超级干货！基于多尺度ORB的图像配准对齐（image alignment），及基于多帧（图像多尺度二值化、梯度恒定性的）图像融合去噪 （附讲解ppt、源码）
keywords：
金字塔融合 图像配准 多尺度ORB orb图像配准 去鬼影 图像融合 图像配齐 图像校正 orb特征的匹配 图像融合去鬼影 image fusion 多帧图像去噪 image alignment 图像对齐 图像融合 orb图像对齐
思量许久，把这个版权属于自己的工作开源出来，耗4，5天写的一个基于多尺度ORB（orbslam2里面的orb）图像对齐，及金字塔多帧融合去噪的工作，配套PPT 源码 及实验图像。
适当修改应该够水一篇中文核心了，想发论文的同学请联系我qq:591883385 给我署名就好。
本作者保留此idea和源码的所有权，未经许可发论文有风险。
本代码只针对几幅图像重合的区域的去噪融合，至于没有重合的区域的边边角角的优化处理暂时没有放出来。
参考文献：
1，Ghost Removal in Exposure Fusion by Temporal Consistency Assessment
2，Reference-guided exposure fusion in dynamic scenes
3，Tom Mertens 的 Exposure Fusion
4，https://github.com/raulmur/ORB_SLAM2
依赖环境
opencv342 X64 Release版本 VS2019
opencv3 vs各版本下载地址： 链接: https://pan.baidu.com/s/1f5oAFqs-u15vkD5LNTcxtw 提取码: 2qj9
上面的百度网盘应该没有opencv版本 VS2019_release_X64_opencv342，可以加QQ群去群文件找:539308722
工作内容ppt介绍：
四个文件的源码：
ORBextractor.h：
#ifndef ORBEXTRACTOR_H
#define ORBEXTRACTOR_H
#include
#include
#include
namespace ORB_pyramid
{
class ExtractorNode
{
public:
ExtractorNode() :bNoMore(false) {}
void DivideNode(ExtractorNode& n1, ExtractorNode& n2, ExtractorNode& n3, ExtractorNode& n4);
std::vector vKeys;
cv::Point2i UL, UR, BL, BR;
std::list::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.
void operator()(cv::InputArray image, cv::InputArray mask,
std::vector& keypoints,
cv::OutputArray descriptors);
int inline GetLevels() {
return nlevels;
}
float inline GetScaleFactor() {
return scaleFactor;
}
std::vector inline GetScaleFactors() {
return mvScaleFactor;
}
std::vector inline GetInverseScaleFactors() {
return mvInvScaleFactor;
}
std::vector inline GetScaleSigmaSquares() {
return mvLevelSigma2;
}
std::vector inline GetInverseScaleSigmaSquares() {
return mvInvLevelSigma2;
}
std::vector mvImagePyramid;
protected:
void ComputePyramid(cv::Mat image);
void ComputeKeyPointsOctTree(std::vector >& allKeypoints);
std::vector DistributeOctTree(const std::vector& vToDistributeKeys, const int& minX,
const int& maxX, const int& minY, const int& maxY, const int& nFeatures, const int& level);
void ComputeKeyPointsOld(std::vector >& allKeypoints);
std::vector pattern;
int nfeatures;
double scaleFactor;
int nlevels;
int iniThFAST;
int minThFAST;
std::vector mnFeaturesPerLevel;
std::vector umax;
std::vector mvScaleFactor;
std::vector mvInvScaleFactor;
std::vector mvLevelSigma2;
std::vector mvInvLevelSigma2;
};
} //namespace ORB_pyramid
#endif
ORBextractor.cpp ：
#include
#include
#include
#include
#include
#include "ORBextractor.h"
using namespace cv;
using namespace std;
namespace ORB_pyramid
{
const int PATCH_SIZE = 31;
const int HALF_PATCH_SIZE = 15;
const int EDGE_THRESHOLD = 19;
static float IC_Angle(const Mat& image, Point2f pt, const vector& u_max)
{
int m_01 = 0, m_10 = 0;
const uchar* center = &image.at(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 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)
{
float angle = (float)kpt.angle * factorPI;
float a = (float)cos(angle), b = (float)sin(angle);
const uchar* center = &img.at(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 Point* pattern0 = (const 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 Mat& image, vector& keypoints, const vector& umax)
{
for (vector::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(UR.x - UL.x) / 2);
const int halfY = ceil(static_cast(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 ORBextractor::DistributeOctTree(const vector& 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(maxX - minX) / (maxY - minY));
const float hX = static_cast(maxX - minX) / nIni;
list lNodes;
vector vpIniNodes;
vpIniNodes.resize(nIni);
for (int i = 0; i < nIni; i++)
{
ExtractorNode ni;
ni.UL = cv::Point2i(hX * static_cast(i), 0);
ni.UR = cv::Point2i(hX * static_cast(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::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 > 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 > 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 vResultKeys;
vResultKeys.reserve(nfeatures);
for (list::iterator lit = lNodes.begin(); lit != lNodes.end(); lit++)
{
vector& 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(vector >& 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;
vector 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;
vector 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::iterator vit = vKeysCell.begin(); vit != vKeysCell.end(); vit++)
{
(*vit).pt.x += j * wCell;
(*vit).pt.y += i * hCell;
vToDistributeKeys.push_back(*vit);
}
}
}
}
vector& 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 >& 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 > > cellKeyPoints(levelRows, vector >(levelCols));
vector > nToRetain(levelRows, vector(levelCols, 0));
vector > nTotal(levelRows, vector(levelCols, 0));
vector > bNoMore(levelRows, vector(levelCols, false));
vector iniXCol(levelCols);
vector 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& 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& 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& keypoints, Mat& descriptors,
const vector& 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& _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 > 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& 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::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 LaplacianBlending.h
#ifndef _LAPLACIANBLENDING_H_
#define _LAPLACIANBLENDING_H_
#include
#include
#include
#include
using namespace cv;
using namespace std;
class LaplacianBlending {
private:
Mat_ under;
Mat_ mid;
Mat_ over;
Mat_ over4;
Mat_ over5;
Mat_ over6;//图像
Mat_ under_blendweight_;
Mat_ mid_blendweight_;
Mat_ over_blendweight_;
Mat_ over4_blendweight_;
Mat_ over5_blendweight_;
Mat_ over6_blendweight_;//6个图像对应的权重
vector > underLapPyr, midLapPyr, overLapPyr, over4LapPyr, over5LapPyr, over6LapPyr, resultLapPyr;//Laplacian 金字塔
Mat underHighestLevel, midHighestLevel, overHighestLevel, over4HighestLevel, over5HighestLevel, over6HighestLevel, resultHighestLevel;
vector > under_weight_GaussianPyramid, mid_weight_GaussianPyramid, over_weight_GaussianPyramid, over4_weight_GaussianPyramid, over5_weight_GaussianPyramid, over6_weight_GaussianPyramid; ; //weight_s are 3-channels for easier multiplication with RGB
int levels;
void buildPyramids() {
buildLaplacianPyramid(under, underLapPyr, underHighestLevel);
buildLaplacianPyramid(mid, midLapPyr, midHighestLevel);
buildLaplacianPyramid(over, overLapPyr, overHighestLevel);
buildLaplacianPyramid(over4, over4LapPyr, over4HighestLevel);
buildLaplacianPyramid(over5, over5LapPyr, over5HighestLevel);
buildLaplacianPyramid(over6, over6LapPyr, over5HighestLevel);
build_under_GaussianPyramid();
build_mid_GaussianPyramid();
build_over_GaussianPyramid();
build_over4_GaussianPyramid();
build_over5_GaussianPyramid();
build_over6_GaussianPyramid();
}
void build_under_GaussianPyramid() {//第一张图像的金字塔
assert(underLapPyr.size() > 0);
under_weight_GaussianPyramid.clear();
Mat currentImg;
cvtColor(under_blendweight_, currentImg, CV_GRAY2BGR);//权重金字塔
under_weight_GaussianPyramid.push_back(currentImg);
currentImg = under_blendweight_;
for (int l = 1; l < levels + 1; l++) {
Mat _down;
if (underLapPyr.size() > l)
pyrDown(currentImg, _down, underLapPyr[l].size());
else
pyrDown(currentImg, _down, underHighestLevel.size());
Mat down;
cvtColor(_down, down, CV_GRAY2BGR);
under_weight_GaussianPyramid.push_back(down);//权重
currentImg = _down;
}
}
void build_mid_GaussianPyramid() {
assert(underLapPyr.size() > 0);
mid_weight_GaussianPyramid.clear();
Mat currentImg;
cvtColor(mid_blendweight_, currentImg, CV_GRAY2BGR);
mid_weight_GaussianPyramid.push_back(currentImg);
currentImg = mid_blendweight_;
for (int l = 1; l < levels + 1; l++) {
Mat _down;
if (underLapPyr.size() > l)
pyrDown(currentImg, _down, underLapPyr[l].size());
else
pyrDown(currentImg, _down, underHighestLevel.size());
Mat down;
cvtColor(_down, down, CV_GRAY2BGR);
mid_weight_GaussianPyramid.push_back(down);
currentImg = _down;
}
}
void build_over_GaussianPyramid() {
assert(underLapPyr.size() > 0);
over_weight_GaussianPyramid.clear();
Mat currentImg;
cvtColor(over_blendweight_, currentImg, CV_GRAY2BGR);
over_weight_GaussianPyramid.push_back(currentImg);
currentImg = over_blendweight_;
for (int l = 1; l < levels + 1; l++) {
Mat _down;
if (underLapPyr.size() > l)
pyrDown(currentImg, _down, underLapPyr[l].size());
else
pyrDown(currentImg, _down, underHighestLevel.size());
Mat down;
cvtColor(_down, down, CV_GRAY2BGR);
over_weight_GaussianPyramid.push_back(down);//权重扩展至3通道在三通道加权
currentImg = _down;
}
}
//
void build_over4_GaussianPyramid() {
assert(underLapPyr.size() > 0);
over4_weight_GaussianPyramid.clear();
Mat currentImg;
cvtColor(over4_blendweight_, currentImg, CV_GRAY2BGR);
over4_weight_GaussianPyramid.push_back(currentImg);
currentImg = over4_blendweight_;
for (int l = 1; l < levels + 1; l++) {
Mat _down;
if (underLapPyr.size() > l)
pyrDown(currentImg, _down, underLapPyr[l].size());
else
pyrDown(currentImg, _down, underHighestLevel.size());
Mat down;
cvtColor(_down, down, CV_GRAY2BGR);
over4_weight_GaussianPyramid.push_back(down);
currentImg = _down;
}
}
//
void build_over5_GaussianPyramid() {
assert(underLapPyr.size() > 0);
over5_weight_GaussianPyramid.clear();
Mat currentImg;
cvtColor(over5_blendweight_, currentImg, CV_GRAY2BGR);
over5_weight_GaussianPyramid.push_back(currentImg);
currentImg = over5_blendweight_;
for (int l = 1; l < levels + 1; l++) {
Mat _down;
if (underLapPyr.size() > l)
pyrDown(currentImg, _down, underLapPyr[l].size());
else
pyrDown(currentImg, _down, underHighestLevel.size());
Mat down;
cvtColor(_down, down, CV_GRAY2BGR);
over5_weight_GaussianPyramid.push_back(down);
currentImg = _down;
}
}
void build_over6_GaussianPyramid() {
assert(underLapPyr.size() > 0);
over6_weight_GaussianPyramid.clear();
Mat currentImg;
cvtColor(over6_blendweight_, currentImg, CV_GRAY2BGR);
over6_weight_GaussianPyramid.push_back(currentImg);
currentImg = over6_blendweight_;
for (int l = 1; l < levels + 1; l++) {
Mat _down;
if (underLapPyr.size() > l)
pyrDown(currentImg, _down, underLapPyr[l].size());
else
pyrDown(currentImg, _down, underHighestLevel.size());
Mat down;
cvtColor(_down, down, CV_GRAY2BGR);
over6_weight_GaussianPyramid.push_back(down);
currentImg = _down;
}
}
void buildLaplacianPyramid(const Mat& img, vector >& lapPyr, Mat& HighestLevel) {
lapPyr.clear();
Mat currentImg = img;
for (int l = 0; l < levels; l++) {
Mat down, up;
pyrDown(currentImg, down);
pyrUp(down, up, currentImg.size());
Mat lap = currentImg - up;
lapPyr.push_back(lap);
currentImg = down;
}
currentImg.copyTo(HighestLevel);
}
Mat_ reconstructImgFromLapPyramid() {
//用金字塔重建上采样
Mat currentImg = resultHighestLevel;
for (int l = levels - 1; l >= 0; l--) {
Mat up;
pyrUp(currentImg, up, resultLapPyr[l].size());
currentImg = up + resultLapPyr[l];
}
return currentImg;
}
void blendLapPyrs() {
Mat w1 = under_weight_GaussianPyramid.back();
Mat w2 = mid_weight_GaussianPyramid.back();
Mat w3 = over_weight_GaussianPyramid.back();
Mat w4 = over4_weight_GaussianPyramid.back();
Mat w5 = over5_weight_GaussianPyramid.back();
Mat w6 = over5_weight_GaussianPyramid.back();
resultHighestLevel = (underHighestLevel.mul(w1) + midHighestLevel.mul(w2) + overHighestLevel.mul(w3) + over4HighestLevel.mul(w4) + over5HighestLevel.mul(w5) + over5HighestLevel.mul(w6)) / (w1 + w2 + w3 + w4 + w5 + w6 + 1e-10);
for (int l = 0; l < levels; l++) {
Mat A = underLapPyr[l].mul(under_weight_GaussianPyramid[l]);
Mat B = midLapPyr[l].mul(mid_weight_GaussianPyramid[l]);
Mat C = overLapPyr[l].mul(over_weight_GaussianPyramid[l]);
Mat D = over4LapPyr[l].mul(over4_weight_GaussianPyramid[l]);
Mat E = over5LapPyr[l].mul(over5_weight_GaussianPyramid[l]);
Mat F = over6LapPyr[l].mul(over6_weight_GaussianPyramid[l]);
Mat W = under_weight_GaussianPyramid[l] + mid_weight_GaussianPyramid[l] + over_weight_GaussianPyramid[l] + over4_weight_GaussianPyramid[l] + over5_weight_GaussianPyramid[l] + over6_weight_GaussianPyramid[l] + 1e-10;
Mat_ blendedLevel = (A + B + C + D + E + F) / W;
resultLapPyr.push_back(blendedLevel);
}
}
public:
LaplacianBlending(const Mat_& _under, const Mat_& _mid, const Mat_& _over, const Mat_& _over4, const Mat_& _over5, const Mat_& _over6, const Mat_& _under_blendweight_, const Mat_& _mid_blendweight_, const Mat_& _over_blendweight_, const Mat_& _over4_blendweight_, const Mat_& _over5_blendweight_, const Mat_& _over6_blendweight_, int _levels) :
under(_under), mid(_mid), over(_over), over4(_over4), over5(_over5), over6(_over6), under_blendweight_(_under_blendweight_), mid_blendweight_(_mid_blendweight_), over_blendweight_(_over_blendweight_), over4_blendweight_(_over4_blendweight_), over5_blendweight_(_over5_blendweight_), over6_blendweight_(_over6_blendweight_), levels(_levels)
{
assert(_under.size() == _over.size());
assert(_under.size() == _under_blendweight_.size());
buildPyramids();
blendLapPyrs();
};
Mat_ blend() {
return reconstructImgFromLapPyramid();
}
};
#endif Main.cpp:
#include
#include
#include
#include
#include
#include
#include "ORBextractor.h"
#include"LaplacianBlending.h"
#include
using namespace std;
using namespace cv;
//宏
#define GhostFree 1
#define PYRAMIDBLENDING 1
#define NFEATURES 1000
#define FSCALEFACTOR 1.2
#define NLEVELS 9
#define FINITHFAST 25
#define FMINTHFAST 15
//#define COLS 4208
//#define ROWS 3120
#define COLS_MINI 842
#define COLS_MINI 624
#define COLS 842
#define ROWS 624
mutex mu_;
/********************************************************
* @brief : 定义的函数
********************************************************/
Mat_ LaplacianBlend(const Mat_&, const Mat_& , const Mat_& ,
const Mat_& , const Mat_& , const Mat_& ,
const Mat_& , const Mat_& , const Mat_& ,
const Mat_& , const Mat_& , const Mat_& );
void ImageReader(Mat*, Mat*, const int&);
void ImageRegistration(Mat*, Mat*, Mat*, Mat*,vector*,Mat* );
void GradAngleCalculation(Mat*, Mat*, Mat*, Mat*);
void Fake_BaseLine_GradAngle_Generator(Mat* , const int& ,
Mat* , Mat* , Mat* , Mat* , Mat* , Mat* );
void Weight_Generator(Mat*, Mat*, Mat*);
void Weight_Normalize(const int&,Mat*, Mat*, Mat*, Mat*, Mat*, Mat*);
void Weight_Fusion(const int&, Mat*, Mat*, Mat*, Mat*, Mat*, Mat*, Mat*, Mat*, Mat*, Mat*, Mat*, Mat*, Mat*);
void MBBP_Generator(Mat*, Mat* , Mat*);
void Weight_Generator_GHOST(Mat* ,Mat* , Mat* , Mat* );
int main()
{
/********************************************************
* @brief : 图像读取 没必要用Vector增加操作
********************************************************/
vector CodeThread;
Mat image1_BGR;
Mat image1_G;
Mat image2_BGR;
Mat image2_G;
Mat image3_BGR;
Mat image3_G;
Mat image5_BGR;
Mat image5_G;
Mat image6_BGR;
Mat image6_G;
Mat BaseLine;
Mat BaseLineG;
CodeThread.push_back(thread(ImageReader, &image1_BGR, &image1_G, 1));
CodeThread.push_back(thread(ImageReader, &image2_BGR, &image2_G, 2));
CodeThread.push_back(thread(ImageReader, &image3_BGR, &image3_G, 3));
CodeThread.push_back(thread(ImageReader, &image5_BGR, &image5_G, 5));
CodeThread.push_back(thread(ImageReader, &image6_BGR, &image6_G, 6));
ImageReader(&BaseLine, &BaseLineG, 4);
/********************************************************
* @brief : 对baseline图像进行特征点检测和描述
********************************************************/
ORB_pyramid::ORBextractor* Pyramid_ORBextractor_BaseLine;
Pyramid_ORBextractor_BaseLine = new ORB_pyramid::ORBextractor(NFEATURES, FSCALEFACTOR, NLEVELS, FINITHFAST, FMINTHFAST);
vector key_points_BaseLine;
Mat descriptors_BaseLine;
(*Pyramid_ORBextractor_BaseLine)(BaseLineG, Mat(), key_points_BaseLine, descriptors_BaseLine);
Mat Registration_1;
Mat Registration_2;
Mat Registration_3;
Mat Registration_5;
Mat Registration_6;
Mat RegistrationGray_1(ROWS, COLS, CV_32FC1, Scalar(0));
Mat RegistrationGray_2(ROWS, COLS, CV_32FC1, Scalar(0));
Mat RegistrationGray_3(ROWS, COLS, CV_32FC1, Scalar(0));
Mat RegistrationGray_4(ROWS, COLS, CV_32FC1, Scalar(0));
Mat RegistrationGray_5(ROWS, COLS, CV_32FC1, Scalar(0));
Mat RegistrationGray_6(ROWS, COLS, CV_32FC1, Scalar(0));
for (auto& ite : CodeThread)
{
ite.join();
}
CodeThread.clear();
// DWORD k = ::GetTickCount();
/********************************************************
* @brief :对待配准图像进行对齐
********************************************************/
CodeThread.push_back(thread(ImageRegistration, &image2_BGR, &image2_G, &Registration_2, &RegistrationGray_2, &key_points_BaseLine, &descriptors_BaseLine));
CodeThread.push_back(thread(ImageRegistration, &image3_BGR, &image3_G, &Registration_3, &RegistrationGray_3, &key_points_BaseLine, &descriptors_BaseLine));
CodeThread.push_back(thread(ImageRegistration, &image5_BGR, &image5_G, &Registration_5, &RegistrationGray_5, &key_points_BaseLine, &descriptors_BaseLine));
CodeThread.push_back(thread(ImageRegistration, &image6_BGR, &image6_G, &Registration_6, &RegistrationGray_6, &key_points_BaseLine, &descriptors_BaseLine));
ImageRegistration(&image1_BGR, &image1_G, &Registration_1, &RegistrationGray_1, &key_points_BaseLine, &descriptors_BaseLine);
Mat BaseLineG_32F(ROWS, COLS, CV_32FC1, Scalar(0));;
for (int i = 0; i < ROWS; ++i)
{
for (int j = 0; j < COLS; ++j)
{
BaseLineG_32F.at(i, j) = (float)(BaseLineG.at(i, j)) / 255.0;
}
}
// cout << ::GetTickCount() - k << endl;
float xfilter_mat[] = {
0.0116601 , 0.0861571 , 0.0116601,
0.0 , 0.0 , 0.0,
-0.0116601 , -0.0861571 , -0.0116601
};
Mat xfilter = Mat(3, 3, CV_32FC1, xfilter_mat);
float yfilter_mat[] = {
0.0116601 , 0.0 , -0.0116601,
0.0861571 , 0.0 , -0.0861571,
0.0116601 , 0.0 , -0.0116601
};
Mat yfilter = Mat(3, 3, CV_32FC1, yfilter_mat);
Mat GradAngle1(ROWS, COLS, CV_32FC1, Scalar(0));
Mat GradAngle2(ROWS, COLS, CV_32FC1, Scalar(0));
Mat GradAngle3(ROWS, COLS, CV_32FC1, Scalar(0));
Mat GradAngle5(ROWS, COLS, CV_32FC1, Scalar(0));
Mat GradAngle6(ROWS, COLS, CV_32FC1, Scalar(0));
Mat GradAngleBaseLine(ROWS, COLS, CV_32FC1, Scalar(0));
for (auto& ite : CodeThread)
{
ite.join();
}
CodeThread.clear();
//imwrite("./image/test/Registration_2.bmp", Registration_2);
//imwrite("./image/test/Registration_1.bmp", Registration_1);
//imwrite("./image/test/Registration_3.bmp", Registration_3);
//imwrite("./image/test/Registration_5.bmp", Registration_5);
//imwrite("./image/test/Registration_6.bmp", Registration_6);
/********************************************************
* @brief :对每个图像求梯度角
********************************************************/
//void GradAngleCalculation(Mat * xfilter, Mat * yfilter, Mat * Registration_images_Gray_Cur, Mat * grad_angle)
CodeThread.push_back(thread(GradAngleCalculation, &xfilter, &yfilter, &RegistrationGray_1, &GradAngle1));
CodeThread.push_back(thread(GradAngleCalculation, &xfilter, &yfilter, &RegistrationGray_2, &GradAngle2));
CodeThread.push_back(thread(GradAngleCalculation, &xfilter, &yfilter, &RegistrationGray_3, &GradAngle3));
CodeThread.push_back(thread(GradAngleCalculation, &xfilter, &yfilter, &RegistrationGray_5, &GradAngle5));
CodeThread.push_back(thread(GradAngleCalculation, &xfilter, &yfilter, &RegistrationGray_6, &GradAngle6));
GradAngleCalculation(&xfilter, &yfilter, &BaseLineG_32F,&GradAngleBaseLine);
Mat Fake_BaseLine_GradAngle(ROWS, COLS, CV_32FC1, Scalar(0));//参考梯度方向
Mat Weight1(ROWS, COLS, CV_32FC1, Scalar(0));
Mat Weight2(ROWS, COLS, CV_32FC1, Scalar(0));
Mat Weight3(ROWS, COLS, CV_32FC1, Scalar(0));
Mat Weight5(ROWS, COLS, CV_32FC1, Scalar(0));
Mat Weight6(ROWS, COLS, CV_32FC1, Scalar(0));
Mat WeightBaseLine(ROWS, COLS, CV_32FC1, Scalar(0));
for (auto& ite : CodeThread)
{
ite.join();
}
CodeThread.clear();
if (GhostFree == 0) {
/********************************************************
* @brief :分两块处理，一般图像都是偶数分辨率不考虑为奇数的极端
电脑开6，7个线程还是比较合适的，分6块处理应更好，这里更多偏向于分块方法的实现
求虚拟的梯度角Mat
********************************************************/
CodeThread.push_back(thread(Fake_BaseLine_GradAngle_Generator, &Fake_BaseLine_GradAngle, 0,
&GradAngle1, &GradAngle2, &GradAngle3, &GradAngleBaseLine,&GradAngle5, &GradAngle6));
Fake_BaseLine_GradAngle_Generator(&Fake_BaseLine_GradAngle, 1,
&GradAngle1, &GradAngle2, &GradAngle3, &GradAngleBaseLine, &GradAngle5, &GradAngle6);
for (auto& ite : CodeThread)
{
ite.join();
}
CodeThread.clear();
/********************************************************
* @brief :根据梯度方向角都求各个图像的权值
********************************************************/
CodeThread.push_back(thread(Weight_Generator, &Fake_BaseLine_GradAngle,&GradAngle1,&Weight1));
CodeThread.push_back(thread(Weight_Generator, &Fake_BaseLine_GradAngle, &GradAngle2, &Weight2));
CodeThread.push_back(thread(Weight_Generator, &Fake_BaseLine_GradAngle, &GradAngle3, &Weight3));
CodeThread.push_back(thread(Weight_Generator, &Fake_BaseLine_GradAngle, &GradAngle5, &Weight5));
CodeThread.push_back(thread(Weight_Generator, &Fake_BaseLine_GradAngle, &GradAngle6, &Weight6));
Weight_Generator(&Fake_BaseLine_GradAngle, &GradAngleBaseLine, &WeightBaseLine);
for (auto& ite : CodeThread)
{
ite.join();
}
CodeThread.clear();
/********************************************************
* @brief :权值归一化
********************************************************/
CodeThread.push_back(thread(Weight_Normalize, 0, &Weight1, &Weight2, &Weight3, &Weight5, &Weight6, &WeightBaseLine));
Weight_Normalize(1, &Weight1, &Weight2, &Weight3, &Weight5, &Weight6, &WeightBaseLine);
for (auto& ite : CodeThread)
{
ite.join();
}
CodeThread.clear();
}
if (GhostFree == 1) {
//分两块处理，一般图像都是偶数分辨率不考虑为奇数的极端
Mat Binary_TH1(ROWS, COLS, CV_32FC1, Scalar(0));
Mat Binary_TH2(ROWS, COLS, CV_32FC1, Scalar(0));
Mat Binary_TH3(ROWS, COLS, CV_32FC1, Scalar(0));
Mat Binary_THBaseLine(ROWS, COLS, CV_32FC1, Scalar(1));
Mat Binary_TH5(ROWS, COLS, CV_32FC1, Scalar(0));
Mat Binary_TH6(ROWS, COLS, CV_32FC1, Scalar(0));
/********************************************************
* @brief :多尺度二值图求解
********************************************************/
CodeThread.push_back(thread(MBBP_Generator, &RegistrationGray_1, &BaseLineG_32F, &Binary_TH1));
CodeThread.push_back(thread(MBBP_Generator, &RegistrationGray_2, &BaseLineG_32F, &Binary_TH2));
CodeThread.push_back(thread(MBBP_Generator, &RegistrationGray_3, &BaseLineG_32F, &Binary_TH3));
CodeThread.push_back(thread(MBBP_Generator, &RegistrationGray_5, &BaseLineG_32F, &Binary_TH5));
MBBP_Generator(&RegistrationGray_5, &BaseLineG_32F, &Binary_TH6);
for (auto& ite : CodeThread)
{
ite.join();
}
CodeThread.clear();
/********************************************************
* @brief :权重求解
********************************************************/
CodeThread.push_back(thread(Weight_Generator_GHOST, &GradAngleBaseLine, &GradAngle1, &Weight1, &Binary_TH1));
CodeThread.push_back(thread(Weight_Generator_GHOST, &GradAngleBaseLine, &GradAngle2, &Weight2, &Binary_TH2));
CodeThread.push_back(thread(Weight_Generator_GHOST, &GradAngleBaseLine, &GradAngle3, &Weight3, &Binary_TH3));
CodeThread.push_back(thread(Weight_Generator_GHOST, &GradAngleBaseLine, &GradAngle5, &Weight5, &Binary_TH5));
WeightBaseLine = 1- WeightBaseLine;
Weight_Generator_GHOST(&GradAngleBaseLine, &GradAngle6, &Weight6, &Binary_TH6);
for (auto& ite : CodeThread)
{
ite.join();
}
CodeThread.clear();
/********************************************************
* @brief :权重归一化
********************************************************/
CodeThread.push_back(thread(Weight_Normalize, 0, &Weight1, &Weight2, &Weight3, &Weight5, &Weight6, &WeightBaseLine));
Weight_Normalize(1, &Weight1, &Weight2, &Weight3, &Weight5, &Weight6, &WeightBaseLine);
for (auto& ite : CodeThread)
{
ite.join();
}
CodeThread.clear();
}
if (PYRAMIDBLENDING == 0)
{
/********************************************************
* @brief :直接加权融合
********************************************************/
Mat newMat(ROWS, COLS, CV_32FC3, Scalar(0));
CodeThread.push_back(thread(Weight_Fusion, 0, &newMat,&Weight1, &Weight2, &Weight3, &Weight5, &Weight6, &WeightBaseLine,
&Registration_1, &Registration_2, &Registration_3, &Registration_5, &Registration_6, &BaseLine));
Weight_Fusion(1, &newMat, &Weight1, &Weight2, &Weight3, &Weight5, &Weight6, &WeightBaseLine,
& Registration_1, &Registration_2, &Registration_3, &Registration_5, &Registration_6, &BaseLine);
for (auto& ite : CodeThread)
{
ite.join();
}
imshow("./image/test/r.bmp", newMat/255);
waitKey();
imwrite("./image/test/r.bmp", newMat);
}
else if (PYRAMIDBLENDING == 1)
{
/********************************************************
* @brief :高斯金字塔融合
********************************************************/
Mat_ newMat = LaplacianBlend(Registration_1, Registration_2, Registration_3, Registration_5, Registration_6, BaseLine, Weight1, Weight2, Weight3, Weight5, Weight6, WeightBaseLine);
imshow("./image/test/r_pyr.bmp", newMat/255);
waitKey();
imwrite("./image/test/r_pyr.bmp", newMat);
}
return 0;
}
Mat_ LaplacianBlend(const Mat_& under, const Mat_& mid, const Mat_& over, const Mat_& over4, const Mat_& over5, const Mat_& over6, const Mat_& W_under, const Mat_& W_mid, const Mat_& W_over, const Mat_& W_over4, const Mat_& W_over5, const Mat_& W_over6)
{
LaplacianBlending lb(under, mid, over, over4, over5, over6, W_under, W_mid, W_over, W_over4, W_over5, W_over6, 7);
return lb.blend();
}
void ImageReader(Mat* Image_BGR, Mat* Image_G, const int& num)
{
int NUM_INT;
NUM_INT = num;
string image_path = "./image/Img3s/" + to_string(NUM_INT) + ".bmp";
*Image_BGR = imread(image_path, 1);
*Image_G = imread(image_path, 0);
}
void ImageRegistration(Mat* image_BGR, Mat* IMG_proG, Mat* Registration, Mat* RegistrationGray, vector* key_points_BaseLine, Mat* descriptors_1)
{
//金字塔ORB检测和特征点匹配
ORB_pyramid::ORBextractor* Pyramid_ORBextractor_;
Pyramid_ORBextractor_ = new ORB_pyramid::ORBextractor(NFEATURES, FSCALEFACTOR, NLEVELS, FINITHFAST, FMINTHFAST);
vector key_points_2;
Mat descriptors_2;
(*Pyramid_ORBextractor_)((*IMG_proG), Mat(), key_points_2, descriptors_2);
Ptr matcher = DescriptorMatcher::create("BruteForce-Hamming");
vector mach;
matcher->match(*descriptors_1, descriptors_2, mach);
//删除错误匹配的特征点
vector InlierMatches;
vector p1, p2;
int MACHSIZE = mach.size();
for (int i = 0; i < MACHSIZE; i++)
{
p1.push_back((*key_points_BaseLine)[mach[i].queryIdx].pt);
p2.push_back(key_points_2[mach[i].trainIdx].pt);
}
vector RANSACStatus;
findFundamentalMat(p1, p2, RANSACStatus, CV_FM_RANSAC);
for (int i = 0; i < MACHSIZE; i++)
{
if (RANSACStatus[i] != 0)
{
InlierMatches.push_back(mach[i]);
}
}
vector IMG_pro_point, Ref_point;
for (int i = 0; i < InlierMatches.size(); i++)
{
IMG_pro_point.push_back((*key_points_BaseLine)[InlierMatches[i].queryIdx].pt);
Ref_point.push_back(key_points_2[InlierMatches[i].trainIdx].pt);
}
//图像配准
Mat Homography = cv::findHomography(Ref_point, IMG_pro_point, CV_RANSAC); //计算单映性矩阵
warpPerspective(*image_BGR, *Registration, Homography, cv::Size(COLS, ROWS));//透视
for (int i = 0; i < ROWS; ++i)
{
for (int j = 0; j < COLS; ++j)
{
RegistrationGray->at(i, j) = (float)(Registration->at(i, j)[0] * 0.2989 + Registration->at(i, j)[1] * 0.5870 + Registration->at(i, j)[2] * 0.1140) / 255.0;
}
}
}
void GradAngleCalculation(Mat* xfilter, Mat* yfilter, Mat* Registration_images_Gray_Cur,Mat* grad_angle)
{
Mat xgrad;//x梯度
filter2D((*Registration_images_Gray_Cur), xgrad, -1, *xfilter);
xgrad = xgrad + 1e-10;//防止分母为0
Mat ygrad;//y梯度
filter2D(*Registration_images_Gray_Cur, ygrad, -1, *yfilter);
//Mat grad_angle(ROWS, COLS, CV_32FC1, Scalar(0));
for (int i = 0; i < ROWS; ++i)
{
for (int j = 0; j < COLS; ++j)
{
grad_angle->at(i, j) = (float)atan2(ygrad.at(i, j), xgrad.at(i, j));
}
}
}
void Fake_BaseLine_GradAngle_Generator(Mat* Fake_BaseLine_GradAngle_Half, const int& type,
Mat* GradAngle_1, Mat* GradAngle_2, Mat* GradAngle_3, Mat* GradAngle_4, Mat* GradAngle_5, Mat* GradAngle_6)
{
if (type == 0)
{
for (int i = 0; i < ROWS / 2; ++i)
{
for (int j = 0; j < COLS; ++j)
{
float Mid_value_[6];
Mid_value_[0] = GradAngle_1->at(i, j);
Mid_value_[1] = GradAngle_2->at(i, j);
Mid_value_[2] = GradAngle_3->at(i, j);
Mid_value_[3] = GradAngle_4->at(i, j);
Mid_value_[4] = GradAngle_5->at(i, j);
Mid_value_[5] = GradAngle_6->at(i, j);
sort(Mid_value_, Mid_value_ + 6);
//mu.lock();
Fake_BaseLine_GradAngle_Half->at(i, j) = Mid_value_[2];
//mu.unlock();
}
}
}
else
{
for (int i = ROWS / 2; i < ROWS; ++i)
{
for (int j = 0; j < COLS; ++j)
{
float Mid_value_[6];
Mid_value_[0] = GradAngle_1->at(i, j);
Mid_value_[1] = GradAngle_2->at(i, j);
Mid_value_[2] = GradAngle_3->at(i, j);
Mid_value_[3] = GradAngle_4->at(i, j);
Mid_value_[4] = GradAngle_5->at(i, j);
Mid_value_[5] = GradAngle_6->at(i, j);
sort(Mid_value_, Mid_value_ + 6);
//mu.lock();
Fake_BaseLine_GradAngle_Half->at(i, j) = Mid_value_[2];
//mu.unlock();
}
}
}
}
void Weight_Generator(Mat* Fake_BaseLine_GradAngle,
Mat* GradAngle,Mat* Weight_element) {
for (int i = 0; i < ROWS; ++i)
{
for (int j = 0; j < COLS; ++j)
{
float difference;
if (abs(GradAngle->at(i, j) - Fake_BaseLine_GradAngle->at(i, j)) > CV_PI)
difference = 2 * CV_PI - abs(GradAngle->at(i, j) - Fake_BaseLine_GradAngle->at(i, j));
else
{
difference = abs(GradAngle->at(i, j) - Fake_BaseLine_GradAngle->at(i, j));
}
Weight_element->at(i, j) = exp((-difference * difference) / (2.0));
}
}
}
void Weight_Normalize(const int& type,Mat* GradAngle_1, Mat* GradAngle_2, Mat* GradAngle_3, Mat* GradAngle_4, Mat* GradAngle_5, Mat* GradAngle_6) {
if (type == 0)
{
for (int i = 0; i < ROWS / 2; ++i)
{
for (int j = 0; j < COLS; ++j)
{
float weight1 = GradAngle_1->at(i, j);
float weight2 = GradAngle_2->at(i, j);
float weight3 = GradAngle_3->at(i, j);
float weight4 = GradAngle_4->at(i, j);
float weight5 = GradAngle_5->at(i, j);
float weight6 = GradAngle_6->at(i, j);
float sum1 = weight1 + weight2 + weight3 + weight4 + weight5 + weight6;
GradAngle_1->at(i, j) = weight1 / sum1;
GradAngle_2->at(i, j) = weight2 / sum1;
GradAngle_3->at(i, j) = weight3 / sum1;
GradAngle_4->at(i, j) = weight4 / sum1;
GradAngle_5->at(i, j) = weight5 / sum1;
GradAngle_6->at(i, j) = weight6 / sum1;
}
}
}
else
{
for (int i = ROWS / 2; i < ROWS; ++i)
{
for (int j = 0; j < COLS; ++j)
{
float weight1 = GradAngle_1->at(i, j);
float weight2 = GradAngle_2->at(i, j);
float weight3 = GradAngle_3->at(i, j);
float weight4 = GradAngle_4->at(i, j);
float weight5 = GradAngle_5->at(i, j);
float weight6 = GradAngle_6->at(i, j);
float sum1 = weight1 + weight2 + weight3 + weight4 + weight5 + weight6;
GradAngle_1->at(i, j) = weight1 / sum1;
GradAngle_2->at(i, j) = weight2 / sum1;
GradAngle_3->at(i, j) = weight3 / sum1;
GradAngle_4->at(i, j) = weight4 / sum1;
GradAngle_5->at(i, j) = weight5 / sum1;
GradAngle_6->at(i, j) = weight6 / sum1;
}
}
}
}
void Weight_Fusion(const int& type, Mat* new1, Mat* W1, Mat* W2, Mat* W3, Mat* W4, Mat* W5, Mat* W6, Mat* A, Mat* B, Mat* C, Mat* D, Mat* E, Mat* F) {
if (type == 0)
{
for (int i = 0; i < ROWS / 2; ++i)
{
for (int j = 0; j < COLS; ++j)
{
new1->at(i, j)[0] = W1->at(i, j) * A->at(i, j)[0]
+ W2->at(i, j) * B->at(i, j)[0]
+ W3->at(i, j) * C->at(i, j)[0]
+ W4->at(i, j) * D->at(i, j)[0]
+ W5->at(i, j) * E->at(i, j)[0]
+ W6->at(i, j) * E->at(i, j)[0];
new1->at(i, j)[1] = W1->at(i, j) * A->at(i, j)[1]
+ W2->at(i, j) * B->at(i, j)[1]
+ W3->at(i, j) * C->at(i, j)[1]
+ W4->at(i, j) * D->at(i, j)[1]
+ W5->at(i, j) * E->at(i, j)[1]
+ W6->at(i, j) * E->at(i, j)[1];
new1->at(i, j)[2] = W1->at(i, j) * A->at(i, j)[2]
+ W2->at(i, j) * B->at(i, j)[2]
+ W3->at(i, j) * C->at(i, j)[2]
+ W4->at(i, j) * D->at(i, j)[2]
+ W5->at(i, j) * E->at(i, j)[2]
+ W6->at(i, j) * E->at(i, j)[2];
}
}
}
else
{
for (int i = ROWS / 2; i < ROWS; ++i)
{
for (int j = 0; j < COLS; ++j)
{
new1->at(i, j)[0] = W1->at(i, j) * A->at(i, j)[0]
+ W2->at(i, j) * B->at(i, j)[0]
+ W3->at(i, j) * C->at(i, j)[0]
+ W4->at(i, j) * D->at(i, j)[0]
+ W5->at(i, j) * E->at(i, j)[0]
+ W6->at(i, j) * E->at(i, j)[0];
new1->at(i, j)[1] = W1->at(i, j) * A->at(i, j)[1]
+ W2->at(i, j) * B->at(i, j)[1]
+ W3->at(i, j) * C->at(i, j)[1]
+ W4->at(i, j) * D->at(i, j)[1]
+ W5->at(i, j) * E->at(i, j)[1]
+ W6->at(i, j) * E->at(i, j)[1];
new1->at(i, j)[2] = W1->at(i, j) * A->at(i, j)[2]
+ W2->at(i, j) * B->at(i, j)[2]
+ W3->at(i, j) * C->at(i, j)[2]
+ W4->at(i, j) * D->at(i, j)[2]
+ W5->at(i, j) * E->at(i, j)[2]
+ W6->at(i, j) * E->at(i, j)[2];
}
}
}
}
void MBBP_Generator(Mat* Registration_images_Gray, Mat* BaseLineGray ,Mat* differ) {
Mat kernel = getStructuringElement(MORPH_CROSS, Size(3, 3), Point(-1, -1));
for (float TH = 0.05; TH < 1; TH = TH + 0.1)
{
Mat BMap;
Mat BaseLineBmap;
cv::threshold((*Registration_images_Gray), BMap, TH, 1.0, THRESH_BINARY);
cv::threshold((*BaseLineGray), BaseLineBmap, TH, 1.0, THRESH_BINARY);
*differ = BMap - BaseLineBmap + (*differ);
}
cv::threshold(*differ, *differ, 0.5, 1.0, THRESH_BINARY);
cv::erode(*differ, *differ, kernel);
Mat kernel1 = getStructuringElement(MORPH_CROSS, Size(5, 5), Point(-1, -1));
cv::dilate(*differ, *differ, kernel1);
*differ = 1 - *differ;
}
void Weight_Generator_GHOST(Mat* Fake_BaseLine_GradAngle,
Mat* GradAngle, Mat* Weight_element, Mat* BTMAP) {
for (int i = 0; i < ROWS; ++i)
{
for (int j = 0; j < COLS; ++j)
{
float difference;
if (abs(GradAngle->at(i, j) - Fake_BaseLine_GradAngle->at(i, j)) > CV_PI)
difference = 2 * CV_PI - abs(GradAngle->at(i, j) - Fake_BaseLine_GradAngle->at(i, j));
else
{
difference = abs(GradAngle->at(i, j) - Fake_BaseLine_GradAngle->at(i, j));
}
Weight_element->at(i, j) = exp((-difference * difference) / (2.0))* BTMAP->at(i, j);
}
}
}
结果图：
输入 图像序列1：
结果图：
输入图像序列2：
结果图：