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 runSaliency(self):
# get the saliency maps using the 3 implemented methods
#rbd = psal.get_saliency_rbd(self.filename).astype('uint8')
ft = psal.get_saliency_ft(self.filename).astype('uint8')
mbd = psal.get_saliency_mbd(self.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(self.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)
cv2.destroyAllWindows()
def saliency_4(frame, threshold=0.5):
#candidate 3
#https://github.com/yhenon/pyimgsaliency
#get the saliency maps using the 3 implemented methods
ft = psal.get_saliency_ft(frame).astype('uint8') #R. Achanta, S. Hemami, F. Estrada and S. Süsstrunk, Frequency-tuned Salient Region Detection, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2009), pp. 1597 - 1604, 2009
#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_ft = psal.binarise_saliency_map(ft, method='adaptive', threshold = threshold)
return ft, 255*binary_ft.astype('uint8')
def saliency_5(frame, threshold=0.5):
#candidate 3
#https://github.com/yhenon/pyimgsaliency
#get the saliency maps using the 3 implemented methods
mbd = psal.get_saliency_mbd(frame).astype('uint8') #Jianming Zhang, Stan Sclaroff, Zhe Lin, Xiaohui Shen, Brian Price and Radomír Měch. "Minimum Barrier Salient Object Detection at 80 FPS."
#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_mbd = psal.binarise_saliency_map(mbd,method='adaptive',threshold=threshold)
return mbd, 255*binary_mbd.astype('uint8')
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)