added improved ORB implementation, convex-convex polygon intersection, eigen2x2 low-level function ...

modules/contrib/src/templatebuffer.hpp

@@ -260,7 +260,7 @@ public:
 	* @param sensitivity: strenght of the sigmoide
 	* @param maxOutputValue: the maximum output value
 	*/
-	inline void normalizeGrayOutputCentredSigmoide(const type meanValue=(type)0.0, const type sensitivity=(type)2.0, const type maxOutputValue=(type)255.0){normalizeGrayOutputCentredSigmoide(meanValue, sensitivity, 255.0, this->Buffer(), this->Buffer(), this->getNBpixels()), maxOutputValue;};
+	inline void normalizeGrayOutputCentredSigmoide(const type meanValue=(type)0.0, const type sensitivity=(type)2.0, const type maxOutputValue=(type)255.0){normalizeGrayOutputCentredSigmoide(meanValue, sensitivity, 255.0, this->Buffer(), this->Buffer(), this->getNBpixels());};
 
 	/**
 	* sigmoide image normalization function (saturates min and max values), in this function, the sigmoide is centered on low values (high saturation of the medium and high values

modules/core/include/opencv2/core/core.hpp

@@ -729,6 +729,8 @@ public:
     _Tp dot(const Point_& pt) const;
     //! dot product computed in double-precision arithmetics
     double ddot(const Point_& pt) const;
+    //! cross-product
+    double cross(const Point_& pt) const;
     //! checks whether the point is inside the specified rectangle
     bool inside(const Rect_<_Tp>& r) const;
     
@@ -1274,6 +1276,7 @@ public:
     _InputArray();
     _InputArray(const Mat& m);
     _InputArray(const MatExpr& expr);
+    template<typename _Tp> _InputArray(const _Tp* vec, int n);
     template<typename _Tp> _InputArray(const vector<_Tp>& vec);
     template<typename _Tp> _InputArray(const vector<vector<_Tp> >& vec);
     _InputArray(const vector<Mat>& vec);
@@ -1323,6 +1326,7 @@ public:
     template<typename _Tp> _OutputArray(vector<vector<_Tp> >& vec);
     _OutputArray(vector<Mat>& vec);
     template<typename _Tp, int m, int n> _OutputArray(Matx<_Tp, m, n>& matx);
+    template<typename _Tp> _OutputArray(_Tp* vec, int n);
     virtual bool fixedSize() const;
     virtual bool fixedType() const;
     virtual bool needed() const;

modules/core/include/opencv2/core/mat.hpp

@@ -1112,6 +1112,9 @@ template<typename _Tp> inline _InputArray::_InputArray(const vector<vector<_Tp>
 
 template<typename _Tp, int m, int n> inline _InputArray::_InputArray(const Matx<_Tp, m, n>& mtx)
     : flags(MATX + DataType<_Tp>::type), obj((void*)&mtx), sz(n, m) {}
+    
+template<typename _Tp> inline _InputArray::_InputArray(const _Tp* vec, int n)
+    : flags(MATX + DataType<_Tp>::type), obj((void*)vec), sz(n, 1) {}
 
 inline _InputArray::_InputArray(const Scalar& s)
     : flags(MATX + CV_64F), obj((void*)&s), sz(1, 4) {} 
@@ -1119,6 +1122,7 @@ inline _InputArray::_InputArray(const Scalar& s)
 template<typename _Tp> inline _OutputArray::_OutputArray(vector<_Tp>& vec) : _InputArray(vec) {}
 template<typename _Tp> inline _OutputArray::_OutputArray(vector<vector<_Tp> >& vec) : _InputArray(vec) {}
 template<typename _Tp, int m, int n> inline _OutputArray::_OutputArray(Matx<_Tp, m, n>& mtx) : _InputArray(mtx) {}
+template<typename _Tp> inline _OutputArray::_OutputArray(_Tp* vec, int n) : _InputArray(vec, n) {}
     
 //////////////////////////////////// Matrix Expressions /////////////////////////////////////////
 

modules/core/include/opencv2/core/operations.hpp

@@ -1599,6 +1599,9 @@ template<typename _Tp> inline _Tp Point_<_Tp>::dot(const Point_& pt) const
 template<typename _Tp> inline double Point_<_Tp>::ddot(const Point_& pt) const
 { return (double)x*pt.x + (double)y*pt.y; }
 
+template<typename _Tp> inline double Point_<_Tp>::cross(const Point_& pt) const
+{ return (double)x*pt.y - (double)y*pt.x; }
+    
 template<typename _Tp> static inline Point_<_Tp>&
 operator += (Point_<_Tp>& a, const Point_<_Tp>& b)
 {

modules/core/src/drawing.cpp

@@ -2050,7 +2050,9 @@ void cv::polylines(InputOutputArray _img, InputArrayOfArrays pts,
                    int thickness, int lineType, int shift )
 {
     Mat img = _img.getMat();
-    int i, ncontours = (int)pts.total();
+    bool manyContours = pts.kind() == _InputArray::STD_VECTOR_VECTOR ||
+                        pts.kind() == _InputArray::STD_VECTOR_MAT;
+    int i, ncontours = manyContours ? (int)pts.total() : 1;
     if( ncontours == 0 )
         return;
     AutoBuffer<Point*> _ptsptr(ncontours);
@@ -2060,7 +2062,9 @@ void cv::polylines(InputOutputArray _img, InputArrayOfArrays pts,
     
     for( i = 0; i < ncontours; i++ )
     {
-        Mat p = pts.getMat(i);
+        Mat p = pts.getMat(manyContours ? i : -1);
+        if( p.total() == 0 )
+            continue;
         CV_Assert(p.checkVector(2, CV_32S) >= 0);
         ptsptr[i] = (Point*)p.data;
         npts[i] = p.rows*p.cols*p.channels()/2;

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

@@ -422,16 +422,15 @@ public:
 
   struct CV_EXPORTS CommonParams
   {
-    enum { DEFAULT_N_LEVELS = 3, DEFAULT_FIRST_LEVEL = 0};
+    enum { DEFAULT_N_LEVELS = 3, DEFAULT_FIRST_LEVEL = 0, HARRIS_SCORE=0, FAST_SCORE=1 };
 
     /** default constructor */
     CommonParams(float scale_factor = 1.2f, unsigned n_levels = DEFAULT_N_LEVELS, int edge_threshold = 31,
-                 unsigned first_level = DEFAULT_FIRST_LEVEL) :
+                 unsigned first_level = DEFAULT_FIRST_LEVEL, int WTA_K=2, int score_type=HARRIS_SCORE) :
       scale_factor_(scale_factor), n_levels_(n_levels), first_level_(first_level >= n_levels ? 0 : first_level),
-      edge_threshold_(edge_threshold)
+      edge_threshold_(edge_threshold), WTA_K_(WTA_K), score_type_(score_type)
     {
-      // No other patch size is supported right now
-      patch_size_ = 31;
+        patch_size_ = 31;
     }
     void read(const FileNode& fn);
     void write(FileStorage& fs) const;
@@ -444,12 +443,16 @@ public:
      * if 1, that means we will also look at the image scale_factor_ times bigger
      */
     unsigned first_level_;
-    /** How far from the boundary the points should be */
+    /** How far from the boundary the points should be. */
     int edge_threshold_;
+    
+    /** How many random points are used to produce each cell of the descriptor (2, 3, 4 ...) */
+    int WTA_K_;
+      
+    /** Type of the score to use (FAST, HARRIS, ...) */  
+    int score_type_; 
 
-    friend class ORB;
-  protected:
-    /** The size of the patch that will be used for orientation and comparisons */
+    /** The size of the patch that will be used for orientation and comparisons (only 31 is supported)*/
     int patch_size_;
   };
 
@@ -483,8 +486,12 @@ public:
   void
   operator()(const cv::Mat &image, const cv::Mat &mask, std::vector<cv::KeyPoint> & keypoints, cv::Mat & descriptors,
              bool useProvidedKeypoints = false);
-
+    
+  void read(const FileNode& fn);
+  void write(FileStorage& fs) const;
+    
 private:
+  
   /** The size of the patch used when comparing regions in the patterns */
   static const int kKernelWidth = 5;
 
@@ -539,28 +546,20 @@ private:
   /** Parameters tuning ORB */
   CommonParams params_;
 
-  /** size of the half patch used for orientation computation, see Rosin - 1999 - Measuring Corner Properties */
-  int half_patch_size_;
-
-  /** pre-computed offsets used for the Harris verification, one vector per scale */
-  std::vector<std::vector<int> > orientation_horizontal_offsets_;
-  std::vector<std::vector<int> > orientation_vertical_offsets_;
-
   /** The steps of the integral images for each scale */
-  std::vector<size_t> integral_image_steps_;
+  vector<size_t> integral_image_steps_;
 
   /** The number of desired features per scale */
-  std::vector<size_t> n_features_per_level_;
+  vector<size_t> n_features_per_level_;
 
   /** The overall number of desired features */
   size_t n_features_;
-
-  /** the end of a row in a circular patch */
-  std::vector<int> u_max_;
-
-  /** The patterns for each level (the patterns are the same, but not their offset */
-  class OrbPatterns;
-  std::vector<OrbPatterns*> patterns_;
+    
+  /** The circular region to compute a feature orientation */
+  vector<int> u_max_;
+  
+  /** Points to compute BRIEF descriptors from */
+  vector<Point> pattern;
 };
 
 /*!
@@ -1551,10 +1550,6 @@ protected:
 private:
   /** the ORB object we use for the computations */
   mutable ORB orb_;
-  /** The parameters used */
-  ORB::CommonParams params_;
-  /** the number of features that need to be retrieved */
-  unsigned n_features_;
 };
 
 class CV_EXPORTS SimpleBlobDetector : public cv::FeatureDetector
@@ -1953,8 +1948,6 @@ protected:
 private:
   /** the ORB object we use for the computations */
   mutable ORB orb_;
-  /** The parameters used */
-  ORB::CommonParams params_;
 };
 
 /*
@@ -2157,6 +2150,17 @@ struct CV_EXPORTS Hamming
 
 typedef Hamming HammingLUT;
 
+template<int cellsize> struct CV_EXPORTS HammingMultilevel
+{
+    typedef unsigned char ValueType;
+    typedef int ResultType;
+    
+    ResultType operator()( const unsigned char* a, const unsigned char* b, int size ) const
+    {
+        return normHamming(a, b, size, cellsize);
+    }
+};
+    
 /****************************************************************************************\
 *                                      DMatch                                            *
 \****************************************************************************************/

modules/features2d/src/descriptors.cpp

@@ -63,7 +63,6 @@ void DescriptorExtractor::compute( const Mat& image, vector<KeyPoint>& keypoints
         return;
     }
 
-
     KeyPointsFilter::runByImageBorder( keypoints, image.size(), 0 );
     KeyPointsFilter::runByKeypointSize( keypoints, std::numeric_limits<float>::epsilon() );
 
@@ -247,8 +246,7 @@ int SurfDescriptorExtractor::descriptorType() const
 
 
 /** Default constructor */
-OrbDescriptorExtractor::OrbDescriptorExtractor(ORB::CommonParams params) :
-  params_(params)
+OrbDescriptorExtractor::OrbDescriptorExtractor(ORB::CommonParams params)
 {
   orb_ = ORB(0, params);
 }
@@ -260,16 +258,15 @@ void OrbDescriptorExtractor::computeImpl(const cv::Mat& image, std::vector<cv::K
 }
 void OrbDescriptorExtractor::read(const cv::FileNode& fn)
 {
-  params_.read(fn);
-  orb_ = ORB(0, params_);
+  orb_.read(fn);
 }
 void OrbDescriptorExtractor::write(cv::FileStorage& fs) const
 {
-  params_.write(fs);
+  orb_.write(fs);
 }
 int OrbDescriptorExtractor::descriptorSize() const
 {
-  return ORB::kBytes;
+  return orb_.descriptorSize();
 }
 int OrbDescriptorExtractor::descriptorType() const
 {

modules/features2d/src/detectors.cpp

@@ -449,43 +449,22 @@ void SurfFeatureDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypoi
 
 ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
 
-void ORB::CommonParams::read(const FileNode& fn)
-{
-  scale_factor_ = fn["scaleFactor"];
-  n_levels_ = int(fn["nLevels"]);
-  first_level_ = int(fn["firstLevel"]);
-  edge_threshold_ = fn["edgeThreshold"];
-  patch_size_ = fn["patchSize"];
-}
-
-void ORB::CommonParams::write(FileStorage& fs) const
-{
-  fs << "scaleFactor" << scale_factor_;
-  fs << "nLevels" << int(n_levels_);
-  fs << "firstLevel" << int(first_level_);
-  fs << "edgeThreshold" << int(edge_threshold_);
-  fs << "patchSize" << int(patch_size_);
-}
-
 /** Default constructor
  * @param n_features the number of desired features
  */
-OrbFeatureDetector::OrbFeatureDetector(size_t n_features, ORB::CommonParams params) :
-  params_(params)
+OrbFeatureDetector::OrbFeatureDetector(size_t n_features, ORB::CommonParams params)
 {
   orb_ = ORB(n_features, params);
 }
 
 void OrbFeatureDetector::read(const FileNode& fn)
 {
-  params_.read(fn);
-  n_features_ = int(fn["nFeatures"]);
+  orb_.read(fn);
 }
 
 void OrbFeatureDetector::write(FileStorage& fs) const
 {
-  params_.write(fs);
-  fs << "nFeatures" << int(n_features_);
+  orb_.write(fs);
 }
 
 void OrbFeatureDetector::detectImpl(const cv::Mat& image, std::vector<cv::KeyPoint>& keypoints, const cv::Mat& mask) const

modules/features2d/src/draw.cpp

@@ -138,6 +138,7 @@ static void _prepareImgAndDrawKeypoints( const Mat& img1, const vector<KeyPoint>
     else
     {
         outImg.create( size, CV_MAKETYPE(img1.depth(), 3) );
+        outImg = Scalar::all(0);
         outImg1 = outImg( Rect(0, 0, img1.cols, img1.rows) );
         outImg2 = outImg( Rect(img1.cols, 0, img2.cols, img2.rows) );
 

modules/features2d/src/evaluation.cpp

@@ -46,6 +46,39 @@
 using namespace cv;
 using namespace std;
 
+template<typename _Tp> static int solveQuadratic(_Tp a, _Tp b, _Tp c, _Tp& x1, _Tp& x2)
+{
+    if( a == 0 )
+    {
+        if( b == 0 )
+        {
+            x1 = x2 = 0;
+            return c == 0;
+        }
+        x1 = x2 = -c/b;
+        return 1;
+    }
+    
+    _Tp d = b*b - 4*a*c;
+    if( d < 0 )
+    {
+        x1 = x2 = 0;
+        return 0;
+    }
+    if( d > 0 )
+    {
+        d = std::sqrt(d);
+        double s = 1/(2*a);
+        x1 = (-b - d)*s;
+        x2 = (-b + d)*s;
+        if( x1 > x2 )
+            std::swap(x1, x2);
+        return 2;
+    }
+    x1 = x2 = -b/(2*a);
+    return 1;
+}
+
 //for android ndk
 #undef _S
 static inline Point2f applyHomography( const Mat_<double>& H, const Point2f& pt )
@@ -109,13 +142,13 @@ EllipticKeyPoint::EllipticKeyPoint( const Point2f& _center, const Scalar& _ellip
     center = _center;
     ellipse = _ellipse;
 
-    Mat_<double> M = getSecondMomentsMatrix(_ellipse), eval;
-    eigen( M, eval );
-    assert( eval.rows == 2 && eval.cols == 1 );
-    axes.width = 1.f / (float)sqrt(eval(0,0));
-    axes.height = 1.f / (float)sqrt(eval(1,0));
-
-    double ac_b2 = ellipse[0]*ellipse[2] - ellipse[1]*ellipse[1];
+    double a = ellipse[0], b = ellipse[1], c = ellipse[2];
+    double ac_b2 = a*c - b*b;
+    double x1, x2;
+    solveQuadratic(1., -(a+c), ac_b2, x1, x2);
+    axes.width = (float)(1/sqrt(x1));
+    axes.height = (float)(1/sqrt(x2));
+    
     boundingBox.width = (float)sqrt(ellipse[2]/ac_b2);
     boundingBox.height = (float)sqrt(ellipse[0]/ac_b2);
 }

modules/features2d/src/fast.cpp

@@ -370,7 +370,7 @@ void cv::FAST(const Mat& img, std::vector<KeyPoint>& keypoints, int threshold, b
                 score > pprev[j-1] && score > pprev[j] && score > pprev[j+1] &&
                 score > curr[j-1] && score > curr[j] && score > curr[j+1]) )
             {
-                keypoints.push_back(KeyPoint((float)j, (float)(i-1), 6.f, -1, (float)score));
+                keypoints.push_back(KeyPoint((float)j, (float)(i-1), 7.f, -1, (float)score));
             }
         }
     }

modules/features2d/src/matchers.cpp

@@ -340,10 +340,20 @@ Ptr<DescriptorMatcher> DescriptorMatcher::create( const string& descriptorMatche
     {
         dm = new BruteForceMatcher<Hamming>();
     }
-    else if( !descriptorMatcherType.compare( "BruteForce-HammingLUT") )
+    else if( !descriptorMatcherType.compare("BruteForce-HammingLUT") )
     {
         dm = new BruteForceMatcher<Hamming>();
     }
+    else if( !descriptorMatcherType.compare("BruteForce-Hamming(2)") )
+    {
+        dm = new BruteForceMatcher<HammingMultilevel<2> >();
+    }
+    else if( !descriptorMatcherType.compare("BruteForce-Hamming(4)") )
+    {
+        dm = new BruteForceMatcher<HammingMultilevel<4> >();
+    }
+    else
+        CV_Error( CV_StsBadArg, "Unknown matcher name" );
 
     return dm;
 }

modules/features2d/src/orb_pattern.hpp

@@ -1,259 +0,0 @@
-//x1,y1,x2,y2
-int ORB::OrbPatterns::bit_pattern_31_[256*4] ={
-8,-3, 9,5/*mean (0), correlation (0)*/,
-4,2, 7,-12/*mean (1.12461e-05), correlation (0.0437584)*/,
--11,9, -8,2/*mean (3.37382e-05), correlation (0.0617409)*/,
-7,-12, 12,-13/*mean (5.62303e-05), correlation (0.0636977)*/,
-2,-13, 2,12/*mean (0.000134953), correlation (0.085099)*/,
-1,-7, 1,6/*mean (0.000528565), correlation (0.0857175)*/,
--2,-10, -2,-4/*mean (0.0188821), correlation (0.0985774)*/,
--13,-13, -11,-8/*mean (0.0363135), correlation (0.0899616)*/,
--13,-3, -12,-9/*mean (0.121806), correlation (0.099849)*/,
-10,4, 11,9/*mean (0.122065), correlation (0.093285)*/,
--13,-8, -8,-9/*mean (0.162787), correlation (0.0942748)*/,
--11,7, -9,12/*mean (0.21561), correlation (0.0974438)*/,
-7,7, 12,6/*mean (0.160583), correlation (0.130064)*/,
--4,-5, -3,0/*mean (0.228171), correlation (0.132998)*/,
--13,2, -12,-3/*mean (0.00997526), correlation (0.145926)*/,
--9,0, -7,5/*mean (0.198234), correlation (0.143636)*/,
-12,-6, 12,-1/*mean (0.0676226), correlation (0.16689)*/,
--3,6, -2,12/*mean (0.166847), correlation (0.171682)*/,
--6,-13, -4,-8/*mean (0.101215), correlation (0.179716)*/,
-11,-13, 12,-8/*mean (0.200641), correlation (0.192279)*/,
-4,7, 5,1/*mean (0.205106), correlation (0.186848)*/,
-5,-3, 10,-3/*mean (0.234908), correlation (0.192319)*/,
-3,-7, 6,12/*mean (0.0709964), correlation (0.210872)*/,
--8,-7, -6,-2/*mean (0.0939834), correlation (0.212589)*/,
--2,11, -1,-10/*mean (0.127778), correlation (0.20866)*/,
--13,12, -8,10/*mean (0.14783), correlation (0.206356)*/,
--7,3, -5,-3/*mean (0.182141), correlation (0.198942)*/,
--4,2, -3,7/*mean (0.188237), correlation (0.21384)*/,
--10,-12, -6,11/*mean (0.14865), correlation (0.23571)*/,
-5,-12, 6,-7/*mean (0.222312), correlation (0.23324)*/,
-5,-6, 7,-1/*mean (0.229082), correlation (0.23389)*/,
-1,0, 4,-5/*mean (0.241577), correlation (0.215286)*/,
-9,11, 11,-13/*mean (0.00338507), correlation (0.251373)*/,
-4,7, 4,12/*mean (0.131005), correlation (0.257622)*/,
-2,-1, 4,4/*mean (0.152755), correlation (0.255205)*/,
--4,-12, -2,7/*mean (0.182771), correlation (0.244867)*/,
--8,-5, -7,-10/*mean (0.186898), correlation (0.23901)*/,
-4,11, 9,12/*mean (0.226226), correlation (0.258255)*/,
-0,-8, 1,-13/*mean (0.0897886), correlation (0.274827)*/,
--13,-2, -8,2/*mean (0.148774), correlation (0.28065)*/,
--3,-2, -2,3/*mean (0.153048), correlation (0.283063)*/,
--6,9, -4,-9/*mean (0.169523), correlation (0.278248)*/,
-8,12, 10,7/*mean (0.225337), correlation (0.282851)*/,
-0,9, 1,3/*mean (0.226687), correlation (0.278734)*/,
-7,-5, 11,-10/*mean (0.00693882), correlation (0.305161)*/,
--13,-6, -11,0/*mean (0.0227283), correlation (0.300181)*/,
-10,7, 12,1/*mean (0.125517), correlation (0.31089)*/,
--6,-3, -6,12/*mean (0.131748), correlation (0.312779)*/,
-10,-9, 12,-4/*mean (0.144827), correlation (0.292797)*/,
--13,8, -8,-12/*mean (0.149202), correlation (0.308918)*/,
--13,0, -8,-4/*mean (0.160909), correlation (0.310013)*/,
-3,3, 7,8/*mean (0.177755), correlation (0.309394)*/,
-5,7, 10,-7/*mean (0.212337), correlation (0.310315)*/,
--1,7, 1,-12/*mean (0.214429), correlation (0.311933)*/,
-3,-10, 5,6/*mean (0.235807), correlation (0.313104)*/,
-2,-4, 3,-10/*mean (0.00494827), correlation (0.344948)*/,
--13,0, -13,5/*mean (0.0549145), correlation (0.344675)*/,
--13,-7, -12,12/*mean (0.103385), correlation (0.342715)*/,
--13,3, -11,8/*mean (0.134222), correlation (0.322922)*/,
--7,12, -4,7/*mean (0.153284), correlation (0.337061)*/,
-6,-10, 12,8/*mean (0.154881), correlation (0.329257)*/,
--9,-1, -7,-6/*mean (0.200967), correlation (0.33312)*/,
--2,-5, 0,12/*mean (0.201518), correlation (0.340635)*/,
--12,5, -7,5/*mean (0.207805), correlation (0.335631)*/,
-3,-10, 8,-13/*mean (0.224438), correlation (0.34504)*/,
--7,-7, -4,5/*mean (0.239361), correlation (0.338053)*/,
--3,-2, -1,-7/*mean (0.240744), correlation (0.344322)*/,
-2,9, 5,-11/*mean (0.242949), correlation (0.34145)*/,
--11,-13, -5,-13/*mean (0.244028), correlation (0.336861)*/,
--1,6, 0,-1/*mean (0.247571), correlation (0.343684)*/,
-5,-3, 5,2/*mean (0.000697256), correlation (0.357265)*/,
--4,-13, -4,12/*mean (0.00213675), correlation (0.373827)*/,
--9,-6, -9,6/*mean (0.0126856), correlation (0.373938)*/,
--12,-10, -8,-4/*mean (0.0152497), correlation (0.364237)*/,
-10,2, 12,-3/*mean (0.0299933), correlation (0.345292)*/,
-7,12, 12,12/*mean (0.0307242), correlation (0.366299)*/,
--7,-13, -6,5/*mean (0.0534975), correlation (0.368357)*/,
--4,9, -3,4/*mean (0.099865), correlation (0.372276)*/,
-7,-1, 12,2/*mean (0.117083), correlation (0.364529)*/,
--7,6, -5,1/*mean (0.126125), correlation (0.369606)*/,
--13,11, -12,5/*mean (0.130364), correlation (0.358502)*/,
--3,7, -2,-6/*mean (0.131691), correlation (0.375531)*/,
-7,-8, 12,-7/*mean (0.160166), correlation (0.379508)*/,
--13,-7, -11,-12/*mean (0.167848), correlation (0.353343)*/,
-1,-3, 12,12/*mean (0.183378), correlation (0.371916)*/,
-2,-6, 3,0/*mean (0.228711), correlation (0.371761)*/,
--4,3, -2,-13/*mean (0.247211), correlation (0.364063)*/,
--1,-13, 1,9/*mean (0.249325), correlation (0.378139)*/,
-7,1, 8,-6/*mean (0.000652272), correlation (0.411682)*/,
-1,-1, 3,12/*mean (0.00248538), correlation (0.392988)*/,
-9,1, 12,6/*mean (0.0206815), correlation (0.386106)*/,
--1,-9, -1,3/*mean (0.0364485), correlation (0.410752)*/,
--13,-13, -10,5/*mean (0.0376068), correlation (0.398374)*/,
-7,7, 10,12/*mean (0.0424202), correlation (0.405663)*/,
-12,-5, 12,9/*mean (0.0942645), correlation (0.410422)*/,
-6,3, 7,11/*mean (0.1074), correlation (0.413224)*/,
-5,-13, 6,10/*mean (0.109256), correlation (0.408646)*/,
-2,-12, 2,3/*mean (0.131691), correlation (0.416076)*/,
-3,8, 4,-6/*mean (0.165081), correlation (0.417569)*/,
-2,6, 12,-13/*mean (0.171874), correlation (0.408471)*/,
-9,-12, 10,3/*mean (0.175146), correlation (0.41296)*/,
--8,4, -7,9/*mean (0.183682), correlation (0.402956)*/,
--11,12, -4,-6/*mean (0.184672), correlation (0.416125)*/,
-1,12, 2,-8/*mean (0.191487), correlation (0.386696)*/,
-6,-9, 7,-4/*mean (0.192668), correlation (0.394771)*/,
-2,3, 3,-2/*mean (0.200157), correlation (0.408303)*/,
-6,3, 11,0/*mean (0.204588), correlation (0.411762)*/,
-3,-3, 8,-8/*mean (0.205904), correlation (0.416294)*/,
-7,8, 9,3/*mean (0.213237), correlation (0.409306)*/,
--11,-5, -6,-4/*mean (0.243444), correlation (0.395069)*/,
--10,11, -5,10/*mean (0.247672), correlation (0.413392)*/,
--5,-8, -3,12/*mean (0.24774), correlation (0.411416)*/,
--10,5, -9,0/*mean (0.00213675), correlation (0.454003)*/,
-8,-1, 12,-6/*mean (0.0293635), correlation (0.455368)*/,
-4,-6, 6,-11/*mean (0.0404971), correlation (0.457393)*/,
--10,12, -8,7/*mean (0.0481107), correlation (0.448364)*/,
-4,-2, 6,7/*mean (0.050641), correlation (0.455019)*/,
--2,0, -2,12/*mean (0.0525978), correlation (0.44338)*/,
--5,-8, -5,2/*mean (0.0629667), correlation (0.457096)*/,
-7,-6, 10,12/*mean (0.0653846), correlation (0.445623)*/,
--9,-13, -8,-8/*mean (0.0858749), correlation (0.449789)*/,
--5,-13, -5,-2/*mean (0.122402), correlation (0.450201)*/,
-8,-8, 9,-13/*mean (0.125416), correlation (0.453224)*/,
--9,-11, -9,0/*mean (0.130128), correlation (0.458724)*/,
-1,-8, 1,-2/*mean (0.132467), correlation (0.440133)*/,
-7,-4, 9,1/*mean (0.132692), correlation (0.454)*/,
--2,1, -1,-4/*mean (0.135695), correlation (0.455739)*/,
-11,-6, 12,-11/*mean (0.142904), correlation (0.446114)*/,
--12,-9, -6,4/*mean (0.146165), correlation (0.451473)*/,
-3,7, 7,12/*mean (0.147627), correlation (0.456643)*/,
-5,5, 10,8/*mean (0.152901), correlation (0.455036)*/,
-0,-4, 2,8/*mean (0.167083), correlation (0.459315)*/,
--9,12, -5,-13/*mean (0.173234), correlation (0.454706)*/,
-0,7, 2,12/*mean (0.18312), correlation (0.433855)*/,
--1,2, 1,7/*mean (0.185504), correlation (0.443838)*/,
-5,11, 7,-9/*mean (0.185706), correlation (0.451123)*/,
-3,5, 6,-8/*mean (0.188968), correlation (0.455808)*/,
--13,-4, -8,9/*mean (0.191667), correlation (0.459128)*/,
--5,9, -3,-3/*mean (0.193196), correlation (0.458364)*/,
--4,-7, -3,-12/*mean (0.196536), correlation (0.455782)*/,
-6,5, 8,0/*mean (0.1972), correlation (0.450481)*/,
--7,6, -6,12/*mean (0.199438), correlation (0.458156)*/,
--13,6, -5,-2/*mean (0.211224), correlation (0.449548)*/,
-1,-10, 3,10/*mean (0.211718), correlation (0.440606)*/,
-4,1, 8,-4/*mean (0.213034), correlation (0.443177)*/,
--2,-2, 2,-13/*mean (0.234334), correlation (0.455304)*/,
-2,-12, 12,12/*mean (0.235684), correlation (0.443436)*/,
--2,-13, 0,-6/*mean (0.237674), correlation (0.452525)*/,
-4,1, 9,3/*mean (0.23962), correlation (0.444824)*/,
--6,-10, -3,-5/*mean (0.248459), correlation (0.439621)*/,
--3,-13, -1,1/*mean (0.249505), correlation (0.456666)*/,
-7,5, 12,-11/*mean (0.00119208), correlation (0.495466)*/,
-4,-2, 5,-7/*mean (0.00372245), correlation (0.484214)*/,
--13,9, -9,-5/*mean (0.00741116), correlation (0.499854)*/,
-7,1, 8,6/*mean (0.0208952), correlation (0.499773)*/,
-7,-8, 7,6/*mean (0.0220085), correlation (0.501609)*/,
--7,-4, -7,1/*mean (0.0233806), correlation (0.496568)*/,
--8,11, -7,-8/*mean (0.0236505), correlation (0.489719)*/,
--13,6, -12,-8/*mean (0.0268781), correlation (0.503487)*/,
-2,4, 3,9/*mean (0.0323324), correlation (0.501938)*/,
-10,-5, 12,3/*mean (0.0399235), correlation (0.494029)*/,
--6,-5, -6,7/*mean (0.0420153), correlation (0.486579)*/,
-8,-3, 9,-8/*mean (0.0548021), correlation (0.484237)*/,
-2,-12, 2,8/*mean (0.0616622), correlation (0.496642)*/,
--11,-2, -10,3/*mean (0.0627755), correlation (0.498563)*/,
--12,-13, -7,-9/*mean (0.0829622), correlation (0.495491)*/,
--11,0, -10,-5/*mean (0.0843342), correlation (0.487146)*/,
-5,-3, 11,8/*mean (0.0929937), correlation (0.502315)*/,
--2,-13, -1,12/*mean (0.113327), correlation (0.48941)*/,
--1,-8, 0,9/*mean (0.132119), correlation (0.467268)*/,
--13,-11, -12,-5/*mean (0.136269), correlation (0.498771)*/,
--10,-2, -10,11/*mean (0.142173), correlation (0.498714)*/,
--3,9, -2,-13/*mean (0.144141), correlation (0.491973)*/,
-2,-3, 3,2/*mean (0.14892), correlation (0.500782)*/,
--9,-13, -4,0/*mean (0.150371), correlation (0.498211)*/,
--4,6, -3,-10/*mean (0.152159), correlation (0.495547)*/,
--4,12, -2,-7/*mean (0.156152), correlation (0.496925)*/,
--6,-11, -4,9/*mean (0.15749), correlation (0.499222)*/,
-6,-3, 6,11/*mean (0.159211), correlation (0.503821)*/,
--13,11, -5,5/*mean (0.162427), correlation (0.501907)*/,
-11,11, 12,6/*mean (0.16652), correlation (0.497632)*/,
-7,-5, 12,-2/*mean (0.169141), correlation (0.484474)*/,
--1,12, 0,7/*mean (0.169456), correlation (0.495339)*/,
--4,-8, -3,-2/*mean (0.171457), correlation (0.487251)*/,
--7,1, -6,7/*mean (0.175), correlation (0.500024)*/,
--13,-12, -8,-13/*mean (0.175866), correlation (0.497523)*/,
--7,-2, -6,-8/*mean (0.178273), correlation (0.501854)*/,
--8,5, -6,-9/*mean (0.181107), correlation (0.494888)*/,
--5,-1, -4,5/*mean (0.190227), correlation (0.482557)*/,
--13,7, -8,10/*mean (0.196739), correlation (0.496503)*/,
-1,5, 5,-13/*mean (0.19973), correlation (0.499759)*/,
-1,0, 10,-13/*mean (0.204465), correlation (0.49873)*/,
-9,12, 10,-1/*mean (0.209334), correlation (0.49063)*/,
-5,-8, 10,-9/*mean (0.211134), correlation (0.503011)*/,
--1,11, 1,-13/*mean (0.212), correlation (0.499414)*/,
--9,-3, -6,2/*mean (0.212168), correlation (0.480739)*/,
--1,-10, 1,12/*mean (0.212731), correlation (0.502523)*/,
--13,1, -8,-10/*mean (0.21327), correlation (0.489786)*/,
-8,-11, 10,-6/*mean (0.214159), correlation (0.488246)*/,
-2,-13, 3,-6/*mean (0.216993), correlation (0.50287)*/,
-7,-13, 12,-9/*mean (0.223639), correlation (0.470502)*/,
--10,-10, -5,-7/*mean (0.224089), correlation (0.500852)*/,
--10,-8, -8,-13/*mean (0.228666), correlation (0.502629)*/,
-4,-6, 8,5/*mean (0.22906), correlation (0.498305)*/,
-3,12, 8,-13/*mean (0.233378), correlation (0.503825)*/,
--4,2, -3,-3/*mean (0.234323), correlation (0.476692)*/,
-5,-13, 10,-12/*mean (0.236392), correlation (0.475462)*/,
-4,-13, 5,-1/*mean (0.236842), correlation (0.504132)*/,
--9,9, -4,3/*mean (0.236977), correlation (0.497739)*/,
-0,3, 3,-9/*mean (0.24314), correlation (0.499398)*/,
--12,1, -6,1/*mean (0.243297), correlation (0.489447)*/,
-3,2, 4,-8/*mean (0.00155196), correlation (0.553496)*/,
--10,-10, -10,9/*mean (0.00239541), correlation (0.54297)*/,
-8,-13, 12,12/*mean (0.0034413), correlation (0.544361)*/,
--8,-12, -6,-5/*mean (0.003565), correlation (0.551225)*/,
-2,2, 3,7/*mean (0.00835583), correlation (0.55285)*/,
-10,6, 11,-8/*mean (0.00885065), correlation (0.540913)*/,
-6,8, 8,-12/*mean (0.0101552), correlation (0.551085)*/,
--7,10, -6,5/*mean (0.0102227), correlation (0.533635)*/,
--3,-9, -3,9/*mean (0.0110211), correlation (0.543121)*/,
--1,-13, -1,5/*mean (0.0113473), correlation (0.550173)*/,
--3,-7, -3,4/*mean (0.0140913), correlation (0.554774)*/,
--8,-2, -8,3/*mean (0.017049), correlation (0.55461)*/,
-4,2, 12,12/*mean (0.01778), correlation (0.546921)*/,
-2,-5, 3,11/*mean (0.0224022), correlation (0.549667)*/,
-6,-9, 11,-13/*mean (0.029161), correlation (0.546295)*/,
-3,-1, 7,12/*mean (0.0303081), correlation (0.548599)*/,
-11,-1, 12,4/*mean (0.0355151), correlation (0.523943)*/,
--3,0, -3,6/*mean (0.0417904), correlation (0.543395)*/,
-4,-11, 4,12/*mean (0.0487292), correlation (0.542818)*/,
-2,-4, 2,1/*mean (0.0575124), correlation (0.554888)*/,
--10,-6, -8,1/*mean (0.0594242), correlation (0.544026)*/,
--13,7, -11,1/*mean (0.0597391), correlation (0.550524)*/,
--13,12, -11,-13/*mean (0.0608974), correlation (0.55383)*/,
-6,0, 11,-13/*mean (0.065126), correlation (0.552006)*/,
-0,-1, 1,4/*mean (0.074224), correlation (0.546372)*/,
--13,3, -9,-2/*mean (0.0808592), correlation (0.554875)*/,
--9,8, -6,-3/*mean (0.0883378), correlation (0.551178)*/,
--13,-6, -8,-2/*mean (0.0901035), correlation (0.548446)*/,
-5,-9, 8,10/*mean (0.0949843), correlation (0.554694)*/,
-2,7, 3,-9/*mean (0.0994152), correlation (0.550979)*/,
--1,-6, -1,-1/*mean (0.10045), correlation (0.552714)*/,
-9,5, 11,-2/*mean (0.100686), correlation (0.552594)*/,
-11,-3, 12,-8/*mean (0.101091), correlation (0.532394)*/,
-3,0, 3,5/*mean (0.101147), correlation (0.525576)*/,
--1,4, 0,10/*mean (0.105263), correlation (0.531498)*/,
-3,-6, 4,5/*mean (0.110785), correlation (0.540491)*/,
--13,0, -10,5/*mean (0.112798), correlation (0.536582)*/,
-5,8, 12,11/*mean (0.114181), correlation (0.555793)*/,
-8,9, 9,-6/*mean (0.117431), correlation (0.553763)*/,
-7,-4, 8,-12/*mean (0.118522), correlation (0.553452)*/,
--10,4, -10,9/*mean (0.12094), correlation (0.554785)*/,
-7,3, 12,4/*mean (0.122582), correlation (0.555825)*/,
-9,-7, 10,-2/*mean (0.124978), correlation (0.549846)*/,
-7,0, 12,-2/*mean (0.127002), correlation (0.537452)*/,
--1,-6, 0,-11/*mean (0.127148), correlation (0.547401)*/
-};

modules/imgproc/include/opencv2/imgproc/imgproc.hpp

@@ -445,6 +445,9 @@ CV_EXPORTS_W void cornerHarris( InputArray src, OutputArray dst, int blockSize,
                                 int ksize, double k,
                                 int borderType=BORDER_DEFAULT );
 
+// low-level function for computing eigenvalues and eigenvectors of 2x2 matrices
+CV_EXPORTS void eigen2x2( const float* a, float* e, int n );
+    
 //! computes both eigenvalues and the eigenvectors of 2x2 derivative covariation matrix  at each pixel. The output is stored as 6-channel matrix.
 CV_EXPORTS_W void cornerEigenValsAndVecs( InputArray src, OutputArray dst,
                                           int blockSize, int ksize,
@@ -1016,6 +1019,10 @@ CV_EXPORTS_W void convexHull( InputArray points, OutputArray hull,
 //! returns true iff the contour is convex. Does not support contours with self-intersection
 CV_EXPORTS_W bool isContourConvex( InputArray contour );
 
+//! finds intersection of two convex polygons
+CV_EXPORTS_W float intersectConvexConvex( InputArray _p1, InputArray _p2,
+                                          OutputArray _p12, bool handleNested=true );
+
 //! fits ellipse to the set of 2D points
 CV_EXPORTS_W RotatedRect fitEllipse( InputArray points );
 

modules/imgproc/src/corner.cpp

@@ -161,11 +161,67 @@ calcHarris( const Mat& _cov, Mat& _dst, double k )
     }
 }
 
+    
+void eigen2x2( const float* cov, float* dst, int n )
+{
+    for( int j = 0; j < n; j++ )
+    {
+        double a = cov[j*3];
+        double b = cov[j*3+1];
+        double c = cov[j*3+2];
+        
+        double u = (a + c)*0.5;
+        double v = std::sqrt((a - c)*(a - c)*0.25 + b*b);
+        double l1 = u + v;
+        double l2 = u - v;
+        
+        double x = b;
+        double y = l1 - a;
+        double e = fabs(x);
+        
+        if( e + fabs(y) < 1e-4 )
+        {
+            y = b;
+            x = l1 - c;
+            e = fabs(x);
+            if( e + fabs(y) < 1e-4 )
+            {
+                e = 1./(e + fabs(y) + FLT_EPSILON);
+                x *= e, y *= e;
+            }
+        }
+        
+        double d = 1./std::sqrt(x*x + y*y + DBL_EPSILON);
+        dst[6*j] = (float)l1;
+        dst[6*j + 2] = (float)(x*d);
+        dst[6*j + 3] = (float)(y*d);
+        
+        x = b;
+        y = l2 - a;
+        e = fabs(x);
+        
+        if( e + fabs(y) < 1e-4 )
+        {
+            y = b;
+            x = l2 - c;
+            e = fabs(x);
+            if( e + fabs(y) < 1e-4 )
+            {
+                e = 1./(e + fabs(y) + FLT_EPSILON);
+                x *= e, y *= e;
+            }
+        }
+        
+        d = 1./std::sqrt(x*x + y*y + DBL_EPSILON);
+        dst[6*j + 1] = (float)l2;
+        dst[6*j + 4] = (float)(x*d);
+        dst[6*j + 5] = (float)(y*d);
+    }
+}
 
 static void
 calcEigenValsVecs( const Mat& _cov, Mat& _dst )
 {
-    int i, j;
     Size size = _cov.size();
     if( _cov.isContinuous() && _dst.isContinuous() )
     {
@@ -173,64 +229,12 @@ calcEigenValsVecs( const Mat& _cov, Mat& _dst )
         size.height = 1;
     }
 
-    for( i = 0; i < size.height; i++ )
+    for( int i = 0; i < size.height; i++ )
     {
         const float* cov = (const float*)(_cov.data + _cov.step*i);
         float* dst = (float*)(_dst.data + _dst.step*i);
 
-        for( j = 0; j < size.width; j++ )
-        {
-            double a = cov[j*3];
-            double b = cov[j*3+1];
-            double c = cov[j*3+2];
-
-            double u = (a + c)*0.5;
-            double v = std::sqrt((a - c)*(a - c)*0.25 + b*b);
-            double l1 = u + v;
-            double l2 = u - v;
-
-            double x = b;
-            double y = l1 - a;
-            double e = fabs(x);
-
-            if( e + fabs(y) < 1e-4 )
-            {
-                y = b;
-                x = l1 - c;
-                e = fabs(x);
-                if( e + fabs(y) < 1e-4 )
-                {
-                    e = 1./(e + fabs(y) + FLT_EPSILON);
-                    x *= e, y *= e;
-                }
-            }
-
-            double d = 1./std::sqrt(x*x + y*y + DBL_EPSILON);
-            dst[6*j] = (float)l1;
-            dst[6*j + 2] = (float)(x*d);
-            dst[6*j + 3] = (float)(y*d);
-
-            x = b;
-            y = l2 - a;
-            e = fabs(x);
-
-            if( e + fabs(y) < 1e-4 )
-            {
-                y = b;
-                x = l2 - c;
-                e = fabs(x);
-                if( e + fabs(y) < 1e-4 )
-                {
-                    e = 1./(e + fabs(y) + FLT_EPSILON);
-                    x *= e, y *= e;
-                }
-            }
-
-            d = 1./std::sqrt(x*x + y*y + DBL_EPSILON);
-            dst[6*j + 1] = (float)l2;
-            dst[6*j + 4] = (float)(x*d);
-            dst[6*j + 5] = (float)(y*d);
-        }
+        eigen2x2(cov, dst, size.width);
     }
 }
 

modules/imgproc/src/geometry.cpp

@@ -328,4 +328,463 @@ cvPointPolygonTest( const CvArr* _contour, CvPoint2D32f pt, int measure_dist )
 }
 
 
+/*
+ This code is described in "Computational Geometry in C" (Second Edition),
+ Chapter 7.  It is not written to be comprehensible without the
+ explanation in that book.
+ 
+ Written by Joseph O'Rourke.
+ Last modified: December 1997
+ Questions to orourke@cs.smith.edu.
+ --------------------------------------------------------------------
+ This code is Copyright 1997 by Joseph O'Rourke.  It may be freely
+ redistributed in its entirety provided that this copyright notice is
+ not removed.
+ --------------------------------------------------------------------
+ */
+
+namespace cv
+{
+typedef enum { Pin, Qin, Unknown } tInFlag;
+
+static int areaSign( Point2f a, Point2f b, Point2f c )
+{
+    static const double eps = 1e-5;
+    double area2 = (b.x - a.x) * (double)(c.y - a.y) - (c.x - a.x ) * (double)(b.y - a.y);
+    return area2 > eps ? 1 : area2 < -eps ? -1 : 0;
+}
+
+//---------------------------------------------------------------------
+// Returns true iff point c lies on the closed segement ab.
+// Assumes it is already known that abc are collinear.
+//---------------------------------------------------------------------
+static bool between( Point2f a, Point2f b, Point2f c )
+{
+    Point2f ba, ca;
+    
+    // If ab not vertical, check betweenness on x; else on y.
+    if ( a.x != b.x )
+        return ((a.x <= c.x) && (c.x <= b.x)) ||
+        ((a.x >= c.x) && (c.x >= b.x));
+    else
+        return ((a.y <= c.y) && (c.y <= b.y)) ||
+        ((a.y >= c.y) && (c.y >= b.y));
+}
+
+static char parallelInt( Point2f a, Point2f b, Point2f c, Point2f d, Point2f& p, Point2f& q )
+{
+    char code = 'e';
+    if( areaSign(a, b, c) != 0 )
+        code = '0';
+    else if( between(a, b, c) && between(a, b, d))
+        p = c, q = d;
+    else if( between(c, d, a) && between(c, d, b))
+        p = a, q = b;
+    else if( between(a, b, c) && between(c, d, b))
+        p = c, q = b;
+    else if( between(a, b, c) && between(c, d, a))
+        p = c, q = a;
+    else if( between(a, b, d) && between(c, d, b))
+        p = d, q = b;
+    else if( between(a, b, d) && between(c, d, a))
+        p = d, q = a;
+    else
+        code = '0';
+    return code;
+}
+
+//---------------------------------------------------------------------
+// segSegInt: Finds the point of intersection p between two closed
+// segments ab and cd.  Returns p and a char with the following meaning:
+// 'e': The segments collinearly overlap, sharing a point.
+// 'v': An endpoint (vertex) of one segment is on the other segment,
+// but 'e' doesn't hold.
+// '1': The segments intersect properly (i.e., they share a point and
+// neither 'v' nor 'e' holds).
+// '0': The segments do not intersect (i.e., they share no points).
+// Note that two collinear segments that share just one point, an endpoint
+// of each, returns 'e' rather than 'v' as one might expect.
+//---------------------------------------------------------------------
+static char segSegInt( Point2f a, Point2f b, Point2f c, Point2f d, Point2f& p, Point2f& q )
+{
+    double  s, t;       // The two parameters of the parametric eqns.
+    double num, denom;  // Numerator and denoninator of equations.
+    char code = '?';    // Return char characterizing intersection.
+    
+    denom = a.x * (double)( d.y - c.y ) +
+    b.x * (double)( c.y - d.y ) +
+    d.x * (double)( b.y - a.y ) +
+    c.x * (double)( a.y - b.y );
+    
+    // If denom is zero, then segments are parallel: handle separately.
+    if (denom == 0.0)
+        return parallelInt(a, b, c, d, p, q);
+    
+    num =    a.x * (double)( d.y - c.y ) +
+    c.x * (double)( a.y - d.y ) +
+    d.x * (double)( c.y - a.y );
+    if ( (num == 0.0) || (num == denom) ) code = 'v';
+    s = num / denom;
+    
+    num = -( a.x * (double)( c.y - b.y ) +
+            b.x * (double)( a.y - c.y ) +
+            c.x * (double)( b.y - a.y ) );
+    if ( (num == 0.0) || (num == denom) ) code = 'v';
+    t = num / denom;
+    
+    if      ( (0.0 < s) && (s < 1.0) &&
+             (0.0 < t) && (t < 1.0) )
+        code = '1';
+    else if ( (0.0 > s) || (s > 1.0) ||
+             (0.0 > t) || (t > 1.0) )
+        code = '0';
+    
+    p.x = a.x + s * ( b.x - a.x );
+    p.y = a.y + s * ( b.y - a.y );
+    
+    return code;
+}
+
+static tInFlag inOut( Point2f p, tInFlag inflag, int aHB, int bHA, Point2f*& result )
+{
+    if( p != result[-1] )
+        *result++ = p;
+    // Update inflag.
+    return aHB > 0 ? Pin : bHA > 0 ? Qin : inflag;
+}
+
+//---------------------------------------------------------------------
+// Advances and prints out an inside vertex if appropriate.
+//---------------------------------------------------------------------
+static int advance( int a, int *aa, int n, bool inside, Point2f v, Point2f*& result )
+{
+    if( inside && v != result[-1] )
+        *result++ = v;
+    (*aa)++;
+    return  (a+1) % n;
+}    
+
+static void addSharedSeg( Point2f p, Point2f q, Point2f*& result )
+{
+    if( p != result[-1] )
+        *result++ = p;
+    if( q != result[-1] )
+        *result++ = q;
+}
+
+
+static int intersectConvexConvex_( const Point2f* P, int n, const Point2f* Q, int m,
+                                   Point2f* result, float* _area )
+{
+    Point2f* result0 = result;
+    // P has n vertices, Q has m vertices.
+    int     a=0, b=0;       // indices on P and Q (resp.)
+    Point2f Origin(0,0);
+    tInFlag inflag=Unknown; // {Pin, Qin, Unknown}: which inside
+    int     aa=0, ba=0;     // # advances on a & b indices (after 1st inter.)
+    bool    FirstPoint=true;// Is this the first point? (used to initialize).
+    Point2f p0;             // The first point.
+    *result++ = Point2f(FLT_MAX, FLT_MAX);
+    
+    do
+    {
+        // Computations of key variables.
+        int a1 = (a + n - 1) % n; // a-1, b-1 (resp.)
+        int b1 = (b + m - 1) % m;
+        
+        Point2f A = P[a] - P[a1], B = Q[b] - Q[b1]; // directed edges on P and Q (resp.)
+        
+        int cross = areaSign( Origin, A, B );    // sign of z-component of A x B
+        int aHB = areaSign( Q[b1], Q[b], P[a] ); // a in H(b).
+        int bHA = areaSign( P[a1], P[a], Q[b] ); // b in H(A);
+        
+        // If A & B intersect, update inflag.
+        Point2f p, q;
+        int code = segSegInt( P[a1], P[a], Q[b1], Q[b], p, q );
+        if( code == '1' || code == 'v' )
+        {
+            if( inflag == Unknown && FirstPoint )
+            {
+                aa = ba = 0;
+                FirstPoint = false;
+                p0 = p;
+                *result++ = p;
+            }
+            inflag = inOut( p, inflag, aHB, bHA, result );
+        }
+        
+        //-----Advance rules-----
+        
+        // Special case: A & B overlap and oppositely oriented.
+        if( code == 'e' && A.ddot(B) < 0 )
+        {
+            addSharedSeg( p, q, result );
+            return (int)(result - result0);
+        }
+        
+        // Special case: A & B parallel and separated.
+        if( cross == 0 && aHB < 0 && bHA < 0 )
+            return (int)(result - result0);
+        
+        // Special case: A & B collinear.
+        else if ( cross == 0 && aHB == 0 && bHA == 0 ) {
+            // Advance but do not output point.
+            if ( inflag == Pin )
+                b = advance( b, &ba, m, inflag == Qin, Q[b], result );
+            else
+                a = advance( a, &aa, n, inflag == Pin, P[a], result );
+        }
+        
+        // Generic cases.
+        else if( cross >= 0 )
+        {
+            if( bHA > 0)
+                a = advance( a, &aa, n, inflag == Pin, P[a], result );
+            else
+                b = advance( b, &ba, m, inflag == Qin, Q[b], result );
+        }
+        else
+        {
+            if( aHB > 0)
+                b = advance( b, &ba, m, inflag == Qin, Q[b], result );
+            else
+                a = advance( a, &aa, n, inflag == Pin, P[a], result );
+        }
+        // Quit when both adv. indices have cycled, or one has cycled twice.
+    }
+    while ( ((aa < n) || (ba < m)) && (aa < 2*n) && (ba < 2*m) );
+    
+    // Deal with special cases: not implemented.
+    if( inflag == Unknown )
+    {
+        // The boundaries of P and Q do not cross.
+        // ...
+    }
+    
+    int i, nr = (int)(result - result0);
+    double area = 0;
+    Point2f prev = result0[nr-1];
+    for( i = 1; i < nr; i++ )
+    {
+        result0[i-1] = result0[i];
+        area += (double)prev.x*result0[i].y - (double)prev.y*result0[i].x;
+        prev = result0[i];
+    }
+    
+    *_area = (float)(area*0.5);
+    
+    if( result0[nr-2] == result0[0] && nr > 1 )
+        nr--;
+    return nr-1;
+}
+    
+}
+    
+float cv::intersectConvexConvex( InputArray _p1, InputArray _p2, OutputArray _p12, bool handleNested )
+{
+    Mat p1 = _p1.getMat(), p2 = _p2.getMat();
+    CV_Assert( p1.depth() == CV_32S || p1.depth() == CV_32F );
+    CV_Assert( p2.depth() == CV_32S || p2.depth() == CV_32F );
+    
+    int n = p1.checkVector(2, p1.depth(), true);
+    int m = p2.checkVector(2, p2.depth(), true);
+    
+    CV_Assert( n >= 0 && m >= 0 );
+    
+    if( n < 2 || m < 2 )
+    {
+        _p12.release();
+        return 0.f;
+    }
+    
+    AutoBuffer<Point2f> _result(n*2 + m*2 + 1);
+    Point2f *fp1 = _result, *fp2 = fp1 + n;
+    Point2f* result = fp2 + m;
+    int orientation = 0;
+    
+    for( int k = 1; k <= 2; k++ )
+    {
+        Mat& p = k == 1 ? p1 : p2;
+        int len = k == 1 ? n : m;
+        Point2f* dst = k == 1 ? fp1 : fp2;
+        
+        Mat temp(p.size(), CV_MAKETYPE(CV_32F, p.channels()), dst);
+        p.convertTo(temp, CV_32F);
+        CV_Assert( temp.ptr<Point2f>() == dst );
+        Point2f diff0 = dst[0] - dst[len-1];
+        for( int i = 1; i < len; i++ )
+        {
+            double s = diff0.cross(dst[i] - dst[i-1]);
+            if( s != 0 )
+            {
+                if( s < 0 )
+                {
+                    orientation++;
+                    flip( temp, temp, temp.rows > 1 ? 0 : 1 );
+                }
+                break;
+            }
+        }
+    }
+    
+    float area = 0.f;
+    int nr = intersectConvexConvex_(fp1, n, fp2, m, result, &area);
+    if( nr == 0 )
+    {
+        if( !handleNested )
+        {
+            _p12.release();
+            return 0.f;
+        }
+        
+        if( pointPolygonTest(_InputArray(fp1, n), fp2[0], false) >= 0 )
+        {
+            result = fp2;
+            nr = m;
+        }
+        else if( pointPolygonTest(_InputArray(fp2, n), fp1[0], false) >= 0 )
+        {
+            result = fp1;
+            nr = n;
+        }
+        else
+        {
+            _p12.release();
+            return 0.f;
+        }
+        area = contourArea(_InputArray(result, nr), false);
+    }
+    
+    if( _p12.needed() )
+    {
+        Mat temp(nr, 1, CV_32FC2, result);
+        // if both input contours were reflected,
+        // let's orient the result as the input vectors
+        if( orientation == 2 )
+            flip(temp, temp, 0);
+
+        temp.copyTo(_p12);
+    }
+    return (float)fabs(area);
+}
+
+/*
+static void testConvConv()
+{
+    static const int P1[] =
+    {
+        0,	0,
+        100, 0,
+        100, 100,
+        0, 100,
+    };
+    
+    static const int Q1[] =
+    {
+        100, 80,
+        50, 80,
+        50, 50,
+        100, 50
+    };
+    
+    static const int P2[] =
+    {
+        0, 0,
+        200, 0,
+        200, 100,
+        100, 200,
+        0, 100
+    };
+    
+    static const int Q2[] =
+    {
+        100, 100,
+        300, 100,
+        300, 200,
+        100, 200
+    };
+    
+    static const int P3[] =
+    {
+        0,   0,
+        100, 0,
+        100, 100,
+        0, 100
+    };
+    
+    static const int Q3[] =
+    {
+        50, 50,
+        150, 50,
+        150, 150,
+        50, 150
+    };
+    
+    static const int P4[] =
+    {
+        0, 160,
+        50, 80,
+        130, 0,
+        190, 20,
+        240, 100,
+        240, 260,
+        190, 290,
+        130, 320,
+        70, 320,
+        30, 290
+    };
+    
+    static const int Q4[] =
+    {
+        160, -30,
+        280, 160,
+        160, 320,
+        0, 220,
+        30, 100
+    };
+    
+    static const void* PQs[] =
+    {
+        P1, Q1, P2, Q2, P3, Q3, P4, Q4
+    };
+    
+    static const int lens[] =
+    {
+        CV_DIM(P1), CV_DIM(Q1),
+        CV_DIM(P2), CV_DIM(Q2),
+        CV_DIM(P3), CV_DIM(Q3),
+        CV_DIM(P4), CV_DIM(Q4)
+    };
+    
+    Mat img(800, 800, CV_8UC3);
+    
+    for( int i = 0; i < CV_DIM(PQs)/2; i++ )
+    {
+        Mat Pm = Mat(lens[i*2]/2, 1, CV_32SC2, (void*)PQs[i*2]) + Scalar(100, 100);
+        Mat Qm = Mat(lens[i*2+1]/2, 1, CV_32SC2, (void*)PQs[i*2+1]) + Scalar(100, 100);
+        Point* P = Pm.ptr<Point>();
+        Point* Q = Qm.ptr<Point>();
+        
+        flip(Pm, Pm, 0);
+        flip(Qm, Qm, 0);
+        
+        Mat Rm;
+        intersectConvexConvex(Pm, Qm, Rm);
+        std::cout << Rm << std::endl << std::endl;
+        
+        img = Scalar::all(0);
+        
+        polylines(img, Pm, true, Scalar(0,255,0), 1, CV_AA, 0);
+        polylines(img, Qm, true, Scalar(0,0,255), 1, CV_AA, 0);
+        Mat temp;
+        Rm.convertTo(temp, CV_32S, 256);
+        polylines(img, temp, true, Scalar(128, 255, 255), 3, CV_AA, 8);
+        
+        namedWindow("test", 1);
+        imshow("test", img);
+        waitKey();
+    }
+}
+*/
+ 
 /* End of file. */

modules/imgproc/src/utils.cpp

@@ -206,6 +206,22 @@ void cv::copyMakeBorder( InputArray _src, OutputArray _dst, int top, int bottom,
     _dst.create( src.rows + top + bottom, src.cols + left + right, src.type() );
     Mat dst = _dst.getMat();
     
+    if( src.isSubmatrix() && (borderType & BORDER_ISOLATED) == 0 )
+    {
+        Size wholeSize;
+        Point ofs;
+        src.locateROI(wholeSize, ofs);
+        int dtop = std::min(ofs.y, top);
+        int dbottom = std::min(wholeSize.height - src.rows - ofs.y, bottom);
+        int dleft = std::min(ofs.x, left);
+        int dright = std::min(wholeSize.width - src.cols - ofs.x, right);
+        src.adjustROI(dtop, dbottom, dleft, dright);
+        top -= dtop;
+        left -= dleft;
+    }
+    
+    borderType &= ~BORDER_ISOLATED;
+    
     if( borderType != BORDER_CONSTANT )
         copyMakeBorder_8u( src.data, src.step, src.size(),
                            dst.data, dst.step, dst.size(),