/// <summary>
/// Computes a tree histogram for a sub-rectangle of the source image defined by the parameters.
/// </summary>
/// <param name="startRow">Starting row of the sub-rectangle</param>
/// <param name="startColumn">Starting column of the sub-rectangle</param>
/// <param name="rows">Number of rows in the sub-rectangle</param>
/// <param name="columns">Number of columns in the sub-rectangle</param>
/// <returns>A tree histogram</returns>
public TreeHistogram ComputeHistogram(int startRow, int startColumn, int rows, int columns)
{
Dictionary <int, TreeNode>[] counts = new Dictionary <int, TreeNode> [_trees];
for (int i = 0; i < counts.Length; i++)
{
counts[i] = new Dictionary <int, TreeNode>();
}
int[] maxIndex = new int[counts.Length];
for (int r = 0, srcR = startRow; r < rows; r++, srcR++)
{
for (int c = 0, srcC = startColumn; c < columns; c++, srcC++)
{
for (byte t = 0; t < _trees; t++)
{
INodeInfo <ImageDataPoint <T>, T[]> info = _data[srcR, srcC, t];
int index = info.TreeIndex;
if (!counts[t].ContainsKey(index))
{
counts[t][index] = new TreeNode(t, 0, info.LeafNodeIndex, info.TreeIndex);
maxIndex[t] = Math.Max(maxIndex[t], index);
}
counts[t][index].Value++;
}
}
}
List <TreeNode> histogram = new List <TreeNode>();
for (byte t = 0; t < _trees; t++)
{
fill(t, 1, histogram, counts[t], maxIndex[t]);
}
return(TreeHistogram.Divide(new TreeHistogram(histogram), rows * columns));
}
/// <summary>
/// Computes a histogram for all trees from the provided points.
/// </summary>
/// <param name="points">Points to classify</param>
/// <returns>The histogram</returns>
public TreeHistogram ComputeHistogram(List <T> points)
{
TreeHistogram histogram = _trees[0].ComputeHistogram(points);
for (int t = 1; t < _numTrees; t++)
{
histogram = TreeHistogram.Union(histogram, _trees[t].ComputeHistogram(points));
}
return(TreeHistogram.Divide(histogram, points.Count));
}
