def calcualteSaliencyMaps(filename):
# get the saliency maps using the 3 implemented methods
rbd = psal.get_saliency_rbd(filename).astype('uint8')
ft = psal.get_saliency_ft(filename).astype('uint8')
mbd = psal.get_saliency_mbd(filename).astype('uint8')
# often, it is desirable to have a binary saliency map
mbd_binary_sal = psal.binarise_saliency_map(mbd,method='adaptive')
ft_binary_sal = psal.binarise_saliency_map(ft,method='adaptive')
rbd_binary_sal = psal.binarise_saliency_map(rbd,method='adaptive')
return (rbd, ft, mbd, rbd_binary_sal, ft_binary_sal, mbd_binary_sal)
def saliency_3(frame, threshold=0.5):
#candidate 3
#https://github.com/yhenon/pyimgsaliency
#Saliency Optimization from Robust Background Detection, Wangjiang Zhu, Shuang Liang, Yichen Wei and Jian Sun, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014
rbd = psal.get_saliency_rbd(frame).astype('uint8')
#often, it is desirable to have a binary saliency map
#check 'binarise.py'
#the method can be either adaptive or fixed
#binarise_saliency_map(saliency_map,method='adaptive',threshold=0.5)
binary_rbd = psal.binarise_saliency_map(rbd, method='adaptive', threshold = threshold)
#openCV cannot display numpy type 0, so convert to uint8 and scale
#cv2.imshow('binary',255 * binary_sal.astype('uint8'))
return rbd, 255*binary_rbd.astype('uint8')
import pyimgsaliency as psal
import cv2
# path to the image
filename = 'bird.jpg'
# get the saliency maps using the 3 implemented methods
rbd = psal.get_saliency_rbd(filename).astype('uint8')
ft = psal.get_saliency_ft(filename).astype('uint8')
mbd = psal.get_saliency_mbd(filename).astype('uint8')
# often, it is desirable to have a binary saliency map
binary_sal = psal.binarise_saliency_map(mbd,method='adaptive')
img = cv2.imread(filename)
cv2.imshow('img',img)
cv2.imshow('rbd',rbd)
cv2.imshow('ft',ft)
cv2.imshow('mbd',mbd)
#openCV cannot display numpy type 0, so convert to uint8 and scale
cv2.imshow('binary',255 * binary_sal.astype('uint8'))
cv2.waitKey(0)
import numpy as np
import pyimgsaliency as psal
import cv2
if '__main__' == __name__:
# path to the image
filename = 'bird.jpg'
# get the saliency maps using the 3 implemented methods
rbd = psal.get_saliency_rbd(filename).astype(np.uint8)
ft = psal.get_saliency_ft(filename).astype(np.uint8)
mbd = psal.get_saliency_mbd(filename).astype(np.uint8)
# often, it is desirable to have a binary saliency map
binary_sal = psal.binarise_saliency_map(mbd, method='adaptive')
img = cv2.imread(filename)
cv2.imshow('img', img)
cv2.imshow('rbd', rbd)
cv2.imshow('ft', ft)
cv2.imshow('mbd', mbd)
# OpenCV cannot display numpy type 0, so convert to uint8 and scale
cv2.imshow('binary', 255 * binary_sal.astype(np.uint8))
cv2.waitKey(0)
import pyimgsaliency as psal
import cv2
# path to the image
filename = '/Users/zmalik/image-similarity-fusion/bird.jpg'
# get the saliency maps using the 3 implemented methods
rbd = psal.get_saliency_rbd(filename).astype('uint8')
ft = psal.get_saliency_ft(filename).astype('uint8')
mbd = psal.get_saliency_mbd(filename).astype('uint8')
# often, it is desirable to have a binary saliency map
binary_sal = psal.binarise_saliency_map(mbd, method='adaptive')
img = cv2.imread(filename)
cv2.imshow('img', img)
cv2.imshow('rbd', rbd)
cv2.imshow('ft', ft)
cv2.imshow('mbd', mbd)
# openCV cannot display numpy type 0, so convert to uint8 and scale
cv2.imshow('binary', 255 * binary_sal.astype('uint8'))
cv2.waitKey(0)
try:
os.mkdir('./input/unsup_labels/')
os.mkdir('./input/unsup_labels/rdb')
os.mkdir('./input/unsup_labels/ft')
os.mkdir('./input/unsup_labels/mbd')
os.mkdir('./input/unsup_labels/hc')
os.mkdir('./input/unsup_labels/rc')
except:
pass
for filename in os.listdir('./input/dataset/MSRA-B'):
if filename.endswith('.jpg'):
# Get RBD saliency map
try:
# Unknown error handler
rbd = get_saliency_rbd('./input/dataset/MSRA-B/' + filename).astype('uint8')
cv2.imwrite('./input/unsup_labels/rdb/' + filename[:-4] + '_ngt.png', rbd)
except:
with open('output/failed.txt', 'a+') as f:
f.write(filename + '\n')
# Get FT saliency map
ft = get_saliency_ft('./input/dataset/MSRA-B/' + filename).astype('uint8')
cv2.imwrite('./input/unsup_labels/ft/' + filename[:-4] + '_ngt.png', ft)
# Get MBD saliency map
mbd = get_saliency_mbd('./input/dataset/MSRA-B/' + filename).astype('uint8')
cv2.imwrite('./input/unsup_labels/mbd/' + filename[:-4] + '_ngt.png', mbd)
# Get HC saliency map
hc = get_saliency_hc('./input/dataset/MSRA-B/' + filename).astype('uint8')