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);

--------------------------------------------------------------------------

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(m1->data.ptr);
?const CvPoint2D64f* to? = reinterpret_cast(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 mask = cvCloneMat(mask0);
?cv::Ptr models, err, tmask;
?cv::Ptr 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;
? ?}
? }