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你可能喜欢opencv源代码分析（37）
#pragma once
#include &opencv2/core/core.hpp&
#include &opencv2/imgproc/imgproc.hpp&
#include &opencv2/features2d/features2d.hpp&
#include &DistanceUtils.h&
Local Binary Similarity Pattern (LBSP) feature extractor
Note 1: both grayscale and RGB/BGR images may be used with this extractor.
Note 2: using LBSP::compute2(...) is logically equivalent to using LBSP::compute(...) followed by LBSP::reshapeDesc(...).
For more details on the different parameters, see G.-A. Bilodeau et al, &Change Detection in Feature Space Using Local
Binary Similarity Patterns&, in CRV 2013.
This algorithm is currently NOT thread-safe.
class LBSP : public cv::DescriptorExtractor {
//! constructor 1, threshold = absolute intensity 'similarity' threshold used when computing comparisons
LBSP(size_t nThreshold);
//! constructor 2, threshold = relative intensity 'similarity' threshold used when computing comparisons
LBSP(float fRelThreshold, size_t nThresholdOffset=0);
//! default destructor
virtual ~LBSP();
//! loads extractor params from the specified file node @@@@ not impl
virtual void read(const cv::FileNode&);
//! writes extractor params to the specified file storage @@@@ not impl
virtual void write(cv::FileStorage&)
//! sets the 'reference' image to be used for inter-frame comparisons (note: if no image is set or if the image is empty, the algorithm will default back to intra-frame comparisons)
virtual void setReference(const cv::Mat&);
//! returns the current descriptor size, in bytes
virtual int descriptorSize()
//! returns the current descriptor data type
virtual int descriptorType()
//! returns whether this extractor is using a relative threshold or not
virtual bool isUsingRelThreshold()
//! returns the current relative threshold used for comparisons (-1 = invalid/not used)
virtual float getRelThreshold()
//! returns the current absolute threshold used for comparisons (-1 = invalid/not used)
virtual size_t getAbsThreshold()
//! similar to DescriptorExtractor::compute(const cv::Mat& image, ...), but in this case, the descriptors matrix has the same shape as the input matrix (possibly slower, but the result can be displayed)
void compute2(const cv::Mat& oImage, std::vector&cv::KeyPoint&& voKeypoints, cv::Mat& oDescriptors)
//! batch version of LBSP::compute2(const cv::Mat& image, ...), also similar to DescriptorExtractor::compute(const std::vector&cv::Mat&& imageCollection, ...)
void compute2(const std::vector&cv::Mat&& voImageCollection, std::vector&std::vector&cv::KeyPoint& && vvoPointCollection, std::vector&cv::Mat&& voDescCollection)
//! utility function, shortcut/lightweight/direct single-point LBSP computation function for extra flexibility (1-channel version)
inline static void computeGrayscaleDescriptor(const cv::Mat& oInputImg, const uchar _ref, const int _x, const int _y, const size_t _t, ushort& _res) {
CV_DbgAssert(!oInputImg.empty());
CV_DbgAssert(oInputImg.type()==CV_8UC1);
CV_DbgAssert(LBSP::DESC_SIZE==2); // @@@ also relies on a constant desc size
CV_DbgAssert(_x&=(int)LBSP::PATCH_SIZE/2 && _y&=(int)LBSP::PATCH_SIZE/2);
CV_DbgAssert(_x&oInputImg.cols-(int)LBSP::PATCH_SIZE/2 && _y&oInputImg.rows-(int)LBSP::PATCH_SIZE/2);
const size_t _step_row = oInputImg.step.p[0];
const uchar* const _data = oInputImg.
#include &LBSP_16bits_dbcross_1ch.i&
//! utility function, shortcut/lightweight/direct single-point LBSP computation function for extra flexibility (3-channels version)
inline static void computeRGBDescriptor(const cv::Mat& oInputImg, const uchar* const _ref,
const int _x, const int _y, const size_t* const _t, ushort* _res) {
CV_DbgAssert(!oInputImg.empty());
CV_DbgAssert(oInputImg.type()==CV_8UC3);
CV_DbgAssert(LBSP::DESC_SIZE==2); // @@@ also relies on a constant desc size
CV_DbgAssert(_x&=(int)LBSP::PATCH_SIZE/2 && _y&=(int)LBSP::PATCH_SIZE/2);
CV_DbgAssert(_x&oInputImg.cols-(int)LBSP::PATCH_SIZE/2 && _y&oInputImg.rows-(int)LBSP::PATCH_SIZE/2);
const size_t _step_row = oInputImg.step.p[0];
const uchar* const _data = oInputImg.
#include &LBSP_16bits_dbcross_3ch3t.i&
//! utility function, shortcut/lightweight/direct single-point LBSP computation function for extra flexibility (3-channels version)
inline static void computeRGBDescriptor(const cv::Mat& oInputImg, const uchar* const _ref,
const int _x, const int _y, const size_t _t, ushort* _res) {
CV_DbgAssert(!oInputImg.empty());
CV_DbgAssert(oInputImg.type()==CV_8UC3);
CV_DbgAssert(LBSP::DESC_SIZE==2); // @@@ also relies on a constant desc size
CV_DbgAssert(_x&=(int)LBSP::PATCH_SIZE/2 && _y&=(int)LBSP::PATCH_SIZE/2);
CV_DbgAssert(_x&oInputImg.cols-(int)LBSP::PATCH_SIZE/2 && _y&oInputImg.rows-(int)LBSP::PATCH_SIZE/2);
const size_t _step_row = oInputImg.step.p[0];
const uchar* const _data = oInputImg.
#include &LBSP_16bits_dbcross_3ch1t.i&
//! utility function, shortcut/lightweight/direct single-point LBSP computation function for extra flexibility (1-channel-RGB version)
inline static void computeSingleRGBDescriptor(const cv::Mat& oInputImg, const uchar _ref, const int _x, const int _y, const size_t _c, const size_t _t, ushort& _res) {
CV_DbgAssert(!oInputImg.empty());
CV_DbgAssert(oInputImg.type()==CV_8UC3 && _c&3);
CV_DbgAssert(LBSP::DESC_SIZE==2); // @@@ also relies on a constant desc size
CV_DbgAssert(_x&=(int)LBSP::PATCH_SIZE/2 && _y&=(int)LBSP::PATCH_SIZE/2);
CV_DbgAssert(_x&oInputImg.cols-(int)LBSP::PATCH_SIZE/2 && _y&oInputImg.rows-(int)LBSP::PATCH_SIZE/2);
const size_t _step_row = oInputImg.step.p[0];
const uchar* const _data = oInputImg.
#include &LBSP_16bits_dbcross_s3ch.i&
//! utility function, used to reshape a descriptors matrix to its input image size via their keypoint locations
static void reshapeDesc(cv::Size oSize, const std::vector&cv::KeyPoint&& voKeypoints, const cv::Mat& oDescriptors, cv::Mat& oOutput);
//! utility function, used to illustrate the difference between two descriptor images
static void calcDescImgDiff(const cv::Mat& oDesc1, const cv::Mat& oDesc2, cv::Mat& oOutput, bool bForceMergeChannels=false);
//! utility function, used to filter out bad keypoints that would trigger out of bounds error because they're too close to the image border
static void validateKeyPoints(std::vector&cv::KeyPoint&& voKeypoints, cv::Size oImgSize);
//! utility function, used to filter out bad pixels in a ROI that would trigger out of bounds error because they're too close to the image border
static void validateROI(cv::Mat& oROI);
//! utility, specifies the pixel size of the pattern used (width and height)
static const size_t PATCH_SIZE = 5;
//! utility, specifies the number of bytes per descriptor (should be the same as calling 'descriptorSize()')
static const size_t DESC_SIZE = 2;
//LBSP描述子是16位，即2个字节
protected:
//! classic 'compute' implementation, based on the regular DescriptorExtractor::computeImpl arguments & expected output
virtual void computeImpl(const cv::Mat& oImage, std::vector&cv::KeyPoint&& voKeypoints, cv::Mat& oDescriptors)
const bool m_bOnlyUsingAbsT //绝对阈值
const float m_fRelT
//相对阈值
const size_t m_nT
cv::Mat m_oRefI
//计算inter-frame comparisons的参考图片
2. LBSP.cpp
#include &LBSP.h&
LBSP::LBSP(size_t nThreshold)
m_bOnlyUsingAbsThreshold(true)
,m_fRelThreshold(0) // unused
,m_nThreshold(nThreshold)
,m_oRefImage() {}
LBSP::LBSP(float fRelThreshold, size_t nThresholdOffset)
m_bOnlyUsingAbsThreshold(false)
,m_fRelThreshold(fRelThreshold)
,m_nThreshold(nThresholdOffset)
,m_oRefImage() {
CV_Assert(m_fRelThreshold&=0);
LBSP::~LBSP() {}
void LBSP::read(const cv::FileNode& /*fn*/) {
// ... = fn[&...&];
void LBSP::write(cv::FileStorage& /*fs*/) const {
//fs && &...& && ...;
void LBSP::setReference(const cv::Mat& img) {
CV_DbgAssert(img.empty() || img.type()==CV_8UC1 || img.type()==CV_8UC3);
m_oRefImage =
//LBSP描述子是16位，即2个字节
int LBSP::descriptorSize() const {
return DESC_SIZE;
int LBSP::descriptorType() const {
return CV_16U;
bool LBSP::isUsingRelThreshold() const {
return !m_bOnlyUsingAbsT
float LBSP::getRelThreshold() const {
return m_fRelT
size_t LBSP::getAbsThreshold() const {
return m_nT
//使用绝对阈值计算LBSP
static inline void lbsp_computeImpl( const cv::Mat& oInputImg,
//需要计算LBSP的输入图片
const cv::Mat& oRefImg,
//计算LBSP的参考图片，也就是使用的各个位置的中间值
const std::vector&cv::KeyPoint&& voKeyPoints, //去除边缘后的有效点
cv::Mat& oDesc,
//存放计算好的LBSP值
size_t _t) {
//_t是绝对阈值
CV_DbgAssert(oRefImg.empty() || (oRefImg.size==oInputImg.size && oRefImg.type()==oInputImg.type()));
CV_DbgAssert(oInputImg.type()==CV_8UC1 || oInputImg.type()==CV_8UC3);
CV_DbgAssert(LBSP::DESC_SIZE==2); // @@@ also relies on a constant desc size
const size_t nChannels = (size_t)oInputImg.channels();
const size_t _step_row = oInputImg.step.p[0];
const uchar* _data = oInputImg.
const uchar* _refdata = oRefImg.empty()?oInputImg.data:oRefImg.
const size_t nKeyPoints = voKeyPoints.size();
if(nChannels==1) {
oDesc.create((int)nKeyPoints,1,CV_16UC1);
for(size_t k=0; k&nKeyP ++k) {
const int _x = (int)voKeyPoints[k].pt.x;
//提取点的纵坐标
const int _y = (int)voKeyPoints[k].pt.y;
//提取点的横坐标
const uchar _ref = _refdata[_step_row*(_y)+_x];
//提取参考点的像素值，即找到计算相应LBSP的中间点
ushort& _res = oDesc.at&ushort&((int)k);
//存放计算的LBSP结果值
#include &LBSP_16bits_dbcross_1ch.i&
//通过该文件具体计算每一个像素位置的LBSP值
else { //nChannels==3
oDesc.create((int)nKeyPoints,1,CV_16UC3);
for(size_t k=0; k&nKeyP ++k) {
const int _x = (int)voKeyPoints[k].pt.x;
const int _y = (int)voKeyPoints[k].pt.y;
const uchar* _ref = _refdata+_step_row*(_y)+3*(_x);
ushort* _res = ((ushort*)(oDesc.data + oDesc.step.p[0]*k));
#include &LBSP_16bits_dbcross_3ch1t.i&
//使用含有相对偏置的相对阈值计算LBSP
static inline void lbsp_computeImpl( const cv::Mat& oInputImg,
const cv::Mat& oRefImg,
const std::vector&cv::KeyPoint&& voKeyPoints,
cv::Mat& oDesc,
float fThreshold,
size_t nThresholdOffset) {
CV_DbgAssert(oRefImg.empty() || (oRefImg.size==oInputImg.size && oRefImg.type()==oInputImg.type()));
CV_DbgAssert(oInputImg.type()==CV_8UC1 || oInputImg.type()==CV_8UC3);
CV_DbgAssert(LBSP::DESC_SIZE==2); // @@@ also relies on a constant desc size
CV_DbgAssert(fThreshold&=0);
const size_t nChannels = (size_t)oInputImg.channels();
const size_t _step_row = oInputImg.step.p[0];
const uchar* _data = oInputImg.
const uchar* _refdata = oRefImg.empty()?oInputImg.data:oRefImg.
const size_t nKeyPoints = voKeyPoints.size();
if(nChannels==1) {
oDesc.create((int)nKeyPoints,1,CV_16UC1);
for(size_t k=0; k&nKeyP ++k) {
const int _x = (int)voKeyPoints[k].pt.x;
//提取点的纵坐标
const int _y = (int)voKeyPoints[k].pt.y;
//提取点的横坐标
const uchar _ref = _refdata[_step_row*(_y)+_x];
//提取参考点的像素值，即找到计算相应LBSP的中间点
ushort& _res = oDesc.at&ushort&((int)k);
//存放计算的LBSP结果值
const size_t _t = (size_t)(_ref*fThreshold)+nThresholdO
#include &LBSP_16bits_dbcross_1ch.i&
else { //nChannels==3
oDesc.create((int)nKeyPoints,1,CV_16UC3);
for(size_t k=0; k&nKeyP ++k) {
const int _x = (int)voKeyPoints[k].pt.x;
const int _y = (int)voKeyPoints[k].pt.y;
const uchar* _ref = _refdata+_step_row*(_y)+3*(_x);
ushort* _res = ((ushort*)(oDesc.data + oDesc.step.p[0]*k));
const size_t _t[3] = {(size_t)(_ref[0]*fThreshold)+nThresholdOffset,(size_t)(_ref[1]*fThreshold)+nThresholdOffset,(size_t)(_ref[2]*fThreshold)+nThresholdOffset};
#include &LBSP_16bits_dbcross_3ch3t.i&
static inline void lbsp_computeImpl2( const cv::Mat& oInputImg,
const cv::Mat& oRefImg,
const std::vector&cv::KeyPoint&& voKeyPoints,
cv::Mat& oDesc,
size_t _t) {
CV_DbgAssert(oRefImg.empty() || (oRefImg.size==oInputImg.size && oRefImg.type()==oInputImg.type()));
CV_DbgAssert(oInputImg.type()==CV_8UC1 || oInputImg.type()==CV_8UC3);
CV_DbgAssert(LBSP::DESC_SIZE==2); // @@@ also relies on a constant desc size
const size_t nChannels = (size_t)oInputImg.channels();
const size_t _step_row = oInputImg.step.p[0];
const uchar* _data = oInputImg.
const uchar* _refdata = oRefImg.empty()?oInputImg.data:oRefImg.
const size_t nKeyPoints = voKeyPoints.size();
if(nChannels==1) {
oDesc.create(oInputImg.size(),CV_16UC1);
for(size_t k=0; k&nKeyP ++k) {
const int _x = (int)voKeyPoints[k].pt.x;
const int _y = (int)voKeyPoints[k].pt.y;
const uchar _ref = _refdata[_step_row*(_y)+_x];
ushort& _res = oDesc.at&ushort&(_y,_x);
#include &LBSP_16bits_dbcross_1ch.i&
else { //nChannels==3
oDesc.create(oInputImg.size(),CV_16UC3);
for(size_t k=0; k&nKeyP ++k) {
const int _x = (int)voKeyPoints[k].pt.x;
const int _y = (int)voKeyPoints[k].pt.y;
const uchar* _ref = _refdata+_step_row*(_y)+3*(_x);
ushort* _res = ((ushort*)(oDesc.data + oDesc.step.p[0]*_y + oDesc.step.p[1]*_x));
#include &LBSP_16bits_dbcross_3ch1t.i&
static inline void lbsp_computeImpl2( const cv::Mat& oInputImg,
const cv::Mat& oRefImg,
const std::vector&cv::KeyPoint&& voKeyPoints,
cv::Mat& oDesc,
float fThreshold,
size_t nThresholdOffset) {
CV_DbgAssert(oRefImg.empty() || (oRefImg.size==oInputImg.size && oRefImg.type()==oInputImg.type()));
CV_DbgAssert(oInputImg.type()==CV_8UC1 || oInputImg.type()==CV_8UC3);
CV_DbgAssert(LBSP::DESC_SIZE==2); // @@@ also relies on a constant desc size
CV_DbgAssert(fThreshold&=0);
const size_t nChannels = (size_t)oInputImg.channels();
const size_t _step_row = oInputImg.step.p[0];
const uchar* _data = oInputImg.
const uchar* _refdata = oRefImg.empty()?oInputImg.data:oRefImg.
const size_t nKeyPoints = voKeyPoints.size();
if(nChannels==1) {
oDesc.create(oInputImg.size(),CV_16UC1);
for(size_t k=0; k&nKeyP ++k) {
const int _x = (int)voKeyPoints[k].pt.x;
const int _y = (int)voKeyPoints[k].pt.y;
const uchar _ref = _refdata[_step_row*(_y)+_x];
ushort& _res = oDesc.at&ushort&(_y,_x);
const size_t _t = (size_t)(_ref*fThreshold)+nThresholdO
#include &LBSP_16bits_dbcross_1ch.i&
else { //nChannels==3
oDesc.create(oInputImg.size(),CV_16UC3);
for(size_t k=0; k&nKeyP ++k) {
const int _x = (int)voKeyPoints[k].pt.x;
const int _y = (int)voKeyPoints[k].pt.y;
const uchar* _ref = _refdata+_step_row*(_y)+3*(_x);
ushort* _res = ((ushort*)(oDesc.data + oDesc.step.p[0]*_y + oDesc.step.p[1]*_x));
const size_t _t[3] = {(size_t)(_ref[0]*fThreshold)+nThresholdOffset,(size_t)(_ref[1]*fThreshold)+nThresholdOffset,(size_t)(_ref[2]*fThreshold)+nThresholdOffset};
#include &LBSP_16bits_dbcross_3ch3t.i&
void LBSP::compute2(const cv::Mat& oImage, std::vector&cv::KeyPoint&& voKeypoints, cv::Mat& oDescriptors) const {
CV_Assert(!oImage.empty());
cv::KeyPointsFilter::runByImageBorder(voKeypoints,oImage.size(),PATCH_SIZE/2);
//去除图像边界的像素点
cv::KeyPointsFilter::runByKeypointSize(voKeypoints,std::numeric_limits&float&::epsilon()); //去除超出一定范围的像素点
if(voKeypoints.empty()) {
oDescriptors.release();
if(m_bOnlyUsingAbsThreshold)
lbsp_computeImpl2(oImage,m_oRefImage,voKeypoints,oDescriptors,m_nThreshold);
lbsp_computeImpl2(oImage,m_oRefImage,voKeypoints,oDescriptors,m_fRelThreshold,m_nThreshold);
void LBSP::compute2(const std::vector&cv::Mat&& voImageCollection, std::vector&std::vector&cv::KeyPoint& && vvoPointCollection, std::vector&cv::Mat&& voDescCollection) const {
CV_Assert(voImageCollection.size() == vvoPointCollection.size());
voDescCollection.resize(voImageCollection.size());
for(size_t i=0; i&voImageCollection.size(); i++)
compute2(voImageCollection[i], vvoPointCollection[i], voDescCollection[i]);
void LBSP::computeImpl(const cv::Mat& oImage, std::vector&cv::KeyPoint&& voKeypoints, cv::Mat& oDescriptors) const {
CV_Assert(!oImage.empty());
cv::KeyPointsFilter::runByImageBorder(voKeypoints,oImage.size(),PATCH_SIZE/2);
cv::KeyPointsFilter::runByKeypointSize(voKeypoints,std::numeric_limits&float&::epsilon());
if(voKeypoints.empty()) {
oDescriptors.release();
if(m_bOnlyUsingAbsThreshold)
lbsp_computeImpl(oImage,m_oRefImage,voKeypoints,oDescriptors,m_nThreshold);
lbsp_computeImpl(oImage,m_oRefImage,voKeypoints,oDescriptors,m_fRelThreshold,m_nThreshold);
//把所有计算好的LBSP描述子恢复为矩阵的形式，即把一行转变成与图片等高等宽的图片形式
void LBSP::reshapeDesc(cv::Size oSize, const std::vector&cv::KeyPoint&& voKeypoints, const cv::Mat& oDescriptors, cv::Mat& oOutput) {
CV_DbgAssert(!voKeypoints.empty());
CV_DbgAssert(!oDescriptors.empty() && oDescriptors.cols==1);
CV_DbgAssert(oSize.width&0 && oSize.height&0);
CV_DbgAssert(DESC_SIZE==2); // @@@ also relies on a constant desc size
CV_DbgAssert(oDescriptors.type()==CV_16UC1 || oDescriptors.type()==CV_16UC3);
const size_t nChannels = (size_t)oDescriptors.channels();
const size_t nKeyPoints = voKeypoints.size();
if(nChannels==1) {
oOutput.create(oSize,CV_16UC1);
oOutput = cv::Scalar_&ushort&(0);
for(size_t k=0; k&nKeyP ++k)
oOutput.at&ushort&(voKeypoints[k].pt) = oDescriptors.at&ushort&((int)k);
else { //nChannels==3
oOutput.create(oSize,CV_16UC3);
oOutput = cv::Scalar_&ushort&(0,0,0);
for(size_t k=0; k&nKeyP ++k) {
ushort* output_ptr = (ushort*)(oOutput.data + oOutput.step.p[0]*(int)voKeypoints[k].pt.y);
const ushort* const desc_ptr = (ushort*)(oDescriptors.data + oDescriptors.step.p[0]*k);
const size_t idx = 3*(int)voKeypoints[k].pt.x;
for(size_t n=0; n&3; ++n)
output_ptr[idx+n] = desc_ptr[n];
void LBSP::calcDescImgDiff(const cv::Mat& oDesc1, const cv::Mat& oDesc2, cv::Mat& oOutput, bool bForceMergeChannels) {
CV_DbgAssert(oDesc1.size()==oDesc2.size() && oDesc1.type()==oDesc2.type());
CV_DbgAssert(DESC_SIZE==2); // @@@ also relies on a constant desc size
CV_DbgAssert(oDesc1.type()==CV_16UC1 || oDesc1.type()==CV_16UC3);
CV_DbgAssert(CV_MAT_DEPTH(oDesc1.type())==CV_16U);
CV_DbgAssert(DESC_SIZE*8&=UCHAR_MAX);
CV_DbgAssert(oDesc1.step.p[0]==oDesc2.step.p[0] && oDesc1.step.p[1]==oDesc2.step.p[1]);
const float fScaleFactor = (float)UCHAR_MAX/(DESC_SIZE*8);
//缩放因子：此处的值为16
const size_t nChannels = CV_MAT_CN(oDesc1.type());
const size_t _step_row = oDesc1.step.p[0];
if(nChannels==1) {
oOutput.create(oDesc1.size(),CV_8UC1);
oOutput = cv::Scalar(0);
for(int i=0; i&oDesc1. ++i) {
const size_t idx = _step_row*i;
const ushort* const desc1_ptr = (ushort*)(oDesc1.data+idx);
const ushort* const desc2_ptr = (ushort*)(oDesc2.data+idx);
for(int j=0; j&oDesc1. ++j)
oOutput.at&uchar&(i,j) = (uchar)(fScaleFactor*hdist(desc1_ptr[j],desc2_ptr[j]));//! hdist
：computes the hamming distance between two N-byte vectors using an 8-bit popcount LUT
else { //nChannels==3
if(bForceMergeChannels)
oOutput.create(oDesc1.size(),CV_8UC1);
oOutput.create(oDesc1.size(),CV_8UC3);
oOutput = cv::Scalar::all(0);
for(int i=0; i&oDesc1. ++i) {
const size_t idx =
_step_row*i;
const ushort* const desc1_ptr = (ushort*)(oDesc1.data+idx);
const ushort* const desc2_ptr = (ushort*)(oDesc2.data+idx);
uchar* output_ptr = oOutput.data + oOutput.step.p[0]*i;
for(int j=0; j&oDesc1. ++j) {
for(size_t n=0;n&3; ++n) {
const size_t idx2 = 3*j+n;
if(bForceMergeChannels)
output_ptr[j] += (uchar)((fScaleFactor*hdist(desc1_ptr[idx2],desc2_ptr[idx2]))/3);
output_ptr[idx2] = (uchar)(fScaleFactor*hdist(desc1_ptr[idx2],desc2_ptr[idx2]));
//去除边界点，以免超出边界
void LBSP::validateKeyPoints(std::vector&cv::KeyPoint&& voKeypoints, cv::Size oImgSize) {
cv::KeyPointsFilter::runByImageBorder(voKeypoints,oImgSize,PATCH_SIZE/2);
//去除感兴趣区域的边界
void LBSP::validateROI(cv::Mat& oROI) {
CV_Assert(!oROI.empty() && oROI.type()==CV_8UC1);
cv::Mat oROI_new(oROI.size(),CV_8UC1,cv::Scalar_&uchar&(0));
const size_t nBorderSize = PATCH_SIZE/2;
const cv::Rect nROI_inner(nBorderSize,nBorderSize,oROI.cols-nBorderSize*2,oROI.rows-nBorderSize*2);
cv::Mat(oROI,nROI_inner).copyTo(cv::Mat(oROI_new,nROI_inner));
oROI = oROI_
3. LBSP_16bits_dbcross_1ch.i
// note: this is the LBSP 16 bit double-cross single channel pattern as used in
// the original article by G.-A. Bilodeau et al.
.. 12 10 14 ..
// must be defined externally:
(size_t, absolute threshold used for comparisons)
(uchar, 'central' value used for comparisons)
(uchar*, single-channel data to be covered by the pattern)
(int, pattern rows location in the image data)
(int, pattern cols location in the image data)
(size_t, step size between rows, including padding)
(ushort, 16 bit result vector)
(function, returns the absolute difference between two uchars)
#ifdef _val
#error &definitions clash detected&
#define _val(x,y) _data[_step_row*(_y+y)+_x+x]
_res = ((L1dist(_val(-1, 1),_ref) & _t) && 15)
+ ((L1dist(_val( 1,-1),_ref) & _t) && 14)
+ ((L1dist(_val( 1, 1),_ref) & _t) && 13)
+ ((L1dist(_val(-1,-1),_ref) & _t) && 12)
+ ((L1dist(_val( 1, 0),_ref) & _t) && 11)
+ ((L1dist(_val( 0,-1),_ref) & _t) && 10)
+ ((L1dist(_val(-1, 0),_ref) & _t) && 9)
+ ((L1dist(_val( 0, 1),_ref) & _t) && 8)
+ ((L1dist(_val(-2,-2),_ref) & _t) && 7)
+ ((L1dist(_val( 2, 2),_ref) & _t) && 6)
+ ((L1dist(_val( 2,-2),_ref) & _t) && 5)
+ ((L1dist(_val(-2, 2),_ref) & _t) && 4)
+ ((L1dist(_val( 0, 2),_ref) & _t) && 3)
+ ((L1dist(_val( 0,-2),_ref) & _t) && 2)
+ ((L1dist(_val( 2, 0),_ref) & _t) && 1)
+ ((L1dist(_val(-2, 0),_ref) & _t));
#undef _val
参考文献：
完整的LBSP源码请点这里：。
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