mirror of
https://github.com/opencv/opencv.git
synced 2024-12-21 13:48:04 +08:00
d58cd9851f
Conflicts: CMakeLists.txt cmake/OpenCVDetectCUDA.cmake doc/tutorials/features2d/feature_flann_matcher/feature_flann_matcher.rst modules/core/src/cmdparser.cpp modules/gpu/CMakeLists.txt modules/gpu/doc/introduction.rst modules/gpu/perf/perf_video.cpp modules/highgui/doc/reading_and_writing_images_and_video.rst modules/ocl/src/cl_context.cpp modules/video/include/opencv2/video/background_segm.hpp samples/cpp/image_sequence.cpp samples/cpp/tutorial_code/ImgTrans/HoughCircle_Demo.cpp samples/python/chessboard.py samples/python/cvutils.py samples/python/demhist.py samples/python/dft.py samples/python/distrans.py samples/python/edge.py samples/python/ffilldemo.py samples/python/fitellipse.py samples/python/houghlines.py samples/python/inpaint.py samples/python/logpolar.py samples/python/morphology.py samples/python/numpy_array.py samples/python/watershed.py
549 lines
19 KiB
C++
549 lines
19 KiB
C++
#include "opencv2/core.hpp"
|
|
|
|
#include "cascadeclassifier.h"
|
|
#include <queue>
|
|
|
|
using namespace std;
|
|
using namespace cv;
|
|
|
|
static const char* stageTypes[] = { CC_BOOST };
|
|
static const char* featureTypes[] = { CC_HAAR, CC_LBP, CC_HOG };
|
|
|
|
CvCascadeParams::CvCascadeParams() : stageType( defaultStageType ),
|
|
featureType( defaultFeatureType ), winSize( cvSize(24, 24) )
|
|
{
|
|
name = CC_CASCADE_PARAMS;
|
|
}
|
|
CvCascadeParams::CvCascadeParams( int _stageType, int _featureType ) : stageType( _stageType ),
|
|
featureType( _featureType ), winSize( cvSize(24, 24) )
|
|
{
|
|
name = CC_CASCADE_PARAMS;
|
|
}
|
|
|
|
//---------------------------- CascadeParams --------------------------------------
|
|
|
|
void CvCascadeParams::write( FileStorage &fs ) const
|
|
{
|
|
string stageTypeStr = stageType == BOOST ? CC_BOOST : string();
|
|
CV_Assert( !stageTypeStr.empty() );
|
|
fs << CC_STAGE_TYPE << stageTypeStr;
|
|
string featureTypeStr = featureType == CvFeatureParams::HAAR ? CC_HAAR :
|
|
featureType == CvFeatureParams::LBP ? CC_LBP :
|
|
featureType == CvFeatureParams::HOG ? CC_HOG :
|
|
0;
|
|
CV_Assert( !stageTypeStr.empty() );
|
|
fs << CC_FEATURE_TYPE << featureTypeStr;
|
|
fs << CC_HEIGHT << winSize.height;
|
|
fs << CC_WIDTH << winSize.width;
|
|
}
|
|
|
|
bool CvCascadeParams::read( const FileNode &node )
|
|
{
|
|
if ( node.empty() )
|
|
return false;
|
|
string stageTypeStr, featureTypeStr;
|
|
FileNode rnode = node[CC_STAGE_TYPE];
|
|
if ( !rnode.isString() )
|
|
return false;
|
|
rnode >> stageTypeStr;
|
|
stageType = !stageTypeStr.compare( CC_BOOST ) ? BOOST : -1;
|
|
if (stageType == -1)
|
|
return false;
|
|
rnode = node[CC_FEATURE_TYPE];
|
|
if ( !rnode.isString() )
|
|
return false;
|
|
rnode >> featureTypeStr;
|
|
featureType = !featureTypeStr.compare( CC_HAAR ) ? CvFeatureParams::HAAR :
|
|
!featureTypeStr.compare( CC_LBP ) ? CvFeatureParams::LBP :
|
|
!featureTypeStr.compare( CC_HOG ) ? CvFeatureParams::HOG :
|
|
-1;
|
|
if (featureType == -1)
|
|
return false;
|
|
node[CC_HEIGHT] >> winSize.height;
|
|
node[CC_WIDTH] >> winSize.width;
|
|
return winSize.height > 0 && winSize.width > 0;
|
|
}
|
|
|
|
void CvCascadeParams::printDefaults() const
|
|
{
|
|
CvParams::printDefaults();
|
|
cout << " [-stageType <";
|
|
for( int i = 0; i < (int)(sizeof(stageTypes)/sizeof(stageTypes[0])); i++ )
|
|
{
|
|
cout << (i ? " | " : "") << stageTypes[i];
|
|
if ( i == defaultStageType )
|
|
cout << "(default)";
|
|
}
|
|
cout << ">]" << endl;
|
|
|
|
cout << " [-featureType <{";
|
|
for( int i = 0; i < (int)(sizeof(featureTypes)/sizeof(featureTypes[0])); i++ )
|
|
{
|
|
cout << (i ? ", " : "") << featureTypes[i];
|
|
if ( i == defaultStageType )
|
|
cout << "(default)";
|
|
}
|
|
cout << "}>]" << endl;
|
|
cout << " [-w <sampleWidth = " << winSize.width << ">]" << endl;
|
|
cout << " [-h <sampleHeight = " << winSize.height << ">]" << endl;
|
|
}
|
|
|
|
void CvCascadeParams::printAttrs() const
|
|
{
|
|
cout << "stageType: " << stageTypes[stageType] << endl;
|
|
cout << "featureType: " << featureTypes[featureType] << endl;
|
|
cout << "sampleWidth: " << winSize.width << endl;
|
|
cout << "sampleHeight: " << winSize.height << endl;
|
|
}
|
|
|
|
bool CvCascadeParams::scanAttr( const string prmName, const string val )
|
|
{
|
|
bool res = true;
|
|
if( !prmName.compare( "-stageType" ) )
|
|
{
|
|
for( int i = 0; i < (int)(sizeof(stageTypes)/sizeof(stageTypes[0])); i++ )
|
|
if( !val.compare( stageTypes[i] ) )
|
|
stageType = i;
|
|
}
|
|
else if( !prmName.compare( "-featureType" ) )
|
|
{
|
|
for( int i = 0; i < (int)(sizeof(featureTypes)/sizeof(featureTypes[0])); i++ )
|
|
if( !val.compare( featureTypes[i] ) )
|
|
featureType = i;
|
|
}
|
|
else if( !prmName.compare( "-w" ) )
|
|
{
|
|
winSize.width = atoi( val.c_str() );
|
|
}
|
|
else if( !prmName.compare( "-h" ) )
|
|
{
|
|
winSize.height = atoi( val.c_str() );
|
|
}
|
|
else
|
|
res = false;
|
|
return res;
|
|
}
|
|
|
|
//---------------------------- CascadeClassifier --------------------------------------
|
|
|
|
bool CvCascadeClassifier::train( const string _cascadeDirName,
|
|
const string _posFilename,
|
|
const string _negFilename,
|
|
int _numPos, int _numNeg,
|
|
int _precalcValBufSize, int _precalcIdxBufSize,
|
|
int _numStages,
|
|
const CvCascadeParams& _cascadeParams,
|
|
const CvFeatureParams& _featureParams,
|
|
const CvCascadeBoostParams& _stageParams,
|
|
bool baseFormatSave )
|
|
{
|
|
// Start recording clock ticks for training time output
|
|
const clock_t begin_time = clock();
|
|
|
|
if( _cascadeDirName.empty() || _posFilename.empty() || _negFilename.empty() )
|
|
CV_Error( CV_StsBadArg, "_cascadeDirName or _bgfileName or _vecFileName is NULL" );
|
|
|
|
string dirName;
|
|
if (_cascadeDirName.find_last_of("/\\") == (_cascadeDirName.length() - 1) )
|
|
dirName = _cascadeDirName;
|
|
else
|
|
dirName = _cascadeDirName + '/';
|
|
|
|
numPos = _numPos;
|
|
numNeg = _numNeg;
|
|
numStages = _numStages;
|
|
if ( !imgReader.create( _posFilename, _negFilename, _cascadeParams.winSize ) )
|
|
{
|
|
cout << "Image reader can not be created from -vec " << _posFilename
|
|
<< " and -bg " << _negFilename << "." << endl;
|
|
return false;
|
|
}
|
|
if ( !load( dirName ) )
|
|
{
|
|
cascadeParams = _cascadeParams;
|
|
featureParams = CvFeatureParams::create(cascadeParams.featureType);
|
|
featureParams->init(_featureParams);
|
|
stageParams = makePtr<CvCascadeBoostParams>();
|
|
*stageParams = _stageParams;
|
|
featureEvaluator = CvFeatureEvaluator::create(cascadeParams.featureType);
|
|
featureEvaluator->init( featureParams, numPos + numNeg, cascadeParams.winSize );
|
|
stageClassifiers.reserve( numStages );
|
|
}
|
|
cout << "PARAMETERS:" << endl;
|
|
cout << "cascadeDirName: " << _cascadeDirName << endl;
|
|
cout << "vecFileName: " << _posFilename << endl;
|
|
cout << "bgFileName: " << _negFilename << endl;
|
|
cout << "numPos: " << _numPos << endl;
|
|
cout << "numNeg: " << _numNeg << endl;
|
|
cout << "numStages: " << numStages << endl;
|
|
cout << "precalcValBufSize[Mb] : " << _precalcValBufSize << endl;
|
|
cout << "precalcIdxBufSize[Mb] : " << _precalcIdxBufSize << endl;
|
|
cascadeParams.printAttrs();
|
|
stageParams->printAttrs();
|
|
featureParams->printAttrs();
|
|
|
|
int startNumStages = (int)stageClassifiers.size();
|
|
if ( startNumStages > 1 )
|
|
cout << endl << "Stages 0-" << startNumStages-1 << " are loaded" << endl;
|
|
else if ( startNumStages == 1)
|
|
cout << endl << "Stage 0 is loaded" << endl;
|
|
|
|
double requiredLeafFARate = pow( (double) stageParams->maxFalseAlarm, (double) numStages ) /
|
|
(double)stageParams->max_depth;
|
|
double tempLeafFARate;
|
|
|
|
for( int i = startNumStages; i < numStages; i++ )
|
|
{
|
|
cout << endl << "===== TRAINING " << i << "-stage =====" << endl;
|
|
cout << "<BEGIN" << endl;
|
|
|
|
if ( !updateTrainingSet( tempLeafFARate ) )
|
|
{
|
|
cout << "Train dataset for temp stage can not be filled. "
|
|
"Branch training terminated." << endl;
|
|
break;
|
|
}
|
|
if( tempLeafFARate <= requiredLeafFARate )
|
|
{
|
|
cout << "Required leaf false alarm rate achieved. "
|
|
"Branch training terminated." << endl;
|
|
break;
|
|
}
|
|
|
|
Ptr<CvCascadeBoost> tempStage = makePtr<CvCascadeBoost>();
|
|
bool isStageTrained = tempStage->train( featureEvaluator,
|
|
curNumSamples, _precalcValBufSize, _precalcIdxBufSize,
|
|
*stageParams );
|
|
cout << "END>" << endl;
|
|
|
|
if(!isStageTrained)
|
|
break;
|
|
|
|
stageClassifiers.push_back( tempStage );
|
|
|
|
// save params
|
|
if( i == 0)
|
|
{
|
|
std::string paramsFilename = dirName + CC_PARAMS_FILENAME;
|
|
FileStorage fs( paramsFilename, FileStorage::WRITE);
|
|
if ( !fs.isOpened() )
|
|
{
|
|
cout << "Parameters can not be written, because file " << paramsFilename
|
|
<< " can not be opened." << endl;
|
|
return false;
|
|
}
|
|
fs << FileStorage::getDefaultObjectName(paramsFilename) << "{";
|
|
writeParams( fs );
|
|
fs << "}";
|
|
}
|
|
// save current stage
|
|
char buf[10];
|
|
sprintf(buf, "%s%d", "stage", i );
|
|
string stageFilename = dirName + buf + ".xml";
|
|
FileStorage fs( stageFilename, FileStorage::WRITE );
|
|
if ( !fs.isOpened() )
|
|
{
|
|
cout << "Current stage can not be written, because file " << stageFilename
|
|
<< " can not be opened." << endl;
|
|
return false;
|
|
}
|
|
fs << FileStorage::getDefaultObjectName(stageFilename) << "{";
|
|
tempStage->write( fs, Mat() );
|
|
fs << "}";
|
|
|
|
// Output training time up till now
|
|
float seconds = float( clock () - begin_time ) / CLOCKS_PER_SEC;
|
|
int days = int(seconds) / 60 / 60 / 24;
|
|
int hours = (int(seconds) / 60 / 60) % 24;
|
|
int minutes = (int(seconds) / 60) % 60;
|
|
int seconds_left = int(seconds) % 60;
|
|
cout << "Training until now has taken " << days << " days " << hours << " hours " << minutes << " minutes " << seconds_left <<" seconds." << endl;
|
|
}
|
|
|
|
if(stageClassifiers.size() == 0)
|
|
{
|
|
cout << "Cascade classifier can't be trained. Check the used training parameters." << endl;
|
|
return false;
|
|
}
|
|
|
|
save( dirName + CC_CASCADE_FILENAME, baseFormatSave );
|
|
|
|
return true;
|
|
}
|
|
|
|
int CvCascadeClassifier::predict( int sampleIdx )
|
|
{
|
|
CV_DbgAssert( sampleIdx < numPos + numNeg );
|
|
for (vector< Ptr<CvCascadeBoost> >::iterator it = stageClassifiers.begin();
|
|
it != stageClassifiers.end(); it++ )
|
|
{
|
|
if ( (*it)->predict( sampleIdx ) == 0.f )
|
|
return 0;
|
|
}
|
|
return 1;
|
|
}
|
|
|
|
bool CvCascadeClassifier::updateTrainingSet( double& acceptanceRatio)
|
|
{
|
|
int64 posConsumed = 0, negConsumed = 0;
|
|
imgReader.restart();
|
|
int posCount = fillPassedSamples( 0, numPos, true, posConsumed );
|
|
if( !posCount )
|
|
return false;
|
|
cout << "POS count : consumed " << posCount << " : " << (int)posConsumed << endl;
|
|
|
|
int proNumNeg = cvRound( ( ((double)numNeg) * ((double)posCount) ) / numPos ); // apply only a fraction of negative samples. double is required since overflow is possible
|
|
int negCount = fillPassedSamples( posCount, proNumNeg, false, negConsumed );
|
|
if ( !negCount )
|
|
return false;
|
|
|
|
curNumSamples = posCount + negCount;
|
|
acceptanceRatio = negConsumed == 0 ? 0 : ( (double)negCount/(double)(int64)negConsumed );
|
|
cout << "NEG count : acceptanceRatio " << negCount << " : " << acceptanceRatio << endl;
|
|
return true;
|
|
}
|
|
|
|
int CvCascadeClassifier::fillPassedSamples( int first, int count, bool isPositive, int64& consumed )
|
|
{
|
|
int getcount = 0;
|
|
Mat img(cascadeParams.winSize, CV_8UC1);
|
|
for( int i = first; i < first + count; i++ )
|
|
{
|
|
for( ; ; )
|
|
{
|
|
bool isGetImg = isPositive ? imgReader.getPos( img ) :
|
|
imgReader.getNeg( img );
|
|
if( !isGetImg )
|
|
return getcount;
|
|
consumed++;
|
|
|
|
featureEvaluator->setImage( img, isPositive ? 1 : 0, i );
|
|
if( predict( i ) == 1.0F )
|
|
{
|
|
getcount++;
|
|
printf("%s current samples: %d\r", isPositive ? "POS":"NEG", getcount);
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
return getcount;
|
|
}
|
|
|
|
void CvCascadeClassifier::writeParams( FileStorage &fs ) const
|
|
{
|
|
cascadeParams.write( fs );
|
|
fs << CC_STAGE_PARAMS << "{"; stageParams->write( fs ); fs << "}";
|
|
fs << CC_FEATURE_PARAMS << "{"; featureParams->write( fs ); fs << "}";
|
|
}
|
|
|
|
void CvCascadeClassifier::writeFeatures( FileStorage &fs, const Mat& featureMap ) const
|
|
{
|
|
featureEvaluator->writeFeatures( fs, featureMap );
|
|
}
|
|
|
|
void CvCascadeClassifier::writeStages( FileStorage &fs, const Mat& featureMap ) const
|
|
{
|
|
char cmnt[30];
|
|
int i = 0;
|
|
fs << CC_STAGES << "[";
|
|
for( vector< Ptr<CvCascadeBoost> >::const_iterator it = stageClassifiers.begin();
|
|
it != stageClassifiers.end(); it++, i++ )
|
|
{
|
|
sprintf( cmnt, "stage %d", i );
|
|
cvWriteComment( fs.fs, cmnt, 0 );
|
|
fs << "{";
|
|
(*it)->write( fs, featureMap );
|
|
fs << "}";
|
|
}
|
|
fs << "]";
|
|
}
|
|
|
|
bool CvCascadeClassifier::readParams( const FileNode &node )
|
|
{
|
|
if ( !node.isMap() || !cascadeParams.read( node ) )
|
|
return false;
|
|
|
|
stageParams = makePtr<CvCascadeBoostParams>();
|
|
FileNode rnode = node[CC_STAGE_PARAMS];
|
|
if ( !stageParams->read( rnode ) )
|
|
return false;
|
|
|
|
featureParams = CvFeatureParams::create(cascadeParams.featureType);
|
|
rnode = node[CC_FEATURE_PARAMS];
|
|
if ( !featureParams->read( rnode ) )
|
|
return false;
|
|
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;
|
|
}
|
|
|
|
// For old Haar Classifier file saving
|
|
#define ICV_HAAR_SIZE_NAME "size"
|
|
#define ICV_HAAR_STAGES_NAME "stages"
|
|
#define ICV_HAAR_TREES_NAME "trees"
|
|
#define ICV_HAAR_FEATURE_NAME "feature"
|
|
#define ICV_HAAR_RECTS_NAME "rects"
|
|
#define ICV_HAAR_TILTED_NAME "tilted"
|
|
#define ICV_HAAR_THRESHOLD_NAME "threshold"
|
|
#define ICV_HAAR_LEFT_NODE_NAME "left_node"
|
|
#define ICV_HAAR_LEFT_VAL_NAME "left_val"
|
|
#define ICV_HAAR_RIGHT_NODE_NAME "right_node"
|
|
#define ICV_HAAR_RIGHT_VAL_NAME "right_val"
|
|
#define ICV_HAAR_STAGE_THRESHOLD_NAME "stage_threshold"
|
|
#define ICV_HAAR_PARENT_NAME "parent"
|
|
#define ICV_HAAR_NEXT_NAME "next"
|
|
|
|
void CvCascadeClassifier::save( const string filename, bool baseFormat )
|
|
{
|
|
FileStorage fs( filename, FileStorage::WRITE );
|
|
|
|
if ( !fs.isOpened() )
|
|
return;
|
|
|
|
fs << FileStorage::getDefaultObjectName(filename) << "{";
|
|
if ( !baseFormat )
|
|
{
|
|
Mat featureMap;
|
|
getUsedFeaturesIdxMap( featureMap );
|
|
writeParams( fs );
|
|
fs << CC_STAGE_NUM << (int)stageClassifiers.size();
|
|
writeStages( fs, featureMap );
|
|
writeFeatures( fs, featureMap );
|
|
}
|
|
else
|
|
{
|
|
//char buf[256];
|
|
CvSeq* weak;
|
|
if ( cascadeParams.featureType != CvFeatureParams::HAAR )
|
|
CV_Error( CV_StsBadFunc, "old file format is used for Haar-like features only");
|
|
fs << ICV_HAAR_SIZE_NAME << "[:" << cascadeParams.winSize.width <<
|
|
cascadeParams.winSize.height << "]";
|
|
fs << ICV_HAAR_STAGES_NAME << "[";
|
|
for( size_t si = 0; si < stageClassifiers.size(); si++ )
|
|
{
|
|
fs << "{"; //stage
|
|
/*sprintf( buf, "stage %d", si );
|
|
CV_CALL( cvWriteComment( fs, buf, 1 ) );*/
|
|
weak = stageClassifiers[si]->get_weak_predictors();
|
|
fs << ICV_HAAR_TREES_NAME << "[";
|
|
for( int wi = 0; wi < weak->total; wi++ )
|
|
{
|
|
int inner_node_idx = -1, total_inner_node_idx = -1;
|
|
queue<const CvDTreeNode*> inner_nodes_queue;
|
|
CvCascadeBoostTree* tree = *((CvCascadeBoostTree**) cvGetSeqElem( weak, wi ));
|
|
|
|
fs << "[";
|
|
/*sprintf( buf, "tree %d", wi );
|
|
CV_CALL( cvWriteComment( fs, buf, 1 ) );*/
|
|
|
|
const CvDTreeNode* tempNode;
|
|
|
|
inner_nodes_queue.push( tree->get_root() );
|
|
total_inner_node_idx++;
|
|
|
|
while (!inner_nodes_queue.empty())
|
|
{
|
|
tempNode = inner_nodes_queue.front();
|
|
inner_node_idx++;
|
|
|
|
fs << "{";
|
|
fs << ICV_HAAR_FEATURE_NAME << "{";
|
|
((CvHaarEvaluator*)featureEvaluator.get())->writeFeature( fs, tempNode->split->var_idx );
|
|
fs << "}";
|
|
|
|
fs << ICV_HAAR_THRESHOLD_NAME << tempNode->split->ord.c;
|
|
|
|
if( tempNode->left->left || tempNode->left->right )
|
|
{
|
|
inner_nodes_queue.push( tempNode->left );
|
|
total_inner_node_idx++;
|
|
fs << ICV_HAAR_LEFT_NODE_NAME << total_inner_node_idx;
|
|
}
|
|
else
|
|
fs << ICV_HAAR_LEFT_VAL_NAME << tempNode->left->value;
|
|
|
|
if( tempNode->right->left || tempNode->right->right )
|
|
{
|
|
inner_nodes_queue.push( tempNode->right );
|
|
total_inner_node_idx++;
|
|
fs << ICV_HAAR_RIGHT_NODE_NAME << total_inner_node_idx;
|
|
}
|
|
else
|
|
fs << ICV_HAAR_RIGHT_VAL_NAME << tempNode->right->value;
|
|
fs << "}"; // ICV_HAAR_FEATURE_NAME
|
|
inner_nodes_queue.pop();
|
|
}
|
|
fs << "]";
|
|
}
|
|
fs << "]"; //ICV_HAAR_TREES_NAME
|
|
fs << ICV_HAAR_STAGE_THRESHOLD_NAME << stageClassifiers[si]->getThreshold();
|
|
fs << ICV_HAAR_PARENT_NAME << (int)si-1 << ICV_HAAR_NEXT_NAME << -1;
|
|
fs << "}"; //stage
|
|
} /* for each stage */
|
|
fs << "]"; //ICV_HAAR_STAGES_NAME
|
|
}
|
|
fs << "}";
|
|
}
|
|
|
|
bool CvCascadeClassifier::load( const string cascadeDirName )
|
|
{
|
|
FileStorage fs( cascadeDirName + CC_PARAMS_FILENAME, FileStorage::READ );
|
|
if ( !fs.isOpened() )
|
|
return false;
|
|
FileNode node = fs.getFirstTopLevelNode();
|
|
if ( !readParams( node ) )
|
|
return false;
|
|
featureEvaluator = CvFeatureEvaluator::create(cascadeParams.featureType);
|
|
featureEvaluator->init( featureParams, numPos + numNeg, cascadeParams.winSize );
|
|
fs.release();
|
|
|
|
char buf[10];
|
|
for ( int si = 0; si < numStages; si++ )
|
|
{
|
|
sprintf( buf, "%s%d", "stage", si);
|
|
fs.open( cascadeDirName + buf + ".xml", FileStorage::READ );
|
|
node = fs.getFirstTopLevelNode();
|
|
if ( !fs.isOpened() )
|
|
break;
|
|
Ptr<CvCascadeBoost> tempStage = makePtr<CvCascadeBoost>();
|
|
|
|
if ( !tempStage->read( node, featureEvaluator, *stageParams ))
|
|
{
|
|
fs.release();
|
|
break;
|
|
}
|
|
stageClassifiers.push_back(tempStage);
|
|
}
|
|
return true;
|
|
}
|
|
|
|
void CvCascadeClassifier::getUsedFeaturesIdxMap( Mat& featureMap )
|
|
{
|
|
int varCount = featureEvaluator->getNumFeatures() * featureEvaluator->getFeatureSize();
|
|
featureMap.create( 1, varCount, CV_32SC1 );
|
|
featureMap.setTo(Scalar(-1));
|
|
|
|
for( vector< Ptr<CvCascadeBoost> >::const_iterator it = stageClassifiers.begin();
|
|
it != stageClassifiers.end(); it++ )
|
|
(*it)->markUsedFeaturesInMap( featureMap );
|
|
|
|
for( int fi = 0, idx = 0; fi < varCount; fi++ )
|
|
if ( featureMap.at<int>(0, fi) >= 0 )
|
|
featureMap.ptr<int>(0)[fi] = idx++;
|
|
}
|