void CameraTab::updateImageInfo()
{
CleanupSettingsModel *model = CleanupSettingsModel::instance();
CleanupParameters *params = model->getCurrentParameters();
TDimension outRes(0, 0);
TPointD outDpi;
params->getOutputImageInfo(outRes, outDpi.x, outDpi.y);
setImageInfo(outRes.lx, outRes.ly, outDpi.x, outDpi.y);
TXshSimpleLevel *sl;
TFrameId fid;
model->getCleanupFrame(sl, fid);
ToonzScene *scene = TApp::instance()->getCurrentScene()->getScene();
TFilePath outputPath(sl ? scene->decodeFilePath(model->getOutputPath(sl, params)) : TFilePath());
setImageInfo(outputPath);
}
void MDDAGClassifier::run(const string& dataFileName, const string& shypFileName,
int numIterations, const string& outResFileName, int numRanksEnclosed)
{
InputData* pData = loadInputData(dataFileName, shypFileName);
if (_verbose > 0)
cout << "Loading strong hypothesis..." << flush;
// The class that loads the weak hypotheses
UnSerialization us;
// Where to put the weak hypotheses
vector<BaseLearner*> weakHypotheses;
// loads them
us.loadHypotheses(shypFileName, weakHypotheses, pData);
// where the results go
vector< ExampleResults* > results;
if (_verbose > 0)
cout << "Classifying..." << flush;
// get the results
computeResults( pData, weakHypotheses, results, numIterations );
const int numClasses = pData->getNumClasses();
if (_verbose > 0)
{
// well.. if verbose = 0 no results are displayed! :)
cout << "Done!" << endl;
vector< vector<float> > rankedError(numRanksEnclosed);
// Get the per-class error for the numRanksEnclosed-th ranks
for (int i = 0; i < numRanksEnclosed; ++i)
getClassError( pData, results, rankedError[i], i );
// output it
cout << endl;
cout << "Error Summary" << endl;
cout << "=============" << endl;
for ( int l = 0; l < numClasses; ++l )
{
// first rank (winner): rankedError[0]
cout << "Class '" << pData->getClassMap().getNameFromIdx(l) << "': "
<< setprecision(4) << rankedError[0][l] * 100 << "%";
// output the others on its side
if (numRanksEnclosed > 1 && _verbose > 1)
{
cout << " (";
for (int i = 1; i < numRanksEnclosed; ++i)
cout << " " << i+1 << ":[" << setprecision(4) << rankedError[i][l] * 100 << "%]";
cout << " )";
}
cout << endl;
}
// the overall error
cout << "\n--> Overall Error: "
<< setprecision(4) << getOverallError(pData, results, 0) * 100 << "%";
// output the others on its side
if (numRanksEnclosed > 1 && _verbose > 1)
{
cout << " (";
for (int i = 1; i < numRanksEnclosed; ++i)
cout << " " << i+1 << ":[" << setprecision(4) << getOverallError(pData, results, i) * 100 << "%]";
cout << " )";
}
cout << endl;
} // verbose
// If asked output the results
if ( !outResFileName.empty() )
{
const int numExamples = pData->getNumExamples();
ofstream outRes(outResFileName.c_str());
outRes << "Instance" << '\t' << "Forecast" << '\t' << "Labels" << '\n';
string exampleName;
for (int i = 0; i < numExamples; ++i)
{
// output the name if it exists, otherwise the number
// of the example
exampleName = pData->getExampleName(i);
if ( exampleName.empty() )
outRes << i << '\t';
else
outRes << exampleName << '\t';
// output the predicted class
outRes << pData->getClassMap().getNameFromIdx( results[i]->getWinner().first ) << '\t';
outRes << '|';
vector<Label>& labels = pData->getLabels(i);
for (vector<Label>::iterator lIt=labels.begin(); lIt != labels.end(); ++lIt) {
if (lIt->y>0)
{
outRes << ' ' << pData->getClassMap().getNameFromIdx(lIt->idx);
}
}
outRes << endl;
}
if (_verbose > 0)
cout << "\nPredictions written on file <" << outResFileName << ">!" << endl;
}
// delete the input data file
if (pData)
delete pData;
vector<ExampleResults*>::iterator it;
for (it = results.begin(); it != results.end(); ++it)
delete (*it);
}
void VJCascadeClassifier::savePosteriors(const string& dataFileName, const string& shypFileName,
const string& outFileName, int numIterations)
{
// loading data
InputData* pData = loadInputData(dataFileName, shypFileName);
const int numOfExamples = pData->getNumExamples();
//get the index of positive label
const NameMap& namemap = pData->getClassMap();
_positiveLabelIndex = namemap.getIdxFromName( _positiveLabelName );
if (_verbose > 0)
cout << "Loading strong hypothesis..." << flush;
// open outfile
ofstream outRes(outFileName.c_str());
if (!outRes.is_open())
{
cout << "Cannot open outfile!!! " << outFileName << endl;
}
// The class that loads the weak hypotheses
UnSerialization us;
// Where to put the weak hypotheses
vector<vector<BaseLearner*> > weakHypotheses;
// For stagewise thresholds
vector<AlphaReal> thresholds(0);
// loads them
//us.loadHypotheses(shypFileName, weakHypotheses, pData);
us.loadCascadeHypotheses(shypFileName, weakHypotheses, thresholds, pData);
// output the number of stages
outRes << "StageNum " << weakHypotheses.size() << endl;
// output original labels
outRes << "Labels";
for(int i=0; i<numOfExamples; ++i )
{
vector<Label>& labels = pData->getLabels(i);
if (labels[_positiveLabelIndex].y>0) // pos label
outRes << " 1";
else
outRes << " 0";
}
outRes << endl;
// store result
vector<CascadeOutputInformation> cascadeData(0);
vector<CascadeOutputInformation>::iterator it;
cascadeData.resize(numOfExamples);
for( it=cascadeData.begin(); it != cascadeData.end(); ++it )
{
it->active=true;
}
for(int stagei=0; stagei < weakHypotheses.size(); ++stagei )
{
// for posteriors
vector<AlphaReal> posteriors(0);
// calculate the posteriors after stage
VJCascadeLearner::calculatePosteriors( pData, weakHypotheses[stagei], posteriors, _positiveLabelIndex );
// update the data (posteriors, active element index etc.)
//VJCascadeLearner::forecastOverAllCascade( pData, posteriors, activeInstances, thresholds[stagei] );
updateCascadeData(pData, weakHypotheses, stagei, posteriors, thresholds, _positiveLabelIndex, cascadeData);
int numberOfActiveInstance = 0;
for( int i = 0; i < numOfExamples; ++i )
if (cascadeData[i].active) numberOfActiveInstance++;
if (_verbose > 0 )
cout << "Number of active instances: " << numberOfActiveInstance << "(" << numOfExamples << ")" << endl;
// output stats
outRes << "Stage " << stagei << " " << weakHypotheses[stagei].size() << endl;
outRes << "Forecast";
for(int i=0; i<numOfExamples; ++i )
{
outRes << " " << cascadeData[i].forecast;
}
outRes << endl;
outRes << "Active";
for(int i=0; i<numOfExamples; ++i )
{
if( cascadeData[i].active)
outRes << " 1";
else
outRes << " 0";
}
outRes << endl;
outRes << "Posteriors";
for(int i=0; i<numOfExamples; ++i )
{
outRes << " " << cascadeData[i].score;
}
outRes << endl;
}
outRes.close();
// free memory allocation
vector<vector<BaseLearner*> >::iterator bvIt;
for( bvIt = weakHypotheses.begin(); bvIt != weakHypotheses.end(); ++bvIt )
{
vector<BaseLearner* >::iterator bIt;
for( bIt = (*bvIt).begin(); bIt != (*bvIt).end(); ++bIt )
delete *bIt;
}
}
