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Using solvePnPRansac() of OpenCV2.4 in OpenCV3 (for some reasons RANSAC is better in OpenCV2.4, tested on Mac OS X). Updated default 3D->3D estimation parameters

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This commit is contained in:
matlabbe
2015-10-01 21:00:16 -04:00
parent 0c8309bb28
commit ee76a02117
4 changed files with 410 additions and 73 deletions
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corelib/include/rtabmap/core/Parameters.h
+5 -5
View File
@@ -320,18 +320,18 @@ class RTABMAP_EXP Parameters
RTABMAP_PARAM(Odom, FeatureType, int, 6, "0=SURF 1=SIFT 2=ORB 3=FAST/FREAK 4=FAST/BRIEF 5=GFTT/FREAK 6=GFTT/BRIEF 7=BRISK.");
RTABMAP_PARAM(Odom, EstimationType, int, 0, "Motion estimation approach: 0:3D->3D, 1:3D->2D (PnP)");
RTABMAP_PARAM(Odom, MaxFeatures, int, 1000, "0 no limits.");
RTABMAP_PARAM(Odom, InlierDistance, float, 0.02, "Maximum distance for visual word correspondences.");
RTABMAP_PARAM(Odom, InlierDistance, float, 0.1, "Maximum distance for visual word correspondences. Used by 3D->3D estimation approach.");
RTABMAP_PARAM(Odom, MinInliers, int, 20, "Minimum visual word correspondences to compute geometry transform.");
RTABMAP_PARAM(Odom, Iterations, int, 100, "Maximum iterations to compute the transform from visual words.");
RTABMAP_PARAM(Odom, RefineIterations, int, 5, "Number of iterations used to refine the transformation found by RANSAC. 0 means that the transformation is not refined.");
RTABMAP_PARAM(Odom, MaxDepth, float, 4.0, "Max depth of the words (0 means no limit).");
RTABMAP_PARAM(Odom, RefineIterations, int, 5, "Number of iterations used to refine the transformation found by RANSAC. 0 means that the transformation is not refined.");
RTABMAP_PARAM(Odom, MaxDepth, float, 0, "Max depth of the words (0 means no limit).");
RTABMAP_PARAM(Odom, ResetCountdown, int, 0, "Automatically reset odometry after X consecutive images on which odometry cannot be computed (value=0 disables auto-reset).");
RTABMAP_PARAM_STR(Odom, RoiRatios, "0.0 0.0 0.0 0.0", "Region of interest ratios [left, right, top, bottom].");
RTABMAP_PARAM(Odom, Force2D, bool, false, "Force 2D transform (3Dof: x,y and yaw).");
RTABMAP_PARAM(Odom, Holonomic, bool, true, "If the robot is holonomic (strafing commands can be issued). If not, y value will be estimated from x and yaw values (y=x*tan(yaw)).");
RTABMAP_PARAM(Odom, FillInfoData, bool, true, "Fill info with data (inliers/outliers features).");
RTABMAP_PARAM(Odom, ImageBufferSize, unsigned int, 1, "Data buffer size (0 min inf).");
RTABMAP_PARAM(Odom, VarianceFromInliersCount, bool, false, "Set variance as the inverse of the number of inliers. Otherwise, the variance is computed as the average 3D position error of the inliers.");
RTABMAP_PARAM(Odom, VarianceFromInliersCount, bool, true, "Set variance as the inverse of the number of inliers. Otherwise, the variance is computed as the average 3D position error of the inliers.");
RTABMAP_PARAM(Odom, PnPReprojError, double, 5.0, "PnP reprojection error.");
RTABMAP_PARAM(Odom, PnPFlags, int, 1, "PnP flags: 0=Iterative, 1=EPNP, 2=P3P");
RTABMAP_PARAM(Odom, ParticleFiltering, bool, false, "Particle filtering to smooth the odometry trajectory.");
@@ -371,7 +371,7 @@ class RTABMAP_EXP Parameters
RTABMAP_PARAM(LccBow, EstimationType, int, 0, "Motion estimation approach: 0:3D->3D, 1:3D->2D (PnP), 2:2D->2D (Epipolar Geometry)");
RTABMAP_PARAM(LccBow, MinInliers, int, 10, "Minimum visual word correspondences to compute geometry transform.");
RTABMAP_PARAM(LccBow, InlierDistance, float, 0.05, "Maximum distance for visual word correspondences.");
RTABMAP_PARAM(LccBow, InlierDistance, float, 0.1, "Maximum distance for visual word correspondences. Used by 3D->3D estimation approach.");
RTABMAP_PARAM(LccBow, Iterations, int, 100, "Maximum iterations to compute the transform from visual words.");
RTABMAP_PARAM(LccBow, RefineIterations, int, 10, "Number of iterations used to refine the transformation found by RANSAC. 0 means that the transformation is not refined.");
RTABMAP_PARAM(LccBow, Force2D, bool, false, "Force 2D transform (3Dof: x,y and yaw).");
corelib/src/util3d_motion_estimation.cpp
+304 -4
View File
@@ -29,6 +29,7 @@ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#include "rtabmap/utilite/UStl.h"
#include "rtabmap/utilite/UMath.h"
#include "rtabmap/utilite/ULogger.h"
#include "rtabmap/core/util3d_transforms.h"
#include "rtabmap/core/util3d_registration.h"
#include "rtabmap/core/util3d_correspondences.h"
@@ -39,6 +40,14 @@ namespace rtabmap
namespace util3d
{
#if CV_MAJOR_VERSION >= 3
void solvePnPRansac(cv::InputArray _opoints, cv::InputArray _ipoints,
cv::InputArray _cameraMatrix, cv::InputArray _distCoeffs,
cv::OutputArray _rvec, cv::OutputArray _tvec, bool useExtrinsicGuess,
int iterationsCount, float reprojectionError, int minInliersCount,
cv::OutputArray _inliers, int flags);
#endif
Transform estimateMotion3DTo2D(
const std::map<int, pcl::PointXYZ> & words3A,
const std::map<int, cv::KeyPoint> & words2B,
@@ -100,7 +109,11 @@ Transform estimateMotion3DTo2D(
cv::Mat tvec = (cv::Mat_<double>(1,3) <<
(double)guessCameraFrame.x(), (double)guessCameraFrame.y(), (double)guessCameraFrame.z());
#if CV_MAJOR_VERSION >= 3
solvePnPRansac(
#else
cv::solvePnPRansac(
#endif
objectPoints,
imagePoints,
K,
@@ -110,11 +123,7 @@ Transform estimateMotion3DTo2D(
true,
iterations,
reprojError,
#if CV_MAJOR_VERSION < 3
0, // min inliers
#else
0.99, // confidence
#endif
inliers,
flagsPnP);
@@ -234,6 +243,297 @@ Transform estimateMotion3DTo3D(
return transform;
}
// Don't know why, but the RANSAC implementation in OpenCV 3 gives me far
// more wrong results than the 2.4x implementation.
// Here is a copy of the RANSAC implementation from OpenCV 2.4.x version
#if CV_MAJOR_VERSION >= 3
namespace pnpransac
{
const int MIN_POINTS_COUNT = 4;
static void project3dPoints(const cv::Mat& points, const cv::Mat& rvec, const cv::Mat& tvec, cv::Mat& modif_points)
{
modif_points.create(1, points.cols, CV_32FC3);
cv::Mat R(3, 3, CV_64FC1);
cv::Rodrigues(rvec, R);
cv::Mat transformation(3, 4, CV_64F);
cv::Mat r = transformation.colRange(0, 3);
R.copyTo(r);
cv::Mat t = transformation.colRange(3, 4);
tvec.copyTo(t);
transform(points, modif_points, transformation);
}
struct CameraParameters
{
void init(cv::Mat _intrinsics, cv::Mat _distCoeffs)
{
_intrinsics.copyTo(intrinsics);
_distCoeffs.copyTo(distortion);
}
cv::Mat intrinsics;
cv::Mat distortion;
};
struct Parameters
{
int iterationsCount;
float reprojectionError;
int minInliersCount;
bool useExtrinsicGuess;
int flags;
CameraParameters camera;
};
template <typename OpointType, typename IpointType>
static void pnpTask(const int curIndex, const std::vector<char>& pointsMask, const cv::Mat& objectPoints, const cv::Mat& imagePoints,
const Parameters& params, std::vector<int>& inliers, int& bestIndex, cv::Mat& rvec, cv::Mat& tvec,
const cv::Mat& rvecInit, const cv::Mat& tvecInit, cv::Mutex& resultsMutex)
{
cv::Mat modelObjectPoints(1, MIN_POINTS_COUNT, CV_MAKETYPE(cv::DataDepth<OpointType>::value, 3));
cv::Mat modelImagePoints(1, MIN_POINTS_COUNT, CV_MAKETYPE(cv::DataDepth<IpointType>::value, 2));
for (int i = 0, colIndex = 0; i < (int)pointsMask.size(); i++)
{
if (pointsMask[i])
{
cv::Mat colModelImagePoints = modelImagePoints(cv::Rect(colIndex, 0, 1, 1));
imagePoints.col(i).copyTo(colModelImagePoints);
cv::Mat colModelObjectPoints = modelObjectPoints(cv::Rect(colIndex, 0, 1, 1));
objectPoints.col(i).copyTo(colModelObjectPoints);
colIndex = colIndex+1;
}
}
//filter same 3d points, hang in solvePnP
double eps = 1e-10;
int num_same_points = 0;
for (int i = 0; i < MIN_POINTS_COUNT; i++)
for (int j = i + 1; j < MIN_POINTS_COUNT; j++)
{
if (norm(modelObjectPoints.at<cv::Vec<OpointType,3> >(0, i) - modelObjectPoints.at<cv::Vec<OpointType,3> >(0, j)) < eps)
num_same_points++;
}
if (num_same_points > 0)
return;
cv::Mat localRvec, localTvec;
rvecInit.copyTo(localRvec);
tvecInit.copyTo(localTvec);
// OpenCV 3
cv::solvePnP(
modelObjectPoints,
modelImagePoints,
params.camera.intrinsics,
params.camera.distortion,
localRvec,
localTvec,
params.useExtrinsicGuess,
params.flags);
std::vector<cv::Point_<OpointType> > projected_points;
projected_points.resize(objectPoints.cols);
projectPoints(objectPoints, localRvec, localTvec, params.camera.intrinsics, params.camera.distortion, projected_points);
cv::Mat rotatedPoints;
project3dPoints(objectPoints, localRvec, localTvec, rotatedPoints);
std::vector<int> localInliers;
for (int i = 0; i < objectPoints.cols; i++)
{
//Although p is a 2D point it needs the same type as the object points to enable the norm calculation
cv::Point_<OpointType> p((OpointType)imagePoints.at<cv::Vec<IpointType,2> >(0, i)[0],
(OpointType)imagePoints.at<cv::Vec<IpointType,2> >(0, i)[1]);
if ((norm(p - projected_points[i]) < params.reprojectionError)
&& (rotatedPoints.at<cv::Vec<OpointType,3> >(0, i)[2] > 0)) //hack
{
localInliers.push_back(i);
}
}
resultsMutex.lock();
if ( (localInliers.size() > inliers.size()) || (localInliers.size() == inliers.size() && curIndex > bestIndex))
{
inliers.clear();
inliers.resize(localInliers.size());
memcpy(&inliers[0], &localInliers[0], sizeof(int) * localInliers.size());
localRvec.copyTo(rvec);
localTvec.copyTo(tvec);
bestIndex = curIndex;
}
resultsMutex.unlock();
}
static void pnpTask(const int curIndex, const std::vector<char>& pointsMask, const cv::Mat& objectPoints, const cv::Mat& imagePoints,
const Parameters& params, std::vector<int>& inliers, int& bestIndex, cv::Mat& rvec, cv::Mat& tvec,
const cv::Mat& rvecInit, const cv::Mat& tvecInit, cv::Mutex& resultsMutex)
{
CV_Assert(objectPoints.depth() == CV_64F || objectPoints.depth() == CV_32F);
CV_Assert(imagePoints.depth() == CV_64F || imagePoints.depth() == CV_32F);
const bool objectDoublePrecision = objectPoints.depth() == CV_64F;
const bool imageDoublePrecision = imagePoints.depth() == CV_64F;
if(objectDoublePrecision)
{
if(imageDoublePrecision)
pnpTask<double, double>(curIndex, pointsMask, objectPoints, imagePoints, params, inliers, bestIndex, rvec, tvec, rvecInit, tvecInit, resultsMutex);
else
pnpTask<double, float>(curIndex, pointsMask, objectPoints, imagePoints, params, inliers, bestIndex, rvec, tvec, rvecInit, tvecInit, resultsMutex);
}
else
{
if(imageDoublePrecision)
pnpTask<float, double>(curIndex, pointsMask, objectPoints, imagePoints, params, inliers, bestIndex, rvec, tvec, rvecInit, tvecInit, resultsMutex);
else
pnpTask<float, float>(curIndex, pointsMask, objectPoints, imagePoints, params, inliers, bestIndex, rvec, tvec, rvecInit, tvecInit, resultsMutex);
}
}
// TBB removed
class PnPSolver
{
public:
void operator()(int begin, int end) const
{
std::vector<char> pointsMask(objectPoints.cols, 0);
for( int i=begin; i!=end; ++i )
{
memset(&pointsMask[0], 0, objectPoints.cols );
memset(&pointsMask[0], 1, MIN_POINTS_COUNT );
generateVar(pointsMask, rng_base_seed + i);
pnpTask(i, pointsMask, objectPoints, imagePoints, parameters,
inliers, bestIndex, rvec, tvec, initRvec, initTvec, syncMutex);
if ((int)inliers.size() >= parameters.minInliersCount)
{
break;
}
}
}
PnPSolver(const cv::Mat& _objectPoints, const cv::Mat& _imagePoints, const Parameters& _parameters,
cv::Mat& _rvec, cv::Mat& _tvec, std::vector<int>& _inliers, int& _bestIndex, uint64 _rng_base_seed):
objectPoints(_objectPoints), imagePoints(_imagePoints), parameters(_parameters),
rvec(_rvec), tvec(_tvec), inliers(_inliers), bestIndex(_bestIndex), rng_base_seed(_rng_base_seed)
{
bestIndex = -1;
rvec.copyTo(initRvec);
tvec.copyTo(initTvec);
}
private:
PnPSolver& operator=(const PnPSolver&);
const cv::Mat& objectPoints;
const cv::Mat& imagePoints;
const Parameters& parameters;
cv::Mat &rvec, &tvec;
std::vector<int>& inliers;
int& bestIndex;
const uint64 rng_base_seed;
cv::Mat initRvec, initTvec;
static cv::Mutex syncMutex;
void generateVar(std::vector<char>& mask, uint64 rng_seed) const
{
cv::RNG generator(rng_seed);
int size = (int)mask.size();
for (int i = 0; i < size; i++)
{
int i1 = generator.uniform(0, size);
int i2 = generator.uniform(0, size);
char curr = mask[i1];
mask[i1] = mask[i2];
mask[i2] = curr;
}
}
};
cv::Mutex PnPSolver::syncMutex;
}
void solvePnPRansac(cv::InputArray _opoints, cv::InputArray _ipoints,
cv::InputArray _cameraMatrix, cv::InputArray _distCoeffs,
cv::OutputArray _rvec, cv::OutputArray _tvec, bool useExtrinsicGuess,
int iterationsCount, float reprojectionError, int minInliersCount,
cv::OutputArray _inliers, int flags)
{
const int _rng_seed = 0;
cv::Mat opoints = _opoints.getMat(), ipoints = _ipoints.getMat();
cv::Mat cameraMatrix = _cameraMatrix.getMat(), distCoeffs = _distCoeffs.getMat();
CV_Assert(opoints.isContinuous());
CV_Assert(opoints.depth() == CV_32F || opoints.depth() == CV_64F);
CV_Assert((opoints.rows == 1 && opoints.channels() == 3) || opoints.cols*opoints.channels() == 3);
CV_Assert(ipoints.isContinuous());
CV_Assert(ipoints.depth() == CV_32F || ipoints.depth() == CV_64F);
CV_Assert((ipoints.rows == 1 && ipoints.channels() == 2) || ipoints.cols*ipoints.channels() == 2);
_rvec.create(3, 1, CV_64FC1);
_tvec.create(3, 1, CV_64FC1);
cv::Mat rvec = _rvec.getMat();
cv::Mat tvec = _tvec.getMat();
cv::Mat objectPoints = opoints.reshape(3, 1), imagePoints = ipoints.reshape(2, 1);
if (minInliersCount <= 0)
minInliersCount = objectPoints.cols;
pnpransac::Parameters params;
params.iterationsCount = iterationsCount;
params.minInliersCount = minInliersCount;
params.reprojectionError = reprojectionError;
params.useExtrinsicGuess = useExtrinsicGuess;
params.camera.init(cameraMatrix, distCoeffs);
params.flags = flags;
std::vector<int> localInliers;
cv::Mat localRvec, localTvec;
rvec.copyTo(localRvec);
tvec.copyTo(localTvec);
int bestIndex;
// TBB not used
if (objectPoints.cols >= pnpransac::MIN_POINTS_COUNT)
{
pnpransac::PnPSolver solver(objectPoints, imagePoints, params,
localRvec, localTvec, localInliers, bestIndex,
_rng_seed);
solver(0, iterationsCount);
}
if (localInliers.size() >= (size_t)pnpransac::MIN_POINTS_COUNT)
{
if (flags != CV_P3P)
{
int i, pointsCount = (int)localInliers.size();
cv::Mat inlierObjectPoints(1, pointsCount, CV_MAKE_TYPE(opoints.depth(), 3)), inlierImagePoints(1, pointsCount, CV_MAKE_TYPE(ipoints.depth(), 2));
for (i = 0; i < pointsCount; i++)
{
int index = localInliers[i];
cv::Mat colInlierImagePoints = inlierImagePoints(cv::Rect(i, 0, 1, 1));
imagePoints.col(index).copyTo(colInlierImagePoints);
cv::Mat colInlierObjectPoints = inlierObjectPoints(cv::Rect(i, 0, 1, 1));
objectPoints.col(index).copyTo(colInlierObjectPoints);
}
solvePnP(inlierObjectPoints, inlierImagePoints, params.camera.intrinsics, params.camera.distortion, localRvec, localTvec, false, flags);
}
localRvec.copyTo(rvec);
localTvec.copyTo(tvec);
if (_inliers.needed())
cv::Mat(localInliers).copyTo(_inliers);
}
else
{
tvec.setTo(cv::Scalar(0));
cv::Mat R = cv::Mat::eye(3, 3, CV_64F);
Rodrigues(R, rvec);
if( _inliers.needed() )
_inliers.release();
}
return;
}
#endif
}
}
guilib/src/DatabaseViewer.cpp
+8
View File
@@ -421,6 +421,8 @@ void DatabaseViewer::readSettings()
// Visual parameters
settings.beginGroup("visual");
ui_->comboBox_estimationType->setCurrentIndex(settings.value("estimationType", ui_->comboBox_estimationType->currentIndex()).toInt());
ui_->comboBox_pnpFlags->setCurrentIndex(settings.value("pnpFlags", ui_->comboBox_pnpFlags->currentIndex()).toInt());
ui_->groupBox_visual_recomputeFeatures->setChecked(settings.value("reextract", ui_->groupBox_visual_recomputeFeatures->isChecked()).toBool());
ui_->comboBox_featureType->setCurrentIndex(settings.value("featureType", ui_->comboBox_featureType->currentIndex()).toInt());
ui_->comboBox_nnType->setCurrentIndex(settings.value("nnType", ui_->comboBox_nnType->currentIndex()).toInt());
@@ -517,6 +519,8 @@ void DatabaseViewer::writeSettings()
// save Visual parameters
settings.beginGroup("visual");
settings.setValue("estimationType", ui_->comboBox_estimationType->currentIndex());
settings.setValue("pnpFlags", ui_->comboBox_pnpFlags->currentIndex());
settings.setValue("reextract", ui_->groupBox_visual_recomputeFeatures->isChecked());
settings.setValue("featureType", ui_->comboBox_featureType->currentIndex());
settings.setValue("nnType", ui_->comboBox_nnType->currentIndex());
@@ -3173,6 +3177,7 @@ void DatabaseViewer::refineConstraintVisually(int from, int to, bool silent, boo
parameters.insert(ParametersPair(Parameters::kLccBowIterations(), uNumber2Str(ui_->spinBox_visual_iteration->value())));
parameters.insert(ParametersPair(Parameters::kLccBowMinInliers(), uNumber2Str(ui_->spinBox_visual_minCorrespondences->value())));
parameters.insert(ParametersPair(Parameters::kLccBowEstimationType(), uNumber2Str(ui_->comboBox_estimationType->currentIndex())));
parameters.insert(ParametersPair(Parameters::kLccBowPnPFlags(), uNumber2Str(ui_->comboBox_pnpFlags->currentIndex())));
parameters.insert(ParametersPair(Parameters::kMemGenerateIds(), "false"));
parameters.insert(ParametersPair(Parameters::kMemRehearsalSimilarity(), "1.0"));
parameters.insert(ParametersPair(Parameters::kKpWordsPerImage(), "0"));
@@ -3203,6 +3208,7 @@ void DatabaseViewer::refineConstraintVisually(int from, int to, bool silent, boo
parameters.insert(ParametersPair(Parameters::kLccBowIterations(), uNumber2Str(ui_->spinBox_visual_iteration->value())));
parameters.insert(ParametersPair(Parameters::kLccBowMinInliers(), uNumber2Str(ui_->spinBox_visual_minCorrespondences->value())));
parameters.insert(ParametersPair(Parameters::kLccBowEstimationType(), uNumber2Str(ui_->comboBox_estimationType->currentIndex())));
parameters.insert(ParametersPair(Parameters::kLccBowPnPFlags(), uNumber2Str(ui_->comboBox_pnpFlags->currentIndex())));
memory_->parseParameters(parameters);
t = memory_->computeVisualTransform(to, from, &rejectedMsg, &inliers, &variance);
}
@@ -3292,6 +3298,7 @@ bool DatabaseViewer::addConstraint(int from, int to, bool silent, bool updateGra
parameters.insert(ParametersPair(Parameters::kLccBowIterations(), uNumber2Str(ui_->spinBox_visual_iteration->value())));
parameters.insert(ParametersPair(Parameters::kLccBowMinInliers(), uNumber2Str(ui_->spinBox_visual_minCorrespondences->value())));
parameters.insert(ParametersPair(Parameters::kLccBowEstimationType(), uNumber2Str(ui_->comboBox_estimationType->currentIndex())));
parameters.insert(ParametersPair(Parameters::kLccBowPnPFlags(), uNumber2Str(ui_->comboBox_pnpFlags->currentIndex())));
parameters.insert(ParametersPair(Parameters::kMemGenerateIds(), "false"));
parameters.insert(ParametersPair(Parameters::kMemRehearsalSimilarity(), "1.0"));
parameters.insert(ParametersPair(Parameters::kKpWordsPerImage(), "0"));
@@ -3329,6 +3336,7 @@ bool DatabaseViewer::addConstraint(int from, int to, bool silent, bool updateGra
parameters.insert(ParametersPair(Parameters::kLccBowIterations(), uNumber2Str(ui_->spinBox_visual_iteration->value())));
parameters.insert(ParametersPair(Parameters::kLccBowMinInliers(), uNumber2Str(ui_->spinBox_visual_minCorrespondences->value())));
parameters.insert(ParametersPair(Parameters::kLccBowEstimationType(), uNumber2Str(ui_->comboBox_estimationType->currentIndex())));
parameters.insert(ParametersPair(Parameters::kLccBowPnPFlags(), uNumber2Str(ui_->comboBox_pnpFlags->currentIndex())));
memory_->parseParameters(parameters);
t = memory_->computeVisualTransform(to, from, &rejectedMsg, &inliers, &variance);
}
guilib/src/ui/DatabaseViewer.ui
+93 -64
View File
@@ -50,8 +50,8 @@
<rect>
<x>0</x>
<y>0</y>
<width>175</width>
<height>173</height>
<width>154</width>
<height>184</height>
</rect>
</property>
<layout class="QGridLayout" name="gridLayout" columnstretch="0,1">
@@ -236,8 +236,8 @@
<rect>
<x>0</x>
<y>0</y>
<width>174</width>
<height>173</height>
<width>154</width>
<height>184</height>
</rect>
</property>
<layout class="QGridLayout" name="gridLayout_2" columnstretch="0,1">
@@ -418,7 +418,7 @@
<x>0</x>
<y>0</y>
<width>1285</width>
<height>25</height>
<height>22</height>
</rect>
</property>
<widget class="QMenu" name="menuFile">
@@ -829,15 +829,15 @@
<item>
<widget class="QToolBox" name="toolBox">
<property name="currentIndex">
<number>3</number>
<number>1</number>
</property>
<widget class="QWidget" name="page">
<property name="geometry">
<rect>
<x>0</x>
<y>0</y>
<width>314</width>
<height>303</height>
<width>312</width>
<height>314</height>
</rect>
</property>
<attribute name="label">
@@ -1056,8 +1056,8 @@
<rect>
<x>0</x>
<y>0</y>
<width>351</width>
<height>347</height>
<width>366</width>
<height>391</height>
</rect>
</property>
<attribute name="label">
@@ -1066,7 +1066,7 @@
<layout class="QVBoxLayout" name="verticalLayout_15">
<item>
<layout class="QGridLayout" name="gridLayout_3" columnstretch="0,1">
<item row="5" column="0">
<item row="6" column="0">
<widget class="QSpinBox" name="spinBox_visual_iteration">
<property name="minimum">
<number>1</number>
@@ -1086,57 +1086,14 @@
</property>
</widget>
</item>
<item row="4" column="1">
<item row="5" column="1">
<widget class="QLabel" name="label_22">
<property name="text">
<string>Max correspondence distance</string>
</property>
</widget>
</item>
<item row="5" column="1">
<widget class="QLabel" name="label_23">
<property name="text">
<string>Iteration</string>
</property>
</widget>
</item>
<item row="2" column="0">
<widget class="QDoubleSpinBox" name="doubleSpinBox_visual_nndr">
<property name="suffix">
<string> m</string>
</property>
<property name="decimals">
<number>1</number>
</property>
<property name="minimum">
<double>0.100000000000000</double>
</property>
<property name="maximum">
<double>1.000000000000000</double>
</property>
<property name="singleStep">
<double>0.100000000000000</double>
</property>
<property name="value">
<double>0.600000000000000</double>
</property>
</widget>
</item>
<item row="2" column="1">
<widget class="QLabel" name="label_26">
<property name="text">
<string>NNDR</string>
</property>
</widget>
</item>
<item row="3" column="1">
<widget class="QLabel" name="label_21">
<property name="text">
<string>Min correspondences</string>
</property>
</widget>
</item>
<item row="3" column="0">
<item row="4" column="0">
<widget class="QSpinBox" name="spinBox_visual_minCorrespondences">
<property name="minimum">
<number>3</number>
@@ -1156,7 +1113,7 @@
</property>
</widget>
</item>
<item row="4" column="0">
<item row="5" column="0">
<widget class="QDoubleSpinBox" name="doubleSpinBox_visual_maxCorrespDistance">
<property name="suffix">
<string> m</string>
@@ -1196,6 +1153,78 @@
</item>
</widget>
</item>
<item row="6" column="1">
<widget class="QLabel" name="label_23">
<property name="text">
<string>Iteration</string>
</property>
</widget>
</item>
<item row="3" column="0">
<widget class="QDoubleSpinBox" name="doubleSpinBox_visual_nndr">
<property name="suffix">
<string> m</string>
</property>
<property name="decimals">
<number>1</number>
</property>
<property name="minimum">
<double>0.100000000000000</double>
</property>
<property name="maximum">
<double>1.000000000000000</double>
</property>
<property name="singleStep">
<double>0.100000000000000</double>
</property>
<property name="value">
<double>0.600000000000000</double>
</property>
</widget>
</item>
<item row="3" column="1">
<widget class="QLabel" name="label_26">
<property name="text">
<string>NNDR</string>
</property>
</widget>
</item>
<item row="4" column="1">
<widget class="QLabel" name="label_21">
<property name="text">
<string>Min correspondences</string>
</property>
</widget>
</item>
<item row="2" column="1">
<widget class="QLabel" name="label_65">
<property name="text">
<string>PnP flags.</string>
</property>
</widget>
</item>
<item row="2" column="0">
<widget class="QComboBox" name="comboBox_pnpFlags">
<property name="currentIndex">
<number>1</number>
</property>
<item>
<property name="text">
<string>Iterative</string>
</property>
</item>
<item>
<property name="text">
<string>EPNP</string>
</property>
</item>
<item>
<property name="text">
<string>P3P</string>
</property>
</item>
</widget>
</item>
</layout>
</item>
<item>
@@ -1346,8 +1375,8 @@
<rect>
<x>0</x>
<y>0</y>
<width>333</width>
<height>333</height>
<width>338</width>
<height>330</height>
</rect>
</property>
<attribute name="label">
@@ -1585,8 +1614,8 @@
<rect>
<x>0</x>
<y>0</y>
<width>320</width>
<height>311</height>
<width>248</width>
<height>343</height>
</rect>
</property>
<attribute name="label">
@@ -1794,7 +1823,7 @@
<x>0</x>
<y>0</y>
<width>201</width>
<height>117</height>
<height>126</height>
</rect>
</property>
<attribute name="label">
@@ -1893,8 +1922,8 @@
<rect>
<x>0</x>
<y>0</y>
<width>285</width>
<height>309</height>
<width>283</width>
<height>322</height>
</rect>
</property>
<attribute name="label">