/// <summary>
/// Con este metodos almacenamos un nuevo simbolo en la base de
/// datos.
///
/// Lanza <c>DuplicateSymbolException</c> si ya existe un simbolo
/// con la misma etiqueta y caracteristicas binarias en la base de datos.
/// </summary>
/// <param name="bitmap">
/// La imagen cuyas caracteristicas aprenderemos.
/// </param>
/// <param name="symbol">
/// El simbolo que representa a la imagen.
/// </param>
public override bool Learn(MathTextBitmap bitmap, MathSymbol symbol)
{
if (characteristics == null)
{
characteristics = CharacteristicFactory.CreateCharacteristicList();
}
FloatBitmap processedBitmap = bitmap.LastProcessedImage;
CheckVector vector = CreateVector(processedBitmap);
int position = symbolsDict.IndexOf(vector);
if (position >= 0)
{
// The vector exists in the list
List <MathSymbol> symbols = symbolsDict[position].Symbols;
if (symbols.Contains(symbol))
{
return(false);
}
else
{
symbols.Add(symbol);
}
}
else
{
vector.Symbols.Add(symbol);
symbolsDict.Add(vector);
}
return(true);
}
/// <summary>
/// Este metodo intenta recuperar los simbolos de la base de datos
/// correspondiente a una imagen.
/// </summary>
/// <param name="image">
/// La imagen cuyos simbolos asociados queremos encontrar.
/// </param>
/// <returns>
/// Los simbolos que se puedieron asociar con la imagen.
/// </returns>
public override List <MathSymbol> Match(MathTextBitmap image)
{
if (characteristics == null)
{
characteristics = CharacteristicFactory.CreateCharacteristicList();
}
List <MathSymbol> res = new List <MathSymbol>();
FloatBitmap processedImage = image.LastProcessedImage;
CheckVector vector = CreateVector(processedImage);
// We have this image vector, now we will compare with the stored ones.
// We consider a threshold.
int threshold = (int)(characteristics.Count * epsilon);
foreach (CheckVector storedVector in symbolsDict)
{
// If the distance is below the threshold, we consider it valid.
if (vector.Distance(storedVector) <= threshold)
{
foreach (MathSymbol symbol in storedVector.Symbols)
{
// We don't want duplicated symbols.
if (!res.Contains(symbol))
{
res.Add(symbol);
}
}
string msg =
String.Format("Distancia({0}, {1}) <= {2}",
vector.ToString(),
storedVector.ToString(),
threshold);
msg += "\nSe añadieron los símbolos almacenados.";
StepDoneInvoker(new StepDoneArgs(msg));
}
else
{
string msg = String.Format("Distancia({0}, {1}) > {2}",
vector.ToString(),
storedVector.ToString(),
threshold);
StepDoneInvoker(new StepDoneArgs(msg));
}
}
return(res);
}
/// <summary>
/// Creates a <c>CharacteristicVector</c> instance for a given
/// <c>FloatBitmap</c> object.
/// </summary>
/// <param name="image">
/// A <see cref="FloatBitmap"/>
/// </param>
/// <returns>
/// A <see cref="CharacteristicVector"/>
/// </returns>
private CheckVector CreateVector(FloatBitmap image)
{
// We create the receptors list.
if (receptors == null)
{
receptors = Receptor.GenerateList(40);
}
CheckVector vector = new CheckVector();
bool checkValue;
int width = image.Width;
int height = image.Height;
foreach (Receptor receptor in receptors)
{
checkValue = receptor.CheckBressard(image);
vector.Values.Add(checkValue);
StepDoneArgs args =
new StepDoneArgs(String.Format("Comprobando receptor {0}: {1}",
String.Format("({0}, {1}) -> ({2}, {3})",
receptor.X0, receptor.Y0,
receptor.X1, receptor.Y1),
checkValue));
StepDoneInvoker(args);
Thread.Sleep(20);
}
foreach (BinaryCharacteristic characteristic in characteristics)
{
checkValue = characteristic.Apply(image);
vector.Values.Add(checkValue);
StepDoneArgs args =
new StepDoneArgs(String.Format("Comprobando característica {0}: {1}",
characteristic.GetType().ToString(),
checkValue));
StepDoneInvoker(args);
Thread.Sleep(20);
}
return(vector);
}
/// <summary>
/// Creates a <c>CharacteristicVector</c> instance for a given
/// <c>FloatBitmap</c> object.
/// </summary>
/// <param name="image">
/// A <see cref="FloatBitmap"/>
/// </param>
/// <returns>
/// A <see cref="CharacteristicVector"/>
/// </returns>
private CheckVector CreateVector(FloatBitmap image)
{
CheckVector vector = new CheckVector();
bool characteristicValue;
foreach (BinaryCharacteristic bc in characteristics)
{
characteristicValue = bc.Apply(image);
vector.Values.Add(characteristicValue);
StepDoneArgs args =
new StepDoneArgs(String.Format("Comprobando {0}: {1}",
bc.GetType(),
characteristicValue));
StepDoneInvoker(args);
}
return(vector);
}
/// <summary>
/// Creates a <c>CharacteristicVector</c> instance for a given
/// <c>FloatBitmap</c> object.
/// </summary>
/// <param name="image">
/// A <see cref="FloatBitmap"/>
/// </param>
/// <returns>
/// A <see cref="CharacteristicVector"/>
/// </returns>
private CheckVector CreateVector(FloatBitmap image)
{
CheckVector vector = new CheckVector();
bool characteristicValue;
foreach(BinaryCharacteristic bc in characteristics)
{
characteristicValue = bc.Apply(image);
vector.Values.Add(characteristicValue);
StepDoneArgs args =
new StepDoneArgs(String.Format("Comprobando {0}: {1}",
bc.GetType(),
characteristicValue));
StepDoneInvoker(args);
}
return vector;
}
