[37] | 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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