OpenCV使用RANSAC的仿射变换估计 estimateAffine2D
OpenCV自带有findHomography这个用RANSAC随机采样求透视变换的方法,很好用，但是没有一个类似的求仿射的。
自带的getAffineTransform只是简单的使用三对点。
而estimateAffine3D使用的是三维坐标,转换起来有点不方便，而且我在使用中发现,即使把z坐标设置为0，有时候求出来的模型竟然100%都是内点，OpenCV的源码,自己提取,封装了一下.用的是SVN的Trunk,主版本2.32
有几个改动:
1.OpenCV的estimator都是继承自CvModelEstimator2,而这个父类并不是导出类,所以只能把代码都再写一遍
2.据我观察,估计时内部用的是64位浮点数,增加计算精度,我把getAffineTransform也再写了一遍,对应64位精度
//Affine2D.hpp
class Affine2DEstimator
{
public:
Affine2DEstimator();
int runKernel( const CvMat* m1, const CvMat* m2, CvMat* model );
bool runRANSAC( const CvMat* m1, const CvMat* m2, CvMat* model,
CvMat* mask, double threshold,
double confidence=0.99, int maxIters=2000 );
bool getSubset( const CvMat* m1, const CvMat* m2,
CvMat* ms1, CvMat* ms2, int maxAttempts=1000 );
bool checkSubset( const CvMat* ms1, int count );
int findInliers( const CvMat* m1, const CvMat* m2,
const CvMat* model, CvMat* error,
CvMat* mask, double threshold );
void computeReprojError( const CvMat* m1, const CvMat* m2, const CvMat* model, CvMat* error );
protected:
CvRNG rng;
int modelPoints;
CvSize modelSize;
int maxBasicSolutions;
bool checkPartialSubsets;
};
int estimateAffine2D(cv::InputArray _from, cv::InputArray _to,
cv::OutputArray _out, cv::OutputArray _inliers,
double param1=3, double param2=0.99);
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int Affine2DEstimator::findInliers( const CvMat* m1, const CvMat* m2,
const CvMat* model, CvMat* _err,
CvMat* _mask, double threshold )
{
int i, count = _err->rows*_err->cols, goodCount = 0;
const float* err = _err->data.fl;
uchar* mask = _mask->data.ptr;
computeReprojError( m1, m2, model, _err );
threshold *= threshold;
for( i = 0; i < count; i++ )
goodCount += mask[i] = err[i] <= threshold;
return goodCount;
}
void Affine2DEstimator::computeReprojError( const CvMat* m1, const CvMat* m2, const CvMat* model, CvMat* error )
{
int count = m1->rows * m1->cols;
const CvPoint2D64f* from = reinterpret_cast<const CvPoint2D64f*>(m1->data.ptr);
const CvPoint2D64f* to = reinterpret_cast<const CvPoint2D64f*>(m2->data.ptr);
const double* F = model->data.db;
float* err = error->data.fl;
for(int i = 0; i < count; i++ )
{
const CvPoint2D64f& f = from[i];
const CvPoint2D64f& t = to[i];
double a = F[0]*f.x + F[1]*f.y + F[2] - t.x;
double b = F[3]*f.x + F[4]*f.y + F[5] - t.y;
err[i] = (float)sqrt(a*a + b*b);
}
}
bool Affine2DEstimator::runRANSAC( const CvMat* m1, const CvMat* m2, CvMat* model,
CvMat* mask0, double reprojThreshold,
double confidence, int maxIters )
{
bool result = false;
cv::Ptr<CvMat> mask = cvCloneMat(mask0);
cv::Ptr<CvMat> models, err, tmask;
cv::Ptr<CvMat> ms1, ms2;
int iter, niters = maxIters;
int count = m1->rows*m1->cols, maxGoodCount = 0;
CV_Assert( CV_ARE_SIZES_EQ(m1, m2) && CV_ARE_SIZES_EQ(m1, mask) );
if( count < modelPoints )
return false;
models = cvCreateMat( modelSize.height*maxBasicSolutions, modelSize.width, CV_64FC1 );
err = cvCreateMat( 1, count, CV_32FC1 );
tmask = cvCreateMat( 1, count, CV_8UC1 );
if( count > modelPoints )
{
ms1 = cvCreateMat( 1, modelPoints, m1->type );
ms2 = cvCreateMat( 1, modelPoints, m2->type );
}
else
{
niters = 1;
ms1 = cvCloneMat(m1);
ms2 = cvCloneMat(m2);
}
for( iter = 0; iter < niters; iter++ )
{
int i, goodCount, nmodels;
if( count > modelPoints )
{
bool found = getSubset( m1, m2, ms1, ms2, 300 );
if( !found )
{
if( iter == 0 )
return false;
break;
}
}
nmodels = runKernel( ms1, ms2, models );
if( nmodels <= 0 )
continue;
for( i = 0; i < nmodels; i++ )
{
CvMat model_i;
cvGetRows( models, &model_i, i*modelSize.height, (i+1)*modelSize.height );
goodCount = findInliers( m1, m2, &model_i, err, tmask, reprojThreshold );
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;
for( i = i0; i <= i1; i++ )
{
// check that the i-th selected point does not belong
// to a line connecting some previously selected points
for( j = 0; j < i; j++ )
{
double dx1 = ptr[j].x - ptr[i].x;
double dy1 = ptr[j].y - ptr[i].y;
for( k = 0; k < j; k++ )
{
double dx2 = ptr[k].x - ptr[i].x;
double dy2 = ptr[k].y - ptr[i].y;
if( fabs(dx2*dy1 - dy2*dx1) <= FLT_EPSILON*(fabs(dx1) + fabs(dy1) + fabs(dx2) + fabs(dy2)))
break;
}
if( k < j )
break;
}
if( j < i )
break;
}
return i >= i1;
}
Affine2DEstimator::Affine2DEstimator() : modelPoints(3),modelSize(cvSize(3, 2)),maxBasicSolutions(1)
{
checkPartialSubsets = true;
rng = cvRNG(-1);
}
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