[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 LearnVariance(LearnAlg,AbsFeaType,AbsFeaDate,WeiBatchNumber,logScale,SkyExclude,LearnNear,DepthDirectory) |
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| 40 | % % This function learned the distance |
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| 41 | |
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| 42 | global GeneralDataFolder ScratchDataFolder LocalFolder ClusterExecutionDirectory... |
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| 43 | ImgFolder VertYNuPatch VertYNuDepth HoriXNuPatch HoriXNuDepth a_default b_default Ox_default Oy_default... |
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| 44 | Horizon_default filename batchSize NuRow_default WeiBatchSize; |
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| 45 | |
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| 46 | statusFilename = [ClusterExecutionDirectory '/matlabExecutionStatus_depth.txt']; |
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| 47 | % parameters setting |
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| 48 | NuPics = size(filename,2); |
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| 49 | NuBatch = ceil(NuPics/batchSize); |
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| 50 | NuRow = NuRow_default; |
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| 51 | %Horizon = Horizon_default; |
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| 52 | %skyBottom = floor(NuRow/2); |
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| 53 | batchRow = 1:WeiBatchSize:NuRow; |
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| 54 | |
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| 55 | |
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| 56 | l = 1; |
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| 57 | for i = batchRow(WeiBatchNumber):min(batchRow(WeiBatchNumber)+WeiBatchSize-1,NuRow) |
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| 58 | %for i = 34:35 |
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| 59 | %i=RowNumber; |
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| 60 | % constructing features for each batch of rows from batch featuresa |
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| 61 | load([ScratchDataFolder '/data/FeatureSuperpixel.mat']); % load the feature relate to position and shape of superpixel |
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| 62 | % load estimated sky |
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| 63 | load([ScratchDataFolder '/data/MaskGSky.mat']); % maskg is the estimated ground maskSky is the estimated sky |
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| 64 | l |
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| 65 | FeaVector = []; |
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| 66 | %FeaWei = []; |
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| 67 | DepthVector = []; |
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| 68 | DepthVectorRobust = []; |
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| 69 | fid = fopen(statusFilename, 'w+'); |
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| 70 | fprintf(fid, 'Currently on row number %i\n', i); |
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| 71 | fclose(fid); %file opening and closing has to be inside the loop, otherwise the file will not appear over afs |
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| 72 | for j = 1:NuBatch |
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| 73 | tic |
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| 74 | load([ScratchDataFolder '/data/feature_Abs_' AbsFeaType int2str(j) '_' AbsFeaDate '.mat']); % 'f' |
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| 75 | %toc |
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| 76 | %for k = trainIndex{j} |
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| 77 | for k = 1:size(f,2)%batchSize |
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| 78 | |
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| 79 | %================== |
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| 80 | % load picsinfo just for the horizontal value |
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| 81 | PicsinfoName = strrep(filename{(j-1)*batchSize+k},'img','picsinfo'); |
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| 82 | temp = dir([GeneralDataFolder '/PicsInfo/' PicsinfoName '.mat']); |
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| 83 | if size(temp,1) == 0 |
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| 84 | a = a_default; |
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| 85 | b = b_default; |
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| 86 | Ox = Ox_default; |
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| 87 | Oy = Oy_default; |
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| 88 | Horizon = Horizon_default; |
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| 89 | else |
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| 90 | load([GeneralDataFolder '/PicsInfo/' PicsinfoName '.mat']); |
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| 91 | end |
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| 92 | %================== |
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| 93 | %tic; |
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| 94 | % generate the range of the row for the same thi (weight value) |
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| 95 | RowskyBottom = ceil(NuRow/2); |
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| 96 | PatchSkyBottom = ceil(VertYNuDepth*(1-Horizon)); |
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| 97 | if i <= RowskyBottom |
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| 98 | PatchRowRatio = PatchSkyBottom/RowskyBottom; |
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| 99 | RowTop = ceil((i-1)*PatchRowRatio+1); |
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| 100 | RowBottom = ceil(i*PatchRowRatio); |
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| 101 | else |
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| 102 | PatchRowRatio = (VertYNuDepth-PatchSkyBottom)/(NuRow-RowskyBottom); |
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| 103 | RowTop = ceil((i-RowskyBottom-1)*PatchRowRatio+1)+PatchSkyBottom; |
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| 104 | RowBottom = ceil((i-RowskyBottom)*PatchRowRatio)+PatchSkyBottom; |
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| 105 | end |
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| 106 | ColumnLeft = 1; |
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| 107 | ColumnRight = HoriXNuDepth; |
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| 108 | |
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| 109 | newFea = genFeaVector(f{k},FeatureSuperpixel{(j-1)*batchSize+k},... |
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| 110 | [RowTop:RowBottom],[ColumnLeft:ColumnRight],(j-1)*batchSize+k,LearnNear); |
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| 111 | if SkyExclude == 1 |
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| 112 | maskSkyPics = maskSky{(j-1)*batchSize+k}; |
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| 113 | newFea = newFea(:,~maskSkyPics(RowTop:RowBottom,ColumnLeft:ColumnRight)); |
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| 114 | end |
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| 115 | |
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| 116 | if size(newFea,2)~=0 |
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| 117 | % load laserdepth |
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| 118 | depthfile = strrep(filename{(j-1)*batchSize+k},'img','depth_sph_corr'); |
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| 119 | load([ScratchDataFolder '/Gridlaserdata/' depthfile '.mat']); |
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| 120 | newDepthLaser = genDepthVector(Position3DGrid(:,:,4),... |
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| 121 | RowTop,RowBottom,ColumnLeft,ColumnRight,(j-1)*batchSize+k); |
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| 122 | newDepthLaser = newDepthLaser(~maskSkyPics(RowTop:RowBottom,ColumnLeft:ColumnRight),1); |
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| 123 | % load learned depth |
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| 124 | depthfile = strrep(filename{i},'img','depth_learned'); |
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| 125 | load([DepthDirectory '/' depthfile '.mat']); |
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| 126 | newDepthLearn = genDepthVector(depthMap,... |
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| 127 | RowTop,RowBottom,ColumnLeft,ColumnRight,(j-1)*batchSize+k); |
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| 128 | newDepthLearn = newDepthLearn(~maskSkyPics(RowTop:RowBottom,ColumnLeft:ColumnRight),1); |
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| 129 | |
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| 130 | newFraDepth = abs( (newDepthLaser - newDepthLearn )./ newDepthLaser)+1; |
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| 131 | newFraDepthRobust = (newDepthLaser - newDepthLearn )./ newDepthLaser; |
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| 132 | % with 1 offset to keep log of newFraDepth positive |
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| 133 | % get rid of Inf in newFraDepth |
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| 134 | MaskInf = isinf(newFraDepth); |
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| 135 | %sum(MaskInf) |
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| 136 | newFraDepth = newFraDepth(~MaskInf); |
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| 137 | % FeaWei = [FeaWei ones(1,size(newFea,2))/size(newFea,2)]; |
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| 138 | newFea = newFea(:,~MaskInf); |
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| 139 | FeaVector =[ FeaVector newFea]; |
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| 140 | DepthVector = [DepthVector; newFraDepth]; |
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| 141 | DepthVectorRobust = [DepthVectorRobust; newFraDepthRobust]; |
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| 142 | end |
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| 143 | %DepthVector = [DepthVector genDepthVector(DepthTrueProj{(j-1)*batchSize+k},i,(j-1)*batchSize+k)]; |
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| 144 | %l = l + 1; |
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| 145 | %toc; |
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| 146 | end |
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| 147 | clear f newFea Position3DGrid; |
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| 148 | toc |
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| 149 | end |
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| 150 | |
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| 151 | clear maskg maskSky FeatureSuperpixel maskSkyPics; |
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| 152 | %FeaVector = FeaVector(:,1:round(end*2/3)); |
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| 153 | %DepthVector = DepthVector(1:round(end*2/3),:); |
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| 154 | % learning part |
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| 155 | %X{i} = [ones(size(FeaVector,2),1) FeaVector'] % add offset feature to complete the feature set |
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| 156 | if logScale == 1 |
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| 157 | target{l} = log(DepthVector); |
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| 158 | targetRobust{l} = log(DepthVectorRobust); |
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| 159 | else |
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| 160 | target{l} = DepthVector;%log(DepthVector); |
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| 161 | targetRobust{l} = DepthVectorRobust;%log(DepthVector); |
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| 162 | end |
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| 163 | clear DepthVector; |
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| 164 | % whos; |
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| 165 | % pack; |
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| 166 | % whos; |
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| 167 | % pause; |
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| 168 | % full feature learninga |
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| 169 | % [thi{l},stats] = robustfit(FeaVector',target{l},'huber'); |
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| 170 | Tsize = size(target{l},1) |
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| 171 | Fsize = size(FeaVector,1)+1 |
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| 172 | % A = [-[ones(Tsize,1) FeaVector'] [ones(Tsize,1) FeaVector'] -speye(Tsize) speye(Tsize) sparse(Tsize,Tsize);... |
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| 173 | % -[ones(Tsize,1) FeaVector'] [ones(Tsize,1) FeaVector'] +speye(Tsize) sparse(Tsize,Tsize) -speye(Tsize)]; |
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| 174 | % bb = [-target{i};-target{i}]; |
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| 175 | % cc = [sparse(Fsize*2,1); ones(Tsize,1); sparse(Tsize*2,1)]; |
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| 176 | if strcmp(LearnAlg,'L1norm') |
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| 177 | % ================== yalmip |
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| 178 | opt = sdpsettings('solver','sedumi'); |
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| 179 | thit = sdpvar(Fsize,1); |
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| 180 | F = set(thit >= 0); |
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| 181 | solvesdp(F,norm([ones(Tsize,1) FeaVector']*thit - target{l},1 ), opt); |
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| 182 | thit = double(thit); |
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| 183 | thi{l} = thit; |
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| 184 | % base line learning |
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| 185 | X_base = ones(size(FeaVector,2),1); |
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| 186 | thit_base = sdpvar(1,1); |
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| 187 | F = set(thit_base >= 0); |
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| 188 | solvesdp(F,norm(X_base*thit - target{l} ,1), opt); |
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| 189 | thit_base = double(thit_base); |
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| 190 | thi_base{l} = thit_base; |
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| 191 | % ======================== |
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| 192 | % error measure |
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| 193 | error{l} = ( abs( (target{l} - [ones(size(FeaVector,2),1) FeaVector']*thi{l})) ); |
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| 194 | error_base{l} = ( abs( (target{l} - X_base*thi_base{l})) ); |
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| 195 | learnRatio{l} = sum(error{l})/sum(error_base{l}); |
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| 196 | elseif strcmp(LearnAlg,'robustfit') |
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| 197 | tic; |
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| 198 | [thi{l},stats] = robustfit(FeaVector',targetRobust{l},'huber'); |
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| 199 | toc; |
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| 200 | % pause; |
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| 201 | % base line learning |
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| 202 | X_base = ones(size(FeaVector,2),1); |
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| 203 | [thi_temp,stats] = robustfit(X_base,targetRobust{l},'huber'); |
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| 204 | thi_base{l} = thi_temp(1); |
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| 205 | % error measure |
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| 206 | error{l} = ( abs( (targetRobust{l} - [ones(size(FeaVector,2),1) FeaVector']*thi{l})) ); |
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| 207 | error_base{l} = ( abs( (targetRobust{l} - X_base*thi_base{l})) ); |
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| 208 | learnRatio{l} = sum(error{l})/sum(error_base{l}); |
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| 209 | elseif strcmp(LearnAlg,'L2norm') |
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| 210 | % ================== yalmip |
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| 211 | opt = sdpsettings('solver','sedumi'); |
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| 212 | thit = sdpvar(Fsize,1); |
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| 213 | F = set(thit >= 0); |
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| 214 | solvesdp(F,norm([ones(Tsize,1) FeaVector']*thit - target{l} ), opt); |
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| 215 | thit = double(thit); |
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| 216 | thi{l} = thit; |
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| 217 | % base line learning |
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| 218 | X_base = ones(size(FeaVector,2),1); |
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| 219 | thit_base = sdpvar(1,1); |
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| 220 | F = set(thit_base >= 0); |
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| 221 | solvesdp(F,norm(X_base*thit_base - target{l} ), opt); |
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| 222 | thit_base = double(thit_base); |
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| 223 | thi_base{l} = thit_base; |
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| 224 | % ======================== |
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| 225 | % thi{l} = [ones(Tsize,1) FeaVector']\target{l}; |
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| 226 | % base line learning |
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| 227 | % X_base = ones(size(FeaVector,2),1); |
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| 228 | % thi_base{l} = X_base\target{l}; |
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| 229 | % error measure |
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| 230 | error{l} = ( ( (target{l} - [ones(size(FeaVector,2),1) FeaVector']*thi{l})).^2 ); |
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| 231 | error_base{l} = ( ( (target{l} - X_base*thi_base{l})).^2 ); |
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| 232 | learnRatio{l} = sqrt(sum(error{l})/sum(error_base{l})); |
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| 233 | end |
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| 234 | |
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| 235 | |
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| 236 | l = l +1; |
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| 237 | % change variable name |
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| 238 | nu = thi; |
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| 239 | nu_base = thi_base; |
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| 240 | |
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| 241 | DateStamp = date; |
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| 242 | save([ScratchDataFolder '/../learned_parameter/Variance/Var_' ImgFolder '_' LearnAlg ... |
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| 243 | '_Nonsky' num2str(SkyExclude) '_Log' num2str(logScale) ... |
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| 244 | '_Near' num2str(LearnNear) '_WeiBatNu' num2str(WeiBatchNumber) ... |
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| 245 | '_' AbsFeaType '_AbsFeaDate' AbsFeaDate '_LearnDate' DateStamp '.mat'],... |
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| 246 | 'nu','nu_base','error','error_base','learnRatio'); |
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| 247 | end |
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| 248 | |
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| 249 | DateStamp = date; |
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| 250 | save([ScratchDataFolder '/../learned_parameter/Variance/Var_' ImgFolder '_' LearnAlg ... |
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| 251 | '_Nonsky' num2str(SkyExclude) '_Log' num2str(logScale) ... |
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| 252 | '_Near' num2str(LearnNear) '_WeiBatNu' num2str(WeiBatchNumber) ... |
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| 253 | '_' AbsFeaType '_AbsFeaDate' AbsFeaDate '_LearnDate' DateStamp '.mat'],... |
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| 254 | 'nu','nu_base','error','error_base','learnRatio'); |
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| 255 | %if logScale == 1 |
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| 256 | % if SkyExclude == 1 |
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| 257 | % save([ScratchDataFolder '/../learned_parameter/Depth_' ImgFolder '_' LearnAlg '_Nonsky_WeiBatNu' num2str(WeiBatchNumber) '_' AbsFeaType '_AbsFeaDate' AbsFeaDate '_LearnDate' DateStamp '.mat'],'thi','thi_base','error','error_base','learnRatio'); |
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| 258 | % else |
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| 259 | % save([ScratchDataFolder '/../learned_parameter/Depth_' ImgFolder '_' LearnAlg '_WeiBatNu' num2str(WeiBatchNumber) '_' AbsFeaType '_AbsFeaDate' AbsFeaDate '_LearnDate' DateStamp '.mat'],'thi','thi_base','error','error_base','learnRatio'); |
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| 260 | % end |
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| 261 | %else |
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| 262 | % if SkyExclude == 1 |
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| 263 | % save([ScratchDataFolder '/../learned_parameter/Depth_' ImgFolder '_' LearnAlg '_Nonsky_WeiBatNu' num2str(WeiBatchNumber) '_' AbsFeaType '_AbsFeaDate' AbsFeaDate '_LearnDate' DateStamp '_linear.mat'],'thi','thi_base','error','error_base','learnRatio'); |
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| 264 | % else |
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| 265 | % save([ScratchDataFolder '/../learned_parameter/Depth_' ImgFolder '_' LearnAlg '_WeiBatNu' num2str(WeiBatchNumber) '_' AbsFeaType '_AbsFeaDate' AbsFeaDate '_LearnDate' DateStamp '_linear.mat'],'thi','thi_base','error','error_base','learnRatio'); |
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| 266 | % end |
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| 267 | %end |
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