1ImageND(3) User Contributed Perl Documentation ImageND(3)
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6 PDL::ImageND - useful image processing in N dimensions
7
9 These routines act on PDLs as N-dimensional objects, not as threaded
10 sets of 0-D or 1-D objects. The file is sort of a catch-all for
11 broadly functional routines, most of which could legitimately be filed
12 elsewhere (and probably will, one day).
13 ImageND is not a part of the PDL core (v2.4) and hence must be
15 explicitly loaded.
16
18 use PDL::ImageND;
19 $b = $a->convolveND($kernel,{bound=>'periodic'});
21 $b = $a->rebin(50,30,10);
22
24 convolve
25 Signature: (a(m); b(n); indx adims(p); indx bdims(q); [o]c(m))
26 N-dimensional convolution (Deprecated; use convolveND)
28 $new = convolve $a, $kernel
30 Convolve an array with a kernel, both of which are N-dimensional. This
32 routine does direct convolution (by copying) but uses quasi-periodic
33 boundary conditions: each dim "wraps around" to the next higher row in
34 the next dim.
35 This routine is kept for backwards compatibility with earlier scripts;
37 for most purposes you want convolveND instead: it runs faster and
38 handles a variety of boundary conditions.
39 convolve does not process bad values. It will set the bad-value flag
41 of all output piddles if the flag is set for any of the input piddles.
42 ninterpol()
44 N-dimensional interpolation routine
45 Signature: ninterpol(point(),data(n),[o]value())
47 $value = ninterpol($point, $data);
49 "ninterpol" uses "interpol" to find a linearly interpolated value in N
51 dimensions, assuming the data is spread on a uniform grid. To use an
52 arbitrary grid distribution, need to find the grid-space point from the
53 indexing scheme, then call "ninterpol" -- this is far from trivial (and
54 ill-defined in general).
55 See also interpND, which includes boundary conditions and allows you to
57 switch the method of interpolation, but which runs somewhat slower.
58 rebin
60 Signature: (a(m); [o]b(n); int ns => n)
61 N-dimensional rebinning algorithm
63 $new = rebin $a, $dim1, $dim2,..;. $new = rebin $a, $template; $new =
65 rebin $a, $template, {Norm => 1};
66 Rebin an N-dimensional array to newly specified dimensions. Specifying
68 `Norm' keeps the sum constant, otherwise the intensities are kept
69 constant. If more template dimensions are given than for the input
70 pdl, these dimensions are created; if less, the final dimensions are
71 maintained as they were.
72 So if $a is a 10 x 10 pdl, then "rebin($a,15)" is a 15 x 10 pdl, while
74 "rebin($a,15,16,17)" is a 15 x 16 x 17 pdl (where the values along the
75 final dimension are all identical).
76 Expansion is performed by sampling; reduction is performed by
78 averaging. If you want different behavior, use PDL::Transform::map
79 instead. PDL::Transform::map runs slower but is more flexible.
80 rebin does not process bad values. It will set the bad-value flag of
82 all output piddles if the flag is set for any of the input piddles.
83 circ_mean_p
85 Calculates the circular mean of an n-dim image and returns the
86 projection. Optionally takes the center to be used.
87 $cmean=circ_mean_p($im);
89 $cmean=circ_mean_p($im,{Center => [10,10]});
90 circ_mean
92 Smooths an image by applying circular mean. Optionally takes the
93 center to be used.
94 circ_mean($im);
96 circ_mean($im,{Center => [10,10]});
97 kernctr
99 `centre' a kernel (auxiliary routine to fftconvolve)
100 $kernel = kernctr($image,$smallk);
102 fftconvolve($image,$kernel);
103 kernctr centres a small kernel to emulate the behaviour of the direct
105 convolution routines.
106 convolveND
108 Signature: (k0(); SV *k; SV *aa; SV *a)
109 Speed-optimized convolution with selectable boundary conditions
111 $new = convolveND($a, $kernel, [ {options} ]);
113 Conolve an array with a kernel, both of which are N-dimensional.
115 If the kernel has fewer dimensions than the array, then the extra array
117 dimensions are threaded over. There are options that control the
118 boundary conditions and method used.
119 The kernel's origin is taken to be at the kernel's center. If your
121 kernel has a dimension of even order then the origin's coordinates get
122 rounded up to the next higher pixel (e.g. (1,2) for a 3x4 kernel).
123 This mimics the behavior of the earlier convolve and fftconvolve
124 routines, so convolveND is a drop-in replacement for them.
125 The kernel may be any size compared to the image, in any dimension.
127 The kernel and the array are not quite interchangeable (as in
129 mathematical convolution): the code is inplace-aware only for the array
130 itself, and the only allowed boundary condition on the kernel is
131 truncation.
132 convolveND is inplace-aware: say "convolveND(inplace $a ,$k)" to modify
134 a variable in-place. You don't reduce the working memory that way --
135 only the final memory.
136 OPTIONS
138 Options are parsed by PDL::Options, so unique abbreviations are
140 accepted.
141 boundary (default: 'truncate')
143 The boundary condition on the array, which affects any pixel closer
144 to the edge than the half-width of the kernel.
145 The boundary conditions are the same as those accepted by range,
147 because this option is passed directly into range. Useful options
148 are 'truncate' (the default), 'extend', and 'periodic'. You can
149 select different boundary conditions for different axes -- see range
150 for more detail.
151 The (default) truncate option marks all the near-boundary pixels as
153 BAD if you have bad values compiled into your PDL and the array's
154 badflag is set.
155 method (default: 'auto')
157 The method to use for the convolution. Acceptable alternatives are
158 'direct', 'fft', or 'auto'. The direct method is an explicit copy-
159 and-multiply operation; the fft method takes the Fourier transform
160 of the input and output kernels. The two methods give the same
161 answer to within double-precision numerical roundoff. The fft
162 method is much faster for large kernels; the direct method is faster
163 for tiny kernels. The tradeoff occurs when the array has about 400x
164 more pixels than the kernel.
165 The default method is 'auto', which chooses direct or fft
167 convolution based on the size of the input arrays.
168 NOTES
170 At the moment there's no way to thread over kernels. That could/should
172 be fixed.
173 The threading over input is cheesy and should probably be fixed:
175 currently the kernel just gets dummy dimensions added to it to match
176 the input dims. That does the right thing tersely but probably runs
177 slower than a dedicated threadloop.
178 The direct copying code uses PP primarily for the generic typing: it
180 includes its own threadloops.
181 convolveND does not process bad values. It will set the bad-value flag
183 of all output piddles if the flag is set for any of the input piddles.
184
186 Copyright (C) Karl Glazebrook and Craig DeForest, 1997, 2003 All rights
187 reserved. There is no warranty. You are allowed to redistribute this
188 software / documentation under certain conditions. For details, see the
189 file COPYING in the PDL distribution. If this file is separated from
190 the PDL distribution, the copyright notice should be included in the
191 file.
192perl v5.30.0 2019-09-05 ImageND(3)