// point-position2.cpp : 定义控制台应用程序的入口点。
#include "stdafx.h"
#include <stdio.h>
#include <iostream>
#include "opencv2/core/core.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include <opencv2/nonfree/features2d.hpp>
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/nonfree/nonfree.hpp"
#include "opencv2/legacy/legacy.hpp"
#include<Eigen/Core>
#include <Eigen/Dense>
#include<math.h>
using namespace cv;int main( int argc, char** argv )
{Mat img_1 = imread("book_in_scene.png");Mat img_2 = imread("book2.png");if( !img_1.data || !img_2.data ){ std::cout<< " --(!) Error reading images " << std::endl; return -1; }//-- Step 1: Detect the keypoints using SURF Detectorint minHessian = 400;SiftFeatureDetector detector( minHessian );//SurfFeatureDetector detector( minHessian );
vector<KeyPoint> keypoints_1, keypoints_2;detector.detect( img_1, keypoints_1 );detector.detect( img_2, keypoints_2 );//-- Step 2: Calculate descriptors (feature vectors)
    SiftDescriptorExtractor extractor;//SurfDescriptorExtractor extractor;
Mat descriptors_1, descriptors_2;extractor.compute( img_1, keypoints_1, descriptors_1 );extractor.compute( img_2, keypoints_2, descriptors_2 );//-- Step 3: Matching descriptor vectors using FLANN matcher
    FlannBasedMatcher matcher;std::vector< DMatch > matches;matcher.match( descriptors_1, descriptors_2, matches );double max_dist = 0; double min_dist = 100;//-- Quick calculation of max and min distances between keypointsfor( int i = 0; i < descriptors_1.rows; i++ ){ double dist = matches[i].distance;if( dist < min_dist ) min_dist = dist;if( dist > max_dist ) max_dist = dist;}//printf("-- Max dist : %f \n", max_dist );//printf("-- Min dist : %f \n", min_dist );//-- Draw only "good" matches (i.e. whose distance is less than 2*min_dist )//-- PS.- radiusMatch can also be used here.std::vector< DMatch > good_matches;for( int i = 0; i < descriptors_1.rows; i++ ){ if( matches[i].distance < 2*min_dist ){ good_matches.push_back( matches[i]); }}//-- Draw only "good" matches
    Mat img_matches;drawMatches( img_1, keypoints_1, img_2, keypoints_2,good_matches, img_matches);//-- Show detected matches//imshow( "Good Matches", img_matches );//imwrite("Lena_match_surf.jpg",img_matches);//imwrite("Lena_match_sift.jpg",img_matches);//good_matches[i].queryIdx保存着第一张图片匹配点的序号，keypoints_1[good_matches[i].queryIdx].pt.x 为该序号对应的点的x坐标。y坐标同理//good_matches[i].trainIdx保存着第二张图片匹配点的序号，keypoints_2[good_matches[i].trainIdx].pt.x 为为该序号对应的点的x坐标。y坐标同理printf( "--Keypoint 1:%f,%f: %d  -- Keypoint 2:%f,%f: %d  \n",  keypoints_1[good_matches[0].queryIdx].pt.x,keypoints_1[good_matches[0].queryIdx].pt.y,good_matches[0].queryIdx, keypoints_2[good_matches[0].trainIdx].pt.x,keypoints_2[good_matches[0].trainIdx].pt.y,good_matches[0].trainIdx );/*_______________________________________________________________________________________________________________________________*/double x_inImage1,y_inImage1,x_inImage2,y_inImage2,y,X,Y,alpha,gamma;//像面坐标(x,y)和图像尺寸（X，Y）以及成像视场角（alpha,gamma）double x1,y1,z1,x2,y2,z2;//双站坐标double alpha1,gamma1;//双站俯仰角和偏转角double alpha2,gamma2;//赋予初始值alpha1=45;alpha1=90;//测试共面gamma1=45;alpha2=270;gamma2=45;X=640;Y=480;double FOVx=10;double FOVy=FOVx*Y/X;x1=0,y1=0,z1=0;x2=0,y2=200,z2=0;/*    //测角偏差补偿x_inImage1=keypoints_1[good_matches[0].queryIdx].pt.x;//目标点坐标由匹配所得y_inImage1=keypoints_1[good_matches[0].queryIdx].pt.y;x_inImage2=keypoints_2[good_matches[0].queryIdx].pt.x;y_inImage2=keypoints_2[good_matches[0].queryIdx].pt.y;double deviation_alpha1=(x_inImage1-X/2)/X*FOVx;double deviation_alpha2=(x_inImage2-X/2)/X*FOVx;double deviation_gamma1=(y_inImage1-Y/2)/X*FOVy;double deviation_gamma2=(y_inImage2-Y/2)/X*FOVy;alpha1=alpha1+deviation_alpha1;alpha2=alpha2+deviation_alpha2;gamma1=gamma1+deviation_gamma1;gamma2=gamma2+deviation_gamma2;
*///开始计算double pi=16*(atan(1.0/5))-4*atan(1.0/239);//精确定义圆周率std::cout<<"pi为:"<<pi<<std::endl;alpha1=alpha1*pi/180;//角度弧度转换gamma1=gamma1*pi/180;alpha2=alpha2*pi/180;gamma2=gamma2*pi/180;//    std::cout<<"cos(alpha1)为:"<<cos(alpha1)<<std::endl;
//    std::cout<<"cos(gamma1)为:"<<cos(gamma1)<<std::endl;double m1=(cos(alpha1))*(cos(gamma1));double n1=(sin(alpha1))*(cos(gamma1));double p1=sin(gamma1);double m2=(cos(alpha2))*(cos(gamma2));double n2=(sin(alpha2))*(cos(gamma2));double p2=sin(gamma2);std::cout<<"方向向量1为:"<<m1<<"，"<<n1<<"，"<<p1<<std::endl;std::cout<<"方向向量2为:"<<m2<<"，"<<n2<<"，"<<p2<<std::endl;double coplane;//共面判断coplane=(x2-x1)*(n1*p2-n2*p1)-(y2-y1)*(m1*p2-m2*p1)+(z2-z1)*(m1*n2-m2*n1);//coplane=0共面if(coplane){//计算公垂线方向向量A1、B1、C1double A1=n1*p2-n2*p1;double B1=p1*m2-p2*m1;double C1=m1*n2-m2*n1;//
        double A2=n2*C1-p2*B1;double B2=p2*A1-m2*C1;double C2=m2*B1-n2*A1;double A3=n1*C1-p1*B1;double B3=p1*A1-m1*C1;double C3=m1*B1-n1*A1;double delta1=n1*(B1*C2-B2*C1)+m1*(A1*C2-A2*C1);double delta2=n2*(B1*C3-B3*C1)+m2*(A1*C3-A3*C1);double D1=A2*(x2-x1)+B2*(y2-y1)+C2*(z2-z1);double D2=A3*(x1-x2)+B3*(y1-y2)+C3*(z1-z2);double Xg,Yg,Zg,Xh,Yh,Zh,Xtarget,Ytarget,Ztarget;//两直线垂足G和H点坐标,目标点在其中点位置。Xg=x1-(D1*m1*C1)/delta1;Yg=y1-(D1*n1*C1)/delta1;Zg=z1+D1*(A1*m1+B1*n1)/delta1;Xh=x2-(D2*m2*C1)/delta2;Yh=y2-(D2*n2*C1)/delta2;Zh=z2+D2*(A1*m2+B1*n2)/delta2;Xtarget=(Xg+Xh)/2;Ytarget=(Yg+Yh)/2;Ztarget=(Zg+Zh)/2;std::cout<<"目标坐标为:"<<Xtarget<<"，"<<Ytarget<<"，"<<Ztarget<<std::endl<<std::endl;}else//两线共面且相交，引入参数t
    {double t;t=(p2*(y1-y2)+n2*(z2-z1))/(n2*p1-p2*n1);double Xtarget,Ytarget,Ztarget;Xtarget=x1+m1*t;Ytarget=y1+n1*t;Ztarget=z1+p1*t;std::cout<<"目标坐标为:"<<Xtarget<<"，"<<Ytarget<<"，"<<Ztarget<<std::endl<<std::endl;}getchar();//waitKey(0);return 0;
}