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