原始slic算法matlab代码

以下是原始slic算法的Matlab代码：

function [L,NumLabels] = slic(I,k,compactness)

% I: input image
% k: desired number of superpixels
% compactness: controls the shape of the superpixels

% Output:
% L: label matrix
% NumLabels: number of superpixels

% Convert input image to Lab color space
I = double(I);
[L,a,b] = RGB2Lab(I(:,:,1),I(:,:,2),I(:,:,3));

% Initialize variables
[h,w,~] = size(I);
S = round(sqrt(h*w/k)); % superpixel size
m = round(S/2); % grid spacing
N = h*w;
L = zeros(h,w);
dist = inf(h,w);
x = 1:w;
y = 1:h;
[X,Y] = meshgrid(x,y);
X = X(:);
Y = Y(:);

% Initialize cluster centers
C = [m:m:w-m m:m:h-m]';
[X,Y] = meshgrid(C,C);
C = [X(:) Y(:)];
NumLabels = size(C,1);

% Perform k-means clustering
for i = 1:10
dist_old = dist;
dist = inf(h,w);
for j = 1:NumLabels
x_min = max(C(j,1)-S,1);
x_max = min(C(j,1)+S,w);
y_min = max(C(j,2)-S,1);
y_max = min(C(j,2)+S,h);
idx = find(X>=x_min & X<=x_max & Y>=y_min & Y<=y_max);
D = (L(idx)-L(C(j,2),C(j,1))).^2 + ...
((a(idx)-a(C(j,2),C(j,1))).^2 + ...
(b(idx)-b(C(j,2),C(j,1))).^2)*(compactness^2);
idx2 = find(D<dist(idx));
dist(idx(idx2)) = D(idx2);
L(idx(idx2)) = j;
end
if dist_old==dist
break;
end
for j = 1:NumLabels
idx = find(L==j);
C(j,:) = round(mean([X(idx) Y(idx)]));
end
end

% Display superpixels
figure;
imshow(I);
hold on;
for i = 1:NumLabels
idx = find(L==i);
plot(X(idx),Y(idx),'o','MarkerSize',2,'MarkerFaceColor',rand(1,3));
end
hold off;

end

注意：这个代码是原始的slic算法，可能存在一些缺陷和不足，建议在使用时进行改进和优化。