superpixels.zip_superpixels_超像素_超像素算法

% function par = imncut_sp; or % [Sp,Seg,V,S,W] = imncut_sp(I,par) % % Compute "superpixels." % An intial segmentation into K1=par.nv segments is performed using cncut. % These initial segments are then subdivided into K2=par.sp "superpixels" % by running kmeans on the cncut eigenvector coordinates, followed by cleanup. % % Input: % I = image % par = parameters % par.nb_r = neighbour hood radius % par.sample_rate = sampling rate % par.mask = mask for bias, label matrix of image size % par.sig_ic = sigma for computing affinity from intervening contour % par.rep = offset for repulsion % par.reg = offset for regularization % par.nv = number of eigenvectors % Output: % Sp = K2-way partition matrix, same size as I, after forcing K2=par.sp segments % Seg = initial K1-way partition matrix, same size as I % [V,S] = eigenvector and eigenvalues from constrained ncuts % W = affinity matrix % Reference: % www.cs.berkeley.edu/~stellayu/thesis.html % Examples: % par = imncut_sp; % return the default parameters % imncut_sp(I); % ncuts using the default parameters % Stella X. Yu, July 13 2003. % Modified by Greg Mori, April 2005. function [Sp,Seg,V,S,W] = imncut_sp(I,par,varargin) if nargin==0, par.nb_r = 8; par.nb_r = 24; par.sample_rate = 0.2; % par.nb_r = 30; % par.sample_rate = 0.2; par.mask = []; par.sig_ic = 0.4; par.sig_pb_ic = 0.2; par.sig_int = 0.15; par.sig_p = 10; par.rep = 0; par.reg = 0; par.nv = 3; par.hwx = 1; par.hwy = 1; par.pb_ic = 1; par.ic = 0; par.int = 0; par.prox = 0; par.count = 0; par.verbose = 0; par.visualize = 0; par.pb_timing = 1; par.sp = 3; Sp = par; return; end if nargin<2, par = ncutim; end if nargin<3 visualize=1; end mask = par.mask; nv = par.nv; VERBOSE = par.verbose; visualize = par.visualize; sz = size(I); sz=sz(1:2); np = prod(sz); [W,samp] = computeW(I,par); % constraints p = find(samp); if isempty(mask), U = []; else U = pargrp(double(mask & samp)); U = U(p,:); end % cuts on samples; and then interpolate to the rest pixels if VERBOSE fprintf('cncutting... '); end [Vp,S] = cncut(W(p,p),U,nv); if VERBOSE fprintf('done\n'); end if VERBOSE fprintf('interpolating... '); end q = find(not(samp)); if isempty(q), Vq = []; else C = W(q,p); C = spmd(C,1./double(sum(C,2)+eps),[]); Vq = C * Vp; end V = zeros(np,nv); V([p;q],:) = [Vp; Vq]; for j=1:nv, z = reshape(V(:,j),sz); z = medfilt2(z,[7,7],'symmetric'); V(:,j) = z(:); end if VERBOSE fprintf('done\n'); end % discretization if VERBOSE fprintf('discretizing... '); end X = getbinsol(V); if VERBOSE fprintf('done\n'); end % visualization if visualize figure; range = [min(V(:)),max(V(:))]; for j=1:nv, z = reshape(V(:,j),sz); subplot(nv,2,j+j-1); imagesc(z,range); axis image; axis off; colormap(gray); title(sprintf('%5.4f',S(j))); z = reshape(X(:,j),sz); subplot(nv,2,j+j); imagesc(im2double(rgb2gray(I)).*z+not(z),[0,1]); axis image; axis off; colormap(gray); end end [mv,C] = max(X,[],2); % TO DO:: Possibly need to do connected components cleanup on Seg as well. Seg = reshape(C,[size(I,1) size(I,2)]); % Force par.sp clusters by running kmeans on clusters from C Sp = zeros(size(C)); N_clusters = par.sp; cc = unique(C)'; c_off=0; for c_i=cc inds_use = find(C==c_i); V_use = V(inds_use,:); % This segment should contribute its fair share, at least 1 though. N_this = max(1,round(N_clusters * length(inds_use)/np)); if N_this>1 Sp_this = kmeans(V_use,N_this,'Start','sample','Replicates',5,'EmptyAction','singleton'); else Sp_this = ones(size(V_use,1),1); end Sp(inds_use) = Sp_this + c_off; c_off = c_off + max(Sp_this); end Sp = reshape(Sp,[size(I,1) size(I,2)]); % Connected components analysis, cut apart disconnected clusters % Clean up Sp. Merge small segments, break disconnected clusters. MIN_SIZE = 30; min_c = min(Sp(:)); max_c = max(Sp(:)); new_num = max_c+1; for c_i=min_c:max_c [L,num] = bwlabel(Sp==c_i,4); if num > 1 | sum(L(:)) < MIN_SIZE for n_i=1:num the_inds = find(L==n_i); if length(the_inds) < MIN_SIZE % Merge each small segment with a nearby one. the_perim = bwperim(imdilate(L==n_i,strel('diamond',1))); the_perim(the_inds)=0; % bwperim has errors on boundary. nhbs = find(the_perim); the_dists = dist2(V(the_inds,:),V(nhbs,:)); the_dists = min(the_dists,[],1); [mind,mind] = min(the_dists); Sp(the_inds) = Sp(nhbs(mind)); else % Renumber large segments. Sp(the_inds) = new_num; new_num = new_num+1; end end end end % Renumber segments. Spv = Sp'; Spv = Spv(:); uu = unique(Spv); N_seg = length(uu); the_map(uu) = 1:N_seg; Spv = the_map(Spv); Sp = reshape(Spv,[size(I,2) size(I,1)])';