ORBExtractor.cpp

/*


   Copyright (c) 2020, Tharaka Ratnayake, email: tharakasachintha.17@cse.mrt.ac.lk
   All rights reserved. https://github.com/tharaka27/ImageStitcherFAST

   Some algorithms used in this code referred to:
   1. OpenCV: http://opencv.org/



   Revision history:
	  April 24th, 2020: initial version.
*/


#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <vector>

#include "ORBExtractor.h"



namespace TORB {
	
	const int PATCH_SIZE = 31;
	const int HALF_PATCH_SIZE = 15;
	const int EDGE_THRESHOLD = 19;


	static float IC_Angle(const cv::Mat& image, cv::Point2f pt, const std::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_PATCH_SIZE; u <= HALF_PATCH_SIZE; ++u)
			m_10 += u * center[u];

		// Go line by line in the circuI853lar patch
		int step = (int)image.step1();
		for (int v = 1; v <= HALF_PATCH_SIZE; ++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 cv::fastAtan2((float)m_01, (float)m_10);
	}



	const float factorPI = (float)(CV_PI / 180.f);
	static void computeOrbDescriptor(const cv::KeyPoint& kpt,
		const cv::Mat& img, const cv::Point* pattern,
		uchar* desc)
	{
		float angle = (float)kpt.angle * factorPI;
		float a = (float)cos(angle), b = (float)sin(angle);

		const uchar* center = &img.at<uchar>(cvRound(kpt.pt.y), cvRound(kpt.pt.x));
		const int step = (int)img.step;

#define GET_VALUE(idx) \
        center[cvRound(pattern[idx].x*b + pattern[idx].y*a)*step + \
               cvRound(pattern[idx].x*a - pattern[idx].y*b)]


		for (int i = 0; i < 32; ++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;
		}

#undef GET_VALUE
	}




	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)*/
	};



	ORBExtractor::ORBExtractor(int _nfeatures, float _scaleFactor, int _nlevels,
		int _iniThFAST, int _minThFAST) :
		nfeatures(_nfeatures), scaleFactor(_scaleFactor), nlevels(_nlevels),
		iniThFAST(_iniThFAST), minThFAST(_minThFAST)
	{
		mvScaleFactor.resize(nlevels);
		mvLevelSigma2.resize(nlevels);
		mvScaleFactor[0] = 1.0f;
		mvLevelSigma2[0] = 1.0f;
		for (int i = 1; i < nlevels; i++)
		{
			mvScaleFactor[i] = mvScaleFactor[i - 1] * scaleFactor;
			mvLevelSigma2[i] = mvScaleFactor[i] * mvScaleFactor[i];
		}

		mvInvScaleFactor.resize(nlevels);
		mvInvLevelSigma2.resize(nlevels);
		for (int i = 0; i < nlevels; i++)
		{
			mvInvScaleFactor[i] = 1.0f / mvScaleFactor[i];
			mvInvLevelSigma2[i] = 1.0f / mvLevelSigma2[i];
		}

		mvImagePyramid.resize(nlevels);

		mnFeaturesPerLevel.resize(nlevels);
		float factor = 1.0f / scaleFactor;
		float nDesiredFeaturesPerScale = nfeatures * (1 - factor) / (1 - (float)pow((double)factor, (double)nlevels));

		int sumFeatures = 0;
		for (int level = 0; level < nlevels - 1; level++)
		{
			mnFeaturesPerLevel[level] = cvRound(nDesiredFeaturesPerScale);
			sumFeatures += mnFeaturesPerLevel[level];
			nDesiredFeaturesPerScale *= factor;
		}
		mnFeaturesPerLevel[nlevels - 1] = std::max(nfeatures - sumFeatures, 0);

		const int npoints = 512;
		const cv::Point* pattern0 = (const cv::Point*)bit_pattern_31_;
		std::copy(pattern0, pattern0 + npoints, std::back_inserter(pattern));

		//This is for orientation
		// pre-compute the end of a row in a circular patch
		umax.resize(HALF_PATCH_SIZE + 1);

		int v, v0, vmax = cvFloor(HALF_PATCH_SIZE * sqrt(2.f) / 2 + 1);
		int vmin = cvCeil(HALF_PATCH_SIZE * sqrt(2.f) / 2);
		const double hp2 = HALF_PATCH_SIZE * HALF_PATCH_SIZE;
		for (v = 0; v <= vmax; ++v)
			umax[v] = cvRound(sqrt(hp2 - v * v));

		// Make sure we are symmetric
		for (v = HALF_PATCH_SIZE, v0 = 0; v >= vmin; --v)
		{
			while (umax[v0] == umax[v0 + 1])
				++v0;
			umax[v] = v0;
			++v0;
		}
	}

	static void computeOrientation(const cv::Mat& image, std::vector<cv::KeyPoint>& keypoints, const std::vector<int>& umax)
	{
		for (std::vector<cv::KeyPoint>::iterator keypoint = keypoints.begin(),
			keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint)
		{
			keypoint->angle = IC_Angle(image, keypoint->pt, umax);
		}
	}



	void ExtractorNode::DivideNode(ExtractorNode& n1, ExtractorNode& n2, ExtractorNode& n3, ExtractorNode& n4)
	{
		const int halfX = ceil(static_cast<float>(UR.x - UL.x) / 2);
		const int halfY = ceil(static_cast<float>(BR.y - UL.y) / 2);

		//Define boundaries of childs
		n1.UL = UL;
		n1.UR = cv::Point2i(UL.x + halfX, UL.y);
		n1.BL = cv::Point2i(UL.x, UL.y + halfY);
		n1.BR = cv::Point2i(UL.x + halfX, UL.y + halfY);
		n1.vKeys.reserve(vKeys.size());

		n2.UL = n1.UR;
		n2.UR = UR;
		n2.BL = n1.BR;
		n2.BR = cv::Point2i(UR.x, UL.y + halfY);
		n2.vKeys.reserve(vKeys.size());

		n3.UL = n1.BL;
		n3.UR = n1.BR;
		n3.BL = BL;
		n3.BR = cv::Point2i(n1.BR.x, BL.y);
		n3.vKeys.reserve(vKeys.size());

		n4.UL = n3.UR;
		n4.UR = n2.BR;
		n4.BL = n3.BR;
		n4.BR = BR;
		n4.vKeys.reserve(vKeys.size());

		//Associate points to childs
		for (size_t i = 0; i < vKeys.size(); i++)
		{
			const cv::KeyPoint& kp = vKeys[i];
			if (kp.pt.x < n1.UR.x)
			{
				if (kp.pt.y < n1.BR.y)
					n1.vKeys.push_back(kp);
				else
					n3.vKeys.push_back(kp);
			}
			else if (kp.pt.y < n1.BR.y)
				n2.vKeys.push_back(kp);
			else
				n4.vKeys.push_back(kp);
		}

		if (n1.vKeys.size() == 1)
			n1.bNoMore = true;
		if (n2.vKeys.size() == 1)
			n2.bNoMore = true;
		if (n3.vKeys.size() == 1)
			n3.bNoMore = true;
		if (n4.vKeys.size() == 1)
			n4.bNoMore = true;

	}


	std::vector<cv::KeyPoint> ORBExtractor::DistributeOctTree(const std::vector<cv::KeyPoint>& vToDistributeKeys, 
		const int& minX,const int& maxX, const int& minY, const int& maxY, const int& N, const int& level)
	{
		// Compute how many initial nodes   
		const int nIni = round(static_cast<float>(maxX - minX) / (maxY - minY));

		const float hX = static_cast<float>(maxX - minX) / nIni;

		std::list<ExtractorNode> lNodes;

		std::vector<ExtractorNode*> vpIniNodes;
		vpIniNodes.resize(nIni);

		for (int i = 0; i < nIni; i++)
		{
			ExtractorNode ni;
			ni.UL = cv::Point2i(hX * static_cast<float>(i), 0);
			ni.UR = cv::Point2i(hX * static_cast<float>(i + 1), 0);
			ni.BL = cv::Point2i(ni.UL.x, maxY - minY);
			ni.BR = cv::Point2i(ni.UR.x, maxY - minY);
			ni.vKeys.reserve(vToDistributeKeys.size());

			lNodes.push_back(ni);
			vpIniNodes[i] = &lNodes.back();
		}

		//Associate points to childs
		for (size_t i = 0; i < vToDistributeKeys.size(); i++)
		{
			const cv::KeyPoint& kp = vToDistributeKeys[i];
			vpIniNodes[kp.pt.x / hX]->vKeys.push_back(kp);
		}

		std::list<ExtractorNode>::iterator lit = lNodes.begin();

		while (lit != lNodes.end())
		{
			if (lit->vKeys.size() == 1)
			{
				lit->bNoMore = true;
				lit++;
			}
			else if (lit->vKeys.empty())
				lit = lNodes.erase(lit);
			else
				lit++;
		}

		bool bFinish = false;

		int iteration = 0;

		std::vector<std::pair<int, ExtractorNode*> > vSizeAndPointerToNode;
		vSizeAndPointerToNode.reserve(lNodes.size() * 4);

		while (!bFinish)
		{
			iteration++;

			int prevSize = lNodes.size();

			lit = lNodes.begin();

			int nToExpand = 0;

			vSizeAndPointerToNode.clear();

			while (lit != lNodes.end())
			{
				if (lit->bNoMore)
				{
					// If node only contains one point do not subdivide and continue
					lit++;
					continue;
				}
				else
				{
					// If more than one point, subdivide
					ExtractorNode n1, n2, n3, n4;
					lit->DivideNode(n1, n2, n3, n4);

					// Add childs if they contain points
					if (n1.vKeys.size() > 0)
					{
						lNodes.push_front(n1);
						if (n1.vKeys.size() > 1)
						{
							nToExpand++;
							vSizeAndPointerToNode.push_back(std::make_pair(n1.vKeys.size(), &lNodes.front()));
							lNodes.front().lit = lNodes.begin();
						}
					}
					if (n2.vKeys.size() > 0)
					{
						lNodes.push_front(n2);
						if (n2.vKeys.size() > 1)
						{
							nToExpand++;
							vSizeAndPointerToNode.push_back(std::make_pair(n2.vKeys.size(), &lNodes.front()));
							lNodes.front().lit = lNodes.begin();
						}
					}
					if (n3.vKeys.size() > 0)
					{
						lNodes.push_front(n3);
						if (n3.vKeys.size() > 1)
						{
							nToExpand++;
							vSizeAndPointerToNode.push_back(std::make_pair(n3.vKeys.size(), &lNodes.front()));
							lNodes.front().lit = lNodes.begin();
						}
					}
					if (n4.vKeys.size() > 0)
					{
						lNodes.push_front(n4);
						if (n4.vKeys.size() > 1)
						{
							nToExpand++;
							vSizeAndPointerToNode.push_back(std::make_pair(n4.vKeys.size(), &lNodes.front()));
							lNodes.front().lit = lNodes.begin();
						}
					}

					lit = lNodes.erase(lit);
					continue;
				}
			}
			// Finish if there are more nodes than required features
		// or all nodes contain just one point
			if ((int)lNodes.size() >= N || (int)lNodes.size() == prevSize)
			{
				bFinish = true;
			}
			else if (((int)lNodes.size() + nToExpand * 3) > N)
			{

				while (!bFinish)
				{

					prevSize = lNodes.size();

					std::vector<std::pair<int, ExtractorNode*> > vPrevSizeAndPointerToNode = vSizeAndPointerToNode;
					vSizeAndPointerToNode.clear();

					sort(vPrevSizeAndPointerToNode.begin(), vPrevSizeAndPointerToNode.end());
					for (int j = vPrevSizeAndPointerToNode.size() - 1; j >= 0; j--)
					{
						ExtractorNode n1, n2, n3, n4;
						vPrevSizeAndPointerToNode[j].second->DivideNode(n1, n2, n3, n4);

						// Add childs if they contain points
						if (n1.vKeys.size() > 0)
						{
							lNodes.push_front(n1);
							if (n1.vKeys.size() > 1)
							{
								vSizeAndPointerToNode.push_back(std::make_pair(n1.vKeys.size(), &lNodes.front()));
								lNodes.front().lit = lNodes.begin();
							}
						}
						if (n2.vKeys.size() > 0)
						{
							lNodes.push_front(n2);
							if (n2.vKeys.size() > 1)
							{
								vSizeAndPointerToNode.push_back(std::make_pair(n2.vKeys.size(), &lNodes.front()));
								lNodes.front().lit = lNodes.begin();
							}
						}
						if (n3.vKeys.size() > 0)
						{
							lNodes.push_front(n3);
							if (n3.vKeys.size() > 1)
							{
								vSizeAndPointerToNode.push_back(std::make_pair(n3.vKeys.size(), &lNodes.front()));
								lNodes.front().lit = lNodes.begin();
							}
						}
						if (n4.vKeys.size() > 0)
						{
							lNodes.push_front(n4);
							if (n4.vKeys.size() > 1)
							{
								vSizeAndPointerToNode.push_back(std::make_pair(n4.vKeys.size(), &lNodes.front()));
								lNodes.front().lit = lNodes.begin();
							}
						}

						lNodes.erase(vPrevSizeAndPointerToNode[j].second->lit);

						if ((int)lNodes.size() >= N)
							break;
					}

					if ((int)lNodes.size() >= N || (int)lNodes.size() == prevSize)
						bFinish = true;

				}
			}
		}

		// Retain the best point in each node
		std::vector<cv::KeyPoint> vResultKeys;
		vResultKeys.reserve(nfeatures);
		for (std::list<ExtractorNode>::iterator lit = lNodes.begin(); lit != lNodes.end(); lit++)
		{
			std::vector<cv::KeyPoint>& vNodeKeys = lit->vKeys;
			cv::KeyPoint* pKP = &vNodeKeys[0];
			float maxResponse = pKP->response;

			for (size_t k = 1; k < vNodeKeys.size(); k++)
			{
				if (vNodeKeys[k].response > maxResponse)
				{
					pKP = &vNodeKeys[k];
					maxResponse = vNodeKeys[k].response;
				}
			}

			vResultKeys.push_back(*pKP);
		}

		return vResultKeys;

	}



	void ORBExtractor::ComputeKeyPointsOctTree(std::vector<std::vector<cv::KeyPoint> >& allKeypoints)
	{

		allKeypoints.resize(nlevels);

		const float W = 30;

		for (int level = 0; level < nlevels; ++level)
		{
			const int minBorderX = EDGE_THRESHOLD - 3;
			const int minBorderY = minBorderX;
			const int maxBorderX = mvImagePyramid[level].cols - EDGE_THRESHOLD + 3;
			const int maxBorderY = mvImagePyramid[level].rows - EDGE_THRESHOLD + 3;

			std::vector<cv::KeyPoint> vToDistributeKeys;
			vToDistributeKeys.reserve(nfeatures * 10);

			const float width = (maxBorderX - minBorderX);
			const float height = (maxBorderY - minBorderY);

			const int nCols = width / W;
			const int nRows = height / W;
			const int wCell = ceil(width / nCols);
			const int hCell = ceil(height / nRows);

			for (int i = 0; i < nRows; i++)
			{
				const float iniY = minBorderY + i * hCell;
				float maxY = iniY + hCell + 6;

				if (iniY >= maxBorderY - 3)
					continue;
				if (maxY > maxBorderY)
					maxY = maxBorderY;

				for (int j = 0; j < nCols; j++)
				{
					const float iniX = minBorderX + j * wCell;
					float maxX = iniX + wCell + 6;
					if (iniX >= maxBorderX - 6)
						continue;
					if (maxX > maxBorderX)
						maxX = maxBorderX;

					std::vector<cv::KeyPoint> vKeysCell;
					FAST(mvImagePyramid[level].rowRange(iniY, maxY).colRange(iniX, maxX),
						vKeysCell, iniThFAST, true);

					if (vKeysCell.empty())
					{
						FAST(mvImagePyramid[level].rowRange(iniY, maxY).colRange(iniX, maxX),
							vKeysCell, minThFAST, true);
					}

					if (!vKeysCell.empty())
					{
						for (std::vector<cv::KeyPoint>::iterator vit = vKeysCell.begin(); vit != vKeysCell.end(); vit++)
						{
							(*vit).pt.x += j * wCell;
							(*vit).pt.y += i * hCell;
							vToDistributeKeys.push_back(*vit);
						}
					}

				}
			}

			std::vector<cv::KeyPoint>& keypoints = allKeypoints[level];
			keypoints.reserve(nfeatures);

			keypoints = DistributeOctTree(vToDistributeKeys, minBorderX, maxBorderX,
				minBorderY, maxBorderY, mnFeaturesPerLevel[level], level);

			const int scaledPatchSize = PATCH_SIZE * mvScaleFactor[level];

			// Add border to coordinates and scale information
			const int nkps = keypoints.size();
			for (int i = 0; i < nkps; i++)
			{
				keypoints[i].pt.x += minBorderX;
				keypoints[i].pt.y += minBorderY;
				keypoints[i].octave = level;
				keypoints[i].size = scaledPatchSize;
			}
		}


		// compute orientations
		for (int level = 0; level < nlevels; ++level)
			computeOrientation(mvImagePyramid[level], allKeypoints[level], umax);

	}


	static void computeDescriptors(const cv::Mat& image, std::vector<cv::KeyPoint>& keypoints, cv::Mat& descriptors,
		const std::vector<cv::Point>& pattern)
	{
		descriptors = cv::Mat::zeros((int)keypoints.size(), 32, CV_8UC1);

		for (size_t i = 0; i < keypoints.size(); i++)
			computeOrbDescriptor(keypoints[i], image, &pattern[0], descriptors.ptr((int)i));
	}


	void ORBExtractor::operator()(cv::InputArray _image, cv::InputArray _mask,
		std::vector<cv::KeyPoint>& _keypoints, cv::OutputArray _descriptors)
	{

		if (_image.empty())
			return;

		cv::Mat image = _image.getMat();
		assert(image.type() == CV_8UC1);

		// Pre-compute the scale pyramid
		ComputePyramid(image);


		std::vector<std::vector< cv::KeyPoint>> allKeypoints;
		ComputeKeyPointsOctTree(allKeypoints);

		cv::Mat descriptors;

		int nkeypoints = 0;
		for (int level = 0; level < nlevels; ++level)
			nkeypoints += (int)allKeypoints[level].size();
		if (nkeypoints == 0)
			_descriptors.release();
		else
		{
			_descriptors.create(nkeypoints, 32, CV_8U);
			descriptors = _descriptors.getMat();
		}

		_keypoints.clear();
		_keypoints.reserve(nkeypoints);

		int offset = 0;
		for (int level = 0; level < nlevels; ++level)
		{
			std::vector<cv::KeyPoint>& keypoints = allKeypoints[level];
			int nkeypointsLevel = (int)keypoints.size();

			if (nkeypointsLevel == 0)
				continue;

			// preprocess the resized image
			cv::Mat workingMat = mvImagePyramid[level].clone();
			GaussianBlur(workingMat, workingMat, cv::Size(7, 7), 2, 2, cv::BORDER_REFLECT_101);

			// Compute the descriptors
			cv::Mat desc = descriptors.rowRange(offset, offset + nkeypointsLevel);
			computeDescriptors(workingMat, keypoints, desc, pattern);

			offset += nkeypointsLevel;

			// Scale keypoint coordinates
			if (level != 0)
			{
				float scale = mvScaleFactor[level]; //getScale(level, firstLevel, scaleFactor);
				for (std::vector<cv::KeyPoint>::iterator keypoint = keypoints.begin(),
					keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint)
					keypoint->pt *= scale;
			}
			// And add the keypoints to the output
			_keypoints.insert(_keypoints.end(), keypoints.begin(), keypoints.end());
		}

	}





	void ORBExtractor::ComputePyramid(cv::Mat image) {
	
		for (int level = 0; level < nlevels; ++level) {

			float scale = mvInvScaleFactor[level];
			cv::Size sz(cvRound((float)image.cols * scale), cvRound((float)image.rows * scale));
			cv::Size wholeSize(sz.width + EDGE_THRESHOLD * 2, sz.height + EDGE_THRESHOLD * 2);
			cv::Mat temp(wholeSize, image.type()), masktemp;
			mvImagePyramid[level] = temp(cv::Rect(EDGE_THRESHOLD, EDGE_THRESHOLD, sz.width, sz.height));

			// Compute the resized image
			if (level != 0)
			{
				resize(mvImagePyramid[level-1], mvImagePyramid[level], sz, 0, 0, cv::INTER_LINEAR);

				copyMakeBorder(mvImagePyramid[level], temp, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD,
					cv::BORDER_REFLECT_101 + cv::BORDER_ISOLATED);
			}
			else
			{
				copyMakeBorder(image, temp, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD,
					cv::BORDER_REFLECT_101);
			}
		}
	
	}


} // namespace TORB