113 lines
3.7 KiB
C++
113 lines
3.7 KiB
C++
#include "opencv2/core/core.hpp"
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#include "opencv2/core/internal.hpp"
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#include "cv.h"
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#include "cascadeclassifier.h"
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using namespace std;
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int main( int argc, char* argv[] )
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{
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CvCascadeClassifier classifier;
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String cascadeDirName, vecName, bgName;
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int numPos = 2000;
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int numNeg = 1000;
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int numStages = 20;
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int precalcValBufSize = 256,
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precalcIdxBufSize = 256;
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bool baseFormatSave = false;
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CvCascadeParams cascadeParams;
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CvCascadeBoostParams stageParams;
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Ptr<CvFeatureParams> featureParams[] = { Ptr<CvFeatureParams>(new CvHaarFeatureParams),
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Ptr<CvFeatureParams>(new CvLBPFeatureParams),
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Ptr<CvFeatureParams>(new CvHOGFeatureParams)
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};
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int fc = sizeof(featureParams)/sizeof(featureParams[0]);
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if( argc == 1 )
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{
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cout << "Usage: " << argv[0] << endl;
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cout << " -data <cascade_dir_name>" << endl;
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cout << " -vec <vec_file_name>" << endl;
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cout << " -bg <background_file_name>" << endl;
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cout << " [-numPos <number_of_positive_samples = " << numPos << ">]" << endl;
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cout << " [-numNeg <number_of_negative_samples = " << numNeg << ">]" << endl;
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cout << " [-numStages <number_of_stages = " << numStages << ">]" << endl;
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cout << " [-precalcValBufSize <precalculated_vals_buffer_size_in_Mb = " << precalcValBufSize << ">]" << endl;
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cout << " [-precalcIdxBufSize <precalculated_idxs_buffer_size_in_Mb = " << precalcIdxBufSize << ">]" << endl;
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cout << " [-baseFormatSave]" << endl;
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cascadeParams.printDefaults();
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stageParams.printDefaults();
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for( int fi = 0; fi < fc; fi++ )
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featureParams[fi]->printDefaults();
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return 0;
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}
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for( int i = 1; i < argc; i++ )
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{
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bool set = false;
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if( !strcmp( argv[i], "-data" ) )
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{
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cascadeDirName = argv[++i];
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}
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else if( !strcmp( argv[i], "-vec" ) )
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{
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vecName = argv[++i];
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}
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else if( !strcmp( argv[i], "-bg" ) )
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{
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bgName = argv[++i];
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}
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else if( !strcmp( argv[i], "-numPos" ) )
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{
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numPos = atoi( argv[++i] );
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}
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else if( !strcmp( argv[i], "-numNeg" ) )
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{
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numNeg = atoi( argv[++i] );
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}
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else if( !strcmp( argv[i], "-numStages" ) )
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{
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numStages = atoi( argv[++i] );
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}
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else if( !strcmp( argv[i], "-precalcValBufSize" ) )
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{
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precalcValBufSize = atoi( argv[++i] );
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}
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else if( !strcmp( argv[i], "-precalcIdxBufSize" ) )
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{
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precalcIdxBufSize = atoi( argv[++i] );
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}
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else if( !strcmp( argv[i], "-baseFormatSave" ) )
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{
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baseFormatSave = true;
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}
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else if ( cascadeParams.scanAttr( argv[i], argv[i+1] ) ) { i++; }
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else if ( stageParams.scanAttr( argv[i], argv[i+1] ) ) { i++; }
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else if ( !set )
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{
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for( int fi = 0; fi < fc; fi++ )
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{
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set = featureParams[fi]->scanAttr(argv[i], argv[i+1]);
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if ( !set )
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{
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i++;
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break;
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}
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}
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}
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}
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classifier.train( cascadeDirName,
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vecName,
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bgName,
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numPos, numNeg,
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precalcValBufSize, precalcIdxBufSize,
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numStages,
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cascadeParams,
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*featureParams[cascadeParams.featureType],
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stageParams,
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baseFormatSave );
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return 0;
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}
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