转载： http://blog.csdn.net/luoshixian099/article/details/48523267
CSDN-勿在浮沙筑高台
没有时间重新复制代码，只能一股脑的复制，所以代码效果不好。。。。。。
为了满足实时性的要求，前面文章中介绍过快速提取特征点算法Fast,以及特征描述子Brief。本篇文章介绍的ORB算法结合了Fast和Brief的速度优势，并做了改进，且ORB是免费。
Ethan Rublee等人2011年在《ORB：An Efficient Alternative to SIFT or SURF》文章中提出了ORB算法。结合Fast与Brief算法，并给Fast特征点增加了方向性，使得特征点具有旋转不变性，并提出了构造金字塔方法，解决尺度不变性，但文章中没有具体详述。实验证明，ORB远优于之前的SIFT与SURF算法。
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论文核心内容概述：
1.构造金字塔，在每层金字塔上采用Fast算法提取特征点，采用Harris角点响应函数，按角点响应值排序，选取前N个特征点。
2. oFast:计算每个特征点的主方向，灰度质心法，计算特征点半径为r的圆形邻域范围内的灰度质心位置。从中心位置到质心位置的向量，定义为该特 征点的主方向。
定义矩的计算公式，x,y∈[-r,r]：
质心位置：
主方向：
3.rBrief:为了解决旋转不变性，把特征点的Patch旋转到主方向上(steered Brief)。通过实验得到，描述子在各个维度上的均值比较离散(偏离0.5)，同时维度间相关性很强，说明特征点描述子区分性不好，影响匹配的效果。论文中提出采取学习的方法，采用300K个训练样本点。每一个特征点，选取Patch大小为wp=31，Patch内每对点都采用wt=5大小的子窗口灰度均值做比较，子窗口的个数即为N=(wp-wt)*(wp-wt),从N个窗口中随机选两个做比较即构成描述子的一个bit,论文中采用M=205590种可能的情况：
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1.对所有样本点，做M种测试，构成M维的描述子，每个维度上非1即0；
2.按均值对M个维度排序（以0.5为中心），组成向量T；
3.贪婪搜索：把向量T中第一个元素移动到R中，然后继续取T的第二个元素，与R中的所有元素做相关性比较，如果相关性大于指定的阈值Threshold, 抛弃T的这个元素，否则加入到R中；
4.重复第3个步骤，直到R中有256个元素，若检测完毕,少于256个元素，则降低阈值，重复上述步骤；
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rBrief:通过上面的步骤取到的256对点，构成的描述子各维度间相关性很低，区分性好；
训练前 训练后
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ORB算法步骤,参考opencv源码:
1.首先构造尺度金字塔；
金字塔共n层，与SIFT不同，每层仅有一副图像；
第s层的尺度为
第s层图像大小:
2.在不同尺度上采用Fast检测特征点；在每一层上按公式计算需要提取的特征点数n,在本层上按Fast角点响应值排序，提取前2n个特征点，然后根据Harris 角点响应值排序， 取前n个特征点，作为本层的特征点；
3.计算每个特征点的主方向（质心法）；
4.旋转每个特征点的Patch到主方向，采用上述步骤3的选取的最优的256对特征点做τ测试，构成256维描述子，占32个字节；
4.采用汉明距离做特征点匹配；
----------OpenCV源码解析-------------------------------------------------------
ORB类定义：位置..features2d.hpp
nfeatures:需要的特征点总数；
scaleFactor:尺度因子；
nlevels:金字塔层数；
edgeThreshold:边界阈值；
firstLevel:起始层；
WTA_K：描述子形成方法,WTA_K=2表示，采用两两比较；
scoreType:角点响应函数，可以选择Harris或者Fast的方法；
patchSize:特征点邻域大小；
/*!
- CV_EXPORTS_W ORB : Feature2D
- {
- :
- { kBytes = 32, HARRIS_SCORE=0, FAST_SCORE=1 };
- CV_WRAP ORB( nfeatures = 500, scaleFactor = 1.2f, nlevels = 8, edgeThreshold = 31,
- firstLevel = 0, WTA_K=2, scoreType=ORB::HARRIS_SCORE, patchSize=31 );
- descriptorSize() ;
- descriptorType() ;
- operator()(InputArray image, InputArray mask, vector<KeyPoint>& keypoints) ;
- operator()( InputArray image, InputArray mask, vector<KeyPoint>& keypoints,
- OutputArray descriptors, useProvidedKeypoints= ) ;
- AlgorithmInfo* info() ;
- :
- computeImpl( Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) ;
- detectImpl( Mat& image, vector<KeyPoint>& keypoints, Mat& mask=Mat() ) ;
- CV_PROP_RW nfeatures;
- CV_PROP_RW scaleFactor;
- CV_PROP_RW nlevels;
- CV_PROP_RW edgeThreshold;
- CV_PROP_RW firstLevel;
- CV_PROP_RW WTA_K;
- CV_PROP_RW scoreType;
- CV_PROP_RW patchSize;
- };
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 keypoints the resulting keypoints
- * @param do_keypoints if true, the keypoints are computed, otherwise used as an input
- */ ORB::operator()( InputArray _image, InputArray _mask, vector<KeyPoint>& _keypoints,
- OutputArray _descriptors, useProvidedKeypoints)
- {
- CV_Assert(patchSize >= 2);
- do_keypoints = !useProvidedKeypoints;
- do_descriptors = _descriptors.needed();
- ( (!do_keypoints && !do_descriptors) || _image.empty() )
- ;
- HARRIS_BLOCK_SIZE = 9;
- halfPatchSize = patchSize / 2;.
- border = std::max(edgeThreshold, std::max(halfPatchSize, HARRIS_BLOCK_SIZE/2))+1;
- Mat image = _image.getMat(), mask = _mask.getMat();
- ( image.type() != CV_8UC1 )
- cvtColor(_image, image, CV_BGR2GRAY);
- levelsNum = ->nlevels;
- ( !do_keypoints )
- {
- levelsNum = 0;
- ( i = 0; i < _keypoints.size(); i++ )
- levelsNum = std::max(levelsNum, std::max(_keypoints[i].octave, 0));
- levelsNum++;
- }
- vector<Mat> imagePyramid(levelsNum), maskPyramid(levelsNum);
- ( level = 0; level < levelsNum; ++level)
- {
- scale = 1/getScale(level, firstLevel, scaleFactor);
- 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));
- 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));
- ( !mask.empty() )
- {
- masktemp = Mat(wholeSize, mask.type());
- maskPyramid[level] = masktemp(Rect(border, border, sz.width, sz.height));
- }
- ( level != firstLevel )
- {
- ( level < firstLevel )
- {
- resize(image, imagePyramid[level], sz, 0, 0, INTER_LINEAR);
- (!mask.empty())
- resize(mask, maskPyramid[level], sz, 0, 0, INTER_LINEAR);
- }
- {
- resize(imagePyramid[level-1], imagePyramid[level], sz, 0, 0, INTER_LINEAR);
- (!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,
- BORDER_REFLECT_101+BORDER_ISOLATED);
- (!mask.empty())
- copyMakeBorder(maskPyramid[level], masktemp, border, border, border, border,
- BORDER_CONSTANT+BORDER_ISOLATED);
- }
- {
- copyMakeBorder(image, temp, border, border, border, border,
- BORDER_REFLECT_101);
- ( !mask.empty() )
- copyMakeBorder(mask, masktemp, border, border, border, border,
- BORDER_CONSTANT+BORDER_ISOLATED);
- }
- }
- vector < vector<KeyPoint> > allKeypoints;
- ( do_keypoints )
- {
- computeKeyPoints(imagePyramid, maskPyramid, allKeypoints,
- nfeatures, firstLevel, scaleFactor,
- edgeThreshold, patchSize, scoreType);
- for (int level = 0; level < n_levels; ++level)
- vector<KeyPoint>& keypoints = all_keypoints[level];
- keypoints.clear();
- keypoint_end = temp.end(); keypoint != keypoint_end; ++keypoint)
- }
- {
- KeyPointsFilter::runByImageBorder(_keypoints, image.size(), edgeThreshold);
- allKeypoints.resize(levelsNum);
- (vector<KeyPoint>::iterator keypoint = _keypoints.begin(),
- keypointEnd = _keypoints.end(); keypoint != keypointEnd; ++keypoint)
- allKeypoints[keypoint->octave].push_back(*keypoint);
- ( level = 0; level < levelsNum; ++level)
- {
- (level == firstLevel)
- ;
- vector<KeyPoint> & keypoints = allKeypoints[level];
- scale = 1/getScale(level, firstLevel, scaleFactor);
- (vector<KeyPoint>::iterator keypoint = keypoints.begin(),
- keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint)
- keypoint->pt *= scale;
- }
- }
- Mat descriptors;
- vector<Point> pattern;
- ( do_descriptors )
- {
- nkeypoints = 0;
- ( level = 0; level < levelsNum; ++level)
- nkeypoints += ()allKeypoints[level].size();
- ( nkeypoints == 0 )
- _descriptors.release();
- {
- _descriptors.create(nkeypoints, descriptorSize(), CV_8U);
- descriptors = _descriptors.getMat();
- }
- npoints = 512;
- Point patternbuf[npoints];
- Point* pattern0 = ( Point*)bit_pattern_31_;
- ( patchSize != 31 )
- {
- pattern0 = patternbuf;
- makeRandomPattern(patchSize, patternbuf, npoints);
- }
- CV_Assert( WTA_K == 2 || WTA_K == 3 || WTA_K == 4 );
- ( WTA_K == 2 )
- std::copy(pattern0, pattern0 + npoints, std::back_inserter(pattern));
- {
- ntuples = descriptorSize()*4;
- initializeOrbPattern(pattern0, pattern, ntuples, WTA_K, npoints);
- }
- }
- _keypoints.clear();
- offset = 0;
- ( level = 0; level < levelsNum; ++level)
- {
- vector<KeyPoint>& keypoints = allKeypoints[level];
- nkeypoints = ()keypoints.size();
- (do_descriptors)
- {
- Mat desc;
- (!descriptors.empty())
- {
- desc = descriptors.rowRange(offset, offset + nkeypoints);
- }
- offset += nkeypoints;
- Mat& workingMat = imagePyramid[level];
- GaussianBlur(workingMat, workingMat, Size(7, 7), 2, 2, BORDER_REFLECT_101);
- computeDescriptors(workingMat, keypoints, desc, pattern, descriptorSize(), WTA_K);
- }
- (level != firstLevel)
- {
- scale = getScale(level, firstLevel, scaleFactor);
- (vector<KeyPoint>::iterator keypoint = keypoints.begin(),
- keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint)
- keypoint->pt *= scale;
- }
- _keypoints.insert(_keypoints.end(), keypoints.begin(), keypoints.end());
- }
- }
（1）提取角点：computeKeyPoints
imagePyramid:即构造好的金字塔
/** Compute the ORB keypoints on an image
- * @param keypoints the resulting keypoints, clustered per level
- computeKeyPoints( vector<Mat>& imagePyramid,
- vector<Mat>& maskPyramid,
- vector<vector<KeyPoint> >& allKeypoints,
- nfeatures, firstLevel, scaleFactor,
- edgeThreshold, patchSize, scoreType )
- {
- nlevels = ()imagePyramid.size();
- vector<> nfeaturesPerLevel(nlevels);
- factor = ()(1.0 / scaleFactor);
- ndesiredFeaturesPerScale = nfeatures*(1 - factor)/(1 - ()pow(()factor, ()nlevels));
- sumFeatures = 0;
- ( level = 0; level < nlevels-1; level++ )
- {
- nfeaturesPerLevel[level] = cvRound(ndesiredFeaturesPerScale);
- sumFeatures += nfeaturesPerLevel[level];
- ndesiredFeaturesPerScale *= factor;
- }
- nfeaturesPerLevel[nlevels-1] = std::max(nfeatures - sumFeatures, 0);
- halfPatchSize = patchSize / 2;
- vector<> umax(halfPatchSize + 2);
- v, v0, vmax = cvFloor(halfPatchSize * sqrt(2.f) / 2 + 1);
- vmin = cvCeil(halfPatchSize * sqrt(2.f) / 2);
- (v = 0; v <= vmax; ++v)
- umax[v] = cvRound(sqrt(()halfPatchSize * halfPatchSize - v * v));
- (v = halfPatchSize, v0 = 0; v >= vmin; --v)
- {
- (umax[v0] == umax[v0 + 1])
- ++v0;
- umax[v] = v0;
- ++v0;
- }
- allKeypoints.resize(nlevels);
- ( level = 0; level < nlevels; ++level)
- {
- featuresNum = nfeaturesPerLevel[level];
- allKeypoints[level].reserve(featuresNum*2);
- vector<KeyPoint> & keypoints = allKeypoints[level];
- FastFeatureDetector fd(20, );
- fd.detect(imagePyramid[level], keypoints, maskPyramid[level]);
- KeyPointsFilter::runByImageBorder(keypoints, imagePyramid[level].size(), edgeThreshold);
- ( scoreType == ORB::HARRIS_SCORE )
- {
- KeyPointsFilter::retainBest(keypoints, 2 * featuresNum);
- HarrisResponses(imagePyramid[level], keypoints, 7, HARRIS_K);
- }
- KeyPointsFilter::retainBest(keypoints, featuresNum);
- sf = getScale(level, firstLevel, scaleFactor);
- (vector<KeyPoint>::iterator keypoint = keypoints.begin(),
- keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint)
- {
- keypoint->octave = level;
- keypoint->size = patchSize*sf;
- }
- computeOrientation(imagePyramid[level], keypoints, halfPatchSize, umax);
- }
- }
static computeOrientation( Mat& image, vector<KeyPoint>& keypoints,
- halfPatchSize, vector<>& umax)
- {
- (vector<KeyPoint>::iterator keypoint = keypoints.begin(),
- keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint)
- {
- keypoint->angle = IC_Angle(image, halfPatchSize, keypoint->pt, umax);
- }
- }
static IC_Angle( Mat& image, half_k, Point2f pt,
- vector<> & u_max)
- {
- m_01 = 0, m_10 = 0;
- uchar* center = &image.at<uchar> (cvRound(pt.y), cvRound(pt.x));
- ( u = -half_k; u <= half_k; ++u)
- m_10 += u * center[u];
- step = ()image.step1();
- ( v = 1; v <= half_k; ++v)
- {
- v_sum = 0;
- d = u_max[v];
- ( u = -d; u <= d; ++u)
- {
- val_plus = center[u + v*step], val_minus = center[u - v*step];
- v_sum += (val_plus - val_minus);
- m_10 += u * (val_plus + val_minus);
- }
- m_01 += v * v_sum;
- }
- fastAtan2(()m_01, ()m_10);
- }
static computeDescriptors( Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors,
- vector<Point>& pattern, dsize, WTA_K)
- {
- CV_Assert(image.type() == CV_8UC1);
- descriptors = Mat::zeros(()keypoints.size(), dsize, CV_8UC1);
- ( i = 0; i < keypoints.size(); i++)
- computeOrbDescriptor(keypoints[i], image, &pattern[0], descriptors.ptr(()i), dsize, WTA_K);
- }
static computeOrbDescriptor( KeyPoint& kpt,
- Mat& img, Point* pattern,
- uchar* desc, dsize, WTA_K)
- {
- angle = kpt.angle;
- angle *= ()(CV_PI/180.f);
- a = ()cos(angle), b = ()sin(angle);
- uchar* center = &img.at<uchar>(cvRound(kpt.pt.y), cvRound(kpt.pt.x));
- step = ()img.step;
- center[cvRound(pattern[idx].x*b + pattern[idx].y*a)*step +
- cvRound(pattern[idx].x*a - pattern[idx].y*b)]
- x, y;
- ix, iy;
- (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))
- ( WTA_K == 2 )
- {
- ( i = 0; i < dsize; ++i, pattern += 16)
- {
- 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;
- }
- }
- ( WTA_K == 3 )
- {
- ( i = 0; i < dsize; ++i, pattern += 12)
- {
- 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;
- }
- }
- ( WTA_K == 4 )
- {
- ( i = 0; i < dsize; ++i, pattern += 16)
- {
- 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;
- ( t1 > t0 ) t0 = t1, u = 1;
- ( 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;
- ( t1 > t0 ) t0 = t1, u = 1;
- ( 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;
- ( t1 > t0 ) t0 = t1, u = 1;
- ( 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;
- ( t1 > t0 ) t0 = t1, u = 1;
- ( t3 > t2 ) t2 = t3, v = 3;
- k = t0 > t2 ? u : v;
- val |= k << 6;
- desc[i] = (uchar)val;
- }
- }
- CV_Error( CV_StsBadSize,
- }
参考：
Ethan Rublee et. ORB：An Efficient Alternative to SIFT or SURF
http://www.cnblogs.com/ronny/p/4083537.html