comparison util/ksvd utils/reggrid.m @ 70:c3eca463202d

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author idamnjanovic
date Wed, 16 Mar 2011 14:16:57 +0000
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69:5f1f436057ca 70:c3eca463202d
1 function [varargout] = reggrid(sz,num,mode)
2 %REGGRID Regular sampling grid.
3 % [I1,I2,...,Ip] = REGGRID([N1 N2 ... Np], NUM) returns the indices
4 % of a regular uniform sampling grid over a p-dimensional matrix with
5 % dimensions N1xN2x...xNp. NUM is the minimal number of required samples,
6 % and it is ensured that the actual number of samples, given by
7 % length(I1)xlength(I2)x...xlength(Ip), is at least as large as NUM.
8 %
9 % [I1,I2,...,Ip] = REGGRID([N1 N2 ... Np], NUM,'MODE') specifies the
10 % method for distributing the samples along each dimension. Valid modes
11 % include 'eqdist' (the default mode) and 'eqnum'. 'eqdist' indicates an
12 % equal distance between the samples in each dimension, while 'eqnum'
13 % indicates an equal number of samples in each dimension.
14 %
15 % Notes about MODE:
16 %
17 % 1. The 'eqnum' mode will generally fail when the p-th root of NUM
18 % (i.e. NUM^(1/p)) is larger than min([N1 N2 ... Np]). Thus 'eqdist' is
19 % the more useful choice for sampling an arbitrary number of samples
20 % from the matrix (up to the total number of matrix entries).
21 %
22 % 2. In both modes, the equality (of the distance between samples, or
23 % the number of samples in each dimension) is only approximate. This is
24 % because REGGRID attempts to maintain the appropriate equality while at
25 % the same time find a sampling pattern where the total number of
26 % samples is as close as possible to NUM. In general, the larger {Ni}
27 % and NUM are, the tighter the equality.
28 %
29 % Example: Sample a set of blocks uniformly from a 2D image.
30 %
31 % n = 512; blocknum = 20000; blocksize = [8 8];
32 % im = rand(n,n);
33 % [i1,i2] = reggrid(size(im)-blocksize+1, blocknum);
34 % blocks = sampgrid(im, blocksize, i1, i2);
35 %
36 % See also SAMPGRID.
37
38 % Ron Rubinstein
39 % Computer Science Department
40 % Technion, Haifa 32000 Israel
41 % ronrubin@cs
42 %
43 % November 2007
44
45 dim = length(sz);
46
47 if (nargin<3)
48 mode = 'eqdist';
49 end
50
51 if (any(sz<1))
52 error(['Invalid matrix size : [' num2str(sz) ']']);
53 end
54
55 if (num > prod(sz))
56 warning(['Invalid number of samples, returning maximum number of samples.']);
57 elseif (num <= 0)
58 if (num < 0)
59 warning('Invalid number of samples, assuming 0 samples.');
60 end
61 for i = 1:length(sz)
62 varargout{i} = [];
63 end
64 return;
65 end
66
67
68 if (strcmp(mode,'eqdist'))
69
70 % approximate distance between samples: total volume divided by number of
71 % samples gives the average volume per sample. then, taking the p-th root
72 % gives the average distance between samples
73 d = (prod(sz)/num)^(1/dim);
74
75 % compute the initial guess for number of samples in each dimension.
76 % then, while total number of samples is too large, decrese the number of
77 % samples by one in the dimension where the samples are the most crowded.
78 % finally, do the opposite process until just passing num, so the final
79 % number of samples is the closest to num from above.
80
81 n = min(max(round(sz/d),1),sz); % set n so that it saturates at 1 and sz
82
83 active_dims = find(n>1); % dimensions where the sample num can be reduced
84 while(prod(n)>num && ~isempty(active_dims))
85 [y,id] = min((sz(active_dims)-1)./n(active_dims));
86 n(active_dims(id)) = n(active_dims(id))-1;
87 if (n(active_dims(id)) < 2)
88 active_dims = find(n>1);
89 end
90 end
91
92 active_dims = find(n<sz); % dimensions where the sample num can be increased
93 while(prod(n)<num && ~isempty(active_dims))
94 [y,id] = max((sz(active_dims)-1)./n(active_dims));
95 n(active_dims(id)) = n(active_dims(id))+1;
96 if (n(active_dims(id)) >= sz(active_dims(id)))
97 active_dims = find(n<sz);
98 end
99 end
100
101 for i = 1:dim
102 varargout{i} = round((1:n(i))/n(i)*sz(i));
103 varargout{i} = varargout{i} - floor((varargout{i}(1)-1)/2);
104 end
105
106 elseif (strcmp(mode,'eqnum'))
107
108 % same idea as above
109 n = min(max( ones(size(sz)) * round(num^(1/dim)) ,1),sz);
110
111 active_dims = find(n>1);
112 while(prod(n)>num && ~isempty(active_dims))
113 [y,id] = min((sz(active_dims)-1)./n(active_dims));
114 n(active_dims(id)) = n(active_dims(id))-1;
115 if (n(active_dims(id)) < 2)
116 active_dims = find(n>1);
117 end
118 end
119
120 active_dims = find(n<sz);
121 while(prod(n)<num && ~isempty(active_dims))
122 [y,id] = max((sz(active_dims)-1)./n(active_dims));
123 n(active_dims(id)) = n(active_dims(id))+1;
124 if (n(active_dims(id)) >= sz(active_dims(id)))
125 active_dims = find(n<sz);
126 end
127 end
128
129 for i = 1:dim
130 varargout{i} = round((1:n(i))/n(i)*sz(i));
131 varargout{i} = varargout{i} - floor((varargout{i}(1)-1)/2);
132 end
133 else
134 error('Invalid sampling mode');
135 end
136