generic-library/opencv
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removed unnecessary param

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This commit is contained in:
Maria Dimashova 2012-04-11 13:48:20 +00:00
parent d8cec20e1b
commit 3db7823682
2 changed files with 4 additions and 10 deletions
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4
modules/legacy/include/opencv2/legacy/legacy.hpp
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@ -1806,7 +1806,7 @@ public:
virtual bool train( const CvMat* samples, const CvMat* sampleIdx=0, virtual bool train( const CvMat* samples, const CvMat* sampleIdx=0,
CvEMParams params=CvEMParams(), CvMat* labels=0 ); CvEMParams params=CvEMParams(), CvMat* labels=0 );
virtual float predict( const CvMat* sample, CV_OUT CvMat* probs, bool isNormalize=true ) const; virtual float predict( const CvMat* sample, CV_OUT CvMat* probs ) const;
#ifndef SWIG #ifndef SWIG
CV_WRAP CvEM( const cv::Mat& samples, const cv::Mat& sampleIdx=cv::Mat(), CV_WRAP CvEM( const cv::Mat& samples, const cv::Mat& sampleIdx=cv::Mat(),
@ -1817,7 +1817,7 @@ public:
CvEMParams params=CvEMParams(), CvEMParams params=CvEMParams(),
CV_OUT cv::Mat* labels=0 ); CV_OUT cv::Mat* labels=0 );
CV_WRAP virtual float predict( const cv::Mat& sample, CV_OUT cv::Mat* probs=0, bool isNormalize=true ) const; CV_WRAP virtual float predict( const cv::Mat& sample, CV_OUT cv::Mat* probs=0 ) const;
CV_WRAP virtual double calcLikelihood( const cv::Mat &sample ) const; CV_WRAP virtual double calcLikelihood( const cv::Mat &sample ) const;
CV_WRAP int getNClusters() const; CV_WRAP int getNClusters() const;

10
modules/legacy/src/em.cpp
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@ -102,15 +102,12 @@ double CvEM::calcLikelihood( const Mat &input_sample ) const
} }
float float
CvEM::predict( const CvMat* _sample, CvMat* _probs, bool isNormalize ) const CvEM::predict( const CvMat* _sample, CvMat* _probs ) const
{ {
Mat prbs0 = cvarrToMat(_probs), prbs = prbs0, sample = cvarrToMat(_sample); Mat prbs0 = cvarrToMat(_probs), prbs = prbs0, sample = cvarrToMat(_sample);
int cls = emObj.predict(sample, _probs ? _OutputArray(prbs) : cv::noArray()); int cls = emObj.predict(sample, _probs ? _OutputArray(prbs) : cv::noArray());
if(_probs) if(_probs)
{ {
if(isNormalize)
normalize(prbs, prbs, 1, 0, NORM_L1);
if( prbs.data != prbs0.data ) if( prbs.data != prbs0.data )
{ {
CV_Assert( prbs.size == prbs0.size ); CV_Assert( prbs.size == prbs0.size );
@ -236,12 +233,9 @@ bool CvEM::train( const Mat& _samples, const Mat& _sample_idx,
} }
float float
CvEM::predict( const Mat& _sample, Mat* _probs, bool isNormalize ) const CvEM::predict( const Mat& _sample, Mat* _probs ) const
{ {
int cls = emObj.predict(_sample, _probs ? _OutputArray(*_probs) : cv::noArray()); int cls = emObj.predict(_sample, _probs ? _OutputArray(*_probs) : cv::noArray());
if(_probs && isNormalize)
normalize(*_probs, *_probs, 1, 0, NORM_L1);
return (float)cls; return (float)cls;
} }