+++ /dev/null
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
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-//
-// 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.
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-// any express or implied warranties, including, but not limited to, the implied
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-// In no event shall the Intel Corporation or contributors be liable for any direct,
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-// (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,
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-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#include "_cv.h"
-
-/* Evaluation of Fundamental Matrix from point correspondences.
- The original code has been written by Valery Mosyagin */
-
-/* The algorithms (except for RANSAC) and the notation have been taken from
- Zhengyou Zhang's research report
- "Determining the Epipolar Geometry and its Uncertainty: A Review"
- that can be found at http://www-sop.inria.fr/robotvis/personnel/zzhang/zzhang-eng.html */
-
-/************************************** 7-point algorithm *******************************/
-static int
-icvFMatrix_7Point( const CvPoint2D64f* m0, const CvPoint2D64f* m1, double* fmatrix )
-{
- double a[7*9], w[7], v[9*9], c[4], r[3];
- double* f1, *f2;
- double t0, t1, t2;
- CvMat A = cvMat( 7, 9, CV_64F, a );
- CvMat V = cvMat( 9, 9, CV_64F, v );
- CvMat W = cvMat( 7, 1, CV_64F, w );
- CvMat coeffs = cvMat( 1, 4, CV_64F, c );
- CvMat roots = cvMat( 1, 3, CV_64F, r );
- int i, k, n;
-
- assert( m0 && m1 && fmatrix );
-
- // form a linear system: i-th row of A(=a) represents
- // the equation: (m1[i], 1)'*F*(m0[i], 1) = 0
- for( i = 0; i < 7; i++ )
- {
- double x0 = m0[i].x, y0 = m0[i].y;
- double x1 = m1[i].x, y1 = m1[i].y;
-
- a[i*9+0] = x1*x0;
- a[i*9+1] = x1*y0;
- a[i*9+2] = x1;
- a[i*9+3] = y1*x0;
- a[i*9+4] = y1*y0;
- a[i*9+5] = y1;
- a[i*9+6] = x0;
- a[i*9+7] = y0;
- a[i*9+8] = 1;
- }
-
- // A*(f11 f12 ... f33)' = 0 is singular (7 equations for 9 variables), so
- // the solution is linear subspace of dimensionality 2.
- // => use the last two singular vectors as a basis of the space
- // (according to SVD properties)
- cvSVD( &A, &W, 0, &V, CV_SVD_MODIFY_A + CV_SVD_V_T );
- f1 = v + 7*9;
- f2 = v + 8*9;
-
- // f1, f2 is a basis => lambda*f1 + mu*f2 is an arbitrary f. matrix.
- // as it is determined up to a scale, normalize lambda & mu (lambda + mu = 1),
- // so f ~ lambda*f1 + (1 - lambda)*f2.
- // use the additional constraint det(f) = det(lambda*f1 + (1-lambda)*f2) to find lambda.
- // it will be a cubic equation.
- // find c - polynomial coefficients.
- for( i = 0; i < 9; i++ )
- f1[i] -= f2[i];
-
- t0 = f2[4]*f2[8] - f2[5]*f2[7];
- t1 = f2[3]*f2[8] - f2[5]*f2[6];
- t2 = f2[3]*f2[7] - f2[4]*f2[6];
-
- c[3] = f2[0]*t0 - f2[1]*t1 + f2[2]*t2;
-
- c[2] = f1[0]*t0 - f1[1]*t1 + f1[2]*t2 -
- f1[3]*(f2[1]*f2[8] - f2[2]*f2[7]) +
- f1[4]*(f2[0]*f2[8] - f2[2]*f2[6]) -
- f1[5]*(f2[0]*f2[7] - f2[1]*f2[6]) +
- f1[6]*(f2[1]*f2[5] - f2[2]*f2[4]) -
- f1[7]*(f2[0]*f2[5] - f2[2]*f2[3]) +
- f1[8]*(f2[0]*f2[4] - f2[1]*f2[3]);
-
- t0 = f1[4]*f1[8] - f1[5]*f1[7];
- t1 = f1[3]*f1[8] - f1[5]*f1[6];
- t2 = f1[3]*f1[7] - f1[4]*f1[6];
-
- c[1] = f2[0]*t0 - f2[1]*t1 + f2[2]*t2 -
- f2[3]*(f1[1]*f1[8] - f1[2]*f1[7]) +
- f2[4]*(f1[0]*f1[8] - f1[2]*f1[6]) -
- f2[5]*(f1[0]*f1[7] - f1[1]*f1[6]) +
- f2[6]*(f1[1]*f1[5] - f1[2]*f1[4]) -
- f2[7]*(f1[0]*f1[5] - f1[2]*f1[3]) +
- f2[8]*(f1[0]*f1[4] - f1[1]*f1[3]);
-
- c[0] = f1[0]*t0 - f1[1]*t1 + f1[2]*t2;
-
- // solve the cubic equation; there can be 1 to 3 roots ...
- n = cvSolveCubic( &coeffs, &roots );
-
- if( n < 1 || n > 3 )
- return n;
-
- for( k = 0; k < n; k++, fmatrix += 9 )
- {
- // for each root form the fundamental matrix
- double lambda = r[k], mu = 1.;
- double s = f1[8]*r[k] + f2[8];
-
- // normalize each matrix, so that F(3,3) (~fmatrix[8]) == 1
- if( fabs(s) > DBL_EPSILON )
- {
- mu = 1./s;
- lambda *= mu;
- fmatrix[8] = 1.;
- }
- else
- fmatrix[8] = 0.;
-
- for( i = 0; i < 8; i++ )
- fmatrix[i] = f1[i]*lambda + f2[i]*mu;
- }
-
- return n;
-}
-
-
-/*************************************** 8-point algorithm ******************************/
-static int
-icvFMatrix_8Point( const CvPoint2D64f* m0, const CvPoint2D64f* m1,
- const uchar* mask, int count, double* fmatrix )
-{
- int result = 0;
- CvMat* A = 0;
-
- double w[9], v[9*9];
- CvMat W = cvMat( 1, 9, CV_64F, w);
- CvMat V = cvMat( 9, 9, CV_64F, v);
- CvMat U, F0, TF;
-
- int i, good_count = 0;
- CvPoint2D64f m0c = {0,0}, m1c = {0,0};
- double t, scale0 = 0, scale1 = 0;
- double* a;
- int a_step;
-
- CV_FUNCNAME( "icvFMatrix_8Point" );
-
- __BEGIN__;
-
- assert( m0 && m1 && fmatrix );
-
- // compute centers and average distances for each of the two point sets
- for( i = 0; i < count; i++ )
- if( !mask || mask[i] )
- {
- double x = m0[i].x, y = m0[i].y;
- m0c.x += x; m0c.y += y;
-
- x = m1[i].x, y = m1[i].y;
- m1c.x += x; m1c.y += y;
- good_count++;
- }
-
- if( good_count < 8 )
- EXIT;
-
- // calculate the normalizing transformations for each of the point sets:
- // after the transformation each set will have the mass center at the coordinate origin
- // and the average distance from the origin will be ~sqrt(2).
- t = 1./good_count;
- m0c.x *= t; m0c.y *= t;
- m1c.x *= t; m1c.y *= t;
-
- for( i = 0; i < count; i++ )
- if( !mask || mask[i] )
- {
- double x = m0[i].x - m0c.x, y = m0[i].y - m0c.y;
- scale0 += sqrt(x*x + y*y);
-
- x = fabs(m1[i].x - m1c.x), y = fabs(m1[i].y - m1c.y);
- scale1 += sqrt(x*x + y*y);
- }
-
- scale0 *= t;
- scale1 *= t;
-
- if( scale0 < FLT_EPSILON || scale1 < FLT_EPSILON )
- EXIT;
-
- scale0 = sqrt(2.)/scale0;
- scale1 = sqrt(2.)/scale1;
-
- CV_CALL( A = cvCreateMat( good_count, 9, CV_64F ));
- a = A->data.db;
- a_step = A->step / sizeof(a[0]);
-
- // form a linear system: for each selected pair of points m0 & m1,
- // the row of A(=a) represents the equation: (m1, 1)'*F*(m0, 1) = 0
- for( i = 0; i < count; i++ )
- {
- if( !mask || mask[i] )
- {
- double x0 = (m0[i].x - m0c.x)*scale0;
- double y0 = (m0[i].y - m0c.y)*scale0;
- double x1 = (m1[i].x - m1c.x)*scale1;
- double y1 = (m1[i].y - m1c.y)*scale1;
-
- a[0] = x1*x0;
- a[1] = x1*y0;
- a[2] = x1;
- a[3] = y1*x0;
- a[4] = y1*y0;
- a[5] = y1;
- a[6] = x0;
- a[7] = y0;
- a[8] = 1;
- a += a_step;
- }
- }
-
- cvSVD( A, &W, 0, &V, CV_SVD_MODIFY_A + CV_SVD_V_T );
-
- for( i = 0; i < 8; i++ )
- {
- if( fabs(w[i]) < FLT_EPSILON )
- break;
- }
-
- if( i < 7 )
- EXIT;
-
- F0 = cvMat( 3, 3, CV_64F, v + 9*8 ); // take the last column of v as a solution of Af = 0
-
- // make F0 singular (of rank 2) by decomposing it with SVD,
- // zeroing the last diagonal element of W and then composing the matrices back.
-
- // use v as a temporary storage for different 3x3 matrices
- W = U = V = TF = F0;
- W.data.db = v;
- U.data.db = v + 9;
- V.data.db = v + 18;
- TF.data.db = v + 27;
-
- cvSVD( &F0, &W, &U, &V, CV_SVD_MODIFY_A + CV_SVD_U_T + CV_SVD_V_T );
- W.data.db[8] = 0.;
-
- // F0 <- U*diag([W(1), W(2), 0])*V'
- cvGEMM( &U, &W, 1., 0, 0., &TF, CV_GEMM_A_T );
- cvGEMM( &TF, &V, 1., 0, 0., &F0, 0/*CV_GEMM_B_T*/ );
-
- // apply the transformation that is inverse
- // to what we used to normalize the point coordinates
- {
- double tt0[] = { scale0, 0, -scale0*m0c.x, 0, scale0, -scale0*m0c.y, 0, 0, 1 };
- double tt1[] = { scale1, 0, -scale1*m1c.x, 0, scale1, -scale1*m1c.y, 0, 0, 1 };
- CvMat T0, T1;
- T0 = T1 = F0;
- T0.data.db = tt0;
- T1.data.db = tt1;
-
- // F0 <- T1'*F0*T0
- cvGEMM( &T1, &F0, 1., 0, 0., &TF, CV_GEMM_A_T );
- F0.data.db = fmatrix;
- cvGEMM( &TF, &T0, 1., 0, 0., &F0, 0 );
-
- // make F(3,3) = 1
- if( fabs(F0.data.db[8]) > FLT_EPSILON )
- cvScale( &F0, &F0, 1./F0.data.db[8] );
- }
-
- result = 1;
-
- __END__;
-
- cvReleaseMat( &A );
- return result;
-}
-
-
-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;
-}
-
-
-/************************************ RANSAC algorithm **********************************/
-static int
-icvFMatrix_RANSAC( const CvPoint2D64f* m0, const CvPoint2D64f* m1,
- uchar* mask, int count, double* fmatrix,
- double threshold, double p,
- unsigned rng_seed, int use_8point )
-{
- int result = 0;
-
- const int max_random_iters = 1000;
- const int sample_size = 7;
- uchar* curr_mask = 0;
- uchar* temp_mask = 0;
-
- CV_FUNCNAME( "icvFMatrix_RANSAC" );
-
- __BEGIN__;
-
- double ff[9*3];
- CvRNG rng = cvRNG(rng_seed);
- int i, j, k, sample_count, max_samples = 500;
- int best_good_count = 0;
-
- assert( m0 && m1 && fmatrix && 0 < p && p < 1 && threshold > 0 );
-
- threshold *= threshold;
-
- CV_CALL( curr_mask = (uchar*)cvAlloc( count ));
- if( !mask && use_8point )
- {
- CV_CALL( temp_mask = (uchar*)cvAlloc( count ));
- mask = temp_mask;
- }
-
- // find the best fundamental matrix (giving the least backprojection error)
- // by picking at most <max_samples> 7-tuples of corresponding points
- // <max_samples> may be updated (decreased) within the loop based on statistics of outliers
- for( sample_count = 0; sample_count < max_samples; sample_count++ )
- {
- int idx[sample_size], n;
- CvPoint2D64f ms0[sample_size], ms1[sample_size];
-
- // choose random <sample_size> (=7) points
- for( i = 0; i < sample_size; i++ )
- {
- for( k = 0; k < max_random_iters; k++ )
- {
- idx[i] = cvRandInt(&rng) % count;
- for( j = 0; j < i; j++ )
- if( idx[j] == idx[i] )
- break;
- if( j == i )
- {
- ms0[i] = m0[idx[i]];
- ms1[i] = m1[idx[i]];
- break;
- }
- }
- if( k >= max_random_iters )
- break;
- }
-
- if( i < sample_size )
- continue;
-
- // find 1 or 3 fundamental matrices out of the 7 point correspondences
- n = icvFMatrix_7Point( ms0, ms1, ff );
-
- if( n < 1 || n > 3 )
- continue;
-
- // for each matrix calculate the backprojection error
- // (distance to the corresponding epipolar lines) for each point and thus find
- // the number of in-liers.
- for( k = 0; k < n; k++ )
- {
- const double* f = ff + k*9;
- int good_count = 0;
-
- for( i = 0; i < count; i++ )
- {
- double d0, d1, s0, s1;
-
- double a = f[0]*m0[i].x + f[1]*m0[i].y + f[2];
- double b = f[3]*m0[i].x + f[4]*m0[i].y + f[5];
- double c = f[6]*m0[i].x + f[7]*m0[i].y + f[8];
-
- s1 = a*a + b*b;
- d1 = m1[i].x*a + m1[i].y*b + c;
-
- a = f[0]*m1[i].x + f[3]*m1[i].y + f[6];
- b = f[1]*m1[i].x + f[4]*m1[i].y + f[7];
- c = f[2]*m1[i].x + f[5]*m1[i].y + f[8];
-
- s0 = a*a + b*b;
- d0 = m0[i].x*a + m0[i].y*b + c;
-
- curr_mask[i] = d1*d1 < threshold*s1 && d0*d0 < threshold*s0;
- good_count += curr_mask[i];
- }
-
- if( good_count > MAX( best_good_count, 6 ) )
- {
- // update the current best fundamental matrix and "goodness" flags
- if( mask )
- memcpy( mask, curr_mask, count );
- memcpy( fmatrix, f, 9*sizeof(f[0]));
- best_good_count = good_count;
-
- max_samples = cvRANSACUpdateNumIters( p,
- (double)(count - good_count)/count, 7, max_samples );
- if( max_samples == 0 )
- break;
- }
- }
- }
-
- if( best_good_count < 7 )
- EXIT;
-
- result = 1;
-
- // optionally, use 8-point algorithm to compute fundamental matrix using only the in-liers
- if( best_good_count >= 8 && use_8point )
- result = icvFMatrix_8Point( m0, m1, mask, count, fmatrix );
-
- __END__;
-
- cvFree( &temp_mask );
- cvFree( &curr_mask );
-
- return result;
-}
-
-
-/***************************** Least Median of Squares algorithm ************************/
-
-static CV_IMPLEMENT_QSORT( icvSortDistances, int, CV_LT )
-
-/* the algorithm is quite similar to RANSAC, but here we choose the matrix that gives
- the least median of d(m0[i], F'*m1[i])^2 + d(m1[i], F*m0[i])^2 (0<=i<count),
- instead of choosing the matrix that gives the least number of outliers (as it is done in RANSAC) */
-static int
-icvFMatrix_LMedS( const CvPoint2D64f* m0, const CvPoint2D64f* m1,
- uchar* mask, int count, double* fmatrix,
- double threshold, double p,
- unsigned rng_seed, int use_8point )
-{
- int result = 0;
-
- const int max_random_iters = 1000;
- const int sample_size = 7;
-
- float* dist = 0;
- uchar* curr_mask = 0;
- uchar* temp_mask = 0;
-
- CV_FUNCNAME( "icvFMatrix_LMedS" );
-
- __BEGIN__;
-
- double ff[9*3];
- CvRNG rng = cvRNG(rng_seed);
- int i, j, k, sample_count, max_samples = 500;
- double least_median = DBL_MAX, median;
- int best_good_count = 0;
-
- assert( m0 && m1 && fmatrix && 0 < p && p < 1 && threshold > 0 );
-
- threshold *= threshold;
-
- CV_CALL( curr_mask = (uchar*)cvAlloc( count ));
- CV_CALL( dist = (float*)cvAlloc( count*sizeof(dist[0]) ));
-
- if( !mask && use_8point )
- {
- CV_CALL( temp_mask = (uchar*)cvAlloc( count ));
- mask = temp_mask;
- }
-
- // find the best fundamental matrix (giving the least backprojection error)
- // by picking at most <max_samples> 7-tuples of corresponding points
- // <max_samples> may be updated (decreased) within the loop based on statistics of outliers
- for( sample_count = 0; sample_count < max_samples; sample_count++ )
- {
- int idx[sample_size], n;
- CvPoint2D64f ms0[sample_size], ms1[sample_size];
-
- // choose random <sample_size> (=7) points
- for( i = 0; i < sample_size; i++ )
- {
- for( k = 0; k < max_random_iters; k++ )
- {
- idx[i] = cvRandInt(&rng) % count;
- for( j = 0; j < i; j++ )
- if( idx[j] == idx[i] )
- break;
- if( j == i )
- {
- ms0[i] = m0[idx[i]];
- ms1[i] = m1[idx[i]];
- break;
- }
- }
- if( k >= max_random_iters )
- break;
- }
-
- if( i < sample_size )
- continue;
-
- // find 1 or 3 fundamental matrix out of the 7 point correspondences
- n = icvFMatrix_7Point( ms0, ms1, ff );
-
- if( n < 1 || n > 3 )
- continue;
-
- // for each matrix calculate the backprojection error
- // (distance to the corresponding epipolar lines) for each point and thus find
- // the number of in-liers.
- for( k = 0; k < n; k++ )
- {
- const double* f = ff + k*9;
- int good_count = 0;
-
- for( i = 0; i < count; i++ )
- {
- double d0, d1, s;
-
- double a = f[0]*m0[i].x + f[1]*m0[i].y + f[2];
- double b = f[3]*m0[i].x + f[4]*m0[i].y + f[5];
- double c = f[6]*m0[i].x + f[7]*m0[i].y + f[8];
-
- s = 1./(a*a + b*b);
- d1 = m1[i].x*a + m1[i].y*b + c;
- d1 = s*d1*d1;
-
- a = f[0]*m1[i].x + f[3]*m1[i].y + f[6];
- b = f[1]*m1[i].x + f[4]*m1[i].y + f[7];
- c = f[2]*m1[i].x + f[5]*m1[i].y + f[8];
-
- s = 1./(a*a + b*b);
- d0 = m0[i].x*a + m0[i].y*b + c;
- d0 = s*d0*d0;
-
- curr_mask[i] = d1 < threshold && d0 < threshold;
- good_count += curr_mask[i];
-
- dist[i] = (float)(d0 + d1);
- }
-
- icvSortDistances( (int*)dist, count, 0 );
- median = (double)dist[count/2];
-
- if( median < least_median )
- {
- double ep, lp, lep;
- int new_max_samples;
-
- // update the current best fundamental matrix and "goodness" flags
- if( mask )
- memcpy( mask, curr_mask, count );
- memcpy( fmatrix, f, 9*sizeof(f[0]));
- least_median = median;
- best_good_count = good_count;
-
- // try to update (decrease) <max_samples>
- ep = (double)(count - good_count)/count;
- lp = log(1. - p);
- lep = log(1. - pow(ep,7.));
- if( lp < lep || lep >= 0 )
- break;
- else
- {
- new_max_samples = cvRound(lp/lep);
- max_samples = MIN( new_max_samples, max_samples );
- }
- }
- }
- }
-
- if( best_good_count < 7 )
- EXIT;
-
- result = 1;
-
- // optionally, use 8-point algorithm to compute fundamental matrix using only the in-liers
- if( best_good_count >= 8 && use_8point )
- result = icvFMatrix_8Point( m0, m1, mask, count, fmatrix );
-
- __END__;
-
- cvFree( &temp_mask );
- cvFree( &curr_mask );
- cvFree( &dist );
-
- return result;
-}
-
-
-CV_IMPL int
-cvFindFundamentalMat( const CvMat* points0, const CvMat* points1,
- CvMat* fmatrix, int method,
- double param1, double param2, CvMat* status )
-{
- const unsigned rng_seed = 0xffffffff;
- int result = 0;
- int pt_alloc_flag[2] = { 0, 0 };
- int i, k;
- CvPoint2D64f* pt[2] = { 0, 0 };
- CvMat* _status = 0;
-
- CV_FUNCNAME( "cvFindFundamentalMat" );
-
- __BEGIN__;
-
- int count, dims;
- int depth, cn;
- uchar* status_data = 0;
- double fmatrix_data0[9*3];
- double* fmatrix_data = 0;
-
- if( !CV_IS_MAT(points0) )
- CV_ERROR( !points0 ? CV_StsNullPtr : CV_StsBadArg, "points0 is not a valid matrix" );
-
- if( !CV_IS_MAT(points1) )
- CV_ERROR( !points1 ? CV_StsNullPtr : CV_StsBadArg, "points1 is not a valid matrix" );
-
- if( !CV_ARE_TYPES_EQ(points0, points1) )
- CV_ERROR( CV_StsUnmatchedFormats, "The matrices of points should have the same data type" );
-
- if( !CV_ARE_SIZES_EQ(points0, points1) )
- CV_ERROR( CV_StsUnmatchedSizes, "The matrices of points should have the same size" );
-
- depth = CV_MAT_DEPTH(points0->type);
- cn = CV_MAT_CN(points0->type);
- if( depth != CV_32S && depth != CV_32F && depth != CV_64F || cn != 1 && cn != 2 && cn != 3 )
- CV_ERROR( CV_StsUnsupportedFormat, "The format of point matrices is unsupported" );
-
- if( points0->rows > points0->cols )
- {
- dims = cn*points0->cols;
- count = points0->rows;
- }
- else
- {
- if( points0->rows > 1 && cn > 1 || points0->rows == 1 && cn == 1 )
- CV_ERROR( CV_StsBadSize, "The point matrices do not have a proper layout (2xn, 3xn, nx2 or nx3)" );
- dims = cn * points0->rows;
- count = points0->cols;
- }
-
- if( dims != 2 && dims != 3 )
- CV_ERROR( CV_StsOutOfRange, "The dimensionality of points must be 2 or 3" );
-
- if( method == CV_FM_7POINT && count != 7 ||
- method != CV_FM_7POINT && count < 7 + (method == CV_FM_8POINT) )
- CV_ERROR( CV_StsOutOfRange,
- "The number of points must be 7 for 7-point algorithm, "
- ">=8 for 8-point algorithm and >=7 for other algorithms" );
-
- if( !CV_IS_MAT(fmatrix) )
- CV_ERROR( !fmatrix ? CV_StsNullPtr : CV_StsBadArg, "fmatrix is not a valid matrix" );
-
- if( CV_MAT_TYPE(fmatrix->type) != CV_32FC1 && CV_MAT_TYPE(fmatrix->type) != CV_64FC1 )
- CV_ERROR( CV_StsUnsupportedFormat, "fundamental matrix must have 32fC1 or 64fC1 type" );
-
- if( fmatrix->cols != 3 || (fmatrix->rows != 3 && (method != CV_FM_7POINT || fmatrix->rows != 9)))
- CV_ERROR( CV_StsBadSize, "fundamental matrix must be 3x3 or 3x9 (for 7-point method only)" );
-
- fmatrix_data = fmatrix->data.db;
- if( !CV_IS_MAT_CONT(fmatrix->type) || CV_MAT_TYPE(fmatrix->type) != CV_64FC1 ||
- method == CV_FM_7POINT && fmatrix->rows != 9 )
- fmatrix_data = fmatrix_data0;
-
- if( status )
- {
- if( !CV_IS_MAT(status) )
- CV_ERROR( CV_StsBadArg, "The output status is not a valid matrix" );
-
- if( status->cols != 1 && status->rows != 1 || status->cols + status->rows - 1 != count )
- CV_ERROR( CV_StsUnmatchedSizes,
- "The status matrix must have the same size as the point matrices" );
-
- if( method == CV_FM_7POINT || method == CV_FM_8POINT )
- cvSet( status, cvScalarAll(1.) );
- else
- {
- status_data = status->data.ptr;
- if( !CV_IS_MAT_CONT(status->type) || !CV_IS_MASK_ARR(status) )
- {
- CV_CALL( _status = cvCreateMat( status->rows, status->cols, CV_8UC1 ));
- status_data = _status->data.ptr;
- }
- }
- }
-
- for( k = 0; k < 2; k++ )
- {
- const CvMat* spt = k == 0 ? points0 : points1;
- CvPoint2D64f* dpt = pt[k] = (CvPoint2D64f*)spt->data.db;
- int plane_stride, stride, elem_size;
-
- if( CV_IS_MAT_CONT(spt->type) && CV_MAT_DEPTH(spt->type) == CV_64F &&
- dims == 2 && (spt->rows == 1 || spt->rows == count) )
- continue;
-
- elem_size = CV_ELEM_SIZE(depth);
-
- if( spt->rows == dims )
- {
- plane_stride = spt->step / elem_size;
- stride = 1;
- }
- else
- {
- plane_stride = 1;
- stride = spt->rows == 1 ? dims : spt->step / elem_size;
- }
-
- CV_CALL( dpt = pt[k] = (CvPoint2D64f*)cvAlloc( count*sizeof(dpt[0]) ));
- pt_alloc_flag[k] = 1;
-
- if( depth == CV_32F )
- {
- const float* xp = spt->data.fl;
- const float* yp = xp + plane_stride;
- const float* zp = dims == 3 ? yp + plane_stride : 0;
-
- for( i = 0; i < count; i++ )
- {
- double x = *xp, y = *yp;
- xp += stride;
- yp += stride;
- if( dims == 3 )
- {
- double z = *zp;
- zp += stride;
- z = z ? 1./z : 1.;
- x *= z;
- y *= z;
- }
- dpt[i].x = x;
- dpt[i].y = y;
- }
- }
- else
- {
- const double* xp = spt->data.db;
- const double* yp = xp + plane_stride;
- const double* zp = dims == 3 ? yp + plane_stride : 0;
-
- for( i = 0; i < count; i++ )
- {
- double x = *xp, y = *yp;
- xp += stride;
- yp += stride;
- if( dims == 3 )
- {
- double z = *zp;
- zp += stride;
- z = z ? 1./z : 1.;
- x *= z;
- y *= z;
- }
- dpt[i].x = x;
- dpt[i].y = y;
- }
- }
- }
-
- if( method == CV_FM_7POINT )
- result = icvFMatrix_7Point( pt[0], pt[1], fmatrix_data );
- else if( method == CV_FM_8POINT )
- result = icvFMatrix_8Point( pt[0], pt[1], 0, count, fmatrix_data );
- else
- {
- if( param1 < 0 )
- CV_ERROR( CV_StsOutOfRange, "param1 (threshold) must be > 0" );
-
- if( param2 < 0 || param2 > 1 )
- CV_ERROR( CV_StsOutOfRange, "param2 (confidence level) must be between 0 and 1" );
-
- if( param2 < DBL_EPSILON || param2 > 1 - DBL_EPSILON )
- param2 = 0.99;
-
- if( method < CV_FM_RANSAC_ONLY )
- result = icvFMatrix_LMedS( pt[0], pt[1], status_data, count, fmatrix_data,
- param1, param2, rng_seed, method & CV_FM_8POINT );
- else
- result = icvFMatrix_RANSAC( pt[0], pt[1], status_data, count, fmatrix_data,
- param1, param2, rng_seed, method & CV_FM_8POINT );
- }
-
- if( result && fmatrix->data.db != fmatrix_data )
- {
- CvMat hdr;
- cvZero( fmatrix );
- hdr = cvMat( MIN(fmatrix->rows, result*3), fmatrix->cols, CV_64F, fmatrix_data );
- cvConvert( &hdr, fmatrix );
- }
-
- if( status && status_data && status->data.ptr != status_data )
- cvConvert( _status, status );
-
- __END__;
-
- cvReleaseMat( &_status );
- for( k = 0; k < 2; k++ )
- if( pt_alloc_flag[k] )
- cvFree( &pt[k] );
-
- return result;
-}
-
-
-CV_IMPL void
-cvComputeCorrespondEpilines( const CvMat* points, int pointImageID,
- const CvMat* fmatrix, CvMat* lines )
-{
- CV_FUNCNAME( "cvComputeCorrespondEpilines" );
-
- __BEGIN__;
-
- int abc_stride, abc_plane_stride, abc_elem_size;
- int plane_stride, stride, elem_size;
- int i, dims, count, depth, cn, abc_dims, abc_count, abc_depth, abc_cn;
- uchar *ap, *bp, *cp;
- const uchar *xp, *yp, *zp;
- double f[9];
- CvMat F = cvMat( 3, 3, CV_64F, f );
-
- if( !CV_IS_MAT(points) )
- CV_ERROR( !points ? CV_StsNullPtr : CV_StsBadArg, "points parameter is not a valid matrix" );
-
- depth = CV_MAT_DEPTH(points->type);
- cn = CV_MAT_CN(points->type);
- if( depth != CV_32F && depth != CV_64F || cn != 1 && cn != 2 && cn != 3 )
- CV_ERROR( CV_StsUnsupportedFormat, "The format of point matrix is unsupported" );
-
- if( points->rows > points->cols )
- {
- dims = cn*points->cols;
- count = points->rows;
- }
- else
- {
- if( points->rows > 1 && cn > 1 || points->rows == 1 && cn == 1 )
- CV_ERROR( CV_StsBadSize, "The point matrix does not have a proper layout (2xn, 3xn, nx2 or nx3)" );
- dims = cn * points->rows;
- count = points->cols;
- }
-
- if( dims != 2 && dims != 3 )
- CV_ERROR( CV_StsOutOfRange, "The dimensionality of points must be 2 or 3" );
-
- if( !CV_IS_MAT(fmatrix) )
- CV_ERROR( !fmatrix ? CV_StsNullPtr : CV_StsBadArg, "fmatrix is not a valid matrix" );
-
- if( CV_MAT_TYPE(fmatrix->type) != CV_32FC1 && CV_MAT_TYPE(fmatrix->type) != CV_64FC1 )
- CV_ERROR( CV_StsUnsupportedFormat, "fundamental matrix must have 32fC1 or 64fC1 type" );
-
- if( fmatrix->cols != 3 || fmatrix->rows != 3 )
- CV_ERROR( CV_StsBadSize, "fundamental matrix must be 3x3" );
-
- if( !CV_IS_MAT(lines) )
- CV_ERROR( !lines ? CV_StsNullPtr : CV_StsBadArg, "lines parameter is not a valid matrix" );
-
- abc_depth = CV_MAT_DEPTH(lines->type);
- abc_cn = CV_MAT_CN(lines->type);
- if( abc_depth != CV_32F && abc_depth != CV_64F || abc_cn != 1 && abc_cn != 3 )
- CV_ERROR( CV_StsUnsupportedFormat, "The format of the matrix of lines is unsupported" );
-
- if( lines->rows > lines->cols )
- {
- abc_dims = abc_cn*lines->cols;
- abc_count = lines->rows;
- }
- else
- {
- if( lines->rows > 1 && abc_cn > 1 || lines->rows == 1 && abc_cn == 1 )
- CV_ERROR( CV_StsBadSize, "The lines matrix does not have a proper layout (3xn or nx3)" );
- abc_dims = abc_cn * lines->rows;
- abc_count = lines->cols;
- }
-
- if( abc_dims != 3 )
- CV_ERROR( CV_StsOutOfRange, "The lines matrix does not have a proper layout (3xn or nx3)" );
-
- if( abc_count != count )
- CV_ERROR( CV_StsUnmatchedSizes, "The numbers of points and lines are different" );
-
- elem_size = CV_ELEM_SIZE(depth);
- abc_elem_size = CV_ELEM_SIZE(abc_depth);
-
- if( points->rows == dims )
- {
- plane_stride = points->step;
- stride = elem_size;
- }
- else
- {
- plane_stride = elem_size;
- stride = points->rows == 1 ? dims*elem_size : points->step;
- }
-
- if( lines->rows == 3 )
- {
- abc_plane_stride = lines->step;
- abc_stride = abc_elem_size;
- }
- else
- {
- abc_plane_stride = abc_elem_size;
- abc_stride = lines->rows == 1 ? 3*abc_elem_size : lines->step;
- }
-
- CV_CALL( cvConvert( fmatrix, &F ));
- if( pointImageID == 2 )
- cvTranspose( &F, &F );
-
- xp = points->data.ptr;
- yp = xp + plane_stride;
- zp = dims == 3 ? yp + plane_stride : 0;
-
- ap = lines->data.ptr;
- bp = ap + abc_plane_stride;
- cp = bp + abc_plane_stride;
-
- for( i = 0; i < count; i++ )
- {
- double x, y, z = 1.;
- double a, b, c, nu;
-
- if( depth == CV_32F )
- {
- x = *(float*)xp; y = *(float*)yp;
- if( zp )
- z = *(float*)zp, zp += stride;
- }
- else
- {
- x = *(double*)xp; y = *(double*)yp;
- if( zp )
- z = *(double*)zp, zp += stride;
- }
-
- xp += stride; yp += stride;
-
- a = f[0]*x + f[1]*y + f[2]*z;
- b = f[3]*x + f[4]*y + f[5]*z;
- c = f[6]*x + f[7]*y + f[8]*z;
- nu = a*a + b*b;
- nu = nu ? 1./sqrt(nu) : 1.;
- a *= nu; b *= nu; c *= nu;
-
- if( abc_depth == CV_32F )
- {
- *(float*)ap = (float)a;
- *(float*)bp = (float)b;
- *(float*)cp = (float)c;
- }
- else
- {
- *(double*)ap = a;
- *(double*)bp = b;
- *(double*)cp = c;
- }
-
- ap += abc_stride;
- bp += abc_stride;
- cp += abc_stride;
- }
-
- __END__;
-}
-
-
-CV_IMPL void
-cvConvertPointsHomogenious( const CvMat* src, CvMat* dst )
-{
- CvMat* temp = 0;
- CvMat* denom = 0;
-
- CV_FUNCNAME( "cvConvertPointsHomogenious" );
-
- __BEGIN__;
-
- int i, s_count, s_dims, d_count, d_dims;
- CvMat _src, _dst, _ones;
- CvMat* ones = 0;
-
- if( !CV_IS_MAT(src) )
- CV_ERROR( !src ? CV_StsNullPtr : CV_StsBadArg,
- "The input parameter is not a valid matrix" );
-
- if( !CV_IS_MAT(dst) )
- CV_ERROR( !dst ? CV_StsNullPtr : CV_StsBadArg,
- "The output parameter is not a valid matrix" );
-
- if( src == dst || src->data.ptr == dst->data.ptr )
- {
- if( src != dst && (!CV_ARE_TYPES_EQ(src, dst) || !CV_ARE_SIZES_EQ(src,dst)) )
- CV_ERROR( CV_StsBadArg, "Invalid inplace operation" );
- EXIT;
- }
-
- if( src->rows > src->cols )
- {
- if( !((src->cols > 1) ^ (CV_MAT_CN(src->type) > 1)) )
- CV_ERROR( CV_StsBadSize, "Either the number of channels or columns or rows must be =1" );
-
- s_dims = CV_MAT_CN(src->type)*src->cols;
- s_count = src->rows;
- }
- else
- {
- if( !((src->rows > 1) ^ (CV_MAT_CN(src->type) > 1)) )
- CV_ERROR( CV_StsBadSize, "Either the number of channels or columns or rows must be =1" );
-
- s_dims = CV_MAT_CN(src->type)*src->rows;
- s_count = src->cols;
- }
-
- if( src->rows == 1 || src->cols == 1 )
- src = cvReshape( src, &_src, 1, s_count );
-
- if( dst->rows > dst->cols )
- {
- if( !((dst->cols > 1) ^ (CV_MAT_CN(dst->type) > 1)) )
- CV_ERROR( CV_StsBadSize,
- "Either the number of channels or columns or rows in the input matrix must be =1" );
-
- d_dims = CV_MAT_CN(dst->type)*dst->cols;
- d_count = dst->rows;
- }
- else
- {
- if( !((dst->rows > 1) ^ (CV_MAT_CN(dst->type) > 1)) )
- CV_ERROR( CV_StsBadSize,
- "Either the number of channels or columns or rows in the output matrix must be =1" );
-
- d_dims = CV_MAT_CN(dst->type)*dst->rows;
- d_count = dst->cols;
- }
-
- if( dst->rows == 1 || dst->cols == 1 )
- dst = cvReshape( dst, &_dst, 1, d_count );
-
- if( s_count != d_count )
- CV_ERROR( CV_StsUnmatchedSizes, "Both matrices must have the same number of points" );
-
- if( CV_MAT_DEPTH(src->type) < CV_32F || CV_MAT_DEPTH(dst->type) < CV_32F )
- CV_ERROR( CV_StsUnsupportedFormat,
- "Both matrices must be floating-point (single or double precision)" );
-
- if( s_dims < 2 || s_dims > 4 || d_dims < 2 || d_dims > 4 )
- CV_ERROR( CV_StsOutOfRange,
- "Both input and output point dimensionality must be 2, 3 or 4" );
-
- if( s_dims < d_dims - 1 || s_dims > d_dims + 1 )
- CV_ERROR( CV_StsUnmatchedSizes,
- "The dimensionalities of input and output point sets differ too much" );
-
- if( s_dims == d_dims - 1 )
- {
- if( d_count == dst->rows )
- {
- ones = cvGetSubRect( dst, &_ones, cvRect( s_dims, 0, 1, d_count ));
- dst = cvGetSubRect( dst, &_dst, cvRect( 0, 0, s_dims, d_count ));
- }
- else
- {
- ones = cvGetSubRect( dst, &_ones, cvRect( 0, s_dims, d_count, 1 ));
- dst = cvGetSubRect( dst, &_dst, cvRect( 0, 0, d_count, s_dims ));
- }
- }
-
- if( s_dims <= d_dims )
- {
- if( src->rows == dst->rows && src->cols == dst->cols )
- {
- if( CV_ARE_TYPES_EQ( src, dst ) )
- cvCopy( src, dst );
- else
- cvConvert( src, dst );
- }
- else
- {
- if( !CV_ARE_TYPES_EQ( src, dst ))
- {
- CV_CALL( temp = cvCreateMat( src->rows, src->cols, dst->type ));
- cvConvert( src, temp );
- src = temp;
- }
- cvTranspose( src, dst );
- }
-
- if( ones )
- cvSet( ones, cvRealScalar(1.) );
- }
- else
- {
- int s_plane_stride, s_stride, d_plane_stride, d_stride, elem_size;
-
- if( !CV_ARE_TYPES_EQ( src, dst ))
- {
- CV_CALL( temp = cvCreateMat( src->rows, src->cols, dst->type ));
- cvConvert( src, temp );
- src = temp;
- }
-
- elem_size = CV_ELEM_SIZE(src->type);
-
- if( s_count == src->cols )
- s_plane_stride = src->step / elem_size, s_stride = 1;
- else
- s_stride = src->step / elem_size, s_plane_stride = 1;
-
- if( d_count == dst->cols )
- d_plane_stride = dst->step / elem_size, d_stride = 1;
- else
- d_stride = dst->step / elem_size, d_plane_stride = 1;
-
- CV_CALL( denom = cvCreateMat( 1, d_count, dst->type ));
-
- if( CV_MAT_DEPTH(dst->type) == CV_32F )
- {
- const float* xs = src->data.fl;
- const float* ys = xs + s_plane_stride;
- const float* zs = 0;
- const float* ws = xs + (s_dims - 1)*s_plane_stride;
-
- float* iw = denom->data.fl;
-
- float* xd = dst->data.fl;
- float* yd = xd + d_plane_stride;
- float* zd = 0;
-
- if( d_dims == 3 )
- {
- zs = ys + s_plane_stride;
- zd = yd + d_plane_stride;
- }
-
- for( i = 0; i < d_count; i++, ws += s_stride )
- {
- float t = *ws;
- iw[i] = t ? t : 1.f;
- }
-
- cvDiv( 0, denom, denom );
-
- if( d_dims == 3 )
- for( i = 0; i < d_count; i++ )
- {
- float w = iw[i];
- float x = *xs * w, y = *ys * w, z = *zs * w;
- xs += s_stride; ys += s_stride; zs += s_stride;
- *xd = x; *yd = y; *zd = z;
- xd += d_stride; yd += d_stride; zd += d_stride;
- }
- else
- for( i = 0; i < d_count; i++ )
- {
- float w = iw[i];
- float x = *xs * w, y = *ys * w;
- xs += s_stride; ys += s_stride;
- *xd = x; *yd = y;
- xd += d_stride; yd += d_stride;
- }
- }
- else
- {
- const double* xs = src->data.db;
- const double* ys = xs + s_plane_stride;
- const double* zs = 0;
- const double* ws = xs + (s_dims - 1)*s_plane_stride;
-
- double* iw = denom->data.db;
-
- double* xd = dst->data.db;
- double* yd = xd + d_plane_stride;
- double* zd = 0;
-
- if( d_dims == 3 )
- {
- zs = ys + s_plane_stride;
- zd = yd + d_plane_stride;
- }
-
- for( i = 0; i < d_count; i++, ws += s_stride )
- {
- double t = *ws;
- iw[i] = t ? t : 1.;
- }
-
- cvDiv( 0, denom, denom );
-
- if( d_dims == 3 )
- for( i = 0; i < d_count; i++ )
- {
- double w = iw[i];
- double x = *xs * w, y = *ys * w, z = *zs * w;
- xs += s_stride; ys += s_stride; zs += s_stride;
- *xd = x; *yd = y; *zd = z;
- xd += d_stride; yd += d_stride; zd += d_stride;
- }
- else
- for( i = 0; i < d_count; i++ )
- {
- double w = iw[i];
- double x = *xs * w, y = *ys * w;
- xs += s_stride; ys += s_stride;
- *xd = x; *yd = y;
- xd += d_stride; yd += d_stride;
- }
- }
- }
-
- __END__;
-
- cvReleaseMat( &denom );
- cvReleaseMat( &temp );
-}
-
-/* End of file. */
projects / opencv / blobdiff