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/** Authors: Ethan Rublee, Vincent Rabaud, Gary Bradski */
#include "precomp.hpp"
#include <iterator>
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
namespace cv
{
const float HARRIS_K = 0.04f;
const int DESCRIPTOR_SIZE = 32;
/**
* Function that computes the Harris responses in a
* blockSize x blockSize patch at given points in an image
*/
static void
HarrisResponses(const Mat& img, vector<KeyPoint>& pts, int blockSize, float harris_k)
{
CV_Assert( img.type() == CV_8UC1 && blockSize*blockSize <= 2048 );
size_t ptidx, ptsize = pts.size();
const uchar* ptr00 = img.ptr<uchar>();
size_t step = img.step/img.elemSize1();
int r = blockSize/2;
float scale = (1 << 2) * blockSize * 255.0f;
scale = 1.0f / scale;
float scale_sq_sq = scale * scale * scale * scale;
AutoBuffer<int> ofsbuf(blockSize*blockSize);
int* ofs = ofsbuf;
for( int i = 0; i < blockSize; i++ )
for( int j = 0; j < blockSize; j++ )
ofs[i*blockSize + j] = (int)(i*step + j);
for( ptidx = 0; ptidx < ptsize; ptidx++ )
{
int x0 = cvRound(pts[ptidx].pt.x - r);
int y0 = cvRound(pts[ptidx].pt.y - r);
const uchar* ptr0 = ptr00 + y0*step + x0;
int a = 0, b = 0, c = 0;
for( int k = 0; k < blockSize*blockSize; k++ )
{
const uchar* ptr = ptr0 + ofs[k];
int Ix = (ptr[1] - ptr[-1])*2 + (ptr[-step+1] - ptr[-step-1]) + (ptr[step+1] - ptr[step-1]);
int Iy = (ptr[step] - ptr[-step])*2 + (ptr[step-1] - ptr[-step-1]) + (ptr[step+1] - ptr[-step+1]);
a += Ix*Ix;
b += Iy*Iy;
c += Ix*Iy;
}
pts[ptidx].response = ((float)a * b - (float)c * c -
harris_k * ((float)a + b) * ((float)a + b))*scale_sq_sq;
}
}
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
struct KeypointResponseGreaterThanThreshold
{
KeypointResponseGreaterThanThreshold(float _value) :
value(_value)
{
}
inline bool operator()(const KeyPoint& kpt) const
{
return kpt.response >= value;
}
float value;
};
struct KeypointResponseGreater
{
inline bool operator()(const KeyPoint& kp1, const KeyPoint& kp2) const
{
return kp1.response > kp2.response;
}
};
static float IC_Angle(const Mat& image, const int half_k, Point2f pt,
const vector<int> & u_max)
{
int m_01 = 0, m_10 = 0;
const uchar* center = &image.at<uchar> (cvRound(pt.y), cvRound(pt.x));
// Treat the center line differently, v=0
for (int u = -half_k; u <= half_k; ++u)
m_10 += u * center[u];
// Go line by line in the circular patch
int step = (int)image.step1();
for (int v = 1; v <= half_k; ++v)
{
// Proceed over the two lines
int v_sum = 0;
int d = u_max[v];
for (int u = -d; u <= d; ++u)
{
int val_plus = center[u + v*step], val_minus = center[u - v*step];
v_sum += (val_plus - val_minus);
m_10 += u * (val_plus + val_minus);
}
m_01 += v * v_sum;
}
return fastAtan2((float)m_01, (float)m_10);
}
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
static void computeOrbDescriptor(const KeyPoint& kpt,
const Mat& img, const Point* pattern,
uchar* desc, int dsize, int WTA_K)
{
float angle = kpt.angle;
//angle = cvFloor(angle/12)*12.f;
angle *= (float)(CV_PI/180.f);
float a = (float)cos(angle), b = (float)sin(angle);
const uchar* center = &img.at<uchar>(cvRound(kpt.pt.y), cvRound(kpt.pt.x));
int step = (int)img.step;
#if 1
#define GET_VALUE(idx) \
center[cvRound(pattern[idx].x*b + pattern[idx].y*a)*step + \
cvRound(pattern[idx].x*a - pattern[idx].y*b)]
#else
float x, y;
int ix, iy;
#define GET_VALUE(idx) \
(x = pattern[idx].x*a - pattern[idx].y*b, \
y = pattern[idx].x*b + pattern[idx].y*a, \
ix = cvFloor(x), iy = cvFloor(y), \
x -= ix, y -= iy, \
cvRound(center[iy*step + ix]*(1-x)*(1-y) + center[(iy+1)*step + ix]*(1-x)*y + \
center[iy*step + ix+1]*x*(1-y) + center[(iy+1)*step + ix+1]*x*y))
#endif
if( WTA_K == 2 )
{
for (int i = 0; i < dsize; ++i, pattern += 16)
{
int t0, t1, val;
t0 = GET_VALUE(0); t1 = GET_VALUE(1);
val = t0 < t1;
t0 = GET_VALUE(2); t1 = GET_VALUE(3);
val |= (t0 < t1) << 1;
t0 = GET_VALUE(4); t1 = GET_VALUE(5);
val |= (t0 < t1) << 2;
t0 = GET_VALUE(6); t1 = GET_VALUE(7);
val |= (t0 < t1) << 3;
t0 = GET_VALUE(8); t1 = GET_VALUE(9);
val |= (t0 < t1) << 4;
t0 = GET_VALUE(10); t1 = GET_VALUE(11);
val |= (t0 < t1) << 5;
t0 = GET_VALUE(12); t1 = GET_VALUE(13);
val |= (t0 < t1) << 6;
t0 = GET_VALUE(14); t1 = GET_VALUE(15);
val |= (t0 < t1) << 7;
desc[i] = (uchar)val;
}
}
else if( WTA_K == 3 )
{
for (int i = 0; i < dsize; ++i, pattern += 12)
{
int t0, t1, t2, val;
t0 = GET_VALUE(0); t1 = GET_VALUE(1); t2 = GET_VALUE(2);
val = t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0);
t0 = GET_VALUE(3); t1 = GET_VALUE(4); t2 = GET_VALUE(5);
val |= (t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0)) << 2;
t0 = GET_VALUE(6); t1 = GET_VALUE(7); t2 = GET_VALUE(8);
val |= (t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0)) << 4;
t0 = GET_VALUE(9); t1 = GET_VALUE(10); t2 = GET_VALUE(11);
val |= (t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0)) << 6;
desc[i] = (uchar)val;
}
}
else if( WTA_K == 4 )
{
for (int i = 0; i < dsize; ++i, pattern += 16)
{
int t0, t1, t2, t3, a, b, k, val;
t0 = GET_VALUE(0); t1 = GET_VALUE(1);
t2 = GET_VALUE(2); t3 = GET_VALUE(3);
a = 0, b = 2;
if( t1 > t0 ) t0 = t1, a = 1;
if( t3 > t2 ) t2 = t3, b = 3;
k = t0 > t2 ? a : b;
val = k;
t0 = GET_VALUE(4); t1 = GET_VALUE(5);
t2 = GET_VALUE(6); t3 = GET_VALUE(7);
a = 0, b = 2;
if( t1 > t0 ) t0 = t1, a = 1;
if( t3 > t2 ) t2 = t3, b = 3;
k = t0 > t2 ? a : b;
val |= k << 2;
t0 = GET_VALUE(8); t1 = GET_VALUE(9);
t2 = GET_VALUE(10); t3 = GET_VALUE(11);
a = 0, b = 2;
if( t1 > t0 ) t0 = t1, a = 1;
if( t3 > t2 ) t2 = t3, b = 3;
k = t0 > t2 ? a : b;
val |= k << 4;
t0 = GET_VALUE(12); t1 = GET_VALUE(13);
t2 = GET_VALUE(14); t3 = GET_VALUE(15);
a = 0, b = 2;
if( t1 > t0 ) t0 = t1, a = 1;
if( t3 > t2 ) t2 = t3, b = 3;
k = t0 > t2 ? a : b;
val |= k << 6;
desc[i] = (uchar)val;
}
}
else
CV_Error( CV_StsBadSize, "Wrong WTA_K. It can be only 2, 3 or 4." );
#undef GET_VALUE
}
static void initializeOrbPattern( const Point* pattern0, vector<Point>& pattern, int ntuples, int tupleSize, int poolSize )
{
RNG rng(0x12345678);
int i, k, k1;
pattern.resize(ntuples*tupleSize);
for( i = 0; i < ntuples; i++ )
{
for( k = 0; k < tupleSize; k++ )
{
for(;;)
{
int idx = rng.uniform(0, poolSize);
Point pt = pattern0[idx];
for( k1 = 0; k1 < k; k1++ )
if( pattern[tupleSize*i + k1] == pt )
break;
if( k1 == k )
{
pattern[tupleSize*i + k] = pt;
break;
}
}
}
}
}
static int 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)*/
};
static void makeRandomPattern(int patchSize, Point* pattern, int npoints)
{
RNG rng(0x34985739); // we always start with a fixed seed,
// to make patterns the same on each run
for( int i = 0; i < npoints; i++ )
{
pattern[i].x = rng.uniform(-patchSize/2, patchSize/2+1);
pattern[i].y = rng.uniform(-patchSize/2, patchSize/2+1);
}
}
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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"];
WTA_K_ = fn["WTA_K"];
if( WTA_K_ == 0 ) WTA_K_ = 2;
score_type_ = fn["scoreType"];
}
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_);
fs << "WTA_K" << WTA_K_;
fs << "scoreType" << score_type_;
}
void ORB::read(const FileNode& fn)
{
CommonParams params;
params.read(fn);
int n_features = int(fn["nFeatures"]);
*this = ORB(n_features, params);
}
void ORB::write(FileStorage& fs) const
{
params_.write(fs);
fs << "nFeatures" << int(n_features_);
}
static inline float get_scale(const ORB::CommonParams& params, int level)
{
return std::pow(params.scale_factor_, float(level) - float(params.first_level_));
}
/** Constructor
* @param detector_params parameters to use
*/
ORB::ORB(size_t n_features, const CommonParams & detector_params) :
params_(detector_params), n_features_(n_features)
{
// fill the extractors and descriptors for the corresponding scales
int n_levels = (int)params_.n_levels_;
float factor = (float)(1.0 / params_.scale_factor_);
float n_desired_features_per_scale = n_features_*(1 - factor)/(1 - (float)pow((double)factor, (double)n_levels));
n_features_per_level_.resize(n_levels);
int sum_n_features = 0;
for( int level = 0; level < n_levels-1; level++ )
{
n_features_per_level_[level] = cvRound(n_desired_features_per_scale);
sum_n_features += n_features_per_level_[level];
n_desired_features_per_scale *= factor;
}
n_features_per_level_[n_levels-1] = n_features - sum_n_features;
// Make sure we forget about what is too close to the boundary
//params_.edge_threshold_ = std::max(params_.edge_threshold_, params_.patch_size_/2 + kKernelWidth / 2 + 2);
// pre-compute the end of a row in a circular patch
int half_patch_size = params_.patch_size_ / 2;
u_max_.resize(half_patch_size + 1);
for (int v = 0; v <= half_patch_size * sqrt(2.f) / 2 + 1; ++v)
u_max_[v] = cvRound(sqrt(float(half_patch_size * half_patch_size - v * v)));
// Make sure we are symmetric
for (int v = half_patch_size, v_0 = 0; v >= half_patch_size * sqrt(2.f) / 2; --v)
{
while (u_max_[v_0] == u_max_[v_0 + 1])
++v_0;
u_max_[v] = v_0;
++v_0;
}
const int npoints = 512;
Point pattern_buf[npoints];
const Point* pattern0 = (const Point*)bit_pattern_31_;
if( params_.patch_size_ != 31 )
{
pattern0 = pattern_buf;
makeRandomPattern(params_.patch_size_, pattern_buf, npoints);
}
CV_Assert( params_.WTA_K_ == 2 || params_.WTA_K_ == 3 || params_.WTA_K_ == 4 );
if( params_.WTA_K_ == 2 )
std::copy(pattern0, pattern0 + npoints, back_inserter(pattern));
else
{
int ntuples = descriptorSize()*4;
initializeOrbPattern(pattern0, pattern, ntuples, params_.WTA_K_, npoints);
}
}
/** destructor to empty the patterns */
ORB::~ORB()
{
}
/** returns the descriptor size in bytes */
int ORB::descriptorSize() const
{
return kBytes;
}
/** Compute the ORB features and descriptors on an image
* @param img the image to compute the features and descriptors on
* @param mask the mask to apply
* @param keypoints the resulting keypoints
*/
void ORB::operator()(const Mat &image, const Mat &mask, vector<KeyPoint> & keypoints)
{
Mat empty_descriptors;
this->operator ()(image, mask, keypoints, empty_descriptors, true, false);
}
/** Compute the ORB features and descriptors on an image
* @param img the image to compute the features and descriptors on
* @param mask the mask to apply
* @param keypoints the resulting keypoints
* @param descriptors the resulting descriptors
* @param useProvidedKeypoints if true, the keypoints are used as an input
*/
void ORB::operator()(const Mat &image, const Mat &mask, vector<KeyPoint> & keypoints,
Mat & descriptors, bool useProvidedKeypoints)
{
this->operator ()(image, mask, keypoints, descriptors, !useProvidedKeypoints, true);
}
//takes keypoints and culls them by the response
static void cull(vector<KeyPoint>& keypoints, size_t n_points)
{
//this is only necessary if the keypoints size is greater than the number of desired points.
if (keypoints.size() > n_points)
{
if (n_points==0) {
keypoints.clear();
return;
}
//first use nth element to partition the keypoints into the best and worst.
std::nth_element(keypoints.begin(), keypoints.begin() + n_points, keypoints.end(), KeypointResponseGreater());
//this is the boundary response, and in the case of FAST may be ambigous
float ambiguous_response = keypoints[n_points - 1].response;
//use std::partition to grab all of the keypoints with the boundary response.
vector<KeyPoint>::const_iterator new_end =
std::partition(keypoints.begin() + n_points, keypoints.end(),
KeypointResponseGreaterThanThreshold(ambiguous_response));
//resize the keypoints, given this new end point. nth_element and partition reordered the points inplace
keypoints.resize(new_end - keypoints.begin());
}
}
/** Compute the ORB features and descriptors on an image
* @param img the image to compute the features and descriptors on
* @param mask the mask to apply
* @param keypoints the resulting keypoints
* @param descriptors the resulting descriptors
* @param do_keypoints if true, the keypoints are computed, otherwise used as an input
* @param do_descriptors if true, also computes the descriptors
*/
void ORB::operator()(const Mat &image_in, const Mat &mask, vector<KeyPoint> & keypoints_in_out,
Mat& descriptors, bool do_keypoints, bool do_descriptors)
{
if (((!do_keypoints) && (!do_descriptors)) || (image_in.empty()))
return;
//ROI handling
const int HARRIS_BLOCK_SIZE = 9;
int half_patch_size = params_.patch_size_ / 2;
int border = std::max(params_.edge_threshold_, std::max(half_patch_size, HARRIS_BLOCK_SIZE/2))+1;
Mat image;
if (image_in.type() != CV_8UC1)
cvtColor(image_in, image, CV_BGR2GRAY);
else
image = image_in;
int n_levels = (int)params_.n_levels_;
if( !do_keypoints )
{
// if we have pre-computed keypoints, they may use more levels than it is set in parameters
// !!!TODO!!! implement more correct method, independent from the used keypoint detector.
// Namely, the detector should provide correct size of each keypoint. Based on the keypoint size
// and the algorithm used (i.e. BRIEF, running on 31x31 patches) we should compute the approximate
// scale-factor that we need to apply. Then we should cluster all the computed scale-factors and
// for each cluster compute the corresponding image.
//
// In short, ultimately the descriptor should
// ignore octave parameter and deal only with the keypoint size.
n_levels = 0;
for( size_t i = 0; i < keypoints_in_out.size(); i++ )
n_levels = std::max(n_levels, std::max(keypoints_in_out[i].octave, 0));
n_levels++;
}
// Pre-compute the scale pyramids
vector<Mat> image_pyramid(n_levels), mask_pyramid(n_levels);
for (int level = 0; level < n_levels; ++level)
{
float scale = 1/get_scale(params_, level);
Size sz(cvRound(image.cols*scale), cvRound(image.rows*scale));
Size wholeSize(sz.width + border*2, sz.height + border*2);
Mat temp(wholeSize, image.type()), masktemp;
image_pyramid[level] = temp(Rect(border, border, sz.width, sz.height));
if( !mask.empty() )
{
masktemp = Mat(wholeSize, mask.type());
mask_pyramid[level] = masktemp(Rect(border, border, sz.width, sz.height));
}
// Compute the resized image
if (level != (int)params_.first_level_)
{
if( level < (int)params_.first_level_ )
{
resize(image, image_pyramid[level], sz, scale, scale, INTER_LINEAR);
if (!mask.empty())
resize(mask, mask_pyramid[level], sz, scale, scale, INTER_LINEAR);
copyMakeBorder(image_pyramid[level], temp, border, border, border, border,
BORDER_REFLECT_101+BORDER_ISOLATED);
}
else
{
float sf = params_.scale_factor_;
resize(image_pyramid[level-1], image_pyramid[level], sz, 1./sf, 1./sf, INTER_LINEAR);
if (!mask.empty())
resize(mask_pyramid[level-1], mask_pyramid[level], sz, 1./sf, 1./sf, INTER_LINEAR);
copyMakeBorder(image_pyramid[level], temp, border, border, border, border,
BORDER_REFLECT_101+BORDER_ISOLATED);
}
}
else
{
copyMakeBorder(image, temp, border, border, border, border,
BORDER_REFLECT_101);
image.copyTo(image_pyramid[level]);
if( !mask.empty() )
mask.copyTo(mask_pyramid[level]);
}
if( !mask.empty() )
copyMakeBorder(mask_pyramid[level], masktemp, border, border, border, border,
BORDER_CONSTANT+BORDER_ISOLATED);
}
// Pre-compute the keypoints (we keep the best over all scales, so this has to be done beforehand
vector < vector<KeyPoint> > all_keypoints;
if (do_keypoints)
{
// Get keypoints, those will be far enough from the border that no check will be required for the descriptor
computeKeyPoints(image_pyramid, mask_pyramid, all_keypoints);
// make sure we have the right number of keypoints keypoints
/*vector<KeyPoint> temp;
for (int level = 0; level < n_levels; ++level)
{
vector<KeyPoint>& keypoints = all_keypoints[level];
temp.insert(temp.end(), keypoints.begin(), keypoints.end());
keypoints.clear();
}
cull(temp, n_features_);
for (vector<KeyPoint>::iterator keypoint = temp.begin(),
keypoint_end = temp.end(); keypoint != keypoint_end; ++keypoint)
all_keypoints[keypoint->octave].push_back(*keypoint);*/
}
else
{
// Remove keypoints very close to the border
KeyPointsFilter::runByImageBorder(keypoints_in_out, image.size(), params_.edge_threshold_);
// Cluster the input keypoints depending on the level they were computed at
all_keypoints.resize(n_levels);
for (vector<KeyPoint>::iterator keypoint = keypoints_in_out.begin(),
keypoint_end = keypoints_in_out.end(); keypoint != keypoint_end; ++keypoint)
all_keypoints[keypoint->octave].push_back(*keypoint);
// Make sure we rescale the coordinates
for (int level = 0; level < n_levels; ++level)
{
if (level == (int)params_.first_level_)
continue;
vector<KeyPoint> & keypoints = all_keypoints[level];
float scale = 1/get_scale(params_, level);
for (vector<KeyPoint>::iterator keypoint = keypoints.begin(),
keypoint_end = keypoints.end(); keypoint != keypoint_end; ++keypoint)
keypoint->pt *= scale;
}
}
if (do_descriptors)
{
int nkeypoints = 0;
for (int level = 0; level < n_levels; ++level)
nkeypoints += (int)all_keypoints[level].size();
if( nkeypoints == 0 )
descriptors.release();
else
descriptors.create(nkeypoints, descriptorSize(), CV_8U);
}
keypoints_in_out.clear();
int offset = 0;
for (int level = 0; level < n_levels; ++level)
{
// Get the features and compute their orientation
vector<KeyPoint>& keypoints = all_keypoints[level];
int nkeypoints = (int)keypoints.size();
// Compute the descriptors
if (do_descriptors)
{
Mat desc = descriptors.rowRange(offset, offset + nkeypoints);
offset += nkeypoints;
// preprocess the resized image
Mat& working_mat = image_pyramid[level];
//boxFilter(working_mat, working_mat, working_mat.depth(), Size(5,5), Point(-1,-1), true, BORDER_REFLECT_101);
GaussianBlur(working_mat, working_mat, Size(7, 7), 2, 2, BORDER_REFLECT_101);
computeDescriptors(working_mat, Mat(), level, keypoints, desc);
}
// Copy to the output data
if (level != (int)params_.first_level_)
{
float scale = get_scale(params_, level);
for (vector<KeyPoint>::iterator keypoint = keypoints.begin(),
keypoint_end = keypoints.end(); keypoint != keypoint_end; ++keypoint)
keypoint->pt *= scale;
}
// And add the keypoints to the output
keypoints_in_out.insert(keypoints_in_out.end(), keypoints.begin(), keypoints.end());
}
}
/** Compute the ORB keypoints on an image
* @param image_pyramid the image pyramid to compute the features and descriptors on
* @param mask_pyramid the masks to apply at every level
* @param keypoints the resulting keypoints, clustered per level
*/
void ORB::computeKeyPoints(const vector<Mat>& image_pyramid,
const vector<Mat>& mask_pyramid,
vector<vector<KeyPoint> >& all_keypoints_out) const
{
all_keypoints_out.resize(params_.n_levels_);
for (int level = 0; level < (int)params_.n_levels_; ++level)
{
int n_features = n_features_per_level_[level];
all_keypoints_out[level].reserve(n_features*2);
vector<KeyPoint> & keypoints = all_keypoints_out[level];
// Detect FAST features, 20 is a good threshold
FastFeatureDetector fd(20, true);
fd.detect(image_pyramid[level], keypoints, mask_pyramid[level]);
// Remove keypoints very close to the border
KeyPointsFilter::runByImageBorder(keypoints, image_pyramid[level].size(), params_.edge_threshold_);
if( params_.score_type_ == CommonParams::HARRIS_SCORE )
{
// Keep more points than necessary as FAST does not give amazing corners
cull(keypoints, 2 * n_features);
// Compute the Harris cornerness (better scoring than FAST)
HarrisResponses(image_pyramid[level], keypoints, 7, HARRIS_K);
}
//cull to the final desired level, using the new Harris scores or the original FAST scores.
cull(keypoints, n_features);
float sf = get_scale(params_, level);
// Set the level of the coordinates
for (vector<KeyPoint>::iterator keypoint = keypoints.begin(),
keypoint_end = keypoints.end(); keypoint != keypoint_end; ++keypoint)
{
keypoint->octave = level;
keypoint->size = params_.patch_size_*sf;
}
computeOrientation(image_pyramid[level], Mat(), level, keypoints);
}
}
/** Compute the ORB keypoint orientations
* @param image the image to compute the features and descriptors on
* @param integral_image the integral image of the iamge (can be empty, but the computation will be slower)
* @param scale the scale at which we compute the orientation
* @param keypoints the resulting keypoints
*/
void ORB::computeOrientation(const Mat& image, const Mat&, unsigned int scale,
vector<KeyPoint>& keypoints) const
{
int half_patch_size = params_.patch_size_/2;
// Process each keypoint
for (vector<KeyPoint>::iterator keypoint = keypoints.begin(),
keypoint_end = keypoints.end(); keypoint != keypoint_end; ++keypoint)
{
keypoint->angle = IC_Angle(image, half_patch_size, keypoint->pt, u_max_);
}
}
/** Compute the integral image and upadte the cached values
* @param image the image to compute the features and descriptors on
* @param level the scale at which we compute the orientation
* @param descriptors the resulting descriptors
*/
void ORB::computeIntegralImage(const Mat&, unsigned int, Mat&)
{
}
/** Compute the ORB decriptors
* @param image the image to compute the features and descriptors on
* @param integral_image the integral image of the image (can be empty, but the computation will be slower)
* @param level the scale at which we compute the orientation
* @param keypoints the keypoints to use
* @param descriptors the resulting descriptors
*/
void ORB::computeDescriptors(const Mat& image, const Mat& integral_image, unsigned int,
vector<KeyPoint>& keypoints, Mat& descriptors) const
{
//convert to grayscale if more than one color
CV_Assert(image.type() == CV_8UC1);
//create the descriptor mat, keypoints.size() rows, BYTES cols
int dsize = descriptorSize();
descriptors = Mat::zeros((int)keypoints.size(), dsize, CV_8UC1);
for (size_t i = 0; i < keypoints.size(); i++)
computeOrbDescriptor(keypoints[i], image, &pattern[0], descriptors.ptr((int)i), dsize, params_.WTA_K_);
}
}