Android学习九---OpenCV4android org.opencv.feature2d

#include <opencv2/core/core.hpp>

#include <opencv2/highgui/highgui.hpp>

#include <opencv2/nonfree/features2d.hpp>

#include <opencv2/legacy/legacy.hpp>

using namespace cv;

int main()

{

Mat image1=imread("../b1.png");

Mat image2=imread("../b2.png");

// 检测surf特征点

vector<KeyPoint> keypoints1,keypoints2;

SurfFeatureDetector detector(400);

detector.detect(image1, keypoints1);

detector.detect(image2, keypoints2);

// 描述surf特征点

SurfDescriptorExtractor surfDesc;

Mat descriptros1,descriptros2;

surfDesc.compute(image1,keypoints1,descriptros1);

surfDesc.compute(image2,keypoints2,descriptros2);

// 计算匹配点数

BruteForceMatcher<L2<float>>matcher;

vector<DMatch> matches;

matcher.match(descriptros1,descriptros2,matches);

std::nth_element(matches.begin(),matches.begin()+24,matches.end());

matches.erase(matches.begin()+25,matches.end());

// 画出匹配图

Mat imageMatches;

drawMatches(image1,keypoints1,image2,keypoints2,matches,

imageMatches,Scalar(255,0,0));

namedWindow("image2");

imshow("image2",image2);

waitKey();

return 0;

}

#include "opencv2/highgui/highgui.hpp"

#include "opencv2/imgproc/imgproc.hpp"

#include "opencv2/nonfree/nonfree.hpp"

#include "opencv2/nonfree/features2d.hpp"

#include <iostream>

#include <stdio.h>

#include <stdlib.h>

using namespace cv;

using namespace std;

int main()

{

initModule_nonfree();//初始化模块，使用SIFT或SURF时用到

Ptr<FeatureDetector> detector = FeatureDetector::create( "SIFT" );//创建SIFT特征检测器

Ptr<DescriptorExtractor> descriptor_extractor = DescriptorExtractor::create( "SIFT" );//创建特征向量生成器

Ptr<DescriptorMatcher> descriptor_matcher = DescriptorMatcher::create( "BruteForce" );//创建特征匹配器

if( detector.empty() || descriptor_extractor.empty() )

cout<<"fail to create detector!";

//读入图像

Mat img1 = imread("desk.jpg");

Mat img2 = imread("desk_glue.jpg");

//特征点检测

double t = getTickCount();//当前滴答数

vector<KeyPoint> keypoints1,keypoints2;

detector->detect( img1, keypoints1 );//检测img1中的SIFT特征点，存储到keypoints1中

detector->detect( img2, keypoints2 );

cout<<"图像1特征点个数:"<<keypoints1.size()<<endl;

cout<<"图像2特征点个数:"<<keypoints2.size()<<endl;

//根据特征点计算特征描述子矩阵，即特征向量矩阵

Mat descriptors1,descriptors2;

descriptor_extractor->compute( img1, keypoints1, descriptors1 );

descriptor_extractor->compute( img2, keypoints2, descriptors2 );

t = ((double)getTickCount() - t)/getTickFrequency();

cout<<"SIFT算法用时："<<t<<"秒"<<endl;

cout<<"图像1特征描述矩阵大小："<<descriptors1.size()

<<"，特征向量个数："<<descriptors1.rows<<"，维数："<<descriptors1.cols<<endl;

cout<<"图像2特征描述矩阵大小："<<descriptors2.size()

<<"，特征向量个数："<<descriptors2.rows<<"，维数："<<descriptors2.cols<<endl;

//画出特征点

Mat img_keypoints1,img_keypoints2;

drawKeypoints(img1,keypoints1,img_keypoints1,Scalar::all(-1),0);

drawKeypoints(img2,keypoints2,img_keypoints2,Scalar::all(-1),0);

//imshow("Src1",img_keypoints1);

//imshow("Src2",img_keypoints2);

//特征匹配

vector<DMatch> matches;//匹配结果

descriptor_matcher->match( descriptors1, descriptors2, matches );//匹配两个图像的特征矩阵

cout<<"Match个数："<<matches.size()<<endl;

//计算匹配结果中距离的最大和最小值

//距离是指两个特征向量间的欧式距离，表明两个特征的差异，值越小表明两个特征点越接近

double max_dist = 0;

double min_dist = 100;

for(int i=0; i<matches.size(); i++)

{

double dist = matches[i].distance;

if(dist < min_dist) min_dist = dist;

if(dist > max_dist) max_dist = dist;

}

cout<<"最大距离："<<max_dist<<endl;

cout<<"最小距离："<<min_dist<<endl;

//筛选出较好的匹配点

vector<DMatch> goodMatches;

for(int i=0; i<matches.size(); i++)

{

if(matches[i].distance < 0.31 * max_dist)

{

goodMatches.push_back(matches[i]);

}

}

cout<<"goodMatch个数："<<goodMatches.size()<<endl;

//画出匹配结果

Mat img_matches;

//红色连接的是匹配的特征点对，绿色是未匹配的特征点

drawMatches(img1,keypoints1,img2,keypoints2,goodMatches,img_matches,

Scalar::all(-1)/*CV_RGB(255,0,0)*/,CV_RGB(0,255,0),Mat(),2);

imshow("MatchSIFT",img_matches);

waitKey(0);

return 0;

}