void extrema_locator::locate(void)
{
find_extrema();
find_objects();
merge_objects();
ex_points_from_objects();
}
CK_RV find_objects_by_name(bee b, const char *name, CK_OBJECT_HANDLE_PTR *found, CK_ULONG_PTR how_many)
{
attrs search_me;
init_attrs(&search_me);
add_attribute(&search_me, CKA_LABEL, (CK_VOID_PTR) name, strlen(name));
return find_objects(b, search_me, found, how_many);
}
int
find_objects(xmlNodePtr curr_node,
struct ldap_object_node **objs,
struct ldap_attr_node **attrs,
struct idinfo *ids)
{
struct ldap_object_node *obj = NULL;
int ret = 0;
if (!curr_node)
/* no objects found */
return 0;
for (; curr_node; curr_node = curr_node->next) {
if (curr_node->type != XML_ELEMENT_NODE)
continue;
if (!strcasecmp((char *)curr_node->name, "element")) {
obj = parse_element_tag(curr_node, objs, attrs, ids);
ret = 1;
} else {
dbg_printf("Descend on %s\n", curr_node->name);
}
if (find_objects(curr_node->xmlChildrenNode,
objs, attrs, ids)) {
ret = 1;
} else if (obj) {
/*
* We have an object, but when we
* looked for children, it did not
* have any. So, we can omit the
* requirement for 'cn' in the
* output LDIF here
*/
if (obj->need_cn) {
dbg_printf("Object class %s does not have"
" any children; not outputting "
"'MUST ( cn )'\n", obj->name);
}
obj->need_cn = 0;
}
}
/* Child objects were found */
return ret;
}
void cmex_object_list(int nlhs, mxArray *plhs[], /**< entlist */
int nrhs, const mxArray *prhs[] ) /**< () */
{
OBJECT *obj;
char criteria[1024]="(undefined)";
FINDPGM *search = NULL;
char *fields[] = {"name","class","parent","flags","location","service","rank","clock","handle"};
FINDLIST *list = NULL;
if (nrhs>0 && mxGetString(prhs[0],criteria,sizeof(criteria))!=0)
output_error("gl('list',type='object'): unable to read search criteria (arg 2)");
else if (nrhs>0 && (search=find_mkpgm(criteria))==NULL)
output_error("gl('list',type='object'): unable to run search '%s'",criteria);
else if (search==NULL && (list=find_objects(NULL,NULL))==NULL)
output_error("gl('list',type='object'): unable to obtain default list");
else if (list==NULL && (list=find_runpgm(NULL,search))==NULL)
output_error("gl('list',type='object'): unable search failed");
else if ((plhs[0] = mxCreateStructMatrix(list->hit_count,1,sizeof(fields)/sizeof(fields[0]),fields))==NULL)
output_error("gl('list',type='object'): unable to allocate memory for result list");
else
{
unsigned int n;
for (n=0, obj=find_first(list); obj!=NULL; n++, obj=find_next(list,obj))
{
char tmp[1024];
mxArray *data;
double *pDouble;
unsigned int *pInt;
mxSetFieldByNumber(plhs[0], n, 0, mxCreateString(object_name(obj)));
mxSetFieldByNumber(plhs[0], n, 1, mxCreateString(obj->oclass->name));
mxSetFieldByNumber(plhs[0], n, 2, mxCreateString(obj->parent?object_name(obj->parent):NONE));
mxSetFieldByNumber(plhs[0], n, 3, mxCreateString(convert_from_set(tmp,sizeof(tmp),&(obj->flags),object_flag_property())?tmp:ERROR));
pDouble = mxGetPr(data=mxCreateDoubleMatrix(1,2,mxREAL)); pDouble[0] = obj->longitude; pDouble[1] = obj->latitude;
mxSetFieldByNumber(plhs[0], n, 4, data);
pDouble = mxGetPr(data=mxCreateDoubleMatrix(1,2,mxREAL)); pDouble[0] = (double)obj->in_svc/TS_SECOND; pDouble[1] = (double)obj->out_svc/TS_SECOND;
mxSetFieldByNumber(plhs[0], n, 5, data);
pInt = (unsigned int*)mxGetPr(data=mxCreateNumericMatrix(1,1,mxINT32_CLASS,mxREAL)); pInt[0] = obj->rank;
mxSetFieldByNumber(plhs[0], n, 6, data);
pDouble = mxGetPr(data=mxCreateDoubleMatrix(1,1,mxREAL)); pDouble[0] = (double)obj->clock/TS_SECOND;
mxSetFieldByNumber(plhs[0], n, 7, data);
mxSetFieldByNumber(plhs[0], n, 8, make_handle(MH_OBJECT,obj));
}
}
}
STATUS original_test_end(int argc, char *argv[])
{
FINDLIST *find = find_objects(FL_GROUP,"class=node;");
OBJECT *obj;
AGGREGATION *aggr;
char *exp = "class=node";
char *agg = "max(V.ang)";
for (obj=find_first(find); obj!=NULL; obj=find_next(find,obj))
output_message("object %s found", object_name(obj));
free(find);
output_message("Aggregation of %s over %s...", agg,exp);
aggr = aggregate_mkgroup(agg,exp);
if (aggr)
output_message("Result is %lf", aggregate_value(aggr));
else
output_message("Aggregation failed!");
if (!saveall("-"))
perror("save failed");
return SUCCESS;
}
NSS_IMPLEMENT nssCryptokiObject *
nssToken_FindCertificateByIssuerAndSerialNumber (
NSSToken *token,
nssSession *sessionOpt,
NSSDER *issuer,
NSSDER *serial,
nssTokenSearchType searchType,
PRStatus *statusOpt
)
{
CK_ATTRIBUTE_PTR attr;
CK_ATTRIBUTE_PTR serialAttr;
CK_ATTRIBUTE cert_template[4];
CK_ULONG ctsize;
nssCryptokiObject **objects;
nssCryptokiObject *rvObject = NULL;
NSS_CK_TEMPLATE_START(cert_template, attr, ctsize);
if (!token) {
PORT_SetError(SEC_ERROR_NO_TOKEN);
if (statusOpt)
*statusOpt = PR_FAILURE;
return NULL;
}
/* Set the search to token/session only if provided */
if (searchType == nssTokenSearchType_SessionOnly) {
NSS_CK_SET_ATTRIBUTE_ITEM(attr, CKA_TOKEN, &g_ck_false);
} else if ((searchType == nssTokenSearchType_TokenOnly) ||
(searchType == nssTokenSearchType_TokenForced)) {
NSS_CK_SET_ATTRIBUTE_ITEM(attr, CKA_TOKEN, &g_ck_true);
}
/* Set the unique id */
NSS_CK_SET_ATTRIBUTE_ITEM(attr, CKA_CLASS, &g_ck_class_cert);
NSS_CK_SET_ATTRIBUTE_ITEM(attr, CKA_ISSUER, issuer);
serialAttr = attr;
NSS_CK_SET_ATTRIBUTE_ITEM(attr, CKA_SERIAL_NUMBER, serial);
NSS_CK_TEMPLATE_FINISH(cert_template, attr, ctsize);
/* get the object handle */
if (searchType == nssTokenSearchType_TokenForced) {
objects = find_objects(token, sessionOpt,
cert_template, ctsize,
1, statusOpt);
} else {
objects = find_objects_by_template(token, sessionOpt,
cert_template, ctsize,
1, statusOpt);
}
if (objects) {
rvObject = objects[0];
nss_ZFreeIf(objects);
}
/*
* NSS used to incorrectly store serial numbers in their decoded form.
* because of this old tokens have decoded serial numbers.
*/
if (!objects) {
NSSItem serialDecode;
PRStatus status;
status = nssToken_decodeSerialItem(serial, &serialDecode);
if (status != PR_SUCCESS) {
return NULL;
}
NSS_CK_SET_ATTRIBUTE_ITEM(serialAttr,CKA_SERIAL_NUMBER,&serialDecode);
if (searchType == nssTokenSearchType_TokenForced) {
objects = find_objects(token, sessionOpt,
cert_template, ctsize,
1, statusOpt);
} else {
objects = find_objects_by_template(token, sessionOpt,
cert_template, ctsize,
1, statusOpt);
}
if (objects) {
rvObject = objects[0];
nss_ZFreeIf(objects);
}
}
return rvObject;
}
void process_image(IplImage* frame, int draw)
{
int i, j;
float t;
uint8_t* pixels;
int nrows, ncols, ldim;
#define MAXNDETECTIONS 2048
int ndetections;
float qs[MAXNDETECTIONS], rs[MAXNDETECTIONS], cs[MAXNDETECTIONS], ss[MAXNDETECTIONS];
static IplImage* gray = 0;
static IplImage* pyr[5] = {0, 0, 0, 0, 0};
/*
...
*/
//
if(!pyr[0])
{
//
gray = cvCreateImage(cvSize(frame->width, frame->height), frame->depth, 1);
//
pyr[0] = gray;
pyr[1] = cvCreateImage(cvSize(frame->width/2, frame->height/2), frame->depth, 1);
pyr[2] = cvCreateImage(cvSize(frame->width/4, frame->height/4), frame->depth, 1);
pyr[3] = cvCreateImage(cvSize(frame->width/8, frame->height/8), frame->depth, 1);
pyr[4] = cvCreateImage(cvSize(frame->width/16, frame->height/16), frame->depth, 1);
}
// get grayscale image
if(frame->nChannels == 3)
cvCvtColor(frame, gray, CV_RGB2GRAY);
else
cvCopy(frame, gray, 0);
// perform detection with the pico library
t = getticks();
if(usepyr)
{
int nd;
//
pyr[0] = gray;
pixels = (uint8_t*)pyr[0]->imageData;
nrows = pyr[0]->height;
ncols = pyr[0]->width;
ldim = pyr[0]->widthStep;
ndetections = find_objects(rs, cs, ss, qs, MAXNDETECTIONS, cascade, angle, pixels, nrows, ncols, ldim, scalefactor, stridefactor, MAX(16, minsize), MIN(128, maxsize));
for(i=1; i<5; ++i)
{
cvResize(pyr[i-1], pyr[i], CV_INTER_LINEAR);
pixels = (uint8_t*)pyr[i]->imageData;
nrows = pyr[i]->height;
ncols = pyr[i]->width;
ldim = pyr[i]->widthStep;
nd = find_objects(&rs[ndetections], &cs[ndetections], &ss[ndetections], &qs[ndetections], MAXNDETECTIONS-ndetections, cascade, angle, pixels, nrows, ncols, ldim, scalefactor, stridefactor, MAX(64, minsize>>i), MIN(128, maxsize>>i));
for(j=ndetections; j<ndetections+nd; ++j)
{
rs[j] = (1<<i)*rs[j];
cs[j] = (1<<i)*cs[j];
ss[j] = (1<<i)*ss[j];
}
ndetections = ndetections + nd;
}
}
else
{
ss[j] = (1<<i)*ss[j];
}
ndetections = ndetections + nd;
}
}
else
{
//
pixels = (uint8_t*)gray->imageData;
nrows = gray->height;
ncols = gray->width;
ldim = gray->widthStep;
//
ndetections = find_objects(rs, cs, ss, qs, MAXNDETECTIONS, cascade, angle, pixels, nrows, ncols, ldim, scalefactor, stridefactor, minsize, MIN(nrows, ncols));
}
if(!noclustering)
ndetections = cluster_detections(rs, cs, ss, qs, ndetections);
t = getticks() - t;
// if the flag is set, draw each detection
if(draw)
for(i=0; i<ndetections; ++i)
if(qs[i]>=qthreshold) // check the confidence threshold
cvCircle(frame, cvPoint(cs[i], rs[i]), ss[i]/2, CV_RGB(255, 0, 0), 4, 8, 0); // we draw circles here since height-to-width ratio of the detected face regions is 1.0f
// if the `verbose` flag is set, print the results to standard output
if(verbose)
int process_image(IplImage* frame, int draw, int print)
{
int i, j;
float t;
uint8_t* pixels;
int nrows, ncols, ldim;
#define MAXNDETECTIONS 2048
int ndetections=0;
float qs[MAXNDETECTIONS], rs[MAXNDETECTIONS], cs[MAXNDETECTIONS], ss[MAXNDETECTIONS];
static IplImage* gray = 0;
static IplImage* pyr[5] = {0, 0, 0, 0, 0};
/*
IMPORTANT:
* these parameters are highly specific for each detection cascade
(determine them experimentally)
*/
// * this function should be generated with picogen from a detection cascade output by picolrn
int (*run_detection_cascade)(float*, int, int, int, void*, int, int, int)
= run_facefinder;
// * detection quality threshold (must be >= 0.0f)
// * you can vary the TPR and FPR with this value
// * if you're experiencing too many false positives, try a larger number here (for example, 7.5f)
float qthreshold = 5.0f;
// * how much to rescale the window during the multiscale detection process
// * increasing this value leads to lower number of detections and higher processing speed
// * for example, set to 1.2f if you're using pico on a mobile device
float scalefactor = 1.1f;
// * how much to move the window between neighboring detections
// * increasing this value leads to lower number of detections and higher processing speed
// * for example, set to 0.05f if you want really high recall
float stridefactor = 0.1f;
// * coarse image pyramid support
// * can improve noise and aliasing problems in some applications
// * set to 1 if pico fails to detect large objects
int usepyr = 0;
/*
...
*/
//
if(!pyr[0])
{
//
gray = cvCreateImage(cvSize(frame->width, frame->height), frame->depth, 1);
//
pyr[0] = gray;
pyr[1] = cvCreateImage(cvSize(frame->width/2, frame->height/2), frame->depth, 1);
pyr[2] = cvCreateImage(cvSize(frame->width/4, frame->height/4), frame->depth, 1);
pyr[3] = cvCreateImage(cvSize(frame->width/8, frame->height/8), frame->depth, 1);
pyr[4] = cvCreateImage(cvSize(frame->width/16, frame->height/16), frame->depth, 1);
}
// get grayscale image
if(frame->nChannels == 3)
cvCvtColor(frame, gray, CV_RGB2GRAY);
else
cvCopy(frame, gray, 0);
// perform detection with the pico library
t = getticks();
if(usepyr)
{
int nd;
//
pyr[0] = gray;
pixels = (uint8_t*)pyr[0]->imageData;
nrows = pyr[0]->height;
ncols = pyr[0]->width;
ldim = pyr[0]->widthStep;
ndetections = find_objects(rs, cs, ss, qs, MAXNDETECTIONS, run_detection_cascade, pixels, nrows, ncols, ldim, scalefactor, stridefactor, MAX(16, minsize), MIN(128, maxsize));
for(i=1; i<5; ++i)
{
cvResize(pyr[i-1], pyr[i], CV_INTER_LINEAR);
pixels = (uint8_t*)pyr[i]->imageData;
nrows = pyr[i]->height;
ncols = pyr[i]->width;
ldim = pyr[i]->widthStep;
nd = find_objects(&rs[ndetections], &cs[ndetections], &ss[ndetections], &qs[ndetections], MAXNDETECTIONS-ndetections, run_detection_cascade, pixels, nrows, ncols, ldim, scalefactor, stridefactor, MAX(64, minsize>>i), MIN(128, maxsize>>i));
for(j=ndetections; j<ndetections+nd; ++j)
{
rs[j] = (1<<i)*rs[j];
cs[j] = (1<<i)*cs[j];
ss[j] = (1<<i)*ss[j];
}
ndetections = ndetections + nd;
}
}
else
{
/*
* input : grayscale image
*/
int pico_facedetection(void* frame, int width, int height,
int maxdetect, float *frs, float *fcs, float *fss)
{
int i, j;
// float t;
uint8_t* pixels;
int nrows, ncols, ldim;
#define MAXNDETECTIONS 2048
int ndetections=0;
float qs[MAXNDETECTIONS], rs[MAXNDETECTIONS], cs[MAXNDETECTIONS], ss[MAXNDETECTIONS];
void* gray = NULL;
void* pyr[5] = {NULL, NULL, NULL, NULL, NULL};
pr_debug(1, "%s: %d %d %d\n", __func__, width, height, maxdetect);
/*
IMPORTANT:
* these parameters are highly specific for each detection cascade
(determine them experimentally)
*/
// * this function should be generated with picogen from a detection cascade output by picolrn
int (*run_detection_cascade)(float*, int, int, int, void*, int, int, int)
= run_facefinder;
// * detection quality threshold (must be >= 0.0f)
// * you can vary the TPR and FPR with this value
// * if you're experiencing too many false positives, try a larger number here (for example, 7.5f)
float qthreshold = 5.0f;
// * how much to rescale the window during the multiscale detection process
// * increasing this value leads to lower number of detections and higher processing speed
// * for example, set to 1.2f if you're using pico on a mobile device
float scalefactor = 1.1f;
// * how much to move the window between neighboring detections
// * increasing this value leads to lower number of detections and higher processing speed
// * for example, set to 0.05f if you want really high recall
float stridefactor = 0.1f;
// * coarse image pyramid support
// * can improve noise and aliasing problems in some applications
// * set to 1 if pico fails to detect large objects
int usepyr = 0;
// perform detection with the pico library
// t = getticks();
if( (gray = malloc(width*height)) == NULL ){
pr_debug(1, "!!!!gray malloc failure\n");
return 0;
}else{
memcpy(gray, frame, width*height);
}
if(usepyr)
{
#if 0
int nd;
//
pyr[0] = gray;
//pyr[1] = cvCreateImage(cvSize(frame->width/2, frame->height/2), frame->depth, 1);
pyr[1] = malloc((width/2)*(height/2));
//pyr[2] = cvCreateImage(cvSize(frame->width/4, frame->height/4), frame->depth, 1);
pyr[2] = malloc((width/4)*(height/4));
//pyr[3] = cvCreateImage(cvSize(frame->width/8, frame->height/8), frame->depth, 1);
pyr[3] = malloc((width/8)*(height/8));
//pyr[4] = cvCreateImage(cvSize(frame->width/16, frame->height/16), frame->depth, 1);
pyr[4] = malloc((width/16)*(height/16));
pixels = (uint8_t*)pyr[0];
nrows = height;
ncols = width;
ldim = width;//widthStep;TODO TODO .... if this is the same width for row-major image, 8 bit gray pixel
ndetections = find_objects(rs, cs, ss, qs, MAXNDETECTIONS, run_detection_cascade,
pixels, nrows, ncols, ldim, scalefactor, stridefactor,
MAX(16, MIN_SIZE), MIN(128, MAX_SIZE));
for(i=1; i<5; ++i)
{
cvResize(pyr[i-1], pyr[i], CV_INTER_LINEAR);
pixels = (uint8_t*)pyr[i];
nrows = height/(1<<i); //1/(2^n)
ncols = width/(1<<i);
ldim = ncols; //pyr[i]->widthStep;
nd = find_objects(&rs[ndetections], &cs[ndetections], &ss[ndetections],
&qs[ndetections], MAXNDETECTIONS-ndetections,
run_detection_cascade, pixels, nrows, ncols, ldim, scalefactor,
stridefactor, MAX(64, MIN_SIZE>>i), MIN(128, MAX_SIZE>>i));
for(j=ndetections; j<ndetections+nd; ++j)
{
rs[j] = (1<<i)*rs[j];
cs[j] = (1<<i)*cs[j];
ss[j] = (1<<i)*ss[j];
}
ndetections = ndetections + nd;
}
for(i=0; i<5; ++i){
if(pyr[i]){
free(pyr[i]);
pyr[i]=NULL;
}
}
#endif
}
else
{
