def test_num_peaks(self):
image = cp.zeros((7, 7), dtype=cp.uint8)
image[1, 1] = 10
image[1, 3] = 11
image[1, 5] = 12
image[3, 5] = 8
image[5, 3] = 7
assert (len(peak.peak_local_max(image, min_distance=1,
threshold_abs=0)) == 5)
peaks_limited = peak.peak_local_max(image,
min_distance=1,
threshold_abs=0,
num_peaks=2)
assert len(peaks_limited) == 2
peaks_limited = cp.asnumpy(peaks_limited)
assert (1, 3) in peaks_limited
assert (1, 5) in peaks_limited
peaks_limited = peak.peak_local_max(image,
min_distance=1,
threshold_abs=0,
num_peaks=4)
peaks_limited = cp.asnumpy(peaks_limited)
assert len(peaks_limited) == 4
assert (1, 3) in peaks_limited
assert (1, 5) in peaks_limited
assert (1, 1) in peaks_limited
assert (3, 5) in peaks_limited
def test_num_peaks_tot_vs_labels_4quadrants(self):
np.random.seed(21)
image = cp.asarray(np.random.uniform(size=(20, 30)))
i, j = cp.mgrid[0:20, 0:30]
labels = 1 + (i >= 10) + (j >= 15) * 2
result = peak.peak_local_max(image,
labels=labels,
min_distance=1,
threshold_rel=0,
num_peaks=cp.inf,
num_peaks_per_label=2)
assert len(result) == 8
result = peak.peak_local_max(image,
labels=labels,
min_distance=1,
threshold_rel=0,
num_peaks=cp.inf,
num_peaks_per_label=1)
assert len(result) == 4
result = peak.peak_local_max(image,
labels=labels,
min_distance=1,
threshold_rel=0,
num_peaks=2,
num_peaks_per_label=2)
assert len(result) == 2
def test_trivial_case(self):
trivial = cp.zeros((25, 25))
peak_indices = peak.peak_local_max(trivial, min_distance=1)
assert type(peak_indices) is cp.ndarray
assert peak_indices.size == 0
with expected_warnings(["indices argument is deprecated"]):
peaks = peak.peak_local_max(trivial, min_distance=1, indices=False)
assert (peaks.astype(bool) == trivial).all()
def test_threshold_rel_default(self):
image = cp.ones((5, 5))
image[2, 2] = 1
assert len(peak.peak_local_max(image)) == 0
image[2, 2] = 2
assert_array_equal(peak.peak_local_max(image), [[2, 2]])
image[2, 2] = 0
with expected_warnings(["When min_distance < 1"]):
assert len(peak.peak_local_max(image,
min_distance=0)) == image.size - 1
def test_input_labels_unmodified(self):
image = cp.zeros((10, 20))
labels = cp.zeros((10, 20), int)
image[5, 5] = 1
labels[5, 5] = 3
labelsin = labels.copy()
with expected_warnings(["indices argument is deprecated"]):
peak.peak_local_max(image,
labels=labels,
footprint=cp.ones((3, 3), bool),
min_distance=1,
threshold_rel=0,
indices=False,
exclude_border=False)
assert cp.all(labels == labelsin)
def test_sorted_peaks(self):
image = cp.zeros((5, 5), dtype=cp.uint8)
image[1, 1] = 20
image[3, 3] = 10
peaks = peak.peak_local_max(image, min_distance=1)
assert peaks.tolist() == [[1, 1], [3, 3]]
image = cp.zeros((3, 10))
# Note: CuPy doesn't support this type of indexing
# image[1, (1, 3, 5, 7)] = (1, 2, 3, 4)
image[1, 1] = 1
image[1, 3] = 2
image[1, 5] = 3
image[1, 7] = 4
peaks = peak.peak_local_max(image, min_distance=1)
assert peaks.tolist() == [[1, 7], [1, 5], [1, 3], [1, 1]]
def test_num_peaks3D(self):
# Issue 1354: the old code only hold for 2D arrays
# and this code would die with IndexError
image = cp.zeros((10, 10, 100))
image[5, 5, ::5] = cp.arange(20)
peaks_limited = peak.peak_local_max(image, min_distance=1, num_peaks=2)
assert len(peaks_limited) == 2
def test_absolute_threshold(self):
image = cp.zeros((5, 5), dtype=cp.uint8)
image[1, 1] = 10
image[3, 3] = 20
peaks = peak.peak_local_max(image, min_distance=1, threshold_abs=10)
assert len(peaks) == 1
assert_array_almost_equal(peaks, [(3, 3)])
def test_empty_non2d_indices(self):
image = cp.zeros((10, 10, 10))
result = peak.peak_local_max(image,
footprint=cp.ones((3, 3, 3), bool),
min_distance=1,
threshold_rel=0,
exclude_border=False)
assert result.shape == (0, image.ndim)
def test_ndarray_indices_false(self):
nd_image = cp.zeros((5, 5, 5))
nd_image[2, 2, 2] = 1
with expected_warnings(["indices argument is deprecated"]):
peaks = peak.peak_local_max(nd_image,
min_distance=1,
indices=False)
assert (peaks == nd_image.astype(bool)).all()
def test_3D(self):
image = cp.zeros((30, 30, 30))
image[15, 15, 15] = 1
image[5, 5, 5] = 1
assert_array_equal(
peak.peak_local_max(image, min_distance=10, threshold_rel=0),
[[15, 15, 15]],
)
assert_array_equal(
peak.peak_local_max(image, min_distance=6, threshold_rel=0),
[[15, 15, 15]],
)
assert sorted(peak.peak_local_max(image, min_distance=10,
threshold_rel=0,
exclude_border=False).tolist()) == \
[[5, 5, 5], [15, 15, 15]]
assert sorted(peak.peak_local_max(image, min_distance=5,
threshold_rel=0).tolist()) == \
[[5, 5, 5], [15, 15, 15]]
def test_linear_warp_polar():
radii = [5, 10, 15, 20]
image = cp.zeros([51, 51])
for rad in radii:
rr, cc, val = circle_perimeter_aa(25, 25, rad)
image[rr, cc] = val
warped = warp_polar(image, radius=25)
profile = warped.mean(axis=0)
peaks = cp.asnumpy(peak_local_max(profile))
assert np.alltrue([peak in radii for peak in peaks])
def test_num_peaks_and_labels(self):
image = cp.zeros((7, 7), dtype=cp.uint8)
labels = cp.zeros((7, 7), dtype=cp.uint8) + 20
image[1, 1] = 10
image[1, 3] = 11
image[1, 5] = 12
image[3, 5] = 8
image[5, 3] = 7
peaks_limited = peak.peak_local_max(image,
min_distance=1,
threshold_abs=0,
labels=labels)
assert len(peaks_limited) == 5
peaks_limited = peak.peak_local_max(image,
min_distance=1,
threshold_abs=0,
labels=labels,
num_peaks=2)
assert len(peaks_limited) == 2
def test_empty(self):
image = cp.zeros((10, 20))
labels = cp.zeros((10, 20), int)
with expected_warnings(["indices argument is deprecated"]):
result = peak.peak_local_max(image,
labels=labels,
footprint=cp.ones((3, 3), bool),
min_distance=1,
threshold_rel=0,
indices=False,
exclude_border=False)
assert cp.all(~result)
def test_noisy_peaks(self):
peak_locations = [(7, 7), (7, 13), (13, 7), (13, 13)]
# image with noise of amplitude 0.8 and peaks of amplitude 1
image = 0.8 * cp.asarray(np.random.rand(20, 20))
for r, c in peak_locations:
image[r, c] = 1
peaks_detected = peak.peak_local_max(image, min_distance=5)
assert len(peaks_detected) == len(peak_locations)
for loc in peaks_detected:
assert tuple(loc) in peak_locations
def test_adjacent_and_same(self):
image = cp.zeros((10, 20))
labels = cp.zeros((10, 20), int)
image[5, 5:6] = 1
labels[5, 5:6] = 1
with expected_warnings(["indices argument is deprecated"]):
result = peak.peak_local_max(image,
labels=labels,
footprint=cp.ones((3, 3), bool),
min_distance=1,
threshold_rel=0,
indices=False,
exclude_border=False)
assert cp.all(result == (labels == 1))
def test_disk(self):
"""regression test of img-1194, footprint = [1]
Test peak.peak_local_max when every point is a local maximum
"""
image = cp.asarray(np.random.uniform(size=(10, 20)))
footprint = cp.asarray([[1]])
with expected_warnings(["indices argument is deprecated"]):
result = peak.peak_local_max(image,
labels=cp.ones((10, 20), int),
footprint=footprint,
min_distance=1,
threshold_rel=0,
threshold_abs=-1,
indices=False,
exclude_border=False)
assert cp.all(result)
with expected_warnings(["indices argument is deprecated"]):
result = peak.peak_local_max(image,
footprint=footprint,
threshold_abs=-1,
indices=False,
exclude_border=False)
assert cp.all(result)
def test_exclude_border_errors():
image = cp.zeros((5, 5))
# exclude_border doesn't have the right cardinality.
with pytest.raises(ValueError):
assert peak.peak_local_max(image, exclude_border=(1, ))
# exclude_border doesn't have the right type
with pytest.raises(TypeError):
assert peak.peak_local_max(image, exclude_border=1.0)
# exclude_border is a tuple of the right cardinality but contains
# non-integer values.
with pytest.raises(ValueError):
assert peak.peak_local_max(image, exclude_border=(1, 'a'))
# exclude_border is a tuple of the right cardinality but contains a
# negative value.
with pytest.raises(ValueError):
assert peak.peak_local_max(image, exclude_border=(1, -1))
# exclude_border is a negative value.
with pytest.raises(ValueError):
assert peak.peak_local_max(image, exclude_border=-1)
def test_exclude_border(indices):
image = cp.zeros((5, 5))
image[indices] = 1
# exclude_border = False, means it will always be found.
assert len(peak.peak_local_max(image, exclude_border=False)) == 1
# exclude_border = 0, means it will always be found.
assert len(peak.peak_local_max(image, exclude_border=0)) == 1
# exclude_border = True, min_distance=1 means it will be found unless it's
# on the edge.
if indices[0] in (0, 4) or indices[1] in (0, 4):
expected_peaks = 0
else:
expected_peaks = 1
assert len(peak.peak_local_max(image, min_distance=1,
exclude_border=True)) == expected_peaks
# exclude_border = (1, 0) means it will be found unless it's on the edge of
# the first dimension.
if indices[0] in (0, 4):
expected_peaks = 0
else:
expected_peaks = 1
assert len(peak.peak_local_max(image,
exclude_border=(1, 0))) == expected_peaks
# exclude_border = (0, 1) means it will be found unless it's on the edge of
# the second dimension.
if indices[1] in (0, 4):
expected_peaks = 0
else:
expected_peaks = 1
assert len(peak.peak_local_max(image,
exclude_border=(0, 1))) == expected_peaks
def test_ndarray_exclude_border(self):
nd_image = cp.zeros((5, 5, 5))
nd_image[[1, 0, 0], [0, 1, 0], [0, 0, 1]] = 1
nd_image[3, 0, 0] = 1
nd_image[2, 2, 2] = 1
expected = cp.zeros_like(nd_image, dtype=bool)
expected[2, 2, 2] = True
expectedNoBorder = np.zeros_like(nd_image, dtype=bool)
expectedNoBorder[2, 2, 2] = True
expectedNoBorder[0, 0, 1] = True
expectedNoBorder[3, 0, 0] = True
expectedNoBorder = cp.asarray(expectedNoBorder)
with expected_warnings(["indices argument is deprecated"]):
result = peak.peak_local_max(nd_image,
min_distance=2,
exclude_border=2,
indices=False)
assert_array_equal(result, expected)
# Check that bools work as expected
assert_array_equal(
peak.peak_local_max(nd_image,
min_distance=2,
exclude_border=2,
indices=False),
peak.peak_local_max(nd_image,
min_distance=2,
exclude_border=True,
indices=False))
assert_array_equal(
peak.peak_local_max(nd_image,
min_distance=2,
exclude_border=0,
indices=False),
peak.peak_local_max(nd_image,
min_distance=2,
exclude_border=False,
indices=False))
# Check both versions with no border
assert_array_equal(
peak.peak_local_max(nd_image,
min_distance=2,
exclude_border=0,
indices=False),
expectedNoBorder,
)
assert_array_equal(
peak.peak_local_max(nd_image,
exclude_border=False,
indices=False), nd_image.astype(bool))
def test_many_objects(self):
mask = np.zeros([500, 500], dtype=bool)
x, y = np.indices((500, 500))
x_c = x // 20 * 20 + 10
y_c = y // 20 * 20 + 10
mask[(x - x_c)**2 + (y - y_c)**2 < 8**2] = True
labels, num_objs = ndimage_cpu.label(mask)
dist = ndimage_cpu.distance_transform_edt(mask)
dist = cp.asarray(dist)
labels = cp.asarray(labels)
local_max = peak.peak_local_max(dist,
min_distance=20,
exclude_border=False,
labels=labels)
assert len(local_max) == 625
def test_two_objects(self):
image = cp.zeros((10, 20))
labels = cp.zeros((10, 20), int)
image[5, 5] = 1
image[5, 15] = 0.5
labels[5, 5] = 1
labels[5, 15] = 2
expected = labels > 0
with expected_warnings(["indices argument is deprecated"]):
result = peak.peak_local_max(image,
labels=labels,
footprint=cp.ones((3, 3), bool),
min_distance=1,
threshold_rel=0,
indices=False,
exclude_border=False)
assert cp.all(result == expected)
def test_log_warp_polar():
radii = [
np.exp(2),
np.exp(3),
np.exp(4),
np.exp(5),
np.exp(5) - 1,
np.exp(5) + 1
]
radii = [int(x) for x in radii]
image = cp.zeros([301, 301])
for rad in radii:
rr, cc, val = circle_perimeter_aa(150, 150, rad)
image[rr, cc] = val
warped = warp_polar(image, radius=200, scaling='log')
profile = warped.mean(axis=0)
peaks_coord = peak_local_max(profile)
peaks_coord.sort(axis=0)
gaps = cp.asnumpy(peaks_coord[1:] - peaks_coord[:-1])
assert np.alltrue([x >= 38 and x <= 40 for x in gaps])
def test_indices_with_labels(self):
image = cp.asarray(np.random.uniform(size=(40, 60)))
i, j = cp.mgrid[0:40, 0:60]
labels = 1 + (i >= 20) + (j >= 30) * 2
i, j = cp.mgrid[-3:4, -3:4]
footprint = i * i + j * j <= 9
expected = cp.zeros(image.shape, float)
for imin, imax in ((0, 20), (20, 40)):
for jmin, jmax in ((0, 30), (30, 60)):
expected[imin:imax,
jmin:jmax] = ndi.maximum_filter(image[imin:imax,
jmin:jmax],
footprint=footprint)
expected = cp.stack(cp.nonzero(expected == image), axis=-1)
expected = expected[cp.argsort(image[tuple(expected.T)])[::-1]]
result = peak.peak_local_max(image,
labels=labels,
min_distance=1,
threshold_rel=0,
footprint=footprint,
exclude_border=False)
result = result[cp.argsort(image[tuple(result.T)])[::-1]]
assert (result == expected).all()
def test_four_quadrants(self):
image = cp.asarray(np.random.uniform(size=(20, 30)))
i, j = cp.mgrid[0:20, 0:30]
labels = 1 + (i >= 10) + (j >= 15) * 2
i, j = cp.mgrid[-3:4, -3:4]
footprint = i * i + j * j <= 9
expected = cp.zeros(image.shape, float)
for imin, imax in ((0, 10), (10, 20)):
for jmin, jmax in ((0, 15), (15, 30)):
expected[imin:imax,
jmin:jmax] = ndi.maximum_filter(image[imin:imax,
jmin:jmax],
footprint=footprint)
expected = expected == image
with expected_warnings(["indices argument is deprecated"]):
result = peak.peak_local_max(image,
labels=labels,
footprint=footprint,
min_distance=1,
threshold_rel=0,
indices=False,
exclude_border=False)
assert cp.all(result == expected)
def test_reorder_labels(self):
image = cp.asarray(np.random.uniform(size=(40, 60)))
i, j = cp.mgrid[0:40, 0:60]
labels = 1 + (i >= 20) + (j >= 30) * 2
labels[labels == 4] = 5
i, j = cp.mgrid[-3:4, -3:4]
footprint = i * i + j * j <= 9
expected = cp.zeros(image.shape, float)
for imin, imax in ((0, 20), (20, 40)):
for jmin, jmax in ((0, 30), (30, 60)):
expected[imin:imax,
jmin:jmax] = ndi.maximum_filter(image[imin:imax,
jmin:jmax],
footprint=footprint)
expected = expected == image
with expected_warnings(["indices argument is deprecated"]):
result = peak.peak_local_max(image,
labels=labels,
min_distance=1,
threshold_rel=0,
footprint=footprint,
indices=False,
exclude_border=False)
assert (result == expected).all()
def test_flat_peak(self):
image = cp.zeros((5, 5), dtype=cp.uint8)
image[1:3, 1:3] = 10
peaks = peak.peak_local_max(image, min_distance=1)
assert len(peaks) == 4
def test_constant_image(self):
image = cp.full((20, 20), 128, dtype=cp.uint8)
peaks = peak.peak_local_max(image, min_distance=1)
assert len(peaks) == 0
