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, H2, 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 | % Parameter |
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50 | [ImgResY ImgResX] = size(H2(:,:,1)); |
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51 | |
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52 | % define image and sample point and patch size infomation |
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53 | gridinfo = [Default.TrainHoriXSize Default.HoriXNuDepth Default.HoriXNuPatch; Default.TrainVerYSize Default.VertYNuDepth Default.VertYNuPatch]; |
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54 | |
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55 | % Grid Info |
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56 | ratio(1:2) = gridinfo(:,1)./gridinfo(:,end); |
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57 | ratio(3:4) = gridinfo(:,1)./gridinfo(:,2); |
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58 | |
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59 | % Patch shape infomation |
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60 | hcol = ones(floor(ratio(2)),1); |
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61 | hrow = ones(1,floor(ratio(1))); |
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62 | |
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63 | if SupMaskFlag == 1 % Need to use irregular Superpixel Mask |
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64 | |
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65 | % calculate how many mask we need |
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66 | NuMask = ceil(gridinfo(:,2)./gridinfo(:,3)); |
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67 | |
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68 | % calcuate the position of the mask |
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69 | hight(1) = round((floor(ratio(2))-1)/2); |
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70 | hight(2) = floor(ratio(2)) - 1 - hight(1); |
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71 | width(1) = round((floor(ratio(1))-1)/2); |
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72 | width(2) = floor(ratio(1)) - 1 - width(1); |
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73 | |
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74 | row_start = 1; |
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75 | f = []; |
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76 | for j = 1:NuMask(1) |
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77 | for k = 1:NuMask(2); |
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78 | f_pics_mask = []; |
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79 | fIndNew = fInd; |
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80 | % first generate the mask respect to the dominate subsuperpixel |
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81 | [mask,SupIndex,PixelMask,PatchMask] = makeSubSupMaskNew(gridinfo, HiSupi, SmallSup, [j; k], width, hight); |
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82 | % [mask,SupIndex,PixelM,PatchMask] = makeSubSupMask(gridinfo, HiSupi, SmallSup, [j; k], width, hight); |
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83 | |
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84 | % calculaing the normalize value |
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85 | % NormalizeValue = conv2(hcol,hrow,mask,'same');%/////////////////////////////////////////////// |
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86 | |
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87 | % generate the 1:34 features for H2 for 1 center and 4 neighbor (left right top bottom) |
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88 | for m = 1:17 |
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89 | |
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90 | % temp = conv2(hcol, hrow, H2(:,:,m).*mask, 'same');%/////////////////////////////////////////// |
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91 | % tt = temp(PixelM)./NormalizeValue(PixelM); |
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92 | % vv =SparseAverageSample2D(H2(:,:,m), floor(ratio(2)), floor(ratio(1)),PixelMask,mask); |
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93 | % [floor(ratio(1)), floor(ratio(2))] |
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94 | f_pics_mask = [f_pics_mask SparseAverageSample2D(H2(:,:,m), floor(ratio(2)), floor(ratio(1)),PixelMask,double(mask))... |
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95 | ./FeaMax(1,fIndNew)]; |
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96 | % f_pics_mask = [f_pics_mask SparseAverageSample2D(H2(:,:,m).*mask, floor(ratio(1)), floor(ratio(2)),PixelMask)... |
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97 | % ./FeaMax(1,fIndNew)]; |
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98 | fIndNew = fIndNew+1; |
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99 | end |
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100 | |
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101 | f(PatchMask, row_start:row_start+size(f_pics_mask,2)-1) = f_pics_mask; |
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102 | end |
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103 | end |
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104 | fInd = fIndNew; |
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105 | |
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106 | else |
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107 | DepthGridSizeY = ImgResY/Default.VertYNuDepth; |
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108 | DepthGridSizeX= ImgResX/Default.HoriXNuDepth; |
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109 | % 1) generating the PixelMask |
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110 | % PixelMask = logical(zeros(ImgResY,ImgResX)); |
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111 | [X Y] = meshgrid(ceil((1/2)*DepthGridSizeX:DepthGridSizeX:ImgResX),... |
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112 | ceil((1/2)*DepthGridSizeY:DepthGridSizeY:ImgResY)); |
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113 | %PixelMask = sub2ind(size(PixelMask),Y(:),X(:)); |
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114 | for m = 1:17 |
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115 | f(:,m) = SparseAverageSample2D(H2(:,:,m),floor(ratio(2)),floor(ratio(1)),[Y(:) X(:)], double(ones(size(H2(:,:,m)))))... |
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116 | ./FeaMax(1,fInd); |
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117 | fInd = fInd +1; |
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118 | end |
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119 | |
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120 | end |
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121 | |
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122 | return; |
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