OpenCV K-d樹實現之FLANN (Fast Library for Approximate Nearest Neighbors) 演算法實現及解析

標籤：opencv   k-d樹   flann   

k-d樹搜尋最近點，在opencv中使用FLANN演算法，其包含：

1：建樹   2.查詢

程式見下：

#include "kdtree.h"
#include <iostream>
#include <iomanip> 
#include "cv.h"
#include "highgui.h"  
#include <fstream>
#include "cv.h"
#include "highgui.h"  
#include <vector>    // <vector > STL標頭檔
#include <stdio.h>
#include "opencv2/objdetect/objdetect.hpp"  
#include "opencv2/features2d/features2d.hpp"  
#include "opencv2/highgui/highgui.hpp"  
#include "opencv2/calib3d/calib3d.hpp"  
#include "opencv2/imgproc/imgproc_c.h"
#include <iostream> 


#define EXAMPLAR_NUM 700
#define EXAMPLAR_DIM 3
/****系統可以搜尋660個3維點雲中的最近點****/
/************儲存CvMat型矩陣****************************
輸入參數：&VecString:n個點雲容器
輸出參數：*Matri:轉換成對應的3*n矩陣
*************************************************************/
void SaveCvmatToTXT(const CvMat*Matri, const char* filename)
{
std::ofstream  file(filename);  //儲存檔案


for (int i = 0; i<Matri->rows; i++)
{
for (int j = 0; j<Matri->cols; j++)
{
file << "    " << cvmGet(Matri, i, j);
}
file << std::endl;
}
file.close();
}
/***********讀取txt文檔儲存CvMat型矩陣****************************
輸入參數：&VecString:n個點雲容器
輸出參數：*Matri:轉換成對應的3*n矩陣
***************************************************************/
void ReadTxtToCvmat(CvMat*Matri, const char* filename)
{
std::ifstream  file(filename);  //儲存檔案
double temp;
for (int i = 0; i<Matri->rows; i++)
{
for (int j = 0; j<Matri->cols; j++)
{
file >> temp;
cvmSet(Matri, i, j,temp);
}
}
file.close();
}

int main(int argc, char *argv[])
{

cv::Mat target(10, 3, CV_32F);  //目標點雲（用之構建k-d樹

ReadTxtToCvmat(&(CvMat)target, "data.txt");
std::cout << "target"<<target<< std::endl;

//1）建立查詢樹 :（此處構建K-d樹）


cv::flann::KDTreeIndexParams indexParams(4);//（此參數用來設定構建的資料結構，此處選擇K-d樹）
cv::flann::Index kdtree(target, indexParams);//此處用target構建k-d樹


//2） 尋找 :
/*cv::Point3f pt ;
std::cout << "Input target point:" << std::endl;
std::cin >> pt.x  >> pt.y >> pt.z;
std::vector<float> query;
query.push_back(pt.x);
query.push_back(pt.y);
    query.push_back(pt.z);*/  //可用來手動輸入單個要搜尋的原始點

cv::Mat source= target;    //原始點
cv::Mat neibours(source.rows, source.cols, CV_32F);//儲存搜尋到的點

int k =1; //number of nearest neighbors 

cv::Mat indices(source.rows, k, CV_32F);   //裝載搜尋到的對應點的索引（即neibours在target這個矩陣的行數）
cv::Mat dists(source.rows,k,CV_32F);         //搜尋到的最近鄰的距離
kdtree.knnSearch(source, indices, dists, k, cv::flann::SearchParams(32));


//std::cout << indices.at<int>(9, 0) << std::endl;
//std::cout << dists.at<float>(9,0) << std::endl;
for (int i = 0; i < neibours.rows; i++)
{
neibours.row(i) = target.row(indices.at<int>(i, 0)) + 0;
}

std::cout << "source=" << source << std::endl;
std::cout << "neibours" << neibours << std::endl;
std::cout << "indices=" << indices << std::endl;
std::cout << "dists=" << dists << std::endl;


system("PAUSE");
return 0;
}

實驗結果：

OpenCV K-d樹實現之FLANN (Fast Library for Approximate Nearest Neighbors) 演算法實現及解析