static void Main(string[] args)
{
List <Video> videos = FileOperations.ReadDatabaseRandomly(Parameters.TrainPath, Parameters.NumVideosEachTree);
//FileOperations.WriteVideos(videos, @"C:\Users\Celebi\Desktop\A");
List <Video> motionVideos = ImageOperations.CreateMotions(videos);
//FileOperations.WriteVideos(motionVideos, @"C:\Users\Celebi\Desktop\B");
motionVideos.CreateIntegralVideos();
Tube tb = new Tube(99);
double result1 = motionVideos[0].GetVolume(new Tube(99));
Console.WriteLine(result1);
//for (int i = tb.Index; i < tb.Index + tb.Length; i++) {
// for(int j = tb.Rect.
//}
//using (StreamWriter writer = File.CreateText(Parameters.GlobalLogPath))
//{
// writer.WriteLine(Parameters.Log());
//}
//Forest fr = new Forest();
//using (StreamWriter writer = File.AppendText(Parameters.GlobalLogPath))
//{
// writer.WriteLine();
// writer.WriteLine("Finished at: " + DateTime.Now);
//}
}
private DecisionNode[] Split(Tube tb)
{
this.Branches = new DecisionNode[2];
List <Video>[] partitions = new List <Video> [2];
partitions[0] = new List <Video>();
partitions[1] = new List <Video>();
for (int i = 0; i < this.Inputs.Count; i++)
{
if (this.Inputs[i].ApplyTube(tb))
{
partitions[0].Add(this.Inputs[i]);
}
else
{
partitions[1].Add(this.Inputs[i]);
}
}
if (partitions[0].Count == 0 || partitions[1].Count == 0)
{
return(null);
}
this.Branches[0] = new DecisionNode(this.Owner, this, partitions[0]);
this.Branches[1] = new DecisionNode(this.Owner, this, partitions[1]);
this.Inputs = null;
this.SplitFeature = tb;
return(this.Branches);
}
private Tube[] CreateFeatureCandidates()
{
Tube[] rVal = new Tube[Parameters.UsedFeatureCountAtEveryNode];
for (int i = 0; i < Parameters.UsedFeatureCountAtEveryNode; i++)
{
rVal[i] = new Tube(199);
}
return(rVal);
}
public DecisionNode[] Split()
{
if (this.Level > Parameters.MaxDepth)
{
return(null);
}
Tube[] features = CreateFeatureCandidates();
Tube bestTube = GetBestFeature(features);
return(Split(bestTube));
}
public static double GetBestDistance(double[] volumes, Tube feature)
{
double rVal = 0;
int counter = 0;
double diff = double.MaxValue;
double mid = volumes.Average(); // best place to start
double mean0, mean1;
List <double>[] parts = new List <double> [2];
parts[0] = new List <double>();
parts[1] = new List <double>();
do
{
for (int i = 0; i < volumes.Length; i++)
{
if (volumes[i] < mid)
{
parts[0].Add(volumes[i]);
}
else
{
parts[1].Add(volumes[i]);
}
}
mean0 = parts[0].Count > 0 ? parts[0].Average() : 0;
mean1 = parts[1].Count > 0 ? parts[1].Average() : 0;
mid = (mean0 + mean1) / 2;
feature.Threshold = mid;
rVal = mean1 - mean0;
counter++;
}while (counter < 4 && diff > Parameters.DiffThreshold);
rVal = rVal / feature.NormalizationScalar;
return(rVal);
}