--------------------------------------------------------------------------------    TILDE: a Temporally Invariant Learned DEtectorThis  software is  the  C++ and  MATLAB implementation  for  the TILDE  keypointdetector presented in [1]. The software  provides both the C++ implementation ofTILDE  which  can  be used  easily  to  detect  keypoints,  and a  MATLAB  basedevaluation framework.This  software is  strictly for  academic  purposes only.   For other  purposes,please  contact  us.  When  using  this software,  please  cite  [1]  and  otherappropriate publications if necessary (see matlab/external/licenses for details).[1] Y.  Verdie, K.   M.  Yi,  P.  Fua,  and V.   Lepetit.  "TILDE:  A Temporally    Invariant Learned DEtector.", Computer Vision and Patern Recognition (CVPR),    2015 IEEE Conference on.Contact:Yannick Verdie : yannick<dot>verdie<at>epfl<dot>chKwang Moo Yi   : kwang<dot>yi<at>epfl<dot>ch--------------------------------------------------------------------------------        << The TILDE C++ Implementation >>The C++  implementation of TILDE provides  an easy-to-use library with  a simpledemo program  to detect and  display the  keypoints. We provide  TILDE keypointslearned with the Webcam dataset in [1].----NOTES: - Code compiles with clang (macOS) and gcc-4.9 (Linux) - The code will generate a static  library, a dynamic library (libTILDE) and an   example code (demo).----REQUIREMENTS: - OpenCV 2.4.9 or higher - CMake 2.8 or higher----USAGE:Build the  libraries and  the demo.  Standard procedure is  as follow  (from theproject root directory): >> cd c++ >> mkdir build >> cd build >> cmake .. >> makeThen you can run the demo code from the build directory with: >> ./Demo/demo----IMPORTANT FUNCTIONS:The main function is getTILDEKeyPoints (see demo.cpp)std::vector<cv::KeyPoint> getTILDEKeyPoints(cv::Mat image,      std::string pathFilter,    bool useApprox,    bool sortKeypoints,    bool keepPositiveOnly,    cv::Mat *score)    <<input parameters>>: - image: a openCV U8C3 Mat object representing the image to process - pathFilter: a std string object giving the name of the filter to apply - useApprox:  a boolean  indicating to  use TILDE  (false) or  the approximated   version of TILDE (true). - sortKeypoints:  (true) we  sort the  keypoints by  decreasing order  of their   scores - keepPositiveOnly: (true) only the keypoints with positive score are returned - score: a  pointer to an  openCV Mat  image, if the  pointer is not  null, the   score map is retuned in addition to the keypoints <<output parameters>>: - std::vector<KeyPoint>: a std vector of OpenCV KeyPoint object (see OpenCV doc   for more details on how to use them)----DIRECTORY STRUCTURE:<c++> : Main project directory  |  |------ <src> : Contains our main library code and a toy example (demo.cpp)  |  |------ <3rdParties> : Contains  the 3rd party codes  which our implementation  |           is dependent on.  |   |------ <filters> : Contains the filters  pre-learned with the Webcam dataset.            When  a number  is added  in the  name of  the filter,  it          denotes that the filter is  for use with the approximation          flag  on (i.e.  it is  the approximated  TILDE). The  name          indicates which dataset was used to learn this filter.  <data> : Contains  a test example testImage.png which is  read by demo.cpp. Also         used by  the MATLAB  evaluation framework (detail  below) to  store the         dataset for evaluation.----NOTE ON THE LICENSE OF 3RD PARTY SOFTWARE:In  case of  3rd  party software  used  in  this project.  Please  refer to  thecorresponding copyright notifications on the top of each code.--------------------------------------------------------------------------------      The MATLAB Evaluation FrameworkThe  MATLAB  evaluation   framework  provides  an  easy  way   to  evaluate  therepeatability of different detectors. We provide the implementations we used forSIFT, SURF, FAST-9,  LCF, EdgeFoci, MSER along with our  own TILDEP and TILDEP24(see [1] for details).----NOTE:Codes run  partially on Mac  OSX (some competitor  methods are not  available onthis platform) and  almost completelly on Linux (all the  competitor methods areavailable  on   Linux  except   EdgeFoci.   We  provided   pre-computed  resultsseparately, available at project web pagehttps://www.epfl.ch/labs/cvlab/research/descriptors-and-keypoints/research-tilde/)In order  to avoid detecting  the same  keypoints multiple times,  This softwareUSES CACHING BY DEFAULT.  It will  save computed keypoints in sub-folders of thedataset folders (detail on the dataset section below). In case you need to resetthe detected keypoints, be sure to erase the cache files.We  have  enhanced  the  implementation  for easier  use  since  the  paper  wassubmitted,  and   we  therefore   recommend  to  use   the  results   from  thisimplementation when comparing.  There may be minor differences  with the resultsreported in [1].----REQUIREMENTS: - MATLAB 2013b or higher (may run on older versions but not tested) - OpenCV 2.4.9 or higher - ImageMagick (for the command 'convert') available both on Mac and Linux - Pkg-config ** Make  sure the  binaries  provided  in <matlab/external/external_codes>  are    compiled for your platform. We also  ship pre-compiled binaries for both Mac    OSX and  Linux. If you want  to use this,  please run link.m to  have proper    soft links pointing to the correct binary file----USAGE:Try running  the following scripts with  MATLAB (they are located  in matlab/srcand should be run from this folder). - link.m: Automatic linking in case of using pre-compiled binaries. - demo.m: Here we provide a demo for using our TILDE detector on Matlab similar      to the demo made in c++.    - evaluate_OxfordEFDataset_2percents.m: Runs  the evaluation  of Oxford  and EF   dataset with the new 2% criteria as reported in the paper [1]    - evaluate_OxfordEFDataset_1000.m: Runs the evaluation of Oxford and EF dataset   with 1000 keypoints and standard overlap measure as reported in the paper [1]    - evaluate_WebcamDataset_2percents.m:  Runs the  evaluation of  the new  Webcam   dataset with the new 2% criteria as reported in the paper [1]----THE DATASET:The   dataset   is   available   for   download  at   the   project   web   pagehttp://cvlab.epfl.ch/research/tilde. Extract  the archive to the  data directoryto use the datasets. - Webcam Dataset includes the following folders:   Chamonix, Courbevoie,Frankfurt, Mexico, Panorama, StLouis - Oxford and EF Dataset includes the following folders:   bark, leuven, rushmore, wall, bikes, notredame, yosemite, boat, obama, trees,   graf, paintedladies, ubcFor    example,    Chamonix   folder    should    be    located   at    <projectroot>/data/Chamonix. Inside each dataset  folder, the following folder structureshould exist.<Sequence Name>  |------ <test>    |------ <image_color>    |------ <image_gray>    |------ <homography>   (only for Oxford and EF)    |------ <features>    (automatically generated when running    |       the benchmark software for caching)    |------ test_imgs.txt    |------ homography.txt (only for Oxford and EF) ** Using pre-computed EdgeFoci results: Simply download and extract the archive    in the  data directory. It should  write the pre-computed files  (caches) in    the corresponding <features> sub-folders of the datasets.----NOTE ON THE LICENSE OF 3RD PARTY SOFTWARE:Implementations in the <matlab/external> directory are mostly adaptations of 3rdparty software into our evaluation framework.  For  the terms of use for the 3rdparty software, please refer to the license files in <matlab/external/licenses>--------------------------------------------------------------------------------