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,而且偏差很大.

只好研究了下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.hppclass 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);

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;elsei0 = 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 pointsfor( 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);}