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 | clear all; close all; clc;
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40 | % This script inputs png image file from the user app and creates a
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41 | % "class" map of the image
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42 | [img] = imread('image_name','png','BackgroundColor',[1, 1, 1]);
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43 | [H W depth] = size(img);
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44 | bitdepth = 255;
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45 | list = [];
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46 | threshold = 20; range = 3;
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47 |
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48 | %Convert to gray scale
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49 | I=rgb2gray(img);
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50 |
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51 | %Compute the histogram of the flattened image
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52 | [amp, bins] = hist(I(:), -4:255);
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53 |
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54 | %Get ride of the white values + zero pad the begining
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55 | amp((length(bins)-4):length(bins)) = 0;
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56 |
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57 | %Gate histogram signal
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58 | for i=1:length(amp)
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59 | if (amp(i) < threshold)
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60 | amp(i) = 0;
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61 | end
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62 | end
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63 |
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64 | %Find maxs until all amplitude values are zero
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65 | [maxx index] = max(amp);
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66 | while( maxx > 5)
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67 | amp((index-range):(index+range)) = 0;
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68 | list = [bins(index) list];
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69 | [maxx index] = max(amp);
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70 | end
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71 | list
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72 |
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73 | %Now go through entire image and label the class for the image: classmap
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74 | classmap = zeros(H,W);
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75 | for i=1:H
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76 | for j=1:W
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77 | for k=1:length(list)
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78 | if ((list(k) - range) <I(i, j)) && (I(i, j) < (list(k)+ range))
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79 | classmap(i, j) = k;
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80 | break;
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81 | end
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82 | end
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83 | end
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84 | end
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85 |
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86 | classmap
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87 |
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88 |
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89 |
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90 |
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91 |
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92 |
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93 |
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94 | |
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