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 | AnchorXTarget = 876; |
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40 | AnchorYTarget = 591; |
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41 | AnchorXField = 1189; |
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42 | AnchorYField = 780; |
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43 | TargetSize = [100 150]; |
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44 | FieldRatio = 3; |
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45 | TargetScale = 1.5; |
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46 | GuessTargetPosition = [97 78]; |
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47 | |
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48 | ITarget_tmp = GrayI606(AnchorXTarget:(AnchorXTarget+TargetSize(1)),AnchorYTarget:(AnchorYTarget+TargetSize(2))); |
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49 | figure; imshow(ITarget_tmp) |
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50 | |
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51 | IField_tmp = GrayI610(AnchorYField:(AnchorYField+TargetSize(1)*FieldRatio),AnchorXField:(AnchorXField+TargetSize(2)*FieldRatio)); |
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52 | figure; imshow(IField_tmp) |
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53 | |
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54 | % example overlay to decide the scale |
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55 | Target2Field = zeros(size(IField_tmp)); |
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56 | Target2Field(GuessTargetPosition(1):(GuessTargetPosition(1)+size(ITarget_tmp,1)-1),GuessTargetPosition(2):(GuessTargetPosition(2)+size(ITarget_tmp,2)-1)) = ITarget_tmp; |
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57 | Target2Field = uint8(Target2Field); |
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58 | figure; imshow(Target2Field) |
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59 | |
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60 | % Recale and do Cross_Corrolation match |
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61 | ITarget_tmp_resized = imresize(ITarget_tmp, TargetScale, 'bicubic'); |
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62 | IField_tmp = GrayI610(AnchorYField:(AnchorYField+size(ITarget_tmp_resized,1)*FieldRatio),AnchorXField:(AnchorXField+size(ITarget_tmp_resized,2)*FieldRatio)); |
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63 | figure; imshow(IField_tmp) |
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64 | |
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65 | res = normxcorr2( ITarget_tmp_resized, IField_tmp ); |
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66 | res_top = size(ITarget_tmp_resized,1); |
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67 | res_left = size(ITarget_tmp_resized,2); |
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68 | res_modified = res(res_top:(end-res_top-1),res_left:(end-res_left-1)); |
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69 | figure; imagesc(abs(res_modified)) |
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70 | |
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71 | % find max position |
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72 | [VCol ICol] = max(res_modified); |
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73 | [V I] = max(VCol) |
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74 | ICol(I) |
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75 | |
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76 | Target2Field = zeros(size(IField_tmp)); |
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77 | Target2Field(ICol(I):(ICol(I)+size(ITarget_tmp_resized,1)-1),I:(I+size(ITarget_tmp_resized,2)-1)) = ITarget_tmp_resized; |
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78 | Target2Field = uint8(Target2Field); |
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79 | Ioverlay = cat(3, IField_tmp,Target2Field,zeros(size(IField_tmp))); |
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80 | figure; imagesc(Ioverlay) |
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