generic-library/opencv
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Boring changes - objdetect.

Browse Source
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
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
modules/objdetect/include/opencv2/objdetect.hpp
View File

@ -141,7 +141,7 @@ public:
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,
CASCADE_SCALE_IMAGE = 2,

4
modules/objdetect/include/opencv2/objdetect/erfilter.hpp
View File

@ -171,7 +171,7 @@ public:
\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
*/
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,
float maxArea = 0.13, float minProbability = 0.2,
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
\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);
}

24
modules/objdetect/src/cascadedetect.cpp
View File

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

9
modules/objdetect/src/erfilter.cpp
View File

@ -179,7 +179,6 @@ ERFilterNM::ERFilterNM()
minProbabilityDiff = 1.;
num_accepted_regions = 0;
num_rejected_regions = 0;
classifier = NULL;
}
// 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( (minProbabilityDiff >= 0.) && (minProbabilityDiff <= 1.) );
Ptr<ERFilterNM> filter = new ERFilterNM();
Ptr<ERFilterNM> filter = makePtr<ERFilterNM>();
if (cb == NULL)
filter->setCallback(new ERClassifierNM1());
filter->setCallback(makePtr<ERClassifierNM1>());
else
filter->setCallback(cb);
@ -1119,11 +1118,11 @@ Ptr<ERFilter> createERFilterNM2(const Ptr<ERFilter::Callback>& cb, float minProb
CV_Assert( (minProbability >= 0.) && (minProbability <= 1.) );
Ptr<ERFilterNM> filter = new ERFilterNM();
Ptr<ERFilterNM> filter = makePtr<ERFilterNM>();
if (cb == NULL)
filter->setCallback(new ERClassifierNM2());
filter->setCallback(makePtr<ERClassifierNM2>());
else
filter->setCallback(cb);

12
modules/objdetect/src/haar.cpp
View File

@ -1536,15 +1536,15 @@ cvHaarDetectObjectsForROC( const CvArr* _img,
maxSize.width = img->cols;
}
temp = cvCreateMat( img->rows, img->cols, CV_8UC1 );
sum = cvCreateMat( img->rows + 1, img->cols + 1, CV_32SC1 );
sqsum = cvCreateMat( img->rows + 1, img->cols + 1, CV_64FC1 );
temp.reset(cvCreateMat( img->rows, img->cols, CV_8UC1 ));
sum.reset(cvCreateMat( img->rows + 1, img->cols + 1, CV_32SC1 ));
sqsum.reset(cvCreateMat( img->rows + 1, img->cols + 1, CV_64FC1 ));
if( !cascade->hid_cascade )
icvCreateHidHaarClassifierCascade(cascade);
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 );
@ -1566,7 +1566,7 @@ cvHaarDetectObjectsForROC( const CvArr* _img,
if( use_ipp )
normImg = cvCreateMat( img->rows, img->cols, CV_32FC1 );
#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 )
{
@ -1635,7 +1635,7 @@ cvHaarDetectObjectsForROC( const CvArr* _img,
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 );
cvIntegral( temp, sumcanny );
}

22
modules/objdetect/src/linemod.cpp
View File

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

6
modules/objdetect/test/test_cascadeandhog.cpp
View File

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