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Added ORB+FastGrid implementation (only with OpenCV2). Updated default parameters: Odom/GuessFromMotion=false, Vis/CorNNType=1. RegistrationVis: octave aware on features matching with motion

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This commit is contained in:
matlabbe
2016-03-08 12:31:32 -05:00
parent e0b25aac8f
commit 62c8be40b9
13 changed files with 1179 additions and 133 deletions
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9
corelib/src/CMakeLists.txt
View File

@@ -68,9 +68,16 @@ SET(SRC_FILES
rtflann/ext/lz4.c
rtflann/ext/lz4hc.c
sqlite3/sqlite3.c
sqlite3/sqlite3.c
)
IF(OpenCV_VERSION_MAJOR EQUAL 2)
SET(SRC_FILES
${SRC_FILES}
opencv/Orb.cpp
)
ENDIF(OpenCV_VERSION_MAJOR EQUAL 2)
SET(INCLUDE_DIRS
${PROJECT_SOURCE_DIR}/utilite/include
${CMAKE_CURRENT_SOURCE_DIR}/../include

39
corelib/src/Features2d.cpp
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@@ -41,6 +41,7 @@ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#include <opencv2/opencv_modules.hpp>
#if CV_MAJOR_VERSION < 3
#include "opencv/Orb.h"
#ifdef HAVE_OPENCV_GPU
#include <opencv2/gpu/gpu.hpp>
#endif
@@ -459,9 +460,6 @@ Feature2D * Feature2D::create(Feature2D::Type type, const ParametersMap & parame
case Feature2D::kFeatureFastFreak:
feature2D = new FAST_FREAK(parameters);
break;
case Feature2D::kFeatureFastOrb:
feature2D = new FAST_ORB(parameters);
break;
case Feature2D::kFeatureGfttFreak:
feature2D = new GFTT_FREAK(parameters);
break;
@@ -545,7 +543,7 @@ std::vector<cv::KeyPoint> Feature2D::generateKeypoints(const cv::Mat & image, co
}
}
if(_subPixWinSize > 0 && _subPixIterations > 0)
if(keypoints.size() && _subPixWinSize > 0 && _subPixIterations > 0)
{
std::vector<cv::Point2f> corners;
cv::KeyPoint::convert(keypoints, corners);
@@ -558,7 +556,7 @@ std::vector<cv::KeyPoint> Feature2D::generateKeypoints(const cv::Mat & image, co
{
keypoints[i].pt = corners[i];
}
UDEBUG("Keypoints extraction time = %f s, keypoints extracted = %d", timer.ticks(), keypoints.size());
UDEBUG("subpixel time = %f s", timer.ticks());
}
return keypoints;
@@ -949,9 +947,9 @@ void ORB::parseParameters(const ParametersMap & parameters)
else
{
#if CV_MAJOR_VERSION < 3
_orb = cv::Ptr<CV_ORB>(new CV_ORB(this->getMaxFeatures(), scaleFactor_, nLevels_, edgeThreshold_, firstLevel_, WTA_K_, scoreType_, patchSize_));
_orb = cv::Ptr<CV_ORB>(new CV_ORB(this->getMaxFeatures(), scaleFactor_, nLevels_, edgeThreshold_, firstLevel_, WTA_K_, scoreType_, patchSize_, parameters));
#else
_orb = CV_ORB::create(this->getMaxFeatures(), scaleFactor_, nLevels_, edgeThreshold_, firstLevel_, WTA_K_, scoreType_, patchSize_);
_orb = CV_ORB::create(this->getMaxFeatures(), scaleFactor_, nLevels_, edgeThreshold_, firstLevel_, WTA_K_, scoreType_, patchSize_, fastThreshold_);
#endif
}
}
@@ -1120,6 +1118,7 @@ void FAST::parseParameters(const ParametersMap & parameters)
#if CV_MAJOR_VERSION < 3
if(gridRows_ > 0 && gridCols_ > 0)
{
UDEBUG("grid max features = %d", this->getMaxFeatures());
cv::Ptr<cv::FeatureDetector> fastAdjuster = cv::Ptr<cv::FastAdjuster>(new cv::FastAdjuster(threshold_, nonmaxSuppression_, minThreshold_, maxThreshold_));
_fast = cv::Ptr<cv::FeatureDetector>(new cv::GridAdaptedFeatureDetector(fastAdjuster, this->getMaxFeatures(), gridRows_, gridCols_));
}
@@ -1275,32 +1274,6 @@ cv::Mat FAST_FREAK::generateDescriptorsImpl(const cv::Mat & image, std::vector<c
return descriptors;
}
//////////////////////////
//FAST-ORB
//////////////////////////
FAST_ORB::FAST_ORB(const ParametersMap & parameters) :
FAST(parameters),
_orb(parameters)
{
parseParameters(parameters);
}
FAST_ORB::~FAST_ORB()
{
}
void FAST_ORB::parseParameters(const ParametersMap & parameters)
{
FAST::parseParameters(parameters);
_orb.parseParameters(parameters);
}
cv::Mat FAST_ORB::generateDescriptorsImpl(const cv::Mat & image, std::vector<cv::KeyPoint> & keypoints) const
{
UASSERT(!image.empty() && image.channels() == 1 && image.depth() == CV_8U);
return _orb.generateDescriptors(image, keypoints);
}
//////////////////////////
//GFTT
//////////////////////////

3
corelib/src/Memory.cpp
View File

@@ -3123,7 +3123,8 @@ Signature * Memory::createSignature(const SensorData & data, const Transform & p
}
cv::Mat depthMask;
if(!data.depthRaw().empty())
if(!data.depthRaw().empty() &&
_feature2D->getType() != Feature2D::kFeatureOrb) // ORB's mask pyramids don't seem to work well
{
if(imageMono.rows % data.depthRaw().rows == 0 &&
imageMono.cols % data.depthRaw().cols == 0 &&

2
corelib/src/Odometry.cpp
View File

@@ -196,7 +196,7 @@ Transform Odometry::process(SensorData & data, OdometryInfo * info)
}
double dt = previousStamp_>0.0f?data.stamp() - previousStamp_:0.0;
Transform guess = dt?Transform::getIdentity():Transform();
Transform guess = dt && guessFromMotion_?Transform::getIdentity():Transform();
UASSERT(dt>0.0 || (dt == 0.0 && previousVelocityTransform_.isNull()));
if(!previousVelocityTransform_.isNull())
{

69
corelib/src/OdometryF2M.cpp
View File

@@ -241,47 +241,54 @@ Transform OdometryF2M::computeTransform(
// fields to update
cv::Mat mapScan = tmpMap.sensorData().laserScanRaw();
std::multimap<int, cv::KeyPoint> mapWords = tmpMap.getWords();
std::multimap<int, cv::Point3f> mapPoints = tmpMap.getWords3();
std::multimap<int, cv::Mat> mapDescriptors = tmpMap.getWordsDescriptors();
//Visual
int added = 0;
int removed = 0;
UDEBUG("keyframeThr=%f inliers=%d features=%d", keyFrameThr_, regInfo.inliers, (int)lastFrame_->sensorData().keypoints().size());
UDEBUG("keyframeThr=%f matches=%d inliers=%d features=%d mp=%d", keyFrameThr_, regInfo.matches, regInfo.inliers, (int)lastFrame_->sensorData().keypoints().size(), (int)mapPoints.size());
if(regPipeline_->isImageRequired() &&
(keyFrameThr_==0 || float(regInfo.inliers) <= keyFrameThr_*float(lastFrame_->sensorData().keypoints().size())))
{
UDEBUG("Update local map");
// update local map
UASSERT(mapWords.size() == mapPoints.size());
UASSERT(mapPoints.size() == mapDescriptors.size());
UASSERT_MSG(lastFrame_->getWordsDescriptors().size() == lastFrame_->getWords3().size(), uFormat("%d vs %d", lastFrame_->getWordsDescriptors().size(), lastFrame_->getWords3().size()).c_str());
// sort by feature response
std::multimap<float, std::pair<int, cv::Point3f> > newIds;
int lastId = 0;
std::multimap<float, std::pair<int, std::pair<cv::KeyPoint, std::pair<cv::Point3f, cv::Mat> > > > newIds;
UASSERT(lastFrame_->getWords3().size() == lastFrame_->getWords().size());
std::multimap<int, cv::KeyPoint>::const_iterator iter2D = lastFrame_->getWords().begin();
for(std::multimap<int, cv::Point3f>::const_iterator iter = lastFrame_->getWords3().begin(); iter!=lastFrame_->getWords3().end(); ++iter, ++iter2D)
std::multimap<int, cv::Mat>::const_iterator iterDesc = lastFrame_->getWordsDescriptors().begin();
for(std::multimap<int, cv::Point3f>::const_iterator iter = lastFrame_->getWords3().begin(); iter!=lastFrame_->getWords3().end(); ++iter, ++iter2D, ++iterDesc)
{
if(iter == lastFrame_->getWords3().begin() ||
(iter != lastFrame_->getWords3().begin() && lastId != iter->first))
if(util3d::isFinite(iter->second))
{
newIds.insert(std::make_pair(iter2D->second.response, std::make_pair(iter->first, iter->second)));
lastId = iter->first;
if(mapPoints.find(iter->first) == mapPoints.end()) // Point not in map
{
newIds.insert(
std::make_pair(iter2D->second.response>0?1.0f/iter2D->second.response:0.0f,
std::make_pair(iter->first,
std::make_pair(iter2D->second,
std::make_pair(iter->second, iterDesc->second)))));
}
}
}
for(std::multimap<float, std::pair<int, cv::Point3f> >::reverse_iterator iter=newIds.rbegin(); iter!=newIds.rend(); ++iter)
for(std::multimap<float, std::pair<int, std::pair<cv::KeyPoint, std::pair<cv::Point3f, cv::Mat> > > >::iterator iter=newIds.begin();
iter!=newIds.end();
++iter)
{
if(maxNewFeatures_ == 0 || added < maxNewFeatures_)
{
if(mapPoints.find(iter->second.first) == mapPoints.end() && util3d::isFinite(iter->second.second))
{
mapPoints.insert(std::make_pair(iter->second.first, util3d::transformPoint(iter->second.second, newFramePose)));
mapDescriptors.insert(std::make_pair(iter->second.first, lastFrame_->getWordsDescriptors().find(iter->second.first)->second));
++added;
}
mapWords.insert(std::make_pair(iter->second.first, iter->second.second.first));
mapPoints.insert(std::make_pair(iter->second.first, util3d::transformPoint(iter->second.second.second.first, newFramePose)));
mapDescriptors.insert(std::make_pair(iter->second.first, iter->second.second.second.second));
++added;
}
}
@@ -290,19 +297,22 @@ Transform OdometryF2M::computeTransform(
{
// remove oldest first, keep matched features
std::set<int> matches(regInfo.matchesIDs.begin(), regInfo.matchesIDs.end());
std::multimap<int, cv::Mat>::iterator iterMapWords = mapDescriptors.begin();
std::multimap<int, cv::Mat>::iterator iterMapDescriptors = mapDescriptors.begin();
std::multimap<int, cv::KeyPoint>::iterator iterMapWords = mapWords.begin();
for(std::multimap<int, cv::Point3f>::iterator iter = mapPoints.begin();
iter!=mapPoints.end() && (int)mapPoints.size() > maximumMapSize_ && mapPoints.size() >= newIds.size();)
{
if(matches.find(iter->first) == matches.end())
{
iter = mapPoints.erase(iter);
iterMapWords = mapDescriptors.erase(iterMapWords);
iterMapDescriptors = mapDescriptors.erase(iterMapDescriptors);
iterMapWords = mapWords.erase(iterMapWords);
++removed;
}
else
{
++iter;
++iterMapDescriptors;
++iterMapWords;
}
}
@@ -372,6 +382,7 @@ Transform OdometryF2M::computeTransform(
*map_ = tmpMap;
map_->sensorData().setLaserScanRaw(mapScan, 0, 0);
map_->setWords(mapWords);
map_->setWords3(mapPoints);
map_->setWordsDescriptors(mapDescriptors);
@@ -419,22 +430,14 @@ Transform OdometryF2M::computeTransform(
if(regPipeline_->isImageRequired() &&
(int)lastFrame_->getWords3().size() >= regPipeline_->getMinVisualCorrespondences())
{
std::multimap<int, cv::Point3f> transformedPoints;
std::multimap<int, cv::Mat> descriptors;
UASSERT(lastFrame_->getWords3().size() == lastFrame_->getWordsDescriptors().size());
std::multimap<int, cv::Mat>::const_iterator descIter = lastFrame_->getWordsDescriptors().begin();
for(std::multimap<int, cv::Point3f>::const_iterator iter = lastFrame_->getWords3().begin();
iter!=lastFrame_->getWords3().end();
++iter,++descIter)
{
if(util3d::isFinite(iter->second))
{
transformedPoints.insert(std::make_pair(iter->first, util3d::transformPoint(iter->second, newFramePose)));
descriptors.insert(std::make_pair(iter->first, descIter->second));
}
}
map_->setWords3(transformedPoints);
map_->setWordsDescriptors(descriptors);
// update local map
UASSERT_MSG(lastFrame_->getWordsDescriptors().size() == lastFrame_->getWords3().size(), uFormat("%d vs %d", lastFrame_->getWordsDescriptors().size(), lastFrame_->getWords3().size()).c_str());
UASSERT(lastFrame_->getWords3().size() == lastFrame_->getWords().size());
map_->setWords(lastFrame_->getWords());
map_->setWords3(lastFrame_->getWords3());
map_->setWordsDescriptors(lastFrame_->getWordsDescriptors());
map_->sensorData().setCameraModels(lastFrame_->sensorData().cameraModels());
map_->sensorData().setStereoCameraModel(lastFrame_->sensorData().stereoCameraModel());
}

13
corelib/src/Parameters.cpp
View File

@@ -134,6 +134,19 @@ ParametersMap Parameters::getDefaultParameters(const std::string & group)
return parameters;
}
ParametersMap Parameters::filterParameters(const ParametersMap & parameters, const std::string & group)
{
ParametersMap output;
for(rtabmap::ParametersMap::const_iterator iter=parameters.begin(); iter!=parameters.end(); ++iter)
{
if(iter->first.compare(group) == 0)
{
output.insert(*iter);
}
}
return output;
}
const std::map<std::string, std::pair<bool, std::string> > & Parameters::getRemovedParameters()
{
if(removedParameters_.empty())

66
corelib/src/RegistrationVis.cpp
View File

@@ -257,7 +257,8 @@ Transform RegistrationVis::computeTransformationImpl(
}
cv::Mat depthMask;
if(!fromSignature.sensorData().depthRaw().empty())
if(!fromSignature.sensorData().depthRaw().empty() &&
detector->getType() != Feature2D::kFeatureOrb) // ORB's mask pyramids don't seem to work well
{
if(fromSignature.sensorData().imageRaw().rows % fromSignature.sensorData().depthRaw().rows == 0 &&
fromSignature.sensorData().imageRaw().cols % fromSignature.sensorData().depthRaw().cols == 0 &&
@@ -430,7 +431,8 @@ Transform RegistrationVis::computeTransformationImpl(
}
cv::Mat depthMask;
if(!toSignature.sensorData().depthRaw().empty())
if(!toSignature.sensorData().depthRaw().empty() &&
detector->getType() != Feature2D::kFeatureOrb) // ORB's mask pyramids don't seem to work well
{
if(toSignature.sensorData().imageRaw().rows % toSignature.sensorData().depthRaw().rows == 0 &&
toSignature.sensorData().imageRaw().cols % toSignature.sensorData().depthRaw().cols == 0 &&
@@ -527,6 +529,8 @@ Transform RegistrationVis::computeTransformationImpl(
kptsTo3D = uValues(toSignature.getWords3());
}
UASSERT(kptsFrom.empty() || int(kptsFrom.size()) == descriptorsFrom.rows);
fromSignature.sensorData().setFeatures(kptsFrom, descriptorsFrom);
toSignature.sensorData().setFeatures(kptsTo, descriptorsTo);
@@ -604,7 +608,7 @@ Transform RegistrationVis::computeTransformationImpl(
UASSERT(indices.size() == pointsToMat.rows);
UASSERT(descriptorsFrom.cols == descriptorsTo.cols);
UASSERT(kptsFrom.empty() || descriptorsFrom.rows == (int)kptsFrom.size());
UASSERT(descriptorsFrom.rows == (int)kptsFrom.size());
UASSERT((int)pointsToMat.rows == descriptorsTo.rows);
UASSERT(pointsToMat.rows == kptsTo.size());
UDEBUG("");
@@ -620,26 +624,43 @@ Transform RegistrationVis::computeTransformationImpl(
{
if(kptsTo3D.empty() || util3d::isFinite(kptsTo3D[i]))
{
int octave = kptsTo[i].octave;
int matchedIndex = -1;
if(indices[i].size() >= 2)
{
cv::Mat descriptors(indices[i].size(), descriptorsFrom.cols, descriptorsFrom.type());
cv::Mat descriptors;
std::vector<int> descriptorsIndices(indices[i].size());
int oi=0;
for(unsigned int j=0; j<indices[i].size(); ++j)
{
descriptorsFrom.row(projectedIndexToDescIndex[indices[i].at(j)]).copyTo(descriptors.row(j));
if(kptsFrom.at(projectedIndexToDescIndex[indices[i].at(j)]).octave>=octave-1 &&
kptsFrom.at(projectedIndexToDescIndex[indices[i].at(j)]).octave<=octave+1)
{
descriptors.push_back(descriptorsFrom.row(projectedIndexToDescIndex[indices[i].at(j)]));
descriptorsIndices[oi++] = indices[i].at(j);
}
}
std::vector<std::vector<cv::DMatch> > matches;
cv::BFMatcher matcher(descriptors.type()==CV_8U?cv::NORM_HAMMING:cv::NORM_L2SQR);
matcher.knnMatch(descriptorsTo.row(i), descriptors, matches, 2);
UASSERT(matches.size() == 1);
UASSERT(matches[0].size() == 2);
if(matches[0].at(0).distance < _nndr * matches[0].at(1).distance)
descriptorsIndices.resize(oi);
if(oi >=2)
{
matchedIndex = indices[i].at(matches[0].at(0).trainIdx);
std::vector<std::vector<cv::DMatch> > matches;
cv::BFMatcher matcher(descriptors.type()==CV_8U?cv::NORM_HAMMING:cv::NORM_L2SQR);
matcher.knnMatch(descriptorsTo.row(i), descriptors, matches, 2);
UASSERT(matches.size() == 1);
UASSERT(matches[0].size() == 2);
if(matches[0].at(0).distance < _nndr * matches[0].at(1).distance)
{
matchedIndex = descriptorsIndices.at(matches[0].at(0).trainIdx);
}
}
else if(oi == 1)
{
matchedIndex = descriptorsIndices[0];
}
}
else if(indices[i].size() == 1)
else if(indices[i].size() == 1 &&
kptsFrom.at(projectedIndexToDescIndex[indices[i].at(0)]).octave >= octave-1 &&
kptsFrom.at(projectedIndexToDescIndex[indices[i].at(0)]).octave <= octave+1)
{
matchedIndex = indices[i].at(0);
}
@@ -699,10 +720,7 @@ Transform RegistrationVis::computeTransformationImpl(
{
if(util3d::isFinite(kptsFrom3D[i]) && addedWordsFrom.find(i) == addedWordsFrom.end())
{
if(kptsFrom.size())
{
wordsFrom.insert(std::make_pair(notMatchedFromId, kptsFrom[i]));
}
wordsFrom.insert(std::make_pair(notMatchedFromId, kptsFrom[i]));
wordsDescFrom.insert(std::make_pair(notMatchedFromId, descriptorsFrom.row(i)));
words3From.insert(std::make_pair(notMatchedFromId, kptsFrom3D[i]));
@@ -753,7 +771,6 @@ Transform RegistrationVis::computeTransformationImpl(
std::multiset<int> fromWordIdsSet(fromWordIds.begin(), fromWordIds.end());
std::multiset<int> toWordIdsSet(toWordIds.begin(), toWordIds.end());
UASSERT(kptsFrom.empty() || fromWordIds.size() == kptsFrom.size());
UASSERT(kptsFrom3D.empty() || fromWordIds.size() == kptsFrom3D.size());
UASSERT(int(fromWordIds.size()) == descriptorsFrom.rows);
int i=0;
@@ -761,10 +778,7 @@ Transform RegistrationVis::computeTransformationImpl(
{
if(fromWordIdsSet.count(*iter) == 1)
{
if(kptsFrom.size())
{
wordsFrom.insert(std::make_pair(*iter, kptsFrom[i]));
}
wordsFrom.insert(std::make_pair(*iter, kptsFrom[i]));
if(kptsFrom3D.size())
{
words3From.insert(std::make_pair(*iter, kptsFrom3D[i]));
@@ -795,14 +809,10 @@ Transform RegistrationVis::computeTransformationImpl(
else if(descriptorsFrom.rows)
{
//just create fake words
UASSERT(kptsFrom.empty() || int(kptsFrom.size()) == descriptorsFrom.rows);
UASSERT(kptsFrom3D.empty() || int(kptsFrom3D.size()) == descriptorsFrom.rows);
for(int i=0; i<descriptorsFrom.rows; ++i)
{
if(kptsFrom.size())
{
wordsFrom.insert(std::make_pair(i, kptsFrom[i]));
}
wordsFrom.insert(std::make_pair(i, kptsFrom[i]));
wordsDescFrom.insert(std::make_pair(i, descriptorsFrom.row(i)));
if(kptsFrom3D.size())
{

963
corelib/src/opencv/Orb.cpp Normal file
View File

@@ -0,0 +1,963 @@
/*********************************************************************
* Software License Agreement (BSD License)
*
* Copyright (c) 2009, Willow Garage, Inc.
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Willow Garage nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*********************************************************************/
/** Authors: Ethan Rublee, Vincent Rabaud, Gary Bradski */
#include <rtabmap/core/Features2d.h>
#include <rtabmap/utilite/UConversion.h>
#include <rtabmap/utilite/UStl.h>
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/imgproc/imgproc_c.h"
#include <algorithm>
#include <iterator>
#include "Orb.h"
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
using namespace cv;
namespace rtabmap
{
const float HARRIS_K = 0.04f;
/**
* Function that computes the Harris responses in a
* blockSize x blockSize patch at given points in an image
*/
static void
HarrisResponses(const Mat& img, std::vector<KeyPoint>& pts, int blockSize, float harris_k)
{
CV_Assert( img.type() == CV_8UC1 && blockSize*blockSize <= 2048 );
size_t ptidx, ptsize = pts.size();
const uchar* ptr00 = img.ptr<uchar>();
int step = (int)(img.step/img.elemSize1());
int r = blockSize/2;
float scale = (1 << 2) * blockSize * 255.0f;
scale = 1.0f / scale;
float scale_sq_sq = scale * scale * scale * scale;
AutoBuffer<int> ofsbuf(blockSize*blockSize);
int* ofs = ofsbuf;
for( int i = 0; i < blockSize; i++ )
for( int j = 0; j < blockSize; j++ )
ofs[i*blockSize + j] = (int)(i*step + j);
for( ptidx = 0; ptidx < ptsize; ptidx++ )
{
int x0 = cvRound(pts[ptidx].pt.x - r);
int y0 = cvRound(pts[ptidx].pt.y - r);
const uchar* ptr0 = ptr00 + y0*step + x0;
int a = 0, b = 0, c = 0;
for( int k = 0; k < blockSize*blockSize; k++ )
{
const uchar* ptr = ptr0 + ofs[k];
int Ix = (ptr[1] - ptr[-1])*2 + (ptr[-step+1] - ptr[-step-1]) + (ptr[step+1] - ptr[step-1]);
int Iy = (ptr[step] - ptr[-step])*2 + (ptr[step-1] - ptr[-step-1]) + (ptr[step+1] - ptr[-step+1]);
a += Ix*Ix;
b += Iy*Iy;
c += Ix*Iy;
}
pts[ptidx].response = ((float)a * b - (float)c * c -
harris_k * ((float)a + b) * ((float)a + b))*scale_sq_sq;
}
}
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
static float IC_Angle(const Mat& image, const int half_k, Point2f pt,
const std::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)
{
// 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);
m_10 += u * (val_plus + val_minus);
}
m_01 += v * v_sum;
}
return fastAtan2((float)m_01, (float)m_10);
}
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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;
float x, y;
int ix, iy;
#if 1
#define GET_VALUE(idx) \
(x = pattern[idx].x*a - pattern[idx].y*b, \
y = pattern[idx].x*b + pattern[idx].y*a, \
ix = cvRound(x), \
iy = cvRound(y), \
*(center + iy*step + ix) )
#else
#define GET_VALUE(idx) \
(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)
{
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
}
static void initializeOrbPattern( const Point* pattern0, std::vector<Point>& pattern, int ntuples, int tupleSize, int poolSize )
{
RNG rng(0x12345678);
int i, k, k1;
pattern.resize(ntuples*tupleSize);
for( i = 0; i < ntuples; i++ )
{
for( k = 0; k < tupleSize; k++ )
{
for(;;)
{
int idx = rng.uniform(0, poolSize);
Point pt = pattern0[idx];
for( k1 = 0; k1 < k; k1++ )
if( pattern[tupleSize*i + k1] == pt )
break;
if( k1 == k )
{
pattern[tupleSize*i + k] = pt;
break;
}
}
}
}
}
static int bit_pattern_31_[256*4] =
{
8,-3, 9,5/*mean (0), correlation (0)*/,
4,2, 7,-12/*mean (1.12461e-05), correlation (0.0437584)*/,
-11,9, -8,2/*mean (3.37382e-05), correlation (0.0617409)*/,
7,-12, 12,-13/*mean (5.62303e-05), correlation (0.0636977)*/,
2,-13, 2,12/*mean (0.000134953), correlation (0.085099)*/,
1,-7, 1,6/*mean (0.000528565), correlation (0.0857175)*/,
-2,-10, -2,-4/*mean (0.0188821), correlation (0.0985774)*/,
-13,-13, -11,-8/*mean (0.0363135), correlation (0.0899616)*/,
-13,-3, -12,-9/*mean (0.121806), correlation (0.099849)*/,
10,4, 11,9/*mean (0.122065), correlation (0.093285)*/,
-13,-8, -8,-9/*mean (0.162787), correlation (0.0942748)*/,
-11,7, -9,12/*mean (0.21561), correlation (0.0974438)*/,
7,7, 12,6/*mean (0.160583), correlation (0.130064)*/,
-4,-5, -3,0/*mean (0.228171), correlation (0.132998)*/,
-13,2, -12,-3/*mean (0.00997526), correlation (0.145926)*/,
-9,0, -7,5/*mean (0.198234), correlation (0.143636)*/,
12,-6, 12,-1/*mean (0.0676226), correlation (0.16689)*/,
-3,6, -2,12/*mean (0.166847), correlation (0.171682)*/,
-6,-13, -4,-8/*mean (0.101215), correlation (0.179716)*/,
11,-13, 12,-8/*mean (0.200641), correlation (0.192279)*/,
4,7, 5,1/*mean (0.205106), correlation (0.186848)*/,
5,-3, 10,-3/*mean (0.234908), correlation (0.192319)*/,
3,-7, 6,12/*mean (0.0709964), correlation (0.210872)*/,
-8,-7, -6,-2/*mean (0.0939834), correlation (0.212589)*/,
-2,11, -1,-10/*mean (0.127778), correlation (0.20866)*/,
-13,12, -8,10/*mean (0.14783), correlation (0.206356)*/,
-7,3, -5,-3/*mean (0.182141), correlation (0.198942)*/,
-4,2, -3,7/*mean (0.188237), correlation (0.21384)*/,
-10,-12, -6,11/*mean (0.14865), correlation (0.23571)*/,
5,-12, 6,-7/*mean (0.222312), correlation (0.23324)*/,
5,-6, 7,-1/*mean (0.229082), correlation (0.23389)*/,
1,0, 4,-5/*mean (0.241577), correlation (0.215286)*/,
9,11, 11,-13/*mean (0.00338507), correlation (0.251373)*/,
4,7, 4,12/*mean (0.131005), correlation (0.257622)*/,
2,-1, 4,4/*mean (0.152755), correlation (0.255205)*/,
-4,-12, -2,7/*mean (0.182771), correlation (0.244867)*/,
-8,-5, -7,-10/*mean (0.186898), correlation (0.23901)*/,
4,11, 9,12/*mean (0.226226), correlation (0.258255)*/,
0,-8, 1,-13/*mean (0.0897886), correlation (0.274827)*/,
-13,-2, -8,2/*mean (0.148774), correlation (0.28065)*/,
-3,-2, -2,3/*mean (0.153048), correlation (0.283063)*/,
-6,9, -4,-9/*mean (0.169523), correlation (0.278248)*/,
8,12, 10,7/*mean (0.225337), correlation (0.282851)*/,
0,9, 1,3/*mean (0.226687), correlation (0.278734)*/,
7,-5, 11,-10/*mean (0.00693882), correlation (0.305161)*/,
-13,-6, -11,0/*mean (0.0227283), correlation (0.300181)*/,
10,7, 12,1/*mean (0.125517), correlation (0.31089)*/,
-6,-3, -6,12/*mean (0.131748), correlation (0.312779)*/,
10,-9, 12,-4/*mean (0.144827), correlation (0.292797)*/,
-13,8, -8,-12/*mean (0.149202), correlation (0.308918)*/,
-13,0, -8,-4/*mean (0.160909), correlation (0.310013)*/,
3,3, 7,8/*mean (0.177755), correlation (0.309394)*/,
5,7, 10,-7/*mean (0.212337), correlation (0.310315)*/,
-1,7, 1,-12/*mean (0.214429), correlation (0.311933)*/,
3,-10, 5,6/*mean (0.235807), correlation (0.313104)*/,
2,-4, 3,-10/*mean (0.00494827), correlation (0.344948)*/,
-13,0, -13,5/*mean (0.0549145), correlation (0.344675)*/,
-13,-7, -12,12/*mean (0.103385), correlation (0.342715)*/,
-13,3, -11,8/*mean (0.134222), correlation (0.322922)*/,
-7,12, -4,7/*mean (0.153284), correlation (0.337061)*/,
6,-10, 12,8/*mean (0.154881), correlation (0.329257)*/,
-9,-1, -7,-6/*mean (0.200967), correlation (0.33312)*/,
-2,-5, 0,12/*mean (0.201518), correlation (0.340635)*/,
-12,5, -7,5/*mean (0.207805), correlation (0.335631)*/,
3,-10, 8,-13/*mean (0.224438), correlation (0.34504)*/,
-7,-7, -4,5/*mean (0.239361), correlation (0.338053)*/,
-3,-2, -1,-7/*mean (0.240744), correlation (0.344322)*/,
2,9, 5,-11/*mean (0.242949), correlation (0.34145)*/,
-11,-13, -5,-13/*mean (0.244028), correlation (0.336861)*/,
-1,6, 0,-1/*mean (0.247571), correlation (0.343684)*/,
5,-3, 5,2/*mean (0.000697256), correlation (0.357265)*/,
-4,-13, -4,12/*mean (0.00213675), correlation (0.373827)*/,
-9,-6, -9,6/*mean (0.0126856), correlation (0.373938)*/,
-12,-10, -8,-4/*mean (0.0152497), correlation (0.364237)*/,
10,2, 12,-3/*mean (0.0299933), correlation (0.345292)*/,
7,12, 12,12/*mean (0.0307242), correlation (0.366299)*/,
-7,-13, -6,5/*mean (0.0534975), correlation (0.368357)*/,
-4,9, -3,4/*mean (0.099865), correlation (0.372276)*/,
7,-1, 12,2/*mean (0.117083), correlation (0.364529)*/,
-7,6, -5,1/*mean (0.126125), correlation (0.369606)*/,
-13,11, -12,5/*mean (0.130364), correlation (0.358502)*/,
-3,7, -2,-6/*mean (0.131691), correlation (0.375531)*/,
7,-8, 12,-7/*mean (0.160166), correlation (0.379508)*/,
-13,-7, -11,-12/*mean (0.167848), correlation (0.353343)*/,
1,-3, 12,12/*mean (0.183378), correlation (0.371916)*/,
2,-6, 3,0/*mean (0.228711), correlation (0.371761)*/,
-4,3, -2,-13/*mean (0.247211), correlation (0.364063)*/,
-1,-13, 1,9/*mean (0.249325), correlation (0.378139)*/,
7,1, 8,-6/*mean (0.000652272), correlation (0.411682)*/,
1,-1, 3,12/*mean (0.00248538), correlation (0.392988)*/,
9,1, 12,6/*mean (0.0206815), correlation (0.386106)*/,
-1,-9, -1,3/*mean (0.0364485), correlation (0.410752)*/,
-13,-13, -10,5/*mean (0.0376068), correlation (0.398374)*/,
7,7, 10,12/*mean (0.0424202), correlation (0.405663)*/,
12,-5, 12,9/*mean (0.0942645), correlation (0.410422)*/,
6,3, 7,11/*mean (0.1074), correlation (0.413224)*/,
5,-13, 6,10/*mean (0.109256), correlation (0.408646)*/,
2,-12, 2,3/*mean (0.131691), correlation (0.416076)*/,
3,8, 4,-6/*mean (0.165081), correlation (0.417569)*/,
2,6, 12,-13/*mean (0.171874), correlation (0.408471)*/,
9,-12, 10,3/*mean (0.175146), correlation (0.41296)*/,
-8,4, -7,9/*mean (0.183682), correlation (0.402956)*/,
-11,12, -4,-6/*mean (0.184672), correlation (0.416125)*/,
1,12, 2,-8/*mean (0.191487), correlation (0.386696)*/,
6,-9, 7,-4/*mean (0.192668), correlation (0.394771)*/,
2,3, 3,-2/*mean (0.200157), correlation (0.408303)*/,
6,3, 11,0/*mean (0.204588), correlation (0.411762)*/,
3,-3, 8,-8/*mean (0.205904), correlation (0.416294)*/,
7,8, 9,3/*mean (0.213237), correlation (0.409306)*/,
-11,-5, -6,-4/*mean (0.243444), correlation (0.395069)*/,
-10,11, -5,10/*mean (0.247672), correlation (0.413392)*/,
-5,-8, -3,12/*mean (0.24774), correlation (0.411416)*/,
-10,5, -9,0/*mean (0.00213675), correlation (0.454003)*/,
8,-1, 12,-6/*mean (0.0293635), correlation (0.455368)*/,
4,-6, 6,-11/*mean (0.0404971), correlation (0.457393)*/,
-10,12, -8,7/*mean (0.0481107), correlation (0.448364)*/,
4,-2, 6,7/*mean (0.050641), correlation (0.455019)*/,
-2,0, -2,12/*mean (0.0525978), correlation (0.44338)*/,
-5,-8, -5,2/*mean (0.0629667), correlation (0.457096)*/,
7,-6, 10,12/*mean (0.0653846), correlation (0.445623)*/,
-9,-13, -8,-8/*mean (0.0858749), correlation (0.449789)*/,
-5,-13, -5,-2/*mean (0.122402), correlation (0.450201)*/,
8,-8, 9,-13/*mean (0.125416), correlation (0.453224)*/,
-9,-11, -9,0/*mean (0.130128), correlation (0.458724)*/,
1,-8, 1,-2/*mean (0.132467), correlation (0.440133)*/,
7,-4, 9,1/*mean (0.132692), correlation (0.454)*/,
-2,1, -1,-4/*mean (0.135695), correlation (0.455739)*/,
11,-6, 12,-11/*mean (0.142904), correlation (0.446114)*/,
-12,-9, -6,4/*mean (0.146165), correlation (0.451473)*/,
3,7, 7,12/*mean (0.147627), correlation (0.456643)*/,
5,5, 10,8/*mean (0.152901), correlation (0.455036)*/,
0,-4, 2,8/*mean (0.167083), correlation (0.459315)*/,
-9,12, -5,-13/*mean (0.173234), correlation (0.454706)*/,
0,7, 2,12/*mean (0.18312), correlation (0.433855)*/,
-1,2, 1,7/*mean (0.185504), correlation (0.443838)*/,
5,11, 7,-9/*mean (0.185706), correlation (0.451123)*/,
3,5, 6,-8/*mean (0.188968), correlation (0.455808)*/,
-13,-4, -8,9/*mean (0.191667), correlation (0.459128)*/,
-5,9, -3,-3/*mean (0.193196), correlation (0.458364)*/,
-4,-7, -3,-12/*mean (0.196536), correlation (0.455782)*/,
6,5, 8,0/*mean (0.1972), correlation (0.450481)*/,
-7,6, -6,12/*mean (0.199438), correlation (0.458156)*/,
-13,6, -5,-2/*mean (0.211224), correlation (0.449548)*/,
1,-10, 3,10/*mean (0.211718), correlation (0.440606)*/,
4,1, 8,-4/*mean (0.213034), correlation (0.443177)*/,
-2,-2, 2,-13/*mean (0.234334), correlation (0.455304)*/,
2,-12, 12,12/*mean (0.235684), correlation (0.443436)*/,
-2,-13, 0,-6/*mean (0.237674), correlation (0.452525)*/,
4,1, 9,3/*mean (0.23962), correlation (0.444824)*/,
-6,-10, -3,-5/*mean (0.248459), correlation (0.439621)*/,
-3,-13, -1,1/*mean (0.249505), correlation (0.456666)*/,
7,5, 12,-11/*mean (0.00119208), correlation (0.495466)*/,
4,-2, 5,-7/*mean (0.00372245), correlation (0.484214)*/,
-13,9, -9,-5/*mean (0.00741116), correlation (0.499854)*/,
7,1, 8,6/*mean (0.0208952), correlation (0.499773)*/,
7,-8, 7,6/*mean (0.0220085), correlation (0.501609)*/,
-7,-4, -7,1/*mean (0.0233806), correlation (0.496568)*/,
-8,11, -7,-8/*mean (0.0236505), correlation (0.489719)*/,
-13,6, -12,-8/*mean (0.0268781), correlation (0.503487)*/,
2,4, 3,9/*mean (0.0323324), correlation (0.501938)*/,
10,-5, 12,3/*mean (0.0399235), correlation (0.494029)*/,
-6,-5, -6,7/*mean (0.0420153), correlation (0.486579)*/,
8,-3, 9,-8/*mean (0.0548021), correlation (0.484237)*/,
2,-12, 2,8/*mean (0.0616622), correlation (0.496642)*/,
-11,-2, -10,3/*mean (0.0627755), correlation (0.498563)*/,
-12,-13, -7,-9/*mean (0.0829622), correlation (0.495491)*/,
-11,0, -10,-5/*mean (0.0843342), correlation (0.487146)*/,
5,-3, 11,8/*mean (0.0929937), correlation (0.502315)*/,
-2,-13, -1,12/*mean (0.113327), correlation (0.48941)*/,
-1,-8, 0,9/*mean (0.132119), correlation (0.467268)*/,
-13,-11, -12,-5/*mean (0.136269), correlation (0.498771)*/,
-10,-2, -10,11/*mean (0.142173), correlation (0.498714)*/,
-3,9, -2,-13/*mean (0.144141), correlation (0.491973)*/,
2,-3, 3,2/*mean (0.14892), correlation (0.500782)*/,
-9,-13, -4,0/*mean (0.150371), correlation (0.498211)*/,
-4,6, -3,-10/*mean (0.152159), correlation (0.495547)*/,
-4,12, -2,-7/*mean (0.156152), correlation (0.496925)*/,
-6,-11, -4,9/*mean (0.15749), correlation (0.499222)*/,
6,-3, 6,11/*mean (0.159211), correlation (0.503821)*/,
-13,11, -5,5/*mean (0.162427), correlation (0.501907)*/,
11,11, 12,6/*mean (0.16652), correlation (0.497632)*/,
7,-5, 12,-2/*mean (0.169141), correlation (0.484474)*/,
-1,12, 0,7/*mean (0.169456), correlation (0.495339)*/,
-4,-8, -3,-2/*mean (0.171457), correlation (0.487251)*/,
-7,1, -6,7/*mean (0.175), correlation (0.500024)*/,
-13,-12, -8,-13/*mean (0.175866), correlation (0.497523)*/,
-7,-2, -6,-8/*mean (0.178273), correlation (0.501854)*/,
-8,5, -6,-9/*mean (0.181107), correlation (0.494888)*/,
-5,-1, -4,5/*mean (0.190227), correlation (0.482557)*/,
-13,7, -8,10/*mean (0.196739), correlation (0.496503)*/,
1,5, 5,-13/*mean (0.19973), correlation (0.499759)*/,
1,0, 10,-13/*mean (0.204465), correlation (0.49873)*/,
9,12, 10,-1/*mean (0.209334), correlation (0.49063)*/,
5,-8, 10,-9/*mean (0.211134), correlation (0.503011)*/,
-1,11, 1,-13/*mean (0.212), correlation (0.499414)*/,
-9,-3, -6,2/*mean (0.212168), correlation (0.480739)*/,
-1,-10, 1,12/*mean (0.212731), correlation (0.502523)*/,
-13,1, -8,-10/*mean (0.21327), correlation (0.489786)*/,
8,-11, 10,-6/*mean (0.214159), correlation (0.488246)*/,
2,-13, 3,-6/*mean (0.216993), correlation (0.50287)*/,
7,-13, 12,-9/*mean (0.223639), correlation (0.470502)*/,
-10,-10, -5,-7/*mean (0.224089), correlation (0.500852)*/,
-10,-8, -8,-13/*mean (0.228666), correlation (0.502629)*/,
4,-6, 8,5/*mean (0.22906), correlation (0.498305)*/,
3,12, 8,-13/*mean (0.233378), correlation (0.503825)*/,
-4,2, -3,-3/*mean (0.234323), correlation (0.476692)*/,
5,-13, 10,-12/*mean (0.236392), correlation (0.475462)*/,
4,-13, 5,-1/*mean (0.236842), correlation (0.504132)*/,
-9,9, -4,3/*mean (0.236977), correlation (0.497739)*/,
0,3, 3,-9/*mean (0.24314), correlation (0.499398)*/,
-12,1, -6,1/*mean (0.243297), correlation (0.489447)*/,
3,2, 4,-8/*mean (0.00155196), correlation (0.553496)*/,
-10,-10, -10,9/*mean (0.00239541), correlation (0.54297)*/,
8,-13, 12,12/*mean (0.0034413), correlation (0.544361)*/,
-8,-12, -6,-5/*mean (0.003565), correlation (0.551225)*/,
2,2, 3,7/*mean (0.00835583), correlation (0.55285)*/,
10,6, 11,-8/*mean (0.00885065), correlation (0.540913)*/,
6,8, 8,-12/*mean (0.0101552), correlation (0.551085)*/,
-7,10, -6,5/*mean (0.0102227), correlation (0.533635)*/,
-3,-9, -3,9/*mean (0.0110211), correlation (0.543121)*/,
-1,-13, -1,5/*mean (0.0113473), correlation (0.550173)*/,
-3,-7, -3,4/*mean (0.0140913), correlation (0.554774)*/,
-8,-2, -8,3/*mean (0.017049), correlation (0.55461)*/,
4,2, 12,12/*mean (0.01778), correlation (0.546921)*/,
2,-5, 3,11/*mean (0.0224022), correlation (0.549667)*/,
6,-9, 11,-13/*mean (0.029161), correlation (0.546295)*/,
3,-1, 7,12/*mean (0.0303081), correlation (0.548599)*/,
11,-1, 12,4/*mean (0.0355151), correlation (0.523943)*/,
-3,0, -3,6/*mean (0.0417904), correlation (0.543395)*/,
4,-11, 4,12/*mean (0.0487292), correlation (0.542818)*/,
2,-4, 2,1/*mean (0.0575124), correlation (0.554888)*/,
-10,-6, -8,1/*mean (0.0594242), correlation (0.544026)*/,
-13,7, -11,1/*mean (0.0597391), correlation (0.550524)*/,
-13,12, -11,-13/*mean (0.0608974), correlation (0.55383)*/,
6,0, 11,-13/*mean (0.065126), correlation (0.552006)*/,
0,-1, 1,4/*mean (0.074224), correlation (0.546372)*/,
-13,3, -9,-2/*mean (0.0808592), correlation (0.554875)*/,
-9,8, -6,-3/*mean (0.0883378), correlation (0.551178)*/,
-13,-6, -8,-2/*mean (0.0901035), correlation (0.548446)*/,
5,-9, 8,10/*mean (0.0949843), correlation (0.554694)*/,
2,7, 3,-9/*mean (0.0994152), correlation (0.550979)*/,
-1,-6, -1,-1/*mean (0.10045), correlation (0.552714)*/,
9,5, 11,-2/*mean (0.100686), correlation (0.552594)*/,
11,-3, 12,-8/*mean (0.101091), correlation (0.532394)*/,
3,0, 3,5/*mean (0.101147), correlation (0.525576)*/,
-1,4, 0,10/*mean (0.105263), correlation (0.531498)*/,
3,-6, 4,5/*mean (0.110785), correlation (0.540491)*/,
-13,0, -10,5/*mean (0.112798), correlation (0.536582)*/,
5,8, 12,11/*mean (0.114181), correlation (0.555793)*/,
8,9, 9,-6/*mean (0.117431), correlation (0.553763)*/,
7,-4, 8,-12/*mean (0.118522), correlation (0.553452)*/,
-10,4, -10,9/*mean (0.12094), correlation (0.554785)*/,
7,3, 12,4/*mean (0.122582), correlation (0.555825)*/,
9,-7, 10,-2/*mean (0.124978), correlation (0.549846)*/,
7,0, 12,-2/*mean (0.127002), correlation (0.537452)*/,
-1,-6, 0,-11/*mean (0.127148), correlation (0.547401)*/
};
static void makeRandomPattern(int patchSize, Point* pattern, int npoints)
{
RNG rng(0x34985739); // we always start with a fixed seed,
// to make patterns the same on each run
for( int i = 0; i < npoints; i++ )
{
pattern[i].x = rng.uniform(-patchSize/2, patchSize/2+1);
pattern[i].y = rng.uniform(-patchSize/2, patchSize/2+1);
}
}
static inline float getScale(int level, int firstLevel, double scaleFactor)
{
return (float)std::pow(scaleFactor, (double)(level - firstLevel));
}
/** Constructor
* @param detector_params parameters to use
*/
CV_ORB::CV_ORB(int _nfeatures, float _scaleFactor, int _nlevels, int _edgeThreshold,
int _firstLevel, int _WTA_K, int _scoreType, int _patchSize, const ParametersMap & _fastParameters) :
nfeatures(_nfeatures), scaleFactor(_scaleFactor), nlevels(_nlevels),
edgeThreshold(_edgeThreshold), firstLevel(_firstLevel), WTA_K(_WTA_K),
scoreType(_scoreType), patchSize(_patchSize), fastParameters(_fastParameters)
{}
int CV_ORB::descriptorSize() const
{
return kBytes;
}
int CV_ORB::descriptorType() const
{
return CV_8U;
}
/** 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
*/
void CV_ORB::operator()(InputArray image, InputArray mask, std::vector<KeyPoint>& keypoints) const
{
(*this)(image, mask, keypoints, noArray(), false);
}
/** Compute the ORB keypoint orientations
* @param image the image to compute the features and descriptors on
* @param integral_image the integral image of the iamge (can be empty, but the computation will be slower)
* @param scale the scale at which we compute the orientation
* @param keypoints the resulting keypoints
*/
static void computeOrientation(const Mat& image, std::vector<KeyPoint>& keypoints,
int halfPatchSize, const std::vector<int>& umax)
{
// Process each keypoint
for (std::vector<KeyPoint>::iterator keypoint = keypoints.begin(),
keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint)
{
keypoint->angle = IC_Angle(image, halfPatchSize, keypoint->pt, umax);
}
}
/** 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 std::vector<Mat>& imagePyramid,
const std::vector<Mat>& maskPyramid,
std::vector<std::vector<KeyPoint> >& allKeypoints,
int nfeatures, int firstLevel, double scaleFactor,
int edgeThreshold, int patchSize, int scoreType,
ParametersMap fastParameters)
{
int nlevels = (int)imagePyramid.size();
std::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++ )
{
nfeaturesPerLevel[level] = cvRound(ndesiredFeaturesPerScale);
sumFeatures += nfeaturesPerLevel[level];
ndesiredFeaturesPerScale *= factor;
}
nfeaturesPerLevel[nlevels-1] = std::max(nfeatures - sumFeatures, 0);
// 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;
std::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);
std::vector<KeyPoint> & keypoints = allKeypoints[level];
// Detect FAST features, maxFeatures is used by the GridAdaptor of FAST
uInsert(fastParameters, ParametersPair(Parameters::kKpMaxFeatures(), uNumber2Str(scoreType == CV_ORB::HARRIS_SCORE?2*featuresNum:featuresNum)));
FAST fast(fastParameters);
keypoints = fast.generateKeypoints(imagePyramid[level], maskPyramid[level]);
// Remove keypoints very close to the border
KeyPointsFilter::runByImageBorder(keypoints, imagePyramid[level].size(), edgeThreshold);
if( scoreType == CV_ORB::HARRIS_SCORE )
{
// Keep more points than necessary as FAST does not give amazing corners
KeyPointsFilter::retainBest(keypoints, 2 * featuresNum);
// Compute the Harris cornerness (better scoring than FAST)
HarrisResponses(imagePyramid[level], keypoints, 7, HARRIS_K);
}
//cull to the final desired level, using the new Harris scores or the original FAST scores.
KeyPointsFilter::retainBest(keypoints, featuresNum);
float sf = getScale(level, firstLevel, scaleFactor);
// Set the level of the coordinates
for (std::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);
}
}
/** Compute the ORB decriptors
* @param image the image to compute the features and descriptors on
* @param integral_image the integral image of the image (can be empty, but the computation will be slower)
* @param level the scale at which we compute the orientation
* @param keypoints the keypoints to use
* @param descriptors the resulting descriptors
*/
static void computeDescriptors(const Mat& image, std::vector<KeyPoint>& keypoints, Mat& descriptors,
const std::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);
}
/** 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 CV_ORB::operator()( InputArray _image, InputArray _mask, std::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;
int halfPatchSize = patchSize / 2;
int border = std::max(edgeThreshold, std::max(halfPatchSize, HARRIS_BLOCK_SIZE/2))+1;
Mat image = _image.getMat(), mask = _mask.getMat();
if( image.type() != CV_8UC1 )
cvtColor(_image, image, CV_BGR2GRAY);
int levelsNum = this->nlevels;
if( !do_keypoints )
{
// 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));
levelsNum++;
}
// Pre-compute the scale pyramids
std::vector<Mat> imagePyramid(levelsNum), maskPyramid(levelsNum);
for (int level = 0; level < levelsNum; ++level)
{
float scale = 1/getScale(level, firstLevel, scaleFactor);
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));
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 )
{
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,
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,
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
std::vector < std::vector<KeyPoint> > allKeypoints;
if( do_keypoints )
{
// Get keypoints, those will be far enough from the border that no check will be required for the descriptor
computeKeyPoints(imagePyramid, maskPyramid, allKeypoints,
nfeatures, firstLevel, scaleFactor,
edgeThreshold, patchSize, scoreType,
fastParameters);
// 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
{
// 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 (std::vector<KeyPoint>::iterator keypoint = _keypoints.begin(),
keypointEnd = _keypoints.end(); keypoint != keypointEnd; ++keypoint)
allKeypoints[keypoint->octave].push_back(*keypoint);
// Make sure we rescale the coordinates
for (int level = 0; level < levelsNum; ++level)
{
if (level == firstLevel)
continue;
std::vector<KeyPoint> & keypoints = allKeypoints[level];
float scale = 1/getScale(level, firstLevel, scaleFactor);
for (std::vector<KeyPoint>::iterator keypoint = keypoints.begin(),
keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint)
keypoint->pt *= scale;
}
}
Mat descriptors;
std::vector<Point> pattern;
if( do_descriptors )
{
int nkeypoints = 0;
for (int level = 0; level < levelsNum; ++level)
nkeypoints += (int)allKeypoints[level].size();
if( nkeypoints == 0 )
_descriptors.release();
else
{
_descriptors.create(nkeypoints, descriptorSize(), CV_8U);
descriptors = _descriptors.getMat();
}
const int npoints = 512;
Point patternbuf[npoints];
const Point* pattern0 = (const Point*)bit_pattern_31_;
if( patchSize != 31 )
{
pattern0 = patternbuf;
makeRandomPattern(patchSize, patternbuf, npoints);
}
CV_Assert( WTA_K == 2 || WTA_K == 3 || WTA_K == 4 );
if( WTA_K == 2 )
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)
{
// Get the features and compute their orientation
std::vector<KeyPoint>& keypoints = allKeypoints[level];
int nkeypoints = (int)keypoints.size();
// Compute the descriptors
if (do_descriptors)
{
Mat desc;
if (!descriptors.empty())
{
desc = descriptors.rowRange(offset, offset + nkeypoints);
}
offset += nkeypoints;
// 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);
computeDescriptors(workingMat, keypoints, desc, pattern, descriptorSize(), WTA_K);
}
// Copy to the output data
if (level != firstLevel)
{
float scale = getScale(level, firstLevel, scaleFactor);
for (std::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());
}
}
void CV_ORB::detectImpl( const Mat& image, std::vector<KeyPoint>& keypoints, const Mat& mask) const
{
(*this)(image, mask, keypoints, noArray(), false);
}
void CV_ORB::computeImpl( const Mat& image, std::vector<KeyPoint>& keypoints, Mat& descriptors) const
{
(*this)(image, Mat(), keypoints, descriptors, true);
}
}

99
corelib/src/opencv/Orb.h Normal file
View File

@@ -0,0 +1,99 @@
/**
* File from OpenCV (see License below), modified by Mathieu Labbe 2016
*/
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef CORELIB_SRC_OPENCV_ORB_H_
#define CORELIB_SRC_OPENCV_ORB_H_
#include <opencv2/features2d/features2d.hpp>
namespace rtabmap {
class FAST;
/*!
ORB implementation.
*/
class CV_ORB : public cv::Feature2D
{
public:
// the size of the signature in bytes
enum { kBytes = 32, HARRIS_SCORE=0, FAST_SCORE=1 };
CV_WRAP explicit CV_ORB(int nfeatures = 500, float scaleFactor = 1.2f, int nlevels = 8, int edgeThreshold = 31,
int firstLevel = 0, int WTA_K=2, int scoreType=CV_ORB::HARRIS_SCORE, int patchSize=31, const ParametersMap & fastParameters=ParametersMap());
// returns the descriptor size in bytes
int descriptorSize() const;
// returns the descriptor type
int descriptorType() const;
// Compute the ORB features and descriptors on an image
void operator()(cv::InputArray image, cv::InputArray mask, std::vector<cv::KeyPoint>& keypoints) const;
// Compute the ORB features and descriptors on an image
void operator()( cv::InputArray image, cv::InputArray mask, std::vector<cv::KeyPoint>& keypoints,
cv::OutputArray descriptors, bool useProvidedKeypoints=false ) const;
protected:
void computeImpl( const cv::Mat& image, std::vector<cv::KeyPoint>& keypoints, cv::Mat& descriptors ) const;
void detectImpl( const cv::Mat& image, std::vector<cv::KeyPoint>& keypoints, const cv::Mat& mask=cv::Mat() ) const;
CV_PROP_RW int nfeatures;
CV_PROP_RW double scaleFactor;
CV_PROP_RW int nlevels;
CV_PROP_RW int edgeThreshold;
CV_PROP_RW int firstLevel;
CV_PROP_RW int WTA_K;
CV_PROP_RW int scoreType;
CV_PROP_RW int patchSize;
ParametersMap fastParameters;
};
}
#endif /* CORELIB_SRC_OPENCV_ORB_H_ */

2
corelib/src/util3d_motion_estimation.cpp
View File

@@ -79,7 +79,7 @@ Transform estimateMotion3DTo2D(
std::map<int, cv::Point3f>::const_iterator iter=words3A.find(ids[i]);
if(iter != words3A.end() && util3d::isFinite(iter->second))
{
cv::Point3f pt = words3A.find(ids[i])->second;
const cv::Point3f & pt = iter->second;
objectPoints[oi].x = pt.x;
objectPoints[oi].y = pt.y;
objectPoints[oi].z = pt.z;