public long realignAll()
{
int i;
long start = CSettings.currentTimeMillis();
CXYVector.realign(_trainingData[0], _mean, _weight);
_newMean = new CXYVector(_trainingData[0]);
for (i = 1; i < _numSamples; i++)
{
CXYVector.realign(_trainingData[i], _mean, _weight);
_newMean.add(_trainingData[i]);
}
_newMean.divide(_numSamples);
return(CSettings.currentTimeMillis() - start);
}
public void normalizeMean()
{
CXYVector.realign(_newMean, _normalShape, _weight);
_energy = _mean.distance(_newMean);
_mean = new CXYVector(_newMean);
}
public bool doStep()
{
if (_stepCounter >= maxStep)
{
return(false);
}
_stepCounter++;
if (isConverge())
{
return(false);
}
int i, j;
// Calculate Movement
int detectedEdgeCnt = calculateMovement(_XdX);
// Compute Change
CXYVector currentShape = new CXYVector(_PDMData.mean);
CMatrix3x3 pose = CXYVector.realign(currentShape, _XdX, _weight);
// Get Residual Adjustment
CXYVector XdX = new CXYVector(_XdX);
XdX.transform(pose.inverse());
double [] dx = XdX.substractDouble(_PDMData.mean);
// Translate Into Model Parameter
double [] db = new double[_PDMData.freedom];
double [,] vectorsPtr = _PDMData.eigen.vectors;
double dimension = _size * 2;
for (i = 0; i < _PDMData.freedom; i++)
{
db[i] = 0;
for (j = 0; j < dimension; j++)
{
db[i] += vectorsPtr[j, i] * dx[j];
}
}
// Find Out If Parameter Is Out Of Limit
double dm = 0.0;
for (i = 0; i < _PDMData.freedom; i++)
{
dm += (db[i] * db[i]) / _PDMData.eigen.values[i];
}
// If It Does, Apply Limit
dm = Math.Sqrt(dm);
if (dm > 3.0)
{
for (i = 0; i < _PDMData.freedom; i++)
{
db[i] *= 3.0 / dm;
}
}
// Update Parameter
currentShape = PDMData.generateNewVariation(-1, db);
CXYVector.realign(currentShape, _XdX, _weight);
//currentShape.transform(pose);
_energy = _ASMResult.distance(currentShape);
_ASMResult = currentShape;
return(detectedEdgeCnt > (_ASMResult.size / 10));
}
