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Boring changes - objdetect.

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This commit is contained in:
Roman Donchenko 2013-08-13 17:45:29 +04:00
parent de6a934f66
commit b12894d95a
7 changed files with 39 additions and 40 deletions
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2
modules/objdetect/include/opencv2/objdetect.hpp
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@ -141,7 +141,7 @@ public:
static Ptr<FeatureEvaluator> create(int type); static Ptr<FeatureEvaluator> create(int type);
}; };
template<> CV_EXPORTS void Ptr<CvHaarClassifierCascade>::delete_obj(); template<> CV_EXPORTS void DefaultDeleter<CvHaarClassifierCascade>::operator ()(CvHaarClassifierCascade* obj) const;
enum { CASCADE_DO_CANNY_PRUNING = 1, enum { CASCADE_DO_CANNY_PRUNING = 1,
CASCADE_SCALE_IMAGE = 2, CASCADE_SCALE_IMAGE = 2,

4
modules/objdetect/include/opencv2/objdetect/erfilter.hpp
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@ -171,7 +171,7 @@ public:
\param nonMaxSuppression Whenever non-maximum suppression is done over the branch probabilities \param nonMaxSuppression Whenever non-maximum suppression is done over the branch probabilities
\param minProbability The minimum probability difference between local maxima and local minima ERs \param minProbability The minimum probability difference between local maxima and local minima ERs
*/ */
CV_EXPORTS Ptr<ERFilter> createERFilterNM1(const Ptr<ERFilter::Callback>& cb = NULL, CV_EXPORTS Ptr<ERFilter> createERFilterNM1(const Ptr<ERFilter::Callback>& cb = Ptr<ERFilter::Callback>(),
int thresholdDelta = 1, float minArea = 0.000025, int thresholdDelta = 1, float minArea = 0.000025,
float maxArea = 0.13, float minProbability = 0.2, float maxArea = 0.13, float minProbability = 0.2,
bool nonMaxSuppression = true, bool nonMaxSuppression = true,
@ -190,7 +190,7 @@ CV_EXPORTS Ptr<ERFilter> createERFilterNM1(const Ptr<ERFilter::Callback>& cb = N
if omitted tries to load a default classifier from file trained_classifierNM2.xml if omitted tries to load a default classifier from file trained_classifierNM2.xml
\param minProbability The minimum probability P(er|character) allowed for retreived ER's \param minProbability The minimum probability P(er|character) allowed for retreived ER's
*/ */
CV_EXPORTS Ptr<ERFilter> createERFilterNM2(const Ptr<ERFilter::Callback>& cb = NULL, CV_EXPORTS Ptr<ERFilter> createERFilterNM2(const Ptr<ERFilter::Callback>& cb = Ptr<ERFilter::Callback>(),
float minProbability = 0.85); float minProbability = 0.85);
} }

24
modules/objdetect/src/cascadedetect.cpp
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@ -467,7 +467,7 @@ bool HaarEvaluator::Feature :: read( const FileNode& node )
HaarEvaluator::HaarEvaluator() HaarEvaluator::HaarEvaluator()
{ {
features = new std::vector<Feature>(); features = makePtr<std::vector<Feature> >();
} }
HaarEvaluator::~HaarEvaluator() HaarEvaluator::~HaarEvaluator()
{ {
@ -492,7 +492,7 @@ bool HaarEvaluator::read(const FileNode& node)
Ptr<FeatureEvaluator> HaarEvaluator::clone() const Ptr<FeatureEvaluator> HaarEvaluator::clone() const
{ {
HaarEvaluator* ret = new HaarEvaluator; Ptr<HaarEvaluator> ret = makePtr<HaarEvaluator>();
ret->origWinSize = origWinSize; ret->origWinSize = origWinSize;
ret->features = features; ret->features = features;
ret->featuresPtr = &(*ret->features)[0]; ret->featuresPtr = &(*ret->features)[0];
@ -582,7 +582,7 @@ bool LBPEvaluator::Feature :: read(const FileNode& node )
LBPEvaluator::LBPEvaluator() LBPEvaluator::LBPEvaluator()
{ {
features = new std::vector<Feature>(); features = makePtr<std::vector<Feature> >();
} }
LBPEvaluator::~LBPEvaluator() LBPEvaluator::~LBPEvaluator()
{ {
@ -603,7 +603,7 @@ bool LBPEvaluator::read( const FileNode& node )
Ptr<FeatureEvaluator> LBPEvaluator::clone() const Ptr<FeatureEvaluator> LBPEvaluator::clone() const
{ {
LBPEvaluator* ret = new LBPEvaluator; Ptr<LBPEvaluator> ret = makePtr<LBPEvaluator>();
ret->origWinSize = origWinSize; ret->origWinSize = origWinSize;
ret->features = features; ret->features = features;
ret->featuresPtr = &(*ret->features)[0]; ret->featuresPtr = &(*ret->features)[0];
@ -662,7 +662,7 @@ bool HOGEvaluator::Feature :: read( const FileNode& node )
HOGEvaluator::HOGEvaluator() HOGEvaluator::HOGEvaluator()
{ {
features = new std::vector<Feature>(); features = makePtr<std::vector<Feature> >();
} }
HOGEvaluator::~HOGEvaluator() HOGEvaluator::~HOGEvaluator()
@ -684,7 +684,7 @@ bool HOGEvaluator::read( const FileNode& node )
Ptr<FeatureEvaluator> HOGEvaluator::clone() const Ptr<FeatureEvaluator> HOGEvaluator::clone() const
{ {
HOGEvaluator* ret = new HOGEvaluator; Ptr<HOGEvaluator> ret = makePtr<HOGEvaluator>();
ret->origWinSize = origWinSize; ret->origWinSize = origWinSize;
ret->features = features; ret->features = features;
ret->featuresPtr = &(*ret->features)[0]; ret->featuresPtr = &(*ret->features)[0];
@ -849,7 +849,7 @@ CascadeClassifier::~CascadeClassifier()
bool CascadeClassifier::empty() const bool CascadeClassifier::empty() const
{ {
return oldCascade.empty() && data.stages.empty(); return !oldCascade && data.stages.empty();
} }
bool CascadeClassifier::load(const String& filename) bool CascadeClassifier::load(const String& filename)
@ -867,13 +867,13 @@ bool CascadeClassifier::load(const String& filename)
fs.release(); fs.release();
oldCascade = Ptr<CvHaarClassifierCascade>((CvHaarClassifierCascade*)cvLoad(filename.c_str(), 0, 0, 0)); oldCascade.reset((CvHaarClassifierCascade*)cvLoad(filename.c_str(), 0, 0, 0));
return !oldCascade.empty(); return !oldCascade.empty();
} }
int CascadeClassifier::runAt( Ptr<FeatureEvaluator>& evaluator, Point pt, double& weight ) int CascadeClassifier::runAt( Ptr<FeatureEvaluator>& evaluator, Point pt, double& weight )
{ {
CV_Assert( oldCascade.empty() ); CV_Assert( !oldCascade );
assert( data.featureType == FeatureEvaluator::HAAR || assert( data.featureType == FeatureEvaluator::HAAR ||
data.featureType == FeatureEvaluator::LBP || data.featureType == FeatureEvaluator::LBP ||
@ -1022,7 +1022,7 @@ bool CascadeClassifier::detectSingleScale( const Mat& image, int stripCount, Siz
#endif #endif
Mat currentMask; Mat currentMask;
if (!maskGenerator.empty()) { if (maskGenerator) {
currentMask=maskGenerator->generateMask(image); currentMask=maskGenerator->generateMask(image);
} }
@ -1097,7 +1097,7 @@ void CascadeClassifier::detectMultiScaleNoGrouping( const Mat& image, std::vecto
{ {
candidates.clear(); candidates.clear();
if (!maskGenerator.empty()) if (maskGenerator)
maskGenerator->initializeMask(image); maskGenerator->initializeMask(image);
if( maxObjectSize.height == 0 || maxObjectSize.width == 0 ) if( maxObjectSize.height == 0 || maxObjectSize.width == 0 )
@ -1350,7 +1350,7 @@ bool CascadeClassifier::read(const FileNode& root)
return featureEvaluator->read(fn); return featureEvaluator->read(fn);
} }
template<> void Ptr<CvHaarClassifierCascade>::delete_obj() template<> void DefaultDeleter<CvHaarClassifierCascade>::operator ()(CvHaarClassifierCascade* obj) const
{ cvReleaseHaarClassifierCascade(&obj); } { cvReleaseHaarClassifierCascade(&obj); }
} // namespace cv } // namespace cv

9
modules/objdetect/src/erfilter.cpp
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@ -179,7 +179,6 @@ ERFilterNM::ERFilterNM()
minProbabilityDiff = 1.; minProbabilityDiff = 1.;
num_accepted_regions = 0; num_accepted_regions = 0;
num_rejected_regions = 0; num_rejected_regions = 0;
classifier = NULL;
} }
// the key method. Takes image on input, vector of ERStat is output for the first stage, // the key method. Takes image on input, vector of ERStat is output for the first stage,
@ -1085,10 +1084,10 @@ Ptr<ERFilter> createERFilterNM1(const Ptr<ERFilter::Callback>& cb, int threshold
CV_Assert( (thresholdDelta >= 0) && (thresholdDelta <= 128) ); CV_Assert( (thresholdDelta >= 0) && (thresholdDelta <= 128) );
CV_Assert( (minProbabilityDiff >= 0.) && (minProbabilityDiff <= 1.) ); CV_Assert( (minProbabilityDiff >= 0.) && (minProbabilityDiff <= 1.) );
Ptr<ERFilterNM> filter = new ERFilterNM(); Ptr<ERFilterNM> filter = makePtr<ERFilterNM>();
if (cb == NULL) if (cb == NULL)
filter->setCallback(new ERClassifierNM1()); filter->setCallback(makePtr<ERClassifierNM1>());
else else
filter->setCallback(cb); filter->setCallback(cb);
@ -1119,11 +1118,11 @@ Ptr<ERFilter> createERFilterNM2(const Ptr<ERFilter::Callback>& cb, float minProb
CV_Assert( (minProbability >= 0.) && (minProbability <= 1.) ); CV_Assert( (minProbability >= 0.) && (minProbability <= 1.) );
Ptr<ERFilterNM> filter = new ERFilterNM(); Ptr<ERFilterNM> filter = makePtr<ERFilterNM>();
if (cb == NULL) if (cb == NULL)
filter->setCallback(new ERClassifierNM2()); filter->setCallback(makePtr<ERClassifierNM2>());
else else
filter->setCallback(cb); filter->setCallback(cb);

12
modules/objdetect/src/haar.cpp
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@ -1536,15 +1536,15 @@ cvHaarDetectObjectsForROC( const CvArr* _img,
maxSize.width = img->cols; maxSize.width = img->cols;
} }
temp = cvCreateMat( img->rows, img->cols, CV_8UC1 ); temp.reset(cvCreateMat( img->rows, img->cols, CV_8UC1 ));
sum = cvCreateMat( img->rows + 1, img->cols + 1, CV_32SC1 ); sum.reset(cvCreateMat( img->rows + 1, img->cols + 1, CV_32SC1 ));
sqsum = cvCreateMat( img->rows + 1, img->cols + 1, CV_64FC1 ); sqsum.reset(cvCreateMat( img->rows + 1, img->cols + 1, CV_64FC1 ));
if( !cascade->hid_cascade ) if( !cascade->hid_cascade )
icvCreateHidHaarClassifierCascade(cascade); icvCreateHidHaarClassifierCascade(cascade);
if( cascade->hid_cascade->has_tilted_features ) if( cascade->hid_cascade->has_tilted_features )
tilted = cvCreateMat( img->rows + 1, img->cols + 1, CV_32SC1 ); tilted.reset(cvCreateMat( img->rows + 1, img->cols + 1, CV_32SC1 ));
result_seq = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvAvgComp), storage ); result_seq = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvAvgComp), storage );
@ -1566,7 +1566,7 @@ cvHaarDetectObjectsForROC( const CvArr* _img,
if( use_ipp ) if( use_ipp )
normImg = cvCreateMat( img->rows, img->cols, CV_32FC1 ); normImg = cvCreateMat( img->rows, img->cols, CV_32FC1 );
#endif #endif
imgSmall = cvCreateMat( img->rows + 1, img->cols + 1, CV_8UC1 ); imgSmall.reset(cvCreateMat( img->rows + 1, img->cols + 1, CV_8UC1 ));
for( factor = 1; ; factor *= scaleFactor ) for( factor = 1; ; factor *= scaleFactor )
{ {
@ -1635,7 +1635,7 @@ cvHaarDetectObjectsForROC( const CvArr* _img,
if( doCannyPruning ) if( doCannyPruning )
{ {
sumcanny = cvCreateMat( img->rows + 1, img->cols + 1, CV_32SC1 ); sumcanny.reset(cvCreateMat( img->rows + 1, img->cols + 1, CV_32SC1 ));
cvCanny( img, temp, 0, 50, 3 ); cvCanny( img, temp, 0, 50, 3 );
cvIntegral( temp, sumcanny ); cvIntegral( temp, sumcanny );
} }

22
modules/objdetect/src/linemod.cpp
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@ -204,11 +204,11 @@ void QuantizedPyramid::selectScatteredFeatures(const std::vector<Candidate>& can
Ptr<Modality> Modality::create(const String& modality_type) Ptr<Modality> Modality::create(const String& modality_type)
{ {
if (modality_type == "ColorGradient") if (modality_type == "ColorGradient")
return new ColorGradient(); return makePtr<ColorGradient>();
else if (modality_type == "DepthNormal") else if (modality_type == "DepthNormal")
return new DepthNormal(); return makePtr<DepthNormal>();
else else
return NULL; return Ptr<Modality>();
} }
Ptr<Modality> Modality::create(const FileNode& fn) Ptr<Modality> Modality::create(const FileNode& fn)
@ -574,7 +574,7 @@ String ColorGradient::name() const
Ptr<QuantizedPyramid> ColorGradient::processImpl(const Mat& src, Ptr<QuantizedPyramid> ColorGradient::processImpl(const Mat& src,
const Mat& mask) const const Mat& mask) const
{ {
return new ColorGradientPyramid(src, mask, weak_threshold, num_features, strong_threshold); return makePtr<ColorGradientPyramid>(src, mask, weak_threshold, num_features, strong_threshold);
} }
void ColorGradient::read(const FileNode& fn) void ColorGradient::read(const FileNode& fn)
@ -889,8 +889,8 @@ String DepthNormal::name() const
Ptr<QuantizedPyramid> DepthNormal::processImpl(const Mat& src, Ptr<QuantizedPyramid> DepthNormal::processImpl(const Mat& src,
const Mat& mask) const const Mat& mask) const
{ {
return new DepthNormalPyramid(src, mask, distance_threshold, difference_threshold, return makePtr<DepthNormalPyramid>(src, mask, distance_threshold, difference_threshold,
num_features, extract_threshold); num_features, extract_threshold);
} }
void DepthNormal::read(const FileNode& fn) void DepthNormal::read(const FileNode& fn)
@ -1828,16 +1828,16 @@ static const int T_DEFAULTS[] = {5, 8};
Ptr<Detector> getDefaultLINE() Ptr<Detector> getDefaultLINE()
{ {
std::vector< Ptr<Modality> > modalities; std::vector< Ptr<Modality> > modalities;
modalities.push_back(new ColorGradient); modalities.push_back(makePtr<ColorGradient>());
return new Detector(modalities, std::vector<int>(T_DEFAULTS, T_DEFAULTS + 2)); return makePtr<Detector>(modalities, std::vector<int>(T_DEFAULTS, T_DEFAULTS + 2));
} }
Ptr<Detector> getDefaultLINEMOD() Ptr<Detector> getDefaultLINEMOD()
{ {
std::vector< Ptr<Modality> > modalities; std::vector< Ptr<Modality> > modalities;
modalities.push_back(new ColorGradient); modalities.push_back(makePtr<ColorGradient>());
modalities.push_back(new DepthNormal); modalities.push_back(makePtr<DepthNormal>());
return new Detector(modalities, std::vector<int>(T_DEFAULTS, T_DEFAULTS + 2)); return makePtr<Detector>(modalities, std::vector<int>(T_DEFAULTS, T_DEFAULTS + 2));
} }
} // namespace linemod } // namespace linemod

6
modules/objdetect/test/test_cascadeandhog.cpp
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@ -426,10 +426,10 @@ int CV_CascadeDetectorTest::detectMultiScale_C( const string& filename,
int di, const Mat& img, int di, const Mat& img,
vector<Rect>& objects ) vector<Rect>& objects )
{ {
Ptr<CvHaarClassifierCascade> c_cascade = cvLoadHaarClassifierCascade(filename.c_str(), cvSize(0,0)); Ptr<CvHaarClassifierCascade> c_cascade(cvLoadHaarClassifierCascade(filename.c_str(), cvSize(0,0)));
Ptr<CvMemStorage> storage = cvCreateMemStorage(); Ptr<CvMemStorage> storage(cvCreateMemStorage());
if( c_cascade.empty() ) if( !c_cascade )
{ {
ts->printf( cvtest::TS::LOG, "cascade %s can not be opened"); ts->printf( cvtest::TS::LOG, "cascade %s can not be opened");
return cvtest::TS::FAIL_INVALID_TEST_DATA; return cvtest::TS::FAIL_INVALID_TEST_DATA;