/// <summary>
/// Adds the data from <paramref name="image"/> to each tree in the forest.
/// </summary>
/// <param name="forest">The forest used for the computation</param>
/// <param name="image">Image to learn from</param>
/// <param name="mode">Mode to use when sampling the image background</param>
public static void Fill <T>(this DecisionForest <ImageDataPoint <T>, T[]> forest, LabeledImage <T> image, BackgroundSampleMode mode)
{
for (int i = 0; i < forest.TreeCount; i++)
{
forest[i].Fill(image, mode);
}
forest.RefreshMetadata();
}
/// <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);
}
/// <summary>
/// Classifies every pixel in the provided image, returning a full distribution over all labels.
/// </summary>
/// <param name="forest">The forest used for the computation</param>
/// <param name="image">Image to classify</param>
/// <returns>The classified image</returns>
public static DistributionImage ClassifySoft <T>(this DecisionForest <ImageDataPoint <T>, T[]> forest, LabeledImage <T> image)
{
DistributionImage dist = new DistributionImage(image.Image.Rows, image.Image.Columns, forest.LabelCount);
dist.ID = image.ID;
for (int t = 0; t < forest.TreeCount; t++)
{
forest[t].ClassifySoft(image, dist);
}
dist.DivideThrough(forest.TreeCount);
dist.Normalize();
return(dist);
}
/// <summary>
/// Computes a histogram for all trees from the provided image.
/// </summary>
/// <param name="forest">The forest used for the computation</param>
/// <param name="image">Image to classify</param>
/// <returns>The histogram</returns>
public static TreeHistogram ComputeHistogram <T>(this DecisionForest <ImageDataPoint <T>, T[]> forest, IMultichannelImage <T> image)
{
int rows = image.Rows;
int columns = image.Columns;
List <ImageDataPoint <T> > points = new List <ImageDataPoint <T> >();
for (short r = 0; r < rows; r++)
{
for (short c = 0; c < columns; c++)
{
points.Add(new ImageDataPoint <T>(image, r, c, -1));
}
}
return(forest.ComputeHistogram(points));
}
/// <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());
}
/// <summary>
/// Classifies every pixel in the provided image with the maximum likelihood label.
/// </summary>
/// <param name="forest">The forest used for the computation</param>
/// <param name="image">Image to classify</param>
/// <returns>The classified image</returns>
public static LabelImage Classify <T>(this DecisionForest <ImageDataPoint <T>, T[]> forest, IMultichannelImage <T> image)
{
return(forest.ClassifySoft(image).ToLabelImage());
}
/// <summary>
/// Computes a histogram for all trees from the provided image.
/// </summary>
/// <param name="forest">The forest used for the computation</param>
/// <param name="image">Image to classify</param>
/// <returns>The histogram</returns>
public static TreeHistogram ComputeHistogram <T>(this DecisionForest <ImageDataPoint <T>, T[]> forest, LabeledImage <T> image)
{
return(forest.ComputeHistogram(image.CreateAllDataPoints(BackgroundSampleMode.Full)));
}