Pysaliency

Pysaliency is a python package for saliency modelling. It aims at providing a unified interface to both the traditional saliency maps used in saliency modeling as well as probabilistic saliency models.

Pysaliency can evaluate most commonly used saliency metrics, including AUC, sAUC, NSS, CC image-based KL divergence, fixation based KL divergence and SIM for saliency map models and log likelihoods and information gain for probabilistic models.

Installation

You can install pysaliency from pypi via

pip install pysaliency

Quickstart

import pysaliency

dataset_location = 'datasets'
model_location = 'models'

mit_stimuli, mit_fixations = pysaliency.external_datasets.get_mit1003(location=dataset_location)
aim = pysaliency.AIM(location=model_location)
saliency_map = aim.saliency_map(mit_stimuli.stimuli[0])

plt.imshow(saliency_map)


auc = aim.AUC(mit_stimuli, mit_fixations)

If you already have saliency maps for some dataset, you can import them into pysaliency easily:

my_model = pysaliency.SaliencyMapModelFromDirectory(mit_stimuli, '/path/to/my/saliency_maps')
auc = my_model.AUC(mit_stimuli, mit_fixations)

Check out the Tutorial for a more detailed introduction!

Included datasets and models

Pysaliency provides several important datasets:

MIT1003
MIT300
CAT2000
Toronto
Koehler
iSUN
SALICON (both the 2015 and the 2017 edition and each with both the original mouse traces and the inferred fixations)
FIGRIM
OSIE
NUSEF (the part with public images)

and some influential models:

AIM
SUN
ContextAwareSaliency
BMS
GBVS
GBVSIttiKoch
Judd
IttiKoch
RARE2012
CovSal

These models are using the original code which is often matlab. Therefore, a matlab licence is required to make use of these models, although quite some of them work with octave, too (see below).

Using Octave

pysaliency will fall back to octave if no matlab is installed. Some models might work with octave, e.g. AIM and GBVSIttiKoch. In Debian/Ubuntu you need to install octave, octave-image, octave-statistics, liboctave-dev.

These models and dataset seem to work with octave:

models
- AIM
- GBVSIttiKoch
datasets
- Toronto
- MIT1003
- MIT300
- SALICON

Dependencies