--- /dev/null
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+// By downloading, copying, installing or using the software you agree to this license.
+// If you do not agree to this license, do not download, install,
+// copy or use the software.
+//
+//
+// Intel License Agreement
+// For Open Source Computer Vision Library
+//
+// Copyright (C) 2000, Intel Corporation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+// * Redistribution's of source code must retain the above copyright notice,
+// this list of conditions and the following disclaimer.
+//
+// * Redistribution's in binary form must reproduce the above copyright notice,
+// this list of conditions and the following disclaimer in the documentation
+// and/or other materials provided with the distribution.
+//
+// * The name of Intel Corporation may not be used to endorse or promote products
+// derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#include "_cv.h"
+#include "_cvmodelest.h"
+#include <algorithm>
+#include <limits>
+
+using namespace std;
+
+
+CvModelEstimator2::CvModelEstimator2(int _modelPoints, CvSize _modelSize, int _maxBasicSolutions)
+{
+ modelPoints = _modelPoints;
+ modelSize = _modelSize;
+ maxBasicSolutions = _maxBasicSolutions;
+ checkPartialSubsets = true;
+ rng = cvRNG(-1);
+}
+
+CvModelEstimator2::~CvModelEstimator2()
+{
+}
+
+void CvModelEstimator2::setSeed( int64 seed )
+{
+ rng = cvRNG(seed);
+}
+
+
+int CvModelEstimator2::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;
+}
+
+
+CV_IMPL int
+cvRANSACUpdateNumIters( double p, double ep,
+ int model_points, int max_iters )
+{
+ int result = 0;
+
+ CV_FUNCNAME( "cvRANSACUpdateNumIters" );
+
+ __BEGIN__;
+
+ double num, denom;
+
+ if( model_points <= 0 )
+ CV_ERROR( CV_StsOutOfRange, "the number of model points should be positive" );
+
+ p = MAX(p, 0.);
+ p = MIN(p, 1.);
+ ep = MAX(ep, 0.);
+ ep = MIN(ep, 1.);
+
+ // avoid inf's & nan's
+ num = MAX(1. - p, DBL_MIN);
+ denom = 1. - pow(1. - ep,model_points);
+ if( denom < DBL_MIN )
+ EXIT;
+
+ num = log(num);
+ denom = log(denom);
+
+ result = denom >= 0 || -num >= max_iters*(-denom) ?
+ max_iters : cvRound(num/denom);
+
+ __END__;
+
+ return result;
+}
+
+bool CvModelEstimator2::runRANSAC( const CvMat* m1, const CvMat* m2, CvMat* model,
+ CvMat* mask, double reprojThreshold,
+ double confidence, int maxIters )
+{
+ bool result = false;
+ CvMat* mask0 = mask, *tmask = 0, *t;
+ CvMat* models = 0, *err = 0;
+ CvMat *ms1 = 0, *ms2 = 0;
+
+ CV_FUNCNAME( "CvModelEstimator2::estimateRansac" );
+
+ __BEGIN__;
+
+ 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 )
+ EXIT;
+
+ 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 = (CvMat*)m1;
+ ms2 = (CvMat*)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 )
+ EXIT;
+ 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) )
+ {
+ CV_SWAP( tmask, mask, t );
+ cvCopy( &model_i, model );
+ maxGoodCount = goodCount;
+ niters = cvRANSACUpdateNumIters( confidence,
+ (double)(count - goodCount)/count, modelPoints, niters );
+ }
+ }
+ }
+
+ if( maxGoodCount > 0 )
+ {
+ if( mask != mask0 )
+ {
+ CV_SWAP( tmask, mask, t );
+ cvCopy( tmask, mask );
+ }
+ result = true;
+ }
+
+ __END__;
+
+ if( ms1 != m1 )
+ cvReleaseMat( &ms1 );
+ if( ms2 != m2 )
+ cvReleaseMat( &ms2 );
+ cvReleaseMat( &models );
+ cvReleaseMat( &err );
+ cvReleaseMat( &tmask );
+ return result;
+}
+
+
+static CV_IMPLEMENT_QSORT( icvSortDistances, int, CV_LT )
+
+bool CvModelEstimator2::runLMeDS( const CvMat* m1, const CvMat* m2, CvMat* model,
+ CvMat* mask, double confidence, int maxIters )
+{
+ const double outlierRatio = 0.45;
+ bool result = false;
+ CvMat* models = 0;
+ CvMat *ms1 = 0, *ms2 = 0;
+ CvMat* err = 0;
+
+ CV_FUNCNAME( "CvModelEstimator2::estimateLMeDS" );
+
+ __BEGIN__;
+
+ int iter, niters = maxIters;
+ int count = m1->rows*m1->cols;
+ double minMedian = DBL_MAX, sigma;
+
+ CV_ASSERT( CV_ARE_SIZES_EQ(m1, m2) && CV_ARE_SIZES_EQ(m1, mask) );
+
+ if( count < modelPoints )
+ EXIT;
+
+ models = cvCreateMat( modelSize.height*maxBasicSolutions, modelSize.width, CV_64FC1 );
+ err = cvCreateMat( 1, count, CV_32FC1 );
+
+ if( count > modelPoints )
+ {
+ ms1 = cvCreateMat( 1, modelPoints, m1->type );
+ ms2 = cvCreateMat( 1, modelPoints, m2->type );
+ }
+ else
+ {
+ niters = 1;
+ ms1 = (CvMat*)m1;
+ ms2 = (CvMat*)m2;
+ }
+
+ niters = cvRound(log(1-confidence)/log(1-pow(1-outlierRatio,(double)modelPoints)));
+ niters = MIN( MAX(niters, 3), maxIters );
+
+ for( iter = 0; iter < niters; iter++ )
+ {
+ int i, nmodels;
+ if( count > modelPoints )
+ {
+ bool found = getSubset( m1, m2, ms1, ms2, 300 );
+ if( !found )
+ {
+ if( iter == 0 )
+ EXIT;
+ 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 );
+ computeReprojError( m1, m2, &model_i, err );
+ icvSortDistances( err->data.i, count, 0 );
+
+ double median = count % 2 != 0 ?
+ err->data.fl[count/2] : (err->data.fl[count/2-1] + err->data.fl[count/2])*0.5;
+
+ if( median < minMedian )
+ {
+ minMedian = median;
+ cvCopy( &model_i, model );
+ }
+ }
+ }
+
+ if( minMedian < DBL_MAX )
+ {
+ sigma = 2.5*1.4826*(1 + 5./(count - modelPoints))*sqrt(minMedian);
+ sigma = MAX( sigma, FLT_EPSILON*100 );
+
+ count = findInliers( m1, m2, model, err, mask, sigma );
+ result = count >= modelPoints;
+ }
+
+ __END__;
+
+ if( ms1 != m1 )
+ cvReleaseMat( &ms1 );
+ if( ms2 != m2 )
+ cvReleaseMat( &ms2 );
+ cvReleaseMat( &models );
+ cvReleaseMat( &err );
+ return result;
+}
+
+
+bool CvModelEstimator2::getSubset( const CvMat* m1, const CvMat* m2,
+ CvMat* ms1, CvMat* ms2, int maxAttempts )
+{
+ int* idx = (int*)cvStackAlloc( modelPoints*sizeof(idx[0]) );
+ 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 CvModelEstimator2::checkSubset( const CvMat* m, int count )
+{
+ int j, k, i, i0, i1;
+ CvPoint2D64f* ptr = (CvPoint2D64f*)m->data.ptr;
+
+ assert( CV_MAT_TYPE(m->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;
+}
+
+
+namespace cv
+{
+
+class Affine3DEstimator : public CvModelEstimator2
+{
+public:
+ Affine3DEstimator() : CvModelEstimator2(4, cvSize(4, 3), 1) {}
+ virtual int runKernel( const CvMat* m1, const CvMat* m2, CvMat* model );
+protected:
+ virtual void computeReprojError( const CvMat* m1, const CvMat* m2, const CvMat* model, CvMat* error );
+ virtual bool checkSubset( const CvMat* ms1, int count );
+};
+
+}
+
+int cv::Affine3DEstimator::runKernel( const CvMat* m1, const CvMat* m2, CvMat* model )
+{
+ const Point3d* from = reinterpret_cast<const Point3d*>(m1->data.ptr);
+ const Point3d* to = reinterpret_cast<const Point3d*>(m2->data.ptr);
+
+ Mat A(12, 12, CV_64F);
+ Mat B(12, 1, CV_64F);
+ A = Scalar(0.0);
+
+ for(int i = 0; i < modelPoints; ++i)
+ {
+ *B.ptr<Point3d>(3*i) = to[i];
+
+ double *aptr = A.ptr<double>(3*i);
+ for(int k = 0; k < 3; ++k)
+ {
+ aptr[3] = 1.0;
+ *reinterpret_cast<Point3d*>(aptr) = from[i];
+ aptr += 16;
+ }
+ }
+
+ CvMat cvA = A;
+ CvMat cvB = B;
+ CvMat cvX;
+ cvReshape(model, &cvX, 1, 12);
+ cvSolve(&cvA, &cvB, &cvX, CV_SVD );
+
+ return 1;
+}
+
+void cv::Affine3DEstimator::computeReprojError( const CvMat* m1, const CvMat* m2, const CvMat* model, CvMat* error )
+{
+ int count = m1->rows * m1->cols;
+ const Point3d* from = reinterpret_cast<const Point3d*>(m1->data.ptr);
+ const Point3d* to = reinterpret_cast<const Point3d*>(m2->data.ptr);
+ const double* F = model->data.db;
+ float* err = error->data.fl;
+
+ for(int i = 0; i < count; i++ )
+ {
+ const Point3d& f = from[i];
+ const Point3d& t = to[i];
+
+ double a = F[0]*f.x + F[1]*f.y + F[ 2]*f.z + F[ 3] - t.x;
+ double b = F[4]*f.x + F[5]*f.y + F[ 6]*f.z + F[ 7] - t.y;
+ double c = F[8]*f.x + F[9]*f.y + F[10]*f.z + F[11] - t.z;
+
+ err[i] = (float)sqrt(a*a + b*b + c*c);
+ }
+}
+
+bool cv::Affine3DEstimator::checkSubset( const CvMat* ms1, int count )
+{
+ CV_Assert( CV_MAT_TYPE(ms1->type) == CV_64FC3 );
+
+ int j, k, i = count - 1;
+ const Point3d* ptr = reinterpret_cast<const Point3d*>(ms1->data.ptr);
+
+ // check that the i-th selected point does not belong
+ // to a line connecting some previously selected points
+
+ for(j = 0; j < i; ++j)
+ {
+ Point3d d1 = ptr[j] - ptr[i];
+ double n1 = norm(d1);
+
+ for(k = 0; k < j; ++k)
+ {
+ Point3d d2 = ptr[k] - ptr[i];
+ double n = norm(d2) * n1;
+
+ if (fabs(d1.dot(d2) / n) > 0.996)
+ break;
+ }
+ if( k < j )
+ break;
+ }
+
+ return j == i;
+}
+
+int cv::estimateAffine3D(const Mat& from, const Mat& to, Mat& out, vector<uchar>& outliers, double param1, double param2)
+{
+ size_t count = from.cols*from.rows*from.channels()/3;
+
+ CV_Assert( count >= 4 && from.isContinuous() && to.isContinuous() &&
+ from.depth() == CV_32F && to.depth() == CV_32F &&
+ ((from.rows == 1 && from.channels() == 3) || from.cols*from.channels() == 3) &&
+ ((to.rows == 1 && to.channels() == 3) || to.cols*to.channels() == 3) &&
+ count == (size_t)to.cols*to.rows*to.channels()/3);
+
+ out.create(3, 4, CV_64F);
+ outliers.resize(count);
+ fill(outliers.begin(), outliers.end(), (uchar)1);
+
+ vector<Point3d> dFrom;
+ vector<Point3d> dTo;
+
+ copy(from.ptr<Point3f>(), from.ptr<Point3f>() + count, back_inserter(dFrom));
+ copy(to.ptr<Point3f>(), to.ptr<Point3f>() + count, back_inserter(dTo));
+
+ CvMat F3x4 = out;
+ CvMat mask = cvMat( 1, count, CV_8U, &outliers[0] );
+ CvMat m1 = cvMat( 1, count, CV_64FC3, &dFrom[0] );
+ CvMat m2 = cvMat( 1, count, CV_64FC3, &dTo[0] );
+
+ const double epsilon = numeric_limits<double>::epsilon();
+ param1 = param1 <= 0 ? 3 : param1;
+ param2 = (param2 < epsilon) ? 0.99 : (param2 > 1 - epsilon) ? 0.99 : param2;
+
+ return Affine3DEstimator().runRANSAC(&m1,& m2, &F3x4, &mask, param1, param2 );
+}
projects / opencv / blobdiff