static void
difference_of_gaussian(struct fmat_t *fm) {
/* this kernel was generated by the gen-gaussian-kernel.py script */
#define KERNEL_A_SIZE 7
#define KERNEL_B_SIZE 13
/* sigma = 1.0 */
static const float kernel_a[] = {
0.00443f, 0.05401f, 0.24204f,
0.39905f,
0.24204f, 0.05401f, 0.00443f,
};
/* sigma = 2.0 */
static const float kernel_b[] = {
0.00222f, 0.00877f, 0.02702f, 0.06482f, 0.12111f, 0.17621f,
0.19968f,
0.17621f, 0.12111f, 0.06482f, 0.02702f, 0.00877f, 0.00222f,
};
/* gaussian smoothing */
struct fmat_t *fma = fmat_new(fm->w, fm->h);
struct fmat_t *fmb = fmat_new(fm->w, fm->h);
seperate_conv(fm, kernel_a, KERNEL_A_SIZE, fma);
seperate_conv(fm, kernel_b, KERNEL_B_SIZE, fmb);
fmat_sub(fma, fmb, fm);
fmat_free(&fma);
fmat_free(&fmb);
}
void
inorm(const struct image_t *src, struct image_t *dst) {
struct fmat_t *fm = fmat_new(src->w, src->h);
struct fmat_t *tfm = fmat_new(src->w, src->h);
struct image_t *timg = image_new(src->w, src->h);
gamma_correction(src, fm);
difference_of_gaussian(fm);
tanh_smooth(fm, tfm);
rescale_to_image(tfm, dst);
image_free(&timg);
fmat_free(&fm);
fmat_free(&tfm);
}
float *fmat_new_pca_part(int d,int n,int nev,
const float *v,float *singvals) {
if(!(nev<=d && nev<=n)) {
fprintf(stderr,"fmat_new_pca_part: asking for too many eigenvalues (%d) wrt %d*%d data\n",nev,n,d);
return NULL;
}
float *pcamat=fmat_new(d,nev);
int ret;
if(n>=d) {
ret=fmat_svd_partial_full(d,n,nev,v,0,singvals,pcamat,NULL,count_cpu());
} else {
fprintf(stderr,"fmat_new_pca_part: warn fewer learning points (%d) than dimensions (%d): transposing\n",n,d);
ret=fmat_svd_partial_full(n,d,nev,v,1,singvals,NULL,pcamat,count_cpu());
}
if(ret<0) {
free(pcamat);
pcamat=NULL;
}
return pcamat;
}
static void
seperate_conv(const struct fmat_t *fm, const float kernel[], unsigned int kernel_size, struct fmat_t *dst) {
struct fmat_t *buf = fmat_new(fm->w, fm->h);
sep_conv_h(fm->data, fm->w, fm->h, kernel, kernel_size, buf->data);
sep_conv_v(buf->data, buf->w, buf->h, kernel, kernel_size, dst->data);
fmat_free(&buf);
}
float *fmat_new_get_columns (const float *a, int nrow, int ncolout, const int *cols) {
int i,j;
float *b = fmat_new (nrow, ncolout);
for(j=0;j<ncolout;j++)
for(i=0;i<nrow;i++)
b[j*nrow+i]=a[i+nrow*cols[j]];
return b;
}
float* fmat_new_mul_full(const float *left, const float *right,
int m, int n, int k,
const char *transp) {
float *result=fmat_new(m,n);
fmat_mul_full(left, right, m, n, k, transp, result);
return result;
}
/*---------------------------------------------------------------------------*/
float *fmat_new_rand_gauss (int nrow, int ncol)
{
long i;
float *m = fmat_new (nrow, ncol);
for (i = 0; i < nrow * ncol; i++)
m[i] = gaussrand ();
return m;
}
float *fmat_get_submatrix (const float *a, int nrow,
int nrow_out,
int ncol) {
long i;
float *b=fmat_new(nrow_out,ncol);
for(i=0;i<ncol;i++)
memcpy(b+i*nrow_out,a+i*nrow,nrow_out*sizeof(*a));
return b;
}
float *fmat_new_get_rows (const float *a, int d, int n,
int nrowout, const int *rows) {
float *b=fmat_new(nrowout,n);
int i,j;
int ii=0;
for(j=0;j<n;j++)
for(i=0;i<nrowout;i++)
b[ii++]=a[rows[i]+d*j];
return b;
}
float *fmat_new_get_rows (const float *a, int d, int n,
int nrowout, const int *rows) {
float *b=fmat_new(nrowout,n);
long i, j;
long ii=0;
for(j=0;j<n;j++) {
const float *aj = a + d*(long)j;
for(i=0;i<nrowout;i++)
b[ii++]=aj[rows[i]];
}
return b;
}
float *fmat_new_vstack(const float *a,int da,
const float *b,int db,
int n) {
int i;
float *c=fmat_new(da+db,n),*ret=c;
for(i=0;i<n;i++) {
memcpy(c,a,da*sizeof(float));
c+=da;
a+=da;
memcpy(c,b,db*sizeof(float));
c+=db;
b+=db;
}
return ret;
}
int fmat_svd_partial_full(int n,int m,int nev,const float *a,int a_transposed,
float *s,float *vout,float *uout,int nt) {
arpack_eigs_t *ae=arpack_eigs_begin(n,nev);
if(!ae) return -100;
int ret=0;
int j,i;
float *ax=NEWA(float,m);
int it;
for(it=0;;it++) {
float *x,*y;
ret=arpack_eigs_step(ae,&x,&y);
printf("arpack iteration %d ret=%d\r",it,ret);
if(ret<0) break; /* error */
if(ret==0) break; /* stop iteration */
/* ret==1 */
if(!a_transposed) {
fmat_mul_v(m,n,a,n,x,ax,nt);
fmat_mul_tv(n,m,a,n,ax,y,nt);
} else {
fmat_mul_tv(m,n,a,m,x,ax,nt);
fmat_mul_v(n,m,a,m,ax,y,nt);
}
fflush(stdout);
}
printf("\n");
free(ax);
float *v=vout ? vout : fmat_new(nev,n);
ret=arpack_eigs_end(ae,s,v);
if(ret>0) {
int nconv=ret;
if(s)
for(j=0;j<nconv;j++)
s[j]=sqrt(s[j]);
if(uout)
for(i=0;i<nconv;i++) {
float *u=uout+m*(long)i;
if(!a_transposed)
fmat_mul_v(m,n,a,n,v+n*(long)i,u,nt);
else
fmat_mul_tv(m,n,a,m,v+n*(long)i,u,nt);
fvec_normalize(u,m,2);
}
}
if(!vout) free(v);
return ret;
}
float *fmat_new_get_columns (const float *a, int nrow, int ncolout, const int *cols) {
float *b = fmat_new (nrow, ncolout);
fmat_get_columns(a, nrow, ncolout, cols, b);
return b;
}
