void
mexFunction(int nout, mxArray *out[],
int nin, const mxArray *in[])
{
enum { X=0,Y } ;
enum { U } ;
int NP, NCP ;
int i,j ;
double *X_pt, *Y_pt, *U_pt ;
#undef small
const double small = 2.220446049250313e-16 ;
/* -----------------------------------------------------------------
* Check the arguments
* -------------------------------------------------------------- */
if (nin != 2) {
vlmxError(vlmxErrNotEnoughInputArguments, NULL) ;
} else if (nout > 1) {
vlmxError(vlmxErrTooManyOutputArguments, NULL) ;
}
if(!vlmxIsMatrix(in[X], 2, -1)) {
mexErrMsgTxt("X must be a 2xNP real matrix") ;
}
if(!vlmxIsMatrix(in[Y], 2, -1)) {
mexErrMsgTxt("Y must be a 2xNCP real matrix") ;
}
NP = getN(X) ;
NCP = getN(Y) ;
X_pt = getPr(X);
Y_pt = getPr(Y) ;
/* Allocate the result. */
out[U] = mxCreateDoubleMatrix(NP, NCP, mxREAL) ;
U_pt = mxGetPr(out[U]) ;
/* -----------------------------------------------------------------
* Check the arguments
* -------------------------------------------------------------- */
for(j = 0 ; j < NCP ; ++j) {
double xcp = *Y_pt++ ;
double ycp = *Y_pt++ ;
for(i = 0 ; i < NP ; ++i) {
double dx = *X_pt++ - xcp ;
double dy = *X_pt++ - ycp ;
double r2 = dx*dx + dy*dy ;
*U_pt++ = (r2 <= small) ? 0 : r2 * log (r2) ;
}
X_pt -= 2*NP ;
}
}
static VlHIKMNode *
xcreate (VlHIKMTree *tree, mxArray const *mnode, int i)
{
mxArray const *mcenters, *msub ;
VlHIKMNode *node ;
vl_size M, node_K ;
vl_uindex k ;
/* sanity checks */
mcenters = mxGetField(mnode, i, "centers") ;
msub = mxGetField(mnode, i, "sub") ;
if (!mcenters ||
mxGetClassID (mcenters) != mxINT32_CLASS ||
!vlmxIsMatrix (mcenters, -1, -1) ) {
mexErrMsgTxt("NODE.CENTERS must be a INT32 matrix.") ;
}
M = mxGetM (mcenters) ;
node_K = mxGetN (mcenters) ;
if (node_K > (vl_size)tree->K) {
mexErrMsgTxt("A node has more clusters than TREE.K.") ;
}
if (tree->M < 0) {
tree->M = M ;
} else if (M != (vl_size)tree->M) {
mexErrMsgTxt("A node CENTERS field has inconsistent dimensionality.") ;
}
node = mxMalloc (sizeof(VlHIKMNode)) ;
node->filter = vl_ikm_new (tree->method) ;
node->children = 0 ;
vl_ikm_init (node->filter, mxGetData(mcenters), M, node_K) ;
/* has any childer? */
if (msub) {
/* sanity checks */
if (mxGetClassID (msub) != mxSTRUCT_CLASS) {
mexErrMsgTxt("NODE.SUB must be a MATLAB structure array.") ;
}
if (mxGetNumberOfElements (msub) != node_K) {
mexErrMsgTxt("NODE.SUB size must correspond to NODE.CENTERS.") ;
}
node-> children = mxMalloc (sizeof(VlHIKMNode *) * node_K) ;
for(k = 0 ; k < node_K ; ++ k) {
node-> children [k] = xcreate (tree, msub, k) ;
}
}
return node ;
}
void
mexFunction(int nout, mxArray *out[],
int nin, const mxArray *in[])
{
enum {IN_FOREST = 0, IN_DATA, IN_QUERY, IN_END} ;
enum {OUT_INDEX = 0, OUT_DISTANCE} ;
int verbose = 0 ;
int opt ;
int next = IN_END ;
mxArray const *optarg ;
VlKDForest * forest ;
mxArray const * forest_array = in[IN_FOREST] ;
mxArray const * data_array = in[IN_DATA] ;
mxArray const * query_array = in[IN_QUERY] ;
mxArray * index_array ;
mxArray * distance_array ;
void * query ;
vl_uint32 * index ;
void * distance ;
vl_size numNeighbors = 1 ;
vl_size numQueries ;
vl_uindex qi, ni;
unsigned int numComparisons = 0 ;
unsigned int maxNumComparisons = 0 ;
VlKDForestNeighbor * neighbors ;
mxClassID dataClass ;
VL_USE_MATLAB_ENV ;
/* -----------------------------------------------------------------
* Check the arguments
* -------------------------------------------------------------- */
if (nin < 3) {
vlmxError(vlmxErrNotEnoughInputArguments, NULL) ;
}
if (nout > 2) {
vlmxError(vlmxErrTooManyOutputArguments, NULL) ;
}
forest = new_kdforest_from_array (forest_array, data_array) ;
dataClass = mxGetClassID (data_array) ;
if (mxGetClassID (query_array) != dataClass) {
vlmxError(vlmxErrInvalidArgument,
"QUERY must have the same storage class as DATA.") ;
}
if (! vlmxIsReal (query_array)) {
vlmxError(vlmxErrInvalidArgument,
"QUERY must be real.") ;
}
if (! vlmxIsMatrix (query_array, forest->dimension, -1)) {
vlmxError(vlmxErrInvalidArgument,
"QUERY must be a matrix with TREE.NUMDIMENSIONS rows.") ;
}
while ((opt = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) {
switch (opt) {
case opt_num_neighs :
if (! vlmxIsScalar(optarg) ||
(numNeighbors = mxGetScalar(optarg)) < 1) {
vlmxError(vlmxErrInvalidArgument,
"NUMNEIGHBORS must be a scalar not smaller than one.") ;
}
break;
case opt_max_num_comparisons :
if (! vlmxIsScalar(optarg)) {
vlmxError(vlmxErrInvalidArgument,
"MAXNUMCOMPARISONS must be a scalar.") ;
}
maxNumComparisons = mxGetScalar(optarg) ;
break;
case opt_verbose :
++ verbose ;
break ;
}
}
vl_kdforest_set_max_num_comparisons (forest, maxNumComparisons) ;
neighbors = vl_malloc (sizeof(VlKDForestNeighbor) * numNeighbors) ;
query = mxGetData (query_array) ;
numQueries = mxGetN (query_array) ;
out[OUT_INDEX] = index_array = mxCreateNumericMatrix
(numNeighbors, numQueries, mxUINT32_CLASS, mxREAL) ;
out[OUT_DISTANCE] = distance_array = mxCreateNumericMatrix
(numNeighbors, numQueries, dataClass, mxREAL) ;
index = mxGetData (index_array) ;
distance = mxGetData (distance_array) ;
if (verbose) {
VL_PRINTF ("vl_kdforestquery: number of queries: %d\n", numQueries) ;
VL_PRINTF ("vl_kdforestquery: number of neighbors per query: %d\n", numNeighbors) ;
VL_PRINTF ("vl_kdforestquery: max num of comparisons per query: %d\n",
vl_kdforest_get_max_num_comparisons (forest)) ;
}
for (qi = 0 ; qi < numQueries ; ++ qi) {
numComparisons += vl_kdforest_query (forest, neighbors, numNeighbors,
query) ;
switch (dataClass) {
case mxSINGLE_CLASS:
{
float * distance_ = (float*) distance ;
for (ni = 0 ; ni < numNeighbors ; ++ni) {
*index++ = neighbors[ni].index + 1 ;
*distance_++ = neighbors[ni].distance ;
}
query = (float*)query + vl_kdforest_get_data_dimension (forest) ;
distance = distance_ ;
break ;
}
case mxDOUBLE_CLASS:
{
double * distance_ = (double*) distance ;
for (ni = 0 ; ni < numNeighbors ; ++ni) {
*index++ = neighbors[ni].index + 1 ;
*distance_++ = neighbors[ni].distance ;
}
query = (double*)query + vl_kdforest_get_data_dimension (forest) ;
distance = distance_ ;
break ;
}
default:
abort() ;
}
}
if (verbose) {
VL_PRINTF ("vl_kdforestquery: number of comparisons per query: %.3f\n",
((double) numComparisons) / numQueries) ;
VL_PRINTF ("vl_kdforestquery: number of comparisons per neighbor: %.3f\n",
((double) numComparisons) / (numQueries * numNeighbors)) ;
}
vl_kdforest_delete (forest) ;
vl_free (neighbors) ;
}
/* driver */
void
mexFunction (int nout VL_UNUSED, mxArray * out[], int nin, const mxArray * in[])
{
enum {IN_DATA = 0, IN_MEANS, IN_COVARIANCES, IN_PRIORS, IN_END} ;
enum {OUT_ENC} ;
int opt ;
int next = IN_END ;
mxArray const *optarg ;
vl_size numClusters = 10;
vl_size dimension ;
vl_size numData ;
int flags = 0 ;
void * covariances = NULL;
void * means = NULL;
void * priors = NULL;
void * data = NULL ;
int verbosity = 0 ;
vl_type dataType ;
mxClassID classID ;
VL_USE_MATLAB_ENV ;
/* -----------------------------------------------------------------
* Check the arguments
* -------------------------------------------------------------- */
if (nin < 4) {
vlmxError (vlmxErrInvalidArgument,
"At least four arguments required.");
}
if (nout > 1) {
vlmxError (vlmxErrInvalidArgument,
"At most one output argument.");
}
classID = mxGetClassID (IN(DATA)) ;
switch (classID) {
case mxSINGLE_CLASS: dataType = VL_TYPE_FLOAT ; break ;
case mxDOUBLE_CLASS: dataType = VL_TYPE_DOUBLE ; break ;
default:
vlmxError (vlmxErrInvalidArgument,
"DATA is neither of class SINGLE or DOUBLE.") ;
}
if (mxGetClassID (IN(MEANS)) != classID) {
vlmxError(vlmxErrInvalidArgument, "MEANS is not of the same class as DATA.") ;
}
if (mxGetClassID (IN(COVARIANCES)) != classID) {
vlmxError(vlmxErrInvalidArgument, "COVARIANCES is not of the same class as DATA.") ;
}
if (mxGetClassID (IN(PRIORS)) != classID) {
vlmxError(vlmxErrInvalidArgument, "PRIORS is not of the same class as DATA.") ;
}
dimension = mxGetM (IN(DATA)) ;
numData = mxGetN (IN(DATA)) ;
numClusters = mxGetN (IN(MEANS)) ;
if (dimension == 0) {
vlmxError (vlmxErrInvalidArgument, "SIZE(DATA,1) is zero.") ;
}
if (!vlmxIsMatrix(IN(MEANS), dimension, numClusters)) {
vlmxError (vlmxErrInvalidArgument, "MEANS is not a matrix or does not have the right size.") ;
}
if (!vlmxIsMatrix(IN(COVARIANCES), dimension, numClusters)) {
vlmxError (vlmxErrInvalidArgument, "COVARIANCES is not a matrix or does not have the right size.") ;
}
if (!vlmxIsVector(IN(PRIORS), numClusters)) {
vlmxError (vlmxErrInvalidArgument, "PRIORS is not a vector or does not have the right size.") ;
}
if (!vlmxIsMatrix(IN(DATA), dimension, numData)) {
vlmxError (vlmxErrInvalidArgument, "DATA is not a matrix or does not have the right size.") ;
}
while ((opt = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) {
switch (opt) {
case opt_verbose : ++ verbosity ; break ;
case opt_normalized: flags |= VL_FISHER_FLAG_NORMALIZED ; break ;
case opt_square_root: flags |= VL_FISHER_FLAG_SQUARE_ROOT ; break ;
case opt_improved: flags |= VL_FISHER_FLAG_IMPROVED ; break ;
case opt_fast: flags |= VL_FISHER_FLAG_FAST ; break ;
default : abort() ;
}
}
/* -----------------------------------------------------------------
* Do the job
* -------------------------------------------------------------- */
data = mxGetPr(IN(DATA)) ;
means = mxGetPr(IN(MEANS)) ;
covariances = mxGetPr(IN(COVARIANCES)) ;
priors = mxGetPr(IN(PRIORS)) ;
if (verbosity) {
mexPrintf("vl_fisher: num data: %d\n", numData) ;
mexPrintf("vl_fisher: num clusters: %d\n", numClusters) ;
mexPrintf("vl_fisher: data dimension: %d\n", dimension) ;
mexPrintf("vl_fisher: code dimension: %d\n", numClusters * dimension) ;
mexPrintf("vl_fisher: normalized: %s\n", VL_YESNO(flags & VL_FISHER_FLAG_NORMALIZED)) ;
mexPrintf("vl_fisher: square root: %s\n", VL_YESNO(flags & VL_FISHER_FLAG_SQUARE_ROOT)) ;
mexPrintf("vl_fisher: normalized: %s\n", VL_YESNO(flags & VL_FISHER_FLAG_NORMALIZED)) ;
mexPrintf("vl_fisher: fast: %s\n", VL_YESNO(flags & VL_FISHER_FLAG_FAST)) ;
}
/* -------------------------------------------------------------- */
/* Encoding */
/* -------------------------------------------------------------- */
OUT(ENC) = mxCreateNumericMatrix (dimension * numClusters * 2, 1, classID, mxREAL) ;
vl_fisher_encode (mxGetData(OUT(ENC)), dataType,
means, dimension, numClusters,
covariances,
priors,
data, numData,
flags) ;
}
void
mexFunction(int nout, mxArray *out[],
int nin, const mxArray *in[])
{
enum {IN_PCX = 0, IN_END} ;
enum {OUT_PARENTS = 0, OUT_COST} ;
enum {INFORMATION, EC} ;
int verbose = 0 ;
int opt ;
int next = IN_END ;
mxArray const *optarg ;
int cluster_null = 0 ;
double *Pcx ;
vl_uint32 nlabels ;
vl_uint32 nvalues ;
mxArray *Pcx_cpy ;
VL_USE_MATLAB_ENV ;
/* -----------------------------------------------------------------
* Check the arguments
* -------------------------------------------------------------- */
if (nin < 1) {
mexErrMsgTxt("One argument required.") ;
} else if (nout > 2) {
mexErrMsgTxt("Too many output arguments.");
}
if (!vlmxIsMatrix(in[IN_PCX], -1, -1)) {
mexErrMsgTxt("PCX must be a real matrix.") ;
}
Pcx_cpy = mxDuplicateArray(in[IN_PCX]);
Pcx = mxGetPr (Pcx_cpy) ;
nlabels = mxGetM (in[IN_PCX]) ;
nvalues = mxGetN (in[IN_PCX]) ;
while ((opt = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) {
switch (opt) {
case opt_verbose :
++ verbose ;
break ;
case opt_cluster_null :
cluster_null = 1 ;
break ;
}
}
if (verbose) {
mexPrintf("aib: cluster null: %d", cluster_null) ;
}
/* -----------------------------------------------------------------
* Do job
* -------------------------------------------------------------- */
{
VlAIB *aib;
double* acost = 0, *cost = 0 ;
vl_uint32 *aparents = 0, *parents = 0 ;
vl_uint32 n ;
out[OUT_PARENTS] = mxCreateNumericMatrix(1, 2*nvalues - 1, mxUINT32_CLASS, mxREAL);
parents = mxGetData(out[OUT_PARENTS]);
if (nout > 1) {
out[OUT_COST] = mxCreateNumericMatrix(1, nvalues, mxDOUBLE_CLASS, mxREAL);
cost = mxGetPr(out[OUT_COST]);
}
aib = vl_aib_new (Pcx, nvalues, nlabels) ;
vl_aib_process (aib);
aparents = vl_aib_get_parents (aib);
acost = vl_aib_get_costs (aib);
memcpy(parents, aparents, sizeof(vl_uint32)*(2*nvalues-1));
if (nout > 1)
memcpy(cost, acost, sizeof(double)*nvalues);
vl_aib_delete(aib);
if (cluster_null) {
cluster_null_nodes (parents, nvalues, (nout == 0) ? 0 : cost) ;
}
/* save back parents */
for (n = 0 ; n < 2 * nvalues - 1 ; ++n) {
if (parents [n] > 2 * nvalues - 1) {
/* map ingored nodes to zero */
parents [n] = 0 ;
} else {
/* MATLAB starts counting from 1 */
++ parents [n] ;
}
}
}
mxDestroyArray(Pcx_cpy);
}
void
mexFunction(int nout, mxArray *out[],
int nin, const mxArray *in[])
{
enum {IN_GRAD=0,IN_FRAMES,IN_END} ;
enum {OUT_DESCRIPTORS} ;
int verbose = 0 ;
int opt ;
int next = IN_END ;
mxArray const *optarg ;
mxArray *grad_array ;
vl_sift_pix *grad ;
int M, N ;
vl_bool floatDescriptors = 0 ;
double magnif = -1 ;
double *ikeys = 0 ;
int nikeys = 0 ;
int i,j ;
VL_USE_MATLAB_ENV ;
/* -----------------------------------------------------------------
* Check the arguments
* -------------------------------------------------------------- */
if (nin < 2) {
mexErrMsgTxt("Two arguments required.") ;
} else if (nout > 1) {
mexErrMsgTxt("Too many output arguments.");
}
if (mxGetNumberOfDimensions (in[IN_GRAD]) != 3 ||
mxGetClassID (in[IN_GRAD]) != mxSINGLE_CLASS ||
mxGetDimensions (in[IN_GRAD])[0] != 2 ) {
mexErrMsgTxt("GRAD must be a 2xMxN matrix of class SINGLE.") ;
}
if (!vlmxIsMatrix(in[IN_FRAMES], 4, -1)) {
mexErrMsgTxt("FRAMES must be a 4xN matrix.") ;
}
nikeys = mxGetN (in[IN_FRAMES]) ;
ikeys = mxGetPr(in[IN_FRAMES]) ;
while ((opt = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) {
switch (opt) {
case opt_verbose :
++ verbose ;
break ;
case opt_magnif :
if (!vlmxIsPlainScalar(optarg) || (magnif = *mxGetPr(optarg)) < 0) {
mexErrMsgTxt("MAGNIF must be a non-negative scalar.") ;
}
break ;
case opt_float_descriptors :
floatDescriptors = 1 ;
break ;
default :
abort() ;
}
}
grad_array = mxDuplicateArray(in[IN_GRAD]) ;
grad = (vl_sift_pix*) mxGetData (grad_array) ;
M = mxGetDimensions(in[IN_GRAD])[1] ;
N = mxGetDimensions(in[IN_GRAD])[2] ;
/* transpose angles */
for (i = 1 ; i < 2*M*N ; i+=2) {
grad [i] = VL_PI/2 - grad [i] ;
}
/* -----------------------------------------------------------------
* Do job
* -------------------------------------------------------------- */
{
VlSiftFilt * filt = 0 ;
void * descr = 0 ;
/* create a filter to process the image */
filt = vl_sift_new (M, N, -1, -1, 0) ;
if (magnif >= 0) vl_sift_set_magnif (filt, magnif) ;
if (verbose) {
mexPrintf("vl_siftdescriptor: filter settings:\n") ;
mexPrintf("vl_siftdescriptor: magnif = %g\n",
vl_sift_get_magnif (filt)) ;
mexPrintf("vl_siftdescriptor: num of frames = %d\n",
nikeys) ;
mexPrintf("vl_siftdescriptor: float descriptor = %d\n",
floatDescriptors) ;
}
{
mwSize dims [2] ;
dims [0] = 128 ;
dims [1] = nikeys ;
out[OUT_DESCRIPTORS]= mxCreateNumericArray
(2, dims,
floatDescriptors ? mxSINGLE_CLASS : mxUINT8_CLASS,
mxREAL) ;
descr = mxGetData(out[OUT_DESCRIPTORS]) ;
}
/* ...............................................................
* Process each octave
* ............................................................ */
for (i = 0 ; i < nikeys ; ++i) {
vl_sift_pix buf [128], rbuf [128] ;
double y = *ikeys++ - 1 ;
double x = *ikeys++ - 1 ;
double s = *ikeys++ ;
double th = VL_PI / 2 - *ikeys++ ;
vl_sift_calc_raw_descriptor (filt,
grad,
buf,
M, N,
x, y, s, th) ;
transpose_descriptor (rbuf, buf) ;
if (! floatDescriptors) {
vl_uint8 * descr_ = descr ;
for (j = 0 ; j < 128 ; ++j) {
float x = 512.0F * rbuf [j] ;
x = (x < 255.0F) ? x : 255.0F ;
*descr_++ = (vl_uint8) (x) ;
}
descr = descr_ ;
} else {
float * descr_ = descr ;
for (j = 0 ; j < 128 ; ++j) {
*descr_++ = 512.0F * rbuf [j] ;
}
descr = descr_ ;
}
}
/* cleanup */
mxDestroyArray (grad_array) ;
vl_sift_delete (filt) ;
} /* job done */
}
void
mexFunction(int nout, mxArray *out[],
int nin, const mxArray *in[])
{
enum {IN_I=0,IN_END} ;
enum {OUT_FRAMES=0, OUT_DESCRIPTORS} ;
int verbose = 0 ;
int opt ;
int next = IN_END ;
mxArray const *optarg ;
vl_sift_pix const *data ;
int M, N ;
int O = - 1 ;
int S = 3 ;
int o_min = 0 ;
double edge_thresh = -1 ;
double peak_thresh = -1 ;
double norm_thresh = -1 ;
double magnif = -1 ;
double window_size = -1 ;
mxArray *ikeys_array = 0 ;
double *ikeys = 0 ;
int nikeys = -1 ;
vl_bool force_orientations = 0 ;
vl_bool floatDescriptors = 0 ;
VL_USE_MATLAB_ENV ;
/* -----------------------------------------------------------------
* Check the arguments
* -------------------------------------------------------------- */
if (nin < 1) {
mexErrMsgTxt("One argument required.") ;
} else if (nout > 2) {
mexErrMsgTxt("Too many output arguments.");
}
if (mxGetNumberOfDimensions (in[IN_I]) != 2 ||
mxGetClassID (in[IN_I]) != mxSINGLE_CLASS ) {
mexErrMsgTxt("I must be a matrix of class SINGLE") ;
}
data = (vl_sift_pix*) mxGetData (in[IN_I]) ;
M = mxGetM (in[IN_I]) ;
N = mxGetN (in[IN_I]) ;
while ((opt = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) {
switch (opt) {
case opt_verbose :
++ verbose ;
break ;
case opt_octaves :
if (!vlmxIsPlainScalar(optarg) || (O = (int) *mxGetPr(optarg)) < 0) {
mexErrMsgTxt("'Octaves' must be a positive integer.") ;
}
break ;
case opt_levels :
if (! vlmxIsPlainScalar(optarg) || (S = (int) *mxGetPr(optarg)) < 1) {
mexErrMsgTxt("'Levels' must be a positive integer.") ;
}
break ;
case opt_first_octave :
if (!vlmxIsPlainScalar(optarg)) {
mexErrMsgTxt("'FirstOctave' must be an integer") ;
}
o_min = (int) *mxGetPr(optarg) ;
break ;
case opt_edge_thresh :
if (!vlmxIsPlainScalar(optarg) || (edge_thresh = *mxGetPr(optarg)) < 1) {
mexErrMsgTxt("'EdgeThresh' must be not smaller than 1.") ;
}
break ;
case opt_peak_thresh :
if (!vlmxIsPlainScalar(optarg) || (peak_thresh = *mxGetPr(optarg)) < 0) {
mexErrMsgTxt("'PeakThresh' must be a non-negative real.") ;
}
break ;
case opt_norm_thresh :
if (!vlmxIsPlainScalar(optarg) || (norm_thresh = *mxGetPr(optarg)) < 0) {
mexErrMsgTxt("'NormThresh' must be a non-negative real.") ;
}
break ;
case opt_magnif :
if (!vlmxIsPlainScalar(optarg) || (magnif = *mxGetPr(optarg)) < 0) {
mexErrMsgTxt("'Magnif' must be a non-negative real.") ;
}
break ;
case opt_window_size :
if (!vlmxIsPlainScalar(optarg) || (window_size = *mxGetPr(optarg)) < 0) {
mexErrMsgTxt("'WindowSize' must be a non-negative real.") ;
}
break ;
case opt_frames :
if (!vlmxIsMatrix(optarg, 4, -1)) {
mexErrMsgTxt("'Frames' must be a 4 x N matrix.x") ;
}
ikeys_array = mxDuplicateArray (optarg) ;
nikeys = mxGetN (optarg) ;
ikeys = mxGetPr (ikeys_array) ;
if (! check_sorted (ikeys, nikeys)) {
qsort (ikeys, nikeys, 4 * sizeof(double), korder) ;
}
break ;
case opt_orientations :
force_orientations = 1 ;
break ;
case opt_float_descriptors :
floatDescriptors = 1 ;
break ;
default :
abort() ;
}
}
/* -----------------------------------------------------------------
* Do job
* -------------------------------------------------------------- */
{
VlSiftFilt *filt ;
vl_bool first ;
double *frames = 0 ;
void *descr = 0 ;
int nframes = 0, reserved = 0, i,j,q ;
/* create a filter to process the image */
filt = vl_sift_new (M, N, O, S, o_min) ;
if (peak_thresh >= 0) vl_sift_set_peak_thresh (filt, peak_thresh) ;
if (edge_thresh >= 0) vl_sift_set_edge_thresh (filt, edge_thresh) ;
if (norm_thresh >= 0) vl_sift_set_norm_thresh (filt, norm_thresh) ;
if (magnif >= 0) vl_sift_set_magnif (filt, magnif) ;
if (window_size >= 0) vl_sift_set_window_size (filt, window_size) ;
if (verbose) {
mexPrintf("vl_sift: filter settings:\n") ;
mexPrintf("vl_sift: octaves (O) = %d\n",
vl_sift_get_noctaves (filt)) ;
mexPrintf("vl_sift: levels (S) = %d\n",
vl_sift_get_nlevels (filt)) ;
mexPrintf("vl_sift: first octave (o_min) = %d\n",
vl_sift_get_octave_first (filt)) ;
mexPrintf("vl_sift: edge thresh = %g\n",
vl_sift_get_edge_thresh (filt)) ;
mexPrintf("vl_sift: peak thresh = %g\n",
vl_sift_get_peak_thresh (filt)) ;
mexPrintf("vl_sift: norm thresh = %g\n",
vl_sift_get_norm_thresh (filt)) ;
mexPrintf("vl_sift: window size = %g\n",
vl_sift_get_window_size (filt)) ;
mexPrintf("vl_sift: float descriptor = %d\n",
floatDescriptors) ;
mexPrintf((nikeys >= 0) ?
"vl_sift: will source frames? yes (%d read)\n" :
"vl_sift: will source frames? no\n", nikeys) ;
mexPrintf("vl_sift: will force orientations? %s\n",
force_orientations ? "yes" : "no") ;
}
/* ...............................................................
* Process each octave
* ............................................................ */
i = 0 ;
first = 1 ;
while (1) {
int err ;
VlSiftKeypoint const* keys = 0 ;
int nkeys = 0 ;
if (verbose) {
mexPrintf ("vl_sift: processing octave %d\n",
vl_sift_get_octave_index (filt)) ;
}
/* Calculate the GSS for the next octave .................... */
if (first) {
err = vl_sift_process_first_octave (filt, data) ;
first = 0 ;
} else {
err = vl_sift_process_next_octave (filt) ;
}
if (err) break ;
if (verbose > 1) {
mexPrintf("vl_sift: GSS octave %d computed\n",
vl_sift_get_octave_index (filt));
}
/* Run detector ............................................. */
if (nikeys < 0) {
vl_sift_detect (filt) ;
keys = vl_sift_get_keypoints (filt) ;
nkeys = vl_sift_get_nkeypoints (filt) ;
i = 0 ;
if (verbose > 1) {
printf ("vl_sift: detected %d (unoriented) keypoints\n", nkeys) ;
}
} else {
nkeys = nikeys ;
}
/* For each keypoint ........................................ */
for (; i < nkeys ; ++i) {
double angles [4] ;
int nangles ;
VlSiftKeypoint ik ;
VlSiftKeypoint const *k ;
/* Obtain keypoint orientations ........................... */
if (nikeys >= 0) {
vl_sift_keypoint_init (filt, &ik,
ikeys [4 * i + 1] - 1,
ikeys [4 * i + 0] - 1,
ikeys [4 * i + 2]) ;
if (ik.o != vl_sift_get_octave_index (filt)) {
break ;
}
k = &ik ;
/* optionally compute orientations too */
if (force_orientations) {
nangles = vl_sift_calc_keypoint_orientations
(filt, angles, k) ;
} else {
angles [0] = VL_PI / 2 - ikeys [4 * i + 3] ;
nangles = 1 ;
}
} else {
k = keys + i ;
nangles = vl_sift_calc_keypoint_orientations
(filt, angles, k) ;
}
/* For each orientation ................................... */
for (q = 0 ; q < nangles ; ++q) {
vl_sift_pix buf [128] ;
vl_sift_pix rbuf [128] ;
/* compute descriptor (if necessary) */
if (nout > 1) {
vl_sift_calc_keypoint_descriptor (filt, buf, k, angles [q]) ;
transpose_descriptor (rbuf, buf) ;
}
/* make enough room for all these keypoints and more */
if (reserved < nframes + 1) {
reserved += 2 * nkeys ;
frames = mxRealloc (frames, 4 * sizeof(double) * reserved) ;
if (nout > 1) {
if (! floatDescriptors) {
descr = mxRealloc (descr, 128 * sizeof(vl_uint8) * reserved) ;
} else {
descr = mxRealloc (descr, 128 * sizeof(float) * reserved) ;
}
}
}
/* Save back with MATLAB conventions. Notice tha the input
* image was the transpose of the actual image. */
frames [4 * nframes + 0] = k -> y + 1 ;
frames [4 * nframes + 1] = k -> x + 1 ;
frames [4 * nframes + 2] = k -> sigma ;
frames [4 * nframes + 3] = VL_PI / 2 - angles [q] ;
if (nout > 1) {
if (! floatDescriptors) {
for (j = 0 ; j < 128 ; ++j) {
float x = 512.0F * rbuf [j] ;
x = (x < 255.0F) ? x : 255.0F ;
((vl_uint8*)descr) [128 * nframes + j] = (vl_uint8) x ;
}
} else {
for (j = 0 ; j < 128 ; ++j) {
float x = 512.0F * rbuf [j] ;
((float*)descr) [128 * nframes + j] = x ;
}
}
}
++ nframes ;
} /* next orientation */
} /* next keypoint */
} /* next octave */
if (verbose) {
mexPrintf ("vl_sift: found %d keypoints\n", nframes) ;
}
/* ...............................................................
* Save back
* ............................................................ */
{
mwSize dims [2] ;
/* create an empty array */
dims [0] = 0 ;
dims [1] = 0 ;
out[OUT_FRAMES] = mxCreateNumericArray
(2, dims, mxDOUBLE_CLASS, mxREAL) ;
/* set array content to be the frames buffer */
dims [0] = 4 ;
dims [1] = nframes ;
mxSetPr (out[OUT_FRAMES], frames) ;
mxSetDimensions (out[OUT_FRAMES], dims, 2) ;
if (nout > 1) {
/* create an empty array */
dims [0] = 0 ;
dims [1] = 0 ;
out[OUT_DESCRIPTORS]= mxCreateNumericArray
(2, dims,
floatDescriptors ? mxSINGLE_CLASS : mxUINT8_CLASS,
mxREAL) ;
/* set array content to be the descriptors buffer */
dims [0] = 128 ;
dims [1] = nframes ;
mxSetData (out[OUT_DESCRIPTORS], descr) ;
mxSetDimensions (out[OUT_DESCRIPTORS], dims, 2) ;
}
}
/* cleanup */
vl_sift_delete (filt) ;
if (ikeys_array)
mxDestroyArray(ikeys_array) ;
} /* end: do job */
}
void
mexFunction(int nout, mxArray *out[],
int nin, const mxArray *in[])
{
enum {IN_DATA, IN_LABELS, IN_LAMBDA, IN_END} ;
enum {OUT_MODEL = 0} ;
int verbose = 0 ;
int opt ;
int next = IN_END ;
mxArray const *optarg ;
double biasMultiplier = 0 ;
double lambda ;
void * data ;
void * preconditioner = NULL ;
vl_size preconditionerDimension = 0 ;
mxClassID dataClass ;
vl_type dataType ;
vl_size numSamples ;
vl_size dimension ;
vl_size numIterations = 0 ;
vl_uindex startingIteration = 1 ;
vl_uint32 * permutation = NULL ;
vl_size permutationSize = 0 ;
VL_USE_MATLAB_ENV ;
/* -----------------------------------------------------------------
* Check the arguments
* -------------------------------------------------------------- */
if (nin < 3) {
vlmxError(vlmxErrInvalidArgument,
"At least three arguments are required.") ;
} else if (nout > 2) {
vlmxError(vlmxErrInvalidArgument,
"Too many output arguments.");
}
dataClass = mxGetClassID(IN(DATA)) ;
if (! vlmxIsMatrix (IN(DATA), -1, -1) ||
! vlmxIsReal (IN(DATA))) {
vlmxError(vlmxErrInvalidArgument,
"DATA must be a real matrix.") ;
}
data = mxGetData (IN(DATA)) ;
dimension = mxGetM(IN(DATA)) ;
numSamples = mxGetN(IN(DATA)) ;
switch (dataClass) {
case mxSINGLE_CLASS : dataType = VL_TYPE_FLOAT ; break ;
case mxDOUBLE_CLASS : dataType = VL_TYPE_DOUBLE ; break ;
default:
vlmxError(vlmxErrInvalidArgument,
"DATA must be either SINGLE or DOUBLE.") ;
}
if (mxGetClassID(IN(LABELS)) != mxINT8_CLASS) {
vlmxError(vlmxErrInvalidArgument, "LABELS must be INT8.") ;
}
if (! vlmxIsVector(IN(LABELS), numSamples)) {
vlmxError(vlmxErrInvalidArgument, "LABELS is not a vector of dimension compatible with DATA.") ;
}
if (! vlmxIsPlainScalar(IN(LAMBDA))) {
vlmxError(vlmxErrInvalidArgument, "LAMBDA is not a plain scalar.") ;
}
lambda = *mxGetPr(IN(LAMBDA)) ;
while ((opt = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) {
switch (opt) {
case opt_bias_multiplier :
if (!vlmxIsPlainScalar(optarg)) {
vlmxError(vlmxErrInvalidArgument, "BIASMULTIPLIER is not a plain scalar.") ;
}
biasMultiplier = *mxGetPr(optarg) ;
break ;
case opt_num_iterations :
if (!vlmxIsPlainScalar(optarg)) {
vlmxError(vlmxErrInvalidArgument, "NUMITERATIONS is not a plain scalar.") ;
}
if (*mxGetPr(optarg) < 0) {
vlmxError(vlmxErrInvalidArgument, "NUMITERATIONS is negative.") ;
}
numIterations = (vl_size) *mxGetPr(optarg) ;
break ;
case opt_starting_iteration :
if (!vlmxIsPlainScalar(optarg)) {
vlmxError(vlmxErrInvalidArgument, "STARTINGITERATION is not a plain scalar.") ;
}
if (*mxGetPr(optarg) < 1) {
vlmxError(vlmxErrInvalidArgument, "STARTINGITERATION is smaller than 1.") ;
}
startingIteration = (vl_size) *mxGetPr(optarg) ;
break ;
case opt_starting_model :
if (!vlmxIsVector(optarg, -1) ||
mxIsComplex(optarg) ||
!mxIsNumeric(optarg)) {
vlmxError(vlmxErrInvalidArgument, "STARTINGMODEL is not a real vector.") ;
}
OUT(MODEL) = mxDuplicateArray(optarg) ;
break ;
case opt_permutation :
if (!vlmxIsVector(optarg, -1) ||
mxIsComplex(optarg) ||
mxGetClassID(optarg) != mxUINT32_CLASS) {
vlmxError(vlmxErrInvalidArgument, "PERMUTATION is not a UINT32 vector.") ;
}
permutationSize = mxGetNumberOfElements(optarg) ;
permutation = mxMalloc(sizeof(vl_uint32) * permutationSize) ;
{
/* adjust indexing */
vl_uint32 const * matlabPermutation = mxGetData(optarg) ;
vl_uindex k ;
for (k = 0 ; k < permutationSize ; ++k) {
permutation[k] = matlabPermutation[k] - 1 ;
if (permutation[k] >= numSamples) {
vlmxError(vlmxErrInconsistentData,
"Permutation indexes out of bounds: PERMUTATION(%d) = %d > %d = number of data samples.",
k + 1, permutation[k] + 1, numSamples) ;
}
}
}
break ;
case opt_preconditioner :
if (!vlmxIsVector(optarg, -1) ||
mxIsComplex(optarg) ||
!mxIsNumeric(optarg)) {
vlmxError(vlmxErrInvalidArgument, "PRECONDITIONER is not a real vector.") ;
}
if (mxGetClassID(optarg) != dataClass) {
vlmxError(vlmxErrInvalidArgument, "PRECODNITIONER storage class does not match the data.") ;
}
preconditioner = mxGetData(optarg) ;
preconditionerDimension = mxGetNumberOfElements(optarg) ;
break ;
case opt_verbose :
++ verbose ;
break ;
}
}
if (preconditioner && preconditionerDimension != (dimension + (biasMultiplier > 0))) {
vlmxError(vlmxErrInvalidArgument, "PRECONDITIONER has incompatible dimension.") ;
}
if (numIterations == 0) {
numIterations = (vl_size) 10 / (lambda + 1) ;
}
if (! OUT(MODEL)) {
OUT(MODEL) = mxCreateNumericMatrix(dimension + (biasMultiplier > 0),
1,
dataClass, mxREAL) ;
} else {
if (mxGetClassID(OUT(MODEL)) != dataClass) {
vlmxError(vlmxErrInvalidArgument, "STARTINGMODEL is not of the same class of DATA.") ;
}
if (mxGetNumberOfElements(OUT(MODEL)) != dimension + (biasMultiplier > 0)) {
vlmxError(vlmxErrInvalidArgument, "STARTINGMODEL has incompatible dimension.") ;
}
}
if (verbose) {
mexPrintf("vl_pegasos: Lambda = %g\n", lambda) ;
mexPrintf("vl_pegasos: BiasMultiplier = %g\n", biasMultiplier) ;
mexPrintf("vl_pegasos: NumIterations = %d\n", numIterations) ;
mexPrintf("vl_pegasos: permutation size = %d\n", permutationSize) ;
mexPrintf("vl_pegasos: using preconditioner = %s\n", VL_YESNO(preconditioner)) ;
}
switch (dataType) {
case VL_TYPE_FLOAT :
vl_pegasos_train_binary_svm_f((float *)mxGetData(OUT(MODEL)),
(float const *)mxGetPr(IN(DATA)), dimension, numSamples,
(vl_int8 const *)mxGetData(IN(LABELS)),
lambda,
biasMultiplier,
startingIteration,
numIterations,
NULL,
permutation,
permutationSize,
(float const*)preconditioner) ;
break ;
case VL_TYPE_DOUBLE:
vl_pegasos_train_binary_svm_d((double *)mxGetData(OUT(MODEL)),
(double const *)mxGetData(IN(DATA)), dimension, numSamples,
(vl_int8 const *)mxGetData(IN(LABELS)),
lambda,
biasMultiplier,
startingIteration,
numIterations,
NULL,
permutation,
permutationSize,
(double const *)preconditioner) ;
break ;
}
if (permutation) vl_free(permutation) ;
}
void
mexFunction(int nout, mxArray *out[],
int nin, const mxArray *in[])
{
enum {IN_DATA = 0, IN_END} ;
enum {OUT_TREE = 0} ;
int verbose = 0 ;
int opt ;
int next = IN_END ;
mxArray const *optarg ;
VlKDForest * forest ;
void * data ;
vl_size numData ;
vl_size dimension ;
mxClassID dataClass ;
vl_type dataType ;
int thresholdingMethod = VL_KDTREE_MEDIAN ;
vl_size numTrees = 1 ;
VL_USE_MATLAB_ENV ;
/* -----------------------------------------------------------------
* Check the arguments
* -------------------------------------------------------------- */
if (nin < 1) {
vlmxError(vlmxErrInvalidArgument,
"At least one argument required") ;
} else if (nout > 2) {
vlmxError(vlmxErrInvalidArgument,
"Too many output arguments");
}
dataClass = mxGetClassID(IN(DATA)) ;
if (! vlmxIsMatrix (IN(DATA), -1, -1) ||
! vlmxIsReal (IN(DATA))) {
vlmxError(vlmxErrInvalidArgument,
"DATA must be a real matrix ") ;
}
switch (dataClass) {
case mxSINGLE_CLASS : dataType = VL_TYPE_FLOAT ; break ;
case mxDOUBLE_CLASS : dataType = VL_TYPE_DOUBLE ; break ;
default:
vlmxError(vlmxErrInvalidArgument,
"DATA must be either SINGLE or DOUBLE") ;
}
while ((opt = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) {
char buffer [1024] ;
switch (opt) {
case opt_threshold_method :
mxGetString (optarg, buffer, sizeof(buffer)/sizeof(buffer[0])) ;
if (! vlmxIsString(optarg, -1)) {
vlmxError(vlmxErrInvalidOption,
"THRESHOLDMETHOD must be a string") ;
}
if (vl_string_casei_cmp(buffer, "median") == 0) {
thresholdingMethod = VL_KDTREE_MEDIAN ;
} else if (vl_string_casei_cmp(buffer, "mean") == 0) {
thresholdingMethod = VL_KDTREE_MEAN ;
} else {
vlmxError(vlmxErrInvalidOption,
"Unknown thresholding method %s", buffer) ;
}
break ;
case opt_num_trees :
if (! vlmxIsScalar(optarg) ||
(numTrees = mxGetScalar(optarg)) < 1) {
vlmxError(vlmxErrInvalidOption,
"NUMTREES must be not smaller than one") ;
}
break ;
case opt_verbose :
++ verbose ;
break ;
}
}
data = mxGetData (IN(DATA)) ;
numData = mxGetN (IN(DATA)) ;
dimension = mxGetM (IN(DATA)) ;
forest = vl_kdforest_new (dataType, dimension, numTrees) ;
vl_kdforest_set_thresholding_method (forest, thresholdingMethod) ;
if (verbose) {
char const * str = 0 ;
mexPrintf("vl_kdforestbuild: data %s [%d x %d]\n",
vl_get_type_name (dataType), dimension, numData) ;
switch (vl_kdforest_get_thresholding_method(forest)) {
case VL_KDTREE_MEAN : str = "mean" ; break ;
case VL_KDTREE_MEDIAN : str = "median" ; break ;
default: abort() ;
}
mexPrintf("vl_kdforestbuild: threshold selection method: %s\n", str) ;
mexPrintf("vl_kdforestbuild: number of trees: %d\n",
vl_kdforest_get_num_trees(forest)) ;
}
/* -----------------------------------------------------------------
* Do job
* -------------------------------------------------------------- */
vl_kdforest_build (forest, numData, data) ;
if (verbose) {
vl_uindex ti ;
for (ti = 0 ; ti < vl_kdforest_get_num_trees(forest) ; ++ ti) {
mexPrintf("vl_kdforestbuild: tree %d: depth %d, num nodes %d\n",
ti,
vl_kdforest_get_depth_of_tree(forest, ti),
vl_kdforest_get_num_nodes_of_tree(forest, ti)) ;
}
}
out[OUT_TREE] = new_array_from_kdforest (forest) ;
vl_kdforest_delete (forest) ;
}
void
mexFunction(int nout, mxArray *out[],
int nin, const mxArray *in[])
{
enum {IN_DATA, IN_LABELS, IN_LAMBDA, IN_END} ;
enum {OUT_MODEL = 0, OUT_BIAS, OUT_INFO} ;
int verbose = 0 ;
int opt ;
int next = IN_END ;
mxArray const *optarg ;
mxArray *inputModel = NULL;
VlSvmPegasos* svm = NULL ;
vl_bool freeModel = VL_TRUE ;
vl_size dataDimension ;
vl_uint32* matlabPermutation ;
void * data ;
mxClassID dataClass ;
vl_type dataType ;
vl_size numSamples ;
vl_uint32 * permutation = NULL ;
vl_size permutationSize = 0 ;
DiagnosticsDispatcher* disp ;
VlSvmDatasetInnerProduct innerProduct = NULL ;
VlSvmDatasetAccumulator accumulator = NULL ;
/* maps */
VlSvmDatasetFeatureMap mapFunc = NULL ;
/* Homkermap */
VlHomogeneousKernelType kernelType = VlHomogeneousKernelChi2 ;
VlHomogeneousKernelMapWindowType windowType = VlHomogeneousKernelMapWindowRectangular ;
double gamma = 1.0 ;
int n = 0 ;
double period = -1 ;
VlSvmDataset* dataset ;
vl_bool homkermap = VL_FALSE ;
void * map = NULL ;
VL_USE_MATLAB_ENV ;
disp = (DiagnosticsDispatcher*) vl_malloc(sizeof(DiagnosticsDispatcher)) ;
disp->diagnosticsHandle = NULL ;
disp->callerRef = NULL ;
disp->verbose = 0 ;
/* -----------------------------------------------------------------
* Check the arguments
* -------------------------------------------------------------- */
if (nin < 3) {
vlmxError(vlmxErrInvalidArgument,
"At least three arguments are required.") ;
} else if (nout > 3) {
vlmxError(vlmxErrInvalidArgument,
"Too many output arguments.");
}
dataClass = mxGetClassID(IN(DATA)) ;
if (! vlmxIsMatrix (IN(DATA), -1, -1) ||
! vlmxIsReal (IN(DATA))) {
vlmxError(vlmxErrInvalidArgument,
"DATA must be a real matrix.") ;
}
data = mxGetData (IN(DATA)) ;
dataDimension = mxGetM(IN(DATA)) ;
numSamples = mxGetN(IN(DATA)) ;
/* Get order of the HOMKERMAP, if used. */
while ((opt = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) {
if (opt == opt_homkermap) {
homkermap = VL_TRUE ;
if (! vlmxIsPlainScalar(optarg)) {
vlmxError(vlmxErrInvalidArgument, "N is not a scalar.") ;
}
n = *mxGetPr(optarg) ;
if (n < 0) {
vlmxError(vlmxErrInvalidArgument, "N is negative.") ;
}
}
}
next = IN_END ;
if (! vlmxIsVector(IN(LABELS), numSamples)) {
vlmxError(vlmxErrInvalidArgument, "LABELS is not a vector of dimension compatible with DATA.") ;
}
switch (dataClass) {
case mxSINGLE_CLASS : dataType = VL_TYPE_FLOAT ; break ;
case mxDOUBLE_CLASS : dataType = VL_TYPE_DOUBLE ; break ;
default:
vlmxError(vlmxErrInvalidArgument,
"DATA must be either SINGLE or DOUBLE.") ;
}
if (mxGetClassID(IN(LABELS)) != mxINT8_CLASS) {
vlmxError(vlmxErrInvalidArgument, "LABELS must be INT8.") ;
}
if (! vlmxIsPlainScalar(IN(LAMBDA))) {
vlmxError(vlmxErrInvalidArgument, "LAMBDA is not a plain scalar.") ;
}
svm = vl_svmpegasos_new ((2*n + 1)*dataDimension,*mxGetPr(IN(LAMBDA))) ;
while ((opt = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) {
switch (opt) {
case opt_bias_multiplier :
if (!vlmxIsPlainScalar(optarg)) {
vlmxError(vlmxErrInvalidArgument, "BIASMULTIPLIER is not a plain scalar.") ;
}
vl_svmpegasos_set_bias_multiplier(svm, *mxGetPr(optarg)) ;
break ;
case opt_max_iterations :
if (!vlmxIsPlainScalar(optarg)) {
vlmxError(vlmxErrInvalidArgument, "MAXITERATIONS is not a plain scalar.") ;
}
if (*mxGetPr(optarg) < 0) {
vlmxError(vlmxErrInvalidArgument, "MAXITERATIONS is negative.") ;
}
vl_svmpegasos_set_maxiterations(svm, (vl_size) *mxGetPr(optarg)) ;
break ;
case opt_epsilon :
if (!vlmxIsPlainScalar(optarg)) {
vlmxError(vlmxErrInvalidArgument, "EPSILON is not a plain scalar.") ;
}
if (*mxGetPr(optarg) < 0) {
vlmxError(vlmxErrInvalidArgument, "EPSILON is negative.") ;
}
vl_svmpegasos_set_epsilon(svm, (double) *mxGetPr(optarg)) ;
break ;
case opt_starting_iteration :
if (!vlmxIsPlainScalar(optarg)) {
vlmxError(vlmxErrInvalidArgument, "STARTINGITERATION is not a plain scalar.") ;
}
if (*mxGetPr(optarg) < 1) {
vlmxError(vlmxErrInvalidArgument, "STARTINGITERATION is smaller than 1.") ;
}
vl_svmpegasos_set_iterations(svm, (vl_size) *mxGetPr(optarg) - 1) ;
break ;
case opt_starting_model :
if (!vlmxIsVector(optarg, -1) ||
mxIsComplex(optarg) ||
mxGetClassID(optarg) != mxDOUBLE_CLASS) {
vlmxError(vlmxErrInvalidArgument, "STARTINGMODEL is not a real vector.") ;
}
inputModel = mxDuplicateArray(optarg) ;
vl_svmpegasos_set_model(svm,(double*) mxGetData(inputModel)) ;
freeModel = VL_FALSE ;
break ;
case opt_starting_bias :
if (!vlmxIsPlainScalar(optarg)) {
vlmxError(vlmxErrInvalidArgument, "STARTINGBIAS is not a plain scalar.") ;
}
vl_svmpegasos_set_bias(svm, (double) *mxGetPr(optarg)) ;
break ;
case opt_permutation :
if (!vlmxIsVector(optarg, -1) ||
mxIsComplex(optarg) ||
mxGetClassID(optarg) != mxUINT32_CLASS) {
vlmxError(vlmxErrInvalidArgument, "PERMUTATION is not a UINT32 vector.") ;
}
permutationSize = mxGetNumberOfElements(optarg) ;
permutation = mxMalloc(sizeof(vl_uint32) * permutationSize) ;
matlabPermutation = mxGetData(optarg) ;
{
/* adjust indexing */
vl_uindex k ;
for (k = 0 ; k < permutationSize ; ++k) {
permutation[k] = matlabPermutation[k] - 1 ;
if (permutation[k] >= numSamples) {
vlmxError(vlmxErrInconsistentData,
"Permutation indexes out of bounds: PERMUTATION(%d) = %d > %d = number of data samples.",
k + 1, permutation[k] + 1, numSamples) ;
}
}
}
vl_svmpegasos_set_permutation(svm,permutation,permutationSize) ;
break ;
case opt_bias_learningrate :
if (!vlmxIsPlainScalar(optarg)) {
vlmxError(vlmxErrInvalidArgument, "BIASLEARNINGRATE is not a plain scalar.") ;
}
if (mxGetClassID(optarg) != mxDOUBLE_CLASS) {
vlmxError(vlmxErrInvalidArgument, "BIASLEARNINGRATE must be double.") ;
}
vl_svmpegasos_set_bias_learningrate(svm, (double)*mxGetPr(optarg)) ;
break ;
case opt_diagnostic :
if( !mxIsClass( optarg , "function_handle")) {
mexErrMsgTxt("DIAGNOSTICSFUNCTION must be a function handle.");
}
disp->diagnosticsHandle = (mxArray*)(optarg) ;
break ;
case opt_diagnostic_caller_ref :
disp->callerRef = (mxArray*)(optarg) ;
break ;
case opt_energy_freq :
if (!vlmxIsPlainScalar(optarg)) {
vlmxError(vlmxErrInvalidArgument, "ENERGYFREQ is not a plain scalar.") ;
}
vl_svmpegasos_set_energy_frequency (svm, (vl_size)*mxGetPr(optarg)) ;
break ;
case opt_verbose :
++ verbose ;
disp->verbose = 1 ;
break ;
case opt_KINTERS:
case opt_KL1:
kernelType = VlHomogeneousKernelIntersection ;
break ;
case opt_KCHI2:
kernelType = VlHomogeneousKernelChi2 ;
break ;
case opt_KJS:
kernelType = VlHomogeneousKernelJS ;
break ;
case opt_period:
if (! vlmxIsPlainScalar(optarg)){
vlmxError(vlmxErrInvalidArgument, "PERIOD is not a scalar.") ;
}
period = *mxGetPr(optarg) ;
if (period <= 0) {
vlmxError(vlmxErrInvalidArgument, "PERIOD is not positive.") ;
}
break ;
case opt_gamma:
if (! vlmxIsPlainScalar(optarg)){
vlmxError(vlmxErrInvalidArgument, "GAMMA is not a scalar.") ;
}
gamma = *mxGetPr(optarg) ;
if (gamma <= 0) {
vlmxError(vlmxErrInvalidArgument, "GAMMA is not positive.") ;
}
break ;
case opt_window:
if (! vlmxIsString(optarg,-1)){
vlmxError(vlmxErrInvalidArgument, "WINDOW is not a string.") ;
} else {
char buffer [1024] ;
mxGetString(optarg, buffer, sizeof(buffer) / sizeof(char)) ;
if (vl_string_casei_cmp("uniform", buffer) == 0) {
windowType = VlHomogeneousKernelMapWindowUniform ;
} else if (vl_string_casei_cmp("rectangular", buffer) == 0) {
windowType = VlHomogeneousKernelMapWindowRectangular ;
} else {
vlmxError(vlmxErrInvalidArgument, "WINDOW=%s is not recognized.", buffer) ;
}
}
break ;
}
}
if (verbose) {
mexPrintf("vl_pegasos: Lambda = %g\n", svm->lambda) ;
mexPrintf("vl_pegasos: BiasMultiplier = %g\n", svm->biasMultiplier) ;
mexPrintf("vl_pegasos: MaxIterations = %d\n", svm->maxIterations) ;
mexPrintf("vl_pegasos: permutation size = %d\n", permutationSize) ;
}
switch (dataType) {
case VL_TYPE_FLOAT :
innerProduct = (VlSvmDatasetInnerProduct)&vl_svmdataset_innerproduct_f ;
accumulator = (VlSvmDatasetAccumulator)&vl_svmdataset_accumulator_f ;
break ;
case VL_TYPE_DOUBLE:
innerProduct = (VlSvmDatasetInnerProduct)&vl_svmdataset_innerproduct_d ;
accumulator = (VlSvmDatasetAccumulator)&vl_svmdataset_accumulator_d ;
break ;
}
dataset = vl_svmdataset_new(data,dataDimension) ;
if (homkermap) {
map = vl_homogeneouskernelmap_new (kernelType, gamma, n, period, windowType) ;
mapFunc = (VlSvmDatasetFeatureMap)&vl_homogeneouskernelmap_evaluate_d ;
vl_svmdataset_set_map(dataset,map,mapFunc,2*n + 1) ;
}
/* -----------------------------------------------------------------
* Do job
* -------------------------------------------------------------- */
if (disp->diagnosticsHandle) {
vl_svmpegasos_set_diagnostic (svm, (VlSvmDiagnostics)&diagnosticDispatcher, disp) ;
}
vl_svmpegasos_train (svm,dataset, numSamples,innerProduct, accumulator,
(vl_int8 const *)mxGetData(IN(LABELS))) ;
/* -----------------------------------------------------------------
* Output
* -------------------------------------------------------------- */
if (nout >= 1) {
double * tempBuffer ;
mwSize dims[2] ;
dims[0] = svm->dimension ;
dims[1] = 1 ;
out[OUT_MODEL] = mxCreateNumericArray(2, dims,
mxDOUBLE_CLASS, mxREAL) ;
tempBuffer = (double*) mxGetData(out[OUT_MODEL]) ;
memcpy(tempBuffer,svm->model,svm->dimension * sizeof(double)) ;
}
if (nout >= 2) {
double * tempBuffer ;
mwSize dims[2] ;
dims[0] = 1 ;
dims[1] = 1 ;
out[OUT_BIAS] = mxCreateNumericArray(2, dims,
mxDOUBLE_CLASS, mxREAL) ;
tempBuffer = (double*) mxGetData(out[OUT_BIAS]) ;
*tempBuffer = svm->bias ;
}
if (nout == 3) {
out[OUT_INFO] = createInfoStruct(svm) ;
}
if (homkermap) {
vl_homogeneouskernelmap_delete(map);
}
vl_svmdataset_delete(dataset) ;
vl_svmpegasos_delete(svm,freeModel) ;
vl_free(disp) ;
}
void
mexFunction(int nout, mxArray *out[],
int nin, const mxArray *in[])
{
vl_size M, N ;
enum {IN_I = 0, IN_PARAM, IN_END} ;
enum {OUT_DT = 0, OUT_INDEXES} ;
vl_uindex * indexes = NULL ;
mxClassID classId ;
double const defaultParam [] = {1.0, 0.0, 1.0, 0.0} ;
double const * param = defaultParam ;
/* -----------------------------------------------------------------
* Check the arguments
* -------------------------------------------------------------- */
if (nin < 1) {
vlmxError(vlmxErrNotEnoughInputArguments, NULL) ;
}
if (nin > 2) {
vlmxError(vlmxErrTooManyInputArguments, NULL) ;
}
if (nout > 2) {
vlmxError(vlmxErrTooManyOutputArguments, NULL) ;
}
classId = mxGetClassID(IN(I)) ;
if (! vlmxIsMatrix(IN(I), -1, -1) ||
(classId != mxSINGLE_CLASS && classId != mxDOUBLE_CLASS)) {
vlmxError(vlmxErrInvalidArgument,
"I is not a SINGLE or DOUBLE matrix.") ;
}
if (nin == 2) {
if (! vlmxIsPlainVector(IN(PARAM), 4)) {
vlmxError(vlmxErrInvalidArgument,
"PARAM is not a 4-dimensional vector.") ;
}
param = mxGetPr (IN(PARAM)) ;
if (param[0] < 0.0 ||
param[2] < 0.0) {
vlmxError(vlmxErrInvalidArgument,
"Either PARAM[0] or PARAM[2] is negative.") ;
}
}
M = mxGetM (IN(I)) ;
N = mxGetN (IN(I)) ;
OUT(DT) = mxCreateNumericMatrix (M, N, classId, mxREAL) ;
if (nout > 1) {
vl_uindex i ;
OUT(INDEXES) = mxCreateDoubleMatrix (M, N, mxREAL) ;
indexes = mxMalloc(sizeof(vl_uindex) * M * N) ;
for (i = 0 ; i < M * N ; ++i) indexes[i] = i + 1 ;
}
/* -----------------------------------------------------------------
* Do the job
* -------------------------------------------------------------- */
switch (classId) {
case mxSINGLE_CLASS:
vl_image_distance_transform_f((float const*)mxGetData(IN(I)),
M, N,
1, M,
(float*)mxGetPr(OUT(DT)),
indexes,
param[2],
param[3]) ;
vl_image_distance_transform_f((float*)mxGetPr(OUT(DT)),
N, M,
M, 1,
(float*)mxGetPr(OUT(DT)),
indexes,
param[0],
param[1]) ;
break ;
case mxDOUBLE_CLASS:
vl_image_distance_transform_d((double const*)mxGetData(IN(I)),
M, N,
1, M,
(double*)mxGetPr(OUT(DT)),
indexes,
param[2],
param[3]) ;
vl_image_distance_transform_d((double*)mxGetPr(OUT(DT)),
N, M,
M, 1,
(double*)mxGetPr(OUT(DT)),
indexes,
param[0],
param[1]) ;
break;
default:
abort() ;
}
if (indexes) {
vl_uindex i ;
double * pt = mxGetPr(OUT(INDEXES)) ;
for (i = 0 ; i < M * N ; ++i) pt[i] = indexes[i] ;
mxFree(indexes) ;
}
}
/* driver */
void
mexFunction(int nout, mxArray *out[],
int nin, const mxArray *in[])
{
typedef int unsigned data_t ;
vl_bool autoComparison = VL_TRUE ;
VlVectorComparisonType comparisonType = VlDistanceL2 ;
enum {IN_X = 0, IN_Y} ;
enum {OUT_D = 0} ;
mwSize numDataX = 0 ;
mwSize numDataY = 0 ;
mwSize dimension ;
mxClassID classId ;
/* for option parsing */
int opt ;
int next ;
mxArray const *optarg ;
VL_USE_MATLAB_ENV ;
if (nout > 1) {
vlmxError(vlmxErrTooManyOutputArguments, NULL) ;
}
if (nin < 1) {
vlmxError(vlmxErrNotEnoughInputArguments, NULL) ;
}
if (! (vlmxIsMatrix (in[IN_X],-1,-1) && vlmxIsReal(in[IN_X]))) {
vlmxError(vlmxErrInvalidArgument, "X must be a real matrix.") ;
}
next = 1 ;
classId = mxGetClassID(in[IN_X]) ;
dimension = mxGetM(in[IN_X]) ;
numDataX = mxGetN(in[IN_X]) ;
if (nin > 1 && vlmxIsMatrix (in[IN_Y],-1,-1) && vlmxIsReal(in[IN_Y])) {
next = 2 ;
autoComparison = VL_FALSE ;
numDataY = mxGetN(in[IN_Y]) ;
if (mxGetClassID(in[IN_Y]) != classId) {
vlmxError(vlmxErrInvalidArgument, "X and Y must have the same class.") ;
}
if (dimension != mxGetM(in[IN_Y])) {
vlmxError(vlmxErrInvalidArgument, "X and Y must have the same number of rows.") ;
}
}
if (classId != mxSINGLE_CLASS && classId != mxDOUBLE_CLASS) {
vlmxError(vlmxErrInvalidArgument,
"X must be either of class SINGLE or DOUBLE.");
}
while ((opt = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) {
switch (opt) {
case opt_L2 : comparisonType = VlDistanceL2 ; break ;
case opt_L1 : comparisonType = VlDistanceL1 ; break ;
case opt_CHI2 : comparisonType = VlDistanceChi2 ; break ;
case opt_HELL : comparisonType = VlDistanceHellinger ; break ;
case opt_JS : comparisonType = VlDistanceJS ; break ;
case opt_KL2 : comparisonType = VlKernelL2 ; break ;
case opt_KL1 : comparisonType = VlKernelL1 ; break ;
case opt_KCHI2 : comparisonType = VlKernelChi2 ; break ;
case opt_KHELL : comparisonType = VlKernelHellinger ; break ;
case opt_KJS : comparisonType = VlKernelJS ; break ;
default:
abort() ;
}
}
/* allocate output */
{
mwSize dims [2] ;
dims[0] = numDataX ;
dims[1] = autoComparison ? numDataX : numDataY ;
out[OUT_D] = mxCreateNumericArray (2, dims, classId, mxREAL) ;
}
/* If either numDataX or numDataY are null, their data pointers are
null as well. This may confuse
vl_eval_vector_comparison_on_all_pairs_*, so we intercept this as
a special case. The same is true if dimension is null.
*/
if (numDataX == 0 || (! autoComparison && numDataY == 0)) {
return ;
}
if (dimension == 0) {
return ;
}
/* make calculation */
switch (classId) {
case mxSINGLE_CLASS:
{
VlFloatVectorComparisonFunction f = vl_get_vector_comparison_function_f (comparisonType) ;
if (autoComparison) {
vl_eval_vector_comparison_on_all_pairs_f ((float*)mxGetData(out[OUT_D]),
dimension,
(float*)mxGetData(in[IN_X]), numDataX,
0, 0,
f) ;
} else {
vl_eval_vector_comparison_on_all_pairs_f ((float*)mxGetData(out[OUT_D]),
dimension,
(float*)mxGetData(in[IN_X]), numDataX,
(float*)mxGetData(in[IN_Y]), numDataY,
f) ;
}
}
break ;
case mxDOUBLE_CLASS:
{
VlDoubleVectorComparisonFunction f = vl_get_vector_comparison_function_d (comparisonType) ;
if (autoComparison) {
vl_eval_vector_comparison_on_all_pairs_d ((double*)mxGetData(out[OUT_D]),
dimension,
(double*)mxGetData(in[IN_X]), numDataX,
0, 0,
f) ;
} else {
vl_eval_vector_comparison_on_all_pairs_d ((double*)mxGetData(out[OUT_D]),
dimension,
(double*)mxGetData(in[IN_X]), numDataX,
(double*)mxGetData(in[IN_Y]), numDataY,
f) ;
}
}
break ;
default:
abort() ;
}
}
void
mexFunction(int nout, mxArray *out[],
int nin, const mxArray *in[])
{
float * image ;
vl_size width, height ;
vl_size cellSize = 16 ;
enum {IN_I = 0, IN_CELLSIZE} ;
enum {OUT_FEATURES = 0} ;
/* -----------------------------------------------------------------
* Check the arguments
* -------------------------------------------------------------- */
if (nin > 2) {
vlmxError(vlmxErrTooManyInputArguments, NULL) ;
}
if (nin < 2) {
vlmxError(vlmxErrNotEnoughInputArguments, NULL) ;
}
if (nout > 1) {
vlmxError(vlmxErrTooManyOutputArguments, NULL) ;
}
if (! mxIsNumeric(IN(I)) ||
! vlmxIsReal(IN(I)) ||
! vlmxIsMatrix(IN(I), -1, -1)) {
vlmxError(vlmxErrInvalidArgument,
"I is not a numeric matrix.") ;
}
if (mxGetClassID(IN(I)) != mxSINGLE_CLASS) {
vlmxError(vlmxErrInvalidArgument,
"I is not of class SINGLE.") ;
}
if (! vlmxIsPlainScalar(IN(CELLSIZE))) {
vlmxError(vlmxErrInvalidArgument,
"CELLSIZE is not a plain scalar.") ;
}
if (mxGetScalar(IN(CELLSIZE)) < 1.0) {
vlmxError(vlmxErrInvalidArgument,
"CELLSIZE is less than 1.") ;
}
cellSize = (vl_size) mxGetScalar(IN(CELLSIZE)) ;
image = mxGetData(IN(I)) ;
width = mxGetN(IN(I)) ;
height = mxGetM(IN(I)) ;
/* do job */
{
/* recall that MATLAB images are transposed */
mwSize dimensions [3] ;
/* get LBP object */
VlLbp * lbp = vl_lbp_new (VlLbpUniform, VL_TRUE) ;
if (lbp == NULL) {
vlmxError(vlmxErrAlloc, NULL) ;
}
/* get output buffer */
dimensions[0] = height / cellSize ;
dimensions[1] = width / cellSize ;
dimensions[2] = vl_lbp_get_dimension(lbp) ;
OUT(FEATURES) = mxCreateNumericArray(3, dimensions, mxSINGLE_CLASS, mxREAL) ;
vl_lbp_process(lbp, mxGetData(OUT(FEATURES)), image, height, width, cellSize) ;
vl_lbp_delete(lbp) ;
}
}
void
mexFunction(int nout, mxArray *out[],
int nin, const mxArray *in[])
{
enum {IN_I=0,IN_END} ;
enum {OUT_FRAMES=0, OUT_DESCRIPTORS} ;
int verbose = 0 ;
int opt ;
int next = IN_END ;
mxArray const *optarg ;
vl_sift_pix const *data ;
int M, N ;
int O = - 1 ;
int S = 3 ;
int o_min = 0 ;
double edge_thresh = -1 ;
double peak_thresh = -1 ;
double norm_thresh = -1 ;
double magnif = -1 ;
double window_size = -1 ;
mxArray *ikeys_array = 0 ;
double *ikeys = 0 ;
int nikeys = -1 ;
vl_bool force_orientations = 0 ;
vl_bool floatDescriptors = 0 ;
VL_USE_MATLAB_ENV ;
/* -----------------------------------------------------------------
* Check the arguments
* -------------------------------------------------------------- */
if (nin < 1) {
mexErrMsgTxt("One argument required.") ;
} else if (nout > 2) {
mexErrMsgTxt("Too many output arguments.");
}
if (mxGetNumberOfDimensions (in[IN_I]) != 2 ||
mxGetClassID (in[IN_I]) != mxSINGLE_CLASS ) {
mexErrMsgTxt("I must be a matrix of class SINGLE") ;
}
data = (vl_sift_pix*) mxGetData (in[IN_I]) ;
M = mxGetM (in[IN_I]) ;
N = mxGetN (in[IN_I]) ;
while ((opt = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) {
switch (opt) {
case opt_verbose :
++ verbose ;
break ;
case opt_frames :
if (!vlmxIsMatrix(optarg, 4, -1)) {
mexErrMsgTxt("'Frames' must be a 4 x N matrix.") ;
}
ikeys_array = mxDuplicateArray (optarg) ;
nikeys = mxGetN (optarg) ;
ikeys = mxGetPr (ikeys_array) ;
if (! check_sorted (ikeys, nikeys)) {
qsort (ikeys, nikeys, 4 * sizeof(double), korder) ;
}
break ;
default :
mexPrintf("F**k you!");
abort() ;
}
}
/* -----------------------------------------------------------------
* Do job
* -------------------------------------------------------------- */
{
VlSiftFilt *filt ;
vl_bool first ;
double *frames = 0 ;
void *descr = 0 ;
int nframes = 0, reserved = 0, i,j,q ;
/* create a filter to process the image */
filt = vl_sift_new (M, N, O, S, o_min) ;
//mexPrintf("%f %f %f \n%f %f %f %f %f\n",(float)O,(float)S,(float)o_min,(float)peak_thresh
// ,(float)edge_thresh,(float)norm_thresh,(float)magnif,(float)window_size);
/* ...............................................................
* Process each octave
* ............................................................ */
i = 0 ;
first = 1 ;
while (first == 1) {
int err ;
VlSiftKeypoint const *keys = 0 ;
int nkeys = 0 ;
err = vl_sift_process_first_octave (filt, data) ;
first = 0 ;
if (err) break ;
/* Run detector ............................................. */
nkeys = nikeys ;
//mexPrintf("Zhu: entering sweeping nkeys, nkeys = %d, i = %d \n", nkeys, i);
/* For each keypoint ........................................ */
for (; i < nkeys ; ++i) {
int h;
vl_sift_pix buf[128];
vl_sift_pix rbuf[128];
double angle;
VlSiftKeypoint ik ;
VlSiftKeypoint const *k ;
/* Obtain keypoint orientations ........................... */
vl_sift_keypoint_init (filt, &ik,
ikeys [4 * i + 1] - 1,
ikeys [4 * i + 0] - 1,
ikeys [4 * i + 2]) ;
//mexPrintf("ikeys: [%f, %f, %f]\n", (float)(ikeys [4 * i + 1] - 1), (float)(ikeys [4 * i + 0] - 1), (float)(ikeys [4 * i + 2]) );
k = &ik ;
/* optionally compute orientations too */
angle = VL_PI / 2 - ikeys [4 * i + 3] ;
q = 0;
/* compute descriptor (if necessary) */
//int h;
//mexPrintf("M = %d, N = %d.\n",M,N);
//for (h = 0; h < 300; h++)
//{
// mexPrintf("%f ",data[h]);
// if (h % 8 == 7) mexPrintf("\n");
//}
if (nout > 1) {
//mexPrintf("angles = %f, x = %f(%d), y = %f(%d), s = %f(%d), o = %d, sigma = %f.\n buf = [",
//angle,k->x,k->ix,k->y,k->iy,k->s,k->is,k->o,k->sigma);
vl_sift_calc_keypoint_descriptor (filt, buf, k, angle) ;
//for (h = 0; h < 128; h++)
//{
// mexPrintf("%f ",(float)buf[h]);
// if (h % 8 == 7) mexPrintf("\n");
//}
//mexPrintf("...].\nrbuf = [");
transpose_descriptor (rbuf, buf) ;
//for (h = 0; h < 128; h++)
//{
// mexPrintf("%f ",(float)rbuf[h]);
// if (h % 8 == 7) mexPrintf("\n");
//}
//mexPrintf("...].\n");
}
/* make enough room for all these keypoints and more */
if (reserved < nframes + 1) {
reserved += 2 * nkeys ;
frames = mxRealloc (frames, 4 * sizeof(double) * reserved) ;
if (nout > 1) {
if (! floatDescriptors) {
descr = mxRealloc (descr, 128 * sizeof(vl_uint8) * reserved) ;
} else {
descr = mxRealloc (descr, 128 * sizeof(float) * reserved) ;
}
}
}
/* Save back with MATLAB conventions. Notice tha the input
* image was the transpose of the actual image. */
frames [4 * nframes + 0] = k -> y + 1 ;
frames [4 * nframes + 1] = k -> x + 1 ;
frames [4 * nframes + 2] = k -> sigma ;
frames [4 * nframes + 3] = VL_PI / 2 - angle;
//mexPrintf("Zhu: %d\n", nframes);
if (nout > 1) {
if (! floatDescriptors) {
for (j = 0 ; j < 128 ; ++j) {
float x = 512.0F * rbuf [j] ;
x = (x < 255.0F) ? x : 255.0F ;
((vl_uint8*)descr) [128 * nframes + j] = (vl_uint8) x ;
}
} else {
for (j = 0 ; j < 128 ; ++j) {
float x = 512.0F * rbuf [j] ;
((float*)descr) [128 * nframes + j] = x ;
}
}
}
++ nframes ;
/* next orientation */
} /* next keypoint */
//break;
//mexPrintf("Zhu: skip subsequent octave\n");
} /* next octave */
//mexPrintf("nframes_tot = %d\n",nframes);
/* ...............................................................
* Save back
* ............................................................ */
{
mwSize dims [2] ;
/* create an empty array */
dims [0] = 0 ;
dims [1] = 0 ;
out[OUT_FRAMES] = mxCreateNumericArray
(2, dims, mxDOUBLE_CLASS, mxREAL) ;
/* set array content to be the frames buffer */
dims [0] = 4 ;
dims [1] = nframes ;
mxSetPr (out[OUT_FRAMES], frames) ;
mxSetDimensions (out[OUT_FRAMES], dims, 2) ;
if (nout > 1) {
/* create an empty array */
dims [0] = 0 ;
dims [1] = 0 ;
out[OUT_DESCRIPTORS]= mxCreateNumericArray
(2, dims,
floatDescriptors ? mxSINGLE_CLASS : mxUINT8_CLASS,
mxREAL) ;
/* set array content to be the descriptors buffer */
dims [0] = 128 ;
dims [1] = nframes ;
mxSetData (out[OUT_DESCRIPTORS], descr) ;
mxSetDimensions (out[OUT_DESCRIPTORS], dims, 2) ;
}
}
/* cleanup */
vl_sift_delete (filt) ;
if (ikeys_array)
mxDestroyArray(ikeys_array) ;
} /* end: do job */
}
void
mexFunction(int nout, mxArray *out[],
int nin, const mxArray *in[])
{
enum {IN_FOREST = 0, IN_DATA, IN_QUERY, IN_END} ;
enum {OUT_INDEX = 0, OUT_DISTANCE} ;
int verbose = 0 ;
int opt ;
int next = IN_END ;
mxArray const *optarg ;
VlKDForest * forest ;
mxArray const * forest_array = in[IN_FOREST] ;
mxArray const * data_array = in[IN_DATA] ;
mxArray const * query_array = in[IN_QUERY] ;
void * query ;
vl_uint32 * index ;
void * distance ;
vl_size numNeighbors = 1 ;
vl_size numQueries ;
unsigned int numComparisons = 0 ;
unsigned int maxNumComparisons = 0 ;
mxClassID dataClass ;
vl_index i ;
VL_USE_MATLAB_ENV ;
/* -----------------------------------------------------------------
* Check the arguments
* -------------------------------------------------------------- */
if (nin < 3) {
vlmxError(vlmxErrNotEnoughInputArguments, NULL) ;
}
if (nout > 2) {
vlmxError(vlmxErrTooManyOutputArguments, NULL) ;
}
forest = new_kdforest_from_array (forest_array, data_array) ;
dataClass = mxGetClassID (data_array) ;
if (mxGetClassID (query_array) != dataClass) {
vlmxError(vlmxErrInvalidArgument,
"QUERY must have the same storage class as DATA.") ;
}
if (! vlmxIsReal (query_array)) {
vlmxError(vlmxErrInvalidArgument,
"QUERY must be real.") ;
}
if (! vlmxIsMatrix (query_array, forest->dimension, -1)) {
vlmxError(vlmxErrInvalidArgument,
"QUERY must be a matrix with TREE.NUMDIMENSIONS rows.") ;
}
while ((opt = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) {
switch (opt) {
case opt_num_neighs :
if (! vlmxIsScalar(optarg) ||
(numNeighbors = mxGetScalar(optarg)) < 1) {
vlmxError(vlmxErrInvalidArgument,
"NUMNEIGHBORS must be a scalar not smaller than one.") ;
}
break;
case opt_max_num_comparisons :
if (! vlmxIsScalar(optarg)) {
vlmxError(vlmxErrInvalidArgument,
"MAXNUMCOMPARISONS must be a scalar.") ;
}
maxNumComparisons = mxGetScalar(optarg) ;
break;
case opt_verbose :
++ verbose ;
break ;
}
}
vl_kdforest_set_max_num_comparisons (forest, maxNumComparisons) ;
query = mxGetData (query_array) ;
numQueries = mxGetN (query_array) ;
out[OUT_INDEX] = mxCreateNumericMatrix (numNeighbors, numQueries, mxUINT32_CLASS, mxREAL) ;
out[OUT_DISTANCE] = mxCreateNumericMatrix (numNeighbors, numQueries, dataClass, mxREAL) ;
index = mxGetData (out[OUT_INDEX]) ;
distance = mxGetData (out[OUT_DISTANCE]) ;
if (verbose) {
VL_PRINTF ("vl_kdforestquery: number of queries: %d\n", numQueries) ;
VL_PRINTF ("vl_kdforestquery: number of neighbors per query: %d\n", numNeighbors) ;
VL_PRINTF ("vl_kdforestquery: max num of comparisons per query: %d\n",
vl_kdforest_get_max_num_comparisons (forest)) ;
}
numComparisons = vl_kdforest_query_with_array (forest, index, numNeighbors, numQueries, distance, query) ;
vl_kdforest_delete(forest) ;
/* adjust for MATLAB indexing */
for (i = 0 ; i < (signed) (numNeighbors * numQueries) ; ++i) { index[i] ++ ; }
if (verbose) {
VL_PRINTF ("vl_kdforestquery: number of comparisons per query: %.3f\n",
((double) numComparisons) / numQueries) ;
VL_PRINTF ("vl_kdforestquery: number of comparisons per neighbor: %.3f\n",
((double) numComparisons) / (numQueries * numNeighbors)) ;
}
}