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OpenCV4按模块学习|feature2d module — 特征检测与匹配

OpenCV4按模块学习|feature2d module — 特征检测与匹配

前言

OpenCV4.1.2 feature2d模块 ——
学习使用OpenCV Contrib modules的SIFT、SURF特征检测，
学习暴力匹配与FLANN匹配。

特征检测

特征检测实际上包含两个步骤：检测和描述。
检测是指将特征点的位置、方向等信息检测出来，描述指的是将这些特征点信息加以描述，形成128维或64维的特征描述子，以用于后续匹配。
OpenCV实现的特征检测接口包括detect()、compute()和detectAndCompute()函数，分别实现特征检测、特征描述，以及检测并描述一步到位。
SIFT和SURF特征检测步骤也大致相似，首先将图像转化为灰度图，然后创建一个对象，接着调用特征检测函数。使用drawKeyPoint()函数可以将检测到的特征点画出来。

SIFT

#include <iostream>
#include "opencv2/core.hpp"
#ifdef HAVE_OPENCV_XFEATURES2D
#include "opencv2/highgui.hpp"
#include "opencv2/features2d.hpp"
#include "opencv2/xfeatures2d.hpp"

using namespace cv;
using namespace cv::xfeatures2d;
using std::cout;
using std::endl;

int main(int argc, char* argv[])
{
	CommandLineParser parser(argc, argv, "{@input | box.png | input image}");
	Mat src = imread(samples::findFile(parser.get<String>("@input")), IMREAD_GRAYSCALE);
	if (src.empty())
	{
		cout << "Could not open or find the image!\n" << endl;
		cout << "Usage: " << argv[0] << " <Input image>" << endl;
		return -1;
	}

	//-- SIFT检测与描述
	Ptr<SIFT> detector = SIFT::create();
	std::vector<KeyPoint> keypoints;
	Mat descriptors;
	detector->detectAndCompute(src, noArray(), keypoints, descriptors);
	cout << keypoints.size() << endl;
	cout << descriptors.size() << endl;

	//-- 画出特征点
	Mat img_keypoints;
	drawKeypoints(src, keypoints, img_keypoints);

	//-- Show detected (drawn) keypoints
	imshow("SIFT Keypoints", img_keypoints);

	waitKey();
	return 0;
}
#else
int main()
{
	std::cout << "This tutorial code needs the xfeatured contrib module to be run." << std::endl;
	return 0;
}
#endif // HAVE_OPENCV_XFEATURES2D

keypoints和descriptors的大小：
603
[128 x 603]

在这里插入图片描述

SURF

#include <iostream>
#include "opencv2/core.hpp"
#ifdef HAVE_OPENCV_XFEATURES2D
#include "opencv2/highgui.hpp"
#include "opencv2/features2d.hpp"
#include "opencv2/xfeatures2d.hpp"

using namespace cv;
using namespace cv::xfeatures2d;
using std::cout;
using std::endl;

int main(int argc, char* argv[])
{
	CommandLineParser parser(argc, argv, "{@input | box.png | input image}");
	Mat src = imread(samples::findFile(parser.get<String>("@input")), IMREAD_GRAYSCALE);
	if (src.empty())
	{
		cout << "Could not open or find the image!\n" << endl;
		cout << "Usage: " << argv[0] << " <Input image>" << endl;
		return -1;
	}

	//-- SURF检测与描述，设置hessianThreshold为400
	int minHessian = 400;
	Ptr<SURF> detector = SURF::create(minHessian);
	std::vector<KeyPoint> keypoints;
	Mat descriptors;
	detector->detectAndCompute(src, noArray(), keypoints, descriptors);
	cout << keypoints.size() << endl;
	cout << descriptors.size() << endl;

	//-- 画出特征点
	Mat img_keypoints;
	drawKeypoints(src, keypoints, img_keypoints);

	//-- Show detected (drawn) keypoints
	imshow("SURF Keypoints", img_keypoints);

	waitKey();
	return 0;
}
#else
int main()
{
	std::cout << "This tutorial code needs the xfeatured contrib module to be run." << std::endl;
	return 0;
}
#endif // HAVE_OPENCV_XFEATURES2D

keypoints和descriptors的大小：
786
[64 x 786]

在这里插入图片描述

特征匹配

特征匹配有暴力匹配和FLANN匹配，其使用步骤大致相同：

  • 首先使用SIFT或SURF对两幅图像进行特征检测
  • 然后使用cv::DescriptorMatcher进行匹配
// 创建一个暴力匹配器
Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create(DescriptorMatcher::BRUTEFORCE);

// 创建一个FLANN BASED匹配器
Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create(DescriptorMatcher::FLANNBASED);
  • 使用cv::drawMatches画出匹配连线

暴力匹配

#include <iostream>
#include "opencv2/core.hpp"
#ifdef HAVE_OPENCV_XFEATURES2D
#include "opencv2/highgui.hpp"
#include "opencv2/features2d.hpp"
#include "opencv2/xfeatures2d.hpp"

using namespace cv;
using namespace cv::xfeatures2d;
using std::cout;
using std::endl;

const char* keys = 
	"{ help h |                  | Print help message. }"
	"{ input1 | box.png          | Path to input image 1. }"
	"{ input2 | box_in_scene.png | Path to input image 2. }";

int main(int argc, char* argv[])
{
	CommandLineParser parser(argc, argv, keys);
	Mat img1 = imread(samples::findFile(parser.get<String>("input1")), IMREAD_GRAYSCALE);
	Mat img2 = imread(samples::findFile(parser.get<String>("input2")), IMREAD_GRAYSCALE);
	if (img1.empty() || img2.empty())
	{
		cout << "Could not open or find the image!\n" << endl;
		parser.printMessage();
		return -1;
	}

	//-- 步骤1: 使用SURF检测关键点
	int minHessian = 400;
	Ptr<SURF> detector = SURF::create(minHessian);
	std::vector<KeyPoint> keypoints1, keypoints2;
	Mat descriptors1, descriptors2;
	detector->detectAndCompute(img1, noArray(), keypoints1, descriptors1);
	detector->detectAndCompute(img2, noArray(), keypoints2, descriptors2);

	//-- 步骤2: 使用暴力匹配器匹配描述子向量
	Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create(DescriptorMatcher::BRUTEFORCE);
	std::vector<DMatch> matches;
	matcher->match(descriptors1, descriptors2, matches);

	//-- 画出匹配
	Mat img_matches;
	drawMatches(img1, keypoints1, img2, keypoints2, matches, img_matches);

	//-- Show detected matches
	imshow("Matches", img_matches);

	waitKey();
	return 0;
}
#else
int main()
{
	std::cout << "This tutorial code needs the xfeatured contrib module to be run." << std::endl;
	return 0;
}
#endif // HAVE_OPENCV_XFEATURES2D

在这里插入图片描述

FLANN匹配

#include <iostream>
#include "opencv2/core.hpp"
#ifdef HAVE_OPENCV_XFEATURES2D
#include "opencv2/highgui.hpp"
#include "opencv2/features2d.hpp"
#include "opencv2/xfeatures2d.hpp"

using namespace cv;
using namespace cv::xfeatures2d;
using std::cout;
using std::endl;

const char* keys =
"{ help h |                  | Print help message. }"
"{ input1 | box.png          | Path to input image 1. }"
"{ input2 | box_in_scene.png | Path to input image 2. }";

int main(int argc, char* argv[])
{
	CommandLineParser parser(argc, argv, keys);
	Mat img1 = imread(samples::findFile(parser.get<String>("input1")), IMREAD_GRAYSCALE);
	Mat img2 = imread(samples::findFile(parser.get<String>("input2")), IMREAD_GRAYSCALE);
	if (img1.empty() || img2.empty())
	{
		cout << "Could not open or find the image!\n" << endl;
		parser.printMessage();
		return -1;
	}

	//-- 步骤1: 使用SURF检测关键点
	int minHessian = 400;
	Ptr<SURF> detector = SURF::create(minHessian);
	std::vector<KeyPoint> keypoints1, keypoints2;
	Mat descriptors1, descriptors2;
	detector->detectAndCompute(img1, noArray(), keypoints1, descriptors1);
	detector->detectAndCompute(img2, noArray(), keypoints2, descriptors2);

	//-- 步骤2: 使用FLANN匹配器匹配描述子向量
	Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create(DescriptorMatcher::FLANNBASED);
	std::vector< std::vector<DMatch> > knn_matches;
	matcher->knnMatch(descriptors1, descriptors2, knn_matches, 2);

	//-- Filter matches using the Lowe's ratio test
	const float ratio_thresh = 0.7f;
	std::vector<DMatch> good_matches;
	for (size_t i = 0; i < knn_matches.size(); i++)
	{
		if (knn_matches[i][0].distance < ratio_thresh * knn_matches[i][1].distance)
		{
			good_matches.push_back(knn_matches[i][0]);
		}
	}

	//-- Draw matches
	Mat img_matches;
	drawMatches(img1, keypoints1, img2, keypoints2, good_matches, img_matches, Scalar::all(-1),
		Scalar::all(-1), std::vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);

	//-- Show detected matches
	imshow("Good Matches", img_matches);

	waitKey();
	return 0;
}
#else
int main()
{
	std::cout << "This tutorial code needs the xfeatured contrib module to be run." << std::endl;
	return 0;
}
#endif // HAVE_OPENCV_XFEATURES2D

在这里插入图片描述

参考

https://docs.opencv.org/4.1.2/d7/d66/tutorial_feature_detection.html.
https://docs.opencv.org/4.1.2/d5/dde/tutorial_feature_description.html.
https://docs.opencv.org/4.1.2/d5/d6f/tutorial_feature_flann_matcher.html.

悦读

道可道，非常道；名可名，非常名。 无名，天地之始，有名，万物之母。 故常无欲，以观其妙，常有欲，以观其徼。 此两者，同出而异名，同谓之玄，玄之又玄，众妙之门。

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