大家好，又见面了，我是你们的朋友全栈君。

KAZE系列笔记：

KAZE算法资源：

1. 论文： http://www.robesafe.com/personal/pablo.alcantarilla/papers/Alcantarilla12eccv.pdf

2. 项目主页：http://www.robesafe.com/personal/pablo.alcantarilla/kaze.html

3. 作者代码：http://www.robesafe.com/personal/pablo.alcantarilla/code/kaze_features_1_4.tar
（需要boost库，另外其计时函数的使用比较复杂，可以用OpenCV的cv::getTickCount代替）

4. Computer Vision Talks的评测：http://computer-vision-talks.com/2013/03/porting-kaze-features-to-opencv/

5. Computer Vision Talks 博主Ievgen Khvedchenia将KAZE集成到OpenCV的cv::Feature2D类，但需要重新编译OpenCV，并且没有实现算法参数调整和按Mask过滤特征点的功能：https://github.com/BloodAxe/opencv/tree/kaze-features

6. 我在Ievgen的项目库中提取出KAZE，封装成继承cv::Feature2D的类，无需重新编译OpenCV，实现了参数调整和Mask过滤的功能: https://github.com/yuhuazou/kaze_opencv (2013-03-28更新，对KAZE代码进行了优化)

7. Matlab 版的接口程序，封装了1.0版的KAZE代码：https://github.com/vlfeat/vlbenchmarks/blob/unstable/%2BlocalFeatures/Kaze.m

2.3 与其他特征算法的比较

2.3.1 与OpenCV API的融合

KAZE算法作者在其项目主页提供了源码，其中包括KAZE的核心算法库以及KAZE特征的提取、匹配和比较等例程，是基于OpenCV实现的。Computer Vision Talks的博主Ievgen Khvedchenia不久前将KAZE代码融合到OpenCV的cv::Feature2D API中，不过他是OpenCV项目的维护者之一，他的目标是在未来的OpenCV版本中加入KAZE。使用他的KAZE类需要重新编译OpenCV，并且目前只是简单地嵌入、还不能调整KAZE类的参数，也不支持Mask过滤。

因为想尽快测试和比较KAZE算法的性能，又不想重新编译OpenCV，我在Ievgen的项目库中将KAZE相关的代码抽离出来，改造为一个相对独立的cv::KAZE类，继承于cv::Feature2D类。这样就可以方便地在OpenCV中使用，并能够通过一致的接口与其它特征算法进行比较。cv::KAZE类包括如下文件：

|--KAZE
	|   kaze_features.cpp				// Class that warps KAZE to cv::Feature2D
	|   kaze_features.h
	|   kaze.cpp						// Implementation of KAZE
	|   kaze.h
	|   kaze_config.cpp					// Configuration variables and options
	|   kaze_config.h
	|   kaze_ipoint.cpp					// Class that defines a point of interest
	|   kaze_ipoint.h
	|   kaze_nldiffusion_functions.cpp	// Functions for non-linear diffusion applications
	|   kaze_nldiffusion_functions.h
	|   kaze_utils.cpp					// Some useful functions
	|   kaze_utils.h

其中kaze_feature.h和kaze_feature.cpp是继承cv::Feature2D的cv::KAZE类，通过这个类将KAZE核心算法库与OpenCV的Feature2D类关联起来。其具体代码如下：

#ifndef _KAZE_FEATURES_H_
#define _KAZE_FEATURES_H_


// Extract from ..\opencv\modules\features2d\src\precomp.hpp
//
#ifdef HAVE_CVCONFIG_H
#include "cvconfig.h"
#endif

#include "opencv2/features2d/features2d.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/imgproc/imgproc_c.h"
#include "opencv2/core/internal.hpp"

#include <algorithm>

#ifdef HAVE_TEGRA_OPTIMIZATION
#include "opencv2/features2d/features2d_tegra.hpp"
#endif
//


#include "kaze_config.h"

/*!
 KAZE features implementation.
 !! Note that it has NOT been warped to cv::Algorithm in oder to avoid rebuilding OpenCV
	So most functions of cv::Algorithm can not be used in cv::KAZE
 http://www.robesafe.com/personal/pablo.alcantarilla/papers/Alcantarilla12eccv.pdf
*/
namespace cv
{
	class CV_EXPORTS_W KAZE : public Feature2D
	{
	public:

		CV_WRAP explicit KAZE();
		KAZE(toptions &_options);

		// returns the descriptor size in bytes
		int descriptorSize() const;

		// returns the descriptor type
		int descriptorType() const;

		// Compute the KAZE features and descriptors on an image
		void operator()( InputArray image, InputArray mask, vector<KeyPoint>& keypoints,
			OutputArray descriptors, bool useProvidedKeypoints=false ) const;

		// Compute the KAZE features with mask
		void operator()(InputArray image, InputArray mask, vector<KeyPoint>& keypoints) const;

		// Compute the KAZE features and descriptors on an image WITHOUT mask
		void operator()(InputArray image, vector<KeyPoint>& keypoints, OutputArray descriptors) const;

		//AlgorithmInfo* info() const;

	protected:

		void detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;

		// !! NOT recommend to use because KAZE descriptors ONLY work with KAZE features
		void computeImpl( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const;

		CV_PROP_RW int nfeatures;

	private:
		toptions options;
	};

	typedef KAZE KazeFeatureDetector;
	//typedef KAZE KazeDescriptorExtractor;	// NOT available because KAZE descriptors ONLY work with KAZE features
}

#endif

/*********************************************************************
* Software License Agreement (BSD License)
*
*  Copyright (c) 2009, Willow Garage, Inc.
*  All rights reserved.
*
*  Redistribution and use in source and binary forms, with or without
*  modification, are permitted provided that the following conditions
*  are met:
*
*   * Redistributions of source code must retain the above copyright
*     notice, this list of conditions and the following disclaimer.
*   * Redistributions in binary form must reproduce the above
*     copyright notice, this list of conditions and the following
*     disclaimer in the documentation and/or other materials provided
*     with the distribution.
*   * Neither the name of the Willow Garage nor the names of its
*     contributors may be used to endorse or promote products derived
*     from this software without specific prior written permission.
*
*  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
*  "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
*  LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
*  FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
*  COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
*  INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
*  BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
*  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
*  CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
*  LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
*  ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
*  POSSIBILITY OF SUCH DAMAGE.
*********************************************************************/

/** Authors: Ievgen Khvedchenia */
/** Modified: Yuhua Zou, 2013-03-20 */

#include <iterator>
#include "kaze_features.h"
#include "kaze.h"



#define DEGREE_TO_RADIAN(x) ((x) * CV_PI / 180.0)
#define RADIAN_TO_DEGREE(x) ((x) * 180.0 / CV_PI)

namespace cv
{
	/***
	 *	Convertions between cv::Keypoint and KAZE::Ipoint
	 */
    static inline void convertPoint(const cv::KeyPoint& kp, Ipoint& aux)
    {
        aux.xf = kp.pt.x;
        aux.yf = kp.pt.y;
        aux.x = fRound(aux.xf);
        aux.y = fRound(aux.yf);

        //cout << "SURF size: " << kpts_surf1_[i].size*.5 << endl;
        aux.octave = kp.octave;

        // Get the radius for visualization
        aux.scale = kp.size*.5/2.5;
        aux.angle = DEGREE_TO_RADIAN(kp.angle);

        //aux.descriptor_size = 64;
    }

    static inline void convertPoint(const Ipoint& src, cv::KeyPoint& kp)
    {
        kp.pt.x = src.xf;
        kp.pt.y = src.yf;

        kp.angle    = RADIAN_TO_DEGREE(src.angle);
        kp.response = src.dresponse;

        kp.octave = src.octave;    
        kp.size = src.scale;
    }

	/***
	 *	runByPixelsMask() for KAZE Ipoint
	 */
	class MaskPredicate
	{
	public:
		MaskPredicate( const Mat& _mask ) : mask(_mask) {}
		bool operator() (const Ipoint& key_pt) const
		{
			return mask.at<uchar>( (int)(key_pt.yf + 0.5f), (int)(key_pt.xf + 0.5f) ) == 0;
		}

	private:
		const Mat mask;
		MaskPredicate& operator=(const MaskPredicate&);
	};

	void runByPixelsMask( std::vector<Ipoint>& keypoints, const Mat& mask )
	{
		if( mask.empty() )
			return;

		keypoints.erase(std::remove_if(keypoints.begin(), keypoints.end(), MaskPredicate(mask)), keypoints.end());
	}

	/***
	 *	Implementation of cv::KAZE
	 */
    KAZE::KAZE()
    {
    }

	KAZE::KAZE(toptions &_options)
	{
		options = _options;
	}

    int KAZE::descriptorSize() const
    {
        return options.extended ? 128 : 64;
    }

    int KAZE::descriptorType() const
    {
        return CV_32F;
    }

    void KAZE::operator()(InputArray _image, InputArray _mask, vector<KeyPoint>& _keypoints,
        OutputArray _descriptors, bool useProvidedKeypoints) const
    {

        bool do_keypoints = !useProvidedKeypoints;
        bool do_descriptors = _descriptors.needed();

        if( (!do_keypoints && !do_descriptors) || _image.empty() )
            return;
		
        cv::Mat img1_8, img1_32;

		// Convert to gray scale iamge and float image
		if (_image.getMat().channels() == 3)
			cv::cvtColor(_image, img1_8, CV_RGB2GRAY);
		else
			_image.getMat().copyTo(img1_8);

        img1_8.convertTo(img1_32, CV_32F, 1.0/255.0,0);

		// Construct KAZE
		toptions opt = options;
        opt.img_width = img1_32.cols;
        opt.img_height = img1_32.rows;

        ::KAZE kazeEvolution(opt);

		// Create nonlinear scale space
        kazeEvolution.Create_Nonlinear_Scale_Space(img1_32);		

		// Feature detection
        std::vector<Ipoint> kazePoints;

        if (do_keypoints)
        {
            kazeEvolution.Feature_Detection(kazePoints);

			if (!_mask.empty())
			{
				runByPixelsMask(kazePoints, _mask.getMat());
			}
        }
        else
        {
            kazePoints.resize(_keypoints.size());
            for (size_t i = 0; i < kazePoints.size(); i++)
            {
                convertPoint(_keypoints[i], kazePoints[i]);    
            }
        }
		
		// Descriptor generation
        if (do_descriptors)
		{
			kazeEvolution.Feature_Description(kazePoints);

            cv::Mat& descriptors = _descriptors.getMatRef();
            descriptors.create(kazePoints.size(), descriptorSize(), descriptorType());

            for (size_t i = 0; i < kazePoints.size(); i++)
            {
                std::copy(kazePoints[i].descriptor.begin(), kazePoints[i].descriptor.end(), (float*)descriptors.row(i).data);
            }
        }

		// Transfer from KAZE::Ipoint to cv::KeyPoint
		if (do_keypoints)
		{
			_keypoints.resize(kazePoints.size());
			for (size_t i = 0; i < kazePoints.size(); i++)
			{
				convertPoint(kazePoints[i], _keypoints[i]);
            }
        }
    }

	void KAZE::operator()(InputArray image, InputArray mask, vector<KeyPoint>& keypoints ) const
	{
		(*this)(image, mask, keypoints, noArray(), false);
	}

	void KAZE::operator()(InputArray image, vector<KeyPoint>& keypoints, OutputArray descriptors) const
	{
		(*this)(image, noArray(), keypoints, descriptors, false);
	}

    void KAZE::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask) const
    {
        (*this)(image, mask, keypoints, noArray(), false);
    }

    void KAZE::computeImpl( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors) const
    {
        (*this)(image, Mat(), keypoints, descriptors, false);		// Regenerate keypoints no matter keypoints is empty or not
    }

}

下面是基于cv::KAZE类的特征提取与图像匹配例程及结果图：

// KazeOpenCV.cpp : 定义控制台应用程序的入口点。
//

#include "predep.h"

#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/calib3d/calib3d.hpp"

#include "KAZE/kaze_features.h"

#pragma comment( lib, cvLIB("core") )
#pragma comment( lib, cvLIB("imgproc") )
#pragma comment( lib, cvLIB("highgui") )
#pragma comment( lib, cvLIB("flann") )
#pragma comment( lib, cvLIB("features2d") )
#pragma comment( lib, cvLIB("calib3d") )


using namespace std;
using namespace cv;


int main(int argc, char** argv[])
{
	Mat img_1 = imread("box.png");
	Mat img_2 = imread("box_in_scene.png");

	std::vector<KeyPoint> keypoints_1, keypoints_2;
	Mat descriptors_1, descriptors_2;

	toptions opt;
	opt.extended = true;		// 1 - 128-bit vector, 0 - 64-bit vector, default: 0
	opt.verbosity = true;		// 1 - show detail information while caculating KAZE, 0 - unshow, default: 0

	KAZE detector_1(opt);
	KAZE detector_2(opt);

	double t2 = 0.0, t1 = 0.0, tkaze = 0.0;
	int64 start_t1 = cv::getTickCount();

	//-- Detect keypoints and calculate descriptors
	detector_1(img_1, keypoints_1, descriptors_1);
	detector_2(img_2, keypoints_2, descriptors_2);

	t2 = cv::getTickCount();
	tkaze = 1000.0 * (t2 - start_t1) / cv::getTickFrequency();

	cout << "\n\n-- Total detection time (ms): " << tkaze << endl;
	printf("-- Keypoint number of img_1 : %d \n", keypoints_1.size() );
	printf("-- Keypoint number of img_2 : %d \n", keypoints_2.size() );

	//-- Matching descriptor vectors using FLANN matcher
	FlannBasedMatcher matcher;
	vector< DMatch > matches;
	matcher.match( descriptors_1, descriptors_2, matches );
	double max_dist = 0; double min_dist = 100;

	//-- 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;
	}

	//-- Find initial good matches (i.e. whose distance is less than 2*min_dist )
	vector< DMatch > good_matches, inliers;
	for( int i = 0; i < descriptors_1.rows; i++ )
	{ 
		if( matches[i].distance < 2*min_dist )	
		{ 
			good_matches.push_back( matches[i]); 
		}
	}

	cout << "-- Computing homography (RANSAC)..." << endl;
	//-- Get the keypoints from the good matches
	vector<Point2f> points1( good_matches.size() ); 
	vector<Point2f> points2( good_matches.size() ); 
	for( size_t i = 0; i < good_matches.size(); i++ )
	{
		points1[i] = keypoints_1[ good_matches[i].queryIdx ].pt;
		points2[i] = keypoints_2[ good_matches[i].trainIdx ].pt;
	}

	//-- Computing homography (RANSAC) and find inliers
	vector<uchar> flags(points1.size(), 0);
	Mat H = findHomography( points1, points2, CV_RANSAC, 3.0, flags );
	//cout << H << endl << endl;
	for (int i = 0; i < good_matches.size(); i++)
	{
		if (flags[i])
		{
			inliers.push_back( good_matches[i] );
		}
	}

	//-- Draw Keypoints
	Mat img_1k, img_2k;
	drawKeypoints(img_1, keypoints_1, img_1k, Scalar::all(-1), DrawMatchesFlags::DRAW_RICH_KEYPOINTS);
	drawKeypoints(img_2, keypoints_2, img_2k, Scalar::all(-1), DrawMatchesFlags::DRAW_RICH_KEYPOINTS);

	//-- Draw inliers
	Mat img_matches;
	drawMatches( img_1, keypoints_1, img_2, keypoints_2,
		inliers, img_matches, Scalar::all(-1), Scalar::all(-1),
		vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );

	printf("-- Number of Matches : %d \n", good_matches.size() );
	printf("-- Number of Inliers : %d \n", inliers.size() );
	printf("-- Match rate : %f \n", inliers.size() / (float)good_matches.size() );

	//-- Localize the object
	//-- Get the corners from the image_1 ( the object to be "detected" )
	vector<Point2f> obj_corners;
	obj_corners.push_back( Point2f(0,0) );
	obj_corners.push_back( Point2f(img_1.cols,0) );
	obj_corners.push_back( Point2f(img_1.cols,img_1.rows) );
	obj_corners.push_back( Point2f(0,img_1.rows) );

	if (!H.empty())
	{
		vector<Point2f> scene_corners;
		perspectiveTransform(obj_corners, scene_corners, H);

		//-- Draw lines between the corners (the mapped object in the scene - image_2 )
		int npts = scene_corners.size();
		for (int i=0; i<npts; i++)
			line( img_matches, scene_corners[i] + Point2f( img_1.cols, 0), 
				scene_corners[(i+1)%npts] + Point2f( img_1.cols, 0), Scalar(0,0,255), 2 );
	}

	//-- Show detected matches
	cout << "-- Show detected matches." << endl;
	namedWindow("Image 1",CV_WINDOW_NORMAL);
	namedWindow("Image 2",CV_WINDOW_NORMAL);
	namedWindow("Good Matches",CV_WINDOW_NORMAL);
	imshow( "Image 1", img_1k );
	imshow( "Image 2", img_2k );
	imshow( "Good Matches", img_matches );
	waitKey(0);
	destroyAllWindows();

	return 0;
}