def spectrum_mask(size):
"""Creates a mask to filter the image of size size"""
import numpy as np
from scipy.ndimage.morphology import distance_transform_edt as distance
ftmask = np.ones(size)
# Set zeros on corners
# ftmask[0, 0] = 0
# ftmask[size[0] - 1, size[1] - 1] = 0
# ftmask[0, size[1] - 1] = 0
# ftmask[size[0] - 1, 0] = 0
ftmask[size[0] // 2, size[1] // 2] = 0
# Distance transform
ftmask = distance(ftmask)
ftmask /= ftmask.max()
# Keep this just in case we want to switch to the opposite filter
ftmask *= -1.0
ftmask += 1.0
ftmask[ftmask >= 0.4] = 1
ftmask[ftmask < 1] = 0
return ftmask
def spectrum_mask(size):
"""Creates a mask to filter the image of size size"""
import numpy as np
from scipy.ndimage.morphology import distance_transform_edt as distance
ftmask = np.ones(size)
# Set zeros on corners
# ftmask[0, 0] = 0
# ftmask[size[0] - 1, size[1] - 1] = 0
# ftmask[0, size[1] - 1] = 0
# ftmask[size[0] - 1, 0] = 0
ftmask[size[0]/2, size[1]/2] = 0
# Distance transform
ftmask = distance(ftmask)
ftmask /= ftmask.max()
# Keep this just in case we want to switch to the opposite filter
ftmask *= -1.0
ftmask += 1.0
ftmask[ftmask >= 0.4] = 1
ftmask[ftmask < 1] = 0
return ftmask
def wfm(FG, GT):
dGT = np.asarray(GT, np.float32)
dGT /= (GT.max() + 1e-8)
E = np.abs(FG - dGT)
Dst, Idxt = distance(dGT, return_indices=True)
Et = E
Et[~GT] = Et[Idxt[~GT]]
EA = imfilter(Et, 'blur')
MIN_E_EA = E
MIN_E_EA[GT & EA < E] = EA[GT & EA < E]
B = np.ones(GT.shape)
B[~GT] = 2.0 - 1 * math.exp(math.log(1 - 0.5) / 5. * Dst[~GT])
Ew = np.multiply(MIN_E_EA, B)
TPw = np.sum(dGT[:] - np.sum(np.sum(Ew[GT])))
FPw = np.sum(np.sum(Ew[~GT]))
R = np.mean(Ew[GT])
P = TPw
def reset_phi(self, mask):
phi = distance(1 - self.mask) - distance(self.mask) + self.mask
return phi
def get_phi(self):
phi = distance(1 - self.mask) - distance(self.mask) + self.mask
return phi
