├── external ├── matconvnet-master │ ├── .gitmodules │ ├── utils │ │ └── proto │ │ │ ├── __init__.py │ │ │ └── get-protos.sh │ ├── matlab │ │ ├── compatibility │ │ │ └── parallel │ │ │ │ ├── labindex.m │ │ │ │ ├── numlabs.m │ │ │ │ └── gather.m │ │ ├── src │ │ │ ├── bits │ │ │ │ ├── data.cpp │ │ │ │ ├── nnbnorm.cpp │ │ │ │ ├── nnconv.cpp │ │ │ │ ├── datamex.cpp │ │ │ │ ├── nnpooling.cpp │ │ │ │ ├── nnnormalize.cpp │ │ │ │ ├── nnbilinearsampler.cpp │ │ │ │ ├── nnsubsample.cpp │ │ │ │ └── nnfullyconnected.cpp │ │ │ ├── vl_nnconv.cpp │ │ │ ├── vl_nnpool.cpp │ │ │ ├── vl_nnbnorm.cpp │ │ │ ├── vl_nnconvt.cpp │ │ │ ├── vl_imreadjpeg.cpp │ │ │ ├── vl_nnnormalize.cpp │ │ │ └── vl_nnbilinearsampler.cpp │ │ ├── xtest │ │ │ ├── suite │ │ │ │ ├── nnsigmoid.m │ │ │ │ ├── nndropout.m │ │ │ │ ├── nnoffset.m │ │ │ │ ├── nnsoftmax.m │ │ │ │ └── nnspnorm.m │ │ │ └── vl_test_bnorm.m │ │ ├── +dagnn │ │ │ ├── @DagNN │ │ │ │ ├── setLayerInputs.m │ │ │ │ ├── setLayerParams.m │ │ │ │ ├── setLayerOutputs.m │ │ │ │ ├── reset.m │ │ │ │ └── removeLayer.m │ │ │ ├── Sigmoid.m │ │ │ ├── SoftMax.m │ │ │ ├── LRN.m │ │ │ ├── NormOffset.m │ │ │ ├── SpatialNorm.m │ │ │ └── ElementWise.m │ │ └── vl_rootnn.m │ ├── doc │ │ ├── figures │ │ │ ├── imnet.pdf │ │ │ └── pepper.pdf │ │ └── site │ │ │ ├── docs │ │ │ ├── figures │ │ │ │ ├── stn-perf.png │ │ │ │ └── stn-samples.png │ │ │ └── js │ │ │ │ ├── mathjaxhelper.js │ │ │ │ └── toggle.js │ │ │ └── theme │ │ │ ├── content.html │ │ │ ├── js │ │ │ └── base.js │ │ │ └── toc.html │ ├── .gitattributes │ ├── matconvnet.xcodeproj │ │ └── project.xcworkspace │ │ │ └── contents.xcworkspacedata │ ├── examples │ │ ├── spatial_transformer │ │ │ └── readme.txt │ │ └── imagenet │ │ │ └── cnn_imagenet_sync_labels.m │ ├── README.md │ └── COPYING ├── vlfeat │ └── toolbox │ │ ├── mex │ │ ├── mexw32 │ │ │ ├── vl.dll │ │ │ ├── msvcr100.dll │ │ │ ├── vl_aib.mexw32 │ │ │ ├── vl_gmm.mexw32 │ │ │ ├── vl_hog.mexw32 │ │ │ ├── vl_lbp.mexw32 │ │ │ ├── vl_liop.mexw32 │ │ │ ├── vl_mser.mexw32 │ │ │ ├── vl_rodr.mexw32 │ │ │ ├── vl_sift.mexw32 │ │ │ ├── vl_slic.mexw32 │ │ │ ├── vl_vlad.mexw32 │ │ │ ├── vl_binsum.mexw32 │ │ │ ├── vl_covdet.mexw32 │ │ │ ├── vl_cummax.mexw32 │ │ │ ├── vl_dsift.mexw32 │ │ │ ├── vl_erfill.mexw32 │ │ │ ├── vl_fisher.mexw32 │ │ │ ├── vl_getpid.mexw32 │ │ │ ├── vl_irodr.mexw32 │ │ │ ├── vl_kmeans.mexw32 │ │ │ ├── vl_tpsumx.mexw32 │ │ │ ├── vl_aibhist.mexw32 │ │ │ ├── vl_alldist.mexw32 │ │ │ ├── vl_alldist2.mexw32 │ │ │ ├── vl_binsearch.mexw32 │ │ │ ├── vl_hikmeans.mexw32 │ │ │ ├── vl_homkermap.mexw32 │ │ │ ├── vl_ihashfind.mexw32 │ │ │ ├── vl_ihashsum.mexw32 │ │ │ ├── vl_ikmeans.mexw32 │ │ │ ├── vl_imdisttf.mexw32 │ │ │ ├── vl_imsmooth.mexw32 │ │ │ ├── vl_inthist.mexw32 │ │ │ ├── vl_localmax.mexw32 │ │ │ ├── vl_simdctrl.mexw32 │ │ │ ├── vl_svmtrain.mexw32 │ │ │ ├── vl_threads.mexw32 │ │ │ ├── vl_twister.mexw32 │ │ │ ├── vl_ubcmatch.mexw32 │ │ │ ├── vl_version.mexw32 │ │ │ ├── vl_ikmeanspush.mexw32 │ │ │ ├── vl_imintegral.mexw32 │ │ │ ├── vl_kdtreebuild.mexw32 │ │ │ ├── vl_kdtreequery.mexw32 │ │ │ ├── vl_quickshift.mexw32 │ │ │ ├── vl_hikmeanspush.mexw32 │ │ │ ├── vl_imwbackwardmx.mexw32 │ │ │ ├── vl_sampleinthist.mexw32 │ │ │ └── vl_siftdescriptor.mexw32 │ │ ├── mexw64 │ │ │ ├── vl.dll │ │ │ ├── msvcr100.dll │ │ │ ├── vl_aib.mexw64 │ │ │ ├── vl_gmm.mexw64 │ │ │ ├── vl_hog.mexw64 │ │ │ ├── vl_lbp.mexw64 │ │ │ ├── vl_liop.mexw64 │ │ │ ├── vl_mser.mexw64 │ │ │ ├── vl_rodr.mexw64 │ │ │ ├── vl_sift.mexw64 │ │ │ ├── vl_slic.mexw64 │ │ │ ├── vl_vlad.mexw64 │ │ │ ├── vl_binsum.mexw64 │ │ │ ├── vl_covdet.mexw64 │ │ │ ├── vl_cummax.mexw64 │ │ │ ├── vl_dsift.mexw64 │ │ │ ├── vl_erfill.mexw64 │ │ │ ├── vl_fisher.mexw64 │ │ │ ├── vl_getpid.mexw64 │ │ │ ├── vl_irodr.mexw64 │ │ │ ├── vl_kmeans.mexw64 │ │ │ ├── vl_tpsumx.mexw64 │ │ │ ├── vl_aibhist.mexw64 │ │ │ ├── vl_alldist.mexw64 │ │ │ ├── vl_alldist2.mexw64 │ │ │ ├── vl_binsearch.mexw64 │ │ │ ├── vl_hikmeans.mexw64 │ │ │ ├── vl_homkermap.mexw64 │ │ │ ├── vl_ihashfind.mexw64 │ │ │ ├── vl_ihashsum.mexw64 │ │ │ ├── vl_ikmeans.mexw64 │ │ │ ├── vl_imdisttf.mexw64 │ │ │ ├── vl_imsmooth.mexw64 │ │ │ ├── vl_inthist.mexw64 │ │ │ ├── vl_localmax.mexw64 │ │ │ ├── vl_simdctrl.mexw64 │ │ │ ├── vl_svmtrain.mexw64 │ │ │ ├── vl_threads.mexw64 │ │ │ ├── vl_twister.mexw64 │ │ │ ├── vl_ubcmatch.mexw64 │ │ │ ├── vl_version.mexw64 │ │ │ ├── vl_ikmeanspush.mexw64 │ │ │ ├── vl_imintegral.mexw64 │ │ │ ├── vl_kdtreebuild.mexw64 │ │ │ ├── vl_kdtreequery.mexw64 │ │ │ ├── vl_quickshift.mexw64 │ │ │ ├── vl_hikmeanspush.mexw64 │ │ │ ├── vl_imwbackwardmx.mexw64 │ │ │ ├── vl_sampleinthist.mexw64 │ │ │ └── vl_siftdescriptor.mexw64 │ │ ├── mexa64 │ │ │ ├── libvl.so │ │ │ ├── vl_aib.mexa64 │ │ │ ├── vl_gmm.mexa64 │ │ │ ├── vl_hog.mexa64 │ │ │ ├── vl_lbp.mexa64 │ │ │ ├── vl_liop.mexa64 │ │ │ ├── vl_mser.mexa64 │ │ │ ├── vl_rodr.mexa64 │ │ │ ├── vl_sift.mexa64 │ │ │ ├── vl_slic.mexa64 │ │ │ ├── vl_vlad.mexa64 │ │ │ ├── vl_binsum.mexa64 │ │ │ ├── vl_covdet.mexa64 │ │ │ ├── vl_cummax.mexa64 │ │ │ ├── vl_dsift.mexa64 │ │ │ ├── vl_erfill.mexa64 │ │ │ ├── vl_fisher.mexa64 │ │ │ ├── vl_getpid.mexa64 │ │ │ ├── vl_irodr.mexa64 │ │ │ ├── vl_kmeans.mexa64 │ │ │ ├── vl_tpsumx.mexa64 │ │ │ ├── vl_aibhist.mexa64 │ │ │ ├── vl_alldist.mexa64 │ │ │ ├── vl_alldist2.mexa64 │ │ │ ├── vl_binsearch.mexa64 │ │ │ ├── vl_hikmeans.mexa64 │ │ │ ├── vl_homkermap.mexa64 │ │ │ ├── vl_ihashfind.mexa64 │ │ │ ├── vl_ihashsum.mexa64 │ │ │ ├── vl_ikmeans.mexa64 │ │ │ ├── vl_imdisttf.mexa64 │ │ │ ├── vl_imsmooth.mexa64 │ │ │ ├── vl_inthist.mexa64 │ │ │ ├── vl_localmax.mexa64 │ │ │ ├── vl_simdctrl.mexa64 │ │ │ ├── vl_svmtrain.mexa64 │ │ │ ├── vl_threads.mexa64 │ │ │ ├── vl_twister.mexa64 │ │ │ ├── vl_ubcmatch.mexa64 │ │ │ ├── vl_version.mexa64 │ │ │ ├── vl_ikmeanspush.mexa64 │ │ │ ├── vl_imintegral.mexa64 │ │ │ ├── vl_kdtreebuild.mexa64 │ │ │ ├── vl_kdtreequery.mexa64 │ │ │ ├── vl_quickshift.mexa64 │ │ │ ├── vl_hikmeanspush.mexa64 │ │ │ ├── vl_imwbackwardmx.mexa64 │ │ │ ├── vl_sampleinthist.mexa64 │ │ │ └── vl_siftdescriptor.mexa64 │ │ ├── mexglx │ │ │ ├── libvl.so │ │ │ ├── vl_aib.mexglx │ │ │ ├── vl_gmm.mexglx │ │ │ ├── vl_hog.mexglx │ │ │ ├── vl_lbp.mexglx │ │ │ ├── vl_liop.mexglx │ │ │ ├── vl_mser.mexglx │ │ │ ├── vl_rodr.mexglx │ │ │ ├── vl_sift.mexglx │ │ │ ├── vl_slic.mexglx │ │ │ ├── vl_vlad.mexglx │ │ │ ├── vl_binsum.mexglx │ │ │ ├── vl_covdet.mexglx │ │ │ ├── vl_cummax.mexglx │ │ │ ├── vl_dsift.mexglx │ │ │ ├── vl_erfill.mexglx │ │ │ ├── vl_fisher.mexglx │ │ │ ├── vl_getpid.mexglx │ │ │ ├── vl_irodr.mexglx │ │ │ ├── vl_kmeans.mexglx │ │ │ ├── vl_tpsumx.mexglx │ │ │ ├── vl_aibhist.mexglx │ │ │ ├── vl_alldist.mexglx │ │ │ ├── vl_alldist2.mexglx │ │ │ ├── vl_binsearch.mexglx │ │ │ ├── vl_hikmeans.mexglx │ │ │ ├── vl_homkermap.mexglx │ │ │ ├── vl_ihashfind.mexglx │ │ │ ├── vl_ihashsum.mexglx │ │ │ ├── vl_ikmeans.mexglx │ │ │ ├── vl_imdisttf.mexglx │ │ │ ├── vl_imsmooth.mexglx │ │ │ ├── vl_inthist.mexglx │ │ │ ├── vl_localmax.mexglx │ │ │ ├── vl_simdctrl.mexglx │ │ │ ├── vl_svmtrain.mexglx │ │ │ ├── vl_threads.mexglx │ │ │ ├── vl_twister.mexglx │ │ │ ├── vl_ubcmatch.mexglx │ │ │ ├── vl_version.mexglx │ │ │ ├── vl_ikmeanspush.mexglx │ │ │ ├── vl_imintegral.mexglx │ │ │ ├── vl_kdtreebuild.mexglx │ │ │ ├── vl_kdtreequery.mexglx │ │ │ ├── vl_quickshift.mexglx │ │ │ ├── vl_hikmeanspush.mexglx │ │ │ ├── vl_imwbackwardmx.mexglx │ │ │ ├── vl_sampleinthist.mexglx │ │ │ └── vl_siftdescriptor.mexglx │ │ ├── mexmaci │ │ │ ├── libvl.dylib │ │ │ ├── vl_aib.mexmaci │ │ │ ├── vl_gmm.mexmaci │ │ │ ├── vl_hog.mexmaci │ │ │ ├── vl_lbp.mexmaci │ │ │ ├── vl_liop.mexmaci │ │ │ ├── vl_mser.mexmaci │ │ │ ├── vl_rodr.mexmaci │ │ │ ├── vl_sift.mexmaci │ │ │ ├── vl_slic.mexmaci │ │ │ ├── vl_vlad.mexmaci │ │ │ ├── vl_aibhist.mexmaci │ │ │ ├── vl_alldist.mexmaci │ │ │ ├── vl_binsum.mexmaci │ │ │ ├── vl_covdet.mexmaci │ │ │ ├── vl_cummax.mexmaci │ │ │ ├── vl_dsift.mexmaci │ │ │ ├── vl_erfill.mexmaci │ │ │ ├── vl_fisher.mexmaci │ │ │ ├── vl_getpid.mexmaci │ │ │ ├── vl_ikmeans.mexmaci │ │ │ ├── vl_inthist.mexmaci │ │ │ ├── vl_irodr.mexmaci │ │ │ ├── vl_kmeans.mexmaci │ │ │ ├── vl_threads.mexmaci │ │ │ ├── vl_tpsumx.mexmaci │ │ │ ├── vl_twister.mexmaci │ │ │ ├── vl_version.mexmaci │ │ │ ├── vl_alldist2.mexmaci │ │ │ ├── vl_binsearch.mexmaci │ │ │ ├── vl_hikmeans.mexmaci │ │ │ ├── vl_homkermap.mexmaci │ │ │ ├── vl_ihashfind.mexmaci │ │ │ ├── vl_ihashsum.mexmaci │ │ │ ├── vl_imdisttf.mexmaci │ │ │ ├── vl_imsmooth.mexmaci │ │ │ ├── vl_localmax.mexmaci │ │ │ ├── vl_simdctrl.mexmaci │ │ │ ├── vl_svmtrain.mexmaci │ │ │ ├── vl_ubcmatch.mexmaci │ │ │ ├── vl_hikmeanspush.mexmaci │ │ │ ├── vl_ikmeanspush.mexmaci │ │ │ ├── vl_imintegral.mexmaci │ │ │ ├── vl_kdtreebuild.mexmaci │ │ │ ├── vl_kdtreequery.mexmaci │ │ │ ├── vl_quickshift.mexmaci │ │ │ ├── vl_imwbackwardmx.mexmaci │ │ │ ├── vl_sampleinthist.mexmaci │ │ │ └── vl_siftdescriptor.mexmaci │ │ └── mexmaci64 │ │ │ ├── libvl.dylib │ │ │ ├── vl_aib.mexmaci64 │ │ │ ├── vl_gmm.mexmaci64 │ │ │ ├── vl_hog.mexmaci64 │ │ │ ├── vl_lbp.mexmaci64 │ │ │ ├── vl_dsift.mexmaci64 │ │ │ ├── vl_irodr.mexmaci64 │ │ │ ├── vl_liop.mexmaci64 │ │ │ ├── vl_mser.mexmaci64 │ │ │ ├── vl_rodr.mexmaci64 │ │ │ ├── vl_sift.mexmaci64 │ │ │ ├── vl_slic.mexmaci64 │ │ │ ├── vl_vlad.mexmaci64 │ │ │ ├── vl_aibhist.mexmaci64 │ │ │ ├── vl_alldist.mexmaci64 │ │ │ ├── vl_alldist2.mexmaci64 │ │ │ ├── vl_binsum.mexmaci64 │ │ │ ├── vl_covdet.mexmaci64 │ │ │ ├── vl_cummax.mexmaci64 │ │ │ ├── vl_erfill.mexmaci64 │ │ │ ├── vl_fisher.mexmaci64 │ │ │ ├── vl_getpid.mexmaci64 │ │ │ ├── vl_hikmeans.mexmaci64 │ │ │ ├── vl_ihashsum.mexmaci64 │ │ │ ├── vl_ikmeans.mexmaci64 │ │ │ ├── vl_imdisttf.mexmaci64 │ │ │ ├── vl_imsmooth.mexmaci64 │ │ │ ├── vl_inthist.mexmaci64 │ │ │ ├── vl_kmeans.mexmaci64 │ │ │ ├── vl_localmax.mexmaci64 │ │ │ ├── vl_simdctrl.mexmaci64 │ │ │ ├── vl_svmtrain.mexmaci64 │ │ │ ├── vl_threads.mexmaci64 │ │ │ ├── vl_tpsumx.mexmaci64 │ │ │ ├── vl_twister.mexmaci64 │ │ │ ├── vl_ubcmatch.mexmaci64 │ │ │ ├── vl_version.mexmaci64 │ │ │ ├── vl_binsearch.mexmaci64 │ │ │ ├── vl_homkermap.mexmaci64 │ │ │ ├── vl_ihashfind.mexmaci64 │ │ │ ├── vl_imintegral.mexmaci64 │ │ │ ├── vl_quickshift.mexmaci64 │ │ │ ├── vl_hikmeanspush.mexmaci64 │ │ │ ├── vl_ikmeanspush.mexmaci64 │ │ │ ├── vl_imwbackwardmx.mexmaci64 │ │ │ ├── vl_kdtreebuild.mexmaci64 │ │ │ ├── vl_kdtreequery.mexmaci64 │ │ │ ├── vl_sampleinthist.mexmaci64 │ │ │ └── vl_siftdescriptor.mexmaci64 │ │ ├── noprefix │ │ ├── assert_equal.m │ │ ├── assert_almost_equal.m │ │ ├── assert_exception.m │ │ ├── pegasos.m │ │ ├── svmpegasos.m │ │ ├── rcos.m │ │ ├── imsc.m │ │ ├── hat.m │ │ ├── imup.m │ │ ├── isoctave.m │ │ ├── tpfp.m │ │ ├── ihat.m │ │ ├── imseg.m │ │ ├── whistc.m │ │ ├── dsigmoid.m │ │ ├── cf.m │ │ ├── ikmeanshist.m │ │ ├── ikmeanspush.m │ │ ├── aibcutpush.m │ │ ├── plotgrid.m │ │ ├── dwaffine.m │ │ ├── figaspect.m │ │ ├── getpid.m │ │ ├── simdctrl.m │ │ ├── xyz2lab.m │ │ ├── numder2.m │ │ ├── flatmap.m │ │ ├── gaussian.m │ │ ├── dgaussian.m │ │ ├── ihashfind.m │ │ ├── imreadbw.m │ │ ├── imreadgray.m │ │ ├── ertr.m │ │ ├── xyz2rgb.m │ │ ├── numder.m │ │ ├── xyz2luv.m │ │ ├── imdown.m │ │ ├── ddgaussian.m │ │ ├── clicksegment.m │ │ ├── waffine.m │ │ ├── linespec2prop.m │ │ ├── ubcread.m │ │ ├── noprefix.m │ │ ├── sigmoid.m │ │ ├── erfill.m │ │ ├── threads.m │ │ ├── lbpfliplr.m │ │ ├── matlabversion.m │ │ ├── histmarg.m │ │ ├── witps.m │ │ ├── rgb2xyz.m │ │ ├── lbp.m │ │ ├── imgrad.m │ │ ├── imwhiten.m │ │ ├── imintegral.m │ │ ├── test.m │ │ ├── plotpoint.m │ │ ├── hikmeanspush.m │ │ ├── aibhist.m │ │ └── binsearch.m │ │ ├── xtest │ │ ├── vl_assert_equal.m │ │ ├── vl_test_slic.m │ │ ├── vl_test_mser.m │ │ ├── vl_test_grad.m │ │ ├── vl_test_plotbox.m │ │ ├── vl_test_hikmeans.m │ │ ├── vl_test_ikmeans.m │ │ ├── vl_test_imwbackward.m │ │ ├── vl_assert_exception.m │ │ ├── vl_test_phow.m │ │ ├── vl_test_ihashsum.m │ │ └── vl_assert_almost_equal.m │ │ ├── misc │ │ ├── vl_isoctave.m │ │ ├── vl_getpid.m │ │ ├── vl_simdctrl.m │ │ ├── vl_version.m │ │ ├── vl_matlabversion.m │ │ ├── vl_ihashfind.m │ │ ├── vl_threads.m │ │ └── vl_numder.m │ │ ├── demo │ │ ├── vl_demo_kmeans_2d.m │ │ ├── vl_demo_sift_or.m │ │ └── vl_demo_cmd.m │ │ ├── info.xml │ │ ├── kmeans │ │ ├── vl_ikmeanspush.m │ │ └── vl_ikmeanshist.m │ │ ├── geometry │ │ ├── vl_ihat.m │ │ └── vl_hat.m │ │ ├── mser │ │ ├── vl_ertr.m │ │ └── vl_erfill.m │ │ ├── aib │ │ └── vl_aibcutpush.m │ │ ├── special │ │ ├── vl_dsigmoid.m │ │ ├── vl_rcos.m │ │ ├── vl_gaussian.m │ │ ├── vl_dgaussian.m │ │ ├── vl_ddgaussian.m │ │ └── vl_sigmoid.m │ │ ├── vl_root.m │ │ ├── imop │ │ ├── vl_imsc.m │ │ ├── vl_imreadbw.m │ │ └── vl_imreadgray.m │ │ └── plotop │ │ └── vl_cf.m └── RF_Class_C │ ├── mexClassRF_train.mexa64 │ ├── mexClassRF_train.mexw32 │ ├── mexClassRF_train.mexw64 │ ├── mexClassRF_predict.mexa64 │ ├── mexClassRF_predict.mexw32 │ ├── mexClassRF_predict.mexw64 │ ├── precompiled_rfsub │ ├── win32 │ │ └── rfsub.o │ ├── win64 │ │ └── rfsub.o │ └── linux64 │ │ └── rfsub.o │ ├── texput.log │ └── test_ClassRF_extensively.m ├── vesselSegmentation ├── Features │ ├── Features │ │ ├── BCOSFIRE │ │ │ ├── Gabor │ │ │ │ ├── inhibfacil.m │ │ │ │ └── onscreen.m │ │ │ ├── COSFIRE │ │ │ │ ├── getHashkey.m │ │ │ │ ├── dilate.mexa64 │ │ │ │ ├── dilate.mexw64 │ │ │ │ ├── maximumOutput.m │ │ │ │ ├── getDoGBankResponse.m │ │ │ │ ├── imshift.m │ │ │ │ ├── getCOSFIREstructure.m │ │ │ │ ├── getDoG.m │ │ │ │ └── getGaborResponse.m │ │ │ ├── Performance │ │ │ │ └── rocarea.m │ │ │ └── Preprocessing │ │ │ │ └── rescaleImage.m │ │ ├── Soares2006.m │ │ ├── soares │ │ │ ├── morlet.m │ │ │ ├── maxmorlet.m │ │ │ └── generatefeaturesredfree.m │ │ └── nguyen │ │ │ ├── noisefiltering.m │ │ │ └── im_seg.m │ └── Util │ │ └── generateFeatureFilter.m ├── README.md ├── Preprocessing │ └── fakepad.m ├── CRF │ ├── CRF_1.0 │ │ ├── maxflowmex.mexw64 │ │ ├── pairwisePart.mexa64 │ │ ├── pairwisePart.mexw64 │ │ ├── fullyCRFwithGivenPairwises.mexa64 │ │ └── fullyCRFwithGivenPairwises.mexw64 │ └── maxflow │ │ ├── make.m │ │ └── instances.inc ├── Util │ ├── resizeImages.m │ ├── Evaluation │ │ ├── evaluateOverTestData.m │ │ └── Metrics │ │ │ └── getAverageMeasures.m │ ├── getMultipleImagesFileNames.m │ ├── openVesselLabels.m │ ├── getLabeledDataFilenames.m │ ├── openMultipleImages.m │ └── encodeFileName.m ├── SOSVM │ ├── Callbacks │ │ └── lossCB.m │ └── Util │ │ ├── getfeatures.m │ │ └── pairwisePotentials.m └── SaveSegmentations.m ├── .gitignore ├── GRAPHICAL_ABSTRACT.png ├── Util ├── files │ ├── generate_dataset_tag.m │ ├── getOnlyFolders.m │ ├── filterFileNames.m │ └── removeFileNamesWithExtension.m ├── eval │ └── plot_froc.m ├── openImagesFromFolder.m └── images │ └── turn_white_outside_fov.m ├── Learning ├── random-forest │ └── classRF_predict_probabilities.m └── standardizeCols.m ├── RedLesionDetection └── util │ ├── evaluation │ ├── getMaLabels.m │ └── is_in_ground_truth.m │ ├── preprocessing │ └── getTopHatTransformation.m │ ├── deep_learning │ ├── get_cnn_training_data.m │ └── prepareCNNforExtractingFeatures.m │ └── classifier │ └── train_val_splits.m ├── Scripts └── figures │ └── script_classifiers_performance_ma_detection.m └── default_configuration └── data_preparation └── config_organize_messidor_2_data.m /external/matconvnet-master/.gitmodules: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /external/matconvnet-master/utils/proto/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /vesselSegmentation/Features/Features/BCOSFIRE/Gabor/inhibfacil.m: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /.gitignore: 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-------------------------------------------------------------------------------- /vesselSegmentation/Util/resizeImages.m: -------------------------------------------------------------------------------- 1 | 2 | function [images] = resizeImages(images, scale) 3 | 4 | for i = 1 : length(images) 5 | images{i} = imresize(images{i}, scale); 6 | end 7 | 8 | end -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/pegasos.m: -------------------------------------------------------------------------------- 1 | function varargout = pegasos(varargin) 2 | % VL_PEGASOS [deprecated] 3 | % VL_PEGASOS is deprecated. Please use VL_SVMTRAIN() instead. 4 | [varargout{1:nargout}] = vl_pegasos(varargin{:}); 5 | -------------------------------------------------------------------------------- /external/matconvnet-master/doc/site/docs/js/mathjaxhelper.js: -------------------------------------------------------------------------------- 1 | /* 2 | #if false 3 | Prevent Unity to try compiling this js 4 | */ 5 | MathJax.Hub.Config({ 6 | "tex2jax": { inlineMath: [ [ '$', '$' ] ] } 7 | }); 8 | /* 9 | #endif 10 | */ -------------------------------------------------------------------------------- /vesselSegmentation/Features/Features/BCOSFIRE/COSFIRE/maximumOutput.m: -------------------------------------------------------------------------------- 1 | function [ output ] = maximumOutput( input ) 2 | for i = 1:length(input) 3 | in(:,:,i) = input{i}; 4 | end 5 | 6 | output = max(in, [], 3); 7 | end 8 | 9 | -------------------------------------------------------------------------------- /external/matconvnet-master/matlab/compatibility/parallel/gather.m: -------------------------------------------------------------------------------- 1 | function x=gather(x) 2 | % GATHER Compatibility stub for the GATHER() function 3 | % GATHER() is a function in the Parallel MATLAB toolbox. MATCONVNET 4 | % can work without it. 5 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/svmpegasos.m: -------------------------------------------------------------------------------- 1 | function varargout = svmpegasos(varargin) 2 | % VL_SVMPEGASOS [deprecated] 3 | % VL_SVMPEGASOS is deprecated. Please use VL_SVMTRAIN() instead. 4 | [varargout{1:nargout}] = vl_svmpegasos(varargin{:}); 5 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/rcos.m: -------------------------------------------------------------------------------- 1 | function varargout = rcos(varargin) 2 | % VL_RCOS RCOS function 3 | % Y = VL_RCOS(R,X) computes the RCOS function with roll-off R. 4 | % 5 | % See also: VL_HELP(). 6 | [varargout{1:nargout}] = vl_rcos(varargin{:}); 7 | -------------------------------------------------------------------------------- /vesselSegmentation/Util/Evaluation/evaluateOverTestData.m: -------------------------------------------------------------------------------- 1 | 2 | function [result] = evaluateOverTestData(param, model, testset) 3 | 4 | % Get results 5 | [result.segmentations, result.qualityMeasures] = getBunchSegmentations(param, testset, model); 6 | 7 | end -------------------------------------------------------------------------------- /external/matconvnet-master/doc/site/docs/js/toggle.js: -------------------------------------------------------------------------------- 1 | function toggle_visibility(id) { 2 | var e = document.getElementById(id); 3 | if(e.style.display == 'block') 4 | e.style.display = 'none'; 5 | else 6 | e.style.display = 'block'; 7 | } 8 | -------------------------------------------------------------------------------- /external/matconvnet-master/matconvnet.xcodeproj/project.xcworkspace/contents.xcworkspacedata: -------------------------------------------------------------------------------- 1 | 2 | 4 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /external/matconvnet-master/doc/site/theme/content.html: -------------------------------------------------------------------------------- 1 | {% if meta.source %} 2 |
3 | {% for filename in meta.source %} 4 | {{ filename }} 5 | {% endfor %} 6 |
7 | {% endif %} 8 | 9 | {{ content }} 10 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/xtest/vl_test_slic.m: -------------------------------------------------------------------------------- 1 | function results = vl_test_slic(varargin) 2 | % VL_TEST_SLIC 3 | vl_test_init ; 4 | 5 | function s = setup() 6 | s.im = im2single(vl_impattern('roofs1')) ; 7 | 8 | function test_slic(s) 9 | segmentation = vl_slic(s.im, 10, 0.1) ; 10 | -------------------------------------------------------------------------------- /Util/files/generate_dataset_tag.m: -------------------------------------------------------------------------------- 1 | 2 | function [datasetTag] = generate_dataset_tag(datasetName) 3 | 4 | % generate data set tag 5 | datasetTag = datasetName; 6 | k = strfind(datasetTag, filesep); 7 | if (~isempty(k)) 8 | datasetTag(k) = '_'; 9 | end 10 | 11 | end -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/imsc.m: -------------------------------------------------------------------------------- 1 | function varargout = imsc(varargin) 2 | % VL_IMSC Scale image 3 | % J=VL_IMSC(I) scales the range of the gray-scale or color image I to 4 | % fit in the interval [0,1]. 5 | % 6 | % See also: VL_HELP(). 7 | [varargout{1:nargout}] = vl_imsc(varargin{:}); 8 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/hat.m: -------------------------------------------------------------------------------- 1 | function varargout = hat(varargin) 2 | % VL_HAT Hat operator 3 | % H = VL_HAT(OM) returns the skew symmetric matrix by taking the "hat" 4 | % of the 3D vector OM. 5 | % 6 | % See also: VL_IHAT(), VL_HELP(). 7 | [varargout{1:nargout}] = vl_hat(varargin{:}); 8 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/imup.m: -------------------------------------------------------------------------------- 1 | function varargout = imup(varargin) 2 | % VL_IMUP Upsample an image by two 3 | % J=VL_IMUP(I) doubles the resolution of the image I by using 4 | % bilinear interpolation. 5 | % 6 | % See also: VL_IMDOWN(), VL_HELP(). 7 | [varargout{1:nargout}] = vl_imup(varargin{:}); 8 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/isoctave.m: -------------------------------------------------------------------------------- 1 | function varargout = isoctave(varargin) 2 | % VL_ISOCTAVE Determines whether Octave is running 3 | % X = VL_ISOCTAVE() returns TRUE if the script is running in the 4 | % Octave environment (instead of MATLAB). 5 | [varargout{1:nargout}] = vl_isoctave(varargin{:}); 6 | -------------------------------------------------------------------------------- /vesselSegmentation/CRF/maxflow/make.m: -------------------------------------------------------------------------------- 1 | %MAKE Compiles the maxflowmex library. 2 | % 3 | % (c) 2008 Michael Rubinstein, WDI R&D and IDC 4 | % $Revision: 130 $ 5 | % $Date: 2008-09-02 17:10:31 -0700 (Tue, 02 Sep 2008) $ 6 | % 7 | 8 | mex maxflowmex.cpp maxflow-v3.0/graph.cpp maxflow-v3.0/maxflow.cpp 9 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/tpfp.m: -------------------------------------------------------------------------------- 1 | function varargout = tpfp(varargin) 2 | % VL_TPFP Compute true positives and false positives 3 | % This is an helper function used by VL_PR(), VL_ROC(), VL_DET(). 4 | % 5 | % See also: VL_PR(), VL_ROC(), VL_DET(), VL_HELP(). 6 | [varargout{1:nargout}] = vl_tpfp(varargin{:}); 7 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/ihat.m: -------------------------------------------------------------------------------- 1 | function varargout = ihat(varargin) 2 | % VL_IHAT Inverse vl_hat operator 3 | % OM = VL_IHAT(H) returns a vector OM such that VL_HAT(OM) = H. 4 | % H hast to be 3x3 skew-symmetric. 5 | % 6 | % See also: VL_HAT(), VL_HELP(). 7 | [varargout{1:nargout}] = vl_ihat(varargin{:}); 8 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/xtest/vl_test_mser.m: -------------------------------------------------------------------------------- 1 | function results = vl_test_mser(varargin) 2 | % VL_TEST_MSER 3 | vl_test_init ; 4 | 5 | function s = setup() 6 | s.im = im2uint8(rgb2gray(vl_impattern('roofs1'))) ; 7 | 8 | function test_mser(s) 9 | [regions,frames] = vl_mser(s.im) ; 10 | mask = vl_erfill(s.im, regions(1)) ; 11 | 12 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/imseg.m: -------------------------------------------------------------------------------- 1 | function varargout = imseg(varargin) 2 | % VL_IMSEG Color an image based on the segmentation 3 | % ISEG = VL_IMSEG(I,LABELS) labels ISEG with the average color from I of 4 | % each cluster indicated by LABELS. 5 | % 6 | % See also: VL_HELP(). 7 | [varargout{1:nargout}] = vl_imseg(varargin{:}); 8 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/whistc.m: -------------------------------------------------------------------------------- 1 | function varargout = whistc(varargin) 2 | % VL_WHISTC Weighted histogram 3 | % H = VL_WHISTC(X,W,EDGES) behaves exactly like HISTC(X,EDGES), but 4 | % weights the samples X by W. Samples that have NaN weight are 5 | % skipped. 6 | % 7 | % See also: HITSC(), VL_HELP(). 8 | [varargout{1:nargout}] = vl_whistc(varargin{:}); 9 | -------------------------------------------------------------------------------- /vesselSegmentation/SOSVM/Callbacks/lossCB.m: -------------------------------------------------------------------------------- 1 | 2 | function [delta] = lossCB(param, y, tildey) 3 | % lossCB Compute the loss 4 | % [delta] = lossCB(param, y, tildey) 5 | % OUTPUT: delta: loss 6 | % INPUT: param: parameters 7 | % y: ground truth labelling 8 | % tildey: estimated labelling 9 | 10 | delta = length(find(y~=tildey)); 11 | 12 | end -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/dsigmoid.m: -------------------------------------------------------------------------------- 1 | function varargout = dsigmoid(varargin) 2 | % VL_DSIGMOID Derivative of the sigmoid function 3 | % Y = VL_DSIGMOID(X) returns the derivative of VL_SIGMOID(X). This is 4 | % calculated as - VL_SIGMOID(X) * (1 - VL_SIGMOID(X)). 5 | % 6 | % See also: VL_SIGMOID(X), VL_HELP(). 7 | [varargout{1:nargout}] = vl_dsigmoid(varargin{:}); 8 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/misc/vl_isoctave.m: -------------------------------------------------------------------------------- 1 | function x = vl_isoctave() 2 | % VL_ISOCTAVE Determines whether Octave is running 3 | % X = VL_ISOCTAVE() returns TRUE if the script is running in the 4 | % Octave environment (instead of MATLAB). 5 | 6 | persistent y ; 7 | 8 | if isempty(y) 9 | y = exist('OCTAVE_VERSION','builtin') ~= 0 ; 10 | end 11 | 12 | x = y ; 13 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/cf.m: -------------------------------------------------------------------------------- 1 | function varargout = cf(varargin) 2 | % VL_CF Creates a copy of a figure 3 | % VL_CF() creates a copy of the current figure and returns VL_CF(H0) 4 | % creates a copy of the figure(s) whose handle is H0. H = 5 | % VL_CF(...) returns the handles of the copies. 6 | % 7 | % See also: VL_HELP(). 8 | [varargout{1:nargout}] = vl_cf(varargin{:}); 9 | -------------------------------------------------------------------------------- /Learning/random-forest/classRF_predict_probabilities.m: -------------------------------------------------------------------------------- 1 | 2 | function [probabilities, y_hat] = classRF_predict_probabilities(features, model) 3 | 4 | % get predictions and votes (call to an external package) 5 | [y_hat, votes] = classRF_predict(features, model); 6 | 7 | % now output the probabilities 8 | probabilities = votes(:,2) ./ sum(votes, 2); 9 | 10 | end -------------------------------------------------------------------------------- /Util/eval/plot_froc.m: -------------------------------------------------------------------------------- 1 | 2 | function plot_froc(per_lesion_sensitivity, fpi, x_axis) 3 | 4 | if (exist('x_axis', 'var')==0) 5 | x_axis = 10.^[-1.5 1.3]; 6 | end 7 | semilogx(fpi, per_lesion_sensitivity, 'LineWidth', 2); 8 | xlim(x_axis); 9 | ylim([0 1]); 10 | grid on 11 | xlabel('FPI'); 12 | ylabel('Per lesion sensitivity'); 13 | 14 | end -------------------------------------------------------------------------------- /external/vlfeat/toolbox/misc/vl_getpid.m: -------------------------------------------------------------------------------- 1 | % VL_GETPID Get MATLAB process ID 2 | % VL_GETPID() returns MATLAB process ID. 3 | % 4 | % Algorithm:: 5 | % VL_GETPID() returns the result of the getpid() system call on 6 | % UNIX-like operating systems and of GetCurrentProcessID() on 7 | % Windows. 8 | % 9 | % See also: VL_HELP(). 10 | 11 | % Authors: Andrea Vedaldi 12 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/ikmeanshist.m: -------------------------------------------------------------------------------- 1 | function varargout = ikmeanshist(varargin) 2 | % VL_IKMEANSHIST Compute histogram of quantized data 3 | % H = VL_IKMEANSHIST(K,ASGN) computes the histogram of the IKM clusters 4 | % activated by cluster assignments ASGN. 5 | % 6 | % See also: VL_IKMEANS(), VL_IKMEANSPUSH(), VL_HELP(). 7 | [varargout{1:nargout}] = vl_ikmeanshist(varargin{:}); 8 | -------------------------------------------------------------------------------- /external/matconvnet-master/matlab/xtest/suite/nnsigmoid.m: -------------------------------------------------------------------------------- 1 | classdef nnsigmoid < nntest 2 | methods (Test) 3 | function basic(test) 4 | x = test.randn(5,5,1,1)/test.range ; 5 | y = vl_nnsigmoid(x) ; 6 | dzdy = test.randn(size(y)) ; 7 | dzdx = vl_nnsigmoid(x,dzdy) ; 8 | test.der(@(x) vl_nnsigmoid(x), x, dzdy, dzdx, 1e-3) ; 9 | end 10 | end 11 | end 12 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/ikmeanspush.m: -------------------------------------------------------------------------------- 1 | function varargout = ikmeanspush(varargin) 2 | % VL_IKMEANSPUSH Project data on integer K-means paritions 3 | % I = VL_IKMEANSPUSH(X,C) projects the data X to the integer K-meanns 4 | % clusters of centers C returning the cluster indeces I. 5 | % 6 | % See also: VL_IKMEANS(), VL_HELP(). 7 | [varargout{1:nargout}] = vl_ikmeanspush(varargin{:}); 8 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/aibcutpush.m: -------------------------------------------------------------------------------- 1 | function varargout = aibcutpush(varargin) 2 | % VL_AIBCUTPUSH Quantize based on VL_AIB cut 3 | % Y = VL_AIBCUTPUSH(MAP, X) maps the data X to elements of the AIB 4 | % cut specified by MAP. 5 | % 6 | % The function is equivalent to Y = MAP(X). 7 | % 8 | % See also: VL_HELP(), VL_AIB(). 9 | [varargout{1:nargout}] = vl_aibcutpush(varargin{:}); 10 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/plotgrid.m: -------------------------------------------------------------------------------- 1 | function varargout = plotgrid(varargin) 2 | % VL_PLOTGRID Plot a 2-D grid 3 | % VL_PLOTGRID(X,Y) plots a grid with vertices (X,Y). X and Y are MxN 4 | % matrices, with one entry per vertex. 5 | % 6 | % H=VL_PLOTGRID(...) returns the handle to the grid object. 7 | % 8 | % See also: VL_HELP(). 9 | [varargout{1:nargout}] = vl_plotgrid(varargin{:}); 10 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/dwaffine.m: -------------------------------------------------------------------------------- 1 | function varargout = dwaffine(varargin) 2 | % VL_DWAFFINE Derivative of an affine warp 3 | % [DWX,DWY]=VL_DWAFFINE(X,Y) returns the derivative of the 2-D affine 4 | % warp [WX; WY] = [A T] [X; Y] with respect to the parameters A,T 5 | % computed at points X,Y. 6 | % 7 | % See also: VL_WAFFINE(), VL_HELP(). 8 | [varargout{1:nargout}] = vl_dwaffine(varargin{:}); 9 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/figaspect.m: -------------------------------------------------------------------------------- 1 | function varargout = figaspect(varargin) 2 | % VL_FIGASPECT Set figure aspect ratio 3 | % VL_FIGASPECT(A) sets the current figure aspect ratio to A. It 4 | % leaves the length of the shortest side unaltered. Both the 5 | % screen and paper positions are affected. 6 | % 7 | % See also: VL_PRINTSIZE(), VL_HELP(). 8 | [varargout{1:nargout}] = vl_figaspect(varargin{:}); 9 | -------------------------------------------------------------------------------- /external/matconvnet-master/examples/spatial_transformer/readme.txt: -------------------------------------------------------------------------------- 1 | Example scripts to train a spatial transformer network [1] 2 | for cluttered MNIST dataset. 3 | 4 | Demonstrates how to initialize and train the network. 5 | 6 | References: 7 | ----------- 8 | 1. Jaderberg, Max, Karen Simonyan, and Andrew Zisserman 9 | Spatial transformer networks 10 | Advances in Neural Information Processing Systems, 2015 11 | -------------------------------------------------------------------------------- /external/matconvnet-master/matlab/xtest/suite/nndropout.m: -------------------------------------------------------------------------------- 1 | classdef nndropout < nntest 2 | methods (Test) 3 | function basic(test) 4 | x = test.randn(4,5,10,3) ; 5 | [y,mask] = vl_nndropout(x) ; 6 | dzdy = test.randn(size(y)) ; 7 | dzdx = vl_nndropout(x,dzdy,'mask',mask) ; 8 | test.der(@(x) vl_nndropout(x,'mask',mask), x, dzdy, dzdx, 1e-3*test.range) ; 9 | end 10 | end 11 | end 12 | 13 | -------------------------------------------------------------------------------- /external/matconvnet-master/matlab/+dagnn/@DagNN/setLayerInputs.m: -------------------------------------------------------------------------------- 1 | function v = setLayerInputs(obj, layer, inputs) 2 | %SETLAYERINPUTS Set or change the inputs to a layer 3 | % Example: NET.SETLAYERINPUTS('layerName', {'input1', 'input2', ...}) 4 | 5 | v = [] ; 6 | l = obj.getLayerIndex(layer) ; 7 | for input = inputs 8 | v(end+1) = obj.addVar(char(input)) ; 9 | end 10 | obj.layers(l).inputs = inputs ; 11 | obj.rebuild() ; 12 | -------------------------------------------------------------------------------- /external/matconvnet-master/matlab/+dagnn/Sigmoid.m: -------------------------------------------------------------------------------- 1 | classdef Sigmoid < dagnn.ElementWise 2 | methods 3 | function outputs = forward(obj, inputs, params) 4 | outputs{1} = vl_nnsigmoid(inputs{1}) ; 5 | end 6 | 7 | function [derInputs, derParams] = backward(obj, inputs, params, derOutputs) 8 | derInputs{1} = vl_nnsigmoid(inputs{1}, derOutputs{1}) ; 9 | derParams = {} ; 10 | end 11 | end 12 | end 13 | -------------------------------------------------------------------------------- /external/matconvnet-master/matlab/xtest/suite/nnoffset.m: -------------------------------------------------------------------------------- 1 | classdef nnoffset < nntest 2 | methods (Test) 3 | function basic(test) 4 | param = [.34, .5] ; 5 | x = test.randn(4,5,10,3) ; 6 | y = vl_nnnoffset(x,param) ; 7 | dzdy = test.randn(size(y)) ; 8 | dzdx = vl_nnnoffset(x,param,dzdy) ; 9 | test.der(@(x) vl_nnnoffset(x,param), x, dzdy, dzdx, 1e-3*test.range) ; 10 | end 11 | end 12 | end -------------------------------------------------------------------------------- /external/matconvnet-master/matlab/+dagnn/@DagNN/setLayerParams.m: -------------------------------------------------------------------------------- 1 | function v = setLayerParams(obj, layer, params) 2 | %SETLAYEPARAMS Set or change the parameters of a layer 3 | % Example: NET.SETLAYERPARAMS('layerName', {'param1', 'param2', ...}) 4 | 5 | v = [] ; 6 | l = obj.getLayerIndex(layer) ; 7 | for param = params 8 | v(end+1) = obj.addParam(char(param)) ; 9 | end 10 | obj.layers(l).params = params ; 11 | obj.rebuild() ; 12 | -------------------------------------------------------------------------------- /vesselSegmentation/Features/Features/BCOSFIRE/Preprocessing/rescaleImage.m: -------------------------------------------------------------------------------- 1 | function image = rescaleImage(image, mn, mx) 2 | if ~isempty(image) 3 | maximum = max(image(:)); 4 | minimum = min(image(:)); 5 | if (maximum - minimum) == 0 6 | image = ones(size(image)) * mx; 7 | else 8 | image = (((image-minimum)/(maximum-minimum) * (mx - mn)) + mn); 9 | end 10 | end 11 | end -------------------------------------------------------------------------------- /external/matconvnet-master/matlab/+dagnn/@DagNN/setLayerOutputs.m: -------------------------------------------------------------------------------- 1 | function v = setLayerOutputs(obj, layer, outputs) 2 | %SETLAYEROUTPUTS Set or change the outputs of a layer 3 | % Example: NET.SETLAYEROUTPUTS('layerName', {'output1', 'output2', ...}) 4 | 5 | v = [] ; 6 | l = obj.getLayerIndex(layer) ; 7 | for output = outputs 8 | v(end+1) = obj.addVar(char(output)) ; 9 | end 10 | obj.layers(l).outputs = outputs ; 11 | obj.rebuild() ; 12 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/getpid.m: -------------------------------------------------------------------------------- 1 | function varargout = getpid(varargin) 2 | % VL_GETPID Get MATLAB process ID 3 | % VL_GETPID() returns MATLAB process ID. 4 | % 5 | % Algorithm:: 6 | % VL_GETPID() returns the result of the getpid() system call on 7 | % UNIX-like operating systems and of GetCurrentProcessID() on 8 | % Windows. 9 | % 10 | % See also: VL_HELP(). 11 | [varargout{1:nargout}] = vl_getpid(varargin{:}); 12 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/simdctrl.m: -------------------------------------------------------------------------------- 1 | function varargout = simdctrl(varargin) 2 | % VL_SIMDCTRL Toggle VLFeat SIMD optimizations 3 | % VL_SIMDCTRL() returns TRUE if VLFeat SIMD optimizations are enabled 4 | % and FALSE otherwise. 5 | % 6 | % VL_SIMDCTRL(TRUE) enables VLFeat SIMD optimizations. VL_SIMDCTRL(FALSE) 7 | % disables them. 8 | % 9 | % See also: VL_HELP(). 10 | [varargout{1:nargout}] = vl_simdctrl(varargin{:}); 11 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/xyz2lab.m: -------------------------------------------------------------------------------- 1 | function varargout = xyz2lab(varargin) 2 | % VL_XYZ2LAB Convert XYZ color space to LAB 3 | % J = VL_XYZ2LAB(I) converts the image from XYZ format to LAB format. 4 | % 5 | % VL_XYZ2LAB(I,IL) uses one of the illuminants A, B, C, E, D50, D55, 6 | % D65, D75, D93. The default illuminatn is E. 7 | % 8 | % See also: VL_XYZ2LUV(), VL_HELP(). 9 | [varargout{1:nargout}] = vl_xyz2lab(varargin{:}); 10 | -------------------------------------------------------------------------------- /RedLesionDetection/util/evaluation/getMaLabels.m: -------------------------------------------------------------------------------- 1 | 2 | function [ma_labels] = getMaLabels(labels, candidates_px) 3 | 4 | % initialize ma labels 5 | ma_labels = zeros(length(candidates_px),1); 6 | % for each candidate 7 | for i = 1 : length(candidates_px) 8 | 9 | % check if the intensity on labels is higher than one 10 | ma_labels(i) = any(labels(candidates_px{i})>0); 11 | 12 | end 13 | 14 | end -------------------------------------------------------------------------------- /Util/files/getOnlyFolders.m: -------------------------------------------------------------------------------- 1 | % Open multiple files from a given directory 2 | function sub_folders = getOnlyFolders(main_folder) 3 | 4 | main_folder_dir = dir(main_folder); 5 | sub_folders = extractfield(main_folder_dir, 'name'); 6 | is_folder = [main_folder_dir.isdir]; 7 | sub_folders = sub_folders(is_folder); 8 | sub_folders(strcmp(sub_folders, '..')) = []; 9 | sub_folders(strcmp(sub_folders, '.')) = []; 10 | 11 | end -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/numder2.m: -------------------------------------------------------------------------------- 1 | function varargout = numder2(varargin) 2 | % VL_NUMDER2 Numerical second derivative 3 | % D = VL_NUMDER2(FUNC, X) computes the numerical second derivative of 4 | % the function FUNC at point X. 5 | % 6 | % D = VL_NUMDER2(FUNC, X, ARG1, ARG2, ...) allow to pass extra 7 | % parameters to the function FUNC. 8 | % 9 | % See also: VL_NUMDER(), VL_HELP(). 10 | [varargout{1:nargout}] = vl_numder2(varargin{:}); 11 | -------------------------------------------------------------------------------- /external/matconvnet-master/doc/site/theme/js/base.js: -------------------------------------------------------------------------------- 1 | 2 | /* Highlight */ 3 | $( document ).ready(function() { 4 | hljs.initHighlightingOnLoad(); 5 | $('table').addClass('table table-striped table-hover'); 6 | }); 7 | 8 | 9 | $('body').scrollspy({ 10 | target: '.bs-sidebar', 11 | }); 12 | 13 | 14 | /* Prevent disabled links from causing a page reload */ 15 | $("li.disabled a").click(function() { 16 | event.preventDefault(); 17 | }); 18 | 19 | 20 | 21 | -------------------------------------------------------------------------------- /external/matconvnet-master/matlab/vl_rootnn.m: -------------------------------------------------------------------------------- 1 | function root = vl_rootnn() 2 | %VL_ROOTNN Get the root path of the MatConvNet toolbox. 3 | % VL_ROOTNN() returns the path to the MatConvNet toolbox. 4 | 5 | % Copyright (C) 2014 Andrea Vedaldi. 6 | % All rights reserved. 7 | % 8 | % This file is part of the VLFeat library and is made available under 9 | % the terms of the BSD license (see the COPYING file). 10 | 11 | root = fileparts(fileparts(mfilename('fullpath'))) ; 12 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/flatmap.m: -------------------------------------------------------------------------------- 1 | function varargout = flatmap(varargin) 2 | % VL_FLATMAP Flatten a tree, assigning the label of the root to each node 3 | % [LABELS CLUSTERS] = VL_FLATMAP(MAP) labels each tree of the forest contained 4 | % in MAP. LABELS contains the linear index of the root node in MAP, CLUSTERS 5 | % instead contains a label between 1 and the number of clusters. 6 | % 7 | % See also: VL_HELP(). 8 | [varargout{1:nargout}] = vl_flatmap(varargin{:}); 9 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/gaussian.m: -------------------------------------------------------------------------------- 1 | function varargout = gaussian(varargin) 2 | % VL_GAUSSIAN Standard Gaussian density function 3 | % Y=VL_GAUSSIAN(X) computes the standard (zero mean, unit variance) 4 | % Gaussian density. 5 | % 6 | % To obtain the Gaussian density of standard deviation S do 7 | % 8 | % Y = 1/S * VL_GAUSSIAN(X/S). 9 | % 10 | % See also: VL_DGAUSSIAN(), VL_DDGAUSSIAN(), VL_HELP(). 11 | [varargout{1:nargout}] = vl_gaussian(varargin{:}); 12 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/demo/vl_demo_kmeans_2d.m: -------------------------------------------------------------------------------- 1 | N = 10000 ; 2 | dimension = 2 ; 3 | data = rand(dimension,N) ; 4 | numClusters = 20 ; 5 | cc=hsv(numClusters); 6 | 7 | [centers, assignments] = vl_kmeans(data, numClusters); 8 | 9 | figure 10 | hold on 11 | for i=1:numClusters 12 | plot(data(1,assignments == i),data(2,assignments == i),'.','color',cc(i,:)); 13 | end 14 | plot(centers(1,:),centers(2,:),'k.','MarkerSize',20) 15 | axis off 16 | vl_demo_print('kmeans_2d_rand',0.6); -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/dgaussian.m: -------------------------------------------------------------------------------- 1 | function varargout = dgaussian(varargin) 2 | % VL_DGAUSSIAN Derivative of the Gaussian density function 3 | % Y=VL_DGAUSSIAN(X) evaluates the derivative of the standard Gaussian 4 | % density. 5 | % 6 | % To obtain the Gaussian density of standard deviation S, do 7 | % 8 | % Y = 1/S^2 * VL_DGAUSSIAN(X/S) . 9 | % 10 | % See also: VL_GAUSSIAN(), VL_DDGAUSSIAN(), VL_HELP(). 11 | [varargout{1:nargout}] = vl_dgaussian(varargin{:}); 12 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/ihashfind.m: -------------------------------------------------------------------------------- 1 | function varargout = ihashfind(varargin) 2 | % VL_IHASHFIND Find labels in an integer hash table 3 | % SEL = VL_IHASHFIND(ID, NEXT, K, X) returns a vector SEL of the 4 | % entires in the hash table ID,NEXT,K corresponding to the labels 5 | % stored as columns of X. 6 | % 7 | % The format is the same as for the functino VL_IHASHSUM(). 8 | % 9 | % See also: VL_IHASHSUM(). 10 | [varargout{1:nargout}] = vl_ihashfind(varargin{:}); 11 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/imreadbw.m: -------------------------------------------------------------------------------- 1 | function varargout = imreadbw(varargin) 2 | % VL_IMREADGRAY Reads an image as gray-scale 3 | % I=VL_IMREADGRAY(FILE) reads the image from file FILE and converts the 4 | % result to a gray scale image (DOUBLE storage class ranging in 5 | % [0,1]). 6 | % 7 | % VL_IMREADGRAY(FILE,FMT) specifies the file format FMT (see IMREAD()). 8 | % 9 | % See also: RGB2DOUBLE(), VL_HELP(). 10 | [varargout{1:nargout}] = vl_imreadbw(varargin{:}); 11 | -------------------------------------------------------------------------------- /external/matconvnet-master/matlab/+dagnn/@DagNN/reset.m: -------------------------------------------------------------------------------- 1 | function reset(obj) 2 | %RESET Reset the DagNN 3 | % RESET(obj) resets the DagNN obj. The function clears any intermediate value stored in the DagNN 4 | % object, including parameter gradients. It also calls the reset 5 | % function of every layer. 6 | 7 | [obj.vars.value] = deal([]) ; 8 | [obj.vars.der] = deal([]) ; 9 | [obj.params.der] = deal([]) ; 10 | for l = 1:numel(obj.layers) 11 | obj.layers(l).block.reset() ; 12 | end 13 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/imreadgray.m: -------------------------------------------------------------------------------- 1 | function varargout = imreadgray(varargin) 2 | % VL_IMREADGRAY Reads an image as gray-scale 3 | % I=VL_IMREADGRAY(FILE) reads the image from file FILE and converts the 4 | % result to a gray scale image (DOUBLE storage class ranging in 5 | % [0,1]). 6 | % 7 | % VL_IMREADGRAY(FILE,FMT) specifies the file format FMT (see IMREAD()). 8 | % 9 | % See also: RGB2DOUBLE(), VL_HELP(). 10 | [varargout{1:nargout}] = vl_imreadgray(varargin{:}); 11 | -------------------------------------------------------------------------------- /Scripts/figures/script_classifiers_performance_ma_detection.m: -------------------------------------------------------------------------------- 1 | 2 | % performance of the classifiers 3 | classifiers = {'L1', 'L2', 'k-sup', 'RF'}; 4 | performances = [0.3049, 0.2049, 0.3529, 0.5108]; 5 | 6 | % Plot 7 | figure, bar(performances) 8 | ax = gca; 9 | set(ax, 'XTickLabels', classifiers) 10 | ylabel('FROC score') 11 | xlabel('Classifiers') 12 | text(1:4,performances,num2str(performances','%0.4f'),... 13 | 'HorizontalAlignment','center',... 14 | 'VerticalAlignment','bottom') 15 | grid on -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/ertr.m: -------------------------------------------------------------------------------- 1 | function varargout = ertr(varargin) 2 | % VL_ERTR Transpose exremal regions frames 3 | % F = VL_ERTR(F) transposes the frames F as returned by VL_MSER(). This 4 | % conversion is required as the VL_MSER algorithm considers the column 5 | % index I as the frist image index, while according standard image 6 | % convention the first coordinate is the abscissa X. 7 | % 8 | % See also: VL_MSER(), VL_HELP(). 9 | [varargout{1:nargout}] = vl_ertr(varargin{:}); 10 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/xyz2rgb.m: -------------------------------------------------------------------------------- 1 | function varargout = xyz2rgb(varargin) 2 | % VL_XYZ2RGB Convert XYZ to RGB 3 | % J = VL_XYZ2RGB(I) the XYZ image I in RGB format. 4 | % 5 | % VL_XYZ2RGB(I,WS) uses the RGB workspace WS. WS is a string in 6 | % 7 | % - CIE: E illuminant and 2.2 gamma 8 | % - Adobe: D65 illuminant and 2.2 gamma 9 | % 10 | % The default workspace is CIE. 11 | % 12 | % See also: VL_RGB2XYZ(), VL_HELP(). 13 | [varargout{1:nargout}] = vl_xyz2rgb(varargin{:}); 14 | -------------------------------------------------------------------------------- /vesselSegmentation/CRF/maxflow/instances.inc: -------------------------------------------------------------------------------- 1 | #include "graph.h" 2 | 3 | #ifdef _MSC_VER 4 | #pragma warning(disable: 4661) 5 | #endif 6 | 7 | // Instantiations: 8 | // IMPORTANT: 9 | // flowtype should be 'larger' than tcaptype 10 | // tcaptype should be 'larger' than captype 11 | 12 | template class Graph; 13 | template class Graph; 14 | template class Graph; 15 | template class Graph; 16 | 17 | -------------------------------------------------------------------------------- /external/matconvnet-master/matlab/xtest/vl_test_bnorm.m: -------------------------------------------------------------------------------- 1 | %% 2 | % Test function to compare nn_bnorm and its GPU/CPU implementation with 3 | % using VLFEAT 4 | %% 5 | 6 | gpu = false; 7 | gpu = true ; 8 | 9 | T = 1 ; 10 | x = randn(64,64,32,32,'single') ; 11 | g = randn(32,1,'single') ; 12 | b = randn(32,1,'single') ; 13 | 14 | if gpu 15 | x = gpuArray(x) ; 16 | g = gpuArray(g) ; 17 | b = gpuArray(b) ; 18 | end 19 | 20 | a=vl_nnbnorm(x,g,b); 21 | a_=vl_nnbnorm_old(x,g,b); 22 | 23 | vl_testsim(a,a_) 24 | -------------------------------------------------------------------------------- /external/matconvnet-master/utils/proto/get-protos.sh: -------------------------------------------------------------------------------- 1 | # FCN 2 | wget -nc "https://raw.githubusercontent.com/longjon/caffe/6e3916766c6b63bff07e2cfadf210ee5e46af807/src/caffe/proto/caffe.proto" --output-document=./caffe_6e3916.proto 3 | protoc ./caffe_6e3916.proto --python_out=./ 4 | 5 | # b590f1d (ResNet) 6 | wget -nc "https://raw.githubusercontent.com/BVLC/caffe/b590f1d27eb5cbd9bc7b9157d447706407c68682/src/caffe/proto/caffe.proto" --output-document=./caffe_b590f1d.proto 7 | protoc ./caffe_b590f1d.proto --python_out=./ 8 | -------------------------------------------------------------------------------- /external/matconvnet-master/matlab/xtest/suite/nnsoftmax.m: -------------------------------------------------------------------------------- 1 | classdef nnsoftmax < nntest 2 | properties (TestParameter) 3 | h = {1 2 3} 4 | w = {1 2} 5 | end 6 | methods (Test) 7 | function basic(test,h,w) 8 | d = 10 ; 9 | n = 3 ; 10 | x = test.randn(h,w,d,n)/test.range ; 11 | y = vl_nnsoftmax(x) ; 12 | dzdy = test.randn(size(y)) ; 13 | dzdx = vl_nnsoftmax(x, dzdy) ; 14 | test.der(@(x) vl_nnsoftmax(x), x, dzdy, dzdx, 1e-2) ; 15 | end 16 | end 17 | end 18 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/info.xml: -------------------------------------------------------------------------------- 1 | 3 | 4 | 5 | 2012a 6 | VLFeat 7 | toolbox 8 | ../doc/images/vl_blue.png 9 | ../doc/matlab/ 10 | ../doc/images/vl_blue.png 11 | 12 | 13 | -------------------------------------------------------------------------------- /external/matconvnet-master/doc/site/theme/toc.html: -------------------------------------------------------------------------------- 1 | 11 | -------------------------------------------------------------------------------- /external/matconvnet-master/matlab/+dagnn/SoftMax.m: -------------------------------------------------------------------------------- 1 | classdef SoftMax < dagnn.ElementWise 2 | methods 3 | function outputs = forward(self, inputs, params) 4 | outputs{1} = vl_nnsoftmax(inputs{1}) ; 5 | end 6 | 7 | function [derInputs, derParams] = backward(self, inputs, params, derOutputs) 8 | derInputs{1} = vl_nnsoftmax(inputs{1}, derOutputs{1}) ; 9 | derParams = {} ; 10 | end 11 | 12 | function obj = SoftMax(varargin) 13 | obj.load(varargin) ; 14 | end 15 | end 16 | end 17 | -------------------------------------------------------------------------------- /external/matconvnet-master/matlab/xtest/suite/nnspnorm.m: -------------------------------------------------------------------------------- 1 | classdef nnspnorm < nntest 2 | methods (Test) 3 | function basic(test) 4 | h = 13 ; 5 | w = 17 ; 6 | d = 4 ; 7 | n = 5 ; 8 | param = [3, 3, 0.1, 0.75] ; 9 | x = test.randn(h,w,d,n) ; 10 | y = vl_nnspnorm(x, param) ; 11 | dzdy = test.rand(h, w, d, n) ; 12 | dzdx = vl_nnspnorm(x, param, dzdy) ; 13 | test.der(@(x) vl_nnspnorm(x,param), x, dzdy, dzdx, test.range * 1e-3) ; 14 | end 15 | end 16 | end 17 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/numder.m: -------------------------------------------------------------------------------- 1 | function varargout = numder(varargin) 2 | % VL_NUMDER Numerical derivative 3 | % D = VL_NUMDER(FUNC, X) computes the numerical derivative of the 4 | % function FUNC at point X. X is a real array and is passed as first 5 | % argument of FUNC. 6 | % 7 | % D = VL_NUMDER(FUNC, X, ARG1, ARG2, ...) passes ARG1, ARG2, ... as 8 | % additional arguments to the function FUNC. 9 | % 10 | % See also: VL_NUMDER2(), VL_HELP(). 11 | [varargout{1:nargout}] = vl_numder(varargin{:}); 12 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/xyz2luv.m: -------------------------------------------------------------------------------- 1 | function varargout = xyz2luv(varargin) 2 | % VL_XYZ2LUV Convert XYZ color space to LUV 3 | % J = VL_XYZ2LUV(I) converts the image I in XYZ format to the image J 4 | % in Luv format. 5 | % 6 | % J = VL_XYZ2LUV(I,IL) uses the specified illuminant. The following 7 | % illuminant are supported: A, B, C, E, D50, D55, D65, D75, D93. IL 8 | % is the name of the illuminant. 9 | % 10 | % See also: VL_XYZ2LAB(), VL_HELP(). 11 | [varargout{1:nargout}] = vl_xyz2luv(varargin{:}); 12 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/kmeans/vl_ikmeanspush.m: -------------------------------------------------------------------------------- 1 | % VL_IKMEANSPUSH Project data on integer K-means paritions 2 | % I = VL_IKMEANSPUSH(X,C) projects the data X to the integer K-meanns 3 | % clusters of centers C returning the cluster indeces I. 4 | % 5 | % See also: VL_IKMEANS(), VL_HELP(). 6 | 7 | % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. 8 | % All rights reserved. 9 | % 10 | % This file is part of the VLFeat library and is made available under 11 | % the terms of the BSD license (see the COPYING file). 12 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/imdown.m: -------------------------------------------------------------------------------- 1 | function varargout = imdown(varargin) 2 | % VL_IMDOWN Downsample an image by two 3 | % J = VL_IMDOWN(I) downsamples the image I by half by discarding 4 | % each other pixel. 5 | % 6 | % VL_IMDOWN() accepts the following options: 7 | % 8 | % Method:: Sample 9 | % 'Sample' downsamples the image by discarding 10 | % pixels. 'Average' instead averages groups of 2x2 pixels. 11 | % 12 | % See also: VL_IMUP(), VL_HELP(). 13 | [varargout{1:nargout}] = vl_imdown(varargin{:}); 14 | -------------------------------------------------------------------------------- /Util/files/filterFileNames.m: -------------------------------------------------------------------------------- 1 | function [filteredNames] = filterFileNames(names, extension) 2 | % filterFileNames Filter an array of image names, removing '..' and '.' from 3 | % the list 4 | % Input: names = cellarray of strings with the file names 5 | % extension = string with the desired extension 6 | % Output: filteredNames = cellarray without '.' and '..' 7 | 8 | IndexC = strfind(names, strcat('.', extension)); 9 | Index = find(not(cellfun('isempty', IndexC))); 10 | filteredNames = names(Index); 11 | 12 | end -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/ddgaussian.m: -------------------------------------------------------------------------------- 1 | function varargout = ddgaussian(varargin) 2 | % VL_DDGAUSSIAN Second derivative of the Gaussian density function 3 | % Y=VL_DDGAUSSIAN(X) computes the second derivative of the standard 4 | % Gaussian density. 5 | % 6 | % To obtain the second derivative of the Gaussian density of 7 | % standard deviation S, do 8 | % 9 | % Y = 1/S^3 * VL_DDGAUSSIAN(X/S) . 10 | % 11 | % See also: VL_GAUSSIAN(), VL_DGAUSSIAN(), VL_HELP(). 12 | [varargout{1:nargout}] = vl_ddgaussian(varargin{:}); 13 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/geometry/vl_ihat.m: -------------------------------------------------------------------------------- 1 | function om = vl_ihat( H ) 2 | % VL_IHAT Inverse vl_hat operator 3 | % OM = VL_IHAT(H) returns a vector OM such that VL_HAT(OM) = H. 4 | % H hast to be 3x3 skew-symmetric. 5 | % 6 | % See also: VL_HAT(), VL_HELP(). 7 | 8 | % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. 9 | % All rights reserved. 10 | % 11 | % This file is part of the VLFeat library and is made available under 12 | % the terms of the BSD license (see the COPYING file). 13 | 14 | om = [H(3,2);H(1,3);H(2,1)] ; 15 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/clicksegment.m: -------------------------------------------------------------------------------- 1 | function varargout = clicksegment(varargin) 2 | % VL_CLICKSEGMENT Select a segment by clicking 3 | % [P1,P2] = VL_CLICKSEGMENT() lets the user select a segment in the 4 | % current figure and returns the starting and ending point P1 and P2. 5 | % 6 | % The user can abort the operation by pressing any key. In this case 7 | % the function returns the empty matrix. 8 | % 9 | % See also: VL_CLICK(), VL_CLICKPOINT(), VL_HELP(). 10 | [varargout{1:nargout}] = vl_clicksegment(varargin{:}); 11 | -------------------------------------------------------------------------------- /vesselSegmentation/Util/getMultipleImagesFileNames.m: -------------------------------------------------------------------------------- 1 | % Open multiple files from a given directory 2 | function allNames = getMultipleImagesFileNames(directory) 3 | % Get all file names 4 | allFiles = dir(directory); 5 | % Get only the names of the images inside the folder 6 | allNames = {allFiles.name}; 7 | allNames(strcmp(allNames, '..')) = []; 8 | allNames(strcmp(allNames, '.')) = []; 9 | allNames(strcmp(allNames, 'Thumbs.db')) = []; 10 | allNames = removeFileNamesWithExtension(allNames, 'mat'); 11 | end 12 | -------------------------------------------------------------------------------- /Learning/standardizeCols.m: -------------------------------------------------------------------------------- 1 | function [S, mu, sigma2] = standardizeCols(M, mu, sigma2) 2 | % function [S,mu,sigma2] = standardize(M, mu, sigma2) 3 | % Make each column of M be zero mean, std 1. 4 | % 5 | % If mu, sigma2 are omitted, they are computed from M 6 | 7 | [nrows ncols] = size(M); 8 | 9 | M = double(M); 10 | if nargin < 2 11 | mu = mean(M); 12 | sigma2 = std(M); 13 | ndx = find(sigma2 < eps); 14 | sigma2(ndx) = 1; 15 | end 16 | 17 | S = M - repmat(mu, [nrows 1]); 18 | if ncols > 0 19 | S = S ./ repmat(sigma2, [nrows 1]); 20 | end 21 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/waffine.m: -------------------------------------------------------------------------------- 1 | function varargout = waffine(varargin) 2 | % VL_WAFFINE Apply affine transformation to points 3 | % Y = VL_WAFFINE(A,T,X) applies the affine transformatio (A,T) to points 4 | % X. X contains one point per column. 5 | % 6 | % [Y1,Y2,...] = VL_WAFFINE(A,T,X1,X2,...) applies the affine 7 | % transformation (A,T) to the points (X1,X2,...). Each array 8 | % X1,X2,... contains one of the coordinates of the points. 9 | % 10 | % See also: VL_HELP(). 11 | [varargout{1:nargout}] = vl_waffine(varargin{:}); 12 | -------------------------------------------------------------------------------- /vesselSegmentation/Features/Features/BCOSFIRE/Gabor/onscreen.m: -------------------------------------------------------------------------------- 1 | function onscreen(img) 2 | % VERSION 5/1/04 3 | % CREATED BY: M.B. Wieling and N. Petkov, Groningen University, 4 | % Department of Computer Science, Intelligent Systems 5 | % 6 | % ONSCREEN displays the image on the screen 7 | % ONSCREEN(IMG) displays 8 | % IMG - a matrix with values between 0 and 1 9 | 10 | colormap('gray'); % set display grayscale 11 | imagesc(img); % display the image 12 | axis image; % make sure the image is in the correct proportion 13 | clear all; -------------------------------------------------------------------------------- /RedLesionDetection/util/evaluation/is_in_ground_truth.m: -------------------------------------------------------------------------------- 1 | function it_is_in = is_in_ground_truth(current_candidate, gt_mas) 2 | 3 | % by default, the MA is not in the region 4 | it_is_in = 0; 5 | 6 | % iterate for each of the MAs in the ground truth 7 | for j = 1 : length(gt_mas.PixelIdxList) 8 | % check if current MA overlaps the j-th MA in the ground truth 9 | if ~isempty(intersect(current_candidate, gt_mas.PixelIdxList{j})) 10 | it_is_in = j; 11 | break 12 | end 13 | end 14 | 15 | end -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/linespec2prop.m: -------------------------------------------------------------------------------- 1 | function varargout = linespec2prop(varargin) 2 | % VL_LINESPEC2PROP Convert PLOT style line specs to line properties 3 | % PROPR = VL_LINESPEC2PROP(SPEC) converts the string SPEC to a cell 4 | % array of properties PROPR. SPEC is in the format of PLOT(). 5 | % 6 | % If SPEC is not a line spec, the string SPEC is returned unaltered 7 | % as the only element of the cell array PROPR. 8 | % 9 | % See also: VL_PLOTFRAME(), PLOT(), VL_HELP(). 10 | [varargout{1:nargout}] = vl_linespec2prop(varargin{:}); 11 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/ubcread.m: -------------------------------------------------------------------------------- 1 | function varargout = ubcread(varargin) 2 | % SIFTREAD Read Lowe's SIFT implementation data files 3 | % [F,D] = VL_UBCREAD(FILE) reads the frames F and the descriptors D 4 | % from FILE in UBC (Lowe's original implementation of SIFT) format 5 | % and returns F and D as defined by VL_SIFT(). 6 | % 7 | % VL_UBCREAD(FILE, 'FORMAT', 'OXFORD') assumes the format used by 8 | % Oxford VGG implementations . 9 | % 10 | % See also: VL_SIFT(), VL_HELP(). 11 | [varargout{1:nargout}] = vl_ubcread(varargin{:}); 12 | -------------------------------------------------------------------------------- /vesselSegmentation/Util/openVesselLabels.m: -------------------------------------------------------------------------------- 1 | function [labels] = openVesselLabels(folder) 2 | 3 | disp(strcat('Loading labels from ', [' '], folder)); 4 | 5 | % Open images, masks and labels 6 | disp('Loading labels'); 7 | labels = openMultipleImages(folder); 8 | 9 | % For each image 10 | for i = 1:length(labels) 11 | % Encode labels as logical matrices 12 | y = labels{i}; 13 | labels{i} = y(:,:,1) > 0; 14 | end 15 | 16 | fprintf('\n'); 17 | disp('Loading finished'); 18 | 19 | end 20 | -------------------------------------------------------------------------------- /RedLesionDetection/util/preprocessing/getTopHatTransformation.m: -------------------------------------------------------------------------------- 1 | 2 | function [tophat] = getTopHatTransformation(I, l) 3 | 4 | % for each angle, compute a morphological closing 5 | angles = 0:15:179; 6 | closings = zeros(size(I,1), size(I,2), length(angles)); 7 | for i = 1 : length(angles) 8 | closings(:,:,i) = imclose(I, strel('line', l, angles(i))); 9 | end 10 | 11 | % compute top hat and remove elements outside the mask and inside the 12 | % od 13 | tophat = (imcomplement(I - min(closings, [], 3)) - 1); 14 | 15 | end -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/noprefix.m: -------------------------------------------------------------------------------- 1 | function varargout = noprefix(varargin) 2 | % VL_NOPREFIX Create a prefix-less version of VLFeat commands 3 | % VL_NOPREFIX() creats prefix-less stubs for VLFeat functions 4 | % (e.g. SIFT for VL_SIFT). This function is seldom used as the stubs 5 | % are included in the VLFeat binary distribution anyways. Moreover, 6 | % on UNIX platforms, the stubs are generally constructed by the 7 | % Makefile. 8 | % 9 | % See also: VL_COMPILE(), VL_HELP(). 10 | [varargout{1:nargout}] = vl_noprefix(varargin{:}); 11 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/sigmoid.m: -------------------------------------------------------------------------------- 1 | function varargout = sigmoid(varargin) 2 | % VL_SIGMOID Sigmoid function 3 | % Y = VL_SIGMOID(X) returns 4 | % 5 | % Y = 1 ./ (1 + EXP(X)) ; 6 | % 7 | % Remark:: 8 | % Useful properties of the sigmoid function are: 9 | % 10 | % - 1 - VL_SIGMOID(X) = VL_SIGMOID(-X) 11 | % - Centered sigmoid: 2 * VL_SIGMOID(X) - 1 ; 12 | % - VL_SIGMOID(X) = (EXP(X/2) - EXP(X/2)) / (EXP(X/2) + EXP(X/2)) 13 | % 14 | % See also: VL_DSIGMOID(), VL_HELP(). 15 | [varargout{1:nargout}] = vl_sigmoid(varargin{:}); 16 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/xtest/vl_test_grad.m: -------------------------------------------------------------------------------- 1 | function results = vl_test_grad(varargin) 2 | % VL_TEST_GRAD 3 | vl_test_init ; 4 | 5 | function s = setup() 6 | s.I = rand(150,253) ; 7 | s.I_small = rand(2,2) ; 8 | 9 | function test_equiv(s) 10 | vl_assert_equal(gradient(s.I), vl_grad(s.I)) ; 11 | 12 | function test_equiv_small(s) 13 | vl_assert_equal(gradient(s.I_small), vl_grad(s.I_small)) ; 14 | 15 | function test_equiv_forward(s) 16 | Ix = diff(s.I,2,1) ; 17 | Iy = diff(s.I,2,1) ; 18 | 19 | vl_assert_equal(gradient(s.I_small), vl_grad(s.I_small)) ; 20 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/xtest/vl_test_plotbox.m: -------------------------------------------------------------------------------- 1 | function results = vl_test_plotbox(varargin) 2 | % VL_TEST_PLOTBOX 3 | vl_test_init ; 4 | 5 | function test_basic(s) 6 | figure(1) ; clf ; 7 | vl_plotbox([-1 -1 1 1]') ; 8 | xlim([-2 2]) ; 9 | ylim([-2 2]) ; 10 | close(1) ; 11 | 12 | function test_multiple(s) 13 | figure(1) ; clf ; 14 | randn('state', 0) ; 15 | vl_plotbox(randn(4,10)) ; 16 | close(1) ; 17 | 18 | function test_style(s) 19 | figure(1) ; clf ; 20 | randn('state', 0) ; 21 | vl_plotbox(randn(4,10), 'r-.', 'LineWidth', 3) ; 22 | close(1) ; 23 | 24 | 25 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/erfill.m: -------------------------------------------------------------------------------- 1 | function varargout = erfill(varargin) 2 | % VL_ERFILL Fill extremal region 3 | % MEMBERS=VL_ERFILL(I,ER) returns the list MEMBERS of the pixels which 4 | % belongs to the extremal region represented by the pixel ER. 5 | % 6 | % The selected region is the one that contains pixel ER and of 7 | % intensity I(ER). 8 | % 9 | % I must be of class UINT8 and ER must be a (scalar) index of the 10 | % region representative point. 11 | % 12 | % See also: VL_MSER(), VL_HELP(). 13 | [varargout{1:nargout}] = vl_erfill(varargin{:}); 14 | -------------------------------------------------------------------------------- /vesselSegmentation/SOSVM/Util/getfeatures.m: -------------------------------------------------------------------------------- 1 | 2 | function [phi] = getfeatures(x, y) 3 | 4 | % Get the feature vectors 5 | X = x{3}; 6 | 7 | % Compute the unary features 8 | phi_u = zeros(size(X, 1), size(X, 2) * 2); 9 | 10 | % Take the Kronecker product of the features with the corresponding 11 | % binary vector, according to the given labeling y 12 | phi_u(y==0, :) = kron(X(y==0, :), [1 0]); 13 | phi_u(y==1, :) = kron(X(y==1, :), [0 1]); 14 | 15 | % Return the unary features 16 | phi = phi_u; 17 | 18 | end 19 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/misc/vl_simdctrl.m: -------------------------------------------------------------------------------- 1 | % VL_SIMDCTRL Toggle VLFeat SIMD optimizations 2 | % VL_SIMDCTRL() returns TRUE if VLFeat SIMD optimizations are enabled 3 | % and FALSE otherwise. 4 | % 5 | % VL_SIMDCTRL(TRUE) enables VLFeat SIMD optimizations. VL_SIMDCTRL(FALSE) 6 | % disables them. 7 | % 8 | % See also: VL_HELP(). 9 | 10 | % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. 11 | % All rights reserved. 12 | % 13 | % This file is part of the VLFeat library and is made available under 14 | % the terms of the BSD license (see the COPYING file). 15 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/misc/vl_version.m: -------------------------------------------------------------------------------- 1 | % VL_VERSION Obtain VLFeat version information 2 | % VL_VERSION() prints VLFeat version number. VL_VERSION('VERBOSE') 3 | % prints additional information. 4 | % 5 | % STR = VL_VERSION(...) returns the ouptut in a string rather than 6 | % printing it. 7 | % 8 | % See also: VL_HELP(). 9 | 10 | % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. 11 | % All rights reserved. 12 | % 13 | % This file is part of the VLFeat library and is made available under 14 | % the terms of the BSD license (see the COPYING file). 15 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/threads.m: -------------------------------------------------------------------------------- 1 | function varargout = threads(varargin) 2 | %VL_THREADS Control VLFeat computational threads 3 | % [NUM,MAXNUM] = VL_THREADS() returns the current number of 4 | % computational threads NUM and the maximum possible number MAXNUM. 5 | % 6 | % VL_THREADS(NUM) sets the current number of threads to the 7 | % specified value. NUM = VL_THREADS(NUM) does the same, but returns 8 | % the *previous* number of computational threads as well. 9 | % 10 | % See also: VL_HELP(). 11 | [varargout{1:nargout}] = vl_threads(varargin{:}); 12 | -------------------------------------------------------------------------------- /vesselSegmentation/Features/Features/nguyen/noisefiltering.m: -------------------------------------------------------------------------------- 1 | function postprocessedimg = noisefiltering(segmentedimg,noisesize) 2 | % Remove all disconnected noisy objects (small sizes) and keep only the vascular network 3 | 4 | if nargin == 1, noisesize = 100; end 5 | 6 | postprocessedimg = zeros(size(segmentedimg)); 7 | [segmentedimg_lb,nobjs] = bwlabel(segmentedimg); 8 | for i = 1:nobjs 9 | cur_obj = find(segmentedimg_lb == i); 10 | cur_size = numel(cur_obj); 11 | if cur_size > noisesize 12 | postprocessedimg(cur_obj) = 1; 13 | end 14 | end 15 | 16 | end -------------------------------------------------------------------------------- /Util/openImagesFromFolder.m: -------------------------------------------------------------------------------- 1 | 2 | function [images] = openImagesFromFolder(folder) 3 | 4 | % Get file names 5 | [allNames] = getImageFilenames(folder); 6 | % Get all the images in the directory and count the number of pixels 7 | images = cell(length(allNames), 1); 8 | for i = 1:length(allNames) 9 | currentfilename = strtrim(allNames{i}); 10 | currentfilename = strrep(currentfilename, '''', ''); 11 | currentImage = imread(strcat(folder, filesep, currentfilename)); 12 | images{i} = currentImage; % Assign the image 13 | end 14 | 15 | end -------------------------------------------------------------------------------- /external/vlfeat/toolbox/misc/vl_matlabversion.m: -------------------------------------------------------------------------------- 1 | function v = vl_matlabversion 2 | % VL_MATLABVERSION Return MATLAB version as an integer 3 | % V = VL_MATLABVERSION() returns MATLAB version encoded as an 4 | % ingeger value. MATLAB version string has the format 5 | % 6 | % MAJOR.MINOR.SERVICEPACK 7 | % 8 | % The integer V has the value 9 | % 10 | % V = MAJOR * 10000 + MINOR * 100 + SERVICEPACK. 11 | % 12 | % For example, in MATLAB 7.10.0 (R2010a), V equals 70100. 13 | % 14 | % See also: VL_HELP(). 15 | 16 | v = [1e4 1e2 1e0] * sscanf(version, '%d.%d.%d') ; 17 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/mser/vl_ertr.m: -------------------------------------------------------------------------------- 1 | function f = vl_ertr(f) ; 2 | % VL_ERTR Transpose exremal regions frames 3 | % F = VL_ERTR(F) transposes the frames F as returned by VL_MSER(). This 4 | % conversion is required as the VL_MSER algorithm considers the column 5 | % index I as the frist image index, while according standard image 6 | % convention the first coordinate is the abscissa X. 7 | % 8 | % See also: VL_MSER(), VL_HELP(). 9 | 10 | if size(f,1) ~= 5 11 | error('F is not in the right format') ; 12 | end 13 | 14 | % adjust convention 15 | f = f([2 1 5 4 3],:) ; 16 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/lbpfliplr.m: -------------------------------------------------------------------------------- 1 | function varargout = lbpfliplr(varargin) 2 | % VL_LBPFLIPLR Flip LBP features left-right 3 | % F = VL_LBPFLIPLR(F) flips the LBP features F from left to 4 | % right. 5 | % 6 | % Example:: 7 | % The following instructions return results almost identical 8 | % assuming that the width of I is exactly divisible by the cell 9 | % size: 10 | % F = VL_LBP(FLIPLR(I), 8) 11 | % F_ = VL_LBPFLIPLR(VL_LBP(I), 8) 12 | % 13 | % See also: VL_LBP(), VL_HELP(). 14 | [varargout{1:nargout}] = vl_lbpfliplr(varargin{:}); 15 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/xtest/vl_test_hikmeans.m: -------------------------------------------------------------------------------- 1 | function results = vl_test_hikmeans(varargin) 2 | % VL_TEST_IKMEANS 3 | vl_test_init ; 4 | 5 | function s = setup() 6 | rand('state',0) ; 7 | s.data = uint8(rand(2,1000) * 255) ; 8 | 9 | function test_basic(s) 10 | [tree, assign] = vl_hikmeans(s.data,3,100) ; 11 | assign_ = vl_hikmeanspush(tree, s.data) ; 12 | vl_assert_equal(assign,assign_) ; 13 | 14 | function test_elkan(s) 15 | [tree, assign] = vl_hikmeans(s.data,3,100,'method','elkan') ; 16 | assign_ = vl_hikmeanspush(tree, s.data) ; 17 | vl_assert_equal(assign,assign_) ; 18 | -------------------------------------------------------------------------------- /vesselSegmentation/Util/getLabeledDataFilenames.m: -------------------------------------------------------------------------------- 1 | 2 | function [images, labels, masks] = getLabeledDataFilenames(folder) 3 | 4 | imagesFolder = strcat(folder, filesep, 'images', filesep); 5 | masksFolder = strcat(folder, filesep, 'masks', filesep); 6 | labelsFolder = strcat(folder, filesep, 'labels', filesep); 7 | 8 | % Open images, masks and labels for the training set 9 | images = getMultipleImagesFileNames(imagesFolder); 10 | masks = getMultipleImagesFileNames(masksFolder); 11 | labels = getMultipleImagesFileNames(labelsFolder); 12 | 13 | end 14 | -------------------------------------------------------------------------------- /external/RF_Class_C/texput.log: -------------------------------------------------------------------------------- 1 | This is pdfTeX, Version 3.1415926-2.5-1.40.14 (TeX Live 2013/Debian) (format=mex 2017.10.12) 27 OCT 2017 11:31 2 | entering extended mode 3 | restricted \write18 enabled. 4 | %&-line parsing enabled. 5 | **mex_ClassificationRF_train.cpp classRF.cpp classTree.o rfutils.o rfsub.o coku 6 | s.o -o mexClassRF_train -lgfortran -lm -DMATLAB 7 | 8 | ! Emergency stop. 9 | <*> mex_ClassificationRF_train.cpp 10 | classRF.cpp classTree.o rfutils.o rfsub.o... 11 | End of file on the terminal! 12 | 13 | No pages of output. 14 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/geometry/vl_hat.m: -------------------------------------------------------------------------------- 1 | function H = vl_hat(om) 2 | % VL_HAT Hat operator 3 | % H = VL_HAT(OM) returns the skew symmetric matrix by taking the "hat" 4 | % of the 3D vector OM. 5 | % 6 | % See also: VL_IHAT(), VL_HELP(). 7 | 8 | % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. 9 | % All rights reserved. 10 | % 11 | % This file is part of the VLFeat library and is made available under 12 | % the terms of the BSD license (see the COPYING file). 13 | 14 | H = [0 -om(3) om(2) ; 15 | om(3) 0 -om(1) ; 16 | -om(2) om(1) 0 ] ; 17 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/xtest/vl_test_ikmeans.m: -------------------------------------------------------------------------------- 1 | function results = vl_test_ikmeans(varargin) 2 | % VL_TEST_IKMEANS 3 | vl_test_init ; 4 | 5 | function s = setup() 6 | rand('state',0) ; 7 | s.data = uint8(rand(2,1000) * 255) ; 8 | 9 | function test_basic(s) 10 | [centers, assign] = vl_ikmeans(s.data,100) ; 11 | assign_ = vl_ikmeanspush(s.data, centers) ; 12 | vl_assert_equal(assign,assign_) ; 13 | 14 | function test_elkan(s) 15 | [centers, assign] = vl_ikmeans(s.data,100,'method','elkan') ; 16 | assign_ = vl_ikmeanspush(s.data, centers) ; 17 | vl_assert_equal(assign,assign_) ; 18 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/aib/vl_aibcutpush.m: -------------------------------------------------------------------------------- 1 | function y = vl_aibcutpush(map, x) 2 | % VL_AIBCUTPUSH Quantize based on VL_AIB cut 3 | % Y = VL_AIBCUTPUSH(MAP, X) maps the data X to elements of the AIB 4 | % cut specified by MAP. 5 | % 6 | % The function is equivalent to Y = MAP(X). 7 | % 8 | % See also: VL_HELP(), VL_AIB(). 9 | 10 | % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. 11 | % All rights reserved. 12 | % 13 | % This file is part of the VLFeat library and is made available under 14 | % the terms of the BSD license (see the COPYING file). 15 | 16 | y = map(x) ; 17 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/matlabversion.m: -------------------------------------------------------------------------------- 1 | function varargout = matlabversion(varargin) 2 | % VL_MATLABVERSION Return MATLAB version as an integer 3 | % V = VL_MATLABVERSION() returns MATLAB version encoded as an 4 | % ingeger value. MATLAB version string has the format 5 | % 6 | % MAJOR.MINOR.SERVICEPACK 7 | % 8 | % The integer V has the value 9 | % 10 | % V = MAJOR * 10000 + MINOR * 100 + SERVICEPACK. 11 | % 12 | % For example, in MATLAB 7.10.0 (R2010a), V equals 70100. 13 | % 14 | % See also: VL_HELP(). 15 | [varargout{1:nargout}] = vl_matlabversion(varargin{:}); 16 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/special/vl_dsigmoid.m: -------------------------------------------------------------------------------- 1 | function y = vl_dsigmoid(x) 2 | % VL_DSIGMOID Derivative of the sigmoid function 3 | % Y = VL_DSIGMOID(X) returns the derivative of VL_SIGMOID(X). This is 4 | % calculated as - VL_SIGMOID(X) * (1 - VL_SIGMOID(X)). 5 | % 6 | % See also: VL_SIGMOID(X), VL_HELP(). 7 | 8 | % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. 9 | % All rights reserved. 10 | % 11 | % This file is part of the VLFeat library and is made available under 12 | % the terms of the BSD license (see the COPYING file). 13 | 14 | t = vl_sigmoid(x) ; 15 | y = t .* (1 - t) ; 16 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/vl_root.m: -------------------------------------------------------------------------------- 1 | function path = vl_root 2 | % VL_ROOT Obtain VLFeat root path 3 | % PATH = VL_ROOT() returns the path to the VLFeat installation. 4 | % 5 | % See also: VL_SETUP(), VL_HELP(). 6 | 7 | % Authors: Andrea Vedaldi and Brian Fulkerson 8 | 9 | % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. 10 | % All rights reserved. 11 | % 12 | % This file is part of the VLFeat library and is made available under 13 | % the terms of the BSD license (see the COPYING file). 14 | 15 | [a,b,c] = fileparts(mfilename('fullpath')) ; 16 | [a,b,c] = fileparts(a) ; 17 | path = a ; 18 | -------------------------------------------------------------------------------- /vesselSegmentation/Features/Features/BCOSFIRE/COSFIRE/getDoGBankResponse.m: -------------------------------------------------------------------------------- 1 | function DoGBank = getDoGBankResponse(inputImage,params) 2 | 3 | sz = size(inputImage); 4 | 5 | DoGBank = zeros(sz(1),sz(2),length(params.sigmalist),length(params.polaritylist)); 6 | 7 | for i = 1:length(params.sigmalist) 8 | for j = 1:length(params.polaritylist) 9 | DoGBank(:,:,i,j) = getDoG(inputImage, ... 10 | params.sigmalist(i), ... 11 | params.polaritylist(j), ... 12 | params.sigmaratio, ... 13 | 0, ... 14 | params.halfwaverect); 15 | end 16 | end -------------------------------------------------------------------------------- /external/matconvnet-master/matlab/+dagnn/LRN.m: -------------------------------------------------------------------------------- 1 | classdef LRN < dagnn.ElementWise 2 | properties 3 | param = [5 1 0.0001/5 0.75] 4 | end 5 | 6 | methods 7 | function outputs = forward(obj, inputs, params) 8 | outputs{1} = vl_nnnormalize(inputs{1}, obj.param) ; 9 | end 10 | 11 | function [derInputs, derParams] = backward(obj, inputs, param, derOutputs) 12 | derInputs{1} = vl_nnnormalize(inputs{1}, obj.param, derOutputs{1}) ; 13 | derParams = {} ; 14 | end 15 | 16 | function obj = LRN(varargin) 17 | obj.load(varargin) ; 18 | end 19 | end 20 | end 21 | -------------------------------------------------------------------------------- /vesselSegmentation/Features/Features/BCOSFIRE/COSFIRE/imshift.m: -------------------------------------------------------------------------------- 1 | function A = imshift(im, shiftRows, shiftCols) 2 | 3 | A = zeros(size(im)); 4 | 5 | if shiftRows >= 0 && shiftCols >= 0 6 | A(1+shiftRows:end,1+shiftCols:end) = im(1:end-shiftRows,1:end-shiftCols); 7 | elseif shiftRows >= 0 && shiftCols < 0 8 | A(1+shiftRows:end,1:end+shiftCols) = im(1:end-shiftRows,1-shiftCols:end); 9 | elseif shiftRows < 0 && shiftCols >= 0 10 | A(1:end+shiftRows,1+shiftCols:end) = im(1-shiftRows:end,1:end-shiftCols); 11 | else 12 | A(1:end+shiftRows,1:end+shiftCols) = im(1-shiftRows:end,1-shiftCols:end); 13 | end -------------------------------------------------------------------------------- /external/matconvnet-master/matlab/+dagnn/NormOffset.m: -------------------------------------------------------------------------------- 1 | classdef NormOffset < dagnn.ElementWise 2 | properties 3 | param = [1 0.5] 4 | end 5 | 6 | methods 7 | function outputs = forward(obj, inputs, params) 8 | outputs{1} = vl_nnnoffset(inputs{1}, obj.param) ; 9 | end 10 | 11 | function [derInputs, derParams] = backward(obj, inputs, param, derOutputs) 12 | derInputs{1} = vl_nnnoffset(inputs{1}, obj.param, derOutputs{1}) ; 13 | derParams = {} ; 14 | end 15 | 16 | function obj = NormOffset(varargin) 17 | obj.load(varargin) ; 18 | end 19 | end 20 | end 21 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/imop/vl_imsc.m: -------------------------------------------------------------------------------- 1 | function J=vl_imsc(I) 2 | % VL_IMSC Scale image 3 | % J=VL_IMSC(I) scales the range of the gray-scale or color image I to 4 | % fit in the interval [0,1]. 5 | % 6 | % See also: VL_HELP(). 7 | 8 | % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. 9 | % All rights reserved. 10 | % 11 | % This file is part of the VLFeat library and is made available under 12 | % the terms of the BSD license (see the COPYING file). 13 | 14 | if ~isfloat(I) 15 | I = im2double(I) ; 16 | end 17 | 18 | J = I - min(I(:)) ; 19 | J = J / max(J(:)) ; 20 | J = max(min(J, 1), 0) ; 21 | -------------------------------------------------------------------------------- /RedLesionDetection/util/deep_learning/get_cnn_training_data.m: -------------------------------------------------------------------------------- 1 | 2 | function [current_windows, current_labels, candidates_coordinates] = get_cnn_training_data(I, candidate, gt_labels, show_windows_in_images, mask) 3 | 4 | % retrieve a set of windows centered on each of the MA centers 5 | [current_windows, candidates_coordinates] = maWindowsExtraction(candidate, I, 32, show_windows_in_images, mask); 6 | 7 | % get the labels 8 | if ~isempty(gt_labels) 9 | [current_labels] = getMaLabels(gt_labels, candidates_coordinates); 10 | else 11 | current_labels = []; 12 | end 13 | 14 | end -------------------------------------------------------------------------------- /external/matconvnet-master/matlab/+dagnn/SpatialNorm.m: -------------------------------------------------------------------------------- 1 | classdef SpatialNorm < dagnn.ElementWise 2 | properties 3 | param = [2 2 10 2] 4 | end 5 | 6 | methods 7 | function outputs = forward(obj, inputs, params) 8 | outputs{1} = vl_nnspnorm(inputs{1}, obj.param) ; 9 | end 10 | 11 | function [derInputs, derParams] = backward(obj, inputs, param, derOutputs) 12 | derInputs{1} = vl_nnspnorm(inputs{1}, obj.param, derOutputs{1}) ; 13 | derParams = {} ; 14 | end 15 | 16 | function obj = SpatialNorm(varargin) 17 | obj.load(varargin) ; 18 | end 19 | end 20 | end 21 | -------------------------------------------------------------------------------- /RedLesionDetection/util/deep_learning/prepareCNNforExtractingFeatures.m: -------------------------------------------------------------------------------- 1 | 2 | function [new_net] = prepareCNNforExtractingFeatures(net) 3 | 4 | % copy the net 5 | new_net = net; 6 | 7 | % last two layers (fully connected) have to be removed 8 | to_remove = []; 9 | % dropout layers as well 10 | for i = 1 : length(net.layers)-2 11 | if (strcmp(net.layers{i}.type, 'dropout')) 12 | to_remove = cat(2, to_remove, i); 13 | end 14 | end 15 | 16 | % now we remove them 17 | new_net.layers(to_remove) = []; 18 | new_net.layers = new_net.layers(1:end-2); 19 | 20 | end -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/histmarg.m: -------------------------------------------------------------------------------- 1 | function varargout = histmarg(varargin) 2 | % VL_HISTMARG Marginal of histogram 3 | % H = VL_HISTMARG(H, DIMS) marginalizes the historgram H w.r.t the 4 | % dimensions DIMS. This is done by summing out all dimensions not 5 | % listed in DIMS and deleting them. 6 | % 7 | % Remark:: 8 | % If DIMS lists only one dimension, the returned histogram H is a 9 | % column vector. Notice that this way of deleting dimensions is 10 | % not always consistent with the SQUEEZE function. 11 | % 12 | % See also: VL_HELP(). 13 | [varargout{1:nargout}] = vl_histmarg(varargin{:}); 14 | -------------------------------------------------------------------------------- /vesselSegmentation/SOSVM/Util/pairwisePotentials.m: -------------------------------------------------------------------------------- 1 | 2 | function [phi_p] = pairwisePotentials(config, x, y) 3 | 4 | % Get the mask 5 | mask = x{2}; 6 | 7 | % Get the pairwise features 8 | pairwiseFeatures = x{4}; 9 | 10 | % Get the pairwises using the MEX implementation 11 | phi_p = - pairwisePart(int32(size(mask, 2)), int32(size(mask, 1)), ... 12 | int16(y), (single(pairwiseFeatures)), int32(size(pairwiseFeatures, 3)), ... 13 | single(config.theta_p.finalValues)); 14 | 15 | if (~isempty(phi_p)) 16 | phi_p = permute(phi_p,[2 1 3]); 17 | end 18 | 19 | end -------------------------------------------------------------------------------- /external/vlfeat/toolbox/kmeans/vl_ikmeanshist.m: -------------------------------------------------------------------------------- 1 | function h = vl_ikmeanshist(K,asgn) 2 | % VL_IKMEANSHIST Compute histogram of quantized data 3 | % H = VL_IKMEANSHIST(K,ASGN) computes the histogram of the IKM clusters 4 | % activated by cluster assignments ASGN. 5 | % 6 | % See also: VL_IKMEANS(), VL_IKMEANSPUSH(), VL_HELP(). 7 | 8 | % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. 9 | % All rights reserved. 10 | % 11 | % This file is part of the VLFeat library and is made available under 12 | % the terms of the BSD license (see the COPYING file). 13 | 14 | h = zeros(K,1) ; 15 | h = vl_binsum(h, 1, double(asgn)) ; 16 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/misc/vl_ihashfind.m: -------------------------------------------------------------------------------- 1 | % VL_IHASHFIND Find labels in an integer hash table 2 | % SEL = VL_IHASHFIND(ID, NEXT, K, X) returns a vector SEL of the 3 | % entires in the hash table ID,NEXT,K corresponding to the labels 4 | % stored as columns of X. 5 | % 6 | % The format is the same as for the functino VL_IHASHSUM(). 7 | % 8 | % See also: VL_IHASHSUM(). 9 | 10 | % Author: Andrea Vedaldi 11 | 12 | % Copyright (C) 2008-12 Andrea Vedaldi. 13 | % All rights reserved. 14 | % 15 | % This file is part of the VLFeat library and is made available under 16 | % the terms of the BSD license (see the COPYING file). 17 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/witps.m: -------------------------------------------------------------------------------- 1 | function varargout = witps(varargin) 2 | % VL_WITPS Inverse thin-plate spline warping 3 | % [X1,X2]=VL_WITPS(XP1,XP2,Y,Yp) computes the inverse thin-plate spline 4 | % (TPS) warp of the points XP1,XP2. 5 | % 6 | % Remark:: 7 | % The inverse of a thin-plate spline in general is NOT a 8 | % thin-plate spline and some splines do not have an inverse. This 9 | % function uses Gauss-Newton to compute a set of points (X1,X2) 10 | % such that [XP1,XP2]=VL_WTPS(X1,X2,Y,Yp). 11 | % 12 | % See also: VL_WTPS(), VL_HELP(). 13 | [varargout{1:nargout}] = vl_witps(varargin{:}); 14 | -------------------------------------------------------------------------------- /Util/files/removeFileNamesWithExtension.m: -------------------------------------------------------------------------------- 1 | 2 | function [fileNames] = removeFileNamesWithExtension(fileNames, extension) 3 | 4 | % newNames = {}; 5 | % 6 | % % for each file name 7 | % for i = 1 : length(fileNames) 8 | % % if the filename contains the extension, it is saved in the list 9 | % filename = fileNames{i}; 10 | % if ~strcmp(filename(end-3:end), strcat('.',extension)) 11 | % newNames = cat(1, newNames, filename); 12 | % end 13 | % 14 | % end 15 | 16 | fileNames(~cellfun(@isempty,regexp(fileNames,strcat('.',extension), 'once'))) = []; 17 | 18 | 19 | end -------------------------------------------------------------------------------- /external/vlfeat/toolbox/special/vl_rcos.m: -------------------------------------------------------------------------------- 1 | function y = vl_rcos(r,x) 2 | % VL_RCOS RCOS function 3 | % Y = VL_RCOS(R,X) computes the RCOS function with roll-off R. 4 | % 5 | % See also: VL_HELP(). 6 | 7 | % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. 8 | % All rights reserved. 9 | % 10 | % This file is part of the VLFeat library and is made available under 11 | % the terms of the BSD license (see the COPYING file). 12 | 13 | x = abs(x) ; 14 | if(r > 0) 15 | y = (x < (1 - r)) + ... 16 | 0.5 * (1 + cos((1 - r - x)/r*pi)) .* ... 17 | (x <= 1) .* (x >= 1 - r) ; 18 | else 19 | y = (abs(x) <= 1) ; 20 | end 21 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/xtest/vl_test_imwbackward.m: -------------------------------------------------------------------------------- 1 | function results = vl_test_imwbackward(varargin) 2 | % VL_TEST_IMWBACKWARD 3 | vl_test_init ; 4 | 5 | function s = setup() 6 | s.I = im2double(imread(fullfile(vl_root,'data','spots.jpg'))) ; 7 | 8 | function test_identity(s) 9 | xr = 1:size(s.I,2) ; 10 | yr = 1:size(s.I,1) ; 11 | [x,y] = meshgrid(xr,yr) ; 12 | vl_assert_almost_equal(s.I, vl_imwbackward(xr,yr,s.I,x,y)) ; 13 | 14 | function test_invalid_args(s) 15 | xr = 1:size(s.I,2) ; 16 | yr = 1:size(s.I,1) ; 17 | [x,y] = meshgrid(xr,yr) ; 18 | vl_assert_exception(@() vl_imwbackward(xr,yr,single(s.I),x,y), 'vl:invalidArgument') ; 19 | -------------------------------------------------------------------------------- /vesselSegmentation/Util/Evaluation/Metrics/getAverageMeasures.m: -------------------------------------------------------------------------------- 1 | 2 | function [averageQualityMeasures] = getAverageMeasures(qualityMeasures) 3 | 4 | averageQualityMeasures.se = mean(qualityMeasures.se); 5 | averageQualityMeasures.sp = mean(qualityMeasures.sp); 6 | averageQualityMeasures.acc = mean(qualityMeasures.acc); 7 | averageQualityMeasures.precision = mean(qualityMeasures.precision); 8 | averageQualityMeasures.recall = mean(qualityMeasures.recall); 9 | averageQualityMeasures.fMeasure = mean(qualityMeasures.fMeasure); 10 | averageQualityMeasures.matthews = mean(qualityMeasures.matthews); 11 | 12 | end -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/rgb2xyz.m: -------------------------------------------------------------------------------- 1 | function varargout = rgb2xyz(varargin) 2 | % VL_RGB2XYZ Convert RGB color space to XYZ 3 | % J=VL_RGB2XYZ(I) converts the CIE RGB image I to the image J in 4 | % CIE XYZ format. CIE RGB has a white point of R=G=B=1.0 5 | % 6 | % VL_RGB2XYZ(I,WS) uses the specified RGB working space WS. The 7 | % function supports the following RGB working spaces: 8 | % 9 | % * `CIE' E illuminant, gamma=2.2 10 | % * `Adobe' D65 illuminant, gamma=2.2 11 | % 12 | % The default workspace is CIE. 13 | % 14 | % See also: VL_XYZ2RGB(), VL_HELP(). 15 | [varargout{1:nargout}] = vl_rgb2xyz(varargin{:}); 16 | -------------------------------------------------------------------------------- /external/matconvnet-master/matlab/+dagnn/ElementWise.m: -------------------------------------------------------------------------------- 1 | classdef ElementWise < dagnn.Layer 2 | %ELEMENTWISE DagNN layers that operate at individual spatial locations 3 | methods 4 | function [outputSizes, transforms] = forwardGeometry(self, inputSizes, paramSizes) 5 | outputSizes = inputSizes ; 6 | transforms = {eye(6)} ; 7 | end 8 | 9 | function rfs = getReceptiveFields(obj) 10 | rfs.size = [1 1] ; 11 | rfs.stride = [1 1] ; 12 | rfs.offset = [1 1] ; 13 | end 14 | 15 | function outputSizes = getOutputSizes(obj, inputSizes) 16 | outputSizes = inputSizes ; 17 | end 18 | end 19 | end 20 | -------------------------------------------------------------------------------- /vesselSegmentation/Util/openMultipleImages.m: -------------------------------------------------------------------------------- 1 | % Open multiple files from a given directory 2 | function [images, allNames] = openMultipleImages(directory) 3 | % Get all file names 4 | allNames = getMultipleImagesFileNames(directory); 5 | % Get all the images in the directory and count the number of pixels 6 | images = cell(length(allNames), 1); 7 | for i = 1:length(allNames) 8 | currentfilename = strtrim(allNames{i}); 9 | currentfilename = strrep(currentfilename, '''', ''); 10 | currentImage = imread(strcat(directory, filesep, currentfilename)); 11 | images{i} = currentImage; % Assign the image 12 | end 13 | end -------------------------------------------------------------------------------- /external/vlfeat/toolbox/xtest/vl_assert_exception.m: -------------------------------------------------------------------------------- 1 | function vl_assert_exception(func, errorId, errorMessage) 2 | % VL_ASSERT_EXCEPTION 3 | 4 | try 5 | func() ; 6 | catch 7 | e = lasterror ; 8 | if nargin >= 2 9 | assert(isequal(e.identifier, errorId), ... 10 | 'Exception ID ''%s'' is not ''%s''.', ... 11 | e.identifier, errorId) ; 12 | end 13 | if nargin >= 3 14 | assert(isequal(e.message, errorMessage), ... 15 | 'Exception message ''%s'' is not ''%s''.', ... 16 | e.message, errorMessage) ; 17 | end 18 | return ; 19 | end 20 | assert(0, 'The function did not generate an exception.') ; 21 | end 22 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/lbp.m: -------------------------------------------------------------------------------- 1 | function varargout = lbp(varargin) 2 | % VL_LBP Local Binary Patterns 3 | % F = VL_LBP(IM, CELLSIZE) computes the Local Binary Pattern (LBP) 4 | % features for image I. 5 | % 6 | % IM is divided in cells of size CELLSIZE. F is a three-dimensional 7 | % array containing one histograms of quantized LBP features per 8 | % cell. The witdh of F is FLOOR(WIDTH/CELLSIZE), where WIDTH is the 9 | % width of the image. The same for the height. The third dimension 10 | % is 58. 11 | % 12 | % See also: LBP, VL_LBPFLIPLR(), 13 | % VL_HELP(). 14 | [varargout{1:nargout}] = vl_lbp(varargin{:}); 15 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/misc/vl_threads.m: -------------------------------------------------------------------------------- 1 | %VL_THREADS Control VLFeat computational threads 2 | % [NUM,MAXNUM] = VL_THREADS() returns the current number of 3 | % computational threads NUM and the maximum possible number MAXNUM. 4 | % 5 | % VL_THREADS(NUM) sets the current number of threads to the 6 | % specified value. NUM = VL_THREADS(NUM) does the same, but returns 7 | % the *previous* number of computational threads as well. 8 | % 9 | % See also: VL_HELP(). 10 | 11 | % Copyright (C) 2013 Andrea Vedaldi. 12 | % All rights reserved. 13 | % 14 | % This file is part of the VLFeat library and is made available under 15 | % the terms of the BSD license (see the COPYING file). 16 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/imgrad.m: -------------------------------------------------------------------------------- 1 | function varargout = imgrad(varargin) 2 | % VL_IMGRAD Image gradient 3 | % [Fx,Fy] = VL_GRAD(F) returns the finite differencies gradient of the 4 | % image F. The function uses central differencies and for all but 5 | % the boundaries pixels, for which it uses forward/backward 6 | % differencies as appropriate. 7 | % 8 | % VL_GRAD(F,'forward') uses only forward differencies (except on the 9 | % lower and right boundaries, where backward difference is used). 10 | % Similarly, VL_GRAD(F,'backward') uses only backward differencies. 11 | % 12 | % See also: GRADIENT(), VL_HELP(). 13 | [varargout{1:nargout}] = vl_imgrad(varargin{:}); 14 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/special/vl_gaussian.m: -------------------------------------------------------------------------------- 1 | function y = vl_gaussian(x) 2 | % VL_GAUSSIAN Standard Gaussian density function 3 | % Y=VL_GAUSSIAN(X) computes the standard (zero mean, unit variance) 4 | % Gaussian density. 5 | % 6 | % To obtain the Gaussian density of standard deviation S do 7 | % 8 | % Y = 1/S * VL_GAUSSIAN(X/S). 9 | % 10 | % See also: VL_DGAUSSIAN(), VL_DDGAUSSIAN(), VL_HELP(). 11 | 12 | % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. 13 | % All rights reserved. 14 | % 15 | % This file is part of the VLFeat library and is made available under 16 | % the terms of the BSD license (see the COPYING file). 17 | 18 | y = 1/sqrt(2*pi)*exp(-0.5*x.^2) ; 19 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/xtest/vl_test_phow.m: -------------------------------------------------------------------------------- 1 | function results = vl_test_phow(varargin) 2 | % VL_TEST_PHOPW 3 | vl_test_init ; 4 | 5 | function s = setup() 6 | s.I = im2double(imread(fullfile(vl_root,'data','spots.jpg'))) ; 7 | s.I = single(s.I) ; 8 | 9 | function test_gray(s) 10 | [f,d] = vl_phow(s.I, 'color', 'gray') ; 11 | assert(size(d,1) == 128) ; 12 | 13 | function test_rgb(s) 14 | [f,d] = vl_phow(s.I, 'color', 'rgb') ; 15 | assert(size(d,1) == 128*3) ; 16 | 17 | function test_hsv(s) 18 | [f,d] = vl_phow(s.I, 'color', 'hsv') ; 19 | assert(size(d,1) == 128*3) ; 20 | 21 | function test_opponent(s) 22 | [f,d] = vl_phow(s.I, 'color', 'opponent') ; 23 | assert(size(d,1) == 128*3) ; 24 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/demo/vl_demo_sift_or.m: -------------------------------------------------------------------------------- 1 | % VL_DEMO_SIFT_OR Demonstrates SIFT orientation detection 2 | 3 | I = vl_impattern('wedge') ; 4 | ur = 1:size(I,2) ; 5 | vr = 1:size(I,1) ; 6 | 7 | % distribute frames on a grid 8 | [u,v] = meshgrid(ur(5:10:end-4),vr(5:10:end-4)) ; 9 | f = [u(:)';v(:)'] ; 10 | K = size(f,2) ; 11 | f = [f ; 4 * ones(1,K) ; 0 * ones(1,K)] ; 12 | 13 | % detect orienntations 14 | f = vl_sift(single(I), 'frames', f, 'orientations') ; 15 | 16 | figure(1) ; clf ; 17 | imagesc(single(I)) ; colormap gray ; hold on ; 18 | vl_plotframe(f,'color','k','linewidth',3) ; 19 | vl_plotframe(f,'color','y','linewidth',2) ; 20 | axis equal ; axis off ; 21 | vl_demo_print('sift_or') ; 22 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/imwhiten.m: -------------------------------------------------------------------------------- 1 | function varargout = imwhiten(varargin) 2 | % VL_IMWHITEN Whiten an image 3 | % J = VL_IMWHITEN(I,ALPHA) approximatively whitens the power spectrum 4 | % of the natural image I. The algorithm assumes that the modulus of 5 | % the spectrum decays as 1/f^ALPHA (f is the frequency). 6 | % 7 | % VL_IMWHITEN(I) uses ALPHA=1 (a typical value for natural images). 8 | % 9 | % VL_IMWHITEN(I,ALPHA,CUTOFF) also applies a low-pass filter with 10 | % cutoff frequency equal to CUTOFF x FN, where FN is the Nyquist 11 | % frequency (half of the sampling frequency). 12 | % 13 | % See also: VL_HELP(). 14 | [varargout{1:nargout}] = vl_imwhiten(varargin{:}); 15 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/special/vl_dgaussian.m: -------------------------------------------------------------------------------- 1 | function y = vl_dgaussian(x) 2 | % VL_DGAUSSIAN Derivative of the Gaussian density function 3 | % Y=VL_DGAUSSIAN(X) evaluates the derivative of the standard Gaussian 4 | % density. 5 | % 6 | % To obtain the Gaussian density of standard deviation S, do 7 | % 8 | % Y = 1/S^2 * VL_DGAUSSIAN(X/S) . 9 | % 10 | % See also: VL_GAUSSIAN(), VL_DDGAUSSIAN(), VL_HELP(). 11 | 12 | % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. 13 | % All rights reserved. 14 | % 15 | % This file is part of the VLFeat library and is made available under 16 | % the terms of the BSD license (see the COPYING file). 17 | 18 | y = -x/sqrt(2*pi) .* exp(-0.5*x.^2) ; 19 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/demo/vl_demo_cmd.m: -------------------------------------------------------------------------------- 1 | function [err,msg] = vl_demo_cmd(name, args) 2 | % DEMO_CMD: Demo: Command line utility 3 | 4 | ext = '' ; 5 | switch mexext 6 | case 'mexmac' 7 | arch = 'mac' ; 8 | case 'mexmaci' 9 | arch = 'maci' ; 10 | case 'mexmaci64' 11 | arch = 'maci64'; 12 | case 'mexglx' 13 | arch = 'glnx86' ; 14 | case 'mexa64' 15 | arch = 'glnxa64' ; 16 | case 'dll' 17 | arch = 'win32' ; 18 | ext = '.exe' ; 19 | case 'mexw32' 20 | arch = 'win32' ; 21 | ext ='.exe' ; 22 | case 'mexw64' 23 | arch = 'win64' ; 24 | ext ='.exe' ; 25 | end 26 | 27 | cmd = fullfile(vl_root,'bin',arch,[name ext]) ; 28 | str = ['"' cmd '" ' args] ; 29 | disp(str); 30 | [err,msg] = system(str) ; 31 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/mser/vl_erfill.m: -------------------------------------------------------------------------------- 1 | % VL_ERFILL Fill extremal region 2 | % MEMBERS=VL_ERFILL(I,ER) returns the list MEMBERS of the pixels which 3 | % belongs to the extremal region represented by the pixel ER. 4 | % 5 | % The selected region is the one that contains pixel ER and of 6 | % intensity I(ER). 7 | % 8 | % I must be of class UINT8 and ER must be a (scalar) index of the 9 | % region representative point. 10 | % 11 | % See also: VL_MSER(), VL_HELP(). 12 | 13 | % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. 14 | % All rights reserved. 15 | % 16 | % This file is part of the VLFeat library and is made available under 17 | % the terms of the BSD license (see the COPYING file). 18 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/imintegral.m: -------------------------------------------------------------------------------- 1 | function varargout = imintegral(varargin) 2 | % VL_IMINTEGRAL Compute integral image 3 | % J = VL_IMINTEGRAL(I) calculates the integral image J of the image 4 | % I. I must a matrix with DOUBLE, SINGLE, UINT32, or INT32 storage 5 | % class. J is given by 6 | % 7 | % J(i,j) = sum(I(1:i,1:j)). 8 | % 9 | % J has the same size as I and the same storage class. 10 | % 11 | % Example:: 12 | % The following identity holds: 13 | % VL_IMINTEGRAL(ONES(3)) = [ 1 2 3 ; 14 | % 2 4 6 ; 15 | % 3 6 9 ] 16 | % 17 | % See also: VL_HELP(). 18 | [varargout{1:nargout}] = vl_imintegral(varargin{:}); 19 | -------------------------------------------------------------------------------- /external/matconvnet-master/examples/imagenet/cnn_imagenet_sync_labels.m: -------------------------------------------------------------------------------- 1 | function imdb = cnn_imagenet_sync_labels(imdb, net) 2 | % CNN_IMAGENET_SYNC_LABELS Match CNN and database labels 3 | % A CNN NET and the image database IMDB may use a different label ordering. 4 | % This function matches classes by name and reorder the labels 5 | % in IMDB to match NET. 6 | 7 | [~,perm] = ismember(imdb.classes.name, net.meta.classes.name); 8 | assert(all(perm ~= 0)); 9 | 10 | imdb.classes.description = imdb.classes.description(perm) ; 11 | imdb.classes.name = imdb.classes.name(perm) ; 12 | ok = imdb.images.label > 0 ; 13 | iperm(perm) = 1:numel(perm) ; 14 | imdb.images.label(ok) = perm(imdb.images.label(ok)) ; 15 | 16 | 17 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/test.m: -------------------------------------------------------------------------------- 1 | function varargout = test(varargin) 2 | % VL_TEST Run test suite 3 | % RESULTS = VL_TEST() runs all VLFeat test suites. The tests 4 | % verify that VLFeat is working correctly. 5 | % 6 | % RESULTS is a structure listing the result of each test. 7 | % It has fileds: 8 | % 9 | % TESTNAME: name of the test 10 | % SUCCEDED: a boolean flag indicating whether the test succeded 11 | % EXCEPTION: the exception generated if the test failed 12 | % 13 | % VL_TEST(SUITE) runs only the specified SUITE. 14 | % 15 | % VL_TEST(SUITE, TEST) runs the specified SUITE/TEST without catching 16 | % the potential exception. Useful to DBSTOP to debug. 17 | [varargout{1:nargout}] = vl_test(varargin{:}); 18 | -------------------------------------------------------------------------------- /vesselSegmentation/Features/Features/BCOSFIRE/COSFIRE/getCOSFIREstructure.m: -------------------------------------------------------------------------------- 1 | function getCOSFIREstructure(operator) 2 | 3 | if isfield(operator,'children') 4 | figure; 5 | ax = axes; 6 | radius = 150; 7 | dim = (2 * radius) + 1; 8 | 9 | for i = 0:1 10 | for c = 1:length(operator.children) 11 | [x y] = pol2cart(operator.tuples(2,c),operator.tuples(1,c)); 12 | showCOSFIREstructure(ax,operator.children(c),1-i,0+i,[radius-y radius+x],dim); 13 | end 14 | end 15 | hold on; 16 | for c = 1:length(operator.children) 17 | circle([radius radius],operator.tuples(1,c),1000,'-',2,[1 0 0],0,2*pi); 18 | end 19 | end -------------------------------------------------------------------------------- /vesselSegmentation/Util/encodeFileName.m: -------------------------------------------------------------------------------- 1 | 2 | function [filefullname] = encodeFileName(root, param, type) 3 | 4 | %training_dataset_uUNARIES_pPAIRWISES 5 | filefullname = strcat(root, filesep, type, '_', param.dataset, '_', ... 6 | 'u', num2str(featuresToNumber(param.unaryFeatures)), '_', ... 7 | 'p', num2str(featuresToNumber(param.pairwiseFeatures)), '.mat'); 8 | 9 | 10 | end 11 | 12 | 13 | 14 | function [number] = featuresToNumber(selectedFeatures) 15 | 16 | % convert x to a string array 17 | str_x = num2str(selectedFeatures); 18 | str_x(isspace(str_x)) = ''; 19 | % now use BIN2DEC to convert the binary 20 | % string to a decimal number 21 | number = bin2dec(str_x); 22 | 23 | end -------------------------------------------------------------------------------- /external/vlfeat/toolbox/xtest/vl_test_ihashsum.m: -------------------------------------------------------------------------------- 1 | function results = vl_test_ihashsum(varargin) 2 | % VL_TEST_IHASHSUM 3 | vl_test_init ; 4 | 5 | function s = setup() 6 | rand('state',0) ; 7 | s.data = uint8(round(16*rand(2,100))) ; 8 | sel = find(all(s.data==0)) ; 9 | s.data(1,sel)=1 ; 10 | 11 | function test_hash(s) 12 | D = size(s.data,1) ; 13 | K = 5 ; 14 | h = zeros(1,K,'uint32') ; 15 | id = zeros(D,K,'uint8'); 16 | next = zeros(1,K,'uint32') ; 17 | [h,id,next] = vl_ihashsum(h,id,next,K,s.data) ; 18 | 19 | sel = vl_ihashfind(id,next,K,s.data) ; 20 | count = double(h(sel)) ; 21 | 22 | [drop,i,j] = unique(s.data','rows') ; 23 | for k=1:size(s.data,2) 24 | count_(k) = sum(j == j(k)) ; 25 | end 26 | vl_assert_equal(count,count_) ; 27 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/plotpoint.m: -------------------------------------------------------------------------------- 1 | function varargout = plotpoint(varargin) 2 | % VL_PLOTPOINT Plot 2 or 3 dimensional points 3 | % VL_PLOTPOINT(V) plots the 2 or 3 dimensional points V. V is a 2xK or 4 | % 3xK array, with one point per column. 5 | % 6 | % H=VL_PLOTPOINT(...) returns the handle H of the plot. 7 | % 8 | % VL_PLOTPOINT() is a simple wrapper around the PLOT() and PLOT3() 9 | % functions. By default, VL_PLOTPOINT(V) plots the points with line 10 | % style '.'. VL_PLOTPOINT(V,...) does not use the default line style; 11 | % rather it passess any extra argument to the underlying plot 12 | % function. 13 | % 14 | % See also: PLOT(), PLOT3(), VL_HELP(). 15 | [varargout{1:nargout}] = vl_plotpoint(varargin{:}); 16 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/special/vl_ddgaussian.m: -------------------------------------------------------------------------------- 1 | function y = vl_ddgaussian(x) 2 | % VL_DDGAUSSIAN Second derivative of the Gaussian density function 3 | % Y=VL_DDGAUSSIAN(X) computes the second derivative of the standard 4 | % Gaussian density. 5 | % 6 | % To obtain the second derivative of the Gaussian density of 7 | % standard deviation S, do 8 | % 9 | % Y = 1/S^3 * VL_DDGAUSSIAN(X/S) . 10 | % 11 | % See also: VL_GAUSSIAN(), VL_DGAUSSIAN(), VL_HELP(). 12 | 13 | % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. 14 | % All rights reserved. 15 | % 16 | % This file is part of the VLFeat library and is made available under 17 | % the terms of the BSD license (see the COPYING file). 18 | 19 | y = (x.^2 - 1)/sqrt(2*pi) .* exp(-0.5*x.^2) ; 20 | -------------------------------------------------------------------------------- /vesselSegmentation/Features/Util/generateFeatureFilter.m: -------------------------------------------------------------------------------- 1 | 2 | function [featureFilter] = generateFeatureFilter(selectedFeatures, sizes) 3 | % generateFeatureFilter Generate a binary array to filter the features 4 | % [featureFilter] = generateFeatureFilter(selectedFeatures, sizes) 5 | % OUTPUT: featureFilter: a binary array indicating which features are going 6 | % to be used 7 | % INPUT: selectedFeatures: a list of the selected features 8 | % sizes: an array with the dimensions of each of the features 9 | 10 | featureFilter = []; 11 | for i = 1 : length(selectedFeatures) 12 | featureFilter = cat(1, featureFilter, ones(sizes(i),1) * selectedFeatures(i)); 13 | end 14 | featureFilter = logical(featureFilter); 15 | 16 | end -------------------------------------------------------------------------------- /external/vlfeat/toolbox/special/vl_sigmoid.m: -------------------------------------------------------------------------------- 1 | function y = vl_sigmoid(x) 2 | % VL_SIGMOID Sigmoid function 3 | % Y = VL_SIGMOID(X) returns 4 | % 5 | % Y = 1 ./ (1 + EXP(X)) ; 6 | % 7 | % Remark:: 8 | % Useful properties of the sigmoid function are: 9 | % 10 | % - 1 - VL_SIGMOID(X) = VL_SIGMOID(-X) 11 | % - Centered sigmoid: 2 * VL_SIGMOID(X) - 1 ; 12 | % - VL_SIGMOID(X) = (EXP(X/2) - EXP(X/2)) / (EXP(X/2) + EXP(X/2)) 13 | % 14 | % See also: VL_DSIGMOID(), VL_HELP(). 15 | 16 | % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. 17 | % All rights reserved. 18 | % 19 | % This file is part of the VLFeat library and is made available under 20 | % the terms of the BSD license (see the COPYING file). 21 | 22 | y = 1 ./ (1 + exp(-x)) ; 23 | -------------------------------------------------------------------------------- /vesselSegmentation/Features/Features/BCOSFIRE/COSFIRE/getDoG.m: -------------------------------------------------------------------------------- 1 | function [output sigma] = getDoG(img,sigma, onoff, sigmaRatio, width, threshold) 2 | % create Difference of Gaussian Kernel 3 | 4 | %sz = size(img) + width + width; 5 | sz = ceil(sigma*3) * 2 + 1; 6 | 7 | g1 = fspecial('gaussian',sz,sigma); 8 | g2 = fspecial('gaussian',sz,sigma*sigmaRatio); 9 | 10 | if onoff == 1 11 | G = g2 - g1; 12 | else 13 | G = g1 - g2; 14 | end 15 | 16 | img = padarray(img,[width width],'both','symmetric'); 17 | 18 | % compute DoG 19 | %output = fftshift(ifft2(fft2(G,sz(1),sz(2)) .* fft2(resultimg))); 20 | output = conv2(img, G, 'same'); 21 | 22 | if nargin == 6 23 | %output(output < threshold) = 0; 24 | output(find(output < threshold)) = 0; 25 | end -------------------------------------------------------------------------------- /Util/images/turn_white_outside_fov.m: -------------------------------------------------------------------------------- 1 | 2 | function I = turn_white_outside_fov(I, mask) 3 | 4 | % if the image is a uint8, then white is 255 5 | if isa(I,'uint8') 6 | white = 255; 7 | else 8 | white = 1; 9 | end 10 | 11 | % resize image 12 | if size(I,1) ~= size(mask,1) 13 | I = imresize(I, size(mask)); 14 | end 15 | 16 | % turn mask into a logical matrix 17 | mask = mask > 0; 18 | % get the blank area 19 | outside_fov = imcomplement(mask); 20 | 21 | % for each of the color channels, turn outside the fov white 22 | for i = 1 : size(I,3) 23 | this_channel = I(:,:,i); 24 | this_channel(outside_fov) = white; 25 | I(:,:,i) = this_channel; 26 | end 27 | 28 | end -------------------------------------------------------------------------------- /external/RF_Class_C/test_ClassRF_extensively.m: -------------------------------------------------------------------------------- 1 | %run plethora of tests 2 | 3 | %compile everything 4 | if strcmpi(computer,'PCWIN') |strcmpi(computer,'PCWIN64') 5 | compile_windows 6 | else 7 | compile_linux 8 | end 9 | 10 | total_train_time=0; 11 | total_test_time=0; 12 | 13 | % 14 | load data/twonorm 15 | % %twonorm, N=300, D=2 16 | for i=1:10 17 | fprintf('%d,',i); 18 | tic; 19 | model=classRF_train(inputs',outputs,1000); 20 | total_train_time=toc; 21 | tic; 22 | y_hat = classRF_predict(inputs',model); 23 | total_test_time=total_test_time+toc; 24 | length(find(y_hat~=outputs))/length(outputs) 25 | %keyboard 26 | end 27 | fprintf('\nnum_tree %d: Avg train time %d, test time %d\n',1000,total_train_time/100,total_test_time/100); 28 | 29 | 30 | -------------------------------------------------------------------------------- /RedLesionDetection/util/classifier/train_val_splits.m: -------------------------------------------------------------------------------- 1 | 2 | function splits = train_val_splits(labels) 3 | 4 | % generate an array of numbers from 1:length(labels) to represent the 5 | % indices, and permute it randomly 6 | indices = 1:1:length(labels); 7 | sorting = indices(randperm(length(indices))); 8 | 9 | % initialize a split array 10 | splits = cell(1,1); 11 | 12 | % the validation size will be equal to 13 | validationSize = floor((1 - 0.7) * length(labels)); 14 | 15 | % first 1:validatioSize will be used for validation 16 | splits{1}.validationIndices = sorting(1:validationSize); 17 | % the remaining images will correspond to the training data 18 | splits{1}.trainingIndices = sorting(validationSize+1:end); 19 | 20 | end -------------------------------------------------------------------------------- /external/vlfeat/toolbox/imop/vl_imreadbw.m: -------------------------------------------------------------------------------- 1 | function I = vl_imreadgray(file,varargin) 2 | % VL_IMREADGRAY Reads an image as gray-scale 3 | % I=VL_IMREADGRAY(FILE) reads the image from file FILE and converts the 4 | % result to a gray scale image (DOUBLE storage class ranging in 5 | % [0,1]). 6 | % 7 | % VL_IMREADGRAY(FILE,FMT) specifies the file format FMT (see IMREAD()). 8 | % 9 | % See also: RGB2DOUBLE(), VL_HELP(). 10 | 11 | % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. 12 | % All rights reserved. 13 | % 14 | % This file is part of the VLFeat library and is made available under 15 | % the terms of the BSD license (see the COPYING file). 16 | 17 | I = imread(file) ; 18 | I = im2double(I) ; 19 | 20 | if(size(I,3) > 1) 21 | I = rgb2gray(I) ; 22 | end 23 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/imop/vl_imreadgray.m: -------------------------------------------------------------------------------- 1 | function I = vl_imreadgray(file,varargin) 2 | % VL_IMREADGRAY Reads an image as gray-scale 3 | % I=VL_IMREADGRAY(FILE) reads the image from file FILE and converts the 4 | % result to a gray scale image (DOUBLE storage class ranging in 5 | % [0,1]). 6 | % 7 | % VL_IMREADGRAY(FILE,FMT) specifies the file format FMT (see IMREAD()). 8 | % 9 | % See also: RGB2DOUBLE(), VL_HELP(). 10 | 11 | % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. 12 | % All rights reserved. 13 | % 14 | % This file is part of the VLFeat library and is made available under 15 | % the terms of the BSD license (see the COPYING file). 16 | 17 | I = imread(file) ; 18 | I = im2double(I) ; 19 | 20 | if(size(I,3) > 1) 21 | I = rgb2gray(I) ; 22 | end 23 | -------------------------------------------------------------------------------- /vesselSegmentation/Features/Features/nguyen/im_seg.m: -------------------------------------------------------------------------------- 1 | function segmentedimg = im_seg(img,mask,W,step) 2 | % img: original color image 3 | % mask: mask of FOV 4 | % W: window size which is chosen as double of typical vessel width 5 | % step: step size for increasing the line length 6 | 7 | img = im2double(img); 8 | mask = im2bw(mask); 9 | 10 | img = 1-img(:,:,2); 11 | img = fakepad(img,mask); 12 | 13 | features = standardize(img,mask); 14 | Ls = 1:step:W; 15 | for j = 1:numel(Ls) 16 | L = Ls(j); 17 | R = get_lineresponse(img,W,L); 18 | R = standardize(R,mask); 19 | features = features+R; 20 | disp(['L = ',num2str(L),' finished!']); 21 | end 22 | segmentedimg = features/(1+numel(Ls)); 23 | t = 0.56; 24 | segmentedimg = im2bw(segmentedimg,t); 25 | 26 | end -------------------------------------------------------------------------------- /default_configuration/data_preparation/config_organize_messidor_2_data.m: -------------------------------------------------------------------------------- 1 | 2 | % CONFIG_ORGANIZE_MESSIDOR_2_DATA 3 | % ------------------------------------------------------------------------- 4 | % This script is called by script_organize_messidor_2_data to set up the 5 | % corresponding parameters to organize MESSIDOR-2 data. 6 | % ------------------------------------------------------------------------- 7 | 8 | % Folder where MESSIDOR-2 data set is. We assume that there is a folder 9 | % MESSIDOR-2/IMAGES inside this root folder. 10 | root_folder = 'C:\Users\USUARIO\Documents\RetinalImageDatasets'; 11 | 12 | % Output folder where files will be saved 13 | output_folder = 'C:\_dr_tbme'; 14 | 15 | % We always set this in true to avoid computing outside the FOV 16 | perform_cropping = true; -------------------------------------------------------------------------------- /external/matconvnet-master/matlab/+dagnn/@DagNN/removeLayer.m: -------------------------------------------------------------------------------- 1 | function removeLayer(obj, layerName) 2 | %REMOVELAYER Remove a layer from the network 3 | % REMOVELAYER(OBJ, NAME) removes the layer NAME from the DagNN object 4 | % OBJ. NAME can be a string or a cell array of strings. 5 | 6 | % Copyright (C) 2015 Karel Lenc and Andrea Vedaldi. 7 | % All rights reserved. 8 | % 9 | % This file is part of the VLFeat library and is made available under 10 | % the terms of the BSD license (see the COPYING file). 11 | 12 | if ischar(layerName), layerName = {layerName}; end; 13 | idxs = obj.getLayerIndex(layerName); 14 | if any(isnan(idxs)) 15 | error('Invalid layer name `%s`', ... 16 | strjoin(layerName(isnan(idxs)), ', ')); 17 | end 18 | obj.layers(idxs) = [] ; 19 | obj.rebuild() ; 20 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/hikmeanspush.m: -------------------------------------------------------------------------------- 1 | function varargout = hikmeanspush(varargin) 2 | % VL_HIKMEANSPUSH Push data down an integer K-means tree 3 | % PATH = VL_HIKMEANSPUSH(TREE,X) quanizes the data X with the 4 | % hierachical integer K-means tree TREE. This is obtained by 5 | % calculating the path of each datum from the root of TREE down to a 6 | % leaf. Here X has a datum for each column and each column of PATH 7 | % represents the corresponding root-to-leaf path. 8 | % 9 | % Example:: 10 | % PATH(:,1) = [2 1 4] means that the first datum follows 11 | % the branches number 2, 1 and 4 starting from the root of the 12 | % HIKM tree ending to a leaf. 13 | % 14 | % See also: VL_HIKMEANS(), VL_HELP(). 15 | [varargout{1:nargout}] = vl_hikmeanspush(varargin{:}); 16 | -------------------------------------------------------------------------------- /vesselSegmentation/SaveSegmentations.m: -------------------------------------------------------------------------------- 1 | 2 | function SaveSegmentations(root, config, results, model, filenames) 3 | 4 | if (sum(config.features.pairwise.pairwiseFeatures)==0) 5 | tag = 'up'; 6 | else 7 | if (strcmp(config.crfVersion, 'fully-connected')) 8 | tag = 'fccrf'; 9 | else 10 | tag = 'lnbcrf'; 11 | end 12 | end 13 | 14 | 15 | for i = 1 : length(results.segmentations); 16 | imwrite(results.segmentations{i}, strcat(root, filesep, filenames{i}, '_', tag, '.png')); 17 | end 18 | results.segmentations = []; 19 | save(strcat(root, filesep, 'results.mat'), 'results'); 20 | save(strcat(root, filesep, 'model.mat'), 'model'); 21 | save(strcat(root, filesep, 'config.mat'), 'config'); 22 | 23 | end -------------------------------------------------------------------------------- /external/matconvnet-master/README.md: -------------------------------------------------------------------------------- 1 | # MatConvNet: CNNs for MATLAB 2 | 3 | **MatConvNet** is a MATLAB toolbox implementing *Convolutional Neural 4 | Networks* (CNNs) for computer vision applications. It is simple, 5 | efficient, and can run and learn state-of-the-art CNNs. Several 6 | example CNNs are included to classify and encode images. Please visit 7 | the [homepage](http://www.vlfeat.org/matconvnet) to know more. 8 | 9 | In case of compilation issues, please read first the 10 | [Installation](http://www.vlfeat.org/matconvnet/install/) and 11 | [FAQ](http://www.vlfeat.org/matconvnet/faq/) section before creating an GitHub 12 | issue. For general inquiries regarding network design and training 13 | related questions, please use the 14 | [Discussion forum](https://groups.google.com/d/forum/matconvnet). 15 | -------------------------------------------------------------------------------- /external/matconvnet-master/COPYING: -------------------------------------------------------------------------------- 1 | Copyright (c) 2014 The MatConvNet team. 2 | All rights reserved. 3 | 4 | Redistribution and use in source and binary forms are permitted 5 | provided that the above copyright notice and this paragraph are 6 | duplicated in all such forms and that any documentation, 7 | advertising materials, and other materials related to such 8 | distribution and use acknowledge that the software was developed 9 | by the . The name of the 10 | may not be used to endorse or promote products derived 11 | from this software without specific prior written permission. 12 | THIS SOFTWARE IS PROVIDED ``AS IS'' AND WITHOUT ANY EXPRESS OR 13 | IMPLIED WARRANTIES, INCLUDING, WITHOUT LIMITATION, THE IMPLIED 14 | WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/aibhist.m: -------------------------------------------------------------------------------- 1 | function varargout = aibhist(varargin) 2 | % VL_AIBHIST Compute histogram over VL_AIB tree 3 | % H = VL_AIBHIST(PARENTS, DATA) computes the histogram of the data 4 | % points DATA on the VL_AIB tree defined by PARENTS. Each element of 5 | % DATA indexes one of the leaves of the VL_AIB tree. 6 | % 7 | % H = VL_AIBHIST(PARENTS, DATA, 'HIST') treats DATA as an histograms. 8 | % In this case each compoment of DATA is the number of occurences of 9 | % the VL_AIB leaves corresponding to that component. 10 | % 11 | % H has the same dimension of parents and counts how many data points 12 | % are descendent of the corresponding node of the VL_AIB tree. 13 | % 14 | % See also: VL_HELP(), VL_AIB(), VL_AIBCUTPUSH(). 15 | [varargout{1:nargout}] = vl_aibhist(varargin{:}); 16 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/plotop/vl_cf.m: -------------------------------------------------------------------------------- 1 | function h = vl_cf(h0) 2 | % VL_CF Creates a copy of a figure 3 | % VL_CF() creates a copy of the current figure and returns VL_CF(H0) 4 | % creates a copy of the figure(s) whose handle is H0. H = 5 | % VL_CF(...) returns the handles of the copies. 6 | % 7 | % See also: VL_HELP(). 8 | 9 | % Authors: Andrea Vedaldi 10 | 11 | % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. 12 | % All rights reserved. 13 | % 14 | % This file is part of the VLFeat library and is made available under 15 | % the terms of the BSD license (see the COPYING file). 16 | 17 | if nargin < 1 18 | h0 = gcf ; 19 | end 20 | 21 | h=zeros(size(h0)) ; 22 | for i=1:numel(h0) 23 | h(i) = copyobj(h0(i),get(h0(i),'Parent')) ; 24 | end 25 | 26 | if nargout < 1 27 | clear h ; 28 | end 29 | -------------------------------------------------------------------------------- /vesselSegmentation/Features/Features/BCOSFIRE/COSFIRE/getGaborResponse.m: -------------------------------------------------------------------------------- 1 | function GaborBankOutput = getGaborResponse(img,params,lambdalist,thetalist) 2 | 3 | [rows cols] = size(img); 4 | 5 | nlambda = length(lambdalist); 6 | ntheta = length(thetalist); 7 | 8 | GaborBankOutput = zeros(rows,cols,ntheta,nlambda); 9 | for lambda = 1:nlambda 10 | for theta = 1:ntheta 11 | GaborBankOutput(:,:,theta,lambda) = ... 12 | Gabor(img,lambdalist(lambda),... 13 | params.aspectratio,... 14 | params.bandwidth,... 15 | {thetalist(theta)},... 16 | params.phaseoffset,... 17 | params.halfwaverect,... 18 | [],... 19 | params.inhibition,... 20 | 1,... 21 | params.thinning); 22 | end 23 | end 24 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/xtest/vl_assert_almost_equal.m: -------------------------------------------------------------------------------- 1 | function vl_assert_almost_equal(x, y, varargin) 2 | epsilon = 1e-6 ; 3 | if length(varargin) > 0 4 | if isnumeric(varargin{1}) 5 | epsilon = varargin{1} ; 6 | varargin(1) = [] ; 7 | end 8 | end 9 | assert(isequal(class(x), class(y)), varargin{:}) ; 10 | assert(isequal(size(x), size(y)), varargin{:}) ; 11 | if isstruct(x) 12 | fx = fieldnames(x) ; 13 | fy = fieldnames(y) ; 14 | assert(isequal(fx,fy), varargin{:}) ; 15 | for i=1:numel(fx) 16 | vl_assert_almost_equal(x.(fx{i}), y.(fy{i}), epsilon, varargin{:}) ; 17 | end 18 | else 19 | i = isnan(x) ; 20 | j = isnan(y) ; 21 | assert(isequal(i,j), varargin{:}) ; 22 | assert(max(abs(x(~i) - y(~i))) < epsilon, varargin{:}) ; 23 | end 24 | end 25 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/misc/vl_numder.m: -------------------------------------------------------------------------------- 1 | function d = vl_numder(func, x, varargin) 2 | % VL_NUMDER Numerical derivative 3 | % D = VL_NUMDER(FUNC, X) computes the numerical derivative of the 4 | % function FUNC at point X. X is a real array and is passed as first 5 | % argument of FUNC. 6 | % 7 | % D = VL_NUMDER(FUNC, X, ARG1, ARG2, ...) passes ARG1, ARG2, ... as 8 | % additional arguments to the function FUNC. 9 | % 10 | % See also: VL_NUMDER2(), VL_HELP(). 11 | 12 | % TODO: uniform sacaling of axis is not a good idea 13 | 14 | dx = 1e-7 ; 15 | N = numel(x) ; 16 | fx = feval(func, x, varargin{:}) ; 17 | d = zeros(length(fx(:)),N) ; 18 | 19 | for n=1:N 20 | e = zeros(size(x)) ; e(n) = 1 ; 21 | fxn = feval(func, x+dx*e, varargin{:}) ; 22 | di = (fxn - fx) / dx ; 23 | d(:,n) = di(:) ; 24 | end 25 | -------------------------------------------------------------------------------- /external/vlfeat/toolbox/noprefix/binsearch.m: -------------------------------------------------------------------------------- 1 | function varargout = binsearch(varargin) 2 | % VL_BINSEARCH Maps data to bins 3 | % IDX = VL_BINSEARCH(B, X) maps the elements of X to the bins 4 | % defined by B. B is a non-decreasing vector of M real numbers 5 | % defining bins [-inf, B(1)), [B(1), B(2)), ... [B(M) +inf] (note 6 | % that the last bin contains +inf). The bins are numbered from 0 to 7 | % M. X is a real (plain) array and IDX is an array with the same 8 | % dimensions of X, specifying the bin correpsonding to each element 9 | % of X. 10 | % 11 | % If B(1) = -inf the bin number 0 is removed and the bin number 1 is 12 | % [-inf B(2)). If B(M) = +inf, the last bin M is the singleton +inf. 13 | % 14 | % See also: VL_BINSUM(), VL_HELP(). 15 | [varargout{1:nargout}] = vl_binsearch(varargin{:}); 16 | --------------------------------------------------------------------------------