private void ReceiveData(Pose input, Envelope envelope)
{
// input: T_x, T_y, T_z, R_x, R_y, R_z
List <float> inputTRTRTransDiff = new List <float>(); //T_x, T_y, T_z, R_x, R_y, R_z, diff_Tx, diff_Ty, diff_Tz, diff_Rx, diff_Ry, diff_Rz
List <float> inputTRTRDiffDiff2 = new List <float>(); //diff_Tx, diff_Ty, diff_Tz, diff_Rx, diff_Ry, diff_Rz, diff2_Tx, diff2_Ty, diff2_Tz, diff2_Rx, diff2_Ry, diff2_Rz
// calculate diff of T_x, T_y, T_z, R_x, R_y, R_z
for (int i = 0; i < input.Count; i++)
{
inputTRTRTransDiff.Add((float)input[i]);
}
for (int i = 0; i < input.Count; i++)
{
inputTRTRTransDiff.Add((float)input[i] - dataQueueTransDiff.Last()[i]);
}
// calculate diff and diff2 of T_x, T_y, T_z, R_x, R_y, R_z
// add diff
for (int i = 0; i < input.Count; i++)
{
inputTRTRDiffDiff2.Add(inputTRTRTransDiff[input.Count + i]);
}
// add diff2
for (int i = 0; i < input.Count; i++)
{
inputTRTRDiffDiff2.Add(inputTRTRDiffDiff2[i] - lastInputTRTRDiffDiff2[i]);
}
// reorder into diff_Tx, diff_Ty, diff2_Tx, diff2_Ty, diff2_Tz, diff_Tz, diff_Rx, diff_Ry, diff_Rz, diff2_Rx, diff2_Ry, diff2_Rz
List <float> inputTTRRDiffDiff2 = deepCopyList(inputTRTRDiffDiff2);
for (int i = 3; i < 6; i++)
{
inputTTRRDiffDiff2[i] = inputTRTRDiffDiff2[i + 3];
}
for (int i = 6; i < 9; i++)
{
inputTTRRDiffDiff2[i] = inputTRTRDiffDiff2[i - 3];
}
lastInputTRTRDiffDiff2 = inputTRTRDiffDiff2;
dataQueueTransDiff.AddLast(inputTRTRTransDiff);
dataQueueTTRRDiffDiff2.AddLast(inputTTRRDiffDiff2);
if (dataQueueTransDiff.Count() > NUM_OF_FRAME)
{
dataQueueTransDiff.RemoveFirst();
}
if (dataQueueTTRRDiffDiff2.Count() > NUM_OF_FRAME)
{
dataQueueTTRRDiffDiff2.RemoveFirst();
}
// flatten the whole list
List <float> networkInput = new List <float>();
List <List <float> > dataQueueList = dataQueueTTRRDiffDiff2.ToList();
//row major
for (int i = 0; i < dataQueueList.Count(); i++)
{
for (int j = 0; j < dataQueueList[i].Count; j++)
{
networkInput.Add(dataQueueList[i][j]);
}
}
try {
// evaluate using the model
HeadPoseObject headPoseObj = new HeadPoseObject(networkInput);
IEnumerable <HeadPoseObject> images = headPoseObj.getHeadPoseIEnumerable();
IDataView imageDataView = mlContext.Data.LoadFromEnumerable(images);
IEnumerable <float[]> probabilities = modelScorer.Score(imageDataView);
var probList = probabilities.ToList();
Out.Post(probList[0][31], envelope.OriginatingTime);
} catch (Exception ex) {
Console.WriteLine(ex.ToString());
}
}
/// <summary>
/// ML.NET uses IEnumerable to pass into the input layer
/// </summary>
/// <returns>IEnumerable of this object</returns>
public IEnumerable <HeadPoseObject> getHeadPoseIEnumerable()
{
IEnumerable <HeadPoseObject> objects = new HeadPoseObject[] { this };
return(objects);
}
