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coding style: vadim comments

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This commit is contained in:
Alex Leontiev
2014-08-05 20:39:53 +03:00
parent 4e14e8c812
commit 01aa659353
1 changed files with 13 additions and 8 deletions
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21
modules/tracking/src/tld_tracker.cpp
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@@ -88,12 +88,11 @@ using namespace tld;
* 11. group decls logically, order of statements * 11. group decls logically, order of statements
* *
* ?10. all in one class * ?10. all in one class
* todo: initializer lists; const methods * todo:
* initializer lists;
* const methods
* *
* ?( ) * ?( )
*
* ?vadim: for{1command} can omit {}; if( a != (b + c) ) vs ( a != ( b + c ) ); if{} for{} method{} oneline:spaces, omit{};
* 1-statement for/if without {}
*/ */
/* design decisions: /* design decisions:
@@ -178,7 +177,7 @@ public:
void setBoudingBox(Rect2d boundingBox){ boundingBox_ = boundingBox; } void setBoudingBox(Rect2d boundingBox){ boundingBox_ = boundingBox; }
double getOriginalVariance(){ return originalVariance_; } double getOriginalVariance(){ return originalVariance_; }
inline double ensembleClassifierNum(const uchar* data); inline double ensembleClassifierNum(const uchar* data);
inline void prepareClassifiers(int rowstep){ for( int i = 0; i < (int)classifiers.size(); i++ ) classifiers[i].prepareClassifier(rowstep); } inline void prepareClassifiers(int rowstep);
double Sr(const Mat_<uchar>& patch); double Sr(const Mat_<uchar>& patch);
double Sc(const Mat_<uchar>& patch); double Sc(const Mat_<uchar>& patch);
void integrateRelabeled(Mat& img, Mat& imgBlurred, const std::vector<TLDDetector::LabeledPatch>& patches); void integrateRelabeled(Mat& img, Mat& imgBlurred, const std::vector<TLDDetector::LabeledPatch>& patches);
@@ -326,7 +325,7 @@ bool TrackerTLDImpl::updateImpl(const Mat& image, Rect2d& boundingBox)
for( int i = 0; i < 2; i++ ) for( int i = 0; i < 2; i++ )
{ {
Rect2d tmpCandid = boundingBox; Rect2d tmpCandid = boundingBox;
if( ( (i == 0) && !(data->failedLastTime) && trackerProxy->update(image, tmpCandid) ) || if( ( (i == 0) && !data->failedLastTime && trackerProxy->update(image, tmpCandid) ) ||
( (i == 1) && detector->detect(imageForDetector, image_blurred, tmpCandid, detectorResults) ) ) ( (i == 1) && detector->detect(imageForDetector, image_blurred, tmpCandid, detectorResults) ) )
{ {
candidates.push_back(tmpCandid); candidates.push_back(tmpCandid);
@@ -395,7 +394,8 @@ bool TrackerTLDImpl::updateImpl(const Mat& image, Rect2d& boundingBox)
if( detectorResults[i].isObject ) if( detectorResults[i].isObject )
{ {
expertResult = nExpert(detectorResults[i].rect); expertResult = nExpert(detectorResults[i].rect);
if( expertResult != detectorResults[i].isObject ){ negRelabeled++; } if( expertResult != detectorResults[i].isObject )
negRelabeled++;
} }
else else
{ {
@@ -758,7 +758,7 @@ void TrackerTLDModel::integrateAdditional(const std::vector<Mat_<uchar> >& eForM
for( int i = 0; i < (int)classifiers.size(); i++ ) for( int i = 0; i < (int)classifiers.size(); i++ )
p += classifiers[i].posteriorProbability(eForEnsemble[k].data, (int)eForEnsemble[k].step[0]); p += classifiers[i].posteriorProbability(eForEnsemble[k].data, (int)eForEnsemble[k].step[0]);
p /= classifiers.size(); p /= classifiers.size();
if( (p > ENSEMBLE_THRESHOLD) != isPositive ) if( ( p > ENSEMBLE_THRESHOLD ) != isPositive )
{ {
if( isPositive ) if( isPositive )
positiveIntoEnsemble++; positiveIntoEnsemble++;
@@ -935,5 +935,10 @@ void TrackerTLDModel::pushIntoModel(const Mat_<uchar>& example, bool positive)
} }
(*proxyN)++; (*proxyN)++;
} }
void TrackerTLDModel::prepareClassifiers(int rowstep)
{
for( int i = 0; i < (int)classifiers.size(); i++ )
classifiers[i].prepareClassifier(rowstep);
}
} /* namespace cv */ } /* namespace cv */