Implements NSS (Natural Scanpath Saliency) scoring function from MIT Benchmark page for python

gistfile1.txt

import numpy.ma as ma

# NOTE: THIS FUNCTION ASUMES BOTH MAPS ARE SAME SIZE! (modify to generalize)
# And also salmap, fixmap are np.arrays or equivalent
def nss(salmap, fixmap, threshold=0):

    # Z-score salmap and binarize fixmap
    salmap_scored = (salmap - salmap.mean())/salmap.std()
    fixmap_mask = fixmap <= threshold  # all these '1' values are going to be eliminated using mask

    # Take mean value of fixation locations in fixmap as your score
    # This means wherever fix-duration is at least threshold we'll use to calculate score
    # If threshold=0 we think of fixation as a binary "there is or isn't"
    
    intermap = ma.masked_array(salmap_scored, mask=fixmap_mask)
    return intermap.mean()