Opencv 4.x version about SURF key point matching code

It is worth noting that in the opencv4.x version, the library introduced by the introduction of SurfDescriptorExtractor and other builds is no longer nonfree/nonfree.hpp, but

#include "opencv2/xfeatures2d/nonfree.hpp"
#include "opencv2/xfeatures2d.hpp"

The SURF::create() method is used to build the detector and extractor. The specific code runs on the opencv4.3.0 version, as follows:

#include "opencv2/core/core.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/xfeatures2d/nonfree.hpp"
#include "opencv2/xfeatures2d.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/imgproc.hpp"
#include <opencv2/imgproc/types_c.h>
#include<opencv2/imgproc/imgproc.hpp>
#include <iostream>
using namespace cv;
using namespace std;
using namespace cv::xfeatures2d;

int main()
{
	//【0】改变console字体颜色
	system("color 1F");


	//【1】载入原始图片
	Mat srcImage1 = imread("E:/Toky/VsProject/ColoNavigation/ColoNavi_Opencv/ColoNavi_Opencv/data/after_remove_hilight/4.jpg", 1);
	Mat srcImage2 = imread("E:/Toky/VsProject/ColoNavigation/ColoNavi_Opencv/ColoNavi_Opencv/data/after_remove_hilight/5.jpg", 1);
	Mat copysrcImage1 = srcImage1.clone();
	Mat copysrcImage2 = srcImage2.clone();

	if (!srcImage1.data || !srcImage2.data)
	{
		printf("读取图片错误，请确定目录下是否有imread函数指定的图片存在~！ \n"); return false;
	}

	//【2】使用SURF算子检测关键点
	int minHessian = 400;//SURF算法中的hessian阈值
	Ptr<SURF> detector = SURF::create(minHessian);//定义一个SurfFeatureDetector（SURF） 特征检测类对象  
	vector<KeyPoint> keypoints_object, keypoints_scene;//vector模板类，存放任意类型的动态数组

	//【3】调用detect函数检测出SURF特征关键点，保存在vector容器中
	detector->detect(srcImage1, keypoints_object);
	detector->detect(srcImage2, keypoints_scene);

	//【4】计算描述符（特征向量）
	Ptr<SURF> extractor = SURF::create();

	Mat descriptors_object, descriptors_scene;
	extractor->compute(srcImage1, keypoints_object, descriptors_object);
	extractor->compute(srcImage2, keypoints_scene, descriptors_scene);

	//【5】使用FLANN匹配算子进行匹配
	FlannBasedMatcher matcher;
	vector< DMatch > matches;
	matcher.match(descriptors_object, descriptors_scene, matches);
	double max_dist = 0; double min_dist = 100;//最小距离和最大距离

	//【6】计算出关键点之间距离的最大值和最小值
	for (int i = 0; i < descriptors_object.rows; i++)
	{
		double dist = matches[i].distance;
		if (dist < min_dist) min_dist = dist;
		if (dist > max_dist) max_dist = dist;
	}

	printf(">Max dist 最大距离 : %f \n", max_dist);
	printf(">Min dist 最小距离 : %f \n", min_dist);

	//【7】存下匹配距离小于3*min_dist的点对
	std::vector< DMatch > good_matches;
	for (int i = 0; i < descriptors_object.rows; i++)
	{
		if (matches[i].distance < 3 * min_dist)
		{
			good_matches.push_back(matches[i]);
		}
	}

	//绘制出匹配到的关键点
	Mat img_matches;
	drawMatches(srcImage1, keypoints_object, srcImage2, keypoints_scene,
		good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
		vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);

	//定义两个局部变量
	vector<Point2f> obj;
	vector<Point2f> scene;

	//从匹配成功的匹配对中获取关键点
	for (unsigned int i = 0; i < good_matches.size(); i++)
	{
		obj.push_back(keypoints_object[good_matches[i].queryIdx].pt);
		scene.push_back(keypoints_scene[good_matches[i].trainIdx].pt);
	}
	vector<unsigned char> listpoints;

	//Mat H = findHomography( obj, scene, CV_RANSAC );//计算透视变换 
	Mat H = findHomography(obj, scene, RANSAC, 3, listpoints);//计算透视变换 


	std::vector< DMatch > goodgood_matches;
	for (int i = 0; i < listpoints.size(); i++)
	{
		if ((int)listpoints[i])
		{

			goodgood_matches.push_back(good_matches[i]);


			cout << (int)listpoints[i] << endl;
		}

	}
	cout << "listpoints大小：" << listpoints.size() << endl;
	cout << "goodgood_matches大小：" << goodgood_matches.size() << endl;
	cout << "good_matches大小：" << good_matches.size() << endl;
	Mat Homgimg_matches;
	drawMatches(copysrcImage1, keypoints_object, copysrcImage2, keypoints_scene,
		goodgood_matches, Homgimg_matches, Scalar::all(-1), Scalar::all(-1),
		vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);

	imshow("去除误匹配点后;", Homgimg_matches);


	//从待测图片中获取角点
	vector<Point2f> obj_corners(4);
	obj_corners[0] = cvPoint(0, 0); obj_corners[1] = cvPoint(srcImage1.cols, 0);
	obj_corners[2] = cvPoint(srcImage1.cols, srcImage1.rows); obj_corners[3] = cvPoint(0, srcImage1.rows);
	vector<Point2f> scene_corners(4);

	//进行透视变换
	perspectiveTransform(obj_corners, scene_corners, H);

	//绘制出角点之间的直线
	line(img_matches, scene_corners[0] + Point2f(static_cast<float>(srcImage1.cols), 0), scene_corners[1] + Point2f(static_cast<float>(srcImage1.cols), 0), Scalar(255, 0, 123), 4);
	line(img_matches, scene_corners[1] + Point2f(static_cast<float>(srcImage1.cols), 0), scene_corners[2] + Point2f(static_cast<float>(srcImage1.cols), 0), Scalar(255, 0, 123), 4);
	line(img_matches, scene_corners[2] + Point2f(static_cast<float>(srcImage1.cols), 0), scene_corners[3] + Point2f(static_cast<float>(srcImage1.cols), 0), Scalar(255, 0, 123), 4);
	line(img_matches, scene_corners[3] + Point2f(static_cast<float>(srcImage1.cols), 0), scene_corners[0] + Point2f(static_cast<float>(srcImage1.cols), 0), Scalar(255, 0, 123), 4);

	//显示最终结果
	imshow("效果图", img_matches);

	waitKey(0);
	return 0;
}

Realization effect:

 

reference:

http://www.mamicode.com/info-detail-1966781.html