| 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 []=PredictPlane(DepthDirectory,logScale,SkyExclude) |
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| 40 | %this function generate the predicted plane |
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| 41 | |
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| 42 | |
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| 43 | % define global variable |
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| 44 | global GeneralDataFolder ScratchDataFolder LocalFolder ClusterExecutionDirectory... |
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| 45 | ImgFolder VertYNuPatch VertYNuDepth HoriXNuPatch HoriXNuDepth a_default b_default Ox_default Oy_default... |
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| 46 | Horizon_default filename batchSize NuRow_default SegVertYSize SegHoriXSize WeiBatchSize; |
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| 47 | |
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| 48 | % load data |
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| 49 | load([ScratchDataFolder '/data/LowResImgIndexSuperpixelSep.mat']); % load LowResImgIndexSuperpixelSep |
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| 50 | load([ScratchDataFolder '/data/DiffLowResImgIndexSuperpixelSep.mat']); % load DiffLowResImgIndexSuperpixelSep(medi$large) |
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| 51 | load([ScratchDataFolder '/data/TextLowResImgIndexSuperpixelSep.mat']); % load TextLowResImgIndexSuperpixelSep using Textrea |
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| 52 | load([ScratchDataFolder '/data/MaskGSky.mat']); % load maskg maskSky from CMU's output |
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| 53 | %load([ScratchDataFolder '/data/maskO.mat']); |
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| 54 | % load useful features |
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| 55 | load([ScratchDataFolder '/data/FeatureSuperpixel.mat']); % load the feature relate to position and shape of superpixe |
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| 56 | |
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| 57 | % prepare to store the predictedM |
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| 58 | system(['mkdir ' ScratchDataFolder '/_predicted_' DepthDirectory]); |
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| 59 | |
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| 60 | % set parameter |
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| 61 | ZTiltFactor = 1; % both can be estimated after group fit that estimate the Norm_floor |
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| 62 | YTiltFactor = 1; |
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| 63 | % initial parameter |
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| 64 | NuPics = size(filename,2); |
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| 65 | for i = 1 : NuPics |
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| 66 | i |
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| 67 | % load picsinfo just for the horizontal value |
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| 68 | PicsinfoName = strrep(filename{i},'img','picsinfo'); |
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| 69 | temp = dir([GeneralDataFolder '/PicsInfo/' PicsinfoName '.mat']); |
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| 70 | if size(temp,1) == 0 |
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| 71 | a = a_default; |
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| 72 | b = b_default; |
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| 73 | Ox = Ox_default; |
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| 74 | Oy = Oy_default; |
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| 75 | Horizon = Horizon_default; |
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| 76 | else |
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| 77 | eload([GeneralDataFolder '/PicsInfo/' PicsinfoName '.mat']); |
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| 78 | end |
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| 79 | |
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| 80 | % load useful feature |
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| 81 | BatchNumber = ceil(i/batchSize); |
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| 82 | PicsNumber = mod(i,batchSize); |
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| 83 | if PicsNumber ==0 |
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| 84 | PicsNumber = 10; |
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| 85 | end |
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| 86 | % load([ScratchDataFolder '/data/feature_sqrt_H4_ray' int2str(BatchNumber) '.mat']); % 'f' |
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| 87 | % f = f{PicsNumber}; |
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| 88 | % f = f(:,1); |
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| 89 | % fsup = FeatureSuperpixel{i}; |
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| 90 | % f = (fsup(1,f))'; |
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| 91 | |
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| 92 | % load depthMap |
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| 93 | depthfile = strrep(filename{i},'img','depth_learned'); % the depth filename |
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| 94 | if logScale == 1 |
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| 95 | if SkyExclude ==1 |
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| 96 | load([ScratchDataFolder '/_LearnDLogScaleNonsky_' DepthDirectory '/' depthfile '.mat']); |
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| 97 | else |
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| 98 | load([ScratchDataFolder '/_LearnDLogScale_' DepthDirectory '/' depthfile '.mat']); |
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| 99 | end |
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| 100 | else |
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| 101 | if SkyExclude ==1 |
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| 102 | load([ScratchDataFolder '/_LearnD_' DepthDirectory '/' depthfile '.mat']); |
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| 103 | else |
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| 104 | load([ScratchDataFolder '/_LearnD_' DepthDirectory '/' depthfile '.mat']); |
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| 105 | end |
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| 106 | end |
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| 107 | |
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| 108 | LearnedDepth = depthMap; clear depthMap; |
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| 109 | |
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| 110 | % initialize parameter |
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| 111 | NuPatch = VertYNuDepth*HoriXNuDepth; |
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| 112 | |
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| 113 | % generate straight line for straight line prior |
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| 114 | Img = imread([GeneralDataFolder '/' ImgFolder '/' filename{i} '.jpg'],'jpg'); |
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| 115 | if prod(size(Img))> SegVertYSize*SegHoriXSize*3 |
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| 116 | Img = imresize(Img,[SegVertYSize SegHoriXSize],'nearest'); |
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| 117 | end |
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| 118 | [ImgYSize ImgXSize dummy] = size(Img); |
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| 119 | [lineSegList] = edgeSegDetection(Img); |
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| 120 | NuSeg = size(lineSegList,1); |
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| 121 | % order the line segment by their length |
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| 122 | lineSegList = lineSegList./repmat([ImgXSize ImgYSize ImgXSize ImgYSize],NuSeg,1)... |
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| 123 | .*repmat([HoriXNuDepth VertYNuDepth HoriXNuDepth VertYNuDepth],NuSeg,1); |
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| 124 | |
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| 125 | % take out the vertical edge so that we may combine it with 1stSmooth in Y direction |
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| 126 | VertLineSegList = lineSegList(abs(round(lineSegList(:,1))-round(lineSegList(:,3))) <=0 & ... |
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| 127 | abs(round(lineSegList(:,2))-round(lineSegList(:,4))) > 0 ,:); |
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| 128 | VertLineSegList = round(VertLineSegList); |
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| 129 | NuVertSeg = size(VertLineSegList,1); |
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| 130 | VertLineMask = zeros(VertYNuDepth, HoriXNuDepth); |
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| 131 | for k = 1:NuVertSeg |
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| 132 | if VertLineSegList(k,2) < VertLineSegList(k,4) |
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| 133 | VertLineMask(max(VertLineSegList(k,2),1):min((VertLineSegList(k,4)-1),VertYNuDepth),... |
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| 134 | min(max(VertLineSegList(k,1),1),HoriXNuDepth)) = 1; |
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| 135 | else |
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| 136 | VertLineMask(max(VertLineSegList(k,4),1):min((VertLineSegList(k,2)-1),VertYNuDepth),... |
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| 137 | min(max(VertLineSegList(k,1),1),HoriXNuDepth)) = 1; |
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| 138 | end |
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| 139 | end |
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| 140 | |
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| 141 | [StraightLinePriorSelectMatrix RayPorjectImgMapY LineIndex newd] = StraightLinePrior(lineSegList,LearnedDepth,a,b,Ox,Oy); |
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| 142 | % |
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| 143 | % generate ray porjection position in the image plane |
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| 144 | RayPorjectImgMapY = ((VertYNuDepth+1-RayPorjectImgMapY)-0.5)/VertYNuDepth - Oy; |
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| 145 | RayPorjectImgMapX = repmat(((1:HoriXNuDepth)-0.5)/HoriXNuDepth - Ox,[VertYNuDepth 1]); |
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| 146 | % generate specific ray for whole pics |
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| 147 | RayCenter = RayImPosition(RayPorjectImgMapY,RayPorjectImgMapX,a,b,Ox,Oy); %[ horiXSizeLowREs VertYSizeLowREs 3] |
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| 148 | Rx = RayCenter(:,:,1); |
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| 149 | Rx = Rx(:); |
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| 150 | Ry = RayCenter(:,:,2); |
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| 151 | Ry = Ry(:); |
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| 152 | Rz = RayCenter(:,:,3); |
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| 153 | Rz = Rz(:); |
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| 154 | |
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| 155 | %===================================================== |
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| 156 | % MRF reshape the 3d cloud |
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| 157 | |
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| 158 | NuSupType = size(LowResImgIndexSuperpixelSep,2)+size(DiffLowResImgIndexSuperpixelSep,2)... |
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| 159 | +size(TextLowResImgIndexSuperpixelSep,2)*size(TextLowResImgIndexSuperpixelSep,3); |
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| 160 | |
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| 161 | for j = 1:NuSupType % total 21 seg : 3 RGB 6*3= 18 texture filters |
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| 162 | |
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| 163 | % pick the specific segmentation |
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| 164 | if j==1 |
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| 165 | sup = LowResImgIndexSuperpixelSep{i,1}; |
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| 166 | elseif j<=3 |
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| 167 | sup = DiffLowResImgIndexSuperpixelSep{i,j-1}; |
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| 168 | else |
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| 169 | Subm = mod((j-3),6); |
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| 170 | if Subm==0 |
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| 171 | Subm=6; |
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| 172 | end |
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| 173 | sup = TextLowResImgIndexSuperpixelSep{i,Subm,ceil((j-3)/6)}; |
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| 174 | end |
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| 175 | |
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| 176 | % Exclude the sky first |
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| 177 | sup(maskSky{i} == 1) = 0; |
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| 178 | [SelectY{j} SelecrX{j} BM{j} Ns{j} MediaM{j} MeanM{j}] = OrientEst(sup, RayCenter); |
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| 179 | end |
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| 180 | |
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| 181 | % set weight for different segmentation |
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| 182 | small=10; med=5; large=1; |
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| 183 | temp =[small; med; large; small*ones(6,1); med*ones(6,1) ;large*ones(6,1)] |
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| 184 | |
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| 185 | % set weight for the VertLineSeg Ground and VertialWalls |
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| 186 | WeiLinSegVertWall = 112; |
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| 187 | WeiLinSegVertGround = 112; |
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| 188 | |
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| 189 | % set weight for the straight line prior |
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| 190 | WeiStraightLine = 1120 |
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| 191 | |
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| 192 | % generate the smooth matrix |
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| 193 | M2ndSmoothY = spdiags([ones(NuPatch,1) -2*ones(NuPatch,1) ones(NuPatch,1)],[-1 0 1],NuPatch,NuPatch); |
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| 194 | M2ndSmoothX = spdiags([ones(NuPatch,1) -2*ones(NuPatch,1) ones(NuPatch,1)],... |
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| 195 | [-VertYNuDepth 0 VertYNuDepth],NuPatch,NuPatch); |
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| 196 | M1stSmoothY = spdiags([ones(NuPatch,1) -ones(NuPatch,1)],[0 1],NuPatch,NuPatch); |
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| 197 | M1stSmoothX = spdiags([ones(NuPatch,1) -ones(NuPatch,1)],[0 VertYNuDepth],NuPatch,NuPatch); |
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| 198 | |
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| 199 | % generate beta |
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| 200 | % beta2ndSmoothGX = Wei2ndSmoothGX'*ones(21,1) * logisticResponse(Wei2ndSmoothGX'*SecXG / (Wei2ndSmoothGX'*ones(21,1)) ); |
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| 201 | beta2ndSmoothGX = Wei2ndSmoothGX'*SecXG; |
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| 202 | %MIN: TO DO |
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| 203 | %MIN: TO DO |
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| 204 | beta2ndSmoothGY = Wei2ndSmoothGY'*SecYG; |
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| 205 | % beta2ndSmoothGY = Wei2ndSmoothGY'*ones(21,1) * logisticResponse(Wei2ndSmoothGY'*SecYG / (Wei2ndSmoothGY'*ones(21,1)) ); |
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| 206 | beta2ndSmoothGY = Wei2ndSmoothGY'*SecYG; |
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| 207 | % beta2ndSmoothVX = Wei2ndSmoothVX'*ones(21,1) * logisticResponse(Wei2ndSmoothVX'*SecXV / (Wei2ndSmoothVX'*ones(21,1)) ); |
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| 208 | beta2ndSmoothVX = Wei2ndSmoothVX'*SecXV; |
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| 209 | % beta2ndSmoothVY = Wei2ndSmoothVY'*ones(21,1) * logisticResponse(Wei2ndSmoothVY'*SecYV / (Wei2ndSmoothVY'*ones(21,1)) ); |
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| 210 | beta2ndSmoothVY = Wei2ndSmoothVY'*SecYV; |
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| 211 | % beta2ndSmoothSX = Wei2ndSmoothSX'*ones(21,1) * logisticResponse(Wei2ndSmoothSX'*SecXS / (Wei2ndSmoothSX'*ones(21,1)) ); |
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| 212 | % beta2ndSmoothSY = Wei2ndSmoothSY'*ones(21,1) * logisticResponse(Wei2ndSmoothSY'*SecYS / (Wei2ndSmoothSY'*ones(21,1)) ); |
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| 213 | |
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| 214 | % beta1stSmoothGY = Wei1stSmoothGY'*ones(21,1)*logisticResponse(Wei1stSmoothGY'*FirstYG/(Wei1stSmoothGY'*ones(21,1)) ); |
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| 215 | beta1stSmoothGY = Wei2ndSmoothGY'*FirstYG; |
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| 216 | % beta1stSmoothVY = Wei1stSmoothVY'*ones(21,1)*logisticResponse(Wei1stSmoothVY'*FirstYV/(Wei1stSmoothVY'*ones(21,1)) ); |
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| 217 | beta1stSmoothVY = Wei2ndSmoothVY'*FirstYV; |
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| 218 | % beta1stSmoothSY = Wei1stSmoothSY'*ones(21,1)*logisticResponse(Wei1stSmoothSY'*FirstYS/(Wei1stSmoothSY'*ones(21,1)) ); |
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| 219 | % beta1stSmoothGX = Wei1stSmoothGX'*ones(21,1)*logisticResponse(Wei1stSmoothGX'*FirstXG/(Wei1stSmoothGX'*ones(21,1)) ); |
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| 220 | beta1stSmoothGX = Wei2ndSmoothGX'*FirstXG; |
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| 221 | % beta1stSmoothVX = Wei1stSmoothVX'*ones(21,1)*logisticResponse(Wei1stSmoothVX'*FirstXV/(Wei1stSmoothVX'*ones(21,1)) ); |
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| 222 | beta1stSmoothVX = Wei2ndSmoothVX'*FirstXV; |
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| 223 | % beta1stSmoothSX = Wei1stSmoothSX'*ones(21,1)*logisticResponse(Wei1stSmoothSX'*FirstXS/(Wei1stSmoothSX'*ones(21,1)) ); |
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| 224 | |
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| 225 | Beta1stSmoothLineGY = VertLineMask.*(maskEstGVS==1)*WeiLinSegVertGround; |
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| 226 | Beta1stSmoothLineGY = (Beta1stSmoothLineGY(:))'; |
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| 227 | Beta1stSmoothLineVWY = VertLineMask.*(maskEstGVS==2)*WeiLinSegVertWall; |
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| 228 | Beta1stSmoothLineVWY = (Beta1stSmoothLineVWY(:))'; |
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| 229 | |
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| 230 | % generate Q |
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| 231 | % Q2ndXx = spdiags((beta2ndSmoothGX+beta2ndSmoothVX+beta2ndSmoothSX)'... |
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| 232 | % ,0,NuPatch,NuPatch)*M2ndSmoothX*spdiags(Rx,0,NuPatch,NuPatch); |
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| 233 | % Q2ndXy = spdiags((beta2ndSmoothGX+beta2ndSmoothVX+beta2ndSmoothSX)'... |
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| 234 | % ,0,NuPatch,NuPatch)*M2ndSmoothX*spdiags(Ry,0,NuPatch,NuPatch); |
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| 235 | % Q2ndXz = spdiags((beta2ndSmoothGX+beta2ndSmoothVX+beta2ndSmoothSX)'... |
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| 236 | % ,0,NuPatch,NuPatch)*M2ndSmoothX*spdiags(Rz,0,NuPatch,NuPatch); |
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| 237 | % Q2ndYx = spdiags((beta2ndSmoothGY+beta2ndSmoothVY+beta2ndSmoothSY)'... |
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| 238 | % ,0,NuPatch,NuPatch)*M2ndSmoothY*spdiags(Rx,0,NuPatch,NuPatch); |
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| 239 | % Q2ndYy = spdiags((beta2ndSmoothGY+beta2ndSmoothVY+beta2ndSmoothSY)'... |
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| 240 | % ,0,NuPatch,NuPatch)*M2ndSmoothY*spdiags(Ry,0,NuPatch,NuPatch); |
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| 241 | % Q2ndYz = spdiags((beta2ndSmoothGY+beta2ndSmoothVY+beta2ndSmoothSY)'... |
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| 242 | % ,0,NuPatch,NuPatch)*M2ndSmoothY*spdiags(Rz,0,NuPatch,NuPatch); |
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| 243 | |
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| 244 | Q2ndXx = spdiags((beta2ndSmoothGX+beta2ndSmoothVX)'... |
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| 245 | ,0,NuPatch,NuPatch)*M2ndSmoothX*spdiags(Rx,0,NuPatch,NuPatch); |
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| 246 | Q2ndXy = spdiags((beta2ndSmoothGX+beta2ndSmoothVX)'... |
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| 247 | ,0,NuPatch,NuPatch)*M2ndSmoothX*spdiags(Ry,0,NuPatch,NuPatch); |
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| 248 | %Q2ndXz = spdiags((beta2ndSmoothGX+beta2ndSmoothVX)'... |
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| 249 | % ,0,NuPatch,NuPatch)*M2ndSmoothX*spdiags(Rz,0,NuPatch,NuPatch); |
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| 250 | Q2ndXz = spdiags((beta2ndSmoothVX)'... |
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| 251 | ,0,NuPatch,NuPatch)*M2ndSmoothX*spdiags(Rz,0,NuPatch,NuPatch); |
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| 252 | Q2ndYx = spdiags((beta2ndSmoothGY+beta2ndSmoothVY)'... |
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| 253 | ,0,NuPatch,NuPatch)*M2ndSmoothY*spdiags(Rx,0,NuPatch,NuPatch); |
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| 254 | Q2ndYy = spdiags((beta2ndSmoothGY+beta2ndSmoothVY)'... |
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| 255 | ,0,NuPatch,NuPatch)*M2ndSmoothY*spdiags(Ry,0,NuPatch,NuPatch); |
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| 256 | %Q2ndYz = spdiags((beta2ndSmoothGY+beta2ndSmoothVY)'... |
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| 257 | % ,0,NuPatch,NuPatch)*M2ndSmoothY*spdiags(Rz,0,NuPatch,NuPatch); |
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| 258 | Q2ndYz = spdiags((beta2ndSmoothVY)'... |
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| 259 | ,0,NuPatch,NuPatch)*M2ndSmoothY*spdiags(Rz,0,NuPatch,NuPatch); |
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| 260 | |
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| 261 | Q2ndLinex = WeiStraightLine*StraightLinePriorSelectMatrix*spdiags(Rx,0,NuPatch,NuPatch); |
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| 262 | Q2ndLiney = WeiStraightLine*StraightLinePriorSelectMatrix*spdiags(Ry,0,NuPatch,NuPatch); |
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| 263 | Q2ndLinez = WeiStraightLine*StraightLinePriorSelectMatrix*spdiags(Rz,0,NuPatch,NuPatch); |
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| 264 | |
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| 265 | % Q1stYz = spdiags((beta1stSmoothVY+beta1stSmoothSY+Beta1stSmoothLineVWY)'... |
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| 266 | % ,0,NuPatch,NuPatch)*M1stSmoothY*spdiags(Rz*ZTiltFactor,0,NuPatch,NuPatch); |
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| 267 | %Q1stYz = spdiags((Beta1stSmoothLineVWY)'... |
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| 268 | % ,0,NuPatch,NuPatch)*M1stSmoothY*spdiags(Rz*ZTiltFactor,0,NuPatch,NuPatch); |
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| 269 | % Q1stYy = spdiags((beta1stSmoothGY+Beta1stSmoothLineGY)'... |
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| 270 | % ,0,NuPatch,NuPatch)*M1stSmoothY*spdiags(Ry*YTiltFactor,0,NuPatch,NuPatch); |
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| 271 | % Q1stXz = spdiags((beta1stSmoothGX+beta1stSmoothVX+beta1stSmoothSX)'... |
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| 272 | % ,0,NuPatch,NuPatch)*M1stSmoothX*spdiags(Rz*ZTiltFactor,0,NuPatch,NuPatch); |
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| 273 | |
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| 274 | |
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| 275 | %Q1stYz = spdiags((beta1stSmoothVY+Beta1stSmoothLineVWY)'... |
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| 276 | % ,0,NuPatch,NuPatch)*M1stSmoothY*spdiags(Rz*ZTiltFactor,0,NuPatch,NuPatch); |
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| 277 | Q1stYz = spdiags((beta1stSmoothVY+Beta1stSmoothLineVWY)'... |
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| 278 | ,0,NuPatch,NuPatch)*M1stSmoothY*spdiags(Rz*ZTiltFactor,0,NuPatch,NuPatch); |
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| 279 | Q1stYy = spdiags((beta1stSmoothGY+Beta1stSmoothLineGY)'... |
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| 280 | ,0,NuPatch,NuPatch)*M1stSmoothY*spdiags(Ry*YTiltFactor,0,NuPatch,NuPatch); |
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| 281 | Q1stXz = spdiags((beta1stSmoothGX+beta1stSmoothVX)'... |
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| 282 | ,0,NuPatch,NuPatch)*M1stSmoothX*spdiags(Rz*ZTiltFactor,0,NuPatch,NuPatch); |
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| 283 | % ============================START MRF OPTMIZATIOM========================================================= |
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| 284 | %================= |
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| 285 | % generate mask for depth difference |
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| 286 | YDiff = repmat(logical([ones(VertYNuDepth-1,1); 0]),1,HoriXNuDepth); |
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| 287 | XDiff = repmat(([ones(1,HoriXNuDepth-1) 0]),VertYNuDepth,1); |
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| 288 | DMatrixY = spdiags([ones(NuPatch,1) -ones(NuPatch,1)],[0 1],NuPatch,NuPatch); |
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| 289 | %DMatrixY = DMatrixY(~maskO,:); |
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| 290 | DMatrixX = spdiags([ones(NuPatch,1) -ones(NuPatch,1)],[0 VertYNuDepth],NuPatch,NuPatch); |
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| 291 | %================= |
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| 292 | tic; |
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| 293 | % lineprog =========================================== |
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| 294 | % B = [DMatrixY;spdiags(maskO(:),[0],NuPatch,NuPatch)]; |
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| 295 | % Q = [Q2ndXz;Q2ndYz]; |
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| 296 | % A = [B -speye(size(B,1)) sparse(size(B,1),size(Q,1));... |
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| 297 | % -B -speye(size(B,1)) sparse(size(B,1),size(Q,1));... |
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| 298 | % Q sparse(size(Q,1),size(B,1)) -speye(size(Q,1));... |
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| 299 | % -Q sparse(size(Q,1),size(B,1)) -speye(size(Q,1));... |
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| 300 | % -speye(NuPatch) sparse(NuPatch,size(B,1)+size(Q,1))]; |
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| 301 | % bb = [B*LearnedDepth(:); - B*LearnedDepth(:);... |
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| 302 | % sparse(size(Q,1)*2,1); -5*ones(NuPatch,1)]; |
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| 303 | % f = [sparse(NuPatch,1); ones(size(B,1)+size(Q,1),1)]; |
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| 304 | % x = linprog(f,A,bb); |
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| 305 | %===================================================== |
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| 306 | |
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| 307 | % quadprog =========================================== |
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| 308 | % only 2nd order smooth use L1 norm |
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| 309 | Q1size = size([Q1stYy; Q1stYz;Q1stXz],1) |
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| 310 | SLsize = size(Q2ndLinez,1) |
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| 311 | Bsize = size([Q2ndXz;Q2ndYz],1) |
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| 312 | % E = [Q1stYy ;Q1stYz; Q1stXz;... |
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| 313 | % spdiags(ones(NuPatch,1),[0],NuPatch,NuPatch);... |
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| 314 | % Q2ndLinez]; |
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| 315 | % e = [sparse(Q1size,1); -LearnedDepth(:); sparse(SLsize,1)]; |
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| 316 | % B = [Q2ndXz ; Q2ndYz]; |
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| 317 | % H = 2*[[Q1stYy;Q1stYz; Q1stXz;... |
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| 318 | % spdiags(ones(NuPatch,1),[0],NuPatch,NuPatch);Q2ndLinez]'*... |
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| 319 | % [Q1stYy;Q1stYz; Q1stXz;... |
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| 320 | % spdiags(ones(NuPatch,1),[0],NuPatch,NuPatch);Q2ndLinez] sparse(NuPatch,Bsize*3);... |
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| 321 | % sparse(Bsize*3,NuPatch+Bsize*3)]; |
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| 322 | % f =[2*[sparse(Q1size,1); -LearnedDepth(:); sparse(SLsize,1)]'*... |
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| 323 | % [Q1stYy;Q1stYz; Q1stXz... |
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| 324 | % spdiags(ones(NuPatch,1),[0],NuPatch,NuPatch); Q2ndLinez] ones(1,Bsize) sparse(1,Bsize*2)]'; |
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| 325 | % Aeq = [[Q2ndXz ; Q2ndYz] -spdiags(ones(Bsize,1),[0],Bsize,Bsize)... |
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| 326 | % spdiags(ones(Bsize,1),[0],Bsize,Bsize) sparse(Bsize,Bsize);... |
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| 327 | % [Q2ndXz ; Q2ndYz] spdiags(ones(Bsize,1),[0],Bsize,Bsize)... |
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| 328 | % sparse(Bsize,Bsize) spdiags(ones(Bsize,1),[0],Bsize,Bsize)]; |
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| 329 | % beq = sparse(Bsize*2,1); |
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| 330 | % lb = [5*ones(NuPatch,1);sparse(Bsize*2,1);... |
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| 331 | % -2*max(LearnedDepth(:))*ones(Bsize,1)]; |
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| 332 | % ub = [max(LearnedDepth(:))*ones(NuPatch,1);... |
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| 333 | % 2*max(LearnedDepth(:))*ones(Bsize*2,1); sparse(Bsize,1)]; |
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| 334 | % options = optimset('LargeScale', 'on', 'Display', 'off'); |
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| 335 | % x = quadprog(H,f,[],[],Aeq,beq,lb,ub,[],options); |
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| 336 | % predictedM = [spdiags(ones(NuPatch,1),[0],NuPatch,NuPatch)... |
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| 337 | % sparse(NuPatch,Bsize*3)]*x; |
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| 338 | % A = [sparse(Bsize*2,NuPatch+Bsize)... |
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| 339 | % spdiags([-ones(Bsize,1);ones(Bsize,1)],[0],Bsize*2,Bsize*2)]; |
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| 340 | % b =sparse(Bsize*2,1); |
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| 341 | %===================================================== |
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| 342 | % predictedM = spdiags([ones(1,NuPatch) sparse(1,size(B,1)+size(Q,1))]',0,NuPatch... |
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| 343 | % ,NuPatch+size(B,1)+size(Q,1))*x; |
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| 344 | % predictedM = (LearnedDepth(:)); |
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| 345 | |
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| 346 | |
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| 347 | %[[Q2ndYz] -spdiags(ones(Bsize,1),[0],Bsize,Bsize)... |
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| 348 | % spdiags(ones(Bsize,1),[0],Bsize,Bsize) sparse(Bsize,Bsize);... |
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| 349 | % [Q2ndYz] spdiags(ones(Bsize,1),[0],Bsize,Bsize)... |
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| 350 | % sparse(Bsize,Bsize) spdiags(ones(Bsize,1),[0],Bsize,Bsize)]*[predictedM; kt; ga; al] == 0; |
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| 351 | |
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| 352 | % [Q2ndXz; Q2ndYz]*predictedM <=kt; |
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| 353 | % [Q2ndXz; Q2ndYz]*predictedM >= -kt; |
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| 354 | % predictedM>=5; |
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| 355 | %ga>=0; |
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| 356 | %al<=0; |
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| 357 | % cvx_end |
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| 358 | % toc; |
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| 359 | |
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| 360 | % post prosessing on straight line |
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| 361 | |
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| 362 | % second optimization |
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| 363 | |
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| 364 | Date =date; |
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| 365 | % predictedM =reshape(predictedM,VertYNuDepth,[]); |
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| 366 | % quadprog Start |
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| 367 | % LearnedDepth = single(LearnedDepth); |
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| 368 | cvx_begin |
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| 369 | cvx_quiet(false); |
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| 370 | variable predictedM(NuPatch,1); |
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| 371 | variable kt(Bsize,1); |
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| 372 | %variable al(Bsize,1); |
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| 373 | %variable ga(Bsize,1); |
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| 374 | variable st(1,1); |
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| 375 | minimize(st);%+ones(1,Bsize)*kt); |
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| 376 | norm([Q2ndYy; Q2ndYz; Q2ndXz; Q2ndLinez;... |
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| 377 | spdiags(ones(NuPatch,1),[0],NuPatch,NuPatch)]... |
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| 378 | *predictedM+[sparse(Q1size+SLsize,1); -LearnedDepth(:)])<=st; |
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| 379 | |
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| 380 | %[[Q2ndYz] -spdiags(ones(Bsize,1),[0],Bsize,Bsize)... |
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| 381 | % spdiags(ones(Bsize,1),[0],Bsize,Bsize) sparse(Bsize,Bsize);... |
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| 382 | % [Q2ndYz] spdiags(ones(Bsize,1),[0],Bsize,Bsize)... |
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| 383 | % sparse(Bsize,Bsize) spdiags(ones(Bsize,1),[0],Bsize,Bsize)]*[predictedM; kt; ga; al] == 0; |
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| 384 | |
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| 385 | % [Q2ndXz; Q2ndYz]*predictedM <=kt; |
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| 386 | % [Q2ndXz; Q2ndYz]*predictedM >= -kt; |
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| 387 | predictedM>=5; |
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| 388 | %ga>=0; |
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| 389 | %al<=0; |
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| 390 | cvx_end |
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| 391 | % toc; |
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| 392 | |
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| 393 | % post prosessing on straight line |
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| 394 | |
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| 395 | % second optimization |
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| 396 | |
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| 397 | Date =date; |
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| 398 | predictedM =reshape(predictedM,VertYNuDepth,[]); |
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| 399 | depthMap = predictedM; |
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| 400 | save([ScratchDataFolder '/_predicted_' DepthDirectory '/' depthfile '_' num2str(logScale) '_' Date '.mat'],'depthMap'); |
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| 401 | clear depthMap; |
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| 402 | |
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| 403 | %===================================================== |
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| 404 | % 2d to would 3d |
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| 405 | [Position3DPredicted] = im_cr2w_cr(predictedM,RayCenter); |
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| 406 | |
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| 407 | % generate new LowResImgIndexSuperpixelSep_deoffset |
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| 408 | %LowResImgIndexSuperpixelSep_deoffset = LowResImgIndexSuperpixelSep{i}; |
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| 409 | |
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| 410 | % add on image feature |
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| 411 | %global Imf; |
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| 412 | %Imf= cat(1,Position3DPredicted,permute(ones(VertY,1)*[1:HoriX],[3 1 2]), permute([1:VertY]'*ones(1,HoriX),[3 1 2]),permute(double(zeros(VertY,HoriX,3)),[3 1 2]),permute(double(LowResImgIndexSuperpixelSep_deoffset),[3 1 2]),permute(double(LowResImgIndexSuperpixelSep{i}),[3 1 2]) ); |
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| 413 | |
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| 414 | % calculate each plane parameter for each superpixel |
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| 415 | %[PlaneParameterPredicted] = fit_all_planes(RayLoResCorner); % hard work around 2min |
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| 416 | |
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| 417 | % generate VRML |
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| 418 | Date = date; |
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| 419 | [VrmlName] = vrml_test_faceset_triangle(filename{i},Position3DPredicted,RayCenter,['PredictM_' DepthDirectory '_' num2str(logScale) '_' num2str(SkyExclude) '_' Date '_LearnedLogScaleNonsky_PredictNonsky_L21st_L12ndSmooth_StraightLineDoblueStitchOrientEst_gravity'],a,b,Ox,Oy); |
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| 420 | system(['gzip -9 -c ' ScratchDataFolder '/vrml/' VrmlName ' > ' ScratchDataFolder '/vrml/' VrmlName '.gz']); |
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| 421 | delete([ScratchDataFolder '/vrml/' VrmlName]); |
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| 422 | %vrml_test_faceset_triangle(filename{i},PlaneParameterPredicted,LowResImgIndexSuperpixelSep{i},LowResImgIndexSuperpixelSep_deoffset,[DepthDirectory '_' Date]); |
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| 423 | end |
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