BRISK特征

      BRISK算法是2011年ICCV上《BRISK:Binary Robust Invariant Scalable Keypoints》文章中，提出来的一种特征提取算法，也是一种二进制的特征描述算子。

       它具有较好的旋转不变性、尺度不变性，较好的鲁棒性等。

      在图像配准应用中，速度比较：SIFT<SURF<BRISK<FREAK<ORB，

      在对有较大模糊的图像配准时，BRISK算法在其中表现最为出色。

代码示例：

#include <opencv2/opencv.hpp>
#include <iostream>

using namespace cv;
using namespace std;


int main(int argc, char** argv) 
{	
	Mat img1 = imread("D:/cv400/data/box.png", 0);
	Mat img2 = imread("D:/cv400/data/box_in_scene.png", 0);
	if (img1.empty() || img2.empty()) 
	{
		cout << "Load image error..." << endl;
		return -1;
	}
	imshow("object image", img1);
	imshow("object in scene", img2);

	// surf featurs extraction
	double t1 = (double)getTickCount();
	//int minHessian = 400;
	Ptr<Feature2D> detector = BRISK::create();
	vector<KeyPoint> keypoints_obj;
	vector<KeyPoint> keypoints_scene;
	Mat descriptor_obj, descriptor_scene;
	detector->detectAndCompute(img1, Mat(), keypoints_obj, descriptor_obj);
	detector->detectAndCompute(img2, Mat(), keypoints_scene, descriptor_scene);

	// matching
	BFMatcher matcher(NORM_L2);
	vector<DMatch> matches;
	matcher.match(descriptor_obj, descriptor_scene, matches);
	double t2 = (double)getTickCount();
	double t = (t2 - t1) / getTickFrequency();
	cout << "spend time : " << t << "s" << endl;
	
	//求匹配点最近距离
	double minDist = 1000;
	for (int i = 0; i < descriptor_obj.rows; i++)
	{
		double dist = matches[i].distance;
		if (dist < minDist) 
			minDist = dist;	
	}
	cout<<"min distance : "<< minDist<<endl;

	//距离较近即匹配较好的点
	vector<DMatch> goodMatches;
	for (int i = 0; i < descriptor_obj.rows; i++)
	{
		double dist = matches[i].distance;
		if (dist < max(2* minDist, 0.02)) 
			goodMatches.push_back(matches[i]);	
	}

	
	//寻找匹配上的关键点的变换
	vector<Point2f> obj;  //目标特征点
	vector<Point2f> objInScene;  //场景中目标特征点
	for (size_t t = 0; t < goodMatches.size(); t++) 
	{
		obj.push_back(keypoints_obj[goodMatches[t].queryIdx].pt);
		objInScene.push_back(keypoints_scene[goodMatches[t].trainIdx].pt);
	}
	Mat imgBH = findHomography(obj, objInScene, RANSAC);

	//映射点
	vector<Point2f> obj_corners(4);
	vector<Point2f> scene_corners(4);
	obj_corners[0] = Point(0, 0);
	obj_corners[1] = Point(img1.cols, 0);
	obj_corners[2] = Point(img1.cols, img1.rows);
	obj_corners[3] = Point(0, img1.rows);
	perspectiveTransform(obj_corners, scene_corners, imgBH);

	//四个点之间画线
	Mat dst;
	cvtColor(img2, dst, COLOR_GRAY2BGR);
	for(int i=0;i<4;i++)
		line(dst, scene_corners[i%4], scene_corners[(i+1)%4], Scalar(0, 0, 255), 2, 8, 0);
	
	imshow("find object in sence", dst);
	waitKey(0);
	return 0;
}

结果：