【特徵匹配】ORB原理與原始碼解析
阿新 • • 發佈：2019-02-12
相關 :
CSDN-勿在浮沙築高臺
為了滿足實時性的要求，前面文章中介紹過快速提取特徵點演算法Fast,以及特徵描述子Brief。本篇文章介紹的ORB演算法結合了Fast和Brief的速度優勢，並做了改進，且ORB是免費。
Ethan Rublee等人2011年在《ORB：An
Efficient Alternative to SIFT or SURF》文章中提出了ORB演算法。結合Fast與Brief演算法，並給Fast特徵點增加了方向性，使得特徵點具有旋轉不變性，並提出了構造金字塔方法，解決尺度不變性，但文章中沒有具體詳述。實驗證明，ORB遠優於之前的SIFT與SURF演算法。
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論文核心內容概述：
1.構造金字塔，在每層金字塔上採用Fast演算法提取特徵點，採用Harris角點響應函式，按角點響應值排序，選取前N個特徵點。
2. oFast:計算每個特徵點的主方向，灰度質心法，計算特徵點半徑為r的圓形鄰域範圍內的灰度質心位置。從中心位置到質心位置的向量，定義為該特 徵點的主方向。
定義矩的計算公式，x,y∈[-r,r]：
質心位置：
主方向：
3.rBrief:為了解決旋轉不變性，把特徵點的Patch旋轉到主方向上(steered Brief)。通過實驗得到，描述子在各個維度上的均值比較離散(偏離0.5)，同時維度間相關性很強，說明特徵點描述子區分性不好，影響匹配的效果。論文中提出採取學習的方法，採用300K個訓練樣本點。每一個特徵點，選取Patch大小為wp=31，Patch內每對點都採用wt=5大小的子視窗灰度均值做比較，子視窗的個數即為N=(wp-wt)*(wp-wt), 從N個視窗中隨機選兩個做比較即構成描述子的一個bit,論文中採用M=205590種可能的情況：
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1.對所有樣本點，做M種測試，構成M維的描述子，每個維度上非1即0；
2.按均值對M個維度排序（以0.5為中心），組成向量T；
3.貪婪搜尋：把向量T中第一個元素移動到R中，然後繼續取T的第二個元素，與R中的所有元素做相關性比較，如果相關性大於指定的閾值Threshold, 拋棄T的這個元素，否則加入到R中；
4.重複第3個步驟，直到R中有256個元素，若檢測完畢,少於256個元素，則降低閾值，重複上述步驟；
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rBrief:通過上面的步驟取到的256對點，構成的描述子各維度間相關性很低，區分性好；
訓練前 訓練後
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ORB演算法步驟,參考opencv原始碼:
1.首先構造尺度金字塔；
金字塔共n層，與SIFT不同，每層僅有一副影象；
第s層的尺度為
第s層影象大小:
2.在不同尺度上採用Fast檢測特徵點；在每一層上按公式計算需要提取的特徵點數n,在本層上按Fast角點響應值排序，提取前2n個特徵點，然後根據Harris 角點響應值排序， 取前n個特徵點，作為本層的特徵點；
3.計算每個特徵點的主方向（質心法）；
4.旋轉每個特徵點的Patch到主方向，採用上述步驟3的選取的最優的256對特徵點做τ測試，構成256維描述子，佔32個位元組；
4.採用漢明距離做特徵點匹配；
----------OpenCV原始碼解析-------------------------------------------------------
ORB類定義：位置..\features2d.hpp
nfeatures:需要的特徵點總數；
scaleFactor:尺度因子；
nlevels:金字塔層數；
edgeThreshold:邊界閾值；
firstLevel:起始層；
WTA_K：描述子形成方法,WTA_K=2表示，採用兩兩比較；
scoreType:角點響應函式，可以選擇Harris或者Fast的方法；
patchSize:特徵點鄰域大小；
/*!
ORB implementation.
*/
class CV_EXPORTS_W ORB : public Feature2D
{
public:
// the size of the signature in bytes
enum { 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,//建構函式
int firstLevel = 0, int WTA_K=2, int scoreType=ORB::HARRIS_SCORE, int patchSize=31 );
// returns the descriptor size in bytes
int descriptorSize() const; //描述子佔用的位元組數，預設32位元組
// returns the descriptor type
int descriptorType() const;//描述子型別，8位整形數
// Compute the ORB features and descriptors on an image
void operator()(InputArray image, InputArray mask, vector<KeyPoint>& keypoints) const;
// Compute the ORB features and descriptors on an image
void operator()( InputArray image, InputArray mask, vector<KeyPoint>& keypoints, //提取特徵點與形成描述子
OutputArray descriptors, bool useProvidedKeypoints=false ) const;
AlgorithmInfo* info() const;
protected:
void computeImpl( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const;//計算描述子
void detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;//檢測特徵點
CV_PROP_RW int nfeatures;//特徵點總數
CV_PROP_RW double scaleFactor;//尺度因子
CV_PROP_RW int nlevels;//金字塔內層數
CV_PROP_RW int edgeThreshold;//邊界閾值
CV_PROP_RW int firstLevel;//開始層數
CV_PROP_RW int WTA_K;//描述子形成方法，預設WTA_K=2，兩兩比較
CV_PROP_RW int scoreType;//角點響應函式
CV_PROP_RW int patchSize;//鄰域Patch大小
};特徵提取及形成描述子：通過這個函式對影象提取Fast特徵點或者計算特徵描述子
_image:輸入影象；
_mask:掩碼影象;
_keypoints:輸入角點；
_descriptors:如果為空，只尋找特徵點，不計算特徵描述子；
_useProvidedKeypoints:如果為true,函式只計算特徵描述子；
/** 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 handling
const int HARRIS_BLOCK_SIZE = 9;//Harris角點響應需要的邊界大小
int halfPatchSize = patchSize / 2;.//鄰域半徑
int border = std::max(edgeThreshold, std::max(halfPatchSize, HARRIS_BLOCK_SIZE/2))+1;//採用最大的邊界
Mat image = _image.getMat(), mask = _mask.getMat();
if( image.type() != CV_8UC1 )
cvtColor(_image, image, CV_BGR2GRAY);//轉灰度圖
int levelsNum = this->nlevels;//金字塔層數
if( !do_keypoints ) //不做特徵點檢測
{
// 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));//提取特徵點的最大層數
levelsNum++;
}
// Pre-compute the scale pyramids
vector<Mat> imagePyramid(levelsNum), maskPyramid(levelsNum);//建立尺度金字塔影象
for (int level = 0; level < levelsNum; ++level)
{
float scale = 1/getScale(level, firstLevel, scaleFactor); //每層對應的尺度
/*
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));//每層對應的影象大小
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 image
if( level != firstLevel ) //得到金字塔每層的影象
{
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,//擴大影象的邊界
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,//擴大影象的四個邊界
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 beforehand
vector < vector<KeyPoint> > allKeypoints;
if( do_keypoints )//提取角點
{
// Get keypoints, those will be far enough from the border that no check will be required for the descriptor
computeKeyPoints(imagePyramid, maskPyramid, allKeypoints, //對每一層影象提取角點，見下面(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 //不提取角點
{
// Remove keypoints very close to the border
KeyPointsFilter::runByImageBorder(_keypoints, image.size(), edgeThreshold);
// Cluster the input keypoints depending on the level they were computed at
allKeypoints.resize(levelsNum);
for (vector<KeyPoint>::iterator keypoint = _keypoints.begin(),
keypointEnd = _keypoints.end(); keypoint != keypointEnd; ++keypoint)
allKeypoints[keypoint->octave].push_back(*keypoint); //把角點資訊存入allKeypoints內
// Make sure we rescale the coordinates
for (int level = 0; level < levelsNum; ++level) //把角點位置資訊縮放到指定層位置上
{
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; //縮放
}
}
Mat descriptors;
vector<Point> pattern;
if( do_descriptors ) //計算特徵描述子
{
int nkeypoints = 0;
for (int level = 0; level < levelsNum; ++level)
nkeypoints += (int)allKeypoints[level].size();//得到所有層的角點總數
if( nkeypoints == 0 )
_descriptors.release();
else
{
_descriptors.create(nkeypoints, descriptorSize(), CV_8U);//建立一個矩陣存放描述子,每一行表示一個角點資訊
descriptors = _descriptors.getMat();
}
const int npoints = 512;//取512個點,共256對,產生256維描述子,32個位元組
Point patternbuf[npoints];
const Point* pattern0 = (const Point*)bit_pattern_31_;//訓練好的256對資料點位置
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使用兩個點之間作比較
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)//依次計算每一層的角點描述子
{
// Get the features and compute their orientation
vector<KeyPoint>& keypoints = allKeypoints[level];
int nkeypoints = (int)keypoints.size();//本層內角點個數
// Compute the descriptors
if (do_descriptors)
{
Mat desc;
if (!descriptors.empty())
{
desc = descriptors.rowRange(offset, offset + nkeypoints);
}
offset += nkeypoints; //偏移量
// preprocess the resized image
Mat& 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);//高斯平滑影象
computeDescriptors(workingMat, keypoints, desc, pattern, descriptorSize(), WTA_K);//計算本層內角點的描述子,（3）
}
// Copy to the output data
if (level != firstLevel) //角點位置資訊返回到原圖上
{
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());//存入描述子資訊，返回
}
}（1）提取角點：computeKeyPoints
imagePyramid:即構造好的金字塔
/** 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(); //金字塔層數
vector<int> nfeaturesPerLevel(nlevels);
// fill the extractors and descriptors for the corresponding scales
float 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++ ) //對每層影象上分配相應角點數
{
nfeaturesPerLevel[level] = cvRound(ndesiredFeaturesPerScale);
sumFeatures += nfeaturesPerLevel[level];
ndesiredFeaturesPerScale *= factor;
}
nfeaturesPerLevel[nlevels-1] = std::max(nfeatures - sumFeatures, 0);//剩下角點數，由最上層影象提取
// 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 patch
int halfPatchSize = patchSize / 2; //計算每個特徵點圓鄰域的位置資訊
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 symmetric
for (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 threshold
FastFeatureDetector fd(20, true);
fd.detect(imagePyramid[level], keypoints, maskPyramid[level]);//Fast角點檢測
// Remove keypoints very close to the border
KeyPointsFilter::runByImageBorder(keypoints, imagePyramid[level].size(), edgeThreshold);//去除鄰近邊界的點
if( scoreType == ORB::HARRIS_SCORE )
{
// Keep more points than necessary as FAST does not give amazing corners
KeyPointsFilter::retainBest(keypoints, 2 * featuresNum);//按Fast強度排序,保留前2*featuresNum個特徵點
// Compute the Harris cornerness (better scoring than FAST)
HarrisResponses(imagePyramid[level], keypoints, 7, HARRIS_K); //計算每個角點的Harris強度響應
}
//cull to the final desired level, using the new Harris scores or the original FAST scores.
KeyPointsFilter::retainBest(keypoints, featuresNum);//按Harris強度排序，保留前featuresNum個
float sf = getScale(level, firstLevel, scaleFactor);
// Set the level of the coordinates
for (vector<KeyPoint>::iterator keypoint = keypoints.begin(),
keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint)
{
keypoint->octave = level; //層資訊
keypoint->size = patchSize*sf; //
}
computeOrientation(imagePyramid[level], keypoints, halfPatchSize, umax); //計算角點的方向，（2）分析
}
}
static void computeOrientation(const Mat& image, vector<KeyPoint>& keypoints,
int halfPatchSize, const vector<int>& umax)
{
// Process each keypoint
for (vector<KeyPoint>::iterator keypoint = keypoints.begin(), //為每個角點計算主方向
keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint)
{
keypoint->angle = IC_Angle(image, halfPatchSize, keypoint->pt, umax);//計算質心方向
}
}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=0
for (int u = -half_k; u <= half_k; ++u)
m_10 += u * center[u];
// Go line by line in the circular patch
int step = (int)image.step1();
for (int v = 1; v <= half_k; ++v) //每次處理對稱的兩行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); //計算m_01時,位置上差一個符號
m_10 += u * (val_plus + val_minus);
}
m_01 += v * v_sum;//計算上下兩行的m_01
}
return fastAtan2((float)m_01, (float)m_10);//計算角度
}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 color
CV_Assert(image.type() == CV_8UC1);
//create the descriptor mat, keypoints.size() rows, BYTES cols
descriptors = 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) \ //取旋轉後一個畫素點的值
center[cvRound(pattern[idx].x*b + pattern[idx].y*a)*step + \
cvRound(pattern[idx].x*a - pattern[idx].y*b)]
#else
float x, y;
int ix, iy;
#define GET_VALUE(idx) \ //取旋轉後一個畫素點，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))
#endif
if( WTA_K == 2 )
{
for (int i = 0; i < dsize; ++i, pattern += 16)//每個特徵描述子長度為32個位元組
{
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;
}
}
else
CV_Error( CV_StsBadSize, "Wrong WTA_K. It can be only 2, 3 or 4." );
#undef GET_VALUE
}參考：
Ethan Rublee et. ORB：An Efficient Alternative to SIFT or SURF
http://www.cnblogs.com/ronny/p/4083537.html