├── tracker_exter ├── matconvnet │ ├── .gitmodules │ ├── .gitattributes │ └── .gitignore ├── pdollar_toolbox │ ├── README.md │ ├── doc │ │ ├── c.png │ │ ├── c++.png │ │ ├── hp.png │ │ ├── mex.png │ │ ├── new.gif │ │ ├── sgi.png │ │ ├── up.png │ │ ├── alpha.png │ │ ├── down.png │ │ ├── left.png │ │ ├── linux.png │ │ ├── right.png │ │ ├── demoicon.gif │ │ ├── favicon.ico │ │ ├── fortran.png │ │ ├── solaris.png │ │ ├── windows.png │ │ ├── matlabicon.gif │ │ ├── simulinkicon.gif │ │ ├── channels │ │ │ ├── Contents.html │ │ │ ├── chnsCompute.html │ │ │ ├── chnsPyramid.html │ │ │ ├── chnsScaling.html │ │ │ └── gradientHist.html │ │ ├── detector │ │ │ ├── acfTrain.html │ │ │ └── acfReadme.html │ │ ├── classify │ │ │ ├── adaBoostTrain.html │ │ │ └── binaryTreeTrain.html │ │ ├── videos │ │ │ └── imagesAlignSeq.html │ │ ├── index.html │ │ └── m2html.css │ ├── channels │ │ ├── Contents.m │ │ ├── chnsCompute.m │ │ ├── chnsPyramid.m │ │ ├── chnsScaling.m │ │ ├── gradientHist.m │ │ ├── private │ │ │ ├── convConst.mexa64 │ │ │ ├── convConst.mexw64 │ │ │ ├── imPadMex.mexa64 │ │ │ ├── imPadMex.mexw64 │ │ │ ├── convConst.mexmaci64 │ │ │ ├── gradientMex.mexa64 │ │ │ ├── gradientMex.mexw64 │ │ │ ├── imPadMex.mexmaci64 │ │ │ ├── gradientMex.mexmaci64 │ │ │ ├── imResampleMex.mexa64 │ │ │ ├── imResampleMex.mexw64 │ │ │ ├── rgbConvertMex.mexa64 │ │ │ ├── rgbConvertMex.mexw64 │ │ │ ├── gradientMexNew.mexmaci64 │ │ │ ├── imResampleMex.mexmaci64 │ │ │ └── rgbConvertMex.mexmaci64 │ │ ├── gradient2.m │ │ └── hogDraw.m │ ├── detector │ │ ├── acfTrain.m │ │ ├── acfReadme.m │ │ ├── models │ │ │ ├── AcfInriaRoc.png │ │ │ ├── AcfCaltech+Roc.png │ │ │ ├── LdcfCaltechRoc.png │ │ │ ├── LdcfInriaRoc.png │ │ │ ├── AcfInriaDetector.mat │ │ │ ├── AcfCaltech+Detector.mat │ │ │ ├── LdcfCaltechDetector.mat │ │ │ └── LdcfInriaDetector.mat │ │ ├── private │ │ │ ├── acfDetect1.mexa64 │ │ │ ├── acfDetect1.mexw64 │ │ │ └── acfDetect1.mexmaci64 │ │ └── Contents.m │ ├── filters │ │ ├── FbDoG.mat │ │ ├── FbGabor.mat │ │ ├── filterSteerable.m │ │ ├── FbCrop.m │ │ ├── filterBinomial1d.m │ │ └── Contents.m │ ├── images │ │ ├── images.mat │ │ ├── private │ │ │ ├── histc2c.mexa64 │ │ │ ├── histc2c.mexw64 │ │ │ ├── histc2c.mexmaci64 │ │ │ ├── assignToBins1.mexa64 │ │ │ ├── assignToBins1.mexw64 │ │ │ ├── imtransform2_c.mexa64 │ │ │ ├── imtransform2_c.mexw64 │ │ │ ├── assignToBins1.mexmaci64 │ │ │ ├── imtransform2_c.mexmaci64 │ │ │ ├── nlfiltersep_max.mexa64 │ │ │ ├── nlfiltersep_max.mexw64 │ │ │ ├── nlfiltersep_sum.mexa64 │ │ │ ├── nlfiltersep_sum.mexw64 │ │ │ ├── nlfiltersep_max.mexmaci64 │ │ │ └── nlfiltersep_sum.mexmaci64 │ │ ├── movieToImages.m │ │ ├── im.m │ │ ├── histMontage.m │ │ ├── makeGif.m │ │ ├── assignToBins.m │ │ └── xcorrn.m │ ├── videos │ │ ├── peds30.seq │ │ ├── imagesAlignSeq.m │ │ ├── peds30-seek.mat │ │ ├── opticalFlowTest.mat │ │ ├── private │ │ │ ├── ktComputeW_c.mexa64 │ │ │ ├── ktComputeW_c.mexw64 │ │ │ ├── ktHistcRgb_c.mexa64 │ │ │ ├── ktHistcRgb_c.mexw64 │ │ │ ├── ktComputeW_c.mexmaci64 │ │ │ ├── ktHistcRgb_c.mexmaci64 │ │ │ ├── opticalFlowHsMex.mexa64 │ │ │ ├── opticalFlowHsMex.mexw64 │ │ │ └── opticalFlowHsMex.mexmaci64 │ │ └── Contents.m │ ├── classify │ │ ├── pcaData.mat │ │ ├── adaBoostTrain.m │ │ ├── binaryTreeTrain.m │ │ ├── private │ │ │ ├── fernsInds1.mexa64 │ │ │ ├── fernsInds1.mexw64 │ │ │ ├── forestInds.mexa64 │ │ │ ├── forestInds.mexw64 │ │ │ ├── meanShift1.mexa64 │ │ │ ├── meanShift1.mexw64 │ │ │ ├── fernsInds1.mexmaci64 │ │ │ ├── forestFindThr.mexa64 │ │ │ ├── forestFindThr.mexw64 │ │ │ ├── forestInds.mexmaci64 │ │ │ ├── meanShift1.mexmaci64 │ │ │ ├── binaryTreeTrain1.mexa64 │ │ │ ├── binaryTreeTrain1.mexw64 │ │ │ ├── forestFindThr.mexmaci64 │ │ │ ├── binaryTreeTrain1.mexmaci64 │ │ │ ├── IDX2order.m │ │ │ ├── meanShiftPost.m │ │ │ └── fernsInds1.c │ │ ├── fernsClfApply.m │ │ ├── fernsRegApply.m │ │ ├── fernsInds.m │ │ ├── binaryTreeApply.m │ │ ├── rbfComputeFtrs.m │ │ └── adaBoostApply.m │ ├── external │ │ ├── m2html │ │ │ ├── mwizard.m │ │ │ ├── mwizard2.m │ │ │ ├── templates │ │ │ │ ├── blue │ │ │ │ │ ├── c.png │ │ │ │ │ ├── c++.png │ │ │ │ │ ├── down.png │ │ │ │ │ ├── hp.png │ │ │ │ │ ├── left.png │ │ │ │ │ ├── mex.png │ │ │ │ │ ├── sgi.png │ │ │ │ │ ├── up.png │ │ │ │ │ ├── Thumbs.db │ │ │ │ │ ├── alpha.png │ │ │ │ │ ├── linux.png │ │ │ │ │ ├── pcode.png │ │ │ │ │ ├── right.png │ │ │ │ │ ├── demoicon.gif │ │ │ │ │ ├── fortran.png │ │ │ │ │ ├── solaris.png │ │ │ │ │ ├── windows.png │ │ │ │ │ ├── matlabicon.gif │ │ │ │ │ ├── simulinkicon.gif │ │ │ │ │ ├── search.tpl │ │ │ │ │ ├── todo.tpl │ │ │ │ │ ├── graph.tpl │ │ │ │ │ └── m2html.css │ │ │ │ ├── brain │ │ │ │ │ ├── c++.png │ │ │ │ │ ├── c.png │ │ │ │ │ ├── hp.png │ │ │ │ │ ├── mex.png │ │ │ │ │ ├── sgi.png │ │ │ │ │ ├── up.png │ │ │ │ │ ├── alpha.png │ │ │ │ │ ├── brain.png │ │ │ │ │ ├── down.png │ │ │ │ │ ├── left.png │ │ │ │ │ ├── linux.png │ │ │ │ │ ├── pcode.png │ │ │ │ │ ├── right.png │ │ │ │ │ ├── demoicon.gif │ │ │ │ │ ├── fortran.png │ │ │ │ │ ├── solaris.png │ │ │ │ │ ├── windows.png │ │ │ │ │ ├── matlabicon.gif │ │ │ │ │ ├── simulinkicon.gif │ │ │ │ │ ├── index.html │ │ │ │ │ ├── graph.tpl │ │ │ │ │ ├── search.tpl │ │ │ │ │ ├── todo.tpl │ │ │ │ │ ├── m2html.css │ │ │ │ │ └── master.tpl │ │ │ │ ├── frame │ │ │ │ │ ├── c++.png │ │ │ │ │ ├── c.png │ │ │ │ │ ├── hp.png │ │ │ │ │ ├── mex.png │ │ │ │ │ ├── sgi.png │ │ │ │ │ ├── up.png │ │ │ │ │ ├── alpha.png │ │ │ │ │ ├── down.png │ │ │ │ │ ├── left.png │ │ │ │ │ ├── linux.png │ │ │ │ │ ├── pcode.png │ │ │ │ │ ├── right.png │ │ │ │ │ ├── demoicon.gif │ │ │ │ │ ├── fortran.png │ │ │ │ │ ├── solaris.png │ │ │ │ │ ├── windows.png │ │ │ │ │ ├── matlabicon.gif │ │ │ │ │ ├── simulinkicon.gif │ │ │ │ │ ├── index.html │ │ │ │ │ ├── graph.tpl │ │ │ │ │ ├── search.tpl │ │ │ │ │ ├── todo.tpl │ │ │ │ │ ├── m2html.css │ │ │ │ │ └── master.tpl │ │ │ │ └── frame-piotr │ │ │ │ │ ├── c.png │ │ │ │ │ ├── hp.png │ │ │ │ │ ├── up.png │ │ │ │ │ ├── c++.png │ │ │ │ │ ├── down.png │ │ │ │ │ ├── left.png │ │ │ │ │ ├── mex.png │ │ │ │ │ ├── new.gif │ │ │ │ │ ├── sgi.png │ │ │ │ │ ├── alpha.png │ │ │ │ │ ├── favicon.ico │ │ │ │ │ ├── fortran.png │ │ │ │ │ ├── linux.png │ │ │ │ │ ├── right.png │ │ │ │ │ ├── solaris.png │ │ │ │ │ ├── windows.png │ │ │ │ │ ├── demoicon.gif │ │ │ │ │ ├── matlabicon.gif │ │ │ │ │ ├── simulinkicon.gif │ │ │ │ │ ├── index.html │ │ │ │ │ ├── graph.tpl │ │ │ │ │ ├── todo.tpl │ │ │ │ │ ├── m2html.css │ │ │ │ │ └── master.tpl │ │ │ ├── private │ │ │ │ ├── m2htmltoolbarimages.mat │ │ │ │ ├── mexexts.m │ │ │ │ ├── openfile.m │ │ │ │ ├── strtok.m │ │ │ │ └── searchindex.m │ │ │ ├── @template │ │ │ │ ├── private │ │ │ │ │ ├── subst.m │ │ │ │ │ ├── loadtpl.m │ │ │ │ │ └── finish.m │ │ │ │ ├── char.m │ │ │ │ ├── display.m │ │ │ │ ├── parse.m │ │ │ │ └── get.m │ │ │ ├── Contents.m │ │ │ ├── LICENSE │ │ │ └── INSTALL │ │ ├── other │ │ │ └── multiprod.m │ │ ├── deprecated │ │ │ ├── clfData.mat │ │ │ ├── clfSvm.m │ │ │ ├── clfDecTree.m │ │ │ ├── mode2.m │ │ │ ├── clfDecTreeFwd.m │ │ │ ├── clfKnn.m │ │ │ ├── localsum_block.m │ │ │ ├── clfEcocCode.m │ │ │ ├── clfKnnFwd.m │ │ │ ├── clfKnnTrain.m │ │ │ ├── clfDecTreeTrain.m │ │ │ ├── filter_gauss_1D.m │ │ │ ├── imtranslate.m │ │ │ ├── makemovie.m │ │ │ ├── imrotate2.m │ │ │ ├── imSubsResize.m │ │ │ ├── randperm2.m │ │ │ ├── clfLda.m │ │ │ └── clfEcoc.m │ │ └── bsd.txt │ └── matlab │ │ ├── private │ │ ├── dijkstra1.mexa64 │ │ ├── dijkstra1.mexw64 │ │ ├── char2img_h015.mat │ │ ├── char2img_h020.mat │ │ ├── char2img_h025.mat │ │ ├── char2img_h030.mat │ │ ├── char2img_h035.mat │ │ ├── char2img_h050.mat │ │ └── dijkstra1.mexmaci64 │ │ ├── cc.m │ │ ├── c.m │ │ ├── ccc.m │ │ ├── fevalDistrDisk.m │ │ ├── num2strs.m │ │ ├── randint2.m │ │ ├── subsToArray.m │ │ ├── diskFill.m │ │ ├── plotGaussEllipses.m │ │ ├── tpsRandom.m │ │ ├── ind2sub2.m │ │ ├── dijkstra.m │ │ ├── randSample.m │ │ ├── figureResized.m │ │ ├── sub2ind2.m │ │ ├── tpsInterpolateIm.m │ │ ├── normpdf2.m │ │ ├── isfield2.m │ │ ├── int2str2.m │ │ └── spBlkDiag.m ├── mtimesx │ ├── mtimesx.c │ ├── mtimesx.m │ ├── mtimesx.mexa64 │ ├── mtimesx.mexw64 │ └── license.txt └── mexResize │ ├── libopencv_core.a │ ├── libopencv_core.so │ ├── mexResize.mexa64 │ ├── mexResize.mexw64 │ ├── libopencv_imgproc.a │ ├── libopencv_imgproc.so │ ├── opencv_core242.dll │ ├── opencv_core242.lib │ ├── opencv_imgproc242.dll │ ├── opencv_imgproc242.lib │ ├── opencv2 │ ├── flann │ │ └── dummy.h │ └── opencv_modules.hpp │ └── compile.m ├── Fig.jpg ├── tracker_featu ├── offline_models │ ├── CNnorm.mat │ └── intensityChannelNorm6.mat ├── integralVecImage.m ├── table_lookup.m ├── get_fhog.m ├── average_feature_region.m ├── sample_patch.m └── get_table_feature.m ├── tracker_utils ├── init_default_params.m ├── get_sequence_results.m ├── get_sequence_frame.m ├── get_sequence_vot.m ├── choose_video.m ├── report_tracking_result.m └── calcRectInt.m ├── setup_paths.m ├── tracker_imple └── resizeDFT2.m └── README.md /tracker_exter/matconvnet/.gitmodules: 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/tracker_exter/pdollar_toolbox/external/m2html/templates/frame-piotr/simulinkicon.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/XU-TIANYANG/GFS-DCF/HEAD/tracker_exter/pdollar_toolbox/external/m2html/templates/frame-piotr/simulinkicon.gif -------------------------------------------------------------------------------- /tracker_featu/integralVecImage.m: -------------------------------------------------------------------------------- 1 | function intImage = integralVecImage(I) 2 | 3 | % Compute the integral image of I. 4 | 5 | if ~isempty(I) 6 | intImage = zeros(size(I,1)+1, size(I,2)+1, size(I,3), size(I,4), 'like', I); 7 | intImage(2:end, 2:end, :, :) = cumsum(cumsum(I,1),2); 8 | else 9 | intImage = []; 10 | end 11 | 12 | -------------------------------------------------------------------------------- /tracker_exter/mexResize/opencv2/flann/dummy.h: -------------------------------------------------------------------------------- 1 | 2 | #ifndef OPENCV_FLANN_DUMMY_H_ 3 | #define OPENCV_FLANN_DUMMY_H_ 4 | 5 | namespace cvflann 6 | { 7 | 8 | #if (defined WIN32 || defined _WIN32 || defined WINCE) && defined CVAPI_EXPORTS 9 | __declspec(dllexport) 10 | #endif 11 | void dummyfunc(); 12 | 13 | } 14 | 15 | 16 | #endif /* OPENCV_FLANN_DUMMY_H_ */ 17 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/matlab/cc.m: -------------------------------------------------------------------------------- 1 | % close all, clc 2 | % 3 | % USAGE 4 | % cc 5 | % 6 | % INPUTS 7 | % 8 | % OUTPUTS 9 | % 10 | % EXAMPLE 11 | % 12 | % See also C, CCC 13 | % 14 | % Piotr's Computer Vision Matlab Toolbox Version 1.5 15 | % Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com] 16 | % Licensed under the Simplified BSD License [see external/bsd.txt] 17 | 18 | close all; clc 19 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/matlab/c.m: -------------------------------------------------------------------------------- 1 | % clc - clear command window. 2 | % 3 | % USAGE 4 | % c 5 | % 6 | % INPUTS 7 | % 8 | % OUTPUTS 9 | % 10 | % EXAMPLE 11 | % 12 | % See also CLC, CC, CCC 13 | % 14 | % Piotr's Computer Vision Matlab Toolbox Version 1.5 15 | % Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com] 16 | % Licensed under the Simplified BSD License [see external/bsd.txt] 17 | 18 | clc 19 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/matlab/ccc.m: -------------------------------------------------------------------------------- 1 | % clear, close all, clc, clear global 2 | % 3 | % USAGE 4 | % ccc 5 | % 6 | % INPUTS 7 | % 8 | % OUTPUTS 9 | % 10 | % EXAMPLE 11 | % 12 | % See also C, CC 13 | % 14 | % Piotr's Computer Vision Matlab Toolbox Version 1.5 15 | % Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com] 16 | % Licensed under the Simplified BSD License [see external/bsd.txt] 17 | 18 | clear; close all; clc; clear all; 19 | -------------------------------------------------------------------------------- /tracker_utils/init_default_params.m: -------------------------------------------------------------------------------- 1 | function params = init_default_params(params) 2 | 3 | % Initialize default parameters 4 | default_params.use_gpu = false; 5 | default_params.gpu_id = []; 6 | default_params.clamp_position = false; 7 | def_param_names = fieldnames(default_params); 8 | for k = 1:numel(def_param_names) 9 | param_name = def_param_names{k}; 10 | if ~isfield(params, param_name) 11 | params.(param_name) = default_params.(param_name); 12 | end 13 | end -------------------------------------------------------------------------------- /tracker_utils/get_sequence_results.m: -------------------------------------------------------------------------------- 1 | function [seq, results] = get_sequence_results(seq) 2 | 3 | if strcmpi(seq.format, 'otb')||strcmpi(seq.format, 'otb_8') 4 | results.type = 'rect'; 5 | results.res = seq.rect_position; 6 | elseif strcmpi(seq.format, 'vot') 7 | seq.handle.quit(seq.handle); 8 | else 9 | error('Uknown sequence format'); 10 | end 11 | 12 | if isfield(seq, 'time') 13 | results.fps = seq.num_frames / seq.time; 14 | else 15 | results.fps = NaN; 16 | end -------------------------------------------------------------------------------- /tracker_exter/mexResize/compile.m: -------------------------------------------------------------------------------- 1 | 2 | % Compiles the mexResize function. 3 | 4 | % Try to use the included mex files first. If they do not work, you can try 5 | % to compile it yourself with this script. If the bellow mex command does 6 | % not work, try to link to your own OpenCV installation. 7 | 8 | if ispc 9 | mex -lopencv_core242 -lopencv_imgproc242 -L./ -I./ mexResize.cpp MxArray.cpp 10 | else 11 | mex -lopencv_core -lopencv_imgproc -L./ -I./ mexResize.cpp MxArray.cpp 12 | end -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/m2html/@template/private/subst.m: -------------------------------------------------------------------------------- 1 | function [tpl, str] = subst(tpl,handle) 2 | %TEMPLATE/SUBST Substitute a replacement field by its value 3 | % STR = SUBST(TPL,HANDLE) substitute all the known fields of variable HANDLE 4 | % in the template TPL. 5 | 6 | % Copyright (C) 2003 Guillaume Flandin 7 | % $Revision: 1.0 $Date: 2003/05/05 22:19:51 $ 8 | 9 | tpl = loadtpl(tpl,handle); 10 | 11 | str = get(tpl,'var',handle); 12 | for i=1:length(tpl.varkeys) 13 | str = strrep(str, strcat('{',tpl.varkeys{i},'}'), tpl.varvals{i}); 14 | end 15 | -------------------------------------------------------------------------------- /tracker_utils/get_sequence_frame.m: -------------------------------------------------------------------------------- 1 | function [seq, im] = get_sequence_frame(seq) 2 | 3 | seq.frame = seq.frame + 1; 4 | 5 | if strcmpi(seq.format, 'otb')||strcmpi(seq.format, 'otb_8') 6 | if seq.frame > seq.num_frames 7 | im = []; 8 | else 9 | im = imread(seq.image_files{seq.frame}); 10 | end 11 | elseif strcmpi(seq.format, 'vot') 12 | [seq.handle, image_file] = seq.handle.frame(seq.handle); 13 | if isempty(image_file) 14 | im = []; 15 | else 16 | im = imread(image_file); 17 | end 18 | else 19 | error('Uknown sequence format'); 20 | end -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/m2html/@template/char.m: -------------------------------------------------------------------------------- 1 | function s = char(tpl) 2 | %TEMPLATE Convert a template object in a one line description string 3 | % S = CHAR(TPL) is a class convertor from Template to a string, used 4 | % in online display. 5 | % 6 | % See also DISPLAY 7 | 8 | % Copyright (C) 2003 Guillaume Flandin 9 | % $Revision: 1.0 $Date: 2003/05/05 22:19:51 $ 10 | 11 | s = ['Template Object: root ''',... 12 | tpl.root,''', ',... 13 | num2str(length(tpl.file)), ' files, ',... 14 | num2str(length(tpl.varkeys)), ' keys, ',... 15 | tpl.unknowns, ' unknowns.']; 16 | -------------------------------------------------------------------------------- /tracker_featu/table_lookup.m: -------------------------------------------------------------------------------- 1 | function out=table_lookup(im,table) 2 | 3 | if isa(im,'uint8') 4 | im = int32(im); 5 | end 6 | 7 | [im_height, im_width, num_im_chan, num_images] = size(im); 8 | 9 | den = int32(8); 10 | fac = int32(32); 11 | offset = int32(1); 12 | 13 | if num_im_chan == 3 14 | RR=im(:,:,1,:);GG=im(:,:,2,:);BB=im(:,:,3,:); 15 | index_im = offset + idivide(RR,den) + fac*idivide(GG,den) + fac*fac*idivide(BB,den); 16 | out = permute(reshape(table(index_im(:),:), im_height, im_width, num_images, size(table,2)), [1 2 4 3]); 17 | else 18 | out = permute(reshape(table(im(:)+1,:), im_height, im_width, num_images, size(table,2)), [1 2 4 3]); 19 | end 20 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/m2html/private/mexexts.m: -------------------------------------------------------------------------------- 1 | function [ext, platform] = mexexts 2 | %MEXEXTS List of Mex files extensions 3 | % MEXEXTS returns a cell array containing the Mex files platform 4 | % dependent extensions and another cell array containing the full names 5 | % of the corresponding platforms. 6 | % 7 | % See also MEX, MEXEXT 8 | 9 | % Copyright (C) 2003 Guillaume Flandin 10 | % $Revision: 1.0 $Date: 2003/29/04 17:33:43 $ 11 | 12 | ext = {'.mexsol' '.mexhpux' '.mexhp7' '.mexrs6' '.mexsg' '.mexaxp' '.mexglx' ... 13 | '.mexlx' '.dll'}; 14 | 15 | platform = {'SunOS' 'HP' 'HP700' 'IBM' 'SGI' 'Alpha' 'Linux x86' 'Linux' 'Windows'}; 16 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/m2html/@template/display.m: -------------------------------------------------------------------------------- 1 | function display(tpl) 2 | %TEMPLATE Display a template object in Matlab window 3 | % DISPLAY(TPL) displays informations about the content of template 4 | % object TPL: 5 | % Template Object: root '.', 2 files, 9 keys, comment unknowns. 6 | % root element of template files, number of template files, number of 7 | % keywords defined and the way of handling unknowns tags. 8 | 9 | % Copyright (C) 2003 Guillaume Flandin 10 | % $Revision: 1.0 $Date: 2003/05/05 22:19:51 $ 11 | 12 | disp(' '); 13 | disp([inputname(1),' = ']); 14 | disp(' '); 15 | for i=1:prod(size(tpl)) 16 | disp([blanks(length(inputname(1))+3) char(tpl(i))]); 17 | end 18 | disp(' '); 19 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/matlab/fevalDistrDisk.m: -------------------------------------------------------------------------------- 1 | function fevalDistrDisk( funNm, jobDir, jobId ) 2 | % Helper for fevalDistr (do no call directly). 3 | % 4 | % USAGE 5 | % fevalDistrDisk( funNm, jobDir, jobId ) 6 | % 7 | % INPUTS 8 | % 9 | % OUTPUTS 10 | % 11 | % EXAMPLE 12 | % 13 | % See also fevalDistr 14 | % 15 | % Piotr's Computer Vision Matlab Toolbox Version 3.02 16 | % Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com] 17 | % Licensed under the Simplified BSD License [see external/bsd.txt] 18 | jobId=[jobDir int2str2(str2double(jobId),10)]; 19 | fIn=[jobId '-in.mat']; if(exist(fIn,'file')~=2), return; end 20 | job=load(fIn); job=job.job; 21 | r=feval(funNm,job{:}); save([jobId '-out'],'r'); %#ok 22 | f=fopen([jobId '-done'],'w'); fclose(f); 23 | end 24 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/matlab/num2strs.m: -------------------------------------------------------------------------------- 1 | function Y = num2strs( X, varargin ) 2 | % Applies num2str to each element of an array X. 3 | % 4 | % USAGE 5 | % Y = num2strs( X, [varargin] ) 6 | % 7 | % INPUTS 8 | % X - array of number to convert to strings 9 | % varargin - [] additional input to num2str 10 | % 11 | % OUTPUTS 12 | % Y - cell array of strings 13 | % 14 | % EXAMPLE 15 | % Y = num2strs( [1.3 2.6; 3 11] ) 16 | % 17 | % See also NUM2STR 18 | % 19 | % Piotr's Computer Vision Matlab Toolbox Version 1.5 20 | % Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com] 21 | % Licensed under the Simplified BSD License [see external/bsd.txt] 22 | 23 | Y = cell(size(X)); 24 | for i=1:numel(X) 25 | Y{i} = num2str( X(i), varargin{:} ); 26 | end 27 | -------------------------------------------------------------------------------- /tracker_exter/mexResize/opencv2/opencv_modules.hpp: -------------------------------------------------------------------------------- 1 | /* 2 | * ** File generated automatically, do not modify ** 3 | * 4 | * This file defines the list of modules available in current build configuration 5 | * 6 | * 7 | */ 8 | 9 | #define HAVE_OPENCV_CALIB3D 10 | #define HAVE_OPENCV_CONTRIB 11 | #define HAVE_OPENCV_CORE 12 | #define HAVE_OPENCV_FEATURES2D 13 | #define HAVE_OPENCV_FLANN 14 | #define HAVE_OPENCV_GPU 15 | #define HAVE_OPENCV_HIGHGUI 16 | #define HAVE_OPENCV_IMGPROC 17 | #define HAVE_OPENCV_LEGACY 18 | #define HAVE_OPENCV_ML 19 | #define HAVE_OPENCV_NONFREE 20 | #define HAVE_OPENCV_OBJDETECT 21 | #define HAVE_OPENCV_PHOTO 22 | #define HAVE_OPENCV_STITCHING 23 | #define HAVE_OPENCV_TS 24 | #define HAVE_OPENCV_VIDEO 25 | #define HAVE_OPENCV_VIDEOSTAB 26 | 27 | 28 | -------------------------------------------------------------------------------- /tracker_featu/get_fhog.m: -------------------------------------------------------------------------------- 1 | function [ feature_image ] = get_fhog( im, fparam, gparam ) 2 | 3 | % Extract FHOG features using pdollar toolbox. 4 | 5 | if ~isfield(fparam, 'nOrients') 6 | fparam.nOrients = 9; 7 | end 8 | if isfield(fparam, 'cell_size') 9 | cell_size = fparam.cell_size; 10 | else 11 | cell_size = gparam.cell_size; 12 | end 13 | 14 | [im_height, im_width, num_im_chan, num_images] = size(im); 15 | feature_image = zeros(floor(im_height/cell_size), floor(im_width/cell_size), fparam.nDim, num_images, 'like', gparam.data_type); 16 | 17 | for k = 1:num_images 18 | hog_image = fhog(single(im(:,:,:,k)), cell_size, fparam.nOrients); 19 | 20 | %the last dimension is all 0 so we can discard it 21 | feature_image(:,:,:,k) = hog_image(:,:,1:end-1); 22 | end 23 | end -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/deprecated/clfSvm.m: -------------------------------------------------------------------------------- 1 | function net = clfSvm(varargin) 2 | % Wrapper for svm that makes svm compatible with nfoldxval. 3 | % 4 | % Requires the SVM toolbox by Anton Schwaighofer. 5 | % 6 | % USAGE 7 | % net = clfSvm(varargin) 8 | % 9 | % INPUTS 10 | % see svm in SVM toolbox by Anton Schwaighofer. 11 | % 12 | % OUTPUTS 13 | % see svm in SVM toolbox by Anton Schwaighofer. 14 | % 15 | % EXAMPLE 16 | % 17 | % See also SVM, NFOLDXVAL 18 | % 19 | % Piotr's Image&Video Toolbox Version 2.0 20 | % Copyright 2008 Piotr Dollar. [pdollar-at-caltech.edu] 21 | % Please email me if you find bugs, or have suggestions or questions! 22 | % Licensed under the Lesser GPL [see external/lgpl.txt] 23 | 24 | net = svm( varargin{:} ); 25 | net.funTrain = @svmtrain; 26 | net.funFwd = @svmfwd; 27 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/m2html/private/openfile.m: -------------------------------------------------------------------------------- 1 | function fid = openfile(filename,permission) 2 | %OPENFILE Open a file in read/write mode, catching errors 3 | % FID = OPENFILE(FILENAME,PERMISSION) opens file FILENAME 4 | % in PERMISSION mode ('r' or 'w') and return a file identifier FID. 5 | % File is opened in text mode: no effect on Unix but useful on Windows. 6 | 7 | % Copyright (C) 2004 Guillaume Flandin 8 | % $Revision: 1.1 $Date: 2004/05/05 17:14:09 $ 9 | 10 | [fid, errmsg] = fopen(filename,[permission 't']); 11 | if ~isempty(errmsg) 12 | switch permission 13 | case 'r' 14 | error(sprintf('Cannot open %s in read mode.',filename)); 15 | case 'w' 16 | error(sprintf('Cannot open %s in write mode.',filename)); 17 | otherwise 18 | error(errmsg); 19 | end 20 | end 21 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/m2html/Contents.m: -------------------------------------------------------------------------------- 1 | % M2HTML Toolbox - A Documentation Generator for Matlab in HTML 2 | % Version 1.4 16-Jun-2004 3 | % 4 | % M2HTML main functions. 5 | % m2html - Documentation System for Matlab M-files in HTML. 6 | % mdot - Wrapper to GraphViz's for dependency graphs. 7 | % mwizard - Graphical user interface for m2html. 8 | % private - Internal functions. 9 | % 10 | % Template toolbox. 11 | % @template - HTML template class 12 | % 13 | % Templates files. 14 | % templates/blue - Default HTML template. 15 | % templates/frame - Identical to but using frames. 16 | % 17 | % Others. 18 | % Changelog, GPL, INSTALL, LICENSE, README, TODO. 19 | 20 | % Copyright (C) 2003-2005 Guillaume Flandin 21 | % $Revision: 1.4 $Date: 2004/06/16 12:45:24 $ 22 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/m2html/@template/private/loadtpl.m: -------------------------------------------------------------------------------- 1 | function tpl = loadtpl(tpl,handle) 2 | %TEMPLATE/LOADTPL Read a template from file 3 | % TPL = LOADTPL(TPL,HANDLE) read the template file associated with the 4 | % handle HANDLE in the template TPL and store it in the variable HANDLE. 5 | 6 | % Copyright (C) 2003 Guillaume Flandin 7 | % $Revision: 1.0 $Date: 2003/05/05 22:19:51 $ 8 | 9 | if ~isempty(get(tpl,'var',handle)) 10 | return; 11 | else 12 | ind = find(ismember(tpl.handles,handle)); 13 | if isempty(ind) 14 | error('[Template] No such template handle.'); 15 | else 16 | filename = tpl.file{ind}; 17 | [fid, errmsg] = fopen(filename,'rt'); 18 | if ~isempty(errmsg) 19 | error(sprintf('Cannot open template file %s.',filename)); 20 | end 21 | tpl = set(tpl,'var',handle,fscanf(fid,'%c')); 22 | fclose(fid); 23 | end 24 | end 25 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/m2html/LICENSE: -------------------------------------------------------------------------------- 1 | M2HTML - Documentation System for Matlab .m files in HTML 2 | Copyright (C) 2003-2005 Guillaume Flandin 3 | 4 | This program is free software; you can redistribute it and/or 5 | modify it under the terms of the GNU General Public License 6 | as published by the Free Software Foundation; either version 2 7 | of the License, or any later version. 8 | 9 | This program is distributed in the hope that it will be useful, 10 | but WITHOUT ANY WARRANTY; without even the implied warranty of 11 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 12 | GNU General Public License for more details. 13 | 14 | You should have received a copy of the GNU General Public License 15 | along with this program; if not, write to the Free Software 16 | Foundation Inc, 59 Temple Pl. - Suite 330, Boston, MA 02111-1307, USA. 17 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/deprecated/clfDecTree.m: -------------------------------------------------------------------------------- 1 | function clf = clfDecTree( p, varargin ) 2 | % Wrapper for treefit that makes decision trees compatible with nfoldxval. 3 | % 4 | % USAGE 5 | % clf = clfDecTree( p, varargin ) 6 | % 7 | % INPUTS 8 | % p - data dimension 9 | % varargin- params for treefit, ex: 'splitmin'',2,'priorprob',ones(1,n)/n 10 | % 11 | % OUTPUTS 12 | % clf - model ready to be trained 13 | % 14 | % EXAMPLE 15 | % 16 | % See also NFOLDXVAL, TREEFIT, CLFDECTREEFWD, CLFDECTREETRAIN 17 | % 18 | % Piotr's Image&Video Toolbox Version 2.0 19 | % Copyright 2008 Piotr Dollar. [pdollar-at-caltech.edu] 20 | % Please email me if you find bugs, or have suggestions or questions! 21 | % Licensed under the Lesser GPL [see external/lgpl.txt] 22 | 23 | clf.p = p; 24 | clf.type = 'dectree'; 25 | clf.params = varargin; 26 | 27 | clf.funTrain = @clfDecTreeTrain; 28 | clf.funFwd = @clfDecTreeFwd; 29 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/deprecated/mode2.m: -------------------------------------------------------------------------------- 1 | % Returns the mode of a vector. 2 | % 3 | % Was mode not part of Matlab before? 4 | % 5 | % USAGE 6 | % y = mode2( x ) 7 | % 8 | % INPUTS 9 | % x - vector of integers 10 | % 11 | % OUTPUTS 12 | % y - mode 13 | % 14 | % EXAMPLE 15 | % x = randint2( 1, 10, [1 3] ) 16 | % mode(x), mode2( x ) 17 | % 18 | % See also MODE 19 | 20 | % Piotr's Image&Video Toolbox Version 1.5 21 | % Written and maintained by Piotr Dollar pdollar-at-cs.ucsd.edu 22 | % Please email me if you find bugs, or have suggestions or questions! 23 | 24 | function y = mode2( x ) 25 | 26 | wid = sprintf('Images:%s:obsoleteFunction',mfilename); 27 | warning(wid,[ '%s is obsolete in Piotr''s toolbox.\n MODE is its '... 28 | 'recommended replacement.'],upper(mfilename)); 29 | 30 | y = mode( x ); 31 | 32 | % [b,i,j] = unique(x); 33 | % [ mval, ind ] = max(hist(j,length(b))); 34 | % y = b(ind); 35 | -------------------------------------------------------------------------------- /setup_paths.m: -------------------------------------------------------------------------------- 1 | function setup_paths() 2 | % Add involved folders into MATLAB path. 3 | 4 | [pathstr, ~, ~] = fileparts(mfilename('fullpath')); 5 | 6 | % Tracker implementation 7 | addpath(genpath([pathstr '/tracker_imple/'])); 8 | 9 | % Utilities 10 | addpath([pathstr '/tracker_utils/']); 11 | 12 | % The feature extraction 13 | addpath(genpath([pathstr '/tracker_featu/'])); 14 | 15 | % Matconvnet 16 | addpath([pathstr '/tracker_exter/matconvnet/matlab/mex/']); 17 | addpath([pathstr '/tracker_exter/matconvnet/matlab']); 18 | addpath([pathstr '/tracker_exter/matconvnet/matlab/simplenn']); 19 | vl_setupnn; 20 | 21 | % PDollar toolbox 22 | addpath(genpath([pathstr '/tracker_exter/pdollar_toolbox/channels'])); 23 | 24 | % Mtimesx 25 | addpath([pathstr '/tracker_exter/mtimesx/']); 26 | 27 | % mexResize 28 | addpath([pathstr '/tracker_exter/mexResize/']); 29 | 30 | % Networks and tables 31 | addpath([pathstr '/tracker_featu/offline_models/']); 32 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/deprecated/clfDecTreeFwd.m: -------------------------------------------------------------------------------- 1 | function Y = clfDecTreeFwd( clf, X ) 2 | % Apply the decision tree to data X. 3 | % 4 | % USAGE 5 | % Y = clfDecTreeFwd( clf, X ) 6 | % 7 | % INPUTS 8 | % clf - trained model 9 | % X - nxp data array 10 | % 11 | % OUTPUTS 12 | % Y - nx1 vector of labels predicted according to the clf 13 | % 14 | % EXAMPLE 15 | % 16 | % See also CLFDECTREE 17 | % 18 | % Piotr's Image&Video Toolbox Version 2.0 19 | % Copyright 2008 Piotr Dollar. [pdollar-at-caltech.edu] 20 | % Please email me if you find bugs, or have suggestions or questions! 21 | % Licensed under the Lesser GPL [see external/lgpl.txt] 22 | 23 | if(~strcmp(clf.type,'dectree')); error( ['incor. type: ' clf.type] ); end; 24 | if( size(X,2)~= clf.p ); error( 'Incorrect data dimension' ); end; 25 | T = clf.T; 26 | 27 | [Y,d,cnames] = treeval( T, X ); 28 | Y = str2double( cnames ); % convert Y back to an int format 29 | -------------------------------------------------------------------------------- /tracker_imple/resizeDFT2.m: -------------------------------------------------------------------------------- 1 | function resizeddft = resizeDFT2(inputdft, desiredSize) 2 | 3 | [imh, imw, n1, n2] = size(inputdft); 4 | imsz = [imh, imw]; 5 | 6 | if any(desiredSize ~= imsz) 7 | minsz = min(imsz, desiredSize); 8 | 9 | scaling = prod(desiredSize)/prod(imsz); 10 | 11 | resizeddft = complex(zeros([desiredSize n1 n2], 'single')); 12 | 13 | mids = ceil(minsz/2); 14 | mide = floor((minsz-1)/2) - 1; 15 | 16 | resizeddft(1:mids(1), 1:mids(2), :, :) = scaling * inputdft(1:mids(1), 1:mids(2), :, :); 17 | resizeddft(1:mids(1), end - mide(2):end, :, :) = scaling * inputdft(1:mids(1), end - mide(2):end, :, :); 18 | resizeddft(end - mide(1):end, 1:mids(2), :, :) = scaling * inputdft(end - mide(1):end, 1:mids(2), :, :); 19 | resizeddft(end - mide(1):end, end - mide(2):end, :, :) = scaling * inputdft(end - mide(1):end, end - mide(2):end, :, :); 20 | else 21 | resizeddft = inputdft; 22 | end 23 | end -------------------------------------------------------------------------------- /tracker_utils/get_sequence_vot.m: -------------------------------------------------------------------------------- 1 | function seq = get_sequence_vot(seq) 2 | if strcmpi(seq.format, 'vot') 3 | if numel(seq.region) > 4 4 | seq.rect8 = round(seq.region(:)); 5 | rect8 = seq.rect8; 6 | x1 = round(min(rect8(1:2:end))); 7 | x2 = round(max(rect8(1:2:end))); 8 | y1 = round(min(rect8(2:2:end))); 9 | y2 = round(max(rect8(2:2:end))); 10 | seq.init_rect = round([x1, y1, x2 - x1, y2 - y1]); 11 | seq.target_mask = single(poly2mask(rect8(1:2:end)-seq.init_rect(1), ... 12 | rect8(2:2:end)-seq.init_rect(2), seq.init_rect(4), seq.init_rect(3))); 13 | seq.t_b_ratio = sum(seq.target_mask(:))/prod(seq.init_rect([4,3])); 14 | else 15 | r = seq.region(:); 16 | seq.rect8 = [r(1),r(2),r(1)+r(3),r(2),r(1)+r(3),r(2)+r(4),r(1),r(2)+r(4)]; 17 | seq.target_mask = single(ones(seq.region([4,3]))); 18 | seq.t_b_ratio = 1; 19 | end 20 | end 21 | end 22 | 23 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/deprecated/clfKnn.m: -------------------------------------------------------------------------------- 1 | function clf = clfKnn( p, k, metric ) 2 | % Create a k nearest neighbor classifier. 3 | % 4 | % USAGE 5 | % clf = clfKnn( p, k, metric ) 6 | % 7 | % INPUTS 8 | % p - data dimension 9 | % k - number of nearest neighbors to look at 10 | % metric - [] distance function, squared euclidean by default 11 | % 12 | % OUTPUTS 13 | % clf - model ready to be trained 14 | % 15 | % EXAMPLE 16 | % 17 | % See also NFOLDXVAL, CLFKNNTRAIN, CLFKNNFWD, CLFKNNDIST 18 | % 19 | % Piotr's Image&Video Toolbox Version 2.0 20 | % Copyright 2008 Piotr Dollar. [pdollar-at-caltech.edu] 21 | % Please email me if you find bugs, or have suggestions or questions! 22 | % Licensed under the Lesser GPL [see external/lgpl.txt] 23 | 24 | if( nargin<3 ); metric = []; end 25 | 26 | clf.p = p; 27 | clf.type = 'knn'; 28 | clf.k = k; 29 | clf.metric = metric; 30 | clf.funTrain = @clfKnnTrain; 31 | clf.funFwd = @clfKnnFwd; 32 | -------------------------------------------------------------------------------- /tracker_utils/choose_video.m: -------------------------------------------------------------------------------- 1 | function video = choose_video(base_path) 2 | % Choose a specific video from 'base_path'. 3 | % Input : base_path[str](root path of a sequence set) 4 | % Output : video[str](selected folder name) 5 | 6 | %process suitable path for system 7 | if ispc(), base_path = strrep(base_path, '\', '/'); end 8 | if base_path(end) ~= '/', base_path(end+1) = '/'; end 9 | 10 | %list all sub-folders 11 | contents = dir(base_path); 12 | names = {}; 13 | for k = 1:numel(contents) 14 | name = contents(k).name; 15 | if isdir([base_path name]) && ~strcmp(name, '.') && ~strcmp(name, '..') 16 | names{end+1} = name; %#ok 17 | end 18 | end 19 | 20 | %no sub-folders found 21 | if isempty(names), video = []; return; end 22 | 23 | %choice GUI 24 | choice = listdlg('ListString',names, 'Name','Choose video', 'SelectionMode','single'); 25 | 26 | if isempty(choice) 27 | video = []; 28 | else 29 | video = names{choice}; 30 | end 31 | 32 | end 33 | 34 | -------------------------------------------------------------------------------- /tracker_featu/average_feature_region.m: -------------------------------------------------------------------------------- 1 | function [ region_image ] = average_feature_region(im, region_size) 2 | % compute cell wise averages, where a cell is a region_size*region_sized 3 | % region in the image. Input can be uint8t, single or double matrices 4 | % of arbitrary dimension 5 | 6 | region_area = region_size.^2; 7 | 8 | if isa(im,'double') || isa(im,'single') || isa(im,'gpuArray') 9 | maxval = 1.0; 10 | elseif isa(im,'unit8') 11 | maxval = 255; 12 | end 13 | 14 | % compute the integral image 15 | iImage = integralVecImage(im); 16 | 17 | % region indices 18 | i1 = (region_size:region_size:size(im,1)) + 1; 19 | i2 = (region_size:region_size:size(im,2)) + 1; 20 | 21 | % sum over region, divided by number of elements, and normalize to [0,1] 22 | % range if integer image 23 | region_image = (iImage(i1,i2,:,:) - iImage(i1,i2-region_size,:,:) - iImage(i1-region_size,i2,:,:) + iImage(i1-region_size,i2-region_size,:,:)) ./ (region_area * maxval); 24 | 25 | end 26 | 27 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/deprecated/localsum_block.m: -------------------------------------------------------------------------------- 1 | % Calculates the sum in non-overlapping blocks of I of size dims. 2 | % 3 | % Similar to localsum except gets sum in non-overlapping windows. 4 | % Equivalent to doing localsum, and then subsampling (except more 5 | % efficient). 6 | % 7 | % USAGE 8 | % I = localsum_block( I, dims ) 9 | % 10 | % INPUTS 11 | % I - matrix to compute sum over 12 | % dims - size of volume to compute sum over 13 | % 14 | % OUTPUTS 15 | % I - resulting array 16 | % 17 | % EXAMPLE 18 | % load trees; I=ind2gray(X,map); 19 | % I2 = localsum_block( I, 11 ); 20 | % figure(1); im(I); figure(2); im(I2); 21 | % 22 | % See also LOCALSUM 23 | 24 | % Piotr's Image&Video Toolbox Version 1.5 25 | % Written and maintained by Piotr Dollar pdollar-at-cs.ucsd.edu 26 | % Please email me if you find bugs, or have suggestions or questions! 27 | 28 | function I = localsum_block( I, dims ) 29 | 30 | I = nlfiltblock_sep( I, dims, @rnlfiltblock_sum ); 31 | -------------------------------------------------------------------------------- /tracker_utils/report_tracking_result.m: -------------------------------------------------------------------------------- 1 | function seq = report_tracking_result(seq, result) 2 | 3 | if strcmpi(seq.format, 'otb')||strcmpi(seq.format, 'otb_8') 4 | seq.rect_position(seq.frame,:) = round([result.center_pos([2,1]) - (result.target_size([2,1]) - 1)/2, result.target_size([2,1])]); 5 | elseif strcmpi(seq.format, 'vot') 6 | if seq.frame > 1 7 | bb_scale = 1; 8 | sz = result.target_size / bb_scale; 9 | tl = result.center_pos - (sz - 1)/2; 10 | br = result.center_pos + (sz - 1)/2; 11 | x1 = tl(2); y1 = tl(1); 12 | x2 = br(2); y2 = br(1); 13 | result_box = round(double([x1 y1 x2 y1 x2 y2 x1 y2])); 14 | if any(isnan(result_box) | isinf(result_box)) 15 | error('Illegal values in the result.') 16 | end 17 | % if any(result_box < 0) 18 | % error('Negative values') 19 | % end 20 | seq.handle = seq.handle.report(seq.handle, result_box); 21 | end 22 | else 23 | error('Uknown sequence format'); 24 | end -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/videos/Contents.m: -------------------------------------------------------------------------------- 1 | % VIDEOS 2 | % See also 3 | % 4 | % Behavior annotation for seq files: 5 | % behaviorAnnotator - Caltech Behavior Annotator. 6 | % behaviorData - Retrieve and manipulate behavior annotation of a video. 7 | % 8 | % Seq files [custom codec free video format]: 9 | % seqIo - Utilities for reading and writing seq files. 10 | % seqReaderPlugin - Plugin for seqIo and videoIO to allow reading of seq files. 11 | % seqWriterPlugin - Plugin for seqIo and videoIO to allow writing of seq files. 12 | % seqPlayer - Simple GUI to play seq files. 13 | % 14 | % Image and sequence alignment: 15 | % imagesAlign - Fast and robust estimation of homography relating two images. 16 | % imagesAlignSeq - Stabilize image sequence using coarse optical flow estimation. 17 | % 18 | % Miscellaneous: 19 | % kernelTracker - Kernel Tracker from Comaniciu, Ramesh and Meer PAMI 2003. 20 | % opticalFlow - Coarse-to-fine optical flow using Lucas&Kanade or Horn&Schunck. 21 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/deprecated/clfEcocCode.m: -------------------------------------------------------------------------------- 1 | function [C,nbits] = clfEcocCode( k ) 2 | % Generates optimal ECOC codes when 3<=nclasses<=7. 3 | % 4 | % USAGE 5 | % [C,nbits] = clfEcocCode( k ) 6 | % 7 | % INPUTS 8 | % k - number of classes 9 | % 10 | % OUTPUTS 11 | % C - code 12 | % nbits - number of bits 13 | % 14 | % EXAMPLE 15 | % 16 | % See also CLFECOC 17 | % 18 | % Piotr's Image&Video Toolbox Version 2.0 19 | % Copyright 2008 Piotr Dollar. [pdollar-at-caltech.edu] 20 | % Please email me if you find bugs, or have suggestions or questions! 21 | % Licensed under the Lesser GPL [see external/lgpl.txt] 22 | 23 | if( k<3 || k>7 ) 24 | error( 'method only works if k is small: 3<=k<=7'); end 25 | 26 | % create C 27 | C = ones(k,2^(k-1)); 28 | for i=2:k 29 | partw = 2^(k-i); nparts = 2^(i-2); 30 | row = [zeros(1,partw) ones(1,partw)]; 31 | row = repmat( row, 1, nparts ); 32 | C(i,:) = row; 33 | end 34 | C = C(:,1:end-1); 35 | nbits = size(C,2); 36 | 37 | % alter C to have entries [-1,1] 38 | C(C==0)=-1; 39 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/deprecated/clfKnnFwd.m: -------------------------------------------------------------------------------- 1 | function Y = clfKnnFwd( clf, X ) 2 | % Apply a k-nearest neighbor classifier to X. 3 | % 4 | % USAGE 5 | % Y = clfKnnFwd( clf, X ) 6 | % 7 | % INPUTS 8 | % clf - trained model 9 | % X - nxp data array 10 | % 11 | % OUTPUTS 12 | % Y - nx1 vector of labels predicted according to the clf 13 | % 14 | % EXAMPLE 15 | % 16 | % See also CLFKNN, CLFKNNTRAIN 17 | % 18 | % Piotr's Image&Video Toolbox Version 2.0 19 | % Copyright 2008 Piotr Dollar. [pdollar-at-caltech.edu] 20 | % Please email me if you find bugs, or have suggestions or questions! 21 | % Licensed under the Lesser GPL [see external/lgpl.txt] 22 | 23 | if( ~strcmp(clf.type,'knn')); error( ['incorrect type: ' clf.type] ); end 24 | if( size(X,2)~= clf.p ); error( 'Incorrect data dimension' ); end 25 | 26 | metric = clf.metric; 27 | Xtrain = clf.Xtrain; 28 | Ytrain = clf.Ytrain; 29 | k = clf.k; 30 | 31 | % get nearest neighbors for each X point 32 | D = pdist2( X, Xtrain, metric ); 33 | Y = clfKnnDist( D, Ytrain, k ); 34 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/m2html/templates/brain/index.html: -------------------------------------------------------------------------------- 1 | 3 | 4 | 5 | Matlab Documentation by M2HTML 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | This is a Matlab Documentation by M2HTML.
19 | Go to menu.html for the documentation of all 20 | the Matlab functions. 21 | 22 | 23 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/m2html/templates/frame/index.html: -------------------------------------------------------------------------------- 1 | 3 | 4 | 5 | Matlab Documentation by M2HTML 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | This is a Matlab Documentation by M2HTML.
19 | Go to menu.html for the documentation of all 20 | the Matlab functions. 21 | 22 | 23 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/deprecated/clfKnnTrain.m: -------------------------------------------------------------------------------- 1 | function clf = clfKnnTrain( clf, X, Y ) 2 | % Train a k nearest neighbor classifier (memorization). 3 | % 4 | % USAGE 5 | % clf = clfKnnTrain( clf, X, Y ) 6 | % 7 | % INPUTS 8 | % clf - model to be trained 9 | % X - nxp data array 10 | % Y - nx1 array of labels 11 | % 12 | % OUTPUTS 13 | % clf - a trained k-nearest neighbor classifier. 14 | % 15 | % EXAMPLE 16 | % 17 | % See also CLFKNN, CLFKNNFWD 18 | % 19 | % Piotr's Image&Video Toolbox Version 2.0 20 | % Copyright 2008 Piotr Dollar. [pdollar-at-caltech.edu] 21 | % Please email me if you find bugs, or have suggestions or questions! 22 | % Licensed under the Lesser GPL [see external/lgpl.txt] 23 | 24 | if( ~strcmp(clf.type,'knn')); error( ['incorrect type: ' clf.type] ); end; 25 | if( size(X,2)~= clf.p ); error( 'Incorrect data dimension' ); end; 26 | 27 | %%% error check 28 | n=size(X,1); Y=double(Y); 29 | [Y,er] = checkNumArgs( Y, [n 1], 0, 0 ); error(er); 30 | 31 | %%% training is memorization 32 | clf.Xtrain = X; 33 | clf.Ytrain = Y; 34 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/classify/fernsClfApply.m: -------------------------------------------------------------------------------- 1 | function [hs,probs] = fernsClfApply( data, ferns, inds ) 2 | % Apply learned fern classifier. 3 | % 4 | % USAGE 5 | % [hs,probs] = fernsClfApply( data, ferns, [inds] ) 6 | % 7 | % INPUTS 8 | % data - [NxF] N length F binary feature vectors 9 | % ferns - learned fern classification model 10 | % inds - [NxM] cached inds (from previous call to fernsInds) 11 | % 12 | % OUTPUTS 13 | % hs - [Nx1] predicted output labels 14 | % probs - [NxH] predicted output label probabilities 15 | % 16 | % EXAMPLE 17 | % 18 | % See also fernsClfTrain, fernsInds 19 | % 20 | % Piotr's Computer Vision Matlab Toolbox Version 2.50 21 | % Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com] 22 | % Licensed under the Simplified BSD License [see external/bsd.txt] 23 | if( nargin<3 || isempty(inds) ) 24 | inds = fernsInds(data,ferns.fids,ferns.thrs); end 25 | [N,M]=size(inds); H=ferns.H; probs=zeros(N,H); 26 | for m=1:M, probs = probs + ferns.pFern(inds(:,m),:,m); end 27 | if(ferns.bayes==0), probs=probs/M; end; [~,hs]=max(probs,[],2); 28 | end 29 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/images/movieToImages.m: -------------------------------------------------------------------------------- 1 | function I = movieToImages( M ) 2 | % Creates a stack of images from a matlab movie M. 3 | % 4 | % Repeatedly calls frame2im. Useful for playback with playMovie. 5 | % 6 | % USAGE 7 | % I = movieToImages( M ) 8 | % 9 | % INPUTS 10 | % M - a matlab movie 11 | % 12 | % OUTPUTS 13 | % I - MxNxT array (of images) 14 | % 15 | % EXAMPLE 16 | % load( 'images.mat' ); [X,map]=gray2ind(video(:,:,1)); 17 | % M = fevalArrays( video, @(x) im2frame(gray2ind(x),map) ); 18 | % I = movieToImages(M); playMovie(I); 19 | % 20 | % See also PLAYMOVIE 21 | % 22 | % Piotr's Computer Vision Matlab Toolbox Version 2.0 23 | % Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com] 24 | % Licensed under the Simplified BSD License [see external/bsd.txt] 25 | 26 | I = fevalArrays( M, @frame2Ii ); 27 | 28 | function I = frame2Ii( F ) 29 | 30 | [I,map] = frame2im( F ); 31 | if( isempty(map) ) 32 | if( size(I,3)==3 ) 33 | classname = class( I ); 34 | I = sum(I,3)/3; 35 | I = feval( classname, I ); 36 | end 37 | else 38 | I = ind2gray( I, map ); 39 | end 40 | -------------------------------------------------------------------------------- /tracker_exter/matconvnet/.gitignore: -------------------------------------------------------------------------------- 1 | *.xcodeproj/*xcuserdata* 2 | *.xcodeproj/project.xcworkspace/*xcuserdata* 3 | *.xcodeproj/project.xcworkspace/xcshareddata/ 4 | mex/* 5 | mex 6 | data 7 | *.o 8 | *.pyc 9 | *~ 10 | index.html 11 | matconvnet-*.tar.gz 12 | local 13 | contrib 14 | 15 | # Documentation 16 | doc/figures/svg/*.pdf 17 | doc/figures/*.idraw 18 | doc/.texpadtmp/* 19 | doc/*.pdf 20 | doc/.build 21 | 22 | # Website 23 | doc/site/docs/mfiles 24 | doc/site/site 25 | doc/site/.build 26 | doc/site/theme/css/bootstrap.min.css 27 | doc/site/theme/css/bootstrap.min.css.map 28 | doc/site/theme/css/font-awesome.min.css 29 | doc/site/theme/fonts/fontawesome-webfont.eot 30 | doc/site/theme/fonts/fontawesome-webfont.svg 31 | doc/site/theme/fonts/fontawesome-webfont.ttf 32 | doc/site/theme/fonts/fontawesome-webfont.woff 33 | doc/site/theme/fonts/fontawesome-webfont.woff2 34 | doc/site/theme/js/bootstrap.min.js 35 | doc/site/theme/js/jquery.min.js 36 | doc/site/theme/js/jquery.min.map 37 | doc/site/theme/js/npm.js 38 | 39 | # Visual C 40 | *.suo 41 | *.user 42 | *.sdf 43 | *.opensdf 44 | doc/figures/svg/*.idraw 45 | 46 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/doc/index.html: -------------------------------------------------------------------------------- 1 | 3 | 4 | 5 | Piotr's Matlab Toolbox 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | Sorry, your browser doesn't support frames. 20 | Go to menu.html for the documentation of all 21 | the Matlab functions. 22 | 23 | 24 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/m2html/templates/frame-piotr/index.html: -------------------------------------------------------------------------------- 1 | 3 | 4 | 5 | Piotr's Matlab Toolbox 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | Sorry, your browser doesn't support frames. 20 | Go to menu.html for the documentation of all 21 | the Matlab functions. 22 | 23 | 24 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/matlab/randint2.m: -------------------------------------------------------------------------------- 1 | function R = randint2( m, n, range ) 2 | % Faster but restricted version of randint. 3 | % 4 | % Generate matrix of uniformly distributed random integers. 5 | % R=randint2(m,n,range) generates an m-by-n matrix of random integers 6 | % between [range(1), range(2)]. Note that randint is part of the 7 | % 'Communications Toolbox' and may not be available on all systems. 8 | % 9 | % To test speed: 10 | % tic, for i=1:1000; R = randint( 100, 10, [0 10] ); end; toc 11 | % tic, for i=1:1000; R = randint2( 100, 10, [0 10] ); end; toc 12 | % 13 | % USAGE 14 | % R = randint2( m, n, range ) 15 | % 16 | % INPUTS 17 | % m - m rows 18 | % n - n cols 19 | % range - range of ints 20 | % 21 | % OUTPUTS 22 | % R - mxn matrix of integers 23 | % 24 | % EXAMPLE 25 | % R = randint2( 2, 5, [0 1] ) 26 | % 27 | % See also RANDINT 28 | % 29 | % Piotr's Computer Vision Matlab Toolbox Version 2.12 30 | % Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com] 31 | % Licensed under the Simplified BSD License [see external/bsd.txt] 32 | 33 | R = rand( m, n ); 34 | R = range(1) + floor( (range(2)-range(1)+1)*R ); 35 | -------------------------------------------------------------------------------- /tracker_utils/calcRectInt.m: -------------------------------------------------------------------------------- 1 | function overlap = calcRectInt(A,B) 2 | % 3 | %each row is a rectangle. 4 | % A(i,:) = [x y w h] 5 | % B(j,:) = [x y w h] 6 | % overlap(i,j) = area of intersection 7 | % normoverlap(i,j) = overlap(i,j) / (area(i)+area(j)-overlap) 8 | % 9 | % Same as built-in rectint, but faster and uses less memory (since avoids repmat). 10 | 11 | 12 | lena = size(A, 1); 13 | lenb = size(B, 1); 14 | lena = min(lena, lenb); 15 | A = A(1:lena, :); 16 | B = B(1:lena, :); 17 | leftA = A(:,1); 18 | bottomA = A(:,2); 19 | rightA = leftA + A(:,3) - 1; 20 | topA = bottomA + A(:,4) - 1; 21 | 22 | leftB = B(:,1); 23 | bottomB = B(:,2); 24 | rightB = leftB + B(:,3) - 1; 25 | topB = bottomB + B(:,4) - 1; 26 | 27 | tmp = (max(0, min(rightA, rightB) - max(leftA, leftB)+1 )) .* (max(0, min(topA, topB) - max(bottomA, bottomB)+1 )); 28 | areaA = A(:,3) .* A(:,4); 29 | areaB = B(:,3) .* B(:,4); 30 | overlap = tmp./(areaA+areaB-tmp); 31 | % if tmp > 0 32 | % 33 | % overlap = tmp; 34 | % 35 | % areaA = A(3) .* A(4); 36 | % areaB = B(3) .* B(4); 37 | % overlap = tmp./(areaA+areaB-tmp); 38 | % else 39 | % overlap = 0; 40 | % end 41 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/matlab/subsToArray.m: -------------------------------------------------------------------------------- 1 | function I = subsToArray( subs, vals, siz, fillVal ) 2 | % Converts subs/vals image representation to array representation. 3 | % 4 | % Constructs array from subs/vals representation. Similar to Matlab's 5 | % sparse command, except doesn't actually produce a sparse matrix. Uses 6 | % different conventions as well. 7 | % 8 | % USAGE 9 | % I = subsToArray( subs, vals, siz, [fillVal] ) 10 | % 11 | % INPUTS 12 | % subs - subscripts of point locations (n x d) 13 | % vals - values at point locations (n x 1) 14 | % siz - image size vector (1xd) - must fully contain subs 15 | % fillVal - [0] value to fill array with at nonspecified locs 16 | % 17 | % OUTPUTS 18 | % I - array of size siz 19 | % 20 | % EXAMPLE 21 | % 22 | % See also SUB2IND2, SPARSE 23 | % 24 | % Piotr's Computer Vision Matlab Toolbox Version 2.0 25 | % Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com] 26 | % Licensed under the Simplified BSD License [see external/bsd.txt] 27 | 28 | if( nargin<4 || isempty(fillVal) ); fillVal=0; end 29 | 30 | inds = sub2ind2( siz, subs ); 31 | I = repmat( fillVal, siz ); 32 | I(inds) = vals; 33 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/m2html/templates/brain/graph.tpl: -------------------------------------------------------------------------------- 1 | 3 | 4 | 5 | Dependency Graph for {MDIR} 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 |

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25 | 26 | 27 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/matlab/diskFill.m: -------------------------------------------------------------------------------- 1 | function diskFill( tDir, nGig ) 2 | % Fill a harddisk with garbage files (useful before discarding disk). 3 | % 4 | % USAGE 5 | % diskFill( tDir, nGig ) 6 | % 7 | % INPUTS 8 | % tDir - target directory to fill with garbage 9 | % nGig - maximum number of gigabytes to write to disk 10 | % 11 | % OUTPUTS 12 | % 13 | % EXAMPLE 14 | % diskFill( 'k:', 500 ) 15 | % 16 | % See also 17 | % 18 | % Piotr's Computer Vision Matlab Toolbox Version 2.61 19 | % Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com] 20 | % Licensed under the Simplified BSD License [see external/bsd.txt] 21 | 22 | nm = sprintf('%s/garbage_%s_%05i_%%05i.mat',tDir,date,round(rand*10^5)); 23 | tid = ticStatus(); 24 | for i=1:nGig 25 | % write up to 1 GB of garbage bytes in chunks of 1 MB 26 | fid=fopen(sprintf(nm,i),'w'); mb=2^20; n=0; o=mb; 27 | while(n<2^30 && o==mb), o=fwrite(fid,rand(mb,1)); n=n+o; end 28 | try fclose(fid); catch, end; tocStatus( tid, i/nGig ); %#ok 29 | % if write failed (k<1), disk is presumably full 30 | if( o 12 | % $Revision: 1.0 $Date: 2003/05/05 22:19:51 $ 13 | 14 | narginchk(3,4); 15 | if nargin == 3 16 | append = 0; 17 | end 18 | 19 | if iscellstr(handle) 20 | for i=1:length(handle) 21 | [tpl, str] = subst(tpl,handle{i}); 22 | tpl = set(tpl,'var',target,str); 23 | end 24 | elseif ischar(handle) 25 | [tpl, str] = subst(tpl,handle); 26 | if append 27 | tpl = set(tpl,'var',target,[get(tpl,'var',target) str]); 28 | else 29 | tpl = set(tpl,'var',target,str); 30 | end 31 | else 32 | error('[Template] Badly formed handle.'); 33 | end 34 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/m2html/templates/brain/search.tpl: -------------------------------------------------------------------------------- 1 | 3 | 4 | 5 | Matlab Search Engine 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 |

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28 | 29 | 30 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/matlab/tpsRandom.m: -------------------------------------------------------------------------------- 1 | function w = tpsRandom( LnInv, bendE ) 2 | % Obtain a random warp with the same bending energy as the original. 3 | % 4 | % USAGE 5 | % w = tpsRandom( LnInv, bendE ) 6 | % 7 | % INPUTS 8 | % LnInv - [see tpsGetWarp] bookstein warping parameters 9 | % bendE - amount of bening energy for random warp to have 10 | % 11 | % OUTPUTS 12 | % w - nonlinear component of warp for use in tpsInterpolate 13 | % 14 | % EXAMPLE 15 | % 16 | % See also TPSGETWARP 17 | % 18 | % Piotr's Computer Vision Matlab Toolbox Version 2.0 19 | % Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com] 20 | % Licensed under the Simplified BSD License [see external/bsd.txt] 21 | 22 | n = size(LnInv,1); 23 | u = rand(n-3,1) - .5; 24 | u = u / norm(u); 25 | u = [u; 0; 0; 0]; 26 | 27 | % get U, sig, sigInv, requires some fanangling 28 | [U,sig] = eig(LnInv); 29 | U = real(U); sig=real(sig); 30 | sig( abs(sig)<.000001)=0; 31 | %sigInv = sig; 32 | %sigInv(abs(sig)>.000001) = 1./sigInv(abs(sig)>.000001); 33 | 34 | % get w (and v?) 35 | %v = sqrt(bendE)* U * sqrt(sigInv) * u; 36 | w = sqrt(bendE)* U * sqrt(sig) * u; 37 | 38 | % % should be eye(N) with 3 0's 39 | % sqrt(sigInv) * U' * LnInv * U * sqrt(sigInv) 40 | % % should be equal 41 | % v' * LnInv * v, bendE 42 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/classify/binaryTreeApply.m: -------------------------------------------------------------------------------- 1 | function hs = binaryTreeApply( X, tree, maxDepth, minWeight, nThreads ) 2 | % Apply learned binary decision tree classifier. 3 | % 4 | % USAGE 5 | % hs = binaryTreeApply( X, tree, [maxDepth], [minWeight], [nThreads] ) 6 | % 7 | % INPUTS 8 | % X - [NxF] N length F feature vectors 9 | % tree - learned tree classification model 10 | % maxDepth - [] maximum depth of tree 11 | % minWeight - [] minimum sample weigth to allow split 12 | % nThreads - [16] max number of computational threads to use 13 | % 14 | % OUTPUTS 15 | % hs - [Nx1] predicted output log ratios 16 | % 17 | % EXAMPLE 18 | % 19 | % See also binaryTreeTrain 20 | % 21 | % Piotr's Computer Vision Matlab Toolbox Version 3.40 22 | % Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com] 23 | % Licensed under the Simplified BSD License [see external/bsd.txt] 24 | 25 | if(nargin<3 || isempty(maxDepth)), maxDepth=0; end 26 | if(nargin<4 || isempty(minWeight)), minWeight=0; end 27 | if(nargin<5 || isempty(nThreads)), nThreads=16; end 28 | if(maxDepth>0), tree.child(tree.depth>=maxDepth) = 0; end 29 | if(minWeight>0), tree.child(tree.weights<=minWeight) = 0; end 30 | hs = tree.hs(forestInds(X,tree.thrs,tree.fids,tree.child,nThreads)); 31 | 32 | end 33 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/filters/filterSteerable.m: -------------------------------------------------------------------------------- 1 | function filterSteerable( theta ) 2 | % Steerable 2D Gaussian derivative filter (for visualization). 3 | % 4 | % This function is a demonstration of steerable filters. The directional 5 | % derivative of G in an arbitrary direction theta can be found by taking a 6 | % linear combination of the directional derivatives dxG and dyG. 7 | % 8 | % USAGE 9 | % filterSteerable( theta ) 10 | % 11 | % INPUTS 12 | % theta - orientation in radians 13 | % 14 | % OUTPUTS 15 | % 16 | % EXAMPLE 17 | % filterSteerable( pi/4 ); 18 | % 19 | % See also filterGauss 20 | % 21 | % Piotr's Computer Vision Matlab Toolbox Version 2.0 22 | % Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com] 23 | % Licensed under the Simplified BSD License [see external/bsd.txt] 24 | 25 | % Get G 26 | [x,y]=meshgrid(-1:.1:1, -1:.1:1 ); 27 | r = sqrt( x.^2 + y.^2 ); 28 | G = exp( -r .* r *2 ); 29 | 30 | % get first derivatives of G. note: d/dx(G)=-2x*G 31 | phi = atan2( y, x ); 32 | dxG = r .* cos(phi) .* G; 33 | dyG = r .* sin(phi) .* G; 34 | 35 | % get directional derivative by taking linear comb in theta 36 | Gtheta = cos(theta)*dxG + sin(theta)*dyG; 37 | 38 | % dislpay (scale for visualization purposes) 39 | GS = cat(3,G,dxG*2,dyG*2,Gtheta*2); 40 | figure(1); montage2( GS ); 41 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/m2html/templates/blue/search.tpl: -------------------------------------------------------------------------------- 1 | 3 | 4 | 5 | Matlab Search Engine 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 |

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28 | 29 | 30 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/m2html/INSTALL: -------------------------------------------------------------------------------- 1 | M2HTML - Documentation System for Matlab .m files in HTML 2 | ========================================================= 3 | 4 | 0/ Requirements: 5 | - Matlab 5.3 or above 6 | - Operating system: any. 7 | 8 | 1/ Download the latest m2html.zip on this website: 9 | 10 | 11 | 2/ Extract files in your Matlab Repository /home/foo/matlab/ : 12 | unzip m2html.zip 13 | 14 | 3/ Add the m2html directory in your Matlab path: 15 | addpath /home/foo/matlab/m2html/ 16 | 17 | 5/ Ready to use ! 18 | help m2html 19 | 20 | Please read the README file. 21 | A tutorial and a FAQ (Frequently Asked Questions) are available online at: 22 | 23 | 24 | 25 | If you want to generate dependency graphs, you need to install GraphViz (a 26 | graph visualization software), free and cross-platform, from: 27 | 28 | 29 | ------------------------------------------------------------------------------- 30 | Matlab is a Registered Trademark of The Mathworks, Inc. 31 | 32 | Copyright (C) 2003-2005 Guillaume Flandin 33 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/m2html/templates/brain/todo.tpl: -------------------------------------------------------------------------------- 1 | 3 | 4 | 5 | To Do List for {MDIR} 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 |
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26 | 27 | 28 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/filters/FbCrop.m: -------------------------------------------------------------------------------- 1 | function FBC = FbCrop( FB, delta ) 2 | % Crop a 2D filterbank (adjusting filter norms). 3 | % 4 | % Takes a filter bank and crops it by cropping off delta pixels from each 5 | % side. Ensures that the mean response of each filter is 0 and that the L1 6 | % norm is 1, i.e. sum(sum(abs(F)))==1. 7 | % 8 | % USAGE 9 | % FBC = FbCrop( FB, delta ) 10 | % 11 | % INPUTS 12 | % FB - original filterbank 13 | % delta - amount to crop by 14 | % 15 | % OUTPUTS 16 | % FBC - cropped filterbank 17 | % 18 | % EXAMPLE 19 | % load FbDoG.mat; FBC=FbCrop(FB,4); 20 | % figure(1); montage2(FB,struct('extraInfo',1)); 21 | % figure(2); montage2(FBC,struct('extraInfo',1)); 22 | % 23 | % See also FBAPPLY2D 24 | % 25 | % Piotr's Computer Vision Matlab Toolbox Version 2.0 26 | % Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com] 27 | % Licensed under the Simplified BSD License [see external/bsd.txt] 28 | 29 | nd = ndims(FB); 30 | if( nd~=2 && nd~=3 ); error('I must an MxNxK array'); end 31 | 32 | cropsiz = size(FB); 33 | cropsiz = [cropsiz(1:2)-2*delta, cropsiz(3)]; 34 | FBC = arrayToDims( FB, cropsiz ); 35 | 36 | for f=1:size(FB,3) 37 | FC = FBC(:,:,f); 38 | FC = FC - sum(sum(FC)) / numel(FC); % 0 mean 39 | FC = FC / sum(sum(abs(FC))); % L1 norm == 1 40 | FBC(:,:,f) = FC; 41 | end 42 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/m2html/templates/frame-piotr/todo.tpl: -------------------------------------------------------------------------------- 1 | 3 | 4 | 5 | To Do List for {MDIR} 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 |
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26 | 27 | 28 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/matlab/ind2sub2.m: -------------------------------------------------------------------------------- 1 | function sub = ind2sub2( siz, ind ) 2 | % Improved version of ind2sub. 3 | % 4 | % Almost the same as ind2sub, except always returns only a single output 5 | % that contains all the index locations. Also handles multiple linear 6 | % indicies at the same time. See help for ind2sub for more info. 7 | % 8 | % USAGE 9 | % sub = ind2sub2( siz, ind ) 10 | % 11 | % INPUTS 12 | % siz - size of array into which ind is an index 13 | % ind - linear index (or vector of indicies) into given array 14 | % 15 | % OUTPUTS 16 | % sub - sub(i,:) is the ith set of subscripts into the array. 17 | % 18 | % EXAMPLE 19 | % sub = ind2sub2( [10,10], 20 ) % 10 2 20 | % sub = ind2sub2( [10,10], [20 19] ) % 10 2; 9 2 21 | % 22 | % See also IND2SUB, SUB2IND2 23 | % 24 | % Piotr's Computer Vision Matlab Toolbox Version 2.0 25 | % Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com] 26 | % Licensed under the Simplified BSD License [see external/bsd.txt] 27 | 28 | if( any(ind>prod(siz)) ); error('index out of range'); end 29 | 30 | % taken almost directly from ind2sub.m 31 | ind = ind(:); 32 | nd = length(siz); 33 | k = [1 cumprod(siz(1:end-1))]; 34 | ind = ind - 1; 35 | sub = zeros(length(ind),nd); 36 | for i = nd:-1:1 37 | sub(:,i) = floor(ind/k(i))+1; 38 | ind = rem(ind,k(i)); 39 | end 40 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/filters/filterBinomial1d.m: -------------------------------------------------------------------------------- 1 | function f = filterBinomial1d( r, show ) 2 | % 1D binomial filter (approximation to Gaussian filter) 3 | % 4 | % Creates a binomial filter of size 2*r+1 x 1. This can be used to 5 | % approximate the Gaussian distribution with sigma=sqrt((2*r+1)/4). For 6 | % large r, should give same output as: 7 | % g = fspecial( 'Gaussian', [2*r+1,1],sqrt((2*r+1)/4) ); 8 | % Given sigma, use r ~= 2*sigma^2. 9 | % 10 | % Use F = f*f' to get the equivalent 2d filter. 11 | % 12 | % USAGE 13 | % f = filterBinomial1d( r, [show] ) 14 | % 15 | % INPUTS 16 | % r - mask will have length 2r+1 and var=(2*r+1)/4 17 | % show - [0] figure to use for optional display 18 | % 19 | % OUTPUTS 20 | % f - 1D binomial filter 21 | % 22 | % EXAMPLE 23 | % r = 10; 24 | % fbinom = filterBinomial1d( r, 1 ); 25 | % fgauss = filterGauss( 2*r+1, [], (2*r+1)/4, 2); 26 | % 27 | % See also FILTERGAUSS 28 | % 29 | % Piotr's Computer Vision Matlab Toolbox Version 2.0 30 | % Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com] 31 | % Licensed under the Simplified BSD License [see external/bsd.txt] 32 | 33 | if( nargin<2 ); show=0; end; 34 | if( mod(r,1)~=0 ); error( 'r must be an integer'); end; 35 | 36 | f = diag(fliplr(pascal(2*r+1))) / 4^r; 37 | 38 | % display 39 | if(show); filterVisualize( f, show ); end; 40 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/filters/Contents.m: -------------------------------------------------------------------------------- 1 | % FILTERS 2 | % See also 3 | % 4 | % Filters: 5 | % filterBinomial1d - 1D binomial filter (approximation to Gaussian filter) 6 | % filterDog2d - Difference of Gaussian (Dog) Filter. 7 | % filterDoog - n-dim difference of offset Gaussian DooG filter (Gaussian derivative). 8 | % filterGabor1d - Creates an even/odd pair of 1D Gabor filters. 9 | % filterGabor2d - Creates an even/odd pair of 2D Gabor filters. 10 | % filterGauss - n-dimensional Gaussian filter. 11 | % filterSteerable - Steerable 2D Gaussian derivative filter (for visualization). 12 | % filterVisualize - Used to visualize a 1D/2D/3D filter. 13 | % 14 | % Operations involving a set of filters (a filter bank or FB): 15 | % FbApply2d - Applies each of the filters in the filterbank FB to the image I. 16 | % FbCrop - Crop a 2D filterbank (adjusting filter norms). 17 | % FbMake - Various 1D/2D/3D filterbanks (hardcoded). 18 | % FbReconstruct2d - Use to see how much image information is preserved in filter outputs. 19 | % FbVisualize - Used to visualize a series of 1D/2D/3D filters. 20 | % 21 | % Simple nonlinear filters: 22 | % medfilt1m - One-dimensional adaptive median filtering with missing values. 23 | % modefilt1 - One-dimensional mode filtering. 24 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # GFS-DCF 2 | Matlab implementation of ICCV2019 paper "Joint Group Feature Selection and Discriminative Filter Learning for Robust Visual Object Tracking" 3 | 4 | ## [Download the Paper](https://www.researchgate.net/publication/334849529_Joint_Group_Feature_Selection_and_Discriminative_Filter_Learning_for_Robust_Visual_Object_Tracking) 5 | >@article{xu2019joint, 6 | title={Joint Group Feature Selection and Discriminative Filter Learning for Robust Visual Object Tracking}, 7 | author={Xu, Tianyang and Feng, Zhen-Hua and Wu, Xiao-Jun and Kittler, Josef}, 8 | journal={arXiv preprint arXiv:1907.13242}, 9 | year={2019}} 10 | 11 | ![image](https://github.com/XU-TIANYANG/GFS-DCF/blob/master/Fig.jpg) 12 | 13 | Dependencies: 14 | MatConvNet, PDollar Toolbox. 15 | Please download the latest MatConvNet (http://www.vlfeat.org/matconvnet/) 16 | in './tracker_exter/matconvnet' 17 | (Set 'opts.enableGpu = true' in 'matconvnet/matlab/vl_compilenn.m') 18 | 19 | Installation: 20 | Run install.m file to compile the libraries and download networks. 21 | 22 | Demo: 23 | Run demo.m 24 | 25 | Raw Results: 26 | [OTB2015(OTB2013)](https://github.com/XU-TIANYANG/cakes/raw/master/GFSDCF_OTB_results.zip) 27 | [TrackingNet Test](https://github.com/XU-TIANYANG/cakes/raw/master/GFSDCF_TrackingNet_results.zip) 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/classify/rbfComputeFtrs.m: -------------------------------------------------------------------------------- 1 | function Xrbf = rbfComputeFtrs( X, rbfBasis ) 2 | % Evaluate features of X given a set of radial basis functions. 3 | % 4 | % See rbfComputeBasis for discussion of rbfs and general usage. 5 | % 6 | % USAGE 7 | % Xrbf = rbfComputeFtrs( X, rbfBasis ) 8 | % 9 | % INPUTS 10 | % X - [N x d] N points of d dimensions each 11 | % rbfBasis - rbfBasis struct (see rbfComputeBasis) 12 | % 13 | % OUTPUTS 14 | % Xrbf - [N x k] computed feature vectors 15 | % 16 | % EXAMPLE 17 | % 18 | % See also RBFDEMO, RBFCOMPUTEBASIS 19 | % 20 | % Piotr's Computer Vision Matlab Toolbox Version 2.50 21 | % Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com] 22 | % Licensed under the Simplified BSD License [see external/bsd.txt] 23 | 24 | % compute distance to each basis function 25 | % mu=[k x d]; onesK=[k x 1]; Xi=[1 x d]; 26 | Xrbf = pdist2(X,rbfBasis.mu','sqeuclidean'); 27 | 28 | % compute gaussian response 29 | if( rbfBasis.globalVar ) 30 | Xrbf = exp( Xrbf / (-2*rbfBasis.var) ); 31 | else 32 | Xrbf = exp( bsxfun(@rdivide, Xrbf, -2*rbfBasis.vars ) ); 33 | end 34 | 35 | % add constant vector of ones as last feature 36 | if( rbfBasis.constant ) 37 | Xrbf(:,end+1) = 1; 38 | end 39 | 40 | % normalize rbfs to sum to 1 41 | if( rbfBasis.normalize ) 42 | Xrbf = bsxfun(@rdivide, Xrbf, sum(Xrbf,2)); 43 | end 44 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/deprecated/filter_gauss_1D.m: -------------------------------------------------------------------------------- 1 | % 1D Gaussian filter. 2 | % 3 | % Equivalent to (but faster then): 4 | % f = fspecial('Gaussian',[2*r+1,1],sigma); 5 | % f = filter_gauss_nD( 2*r+1, r+1, sigma^2 ); 6 | % 7 | % USAGE 8 | % f = filter_gauss_1D( r, sigma, [show] ) 9 | % 10 | % INPUTS 11 | % r - filter size=2r+1, if r=[] -> r=ceil(2.25*sigma) 12 | % sigma - standard deviation of filter 13 | % show - [0] figure to use for optional display 14 | % 15 | % OUTPUTS 16 | % f - 1D Gaussian filter 17 | % 18 | % EXAMPLE 19 | % f1 = filter_gauss_1D( 10, 2, 1 ); 20 | % f2 = filter_gauss_nD( 21, [], 2^2, 2); 21 | % 22 | % See also FILTER_BINOMIAL_1D, FILTER_GAUSS_ND, FSPECIAL 23 | 24 | % Piotr's Image&Video Toolbox Version 1.5 25 | % Written and maintained by Piotr Dollar pdollar-at-cs.ucsd.edu 26 | % Please email me if you find bugs, or have suggestions or questions! 27 | 28 | function f = filter_gauss_1D( r, sigma, show ) 29 | 30 | if( nargin<3 || isempty(show) ); show=0; end 31 | if( isempty(r) ); r = ceil(sigma*2.25); end 32 | if( mod(r,1)~=0 ); error( 'r must be an integer'); end 33 | 34 | % compute filter 35 | x = -r:r; 36 | f = exp(-(x.*x)/(2*sigma*sigma))'; 37 | f(f1), isArray=1; elseif(isArray==0), m=n; end 32 | if(k>m), error('Too many samples requested.'); end 33 | if(k==1), r=1+floor(m*rand); else r=randperm(m); r=r(1:k); end 34 | if(isArray), r=n(r); end 35 | 36 | end 37 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/deprecated/imtranslate.m: -------------------------------------------------------------------------------- 1 | % Translate an image to subpixel accuracy. 2 | % 3 | % Note that for subplixel accuracy cannot use nearest neighbor interp. 4 | % 5 | % USAGE 6 | % IR = imtranslate( I, dx, dy, [method], [bbox] ) 7 | % 8 | % INPUTS 9 | % I - 2D image [converted to double] 10 | % dx - x translation (right) 11 | % dy - y translation (up) 12 | % method - ['linear'] 'nearest', 'linear', 'spline', 'cubic' 13 | % bbox - ['loose'] 'loose' or 'crop' 14 | % 15 | % OUTPUTS 16 | % IR - translated image 17 | % 18 | % EXAMPLE 19 | % load trees; 20 | % XT = imtranslate(X,0,1.5,'bicubic','crop'); 21 | % figure(1); im(X,[0 255]); figure(2); im(XT,[0 255]); 22 | % 23 | % See also IMROTATE2 24 | 25 | % Piotr's Image&Video Toolbox Version 1.5 26 | % Written and maintained by Piotr Dollar pdollar-at-cs.ucsd.edu 27 | % Please email me if you find bugs, or have suggestions or questions! 28 | 29 | function IR = imtranslate( I, dx, dy, method, bbox ) 30 | 31 | if( ~isa( I, 'double' ) ); I = double(I); end 32 | if( nargin<4 || isempty(method)); method='linear'; end 33 | if( nargin<5 || isempty(bbox) ); bbox='loose'; end 34 | if( strcmp(method,'bilinear') || strcmp(method,'lin')); method='linear';end 35 | 36 | % convert arguments for apply_homography 37 | H = [eye(2) [dy; dx]; 0 0 1]; 38 | IR = apply_homography( I, H, method, bbox ); 39 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/channels/gradient2.m: -------------------------------------------------------------------------------- 1 | function [Gx,Gy] = gradient2( I ) 2 | % Compute numerical gradients along x and y directions. 3 | % 4 | % For 2D arrays identical to Matlab's gradient() with a spacing value of 5 | % h=1 but ~10-20x faster (due to mexed implementation). Like gradient(), 6 | % computes centered derivatives in interior of image and uncentered 7 | % derivatives along boundaries. For 3D arrays computes x and y gradient 8 | % separately for each channel and concatenates the results. 9 | % 10 | % This code requires SSE2 to compile and run (most modern Intel and AMD 11 | % processors support SSE2). Please see: http://en.wikipedia.org/wiki/SSE2. 12 | % 13 | % USAGE 14 | % [Gx,Gy] = gradient2( I ) 15 | % 16 | % INPUTS 17 | % I - [hxwxk] input k channel single image 18 | % 19 | % OUTPUTS 20 | % Gx - [hxwxk] x-gradient (horizontal) 21 | % Gy - [hxwxk] y-gradient (vertical) 22 | % 23 | % EXAMPLE 24 | % I=single(imread('peppers.png'))/255; 25 | % tic, [Gx1,Gy1]=gradient(I,1); toc 26 | % tic, [Gx2,Gy2]=gradient2(I); toc 27 | % isequal(Gx1,Gx2), isequal(Gy1,Gy2) 28 | % 29 | % See also gradient, gradientMag 30 | % 31 | % Piotr's Computer Vision Matlab Toolbox Version 3.00 32 | % Copyright 2014 Piotr Dollar & Ron Appel. [pdollar-at-gmail.com] 33 | % Licensed under the Simplified BSD License [see external/bsd.txt] 34 | 35 | [Gx,Gy]=gradientMex('gradient2',I); 36 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/matlab/figureResized.m: -------------------------------------------------------------------------------- 1 | function varargout = figureResized( screenratio, varargin ) 2 | % Creates a figures that takes up certain area of screen. 3 | % 4 | % Almost same as figure, except get to specify what fraction of available 5 | % screen space figure should take up. Figure appears in center of screen. 6 | % 7 | % USAGE 8 | % varargout = figureResized( screenratio, varargin ) 9 | % 10 | % INPUTS 11 | % screenratio - controls fraction of screen image takes out (<=.8) 12 | % varargin - parameters to figure 13 | % 14 | % OUTPUTS 15 | % varargout - out from figure 16 | % 17 | % EXAMPLE 18 | % figureResized( .75 ) 19 | % 20 | % See also FIGURE 21 | % 22 | % Piotr's Computer Vision Matlab Toolbox Version 2.0 23 | % Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com] 24 | % Licensed under the Simplified BSD License [see external/bsd.txt] 25 | 26 | if( nargin<1 ); screenratio=.3; end 27 | if( screenratio>1 ); error('screenratio must be <= 1'); end 28 | 29 | % call figure 30 | h = figure( varargin{:} ); 31 | if( nargout ); varargout = {h}; end; 32 | 33 | % get dimensions of screen and what want figure to be 34 | units = get(0,'Units'); 35 | ss = get(0,'ScreenSize'); 36 | st = (1 - screenratio)/2; 37 | pos = [st*ss(3), st*ss(4), screenratio*ss(3), screenratio*ss(4)]; 38 | 39 | % set dimensions of figure 40 | set( h, 'Units', units ); 41 | set( h, 'Position', pos ); 42 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/m2html/@template/private/finish.m: -------------------------------------------------------------------------------- 1 | function str = finish(str,unknowns) 2 | %TEMPLATE/FINISH Apply given strategy to unknown fields in a string 3 | % STR = FINISH(STR,UNKNOWNS) applies on string STR the strategy defined 4 | % in UNKNOWNS to unknowns fields '{UNKNOWNS_FIELDS}'. 5 | % UNKNOWNS may be: 6 | % * 'keep' to do nothing 7 | % * 'remove' to remove all undefined fields 8 | % * 'comment' to replace undefined fields by a warning HTML comment. 9 | % This function uses Matlab REGEXPREP function coming with R13. If you 10 | % hold an older version, please comment lines 38 and 42: then you can 11 | % only apply the 'keep' strategy. 12 | 13 | % Copyright (C) 2003 Guillaume Flandin 14 | % $Revision: 1.0 $Date: 2003/05/05 22:19:51 $ 15 | 16 | narginchk(2,2); 17 | 18 | switch lower(unknowns) 19 | case 'keep' 20 | %- do nothing 21 | case 'remove' 22 | %%%%%%%%%%%%%%%%%%%%%%%% WIH REGEXP ONLY %%%%%%%%%%%%%%%%%%%% 23 | % str = regexprep(str,'{[^ \t\r\n}]+}',''); 24 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 25 | case 'comment' 26 | %%%%%%%%%%%%%%%%%%%%%%%% WIH REGEXP ONLY %%%%%%%%%%%%%%%%%%%% 27 | % str = regexprep(str,'{[^ \t\r\n}]+}',''); 28 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 29 | otherwise 30 | error('[Template] Unknown action.'); 31 | end 32 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/m2html/templates/frame-piotr/m2html.css: -------------------------------------------------------------------------------- 1 | body { 2 | background: white; 3 | color: black; 4 | font-family: arial,sans-serif; 5 | margin: 0; 6 | padding: 1ex; 7 | } 8 | 9 | div.fragment { 10 | width: 98%; 11 | border: 1px solid #CCCCCC; 12 | background-color: #f5f5f5; 13 | padding-left: 4px; 14 | margin: 4px; 15 | } 16 | 17 | div.box { 18 | width: 98%; 19 | background-color: #f5f5f5; 20 | border: 1px solid #CCCCCC; 21 | color: black; 22 | padding: 4px; 23 | } 24 | 25 | .comment { 26 | color: #228B22; 27 | } 28 | .string { 29 | color: #B20000; 30 | } 31 | .keyword { 32 | color: #0000FF; 33 | } 34 | 35 | .keywordtype { color: #604020; } 36 | .keywordflow { color: #e08000; } 37 | .preprocessor { color: #806020; } 38 | .stringliteral { color: #002080; } 39 | .charliteral { color: #008080; } 40 | 41 | a { 42 | text-decoration: none; 43 | } 44 | 45 | a:hover { 46 | background-color: #006699; 47 | color:#FFFFFF; 48 | } 49 | 50 | a.code { 51 | font-weight: normal; 52 | color: #A020F0; 53 | } 54 | 55 | a.code:hover { 56 | background-color: #FF0000; 57 | color: #FFFFFF; 58 | } 59 | 60 | h1 { 61 | background: transparent; 62 | color: #006699; 63 | font-size: x-large; 64 | text-align: center; 65 | } 66 | 67 | h2 { 68 | background: transparent; 69 | color: #006699; 70 | font-size: large; 71 | } 72 | 73 | address { 74 | font-size:small; 75 | } 76 | -------------------------------------------------------------------------------- /tracker_exter/mtimesx/license.txt: -------------------------------------------------------------------------------- 1 | Copyright (c) 2011, James Tursa 2 | All rights reserved. 3 | 4 | Redistribution and use in source and binary forms, with or without 5 | modification, are permitted provided that the following conditions are 6 | met: 7 | 8 | * Redistributions of source code must retain the above copyright 9 | notice, this list of conditions and the following disclaimer. 10 | * Redistributions in binary form must reproduce the above copyright 11 | notice, this list of conditions and the following disclaimer in 12 | the documentation and/or other materials provided with the distribution 13 | 14 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 15 | AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE 16 | IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE 17 | ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE 18 | LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR 19 | CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF 20 | SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS 21 | INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN 22 | CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) 23 | ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE 24 | POSSIBILITY OF SUCH DAMAGE. 25 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/classify/adaBoostApply.m: -------------------------------------------------------------------------------- 1 | function hs = adaBoostApply( X, model, maxDepth, minWeight, nThreads ) 2 | % Apply learned boosted decision tree classifier. 3 | % 4 | % USAGE 5 | % hs = adaBoostApply( X, model, [maxDepth], [minWeight], [nThreads] ) 6 | % 7 | % INPUTS 8 | % X - [NxF] N length F feature vectors 9 | % model - learned boosted tree classifier 10 | % maxDepth - [] maximum depth of tree 11 | % minWeight - [] minimum sample weigth to allow split 12 | % nThreads - [16] max number of computational threads to use 13 | % 14 | % OUTPUTS 15 | % hs - [Nx1] predicted output log ratios 16 | % 17 | % EXAMPLE 18 | % 19 | % See also adaBoostTrain 20 | % 21 | % Piotr's Computer Vision Matlab Toolbox Version 3.40 22 | % Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com] 23 | % Licensed under the Simplified BSD License [see external/bsd.txt] 24 | 25 | if(nargin<3 || isempty(maxDepth)), maxDepth=0; end 26 | if(nargin<4 || isempty(minWeight)), minWeight=0; end 27 | if(nargin<5 || isempty(nThreads)), nThreads=16; end 28 | if(maxDepth>0), model.child(model.depth>=maxDepth) = 0; end 29 | if(minWeight>0), model.child(model.weights<=minWeight) = 0; end 30 | nWeak=size(model.fids,2); N=size(X,1); hs=zeros(N,1); nt=nThreads; 31 | for i=1:nWeak 32 | ids = forestInds(X,model.thrs(:,i),model.fids(:,i),model.child(:,i),nt); 33 | hs = hs + model.hs(ids,i); 34 | end 35 | 36 | end 37 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/m2html/templates/frame-piotr/master.tpl: -------------------------------------------------------------------------------- 1 | 3 | 4 | 5 | Piotr's Image & Video Toolbox for Matlab 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 |

Piotr's Image & Video Toolbox for Matlab

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29 | 30 | 31 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/m2html/templates/blue/todo.tpl: -------------------------------------------------------------------------------- 1 | 3 | 4 | 5 | To Do List for {MDIR} 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 |
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To Do List for {MDIR}

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{MFILE}:

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27 | 28 | 29 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/channels/hogDraw.m: -------------------------------------------------------------------------------- 1 | function V = hogDraw( H, w, fhog ) 2 | % Create visualization of hog descriptor. 3 | % 4 | % USAGE 5 | % V = hogDraw( H, [w], [fhog] ) 6 | % 7 | % INPUTS 8 | % H - [m n oBin*4] computed hog features 9 | % w - [15] width for each glyph 10 | % fhog - [0] if true draw features returned by fhog 11 | % 12 | % OUTPUTS 13 | % V - [m*w n*w] visualization of hog features 14 | % 15 | % EXAMPLE 16 | % 17 | % See also hog, fhog 18 | % 19 | % Piotr's Computer Vision Matlab Toolbox Version 3.23 20 | % Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com] 21 | % Licensed under the Simplified BSD License [see external/bsd.txt] 22 | 23 | % fold normalizations 24 | if(nargin<3 || isempty(fhog)), fhog=0; end 25 | m=size(H,3); if(fhog), m=(m-5)/3; H=H(:,:,1:m*3); m=3; else m=4; end 26 | s=size(H); s(3)=s(3)/m; w0=H; H=zeros(s); 27 | for o=0:m-1, H=H+w0(:,:,(1:s(3))+o*s(3)); end; 28 | 29 | % construct a "glyph" for each orientaion 30 | if(nargin<2 || isempty(w)), w=15; end 31 | bar=zeros(w,w); bar(:,round(.45*w):round(.55*w))=1; 32 | bars=zeros([size(bar) s(3)]); 33 | for o=1:s(3), bars(:,:,o)=imrotate(bar,-(o-1)*180/s(3),'crop'); end 34 | 35 | % make pictures of positive weights by adding up weighted glyphs 36 | H(H<0)=0; V=zeros(w*s(1:2)); 37 | for r=1:s(1), rs=(1:w)+(r-1)*w; 38 | for c=1:s(2), cs=(1:w)+(c-1)*w; 39 | for o=1:s(3), V(rs,cs)=V(rs,cs)+bars(:,:,o)*H(r,c,o); end 40 | end 41 | end 42 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/m2html/templates/blue/graph.tpl: -------------------------------------------------------------------------------- 1 | 3 | 4 | 5 | Dependency Graph for {MDIR} 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 |
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27 | 28 | 29 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/images/im.m: -------------------------------------------------------------------------------- 1 | function varargout = im( I, range, extraInf ) 2 | % Function for displaying grayscale images. 3 | % 4 | % Handy function for showing a grayscale or color image with a colorbar. 5 | % 6 | % USAGE 7 | % h = im( I, [range], [extraInf] ) 8 | % 9 | % INPUTS 10 | % I - image in a valid format for imagesc 11 | % range - [] minval/maxval for imagesc 12 | % extraInf - [1] if 1 then colorbar is shown as well as tick marks 13 | % 14 | % OUTPUTS 15 | % h - handle for image graphics object 16 | % 17 | % EXAMPLE 18 | % load clown; im( X ) 19 | % 20 | % See also imshow, imview, impixelinfo, imtool, imagesc 21 | % 22 | % Piotr's Computer Vision Matlab Toolbox Version 2.41 23 | % Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com] 24 | % Licensed under the Simplified BSD License [see external/bsd.txt] 25 | if( nargin<1 || isempty(I)); I=0; end; 26 | if( nargin<2 || isempty(range)), range=[]; end 27 | if( nargin<3 || isempty(extraInf)); extraInf=1; end; 28 | % display image using imagesc 29 | if(isempty(range)), h=imagesc(I); else h=imagesc(I,range); end 30 | % set basic and optional properties 31 | colormap(gray); title(inputname(1)); axis('image'); 32 | if( extraInf ), colorbar; else set(gca,'XTick',[],'YTick',[]); end 33 | % output h only if output argument explicitly requested 34 | if(nargout>0), varargout={h}; end 35 | end 36 | 37 | % whitebg('black'); set(gcf,'color', [0 0 0]); %black background 38 | % set(gcf,'menubar','none'); % no menu 39 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/matlab/sub2ind2.m: -------------------------------------------------------------------------------- 1 | function ind = sub2ind2( siz, sub ) 2 | % Improved version of sub2ind. 3 | % 4 | % Almost the same as sub2ind, except always returns only a single output 5 | % that contains all the subscript locations. Also handles multiple linear 6 | % subscripts at the same time more conveniently then matlab's version. See 7 | % help for sub2ind for more info. 8 | % 9 | % USAGE 10 | % ind = sub2ind2( siz, sub ) 11 | % 12 | % INPUTS 13 | % siz - size of array into which sub is an index 14 | % sub - sub(i,:) is the ith set of subscripts into the array. 15 | % 16 | % OUTPUTS 17 | % ind - linear index (or vector of indicies) into given array 18 | % 19 | % EXAMPLE 20 | % ind = sub2ind2( [10,10], [10 2] ) % 20 21 | % ind = sub2ind2( [10,10], [9 2; 10 2] ) % 19, 20 22 | % 23 | % See also SUB2IND, IND2SUB2, SUBSTOARRAY 24 | % 25 | % Piotr's Computer Vision Matlab Toolbox Version 2.0 26 | % Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com] 27 | % Licensed under the Simplified BSD License [see external/bsd.txt] 28 | 29 | if(isempty(sub)); ind=[]; return; end; 30 | n = length(siz); 31 | 32 | % error check (commented out to speed up substantially) 33 | if( size(sub,2)~=n ) 34 | error('Incorrect dimension for sub'); 35 | % for i = 1:n; if( any( sub(:,i)<1 ) || any( sub(:,i)>siz(i) ) ) 36 | % error('subscript out of range'); end; 37 | end 38 | 39 | k = [1 cumprod(siz(1:end-1))]; 40 | ind = 1; 41 | for i = 1:n; ind = ind + (sub(:,i)-1)*k(i); end 42 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/deprecated/makemovie.m: -------------------------------------------------------------------------------- 1 | % [3D] Used to convert a stack of T images into a movie. 2 | % 3 | % To display same data statically use montage. 4 | % 5 | % USAGE 6 | % M = makemovies( IS ) 7 | % 8 | % INPUTS 9 | % IS - MxNxT or MxNx1xT or MxNx3xT array of movies. 10 | % 11 | % OUTPUTS 12 | % M - resulting movie 13 | % 14 | % EXAMPLE 15 | % load( 'images.mat' ); 16 | % M = makemovie( videos(:,:,:,1) ); 17 | % movie( M ); 18 | % 19 | % See also MONTAGE2, MAKEMOVIES, PLAYMOVIE, CELL2ARRAY, FEVALARRAYS, 20 | % IMMOVIE, MOVIE2AVI 21 | 22 | % Piotr's Image&Video Toolbox Version NEW 23 | % Written and maintained by Piotr Dollar pdollar-at-cs.ucsd.edu 24 | % Please email me if you find bugs, or have suggestions or questions! 25 | 26 | function M = makemovie( IS ) 27 | 28 | % get images format (if image stack is MxNxT convert to MxNx1xT) 29 | if (ndims(IS)==3); IS = permute(IS, [1,2,4,3] ); end 30 | siz = size(IS); nch = siz(3); nd = ndims(IS); 31 | if ( nd~=4 ); error('unsupported dimension of IS'); end 32 | if( nch~=1 && nch~=3 ); error('illegal image stack format'); end; 33 | 34 | % normalize for maximum contrast 35 | if( isa(IS,'double') ); IS = IS - min(IS(:)); IS = IS / max(IS(:)); end 36 | 37 | % make movie 38 | for i=1:siz(4) 39 | Ii=IS(:,:,:,i); 40 | if( nch==1 ); [Ii,Mi] = gray2ind( Ii ); else Mi=[]; end 41 | if i==1 42 | M=repmat(im2frame( Ii, Mi ),[1,siz(4)]); 43 | else 44 | M(i) = im2frame( Ii, Mi ); 45 | end 46 | end 47 | 48 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/matlab/tpsInterpolateIm.m: -------------------------------------------------------------------------------- 1 | function [IR,boundX,boundY] = tpsInterpolateIm( Isrc, warp, holeVal ) 2 | % Interpolate Isrc according to the warp from Isrc->Idst. 3 | % 4 | % Use tpsGetWarp to obtain the warp. 5 | % 6 | % USAGE 7 | % IR = tpsInterpolateIm( Isrc, warp, [holeVal] ) 8 | % 9 | % INPUTS 10 | % Isrc - image to interpolate 11 | % warp - [see tpsGetWarp] bookstein warping parameters 12 | % holeVal - [0] Value of the empty warps 13 | % 14 | % OUTPUTS 15 | % IR - warped image 16 | % 17 | % EXAMPLE 18 | % xsS=[0 0 1 1 2 2]; ysS=[0 2 0 2 0 2]; ysD=[0 2 .5 1.5 0 2]; 19 | % warp = tpsGetWarp(0,xsS*100,ysS*100,xsS*100,ysD*100); 20 | % load clown; I=padarray(X,[1 1],0,'both'); clear X caption map; 21 | % IR = tpsInterpolateIm( I, warp ); 22 | % figure(1); clf; im(I); figure(2); clf; im(IR); 23 | % 24 | % See also TPSGETWARP, TEXTUREMAP 25 | % 26 | % Piotr's Computer Vision Matlab Toolbox Version 2.0 27 | % Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com] 28 | % Licensed under the Simplified BSD License [see external/bsd.txt] 29 | 30 | if( nargin<3 || isempty(holeVal) ); holeVal=0; end 31 | 32 | % warp grid points 33 | [ gxs, gys ] = meshgrid( 1:size(Isrc,2), 1:size(Isrc,1) ); 34 | [ gxsTar, gysTar ] = tpsInterpolate( warp, gxs(:), gys(:), 0 ); 35 | gxsTar = reshape( gxsTar, size(Isrc) ); 36 | gysTar = reshape( gysTar, size(Isrc) ); 37 | 38 | % use texture mapping to generate target image 39 | [IR,boundX,boundY]=textureMap(double(Isrc),gysTar,gxsTar,'loose',holeVal); 40 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/images/histMontage.m: -------------------------------------------------------------------------------- 1 | function histMontage( HS, mm, nn ) 2 | % Used to display multiple 1D histograms. 3 | % 4 | % USAGE 5 | % histMontage( HS, mm, nn ) 6 | % 7 | % INPUTS 8 | % HS - HS(i,j) is the jth bin in the ith histogram 9 | % mm - [] #images/row (if [] then calculated based on nn) 10 | % nn - [] #images/col (if [] then calculated based on mm) 11 | % 12 | % OUTPUTS 13 | % 14 | % EXAMPLE 15 | % h = histc2( randn(2000,1), 20 )'; clf; histMontage([h; h]); 16 | % 17 | % See also HISTC, HISTC2 18 | % 19 | % Piotr's Computer Vision Matlab Toolbox Version 2.0 20 | % Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com] 21 | % Licensed under the Simplified BSD License [see external/bsd.txt] 22 | 23 | [nhist, nbins] = size(HS); 24 | if( nhist>100 || nhist*nbins>10000 ) 25 | error('Too much histogram data to display!'); end; 26 | 27 | % get layout of images (mm=#images/row, nn=#images/col) 28 | if (nargin<3 || isempty(mm) || isempty(nn)) 29 | if (nargin==1 || (nargin==2 && isempty(mm)) || (nargin==3 && ... 30 | isempty(mm) && isempty(nn)) ) 31 | nn = round(sqrt(nhist)); 32 | mm = ceil( nhist / nn ); 33 | elseif (isempty(mm)) 34 | mm = ceil( nhist / nn ); 35 | else 36 | nn = ceil( nhist / mm ); 37 | end; 38 | end; 39 | 40 | % plot each histogram 41 | clf; 42 | for q=1:nhist 43 | if( nhist>1 ); subplot( mm, nn, q ); end; 44 | bar( HS(q,:), 1 ); shading('flat'); 45 | ylim( [0,1] ); set( gca, 'YTick', [] ); 46 | xlim( [.5, nbins+.5] ); set( gca, 'XTick', [] ); 47 | end; 48 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/deprecated/imrotate2.m: -------------------------------------------------------------------------------- 1 | % Custom version of imrotate that demonstrates use of apply_homography. 2 | % 3 | % Works exactly the same as imrotate. For usage see imrotate. 4 | % 5 | % USAGE 6 | % IR = imrotate2( I, angle, [method], [bbox] ) 7 | % 8 | % INPUTS 9 | % I - 2D image [converted to double] 10 | % angle - angle to rotate in degrees 11 | % method - ['linear'] 'nearest', 'linear', 'spline', 'cubic' 12 | % bbox - ['loose'] 'loose' or 'crop' 13 | % 14 | % OUTPUTS 15 | % IR - rotated image 16 | % 17 | % EXAMPLE 18 | % load trees; 19 | % tic; X1 = imrotate( X, 55, 'bicubic' ); toc, 20 | % tic; X2 = imrotate2( X, 55, 'bicubic' ); toc 21 | % clf; subplot(2,2,1); im(X); subplot(2,2,2); im(X1-X2); 22 | % subplot(2,2,3); im(X1); subplot(2,2,4); im(X2); 23 | % 24 | % See also IMROTATE 25 | 26 | % Piotr's Image&Video Toolbox Version 1.5 27 | % Written and maintained by Piotr Dollar pdollar-at-cs.ucsd.edu 28 | % Please email me if you find bugs, or have suggestions or questions! 29 | 30 | function IR = imrotate2( I, angle, method, bbox ) 31 | 32 | if( ~isa( I, 'double' ) ); I = double(I); end 33 | if( nargin<3 || isempty(method)); method='linear'; end 34 | if( nargin<4 || isempty(bbox) ); bbox='loose'; end 35 | if( strcmp(method,'bilinear') || strcmp(method,'lin')); method='linear';end 36 | 37 | % convert arguments for apply_homography 38 | angle_rads = angle /180 * pi; 39 | R = rotationMatrix( angle_rads ); 40 | H = [R [0;0]; 0 0 1]; 41 | IR = apply_homography( I, H, method, bbox ); 42 | -------------------------------------------------------------------------------- /tracker_featu/sample_patch.m: -------------------------------------------------------------------------------- 1 | function resized_patch = sample_patch(im, pos, sample_sz, output_sz, gparams) 2 | 3 | if nargin < 4 4 | output_sz = []; 5 | end 6 | if nargin < 5 || ~isfield(gparams, 'use_mexResize') 7 | gparams.use_mexResize = false; 8 | end 9 | 10 | % Pos should be integer when input, but floor in just in case. 11 | pos = floor(pos); 12 | 13 | % Downsample factor 14 | resize_factor = min(sample_sz ./ output_sz); 15 | df = max(floor(resize_factor - 0.1), 1); 16 | if df > 1 17 | % pos = 1 + of + df * (npos - 1) 18 | 19 | % compute offset and new center position 20 | os = mod(pos - 1, df); 21 | pos = (pos - 1 - os) / df + 1; 22 | 23 | % new sample size 24 | sample_sz = sample_sz / df; 25 | 26 | % donwsample image 27 | im = im(1+os(1):df:end, 1+os(2):df:end, :); 28 | end 29 | 30 | % make sure the size is not too small and round it 31 | sample_sz = max(round(sample_sz), 2); 32 | 33 | xs = pos(2) + (1:sample_sz(2)) - floor((sample_sz(2)+1)/2); 34 | ys = pos(1) + (1:sample_sz(1)) - floor((sample_sz(1)+1)/2); 35 | 36 | %check for out-of-bounds coordinates, and set them to the values at 37 | %the borders 38 | xs(xs < 1) = 1; 39 | ys(ys < 1) = 1; 40 | xs(xs > size(im,2)) = size(im,2); 41 | ys(ys > size(im,1)) = size(im,1); 42 | 43 | %extract image 44 | im_patch = im(ys, xs, :); 45 | 46 | if isempty(output_sz) || isequal(sample_sz(:), output_sz(:)) 47 | resized_patch = im_patch; 48 | else 49 | resized_patch = mexResize(im_patch, output_sz, 'linear'); 50 | end 51 | 52 | end 53 | 54 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/deprecated/imSubsResize.m: -------------------------------------------------------------------------------- 1 | % Resizes subs by resizVals. 2 | % 3 | % Resizes subs in subs/vals image representation by resizVals. 4 | % 5 | % This essentially replaces each sub by sub.*resizVals. The only subtlety 6 | % is that in images the leftmost sub value is .5, so for example when 7 | % resizing by a factor of 2, the first pixel is replaced by 2 pixels and so 8 | % location 1 in the original image goes to location 1.5 in the second 9 | % image, NOT 2. It may be necessary to round the values afterward. 10 | % 11 | % USAGE 12 | % subs = imSubsResize( subs, resizVals, [zeroPnt] ) 13 | % 14 | % INPUTS 15 | % subs - subscripts of point locations (n x d) 16 | % resizVals - k element vector of shrinking factors 17 | % zeroPnt - [.5] See comment above. 18 | % 19 | % OUTPUTS 20 | % subs - transformed subscripts of point locations (n x d) 21 | % 22 | % EXAMPLE 23 | % subs = imSubsResize( [1 1; 2 2], [2 2] ) 24 | % 25 | % 26 | % See also IMSUBSTOARRAY 27 | 28 | % Piotr's Image&Video Toolbox Version NEW 29 | % Written and maintained by Piotr Dollar pdollar-at-cs.ucsd.edu 30 | % Please email me if you find bugs, or have suggestions or questions! 31 | 32 | function subs = imSubsResize( subs, resizVals, zeroPnt ) 33 | 34 | if( nargin<3 || isempty(zeroPnt) ); zeroPnt=.5; end 35 | 36 | [n d] = size(subs); 37 | [resizVals,er] = checkNumArgs( resizVals, [1 d], -1, 2 ); error(er); 38 | 39 | % transform subs 40 | resizVals = repmat( resizVals, [n, 1] ); 41 | subs = (subs - zeroPnt) .* resizVals + zeroPnt; 42 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/m2html/private/strtok.m: -------------------------------------------------------------------------------- 1 | function [token, remainder, quotient] = strtok(string, delimiters) 2 | %Modified version of STRTOK to also return the quotient 3 | % string = [quotient token remainder] 4 | %STRTOK Find token in string. 5 | % STRTOK(S) returns the first token in the string S delimited 6 | % by "white space". Any leading white space characters are ignored. 7 | % 8 | % STRTOK(S,D) returns the first token delimited by one of the 9 | % characters in D. Any leading delimiter characters are ignored. 10 | % 11 | % [T,R] = STRTOK(...) also returns the remainder of the original 12 | % string. 13 | % If the token is not found in S then R is an empty string and T 14 | % is same as S. 15 | % 16 | % Copyright 1984-2002 The MathWorks, Inc. 17 | % $Revision: 5.14 $ $Date: 2002/04/09 00:33:38 $ 18 | 19 | token = []; remainder = []; quotient = string; 20 | 21 | len = length(string); 22 | if len == 0 23 | return 24 | end 25 | 26 | if (nargin == 1) 27 | delimiters = [9:13 32]; % White space characters 28 | end 29 | 30 | i = 1; 31 | while (any(string(i) == delimiters)) 32 | i = i + 1; 33 | if (i > len), return, end 34 | end 35 | start = i; 36 | while (~any(string(i) == delimiters)) 37 | i = i + 1; 38 | if (i > len), break, end 39 | end 40 | sfinish = i - 1; 41 | 42 | token = string(start:sfinish); 43 | 44 | if (nargout >= 2) 45 | remainder = string(sfinish + 1:length(string)); 46 | end 47 | 48 | if (nargout == 3 & start > 1) 49 | quotient = string(1:start-1); 50 | else 51 | quotient = []; 52 | end 53 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/matlab/normpdf2.m: -------------------------------------------------------------------------------- 1 | function ps = normpdf2( xs, m, C ) 2 | % Normal prob. density function (pdf) with arbitrary covariance matrix. 3 | % 4 | % Evaluate the multi-variate density with mean vector m and covariance 5 | % matrix C for the input vector xs. Assumes that the N datapoints are d 6 | % dimensional. Then m is dx1 or 1xd, C is dxd, and xs is dxN or NxD where 7 | % N is the number of samples to be evaluated. 8 | % 9 | % USAGE 10 | % ps = normpdf2( xs, m, C ) 11 | % 12 | % INPUTS 13 | % xs - points to evaluated (Nxd or dxN) 14 | % m - mean vector (dx1 or 1xd) 15 | % C - Covariance matrix (dxd) 16 | % 17 | % OUTPUTS 18 | % ps - probability density at each x (Nx1) 19 | % 20 | % EXAMPLE 21 | % ps = normpdf2( randn(10,2), [0 0], eye(2) ) 22 | % 23 | % See also NORMPDF 24 | % 25 | % Piotr's Computer Vision Matlab Toolbox Version 2.30 26 | % Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com] 27 | % Licensed under the Simplified BSD License [see external/bsd.txt] 28 | 29 | % get dimensions of data 30 | d=length(m); 31 | if( size(xs,1)~=d ); xs=xs'; end 32 | N=size(xs,2); 33 | 34 | if( d==1 ) % fast special case 35 | ps = 1/sqrt(2*pi*C) * exp(-(xs-m).*(xs-m)/(2*C))'; 36 | 37 | elseif( rcond(C) 39 | ps = zeros(N,1); 40 | 41 | else % get probabilities 42 | xs = (xs-m(:)*ones(1,N))'; 43 | denom = (2*pi)^(d/2)*sqrt(abs(det(C))); 44 | mahal = sum( (xs/C).*xs, 2 ); 45 | numer = exp(-0.5*mahal); 46 | ps = numer/denom; 47 | end 48 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/matlab/isfield2.m: -------------------------------------------------------------------------------- 1 | function tf = isfield2( S, fs, isinit ) 2 | % Similar to isfield but also test whether fields are initialized. 3 | % 4 | % A more comprehensive test of what fields are present [and optionally 5 | % initialized] in a stuct S. fs is either a single field name or a cell 6 | % array of field names. The presence of all fields in fs are tested for in 7 | % S, tf is true iif all fs are present. Additionally, if isinit==1, then tf 8 | % is true iff every field fs of every element of S is nonempty (test done 9 | % using isempty). 10 | % 11 | % USAGE 12 | % tf = isfield2( S, fs, [isinit] ) 13 | % 14 | % INPUTS 15 | % S - struct array 16 | % fs - cell of string name or string 17 | % isinit - [0] if true additionally test if all fields are initialized 18 | % 19 | % OUTPUTS 20 | % tf - true or false, depending on results of above tests 21 | % 22 | % EXAMPLE 23 | % isfield2( struct('a',1,'b',2), {'a','b'}, 1 ) 24 | % 25 | % See also ISFIELD 26 | % 27 | % Piotr's Computer Vision Matlab Toolbox Version 2.10 28 | % Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com] 29 | % Licensed under the Simplified BSD License [see external/bsd.txt] 30 | 31 | tf = all( isfield(S,fs) ); 32 | if( ~tf || nargin<3 || ~isinit ); return; end 33 | 34 | % now optionally check if fields are initialized 35 | if( iscell(fs) ) 36 | for i=1:length(fs) 37 | for j=1:numel(S) 38 | if( isempty(S(j).(fs{i})) ); tf=false; return; end; 39 | end; 40 | end; 41 | else 42 | for j=1:numel(S) 43 | if( isempty(S(j).(fs)) ); tf=false; return; end; 44 | end; 45 | end 46 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/matlab/int2str2.m: -------------------------------------------------------------------------------- 1 | function nstr = int2str2( n, nDigits ) 2 | % Convert integer to string of given length; improved version of int2str. 3 | % 4 | % Pads string with zeros on the left. For integers similar to 5 | % sprintf( '%03i', n ); %for nDigits=3 6 | % If input n is an array, output is a cell array of strings of the same 7 | % dimension as n. Works also for non integers (pads to given length). 8 | % 9 | % USAGE 10 | % nstr = int2str2( n, [nDigits] ) 11 | % 12 | % INPUTS 13 | % n - integer to convert to string 14 | % nDigits - [0] minimum number of digits to use 15 | % 16 | % OUTPUTS 17 | % nstr - string repr. of n (or cell array of strings if n is array) 18 | % 19 | % EXAMPLE 20 | % s = int2str2( 3, 3 ) % s='003' 21 | % 22 | % See also INT2STR 23 | % 24 | % Piotr's Computer Vision Matlab Toolbox Version 1.5 25 | % Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com] 26 | % Licensed under the Simplified BSD License [see external/bsd.txt] 27 | 28 | if( nargin<2 ); nDigits=0; end 29 | 30 | nel = numel( n ); 31 | negvals=(n<0); n=abs(n); 32 | if( nel==1 ) % for a single int 33 | nstr = num2str( n ); 34 | if( nDigits > size(nstr,2) ) 35 | nstr = [repmat( '0', 1, nDigits-size(nstr,2) ), nstr]; 36 | end; 37 | if(negvals); nstr=['-' nstr]; end 38 | 39 | else % for array of ints 40 | nstr = cell(size(n)); 41 | for i=1:nel 42 | nstr{i} = num2str( n(i) ); 43 | if( nDigits > size(nstr{i},2) ) 44 | nstr{i} = [repmat( '0', 1, nDigits-size(nstr{i},2) ), nstr{i}]; 45 | end; 46 | if(negvals(i)); nstr{i}=['-' nstr{i}]; end 47 | end 48 | end 49 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/m2html/@template/get.m: -------------------------------------------------------------------------------- 1 | function varargout = get(tpl,action,varargin) 2 | %TEMPLATE/GET Access data stored in a Template object 3 | % TPL = GET(TPL,ACTION,VARARGIN) 4 | % ACTION 'var' 5 | % ACTION 'undefined' 6 | 7 | % Copyright (C) 2003 Guillaume Flandin 8 | % $Revision: 1.0 $Date: 2003/05/05 22:19:51 $ 9 | 10 | narginchk(2,3); 11 | 12 | switch lower(action) 13 | case 'var' 14 | narginchk(2,3); 15 | if nargin == 2 16 | varargout{1} = tpl.varvals; 17 | elseif iscellstr(varargin{1}) 18 | varargout{1} = {}; 19 | for i=1:length(varargin{1}) 20 | key = find(ismember(tpl.varkeys,varargin{1}{i})); 21 | if isempty(key) 22 | %error('[Template] No such variable name.'); 23 | varargout{1}{end+1} = ''; 24 | else 25 | varargout{1}{end+1} = tpl.varvals{key}; 26 | end 27 | end 28 | elseif ischar(varargin{1}) 29 | varargout{1} = char(get(tpl,'var',cellstr(varargin{1}))); 30 | else 31 | varargout{1} = ''; 32 | end 33 | case 'undefined' 34 | narginchk(3,3); 35 | tpl = loadtpl(tpl,varargin{1}); 36 | str = get(tpl,'var',varargin{1}); 37 | varargout{1} = {}; 38 | 39 | %%%%%%%%%%%%%%%%%%%%%%%% WIH REGEXP ONLY %%%%%%%%%%%%%%%%%%%% 40 | % [b, e] = regexp(str,'{[^ \t\r\n}]+}'); 41 | % for i=1:length(b) 42 | % if ~any(ismember(tpl.varkeys,str(b(i)+1:e(i)-1))) 43 | % varargout{1}{end+1} = str(b(i)+1:e(i)-1); 44 | % end 45 | % end 46 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 47 | 48 | otherwise 49 | varargout{1} = finish(get(tpl,'var',action),tpl.unknowns); 50 | end 51 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/deprecated/randperm2.m: -------------------------------------------------------------------------------- 1 | % Returns a random permutation of integers. 2 | % 3 | % randperm2(n) is a random permutation of the integers from 1 to n. For 4 | % example, randperm2(6) might be [2 4 5 6 1 3]. randperm2(n,k) is only 5 | % returns the first k elements of the permuation, so for example 6 | % randperm2(6) might be [2 4]. This is a faster version of randperm.m if 7 | % only need first k<= minCl ); break; end; 41 | 42 | % otherwise discard smallest [last] cluster 43 | C( end, : ) = []; 44 | for i=find(IDX==k)' 45 | D = pdist2( X(i,:), C ); 46 | [mind IDx] = min(D,[],2); 47 | IDX(i) = IDx; 48 | end; 49 | k = k-1; 50 | 51 | tocStatus( ticId, (kinit-k)/kinit ); 52 | end 53 | tocStatus( ticId, 1 ); 54 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/images/makeGif.m: -------------------------------------------------------------------------------- 1 | function makeGif( M, fileName, prm ) 2 | % Writes a matlab movie to an animated GIF. 3 | % 4 | % USAGE 5 | % makeGif( M, fileName, prm ) 6 | % 7 | % INPUTS 8 | % M - Matlab movie 9 | % fileName - file name of the output gif file 10 | % prm 11 | % .fps - number of frames per second 12 | % .nColor - number of indexes colors 13 | % .scale - scale to resize the frames at 14 | % .loop - number of times to repeat the movie (Inf possible) 15 | % 16 | % OUTPUTS 17 | % 18 | % EXAMPLE 19 | % load( 'images.mat' ); 20 | % M = playMovie( video, [], 1 ); 21 | % makeGif( M, 'mouse.gif', struct('scale',0.5) ); 22 | % 23 | % See also PLAYMOVIE, MONTAGE2 24 | % 25 | % Piotr's Computer Vision Matlab Toolbox Version 2.0 26 | % Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com] 27 | % Licensed under the Simplified BSD License [see external/bsd.txt] 28 | 29 | dfs = {'fps',30, 'nColor',256, 'scale',1, 'loop', 0}; 30 | prm = getPrmDflt( prm, dfs ); 31 | fps=prm.fps; nColor=prm.nColor; scale=prm.scale; loop=prm.loop; 32 | 33 | if scale~=1 34 | for i=1:length(M); M(i).cdata=imresize(M(i).cdata,scale, 'bicubic'); end 35 | end 36 | 37 | if ndims(M(1).cdata)==3 38 | for i=1:length(M) 39 | [M(i).cdata,M(i).colormap] = rgb2ind(M(i).cdata, nColor); 40 | end 41 | else 42 | for i=1:length(M) 43 | M(i).colormap = repmat(0:1/256:1,[3 1])'; 44 | end 45 | end 46 | 47 | imwrite(M(1).cdata,M(1).colormap,fileName,'gif','LoopCount',loop); 48 | for i=2:length(M) 49 | imwrite(M(i).cdata,M(i).colormap,fileName,'gif','DelayTime',1/fps,... 50 | 'WriteMode','append'); 51 | end 52 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/m2html/templates/blue/m2html.css: -------------------------------------------------------------------------------- 1 | body { 2 | background: white; 3 | color: black; 4 | font-family: arial,sans-serif; 5 | margin: 0; 6 | padding: 1ex; 7 | } 8 | 9 | div.fragment { 10 | width: 98%; 11 | border: 1px solid #CCCCCC; 12 | background-color: #f5f5f5; 13 | padding-left: 4px; 14 | margin: 4px; 15 | } 16 | 17 | div.box { 18 | width: 98%; 19 | background-color: #f5f5f5; 20 | border: 1px solid #CCCCCC; 21 | color: black; 22 | padding: 4px; 23 | } 24 | 25 | .comment { 26 | color: #228B22; 27 | } 28 | .string { 29 | color: #B20000; 30 | } 31 | .keyword { 32 | color: #0000FF; 33 | } 34 | 35 | .keywordtype { color: #604020; } 36 | .keywordflow { color: #e08000; } 37 | .preprocessor { color: #806020; } 38 | .stringliteral { color: #002080; } 39 | .charliteral { color: #008080; } 40 | 41 | a { 42 | text-decoration: none; 43 | } 44 | 45 | a:hover { 46 | background-color: #006699; 47 | color:#FFFFFF; 48 | } 49 | 50 | a.code { 51 | font-weight: normal; 52 | color: #A020F0; 53 | } 54 | 55 | a.code:hover { 56 | background-color: #FF0000; 57 | color: #FFFFFF; 58 | } 59 | 60 | h1 { 61 | background: transparent; 62 | color: #006699; 63 | font-size: x-large; 64 | text-align: center; 65 | } 66 | 67 | h2 { 68 | background: transparent; 69 | color: #006699; 70 | font-size: large; 71 | } 72 | 73 | address { 74 | font-size:small; 75 | } 76 | 77 | form.search { 78 | margin-bottom: 0px; 79 | margin-top: 0px; 80 | } 81 | input.search { 82 | font-size: 75%; 83 | color: #000080; 84 | font-weight: normal; 85 | background-color: #eeeeff; 86 | } 87 | 88 | li { 89 | padding-left:5px; 90 | } -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/m2html/templates/frame/m2html.css: -------------------------------------------------------------------------------- 1 | body { 2 | background: white; 3 | color: black; 4 | font-family: arial,sans-serif; 5 | margin: 0; 6 | padding: 1ex; 7 | } 8 | 9 | div.fragment { 10 | width: 98%; 11 | border: 1px solid #CCCCCC; 12 | background-color: #f5f5f5; 13 | padding-left: 4px; 14 | margin: 4px; 15 | } 16 | 17 | div.box { 18 | width: 98%; 19 | background-color: #f5f5f5; 20 | border: 1px solid #CCCCCC; 21 | color: black; 22 | padding: 4px; 23 | } 24 | 25 | .comment { 26 | color: #228B22; 27 | } 28 | .string { 29 | color: #B20000; 30 | } 31 | .keyword { 32 | color: #0000FF; 33 | } 34 | 35 | .keywordtype { color: #604020; } 36 | .keywordflow { color: #e08000; } 37 | .preprocessor { color: #806020; } 38 | .stringliteral { color: #002080; } 39 | .charliteral { color: #008080; } 40 | 41 | a { 42 | text-decoration: none; 43 | } 44 | 45 | a:hover { 46 | background-color: #006699; 47 | color:#FFFFFF; 48 | } 49 | 50 | a.code { 51 | font-weight: normal; 52 | color: #A020F0; 53 | } 54 | 55 | a.code:hover { 56 | background-color: #FF0000; 57 | color: #FFFFFF; 58 | } 59 | 60 | h1 { 61 | background: transparent; 62 | color: #006699; 63 | font-size: x-large; 64 | text-align: center; 65 | } 66 | 67 | h2 { 68 | background: transparent; 69 | color: #006699; 70 | font-size: large; 71 | } 72 | 73 | address { 74 | font-size:small; 75 | } 76 | 77 | form.search { 78 | margin-bottom: 0px; 79 | margin-top: 0px; 80 | } 81 | input.search { 82 | font-size: 75%; 83 | color: #000080; 84 | font-weight: normal; 85 | background-color: #eeeeff; 86 | } 87 | 88 | li { 89 | padding-left:5px; 90 | } -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/bsd.txt: -------------------------------------------------------------------------------- 1 | Copyright (c) 2012, Piotr Dollar 2 | All rights reserved. 3 | 4 | Redistribution and use in source and binary forms, with or without 5 | modification, are permitted provided that the following conditions are met: 6 | 7 | 1. Redistributions of source code must retain the above copyright notice, this 8 | list of conditions and the following disclaimer. 9 | 2. Redistributions in binary form must reproduce the above copyright notice, 10 | this list of conditions and the following disclaimer in the documentation 11 | and/or other materials provided with the distribution. 12 | 13 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND 14 | ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED 15 | WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE 16 | DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR 17 | ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES 18 | (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; 19 | LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND 20 | ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT 21 | (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS 22 | SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 23 | 24 | The views and conclusions contained in the software and documentation are those 25 | of the authors and should not be interpreted as representing official policies, 26 | either expressed or implied, of the FreeBSD Project. 27 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/classify/private/fernsInds1.c: -------------------------------------------------------------------------------- 1 | /******************************************************************************* 2 | * Piotr's Computer Vision Matlab Toolbox Version 2.50 3 | * Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com] 4 | * Licensed under the Simplified BSD License [see external/bsd.txt] 5 | *******************************************************************************/ 6 | #include "mex.h" 7 | #include 8 | typedef unsigned int uint; 9 | 10 | void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) { 11 | int N, F, M, S, n, f, m, s; 12 | double *data, *thrs; 13 | uint *fids, *inds; 14 | 15 | /* Error checking on arguments */ 16 | if( nrhs!=3) mexErrMsgTxt("Three input arguments required."); 17 | if( nlhs>1 ) mexErrMsgTxt("Too many output arguments."); 18 | if( !mxIsClass(prhs[0], "double") || !mxIsClass(prhs[1], "uint32") 19 | || !mxIsClass(prhs[2], "double")) 20 | mexErrMsgTxt("Input arrays are of incorrect type."); 21 | 22 | /* extract inputs */ 23 | data = (double*) mxGetData(prhs[0]); /* N x F */ 24 | fids = (uint*) mxGetData(prhs[1]); /* M x S */ 25 | thrs = (double*) mxGetData(prhs[2]); /* N x F */ 26 | N=mxGetM(prhs[0]); F=mxGetN(prhs[0]); 27 | M=mxGetM(prhs[1]); S=mxGetN(prhs[1]); 28 | 29 | /* create outputs */ 30 | plhs[0] = mxCreateNumericMatrix(N, M, mxUINT32_CLASS, mxREAL); 31 | inds = (uint*) mxGetData(plhs[0]); /* N x M */ 32 | 33 | /* compute inds */ 34 | for(m=0; m 14 | % $Revision: 1.0 $Date: 2003/04/10 18:32:48 $ 15 | 16 | error(nargchk(1,2,nargin)); 17 | if nargin == 1, szmin = 2; end 18 | 19 | %- Delimiters used in strtok 20 | strtok_delim = sprintf(' \t\n\r(){}[]<>+-*^$~#!|\\@&/.,:;="''%%'); 21 | 22 | %- Open for reading the M-file 23 | fid = openfile(mfile,'r'); 24 | 25 | %- Initialize keywords list 26 | s = {}; 27 | 28 | %- Loop over lines 29 | while 1 30 | tline = fgetl(fid); 31 | if ~ischar(tline), break, end 32 | 33 | %- Extract keywords in each line 34 | while 1 35 | [w, tline] = strtok(tline,strtok_delim); 36 | if isempty(w), break, end; 37 | %- Check the length of the keyword 38 | if length(w) > szmin 39 | s{end+1} = w; 40 | end 41 | end 42 | end 43 | 44 | %- Close the M-file 45 | fclose(fid); 46 | 47 | %- Remove repeted keywords 48 | [s, i, j] = unique(s); 49 | 50 | %- Compute occurency frenquency if required 51 | if nargout == 2, 52 | if ~isempty(s) 53 | freq = histc(j,1:length(i)); 54 | else 55 | freq = []; 56 | end 57 | end 58 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/images/assignToBins.m: -------------------------------------------------------------------------------- 1 | function B = assignToBins( A, edges ) 2 | % Quantizes A according to values in edges. 3 | % 4 | % assignToBins replaces each value in A with a value between [0,nBins] where 5 | % nBins=length(edges)-1. edges must be a vector of monotonically 6 | % increasing values. Each element v in A gets converted to a discrete 7 | % value q such that edges(q)<=v< edges(q+1). If v==edges(end) then q=nBins. 8 | % If v does not fall into any bin, then q=0. See histc2 for more details 9 | % about edges. For even spaced edges can get away with rounding A 10 | % appropriately, see example below. 11 | % 12 | % USAGE 13 | % B = assignToBins( A, edges ) 14 | % 15 | % INPUTS 16 | % A - numeric array of arbitrary dimension 17 | % edges - quantization bounds, see histc2 18 | % 19 | % OUTPUTS 20 | % B - size(A) array of quantization levels, ints between [0,nBins] 21 | % 22 | % EXAMPLE 23 | % A = rand(5,5); 24 | % B1 = assignToBins(A,[0:.1:1]); 25 | % B2 = ceil(A*10); B1-B2 26 | % 27 | % See also HISTC2 28 | % 29 | % Piotr's Computer Vision Matlab Toolbox Version 2.0 30 | % Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com] 31 | % Licensed under the Simplified BSD License [see external/bsd.txt] 32 | 33 | if(~isa(A,'double')); A = double(A); end; 34 | 35 | if( length(edges)==1 ) % if nBins given instead of edges calculate edges 36 | edges = linspace( min(A(:))-eps, max(A(:))+eps, edges+1 ); end; 37 | 38 | B = assignToBins1( A, edges ); % assign bin number 39 | B = B + 1; % convert to 1 indexed 40 | B = reshape( B, size(A) ); % resize B to have correct shape 41 | B( B==(length(edges)) ) = 0; % vals outside or range get bin 0 42 | -------------------------------------------------------------------------------- /tracker_featu/get_table_feature.m: -------------------------------------------------------------------------------- 1 | function [ feature_map ] = get_table_feature( im, fparam, gparam) 2 | %get per-pixel features using a lookup table, if the gparam feature cell 3 | %size is set to something large than one, the resulting data will be 4 | %averaged in cells of the specified size. 5 | % 6 | %tables are loaded dynamically when needed from the lookup_tables folder, 7 | %and stored in a persistent variable 8 | 9 | persistent tables; 10 | 11 | if isempty(tables) 12 | tables = {}; 13 | end 14 | 15 | cell_size = fparam.cell_size; 16 | 17 | tab_ind = 0; 18 | for k = 1:length(tables) 19 | if isfield(tables{k}, fparam.tablename) 20 | tab_ind = k; 21 | break; 22 | end 23 | end 24 | 25 | if tab_ind == 0 26 | tables{end+1} = load([fparam.tablename]); 27 | tab_ind = length(tables); 28 | end 29 | 30 | if strcmp(tables{tab_ind}.inputType,'color') 31 | if size(im,3) ~= 3 32 | except = MException('cannot get colorfeature from non color image'); 33 | raise(except); 34 | end 35 | elseif strcmp(tables{tab_ind}.inputType,'gray') 36 | if size(im,3) == 3 37 | im_gray = zeros(size(im,1),size(im,2),1,size(im,4),'uint8'); 38 | for k = 1:size(im,4) 39 | im_gray(:,:,:,k) = rgb2gray(im(:,:,:,k)); 40 | end 41 | im = im_gray; 42 | end 43 | end 44 | 45 | % Extract features from table 46 | if gparam.use_gpu 47 | feature_map = gpuArray(table_lookup(im,tables{tab_ind}.(fparam.tablename))); 48 | else 49 | feature_map = table_lookup(im,tables{tab_ind}.(fparam.tablename)); 50 | end 51 | 52 | if cell_size > 1 53 | feature_map = average_feature_region(feature_map, cell_size); 54 | end 55 | 56 | end 57 | 58 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/deprecated/clfLda.m: -------------------------------------------------------------------------------- 1 | function clf = clfLda( p, type, prior ) 2 | % Create a Linear Discriminant Analysis (LDA) classifier. 3 | % 4 | % Same algorithm as matlab's function 'classify' (in the statistics 5 | % toolbox). Nice to have in form that actually stores a model that can be 6 | % applied multiple times. For the meaning and usage of prior and type see 7 | % classify.m 8 | % 9 | % USAGE 10 | % clf = clfLda( p, [type], [prior] ) 11 | % 12 | % INPUTS 13 | % p - data dimension 14 | % type - ['linear'] 'linear', 'quadratic', 'mahalanobis' 15 | % prior - [] prior to use 16 | % 17 | % OUTPUTS 18 | % clf - an LDA model ready to be trained (see clfLdaTrain) 19 | % 20 | % EXAMPLE 21 | % 22 | % See also NFOLDXVAL, CLASSIFY, CLFLDATRAIN, CLFLDAFWD 23 | % 24 | % Piotr's Image&Video Toolbox Version 2.0 25 | % Copyright 2008 Piotr Dollar. [pdollar-at-caltech.edu] 26 | % Please email me if you find bugs, or have suggestions or questions! 27 | % Licensed under the Lesser GPL [see external/lgpl.txt] 28 | 29 | if( nargin<3 ); prior=[]; end 30 | 31 | %%% get type 32 | if( nargin < 2 || isempty(type) ) 33 | type = 1; % 'linear' 34 | elseif ischar(type) 35 | i = strmatch(lower(type), {'linear','quadratic','mahalanobis'}); 36 | if( length(i)>1 ); 37 | error('Ambiguous value for TYPE: %s.', type); 38 | elseif( isempty(i)); 39 | error('Unknown value for TYPE: %s.', type); 40 | end; 41 | type = i; 42 | else 43 | error('TYPE must be a string.'); 44 | end 45 | 46 | %%% save clfLda parameters 47 | clf.prior = prior; 48 | clf.p = p; 49 | clf.type = 'lda'; 50 | clf.clfLdaType = type; 51 | clf.funTrain = @clfLdaTrain; 52 | clf.funFwd = @clfLdaFwd; 53 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/deprecated/clfEcoc.m: -------------------------------------------------------------------------------- 1 | function clf = clfEcoc(p,clfInit,clfparams,nclasses,use01targets) 2 | % Wrapper for ecoc that makes ecoc compatible with nfoldxval. 3 | % 4 | % Requires the SVM toolbox by Anton Schwaighofer. 5 | % 6 | % USAGE 7 | % clf = clfEcoc(p,clfInit,clfparams,nclasses,use01targets) 8 | % 9 | % INPUTS 10 | % p - data dimension 11 | % clfInit - binary classifier init (see nfoldxval) 12 | % clfparams - binary classifier parameters (see nfoldxval) 13 | % nclasses - num of classes (currently 3<=nclasses<=7 suppored) 14 | % use01targets - see ecoc 15 | % 16 | % OUTPUTS 17 | % clf - see ecoc 18 | % 19 | % EXAMPLE 20 | % 21 | % See also ECOC, NFOLDXVAL, CLFECOCCODE 22 | % 23 | % Piotr's Image&Video Toolbox Version 2.0 24 | % Copyright 2008 Piotr Dollar. [pdollar-at-caltech.edu] 25 | % Please email me if you find bugs, or have suggestions or questions! 26 | % Licensed under the Lesser GPL [see external/lgpl.txt] 27 | 28 | if( nclasses<3 || nclasses>7 ) 29 | error( 'currently only works if 3<=nclasses<=7'); end; 30 | if( nargin<5 || isempty(use01targets)); use01targets=0; end; 31 | 32 | % create code (limited for now) 33 | [C,nbits] = clfEcocCode( nclasses ); 34 | clf = ecoc(nclasses, nbits, C, use01targets ); % didn't use to pass use01? 35 | clf.verbosity = 0; % don't diplay output 36 | 37 | % initialize and temporarily store binary learner 38 | clf.templearner = feval( clfInit, p, clfparams{:} ); 39 | 40 | % ecoctrain2 is custom version of ecoctrain 41 | clf.funTrain = @clfEcocTrain; 42 | clf.funFwd = @ecocfwd; 43 | 44 | function clf = clfEcocTrain( clf, varargin ) 45 | 46 | clf = ecoctrain( clf, clf.templearner, varargin{:} ); 47 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/m2html/templates/brain/m2html.css: -------------------------------------------------------------------------------- 1 | body { 2 | background: #f5f5f5; 3 | color: black; 4 | font-family: arial,sans-serif; 5 | margin: 0; 6 | padding: 1ex; 7 | } 8 | 9 | div.fragment { 10 | width: 98%; 11 | border: 1px solid #CCCCCC; 12 | background-color: #FFFFFF; 13 | padding-left: 4px; 14 | margin: 4px; 15 | background-repeat: no-repeat; 16 | background-position: -15% 0%; 17 | } 18 | 19 | div.box { 20 | width: 98%; 21 | background-color: #FFFFFF; 22 | border: 1px solid #CCCCCC; 23 | color: black; 24 | padding: 4px; 25 | } 26 | 27 | .comment { 28 | color: #228B22; 29 | } 30 | .string { 31 | color: #B20000; 32 | } 33 | .keyword { 34 | color: #0000FF; 35 | } 36 | 37 | .keywordtype { color: #604020; } 38 | .keywordflow { color: #e08000; } 39 | .preprocessor { color: #806020; } 40 | .stringliteral { color: #002080; } 41 | .charliteral { color: #008080; } 42 | 43 | a { 44 | text-decoration: none; 45 | } 46 | 47 | a:hover { 48 | background-color: #006699; 49 | color:#FFFFFF; 50 | } 51 | 52 | a.code { 53 | font-weight: normal; 54 | color: #A020F0; 55 | } 56 | 57 | a.code:hover { 58 | background-color: #FF0000; 59 | color: #FFFFFF; 60 | } 61 | 62 | h1 { 63 | background: transparent; 64 | color: #006699; 65 | font-size: x-large; 66 | text-align: center; 67 | } 68 | 69 | h2 { 70 | background: transparent; 71 | color: #006699; 72 | font-size: large; 73 | } 74 | 75 | address { 76 | font-size:small; 77 | } 78 | 79 | form.search { 80 | margin-bottom: 0px; 81 | margin-top: 0px; 82 | } 83 | input.search { 84 | font-size: 75%; 85 | color: #000080; 86 | font-weight: normal; 87 | background-color: #eeeeff; 88 | } 89 | 90 | li { 91 | padding-left:5px; 92 | } 93 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/external/m2html/templates/brain/master.tpl: -------------------------------------------------------------------------------- 1 | 3 | 4 | 5 | Matlab Index 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 |

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43 | 44 | 45 | -------------------------------------------------------------------------------- /tracker_exter/pdollar_toolbox/images/xcorrn.m: -------------------------------------------------------------------------------- 1 | function C = xcorrn( A, T, shape ) 2 | % n-dimensional cross-correlation. Generalized version of xcorr2. 3 | % 4 | % For 2 dimensional inputs this function is exactly the same as xcorr2, but 5 | % also works in higher dimensions. Can also be more efficient because it 6 | % performs convolution using convnFast. Note that xcorr2 is part of the 7 | % 'Signal Processing Toolbox' and may not be available on all systems. 8 | % 9 | % The order of parameters is reversed from normxcorrn. This is to be 10 | % compatible with the matlab functions normxcorr2 and xcorr2 (which take 11 | % parameters in different orders). 12 | % 13 | % USAGE 14 | % C = xcorrn( A, T, [shape] ) 15 | % 16 | % INPUTS 17 | % A - first d-dimensional matrix 18 | % T - second d-dimensional matrix 19 | % shape - ['full'] 'valid', or 'same' (see convn) 20 | % 21 | % OUTPUTS 22 | % C - correlation matrix 23 | % 24 | % EXAMPLE 25 | % T=gaussSmooth(rand(20),2); A=repmat(T,[3 3]); 26 | % C1=xcorr2(A,T); C2=xcorrn(A,T); C3=rot90(xcorrn(T,A),2); 27 | % figure(1); im(C1); figure(2); im(C2); figure(3); im(C3); 28 | % 29 | % See also XCORR2, NORMXCORRN, XEUCN, CONVNFAST 30 | % 31 | % Piotr's Computer Vision Matlab Toolbox Version 2.12 32 | % Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com] 33 | % Licensed under the Simplified BSD License [see external/bsd.txt] 34 | 35 | if( nargin<3 || isempty(shape)); shape='full'; end 36 | nd = ndims(A); 37 | if(nd~=ndims(A)); error('xcorrn: T and A must have same ndims'); end; 38 | 39 | % flip for conv purposes 40 | if(nd==2); T=rot90(T,2); else for d=1:nd; T=flipdim(T,d); end; end 41 | 42 | % convolve [in frequency or spatial domain] 43 | C = convnFast( A, T, shape ); 44 | --------------------------------------------------------------------------------