if( goodCount > MAX(maxGoodCount, modelPoints-1) )
{
std::swap(tmask, mask);
cvCopy( &model_i, model );
maxGoodCount = goodCount;
niters = cvRANSACUpdateNumIters( confidence,
(double)(count - goodCount)/count, modelPoints, niters );
}
}
}
if( maxGoodCount > 0 )
{
if( mask != mask0 )
cvCopy( mask, mask0 );
result = true;
}
return result;
}
Mat getAffineTransform64f( const Point2d src[], const Point2d dst[] )
{
Mat M(2, 3, CV_64F), X(6, 1, CV_64F, M.data);
double a[6*6], b[6];
Mat A(6, 6, CV_64F, a), B(6, 1, CV_64F, b);
for( int i = 0; i < 3; i++ )
{
int j = i*12;
int k = i*12+6;
a[j] = a[k+3] = src[i].x;
a[j+1] = a[k+4] = src[i].y;
a[j+2] = a[k+5] = 1;
a[j+3] = a[j+4] = a[j+5] = 0;
a[k] = a[k+1] = a[k+2] = 0;
b[i*2] = dst[i].x;
b[i*2+1] = dst[i].y;
}
solve( A, B, X );
return M;
}
int Affine2DEstimator::runKernel( const CvMat* m1, const CvMat* m2, CvMat* model )
{
const Point2d* from = reinterpret_cast<const Point2d*>(m1->data.ptr);
const Point2d* to = reinterpret_cast<const Point2d*>(m2->data.ptr);
Mat M0 = cv::cvarrToMat(model);
Mat M=getAffineTransform64f(from,to);
CV_Assert( M.size() == M0.size() );
M.convertTo(M0, M0.type());
return model!=NULL?1:0;
}
int estimateAffine2D(InputArray _from, InputArray _to,
OutputArray _out, OutputArray _inliers,
double param1, double param2)
{
Mat from = _from.getMat(), to = _to.getMat();
int count = from.checkVector(2, CV_32F);
CV_Assert( count >= 0 && to.checkVector(2, CV_32F) == count );
_out.create(2, 3, CV_64F);
Mat out = _out.getMat();
_inliers.create(count, 1, CV_8U, -1, true);
Mat inliers = _inliers.getMat();
inliers = Scalar::all(1);
Mat dFrom, dTo;
from.convertTo(dFrom, CV_64F);
to.convertTo(dTo, CV_64F);
CvMat F2x3 = out;
CvMat mask = inliers;
CvMat m1 = dFrom;
CvMat m2 = dTo;
const double epsilon = numeric_limits<double>::epsilon();
param1 = param1 <= 0 ? 3 : param1;
param2 = (param2 < epsilon) ? 0.99 : (param2 > 1 - epsilon) ? 0.99 : param2;
return Affine2DEstimator().runRANSAC(&m1, &m2, &F2x3, &mask, param1, param2 );
}
bool Affine2DEstimator::getSubset( const CvMat* m1, const CvMat* m2,
CvMat* ms1, CvMat* ms2, int maxAttempts )
{
cv::AutoBuffer<int> _idx(modelPoints);
int* idx = _idx;
int i = 0, j, k, idx_i, iters = 0;
int type = CV_MAT_TYPE(m1->type), elemSize = CV_ELEM_SIZE(type);
const int *m1ptr = m1->data.i, *m2ptr = m2->data.i;
int *ms1ptr = ms1->data.i, *ms2ptr = ms2->data.i;
int count = m1->cols*m1->rows;
assert( CV_IS_MAT_CONT(m1->type & m2->type) && (elemSize % sizeof(int) == 0) );
elemSize /= sizeof(int);
for(; iters < maxAttempts; iters++)
{
for( i = 0; i < modelPoints && iters < maxAttempts; )
{
idx[i] = idx_i = cvRandInt(&rng) % count;
for( j = 0; j < i; j++ )
if( idx_i == idx[j] )
break;
if( j < i )
continue;
for( k = 0; k < elemSize; k++ )
{
ms1ptr[i*elemSize + k] = m1ptr[idx_i*elemSize + k];
ms2ptr[i*elemSize + k] = m2ptr[idx_i*elemSize + k];
}
if( checkPartialSubsets && (!checkSubset( ms1, i+1 ) || !checkSubset( ms2, i+1 )))
{
iters++;
continue;
}
i++;
}
if( !checkPartialSubsets && i == modelPoints &&
(!checkSubset( ms1, i ) || !checkSubset( ms2, i )))
continue;
break;
}
return i == modelPoints && iters < maxAttempts;
}
bool Affine2DEstimator::checkSubset( const CvMat* ms1, int count )
{
int j, k, i, i0, i1;
CvPoint2D64f* ptr = (CvPoint2D64f*)ms1->data.ptr;
assert( CV_MAT_TYPE(ms1->type) == CV_64FC2 );
if( checkPartialSubsets )
i0 = i1 = count - 1;
else
i0 = 0, i1 = count - 1;