opencv3 特征检测与匹配及寻找目标

1.算法：surf特征提取算法

SURF算法是著名的尺度不变特征检测器SIFT(Scale-Invariant Features Transform)的高效变种，它为每个检测到的特征定义了位置和尺度，其中尺度的值可用于定义围绕特征点的窗口大小，使得每个特征点都与众不同。这里便是使用SURF算法提取两幅图像中的特征点描述子，并调用OpenCV中的函数进行匹配，

2 代码

//特征检测与匹配
#include <iostream>
#include <stdio.h>
#include "opencv2/core.hpp"
#include "opencv2/core/utility.hpp"
#include "opencv2/core/ocl.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/features2d.hpp"
#include "opencv2/calib3d.hpp"
#include "opencv2/imgproc.hpp"
#include"opencv2/flann.hpp"
#include"opencv2/xfeatures2d.hpp"
#include"opencv2/ml.hpp"

using namespace cv;
using namespace std;
using namespace cv::xfeatures2d;
using namespace cv::ml;

int main()
{
     //对BGR空间的图像直接进行计算很费时间，所以，需要转换为灰度图  
    Mat a = imread("/home/daniel/catkin_ws/src/cvbridge/src/1.jpg", IMREAD_GRAYSCALE);    //读取灰度图像
    Mat b = imread("/home/daniel/catkin_ws/src/cvbridge/src/2.jpg", IMREAD_GRAYSCALE);

    Ptr<SURF> surf;      //创建方式和2中的不一样
    surf = SURF::create(800);
    //先对原图的描述子进行保留 
    BFMatcher matcher;
    Mat c, d;
    // 首先对两幅图像进行特征点的检测和描述子的计算 
    vector<KeyPoint>key1, key2;
    vector<DMatch> matches;

    surf->detectAndCompute(a, Mat(), key1, c);
    surf->detectAndCompute(b, Mat(), key2, d);

    matcher.match(c, d, matches);       //匹配

    sort(matches.begin(), matches.end());  //筛选匹配点


    vector< DMatch > good_matches;  //采集优秀的匹配点            
    int ptsPairs = std::min(50, (int)(matches.size() * 0.15));
    cout << ptsPairs << endl;
    for (int i = 0; i < ptsPairs; i++)
    {
        good_matches.push_back(matches[i]);
    }
    Mat outimg;
    drawMatches(a, key1, b, key2, good_matches, outimg, Scalar::all(-1), Scalar::all(-1),vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);  //绘制匹配点

    std::vector<Point2f> obj;
    std::vector<Point2f> scene;

    for (size_t i = 0; i < good_matches.size(); i++)
    {
        obj.push_back(key1[good_matches[i].queryIdx].pt);
        scene.push_back(key2[good_matches[i].trainIdx].pt);
    }

    std::vector<Point2f> obj_corners(4);
    obj_corners[0] = Point(0, 0);
    obj_corners[1] = Point(a.cols, 0);
    obj_corners[2] = Point(a.cols, a.rows);
    obj_corners[3] = Point(0, a.rows);
    std::vector<Point2f> scene_corners(4);

    Mat H = findHomography(obj, scene, RANSAC);      //寻找匹配的图像
    perspectiveTransform(obj_corners, scene_corners, H);

    line(outimg,scene_corners[0] + Point2f((float)a.cols, 0), scene_corners[1] + Point2f((float)a.cols, 0),Scalar(0, 255, 0), 2, LINE_AA);       //绘制
    line(outimg,scene_corners[1] + Point2f((float)a.cols, 0), scene_corners[2] + Point2f((float)a.cols, 0),Scalar(0, 255, 0), 2, LINE_AA);
    line(outimg,scene_corners[2] + Point2f((float)a.cols, 0), scene_corners[3] + Point2f((float)a.cols, 0),Scalar(0, 255, 0), 2, LINE_AA);
    line(outimg,scene_corners[3] + Point2f((float)a.cols, 0), scene_corners[0] + Point2f((float)a.cols, 0),Scalar(0, 255, 0), 2, LINE_AA);
   // if(!outimg.empty())
    imshow("aaaa",outimg);
    cvWaitKey(0);
}

3 错误集锦

这个错误折腾了好几天，一直还以为是opencv版本的问题，所以就一直各种卸载安装，最终解决办法：
我是在ubuntu下做的，就把catkin_ws工作空间下的devel和build文件夹删了，重新执行

catkin_make
rosrun cvbridge cvbridge

终于好了。。。