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 []=gen_feature_general(tskName,imgFolder,trainSet,learnType,learnSkyEx,learnLog,learnNear,... |
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40 | learnAlg,learnDate,absFeaType,absFeaDate,HistFeaType,histFeaDate, ... |
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41 | generalDataFolder, scratchDataFolder, localFolder, clusterExecutionDir, batchNumber,Absolute) |
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42 | |
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43 | % This function calculate the feature of each subsuperpixel using texture |
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44 | % infomation |
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45 | |
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46 | % decide the Hist |
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47 | if strcmp(HistFeaType,'Whole') |
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48 | Hist = 1; |
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49 | HistFeaType |
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50 | else |
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51 | Hist = 0; |
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52 | end |
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53 | |
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54 | if Hist ~= 1 && Absolute ~=1 |
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55 | return |
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56 | end |
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57 | |
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58 | if nargin < 1 |
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59 | batchNumber = 1; |
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60 | elseif nargin < 2 |
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61 | Hist = 1; |
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62 | Absolute =1; |
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63 | elseif nargin < 3 |
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64 | Absolute =1; |
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65 | end |
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66 | |
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67 | global GeneralDataFolder ScratchDataFolder LocalFolder ClusterExecutionDirectory... |
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68 | ImgFolder VertYNuPatch VertYNuDepth HoriXNuPatch HoriXNuDepth a_default b_default Ox_default Oy_default... |
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69 | Horizon_default filename batchSize NuRow_default SegVertYSize SegHoriXSize WeiBatchSize PopUpVertY PopUpHoriX taskName... |
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70 | TrainVerYSize TrainHoriXSize MempryFactor; |
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71 | |
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72 | |
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73 | % load estimated sky |
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74 | %load([ScratchDataFolder '/data/MaskGSky.mat']); % maskg is the estimated ground maskSky is the estimated sky |
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75 | |
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76 | % load([ScratchDataFolder '/data/filename.mat']);% load the filename |
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77 | % load([ScratchDataFolder '/data/PlaneParameterTure.mat']); % planeParameter |
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78 | load([ScratchDataFolder '/data/LowResImgIndexSuperpixelSep.mat']); % superpixel_index |
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79 | %load([ScratchDataFolder '/data/MediResImgIndexSuperpixelSep.mat']); % MediResImgIndexSuperpixelSep |
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80 | load([ScratchDataFolder '/data/DiffLowResImgIndexSuperpixelSep.mat']); % DiffLowResImgIndexSuperpixelSep |
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81 | DiffLowResImgIndexSuperpixelSep = DiffLowResImgIndexSuperpixelSep(:,1);% need only the middle scale segmentation |
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82 | |
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83 | %load([ScratchDataFolder '/data/FeatureSuperpixel.mat']); %load feature of superpixel |
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84 | % load FeaMax to do normalizeing |
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85 | load([GeneralDataFolder '/FeaMax.mat']); |
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86 | FeaMax = 10.^floor(log10(FeaMax)); |
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87 | |
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88 | % prepare data step |
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89 | nu_pics = size(filename,2); % number of pictures |
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90 | |
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91 | % ====================== change able parameter ===================== |
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92 | %batchSize = 10;% decide the batch size as 10 image pre batch |
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93 | % ================================================================== |
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94 | |
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95 | % for batchImg = 1:batchSize:nu_pics |
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96 | batchImg = 1:batchSize:nu_pics; |
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97 | |
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98 | f = []; % total feature: feature of superpixel followed by texture feature of patch |
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99 | f_pics = []; |
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100 | PicsNu = 1 |
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101 | for i = batchImg(batchNumber):min(batchImg(batchNumber)+batchSize-1, nu_pics) |
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102 | % load MediResImgIndexSuperpixelSep |
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103 | load([ScratchDataFolder '/data/MedSeg/MediResImgIndexSuperpixelSep' num2str(i) '.mat']); |
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104 | |
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105 | % calculate all the features, ray, plane parameter, and row column value |
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106 | img = imread([GeneralDataFolder '/' ImgFolder '/' filename{i} '.jpg']);% read in the hi resolution image of the ith file |
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107 | size(img) |
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108 | % change the resolution in to exactly [TrainVerYSize TrainHoriXSize] |
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109 | |
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110 | if ~all(size(img) == [TrainVerYSize TrainHoriXSize 3]) |
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111 | disp('resize to 2272 1704') |
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112 | img = imresize(img,[TrainVerYSize TrainHoriXSize],'bilinear'); |
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113 | end |
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114 | % the images resolusion must be bigger then a certain size to have reasonable predicted depth |
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115 | % if prod(size(img))<SegVertYSize*SegHoriXSize*3 |
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116 | % img = imresize(img,[SegVertYSize SegHoriXSize],'bilinear'); |
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117 | % the images resolusion must be smaller then a certain size to avoid out of memory |
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118 | % elseif prod(size(img)) > TrainVerYSize*TrainHoriXSize*3*(MempryFactor); |
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119 | % disp('origin size') |
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120 | % size(img) |
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121 | % img = imresize(img,[TrainVerYSize TrainHoriXSize],'bilinear'); |
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122 | % disp('image too big') |
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123 | % size(img) |
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124 | % end |
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125 | [vertical_size_hi_res horizontal_size_hi_res t] = size(img); clear t; |
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126 | % get the horizontal(vertical_size_hi_res) and vertical(horizontal_size_hi_res) size of hi resolution image |
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127 | sup_hi_res = imresize(MediResImgIndexSuperpixelSep, ... |
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128 | [vertical_size_hi_res horizontal_size_hi_res],'nearest');% enlarge the low res superpixel into hi res superpixel |
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129 | clear MediResImgIndexSuperpixelSep; |
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130 | |
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131 | % generate the superpixel in the depth_grid size |
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132 | sup = imresize(LowResImgIndexSuperpixelSep{i,1},[VertYNuDepth HoriXNuDepth],'nearest'); |
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133 | |
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134 | % load picsinfo just for the horizontal value |
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135 | PicsinfoName = strrep(filename{i},'img','picsinfo'); |
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136 | temp = dir([GeneralDataFolder '/PicsInfo/' PicsinfoName '.mat']); |
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137 | if size(temp,1) == 0 |
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138 | a = a_default; |
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139 | b = b_default; |
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140 | Ox = Ox_default; |
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141 | Oy = Oy_default; |
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142 | Horizon = Horizon_default; |
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143 | else |
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144 | load([GeneralDataFolder '/PicsInfo/' PicsinfoName '.mat']); |
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145 | end |
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146 | |
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147 | RayCenter = GenerateRay(HoriXNuDepth,VertYNuDepth,'center',a,b,Ox,Oy); %[ horiXSizeLowREs VertYSizeLowREs 3] |
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148 | |
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149 | Default=SetupDefault(tskName,imgFolder,trainSet,learnType,learnSkyEx,learnLog,learnNear,learnAlg,learnDate,absFeaType, ... |
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150 | absFeaDate,HistFeaType,histFeaDate,generalDataFolder,scratchDataFolder,localFolder,clusterExecutionDir); |
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151 | [TextureFeature]=GenTextureFeature(Default, img, sup, sup_hi_res, 1); |
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152 | |
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153 | f_pics=TextureFeature.Abs; |
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154 | |
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155 | %% write ground boundary here |
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156 | if Absolute == 1 |
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157 | % superpixel features |
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158 | %fsup = FeatureSuperpixel{i}; |
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159 | %f_pics = [f_pics fsup(:,f_pics(:,1))']; |
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160 | % other features without relation with neiborfeatures |
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161 | % 1) the closest ground position to the (i, j) patch |
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162 | % How can we remove DiffLowResImgIndexSuperpixelSep since generate another superpixel takes time |
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163 | big_sup = imresize(DiffLowResImgIndexSuperpixelSep{i,1},[VertYNuDepth HoriXNuDepth],'nearest'); |
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164 | %%%%% GroundSupIndex = unique(big_sup(maskg{i})); |
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165 | DefaultGroundMask = [zeros(floor(VertYNuDepth/2), HoriXNuDepth); ones(VertYNuDepth-floor(VertYNuDepth/2), HoriXNuDepth)]; |
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166 | GroundSupIndex = unique(big_sup(logical(DefaultGroundMask))); |
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167 | |
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168 | % NonGround = []; |
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169 | % for j = 1:HoriXNuDepth |
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170 | % Ground(j) = analysesupinpatch(big_sup(round(gridinfo(2,2)*(1-Horizon)):end,j)); |
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171 | % NonGround(j) = analysesupinpatch(big_sup(round(1:(gridinfo(2,2)*(1-Horizon)-1)),j)); |
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172 | % NonGround = [NonGround (unique(big_sup(round(1:(gridinfo(2,2)*(1-Horizon)-1)),j)))']; |
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173 | % end |
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174 | % Ground = setdiff(Ground,NonGround); |
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175 | for j = 1:HoriXNuDepth |
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176 | Gmask = logical(zeros(size( big_sup(:,j)))); |
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177 | for k=GroundSupIndex' |
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178 | Gmask(big_sup(:,j) == k) = true; |
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179 | end |
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180 | [rowSub colSub ] = find(Gmask); |
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181 | minRow = min(rowSub); |
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182 | % if size(minRow,1) == 0 |
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183 | % Gmask = logical(zeros(size( big_sup(:,j)))); |
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184 | % MaybeGround = unique(big_sup(round(gridinfo(2,2)*(1-Horizon)):end,j)); |
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185 | % MaybeGround = setdiff(MaybeGround,NonGround); |
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186 | % for k=MaybeGround |
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187 | % Gmask(big_sup(:,j) == k) = true; |
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188 | % end |
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189 | % [rowSub colSub ] = find(Gmask); |
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190 | % minRow = min(rowSub); |
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191 | % if size(minRow,1) == 0 |
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192 | % lastguessGround = analysesupinpatch(big_sup(round(gridinfo(2,2)*(1-Horizon)):end,j)); |
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193 | % [rowSub colSub ] = find(big_sup(round(gridinfo(2,2)*(1-Horizon)):end,j)==lastguessGround); |
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194 | % minRow = min(rowSub); |
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195 | % end |
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196 | % end |
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197 | %minRow = max(minRow,round(gridinfo(2,2)*(1-Horizon))); |
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198 | |
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199 | GroundVertEdge(:,j) = (1-(VertYNuDepth-1)/VertYNuDepth)./RayCenter(:,j,3); |
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200 | if size(minRow,1) ~= 0 |
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201 | GroundVertEdge((minRow+1):VertYNuDepth,j) = (1-((minRow+1):VertYNuDepth)'/VertYNuDepth)./RayCenter((minRow+1):VertYNuDepth,j,3); |
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202 | GroundVertEdge(1:(minRow),j) = (1- minRow/VertYNuDepth)./RayCenter(1:(minRow),j,3); |
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203 | end |
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204 | end |
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205 | GroundVertEdgePics = GroundVertEdge; |
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206 | |
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207 | |
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208 | %f_pics(:,end+1) = max([GroundVertEdgePics(:) repmat((1:VertYNuDepth)',[HoriXNuDepth 1])],[],2); |
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209 | %f_pics(:,end+1) = Gr undVertEdgePics(:);%./reshape(RayCenter(:,:,3),[],1); |
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210 | f_pics(:,end+1) = min([GroundVertEdgePics(:)... |
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211 | repmat((VertYNuDepth:-1:1)'/VertYNuDepth,[HoriXNuDepth 1])./reshape(RayCenter(:,:,3),[],1)],[],2); |
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212 | |
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213 | disp(['Image Number ' num2str(i)]); |
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214 | %size(f_pics) |
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215 | %size(sup(:)) |
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216 | f{PicsNu} = [sup(:) f_pics]; |
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217 | |
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218 | end |
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219 | |
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220 | PicsNu = PicsNu + 1 |
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221 | batchNumber |
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222 | |
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223 | end |
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224 | save([ScratchDataFolder '/data/feature_Abs_Whole' num2str(batchNumber) '_.mat'],'f'); |
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225 | return; |
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