function [I limits map] = sc(I, varargin)
%SC Display/output truecolor images with a range of colormaps
%
% Examples:
% sc(image)
% sc(image, limits)
% sc(image, map)
% sc(image, limits, map)
% sc(image, map, limits)
% sc(..., col1, mask1, col2, mask2,...)
% out = sc(...)
% [out clim map] = sc(...)
% sc
%
% Generates a truecolor RGB image based on the input values in 'image' and
% any maximum and minimum limits specified, using the colormap specified.
% The image is displayed on screen if there is no output argument.
%
% SC has these advantages over MATLAB image rendering functions:
% - images can be displayed or output; makes combining/overlaying images
% simple.
% - images are rendered/output in truecolor (RGB [0,1]); no nasty
% discretization of the input data.
% - many special, built-in colormaps for viewing various types of data.
% - linearly interpolates user defined linear and non-linear colormaps.
% - no border and automatic, integer magnification (unless figure is
% docked or maximized) for better display.
% - multiple images can be generated simultaneously.
%
% For a demonstration, simply call SC without any input arguments.
%
% If you don't like the display behaviour of SC, e.g. the fixed aspect
% ratio, no border and axes, then you can replicate the behaviour of
% IMAGESC using the wrapper function IMSC.
%
% IN:
% image - MxNxCxP or 3xMxNxP image array. MxN are the dimensions of the
% image(s), C is the number of channels, and P the number of
% images. If P > 1, images can only be exported, not displayed.
% limits - [min max] where values in image less than min will be set to
% min and values greater than max will be set to max.
% map - Kx3 or Kx4 user defined colormap matrix, where the optional 4th
% column is the relative distance between colours along the scale,
% or a string containing the name of the colormap to use to create
% the output image. Default: 'none', which is RGB for 3-channel
% images, grayscale otherwise. Conversion of multi-channel images
% to intensity for intensity-based colormaps is done using the L2
% norm. Most MATLAB colormaps are supported. All named colormaps
% can be reversed by prefixing '-' to the string. This maintains
% integrity of the colorbar. Special, non-MATLAB colormaps are:
% 'hicontrast' - a high contrast colormap for intensity images that
% maintains intensity scale when converted to
% grayscale, for example when printing in black &
% white.
% 'prob' - first channel is plotted as hue, and the other channels
% modulate intensity. Useful for laying probabilites over
% images.
% 'prob_jet' - first channel is plotted as jet colormap, and the other
% channels modulate intensity.
% 'diff' - intensity values are marked blue for > 0 and red for < 0.
% Darker colour means larger absolute value. For multi-
% channel images, the L2 norm of the other channels sets
% green level. 3 channel images are converted to YUV and
% images with more that 3 channels are projected onto the
% principle components first.
% 'compress' - compress many channels to RGB while maximizing
% variance.
% 'contrast' - apply the CONTRAST function to create an approximately
% equal intensity distribution.
% 'flow' - display two channels representing a 2d Cartesian vector as
% hue for angle and intensity for magnitude (darker colour
% indicates a larger magnitude).
% 'phase' - first channel is intensity, second channel is phase in
% radians. Darker colour means greater intensity, hue
% represents phase from 0 to 2 pi.
% 'stereo' - pair of concatenated images used to generate a red/cyan
% anaglyph.
% 'stereo_col' - pair of concatenated RGB images used to generate a
% colour anaglyph.
% 'segment' - given concatenated image and segmentation (index image),
% colour each segment with the mean colour of the image in
% that segment.
% 'rand' - gives an index image a random colormap. Useful for viewing
% segmentations.
% 'rgb2gray' - converts an RGB image to grayscale in the same fashion
% as MATLAB's rgb2gray (in the image processing toolbox).
% col/mask pairs - Pairs of parameters for coloring specific parts of the
% image differently. The first (col) parameter can be
% a MATLAB color specifier, e.g. 'b' or [0.5 0 1], or
% one of the colormaps named above, or an MxNx3 RGB
% image. The second (mask) paramater should be an MxN
% logical array indicating those pixels (true) whose
% color should come from the specified color parameter.
% If there is only one col parameter, without a mask
% pair, then mask = any(isnan(I, 3)), i.e. the mask is
% assumed to indicate the location of NaNs. Note that
% col/mask pairs are applied in order, painting over
% previous pixel values.
%
% OUT:
% out - MxNx3xP truecolour (double) RGB image array in range [0, 1].
% clim - 1x2 vector of limits of the input array mapped to linearly
% [0, 1] in out. [] if this mapping is not available, e.g. if
% output image is multi-spectral.
% map - 256x3 colormap of the output. [] is limits is [].
%
% See also IMAGE, IMAGESC, IMSC, COLORMAP, COLORBAR.
% Copyright: Oliver Woodford, 2007-2010
%% Check for arguments
if nargin == 0
% If there are no input arguments then run the demo
if nargout > 0
error('Output expected from no inputs!');
end
demo; % Run the demo
return
end
%% Size our image(s)
[y x c n] = size(I);
I = reshape(I, y, x, c, n);
%% Check if image is given with RGB colour along the first dimension
if y == 3 && c > 3
% Flip colour to 3rd dimension
I = permute(I, [2 3 1 4]);
[y x c n] = size(I);
end
%% Don't do much if I is empty
if isempty(I)
if nargout == 0
% Clear the current axes if we were supposed to display the image
cla; axis off;
else
% Create an empty array with the correct dimensions
I = zeros(y, x, (c~=0)*3, n);
end
return
end
%% Check for multiple images
% If we have a non-singleton 4th dimension we want to display the images in
% a 3x4 grid and use buttons to cycle through them
if n > 1
% Return transformed images in an YxXx3xN array
A = zeros(y, x, 3, n);
for a = 1:n
A(:,:,:,a) = sc(I(:,:,:,a), varargin{:});
end
I = A;
if nargout == 0
clear I
if n > 12
sz = [3 4];
else
sz = [];
end
imdisp(A, 'Size', sz, 'Border', 0.01);
end
return
end
%% Parse the input arguments coming after I (1st input)
[map limits mask] = parse_inputs(I, varargin, y, x);
%% Call the rendering function
if isnumeric(map) && (size(map, 2) == 3 || size(map, 2) == 4)
% Table-based colormap
[I limits map] = table_wrapper(I, map, limits);
elseif ischar(map)
% Predefined colormap
[I limits map] = colormap_switch(I, map, limits);
else
error('map input invalid.');
end
%% Update any masked pixels
I = reshape(I, y*x, 3);
for a = 1:size(mask, 2)
I(mask{2,a},1) = mask{1,a}(:,1);
I(mask{2,a},2) = mask{1,a}(:,2);
I(mask{2,a},3) = mask{1,a}(:,3);
end
I = reshape(I, [y x 3]); % Reshape to correct size
%% Only display if the output isn't used
if nargout == 0
% Display the image
hIm = imdisp(I, 'Border', 0);
% Set the colormap (if valid)
if ~isempty(limits)
set(gcf, 'Colormap', map);
set(gca, 'CLim', limits);
set(hIm, 'CDataMapping', 'scaled');
end
% Don't print out the matrix if we've forgotten the ";"
clear I
end
return
%% Colormap switch
function [I limits map] = colormap_switch(A, cmap, limits)
% Reshape
[y x c] = size(A);
I = full(real(double(reshape(A, y*x, 1, c))));
% If map starts with a '-' sign, invert the colormap
reverseMap = cmap(1) == '-';
% If the map ends with a '*', attempt to make map convert linearly to
% grayscale
grayMap = cmap(end) == '*';
% Large switch statement for all the colormaps
switch lower(cmap(1+reverseMap:end-grayMap))
%% Prism
case 'prism'
% Similar to the MATLAB internal prism colormap, but only works on
% index images, assigning each index (or rounded float) to a
% different colour
[I limits] = index_im(I);
% Generate prism colormap
map = prism(6);
if reverseMap
map = map(end:-1:1,:); % Reverse the map
end
% Lookup the colours
I = mod(I, 6) + 1;
I = map(I,:);
%% Rand
case 'rand'
% Assigns a random colour to each index
[I limits num_vals] = index_im(I);
% Generate random colormap
map = rand(num_vals, 3);
% Lookup the colours
I = map(I,:);
%% Diff
case 'diff'
% Show positive as blue and negative as red, white is 0
switch c
case 1
I(:,2:3) = 0;
case 2
% Second channel can only have absolute value
I(:,3) = abs(I(:,2));
case 3
% Diff of RGB images - convert to YUV first
I = rgb2yuv(I);
I(:,3) = sqrt(sum(I(:,2:end) .^ 2, 2)) ./ sqrt(2);
otherwise
% Use difference along principle component, and other
% channels to modulate second channel
I = calc_prin_comps(I);
I(:,3) = sqrt(sum(I(:,2:end) .^ 2, 2)) ./ sqrt(c - 1);
I(:,4:end) = [];
end
% Generate limits
if isempty(limits)
limits = [min(I(:,1)) max(I(:,1))];
end
limits = max(abs(limits));
if limits
% Scale
if c > 1
I(:,[1 3]) = I(:,[1 3]) / limits;
else
I = I / (limits * 0.5);
end
else
limits = 1;
end
% Colour
M = I(:,1) > 0;
I(:,2) = -I(:,1) .* ~M;
I(:,1) = I(:,1) .* M;
if reverseMap
% Swap first two channels
I = I(:,[2 1 3]);
end
%I = 1 - I * [1 0.4 1; 0.4 1 1; 1 1 0.4]; % (Green/Red)
I = 1 - I * [1 1 0.4; 0.4 1 1; 1 0.4 1]; % (Blue/Red)
I = min(max(I, 0), 1);
limits = [-limits limits]; % For colorbar
%% Flow
case 'flow'
% Calculate amplitude and phase, and use 'phase'
if c ~= 2
error('''flow'' requires two channels');
end
A = sqrt(sum(I .^ 2, 3));
if isempty(limits)
limits = [min(A) max(A)*2];
else
limits = [0 max(abs(limits)*sqrt(2))*2];
end
I(:,1) = atan2(I(:,2), I(:,1));
I(:,2) = A;
if reverseMap
% Invert the amplitude
I(:,2) = -I(:,2);
limits = -limits([2 1]);
end
I = phase_helper(I, limits, 2); % Last parameter tunes how saturated colors can get
% Set NaNs (unknown flow) to 0
I(isnan(I)) = reverseMap;
limits = []; % This colormap doesn't have a valid colorbar
%% Phase
case 'phase'
% Plot amplitude as intensity and angle as hue
if c < 2
error('''phase'' requires two channels');
end
if isempty(limits)
limits = [min(I(:,1)) max(I(:,1))];
end
if reverseMap
% Invert the phase
I(:,2) = -I(:,2);
end
I = I(:,[2 1]);
if diff(limits)
I = phase_helper(I, limits, 1.3); % Last parameter tunes how saturated colors can get
else
% No intensity - just cycle hsv
I = real2rgb(mod(I(:,1) / (2 * pi), 1), 'hsv');
end
limits = []; % This colormap doesn't have a valid colorbar
%% RGB2Gray
case {'rgb2gray', 'rgb2grey'}
% Compress RGB to greyscale
[I limits] = rgb2gray(I, limits, reverseMap);
%% RGB2YUV
case 'rgb2yuv'
% Convert RGB to YUV - not for displaying or saving to disk!
[I limits] = rgb2yuv(I);
%% YUV2RGB
case 'yuv2rgb'
% Convert YUV to RGB - undo conversion of rgb2yuv
if c ~= 3
error('''yuv2rgb'' requires a 3 channel image');
end
I = I(:,:) * [1 1 1; 0, -0.39465, 2.03211; 1.13983, -0.58060 0];
I = rescale(I, limits);
limits = []; % This colormap doesn't have a valid colorbar
%% Prob
case 'prob'
% Plot first channel as grey variation of 'bled' and modulate
% according to other channels
if c > 1
A = rgb2gray(I(:,:,2:end), [], false);
I = I(:,:,1);
else
A = 0.5;
end
cmap2 = cmap;
cmap2((1:4)+reverseMap) = 'bled';
[I limits] = real2rgb(I, cmap2, limits);
I = rescale(A + I, [-0.1 1.3]);
%% Prob_jet
case 'prob_jet'
% Plot first channel as 'jet' and modulate according to other
% channels
if c > 1
A = rgb2gray(I(:,:,2:end), [], false);
I = I(:,:,1);
else
A = 0.5;
end
cmap2 = cmap;
cmap2(reverseMap+(1:5)) = [];
[I limits] = real2rgb(I, cmap2, limits);
I = rescale(A + I, [0.2 1.8]);
%% Compress
case 'compress'
% Compress to RGB, maximizing variance
% Determine and scale to limits
I = rescale(I, limits);
if reverseMap
% Invert after everything
I = 1 - I;
end
% Zero mean
meanCol = mean(I, 1);
isBsx = exist('bsxfun', 'builtin');
if isBsx
I = bsxfun(@minus, I, meanCol);
else
I = I - meanCol(ones(x*y, 1, 'uint8'),:);
end
% Calculate top 3 principle components
I = calc_prin_comps(I, 3);
% Normalize each channel independently
if isBsx
I = bsxfun(@minus, I, min(I, [], 1));
I = bsxfun(@times, I, 1./max(I, [], 1));
else
for a = 1:3
I(:,a) = I(:,a) - min(I(:,a));
I(:,a) = I(:,a) / max(I(:,a));
end
end
% Put components in order of human eyes' response to channels
I = I(:,[2 1 3]);
limits = []; % This colormap doesn't have a valid colorbar
%% Contrast
case 'contrast'
% Use MATLAB's CONTRAST function to spread the gray levels evenly
[I limits] = intensity(I, limits, 0);
map = contrast(I, 256);
if reverseMap
map = map(end:-1:1,:);
end
I = real2rgb(I, map, [0 1]);
%% Stereo (anaglyph)
case 'stereo'
% Convert 2 colour images to intensity images
% Show first channel as red and second channel as cyan
A = rgb2gray(I(:,1:floor(end/2)), limits, false);
I = rgb2gray(I(:,floor(end/2)+1:end), limits, false);
if reverseMap
I(:,2:3) = A(:,1:2); % Make first image cyan
else
I(:,1) = A(:,1); % Make first image red
end
limits = []; % This colormap doesn't have a valid colorbar
%% Coloured anaglyph
case 'stereo_col'
if c ~= 6
error('''stereo_col'' requires a 6 channel image');
end
I = rescale(I, limits);
% Red channel from one image, green and blue from the other
if reverseMap
I(:,1) = I(:,4); % Make second image red
else
I(:,2:3) = I(:,5:6); % Make first image red
end
I = I(:,1:3);
limits = []; % This colormap doesn't have a valid colorbar
%% Coloured segments
case 'segment'
% Last channel assumed to be an index image. Output each segment as
% a mean of the other channels in the segment.
if c < 2
error('''segment'' requires at least 2 channels');
end
[S num_vals num_vals] = index_im(I(:,end));
% Generate the means
R = 1 ./ (accumarray(S, 1, [num_vals 1]) + 1e-300);
for a = c-1:-1:1
J(:,1,a) = accumarray(S, I(:,a), [num_vals 1]) .* R;
end
if c == 4
J = rescale(J, limits);
limits = [];
else
[J limits map] = table_wrapper(J, 'gray', limits);
end
% Generate the image
I = J(S,:);
%% None
case 'none'
% No colormap - just output the image
if c == 3
I = rescale(I, limits);
limits = [];
if reverseMap
I = 1 - I;
end
else
cmap((1:4)+reverseMap) = 'gray';
[I limits map] = table_wrapper(I, cmap, limits);
end
%% Grey
case 'grey'
cmap(3+reverseMap) = 'a';
[I limits map] = table_wrapper(I, cmap, limits);
%% Colourcube
case {'colorcube2', 'colourcube2'}
% Psychedelic colormap inspired by MATLAB's version
[I limits] = intensity(I, limits, reverseMap); % Intensity map
step = 4;
I = I * (step * (1 - eps));
J = I * step;
K = floor(J);
I = cat(3, mod(K, step)/(step-1), J - floor(K), mod(floor(I), step)/(step-1));
%% Functional colormap
otherwise
[I limits map] = table_wrapper(I, cmap, limits);
end
I = reshape(I, y, x, 3);
% Fix up the colormap
if nargout > 2
if isempty(limits)
map = [];
elseif ~exist('map', 'var')
map = (0:255) * ((limits(2) - limits(1)) / 255) + limits(1);
map = squeeze(colormap_switch(map, cmap, limits));
end
end
return
%% Parse input variables
function [map limits mask] = parse_inputs(I, inputs, y, x)
% Check the first two arguments for the colormap and limits
ninputs = numel(inputs);
map = 'none';
limits = [];
mask = 1;
for a = 1:min(2, ninputs)
if ischar(inputs{a}) && numel(inputs{a}) > 1
% Name of colormap
map = inputs{a};
elseif isnumeric(inputs{a})
[p q r] = size(inputs{a});
if (p * q * r) == 2
% Limits
limits = double(inputs{a});
elseif p > 1 && (q == 3 || q == 4) && r == 1
% Table-based colormap
map = inputs{a};
else
break;
end
else
break;
end
mask = mask + 1;
end
% Check for following inputs
if mask > ninputs
mask = cell(2, 0);
return
end
% Following inputs must either be colour/mask pairs, or a colour for NaNs
if ninputs - mask == 0
mask = cell(2, 1);
mask{1} = inputs{end};
mask{2} = ~all(isfinite(I), 3);
elseif mod(ninputs-mask, 2) == 1
mask = reshape(inputs(mask:end), 2, []);
else
error('Error parsing inputs');
end
% Go through pairs and generate
for a = 1:size(mask, 2)
% Generate any masks from functions
if isa(mask{2,a}, 'function_handle')
mask{2,a} = mask{2,a}(I);
end
if ~islogical(mask{2,a})
error('Mask is not a logical array');
end
if ~isequal(size(mask{2,a}), [y x])
error('Mask does not match image size');
end
if ischar(mask{1,a})
if numel(mask{1,a}) == 1
% Generate colours from MATLAB colour strings
mask{1,a} = rem(floor((strfind('kbgcrmyw', mask{1,a}) - 1) * [0.25 0.5 1]), 2);
else
% Assume it's a colormap name
mask{1,a} = sc(I, mask{1,a});
end
end
mask{1,a} = reshape(mask{1,a}, [], 3);
if size(mask{1,a}, 1) ~= y*x && size(mask{1,a}, 1) ~= 1
error('Replacement color/image of unexpected dimensions');
end
if size(mask{1,a}, 1) ~= 1
mask{1,a} = mask{1,a}(mask{2,a},:);
end
end
return
%% RGB2gray
function [B limits] = rgb2gray(A, limits, reverseMap)
% Compress RGB to grayscale
c = size(A, 3);
B = A;
if c == 3
B = reshape(reshape(B, [], 3) * [0.299; 0.587; 0.114], size(B, 1), size(B, 2));
end
[B limits] = intensity(B, limits, reverseMap);
% Replicate channels
B = B(:,:,[1 1 1]);
return
%% RGB2YUV
function [B limits] = rgb2yuv(A)
% Convert RGB to YUV - not for displaying or saving to disk!
c = size(A, 3);
if c ~= 3
error('rgb2yuv requires a 3 channel image');
end
B = reshape(A, [], c) * [0.299, -0.14713, 0.615; 0.587, -0.28886, -0.51498; 0.114, 0.436, -0.10001];
limits = []; % This colormap doesn't have a valid colorbar
return
%% Phase helper
function I = phase_helper(I, limits, n)
I = squeeze(I);
I(:,1) = mod(I(:,1)/(2*pi), 1);
I(:,2) = I(:,2) - limits(1);
I(:,2) = I(:,2) * (n / (limits(2) - limits(1)));
I(:,3) = n - I(:,2);
I(:,[2 3]) = min(max(I(:,[2 3]), 0), 1);
I = hsv2rgb(reshape(I, [], 1, 3));
return
%% Real2rgb wrapper
function [B limits map] = table_wrapper(A, cmap, limits)
% Convert to intensity
[B limits] = intensity(A, limits, 0);
% Convert to colour using the specified colormap
[B map map] = real2rgb(B, cmap, [0 1]);
return
%% Intensity maps
function [I limits] = intensity(I, limits, reverseMap)
% Squash to 1d using L2 norm
if size(I, 3) > 1
I = sqrt(sum(I .^ 2, 3));
end
% Determine and scale to limits
[I limits] = rescale(I, limits);
if limits(1) == limits(2)
limits(2) = limits(1) + 1;
end
if reverseMap
% Invert after everything
I = 1 - I;
end
return
%% Index images
function [J limits num_vals] = index_im(I)
% Returns an index image
if size(I, 2) ~= 1
error('Index maps only work on single channel images');
end
J = round(I);
rescaled = any(abs(I - J) > 0.01);
if rescaled
% Appears not to be an index image. Rescale over 256 indices
m = min(I);
m = m * (1 - sign(m) * eps);
I = I - m;
I = I * (256 / max(I(:)));
J = ceil(I);
num_vals = 256;
elseif nargout > 2
% Output the number of values
J = J - (min(J) - 1);
num_vals = max(J);
end
% These colormaps don't have valid colorbars
limits = [];
return
%% Calculate principle components
function I = calc_prin_comps(I, numComps)
c = size(I, 3);
I = reshape(I, [], c);
if nargin < 2
numComps = c;
end
% Do SVD
[I S] = svd(I, 0);
% Calculate projection of data onto components
S = diag(S);
S = S(1:numComps)';
if exist('bsxfun', 'builtin')
I = bsxfun(@times, I(:,1:numComps), S);
else
I = I(:,1:numComps) .* S(ones(size(I, 1), 1, 'uint8'),:);
end
return
%% Demo function to show capabilities of sc
function demo
%% Demo gray & lack of border
figure; fig = gcf; Z = peaks(256); sc(Z);
display_text([...
' Lets take a standard, MATLAB, real-valued function:\n\n peaks(256)\n\n'...
' Calling:\n\n figure\n Z = peaks(256);\n sc(Z)\n\n'...
' gives (see figure). SC automatically scales intensity to fill the\n'...
' truecolor range of [0 1].\n\n'...
' If your figure isn''t docked, then the image will have no border, and\n'...
' will be magnified by an integer factor (in this case, 2) so that the\n'...
' image is a reasonable size.']);
%% Demo colour image display
figure(fig); clf;
load mandrill; mandrill = ind2rgb(X, map); sc(mandrill);
display_text([...
' That wasn''t so interesting. The default colormap is ''none'', which\n'...
' produces RGB images given a 3-channel input image, otherwise it produces\n'...
' a grayscale image. So calling:\n\n load mandrill\n'...
' mandrill = ind2rgb(X, map);\n sc(mandrill)\n\n gives (see figure).']);
%% Demo discretization
figure(fig); clf;
subplot(121); sc(Z, 'jet'); label(Z, 'sc(Z, ''jet'')');
subplot(122); imagesc(Z); axis image off; colormap(jet(64)); % Fix the fact we change the default depth
label(Z, 'imagesc(Z); axis image off; colormap(''jet'');');
display_text([...
' However, if we want to display intensity images in color we can use any\n'...
' of the MATLAB colormaps implemented (most of them) to give truecolor\n'...
' images. For example, to use ''jet'' simply call:\n\n'...
' sc(Z, ''jet'')\n\n'...
' The MATLAB alternative, shown on the right, is:\n\n'...
' imagesc(Z)\n axis equal off\n colormap(jet)\n\n'...
' which generates noticeable discretization artifacts.']);
%% Demo intensity colormaps
figure(fig); clf;
subplot(221); sc(Z, 'hsv'); label(Z, 'sc(Z, ''hsv'')');
subplot(222); sc(Z, 'colorcube'); label(Z, 'sc(Z, ''colorcube'')');
subplot(223); sc(Z, 'hicontrast'); label(Z, 'sc(Z, ''hicontrast'')');
subplot(224); sc(Z-round(Z), 'diff'); label(Z, 'sc(Z-round(Z), ''diff'')');
display_text([...
' There are several other intensity colormaps to choose from. Calling:\n\n'...
' help sc\n\n'...
' will give you a list of them. Here are several others demonstrated.']);
%% Demo saturation limits & colormap reversal
figure(fig); clf;
subplot(121); sc(Z, [0 max(Z(:))], '-hot'); label(Z, 'sc(Z, [0 max(Z(:))], ''-hot'')');
subplot(122); sc(mandrill, [-0.5 0.5]); label(mandrill, 'sc(mandrill, [-0.5 0.5])');
display_text([...
' SC can also rescale intensity, given an upper and lower bound provided\n'...
' by the user, and invert most colormaps simply by prefixing a ''-'' to the\n'...
' colormap name. For example:\n\n'...
' sc(Z, [0 max(Z(:))], ''-hot'');\n'...
' sc(mandrill, [-0.5 0.5]);\n\n'...
' Note that the order of the colormap and limit arguments are\n'...
' interchangable.']);
%% Demo prob
load gatlin;
gatlin = X;
figure(fig); clf; im = cat(3, abs(Z)', gatlin(1:256,end-255:end)); sc(im, 'prob');
label(im, 'sc(cat(3, prob, gatlin), ''prob'')');
display_text([...
' SC outputs the recolored data as a truecolor RGB image. This makes it\n'...
' easy to combine colormaps, either arithmetically, or by masking regions.\n'...
' For example, we could combine an image and a probability map\n'...
' arithmetically as follows:\n\n'...
' load gatlin\n'...
' gatlin = X(1:256,end-255:end);\n'...
' prob = abs(Z)'';\n'...
' im = sc(prob, ''hsv'') .* sc(prob, ''gray'') + sc(gatlin, ''rgb2gray'');\n'...
' sc(im, [-0.1 1.3]);\n\n'...
' In fact, that particular colormap has already been implemented in SC.\n'...
' Simply call:\n\n'...
' sc(cat(3, prob, gatlin), ''prob'');']);
%% Demo colorbar
colorbar;
display_text([...
' SC also makes possible the generation of a colorbar in the normal way, \n'...
' with all the colours and data values correct. Simply call:\n\n'...
' colorbar\n\n'...
' The colorbar doesn''t work with all colormaps, but when it does,\n'...
' inverting the colormap (using ''-map'') maintains the integrity of the\n'...
' colorbar (i.e. it works correctly) - unlike if you invert the input data.']);
%% Demo combine by masking
figure(fig); clf;
sc(Z, [0 max(Z(:))], '-hot', sc(Z-round(Z), 'diff'), Z < 0);
display_text([...
' It''s just as easy to combine generated images by masking too. Here''s an\n'...
' example:\n\n'...
' im = cat(4, sc(Z, [0 max(Z(:))], ''-hot''), sc(Z-round(Z), ''diff''));\n'...
' mask = repmat(Z < 0, [1 1 3]);\n'...
' mask = cat(4, mask, ~mask);\n'...
' im = sum(im .* mask, 4);\n'...
' sc(im)\n\n'...
' In fact, SC can also do this for you, by adding image/colormap and mask\n'...
' pairs to the end of the argument list, as follows:\n\n'...
' sc(Z, [0 max(Z(:))], ''-hot'', sc(Z-round(Z), ''diff''), Z < 0);\n\n'...
' A benefit of the latter approach is that you can still display a\n'...
' colorbar for the first colormap.']);
%% Demo user defined colormap
figure(fig); clf; sc(abs(Z), rand(10, 3)); colorbar;
display_text([...
' SC can also use user defined colormaps to display indexed images.\n'...
' These can be defined as a linear colormap. For example:\n\n'...
' sc(abs(Z), rand(10, 3))\n colorbar;\n\n'...
' Note that the colormap is automatically linearly interpolated.']);
%% Demo non-linear user defined colormap
figure(fig); clf; sc(abs(Z), [rand(10, 3) exp((1:10)/2)']); colorbar;
display_text([...
' Non-linear colormaps can also be defined by the user, by including the\n'...
' relative distance between the given colormap points on the colormap\n'...
' scale in the fourth column of the colormap matrix. For example:\n\n'...
' sc(abs(Z), [rand(10, 3) exp((1:10)/2)''])\n colorbar;\n\n'...
' Note that the colormap is still linearly interpolated between points.']);
%% Demo compress
load mri;
mri = D;
figure(fig); clf;
sc(squeeze(mri(:,:,:,1:6)), 'compress');
display_text([...
' SC has many colormaps for displaying multi-dimensional data, including\n'...
' ''flow'' for optic flow fields, ''phase'' for edge maps with phase, and\n'...
' ''stereo'' for generating anaglyphs.\n\n'...
' For images with more than 3 channels, SC can compress these images to RGB\n'...
' while maintaining the maximum amount of variance in the data. For\n'...
' example, this 6 channel image:\n\n'...
' load mri\n mri = D;\n sc(squeeze(mri(:,:,:,1:6), ''compress'')']);
%% Demo texture map
figure(fig); clf;
surf(Z, sc(Z, 'hicontrast'), 'edgecolor', 'none');
display_text([...
' Further benefits of SC outputting the image as an array (on top of being\n'...
' able to combine images) are that the image can be saved straight to disk\n'...
' using imwrite(), or can be used to texture map a surface, thus:\n\n'...
' tex = sc(Z, ''hicontrast'');\n'...
' surf(Z, tex, ''edgecolor'', ''none'');']);
%% Demo multiple images
close(fig); % Only way to get round loss of focus (bug?)
figure(fig); clf; sc(mri, 'bone');
display_text([...
' Finally, SC can process multiple images when passed in as a 4d array,\n'...
' either for export or for display. In the latter case, images are\n'...
' displayed as a montage of up to 12 images. For example:\n\n'...
' sc(mri, ''bone'')']);
display_text([...
' Any additional images can then be viewed by scrolling through them using\n'...
' using the scroll (arrow) keys. Try it and see.']);
clc; fprintf('End of demo.\n');
return
%% Some helper functions for the demo
function display_text(str)
clc;
fprintf([str '\n\n']);
fprintf('Press a key to go on.\n');
figure(gcf);
waitforbuttonpress;
return
function label(im, str)
text(size(im, 2)/2, size(im, 1)+12, str,...
'Interpreter', 'none', 'HorizontalAlignment', 'center', 'VerticalAlignment', 'middle');
return
