2D特征框架功能說明

目標(biāo)

在本教程中，您將學(xué)習(xí)如何：

• 使用cv :: DescriptorExtractor接口來查找與關(guān)鍵點(diǎn)相對應(yīng)的特征向量。特別：使用cv :: xfeatures2d :: SURF及其函數(shù)cv :: xfeatures2d :: SURF :: compute來執(zhí)行所需的計(jì)算。使用cv :: BFMatcher來匹配特征向量使用函數(shù)cv :: drawMatches來繪制檢測到的匹配項(xiàng)。

Code

本教程代碼如下所示。

#include <stdio.h>
#include <iostream>
#include "opencv2/core.hpp"
#include "opencv2/features2d.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/xfeatures2d.hpp"
using namespace cv;
using namespace cv::xfeatures2d;
void readme();
/* @function main */
int main( int argc, char** argv )
{
  if( argc != 3 )
   { return -1; }
  Mat img_1 = imread( argv[1], IMREAD_GRAYSCALE );
  Mat img_2 = imread( argv[2], IMREAD_GRAYSCALE );
  if( !img_1.data || !img_2.data )
   { return -1; }
  //-- Step 1: Detect the keypoints using SURF Detector, compute the descriptors
  int minHessian = 400;
  Ptr<SURF> detector = SURF::create();
  detector->setHessianThreshold(minHessian);
  std::vector<KeyPoint> keypoints_1, keypoints_2;
  Mat descriptors_1, descriptors_2;
  detector->detectAndCompute( img_1, Mat(), keypoints_1, descriptors_1 );
  detector->detectAndCompute( img_2, Mat(), keypoints_2, descriptors_2 );
  //-- Step 2: Matching descriptor vectors with a brute force matcher
  BFMatcher matcher(NORM_L2);
  std::vector< DMatch > matches;
  matcher.match( descriptors_1, descriptors_2, matches );
  //-- Draw matches
  Mat img_matches;
  drawMatches( img_1, keypoints_1, img_2, keypoints_2, matches, img_matches );
  //-- Show detected matches
  imshow("Matches", img_matches );
  waitKey(0);
  return 0;
  }
 /* @function readme */
 void readme()
 { std::cout << " Usage: ./SURF_descriptor <img1> <img2>" << std::endl; }

結(jié)果

這是在兩個(gè)原始圖像應(yīng)用BruteForce匹配器之后的結(jié)果：