matlab 超像素分割代码

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)) 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