private float[,] DoTrain(ref GestureDetector gd)
{
/* arrange the key frame data in m_skRecorder.m_dataList */
m_postFeature.Reset();
m_postFeature.RegisterJoints(m_jointIdx); // set the feature type and joint indices to calculate features
for (int i = 0; i < m_skRecorder.FrameCount; i++)
{
int tag = m_skRecorder.m_tagList[i];
if (tag == 0)
{
continue;
}
CopyCurFrameJointDataRef(i);
m_postFeature.UpdateFeatures(); // calculate the features
for (int j = 0; j < JointCount; j++)
{
gd.m_posts[tag - 1][j].AddTrainData(m_postFeature.m_jointVec[(int)m_jointIdx[j]]);
}
}
/* train the posture templates */
float[,] stdArray = new float[m_tagCount, JointCount];
for (int i = 0; i < m_tagCount; i++)
{
for (int j = 0; j < JointCount; j++)
{
stdArray[i, j] = gd.m_posts[i][j].Train();
}
}
// train time interval coefficients
TrainTimeIntv(gd);
return(stdArray);
}
/// the main work flow. called once per frame
void Update()
{
if (Pause)
{
return;
}
if (UseRealtimeData && !m_skReader.UpdateSkeletonData())
{
return;
}
m_postFeature.UpdateFeatures();
foreach (GestureDetector gd in m_gestures)
{
if (!gd.CheckPositionConfidence())
{
continue; // if data not confident, won't do detection
}
if (gd.Detect())
{
if (m_gestureDetected != null)
{
m_gestureDetected(this, new GestureEventArgs(gd.m_name, gd.PostureCount == 1)); // trigger the event
}
}
}
}