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 [Pair ImgInfo matches fail]=PoseMatchEst(defaultPara, ImgInfo) |
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40 | |
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41 | % This function estimate the relative Pose of the camera using first camera coordinate |
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42 | % as world coordinate |
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43 | |
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44 | % Input: |
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45 | % default - camera intrinsic, etc |
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46 | % ImgInfo - Exif, Model info, GPS, IMU info |
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47 | % |
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48 | % Return: |
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49 | % R - rotation - (R*Posi2+ T to A's coordinate) |
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50 | % T - translation |
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51 | |
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52 | % step outline |
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53 | % 1) extract Measuesd Position and orientation from GPS or IMU info |
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54 | % 2) Using Measures R and T and Mono-Depth to define mach search space constrain |
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55 | % 3) Do match search with all combinations satisfying Constrain from 2) using ralative threshould |
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56 | % 4) Ransac |
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57 | % 5) Bundle Adjustment |
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58 | % 6) up to scale translation reconstruction |
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59 | % 7) matches 3D triangulation |
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60 | % 8) Modified ImgInfo.Model.Depth up to accurate scale |
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61 | |
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62 | % initialize variables |
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63 | fail = 0; |
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64 | Pair.R = []; |
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65 | Pair.t = []; |
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66 | Pair.Xim = []; |
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67 | Pair.DepthScale = []; |
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68 | |
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69 | Img1 = ImgInfo(1).ExifInfo.IDName; |
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70 | Img2 = ImgInfo(2).ExifInfo.IDName; |
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71 | I1=imreadbw([defaultPara.Fdir '/pgm/' Img1 '.pgm']); % function from sift |
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72 | I2=imreadbw([defaultPara.Fdir '/pgm/' Img2 '.pgm']); % function from sift |
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73 | [f1] = readSurf(Img1, defaultPara.Fdir, 'Dense'); % Dense features |
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74 | [f2] = readSurf(Img2, defaultPara.Fdir, 'Dense'); % Dense features |
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75 | [D1 IND] = PorjPosi2Depth(size(I1), size(ImgInfo(1).Model.Depth.FitDepth), f1, ImgInfo(1).Model.Depth.FitDepth); |
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76 | [D2 IND] = PorjPosi2Depth(size(I2), size(ImgInfo(2).Model.Depth.FitDepth), f2, ImgInfo(1).Model.Depth.FitDepth); |
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77 | |
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78 | % 1) extract Measuesd Position and orientation from GPS or IMU info |
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79 | % Depends on what data we have, MeasR or MeasT, or both might be empty |
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80 | [MeasR MeasT] = InitPoseMeas(defaultPara, ImgInfo(1), ImgInfo(2)); |
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81 | |
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82 | if ~isempty(MeasR) |
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83 | % 2) Using Measures R and T and Mono-Depth to define match search space constrain |
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84 | % read in all surf features |
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85 | [ Rc1, Rc2, ConS1, ConS2, ConSRough1, ConSRough2] = CalMatchSearchRegin(defaultPara, MeasR, MeasT, I1, I2, f1, f2, D1, D2, 1, defaultPara.Flag.FlagDisp); |
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86 | |
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87 | % write the match search space constrain in to files for surfMatchRConS.sh script to read |
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88 | Vector2Ipoint([Rc1; ConS1],[defaultPara.Fdir '/surf/'],['RConS_' Img1]); |
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89 | Vector2Ipoint([Rc2; ConS2],[defaultPara.Fdir '/surf/'],['RConS_' Img2]); |
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90 | Vector2Ipoint([ConSRough1],[defaultPara.Fdir '/surf/'],['RConSRough_' Img1]); |
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91 | Vector2Ipoint([ConSRough2],[defaultPara.Fdir '/surf/'],['RConSRough_' Img2]); |
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92 | |
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93 | % 3) Do match search with all combinations satisfying Constrain from 2) using ralative threshould |
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94 | cd match |
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95 | [status, result] = system(['ls ' defaultPara.Fdir '/surf_matches/' Img1 '-' Img2 '.matchRConSDense_' num2str(defaultPara.AbsThre) '_' num2str(defaultPara.RatioThre)]); |
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96 | [statusReverse, resultReverse] ... |
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97 | = system(['ls ' defaultPara.Fdir '/surf_matches/' Img2 '-' Img1 '.matchRConSDense_' num2str(defaultPara.AbsThre) '_' num2str(defaultPara.RatioThre)]); |
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98 | if status && statusReverse |
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99 | SurfMatchTime = tic; |
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100 | system(['./surfMatchRConS.sh ' defaultPara.Fdir ' ' Img1 ' ' Img2 ' Dense ' num2str(defaultPara.AbsThre) ' ' num2str(defaultPara.RatioThre)]); |
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101 | disp([' ' num2str( toc( SurfMatchTime)) ' seconds.']); |
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102 | end |
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103 | cd .. |
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104 | else |
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105 | cd match |
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106 | [status, result] = system(['ls ' defaultPara.Fdir '/surf_matches/' Img1 '-' Img2 '.matchDense_' num2str(defaultPara.AbsThre) '_' num2str(defaultPara.RatioThre)]); |
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107 | [statusReverse, resultReverse] ... |
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108 | = system(['ls ' defaultPara.Fdir '/surf_matches/' Img2 '-' Img1 '.matchDense_' num2str(defaultPara.AbsThre) '_' num2str(defaultPara.RatioThre)]); |
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109 | if status && statusReverse |
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110 | SurfMatchTime = tic; |
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111 | system(['./surfMatch.sh ' defaultPara.Fdir ' ' Img1 ' ' Img2 ' Dense ' num2str(defaultPara.AbsThre) ' ' num2str(defaultPara.RatioThre)]); |
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112 | disp([' ' num2str( toc( SurfMatchTime)) ' seconds.']); |
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113 | end |
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114 | cd .. |
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115 | end |
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116 | % 4. Ransac |
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117 | if ~isempty(MeasR) |
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118 | [f1, f2, matches] = readSurfMatches(Img1, Img2, defaultPara.Fdir, ... |
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119 | [ defaultPara.Type 'Dense_' num2str(defaultPara.AbsThre) '_' num2str(defaultPara.RatioThre)], 1, 1); |
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120 | else |
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121 | [f1, f2, matches] = readSurfMatches(Img1, Img2, defaultPara.Fdir, ... |
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122 | [ 'Dense_' num2str(defaultPara.AbsThre) '_' num2str(defaultPara.RatioThre)], 1, 1); |
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123 | end |
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124 | if isempty(matches) |
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125 | disp('Zeros Surf matches'); |
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126 | fail = 1; |
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127 | return; |
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128 | end |
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129 | [D1 IND1] = PorjPosi2Depth(size(I1), size(ImgInfo(1).Model.Depth.FitDepth), f1(:,matches(1,:)), ImgInfo(1).Model.Depth.FitDepth); |
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130 | [D2 IND2] = PorjPosi2Depth(size(I2), size(ImgInfo(2).Model.Depth.FitDepth), f2(:,matches(2,:)), ImgInfo(1).Model.Depth.FitDepth); |
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131 | |
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132 | % Ensemble method to determine confidence of inliers |
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133 | fittingfn = @fundmatrix; |
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134 | distfnEnsmble = @fundistEnsmble; |
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135 | degenfn = @isdegenerate; |
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136 | nmatches = size(matches, 2); |
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137 | x = [[f1(:, matches(1,:)); ones(1, nmatches)]; [f2(:, matches(2,:)); ones(1, nmatches)]]; |
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138 | [ SampsonDist ] = EnsembleRansac(defaultPara, x, fittingfn, distfnEnsmble, degenfn, 8, ones(1,nmatches)', min(nmatches*10, defaultPara.MAXEnsembleSamples), 0); |
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139 | kurtosisValue =kurtosis(SampsonDist'); |
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140 | |
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141 | % Ransac |
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142 | [F0, inliers, NewDist, fail, ind]=GeneralRansac(defaultPara, f1, f2, matches, D1, D2, kurtosisValue', 8); |
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143 | if defaultPara.Flag.FlagDisp |
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144 | figure; plotmatches(I1,I2,f1, f2,matches(:,inliers), 'Stacking', 'v', 'Interactive', defaultPara.Flag.FlagDisp); |
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145 | end |
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146 | % *** Stop maunally to pick out the bad matches*** ----------------- |
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147 | matches = matches(:,inliers); |
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148 | if isempty(matches) |
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149 | disp('Zeros Matches After Ransac'); |
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150 | fail = 2; |
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151 | return; |
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152 | end |
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153 | % ------------------------------------------------------------------ |
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154 | |
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155 | x_calib = [ inv(defaultPara.InrinsicK1)*[ f1(:,matches(1,:)); ones(1,length(matches))];... |
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156 | inv(defaultPara.InrinsicK2)*[ f2(:,matches(2,:)); ones(1,length(matches))]]; |
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157 | |
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158 | % Estimate F using NonLine LS on every inlier |
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159 | MatchDensityWeights1 = CalMatchDensityWeights(f1(:,matches(1,:)), max(size(I1))/defaultPara.radius2imageSizeRatio); |
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160 | MatchDensityWeights2 = CalMatchDensityWeights(f2(:,matches(2,:)), max(size(I2))/defaultPara.radius2imageSizeRatio); |
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161 | MatchDensityWeights =mean([MatchDensityWeights1; MatchDensityWeights2], 1); |
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162 | F = getFnpt( F0, f1(:, matches(1,:))', f2(:, matches(2,:))', MatchDensityWeights); |
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163 | E = defaultPara.InrinsicK2'*F*defaultPara.InrinsicK1; % Camera essential Matrix |
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164 | if ~isempty(MeasR) |
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165 | [ R0, T0, lamda1, lamda2, inlier, Error] = EstPose( defaultPara, E, x_calib, [], MeasR(1:3,:)); |
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166 | else |
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167 | [ R0, T0, lamda1, lamda2, inlier, Error] = EstPose( defaultPara, E, x_calib, [], []); |
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168 | end |
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169 | T0 = [T0; - R0'*T0]; |
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170 | R0 = [R0; R0']; |
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171 | matches = matches(:,inlier); % delet matches give negative depths |
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172 | x_calib = x_calib(:,inlier); |
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173 | lamda1 = lamda1(inlier); |
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174 | lamda2 = lamda2(inlier); |
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175 | |
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176 | % Estimated X_obj by triangulation |
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177 | X_obj_1 = x_calib(1:3,:).*repmat(lamda1, 3, 1); |
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178 | X_obj_2 = R0(4:6,:)*(x_calib(4:6,:).*repmat(lamda2, 3, 1)) + repmat(T0(4:6), 1, size(matches,2)); |
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179 | X_obj = (X_obj_1+X_obj_2)/2; |
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180 | |
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181 | % 5. Bundle Adjustment |
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182 | [R T X_obj_BA X_im_BA dist1_BA dist2_BA]=SparseBAWraper(defaultPara, R0(1:3,:), T0(1:3), [f1(:,matches(1,:)); f2(:,matches(2,:))], X_obj, ImgInfo, 1); |
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183 | if all(isnan( dist1_BA)) || isempty(R) || any(isnan(R(:))) |
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184 | disp('BA failed'); |
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185 | fail = 3; |
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186 | return; |
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187 | end |
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188 | while length(X_im_BA) >= defaultPara.MinimumNumMatches |
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189 | outlier_thre1 = prctile(dist1_BA,90); |
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190 | outlier_thre2 = prctile(dist2_BA,90); |
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191 | Outlier = logical(zeros( size( dist1_BA))); |
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192 | if max(dist1_BA) >= defaultPara.ReProjErrorThre |
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193 | % Outlier = Outlier | dist1_BA > max( outlier_thre1, defaultPara.ReProjErrorThre); |
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194 | Outlier = Outlier | dist1_BA > outlier_thre1; |
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195 | end |
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196 | if max(dist2_BA) >= defaultPara.ReProjErrorThre |
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197 | % Outlier = Outlier | dist2_BA > max( outlier_thre2, defaultPara.ReProjErrorThre); |
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198 | Outlier = Outlier | dist2_BA > outlier_thre2; |
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199 | end |
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200 | matches(:,Outlier) = []; |
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201 | if isempty(matches) |
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202 | disp('Zeros Matches After BA Pruning'); |
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203 | fail = 4; |
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204 | return; |
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205 | end |
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206 | if all( Outlier == 0) |
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207 | % Non Outlier detected for BA |
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208 | break; |
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209 | end |
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210 | lamda1(Outlier) = []; |
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211 | lamda2(Outlier) = []; |
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212 | X_obj_BA(:,Outlier) = []; |
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213 | x_calib(:,Outlier) = []; |
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214 | [R T X_obj_BA X_im_BA dist1_BA dist2_BA]=SparseBAWraper(defaultPara, R, T, [f1(:,matches(1,:)); f2(:,matches(2,:))], X_obj_BA, ImgInfo, 1); |
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215 | if all(isnan( dist1_BA)) || isempty(R) || any(isnan(R(:))) |
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216 | disp('BA failed'); |
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217 | fail = 5; |
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218 | return; |
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219 | end |
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220 | end |
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221 | if defaultPara.Flag.FlagDisp |
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222 | figure; plotmatches(I1,I2,f1, f2, matches, 'Stacking', 'v', 'Interactive', defaultPara.Flag.FlagDisp); |
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223 | end |
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224 | |
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225 | % 6. find T up to scale |
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226 | |
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227 | % 7. Triangulation |
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228 | % modified the x_calib So that perfact triangulation but the image is distorted a little bit |
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229 | tempf1 = X_im_BA(1:2,:); |
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230 | tempf2 = X_im_BA(3:4,:); |
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231 | x_calib = [ inv(defaultPara.InrinsicK1)*[ tempf1; ones(1,length(tempf1))];... |
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232 | inv(defaultPara.InrinsicK2)*[ tempf2; ones(1,length(tempf2))]]; |
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233 | % ------------------ |
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234 | [ lamda1 lamda2 Error] = triangulation( defaultPara, R, T, x_calib); |
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235 | |
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236 | % 8. modify ImgInfo.Model.Depth .... (not sure do it or not??????) |
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237 | [D1 IND1] = PorjPosi2Depth(size(I1), size(ImgInfo(1).Model.Depth.FitDepth), f1(:,matches(1,:)), ImgInfo(1).Model.Depth.FitDepth); |
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238 | [D2 IND2] = PorjPosi2Depth(size(I2), size(ImgInfo(2).Model.Depth.FitDepth), f2(:,matches(2,:)), ImgInfo(2).Model.Depth.FitDepth); |
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239 | Depth1ProjDepthRatio = sqrt(sum(x_calib(1:3,:).^2, 1)); |
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240 | Depth2ProjDepthRatio = sqrt(sum(x_calib(4:6,:).^2, 1)); |
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241 | DProj1 = D1./Depth1ProjDepthRatio; |
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242 | DProj2 = D2./Depth2ProjDepthRatio; |
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243 | [DepthScale1] = UniformDepthScale( defaultPara, DProj1, lamda1, ones(1,length(lamda1))); |
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244 | [DepthScale2] = UniformDepthScale( defaultPara, DProj2, lamda2, ones(1,length(lamda2)) ); |
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245 | %if DepthScale1 > 20 | DepthScale1 <0.05 | DepthScale2 > 20 | DepthScale2 <0.05 %//Min used to use 10 and 0.2 |
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246 | % disp('Unrealistic in Rescaleing the depth, Check matchings'); |
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247 | % fail = -1; |
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248 | %end |
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249 | |
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250 | Pair.lamda = [lamda1; lamda2]; |
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251 | Pair.DepthScale = [DepthScale1; DepthScale2]; |
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252 | Pair.R = R; |
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253 | Pair.T = T; |
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254 | Pair.Xim = [f1(:,matches(1,:)); f2(:,matches(2,:))]; |
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255 | |
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256 | % check is triangulation reasonable |
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257 | if defaultPara.Flag.FlagDisp |
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258 | figure(50); clf; title('Closest point Match Point'); hold on; |
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259 | ClosestMatchPosition2 = x_calib(4:6,:).*repmat( lamda2, 3,1); |
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260 | ClosestMatchPosition1 = R*(x_calib(1:3,:).*repmat( lamda1, 3,1)) + repmat(T, 1, length(lamda1)); |
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261 | MonoStichPosition2 = x_calib(4:6,:).*repmat( DProj2.*DepthScale2, 3,1); |
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262 | MonoStichPosition1 = R*(x_calib(1:3,:).*repmat( DProj1.*DepthScale1, 3,1)) + repmat(T, 1, length(DProj1)); |
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263 | % ===================== |
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264 | [VDepth HDepth] = size(ImgInfo(2).Model.Depth.FitDepth); |
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265 | [VImg HImg] = size(I1); |
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266 | VIndexDepthRes = repmat((1:VDepth)', [1 HDepth]); |
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267 | HIndexDepthRes = repmat((1:HDepth), [VDepth 1]); |
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268 | VIndexImgRes = ( VIndexDepthRes -0.5)/VDepth*VImg; |
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269 | HIndexImgRes = ( HIndexDepthRes -0.5)/HDepth*HImg; |
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270 | ImgPositionPix = cat(3, HIndexImgRes, VIndexImgRes); |
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271 | All_x_calib = inv(defaultPara.InrinsicK1)*[ reshape( permute(ImgPositionPix, [ 3 1 2]), 2, []); ones(1, VDepth*HDepth)];% |
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272 | All_Ray = All_x_calib./repmat( sqrt(sum(All_x_calib.^2, 1)), 3, 1); |
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273 | All_Ray = repmat( All_Ray, 2, 1); |
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274 | % ==================== |
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275 | ReScaledPosi2 = All_Ray(4:6,:).*repmat( ImgInfo(2).Model.Depth.FitDepth(:)'*DepthScale2, 3,1); |
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276 | ReScaledPosi1 = R*(All_Ray(1:3,:).*repmat( ImgInfo(1).Model.Depth.FitDepth(:)'*DepthScale1, 3,1)) + repmat(T, 1, length(All_Ray)); |
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277 | ReScaledPosi2(:,IND2) = []; |
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278 | ReScaledPosi1(:,IND1) = []; |
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279 | scatter3(ReScaledPosi2(1,:)', ReScaledPosi2(3,:)', ReScaledPosi2(2,:)', 0.5*ones(1,size( ReScaledPosi2,2))); |
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280 | scatter3(ReScaledPosi1(1,:)', ReScaledPosi1(3,:)', ReScaledPosi1(2,:)', 1*ones(1,size( ReScaledPosi1,2))); |
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281 | scatter3(ClosestMatchPosition2(1,:)', ClosestMatchPosition2(3,:)', ClosestMatchPosition2(2,:)', 40, 'g'); |
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282 | scatter3(ClosestMatchPosition1(1,:)', ClosestMatchPosition1(3,:)', ClosestMatchPosition1(2,:)', 40, 'b'); |
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283 | line( [ ClosestMatchPosition2(1,:); ClosestMatchPosition1(1,:)], ... |
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284 | [ ClosestMatchPosition2(3,:); ClosestMatchPosition1(3,:)], ... |
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285 | [ ClosestMatchPosition2(2,:); ClosestMatchPosition1(2,:)]); |
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286 | % line( [ MonoStichPosition2(1,:); MonoStichPosition1(1,:)], ... |
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287 | % [ MonoStichPosition2(3,:); MonoStichPosition1(3,:)], ... |
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288 | % [ MonoStichPosition2(2,:); MonoStichPosition1(2,:)]); |
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289 | if ~isempty(ImgInfo(1).Model.Constrain.RayMatche) |
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290 | ClosestMatchPosition1Hist = R*(ImgInfo(1).Model.Constrain.RayMatche'.*repmat(ImgInfo(1).Model.Constrain.Depth_modified , 3, 1)) + repmat(T, 1, length(ImgInfo(1).Model.Constrain.RayMatche)); |
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291 | scatter3(ClosestMatchPosition1Hist(1,:)', ClosestMatchPosition1Hist(3,:)', ClosestMatchPosition1Hist(2,:)', 40, 'y'); |
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292 | end |
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293 | if ~isempty(ImgInfo(2).Model.Constrain.RayMatche) |
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294 | ClosestMatchPosition2Hist = ImgInfo(2).Model.Constrain.RayMatche'.*repmat(ImgInfo(2).Model.Constrain.Depth_modified , 3, 1); |
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295 | scatter3(ClosestMatchPosition2Hist(1,:)', ClosestMatchPosition2Hist(3,:)', ClosestMatchPosition2Hist(2,:)', 40, 'y'); |
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296 | end |
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297 | |
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298 | figure(51); clf; title('Closest point Match Point'); hold on; |
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299 | RawReScaledPosi2 = All_Ray(4:6,:).*repmat( ImgInfo(2).Model.Depth.RawDepth(:)'*DepthScale2, 3,1); |
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300 | RawReScaledPosi1 = R*(All_Ray(1:3,:).*repmat( ImgInfo(1).Model.Depth.RawDepth(:)'*DepthScale1, 3,1)) + repmat(T, 1, length(All_Ray)); |
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301 | RawReScaledPosi2(:,IND2) = []; |
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302 | RawReScaledPosi1(:,IND1) = []; |
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303 | scatter3(RawReScaledPosi2(1,:)', RawReScaledPosi2(3,:)', RawReScaledPosi2(2,:)', 1*ones(1,size( RawReScaledPosi2,2))); |
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304 | scatter3(RawReScaledPosi1(1,:)', RawReScaledPosi1(3,:)', RawReScaledPosi1(2,:)', 0.5*ones(1,size( RawReScaledPosi1,2))); |
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305 | scatter3(ClosestMatchPosition2(1,:)', ClosestMatchPosition2(3,:)', ClosestMatchPosition2(2,:)', 40, 'g'); |
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306 | scatter3(ClosestMatchPosition2(1,:)', ClosestMatchPosition2(3,:)', ClosestMatchPosition2(2,:)', 40, 'b'); |
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307 | line( [ ClosestMatchPosition2(1,:); ClosestMatchPosition1(1,:)], ... |
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308 | [ ClosestMatchPosition2(3,:); ClosestMatchPosition1(3,:)], ... |
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309 | [ ClosestMatchPosition2(2,:); ClosestMatchPosition1(2,:)]); |
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310 | line( [ MonoStichPosition2(1,:); MonoStichPosition1(1,:)], ... |
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311 | [ MonoStichPosition2(3,:); MonoStichPosition1(3,:)], ... |
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312 | [ MonoStichPosition2(2,:); MonoStichPosition1(2,:)]); |
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313 | end |
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314 | return; |
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315 | |
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