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)
assert_array_equal(out_array_big, expected_dilation)
grey.erosion(image, out=out_array)
testing.assert_array_equal(out_array_big, expected_erosion)
def find_peaks_in_subspace(self, subspace, t_abs, prominence, min_dist):
"""
Retrieve locations with prominent local maxima from one part of the accumulator
"""
de = dilation(subspace + 1, ) / erosion(subspace + 1)
p = peak_local_max(subspace,
threshold_abs=t_abs,
min_distance=min_dist,
exclude_border=False)
r, c = p[:, 0], p[:, 1]
v = subspace[r, c]
valid = de[r, c] > prominence
peaks = np.empty([np.sum(valid), 2], np.float64)
values = v[valid]
# Subpixel correction of the peak
s = np.pad(subspace, ((1, 1), (1, 1)),
constant_values=((0, 0), (0, 0)))
for i, pi in enumerate(p[valid]):
neighborhood = s[pi[0]:pi[0] + 3, pi[1]:pi[1] + 3]
neighborhood = neighborhood / np.sum(neighborhood)
weight_r = np.tile(np.array([-1.0, 0, 1]), (3, 1)).T + pi[0]
weight_c = np.tile(np.array([-1.0, 0, 1]), (3, 1)) + pi[1]
peaks[i, 1] = np.sum(neighborhood * weight_r).astype(float)
peaks[i, 0] = np.sum(neighborhood * weight_c).astype(float)
return peaks, values
def test_selem_overflow():
strel = np.ones((17, 17), dtype=np.uint8)
img = np.zeros((20, 20), dtype=bool)
img[2:19, 2:19] = True
binary_res = binary.binary_erosion(img, strel)
grey_res = img_as_bool(grey.erosion(img, strel))
testing.assert_array_equal(binary_res, grey_res)
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():
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_selem_overflow():
strel = np.ones((17, 17), dtype=np.uint8)
img = np.zeros((20, 20))
img[2:19, 2:19] = 1
binary_res = binary.binary_erosion(img, strel)
grey_res = img_as_bool(grey.erosion(img, strel))
testing.assert_array_equal(binary_res, grey_res)
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_selem_overflow():
strel = np.ones((17, 17), dtype=np.uint8)
img = np.zeros((20, 20))
img[2:19, 2:19] = 1
binary_res = binary.binary_erosion(img, strel)
with expected_warnings(['precision loss']):
grey_res = img_as_bool(grey.erosion(img, strel))
testing.assert_array_equal(binary_res, grey_res)
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_erosion():
# compare the result of maximum filter with erode
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.minimum(image=image, selem=elem, out=out, mask=mask)
cm = grey.erosion(image=image, selem=elem)
assert_equal(out, cm)
def test_compare_with_grey_erosion():
# compare the result of maximum filter with erode
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.minimum(image=image, selem=elem, out=out, mask=mask)
cm = grey.erosion(image=image, selem=elem)
assert_equal(out, cm)
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_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_1d_erosion():
image = np.array([1, 2, 3, 2, 1])
expected = np.array([1, 1, 2, 1, 1])
eroded = grey.erosion(image)
testing.assert_array_equal(eroded, expected)
def test_binary_erosion():
strel = selem.square(3)
binary_res = binary.binary_erosion(bw_img, strel)
with expected_warnings(['precision loss']):
grey_res = img_as_bool(grey.erosion(bw_img, strel))
testing.assert_array_equal(binary_res, grey_res)
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_1d_erosion():
image = np.array([1, 2, 3, 2, 1])
expected = np.array([1, 1, 2, 1, 1])
eroded = grey.erosion(image)
testing.assert_array_equal(eroded, expected)
def test_binary_erosion():
strel = selem.square(3)
binary_res = binary.binary_erosion(bw_img, strel)
grey_res = img_as_bool(grey.erosion(bw_img, strel))
testing.assert_array_equal(binary_res, grey_res)
def test_non_square_image():
strel = selem.square(3)
binary_res = binary.binary_erosion(bw_img[:100, :200], strel)
grey_res = img_as_bool(grey.erosion(bw_img[:100, :200], strel))
testing.assert_array_equal(binary_res, grey_res)
def test_non_square_image():
strel = selem.square(3)
binary_res = binary.binary_erosion(bw_lena[:100, :200], strel)
grey_res = img_as_bool(grey.erosion(bw_lena[:100, :200], strel))
testing.assert_array_equal(binary_res, grey_res)
def test_binary_erosion():
strel = selem.square(3)
binary_res = binary.binary_erosion(bw_lena, strel)
grey_res = img_as_bool(grey.erosion(bw_lena, 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_binary_erosion():
strel = selem.square(3)
binary_res = binary.binary_erosion(bw_lena, strel)
grey_res = grey.erosion(bw_lena, strel)
testing.assert_array_equal(binary_res, grey_res)