[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 [f, fInd] = AbsFeatureGenMex(Default, SmallSup, HiSupi, SupMaskFlag, FeaMax, fInd) |
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| 40 | |
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| 41 | % This function generate the average of the feature within a certain mask of a sample point |
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| 42 | % Input-- |
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| 43 | % H2: texture filter output |
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| 44 | % HiSup: the Hi resolution superpixel index matrix |
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| 45 | % SupMaskFlag: if SupMaskFlag is 1, we calculate the averaging using superpixel mask (irregular mask) |
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| 46 | % Output-- |
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| 47 | % f: Feature matrix of size(No od depth point, No of feature vector) |
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| 48 | |
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| 49 | global H2; |
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| 50 | |
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| 51 | % Parameter |
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| 52 | [ImgResY ImgResX] = size(H2(:,:,1)); |
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| 53 | |
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| 54 | % define image and sample point and patch size infomation |
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| 55 | gridinfo = [Default.TrainHoriXSize Default.HoriXNuDepth Default.HoriXNuPatch; Default.TrainVerYSize Default.VertYNuDepth Default.VertYNuPatch]; |
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| 56 | |
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| 57 | % Grid Info |
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| 58 | ratio(1:2) = floor(gridinfo(:,1)./gridinfo(:,end) ); |
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| 59 | ratio(3:4) = floor( gridinfo(:,1)./gridinfo(:,2) ); |
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| 60 | |
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| 61 | % Patch shape infomation |
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| 62 | hcol = ones(floor(ratio(2)),1); |
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| 63 | hrow = ones(1,floor(ratio(1))); |
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| 64 | |
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| 65 | if SupMaskFlag == 1 % Need to use irregular Superpixel Mask |
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| 66 | |
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| 67 | % calculate how many mask we need |
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| 68 | NuMask = ceil(gridinfo(:,2)./gridinfo(:,3)); |
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| 69 | |
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| 70 | % calcuate the position of the mask |
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| 71 | hight(1) = round((ratio(2)-1)/2); |
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| 72 | hight(2) = ratio(2) - 1 - hight(1); |
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| 73 | width(1) = round((ratio(1)-1)/2); |
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| 74 | width(2) = ratio(1) - 1 - width(1); |
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| 75 | |
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| 76 | row_start = 1; |
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| 77 | f = []; |
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| 78 | f_pics_mask = zeros( gridinfo(1,3)*gridinfo(2,3), 17); |
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| 79 | for j = 1:NuMask(1) |
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| 80 | for k = 1:NuMask(2); |
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| 81 | fIndNew = fInd; |
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| 82 | % first generate the mask respect to the dominate subsuperpixel |
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| 83 | [mask,PixelMask,PatchMask] = makeSubSupMaskNew2(gridinfo, HiSupi, SmallSup, [j; k], width, hight); |
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| 84 | % [mask,SupIndex,PixelM,PatchMask] = makeSubSupMask(gridinfo, HiSupi, SmallSup, [j; k], width, hight); |
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| 85 | |
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| 86 | |
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| 87 | % calculaing the normalize value |
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| 88 | % NormalizeValue = conv2(hcol,hrow,mask,'same');%/////////////////////////////////////////////// |
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| 89 | |
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| 90 | % generate the 1:34 features for H2 for 1 center and 4 neighbor (left right top bottom) |
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| 91 | for m = 1:17 |
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| 92 | |
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| 93 | % temp = conv2(hcol, hrow, H2(:,:,m).*mask, 'same');%/////////////////////////////////////////// |
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| 94 | % tt = temp(PixelM)./NormalizeValue(PixelM); |
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| 95 | % vv =SparseAverageSample2D(H2(:,:,m), floor(ratio(2)), floor(ratio(1)),PixelMask,mask); |
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| 96 | % [floor(ratio(1)), floor(ratio(2))] |
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| 97 | f_pics_mask(:,m) = ... |
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| 98 | SparseAverageSample2DOptimized(H2(:,:,m),... |
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| 99 | ratio(2), ratio(1), ... |
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| 100 | PixelMask,double(mask))... |
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| 101 | ./FeaMax(1,fIndNew); |
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| 102 | fIndNew = fIndNew+1; |
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| 103 | end |
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| 104 | |
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| 105 | f(PatchMask, row_start:row_start+size(f_pics_mask,2)-1) = f_pics_mask; |
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| 106 | end |
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| 107 | end |
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| 108 | fInd = fIndNew; |
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| 109 | |
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| 110 | else |
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| 111 | DepthGridSizeY = ImgResY/Default.VertYNuDepth; |
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| 112 | DepthGridSizeX= ImgResX/Default.HoriXNuDepth; |
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| 113 | % 1) generating the PixelMask |
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| 114 | % PixelMask = logical(zeros(ImgResY,ImgResX)); |
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| 115 | [X Y] = meshgrid(ceil((1/2)*DepthGridSizeX:DepthGridSizeX:ImgResX),... |
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| 116 | ceil((1/2)*DepthGridSizeY:DepthGridSizeY:ImgResY)); |
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| 117 | %PixelMask = sub2ind(size(PixelMask),Y(:),X(:)); |
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| 118 | for m = 1:17 |
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| 119 | f(:,m) = SparseAverageSample2DOptimized(H2(:,:,m),ratio(2),ratio(1),[Y(:) X(:)], double(ones(size(H2(:,:,m)))))... |
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| 120 | ./FeaMax(1,fInd); |
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| 121 | fInd = fInd +1; |
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| 122 | end |
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| 123 | |
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| 124 | end |
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| 125 | |
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| 126 | return; |
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