def __itti_saliency_map(self, image_src):
self.__set_timer()
image = io.imread(image_src)
print "itti saliency started..."
s_map = SaliencyMap(image)
self.__print_timer()
print "itti saliency done."
return s_map.map
def run(image, mask=None, smoothing=False, show=False, show_now=True):
if mask is None:
mask = np.ones_like(image)
im_orig = image.copy()
else:
image, mask = tools.crop_to_bbox(image, mask)
im_orig = image.copy()
mean_v = int(image[np.nonzero(mask)].mean())
image = np.where(mask, image, mean_v)
mask = mask.astype(np.uint8)
if smoothing:
image = tools.smoothing(image)
rgb_image = cv2.cvtColor(image, cv2.COLOR_BAYER_GR2BGR).astype(np.float32)
saliency = SaliencyMap(rgb_image)
saliency_map = saliency.map
saliency_map *= mask
im_orig *= mask
saliency_map = skiexp.rescale_intensity(saliency_map, out_range=(0, 1))
if show:
if smoothing:
plt.subplot(131), plt.imshow(
im_orig, 'gray', interpolation='nearest'), plt.title('input')
plt.subplot(132), plt.imshow(
image, 'gray', interpolation='nearest'), plt.title('smoothed')
plt.subplot(133), plt.imshow(
saliency_map, 'gray',
interpolation='nearest'), plt.title('saliency')
else:
plt.subplot(121), plt.imshow(
im_orig, 'gray', interpolation='nearest'), plt.title('input')
plt.subplot(122), plt.imshow(
saliency_map, 'gray',
interpolation='nearest'), plt.title('saliency')
if show_now:
plt.show()
return im_orig, image, saliency_map
def buildSM(self, gazepath, lblpath):
'''
build saliency map from gaze labels
'''
'''
# convert black pixels in lbl to white
with open(lblpath, 'r+b') as f:
with PIL.Image.open(f) as image:
for width in xrange(image.size[0]):
for length in xrange(image.size[1]):
pixel = image.getpixel((width, length))
if pixel == (0, 0, 0):
image.putpixel((width, length), (255, 255, 255))
'''
oi = OpencvIo()
gaze = oi.imread(lblpath)
sm = SaliencyMap(gaze)
oi.imwrite(sm.map, gazepath)
# threshold SM
with open(gazepath, 'r+b') as f:
with PIL.Image.open(f).convert('RGB') as image:
for length in xrange(image.size[0]):
for width in xrange(image.size[1]):
pixel = image.getpixel((length, width))
# putpixel() is slow, learn to use paste for single pixel placement if possible
if pixel[0] < 50:
image.putpixel((length, width), (0, 0, 0))
elif 50 <= pixel[0] < 100:
image.putpixel((length, width), (100, 100, 100))
elif 100 <= pixel[0] < 150:
image.putpixel((length, width), (175, 175, 175))
else:
image.putpixel((length, width), (255, 255, 255))
image.save(gazepath, image.format)
from saliency_map import SaliencyMap
from utils import *
from os import listdir
from os.path import isfile, join
import numpy as np
onlyfiles = [f for f in sorted(listdir('stimuli')) if isfile(join('stimuli', f))]
oi = OpencvIo()
u = Util()
## option 1: using saliency_map.py
for f in onlyfiles:
maps = 0
print('Doing saliency map of file {}'.format(f))
src = oi.imread('stimuli/'+f)
sm = SaliencyMap(src)
# np.savetxt('results/'+f+'.txt',sm.map)
cv.imwrite( 'results/sm'+f, np.uint8(u.normalize_range(sm.map)))
cv.imwrite( 'results/cmcolor'+f, np.uint8(u.normalize_range(sm.cm.maps['color'])))
cv.imwrite( 'results/cmintensity'+f, np.uint8(u.normalize_range(sm.cm.maps['intensity'])))
cv.imwrite( 'results/cmorientation'+f, np.uint8(u.normalize_range(sm.cm.maps['orientation'])))
maps+=4
for i in sm.fm.maps['intensity']:
maps+=1
cv.imwrite( 'results/fmintensity'+ str(maps) + f, np.uint8(u.normalize_range(i.astype('float64'))))
for i in sm.fm.maps['colors']['bg']:
maps+=1
cv.imwrite( 'results/fmcolorsbg'+ str(maps) + f, np.uint8(u.normalize_range(i.astype('float64'))))
for i in sm.fm.maps['colors']['ry']:
maps+=1
cv.imwrite( 'results/fmcolorsry'+ str(maps) + f, np.uint8(u.normalize_range(i.astype('float64'))))