[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 FeaSupNeighList=findBoundaryFeaturesMore(Sup_low,Sup_medi,nList,seglist, FeaMax, slack) |
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| 40 | %tic |
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| 41 | %load ../debugFeat.mat |
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| 42 | global H2; |
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| 43 | FeaSize = size(H2,1) |
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| 44 | |
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| 45 | NuSup = setdiff(unique(Sup_low)',0); |
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| 46 | NuSup = sort(NuSup); |
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| 47 | |
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| 48 | [currMask1, xVec1, yVec1, currMask2, xVec2, yVec2]=creatingMasks(Sup_low,Sup_medi); |
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| 49 | % load /afs/cs/group/reconstruction3d/scratch/maskSup1.mat |
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| 50 | % load /afs/cs/group/reconstruction3d/scratch/maskSup2.mat |
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| 51 | |
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| 52 | mask = zeros(size(Sup_medi,1),size(Sup_medi,2)); |
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| 53 | bMask = mask; |
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| 54 | bCurrNeighMask = mask; |
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| 55 | |
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| 56 | %nRows = size(mask,1); |
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| 57 | %nCols = size(mask,2); |
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| 58 | %newMask = zeros(nRows,nCols,2); |
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| 59 | %colCount = 1:nCols; |
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| 60 | %rowCount = (1:nRows)'; |
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| 61 | %newMask(:,:,1) = repmat(colCount,nRows,1); |
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| 62 | %newMask(:,:,2) = repmat(rowCount,1,nCols); |
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| 63 | |
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| 64 | currNeighMask=zeros(size(Sup_medi,1),size(Sup_medi,2)); |
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| 65 | xV=zeros(1,size(mask,2)); |
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| 66 | yV=zeros(1,size(mask,1)); |
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| 67 | FeaSupNeighList = zeros(size(NuSup,2),FeaSize); |
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| 68 | |
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| 69 | countSup=1; |
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| 70 | for i = NuSup |
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| 71 | nListCurr=find(nList(:,1)==i); |
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| 72 | posInNuSup=find(NuSup==i); |
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| 73 | if (posInNuSup<=500) |
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| 74 | mask = full(currMask1{posInNuSup}); |
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| 75 | xV=xVec1{posInNuSup}; |
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| 76 | yV=yVec1{posInNuSup}; |
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| 77 | else |
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| 78 | mask = full(currMask2{posInNuSup-500}); |
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| 79 | xV=xVec2{posInNuSup-500}; |
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| 80 | yV=yVec2{posInNuSup-500}; |
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| 81 | end |
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| 82 | %% find midpoint of this superpixel |
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| 83 | %% Notice that the sum command sums the columns of a matrix |
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| 84 | x(1)=findSupMid(xV); |
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| 85 | y(1)=findSupMid(yV); |
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| 86 | |
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| 87 | |
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| 88 | for j=1:length(nListCurr) |
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| 89 | neighNum=nList(nListCurr(j),2); |
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| 90 | posInNuSup=find(NuSup==neighNum); |
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| 91 | if (posInNuSup<=500) |
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| 92 | currNeighMask = full(currMask1{posInNuSup}); |
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| 93 | xV=xVec1{posInNuSup}; |
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| 94 | yV=yVec1{posInNuSup}; |
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| 95 | else |
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| 96 | currNeighMask = full(currMask2{posInNuSup-500}); |
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| 97 | xV=xVec2{posInNuSup-500}; |
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| 98 | yV=yVec2{posInNuSup-500}; |
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| 99 | end |
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| 100 | |
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| 101 | %% Now we find the mid-point of the neighbor superpixel |
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| 102 | |
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| 103 | x(2)=findSupMid(xV); |
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| 104 | y(2)=findSupMid(yV); |
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| 105 | |
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| 106 | %iPos=find(FeaSupList(1,:)==i); |
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| 107 | %jPos=find(FeaSupList(1,:)==neighNum); |
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| 108 | |
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| 109 | %Xi = FeaSupList(2:end,iPos); |
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| 110 | %Xj = FeaSupList(2:end,jPos); |
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| 111 | |
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| 112 | %FeaSupNeighList(countSup,3:(3+(length(Xi))-1)) =abs(Xi-Xj)'; |
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| 113 | |
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| 114 | % %% Now we calculate the features with just average over the |
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| 115 | % %% boundary |
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| 116 | % [xCen,yCen]=findIntersectionPoint(mask,currNeighMask,x(1:2),y(1:2),newMask); |
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| 117 | xCen = mean(x); |
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| 118 | yCen = mean(y); |
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| 119 | R = [sqrt((xCen-x(1))^2+(yCen-y(1))^2);sqrt((x(2)-xCen)^2+(y(2)-yCen)^2)]; |
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| 120 | R=R-slack*R; |
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| 121 | |
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| 122 | bMask = findCloserPointsSquare(mask,xCen,yCen,R(1)); |
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| 123 | bCurrNeighMask = findCloserPointsSquare(currNeighMask,xCen,yCen,R(2)); |
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| 124 | |
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| 125 | % bMask = findCloserPointsSquare(mask,xCen,yCen,R(1)); |
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| 126 | % bCurrNeighMask = findCloserPointsSquare(currNeighMask,xCen,yCen,R(2)); |
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| 127 | % |
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| 128 | if ((sum(sum(bMask))==0)||(sum(sum(bCurrNeighMask))==0)) |
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| 129 | bXi = zeros(1,FeaSize); |
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| 130 | bXj = zeros(1,FeaSize); |
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| 131 | else |
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| 132 | bXi = (mean(H2(:,logical(bMask))')./FeaMax(1,2:18)); %% Min change this to 18 |
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| 133 | bXj = (mean(H2(:,logical(bCurrNeighMask))')./FeaMax(1,2:18)); %% Min change this to 18 |
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| 134 | end |
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| 135 | |
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| 136 | FeaSupNeighList(countSup,1:FeaSize) =abs(bXi-bXj); |
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| 137 | %% Now we check if any of the lines intersects the current |
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| 138 | %% super-pixel and its current neighbor |
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| 139 | FeaSupNeighList(countSup,FeaSize+1)=checkIfALineCrosses(seglist,x,y); |
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| 140 | % for k=1:size(seglist,1) |
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| 141 | % x(3)=seglist(k,1); |
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| 142 | % y(3)=seglist(k,2); |
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| 143 | % x(4)=seglist(k,3); |
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| 144 | % y(4)=seglist(k,4); |
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| 145 | % if(lineSegIntersect(x,y)) |
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| 146 | % bndry_features(countSup,3)=1; |
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| 147 | % break; |
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| 148 | % end |
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| 149 | % end |
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| 150 | |
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| 151 | % FeaSupNeighList(countSup,(3+length(Xi)+length(bXi))) = bndry_features(countSup,3); |
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| 152 | % FeaSupNeighList(countSup,(3+length(Xi))) = bndry_features(countSup,3); |
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| 153 | countSup=countSup+1; |
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| 154 | end |
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| 155 | end |
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| 156 | |
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| 157 | size(FeaSupNeighList) |
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