/// <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);
}
