/// <summary>
/// Ретинекс
/// </summary>
/// <param name="bm">Картинка</param>
/// <returns></returns>
public static Bitmap Retin(Bitmap bm)
{
Matrix m = ImgConverter.BmpToMatr(bm);
Matrix filter = new Matrix(5, 5) + 10;
Matrix filter2 = new Matrix(5, 5);
filter2[2, 2] = 1;
double sum = 0;
for (int i = 1; i < 4; i++)
{
for (int j = 1; j < 4; j++)
{
filter[i, j] = 12;
}
}
filter[2, 2] = 18;
for (int i = 0; i < 5; i++)
{
for (int j = 0; j < 5; j++)
{
sum += filter[i, j];
}
}
filter /= 1.7 * sum;
Matrix bb = ImgFilters.SpaceFilter(m, filter);
Matrix G = MathFunc.lg(bb + 0.001);
m = ImgFilters.SpaceFilter(m, filter2);
double mean, sigm;
m = MathFunc.lg(m + 0.001);
m -= G;
m -= Statistic.MinimalValue(m.Spagetiz());
m /= Statistic.MaximalValue(m.Spagetiz());
mean = Statistic.ExpectedValue(m.Spagetiz());
sigm = 0.9 / Statistic.Std(m.Spagetiz());
m = NeuroFunc.Sigmoid(sigm * (m - mean));
return(ImgConverter.MatrixToBitmap(m));
}
//Элемент контрасного фильтра
private static Matrix FilterContrast(Matrix img, int x, int y, int dx, int dy)
{
Vector vect;
Matrix matr = new Matrix(y, x);
for (int i = 0; i < y; i++)
{
for (int j = 0; j < x; j++)
{
matr[i, j] = img.Matr[dy + i, dx + j];
}
}
matr = MathFunc.lg(matr * 255 + 1);
vect = matr.Spagetiz();
double cko = 3 * Statistic.Std(vect), m = Statistic.ExpectedValue(vect);
return(NeuroFunc.Sigmoid((matr - m) / (cko + 0.01), 3));
}
/// <summary>
/// Распознавание векторов
/// </summary>
/// <param name="x">Вектора</param>
public Vector RecognitionAll(Vector x)
{
Vector[] vecs = new Vector[x.N];
for (int i = 0; i < x.N; i++)
{
vecs[i] = new Vector
(
new double[]
{
1, x[i]
}
);
}
Vector outp = _lr.Predict(vecs);
return(NeuroFunc.Sigmoid(outp));
}
/// <summary>
/// Функция активации
/// </summary>
/// <param name="inp">Выход линейного слоя</param>
public override Vector FActivation(Vector inp)
{
return(NeuroFunc.Sigmoid(inp));
}
/// <summary>
/// Распознавание вектора
/// </summary>
/// <param name="x">Вектор</param>
public double Recognition(Vector x)
{
double outp = _lr.Predict(x.AddOne());
return(NeuroFunc.Sigmoid(outp));
}
