void MultiColorAdjustment::setAdjustmentForLed(const std::string& id, const unsigned startLed, const unsigned endLed)
{
assert(startLed <= endLed);
assert(endLed < _ledAdjustments.size());
// Get the identified adjustment (don't care if is nullptr)
ColorAdjustment * adjustment = getAdjustment(id);
for (unsigned iLed=startLed; iLed<=endLed; ++iLed)
{
_ledAdjustments[iLed] = adjustment;
}
}
//*************************************************************************************************
//! Saves the current active adjustment to the KWL file. If an adjustment of the same
//! description already exists in the KWL, it is replaced by the current adjustment.
//*************************************************************************************************
void ossimAdjustableParameterInterface::saveCurrentAdjustmentOnly(ossimKeywordlist &kwl,
const ossimString &prefix)
{
// Fetch current adjustment:
ossimAdjustmentInfo adjTosave;
getAdjustment(adjTosave);
ossimString currentLabel = adjTosave.getDescription();
// Fetch the number of adjustments specified in the KWL:
int numberOfAdjustments = 0;
const char* value = kwl.find(prefix, NUMBER_OF_ADJUSTMENTS_KW);
if (value)
{
numberOfAdjustments = atoi(value);
}
// Loop to read the descriptions of each adjustment in the KWL to see if a match exists. If
// we reach the end, insert it there:
// int saved_adj_idx;
ossimAdjustmentInfo adjInfo;
int i = 0;
while (true)
{
ossimString adjPrefix = prefix+(ossimString(ADJUSTMENT_PREFIX)+ossimString::toString(i)+".");
if (i < numberOfAdjustments)
{
adjInfo.loadState(kwl, adjPrefix);
}
if ((i == numberOfAdjustments) || (adjInfo.getDescription() == currentLabel))
{
// match found or end of list, insert the new adjustment at this position:
adjTosave.saveState(kwl, adjPrefix);
// saved_adj_idx = i;
// Need to bump the number of adjustments by one if we added a new one:
if (i == numberOfAdjustments)
{
kwl.add(prefix, NUMBER_OF_ADJUSTMENTS_KW, ++numberOfAdjustments);
}
break;
}
++i;
}
//// Sync up this object with the contents of the KWL: NOT NEEDED???
//loadAdjustments(kwl, prefix);
//setCurrentAdjustment(saved_adj_idx);
}
void ossimAdjustableParameterInterface::getAdjustment(ossimAdjustmentInfo& adj) const
{
getAdjustment(theCurrentAdjustment, adj);
}
double
ossimRpcProjection::optimizeFit(const ossimTieGptSet& tieSet, double* /* targetVariance */)
{
#if 1
//NOTE : ignore targetVariance
ossimRefPtr<ossimRpcSolver> solver = new ossimRpcSolver(false, false); //TBD : choices should be part of setupFromString
std::vector<ossimDpt> imagePoints;
std::vector<ossimGpt> groundPoints;
tieSet.getSlaveMasterPoints(imagePoints, groundPoints);
solver->solveCoefficients(imagePoints, groundPoints);
ossimRefPtr< ossimImageGeometry > optProj = solver->createRpcProjection();
if (!optProj)
{
ossimNotify(ossimNotifyLevel_FATAL) << "FATAL ossimRpcProjection::optimizeFit(): error when optimizing the RPC with given tie points"
<< std::endl;
return -1.0;
}
if(optProj->hasProjection())
{
ossimKeywordlist kwl;
optProj->getProjection()->saveState(kwl);
this->loadState(kwl);
}
return std::pow(solver->getRmsError(), 2); //variance in pixel^2
#else
// COPIED from ossimRpcProjection
//
//
//use a simple Levenberg-Marquardt non-linear optimization
//note : please limit the number of tie points
//
//INPUTS: requires Jacobian matrix (partial derivatives with regards to parameters)
//OUTPUTS: will also compute parameter covariance matrix
//
//TBD: use targetVariance!
int np = getNumberOfAdjustableParameters();
int nobs = tieSet.size();
//setup initail values
int iter=0;
int iter_max = 200;
double minResidue = 1e-10; //TBC
double minDelta = 1e-10; //TBC
//build Least Squares initial normal equation
// don't waste memory, add samples one at a time
NEWMAT::SymmetricMatrix A;
NEWMAT::ColumnVector residue;
NEWMAT::ColumnVector projResidue;
double deltap_scale = 1e-4; //step_Scale is 1.0 because we expect parameters to be between -1 and 1
buildNormalEquation(tieSet, A, residue, projResidue, deltap_scale);
double ki2=residue.SumSquare();
//get current adjustment (between -1 and 1 normally) and convert to ColumnVector
ossimAdjustmentInfo cadj;
getAdjustment(cadj);
std::vector< ossimAdjustableParameterInfo >& parmlist = cadj.getParameterList();
NEWMAT::ColumnVector cparm(np), nparm(np);
for(int n=0;n<np;++n)
{
cparm(n+1) = parmlist[n].getParameter();
}
double damping_speed = 2.0;
//find max diag element for A
double maxdiag=0.0;
for(int d=1;d<=np;++d) {
if (maxdiag < A(d,d)) maxdiag=A(d,d);
}
double damping = 1e-3 * maxdiag;
double olddamping = 0.0;
bool found = false;
//DEBUG TBR
cout<<"rms="<<sqrt(ki2/nobs)<<" ";
cout.flush();
while ( (!found) && (iter < iter_max) ) //non linear optimization loop
{
bool decrease = false;
do
{
//add damping update to normal matrix
for(int d=1;d<=np;++d) A(d,d) += damping - olddamping;
olddamping = damping;
NEWMAT::ColumnVector deltap = solveLeastSquares(A, projResidue);
if (deltap.NormFrobenius() <= minDelta)
{
found = true;
} else {
//update adjustment
nparm = cparm + deltap;
for(int n=0;n<np;++n)
{
setAdjustableParameter(n, nparm(n+1), false); //do not update now, wait
}
adjustableParametersChanged();
//check residue is reduced
NEWMAT::ColumnVector newresidue = getResidue(tieSet);
double newki2=newresidue.SumSquare();
double res_reduction = (ki2 - newki2) / (deltap.t()*(deltap*damping + projResidue)).AsScalar();
//DEBUG TBR
cout<<sqrt(newki2/nobs)<<" ";
cout.flush();
if (res_reduction > 0)
{
//accept new parms
cparm = nparm;
ki2=newki2;
deltap_scale = max(1e-15, deltap.NormInfinity()*1e-4);
buildNormalEquation(tieSet, A, residue, projResidue, deltap_scale);
olddamping = 0.0;
found = ( projResidue.NormInfinity() <= minResidue );
//update damping factor
damping *= std::max( 1.0/3.0, 1.0-std::pow((2.0*res_reduction-1.0),3));
damping_speed = 2.0;
decrease = true;
} else {
//cancel parameter update
for(int n=0;n<np;++n)
{
setAdjustableParameter(n, nparm(n+1), false); //do not update right now
}
adjustableParametersChanged();
damping *= damping_speed;
damping_speed *= 2.0;
}
}
} while (!decrease && !found);
++iter;
}
//DEBUG TBR
cout<<endl;
//compute parameter correlation
// use normal matrix inverse
//TBD
return ki2/nobs;
#endif
}
