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

Author: alalek

modules/xfeatures2d/include/opencv2/xfeatures2d.hpp

@@ -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

@@ -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

@@ -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

@@ -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

@@ -53,13 +53,13 @@ const string IMAGE_FILENAME = "tsukuba.png";
 
 namespace opencv_test { namespace {
 
-#ifdef OPENCV_ENABLE_NONFREE
-TEST( Features2d_Detector_SIFT, regression)
+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() );
@@ -94,14 +94,14 @@ 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,
                                                 SIFT::create() );
     test.safe_run();
 }
 
+#ifdef OPENCV_ENABLE_NONFREE
 TEST( Features2d_DescriptorExtractor_SURF, regression )
 {
 #ifdef HAVE_OPENCL
@@ -375,8 +375,9 @@ class CV_DetectPlanarTest : public cvtest::BaseTest
     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 @@ class FeatureDetectorUsingMaskTest : public cvtest::BaseTest
     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());