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#include "precomp.hpp"
#if 0
#define dprintf(x) printf x
#define print_matrix(x) print(x)
#else
#define dprintf(x)
#define print_matrix(x)
#endif
/*
****Error Message********************************************************************************************************************
Downhill Simplex method in OpenCV dev 3.0.0 getting this error:
OpenCV Error: Assertion failed (dims <= 2 && data && (unsigned)i0 < (unsigned)(s ize.p[0] * size.p[1])
&& elemSize() == (((((DataType<_Tp>::type) & ((512 - 1) << 3)) >> 3) + 1) << ((((sizeof(size_t)/4+1)16384|0x3a50)
>> ((DataType<_Tp>::typ e) & ((1 << 3) - 1))2) & 3))) in Mat::at,
file C:\builds\master_PackSlave-w in32-vc12-shared\opencv\modules\core\include\opencv2/core/mat.inl.hpp, line 893
****Problem and Possible Fix*********************************************************************************************************
DownhillSolverImpl::innerDownhillSimplex something looks broken here:
Mat_<double> coord_sum(1,ndim,0.0),buf(1,ndim,0.0),y(1,ndim,0.0);
fcount = 0;
for(i=0;i<ndim+1;++i)
{
y(i) = f->calc(p[i]);
}
y has only ndim elements, while the loop goes over ndim+1
Edited the following for possible fix:
Replaced y(1,ndim,0.0) ------> y(1,ndim+1,0.0)
***********************************************************************************************************************************
The code below was used in tesing the source code.
Created by @SareeAlnaghy
#include <iostream>
#include <cstdlib>
#include <cmath>
#include <algorithm>
#include <opencv2\optim\optim.hpp>
using namespace std;
using namespace cv;
void test(Ptr<optim::DownhillSolver> MinProblemSolver, Ptr<optim::MinProblemSolver::Function> ptr_F, Mat &P, Mat &step)
{
try{
MinProblemSolver->setFunction(ptr_F);
MinProblemSolver->setInitStep(step);
double res = MinProblemSolver->minimize(P);
cout << "res " << res << endl;
}
catch (exception e)
{
cerr << "Error:: " << e.what() << endl;
}
}
int main()
{
class DistanceToLines :public optim::MinProblemSolver::Function {
public:
double calc(const double* x)const{
return x[0] * x[0] + x[1] * x[1];
}
};
Mat P = (Mat_<double>(1, 2) << 1.0, 1.0);
Mat step = (Mat_<double>(2, 1) << -0.5, 0.5);
Ptr<optim::MinProblemSolver::Function> ptr_F(new DistanceToLines());
Ptr<optim::DownhillSolver> MinProblemSolver = optim::createDownhillSolver();
test(MinProblemSolver, ptr_F, P, step);
system("pause");
return 0;
}
****Suggesttion for imporving Simplex implentation***************************************************************************************
Currently the downhilll simplex method outputs the function value that is minimized. It should also return the coordinate points where the
function is minimized. This is very useful in many applications such as using back projection methods to find a point of intersection of
multiple lines in three dimensions as not all lines intersect in three dimensions.
*/
namespace cv
{
class DownhillSolverImpl : public DownhillSolver
{
public:
DownhillSolverImpl()
{
_Function=Ptr<Function>();
_step=Mat_<double>();
}
void getInitStep(OutputArray step) const { _step.copyTo(step); }
void setInitStep(InputArray step)
{
// set dimensionality and make a deep copy of step
Mat m = step.getMat();
dprintf(("m.cols=%d\nm.rows=%d\n", m.cols, m.rows));
if( m.rows == 1 )
m.copyTo(_step);
else
transpose(m, _step);
}
Ptr<MinProblemSolver::Function> getFunction() const { return _Function; }
void setFunction(const Ptr<Function>& f) { _Function=f; }
TermCriteria getTermCriteria() const { return _termcrit; }
void setTermCriteria( const TermCriteria& termcrit )
{
CV_Assert( termcrit.type == (TermCriteria::MAX_ITER + TermCriteria::EPS) &&
termcrit.epsilon > 0 &&
termcrit.maxCount > 0 );
_termcrit=termcrit;
}
double minimize( InputOutputArray x_ )
{
dprintf(("hi from minimize\n"));
CV_Assert( !_Function.empty() );
CV_Assert( std::min(_step.cols, _step.rows) == 1 &&
std::max(_step.cols, _step.rows) >= 2 &&
_step.type() == CV_64FC1 );
dprintf(("termcrit:\n\ttype: %d\n\tmaxCount: %d\n\tEPS: %g\n",_termcrit.type,_termcrit.maxCount,_termcrit.epsilon));
dprintf(("step\n"));
print_matrix(_step);
Mat x = x_.getMat(), simplex;
createInitialSimplex(x, simplex, _step);
int count = 0;
double res = innerDownhillSimplex(simplex,_termcrit.epsilon, _termcrit.epsilon,
count, _termcrit.maxCount);
dprintf(("%d iterations done\n",count));
if( !x.empty() )
{
Mat simplex_0m(x.rows, x.cols, CV_64F, simplex.ptr<double>());
simplex_0m.convertTo(x, x.type());
}
else
{
int x_type = x_.fixedType() ? x_.type() : CV_64F;
simplex.row(0).convertTo(x_, x_type);
}
return res;
}
protected:
Ptr<MinProblemSolver::Function> _Function;
TermCriteria _termcrit;
Mat _step;
inline void updateCoordSum(const Mat& p, Mat& coord_sum)
{
int i, j, m = p.rows, n = p.cols;
double* coord_sum_ = coord_sum.ptr<double>();
CV_Assert( coord_sum.cols == n && coord_sum.rows == 1 );
for( j = 0; j < n; j++ )
coord_sum_[j] = 0.;
for( i = 0; i < m; i++ )
{
const double* p_i = p.ptr<double>(i);
for( j = 0; j < n; j++ )
coord_sum_[j] += p_i[j];
}
dprintf(("\nupdated coord sum:\n"));
print_matrix(coord_sum);
}
inline void createInitialSimplex( const Mat& x0, Mat& simplex, Mat& step )
{
int i, j, ndim = step.cols;
CV_Assert( _Function->getDims() == ndim );
Mat x = x0;
if( x0.empty() )
x = Mat::zeros(1, ndim, CV_64F);
CV_Assert( (x.cols == 1 && x.rows == ndim) || (x.cols == ndim && x.rows == 1) );
CV_Assert( x.type() == CV_32F || x.type() == CV_64F );
simplex.create(ndim + 1, ndim, CV_64F);
Mat simplex_0m(x.rows, x.cols, CV_64F, simplex.ptr<double>());
x.convertTo(simplex_0m, CV_64F);
double* simplex_0 = simplex.ptr<double>();
const double* step_ = step.ptr<double>();
for( i = 1; i <= ndim; i++ )
{
double* simplex_i = simplex.ptr<double>(i);
for( j = 0; j < ndim; j++ )
simplex_i[j] = simplex_0[j];
simplex_i[i-1] += 0.5*step_[i-1];
}
for( j = 0; j < ndim; j++ )
simplex_0[j] -= 0.5*step_[j];
dprintf(("\nthis is simplex\n"));
print_matrix(simplex);
}
/*
Performs the actual minimization of MinProblemSolver::Function f (after the initialization was done)
The matrix p[ndim+1][1..ndim] represents ndim+1 vertices that
form a simplex - each row is an ndim vector.
On output, fcount gives the number of function evaluations taken.
*/
double innerDownhillSimplex( Mat& p, double MinRange, double MinError, int& fcount, int nmax )
{
int i, j, ndim = p.cols;
Mat coord_sum(1, ndim, CV_64F), buf(1, ndim, CV_64F), y(1, ndim+1, CV_64F);
double* y_ = y.ptr<double>();
fcount = ndim+1;
for( i = 0; i <= ndim; i++ )
y_[i] = calc_f(p.ptr<double>(i));
updateCoordSum(p, coord_sum);
for (;;)
{
// find highest (worst), next-to-worst, and lowest
// (best) points by going through all of them.
int ilo = 0, ihi, inhi;
if( y_[0] > y_[1] )
{
ihi = 0; inhi = 1;
}
else
{
ihi = 1; inhi = 0;
}
for( i = 0; i <= ndim; i++ )
{
double yval = y_[i];
if (yval <= y_[ilo])
ilo = i;
if (yval > y_[ihi])
{
inhi = ihi;
ihi = i;
}
else if (yval > y_[inhi] && i != ihi)
inhi = i;
}
CV_Assert( ihi != inhi );
if( ilo == inhi || ilo == ihi )
{
for( i = 0; i <= ndim; i++ )
{
double yval = y_[i];
if( yval == y_[ilo] && i != ihi && i != inhi )
{
ilo = i;
break;
}
}
}
dprintf(("\nthis is y on iteration %d:\n",fcount));
print_matrix(y);
// check stop criterion
double error = fabs(y_[ihi] - y_[ilo]);
double range = 0;
for( j = 0; j < ndim; j++ )
{
double minval, maxval;
minval = maxval = p.at<double>(0, j);
for( i = 1; i <= ndim; i++ )
{
double pval = p.at<double>(i, j);
minval = std::min(minval, pval);
maxval = std::max(maxval, pval);
}
range = std::max(range, fabs(maxval - minval));
}
if( range <= MinRange || error <= MinError || fcount >= nmax )
{
// Put best point and value in first slot.
std::swap(y_[0], y_[ilo]);
for( j = 0; j < ndim; j++ )
{
std::swap(p.at<double>(0, j), p.at<double>(ilo, j));
}
break;
}
double y_lo = y_[ilo], y_nhi = y_[inhi], y_hi = y_[ihi];
// Begin a new iteration. First, reflect the worst point about the centroid of others
double alpha = -1.0;
double y_alpha = tryNewPoint(p, coord_sum, ihi, alpha, buf, fcount);
dprintf(("\ny_lo=%g, y_nhi=%g, y_hi=%g, y_alpha=%g, p_alpha:\n", y_lo, y_nhi, y_hi, y_alpha));
print_matrix(buf);
if( y_alpha < y_nhi )
{
if( y_alpha < y_lo )
{
// If that's better than the best point, go twice as far in that direction
double beta = -2.0;
double y_beta = tryNewPoint(p, coord_sum, ihi, beta, buf, fcount);
dprintf(("\ny_beta=%g, p_beta:\n", y_beta));
print_matrix(buf);
if( y_beta < y_alpha )
{
alpha = beta;
y_alpha = y_beta;
}
}
replacePoint(p, coord_sum, y, ihi, alpha, y_alpha);
}
else
{
// The new point is worse than the second-highest,
// do not go so far in that direction
double gamma = 0.5;
double y_gamma = tryNewPoint(p, coord_sum, ihi, gamma, buf, fcount);
dprintf(("\ny_gamma=%g, p_gamma:\n", y_gamma));
print_matrix(buf);
if( y_gamma < y_hi )
replacePoint(p, coord_sum, y, ihi, gamma, y_gamma);
else
{
// Can't seem to improve things.
// Contract the simplex to good point
// in hope to find a simplex landscape.
for( i = 0; i <= ndim; i++ )
{
if (i != ilo)
{
for( j = 0; j < ndim; j++ )
p.at<double>(i, j) = 0.5*(p.at<double>(i, j) + p.at<double>(ilo, j));
y_[i] = calc_f(p.ptr<double>(i));
}
}
fcount += ndim;
updateCoordSum(p, coord_sum);
}
}
dprintf(("\nthis is simplex on iteration %d\n",fcount));
print_matrix(p);
}
return y_[0];
}
inline double calc_f(const double* ptr)
{
double res = _Function->calc(ptr);
CV_Assert( !cvIsNaN(res) && !cvIsInf(res) );
return res;
}
double tryNewPoint( Mat& p, Mat& coord_sum, int ihi, double alpha_, Mat& ptry, int& fcount )
{
int j, ndim = p.cols;
double alpha = (1.0 - alpha_)/ndim;
double beta = alpha - alpha_;
double* p_ihi = p.ptr<double>(ihi);
double* ptry_ = ptry.ptr<double>();
double* coord_sum_ = coord_sum.ptr<double>();
for( j = 0; j < ndim; j++ )
ptry_[j] = coord_sum_[j]*alpha - p_ihi[j]*beta;
fcount++;
return calc_f(ptry_);
}
void replacePoint( Mat& p, Mat& coord_sum, Mat& y, int ihi, double alpha_, double ytry )
{
int j, ndim = p.cols;
double alpha = (1.0 - alpha_)/ndim;
double beta = alpha - alpha_;
double* p_ihi = p.ptr<double>(ihi);
double* coord_sum_ = coord_sum.ptr<double>();
for( j = 0; j < ndim; j++ )
p_ihi[j] = coord_sum_[j]*alpha - p_ihi[j]*beta;
y.at<double>(ihi) = ytry;
updateCoordSum(p, coord_sum);
}
};
// both minRange & minError are specified by termcrit.epsilon;
// In addition, user may specify the number of iterations that the algorithm does.
Ptr<DownhillSolver> DownhillSolver::create( const Ptr<MinProblemSolver::Function>& f,
InputArray initStep, TermCriteria termcrit )
{
Ptr<DownhillSolver> DS = makePtr<DownhillSolverImpl>();
DS->setFunction(f);
DS->setInitStep(initStep);
DS->setTermCriteria(termcrit);
return DS;
}
}