//---------------------------------------------------------
bool CGrid_Normalise::On_Execute(void)
{
CSG_Grid *pGrid = Parameters("INPUT")->asGrid();
if( pGrid->Get_StdDev() <= 0.0 )
{
return( false );
}
if( pGrid != Parameters("OUTPUT")->asGrid() )
{
pGrid = Parameters("OUTPUT")->asGrid();
pGrid ->Assign(Parameters("INPUT")->asGrid());
}
pGrid->Fmt_Name("%s (%s)", pGrid->Get_Name(), _TL("Normalized"));
//-----------------------------------------------------
double Minimum, Maximum, Offset, Scale;
Minimum = Parameters("RANGE")->asRange()->Get_Min();
Maximum = Parameters("RANGE")->asRange()->Get_Max();
Offset = pGrid->Get_Min();
Scale = (Maximum - Minimum) / pGrid->Get_Range();
for(int y=0; y<Get_NY() && SG_UI_Process_Set_Progress(y, Get_NY()); y++)
{
for(int x=0; x<Get_NX(); x++)
{
if( !pGrid->is_NoData(x, y) )
{
pGrid->Set_Value(x, y, Minimum + Scale * (pGrid->asDouble(x, y) - Offset));
}
}
}
//-----------------------------------------------------
if( pGrid == Parameters("INPUT")->asGrid() )
{
DataObject_Update(pGrid);
}
return( true );
}
//---------------------------------------------------------
bool CPanSharp_PCA::On_Execute(void)
{
//-----------------------------------------------------
bool bResult;
CSG_Parameters Tool_Parms;
CSG_Table Eigen;
//-----------------------------------------------------
// get the principal components for the low resolution bands
SG_RUN_TOOL_KEEP_PARMS(bResult, "statistics_grid", 8, Tool_Parms,
SG_TOOL_PARAMETER_SET("GRIDS" , Parameters("GRIDS" ))
&& SG_TOOL_PARAMETER_SET("METHOD" , Parameters("METHOD"))
&& SG_TOOL_PARAMETER_SET("EIGEN" , &Eigen)
&& SG_TOOL_PARAMETER_SET("COMPONENTS", 0) // get all components
);
if( !bResult )
{
return( false );
}
//-----------------------------------------------------
CSG_Parameter_Grid_List *pPCA = Tool_Parms.Get_Parameter("PCA")->asGridList();
int i, n = pPCA->Get_Grid_Count();
CSG_Grid *PCA = new CSG_Grid[n];
CSG_Grid *pPan = Parameters("PAN")->asGrid();
//-----------------------------------------------------
// replace first principal component with the high resolution panchromatic band
Process_Set_Text(_TL("Replace first PC with PAN"));
double Offset_Pan, Offset, Scale;
if( Parameters("PAN_MATCH")->asInt() == 0 ) // scale PAN band to fit first PC histogram
{
Offset_Pan = pPan->Get_Min();
Offset = pPCA->Get_Grid(0)->Get_Min();
Scale = pPCA->Get_Grid(0)->Get_Range() / pPan->Get_Range();
}
else
{
Offset_Pan = pPan->Get_Mean();
Offset = pPCA->Get_Grid(0)->Get_Mean();
Scale = pPCA->Get_Grid(0)->Get_StdDev() / pPan->Get_StdDev();
}
PCA[0].Create(Get_System());
for(int y=0; y<Get_NY() && Set_Progress(y); y++)
{
#pragma omp parallel for
for(int x=0; x<Get_NX(); x++)
{
if( pPan->is_NoData(x, y) )
{
PCA[0].Set_NoData(x, y);
}
else
{
PCA[0].Set_Value(x, y, Offset + Scale * (pPan->asDouble(x, y) - Offset_Pan));
}
}
}
//-----------------------------------------------------
// resample all other PCs to match the high resolution of the PAN band
TSG_Grid_Resampling Resampling = Get_Resampling(Parameters("RESAMPLING")->asInt());
for(i=1; i<n; i++)
{
Process_Set_Text("%s: %s ...", _TL("Resampling"), pPCA->Get_Grid(i)->Get_Name());
PCA[i].Create(Get_System());
PCA[i].Assign(pPCA->Get_Grid(i), Resampling);
delete(pPCA->Get_Grid(i)); // PCA tool was unmanaged, so we have to delete the output
}
delete(pPCA->Get_Grid(0));
pPCA->Del_Items();
for(i=0; i<n; i++)
{
pPCA->Add_Item(&PCA[i]);
}
//-----------------------------------------------------
// inverse principal component rotation for the high resolution bands
SG_RUN_TOOL_KEEP_PARMS(bResult, "statistics_grid", 10, Tool_Parms,
SG_TOOL_PARAMETER_SET("PCA" , Tool_Parms("PCA"))
&& SG_TOOL_PARAMETER_SET("GRIDS", Parameters("SHARPEN"))
&& SG_TOOL_PARAMETER_SET("EIGEN", &Eigen)
);
delete[](PCA);
if( !bResult )
{
return( false );
}
CSG_Parameter_Grid_List *pHiRes = Parameters("SHARPEN")->asGridList();
CSG_Parameter_Grid_List *pLoRes = Parameters("GRIDS" )->asGridList();
CSG_Parameter_Grid_List *pGrids = Tool_Parms("GRIDS" )->asGridList();
if( !Parameters("OVERWRITE")->asBool() )
{
pHiRes->Del_Items();
}
for(i=0; i<pLoRes->Get_Grid_Count() && i<pGrids->Get_Grid_Count(); i++)
{
if( pHiRes->Get_Grid(i) )
{
pHiRes->Get_Grid(i)->Assign(pGrids->Get_Grid(i));
delete(pGrids->Get_Grid(i));
}
else
{
pHiRes->Add_Item(pGrids->Get_Grid(i));
}
pHiRes->Get_Grid(i)->Set_Name(pLoRes->Get_Grid(i)->Get_Name());
}
return( true );
}
//---------------------------------------------------------
bool CPanSharp_IHS::On_Execute(void)
{
//-----------------------------------------------------
TSG_Grid_Resampling Resampling = Get_Resampling(Parameters("RESAMPLING")->asInt());
//-----------------------------------------------------
int y;
CSG_Grid *pPan = Parameters("PAN")->asGrid();
//-----------------------------------------------------
Process_Set_Text("%s: %s ...", _TL("Resampling"), Parameters("R")->asGrid()->Get_Name());
CSG_Grid *pR = Parameters("R_SHARP")->asGrid();
pR->Assign (Parameters("R")->asGrid(), Resampling);
pR->Set_Name(Parameters("R")->asGrid()->Get_Name());
Process_Set_Text("%s: %s ...", _TL("Resampling"), Parameters("G")->asGrid()->Get_Name());
CSG_Grid *pG = Parameters("G_SHARP")->asGrid();
pG->Assign (Parameters("G")->asGrid(), Resampling);
pG->Set_Name(Parameters("G")->asGrid()->Get_Name());
Process_Set_Text("%s: %s ...", _TL("Resampling"), Parameters("B")->asGrid()->Get_Name());
CSG_Grid *pB = Parameters("B_SHARP")->asGrid();
pB->Assign (Parameters("B")->asGrid(), Resampling);
pB->Set_Name(Parameters("B")->asGrid()->Get_Name());
//-----------------------------------------------------
Process_Set_Text(_TL("RGB to IHS"));
double rMin = pR->Get_Min(), rRange = pR->Get_Range();
double gMin = pG->Get_Min(), gRange = pG->Get_Range();
double bMin = pB->Get_Min(), bRange = pB->Get_Range();
for(y=0; y<pPan->Get_NY() && Set_Progress(y, pPan->Get_NY()); y++)
{
#pragma omp parallel for
for(int x=0; x<pPan->Get_NX(); x++)
{
bool bNoData = true;
if( pPan->is_NoData(x, y) || pR->is_NoData(x, y) || pG->is_NoData(x, y) || pB->is_NoData(x, y) )
{
pR->Set_NoData(x, y);
pG->Set_NoData(x, y);
pB->Set_NoData(x, y);
}
else
{
double r = (pR->asDouble(x, y) - rMin) / rRange; if( r < 0.0 ) r = 0.0; else if( r > 1.0 ) r = 1.0;
double g = (pG->asDouble(x, y) - gMin) / gRange; if( g < 0.0 ) g = 0.0; else if( g > 1.0 ) g = 1.0;
double b = (pB->asDouble(x, y) - bMin) / bRange; if( b < 0.0 ) b = 0.0; else if( b > 1.0 ) b = 1.0;
double h, s, i = r + g + b;
if( i <= 0.0 )
{
h = 0.0;
s = 0.0;
}
else
{
if( r == g && g == b ) { h = 0.0; }
else if( b < r && b < g ) { h = (g - b) / (i - 3 * b) ; }
else if( r < g && r < b ) { h = (b - r) / (i - 3 * r) + 1; }
else { h = (r - g) / (i - 3 * g) + 2; }
if ( 0.0 <= h && h < 1.0 ) { s = (i - 3 * b) / i; }
else if( 1.0 <= h && h < 2.0 ) { s = (i - 3 * r) / i; }
else { s = (i - 3 * g) / i; }
}
pR->Set_Value(x, y, i);
pG->Set_Value(x, y, s);
pB->Set_Value(x, y, h);
}
}
}
//-----------------------------------------------------
double Offset_Pan, Offset, Scale;
if( Parameters("PAN_MATCH")->asInt() == 0 )
{
Offset_Pan = pPan->Get_Min();
Offset = pR->Get_Min();
Scale = pR->Get_Range() / pPan->Get_Range();
}
else
{
Offset_Pan = pPan->Get_Mean();
Offset = pR->Get_Mean();
Scale = pR->Get_StdDev() / pPan->Get_StdDev();
}
//-----------------------------------------------------
Process_Set_Text(_TL("IHS to RGB"));
for(y=0; y<pPan->Get_NY() && Set_Progress(y, pPan->Get_NY()); y++)
{
#pragma omp parallel for
for(int x=0; x<pPan->Get_NX(); x++)
{
if( !pR->is_NoData(x, y) )
{
double i = Offset + Scale * (pPan->asDouble(x, y) - Offset_Pan);
double s = pG ->asDouble(x, y);
double h = pB ->asDouble(x, y);
double r, g, b;
if ( 0.0 <= h && h < 1.0 )
{
r = i * (1 + 2 * s - 3 * s * h) / 3;
g = i * (1 - s + 3 * s * h) / 3;
b = i * (1 - s ) / 3;
}
else if( 1.0 <= h && h < 2.0 )
{
r = i * (1 - s ) / 3;
g = i * (1 + 2 * s - 3 * s * (h - 1)) / 3;
b = i * (1 - s + 3 * s * (h - 1)) / 3;
}
else
{
r = i * (1 - s + 3 * s * (h - 2)) / 3;
g = i * (1 - s ) / 3;
b = i * (1 + 2 * s - 3 * s * (h - 2)) / 3;
}
pR->Set_Value(x, y, rMin + r * rRange);
pG->Set_Value(x, y, gMin + g * gRange);
pB->Set_Value(x, y, bMin + b * bRange);
}
}
}
//-----------------------------------------------------
return( true );
}
//---------------------------------------------------------
CSG_String CPresence_Prediction::Get_Feature(int x, int y, int i)
{
CSG_Grid *pFeature = m_Features[i].pGrid;
if( m_nNumClasses > 1 && m_Features[i].bNumeric )
{
return( CSG_String::Format("%d", (int)(m_nNumClasses * (pFeature->asDouble(x, y) - pFeature->Get_Min()) / pFeature->Get_Range())) );
}
return( SG_Get_String(pFeature->asDouble(x, y), -2) );
}
//---------------------------------------------------------
bool CPresence_Prediction::On_Execute(void)
{
//-----------------------------------------------------
EventSet DL_Events ; m_DL_Events = &DL_Events ;
GISTrainer DL_Trainer; m_DL_Trainer = &DL_Trainer;
MaxEntModel DL_Model ; m_DL_Model = &DL_Model ;
m_YT_Model.clear();
//-----------------------------------------------------
CSG_Grid *pPrediction = Parameters("PREDICTION" )->asGrid();
CSG_Grid *pProbability = Parameters("PROBABILITY")->asGrid();
if( !pPrediction ->Get_Range() ) DataObject_Set_Colors(pPrediction , 11, SG_COLORS_YELLOW_GREEN);
if( !pProbability->Get_Range() ) DataObject_Set_Colors(pProbability, 11, SG_COLORS_YELLOW_GREEN);
m_Method = Parameters("METHOD" )->asInt ();
m_nNumClasses = Parameters("NUM_CLASSES" )->asInt ();
m_bYT_Weights = Parameters("YT_NUMASREAL")->asBool();
//-----------------------------------------------------
CSG_Array Features;
if( !Get_Features(Features) )
{
Error_Set(_TL("invalid features"));
return( false );
}
//-----------------------------------------------------
if( m_Method == 0 && SG_File_Exists(Parameters("YT_FILE_LOAD")->asString()) )
{
if( !Get_File(Parameters("YT_FILE_LOAD")->asString()) )
{
return( false );
}
}
else if( !Get_Training() )
{
return( false );
}
//-----------------------------------------------------
Process_Set_Text(_TL("prediction"));
for(int y=0; y<Get_NY() && Set_Progress(y); y++)
{
#pragma omp parallel for
for(int x=0; x<Get_NX(); x++)
{
int i;
CSG_Strings Values;
for(i=0; i<m_nFeatures; i++)
{
if( !m_Features[i].pGrid->is_NoData(x, y) )
{
Values.Add(Get_Feature(x, y, i));
}
else
{
break;
}
}
if( Values.Get_Count() != m_nFeatures )
{
pPrediction ->Set_NoData(x, y);
pProbability->Set_NoData(x, y);
}
else switch( m_Method )
{
//---------------------------------------------
default: // Kyoshida
{
ME_Sample Sample;
for(i=0; i<m_nFeatures; i++)
{
if( m_bYT_Weights && m_Features[i].bNumeric )
{
Sample.add_feature(m_Features[i].Name, m_Features[i].pGrid->asDouble(x, y));
}
else
{
Sample.add_feature(Values[i].b_str());
}
}
vector<double> Probs = m_YT_Model.classify(Sample);
pPrediction ->Set_Value(x, y, m_YT_Model.get_class_id(Sample.label) == 0 ? 1 : 0);
pProbability->Set_Value(x, y, Probs[0]);
}
break;
//---------------------------------------------
case 1: // Dekang Lin
{
MaxEntEvent Event; Event.count(1);
for(i=0; i<m_nFeatures; i++)
{
Event.push_back(m_DL_Trainer->getId(Values[i].b_str()));
}
vector<double> Probs;
pPrediction ->Set_Value(x, y, m_DL_Model->getProbs(Event, Probs) == 0 ? 1 : 0);
pProbability->Set_Value(x, y, Probs[0]);
}
break;
}
}
}
return( true );
}