def get_saliency(img):
#Calculates img saliency, using NVT algorithm (Itti, 1998)
intensty = saliency.intensityConspicuity(img)
gabor = saliency.gaborConspicuity(img, 4)
im = saliency.makeNormalizedColorChannels(img)
rg = saliency.rgConspicuity(im)
by = saliency.byConspicuity(im)
c = rg + by
sal = 1./3 * (saliency.N(intensty) + saliency.N(c) + saliency.N(gabor))
sal = cv2.resize(sal, dsize=(img.shape[1],img.shape[0]))
#sal = (sal + magno)/2
sal = sal.astype(np.uint8)
return sal
res,resolution,image=vrep.simxGetVisionSensorImage(clientID,v0,0,vrep.simx_opmode_buffer)
if res==vrep.simx_return_ok:
# converting image to rgb np array
img = image
img = np.array(img, dtype=np.uint8)
img.resize([resolution[0], resolution[1], 3])
temp = np.copy(img[:,:,2])
img[:, :, 2] = img[:, :, 0]
img[:, :, 0] = temp
img = np.flipud(img)
cv2.imshow('felipe', img)
cv2.waitKey(2)
#calculating saliency
intensty = saliency.intensityConspicuity(img)
gabor = saliency.gaborConspicuity(img, 4)
im = saliency.makeNormalizedColorChannels(img)
rg = saliency.rgConspicuity(im)
by = saliency.byConspicuity(im)
c = rg + by
sal = 1./3 * (saliency.N(intensty) + saliency.N(c) + saliency.N(gabor))
sal = cv2.resize(sal, dsize=(img.shape[1],img.shape[0]))
#sal = (sal + magno)/2
sal = sal.astype(np.uint8)
# finding the most salient point
#max_sal_arg = np.unravel_index(np.argmax(sal), sal.shape)
#print(max_sal_arg)