[37] | 1 | % * This code was used in the following articles:
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| 2 | % * [1] Learning 3-D Scene Structure from a Single Still Image,
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| 3 | % * Ashutosh Saxena, Min Sun, Andrew Y. Ng,
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| 4 | % * In ICCV workshop on 3D Representation for Recognition (3dRR-07), 2007.
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| 5 | % * (best paper)
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| 6 | % * [2] 3-D Reconstruction from Sparse Views using Monocular Vision,
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| 7 | % * Ashutosh Saxena, Min Sun, Andrew Y. Ng,
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| 8 | % * In ICCV workshop on Virtual Representations and Modeling
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| 9 | % * of Large-scale environments (VRML), 2007.
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| 10 | % * [3] 3-D Depth Reconstruction from a Single Still Image,
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| 11 | % * Ashutosh Saxena, Sung H. Chung, Andrew Y. Ng.
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| 12 | % * International Journal of Computer Vision (IJCV), Aug 2007.
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| 13 | % * [6] Learning Depth from Single Monocular Images,
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| 14 | % * Ashutosh Saxena, Sung H. Chung, Andrew Y. Ng.
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| 15 | % * In Neural Information Processing Systems (NIPS) 18, 2005.
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| 16 | % *
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| 17 | % * These articles are available at:
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| 18 | % * http://make3d.stanford.edu/publications
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| 19 | % *
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| 20 | % * We request that you cite the papers [1], [3] and [6] in any of
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| 21 | % * your reports that uses this code.
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| 22 | % * Further, if you use the code in image3dstiching/ (multiple image version),
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| 23 | % * then please cite [2].
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| 24 | % *
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| 25 | % * If you use the code in third_party/, then PLEASE CITE and follow the
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| 26 | % * LICENSE OF THE CORRESPONDING THIRD PARTY CODE.
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| 27 | % *
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| 28 | % * Finally, this code is for non-commercial use only. For further
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| 29 | % * information and to obtain a copy of the license, see
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| 30 | % *
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| 31 | % * http://make3d.stanford.edu/publications/code
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| 32 | % *
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| 33 | % * Also, the software distributed under the License is distributed on an
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| 34 | % * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
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| 35 | % * express or implied. See the License for the specific language governing
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| 36 | % * permissions and limitations under the License.
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| 37 | % *
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| 38 | % */
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| 39 | function [MedSup, Sup, Default SupNeighborTableFlag]=gen_Sup_efficient(Default, img, SelectSegmentationPara); |
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| 40 | % this function generate superpixel using default parameter |
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| 41 | % but can also change to manually input parameter |
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| 42 | |
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| 43 | %%% Jeff's Comments (Min modified) |
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| 44 | % Send that image to the CMU |
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| 45 | % segmentation program with params 0.8*[sigm, k, min]. If |
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| 46 | % SelectSegmenationPara is true, then display the results and ask |
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| 47 | % the user to enter new sigm, k, and min; repeat until user is happy. |
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| 48 | % |
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| 49 | % Output the MedSup(only the smallest scale) and Sup(three scale) |
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| 50 | %%%% |
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| 51 | |
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| 52 | % default parameter |
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| 53 | if nargin < 3 |
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| 54 | SelectSegmentationPara = 0; % if SelectSegmentationPara == 1, enable the parameter interation with user. |
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| 55 | end |
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| 56 | DisplayFlag = Default.Flag.DisplayFlag; % set to display or not |
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| 57 | |
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| 58 | scale =[0.8 1.6 5]; % use different scale to generate small(0.8) middle(1.6) 5(large) scale of superpixel |
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| 59 | |
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| 60 | [VertYSizeHiREs HoriXSizeHiREs dummy]= size(img);% find the dimension size of the Hi Resolution image |
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| 61 | clear dummy; |
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| 62 | |
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| 63 | % using a fixed range of median size image [SegVertYSize SegHoriXSize ] |
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| 64 | % to generate superpixel to reduce computation |
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| 65 | if VertYSizeHiREs*HoriXSizeHiREs > Default.SegVertYSize*Default.SegHoriXSize |
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| 66 | |
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| 67 | % Downsample high resolution image to a fixed median size image |
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| 68 | %**** +4 because segmentImgOpt gives constant additinal rows and column |
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| 69 | %**** so add 4 rows and columns a prior then delete then at line 55 |
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| 70 | img = imresize(img,[Default.SegVertYSize+4 Default.SegHoriXSize+4 ],'nearest'); |
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| 71 | else |
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| 72 | Default.SegVertYSize = VertYSizeHiREs-4; |
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| 73 | Default.SegHoriXSize = HoriXSizeHiREs-4; |
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| 74 | end |
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| 75 | |
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| 76 | % generate superpixel of each image |
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| 77 | for j = 1:3% number of scale of superpixel |
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| 78 | |
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| 79 | % choose superpixel of the images |
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| 80 | % default segmentation parameter |
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| 81 | ok = 0; % ok ==1 means accept the segmentation |
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| 82 | while 1 |
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| 83 | |
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| 84 | % call the efficient segment function writen in C++ from MIT |
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| 85 | % Output the high resolution image ( + 1 since the smallest index can be zero) |
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| 86 | if j ==1 |
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| 87 | a = segmentImgOpt( Default.sigm*scale(j), Default.k*scale(j), Default.minp*scale(j), img,... |
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| 88 | [ Default.OutPutFolder Default.filename{1} '.ppm'],Default.PpmOption) + 1; |
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| 89 | else |
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| 90 | a = segmentImgOpt( Default.sigm*scale(j), Default.k*scale(j), Default.minp*scale(j), img,... |
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| 91 | [ Default.OutPutFolder Default.filename{1} '.ppm'], 0) + 1; |
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| 92 | end |
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| 93 | a = a(3:(end-2),3:(end-2)); %*** clean the edge superpixel index errors *** |
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| 94 | |
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| 95 | % Arrange the superpixel index in order |
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| 96 | if j == 1 % For the smallest Scale |
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| 97 | |
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| 98 | ma = max(a(:)); |
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| 99 | Unique_a = unique(a); |
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| 100 | SparseIndex = sparse(ma,1); |
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| 101 | SparseIndex(Unique_a) = 1:size(Unique_a); |
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| 102 | MedSup = full(SparseIndex(a)); |
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| 103 | |
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| 104 | %Downsample to size as prediected depth map |
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| 105 | Sup{j} = imresize(MedSup,[Default.VertYNuDepth Default.HoriXNuDepth],'nearest'); |
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| 106 | % clean superpixel section ==================================================================== |
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| 107 | % merage all small and disconneted points in 1st scale segmentation |
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| 108 | [Sup{j} SupNeighborTableFlag] = premergAllsuperpixel_efficient(Sup{j}, Default); |
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| 109 | % ============================================================== |
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| 110 | % =============================== |
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| 111 | |
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| 112 | else % o/w don't need the MedSup |
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| 113 | |
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| 114 | %Downsample to size size as prediected depth map |
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| 115 | a = imresize(a,[Default.VertYNuDepth Default.HoriXNuDepth],'nearest'); |
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| 116 | ma = max(a(:)); |
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| 117 | Unique_a = unique(a); |
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| 118 | SparseIndex = sparse(ma,1); |
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| 119 | SparseIndex(Unique_a) = 1:size(Unique_a); |
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| 120 | Sup{j} = full(SparseIndex(a)); |
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| 121 | end |
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| 122 | clear a SparseIndex Unique_a ma; |
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| 123 | |
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| 124 | |
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| 125 | % show superpixel |
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| 126 | if DisplayFlag |
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| 127 | figure(1); |
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| 128 | imagesc(Sup{j}); |
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| 129 | newmap = rand(max(max(Sup{j})),3); |
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| 130 | colormap(newmap); |
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| 131 | end |
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| 132 | |
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| 133 | % check if need to select segmentation parameter |
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| 134 | if SelectSegmentationPara==1; |
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| 135 | ok = input('Is the segmentation of image OK');% input new segmentation parameter |
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| 136 | else |
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| 137 | ok =1 ;% accept default segmentation parameter |
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| 138 | end |
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| 139 | |
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| 140 | if ok==1; |
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| 141 | break; |
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| 142 | end |
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| 143 | |
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| 144 | % Get the user selected parameter |
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| 145 | sigm = input('type sigm of segmentation'); |
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| 146 | k = input('type k of segmentation'); |
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| 147 | minp = input('type min of segmentation'); |
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| 148 | |
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| 149 | end % end of while 1 |
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| 150 | |
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| 151 | end % end of for j=1:3 |
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| 152 | |
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| 153 | return; |
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