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opencv
Commit 01d3848f authored by Vadim Pisarevsky's avatar Vadim Pisarevsky
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all the tests now pass except for MSER

parent 162384a8
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Showing with 730 additions and 21 deletions
modules/core/include/opencv2/core.hpp
View file @ 01d3848f
......@@ -874,6 +874,9 @@ public:
virtual ~Algorithm();
String name() const;
virtual void set(int, double);
virtual double get(int) const;
template<typename _Tp> typename ParamType<_Tp>::member_type get(const String& name) const;
template<typename _Tp> typename ParamType<_Tp>::member_type get(const char* name) const;
......
modules/core/src/algorithm.cpp
View file @ 01d3848f
......@@ -179,6 +179,9 @@ String Algorithm::name() const
return info()->name();
}
void Algorithm::set(int, double) {}
double Algorithm::get(int) const { return 0.; }
void Algorithm::set(const String& parameter, int value)
{
info()->set(this, parameter.c_str(), ParamType<int>::type, &value);
......
modules/features2d/include/opencv2/features2d.hpp
View file @ 01d3848f
......@@ -213,9 +213,10 @@ CV_EXPORTS void FAST( InputArray image, CV_OUT std::vector<KeyPoint>& keypoints,
class CV_EXPORTS_W FastFeatureDetector : public Feature2D
{
public:
enum Type
enum
{
TYPE_5_8 = 0, TYPE_7_12 = 1, TYPE_9_16 = 2
TYPE_5_8 = 0, TYPE_7_12 = 1, TYPE_9_16 = 2,
THRESHOLD = 10000, NONMAX_SUPPRESSION=10001, FAST_N=10002,
};
CV_WRAP static Ptr<FastFeatureDetector> create( int threshold=10,
......
modules/features2d/perf/opencl/perf_fast.cpp
View file @ 01d3848f
......@@ -32,11 +32,8 @@ OCL_PERF_TEST_P(FASTFixture, FastDetect, testing::Combine(
mframe.copyTo(frame);
declare.in(frame);
Ptr<FeatureDetector> fd = Algorithm::create<FeatureDetector>("Feature2D.FAST");
Ptr<FeatureDetector> fd = FastFeatureDetector::create(20, true, type);
ASSERT_FALSE( fd.empty() );
fd->set("threshold", 20);
fd->set("nonmaxSuppression", true);
fd->set("type", type);
vector<KeyPoint> points;
OCL_TEST_CYCLE() fd->detect(frame, points);
......
modules/features2d/perf/perf_fast.cpp
View file @ 01d3848f
......@@ -30,11 +30,8 @@ PERF_TEST_P(fast, detect, testing::Combine(
declare.in(frame);
Ptr<FeatureDetector> fd = Algorithm::create<FeatureDetector>("Feature2D.FAST");
Ptr<FeatureDetector> fd = FastFeatureDetector::create(20, true, type);
ASSERT_FALSE( fd.empty() );
fd->set("threshold", 20);
fd->set("nonmaxSuppression", true);
fd->set("type", type);
vector<KeyPoint> points;
TEST_CYCLE() fd->detect(frame, points);
......
modules/features2d/src/brisk.cpp
View file @ 01d3848f
......@@ -2099,7 +2099,7 @@ BriskLayer::BriskLayer(const BriskLayer& layer, int mode)
void
BriskLayer::getAgastPoints(int threshold, std::vector<KeyPoint>& keypoints)
{
fast_9_16_ = FastFeatureDetector::create(threshold);
fast_9_16_->set(FastFeatureDetector::THRESHOLD, threshold);
fast_9_16_->detect(img_, keypoints);
// also write scores
......
modules/features2d/src/fast.cpp
View file @ 01d3848f
......@@ -383,6 +383,30 @@ public:
KeyPointsFilter::runByPixelsMask( keypoints, mask );
}
void set(int prop, double value)
{
if(prop == THRESHOLD)
threshold = cvRound(value);
else if(prop == NONMAX_SUPPRESSION)
nonmaxSuppression = value != 0;
else if(prop == FAST_N)
type = cvRound(value);
else
CV_Error(Error::StsBadArg, "");
}
double get(int prop) const
{
if(prop == THRESHOLD)
return threshold;
if(prop == NONMAX_SUPPRESSION)
return nonmaxSuppression;
if(prop == FAST_N)
return type;
CV_Error(Error::StsBadArg, "");
return 0;
}
int threshold;
bool nonmaxSuppression;
int type;
......
modules/features2d/src/mser.cpp
View file @ 01d3848f
......@@ -800,9 +800,12 @@ extractMSER_8uC3( const Mat& src, Mat& labels,
double emean = 0;
Mat dx( src.rows, src.cols-1, CV_32FC1 );
Mat dy( src.rows, src.cols, CV_32FC1 );
Ne = preprocessMSER_8UC3( map, edge, emean, src, dx, dy, Ne, params.edgeBlurSize );
emean = emean / (double)Ne;
std::sort(edge, edge + Ne, LessThanEdge());
MSCREdge* edge_ub = edge+Ne;
MSCREdge* edgeptr = edge;
TempMSCR* mscrptr = mscr;
......
modules/features2d/test/test_descriptors_regression.cpp
View file @ 01d3848f
......@@ -106,8 +106,6 @@ public:
~CV_DescriptorExtractorTest()
{
if(!detector.empty())
detector.release();
}
protected:
virtual void createDescriptorExtractor() {}
......@@ -333,7 +331,7 @@ TEST( Features2d_DescriptorExtractor_KAZE, regression )
{
CV_DescriptorExtractorTest< L2<float> > test( "descriptor-kaze", 0.03f,
KAZE::create(),
L2<float>() );
L2<float>(), KAZE::create() );
test.safe_run();
}
......
modules/features2d/test/test_keypoints.cpp
View file @ 01d3848f
......@@ -41,6 +41,7 @@
#include "test_precomp.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/core/core_c.h"
using namespace std;
using namespace cv;
......@@ -48,6 +49,683 @@ using namespace cv;
const string FEATURES2D_DIR = "features2d";
const string IMAGE_FILENAME = "tsukuba.png";
const int TABLE_SIZE = 400;
static const float chitab3[]=
{
0.f, 0.0150057f, 0.0239478f, 0.0315227f,
0.0383427f, 0.0446605f, 0.0506115f, 0.0562786f,
0.0617174f, 0.0669672f, 0.0720573f, 0.0770099f,
0.081843f, 0.0865705f, 0.0912043f, 0.0957541f,
0.100228f, 0.104633f, 0.108976f, 0.113261f,
0.117493f, 0.121676f, 0.125814f, 0.12991f,
0.133967f, 0.137987f, 0.141974f, 0.145929f,
0.149853f, 0.15375f, 0.15762f, 0.161466f,
0.165287f, 0.169087f, 0.172866f, 0.176625f,
0.180365f, 0.184088f, 0.187794f, 0.191483f,
0.195158f, 0.198819f, 0.202466f, 0.2061f,
0.209722f, 0.213332f, 0.216932f, 0.220521f,
0.2241f, 0.22767f, 0.231231f, 0.234783f,
0.238328f, 0.241865f, 0.245395f, 0.248918f,
0.252435f, 0.255947f, 0.259452f, 0.262952f,
0.266448f, 0.269939f, 0.273425f, 0.276908f,
0.280386f, 0.283862f, 0.287334f, 0.290803f,
0.29427f, 0.297734f, 0.301197f, 0.304657f,
0.308115f, 0.311573f, 0.315028f, 0.318483f,
0.321937f, 0.32539f, 0.328843f, 0.332296f,
0.335749f, 0.339201f, 0.342654f, 0.346108f,
0.349562f, 0.353017f, 0.356473f, 0.35993f,
0.363389f, 0.366849f, 0.37031f, 0.373774f,
0.377239f, 0.380706f, 0.384176f, 0.387648f,
0.391123f, 0.3946f, 0.39808f, 0.401563f,
0.405049f, 0.408539f, 0.412032f, 0.415528f,
0.419028f, 0.422531f, 0.426039f, 0.429551f,
0.433066f, 0.436586f, 0.440111f, 0.44364f,
0.447173f, 0.450712f, 0.454255f, 0.457803f,
0.461356f, 0.464915f, 0.468479f, 0.472049f,
0.475624f, 0.479205f, 0.482792f, 0.486384f,
0.489983f, 0.493588f, 0.4972f, 0.500818f,
0.504442f, 0.508073f, 0.511711f, 0.515356f,
0.519008f, 0.522667f, 0.526334f, 0.530008f,
0.533689f, 0.537378f, 0.541075f, 0.54478f,
0.548492f, 0.552213f, 0.555942f, 0.55968f,
0.563425f, 0.56718f, 0.570943f, 0.574715f,
0.578497f, 0.582287f, 0.586086f, 0.589895f,
0.593713f, 0.597541f, 0.601379f, 0.605227f,
0.609084f, 0.612952f, 0.61683f, 0.620718f,
0.624617f, 0.628526f, 0.632447f, 0.636378f,
0.64032f, 0.644274f, 0.648239f, 0.652215f,
0.656203f, 0.660203f, 0.664215f, 0.668238f,
0.672274f, 0.676323f, 0.680384f, 0.684457f,
0.688543f, 0.692643f, 0.696755f, 0.700881f,
0.70502f, 0.709172f, 0.713339f, 0.717519f,
0.721714f, 0.725922f, 0.730145f, 0.734383f,
0.738636f, 0.742903f, 0.747185f, 0.751483f,
0.755796f, 0.760125f, 0.76447f, 0.768831f,
0.773208f, 0.777601f, 0.782011f, 0.786438f,
0.790882f, 0.795343f, 0.799821f, 0.804318f,
0.808831f, 0.813363f, 0.817913f, 0.822482f,
0.827069f, 0.831676f, 0.836301f, 0.840946f,
0.84561f, 0.850295f, 0.854999f, 0.859724f,
0.864469f, 0.869235f, 0.874022f, 0.878831f,
0.883661f, 0.888513f, 0.893387f, 0.898284f,
0.903204f, 0.908146f, 0.913112f, 0.918101f,
0.923114f, 0.928152f, 0.933214f, 0.938301f,
0.943413f, 0.94855f, 0.953713f, 0.958903f,
0.964119f, 0.969361f, 0.974631f, 0.979929f,
0.985254f, 0.990608f, 0.99599f, 1.0014f,
1.00684f, 1.01231f, 1.01781f, 1.02335f,
1.02891f, 1.0345f, 1.04013f, 1.04579f,
1.05148f, 1.05721f, 1.06296f, 1.06876f,
1.07459f, 1.08045f, 1.08635f, 1.09228f,
1.09826f, 1.10427f, 1.11032f, 1.1164f,
1.12253f, 1.1287f, 1.1349f, 1.14115f,
1.14744f, 1.15377f, 1.16015f, 1.16656f,
1.17303f, 1.17954f, 1.18609f, 1.19269f,
1.19934f, 1.20603f, 1.21278f, 1.21958f,
1.22642f, 1.23332f, 1.24027f, 1.24727f,
1.25433f, 1.26144f, 1.26861f, 1.27584f,
1.28312f, 1.29047f, 1.29787f, 1.30534f,
1.31287f, 1.32046f, 1.32812f, 1.33585f,
1.34364f, 1.3515f, 1.35943f, 1.36744f,
1.37551f, 1.38367f, 1.39189f, 1.4002f,
1.40859f, 1.41705f, 1.42561f, 1.43424f,
1.44296f, 1.45177f, 1.46068f, 1.46967f,
1.47876f, 1.48795f, 1.49723f, 1.50662f,
1.51611f, 1.52571f, 1.53541f, 1.54523f,
1.55517f, 1.56522f, 1.57539f, 1.58568f,
1.59611f, 1.60666f, 1.61735f, 1.62817f,
1.63914f, 1.65025f, 1.66152f, 1.67293f,
1.68451f, 1.69625f, 1.70815f, 1.72023f,
1.73249f, 1.74494f, 1.75757f, 1.77041f,
1.78344f, 1.79669f, 1.81016f, 1.82385f,
1.83777f, 1.85194f, 1.86635f, 1.88103f,
1.89598f, 1.91121f, 1.92674f, 1.94257f,
1.95871f, 1.97519f, 1.99201f, 2.0092f,
2.02676f, 2.04471f, 2.06309f, 2.08189f,
2.10115f, 2.12089f, 2.14114f, 2.16192f,
2.18326f, 2.2052f, 2.22777f, 2.25101f,
2.27496f, 2.29966f, 2.32518f, 2.35156f,
2.37886f, 2.40717f, 2.43655f, 2.46709f,
2.49889f, 2.53206f, 2.56673f, 2.60305f,
2.64117f, 2.6813f, 2.72367f, 2.76854f,
2.81623f, 2.86714f, 2.92173f, 2.98059f,
3.04446f, 3.1143f, 3.19135f, 3.27731f,
3.37455f, 3.48653f, 3.61862f, 3.77982f,
3.98692f, 4.2776f, 4.77167f, 133.333f
};
struct MSCRNode;
struct TempMSCR
{
MSCRNode* head;
MSCRNode* tail;
double m; // the margin used to prune area later
int size;
};
struct MSCRNode
{
MSCRNode* shortcut;
// to make the finding of root less painful
MSCRNode* prev;
MSCRNode* next;
// a point double-linked list
TempMSCR* tmsr;
// the temporary msr (set to NULL at every re-initialise)
TempMSCR* gmsr;
// the global msr (once set, never to NULL)
int index;
// the index of the node, at this point, it should be x at the first 16-bits, and y at the last 16-bits.
int rank;
int reinit;
int size, sizei;
double dt, di;
double s;
};
struct MSCREdge
{
double chi;
MSCRNode* left;
MSCRNode* right;
};
static double ChiSquaredDistance( uchar* x, uchar* y )
{
return (double)((x[0]-y[0])*(x[0]-y[0]))/(double)(x[0]+y[0]+1e-10)+
(double)((x[1]-y[1])*(x[1]-y[1]))/(double)(x[1]+y[1]+1e-10)+
(double)((x[2]-y[2])*(x[2]-y[2]))/(double)(x[2]+y[2]+1e-10);
}
static void initMSCRNode( MSCRNode* node )
{
node->gmsr = node->tmsr = NULL;
node->reinit = 0xffff;
node->rank = 0;
node->sizei = node->size = 1;
node->prev = node->next = node->shortcut = node;
}
// the preprocess to get the edge list with proper gaussian blur
static int preprocessMSER_8UC3( MSCRNode* node,
MSCREdge* edge,
double* total,
CvMat* src,
CvMat* mask,
CvMat* dx,
CvMat* dy,
int Ne,
int edgeBlurSize )
{
int srccpt = src->step-src->cols*3;
uchar* srcptr = src->data.ptr;
uchar* lastptr = src->data.ptr+3;
double* dxptr = dx->data.db;
for ( int i = 0; i < src->rows; i++ )
{
for ( int j = 0; j < src->cols-1; j++ )
{
*dxptr = ChiSquaredDistance( srcptr, lastptr );
dxptr++;
srcptr += 3;
lastptr += 3;
}
srcptr += srccpt+3;
lastptr += srccpt+3;
}
srcptr = src->data.ptr;
lastptr = src->data.ptr+src->step;
double* dyptr = dy->data.db;
for ( int i = 0; i < src->rows-1; i++ )
{
for ( int j = 0; j < src->cols; j++ )
{
*dyptr = ChiSquaredDistance( srcptr, lastptr );
dyptr++;
srcptr += 3;
lastptr += 3;
}
srcptr += srccpt;
lastptr += srccpt;
}
// get dx and dy and blur it
if ( edgeBlurSize >= 1 )
{
Mat _dx(dx->rows, dx->cols, dx->type, dx->data.ptr, dx->step);
Mat _dy(dy->rows, dy->cols, dy->type, dy->data.ptr, dy->step);
GaussianBlur( _dx, _dx, Size(edgeBlurSize, edgeBlurSize), 0 );
GaussianBlur( _dy, _dy, Size(edgeBlurSize, edgeBlurSize), 0 );
}
dxptr = dx->data.db;
dyptr = dy->data.db;
// assian dx, dy to proper edge list and initialize mscr node
// the nasty code here intended to avoid extra loops
if ( mask )
{
Ne = 0;
int maskcpt = mask->step-mask->cols+1;
uchar* maskptr = mask->data.ptr;
MSCRNode* nodeptr = node;
initMSCRNode( nodeptr );
nodeptr->index = 0;
*total += edge->chi = *dxptr;
if ( maskptr[0] && maskptr[1] )
{
edge->left = nodeptr;
edge->right = nodeptr+1;
edge++;
Ne++;
}
dxptr++;
nodeptr++;
maskptr++;
for ( int i = 1; i < src->cols-1; i++ )
{
initMSCRNode( nodeptr );
nodeptr->index = i;
if ( maskptr[0] && maskptr[1] )
{
*total += edge->chi = *dxptr;
edge->left = nodeptr;
edge->right = nodeptr+1;
edge++;
Ne++;
}
dxptr++;
nodeptr++;
maskptr++;
}
initMSCRNode( nodeptr );
nodeptr->index = src->cols-1;
nodeptr++;
maskptr += maskcpt;
for ( int i = 1; i < src->rows-1; i++ )
{
initMSCRNode( nodeptr );
nodeptr->index = i<<16;
if ( maskptr[0] )
{
if ( maskptr[-mask->step] )
{
*total += edge->chi = *dyptr;
edge->left = nodeptr-src->cols;
edge->right = nodeptr;
edge++;
Ne++;
}
if ( maskptr[1] )
{
*total += edge->chi = *dxptr;
edge->left = nodeptr;
edge->right = nodeptr+1;
edge++;
Ne++;
}
}
dyptr++;
dxptr++;
nodeptr++;
maskptr++;
for ( int j = 1; j < src->cols-1; j++ )
{
initMSCRNode( nodeptr );
nodeptr->index = (i<<16)|j;
if ( maskptr[0] )
{
if ( maskptr[-mask->step] )
{
*total += edge->chi = *dyptr;
edge->left = nodeptr-src->cols;
edge->right = nodeptr;
edge++;
Ne++;
}
if ( maskptr[1] )
{
*total += edge->chi = *dxptr;
edge->left = nodeptr;
edge->right = nodeptr+1;
edge++;
Ne++;
}
}
dyptr++;
dxptr++;
nodeptr++;
maskptr++;
}
initMSCRNode( nodeptr );
nodeptr->index = (i<<16)|(src->cols-1);
if ( maskptr[0] && maskptr[-mask->step] )
{
*total += edge->chi = *dyptr;
edge->left = nodeptr-src->cols;
edge->right = nodeptr;
edge++;
Ne++;
}
dyptr++;
nodeptr++;
maskptr += maskcpt;
}
initMSCRNode( nodeptr );
nodeptr->index = (src->rows-1)<<16;
if ( maskptr[0] )
{
if ( maskptr[1] )
{
*total += edge->chi = *dxptr;
edge->left = nodeptr;
edge->right = nodeptr+1;
edge++;
Ne++;
}
if ( maskptr[-mask->step] )
{
*total += edge->chi = *dyptr;
edge->left = nodeptr-src->cols;
edge->right = nodeptr;
edge++;
Ne++;
}
}
dxptr++;
dyptr++;
nodeptr++;
maskptr++;
for ( int i = 1; i < src->cols-1; i++ )
{
initMSCRNode( nodeptr );
nodeptr->index = ((src->rows-1)<<16)|i;
if ( maskptr[0] )
{
if ( maskptr[1] )
{
*total += edge->chi = *dxptr;
edge->left = nodeptr;
edge->right = nodeptr+1;
edge++;
Ne++;
}
if ( maskptr[-mask->step] )
{
*total += edge->chi = *dyptr;
edge->left = nodeptr-src->cols;
edge->right = nodeptr;
edge++;
Ne++;
}
}
dxptr++;
dyptr++;
nodeptr++;
maskptr++;
}
initMSCRNode( nodeptr );
nodeptr->index = ((src->rows-1)<<16)|(src->cols-1);
if ( maskptr[0] && maskptr[-mask->step] )
{
*total += edge->chi = *dyptr;
edge->left = nodeptr-src->cols;
edge->right = nodeptr;
Ne++;
}
} else {
MSCRNode* nodeptr = node;
initMSCRNode( nodeptr );
nodeptr->index = 0;
*total += edge->chi = *dxptr;
dxptr++;
edge->left = nodeptr;
edge->right = nodeptr+1;
edge++;
nodeptr++;
for ( int i = 1; i < src->cols-1; i++ )
{
initMSCRNode( nodeptr );
nodeptr->index = i;
*total += edge->chi = *dxptr;
dxptr++;
edge->left = nodeptr;
edge->right = nodeptr+1;
edge++;
nodeptr++;
}
initMSCRNode( nodeptr );
nodeptr->index = src->cols-1;
nodeptr++;
for ( int i = 1; i < src->rows-1; i++ )
{
initMSCRNode( nodeptr );
nodeptr->index = i<<16;
*total += edge->chi = *dyptr;
dyptr++;
edge->left = nodeptr-src->cols;
edge->right = nodeptr;
edge++;
*total += edge->chi = *dxptr;
dxptr++;
edge->left = nodeptr;
edge->right = nodeptr+1;
edge++;
nodeptr++;
for ( int j = 1; j < src->cols-1; j++ )
{
initMSCRNode( nodeptr );
nodeptr->index = (i<<16)|j;
*total += edge->chi = *dyptr;
dyptr++;
edge->left = nodeptr-src->cols;
edge->right = nodeptr;
edge++;
*total += edge->chi = *dxptr;
dxptr++;
edge->left = nodeptr;
edge->right = nodeptr+1;
edge++;
nodeptr++;
}
initMSCRNode( nodeptr );
nodeptr->index = (i<<16)|(src->cols-1);
*total += edge->chi = *dyptr;
dyptr++;
edge->left = nodeptr-src->cols;
edge->right = nodeptr;
edge++;
nodeptr++;
}
initMSCRNode( nodeptr );
nodeptr->index = (src->rows-1)<<16;
*total += edge->chi = *dxptr;
dxptr++;
edge->left = nodeptr;
edge->right = nodeptr+1;
edge++;
*total += edge->chi = *dyptr;
dyptr++;
edge->left = nodeptr-src->cols;
edge->right = nodeptr;
edge++;
nodeptr++;
for ( int i = 1; i < src->cols-1; i++ )
{
initMSCRNode( nodeptr );
nodeptr->index = ((src->rows-1)<<16)|i;
*total += edge->chi = *dxptr;
dxptr++;
edge->left = nodeptr;
edge->right = nodeptr+1;
edge++;
*total += edge->chi = *dyptr;
dyptr++;
edge->left = nodeptr-src->cols;
edge->right = nodeptr;
edge++;
nodeptr++;
}
initMSCRNode( nodeptr );
nodeptr->index = ((src->rows-1)<<16)|(src->cols-1);
*total += edge->chi = *dyptr;
edge->left = nodeptr-src->cols;
edge->right = nodeptr;
}
return Ne;
}
class LessThanEdge
{
public:
bool operator()(const MSCREdge& a, const MSCREdge& b) const { return a.chi < b.chi; }
};
// to find the root of one region
static MSCRNode* findMSCR( MSCRNode* x )
{
MSCRNode* prev = x;
MSCRNode* next;
for ( ; ; )
{
next = x->shortcut;
x->shortcut = prev;
if ( next == x ) break;
prev= x;
x = next;
}
MSCRNode* root = x;
for ( ; ; )
{
prev = x->shortcut;
x->shortcut = root;
if ( prev == x ) break;
x = prev;
}
return root;
}
struct MSERParams
{
MSERParams( int _delta=5, int _min_area=60, int _max_area=14400,
double _max_variation=0.25, double _min_diversity=.2,
int _max_evolution=200, double _area_threshold=1.01,
double _min_margin=0.003, int _edge_blur_size=5 )
{
delta = _delta;
minArea = _min_area;
maxArea = _max_area;
maxVariation = _max_variation;
minDiversity = _min_diversity;
maxEvolution = _max_evolution;
areaThreshold = _area_threshold;
minMargin = _min_margin;
edgeBlurSize = _edge_blur_size;
}
int delta;
int minArea;
int maxArea;
double maxVariation;
double minDiversity;
int maxEvolution;
double areaThreshold;
double minMargin;
int edgeBlurSize;
};
// the stable mscr should be:
// bigger than minArea and smaller than maxArea
// differ from its ancestor more than minDiversity
static bool MSCRStableCheck( MSCRNode* x, MSERParams params )
{
if ( x->size <= params.minArea || x->size >= params.maxArea )
return 0;
if ( x->gmsr == NULL )
return 1;
double div = (double)(x->size-x->gmsr->size)/(double)x->size;
return div > params.minDiversity;
}
static void
extractMSER_8UC3( CvMat* src,
CvMat* mask,
vector<vector<Point> >& msers,
MSERParams params )
{
msers.clear();
MSCRNode* map = (MSCRNode*)cvAlloc( src->cols*src->rows*sizeof(map[0]) );
int Ne = src->cols*src->rows*2-src->cols-src->rows;
MSCREdge* edge = (MSCREdge*)cvAlloc( Ne*sizeof(edge[0]) );
TempMSCR* mscr = (TempMSCR*)cvAlloc( src->cols*src->rows*sizeof(mscr[0]) );
double emean = 0;
CvMat* dx = cvCreateMat( src->rows, src->cols-1, CV_64FC1 );
CvMat* dy = cvCreateMat( src->rows-1, src->cols, CV_64FC1 );
Ne = preprocessMSER_8UC3( map, edge, &emean, src, mask, dx, dy, Ne, params.edgeBlurSize );
emean = emean / (double)Ne;
std::sort(edge, edge + Ne, LessThanEdge());
MSCREdge* edge_ub = edge+Ne;
MSCREdge* edgeptr = edge;
TempMSCR* mscrptr = mscr;
// the evolution process
for ( int i = 0; i < params.maxEvolution; i++ )
{
double k = (double)i/(double)params.maxEvolution*(TABLE_SIZE-1);
int ti = cvFloor(k);
double reminder = k-ti;
double thres = emean*(chitab3[ti]*(1-reminder)+chitab3[ti+1]*reminder);
// to process all the edges in the list that chi < thres
while ( edgeptr < edge_ub && edgeptr->chi < thres )
{
MSCRNode* lr = findMSCR( edgeptr->left );
MSCRNode* rr = findMSCR( edgeptr->right );
// get the region root (who is responsible)
if ( lr != rr )
{
// rank idea take from: N-tree Disjoint-Set Forests for Maximally Stable Extremal Regions
if ( rr->rank > lr->rank )
{
MSCRNode* tmp;
CV_SWAP( lr, rr, tmp );
} else if ( lr->rank == rr->rank ) {
// at the same rank, we will compare the size
if ( lr->size > rr->size )
{
MSCRNode* tmp;
CV_SWAP( lr, rr, tmp );
}
lr->rank++;
}
rr->shortcut = lr;
lr->size += rr->size;
// join rr to the end of list lr (lr is a endless double-linked list)
lr->prev->next = rr;
lr->prev = rr->prev;
rr->prev->next = lr;
rr->prev = lr;
// area threshold force to reinitialize
if ( lr->size > (lr->size-rr->size)*params.areaThreshold )
{
lr->sizei = lr->size;
lr->reinit = i;
if ( lr->tmsr != NULL )
{
lr->tmsr->m = lr->dt-lr->di;
lr->tmsr = NULL;
}
lr->di = edgeptr->chi;
lr->s = 1e10;
}
lr->dt = edgeptr->chi;
if ( i > lr->reinit )
{
double s = (double)(lr->size-lr->sizei)/(lr->dt-lr->di);
if ( s < lr->s )
{
// skip the first one and check stablity
if ( i > lr->reinit+1 && MSCRStableCheck( lr, params ) )
{
if ( lr->tmsr == NULL )
{
lr->gmsr = lr->tmsr = mscrptr;
mscrptr++;
}
lr->tmsr->size = lr->size;
lr->tmsr->head = lr;
lr->tmsr->tail = lr->prev;
lr->tmsr->m = 0;
}
lr->s = s;
}
}
}
edgeptr++;
}
if ( edgeptr >= edge_ub )
break;
}
for ( TempMSCR* ptr = mscr; ptr < mscrptr; ptr++ )
// to prune area with margin less than minMargin
if ( ptr->m > params.minMargin )
{
vector<Point> mser;
MSCRNode* lpt = ptr->head;
for ( int i = 0; i < ptr->size; i++ )
{
Point pt;
pt.x = (lpt->index)&0xffff;
pt.y = (lpt->index)>>16;
lpt = lpt->next;
mser.push_back(pt);
}
msers.push_back(mser);
}
cvReleaseMat( &dx );
cvReleaseMat( &dy );
cvFree( &mscr );
cvFree( &edge );
cvFree( &map );
}
/****************************************************************************************\
* Test for KeyPoint *
\****************************************************************************************/
......@@ -74,6 +752,11 @@ protected:
}
vector<KeyPoint> keypoints;
vector<vector<Point> > msers;
CvMat src = image;
extractMSER_8UC3( &src, 0, msers, MSERParams());
detector->detect(image, keypoints);
if(keypoints.empty())
......
modules/features2d/test/test_matchers_algorithmic.cpp
View file @ 01d3848f
......@@ -533,13 +533,13 @@ void CV_DescriptorMatcherTest::run( int )
TEST( Features2d_DescriptorMatcher_BruteForce, regression )
{
CV_DescriptorMatcherTest test( "descriptor-matcher-brute-force",
DescriptorMatcher::create("BFMatcher"), 0.01f );
DescriptorMatcher::create("BruteForce"), 0.01f );
test.safe_run();
}
TEST( Features2d_DescriptorMatcher_FlannBased, regression )
{
CV_DescriptorMatcherTest test( "descriptor-matcher-flann-based",
DescriptorMatcher::create("FlannBasedMatcher"), 0.04f );
DescriptorMatcher::create("FlannBased"), 0.04f );
test.safe_run();
}
modules/features2d/test/test_rotation_and_scale_invariance.cpp
View file @ 01d3848f
......@@ -595,7 +595,7 @@ protected:
TEST(Features2d_RotationInvariance_Detector_BRISK, regression)
{
DetectorRotationInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.BRISK"),
DetectorRotationInvarianceTest test(BRISK::create(),
0.32f,
0.76f);
test.safe_run();
......@@ -603,7 +603,7 @@ TEST(Features2d_RotationInvariance_Detector_BRISK, regression)
TEST(Features2d_RotationInvariance_Detector_ORB, regression)
{
DetectorRotationInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.ORB"),
DetectorRotationInvarianceTest test(ORB::create(),
0.47f,
0.76f);
test.safe_run();
......
modules/stitching/src/matchers.cpp
View file @ 01d3848f
......@@ -321,7 +321,7 @@ SurfFeaturesFinder::SurfFeaturesFinder(double hess_thresh, int num_octaves, int
{
if (num_octaves_descr == num_octaves && num_layers_descr == num_layers)
{
surf = Algorithm::create<Feature2D>("Feature2D.SURF");
surf = xfeatures2d::SURF::create();
if( !surf )
CV_Error( Error::StsNotImplemented, "OpenCV was built without SURF support" );
surf->set("hessianThreshold", hess_thresh);
......@@ -330,8 +330,8 @@ SurfFeaturesFinder::SurfFeaturesFinder(double hess_thresh, int num_octaves, int
}
else
{
detector_ = Algorithm::create<FeatureDetector>("Feature2D.SURF");
extractor_ = Algorithm::create<DescriptorExtractor>("Feature2D.SURF");
detector_ = xfeatures2d::SURF::create();
extractor_ = xfeatures2d::SURF::create();
if( !detector_ || !extractor_ )
CV_Error( Error::StsNotImplemented, "OpenCV was built without SURF support" );
......
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