public double RMSE(PoseEstimate other)
{
if (other == null)
{
throw new ArgumentNullException("other");
}
double result = this.MSE(other);
result = Math.Sqrt(result);
return(result);
}
public double MSE(PoseEstimate other)
{
if (other == null)
{
throw new ArgumentNullException("other");
}
double result = Math.Pow(this.Dx - other.Dx, 2);
result += Math.Pow(this.Dy - other.Dy, 2);
result += Math.Pow(this.Ds - other.Ds, 2);
result += Math.Pow(this.Do - other.Do, 2);
result /= 4.0;
return(result);
}
private List <MDMatch> clusterBasedOnPoseEstimation(List <MDMatch> matches, KeyPoints queryKeyPoints, PreProcessedImage trainingData)
{
// If no training data is provided then we cannot compute pose (LeafAnalysisV1), hence just return back the original set
if (trainingData == null ||
matches == null ||
!matches.Any())
{
return(matches);
}
Dictionary <MDMatch, List <MDMatch> > clusters = new Dictionary <MDMatch, List <MDMatch> >(matches.Count);
List <PoseEstimate> poseEstimates = new List <PoseEstimate>(matches.Count);
foreach (MDMatch match in matches)
{
MKeyPoint queryKeyPoint = queryKeyPoints.Points[match.QueryIdx];
MKeyPoint trainingKeyPoint = trainingData.KeyPoints.Points[match.TrainIdx];
PoseEstimate estimate = new PoseEstimate();
estimate.Match = match;
estimate.Dx = trainingKeyPoint.Point.X - queryKeyPoint.Point.X;
estimate.Dy = trainingKeyPoint.Point.Y - queryKeyPoint.Point.Y;
estimate.Ds = trainingKeyPoint.Octave / queryKeyPoint.Octave;
estimate.Do = trainingKeyPoint.Angle - queryKeyPoint.Angle;
poseEstimates.Add(estimate);
// Initialize clusters for each individual match
// Next we will add other matches which belong to this cluster
clusters.Add(match, new List <MDMatch>(new MDMatch[] { match }));
}
const double errorThreshold = 5;
// Compute cluster membership
foreach (PoseEstimate estimate in poseEstimates)
{
foreach (PoseEstimate otherEstimate in poseEstimates)
{
// Ignore self
if (estimate == otherEstimate)
{
continue;
}
double error = estimate.RMSE(otherEstimate);
//Console.WriteLine("Error: " + trainingData.Category + ": " + error);
if (error < errorThreshold)
{
clusters[estimate.Match].Add(otherEstimate.Match);
}
}
}
// Finally pick the largest cluster
List <MDMatch> result = null;
int sizeOfCluster = -1;;
foreach (KeyValuePair <MDMatch, List <MDMatch> > cluster in clusters)
{
if (cluster.Value.Count == sizeOfCluster)
{
// Tie breaker: choose the cluster with smaller overall distances
if (result.Sum(item => item.Distance) > cluster.Value.Sum(item => item.Distance))
{
result = cluster.Value;
}
}
else if (cluster.Value.Count > sizeOfCluster)
{
sizeOfCluster = cluster.Value.Count;
result = cluster.Value;
}
}
return(result);
}