Commit 1d77931b authored by Alexander Alekhin's avatar Alexander Alekhin
Browse files

Merge remote-tracking branch 'upstream/3.4' into merge-3.4

parents 698f805c f3982616

modules/xfeatures2d/include/opencv2/xfeatures2d.hpp

+33 −0
Original line number Diff line number Diff line
@@ -67,6 +67,39 @@ namespace cv 
namespace xfeatures2d

{





/** @brief Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform

(SIFT) algorithm by D. Lowe @cite Lowe04 .

*/

class CV_EXPORTS_W SIFT : public Feature2D

{

public:

    /**

    @param nfeatures The number of best features to retain. The features are ranked by their scores

    (measured in SIFT algorithm as the local contrast)



    @param nOctaveLayers The number of layers in each octave. 3 is the value used in D. Lowe paper. The

    number of octaves is computed automatically from the image resolution.



    @param contrastThreshold The contrast threshold used to filter out weak features in semi-uniform

    (low-contrast) regions. The larger the threshold, the less features are produced by the detector.



    @param edgeThreshold The threshold used to filter out edge-like features. Note that the its meaning

    is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are

    filtered out (more features are retained).



    @param sigma The sigma of the Gaussian applied to the input image at the octave \#0. If your image

    is captured with a weak camera with soft lenses, you might want to reduce the number.

    */

    CV_WRAP static Ptr<SIFT> create(int nfeatures = 0, int nOctaveLayers = 3,

        double contrastThreshold = 0.04, double edgeThreshold = 10,

        double sigma = 1.6);

};



typedef SIFT SiftFeatureDetector;

typedef SIFT SiftDescriptorExtractor;





//! @addtogroup xfeatures2d_experiment

//! @{

modules/xfeatures2d/include/opencv2/xfeatures2d/nonfree.hpp

+0 −34
Original line number Diff line number Diff line
@@ -50,40 +50,6 @@ namespace cv 
namespace xfeatures2d

{



//! @addtogroup xfeatures2d_nonfree

//! @{



/** @brief Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform

(SIFT) algorithm by D. Lowe @cite Lowe04 .

 */

class CV_EXPORTS_W SIFT : public Feature2D

{

public:

    /**

    @param nfeatures The number of best features to retain. The features are ranked by their scores

    (measured in SIFT algorithm as the local contrast)



    @param nOctaveLayers The number of layers in each octave. 3 is the value used in D. Lowe paper. The

    number of octaves is computed automatically from the image resolution.



    @param contrastThreshold The contrast threshold used to filter out weak features in semi-uniform

    (low-contrast) regions. The larger the threshold, the less features are produced by the detector.



    @param edgeThreshold The threshold used to filter out edge-like features. Note that the its meaning

    is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are

    filtered out (more features are retained).



    @param sigma The sigma of the Gaussian applied to the input image at the octave \#0. If your image

    is captured with a weak camera with soft lenses, you might want to reduce the number.

     */

    CV_WRAP static Ptr<SIFT> create( int nfeatures = 0, int nOctaveLayers = 3,

                                    double contrastThreshold = 0.04, double edgeThreshold = 10,

                                    double sigma = 1.6);

};



typedef SIFT SiftFeatureDetector;

typedef SIFT SiftDescriptorExtractor;



/** @brief Class for extracting Speeded Up Robust Features from an image @cite Bay06 .



The algorithm parameters:

modules/xfeatures2d/src/freak.cpp

+9 −15
Original line number Diff line number Diff line
@@ -283,8 +283,8 @@ void FREAK_Impl::buildPattern() 
        const float dx = patternLookup[orientationPairs[m].i].x-patternLookup[orientationPairs[m].j].x;

        const float dy = patternLookup[orientationPairs[m].i].y-patternLookup[orientationPairs[m].j].y;

        const float norm_sq = (dx*dx+dy*dy);

        orientationPairs[m].weight_dx = int((dx/(norm_sq))*4096.0+0.5);

        orientationPairs[m].weight_dy = int((dy/(norm_sq))*4096.0+0.5);

        orientationPairs[m].weight_dx = cvRound((dx/(norm_sq))*4096.0);

        orientationPairs[m].weight_dy = cvRound((dy/(norm_sq))*4096.0);

    }



    // build the list of description pairs
@@ -482,7 +482,7 @@ void FREAK_Impl::computeDescriptors( InputArray _image, std::vector<KeyPoint>& k 
    }

    else

    {

        const int scIdx = std::max( (int)(1.0986122886681*sizeCst+0.5) ,0);

        const int scIdx = std::max( cvRound(1.0986122886681*sizeCst) ,0);

        for( size_t k = keypoints.size(); k--; )

        {

            kpScaleIdx[k] = scIdx; // equivalent to the formule when the scale is normalized with a constant size of keypoints[k].size=3*SMALLEST_KP_SIZE
@@ -539,10 +539,7 @@ void FREAK_Impl::computeDescriptors( InputArray _image, std::vector<KeyPoint>& k 


                keypoints[k].angle = static_cast<float>(atan2((float)direction1,(float)direction0)*(180.0/CV_PI));//estimate orientation



                if(keypoints[k].angle < 0.f)

                    thetaIdx = int(FREAK_NB_ORIENTATION*keypoints[k].angle*(1/360.0)-0.5);

                else

                    thetaIdx = int(FREAK_NB_ORIENTATION*keypoints[k].angle*(1/360.0)+0.5);

                thetaIdx = cvRound(FREAK_NB_ORIENTATION*keypoints[k].angle*(1/360.0));



                if( thetaIdx < 0 )

                    thetaIdx += FREAK_NB_ORIENTATION;
@@ -596,10 +593,7 @@ void FREAK_Impl::computeDescriptors( InputArray _image, std::vector<KeyPoint>& k 


                keypoints[k].angle = static_cast<float>(atan2((float)direction1,(float)direction0)*(180.0/CV_PI)); //estimate orientation



                if(keypoints[k].angle < 0.f)

                    thetaIdx = int(FREAK_NB_ORIENTATION*keypoints[k].angle*(1/360.0)-0.5);

                else

                    thetaIdx = int(FREAK_NB_ORIENTATION*keypoints[k].angle*(1/360.0)+0.5);

                thetaIdx = cvRound(FREAK_NB_ORIENTATION*keypoints[k].angle*(1/360.0));



                if( thetaIdx < 0 )

                    thetaIdx += FREAK_NB_ORIENTATION;
@@ -671,10 +665,10 @@ imgType FREAK_Impl::meanIntensity( InputArray _image, InputArray _integral, 
    // expected case:



    // calculate borders

    const int x_left = int(xf-radius+0.5);

    const int y_top = int(yf-radius+0.5);

    const int x_right = int(xf+radius+1.5);//integral image is 1px wider

    const int y_bottom = int(yf+radius+1.5);//integral image is 1px higher

    const int x_left = cvRound(xf-radius);

    const int y_top = cvRound(yf-radius);

    const int x_right = cvRound(xf+radius+1);//integral image is 1px wider

    const int y_bottom = cvRound(yf+radius+1);//integral image is 1px higher

    iiType ret_val;



    ret_val = integral.at<iiType>(y_bottom,x_right);//bottom right corner

modules/xfeatures2d/src/sift.cpp

+0 −11
Original line number Diff line number Diff line
@@ -114,8 +114,6 @@ namespace cv 
namespace xfeatures2d

{



#ifdef OPENCV_ENABLE_NONFREE



/*!

 SIFT implementation.
@@ -1202,14 +1200,5 @@ void SIFT_Impl::detectAndCompute(InputArray _image, InputArray _mask, 
    }

}



#else // ! #ifdef OPENCV_ENABLE_NONFREE

Ptr<SIFT> SIFT::create( int, int, double, double, double )

{

    CV_Error(Error::StsNotImplemented,

        "This algorithm is patented and is excluded in this configuration; "

        "Set OPENCV_ENABLE_NONFREE CMake option and rebuild the library");

}

#endif



}

}

modules/xfeatures2d/test/test_features2d.cpp

+6 −5
Original line number Diff line number Diff line
@@ -53,13 +53,13 @@ const string IMAGE_FILENAME = "tsukuba.png"; 


namespace opencv_test { namespace {



#ifdef OPENCV_ENABLE_NONFREE

TEST( Features2d_Detector_SIFT, regression )

{

    CV_FeatureDetectorTest test( "detector-sift", SIFT::create() );

    test.safe_run();

}



#ifdef OPENCV_ENABLE_NONFREE

TEST( Features2d_Detector_SURF, regression )

{

    CV_FeatureDetectorTest test( "detector-surf", SURF::create() );
@@ -94,7 +94,6 @@ TEST( Features2d_Detector_Harris_Laplace_Affine, regression ) 
/*

 * Descriptors

 */

#ifdef OPENCV_ENABLE_NONFREE

TEST( Features2d_DescriptorExtractor_SIFT, regression )

{

    CV_DescriptorExtractorTest<L1<float> > test( "descriptor-sift", 1.0f,
@@ -102,6 +101,7 @@ TEST( Features2d_DescriptorExtractor_SIFT, regression ) 
    test.safe_run();

}



#ifdef OPENCV_ENABLE_NONFREE

TEST( Features2d_DescriptorExtractor_SURF, regression )

{

#ifdef HAVE_OPENCL
@@ -375,8 +375,9 @@ protected: 
    Ptr<Feature2D> f2d;

};



#ifdef OPENCV_ENABLE_NONFREE

TEST(Features2d_SIFTHomographyTest, regression) { CV_DetectPlanarTest test("SIFT", 80, SIFT::create()); test.safe_run(); }



#ifdef OPENCV_ENABLE_NONFREE

TEST(Features2d_SURFHomographyTest, regression) { CV_DetectPlanarTest test("SURF", 80, SURF::create()); test.safe_run(); }

#endif
@@ -441,13 +442,13 @@ protected: 
    Ptr<FeatureDetector> featureDetector_;

};



#ifdef OPENCV_ENABLE_NONFREE

TEST(Features2d_SIFT_using_mask, regression)

{

    FeatureDetectorUsingMaskTest test(SIFT::create());

    test.safe_run();

}



#ifdef OPENCV_ENABLE_NONFREE

TEST(DISABLED_Features2d_SURF_using_mask, regression)

{

    FeatureDetectorUsingMaskTest test(SURF::create());