bool DataSetVOC::loadBBoxes(CStr &nameNE, vector<Vec4i> &boxes, vecI &clsIdx)
{
string fName = format(_S(annoPathW), _S(nameNE));
FileStorage fs(fName, FileStorage::READ);
FileNode fn = fs["annotation"]["object"];
boxes.clear();
clsIdx.clear();
if (fn.isSeq()){
for (FileNodeIterator it = fn.begin(), it_end = fn.end(); it != it_end; it++)
loadBox(*it, boxes, clsIdx);
}
else
loadBox(fn, boxes, clsIdx);
return true;
}
bool CvCascadeClassifier::readStages( const FileNode &node)
{
FileNode rnode = node[CC_STAGES];
if (!rnode.empty() || !rnode.isSeq())
return false;
stageClassifiers.reserve(numStages);
FileNodeIterator it = rnode.begin();
for( int i = 0; i < min( (int)rnode.size(), numStages ); i++, it++ )
{
Ptr<CvCascadeBoost> tempStage = makePtr<CvCascadeBoost>();
if ( !tempStage->read( *it, featureEvaluator, *stageParams) )
return false;
stageClassifiers.push_back(tempStage);
}
return true;
}
virtual int readRunParams( FileStorage& fs )
{
int code = CV_StereoMatchingTest::readRunParams(fs);
FileNode fn = fs.getFirstTopLevelNode();
assert(fn.isSeq());
for( int i = 0; i < (int)fn.size(); i+=4 )
{
string caseName = fn[i], datasetName = fn[i+1];
RunParams params;
string ndisp = fn[i+2]; params.ndisp = atoi(ndisp.c_str());
string iterCount = fn[i+3]; params.iterCount = atoi(iterCount.c_str());
caseNames.push_back( caseName );
caseDatasets.push_back( datasetName );
caseRunParams.push_back( params );
}
return code;
}
virtual int readRunParams( FileStorage& fs )
{
int code = CV_StereoMatchingTest::readRunParams(fs);
FileNode fn = fs.getFirstTopLevelNode();
assert(fn.isSeq());
for( int i = 0; i < (int)fn.size(); i+=5 )
{
string caseName = fn[i], datasetName = fn[i+1];
RunParams params;
string ndisp = fn[i+2]; params.ndisp = atoi(ndisp.c_str());
string winSize = fn[i+3]; params.winSize = atoi(winSize.c_str());
string fullDP = fn[i+4]; params.fullDP = atoi(fullDP.c_str()) == 0 ? false : true;
caseNames.push_back( caseName );
caseDatasets.push_back( datasetName );
caseRunParams.push_back( params );
}
return code;
}
bool CvCascadeClassifier::readStages( const FileNode &node)
{
FileNode rnode = node[CC_STAGES];
if (!rnode.empty() || !rnode.isSeq())
return false;
stageClassifiers.reserve(numStages);
FileNodeIterator it = rnode.begin();
for( int i = 0; i < min( (int)rnode.size(), numStages ); i++, it++ )
{
CvCascadeBoost* tempStage = new CvCascadeBoost;
if ( !tempStage->read( *it, (CvFeatureEvaluator *)featureEvaluator, *((CvCascadeBoostParams*)stageParams) ) )
{
delete tempStage;
return false;
}
stageClassifiers.push_back(tempStage);
}
return true;
}
int CV_StereoMatchingTest::readDatasetsParams( FileStorage& fs )
{
if( !fs.isOpened() )
{
ts->printf( CvTS::LOG, "datasetsParams can not be read " );
return CvTS::FAIL_INVALID_TEST_DATA;
}
datasetsParams.clear();
FileNode fn = fs.getFirstTopLevelNode();
assert(fn.isSeq());
for( int i = 0; i < (int)fn.size(); i+=3 )
{
string name = fn[i];
DatasetParams params;
string sf = fn[i+1]; params.dispScaleFactor = atoi(sf.c_str());
string uv = fn[i+2]; params.dispUnknVal = atoi(uv.c_str());
datasetsParams[name] = params;
}
return CvTS::OK;
}
