================================================================================ What's in this package ??

Torch 5 Torch5 provides a Matlab-like environment for state-of-the-art machine learning algorithms. It is easy to use and provides a very efficient implementation, thanks to an easy and fast scripting language (Lua) and a underlying C implementation.

             The Torch 5 library is re-distributed here for simplicity of 
             installation.
             The original package can be found here:
             http://torch5.sourceforge.net

             The distribution has been slightly modified, in particular, 
             the original Lua kernel has been patched for multi-threaded 
             applications.

             Torch is licensed under a BSD license:
             http://torch5.sourceforge.net/manual/License.html

xLearn xLearn is an extension library for torch. It provides dozens of tools/modules for vision, image processing, and machine learning for vision

luaFlow luaFlow is a unified flow-graph description environment for [beta] vision / image-processing types of applications. One of its primary objectives is to abstract computing platforms, by providing a unified, high-level description flow.

xFlow a serializing language for luaFlow, that allows algorithms to [beta] be imported/exported from/to other software frameworks

neuFlow neuFlow is the compiler toolkit for the neuFlow processor, developped at New York University / Yale University. The neuFlow processor is dataflow computer optimized for vision and bio-inspired models of vision. The neuFlow compiler currently converts xLearn/torch algorithms to native neuFlow's bytecode. Soon to appear is a luaFlow>neuFlow compiler, which would
simplify retargetting. It is quite important to have access to a neuFlow device to be able to experiment with it: for more info/support, to get a neuFlow-enabled board, please contact [email protected]

thread the Lua core is patched with LuaThread to allow multithreaded apps

LuaJIT the entire framework can be built against LuaJIT for improved performance

opencv a wrapper for OpenCV, for now just a couple of functions, super easy to extend

debugger the open-source debugger framework for Lua (activated by requiring 'debug')

camiface a wrapper for libcamiface, to interface webcams on MacOS

video4linux a wrapper for libv4l2, to interface webcams in Linux

mstsegm a wrapper around P. Felzenszwalb’s image segmentation lib

powerwatersegm a wrapper around C. Couprie’s Powerwatershed lib

stereo a wrapper around P. Felzenszwalb’s BP-based stereo code

opticalFlow a wrapper around C. Liu’s great optical-flow estimator

kinect a wrapper around Microsoft's kinect device

pink a wrapper around M. Couprie's Morphology library

================================================================================ INSTALL

(1-LINUX) install dependencies (compilation tools, cmake, QT4): $ sudo apt-get install binutils gcc g++ cmake libqt4-core libqt4-dev libqt4-gui libreadline5-dev libpcap-dev

optionally, install OpenCV 2.x, to get access to extra packages:
http://sourceforge.net/projects/opencvlibrary/files/opencv-unix/2.1/

(1-MACOS) install dependencies (readline, cmake, QT4) $ sudo port install readline-5 cmake qt4-mac-devel

you might want to use a prebuilt version of QT4, to avoid the 2 hour
build time... (I still don't understand
why MacPort relies on sources rather than binaries...)
I'm keeping a working version here (installs in 5mins):
http://data.clement.farabet.net/qt/qt-mac-cocoa-opensource-4.5.3.dmg

optionally, install OpenCV 2.x, to get access to extra packages
on Snow Leopard, that works: 
sudo port install opencv +sl_64bit

(2-COMMON) once the dependencies are installed, just run: $ make $ [sudo] make install for the default install

or just
$ make help
for more info about the options/submodules

example of a local install:
$ make install INSTALL_PREFIX=~/local

================================================================================ DOC

The documentation is built and installed, and then available at: /usr/local/share/torch/html/index.html or INSTALL_PREFIX/share/torch/html/index.html

All details about installation are available there.