private float[] ExtractInputFeatures(Dictionary <string, Vector3> points)
{
points = new Dictionary <string, Vector3>(points); // Defensive copy
var alignmentPoints = new Dictionary <string, Vector3>();
foreach (var id in handTemplate.AlignmentMarkerList)
{
alignmentPoints.Add(id, points[id]);
Debug.Assert(!float.IsNaN(points[id].x), "Alignment marker is NaN");
}
double[,] R;
double[] offset;
handTemplate.AlignTransform(alignmentPoints, points, out R, out offset);
var markerList = handTemplate.MarkerList;
float[] features = new float[markerList.Count * 3];
for (int i = 0; i < markerList.Count; i++)
{
Vector3 pos = points[markerList[i]];
features[3 * i + 0] = pos.x;
features[3 * i + 1] = pos.y;
features[3 * i + 2] = pos.z;
if (float.IsNaN(pos.x))
{
Debug.Assert(!float.IsNaN(pos.x), "NaNs detected in the neural network input (might happen during warmup)");
features[3 * i + 0] = features[3 * i + 1] = features[3 * i + 2] = 0;
}
}
return(features);
}
private float[] ExtractInputFeatures(Dictionary <string, Vector3> points, out double[,] R, out double[] offset)
{
points = new Dictionary <string, Vector3>(points); // Defensive copy
handTemplate.AlignTransform(points, points, out R, out offset);
var pointList = new List <Vector3>(handTemplate.MarkerList.Count);
foreach (var marker in handTemplate.MarkerList)
{
if (points.ContainsKey(marker))
{
pointList.Add(points[marker]);
}
else
{
// Missing values are set to 0
pointList.Add(Vector3.zero);
}
}
// Flatten input vectors
float[] features = new float[pointList.Count * 3];
for (int i = 0; i < pointList.Count; i++)
{
features[3 * i + 0] = pointList[i].x;
features[3 * i + 1] = pointList[i].y;
features[3 * i + 2] = pointList[i].z;
}
return(features);
}
