OpenCV 中SURF +SIFT+RANSAC算法

看到OpenCV2.4.6里面ORB特征提取算法也在里面了，套用给的SURF特征例子程序改为ORB特征一直提示错误，类型不匹配神马的，由于没有找到示例程序，只能自己找答案。

经过查找发现：

描述符数据类型有是float的，比如说SIFT，SURF描述符，还有是uchar的，比如说有ORB，BRIEF

对于float 匹配方式有：

FlannBased

BruteForce >

BruteForce >

BruteForce >

对于uchar有：

BruteForce

BruteForce

BruteForceMatcher< L2 > matcher;//改动的地方

BruteForceMatcher< L2 > matcher;//改动的地方

完整代码如下：

经过查找发现：

描述符数据类型有是float的，比如说SIFT，SURF描述符，还有是uchar的，比如说有ORB，BRIEF

对于float 匹配方式有：

FlannBased

BruteForce >

BruteForce >

BruteForce >

对于uchar有：

BruteForce

BruteForce

BruteForceMatcher< L2 > matcher;//改动的地方

BruteForceMatcher< L2 > matcher;//改动的地方

完整代码如下：

#include "StdAfx.h"
#include <windows.h>
#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/nonfree/features2d.hpp>
#include <opencv2/calib3d/calib3d.hpp>
#include <opencv2/legacy/legacy.hpp>
#include<opencv2/nonfree/nonfree.hpp>

int main(int argc, char** argv)
{
    initModule_nonfree();//初始化模块，使用SIFT或SURF时用到
    Ptr detector = FeatureDetector::create( "SURF" );//创建SIFT特征检测器，可改成SURF/ORB
    Ptr descriptor_extractor = DescriptorExtractor::create( "SURF" );//创建特征向量生成器，可改成SURF/ORB
    Ptr 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("E:/Book/3.jpg");
    Mat img2 = imread("E:/Book/4.jpg");

    Size imgSize(320,240);
    resize(img1, img1, imgSize);
    resize(img2, img2, imgSize);

    //特征点检测
    double t = getTickCount();//当前滴答数
    vector m_LeftKey,m_RightKey;
    detector->detect( img1, m_LeftKey );//检测img1中的SIFT特征点，存储到m_LeftKey中
    detector->detect( img2, m_RightKey );
    cout<<"图像1特征点个数:"<
    cout<<"图像2特征点个数:"<

    //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, m_LeftKey, descriptors1 );
    descriptor_extractor->compute( img2, m_RightKey, descriptors2 );
    t = ((double)getTickCount() - t)/getTickFrequency();
    cout<<"SIFT算法用时："<"秒"<

    cout<<"图像1特征描述矩阵大小："<
        <<"，特征向量个数："<"，维数："<
    cout<<"图像2特征描述矩阵大小："<
        <<"，特征向量个数："<"，维数："<

    //画出特征点
    Mat img_m_LeftKey,img_m_RightKey;
    drawKeypoints(img1,m_LeftKey,img_m_LeftKey,Scalar::all(-1),0);
    drawKeypoints(img2,m_RightKey,img_m_RightKey,Scalar::all(-1),0);
    //imshow("Src1",img_m_LeftKey);
    //imshow("Src2",img_m_RightKey);

    //特征匹配
    vector 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："<

    //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
    {
        double dist = matches[i].distance;
        if(dist < min_dist) min_dist = dist;
        if(dist > max_dist) max_dist = dist;
    }
    cout<<"最大距离："<
    cout<<"最小距离："<

    //筛选出较好的匹配点
    vector goodMatches;
    for(int i=0; i
    {
        if(matches[i].distance < 0.6 * max_dist)
        {
            goodMatches.push_back(matches[i]);
        }
    }
    cout<<"goodMatch个数："<

    //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,m_LeftKey,img2,m_RightKey,goodMatches,img_matches,
        Scalar::all(-1)/*CV_RGB(255,0,0)*/,CV_RGB(0,255,0),Mat(),2);

    imshow("MatchSIFT",img_matches);
    IplImage result=img_matches;

    waitKey(10);


    //RANSAC匹配过程
    vector m_Matches=goodMatches;
    // 分配空间
    int ptCount = (int)m_Matches.size();

    if ( ptCount<100)
    {
        cout<<"没有找到足够的匹配点"<
        waitKey(0);
        return 0;
    }
    Mat p1(ptCount, 2, CV_32F);
    Mat p2(ptCount, 2, CV_32F);

    // 把Keypoint转换为Mat
    Point2f pt;
    for (int i=0; i
    {
        pt = m_LeftKey[m_Matches[i].queryIdx].pt;
        p1.at<float>(i, 0) = pt.x;
        p1.at<float>(i, 1) = pt.y;

        pt = m_RightKey[m_Matches[i].trainIdx].pt;
        p2.at<float>(i, 0) = pt.x;
        p2.at<float>(i, 1) = pt.y;
    }

    // 用RANSAC方法计算F
    Mat m_Fundamental;
    vector m_RANSACStatus;       // 这个变量用于存储RANSAC后每个点的状态
    findFundamentalMat(p1, p2, m_RANSACStatus, FM_RANSAC);

    // 计算野点个数

    int OutlinerCount = 0;
    for (int i=0; i
    {
        if (m_RANSACStatus[i] == 0)    // 状态为0表示野点
        {
            OutlinerCount++;
        }
    }
    int InlinerCount = ptCount - OutlinerCount;   // 计算内点
    cout<<"内点数为："<


    // 这三个变量用于保存内点和匹配关系
    vector m_LeftInlier;
    vector m_RightInlier;
    vector m_InlierMatches;

    m_InlierMatches.resize(InlinerCount);
    m_LeftInlier.resize(InlinerCount);
    m_RightInlier.resize(InlinerCount);
    InlinerCount=0;
    float inlier_minRx=img1.cols;        //用于存储内点中右图最小横坐标，以便后续融合

    for (int i=0; i
    {
        if (m_RANSACStatus[i] != 0)
        {
            m_LeftInlier[InlinerCount].x = p1.at<float>(i, 0);
            m_LeftInlier[InlinerCount].y = p1.at<float>(i, 1);
            m_RightInlier[InlinerCount].x = p2.at<float>(i, 0);
            m_RightInlier[InlinerCount].y = p2.at<float>(i, 1);
            m_InlierMatches[InlinerCount].queryIdx = InlinerCount;
            m_InlierMatches[InlinerCount].trainIdx = InlinerCount;

            if(m_RightInlier[InlinerCount].x//存储内点中右图最小横坐标

            InlinerCount++;
        }
    }

    // 把内点转换为drawMatches可以使用的格式
    vector key1(InlinerCount);
    vector key2(InlinerCount);
    KeyPoint::convert(m_LeftInlier, key1);
    KeyPoint::convert(m_RightInlier, key2);

    // 显示计算F过后的内点匹配
    Mat OutImage;
    drawMatches(img1, key1, img2, key2, m_InlierMatches, OutImage);
    cvNamedWindow( "Match features", 1);
    cvShowImage("Match features", &IplImage(OutImage));
    waitKey(10);

    cvDestroyAllWindows();

    //矩阵H用以存储RANSAC得到的单应矩阵
    Mat H = findHomography( m_LeftInlier, m_RightInlier, RANSAC );

    //存储左图四角，及其变换到右图位置
    std::vector obj_corners(4);
    obj_corners[0] = Point(0,0); obj_corners[1] = Point( img1.cols, 0 );
    obj_corners[2] = Point( img1.cols, img1.rows ); obj_corners[3] = Point( 0, img1.rows );
    std::vector scene_corners(4);
    perspectiveTransform( obj_corners, scene_corners, H);

    //画出变换后图像位置
    Point2f offset( (float)img1.cols, 0);
    line( OutImage, scene_corners[0]+offset, scene_corners[1]+offset, Scalar( 0, 255, 0), 4 );
    line( OutImage, scene_corners[1]+offset, scene_corners[2]+offset, Scalar( 0, 255, 0), 4 );
    line( OutImage, scene_corners[2]+offset, scene_corners[3]+offset, Scalar( 0, 255, 0), 4 );
    line( OutImage, scene_corners[3]+offset, scene_corners[0]+offset, Scalar( 0, 255, 0), 4 );
    imshow( "Good Matches & Object detection", OutImage );

    waitKey(10);
    imwrite("warp_position.jpg",OutImage);


    int drift = scene_corners[1].x;                                                        //储存偏移量

    //新建一个矩阵存储配准后四角的位置
    int width = int(max(abs(scene_corners[1].x), abs(scene_corners[2].x)));
    int height= img1.rows;                                                                  //或者：int height = int(max(abs(scene_corners[2].y), abs(scene_corners[3].y)));
    float origin_x=0,origin_y=0;
    if(scene_corners[0].x<0) {
        if (scene_corners[3].x<0) origin_x+=min(scene_corners[0].x,scene_corners[3].x);
        else origin_x+=scene_corners[0].x;}
    width-=int(origin_x);
    if(scene_corners[0].y<0) {
        if (scene_corners[1].y) origin_y+=min(scene_corners[0].y,scene_corners[1].y);
        else origin_y+=scene_corners[0].y;}
    //可选：height-=int(origin_y);
    Mat imageturn=Mat::zeros(width,height,img1.type());

    //获取新的变换矩阵，使图像完整显示
    for (int i=0;i<4;i++) {scene_corners[i].x -= origin_x; }     //可选：scene_corners[i].y -= (float)origin_y; }
    Mat H1=getPerspectiveTransform(obj_corners, scene_corners);

    //进行图像变换，显示效果
    warpPerspective(img1,imageturn,H1,Size(width,height));
    imshow("image_Perspective", imageturn);
    waitKey(10);


    //图像融合
    int width_ol=width-int(inlier_minRx-origin_x);
    int start_x=int(inlier_minRx-origin_x);
    cout<<"width: "<
    cout<<"img1.width: "<
    cout<<"start_x: "<
    cout<<"width_ol: "<

    uchar* ptr=imageturn.data;
    double alpha=0, beta=1;
    for (int row=0;row
    {
        ptr=imageturn.data+row*imageturn.step+(start_x)*imageturn.elemSize();
        for(int col=0;col
        {
            uchar* ptr_c1=ptr+imageturn.elemSize1();
            uchar*  ptr_c2=ptr_c1+imageturn.elemSize1();
            uchar* ptr2=img2.data+row*img2.step+(col+int(inlier_minRx))*img2.elemSize();
            uchar* ptr2_c1=ptr2+img2.elemSize1();
            uchar* ptr2_c2=ptr2_c1+img2.elemSize1();

            alpha=double(col)/double(width_ol); beta=1-alpha;

            if (*ptr==0&&*ptr_c1==0&&*ptr_c2==0)
            {
                *ptr=(*ptr2);
                *ptr_c1=(*ptr2_c1);
                *ptr_c2=(*ptr2_c2);
            }

            *ptr=(*ptr)*beta+(*ptr2)*alpha;
            *ptr_c1=(*ptr_c1)*beta+(*ptr2_c1)*alpha;
            *ptr_c2=(*ptr_c2)*beta+(*ptr2_c2)*alpha;

            ptr+=imageturn.elemSize();
    }   }

    //imshow("image_overlap", imageturn);
    //waitKey(0);

    Mat img_result=Mat::zeros(height,width+img2.cols-drift,img1.type());
    uchar* ptr_r=imageturn.data;

    for (int row=0;row
    {
        ptr_r=img_result.data+row*img_result.step;

        for(int col=0;col
        {
            uchar* ptr_rc1=ptr_r+imageturn.elemSize1();
            uchar* ptr_rc2=ptr_rc1+imageturn.elemSize1();

            uchar* ptr=imageturn.data+row*imageturn.step+col*imageturn.elemSize();
            uchar* ptr_c1=ptr+imageturn.elemSize1();
            uchar* ptr_c2=ptr_c1+imageturn.elemSize1();

            *ptr_r=*ptr;
            *ptr_rc1=*ptr_c1;
            *ptr_rc2=*ptr_c2;

            ptr_r+=img_result.elemSize();
        }

        ptr_r=img_result.data+row*img_result.step+imageturn.cols*img_result.elemSize();
        for(int col=imageturn.cols;col
        {
            uchar* ptr_rc1=ptr_r+imageturn.elemSize1();
            uchar*  ptr_rc2=ptr_rc1+imageturn.elemSize1();

            uchar* ptr2=img2.data+row*img2.step+(col-imageturn.cols+drift)*img2.elemSize();
            uchar* ptr2_c1=ptr2+img2.elemSize1();
            uchar* ptr2_c2=ptr2_c1+img2.elemSize1();

            *ptr_r=*ptr2;
            *ptr_rc1=*ptr2_c1;
            *ptr_rc2=*ptr2_c2;

            ptr_r+=img_result.elemSize();
        }
    }

    //imshow("image_result", img_result);
    //imwrite("final_result.jpg",img_result);
    waitKey(0);

    return 0;
}

原文出自http://blog.csdn.net/kezunhai/article/details/47438595