学习OpenCV——ORB简化版&Location加速版

根据前面surf简化版的结构，重新把ORB检测的代码给简化以下，发现虽然速度一样，确实能省好多行代码，关键是有

BruteForceMatcher<HammingLUT>matcher的帮忙，直接省的写了一个函数；

NB类型：class gpu::BruteForceMatcher_GPU

再加上findHomography，之后perspectiveTransform就可以location，但是这样速度很慢；

于是改动一下，求matches的keypoints的x与y坐标和的平均值，基本上就是对象中心！！！

以这个点为中心画与原对象大小相同的矩形框，就可以定位出大概位置，但是肯定不如透视变换准确，而且不具有尺度不变性。

但是鲁棒性应该更好，因为，只要能match成功，基本都能定位中心，但是透视变换有时却因为尺度变换过大等因素，画出很不靠谱的矩形框！

#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/imgproc/imgproc_c.h"
#include "opencv2/imgproc/imgproc.hpp"

#include <string>
#include <vector>
#include <iostream>

using namespace cv;
using namespace std;

char* image_filename1 = "D:/src.jpg";
char* image_filename2 = "D:/Demo.jpg";

int main()
{
    Mat img1 = imread( image_filename1, CV_LOAD_IMAGE_GRAYSCALE );
    Mat img2 = imread( image_filename2, CV_LOAD_IMAGE_GRAYSCALE );

    int64 st,et;
    ORB orb1(30,ORB::CommonParams(1.2,1));
    ORB orb2(100,ORB::CommonParams(1.2,1));

    vector<KeyPoint>keys1,keys2;
    Mat descriptor1,descriptor2;
    orb1(img1,Mat(),keys1,descriptor1,false);
    st=getTickCount();
    orb2(img2,Mat(),keys2,descriptor2,false);
    et=getTickCount()-st;
    et=et*1000/(double)getTickFrequency();
    cout<<"extract time:"<<et<<"ms"<<endl;

    vector<DMatch> matches;
         //<em>class </em><tt class="descclassname">gpu::</tt><tt class="descname"><span class="highlighted">BruteForce</span>Matcher_GPU</tt>
    BruteForceMatcher<HammingLUT>matcher;//BruteForceMatcher支持<Hamming> <L1<float>> <L2<float>>
    //FlannBasedMatcher matcher;不支持
    st=getTickCount();
    matcher.match(descriptor1,descriptor2,matches);
    et=getTickCount()-st;
    et=et*1000/getTickFrequency();
    cout<<"match time:"<<et<<"ms"<<endl;


    Mat img_matches;
    drawMatches( img1, keys1, img2, keys2,
                matches, img_matches, Scalar::all(-1), Scalar::all(-1),
                vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
    imshow("match",img_matches);


    cout<<"match size:"<<matches.size()<<endl;
    /*
    Mat showImg;
    drawMatches(img1,keys1,img2,keys2,matchs,showImg);
    imshow( "win", showImg );
    */
    waitKey(0);


    st=getTickCount();
    vector<Point2f>pt1;
    vector<Point2f>pt2;
    float x=0,y=0;
    for(size_t i=0;i<matches.size();i++)
    {
        pt1.push_back(keys1[matches[i].queryIdx].pt);
        pt2.push_back(keys2[matches[i].trainIdx].pt);
        x+=keys2[matches[i].trainIdx].pt.x;
        y+=keys2[matches[i].trainIdx].pt.y;
    }
    x=x/matches.size();
    y=y/matches.size();

    Mat homo;
    homo=findHomography(pt1,pt2,CV_RANSAC);



    vector<Point2f>src_cornor(4);
    vector<Point2f>dst_cornor(4);
    src_cornor[0]=cvPoint(0,0);
    src_cornor[1]=cvPoint(img1.cols,0);
    src_cornor[2]=cvPoint(img1.cols,img1.rows);
    src_cornor[3]=cvPoint(0,img1.rows);
    perspectiveTransform(src_cornor,dst_cornor,homo);

    Mat img=imread(image_filename2,1);

    line(img,dst_cornor[0],dst_cornor[1],Scalar(255,0,0),2);
    line(img,dst_cornor[1],dst_cornor[2],Scalar(255,0,0),2);
    line(img,dst_cornor[2],dst_cornor[3],Scalar(255,0,0),2);
    line(img,dst_cornor[3],dst_cornor[0],Scalar(255,0,0),2);
    /*
    line(img,cvPoint((int)dst_cornor[0].x,(int)dst_cornor[0].y),cvPoint((int)dst_cornor[1].x,(int)dst_cornor[1].y),Scalar(255,0,0),2);
    line(img,cvPoint((int)dst_cornor[1].x,(int)dst_cornor[1].y),cvPoint((int)dst_cornor[2].x,(int)dst_cornor[2].y),Scalar(255,0,0),2);
    line(img,cvPoint((int)dst_cornor[2].x,(int)dst_cornor[2].y),cvPoint((int)dst_cornor[3].x,(int)dst_cornor[3].y),Scalar(255,0,0),2);
    line(img,cvPoint((int)dst_cornor[3].x,(int)dst_cornor[3].y),cvPoint((int)dst_cornor[0].x,(int)dst_cornor[0].y),Scalar(255,0,0),2);
    */

    circle(img,Point(x,y),10,Scalar(0,0,255),3,CV_FILLED);
    line(img,Point(x-img1.cols/2,y-img1.rows/2),Point(x+img1.cols/2,y-img1.rows/2),Scalar(0,0,255),2);
    line(img,Point(x+img1.cols/2,y-img1.rows/2),Point(x+img1.cols/2,y+img1.rows/2),Scalar(0,0,255),2);
    line(img,Point(x+img1.cols/2,y+img1.rows/2),Point(x-img1.cols/2,y+img1.rows/2),Scalar(0,0,255),2);
    line(img,Point(x-img1.cols/2,y+img1.rows/2),Point(x-img1.cols/2,y-img1.rows/2),Scalar(0,0,255),2);

    imshow("location",img);

    et=getTickCount()-st;
    et=et*1000/getTickFrequency();
    cout<<"location time:"<<et<<"ms"<<endl;

    waitKey(0);
}

 

from: http://blog.csdn.net/yangtrees/article/details/7545820