from Script.lab1.noise import gauss_noise
def bilateral(img: np.ndarray, sigmad: float = 200, sigmar: float = 200):
img = np.array(img, dtype=np.float)
g = np.zeros(img.shape, dtype=np.float)
img = samepadding(img, np.eye(5))
for i0 in range(0, img.shape[0] - 4):
for j0 in range(0, img.shape[1] - 4):
i = i0 + 2
j = j0 + 2
sumup = 0
sumdown = 0
for k in range(i0, i0 + 5):
for l in range(j0, j0 + 5):
d = np.exp(-(((i - k)**2 + (j - l)**2) / (2 * sigmad**2)))
r = np.exp(-((img[i][j] - img[k][l])**2) / (2 * sigmar**2))
w = d * r
sumdown += w
sumup += w * img[k][l]
g[i0][j0] = sumup / sumdown
g = np.array(g, dtype='u1')
return g
img = get()
show(img)
img = gauss_noise(img, SNR=0.95)
img1 = bilateral(img)
show(img1)
def test_sobel():
img = get()
img = sobel(img)
show(img)
#test_sobel()