/// <summary>
/// Creates template from the input image by using provided parameters.
/// </summary>
/// <param name="orientation">Orientation image.</param>
/// <param name="maxNumberOfFeatures">Maximum number of features per template. The features will be extracted so that their locations are semi-uniformly spread.</param>
/// <param name="classLabel">Template class label.</param>
/// <param name="featureImportanceFunc">Function which returns feature's strength.</param>
public void Initialize(Gray <int>[,] orientation, int maxNumberOfFeatures, string classLabel, Func <Feature, int> featureImportanceFunc = null)
{
maxNumberOfFeatures = System.Math.Max(0, System.Math.Min(maxNumberOfFeatures, GlobalParameters.MAX_NUM_OF_FEATURES));
featureImportanceFunc = (featureImportanceFunc != null) ? featureImportanceFunc: (feature) => 0;
Gray <byte>[,] importantQuantizedOrient = FeatureMap.Calculate(orientation, 0);
List <Feature> features = ExtractTemplate(importantQuantizedOrient, maxNumberOfFeatures, featureImportanceFunc);
BoundingRect = GetBoundingRectangle(features);
//if (boundingRect.X == 1 && boundingRect.Y == 1 && boundingRect.Width == 18)
// Console.WriteLine();
for (int i = 0; i < features.Count; i++)
{
features[i].X -= BoundingRect.X;
features[i].Y -= BoundingRect.Y;
//if(features[i].X < 0 || features[i].Y < 0)
// Console.WriteLine();
//PATCH!!!
features[i].X = System.Math.Max(0, features[i].X);
features[i].Y = System.Math.Max(0, features[i].Y);
}
this.Features = features.ToArray();
this.Size = BoundingRect.Size;
this.ClassLabel = classLabel;
}
private Image <Gray <byte> >[][,] calculate(Gray <int>[,] orientationDegImg)
{
var linearMaps = new Image <Gray <byte> > [GlobalParameters.NUM_OF_QUNATIZED_ORIENTATIONS][, ];
Gray <byte>[,] sprededQuantizedOrient = FeatureMap.Calculate(orientationDegImg, this.NeigborhoodSize);
for (int orient = 0; orient < GlobalParameters.NUM_OF_QUNATIZED_ORIENTATIONS; orient++)
{
var responseMap = computeResponseMap(sprededQuantizedOrient, orient);
linearMaps[orient] = linearizeResponseMap(responseMap);
//responseMap.Save("C:/RM_" + orient + ".bmp");
}
return(linearMaps);
}
private Image <Gray, Byte>[][,] calculate(Image <Gray, int> orientationDegImg)
{
Image <Gray, Byte>[][,] linearMaps = new Image <Gray, byte> [GlobalParameters.NUM_OF_QUNATIZED_ORIENTATIONS][, ];
using (Image <Gray, Byte> sprededQuantizedOrient = FeatureMap.Calculate(orientationDegImg, this.NeigborhoodSize))
{
for (int orient = 0; orient < GlobalParameters.NUM_OF_QUNATIZED_ORIENTATIONS; orient++)
{
using (var responseMap = computeResponseMap(sprededQuantizedOrient, orient))
{
linearMaps[orient] = linearizeResponseMap(responseMap);
//responseMap.Save("C:/RM_" + orient + ".bmp");
}
}
}
return(linearMaps);
}