ORBSLAM2中很多模块可以单独拿出来给自己的程序使用，博主在这里将ORB中特征点提取模块提取出来，供后续自己开发。
与OpenCV库中提供的ORB特征点提取函数相比，ORBSLAM中特征点分布均匀，ORBSLAM将图像划分成若干个区域，在每个区域内设置提取特征点的数量，并设置提取数量的阈值，保证整幅图片特征点数量更均匀。
ORB特征点如何提取在这里不做赘述，很多博客和高博的《视觉SLAM十四讲》讲的都非常清楚。（PS:在这里想推荐一下高博最近新出的《视觉SLAM十四讲 第二版》，大致看了下自认为与第一版相比改变较大，新添加了很多原理性程序，比如手写高斯牛顿法，手写特征点提取，而不仅仅是像第一版用调库的方法实现。）
不多废话，直接上代码：
头文件函数，博主在这里改了一下命名空间的名字，其余没有任何变化，因为这个模块本身在程序中就是单独的一块存在，并不依赖其他功能函数。在头文件中可以看到，作者重载了()运算符，便于调用。
#ifndef ORBEXTRACTOR_H
#define ORBEXTRACTOR_H
#include <vector>
#include <list>
#include <opencv/cv.h>
namespace myORB
{
class ExtractorNode
{
public:
ExtractorNode():bNoMore(false){}
void DivideNode(ExtractorNode &n1, ExtractorNode &n2, ExtractorNode &n3, ExtractorNode &n4);
std::vector<cv::KeyPoint> vKeys;
cv::Point2i UL, UR, BL, BR;
std::list<ExtractorNode>::iterator lit;
bool bNoMore;
};
class ORBextractor
{
public:
enum {HARRIS_SCORE=0, FAST_SCORE=1 };
ORBextractor(int nfeatures, float scaleFactor, int nlevels,
int iniThFAST, int minThFAST);
~ORBextractor(){}
// Compute the ORB features and descriptors on an image.
// ORB are dispersed on the image using an octree.
// Mask is ignored in the current implementation.
// 重载（）运算符，作为对外接口
void operator()( cv::InputArray image, cv::InputArray mask,
std::vector<cv::KeyPoint>& keypoints,
cv::OutputArray descriptors);
int inline GetLevels(){
return nlevels;}
float inline GetScaleFactor(){
return scaleFactor;}
std::vector<float> inline GetScaleFactors(){
return mvScaleFactor;
}
std::vector<float> inline GetInverseScaleFactors(){
return mvInvScaleFactor;
}
std::vector<float> inline GetScaleSigmaSquares(){
return mvLevelSigma2;
}
std::vector<float> inline GetInverseScaleSigmaSquares(){
return mvInvLevelSigma2;
}
//存放各层级图片
std::vector<cv::Mat> mvImagePyramid;
protected:
//计算高斯金字塔
void ComputePyramid(cv::Mat image);
///对图像金字塔中的每一层图像进行特征点的计算。具体的计算过程是将图像网格分割为小区域，
/// 每一个小区域独立使用 FAST 角点检测。检测完成之后使用 DistributeOctTree 函数对检测得到的所有角点进行筛选，使得角点分布均匀。
void ComputeKeyPointsOctTree(std::vector<std::vector<cv::KeyPoint> >& allKeypoints);
//将关键点分配到四叉树
std::vector<cv::KeyPoint> DistributeOctTree(const std::vector<cv::KeyPoint>& vToDistributeKeys, const int &minX,
const int &maxX, const int &minY, const int &maxY, const int &nFeatures, const int &level);
void ComputeKeyPointsOld(std::vector<std::vector<cv::KeyPoint> >& allKeypoints);
//存储关键点附近patch的点对
std::vector<cv::Point> pattern;
int nfeatures; //特征点的个数
double scaleFactor; //相邻层图像的比例系数
int nlevels; //构造金字塔的层数
int iniThFAST; //检测fast角点阈值
int minThFAST; //没有检测到角点的前提下降低阈值
//每层特征点的数量
std::vector<int> mnFeaturesPerLevel;
//patch圆的最大坐标
std::vector<int> umax;
//每层相对于原始图像的缩放比例
std::vector<float> mvScaleFactor;
//每层相对于原始图像缩放比例的倒数
std::vector<float> mvInvScaleFactor;
//每层相对于原始图像的缩放比例的平方
std::vector<float> mvLevelSigma2;
std::vector<float> mvInvLevelSigma2;
};
} //namespace ORB_SLAM
#endif
接下来是cpp文件
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <vector>
#include "myORBextractor.h"
using namespace cv;
using namespace std;
namespace myORB
{
const int PATCH_SIZE = 31;
const int HALF_PATCH_SIZE = 15;
const int EDGE_THRESHOLD = 19;
// 计算特征点的角度
// u_max：patch的最大半径
static float IC_Angle(const Mat& image, Point2f pt, const vector<int> & u_max)
{
int m_01 = 0, m_10 = 0;
//计算图像中心位置
const uchar* center = &image.at<uchar> (cvRound(pt.y), cvRound(pt.x));
// Treat the center line differently, v=0
//对于v=0这行单独计算
for (int u = -HALF_PATCH_SIZE; u <= HALF_PATCH_SIZE; ++u)
m_10 += u * center[u];
// Go line by line in the circular patch
//注意step并不一定是图像的宽度
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 fastAtan2((float)m_01, (float)m_10);
}
const float factorPI = (float)(CV_PI/180.f);
static void computeOrbDescriptor(const KeyPoint& kpt,
const Mat& img, const Point* pattern,
uchar* desc)
{
//转化为弧度
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)*/
};
// _nfeatures:特征点数量
// _scaleFactor:相邻图像层比例系数
// _nlevels：金字塔层数
// _iniThFAST：检测Fast角点阈值
// _minThFAST:没有检测到角点的前提下降低阈值
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); //每层相对于原始图像的缩放系数，第一层为1.0
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; //尺度因子的倒数
// 第一层：nfeatures*(1 - factor)/(1 - (float)pow((double)factor, (double)nlevels))
// 第二层：nfeatures*(1 - factor)/(1 - (float)pow((double)factor, (double)nlevels))*q
// 第三层：nfeatures*(1 - factor)/(1 - (float)pow((double)factor, (double)nlevels))*q*q
// .........
// 第nlevels层：nfeatures*(1 - factor)/(1 - (float)pow((double)factor, (double)nlevels))*q^nlevels
// 那么前nlevels层的和为总的特征点数量nfeatures（等比数列的前n相和）
// 可以看出ORB特征点是如何实现尺度不变性的，原始图像那一层特征点最多，依次递减
// 主要是将每层的特征点数量进行均匀控制
float nDesiredFeaturesPerScale = nfeatures*(1 - factor)/(1 - (float)pow((double)factor, (double)nlevels));//第一层特征点数量
int sumFeatures = 0;
for( int level = 0; level < nlevels-1; level++ )
{
//cvRound()：返回跟参数最接近的整数值，即四舍五入；
mnFeaturesPerLevel[level] = cvRound(nDesiredFeaturesPerScale); //mnFeaturesPerLevel数组存储每层特征点的数量
sumFeatures += mnFeaturesPerLevel[level];
nDesiredFeaturesPerScale *= factor;
}
//每层都是四舍五入的特征点,加起来的总数有可能比设定的总数要多，这样最上一层就设为0
mnFeaturesPerLevel[nlevels-1] = std::max(nfeatures - sumFeatures, 0);
const int npoints = 512;
const Point* pattern0 = (const Point*)bit_pattern_31_;
//标准库提供的back_inserter模板函数很方便，因为它为container返回一个back_insert_iterator迭代器，这样，复制的元素都被追加到container的末尾了
std::copy(pattern0, pattern0 + npoints, std::back_inserter(pattern));
//This is for orientation 计算特征点方向
// pre-compute the end of a row in a circular patch
//用于计算特征方向时，每个v坐标对应最大的u坐标
umax.resize(HALF_PATCH_SIZE + 1);
//cvFloor是取不大于参数的最大整数值
int v, v0, vmax = cvFloor(HALF_PATCH_SIZE * sqrt(2.f) / 2 + 1);
//cvCeil 是取不小于参数的最小整数值
int vmin = cvCeil(HALF_PATCH_SIZE * sqrt(2.f) / 2); //利用勾股定理计算
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;
}
}
//计算图像所有特征点的方向
static void computeOrientation(const Mat& image, vector<KeyPoint>& keypoints, const vector<int>& umax)
{
for (vector<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;
}
vector<cv::KeyPoint> ORBextractor::DistributeOctTree(const 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;
list<ExtractorNode> lNodes;
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);
}
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;
vector<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(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(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(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(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();
vector<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(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(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(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(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
vector<cv::KeyPoint> vResultKeys;
vResultKeys.reserve(nfeatures);
for(list<ExtractorNode>::iterator lit=lNodes.begin(); lit!=lNodes.end(); lit++)
{
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;
}
/**
* @brief Assigns an initializer list to a %vector.
* @param __l An initializer_list.
*
* This function fills a %vector with copies of the elements in the
* initializer list @a __l.
*
* Note that the assignment completely changes the %vector and
* that the resulting %vector's size is the same as the number
* of elements assigned. Old data may be lost.
*/
void ORBextractor::ComputeKeyPointsOctTree(vector<vector<KeyPoint> >& allKeypoints)
{
allKeypoints.resize(nlevels);
//设定每个格子的大小
const float W = 30;
for (int level = 0; level < nlevels; ++level)
{
//设定该层图像中检测的X,Y最大最小坐标
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;
//用于分配的关键点
vector<cv::KeyPoint> vToDistributeKeys;
vToDistributeKeys.reserve(nfeatures*10);
const float width = (maxBorderX-minBorderX);
const float height = (maxBorderY-minBorderY);
//待检测区域格子的行列数
const int nCols = width/W;
const int nRows = height/W;
//重新计算每个格子的大小
//ceil()返回大于参数的最小整数
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;
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(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);
}
}
}
}
vector<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);
}
void ORBextractor::ComputeKeyPointsOld(std::vector<std::vector<KeyPoint> > &allKeypoints)
{
allKeypoints.resize(nlevels);
float imageRatio = (float)mvImagePyramid[0].cols/mvImagePyramid[0].rows;
for (int level = 0; level < nlevels; ++level)
{
const int nDesiredFeatures = mnFeaturesPerLevel[level];
const int levelCols = sqrt((float)nDesiredFeatures/(5*imageRatio));
const int levelRows = imageRatio*levelCols;
const int minBorderX = EDGE_THRESHOLD;
const int minBorderY = minBorderX;
const int maxBorderX = mvImagePyramid[level].cols-EDGE_THRESHOLD;
const int maxBorderY = mvImagePyramid[level].rows-EDGE_THRESHOLD;
const int W = maxBorderX - minBorderX;
const int H = maxBorderY - minBorderY;
const int cellW = ceil((float)W/levelCols);
const int cellH = ceil((float)H/levelRows);
const int nCells = levelRows*levelCols;
const int nfeaturesCell = ceil((float)nDesiredFeatures/nCells);
vector<vector<vector<KeyPoint> > > cellKeyPoints(levelRows, vector<vector<KeyPoint> >(levelCols));
vector<vector<int> > nToRetain(levelRows,vector<int>(levelCols,0));
vector<vector<int> > nTotal(levelRows,vector<int>(levelCols,0));
vector<vector<bool> > bNoMore(levelRows,vector<bool>(levelCols,false));
vector<int> iniXCol(levelCols);
vector<int> iniYRow(levelRows);
int nNoMore = 0;
int nToDistribute = 0;
float hY = cellH + 6;
for(int i=0; i<levelRows; i++)
{
const float iniY = minBorderY + i*cellH - 3;
iniYRow[i] = iniY;
if(i == levelRows-1)
{
hY = maxBorderY+3-iniY;
if(hY<=0)
continue;
}
float hX = cellW + 6;
for(int j=0; j<levelCols; j++)
{
float iniX;
if(i==0)
{
iniX = minBorderX + j*cellW - 3;
iniXCol[j] = iniX;
}
else
{
iniX = iniXCol[j];
}
if(j == levelCols-1)
{
hX = maxBorderX+3-iniX;
if(hX<=0)
continue;
}
Mat cellImage = mvImagePyramid[level].rowRange(iniY,iniY+hY).colRange(iniX,iniX+hX);
cellKeyPoints[i][j].reserve(nfeaturesCell*5);
FAST(cellImage,cellKeyPoints[i][j],iniThFAST,true);
if(cellKeyPoints[i][j].size()<=3)
{
cellKeyPoints[i][j].clear();
FAST(cellImage,cellKeyPoints[i][j],minThFAST,true);
}
const int nKeys = cellKeyPoints[i][j].size();
nTotal[i][j] = nKeys;
if(nKeys>nfeaturesCell)
{
nToRetain[i][j] = nfeaturesCell;
bNoMore[i][j] = false;
}
else
{
nToRetain[i][j] = nKeys;
nToDistribute += nfeaturesCell-nKeys;
bNoMore[i][j] = true;
nNoMore++;
}
}
}
// Retain by score
while(nToDistribute>0 && nNoMore<nCells)
{
int nNewFeaturesCell = nfeaturesCell + ceil((float)nToDistribute/(nCells-nNoMore));
nToDistribute = 0;
for(int i=0; i<levelRows; i++)
{
for(int j=0; j<levelCols; j++)
{
if(!bNoMore[i][j])
{
if(nTotal[i][j]>nNewFeaturesCell)
{
nToRetain[i][j] = nNewFeaturesCell;
bNoMore[i][j] = false;
}
else
{
nToRetain[i][j] = nTotal[i][j];
nToDistribute += nNewFeaturesCell-nTotal[i][j];
bNoMore[i][j] = true;
nNoMore++;
}
}
}
}
}
vector<KeyPoint> & keypoints = allKeypoints[level];
keypoints.reserve(nDesiredFeatures*2);
const int scaledPatchSize = PATCH_SIZE*mvScaleFactor[level];
// Retain by score and transform coordinates
for(int i=0; i<levelRows; i++)
{
for(int j=0; j<levelCols; j++)
{
vector<KeyPoint> &keysCell = cellKeyPoints[i][j];
KeyPointsFilter::retainBest(keysCell,nToRetain[i][j]);
if((int)keysCell.size()>nToRetain[i][j])
keysCell.resize(nToRetain[i][j]);
for(size_t k=0, kend=keysCell.size(); k<kend; k++)
{
keysCell[k].pt.x+=iniXCol[j];
keysCell[k].pt.y+=iniYRow[i];
keysCell[k].octave=level;
keysCell[k].size = scaledPatchSize;
keypoints.push_back(keysCell[k]);
}
}
}
if((int)keypoints.size()>nDesiredFeatures)
{
KeyPointsFilter::retainBest(keypoints,nDesiredFeatures);
keypoints.resize(nDesiredFeatures);
}
}
// and compute orientations
for (int level = 0; level < nlevels; ++level)
computeOrientation(mvImagePyramid[level], allKeypoints[level], umax);
}
static void computeDescriptors(const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors,
const vector<Point>& pattern)
{
descriptors = 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()( InputArray _image, InputArray _mask, vector<KeyPoint>& _keypoints,
OutputArray _descriptors)
{
if(_image.empty())
return;
Mat image = _image.getMat();
assert(image.type() == CV_8UC1 );
// Pre-compute the scale pyramid
ComputePyramid(image);
vector < vector<KeyPoint> > allKeypoints;
ComputeKeyPointsOctTree(allKeypoints);
//ComputeKeyPointsOld(allKeypoints);
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)
{
vector<KeyPoint>& keypoints = allKeypoints[level];
int nkeypointsLevel = (int)keypoints.size();
if(nkeypointsLevel==0)
continue;
// preprocess the resized image
Mat workingMat = mvImagePyramid[level].clone();
GaussianBlur(workingMat, workingMat, Size(7, 7), 2, 2, BORDER_REFLECT_101);
// Compute the descriptors
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 (vector<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];
Size sz(cvRound((float)image.cols*scale), cvRound((float)image.rows*scale));
Size wholeSize(sz.width + EDGE_THRESHOLD*2, sz.height + EDGE_THRESHOLD*2);
Mat temp(wholeSize, image.type()), masktemp;
mvImagePyramid[level] = temp(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, INTER_LINEAR);
copyMakeBorder(mvImagePyramid[level], temp, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD,
BORDER_REFLECT_101+BORDER_ISOLATED);
}
else
{
copyMakeBorder(image, temp, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD,
BORDER_REFLECT_101);
}
}
}
} //namespace ORB_SLAM
最后是博主用来测试的主函数：
#include <iostream>
#include <string>
#include <vector>
#include <chrono>
#include <opencv2/opencv.hpp>
#include "myORBextractor.h"
using namespace std;
//设置特征点提取需要的一些参数
int nFeatures = 1000;//图像金字塔上特征点的数量
int nLevels = 8;//图像金字塔层数
float fScaleFactor = 1.2;//金字塔比例因子
int fIniThFAST = 20;//检测fast角点阈值
int fMinThFAST = 8; //最低阈值
int main(int argc, char** argv) {
cv::Mat image = cv::imread(argv[1],0);
vector<cv::KeyPoint> Keypoints;
cv::Mat mDescriptors;
myORB::ORBextractor ORBextractor = myORB::ORBextractor(nFeatures,fScaleFactor,nLevels,fIniThFAST,fMinThFAST);
cout << endl << "ORB Extractor Parameters: " << endl;
cout << "- Number of Features: " << nFeatures << endl;
cout << "- Scale Levels: " << nLevels << endl;
cout << "- Scale Factor: " << fScaleFactor << endl;
cout << "- Initial Fast Threshold: " << fIniThFAST << endl;
cout << "- Minimum Fast Threshold: " << fMinThFAST << endl;
ORBextractor(image, cv::Mat(), Keypoints, mDescriptors);
cv::Mat outimg;
cv::drawKeypoints(image, Keypoints, outimg);
cv::imshow("ORB features", outimg);
cv::waitKey(0);
return 0;
}下面两幅图是用OpenCV提取的特征点和用ORBSLAM提取的特征点对比：
这是用ORBSLAM提取的特征点：
这是用OpenCV提取的特征点：
