opencv surf讲解

先介绍利用SURF特征的特征描述办法，其操作封装在类SurfFeatureDetector中，利用类内的detect函数可以检测出SURF特征的关键点，保存在vector容器中。第二部利用SurfDescriptorExtractor类进行特征向量的相关计算。将之前的vector变量变成向量矩阵形式保存在Mat中。最后强行匹配两幅图像的特征向量，利用了类BruteForceMatcher中的函数match。代码如下：

/**
 * @file SURF_descriptor
 * @brief SURF detector + descritpor + BruteForce Matcher + drawing matches with OpenCV functions
 * @author A. Huaman
 */
 
#include <stdio.h>
#include <iostream>
#include "opencv2/core/core.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
 
using namespace cv;
 
void readme();
 
/**
 * @function main
 * @brief Main function
 */
int main( int argc, char** argv )
{
  if( argc != 3 )
  { return -1; }
 
  Mat img_1 = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );
  Mat img_2 = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );
  
  if( !img_1.data || !img_2.data )
  { return -1; }
 
  //-- Step 1: Detect the keypoints using SURF Detector
  int minHessian = 400;
 
  SurfFeatureDetector detector( minHessian );
 
  std::vector<KeyPoint> keypoints_1, keypoints_2;
 
  detector.detect( img_1, keypoints_1 );
  detector.detect( img_2, keypoints_2 );
 
  //-- Step 2: Calculate descriptors (feature vectors)
  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 with a brute force matcher
  BruteForceMatcher< L2<float> > matcher;
  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; }