--- /dev/null
+#include "cascadeclassifier.h"
+#include <queue>
+
+using namespace std;
+
+static const char* stageTypes[] = { CC_BOOST };
+static const char* featureTypes[] = { CC_HAAR, CC_LBP };
+
+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 : 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 : -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 )
+{
+ if( _cascadeDirName.empty() || _posFilename.empty() || _negFilename.empty() )
+ CV_Error( CV_StsBadArg, "_cascadeDirName or _bgfileName or _vecFileName is NULL" );
+
+ String dirName;
+ if ( _cascadeDirName.find('/') )
+ dirName = _cascadeDirName + '/';
+ else
+ dirName = _cascadeDirName + '\\';
+
+ numPos = _numPos;
+ numNeg = _numNeg;
+ numStages = _numStages;
+ if ( !imgReader.create( _posFilename, _negFilename, cascadeParams.winSize ) )
+ return false;
+ if ( !load( dirName ) )
+ {
+ cascadeParams = _cascadeParams;
+ featureParams = CvFeatureParams::create(cascadeParams.featureType);
+ featureParams->init(_featureParams);
+ stageParams = new CvCascadeBoostParams;
+ *stageParams = _stageParams;
+ featureEvaluator = CvFeatureEvaluator::create(cascadeParams.featureType);
+ featureEvaluator->init( (CvFeatureParams*)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;
+ }
+
+ CvCascadeBoost* tempStage = new CvCascadeBoost;
+ tempStage->train( (CvFeatureEvaluator*)featureEvaluator,
+ curNumSamples, _precalcValBufSize, _precalcIdxBufSize,
+ *((CvCascadeBoostParams*)stageParams) );
+ stageClassifiers.push_back( tempStage );
+
+ cout << "END>" << endl;
+
+ // save params
+ String filename;
+ if ( i == 0)
+ {
+ filename = dirName + CC_PARAMS_FILENAME;
+ FileStorage fs( filename, FileStorage::WRITE);
+ if ( !fs.isOpened() )
+ return false;
+ fs << FileStorage::getDefaultObjectName(filename) << "{";
+ writeParams( fs );
+ fs << "}";
+ }
+ // save temp stage
+ char buf[10];
+ sprintf(buf, "%s%d", "stage", i );
+ filename = dirName + buf + ".xml";
+ FileStorage fs( filename, FileStorage::WRITE );
+ if ( !fs.isOpened() )
+ return false;
+ fs << FileStorage::getDefaultObjectName(filename) << "{";
+ tempStage->write( fs, Mat() );
+ fs << "}";
+ }
+ 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 = fillPassedSamles( 0, numPos, true, posConsumed );
+ if( !posCount )
+ return false;
+ cout << "POS count : consumed " << posCount << " : " << (int)posConsumed << endl;
+
+ int negCount = fillPassedSamles( numPos, numNeg, 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::fillPassedSamles( 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++;
+ 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
+{
+ ((CvFeatureEvaluator*)((Ptr<CvFeatureEvaluator>)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 );
+ CV_CALL( cvWriteComment( fs, cmnt, 0 ) );*/
+ fs << "{";
+ ((CvCascadeBoost*)((Ptr<CvCascadeBoost>)*it))->write( fs, featureMap );
+ fs << "}";
+ }
+ fs << "]";
+}
+
+bool CvCascadeClassifier::readParams( const FileNode &node )
+{
+ if ( !node.isMap() || !cascadeParams.read( node ) )
+ return false;
+
+ stageParams = new 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++ )
+ {
+ CvCascadeBoost* tempStage = new CvCascadeBoost;
+ if ( !tempStage->read( *it, (CvFeatureEvaluator *)featureEvaluator, *((CvCascadeBoostParams*)stageParams) ) )
+ {
+ delete tempStage;
+ 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*)((CvFeatureEvaluator*)featureEvaluator))->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( ((CvFeatureParams*)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;
+ CvCascadeBoost *tempStage = new CvCascadeBoost;
+
+ if ( !tempStage->read( node, (CvFeatureEvaluator*)featureEvaluator, *((CvCascadeBoostParams*)stageParams )) )
+ {
+ delete tempStage;
+ fs.release();
+ break;
+ }
+ stageClassifiers.push_back(tempStage);
+ }
+ return true;
+}
+
+void CvCascadeClassifier::getUsedFeaturesIdxMap( Mat& featureMap )
+{
+ featureMap.create( 1, featureEvaluator->getNumFeatures(), CV_32SC1 );
+ featureMap.setTo(Scalar(-1));
+
+ for( vector< Ptr<CvCascadeBoost> >::const_iterator it = stageClassifiers.begin();
+ it != stageClassifiers.end(); it++ )
+ ((CvCascadeBoost*)((Ptr<CvCascadeBoost>)(*it)))->markUsedFeaturesInMap( featureMap );
+
+ for( int fi = 0, idx = 0; fi < featureEvaluator->getNumFeatures(); fi++ )
+ if ( featureMap.at<int>(0, fi) >= 0 )
+ featureMap.ptr<int>(0)[fi] = idx++;
+}
projects / opencv / blobdiff