/// <summary>
/// Classifies each pixel in the image and returns the leaf nodes which they end up in.
/// </summary>
/// <param name="forest">The forest used for the computation</param>
/// <param name="image">Image to classify</param>
/// <returns>A leaf image</returns>
public static LeafImage <T> ComputeLeafImage <T>(this DecisionForest <ImageDataPoint <T>, T[]> forest, IMultichannelImage <T> image)
{
LeafImage <T> leafImage = new LeafImage <T>(image.Rows, image.Columns, forest.TreeCount);
leafImage.ID = image.ID;
for (int i = 0; i < forest.TreeCount; i++)
{
forest[i].FillLeafImage(leafImage, image);
}
return(leafImage);
}
internal static void FillLeafImage <T>(this DecisionTree <ImageDataPoint <T>, T[]> tree, LeafImage <T> leafImage, IMultichannelImage <T> image)
{
INodeInfo <ImageDataPoint <T>, T[]>[, ,] array = leafImage.RawArray;
int rows = image.Rows;
int columns = image.Columns;
List <ImageDataPoint <T> > points = new List <ImageDataPoint <T> >();
List <int> indices = new List <int>();
int i = 0;
for (short r = 0; r < rows; r++)
{
for (short c = 0; c < columns; c++, i++)
{
points.Add(new ImageDataPoint <T>(image, r, c, -1));
indices.Add(i);
}
}
INodeInfo <ImageDataPoint <T>, T[]>[] info = new INodeInfo <ImageDataPoint <T>, T[]> [points.Count];
DecisionTree <ImageDataPoint <T>, T[]> .assignLabels(tree._root, points, info, indices);
i = 0;
int treeLabel = tree.TreeLabel;
for (short r = 0; r < rows; r++)
{
for (short c = 0; c < columns; c++, i++)
{
array[r, c, treeLabel] = info[i];
}
}
}
