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：
- 可重复性（Repeatability）：相同的“区域”可以在不同的图像中被找到。
- 可区别性（Distinctiveness）：不同的“区域”有不同的表达。
- 高效率（Efficiency）：同一图像中，特征点的数量应远小于像素的数量。
- 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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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
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。
- 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
2.4 Opencv源码解析
- API简介
//ORB类定义：位置..\features2d.hpp
static 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 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,//构造函数
int firstLevel = 0, int WTA_K=2, int scoreType=ORB::HARRIS_SCORE, int patchSize=31 );
// returns the descriptor size in bytes
int descriptorSize() const; //描述子占用的字节数，默认32字节
// returns the descriptor type
int descriptorType() const;//描述子类型，8位整形数
// 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 handling
const 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 pyramids
vector<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 image
if( 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 beforehand
vector < 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 descriptor
computeKeyPoints(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 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); //把角点信息存入allKeypoints内
// Make sure we rescale the coordinates
for (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 orientation
vector<KeyPoint>& keypoints = allKeypoints[level];
int nkeypoints = (int)keypoints.size();//本层内角点个数
// 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 data
if (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 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++ ) //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 patch
int 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 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角点检测
// Remove keypoints very close to the border
KeyPointsFilter::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 corners
KeyPointsFilter::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 coordinates
for (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 keypoint
for (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=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) //每次处理对称的两行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); //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 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)]
#else
float 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))
#endif
if( 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;
}
}
else
CV_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;
}
- 后续：
参考：
1. ORB原理与Opencv源码解析
2. ORBSLAM2学习（一）：ORB算法原理
