Chiebot-Mirror
/
opencv
8
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Boring changes - objdetect.

Browse Source
pull/1281/head
Roman Donchenko 12 years ago
parent de6a934f66
commit b12894d95a
7 changed files with 39 additions and 40 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 2
      modules/objdetect/include/opencv2/objdetect.hpp
  2. 4
      modules/objdetect/include/opencv2/objdetect/erfilter.hpp
  3. 24
      modules/objdetect/src/cascadedetect.cpp
  4. 9
      modules/objdetect/src/erfilter.cpp
  5. 12
      modules/objdetect/src/haar.cpp
  6. 22
      modules/objdetect/src/linemod.cpp
  7. 6
      modules/objdetect/test/test_cascadeandhog.cpp

2
modules/objdetect/include/opencv2/objdetect.hpp
Unescape 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
Unescape 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
Unescape 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
Unescape 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
Unescape 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
Unescape 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
Unescape 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;

Write Preview
Loading…
Cancel
Save