├── .gitattributes ├── LICENSE ├── MBS ├── BG.m ├── MBS.m ├── MBS.mexw64 ├── MxArray.obj ├── demo.JPG ├── demo.m ├── doMBS.m ├── getParam.m ├── mex │ ├── MBS.cpp │ ├── MBS.hpp │ ├── MxArray.cpp │ ├── MxArray.hpp │ ├── MxArray.obj │ ├── compile.m │ ├── compile_win.m │ └── mexopencv.hpp └── readme.txt ├── README.md ├── blendTexture.m ├── build_graphCutMex.m ├── calcDist.m ├── calcHomo.m ├── gradient_blend.m ├── graphCutMex.cpp ├── graphCutMex.h ├── graphCutMex.m ├── graphCutMex.mexw64 ├── homographyAlign.m ├── main.m ├── matchDelete.m ├── maxflow-v3.03.src ├── CHANGES.TXT ├── GPL.TXT ├── README.TXT ├── block.h ├── graph.cpp ├── graph.h ├── instances.inc └── maxflow.cpp ├── mbs_saliency.m ├── modelspecific ├── hnormalise.m ├── homography_degen.m ├── homography_fit.m ├── homography_res.m ├── iscolinear.m ├── normalise2dpts.m ├── vgg_H_from_x_lin.m ├── vgg_condition_2d.m ├── vgg_conditioner_from_pts.m └── vgg_get_nonhomg.m ├── randIndex.m ├── ransacx.m ├── registerTexture.m ├── siftMatch.m └── vlfeat-0.9.21 └── README.txt /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | *.cpp linguist-language=MATLAB 4 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2019 tianli liao 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /MBS/BG.m: -------------------------------------------------------------------------------- 1 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 2 | % Implemetation of the saliency detction method described in paper 3 | % "Minimum Barrier Salient Object Detection at 80 FPS", Jianming Zhang, 4 | % Stan Sclaroff, Zhe Lin, Xiaohui Shen, Brian Price, Radomir Mech, ICCV, 5 | % 2015 6 | % 7 | % Copyright (C) 2015 Jianming Zhang 8 | % 9 | % This program is free software: you can redistribute it and/or modify 10 | % it under the terms of the GNU General Public License as published by 11 | % the Free Software Foundation, either version 3 of the License, or 12 | % (at your option) any later version. 13 | % 14 | % This program is distributed in the hope that it will be useful, 15 | % but WITHOUT ANY WARRANTY; without even the implied warranty of 16 | % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 17 | % GNU General Public License for more details. 18 | % 19 | % You should have received a copy of the GNU General Public License 20 | % along with this program. If not, see . 21 | % 22 | % If you have problems about this software, please contact: 23 | % jimmie33@gmail.com 24 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 25 | 26 | function bgMap = BG(I, reg, m_ratio) 27 | 28 | I = single(I); 29 | rowMargin = round(m_ratio*size(I,1)); 30 | colMargin = round(m_ratio*size(I,2)); 31 | mRegion = []; 32 | mRegion{end+1} = I(1:rowMargin,:,:); 33 | mRegion{end+1} = I(end-rowMargin+1:end,:,:); 34 | mRegion{end+1} = I(:,1:colMargin,:); 35 | mRegion{end+1} = I(:,end-colMargin+1:end,:); 36 | 37 | tmpMap = zeros(size(I,1)*size(I,2),4); 38 | for i = 1:4 39 | R = reshape(mRegion{i}, size(mRegion{i},1)*size(mRegion{i},2),[]); 40 | covMat = cov(R) + reg*eye(size(I,3)); 41 | [U,S,V] = svd(covMat); 42 | P = U*diag(1./diag(sqrt(S))); 43 | V = sum(abs(bsxfun(@minus, reshape(I, size(I,1)*size(I,2),[]), mean(R))*P),2); 44 | tmpMap(:,i) = mat2gray(V); 45 | end 46 | 47 | bgMap = sum(tmpMap,2) - max(tmpMap, [], 2); 48 | bgMap = reshape(bgMap,size(I,1),size(I,2)); 49 | bgMap = mat2gray(bgMap); 50 | 51 | -------------------------------------------------------------------------------- /MBS/MBS.m: -------------------------------------------------------------------------------- 1 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 2 | % Implemetation of the saliency detction method described in paper 3 | % "Minimum Barrier Salient Object Detection at 80 FPS", Jianming Zhang, 4 | % Stan Sclaroff, Zhe Lin, Xiaohui Shen, Brian Price, Radomir Mech, ICCV, 5 | % 2015 6 | % 7 | % Copyright (C) 2015 Jianming Zhang 8 | % 9 | % This program is free software: you can redistribute it and/or modify 10 | % it under the terms of the GNU General Public License as published by 11 | % the Free Software Foundation, either version 3 of the License, or 12 | % (at your option) any later version. 13 | % 14 | % This program is distributed in the hope that it will be useful, 15 | % but WITHOUT ANY WARRANTY; without even the implied warranty of 16 | % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 17 | % GNU General Public License for more details. 18 | % 19 | % You should have received a copy of the GNU General Public License 20 | % along with this program. If not, see . 21 | % 22 | % If you have problems about this software, please contact: 23 | % jimmie33@gmail.com 24 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 25 | 26 | % usage: MBS I 27 | % 28 | % I: uint8 3-channle image 29 | % use_lab: boolean flag for using the Lab color space (true by default) 30 | % remove_border: boolean flag for removing artificial border (true by default) 31 | % use_geodesice: boolean flag for replacing MBD with geodesic distance (false by default) -------------------------------------------------------------------------------- /MBS/MBS.mexw64: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tlliao/Perception-based-seam-cutting/f1a839e0b24214773b26905719a492e49d0aa5ec/MBS/MBS.mexw64 -------------------------------------------------------------------------------- /MBS/MxArray.obj: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tlliao/Perception-based-seam-cutting/f1a839e0b24214773b26905719a492e49d0aa5ec/MBS/MxArray.obj -------------------------------------------------------------------------------- /MBS/demo.JPG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tlliao/Perception-based-seam-cutting/f1a839e0b24214773b26905719a492e49d0aa5ec/MBS/demo.JPG -------------------------------------------------------------------------------- /MBS/demo.m: -------------------------------------------------------------------------------- 1 | 2 | % MB 3 | paramMB = getParam(); 4 | paramMB.use_backgroundness = false; 5 | % MB+ 6 | paramMBplus = getParam(); 7 | paramMBplus.verbose = true; % to show run time for each step 8 | 9 | % Geodesic 10 | paramGeo = getParam(); 11 | paramGeo.use_backgroundness = false; 12 | paramGeo.use_geodesic = true; 13 | 14 | I = imread('demo.jpg'); 15 | 16 | paramMBD.use_backgroudness = true; 17 | [pMap1, dMap1] = doMBS(I, paramMB); 18 | [pMapG, dMapG] = doMBS(I, paramGeo); 19 | [pMap2] = doMBS(I, paramMBplus); 20 | 21 | % display results 22 | fh = figure(1); 23 | set(fh, 'OuterPosition', [500,600,800,300]); 24 | 25 | subplot(1,3,1) 26 | imshow(I); 27 | title('Input') 28 | 29 | subplot(1,3,2) 30 | imshow(pMap1) 31 | title('MB') 32 | 33 | subplot(1,3,3) 34 | imshow(pMap2) 35 | title('MB+') 36 | 37 | 38 | fh = figure(2); 39 | set(fh, 'OuterPosition', [500,100,800,500]); 40 | 41 | subplot(2,3,1) 42 | imshow(I); 43 | title('Input') 44 | 45 | subplot(2,3,2) 46 | imshow(dMap1) 47 | title('MBD Map') 48 | 49 | subplot(2,3,3) 50 | imshow(pMap1) 51 | title('MB') 52 | 53 | subplot(2,3,5) 54 | imshow(dMapG) 55 | title('Geodesic Distance Map') 56 | subplot(2,3,6) 57 | imshow(pMapG) 58 | title('Geodesic Saliency Map') 59 | 60 | -------------------------------------------------------------------------------- /MBS/doMBS.m: -------------------------------------------------------------------------------- 1 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 2 | % Implemetation of the saliency detction method described in paper 3 | % "Minimum Barrier Salient Object Detection at 80 FPS", Jianming Zhang, 4 | % Stan Sclaroff, Zhe Lin, Xiaohui Shen, Brian Price, Radomir Mech, ICCV, 5 | % 2015 6 | % 7 | % Copyright (C) 2015 Jianming Zhang 8 | % 9 | % This program is free software: you can redistribute it and/or modify 10 | % it under the terms of the GNU General Public License as published by 11 | % the Free Software Foundation, either version 3 of the License, or 12 | % (at your option) any later version. 13 | % 14 | % This program is distributed in the hope that it will be useful, 15 | % but WITHOUT ANY WARRANTY; without even the implied warranty of 16 | % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 17 | % GNU General Public License for more details. 18 | % 19 | % You should have received a copy of the GNU General Public License 20 | % along with this program. If not, see . 21 | % 22 | % If you have problems about this software, please contact: 23 | % jimmie33@gmail.com 24 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 25 | 26 | 27 | function [pMap, dMap] = doMBS(I, param) 28 | 29 | if ~exist('param','var') 30 | param = getParam(); 31 | end 32 | 33 | scale = param.MAX_DIM/max(size(I,1),size(I,2)); 34 | Ir = imresize(I, scale); 35 | 36 | % check the type of the Ir 37 | if ~isa(Ir, 'uint8') 38 | Ir = mat2gray(Ir); 39 | Ir = uint8(Ir*255); 40 | end 41 | if size(Ir, 3) == 1 42 | Ir = repmat(Ir,[1,1,3]); 43 | end 44 | 45 | % compute saliency 46 | tic 47 | dMap = MBS(Ir, param.use_lab, param.remove_border, param.use_geodesic); 48 | t1 = toc; 49 | 50 | 51 | if param.use_backgroundness 52 | tic 53 | bgMap = BG(Ir, param.COV_REG,param.MARGIN_RATIO); 54 | t2 = toc; 55 | dMap = dMap + bgMap; 56 | end 57 | pMap = dMap; 58 | 59 | % postprocess 60 | tic 61 | if param.center_bias 62 | cmap = imresize(param.cmap, [size(pMap,1), size(pMap,2)]); 63 | pMap = pMap.*cmap; 64 | end 65 | pMap = mat2gray(pMap); 66 | radius = floor(param.smooth_alpha*sqrt(mean(pMap(:)))); 67 | pMap = morphSmooth(pMap, max(radius, 3)); 68 | pMap = enhanceContrast(pMap, param.contrast_b); 69 | t3 = toc; 70 | 71 | pMap = imresize(pMap, [size(I,1), size(I,2)]); 72 | pMap = mat2gray(pMap); 73 | 74 | if param.verbose 75 | fprintf('computing MBD map: %f\n', t1); 76 | if param.use_backgroundness 77 | fprintf('computing BG map: %f\n', t2); 78 | end 79 | fprintf('postprocessing: %f\n', t3); 80 | end 81 | 82 | 83 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 84 | function sMap = morphSmooth(I,width) 85 | % opening by reconstruction followed by closing by reconstruction 86 | % see the following material for detailed explanations 87 | % http://www.mathworks.com/products/demos/image/watershed/ipexwatershed.html 88 | I = uint8(I*255); 89 | se = strel('square',width); 90 | Ie = imerode(I, se); 91 | Iobr = imreconstruct(Ie, I); 92 | Iobrd = imdilate(Iobr, se); 93 | Iobrcbr = imreconstruct(imcomplement(Iobrd), imcomplement(Iobr)); 94 | Iobrcbr = imcomplement(Iobrcbr); 95 | sMap = mat2gray(Iobrcbr); 96 | 97 | function pMap = enhanceContrast(I, b) 98 | 99 | t = 0.5*max(I(:)); 100 | v1 = mean(I(I>=t)); 101 | v2 = mean(I(I. 21 | % 22 | % If you have problems about this software, please contact: 23 | % jimmie33@gmail.com 24 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 25 | 26 | 27 | function param = getParam() 28 | 29 | param.MAX_DIM = 300; % max image dimension in computation 30 | param.use_lab = true; % use Lab color space 31 | param.remove_border = true; % detect and remove artificial image frames 32 | param.use_geodesic = false; % flag for replacing geodesic distance with MBD 33 | param.use_backgroundness = true; % MB+ 34 | param.COV_REG = 50; % covariance regularization term for the maximum value of 255*255 35 | param.MARGIN_RATIO = 0.1; % the boundary margion for computing backgroundness map 36 | param.cmap = getCenterMap(param.MAX_DIM); % the center distance map for center bias 37 | param.center_bias = true; 38 | param.smooth_alpha = 50; % see eqn. 9 39 | param.contrast_b = 10; % see eqn. 11 40 | param.verbose = false; 41 | 42 | function cmap = getCenterMap(dim) 43 | X = [1:dim] - dim/2; X = repmat(X.^2,[dim,1]); 44 | Y = X'; 45 | cmap = sqrt(X+Y); 46 | cmap = 1-mat2gray(cmap); -------------------------------------------------------------------------------- /MBS/mex/MBS.cpp: -------------------------------------------------------------------------------- 1 | /***************************************************************************** 2 | * Implemetation of the saliency detction method described in paper 3 | * "Minimum Barrier Salient Object Detection at 80 FPS", Jianming Zhang, 4 | * Stan Sclaroff, Zhe Lin, Xiaohui Shen, Brian Price, Radomir Mech, ICCV, 5 | * 2015 6 | * 7 | * Copyright (C) 2015 Jianming Zhang 8 | * 9 | * This program is free software: you can redistribute it and/or modify 10 | * it under the terms of the GNU General Public License as published by 11 | * the Free Software Foundation, either version 3 of the License, or 12 | * (at your option) any later version. 13 | * 14 | * This program is distributed in the hope that it will be useful, 15 | * but WITHOUT ANY WARRANTY; without even the implied warranty of 16 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 17 | * GNU General Public License for more details. 18 | * 19 | * You should have received a copy of the GNU General Public License 20 | * along with this program. If not, see . 21 | * 22 | * If you have problems about this software, please contact: 23 | * jimmie33@gmail.com 24 | *******************************************************************************/ 25 | 26 | #include 27 | #include 28 | #include "opencv2/opencv.hpp" 29 | #include "mexopencv.hpp" 30 | #include "MBS.hpp" 31 | #include 32 | #include 33 | 34 | using namespace std; 35 | using namespace cv; 36 | 37 | #define MAX_IMG_DIM 300 38 | #define TOLERANCE 0.01 39 | #define FRAME_MAX 20 40 | #define SOBEL_THRESH 0.4 41 | 42 | MBS::MBS(const Mat& src) 43 | :mAttMapCount(0) 44 | { 45 | mSrc=src.clone(); 46 | mSaliencyMap = Mat::zeros(src.size(), CV_32FC1); 47 | 48 | split(mSrc, mFeatureMaps); 49 | 50 | for (int i = 0; i < mFeatureMaps.size(); i++) 51 | { 52 | //normalize(mFeatureMaps[i], mFeatureMaps[i], 255.0, 0.0, NORM_MINMAX); 53 | medianBlur(mFeatureMaps[i], mFeatureMaps[i], 5); 54 | } 55 | } 56 | 57 | void MBS::computeSaliency(bool use_geodesic) 58 | { 59 | 60 | if (use_geodesic) 61 | mMBSMap = fastGeodesic(mFeatureMaps); 62 | else 63 | mMBSMap = fastMBS(mFeatureMaps); 64 | normalize(mMBSMap, mMBSMap, 0.0, 1.0, NORM_MINMAX); 65 | mSaliencyMap = mMBSMap; 66 | } 67 | 68 | Mat MBS::getSaliencyMap() 69 | { 70 | Mat ret; 71 | normalize(mSaliencyMap, ret, 0.0, 255.0, NORM_MINMAX); 72 | ret.convertTo(ret, CV_8UC1); 73 | return ret; 74 | } 75 | 76 | 77 | void rasterScan(const Mat& featMap, Mat& map, Mat& lb, Mat& ub) 78 | { 79 | Size sz = featMap.size(); 80 | float *pMapup = (float*)map.data + 1; 81 | float *pMap = pMapup + sz.width; 82 | uchar *pFeatup = featMap.data + 1; 83 | uchar *pFeat = pFeatup + sz.width; 84 | uchar *pLBup = lb.data + 1; 85 | uchar *pLB = pLBup + sz.width; 86 | uchar *pUBup = ub.data + 1; 87 | uchar *pUB = pUBup + sz.width; 88 | 89 | float mapPrev; 90 | float featPrev; 91 | uchar lbPrev, ubPrev; 92 | 93 | float lfV, upV; 94 | int flag; 95 | for (int r = 1; r < sz.height - 1; r++) 96 | { 97 | mapPrev = *(pMap - 1); 98 | featPrev = *(pFeat - 1); 99 | lbPrev = *(pLB - 1); 100 | ubPrev = *(pUB - 1); 101 | 102 | 103 | for (int c = 1; c < sz.width - 1; c++) 104 | { 105 | lfV = MAX(*pFeat, ubPrev) - MIN(*pFeat, lbPrev);//(*pFeat >= lbPrev && *pFeat <= ubPrev) ? mapPrev : mapPrev + abs((float)(*pFeat) - featPrev); 106 | upV = MAX(*pFeat, *pUBup) - MIN(*pFeat, *pLBup);//(*pFeat >= *pLBup && *pFeat <= *pUBup) ? *pMapup : *pMapup + abs((float)(*pFeat) - (float)(*pFeatup)); 107 | 108 | flag = 0; 109 | if (lfV < *pMap) 110 | { 111 | *pMap = lfV; 112 | flag = 1; 113 | } 114 | if (upV < *pMap) 115 | { 116 | *pMap = upV; 117 | flag = 2; 118 | } 119 | 120 | switch (flag) 121 | { 122 | case 0: // no update 123 | break; 124 | case 1: // update from left 125 | *pLB = MIN(*pFeat, lbPrev); 126 | *pUB = MAX(*pFeat, ubPrev); 127 | break; 128 | case 2: // update from up 129 | *pLB = MIN(*pFeat, *pLBup); 130 | *pUB = MAX(*pFeat, *pUBup); 131 | break; 132 | default: 133 | break; 134 | } 135 | 136 | mapPrev = *pMap; 137 | pMap++; pMapup++; 138 | featPrev = *pFeat; 139 | pFeat++; pFeatup++; 140 | lbPrev = *pLB; 141 | pLB++; pLBup++; 142 | ubPrev = *pUB; 143 | pUB++; pUBup++; 144 | } 145 | pMapup += 2; pMap += 2; 146 | pFeat += 2; pFeatup += 2; 147 | pLBup += 2; pLB += 2; 148 | pUBup += 2; pUB += 2; 149 | } 150 | } 151 | 152 | void invRasterScan(const Mat& featMap, Mat& map, Mat& lb, Mat& ub) 153 | { 154 | Size sz = featMap.size(); 155 | int datalen = sz.width*sz.height; 156 | float *pMapdn = (float*)map.data + datalen - 2; 157 | float *pMap = pMapdn - sz.width; 158 | uchar *pFeatdn = featMap.data + datalen - 2; 159 | uchar *pFeat = pFeatdn - sz.width; 160 | uchar *pLBdn = lb.data + datalen - 2; 161 | uchar *pLB = pLBdn - sz.width; 162 | uchar *pUBdn = ub.data + datalen - 2; 163 | uchar *pUB = pUBdn - sz.width; 164 | 165 | float mapPrev; 166 | float featPrev; 167 | uchar lbPrev, ubPrev; 168 | 169 | float rtV, dnV; 170 | int flag; 171 | for (int r = 1; r < sz.height - 1; r++) 172 | { 173 | mapPrev = *(pMap + 1); 174 | featPrev = *(pFeat + 1); 175 | lbPrev = *(pLB + 1); 176 | ubPrev = *(pUB + 1); 177 | 178 | for (int c = 1; c < sz.width - 1; c++) 179 | { 180 | rtV = MAX(*pFeat, ubPrev) - MIN(*pFeat, lbPrev);//(*pFeat >= lbPrev && *pFeat <= ubPrev) ? mapPrev : mapPrev + abs((float)(*pFeat) - featPrev); 181 | dnV = MAX(*pFeat, *pUBdn) - MIN(*pFeat, *pLBdn);//(*pFeat >= *pLBdn && *pFeat <= *pUBdn) ? *pMapdn : *pMapdn + abs((float)(*pFeat) - (float)(*pFeatdn)); 182 | 183 | flag = 0; 184 | if (rtV < *pMap) 185 | { 186 | *pMap = rtV; 187 | flag = 1; 188 | } 189 | if (dnV < *pMap) 190 | { 191 | *pMap = dnV; 192 | flag = 2; 193 | } 194 | 195 | switch (flag) 196 | { 197 | case 0: // no update 198 | break; 199 | case 1: // update from right 200 | *pLB = MIN(*pFeat, lbPrev); 201 | *pUB = MAX(*pFeat, ubPrev); 202 | break; 203 | case 2: // update from down 204 | *pLB = MIN(*pFeat, *pLBdn); 205 | *pUB = MAX(*pFeat, *pUBdn); 206 | break; 207 | default: 208 | break; 209 | } 210 | 211 | mapPrev = *pMap; 212 | pMap--; pMapdn--; 213 | featPrev = *pFeat; 214 | pFeat--; pFeatdn--; 215 | lbPrev = *pLB; 216 | pLB--; pLBdn--; 217 | ubPrev = *pUB; 218 | pUB--; pUBdn--; 219 | } 220 | 221 | 222 | pMapdn -= 2; pMap -= 2; 223 | pFeatdn -= 2; pFeat -= 2; 224 | pLBdn -= 2; pLB -= 2; 225 | pUBdn -= 2; pUB -= 2; 226 | } 227 | } 228 | 229 | cv::Mat fastMBS(const std::vector featureMaps) 230 | { 231 | assert(featureMaps[0].type() == CV_8UC1); 232 | 233 | Size sz = featureMaps[0].size(); 234 | Mat ret = Mat::zeros(sz, CV_32FC1); 235 | if (sz.width < 3 || sz.height < 3) 236 | return ret; 237 | 238 | for (int i = 0; i < featureMaps.size(); i++) 239 | { 240 | Mat map = Mat::zeros(sz, CV_32FC1); 241 | Mat mapROI(map, Rect(1, 1, sz.width - 2, sz.height - 2)); 242 | mapROI.setTo(Scalar(100000)); 243 | Mat lb = featureMaps[i].clone(); 244 | Mat ub = featureMaps[i].clone(); 245 | 246 | rasterScan(featureMaps[i], map, lb, ub); 247 | invRasterScan(featureMaps[i], map, lb, ub); 248 | rasterScan(featureMaps[i], map, lb, ub); 249 | 250 | ret += map; 251 | } 252 | 253 | return ret; 254 | 255 | } 256 | 257 | float getThreshForGeo(const Mat& src) 258 | { 259 | float ret; 260 | Size sz = src.size(); 261 | 262 | uchar *pFeatup = src.data + 1; 263 | uchar *pFeat = pFeatup + sz.width; 264 | uchar *pfeatdn = pFeat + sz.width; 265 | 266 | float featPrev; 267 | 268 | for (int r = 1; r < sz.height - 1; r++) 269 | { 270 | featPrev = *(pFeat - 1); 271 | 272 | for (int c = 1; c < sz.width - 1; c++) 273 | { 274 | float temp = MIN(abs(*pFeat-featPrev),abs(*pFeat-*(pFeat+1))); 275 | temp = MIN(temp,abs(*pFeat-*pFeatup)); 276 | temp = MIN(temp,abs(*pFeat-*pfeatdn)); 277 | ret += temp; 278 | 279 | featPrev = *pFeat; 280 | pFeat++; pFeatup++; pfeatdn++; 281 | } 282 | pFeat += 2; pFeatup += 2; pfeatdn += 2; 283 | } 284 | return ret / ((sz.width - 2)*(sz.height - 2)); 285 | } 286 | 287 | void rasterScanGeo(const Mat& featMap, Mat& map, float thresh) 288 | { 289 | Size sz = featMap.size(); 290 | float *pMapup = (float*)map.data + 1; 291 | float *pMap = pMapup + sz.width; 292 | uchar *pFeatup = featMap.data + 1; 293 | uchar *pFeat = pFeatup + sz.width; 294 | 295 | float mapPrev; 296 | float featPrev; 297 | 298 | float lfV, upV; 299 | int flag; 300 | for (int r = 1; r < sz.height - 1; r++) 301 | { 302 | mapPrev = *(pMap - 1); 303 | featPrev = *(pFeat - 1); 304 | 305 | 306 | for (int c = 1; c < sz.width - 1; c++) 307 | { 308 | lfV = (abs(featPrev - *pFeat)>thresh ? abs(featPrev - *pFeat):0.0f) + mapPrev; 309 | upV = (abs(*pFeatup - *pFeat)>thresh ? abs(*pFeatup - *pFeat):0.0f) + *pMapup; 310 | 311 | if (lfV < *pMap) 312 | { 313 | *pMap = lfV; 314 | } 315 | if (upV < *pMap) 316 | { 317 | *pMap = upV; 318 | } 319 | 320 | mapPrev = *pMap; 321 | pMap++; pMapup++; 322 | featPrev = *pFeat; 323 | pFeat++; pFeatup++; 324 | } 325 | pMapup += 2; pMap += 2; 326 | pFeat += 2; pFeatup += 2; 327 | } 328 | } 329 | 330 | void invRasterScanGeo(const Mat& featMap, Mat& map, float thresh) 331 | { 332 | Size sz = featMap.size(); 333 | int datalen = sz.width*sz.height; 334 | float *pMapdn = (float*)map.data + datalen - 2; 335 | float *pMap = pMapdn - sz.width; 336 | uchar *pFeatdn = featMap.data + datalen - 2; 337 | uchar *pFeat = pFeatdn - sz.width; 338 | 339 | float mapPrev; 340 | float featPrev; 341 | 342 | float rtV, dnV; 343 | int flag; 344 | for (int r = 1; r < sz.height - 1; r++) 345 | { 346 | mapPrev = *(pMap + 1); 347 | featPrev = *(pFeat + 1); 348 | 349 | for (int c = 1; c < sz.width - 1; c++) 350 | { 351 | rtV = (abs(featPrev - *pFeat)>thresh ? abs(featPrev - *pFeat):0.0f) + mapPrev; 352 | dnV = (abs(*pFeatdn - *pFeat)>thresh ? abs(*pFeatdn - *pFeat):0.0f) + *pMapdn; 353 | 354 | if (rtV < *pMap) 355 | { 356 | *pMap = rtV; 357 | } 358 | if (dnV < *pMap) 359 | { 360 | *pMap = dnV; 361 | } 362 | 363 | mapPrev = *pMap; 364 | pMap--; pMapdn--; 365 | featPrev = *pFeat; 366 | pFeat--; pFeatdn--; 367 | } 368 | 369 | 370 | pMapdn -= 2; pMap -= 2; 371 | pFeatdn -= 2; pFeat -= 2; 372 | } 373 | } 374 | 375 | cv::Mat fastGeodesic(const std::vector featureMaps) 376 | { 377 | assert(featureMaps[0].type() == CV_8UC1); 378 | 379 | Size sz = featureMaps[0].size(); 380 | Mat ret = Mat::zeros(sz, CV_32FC1); 381 | if (sz.width < 3 || sz.height < 3) 382 | return ret; 383 | 384 | 385 | for (int i = 0; i < featureMaps.size(); i++) 386 | { 387 | // determines the threshold for clipping 388 | float thresh = getThreshForGeo(featureMaps[i]); 389 | //cout << thresh << endl; 390 | Mat map = Mat::zeros(sz, CV_32FC1); 391 | Mat mapROI(map, Rect(1, 1, sz.width - 2, sz.height - 2)); 392 | mapROI.setTo(Scalar(1000000000)); 393 | 394 | rasterScanGeo(featureMaps[i], map, thresh); 395 | invRasterScanGeo(featureMaps[i], map, thresh); 396 | rasterScanGeo(featureMaps[i], map, thresh); 397 | 398 | ret += map; 399 | } 400 | 401 | return ret; 402 | 403 | } 404 | 405 | int findFrameMargin(const Mat& img, bool reverse) 406 | { 407 | Mat edgeMap, edgeMapDil, edgeMask; 408 | Sobel(img, edgeMap, CV_16SC1, 0, 1); 409 | edgeMap = abs(edgeMap); 410 | edgeMap.convertTo(edgeMap, CV_8UC1); 411 | edgeMask = edgeMap < (SOBEL_THRESH * 255.0); 412 | dilate(edgeMap, edgeMapDil, Mat(), Point(-1, -1), 2); 413 | edgeMap = edgeMap == edgeMapDil; 414 | edgeMap.setTo(Scalar(0.0), edgeMask); 415 | 416 | 417 | if (!reverse) 418 | { 419 | for (int i = edgeMap.rows - 1; i >= 0; i--) 420 | if (mean(edgeMap.row(i))[0] > 0.6*255.0) 421 | return i + 1; 422 | } 423 | else 424 | { 425 | for (int i = 0; i < edgeMap.rows; i++) 426 | if (mean(edgeMap.row(i))[0] > 0.6*255.0) 427 | return edgeMap.rows - i; 428 | } 429 | 430 | return 0; 431 | } 432 | 433 | bool removeFrame(const cv::Mat& inImg, cv::Mat& outImg, cv::Rect &roi) 434 | { 435 | if (inImg.rows < 2 * (FRAME_MAX + 3) || inImg.cols < 2 * (FRAME_MAX + 3)) 436 | { 437 | roi = Rect(0, 0, inImg.cols, inImg.rows); 438 | outImg = inImg; 439 | return false; 440 | } 441 | 442 | Mat imgGray; 443 | cvtColor(inImg, imgGray, CV_RGB2GRAY); 444 | 445 | int up, dn, lf, rt; 446 | 447 | up = findFrameMargin(imgGray.rowRange(0, FRAME_MAX), false); 448 | dn = findFrameMargin(imgGray.rowRange(imgGray.rows - FRAME_MAX, imgGray.rows), true); 449 | lf = findFrameMargin(imgGray.colRange(0, FRAME_MAX).t(), false); 450 | rt = findFrameMargin(imgGray.colRange(imgGray.cols - FRAME_MAX, imgGray.cols).t(), true); 451 | 452 | int margin = MAX(up, MAX(dn, MAX(lf, rt))); 453 | if ( margin == 0 ) 454 | { 455 | roi = Rect(0, 0, imgGray.cols, imgGray.rows); 456 | outImg = inImg; 457 | return false; 458 | } 459 | 460 | int count = 0; 461 | count = up == 0 ? count : count + 1; 462 | count = dn == 0 ? count : count + 1; 463 | count = lf == 0 ? count : count + 1; 464 | count = rt == 0 ? count : count + 1; 465 | 466 | // cut four border region if at least 2 border frames are detected 467 | if (count > 1) 468 | { 469 | margin += 2; 470 | roi = Rect(margin, margin, inImg.cols - 2*margin, inImg.rows - 2*margin); 471 | outImg = Mat(inImg, roi); 472 | 473 | return true; 474 | } 475 | 476 | // otherwise, cut only one border 477 | up = up == 0 ? up : up + 2; 478 | dn = dn == 0 ? dn : dn + 2; 479 | lf = lf == 0 ? lf : lf + 2; 480 | rt = rt == 0 ? rt : rt + 2; 481 | 482 | 483 | roi = Rect(lf, up, inImg.cols - lf - rt, inImg.rows - up - dn); 484 | outImg = Mat(inImg, roi); 485 | 486 | return true; 487 | 488 | } 489 | 490 | Mat doWork( 491 | const Mat& src, 492 | bool use_lab, 493 | bool remove_border, 494 | bool use_geodesic 495 | ) 496 | { 497 | Mat src_small; 498 | float w = (float)src.cols, h = (float)src.rows; 499 | float maxD = max(w,h); 500 | resize(src,src_small,Size((int)(MAX_IMG_DIM*w/maxD),(int)(MAX_IMG_DIM*h/maxD)),0.0,0.0,INTER_AREA);// standard: width: 300 pixel 501 | Mat srcRoi; 502 | Rect roi; 503 | // detect and remove the artifical frame of the image 504 | if (remove_border) 505 | removeFrame(src_small, srcRoi, roi); 506 | else 507 | { 508 | srcRoi = src_small; 509 | roi = Rect(0, 0, src_small.cols, src_small.rows); 510 | } 511 | 512 | 513 | if (use_lab) 514 | cvtColor(srcRoi, srcRoi, CV_RGB2Lab); 515 | 516 | /* Computing saliency */ 517 | MBS mbs(srcRoi); 518 | mbs.computeSaliency(use_geodesic); 519 | 520 | Mat resultRoi=mbs.getSaliencyMap(); 521 | Mat result = Mat::zeros(src_small.size(), CV_32FC1); 522 | 523 | normalize(resultRoi, Mat(result, roi), 0.0, 1.0, NORM_MINMAX); 524 | 525 | resize(result, result, src.size()); 526 | return result; 527 | } 528 | 529 | 530 | /** 531 | * Main entry called from Matlab 532 | * @param nlhs number of left-hand-side arguments 533 | * @param plhs pointers to mxArrays in the left-hand-side 534 | * @param nrhs number of right-hand-side arguments 535 | * @param prhs pointers to mxArrays in the right-hand-side 536 | */ 537 | void mexFunction( int nlhs, mxArray *plhs[], 538 | int nrhs, const mxArray *prhs[] ) 539 | { 540 | // Check the number of arguments 541 | if (nrhs<1 || nrhs > 4 || nlhs>1) 542 | mexErrMsgIdAndTxt("mexopencv:error","Wrong number of arguments"); 543 | 544 | // Decide second arguments 545 | bool use_geodesic = false; 546 | bool use_lab = true; 547 | bool remove_border = true; 548 | if (nrhs >= 2) 549 | use_lab = mxIsLogicalScalarTrue(prhs[1]); 550 | if (nrhs >= 3) 551 | remove_border = mxIsLogicalScalarTrue(prhs[2]); 552 | if (nrhs >= 4) 553 | use_geodesic = mxIsLogicalScalarTrue(prhs[3]); 554 | 555 | // Apply 556 | Mat src(((MxArray)prhs[0]).toMat()), dst; 557 | dst = doWork(src, use_lab, remove_border, use_geodesic); 558 | plhs[0] = MxArray(dst); 559 | } 560 | -------------------------------------------------------------------------------- /MBS/mex/MBS.hpp: -------------------------------------------------------------------------------- 1 | /***************************************************************************** 2 | * Implemetation of the saliency detction method described in paper 3 | * "Minimum Barrier Salient Object Detection at 80 FPS", Jianming Zhang, 4 | * Stan Sclaroff, Zhe Lin, Xiaohui Shen, Brian Price, Radomir Mech, ICCV, 5 | * 2015 6 | * 7 | * Copyright (C) 2015 Jianming Zhang 8 | * 9 | * This program is free software: you can redistribute it and/or modify 10 | * it under the terms of the GNU General Public License as published by 11 | * the Free Software Foundation, either version 3 of the License, or 12 | * (at your option) any later version. 13 | * 14 | * This program is distributed in the hope that it will be useful, 15 | * but WITHOUT ANY WARRANTY; without even the implied warranty of 16 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 17 | * GNU General Public License for more details. 18 | * 19 | * You should have received a copy of the GNU General Public License 20 | * along with this program. If not, see . 21 | * 22 | * If you have problems about this software, please contact: 23 | * jimmie33@gmail.com 24 | *******************************************************************************/ 25 | 26 | #ifndef BMS_H 27 | #define BMS_H 28 | 29 | #ifdef IMDEBUG 30 | #include 31 | #endif 32 | #include 33 | #include 34 | #include 35 | 36 | 37 | static cv::RNG MBS_RNG; 38 | 39 | class MBS 40 | { 41 | public: 42 | MBS (const cv::Mat& src); 43 | cv::Mat getSaliencyMap(); 44 | void computeSaliency(bool use_geodesic = false); 45 | cv::Mat getMBSMap() const { return mMBSMap; } 46 | private: 47 | cv::Mat mSaliencyMap; 48 | cv::Mat mMBSMap; 49 | int mAttMapCount; 50 | cv::Mat mBorderPriorMap; 51 | cv::Mat mSrc; 52 | std::vector mFeatureMaps; 53 | void whitenFeatMap(float reg); 54 | void computeBorderPriorMap(float reg, float marginRatio); 55 | }; 56 | 57 | cv::Mat computeCWS(const cv::Mat src, float reg, float marginRatio); 58 | cv::Mat fastMBS(const std::vector featureMaps); 59 | cv::Mat fastGeodesic(const std::vector featureMaps); 60 | 61 | int findFrameMargin(const cv::Mat& img, bool reverse); 62 | bool removeFrame(const cv::Mat& inImg, cv::Mat& outImg, cv::Rect &roi); 63 | 64 | #endif -------------------------------------------------------------------------------- /MBS/mex/MxArray.cpp: -------------------------------------------------------------------------------- 1 | /** Implemenation of MxArray. 2 | * @file MxArray.cpp 3 | * @author Kota Yamaguchi 4 | * @date 2012 5 | */ 6 | #include "MxArray.hpp" 7 | 8 | namespace { // namespace 9 | 10 | /** Field names for cv::Moments. 11 | */ 12 | const char *cv_moments_fields[10] = {"m00", "m10", "m01", "m20", "m11", "m02", 13 | "m30", "m21", "m12", "m03"}; 14 | /** Field names for cv::RotatedRect. 15 | */ 16 | const char *cv_rotated_rect_fields[3] = {"center", "size", "angle"}; 17 | /** Field names for cv::TermCriteria. 18 | */ 19 | const char *cv_term_criteria_fields[3] = {"type", "maxCount", "epsilon"}; 20 | /** Field names for cv::Keypoint. 21 | */ 22 | const char *cv_keypoint_fields[6] = {"pt", "size", "angle", "response", 23 | "octave", "class_id"}; 24 | /** Field names for cv::DMatch. 25 | */ 26 | const char *cv_dmatch_fields[4] = {"queryIdx", "trainIdx", "imgIdx", 27 | "distance"}; 28 | 29 | /** Translates data type definition used in OpenCV to that of Matlab. 30 | * @param classid data type of matlab's mxArray. e.g., mxDOUBLE_CLASS. 31 | * @return opencv's data type. e.g., CV_8U. 32 | */ 33 | const ConstMap DepthOf = ConstMap 34 | (mxDOUBLE_CLASS, CV_64F) 35 | (mxSINGLE_CLASS, CV_32F) 36 | (mxINT8_CLASS, CV_8S) 37 | (mxUINT8_CLASS, CV_8U) 38 | (mxINT16_CLASS, CV_16S) 39 | (mxUINT16_CLASS, CV_16U) 40 | (mxINT32_CLASS, CV_32S) 41 | (mxUINT32_CLASS, CV_32S) 42 | (mxLOGICAL_CLASS, CV_8U); 43 | 44 | /** Translates data type definition used in Matlab to that of OpenCV. 45 | * @param depth data depth of opencv's Mat class. e.g., CV_32F. 46 | * @return data type of matlab's mxArray. e.g., mxDOUBLE_CLASS. 47 | */ 48 | const ConstMap ClassIDOf = ConstMap 49 | (CV_64F, mxDOUBLE_CLASS) 50 | (CV_32F, mxSINGLE_CLASS) 51 | (CV_8S, mxINT8_CLASS) 52 | (CV_8U, mxUINT8_CLASS) 53 | (CV_16S, mxINT16_CLASS) 54 | (CV_16U, mxUINT16_CLASS) 55 | (CV_32S, mxINT32_CLASS); 56 | 57 | /** Comparison operator for sparse matrix elements. 58 | */ 59 | struct CompareSparseMatNode { 60 | bool operator () (const cv::SparseMat::Node* rhs, 61 | const cv::SparseMat::Node* lhs) 62 | { 63 | if (rhs->idx[1] < lhs->idx[1]) 64 | return true; 65 | if (rhs->idx[1] == lhs->idx[1] && rhs->idx[0] < lhs->idx[0]) 66 | return true; 67 | return false; 68 | } 69 | }; 70 | 71 | /** InvTermCritType map for option processing. 72 | */ 73 | const ConstMap InvTermCritType = ConstMap 74 | (cv::TermCriteria::COUNT, "Count") 75 | (cv::TermCriteria::EPS, "EPS") 76 | (cv::TermCriteria::COUNT+cv::TermCriteria::EPS, "Count+EPS"); 77 | 78 | /** TermCritType map for option processing. 79 | */ 80 | const ConstMap TermCritType = ConstMap 81 | ("Count", cv::TermCriteria::COUNT) 82 | ("EPS", cv::TermCriteria::EPS) 83 | ("Count+EPS", cv::TermCriteria::COUNT+cv::TermCriteria::EPS); 84 | 85 | } // namespace 86 | 87 | MxArray& MxArray::operator=(const MxArray& rhs) 88 | { 89 | if (this != &rhs) 90 | this->p_ = rhs.p_; 91 | return *this; 92 | } 93 | 94 | MxArray::MxArray(const int i) 95 | : p_(mxCreateDoubleScalar(static_cast(i))) 96 | { 97 | if (!p_) 98 | mexErrMsgIdAndTxt("mexopencv:error", "Allocation error"); 99 | } 100 | 101 | MxArray::MxArray(const double d) : p_(mxCreateDoubleScalar(d)) 102 | { 103 | if (!p_) 104 | mexErrMsgIdAndTxt("mexopencv:error", "Allocation error"); 105 | } 106 | 107 | MxArray::MxArray(const bool b) : p_(mxCreateLogicalScalar(b)) 108 | { 109 | if (!p_) 110 | mexErrMsgIdAndTxt("mexopencv:error", "Allocation error"); 111 | } 112 | 113 | MxArray::MxArray(const std::string& s) : p_(mxCreateString(s.c_str())) 114 | { 115 | if (!p_) 116 | mexErrMsgIdAndTxt("mexopencv:error", "Allocation error"); 117 | } 118 | 119 | MxArray::MxArray(const cv::Mat& mat, mxClassID classid, bool transpose) 120 | { 121 | if (mat.empty()) 122 | { 123 | p_ = mxCreateNumericArray(0, 0, mxDOUBLE_CLASS, mxREAL); 124 | if (!p_) 125 | mexErrMsgIdAndTxt("mexopencv:error", "Allocation error"); 126 | return; 127 | } 128 | cv::Mat input = (mat.dims == 2 && transpose) ? mat.t() : mat; 129 | // Create a new mxArray. 130 | const int nchannels = input.channels(); 131 | const int* dims_ = input.size; 132 | std::vector d(dims_, dims_ + input.dims); 133 | d.push_back(nchannels); 134 | classid = (classid == mxUNKNOWN_CLASS) 135 | ? ClassIDOf[input.depth()] : classid; 136 | std::swap(d[0], d[1]); 137 | if (classid == mxLOGICAL_CLASS) 138 | { 139 | // OpenCV's logical true is any nonzero while matlab's true is 1. 140 | cv::compare(input, 0, input, cv::CMP_NE); 141 | input.setTo(1, input); 142 | p_ = mxCreateLogicalArray(d.size(), &d[0]); 143 | } 144 | else { 145 | p_ = mxCreateNumericArray(d.size(), &d[0], classid, mxREAL); 146 | } 147 | if (!p_) 148 | mexErrMsgIdAndTxt("mexopencv:error", "Allocation error"); 149 | // Copy each channel. 150 | std::vector channels; 151 | split(input, channels); 152 | std::vector si(d.size(), 0); // subscript index. 153 | int type = CV_MAKETYPE(DepthOf[classid], 1); // destination type. 154 | for (int i = 0; i < nchannels; ++i) 155 | { 156 | si[si.size() - 1] = i; // last dim is a channel index. 157 | void *ptr = reinterpret_cast( 158 | reinterpret_cast(mxGetData(p_)) + 159 | mxGetElementSize(p_) * subs(si)); 160 | cv::Mat m(input.dims, dims_, type, ptr); 161 | channels[i].convertTo(m, type); // Write to mxArray through m. 162 | } 163 | } 164 | 165 | MxArray::MxArray(const cv::SparseMat& mat) 166 | { 167 | if (mat.dims() != 2) 168 | mexErrMsgIdAndTxt("mexopencv:error", "cv::Mat is not 2D"); 169 | if (mat.type() != CV_32FC1) 170 | mexErrMsgIdAndTxt("mexopencv:error", "cv::Mat is not float"); 171 | // Create a sparse array. 172 | int m = mat.size(0), n = mat.size(1), nnz = mat.nzcount(); 173 | p_ = mxCreateSparse(m, n, nnz, mxREAL); 174 | if (!p_) 175 | mexErrMsgIdAndTxt("mexopencv:error", "Allocation error"); 176 | mwIndex *ir = mxGetIr(p_); 177 | mwIndex *jc = mxGetJc(p_); 178 | if (ir == NULL || jc == NULL) 179 | mexErrMsgIdAndTxt("mexopencv:error", "Unknown error"); 180 | // Sort nodes before we put elems into mxArray. 181 | std::vector nodes; 182 | nodes.reserve(nnz); 183 | for (cv::SparseMatConstIterator it = mat.begin(); it != mat.end(); ++it) 184 | nodes.push_back(it.node()); 185 | std::sort(nodes.begin(), nodes.end(), CompareSparseMatNode()); 186 | // Copy data. 187 | double *pr = mxGetPr(p_); 188 | int i = 0; 189 | jc[0] = 0; 190 | for (std::vector::const_iterator 191 | it = nodes.begin(); it != nodes.end(); ++it) 192 | { 193 | mwIndex row = (*it)->idx[0], col = (*it)->idx[1]; 194 | ir[i] = row; 195 | jc[col+1] = i+1; 196 | pr[i] = static_cast(mat.value(*it)); 197 | ++i; 198 | } 199 | } 200 | 201 | MxArray::MxArray(const cv::Moments& m) : 202 | p_(mxCreateStructMatrix(1, 1, 10, cv_moments_fields)) 203 | { 204 | if (!p_) 205 | mexErrMsgIdAndTxt("mexopencv:error", "Allocation error"); 206 | set("m00", m.m00); 207 | set("m10", m.m10); 208 | set("m01", m.m01); 209 | set("m20", m.m20); 210 | set("m11", m.m11); 211 | set("m02", m.m02); 212 | set("m30", m.m30); 213 | set("m12", m.m12); 214 | set("m21", m.m21); 215 | set("m03", m.m03); 216 | } 217 | 218 | MxArray::MxArray(const cv::KeyPoint& p) : 219 | p_(mxCreateStructMatrix(1, 1, 6, cv_keypoint_fields)) 220 | { 221 | if (!p_) 222 | mexErrMsgIdAndTxt("mexopencv:error", "Allocation error"); 223 | set("pt", p.pt); 224 | set("size", p.size); 225 | set("angle", p.angle); 226 | set("response", p.response); 227 | set("octave", p.octave); 228 | set("class_id", p.class_id); 229 | } 230 | 231 | MxArray::MxArray(const std::vector& v) : 232 | p_(mxCreateStructMatrix(1, v.size(), 6, cv_keypoint_fields)) 233 | { 234 | if (!p_) 235 | mexErrMsgIdAndTxt("mexopencv:error", "Allocation error"); 236 | for (size_t i = 0; i < v.size(); ++i) 237 | { 238 | set("pt", v[i].pt, i); 239 | set("size", v[i].size, i); 240 | set("angle", v[i].angle, i); 241 | set("response", v[i].response, i); 242 | set("octave", v[i].octave, i); 243 | set("class_id", v[i].class_id, i); 244 | } 245 | } 246 | 247 | MxArray::MxArray(const cv::DMatch& m) : 248 | p_(mxCreateStructMatrix(1, 1, 4, cv_keypoint_fields)) 249 | { 250 | if (!p_) 251 | mexErrMsgIdAndTxt("mexopencv:error", "Allocation error"); 252 | set("queryIdx", m.queryIdx); 253 | set("trainIdx", m.trainIdx); 254 | set("imgIdx", m.imgIdx); 255 | set("distance", m.distance); 256 | } 257 | 258 | MxArray::MxArray(const std::vector& v) : 259 | p_(mxCreateStructMatrix(1, v.size(), 4, cv_dmatch_fields)) 260 | { 261 | if (!p_) 262 | mexErrMsgIdAndTxt("mexopencv:error", "Allocation error"); 263 | for (size_t i = 0; i < v.size(); ++i) 264 | { 265 | set("queryIdx", v[i].queryIdx, i); 266 | set("trainIdx", v[i].trainIdx, i); 267 | set("imgIdx", v[i].imgIdx, i); 268 | set("distance", v[i].distance, i); 269 | } 270 | } 271 | 272 | MxArray::MxArray(const cv::RotatedRect& m) : 273 | p_(mxCreateStructMatrix(1, 1, 3, cv_rotated_rect_fields)) 274 | { 275 | if (!p_) 276 | mexErrMsgIdAndTxt("mexopencv:error", "Allocation error"); 277 | set("center", m.center); 278 | set("size", m.size); 279 | set("angle", m.angle); 280 | } 281 | 282 | MxArray::MxArray(const cv::TermCriteria& t) : 283 | p_(mxCreateStructMatrix(1, 1, 3, cv_term_criteria_fields)) 284 | { 285 | if (!p_) 286 | mexErrMsgIdAndTxt("mexopencv:error", "Allocation error"); 287 | set("type", InvTermCritType[t.type]); 288 | set("maxCount", t.maxCount); 289 | set("epsilon", t.epsilon); 290 | } 291 | 292 | MxArray::MxArray(const char** fields, int nfields, int m, int n) : 293 | p_(mxCreateStructMatrix(m, n, nfields, fields)) 294 | { 295 | if (!p_) 296 | mexErrMsgIdAndTxt("mexopencv:error", "Allocation error"); 297 | } 298 | 299 | int MxArray::toInt() const { 300 | if (numel() != 1) 301 | mexErrMsgIdAndTxt("mexopencv:error", "MxArray is not a scalar"); 302 | return at(0); 303 | } 304 | 305 | double MxArray::toDouble() const { 306 | if (numel() != 1) 307 | mexErrMsgIdAndTxt("mexopencv:error", "MxArray is not a scalar"); 308 | return at(0); 309 | } 310 | 311 | bool MxArray::toBool() const { 312 | if (numel() != 1) 313 | mexErrMsgIdAndTxt("mexopencv:error", "MxArray is not a scalar"); 314 | return at(0); 315 | } 316 | 317 | std::string MxArray::toString() const 318 | { 319 | if (!isChar()) 320 | mexErrMsgIdAndTxt("mexopencv:error", "MxArray not of type char"); 321 | char *pc = mxArrayToString(p_); 322 | std::string s(pc); 323 | mxFree(pc); 324 | return s; 325 | } 326 | 327 | cv::Mat MxArray::toMat(int depth, bool transpose) const 328 | { 329 | // Create cv::Mat object. 330 | std::vector d(dims(), dims()+ndims()); 331 | int ndims = (d.size()>2) ? d.size()-1 : d.size(); 332 | int nchannels = (d.size()>2) ? *(d.end()-1) : 1; 333 | depth = (depth==CV_USRTYPE1) ? DepthOf[classID()] : depth; 334 | std::swap(d[0], d[1]); 335 | cv::Mat mat(ndims, &d[0], CV_MAKETYPE(depth, nchannels)); 336 | // Copy each channel. 337 | std::vector channels(nchannels); 338 | std::vector si(d.size(), 0); // subscript index 339 | int type = CV_MAKETYPE(DepthOf[classID()], 1); // Source type 340 | for (int i = 0; i( 344 | reinterpret_cast(mxGetData(p_))+ 345 | mxGetElementSize(p_)*subs(si)); 346 | cv::Mat m(ndims, &d[0], type, pd); 347 | // Read from mxArray through m 348 | m.convertTo(channels[i], CV_MAKETYPE(depth, 1)); 349 | } 350 | cv::merge(channels, mat); 351 | return (mat.dims==2 && transpose) ? cv::Mat(mat.t()) : mat; 352 | } 353 | 354 | cv::MatND MxArray::toMatND(int depth, bool transpose) const 355 | { 356 | // Create cv::Mat object. 357 | std::vector d(dims(), dims()+ndims()); 358 | std::swap(d[0], d[1]); 359 | cv::MatND m(ndims(), &d[0], CV_MAKETYPE(DepthOf[classID()], 1), 360 | mxGetData(p_)); 361 | // Copy. 362 | cv::MatND mat; 363 | depth = (depth==CV_USRTYPE1) ? CV_MAKETYPE(DepthOf[classID()], 1) : depth; 364 | m.convertTo(mat, CV_MAKETYPE(depth, 1)); 365 | return (mat.dims==2 && transpose) ? cv::Mat(mat.t()) : mat; 366 | } 367 | 368 | cv::SparseMat MxArray::toSparseMat() const 369 | { 370 | // Check if it's sparse. 371 | if (!isSparse() || !isDouble()) 372 | mexErrMsgIdAndTxt("mexopencv:error", "MxArray is not sparse"); 373 | mwIndex *ir = mxGetIr(p_); 374 | mwIndex *jc = mxGetJc(p_); 375 | if (ir == NULL || jc == NULL) 376 | mexErrMsgIdAndTxt("mexopencv:error", "Unknown error"); 377 | // Create cv::SparseMat. 378 | int m = mxGetM(p_), n = mxGetN(p_); 379 | int dims[] = {m, n}; 380 | cv::SparseMat mat(2, dims, CV_32F); 381 | // Copy data. 382 | double *pr = mxGetPr(p_); 383 | for (int j=0; j(ir[i], j) = static_cast(pr[i]); 389 | } 390 | return mat; 391 | } 392 | 393 | cv::Moments MxArray::toMoments(mwIndex index) const 394 | { 395 | return cv::Moments( 396 | (isField("m00")) ? at("m00", index).toDouble() : 0, 397 | (isField("m10")) ? at("m10", index).toDouble() : 0, 398 | (isField("m01")) ? at("m01", index).toDouble() : 0, 399 | (isField("m20")) ? at("m20", index).toDouble() : 0, 400 | (isField("m11")) ? at("m11", index).toDouble() : 0, 401 | (isField("m02")) ? at("m02", index).toDouble() : 0, 402 | (isField("m30")) ? at("m30", index).toDouble() : 0, 403 | (isField("m12")) ? at("m12", index).toDouble() : 0, 404 | (isField("m21")) ? at("m21", index).toDouble() : 0, 405 | (isField("m03")) ? at("m03", index).toDouble() : 0 406 | ); 407 | } 408 | 409 | cv::KeyPoint MxArray::toKeyPoint(mwIndex index) const 410 | { 411 | return cv::KeyPoint( 412 | at("pt", index).toPoint2f(), 413 | at("size", index).toDouble(), 414 | (isField("angle")) ? at("angle", index).toDouble() : -1, 415 | (isField("response")) ? at("response", index).toDouble() : 0, 416 | (isField("octave")) ? at("octave", index).toInt() : 0, 417 | (isField("class_id")) ? at("class_id", index).toInt() : -1 418 | ); 419 | } 420 | 421 | cv::DMatch MxArray::toDMatch(mwIndex index) const 422 | { 423 | return cv::DMatch( 424 | (isField("queryIdx")) ? at("queryIdx", index).toInt() : 0, 425 | (isField("trainIdx")) ? at("trainIdx", index).toInt() : 0, 426 | (isField("imgIdx")) ? at("imgIdx", index).toInt() : 0, 427 | (isField("distance")) ? at("distance", index).toDouble() : 0 428 | ); 429 | } 430 | 431 | cv::Range MxArray::toRange() const 432 | { 433 | cv::Range r; 434 | if (isNumeric() && numel()==2) 435 | r = cv::Range(at(0), at(1)); 436 | else if (isChar() && toString()==":") 437 | r = cv::Range::all(); 438 | else 439 | mexErrMsgIdAndTxt("mexopencv:error", "Invalid range value"); 440 | return r; 441 | } 442 | 443 | cv::TermCriteria MxArray::toTermCriteria(mwIndex index) const 444 | { 445 | MxArray _type(at("type", index)); 446 | return cv::TermCriteria( 447 | (_type.isChar()) ? TermCritType[_type.toString()] : _type.toInt(), 448 | at("maxCount", index).toInt(), 449 | at("epsilon", index).toDouble() 450 | ); 451 | } 452 | 453 | std::string MxArray::fieldname(int index) const 454 | { 455 | const char *f = mxGetFieldNameByNumber(p_, index); 456 | if (!f) 457 | mexErrMsgIdAndTxt("mexopencv:error", 458 | "Failed to get field name at %d\n", index); 459 | return std::string(f); 460 | } 461 | 462 | std::vector MxArray::fieldnames() const 463 | { 464 | if (!isStruct()) 465 | mexErrMsgIdAndTxt("mexopencv:error", "MxArray is not a struct array"); 466 | int n = nfields(); 467 | std::vector v; 468 | v.reserve(n); 469 | for (int i = 0; i < n; ++i) 470 | v.push_back(fieldname(i)); 471 | return v; 472 | } 473 | 474 | mwIndex MxArray::subs(mwIndex i, mwIndex j) const 475 | { 476 | if (i < 0 || i >= rows() || j < 0 || j >= cols()) 477 | mexErrMsgIdAndTxt("mexopencv:error", "Subscript out of range"); 478 | mwIndex s[] = {i, j}; 479 | return mxCalcSingleSubscript(p_, 2, s); 480 | } 481 | 482 | mwIndex MxArray::subs(const std::vector& si) const 483 | { 484 | return mxCalcSingleSubscript(p_, si.size(), &si[0]); 485 | } 486 | 487 | MxArray MxArray::at(const std::string& fieldName, mwIndex index) const 488 | { 489 | if (!isStruct()) 490 | mexErrMsgIdAndTxt("mexopencv:error", "MxArray is not struct"); 491 | if (index < 0 || numel() <= index) 492 | mexErrMsgIdAndTxt("mexopencv:error", "Out of range in struct array"); 493 | mxArray* pm = mxGetField(p_, index, fieldName.c_str()); 494 | if (!pm) 495 | mexErrMsgIdAndTxt("mexopencv:error", 496 | "Field '%s' doesn't exist", 497 | fieldName.c_str()); 498 | return MxArray(pm); 499 | } 500 | 501 | template <> 502 | MxArray MxArray::at(mwIndex index) const 503 | { 504 | if (!isCell()) 505 | mexErrMsgIdAndTxt("mexopencv:error", "MxArray is not cell"); 506 | return MxArray(mxGetCell(p_, index)); 507 | } 508 | 509 | template <> 510 | void MxArray::set(mwIndex index, const MxArray& value) 511 | { 512 | if (index < 0 || numel() <= index) 513 | mexErrMsgIdAndTxt("mexopencv:error", "Accessing invalid range"); 514 | if (!isCell()) 515 | mexErrMsgIdAndTxt("mexopencv:error", "Not cell array"); 516 | mxSetCell(const_cast(p_), index, 517 | static_cast(value)); 518 | } 519 | 520 | template <> 521 | std::vector MxArray::toVector() const 522 | { 523 | if (isCell()) 524 | { 525 | int n = numel(); 526 | std::vector v; 527 | v.reserve(n); 528 | for (int i = 0; i < n; ++i) 529 | v.push_back(MxArray(mxGetCell(p_, i))); 530 | return v; 531 | } 532 | else 533 | return std::vector(1, *this); 534 | } 535 | 536 | template <> 537 | std::vector MxArray::toVector() const 538 | { 539 | return toVector( 540 | std::const_mem_fun_ref_t(&MxArray::toString)); 541 | } 542 | 543 | template <> 544 | std::vector MxArray::toVector() const 545 | { 546 | std::vector v(toVector()); 547 | std::vector vm; 548 | vm.reserve(v.size()); 549 | for (std::vector::iterator it = v.begin(); it < v.end(); ++it) 550 | vm.push_back((*it).toMat()); 551 | return vm; 552 | } 553 | 554 | template <> 555 | std::vector MxArray::toVector() const 556 | { 557 | return toVector( 558 | std::const_mem_fun_ref_t(&MxArray::toPoint)); 559 | } 560 | 561 | template <> 562 | std::vector MxArray::toVector() const 563 | { 564 | return toVector( 565 | std::const_mem_fun_ref_t(&MxArray::toPoint2f)); 566 | } 567 | 568 | template <> 569 | std::vector MxArray::toVector() const 570 | { 571 | return toVector( 572 | std::const_mem_fun_ref_t(&MxArray::toPoint3f)); 573 | } 574 | 575 | template <> 576 | std::vector MxArray::toVector() const 577 | { 578 | int n = numel(); 579 | std::vector v; 580 | v.reserve(n); 581 | if (isCell()) 582 | for (int i = 0; i < n; ++i) 583 | v.push_back(at(i).toKeyPoint()); 584 | else if (isStruct()) 585 | for (int i = 0; i < n; ++i) 586 | v.push_back(toKeyPoint(i)); 587 | else 588 | mexErrMsgIdAndTxt("mexopencv:error", 589 | "MxArray unable to convert to std::vector"); 590 | return v; 591 | } 592 | 593 | template <> 594 | std::vector MxArray::toVector() const 595 | { 596 | int n = numel(); 597 | std::vector v; 598 | v.reserve(n); 599 | if (isCell()) 600 | for (int i = 0; i < n; ++i) 601 | v.push_back(at(i).toDMatch()); 602 | else if (isStruct()) 603 | for (int i = 0; i < n; ++i) 604 | v.push_back(toDMatch(i)); 605 | else 606 | mexErrMsgIdAndTxt("mexopencv:error", 607 | "MxArray unable to convert to std::vector"); 608 | return v; 609 | } 610 | -------------------------------------------------------------------------------- /MBS/mex/MxArray.obj: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tlliao/Perception-based-seam-cutting/f1a839e0b24214773b26905719a492e49d0aa5ec/MBS/mex/MxArray.obj -------------------------------------------------------------------------------- /MBS/mex/compile.m: -------------------------------------------------------------------------------- 1 | % make the mex file 2 | % Jianming Zhang 3 | % 3/22/2016 4 | 5 | function compile() 6 | 7 | % set the paths to your opencv libs (tested using Opencv2.4) 8 | opts.opencv_include_path = 'D:\opencv2.4.9\opencv\build\include'; % OpenCV include path 9 | opts.opencv_lib_path = 'D:\opencv2.4.9\opencv\build\x64\vc10\lib'; % OpenCV lib path 10 | 11 | opts.clean = false; % clean mode 12 | opts.dryrun = false; % dry run mode 13 | opts.verbose = 1; % output verbosity 14 | opts.debug = false; % enable debug symbols in MEX-files 15 | 16 | 17 | % Clean 18 | if opts.clean 19 | if opts.verbose > 0 20 | fprintf('Cleaning all generated files...\n'); 21 | end 22 | 23 | cmd = fullfile('../',['*.' mexext]); 24 | if opts.verbose > 0, disp(cmd); end 25 | if ~opts.dryrun, delete(cmd); end 26 | 27 | cmd = fullfile('*.o'); 28 | if opts.verbose > 0, disp(cmd); end 29 | if ~opts.dryrun, delete(cmd); end 30 | 31 | return; 32 | end 33 | 34 | % compile flags 35 | [cv_cflags,cv_libs] = pkg_config(opts); 36 | mex_flags = sprintf('%s %s', cv_cflags, cv_libs); 37 | if opts.verbose > 1 38 | mex_flags = ['-v ' mex_flags]; % verbose mex output 39 | end 40 | if opts.debug 41 | mex_flags = ['-g ' mex_flags]; % debug vs. optimized builds 42 | end 43 | compstr = computer; 44 | is64bit = strcmp(compstr(end-1:end),'64'); 45 | if (is64bit) 46 | mex_flags = ['-largeArrayDims ' mex_flags]; 47 | end 48 | 49 | % Compile MxArray and BMS 50 | src = 'mex\MxArray.cpp'; 51 | 52 | cmd = sprintf('mex %s -c %s', mex_flags, src); 53 | if opts.verbose > 0, disp(cmd); end 54 | if ~opts.dryrun, eval(cmd); end 55 | 56 | src = {'mex\MBS.cpp'}; 57 | % Compile the mex file 58 | for i = 1:numel(src) 59 | obj = 'MxArray.obj'; 60 | cmd = sprintf('mex %s %s %s -outdir ../', mex_flags, src{i}, obj); 61 | if opts.verbose > 0, disp(cmd); end 62 | if ~opts.dryrun, eval(cmd); end 63 | end 64 | 65 | end 66 | 67 | % 68 | % Helper functions for windows 69 | % 70 | function [cflags,libs] = pkg_config(opts) 71 | %PKG_CONFIG constructs OpenCV-related option flags 72 | I_path = opts.opencv_include_path; 73 | L_path = opts.opencv_lib_path; 74 | l_options = strcat({' -l'}, lib_names(L_path)); 75 | %if opts.debug 76 | % l_options = strcat(l_options,'d'); % link against debug binaries 77 | %end 78 | l_options = [l_options{:}]; 79 | 80 | if ~exist(I_path,'dir') 81 | error('OpenCV include path not found: %s', I_path); 82 | end 83 | if ~exist(L_path,'dir') 84 | error('OpenCV library path not found: %s', L_path); 85 | end 86 | 87 | cflags = sprintf('-I''%s''', I_path); 88 | libs = sprintf('-L''%s'' %s', L_path, l_options); 89 | end 90 | 91 | function l = lib_names(L_path) 92 | %LIB_NAMES return library names 93 | l = {'opencv_core', 'opencv_imgproc', 'opencv_highgui'}; 94 | end -------------------------------------------------------------------------------- /MBS/mex/compile_win.m: -------------------------------------------------------------------------------- 1 | % make the mex file 2 | % Jianming Zhang 3 | % 3/22/2016 4 | 5 | function compile_win() 6 | 7 | % set the values 8 | opts.opencv_include_path = 'D:\Program Files\opencv_2.4.9\opencv\build\include'; % OpenCV include path 9 | opts.opencv_lib_path = 'D:\Program Files\opencv_2.4.9\opencv\build\x64\vc12\lib'; % OpenCV lib path 10 | 11 | opts.clean = false; % clean mode 12 | opts.dryrun = false; % dry run mode 13 | opts.verbose = 1; % output verbosity 14 | opts.debug = false; % enable debug symbols in MEX-files 15 | 16 | 17 | % Clean 18 | if opts.clean 19 | if opts.verbose > 0 20 | fprintf('Cleaning all generated files...\n'); 21 | end 22 | 23 | cmd = fullfile('../',['*.' mexext]); 24 | if opts.verbose > 0, disp(cmd); end 25 | if ~opts.dryrun, delete(cmd); end 26 | 27 | cmd = fullfile('*.obj'); 28 | if opts.verbose > 0, disp(cmd); end 29 | if ~opts.dryrun, delete(cmd); end 30 | 31 | return; 32 | end 33 | 34 | % compile flags 35 | [cv_cflags,cv_libs] = pkg_config(opts); 36 | mex_flags = sprintf('%s %s', cv_cflags, cv_libs); 37 | if opts.verbose > 1 38 | mex_flags = ['-v ' mex_flags]; % verbose mex output 39 | end 40 | if opts.debug 41 | mex_flags = ['-g ' mex_flags]; % debug vs. optimized builds 42 | end 43 | compstr = computer; 44 | is64bit = strcmp(compstr(end-1:end),'64'); 45 | if (is64bit) 46 | mex_flags = ['-largeArrayDims ' mex_flags]; 47 | end 48 | 49 | % Compile MxArray and BMS 50 | src = 'MxArray.cpp'; 51 | 52 | cmd = sprintf('mex %s -c %s', mex_flags, src); 53 | if opts.verbose > 0, disp(cmd); end 54 | if ~opts.dryrun, eval(cmd); end 55 | 56 | src = {'MBS.cpp'}; 57 | % Compile the mex file 58 | for i = 1:numel(src) 59 | obj = 'MxArray.obj'; 60 | cmd = sprintf('mex %s %s %s -outdir ../', mex_flags, src{i}, obj); 61 | if opts.verbose > 0, disp(cmd); end 62 | if ~opts.dryrun, eval(cmd); end 63 | end 64 | 65 | end 66 | 67 | % 68 | % Helper functions for windows 69 | % 70 | function [cflags,libs] = pkg_config(opts) 71 | %PKG_CONFIG constructs OpenCV-related option flags 72 | I_path = opts.opencv_include_path; 73 | L_path = opts.opencv_lib_path; 74 | l_options = strcat({' -l'}, lib_names(L_path)); 75 | %if opts.debug 76 | % l_options = strcat(l_options,'d'); % link against debug binaries 77 | %end 78 | l_options = [l_options{:}]; 79 | 80 | if ~exist(I_path,'dir') 81 | error('OpenCV include path not found: %s', I_path); 82 | end 83 | if ~exist(L_path,'dir') 84 | error('OpenCV library path not found: %s', L_path); 85 | end 86 | 87 | cflags = sprintf('-I''%s''', I_path); 88 | libs = sprintf('-L''%s'' %s', L_path, l_options); 89 | end 90 | 91 | function l = lib_names(L_path) 92 | %LIB_NAMES return library names 93 | d = dir( fullfile(L_path,'opencv_*.lib') ); 94 | l = regexp({d.name}, '(opencv_core.+)\.lib|(opencv_imgproc.+)\.lib|(opencv_highgui.+)\.lib', 'tokens', 'once'); 95 | l = [l{:}]; 96 | end -------------------------------------------------------------------------------- /MBS/mex/mexopencv.hpp: -------------------------------------------------------------------------------- 1 | /** 2 | * @file mexopencv.hpp 3 | * @brief Global constant definitions 4 | * @author Kota Yamaguchi 5 | * @date 2012 6 | * 7 | * The header file for a Matlab mex function that uses OpenCV library. 8 | * The file includes definition of MxArray class that converts between mxArray 9 | * and a couple of std:: and cv:: data types including cv::Mat. 10 | */ 11 | #ifndef __MEXOPENCV_HPP__ 12 | #define __MEXOPENCV_HPP__ 13 | 14 | #include "MxArray.hpp" 15 | 16 | // Global constants 17 | 18 | /** BorderType map for option processing 19 | */ 20 | const ConstMap BorderType = ConstMap 21 | ("Replicate", cv::BORDER_REPLICATE) 22 | ("Constant", cv::BORDER_CONSTANT) 23 | ("Reflect", cv::BORDER_REFLECT) 24 | ("Wrap", cv::BORDER_WRAP) 25 | ("Reflect101", cv::BORDER_REFLECT_101) 26 | ("Transparent", cv::BORDER_TRANSPARENT) 27 | ("Default", cv::BORDER_DEFAULT) 28 | ("Isolated", cv::BORDER_ISOLATED); 29 | 30 | /** Interpolation type map for option processing 31 | */ 32 | const ConstMap InterType = ConstMap 33 | ("Nearest", cv::INTER_NEAREST) //!< nearest neighbor interpolation 34 | ("Linear", cv::INTER_LINEAR) //!< bilinear interpolation 35 | ("Cubic", cv::INTER_CUBIC) //!< bicubic interpolation 36 | ("Area", cv::INTER_AREA) //!< area-based (or super) interpolation 37 | ("Lanczos4", cv::INTER_LANCZOS4) //!< Lanczos interpolation over 8x8 neighborhood 38 | ("Max", cv::INTER_MAX); 39 | //("WarpInverseMap", cv::WARP_INVERSE_MAP); 40 | 41 | /** Thresholding type map for option processing 42 | */ 43 | const ConstMap ThreshType = ConstMap 44 | ("Binary", cv::THRESH_BINARY) 45 | ("BinaryInv", cv::THRESH_BINARY_INV) 46 | ("Trunc", cv::THRESH_TRUNC) 47 | ("ToZero", cv::THRESH_TOZERO) 48 | ("ToZeroInv", cv::THRESH_TOZERO_INV) 49 | ("Mask", cv::THRESH_MASK); 50 | //("Otsu", cv::THRESH_OTSU); 51 | 52 | /** Distance types for Distance Transform and M-estimators 53 | */ 54 | const ConstMap DistType = ConstMap 55 | ("User", CV_DIST_USER) 56 | ("L1", CV_DIST_L1) 57 | ("L2", CV_DIST_L2) 58 | ("C", CV_DIST_C) 59 | ("L12", CV_DIST_L12) 60 | ("Fair", CV_DIST_FAIR) 61 | ("Welsch", CV_DIST_WELSCH) 62 | ("Huber", CV_DIST_HUBER); 63 | 64 | /** Line type for drawing 65 | */ 66 | const ConstMap LineType = ConstMap 67 | ("8", 8) 68 | ("4", 4) 69 | ("AA", CV_AA); 70 | 71 | /** Font faces for drawing 72 | */ 73 | const ConstMap FontFace = ConstMap 74 | ("HersheySimplex", CV_FONT_HERSHEY_SIMPLEX) 75 | ("HersheyPlain", CV_FONT_HERSHEY_PLAIN) 76 | ("HersheyDuplex", CV_FONT_HERSHEY_DUPLEX) 77 | ("HersheyComplex", CV_FONT_HERSHEY_COMPLEX) 78 | ("HersheyTriplex", CV_FONT_HERSHEY_TRIPLEX) 79 | ("HersheyComplexSmall", CV_FONT_HERSHEY_COMPLEX_SMALL) 80 | ("HersheyScriptSimplex", CV_FONT_HERSHEY_SCRIPT_SIMPLEX) 81 | ("HersheyScriptComplex", CV_FONT_HERSHEY_SCRIPT_COMPLEX); 82 | 83 | /** Font styles for drawing 84 | */ 85 | const ConstMap FontStyle = ConstMap 86 | ("Regular", 0) 87 | ("Italic", CV_FONT_ITALIC); 88 | #endif 89 | -------------------------------------------------------------------------------- /MBS/readme.txt: -------------------------------------------------------------------------------- 1 | This is a matlab implementation of the method described in: 2 | 3 | "Minimum Barrier Salient Object Detection at 80 FPS", Jianming Zhang, 4 | Stan Sclaroff, Zhe Lin, Xiaohui Shen, Brian Price, Radomir Mech, ICCV, 2015 5 | 6 | Contact: jimmie33@gmail.com 7 | 8 | Prerequisite: OpenCV 2.4+ 9 | 10 | Usage: 11 | 12 | 1. Go to the folder "mex" 13 | 2. modify the opencv include and lib paths in "compile.m/compile_win.m" 14 | (for Linux/Windows) 15 | 3. run "compile/compile_win" in matlab (for Linux/Windows) 16 | 4. Go to the root folder 17 | 5. run "demo" 18 | 19 | 20 | This matlab implementation is provided for research purpose only. For fully 21 | reproducing the results in our ICCV paper, please use the original Windows 22 | executable program. 23 | 24 | The matlab implementation is slower than the window executable, mainly due 25 | to the morphological post-processing step. We use the highly optimized IPP 26 | library for the morphological operations in our C++ implementation, which 27 | are much faster than the corresponding Matlab functions. 28 | 29 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Perception-based seam cutting for image stitching 2 | 3 | This repository is our implementation of the paper, Perception-based seam cutting for image stitching. If you use any code or data from our work, please cite our paper. 4 | 5 | ### Usage 6 | 7 | 1. Download code, add images in the folder "Imgs" in the main path and run the "main.m". 8 | 9 | 2. This codehas been tested on 64bit Windows; for other platforms, you'll need to compile your own mex files for "graphCutMex" and saliency detection library in "MBS". 10 | 11 | ### Citation 12 | ``` 13 | @article{li2018perception, 14 | title={Perception-based seam cutting for image stitching}, 15 | author={Li, Nan and Liao, Tianli and Wang, Chao}, 16 | journal={Signal, Image and Video Processing}, 17 | volume={12}, 18 | number={5}, 19 | pages={967--974}, 20 | year={2018}, 21 | publisher={Springer} 22 | } 23 | ``` 24 | 25 | ### Contact 26 | 27 | If you have any comments, suggestions, or questions, please contact me (tianli.liao@haut.edu.cn). 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | -------------------------------------------------------------------------------- /blendTexture.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tlliao/Perception-based-seam-cutting/f1a839e0b24214773b26905719a492e49d0aa5ec/blendTexture.m -------------------------------------------------------------------------------- /build_graphCutMex.m: -------------------------------------------------------------------------------- 1 | function build_graphCutMex 2 | % build_graphCutMex builds package graphCutMex 3 | % 4 | % Anton Osokin (firstname.lastname@gmail.com), 19.05.2013 5 | 6 | maxFlowPath = 'maxflow-v3.03.src'; 7 | 8 | mexCmd = ['mex graphCutMex.cpp -output graphCutMex -largeArrayDims ', '-I', maxFlowPath]; 9 | eval(mexCmd); 10 | -------------------------------------------------------------------------------- /calcDist.m: -------------------------------------------------------------------------------- 1 | function d = calcDist(H,pts1,pts2) 2 | % Project PTS1 to PTS3 using H, then calcultate the distances between 3 | % PTS2 and PTS3 4 | 5 | n = size(pts1,2); 6 | pts3 = H*[pts1;ones(1,n)]; 7 | pts3 = pts3(1:2,:)./repmat(pts3(3,:),2,1); 8 | d = sum((pts2-pts3).^2,1); 9 | 10 | end -------------------------------------------------------------------------------- /calcHomo.m: -------------------------------------------------------------------------------- 1 | function H = calcHomo(pts1,pts2) 2 | %% use Direct linear tranformation (DLT) to calculate homography 3 | % approxmation: H*[pts1; ones(1,size(pts1,2))] = [pts2; ones(1,size(pts2,2))] 4 | % Normalise point distribution. 5 | data_pts = [ pts1; ones(1,size(pts1,2)) ; pts2; ones(1,size(pts2,2)) ]; 6 | [ dat_norm_pts1,T1 ] = normalise2dpts(data_pts(1:3,:)); 7 | [ dat_norm_pts2,T2 ] = normalise2dpts(data_pts(4:6,:)); 8 | data_norm = [ dat_norm_pts1 ; dat_norm_pts2 ]; 9 | 10 | %----------------------- 11 | % Global homography (H). 12 | %----------------------- 13 | %fprintf('DLT (projective transform) on inliers\n'); 14 | % Refine homography using DLT on inliers. 15 | %fprintf('> Refining homography (H) using DLT...'); 16 | [ h,~,~,~ ] = feval('homography_fit',data_norm); 17 | H = T2\(reshape(h,3,3)*T1); 18 | 19 | end -------------------------------------------------------------------------------- /gradient_blend.m: -------------------------------------------------------------------------------- 1 | function result = gradient_blend(source, mask, target) 2 | % Uses gradient-domain editing to blend a source image into a target image. 3 | % The target image is the background onto which a region of the source 4 | % image is copied and blended; the region of the source image to be 5 | % transferred is given by a binary mask. 6 | % 7 | % Inputs: 8 | % source: The image from which pixels will be transferred onto the target. 9 | % mask: A binary matrix specifying the region of the source image that 10 | % should be blended into the target. 11 | % target: The image into which the selected source region is blended; 12 | % this serves as the background for blending. 13 | % 14 | % Outputs: 15 | % result: An image of the same dimensions as the source/target, 16 | % representing the output of the gradient domain blending. 17 | % 18 | % This function assumes that the inputs, source, mask, and target, all have 19 | % the same width and height. 20 | 21 | 22 | % To simplify edge cases, we add a 1-pixel border around the source, 23 | % target, and mask. For the source and target images, the extra 1-pixel 24 | % border is created by copying pixel values along the edge, essentially 25 | % extending the original images. For the mask, the extra 1-pixel border 26 | % just consists of all 0s, since we do not want that border to be selected. 27 | % This border is removed after blending. 28 | source = padarray(source, [1,1], 'symmetric'); 29 | target = padarray(target, [1,1], 'symmetric'); 30 | mask = padarray(mask, [1,1]); 31 | 32 | [t_rows, t_cols, ~] = size(target); 33 | 34 | % We reshape the source and target to have dimensions t_rows*t_cols x 3, 35 | % turning each color channel into a column vector. This greatly simplifies 36 | % later computations, which are performed across all color channels 37 | % simultaneously. 38 | s = reshape(source, t_rows*t_cols, []); 39 | t = reshape(target, t_rows*t_cols, []); 40 | 41 | % Allocate the RHS vector b. 42 | b = zeros(t_rows*t_cols, 3); 43 | 44 | disp('Constructing the matrix A...'); 45 | tic 46 | 47 | % We construct the matrix A efficiently from a set of three vectors: 48 | % row_vec has entries that represent row indexes of A; col_vec has entries 49 | % that represent column indexes of A, and value_vec has entries that 50 | % represent the values at specific positions inside A. These three vectors 51 | % are correlated, such that the final matrix A will have (row_vec(index), 52 | % col_vec(index)) = value_vec(index). Thus, for each entry in A, we add one 53 | % entry into each of row_vec, col_vec, and value_vec. 54 | row_vec = zeros(t_rows*t_cols, 1); 55 | col_vec = zeros(t_rows*t_cols, 1); 56 | value_vec = zeros(t_rows*t_cols, 1); 57 | 58 | % Each row of the sparse matrix A represents a linear equation; this 59 | % variable is used to keep track of the current row inside A. 60 | equation_num = 1; 61 | 62 | % The matrix A has one equation for each pixel in the target image; we 63 | % iterate through them, and insert the appropriate values in the matrix A 64 | % and the corresponding values in b: 65 | for index = 1:t_rows*t_cols 66 | if mask(index) 67 | % 68 | b(index,:) = 4*s(index,:) - s(index-1,:) - s(index+1,:) - s(index+t_rows,:) - s(index-t_rows,:); 69 | 70 | % Insert a 4 into A at the index of the current central pixel. 71 | row_vec(equation_num) = index; 72 | col_vec(equation_num) = index; 73 | value_vec(equation_num) = 4; 74 | equation_num = equation_num + 1; 75 | 76 | % Insert a -1 for the pixel below the current pixel: 77 | row_vec(equation_num) = index; 78 | col_vec(equation_num) = index + 1; 79 | value_vec(equation_num) = -1; 80 | equation_num = equation_num + 1; 81 | 82 | % Insert a -1 for the pixel above the current pixel: 83 | row_vec(equation_num) = index; 84 | col_vec(equation_num) = index - 1; 85 | value_vec(equation_num) = -1; 86 | equation_num = equation_num + 1; 87 | 88 | % Insert a -1 for the pixel to the left of the current pixel: 89 | row_vec(equation_num) = index; 90 | col_vec(equation_num) = index - t_rows; 91 | value_vec(equation_num) = -1; 92 | equation_num = equation_num + 1; 93 | 94 | % Insert a -1 for the pixel to the right of the current pixel: 95 | row_vec(equation_num) = index; 96 | col_vec(equation_num) = index + t_rows; 97 | value_vec(equation_num) = -1; 98 | equation_num = equation_num + 1; 99 | else 100 | % If the current pixel location is not in the mask, the final value 101 | % in the blended image is the same as the original value in the 102 | % target image, so we insert a 1 in the matrix A, and copy the 103 | % target value into the appropriate position of the vector b: 104 | row_vec(equation_num) = index; 105 | col_vec(equation_num) = index; 106 | value_vec(equation_num) = 1; 107 | equation_num = equation_num + 1; 108 | 109 | b(index,:) = t(index,:); 110 | end 111 | end 112 | 113 | % We create the sparse matrix efficiently: 114 | A = sparse(row_vec, col_vec, value_vec, t_rows*t_cols, t_rows*t_cols); 115 | 116 | toc 117 | disp('Finished constructing the matrix A...') 118 | 119 | % Solve for each color channel: 120 | f_red = A \ b(:,1); 121 | f_green = A \ b(:,2); 122 | f_blue = A \ b(:,3); 123 | 124 | % Reshape to the original size: 125 | f_red = reshape(f_red, [t_rows, t_cols]); 126 | f_green = reshape(f_green, [t_rows, t_cols]); 127 | f_blue = reshape(f_blue, [t_rows, t_cols]); 128 | 129 | % Stack the channels back together: 130 | result = zeros(t_rows, t_cols, 3); 131 | result(:,:,1) = f_red; 132 | result(:,:,2) = f_green; 133 | result(:,:,3) = f_blue; 134 | 135 | % Chop off the border: 136 | result = result(2:t_rows-1, 2:t_cols-1, :); 137 | 138 | end -------------------------------------------------------------------------------- /graphCutMex.cpp: -------------------------------------------------------------------------------- 1 | 2 | #include "graphCutMex.h" 3 | #include "mex.h" 4 | 5 | #include 6 | #include 7 | 8 | #define INFTY INT_MAX 9 | 10 | //define types 11 | typedef double EnergyType; 12 | mxClassID MATLAB_ENERGYTERM_TYPE = mxDOUBLE_CLASS; 13 | 14 | typedef double EnergyTermType; 15 | mxClassID MATLAB_ENERGY_TYPE = mxDOUBLE_CLASS; 16 | 17 | typedef double LabelType; 18 | mxClassID MATLAB_LABEL_TYPE = mxDOUBLE_CLASS; 19 | /* 20 | typedef int EnergyType; 21 | mxClassID MATLAB_ENERGYTERM_TYPE = mxINT32_CLASS; 22 | 23 | typedef int EnergyTermType; 24 | mxClassID MATLAB_ENERGY_TYPE = mxINT32_CLASS; 25 | 26 | typedef int LabelType; 27 | mxClassID MATLAB_LABEL_TYPE = mxINT32_CLASS; 28 | */ 29 | 30 | typedef Graph GraphType; 31 | 32 | double round(double a); 33 | int isInteger(double a); 34 | 35 | #define MATLAB_ASSERT(expr,msg) if (!(expr)) { mexErrMsgTxt(msg);} 36 | 37 | #if !defined(MX_API_VER) || MX_API_VER < 0x07030000 38 | typedef int mwSize; 39 | typedef int mwIndex; 40 | #endif 41 | 42 | 43 | 44 | void mexFunction(int nlhs, mxArray *plhs[], 45 | int nrhs, const mxArray *prhs[]) 46 | { 47 | MATLAB_ASSERT( nrhs == 2, "graphCutMex: Wrong number of input parameters: expected 2"); 48 | MATLAB_ASSERT( nlhs <= 2, "graphCutMex: Too many output arguments: expected 2 or less"); 49 | 50 | //Fix input parameter order: 51 | const mxArray *uInPtr = (nrhs >= 1) ? prhs[0] : NULL; //unary 52 | const mxArray *pInPtr = (nrhs >= 2) ? prhs[1] : NULL; //pairwise 53 | 54 | //Fix output parameter order: 55 | mxArray **cOutPtr = (nlhs >= 1) ? &plhs[0] : NULL; //cut 56 | mxArray **lOutPtr = (nlhs >= 2) ? &plhs[1] : NULL; //labels 57 | 58 | //node number 59 | int numNodes; 60 | 61 | // get unary potentials 62 | MATLAB_ASSERT(mxGetNumberOfDimensions(uInPtr) == 2, "graphCutMex: The first paramater is not 2-dimensional"); 63 | MATLAB_ASSERT(mxGetClassID(uInPtr) == MATLAB_ENERGYTERM_TYPE, "graphCutMex: Unary potentials are of wrong type"); 64 | MATLAB_ASSERT(mxGetPi(uInPtr) == NULL, "graphCutMex: Unary potentials should not be complex"); 65 | 66 | numNodes = mxGetM(uInPtr); 67 | 68 | MATLAB_ASSERT(numNodes >= 1, "graphCutMex: The number of nodes is not positive"); 69 | MATLAB_ASSERT(mxGetN(uInPtr) == 2, "graphCutMex: The first paramater is not of size #nodes x 2"); 70 | 71 | EnergyTermType* termW = (EnergyTermType*)mxGetData(uInPtr); 72 | 73 | //get pairwise potentials 74 | MATLAB_ASSERT(mxGetNumberOfDimensions(pInPtr) == 2, "graphCutMex: The second paramater is not 2-dimensional"); 75 | 76 | mwSize numEdges = mxGetM(pInPtr); 77 | 78 | MATLAB_ASSERT( mxGetN(pInPtr) == 4, "graphCutMex: The second paramater is not of size #edges x 4"); 79 | MATLAB_ASSERT(mxGetClassID(pInPtr) == MATLAB_ENERGYTERM_TYPE, "graphCutMex: Pairwise potentials are of wrong type"); 80 | 81 | EnergyTermType* edges = (EnergyTermType*)mxGetData(pInPtr); 82 | for(int i = 0; i < numEdges; i++) 83 | { 84 | MATLAB_ASSERT(1 <= round(edges[i]) && round(edges[i]) <= numNodes, "graphCutMex: error in pairwise terms array: wrong vertex index"); 85 | MATLAB_ASSERT(isInteger(edges[i]), "graphCutMex: error in pairwise terms array: wrong vertex index"); 86 | MATLAB_ASSERT(1 <= round(edges[i + numEdges]) && round(edges[i + numEdges]) <= numNodes, "graphCutMex: error in pairwise terms array: wrong vertex index"); 87 | MATLAB_ASSERT(isInteger(edges[i + numEdges]), "graphCutMex: error in pairwise terms array: wrong vertex index"); 88 | MATLAB_ASSERT(edges[i + 2 * numEdges] + edges[i + 3 * numEdges] >= 0, "graphCutMex: error in pairwise terms array: nonsubmodular edge"); 89 | } 90 | 91 | 92 | // start computing 93 | if (nlhs == 0){ 94 | return; 95 | } 96 | 97 | //prepare graph 98 | GraphType *g = new GraphType( numNodes, numEdges); 99 | 100 | for(int i = 0; i < numNodes; i++) 101 | { 102 | g -> add_node(); 103 | g -> add_tweights( i, termW[i], termW[numNodes + i]); 104 | } 105 | 106 | for(int i = 0; i < numEdges; i++) 107 | if(edges[i] < 1 || edges[i] > numNodes || edges[numEdges + i] < 1 || edges[numEdges + i] > numNodes || edges[i] == edges[numEdges + i] || !isInteger(edges[i]) || !isInteger(edges[numEdges + i])){ 108 | mexWarnMsgIdAndTxt("graphCutMex:pairwisePotentials", "Some edge has invalid vertex numbers and therefore it is ignored"); 109 | } 110 | else 111 | if(edges[2 * numEdges + i] + edges[3 * numEdges + i] < 0){ 112 | mexWarnMsgIdAndTxt("graphCutMex:pairwisePotentials", "Some edge is non-submodular and therefore it is ignored"); 113 | } 114 | else 115 | { 116 | if (edges[2 * numEdges + i] >= 0 && edges[3 * numEdges + i] >= 0) 117 | g -> add_edge((GraphType::node_id)round(edges[i] - 1), (GraphType::node_id)round(edges[numEdges + i] - 1), edges[2 * numEdges + i], edges[3 * numEdges + i]); 118 | else 119 | if (edges[2 * numEdges + i] <= 0 && edges[3 * numEdges + i] >= 0) 120 | { 121 | g -> add_edge((GraphType::node_id)round(edges[i] - 1), (GraphType::node_id)round(edges[numEdges + i] - 1), 0, edges[3 * numEdges + i] + edges[2 * numEdges + i]); 122 | g -> add_tweights((GraphType::node_id)round(edges[i] - 1), 0, edges[2 * numEdges + i]); 123 | g -> add_tweights((GraphType::node_id)round(edges[numEdges + i] - 1),0 , -edges[2 * numEdges + i]); 124 | } 125 | else 126 | if (edges[2 * numEdges + i] >= 0 && edges[3 * numEdges + i] <= 0) 127 | { 128 | g -> add_edge((GraphType::node_id)round(edges[i] - 1), (GraphType::node_id)round(edges[numEdges + i] - 1), edges[3 * numEdges + i] + edges[2 * numEdges + i], 0); 129 | g -> add_tweights((GraphType::node_id)round(edges[i] - 1),0 , -edges[3 * numEdges + i]); 130 | g -> add_tweights((GraphType::node_id)round(edges[numEdges + i] - 1), 0, edges[3 * numEdges + i]); 131 | } 132 | else 133 | mexWarnMsgIdAndTxt("graphCutMex:pairwisePotentials", "Something strange with an edge and therefore it is ignored"); 134 | } 135 | 136 | //compute flow 137 | EnergyType flow = g -> maxflow(); 138 | 139 | //output minimum value 140 | if (cOutPtr != NULL){ 141 | *cOutPtr = mxCreateNumericMatrix(1, 1, MATLAB_ENERGY_TYPE, mxREAL); 142 | *(EnergyType*)mxGetData(*cOutPtr) = (EnergyType)flow; 143 | } 144 | 145 | //output minimum cut 146 | if (lOutPtr != NULL){ 147 | *lOutPtr = mxCreateNumericMatrix(numNodes, 1, MATLAB_LABEL_TYPE, mxREAL); 148 | LabelType* segment = (LabelType*)mxGetData(*lOutPtr); 149 | for(int i = 0; i < numNodes; i++) 150 | segment[i] = g -> what_segment(i); 151 | } 152 | 153 | delete g; 154 | } 155 | 156 | double round(double a) 157 | { 158 | return floor(a + 0.5); 159 | } 160 | 161 | 162 | int isInteger(double a) 163 | { 164 | return (abs(a - round(a)) < 1e-6); 165 | } 166 | -------------------------------------------------------------------------------- /graphCutMex.h: -------------------------------------------------------------------------------- 1 | 2 | #ifndef __GRAPHCUTMEX_H__ 3 | #define __GRAPHCUTMEX_H__ 4 | 5 | #include "graph.h" 6 | #include "graph.cpp" 7 | #include "maxflow.cpp" 8 | 9 | #endif 10 | -------------------------------------------------------------------------------- /graphCutMex.m: -------------------------------------------------------------------------------- 1 | % graphCutMex - Matlab wrapper to the implementation of min-cut algorithm by Yuri Boykov and Vladimir Kolmogorov: 2 | % http://pub.ist.ac.at/~vnk/software/maxflow-v3.03.src.zip 3 | % This version can automatically perform reparametrization on all submodular edges. 4 | % 5 | % The algorithm is described in: 6 | % Yuri Boykov and Vladimir Kolmogorov, 'An Experimental Comparison of Min-Cut/Max-Flow Algorithms for 7 | % Energy Minimization in Vision', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8 | % 26, no. 9, pp. 1124-1137, Sept. 2004. 9 | % 10 | % Usage: 11 | % [cut] = graphCutMex(termWeights, edgeWeights); 12 | % [cut, labels] = graphCutMex(termWeights, edgeWeights); 13 | % 14 | % Inputs: 15 | % termWeights - the edges connecting the source and the sink with the regular nodes (array of type double, size : [numNodes, 2]) 16 | % termWeights(i, 1) is the weight of the edge connecting the source with node #i 17 | % termWeights(i, 2) is the weight of the edge connecting node #i with the sink 18 | % numNodes is determined from the size of termWeights. 19 | % edgeWeights - the edges connecting regular nodes with each other (array of type double, array size [numEdges, 4]) 20 | % edgeWeights(i, 3) connects node #edgeWeights(i, 1) to node #edgeWeights(i, 2) 21 | % edgeWeights(i, 4) connects node #edgeWeights(i, 2) to node #edgeWeights(i, 1) 22 | % The only requirement on edge weights is submodularity: edgeWeights(i, 3) + edgeWeights(i, 4) >= 0 23 | % 24 | % Outputs: 25 | % cut - the minimum cut value (type double) 26 | % labels - a vector of length numNodes, where labels(i) is 0 or 1 if node #i belongs to S (source) or T (sink) respectively. 27 | % 28 | % To build the code in Matlab choose reasonable compiler and run build_graphCutMex.m 29 | % Run example_graphCutMex.m to test the code 30 | % 31 | % Anton Osokin (firstname.lastname@gmail.com), 19.05.2013 32 | -------------------------------------------------------------------------------- /graphCutMex.mexw64: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tlliao/Perception-based-seam-cutting/f1a839e0b24214773b26905719a492e49d0aa5ec/graphCutMex.mexw64 -------------------------------------------------------------------------------- /homographyAlign.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tlliao/Perception-based-seam-cutting/f1a839e0b24214773b26905719a492e49d0aa5ec/homographyAlign.m -------------------------------------------------------------------------------- /main.m: -------------------------------------------------------------------------------- 1 | clear; clc; close all; 2 | %% Setup VLFeat toolbox. 3 | %---------------------- 4 | addpath('modelspecific'); 5 | addpath('MBS'); % for saliency detection 6 | cd vlfeat-0.9.21/toolbox; 7 | feval('vl_setup'); 8 | cd ../..; 9 | 10 | imgpath = 'Imgs\'; 11 | 12 | img_format = '*.jpg'; 13 | outpath = [imgpath, 'results\'];%testpatch 14 | dir_folder = dir(strcat(imgpath, img_format)); 15 | if ~exist(outpath,'dir'); mkdir(outpath); end 16 | 17 | path1 = sprintf('%s%s',imgpath, dir_folder(1).name); % 18 | path2 = sprintf('%s%s',imgpath, dir_folder(2).name); % 19 | img1 = im2double(imread(path1)); % target image 20 | img2 = im2double(imread(path2)); % reference image 21 | 22 | %% saliency detection 23 | 24 | pMap_1 = mbs_saliency(img1); 25 | pMap_2 = mbs_saliency(img2); 26 | 27 | %% image alignment 28 | fprintf('> image alignment...');tic; 29 | [warped_img1, warped_pmap1, warped_img2, warped_pmap2] = registerTexture(img1, pMap_1, img2, pMap_2, imgpath); 30 | fprintf('done (%fs)\n', toc); 31 | 32 | %% image composition 33 | fprintf('> seam cutting...');tic; 34 | imgout = blendTexture(warped_img1, warped_pmap1, warped_img2, warped_pmap2); 35 | fprintf('done (%fs)\n', toc); 36 | -------------------------------------------------------------------------------- /matchDelete.m: -------------------------------------------------------------------------------- 1 | function [matches1, matches2] = matchDelete(pts1, pts2, height, width) 2 | 3 | %% delete the wrong matches (one-to-more) 4 | [~, ind_1] = unique(pts1', 'rows'); 5 | pts1 = pts1(:,ind_1'); 6 | pts2 = pts2(:,ind_1'); 7 | [~, ind_2] = unique(pts2', 'rows'); 8 | pts1 = pts1(:,ind_2'); 9 | pts2 = pts2(:,ind_2'); 10 | 11 | %% use histogram (horizontal and vertical orientation) delete outliers 12 | thr = 0.1; 13 | % horizontal histogram 14 | xbins = (-width+width*thr/2:width*thr:width-width*thr/2); 15 | counts1 = hist(pts1(1,:)-pts2(1,:), xbins); 16 | [~,ia1] = max(counts1); 17 | C1 = find(pts1(1,:)-pts2(1,:)>=max(-width,-width+(ia1-2)*width*thr) & pts1(1,:)-pts2(1,:)<=min(width,-width+(ia1+1)*width*thr)); 18 | % vertical histogram 19 | ybins = (-height+height*thr/2: height*thr: height-height*thr/2); 20 | counts2 = hist(pts1(2,:)-pts2(2,:), ybins); 21 | [~, ia2] = max(counts2); 22 | C2 = find(pts1(2,:)-pts2(2,:)>=max(-height,-height+(ia2-2)*height*thr) & pts1(2,:)-pts2(2,:)<=min(height, -height+(ia2+1)*height*thr)); 23 | % final inliers after 1st filter 24 | C = intersect(C1,C2); 25 | pts1 = pts1(:,C); 26 | pts2 = pts2(:,C); 27 | 28 | %% RANSAC delete 29 | coef.minPtNum = 4; %max(min(round(size(pts1,2)/4),10),4); 30 | coef.iterNum = 1000; 31 | coef.thDist = 5; 32 | coef.thInlrRatio = .1; 33 | 34 | [~,corrPtIdx1] = ransacx(pts2, pts1, coef); %, @DLT_Homo,@calcDist); 35 | matches1 = pts1(:, corrPtIdx1); 36 | matches2 = pts2(:, corrPtIdx1); 37 | 38 | end -------------------------------------------------------------------------------- /maxflow-v3.03.src/CHANGES.TXT: -------------------------------------------------------------------------------- 1 | List of changes from version 3.02: 2 | 3 | - put under GPL license 4 | 5 | List of changes from version 3.01: 6 | 7 | - fixed a bug: using add_node() or add_edge() after the first maxflow() with the reuse_trees option 8 | could have caused segmentation fault (if nodes or arcs are reallocated). Thanks to Jan Lellmann for pointing out this bug. 9 | - updated block.h to suppress compilation warnings 10 | 11 | List of changes from version 3.0: 12 | - Moved line 13 | #include "instances.inc" 14 | to the end of cpp files to make it compile under GNU c++ compilers 4.2(?) and above 15 | 16 | List of changes from version 2.2: 17 | 18 | - Added functions for accessing graph structure, residual capacities, etc. 19 | (They are needed for implementing maxflow-based algorithms such as primal-dual algorithm for convex MRFs.) 20 | - Added option of reusing trees. 21 | - node_id's are now integers starting from 0. Thus, it is not necessary to store node_id's in a separate array. 22 | - Capacity types are now templated. 23 | - Fixed bug in block.h. (After Block::Reset, ScanFirst() and ScanNext() did not work properly). 24 | - Implementation with a forward star representation of the graph is no longer supported. (It needs less memory, but slightly slower than adjacency list representation.) If you still wish to use it, download version 2.2. 25 | - Note: version 3.0 is released under a different license than version 2.2. 26 | 27 | List of changes from version 2.1: 28 | 29 | - Put the code under GPL license 30 | 31 | List of changes from version 2.02: 32 | 33 | - Fixed a bug in the implementation that uses forward star representation 34 | 35 | List of changes from version 2.01: 36 | 37 | - Added new interface function - Graph::add_tweights(Node_id, captype, captype) 38 | (necessary for the "ENERGY" software package) 39 | 40 | -------------------------------------------------------------------------------- /maxflow-v3.03.src/GPL.TXT: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. 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If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /maxflow-v3.03.src/README.TXT: -------------------------------------------------------------------------------- 1 | ################################################################### 2 | # # 3 | # MAXFLOW - software for computing mincut/maxflow in a graph # 4 | # Version 3.03 # 5 | # http://http://pub.ist.ac.at/~vnk/software.html # 6 | # # 7 | # Yuri Boykov (yuri@csd.uwo.ca) # 8 | # Vladimir Kolmogorov (vnk@ist.ac.at) # 9 | # 2001-2006 # 10 | # # 11 | ################################################################### 12 | 13 | 1. Introduction. 14 | 15 | This software library implements the maxflow algorithm described in 16 | 17 | "An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision." 18 | Yuri Boykov and Vladimir Kolmogorov. 19 | In IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 20 | September 2004 21 | 22 | This algorithm was developed by Yuri Boykov and Vladimir Kolmogorov 23 | at Siemens Corporate Research. To make it available for public use, 24 | it was later reimplemented by Vladimir Kolmogorov based on open publications. 25 | 26 | If you use this software for research purposes, you should cite 27 | the aforementioned paper in any resulting publication. 28 | 29 | ---------------------------------------------------------------------- 30 | 31 | REUSING TREES: 32 | 33 | Starting with version 3.0, there is a also an option of reusing search 34 | trees from one maxflow computation to the next, as described in 35 | 36 | "Efficiently Solving Dynamic Markov Random Fields Using Graph Cuts." 37 | Pushmeet Kohli and Philip H.S. Torr 38 | International Conference on Computer Vision (ICCV), 2005 39 | 40 | If you use this option, you should cite 41 | the aforementioned paper in any resulting publication. 42 | 43 | Tested under windows, Visual C++ 6.0 compiler and unix (SunOS 5.8 44 | and RedHat Linux 7.0, GNU c++ compiler). 45 | 46 | ################################################################## 47 | 48 | 2. License & disclaimer. 49 | 50 | Copyright 2001-2006 Vladimir Kolmogorov (vnk@ist.ac.at), Yuri Boykov (yuri@csd.uwo.ca). 51 | 52 | This software is under the GPL license. 53 | If you require another license, you may consider using version 2.21 54 | (which implements exactly the same algorithm, but does not have the option of reusing search trees). 55 | 56 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 57 | "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT 58 | LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR 59 | A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT 60 | OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, 61 | SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT 62 | LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, 63 | DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY 64 | THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT 65 | (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE 66 | OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 67 | 68 | ################################################################## 69 | 70 | 3. Example usage. 71 | 72 | This section shows how to use the library to compute 73 | a minimum cut on the following graph: 74 | 75 | SOURCE 76 | / \ 77 | 1/ \2 78 | / 3 \ 79 | node0 -----> node1 80 | | <----- | 81 | | 4 | 82 | \ / 83 | 5\ /6 84 | \ / 85 | SINK 86 | 87 | /////////////////////////////////////////////////// 88 | 89 | #include 90 | #include "graph.h" 91 | 92 | int main() 93 | { 94 | typedef Graph GraphType; 95 | GraphType *g = new GraphType(/*estimated # of nodes*/ 2, /*estimated # of edges*/ 1); 96 | 97 | g -> add_node(); 98 | g -> add_node(); 99 | 100 | g -> add_tweights( 0, /* capacities */ 1, 5 ); 101 | g -> add_tweights( 1, /* capacities */ 2, 6 ); 102 | g -> add_edge( 0, 1, /* capacities */ 3, 4 ); 103 | 104 | int flow = g -> maxflow(); 105 | 106 | printf("Flow = %d\n", flow); 107 | printf("Minimum cut:\n"); 108 | if (g->what_segment(0) == GraphType::SOURCE) 109 | printf("node0 is in the SOURCE set\n"); 110 | else 111 | printf("node0 is in the SINK set\n"); 112 | if (g->what_segment(1) == GraphType::SOURCE) 113 | printf("node1 is in the SOURCE set\n"); 114 | else 115 | printf("node1 is in the SINK set\n"); 116 | 117 | delete g; 118 | 119 | return 0; 120 | } 121 | 122 | 123 | /////////////////////////////////////////////////// 124 | -------------------------------------------------------------------------------- /maxflow-v3.03.src/block.h: -------------------------------------------------------------------------------- 1 | /* block.h */ 2 | /* Vladimir Kolmogorov vnk@ist.ac.at */ 3 | /* Last modified 08/05/2012 */ 4 | /* 5 | Template classes Block and DBlock 6 | Implement adding and deleting items of the same type in blocks. 7 | 8 | If there there are many items then using Block or DBlock 9 | is more efficient than using 'new' and 'delete' both in terms 10 | of memory and time since 11 | (1) On some systems there is some minimum amount of memory 12 | that 'new' can allocate (e.g., 64), so if items are 13 | small that a lot of memory is wasted. 14 | (2) 'new' and 'delete' are designed for items of varying size. 15 | If all items has the same size, then an algorithm for 16 | adding and deleting can be made more efficient. 17 | (3) All Block and DBlock functions are inline, so there are 18 | no extra function calls. 19 | 20 | Differences between Block and DBlock: 21 | (1) DBlock allows both adding and deleting items, 22 | whereas Block allows only adding items. 23 | (2) Block has an additional operation of scanning 24 | items added so far (in the order in which they were added). 25 | (3) Block allows to allocate several consecutive 26 | items at a time, whereas DBlock can add only a single item. 27 | 28 | Note that no constructors or destructors are called for items. 29 | 30 | Example usage for items of type 'MyType': 31 | 32 | /////////////////////////////////////////////////// 33 | #include "block.h" 34 | #define BLOCK_SIZE 1024 35 | typedef struct { int a, b; } MyType; 36 | MyType *ptr, *array[10000]; 37 | 38 | ... 39 | 40 | Block *block = new Block(BLOCK_SIZE); 41 | 42 | // adding items 43 | for (int i=0; i New(); 46 | ptr -> a = ptr -> b = rand(); 47 | } 48 | 49 | // reading items 50 | for (ptr=block->ScanFirst(); ptr; ptr=block->ScanNext()) 51 | { 52 | printf("%d %d\n", ptr->a, ptr->b); 53 | } 54 | 55 | delete block; 56 | 57 | ... 58 | 59 | DBlock *dblock = new DBlock(BLOCK_SIZE); 60 | 61 | // adding items 62 | for (int i=0; i New(); 65 | } 66 | 67 | // deleting items 68 | for (int i=0; i Delete(array[i]); 71 | } 72 | 73 | // adding items 74 | for (int i=0; i New(); 77 | } 78 | 79 | delete dblock; 80 | 81 | /////////////////////////////////////////////////// 82 | 83 | Note that DBlock deletes items by marking them as 84 | empty (i.e., by adding them to the list of free items), 85 | so that this memory could be used for subsequently 86 | added items. Thus, at each moment the memory allocated 87 | is determined by the maximum number of items allocated 88 | simultaneously at earlier moments. All memory is 89 | deallocated only when the destructor is called. 90 | */ 91 | 92 | #ifndef __BLOCK_H__ 93 | #define __BLOCK_H__ 94 | 95 | #include 96 | 97 | /***********************************************************************/ 98 | /***********************************************************************/ 99 | /***********************************************************************/ 100 | 101 | template class Block 102 | { 103 | public: 104 | /* Constructor. Arguments are the block size and 105 | (optionally) the pointer to the function which 106 | will be called if allocation failed; the message 107 | passed to this function is "Not enough memory!" */ 108 | Block(int size, void (*err_function)(const char *) = NULL) { first = last = NULL; block_size = size; error_function = err_function; } 109 | 110 | /* Destructor. Deallocates all items added so far */ 111 | ~Block() { while (first) { block *next = first -> next; delete[] ((char*)first); first = next; } } 112 | 113 | /* Allocates 'num' consecutive items; returns pointer 114 | to the first item. 'num' cannot be greater than the 115 | block size since items must fit in one block */ 116 | Type *New(int num = 1) 117 | { 118 | Type *t; 119 | 120 | if (!last || last->current + num > last->last) 121 | { 122 | if (last && last->next) last = last -> next; 123 | else 124 | { 125 | block *next = (block *) new char [sizeof(block) + (block_size-1)*sizeof(Type)]; 126 | if (!next) { if (error_function) (*error_function)("Not enough memory!"); exit(1); } 127 | if (last) last -> next = next; 128 | else first = next; 129 | last = next; 130 | last -> current = & ( last -> data[0] ); 131 | last -> last = last -> current + block_size; 132 | last -> next = NULL; 133 | } 134 | } 135 | 136 | t = last -> current; 137 | last -> current += num; 138 | return t; 139 | } 140 | 141 | /* Returns the first item (or NULL, if no items were added) */ 142 | Type *ScanFirst() 143 | { 144 | for (scan_current_block=first; scan_current_block; scan_current_block = scan_current_block->next) 145 | { 146 | scan_current_data = & ( scan_current_block -> data[0] ); 147 | if (scan_current_data < scan_current_block -> current) return scan_current_data ++; 148 | } 149 | return NULL; 150 | } 151 | 152 | /* Returns the next item (or NULL, if all items have been read) 153 | Can be called only if previous ScanFirst() or ScanNext() 154 | call returned not NULL. */ 155 | Type *ScanNext() 156 | { 157 | while (scan_current_data >= scan_current_block -> current) 158 | { 159 | scan_current_block = scan_current_block -> next; 160 | if (!scan_current_block) return NULL; 161 | scan_current_data = & ( scan_current_block -> data[0] ); 162 | } 163 | return scan_current_data ++; 164 | } 165 | 166 | struct iterator; // for overlapping scans 167 | Type *ScanFirst(iterator& i) 168 | { 169 | for (i.scan_current_block=first; i.scan_current_block; i.scan_current_block = i.scan_current_block->next) 170 | { 171 | i.scan_current_data = & ( i.scan_current_block -> data[0] ); 172 | if (i.scan_current_data < i.scan_current_block -> current) return i.scan_current_data ++; 173 | } 174 | return NULL; 175 | } 176 | Type *ScanNext(iterator& i) 177 | { 178 | while (i.scan_current_data >= i.scan_current_block -> current) 179 | { 180 | i.scan_current_block = i.scan_current_block -> next; 181 | if (!i.scan_current_block) return NULL; 182 | i.scan_current_data = & ( i.scan_current_block -> data[0] ); 183 | } 184 | return i.scan_current_data ++; 185 | } 186 | 187 | /* Marks all elements as empty */ 188 | void Reset() 189 | { 190 | block *b; 191 | if (!first) return; 192 | for (b=first; ; b=b->next) 193 | { 194 | b -> current = & ( b -> data[0] ); 195 | if (b == last) break; 196 | } 197 | last = first; 198 | } 199 | 200 | /***********************************************************************/ 201 | 202 | private: 203 | 204 | typedef struct block_st 205 | { 206 | Type *current, *last; 207 | struct block_st *next; 208 | Type data[1]; 209 | } block; 210 | 211 | int block_size; 212 | block *first; 213 | block *last; 214 | public: 215 | struct iterator 216 | { 217 | block *scan_current_block; 218 | Type *scan_current_data; 219 | }; 220 | private: 221 | block *scan_current_block; 222 | Type *scan_current_data; 223 | 224 | void (*error_function)(const char *); 225 | }; 226 | 227 | /***********************************************************************/ 228 | /***********************************************************************/ 229 | /***********************************************************************/ 230 | 231 | template class DBlock 232 | { 233 | public: 234 | /* Constructor. Arguments are the block size and 235 | (optionally) the pointer to the function which 236 | will be called if allocation failed; the message 237 | passed to this function is "Not enough memory!" */ 238 | DBlock(int size, void (*err_function)(const char *) = NULL) { first = NULL; first_free = NULL; block_size = size; error_function = err_function; } 239 | 240 | /* Destructor. Deallocates all items added so far */ 241 | ~DBlock() { while (first) { block *next = first -> next; delete[] ((char*)first); first = next; } } 242 | 243 | /* Allocates one item */ 244 | Type *New() 245 | { 246 | block_item *item; 247 | 248 | if (!first_free) 249 | { 250 | block *next = first; 251 | first = (block *) new char [sizeof(block) + (block_size-1)*sizeof(block_item)]; 252 | if (!first) { if (error_function) (*error_function)("Not enough memory!"); exit(1); } 253 | first_free = & (first -> data[0] ); 254 | for (item=first_free; item next_free = item + 1; 256 | item -> next_free = NULL; 257 | first -> next = next; 258 | } 259 | 260 | item = first_free; 261 | first_free = item -> next_free; 262 | return (Type *) item; 263 | } 264 | 265 | /* Deletes an item allocated previously */ 266 | void Delete(Type *t) 267 | { 268 | ((block_item *) t) -> next_free = first_free; 269 | first_free = (block_item *) t; 270 | } 271 | 272 | /***********************************************************************/ 273 | 274 | private: 275 | 276 | typedef union block_item_st 277 | { 278 | Type t; 279 | block_item_st *next_free; 280 | } block_item; 281 | 282 | typedef struct block_st 283 | { 284 | struct block_st *next; 285 | block_item data[1]; 286 | } block; 287 | 288 | int block_size; 289 | block *first; 290 | block_item *first_free; 291 | 292 | void (*error_function)(const char *); 293 | }; 294 | 295 | 296 | #endif 297 | 298 | -------------------------------------------------------------------------------- /maxflow-v3.03.src/graph.cpp: -------------------------------------------------------------------------------- 1 | /* graph.cpp */ 2 | 3 | #ifndef __GRAPH_CPP__ 4 | #define __GRAPH_CPP__ 5 | 6 | #include 7 | #include 8 | #include 9 | #include "graph.h" 10 | 11 | /* 12 | special constants for node->parent. Duplicated in maxflow.cpp, both should match! 13 | */ 14 | #define TERMINAL ( (arc *) 1 ) /* to terminal */ 15 | #define ORPHAN ( (arc *) 2 ) /* orphan */ 16 | 17 | template 18 | Graph::Graph(int node_num_max, int edge_num_max, void (*err_function)(const char *)) 19 | : node_num(0), 20 | nodeptr_block(NULL), 21 | error_function(err_function) 22 | { 23 | if (node_num_max < 16) node_num_max = 16; 24 | if (edge_num_max < 16) edge_num_max = 16; 25 | 26 | nodes = (node*) malloc(node_num_max*sizeof(node)); 27 | arcs = (arc*) malloc(2*edge_num_max*sizeof(arc)); 28 | if (!nodes || !arcs) { if (error_function) (*error_function)("Not enough memory!"); exit(1); } 29 | 30 | node_last = nodes; 31 | node_max = nodes + node_num_max; 32 | arc_last = arcs; 33 | arc_max = arcs + 2*edge_num_max; 34 | 35 | maxflow_iteration = 0; 36 | flow = 0; 37 | } 38 | 39 | template 40 | Graph::~Graph() 41 | { 42 | if (nodeptr_block) 43 | { 44 | delete nodeptr_block; 45 | nodeptr_block = NULL; 46 | } 47 | free(nodes); 48 | free(arcs); 49 | } 50 | 51 | template 52 | void Graph::reset() 53 | { 54 | node_last = nodes; 55 | arc_last = arcs; 56 | node_num = 0; 57 | 58 | if (nodeptr_block) 59 | { 60 | delete nodeptr_block; 61 | nodeptr_block = NULL; 62 | } 63 | 64 | maxflow_iteration = 0; 65 | flow = 0; 66 | } 67 | 68 | template 69 | void Graph::reallocate_nodes(int num) 70 | { 71 | int node_num_max = (int)(node_max - nodes); 72 | node* nodes_old = nodes; 73 | 74 | node_num_max += node_num_max / 2; 75 | if (node_num_max < node_num + num) node_num_max = node_num + num; 76 | nodes = (node*) realloc(nodes_old, node_num_max*sizeof(node)); 77 | if (!nodes) { if (error_function) (*error_function)("Not enough memory!"); exit(1); } 78 | 79 | node_last = nodes + node_num; 80 | node_max = nodes + node_num_max; 81 | 82 | if (nodes != nodes_old) 83 | { 84 | node* i; 85 | arc* a; 86 | for (i=nodes; inext) i->next = (node*) ((char*)i->next + (((char*) nodes) - ((char*) nodes_old))); 89 | } 90 | for (a=arcs; ahead = (node*) ((char*)a->head + (((char*) nodes) - ((char*) nodes_old))); 93 | } 94 | } 95 | } 96 | 97 | template 98 | void Graph::reallocate_arcs() 99 | { 100 | int arc_num_max = (int)(arc_max - arcs); 101 | int arc_num = (int)(arc_last - arcs); 102 | arc* arcs_old = arcs; 103 | 104 | arc_num_max += arc_num_max / 2; if (arc_num_max & 1) arc_num_max ++; 105 | arcs = (arc*) realloc(arcs_old, arc_num_max*sizeof(arc)); 106 | if (!arcs) { if (error_function) (*error_function)("Not enough memory!"); exit(1); } 107 | 108 | arc_last = arcs + arc_num; 109 | arc_max = arcs + arc_num_max; 110 | 111 | if (arcs != arcs_old) 112 | { 113 | node* i; 114 | arc* a; 115 | for (i=nodes; ifirst) i->first = (arc*) ((char*)i->first + (((char*) arcs) - ((char*) arcs_old))); 118 | if (i->parent && i->parent != ORPHAN && i->parent != TERMINAL) i->parent = (arc*) ((char*)i->parent + (((char*) arcs) - ((char*) arcs_old))); 119 | } 120 | for (a=arcs; anext) a->next = (arc*) ((char*)a->next + (((char*) arcs) - ((char*) arcs_old))); 123 | a->sister = (arc*) ((char*)a->sister + (((char*) arcs) - ((char*) arcs_old))); 124 | } 125 | } 126 | } 127 | 128 | #ifndef __INSTANCES_INC__ 129 | #define __INSTANCES_INC__ 130 | 131 | #include "instances.inc" 132 | 133 | #endif 134 | 135 | #endif 136 | -------------------------------------------------------------------------------- /maxflow-v3.03.src/graph.h: -------------------------------------------------------------------------------- 1 | /* graph.h */ 2 | /* 3 | Copyright Vladimir Kolmogorov (vnk@ist.ac.at), Yuri Boykov (yuri@csd.uwo.ca) 4 | 5 | This file is part of MAXFLOW. 6 | 7 | MAXFLOW is free software: you can redistribute it and/or modify 8 | it under the terms of the GNU General Public License as published by 9 | the Free Software Foundation, either version 3 of the License, or 10 | (at your option) any later version. 11 | 12 | MAXFLOW is distributed in the hope that it will be useful, 13 | but WITHOUT ANY WARRANTY; without even the implied warranty of 14 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 15 | GNU General Public License for more details. 16 | 17 | You should have received a copy of the GNU General Public License 18 | along with MAXFLOW. If not, see . 19 | 20 | ======================== 21 | 22 | version 3.03 23 | 24 | This software library implements the maxflow algorithm 25 | described in 26 | 27 | "An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision." 28 | Yuri Boykov and Vladimir Kolmogorov. 29 | In IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 30 | September 2004 31 | 32 | This algorithm was developed by Yuri Boykov and Vladimir Kolmogorov 33 | at Siemens Corporate Research. To make it available for public use, 34 | it was later reimplemented by Vladimir Kolmogorov based on open publications. 35 | 36 | If you use this software for research purposes, you should cite 37 | the aforementioned paper in any resulting publication. 38 | 39 | ---------------------------------------------------------------------- 40 | 41 | REUSING TREES: 42 | 43 | Starting with version 3.0, there is a also an option of reusing search 44 | trees from one maxflow computation to the next, as described in 45 | 46 | "Efficiently Solving Dynamic Markov Random Fields Using Graph Cuts." 47 | Pushmeet Kohli and Philip H.S. Torr 48 | International Conference on Computer Vision (ICCV), 2005 49 | 50 | If you use this option, you should cite 51 | the aforementioned paper in any resulting publication. 52 | */ 53 | 54 | 55 | 56 | /* 57 | For description, license, example usage see README.TXT. 58 | */ 59 | 60 | #ifndef __GRAPH_H__ 61 | #define __GRAPH_H__ 62 | 63 | #include 64 | #include "block.h" 65 | 66 | #include 67 | // NOTE: in UNIX you need to use -DNDEBUG preprocessor option to supress assert's!!! 68 | 69 | 70 | 71 | // captype: type of edge capacities (excluding t-links) 72 | // tcaptype: type of t-links (edges between nodes and terminals) 73 | // flowtype: type of total flow 74 | // 75 | // Current instantiations are in instances.inc 76 | template class Graph 77 | { 78 | public: 79 | typedef enum 80 | { 81 | SOURCE = 0, 82 | SINK = 1 83 | } termtype; // terminals 84 | typedef int node_id; 85 | 86 | ///////////////////////////////////////////////////////////////////////// 87 | // BASIC INTERFACE FUNCTIONS // 88 | // (should be enough for most applications) // 89 | ///////////////////////////////////////////////////////////////////////// 90 | 91 | // Constructor. 92 | // The first argument gives an estimate of the maximum number of nodes that can be added 93 | // to the graph, and the second argument is an estimate of the maximum number of edges. 94 | // The last (optional) argument is the pointer to the function which will be called 95 | // if an error occurs; an error message is passed to this function. 96 | // If this argument is omitted, exit(1) will be called. 97 | // 98 | // IMPORTANT: It is possible to add more nodes to the graph than node_num_max 99 | // (and node_num_max can be zero). However, if the count is exceeded, then 100 | // the internal memory is reallocated (increased by 50%) which is expensive. 101 | // Also, temporarily the amount of allocated memory would be more than twice than needed. 102 | // Similarly for edges. 103 | // If you wish to avoid this overhead, you can download version 2.2, where nodes and edges are stored in blocks. 104 | Graph(int node_num_max, int edge_num_max, void (*err_function)(const char *) = NULL); 105 | 106 | // Destructor 107 | ~Graph(); 108 | 109 | // Adds node(s) to the graph. By default, one node is added (num=1); then first call returns 0, second call returns 1, and so on. 110 | // If num>1, then several nodes are added, and node_id of the first one is returned. 111 | // IMPORTANT: see note about the constructor 112 | node_id add_node(int num = 1); 113 | 114 | // Adds a bidirectional edge between 'i' and 'j' with the weights 'cap' and 'rev_cap'. 115 | // IMPORTANT: see note about the constructor 116 | void add_edge(node_id i, node_id j, captype cap, captype rev_cap); 117 | 118 | // Adds new edges 'SOURCE->i' and 'i->SINK' with corresponding weights. 119 | // Can be called multiple times for each node. 120 | // Weights can be negative. 121 | // NOTE: the number of such edges is not counted in edge_num_max. 122 | // No internal memory is allocated by this call. 123 | void add_tweights(node_id i, tcaptype cap_source, tcaptype cap_sink); 124 | 125 | 126 | // Computes the maxflow. Can be called several times. 127 | // FOR DESCRIPTION OF reuse_trees, SEE mark_node(). 128 | // FOR DESCRIPTION OF changed_list, SEE remove_from_changed_list(). 129 | flowtype maxflow(bool reuse_trees = false, Block* changed_list = NULL); 130 | 131 | // After the maxflow is computed, this function returns to which 132 | // segment the node 'i' belongs (Graph::SOURCE or Graph::SINK). 133 | // 134 | // Occasionally there may be several minimum cuts. If a node can be assigned 135 | // to both the source and the sink, then default_segm is returned. 136 | termtype what_segment(node_id i, termtype default_segm = SOURCE); 137 | 138 | 139 | 140 | ////////////////////////////////////////////// 141 | // ADVANCED INTERFACE FUNCTIONS // 142 | // (provide access to the graph) // 143 | ////////////////////////////////////////////// 144 | 145 | private: 146 | struct node; 147 | struct arc; 148 | 149 | public: 150 | 151 | //////////////////////////// 152 | // 1. Reallocating graph. // 153 | //////////////////////////// 154 | 155 | // Removes all nodes and edges. 156 | // After that functions add_node() and add_edge() must be called again. 157 | // 158 | // Advantage compared to deleting Graph and allocating it again: 159 | // no calls to delete/new (which could be quite slow). 160 | // 161 | // If the graph structure stays the same, then an alternative 162 | // is to go through all nodes/edges and set new residual capacities 163 | // (see functions below). 164 | void reset(); 165 | 166 | //////////////////////////////////////////////////////////////////////////////// 167 | // 2. Functions for getting pointers to arcs and for reading graph structure. // 168 | // NOTE: adding new arcs may invalidate these pointers (if reallocation // 169 | // happens). So it's best not to add arcs while reading graph structure. // 170 | //////////////////////////////////////////////////////////////////////////////// 171 | 172 | // The following two functions return arcs in the same order that they 173 | // were added to the graph. NOTE: for each call add_edge(i,j,cap,cap_rev) 174 | // the first arc returned will be i->j, and the second j->i. 175 | // If there are no more arcs, then the function can still be called, but 176 | // the returned arc_id is undetermined. 177 | typedef arc* arc_id; 178 | arc_id get_first_arc(); 179 | arc_id get_next_arc(arc_id a); 180 | 181 | // other functions for reading graph structure 182 | int get_node_num() { return node_num; } 183 | int get_arc_num() { return (int)(arc_last - arcs); } 184 | void get_arc_ends(arc_id a, node_id& i, node_id& j); // returns i,j to that a = i->j 185 | 186 | /////////////////////////////////////////////////// 187 | // 3. Functions for reading residual capacities. // 188 | /////////////////////////////////////////////////// 189 | 190 | // returns residual capacity of SOURCE->i minus residual capacity of i->SINK 191 | tcaptype get_trcap(node_id i); 192 | // returns residual capacity of arc a 193 | captype get_rcap(arc* a); 194 | 195 | ///////////////////////////////////////////////////////////////// 196 | // 4. Functions for setting residual capacities. // 197 | // NOTE: If these functions are used, the value of the flow // 198 | // returned by maxflow() will not be valid! // 199 | ///////////////////////////////////////////////////////////////// 200 | 201 | void set_trcap(node_id i, tcaptype trcap); 202 | void set_rcap(arc* a, captype rcap); 203 | 204 | //////////////////////////////////////////////////////////////////// 205 | // 5. Functions related to reusing trees & list of changed nodes. // 206 | //////////////////////////////////////////////////////////////////// 207 | 208 | // If flag reuse_trees is true while calling maxflow(), then search trees 209 | // are reused from previous maxflow computation. 210 | // In this case before calling maxflow() the user must 211 | // specify which parts of the graph have changed by calling mark_node(): 212 | // add_tweights(i),set_trcap(i) => call mark_node(i) 213 | // add_edge(i,j),set_rcap(a) => call mark_node(i); mark_node(j) 214 | // 215 | // This option makes sense only if a small part of the graph is changed. 216 | // The initialization procedure goes only through marked nodes then. 217 | // 218 | // mark_node(i) can either be called before or after graph modification. 219 | // Can be called more than once per node, but calls after the first one 220 | // do not have any effect. 221 | // 222 | // NOTE: 223 | // - This option cannot be used in the first call to maxflow(). 224 | // - It is not necessary to call mark_node() if the change is ``not essential'', 225 | // i.e. sign(trcap) is preserved for a node and zero/nonzero status is preserved for an arc. 226 | // - To check that you marked all necessary nodes, you can call maxflow(false) after calling maxflow(true). 227 | // If everything is correct, the two calls must return the same value of flow. (Useful for debugging). 228 | void mark_node(node_id i); 229 | 230 | // If changed_list is not NULL while calling maxflow(), then the algorithm 231 | // keeps a list of nodes which could potentially have changed their segmentation label. 232 | // Nodes which are not in the list are guaranteed to keep their old segmentation label (SOURCE or SINK). 233 | // Example usage: 234 | // 235 | // typedef Graph G; 236 | // G* g = new Graph(nodeNum, edgeNum); 237 | // Block* changed_list = new Block(128); 238 | // 239 | // ... // add nodes and edges 240 | // 241 | // g->maxflow(); // first call should be without arguments 242 | // for (int iter=0; iter<10; iter++) 243 | // { 244 | // ... // change graph, call mark_node() accordingly 245 | // 246 | // g->maxflow(true, changed_list); 247 | // G::node_id* ptr; 248 | // for (ptr=changed_list->ScanFirst(); ptr; ptr=changed_list->ScanNext()) 249 | // { 250 | // G::node_id i = *ptr; assert(i>=0 && iremove_from_changed_list(i); 252 | // // do something with node i... 253 | // if (g->what_segment(i) == G::SOURCE) { ... } 254 | // } 255 | // changed_list->Reset(); 256 | // } 257 | // delete changed_list; 258 | // 259 | // NOTE: 260 | // - If changed_list option is used, then reuse_trees must be used as well. 261 | // - In the example above, the user may omit calls g->remove_from_changed_list(i) and changed_list->Reset() in a given iteration. 262 | // Then during the next call to maxflow(true, &changed_list) new nodes will be added to changed_list. 263 | // - If the next call to maxflow() does not use option reuse_trees, then calling remove_from_changed_list() 264 | // is not necessary. ("changed_list->Reset()" or "delete changed_list" should still be called, though). 265 | void remove_from_changed_list(node_id i) 266 | { 267 | assert(i>=0 && i 0 then tr_cap is residual capacity of the arc SOURCE->node 297 | // otherwise -tr_cap is residual capacity of the arc node->SINK 298 | 299 | }; 300 | 301 | struct arc 302 | { 303 | node *head; // node the arc points to 304 | arc *next; // next arc with the same originating node 305 | arc *sister; // reverse arc 306 | 307 | captype r_cap; // residual capacity 308 | }; 309 | 310 | struct nodeptr 311 | { 312 | node *ptr; 313 | nodeptr *next; 314 | }; 315 | static const int NODEPTR_BLOCK_SIZE = 128; 316 | 317 | node *nodes, *node_last, *node_max; // node_last = nodes+node_num, node_max = nodes+node_num_max; 318 | arc *arcs, *arc_last, *arc_max; // arc_last = arcs+2*edge_num, arc_max = arcs+2*edge_num_max; 319 | 320 | int node_num; 321 | 322 | DBlock *nodeptr_block; 323 | 324 | void (*error_function)(const char *); // this function is called if a error occurs, 325 | // with a corresponding error message 326 | // (or exit(1) is called if it's NULL) 327 | 328 | flowtype flow; // total flow 329 | 330 | // reusing trees & list of changed pixels 331 | int maxflow_iteration; // counter 332 | Block *changed_list; 333 | 334 | ///////////////////////////////////////////////////////////////////////// 335 | 336 | node *queue_first[2], *queue_last[2]; // list of active nodes 337 | nodeptr *orphan_first, *orphan_last; // list of pointers to orphans 338 | int TIME; // monotonically increasing global counter 339 | 340 | ///////////////////////////////////////////////////////////////////////// 341 | 342 | void reallocate_nodes(int num); // num is the number of new nodes 343 | void reallocate_arcs(); 344 | 345 | // functions for processing active list 346 | void set_active(node *i); 347 | node *next_active(); 348 | 349 | // functions for processing orphans list 350 | void set_orphan_front(node* i); // add to the beginning of the list 351 | void set_orphan_rear(node* i); // add to the end of the list 352 | 353 | void add_to_changed_list(node* i); 354 | 355 | void maxflow_init(); // called if reuse_trees == false 356 | void maxflow_reuse_trees_init(); // called if reuse_trees == true 357 | void augment(arc *middle_arc); 358 | void process_source_orphan(node *i); 359 | void process_sink_orphan(node *i); 360 | 361 | void test_consistency(node* current_node=NULL); // debug function 362 | }; 363 | 364 | 365 | 366 | 367 | 368 | 369 | 370 | 371 | 372 | 373 | 374 | /////////////////////////////////////// 375 | // Implementation - inline functions // 376 | /////////////////////////////////////// 377 | 378 | 379 | 380 | template 381 | inline typename Graph::node_id Graph::add_node(int num) 382 | { 383 | assert(num > 0); 384 | 385 | if (node_last + num > node_max) reallocate_nodes(num); 386 | 387 | memset(node_last, 0, num*sizeof(node)); 388 | 389 | node_id i = node_num; 390 | node_num += num; 391 | node_last += num; 392 | return i; 393 | } 394 | 395 | template 396 | inline void Graph::add_tweights(node_id i, tcaptype cap_source, tcaptype cap_sink) 397 | { 398 | assert(i >= 0 && i < node_num); 399 | 400 | tcaptype delta = nodes[i].tr_cap; 401 | if (delta > 0) cap_source += delta; 402 | else cap_sink -= delta; 403 | flow += (cap_source < cap_sink) ? cap_source : cap_sink; 404 | nodes[i].tr_cap = cap_source - cap_sink; 405 | } 406 | 407 | template 408 | inline void Graph::add_edge(node_id _i, node_id _j, captype cap, captype rev_cap) 409 | { 410 | assert(_i >= 0 && _i < node_num); 411 | assert(_j >= 0 && _j < node_num); 412 | assert(_i != _j); 413 | assert(cap >= 0); 414 | assert(rev_cap >= 0); 415 | 416 | if (arc_last == arc_max) reallocate_arcs(); 417 | 418 | arc *a = arc_last ++; 419 | arc *a_rev = arc_last ++; 420 | 421 | node* i = nodes + _i; 422 | node* j = nodes + _j; 423 | 424 | a -> sister = a_rev; 425 | a_rev -> sister = a; 426 | a -> next = i -> first; 427 | i -> first = a; 428 | a_rev -> next = j -> first; 429 | j -> first = a_rev; 430 | a -> head = j; 431 | a_rev -> head = i; 432 | a -> r_cap = cap; 433 | a_rev -> r_cap = rev_cap; 434 | } 435 | 436 | template 437 | inline typename Graph::arc* Graph::get_first_arc() 438 | { 439 | return arcs; 440 | } 441 | 442 | template 443 | inline typename Graph::arc* Graph::get_next_arc(arc* a) 444 | { 445 | return a + 1; 446 | } 447 | 448 | template 449 | inline void Graph::get_arc_ends(arc* a, node_id& i, node_id& j) 450 | { 451 | assert(a >= arcs && a < arc_last); 452 | i = (node_id) (a->sister->head - nodes); 453 | j = (node_id) (a->head - nodes); 454 | } 455 | 456 | template 457 | inline tcaptype Graph::get_trcap(node_id i) 458 | { 459 | assert(i>=0 && i 464 | inline captype Graph::get_rcap(arc* a) 465 | { 466 | assert(a >= arcs && a < arc_last); 467 | return a->r_cap; 468 | } 469 | 470 | template 471 | inline void Graph::set_trcap(node_id i, tcaptype trcap) 472 | { 473 | assert(i>=0 && i 478 | inline void Graph::set_rcap(arc* a, captype rcap) 479 | { 480 | assert(a >= arcs && a < arc_last); 481 | a->r_cap = rcap; 482 | } 483 | 484 | 485 | template 486 | inline typename Graph::termtype Graph::what_segment(node_id i, termtype default_segm) 487 | { 488 | if (nodes[i].parent) 489 | { 490 | return (nodes[i].is_sink) ? SINK : SOURCE; 491 | } 492 | else 493 | { 494 | return default_segm; 495 | } 496 | } 497 | 498 | template 499 | inline void Graph::mark_node(node_id _i) 500 | { 501 | node* i = nodes + _i; 502 | if (!i->next) 503 | { 504 | /* it's not in the list yet */ 505 | if (queue_last[1]) queue_last[1] -> next = i; 506 | else queue_first[1] = i; 507 | queue_last[1] = i; 508 | i -> next = i; 509 | } 510 | i->is_marked = 1; 511 | } 512 | 513 | 514 | #endif 515 | -------------------------------------------------------------------------------- /maxflow-v3.03.src/instances.inc: -------------------------------------------------------------------------------- 1 | #include "graph.h" 2 | 3 | #ifdef _MSC_VER 4 | #pragma warning(disable: 4661) 5 | #endif 6 | 7 | // Instantiations: 8 | // IMPORTANT: 9 | // flowtype should be 'larger' than tcaptype 10 | // tcaptype should be 'larger' than captype 11 | 12 | template class Graph; 13 | template class Graph; 14 | template class Graph; 15 | template class Graph; 16 | 17 | -------------------------------------------------------------------------------- /maxflow-v3.03.src/maxflow.cpp: -------------------------------------------------------------------------------- 1 | /* maxflow.cpp */ 2 | 3 | #ifndef __MAXFLOW_CPP__ 4 | #define __MAXFLOW_CPP__ 5 | 6 | 7 | #include 8 | #include "graph.h" 9 | 10 | 11 | /* 12 | special constants for node->parent. Duplicated in graph.cpp, both should match! 13 | */ 14 | #define TERMINAL ( (arc *) 1 ) /* to terminal */ 15 | #define ORPHAN ( (arc *) 2 ) /* orphan */ 16 | 17 | 18 | #define INFINITE_D ((int)(((unsigned)-1)/2)) /* infinite distance to the terminal */ 19 | 20 | /***********************************************************************/ 21 | 22 | /* 23 | Functions for processing active list. 24 | i->next points to the next node in the list 25 | (or to i, if i is the last node in the list). 26 | If i->next is NULL iff i is not in the list. 27 | 28 | There are two queues. Active nodes are added 29 | to the end of the second queue and read from 30 | the front of the first queue. If the first queue 31 | is empty, it is replaced by the second queue 32 | (and the second queue becomes empty). 33 | */ 34 | 35 | 36 | template 37 | inline void Graph::set_active(node *i) 38 | { 39 | if (!i->next) 40 | { 41 | /* it's not in the list yet */ 42 | if (queue_last[1]) queue_last[1] -> next = i; 43 | else queue_first[1] = i; 44 | queue_last[1] = i; 45 | i -> next = i; 46 | } 47 | } 48 | 49 | /* 50 | Returns the next active node. 51 | If it is connected to the sink, it stays in the list, 52 | otherwise it is removed from the list 53 | */ 54 | template 55 | inline typename Graph::node* Graph::next_active() 56 | { 57 | node *i; 58 | 59 | while ( 1 ) 60 | { 61 | if (!(i=queue_first[0])) 62 | { 63 | queue_first[0] = i = queue_first[1]; 64 | queue_last[0] = queue_last[1]; 65 | queue_first[1] = NULL; 66 | queue_last[1] = NULL; 67 | if (!i) return NULL; 68 | } 69 | 70 | /* remove it from the active list */ 71 | if (i->next == i) queue_first[0] = queue_last[0] = NULL; 72 | else queue_first[0] = i -> next; 73 | i -> next = NULL; 74 | 75 | /* a node in the list is active iff it has a parent */ 76 | if (i->parent) return i; 77 | } 78 | } 79 | 80 | /***********************************************************************/ 81 | 82 | template 83 | inline void Graph::set_orphan_front(node *i) 84 | { 85 | nodeptr *np; 86 | i -> parent = ORPHAN; 87 | np = nodeptr_block -> New(); 88 | np -> ptr = i; 89 | np -> next = orphan_first; 90 | orphan_first = np; 91 | } 92 | 93 | template 94 | inline void Graph::set_orphan_rear(node *i) 95 | { 96 | nodeptr *np; 97 | i -> parent = ORPHAN; 98 | np = nodeptr_block -> New(); 99 | np -> ptr = i; 100 | if (orphan_last) orphan_last -> next = np; 101 | else orphan_first = np; 102 | orphan_last = np; 103 | np -> next = NULL; 104 | } 105 | 106 | /***********************************************************************/ 107 | 108 | template 109 | inline void Graph::add_to_changed_list(node *i) 110 | { 111 | if (changed_list && !i->is_in_changed_list) 112 | { 113 | node_id* ptr = changed_list->New(); 114 | *ptr = (node_id)(i - nodes); 115 | i->is_in_changed_list = true; 116 | } 117 | } 118 | 119 | /***********************************************************************/ 120 | 121 | template 122 | void Graph::maxflow_init() 123 | { 124 | node *i; 125 | 126 | queue_first[0] = queue_last[0] = NULL; 127 | queue_first[1] = queue_last[1] = NULL; 128 | orphan_first = NULL; 129 | 130 | TIME = 0; 131 | 132 | for (i=nodes; i next = NULL; 135 | i -> is_marked = 0; 136 | i -> is_in_changed_list = 0; 137 | i -> TS = TIME; 138 | if (i->tr_cap > 0) 139 | { 140 | /* i is connected to the source */ 141 | i -> is_sink = 0; 142 | i -> parent = TERMINAL; 143 | set_active(i); 144 | i -> DIST = 1; 145 | } 146 | else if (i->tr_cap < 0) 147 | { 148 | /* i is connected to the sink */ 149 | i -> is_sink = 1; 150 | i -> parent = TERMINAL; 151 | set_active(i); 152 | i -> DIST = 1; 153 | } 154 | else 155 | { 156 | i -> parent = NULL; 157 | } 158 | } 159 | } 160 | 161 | template 162 | void Graph::maxflow_reuse_trees_init() 163 | { 164 | node* i; 165 | node* j; 166 | node* queue = queue_first[1]; 167 | arc* a; 168 | nodeptr* np; 169 | 170 | queue_first[0] = queue_last[0] = NULL; 171 | queue_first[1] = queue_last[1] = NULL; 172 | orphan_first = orphan_last = NULL; 173 | 174 | TIME ++; 175 | 176 | while ((i=queue)) 177 | { 178 | queue = i->next; 179 | if (queue == i) queue = NULL; 180 | i->next = NULL; 181 | i->is_marked = 0; 182 | set_active(i); 183 | 184 | if (i->tr_cap == 0) 185 | { 186 | if (i->parent) set_orphan_rear(i); 187 | continue; 188 | } 189 | 190 | if (i->tr_cap > 0) 191 | { 192 | if (!i->parent || i->is_sink) 193 | { 194 | i->is_sink = 0; 195 | for (a=i->first; a; a=a->next) 196 | { 197 | j = a->head; 198 | if (!j->is_marked) 199 | { 200 | if (j->parent == a->sister) set_orphan_rear(j); 201 | if (j->parent && j->is_sink && a->r_cap > 0) set_active(j); 202 | } 203 | } 204 | add_to_changed_list(i); 205 | } 206 | } 207 | else 208 | { 209 | if (!i->parent || !i->is_sink) 210 | { 211 | i->is_sink = 1; 212 | for (a=i->first; a; a=a->next) 213 | { 214 | j = a->head; 215 | if (!j->is_marked) 216 | { 217 | if (j->parent == a->sister) set_orphan_rear(j); 218 | if (j->parent && !j->is_sink && a->sister->r_cap > 0) set_active(j); 219 | } 220 | } 221 | add_to_changed_list(i); 222 | } 223 | } 224 | i->parent = TERMINAL; 225 | i -> TS = TIME; 226 | i -> DIST = 1; 227 | } 228 | 229 | //test_consistency(); 230 | 231 | /* adoption */ 232 | while ((np=orphan_first)) 233 | { 234 | orphan_first = np -> next; 235 | i = np -> ptr; 236 | nodeptr_block -> Delete(np); 237 | if (!orphan_first) orphan_last = NULL; 238 | if (i->is_sink) process_sink_orphan(i); 239 | else process_source_orphan(i); 240 | } 241 | /* adoption end */ 242 | 243 | //test_consistency(); 244 | } 245 | 246 | template 247 | void Graph::augment(arc *middle_arc) 248 | { 249 | node *i; 250 | arc *a; 251 | tcaptype bottleneck; 252 | 253 | 254 | /* 1. Finding bottleneck capacity */ 255 | /* 1a - the source tree */ 256 | bottleneck = middle_arc -> r_cap; 257 | for (i=middle_arc->sister->head; ; i=a->head) 258 | { 259 | a = i -> parent; 260 | if (a == TERMINAL) break; 261 | if (bottleneck > a->sister->r_cap) bottleneck = a -> sister -> r_cap; 262 | } 263 | if (bottleneck > i->tr_cap) bottleneck = i -> tr_cap; 264 | /* 1b - the sink tree */ 265 | for (i=middle_arc->head; ; i=a->head) 266 | { 267 | a = i -> parent; 268 | if (a == TERMINAL) break; 269 | if (bottleneck > a->r_cap) bottleneck = a -> r_cap; 270 | } 271 | if (bottleneck > - i->tr_cap) bottleneck = - i -> tr_cap; 272 | 273 | 274 | /* 2. Augmenting */ 275 | /* 2a - the source tree */ 276 | middle_arc -> sister -> r_cap += bottleneck; 277 | middle_arc -> r_cap -= bottleneck; 278 | for (i=middle_arc->sister->head; ; i=a->head) 279 | { 280 | a = i -> parent; 281 | if (a == TERMINAL) break; 282 | a -> r_cap += bottleneck; 283 | a -> sister -> r_cap -= bottleneck; 284 | if (!a->sister->r_cap) 285 | { 286 | set_orphan_front(i); // add i to the beginning of the adoption list 287 | } 288 | } 289 | i -> tr_cap -= bottleneck; 290 | if (!i->tr_cap) 291 | { 292 | set_orphan_front(i); // add i to the beginning of the adoption list 293 | } 294 | /* 2b - the sink tree */ 295 | for (i=middle_arc->head; ; i=a->head) 296 | { 297 | a = i -> parent; 298 | if (a == TERMINAL) break; 299 | a -> sister -> r_cap += bottleneck; 300 | a -> r_cap -= bottleneck; 301 | if (!a->r_cap) 302 | { 303 | set_orphan_front(i); // add i to the beginning of the adoption list 304 | } 305 | } 306 | i -> tr_cap += bottleneck; 307 | if (!i->tr_cap) 308 | { 309 | set_orphan_front(i); // add i to the beginning of the adoption list 310 | } 311 | 312 | 313 | flow += bottleneck; 314 | } 315 | 316 | /***********************************************************************/ 317 | 318 | template 319 | void Graph::process_source_orphan(node *i) 320 | { 321 | node *j; 322 | arc *a0, *a0_min = NULL, *a; 323 | int d, d_min = INFINITE_D; 324 | 325 | /* trying to find a new parent */ 326 | for (a0=i->first; a0; a0=a0->next) 327 | if (a0->sister->r_cap) 328 | { 329 | j = a0 -> head; 330 | if (!j->is_sink && (a=j->parent)) 331 | { 332 | /* checking the origin of j */ 333 | d = 0; 334 | while ( 1 ) 335 | { 336 | if (j->TS == TIME) 337 | { 338 | d += j -> DIST; 339 | break; 340 | } 341 | a = j -> parent; 342 | d ++; 343 | if (a==TERMINAL) 344 | { 345 | j -> TS = TIME; 346 | j -> DIST = 1; 347 | break; 348 | } 349 | if (a==ORPHAN) { d = INFINITE_D; break; } 350 | j = a -> head; 351 | } 352 | if (dhead; j->TS!=TIME; j=j->parent->head) 361 | { 362 | j -> TS = TIME; 363 | j -> DIST = d --; 364 | } 365 | } 366 | } 367 | } 368 | 369 | if (i->parent = a0_min) 370 | { 371 | i -> TS = TIME; 372 | i -> DIST = d_min + 1; 373 | } 374 | else 375 | { 376 | /* no parent is found */ 377 | add_to_changed_list(i); 378 | 379 | /* process neighbors */ 380 | for (a0=i->first; a0; a0=a0->next) 381 | { 382 | j = a0 -> head; 383 | if (!j->is_sink && (a=j->parent)) 384 | { 385 | if (a0->sister->r_cap) set_active(j); 386 | if (a!=TERMINAL && a!=ORPHAN && a->head==i) 387 | { 388 | set_orphan_rear(j); // add j to the end of the adoption list 389 | } 390 | } 391 | } 392 | } 393 | } 394 | 395 | template 396 | void Graph::process_sink_orphan(node *i) 397 | { 398 | node *j; 399 | arc *a0, *a0_min = NULL, *a; 400 | int d, d_min = INFINITE_D; 401 | 402 | /* trying to find a new parent */ 403 | for (a0=i->first; a0; a0=a0->next) 404 | if (a0->r_cap) 405 | { 406 | j = a0 -> head; 407 | if (j->is_sink && (a=j->parent)) 408 | { 409 | /* checking the origin of j */ 410 | d = 0; 411 | while ( 1 ) 412 | { 413 | if (j->TS == TIME) 414 | { 415 | d += j -> DIST; 416 | break; 417 | } 418 | a = j -> parent; 419 | d ++; 420 | if (a==TERMINAL) 421 | { 422 | j -> TS = TIME; 423 | j -> DIST = 1; 424 | break; 425 | } 426 | if (a==ORPHAN) { d = INFINITE_D; break; } 427 | j = a -> head; 428 | } 429 | if (dhead; j->TS!=TIME; j=j->parent->head) 438 | { 439 | j -> TS = TIME; 440 | j -> DIST = d --; 441 | } 442 | } 443 | } 444 | } 445 | 446 | if (i->parent = a0_min) 447 | { 448 | i -> TS = TIME; 449 | i -> DIST = d_min + 1; 450 | } 451 | else 452 | { 453 | /* no parent is found */ 454 | add_to_changed_list(i); 455 | 456 | /* process neighbors */ 457 | for (a0=i->first; a0; a0=a0->next) 458 | { 459 | j = a0 -> head; 460 | if (j->is_sink && (a=j->parent)) 461 | { 462 | if (a0->r_cap) set_active(j); 463 | if (a!=TERMINAL && a!=ORPHAN && a->head==i) 464 | { 465 | set_orphan_rear(j); // add j to the end of the adoption list 466 | } 467 | } 468 | } 469 | } 470 | } 471 | 472 | /***********************************************************************/ 473 | 474 | template 475 | flowtype Graph::maxflow(bool reuse_trees, Block* _changed_list) 476 | { 477 | node *i, *j, *current_node = NULL; 478 | arc *a; 479 | nodeptr *np, *np_next; 480 | 481 | if (!nodeptr_block) 482 | { 483 | nodeptr_block = new DBlock(NODEPTR_BLOCK_SIZE, error_function); 484 | } 485 | 486 | changed_list = _changed_list; 487 | if (maxflow_iteration == 0 && reuse_trees) { if (error_function) (*error_function)("reuse_trees cannot be used in the first call to maxflow()!"); exit(1); } 488 | if (changed_list && !reuse_trees) { if (error_function) (*error_function)("changed_list cannot be used without reuse_trees!"); exit(1); } 489 | 490 | if (reuse_trees) maxflow_reuse_trees_init(); 491 | else maxflow_init(); 492 | 493 | // main loop 494 | while ( 1 ) 495 | { 496 | // test_consistency(current_node); 497 | 498 | if ((i=current_node)) 499 | { 500 | i -> next = NULL; /* remove active flag */ 501 | if (!i->parent) i = NULL; 502 | } 503 | if (!i) 504 | { 505 | if (!(i = next_active())) break; 506 | } 507 | 508 | /* growth */ 509 | if (!i->is_sink) 510 | { 511 | /* grow source tree */ 512 | for (a=i->first; a; a=a->next) 513 | if (a->r_cap) 514 | { 515 | j = a -> head; 516 | if (!j->parent) 517 | { 518 | j -> is_sink = 0; 519 | j -> parent = a -> sister; 520 | j -> TS = i -> TS; 521 | j -> DIST = i -> DIST + 1; 522 | set_active(j); 523 | add_to_changed_list(j); 524 | } 525 | else if (j->is_sink) break; 526 | else if (j->TS <= i->TS && 527 | j->DIST > i->DIST) 528 | { 529 | /* heuristic - trying to make the distance from j to the source shorter */ 530 | j -> parent = a -> sister; 531 | j -> TS = i -> TS; 532 | j -> DIST = i -> DIST + 1; 533 | } 534 | } 535 | } 536 | else 537 | { 538 | /* grow sink tree */ 539 | for (a=i->first; a; a=a->next) 540 | if (a->sister->r_cap) 541 | { 542 | j = a -> head; 543 | if (!j->parent) 544 | { 545 | j -> is_sink = 1; 546 | j -> parent = a -> sister; 547 | j -> TS = i -> TS; 548 | j -> DIST = i -> DIST + 1; 549 | set_active(j); 550 | add_to_changed_list(j); 551 | } 552 | else if (!j->is_sink) { a = a -> sister; break; } 553 | else if (j->TS <= i->TS && 554 | j->DIST > i->DIST) 555 | { 556 | /* heuristic - trying to make the distance from j to the sink shorter */ 557 | j -> parent = a -> sister; 558 | j -> TS = i -> TS; 559 | j -> DIST = i -> DIST + 1; 560 | } 561 | } 562 | } 563 | 564 | TIME ++; 565 | 566 | if (a) 567 | { 568 | i -> next = i; /* set active flag */ 569 | current_node = i; 570 | 571 | /* augmentation */ 572 | augment(a); 573 | /* augmentation end */ 574 | 575 | /* adoption */ 576 | while ((np=orphan_first)) 577 | { 578 | np_next = np -> next; 579 | np -> next = NULL; 580 | 581 | while ((np=orphan_first)) 582 | { 583 | orphan_first = np -> next; 584 | i = np -> ptr; 585 | nodeptr_block -> Delete(np); 586 | if (!orphan_first) orphan_last = NULL; 587 | if (i->is_sink) process_sink_orphan(i); 588 | else process_source_orphan(i); 589 | } 590 | 591 | orphan_first = np_next; 592 | } 593 | /* adoption end */ 594 | } 595 | else current_node = NULL; 596 | } 597 | // test_consistency(); 598 | 599 | if (!reuse_trees || (maxflow_iteration % 64) == 0) 600 | { 601 | delete nodeptr_block; 602 | nodeptr_block = NULL; 603 | } 604 | 605 | maxflow_iteration ++; 606 | return flow; 607 | } 608 | 609 | /***********************************************************************/ 610 | 611 | 612 | template 613 | void Graph::test_consistency(node* current_node) 614 | { 615 | node *i; 616 | arc *a; 617 | int r; 618 | int num1 = 0, num2 = 0; 619 | 620 | // test whether all nodes i with i->next!=NULL are indeed in the queue 621 | for (i=nodes; inext || i==current_node) num1 ++; 624 | } 625 | for (r=0; r<3; r++) 626 | { 627 | i = (r == 2) ? current_node : queue_first[r]; 628 | if (i) 629 | for ( ; ; i=i->next) 630 | { 631 | num2 ++; 632 | if (i->next == i) 633 | { 634 | if (r<2) assert(i == queue_last[r]); 635 | else assert(i == current_node); 636 | break; 637 | } 638 | } 639 | } 640 | assert(num1 == num2); 641 | 642 | for (i=nodes; iparent == NULL) {} 646 | else if (i->parent == ORPHAN) {} 647 | else if (i->parent == TERMINAL) 648 | { 649 | if (!i->is_sink) assert(i->tr_cap > 0); 650 | else assert(i->tr_cap < 0); 651 | } 652 | else 653 | { 654 | if (!i->is_sink) assert (i->parent->sister->r_cap > 0); 655 | else assert (i->parent->r_cap > 0); 656 | } 657 | // test whether passive nodes in search trees have neighbors in 658 | // a different tree through non-saturated edges 659 | if (i->parent && !i->next) 660 | { 661 | if (!i->is_sink) 662 | { 663 | assert(i->tr_cap >= 0); 664 | for (a=i->first; a; a=a->next) 665 | { 666 | if (a->r_cap > 0) assert(a->head->parent && !a->head->is_sink); 667 | } 668 | } 669 | else 670 | { 671 | assert(i->tr_cap <= 0); 672 | for (a=i->first; a; a=a->next) 673 | { 674 | if (a->sister->r_cap > 0) assert(a->head->parent && a->head->is_sink); 675 | } 676 | } 677 | } 678 | // test marking invariants 679 | if (i->parent && i->parent!=ORPHAN && i->parent!=TERMINAL) 680 | { 681 | assert(i->TS <= i->parent->head->TS); 682 | if (i->TS == i->parent->head->TS) assert(i->DIST > i->parent->head->DIST); 683 | } 684 | } 685 | } 686 | 687 | #ifndef __INSTANCES_INC__ 688 | #define __INSTANCES_INC__ 689 | 690 | #include "instances.inc" 691 | 692 | #endif 693 | 694 | #endif 695 | 696 | -------------------------------------------------------------------------------- /mbs_saliency.m: -------------------------------------------------------------------------------- 1 | function [pMap] = mbs_saliency(img) 2 | 3 | % This is a matlab implementation of the method described in: 4 | % 5 | % "Minimum Barrier Salient Object Detection at 80 FPS", Jianming Zhang, 6 | % Stan Sclaroff, Zhe Lin, Xiaohui Shen, Brian Price, Radomir Mech, ICCV, 2015 7 | % 8 | % Contact: jimmie33@gmail.com 9 | % 10 | % Prerequisite: OpenCV 2.4+ 11 | % 12 | % Usage: 13 | % 14 | % 1. Go to the folder "mex" 15 | % 2. modify the opencv include and lib paths in "compile.m/compile_win.m" 16 | % (for Linux/Windows) 17 | % 3. run "compile/compile_win" in matlab (for Linux/Windows) 18 | % 4. Go to the root folder 19 | % 5. run "demo" 20 | % 21 | % 22 | % This matlab implementation is provided for research purpose only. For fully 23 | % reproducing the results in our ICCV paper, please use the original Windows 24 | % executable program. 25 | % 26 | % The matlab implementation is slower than the window executable, mainly due 27 | % to the morphological post-processing step. We use the highly optimized IPP 28 | % library for the morphological operations in our C++ implementation, which 29 | % are much faster than the corresponding Matlab functions. 30 | 31 | % MB+ 32 | paramMBplus = getParam(); 33 | paramMBplus.verbose = true; % to show run time for each step 34 | 35 | % Geodesic 36 | % paramGeo = getParam(); 37 | % paramGeo.use_backgroundness = false; 38 | % paramGeo.use_geodesic = true; 39 | 40 | I = img; 41 | 42 | % paramMBD.use_backgroudness = true; 43 | % [pMap1, dMap1] = doMBS(I, paramMB); 44 | % [pMapG, dMapG] = doMBS(I, paramGeo); 45 | pMap = doMBS(I, paramMBplus); 46 | 47 | 48 | end -------------------------------------------------------------------------------- /modelspecific/hnormalise.m: -------------------------------------------------------------------------------- 1 | % HNORMALISE - Normalises array of homogeneous coordinates to a scale of 1 2 | % 3 | % Usage: nx = hnormalise(x) 4 | % 5 | % Argument: 6 | % x - an Nxnpts array of homogeneous coordinates. 7 | % 8 | % Returns: 9 | % nx - an Nxnpts array of homogeneous coordinates rescaled so 10 | % that the scale values nx(N,:) are all 1. 11 | % 12 | % Note that any homogeneous coordinates at infinity (having a scale value of 13 | % 0) are left unchanged. 14 | 15 | % Peter Kovesi 16 | % School of Computer Science & Software Engineering 17 | % The University of Western Australia 18 | % pk at csse uwa edu au 19 | % http://www.csse.uwa.edu.au/~pk 20 | % 21 | % February 2004 22 | 23 | function nx = hnormalise(x) 24 | 25 | [rows,npts] = size(x); 26 | nx = x; 27 | 28 | % Find the indices of the points that are not at infinity 29 | finiteind = find(abs(x(rows,:)) > eps); 30 | 31 | if length(finiteind) ~= npts 32 | warning('Some points are at infinity'); 33 | end 34 | 35 | % Normalise points not at infinity 36 | for r = 1:rows-1 37 | nx(r,finiteind) = x(r,finiteind)./x(rows,finiteind); 38 | end 39 | nx(rows,finiteind) = 1; 40 | 41 | -------------------------------------------------------------------------------- /modelspecific/homography_degen.m: -------------------------------------------------------------------------------- 1 | function r = homography_degen(X) 2 | 3 | x1 = X(1:3,:); % Extract x1 and x2 from x 4 | x2 = X(4:6,:); 5 | 6 | r = ... 7 | iscolinear(x1(:,1),x1(:,2),x1(:,3)) | ... 8 | iscolinear(x1(:,1),x1(:,2),x1(:,4)) | ... 9 | iscolinear(x1(:,1),x1(:,3),x1(:,4)) | ... 10 | iscolinear(x1(:,2),x1(:,3),x1(:,4)) | ... 11 | iscolinear(x2(:,1),x2(:,2),x2(:,3)) | ... 12 | iscolinear(x2(:,1),x2(:,2),x2(:,4)) | ... 13 | iscolinear(x2(:,1),x2(:,3),x2(:,4)) | ... 14 | iscolinear(x2(:,2),x2(:,3),x2(:,4)); 15 | 16 | end 17 | 18 | 19 | % function r = homography_degen(X) 20 | % 21 | % r = any(pdist(X(1:2,:)') < eps); 22 | % 23 | % r = r + any(pdist(X(3:4,:)') < eps); 24 | % 25 | % end 26 | -------------------------------------------------------------------------------- /modelspecific/homography_fit.m: -------------------------------------------------------------------------------- 1 | function [P A C1 C2] = homography_fit(X,A,W,C1,C2) 2 | 3 | x1 = X(1:3,:); 4 | x2 = X(4:6,:); 5 | 6 | if nargin == 5 7 | H = vgg_H_from_x_lin(x1,x2,A,W,C1,C2); 8 | else 9 | [H A C1 C2] = vgg_H_from_x_lin(x1,x2); 10 | end 11 | 12 | % % Denormalise 13 | % H = T2\H*T1; 14 | 15 | P = H(:); 16 | 17 | 18 | % % Note that it may have not been possible to normalise 19 | % % the points if one was at infinity so the following does not 20 | % % assume that scale parameter w = 1. 21 | % 22 | % Npts = length(x1); 23 | % A = zeros(3*Npts,9); 24 | % 25 | % O = [0 0 0]; 26 | % for n = 1:Npts 27 | % X = x1(:,n)'; 28 | % x = x2(1,n); y = x2(2,n); w = x2(3,n); 29 | % A(3*n-2,:) = [ O -w*X y*X]; 30 | % A(3*n-1,:) = [ w*X O -x*X]; 31 | % A(3*n ,:) = [-y*X x*X O ]; 32 | % end 33 | % 34 | % [U,D,V] = svd(A,0); % 'Economy' decomposition for speed 35 | % 36 | % % Extract homography 37 | % % H = reshape(V(:,9),3,3)'; 38 | % P = V(:,9); 39 | 40 | end 41 | 42 | 43 | % function P = homography_fit(X) 44 | % % Homography fitting function using Direct Linear Transformation (DLT) (see Hartley and Zisserman). 45 | % % X is a 5 by 4 matrix. 46 | % % X(1,i) is the x coordinate in the 1st image of the i-th correspondence. 47 | % % X(2,i) is the y coordinate in the 1st image of the i-th correspondence. 48 | % % X(3,i) is the x coordinate in the 2nd image of the i-th correspondence. 49 | % % X(4,i) is the y coordinate in the 2nd image of the i-th correspondence. 50 | % % X(5,i) is max(width_of_image,height_of_image) (for increasing stability via normalisation). 51 | % % P is the resulting homography relation (a matrix) reshaped to a vector. 52 | % 53 | % A = zeros(8,9); 54 | % 55 | % for i=1:4 56 | % x1 = X(1,i)/X(5,i); 57 | % y1 = X(2,i)/X(5,i); 58 | % x2 = X(3,i)/X(5,i); 59 | % y2 = X(4,i)/X(5,i); 60 | % A((i-1)*2+1,:) = [ 0 0 0 -x2 -y2 -1 y1*x2 y1*y2 y1 ]; 61 | % A((i-1)*2+2,:) = [ x2 y2 1 0 0 0 -x1*x2 -x1*y2 -x1 ]; 62 | % end 63 | % 64 | % [ u s v ] = svd(A); 65 | % 66 | % H = reshape(v(:,end),3,3)'; 67 | % 68 | % N = [ 1/X(5,1) 0 0 ; 0 1/X(5,1) 0 ; 0 0 1 ]; 69 | % 70 | % H = N\(H*N); % Equivalent to H = inv(N)*H*N; 71 | % 72 | % P = reshape(H',9,1); 73 | % 74 | % end 75 | -------------------------------------------------------------------------------- /modelspecific/homography_res.m: -------------------------------------------------------------------------------- 1 | function [dist, H] = homography_res(H, X) 2 | 3 | H = reshape(H,3,3); 4 | 5 | 6 | x1 = X(1:3,:); % Extract x1 and x2 from x 7 | x2 = X(4:6,:); 8 | n = size(x1,2); 9 | 10 | % Calculate, in both directions, the transfered points 11 | Hx1 = H*x1; 12 | invHx2 = H\x2; 13 | 14 | % Normalise so that the homogeneous scale parameter for all coordinates 15 | % is 1. 16 | 17 | x1 = hnormalise(x1); 18 | x2 = hnormalise(x2); 19 | Hx1 = hnormalise(Hx1); 20 | invHx2 = hnormalise(invHx2); 21 | 22 | dist = sum((x1-invHx2).^2) + sum((x2-Hx1).^2); 23 | 24 | dist = reshape(dist,n,1); 25 | 26 | H = H(:); 27 | 28 | end 29 | 30 | 31 | 32 | % function [dist, P] = homography_res(P, X) 33 | % % Function to calculate distances between a homography and a set of point 34 | % % correspondences. Implmenents the symmetric transfer error. See 35 | % % [Harley and Zisserman 2nd ed., pg 94]. 36 | % % X is a 4 by npts matrix. 37 | % % X(1,i) is the x coordinate in the 1st image of the i-th correspondence. 38 | % % X(2,i) is the y coordinate in the 1st image of the i-th correspondence. 39 | % % X(3,i) is the x coordinate in the 2nd image of the i-th correspondence. 40 | % % X(4,i) is the y coordinate in the 2nd image of the i-th correspondence. 41 | % 42 | % [dummy npts] = size(X); 43 | % dist = zeros(npts,1); 44 | % 45 | % % Forward. 46 | % H = reshape(P,3,3)'; 47 | % hom = H*[ X(3:4,:) ; ones(1,size(X,2)) ]; 48 | % inhom = [ hom(1,:)./hom(3,:) ; hom(2,:)./hom(3,:) ]; 49 | % for i=1:npts 50 | % dist(i) = norm(X(1:2,i) - inhom(:,i))^2; 51 | % end 52 | % 53 | % % Reverse. 54 | % % invH = inv(H); 55 | % % hom = invH*[ X(1:2,:) ; ones(1,size(X,2)) ]; 56 | % 57 | % hom = H\[ X(1:2,:) ; ones(1,size(X,2)) ]; 58 | % 59 | % inhom = [ hom(1,:)./hom(3,:) ; hom(2,:)./hom(3,:) ]; 60 | % for i=1:npts 61 | % dist(i) = dist(i) + norm(X(3:4,i) - inhom(:,i))^2; 62 | % end 63 | % 64 | % end 65 | -------------------------------------------------------------------------------- /modelspecific/iscolinear.m: -------------------------------------------------------------------------------- 1 | % ISCOLINEAR - are 3 points colinear 2 | % 3 | % Usage: r = iscolinear(p1, p2, p3, flag) 4 | % 5 | % Arguments: 6 | % p1, p2, p3 - Points in 2D or 3D. 7 | % flag - An optional parameter set to 'h' or 'homog' 8 | % indicating that p1, p2, p3 are homogneeous 9 | % coordinates with arbitrary scale. If this is 10 | % omitted it is assumed that the points are 11 | % inhomogeneous, or that they are homogeneous with 12 | % equal scale. 13 | % 14 | % Returns: 15 | % r = 1 if points are co-linear, 0 otherwise 16 | 17 | % Copyright (c) 2004-2005 Peter Kovesi 18 | % School of Computer Science & Software Engineering 19 | % The University of Western Australia 20 | % http://www.csse.uwa.edu.au/ 21 | % 22 | % Permission is hereby granted, free of charge, to any person obtaining a copy 23 | % of this software and associated documentation files (the "Software"), to deal 24 | % in the Software without restriction, subject to the following conditions: 25 | % 26 | % The above copyright notice and this permission notice shall be included in 27 | % all copies or substantial portions of the Software. 28 | % 29 | % The Software is provided "as is", without warranty of any kind. 30 | 31 | % February 2004 32 | % January 2005 - modified to allow for homogeneous points of arbitrary 33 | % scale (thanks to Michael Kirchhof) 34 | 35 | 36 | function r = iscolinear(p1, p2, p3, flag) 37 | 38 | if nargin == 3 % Assume inhomogeneous coords 39 | flag = 'inhomog'; 40 | end 41 | 42 | if ~all(size(p1)==size(p2)) | ~all(size(p1)==size(p3)) | ... 43 | ~(length(p1)==2 | length(p1)==3) 44 | error('points must have the same dimension of 2 or 3'); 45 | end 46 | 47 | % If data is 2D, assume they are 2D inhomogeneous coords. Make them 48 | % homogeneous with scale 1. 49 | if length(p1) == 2 50 | p1(3) = 1; p2(3) = 1; p3(3) = 1; 51 | end 52 | 53 | if flag(1) == 'h' 54 | % Apply test that allows for homogeneous coords with arbitrary 55 | % scale. p1 X p2 generates a normal vector to plane defined by 56 | % origin, p1 and p2. If the dot product of this normal with p3 57 | % is zero then p3 also lies in the plane, hence co-linear. 58 | r = abs(dot(cross(p1, p2),p3)) < eps; 59 | else 60 | % Assume inhomogeneous coords, or homogeneous coords with equal 61 | % scale. 62 | r = norm(cross(p2-p1, p3-p1)) < eps; 63 | end 64 | 65 | -------------------------------------------------------------------------------- /modelspecific/normalise2dpts.m: -------------------------------------------------------------------------------- 1 | % NORMALISE2DPTS - normalises 2D homogeneous points 2 | % 3 | % Function translates and normalises a set of 2D homogeneous points 4 | % so that their centroid is at the origin and their mean distance from 5 | % the origin is sqrt(2). This process typically improves the 6 | % conditioning of any equations used to solve homographies, fundamental 7 | % matrices etc. 8 | % 9 | % Usage: [newpts, T] = normalise2dpts(pts) 10 | % 11 | % Argument: 12 | % pts - 3xN array of 2D homogeneous coordinates 13 | % 14 | % Returns: 15 | % newpts - 3xN array of transformed 2D homogeneous coordinates. The 16 | % scaling parameter is normalised to 1 unless the point is at 17 | % infinity. 18 | % T - The 3x3 transformation matrix, newpts = T*pts 19 | % 20 | % If there are some points at infinity the normalisation transform 21 | % is calculated using just the finite points. Being a scaling and 22 | % translating transform this will not affect the points at infinity. 23 | 24 | % Peter Kovesi 25 | % School of Computer Science & Software Engineering 26 | % The University of Western Australia 27 | % pk at csse uwa edu au 28 | % http://www.csse.uwa.edu.au/~pk 29 | % 30 | % May 2003 - Original version 31 | % February 2004 - Modified to deal with points at infinity. 32 | % December 2008 - meandist calculation modified to work with Octave 3.0.1 33 | % (thanks to Ron Parr) 34 | 35 | 36 | function [newpts, T] = normalise2dpts(pts) 37 | 38 | if size(pts,1) ~= 3 39 | error('pts must be 3xN'); 40 | end 41 | 42 | % Find the indices of the points that are not at infinity 43 | finiteind = find(abs(pts(3,:)) > eps); 44 | 45 | if length(finiteind) ~= size(pts,2) 46 | disp('Some points are at infinity'); 47 | end 48 | 49 | % For the finite points ensure homogeneous coords have scale of 1 50 | pts(1,finiteind) = pts(1,finiteind)./pts(3,finiteind); 51 | pts(2,finiteind) = pts(2,finiteind)./pts(3,finiteind); 52 | pts(3,finiteind) = 1; 53 | 54 | c = mean(pts(1:2,finiteind)')'; % Centroid of finite points 55 | newp(1,finiteind) = pts(1,finiteind)-c(1); % Shift origin to centroid. 56 | newp(2,finiteind) = pts(2,finiteind)-c(2); 57 | 58 | dist = sqrt(newp(1,finiteind).^2 + newp(2,finiteind).^2); 59 | meandist = mean(dist(:)); % Ensure dist is a column vector for Octave 3.0.1 60 | 61 | scale = sqrt(2)/meandist; 62 | 63 | T = [scale 0 -scale*c(1) 64 | 0 scale -scale*c(2) 65 | 0 0 1 ]; 66 | 67 | newpts = T*pts; 68 | 69 | end -------------------------------------------------------------------------------- /modelspecific/vgg_H_from_x_lin.m: -------------------------------------------------------------------------------- 1 | function [H, A, C1, C2] = x(xs1,xs2,A,W,C1,C2) 2 | % H = vgg_H_from_x_lin(xs1,xs2) 3 | % 4 | % Compute H using linear method (see Hartley & Zisserman Alg 3.2 page 92 in 5 | % 1st edition, Alg 4.2 page 109 in 2nd edition). 6 | % Point preconditioning is inside the function. 7 | % 8 | % The format of the xs [p1 p2 p3 ... pn], where each p is a 2 or 3 9 | % element column vector. 10 | 11 | [r,c] = size(xs1); 12 | 13 | if (size(xs1) ~= size(xs2)) 14 | error ('Input point sets are different sizes!') 15 | end 16 | 17 | if (size(xs1,1) == 2) 18 | xs1 = [xs1 ; ones(1,size(xs1,2))]; 19 | xs2 = [xs2 ; ones(1,size(xs2,2))]; 20 | end 21 | 22 | 23 | % condition points 24 | if nargin == 2 25 | C1 = vgg_conditioner_from_pts(xs1); 26 | C2 = vgg_conditioner_from_pts(xs2); 27 | xs1 = vgg_condition_2d(xs1,C1); 28 | xs2 = vgg_condition_2d(xs2,C2); 29 | end 30 | 31 | if nargin == 6 32 | B = A; 33 | B(1:2:end,:)=W*A(1:2:end,:); 34 | B(2:2:end,:)=W*A(2:2:end,:); 35 | 36 | % Extract nullspace 37 | [u,s,v] = svd(B, 0); s = diag(s); 38 | else 39 | A = []; 40 | ooo = zeros(1,3); 41 | for k=1:c 42 | p1 = xs1(:,k); 43 | p2 = xs2(:,k); 44 | A = [ A; 45 | p1'*p2(3) ooo -p1'*p2(1) 46 | ooo p1'*p2(3) -p1'*p2(2) 47 | ]; 48 | end 49 | 50 | % Extract nullspace 51 | [u,s,v] = svd(A, 0); s = diag(s); 52 | end 53 | 54 | nullspace_dimension = sum(s < eps * s(1) * 1e3); 55 | if nullspace_dimension > 1 56 | fprintf('Nullspace is a bit roomy...'); 57 | end 58 | 59 | h = v(:,9); 60 | 61 | H = reshape(h,3,3)'; 62 | 63 | %decondition 64 | H = C2\H*C1; 65 | 66 | H = H/H(3,3); 67 | end 68 | -------------------------------------------------------------------------------- /modelspecific/vgg_condition_2d.m: -------------------------------------------------------------------------------- 1 | function pc = z(p,C) 2 | % function pc = vgg_condition_2d(p,C); 3 | % 4 | % Condition a set of 2D homogeneous or nonhomogeneous points using conditioner C 5 | 6 | [r,c] = size(p); 7 | if r == 2 8 | pc = vgg_get_nonhomg(C * vgg_get_homg(p)); 9 | elseif r == 3 10 | pc = C * p; 11 | else 12 | error ('rows != 2 or 3'); 13 | end 14 | 15 | end -------------------------------------------------------------------------------- /modelspecific/vgg_conditioner_from_pts.m: -------------------------------------------------------------------------------- 1 | function T = y(Pts,isotropic) 2 | 3 | % VGG_CONDITIONER_FROM_PTS - Returns a conditioning matrix for points 4 | % 5 | % T = vgg_conditioner_from_pts(Pts [,isotropic]) 6 | % 7 | % Returns a DxD matrix that normalizes Pts to have mean 0 and stddev sqrt(2) 8 | % 9 | % 10 | %IN: 11 | % Pts - DxK list of K projective points. Last row is ignored. 12 | % isotropic - optional; if present then T(1,1)==T(2,2)==...==T(D-1,D-1). 13 | % 14 | % 15 | %OUT: 16 | % T - DxD conditioning matrix 17 | 18 | % Yoni, Thu Feb 14 12:24:48 2002 19 | 20 | Dim=size(Pts,1); 21 | 22 | Pts=vgg_get_nonhomg(Pts); 23 | Pts=Pts(1:Dim-1,:); 24 | 25 | m=mean(Pts,2); 26 | s=std(Pts'); 27 | s=s+(s==0); 28 | 29 | if nargin==1 30 | T=[ diag(sqrt(2)./s) -diag(sqrt(2)./s)*m]; 31 | else % isotropic; added by TW 32 | T=[ diag(sqrt(2)./(ones(1,Dim-1)*mean(s))) -diag(sqrt(2)./s)*m]; 33 | end 34 | T(Dim,:)=0; 35 | T(Dim,Dim)=1; 36 | -------------------------------------------------------------------------------- /modelspecific/vgg_get_nonhomg.m: -------------------------------------------------------------------------------- 1 | function x = vgg_get_nonhomg(x) 2 | % p = vgg_get_nonhomg(h) 3 | % 4 | % Convert a set of homogeneous points to non-homogeneous form 5 | % Points are stored as column vectors, stacked horizontally, e.g. 6 | % [x0 x1 x2 ... xn ; 7 | % y0 y1 y2 ... yn ; 8 | % w0 w1 w2 ... wn ] 9 | 10 | % Modified by TW 11 | 12 | if isempty(x) 13 | x = []; 14 | return; 15 | end 16 | 17 | d = size(x,1) - 1; 18 | x = x(1:d,:)./(ones(d,1)*x(end,:)); 19 | 20 | return -------------------------------------------------------------------------------- /randIndex.m: -------------------------------------------------------------------------------- 1 | function index = randIndex(maxIndex,len) 2 | %INDEX = RANDINDEX(MAXINDEX,LEN) 3 | % randomly, non-repeatedly select LEN integers from 1:MAXINDEX 4 | 5 | if len > maxIndex 6 | index = []; 7 | return 8 | end 9 | 10 | index = zeros(1,len); 11 | available = 1:maxIndex; 12 | rs = ceil(rand(1,len).*(maxIndex:-1:maxIndex-len+1)); 13 | for p = 1:len 14 | while rs(p) == 0 15 | rs(p) = ceil(rand(1)*(maxIndex-p+1)); 16 | end 17 | index(p) = available(rs(p)); 18 | available(rs(p)) = []; 19 | end -------------------------------------------------------------------------------- /ransacx.m: -------------------------------------------------------------------------------- 1 | function [f,inlierIdx] = ransacx( x,y, ransacCoef )%, funcFindF, funcDist) 2 | %[f inlierIdx] = ransacx( x,y,ransacCoef,funcFindF,funcDist ) 3 | % Use RANdom SAmple Consensus to find a fit from X to Y. 4 | % X is M*n matrix including n points with dim M, Y is N*n; 5 | % The fit, f, and the indices of inliers, are returned. 6 | % 7 | % RANSACCOEF is a struct with following fields: 8 | % minPtNum,iterNum,thDist,thInlrRatio 9 | % MINPTNUM is the minimum number of points with whom can we 10 | % find a fit. For line fitting, it's 2. For homography, it's 4. 11 | % ITERNUM is the number of iteration, 12 | % THDIST is the inlier distance threshold and 13 | % ROUND(THINLRRATIO*n) is the inlier number threshold. 14 | % 15 | % FUNCFINDF is a func handle, f1 = funcFindF(x1,y1) 16 | % x1 is M*n1 and y1 is N*n1, n1 >= ransacCoef.minPtNum 17 | % f1 can be of any type. 18 | 19 | % FUNCDIST is a func handle, d = funcDist(f,x1,y1) 20 | % It uses f returned by FUNCFINDF, and return the distance 21 | % between f and the points, d is 1*n1. 22 | % For line fitting, it should calculate the dist between the line and the 23 | % points [x1;y1]; for homography, it should project x1 to y2 then 24 | % calculate the dist between y1 and y2. 25 | % Yan Ke @ THUEE, 20110123, xjed09@gmail.com 26 | 27 | 28 | minPtNum = ransacCoef.minPtNum; 29 | iterNum = ransacCoef.iterNum; 30 | thInlrRatio = ransacCoef.thInlrRatio; 31 | thDist = ransacCoef.thDist; 32 | ptNum = size(x,2); 33 | thInlr = round(thInlrRatio*ptNum); 34 | 35 | inlrNum = zeros(1,iterNum); 36 | fLib = cell(1,iterNum); 37 | 38 | parfor p = 1:iterNum 39 | % 1. fit using random points 40 | sampleIdx = randIndex(ptNum,minPtNum); 41 | f1 = calcHomo(x(:,sampleIdx),y(:,sampleIdx));%funcFindF(x(:,sampleIdx),y(:,sampleIdx)); % For homography: f1 is H 42 | 43 | % 2. count the inliers, if more than thInlr, refit; else iterate 44 | dist = calcDist(f1,x,y);%funcDist(f1,x,y); 45 | inlier1 = find(dist < thDist); %caculate count of inlier 46 | % if size(distance,1)==0 47 | % inlrNum(p) = length(inlier1); %original RANSAC =func(inlier1,distance,para):para=0:length;para=1:distance 48 | % else 49 | % if length(inlier1)>=4 50 | % inlrNum(p) = length(inlier1); %sum(distance(:,inlier1)); %distance RANSAC 51 | % fLib{p} = inlier1; % re-caculate H 52 | % else 53 | % inlrNum(p) = 0; 54 | % end 55 | % end 56 | % inlrNum(p) = length(inlier1); 57 | if length(inlier1) < thInlr, continue; end 58 | inlrNum(p) = length(inlier1); 59 | fLib{p} = inlier1; % re-caculate H 60 | end 61 | 62 | % 3. choose the coef with the most inliers 63 | [max_inlier, idx] = max(inlrNum); 64 | if max_inlier==0 65 | inlierIdx = []; 66 | f = []; 67 | return; 68 | end 69 | f = calcHomo(x(:,fLib{idx}),y(:,fLib{idx})); %most inliers 70 | dist = calcDist(f,x,y); % find match point 71 | inlierIdx = find(dist < thDist); 72 | f = calcHomo(x(:,inlierIdx),y(:,inlierIdx)); 73 | % fprintf('> Select %d matches in 2nd round.\n', length(inlierIdx)); 74 | 75 | end -------------------------------------------------------------------------------- /registerTexture.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tlliao/Perception-based-seam-cutting/f1a839e0b24214773b26905719a492e49d0aa5ec/registerTexture.m -------------------------------------------------------------------------------- /siftMatch.m: -------------------------------------------------------------------------------- 1 | function [pts1, pts2] = siftMatch( img1, img2 ) 2 | %-------------------------------------- 3 | % SIFT keypoint detection and matching. 4 | %-------------------------------------- 5 | fprintf(' Keypoint detection and matching...');tic; 6 | [ kp1,ds1 ] = vl_sift(single(rgb2gray(img1)),'PeakThresh', 0,'edgethresh',500); 7 | [ kp2,ds2 ] = vl_sift(single(rgb2gray(img2)),'PeakThresh', 0,'edgethresh',500); 8 | matches = vl_ubcmatch(ds1, ds2); 9 | fprintf('done (%fs)\n',toc); 10 | 11 | % extract match points' position 12 | pts1 = kp1(1:2,matches(1,:)); 13 | pts2 = kp2(1:2,matches(2,:)); 14 | 15 | end 16 | 17 | -------------------------------------------------------------------------------- /vlfeat-0.9.21/README.txt: -------------------------------------------------------------------------------- 1 | VLFeat 0.9.21 binary package can be downloaded from: 2 | http://www.vlfeat.org/ --------------------------------------------------------------------------------