opencv-图片特征点提取

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我是靠谱客的博主傻傻小霸王，最近开发中收集的这篇文章主要介绍opencv-图片特征点提取，觉得挺不错的，现在分享给大家，希望可以做个参考。

概述

#include "opencv2/opencv.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/nonfree/features2d.hpp"
#include "opencv2/flann/miniflann.hpp"
#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include <stdio.h>
#include <iostream>
using namespace cv;
using namespace std;
cv::Mat flannMatchExtract(cv::Mat srcImage1, cv::Mat srcImage2)
{
CV_Assert(srcImage1.data != 0 && srcImage2.data != 0);
// 构造SURF检测器
int hessPara = 300;
SurfFeatureDetector
detector(hessPara);
vector<KeyPoint> kPoint1, kPoint2;
// 特征点检测
detector.detect(srcImage1,kPoint1);
detector.detect(srcImage2,kPoint2);
SurfDescriptorExtractor extractor;
// 描述子提取
cv::Mat despMat1, despMat2;
extractor.compute(srcImage1,kPoint1, despMat1);
extractor.compute(srcImage2,kPoint2, despMat2);
// Flann 特征点匹配
FlannBasedMatcher matcher;
vector< DMatch > matches;
matcher.match( despMat1, despMat2, matches );
double max_dist = 0; double min_dist = 100;
// 距离判断-最优匹配点
for( int i = 0; i < despMat1.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 );
// 最佳匹配点
vector< DMatch > matchVec;
// 检测点
for( int i = 0; i < despMat1.rows; i++ )
{
if( matches[i].distance < 3*min_dist )
{
matchVec.push_back( matches[i]);
}
}
// 绘制检测点
cv::Mat matchMat, matchMat2;
drawMatches( srcImage1, kPoint1, srcImage2, kPoint2,
matchVec, matchMat, Scalar::all(-1),
Scalar::all(-1),vector<char>(),
DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
imshow( "matchMat", matchMat);
// 特征点一致性检测
vector<Point2f> obj;
vector<Point2f> scene;
for( int i = 0; i < matchVec.size(); i++ )
{
obj.push_back( kPoint1[ matchVec[i].queryIdx ].pt );
scene.push_back( kPoint2[ matchVec[i].trainIdx ].pt );
}
// 随机一致性
Mat H = findHomography( obj, scene, CV_RANSAC );
// 构造变换矩阵
Point2f objCorner[4] = { cvPoint(0,0),
cvPoint( srcImage1.cols, 0 ),
cvPoint( srcImage1.cols, srcImage1.rows ),
cvPoint( 0, srcImage1.rows )
};
// 绘制匹配点
cv::Point sceneCors[4];
for( int i = 0; i < 4; i++ )
{
double x = objCorner[i].x;
double y = objCorner[i].y;
// 映射矩阵构造
double Z = 1./( H.at<double>(2,0)*x +
H.at<double>(2,1)*y + H.at<double>(2,2) );
double X = ( H.at<double>(0,0)*x +
H.at<double>(0,1)*y + H.at<double>(0,2) )*Z;
double Y = ( H.at<double>(1,0)*x +
H.at<double>(1,1)*y + H.at<double>(1,2) )*Z;
sceneCors[i] = cvPoint( cvRound(X) +
srcImage1.cols, cvRound(Y) );
}
// 绘制匹配出目标
line( matchMat, sceneCors[0],
sceneCors[1], Scalar(0, 255, 0), 2 );
line( matchMat, sceneCors[1],
sceneCors[2], Scalar( 0, 255, 0), 2 );
line( matchMat, sceneCors[2],
sceneCors[3], Scalar( 0, 255, 0), 2 );
line( matchMat, sceneCors[3],
sceneCors[0], Scalar( 0, 255, 0), 2 );
imshow( "ObjectMat", matchMat );
return matchMat;
}
int main()
{
cv::Mat srcImage1 =
imread("hand1.jpg");
cv::Mat srcImage2 =
imread("hand2.jpg");
if (!srcImage1.data || !srcImage2.data)
return -1;
cv::Mat resMatchMat = flannMatchExtract(srcImage1, srcImage2);
cv::waitKey(0);
return 0;
}