Opencv特征检测之ORB算法原理及应用详解

特征是图像信息的另一种数字表达形式。一组好的特征对于在指定
任务上的最终表现至关重要。
视觉里程（VO）的主要问题是如何根据图像特征来估计相机运动。但是，整幅图像用来计算分析通常比较耗时，故而转换为分析图像中的特征点的运动。
计算机视觉领域的研究者们在长年的研究中，设计了许多更加稳定的局部图像特征，如著名的SIFT, SURF,ORB 等等。相比于朴素的角点，这些人工设计的特征点能够拥有如下的性质：
1. 可重复性（Repeatability）：相同的“区域”可以在不同的图像中被找到。
2. 可区别性（Distinctiveness）：不同的“区域”有不同的表达。
3. 高效率（Efficiency）：同一图像中，特征点的数量应远小于像素的数量。
4. 本地性（Locality）：特征仅与一小片图像区域相关。
特征点由关键点（Key-point）和描述子（Descriptor）两部分组成。比方说，当我们谈论 SIFT 特征时，是指“提取 SIFT 关键点，并计算 SIFT 描述子”两件事情。关键点是指该特征点在图像里的位置，有些特征点还具有朝向、大小等信息。描述子通常是一个向量，按照某种人为设计的方式，描述了该关键点周围像素的信息。描述子是按照“外观相似的特征应该有相似的描述子”的原则设计的。因此，只要两个特征点的描述子在向量空间上的距离相近，就可以认为它们是同样的特征点。
下文将详细讲述ORB算法（关键点+特征描述子）原理。

1. ORB 简介

ORB论文：https://www.gwylab.com/download/ORB_2012.pdf
历史上，研究者提出过许多图像特征。它们有些很精确，在相机的运动和光照变化下仍具有相似表达，但相应地需要较大的计算量。其中，SIFT(尺度不变特征变换，Scale Invariant FeatureTransform) 当属最为经典的一种。它充分考虑了在图像变换过程中出现的光照，尺度，旋转等变化，但随之而来的是极大的计算量。由于整个 SLAM 过程中，图像特征的提取与匹配仅仅是诸多环节中的一个，到目前（2016 年）为止，普通 PC 的 CPU还无法实时地计算 SIFT 特征，进行定位与建图。所以在 SLAM 中我们甚少使用这种“奢侈”的图像特征。
另一些特征，则考虑适当降低精度和鲁棒性，提升计算的速度。例如 FAST 关键点属于计算特别快的一种特征点（注意这里“关键点”的用词，说明它没有描述子）。而 ORB（Oriented FAST and Rotated BRIEF）特征则是目前看来非常具有代表性的实时图像特征。它改进了 FAST 检测子 [33] 不具有方向性的问题，并采用速度极快的二进制描述子BRIEF，使整个图像特征提取的环节大大加速。根据作者在论文中的测试，在同一幅图像中同时提取约 1000 个特征点的情况下，ORB 约要花费 15.3ms，SURF 约花费 217.3ms，SIFT 约花费 5228.7ms。由此可以看出 ORB 在保持了特征子具有旋转，尺度不变性的同时，速度方面提升明显，对于实时性要求很高的 SLAM 来说是一个很好的选择。大部分特征提取都具有较好的并行性，可以通过 GPU 等设备来加速计算。经过 GPU加速后的 SIFT，就可以满足实时计算要求。但是，引入 GPU 将带来整个 SLAM 成本的提升。由此带来的性能提升，是否足以抵去付出的计算成本，需要系统的设计人员仔细考量。在目前的 SLAM 方案中，ORB 是质量与性能之间较好的折中，因此我们以 ORB 为代表，介绍提取特征的整个过程。

2. ORB原理

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$\quad$ $\quad$ ORB特征亦由关键点和描述子两部分组成。它的关键点称为“OrientedFAST”，是一种改进的FAST角点，什么是FAST角点我们将在下文介绍。它的描述子称为BRIEF （BinaryRobustIndependentElementaryFeatures）。因此，提取ORB特征分为两个步骤：

FAST 角点提取：找出图像中的” 角点”。相较于原版的 FAST, ORB 中计算了特征
点的主方向，为后续的 BRIEF 描述子增加了旋转不变特性。
BRIEF 描述子：对前一步提取出特征点的周围图像区域进行描述。
下面我们分别介绍 FAST 和 BRIEF。

2.1 FAST关键点

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2.2 BRIEF 描述子

在提取 Oriented FAST 关键点后，我们对每个点计算其描述子。ORB 使用改进的BRIEF 特征描述。我们先来讲 BRIEF 是什么。
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具体实现：
比较好的博客：ORB算法与opencv实现

2.3 ORB算法具体实现

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2.4 Opencv源码解析

API简介

 //ORB类定义：位置..\features2d.hppstatic Ptr<ORB> cv::ORB::create (int     nfeatures = 500,             //需要的特征点总数；float   scaleFactor = 1.2f,         //尺度因子；       int     nlevels = 8,                //金字塔层数；int     edgeThreshold = 31,         //边界阈值；int     firstLevel = 0,             //起始层；int     WTA_K = 2,                  //描述子形成方法,WTA_K=2表示，采用两两比较；int     scoreType = ORB::HARRIS_SCORE,  //角点响应函数，可以选择Harris或者Fast的方法；                   int     patchSize = 31,            //特征点邻域大小；int     fastThreshold = 20)        //FAST阈值

源码：
头文件类定义如下：

/*!ORB implementation.
*/
class CV_EXPORTS_W ORB : public Feature2D
{
public:// the size of the signature in bytesenum { kBytes = 32, HARRIS_SCORE=0, FAST_SCORE=1 };CV_WRAP explicit ORB(int nfeatures = 500, float scaleFactor = 1.2f, int nlevels = 8, int edgeThreshold = 31,//构造函数int firstLevel = 0, int WTA_K=2, int scoreType=ORB::HARRIS_SCORE, int patchSize=31 );// returns the descriptor size in bytesint descriptorSize() const;   //描述子占用的字节数，默认32字节// returns the descriptor typeint descriptorType() const;//描述子类型，8位整形数// Compute the ORB features and descriptors on an imagevoid operator()(InputArray image, InputArray mask, vector<KeyPoint>& keypoints) const;// Compute the ORB features and descriptors on an imagevoid operator()( InputArray image, InputArray mask, vector<KeyPoint>& keypoints,    //提取特征点与形成描述子OutputArray descriptors, bool useProvidedKeypoints=false ) const;AlgorithmInfo* info() const;protected:void computeImpl( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const;//计算描述子void detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;//检测特征点CV_PROP_RW int nfeatures;//特征点总数CV_PROP_RW double scaleFactor;//尺度因子CV_PROP_RW int nlevels;//金字塔内层数CV_PROP_RW int edgeThreshold;//边界阈值CV_PROP_RW int firstLevel;//开始层数CV_PROP_RW int WTA_K;//描述子形成方法，默认WTA_K=2，两两比较CV_PROP_RW int scoreType;//角点响应函数CV_PROP_RW int patchSize;//邻域Patch大小
};

特征提取及形成描述子：通过这个函数对图像提取Fast特征点或者计算特征描述子

_image:输入图像；
_mask:掩码图像;
_keypoints:输入角点；
_descriptors:如果为空，只寻找特征点，不计算特征描述子；
_useProvidedKeypoints:如果为true,函数只计算特征描述子；

/** Compute the ORB features and descriptors on an image* @param img the image to compute the features and descriptors on* @param mask the mask to apply* @param keypoints the resulting keypoints* @param descriptors the resulting descriptors* @param do_keypoints if true, the keypoints are computed, otherwise used as an input* @param do_descriptors if true, also computes the descriptors*/
void ORB::operator()( InputArray _image, InputArray _mask, vector<KeyPoint>& _keypoints,OutputArray _descriptors, bool useProvidedKeypoints) const
{CV_Assert(patchSize >= 2);bool do_keypoints = !useProvidedKeypoints;bool do_descriptors = _descriptors.needed();if( (!do_keypoints && !do_descriptors) || _image.empty() )return;//ROI handlingconst int HARRIS_BLOCK_SIZE = 9;//Harris角点响应需要的边界大小int halfPatchSize = patchSize / 2;.//邻域半径int border = std::max(edgeThreshold, std::max(halfPatchSize, HARRIS_BLOCK_SIZE/2))+1;//采用最大的边界Mat image = _image.getMat(), mask = _mask.getMat();if( image.type() != CV_8UC1 )cvtColor(_image, image, CV_BGR2GRAY);//转灰度图int levelsNum = this->nlevels;//金字塔层数if( !do_keypoints )   //不做特征点检测{// if we have pre-computed keypoints, they may use more levels than it is set in parameters// !!!TODO!!! implement more correct method, independent from the used keypoint detector.// Namely, the detector should provide correct size of each keypoint. Based on the keypoint size// and the algorithm used (i.e. BRIEF, running on 31x31 patches) we should compute the approximate// scale-factor that we need to apply. Then we should cluster all the computed scale-factors and// for each cluster compute the corresponding image.//// In short, ultimately the descriptor should// ignore octave parameter and deal only with the keypoint size.levelsNum = 0;for( size_t i = 0; i < _keypoints.size(); i++ )levelsNum = std::max(levelsNum, std::max(_keypoints[i].octave, 0));//提取特征点的最大层数levelsNum++;}// Pre-compute the scale pyramidsvector<Mat> imagePyramid(levelsNum), maskPyramid(levelsNum);//创建尺度金字塔图像for (int level = 0; level < levelsNum; ++level){float scale = 1/getScale(level, firstLevel, scaleFactor);  //每层对应的尺度/*static inline float getScale(int level, int firstLevel, double scaleFactor){return (float)std::pow(scaleFactor, (double)(level - firstLevel));}	*/Size sz(cvRound(image.cols*scale), cvRound(image.rows*scale));//每层对应的图像大小Size wholeSize(sz.width + border*2, sz.height + border*2);Mat temp(wholeSize, image.type()), masktemp;imagePyramid[level] = temp(Rect(border, border, sz.width, sz.height));if( !mask.empty() ){masktemp = Mat(wholeSize, mask.type());maskPyramid[level] = masktemp(Rect(border, border, sz.width, sz.height));}// Compute the resized imageif( level != firstLevel )    //得到金字塔每层的图像{if( level < firstLevel ){resize(image, imagePyramid[level], sz, 0, 0, INTER_LINEAR);if (!mask.empty())resize(mask, maskPyramid[level], sz, 0, 0, INTER_LINEAR);}else{resize(imagePyramid[level-1], imagePyramid[level], sz, 0, 0, INTER_LINEAR);if (!mask.empty()){resize(maskPyramid[level-1], maskPyramid[level], sz, 0, 0, INTER_LINEAR);threshold(maskPyramid[level], maskPyramid[level], 254, 0, THRESH_TOZERO);}}copyMakeBorder(imagePyramid[level], temp, border, border, border, border,//扩大图像的边界BORDER_REFLECT_101+BORDER_ISOLATED);if (!mask.empty())copyMakeBorder(maskPyramid[level], masktemp, border, border, border, border,BORDER_CONSTANT+BORDER_ISOLATED);}else{copyMakeBorder(image, temp, border, border, border, border,//扩大图像的四个边界BORDER_REFLECT_101);if( !mask.empty() )copyMakeBorder(mask, masktemp, border, border, border, border,BORDER_CONSTANT+BORDER_ISOLATED);}}// Pre-compute the keypoints (we keep the best over all scales, so this has to be done beforehandvector < vector<KeyPoint> > allKeypoints;if( do_keypoints )//提取角点{// Get keypoints, those will be far enough from the border that no check will be required for the descriptorcomputeKeyPoints(imagePyramid, maskPyramid, allKeypoints,  //对每一层图像提取角点，见下面(1)的分析nfeatures, firstLevel, scaleFactor,edgeThreshold, patchSize, scoreType);// make sure we have the right number of keypoints keypoints/*vector<KeyPoint> temp;for (int level = 0; level < n_levels; ++level){vector<KeyPoint>& keypoints = all_keypoints[level];temp.insert(temp.end(), keypoints.begin(), keypoints.end());keypoints.clear();}KeyPoint::retainBest(temp, n_features_);for (vector<KeyPoint>::iterator keypoint = temp.begin(),keypoint_end = temp.end(); keypoint != keypoint_end; ++keypoint)all_keypoints[keypoint->octave].push_back(*keypoint);*/}else  //不提取角点{// Remove keypoints very close to the borderKeyPointsFilter::runByImageBorder(_keypoints, image.size(), edgeThreshold);// Cluster the input keypoints depending on the level they were computed atallKeypoints.resize(levelsNum);for (vector<KeyPoint>::iterator keypoint = _keypoints.begin(),keypointEnd = _keypoints.end(); keypoint != keypointEnd; ++keypoint)allKeypoints[keypoint->octave].push_back(*keypoint);    //把角点信息存入allKeypoints内// Make sure we rescale the coordinatesfor (int level = 0; level < levelsNum; ++level)   //把角点位置信息缩放到指定层位置上{if (level == firstLevel)continue;vector<KeyPoint> & keypoints = allKeypoints[level];float scale = 1/getScale(level, firstLevel, scaleFactor);for (vector<KeyPoint>::iterator keypoint = keypoints.begin(),keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint)keypoint->pt *= scale;   //缩放}}Mat descriptors;        vector<Point> pattern;if( do_descriptors ) //计算特征描述子{int nkeypoints = 0;for (int level = 0; level < levelsNum; ++level)nkeypoints += (int)allKeypoints[level].size();//得到所有层的角点总数if( nkeypoints == 0 )_descriptors.release();else{_descriptors.create(nkeypoints, descriptorSize(), CV_8U);//创建一个矩阵存放描述子,每一行表示一个角点信息descriptors = _descriptors.getMat();}const int npoints = 512;//取512个点,共256对,产生256维描述子,32个字节Point patternbuf[npoints];const Point* pattern0 = (const Point*)bit_pattern_31_;//训练好的256对数据点位置if( patchSize != 31 ){pattern0 = patternbuf;makeRandomPattern(patchSize, patternbuf, npoints);}CV_Assert( WTA_K == 2 || WTA_K == 3 || WTA_K == 4 );if( WTA_K == 2 )  //WTA_K=2使用两个点之间作比较std::copy(pattern0, pattern0 + npoints, std::back_inserter(pattern));else{int ntuples = descriptorSize()*4;initializeOrbPattern(pattern0, pattern, ntuples, WTA_K, npoints);}}_keypoints.clear();int offset = 0;for (int level = 0; level < levelsNum; ++level)//依次计算每一层的角点描述子{// Get the features and compute their orientationvector<KeyPoint>& keypoints = allKeypoints[level];int nkeypoints = (int)keypoints.size();//本层内角点个数// Compute the descriptorsif (do_descriptors){Mat desc;if (!descriptors.empty()){desc = descriptors.rowRange(offset, offset + nkeypoints);}offset += nkeypoints;  //偏移量// preprocess the resized imageMat& workingMat = imagePyramid[level];//boxFilter(working_mat, working_mat, working_mat.depth(), Size(5,5), Point(-1,-1), true, BORDER_REFLECT_101);GaussianBlur(workingMat, workingMat, Size(7, 7), 2, 2, BORDER_REFLECT_101);//高斯平滑图像computeDescriptors(workingMat, keypoints, desc, pattern, descriptorSize(), WTA_K);//计算本层内角点的描述子,（3）}// Copy to the output dataif (level != firstLevel)  //角点位置信息返回到原图上{float scale = 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());//存入描述子信息，返回}
}

2.4.1 提取角点

imagePyramid:即构造好的金字塔

/** Compute the ORB keypoints on an image* @param image_pyramid the image pyramid to compute the features and descriptors on* @param mask_pyramid the masks to apply at every level* @param keypoints the resulting keypoints, clustered per level*/
static void computeKeyPoints(const vector<Mat>& imagePyramid,const vector<Mat>& maskPyramid,vector<vector<KeyPoint> >& allKeypoints,int nfeatures, int firstLevel, double scaleFactor,int edgeThreshold, int patchSize, int scoreType )
{int nlevels = (int)imagePyramid.size();  //金字塔层数vector<int> nfeaturesPerLevel(nlevels);// fill the extractors and descriptors for the corresponding scalesfloat factor = (float)(1.0 / scaleFactor);float ndesiredFeaturesPerScale = nfeatures*(1 - factor)/(1 - (float)pow((double)factor, (double)nlevels));//int sumFeatures = 0;for( int level = 0; level < nlevels-1; level++ )   //对每层图像上分配相应角点数{nfeaturesPerLevel[level] = cvRound(ndesiredFeaturesPerScale);sumFeatures += nfeaturesPerLevel[level];ndesiredFeaturesPerScale *= factor;}nfeaturesPerLevel[nlevels-1] = std::max(nfeatures - sumFeatures, 0);//剩下角点数，由最上层图像提取// Make sure we forget about what is too close to the boundary//edge_threshold_ = std::max(edge_threshold_, patch_size_/2 + kKernelWidth / 2 + 2);// pre-compute the end of a row in a circular patchint halfPatchSize = patchSize / 2;           //计算每个特征点圆邻域的位置信息vector<int> umax(halfPatchSize + 2);int v, v0, vmax = cvFloor(halfPatchSize * sqrt(2.f) / 2 + 1);int vmin = cvCeil(halfPatchSize * sqrt(2.f) / 2);for (v = 0; v <= vmax; ++v)           //umax[v] = cvRound(sqrt((double)halfPatchSize * halfPatchSize - v * v));// Make sure we are symmetricfor (v = halfPatchSize, v0 = 0; v >= vmin; --v){while (umax[v0] == umax[v0 + 1])++v0;umax[v] = v0;++v0;}allKeypoints.resize(nlevels);for (int level = 0; level < nlevels; ++level){int featuresNum = nfeaturesPerLevel[level];allKeypoints[level].reserve(featuresNum*2);vector<KeyPoint> & keypoints = allKeypoints[level];// Detect FAST features, 20 is a good thresholdFastFeatureDetector fd(20, true);      fd.detect(imagePyramid[level], keypoints, maskPyramid[level]);//Fast角点检测// Remove keypoints very close to the borderKeyPointsFilter::runByImageBorder(keypoints, imagePyramid[level].size(), edgeThreshold);//去除邻近边界的点if( scoreType == ORB::HARRIS_SCORE ){// Keep more points than necessary as FAST does not give amazing cornersKeyPointsFilter::retainBest(keypoints, 2 * featuresNum);//按Fast强度排序,保留前2*featuresNum个特征点// Compute the Harris cornerness (better scoring than FAST)HarrisResponses(imagePyramid[level], keypoints, 7, HARRIS_K); //计算每个角点的Harris强度响应}//cull to the final desired level, using the new Harris scores or the original FAST scores.KeyPointsFilter::retainBest(keypoints, featuresNum);//按Harris强度排序，保留前featuresNum个float sf = getScale(level, firstLevel, scaleFactor);// Set the level of the coordinatesfor (vector<KeyPoint>::iterator keypoint = keypoints.begin(),keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint){keypoint->octave = level;  //层信息keypoint->size = patchSize*sf; //}computeOrientation(imagePyramid[level], keypoints, halfPatchSize, umax);  //计算角点的方向，（2）分析}
}

2.4.2 质心法计算角点主方向

static void computeOrientation(const Mat& image, vector<KeyPoint>& keypoints,int halfPatchSize, const vector<int>& umax)
{// Process each keypointfor (vector<KeyPoint>::iterator keypoint = keypoints.begin(),  //为每个角点计算主方向keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint){keypoint->angle = IC_Angle(image, halfPatchSize, keypoint->pt, umax);//计算质心方向}
}

static float IC_Angle(const Mat& image, const int half_k, 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=0for (int u = -half_k; u <= half_k; ++u)m_10 += u * center[u];// Go line by line in the circular patchint step = (int)image.step1();for (int v = 1; v <= half_k; ++v)    //每次处理对称的两行v{// Proceed over the two linesint 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m_01时,位置上差一个符号m_10 += u * (val_plus + val_minus);}m_01 += v * v_sum;//计算上下两行的m_01}return fastAtan2((float)m_01, (float)m_10);//计算角度
}

2.4.3 计算特征点描述子

static void computeDescriptors(const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors,const vector<Point>& pattern, int dsize, int WTA_K)
{//convert to grayscale if more than one colorCV_Assert(image.type() == CV_8UC1);//create the descriptor mat, keypoints.size() rows, BYTES colsdescriptors = Mat::zeros((int)keypoints.size(), dsize, CV_8UC1);for (size_t i = 0; i < keypoints.size(); i++)computeOrbDescriptor(keypoints[i], image, &pattern[0], descriptors.ptr((int)i), dsize, WTA_K);
}

static void computeOrbDescriptor(const KeyPoint& kpt,const Mat& img, const Point* pattern,uchar* desc, int dsize, int WTA_K)
{float angle = kpt.angle; //angle = cvFloor(angle/12)*12.f;angle *= (float)(CV_PI/180.f);float a = (float)cos(angle), b = (float)sin(angle);const uchar* center = &img.at<uchar>(cvRound(kpt.pt.y), cvRound(kpt.pt.x));int step = (int)img.step;#if 1#define GET_VALUE(idx) \       //取旋转后一个像素点的值center[cvRound(pattern[idx].x*b + pattern[idx].y*a)*step + \cvRound(pattern[idx].x*a - pattern[idx].y*b)]
#elsefloat x, y;int ix, iy;#define GET_VALUE(idx) \ //取旋转后一个像素点，插值法(x = pattern[idx].x*a - pattern[idx].y*b, \y = pattern[idx].x*b + pattern[idx].y*a, \ix = cvFloor(x), iy = cvFloor(y), \x -= ix, y -= iy, \cvRound(center[iy*step + ix]*(1-x)*(1-y) + center[(iy+1)*step + ix]*(1-x)*y + \center[iy*step + ix+1]*x*(1-y) + center[(iy+1)*step + ix+1]*x*y))
#endifif( WTA_K == 2 ){for (int i = 0; i < dsize; ++i, pattern += 16)//每个特征描述子长度为32个字节{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;}}else if( WTA_K == 3 ){for (int i = 0; i < dsize; ++i, pattern += 12){int t0, t1, t2, val;t0 = GET_VALUE(0); t1 = GET_VALUE(1); t2 = GET_VALUE(2);val = t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0);t0 = GET_VALUE(3); t1 = GET_VALUE(4); t2 = GET_VALUE(5);val |= (t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0)) << 2;t0 = GET_VALUE(6); t1 = GET_VALUE(7); t2 = GET_VALUE(8);val |= (t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0)) << 4;t0 = GET_VALUE(9); t1 = GET_VALUE(10); t2 = GET_VALUE(11);val |= (t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0)) << 6;desc[i] = (uchar)val;}}else if( WTA_K == 4 ){for (int i = 0; i < dsize; ++i, pattern += 16){int t0, t1, t2, t3, u, v, k, val;t0 = GET_VALUE(0); t1 = GET_VALUE(1);t2 = GET_VALUE(2); t3 = GET_VALUE(3);u = 0, v = 2;if( t1 > t0 ) t0 = t1, u = 1;if( t3 > t2 ) t2 = t3, v = 3;k = t0 > t2 ? u : v;val = k;t0 = GET_VALUE(4); t1 = GET_VALUE(5);t2 = GET_VALUE(6); t3 = GET_VALUE(7);u = 0, v = 2;if( t1 > t0 ) t0 = t1, u = 1;if( t3 > t2 ) t2 = t3, v = 3;k = t0 > t2 ? u : v;val |= k << 2;t0 = GET_VALUE(8); t1 = GET_VALUE(9);t2 = GET_VALUE(10); t3 = GET_VALUE(11);u = 0, v = 2;if( t1 > t0 ) t0 = t1, u = 1;if( t3 > t2 ) t2 = t3, v = 3;k = t0 > t2 ? u : v;val |= k << 4;t0 = GET_VALUE(12); t1 = GET_VALUE(13);t2 = GET_VALUE(14); t3 = GET_VALUE(15);u = 0, v = 2;if( t1 > t0 ) t0 = t1, u = 1;if( t3 > t2 ) t2 = t3, v = 3;k = t0 > t2 ? u : v;val |= k << 6;desc[i] = (uchar)val;}}elseCV_Error( CV_StsBadSize, "Wrong WTA_K. It can be only 2, 3 or 4." );#undef GET_VALUE
}

3. ORB特征匹配

#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/highgui/highgui.hpp>using namespace std;
using namespace cv;int main ( int argc, char** argv )
{if ( argc != 3 ){cout<<"usage: feature_extraction img1 img2"<<endl;return 1;}//-- 读取图像Mat img_1 = imread ( argv[1], CV_LOAD_IMAGE_COLOR );Mat img_2 = imread ( argv[2], CV_LOAD_IMAGE_COLOR );//-- 初始化std::vector<KeyPoint> keypoints_1, keypoints_2;Mat descriptors_1, descriptors_2;Ptr<ORB> orb = ORB::create ( 500, 1.2f, 8, 31, 0, 2, ORB::HARRIS_SCORE,31,20 );//-- 第一步: 检测 Oriented FAST 角点位置orb->detect ( img_1,keypoints_1 );orb->detect ( img_2,keypoints_2 );//-- 第二步: 根据角点位置计算 BRIEF 描述子orb->compute ( img_1, keypoints_1, descriptors_1 );orb->compute ( img_2, keypoints_2, descriptors_2 );Mat outimg1;drawKeypoints( img_1, keypoints_1, outimg1, Scalar::all(-1), DrawMatchesFlags::DEFAULT );imshow("ORB特征点",outimg1);//-- 第三步: 对两幅图像中的BRIEF描述子进行匹配，使用 Hamming 距离vector<DMatch> matches;BFMatcher matcher ( NORM_HAMMING );matcher.match ( descriptors_1, descriptors_2, matches );//-- 第四步:匹配点对筛选double min_dist=10000, max_dist=0;// 找出所有匹配之间的最小距离和最大距离，即是最相似的和最不相似的两组点之间的距离for ( int i = 0; i < descriptors_1.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 );// 当描述子之间的距离大于两倍的最小距离时，即认为匹配有误。// 但有时候最小距离会非常小，设置一个经验值作为下限。std::vector< DMatch > good_matches;for ( int i = 0; i < descriptors_1.rows; i++ ){if ( matches[i].distance <= max ( 2*min_dist, 30.0 ) ){good_matches.push_back ( matches[i] );}}//-- 第五步: 绘制匹配结果Mat img_match;Mat img_goodmatch;drawMatches ( img_1, keypoints_1, img_2, keypoints_2, matches, img_match );drawMatches ( img_1, keypoints_1, img_2, keypoints_2, good_matches, img_goodmatch );imshow ( "所有匹配点对", img_match );imshow ( "优化后匹配点对", img_goodmatch );waitKey(0);return 0;
}