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 [TextSup]=gen_TextSup_efficient_GlobalH2( Default, SelectSegmentationPara); |
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40 | % process H's superpixels into Hi and Medi Resolution |
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41 | % this function generate superpixel using default parameter |
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42 | % but can also change to manually input parameter |
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43 | |
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44 | % set to global for gen_TextSup_efficient_GlobalH2 only |
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45 | global H2; |
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46 | |
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47 | % default parameter |
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48 | if nargin < 3 |
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49 | SelectSegmentationPara = 0; |
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50 | end |
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51 | DisplayFlag = Default.Flag.DisplayFlag; % set to display or not |
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52 | |
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53 | scale_sigm =[ 1 1.6]; |
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54 | scale_k = [ 1.6 3]; |
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55 | scale_minV = [ 1 3]; |
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56 | |
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57 | %==================== choose 6 different feature channels |
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58 | Pick= [1 10 11; |
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59 | 1 2 5; |
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60 | 1 3 7; |
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61 | 10 14 17; |
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62 | 12 15 13; |
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63 | 10 10 11]; |
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64 | NuPick = size(Pick,1); |
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65 | reduce = 1; %100 percentage (used to reduce the size to process superpixel) |
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66 | % ================================ |
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67 | |
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68 | % find the dimension size of the Hi Resolution H |
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69 | [VertYSizeHiREs HoriXSizeHiREs dummy]= size(H2); |
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70 | clear dummy; |
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71 | |
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72 | % using a median size image to generate superpixel to reduce computation |
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73 | % intensity (the median size has a upper threshould SegVertYSize SegHoriXSize) |
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74 | if VertYSizeHiREs*HoriXSizeHiREs > Default.SegVertYSize*Default.SegHoriXSize |
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75 | |
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76 | % Downsample high resolution image to a median size image |
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77 | H2 = imresize(H2,([Default.SegVertYSize Default.SegHoriXSize ]*reduce+4),'nearest'); % +4 because edge error |
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78 | end |
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79 | [VertYImg HoriXImg dummy]= size(H2); |
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80 | clear dummy; |
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81 | |
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82 | %======================================== |
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83 | temp = max(H2,[],1); |
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84 | temp = max(temp,[],2); |
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85 | H2 = H2./repmat(temp,[VertYImg HoriXImg 1]); |
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86 | %======================================= |
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87 | |
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88 | % Process 6 different feature channel superpixel each with large and median scale |
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89 | for m=1:NuPick |
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90 | |
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91 | img=H2(:,:,Pick(m,:)); |
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92 | |
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93 | if DisplayFlag |
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94 | figure(1); image(img); |
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95 | end |
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96 | |
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97 | %================================= |
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98 | % choose superpixel of the images |
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99 | % default segmentation parameter |
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100 | for j = 1:2% number of scale of superpixel |
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101 | |
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102 | ok = 0; % ok ==1 means accept the segmentation |
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103 | while 1 |
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104 | % call the efficient segment function writen in C++ from MIT |
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105 | % Output the high resolution image ( + 1 since the smallest index can be zero) |
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106 | a = segmentImgOpt( Default.sigm*scale_sigm(j), Default.k*scale_k(j), Default.minp*scale_minV(j), uint8(img*255), ... |
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107 | [ Default.OutPutFolder Default.filename{1} '.ppm'], 0) + 1; |
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108 | a = a(2:(end-2),2:(end-2)); % clean the edge superpixel index errors |
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109 | |
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110 | % Arrange the superpixel index in order |
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111 | %Downsample to size size as prediected depth map |
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112 | a = imresize(a,[Default.VertYNuDepth Default.HoriXNuDepth],'nearest'); |
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113 | ma = max(a(:)); |
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114 | Unique_a = unique(a); |
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115 | SparseIndex = sparse(ma,1); |
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116 | SparseIndex(Unique_a) = 1:size(Unique_a); |
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117 | TextSup{m,j} = full(SparseIndex(a)); |
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118 | clear a SparseIndex Unique_a ma; |
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119 | |
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120 | % clean superpixel section ==================================================================== |
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121 | % merage all small point in higher scale segmentation |
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122 | % if j ~= 1 |
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123 | % TextSup{m,j} = premergAllsuperpixel(TextSup{m,j}); |
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124 | % end |
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125 | % ============================================================================================= |
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126 | |
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127 | % show superpixel |
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128 | if DisplayFlag |
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129 | figure(1); |
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130 | imagesc(TextSup{m,j}); |
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131 | newmap = rand(max(max(TextSup{m,j})),3); |
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132 | colormap(newmap); |
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133 | end |
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134 | |
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135 | % check if need to select segmentation parameter |
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136 | if SelectSegmentationPara==1; |
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137 | ok = input('Is the segmentation of image OK');% input new segmentation parameter |
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138 | else |
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139 | ok =1 ;% accept default segmentation parameter |
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140 | end |
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141 | |
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142 | if ok==1; |
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143 | break; |
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144 | end |
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145 | |
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146 | % Get the user selected parameter |
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147 | sigm = input('type sigm of segmentation'); |
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148 | k = input('type k of segmentation'); |
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149 | minp = input('type min of segmentation'); |
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150 | |
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151 | end % end of while 1 |
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152 | end % end of j = 1:2 (large and median scale) |
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153 | end % end of m=1:NuPick (NuPick different feature channel) |
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154 | |
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155 | H2 = H2.*repmat(temp,[VertYImg HoriXImg 1]); |
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156 | % save([ScratchDataFolder '/data/TextLowResImgIndexSuperpixelSepi' num2str(BatchNu) '.mat'], 'TextLowResImgIndexSuperpixelSep'); |
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157 | return; |
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