91 lines
3.1 KiB
Python
91 lines
3.1 KiB
Python
import numpy as np
|
|
from skimage.segmentation import chan_vese
|
|
|
|
from skimage._shared import testing
|
|
from skimage._shared.testing import assert_array_equal
|
|
|
|
|
|
def test_chan_vese_flat_level_set():
|
|
# because the algorithm evolves the level set around the
|
|
# zero-level, it the level-set has no zero level, the algorithm
|
|
# will not produce results in theory. However, since a continuous
|
|
# approximation of the delta function is used, the algorithm
|
|
# still affects the entirety of the level-set. Therefore with
|
|
# infinite time, the segmentation will still converge.
|
|
img = np.zeros((10, 10))
|
|
img[3:6, 3:6] = np.ones((3, 3))
|
|
ls = np.full((10, 10), 1000)
|
|
result = chan_vese(img, mu=0.0, tol=1e-3, init_level_set=ls)
|
|
assert_array_equal(result.astype(float), np.ones((10, 10)))
|
|
result = chan_vese(img, mu=0.0, tol=1e-3, init_level_set=-ls)
|
|
assert_array_equal(result.astype(float), np.zeros((10, 10)))
|
|
|
|
|
|
def test_chan_vese_small_disk_level_set():
|
|
img = np.zeros((10, 10))
|
|
img[3:6, 3:6] = np.ones((3, 3))
|
|
result = chan_vese(img, mu=0.0, tol=1e-3, init_level_set="small disk")
|
|
assert_array_equal(result.astype(float), img)
|
|
|
|
|
|
def test_chan_vese_simple_shape():
|
|
img = np.zeros((10, 10))
|
|
img[3:6, 3:6] = np.ones((3, 3))
|
|
result = chan_vese(img, mu=0.0, tol=1e-8).astype(float)
|
|
assert_array_equal(result, img)
|
|
|
|
|
|
def test_chan_vese_extended_output():
|
|
img = np.zeros((10, 10))
|
|
img[3:6, 3:6] = np.ones((3, 3))
|
|
result = chan_vese(img, mu=0.0, tol=1e-8, extended_output=True)
|
|
assert_array_equal(len(result), 3)
|
|
|
|
|
|
def test_chan_vese_remove_noise():
|
|
ref = np.zeros((10, 10))
|
|
ref[1:6, 1:6] = np.array([[0, 1, 1, 1, 0],
|
|
[1, 1, 1, 1, 1],
|
|
[1, 1, 1, 1, 1],
|
|
[1, 1, 1, 1, 1],
|
|
[0, 1, 1, 1, 0]])
|
|
img = ref.copy()
|
|
img[8, 3] = 1
|
|
result = chan_vese(img, mu=0.3, tol=1e-3, max_iter=100, dt=10,
|
|
init_level_set="disk").astype(float)
|
|
assert_array_equal(result, ref)
|
|
|
|
|
|
def test_chan_vese_incorrect_image_type():
|
|
img = np.zeros((10, 10, 3))
|
|
ls = np.zeros((10, 9))
|
|
with testing.raises(ValueError):
|
|
chan_vese(img, mu=0.0, init_level_set=ls)
|
|
|
|
|
|
def test_chan_vese_gap_closing():
|
|
ref = np.zeros((20, 20))
|
|
ref[8:15, :] = np.ones((7, 20))
|
|
img = ref.copy()
|
|
img[:, 6] = np.zeros((20))
|
|
result = chan_vese(img, mu=0.7, tol=1e-3, max_iter=1000, dt=1000,
|
|
init_level_set="disk").astype(float)
|
|
assert_array_equal(result, ref)
|
|
|
|
|
|
def test_chan_vese_incorrect_level_set():
|
|
img = np.zeros((10, 10))
|
|
ls = np.zeros((10, 9))
|
|
with testing.raises(ValueError):
|
|
chan_vese(img, mu=0.0, init_level_set=ls)
|
|
with testing.raises(ValueError):
|
|
chan_vese(img, mu=0.0, init_level_set="a")
|
|
|
|
|
|
def test_chan_vese_blank_image():
|
|
img = np.zeros((10, 10))
|
|
level_set = np.random.rand(10, 10)
|
|
ref = level_set > 0
|
|
result = chan_vese(img, mu=0.0, tol=0.0, init_level_set=level_set)
|
|
assert_array_equal(result, ref)
|