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

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This commit is contained in:
Vadim Pisarevsky 2011-11-08 12:01:49 +00:00
parent 5a702d7d9d
commit 2e9f5c434b
18 changed files with 1518 additions and 1144 deletions
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2
modules/contrib/src/templatebuffer.hpp
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@ -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

4
modules/core/include/opencv2/core/core.hpp
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@ -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;

4
modules/core/include/opencv2/core/mat.hpp
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@ -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 /////////////////////////////////////////

3
modules/core/include/opencv2/core/operations.hpp
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@ -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)
{

8
modules/core/src/drawing.cpp
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@ -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;

68
modules/features2d/include/opencv2/features2d/features2d.hpp
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@ -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 *
\****************************************************************************************/

11
modules/features2d/src/descriptors.cpp
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@ -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
{

27
modules/features2d/src/detectors.cpp
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@ -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

1
modules/features2d/src/draw.cpp
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@ -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) );

47
modules/features2d/src/evaluation.cpp
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@ -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);
}

2
modules/features2d/src/fast.cpp
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@ -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));
}
}
}

12
modules/features2d/src/matchers.cpp
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@ -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;
}

1618
modules/features2d/src/orb.cpp
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File diff suppressed because it is too large Load Diff

259
modules/features2d/src/orb_pattern.hpp
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@ -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)*/
};

7
modules/imgproc/include/opencv2/imgproc/imgproc.hpp
View File

@ -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 );

114
modules/imgproc/src/corner.cpp
View File

@ -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);
}
}

459
modules/imgproc/src/geometry.cpp
View File

@ -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. */

16
modules/imgproc/src/utils.cpp
View File

@ -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(),