imRAG.zip_Matlab 邻接区域_imRAG_区域邻接图_图像分割计算

function varargout = imRAG(img, varargin) %IMRAG Region adjacency graph of a labeled image % % Usage: % ADJ = imRAG(IMG); % computes region adjacencies graph of labeled 2D or 3D image IMG. % The result is a N*2 array, containing 2 indices for each couple of % neighbor regions. Two regions are considered as neighbor if they are % separated by a black (i. e. with color 0) pixel in the horizontal or % vertical direction. % ADJ has the format [LBL1 LBL2], LBL1 and LBL2 being vertical arrays the % same size. % % LBL1 is given in ascending order, LBL2 is given in ascending order for % each LBL1. Ex: % [1 2] % [1 3] % [1 4] % [2 3] % [2 5] % [3 4] % % [NODES, ADJ] = imRAG(IMG); % Return two arrays: the first one is a [N*2] array containing centroids % of the N labeled region, and ADJ is the adjacency previously described. % For 3D images, the nodes array is [N*3]. % % Example (requires image processing toolbox) % % read and display an image with several objects % img = imread('coins.png'); % figure(1); clf; % imshow(img); hold on; % % % compute the Skeleton by influence zones using watershed % bin = imfill(img>100, 'holes'); % dist = bwdist(bin); % wat = watershed(dist, 4); % % % compute overlay image for display % tmp = uint8(double(img).*(wat>0)); % ovr = uint8(cat(3, max(img, uint8(255*(wat==0))), tmp, tmp)); % imshow(ovr); % % % show the resulting graph % [n e] = imRAG(wat); % for i=1:size(e, 1) % plot(n(e(i,:), 1), n(e(i,:), 2), 'linewidth', 4, 'color', 'g'); % end % plot(n(:,1), n(:,2), 'bo', 'markerfacecolor', 'b'); % % % ------ % Author: David Legland % e-mail: david.legland@grignon.inra.fr % Created: 2004-02-20, % Copyright 2007 INRA - BIA PV Nantes - MIAJ Jouy-en-Josas. % History % 2007-10-12 update doc % 2007-10-17 add example % 2010-03-08 replace calls to regionprops by local centroid computation % 2010-07-29 update doc %% Initialisations % convert to double, to avoid repetitive type cast img = double(img); % get greatest region label N = max(img(:)); % size of image dim = size(img); % number of dimensions nd = length(dim); % initialize array edges = []; %% Main processing if nd==2 % compute matrix of absolute differences in the first direction diff1 = abs(diff(img, 1, 1)); % find non zero values (region changes) [i1 i2] = find(diff1); % delete values close to border i2 = i2(i1<dim(1)-1); i1 = i1(i1<dim(1)-1); % get values of consecutive changes val1 = diff1(sub2ind(size(diff1), i1, i2)); val2 = diff1(sub2ind(size(diff1), i1+1, i2)); % find changes separated with 2 pixels ind = find(val2 & val1~=val2); edges = unique([val1(ind) val2(ind)], 'rows'); % compute matrix of absolute differences in the second direction diff2 = abs(diff(img, 1, 2)); % find non zero values (region changes) [i1 i2] = find(diff2); % delete values close to border i1 = i1(i2<dim(2)-1); i2 = i2(i2<dim(2)-1); % get values of consecutive changes val1 = diff2(sub2ind(size(diff2), i1, i2)); val2 = diff2(sub2ind(size(diff2), i1, i2+1)); % find changes separated with 2 pixels ind = find(val2 & val1~=val2); edges = [edges ; unique([val1(ind) val2(ind)], 'rows')]; elseif nd==3 % compute matrix of absolute differences in the first direction diff1 = abs(diff(img, 1, 1)); % find non zero values (region changes) [i1 i2 i3] = ind2sub(size(diff1), find(diff1)); % delete values close to border i2 = i2(i1<dim(1)-1); i3 = i3(i1<dim(1)-1); i1 = i1(i1<dim(1)-1); % get values of consecutive changes val1 = diff1(sub2ind(size(diff1), i1, i2, i3)); val2 = diff1(sub2ind(size(diff1), i1+1, i2, i3)); % find changes separated with 2 pixels ind = find(val2 & val1~=val2); edges = unique([val1(ind) val2(ind)], 'rows'); % compute matrix of absolute differences in the second direction diff2 = abs(diff(img, 1, 2)); % find non zero values (region changes) [i1 i2 i3] = ind2sub(size(diff2), find(diff2)); % delete values close to border i1 = i1(i2<dim(2)-1); i3 = i3(i2<dim(2)-1); i2 = i2(i2<dim(2)-1); % get values of consecutive changes val1 = diff2(sub2ind(size(diff2), i1, i2, i3)); val2 = diff2(sub2ind(size(diff2), i1, i2+1, i3)); % find changes separated with 2 pixels ind = find(val2 & val1~=val2); edges = [edges ; unique([val1(ind) val2(ind)], 'rows')]; % compute matrix of absolute differences in the third direction diff3 = abs(diff(img, 1, 3)); % find non zero values (region changes) [i1 i2 i3] = ind2sub(size(diff3), find(diff3)); % delete values close to border i1 = i1(i3<dim(3)-1); i2 = i2(i3<dim(3)-1); i3 = i3(i3<dim(3)-1); % get values of consecutive changes val1 = diff3(sub2ind(size(diff3), i1, i2, i3)); val2 = diff3(sub2ind(size(diff3), i1, i2, i3+1)); % find changes separated with 2 pixels ind = find(val2 & val1~=val2); edges = [edges ; unique([val1(ind) val2(ind)], 'rows')]; end % format output to have increasing order of n1, n1<n2, and % increasing order of n2 for n1=constant. edges = sortrows(sort(edges, 2)); % remove eventual double edges edges = unique(edges, 'rows'); %% Output processing if nargout == 1 varargout{1} = edges; elseif nargout == 2 % Also compute region centroids points = zeros(N, nd); labels = unique(img); labels(labels==0) = []; if nd==2 % compute 2D centroids for i=1:length(labels) label = labels(i); [iy ix] = ind2sub(dim, find(img==label)); points(label, 1) = mean(ix); points(label, 2) = mean(iy); end else % compute 3D centroids for i=1:length(labels) label = labels(i); [iy ix iz] = ind2sub(dim, find(img==label)); points(label, 1) = mean(ix); points(label, 2) = mean(iy); points(label, 3) = mean(iz); end end % setup output arguments varargout{1} = points; varargout{2} = edges; end