x => x.FaceRecognitionSystemId == frsId).ToList();
// лучше добавить и использовать FK:
List <Guid> test = testDatabaseUserList.Select(x => x.ImageId).ToList();
var imageList = db.Images.Where(x => test.Any(t => t == x.ImageId)).OrderBy(x => x.User.Username).ToList();
var etalonList = db.Etalons.Where(e => e.FaceRecognitionSystemId == frsId).ToList();
var maxEtalonsForUser = etalonList.GroupBy(x => x.UserId).Select(x => x.Count()).FirstOrDefault();
List <double> resultList = new List <double>();
for (int i = 0; i < maxEtalonsForUser; i++)
{
var etalonForUserTempList = etalonList.GroupBy(x => x.UserId).SelectMany(x => x.Take(i + 1)).ToList();
var performance = calcPerformance(imageList, leftMatrix, averageMatrix, rightMatrix, db, etalonForUserTempList);
resultList.Add(performance);
}
}
private static void Main(string[] args)
{
using (FrcContext context = new FrcContext(new DbContextOptionsBuilder <FrcContext>().UseNpgsql(
"Host = localhost; Port = 5442; Database = FrcTest; Username = postgres; Password = postgres").Options))
{
FrcContextInitializer.Initialize(context);
}
}
public void startAddingDatabase()
{
var db = new FrcContext();
proccessDatabase(db);
Console.ReadKey();
db.Dispose();
}
public List <FaceRecognitionSystem> GetFrsList()
{
var db = new FrcContext();
var frsList = db.FaceRecognitionSystems.ToList();
db.Dispose();
return(frsList);
}
public List <DatabaseTestUser> GetList(Guid frsId)
{
var db = new FrcContext();
var frsList = db.DatabaseTestUsers.Where(x => x.FaceRecognitionSystemId == frsId).ToList();
db.Dispose();
return(frsList);
}
public Image Get(Guid imageId)
{
var db = new FrcContext();
var imageEntity = db.Images.Where(x => x.ImageId == imageId).FirstOrDefault();
db.Dispose();
return(imageEntity);
}
private MatrixString[] paramLDA(Guid ldaId, FrcContext db, Guid frsId)
{
var ldaEntity = db.LDAs.Where(x => x.LDAId == ldaId).FirstOrDefault();
if (ldaEntity == null)
{
throw new Exception("LDA entity is not exist");
}
var averageMatrixString = db.MatrixStrings.Where(x => x.MatrixStringId == ldaEntity.AverageImageMatrixId).FirstOrDefault();
var leftMatrixString = db.MatrixStrings.Where(x => x.MatrixStringId == ldaEntity.LeftMatrixId).FirstOrDefault();
var rightMatrixString = db.MatrixStrings.Where(x => x.MatrixStringId == ldaEntity.RightMatrixId).FirstOrDefault();
return(new MatrixString[] { averageMatrixString, leftMatrixString, rightMatrixString });
}
private void registerLDA(Guid ldaId, FrcContext db, List <Guid> imageIdList, Guid frsId)
{
var ldaEntity = db.LDAs.Where(x => x.LDAId == ldaId).FirstOrDefault();
if (ldaEntity == null)
{
throw new Exception("LDA entity is not exist");
}
var averageMatrixString = db.MatrixStrings.Where(x => x.MatrixStringId == ldaEntity.AverageImageMatrixId).FirstOrDefault();
var leftMatrixString = db.MatrixStrings.Where(x => x.MatrixStringId == ldaEntity.LeftMatrixId).FirstOrDefault();
var rightMatrixString = db.MatrixStrings.Where(x => x.MatrixStringId == ldaEntity.RightMatrixId).FirstOrDefault();
var averageMatrix = MatrixHelper.MatrixString2Matrix(averageMatrixString);
var leftMatrix = MatrixHelper.MatrixString2Matrix(leftMatrixString);
var rightMatrix = MatrixHelper.MatrixString2Matrix(rightMatrixString);
foreach (var imageId in imageIdList)
{
var imageEntity = db.Images.Where(x => x.ImageId == imageId).FirstOrDefault();
if (imageEntity == null)
{
continue;
}
var imageMatrix = DenseMatrix.OfArray(ImageHelper.ImageByteArray2pixelArray(imageEntity.ImageByteArray));
var featureMatrix = leftMatrix * (imageMatrix - averageMatrix) * rightMatrix;
var featureMatrixString = MatrixHelper.convertToMatrixString(featureMatrix);
db.MatrixStrings.Add(featureMatrixString);
db.SaveChanges();
var etalon = new Entities.Etalon
{
EtalonId = Guid.NewGuid(),
FaceRecognitionSystemId = frsId,
FeatureMatrixId = featureMatrixString.MatrixStringId,
ImageId = imageEntity.ImageId,
RegistredDT = DateTime.UtcNow,
UserId = imageEntity.UserId,
};
db.Etalons.Add(etalon);
db.SaveChanges();
}
}
private void proccessDatabase(FrcContext db)
{
var orl = db.ImageDatabases.Where(x => x.DatabaseName == imageDatabaseName).FirstOrDefault();
if (orl != null)
{
Console.WriteLine("The database {0} already added;", imageDatabaseName);
return;
}
analysisDatabase(inputDatabaseDir, imageDatabaseName);
db.ImageDatabases.Add(imageDatabase);
db.Users.AddRange(userList.AsEnumerable());
db.Images.AddRange(imageList.AsEnumerable());
db.SaveChanges();
Console.WriteLine("The database {0} successfully added;", imageDatabaseName);
}
// to do: продумать как можно лучше работать с массивами пользователей,
// чтобы не пришлось по сто раз извлекать для каждой регистрации в БД.
public void RegisterUserList(List <Guid> imageIdList, Guid frsId)
{
var db = new FrcContext();
var frs = db.FaceRecognitionSystems.Where(x => x.FaceRecognitionSystemId == frsId).FirstOrDefault();
if (frs == null)
{
throw new Exception("Face recognition system is not exist");
}
switch (frs.Type)
{
case "LDA":
registerLDA(frs.TypeSystemId, db, imageIdList, frsId);
break;
default:
throw new NotImplementedException();
}
db.Dispose();
}
// Вообще лучше инкапсулировать всё что связано с LDA в отдельный модуль.
public void TestFromDatabase(Guid frsId)
{
var db = new FrcContext();
var frs = db.FaceRecognitionSystems.Where(x => x.FaceRecognitionSystemId == frsId).FirstOrDefault();
if (frs == null)
{
throw new Exception("Face recognition system is not exist");
}
switch (frs.Type)
{
case "LDA":
testLDA(frs.TypeSystemId, db, frsId);
break;
default:
throw new NotImplementedException();
}
db.Dispose();
}
public Guid Create()
{
var db = new FrcContext();
var imdb = db.ImageDatabases.Where(x => x.DatabaseName == md.databaseName).FirstOrDefault();
if (imdb == null)
{
throw new Exception("Image database is not exist");
}
totalTrainImageForUser = (int)Math.Floor(
(double)imdb.TotalImageForUser * (Constants.HUNDRED_PERCENT - md.databaseTestImagesPercent) / Constants.HUNDRED_PERCENT);
totalUserForTrain = (int)Math.Floor(
(double)imdb.TotalUser * (Constants.HUNDRED_PERCENT - md.databaseTestUsersForOpenTaskPercent) / Constants.HUNDRED_PERCENT);
if (totalTrainImageForUser == 0 || totalUserForTrain == 0)
{
throw new Exception("Not enaught images for user");
}
// to do: обращаться ко всем элементам не очень так как для больших БД может не хватить памяти:
var userListOfListsForTrain = db.Users.OrderBy(x => x.Username).Take(totalUserForTrain)
.Select(x => x.Images.OrderBy(y => y.ImageName).Take(totalTrainImageForUser)).ToList();
foreach (var userImageList in userListOfListsForTrain)
{
List <Matrix <double> > userMatrixList = new List <Matrix <double> >();
foreach (var userImage in userImageList)
{
var imageMatrix = ImageHelper.ImageByteArray2pixelArray(userImage.ImageByteArray);
userMatrixList.Add(DenseMatrix.OfArray(imageMatrix));
}
trainImageLists.Add(userMatrixList);
}
Guid frsId;
switch (md.trainName)
{
case "LDA":
frsId = trainLDA(imdb, db);
break;
default:
throw new NotImplementedException();
}
// Записать все изображения пользователей, которые предназначены для тестирования БД на закрытом множестве.
var userListOfListsForTest = db.Users.OrderBy(x => x.Username).Take(totalUserForTrain)
.Select(x => x.Images.OrderBy(y => y.ImageName).Skip(totalTrainImageForUser)).ToList();
var testList = userListOfListsForTest.SelectMany(x => x.Select(i => new DatabaseTestUser
{
DatabaseTestUserId = Guid.NewGuid(),
FaceRecognitionSystemId = frsId,
ImageId = i.ImageId,
})).ToList();
db.DatabaseTestUsers.AddRange(testList);
db.SaveChanges();
db.Dispose();
// Зарегестрировать все изображения пользователей, которые учавствовали в обучении.
var registrator = new FaceRecognitionRegistrator();
var imageIdList = userListOfListsForTrain.SelectMany(x => x.Select(y => y.ImageId)).ToList();
registrator.RegisterUserList(imageIdList, frsId);
return(frsId);
}
private Guid trainLDA(ImageDatabase imdb, FrcContext db)
{
if (!imdb.isSameImageSize || !imdb.isSameTotalImageForUser)
{
throw new NotImplementedException();
}
// 1) Находим средний образ для всей базы исходных данных:
var averageMatrix = Matrix <double> .Build.Dense(imdb.ImageHeight, imdb.ImageWidth);
foreach (var userMatrixList in trainImageLists)
{
foreach (var userImage in userMatrixList)
{
averageMatrix = averageMatrix + userImage;
}
}
var Xaverage = averageMatrix / (totalUserForTrain * totalTrainImageForUser);
// 2) Находим средний образ для каждого класса:
List <Matrix <double> > XclassAverageList = new List <Matrix <double> >();
foreach (var userMatrixList in trainImageLists)
{
var averageClassMatrix = Matrix <double> .Build.Dense(imdb.ImageHeight, imdb.ImageWidth);
foreach (var userImage in userMatrixList)
{
averageClassMatrix = averageClassMatrix + userImage;
}
averageClassMatrix = averageClassMatrix / totalTrainImageForUser;
XclassAverageList.Add(averageClassMatrix);
}
// 3) Вычислим матрицу внутриклассовой(w) ковариации относительно строк(R):
var SwR = Matrix <double> .Build.Dense(imdb.ImageHeight, imdb.ImageHeight);
for (int i = 0; i < trainImageLists.Count; i++)
{
foreach (var userImage in trainImageLists[i])
{
SwR = SwR + (userImage - XclassAverageList[i]) * (userImage - XclassAverageList[i]).Transpose();
}
}
// 4) Вычислим матрицу межклассовой(b) ковариации относительно строк(R):
var SbR = Matrix <double> .Build.Dense(imdb.ImageHeight, imdb.ImageHeight);
foreach (var XclassAverage in XclassAverageList)
{
SbR = SbR + (XclassAverage - Xaverage) * (XclassAverage - Xaverage).Transpose();
}
// 5) Вычислим матрицу внутриклассовой (w) ковариации относительно столбцов (C):
var SwC = Matrix <double> .Build.Dense(imdb.ImageWidth, imdb.ImageWidth);
for (int i = 0; i < trainImageLists.Count; i++)
{
foreach (var userImage in trainImageLists[i])
{
SwC = SwC + (userImage - XclassAverageList[i]).Transpose() * (userImage - XclassAverageList[i]);
}
}
// 6) Вычислим матрицу межклассовой (b) ковариации относительно столбцов (C):
var SbC = Matrix <double> .Build.Dense(imdb.ImageWidth, imdb.ImageWidth);
foreach (var XclassAverage in XclassAverageList)
{
SbC = SbC + (XclassAverage - Xaverage).Transpose() * (XclassAverage - Xaverage);
}
// 7) Сформируем общую матрицу рассеяния относительно строк:
var StotalR = SwR.Inverse() * SbR;
// Регуляризация (стр. 349):
StotalR = StotalR + Constants.SMALL_VALUE * Matrix <double> .Build.Dense(imdb.ImageHeight, imdb.ImageHeight);
// 8) Сформируем общую матрицу рассеяния относительно столбцов:
var StotalC = SwC.Inverse() * SbC;
// Регуляризация (стр. 349):
StotalC = StotalC + Constants.SMALL_VALUE * Matrix <double> .Build.Dense(imdb.ImageWidth, imdb.ImageWidth);
// 9) Решим задачу на собственные значения:
var ReigResult = StotalR.Evd();
var CeigResult = StotalC.Evd();
// 10) Пропускаем сортировку по eigenvalues и ограничение по 95%. Берём только то, что написано в мнемоническом описании:
// 11) Подготовка матриц для преобразования Карунена-Лоева:
List <Vector <double> > rVectorList = new List <Vector <double> >();
for (int i = 0; i < md.trainMartixRightDimension; i++)
{
rVectorList.Add(ReigResult.EigenVectors.Column(i));
}
// to do: значения матриц R как-то оказываются слева. Нужно переименовывать везде, а лучше ещё раз уточнить формулы:
var eigMatrixLeft = Matrix <double> .Build.DenseOfColumnVectors(rVectorList.ToArray()).Transpose();
List <Vector <double> > cVectorList = new List <Vector <double> >();
for (int i = 0; i < md.trainMartixLeftDimension; i++)
{
cVectorList.Add(CeigResult.EigenVectors.Column(i));
}
var eigMatrixRight = Matrix <double> .Build.DenseOfColumnVectors(cVectorList.ToArray());
var averageImageMatrixString = MatrixHelper.convertToMatrixString(Xaverage);
var leftMatrixString = MatrixHelper.convertToMatrixString(eigMatrixLeft);
var rightMatrixString = MatrixHelper.convertToMatrixString(eigMatrixRight);
db.MatrixStrings.Add(averageImageMatrixString);
db.MatrixStrings.Add(leftMatrixString);
db.MatrixStrings.Add(rightMatrixString);
db.SaveChanges();
var ldaEntity = new LDA
{
AverageImageMatrixId = averageImageMatrixString.MatrixStringId,
LDAId = Guid.NewGuid(),
LeftMatrixId = leftMatrixString.MatrixStringId,
RightMatrixId = rightMatrixString.MatrixStringId,
};
db.LDAs.Add(ldaEntity);
db.SaveChanges();
var frs = new Entities.FaceRecognitionSystem
{
FaceRecognitionSystemId = Guid.NewGuid(),
MnemonicDescription = md.originalDescription,
Type = "LDA",
TypeSystemId = ldaEntity.LDAId,
InputImageHeight = imdb.ImageHeight,
InputImageWidth = imdb.ImageWidth,
CreatedDT = DateTime.UtcNow,
};
db.FaceRecognitionSystems.Add(frs);
db.SaveChanges();
return(frs.FaceRecognitionSystemId);
}
private Guid CompareToEtalonList(DenseMatrix featureMatrix, List <Etalon> etalonForUserTempList, FrcContext db)
{
var test = etalonForUserTempList.Select(x => x.FeatureMatrixId).ToList();
var matrixStringList = db.MatrixStrings.Where(x => test.Any(y => y == x.MatrixStringId)).ToList();
List <double> comparisonDist = new List <double>();
foreach (var etalonMatrix in matrixStringList)
{
var etalon = MatrixHelper.MatrixString2Matrix(etalonMatrix);
var compare = etalon - featureMatrix;
var sqrDist = compare.AsColumnMajorArray().Sum(x => x * x);
comparisonDist.Add(sqrDist);
}
var index = comparisonDist.IndexOf(comparisonDist.Min());
var answerMatrixId = matrixStringList[index].MatrixStringId;
return(db.Etalons.Where(x => x.FeatureMatrixId == answerMatrixId).FirstOrDefault().UserId);
}
private double calcPerformance(List <Image> imageList, DenseMatrix leftMatrix, DenseMatrix averageMatrix, DenseMatrix rightMatrix, FrcContext db, List <Etalon> etalonForUserTempList)
{
// тестируем изображения лиц:
int correct = 0;
foreach (var image in imageList)
{
var imageMatrix = DenseMatrix.OfArray(ImageHelper.ImageByteArray2pixelArray(image.ImageByteArray));
var featureMatrix = leftMatrix * (imageMatrix - averageMatrix) * rightMatrix;
var userId = CompareToEtalonList(featureMatrix, etalonForUserTempList, db);
if (userId == image.UserId)
{
correct += 1;
}
}
double performance = (double)correct / imageList.Count * Constants.HUNDRED_PERCENT;
return(performance);
}
