opencv AKAZE 局部特征匹配算法

Posted 2021-08-07 qianbo_insist

AKAZE 局部特征匹配
级联分类器使用
等比例缩放图片
给图片加logo
鱼眼校正
智能答卷识别
opencv滤镜效果
灰度图像增强方式

基础知识点

AKAZE特征提取算法是局部特征描述子算法，是SIFT算法的改进、采用非线性扩散滤波迭代来提取与构建尺度空间、采用与SIFT类似的方法寻找特征点，
1 在描述子生成阶段采用ORB类似的方法生成描述子
2 描述子比ORB多了旋转不变性特征
3 ORB采用LDB方法，KAZE采用 M-LDB。

orb算法获取尺度不变性，构建了图像金字塔，在金字塔的每一层上都检测关键点。AKAZE则是改进算法。实际上，把AKAKE改成ORB就是执行ORB算法

//opencv里面，修改算法名称即可

 Ptr<ORB> detector = ORB::create(minHessian);/
 Ptr<AKAZE> detector = AKAZE::create();

找出关键点很简单，就是下面做法

vector<KeyPoint> keypoints;
detector->detect(src, keypoints, Mat());//找出关键点

show me the code

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

using namespace cv;
using namespace std;

int main(int argc, char** argv) {
	Mat img1 = imread("in.jpg", IMREAD_GRAYSCALE);
	Mat img2 = imread("big2.jpg", IMREAD_GRAYSCALE);
	if (img1.empty() || img2.empty()) {
		printf("could not load images...\\n");
		return -1;

	}
	imshow("box image", img1);
	imshow("scene image", img2);


	// extract akaze features
	Ptr<AKAZE> detector = AKAZE::create();
	vector<KeyPoint> keypoints_obj;
	vector<KeyPoint> keypoints_scene;
	Mat descriptor_obj, descriptor_scene;
	detector->detectAndCompute(img1, Mat(), keypoints_obj, descriptor_obj);
	detector->detectAndCompute(img2, Mat(), keypoints_scene, descriptor_scene);


	// matching
	FlannBasedMatcher matcher(new flann::LshIndexParams(20, 10, 2));
	//FlannBasedMatcher matcher;
	vector<DMatch> matches;
	matcher.match(descriptor_obj, descriptor_scene, matches);

	// draw matches(key points)
	Mat akazeMatchesImg;
	/*
	drawMatches(img1, keypoints_obj, img2, keypoints_scene, matches, akazeMatchesImg);
	imshow("akaze match result", akazeMatchesImg);*/

	vector<DMatch> goodMatches;
	double minDist = 100000, maxDist = 0;
	for (int i = 0; i < descriptor_obj.rows; i++) {
		double dist = matches[i].distance;
		if (dist < minDist) {
			minDist = dist;

		}
		if (dist > maxDist) {
			maxDist = dist;

		}

	}
	printf("min distance : %f", minDist);

	for (int i = 0; i < descriptor_obj.rows; i++) {
		double dist = matches[i].distance;
		if (dist < max(1.5*minDist, 0.02)) {
			goodMatches.push_back(matches[i]);

		}
	}
	drawMatches(img1, keypoints_obj, img2, keypoints_scene, goodMatches, akazeMatchesImg, Scalar::all(-1),
		Scalar::all(-1), vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);
	imshow("good match result", akazeMatchesImg);

	waitKey(0);
	return 0;
}

下面为大图

小图