FLANN is a library for performing fast approximate nearest neighbor searches in high dimensional spaces. It contains a collection of algorithms we found to work best for nearest neighbor search and a system for automatically choosing the best algorithm and optimum parameters depending on the dataset. FLANN is written in C++ and contains bindings for the following languages: C, MATLAB, Python, and Ruby.

Check FLANN web page here.

Documentation on how to use the library can be found in the doc/manual.pdf file included in the release archives.

More information and experimental results can be found in the following paper:

• Marius Muja and David G. Lowe, "Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration", in International Conference on Computer Vision Theory and Applications (VISAPP'09), 2009 (PDF) (BibTex)

The latest version of FLANN can be downloaded from here:

• Version 1.8.4 (15 January 2013) flann-1.8.4-src.zip (Source code)
  User manual
  Changelog

If you want to try out the latest changes or contribute to FLANN, then it's recommended that you checkout the git source repository: git clone git://github.com/mariusmuja/flann.git

If you just want to browse the repository, you can do so by going here.

FLANN is distributed under the terms of the BSD License.

Please report bugs or feature requests using github's issue tracker.

#How to install? To say you have ubuntu, then:

sudo apt-get install python-dev cmake gcc openssh-server git
wget https://github.com/pypa/pip/raw/master/contrib/get-pip.py
sudo python ./get-pip.py
sudo pip install numpy scipy
git clone https://github.com/mariusmuja/flann.git

Go to folder flann

mkdir build
cd build
cmake ..
make all
sudo make install

To test, create a python file, eg yourPython.py with following content:

#coding=utf-8
from pyflann import *
from numpy import *
from numpy.random import *

dataset = rand(10000, 400)
testset = rand(1000, 400)
flann = FLANN()
#建索引，自动调参数,　会运行很多词建立很多索引，目的是找到最佳参数
params = flann.build_index(dataset, algorithm="autotuned", target_precision=0.9, log_level = "info");
#打印最佳参数，以后就可以用最佳参数，直接建立一个索引
print params
#保存索引，以后就可以直接load index
flann.save_index('./temp.index.bin')
#测试建立的索引，找出最近的５五个
result, dists = flann.nn_index(testset,5, checks=params["checks"]);
#打印检索结果，包含每个测试向量的　5 个临近向量的　index ，就是在dataset 里面的序列号
print result
#打印出　测试向量到　5个临近向量的距离
print dists

Then python ./yourPython.py

Done!