def test_uint16():
im16, eroded16, dilated16, opened16, closed16 = (
map(img_as_uint, [im, eroded, dilated, opened, closed]))
np.testing.assert_allclose(grey.erosion(im16), eroded16)
np.testing.assert_allclose(grey.dilation(im16), dilated16)
np.testing.assert_allclose(grey.opening(im16), opened16)
np.testing.assert_allclose(grey.closing(im16), closed16)
def test_uint16():
with expected_warnings(['Possible precision loss']):
im16, eroded16, dilated16, opened16, closed16 = (
map(img_as_uint, [im, eroded, dilated, opened, closed]))
np.testing.assert_allclose(grey.erosion(im16), eroded16)
np.testing.assert_allclose(grey.dilation(im16), dilated16)
np.testing.assert_allclose(grey.opening(im16), opened16)
np.testing.assert_allclose(grey.closing(im16), closed16)
def test_discontiguous_out_array():
image = np.array([[5, 6, 2],
[7, 2, 2],
[3, 5, 1]], np.uint8)
out_array_big = np.zeros((5, 5), np.uint8)
out_array = out_array_big[::2, ::2]
expected_dilation = np.array([[7, 0, 6, 0, 6],
[0, 0, 0, 0, 0],
[7, 0, 7, 0, 2],
[0, 0, 0, 0, 0],
[7, 0, 5, 0, 5]], np.uint8)
expected_erosion = np.array([[5, 0, 2, 0, 2],
[0, 0, 0, 0, 0],
[2, 0, 2, 0, 1],
[0, 0, 0, 0, 0],
[3, 0, 1, 0, 1]], np.uint8)
grey.dilation(image, out=out_array)
testing.assert_array_equal(out_array_big, expected_dilation)
grey.erosion(image, out=out_array)
testing.assert_array_equal(out_array_big, expected_erosion)
def test_compare_with_grey_dilation():
# compare the result of maximum filter with dilate
image = (np.random.rand(100, 100) * 256).astype(np.uint8)
out = np.empty_like(image)
mask = np.ones(image.shape, dtype=np.uint8)
for r in range(3, 20, 2):
elem = np.ones((r, r), dtype=np.uint8)
rank.maximum(image=image, selem=elem, out=out, mask=mask)
cm = grey.dilation(image=image, selem=elem)
assert_equal(out, cm)
def test_dilate_erode_symmetry(self):
for s in self.selems:
c = grey.erosion(self.black_pixel, s)
d = grey.dilation(self.white_pixel, s)
assert np.all(c == (255 - d))
def test_float():
np.testing.assert_allclose(grey.erosion(im), eroded)
np.testing.assert_allclose(grey.dilation(im), dilated)
np.testing.assert_allclose(grey.opening(im), opened)
np.testing.assert_allclose(grey.closing(im), closed)
def test_binary_dilation():
strel = selem.square(3)
binary_res = binary.binary_dilation(bw_img, strel)
grey_res = img_as_bool(grey.dilation(bw_img, strel))
testing.assert_array_equal(binary_res, grey_res)
def test_binary_dilation():
strel = selem.square(3)
binary_res = binary.binary_dilation(bw_img, strel)
grey_res = img_as_bool(grey.dilation(bw_img, strel))
testing.assert_array_equal(binary_res, grey_res)
def test_dilate_erode_symmetry(self):
for s in self.selems:
c = grey.erosion(self.black_pixel, s)
d = grey.dilation(self.white_pixel, s)
assert np.all(c == (255 - d))
def test_float():
np.testing.assert_allclose(grey.erosion(im), eroded)
np.testing.assert_allclose(grey.dilation(im), dilated)
np.testing.assert_allclose(grey.opening(im), opened)
np.testing.assert_allclose(grey.closing(im), closed)
def test_binary_dilation():
strel = selem.square(3)
binary_res = binary.binary_dilation(bw_img, strel)
with expected_warnings(['precision loss']):
grey_res = img_as_bool(grey.dilation(bw_img, strel))
testing.assert_array_equal(binary_res, grey_res)
def test_binary_dilation():
strel = selem.square(3)
binary_res = binary.binary_dilation(bw_lena, strel)
grey_res = grey.dilation(bw_lena, strel)
testing.assert_array_equal(binary_res, grey_res)