From: Maria Dimashova <no@email>
Date: Wed, 11 Apr 2012 13:48:20 +0000 (+0000)
Subject: removed unnecessary param
X-Git-Tag: accepted/tizen/6.0/unified/20201030.111113~1314^2~2130
X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=3db78236826b9cdf875d740e3b22a82a1b041a11;p=platform%2Fupstream%2Fopencv.git

removed unnecessary param
---

diff --git a/modules/legacy/include/opencv2/legacy/legacy.hpp b/modules/legacy/include/opencv2/legacy/legacy.hpp
index 36e8487..8df5a23 100644
--- a/modules/legacy/include/opencv2/legacy/legacy.hpp
+++ b/modules/legacy/include/opencv2/legacy/legacy.hpp
@@ -1806,7 +1806,7 @@ public:
     virtual bool train( const CvMat* samples, const CvMat* sampleIdx=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
     CV_WRAP CvEM( const cv::Mat& samples, const cv::Mat& sampleIdx=cv::Mat(),
@@ -1817,7 +1817,7 @@ public:
                                 CvEMParams params=CvEMParams(),
                                 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 int getNClusters() const;
diff --git a/modules/legacy/src/em.cpp b/modules/legacy/src/em.cpp
index dbf774d..543df09 100644
--- a/modules/legacy/src/em.cpp
+++ b/modules/legacy/src/em.cpp
@@ -102,15 +102,12 @@ double CvEM::calcLikelihood( const Mat &input_sample ) const
 }
 
 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);
 	int cls = emObj.predict(sample, _probs ? _OutputArray(prbs) : cv::noArray());
     if(_probs)
     {
-        if(isNormalize)
-            normalize(prbs, prbs, 1, 0, NORM_L1);
-        
         if( prbs.data != prbs0.data )
         {
             CV_Assert( prbs.size == prbs0.size );
@@ -236,12 +233,9 @@ bool CvEM::train( const Mat& _samples, const Mat& _sample_idx,
 }
 
 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());
-    if(_probs && isNormalize)
-        normalize(*_probs, *_probs, 1, 0, NORM_L1);
-
     return (float)cls;
 }