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 [] = GenAveFeaSup(batchNumber, Nei, AbsFeaType, AbsFeaDate) |
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40 | |
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41 | % This function calculate the feature of each subsuperpixel using texture |
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42 | % infomation |
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43 | |
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44 | if nargin < 1 |
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45 | batchNumber = 1; |
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46 | end |
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47 | |
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48 | global GeneralDataFolder ScratchDataFolder LocalFolder ClusterExecutionDirectory... |
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49 | ImgFolder VertYNuPatch VertYNuDepth HoriXNuPatch HoriXNuDepth a_default b_default Ox_default Oy_default... |
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50 | Horizon_default filename batchSize NuRow_default SegVertYSize SegHoriXSize WeiBatchSize PopUpVertY PopUpHoriX taskName; |
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51 | |
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52 | % ================================================================================== |
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53 | % may chage for different usage |
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54 | %load([ScratchDataFolder '/data/LowResImgIndexSuperpixelSep.mat']); % superpixel_index |
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55 | %load([ScratchDataFolder '/data/DiffLowResImgIndexSuperpixelSep.mat']); |
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56 | %load([ScratchDataFolder '/data/TextLowResImgIndexSuperpixelSep.mat']); |
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57 | % ================================================================================== |
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58 | % load FeaMax to do normalizeing |
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59 | load([GeneralDataFolder '/FeaMax.mat']); |
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60 | FeaMax = 10.^floor(log10(FeaMax)); |
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61 | |
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62 | %load([ScratchDataFolder '/data/MaskGSky.mat']); % load maskg maskSky from CMU's output |
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63 | %clear maskg; |
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64 | |
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65 | % prepare data step |
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66 | nu_pics = size(filename,2); % number of picture |
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67 | |
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68 | batchImg = 1:batchSize:nu_pics; |
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69 | l = 1; |
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70 | for i = batchImg(batchNumber):min(batchImg(batchNumber)+batchSize-1, nu_pics) |
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71 | %for i = [10:18 54:62] |
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72 | tic |
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73 | i |
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74 | % ================================================================================== |
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75 | % may chage for different usage |
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76 | %load([ScratchDataFolder '/data/LowResImgIndexSuperpixelSep.mat']); % superpixel_index |
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77 | load([ScratchDataFolder '/data/DiffLowResImgIndexSuperpixelSep.mat']); |
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78 | %load([ScratchDataFolder '/data/TextLowResImgIndexSuperpixelSep.mat']); |
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79 | % ================================================================================== |
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80 | % load([ScratchDataFolder '/data/MedSeg/MediResImgIndexSuperpixelSep' num2str(i) '.mat']); |
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81 | % Sup = LowResImgIndexSuperpixelSep{i}; clear LowResImgIndexSuperpixelSep; |
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82 | % MedSup = MediResImgIndexSuperpixelSep; clear MediResImgIndexSuperpixelSep; |
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83 | DiffSup = DiffLowResImgIndexSuperpixelSep(i,end); clear DiffLowResImgIndexSuperpixelSep; |
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84 | % TextSup = TextLowResImgIndexSuperpixelSep(i,:,2:end); clear TextLowResImgIndexSuperpixelSep; |
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85 | batchNumber,% MedSup = imresize(MedSup, [vertical_size_hi_res horizontal_size_hi_res]); |
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86 | |
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87 | % load MediResImgIndexSuperpixelSep |
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88 | % decide to claen the Sup or not (means imclosing) |
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89 | %[Sup,MedSup]=CleanedSup(Sup,MedSup,maskSky{i}); |
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90 | load([ScratchDataFolder '/data/CleanSup/CleanSup' num2str(i) '.mat']); |
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91 | % check if the MedSup and Sup have the same index |
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92 | % !!!!!!!Don't need to check it since later working on Sup scale only |
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93 | % Res = setdiff(unique(MedSup(:)),unique(Sup(:))); |
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94 | % if ~isempty(Res) |
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95 | % disp('error index from MedSup to Sup'); |
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96 | % return; |
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97 | % end |
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98 | |
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99 | MedSup = double(MedSup); |
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100 | Sup = double(Sup); |
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101 | maskSky{i} = Sup == 0; % the new skymap |
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102 | |
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103 | % load picsinfo just for the horizontal value |
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104 | PicsinfoName = strrep(filename{l},'img','picsinfo'); |
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105 | temp = dir([GeneralDataFolder '/PicsInfo/' PicsinfoName '.mat']); |
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106 | if size(temp,1) == 0 |
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107 | a = a_default; |
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108 | b = b_default; |
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109 | Ox = Ox_default; |
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110 | Oy = Oy_default; |
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111 | Horizon = Horizon_default; |
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112 | else |
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113 | load([GeneralDataFolder '/PicsInfo/' PicsinfoName '.mat']); |
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114 | end |
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115 | |
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116 | % 1) Analyze Sup : SupFact with NuPatchEachSup and all 13 features calcuate in gen_fsup_new |
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117 | [SupFact, nList] = AnalyzeSup(Sup,maskSky{i}); % Calculate for H2 |
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118 | % load([ScratchDataFolder '/data/temp/List' num2str(i) '.mat']); |
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119 | % ====================== |
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120 | BounaryPHori = conv2(Sup,[1 -1],'same') ~=0; |
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121 | BounaryPHori(:,end) = 0; |
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122 | BounaryPVert = conv2(Sup,[1; -1],'same') ~=0; |
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123 | BounaryPVert(end,:) = 0; |
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124 | ClosestNList = [ Sup(find(BounaryPHori==1)) Sup(find(BounaryPHori==1)+VertYNuDepth);... |
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125 | Sup(find(BounaryPVert==1)) Sup(find(BounaryPVert==1)+1)]; |
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126 | ClosestNList = sort(ClosestNList,2); |
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127 | ClosestNList = unique(ClosestNList,'rows'); |
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128 | ClosestNList(ClosestNList(:,1) == 0,:) = []; |
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129 | % MedBounaryPHori = conv2(MedSup,[1 -1],'same') ~=0; |
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130 | % MedBounaryPHori(:,end) = 0; |
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131 | % MedBounaryPVert = conv2(MedSup,[1; -1],'same') ~=0; |
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132 | % MedBounaryPVert(end,:) = 0; |
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133 | % MedClosestNList = [ MedSup(find(MedBounaryPHori==1)) MedSup(find(MedBounaryPHori==1)+SegVertYSize);... |
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134 | % MedSup(find(MedBounaryPVert==1)) MedSup(find(MedBounaryPVert==1)+1)]; |
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135 | % MedClosestNList = sort(MedClosestNList,2); |
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136 | % MedClosestNList = unique(MedClosestNList,'rows'); |
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137 | % MedClosestNList(MedClosestNList(:,1) == 0,:) = []; |
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138 | % nList = [ClosestNList; MedClosestNList]; |
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139 | % nList = unique(nList,'rows'); |
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140 | nList = ClosestNList; |
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141 | % ======================================================================================================= |
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142 | % calculate all the features, ray, plane parameter, and row column value |
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143 | img = imread([GeneralDataFolder '/' ImgFolder '/' filename{l} '.jpg']);% read hi resolution image |
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144 | |
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145 | % check if the images resolusion is smaller then a certain size |
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146 | if prod(size(img))<SegVertYSize*SegHoriXSize*3 |
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147 | disp('imresize hard work'); |
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148 | img = imresize(img,[SegVertYSize SegHoriXSize],'bilinear'); |
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149 | end |
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150 | [vertical_size_hi_res horizontal_size_hi_res t] = size(img); clear t; |
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151 | |
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152 | % generate lineseg |
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153 | % seglist=edgeSegDetection(img,i); |
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154 | |
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155 | % generate the texture features |
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156 | disp('going to cal Fea') |
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157 | |
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158 | % Start Sup relation with the 7 * 2 = 14 Multi Sup |
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159 | [MultiScaleSupTable] = MultiScalAnalyze( Sup, permute( cat( 3, DiffSup{1,1}),...% DiffSup{1,2},... |
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160 | [3 1 2])); |
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161 | % TextSup{1,1,1}, TextSup{1,1,2},... |
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162 | % TextSup{1,2,1}, TextSup{1,2,2},... |
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163 | % TextSup{1,3,1}, TextSup{1,3,2},... |
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164 | % TextSup{1,4,1}, TextSup{1,4,2},... |
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165 | % TextSup{1,5,1}, TextSup{1,5,2},... |
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166 | % TextSup{1,6,1}, TextSup{1,6,2}),... |
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167 | clear DiffSup;% TextSup; |
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168 | |
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169 | global H2; |
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170 | [H2] = calculateFilterBanks_old(img); % (hard work 1min) use Ashutaosh's code |
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171 | clear img; |
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172 | H2 = permute(H2,[3 1 2]); |
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173 | |
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174 | % run Rajiv Code |
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175 | % FeaNList = findBoundaryFeaturesMore( Sup, MedSup, nList(:,1:2), seglist, FeaMax, 0); |
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176 | % clear seglist; |
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177 | |
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178 | % Calculate the AveSupFea % H1 first |
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179 | [SupFea ] = AveSupFea(Sup, MedSup, SupFact, MultiScaleSupTable, FeaMax, 1); % Maight can be faster when doing calcuating fea from MultiScaleSupTable |
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180 | H2 = H2.^2; % then H2 |
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181 | [TempSupFea ] = AveSupFea(Sup, MedSup, SupFact, MultiScaleSupTable, FeaMax, 2); % Maight can be faster when doing calcuating fea from MultiScaleSupTable |
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182 | SupFea = [SupFea TempSupFea(:,2:end)]; % Maight can be faster when doing calcuating fea from MultiScaleSupTable |
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183 | H2 = H2.^2; % then H4 |
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184 | TempSupFea = AveSupFea(Sup, MedSup, SupFact, MultiScaleSupTable, FeaMax, 4); |
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185 | SupFea = [SupFea TempSupFea(:,2:end)]; % Maight can be faster when doing calcuating fea from MultiScaleSupTable |
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186 | clear MultiScaleSupTable TempSupFea; |
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187 | clear global H2; |
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188 | toc |
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189 | |
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190 | % gnerate Feature as the same row of nList , and |
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191 | tic |
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192 | if Nei |
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193 | load([ScratchDataFolder '/data/feature_Abs_' AbsFeaType int2str(batchNumber) '_' AbsFeaDate '.mat']); % 'f' |
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194 | else |
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195 | f = []; |
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196 | end |
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197 | |
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198 | % add new option to include Col feature 103*2 col features and 103*2 row features and 103*4 Nei features |
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199 | [FeaNList, nList] = GenFeaParaNList( Sup, MedSup, maskSky{i}, nList, SupFact, SupFea, FeaMax, Nei, f{i-10*(batchNumber-1)}, i); |
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200 | clear f; |
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201 | % FeaNList = [ FeaNListTemp FeaNList]; |
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202 | toc |
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203 | |
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204 | |
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205 | disp([ScratchDataFolder '/data/SupFea/FeaNList' num2str(i) 'new.mat']); |
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206 | save([ScratchDataFolder '/data/SupFea/FeaNList' num2str(i) 'new.mat'],'FeaNList','nList'); |
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207 | %return; |
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208 | % save([ScratchDataFolder '/data/SupFea/DiffA_Alpha' num2str(i) '.mat'],'DiffA','DiffAlpha'); |
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209 | clear FeaNListTemp nList Sup MedSup nList SupFact SupFea FeaNList; |
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210 | l= l+1; |
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211 | end |
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212 | %save([ScratchDataFolder '/data/SupFea/FeaNList' num2str(i) '.mat'],'FeaNList','nList'); |
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213 | %save([ScratchDataFolder '/data/SupFea/DiffA_Alpha' num2str(i) '.mat'],'DiffA','DiffAlpha'); |
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214 | return; |
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