opencv 33.AKAZE

KAZE与AKAZE都是OpenCv中集成的API算法，AKAZE是KAZE的加速版本（A表示Accelerated(加速的)）。且AKAZE的执行效率要优于KAZE。
与SURF和SIFT不同(基于线性)，KAZE(AKAZE)是基于非线性插值的方法，这一点在图像处理方面来说确实比SURF和SIFT要好，毕竟图像大多是不连续的。

KAZE(AKAZE)基本步骤与介绍：

1、AOS（加性算子分裂算法(Additive Operator Splitting, AOS)）构造尺度空间；------非线性方法构造，SURF与SIFT基于线性方法构造。
2、Hessian矩阵特征点检测；
3、方向基于一阶微分图像；------图像的旋转不变性（旋转角度）。
4、描述子生成。---------归一化处理。

与SIFT和SURF的比较：

1、更加稳定；
2、非线性尺度空间；
3、AKAZE比KAZE素的更快；

具体的算法分析和原理可参考最后链接

//31 AKAZE
void StartOp2::ImageProcess2_31()
{
	Mat src = imread("../../Images/22.jpg", IMREAD_GRAYSCALE);
	if (!src.data) {

	}
	imshow("image1", src);

	// akaze detection
	//Ptr<KAZE> detector = KAZE::create();
	Ptr<AKAZE> detector = AKAZE::create();
	vector<KeyPoint> keypoints;
	double t1 = getTickCount();
	detector->detect(src, keypoints, Mat());
	double t2 = getTickCount();
	double tkaze = 1000 * (t2 - t1) / getTickFrequency();
	printf("KAZE Time consume(ms) : %f", tkaze);

	Mat keypointImg;
	drawKeypoints(src, keypoints, keypointImg, Scalar::all(-1), DrawMatchesFlags::DEFAULT);
	imshow("kaze key points", keypointImg);


	//AKAZE match
	Mat img1 = imread("../../Images/22.jpg", IMREAD_GRAYSCALE);
	Mat img2 = imread("../../Images/23.jpg", IMREAD_GRAYSCALE);
	if (img1.empty() || img2.empty()) {
		printf("could not load images...\n");
	}
	//imshow("box image", img1);
	//imshow("scene image", img2);

	// extract akaze features
	Ptr<AKAZE> detector1 = AKAZE::create();
	vector<KeyPoint> keypoints_obj;
	vector<KeyPoint> keypoints_scene;
	Mat descriptor_obj, descriptor_scene;
	//double t1 = getTickCount();
	detector1->detectAndCompute(img1, Mat(), keypoints_obj, descriptor_obj);
	detector1->detectAndCompute(img2, Mat(), keypoints_scene, descriptor_scene);
	//double t2 = getTickCount();
	//double tkaze = 1000 * (t2 - t1) / getTickFrequency();
	//printf("AKAZE Time consume(ms) : %f\n", tkaze);

	// 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);

	//draw good matches
	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);
}

参考：

https://blog.youkuaiyun.com/chenyusiyuan/article/details/8710462
http://www.pianshen.com/article/9621127314/