www.pudn.com > OpenCV-Intel.zip > cvfundam.cpp
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#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}, m0q = {0,0}, m1q = {0,0};
double t, scale0, scale1;
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;
m0q.x += x*x; m0q.y += y*y;
x = m1[i].x, y = m1[i].y;
m1c.x += x; m1c.y += y;
m1q.x += x*x; m1q.y += 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;
scale0 = t * sqrt( m0q.x + m0q.y - good_count*(m0c.x*m0c.x + m0c.y*m0c.y) );
m1c.x *= t; m1c.y *= t;
scale1 = t * sqrt( m1q.x + m1q.y - good_count*(m1c.x*m1c.x + m1c.y*m1c.y) );
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;
}
/************************************ 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 7-tuples of corresponding points
// 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 (=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 ) )
{
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]));
best_good_count = good_count;
// try to update (decrease)
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( (void**)&temp_mask );
cvFree( (void**)&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 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 7-tuples of corresponding points
// 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 (=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)
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( (void**)&temp_mask );
cvFree( (void**)&curr_mask );
cvFree( (void**)&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( (void**)&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. */