93 lines
2.9 KiB
C++
93 lines
2.9 KiB
C++
#include "opencv2/core.hpp"
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#include "traincascade_features.h"
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#include "cascadeclassifier.h"
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using namespace std;
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using namespace cv;
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float calcNormFactor( const Mat& sum, const Mat& sqSum )
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{
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CV_DbgAssert( sum.cols > 3 && sqSum.rows > 3 );
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Rect normrect( 1, 1, sum.cols - 3, sum.rows - 3 );
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size_t p0, p1, p2, p3;
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CV_SUM_OFFSETS( p0, p1, p2, p3, normrect, sum.step1() )
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double area = normrect.width * normrect.height;
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const int *sp = sum.ptr<int>();
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int valSum = sp[p0] - sp[p1] - sp[p2] + sp[p3];
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const double *sqp = sqSum.ptr<double>();
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double valSqSum = sqp[p0] - sqp[p1] - sqp[p2] + sqp[p3];
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return (float) sqrt( (double) (area * valSqSum - (double)valSum * valSum) );
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}
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CvParams::CvParams() : name( "params" ) {}
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void CvParams::printDefaults() const
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{ cout << "--" << name << "--" << endl; }
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void CvParams::printAttrs() const {}
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bool CvParams::scanAttr( const string, const string ) { return false; }
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//---------------------------- FeatureParams --------------------------------------
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CvFeatureParams::CvFeatureParams() : maxCatCount( 0 ), featSize( 1 )
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{
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name = CC_FEATURE_PARAMS;
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}
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void CvFeatureParams::init( const CvFeatureParams& fp )
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{
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maxCatCount = fp.maxCatCount;
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featSize = fp.featSize;
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}
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void CvFeatureParams::write( FileStorage &fs ) const
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{
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fs << CC_MAX_CAT_COUNT << maxCatCount;
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fs << CC_FEATURE_SIZE << featSize;
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}
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bool CvFeatureParams::read( const FileNode &node )
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{
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if ( node.empty() )
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return false;
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maxCatCount = node[CC_MAX_CAT_COUNT];
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featSize = node[CC_FEATURE_SIZE];
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return ( maxCatCount >= 0 && featSize >= 1 );
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}
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Ptr<CvFeatureParams> CvFeatureParams::create( int featureType )
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{
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return featureType == HAAR ? Ptr<CvFeatureParams>(new CvHaarFeatureParams) :
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featureType == LBP ? Ptr<CvFeatureParams>(new CvLBPFeatureParams) :
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featureType == HOG ? Ptr<CvFeatureParams>(new CvHOGFeatureParams) :
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Ptr<CvFeatureParams>();
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}
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//------------------------------------- FeatureEvaluator ---------------------------------------
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void CvFeatureEvaluator::init(const CvFeatureParams *_featureParams,
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int _maxSampleCount, Size _winSize )
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{
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CV_Assert(_maxSampleCount > 0);
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featureParams = (CvFeatureParams *)_featureParams;
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winSize = _winSize;
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numFeatures = 0;
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cls.create( (int)_maxSampleCount, 1, CV_32FC1 );
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generateFeatures();
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}
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void CvFeatureEvaluator::setImage(const Mat &img, uchar clsLabel, int idx)
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{
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CV_Assert(img.cols == winSize.width);
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CV_Assert(img.rows == winSize.height);
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CV_Assert(idx < cls.rows);
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cls.ptr<float>(idx)[0] = clsLabel;
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}
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Ptr<CvFeatureEvaluator> CvFeatureEvaluator::create(int type)
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{
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return type == CvFeatureParams::HAAR ? Ptr<CvFeatureEvaluator>(new CvHaarEvaluator) :
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type == CvFeatureParams::LBP ? Ptr<CvFeatureEvaluator>(new CvLBPEvaluator) :
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type == CvFeatureParams::HOG ? Ptr<CvFeatureEvaluator>(new CvHOGEvaluator) :
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Ptr<CvFeatureEvaluator>();
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}
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