/GAfilters

Geometry Aware Convolutional Filters for Omnidirectional Images Representation

Primary LanguagePythonMIT LicenseMIT

Geometry Aware Convolutional Filters for Omnidirectional Images Representation

Implemtentation of the Geometry Aware Convolutional Filters for Omnidirectional Images Representation ICML 2019 paper

Installation

  • to install all the dependencies run:
pip install -r requirements.txt
  • add the path to the code to the PYTHONPATH environment variable as shown below:
export PYTHONPATH=<path_to_the_code>:$PYTHONPATH

Usage

This code implements the classification experiment described in the paper. In order to train a model download the dataset, as described in the following section and run

python classification/run_classification.py --exp=<exp_type>
  • exp_type - type of the experiment which can be one of the following: [cubic|fisheye|spherical|modspherical]
  • by default the experiment with cube-map projection will be executed
  • you may need to adjust classification\config in order to run custom experiments

Datasets

Download the datasets as follows:

  • Cube-map projection [94.7 MB] -- required for running the code with --exp=cubic
cd data
wget --no-check-certificate -O MNISTcubic.zip https://drive.switch.ch/index.php/s/sVe1wFtqaVRwmqn/download
unzip MNISTcubic.zip
rm MNISTcubic.zip
cd ../
  • Fish-eye projection [62.1 MB] -- required for running the code with --exp=fisheye
cd data
wget --no-check-certificate -O fisheye.zip https://drive.switch.ch/index.php/s/WSEy61zestEVyAQ/download
unzip fisheye.zip
rm fisheye.zip
cd ../
  • Spherical projection [34.5 MB] -- required for running the code with --exp=spherical
cd data
wget --no-check-certificate -O MNISTomni.zip https://drive.switch.ch/index.php/s/5Kg8DTmhMep3iXi/download
unzip MNISTomni.zip
rm MNISTomni.zip
cd ../
  • Modified Spherical projection [131.8 MB] -- required for running the code with --exp=modspherical
cd data
wget --no-check-certificate -O MNISTrandom_projection.zip https://drive.switch.ch/index.php/s/vFsZY38smcu7jA6/download
unzip MNISTrandom_projection.zip
rm MNISTrandom_projection.zip
cd ../

References

If you are using the code please cite the following paper:

@inproceedings{KhasanovaICML19,
	author    = {Reanta Khasanova and Pascal Frossard},
	title     = {Geometry Aware Convolutional Filters for Omnidirectional Images Representation},
	booktitle = {International Conference on Machine Learning},
	year      = {2019}
}