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Created Feb 11, 2016

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Simple Itty-Koch-Style Saliency Maps
# coding=utf-8
# Copyright 2013 Johannes Bauer, Universitaet Hamburg
# This file is free software. Do with it whatever you like.
# It comes with no warranty, explicit or implicit, whatsoever.
# This python script implements an early version of Itti and Koch's
# saliency model. Specifically, it was written according to the
# information contained in the following paper:
# Laurent Itti, Christof Koch, and Ernst Niebur. A model of
# Saliency-Based visual attention for rapid scene analysis. IEEE
# Transactions on Pattern Analysis and Machine Intelligence,
# 20(11):1254–1259, 1998.
# If you find it useful or if you have any questions, do not
# hesitate to contact me at
# bauer at informatik dot uni dash hamburg dot de.
# For information on how to use this script, type
# > python -h
# on the command line.
import math
import logging
import cv2
import numpy
from scipy.ndimage.filters import maximum_filter
import os.path
import sys
if sys.version_info[0] != 2:
raise Exception("This script was written for Python version 2. You're running Python %s." % sys.version)
logger = logging.getLogger(__name__)
def features(image, channel, levels=9, start_size=(640,480), ):
Extracts features by down-scaling the image levels times,
transforms the image by applying the function channel to
each scaled version and computing the difference between
the scaled, transformed versions.
image : the image
channel : a function which transforms the image into
another image of the same size
levels : number of scaling levels
start_size : tuple. The size of the biggest image in
the scaling pyramid. The image is first
scaled to that size and then scaled by half
levels times. Therefore, both entries in
start_size must be divisible by 2^levels.
image = channel(image)
if image.shape != start_size:
image = cv2.resize(image, dsize=start_size)
scales = [image]
for l in xrange(levels - 1):
logger.debug("scaling at level %d", l)
features = []
for i in xrange(1, levels - 5):
big = scales[i]
for j in (3,4):
logger.debug("computing features for levels %d and %d", i, i + j)
small = scales[i + j]
srcsize = small.shape[1],small.shape[0]
dstsize = big.shape[1],big.shape[0]
logger.debug("Shape source: %s, Shape target :%s", srcsize, dstsize)
scaled = cv2.resize(src=small, dsize=dstsize)
features.append(((i+1,j+1),cv2.absdiff(big, scaled)))
return features
def intensity(image):
Converts a color image into grayscale.
Used as `channel' argument to function `features'
return cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
def makeGaborFilter(dims, lambd, theta, psi, sigma, gamma):
Creates a Gabor filter (an array) with parameters labmbd, theta,
psi, sigma, and gamma of size dims. Returns a function which
can be passed to `features' as `channel' argument.
In some versions of OpenCV, sizes greater than (11,11) will lead
to segfaults (see
def xpf(i,j):
return i*math.cos(theta) + j*math.sin(theta)
def ypf(i,j):
return -i*math.sin(theta) + j*math.cos(theta)
def gabor(i,j):
xp = xpf(i,j)
yp = ypf(i,j)
return math.exp(-(xp**2 + gamma**2*yp**2)/2*sigma**2) * math.cos(2*math.pi*xp/lambd + psi)
halfwidth = dims[0]/2
halfheight = dims[1]/2
kernel = numpy.array([[gabor(halfwidth - i,halfheight - j) for j in range(dims[1])] for i in range(dims[1])])
def theFilter(image):
return cv2.filter2D(src = image, ddepth = -1, kernel = kernel, )
return theFilter
def intensityConspicuity(image):
Creates the conspicuity map for the channel `intensity'.
fs = features(image = im, channel = intensity)
return sumNormalizedFeatures(fs)
def gaborConspicuity(image, steps):
Creates the conspicuity map for the channel `orientations'.
gaborConspicuity = numpy.zeros((60,80), numpy.uint8)
for step in range(steps):
theta = step * (math.pi/steps)
gaborFilter = makeGaborFilter(dims=(10,10), lambd=2.5, theta=theta, psi=math.pi/2, sigma=2.5, gamma=.5)
gaborFeatures = features(image = intensity(im), channel = gaborFilter)
summedFeatures = sumNormalizedFeatures(gaborFeatures)
gaborConspicuity += N(summedFeatures)
return gaborConspicuity
def rgConspicuity(image):
Creates the conspicuity map for the sub channel `red-green conspicuity'.
of the color channel.
def rg(image):
r,g,_,__ = cv2.split(image)
return cv2.absdiff(r,g)
fs = features(image = image, channel = rg)
return sumNormalizedFeatures(fs)
def byConspicuity(image):
Creates the conspicuity map for the sub channel `blue-yellow conspicuity'.
of the color channel.
def by(image):
_,__,b,y = cv2.split(image)
return cv2.absdiff(b,y)
fs = features(image = image, channel = by)
return sumNormalizedFeatures(fs)
def sumNormalizedFeatures(features, levels=9, startSize=(640,480)):
Normalizes the feature maps in argument features and combines them into one.
features : list of feature maps (images)
levels : the levels of the Gaussian pyramid used to
calculate the feature maps.
startSize : the base size of the Gaussian pyramit used to
calculate the feature maps.
a combined feature map.
commonWidth = startSize[0] / 2**(levels/2 - 1)
commonHeight = startSize[1] / 2**(levels/2 - 1)
commonSize = commonWidth, commonHeight"Size of conspicuity map: %s", commonSize)
consp = N(cv2.resize(features[0][1], commonSize))
for f in features[1:]:
resized = N(cv2.resize(f[1], commonSize))
consp = cv2.add(consp, resized)
return consp
def N(image):
Normalization parameter as per Itti et al. (1998).
returns a normalized feature map image.
M = 8. # an arbitrary global maximum to which the image is scaled.
# (When saving saliency maps as images, pixel values may become
# too large or too small for the chosen image format depending
# on this constant)
image = cv2.convertScaleAbs(image, alpha=M/image.max(), beta=0.)
w,h = image.shape
maxima = maximum_filter(image, size=(w/10,h/1))
maxima = (image == maxima)
mnum = maxima.sum()
logger.debug("Found %d local maxima.", mnum)
maxima = numpy.multiply(maxima, image)
mbar = float(maxima.sum()) / mnum
logger.debug("Average of local maxima: %f. Global maximum: %f", mbar, M)
return image * (M-mbar)**2
def makeNormalizedColorChannels(image, thresholdRatio=10.):
Creates a version of the (3-channel color) input image in which each of
the (4) channels is normalized. Implements color opponencies as per
Itti et al. (1998).
image : input image (3 color channels)
thresholdRatio : the threshold below which to set all color values
to zero.
an output image with four normalized color channels for red, green,
blue and yellow.
intens = intensity(image)
threshold = intens.max() / thresholdRatio
logger.debug("Threshold: %d", threshold)
r,g,b = cv2.split(image)
cv2.threshold(src=r, dst=r, thresh=threshold, maxval=0.0, type=cv2.THRESH_TOZERO)
cv2.threshold(src=g, dst=g, thresh=threshold, maxval=0.0, type=cv2.THRESH_TOZERO)
cv2.threshold(src=b, dst=b, thresh=threshold, maxval=0.0, type=cv2.THRESH_TOZERO)
R = r - (g + b) / 2
G = g - (r + b) / 2
B = b - (g + r) / 2
Y = (r + g) / 2 - cv2.absdiff(r,g) / 2 - b
# Negative values are set to zero.
cv2.threshold(src=R, dst=R, thresh=0., maxval=0.0, type=cv2.THRESH_TOZERO)
cv2.threshold(src=G, dst=G, thresh=0., maxval=0.0, type=cv2.THRESH_TOZERO)
cv2.threshold(src=B, dst=B, thresh=0., maxval=0.0, type=cv2.THRESH_TOZERO)
cv2.threshold(src=Y, dst=Y, thresh=0., maxval=0.0, type=cv2.THRESH_TOZERO)
image = cv2.merge((R,G,B,Y))
return image
def markMaxima(saliency):
Mark the maxima in a saliency map (a gray-scale image).
maxima = maximum_filter(saliency, size=(20,20))
maxima = numpy.array(saliency == maxima, dtype=numpy.float64) * 255
r = cv2.max(saliency, maxima)
g = saliency
b = saliency
marked = cv2.merge((b,g,r))
return marked
if __name__ == "__main__":
import argparse
import sys
parser = argparse.ArgumentParser(description = "Simple Itti-Koch-style conspicuity.")
parser.add_argument('--fileList', type=str, dest='fileList', action='store', help='Text file containing input file names, one per line.')
parser.add_argument('--inputFile', type=str, dest='inputFile', action='store', help='File to compute compute saliency list for. Need either --fileList or --inputFile.')
parser.add_argument('--intensityOutput', type=str, dest='intensityOutput', action='store', help="Filename for intensity conspicuity map,")
parser.add_argument('--gaborOutput', type=str, dest='gaborOutput', action='store', help="Filename for intensity conspicuity map,")
parser.add_argument('--rgOutput', type=str, dest='rgOutput', action='store', help="Filename for rg conspicuity map,")
parser.add_argument('--byOutput', type=str, dest='byOutput', action='store', help="Filename for by conspicuity map,")
parser.add_argument('--cOutput', type=str, dest='cOutput', action='store', help="Filename for color conspicuity map,")
parser.add_argument('--saliencyOutput', type=str, dest='saliencyOutput', action='store', help="Filename for saliency map,")
parser.add_argument("--markMaxima", action='store_true', help="Mark maximum saliency in output image.")
args = parser.parse_args()
if args.fileList is None and args.inputFile is None:
logger.error("Need either --fileList or --inputFile cmd line arguments.")
elif args.fileList is not None and args.inputFile is not None:
logger.error("Need only one of --fileList or --inputFile cmd line arguments.")
if args.fileList:
# we are reading filenames from a file.
filenames = (filename[:-1] for filename in open(args.fileList)) # remove end-of line character
# filenames were given on the command line.
filenames = [args.inputFile]
for filename in filenames:
im = cv2.imread(filename, cv2.COLOR_BGR2RGB) # assume BGR, convert to RGB---more intuitive code.
if im is None:
logger.fatal("Could not load file \"%s.\"", filename)
intensty = intensityConspicuity(im)
gabor = gaborConspicuity(im, 4)
im = makeNormalizedColorChannels(im)
rg = rgConspicuity(im)
by = byConspicuity(im)
c = rg + by
saliency = 1./3 * (N(intensty) + N(c) + N(gabor))
if args.markMaxima:
saliency = markMaxima(saliency)
def writeCond(outFileName, image):
name,_ = os.path.splitext(os.path.basename(filename))
if outFileName and args.fileList:
cv2.imwrite(outFileName % name, image)
elif outFileName:
cv2.imwrite(outFileName, image)
writeCond(args.intensityOutput, intensty)
writeCond(args.gaborOutput, gabor)
writeCond(args.rgOutput, rg)
writeCond(args.byOutput, by)
writeCond(args.cOutput, .25 * c)
writeCond(args.saliencyOutput, saliency)

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commented Feb 11, 2016

This script is an implementation of Itti-Koch style saliency maps. It is based on this paper and uses Python 2 and OpenCV.

The reason I wrote this is that I needed an implementation that I could play with. Also, I wanted a simple, standalone, easy to use script where I didn't have to worry about parameter settings or scientifically controversial assumptions.

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