OpenCv-C++-KAZE(AKAZE)局部特征匹配（二）

上一篇已经做出了KAZE(AKAZE)局部特征的检测，就差匹配没有做到。
那么，现在来实现一下：

放上代码：

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

using namespace cv;
using namespace std;

Mat img1,img2;
int main(int argc, char** argv)
{
	img1 = imread("D:/test/box.png", IMREAD_GRAYSCALE);
	img2 = imread("D:/test/box_in_scene.png", IMREAD_GRAYSCALE);
	if (!img1.data||!img2.data)
	{
		printf("图片未找到...");
		return -1;
	}
	imshow("input box", img1);
	imshow("input box_in_scene", img2);
	double t1 = getTickCount();
	//检测特征点(非线性)
	Ptr<AKAZE>detector = AKAZE::create();
	//Ptr<KAZE>detector = KAZE::create();//KAZE检测
	
	//存放描述子
	Mat descriptor_obj,descriptor_scene;

	//img1特征点检测并计算描述子
	vector<KeyPoint> keypoints_obj;
	detector->detectAndCompute(img1, Mat(), keypoints_obj, descriptor_obj);
	
	//img2特征点检测并计算描述子
	vector<KeyPoint> keypoints_scene;
	detector->detectAndCompute(img2, Mat(), keypoints_scene,descriptor_scene);
	double t2 = getTickCount();
	double t = (t2 - t1) * 1000 / getTickFrequency();//结果转化为毫秒
	printf("特征点寻找所花费时间(ms):%f", t);

	//使用FLANN匹配器比较两个关键点的匹配度
	FlannBasedMatcher fbMatcher(new flann::LshIndexParams(20,10,2));//用LshIndexParams
	
    /*这里不能使用FlannBasedMatcher fbMatcher();这条语句，因为它不支持CV_8UC1类型，
	会报错，OpenCv暂时还没有解决这一问题。*/
	//也可以使用暴力匹配(BFMatcher bfmatches;)

	BFMatcher bfmatches;
	vector<DMatch>matches;
	fbMatcher.match(descriptor_obj, descriptor_scene, matches);


	//绘制匹配线
	Mat resultImg;
	drawMatches(img1, keypoints_obj, img2, keypoints_scene, matches, resultImg,
	Scalar::all(-1), Scalar::all(-1),vector<char>(),DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);
	
	/*最后一个参数使用DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS就可以把检测到的特征点隐去，
	只留下匹配到的特征点。*/

	imshow("AKAZE Matches", resultImg);

	/*那么上面的操作所显示的结果匹配到的特征点很多，为了减少多余的特征点，下面进行
	如下操作*/

	vector<DMatch>goodmatches;//保存从众多匹配点中找出的最优点
	/*
	1、遍历整个描述子；
	2、从描述子中找出最优匹配点(距离最小)
	*/
	double minDist = 1000;//初始化
	double maxDist = 0;
	for (int i = 0; i < descriptor_obj.rows; i++)
	{
		double dist = matches[i].distance;
		if (dist > maxDist)
		{
			maxDist = dist;
		}
		if (dist < minDist)
		{
			minDist = dist;
		}

	}
	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]);
		}
		
	}
	Mat goodmatchesImg;
	drawMatches(img1, keypoints_obj, img2, keypoints_scene, goodmatches, goodmatchesImg,
		Scalar::all(-1), Scalar::all(-1), vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);
	imshow("goodmatchesImg", goodmatchesImg);


  //用线匹配不直观，用透视矩阵来做
	//-------------------平面对象识别(将匹配到的内容替换为矩形)--------------------------
	//生成透视变换矩阵
	vector<Point2f> obj;
	vector<Point2f> objinscene;

	for (size_t i = 0; i < goodmatches.size(); i++)
	{
		obj.push_back(keypoints_obj[goodmatches[i].queryIdx].pt);
		objinscene.push_back(keypoints_scene[goodmatches[i].trainIdx].pt);
	}
	Mat H = findHomography(obj, objinscene, RANSAC);     //生成透视变换矩阵

	vector<Point2f> obj_corner(4);//源图片4个角的坐标
	vector<Point2f> objinscene_corner(4);
	obj_corner[0] = Point(0,0);
	obj_corner[1] = Point(img1.cols, 0);
	obj_corner[2] = Point(img1.cols, img1.rows);
	obj_corner[3] = Point(0, img1.rows);


	//------------------透视变换---------------------
	perspectiveTransform(obj_corner, objinscene_corner, H);
	Mat pptfImg= goodmatchesImg.clone();
	line(pptfImg, objinscene_corner[0] + Point2f(img1.cols, 0), objinscene_corner[1] + Point2f(img1.cols, 0), Scalar(0, 0, 255), 2, 8, 0);
	line(pptfImg, objinscene_corner[1] + Point2f(img1.cols, 0), objinscene_corner[2] + Point2f(img1.cols, 0), Scalar(0, 0, 255), 2, 8, 0);
	line(pptfImg, objinscene_corner[2] + Point2f(img1.cols, 0), objinscene_corner[3] + Point2f(img1.cols, 0), Scalar(0, 0, 255), 2, 8, 0);
	line(pptfImg, objinscene_corner[3] + Point2f(img1.cols, 0), objinscene_corner[0] + Point2f(img1.cols, 0), Scalar(0, 0, 255), 2, 8, 0);

	imshow("pptfImg", pptfImg);

	waitKey(0);
	return 0;
}

具体说明已经在注释中有说明。
运行结果：