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imnorm.m
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Fri, May 10, 01:19

imnorm.m
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function [img, minval, maxval] = imnorm(img, minval, maxval, mode, minrange, maxrange)
% IMNORM normalizes the pixel value of an image.
%
% [IMG] = IMNORM(ORIG) normalizes the pixel value of ORIG such that it ranges
% between 0 and 1 in IMG. A linear scaling between the min and max values of ORIG
% is applied. Non-finite values are replaced by NaN.
%
% [...] = IMNORM(STACK) normalizes the entire stack as a whole.
%
% [IMG, MIN, MAX] = IMNORM(...) returns in addition the MIN and MAX values found.
%
% [...] = IMNORM(ORIG, MIN, MAX) normalizes the values between MIN and MAX in ORIG
% such that it ranges between 0 and 1 in IMG. Values outside [MIN, MAX] are clipped.
%
% [...] = IMNORM(ORIG, MIN, MAX, MODE) performs the normalization according to the
% defined MODE: either 'column', 'row' or 'slice' -wise. Provide empty values for
% MIN and MAX to utilize the extrema values in ORIG.
%
% [...] = IMNORM(ORIG, MIN, MAX, MODE, LBOUND, UBOUND) performs the normalization
% such that the pixel value in IMG ranges between LBOUND and UBOUND. Provide an
% empty string for MODE to ignore this parameter.
%
% Gonczy & Naef labs, EPFL
% Simon Blanchoud
% 15.05.2014
% Input checking and default values
if (nargin == 1)
minval = [];
maxval = [];
mode = '';
elseif (nargin < 4)
mode = '';
end
% In case we have no data
if (nargin == 0 || isempty(img))
return;
end
% The size of the image
[h, w, p] = size(img);
% Get the type of img as we need to convert it to double for the division
class_type = class(img);
is_double = (class_type(1) == 'd');
% In that case, convert it
if (~is_double)
img = double(img);
end
% More input checking
if (nargin < 6)
if (is_double)
minrange = 0;
maxrange = 1;
else
minrange = intmin(class_type);
maxrange = intmax(class_type);
end
end
% And where there are non-finite elements, we replace them with NaN which do not
% conflict with min() and max()
img(~isfinite(img)) = NaN;
% Convert to lower case, just in case !
mode = lower(mode);
% In case we do not have a minimal value, we need to compute it
if (isempty(minval))
% The simplest case, we get the overall minimum
if (isempty(mode))
minval = min(img(:));
else
% Otherwise, we get it along the corresponding dimension. In addition, we
% need to reconstruct a matrix of the proper size for the normalization.
switch mode(1)
case 'r'
minval = repmat(min(img,[],2), [1, w, 1]);
case 'c'
minval = repmat(min(img,[],1), [h, 1, 1]);
case 's'
minval = repmat(min(img,[],3), [1, 1, p]);
case 'a'
minval = min(img(:));
% Here we have a problem, so notify it and ignore the provided mode
otherwise
warning('CAST:imnorm', ['Normalization mode ' mode ' is unknown. Ignoring it.']);
minval = min(img(:));
% And remove the weird mode
mode = 'a';
end
end
end
% Here we have exactly the same, except for the max value
if (isempty(maxval))
if (isempty(mode))
maxval = max(img(:));
else
switch mode(1)
case 'r'
maxval = repmat(max(img,[],2), [1, w, 1]);
case 'c'
maxval = repmat(max(img,[],1), [h, 1, 1]);
case 's'
maxval = repmat(max(img,[],3), [1, 1, p]);
case 'a'
maxval = max(img(:));
% Here we have a problem, so notify it and ignor the provided mode
otherwise
warning('CAST:imnorm', ['Normalization mode ' mode ' is unknown. Ignoring it.']);
maxval = max(img(:));
end
end
end
% Cast all variables to double just in case
minval = double(minval);
maxval = double(maxval);
minrange = double(minrange);
maxrange = double(maxrange);
% Now for the normalization per se. Because we kept matrices of the proper size,
% we can compute it element-wise.
img = (img - (minval - minrange)) .* ((maxrange - minrange) ./ (maxval - minval));
% Clip the values outside of the defined range
img(img < minrange) = minrange;
img(img > maxrange) = maxrange;
% And convert back to the original type
if (~is_double)
img = cast(img, class_type);
end
return;
end

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