Superpixels demo

This program demonstrates superpixels segmentation using OpenCV classes cv.SuperpixelSEEDS, cv.SuperpixelSLIC, and cv.SuperpixelLSC.

Sources:

Algorithm
Load image
Create segmentation object
Segment image
Display output

Algorithm

choose one of: 'SEEDS', 'SLIC', 'SLICO', 'MSLIC', 'LSC'

ALG = 'SEEDS';

Load image

src = cv.imread(fullfile(mexopencv.root(),'test','test1.png'), 'Color',true);
assert(~isempty(src), 'Could not open image');
src = cv.resize(src, 0.5, 0.5, 'Interpolation','Area');

smooth image a bit

if true
    src = cv.GaussianBlur(src, 'KSize',[3 3]);
end

convert to LAB color space

if true
    converted = cv.cvtColor(src, 'RGB2Lab');
else
    converted = cv.cvtColor(src, 'BGR2HSV');
end

Create segmentation object

num_superpixels = 300;  % SEEDS Number of Superpixels
num_levels = 4;         % SEEDS Number of Levels
prior = 2;              % SEEDS Smoothing Prior
num_histogram_bins = 5; % SEEDS histogram bins
double_step = false;    % SEEDS two steps
region_size = 20;       % SLIC/SLICO/MSLIC/LSC average superpixel size
ruler = 15.0;           % SLIC/MSLIC smoothness (spatial regularization)
ratio = 0.075;          % LSC compactness
min_element_size = 25;  % SLIC/SLICO/MSLIC/LSC minimum component size percentage
num_iterations = 10;    % Iterations

switch ALG
    case 'SEEDS'
        superpix = cv.SuperpixelSEEDS(...
            size(converted), num_superpixels, num_levels, 'Prior',prior, ...
            'HistogramBins',num_histogram_bins, 'DoubleStep',double_step);
    case {'SLIC', 'SLICO', 'MSLIC'}
        superpix = cv.SuperpixelSLIC(converted, 'Algorithm',ALG, ...
            'RegionSize',region_size, 'Ruler',ruler);
    case 'LSC'
        superpix = cv.SuperpixelLSC(converted, ...
            'RegionSize',region_size, 'Ratio',ratio);
    otherwise
        error('Unrecognized algorithm %s', ALG);
end

Segment image

tic
if strcmp(ALG, 'SEEDS')
    superpix.iterate(converted, 'NumIterations',num_iterations);
else
    superpix.iterate('NumIterations',num_iterations);

    % merge small superpixels to neighbouring ones
    if min_element_size > 0
        superpix.enforceLabelConnectivity('MinElementSize',min_element_size);
    end
end
npix = superpix.getNumberOfSuperpixels();
fprintf('%s segmentation with %d superpixels\n', ALG, npix);
toc

SEEDS segmentation with 176 superpixels
Elapsed time is 0.027262 seconds.

get the contours for displaying

mask = superpix.getLabelContourMask('ThickLine',true);
zr = false(size(mask));
result = imlincomb(0.8,src, 0.2,255*ones(size(src),'uint8'));
result(cat(3,mask,zr,zr)) = 0;
result(cat(3,zr,mask,zr)) = 0;
result(cat(3,zr,zr,mask)) = 255;

retrieve the segmentation result

labels = superpix.getLabels();
if false
    % use the last x bits to determine the color. Note that this does not
    % guarantee that 2 neighboring superpixels have different colors.
    bits = 2;
    L = bitand(labels, bitshift(1,bits)-1) * bitshift(1,16-bits);
    L = double(L) ./ double(max(L(:)));
else
    labels = double(labels) + 1;
    L = label2rgb(labels, jet(npix), 'k', 'shuffle');
end

Display output