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2022-04-05 11:42:28 +03:00
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/******************************************************************************
* Author: Laurent Kneip *
* Contact: kneip.laurent@gmail.com *
* License: Copyright (c) 2013 Laurent Kneip, ANU. 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 ANU 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 ANU OR THE 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. *
******************************************************************************/
#include "random_generators.hpp"
#include "time_measurement.hpp"
#include <math.h>
#include <iostream>
using namespace Eigen;
void
opengv::initializeRandomSeed()
{
struct timeval tic;
gettimeofday( &tic, 0 );
srand ( tic.tv_usec );
}
Eigen::Vector3d
opengv::generateRandomPoint( double maximumDepth, double minimumDepth )
{
Eigen::Vector3d cleanPoint;
cleanPoint[0] = (((double) rand())/ ((double) RAND_MAX)-0.5)*2.0;
cleanPoint[1] = (((double) rand())/ ((double) RAND_MAX)-0.5)*2.0;
cleanPoint[2] = (((double) rand())/ ((double) RAND_MAX)-0.5)*2.0;
Eigen::Vector3d direction = cleanPoint / cleanPoint.norm();
cleanPoint =
(maximumDepth-minimumDepth) * cleanPoint + minimumDepth * direction;
return cleanPoint;
}
Eigen::Vector3d
opengv::generateRandomPointPlane()
{
Eigen::Vector3d cleanPoint;
cleanPoint[0] = (((double) rand())/ ((double) RAND_MAX)-0.5)*2.0;
cleanPoint[1] = (((double) rand())/ ((double) RAND_MAX)-0.5)*2.0;
cleanPoint[2] = (((double) rand())/ ((double) RAND_MAX)-0.5)*2.0;
cleanPoint[0] = 6*cleanPoint[0];
cleanPoint[1] = 6*cleanPoint[1];
cleanPoint[2] = 2*cleanPoint[2]-6.0;
return cleanPoint;
}
Eigen::Vector3d
opengv::addNoise( double noiseLevel, Eigen::Vector3d cleanPoint )
{
//compute a vector in the normal plane (based on good conditioning)
Eigen::Vector3d normalVector1;
if(
(fabs(cleanPoint[0]) > fabs(cleanPoint[1])) &&
(fabs(cleanPoint[0]) > fabs(cleanPoint[2])) )
{
normalVector1[1] = 1.0;
normalVector1[2] = 0.0;
normalVector1[0] = -cleanPoint[1]/cleanPoint[0];
}
else
{
if(
(fabs(cleanPoint[1]) > fabs(cleanPoint[0])) &&
(fabs(cleanPoint[1]) > fabs(cleanPoint[2])) )
{
normalVector1[2] = 1.0;
normalVector1[0] = 0.0;
normalVector1[1] = -cleanPoint[2]/cleanPoint[1];
}
else
{
normalVector1[0] = 1.0;
normalVector1[1] = 0.0;
normalVector1[2] = -cleanPoint[0]/cleanPoint[2];
}
}
normalVector1 = normalVector1 / normalVector1.norm();
Eigen::Vector3d normalVector2 = cleanPoint.cross(normalVector1);
double noiseX =
noiseLevel * (((double) rand())/ ((double) RAND_MAX)-0.5)*2.0 / 1.4142;
double noiseY =
noiseLevel * (((double) rand())/ ((double) RAND_MAX)-0.5)*2.0 / 1.4142;
Eigen::Vector3d noisyPoint =
800 * cleanPoint + noiseX *normalVector1 + noiseY * normalVector2;
noisyPoint = noisyPoint / noisyPoint.norm();
return noisyPoint;
}
Eigen::Vector3d
opengv::generateRandomTranslation( double maximumParallax )
{
Eigen::Vector3d translation;
translation[0] = (((double) rand())/ ((double) RAND_MAX)-0.5)*2.0;
translation[1] = (((double) rand())/ ((double) RAND_MAX)-0.5)*2.0;
translation[2] = (((double) rand())/ ((double) RAND_MAX)-0.5)*2.0;
return maximumParallax * translation;
}
Eigen::Vector3d
opengv::generateRandomDirectionTranslation( double parallax )
{
Eigen::Matrix3d rotation = generateRandomRotation();
Eigen::Vector3d translation;
translation << 1.0, 0.0, 0.0;
translation = rotation * translation;
translation = parallax * translation;
return translation;
}
Eigen::Matrix3d
opengv::generateRandomRotation( double maxAngle )
{
Eigen::Vector3d rpy;
rpy[0] = ((double) rand())/ ((double) RAND_MAX);
rpy[1] = ((double) rand())/ ((double) RAND_MAX);
rpy[2] = ((double) rand())/ ((double) RAND_MAX);
rpy[0] = maxAngle*2.0*(rpy[0]-0.5);
rpy[1] = maxAngle*2.0*(rpy[1]-0.5);
rpy[2] = maxAngle*2.0*(rpy[2]-0.5);
Eigen::Matrix3d R1;
R1(0,0) = 1.0;
R1(0,1) = 0.0;
R1(0,2) = 0.0;
R1(1,0) = 0.0;
R1(1,1) = cos(rpy[0]);
R1(1,2) = -sin(rpy[0]);
R1(2,0) = 0.0;
R1(2,1) = -R1(1,2);
R1(2,2) = R1(1,1);
Eigen::Matrix3d R2;
R2(0,0) = cos(rpy[1]);
R2(0,1) = 0.0;
R2(0,2) = sin(rpy[1]);
R2(1,0) = 0.0;
R2(1,1) = 1.0;
R2(1,2) = 0.0;
R2(2,0) = -R2(0,2);
R2(2,1) = 0.0;
R2(2,2) = R2(0,0);
Eigen::Matrix3d R3;
R3(0,0) = cos(rpy[2]);
R3(0,1) = -sin(rpy[2]);
R3(0,2) = 0.0;
R3(1,0) =-R3(0,1);
R3(1,1) = R3(0,0);
R3(1,2) = 0.0;
R3(2,0) = 0.0;
R3(2,1) = 0.0;
R3(2,2) = 1.0;
Eigen::Matrix3d rotation = R3 * R2 * R1;
rotation.col(0) = rotation.col(0) / rotation.col(0).norm();
rotation.col(2) = rotation.col(0).cross(rotation.col(1));
rotation.col(2) = rotation.col(2) / rotation.col(2).norm();
rotation.col(1) = rotation.col(2).cross(rotation.col(0));
rotation.col(1) = rotation.col(1) / rotation.col(1).norm();
return rotation;
}
Eigen::Matrix3d
opengv::generateRandomRotation()
{
Eigen::Vector3d rpy;
rpy[0] = ((double) rand())/ ((double) RAND_MAX);
rpy[1] = ((double) rand())/ ((double) RAND_MAX);
rpy[2] = ((double) rand())/ ((double) RAND_MAX);
rpy[0] = 2*M_PI*(rpy[0]-0.5);
rpy[1] = M_PI*(rpy[1]-0.5);
rpy[2] = 2*M_PI*(rpy[2]-0.5);
Eigen::Matrix3d R1;
R1(0,0) = 1.0;
R1(0,1) = 0.0;
R1(0,2) = 0.0;
R1(1,0) = 0.0;
R1(1,1) = cos(rpy[0]);
R1(1,2) = -sin(rpy[0]);
R1(2,0) = 0.0;
R1(2,1) = -R1(1,2);
R1(2,2) = R1(1,1);
Eigen::Matrix3d R2;
R2(0,0) = cos(rpy[1]);
R2(0,1) = 0.0;
R2(0,2) = sin(rpy[1]);
R2(1,0) = 0.0;
R2(1,1) = 1.0;
R2(1,2) = 0.0;
R2(2,0) = -R2(0,2);
R2(2,1) = 0.0;
R2(2,2) = R2(0,0);
Eigen::Matrix3d R3;
R3(0,0) = cos(rpy[2]);
R3(0,1) = -sin(rpy[2]);
R3(0,2) = 0.0;
R3(1,0) =-R3(0,1);
R3(1,1) = R3(0,0);
R3(1,2) = 0.0;
R3(2,0) = 0.0;
R3(2,1) = 0.0;
R3(2,2) = 1.0;
Eigen::Matrix3d rotation = R3 * R2 * R1;
rotation.col(0) = rotation.col(0) / rotation.col(0).norm();
rotation.col(2) = rotation.col(0).cross(rotation.col(1));
rotation.col(2) = rotation.col(2) / rotation.col(2).norm();
rotation.col(1) = rotation.col(2).cross(rotation.col(0));
rotation.col(1) = rotation.col(1) / rotation.col(1).norm();
return rotation;
}