Manual Reference Pages  PDL::IMAGEND (3)
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NAME
PDL::ImageND  useful image processing in N dimensions
CONTENTS
DESCRIPTION
These routines act on PDLs as Ndimensional objects, not as threaded
sets of 0D or 1D objects. The file is sort of a catchall for
broadly functional routines, most of which could legitimately
be filed elsewhere (and probably will, one day).
ImageND is not a part of the PDL core (v2.4) and hence must be explicitly
loaded.
SYNOPSIS
use PDL::ImageND;
$b = $a>convolveND($kernel,{bound=>periodic});
$b = $a>rebin(50,30,10);
FUNCTIONS
convolve
Signature: (a(m); b(n); indx adims(p); indx bdims(q); [o]c(m))
Ndimensional convolution (Deprecated; use convolveND)
$new = convolve $a, $kernel
Convolve an array with a kernel, both of which are Ndimensional. This
routine does direct convolution (by copying) but uses quasiperiodic
boundary conditions: each dim wraps around to the next higher row in
the next dim.
This routine is kept for backwards compatibility with earlier scripts;
for most purposes you want convolveND instead:
it runs faster and handles a variety of boundary conditions.
convolve does not process bad values.
It will set the badvalue flag of all output piddles if the flag is set for any of the input piddles.
Ndimensional interpolation routine
Signature: ninterpol(point(),data(n),[o]value())
$value = ninterpol($point, $data);
ninterpol uses interpol to find a linearly interpolated value in
N dimensions, assuming the data is spread on a uniform grid. To use
an arbitrary grid distribution, need to find the gridspace point from
the indexing scheme, then call ninterpol — this is far from
trivial (and illdefined in general).
See also interpND, which includes boundary
conditions and allows you to switch the method of interpolation, but
which runs somewhat slower.
rebin
Signature: (a(m); [o]b(n); int ns => n)
Ndimensional rebinning algorithm
$new = rebin $a, $dim1, $dim2,..;.
$new = rebin $a, $template;
$new = rebin $a, $template, {Norm => 1};
Rebin an Ndimensional array to newly specified dimensions.
Specifying ‘Norm’ keeps the sum constant, otherwise the intensities
are kept constant. If more template dimensions are given than for the
input pdl, these dimensions are created; if less, the final dimensions
are maintained as they were.
So if $a is a 10 x 10 pdl, then rebin($a,15) is a 15 x 10 pdl,
while rebin($a,15,16,17) is a 15 x 16 x 17 pdl (where the values
along the final dimension are all identical).
Expansion is performed by sampling; reduction is performed by averaging.
If you want different behavior, use PDL::Transform::map
instead. PDL::Transform::map runs slower but is more flexible.
rebin does not process bad values.
It will set the badvalue flag of all output piddles if the flag is set for any of the input piddles.
circ_mean_p
Calculates the circular mean of an ndim image and returns
the projection. Optionally takes the center to be used.
$cmean=circ_mean_p($im);
$cmean=circ_mean_p($im,{Center => [10,10]});
circ_mean
Smooths an image by applying circular mean.
Optionally takes the center to be used.
circ_mean($im);
circ_mean($im,{Center => [10,10]});
kernctr
‘centre’ a kernel (auxiliary routine to fftconvolve)
$kernel = kernctr($image,$smallk);
fftconvolve($image,$kernel);
kernctr centres a small kernel to emulate the behaviour of the direct
convolution routines.
convolveND
Signature: (k0(); SV *k; SV *aa; SV *a)
Speedoptimized convolution with selectable boundary conditions
$new = convolveND($a, $kernel, [ {options} ]);
Conolve an array with a kernel, both of which are Ndimensional.
If the kernel has fewer dimensions than the array, then the extra array
dimensions are threaded over. There are options that control the boundary
conditions and method used.
The kernel’s origin is taken to be at the kernel’s center. If your
kernel has a dimension of even order then the origin’s coordinates get
rounded up to the next higher pixel (e.g. (1,2) for a 3x4 kernel).
This mimics the behavior of the earlier convolve and
fftconvolve routines, so convolveND is a dropin
replacement for them.
The kernel may be any size compared to the image, in any dimension.
The kernel and the array are not quite interchangeable (as in mathematical
convolution): the code is inplaceaware only for the array itself, and
the only allowed boundary condition on the kernel is truncation.
convolveND is inplaceaware: say convolveND(inplace $a ,$k) to modify
a variable inplace. You don’t reduce the working memory that way — only
the final memory.
OPTIONS
Options are parsed by PDL::Options, so unique abbreviations are accepted.

boundary (default: ’truncate’)

The boundary condition on the array, which affects any pixel closer
to the edge than the halfwidth of the kernel.
The boundary conditions are the same as those accepted by
range, because this option is passed directly
into range. Useful options are ’truncate’ (the
default), ’extend’, and ’periodic’. You can select different boundary
conditions for different axes — see range for more
detail.
The (default) truncate option marks all the nearboundary pixels as BAD if
you have bad values compiled into your PDL and the array’s badflag is set.

method (default: ’auto’)

The method to use for the convolution. Acceptable alternatives are
’direct’, ’fft’, or ’auto’. The direct method is an explicit
copyandmultiply operation; the fft method takes the Fourier
transform of the input and output kernels. The two methods give the
same answer to within doubleprecision numerical roundoff. The fft
method is much faster for large kernels; the direct method is faster
for tiny kernels. The tradeoff occurs when the array has about 400x
more pixels than the kernel.
The default method is ’auto’, which chooses direct or fft convolution
based on the size of the input arrays.


NOTES
At the moment there’s no way to thread over kernels. That could/should
be fixed.
The threading over input is cheesy and should probably be fixed:
currently the kernel just gets dummy dimensions added to it to match
the input dims. That does the right thing tersely but probably runs slower
than a dedicated threadloop.
The direct copying code uses PP primarily for the generic typing: it includes
its own threadloops.
convolveND does not process bad values.
It will set the badvalue flag of all output piddles if the flag is set for any of the input piddles.
AUTHORS
Copyright (C) Karl Glazebrook and Craig DeForest, 1997, 2003
All rights reserved. There is no warranty. You are allowed
to redistribute this software / documentation under certain
conditions. For details, see the file COPYING in the PDL
distribution. If this file is separated from the PDL distribution,
the copyright notice should be included in the file.
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