OpenCV的ORB特征提取算法

看到OpenCV2.3.1里面ORB特征提取算法也在里面了，套用给的SURF特征例子程序改为ORB特征一直提示错误，类型不匹配神马的，由于没有找到示例程序，只能自己找答案。
（ORB特征论文：ORB： an efficient alternative to SIFT or SURF.点击下载论文）经过查找发现：
描述符数据类型有是float的，比如说SIFT，SURF描述符，还有是uchar的，比如说有ORB，BRIEF
对于float 匹配方式有：
FlannBased
BruteForce<L2<float> >
BruteForce<SL2<float> >
BruteForce<L1<float> >
对于uchar有：
BruteForce<Hammin>
BruteForce<HammingLUT>
BruteForceMatcher< L2<float> > matcher;//改动的地方
BruteForceMatcher< L2<float> > matcher;//改动的地方
完整代码如下：

#include <iostream>   
#include "opencv2/core/core.hpp"   
#include "opencv2/features2d/features2d.hpp"   
#include "opencv2/highgui/highgui.hpp"   
#include <iostream>   
#include <vector>   
using namespace cv;  
using namespace std;  
int main()  
{  
    Mat img_1 = imread("D:\\image\\img1.jpg");  
    Mat img_2 = imread("D:\\image\\img2.jpg");  
    if (!img_1.data || !img_2.data)  
    {  
        cout << "error reading images " << endl;  
        return -1;  
    }  
  
    ORB orb;  
    vector<KeyPoint> keyPoints_1, keyPoints_2;  
    Mat descriptors_1, descriptors_2;  
  
    orb(img_1, Mat(), keyPoints_1, descriptors_1);  
    orb(img_2, Mat(), keyPoints_2, descriptors_2);  
      
    BruteForceMatcher<HammingLUT> matcher;  
    vector<DMatch> matches;  
    matcher.match(descriptors_1, descriptors_2, matches);  
  
    double max_dist = 0; double min_dist = 100;  
    //-- Quick calculation of max and min distances between keypoints   
    for( int i = 0; i < descriptors_1.rows; i++ )  
    {   
        double dist = matches[i].distance;  
        if( dist < min_dist ) min_dist = dist;  
        if( dist > max_dist ) max_dist = dist;  
    }  
    printf("-- Max dist : %f \n", max_dist );  
    printf("-- Min dist : %f \n", min_dist );  
    //-- Draw only "good" matches (i.e. whose distance is less than 0.6*max_dist )   
    //-- PS.- radiusMatch can also be used here.   
    std::vector< DMatch > good_matches;  
    for( int i = 0; i < descriptors_1.rows; i++ )  
    {   
        if( matches[i].distance < 0.6*max_dist )  
        {   
            good_matches.push_back( matches[i]);   
        }  
    }  
  
    Mat img_matches;  
    drawMatches(img_1, keyPoints_1, img_2, keyPoints_2,  
        good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),  
        vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);  
    imshow( "Match", img_matches);  
    cvWaitKey();  
    return 0;  
}

另外: SURF SIFT

/*
SIFT sift;
sift(img_1, Mat(), keyPoints_1, descriptors_1);
sift(img_2, Mat(), keyPoints_2, descriptors_2);
BruteForceMatcher<L2<float> >  matcher;
*/

/*
SURF surf;
surf(img_1, Mat(), keyPoints_1);
surf(img_2, Mat(), keyPoints_2);
SurfDescriptorExtractor extrator;
extrator.compute(img_1, keyPoints_1, descriptors_1);
extrator.compute(img_2, keyPoints_2, descriptors_2);
BruteForceMatcher<L2<float> >  matcher;
*/

效果：



另外一个是寻找目标匹配
在右边的场景图里面寻找左边那幅图的starbucks标志

效果如下：



需要在之前的那个imshow之前加上如下代码即可完成一个简单的功能展示：

// localize the object   
std::vector<Point2f> obj;  
std::vector<Point2f> scene;  
  
for (size_t i = 0; i < good_matches.size(); ++i)  
{  
    // get the keypoints from the good matches   
    obj.push_back(keyPoints_1[ good_matches[i].queryIdx ].pt);  
    scene.push_back(keyPoints_2[ good_matches[i].trainIdx ].pt);  
}  
Mat H = findHomography( obj, scene, CV_RANSAC );  
  
// get the corners from the image_1   
std::vector<Point2f> obj_corners(4);  
obj_corners[0] = cvPoint(0,0);  
obj_corners[1] = cvPoint( img_1.cols, 0);  
obj_corners[2] = cvPoint( img_1.cols, img_1.rows);  
obj_corners[3] = cvPoint( 0, img_1.rows);  
std::vector<Point2f> scene_corners(4);  
  
perspectiveTransform( obj_corners, scene_corners, H);  
  
// draw lines between the corners (the mapped object in the scene - image_2)   
line( img_matches, scene_corners[0] + Point2f( img_1.cols, 0), scene_corners[1] + Point2f( img_1.cols, 0),Scalar(0,255,0));  
line( img_matches, scene_corners[1] + Point2f( img_1.cols, 0), scene_corners[2] + Point2f( img_1.cols, 0),Scalar(0,255,0));  
line( img_matches, scene_corners[2] + Point2f( img_1.cols, 0), scene_corners[3] + Point2f( img_1.cols, 0),Scalar(0,255,0));  
line( img_matches, scene_corners[3] + Point2f( img_1.cols, 0), scene_corners[0] + Point2f( img_1.cols, 0),Scalar(0,255,0));