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Repository for "TILDE: A Temporally Invariant Learned DEtector", CVPR2015
This project forked from vcg-uvic/tilde
Repository for "TILDE: A Temporally Invariant Learned DEtector", CVPR2015
-------------------------------------------------------------------------------- TILDE: a Temporally Invariant Learned DEtector This software is the C++ and MATLAB implementation for the TILDE keypoint detector presented in [1]. The software provides both the C++ implementation of TILDE which can be used easily to detect keypoints, and a MATLAB based evaluation framework. This software is strictly for academic purposes only. For other purposes, please contact us. When using this software, please cite [1] and other appropriate 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>ch Kwang 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 simple demo program to detect and display the keypoints. We provide TILDE keypoints learned 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 the project root directory): >> cd c++ >> mkdir build >> cd build >> cmake .. >> make Then 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 the corresponding copyright notifications on the top of each code. -------------------------------------------------------------------------------- The MATLAB Evaluation Framework The MATLAB evaluation framework provides an easy way to evaluate the repeatability of different detectors. We provide the implementations we used for SIFT, 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 on this platform) and almost completelly on Linux (all the competitor methods are available on Linux except EdgeFoci. We provided pre-computed results separately, available at project web page http://cvlab.epfl.ch/research/tilde) In order to avoid detecting the same keypoints multiple times, This software USES CACHING BY DEFAULT. It will save computed keypoints in sub-folders of the dataset folders (detail on the dataset section below). In case you need to reset the detected keypoints, be sure to erase the cache files. We have enhanced the implementation for easier use since the paper was submitted, and we therefore recommend to use the results from this implementation when comparing. There may be minor differences with the results reported 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/src and 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 page http://cvlab.epfl.ch/research/tilde. Extract the archive to the data directory to 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, ubc For example, Chamonix folder should be located at <project root>/data/Chamonix. Inside each dataset folder, the following folder structure should 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 3rd party software into our evaluation framework. For the terms of use for the 3rd party software, please refer to the license files in <matlab/external/licenses> --------------------------------------------------------------------------------
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