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_DiffSup(sigm,k,min,SelectSegmentationPara); |
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40 | % this function generate superpixel using default parameter |
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41 | % but can also change to manually input parameter |
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42 | |
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43 | % This file generates multiple segmentation (one of them using RGB) |
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44 | |
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45 | %%% |
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46 | % looks like gen_Sup_new but with different scaling params. |
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47 | % should we delete this file? |
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48 | %%% |
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49 | |
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50 | |
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51 | % default parameter |
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52 | if nargin < 4 |
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53 | SelectSegmentationPara = 0; |
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54 | end |
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55 | |
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56 | % declaim global variable |
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57 | global GeneralDataFolder ScratchDataFolder LocalFolder ClusterExecutionDirectory... |
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58 | ImgFolder VertYNuPatch VertYNuDepth HoriXNuPatch HoriXNuDepth a_default b_default Ox_default Oy_default... |
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59 | Horizon_default filename batchSize NuRow_default SegVertYSize SegHoriXSize; |
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60 | |
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61 | scale_sigm =[1 1.6]; |
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62 | scale_k = [1.6 5]; |
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63 | scale_min = [1 5]; |
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64 | |
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65 | % generate superpixel of each image |
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66 | |
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67 | NuPics = size(filename,2); |
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68 | for i = 1:NuPics |
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69 | i |
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70 | % load([ScratchDataFolder '/data/LowResImgIndexSuperpixelSep.mat']); |
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71 | |
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72 | % for j = 1:2%3% number of scale of superpixel |
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73 | |
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74 | |
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75 | % sigm_new = |
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76 | % load image and process it to Hi Medi and Low Resolution |
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77 | Img = imread([GeneralDataFolder '/' ImgFolder '/' filename{i} '.jpg']); % Readin the high resolution image |
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78 | [VertYSizeHiREs HoriXSizeHiREs dummy]= size(Img);% find the dimension size of the Hi Resolution image |
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79 | clear dummy; |
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80 | % Loadin the GroundTruth data to know the depthMap size |
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81 | % depthfile = strrep(filename{i},'img','depth'); % the depth filename(without .file extension) associate with the *jpg file |
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82 | % load([GeneralDataFolder '/depthMap/' depthfile '.mat']); |
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83 | % [VertYSizeLowREs HoriXSizeLowREs]= size(depthMap);% find the dimension size of the depth data |
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84 | % in the new laser data we have scatter depthmap so use a |
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85 | % predecided LowRes |
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86 | VertYSizeLowREs = VertYNuDepth; |
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87 | HoriXSizeLowREs = HoriXNuDepth; |
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88 | |
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89 | % using a median size image to generate superpixel to reduce computation |
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90 | % intensity (the median size has a upper threshould SegVertYSize SegHoriXSize) |
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91 | if VertYSizeHiREs*HoriXSizeHiREs > SegVertYSize*SegHoriXSize |
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92 | Img = imresize(Img,[SegVertYSize SegHoriXSize ],'nearest'); % Downsample high resolution image to a median size image |
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93 | imwrite(Img,[ScratchDataFolder '/ppm/' filename{i} '_Diff.ppm'],'ppm');% store median Resolution image to PPM format to feed in CMU C++ function |
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94 | else |
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95 | imwrite(Img,[ScratchDataFolder '/ppm/' filename{i} '_Diff.ppm'],'ppm');% store median Resolution image to PPM format to feed in CMU C++ function |
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96 | end |
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97 | |
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98 | for j = 1:2%3% number of scale of superpixel |
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99 | % choose superpixel of the images |
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100 | % default segmentation parameter |
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101 | ok = 0; % ok ==1 means accept the segmentation |
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102 | while 1 |
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103 | % call segment function writen in C++ from MIT |
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104 | system([LocalFolder '/../third_party/Superpixels/segment ' num2str(sigm*scale_sigm(j)) ' ' num2str(k*scale_k(j)) ... |
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105 | ' ' num2str(min*scale_min(j)) ' ' ScratchDataFolder '/ppm/' filename{i} '_Diff.ppm' ' ' ... |
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106 | ScratchDataFolder '/ppm/' filename{i} '_' num2str(sigm*scale_sigm(j)) '_' ... |
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107 | num2str(k*scale_k(j)) '_' num2str(min*scale_min(j)) '.ppm']); |
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108 | MediResImgSuperpixel = imread([ScratchDataFolder '/ppm/' filename{i} '_' num2str(sigm*scale_sigm(j)) '_' num2str(k*scale_k(j)) '_' num2str(min*scale_min(j)) '.ppm']); % Readin the high resolution image |
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109 | figure(1); image(MediResImgSuperpixel); % show the superpixel in Medi Resolution |
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110 | |
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111 | % check if need to select segmentation parameter |
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112 | if SelectSegmentationPara==1; |
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113 | ok = input('Is the segmentation of image OK');% input new segmentation parameter |
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114 | else |
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115 | ok =1 ;% accept default segmentation parameter |
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116 | end |
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117 | |
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118 | % finish segmentation clean up the ppm folder. |
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119 | if ok==1; |
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120 | delete([ScratchDataFolder '/ppm/' filename{i} '_' num2str(sigm*scale_sigm(j)) '_' num2str(k*scale_k(j)) '_' num2str(min*scale_min(j)) '.ppm']); |
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121 | |
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122 | break; |
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123 | end |
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124 | sigm = input('type sigm of segmentation'); |
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125 | k = input('type k of segmentation'); |
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126 | min = input('type min of segmentation'); |
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127 | |
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128 | end |
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129 | |
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130 | % index superpixel |
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131 | [MediResImgIndexSuperpixelSepTemp dummy]= suprgb2ind(MediResImgSuperpixel); clear dummy; |
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132 | DiffLowResImgIndexSuperpixelSepTemp = imresize(MediResImgIndexSuperpixelSepTemp,[VertYSizeLowREs HoriXSizeLowREs],'nearest'); %Downsample high resolution image to the same pixel size of predict Depth data |
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133 | |
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134 | % merage the superpixel according to diff segmentation |
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135 | %NuSup = size(unique(LowResImgIndexSuperpixelSep),1); |
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136 | % LowSup = LowResImgIndexSuperpixelSep{i,1}; |
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137 | % Sup = zeros(size(LowSup)); |
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138 | % for l = (unique(LowSup))' |
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139 | % masksup = LowSup == l; |
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140 | % Index = analysesupinpatch(DiffLowResImgIndexSuperpixelSepTemp(masksup)); |
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141 | % Sup(masksup)= Index; |
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142 | % end |
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143 | % DiffLowResImgIndexSuperpixelSep{i,j} = Sup; |
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144 | % |
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145 | % merage all small point in higher scale segmentation |
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146 | if j == 1 |
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147 | DiffLowResImgIndexSuperpixelSep{i,j} = DiffLowResImgIndexSuperpixelSepTemp |
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148 | else |
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149 | DiffLowResImgIndexSuperpixelSep{i,j} = premergAllsuperpixel(DiffLowResImgIndexSuperpixelSepTemp); |
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150 | end |
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151 | %if j == 1; |
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152 | % MediResImgIndexSuperpixelSep{i} = MediResImgIndexSuperpixelSepTemp; |
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153 | %end |
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154 | % refining superpixel |
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155 | % superpixel segmentation LowResImgSeperatedSuperpixel |
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156 | %LowResImgsuperpixel = imresize(MediResImgSuperpixel,[VertYSizeLowREs HoriXSizeLowREs],'nearest'); %Downsample high resolution image to the same pixel size of GroundTruth data |
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157 | %[LowResImgIndexSuperpixel LowResImgIndexSuperpixel_list]= suprgb2ind(LowResImgsuperpixel); |
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158 | |
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159 | % comment: cmu's superpixel might be connected. use premergsuperpixel to |
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160 | % deal with nonconnected superpixels and very small superpixels |
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161 | %[LowResImgIndexSuperpixelSepTemp]=premergsuperpixel(LowResImgIndexSuperpixel); % hard work 1min |
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162 | |
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163 | % reorder the index number of the LowResImgIndexSuperpixelSep |
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164 | %[LowResImgIndexSuperpixelSep{i,j} LowResImgIndexSuperpixelSep_list]= ordersup(LowResImgIndexSuperpixelSepTemp); |
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165 | |
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166 | % show superpixel |
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167 | figure(2); |
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168 | imagesc(DiffLowResImgIndexSuperpixelSep{i,j}); |
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169 | newmap = rand(max(max(DiffLowResImgIndexSuperpixelSep{i,j})),3); |
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170 | colormap(newmap); |
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171 | |
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172 | % process the MediResImgSuperpixel to have the same number of |
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173 | % LowResImgIndexSuperpixelSep |
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174 | % if j==1 |
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175 | % tic |
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176 | % [MediResImgIndexSuperpixel dummy]= suprgb2ind(MediResImgSuperpixel); clear dummy; |
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177 | % MediResImgIndexSuperpixelSep = imresize(LowResImgIndexSuperpixelSep{i,1},size(MediResImgIndexSuperpixel),'nearest'); |
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178 | % NuSupMedi = max(max(MediResImgIndexSuperpixel)); |
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179 | % LowToMediResImgIndexSuperpixel = zeros(size(MediResImgIndexSuperpixel)); |
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180 | % for k = 1:NuSupMedi |
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181 | % mask = MediResImgIndexSuperpixel==k; |
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182 | % LowToMediResImgIndexSuperpixel(mask) = analysesupinpatch(MediResImgIndexSuperpixelSep(mask)); |
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183 | % % [list_sup] = analysesupinpatch(MediResImgIndexSuperpixelSep(mask)); |
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184 | % % [I C] = max(list_sup(2,:)); |
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185 | % % LowToMediResImgIndexSuperpixel(mask) = list_sup(1,C); |
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186 | % end |
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187 | % LowToMediResImgIndexSuperpixelSep{i} =... |
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188 | % premergAllsuperpixel(LowToMediResImgIndexSuperpixel); |
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189 | % toc |
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190 | % end |
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191 | end |
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192 | delete([ScratchDataFolder '/ppm/' filename{i} '_Diff.ppm']); |
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193 | save([ScratchDataFolder '/data/DiffLowResImgIndexSuperpixelSep.mat'], 'DiffLowResImgIndexSuperpixelSep'); |
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194 | end |
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195 | |
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196 | % save result for later application |
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197 | save([ScratchDataFolder '/data/DiffLowResImgIndexSuperpixelSep.mat'], 'DiffLowResImgIndexSuperpixelSep'); |
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198 | %save([ScratchDataFolder '/data/MediResImgIndexSuperpixelSep.mat'], 'MediResImgIndexSuperpixelSep'); |
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199 | |
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200 | return; |
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