[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 [] = OcclusionMatchRefineMent(defaultPara, Wrlname, PairList) |
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| 40 | |
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| 41 | % This function do three thing |
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| 42 | % 1) detect the occlusion from the current model |
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| 43 | % 2) Match the Occlued area to find any match |
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| 44 | % 3) reinference including the new matches information |
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| 45 | |
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| 46 | % Input: |
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| 47 | % 1) defaultPara - all parameter share for the whole stitching3d folder |
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| 48 | % 2) Wrlname - the name of the model, different by the PairList |
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| 49 | % 3) PairList - the linearly order of image that been added to the model |
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| 50 | |
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| 51 | % Fixed constant --- need to be put in defaultPara by Min later |
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| 52 | NegI = diag([1 1 -1]); |
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| 53 | PostFixStr = 'NonMono' |
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| 54 | PostFixStrAfter = 'NonMonoOccluMatched' |
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| 55 | % 1) Run occlusion detection and Matcheing for every pair of images |
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| 56 | |
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| 57 | ImgList = unique(PairList); |
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| 58 | NumImg = length(ImgList); |
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| 59 | |
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| 60 | for i = 1:NumImg |
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| 61 | for j = (i+1):NumImg |
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| 62 | |
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| 63 | % detect occlusion for a pair of image |
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| 64 | |
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| 65 | [ImgInfo1 ImgInfo2 Pair GlobalScale] = ... % What to do with Pairs haven't been matched ??????????????????? |
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| 66 | LoadDataForFindOcclu(defaultPara, Wrlname, ImgList{i}, ImgList{j}, ... |
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| 67 | 0, PostFixStr); % load all imformation needed for occlusion detection |
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| 68 | |
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| 69 | % Define variables |
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| 70 | H = 2274; |
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| 71 | V = 1704; |
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| 72 | Img1 = ImgInfo1.ExifInfo.IDName; |
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| 73 | Img2 = ImgInfo2.ExifInfo.IDName; |
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| 74 | I1=imreadbw([defaultPara.Fdir '/pgm/' Img1 '.pgm']); % function from sift |
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| 75 | I2=imreadbw([defaultPara.Fdir '/pgm/' Img2 '.pgm']); % function from sift |
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| 76 | ImgScale1 = size(I1); |
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| 77 | ImgScale2 = size(I2); |
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| 78 | |
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| 79 | % time consuming about 5mins |
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| 80 | if ~defaultPara.Flag.FlagPreloadOccluDetect |
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| 81 | [Region1 PointPix1 POriReprojM1 PoccluM1 OccluDist1 Region2 PointPix2 POriReprojM2 PoccluM2 OccluDist2] = ... |
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| 82 | FindOccluPair(defaultPara, ImgInfo1, ImgInfo2, Pair, GlobalScale, 1); % detect occlsion |
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| 83 | save([ defaultPara.Fdir '/data/' Img1 '_' Img2 '_OccluDetect.mat'],... |
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| 84 | 'Region1','PointPix1','POriReprojM1','PoccluM1','OccluDist1','Region2','PointPix2','POriReprojM2','PoccluM2','OccluDist2'); |
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| 85 | else |
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| 86 | load([defaultPara.Fdir '/data/' Img1 '_' Img2 '_OccluDetect.mat']); |
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| 87 | end |
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| 88 | |
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| 89 | % Define occlusion if OccluDist > CentainThreshold |
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| 90 | Mask1 = OccluDist1 > defaultPara.OccluDistThre; |
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| 91 | Mask2 = OccluDist2 > defaultPara.OccluDistThre; |
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| 92 | [Region1 PointPix1 POriReprojM1 PoccluM1 Region2 PointPix2 POriReprojM2 PoccluM2] = ... |
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| 93 | OccluPruning(Mask1, Mask2, Region1, PointPix1, POriReprojM1, PoccluM1, OccluDist1, ... |
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| 94 | Region2, PointPix2, POriReprojM2, PoccluM2, OccluDist2); |
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| 95 | |
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| 96 | |
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| 97 | % match occlusion part for a pair of image |
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| 98 | % Prepare the Constrain to run the matching |
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| 99 | [ Rc1 ConS1 ConSRough1] = EndPoint2BoxConS(defaultPara, H, V, POriReprojM1, PoccluM1); |
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| 100 | [ Rc2 ConS2 ConSRough2] = EndPoint2BoxConS(defaultPara, H, V, POriReprojM2, PoccluM2); |
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| 101 | Vector2Ipoint([Rc1; ConS1],[defaultPara.Fdir '/surf/'],['RConS_' Img1]); |
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| 102 | Vector2Ipoint([Rc2; ConS2],[defaultPara.Fdir '/surf/'],['RConS_' Img2]); |
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| 103 | Vector2Ipoint([ConSRough1],[defaultPara.Fdir '/surf/'],['RConSRough_' Img1]); |
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| 104 | Vector2Ipoint([ConSRough2],[defaultPara.Fdir '/surf/'],['RConSRough_' Img2]); |
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| 105 | tic; |
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| 106 | cd match |
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| 107 | % system(['./surfOccluMatch.sh ' defaultPara.Fdir ' ' Img1 ' ' Img2 ' OccluDense ' '0.1 0.2']); % Parameter still need to be changed//Min |
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| 108 | cd .. |
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| 109 | toc |
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| 110 | [f1, f2, matches] = readSurfMatches(Img1, Img2, defaultPara.Fdir, [ defaultPara.Type 'OccluDense'], 1, 1); |
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| 111 | figure(200); plotmatches(I1,I2,f1, f2,matches, 'Stacking','v','Interactive', 3); |
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| 112 | saveas(200,[ defaultPara.ScratchFolder Img1 '_' Img2 '_OccluMatches'],'jpg'); |
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| 113 | |
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| 114 | % Process the matches |
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| 115 | % Pruning by epipolarline |
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| 116 | [inlier] = EpipoPrune(defaultPara, Pair, [f1(:,matches(1,:)); f2(:,matches(2,:))], (ImgScale1+ImgScale2)/2); |
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| 117 | matches = matches(:,inlier); |
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| 118 | figure(201); plotmatches(I1,I2,f1, f2,matches, 'Stacking','v','Interactive', 3); |
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| 119 | saveas(201,[ defaultPara.ScratchFolder Img1 '_' Img2 '_OccluMatchesPrune'],'jpg'); |
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| 120 | |
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| 121 | |
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| 122 | % Triangulation |
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| 123 | if ~isempty(matches) |
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| 124 | tempf1 = f1(:,matches(1,:)); |
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| 125 | tempf2 = f2(:,matches(2,:)); |
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| 126 | x_calib = [ inv(defaultPara.InrinsicK1)*[ tempf1; ones(1,size(tempf1,2))];... |
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| 127 | inv(defaultPara.InrinsicK2)*[ tempf2; ones(1,size(tempf2,2))]]; |
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| 128 | [ lamda1 lamda2 Error] = triangulation( defaultPara, Pair.R, Pair.T, x_calib); |
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| 129 | % notice lamda re-scale to local model scale |
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| 130 | lamda1 = lamda1./GlobalScale(1); |
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| 131 | lamda2 = lamda2./GlobalScale(2); |
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| 132 | % Storage the match result for later ReInference |
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| 133 | AddMatch2Model(defaultPara, Wrlname, lamda1, f1(:,matches(1,:)), ImgInfo1, ImgScale1, i, j, PostFixStrAfter); |
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| 134 | AddMatch2Model(defaultPara, Wrlname, lamda2, f2(:,matches(2,:)), ImgInfo2, ImgScale2, i, j, PostFixStrAfter); |
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| 135 | end |
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| 136 | |
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| 137 | % storage the new Triangulated information |
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| 138 | end |
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| 139 | end |
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| 140 | |
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| 141 | % 2) ReInference of each image individiually |
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| 142 | |
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| 143 | for i = 1:NumImg |
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| 144 | |
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| 145 | % Data preparing and ReInfernece |
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| 146 | Default.Wrlname{1} = [Wrlname '_' ImgList{i} '_OccluMatches']; |
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| 147 | load([defaultPara.Fdir '/data/' ImgList{i} '/' Wrlname '_' ImgList{i} '_' PostFixStrAfter '.mat']); |
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| 148 | R = LoadModelStatus( defaultPara.Fdir, Wrlname, ImgList{i}, 'R'); |
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| 149 | T = LoadModelStatus( defaultPara.Fdir, Wrlname, ImgList{i}, 'T'); |
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| 150 | Scale = LoadModelStatus(defaultPara.Fdir, Wrlname, ImgList{i}, 'Scale'); |
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| 151 | SingleImgReInference(defaultPara, model, ImgList{i}, LoadModelStatus(defaultPara.Fdir, Wrlname, ImgList{i}, 'GroundLevel'),Wrlname, ... |
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| 152 | R, T, Scale ,PostFixStrAfter); |
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| 153 | |
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| 154 | % Build Meta Wrl file |
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| 155 | |
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| 156 | Path = [ defaultPara.OutPutFolder Wrlname 'Occlu.wrl']; |
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| 157 | InLinePath = ['./' ImgList{i} '/' Default.Wrlname{1} '.wrl']; |
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| 158 | % Possibly to be wrong ========== |
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| 159 | % Angle = recoverAlphasFromU(reshape(R,1,[])); |
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| 160 | % Q = GetQauternionFrom2Rotation(zeros(3,1), Angle, false);%[1 0 0 0]'; |
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| 161 | Q = Rotation2Q(NegI*R*NegI);% for Wrl (-) z component; |
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| 162 | if any(isnan(Q)) |
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| 163 | Q = zeros(4,1); |
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| 164 | end |
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| 165 | % =============================== |
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| 166 | WRLT = T; |
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| 167 | WRLT(3) = -WRLT(3);% for Wrl (-) z component; |
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| 168 | Scale = LoadModelStatus(defaultPara.Fdir, Wrlname, ImgList{i}, 'Scale'); |
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| 169 | if i == 1 |
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| 170 | BuildVrmlMetaModel(1, defaultPara.OutPutFolder, Path, InLinePath, Q, WRLT, repmat(Scale,3,1)); |
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| 171 | else |
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| 172 | BuildVrmlMetaModel(0, defaultPara.OutPutFolder, Path, InLinePath, Q, WRLT, repmat(Scale,3,1)); |
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| 173 | end |
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| 174 | end |
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| 175 | |
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| 176 | return; |
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