DS.rar_Ds.rar_segmentation dbscan

function [Segmented, Array]=DS(~,flag,open,EdHist) % This function applies the Delaunay-based image segmentation, % which is a fully automated process that does not require initial % estimate of number of clusters. % % The core idea is to apply Delaunay triangulation to the image % histogram instead of the image itself. That reduces the sites required % to construct the diagram to merely 255 at most (uint8) resulting in a % fast image segmentation. % % I don't claim it is the optimum way to segment an image, which is why % I will be more than happy receiving constructive comments or reporting % any bug in the program. % % For further description of the theoritical foundation of the algorithm % and for citation purposes please refer to my journal paper: % % - A. Cheddad, D. Mohamad and A. Abd Manaf, "Exploiting Voronoi diagram % properties in face segmentation and features extraction," Pattern % Recognition, 41 (12)(2008)3842-3859, Elsevier Science. % % % Inputs, % Im : The image, class preferable uint8 or uint16, the function accepts % RGB as well as grayscale images % % flag : (1) segmentation of image complement % (0) direct segmentation [default] % % open: (1) apply grayscale morphological opening % (0) don't apply [default] % EdHist:(1) apply histogram equalization [default] % (0) don't apply % % Outputs, % Segmented: the segmented image % Array: Array containing grayscale values (Delaunay vertices) used for % segmentation. You can use that to call homogeneous regions, % e.g., imshow(Segmented(:,:,1)<=Array(i) & Segmented(:,:,1)>Array(i-1),[]); % % Example, % [Segmented, Array]=DS('Image.png'); with the default options % [Segmented, Array]=DS('Image.png',0,1,1); % % Function was written by Abbas Cheddad, University of Technology Malaysia (July 2005) % Revised on 05-August- 2010 Ume? Universitet, Sweden % % URL: www.abbascheddad.net % www.infm.ulst.ac.uk/~abbasc/ (might be obsolete end of 2010) % % If you run into an error as below: % % (??? Error using ==> convhull % Error computing the convex hull. The points may be collinear.) % % That is because the histogram did not yield enough scattered sites (collinearity) that % enable forming the Delaunay triangulation. % Try re-setting the parameters. % The segmentation ranges from coarse to fine, if the image has an abnormal % histogram that might result in undersegmentation. % if nargin < 2, flag=0;open=0;EdHist=1; end tic [file, pathname] = uigetfile('*.jpg','Load Image'); cd(pathname); Im=imread(file); % Im=imread(Im); %%%%%%%%%%%%%%%%%%%%Pre-processing step%%%%%%%%%%%%%%% if length(size(Im))==3 %colour transform into NTSC coulour space allowing us to work on the %intensity channel Im=rgb2ntsc(Im); end I2=im2uint8(Im(:,:,1)); Store=I2; if open==1 se=strel('square',4); I2=imopen(I2,se); end if flag==1 I2=imcomplement(I2); end if EdHist==1 I2=histeq(medfilt2(I2)); end Orig=I2; %%%%%%%%%%%%%%%%%%%%Getting segmentation values%%%%%%%%%%%%%%% [x, xx]=imhist(I2); C=[x,xx]; %%%%%% k=convhull(C(:,1),C(:,2)); X_conv=C(k,1); Y_conv=C(k,2); Maxim_C=max(C(:,1)); Max_Conv=max(X_conv(:,1)); %Getting the 1st maxima [Point,~]=find(X_conv==Max_Conv); Point_1=Y_conv(Point,1); X_conv(Point,1)=0; Max_Conv=max(X_conv(:,1)); %Getting the 2nd maxima [Point,~]=find(X_conv==Max_Conv); Point_2=Y_conv(Point); C(1:min(Point_1,Point_2),1)=Maxim_C; C(max(Point_1,Point_2):256,1)=Maxim_C; C(:,1)=sqrt((C(:,1)-Maxim_C).^2); Temp_C=C(k,2); Temp_C=sort(Temp_C); %%%%%%%% %T=C(:,1); f=convhull(C(:,1),C(:,2)); Arr=C(f,2); Arr2=C(f,1); [count,~]=find(C(f,1)~=0); Array=Arr(count); %%%%%%%%%%%% Temp=C(f,1); Temp2=C(f,2); cell1=0; cell2=0; for i=1:length(Temp) if Temp(i,1)~=0 cell1=Temp(i,1); i=length(Temp); end end for i=length(Temp):-1:1 if Temp(i,1)~=0 cell2=Temp(i,1); i=1; end end Max_C=max(C(:,1)); if (cell1>0 && cell2>0) Point_New=find(C(:,1)==cell1); Point_New=Point_New(1); Point_New2=find(C(:,1)==cell2); Point_New2=Point_New2(end); C(1:Point_New,1)=Max_C; C(Point_New2:length(C(:,1)),1)=Max_C; C=C(1:256,:); C(:,1)=sqrt((C(:,1)-Maxim_C).^2); end Array=[Array;Temp_C]; Array=sort(Array); for i=1:length(Array(:))-1 if Array(i)==Array(i+1) Array(i+1)=0; end end Array=sort(Array); [D,~]=(find(Array>0)); Array=Array(D); Orig(Orig<Array(1,1))=0; Orig(Orig>Array(length(Array(:)),1))=0; d=[]; %%%%%%%%%%%%%%%%%%%%Segmentation of the image%%%%%%%%%%%%%%% for i=1:length(Array(:))-1 if i==1 Orig(Orig>=Array(i) & Orig<=Array(i+1))=Array(i+1); d=[d;Array(i+1)]; else Orig(Orig>Array(i) & Orig<=Array(i+1))=Array(i+1); d=[d;Array(i+1)]; end end d=sort(d); Orig_Color=[]; if length(size(Im))==3 Im(:,:,1)=im2double(Orig); Orig_Color=ntsc2rgb(Im); [x xx]=imhist(im2uint8(Im(:,:,1))); Array=xx(x>0); else [x, xx]=imhist(im2uint8(Orig(:,:,1)));% Corrected! Array=xx(x>0); end Segmented=im2uint8(Orig); Time=toc; % Calculating additional info, i.e., comparision of the number of % grayvalues before and after the segmentation [~, xx]=imhist(Store); Count=xx(xx>0); %%%%Display the segmented regions as binary %if length(size(Segmented))==3 %Segmented=rgb2ntsc(Segmented); %end % Segmented=im2double(Segmented(:,:,1)); % [n nn]=hist(Segmented); % for i=1:length(nn) % if i=1 % figure, imshow(A(:,:,1)<=nn(i),[]) % else % figure, imshow(A(:,:,1)<=nn(i) & A(:,:,1)>nn(i-1),[]) % end % end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% figure, imshow(Segmented), title(sprintf('Delaunay based unsupervised segmentation, excusion time: %s Sec',num2str(Time))) if ~isempty(Orig_Color) figure, imshow(Orig_Color),title(sprintf('Color Delaunay based unsupervised segmentation , excusion time: %s Sec',num2str(Time))); imwrite(Orig_Color,'Segmented_Color.tif') end msgbox(sprintf('The number of gray values in the original was: %d which is reduced to: %d with the segmentation process.',length(Count),length(Array)),'Title','modal'); function varargout = DSeg(varargin) % DSEG MATLAB code for DSeg.fig % DSEG, by itself, creates a new DSEG or raises the existing % singleton*. % % H = DSEG returns the handle to a new DSEG or the handle to % the existing singleton*. % % DSEG('CALLBACK',hObject,eventData,handles,...) calls the local % function named CALLBACK in DSEG.M with the given input arguments. % % DSEG('Property','Value',...) creates a new DSEG or raises the % existing singleton*. Starting from the left, property value pairs are % applied to the GUI before DSeg_OpeningFcn gets called. An % unrecognized property name or invalid value makes property application % stop. All inputs are passed to DSeg_OpeningFcn via varargin. % % *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one % instance to run (singleton)". % % See also: GUIDE, GUIDATA, GUIHANDLES % Edit the above text to modify the response to help DSeg % Last Modified by GUIDE v2.5 14-Jun-2011 16:56:58 % Begin initialization code - DO NOT EDIT gui_Singleton = 1; gui_State = struct('gui_Name', mfilename, ... 'gui_Singleton', gui_Singleton, ... 'gui_OpeningFcn', @DSeg_OpeningFcn, ... 'gui_OutputFcn', @DSeg_OutputFcn, ... 'gui_LayoutFcn', [] , ... 'gui_Callback', []); if nargin && ischar(varargin{1}) gui_State.gui_Callback = str2func(varargin{1}); end if nargout [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:}); else gui_mainfcn(gui_State, varargin{:}); end % End initialization code - DO NOT EDIT % --- Executes just before DSeg is made vi