dimensionedTensor hinv(const dimensionedTensor& dt)
{
return dimensionedTensor
(
"hinv("+dt.name()+')',
dimless/dt.dimensions(),
hinv(dt.value())
);
}
Img*
dssread(char *file)
{
int nx, ny, scale, sumall;
Pix *p, *pend;
uint8_t buf[21];
Biobuf *bp;
Img *ip;
if(debug)
Bprint(&bout, "reading %s\n", file);
bp = Bopen(file, OREAD);
if(bp == 0)
return 0;
if(Bread(bp, buf, sizeof(buf)) != sizeof(buf) ||
buf[0] != 0xdd || buf[1] != 0x99){
werrstr("bad format");
return 0;
}
nx = getlong(buf+2);
ny = getlong(buf+6);
scale = getlong(buf+10);
sumall = getlong(buf+14);
if(debug)
fprint(2, "%s: nx=%d, ny=%d, scale=%d, sumall=%d, nbitplanes=%d,%d,%d\n",
file, nx, ny, scale, sumall, buf[18], buf[19], buf[20]);
ip = malloc(sizeof(Img) + (nx*ny-1)*sizeof(int));
if(ip == 0){
Bterm(bp);
werrstr("no memory");
return 0;
}
ip->nx = nx;
ip->ny = ny;
dodecode(bp, ip->a, nx, ny, buf+18);
ip->a[0] = sumall; /* sum of all pixels */
Bterm(bp);
if(scale > 1){
p = ip->a;
pend = &ip->a[nx*ny];
while(p < pend)
*p++ *= scale;
}
hinv(ip->a, nx, ny);
return ip;
}
void Generic_ordinal::invert()
{
std::vector<ftype> hinv( hist.size() );
hinv[0] = range_.min();
hinv[hist.size()-1] = range_.max();
for ( int i = 1; i < hist.size()-1; i++ ) {
ftype target = ftype(i)/nranges;
ftype guess = 0.5 * ( range_.max() + range_.min() );
ftype step = 0.5 * ( range_.max() - range_.min() );
for ( int j = 0; j < 10; j++ ) {
if ( ordinal( guess ) > target ) guess -= step;
else guess += step;
step *= 0.5;
}
hinv[i] = Util::bound( range_.min(), guess, range_.max() );
}
range_ = Range<ftype>( 0.0, 1.0 );
hist = hinv;
}
