单张图片特征点

最新推荐文章于 2023-10-17 22:01:18 发布

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最新推荐文章于 2023-10-17 22:01:18 发布

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分类专栏： opencv

本文链接：https://blog.youkuaiyun.com/cbacq/article/details/44978587

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本文介绍如何利用OpenCV实现图像特征检测（SURF算法）与关键点匹配，包括灰度变换、特征点提取及画图展示。

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先放着

// opencv_character.cpp : 定义控制台应用程序的入口点。
//

#include "stdafx.h"
#include <iostream>   
#include "opencv2/core/core.hpp"   
#include "opencv2/features2d/features2d.hpp"   
#include "opencv2/highgui/highgui.hpp"
#include <opencv2/legacy/legacy.hpp>
#include <opencv2/nonfree/features2d.hpp>
#include <opencv2/nonfree/nonfree.hpp>
#include <iostream>   
#include <vector>   
using namespace cv;
using namespace std;
int main()
{

	cv::Mat  image, image1 = cv::imread("./xxx1.png");
	//灰度变换
	cv::cvtColor(image1, image, CV_BGR2GRAY);
	std::vector<cv::KeyPoint> keypoints;
	
	cv::SurfFeatureDetector surf(2500);
	surf.detect(image, keypoints);提取特征点
	cv::drawKeypoints(image, keypoints, image, cv::Scalar::all(255), cv::DrawMatchesFlags::DRAW_RICH_KEYPOINTS);//画特征点

	cv::namedWindow("surf");
	cv::imshow("surf", image);
	cv::waitKey(0);
	return 0;



	/*Mat img_1 = imread("./xxx.png");
	Mat img_2 = imread("./xxx1.png");
	if (!img_1.data || !img_2.data)
	{
		cout << "error reading images " << endl;
		return -1;
	}

	ORB orb;
	vector<KeyPoint> keyPoints_1, keyPoints_2;
	Mat descriptors_1, descriptors_2;

	orb(img_1, Mat(), keyPoints_1, descriptors_1);
	orb(img_2, Mat(), keyPoints_2, descriptors_2);

	
	BruteForceMatcher<L2<float>> matcher;
	vector<DMatch> matches;
	matcher.match(descriptors_1, descriptors_2, matches);

	double max_dist = 0; double min_dist = 10;
	//-- Quick calculation of max and min distances between keypoints   
	for (int i = 0; i < descriptors_1.rows; i++)
	{
		double dist = matches[i].distance;
		if (dist < min_dist) min_dist = dist;
		if (dist > max_dist) max_dist = dist;
	}
	printf("-- Max dist : %f \n", max_dist);
	printf("-- Min dist : %f \n", min_dist);
	//-- Draw only "good" matches (i.e. whose distance is less than 0.6*max_dist )   
	//-- PS.- radiusMatch can also be used here.   
	std::vector< DMatch > good_matches;
	for (int i = 0; i < descriptors_1.rows; i++)
	{
		if (matches[i].distance < 0.6*max_dist)
		{
			good_matches.push_back(matches[i]);
		}
	}

	Mat img_matches;
	drawMatches(img_1, keyPoints_1, img_2, keyPoints_2,
		good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
		vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);
	imshow("Match", img_matches);
	cvWaitKey();
	return 0;*/
}