[37] | 1 | % im2pointsLocally computes image statistics from several images |
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| 2 | % and finds the projections of laser points |
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| 3 | % |
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| 4 | % The version for local computation on each machine |
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| 5 | % |
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| 6 | % The computation core is taken from im2points.m |
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| 7 | % computes average image and image of standard deviations |
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| 8 | % requires configdata.m |
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| 9 | % The name of the experiment has to be specified |
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| 10 | % it determines the location of files etc ... |
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| 11 | % |
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| 12 | % the scripts serves as a template for a multiprocessing |
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| 13 | % it assumes a vector of camera IDs CamIds to be known |
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| 14 | % indexes in the CamIds are supposed to be correct |
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| 15 | % |
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| 16 | |
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| 17 | donefile = '.done'; |
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| 18 | |
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| 19 | addpath /home/svoboda/Work/BlueCCal/BlueCFindingPoints |
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| 20 | |
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| 21 | % config = localconfig('BlueCHoengg') |
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| 22 | config = localconfig('BlueCRZ') |
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| 23 | |
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| 24 | STEP4STAT = 5; % step for computing average and std images, if 1 then all images taken |
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| 25 | |
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| 26 | im.dir = config.paths.data; |
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| 27 | im.ext = config.files.imgext; |
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| 28 | |
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| 29 | % get the information about machine |
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| 30 | machinename = getenv('HOST'); |
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| 31 | CamsIds = str2num(machinename(find(machinename>47 & machinename<58))); |
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| 32 | |
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| 33 | NoCams = size(CamsIds,2); |
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| 34 | CamsIds |
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| 35 | |
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| 36 | % load image names |
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| 37 | for i=1:NoCams, |
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| 38 | seq(i).camId = CamsIds(i); |
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| 39 | if seq(i).camId > -1 |
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| 40 | seq(i).data = dir([sprintf(im.dir,seq(i).camId),sprintf(config.files.imnames,seq(i).camId),im.ext]); |
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| 41 | [sprintf(im.dir,seq(i).camId),sprintf(config.files.imnames,seq(i).camId),im.ext] |
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| 42 | else |
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| 43 | seq(i).data = dir([im.dir,sprintf(config.files.imnames),im.ext]); |
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| 44 | end |
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| 45 | seq(i).size = size(seq(i).data,1); |
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| 46 | if seq(i).size<4 |
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| 47 | i, seq |
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| 48 | error('Not enough images found. Wrong image path or name pattern?'); |
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| 49 | end |
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| 50 | end |
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| 51 | |
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| 52 | % Expected number of 3D points is equal to the number of frames. |
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| 53 | % In fact, some frames might be without any calibration point |
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| 54 | |
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| 55 | % Because of non-consistent stopping of capturing, the sequences might |
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| 56 | % have different number of images, select the minimal value as the right one |
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| 57 | NoPoints = min([seq.size]); |
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| 58 | |
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| 59 | % compute the average images that will be used for finding LEDs |
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| 60 | % if not already computed |
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| 61 | |
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| 62 | pointsIdx = [1:STEP4STAT:NoPoints]; |
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| 63 | |
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| 64 | t = cputime; |
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| 65 | for i=1:NoCams, |
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| 66 | if ~exist(sprintf(config.files.avIM,seq(i).camId)), |
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| 67 | disp(sprintf('The average image of the camera %d is being computed',seq(i).camId)); |
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| 68 | avIM = zeros(size(imread([sprintf(im.dir,seq(i).camId),seq(i).data(1).name]))); |
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| 69 | for j=pointsIdx, |
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| 70 | IM = imread([sprintf(im.dir,seq(i).camId),seq(i).data(j).name]); |
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| 71 | avIM = avIM + double(IM); |
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| 72 | end |
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| 73 | avIM = uint8(round(avIM./size(pointsIdx,2))); |
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| 74 | imwrite(avIM,sprintf(config.files.avIM,seq(i).camId)); |
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| 75 | else disp('Average files already exist'); |
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| 76 | end |
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| 77 | end |
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| 78 | disp(sprintf('Elapsed time for average images: %d [sec]',cputime-t)) |
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| 79 | % compute the standard deviations images that will be used for finding LEDs |
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| 80 | % if not already computed |
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| 81 | t = cputime; |
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| 82 | for i=1:NoCams, |
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| 83 | if ~exist(sprintf(config.files.stdIM,seq(i).camId)), |
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| 84 | avIM = double(imread(sprintf(config.files.avIM,seq(i).camId))); |
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| 85 | disp(sprintf('The image of standard deviations of the camera %d is being computed',seq(i).camId)); |
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| 86 | stdIM = zeros(size(imread([sprintf(im.dir,seq(i).camId),seq(i).data(1).name]))); |
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| 87 | for j=pointsIdx, |
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| 88 | IM = imread([sprintf(im.dir,seq(i).camId),seq(i).data(j).name]); |
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| 89 | stdIM = stdIM + (double(IM)-avIM).^2; |
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| 90 | end |
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| 91 | stdIM = uint8(round(sqrt(stdIM./(size(pointsIdx,2)-1)))); |
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| 92 | imwrite(stdIM,sprintf(config.files.stdIM,seq(i).camId)); |
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| 93 | else |
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| 94 | disp('Image of standard deviations already exist') |
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| 95 | end |
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| 96 | end |
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| 97 | disp(sprintf('Elapsed time for computation of images [sec]: %d',cputime-t)) |
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| 98 | |
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| 99 | % find points in the images |
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| 100 | Ws = []; |
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| 101 | IdWs = []; |
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| 102 | Res = []; |
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| 103 | |
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| 104 | IdMat = ones(NoCams,NoPoints); |
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| 105 | % IdMat is very important for Martinec&Pajdla filling [ECCV2002] |
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| 106 | % it is a NoCams x NoPoints matrix, |
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| 107 | % IdMat(i,j) = 0 -> no j-th point in i-th |
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| 108 | % IdMat(i,j) = 1 -> point successfully detected |
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| 109 | |
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| 110 | for i=1:NoCams, |
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| 111 | Points = []; |
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| 112 | avIM = imread(sprintf(config.files.avIM,seq(i).camId)); |
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| 113 | stdIM = imread(sprintf(config.files.stdIM,seq(i).camId)); |
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| 114 | for j=1:NoPoints, |
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| 115 | [pos,err] = getpoint([sprintf(im.dir,seq(i).camId),seq(i).data(j).name], 0, config.imgs, avIM, stdIM); |
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| 116 | if err |
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| 117 | IdMat(i,j) = 0; |
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| 118 | Points = [Points, [NaN; NaN; NaN]]; |
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| 119 | else |
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| 120 | Points = [Points, [pos; 1]]; |
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| 121 | end |
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| 122 | end |
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| 123 | Ws = [Ws; Points]; |
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| 124 | Res= [Res; size(avIM,2), size(avIM,1)]; |
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| 125 | end |
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| 126 | |
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| 127 | idx = '.'; |
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| 128 | for i=CamsIds, |
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| 129 | idx = sprintf('%s%02d',idx,i); |
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| 130 | end |
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| 131 | |
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| 132 | save([config.files.points,idx], 'Ws','-ASCII') |
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| 133 | % save(config.files.IdPoints,'IdWs','-ASCII') |
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| 134 | save([config.files.Res,idx], 'Res', '-ASCII') |
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| 135 | save([config.files.IdMat,idx], 'IdMat', '-ASCII') |
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| 136 | |
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| 137 | % write auxiliary file that is done |
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| 138 | done=1; |
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| 139 | save(donefile,'done','-ascii'); |
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| 140 | |
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| 141 | % exit the Matlab |
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| 142 | % this script is to be used in the batch mode |
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| 143 | % hence exit at the end is necessary |
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| 144 | exit; |
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