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];
}
}
}
/// <summary>
/// Classifies each pixel in <paramref name="image"/> and stores the results in <paramref name="dist"/>.
/// </summary>
/// <param name="tree">The tree used for the computation</param>
/// <param name="image">Image to classify</param>
/// <param name="dist">Image which is used to store the distributions</param>
public static void ClassifySoft <T>(this DecisionTree <ImageDataPoint <T>, T[]> tree, IMultichannelImage <T> image, DistributionImage dist)
{
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;
for (short r = 0; r < rows; r++)
{
for (short c = 0; c < columns; c++, i++)
{
dist.Add(r, c, info[i].Distribution);
}
}
}
/// <summary>
/// Classifies points from <paramref name="labeledImage"/> (using the mask if present) and adds the distributions at each pixel to <paramref name="dist"/>.
/// </summary>
/// <param name="tree">The tree used for the computation</param>
/// <param name="labeledImage">The image to classify</param>
/// <param name="dist">Image which is used to store the distributions</param>
public static void ClassifySoft <T>(this DecisionTree <ImageDataPoint <T>, T[]> tree, LabeledImage <T> labeledImage, DistributionImage dist)
{
List <ImageDataPoint <T> > points = labeledImage.CreateAllDataPoints(BackgroundSampleMode.Full);
List <int> indices = new List <int>();
for (int i = 0; i < points.Count; i++)
{
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);
for (int i = 0; i < info.Length; i++)
{
dist.Add(points[i].Row, points[i].Column, info[i].Distribution);
}
}
/// <summary>
/// Get the node information for all points from all trees.
/// </summary>
/// <typeparam name="T">The type of the data point</typeparam>
/// <typeparam name="D">The underlying feature type of the data point</typeparam>
/// <param name="forest">The forest to query</param>
/// <param name="points">The points to get info for</param>
/// <returns>The desired info</returns>
public static INodeInfo <T, D>[][] GetNodeInfo <T, D>(this DecisionForest <T, D> forest, List <T> points) where T : IDataPoint <D>
{
List <Task <INodeInfo <T, D>[]> > tasks = new List <Task <INodeInfo <T, D>[]> >();
TaskFactory <INodeInfo <T, D>[]> factory = new TaskFactory <INodeInfo <T, D>[]>();
for (int t = 0; t < forest.TreeCount; t++)
{
tasks.Add(factory.StartNew(arg =>
{
DecisionTree <T, D> tree = (DecisionTree <T, D>)arg;
INodeInfo <T, D>[] info = new INodeInfo <T, D> [points.Count];
DecisionTree <T, D> .assignLabels(tree._root, points, info, Enumerable.Range(0, points.Count).ToList());
return(info);
}, forest[t]));
}
Task.WaitAll(tasks.ToArray());
return(tasks.Select(o => o.Result).ToArray());
}