├── ECO_tir ├── LICENSE ├── VOT_integration │ ├── benchmark_wrapper │ │ ├── benchmark_tracker_wrapper.m │ │ ├── setup_tracker_paths.m │ │ └── vot.m │ └── configuration_file │ │ ├── tracker_ECO.m │ │ └── tracker_ECO_tir.m ├── external_libs │ ├── mexResize │ │ ├── MxArray.cpp │ │ ├── MxArray.hpp │ │ ├── compile.m │ │ ├── libopencv_core.a │ │ ├── libopencv_core.so │ │ ├── libopencv_imgproc.a │ │ ├── libopencv_imgproc.so │ │ ├── mexResize.cpp │ │ ├── mexResize.mexa64 │ │ ├── mexResize.mexw64 │ │ ├── opencv2 │ │ │ ├── core │ │ │ │ ├── core.hpp │ │ │ │ ├── core_c.h │ │ │ │ ├── devmem2d.hpp │ │ │ │ ├── eigen.hpp │ │ │ │ ├── gpumat.hpp │ │ │ │ ├── internal.hpp │ │ │ │ ├── mat.hpp │ │ │ │ ├── opengl_interop.hpp │ │ │ │ ├── operations.hpp │ │ │ │ ├── types_c.h │ │ │ │ ├── version.hpp │ │ │ │ └── wimage.hpp │ │ │ ├── features2d │ │ │ │ └── features2d.hpp │ │ │ ├── flann │ │ │ │ ├── all_indices.h │ │ │ │ ├── allocator.h │ │ │ │ ├── any.h │ │ │ │ ├── autotuned_index.h │ │ │ │ ├── composite_index.h │ │ │ │ ├── config.h │ │ │ │ ├── defines.h │ │ │ │ ├── dist.h │ │ │ │ ├── dummy.h │ │ │ │ ├── dynamic_bitset.h │ │ │ │ ├── flann.hpp │ │ │ │ ├── flann_base.hpp │ │ │ │ ├── general.h │ │ │ │ ├── ground_truth.h │ │ │ │ ├── hdf5.h │ │ │ │ ├── heap.h │ │ │ │ ├── hierarchical_clustering_index.h │ │ │ │ ├── index_testing.h │ │ │ │ ├── kdtree_index.h │ │ │ │ ├── kdtree_single_index.h │ │ │ │ ├── kmeans_index.h │ │ │ │ ├── linear_index.h │ │ │ │ ├── logger.h │ │ │ │ ├── lsh_index.h │ │ │ │ ├── lsh_table.h │ │ │ │ ├── matrix.h │ │ │ │ ├── miniflann.hpp │ │ │ │ ├── nn_index.h │ │ │ │ ├── object_factory.h │ │ │ │ ├── params.h │ │ │ │ ├── random.h │ │ │ │ ├── result_set.h │ │ │ │ ├── sampling.h │ │ │ │ ├── saving.h │ │ │ │ ├── simplex_downhill.h │ │ │ │ └── timer.h │ │ │ ├── imgproc │ │ │ │ ├── imgproc.hpp │ │ │ │ ├── imgproc_c.h │ │ │ │ └── types_c.h │ │ │ ├── opencv.hpp │ │ │ └── opencv_modules.hpp │ │ ├── opencv_core242.dll │ │ ├── opencv_core242.lib │ │ ├── opencv_imgproc242.dll │ │ └── opencv_imgproc242.lib │ ├── mtimesx │ │ ├── license.txt │ │ ├── mtimesx.c │ │ ├── mtimesx.m │ │ ├── mtimesx.mexa64 │ │ ├── mtimesx_RealTimesReal.c │ │ ├── mtimesx_build.m │ │ ├── mtimesx_sparse.m │ │ ├── mtimesx_test_ddequal.m │ │ ├── mtimesx_test_ddspeed.m │ │ ├── mtimesx_test_dsequal.m │ │ ├── mtimesx_test_dsspeed.m │ │ ├── mtimesx_test_nd.m │ │ ├── mtimesx_test_sdequal.m │ │ ├── mtimesx_test_sdspeed.m │ │ ├── mtimesx_test_ssequal.m │ │ └── mtimesx_test_ssspeed.m │ └── pdollar_toolbox │ │ ├── README.md │ │ ├── channels │ │ ├── Contents.m │ │ ├── chnsCompute.m │ │ ├── chnsPyramid.m │ │ ├── chnsScaling.m │ │ ├── convBox.m │ │ ├── convMax.m │ │ ├── convTri.m │ │ ├── fhog.m │ │ ├── gradient2.m │ │ ├── gradientHist.m │ │ ├── gradientMag.m │ │ ├── hog.m │ │ ├── hogDraw.m │ │ ├── imPad.m │ │ ├── imResample.m │ │ ├── private │ │ │ ├── chnsTestCpp.cpp │ │ │ ├── convConst.cpp │ │ │ ├── convConst.mexa64 │ │ │ ├── convConst.mexmaci64 │ │ │ ├── convConst.mexw64 │ │ │ ├── gradientMex.cpp │ │ │ ├── gradientMex.mexa64 │ │ │ ├── gradientMex.mexmaci64 │ │ │ ├── gradientMex.mexw64 │ │ │ ├── gradientMexNew.mexmaci64 │ │ │ ├── imPadMex.cpp │ │ │ ├── imPadMex.mexa64 │ │ │ ├── imPadMex.mexmaci64 │ │ │ ├── imPadMex.mexw64 │ │ │ ├── imResampleMex.cpp │ │ │ ├── imResampleMex.mexa64 │ │ │ ├── imResampleMex.mexmaci64 │ │ │ ├── imResampleMex.mexw64 │ │ │ ├── rgbConvertMex.cpp │ │ │ ├── rgbConvertMex.mexa64 │ │ │ ├── rgbConvertMex.mexmaci64 │ │ │ ├── rgbConvertMex.mexw64 │ │ │ ├── sse.hpp │ │ │ └── wrappers.hpp │ │ └── rgbConvert.m │ │ ├── classify │ │ ├── Contents.m │ │ ├── adaBoostApply.m │ │ ├── adaBoostTrain.m │ │ ├── binaryTreeApply.m │ │ ├── binaryTreeTrain.m │ │ ├── confMatrix.m │ │ ├── confMatrixShow.m │ │ ├── demoCluster.m │ │ ├── demoGenData.m │ │ ├── distMatrixShow.m │ │ ├── fernsClfApply.m │ │ ├── fernsClfTrain.m │ │ ├── fernsInds.m │ │ ├── fernsRegApply.m │ │ ├── fernsRegTrain.m │ │ ├── forestApply.m │ │ ├── forestTrain.m │ │ ├── kmeans2.m │ │ ├── meanShift.m │ │ ├── meanShiftIm.m │ │ ├── meanShiftImExplore.m │ │ ├── pca.m │ │ ├── pcaApply.m │ │ ├── pcaData.mat │ │ ├── pcaRandVec.m │ │ ├── pcaVisualize.m │ │ ├── pdist2.m │ │ ├── private │ │ │ ├── IDX2order.m │ │ │ ├── binaryTreeTrain1.cpp │ │ │ ├── binaryTreeTrain1.mexa64 │ │ │ ├── binaryTreeTrain1.mexmaci64 │ │ │ ├── binaryTreeTrain1.mexw64 │ │ │ ├── fernsInds1.c │ │ │ ├── fernsInds1.mexa64 │ │ │ ├── fernsInds1.mexmaci64 │ │ │ ├── fernsInds1.mexw64 │ │ │ ├── forestFindThr.cpp │ │ │ ├── forestFindThr.mexa64 │ │ │ ├── forestFindThr.mexmaci64 │ │ │ ├── forestFindThr.mexw64 │ │ │ ├── forestInds.cpp │ │ │ ├── forestInds.mexa64 │ │ │ ├── forestInds.mexmaci64 │ │ │ ├── forestInds.mexw64 │ │ │ ├── meanShift1.c │ │ │ ├── meanShift1.mexa64 │ │ │ ├── meanShift1.mexmaci64 │ │ │ ├── meanShift1.mexw64 │ │ │ └── meanShiftPost.m │ │ ├── rbfComputeBasis.m │ │ ├── rbfComputeFtrs.m │ │ ├── rbfDemo.m │ │ ├── softMin.m │ │ └── visualizeData.m │ │ ├── detector │ │ ├── Contents.m │ │ ├── acfDemoCal.m │ │ ├── acfDemoInria.m │ │ ├── acfDetect.m │ │ ├── acfModify.m │ │ ├── acfReadme.m │ │ ├── acfSweeps.m │ │ ├── acfTest.m │ │ ├── acfTrain.m │ │ ├── bbApply.m │ │ ├── bbGt.m │ │ ├── bbLabeler.m │ │ ├── bbNms.m │ │ ├── models │ │ │ ├── AcfCaltech+Detector.mat │ │ │ ├── AcfCaltech+Log.txt │ │ │ ├── AcfCaltech+Roc.png │ │ │ ├── AcfInriaDetector.mat │ │ │ ├── AcfInriaLog.txt │ │ │ ├── AcfInriaRoc.png │ │ │ ├── LdcfCaltechDetector.mat │ │ │ ├── LdcfCaltechLog.txt │ │ │ ├── LdcfCaltechRoc.png │ │ │ ├── LdcfInriaDetector.mat │ │ │ ├── LdcfInriaLog.txt │ │ │ └── LdcfInriaRoc.png │ │ └── private │ │ │ ├── acfDetect1.cpp │ │ │ ├── acfDetect1.mexa64 │ │ │ ├── acfDetect1.mexmaci64 │ │ │ └── acfDetect1.mexw64 │ │ ├── doc │ │ ├── alpha.png │ │ ├── c++.png │ │ ├── c.png │ │ ├── channels │ │ │ ├── Contents.html │ │ │ ├── chnsCompute.html │ │ │ ├── chnsPyramid.html │ │ │ ├── chnsScaling.html │ │ │ ├── convBox.html │ │ │ ├── convMax.html │ │ │ ├── convTri.html │ │ │ ├── fhog.html │ │ │ ├── gradient2.html │ │ │ ├── gradientHist.html │ │ │ ├── gradientMag.html │ │ │ ├── hog.html │ │ │ ├── hogDraw.html │ │ │ ├── imPad.html │ │ │ ├── imResample.html │ │ │ ├── menu.html │ │ │ └── rgbConvert.html │ │ ├── classify │ │ │ ├── Contents.html │ │ │ ├── adaBoostApply.html │ │ │ ├── adaBoostTrain.html │ │ │ ├── binaryTreeApply.html │ │ │ ├── binaryTreeTrain.html │ │ │ ├── confMatrix.html │ │ │ ├── confMatrixShow.html │ │ │ ├── demoCluster.html │ │ │ ├── demoGenData.html │ │ │ ├── distMatrixShow.html │ │ │ ├── fernsClfApply.html │ │ │ ├── fernsClfTrain.html │ │ │ ├── fernsInds.html │ │ │ ├── fernsRegApply.html │ │ │ ├── fernsRegTrain.html │ │ │ ├── forestApply.html │ │ │ ├── forestTrain.html │ │ │ ├── kmeans2.html │ │ │ ├── meanShift.html │ │ │ ├── meanShiftIm.html │ │ │ ├── meanShiftImExplore.html │ │ │ ├── menu.html │ │ │ ├── pca.html │ │ │ ├── pcaApply.html │ │ │ ├── pcaRandVec.html │ │ │ ├── pcaVisualize.html │ │ │ ├── pdist2.html │ │ │ ├── rbfComputeBasis.html │ │ │ ├── rbfComputeFtrs.html │ │ │ ├── rbfDemo.html │ │ │ ├── softMin.html │ │ │ └── visualizeData.html │ │ ├── demoicon.gif │ │ ├── detector │ │ │ ├── Contents.html │ │ │ ├── acfDemoCal.html │ │ │ ├── acfDemoInria.html │ │ │ ├── acfDetect.html │ │ │ ├── acfModify.html │ │ │ ├── acfReadme.html │ │ │ ├── acfSweeps.html │ │ │ ├── acfTest.html │ │ │ ├── acfTrain.html │ │ │ ├── bbApply.html │ │ │ ├── bbGt.html │ │ │ ├── bbLabeler.html │ │ │ ├── bbNms.html │ │ │ └── menu.html │ │ ├── down.png │ │ ├── favicon.ico │ │ ├── filters │ │ │ ├── Contents.html │ │ │ ├── FbApply2d.html │ │ │ ├── FbCrop.html │ │ │ ├── FbMake.html │ │ │ ├── FbReconstruct2d.html │ │ │ ├── FbVisualize.html │ │ │ ├── filterBinomial1d.html │ │ │ ├── filterDog2d.html │ │ │ ├── filterDoog.html │ │ │ ├── filterGabor1d.html │ │ │ ├── filterGabor2d.html │ │ │ ├── filterGauss.html │ │ │ ├── filterSteerable.html │ │ │ ├── filterVisualize.html │ │ │ ├── medfilt1m.html │ │ │ ├── menu.html │ │ │ └── modefilt1.html │ │ ├── fortran.png │ │ ├── history.txt │ │ ├── hp.png │ │ ├── images │ │ │ ├── Contents.html │ │ │ ├── assignToBins.html │ │ │ ├── clusterMontage.html │ │ │ ├── convnFast.html │ │ │ ├── filmStrip.html │ │ │ ├── gaussSmooth.html │ │ │ ├── histMontage.html │ │ │ ├── histc2.html │ │ │ ├── histcImLoc.html │ │ │ ├── histcImWin.html │ │ │ ├── im.html │ │ │ ├── imMlGauss.html │ │ │ ├── imNormalize.html │ │ │ ├── imRectRot.html │ │ │ ├── imShrink.html │ │ │ ├── imtransform2.html │ │ │ ├── imwrite2.html │ │ │ ├── jitterImage.html │ │ │ ├── localSum.html │ │ │ ├── makeGif.html │ │ │ ├── maskCircle.html │ │ │ ├── maskEllipse.html │ │ │ ├── maskGaussians.html │ │ │ ├── maskSphere.html │ │ │ ├── menu.html │ │ │ ├── montage2.html │ │ │ ├── movieToImages.html │ │ │ ├── nonMaxSupr.html │ │ │ ├── nonMaxSuprList.html │ │ │ ├── nonMaxSuprWin.html │ │ │ ├── normxcorrn.html │ │ │ ├── playMovie.html │ │ │ ├── textureMap.html │ │ │ ├── xcorrn.html │ │ │ └── xeucn.html │ │ ├── index.html │ │ ├── left.png │ │ ├── linux.png │ │ ├── m2html.css │ │ ├── matlab │ │ │ ├── Contents.html │ │ │ ├── arrayCrop.html │ │ │ ├── arrayToDims.html │ │ │ ├── c.html │ │ │ ├── cc.html │ │ │ ├── ccc.html │ │ │ ├── cell2array.html │ │ │ ├── char2img.html │ │ │ ├── checkNumArgs.html │ │ │ ├── dijkstra.html │ │ │ ├── dirSynch.html │ │ │ ├── diskFill.html │ │ │ ├── dispMatrixIm.html │ │ │ ├── fevalArrays.html │ │ │ ├── fevalDistr.html │ │ │ ├── fevalDistrDisk.html │ │ │ ├── fevalImages.html │ │ │ ├── fevalMats.html │ │ │ ├── figureResized.html │ │ │ ├── gauss2ellipse.html │ │ │ ├── getPrmDflt.html │ │ │ ├── imLabel.html │ │ │ ├── ind2sub2.html │ │ │ ├── int2str2.html │ │ │ ├── isfield2.html │ │ │ ├── mat2cell2.html │ │ │ ├── medianw.html │ │ │ ├── menu.html │ │ │ ├── multiDiv.html │ │ │ ├── multiTimes.html │ │ │ ├── normpdf2.html │ │ │ ├── num2strs.html │ │ │ ├── plotEllipse.html │ │ │ ├── plotGaussEllipses.html │ │ │ ├── plotRoc.html │ │ │ ├── randSample.html │ │ │ ├── randint2.html │ │ │ ├── rotationMatrix.html │ │ │ ├── simpleCache.html │ │ │ ├── spBlkDiag.html │ │ │ ├── sub2ind2.html │ │ │ ├── subsToArray.html │ │ │ ├── text2.html │ │ │ ├── ticStatus.html │ │ │ ├── tocStatus.html │ │ │ ├── tpsGetWarp.html │ │ │ ├── tpsInterpolate.html │ │ │ ├── tpsInterpolateIm.html │ │ │ ├── tpsRandom.html │ │ │ ├── txt2img.html │ │ │ └── uniqueColors.html │ │ ├── matlabicon.gif │ │ ├── menu.html │ │ ├── mex.png │ │ ├── new.gif │ │ ├── overview.html │ │ ├── right.png │ │ ├── sgi.png │ │ ├── simulinkicon.gif │ │ ├── solaris.png │ │ ├── up.png │ │ ├── videos │ │ │ ├── Contents.html │ │ │ ├── behaviorAnnotator.html │ │ │ ├── behaviorData.html │ │ │ ├── imagesAlign.html │ │ │ ├── imagesAlignSeq.html │ │ │ ├── kernelTracker.html │ │ │ ├── menu.html │ │ │ ├── opticalFlow.html │ │ │ ├── seqIo.html │ │ │ ├── seqPlayer.html │ │ │ ├── seqReaderPlugin.html │ │ │ └── seqWriterPlugin.html │ │ └── windows.png │ │ ├── external │ │ ├── bsd.txt │ │ ├── deprecated │ │ │ ├── apply_homography.m │ │ │ ├── clfData.mat │ │ │ ├── clfDecTree.m │ │ │ ├── clfDecTreeFwd.m │ │ │ ├── clfDecTreeTrain.m │ │ │ ├── clfEcoc.m │ │ │ ├── clfEcocCode.m │ │ │ ├── clfKnn.m │ │ │ ├── clfKnnDist.m │ │ │ ├── clfKnnFwd.m │ │ │ ├── clfKnnTrain.m │ │ │ ├── clfLda.m │ │ │ ├── clfLdaFwd.m │ │ │ ├── clfLdaTrain.m │ │ │ ├── clfSvm.m │ │ │ ├── demoClassify.m │ │ │ ├── diffTracker.m │ │ │ ├── filter_gauss_1D.m │ │ │ ├── getargs.m │ │ │ ├── imRectLite.m │ │ │ ├── imSubsResize.m │ │ │ ├── imrotate2.m │ │ │ ├── imtranslate.m │ │ │ ├── imwrite2split.m │ │ │ ├── localsum_block.m │ │ │ ├── makemovie.m │ │ │ ├── mode2.m │ │ │ ├── montages2.m │ │ │ ├── nfoldxval.m │ │ │ ├── normxcorrn_fg.m │ │ │ ├── pca_apply.m │ │ │ ├── pca_apply_large.m │ │ │ ├── playmovies.m │ │ │ ├── randperm2.m │ │ │ └── subsampleMatrix.m │ │ ├── history.txt │ │ ├── m2html │ │ │ ├── @template │ │ │ │ ├── char.m │ │ │ │ ├── display.m │ │ │ │ ├── get.m │ │ │ │ ├── parse.m │ │ │ │ ├── private │ │ │ │ │ ├── finish.m │ │ │ │ │ ├── loadtpl.m │ │ │ │ │ └── subst.m │ │ │ │ ├── set.m │ │ │ │ └── template.m │ │ │ ├── Changelog │ │ │ ├── Contents.m │ │ │ ├── GPL │ │ │ ├── INSTALL │ │ │ ├── LICENSE │ │ │ ├── README │ │ │ ├── TODO │ │ │ ├── m2html.m │ │ │ ├── mdot.m │ │ │ ├── mwizard.m │ │ │ ├── mwizard2.m │ │ │ ├── private │ │ │ │ ├── doxyread.m │ │ │ │ ├── doxysearch.m │ │ │ │ ├── doxywrite.m │ │ │ │ ├── m2htmltoolbarimages.mat │ │ │ │ ├── mexexts.m │ │ │ │ ├── mfileparse.m │ │ │ │ ├── openfile.m │ │ │ │ ├── searchindex.m │ │ │ │ ├── splitcode.m │ │ │ │ └── strtok.m │ │ │ └── templates │ │ │ │ ├── blue │ │ │ │ ├── Thumbs.db │ │ │ │ ├── alpha.png │ │ │ │ ├── c++.png │ │ │ │ ├── c.png │ │ │ │ ├── demoicon.gif │ │ │ │ ├── down.png │ │ │ │ ├── doxysearch.php │ │ │ │ ├── fortran.png │ │ │ │ ├── graph.tpl │ │ │ │ ├── hp.png │ │ │ │ ├── left.png │ │ │ │ ├── linux.png │ │ │ │ ├── m2html.css │ │ │ │ ├── master.tpl │ │ │ │ ├── matlabicon.gif │ │ │ │ ├── mdir.tpl │ │ │ │ ├── mex.png │ │ │ │ ├── mfile.tpl │ │ │ │ ├── pcode.png │ │ │ │ ├── right.png │ │ │ │ ├── search.tpl │ │ │ │ ├── sgi.png │ │ │ │ ├── simulinkicon.gif │ │ │ │ ├── solaris.png │ │ │ │ ├── todo.tpl │ │ │ │ ├── up.png │ │ │ │ └── windows.png │ │ │ │ ├── brain │ │ │ │ ├── alpha.png │ │ │ │ ├── brain.png │ │ │ │ ├── c++.png │ │ │ │ ├── c.png │ │ │ │ ├── demoicon.gif │ │ │ │ ├── down.png │ │ │ │ ├── doxysearch.php │ │ │ │ ├── fortran.png │ │ │ │ ├── graph.tpl │ │ │ │ ├── hp.png │ │ │ │ ├── index.html │ │ │ │ ├── left.png │ │ │ │ ├── linux.png │ │ │ │ ├── m2html.css │ │ │ │ ├── master.tpl │ │ │ │ ├── matlabicon.gif │ │ │ │ ├── mdir.tpl │ │ │ │ ├── menu.css │ │ │ │ ├── mex.png │ │ │ │ ├── mfile.tpl │ │ │ │ ├── pcode.png │ │ │ │ ├── right.png │ │ │ │ ├── search.tpl │ │ │ │ ├── sgi.png │ │ │ │ ├── simulinkicon.gif │ │ │ │ ├── solaris.png │ │ │ │ ├── todo.tpl │ │ │ │ ├── up.png │ │ │ │ └── windows.png │ │ │ │ ├── frame-piotr │ │ │ │ ├── alpha.png │ │ │ │ ├── c++.png │ │ │ │ ├── c.png │ │ │ │ ├── demoicon.gif │ │ │ │ ├── down.png │ │ │ │ ├── favicon.ico │ │ │ │ ├── fortran.png │ │ │ │ ├── graph.tpl │ │ │ │ ├── hp.png │ │ │ │ ├── index.html │ │ │ │ ├── left.png │ │ │ │ ├── linux.png │ │ │ │ ├── m2html.css │ │ │ │ ├── master.tpl │ │ │ │ ├── matlabicon.gif │ │ │ │ ├── mdir.tpl │ │ │ │ ├── mex.png │ │ │ │ ├── mfile.tpl │ │ │ │ ├── new.gif │ │ │ │ ├── overview.html │ │ │ │ ├── right.png │ │ │ │ ├── sgi.png │ │ │ │ ├── simulinkicon.gif │ │ │ │ ├── solaris.png │ │ │ │ ├── todo.tpl │ │ │ │ ├── up.png │ │ │ │ └── windows.png │ │ │ │ ├── frame │ │ │ │ ├── alpha.png │ │ │ │ ├── c++.png │ │ │ │ ├── c.png │ │ │ │ ├── demoicon.gif │ │ │ │ ├── down.png │ │ │ │ ├── doxysearch.php │ │ │ │ ├── fortran.png │ │ │ │ ├── graph.tpl │ │ │ │ ├── hp.png │ │ │ │ ├── index.html │ │ │ │ ├── left.png │ │ │ │ ├── linux.png │ │ │ │ ├── m2html.css │ │ │ │ ├── master.tpl │ │ │ │ ├── matlabicon.gif │ │ │ │ ├── mdir.tpl │ │ │ │ ├── mex.png │ │ │ │ ├── mfile.tpl │ │ │ │ ├── pcode.png │ │ │ │ ├── right.png │ │ │ │ ├── search.tpl │ │ │ │ ├── sgi.png │ │ │ │ ├── simulinkicon.gif │ │ │ │ ├── solaris.png │ │ │ │ ├── todo.tpl │ │ │ │ ├── up.png │ │ │ │ └── windows.png │ │ │ │ └── menu-for-frame-piotr.html │ │ ├── other │ │ │ ├── compareStructs.m │ │ │ ├── matrix2latex.m │ │ │ ├── multiprod.m │ │ │ └── savefig.m │ │ ├── toolboxCompile.m │ │ ├── toolboxGenDoc.m │ │ ├── toolboxHeader.m │ │ └── toolboxUpdateHeader.m │ │ ├── filters │ │ ├── Contents.m │ │ ├── FbApply2d.m │ │ ├── FbCrop.m │ │ ├── FbDoG.mat │ │ ├── FbGabor.mat │ │ ├── FbMake.m │ │ ├── FbReconstruct2d.m │ │ ├── FbVisualize.m │ │ ├── filterBinomial1d.m │ │ ├── filterDog2d.m │ │ ├── filterDoog.m │ │ ├── filterGabor1d.m │ │ ├── filterGabor2d.m │ │ ├── filterGauss.m │ │ ├── filterSteerable.m │ │ ├── filterVisualize.m │ │ ├── medfilt1m.m │ │ └── modefilt1.m │ │ ├── images │ │ ├── Contents.m │ │ ├── assignToBins.m │ │ ├── clusterMontage.m │ │ ├── convnFast.m │ │ ├── filmStrip.m │ │ ├── gaussSmooth.m │ │ ├── histMontage.m │ │ ├── histc2.m │ │ ├── histcImLoc.m │ │ ├── histcImWin.m │ │ ├── im.m │ │ ├── imMlGauss.m │ │ ├── imNormalize.m │ │ ├── imRectRot.m │ │ ├── imShrink.m │ │ ├── images.mat │ │ ├── imtransform2.m │ │ ├── imwrite2.m │ │ ├── jitterImage.m │ │ ├── localSum.m │ │ ├── makeGif.m │ │ ├── maskCircle.m │ │ ├── maskEllipse.m │ │ ├── maskGaussians.m │ │ ├── maskSphere.m │ │ ├── montage2.m │ │ ├── movieToImages.m │ │ ├── nonMaxSupr.m │ │ ├── nonMaxSuprList.m │ │ ├── nonMaxSuprWin.m │ │ ├── normxcorrn.m │ │ ├── playMovie.m │ │ ├── private │ │ │ ├── assignToBins1.c │ │ │ ├── assignToBins1.mexa64 │ │ │ ├── assignToBins1.mexmaci64 │ │ │ ├── assignToBins1.mexw64 │ │ │ ├── histc2c.c │ │ │ ├── histc2c.mexa64 │ │ │ ├── histc2c.mexmaci64 │ │ │ ├── histc2c.mexw64 │ │ │ ├── imtransform2_c.c │ │ │ ├── imtransform2_c.mexa64 │ │ │ ├── imtransform2_c.mexmaci64 │ │ │ ├── imtransform2_c.mexw64 │ │ │ ├── nlfiltersep.m │ │ │ ├── nlfiltersep_max.c │ │ │ ├── nlfiltersep_max.mexa64 │ │ │ ├── nlfiltersep_max.mexmaci64 │ │ │ ├── nlfiltersep_max.mexw64 │ │ │ ├── nlfiltersep_sum.c │ │ │ ├── nlfiltersep_sum.mexa64 │ │ │ ├── nlfiltersep_sum.mexmaci64 │ │ │ └── nlfiltersep_sum.mexw64 │ │ ├── textureMap.m │ │ ├── xcorrn.m │ │ └── xeucn.m │ │ ├── matlab │ │ ├── Contents.m │ │ ├── arrayCrop.m │ │ ├── arrayToDims.m │ │ ├── c.m │ │ ├── cc.m │ │ ├── ccc.m │ │ ├── cell2array.m │ │ ├── char2img.m │ │ ├── checkNumArgs.m │ │ ├── dijkstra.m │ │ ├── dirSynch.m │ │ ├── diskFill.m │ │ ├── dispMatrixIm.m │ │ ├── fevalArrays.m │ │ ├── fevalDistr.m │ │ ├── fevalDistrDisk.m │ │ ├── fevalImages.m │ │ ├── fevalMats.m │ │ ├── figureResized.m │ │ ├── gauss2ellipse.m │ │ ├── getPrmDflt.m │ │ ├── imLabel.m │ │ ├── ind2sub2.m │ │ ├── int2str2.m │ │ ├── isfield2.m │ │ ├── mat2cell2.m │ │ ├── medianw.m │ │ ├── multiDiv.m │ │ ├── multiTimes.m │ │ ├── normpdf2.m │ │ ├── num2strs.m │ │ ├── plotEllipse.m │ │ ├── plotGaussEllipses.m │ │ ├── plotRoc.m │ │ ├── private │ │ │ ├── char2img_h015.mat │ │ │ ├── char2img_h020.mat │ │ │ ├── char2img_h025.mat │ │ │ ├── char2img_h030.mat │ │ │ ├── char2img_h035.mat │ │ │ ├── char2img_h050.mat │ │ │ ├── dijkstra1.cpp │ │ │ ├── dijkstra1.mexa64 │ │ │ ├── dijkstra1.mexmaci64 │ │ │ ├── dijkstra1.mexw64 │ │ │ ├── fibheap.cpp │ │ │ └── fibheap.h │ │ ├── randSample.m │ │ ├── randint2.m │ │ ├── rotationMatrix.m │ │ ├── simpleCache.m │ │ ├── spBlkDiag.m │ │ ├── sub2ind2.m │ │ ├── subsToArray.m │ │ ├── text2.m │ │ ├── ticStatus.m │ │ ├── tocStatus.m │ │ ├── tpsGetWarp.m │ │ ├── tpsInterpolate.m │ │ ├── tpsInterpolateIm.m │ │ ├── tpsRandom.m │ │ ├── txt2img.m │ │ └── uniqueColors.m │ │ └── videos │ │ ├── Contents.m │ │ ├── behaviorAnnotator.m │ │ ├── behaviorData.m │ │ ├── imagesAlign.m │ │ ├── imagesAlignSeq.m │ │ ├── kernelTracker.m │ │ ├── opticalFlow.m │ │ ├── opticalFlowTest.mat │ │ ├── peds30-seek.mat │ │ ├── peds30.seq │ │ ├── private │ │ ├── ktComputeW_c.c │ │ ├── ktComputeW_c.mexa64 │ │ ├── ktComputeW_c.mexmaci64 │ │ ├── ktComputeW_c.mexw64 │ │ ├── ktHistcRgb_c.c │ │ ├── ktHistcRgb_c.mexa64 │ │ ├── ktHistcRgb_c.mexmaci64 │ │ ├── ktHistcRgb_c.mexw64 │ │ ├── opticalFlowHsMex.cpp │ │ ├── opticalFlowHsMex.mexa64 │ │ ├── opticalFlowHsMex.mexmaci64 │ │ └── opticalFlowHsMex.mexw64 │ │ ├── seqIo.m │ │ ├── seqPlayer.m │ │ ├── seqReaderPlugin.m │ │ └── seqWriterPlugin.m ├── feature_extraction │ ├── extract_features.m │ ├── get_cnn_layers.m │ ├── get_colorspace.m │ ├── get_feature_extract_info.m │ ├── get_fhog.m │ ├── get_table_feature.m │ ├── init_features.m │ ├── load_cnn.m │ ├── load_cnn_model.m │ ├── load_cnn_model2.m │ ├── lookup_tables │ │ ├── CNnorm.mat │ │ ├── intensityChannelNorm11.mat │ │ ├── intensityChannelNorm16.mat │ │ └── intensityChannelNorm6.mat │ ├── networks │ │ └── README.md │ ├── sample_patch.m │ ├── set_cnn_input_size.m │ └── table_lookup.m ├── implementation │ ├── dim_reduction │ │ ├── init_projection_matrix.m │ │ └── project_sample.m │ ├── fourier_tools │ │ ├── cfft2.m │ │ ├── cifft2.m │ │ ├── compact_fourier_coeff.m │ │ ├── cubic_spline_fourier.m │ │ ├── full_fourier_coeff.m │ │ ├── interpolate_dft.m │ │ ├── resizeDFT.m │ │ ├── sample_fs.m │ │ ├── shift_sample.m │ │ └── symmetrize_filter.m │ ├── initialization │ │ ├── get_interp_fourier.m │ │ ├── get_reg_filter.m │ │ ├── init_default_params.m │ │ └── init_feature_params.m │ ├── localization │ │ └── optimize_scores.m │ ├── sample_space_model │ │ ├── find_gram_vector.m │ │ ├── find_gram_vector_gpu.m │ │ ├── merge_samples.m │ │ ├── update_distance_matrix.m │ │ ├── update_prior_weights.m │ │ ├── update_sample_space_model.m │ │ └── update_sample_space_model_gpu.m │ ├── scale_filter │ │ ├── extract_scale_sample.m │ │ ├── feature_projection_scale.m │ │ ├── init_scale_filter.m │ │ ├── scale_filter_track.m │ │ └── scale_filter_update.m │ ├── tracker.m │ ├── tracker_vot.m │ └── training │ │ ├── diag_precond.m │ │ ├── inner_product_filter.m │ │ ├── inner_product_joint.m │ │ ├── lhs_operation.m │ │ ├── lhs_operation_gpu.m │ │ ├── lhs_operation_joint.m │ │ ├── lhs_operation_joint_gpu.m │ │ ├── pcg_ccot.m │ │ ├── train_filter.m │ │ ├── train_filter_gpu.m │ │ ├── train_joint.m │ │ └── train_joint_gpu.m ├── install.m ├── runfiles │ └── OTB_DEEP_settings_tircnn.m ├── setup_paths.m └── utils │ ├── get_sequence_frame.m │ ├── get_sequence_info.m │ ├── get_sequence_info.m~ │ ├── get_sequence_results.m │ ├── load_video_info.m │ ├── load_video_info_vot.m │ ├── load_video_info_vot1.m │ ├── read_vot_regions0.m │ ├── report_tracking_result.m │ └── report_tracking_result_local.m ├── GAN_code ├── LICENSE ├── README.md ├── README.md~ ├── checkpoints │ └── thermal_pix2pix_1ch_all_batch4 │ │ ├── loss_log.txt │ │ ├── opt.txt │ │ └── web │ │ ├── images │ │ ├── epoch001_fake_B.png │ │ ├── epoch001_real_A.png │ │ └── epoch001_real_B.png │ │ └── index.html ├── data │ ├── __init__.py │ ├── __init__.pyc │ ├── __pycache__ │ │ ├── __init__.cpython-36.pyc │ │ ├── base_data_loader.cpython-36.pyc │ │ ├── base_dataset.cpython-36.pyc │ │ ├── custom_dataset_data_loader.cpython-36.pyc │ │ ├── data_loader.cpython-36.pyc │ │ ├── image_folder.cpython-36.pyc │ │ └── single_dataset.cpython-36.pyc │ ├── aligned_dataset.py │ ├── aligned_dataset.pyc │ ├── base_data_loader.py │ ├── base_data_loader.pyc │ ├── base_dataset (copy).py │ ├── base_dataset.py │ ├── base_dataset.pyc │ ├── custom_dataset_data_loader.py │ ├── custom_dataset_data_loader.pyc │ ├── data_loader.py │ ├── data_loader.pyc │ ├── image_folder.py │ ├── image_folder.pyc │ ├── single_dataset.py │ ├── single_dataset.pyc │ ├── single_dataset_ori.py │ ├── unaligned_dataset.py │ └── unaligned_dataset.pyc ├── datasets │ ├── bibtex │ │ ├── cityscapes.tex │ │ ├── facades.tex │ │ ├── handbags.tex │ │ └── shoes.tex │ ├── combine_A_and_B.py │ ├── download_cyclegan_dataset.sh │ └── download_pix2pix_dataset.sh ├── imgs │ ├── edges2cats.jpg │ └── horse2zebra.gif ├── main ├── models │ ├── __init__.py │ ├── __init__.pyc │ ├── __pycache__ │ │ ├── __init__.cpython-36.pyc │ │ ├── base_model.cpython-36.pyc │ │ ├── models.cpython-36.pyc │ │ ├── networks.cpython-36.pyc │ │ └── test_model.cpython-36.pyc │ ├── base_model.py │ ├── base_model.pyc │ ├── cycle_gan_model.py │ ├── models.py │ ├── models.pyc │ ├── networks.py │ ├── networks.pyc │ ├── pix2pix_model.py │ ├── pix2pix_model.pyc │ ├── test_model.py │ └── test_model.pyc ├── options │ ├── __init__.py │ ├── __init__.pyc │ ├── __pycache__ │ │ ├── __init__.cpython-36.pyc │ │ ├── __init__.cpython-37.pyc │ │ ├── base_options.cpython-36.pyc │ │ ├── base_options.cpython-37.pyc │ │ ├── test_options.cpython-36.pyc │ │ └── test_options.cpython-37.pyc │ ├── base_options.py │ ├── base_options.pyc │ ├── test_options.py │ ├── test_options.pyc │ ├── train_options.py │ └── train_options.pyc ├── pretrained_models │ ├── download_cyclegan_model.sh │ └── download_pix2pix_model.sh ├── scripts │ ├── test_cyclegan.sh │ ├── test_pix2pix.sh │ ├── test_single.sh │ ├── train_cyclegan.sh │ └── train_pix2pix.sh ├── test.py ├── test_pix2pix_got.py ├── test_pix2pix_got2.py ├── test_pix2pix_imagenetvid.py ├── train.py └── util │ ├── __init__.py │ ├── __init__.pyc │ ├── __pycache__ │ ├── __init__.cpython-36.pyc │ ├── __init__.cpython-37.pyc │ ├── html.cpython-36.pyc │ ├── util.cpython-36.pyc │ ├── util.cpython-37.pyc │ └── visualizer.cpython-36.pyc │ ├── get_data.py │ ├── html.py │ ├── html.pyc │ ├── image_pool.py │ ├── image_pool.pyc │ ├── util.py │ ├── util.pyc │ ├── visualizer.py │ ├── visualizer.pyc │ └── visualizer_ori.py ├── Ganres.png ├── README.md ├── aar.png └── histgrad.png /ECO_tir/VOT_integration/benchmark_wrapper/setup_tracker_paths.m: -------------------------------------------------------------------------------- 1 | function setup_tracker_paths(tracker_name) 2 | 3 | [wrapper_path, name, ext] = fileparts(mfilename('fullpath')); 4 | 5 | addpath(wrapper_path) 6 | 7 | cd_ind = strfind(wrapper_path, filesep()); 8 | repo_path = wrapper_path(1:cd_ind(end-1)-1); 9 | 10 | addpath(repo_path); 11 | setup_paths(); -------------------------------------------------------------------------------- /ECO_tir/VOT_integration/configuration_file/tracker_ECO.m: -------------------------------------------------------------------------------- 1 | % Copy this template configuration file to your VOT workspace. 2 | % Enter the full path to the ECO repository root folder. 3 | 4 | ECO_repo_path = ######## 5 | 6 | tracker_label = 'ECO'; 7 | tracker_command = generate_matlab_command('benchmark_tracker_wrapper(''ECO'', ''VOT2016_DEEP_settings'', true)', {[ECO_repo_path '/VOT_integration/benchmark_wrapper']}); 8 | tracker_interpreter = 'matlab'; -------------------------------------------------------------------------------- /ECO_tir/VOT_integration/configuration_file/tracker_ECO_tir.m: -------------------------------------------------------------------------------- 1 | 2 | % Copy this template configuration file to your VOT workspace. 3 | % Enter the full path to the ECO repository root folder. 4 | 5 | ECO_repo_path = '/home/lichao/papers/ECO/ECO'; 6 | 7 | tracker_label = 'OTB_DEEP_settings_tircnn'; 8 | tracker_command = generate_matlab_command('benchmark_tracker_wrapper(''ECO'', ''OTB_DEEP_settings_tircnn'', true)', {[ECO_repo_path '/VOT_integration/benchmark_wrapper']}); 9 | tracker_interpreter = 'matlab'; 10 | -------------------------------------------------------------------------------- /ECO_tir/external_libs/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 -------------------------------------------------------------------------------- /ECO_tir/external_libs/mexResize/libopencv_core.a: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhanglichao/generatedTIR_tracking/a92c477284d2920a1cb59812a58eb5826f962eea/ECO_tir/external_libs/mexResize/libopencv_core.a -------------------------------------------------------------------------------- /ECO_tir/external_libs/mexResize/libopencv_core.so: -------------------------------------------------------------------------------- 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(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 | -------------------------------------------------------------------------------- /ECO_tir/external_libs/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 | 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[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 | -------------------------------------------------------------------------------- /ECO_tir/external_libs/pdollar_toolbox/classify/binaryTreeTrain.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhanglichao/generatedTIR_tracking/a92c477284d2920a1cb59812a58eb5826f962eea/ECO_tir/external_libs/pdollar_toolbox/classify/binaryTreeTrain.m -------------------------------------------------------------------------------- /ECO_tir/external_libs/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 | -------------------------------------------------------------------------------- /ECO_tir/external_libs/pdollar_toolbox/classify/fernsInds.m: -------------------------------------------------------------------------------- 1 | function inds = fernsInds( data, fids, thrs ) 2 | % Compute indices for each input by each fern. 3 | % 4 | % USAGE 5 | % inds = fernsInds( data, fids, thrs ) 6 | % 7 | % INPUTS 8 | % data - [NxF] N length F binary feature vectors 9 | % fids - [MxS] feature ids for each fern for each depth 10 | % thrs - [MxS] threshold corresponding to each fid 11 | % 12 | % OUTPUTS 13 | % inds - [NxM] computed indices for each input by each fern 14 | % 15 | % EXAMPLE 16 | % 17 | % See also fernsClfTrain, fernsClfApply 18 | % 19 | % Piotr's Computer Vision Matlab Toolbox Version 2.50 20 | % Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com] 21 | % Licensed under the Simplified BSD License [see external/bsd.txt] 22 | 23 | inds = fernsInds1( data, fids, thrs ); 24 | 25 | %%% OLD MATLAB CODE -- NOW IN MEX 26 | % [M,S]=size(fids); N=size(data,1); 27 | % inds = zeros(N,M,'uint32'); 28 | % for n=1:N 29 | % for m=1:M 30 | % for s=1:S 31 | % inds(n,m)=inds(n,m)*2; 32 | % if( data(n,fids(m,s)) 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 | -------------------------------------------------------------------------------- /ECO_tir/external_libs/pdollar_toolbox/doc/left.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhanglichao/generatedTIR_tracking/a92c477284d2920a1cb59812a58eb5826f962eea/ECO_tir/external_libs/pdollar_toolbox/doc/left.png 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-------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /ECO_tir/external_libs/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 | -------------------------------------------------------------------------------- /ECO_tir/external_libs/pdollar_toolbox/external/deprecated/clfDecTreeTrain.m: -------------------------------------------------------------------------------- 1 | function clf = clfDecTreeTrain( clf, X, Y ) 2 | % Train a decision tree classifier. 3 | % 4 | % USAGE 5 | % clf = clfDecTreeTrain( 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 binary decision tree clf 14 | % 15 | % EXAMPLE 16 | % 17 | % See also CLFDECTREE 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,'dectree')); error( ['incor. type: ' clf.type] ); end 25 | if( size(X,2)~= clf.p ); error( 'Incorrect data dimension' ); end 26 | 27 | % apply treefit 28 | Y = int2str2( Y ); % convert Y to string format for treefit. 29 | params = clf.params; 30 | T = treefit(X,Y,'method','classification',params{:}); 31 | 32 | % apply cross validation (on training data), and prune 33 | [c,s,n,best] = treetest(T,'cross',X,Y); 34 | T = treeprune(T,'level',best); 35 | 36 | clf.T = T; 37 | -------------------------------------------------------------------------------- /ECO_tir/external_libs/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 | -------------------------------------------------------------------------------- /ECO_tir/external_libs/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 | -------------------------------------------------------------------------------- /ECO_tir/external_libs/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 | -------------------------------------------------------------------------------- /ECO_tir/external_libs/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 | -------------------------------------------------------------------------------- /ECO_tir/external_libs/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 | -------------------------------------------------------------------------------- /ECO_tir/external_libs/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(f 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 | -------------------------------------------------------------------------------- /ECO_tir/external_libs/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 | -------------------------------------------------------------------------------- /ECO_tir/external_libs/pdollar_toolbox/external/m2html/@template/parse.m: -------------------------------------------------------------------------------- 1 | function [tpl, str] = parse(tpl,target,handle,append) 2 | %TEMPLATE/PARSE Fill in replacement fields with the class properties 3 | % [TPL, STR] = PARSE(TPL,TARGET,HANDLE) fills in the replacement field 4 | % HANDLE using previously defined variables of template TPL and store 5 | % it in field TARGET. HANDLE can also be a cell array of field names. 6 | % Output is also provided in output STR (content of TARGET). 7 | % [TPL, STR] = PARSE(TPL,TARGET,HANDLE,APPEND) allows to specify if 8 | % TARGET field is reseted before being filled or if new content is 9 | % appended to the previous one. 10 | 11 | % Copyright (C) 2003 Guillaume Flandin 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 | -------------------------------------------------------------------------------- /ECO_tir/external_libs/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 | -------------------------------------------------------------------------------- /ECO_tir/external_libs/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 | -------------------------------------------------------------------------------- /ECO_tir/external_libs/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 | -------------------------------------------------------------------------------- /ECO_tir/external_libs/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 | -------------------------------------------------------------------------------- /ECO_tir/external_libs/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 | -------------------------------------------------------------------------------- /ECO_tir/external_libs/pdollar_toolbox/external/m2html/mwizard.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhanglichao/generatedTIR_tracking/a92c477284d2920a1cb59812a58eb5826f962eea/ECO_tir/external_libs/pdollar_toolbox/external/m2html/mwizard.m -------------------------------------------------------------------------------- /ECO_tir/external_libs/pdollar_toolbox/external/m2html/mwizard2.m: -------------------------------------------------------------------------------- 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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 | -------------------------------------------------------------------------------- /ECO_tir/external_libs/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 | -------------------------------------------------------------------------------- /ECO_tir/external_libs/pdollar_toolbox/external/m2html/templates/blue/Thumbs.db: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhanglichao/generatedTIR_tracking/a92c477284d2920a1cb59812a58eb5826f962eea/ECO_tir/external_libs/pdollar_toolbox/external/m2html/templates/blue/Thumbs.db -------------------------------------------------------------------------------- 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^ Master index ^
16 |

TODO list for {MDIR}

17 | 18 |

{MFILE}:

19 | 24 | 25 |
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Dependency Graph for {MDIR}

16 | 17 |
18 | Dependency Graph for {MDIR} 19 | 20 | {GRAPH_MAP} 21 | 22 |
23 | 24 |
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^ Master index ^
16 |

TODO list for {MDIR}

17 | 18 |

{MFILE}:

19 | 24 | 25 |
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Dependency Graph for {MDIR}

16 | 17 |
18 | Dependency Graph for {MDIR} 19 | 20 | {GRAPH_MAP} 21 | 22 |
23 | 24 |
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19 | Go to menu.html for the documentation of all 20 | the Matlab functions. 21 | 22 | 23 | -------------------------------------------------------------------------------- /ECO_tir/external_libs/pdollar_toolbox/external/m2html/templates/frame/left.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhanglichao/generatedTIR_tracking/a92c477284d2920a1cb59812a58eb5826f962eea/ECO_tir/external_libs/pdollar_toolbox/external/m2html/templates/frame/left.png -------------------------------------------------------------------------------- /ECO_tir/external_libs/pdollar_toolbox/external/m2html/templates/frame/linux.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhanglichao/generatedTIR_tracking/a92c477284d2920a1cb59812a58eb5826f962eea/ECO_tir/external_libs/pdollar_toolbox/external/m2html/templates/frame/linux.png -------------------------------------------------------------------------------- /ECO_tir/external_libs/pdollar_toolbox/external/m2html/templates/frame/matlabicon.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhanglichao/generatedTIR_tracking/a92c477284d2920a1cb59812a58eb5826f962eea/ECO_tir/external_libs/pdollar_toolbox/external/m2html/templates/frame/matlabicon.gif -------------------------------------------------------------------------------- /ECO_tir/external_libs/pdollar_toolbox/external/m2html/templates/frame/mex.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhanglichao/generatedTIR_tracking/a92c477284d2920a1cb59812a58eb5826f962eea/ECO_tir/external_libs/pdollar_toolbox/external/m2html/templates/frame/mex.png -------------------------------------------------------------------------------- /ECO_tir/external_libs/pdollar_toolbox/external/m2html/templates/frame/pcode.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhanglichao/generatedTIR_tracking/a92c477284d2920a1cb59812a58eb5826f962eea/ECO_tir/external_libs/pdollar_toolbox/external/m2html/templates/frame/pcode.png -------------------------------------------------------------------------------- /ECO_tir/external_libs/pdollar_toolbox/external/m2html/templates/frame/right.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhanglichao/generatedTIR_tracking/a92c477284d2920a1cb59812a58eb5826f962eea/ECO_tir/external_libs/pdollar_toolbox/external/m2html/templates/frame/right.png -------------------------------------------------------------------------------- /ECO_tir/external_libs/pdollar_toolbox/external/m2html/templates/frame/search.tpl: -------------------------------------------------------------------------------- 1 | 3 | 4 | 5 | Matlab Search Engine 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 |

Search Engine

16 | 17 |
18 | Search for 19 | 20 |
21 | 22 | 26 | 27 |
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^ Master index ^
16 |

TODO list for {MDIR}

17 | 18 |

{MFILE}:

19 | 24 | 25 |
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26 | 27 | 28 | -------------------------------------------------------------------------------- /ECO_tir/external_libs/pdollar_toolbox/external/m2html/templates/frame/up.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhanglichao/generatedTIR_tracking/a92c477284d2920a1cb59812a58eb5826f962eea/ECO_tir/external_libs/pdollar_toolbox/external/m2html/templates/frame/up.png -------------------------------------------------------------------------------- /ECO_tir/external_libs/pdollar_toolbox/external/m2html/templates/frame/windows.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhanglichao/generatedTIR_tracking/a92c477284d2920a1cb59812a58eb5826f962eea/ECO_tir/external_libs/pdollar_toolbox/external/m2html/templates/frame/windows.png -------------------------------------------------------------------------------- /ECO_tir/external_libs/pdollar_toolbox/external/other/multiprod.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhanglichao/generatedTIR_tracking/a92c477284d2920a1cb59812a58eb5826f962eea/ECO_tir/external_libs/pdollar_toolbox/external/other/multiprod.m -------------------------------------------------------------------------------- /ECO_tir/external_libs/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 | -------------------------------------------------------------------------------- /ECO_tir/external_libs/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 | -------------------------------------------------------------------------------- /ECO_tir/external_libs/pdollar_toolbox/filters/FbDoG.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhanglichao/generatedTIR_tracking/a92c477284d2920a1cb59812a58eb5826f962eea/ECO_tir/external_libs/pdollar_toolbox/filters/FbDoG.mat -------------------------------------------------------------------------------- /ECO_tir/external_libs/pdollar_toolbox/filters/FbGabor.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhanglichao/generatedTIR_tracking/a92c477284d2920a1cb59812a58eb5826f962eea/ECO_tir/external_libs/pdollar_toolbox/filters/FbGabor.mat -------------------------------------------------------------------------------- /ECO_tir/external_libs/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 | -------------------------------------------------------------------------------- /ECO_tir/external_libs/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 | -------------------------------------------------------------------------------- /ECO_tir/external_libs/pdollar_toolbox/images/images.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhanglichao/generatedTIR_tracking/a92c477284d2920a1cb59812a58eb5826f962eea/ECO_tir/external_libs/pdollar_toolbox/images/images.mat -------------------------------------------------------------------------------- /ECO_tir/external_libs/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 | -------------------------------------------------------------------------------- /ECO_tir/external_libs/pdollar_toolbox/images/private/assignToBins1.mexa64: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhanglichao/generatedTIR_tracking/a92c477284d2920a1cb59812a58eb5826f962eea/ECO_tir/external_libs/pdollar_toolbox/images/private/assignToBins1.mexa64 -------------------------------------------------------------------------------- /ECO_tir/external_libs/pdollar_toolbox/images/private/assignToBins1.mexmaci64: 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https://raw.githubusercontent.com/zhanglichao/generatedTIR_tracking/a92c477284d2920a1cb59812a58eb5826f962eea/ECO_tir/external_libs/pdollar_toolbox/images/private/nlfiltersep_sum.mexw64 -------------------------------------------------------------------------------- /ECO_tir/external_libs/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 | -------------------------------------------------------------------------------- /ECO_tir/external_libs/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 | -------------------------------------------------------------------------------- /ECO_tir/external_libs/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 | -------------------------------------------------------------------------------- /ECO_tir/external_libs/pdollar_toolbox/matlab/dijkstra.m: -------------------------------------------------------------------------------- 1 | function [D,P] = dijkstra( G, varargin ) 2 | % Runs Dijkstra's shortest path algorithm on a distance matrix. 3 | % 4 | % Runs Dijkstra's on the given SPARSE nxn distance matrix G, where missing 5 | % values mean no edge (infinite distance). Uses a Finonacci heap resulting 6 | % in fast computation. Finds the shortest path distance from every point 7 | % S(i) in the 1xp source vector S to every other point j, resulting in a 8 | % pxn distance matrix D. P(i,j) contains the second to last node on the 9 | % path from S(i) to j. If point j is not reachable from point S(i) then 10 | % D(i,j)=inf and P(i,j)=-1. 11 | % 12 | % USAGE 13 | % [D P] = dijkstra( G, [S] ) 14 | % 15 | % INPUT 16 | % G - sparse nxn distance matrix 17 | % S - 1xp array of source indices i 18 | % 19 | % OUPUT 20 | % D - pxn - shortest path lengths from S(i) to j 21 | % P - pxn - indicies of second to last node on path from S(i) to j 22 | % 23 | % EXAMPLE 24 | % n=11; G=sparse(n,n); for i=1:n-1, G(i,i+1)=1; end; G=G+G'; 25 | % [D,P] = dijkstra(G,5), % D=[4:-1:0 1:6]; P=[2:5 -1 5:10]; 26 | % 27 | % See also 28 | % 29 | % Piotr's Computer Vision Matlab Toolbox Version 3.20 30 | % Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com] 31 | % Licensed under the Simplified BSD License [see external/bsd.txt] 32 | 33 | [D,P] = dijkstra1( G, varargin{:} ); 34 | -------------------------------------------------------------------------------- /ECO_tir/external_libs/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 22 | f=fopen([jobId '-done'],'w'); fclose(f); 23 | end 24 | -------------------------------------------------------------------------------- /ECO_tir/external_libs/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 | -------------------------------------------------------------------------------- /ECO_tir/external_libs/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 | -------------------------------------------------------------------------------- /ECO_tir/external_libs/pdollar_toolbox/matlab/plotGaussEllipses.m: -------------------------------------------------------------------------------- 1 | function hs = plotGaussEllipses( mus, Cs, rad ) 2 | % Plots 2D ellipses derived from 2D Gaussians specified by mus & Cs. 3 | % 4 | % USAGE 5 | % hs = plotGaussEllipses( mus, Cs, [rad] ) 6 | % 7 | % INPUTS 8 | % mus - k x 2 matrix of means 9 | % Cs - 2 x 2 x k covariance matricies 10 | % rad - [2] Number of std to create the ellipse to 11 | % 12 | % OUTPUTS 13 | % hs - handles to ellipses 14 | % 15 | % EXAMPLE 16 | % plotGaussEllipses( [ 10 10; 10 10 ], cat(3,eye(2),eye(2)*2) ); 17 | % 18 | % See also PLOTELLIPSE, GAUSS2ELLIPSE 19 | % 20 | % Piotr's Computer Vision Matlab Toolbox Version 2.0 21 | % Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com] 22 | % Licensed under the Simplified BSD License [see external/bsd.txt] 23 | 24 | if (nargin<3 || isempty(rad) ); rad=2; end 25 | colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']; nc = length(colors); 26 | 27 | washeld = ishold; if (~washeld); hold('on'); end 28 | hs = zeros( size(mus,1),1 ); 29 | for i=1:size( mus,1) 30 | [ cRow, ccol, ra, rb, phi ] = gauss2ellipse( mus(i,:), Cs(:,:,i), rad ); 31 | hs(i)=plotEllipse( cRow, ccol, ra, rb, phi, colors( mod(i-1,nc)+1) ); 32 | end 33 | if (~washeld); hold('off'); end 34 | -------------------------------------------------------------------------------- /ECO_tir/external_libs/pdollar_toolbox/matlab/private/char2img_h015.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhanglichao/generatedTIR_tracking/a92c477284d2920a1cb59812a58eb5826f962eea/ECO_tir/external_libs/pdollar_toolbox/matlab/private/char2img_h015.mat -------------------------------------------------------------------------------- /ECO_tir/external_libs/pdollar_toolbox/matlab/private/char2img_h020.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhanglichao/generatedTIR_tracking/a92c477284d2920a1cb59812a58eb5826f962eea/ECO_tir/external_libs/pdollar_toolbox/matlab/private/char2img_h020.mat -------------------------------------------------------------------------------- /ECO_tir/external_libs/pdollar_toolbox/matlab/private/char2img_h025.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhanglichao/generatedTIR_tracking/a92c477284d2920a1cb59812a58eb5826f962eea/ECO_tir/external_libs/pdollar_toolbox/matlab/private/char2img_h025.mat -------------------------------------------------------------------------------- /ECO_tir/external_libs/pdollar_toolbox/matlab/private/char2img_h030.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhanglichao/generatedTIR_tracking/a92c477284d2920a1cb59812a58eb5826f962eea/ECO_tir/external_libs/pdollar_toolbox/matlab/private/char2img_h030.mat -------------------------------------------------------------------------------- /ECO_tir/external_libs/pdollar_toolbox/matlab/private/char2img_h035.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhanglichao/generatedTIR_tracking/a92c477284d2920a1cb59812a58eb5826f962eea/ECO_tir/external_libs/pdollar_toolbox/matlab/private/char2img_h035.mat -------------------------------------------------------------------------------- /ECO_tir/external_libs/pdollar_toolbox/matlab/private/char2img_h050.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhanglichao/generatedTIR_tracking/a92c477284d2920a1cb59812a58eb5826f962eea/ECO_tir/external_libs/pdollar_toolbox/matlab/private/char2img_h050.mat -------------------------------------------------------------------------------- /ECO_tir/external_libs/pdollar_toolbox/matlab/private/dijkstra1.mexa64: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhanglichao/generatedTIR_tracking/a92c477284d2920a1cb59812a58eb5826f962eea/ECO_tir/external_libs/pdollar_toolbox/matlab/private/dijkstra1.mexa64 -------------------------------------------------------------------------------- /ECO_tir/external_libs/pdollar_toolbox/matlab/private/dijkstra1.mexmaci64: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhanglichao/generatedTIR_tracking/a92c477284d2920a1cb59812a58eb5826f962eea/ECO_tir/external_libs/pdollar_toolbox/matlab/private/dijkstra1.mexmaci64 -------------------------------------------------------------------------------- /ECO_tir/external_libs/pdollar_toolbox/matlab/private/dijkstra1.mexw64: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhanglichao/generatedTIR_tracking/a92c477284d2920a1cb59812a58eb5826f962eea/ECO_tir/external_libs/pdollar_toolbox/matlab/private/dijkstra1.mexw64 -------------------------------------------------------------------------------- /ECO_tir/external_libs/pdollar_toolbox/matlab/randSample.m: -------------------------------------------------------------------------------- 1 | function r = randSample( n, k, isArray ) 2 | % Generate values sampled uniformly without replacement from 1:n. 3 | % 4 | % If n is an integer, samples k values from 1:n without replacement. If n 5 | % is an array, samples k values without replacement from n. This function 6 | % implements much of the same functionality as randsample.m in the 7 | % Statistics Toolbox. 8 | % 9 | % USAGE 10 | % r = randSample( n, k, [isArray] ) 11 | % 12 | % INPUTS 13 | % n - specifies sampling source (if array) or range (if scalar) 14 | % k - the number of samples to draw 15 | % isArray - [0] if true treat n as an array (even if has single elt) 16 | % 17 | % OUTPUTS 18 | % r - k uniformly generated random samples 19 | % 20 | % EXAMPLE 21 | % r = randSample( 10, 5 ) 22 | % r = randSample( [ 5 6 8 9 32 45 ], 2 ) 23 | % 24 | % See also randperm 25 | % 26 | % Piotr's Computer Vision Matlab Toolbox Version 2.50 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 ), isArray=0; end 31 | m=length(n); if(m>1), 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 | -------------------------------------------------------------------------------- /ECO_tir/external_libs/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 | -------------------------------------------------------------------------------- /ECO_tir/external_libs/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 | -------------------------------------------------------------------------------- /ECO_tir/external_libs/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 | -------------------------------------------------------------------------------- /ECO_tir/external_libs/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 | -------------------------------------------------------------------------------- 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extract_info.img_input_sizes = {}; 6 | for feat_ind = 1:length(features) 7 | % if not equals any previously stored size 8 | if ~any(cellfun(@(sz) isequal(features{feat_ind}.img_sample_sz, sz), extract_info.img_sample_sizes)) 9 | extract_info.img_sample_sizes{end+1} = features{feat_ind}.img_sample_sz; 10 | extract_info.img_input_sizes{end+1} = features{feat_ind}.img_input_sz; 11 | end 12 | end -------------------------------------------------------------------------------- /ECO_tir/feature_extraction/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 -------------------------------------------------------------------------------- /ECO_tir/feature_extraction/load_cnn.m: -------------------------------------------------------------------------------- 1 | function net = load_cnn(fparams, im_size) 2 | 3 | net = load(['networks/' fparams.nn_name]); 4 | net = vl_simplenn_tidy(net); 5 | net.layers = net.layers(1:max(fparams.output_layer)); 6 | 7 | if strcmpi(fparams.input_size_mode, 'cnn_default') 8 | base_input_sz = net.meta.normalization.imageSize(1:2); 9 | elseif strcmpi(fparams.input_size_mode, 'adaptive') 10 | base_input_sz = im_size(1:2); 11 | else 12 | error('Unknown input_size_mode'); 13 | end 14 | 15 | net.meta.normalization.imageSize(1:2) = round(base_input_sz .* fparams.input_size_scale); 16 | net.meta.normalization.averageImageOrig = net.meta.normalization.averageImage; 17 | 18 | if isfield(net.meta,'inputSize') 19 | net.meta.inputSize = base_input_sz; 20 | end 21 | 22 | if size(net.meta.normalization.averageImage,1) > 1 || size(net.meta.normalization.averageImage,2) > 1 23 | net.meta.normalization.averageImage = imresize(single(net.meta.normalization.averageImage), net.meta.normalization.imageSize(1:2)); 24 | end 25 | 26 | net.info = vl_simplenn_display(net); 27 | end -------------------------------------------------------------------------------- /ECO_tir/feature_extraction/load_cnn_model.m: -------------------------------------------------------------------------------- 1 | function net = load_cnn_model(fparams, im_size) 2 | 3 | net = fparams.net; 4 | 5 | if strcmpi(fparams.input_size_mode, 'cnn_default') 6 | base_input_sz = net.meta.normalization.imageSize(1:2); 7 | elseif strcmpi(fparams.input_size_mode, 'adaptive') 8 | base_input_sz = im_size(1:2); 9 | else 10 | error('Unknown input_size_mode'); 11 | end 12 | 13 | net.meta.normalization.imageSize(1:2) = round(base_input_sz .* fparams.input_size_scale); 14 | net.meta.normalization.averageImageOrig = net.meta.normalization.averageImage; 15 | 16 | if isfield(net.meta,'inputSize') 17 | net.meta.inputSize = base_input_sz; 18 | end 19 | 20 | if size(net.meta.normalization.averageImage,1) > 1 || size(net.meta.normalization.averageImage,2) > 1 21 | net.meta.normalization.averageImage = imresize(single(net.meta.normalization.averageImage), net.meta.normalization.imageSize(1:2)); 22 | end 23 | 24 | net.info = vl_simplenn_display(net); 25 | end -------------------------------------------------------------------------------- /ECO_tir/feature_extraction/load_cnn_model2.m: -------------------------------------------------------------------------------- 1 | function net = load_cnn_model2(fparams, im_size) 2 | 3 | net = load(fparams.nn_name); 4 | net = vl_simplenn_tidy(net); 5 | net.layers = net.layers(1:max(fparams.output_layer)); 6 | 7 | if strcmpi(fparams.input_size_mode, 'cnn_default') 8 | base_input_sz = net.meta.normalization.imageSize(1:2); 9 | elseif strcmpi(fparams.input_size_mode, 'adaptive') 10 | base_input_sz = im_size(1:2); 11 | else 12 | error('Unknown input_size_mode'); 13 | end 14 | 15 | net.meta.normalization.imageSize(1:2) = round(base_input_sz .* fparams.input_size_scale); 16 | net.meta.normalization.averageImageOrig = net.meta.normalization.averageImage; 17 | 18 | if isfield(net.meta,'inputSize') 19 | net.meta.inputSize = base_input_sz; 20 | end 21 | 22 | if size(net.meta.normalization.averageImage,1) > 1 || size(net.meta.normalization.averageImage,2) > 1 23 | net.meta.normalization.averageImage = imresize(single(net.meta.normalization.averageImage), net.meta.normalization.imageSize(1:2)); 24 | end 25 | 26 | net.info = vl_simplenn_display(net); 27 | end -------------------------------------------------------------------------------- /ECO_tir/feature_extraction/lookup_tables/CNnorm.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhanglichao/generatedTIR_tracking/a92c477284d2920a1cb59812a58eb5826f962eea/ECO_tir/feature_extraction/lookup_tables/CNnorm.mat -------------------------------------------------------------------------------- /ECO_tir/feature_extraction/lookup_tables/intensityChannelNorm11.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhanglichao/generatedTIR_tracking/a92c477284d2920a1cb59812a58eb5826f962eea/ECO_tir/feature_extraction/lookup_tables/intensityChannelNorm11.mat -------------------------------------------------------------------------------- /ECO_tir/feature_extraction/lookup_tables/intensityChannelNorm16.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhanglichao/generatedTIR_tracking/a92c477284d2920a1cb59812a58eb5826f962eea/ECO_tir/feature_extraction/lookup_tables/intensityChannelNorm16.mat -------------------------------------------------------------------------------- /ECO_tir/feature_extraction/lookup_tables/intensityChannelNorm6.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhanglichao/generatedTIR_tracking/a92c477284d2920a1cb59812a58eb5826f962eea/ECO_tir/feature_extraction/lookup_tables/intensityChannelNorm6.mat -------------------------------------------------------------------------------- /ECO_tir/feature_extraction/networks/README.md: -------------------------------------------------------------------------------- 1 | ## The folder to put the fine-tuned models 2 | -------------------------------------------------------------------------------- /ECO_tir/feature_extraction/set_cnn_input_size.m: -------------------------------------------------------------------------------- 1 | function net = set_cnn_input_size(net, im_size) 2 | 3 | net.meta.normalization.imageSize(1:2) = round(im_size(1:2)); 4 | net.meta.normalization.averageImage = imresize(single(net.meta.normalization.averageImageOrig), net.meta.normalization.imageSize(1:2)); 5 | 6 | net.info = vl_simplenn_display(net); 7 | end -------------------------------------------------------------------------------- /ECO_tir/feature_extraction/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 | -------------------------------------------------------------------------------- /ECO_tir/implementation/dim_reduction/init_projection_matrix.m: -------------------------------------------------------------------------------- 1 | function projection_matrix = init_projection_matrix(init_sample, compressed_dim, params) 2 | 3 | % Initialize the projection matrix. 4 | 5 | % Convert the compressed dimensions to a cell 6 | compressed_dim_cell = permute(num2cell(compressed_dim), [2 3 1]); 7 | 8 | % Reshape the sample 9 | x = cellfun(@(x) reshape(x, [], size(x,3)), init_sample, 'uniformoutput', false); 10 | x = cellfun(@(x) bsxfun(@minus, x, mean(x, 1)), x, 'uniformoutput', false); 11 | 12 | if strcmpi(params.proj_init_method, 'pca') 13 | [projection_matrix, ~, ~] = cellfun(@(x) svd(x' * x), x, 'uniformoutput', false); 14 | projection_matrix = cellfun(@(P, dim) cast(P(:,1:dim), 'like', params.data_type), projection_matrix, compressed_dim_cell, 'uniformoutput', false); 15 | elseif strcmpi(params.proj_init_method, 'rand_uni') 16 | projection_matrix = cellfun(@(x, dim) randn(size(x,2), dim, 'like', params.data_type), x, compressed_dim_cell, 'uniformoutput', false); 17 | projection_matrix = cellfun(@(P) bsxfun(@rdivide, P, sqrt(sum(P.^2,1))), projection_matrix, 'uniformoutput', false); 18 | elseif strcmpi(params.proj_init_method, 'none') 19 | projection_matrix = []; 20 | else 21 | error('Unknown initialization method for the projection matrix: %s', params.proj_init_method); 22 | end -------------------------------------------------------------------------------- /ECO_tir/implementation/dim_reduction/project_sample.m: -------------------------------------------------------------------------------- 1 | function x = project_sample(x, P) 2 | 3 | if isempty(P) 4 | return; 5 | end 6 | 7 | if isa(x{1}, 'gpuArray') 8 | for k = 1:length(x) 9 | x{k} = reshape(pagefun(@mtimes, reshape(x{k}, [], size(x{k},3), size(x{k},4)), P{k}), size(x{k},1), size(x{k},2), [], size(x{k},4)); 10 | end 11 | else 12 | for k = 1:length(x) 13 | x{k} = reshape(mtimesx(reshape(x{k}, [], size(x{k},3), size(x{k},4)), P{k}, 'speed'), size(x{k},1), size(x{k},2), [], size(x{k},4)); 14 | end 15 | end -------------------------------------------------------------------------------- /ECO_tir/implementation/fourier_tools/cfft2.m: -------------------------------------------------------------------------------- 1 | function xf = cfft2(x) 2 | 3 | % Find the data type 4 | data_type_complex = complex(zeros(1, 'like', x)); 5 | 6 | % calculate output size 7 | in_sz = size(x); 8 | 9 | % if both dimensions are odd 10 | if all(mod(in_sz(1:2), 2) == 1) 11 | xf = fftshift(fftshift(fft2(x), 1), 2); 12 | else 13 | out_sz = in_sz; 14 | out_sz(1:2) = out_sz(1:2) + mod(out_sz(1:2)+1,2); 15 | 16 | % allocate 17 | xf = zeros(out_sz, 'like', data_type_complex); 18 | 19 | xf(1:in_sz(1),1:in_sz(2),:,:) = fftshift(fftshift(fft2(x), 1), 2); 20 | 21 | if out_sz(1) ~= in_sz(1) 22 | xf(end,:,:,:) = conj(xf(1,end:-1:1,:,:)); 23 | end 24 | if out_sz(2) ~= in_sz(2) 25 | xf(:,end,:,:) = conj(xf(end:-1:1,1,:,:)); 26 | end 27 | end -------------------------------------------------------------------------------- /ECO_tir/implementation/fourier_tools/cifft2.m: -------------------------------------------------------------------------------- 1 | function x = cifft2(xf) 2 | 3 | if isa(xf, 'gpuArray') 4 | x = real(ifft2(ifftshift(ifftshift(xf, 1), 2))); 5 | else 6 | x = ifft2(ifftshift(ifftshift(xf, 1), 2), 'symmetric'); 7 | end -------------------------------------------------------------------------------- /ECO_tir/implementation/fourier_tools/compact_fourier_coeff.m: -------------------------------------------------------------------------------- 1 | function xf = compact_fourier_coeff(xf) 2 | 3 | % Creates a compact fourier series representation by removing the strict right 4 | % half plane. 5 | 6 | if iscell(xf) 7 | xf = cellfun(@(xf) xf(:,1:(size(xf,2)+1)/2,:), xf, 'uniformoutput', false); 8 | else 9 | xf = xf(:,1:(size(xf,2)+1)/2,:); 10 | end -------------------------------------------------------------------------------- /ECO_tir/implementation/fourier_tools/cubic_spline_fourier.m: -------------------------------------------------------------------------------- 1 | function bf = cubic_spline_fourier(f, a) 2 | 3 | % The continuous Fourier transform of a cubic spline kernel. 4 | 5 | bf = -(- 12*a + 12*exp(-pi*f*2i) + 12*exp(pi*f*2i) + 6*a*exp(-pi*f*4i) + ... 6 | 6*a*exp(pi*f*4i) + f.*(pi*exp(-pi*f*2i)*12i) - f.*(pi*exp(pi*f*2i)*12i) + ... 7 | a*f.*(pi*exp(-pi*f*2i)*16i) - a*f.*(pi*exp(pi*f*2i)*16i) + ... 8 | a*f.*(pi*exp(-pi*f*4i)*4i) - a*f.*(pi*exp(pi*f*4i)*4i) - 24)./(16*f.^4*pi^4); 9 | 10 | bf(f == 0) = 1; -------------------------------------------------------------------------------- /ECO_tir/implementation/fourier_tools/full_fourier_coeff.m: -------------------------------------------------------------------------------- 1 | function xf = full_fourier_coeff(xf) 2 | 3 | % Reconstructs the full Fourier series coefficients. 4 | 5 | if iscell(xf) 6 | xf = cellfun(@(xf) cat(2, xf, conj(rot90(xf(:,1:end-1,:), 2))), xf, 'uniformoutput', false); 7 | else 8 | xf = cat(2, xf, conj(rot90(xf(:,1:end-1,:), 2))); 9 | end -------------------------------------------------------------------------------- /ECO_tir/implementation/fourier_tools/interpolate_dft.m: -------------------------------------------------------------------------------- 1 | function xf = interpolate_dft(xf, interp1_fs, interp2_fs) 2 | 3 | % Performs the implicit interpolation in the Fourier domain of a sample by 4 | % multiplying with the Fourier coefficients of the interpolation function. 5 | 6 | xf = cellfun(@(xf, interp1_fs, interp2_fs) bsxfun(@times, bsxfun(@times, xf, interp1_fs), interp2_fs), ... 7 | xf, interp1_fs, interp2_fs, 'uniformoutput', false); 8 | -------------------------------------------------------------------------------- /ECO_tir/implementation/fourier_tools/resizeDFT.m: -------------------------------------------------------------------------------- 1 | function resizeddft = resizeDFT(inputdft, desiredLen) 2 | 3 | % Resizes a one-dimensional DFT to the desired length. 4 | 5 | len = length(inputdft); 6 | minsz = min(len, desiredLen); 7 | 8 | scaling = desiredLen/len; 9 | 10 | if size(inputdft, 1) > 1 11 | newSize = [desiredLen 1]; 12 | else 13 | newSize = [1 desiredLen]; 14 | end 15 | 16 | resizeddft = complex(zeros(newSize, 'single')); 17 | 18 | mids = ceil(minsz/2); 19 | mide = floor((minsz-1)/2) - 1; 20 | 21 | resizeddft(1:mids) = scaling * inputdft(1:mids); 22 | resizeddft(end - mide:end) = scaling * inputdft(end - mide:end); 23 | end -------------------------------------------------------------------------------- /ECO_tir/implementation/fourier_tools/sample_fs.m: -------------------------------------------------------------------------------- 1 | function x = sample_fs(xf, grid_sz) 2 | 3 | % Samples the Fourier series 4 | 5 | sz = [size(xf,1) size(xf,2)]; 6 | 7 | if nargin < 2 || all(sz == grid_sz) 8 | x = prod(sz) * cifft2(xf); 9 | else 10 | if any(grid_sz < sz) 11 | error('The grid size must be larger than or equal to the signal size') 12 | end 13 | tot_pad = grid_sz - sz; 14 | pad_sz = ceil(tot_pad/2); 15 | xf_pad = padarray(xf, pad_sz); 16 | if any(mod(tot_pad,2) == 1) 17 | % Handle odd padding 18 | xf_pad = xf_pad(1:end-mod(tot_pad(1),2), 1:end-mod(tot_pad(2),2), :, :); 19 | end 20 | x = prod(grid_sz) * cifft2(xf_pad); 21 | end 22 | 23 | 24 | -------------------------------------------------------------------------------- /ECO_tir/implementation/fourier_tools/shift_sample.m: -------------------------------------------------------------------------------- 1 | function xf = shift_sample(xf, shift, kx, ky) 2 | 3 | % Shift a sample in the Fourier domain. The shift should be normalized to 4 | % the range [-pi, pi]. 5 | 6 | shift_exp_y = cellfun(@(ky) exp((1i * shift(1)) * ky), ky, 'uniformoutput', false); 7 | shift_exp_x = cellfun(@(kx) exp((1i * shift(2)) * kx), kx, 'uniformoutput', false); 8 | xf = cellfun(@(xf, sy, sx) bsxfun(@times, bsxfun(@times, xf, sy), sx), xf, shift_exp_y, shift_exp_x, 'uniformoutput', false); -------------------------------------------------------------------------------- /ECO_tir/implementation/fourier_tools/symmetrize_filter.m: -------------------------------------------------------------------------------- 1 | function hf = symmetrize_filter(hf) 2 | 3 | % Ensure hermetian symmetry. 4 | 5 | for k = 1:length(hf) 6 | dc_ind = (size(hf{k},1) + 1) / 2; 7 | hf{k}(dc_ind+1:end,end,:) = conj(flipud(hf{k}(1:dc_ind-1,end,:))); 8 | end -------------------------------------------------------------------------------- /ECO_tir/implementation/initialization/init_default_params.m: -------------------------------------------------------------------------------- 1 | function params = init_default_params(params) 2 | 3 | % Initialize default parameters 4 | 5 | default_params.use_gpu = false; 6 | default_params.gpu_id = []; 7 | default_params.interpolation_method = 'none'; 8 | default_params.interpolation_centering = false; 9 | default_params.interpolation_windowing = false; 10 | default_params.clamp_position = false; 11 | default_params.update_projection_matrix = true; 12 | default_params.proj_init_method = 'pca'; 13 | default_params.use_detection_sample = true; 14 | default_params.use_projection_matrix = true; 15 | default_params.use_sample_merge = true; 16 | default_params.CG_use_FR = true; 17 | default_params.CG_standard_alpha = true; 18 | 19 | def_param_names = fieldnames(default_params); 20 | for k = 1:numel(def_param_names) 21 | param_name = def_param_names{k}; 22 | if ~isfield(params, param_name) 23 | params.(param_name) = default_params.(param_name); 24 | end 25 | end 26 | 27 | if params.use_projection_matrix == false 28 | params.proj_init_method = 'none'; 29 | params.update_projection_matrix = false; 30 | end 31 | 32 | params.visualization = params.visualization || params.debug; -------------------------------------------------------------------------------- /ECO_tir/implementation/initialization/init_feature_params.m: -------------------------------------------------------------------------------- 1 | function feature_params = init_feature_params(features, feature_info) 2 | 3 | % Initialize some feature parameters. 4 | 5 | feature_dim = feature_info.dim; 6 | num_features = length(features); 7 | num_feature_blocks = length(feature_dim); 8 | 9 | feature_params.compressed_dim_block = cell(num_features, 1); 10 | 11 | for k = 1:num_features 12 | if ~isfield(features{k}.fparams, 'compressed_dim') 13 | features{k}.fparams.compressed_dim = features{k}.fparams.nDim; 14 | end 15 | 16 | feature_params.compressed_dim_block{k} = features{k}.fparams.compressed_dim(:); 17 | end 18 | 19 | feature_params.compressed_dim = cell2mat(feature_params.compressed_dim_block); -------------------------------------------------------------------------------- /ECO_tir/implementation/sample_space_model/find_gram_vector_gpu.m: -------------------------------------------------------------------------------- 1 | function gram_vector = find_gram_vector_gpu(samplesf, new_sample, num_training_samples, params) 2 | % Find the inner product of the new sample with the existing samples. TO be 3 | % used for distance calculation 4 | 5 | % Note: Since getting the 'exact' distance between the samples is not important, 6 | % the inner product computation is done by using only the half spectrum for 7 | % efficiency. In practice the error incurred by this is negligible. Also 8 | % since we wannt to merge samples that are similar, and finding the best 9 | % match is not important, small error in the distance computation doesn't 10 | % matter 11 | 12 | gram_vector = inf(params.nSamples,1,'single'); 13 | 14 | num_feature_blocks = numel(new_sample); 15 | 16 | if num_training_samples > 0 17 | ip = zeros(1,num_training_samples,'like', params.data_type_complex); 18 | for k = 1:num_feature_blocks 19 | ip = ip + 2 * (new_sample{k}(:)' * reshape(samplesf{k}(:,:,:,1:num_training_samples), [],num_training_samples)); 20 | end 21 | 22 | gram_vector(1:num_training_samples) = real(gather(ip)); 23 | end 24 | 25 | end 26 | 27 | 28 | -------------------------------------------------------------------------------- /ECO_tir/implementation/sample_space_model/merge_samples.m: -------------------------------------------------------------------------------- 1 | function merged_sample = merge_samples(sample1, sample2, w1, w2, sample_merge_type) 2 | % Merge sample1 and sample2 using weights w1 and w2. The type of merging is 3 | % decided by sample_merge_type. 4 | % The sample_merge_type can be 5 | % 1) Merge: The output is the weighted sum of the input samples 6 | % 2) Replace: The output is the first sample. ie w2 is assumed to be 0 7 | 8 | 9 | % Normalise the weights so that they sum to one 10 | alpha1 = w1/(w1+w2); 11 | alpha2 = 1 - alpha1; 12 | 13 | % Build the merged sample 14 | if strcmpi(sample_merge_type, 'replace') 15 | merged_sample = sample1; 16 | elseif strcmpi(sample_merge_type, 'merge') 17 | num_feature_blocks = numel(sample1); 18 | 19 | merged_sample = cell(1, 1, num_feature_blocks); 20 | for k = 1:num_feature_blocks 21 | merged_sample{k} = alpha1*sample1{k} + alpha2*sample2{k}; 22 | end 23 | else 24 | error('Invalid sample merge type'); 25 | end 26 | -------------------------------------------------------------------------------- /ECO_tir/implementation/scale_filter/feature_projection_scale.m: -------------------------------------------------------------------------------- 1 | function z = feature_projection_scale(x, projection_matrix, cos_window) 2 | 3 | % do the windowing of the output 4 | z = bsxfun(@times, cos_window, projection_matrix * x); 5 | end -------------------------------------------------------------------------------- /ECO_tir/implementation/training/diag_precond.m: -------------------------------------------------------------------------------- 1 | function hf_out = diag_precond(hf, M_diag) 2 | 3 | % This is the preconditioner operation in Conjugate Gradient 4 | 5 | hf_out = cellfun(@rdivide, hf, M_diag, 'uniformoutput',false); 6 | 7 | end -------------------------------------------------------------------------------- /ECO_tir/implementation/training/inner_product_filter.m: -------------------------------------------------------------------------------- 1 | function ip = inner_product_filter(xf, yf) 2 | 3 | % Computes the inner product between two filters. 4 | 5 | ip = 0; 6 | for k = 1:length(xf) 7 | ip = ip + 2*gather(xf{k}(:)' * yf{k}(:)) - gather(reshape(xf{k}(:,end,:), [], 1, 1)' * reshape(yf{k}(:,end,:), [], 1, 1)); 8 | end 9 | ip = real(ip); 10 | -------------------------------------------------------------------------------- /ECO_tir/implementation/training/inner_product_joint.m: -------------------------------------------------------------------------------- 1 | function ip = inner_product_joint(xf, yf) 2 | 3 | % Computes the joint inner product between two filters and projection matrices. 4 | 5 | ip = 0; 6 | for k = 1:size(xf,3) 7 | % Filter part 8 | ip = ip + 2*gather(xf{1,1,k}(:)' * yf{1,1,k}(:)) - gather(reshape(xf{1,1,k}(:,end,:), [], 1, 1)' * reshape(yf{1,1,k}(:,end,:), [], 1, 1)); 9 | 10 | % Projection matrix part 11 | ip = ip + gather(xf{2,1,k}(:)' * yf{2,1,k}(:)); 12 | end 13 | ip = real(ip); -------------------------------------------------------------------------------- /ECO_tir/implementation/training/train_filter.m: -------------------------------------------------------------------------------- 1 | function [hf, res_norms, CG_state] = train_filter(hf, samplesf, yf, reg_filter, sample_weights, sample_energy, reg_energy, params, CG_opts, CG_state) 2 | 3 | % Do Conjugate gradient optimization of the filter. 4 | 5 | % Construct the right hand side vector 6 | rhs_samplef = cellfun(@(xf) permute(mtimesx(sample_weights, 'T', xf, 'speed'), [3 4 2 1]), samplesf, 'uniformoutput', false); 7 | rhs_samplef = cellfun(@(xf, yf) bsxfun(@times, conj(xf), yf), rhs_samplef, yf, 'uniformoutput', false); 8 | 9 | % Construct preconditioner 10 | diag_M = cellfun(@(m, reg_energy) (1-params.precond_reg_param) * bsxfun(@plus, params.precond_data_param * m, (1-params.precond_data_param) * mean(m,3)) + params.precond_reg_param*reg_energy, sample_energy, reg_energy, 'uniformoutput',false); 11 | 12 | % do conjugate gradient 13 | [hf, res_norms, CG_state] = pcg_ccot(... 14 | @(x) lhs_operation(x, samplesf, reg_filter, sample_weights),... 15 | rhs_samplef, CG_opts, ... 16 | @(x) diag_precond(x, diag_M), ... 17 | [], @inner_product_filter, ... 18 | hf, CG_state); 19 | 20 | res_norms = res_norms/sqrt(inner_product_filter(rhs_samplef,rhs_samplef)); -------------------------------------------------------------------------------- /ECO_tir/implementation/training/train_filter_gpu.m: -------------------------------------------------------------------------------- 1 | function [hf, res_norms, CG_state] = train_filter_gpu(hf, samplesf, yf, reg_filter, sample_weights, sample_energy, reg_energy, params, CG_opts, CG_state) 2 | 3 | % Do Conjugate gradient optimization of the filter. 4 | 5 | sample_weights = permute(sample_weights, [2 3 4 1]); 6 | 7 | % Construct the right hand side vector 8 | rhs_samplef = cellfun(@(xf) sum(bsxfun(@times, sample_weights, xf), 4), samplesf, 'uniformoutput', false); 9 | rhs_samplef = cellfun(@(xf, yf) bsxfun(@times, conj(xf), yf), rhs_samplef, yf, 'uniformoutput', false); 10 | 11 | % Construct preconditioner 12 | diag_M = cellfun(@(m, reg_energy) (1-params.precond_reg_param) * bsxfun(@plus, params.precond_data_param * m, (1-params.precond_data_param) * mean(m,3)) + params.precond_reg_param*reg_energy, sample_energy, reg_energy, 'uniformoutput',false); 13 | 14 | % do conjugate gradient 15 | [hf, res_norms, CG_state] = pcg_ccot(... 16 | @(x) lhs_operation_gpu(x, samplesf, reg_filter, sample_weights),... 17 | rhs_samplef, CG_opts, ... 18 | @(x) diag_precond(x, diag_M), ... 19 | [], @inner_product_filter, ... 20 | hf, CG_state); 21 | 22 | res_norms = res_norms/sqrt(inner_product_filter(rhs_samplef,rhs_samplef)); -------------------------------------------------------------------------------- /ECO_tir/setup_paths.m: -------------------------------------------------------------------------------- 1 | function setup_paths() 2 | 3 | % Add the neccesary paths 4 | 5 | [pathstr, name, ext] = fileparts(mfilename('fullpath')); 6 | 7 | % Tracker implementation 8 | addpath(genpath([pathstr '/implementation/'])); 9 | 10 | % Runfiles 11 | addpath(genpath([pathstr '/runfiles/'])); 12 | 13 | % Utilities 14 | addpath([pathstr '/utils/']); 15 | 16 | % The feature extraction 17 | addpath(genpath([pathstr '/feature_extraction/'])); 18 | 19 | % Matconvnet 20 | % addpath([pathstr '/external_libs/matconvnet/matlab/mex/']); 21 | % addpath([pathstr '/external_libs/matconvnet/matlab']); 22 | % addpath([pathstr '/external_libs/matconvnet/matlab/simplenn']); 23 | addpath('/home/lichao/softwares/matconvnet25/matlab/mex/'); 24 | addpath('/home/lichao/softwares/matconvnet25/matlab'); 25 | addpath('/home/lichao/softwares/matconvnet25/matlab/simplenn'); 26 | vl_setupnn 27 | 28 | % PDollar toolbox 29 | addpath(genpath([pathstr '/external_libs/pdollar_toolbox/channels'])); 30 | 31 | % Mtimesx 32 | addpath([pathstr '/external_libs/mtimesx/']); 33 | 34 | % mexResize 35 | addpath([pathstr '/external_libs/mexResize/']); 36 | -------------------------------------------------------------------------------- /ECO_tir/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') 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_local') 12 | if seq.frame > seq.num_frames 13 | im = []; 14 | else 15 | im = imread(seq.image_files{seq.frame}); 16 | end 17 | elseif strcmpi(seq.format, 'vot') 18 | [seq.handle, image_file] = seq.handle.frame(seq.handle); 19 | if isempty(image_file) 20 | im = []; 21 | else 22 | im = imread(image_file); 23 | end 24 | else 25 | error('Uknown sequence format'); 26 | end 27 | -------------------------------------------------------------------------------- /ECO_tir/utils/get_sequence_results.m: -------------------------------------------------------------------------------- 1 | function [seq, results] = get_sequence_results(seq) 2 | 3 | if strcmpi(seq.format, 'otb') 4 | results.type = 'rect'; 5 | results.res = seq.rect_position; 6 | elseif strcmpi(seq.format, 'vot_local') 7 | 8 | elseif strcmpi(seq.format, 'vot') 9 | seq.handle.quit(seq.handle); 10 | else 11 | error('Uknown sequence format'); 12 | end 13 | 14 | if isfield(seq, 'time') 15 | results.fps = seq.num_frames / seq.time; 16 | else 17 | results.fps = NaN; 18 | end 19 | -------------------------------------------------------------------------------- /ECO_tir/utils/load_video_info.m: -------------------------------------------------------------------------------- 1 | function [seq, ground_truth] = load_video_info(video_path) 2 | 3 | ground_truth = dlmread([video_path '/groundtruth_rect.txt']); 4 | 5 | seq.format = 'otb'; 6 | seq.len = size(ground_truth, 1); 7 | seq.init_rect = ground_truth(1,:); 8 | 9 | img_path = [video_path '/img/']; 10 | 11 | if exist([img_path num2str(1, '%04i.png')], 'file'), 12 | img_files = num2str((1:seq.len)', [img_path '%04i.png']); 13 | elseif exist([img_path num2str(1, '%04i.jpg')], 'file'), 14 | img_files = num2str((1:seq.len)', [img_path '%04i.jpg']); 15 | elseif exist([img_path num2str(1, '%04i.bmp')], 'file'), 16 | img_files = num2str((1:seq.len)', [img_path '%04i.bmp']); 17 | else 18 | error('No image files to load.') 19 | end 20 | 21 | seq.s_frames = cellstr(img_files); 22 | 23 | end 24 | 25 | -------------------------------------------------------------------------------- /ECO_tir/utils/load_video_info_vot1.m: -------------------------------------------------------------------------------- 1 | function seq = load_video_info_vot1(ground_truth) 2 | 3 | % ground_truth = dlmread([video_path '/groundtruth.txt']); 4 | 5 | seq.format = 'otb'; 6 | seq.len = size(ground_truth, 1); 7 | init_region = ground_truth(1,:); 8 | 9 | % If the provided region is a polygon ... 10 | if numel(init_region) > 4 11 | cx = mean(init_region(1:2:end)); 12 | cy = mean(init_region(2:2:end)); 13 | x1 = min(init_region(1:2:end)); 14 | x2 = max(init_region(1:2:end)); 15 | y1 = min(init_region(2:2:end)); 16 | y2 = max(init_region(2:2:end)); 17 | A1 = norm(init_region(1:2) - init_region(3:4)) * norm(init_region(3:4) - init_region(5:6)); 18 | A2 = (x2 - x1) * (y2 - y1); 19 | s = sqrt(A1/A2); 20 | w = s * (x2 - x1) + 1; 21 | h = s * (y2 - y1) + 1; 22 | seq.init_rect = [cx-w/2,cy-h/2,w,h]; 23 | else 24 | x1 = init_region(1); 25 | y1 = init_region(2); 26 | w = init_region(3); 27 | h = init_region(4); 28 | seq.init_rect = [x1 y1 w h]; 29 | end 30 | 31 | 32 | 33 | end 34 | 35 | -------------------------------------------------------------------------------- /ECO_tir/utils/read_vot_regions0.m: -------------------------------------------------------------------------------- 1 | function regions = read_vot_regions0(regions) 2 | % READ_VOT_REGIONS 3 | % reads vot ground-truth regions and transforms them to axis-aligned 4 | 5 | % regions = dlmread(filename); 6 | if size(regions,2) > 4, 7 | cx = mean(regions(:,1:2:end),2); 8 | cy = mean(regions(:,2:2:end),2); 9 | x1 = round(min(regions(:, 1:2:end), [], 2)); 10 | x2 = round(max(regions(:, 1:2:end), [], 2)); 11 | y1 = round(min(regions(:, 2:2:end), [], 2)); 12 | y2 = round(max(regions(:, 2:2:end), [], 2)); 13 | % regions = round([x1, y1, x2 - x1, y2 - y1]); 14 | x1y1x2y2 = regions(:,1:2) - regions(:,3:4); 15 | x2y2x3y3 = regions(:,3:4) - regions(:,5:6); 16 | A1 = sqrt(sum(x1y1x2y2.*x1y1x2y2,2)).* sqrt(sum(x2y2x3y3.*x2y2x3y3,2)); 17 | A2 = (x2 - x1) .* (y2 - y1); 18 | s = sqrt(A1./A2); 19 | w = s .* (x2 - x1) + 1; 20 | h = s .* (y2 - y1) + 1; 21 | regions = [cx-w/2,cy-h/2,w,h]; 22 | end 23 | 24 | end % endfunction -------------------------------------------------------------------------------- /ECO_tir/utils/report_tracking_result_local.m: -------------------------------------------------------------------------------- 1 | function results = report_tracking_result_local(seq, result, imgbounds) 2 | 3 | if strcmpi(seq.format, 'vot_local') 4 | bb_scale = 1; 5 | sz = result.target_size / bb_scale; 6 | tl = result.center_pos - (sz - 1)/2; 7 | br = result.center_pos + (sz - 1)/2; 8 | x1 = tl(2); y1 = tl(1); 9 | x2 = br(2); y2 = br(1); 10 | result_box = round(double([x1 y1 x2 y1 x2 y2 x1 y2])); 11 | if any(isnan(result_box) | isinf(result_box)) 12 | error('Illegal values in the result.') 13 | end 14 | % if any(result_box < 0) 15 | % error('Negative values') 16 | % end 17 | results.res = result_box; results.frame = seq.frame; 18 | if seq.reset 19 | % from vot-toolkit experiment_supervised 20 | % imgbounds = [size(im,2),size(im,1)]; 21 | o = region_overlap(result_box, seq.gt(seq.frame,:), imgbounds); 22 | if o(1) <= 0 23 | results.first_failure = seq.frame; 24 | break; 25 | end 26 | end 27 | else 28 | error('Uknown sequence format'); 29 | end 30 | -------------------------------------------------------------------------------- /GAN_code/README.md: -------------------------------------------------------------------------------- 1 | 2 | ## Exemplar command for testing on GOT-10k dataset 3 | 4 | python test_pix2pix_got.py --dataroot /home/lichao/tracking/datasets/GOT-10k/train --name thermal_pix2pix_1ch_all_batch4 --model test --dataset_mode single --which_model_netG unet_256 --which_direction AtoB --norm batch --how_many 10000000 --which_epoch 25 --results_dir /home/lichao/tracking/datasets/GOT-10k_i/train --output_nc 1 --no_dropout --loadSize 256 --gpu_ids 1 5 | 6 | 7 | 8 | ## Reference 9 | 10 | 11 | The original project is in the [website](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix). 12 | 13 | -------------------------------------------------------------------------------- /GAN_code/README.md~: -------------------------------------------------------------------------------- 1 | 2 | ## Examplar command for testing on GOT-10k dataset 3 | 4 | python test_pix2pix_got.py --dataroot /home/lichao/tracking/datasets/GOT-10k/train --name thermal_pix2pix_1ch_all_batch4 --model test --dataset_mode single --which_model_netG unet_256 --which_direction AtoB --norm batch --how_many 10000000 --which_epoch 25 --results_dir /home/lichao/tracking/datasets/GOT-10k_i/train --output_nc 1 --no_dropout --loadSize 256 --gpu_ids 1 5 | 6 | 7 | 8 | ## Reference 9 | 10 | 11 | The original project is in the [website](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix). 12 | 13 | -------------------------------------------------------------------------------- /GAN_code/checkpoints/thermal_pix2pix_1ch_all_batch4/opt.txt: -------------------------------------------------------------------------------- 1 | ------------ Options ------------- 2 | aspect_ratio: 1.0 3 | batchSize: 1 4 | checkpoints_dir: ./checkpoints 5 | dataroot: /home/lichao/tracking/datasets/GOT-10k/train 6 | dataset_mode: single 7 | display_id: 1 8 | display_port: 8097 9 | display_winsize: 256 10 | fineSize: 256 11 | gpu_ids: [3] 12 | how_many: 10000000 13 | init_type: normal 14 | input_nc: 3 15 | isTrain: False 16 | loadSize: 256 17 | max_dataset_size: inf 18 | model: test 19 | nThreads: 2 20 | n_layers_D: 3 21 | name: thermal_pix2pix_1ch_all_batch4 22 | ndf: 64 23 | ngf: 64 24 | no_dropout: True 25 | no_flip: False 26 | norm: batch 27 | ntest: inf 28 | output_nc: 1 29 | phase: test 30 | resize_or_crop: resize_and_crop 31 | results_dir: /home/lichao/tracking/datasets/GOT-10k_i_/train 32 | serial_batches: False 33 | which_direction: AtoB 34 | which_epoch: 25 35 | which_model_netD: basic 36 | which_model_netG: unet_256 37 | -------------- End ---------------- 38 | -------------------------------------------------------------------------------- /GAN_code/checkpoints/thermal_pix2pix_1ch_all_batch4/web/images/epoch001_fake_B.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhanglichao/generatedTIR_tracking/a92c477284d2920a1cb59812a58eb5826f962eea/GAN_code/checkpoints/thermal_pix2pix_1ch_all_batch4/web/images/epoch001_fake_B.png -------------------------------------------------------------------------------- /GAN_code/checkpoints/thermal_pix2pix_1ch_all_batch4/web/images/epoch001_real_A.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhanglichao/generatedTIR_tracking/a92c477284d2920a1cb59812a58eb5826f962eea/GAN_code/checkpoints/thermal_pix2pix_1ch_all_batch4/web/images/epoch001_real_A.png -------------------------------------------------------------------------------- /GAN_code/checkpoints/thermal_pix2pix_1ch_all_batch4/web/images/epoch001_real_B.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhanglichao/generatedTIR_tracking/a92c477284d2920a1cb59812a58eb5826f962eea/GAN_code/checkpoints/thermal_pix2pix_1ch_all_batch4/web/images/epoch001_real_B.png -------------------------------------------------------------------------------- /GAN_code/checkpoints/thermal_pix2pix_1ch_all_batch4/web/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | Experiment name = thermal_pix2pix_1ch_all_batch4 5 | 6 | 7 | 8 |

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PIL import Image 5 | 6 | 7 | class SingleDataset(BaseDataset): 8 | def initialize(self, opt): 9 | self.opt = opt 10 | self.root = opt.dataroot 11 | self.dir_A = os.path.join(opt.dataroot) 12 | 13 | self.A_paths = make_dataset(self.dir_A) 14 | 15 | self.A_paths = sorted(self.A_paths) 16 | 17 | self.transform = get_transform(opt) 18 | 19 | def __getitem__(self, index): 20 | A_path = self.A_paths[index] 21 | A_img = Image.open(A_path).convert('RGB') 22 | A = self.transform(A_img) 23 | if self.opt.which_direction == 'BtoA': 24 | input_nc = self.opt.output_nc 25 | else: 26 | input_nc = self.opt.input_nc 27 | 28 | if input_nc == 1: # RGB to gray 29 | tmp = A[0, ...] * 0.299 + A[1, ...] * 0.587 + A[2, ...] * 0.114 30 | A = tmp.unsqueeze(0) 31 | [w1,h1] = A_img.size 32 | return {'A': A, 'A_paths': A_path, 'A_sz': [w1,h1]} 33 | 34 | def __len__(self): 35 | return len(self.A_paths) 36 | 37 | def name(self): 38 | return 'SingleImageDataset' 39 | -------------------------------------------------------------------------------- /GAN_code/data/single_dataset.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhanglichao/generatedTIR_tracking/a92c477284d2920a1cb59812a58eb5826f962eea/GAN_code/data/single_dataset.pyc -------------------------------------------------------------------------------- /GAN_code/data/single_dataset_ori.py: -------------------------------------------------------------------------------- 1 | import os.path 2 | from data.base_dataset import BaseDataset, get_transform 3 | from data.image_folder import make_dataset 4 | from PIL import Image 5 | 6 | 7 | class SingleDataset(BaseDataset): 8 | def initialize(self, opt): 9 | self.opt = opt 10 | self.root = opt.dataroot 11 | self.dir_A = os.path.join(opt.dataroot) 12 | 13 | self.A_paths = make_dataset(self.dir_A) 14 | 15 | self.A_paths = sorted(self.A_paths) 16 | 17 | self.transform = get_transform(opt) 18 | 19 | def __getitem__(self, index): 20 | A_path = self.A_paths[index] 21 | A_img = Image.open(A_path).convert('RGB') 22 | A = self.transform(A_img) 23 | if self.opt.which_direction == 'BtoA': 24 | input_nc = self.opt.output_nc 25 | else: 26 | input_nc = self.opt.input_nc 27 | 28 | if input_nc == 1: # RGB to gray 29 | tmp = A[0, ...] * 0.299 + A[1, ...] * 0.587 + A[2, ...] * 0.114 30 | A = tmp.unsqueeze(0) 31 | 32 | return {'A': A, 'A_paths': A_path} 33 | 34 | def __len__(self): 35 | return len(self.A_paths) 36 | 37 | def name(self): 38 | return 'SingleImageDataset' 39 | -------------------------------------------------------------------------------- /GAN_code/data/unaligned_dataset.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhanglichao/generatedTIR_tracking/a92c477284d2920a1cb59812a58eb5826f962eea/GAN_code/data/unaligned_dataset.pyc -------------------------------------------------------------------------------- /GAN_code/datasets/bibtex/cityscapes.tex: -------------------------------------------------------------------------------- 1 | @inproceedings{Cordts2016Cityscapes, 2 | title={The Cityscapes Dataset for Semantic Urban Scene Understanding}, 3 | author={Cordts, Marius and Omran, Mohamed and Ramos, Sebastian and Rehfeld, Timo and Enzweiler, Markus and Benenson, Rodrigo and Franke, Uwe and Roth, Stefan and Schiele, Bernt}, 4 | booktitle={Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, 5 | year={2016} 6 | } 7 | -------------------------------------------------------------------------------- /GAN_code/datasets/bibtex/facades.tex: -------------------------------------------------------------------------------- 1 | @INPROCEEDINGS{Tylecek13, 2 | author = {Radim Tyle{\v c}ek, Radim {\v S}{\' a}ra}, 3 | title = {Spatial Pattern Templates for Recognition of Objects with Regular Structure}, 4 | booktitle = {Proc. GCPR}, 5 | year = {2013}, 6 | address = {Saarbrucken, Germany}, 7 | } 8 | -------------------------------------------------------------------------------- /GAN_code/datasets/bibtex/handbags.tex: -------------------------------------------------------------------------------- 1 | @inproceedings{zhu2016generative, 2 | title={Generative Visual Manipulation on the Natural Image Manifold}, 3 | author={Zhu, Jun-Yan and Kr{\"a}henb{\"u}hl, Philipp and Shechtman, Eli and Efros, Alexei A.}, 4 | booktitle={Proceedings of European Conference on Computer Vision (ECCV)}, 5 | year={2016} 6 | } 7 | 8 | @InProceedings{xie15hed, 9 | author = {"Xie, Saining and Tu, Zhuowen"}, 10 | Title = {Holistically-Nested Edge Detection}, 11 | Booktitle = "Proceedings of IEEE International Conference on Computer Vision", 12 | Year = {2015}, 13 | } 14 | -------------------------------------------------------------------------------- /GAN_code/datasets/bibtex/shoes.tex: -------------------------------------------------------------------------------- 1 | @InProceedings{fine-grained, 2 | author = {A. Yu and K. Grauman}, 3 | title = {{F}ine-{G}rained {V}isual {C}omparisons with {L}ocal {L}earning}, 4 | booktitle = {Computer Vision and Pattern Recognition (CVPR)}, 5 | month = {June}, 6 | year = {2014} 7 | } 8 | 9 | @InProceedings{xie15hed, 10 | author = {"Xie, Saining and Tu, Zhuowen"}, 11 | Title = {Holistically-Nested Edge Detection}, 12 | Booktitle = "Proceedings of IEEE International Conference on Computer Vision", 13 | Year = {2015}, 14 | } 15 | -------------------------------------------------------------------------------- /GAN_code/datasets/download_cyclegan_dataset.sh: -------------------------------------------------------------------------------- 1 | FILE=$1 2 | 3 | if [[ $FILE != "ae_photos" && $FILE != "apple2orange" && $FILE != "summer2winter_yosemite" && $FILE != "horse2zebra" && $FILE != "monet2photo" && $FILE != "cezanne2photo" && $FILE != "ukiyoe2photo" && $FILE != "vangogh2photo" && $FILE != "maps" && $FILE != "cityscapes" && $FILE != "facades" && $FILE != "iphone2dslr_flower" && $FILE != "ae_photos" ]]; then 4 | echo "Available datasets are: apple2orange, summer2winter_yosemite, horse2zebra, monet2photo, cezanne2photo, ukiyoe2photo, vangogh2photo, maps, cityscapes, facades, iphone2dslr_flower, ae_photos" 5 | exit 1 6 | fi 7 | 8 | URL=https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/$FILE.zip 9 | ZIP_FILE=./datasets/$FILE.zip 10 | TARGET_DIR=./datasets/$FILE/ 11 | wget -N $URL -O $ZIP_FILE 12 | mkdir $TARGET_DIR 13 | unzip $ZIP_FILE -d ./datasets/ 14 | rm $ZIP_FILE 15 | -------------------------------------------------------------------------------- /GAN_code/datasets/download_pix2pix_dataset.sh: -------------------------------------------------------------------------------- 1 | FILE=$1 2 | URL=https://people.eecs.berkeley.edu/~tinghuiz/projects/pix2pix/datasets/$FILE.tar.gz 3 | TAR_FILE=./datasets/$FILE.tar.gz 4 | TARGET_DIR=./datasets/$FILE/ 5 | wget -N $URL -O $TAR_FILE 6 | mkdir $TARGET_DIR 7 | tar -zxvf $TAR_FILE -C 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'cycle_gan': 5 | assert(opt.dataset_mode == 'unaligned') 6 | from .cycle_gan_model import CycleGANModel 7 | model = CycleGANModel() 8 | elif opt.model == 'pix2pix': 9 | assert(opt.dataset_mode == 'aligned') 10 | from .pix2pix_model import Pix2PixModel 11 | model = Pix2PixModel() 12 | elif opt.model == 'test': 13 | assert(opt.dataset_mode == 'single') 14 | from .test_model import TestModel 15 | model = TestModel() 16 | else: 17 | raise ValueError("Model [%s] not recognized." % opt.model) 18 | model.initialize(opt) 19 | print("model [%s] was created" % (model.name())) 20 | return model 21 | -------------------------------------------------------------------------------- /GAN_code/models/models.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhanglichao/generatedTIR_tracking/a92c477284d2920a1cb59812a58eb5826f962eea/GAN_code/models/models.pyc 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TestOptions(BaseOptions): 5 | def initialize(self): 6 | BaseOptions.initialize(self) 7 | self.parser.add_argument('--ntest', type=int, default=float("inf"), help='# of test examples.') 8 | self.parser.add_argument('--results_dir', type=str, default='./results/', help='saves results here.') 9 | self.parser.add_argument('--aspect_ratio', type=float, default=1.0, help='aspect ratio of result images') 10 | self.parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc') 11 | self.parser.add_argument('--which_epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model') 12 | self.parser.add_argument('--how_many', type=int, default=50, help='how many test images to run') 13 | self.isTrain = False 14 | -------------------------------------------------------------------------------- /GAN_code/options/test_options.pyc: -------------------------------------------------------------------------------- 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URL=https://people.eecs.berkeley.edu/~taesung_park/pytorch-CycleGAN-and-pix2pix/models/$FILE.pth 10 | 11 | wget -N $URL -O $MODEL_FILE 12 | 13 | 14 | -------------------------------------------------------------------------------- /GAN_code/pretrained_models/download_pix2pix_model.sh: -------------------------------------------------------------------------------- 1 | FILE=$1 2 | 3 | echo "Note: available models are edges2shoes, sat2map, and facades_label2photo" 4 | 5 | echo "Specified [$FILE]" 6 | 7 | mkdir -p ./checkpoints/${FILE}_pretrained 8 | MODEL_FILE=./checkpoints/${FILE}_pretrained/latest_net_G.pth 9 | URL=https://people.eecs.berkeley.edu/~taesung_park/pytorch-CycleGAN-and-pix2pix/pix2pix_models/$FILE.pth 10 | 11 | wget -N $URL -O $MODEL_FILE 12 | 13 | 14 | -------------------------------------------------------------------------------- /GAN_code/scripts/test_cyclegan.sh: -------------------------------------------------------------------------------- 1 | python test.py --dataroot ./datasets/maps --name maps_cyclegan --model cycle_gan --phase test --no_dropout 2 | -------------------------------------------------------------------------------- /GAN_code/scripts/test_pix2pix.sh: -------------------------------------------------------------------------------- 1 | python test.py --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix --which_model_netG unet_256 --which_direction BtoA --dataset_mode aligned --norm batch 2 | -------------------------------------------------------------------------------- /GAN_code/scripts/test_single.sh: -------------------------------------------------------------------------------- 1 | python test.py --dataroot ./datasets/facades/testB/ --name facades_pix2pix --model test --which_model_netG unet_256 --which_direction BtoA --dataset_mode single --norm batch 2 | -------------------------------------------------------------------------------- /GAN_code/scripts/train_cyclegan.sh: 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