Может кто-нибудь объяснить мне, каких параметров ожидает следующая функция и для чего они используются?У меня есть проблемы, чтобы понять данное описание.Я думаю, что я передаю неправильные аргументы, которые приводят к исключениям памяти.К сожалению, исключения не могут быть отлажены, потому что это внешний код из библиотеки.
Эта функция используется для вычисления трехмерных положений вершин, учитывая только проекцию этих точек на различные изображения, которые показывают трехмерную сцену с разных видов.
Здесь заголовок функции:
int sba_motstr_levmar_x(
const int n, /* number of points */
const int ncon,/* number of points (starting from the 1st) whose parameters should not be modified.
* All B_ij (see below) with i<ncon are assumed to be zero
*/
const int m, /* number of images */
const int mcon,/* number of images (starting from the 1st) whose parameters should not be modified.
* All A_ij (see below) with j<mcon are assumed to be zero
*/
char *vmask, /* visibility mask: vmask[i, j]=1 if point i visible in image j, 0 otherwise. nxm */
double *p, /* initial parameter vector p0: (a1, ..., am, b1, ..., bn).
* aj are the image j parameters, bi are the i-th point parameters,
* size m*cnp + n*pnp
*/
const int cnp,/* number of parameters for ONE camera; e.g. 6 for Euclidean cameras */
const int pnp,/* number of parameters for ONE point; e.g. 3 for Euclidean points */
double *x, /* measurements vector: (x_11^T, .. x_1m^T, ..., x_n1^T, .. x_nm^T)^T where
* x_ij is the projection of the i-th point on the j-th image.
* NOTE: some of the x_ij might be missing, if point i is not visible in image j;
* see vmask[i, j], max. size n*m*mnp
*/
double *covx, /* measurements covariance matrices: (Sigma_x_11, .. Sigma_x_1m, ..., Sigma_x_n1, .. Sigma_x_nm),
* where Sigma_x_ij is the mnp x mnp covariance of x_ij stored row-by-row. Set to NULL if no
* covariance estimates are available (identity matrices are implicitly used in this case).
* NOTE: a certain Sigma_x_ij is missing if the corresponding x_ij is also missing;
* see vmask[i, j], max. size n*m*mnp*mnp
*/
const int mnp,/* number of parameters for EACH measurement; usually 2 */
void (*func)(double *p, struct sba_crsm *idxij, int *rcidxs, int *rcsubs, double *hx, void *adata),
/* functional relation describing measurements. Given a parameter vector p,
* computes a prediction of the measurements \hat{x}. p is (m*cnp + n*pnp)x1,
* \hat{x} is (n*m*mnp)x1, maximum
* rcidxs, rcsubs are max(m, n) x 1, allocated by the caller and can be used
* as working memory
*/
void (*fjac)(double *p, struct sba_crsm *idxij, int *rcidxs, int *rcsubs, double *jac, void *adata),
/* function to evaluate the sparse Jacobian dX/dp.
* The Jacobian is returned in jac as
* (dx_11/da_1, ..., dx_1m/da_m, ..., dx_n1/da_1, ..., dx_nm/da_m,
* dx_11/db_1, ..., dx_1m/db_1, ..., dx_n1/db_n, ..., dx_nm/db_n), or
* (using HZ's notation),
* jac=(A_11, B_11, ..., A_1m, B_1m, ..., A_n1, B_n1, ..., A_nm, B_nm)
* Notice that depending on idxij, some of the A_ij and B_ij might be missing.
* Note also that A_ij and B_ij are mnp x cnp and mnp x pnp matrices resp. and
* should be stored in jac in row-major order.
* rcidxs, rcsubs are max(m, n) x 1, allocated by the caller and can be used
* as working memory
*
* If NULL, the Jacobian is approximated by repetitive func calls and finite
* differences. This is computationally inefficient and thus NOT recommended.
*/
void *adata, /* pointer to possibly additional data, passed uninterpreted to func, fjac */
const int itmax, /* I: maximum number of iterations. itmax==0 signals Jacobian verification followed by immediate return */
const int verbose, /* I: verbosity */
const double opts[SBA_OPTSSZ],
/* I: minim. options [\mu, \epsilon1, \epsilon2, \epsilon3, \epsilon4]. Respectively the scale factor for
* initial \mu, stopping thresholds for ||J^T e||_inf, ||dp||_2, ||e||_2 and (||e||_2-||e_new||_2)/||e||_2
*/
double info[SBA_INFOSZ]
/* O: information regarding the minimization. Set to NULL if don't care
* info[0]=||e||_2 at initial p.
* info[1-4]=[ ||e||_2, ||J^T e||_inf, ||dp||_2, mu/max[J^T J]_ii ], all computed at estimated p.
* info[5]= # iterations,
* info[6]=reason for terminating: 1 - stopped by small gradient J^T e
* 2 - stopped by small dp
* 3 - stopped by itmax
* 4 - stopped by small relative reduction in ||e||_2
* 5 - stopped by small ||e||_2
* 6 - too many attempts to increase damping. Restart with increased mu
* 7 - stopped by invalid (i.e. NaN or Inf) "func" values; a user error
* info[7]= # function evaluations
* info[8]= # Jacobian evaluations
* info[9]= # number of linear systems solved, i.e. number of attempts for reducing error
*/
)
.":
http://www.ics.forth.gr/~lourakis/sba/#documentation
Это также официальная домашняя страница этой библиотеки.