/// <summary>
/// Classifies <paramref name="image"/>, creating a distribution over all labels at each pixel.
/// </summary>
/// <param name="tree">The tree used for the computation</param>
/// <param name="image">Image to classify</param>
/// <returns>Distributions at each pixel</returns>
public static DistributionImage ClassifySoft <T>(this DecisionTree <ImageDataPoint <T>, T[]> tree, IMultichannelImage <T> image)
{
DistributionImage dist = new DistributionImage(image.Rows, image.Columns, tree.LabelCount);
tree.ClassifySoft(image, dist);
dist.Normalize();
return(dist);
}
/// <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 label distribution at each pixel by combining its node distributions.
/// </summary>
/// <returns>Distribution image</returns>
public DistributionImage ComputeDistributionImage()
{
DistributionImage dist = new DistributionImage(_rows, _columns, _data[0, 0, 0].Distribution.Length);
dist.ID = ID;
for (int r = 0; r < _rows; r++)
{
for (int c = 0; c < _columns; c++)
{
for (int t = 0; t < _trees; t++)
{
dist.Add(r, c, _data[r, c, t].Distribution);
}
}
}
dist.DivideThrough(_trees);
dist.Normalize();
return(dist);
}
/// <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);
}
}
