SURFキーポイントマッチングコードに関するOpencv4.xバージョン

opencv4.xバージョンでは、SurfDescriptorExtractorおよびその他のビルドの導入によって導入されたライブラリがnonfree / nonfree.hppではなくなったことは、注目に値します。

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

SURF :: create（）メソッドは、検出器と抽出器を構築するために使用されます。特定のコードは、次のようにopencv4.3.0バージョンで実行されます。

#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;
}

実現効果：

 

参照：

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