特征检测与特征匹配

其他 2018-11-22 23:21:40 阅读次数: 0

一、使用surf算法实现

1、绘制关键点函数

void drawKeyPoints(const Mat &image,const vector<KeyPoint>&keypoints,Mat &outImage,const Scalar &color=Scalar::all(-1),int flags = DrawMatchesFlags::DEFAULT)

参数一：输入图像

参数二：跟据图像得到的特征点

参数三：输出图像，其内容取决于参数五

参数四：关键点的颜色

参数五：绘制关键点的特征标识符

DEFAULT=0；对每一个关键点只绘制中间点

DRAW_OVER_OUTIMG=1；不创建输出图像阵列，而是在输出图像上绘制匹配对

NOT_DRAW_SINGLE_POINTS=2；单点特征点不被绘制

DRAW_RITCH_KEYPOINTS=4；对每一个关键点，绘制待大小和方向的关键点圆圈

2、绘制相匹配的两图像的特征点

（1）、void drawMatches(const Mat &img1,

const vetor<KeyPoint>& keypoints1,

const Mat &img2,

const vector<KeyPoint>&keypoints2,

const vector<DMatch>&matches1to2,

Mat &outImg,

const Scalar &machColor=Scalar::all(-1),

const Scalar &singlePointColor=Scalar::all(-1),

const vector<char>&matchesMask=vector<char>(),

int flags=DrawMatchesFlags::DEFAULT)

（2）、

void drawMatches(const Mat &img1,

const vetor<KeyPoint>& keypoints1,

const Mat &img2,

const vector<KeyPoint>&keypoints2,

const vector<vector<DMatch> >&matches1to2,

Mat &outImg,

const Scalar &matchColor=Scalar::all(-1),

const Scalar &singlePointColor=Scalar::all(-1),

const vector<vector<char> >&matchesMask=vector<char>(),

int flags=DrawMatchesFlags::DEFAULT)

参数一：第一幅图像

参数二：第一幅图像的特征点

参数三：第二幅图像

参数四：第二幅图像的特征点

参数五：第一幅图像到第二幅图像的特征点

参数六：输出图像，其内容取决于参数十flags

参数七：匹配的颜色，即线和关键点的颜色

参数八：单一特征点的颜色

参数九：确定哪些匹配要进行掩摸

参数十：标识符

DEFAULT=0；对每一个关键点只绘制中间点

DRAW_OVER_OUTIMG=1；不创建输出图像阵列，而是在输出图像上绘制匹配对

NOT_DRAW_SINGLE_POINTS=2；单点特征点不被绘制

DRAW_RITCH_KEYPOINTS=4；对每一个关键点，绘制待大小和方向的关键点圆圈

示例1：绘制特征点

#include <opencv2/core/core.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/nonfree/nonfree.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <iostream>

using namespace std;
using namespace cv;

int main(int argc,char *argv[])
{
	Mat srcImage1 = imread(argv[1]);
	Mat srcImage2 = imread(argv[2]);
	if(!srcImage1.data||!srcImage2.data)
	{
		cout<<"读取图片出错"<<endl;
		return -1;
	}
	imshow("srcImage1",srcImage1);
	imshow("srcImage2",srcImage2);
	
	int minHessian = 400;
	SurfFeatureDetector detector(minHessian);
	vector<KeyPoint>keyPoint1,keyPoint2;
	detector.detect(srcImage1,keyPoint1);
	detector.detect(srcImage2,keyPoint2);
	
	Mat img1,img2;
	drawKeypoints(srcImage1,keyPoint1,img1,Scalar::all(-1),DrawMatchesFlags::DEFAULT);
	drawKeypoints(srcImage2,keyPoint1,img2,Scalar::all(-1),DrawMatchesFlags::DEFAULT);
	
	imshow("img1",img1);
	imshow("img2",img2);
	waitKey(0);
	return 0;
}

示例2：特征提取

#include <opencv2/core/core.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/nonfree/nonfree.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <iostream>

using namespace std;
using namespace cv;

int main(int argc,char *argv[])
{
	Mat srcImage1 = imread(argv[1]);
	Mat srcImage2 = imread(argv[2]);
	if(!srcImage1.data||!srcImage2.data)
	{
		cout<<"读取图片出错"<<endl;
		return -1;
	}
	imshow("srcImage1",srcImage1);
	imshow("srcImage2",srcImage2);
	
	int minHessian = 700;
	SurfFeatureDetector detector(minHessian);
	vector<KeyPoint>keyPoint1,keyPoint2;
	detector.detect(srcImage1,keyPoint1);
	detector.detect(srcImage2,keyPoint2);

	//计算特征向量
	SurfDescriptorExtractor extractor;
	Mat descriptor1,descriptor2;
	extractor.compute(srcImage1,keyPoint1,descriptor1);	
	extractor.compute(srcImage2,keyPoint2,descriptor2);
	
	//实例化一个匹配器
	BruteForceMatcher<L2<float> >matcher;
	vector<DMatch >matches;
	matcher.match(descriptor1,descriptor2,matches);
	Mat imageMatches;
	drawMatches(srcImage1,keyPoint1,srcImage2,keyPoint2,matches,imageMatches);
	imshow("匹配图",imageMatches);
	waitKey(0);
	return 0;
}

二、使用flann算法实现

void DescriptorMatcher::match(

const Mat &queryDescriptors,//查询描述符集

const Mat &trainDescriptors,//训练描述符集

vector<DMatch>&matches,//匹配结果集

const Mat& mask=Mat())//指定输入查询和训练描述符允许匹配的掩摸

void DescriptorMatcher::match(

const Mat &queryDescriptors,//查询描述符集

vector<DMatch>&matches,//匹配结果集

const vector<Mat>&masks = vector<Mat>())//一组掩摸，指定输入查询和训练描述符允许匹配的掩摸


#include <opencv2/core/core.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/nonfree/nonfree.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/legacy/legacy.hpp>
#include <iostream>

using namespace std;
using namespace cv;

int main(int argc,char *argv[])
{
	Mat srcImage1 = imread(argv[1]);
	Mat srcImage2 = imread(argv[2]);
	if(!srcImage1.data||!srcImage2.data)
	{
		cout<<"读取图片出错"<<endl;
		return -1;
	}
	imshow("srcImage1",srcImage1);
	imshow("srcImage2",srcImage2);
	
	int minHessian = 300;
	SurfFeatureDetector detector(minHessian);
	vector<KeyPoint>keyPoint1,keyPoint2;
	detector.detect(srcImage1,keyPoint1);
	detector.detect(srcImage2,keyPoint2);

	//计算特征向量
	SurfDescriptorExtractor extractor;
	Mat descriptor1,descriptor2;
	extractor.compute(srcImage1,keyPoint1,descriptor1);	
	extractor.compute(srcImage2,keyPoint2,descriptor2);
	
	//实例化一个匹配器
	FlannBasedMatcher matcher;
	vector<DMatch >matches;
	matcher.match(descriptor1,descriptor2,matches);
	double max_dist =0,min_dist =100;
	for(int i=0;i<descriptors1.rows;i++)
	{
		double dist = matches[i].distance;
		if(dist < min_dist) min_dist = dist;
		if(dist > max_dist) max_dist = dist;
	}
	printf("max_dist:%f\n",max_dist);
	printf("min_dist:%f\n",min_dist);
	
	vector<DMatch>good_matches;
	for(int i=0;i<descriptor1.rows;i++)
	{
		if(matches[i].distance < 2*min_dist)
		{good_matches.push_back(matches[i]);}
	}
	Mat imageMatches;
	drawMatches(srcImage1,keyPoint1,srcImage2,keyPoint2,
	good_matches,imageMatches,Scalar::all(-1),Scalar::all(-1),
	vector<char>(),DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);
	for(int i=0;i<good_matches.size();i++)
	{
			printf("符合条件的匹配点[%d]特征点1:%d--特征点2:%d\n",
			i,good_matches[i].queryIdx,good_matches[i].trainIdx);
	}
	
	imshow("匹配图",imageMatches);
	waitKey(0);
	return 0;
}

三、使用surf检测特征点，flann匹配

#include <opencv2/core/core.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/nonfree/nonfree.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <iostream>

using namespace std;
using namespace cv;

int main(int argc,char *argv[])
{
	//载入图像并转化为灰度图
	Mat trainImage = imread(argv[1]);
	Mat trainImage_gray;
	imshow("原始图",trainImage);
	cvtColor(trainImage,trainImage_gray,COLOR_BGR2GRAY);
	
	//检测surf关键点、提取训练图像描述符
	vector<KeyPoint> train_keyPoint;
	Mat trainDescriptor;
	SurfFeaturDetector featureDetector(80);
	featureDetector.detect(trainImage_gray,train_keyPoint);
	SurfDescriptorExtractor featureExtractor;
	featureExtractor.compute(trainImage_gray,train_keyPoint,trainDescriptor);
		
	//创建基于FLANN的描述符匹配对象
	FlannBaseMatcher matcher;
	vector<Mat>train_desc_collection(1,trainDescriptor);
	matcher,add(train_desc_collection);
	matcher.train();
	
	//创建视频对象、定义频率
	VideoCapture cap(0);
	unsigned int frameCount = 0;
	//循环处理，直到按下q键
	while(char(waitKey(1)) != 'q')
	{
		int64 time0 = getTickCount();
		Mat testImage,testImage_gray;
		cap >> testImage;
		if(testImage.empty())
			continue;
		
		cvtColor(testImage,testImage_gray,COLOR_BGR2GRAY);
		vector<KeyPoint> test_keyPoint;
		Mat testDescriptor;
		featureDetector.detect(testImage_gray,test_keyPoint);
		featureExtractor.compute(testImage_gray,test_keyPoint,testDescriptor);
		
		//匹配训练和测试描述符
		vector<vector<DMatch> >matches;
		matcher.knnMatch(testDescriptor,matches,2);
		
		//根据劳氏算法，得到优秀的匹配点
		vector<DMatch>goodMatches;
		for(unsigned int i=0;i<matches,size();i++)
		{
			if(matches[i][0].distance < 0.6*matches[i][1].distance)
				goodMatches.push_back(matches[i][0])
		}
		//绘制匹配点并显示窗口
		Mat dstImage;
		drawMatches(testImage,test_keyPoint,trainImage,
		train_keyPoint,goodMatches,dstImage);
		imshow("匹配窗口",dstImage);
		
		cout<<"当前频率为："<<getTickFrequency()/(getTickCount-time0)<<endl;
	}
	return 0;
}

四、SIFT算法暴力匹配（比SURF算法慢3倍）

#include <opencv2/core/core.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/nonfree/nonfree.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <iostream>

using namespace std;
using namespace cv;

int main(int argc,char *argv[])
{
	//载入图像并转化为灰度图
	Mat trainImage = imread(argv[1]);
	Mat trainImage_gray;
	imshow("原始图",trainImage);
	cvtColor(trainImage,trainImage_gray,COLOR_BGR2GRAY);
	
	//检测surf关键点、提取训练图像描述符
	vector<KeyPoint> train_keyPoint;
	Mat trainDescriptor;
	SiftFeaturDetector featureDetector(80);
	featureDetector.detect(trainImage_gray,train_keyPoint);
	SiftDescriptorExtractor featureExtractor;
	featureExtractor.compute(trainImage_gray,train_keyPoint,trainDescriptor);
		
	//创建基于FLANN的描述符匹配对象
	BFMatcher matcher;
	vector<Mat>train_desc_collection(1,trainDescriptor);
	matcher,add(train_desc_collection);
	matcher.train();
	
	//创建视频对象、定义频率
	VideoCapture cap(0);
	unsigned int frameCount = 0;
	//循环处理，直到按下q键
	while(char(waitKey(1)) != 'q')
	{
		int64 time0 = getTickCount();
		Mat testImage,testImage_gray;
		cap >> testImage;
		if(testImage.empty())
			continue;
		
		cvtColor(testImage,testImage_gray,COLOR_BGR2GRAY);
		vector<KeyPoint> test_keyPoint;
		Mat testDescriptor;
		featureDetector.detect(testImage_gray,test_keyPoint);
		featureExtractor.compute(testImage_gray,test_keyPoint,testDescriptor);
		
		//匹配训练和测试描述符
		vector<vector<DMatch> >matches;
		matcher.knnMatch(testDescriptor,matches,2);
		
		//根据劳氏算法，得到优秀的匹配点
		vector<DMatch>goodMatches;
		for(unsigned int i=0;i<matches,size();i++)
		{
			if(matches[i][0].distance < 0.6*matches[i][1].distance)
				goodMatches.push_back(matches[i][0])
		}
		//绘制匹配点并显示窗口
		Mat dstImage;
		drawMatches(testImage,test_keyPoint,trainImage,
		train_keyPoint,goodMatches,dstImage);
		imshow("匹配窗口",dstImage);
		
		cout<<"当前频率为："<<getTickFrequency()/(getTickCount-time0)<<endl;
	}
	return 0;
}

五、寻找已知物体

步骤：

（1）、使用函数findHomography寻找匹配上的关键点的变换

（2）、使用函数perspectiveTranform来映射点

1、寻找透视变换矩阵

void findHomography(

InputArray srcPoints,

InputArray dstPoints,

int method = 0,

double ransacReprojThreshold = 3,

OutputArray mask = noArray()

)

参数一：源平面上对应的点

参数二：目标平面对应的点

参数三：可选标识符

0  使用所有点的常规方法

CV_RANSAC 基于RANSAC鲁棒性的方法

CV_LMEDS 最小中值鲁棒性方法

参数四：取值范围1-10，处理点对为内层时，允许重投影误差的最大值。

参数五：可选掩摸，输入掩摸值会忽略鲁棒性

2、进行透视矩阵变换

void perspectiveTransform(InputArray src,InputArray dst,OutputArray m)

参数一：输入图像

参数二：输出结果

参数三：变换矩阵3*3或4*4


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