fanfuhan OpenCV 教學106 ~ opencv-106-AKAZE特徵與描述子

資料來源: https://fanfuhan.github.io/

https://fanfuhan.github.io/2019/05/21/opencv-106/

GITHUB:https://github.com/jash-git/fanfuhan_ML_OpenCV

AKAZE特徵提取算法是局部特徵描述子算法，可以看成是SIFT算法的改進，採用差分擴散分解迭代來提取與嵌入尺度空間，採用與SIFT類似的方法尋找特徵點，在描述子生成階段採用ORB類似的方法生成描述子，但是描述子比ORB多了旋轉不變性特徵。ORB採用LDB方法，AKAZE採用M-LDB。

C++

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

using namespace cv;
using namespace std;

int main(int argc, char** argv) {
	Mat box = imread("D:/images/box.png");
	Mat box_in_sence = imread("D:/images/box_in_scene.png");

	// 创建AKAZE
	auto akaze_detector = AKAZE::create();
	vector<KeyPoint> kpts_01, kpts_02;
	Mat descriptors1, descriptors2;
	akaze_detector->detectAndCompute(box, Mat(), kpts_01, descriptors1);
	akaze_detector->detectAndCompute(box_in_sence, Mat(), kpts_02, descriptors2);

	// 定义描述子匹配 - 暴力匹配
	Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create(DescriptorMatcher::BRUTEFORCE);
	std::vector< DMatch > matches;
	matcher->match(descriptors1, descriptors2, matches);

	// 绘制匹配
	Mat img_matches;
	drawMatches(box, kpts_01, box_in_sence, kpts_02, matches, img_matches);
	imshow("AKAZE-Matches", img_matches);
	imwrite("D:/result.png", img_matches);

	waitKey(0);
	return 0;
}

Python

"""
AKAZE特征与描述子
"""

import cv2 as cv

box = cv.imread("images/box.png")
box_in_scene = cv.imread("images/box_in_scene.png")

# 创建AKAZE特征检测器
akaze = cv.AKAZE_create()

# 得到特征关键点和描述子
kp1, des1 = akaze.detectAndCompute(box, None)
kp2, des2 = akaze.detectAndCompute(box_in_scene, None)

# 暴力匹配
bf = cv.BFMatcher(cv.NORM_HAMMING, crossCheck=True)
matchers = bf.match(des1, des2)

# 绘制匹配
result = cv.drawMatches(box, kp1, box_in_scene, kp2, matchers, None)
cv.imshow("orb-match", result)

cv.waitKey(0)
cv.destroyAllWindows()