[opencv]KAZE、AKAZE特征检测、匹配与对象查找

AkAZE是KAZE的加速版

与SIFT,SUFR比较:

1.更加稳定

2.非线性尺度空间

3.AKAZE速度更加快

4.比较新的算法,只有Opencv新的版本才可以用

AKAZE局部匹配介绍

1.AOS构造尺度空间

2.Hessian矩阵特征点

3.方向指定基于一阶微分图像

4.描述子生成

特征点查找和绘制：把surf中的surf改成KAZE或AKAZE即可

#include <opencv2/opencv.hpp>
#include <opencv2/features2d.hpp>
#include <iostream>

using namespace cv;
using namespace cv::features2d;
using namespace std;

int Akaze feature detection(){
Mat src = imread("test.jpg", IMREAD_GRAYSCALE);
    if (src.empty()) {
        printf("could not load image...\n");
        return -1;
    }
    namedWindow("input image", CV_WINDOW_AUTOSIZE);
    imshow("input image", src);

    // AKAZE特征点检测
    Ptr<AKAZE> detector = AKAZE::create();//创建一个AKAZE类对象并初始化
    vector<KeyPoint> keypoints;
    detector->detect(src, keypoints, Mat());//找Mat src = imread("test.jpg", IMREAD_GRAYSCALE);
    if (src.empty()) {
        printf("could not load image...\n");
        return -1;
    }
    namedWindow("input image", CV_WINDOW_AUTOSIZE);
    imshow("input image", src);

    // AKAZE特征点检测
    Ptr<AKAZE> detector = AKAZE::create();//创建一个AKAZE类对象并初始化
    vector<KeyPoint> keypoints;
    detector->detect(src, keypoints, Mat());//找出关键点

    // 绘制关键点
    Mat keypoint_img;
    drawKeypoints(src, keypoints, keypoint_img, Scalar::all(-1), DrawMatchesFlags::DEFAULT);
    imshow("KeyPoints Image", keypoint_img);

    waitKey(0);
    return 0;
}

匹配：

int featurematching{

Mat img1 = imread("/home/leoxae/KeekoRobot/TestPic/qrcodetest/13.png");
Mat img2 = imread("/home/leoxae/KeekoRobot/TestPic/qrcodetest/13.png");

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);
}