= dbqlsh( dataset, testset, param_dbq, ' ', 2, 10)ĭisp( 'Test kdbqlsh, Press any key to continue. = itqlsh( dataset, testset, param_itq, ' ', 2, 10)ĭisp( 'Test dbqlsh, Press any key to continue. = shlsh( dataset, testset, param_sh, ' ', 2, 10)ĭisp( 'Test itqlsh, Press any key to continue. Input( 'Test shlsh, Press any key to continue. = psdlsh( dataset, testset, param_psdL2, ' ', 2, 10) Input( 'Test psdlsh with param_psdL2.T = 2, Press any key to continue. = psdlsh( dataset, testset, param_psdL1, ' ', 1, 10) Input( 'Test psdlsh with param_psdL1.T = 1, Press any key to continue. = thlsh( dataset, testset, param_th, ' ', 2, 10) Input( 'Test thlsh, Press any key to continue. = rhplsh( dataset, testset, param_rhp, ' ', 2, 10) Input( 'Test rhplsh, Press any key to continue. ')ĭataset = dataset - repmat( mean( dataset), size( dataset, 1), 1) % matlab_example.m disp( 'prepare test data. In Windows, the py module name is pylshbox, but in linux, it will be libpylshbox. Print '' print 'Test itqLsh' itq_mat = pylshbox. Print '' print 'Test shLsh' sh_mat = pylshbox. Print '' print 'Test psdlsh with param.T = 2' psdL2_mat = pylshbox. Print '' print 'Test psdlsh with param.T = 1' psdL1_mat = pylshbox. Print '' print 'Test thLsh' th_mat = pylshbox. Print '' print 'Test rhpLsh' rhp_mat = pylshbox. Print '' print 'Test rbsLsh' rbs_mat = pylshbox. #!/usr/bin/env python # -*- coding: utf-8 -*- # pylshbox_example.py import pylshbox import numpy as np print 'prepare test data' float_mat = np.
Imagezilla lsh code#
You can get the sample dataset audio.data from, if the link is invalid, you can also get it from LSHBOX-sample-data.įOR EXAMPLE, YOU CAN RUN THE FOLLOWING CODE IN COMMAND LINE AFTER BUILD ALL THE TOOLS: Std::cout > res = scanner.topk().getTopk() // for (std::vector >::iterator it = res.begin() it != res.end() ++it) // // std::cout << "DISTANCE COMPARISON TIMES: " << scanner.cnt() << std::endl # include int main( int argc, char const *argv) ** * itqlsh_test.cpp * * Example of using Iterative Quantization LSH index for L2 distance. python/README.md.ĭuring compilation, create a new directory named build in the main directory, then choose a appropriate compiler and switch to the build directory, finally, execute the following command according to your machine: For more detailed information, you can view the document. In some cases, if you want or need to compile it by yourself with Python and MATLAB, please delete the comment of the last two lines in file CMakeLists.txt, and you will find the compiling progress of python must rely on Boost library or some part of this library. CMake can be downloaded from CMake' website. If you want to test or contribute, CMAKE, a cross-platform, open-source build system, is usded to build some tools for the purpose. You only need to add the include directory or modify the program search path, then you can use this library directly in C, C++, Python or MATLAB. If you want to integrate LSHBOX into you application, it don't need compile. And it also can be easily used in many contexts through the Python and MATLAB bindings provided with this toolbox.
Imagezilla lsh free#
Please feel free to contact us [ or if you have any questions. We hope that there are more people that join in the test or contribute more algrithms.
Imagezilla lsh mac#
We tested LSHBOX with VS2010 in Windows 7/8 32bit/64bit and with g++ in Linux, Mac test will be done in the future. Part of the code depends on the C++11, So I think your compiler should support this feature. In addition, File-Based-ITQ is an File Based ITQ example for LSHBOX. LSHBOX-sample datasets: a dataset for performance tests.LSHBOX-3rdparty: 3rdparty of LSHBOX, it is for compilation.There are two repositories for compilation and performance tests, they are: K-means Based Double-Bit Quantization Hashing (KDBQ).The following LSH algrithms have been implemented in LSHBOX, they are: LSHBOX is a simple but robust C++ toolbox that provides several LSH algrithms, in addition, it can be integrated into Python and MATLAB languages. Locality-Sensitive Hashing (LSH) is an efficient method for large scale image retrieval, and it achieves great performance in approximate nearest neighborhood searching. Other files related to DBQ have been updated synchronously. And the Python interface will be added into LSHBOX-0.9 later. We implement DBQ by C++ but also provide MATLAB interface. Other files related to KDBQ have been updated synchronously.Ī new LSH method, Double-Bit Quantization Hashing (DBQ), was added into LSHBOX-0.9 on June 4th, 2015. We implement KDBQ by C++ but also provide MATLAB interface. LSHBOX-0.9 A C++ Toolbox of Locality-Sensitive Hashing for Large Scale Image Retrieval, Also Support Python and MATLAB.Ī new LSH method, K-means Based Double-Bit Quantization for Hashing (KDBQ), was added into LSHBOX-0.9 on July 4th, 2015.
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