Merge remote-tracking branch 'upstream/master' into bow_desc

Conflicts:
	modules/features2d/src/bagofwords.cpp

modules/features2d/CMakeLists.txt

@@ -1,3 +1,2 @@
 set(the_description "2D Features Framework")
 ocv_define_module(features2d opencv_imgproc opencv_flann OPTIONAL opencv_highgui)
-

modules/features2d/doc/common_interfaces_of_descriptor_extractors.rst

@@ -9,7 +9,10 @@ represented as vectors in a multidimensional space. All objects that implement t
 descriptor extractors inherit the
 :ocv:class:`DescriptorExtractor` interface.
 
+.. note::
 
+   * An example explaining keypoint extraction can be found at opencv_source_code/samples/cpp/descriptor_extractor_matcher.cpp
+   * An example on descriptor evaluation can be found at opencv_source_code/samples/cpp/detector_descriptor_evaluation.cpp
 
 DescriptorExtractor
 -------------------
@@ -82,9 +85,10 @@ The current implementation supports the following types of a descriptor extracto
 
  * ``"SIFT"`` -- :ocv:class:`SIFT`
  * ``"SURF"`` -- :ocv:class:`SURF`
- * ``"ORB"`` -- :ocv:class:`ORB`
- * ``"BRISK"`` -- :ocv:class:`BRISK`
  * ``"BRIEF"`` -- :ocv:class:`BriefDescriptorExtractor`
+ * ``"BRISK"`` -- :ocv:class:`BRISK`
+ * ``"ORB"`` -- :ocv:class:`ORB`
+ * ``"FREAK"`` -- :ocv:class:`FREAK`
 
 A combined format is also supported: descriptor extractor adapter name ( ``"Opponent"`` --
 :ocv:class:`OpponentColorDescriptorExtractor` ) + descriptor extractor name (see above),
@@ -141,4 +145,6 @@ Strecha C., Fua P. *BRIEF: Binary Robust Independent Elementary Features* ,
         ...
     };
 
+.. note::
 
+   * A complete BRIEF extractor sample can be found at opencv_source_code/samples/cpp/brief_match_test.cpp

modules/features2d/doc/common_interfaces_of_descriptor_matchers.rst

@@ -9,6 +9,11 @@ that are represented as vectors in a multidimensional space. All objects that im
 descriptor matchers inherit the
 :ocv:class:`DescriptorMatcher` interface.
 
+.. note::
+
+   * An example explaining keypoint matching can be found at opencv_source_code/samples/cpp/descriptor_extractor_matcher.cpp
+   * An example on descriptor matching evaluation can be found at opencv_source_code/samples/cpp/detector_descriptor_matcher_evaluation.cpp
+   * An example on one to many image matching can be found at opencv_source_code/samples/cpp/matching_to_many_images.cpp
 
 DescriptorMatcher
 -----------------
@@ -271,4 +276,3 @@ Flann-based descriptor matcher. This matcher trains :ocv:class:`flann::Index_` o
     };
 
 ..
-

modules/features2d/doc/common_interfaces_of_feature_detectors.rst

@@ -8,6 +8,9 @@ between different algorithms solving the same problem. All objects that implemen
 inherit the
 :ocv:class:`FeatureDetector` interface.
 
+.. note::
+
+   * An example explaining keypoint detection can be found at opencv_source_code/samples/cpp/descriptor_extractor_matcher.cpp
 
 FeatureDetector
 ---------------
@@ -166,7 +169,7 @@ StarFeatureDetector
 -------------------
 .. ocv:class:: StarFeatureDetector : public FeatureDetector
 
-The class implements the keypoint detector introduced by K. Konolige, synonym of ``StarDetector``.  ::
+The class implements the keypoint detector introduced by [Agrawal08]_, synonym of ``StarDetector``.  ::
 
     class StarFeatureDetector : public FeatureDetector
     {
@@ -180,6 +183,9 @@ The class implements the keypoint detector introduced by K. Konolige, synonym of
         ...
     };
 
+.. [Agrawal08] Agrawal, M., Konolige, K., & Blas, M. R. (2008). Censure: Center surround extremas for realtime feature detection and matching. In Computer Vision–ECCV 2008 (pp. 102-115). Springer Berlin Heidelberg.
+
+
 DenseFeatureDetector
 --------------------
 .. ocv:class:: DenseFeatureDetector : public FeatureDetector

modules/features2d/doc/common_interfaces_of_generic_descriptor_matchers.rst

@@ -11,7 +11,11 @@ Every descriptor with the
 :ocv:class:`VectorDescriptorMatcher` ).
 There are descriptors such as the One-way descriptor and Ferns that have the ``GenericDescriptorMatcher`` interface implemented but do not support ``DescriptorExtractor``.
 
+.. note::
 
+   * An example explaining keypoint description can be found at opencv_source_code/samples/cpp/descriptor_extractor_matcher.cpp
+   * An example on descriptor matching evaluation can be found at opencv_source_code/samples/cpp/detector_descriptor_matcher_evaluation.cpp
+   * An example on one to many image matching can be found at opencv_source_code/samples/cpp/matching_to_many_images.cpp
 
 GenericDescriptorMatcher
 ------------------------
@@ -270,5 +274,3 @@ Example: ::
 
     VectorDescriptorMatcher matcher( new SurfDescriptorExtractor,
                                      new BruteForceMatcher<L2<float> > );
-
-

modules/features2d/doc/drawing_function_of_keypoints_and_matches.rst

@@ -83,4 +83,4 @@ Draws keypoints.
 
     :param flags: Flags setting drawing features. Possible  ``flags``  bit values are defined by  ``DrawMatchesFlags``. See details above in  :ocv:func:`drawMatches` .
 
-.. note:: For Python API, flags are modified as `cv2.DRAW_MATCHES_FLAGS_DEFAULT`, `cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS`, `cv2.DRAW_MATCHES_FLAGS_DRAW_OVER_OUTIMG`, `cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS` 
+.. note:: For Python API, flags are modified as `cv2.DRAW_MATCHES_FLAGS_DEFAULT`, `cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS`, `cv2.DRAW_MATCHES_FLAGS_DRAW_OVER_OUTIMG`, `cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS`

modules/features2d/doc/feature_detection_and_description.rst

@@ -3,6 +3,10 @@ Feature Detection and Description
 
 .. highlight:: cpp
 
+.. note::
+
+   * An example explaining keypoint detection and description can be found at opencv_source_code/samples/cpp/descriptor_extractor_matcher.cpp
+
 FAST
 ----
 Detects corners using the FAST algorithm
@@ -58,6 +62,10 @@ Maximally stable extremal region extractor. ::
 The class encapsulates all the parameters of the MSER extraction algorithm (see
 http://en.wikipedia.org/wiki/Maximally_stable_extremal_regions). Also see http://code.opencv.org/projects/opencv/wiki/MSER for useful comments and parameters description.
 
+.. note::
+
+   * (Python) A complete example showing the use of the MSER detector can be found at opencv_source_code/samples/python2/mser.py
+
 
 ORB
 ---
@@ -112,7 +120,7 @@ Finds keypoints in an image and computes their descriptors
     :param descriptors: The output descriptors. Pass ``cv::noArray()`` if you do not need it.
 
     :param useProvidedKeypoints: If it is true, then the method will use the provided vector of keypoints instead of detecting them.
-    
+
 
 BRISK
 -----
@@ -182,6 +190,10 @@ Class implementing the FREAK (*Fast Retina Keypoint*) keypoint descriptor, descr
 
 .. [AOV12] A. Alahi, R. Ortiz, and P. Vandergheynst. FREAK: Fast Retina Keypoint. In IEEE Conference on Computer Vision and Pattern Recognition, 2012. CVPR 2012 Open Source Award Winner.
 
+.. note::
+
+   * An example on how to use the FREAK descriptor can be found at opencv_source_code/samples/cpp/freak_demo.cpp
+
 FREAK::FREAK
 ------------
 The FREAK constructor

modules/features2d/doc/object_categorization.rst

@@ -5,6 +5,12 @@ Object Categorization
 
 This section describes approaches based on local 2D features and used to categorize objects.
 
+.. note::
+
+   * A complete Bag-Of-Words sample can be found at opencv_source_code/samples/cpp/bagofwords_classification.cpp
+
+   * (Python) An example using the features2D framework to perform object categorization can be found at opencv_source_code/samples/python2/find_obj.py
+
 BOWTrainer
 ----------
 .. ocv:class:: BOWTrainer
@@ -205,4 +211,3 @@ BOWImgDescriptorExtractor::descriptorType
 Returns an image descriptor type.
 
 .. ocv:function:: int BOWImgDescriptorExtractor::descriptorType() const
-

modules/features2d/include/opencv2/features2d.hpp

@@ -517,7 +517,7 @@ CV_EXPORTS void FAST( InputArray image, CV_OUT std::vector<KeyPoint>& keypoints,
 class CV_EXPORTS_W FastFeatureDetector : public FeatureDetector
 {
 public:
-    enum
+    enum Type
     {
       TYPE_5_8 = 0, TYPE_7_12 = 1, TYPE_9_16 = 2
     };
@@ -646,7 +646,7 @@ public:
      * gridRows            Grid rows count.
      * gridCols            Grid column count.
      */
-    CV_WRAP GridAdaptedFeatureDetector( const Ptr<FeatureDetector>& detector=0,
+    CV_WRAP GridAdaptedFeatureDetector( const Ptr<FeatureDetector>& detector=Ptr<FeatureDetector>(),
                                         int maxTotalKeypoints=1000,
                                         int gridRows=4, int gridCols=4 );
 
@@ -961,7 +961,7 @@ struct CV_EXPORTS Hamming
 
 typedef Hamming HammingLUT;
 
-template<int cellsize> struct CV_EXPORTS HammingMultilevel
+template<int cellsize> struct HammingMultilevel
 {
     enum { normType = NORM_HAMMING + (cellsize>1) };
     typedef unsigned char ValueType;
@@ -1143,8 +1143,8 @@ protected:
 class CV_EXPORTS_W FlannBasedMatcher : public DescriptorMatcher
 {
 public:
-    CV_WRAP FlannBasedMatcher( const Ptr<flann::IndexParams>& indexParams=new flann::KDTreeIndexParams(),
-                       const Ptr<flann::SearchParams>& searchParams=new flann::SearchParams() );
+    CV_WRAP FlannBasedMatcher( const Ptr<flann::IndexParams>& indexParams=makePtr<flann::KDTreeIndexParams>(),
+                       const Ptr<flann::SearchParams>& searchParams=makePtr<flann::SearchParams>() );
 
     virtual void add( const std::vector<Mat>& descriptors );
     virtual void clear();

modules/features2d/include/opencv2/features2d/features2d.hpp

@@ -45,4 +45,4 @@
 #error this is a compatibility header which should not be used inside the OpenCV library
 #endif
 
-#include "opencv2/features2d.hpp"
+#include "opencv2/features2d.hpp"

modules/features2d/perf/perf_fast.cpp

@@ -41,4 +41,3 @@ PERF_TEST_P(fast, detect, testing::Combine(
 
     SANITY_CHECK_KEYPOINTS(points);
 }
-

modules/features2d/perf/perf_precomp.cpp

@@ -1 +0,0 @@
-#include "perf_precomp.hpp"

modules/features2d/src/bagofwords.cpp

@@ -204,4 +204,14 @@ void BOWImgDescriptorExtractor::compute( const Mat& descriptors, Mat& imgDescrip
     imgDescriptor /= descriptors.rows;
 }
 
+int BOWImgDescriptorExtractor::descriptorSize() const
+{
+    return vocabulary.empty() ? 0 : vocabulary.rows;
+}
+
+int BOWImgDescriptorExtractor::descriptorType() const
+{
+    return CV_32FC1;
+}
+
 }

modules/features2d/src/brisk.cpp

@@ -2003,7 +2003,7 @@ BriskLayer::BriskLayer(const cv::Mat& img_in, float scale_in, float offset_in)
   scale_ = scale_in;
   offset_ = offset_in;
   // create an agast detector
-  fast_9_16_ = new FastFeatureDetector(1, true, FastFeatureDetector::TYPE_9_16);
+  fast_9_16_ = makePtr<FastFeatureDetector>(1, true, FastFeatureDetector::TYPE_9_16);
   makeOffsets(pixel_5_8_, (int)img_.step, 8);
   makeOffsets(pixel_9_16_, (int)img_.step, 16);
 }
@@ -2025,7 +2025,7 @@ BriskLayer::BriskLayer(const BriskLayer& layer, int mode)
     offset_ = 0.5f * scale_ - 0.5f;
   }
   scores_ = cv::Mat::zeros(img_.rows, img_.cols, CV_8U);
-  fast_9_16_ = new FastFeatureDetector(1, false, FastFeatureDetector::TYPE_9_16);
+  fast_9_16_ = makePtr<FastFeatureDetector>(1, false, FastFeatureDetector::TYPE_9_16);
   makeOffsets(pixel_5_8_, (int)img_.step, 8);
   makeOffsets(pixel_9_16_, (int)img_.step, 16);
 }

modules/features2d/src/descriptors.cpp

@@ -99,7 +99,7 @@ Ptr<DescriptorExtractor> DescriptorExtractor::create(const String& descriptorExt
     {
         size_t pos = String("Opponent").size();
         String type = descriptorExtractorType.substr(pos);
-        return new OpponentColorDescriptorExtractor(DescriptorExtractor::create(type));
+        return makePtr<OpponentColorDescriptorExtractor>(DescriptorExtractor::create(type));
     }
 
     return Algorithm::create<DescriptorExtractor>("Feature2D." + descriptorExtractorType);
@@ -119,7 +119,7 @@ CV_WRAP void Feature2D::compute( const Mat& image, CV_OUT CV_IN_OUT std::vector<
 OpponentColorDescriptorExtractor::OpponentColorDescriptorExtractor( const Ptr<DescriptorExtractor>& _descriptorExtractor ) :
         descriptorExtractor(_descriptorExtractor)
 {
-    CV_Assert( !descriptorExtractor.empty() );
+    CV_Assert( descriptorExtractor );
 }
 
 static void convertBGRImageToOpponentColorSpace( const Mat& bgrImage, std::vector<Mat>& opponentChannels )
@@ -249,7 +249,7 @@ int OpponentColorDescriptorExtractor::descriptorType() const
 
 bool OpponentColorDescriptorExtractor::empty() const
 {
-    return descriptorExtractor.empty() || (DescriptorExtractor*)(descriptorExtractor)->empty();
+    return !descriptorExtractor || descriptorExtractor->empty();
 }
 
 }

modules/features2d/src/detectors.cpp

@@ -90,19 +90,19 @@ Ptr<FeatureDetector> FeatureDetector::create( const String& detectorType )
 {
     if( detectorType.find("Grid") == 0 )
     {
-        return new GridAdaptedFeatureDetector(FeatureDetector::create(
+        return makePtr<GridAdaptedFeatureDetector>(FeatureDetector::create(
                                 detectorType.substr(strlen("Grid"))));
     }
 
     if( detectorType.find("Pyramid") == 0 )
     {
-        return new PyramidAdaptedFeatureDetector(FeatureDetector::create(
+        return makePtr<PyramidAdaptedFeatureDetector>(FeatureDetector::create(
                                 detectorType.substr(strlen("Pyramid"))));
     }
 
     if( detectorType.find("Dynamic") == 0 )
     {
-        return new DynamicAdaptedFeatureDetector(AdjusterAdapter::create(
+        return makePtr<DynamicAdaptedFeatureDetector>(AdjusterAdapter::create(
                                 detectorType.substr(strlen("Dynamic"))));
     }
 
@@ -190,7 +190,7 @@ GridAdaptedFeatureDetector::GridAdaptedFeatureDetector( const Ptr<FeatureDetecto
 
 bool GridAdaptedFeatureDetector::empty() const
 {
-    return detector.empty() || (FeatureDetector*)detector->empty();
+    return !detector || detector->empty();
 }
 
 struct ResponseComparator
@@ -295,7 +295,7 @@ PyramidAdaptedFeatureDetector::PyramidAdaptedFeatureDetector( const Ptr<FeatureD
 
 bool PyramidAdaptedFeatureDetector::empty() const
 {
-    return detector.empty() || (FeatureDetector*)detector->empty();
+    return !detector || detector->empty();
 }
 
 void PyramidAdaptedFeatureDetector::detectImpl( const Mat& image, std::vector<KeyPoint>& keypoints, const Mat& mask ) const

modules/features2d/src/dynamic.cpp

@@ -51,7 +51,7 @@ DynamicAdaptedFeatureDetector::DynamicAdaptedFeatureDetector(const Ptr<AdjusterA
 
 bool DynamicAdaptedFeatureDetector::empty() const
 {
-    return adjuster_.empty() || adjuster_->empty();
+    return !adjuster_ || adjuster_->empty();
 }
 
 void DynamicAdaptedFeatureDetector::detectImpl(const Mat& image, std::vector<KeyPoint>& keypoints, const Mat& mask) const
@@ -124,7 +124,7 @@ bool FastAdjuster::good() const
 
 Ptr<AdjusterAdapter> FastAdjuster::clone() const
 {
-    Ptr<AdjusterAdapter> cloned_obj = new FastAdjuster( init_thresh_, nonmax_, min_thresh_, max_thresh_ );
+    Ptr<AdjusterAdapter> cloned_obj(new FastAdjuster( init_thresh_, nonmax_, min_thresh_, max_thresh_ ));
     return cloned_obj;
 }
 
@@ -158,7 +158,7 @@ bool StarAdjuster::good() const
 
 Ptr<AdjusterAdapter> StarAdjuster::clone() const
 {
-    Ptr<AdjusterAdapter> cloned_obj = new StarAdjuster( init_thresh_, min_thresh_, max_thresh_ );
+    Ptr<AdjusterAdapter> cloned_obj(new StarAdjuster( init_thresh_, min_thresh_, max_thresh_ ));
     return cloned_obj;
 }
 
@@ -195,7 +195,7 @@ bool SurfAdjuster::good() const
 
 Ptr<AdjusterAdapter> SurfAdjuster::clone() const
 {
-    Ptr<AdjusterAdapter> cloned_obj = new SurfAdjuster( init_thresh_, min_thresh_, max_thresh_ );
+    Ptr<AdjusterAdapter> cloned_obj(new SurfAdjuster( init_thresh_, min_thresh_, max_thresh_ ));
     return cloned_obj;
 }
 
@@ -205,15 +205,15 @@ Ptr<AdjusterAdapter> AdjusterAdapter::create( const String& detectorType )
 
     if( !detectorType.compare( "FAST" ) )
     {
-        adapter = new FastAdjuster();
+        adapter = makePtr<FastAdjuster>();
     }
     else if( !detectorType.compare( "STAR" ) )
     {
-        adapter = new StarAdjuster();
+        adapter = makePtr<StarAdjuster>();
     }
     else if( !detectorType.compare( "SURF" ) )
     {
-        adapter = new SurfAdjuster();
+        adapter = makePtr<SurfAdjuster>();
     }
 
     return adapter;

modules/features2d/src/evaluation.cpp

@@ -257,7 +257,7 @@ struct IntersectAreaCounter
     {
         CV_Assert( miny < maxy );
         CV_Assert( dr > FLT_EPSILON );
-        
+
         int temp_bua = bua, temp_bna = bna;
         for( int i = range.begin(); i != range.end(); i++ )
         {
@@ -461,7 +461,7 @@ void cv::evaluateFeatureDetector( const Mat& img1, const Mat& img2, const Mat& H
     keypoints1 = _keypoints1 != 0 ? _keypoints1 : &buf1;
     keypoints2 = _keypoints2 != 0 ? _keypoints2 : &buf2;
 
-    if( (keypoints1->empty() || keypoints2->empty()) && fdetector.empty() )
+    if( (keypoints1->empty() || keypoints2->empty()) && !fdetector )
         CV_Error( Error::StsBadArg, "fdetector must not be empty when keypoints1 or keypoints2 is empty" );
 
     if( keypoints1->empty() )
@@ -575,7 +575,7 @@ void cv::evaluateGenericDescriptorMatcher( const Mat& img1, const Mat& img2, con
     if( keypoints1.empty() )
         CV_Error( Error::StsBadArg, "keypoints1 must not be empty" );
 
-    if( matches1to2->empty() && dmatcher.empty() )
+    if( matches1to2->empty() && !dmatcher )
         CV_Error( Error::StsBadArg, "dmatch must not be empty when matches1to2 is empty" );
 
     bool computeKeypoints2ByPrj = keypoints2.empty();

modules/features2d/src/fast_score.cpp

@@ -357,4 +357,3 @@ int cornerScore<8>(const uchar* ptr, const int pixel[], int threshold)
 }
 
 } // namespace cv
-

modules/features2d/src/features2d_init.cpp

@@ -176,7 +176,7 @@ CV_INIT_ALGORITHM(DenseFeatureDetector, "Feature2D.Dense",
                   obj.info()->addParam(obj, "varyImgBoundWithScale", obj.varyImgBoundWithScale));
 
 CV_INIT_ALGORITHM(GridAdaptedFeatureDetector, "Feature2D.Grid",
-                  obj.info()->addParam(obj, "detector", obj.detector);
+                  obj.info()->addParam<FeatureDetector>(obj, "detector", obj.detector, false, 0, 0); // Extra params added to avoid VS2013 fatal error in opencv2/core.hpp (decl. of addParam)
                   obj.info()->addParam(obj, "maxTotalKeypoints", obj.maxTotalKeypoints);
                   obj.info()->addParam(obj, "gridRows", obj.gridRows);
                   obj.info()->addParam(obj, "gridCols", obj.gridCols));

modules/features2d/src/matchers.cpp

@@ -326,7 +326,7 @@ BFMatcher::BFMatcher( int _normType, bool _crossCheck )
 
 Ptr<DescriptorMatcher> BFMatcher::clone( bool emptyTrainData ) const
 {
-    BFMatcher* matcher = new BFMatcher(normType, crossCheck);
+    Ptr<BFMatcher> matcher = makePtr<BFMatcher>(normType, crossCheck);
     if( !emptyTrainData )
     {
         matcher->trainDescCollection.resize(trainDescCollection.size());
@@ -458,31 +458,31 @@ void BFMatcher::radiusMatchImpl( const Mat& queryDescriptors, std::vector<std::v
  */
 Ptr<DescriptorMatcher> DescriptorMatcher::create( const String& descriptorMatcherType )
 {
-    DescriptorMatcher* dm = 0;
+    Ptr<DescriptorMatcher> dm;
     if( !descriptorMatcherType.compare( "FlannBased" ) )
     {
-        dm = new FlannBasedMatcher();
+        dm = makePtr<FlannBasedMatcher>();
     }
     else if( !descriptorMatcherType.compare( "BruteForce" ) ) // L2
     {
-        dm = new BFMatcher(NORM_L2);
+        dm = makePtr<BFMatcher>(int(NORM_L2)); // anonymous enums can't be template parameters
     }
     else if( !descriptorMatcherType.compare( "BruteForce-SL2" ) ) // Squared L2
     {
-        dm = new BFMatcher(NORM_L2SQR);
+        dm = makePtr<BFMatcher>(int(NORM_L2SQR));
     }
     else if( !descriptorMatcherType.compare( "BruteForce-L1" ) )
     {
-        dm = new BFMatcher(NORM_L1);
+        dm = makePtr<BFMatcher>(int(NORM_L1));
     }
     else if( !descriptorMatcherType.compare("BruteForce-Hamming") ||
              !descriptorMatcherType.compare("BruteForce-HammingLUT") )
     {
-        dm = new BFMatcher(NORM_HAMMING);
+        dm = makePtr<BFMatcher>(int(NORM_HAMMING));
     }
     else if( !descriptorMatcherType.compare("BruteForce-Hamming(2)") )
     {
-        dm = new BFMatcher(NORM_HAMMING2);
+        dm = makePtr<BFMatcher>(int(NORM_HAMMING2));
     }
     else
         CV_Error( Error::StsBadArg, "Unknown matcher name" );
@@ -497,8 +497,8 @@ Ptr<DescriptorMatcher> DescriptorMatcher::create( const String& descriptorMatche
 FlannBasedMatcher::FlannBasedMatcher( const Ptr<flann::IndexParams>& _indexParams, const Ptr<flann::SearchParams>& _searchParams )
     : indexParams(_indexParams), searchParams(_searchParams), addedDescCount(0)
 {
-    CV_Assert( !_indexParams.empty() );
-    CV_Assert( !_searchParams.empty() );
+    CV_Assert( _indexParams );
+    CV_Assert( _searchParams );
 }
 
 void FlannBasedMatcher::add( const std::vector<Mat>& descriptors )
@@ -522,17 +522,17 @@ void FlannBasedMatcher::clear()
 
 void FlannBasedMatcher::train()
 {
-    if( flannIndex.empty() || mergedDescriptors.size() < addedDescCount )
+    if( !flannIndex || mergedDescriptors.size() < addedDescCount )
     {
         mergedDescriptors.set( trainDescCollection );
-        flannIndex = new flann::Index( mergedDescriptors.getDescriptors(), *indexParams );
+        flannIndex = makePtr<flann::Index>( mergedDescriptors.getDescriptors(), *indexParams );
     }
 }
 
 void FlannBasedMatcher::read( const FileNode& fn)
 {
-     if (indexParams.empty())
-         indexParams = new flann::IndexParams();
+     if (!indexParams)
+         indexParams = makePtr<flann::IndexParams>();
 
      FileNode ip = fn["indexParams"];
      CV_Assert(ip.type() == FileNode::SEQ);
@@ -570,8 +570,8 @@ void FlannBasedMatcher::read( const FileNode& fn)
         };
      }
 
-     if (searchParams.empty())
-         searchParams = new flann::SearchParams();
+     if (!searchParams)
+         searchParams = makePtr<flann::SearchParams>();
 
      FileNode sp = fn["searchParams"];
      CV_Assert(sp.type() == FileNode::SEQ);
@@ -725,7 +725,7 @@ bool FlannBasedMatcher::isMaskSupported() const
 
 Ptr<DescriptorMatcher> FlannBasedMatcher::clone( bool emptyTrainData ) const
 {
-    FlannBasedMatcher* matcher = new FlannBasedMatcher(indexParams, searchParams);
+    Ptr<FlannBasedMatcher> matcher = makePtr<FlannBasedMatcher>(indexParams, searchParams);
     if( !emptyTrainData )
     {
         CV_Error( Error::StsNotImplemented, "deep clone functionality is not implemented, because "
@@ -1066,7 +1066,7 @@ Ptr<GenericDescriptorMatcher> GenericDescriptorMatcher::create( const String& ge
     Ptr<GenericDescriptorMatcher> descriptorMatcher =
         Algorithm::create<GenericDescriptorMatcher>("DescriptorMatcher." + genericDescritptorMatcherType);
 
-    if( !paramsFilename.empty() && !descriptorMatcher.empty() )
+    if( !paramsFilename.empty() && descriptorMatcher )
     {
         FileStorage fs = FileStorage( paramsFilename, FileStorage::READ );
         if( fs.isOpened() )
@@ -1086,7 +1086,7 @@ VectorDescriptorMatcher::VectorDescriptorMatcher( const Ptr<DescriptorExtractor>
                                                   const Ptr<DescriptorMatcher>& _matcher )
                                 : extractor( _extractor ), matcher( _matcher )
 {
-    CV_Assert( !extractor.empty() && !matcher.empty() );
+    CV_Assert( extractor && matcher );
 }
 
 VectorDescriptorMatcher::~VectorDescriptorMatcher()
@@ -1152,14 +1152,14 @@ void VectorDescriptorMatcher::write (FileStorage& fs) const
 
 bool VectorDescriptorMatcher::empty() const
 {
-    return extractor.empty() || extractor->empty() ||
-           matcher.empty() || matcher->empty();
+    return !extractor || extractor->empty() ||
+           !matcher || matcher->empty();
 }
 
 Ptr<GenericDescriptorMatcher> VectorDescriptorMatcher::clone( bool emptyTrainData ) const
 {
     // TODO clone extractor
-    return new VectorDescriptorMatcher( extractor, matcher->clone(emptyTrainData) );
+    return makePtr<VectorDescriptorMatcher>( extractor, matcher->clone(emptyTrainData) );
 }
 
 }

modules/features2d/src/orb.cpp

@@ -138,13 +138,16 @@ static void computeOrbDescriptor(const KeyPoint& kpt,
     const uchar* center = &img.at<uchar>(cvRound(kpt.pt.y), cvRound(kpt.pt.x));
     int step = (int)img.step;
 
-#if 1
-    #define GET_VALUE(idx) \
-        center[cvRound(pattern[idx].x*b + pattern[idx].y*a)*step + \
-               cvRound(pattern[idx].x*a - pattern[idx].y*b)]
-#else
     float x, y;
     int ix, iy;
+#if 1
+#define GET_VALUE(idx) \
+       (x = pattern[idx].x*a - pattern[idx].y*b, \
+        y = pattern[idx].x*b + pattern[idx].y*a, \
+        ix = cvRound(x), \
+        iy = cvRound(y), \
+        *(center + iy*step + ix) )
+#else
     #define GET_VALUE(idx) \
         (x = pattern[idx].x*a - pattern[idx].y*b, \
         y = pattern[idx].x*b + pattern[idx].y*a, \

modules/features2d/src/precomp.cpp

@@ -1,44 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                        Intel License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000, Intel Corporation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of Intel Corporation may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#include "precomp.hpp"
-
-/* End of file. */

modules/features2d/test/test_brisk.cpp

@@ -93,4 +93,3 @@ void CV_BRISKTest::run( int )
 }
 
 TEST(Features2d_BRISK, regression) { CV_BRISKTest test; test.safe_run(); }
-

modules/features2d/test/test_descriptors_regression.cpp

@@ -80,10 +80,10 @@ static Mat readMatFromBin( const string& filename )
         size_t elements_read4 = fread( (void*)&dataSize, sizeof(int), 1, f );
         CV_Assert(elements_read1 == 1 && elements_read2 == 1 && elements_read3 == 1 && elements_read4 == 1);
 
-        size_t step = dataSize / rows / CV_ELEM_SIZE(type);
-        CV_Assert(step >= (size_t)cols);
+        int step = dataSize / rows / CV_ELEM_SIZE(type);
+        CV_Assert(step >= cols);
 
-        Mat m = Mat( rows, step, type).colRange(0, cols);
+        Mat m = Mat(rows, step, type).colRange(0, cols);
 
         size_t elements_read = fread( m.ptr(), 1, dataSize, f );
         CV_Assert(elements_read == (size_t)(dataSize));
@@ -141,7 +141,7 @@ protected:
 
     void emptyDataTest()
     {
-        assert( !dextractor.empty() );
+        assert( dextractor );
 
         // One image.
         Mat image;
@@ -186,7 +186,7 @@ protected:
 
     void regressionTest()
     {
-        assert( !dextractor.empty() );
+        assert( dextractor );
 
         // Read the test image.
         string imgFilename =  string(ts->get_data_path()) + FEATURES2D_DIR + "/" + IMAGE_FILENAME;
@@ -267,7 +267,7 @@ protected:
     void run(int)
     {
         createDescriptorExtractor();
-        if( dextractor.empty() )
+        if( !dextractor )
         {
             ts->printf(cvtest::TS::LOG, "Descriptor extractor is empty.\n");
             ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );

modules/features2d/test/test_detectors_regression.cpp

@@ -230,7 +230,7 @@ void CV_FeatureDetectorTest::regressionTest()
 
 void CV_FeatureDetectorTest::run( int /*start_from*/ )
 {
-    if( fdetector.empty() )
+    if( !fdetector )
     {
         ts->printf( cvtest::TS::LOG, "Feature detector is empty.\n" );
         ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );

modules/features2d/test/test_fast.cpp

@@ -135,4 +135,3 @@ void CV_FastTest::run( int )
 }
 
 TEST(Features2d_FAST, regression) { CV_FastTest test; test.safe_run(); }
-

modules/features2d/test/test_keypoints.cpp

@@ -62,7 +62,7 @@ protected:
     virtual void run(int)
     {
         cv::initModule_features2d();
-        CV_Assert(!detector.empty());
+        CV_Assert(detector);
         string imgFilename = string(ts->get_data_path()) + FEATURES2D_DIR + "/" + IMAGE_FILENAME;
 
         // Read the test image.
@@ -166,5 +166,3 @@ TEST(Features2d_Detector_Keypoints_Dense, validation)
     CV_FeatureDetectorKeypointsTest test(Algorithm::create<FeatureDetector>("Feature2D.Dense"));
     test.safe_run();
 }
-
-

modules/features2d/test/test_mser.cpp

@@ -205,4 +205,3 @@ void CV_MserTest::run(int)
 }
 
 TEST(Features2d_MSER, DISABLED_regression) { CV_MserTest test; test.safe_run(); }
-

modules/features2d/test/test_precomp.cpp

@@ -1 +0,0 @@
-#include "test_precomp.hpp"

modules/features2d/test/test_rotation_and_scale_invariance.cpp

@@ -196,7 +196,7 @@ public:
         minKeyPointMatchesRatio(_minKeyPointMatchesRatio),
         minAngleInliersRatio(_minAngleInliersRatio)
     {
-        CV_Assert(!featureDetector.empty());
+        CV_Assert(featureDetector);
     }
 
 protected:
@@ -307,8 +307,8 @@ public:
         normType(_normType),
         minDescInliersRatio(_minDescInliersRatio)
     {
-        CV_Assert(!featureDetector.empty());
-        CV_Assert(!descriptorExtractor.empty());
+        CV_Assert(featureDetector);
+        CV_Assert(descriptorExtractor);
     }
 
 protected:
@@ -392,7 +392,7 @@ public:
         minKeyPointMatchesRatio(_minKeyPointMatchesRatio),
         minScaleInliersRatio(_minScaleInliersRatio)
     {
-        CV_Assert(!featureDetector.empty());
+        CV_Assert(featureDetector);
     }
 
 protected:
@@ -510,8 +510,8 @@ public:
         normType(_normType),
         minDescInliersRatio(_minDescInliersRatio)
     {
-        CV_Assert(!featureDetector.empty());
-        CV_Assert(!descriptorExtractor.empty());
+        CV_Assert(featureDetector);
+        CV_Assert(descriptorExtractor);
     }
 
 protected: