最新版的OpenCV中新增加的ORB特征的使用

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

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));