%%%%这是一个关于超像素分割的代码  从聚类尺寸哪里开始出现zeros 而且总是报错 求解决。

function klabels = PerformSuperpixelSLIC(img_Lab, kseedsl, kseedsa, kseedsb, kseedsx, kseedsy, STEP, compactness)

[m_height, m_width, m_channel] = size(img_Lab);

numseeds = size(kseedsl);

img_Lab = double(img_Lab);

%像素标签格式为(x, y) (行, 列)

klabels = zeros(m_height, m_width);

%聚类尺寸

clustersize = zeros((numseeds, 1);

inv = zeros(numseeds, 1);

sigmal = zeros(numseeds, 1);

sigmaa = zeros(numseeds, 1);

sigmab = zeros(numseeds, 1);

sigmax = zeros(numseeds, 1);

sigmay = zeros(numseeds, 1);

invwt = 1/((double(STEP)/double(compactness))*(double(STEP)/double(compactness)));

%invwt = double(compactness)/double(STEP);

distvec = 100000*ones(m_height, m_width);

numk = numseeds;

for itr = 1: 10   %迭代次数

sigmal = zeros(numseeds, 1);

sigmaa = zeros(numseeds, 1);

sigmab = zeros(numseeds, 1);

sigmax = zeros(numseeds, 1);

sigmay = zeros(numseeds, 1);

clustersize = zeros(numseeds, 1);

inv = zeros(numseeds, 1);

distvec = double(100000*ones(m_height, m_width));

%根据当前种子点信息计算每一个像素的归属

for n = 1: numk

y1 = max(1, kseedsy(n, 1)-STEP);

y2 = min(m_height, kseedsy(n, 1)+STEP);

x1 = max(1, kseedsx(n, 1)-STEP);

x2 = min(m_width, kseedsx(n, 1)+STEP);

%按像素计算距离

for y = y1: y2

for x = x1: x2

%dist_lab = abs(img_Lab(y, x, 1)-kseedsl(n))+abs(img_Lab(y, x, 2)-kseedsa(n))+abs(img_Lab(y, x, 3)-kseedsb(n));

dist_lab = (img_Lab(y, x, 1)-kseedsl(n, 1))^2+(img_Lab(y, x, 2)-kseedsa(n, 1))^2+(img_Lab(y, x, 3)-kseedsb(n, 1))^2;

dist_xy = (double(y)-kseedsy(n, 1))*(double(y)-kseedsy(n, 1)) + (double(x)-kseedsx(n, 1))*(double(x)-kseedsx(n, 1));

%dist_xy = abs(y-kseedsy(n)) + abs(x-kseedsx(n));

%距离 = lab色彩空间距离 + 空间距离权重×空间距离

dist = dist_lab + dist_xy*invwt;

%在周围最多四个种子点中找到最相似的 标记后存入klabels

%m = (y-1)*m_width+x;

if (dist

distvec(y, x) = dist;

klabels(y, x) = n;

end

end

end

end

%重新计算种子点位置 使其向梯度最小地方移动

ind = 1;

for r = 1: m_height

for c = 1: m_width

sigmal(klabels(r, c),1) = sigmal(klabels(r, c),1)+img_Lab(r, c, 1);

sigmaa(klabels(r, c),1) = sigmaa(klabels(r, c),1)+img_Lab(r, c, 2);

sigmab(klabels(r, c),1) = sigmab(klabels(r, c),1)+img_Lab(r, c, 3);

sigmax(klabels(r, c),1) = sigmax(klabels(r, c),1)+c;

sigmay(klabels(r, c),1) = sigmay(klabels(r, c),1)+r;

clustersize(klabels(r, c),1) = clustersize(klabels(r, c),1)+1;

end

end

for m = 1: numseeds

if (clustersize(m, 1)<=0)

clustersize(m, 1) = 1;

end

inv(m, 1) = 1/clustersize(m, 1);

end

for m = 1: numseeds

kseedsl(m, 1) = sigmal(m, 1)*inv(m, 1);

kseedsa(m, 1) = sigmaa(m, 1)*inv(m, 1);

kseedsb(m, 1) = sigmab(m, 1)*inv(m, 1);

kseedsx(m, 1) = sigmax(m, 1)*inv(m, 1);

kseedsy(m, 1) = sigmay(m, 1)*inv(m, 1);

end

end

end

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