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 [] = extractdata(); |
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40 | |
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41 | global GeneralDataFolder ScratchDataFolder LocalFolder ClusterExecutionDirectory... |
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42 | ImgFolder VertYNuPatch VertYNuDepth HoriXNuPatch HoriXNuDepth a_default b_default Ox_default Oy_default... |
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43 | Horizon_default filename batchSize NuRow_default SegVertYSize SegHoriXSize WeiBatchSize PopUpVertY PopUpHoriX taskName; |
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44 | |
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45 | %SearchFolder{1} = 'MinTest'; |
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46 | %SearchFolder{2} = 'Min96Int'; |
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47 | %SearchFolder{3} = 'SUCampus2'; |
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48 | SearchFolder{1} = 'AshOldTest'; |
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49 | SearchFolder{2} = 'AugTest'; |
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50 | SearchFolder{3} = 'MinDataOct21'; |
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51 | SearchFolder{4} = 'AshOldNewAdd'; |
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52 | %chFolder{1} = 'MinT'; |
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53 | NuFolder = 4; |
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54 | BatchSize = 10; |
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55 | |
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56 | for j = 1:NuFolder |
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57 | % if ~isempty(dir([ScratchDataFolder '/../' SearchFolder{j} '/data/TextLowResImgIndexSuperpixelSep.mat'])) |
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58 | % Text(j) = true; |
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59 | % else |
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60 | % Text(j) = false; |
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61 | % disp('Texture false'); |
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62 | % return; |
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63 | % end |
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64 | % if ~isempty(dir([ScratchDataFolder '/../' SearchFolder{j} '/Gridlaserdata'])) |
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65 | % Laserdepth(j) = true; |
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66 | % end |
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67 | if ~isempty(dir([ScratchDataFolder '/../' SearchFolder{j} '/data/feature_Abs_Whole*'])) |
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68 | Features(j) = true; |
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69 | else |
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70 | Features(j) = false; |
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71 | disp('feature false'); |
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72 | return; |
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73 | end |
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74 | end |
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75 | filename_ori= filename; |
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76 | NuPics = size(filename_ori,2) |
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77 | for i = 1: NuPics |
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78 | for j = 1 :NuFolder |
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79 | load([ScratchDataFolder '/../' SearchFolder{j} '/data/filename.mat']); |
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80 | foundFlag = 0; |
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81 | TargetNuPics = size(filename,2); |
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82 | for k = 1:TargetNuPics |
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83 | if strcmp(filename_ori{i}, filename{k}) |
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84 | filename_ori{i} |
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85 | filename{k} |
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86 | foundFlag = 1; |
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87 | break; |
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88 | end |
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89 | end |
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90 | if foundFlag |
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91 | break; |
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92 | end |
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93 | end |
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94 | if foundFlag == 0 |
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95 | disp('not found'); |
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96 | return; |
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97 | end |
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98 | j |
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99 | % find k the corresponding batch BaInd and Ind in size the Batch |
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100 | BaInd = ceil(k/BatchSize); |
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101 | Ind = k - BatchSize*(BaInd-1); |
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102 | if Features(j) |
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103 | ff = dir([ScratchDataFolder '/../' SearchFolder{j} '/data/feature_Abs_Whole' num2str(BaInd) '_*']); |
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104 | load([ScratchDataFolder '/../' SearchFolder{j} '/data/' ff(1).name]); |
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105 | end |
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106 | % load everything we need |
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107 | load([ScratchDataFolder '/../' SearchFolder{j} '/data/LowResImgIndexSuperpixelSep.mat']); |
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108 | load([ScratchDataFolder '/../' SearchFolder{j} '/data/DiffLowResImgIndexSuperpixelSep.mat']); |
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109 | load([ScratchDataFolder '/../' SearchFolder{j} '/data/MedSeg/MediResImgIndexSuperpixelSep' num2str(k) '.mat']); |
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110 | load([ScratchDataFolder '/../' SearchFolder{j} '/data/MaskGSky.mat']); |
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111 | load([ScratchDataFolder '/../' SearchFolder{j} '/data/FeatureSuperpixel.mat']); |
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112 | % depthfile = strrep(filename_ori{i},'img','depth_learned'); |
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113 | % load([ScratchDataFolder '/../' SearchFolder{j} '/_LearnDNonsky_depth/' depthfile '.mat']); |
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114 | %if Laserdepth(j) |
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115 | % depthfile = strrep(filename_ori{i},'img','depth_sph_corr'); |
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116 | % system(['cp ' ScratchDataFolder '/../' SearchFolder{j} '/Gridlaserdata/' depthfile '.mat ' ... |
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117 | % ScratchDataFolder '/Gridlaserdata/' depthfile '.mat']); |
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118 | %end |
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119 | |
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120 | GroupLowResImgIndexSuperpixelSep{i,:} = LowResImgIndexSuperpixelSep{k}; |
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121 | GroupDiffLowResImgIndexSuperpixelSep(i,:) = DiffLowResImgIndexSuperpixelSep(k,:); |
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122 | %if Text(j) |
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123 | % load([ScratchDataFolder '/../' SearchFolder{j} '/data/TextLowResImgIndexSuperpixelSep.mat']); |
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124 | % GroupTextLowResImgIndexSuperpixelSep(i,:,:) = TextLowResImgIndexSuperpixelSep(k,:,:); |
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125 | %end |
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126 | if Features(j) |
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127 | i |
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128 | i - BatchSize*(ceil(i/BatchSize)-1) |
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129 | Groupf{i - BatchSize*(ceil(i/BatchSize)-1)} = f{Ind}; |
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130 | whos('Groupf') |
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131 | clear f; |
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132 | end |
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133 | GroupmaskSky{i} = maskSky{k}; |
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134 | Groupmaskg{i} = maskg{k}; |
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135 | GroupFeatureSuperpixel{i} = FeatureSuperpixel{k}; |
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136 | |
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137 | % save Medi and depthMap first |
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138 | save([ScratchDataFolder '/data/MedSeg/MediResImgIndexSuperpixelSep' num2str(i) '.mat' ],'MediResImgIndexSuperpixelSep'); |
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139 | % save([ScratchDataFolder '/_LearnDNonsky_depth/' depthfile '.mat'],'depthMap'); |
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140 | if Features(j) && ~rem(i,BatchSize) |
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141 | f = Groupf; |
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142 | clear Groupf; |
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143 | whos('f') |
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144 | save([ScratchDataFolder '/data/feature_Abs_Whole' num2str(ceil(i/BatchSize)) '.mat' ],'f'); |
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145 | clear f; |
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146 | end |
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147 | |
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148 | LowResImgIndexSuperpixelSep = GroupLowResImgIndexSuperpixelSep; |
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149 | DiffLowResImgIndexSuperpixelSep = GroupDiffLowResImgIndexSuperpixelSep; |
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150 | maskSky = GroupmaskSky; |
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151 | maskg = Groupmaskg; |
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152 | FeatureSuperpixel = GroupFeatureSuperpixel; |
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153 | save([ScratchDataFolder '/data/LowResImgIndexSuperpixelSep.mat' ],'LowResImgIndexSuperpixelSep'); |
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154 | save([ScratchDataFolder '/data/DiffLowResImgIndexSuperpixelSep.mat' ],'DiffLowResImgIndexSuperpixelSep'); |
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155 | %if Text(j) |
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156 | % TextLowResImgIndexSuperpixelSep = GroupTextLowResImgIndexSuperpixelSep; |
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157 | % save([ScratchDataFolder '/data/TextLowResImgIndexSuperpixelSep.mat' ],'TextLowResImgIndexSuperpixelSep'); |
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158 | %end |
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159 | save([ScratchDataFolder '/data/MaskGSky.mat' ],'maskSky','maskg'); |
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160 | save([ScratchDataFolder '/data/FeatureSuperpixel.mat' ],'FeatureSuperpixel'); |
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161 | end |
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162 | LowResImgIndexSuperpixelSep = GroupLowResImgIndexSuperpixelSep; |
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163 | DiffLowResImgIndexSuperpixelSep = GroupDiffLowResImgIndexSuperpixelSep; |
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164 | maskSky = GroupmaskSky; |
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165 | maskg = Groupmaskg; |
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166 | FeatureSuperpixel = GroupFeatureSuperpixel; |
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167 | save([ScratchDataFolder '/data/LowResImgIndexSuperpixelSep.mat' ],'LowResImgIndexSuperpixelSep'); |
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168 | save([ScratchDataFolder '/data/DiffLowResImgIndexSuperpixelSep.mat' ],'DiffLowResImgIndexSuperpixelSep'); |
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169 | %if Text(j) |
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170 | % TextLowResImgIndexSuperpixelSep = GroupTextLowResImgIndexSuperpixelSep; |
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171 | % save([ScratchDataFolder '/data/TextLowResImgIndexSuperpixelSep.mat' ],'TextLowResImgIndexSuperpixelSep'); |
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172 | %end |
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173 | save([ScratchDataFolder '/data/MaskGSky.mat' ],'maskSky','maskg'); |
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174 | save([ScratchDataFolder '/data/FeatureSuperpixel.mat' ],'FeatureSuperpixel'); |
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