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Step-by-step instruction to compile FIRE on Ubuntu 10.4.
To facilitate installation, I provide here a description how to compile the FIRE server on Ubuntu 10.4 from scratch.
The description applies to FIRE V 2.3 (SVN checkout of July 26, 2010).
- default Ubuntu 10.4 installation (in May 2010 in virtualbox)
- upgrade to recent state (sudo apt-get update; sudo apt-get dist-upgrade) on July 26, 2010
- sudo apt-get install subversion g++ libmagick++-dev
- svn checkout http://fire-cbir.googlecode.com/svn/trunk/ fire-cbir
- cd fire-cbir
After that the bin-Directory contain a total of 47 binaries and the FIRE server is ready to use.
Integration with Apache
- sudo apt-get install apache2
- cd ~/fire-cbir/
- sudo cp Python/firesocket.py /usr/lib/cgi-bin
- cd WebInterface
- sudo cp fire.py feature.py img.py config.py fire-template.html /usr/lib/cgi-bin
- sudo mkdir /usr/lib/cgi-bin/images
- sudo cp neutral.png positive.png negative.png fire-logo.png i6.png /usr/lib/cgi-bin/images
Now all the files should be in place for the FIRE webinterface to work
Setting up a dataset
- create a directory mkdir ~/fire-img
- put a set of medium sized images (e.g. 500x500 pixels) into this directory. For a start use about 100 images.
- convert all images into jpg (not necessary but often avoids problems)
- cd ~/fire-img
- mogrify -format jpg
- extract color histograms
- ~/fire-cbir/bin/extractcolorhistograms --color --images ~/fire-img/
- create a filelist for this dataset
- cd ~/fire-img/
- echo FIRE_filelist > filelist
- echo suffix color.histo.gz >> filelist
- echo path this >> filelist
*.jpg | sed 's/^/file /' >> filelist
Now you have a dataset where every image is represented using a color histogram. Other features can be added by extracting them and adding the corresponding suffix lines to the filelist.
Starting the FIRE server and accessing it through the webinterface
- cd ~/fire-cbir
- ./bin/fire -f ~/fire-img/filelist -r 10
- Access the fire webinterface throught http://localhost/cgi-bin/fire.py
- There you should be able to click an image and get similar images (according to color histograms)