void dgauy_gaux_filter(float sigma){
int i,j,k,length_gaussian,length_deriv;
float gaussian[SIZE1],deriv_gaussian[SIZE1];
matrix dgauy, dgauy_gaux, total_resul, m;
float rmin,rmax,rng;
/* Fill in the image with background color */
for(i=0; i<IMAGE_WIDTH; i++)
for(j=0; j<IMAGE_HEIGHT; j++)
XPutPixel(theXImage_2,i,j,bg);
/* Clear the drawing window so the image is displayed again */
XClearArea(XtDisplay(draw_1),XtWindow(draw_2),0,0,0,0,True);
/* Associate the watch cursor with the main window */
XDefineCursor(XtDisplay(draw_1), XtWindow(main_window), theCursor);
/* Flush the request buffer and wait for all events */
/* and errors to be processed by the server. */
XSync(XtDisplay(draw_1), False);
get_gaussian(sigma,gaussian,&length_gaussian);
get_derigaussian(sigma,deriv_gaussian,&length_deriv);
for(i=0;i<IMAGE_WIDTH;i++)
for(j=0;j<IMAGE_HEIGHT;j++)
m[i][j]= XGetPixel(theXImage_1,i,j);
/* Calculates convolution of the image with the derivative of a */
convo_vectory(m,deriv_gaussian,length_deriv,&rmax,&rmin,dgauy);
/*Calculate the smoothing of a Gaussian(y) in x direction */
convo_vectorx(dgauy,gaussian,length_gaussian,&rmax,&rmin,
dgauy_gaux);
rng=(rmax-rmin);
for(i=0;i<IMAGE_WIDTH;i++)
for(j=0;j<IMAGE_HEIGHT;j++)
total_resul[i][j]=sqrt(pow(dgauy_gaux[i][j],2));
for(i=0;i<IMAGE_WIDTH;i++)
for(j=0;j<IMAGE_HEIGHT;j++)
XPutPixel(theXImage_2,i,j,(unsigned long)
(((float)(total_resul[i][j]-rmin)/rng)*255.0));
/* Copy image into pixmap */
XPutImage(XtDisplay(draw_2), thePixmap_2,image_gc_2, theXImage_2,
0, 0, 0, 0, theXImage_2->width, theXImage_2->height);
/* Disassociate the watch cursor from the main window */
XUndefineCursor(XtDisplay(draw_1), XtWindow(main_window));
/* Clear the drawing window so the image is displayed again */
XClearArea(XtDisplay(draw_2),XtWindow(draw_2),0,0,0,0,True);
}
void bptt_update_connections(Net *net,Connections *c,int t)
{
Group *gfrom,*gto;
Real *invec,*outvec,*w,dot,*outs,weight;
int i,j,nto,nfrom,d;
/* if we reset activation and t=0, don't do anything */
if (c->to->resetActivation==1 && t==0)
return;
gfrom=c->from;
gto=c->to;
invec=gto->inputs[t];
/* we won't be here with t==0 unless we're not resetting */
nto=gto->numUnits;
nfrom=gfrom->numUnits;
if (c->weightNoiseType==NO_NOISE)
{
for(i=0;i<nto;i++)
{
if (t==0) /* already zeroed if t=0 */
{
d=net->time-1;
}
else d= t - gto->delays[i];
if (d>=0)
{
outvec=gfrom->outputs[d];
w=c->weights[i];
dot=0.0;
outs=outvec;
for(j=0;j<nfrom;j++)
{
dot += *w++ * *outs++;
}
invec[i] +=dot;
}
}
}
else if (c->weightNoiseType==ADDITIVE_NOISE)
{
for(i=0;i<nto;i++)
{
if (t==0) /* already zeroed if t=0 */
{
d=net->time-1;
}
else d= t - gto->delays[i];
if (d>=0)
{
outvec=gfrom->outputs[d];
w=c->weights[i];
dot=0.0;
outs=outvec;
for(j=0;j<nfrom;j++)
{
weight = *w++ +
(get_gaussian() * c->weightNoise);
dot += weight * *outs++;
}
invec[i] +=dot;
}
}
}
else if (c->weightNoiseType==MULTIPLICATIVE_NOISE)
{
for(i=0;i<nto;i++)
{
if (t==0) /* already zeroed if t=0 */
{
d=net->time-1;
}
else d= t - gto->delays[i];
if (d>=0)
{
outvec=gfrom->outputs[d];
w=c->weights[i];
dot=0.0;
outs=outvec;
for(j=0;j<nfrom;j++)
{
weight = *w++ *
(1.0 + (get_gaussian() * c->weightNoise));
dot += weight * *outs++;
}
invec[i] +=dot;
}
}
}
else
Error0("Unknown weightNoiseType");
}
void dbm_forward(Net *net,Example *ex,int doclamp)
{
int i,t,nu,nt,ng;
int nc,c,g;
Group *gto;
float prev;
nt=net->time;
ng=net->numGroups;
nc=net->numConnections;
for(t=0;t<nt;t++)
{
/* first, zero out the input field */
for(g=0;g<ng;g++)
{
gto=net->groups[g];
nu=gto->numUnits;
for(i=0;i<nu;i++)
{
gto->inputs[t][i]=0;
}
gto->temperature =
1.0/(gto->temperature + gto->temporg *
pow(gto->tempmult,t));
}
for(c=0;c<nc;c++)
{
if (!is_group_in_net(net->connections[c]->from,net))
Error1("dbm_update: Group %s is not in net",
net->connections[c]->from->name);
if (!is_group_in_net(net->connections[c]->to,net))
Error1("dbm_forward: Group %s is not in net",
net->connections[c]->to->name);
dbm_update_connections(net,net->connections[c],t);
}
/* now, having computed all the inputs from the
previous time tick's outputs, update the current
time tick's outputs from its inputs (got that?). */
for(g=0;g<ng;g++)
{
gto=net->groups[g];
apply_example_clamps(ex,gto,t);
nu=gto->numUnits;
for(i=0;i<nu;i++)
{
/* is it a bias */
if (gto->bias)
gto->outputs[t][i]=gto->biasValue;
/* does it have a target */
else if (VAL(gto->exampleData[i]))
{
gto->outputs[t][i]=gto->exampleData[i];
if (gto->clampNoise != 0)
{
gto->outputs[t][i] +=
gto->clampNoise * get_gaussian();
gto->outputs[t][i]=
clip_output(gto,gto->outputs[t][i]);
}
}
else if (VAL(gto->exampleData[i]) &&
(doclamp == CLAMP_TARGETS))
{
gto->outputs[t][i]=gto->exampleData[i];
if (gto->clampNoise != 0)
{
gto->outputs[t][i] +=
gto->clampNoise * get_gaussian();
gto->outputs[t][i]=
clip_output(gto,gto->outputs[t][i]);
}
}
/* else compute normally */
else
{
if (t==0) prev=0.0;
else prev = gto->outputs[t-1][i];
gto->outputs[t][i] =
(1.0-net->integrationConstant) * prev +
net->integrationConstant *
bptt_unit_activation(gto,i,t);
if (gto->activationNoise != 0)
{
gto->outputs[t][i]
+= gto->activationNoise * get_gaussian();
gto->outputs[t][i]=
clip_output(gto,gto->outputs[t][i]);
}
}
}
}
}
return;
}
/* total Gradient= sqrt(pow(dgauxgauy,2)+pow(dgauygaux,2)) */
void total_gradient_filter(float sigma){
int i,j,k,length_gaussian,length_deriv;
float gaussian[SIZE1],deriv_gaussian[SIZE1];
matrix dgaux, dgauy, dgaux_gauy, dgauy_gaux,
total_gradient, m;
float rmin,rmax,rng;
if (yhs_files_open == 0 || yhs_filename[0] == 0 || yhs_files_open > 19 || running == 1) return;
running=1;
XtUnmapWidget(sigma_dialog);
XtUnmanageChild(sigma_dialog);
XFlush(XtDisplay(sigma_dialog));
fprintf(stderr,"\nProcessing filter...");
get_gaussian(sigma,gaussian,&length_gaussian);
get_derigaussian(sigma,deriv_gaussian,&length_deriv);
for(i=0;i<IMAGE_WIDTH;i++)
for(j=0;j<IMAGE_HEIGHT;j++)
m[i][j]= array1[i][j];
/* calculates the convolution of the image with the derivative of a */
/* Gaussian for the rows */
convo_vectorx(m,deriv_gaussian,length_deriv,&rmax,&rmin,dgaux);
/*convo_vectorx(m,gaussian,length_gaussian,&rmax,&rmin,gaussian_x);
convo_vectory(gaussian_x,deriv_gaussian,length_deriv,&rmax,&rmin,final_gaussian_x);*/
/* calculate the smoothing of a gaussian(x) in y direction. */
convo_vectory(dgaux,gaussian,length_gaussian,&rmax,&rmin,
dgaux_gauy);
fprintf(stderr,"Maximum is %f, Minimum is %f..",rmax,rmin);
/* calculates the convolution of the image with the derivative of a */
/* gaussian for the columns */
convo_vectory(m,deriv_gaussian,length_deriv,&rmax,&rmin,dgauy);
/* convo_vectory(m,gaussian,length_gaussian,&rmax,&rmin,gaussian_y);
convo_vectorx(gaussian_y,deriv_gaussian,length_deriv,&rmax,&rmin,final_gaussian_y);*/
/* calculates the smoothing of a gaussian(y) in x direction */
convo_vectorx(dgauy, gaussian, length_gaussian, &rmax, &rmin,
dgauy_gaux);
for(i=0;i<IMAGE_WIDTH;i++)
for(j=0;j<IMAGE_HEIGHT;j++){
total_gradient[i][j]=sqrt(pow(dgaux_gauy[i][j],2)+
pow(dgauy_gaux[i][j],2));
if (i==0 && j==0){rmax = total_gradient[0][0];
rmin = rmax;}
if (total_gradient[i][j]>rmax) rmax=total_gradient[i][j];
if (total_gradient[i][j]<rmin) rmin=total_gradient[i][j];
}
rng=(rmax-rmin);
for(i=0;i<IMAGE_WIDTH;i++)
for(j=0;j<IMAGE_HEIGHT;j++)
array2[i][j]=(int)(((float)(total_gradient[i][j]-rmin)/rng)*255.0);
/* Contorno de la Imagen a negro */
for(i=0; i<IMAGE_WIDTH; i++)
for(j=0; j< IMAGE_HEIGHT; j++) {
array2[i][0]=0;
array2[i][1]=0;
array2[i][511]=0;
array2[0][j]=0;
array2[1][j]=0;
array2[511][j]=0;
};
fprintf(stderr,"..completed.\nProcessing watershed...");
watershed_callback(NULL,NULL,NULL);
fprintf(stderr,"..completed.\n");
running=0;
}
/* Procedure that aply a gaussian matrix filter to the image */
void gaussian_filter(float sigma){
int i,j,start,length_gaussian,lon;
float value, rmax,rmin,rng, mask_gaussian[SIZE2][SIZE2];
float gaussian[SIZE1];
matrix m,resul;
/* Fill in the image with background color */
for(i=0; i<IMAGE_WIDTH; i++)
for(j=0; j<IMAGE_HEIGHT; j++)
XPutPixel(theXImage_2,i,j,bg);
/* Clear the drawing window so the image is displayed again */
XClearArea(XtDisplay(draw_1),XtWindow(draw_2),0,0,0,0,True);
/* Associate the watch cursor with the main window */
XDefineCursor(XtDisplay(draw_1), XtWindow(main_window), theCursor);
/* Flush the request buffer and wait for all events */
/* and errors to be processed by the server. */
XSync(XtDisplay(draw_1), False);
/* Storing the matrix we want to process in m */
for(i=0;i<IMAGE_WIDTH;i++)
for(j=0;j<IMAGE_HEIGHT;j++)
m[i][j]= XGetPixel(theXImage_1,i,j);
get_gaussian(sigma, gaussian, &length_gaussian);
fprintf(stderr,"la long es %i \n",length_gaussian);
/* Only the values higher than 0.01 are desirable */
start=0;
value=pow(gaussian[0],2);
while (value<0.01){
start += 1;
value = pow(gaussian[start],2);
}
for(i=start;i<=length_gaussian-start;i++)
for(j=start;j<=length_gaussian-start;j++){
mask_gaussian[i-start][j-start]= gaussian[i]*gaussian[j];
fprintf(stderr,"la %i,%i es %f \n",i-start,j-start,
mask_gaussian[i-start][j-start]);
}
length_gaussian = length_gaussian - (2*start);
fprintf(stderr,"la long es %i \n",length_gaussian);
convolution(m, mask_gaussian, length_gaussian, &rmax, &rmin, resul);
rng = (float) (rmax - rmin);
lon = length_gaussian/2;
/* Now compute the convolution, scaling. */
for (i=lon; i<IMAGE_WIDTH-lon; i++)
for (j=lon; j<IMAGE_HEIGHT-lon; j++)
XPutPixel(theXImage_2,i,j,
(unsigned long) (((float)(resul[i][j]-rmin)/rng)*255.0));
/* Copy image into pixmap. */
XPutImage(XtDisplay(draw_2), thePixmap_2,image_gc_2, theXImage_2,
0, 0, 0, 0, theXImage_2->width, theXImage_2->height);
/* Disassociate the watch cursor from the main window */
XUndefineCursor(XtDisplay(draw_1), XtWindow(main_window));
/* Clear the drawing window so the image is displayed again */
XClearArea(XtDisplay(draw_2),XtWindow(draw_2),0,0,0,0,True);
}
