orb-slam系列 Tracking线程 orb特征提取(三)

帧对象的构造

主要功能
1）ORB extraction的入口
2）UndistortKeyPoints();
3）ComputeStereoMatches();
4）AssignFeaturesToGrid();

Frame::Frame(const cv::Mat &imLeft, const cv::Mat &imRight, const double &timeStamp, ORBextractor* extractorLeft, ORBextractor* extractorRight, ORBVocabulary* voc, cv::Mat &K, cv::Mat &distCoef, const float &bf, const float &thDepth)
    :mpORBvocabulary(voc),mpORBextractorLeft(extractorLeft),mpORBextractorRight(extractorRight), mTimeStamp(timeStamp), mK(K.clone()),mDistCoef(distCoef.clone()), mbf(bf), mThDepth(thDepth),
     mpReferenceKF(static_cast<KeyFrame*>(NULL))
{
    // Frame ID
    mnId=nNextId++;

    // Scale Level Info
    mnScaleLevels = mpORBextractorLeft->GetLevels();
    mfScaleFactor = mpORBextractorLeft->GetScaleFactor();
    mfLogScaleFactor = log(mfScaleFactor);
    mvScaleFactors = mpORBextractorLeft->GetScaleFactors();
    mvInvScaleFactors = mpORBextractorLeft->GetInverseScaleFactors();
    mvLevelSigma2 = mpORBextractorLeft->GetScaleSigmaSquares();
    mvInvLevelSigma2 = mpORBextractorLeft->GetInverseScaleSigmaSquares();

    // ORB extraction
    thread threadLeft(&Frame::ExtractORB,this,0,imLeft);
    thread threadRight(&Frame::ExtractORB,this,1,imRight);
    threadLeft.join();
    threadRight.join();

    N = mvKeys.size();

    if(mvKeys.empty())
        return;

    UndistortKeyPoints();

    ComputeStereoMatches();

    mvpMapPoints = vector<MapPoint*>(N,static_cast<MapPoint*>(NULL));    
    mvbOutlier = vector<bool>(N,false);


    // This is done only for the first Frame (or after a change in the calibration)
    if(mbInitialComputations)
    {
        ComputeImageBounds(imLeft);

        mfGridElementWidthInv=static_cast<float>(FRAME_GRID_COLS)/(mnMaxX-mnMinX);
        mfGridElementHeightInv=static_cast<float>(FRAME_GRID_ROWS)/(mnMaxY-mnMinY);

        fx = K.at<float>(0,0);
        fy = K.at<float>(1,1);
        cx = K.at<float>(0,2);
        cy = K.at<float>(1,2);
        invfx = 1.0f/fx;
        invfy = 1.0f/fy;

        mbInitialComputations=false;
    }

    mb = mbf/fx;

    AssignFeaturesToGrid();
}

orb特征入口

// ORB extraction
    thread threadLeft(&Frame::ExtractORB,this,0,imLeft);
    thread threadRight(&Frame::ExtractORB,this,1,imRight);

/*****************************************************************
*      函数属性：类Frame的成员函数ExtractORB(int flag, const cv::Mat &im)
 *     函数功能：
 *                 调用ORB提取器的()运算符，将得到该帧图像的关键点和描述子
 *                 将该帧图像的关键点存储到mvKeys
 *                     该帧图像的描述子存储到mDescriptors
 *     函数参数介绍：
 *                 flag：提取图像的标志  0代表左提取   1代表右提取
 *                 im：待提取ORB特征的图像(灰度图)
 *     备注：NULL
 ******************************************************************/
void Frame::ExtractORB(int flag, const cv::Mat &im)
{
  
    if(flag==0)
        (*mpORBextractorLeft)(im,cv::Mat(),mvKeys,mDescriptors);
    else
        (*mpORBextractorRight)(im,cv::Mat(),mvKeysRight,mDescriptorsRight);
}

ExtractORB函数中调用了重载的()操作符函数，该函数代码如下

void ORBextractor::operator()( InputArray _image, InputArray _mask, vector<KeyPoint>& _keypoints,
                      OutputArray _descriptors)
{ 
    if(_image.empty())
        return;

    Mat image = _image.getMat();
    assert(image.type() == CV_8UC1 );

    // Pre-compute the scale pyramid  构建高斯金字塔
    ComputePyramid(image);
    //计算关键点并生成四叉树
    vector < vector<KeyPoint> > allKeypoints;
    ComputeKeyPointsOctTree(allKeypoints);
    //ComputeKeyPointsOld(allKeypoints);

    Mat descriptors;

    int nkeypoints = 0;
    for (int level = 0; level < nlevels; ++level)
        nkeypoints += (int)allKeypoints[level].size();
    if( nkeypoints == 0 )
        _descriptors.release();
    else
    {
        _descriptors.create(nkeypoints, 32, CV_8U);
        descriptors = _descriptors.getMat();
    }

    _keypoints.clear();
    _keypoints.reserve(nkeypoints);
    //计算每个关键点对应的描述子
    int offset = 0;
    for (int level = 0; level < nlevels; ++level)
    {
        vector<KeyPoint>& keypoints = allKeypoints[level];
        int nkeypointsLevel = (int)keypoints.size();

        if(nkeypointsLevel==0)
            continue;

        // preprocess the resized image
        Mat workingMat = mvImagePyramid[level].clone();
	//进行高斯模糊，用BORDER_REFLECT_101方法处理边缘
        GaussianBlur(workingMat, workingMat, Size(7, 7), 2, 2, BORDER_REFLECT_101);

        // Compute the descriptors
        Mat desc = descriptors.rowRange(offset, offset + nkeypointsLevel);  //offset是偏移量，此处取出的是该层的描述子，
	//计算描述子
        computeDescriptors(workingMat, keypoints, desc, pattern);

        offset += nkeypointsLevel;

        // Scale keypoint coordinates
        if (level != 0)
        {
            float scale = mvScaleFactor[level]; //getScale(level, firstLevel, scaleFactor);
            for (vector<KeyPoint>::iterator keypoint = keypoints.begin(),
                 keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint)
                keypoint->pt *= scale;
        }
        // And add the keypoints to the output
        _keypoints.insert(_keypoints.end(), keypoints.begin(), keypoints.end());
    }
}

构建高斯金金字塔

    // Pre-compute the scale pyramid  构建高斯金字塔
    ComputePyramid(image);

void ORBextractor::ComputePyramid(cv::Mat image)
{
    for (int level = 0; level < nlevels; ++level)
    {
        float scale = mvInvScaleFactor[level];
        Size sz(cvRound((float)image.cols*scale), cvRound((float)image.rows*scale));
        // Compute the resized image
        if( level != 0 )
        {
            resize(mvImagePyramid[level-1], mvImagePyramid[level], sz, 0, 0, INTER_LINEAR); //INTER_LINEAR是指用双线性插值的方式进行插值。
        }
        else
        {
	  //此函数将图像转化为更大的图像，并用BORDER_REFLECT_101的方式来处理边缘图像
          mvImagePyramid[level] = image;
        }
    }
}

其中mvImagePyramid[level] = image 表示该图像level的缩放图像

四叉树

    vector < vector<KeyPoint> > allKeypoints;
    ComputeKeyPointsOctTree(allKeypoints);

void ORBextractor::ComputeKeyPointsOctTree(vector<vector<KeyPoint> >& allKeypoints)
{
    allKeypoints.resize(nlevels);
    //设定每个格子的大小
    const float W = 30;
    //每层提想分别提取特征点
    for (int level = 0; level < nlevels; ++level)
    {
        //设定该层图像中检测的X，Y最大最小坐标   由于PATCH 15 和EDGE_THRESHOLD 19 相差4 所以应该减去3,从每副图像的第16个元素开始查询FAST关键点
        //为何要去除边缘呢，是因为边缘的特征点不具有代表性
        const int minBorderX = EDGE_THRESHOLD-3;
        const int minBorderY = minBorderX;
        const int maxBorderX = mvImagePyramid[level].cols-EDGE_THRESHOLD+3;
        const int maxBorderY = mvImagePyramid[level].rows-EDGE_THRESHOLD+3;
        // 用于分配的关键点
        vector<cv::KeyPoint> vToDistributeKeys;
        vToDistributeKeys.reserve(nfeatures*10);
        
        const float width = (maxBorderX-minBorderX);
        const float height = (maxBorderY-minBorderY);
        // 将待检测区域划分为格子的行列数
        const int nCols = width/W;
        const int nRows = height/W;
	// 重新计算每个格子的大小
        const int wCell = ceil(width/nCols);
        const int hCell = ceil(height/nRows);
	// 在每个格子内进行fast特征检测
        for(int i=0; i<nRows; i++)
        {
            const float iniY =minBorderY+i*hCell;
	    //为什么加6呢，是因为在FAST角点检测时是需要3为半径的圆周围的16个点进行比较的，因此会加6
            float maxY = iniY+hCell+6;

            if(iniY>=maxBorderY-3)
                continue;
            if(maxY>maxBorderY)
                maxY = maxBorderY;

            for(int j=0; j<nCols; j++)
            {
                const float iniX =minBorderX+j*wCell;
                float maxX = iniX+wCell+6;
                if(iniX>=maxBorderX-6)
                    continue;
                if(maxX>maxBorderX)
                    maxX = maxBorderX;


                //每个单元格内检测
                vector<cv::KeyPoint> vKeysCell;
                FAST(mvImagePyramid[level].rowRange(iniY,maxY).colRange(iniX,maxX),
                     vKeysCell,iniThFAST,true);
                //如果检测为空就降低阈值在进行检测
                if(vKeysCell.empty())
                {
                    FAST(mvImagePyramid[level].rowRange(iniY,maxY).colRange(iniX,maxX),
                         vKeysCell,minThFAST,true);
                }
                //如果检测不为空就将检测到的特征点放到vToDistributeKeys中
                if(!vKeysCell.empty())
                {
                    for(vector<cv::KeyPoint>::iterator vit=vKeysCell.begin(); vit!=vKeysCell.end();vit++)
                    {
                        (*vit).pt.x+=j*wCell;
                        (*vit).pt.y+=i*hCell;
                        vToDistributeKeys.push_back(*vit);
                    }
                }

            }
        }

        vector<KeyPoint> & keypoints = allKeypoints[level];
        keypoints.reserve(nfeatures);
        //vToDistributeKeys存储该层的关键点 minBorderX 最小x坐标    maxBorderX 最大x坐标 minBorderY最小y坐标 maxBorderY最大y坐标 
	//mnFeaturesPerLevel[level]该层检测到的特征点数量 level当前特征点所在的层数
        keypoints = DistributeOctTree(vToDistributeKeys, minBorderX, maxBorderX,
                                      minBorderY, maxBorderY,mnFeaturesPerLevel[level], level);
        //计算特征点Patch的大小，根据每层的尺度的不同而不同
        const int scaledPatchSize = PATCH_SIZE*mvScaleFactor[level];

        // Add border to coordinates and scale information  将边界信息考虑进去计算特征点的位置
        const int nkps = keypoints.size();
        for(int i=0; i<nkps ; i++)
        {
            keypoints[i].pt.x+=minBorderX;//特征点的x坐标
            keypoints[i].pt.y+=minBorderY;//特征点的y坐标
            keypoints[i].octave=level;          //特征点所在的层数
            keypoints[i].size = scaledPatchSize;//特征点Patch的大小将来计算描述子时使用
        }
    }

    // compute orientations  计算特征点的方向
    for (int level = 0; level < nlevels; ++level)
        computeOrientation(mvImagePyramid[level], allKeypoints[level], umax);
}

1）分为长宽30像素点的格子
2）在每个格子进行fast检测，圆周围的16个点进行比较

//每个单元格内检测
                vector<cv::KeyPoint> vKeysCell;
                FAST(mvImagePyramid[level].rowRange(iniY,maxY).colRange(iniX,maxX),
                     vKeysCell,iniThFAST,true);
                //如果检测为空就降低阈值在进行检测
                if(vKeysCell.empty())
                {
                    FAST(mvImagePyramid[level].rowRange(iniY,maxY).colRange(iniX,maxX),
                         vKeysCell,minThFAST,true);
                }
                //如果检测不为空就将检测到的特征点放到vToDistributeKeys中
                if(!vKeysCell.empty())
                {
                    for(vector<cv::KeyPoint>::iterator vit=vKeysCell.begin(); vit!=vKeysCell.end();vit++)
                    {
                        (*vit).pt.x+=j*wCell;
                        (*vit).pt.y+=i*hCell;
                        vToDistributeKeys.push_back(*vit);
                    }
                }

vit是关键点，位置赋值vToDistributeKeys中
接下来

vector<KeyPoint> & keypoints = allKeypoints[level];
        keypoints.reserve(nfeatures);
        //vToDistributeKeys存储该层的关键点 minBorderX 最小x坐标 maxBorderX 最大x坐标 minBorderY最小y坐标 maxBorderY最大y坐标 
	    //mnFeaturesPerLevel[level]该层检测到的特征点数量 level当前特征点所在的层数
        keypoints = DistributeOctTree(vToDistributeKeys, minBorderX, maxBorderX,
                                      minBorderY, maxBorderY,mnFeaturesPerLevel[level], level);

DistributeOctTree()函数 四分树

vector<cv::KeyPoint> ORBextractor::DistributeOctTree(const vector<cv::KeyPoint>& vToDistributeKeys, const int &minX,
                                       const int &maxX, const int &minY, const int &maxY, const int &N, const int &level)
{
    // Compute how many initial nodes   计算有多少个初始的节点数量，为了满足每个节点是正方形的，根节点的数量
    const int nIni = round(static_cast<float>(maxX-minX)/(maxY-minY));
    //得到每个节点之间的距离
    const float hX = static_cast<float>(maxX-minX)/nIni;
    //分开的块
    list<ExtractorNode> lNodes;

    vector<ExtractorNode*> vpIniNodes;
    vpIniNodes.resize(nIni);
    //初始化各个节点
    for(int i=0; i<nIni; i++)
    {   
        //四叉树是每次根据特定条件将一个结点分成四等分，四个区域左上(UL)，右上(UR)，左下(BL)，右下(BR
        ExtractorNode ni;
        ni.UL = cv::Point2i(hX*static_cast<float>(i),0);
        ni.UR = cv::Point2i(hX*static_cast<float>(i+1),0);
        ni.BL = cv::Point2i(ni.UL.x,maxY-minY);
        ni.BR = cv::Point2i(ni.UR.x,maxY-minY);
        ni.vKeys.reserve(vToDistributeKeys.size());

        lNodes.push_back(ni);
        vpIniNodes[i] = &lNodes.back();
    }

    //Associate points to childs   把关键点分配到各个节点中
    for(size_t i=0;i<vToDistributeKeys.size();i++)
    {
        const cv::KeyPoint &kp = vToDistributeKeys[i];
        vpIniNodes[kp.pt.x/hX]->vKeys.push_back(kp);
    }

    list<ExtractorNode>::iterator lit = lNodes.begin();

    while(lit!=lNodes.end())
    {
      //如果节点中只有一个特征点时，不在进行划分
        if(lit->vKeys.size()==1)
        {
            lit->bNoMore=true;
            lit++;
        }
        else if(lit->vKeys.empty()) //如果该节点中无特征点时，就删除该节点
            lit = lNodes.erase(lit);
        else
            lit++;
    }

    bool bFinish = false;

    int iteration = 0;

    vector<pair<int,ExtractorNode*> > vSizeAndPointerToNode;//节点及对应包含的特征数
    vSizeAndPointerToNode.reserve(lNodes.size()*4);

    while(!bFinish)
    {
        iteration++;
        // 初始节点个数，用于判断是否节点再一次进行了划分
        int prevSize = lNodes.size();

        lit = lNodes.begin();
        // 表示节点分解次数
        int nToExpand = 0;

        vSizeAndPointerToNode.clear();
        
        while(lit!=lNodes.end())
        {
            if(lit->bNoMore)
            {
                // If node only contains one point do not subdivide and continue  表面只有一个特征点，不在进行划分
                lit++;
                continue;
            }
            else
            {
                // If more than one point, subdivide   如果有超过一个节点就继续划分
                ExtractorNode n1,n2,n3,n4;
                lit->DivideNode(n1,n2,n3,n4);//将该节点划分为4个节点

                // Add childs if they contain points  如果子节点中包含着特征点就添加该节点，否则删除节点
                if(n1.vKeys.size()>0)
                {
                    lNodes.push_front(n1);                    
                    if(n1.vKeys.size()>1)
                    {
                        nToExpand++;
                        vSizeAndPointerToNode.push_back(make_pair(n1.vKeys.size(),&lNodes.front()));
                        lNodes.front().lit = lNodes.begin();
                    }
                }
                if(n2.vKeys.size()>0)
                {
                    lNodes.push_front(n2);
                    if(n2.vKeys.size()>1)
                    {
                        nToExpand++;
                        vSizeAndPointerToNode.push_back(make_pair(n2.vKeys.size(),&lNodes.front()));
                        lNodes.front().lit = lNodes.begin();
                    }
                }
                if(n3.vKeys.size()>0)
                {
                    lNodes.push_front(n3);
                    if(n3.vKeys.size()>1)
                    {
                        nToExpand++;
                        vSizeAndPointerToNode.push_back(make_pair(n3.vKeys.size(),&lNodes.front()));
                        lNodes.front().lit = lNodes.begin();
                    }
                }
                if(n4.vKeys.size()>0)
                {
                    lNodes.push_front(n4);
                    if(n4.vKeys.size()>1)
                    {
                        nToExpand++;
                        vSizeAndPointerToNode.push_back(make_pair(n4.vKeys.size(),&lNodes.front()));
                        lNodes.front().lit = lNodes.begin();
                    }
                }
                //删除该节点，因为有字节点包含了这些特征点了，所以为了减少冗余节点就删除该父节点
                lit=lNodes.erase(lit);
                continue;
            }
        }       

        // Finish if there are more nodes than required features
        // or all nodes contain just one point// 当节点个数大于需分配的特征数或者所有的节点只有一个特征点（节点不能划分）的时候，则结束。
        if((int)lNodes.size()>=N || (int)lNodes.size()==prevSize)
        {
            bFinish = true;
        }
        else if(((int)lNodes.size()+nToExpand*3)>N)//节点展开次数乘以3用于表明下一次的节点分解可能超过特征数，即为最后一次分解
        {

            while(!bFinish)
            {

                prevSize = lNodes.size();

                vector<pair<int,ExtractorNode*> > vPrevSizeAndPointerToNode = vSizeAndPointerToNode;
                vSizeAndPointerToNode.clear();

                sort(vPrevSizeAndPointerToNode.begin(),vPrevSizeAndPointerToNode.end());
                for(int j=vPrevSizeAndPointerToNode.size()-1;j>=0;j--)
                {
                    ExtractorNode n1,n2,n3,n4;
                    vPrevSizeAndPointerToNode[j].second->DivideNode(n1,n2,n3,n4);

                    // Add childs if they contain points  // 划分之后进一步的判断
                    if(n1.vKeys.size()>0)
                    {
                        lNodes.push_front(n1);
                        if(n1.vKeys.size()>1)
                        {
                            vSizeAndPointerToNode.push_back(make_pair(n1.vKeys.size(),&lNodes.front()));
                            lNodes.front().lit = lNodes.begin();
                        }
                    }
                    if(n2.vKeys.size()>0)
                    {
                        lNodes.push_front(n2);
                        if(n2.vKeys.size()>1)
                        {
                            vSizeAndPointerToNode.push_back(make_pair(n2.vKeys.size(),&lNodes.front()));
                            lNodes.front().lit = lNodes.begin();
                        }
                    }
                    if(n3.vKeys.size()>0)
                    {
                        lNodes.push_front(n3);
                        if(n3.vKeys.size()>1)
                        {
                            vSizeAndPointerToNode.push_back(make_pair(n3.vKeys.size(),&lNodes.front()));
                            lNodes.front().lit = lNodes.begin();
                        }
                    }
                    if(n4.vKeys.size()>0)
                    {
                        lNodes.push_front(n4);
                        if(n4.vKeys.size()>1)
                        {
                            vSizeAndPointerToNode.push_back(make_pair(n4.vKeys.size(),&lNodes.front()));
                            lNodes.front().lit = lNodes.begin();
                        }
                    }

                    lNodes.erase(vPrevSizeAndPointerToNode[j].second->lit);

                    if((int)lNodes.size()>=N)
                        break;
                }

                if((int)lNodes.size()>=N || (int)lNodes.size()==prevSize)
                    bFinish = true;

            }
        }
    }

    // Retain the best point in each node  保留每个节点下最好的特征点
    vector<cv::KeyPoint> vResultKeys;
    vResultKeys.reserve(nfeatures);
    for(list<ExtractorNode>::iterator lit=lNodes.begin(); lit!=lNodes.end(); lit++)
    {
        vector<cv::KeyPoint> &vNodeKeys = lit->vKeys;
        cv::KeyPoint* pKP = &vNodeKeys[0];
        float maxResponse = pKP->response;

        for(size_t k=1;k<vNodeKeys.size();k++)
        {
            if(vNodeKeys[k].response>maxResponse)
            {
                pKP = &vNodeKeys[k];
                maxResponse = vNodeKeys[k].response;
            }
        }

        vResultKeys.push_back(*pKP);
    }

    return vResultKeys;
}

const int nIni = round(static_cast<float>(maxX-minX)/(maxY-minY));

四舍五入根节点 正方形

for(int i=0; i<nIni; i++)
    {   
        //四叉树是每次根据特定条件将一个结点分成四等分，四个区域左上(UL)，右上(UR)，左下(BL)，右下(BR
        ExtractorNode ni;
        ni.UL = cv::Point2i(hX*static_cast<float>(i),0);
        ni.UR = cv::Point2i(hX*static_cast<float>(i+1),0);
        ni.BL = cv::Point2i(ni.UL.x,maxY-minY);
        ni.BR = cv::Point2i(ni.UR.x,maxY-minY);
        ni.vKeys.reserve(vToDistributeKeys.size());

        lNodes.push_back(ni);
        vpIniNodes[i] = &lNodes.back();
    }
      //Associate points to childs   把关键点分配到各个节点中
    for(size_t i=0;i<vToDistributeKeys.size();i++)
    {
        const cv::KeyPoint &kp = vToDistributeKeys[i];
        vpIniNodes[kp.pt.x/hX]->vKeys.push_back(kp);
    }

不断分成四分 知道每个小框中只有一个节点
vResultKeys.push_back(*pKP) 存入返回

computeOrientation 计算方向

    // compute orientations  计算特征点的方向
    for (int level = 0; level < nlevels; ++level)
        computeOrientation(mvImagePyramid[level], allKeypoints[level], umax);

static void computeOrientation(const Mat& image, vector<KeyPoint>& keypoints, const vector<int>& umax)
{
    for (vector<KeyPoint>::iterator keypoint = keypoints.begin(),
         keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint)
    {
        keypoint->angle = IC_Angle(image, keypoint->pt, umax);
    }
}

// 灰度质心法计算特征点方向    image待提取特征点的图片  pt是特征点的位置 
static float IC_Angle(const Mat& image, Point2f pt,  const vector<int> & u_max)
{
    int m_01 = 0, m_10 = 0;

    const uchar* center = &image.at<uchar> (cvRound(pt.y), cvRound(pt.x));

    // Treat the center line differently, v=0  v=0这一行是特殊的，需要单独计算
    for (int u = -HALF_PATCH_SIZE; u <= HALF_PATCH_SIZE; ++u)
        m_10 += u * center[u];//m_10=SUM(u*I(u,v))    m_01=SUM(v*I(u,v))

    // Go line by line in the circuI853lar patch
    int step = (int)image.step1();
    for (int v = 1; v <= HALF_PATCH_SIZE; ++v)
    {
        // Proceed over the two lines
        int v_sum = 0;
        int d = u_max[v];
        for (int u = -d; u <= d; ++u)
        {
            int val_plus = center[u + v*step], val_minus = center[u - v*step];
            v_sum += (val_plus - val_minus);  //计算上下的时候是有符号的，所以这边是减
            m_10 += u * (val_plus + val_minus);//将(u,v)和(u,-v)两个点的像素一起计算   这边加是由于u已经确定好了符号
        }
        m_01 += v * v_sum;  //将x=v这条直线上所有的坐标点的像素值求和在进行计算
    }

    return fastAtan2((float)m_01, (float)m_10);
}

灰度质心法

computeOrbDescriptor

static void computeOrbDescriptor(const KeyPoint& kpt,
                                 const Mat& img, const Point* pattern,
                                 uchar* desc)
{
  //取关键点的方向角度并进一步转化为弧度
    float angle = (float)kpt.angle*factorPI;
    float a = (float)cos(angle), b = (float)sin(angle);
    
    const uchar* center = &img.at<uchar>(cvRound(kpt.pt.y), cvRound(kpt.pt.x));
    const int step = (int)img.step;
    //使得该关键点具有旋转不变性
    #define GET_VALUE(idx) \
        center[cvRound(pattern[idx].x*b + pattern[idx].y*a)*step + \
               cvRound(pattern[idx].x*a - pattern[idx].y*b)]

     //生成8*32 = 256维的向量代表这个关键点，并通过将点的坐标转换到以描述子主方向为X轴的坐标系下，从而使得描述子具有旋转不变性。
    for (int i = 0; i < 32; ++i, pattern += 16)
    {
        int t0, t1, val;
        t0 = GET_VALUE(0); t1 = GET_VALUE(1);
        val = t0 < t1;
        t0 = GET_VALUE(2); t1 = GET_VALUE(3);
        val |= (t0 < t1) << 1;
        t0 = GET_VALUE(4); t1 = GET_VALUE(5);
        val |= (t0 < t1) << 2;
        t0 = GET_VALUE(6); t1 = GET_VALUE(7);
        val |= (t0 < t1) << 3;
        t0 = GET_VALUE(8); t1 = GET_VALUE(9);
        val |= (t0 < t1) << 4;
        t0 = GET_VALUE(10); t1 = GET_VALUE(11);
        val |= (t0 < t1) << 5;
        t0 = GET_VALUE(12); t1 = GET_VALUE(13);
        val |= (t0 < t1) << 6;
        t0 = GET_VALUE(14); t1 = GET_VALUE(15);
        val |= (t0 < t1) << 7;

        desc[i] = (uchar)val;
    }

    #undef GET_VALUE
}

注意角度