学习OpenCV——Surf简化版

之前写过一遍关于学习surf算法的blog：http://blog.csdn.net/sangni007/article/details/7482960

但是代码比较麻烦，而且其中还涉及到flann算法（其中的Random KDTree+KNN），虽然能看明白，但是比较费劲，今天在文档中找到一个简化版本：

1.SurfFeatureDetector detector( minHessian );构造surf检测器；

   detector.detect( img_1, keypoints_1 ); detector.detect( img_2, keypoints_2 );检测

2.SurfDescriptorExtractor extractor;提取描述结构

   Mat descriptors_1, descriptors_2;

   extractor.compute( img_1, keypoints_1, descriptors_1 ); extractor.compute( img_2, keypoints_2, descriptors_2 );

3.BruteForceMatcher< L2<float> > matcher;牛逼的匹配结构啊！！！！可以直接暴力测量距离

   std::vector< DMatch > matches; 

   matcher.match( descriptors_1, descriptors_2, matches );

 文档：http://opencv.itseez.com/modules/gpu/doc/feature_detection_and_description.html?highlight=bruteforce#gpu::BruteForceMatcher_GPU

PS：OpenCV 你是在太强悍了！！！只有我想不到，木有你办不到的啊！ 我真心跪了！

/**
 * @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;
using namespace std;

void readme();

/**
 * @function main
 * @brief Main function
 */
int main( int argc, char** argv )
{
  //if( argc != 3 )
  //{ return -1; }

  Mat img_1 = imread( "D:/src.jpg", CV_LOAD_IMAGE_GRAYSCALE );
  Mat img_2 = imread( "D:/Demo.jpg", CV_LOAD_IMAGE_GRAYSCALE );

  if( !img_1.data || !img_2.data )
  { return -1; }

  //-- Step 1: Detect the keypoints using SURF Detector
  int minHessian = 400;

  double t=getTickCount();

  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 );
  t=getTickCount()-t;
  t=t*1000/getTickFrequency();
  //-- Draw matches
  Mat img_matches;
  drawMatches( img_1, keypoints_1, img_2, keypoints_2, matches, img_matches );
  cout<<"Cost Time:"<<t<<endl;
  //-- Show detected matches
  imshow("Matches", img_matches );

  waitKey(0);

  return 0;
}

/**
 * @function readme
 */
void readme()
{ std::cout << " Usage: ./SURF_descriptor <img1> <img2>" << std::endl; }

图像中match的keypoints没有经过过滤。导致匹配点过多

文档地址：http://opencv.itseez.com/doc/tutorials/features2d/feature_description/feature_description.html?highlight=description

文档中还有一个版本带定位的和过滤Match的，

：http://opencv.itseez.com/doc/tutorials/features2d/feature_homography/feature_homography.html?highlight=drawmatchesflags

from: http://blog.csdn.net/yangtrees/article/details/7544133