source: proiecte/pmake3d/make3d_original/Make3dSingleImageStanford_version0.1/LearningCode/Features/OldBatchVersion/gen_DiffSup.m @ 37

% *  This code was used in the following articles:
% *  [1] Learning 3-D Scene Structure from a Single Still Image, 
% *      Ashutosh Saxena, Min Sun, Andrew Y. Ng, 
% *      In ICCV workshop on 3D Representation for Recognition (3dRR-07), 2007.
% *      (best paper)
% *  [2] 3-D Reconstruction from Sparse Views using Monocular Vision, 
% *      Ashutosh Saxena, Min Sun, Andrew Y. Ng, 
% *      In ICCV workshop on Virtual Representations and Modeling 
% *      of Large-scale environments (VRML), 2007. 
% *  [3] 3-D Depth Reconstruction from a Single Still Image, 
% *      Ashutosh Saxena, Sung H. Chung, Andrew Y. Ng. 
% *      International Journal of Computer Vision (IJCV), Aug 2007. 
% *  [6] Learning Depth from Single Monocular Images, 
% *      Ashutosh Saxena, Sung H. Chung, Andrew Y. Ng. 
% *      In Neural Information Processing Systems (NIPS) 18, 2005.
% *
% *  These articles are available at:
% *  http://make3d.stanford.edu/publications
% * 
% *  We request that you cite the papers [1], [3] and [6] in any of
% *  your reports that uses this code. 
% *  Further, if you use the code in image3dstiching/ (multiple image version),
% *  then please cite [2].
% *  
% *  If you use the code in third_party/, then PLEASE CITE and follow the
% *  LICENSE OF THE CORRESPONDING THIRD PARTY CODE.
% *
% *  Finally, this code is for non-commercial use only.  For further 
% *  information and to obtain a copy of the license, see 
% *
% *  http://make3d.stanford.edu/publications/code
% *
% *  Also, the software distributed under the License is distributed on an 
% * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either 
% *  express or implied.   See the License for the specific language governing 
% *  permissions and limitations under the License.
% *
% */
function []=gen_DiffSup(sigm,k,min,SelectSegmentationPara);
% this function generate superpixel using default parameter
% but can also change to manually input parameter

% This file generates multiple segmentation (one of them using RGB)

%%%
% looks like gen_Sup_new but with different scaling params.
% should we delete this file?
%%%


% default parameter
if nargin < 4
    SelectSegmentationPara = 0;
end

% declaim global variable
global GeneralDataFolder ScratchDataFolder LocalFolder ClusterExecutionDirectory...
    ImgFolder VertYNuPatch VertYNuDepth HoriXNuPatch HoriXNuDepth a_default b_default Ox_default Oy_default...
    Horizon_default filename batchSize NuRow_default SegVertYSize SegHoriXSize;

scale_sigm =[1 1.6];
scale_k = [1.6 5];
scale_min = [1 5];

% generate superpixel of each image

NuPics = size(filename,2);
for i = 1:NuPics
    i 
  %  load([ScratchDataFolder '/data/LowResImgIndexSuperpixelSep.mat']);

%     for j = 1:2%3% number of scale of superpixel

        
%         sigm_new =
        % load image and process it to Hi Medi and Low Resolution
        Img = imread([GeneralDataFolder '/' ImgFolder '/' filename{i} '.jpg']); % Readin the high resolution image
        [VertYSizeHiREs HoriXSizeHiREs dummy]= size(Img);% find the dimension size of the Hi Resolution image
        clear dummy;
        % Loadin the GroundTruth data to know the depthMap size
%         depthfile = strrep(filename{i},'img','depth'); % the depth filename(without .file extension) associate with the *jpg file
%         load([GeneralDataFolder '/depthMap/' depthfile '.mat']); 
%         [VertYSizeLowREs HoriXSizeLowREs]= size(depthMap);% find the dimension size of the depth data
        % in the new laser data we have scatter depthmap so use a
        % predecided LowRes
        VertYSizeLowREs = VertYNuDepth;
        HoriXSizeLowREs = HoriXNuDepth;
        
        % using a median size image to generate superpixel to reduce computation
        % intensity (the median size has a upper threshould SegVertYSize SegHoriXSize)
        if VertYSizeHiREs*HoriXSizeHiREs > SegVertYSize*SegHoriXSize
            Img = imresize(Img,[SegVertYSize SegHoriXSize ],'nearest'); % Downsample high resolution image to a median size image
            imwrite(Img,[ScratchDataFolder '/ppm/' filename{i} '_Diff.ppm'],'ppm');% store median Resolution image to PPM format to feed in CMU C++ function
        else
            imwrite(Img,[ScratchDataFolder '/ppm/' filename{i} '_Diff.ppm'],'ppm');% store median Resolution image to PPM format to feed in CMU C++ function
        end    
        
    for j = 1:2%3% number of scale of superpixel    
        % choose superpixel of the images
        % default segmentation parameter
        ok = 0; % ok ==1 means accept the segmentation
        while 1
            % call segment function writen in C++ from MIT
            system([LocalFolder '/../third_party/Superpixels/segment ' num2str(sigm*scale_sigm(j)) ' ' num2str(k*scale_k(j)) ...
                ' ' num2str(min*scale_min(j)) ' ' ScratchDataFolder '/ppm/' filename{i} '_Diff.ppm' ' ' ...
                ScratchDataFolder '/ppm/' filename{i} '_' num2str(sigm*scale_sigm(j)) '_' ...
                num2str(k*scale_k(j)) '_' num2str(min*scale_min(j)) '.ppm']);
            MediResImgSuperpixel = imread([ScratchDataFolder '/ppm/' filename{i} '_' num2str(sigm*scale_sigm(j)) '_' num2str(k*scale_k(j)) '_' num2str(min*scale_min(j)) '.ppm']); % Readin the high resolution image
            figure(1); image(MediResImgSuperpixel); % show the superpixel in Medi Resolution
    
            % check if need to select segmentation parameter
            if SelectSegmentationPara==1;
                ok = input('Is the segmentation of image OK');% input new segmentation parameter
            else    
                ok =1 ;% accept default segmentation parameter
            end
    
            % finish segmentation clean up the ppm folder.
            if ok==1;
                delete([ScratchDataFolder '/ppm/' filename{i} '_' num2str(sigm*scale_sigm(j)) '_' num2str(k*scale_k(j)) '_' num2str(min*scale_min(j)) '.ppm']);
                
                break;
            end
            sigm = input('type sigm of segmentation');
            k = input('type k of segmentation');
            min = input('type min of segmentation');
            
        end
    
        % index superpixel
        [MediResImgIndexSuperpixelSepTemp dummy]= suprgb2ind(MediResImgSuperpixel); clear dummy;
        DiffLowResImgIndexSuperpixelSepTemp = imresize(MediResImgIndexSuperpixelSepTemp,[VertYSizeLowREs HoriXSizeLowREs],'nearest'); %Downsample high resolution image to the same pixel size of predict Depth data
       
        % merage the superpixel according to diff segmentation
        %NuSup = size(unique(LowResImgIndexSuperpixelSep),1);
%         LowSup = LowResImgIndexSuperpixelSep{i,1};
%         Sup = zeros(size(LowSup));
%         for l = (unique(LowSup))'
%             masksup = LowSup == l;
%             Index = analysesupinpatch(DiffLowResImgIndexSuperpixelSepTemp(masksup));
%             Sup(masksup)= Index;
%         end
%         DiffLowResImgIndexSuperpixelSep{i,j} = Sup;
%  
        % merage all small point in higher scale segmentation
        if j == 1
           DiffLowResImgIndexSuperpixelSep{i,j} = DiffLowResImgIndexSuperpixelSepTemp
        else
           DiffLowResImgIndexSuperpixelSep{i,j} = premergAllsuperpixel(DiffLowResImgIndexSuperpixelSepTemp);
        end
        %if j == 1;
        %   MediResImgIndexSuperpixelSep{i} = MediResImgIndexSuperpixelSepTemp;
        %end
        % refining superpixel
        % superpixel segmentation LowResImgSeperatedSuperpixel
        %LowResImgsuperpixel = imresize(MediResImgSuperpixel,[VertYSizeLowREs HoriXSizeLowREs],'nearest'); %Downsample high resolution image to the same pixel size of GroundTruth data
        %[LowResImgIndexSuperpixel  LowResImgIndexSuperpixel_list]= suprgb2ind(LowResImgsuperpixel);

        % comment: cmu's superpixel might be connected. use premergsuperpixel to
        % deal with nonconnected superpixels and very small superpixels
        %[LowResImgIndexSuperpixelSepTemp]=premergsuperpixel(LowResImgIndexSuperpixel); % hard work 1min
        
        % reorder the index number of the LowResImgIndexSuperpixelSep
        %[LowResImgIndexSuperpixelSep{i,j}  LowResImgIndexSuperpixelSep_list]= ordersup(LowResImgIndexSuperpixelSepTemp);
        
        % show superpixel
        figure(2);
        imagesc(DiffLowResImgIndexSuperpixelSep{i,j});
        newmap = rand(max(max(DiffLowResImgIndexSuperpixelSep{i,j})),3);
        colormap(newmap);
        
        % process the MediResImgSuperpixel to have the same number of
        % LowResImgIndexSuperpixelSep
%         if j==1 
%             tic
%             [MediResImgIndexSuperpixel dummy]= suprgb2ind(MediResImgSuperpixel); clear dummy;
%             MediResImgIndexSuperpixelSep = imresize(LowResImgIndexSuperpixelSep{i,1},size(MediResImgIndexSuperpixel),'nearest');
%             NuSupMedi = max(max(MediResImgIndexSuperpixel));
%             LowToMediResImgIndexSuperpixel = zeros(size(MediResImgIndexSuperpixel));
%             for k = 1:NuSupMedi
%                 mask = MediResImgIndexSuperpixel==k;
%                 LowToMediResImgIndexSuperpixel(mask) = analysesupinpatch(MediResImgIndexSuperpixelSep(mask));
% %                 [list_sup] = analysesupinpatch(MediResImgIndexSuperpixelSep(mask));
% %                 [I C] = max(list_sup(2,:));
% %                 LowToMediResImgIndexSuperpixel(mask) = list_sup(1,C);
%             end
%             LowToMediResImgIndexSuperpixelSep{i} =...
%             premergAllsuperpixel(LowToMediResImgIndexSuperpixel);
%             toc
%         end    
    end    
    delete([ScratchDataFolder '/ppm/' filename{i} '_Diff.ppm']);
    save([ScratchDataFolder '/data/DiffLowResImgIndexSuperpixelSep.mat'], 'DiffLowResImgIndexSuperpixelSep');
end    

% save result for later application
save([ScratchDataFolder '/data/DiffLowResImgIndexSuperpixelSep.mat'], 'DiffLowResImgIndexSuperpixelSep');
%save([ScratchDataFolder '/data/MediResImgIndexSuperpixelSep.mat'], 'MediResImgIndexSuperpixelSep');

return;