Merge pull request #3339 from vpisarev:refactor_features2d_take4

doc/tutorials/features2d/detection_of_planar_objects/detection_of_planar_objects.rst

@@ -16,24 +16,15 @@ The goal of this tutorial is to learn how to use *features2d* and *calib3d* modu
         Mat img2 = imread(argv[2], IMREAD_GRAYSCALE);
 
 #.
-    Detect keypoints in both images. ::
+    Detect keypoints in both images and compute descriptors for each of the keypoints. ::
 
         // detecting keypoints
-        FastFeatureDetector detector(15);
+        Ptr<Feature2D> surf = SURF::create();
         vector<KeyPoint> keypoints1;
-        detector.detect(img1, keypoints1);
-
-        ... // do the same for the second image
-
-#.
-    Compute descriptors for each of the keypoints. ::
-
-        // computing descriptors
-        SurfDescriptorExtractor extractor;
         Mat descriptors1;
-        extractor.compute(img1, keypoints1, descriptors1);
+        surf->detectAndCompute(img1, Mat(), keypoints1, descriptors1);
 
-        ... // process keypoints from the second image as well
+        ... // do the same for the second image
 
 #.
     Now, find the closest matches between descriptors from the first image to the second: ::

doc/user_guide/ug_features2d.rst

@@ -65,18 +65,18 @@ Let us break the code down. ::
 We load two images and check if they are loaded correctly.::
 
     // detecting keypoints
-    FastFeatureDetector detector(15);
+    Ptr<FeatureDetector> detector = FastFeatureDetector::create(15);
     vector<KeyPoint> keypoints1, keypoints2;
-    detector.detect(img1, keypoints1);
-    detector.detect(img2, keypoints2);
+    detector->detect(img1, keypoints1);
+    detector->detect(img2, keypoints2);
 
 First, we create an instance of a keypoint detector. All detectors inherit the abstract ``FeatureDetector`` interface, but the constructors are algorithm-dependent. The first argument to each detector usually controls the balance between the amount of keypoints and their stability. The range of values is different for different detectors (For instance, *FAST* threshold has the meaning of pixel intensity difference and usually varies in the region *[0,40]*. *SURF* threshold is applied to a Hessian of an image and usually takes on values larger than *100*), so use defaults in case of doubt. ::
 
     // computing descriptors
-    SurfDescriptorExtractor extractor;
+    Ptr<SURF> extractor = SURF::create();
     Mat descriptors1, descriptors2;
-    extractor.compute(img1, keypoints1, descriptors1);
-    extractor.compute(img2, keypoints2, descriptors2);
+    extractor->compute(img1, keypoints1, descriptors1);
+    extractor->compute(img2, keypoints2, descriptors2);
 
 We create an instance of descriptor extractor. The most of OpenCV descriptors inherit ``DescriptorExtractor`` abstract interface. Then we compute descriptors for each of the keypoints. The output ``Mat`` of the ``DescriptorExtractor::compute`` method contains a descriptor in a row *i* for each *i*-th keypoint. Note that the method can modify the keypoints vector by removing the keypoints such that a descriptor for them is not defined (usually these are the keypoints near image border). The method makes sure that the ouptut keypoints and descriptors are consistent with each other (so that the number of keypoints is equal to the descriptors row count). ::
 

modules/calib3d/include/opencv2/calib3d.hpp

@@ -185,7 +185,7 @@ CV_EXPORTS_W void drawChessboardCorners( InputOutputArray image, Size patternSiz
 //! finds circles' grid pattern of the specified size in the image
 CV_EXPORTS_W bool findCirclesGrid( InputArray image, Size patternSize,
                                    OutputArray centers, int flags = CALIB_CB_SYMMETRIC_GRID,
-                                   const Ptr<FeatureDetector> &blobDetector = makePtr<SimpleBlobDetector>());
+                                   const Ptr<FeatureDetector> &blobDetector = SimpleBlobDetector::create());
 
 //! finds intrinsic and extrinsic camera parameters from several fews of a known calibration pattern.
 CV_EXPORTS_W double calibrateCamera( InputArrayOfArrays objectPoints,

modules/calib3d/src/solvepnp.cpp

@@ -115,6 +115,9 @@ bool cv::solvePnP( InputArray _opoints, InputArray _ipoints,
         cv::Mat R, rvec = _rvec.getMat(), tvec = _tvec.getMat();
         double f = PnP.compute_pose(R, tvec);
         cv::Rodrigues(R, rvec);
+        if(cameraMatrix.type() == CV_32F)
+            cameraMatrix.at<float>(0,0) = cameraMatrix.at<float>(1,1) = (float)f;
+        else
             cameraMatrix.at<double>(0,0) = cameraMatrix.at<double>(1,1) = f;
         return true;
     }

modules/calib3d/src/upnp.h

@@ -73,12 +73,12 @@ private:
       {
           for(int i = 0; i < number_of_correspondences; i++)
           {
-            pws[3 * i    ] = opoints.at<OpointType>(0,i).x;
-            pws[3 * i + 1] = opoints.at<OpointType>(0,i).y;
-            pws[3 * i + 2] = opoints.at<OpointType>(0,i).z;
+            pws[3 * i    ] = opoints.at<OpointType>(i).x;
+            pws[3 * i + 1] = opoints.at<OpointType>(i).y;
+            pws[3 * i + 2] = opoints.at<OpointType>(i).z;
 
-            us[2 * i    ] = ipoints.at<IpointType>(0,i).x;
-            us[2 * i + 1] = ipoints.at<IpointType>(0,i).y;
+            us[2 * i    ] = ipoints.at<IpointType>(i).x;
+            us[2 * i + 1] = ipoints.at<IpointType>(i).y;
           }
       }
 

modules/core/include/opencv2/core.hpp

@@ -874,6 +874,9 @@ public:
     virtual ~Algorithm();
     String name() const;
 
+    virtual void set(int, double);
+    virtual double get(int) const;
+
     template<typename _Tp> typename ParamType<_Tp>::member_type get(const String& name) const;
     template<typename _Tp> typename ParamType<_Tp>::member_type get(const char* name) const;
 

modules/core/src/algorithm.cpp

@@ -179,6 +179,9 @@ String Algorithm::name() const
     return info()->name();
 }
 
+void Algorithm::set(int, double) {}
+double Algorithm::get(int) const { return 0.; }
+
 void Algorithm::set(const String& parameter, int value)
 {
     info()->set(this, parameter.c_str(), ParamType<int>::type, &value);

modules/features2d/doc/common_interfaces_of_feature_detectors.rst

@@ -59,100 +59,60 @@ Detects keypoints in an image (first variant) or image set (second variant).
 
     :param masks: Masks for each input image specifying where to look for keypoints (optional). ``masks[i]`` is a mask for ``images[i]``.
 
-FeatureDetector::create
------------------------
-Creates a feature detector by its name.
-
-.. ocv:function:: Ptr<FeatureDetector> FeatureDetector::create( const String& detectorType )
-
-.. ocv:pyfunction:: cv2.FeatureDetector_create(detectorType) -> retval
-
-    :param detectorType: Feature detector type.
-
-The following detector types are supported:
-
-* ``"FAST"`` -- :ocv:class:`FastFeatureDetector`
-* ``"ORB"`` -- :ocv:class:`ORB`
-* ``"BRISK"`` -- :ocv:class:`BRISK`
-* ``"MSER"`` -- :ocv:class:`MSER`
-* ``"GFTT"`` -- :ocv:class:`GoodFeaturesToTrackDetector`
-* ``"HARRIS"`` -- :ocv:class:`GoodFeaturesToTrackDetector` with Harris detector enabled
-* ``"SimpleBlob"`` -- :ocv:class:`SimpleBlobDetector`
-
 FastFeatureDetector
 -------------------
-.. ocv:class:: FastFeatureDetector : public FeatureDetector
+.. ocv:class:: FastFeatureDetector : public Feature2D
 
 Wrapping class for feature detection using the
 :ocv:func:`FAST` method. ::
 
-    class FastFeatureDetector : public FeatureDetector
+    class FastFeatureDetector : public Feature2D
     {
     public:
-        FastFeatureDetector( int threshold=1, bool nonmaxSuppression=true, type=FastFeatureDetector::TYPE_9_16 );
-        virtual void read( const FileNode& fn );
-        virtual void write( FileStorage& fs ) const;
-    protected:
-        ...
+        static Ptr<FastFeatureDetector> create( int threshold=1, bool nonmaxSuppression=true, type=FastFeatureDetector::TYPE_9_16 );
     };
 
-GoodFeaturesToTrackDetector
+GFTTDetector
 ---------------------------
-.. ocv:class:: GoodFeaturesToTrackDetector : public FeatureDetector
+.. ocv:class:: GFTTDetector : public FeatureDetector
 
 Wrapping class for feature detection using the
 :ocv:func:`goodFeaturesToTrack` function. ::
 
-    class GoodFeaturesToTrackDetector : public FeatureDetector
-    {
-    public:
-        class Params
+    class GFTTDetector : public Feature2D
     {
     public:
-            Params( int maxCorners=1000, double qualityLevel=0.01,
-                    double minDistance=1., int blockSize=3,
+        enum { USE_HARRIS_DETECTOR=10000 };
+        static Ptr<GFTTDetector> create( int maxCorners=1000, double qualityLevel=0.01,
+                                         double minDistance=1, int blockSize=3,
                                          bool useHarrisDetector=false, double k=0.04 );
-            void read( const FileNode& fn );
-            void write( FileStorage& fs ) const;
-
-            int maxCorners;
-            double qualityLevel;
-            double minDistance;
-            int blockSize;
-            bool useHarrisDetector;
-            double k;
     };
 
-        GoodFeaturesToTrackDetector( const GoodFeaturesToTrackDetector::Params& params=
-                                                GoodFeaturesToTrackDetector::Params() );
-        GoodFeaturesToTrackDetector( int maxCorners, double qualityLevel,
-                                     double minDistance, int blockSize=3,
-                                     bool useHarrisDetector=false, double k=0.04 );
-        virtual void read( const FileNode& fn );
-        virtual void write( FileStorage& fs ) const;
-    protected:
-        ...
-    };
-
-MserFeatureDetector
+MSER
 -------------------
-.. ocv:class:: MserFeatureDetector : public FeatureDetector
+.. ocv:class:: MSER : public Feature2D
 
-Wrapping class for feature detection using the
-:ocv:class:`MSER` class. ::
+Maximally stable region detector ::
 
-    class MserFeatureDetector : public FeatureDetector
+    class MSER : public Feature2D
     {
     public:
-        MserFeatureDetector( CvMSERParams params=cvMSERParams() );
-        MserFeatureDetector( int delta, int minArea, int maxArea,
-                             double maxVariation, double minDiversity,
-                             int maxEvolution, double areaThreshold,
-                             double minMargin, int edgeBlurSize );
-        virtual void read( const FileNode& fn );
-        virtual void write( FileStorage& fs ) const;
-    protected:
-        ...
+        enum
+        {
+            DELTA=10000, MIN_AREA=10001, MAX_AREA=10002, PASS2_ONLY=10003,
+            MAX_EVOLUTION=10004, AREA_THRESHOLD=10005,
+            MIN_MARGIN=10006, EDGE_BLUR_SIZE=10007
+        };
+
+        //! the full constructor
+        static Ptr<MSER> create( int _delta=5, int _min_area=60, int _max_area=14400,
+              double _max_variation=0.25, double _min_diversity=.2,
+              int _max_evolution=200, double _area_threshold=1.01,
+              double _min_margin=0.003, int _edge_blur_size=5 );
+
+        virtual void detectRegions( InputArray image,
+                                    std::vector<std::vector<Point> >& msers,
+                                    std::vector<Rect>& bboxes ) = 0;
     };
 
 SimpleBlobDetector
@@ -189,10 +149,8 @@ Class for extracting blobs from an image. ::
         float minConvexity, maxConvexity;
     };
 
-    SimpleBlobDetector(const SimpleBlobDetector::Params &parameters = SimpleBlobDetector::Params());
-
-    protected:
-        ...
+    static Ptr<SimpleBlobDetector> create(const SimpleBlobDetector::Params
+                    &parameters = SimpleBlobDetector::Params());
     };
 
 The class implements a simple algorithm for extracting blobs from an image:

modules/features2d/doc/feature_detection_and_description.rst

@@ -14,11 +14,6 @@ Detects corners using the FAST algorithm
 .. ocv:function:: void FAST( InputArray image, vector<KeyPoint>& keypoints, int threshold, bool nonmaxSuppression=true )
 .. ocv:function:: void FAST( InputArray image, vector<KeyPoint>& keypoints, int threshold, bool nonmaxSuppression, int type )
 
-.. ocv:pyfunction:: cv2.FastFeatureDetector([, threshold[, nonmaxSuppression]]) -> <FastFeatureDetector object>
-.. ocv:pyfunction:: cv2.FastFeatureDetector(threshold, nonmaxSuppression, type) -> <FastFeatureDetector object>
-.. ocv:pyfunction:: cv2.FastFeatureDetector.detect(image[, mask]) -> keypoints
-
-
     :param image: grayscale image where keypoints (corners) are detected.
 
     :param keypoints: keypoints detected on the image.
@@ -55,7 +50,7 @@ Maximally stable extremal region extractor. ::
         // runs the extractor on the specified image; returns the MSERs,
         // each encoded as a contour (vector<Point>, see findContours)
         // the optional mask marks the area where MSERs are searched for
-        void operator()( const Mat& image, vector<vector<Point> >& msers, const Mat& mask ) const;
+        void detectRegions( InputArray image, vector<vector<Point> >& msers, vector<Rect>& bboxes ) const;
     };
 
 The class encapsulates all the parameters of the MSER extraction algorithm (see

modules/features2d/perf/opencl/perf_fast.cpp

@@ -32,11 +32,8 @@ OCL_PERF_TEST_P(FASTFixture, FastDetect, testing::Combine(
     mframe.copyTo(frame);
     declare.in(frame);
 
-    Ptr<FeatureDetector> fd = Algorithm::create<FeatureDetector>("Feature2D.FAST");
+    Ptr<FeatureDetector> fd = FastFeatureDetector::create(20, true, type);
     ASSERT_FALSE( fd.empty() );
-    fd->set("threshold", 20);
-    fd->set("nonmaxSuppression", true);
-    fd->set("type", type);
     vector<KeyPoint> points;
 
     OCL_TEST_CYCLE() fd->detect(frame, points);

modules/features2d/perf/opencl/perf_orb.cpp

@@ -22,10 +22,10 @@ OCL_PERF_TEST_P(ORBFixture, ORB_Detect, ORB_IMAGES)
     mframe.copyTo(frame);
 
     declare.in(frame);
-    ORB detector(1500, 1.3f, 1);
+    Ptr<ORB> detector = ORB::create(1500, 1.3f, 1);
     vector<KeyPoint> points;
 
-    OCL_TEST_CYCLE() detector(frame, mask, points);
+    OCL_TEST_CYCLE() detector->detect(frame, points, mask);
 
     std::sort(points.begin(), points.end(), comparators::KeypointGreater());
     SANITY_CHECK_KEYPOINTS(points, 1e-5);
@@ -44,14 +44,14 @@ OCL_PERF_TEST_P(ORBFixture, ORB_Extract, ORB_IMAGES)
 
     declare.in(frame);
 
-    ORB detector(1500, 1.3f, 1);
+    Ptr<ORB> detector = ORB::create(1500, 1.3f, 1);
     vector<KeyPoint> points;
-    detector(frame, mask, points);
+    detector->detect(frame, points, mask);
     std::sort(points.begin(), points.end(), comparators::KeypointGreater());
 
     UMat descriptors;
 
-    OCL_TEST_CYCLE() detector(frame, mask, points, descriptors, true);
+    OCL_TEST_CYCLE() detector->compute(frame, points, descriptors);
 
     SANITY_CHECK(descriptors);
 }
@@ -68,12 +68,12 @@ OCL_PERF_TEST_P(ORBFixture, ORB_Full, ORB_IMAGES)
     mframe.copyTo(frame);
 
     declare.in(frame);
-    ORB detector(1500, 1.3f, 1);
+    Ptr<ORB> detector = ORB::create(1500, 1.3f, 1);
 
     vector<KeyPoint> points;
     UMat descriptors;
 
-    OCL_TEST_CYCLE() detector(frame, mask, points, descriptors, false);
+    OCL_TEST_CYCLE() detector->detectAndCompute(frame, mask, points, descriptors, false);
 
     ::perf::sort(points, descriptors);
     SANITY_CHECK_KEYPOINTS(points, 1e-5);

modules/features2d/perf/perf_fast.cpp

@@ -30,11 +30,8 @@ PERF_TEST_P(fast, detect, testing::Combine(
 
     declare.in(frame);
 
-    Ptr<FeatureDetector> fd = Algorithm::create<FeatureDetector>("Feature2D.FAST");
+    Ptr<FeatureDetector> fd = FastFeatureDetector::create(20, true, type);
     ASSERT_FALSE( fd.empty() );
-    fd->set("threshold", 20);
-    fd->set("nonmaxSuppression", true);
-    fd->set("type", type);
     vector<KeyPoint> points;
 
     TEST_CYCLE() fd->detect(frame, points);

modules/features2d/perf/perf_orb.cpp

@@ -22,10 +22,10 @@ PERF_TEST_P(orb, detect, testing::Values(ORB_IMAGES))
 
     Mat mask;
     declare.in(frame);
-    ORB detector(1500, 1.3f, 1);
+    Ptr<ORB> detector = ORB::create(1500, 1.3f, 1);
     vector<KeyPoint> points;
 
-    TEST_CYCLE() detector(frame, mask, points);
+    TEST_CYCLE() detector->detect(frame, points, mask);
 
     sort(points.begin(), points.end(), comparators::KeypointGreater());
     SANITY_CHECK_KEYPOINTS(points, 1e-5);
@@ -42,14 +42,14 @@ PERF_TEST_P(orb, extract, testing::Values(ORB_IMAGES))
     Mat mask;
     declare.in(frame);
 
-    ORB detector(1500, 1.3f, 1);
+    Ptr<ORB> detector = ORB::create(1500, 1.3f, 1);
     vector<KeyPoint> points;
-    detector(frame, mask, points);
+    detector->detect(frame, points, mask);
     sort(points.begin(), points.end(), comparators::KeypointGreater());
 
     Mat descriptors;
 
-    TEST_CYCLE() detector(frame, mask, points, descriptors, true);
+    TEST_CYCLE() detector->compute(frame, points, descriptors);
 
     SANITY_CHECK(descriptors);
 }
@@ -64,12 +64,12 @@ PERF_TEST_P(orb, full, testing::Values(ORB_IMAGES))
 
     Mat mask;
     declare.in(frame);
-    ORB detector(1500, 1.3f, 1);
+    Ptr<ORB> detector = ORB::create(1500, 1.3f, 1);
 
     vector<KeyPoint> points;
     Mat descriptors;
 
-    TEST_CYCLE() detector(frame, mask, points, descriptors, false);
+    TEST_CYCLE() detector->detectAndCompute(frame, mask, points, descriptors, false);
 
     perf::sort(points, descriptors);
     SANITY_CHECK_KEYPOINTS(points, 1e-5);

modules/features2d/src/akaze.cpp

@@ -52,22 +52,15 @@ http://www.robesafe.com/personal/pablo.alcantarilla/papers/Alcantarilla13bmvc.pd
 #include "kaze/AKAZEFeatures.h"
 
 #include <iostream>
-using namespace std;
 
 namespace cv
 {
-    AKAZE::AKAZE()
-        : descriptor(DESCRIPTOR_MLDB)
-        , descriptor_channels(3)
-        , descriptor_size(0)
-        , threshold(0.001f)
-        , octaves(4)
-        , sublevels(4)
-        , diffusivity(DIFF_PM_G2)
-    {
-    }
+    using namespace std;
 
-    AKAZE::AKAZE(int _descriptor_type, int _descriptor_size, int _descriptor_channels,
+    class AKAZE_Impl : public AKAZE
+    {
+    public:
+        AKAZE_Impl(int _descriptor_type, int _descriptor_size, int _descriptor_channels,
                  float _threshold, int _octaves, int _sublevels, int _diffusivity)
         : descriptor(_descriptor_type)
         , descriptor_channels(_descriptor_channels)
@@ -77,25 +70,24 @@ namespace cv
         , sublevels(_sublevels)
         , diffusivity(_diffusivity)
         {
-
         }
 
-    AKAZE::~AKAZE()
+        virtual ~AKAZE_Impl()
         {
 
         }
 
         // returns the descriptor size in bytes
-    int AKAZE::descriptorSize() const
+        int descriptorSize() const
         {
             switch (descriptor)
             {
-        case cv::DESCRIPTOR_KAZE:
-        case cv::DESCRIPTOR_KAZE_UPRIGHT:
+            case DESCRIPTOR_KAZE:
+            case DESCRIPTOR_KAZE_UPRIGHT:
                 return 64;
 
-        case cv::DESCRIPTOR_MLDB:
-        case cv::DESCRIPTOR_MLDB_UPRIGHT:
+            case DESCRIPTOR_MLDB:
+            case DESCRIPTOR_MLDB_UPRIGHT:
                 // We use the full length binary descriptor -> 486 bits
                 if (descriptor_size == 0)
                 {
@@ -114,16 +106,16 @@ namespace cv
         }
 
         // returns the descriptor type
-    int AKAZE::descriptorType() const
+        int descriptorType() const
         {
             switch (descriptor)
             {
-        case cv::DESCRIPTOR_KAZE:
-        case cv::DESCRIPTOR_KAZE_UPRIGHT:
+            case DESCRIPTOR_KAZE:
+            case DESCRIPTOR_KAZE_UPRIGHT:
                     return CV_32F;
 
-        case cv::DESCRIPTOR_MLDB:
-        case cv::DESCRIPTOR_MLDB_UPRIGHT:
+            case DESCRIPTOR_MLDB:
+            case DESCRIPTOR_MLDB_UPRIGHT:
                     return CV_8U;
 
                 default:
@@ -132,38 +124,35 @@ namespace cv
         }
 
         // returns the default norm type
-    int AKAZE::defaultNorm() const
+        int defaultNorm() const
         {
             switch (descriptor)
             {
-        case cv::DESCRIPTOR_KAZE:
-        case cv::DESCRIPTOR_KAZE_UPRIGHT:
-            return cv::NORM_L2;
+            case DESCRIPTOR_KAZE:
+            case DESCRIPTOR_KAZE_UPRIGHT:
+                return NORM_L2;
 
-        case cv::DESCRIPTOR_MLDB:
-        case cv::DESCRIPTOR_MLDB_UPRIGHT:
-            return cv::NORM_HAMMING;
+            case DESCRIPTOR_MLDB:
+            case DESCRIPTOR_MLDB_UPRIGHT:
+                return NORM_HAMMING;
 
             default:
                 return -1;
             }
         }
 
-
-    void AKAZE::operator()(InputArray image, InputArray mask,
+        void detectAndCompute(InputArray image, InputArray mask,
                               std::vector<KeyPoint>& keypoints,
                               OutputArray descriptors,
-        bool useProvidedKeypoints) const
+                              bool useProvidedKeypoints)
         {
-        cv::Mat img = image.getMat();
+            Mat img = image.getMat();
             if (img.type() != CV_8UC1)
                 cvtColor(image, img, COLOR_BGR2GRAY);
 
             Mat img1_32;
             img.convertTo(img1_32, CV_32F, 1.0 / 255.0, 0);
 
-        cv::Mat& desc = descriptors.getMatRef();
-
             AKAZEOptions options;
             options.descriptor = descriptor;
             options.descriptor_channels = descriptor_channels;
@@ -185,72 +174,56 @@ namespace cv
 
             if (!mask.empty())
             {
-            cv::KeyPointsFilter::runByPixelsMask(keypoints, mask.getMat());
+                KeyPointsFilter::runByPixelsMask(keypoints, mask.getMat());
             }
 
+            if( descriptors.needed() )
+            {
+                Mat& desc = descriptors.getMatRef();
                 impl.Compute_Descriptors(keypoints, desc);
 
                 CV_Assert((!desc.rows || desc.cols == descriptorSize()));
                 CV_Assert((!desc.rows || (desc.type() == descriptorType())));
             }
+        }
 
-    void AKAZE::detectImpl(InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask) const
-    {
-        cv::Mat img = image.getMat();
-        if (img.type() != CV_8UC1)
-            cvtColor(image, img, COLOR_BGR2GRAY);
-
-        Mat img1_32;
-        img.convertTo(img1_32, CV_32F, 1.0 / 255.0, 0);
-
-        AKAZEOptions options;
-        options.descriptor = descriptor;
-        options.descriptor_channels = descriptor_channels;
-        options.descriptor_size = descriptor_size;
-        options.img_width = img.cols;
-        options.img_height = img.rows;
-        options.dthreshold = threshold;
-        options.omax = octaves;
-        options.nsublevels = sublevels;
-        options.diffusivity = diffusivity;
-
-        AKAZEFeatures impl(options);
-        impl.Create_Nonlinear_Scale_Space(img1_32);
-        impl.Feature_Detection(keypoints);
-
-        if (!mask.empty())
+        void write(FileStorage& fs) const
         {
-            cv::KeyPointsFilter::runByPixelsMask(keypoints, mask.getMat());
-        }
+            fs << "descriptor" << descriptor;
+            fs << "descriptor_channels" << descriptor_channels;
+            fs << "descriptor_size" << descriptor_size;
+            fs << "threshold" << threshold;
+            fs << "octaves" << octaves;
+            fs << "sublevels" << sublevels;
+            fs << "diffusivity" << diffusivity;
         }
 
-    void AKAZE::computeImpl(InputArray image, std::vector<KeyPoint>& keypoints, OutputArray descriptors) const
+        void read(const FileNode& fn)
         {
-        cv::Mat img = image.getMat();
-        if (img.type() != CV_8UC1)
-            cvtColor(image, img, COLOR_BGR2GRAY);
-
-        Mat img1_32;
-        img.convertTo(img1_32, CV_32F, 1.0 / 255.0, 0);
-
-        cv::Mat& desc = descriptors.getMatRef();
-
-        AKAZEOptions options;
-        options.descriptor = descriptor;
-        options.descriptor_channels = descriptor_channels;
-        options.descriptor_size = descriptor_size;
-        options.img_width = img.cols;
-        options.img_height = img.rows;
-        options.dthreshold = threshold;
-        options.omax = octaves;
-        options.nsublevels = sublevels;
-        options.diffusivity = diffusivity;
+            descriptor = (int)fn["descriptor"];
+            descriptor_channels = (int)fn["descriptor_channels"];
+            descriptor_size = (int)fn["descriptor_size"];
+            threshold = (float)fn["threshold"];
+            octaves = (int)fn["octaves"];
+            sublevels = (int)fn["sublevels"];
+            diffusivity = (int)fn["diffusivity"];
+        }
 
-        AKAZEFeatures impl(options);
-        impl.Create_Nonlinear_Scale_Space(img1_32);
-        impl.Compute_Descriptors(keypoints, desc);
+        int descriptor;
+        int descriptor_channels;
+        int descriptor_size;
+        float threshold;
+        int octaves;
+        int sublevels;
+        int diffusivity;
+    };
 
-        CV_Assert((!desc.rows || desc.cols == descriptorSize()));
-        CV_Assert((!desc.rows || (desc.type() == descriptorType())));
+    Ptr<AKAZE> AKAZE::create(int descriptor_type,
+                             int descriptor_size, int descriptor_channels,
+                             float threshold, int octaves,
+                             int sublevels, int diffusivity)
+    {
+        return makePtr<AKAZE_Impl>(descriptor_type, descriptor_size, descriptor_channels,
+                                   threshold, octaves, sublevels, diffusivity);
     }
 }

modules/features2d/src/blobdetector.cpp

@@ -55,7 +55,31 @@
 #  endif
 #endif
 
-using namespace cv;
+namespace cv
+{
+
+class CV_EXPORTS_W SimpleBlobDetectorImpl : public SimpleBlobDetector
+{
+public:
+
+  explicit SimpleBlobDetectorImpl(const SimpleBlobDetector::Params &parameters = SimpleBlobDetector::Params());
+
+  virtual void read( const FileNode& fn );
+  virtual void write( FileStorage& fs ) const;
+
+protected:
+  struct CV_EXPORTS Center
+  {
+      Point2d location;
+      double radius;
+      double confidence;
+  };
+
+  virtual void detect( InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask=noArray() );
+  virtual void findBlobs(InputArray image, InputArray binaryImage, std::vector<Center> &centers) const;
+
+  Params params;
+};
 
 /*
 *  SimpleBlobDetector
@@ -148,22 +172,22 @@ void SimpleBlobDetector::Params::write(cv::FileStorage& fs) const
     fs << "maxConvexity" << maxConvexity;
 }
 
-SimpleBlobDetector::SimpleBlobDetector(const SimpleBlobDetector::Params &parameters) :
+SimpleBlobDetectorImpl::SimpleBlobDetectorImpl(const SimpleBlobDetector::Params &parameters) :
 params(parameters)
 {
 }
 
-void SimpleBlobDetector::read( const cv::FileNode& fn )
+void SimpleBlobDetectorImpl::read( const cv::FileNode& fn )
 {
     params.read(fn);
 }
 
-void SimpleBlobDetector::write( cv::FileStorage& fs ) const
+void SimpleBlobDetectorImpl::write( cv::FileStorage& fs ) const
 {
     params.write(fs);
 }
 
-void SimpleBlobDetector::findBlobs(InputArray _image, InputArray _binaryImage, std::vector<Center> &centers) const
+void SimpleBlobDetectorImpl::findBlobs(InputArray _image, InputArray _binaryImage, std::vector<Center> &centers) const
 {
     Mat image = _image.getMat(), binaryImage = _binaryImage.getMat();
     (void)image;
@@ -277,7 +301,7 @@ void SimpleBlobDetector::findBlobs(InputArray _image, InputArray _binaryImage, s
 #endif
 }
 
-void SimpleBlobDetector::detectImpl(InputArray image, std::vector<cv::KeyPoint>& keypoints, InputArray) const
+void SimpleBlobDetectorImpl::detect(InputArray image, std::vector<cv::KeyPoint>& keypoints, InputArray)
 {
     //TODO: support mask
     keypoints.clear();
@@ -340,3 +364,10 @@ void SimpleBlobDetector::detectImpl(InputArray image, std::vector<cv::KeyPoint>&
         keypoints.push_back(kpt);
     }
 }
+
+Ptr<SimpleBlobDetector> SimpleBlobDetector::create(const SimpleBlobDetector::Params& params)
+{
+    return makePtr<SimpleBlobDetectorImpl>(params);
+}
+
+}

modules/features2d/src/fast.cpp

@@ -359,19 +359,17 @@ void FAST(InputArray _img, std::vector<KeyPoint>& keypoints, int threshold, bool
 {
     FAST(_img, keypoints, threshold, nonmax_suppression, FastFeatureDetector::TYPE_9_16);
 }
-/*
- *   FastFeatureDetector
- */
-FastFeatureDetector::FastFeatureDetector( int _threshold, bool _nonmaxSuppression )
-    : threshold(_threshold), nonmaxSuppression(_nonmaxSuppression), type(FastFeatureDetector::TYPE_9_16)
-{}
 
-FastFeatureDetector::FastFeatureDetector( int _threshold, bool _nonmaxSuppression, int _type )
-: threshold(_threshold), nonmaxSuppression(_nonmaxSuppression), type((short)_type)
-{}
 
-void FastFeatureDetector::detectImpl( InputArray _image, std::vector<KeyPoint>& keypoints, InputArray _mask ) const
+class FastFeatureDetector_Impl : public FastFeatureDetector
 {
+public:
+    FastFeatureDetector_Impl( int _threshold, bool _nonmaxSuppression, int _type )
+    : threshold(_threshold), nonmaxSuppression(_nonmaxSuppression), type((short)_type)
+    {}
+
+    void detect( InputArray _image, std::vector<KeyPoint>& keypoints, InputArray _mask )
+    {
         Mat mask = _mask.getMat(), grayImage;
         UMat ugrayImage;
         _InputArray gray = _image;
@@ -383,6 +381,41 @@ void FastFeatureDetector::detectImpl( InputArray _image, std::vector<KeyPoint>&
         }
         FAST( gray, keypoints, threshold, nonmaxSuppression, type );
         KeyPointsFilter::runByPixelsMask( keypoints, mask );
+    }
+
+    void set(int prop, double value)
+    {
+        if(prop == THRESHOLD)
+            threshold = cvRound(value);
+        else if(prop == NONMAX_SUPPRESSION)
+            nonmaxSuppression = value != 0;
+        else if(prop == FAST_N)
+            type = cvRound(value);
+        else
+            CV_Error(Error::StsBadArg, "");
+    }
+
+    double get(int prop) const
+    {
+        if(prop == THRESHOLD)
+            return threshold;
+        if(prop == NONMAX_SUPPRESSION)
+            return nonmaxSuppression;
+        if(prop == FAST_N)
+            return type;
+        CV_Error(Error::StsBadArg, "");
+        return 0;
+    }
+
+    int threshold;
+    bool nonmaxSuppression;
+    int type;
+};
+
+Ptr<FastFeatureDetector> FastFeatureDetector::create( int threshold, bool nonmaxSuppression, int type )
+{
+    return makePtr<FastFeatureDetector_Impl>(threshold, nonmaxSuppression, type);
 }
 
+
 }

modules/features2d/src/detectors.cpp → modules/features2d/src/feature2d.cpp

@@ -7,10 +7,11 @@
 //  copy or use the software.
 //
 //
-//                        Intel License Agreement
+//                          License Agreement
 //                For Open Source Computer Vision Library
 //
-// Copyright (C) 2000, Intel Corporation, all rights reserved.
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
 // Third party copyrights are property of their respective owners.
 //
 // Redistribution and use in source and binary forms, with or without modification,
@@ -23,7 +24,7 @@
 //     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
+//   * The name of the copyright holders 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
@@ -44,118 +45,125 @@
 namespace cv
 {
 
-/*
- *  FeatureDetector
- */
+using std::vector;
 
-FeatureDetector::~FeatureDetector()
-{}
+Feature2D::~Feature2D() {}
 
-void FeatureDetector::detect( InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask ) const
+/*
+ * Detect keypoints in an image.
+ * image        The image.
+ * keypoints    The detected keypoints.
+ * mask         Mask specifying where to look for keypoints (optional). Must be a char
+ *              matrix with non-zero values in the region of interest.
+ */
+void Feature2D::detect( InputArray image,
+                        std::vector<KeyPoint>& keypoints,
+                        InputArray mask )
 {
-    keypoints.clear();
-
     if( image.empty() )
+    {
+        keypoints.clear();
         return;
-
-    CV_Assert( mask.empty() || (mask.type() == CV_8UC1 && mask.size() == image.size()) );
-
-    detectImpl( image, keypoints, mask );
+    }
+    detectAndCompute(image, mask, keypoints, noArray(), false);
 }
 
-void FeatureDetector::detect(InputArrayOfArrays _imageCollection, std::vector<std::vector<KeyPoint> >& pointCollection,
-                             InputArrayOfArrays _masks ) const
+
+void Feature2D::detect( InputArrayOfArrays _images,
+                        std::vector<std::vector<KeyPoint> >& keypoints,
+                        InputArrayOfArrays _masks )
 {
-    if (_imageCollection.isUMatVector())
-    {
-        std::vector<UMat> uimageCollection, umasks;
-        _imageCollection.getUMatVector(uimageCollection);
-        _masks.getUMatVector(umasks);
+    vector<Mat> images, masks;
 
-        pointCollection.resize( uimageCollection.size() );
-        for( size_t i = 0; i < uimageCollection.size(); i++ )
-            detect( uimageCollection[i], pointCollection[i], umasks.empty() ? noArray() : umasks[i] );
+    _images.getMatVector(images);
+    size_t i, nimages = images.size();
 
-        return;
+    if( !_masks.empty() )
+    {
+        _masks.getMatVector(masks);
+        CV_Assert(masks.size() == nimages);
     }
 
-    std::vector<Mat> imageCollection, masks;
-    _imageCollection.getMatVector(imageCollection);
-    _masks.getMatVector(masks);
+    keypoints.resize(nimages);
 
-    pointCollection.resize( imageCollection.size() );
-    for( size_t i = 0; i < imageCollection.size(); i++ )
-        detect( imageCollection[i], pointCollection[i], masks.empty() ? noArray() : masks[i] );
+    for( i = 0; i < nimages; i++ )
+    {
+        detect(images[i], keypoints[i], masks.empty() ? Mat() : masks[i] );
+    }
 }
 
-/*void FeatureDetector::read( const FileNode& )
-{}
-
-void FeatureDetector::write( FileStorage& ) const
-{}*/
-
-bool FeatureDetector::empty() const
+/*
+ * Compute the descriptors for a set of keypoints in an image.
+ * image        The image.
+ * keypoints    The input keypoints. Keypoints for which a descriptor cannot be computed are removed.
+ * descriptors  Copmputed descriptors. Row i is the descriptor for keypoint i.
+ */
+void Feature2D::compute( InputArray image,
+                         std::vector<KeyPoint>& keypoints,
+                         OutputArray descriptors )
 {
-    return false;
+    if( image.empty() )
+    {
+        descriptors.release();
+        return;
+    }
+    detectAndCompute(image, noArray(), keypoints, descriptors, true);
 }
 
-void FeatureDetector::removeInvalidPoints( const Mat& mask, std::vector<KeyPoint>& keypoints )
+void Feature2D::compute( InputArrayOfArrays _images,
+                         std::vector<std::vector<KeyPoint> >& keypoints,
+                         OutputArrayOfArrays _descriptors )
 {
-    KeyPointsFilter::runByPixelsMask( keypoints, mask );
-}
+    if( !_descriptors.needed() )
+        return;
 
-Ptr<FeatureDetector> FeatureDetector::create( const String& detectorType )
-{
-    if( detectorType.compare( "HARRIS" ) == 0 )
+    vector<Mat> images;
+
+    _images.getMatVector(images);
+    size_t i, nimages = images.size();
+
+    CV_Assert( keypoints.size() == nimages );
+    CV_Assert( _descriptors.kind() == _InputArray::STD_VECTOR_MAT );
+
+    vector<Mat>& descriptors = *(vector<Mat>*)_descriptors.getObj();
+    descriptors.resize(nimages);
+
+    for( i = 0; i < nimages; i++ )
     {
-        Ptr<FeatureDetector> fd = FeatureDetector::create("GFTT");
-        fd->set("useHarrisDetector", true);
-        return fd;
+        compute(images[i], keypoints[i], descriptors[i]);
     }
-
-    return Algorithm::create<FeatureDetector>("Feature2D." + detectorType);
 }
 
 
-GFTTDetector::GFTTDetector( int _nfeatures, double _qualityLevel,
-                            double _minDistance, int _blockSize,
-                            bool _useHarrisDetector, double _k )
-    : nfeatures(_nfeatures), qualityLevel(_qualityLevel), minDistance(_minDistance),
-    blockSize(_blockSize), useHarrisDetector(_useHarrisDetector), k(_k)
+/* Detects keypoints and computes the descriptors */
+void Feature2D::detectAndCompute( InputArray, InputArray,
+                                  std::vector<KeyPoint>&,
+                                  OutputArray,
+                                  bool )
 {
+    CV_Error(Error::StsNotImplemented, "");
 }
 
-void GFTTDetector::detectImpl( InputArray _image, std::vector<KeyPoint>& keypoints, InputArray _mask) const
+int Feature2D::descriptorSize() const
 {
-    std::vector<Point2f> corners;
-
-    if (_image.isUMat())
-    {
-        UMat ugrayImage;
-        if( _image.type() != CV_8U )
-            cvtColor( _image, ugrayImage, COLOR_BGR2GRAY );
-        else
-            ugrayImage = _image.getUMat();
-
-        goodFeaturesToTrack( ugrayImage, corners, nfeatures, qualityLevel, minDistance, _mask,
-                             blockSize, useHarrisDetector, k );
-    }
-    else
-    {
-        Mat image = _image.getMat(), grayImage = image;
-        if( image.type() != CV_8U )
-            cvtColor( image, grayImage, COLOR_BGR2GRAY );
+    return 0;
+}
 
-        goodFeaturesToTrack( grayImage, corners, nfeatures, qualityLevel, minDistance, _mask,
-                             blockSize, useHarrisDetector, k );
-    }
+int Feature2D::descriptorType() const
+{
+    return CV_32F;
+}
 
-    keypoints.resize(corners.size());
-    std::vector<Point2f>::const_iterator corner_it = corners.begin();
-    std::vector<KeyPoint>::iterator keypoint_it = keypoints.begin();
-    for( ; corner_it != corners.end(); ++corner_it, ++keypoint_it )
-        *keypoint_it = KeyPoint( *corner_it, (float)blockSize );
+int Feature2D::defaultNorm() const
+{
+    int tp = descriptorType();
+    return tp == CV_8U ? NORM_HAMMING : NORM_L2;
+}
 
+// Return true if detector object is empty
+bool Feature2D::empty() const
+{
+    return true;
 }
 
 }

modules/features2d/src/descriptors.cpp → modules/features2d/src/gftt.cpp

@@ -40,71 +40,87 @@
 //M*/
 
 #include "precomp.hpp"
-#include <limits>
 
 namespace cv
 {
 
-/****************************************************************************************\
-*                                 DescriptorExtractor                                    *
-\****************************************************************************************/
-/*
- *   DescriptorExtractor
- */
-DescriptorExtractor::~DescriptorExtractor()
-{}
-
-void DescriptorExtractor::compute( InputArray image, std::vector<KeyPoint>& keypoints, OutputArray descriptors ) const
+class GFTTDetector_Impl : public GFTTDetector
 {
-    if( image.empty() || keypoints.empty() )
+public:
+    GFTTDetector_Impl( int _nfeatures, double _qualityLevel,
+                      double _minDistance, int _blockSize,
+                      bool _useHarrisDetector, double _k )
+        : nfeatures(_nfeatures), qualityLevel(_qualityLevel), minDistance(_minDistance),
+        blockSize(_blockSize), useHarrisDetector(_useHarrisDetector), k(_k)
     {
-        descriptors.release();
-        return;
     }
 
-    KeyPointsFilter::runByImageBorder( keypoints, image.size(), 0 );
-    KeyPointsFilter::runByKeypointSize( keypoints, std::numeric_limits<float>::epsilon() );
+    void set(int prop, double value)
+    {
+        if( prop == USE_HARRIS_DETECTOR )
+            useHarrisDetector = value != 0;
+        else
+            CV_Error(Error::StsBadArg, "");
+    }
 
-    computeImpl( image, keypoints, descriptors );
-}
+    double get(int prop) const
+    {
+        double value = 0;
+        if( prop == USE_HARRIS_DETECTOR )
+            value = useHarrisDetector;
+        else
+            CV_Error(Error::StsBadArg, "");
+        return value;
+    }
 
-void DescriptorExtractor::compute( InputArrayOfArrays _imageCollection, std::vector<std::vector<KeyPoint> >& pointCollection, OutputArrayOfArrays _descCollection ) const
-{
-    std::vector<Mat> imageCollection, descCollection;
-    _imageCollection.getMatVector(imageCollection);
-    _descCollection.getMatVector(descCollection);
-    CV_Assert( imageCollection.size() == pointCollection.size() );
-    descCollection.resize( imageCollection.size() );
-    for( size_t i = 0; i < imageCollection.size(); i++ )
-        compute( imageCollection[i], pointCollection[i], descCollection[i] );
-}
+    void detect( InputArray _image, std::vector<KeyPoint>& keypoints, InputArray _mask )
+    {
+        std::vector<Point2f> corners;
 
-/*void DescriptorExtractor::read( const FileNode& )
-{}
+        if (_image.isUMat())
+        {
+            UMat ugrayImage;
+            if( _image.type() != CV_8U )
+                cvtColor( _image, ugrayImage, COLOR_BGR2GRAY );
+            else
+                ugrayImage = _image.getUMat();
 
-void DescriptorExtractor::write( FileStorage& ) const
-{}*/
+            goodFeaturesToTrack( ugrayImage, corners, nfeatures, qualityLevel, minDistance, _mask,
+                                 blockSize, useHarrisDetector, k );
+        }
+        else
+        {
+            Mat image = _image.getMat(), grayImage = image;
+            if( image.type() != CV_8U )
+                cvtColor( image, grayImage, COLOR_BGR2GRAY );
 
-bool DescriptorExtractor::empty() const
-{
-    return false;
-}
+            goodFeaturesToTrack( grayImage, corners, nfeatures, qualityLevel, minDistance, _mask,
+                                blockSize, useHarrisDetector, k );
+        }
 
-void DescriptorExtractor::removeBorderKeypoints( std::vector<KeyPoint>& keypoints,
-                                                 Size imageSize, int borderSize )
-{
-    KeyPointsFilter::runByImageBorder( keypoints, imageSize, borderSize );
-}
+        keypoints.resize(corners.size());
+        std::vector<Point2f>::const_iterator corner_it = corners.begin();
+        std::vector<KeyPoint>::iterator keypoint_it = keypoints.begin();
+        for( ; corner_it != corners.end(); ++corner_it, ++keypoint_it )
+            *keypoint_it = KeyPoint( *corner_it, (float)blockSize );
 
-Ptr<DescriptorExtractor> DescriptorExtractor::create(const String& descriptorExtractorType)
-{
-    return Algorithm::create<DescriptorExtractor>("Feature2D." + descriptorExtractorType);
-}
+    }
+
+    int nfeatures;
+    double qualityLevel;
+    double minDistance;
+    int blockSize;
+    bool useHarrisDetector;
+    double k;
+};
 
 
-CV_WRAP void Feature2D::compute( InputArray image, CV_OUT CV_IN_OUT std::vector<KeyPoint>& keypoints, OutputArray descriptors ) const
+Ptr<GFTTDetector> GFTTDetector::create( int _nfeatures, double _qualityLevel,
+                         double _minDistance, int _blockSize,
+                         bool _useHarrisDetector, double _k )
 {
-   DescriptorExtractor::compute(image, keypoints, descriptors);
+    return makePtr<GFTTDetector_Impl>(_nfeatures, _qualityLevel,
+                                      _minDistance, _blockSize, _useHarrisDetector, _k);
 }
 
 }

modules/features2d/src/kaze.cpp

@@ -52,17 +52,11 @@ http://www.robesafe.com/personal/pablo.alcantarilla/papers/Alcantarilla12eccv.pd
 
 namespace cv
 {
-    KAZE::KAZE()
-        : extended(false)
-        , upright(false)
-        , threshold(0.001f)
-        , octaves(4)
-        , sublevels(4)
-        , diffusivity(DIFF_PM_G2)
-    {
-    }
 
-    KAZE::KAZE(bool _extended, bool _upright, float _threshold, int _octaves,
+    class KAZE_Impl : public KAZE
+    {
+    public:
+        KAZE_Impl(bool _extended, bool _upright, float _threshold, int _octaves,
                    int _sublevels, int _diffusivity)
         : extended(_extended)
         , upright(_upright)
@@ -71,40 +65,32 @@ namespace cv
         , sublevels(_sublevels)
         , diffusivity(_diffusivity)
         {
-
         }
-    KAZE::~KAZE()
-    {
 
-    }
+        virtual ~KAZE_Impl() {}
 
         // returns the descriptor size in bytes
-    int KAZE::descriptorSize() const
+        int descriptorSize() const
         {
             return extended ? 128 : 64;
         }
 
         // returns the descriptor type
-    int KAZE::descriptorType() const
+        int descriptorType() const
         {
             return CV_32F;
         }
 
         // returns the default norm type
-    int KAZE::defaultNorm() const
+        int defaultNorm() const
         {
             return NORM_L2;
         }
 
-    void KAZE::operator()(InputArray image, InputArray mask, std::vector<KeyPoint>& keypoints) const
-    {
-        detectImpl(image, keypoints, mask);
-    }
-
-    void KAZE::operator()(InputArray image, InputArray mask,
+        void detectAndCompute(InputArray image, InputArray mask,
                               std::vector<KeyPoint>& keypoints,
                               OutputArray descriptors,
-        bool useProvidedKeypoints) const
+                              bool useProvidedKeypoints)
         {
             cv::Mat img = image.getMat();
             if (img.type() != CV_8UC1)
@@ -113,8 +99,6 @@ namespace cv
             Mat img1_32;
             img.convertTo(img1_32, CV_32F, 1.0 / 255.0, 0);
 
-        cv::Mat& desc = descriptors.getMatRef();
-
             KAZEOptions options;
             options.img_width = img.cols;
             options.img_height = img.rows;
@@ -138,67 +122,49 @@ namespace cv
                 cv::KeyPointsFilter::runByPixelsMask(keypoints, mask.getMat());
             }
 
+            if( descriptors.needed() )
+            {
+                Mat& desc = descriptors.getMatRef();
                 impl.Feature_Description(keypoints, desc);
 
                 CV_Assert((!desc.rows || desc.cols == descriptorSize()));
                 CV_Assert((!desc.rows || (desc.type() == descriptorType())));
             }
+        }
 
-    void KAZE::detectImpl(InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask) const
+        void write(FileStorage& fs) const
         {
-        Mat img = image.getMat();
-        if (img.type() != CV_8UC1)
-            cvtColor(image, img, COLOR_BGR2GRAY);
-
-        Mat img1_32;
-        img.convertTo(img1_32, CV_32F, 1.0 / 255.0, 0);
-
-        KAZEOptions options;
-        options.img_width = img.cols;
-        options.img_height = img.rows;
-        options.extended = extended;
-        options.upright = upright;
-        options.dthreshold = threshold;
-        options.omax = octaves;
-        options.nsublevels = sublevels;
-        options.diffusivity = diffusivity;
-
-        KAZEFeatures impl(options);
-        impl.Create_Nonlinear_Scale_Space(img1_32);
-        impl.Feature_Detection(keypoints);
+            fs << "extended" << (int)extended;
+            fs << "upright" << (int)upright;
+            fs << "threshold" << threshold;
+            fs << "octaves" << octaves;
+            fs << "sublevels" << sublevels;
+            fs << "diffusivity" << diffusivity;
+        }
 
-        if (!mask.empty())
+        void read(const FileNode& fn)
         {
-            cv::KeyPointsFilter::runByPixelsMask(keypoints, mask.getMat());
-        }
+            extended = (int)fn["extended"] != 0;
+            upright = (int)fn["upright"] != 0;
+            threshold = (float)fn["threshold"];
+            octaves = (int)fn["octaves"];
+            sublevels = (int)fn["sublevels"];
+            diffusivity = (int)fn["diffusivity"];
         }
 
-    void KAZE::computeImpl(InputArray image, std::vector<KeyPoint>& keypoints, OutputArray descriptors) const
+        bool extended;
+        bool upright;
+        float threshold;
+        int octaves;
+        int sublevels;
+        int diffusivity;
+    };
+
+    Ptr<KAZE> KAZE::create(bool extended, bool upright,
+                            float threshold,
+                            int octaves, int sublevels,
+                            int diffusivity)
     {
-        cv::Mat img = image.getMat();
-        if (img.type() != CV_8UC1)
-            cvtColor(image, img, COLOR_BGR2GRAY);
-
-        Mat img1_32;
-        img.convertTo(img1_32, CV_32F, 1.0 / 255.0, 0);
-
-        cv::Mat& desc = descriptors.getMatRef();
-
-        KAZEOptions options;
-        options.img_width = img.cols;
-        options.img_height = img.rows;
-        options.extended = extended;
-        options.upright = upright;
-        options.dthreshold = threshold;
-        options.omax = octaves;
-        options.nsublevels = sublevels;
-        options.diffusivity = diffusivity;
-
-        KAZEFeatures impl(options);
-        impl.Create_Nonlinear_Scale_Space(img1_32);
-        impl.Feature_Description(keypoints, desc);
-
-        CV_Assert((!desc.rows || desc.cols == descriptorSize()));
-        CV_Assert((!desc.rows || (desc.type() == descriptorType())));
+        return makePtr<KAZE_Impl>(extended, upright, threshold, octaves, sublevels, diffusivity);
     }
 }

modules/features2d/src/kaze/AKAZEConfig.h

@@ -8,23 +8,8 @@
 #ifndef __OPENCV_FEATURES_2D_AKAZE_CONFIG_H__
 #define __OPENCV_FEATURES_2D_AKAZE_CONFIG_H__
 
-/* ************************************************************************* */
-// OpenCV
-#include "../precomp.hpp"
-#include <opencv2/features2d.hpp>
-
-/* ************************************************************************* */
-/// Lookup table for 2d gaussian (sigma = 2.5) where (0,0) is top left and (6,6) is bottom right
-const float gauss25[7][7] = {
-    { 0.02546481f, 0.02350698f, 0.01849125f, 0.01239505f, 0.00708017f, 0.00344629f, 0.00142946f },
-    { 0.02350698f, 0.02169968f, 0.01706957f, 0.01144208f, 0.00653582f, 0.00318132f, 0.00131956f },
-    { 0.01849125f, 0.01706957f, 0.01342740f, 0.00900066f, 0.00514126f, 0.00250252f, 0.00103800f },
-    { 0.01239505f, 0.01144208f, 0.00900066f, 0.00603332f, 0.00344629f, 0.00167749f, 0.00069579f },
-    { 0.00708017f, 0.00653582f, 0.00514126f, 0.00344629f, 0.00196855f, 0.00095820f, 0.00039744f },
-    { 0.00344629f, 0.00318132f, 0.00250252f, 0.00167749f, 0.00095820f, 0.00046640f, 0.00019346f },
-    { 0.00142946f, 0.00131956f, 0.00103800f, 0.00069579f, 0.00039744f, 0.00019346f, 0.00008024f }
-};
-
+namespace cv
+{
 /* ************************************************************************* */
 /// AKAZE configuration options structure
 struct AKAZEOptions {
@@ -37,12 +22,12 @@ struct AKAZEOptions {
         , soffset(1.6f)
         , derivative_factor(1.5f)
         , sderivatives(1.0)
-        , diffusivity(cv::DIFF_PM_G2)
+        , diffusivity(KAZE::DIFF_PM_G2)
 
         , dthreshold(0.001f)
         , min_dthreshold(0.00001f)
 
-        , descriptor(cv::DESCRIPTOR_MLDB)
+        , descriptor(AKAZE::DESCRIPTOR_MLDB)
         , descriptor_size(0)
         , descriptor_channels(3)
         , descriptor_pattern_size(10)
@@ -75,4 +60,6 @@ struct AKAZEOptions {
     int kcontrast_nbins;            ///< Number of bins for the contrast factor histogram
 };
 
+}
+
 #endif

modules/features2d/src/kaze/AKAZEFeatures.h

@@ -11,10 +11,12 @@
 
 /* ************************************************************************* */
 // Includes
-#include "../precomp.hpp"
 #include "AKAZEConfig.h"
 #include "TEvolution.h"
 
+namespace cv
+{
+
 /* ************************************************************************* */
 // AKAZE Class Declaration
 class AKAZEFeatures {
@@ -59,4 +61,6 @@ public:
 void generateDescriptorSubsample(cv::Mat& sampleList, cv::Mat& comparisons,
                                  int nbits, int pattern_size, int nchannels);
 
+}
+
 #endif

modules/features2d/src/kaze/KAZEConfig.h

@@ -12,12 +12,14 @@
 #include "../precomp.hpp"
 #include <opencv2/features2d.hpp>
 
+namespace cv
+{
 //*************************************************************************************
 
 struct KAZEOptions {
 
     KAZEOptions()
-        : diffusivity(cv::DIFF_PM_G2)
+        : diffusivity(KAZE::DIFF_PM_G2)
 
         , soffset(1.60f)
         , omax(4)
@@ -49,4 +51,6 @@ struct KAZEOptions {
     bool extended;
 };
 
+}
+
 #endif

modules/features2d/src/kaze/KAZEFeatures.h

@@ -17,10 +17,13 @@
 #include "fed.h"
 #include "TEvolution.h"
 
+namespace cv
+{
+
 /* ************************************************************************* */
 // KAZE Class Declaration
-class KAZEFeatures {
-
+class KAZEFeatures
+{
 private:
 
     /// Parameters of the Nonlinear diffusion class
@@ -56,4 +59,6 @@ public:
     void Do_Subpixel_Refinement(std::vector<cv::KeyPoint>& kpts);
 };
 
+}
+
 #endif

modules/features2d/src/kaze/TEvolution.h

@@ -8,10 +8,13 @@
 #ifndef __OPENCV_FEATURES_2D_TEVOLUTION_H__
 #define __OPENCV_FEATURES_2D_TEVOLUTION_H__
 
+namespace cv
+{
+
 /* ************************************************************************* */
 /// KAZE/A-KAZE nonlinear diffusion filtering evolution
-struct TEvolution {
-
+struct TEvolution
+{
   TEvolution() {
     etime = 0.0f;
     esigma = 0.0f;
@@ -20,11 +23,11 @@ struct TEvolution {
     sigma_size = 0;
   }
 
-  cv::Mat Lx, Ly;           ///< First order spatial derivatives
-  cv::Mat Lxx, Lxy, Lyy;    ///< Second order spatial derivatives
-  cv::Mat Lt;               ///< Evolution image
-  cv::Mat Lsmooth;          ///< Smoothed image
-  cv::Mat Ldet;             ///< Detector response
+  Mat Lx, Ly;           ///< First order spatial derivatives
+  Mat Lxx, Lxy, Lyy;    ///< Second order spatial derivatives
+  Mat Lt;               ///< Evolution image
+  Mat Lsmooth;          ///< Smoothed image
+  Mat Ldet;             ///< Detector response
   float etime;              ///< Evolution time
   float esigma;             ///< Evolution sigma. For linear diffusion t = sigma^2 / 2
   int octave;               ///< Image octave
@@ -32,4 +35,6 @@ struct TEvolution {
   int sigma_size;           ///< Integer esigma. For computing the feature detector responses
 };
 
+}
+
 #endif

modules/features2d/src/kaze/nldiffusion_functions.h

@@ -11,43 +11,37 @@
 #ifndef __OPENCV_FEATURES_2D_NLDIFFUSION_FUNCTIONS_H__
 #define __OPENCV_FEATURES_2D_NLDIFFUSION_FUNCTIONS_H__
 
-/* ************************************************************************* */
-// Includes
-#include "../precomp.hpp"
-
 /* ************************************************************************* */
 // Declaration of functions
 
-namespace cv {
-    namespace details {
-        namespace kaze {
+namespace cv
+{
+
+// Gaussian 2D convolution
+void gaussian_2D_convolution(const cv::Mat& src, cv::Mat& dst, int ksize_x, int ksize_y, float sigma);
 
-            // Gaussian 2D convolution
-            void gaussian_2D_convolution(const cv::Mat& src, cv::Mat& dst, int ksize_x, int ksize_y, float sigma);
+// Diffusivity functions
+void pm_g1(const cv::Mat& Lx, const cv::Mat& Ly, cv::Mat& dst, float k);
+void pm_g2(const cv::Mat& Lx, const cv::Mat& Ly, cv::Mat& dst, float k);
+void weickert_diffusivity(const cv::Mat& Lx, const cv::Mat& Ly, cv::Mat& dst, float k);
+void charbonnier_diffusivity(const cv::Mat& Lx, const cv::Mat& Ly, cv::Mat& dst, float k);
 
-            // Diffusivity functions
-            void pm_g1(const cv::Mat& Lx, const cv::Mat& Ly, cv::Mat& dst, float k);
-            void pm_g2(const cv::Mat& Lx, const cv::Mat& Ly, cv::Mat& dst, float k);
-            void weickert_diffusivity(const cv::Mat& Lx, const cv::Mat& Ly, cv::Mat& dst, float k);
-            void charbonnier_diffusivity(const cv::Mat& Lx, const cv::Mat& Ly, cv::Mat& dst, float k);
+float compute_k_percentile(const cv::Mat& img, float perc, float gscale, int nbins, int ksize_x, int ksize_y);
 
-            float compute_k_percentile(const cv::Mat& img, float perc, float gscale, int nbins, int ksize_x, int ksize_y);
+// Image derivatives
+void compute_scharr_derivatives(const cv::Mat& src, cv::Mat& dst, int xorder, int yorder, int scale);
+void compute_derivative_kernels(cv::OutputArray _kx, cv::OutputArray _ky, int dx, int dy, int scale);
+void image_derivatives_scharr(const cv::Mat& src, cv::Mat& dst, int xorder, int yorder);
 
-            // Image derivatives
-            void compute_scharr_derivatives(const cv::Mat& src, cv::Mat& dst, int xorder, int yorder, int scale);
-            void compute_derivative_kernels(cv::OutputArray _kx, cv::OutputArray _ky, int dx, int dy, int scale);
-            void image_derivatives_scharr(const cv::Mat& src, cv::Mat& dst, int xorder, int yorder);
+// Nonlinear diffusion filtering scalar step
+void nld_step_scalar(cv::Mat& Ld, const cv::Mat& c, cv::Mat& Lstep, float stepsize);
 
-            // Nonlinear diffusion filtering scalar step
-            void nld_step_scalar(cv::Mat& Ld, const cv::Mat& c, cv::Mat& Lstep, float stepsize);
+// For non-maxima suppresion
+bool check_maximum_neighbourhood(const cv::Mat& img, int dsize, float value, int row, int col, bool same_img);
 
-            // For non-maxima suppresion
-            bool check_maximum_neighbourhood(const cv::Mat& img, int dsize, float value, int row, int col, bool same_img);
+// Image downsampling
+void halfsample_image(const cv::Mat& src, cv::Mat& dst);
 
-            // Image downsampling
-            void halfsample_image(const cv::Mat& src, cv::Mat& dst);
-        }
-    }
 }
 
 #endif

modules/features2d/test/test_brisk.cpp

@@ -72,7 +72,7 @@ void CV_BRISKTest::run( int )
   cvtColor(image1, gray1, COLOR_BGR2GRAY);
   cvtColor(image2, gray2, COLOR_BGR2GRAY);
 
-  Ptr<FeatureDetector> detector = Algorithm::create<FeatureDetector>("Feature2D.BRISK");
+  Ptr<FeatureDetector> detector = BRISK::create();
 
   vector<KeyPoint> keypoints1;
   vector<KeyPoint> keypoints2;

modules/features2d/test/test_descriptors_regression.cpp

@@ -106,8 +106,6 @@ public:
 
     ~CV_DescriptorExtractorTest()
     {
-        if(!detector.empty())
-            detector.release();
     }
 protected:
     virtual void createDescriptorExtractor() {}
@@ -314,31 +312,34 @@ private:
 
 TEST( Features2d_DescriptorExtractor_BRISK, regression )
 {
-    CV_DescriptorExtractorTest<Hamming> test( "descriptor-brisk",  (CV_DescriptorExtractorTest<Hamming>::DistanceType)2.f,
-                                                 DescriptorExtractor::create("BRISK") );
+    CV_DescriptorExtractorTest<Hamming> test( "descriptor-brisk",
+                                             (CV_DescriptorExtractorTest<Hamming>::DistanceType)2.f,
+                                            BRISK::create() );
     test.safe_run();
 }
 
 TEST( Features2d_DescriptorExtractor_ORB, regression )
 {
     // TODO adjust the parameters below
-    CV_DescriptorExtractorTest<Hamming> test( "descriptor-orb",  (CV_DescriptorExtractorTest<Hamming>::DistanceType)12.f,
-                                                 DescriptorExtractor::create("ORB") );
+    CV_DescriptorExtractorTest<Hamming> test( "descriptor-orb",
+                                              (CV_DescriptorExtractorTest<Hamming>::DistanceType)12.f,
+                                             ORB::create() );
     test.safe_run();
 }
 
 TEST( Features2d_DescriptorExtractor_KAZE, regression )
 {
     CV_DescriptorExtractorTest< L2<float> > test( "descriptor-kaze",  0.03f,
-                                                 DescriptorExtractor::create("KAZE"),
-                                                 L2<float>(), FeatureDetector::create("KAZE"));
+                                                 KAZE::create(),
+                                                 L2<float>(), KAZE::create() );
     test.safe_run();
 }
 
 TEST( Features2d_DescriptorExtractor_AKAZE, regression )
 {
-    CV_DescriptorExtractorTest<Hamming> test( "descriptor-akaze",  (CV_DescriptorExtractorTest<Hamming>::DistanceType)12.f,
-                                              DescriptorExtractor::create("AKAZE"),
-                                              Hamming(), FeatureDetector::create("AKAZE"));
+    CV_DescriptorExtractorTest<Hamming> test( "descriptor-akaze",
+                                              (CV_DescriptorExtractorTest<Hamming>::DistanceType)12.f,
+                                              AKAZE::create(),
+                                              Hamming(), AKAZE::create());
     test.safe_run();
 }

modules/features2d/test/test_detectors_regression.cpp

@@ -249,48 +249,50 @@ void CV_FeatureDetectorTest::run( int /*start_from*/ )
 
 TEST( Features2d_Detector_BRISK, regression )
 {
-    CV_FeatureDetectorTest test( "detector-brisk", FeatureDetector::create("BRISK") );
+    CV_FeatureDetectorTest test( "detector-brisk", BRISK::create() );
     test.safe_run();
 }
 
 TEST( Features2d_Detector_FAST, regression )
 {
-    CV_FeatureDetectorTest test( "detector-fast", FeatureDetector::create("FAST") );
+    CV_FeatureDetectorTest test( "detector-fast", FastFeatureDetector::create() );
     test.safe_run();
 }
 
 TEST( Features2d_Detector_GFTT, regression )
 {
-    CV_FeatureDetectorTest test( "detector-gftt", FeatureDetector::create("GFTT") );
+    CV_FeatureDetectorTest test( "detector-gftt", GFTTDetector::create() );
     test.safe_run();
 }
 
 TEST( Features2d_Detector_Harris, regression )
 {
-    CV_FeatureDetectorTest test( "detector-harris", FeatureDetector::create("HARRIS") );
+    Ptr<FeatureDetector> gftt = GFTTDetector::create();
+    gftt->set(GFTTDetector::USE_HARRIS_DETECTOR, 1);
+    CV_FeatureDetectorTest test( "detector-harris", gftt);
     test.safe_run();
 }
 
 TEST( Features2d_Detector_MSER, DISABLED_regression )
 {
-    CV_FeatureDetectorTest test( "detector-mser", FeatureDetector::create("MSER") );
+    CV_FeatureDetectorTest test( "detector-mser", MSER::create() );
     test.safe_run();
 }
 
 TEST( Features2d_Detector_ORB, regression )
 {
-    CV_FeatureDetectorTest test( "detector-orb", FeatureDetector::create("ORB") );
+    CV_FeatureDetectorTest test( "detector-orb", ORB::create() );
     test.safe_run();
 }
 
 TEST( Features2d_Detector_KAZE, regression )
 {
-    CV_FeatureDetectorTest test( "detector-kaze", FeatureDetector::create("KAZE") );
+    CV_FeatureDetectorTest test( "detector-kaze", KAZE::create() );
     test.safe_run();
 }
 
 TEST( Features2d_Detector_AKAZE, regression )
 {
-    CV_FeatureDetectorTest test( "detector-akaze", FeatureDetector::create("AKAZE") );
+    CV_FeatureDetectorTest test( "detector-akaze", AKAZE::create() );
     test.safe_run();
 }

modules/features2d/test/test_keypoints.cpp

@@ -41,6 +41,7 @@
 
 #include "test_precomp.hpp"
 #include "opencv2/highgui.hpp"
+#include "opencv2/core/core_c.h"
 
 using namespace std;
 using namespace cv;
@@ -61,7 +62,6 @@ public:
 protected:
     virtual void run(int)
     {
-        cv::initModule_features2d();
         CV_Assert(detector);
         string imgFilename = string(ts->get_data_path()) + FEATURES2D_DIR + "/" + IMAGE_FILENAME;
 
@@ -121,51 +121,54 @@ protected:
 
 TEST(Features2d_Detector_Keypoints_BRISK, validation)
 {
-    CV_FeatureDetectorKeypointsTest test(Algorithm::create<FeatureDetector>("Feature2D.BRISK"));
+    CV_FeatureDetectorKeypointsTest test(BRISK::create());
     test.safe_run();
 }
 
 TEST(Features2d_Detector_Keypoints_FAST, validation)
 {
-    CV_FeatureDetectorKeypointsTest test(Algorithm::create<FeatureDetector>("Feature2D.FAST"));
+    CV_FeatureDetectorKeypointsTest test(FastFeatureDetector::create());
     test.safe_run();
 }
 
 TEST(Features2d_Detector_Keypoints_HARRIS, validation)
 {
-    CV_FeatureDetectorKeypointsTest test(Algorithm::create<FeatureDetector>("Feature2D.HARRIS"));
+
+    CV_FeatureDetectorKeypointsTest test(GFTTDetector::create(1000, 0.01, 1, 3, true, 0.04));
     test.safe_run();
 }
 
 TEST(Features2d_Detector_Keypoints_GFTT, validation)
 {
-    CV_FeatureDetectorKeypointsTest test(Algorithm::create<FeatureDetector>("Feature2D.GFTT"));
+    Ptr<FeatureDetector> gftt = GFTTDetector::create();
+    gftt->set(GFTTDetector::USE_HARRIS_DETECTOR, 1);
+    CV_FeatureDetectorKeypointsTest test(gftt);
     test.safe_run();
 }
 
 TEST(Features2d_Detector_Keypoints_MSER, validation)
 {
-    CV_FeatureDetectorKeypointsTest test(Algorithm::create<FeatureDetector>("Feature2D.MSER"));
+    CV_FeatureDetectorKeypointsTest test(MSER::create());
     test.safe_run();
 }
 
 TEST(Features2d_Detector_Keypoints_ORB, validation)
 {
-    CV_FeatureDetectorKeypointsTest test(Algorithm::create<FeatureDetector>("Feature2D.ORB"));
+    CV_FeatureDetectorKeypointsTest test(ORB::create());
     test.safe_run();
 }
 
 TEST(Features2d_Detector_Keypoints_KAZE, validation)
 {
-    CV_FeatureDetectorKeypointsTest test(Algorithm::create<FeatureDetector>("Feature2D.KAZE"));
+    CV_FeatureDetectorKeypointsTest test(KAZE::create());
     test.safe_run();
 }
 
 TEST(Features2d_Detector_Keypoints_AKAZE, validation)
 {
-    CV_FeatureDetectorKeypointsTest test_kaze(cv::Ptr<FeatureDetector>(new cv::AKAZE(cv::DESCRIPTOR_KAZE)));
+    CV_FeatureDetectorKeypointsTest test_kaze(AKAZE::create(AKAZE::DESCRIPTOR_KAZE));
     test_kaze.safe_run();
 
-    CV_FeatureDetectorKeypointsTest test_mldb(cv::Ptr<FeatureDetector>(new cv::AKAZE(cv::DESCRIPTOR_MLDB)));
+    CV_FeatureDetectorKeypointsTest test_mldb(AKAZE::create(AKAZE::DESCRIPTOR_MLDB));
     test_mldb.safe_run();
 }

modules/features2d/test/test_matchers_algorithmic.cpp

@@ -532,12 +532,14 @@ void CV_DescriptorMatcherTest::run( int )
 
 TEST( Features2d_DescriptorMatcher_BruteForce, regression )
 {
-    CV_DescriptorMatcherTest test( "descriptor-matcher-brute-force", Algorithm::create<DescriptorMatcher>("DescriptorMatcher.BFMatcher"), 0.01f );
+    CV_DescriptorMatcherTest test( "descriptor-matcher-brute-force",
+                                  DescriptorMatcher::create("BruteForce"), 0.01f );
     test.safe_run();
 }
 
 TEST( Features2d_DescriptorMatcher_FlannBased, regression )
 {
-    CV_DescriptorMatcherTest test( "descriptor-matcher-flann-based", Algorithm::create<DescriptorMatcher>("DescriptorMatcher.FlannBasedMatcher"), 0.04f );
+    CV_DescriptorMatcherTest test( "descriptor-matcher-flann-based",
+                                  DescriptorMatcher::create("FlannBased"), 0.04f );
     test.safe_run();
 }

modules/features2d/test/test_mser.cpp

@@ -43,6 +43,8 @@
 #include "test_precomp.hpp"
 #include "opencv2/imgproc/imgproc_c.h"
 
+#if 0
+
 #include <vector>
 #include <string>
 using namespace std;
@@ -205,3 +207,5 @@ void CV_MserTest::run(int)
 }
 
 TEST(Features2d_MSER, DISABLED_regression) { CV_MserTest test; test.safe_run(); }
+
+#endif

modules/features2d/test/test_orb.cpp

@@ -47,10 +47,8 @@ using namespace cv;
 
 TEST(Features2D_ORB, _1996)
 {
-    Ptr<FeatureDetector> fd = FeatureDetector::create("ORB");
-    fd->set("nFeatures", 10000);//setting a higher maximum to make effect of threshold visible
-    fd->set("fastThreshold", 20);//more features than the default
-    Ptr<DescriptorExtractor> de = DescriptorExtractor::create("ORB");
+    Ptr<FeatureDetector> fd = ORB::create(10000, 1.2f, 8, 31, 0, 2, ORB::HARRIS_SCORE, 31, 20);
+    Ptr<DescriptorExtractor> de = fd;
 
     Mat image = imread(string(cvtest::TS::ptr()->get_data_path()) + "shared/lena.png");
     ASSERT_FALSE(image.empty());

modules/features2d/test/test_rotation_and_scale_invariance.cpp

@@ -595,7 +595,7 @@ protected:
 
 TEST(Features2d_RotationInvariance_Detector_BRISK, regression)
 {
-    DetectorRotationInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.BRISK"),
+    DetectorRotationInvarianceTest test(BRISK::create(),
                                         0.32f,
                                         0.76f);
     test.safe_run();
@@ -603,7 +603,7 @@ TEST(Features2d_RotationInvariance_Detector_BRISK, regression)
 
 TEST(Features2d_RotationInvariance_Detector_ORB, regression)
 {
-    DetectorRotationInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.ORB"),
+    DetectorRotationInvarianceTest test(ORB::create(),
                                         0.47f,
                                         0.76f);
     test.safe_run();
@@ -615,19 +615,15 @@ TEST(Features2d_RotationInvariance_Detector_ORB, regression)
 
 TEST(Features2d_RotationInvariance_Descriptor_BRISK, regression)
 {
-    DescriptorRotationInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.BRISK"),
-                                          Algorithm::create<DescriptorExtractor>("Feature2D.BRISK"),
-                                          Algorithm::create<DescriptorExtractor>("Feature2D.BRISK")->defaultNorm(),
-                                          0.99f);
+    Ptr<Feature2D> f2d = BRISK::create();
+    DescriptorRotationInvarianceTest test(f2d, f2d, f2d->defaultNorm(), 0.99f);
     test.safe_run();
 }
 
 TEST(Features2d_RotationInvariance_Descriptor_ORB, regression)
 {
-    DescriptorRotationInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.ORB"),
-                                          Algorithm::create<DescriptorExtractor>("Feature2D.ORB"),
-                                          Algorithm::create<DescriptorExtractor>("Feature2D.ORB")->defaultNorm(),
-                                          0.99f);
+    Ptr<Feature2D> f2d = ORB::create();
+    DescriptorRotationInvarianceTest test(f2d, f2d, f2d->defaultNorm(), 0.99f);
     test.safe_run();
 }
 
@@ -646,25 +642,19 @@ TEST(Features2d_RotationInvariance_Descriptor_ORB, regression)
 
 TEST(Features2d_ScaleInvariance_Detector_BRISK, regression)
 {
-    DetectorScaleInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.BRISK"),
-                                     0.08f,
-                                     0.49f);
+    DetectorScaleInvarianceTest test(BRISK::create(), 0.08f, 0.49f);
     test.safe_run();
 }
 
 TEST(Features2d_ScaleInvariance_Detector_KAZE, regression)
 {
-    DetectorScaleInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.KAZE"),
-        0.08f,
-        0.49f);
+    DetectorScaleInvarianceTest test(KAZE::create(), 0.08f, 0.49f);
     test.safe_run();
 }
 
 TEST(Features2d_ScaleInvariance_Detector_AKAZE, regression)
 {
-    DetectorScaleInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.AKAZE"),
-        0.08f,
-        0.49f);
+    DetectorScaleInvarianceTest test(AKAZE::create(), 0.08f, 0.49f);
     test.safe_run();
 }
 

modules/java/generator/src/cpp/features2d_manual.hpp

@@ -90,67 +90,69 @@ public:
     //not supported: SimpleBlob, Dense
     CV_WRAP static javaFeatureDetector* create( int detectorType )
     {
-        String name;
+        //String name;
         if (detectorType > DYNAMICDETECTOR)
         {
-            name = "Dynamic";
+            //name = "Dynamic";
             detectorType -= DYNAMICDETECTOR;
         }
         if (detectorType > PYRAMIDDETECTOR)
         {
-            name = "Pyramid";
+            //name = "Pyramid";
             detectorType -= PYRAMIDDETECTOR;
         }
         if (detectorType > GRIDDETECTOR)
         {
-            name = "Grid";
+            //name = "Grid";
             detectorType -= GRIDDETECTOR;
         }
 
+        Ptr<FeatureDetector> fd;
         switch(detectorType)
         {
         case FAST:
-            name = name + "FAST";
-            break;
-        case STAR:
-            name = name + "STAR";
-            break;
-        case SIFT:
-            name = name + "SIFT";
-            break;
-        case SURF:
-            name = name + "SURF";
+            fd = FastFeatureDetector::create();
             break;
+        //case STAR:
+        //    fd = xfeatures2d::StarDetector::create();
+        //    break;
+        //case SIFT:
+        //    name = name + "SIFT";
+        //    break;
+        //case SURF:
+        //    name = name + "SURF";
+        //    break;
         case ORB:
-            name = name + "ORB";
+            fd = ORB::create();
             break;
         case MSER:
-            name = name + "MSER";
+            fd = MSER::create();
             break;
         case GFTT:
-            name = name + "GFTT";
+            fd = GFTTDetector::create();
             break;
         case HARRIS:
-            name = name + "HARRIS";
+            fd = GFTTDetector::create();
+            fd->set(GFTTDetector::USE_HARRIS_DETECTOR, 1);
             break;
         case SIMPLEBLOB:
-            name = name + "SimpleBlob";
-            break;
-        case DENSE:
-            name = name + "Dense";
+            fd = SimpleBlobDetector::create();
             break;
+        //case DENSE:
+        //    name = name + "Dense";
+        //    break;
         case BRISK:
-            name = name + "BRISK";
+            fd = BRISK::create();
             break;
         case AKAZE:
-            name = name + "AKAZE";
+            fd = AKAZE::create();
             break;
         default:
             CV_Error( Error::StsBadArg, "Specified feature detector type is not supported." );
             break;
         }
 
-        return new javaFeatureDetector(FeatureDetector::create(name));
+        return new javaFeatureDetector(fd);
     }
 
     CV_WRAP void write( const String& fileName ) const
@@ -332,43 +334,44 @@ public:
     //not supported: Calonder
     CV_WRAP static javaDescriptorExtractor* create( int extractorType )
     {
-        String name;
+        //String name;
 
         if (extractorType > OPPONENTEXTRACTOR)
         {
-            name = "Opponent";
+            //name = "Opponent";
             extractorType -= OPPONENTEXTRACTOR;
         }
 
+        Ptr<DescriptorExtractor> de;
         switch(extractorType)
         {
-        case SIFT:
-            name = name + "SIFT";
-            break;
-        case SURF:
-            name = name + "SURF";
-            break;
+        //case SIFT:
+        //    name = name + "SIFT";
+        //    break;
+        //case SURF:
+        //    name = name + "SURF";
+        //    break;
         case ORB:
-            name = name + "ORB";
-            break;
-        case BRIEF:
-            name = name + "BRIEF";
+            de = ORB::create();
             break;
+        //case BRIEF:
+        //    name = name + "BRIEF";
+        //    break;
         case BRISK:
-            name = name + "BRISK";
-            break;
-        case FREAK:
-            name = name + "FREAK";
+            de = BRISK::create();
             break;
+        //case FREAK:
+        //    name = name + "FREAK";
+        //    break;
         case AKAZE:
-            name = name + "AKAZE";
+            de = AKAZE::create();
             break;
         default:
             CV_Error( Error::StsBadArg, "Specified descriptor extractor type is not supported." );
             break;
         }
 
-        return new javaDescriptorExtractor(DescriptorExtractor::create(name));
+        return new javaDescriptorExtractor(de);
     }
 
     CV_WRAP void write( const String& fileName ) const

modules/java/generator/src/cpp/jni_part.cpp

@@ -24,15 +24,9 @@ JNI_OnLoad(JavaVM* vm, void* )
         return -1;
 
     bool init = true;
-#ifdef HAVE_OPENCV_FEATURES2D
-    init &= cv::initModule_features2d();
-#endif
 #ifdef HAVE_OPENCV_VIDEO
     init &= cv::initModule_video();
 #endif
-#ifdef HAVE_OPENCV_CONTRIB
-    init &= cv::initModule_contrib();
-#endif
 
     if(!init)
         return -1;

modules/python/test/test.py

@@ -91,7 +91,7 @@ class Hackathon244Tests(NewOpenCVTests):
         self.assertEqual(cv2.countNonZero(inliers), pattern_size[0]*pattern_size[1])
 
     def test_fast(self):
-        fd = cv2.FastFeatureDetector(30, True)
+        fd = cv2.FastFeatureDetector_create(30, True)
         img = self.get_sample("samples/cpp/right02.jpg", 0)
         img = cv2.medianBlur(img, 3)
         imgc = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)

modules/stitching/src/matchers.cpp

@@ -48,8 +48,7 @@ using namespace cv::cuda;
 
 #ifdef HAVE_OPENCV_XFEATURES2D
 #include "opencv2/xfeatures2d.hpp"
-
-static bool makeUseOfXfeatures2d = xfeatures2d::initModule_xfeatures2d();
+using xfeatures2d::SURF;
 #endif
 
 namespace {
@@ -321,30 +320,34 @@ void FeaturesFinder::operator ()(InputArray image, ImageFeatures &features, cons
 SurfFeaturesFinder::SurfFeaturesFinder(double hess_thresh, int num_octaves, int num_layers,
                                        int num_octaves_descr, int num_layers_descr)
 {
+#ifdef HAVE_OPENCV_XFEATURES2D
     if (num_octaves_descr == num_octaves && num_layers_descr == num_layers)
     {
-        surf = Algorithm::create<Feature2D>("Feature2D.SURF");
+        surf = SURF::create();
         if( !surf )
             CV_Error( Error::StsNotImplemented, "OpenCV was built without SURF support" );
-        surf->set("hessianThreshold", hess_thresh);
-        surf->set("nOctaves", num_octaves);
-        surf->set("nOctaveLayers", num_layers);
+        surf->set(SURF::HESSIAN_THRESHOLD, hess_thresh);
+        surf->set(SURF::NOCTAVES, num_octaves);
+        surf->set(SURF::NOCTAVE_LAYERS, num_layers);
     }
     else
     {
-        detector_ = Algorithm::create<FeatureDetector>("Feature2D.SURF");
-        extractor_ = Algorithm::create<DescriptorExtractor>("Feature2D.SURF");
+        detector_ = SURF::create();
+        extractor_ = SURF::create();
 
         if( !detector_ || !extractor_ )
             CV_Error( Error::StsNotImplemented, "OpenCV was built without SURF support" );
 
-        detector_->set("hessianThreshold", hess_thresh);
-        detector_->set("nOctaves", num_octaves);
-        detector_->set("nOctaveLayers", num_layers);
+        detector_->set(SURF::HESSIAN_THRESHOLD, hess_thresh);
+        detector_->set(SURF::NOCTAVES, num_octaves);
+        detector_->set(SURF::NOCTAVE_LAYERS, num_layers);
 
-        extractor_->set("nOctaves", num_octaves_descr);
-        extractor_->set("nOctaveLayers", num_layers_descr);
+        extractor_->set(SURF::NOCTAVES, num_octaves_descr);
+        extractor_->set(SURF::NOCTAVE_LAYERS, num_layers_descr);
     }
+#else
+    CV_Error( Error::StsNotImplemented, "OpenCV was built without SURF support" );
+#endif
 }
 
 void SurfFeaturesFinder::find(InputArray image, ImageFeatures &features)
@@ -367,7 +370,7 @@ void SurfFeaturesFinder::find(InputArray image, ImageFeatures &features)
     else
     {
         UMat descriptors;
-        (*surf)(gray_image, Mat(), features.keypoints, descriptors);
+        surf->detectAndCompute(gray_image, Mat(), features.keypoints, descriptors);
         features.descriptors = descriptors.reshape(1, (int)features.keypoints.size());
     }
 }
@@ -375,7 +378,7 @@ void SurfFeaturesFinder::find(InputArray image, ImageFeatures &features)
 OrbFeaturesFinder::OrbFeaturesFinder(Size _grid_size, int n_features, float scaleFactor, int nlevels)
 {
     grid_size = _grid_size;
-    orb = makePtr<ORB>(n_features * (99 + grid_size.area())/100/grid_size.area(), scaleFactor, nlevels);
+    orb = ORB::create(n_features * (99 + grid_size.area())/100/grid_size.area(), scaleFactor, nlevels);
 }
 
 void OrbFeaturesFinder::find(InputArray image, ImageFeatures &features)
@@ -395,7 +398,7 @@ void OrbFeaturesFinder::find(InputArray image, ImageFeatures &features)
     }
 
     if (grid_size.area() == 1)
-        (*orb)(gray_image, Mat(), features.keypoints, features.descriptors);
+        orb->detectAndCompute(gray_image, Mat(), features.keypoints, features.descriptors);
     else
     {
         features.keypoints.clear();
@@ -425,7 +428,7 @@ void OrbFeaturesFinder::find(InputArray image, ImageFeatures &features)
                 //     << " gray_image_part.dims=" << gray_image_part.dims << ", "
                 //     << " gray_image_part.data=" << ((size_t)gray_image_part.data) << "\n");
 
-                (*orb)(gray_image_part, UMat(), points, descriptors);
+                orb->detectAndCompute(gray_image_part, UMat(), points, descriptors);
 
                 features.keypoints.reserve(features.keypoints.size() + points.size());
                 for (std::vector<KeyPoint>::iterator kp = points.begin(); kp != points.end(); ++kp)

modules/videostab/src/global_motion.cpp

@@ -671,7 +671,7 @@ Mat ToFileMotionWriter::estimate(const Mat &frame0, const Mat &frame1, bool *ok)
 KeypointBasedMotionEstimator::KeypointBasedMotionEstimator(Ptr<MotionEstimatorBase> estimator)
     : ImageMotionEstimatorBase(estimator->motionModel()), motionEstimator_(estimator)
 {
-    setDetector(makePtr<GoodFeaturesToTrackDetector>());
+    setDetector(GFTTDetector::create());
     setOpticalFlowEstimator(makePtr<SparsePyrLkOptFlowEstimator>());
     setOutlierRejector(makePtr<NullOutlierRejector>());
 }

modules/world/src/world_init.cpp

@@ -47,12 +47,6 @@ bool cv::initAll()
     return true
 #ifdef HAVE_OPENCV_VIDEO
     && initModule_video()
-#endif
-#ifdef HAVE_OPENCV_FEATURES2D
-    && initModule_features2d()
-#endif
-#ifdef HAVE_OPENCV_XFEATURES2D
-    && xfeatures2d::initModule_xfeatures2d()
 #endif
     ;
 }

samples/android/tutorial-2-mixedprocessing/jni/jni_part.cpp

@@ -16,8 +16,8 @@ JNIEXPORT void JNICALL Java_org_opencv_samples_tutorial2_Tutorial2Activity_FindF
     Mat& mRgb = *(Mat*)addrRgba;
     vector<KeyPoint> v;
 
-    FastFeatureDetector detector(50);
-    detector.detect(mGr, v);
+    Ptr<FeatureDetector> detector = FastFeatureDetector::create(50);
+    detector->detect(mGr, v);
     for( unsigned int i = 0; i < v.size(); i++ )
     {
         const KeyPoint& kp = v[i];

samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/src/RobustMatcher.h

@@ -19,23 +19,23 @@ public:
   RobustMatcher() : ratio_(0.8f)
   {
     // ORB is the default feature
-    detector_ = new cv::OrbFeatureDetector();
-    extractor_ = new cv::OrbDescriptorExtractor();
+    detector_ = cv::ORB::create();
+    extractor_ = cv::ORB::create();
 
     // BruteFroce matcher with Norm Hamming is the default matcher
-    matcher_ = new cv::BFMatcher(cv::NORM_HAMMING, false);
+    matcher_ = cv::makePtr<cv::BFMatcher>((int)cv::NORM_HAMMING, false);
 
   }
   virtual ~RobustMatcher();
 
   // Set the feature detector
-  void setFeatureDetector(cv::FeatureDetector * detect) {  detector_ = detect; }
+  void setFeatureDetector(const cv::Ptr<cv::FeatureDetector>& detect) {  detector_ = detect; }
 
   // Set the descriptor extractor
-  void setDescriptorExtractor(cv::DescriptorExtractor * desc) { extractor_ = desc; }
+  void setDescriptorExtractor(const cv::Ptr<cv::DescriptorExtractor>& desc) { extractor_ = desc; }
 
   // Set the matcher
-  void setDescriptorMatcher(cv::DescriptorMatcher * match) {  matcher_ = match; }
+  void setDescriptorMatcher(const cv::Ptr<cv::DescriptorMatcher>& match) {  matcher_ = match; }
 
   // Compute the keypoints of an image
   void computeKeyPoints( const cv::Mat& image, std::vector<cv::KeyPoint>& keypoints);
@@ -69,11 +69,11 @@ public:
 
 private:
   // pointer to the feature point detector object
-  cv::FeatureDetector * detector_;
+  cv::Ptr<cv::FeatureDetector> detector_;
   // pointer to the feature descriptor extractor object
-  cv::DescriptorExtractor * extractor_;
+  cv::Ptr<cv::DescriptorExtractor> extractor_;
   // pointer to the matcher object
-  cv::DescriptorMatcher * matcher_;
+  cv::Ptr<cv::DescriptorMatcher> matcher_;
   // max ratio between 1st and 2nd NN
   float ratio_;
 };

samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/src/main_registration.cpp

@@ -13,13 +13,16 @@
 #include "ModelRegistration.h"
 #include "Utils.h"
 
+using namespace cv;
+using namespace std;
+
 /**  GLOBAL VARIABLES  **/
 
-std::string tutorial_path = "../../samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/"; // path to tutorial
+string tutorial_path = "../../samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/"; // path to tutorial
 
-std::string img_path = tutorial_path + "Data/resized_IMG_3875.JPG";  // image to register
-std::string ply_read_path = tutorial_path + "Data/box.ply";          // object mesh
-std::string write_path = tutorial_path + "Data/cookies_ORB.yml";     // output file
+string img_path = tutorial_path + "Data/resized_IMG_3875.JPG";  // image to register
+string ply_read_path = tutorial_path + "Data/box.ply";          // object mesh
+string write_path = tutorial_path + "Data/cookies_ORB.yml";     // output file
 
 // Boolean the know if the registration it's done
 bool end_registration = false;
@@ -39,10 +42,10 @@ int n = 8;
 int pts[] = {1, 2, 3, 4, 5, 6, 7, 8}; // 3 -> 4
 
 // Some basic colors
-cv::Scalar red(0, 0, 255);
-cv::Scalar green(0,255,0);
-cv::Scalar blue(255,0,0);
-cv::Scalar yellow(0,255,255);
+Scalar red(0, 0, 255);
+Scalar green(0,255,0);
+Scalar blue(255,0,0);
+Scalar yellow(0,255,255);
 
 /*
  * CREATE MODEL REGISTRATION OBJECT
@@ -61,13 +64,13 @@ void help();
 // Mouse events for model registration
 static void onMouseModelRegistration( int event, int x, int y, int, void* )
 {
-  if  ( event == cv::EVENT_LBUTTONUP )
+  if  ( event == EVENT_LBUTTONUP )
   {
       int n_regist = registration.getNumRegist();
       int n_vertex = pts[n_regist];
 
-      cv::Point2f point_2d = cv::Point2f((float)x,(float)y);
-      cv::Point3f point_3d = mesh.getVertex(n_vertex-1);
+      Point2f point_2d = Point2f((float)x,(float)y);
+      Point3f point_3d = mesh.getVertex(n_vertex-1);
 
       bool is_registrable = registration.is_registrable();
       if (is_registrable)
@@ -92,23 +95,23 @@ int main()
 
   //Instantiate robust matcher: detector, extractor, matcher
   RobustMatcher rmatcher;
-  cv::FeatureDetector * detector = new cv::OrbFeatureDetector(numKeyPoints);
+  Ptr<FeatureDetector> detector = ORB::create(numKeyPoints);
   rmatcher.setFeatureDetector(detector);
 
   /**  GROUND TRUTH OF THE FIRST IMAGE  **/
 
   // Create & Open Window
-  cv::namedWindow("MODEL REGISTRATION", cv::WINDOW_KEEPRATIO);
+  namedWindow("MODEL REGISTRATION", WINDOW_KEEPRATIO);
 
   // Set up the mouse events
-  cv::setMouseCallback("MODEL REGISTRATION", onMouseModelRegistration, 0 );
+  setMouseCallback("MODEL REGISTRATION", onMouseModelRegistration, 0 );
 
   // Open the image to register
-  cv::Mat img_in = cv::imread(img_path, cv::IMREAD_COLOR);
-  cv::Mat img_vis = img_in.clone();
+  Mat img_in = imread(img_path, IMREAD_COLOR);
+  Mat img_vis = img_in.clone();
 
   if (!img_in.data) {
-    std::cout << "Could not open or find the image" << std::endl;
+    cout << "Could not open or find the image" << endl;
     return -1;
   }
 
@@ -116,18 +119,18 @@ int main()
   int num_registrations = n;
   registration.setNumMax(num_registrations);
 
-  std::cout << "Click the box corners ..." << std::endl;
-  std::cout << "Waiting ..." << std::endl;
+  cout << "Click the box corners ..." << endl;
+  cout << "Waiting ..." << endl;
 
   // Loop until all the points are registered
-  while ( cv::waitKey(30) < 0 )
+  while ( waitKey(30) < 0 )
   {
     // Refresh debug image
     img_vis = img_in.clone();
 
     // Current registered points
-    std::vector<cv::Point2f> list_points2d = registration.get_points2d();
-    std::vector<cv::Point3f> list_points3d = registration.get_points3d();
+    vector<Point2f> list_points2d = registration.get_points2d();
+    vector<Point3f> list_points3d = registration.get_points3d();
 
     // Draw current registered points
     drawPoints(img_vis, list_points2d, list_points3d, red);
@@ -139,7 +142,7 @@ int main()
       // Draw debug text
       int n_regist = registration.getNumRegist();
       int n_vertex = pts[n_regist];
-      cv::Point3f current_poin3d = mesh.getVertex(n_vertex-1);
+      Point3f current_poin3d = mesh.getVertex(n_vertex-1);
 
       drawQuestion(img_vis, current_poin3d, green);
       drawCounter(img_vis, registration.getNumRegist(), registration.getNumMax(), red);
@@ -153,43 +156,43 @@ int main()
     }
 
     // Show the image
-    cv::imshow("MODEL REGISTRATION", img_vis);
+    imshow("MODEL REGISTRATION", img_vis);
   }
 
   /** COMPUTE CAMERA POSE **/
 
-  std::cout << "COMPUTING POSE ..." << std::endl;
+  cout << "COMPUTING POSE ..." << endl;
 
   // The list of registered points
-  std::vector<cv::Point2f> list_points2d = registration.get_points2d();
-  std::vector<cv::Point3f> list_points3d = registration.get_points3d();
+  vector<Point2f> list_points2d = registration.get_points2d();
+  vector<Point3f> list_points3d = registration.get_points3d();
 
   // Estimate pose given the registered points
-  bool is_correspondence = pnp_registration.estimatePose(list_points3d, list_points2d, cv::SOLVEPNP_ITERATIVE);
+  bool is_correspondence = pnp_registration.estimatePose(list_points3d, list_points2d, SOLVEPNP_ITERATIVE);
   if ( is_correspondence )
   {
-    std::cout << "Correspondence found" << std::endl;
+    cout << "Correspondence found" << endl;
 
     // Compute all the 2D points of the mesh to verify the algorithm and draw it
-    std::vector<cv::Point2f> list_points2d_mesh = pnp_registration.verify_points(&mesh);
+    vector<Point2f> list_points2d_mesh = pnp_registration.verify_points(&mesh);
     draw2DPoints(img_vis, list_points2d_mesh, green);
 
   } else {
-    std::cout << "Correspondence not found" << std::endl << std::endl;
+    cout << "Correspondence not found" << endl << endl;
   }
 
   // Show the image
-  cv::imshow("MODEL REGISTRATION", img_vis);
+  imshow("MODEL REGISTRATION", img_vis);
 
   // Show image until ESC pressed
-  cv::waitKey(0);
+  waitKey(0);
 
 
    /** COMPUTE 3D of the image Keypoints **/
 
   // Containers for keypoints and descriptors of the model
-  std::vector<cv::KeyPoint> keypoints_model;
-  cv::Mat descriptors;
+  vector<KeyPoint> keypoints_model;
+  Mat descriptors;
 
   // Compute keypoints and descriptors
   rmatcher.computeKeyPoints(img_in, keypoints_model);
@@ -197,8 +200,8 @@ int main()
 
   // Check if keypoints are on the surface of the registration image and add to the model
   for (unsigned int i = 0; i < keypoints_model.size(); ++i) {
-    cv::Point2f point2d(keypoints_model[i].pt);
-    cv::Point3f point3d;
+    Point2f point2d(keypoints_model[i].pt);
+    Point3f point3d;
     bool on_surface = pnp_registration.backproject2DPoint(&mesh, point2d, point3d);
     if (on_surface)
     {
@@ -219,12 +222,12 @@ int main()
   img_vis = img_in.clone();
 
   // The list of the points2d of the model
-  std::vector<cv::Point2f> list_points_in = model.get_points2d_in();
-  std::vector<cv::Point2f> list_points_out = model.get_points2d_out();
+  vector<Point2f> list_points_in = model.get_points2d_in();
+  vector<Point2f> list_points_out = model.get_points2d_out();
 
   // Draw some debug text
-  std::string num = IntToString((int)list_points_in.size());
-  std::string text = "There are " + num + " inliers";
+  string num = IntToString((int)list_points_in.size());
+  string text = "There are " + num + " inliers";
   drawText(img_vis, text, green);
 
   // Draw some debug text
@@ -240,26 +243,26 @@ int main()
   draw2DPoints(img_vis, list_points_out, red);
 
   // Show the image
-  cv::imshow("MODEL REGISTRATION", img_vis);
+  imshow("MODEL REGISTRATION", img_vis);
 
   // Wait until ESC pressed
-  cv::waitKey(0);
+  waitKey(0);
 
   // Close and Destroy Window
-  cv::destroyWindow("MODEL REGISTRATION");
+  destroyWindow("MODEL REGISTRATION");
 
-  std::cout << "GOODBYE" << std::endl;
+  cout << "GOODBYE" << endl;
 
 }
 
 /**********************************************************************************************************/
 void help()
 {
-  std::cout
-  << "--------------------------------------------------------------------------"   << std::endl
-  << "This program shows how to create your 3D textured model. "                    << std::endl
-  << "Usage:"                                                                       << std::endl
-  << "./cpp-tutorial-pnp_registration"                                              << std::endl
-  << "--------------------------------------------------------------------------"   << std::endl
-  << std::endl;
+  cout
+  << "--------------------------------------------------------------------------"   << endl
+  << "This program shows how to create your 3D textured model. "                    << endl
+  << "Usage:"                                                                       << endl
+  << "./cpp-tutorial-pnp_registration"                                              << endl
+  << "--------------------------------------------------------------------------"   << endl
+  << endl;
 }

samples/cpp/tutorial_code/features2D/AKAZE_match.cpp

@@ -22,9 +22,9 @@ int main(void)
     vector<KeyPoint> kpts1, kpts2;
     Mat desc1, desc2;
 
-    AKAZE akaze;
-    akaze(img1, noArray(), kpts1, desc1);
-    akaze(img2, noArray(), kpts2, desc2);
+    Ptr<AKAZE> akaze = AKAZE::create();
+    akaze->detectAndCompute(img1, noArray(), kpts1, desc1);
+    akaze->detectAndCompute(img2, noArray(), kpts2, desc2);
 
     BFMatcher matcher(NORM_HAMMING);
     vector< vector<DMatch> > nn_matches;