import numpy as np from scipy._lib._array_api import ( is_cupy, is_numpy, is_torch, array_namespace, xp_assert_close, xp_assert_equal, assert_array_almost_equal ) import pytest from pytest import raises as assert_raises from scipy import ndimage from . import types from scipy.conftest import array_api_compatible skip_xp_backends = pytest.mark.skip_xp_backends xfail_xp_backends = pytest.mark.xfail_xp_backends pytestmark = [array_api_compatible, pytest.mark.usefixtures("skip_xp_backends"), pytest.mark.usefixtures("xfail_xp_backends"), skip_xp_backends(cpu_only=True, exceptions=['cupy', 'jax.numpy'],)] class TestNdimageMorphology: @xfail_xp_backends('cupy', reason='CuPy does not have distance_transform_bf.') @pytest.mark.parametrize('dtype', types) def test_distance_transform_bf01(self, dtype, xp): dtype = getattr(xp, dtype) # brute force (bf) distance transform data = xp.asarray([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=dtype) out, ft = ndimage.distance_transform_bf(data, 'euclidean', return_indices=True) expected = [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 2, 4, 2, 1, 0, 0], [0, 0, 1, 4, 8, 4, 1, 0, 0], [0, 0, 1, 2, 4, 2, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]] expected = xp.asarray(expected) assert_array_almost_equal(out * out, expected) expected = [[[0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1], [2, 2, 2, 2, 1, 2, 2, 2, 2], [3, 3, 3, 2, 1, 2, 3, 3, 3], [4, 4, 4, 4, 6, 4, 4, 4, 4], [5, 5, 6, 6, 7, 6, 6, 5, 5], [6, 6, 6, 7, 7, 7, 6, 6, 6], [7, 7, 7, 7, 7, 7, 7, 7, 7], [8, 8, 8, 8, 8, 8, 8, 8, 8]], [[0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 2, 4, 6, 6, 7, 8], [0, 1, 1, 2, 4, 6, 7, 7, 8], [0, 1, 1, 1, 6, 7, 7, 7, 8], [0, 1, 2, 2, 4, 6, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8]]] expected = xp.asarray(expected) assert_array_almost_equal(ft, expected) @xfail_xp_backends('cupy', reason='CuPy does not have distance_transform_bf.') @pytest.mark.parametrize('dtype', types) def test_distance_transform_bf02(self, dtype, xp): dtype = getattr(xp, dtype) data = xp.asarray([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=dtype) out, ft = ndimage.distance_transform_bf(data, 'cityblock', return_indices=True) expected = [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 2, 2, 2, 1, 0, 0], [0, 0, 1, 2, 3, 2, 1, 0, 0], [0, 0, 1, 2, 2, 2, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]] expected = xp.asarray(expected) assert_array_almost_equal(out, expected) expected = [[[0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1], [2, 2, 2, 2, 1, 2, 2, 2, 2], [3, 3, 3, 3, 1, 3, 3, 3, 3], [4, 4, 4, 4, 7, 4, 4, 4, 4], [5, 5, 6, 7, 7, 7, 6, 5, 5], [6, 6, 6, 7, 7, 7, 6, 6, 6], [7, 7, 7, 7, 7, 7, 7, 7, 7], [8, 8, 8, 8, 8, 8, 8, 8, 8]], [[0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 2, 4, 6, 6, 7, 8], [0, 1, 1, 1, 4, 7, 7, 7, 8], [0, 1, 1, 1, 4, 7, 7, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8]]] expected = xp.asarray(expected) assert_array_almost_equal(expected, ft) @xfail_xp_backends('cupy', reason='CuPy does not have distance_transform_bf.') @pytest.mark.parametrize('dtype', types) def test_distance_transform_bf03(self, dtype, xp): dtype = getattr(xp, dtype) data = xp.asarray([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=dtype) out, ft = ndimage.distance_transform_bf(data, 'chessboard', return_indices=True) expected = [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 2, 1, 1, 0, 0], [0, 0, 1, 2, 2, 2, 1, 0, 0], [0, 0, 1, 1, 2, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]] expected = xp.asarray(expected) assert_array_almost_equal(out, expected) expected = [[[0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1], [2, 2, 2, 2, 1, 2, 2, 2, 2], [3, 3, 4, 2, 2, 2, 4, 3, 3], [4, 4, 5, 6, 6, 6, 5, 4, 4], [5, 5, 6, 6, 7, 6, 6, 5, 5], [6, 6, 6, 7, 7, 7, 6, 6, 6], [7, 7, 7, 7, 7, 7, 7, 7, 7], [8, 8, 8, 8, 8, 8, 8, 8, 8]], [[0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 2, 5, 6, 6, 7, 8], [0, 1, 1, 2, 6, 6, 7, 7, 8], [0, 1, 1, 2, 6, 7, 7, 7, 8], [0, 1, 2, 2, 6, 6, 7, 7, 8], [0, 1, 2, 4, 5, 6, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8]]] expected = xp.asarray(expected) assert_array_almost_equal(ft, expected) @skip_xp_backends( np_only=True, reason='inplace distances= arrays are numpy-specific' ) @pytest.mark.parametrize('dtype', types) def test_distance_transform_bf04(self, dtype, xp): dtype = getattr(xp, dtype) data = xp.asarray([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=dtype) tdt, tft = ndimage.distance_transform_bf(data, return_indices=1) dts = [] fts = [] dt = xp.zeros(data.shape, dtype=xp.float64) ndimage.distance_transform_bf(data, distances=dt) dts.append(dt) ft = ndimage.distance_transform_bf( data, return_distances=False, return_indices=1) fts.append(ft) ft = np.indices(data.shape, dtype=xp.int32) ndimage.distance_transform_bf( data, return_distances=False, return_indices=True, indices=ft) fts.append(ft) dt, ft = ndimage.distance_transform_bf( data, return_indices=1) dts.append(dt) fts.append(ft) dt = xp.zeros(data.shape, dtype=xp.float64) ft = ndimage.distance_transform_bf( data, distances=dt, return_indices=True) dts.append(dt) fts.append(ft) ft = np.indices(data.shape, dtype=xp.int32) dt = ndimage.distance_transform_bf( data, return_indices=True, indices=ft) dts.append(dt) fts.append(ft) dt = xp.zeros(data.shape, dtype=xp.float64) ft = np.indices(data.shape, dtype=xp.int32) ndimage.distance_transform_bf( data, distances=dt, return_indices=True, indices=ft) dts.append(dt) fts.append(ft) for dt in dts: assert_array_almost_equal(tdt, dt) for ft in fts: assert_array_almost_equal(tft, ft) @xfail_xp_backends('cupy', reason='CuPy does not have distance_transform_bf.') @pytest.mark.parametrize('dtype', types) def test_distance_transform_bf05(self, dtype, xp): dtype = getattr(xp, dtype) data = xp.asarray([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=dtype) out, ft = ndimage.distance_transform_bf( data, 'euclidean', return_indices=True, sampling=[2, 2]) expected = [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 4, 4, 4, 0, 0, 0], [0, 0, 4, 8, 16, 8, 4, 0, 0], [0, 0, 4, 16, 32, 16, 4, 0, 0], [0, 0, 4, 8, 16, 8, 4, 0, 0], [0, 0, 0, 4, 4, 4, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]] expected = xp.asarray(expected) assert_array_almost_equal(out * out, expected) expected = [[[0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1], [2, 2, 2, 2, 1, 2, 2, 2, 2], [3, 3, 3, 2, 1, 2, 3, 3, 3], [4, 4, 4, 4, 6, 4, 4, 4, 4], [5, 5, 6, 6, 7, 6, 6, 5, 5], [6, 6, 6, 7, 7, 7, 6, 6, 6], [7, 7, 7, 7, 7, 7, 7, 7, 7], [8, 8, 8, 8, 8, 8, 8, 8, 8]], [[0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 2, 4, 6, 6, 7, 8], [0, 1, 1, 2, 4, 6, 7, 7, 8], [0, 1, 1, 1, 6, 7, 7, 7, 8], [0, 1, 2, 2, 4, 6, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8]]] expected = xp.asarray(expected) assert_array_almost_equal(ft, expected) @xfail_xp_backends('cupy', reason='CuPy does not have distance_transform_bf.') @pytest.mark.parametrize('dtype', types) def test_distance_transform_bf06(self, dtype, xp): dtype = getattr(xp, dtype) data = xp.asarray([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=dtype) out, ft = ndimage.distance_transform_bf( data, 'euclidean', return_indices=True, sampling=[2, 1]) expected = [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 4, 1, 0, 0, 0], [0, 0, 1, 4, 8, 4, 1, 0, 0], [0, 0, 1, 4, 9, 4, 1, 0, 0], [0, 0, 1, 4, 8, 4, 1, 0, 0], [0, 0, 0, 1, 4, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]] expected = xp.asarray(expected) assert_array_almost_equal(out * out, expected) expected = [[[0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1], [2, 2, 2, 2, 2, 2, 2, 2, 2], [3, 3, 3, 3, 2, 3, 3, 3, 3], [4, 4, 4, 4, 4, 4, 4, 4, 4], [5, 5, 5, 5, 6, 5, 5, 5, 5], [6, 6, 6, 6, 7, 6, 6, 6, 6], [7, 7, 7, 7, 7, 7, 7, 7, 7], [8, 8, 8, 8, 8, 8, 8, 8, 8]], [[0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 2, 6, 6, 6, 7, 8], [0, 1, 1, 1, 6, 7, 7, 7, 8], [0, 1, 1, 1, 7, 7, 7, 7, 8], [0, 1, 1, 1, 6, 7, 7, 7, 8], [0, 1, 2, 2, 4, 6, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8]]] expected = xp.asarray(expected) assert_array_almost_equal(ft, expected) def test_distance_transform_bf07(self, xp): if is_cupy(xp): pytest.xfail("CuPy does not have distance_transform_bf.") # test input validation per discussion on PR #13302 data = xp.asarray([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]]) with assert_raises(RuntimeError): ndimage.distance_transform_bf( data, return_distances=False, return_indices=False ) @pytest.mark.parametrize('dtype', types) def test_distance_transform_cdt01(self, dtype, xp): dtype = getattr(xp, dtype) if is_cupy(xp): pytest.xfail("CuPy does not have distance_transform_cdt.") # chamfer type distance (cdt) transform data = xp.asarray([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=dtype) out, ft = ndimage.distance_transform_cdt( data, 'cityblock', return_indices=True) bf = ndimage.distance_transform_bf(data, 'cityblock') assert_array_almost_equal(bf, out) expected = [[[0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1], [2, 2, 2, 1, 1, 1, 2, 2, 2], [3, 3, 2, 1, 1, 1, 2, 3, 3], [4, 4, 4, 4, 1, 4, 4, 4, 4], [5, 5, 5, 5, 7, 7, 6, 5, 5], [6, 6, 6, 6, 7, 7, 6, 6, 6], [7, 7, 7, 7, 7, 7, 7, 7, 7], [8, 8, 8, 8, 8, 8, 8, 8, 8]], [[0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 1, 1, 4, 7, 7, 7, 8], [0, 1, 1, 1, 4, 5, 6, 7, 8], [0, 1, 2, 2, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8]]] expected = xp.asarray(expected) assert_array_almost_equal(ft, expected) @pytest.mark.parametrize('dtype', types) def test_distance_transform_cdt02(self, dtype, xp): dtype = getattr(xp, dtype) if is_cupy(xp): pytest.xfail("CuPy does not have distance_transform_cdt.") data = xp.asarray([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=dtype) out, ft = ndimage.distance_transform_cdt(data, 'chessboard', return_indices=True) bf = ndimage.distance_transform_bf(data, 'chessboard') assert_array_almost_equal(bf, out) expected = [[[0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1], [2, 2, 2, 1, 1, 1, 2, 2, 2], [3, 3, 2, 2, 1, 2, 2, 3, 3], [4, 4, 3, 2, 2, 2, 3, 4, 4], [5, 5, 4, 6, 7, 6, 4, 5, 5], [6, 6, 6, 6, 7, 7, 6, 6, 6], [7, 7, 7, 7, 7, 7, 7, 7, 7], [8, 8, 8, 8, 8, 8, 8, 8, 8]], [[0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 2, 3, 4, 6, 7, 8], [0, 1, 1, 2, 2, 6, 6, 7, 8], [0, 1, 1, 1, 2, 6, 7, 7, 8], [0, 1, 1, 2, 6, 6, 7, 7, 8], [0, 1, 2, 2, 5, 6, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8]]] expected = xp.asarray(expected) assert_array_almost_equal(ft, expected) @skip_xp_backends( np_only=True, reason='inplace indices= arrays are numpy-specific' ) @pytest.mark.parametrize('dtype', types) def test_distance_transform_cdt03(self, dtype, xp): dtype = getattr(xp, dtype) data = xp.asarray([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=dtype) tdt, tft = ndimage.distance_transform_cdt(data, return_indices=True) dts = [] fts = [] dt = xp.zeros(data.shape, dtype=xp.int32) ndimage.distance_transform_cdt(data, distances=dt) dts.append(dt) ft = ndimage.distance_transform_cdt( data, return_distances=False, return_indices=True) fts.append(ft) ft = xp.asarray(np.indices(data.shape, dtype=np.int32)) ndimage.distance_transform_cdt( data, return_distances=False, return_indices=True, indices=ft) fts.append(ft) dt, ft = ndimage.distance_transform_cdt( data, return_indices=True) dts.append(dt) fts.append(ft) dt = xp.zeros(data.shape, dtype=xp.int32) ft = ndimage.distance_transform_cdt( data, distances=dt, return_indices=True) dts.append(dt) fts.append(ft) ft = xp.asarray(np.indices(data.shape, dtype=np.int32)) dt = ndimage.distance_transform_cdt( data, return_indices=True, indices=ft) dts.append(dt) fts.append(ft) dt = xp.zeros(data.shape, dtype=xp.int32) ft = xp.asarray(np.indices(data.shape, dtype=np.int32)) ndimage.distance_transform_cdt(data, distances=dt, return_indices=True, indices=ft) dts.append(dt) fts.append(ft) for dt in dts: assert_array_almost_equal(tdt, dt) for ft in fts: assert_array_almost_equal(tft, ft) @skip_xp_backends( np_only=True, reason='XXX: does not raise unless indices is a numpy array' ) def test_distance_transform_cdt04(self, xp): # test input validation per discussion on PR #13302 data = xp.asarray([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]]) indices_out = xp.zeros((data.ndim,) + data.shape, dtype=xp.int32) with assert_raises(RuntimeError): ndimage.distance_transform_bf( data, return_distances=True, return_indices=False, indices=indices_out ) @pytest.mark.parametrize('dtype', types) def test_distance_transform_cdt05(self, dtype, xp): dtype = getattr(xp, dtype) if is_cupy(xp): pytest.xfail("CuPy does not have distance_transform_cdt.") elif is_torch(xp): pytest.xfail("int overflow") # test custom metric type per discussion on issue #17381 data = xp.asarray([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=dtype) metric_arg = xp.ones((3, 3)) actual = ndimage.distance_transform_cdt(data, metric=metric_arg) assert xp.sum(actual) == -21 @pytest.mark.parametrize('dtype', types) def test_distance_transform_edt01(self, dtype, xp): dtype = getattr(xp, dtype) if is_cupy(xp): pytest.xfail("CuPy does not have distance_transform_bf") # euclidean distance transform (edt) data = xp.asarray([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=dtype) out, ft = ndimage.distance_transform_edt(data, return_indices=True) bf = ndimage.distance_transform_bf(data, 'euclidean') assert_array_almost_equal(bf, out) # np-specific check np_ft = np.asarray(ft) dt = np_ft - np.indices(np_ft.shape[1:], dtype=np_ft.dtype) dt = dt.astype(np.float64) np.multiply(dt, dt, dt) dt = np.add.reduce(dt, axis=0) np.sqrt(dt, dt) dt = xp.asarray(dt) assert_array_almost_equal(bf, dt) @skip_xp_backends( np_only=True, reason='inplace distances= are numpy-specific' ) @pytest.mark.parametrize('dtype', types) def test_distance_transform_edt02(self, dtype, xp): dtype = getattr(xp, dtype) data = xp.asarray([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=dtype) tdt, tft = ndimage.distance_transform_edt(data, return_indices=True) dts = [] fts = [] dt = xp.zeros(data.shape, dtype=xp.float64) ndimage.distance_transform_edt(data, distances=dt) dts.append(dt) ft = ndimage.distance_transform_edt( data, return_distances=0, return_indices=True) fts.append(ft) ft = np.indices(data.shape, dtype=xp.int32) ft = xp.asarray(ft) ndimage.distance_transform_edt( data, return_distances=False, return_indices=True, indices=ft) fts.append(ft) dt, ft = ndimage.distance_transform_edt( data, return_indices=True) dts.append(dt) fts.append(ft) dt = xp.zeros(data.shape, dtype=xp.float64) ft = ndimage.distance_transform_edt( data, distances=dt, return_indices=True) dts.append(dt) fts.append(ft) ft = np.indices(data.shape, dtype=xp.int32) ft = xp.asarray(ft) dt = ndimage.distance_transform_edt( data, return_indices=True, indices=ft) dts.append(dt) fts.append(ft) dt = xp.zeros(data.shape, dtype=xp.float64) ft = np.indices(data.shape, dtype=xp.int32) ft = xp.asarray(ft) ndimage.distance_transform_edt( data, distances=dt, return_indices=True, indices=ft) dts.append(dt) fts.append(ft) for dt in dts: assert_array_almost_equal(tdt, dt) for ft in fts: assert_array_almost_equal(tft, ft) @pytest.mark.parametrize('dtype', types) def test_distance_transform_edt03(self, dtype, xp): dtype = getattr(xp, dtype) if is_cupy(xp): pytest.xfail("CuPy does not have distance_transform_bf") data = xp.asarray([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=dtype) ref = ndimage.distance_transform_bf(data, 'euclidean', sampling=[2, 2]) out = ndimage.distance_transform_edt(data, sampling=[2, 2]) assert_array_almost_equal(ref, out) @pytest.mark.parametrize('dtype', types) def test_distance_transform_edt4(self, dtype, xp): dtype = getattr(xp, dtype) if is_cupy(xp): pytest.xfail("CuPy does not have distance_transform_bf") data = xp.asarray([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=dtype) ref = ndimage.distance_transform_bf(data, 'euclidean', sampling=[2, 1]) out = ndimage.distance_transform_edt(data, sampling=[2, 1]) assert_array_almost_equal(ref, out) def test_distance_transform_edt5(self, xp): # Ticket #954 regression test out = ndimage.distance_transform_edt(False) assert_array_almost_equal(out, [0.]) @skip_xp_backends( np_only=True, reason='XXX: does not raise unless indices is a numpy array' ) def test_distance_transform_edt6(self, xp): # test input validation per discussion on PR #13302 data = xp.asarray([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]]) distances_out = xp.zeros(data.shape, dtype=xp.float64) with assert_raises(RuntimeError): ndimage.distance_transform_bf( data, return_indices=True, return_distances=False, distances=distances_out ) def test_generate_structure01(self, xp): struct = ndimage.generate_binary_structure(0, 1) assert struct == 1 def test_generate_structure02(self, xp): struct = ndimage.generate_binary_structure(1, 1) assert_array_almost_equal(struct, [1, 1, 1]) def test_generate_structure03(self, xp): struct = ndimage.generate_binary_structure(2, 1) assert_array_almost_equal(struct, [[0, 1, 0], [1, 1, 1], [0, 1, 0]]) def test_generate_structure04(self, xp): struct = ndimage.generate_binary_structure(2, 2) assert_array_almost_equal(struct, [[1, 1, 1], [1, 1, 1], [1, 1, 1]]) def test_iterate_structure01(self, xp): struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] struct = xp.asarray(struct) out = ndimage.iterate_structure(struct, 2) expected = np.asarray([[0, 0, 1, 0, 0], [0, 1, 1, 1, 0], [1, 1, 1, 1, 1], [0, 1, 1, 1, 0], [0, 0, 1, 0, 0]], dtype=bool) expected = xp.asarray(expected) assert_array_almost_equal(out, expected) def test_iterate_structure02(self, xp): struct = [[0, 1], [1, 1], [0, 1]] struct = xp.asarray(struct) out = ndimage.iterate_structure(struct, 2) expected = np.asarray([[0, 0, 1], [0, 1, 1], [1, 1, 1], [0, 1, 1], [0, 0, 1]], dtype=bool) expected = xp.asarray(expected) assert_array_almost_equal(out, expected) def test_iterate_structure03(self, xp): struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] struct = xp.asarray(struct) out = ndimage.iterate_structure(struct, 2, 1) expected = [[0, 0, 1, 0, 0], [0, 1, 1, 1, 0], [1, 1, 1, 1, 1], [0, 1, 1, 1, 0], [0, 0, 1, 0, 0]] expected = np.asarray(expected, dtype=bool) expected = xp.asarray(expected) assert_array_almost_equal(out[0], expected) assert out[1] == [2, 2] @pytest.mark.parametrize('dtype', types) def test_binary_erosion01(self, dtype, xp): dtype = getattr(xp, dtype) data = xp.ones([], dtype=dtype) out = ndimage.binary_erosion(data) assert out == xp.asarray(1, dtype=out.dtype) @pytest.mark.parametrize('dtype', types) def test_binary_erosion02(self, dtype, xp): dtype = getattr(xp, dtype) data = xp.ones([], dtype=dtype) out = ndimage.binary_erosion(data, border_value=1) assert out == xp.asarray(1, dtype=out.dtype) @pytest.mark.parametrize('dtype', types) def test_binary_erosion03(self, dtype, xp): dtype = getattr(xp, dtype) data = xp.ones([1], dtype=dtype) out = ndimage.binary_erosion(data) assert_array_almost_equal(out, xp.asarray([0])) @pytest.mark.parametrize('dtype', types) def test_binary_erosion04(self, dtype, xp): dtype = getattr(xp, dtype) data = xp.ones([1], dtype=dtype) out = ndimage.binary_erosion(data, border_value=1) assert_array_almost_equal(out, xp.asarray([1])) @pytest.mark.parametrize('dtype', types) def test_binary_erosion05(self, dtype, xp): dtype = getattr(xp, dtype) data = xp.ones([3], dtype=dtype) out = ndimage.binary_erosion(data) assert_array_almost_equal(out, xp.asarray([0, 1, 0])) @pytest.mark.parametrize('dtype', types) def test_binary_erosion06(self, dtype, xp): dtype = getattr(xp, dtype) data = xp.ones([3], dtype=dtype) out = ndimage.binary_erosion(data, border_value=1) assert_array_almost_equal(out, xp.asarray([1, 1, 1])) @pytest.mark.parametrize('dtype', types) def test_binary_erosion07(self, dtype, xp): dtype = getattr(xp, dtype) data = xp.ones([5], dtype=dtype) out = ndimage.binary_erosion(data) assert_array_almost_equal(out, xp.asarray([0, 1, 1, 1, 0])) @pytest.mark.parametrize('dtype', types) def test_binary_erosion08(self, dtype, xp): dtype = getattr(xp, dtype) data = xp.ones([5], dtype=dtype) out = ndimage.binary_erosion(data, border_value=1) assert_array_almost_equal(out, xp.asarray([1, 1, 1, 1, 1])) @pytest.mark.parametrize('dtype', types) def test_binary_erosion09(self, dtype, xp): data = np.ones([5], dtype=dtype) data[2] = 0 data = xp.asarray(data) out = ndimage.binary_erosion(data) assert_array_almost_equal(out, xp.asarray([0, 0, 0, 0, 0])) @pytest.mark.parametrize('dtype', types) def test_binary_erosion10(self, dtype, xp): data = np.ones([5], dtype=dtype) data[2] = 0 data = xp.asarray(data) out = ndimage.binary_erosion(data, border_value=1) assert_array_almost_equal(out, xp.asarray([1, 0, 0, 0, 1])) @pytest.mark.parametrize('dtype', types) def test_binary_erosion11(self, dtype, xp): data = np.ones([5], dtype=dtype) data[2] = 0 data = xp.asarray(data) struct = xp.asarray([1, 0, 1]) out = ndimage.binary_erosion(data, struct, border_value=1) assert_array_almost_equal(out, xp.asarray([1, 0, 1, 0, 1])) @pytest.mark.parametrize('dtype', types) def test_binary_erosion12(self, dtype, xp): data = np.ones([5], dtype=dtype) data[2] = 0 data = xp.asarray(data) struct = xp.asarray([1, 0, 1]) out = ndimage.binary_erosion(data, struct, border_value=1, origin=-1) assert_array_almost_equal(out, xp.asarray([0, 1, 0, 1, 1])) @pytest.mark.parametrize('dtype', types) def test_binary_erosion13(self, dtype, xp): data = np.ones([5], dtype=dtype) data[2] = 0 data = xp.asarray(data) struct = xp.asarray([1, 0, 1]) out = ndimage.binary_erosion(data, struct, border_value=1, origin=1) assert_array_almost_equal(out, xp.asarray([1, 1, 0, 1, 0])) @pytest.mark.parametrize('dtype', types) def test_binary_erosion14(self, dtype, xp): data = np.ones([5], dtype=dtype) data[2] = 0 data = xp.asarray(data) struct = xp.asarray([1, 1]) out = ndimage.binary_erosion(data, struct, border_value=1) assert_array_almost_equal(out, xp.asarray([1, 1, 0, 0, 1])) @pytest.mark.parametrize('dtype', types) def test_binary_erosion15(self, dtype, xp): data = np.ones([5], dtype=dtype) data[2] = 0 data = xp.asarray(data) struct = xp.asarray([1, 1]) out = ndimage.binary_erosion(data, struct, border_value=1, origin=-1) assert_array_almost_equal(out, xp.asarray([1, 0, 0, 1, 1])) @pytest.mark.parametrize('dtype', types) def test_binary_erosion16(self, dtype, xp): dtype = getattr(xp, dtype) data = xp.ones([1, 1], dtype=dtype) out = ndimage.binary_erosion(data, border_value=1) assert_array_almost_equal(out, xp.asarray([[1]])) @pytest.mark.parametrize('dtype', types) def test_binary_erosion17(self, dtype, xp): dtype = getattr(xp, dtype) data = xp.ones([1, 1], dtype=dtype) out = ndimage.binary_erosion(data) assert_array_almost_equal(out, xp.asarray([[0]])) @pytest.mark.parametrize('dtype', types) def test_binary_erosion18(self, dtype, xp): dtype = getattr(xp, dtype) data = xp.ones([1, 3], dtype=dtype) out = ndimage.binary_erosion(data) assert_array_almost_equal(out, xp.asarray([[0, 0, 0]])) @pytest.mark.parametrize('dtype', types) def test_binary_erosion19(self, dtype, xp): dtype = getattr(xp, dtype) data = xp.ones([1, 3], dtype=dtype) out = ndimage.binary_erosion(data, border_value=1) assert_array_almost_equal(out, xp.asarray([[1, 1, 1]])) @pytest.mark.parametrize('dtype', types) def test_binary_erosion20(self, dtype, xp): dtype = getattr(xp, dtype) data = xp.ones([3, 3], dtype=dtype) out = ndimage.binary_erosion(data) assert_array_almost_equal(out, xp.asarray([[0, 0, 0], [0, 1, 0], [0, 0, 0]])) @pytest.mark.parametrize('dtype', types) def test_binary_erosion21(self, dtype, xp): dtype = getattr(xp, dtype) data = xp.ones([3, 3], dtype=dtype) out = ndimage.binary_erosion(data, border_value=1) assert_array_almost_equal(out, xp.asarray([[1, 1, 1], [1, 1, 1], [1, 1, 1]])) @pytest.mark.parametrize('dtype', types) def test_binary_erosion22(self, dtype, xp): dtype = getattr(xp, dtype) expected = [[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] expected = xp.asarray(expected) data = xp.asarray([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 1, 1, 1, 1, 1, 1], [0, 0, 1, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0]], dtype=dtype) out = ndimage.binary_erosion(data, border_value=1) assert_array_almost_equal(out, expected) @pytest.mark.parametrize('dtype', types) def test_binary_erosion23(self, dtype, xp): dtype = getattr(xp, dtype) struct = ndimage.generate_binary_structure(2, 2) struct = xp.asarray(struct) expected = [[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] expected = xp.asarray(expected) data = xp.asarray([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 1, 1, 1, 1, 1, 1], [0, 0, 1, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0]], dtype=dtype) out = ndimage.binary_erosion(data, struct, border_value=1) assert_array_almost_equal(out, expected) @pytest.mark.parametrize('dtype', types) def test_binary_erosion24(self, dtype, xp): dtype = getattr(xp, dtype) struct = xp.asarray([[0, 1], [1, 1]]) expected = [[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 1, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0]] expected = xp.asarray(expected) data = xp.asarray([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 1, 1, 1, 1, 1, 1], [0, 0, 1, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0]], dtype=dtype) out = ndimage.binary_erosion(data, struct, border_value=1) assert_array_almost_equal(out, expected) @pytest.mark.parametrize('dtype', types) def test_binary_erosion25(self, dtype, xp): dtype = getattr(xp, dtype) struct = [[0, 1, 0], [1, 0, 1], [0, 1, 0]] struct = xp.asarray(struct) expected = [[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] expected = xp.asarray(expected) data = xp.asarray([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 1, 1, 1, 0, 1, 1], [0, 0, 1, 0, 1, 1, 0, 0], [0, 1, 0, 1, 1, 1, 1, 0], [0, 1, 1, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0]], dtype=dtype) out = ndimage.binary_erosion(data, struct, border_value=1) assert_array_almost_equal(out, expected) @pytest.mark.parametrize('dtype', types) def test_binary_erosion26(self, dtype, xp): dtype = getattr(xp, dtype) struct = [[0, 1, 0], [1, 0, 1], [0, 1, 0]] struct = xp.asarray(struct) expected = [[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 1, 0, 0, 1], [0, 0, 1, 0, 0, 0, 0, 0], [0, 1, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1]] expected = xp.asarray(expected) data = xp.asarray([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 1, 1, 1, 0, 1, 1], [0, 0, 1, 0, 1, 1, 0, 0], [0, 1, 0, 1, 1, 1, 1, 0], [0, 1, 1, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0]], dtype=dtype) out = ndimage.binary_erosion(data, struct, border_value=1, origin=(-1, -1)) assert_array_almost_equal(out, expected) def test_binary_erosion27(self, xp): if is_cupy(xp): pytest.xfail("CuPy: NotImplementedError: only brute_force iteration") struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] struct = xp.asarray(struct) expected = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]] expected = xp.asarray(expected) data = np.asarray([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]], dtype=bool) data = xp.asarray(data) out = ndimage.binary_erosion(data, struct, border_value=1, iterations=2) assert_array_almost_equal(out, expected) @skip_xp_backends( np_only=True, reason='inplace out= arguments are numpy-specific' ) def test_binary_erosion28(self, xp): struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] struct = xp.asarray(struct) expected = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]] expected = np.asarray(expected, dtype=bool) expected = xp.asarray(expected) data = np.asarray([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]], dtype=bool) data = xp.asarray(data) out = np.zeros(data.shape, dtype=bool) out = xp.asarray(out) ndimage.binary_erosion(data, struct, border_value=1, iterations=2, output=out) assert_array_almost_equal(out, expected) def test_binary_erosion29(self, xp): if is_cupy(xp): pytest.xfail("CuPy: NotImplementedError: only brute_force iteration") struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] struct = xp.asarray(struct) expected = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]] expected = xp.asarray(expected) data = np.asarray([[0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0]], dtype=bool) data = xp.asarray(data) out = ndimage.binary_erosion(data, struct, border_value=1, iterations=3) assert_array_almost_equal(out, expected) @skip_xp_backends( np_only=True, reason='inplace out= arguments are numpy-specific' ) def test_binary_erosion30(self, xp): if is_cupy(xp): pytest.xfail("CuPy: NotImplementedError: only brute_force iteration") struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] struct = xp.asarray(struct) expected = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]] expected = np.asarray(expected, dtype=bool) expected = xp.asarray(expected) data = np.asarray([[0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0]], dtype=bool) data = xp.asarray(data) out = np.zeros(data.shape, dtype=bool) out = xp.asarray(out) ndimage.binary_erosion(data, struct, border_value=1, iterations=3, output=out) assert_array_almost_equal(out, expected) # test with output memory overlap ndimage.binary_erosion(data, struct, border_value=1, iterations=3, output=data) assert_array_almost_equal(data, expected) @skip_xp_backends( np_only=True, reason='inplace out= arguments are numpy-specific' ) def test_binary_erosion31(self, xp): struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] struct = xp.asarray(struct) expected = [[0, 0, 1, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 0], [1, 1, 1, 1, 1, 0, 1], [0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 1]] expected = np.asarray(expected, dtype=bool) expected = xp.asarray(expected) data = np.asarray([[0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0]], dtype=bool) data = xp.asarray(data) out = np.zeros(data.shape, dtype=bool) out = xp.asarray(out) ndimage.binary_erosion(data, struct, border_value=1, iterations=1, output=out, origin=(-1, -1)) assert_array_almost_equal(out, expected) def test_binary_erosion32(self, xp): if is_cupy(xp): pytest.xfail("CuPy: NotImplementedError: only brute_force iteration") struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] struct = xp.asarray(struct) expected = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]] expected = xp.asarray(expected) data = np.asarray([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]], dtype=bool) data = xp.asarray(data) out = ndimage.binary_erosion(data, struct, border_value=1, iterations=2) assert_array_almost_equal(out, expected) def test_binary_erosion33(self, xp): if is_cupy(xp): pytest.xfail("CuPy: NotImplementedError: only brute_force iteration") struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] struct = xp.asarray(struct) expected = [[0, 0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]] expected = xp.asarray(expected) mask = [[1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1]] mask = xp.asarray(mask) data = np.asarray([[0, 0, 0, 0, 0, 1, 1], [0, 0, 0, 1, 0, 0, 1], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]], dtype=bool) data = xp.asarray(data) out = ndimage.binary_erosion(data, struct, border_value=1, mask=mask, iterations=-1) assert_array_almost_equal(out, expected) def test_binary_erosion34(self, xp): struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] struct = xp.asarray(struct) expected = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]] expected = xp.asarray(expected) mask = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 0, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]] mask = xp.asarray(mask) data = np.asarray([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]], dtype=bool) data = xp.asarray(data) out = ndimage.binary_erosion(data, struct, border_value=1, mask=mask) assert_array_almost_equal(out, expected) @skip_xp_backends( np_only=True, reason='inplace out= arguments are numpy-specific' ) def test_binary_erosion35(self, xp): struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] struct = xp.asarray(struct) mask = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 0, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]] mask = np.asarray(mask, dtype=bool) mask = xp.asarray(mask) data = np.asarray([[0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0]], dtype=bool) data = xp.asarray(data) tmp = [[0, 0, 1, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 0], [1, 1, 1, 1, 1, 0, 1], [0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 1]] tmp = np.asarray(tmp, dtype=bool) tmp = xp.asarray(tmp) expected = xp.logical_and(tmp, mask) tmp = xp.logical_and(data, xp.logical_not(mask)) expected = xp.logical_or(expected, tmp) out = np.zeros(data.shape, dtype=bool) out = xp.asarray(out) ndimage.binary_erosion(data, struct, border_value=1, iterations=1, output=out, origin=(-1, -1), mask=mask) assert_array_almost_equal(out, expected) def test_binary_erosion36(self, xp): if is_cupy(xp): pytest.xfail("CuPy: NotImplementedError: only brute_force iteration") struct = [[0, 1, 0], [1, 0, 1], [0, 1, 0]] struct = xp.asarray(struct) mask = [[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 0, 1, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] mask = np.asarray(mask, dtype=bool) mask = xp.asarray(mask) tmp = [[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 1, 0, 0, 1], [0, 0, 1, 0, 0, 0, 0, 0], [0, 1, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1]] tmp = np.asarray(tmp, dtype=bool) tmp = xp.asarray(tmp) data = np.asarray([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 1, 1, 1, 0, 1, 1], [0, 0, 1, 0, 1, 1, 0, 0], [0, 1, 0, 1, 1, 1, 1, 0], [0, 1, 1, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0]], dtype=bool) data = xp.asarray(data) expected = xp.logical_and(tmp, mask) tmp = xp.logical_and(data, xp.logical_not(mask)) expected = xp.logical_or(expected, tmp) out = ndimage.binary_erosion(data, struct, mask=mask, border_value=1, origin=(-1, -1)) assert_array_almost_equal(out, expected) @skip_xp_backends( np_only=True, reason='inplace out= arguments are numpy-specific' ) def test_binary_erosion37(self, xp): a = np.asarray([[1, 0, 1], [0, 1, 0], [1, 0, 1]], dtype=bool) a = xp.asarray(a) b = xp.zeros_like(a) out = ndimage.binary_erosion(a, structure=a, output=b, iterations=0, border_value=True, brute_force=True) assert out is b xp_assert_equal( ndimage.binary_erosion(a, structure=a, iterations=0, border_value=True), b) def test_binary_erosion38(self, xp): data = np.asarray([[1, 0, 1], [0, 1, 0], [1, 0, 1]], dtype=bool) data = xp.asarray(data) iterations = 2.0 with assert_raises(TypeError): _ = ndimage.binary_erosion(data, iterations=iterations) @skip_xp_backends( np_only=True, reason='inplace out= arguments are numpy-specific' ) def test_binary_erosion39(self, xp): iterations = np.int32(3) struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] struct = xp.asarray(struct) expected = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]] expected = xp.asarray(expected, dtype=bool) expected = xp.asarray(expected) data = np.asarray([[0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0]], dtype=bool) data = xp.asarray(data) out = np.zeros(data.shape, dtype=bool) out = xp.asarray(out) ndimage.binary_erosion(data, struct, border_value=1, iterations=iterations, output=out) assert_array_almost_equal(out, expected) @skip_xp_backends( np_only=True, reason='inplace out= arguments are numpy-specific' ) def test_binary_erosion40(self, xp): iterations = np.int64(3) struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] struct = xp.asarray(struct) expected = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]] expected = np.asarray(expected, dtype=bool) expected = xp.asarray(expected) data = np.asarray([[0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0]], dtype=bool) data = xp.asarray(data) out = np.zeros(data.shape, dtype=bool) out = xp.asarray(out) ndimage.binary_erosion(data, struct, border_value=1, iterations=iterations, output=out) assert_array_almost_equal(out, expected) @pytest.mark.parametrize('dtype', types) def test_binary_dilation01(self, dtype, xp): dtype = getattr(xp, dtype) data = xp.ones([], dtype=dtype) out = ndimage.binary_dilation(data) assert out == xp.asarray(1, dtype=out.dtype) @pytest.mark.parametrize('dtype', types) def test_binary_dilation02(self, dtype, xp): dtype = getattr(xp, dtype) data = xp.zeros([], dtype=dtype) out = ndimage.binary_dilation(data) assert out == xp.asarray(False) @pytest.mark.parametrize('dtype', types) def test_binary_dilation03(self, dtype, xp): dtype = getattr(xp, dtype) data = xp.ones([1], dtype=dtype) out = ndimage.binary_dilation(data) assert_array_almost_equal(out, xp.asarray([1], dtype=out.dtype)) @pytest.mark.parametrize('dtype', types) def test_binary_dilation04(self, dtype, xp): dtype = getattr(xp, dtype) data = xp.zeros([1], dtype=dtype) out = ndimage.binary_dilation(data) assert_array_almost_equal(out, xp.asarray([0])) @pytest.mark.parametrize('dtype', types) def test_binary_dilation05(self, dtype, xp): dtype = getattr(xp, dtype) data = xp.ones([3], dtype=dtype) out = ndimage.binary_dilation(data) assert_array_almost_equal(out, xp.asarray([1, 1, 1])) @pytest.mark.parametrize('dtype', types) def test_binary_dilation06(self, dtype, xp): dtype = getattr(xp, dtype) data = xp.zeros([3], dtype=dtype) out = ndimage.binary_dilation(data) assert_array_almost_equal(out, xp.asarray([0, 0, 0])) @pytest.mark.parametrize('dtype', types) def test_binary_dilation07(self, dtype, xp): data = np.zeros([3], dtype=dtype) data[1] = 1 data = xp.asarray(data) out = ndimage.binary_dilation(data) assert_array_almost_equal(out, xp.asarray([1, 1, 1])) @pytest.mark.parametrize('dtype', types) def test_binary_dilation08(self, dtype, xp): data = np.zeros([5], dtype=dtype) data[1] = 1 data[3] = 1 data = xp.asarray(data) out = ndimage.binary_dilation(data) assert_array_almost_equal(out, xp.asarray([1, 1, 1, 1, 1])) @pytest.mark.parametrize('dtype', types) def test_binary_dilation09(self, dtype, xp): data = np.zeros([5], dtype=dtype) data[1] = 1 data = xp.asarray(data) out = ndimage.binary_dilation(data) assert_array_almost_equal(out, xp.asarray([1, 1, 1, 0, 0])) @pytest.mark.parametrize('dtype', types) def test_binary_dilation10(self, dtype, xp): data = np.zeros([5], dtype=dtype) data[1] = 1 data = xp.asarray(data) out = ndimage.binary_dilation(data, origin=-1) assert_array_almost_equal(out, xp.asarray([0, 1, 1, 1, 0])) @pytest.mark.parametrize('dtype', types) def test_binary_dilation11(self, dtype, xp): data = np.zeros([5], dtype=dtype) data[1] = 1 data = xp.asarray(data) out = ndimage.binary_dilation(data, origin=1) assert_array_almost_equal(out, xp.asarray([1, 1, 0, 0, 0])) @pytest.mark.parametrize('dtype', types) def test_binary_dilation12(self, dtype, xp): data = np.zeros([5], dtype=dtype) data[1] = 1 data = xp.asarray(data) struct = xp.asarray([1, 0, 1]) out = ndimage.binary_dilation(data, struct) assert_array_almost_equal(out, xp.asarray([1, 0, 1, 0, 0])) @pytest.mark.parametrize('dtype', types) def test_binary_dilation13(self, dtype, xp): data = np.zeros([5], dtype=dtype) data[1] = 1 data = xp.asarray(data) struct = xp.asarray([1, 0, 1]) out = ndimage.binary_dilation(data, struct, border_value=1) assert_array_almost_equal(out, xp.asarray([1, 0, 1, 0, 1])) @pytest.mark.parametrize('dtype', types) def test_binary_dilation14(self, dtype, xp): data = np.zeros([5], dtype=dtype) data[1] = 1 data = xp.asarray(data) struct = xp.asarray([1, 0, 1]) out = ndimage.binary_dilation(data, struct, origin=-1) assert_array_almost_equal(out, xp.asarray([0, 1, 0, 1, 0])) @pytest.mark.parametrize('dtype', types) def test_binary_dilation15(self, dtype, xp): data = np.zeros([5], dtype=dtype) data[1] = 1 data = xp.asarray(data) struct = xp.asarray([1, 0, 1]) out = ndimage.binary_dilation(data, struct, origin=-1, border_value=1) assert_array_almost_equal(out, xp.asarray([1, 1, 0, 1, 0])) @pytest.mark.parametrize('dtype', types) def test_binary_dilation16(self, dtype, xp): dtype = getattr(xp, dtype) data = xp.ones([1, 1], dtype=dtype) out = ndimage.binary_dilation(data) assert_array_almost_equal(out, xp.asarray([[1]])) @pytest.mark.parametrize('dtype', types) def test_binary_dilation17(self, dtype, xp): dtype = getattr(xp, dtype) data = xp.zeros([1, 1], dtype=dtype) out = ndimage.binary_dilation(data) assert_array_almost_equal(out, xp.asarray([[0]])) @pytest.mark.parametrize('dtype', types) def test_binary_dilation18(self, dtype, xp): dtype = getattr(xp, dtype) data = xp.ones([1, 3], dtype=dtype) out = ndimage.binary_dilation(data) assert_array_almost_equal(out, xp.asarray([[1, 1, 1]])) @pytest.mark.parametrize('dtype', types) def test_binary_dilation19(self, dtype, xp): dtype = getattr(xp, dtype) data = xp.ones([3, 3], dtype=dtype) out = ndimage.binary_dilation(data) assert_array_almost_equal(out, xp.asarray([[1, 1, 1], [1, 1, 1], [1, 1, 1]])) @pytest.mark.parametrize('dtype', types) def test_binary_dilation20(self, dtype, xp): data = np.zeros([3, 3], dtype=dtype) data[1, 1] = 1 data = xp.asarray(data) out = ndimage.binary_dilation(data) assert_array_almost_equal(out, xp.asarray([[0, 1, 0], [1, 1, 1], [0, 1, 0]])) @pytest.mark.parametrize('dtype', types) def test_binary_dilation21(self, dtype, xp): struct = ndimage.generate_binary_structure(2, 2) struct = xp.asarray(struct) data = np.zeros([3, 3], dtype=dtype) data[1, 1] = 1 data = xp.asarray(data) out = ndimage.binary_dilation(data, struct) assert_array_almost_equal(out, xp.asarray([[1, 1, 1], [1, 1, 1], [1, 1, 1]])) @pytest.mark.parametrize('dtype', types) def test_binary_dilation22(self, dtype, xp): dtype = getattr(xp, dtype) expected = [[0, 1, 0, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] expected = xp.asarray(expected) data = xp.asarray([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], dtype=dtype) out = ndimage.binary_dilation(data) assert_array_almost_equal(out, expected) @pytest.mark.parametrize('dtype', types) def test_binary_dilation23(self, dtype, xp): dtype = getattr(xp, dtype) expected = [[1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0, 0, 1], [1, 1, 0, 0, 0, 1, 0, 1], [1, 0, 0, 1, 1, 1, 1, 1], [1, 0, 1, 1, 1, 1, 0, 1], [1, 1, 1, 1, 1, 1, 1, 1], [1, 0, 1, 0, 0, 1, 0, 1], [1, 1, 1, 1, 1, 1, 1, 1]] expected = xp.asarray(expected) data = xp.asarray([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], dtype=dtype) out = ndimage.binary_dilation(data, border_value=1) assert_array_almost_equal(out, expected) @pytest.mark.parametrize('dtype', types) def test_binary_dilation24(self, dtype, xp): dtype = getattr(xp, dtype) expected = [[1, 1, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 0, 0, 0], [1, 1, 1, 1, 1, 1, 0, 0], [0, 1, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] expected = xp.asarray(expected) data = xp.asarray([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], dtype=dtype) out = ndimage.binary_dilation(data, origin=(1, 1)) assert_array_almost_equal(out, expected) @pytest.mark.parametrize('dtype', types) def test_binary_dilation25(self, dtype, xp): dtype = getattr(xp, dtype) expected = [[1, 1, 0, 0, 0, 0, 1, 1], [1, 0, 0, 0, 1, 0, 1, 1], [0, 0, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 0, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1], [0, 1, 0, 0, 1, 0, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1]] expected = xp.asarray(expected) data = xp.asarray([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], dtype=dtype) out = ndimage.binary_dilation(data, origin=(1, 1), border_value=1) assert_array_almost_equal(out, expected) @pytest.mark.parametrize('dtype', types) def test_binary_dilation26(self, dtype, xp): dtype = getattr(xp, dtype) struct = ndimage.generate_binary_structure(2, 2) expected = [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 1, 1, 1, 0], [0, 0, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0]] struct = xp.asarray(struct) expected = xp.asarray(expected) data = xp.asarray([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], dtype=dtype) out = ndimage.binary_dilation(data, struct) assert_array_almost_equal(out, expected) @pytest.mark.parametrize('dtype', types) def test_binary_dilation27(self, dtype, xp): dtype = getattr(xp, dtype) struct = [[0, 1], [1, 1]] expected = [[0, 1, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 1, 1, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] struct = xp.asarray(struct) expected = xp.asarray(expected) data = xp.asarray([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], dtype=dtype) out = ndimage.binary_dilation(data, struct) assert_array_almost_equal(out, expected) @pytest.mark.parametrize('dtype', types) def test_binary_dilation28(self, dtype, xp): dtype = getattr(xp, dtype) expected = [[1, 1, 1, 1], [1, 0, 0, 1], [1, 0, 0, 1], [1, 1, 1, 1]] expected = xp.asarray(expected) data = xp.asarray([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], dtype=dtype) out = ndimage.binary_dilation(data, border_value=1) assert_array_almost_equal(out, expected) def test_binary_dilation29(self, xp): if is_cupy(xp): pytest.xfail("CuPy: NotImplementedError: only brute_force iteration") struct = [[0, 1], [1, 1]] expected = [[0, 0, 0, 0, 0], [0, 0, 0, 1, 0], [0, 0, 1, 1, 0], [0, 1, 1, 1, 0], [0, 0, 0, 0, 0]] struct = xp.asarray(struct) expected = xp.asarray(expected) data = np.asarray([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 1, 0], [0, 0, 0, 0, 0]], dtype=bool) data = xp.asarray(data) out = ndimage.binary_dilation(data, struct, iterations=2) assert_array_almost_equal(out, expected) @skip_xp_backends(np_only=True, reason='output= arrays are numpy-specific') def test_binary_dilation30(self, xp): if is_cupy(xp): pytest.xfail("CuPy: NotImplementedError: only brute_force iteration") struct = [[0, 1], [1, 1]] expected = [[0, 0, 0, 0, 0], [0, 0, 0, 1, 0], [0, 0, 1, 1, 0], [0, 1, 1, 1, 0], [0, 0, 0, 0, 0]] struct = xp.asarray(struct) expected = xp.asarray(expected) data = xp.asarray([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 1, 0], [0, 0, 0, 0, 0]], dtype=bool) data = xp.asarray(data) out = np.zeros(data.shape, dtype=bool) out = xp.asarray(out) ndimage.binary_dilation(data, struct, iterations=2, output=out) assert_array_almost_equal(out, expected) def test_binary_dilation31(self, xp): if is_cupy(xp): pytest.xfail("CuPy: NotImplementedError: only brute_force iteration") struct = [[0, 1], [1, 1]] expected = [[0, 0, 0, 1, 0], [0, 0, 1, 1, 0], [0, 1, 1, 1, 0], [1, 1, 1, 1, 0], [0, 0, 0, 0, 0]] struct = xp.asarray(struct) expected = xp.asarray(expected) data = np.asarray([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 1, 0], [0, 0, 0, 0, 0]], dtype=bool) data = xp.asarray(data) out = ndimage.binary_dilation(data, struct, iterations=3) assert_array_almost_equal(out, expected) @skip_xp_backends(np_only=True, reason='output= arrays are numpy-specific') def test_binary_dilation32(self, xp): if is_cupy(xp): pytest.xfail("CuPy: NotImplementedError: only brute_force iteration") struct = [[0, 1], [1, 1]] expected = [[0, 0, 0, 1, 0], [0, 0, 1, 1, 0], [0, 1, 1, 1, 0], [1, 1, 1, 1, 0], [0, 0, 0, 0, 0]] struct = xp.asarray(struct) expected = xp.asarray(expected) data = np.asarray([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 1, 0], [0, 0, 0, 0, 0]], dtype=bool) data = xp.asarray(data) out = np.zeros(data.shape, dtype=bool) out = xp.asarray(out) ndimage.binary_dilation(data, struct, iterations=3, output=out) assert_array_almost_equal(out, expected) def test_binary_dilation33(self, xp): if is_cupy(xp): pytest.xfail("CuPy: NotImplementedError: only brute_force iteration") struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] struct = xp.asarray(struct) expected = np.asarray([[0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0], [0, 1, 1, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], dtype=bool) expected = xp.asarray(expected) mask = np.asarray([[0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0], [0, 1, 1, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], dtype=bool) mask = xp.asarray(mask) data = np.asarray([[0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], dtype=bool) data = xp.asarray(data) out = ndimage.binary_dilation(data, struct, iterations=-1, mask=mask, border_value=0) assert_array_almost_equal(out, expected) @skip_xp_backends( np_only=True, reason='inplace output= arrays are numpy-specific', ) def test_binary_dilation34(self, xp): if is_cupy(xp): pytest.xfail("CuPy: NotImplementedError: only brute_force iteration") struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] struct = xp.asarray(struct) expected = [[0, 1, 0, 0, 0, 0, 0, 0], [0, 1, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] mask = np.asarray([[0, 1, 0, 0, 0, 0, 0, 0], [0, 1, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], dtype=bool) mask = xp.asarray(mask) data = np.zeros(mask.shape, dtype=bool) data = xp.asarray(data) out = ndimage.binary_dilation(data, struct, iterations=-1, mask=mask, border_value=1) assert_array_almost_equal(out, expected) @pytest.mark.parametrize('dtype', types) def test_binary_dilation35(self, dtype, xp): dtype = getattr(xp, dtype) tmp = [[1, 1, 0, 0, 0, 0, 1, 1], [1, 0, 0, 0, 1, 0, 1, 1], [0, 0, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 0, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1], [0, 1, 0, 0, 1, 0, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1]] data = np.asarray([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]]) mask = [[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] mask = np.asarray(mask, dtype=bool) expected = np.logical_and(tmp, mask) tmp = np.logical_and(data, np.logical_not(mask)) expected = np.logical_or(expected, tmp) mask = xp.asarray(mask) expected = xp.asarray(expected) data = xp.asarray([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], dtype=dtype) out = ndimage.binary_dilation(data, mask=mask, origin=(1, 1), border_value=1) assert_array_almost_equal(out, expected) def test_binary_dilation36(self, xp): # gh-21009 data = np.zeros([], dtype=bool) data = xp.asarray(data) out = ndimage.binary_dilation(data, iterations=-1) assert out == xp.asarray(False) def test_binary_propagation01(self, xp): struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] struct = xp.asarray(struct) expected = np.asarray([[0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0], [0, 1, 1, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], dtype=bool) expected = xp.asarray(expected) mask = np.asarray([[0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0], [0, 1, 1, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], dtype=bool) mask = xp.asarray(mask) data = np.asarray([[0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], dtype=bool) data = xp.asarray(data) out = ndimage.binary_propagation(data, struct, mask=mask, border_value=0) assert_array_almost_equal(out, expected) def test_binary_propagation02(self, xp): struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] expected = [[0, 1, 0, 0, 0, 0, 0, 0], [0, 1, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] expected = xp.asarray(expected) struct = xp.asarray(struct) mask = np.asarray([[0, 1, 0, 0, 0, 0, 0, 0], [0, 1, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], dtype=bool) mask = xp.asarray(mask) data = np.zeros(mask.shape, dtype=bool) data = xp.asarray(data) out = ndimage.binary_propagation(data, struct, mask=mask, border_value=1) assert_array_almost_equal(out, expected) def test_binary_propagation03(self, xp): # gh-21009 data = xp.asarray(np.zeros([], dtype=bool)) expected = xp.asarray(np.zeros([], dtype=bool)) out = ndimage.binary_propagation(data) assert out == expected @pytest.mark.parametrize('dtype', types) def test_binary_opening01(self, dtype, xp): dtype = getattr(xp, dtype) expected = [[0, 1, 0, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 1, 1, 1, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] expected = xp.asarray(expected) data = xp.asarray([[0, 1, 0, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 1, 1, 0], [0, 0, 1, 1, 0, 1, 0, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], dtype=dtype) out = ndimage.binary_opening(data) assert_array_almost_equal(out, expected) @pytest.mark.parametrize('dtype', types) def test_binary_opening02(self, dtype, xp): dtype = getattr(xp, dtype) struct = ndimage.generate_binary_structure(2, 2) expected = [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] expected = xp.asarray(expected) struct = xp.asarray(struct) data = xp.asarray([[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 0, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0]], dtype=dtype) out = ndimage.binary_opening(data, struct) assert_array_almost_equal(out, expected) @pytest.mark.parametrize('dtype', types) def test_binary_closing01(self, dtype, xp): dtype = getattr(xp, dtype) expected = [[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 0, 0, 0, 0, 0], [0, 1, 1, 1, 0, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] expected = xp.asarray(expected) data = xp.asarray([[0, 1, 0, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 1, 1, 0], [0, 0, 1, 1, 0, 1, 0, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], dtype=dtype) out = ndimage.binary_closing(data) assert_array_almost_equal(out, expected) @pytest.mark.parametrize('dtype', types) def test_binary_closing02(self, dtype, xp): dtype = getattr(xp, dtype) struct = ndimage.generate_binary_structure(2, 2) expected = [[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0]] expected = xp.asarray(expected) struct = xp.asarray(struct) data = xp.asarray([[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 0, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0]], dtype=dtype) out = ndimage.binary_closing(data, struct) assert_array_almost_equal(out, expected) def test_binary_fill_holes01(self, xp): expected = np.asarray([[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], dtype=bool) expected = xp.asarray(expected) data = np.asarray([[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], dtype=bool) data = xp.asarray(data) out = ndimage.binary_fill_holes(data) assert_array_almost_equal(out, expected) def test_binary_fill_holes02(self, xp): expected = np.asarray([[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], dtype=bool) expected = xp.asarray(expected) data = np.asarray([[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], dtype=bool) data = xp.asarray(data) out = ndimage.binary_fill_holes(data) assert_array_almost_equal(out, expected) def test_binary_fill_holes03(self, xp): expected = np.asarray([[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0], [0, 1, 1, 1, 0, 1, 1, 1], [0, 1, 1, 1, 0, 1, 1, 1], [0, 1, 1, 1, 0, 1, 1, 1], [0, 0, 1, 0, 0, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0]], dtype=bool) expected = xp.asarray(expected) data = np.asarray([[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0], [0, 1, 0, 1, 0, 1, 1, 1], [0, 1, 0, 1, 0, 1, 0, 1], [0, 1, 0, 1, 0, 1, 0, 1], [0, 0, 1, 0, 0, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0]], dtype=bool) data = xp.asarray(data) out = ndimage.binary_fill_holes(data) assert_array_almost_equal(out, expected) @skip_xp_backends(cpu_only=True) @skip_xp_backends( "cupy", reason="these filters do not yet have axes support in CuPy") @skip_xp_backends( "jax.numpy", reason="these filters are not implemented in JAX.numpy") @pytest.mark.parametrize('border_value',[0, 1]) @pytest.mark.parametrize('origin', [(0, 0), (-1, 0)]) @pytest.mark.parametrize('expand_axis', [0, 1, 2]) @pytest.mark.parametrize('func_name', ["binary_erosion", "binary_dilation", "binary_opening", "binary_closing", "binary_hit_or_miss", "binary_propagation", "binary_fill_holes"]) def test_binary_axes(self, xp, func_name, expand_axis, origin, border_value): struct = np.asarray([[0, 1, 0], [1, 1, 1], [0, 1, 0]], bool) struct = xp.asarray(struct) data = np.asarray([[0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 1, 0, 1, 0], [0, 1, 0, 1, 1, 0, 1], [0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0]], bool) data = xp.asarray(data) if func_name == "binary_hit_or_miss": kwargs = dict(origin1=origin, origin2=origin) else: kwargs = dict(origin=origin) border_supported = func_name not in ["binary_hit_or_miss", "binary_fill_holes"] if border_supported: kwargs['border_value'] = border_value elif border_value != 0: pytest.skip('border_value !=0 unsupported by this function') func = getattr(ndimage, func_name) expected = func(data, struct, **kwargs) # replicate data and expected result along a new axis n_reps = 5 expected = xp.stack([expected] * n_reps, axis=expand_axis) data = xp.stack([data] * n_reps, axis=expand_axis) # filter all axes except expand_axis axes = [0, 1, 2] axes.remove(expand_axis) if is_numpy(xp) or is_cupy(xp): out = xp.asarray(np.zeros(data.shape, bool)) func(data, struct, output=out, axes=axes, **kwargs) else: # inplace output= is unsupported by JAX out = func(data, struct, axes=axes, **kwargs) xp_assert_close(out, expected) def test_grey_erosion01(self, xp): array = xp.asarray([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = xp.asarray([[1, 0, 1], [1, 1, 0]]) output = ndimage.grey_erosion(array, footprint=footprint) assert_array_almost_equal(output, xp.asarray([[2, 2, 1, 1, 1], [2, 3, 1, 3, 1], [5, 5, 3, 3, 1]])) @skip_xp_backends("jax.numpy", reason="output array is read-only.") @xfail_xp_backends("cupy", reason="https://github.com/cupy/cupy/issues/8398") def test_grey_erosion01_overlap(self, xp): array = xp.asarray([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = xp.asarray([[1, 0, 1], [1, 1, 0]]) ndimage.grey_erosion(array, footprint=footprint, output=array) assert_array_almost_equal(array, xp.asarray([[2, 2, 1, 1, 1], [2, 3, 1, 3, 1], [5, 5, 3, 3, 1]]) ) def test_grey_erosion02(self, xp): array = xp.asarray([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = xp.asarray([[1, 0, 1], [1, 1, 0]]) structure = xp.asarray([[0, 0, 0], [0, 0, 0]]) output = ndimage.grey_erosion(array, footprint=footprint, structure=structure) assert_array_almost_equal(output, xp.asarray([[2, 2, 1, 1, 1], [2, 3, 1, 3, 1], [5, 5, 3, 3, 1]]) ) def test_grey_erosion03(self, xp): array = xp.asarray([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = xp.asarray([[1, 0, 1], [1, 1, 0]]) structure = xp.asarray([[1, 1, 1], [1, 1, 1]]) output = ndimage.grey_erosion(array, footprint=footprint, structure=structure) assert_array_almost_equal(output, xp.asarray([[1, 1, 0, 0, 0], [1, 2, 0, 2, 0], [4, 4, 2, 2, 0]]) ) def test_grey_dilation01(self, xp): array = xp.asarray([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = xp.asarray([[0, 1, 1], [1, 0, 1]]) output = ndimage.grey_dilation(array, footprint=footprint) assert_array_almost_equal(output, xp.asarray([[7, 7, 9, 9, 5], [7, 9, 8, 9, 7], [8, 8, 8, 7, 7]]), ) def test_grey_dilation02(self, xp): array = xp.asarray([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = xp.asarray([[0, 1, 1], [1, 0, 1]]) structure = xp.asarray([[0, 0, 0], [0, 0, 0]]) output = ndimage.grey_dilation(array, footprint=footprint, structure=structure) assert_array_almost_equal(output, xp.asarray([[7, 7, 9, 9, 5], [7, 9, 8, 9, 7], [8, 8, 8, 7, 7]]), ) def test_grey_dilation03(self, xp): array = xp.asarray([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = xp.asarray([[0, 1, 1], [1, 0, 1]]) structure = xp.asarray([[1, 1, 1], [1, 1, 1]]) output = ndimage.grey_dilation(array, footprint=footprint, structure=structure) assert_array_almost_equal(output, xp.asarray([[8, 8, 10, 10, 6], [8, 10, 9, 10, 8], [9, 9, 9, 8, 8]]), ) def test_grey_opening01(self, xp): array = xp.asarray([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = xp.asarray([[1, 0, 1], [1, 1, 0]]) tmp = ndimage.grey_erosion(array, footprint=footprint) expected = ndimage.grey_dilation(tmp, footprint=footprint) output = ndimage.grey_opening(array, footprint=footprint) assert_array_almost_equal(output, expected) def test_grey_opening02(self, xp): array = xp.asarray([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = xp.asarray([[1, 0, 1], [1, 1, 0]]) structure = xp.asarray([[0, 0, 0], [0, 0, 0]]) tmp = ndimage.grey_erosion(array, footprint=footprint, structure=structure) expected = ndimage.grey_dilation(tmp, footprint=footprint, structure=structure) output = ndimage.grey_opening(array, footprint=footprint, structure=structure) assert_array_almost_equal(output, expected) def test_grey_closing01(self, xp): array = xp.asarray([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = xp.asarray([[1, 0, 1], [1, 1, 0]]) tmp = ndimage.grey_dilation(array, footprint=footprint) expected = ndimage.grey_erosion(tmp, footprint=footprint) output = ndimage.grey_closing(array, footprint=footprint) assert_array_almost_equal(expected, output) def test_grey_closing02(self, xp): array = xp.asarray([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = xp.asarray([[1, 0, 1], [1, 1, 0]]) structure = xp.asarray([[0, 0, 0], [0, 0, 0]]) tmp = ndimage.grey_dilation(array, footprint=footprint, structure=structure) expected = ndimage.grey_erosion(tmp, footprint=footprint, structure=structure) output = ndimage.grey_closing(array, footprint=footprint, structure=structure) assert_array_almost_equal(expected, output) @skip_xp_backends(np_only=True, reason='output= arrays are numpy-specific') def test_morphological_gradient01(self, xp): array = xp.asarray([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = xp.asarray([[1, 0, 1], [1, 1, 0]]) structure = xp.asarray([[0, 0, 0], [0, 0, 0]]) tmp1 = ndimage.grey_dilation(array, footprint=footprint, structure=structure) tmp2 = ndimage.grey_erosion(array, footprint=footprint, structure=structure) expected = tmp1 - tmp2 output = xp.zeros(array.shape, dtype=array.dtype) ndimage.morphological_gradient(array, footprint=footprint, structure=structure, output=output) assert_array_almost_equal(expected, output) def test_morphological_gradient02(self, xp): array = xp.asarray([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = xp.asarray([[1, 0, 1], [1, 1, 0]]) structure = xp.asarray([[0, 0, 0], [0, 0, 0]]) tmp1 = ndimage.grey_dilation(array, footprint=footprint, structure=structure) tmp2 = ndimage.grey_erosion(array, footprint=footprint, structure=structure) expected = tmp1 - tmp2 output = ndimage.morphological_gradient(array, footprint=footprint, structure=structure) assert_array_almost_equal(expected, output) @skip_xp_backends(np_only=True, reason='output= arrays are numpy-specific') def test_morphological_laplace01(self, xp): array = xp.asarray([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = xp.asarray([[1, 0, 1], [1, 1, 0]]) structure = xp.asarray([[0, 0, 0], [0, 0, 0]]) tmp1 = ndimage.grey_dilation(array, footprint=footprint, structure=structure) tmp2 = ndimage.grey_erosion(array, footprint=footprint, structure=structure) expected = tmp1 + tmp2 - 2 * array output = xp.zeros(array.shape, dtype=array.dtype) ndimage.morphological_laplace(array, footprint=footprint, structure=structure, output=output) assert_array_almost_equal(expected, output) def test_morphological_laplace02(self, xp): array = xp.asarray([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = xp.asarray([[1, 0, 1], [1, 1, 0]]) structure = xp.asarray([[0, 0, 0], [0, 0, 0]]) tmp1 = ndimage.grey_dilation(array, footprint=footprint, structure=structure) tmp2 = ndimage.grey_erosion(array, footprint=footprint, structure=structure) expected = tmp1 + tmp2 - 2 * array output = ndimage.morphological_laplace(array, footprint=footprint, structure=structure) assert_array_almost_equal(expected, output) @skip_xp_backends("jax.numpy", reason="output array is read-only.") def test_white_tophat01(self, xp): array = xp.asarray([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = xp.asarray([[1, 0, 1], [1, 1, 0]]) structure = xp.asarray([[0, 0, 0], [0, 0, 0]]) tmp = ndimage.grey_opening(array, footprint=footprint, structure=structure) expected = array - tmp output = xp.zeros(array.shape, dtype=array.dtype) ndimage.white_tophat(array, footprint=footprint, structure=structure, output=output) assert_array_almost_equal(expected, output) def test_white_tophat02(self, xp): array = xp.asarray([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = xp.asarray([[1, 0, 1], [1, 1, 0]]) structure = xp.asarray([[0, 0, 0], [0, 0, 0]]) tmp = ndimage.grey_opening(array, footprint=footprint, structure=structure) expected = array - tmp output = ndimage.white_tophat(array, footprint=footprint, structure=structure) assert_array_almost_equal(expected, output) @xfail_xp_backends('cupy', reason="cupy#8399") def test_white_tophat03(self, xp): array = np.asarray([[1, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 0, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 1]], dtype=bool) array = xp.asarray(array) structure = np.ones((3, 3), dtype=bool) structure = xp.asarray(structure) expected = np.asarray([[0, 1, 1, 0, 0, 0, 0], [1, 0, 0, 1, 1, 1, 0], [1, 0, 0, 1, 1, 1, 0], [0, 1, 1, 0, 0, 0, 1], [0, 1, 1, 0, 1, 0, 1], [0, 1, 1, 0, 0, 0, 1], [0, 0, 0, 1, 1, 1, 1]], dtype=bool) expected = xp.asarray(expected) output = ndimage.white_tophat(array, structure=structure) xp_assert_equal(expected, output) @skip_xp_backends("jax.numpy", reason="output array is read-only.") def test_white_tophat04(self, xp): array = np.eye(5, dtype=bool) structure = np.ones((3, 3), dtype=bool) array = xp.asarray(array) structure = xp.asarray(structure) # Check that type mismatch is properly handled output = xp.empty_like(array, dtype=xp.float64) ndimage.white_tophat(array, structure=structure, output=output) @skip_xp_backends("jax.numpy", reason="output array is read-only.") def test_black_tophat01(self, xp): array = xp.asarray([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = xp.asarray([[1, 0, 1], [1, 1, 0]]) structure = xp.asarray([[0, 0, 0], [0, 0, 0]]) tmp = ndimage.grey_closing(array, footprint=footprint, structure=structure) expected = tmp - array output = xp.zeros(array.shape, dtype=array.dtype) ndimage.black_tophat(array, footprint=footprint, structure=structure, output=output) assert_array_almost_equal(expected, output) def test_black_tophat02(self, xp): array = xp.asarray([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = xp.asarray([[1, 0, 1], [1, 1, 0]]) structure = xp.asarray([[0, 0, 0], [0, 0, 0]]) tmp = ndimage.grey_closing(array, footprint=footprint, structure=structure) expected = tmp - array output = ndimage.black_tophat(array, footprint=footprint, structure=structure) assert_array_almost_equal(expected, output) @xfail_xp_backends('cupy', reason="cupy/cupy#8399") def test_black_tophat03(self, xp): array = np.asarray([[1, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 0, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 1]], dtype=bool) array = xp.asarray(array) structure = np.ones((3, 3), dtype=bool) structure = xp.asarray(structure) expected = np.asarray([[0, 1, 1, 1, 1, 1, 1], [1, 0, 0, 0, 0, 0, 1], [1, 0, 0, 0, 0, 0, 1], [1, 0, 0, 0, 0, 0, 1], [1, 0, 0, 0, 1, 0, 1], [1, 0, 0, 0, 0, 0, 1], [1, 1, 1, 1, 1, 1, 0]], dtype=bool) expected = xp.asarray(expected) output = ndimage.black_tophat(array, structure=structure) xp_assert_equal(expected, output) @skip_xp_backends("jax.numpy", reason="output array is read-only.") def test_black_tophat04(self, xp): array = xp.asarray(np.eye(5, dtype=bool)) structure = xp.asarray(np.ones((3, 3), dtype=bool)) # Check that type mismatch is properly handled output = xp.empty_like(array, dtype=xp.float64) ndimage.black_tophat(array, structure=structure, output=output) @skip_xp_backends(cpu_only=True) @skip_xp_backends( "cupy", reason="these filters do not yet have axes support in CuPy") @skip_xp_backends( "jax.numpy", reason="these filters are not implemented in JAX.numpy") @pytest.mark.parametrize('origin', [(0, 0), (-1, 0)]) @pytest.mark.parametrize('expand_axis', [0, 1, 2]) @pytest.mark.parametrize('mode', ['reflect', 'constant', 'nearest', 'mirror', 'wrap']) @pytest.mark.parametrize('footprint_mode', ['size', 'footprint', 'structure']) @pytest.mark.parametrize('func_name', ["grey_erosion", "grey_dilation", "grey_opening", "grey_closing", "morphological_laplace", "morphological_gradient", "white_tophat", "black_tophat"]) def test_grey_axes(self, xp, func_name, expand_axis, origin, footprint_mode, mode): data = xp.asarray([[0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 4, 0, 0, 0], [0, 0, 2, 1, 0, 2, 0], [0, 3, 0, 6, 5, 0, 1], [0, 4, 5, 3, 3, 4, 0], [0, 0, 9, 3, 0, 0, 0], [0, 0, 0, 2, 0, 0, 0]]) kwargs = dict(origin=origin, mode=mode) if footprint_mode == 'size': kwargs['size'] = (2, 3) else: kwargs['footprint'] = xp.asarray([[1, 0, 1], [1, 1, 0]]) if footprint_mode == 'structure': kwargs['structure'] = xp.ones_like(kwargs['footprint']) func = getattr(ndimage, func_name) expected = func(data, **kwargs) # replicate data and expected result along a new axis n_reps = 5 expected = xp.stack([expected] * n_reps, axis=expand_axis) data = xp.stack([data] * n_reps, axis=expand_axis) # filter all axes except expand_axis axes = [0, 1, 2] axes.remove(expand_axis) if is_numpy(xp) or is_cupy(xp): out = xp.zeros(expected.shape, dtype=expected.dtype) func(data, output=out, axes=axes, **kwargs) else: # inplace output= is unsupported by JAX out = func(data, axes=axes, **kwargs) xp_assert_close(out, expected) @pytest.mark.parametrize('dtype', types) def test_hit_or_miss01(self, dtype, xp): if not (is_numpy(xp) or is_cupy(xp)): pytest.xfail("inplace output= is numpy-specific") dtype = getattr(xp, dtype) struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] struct = xp.asarray(struct) expected = [[0, 0, 0, 0, 0], [0, 1, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]] expected = xp.asarray(expected) data = xp.asarray([[0, 1, 0, 0, 0], [1, 1, 1, 0, 0], [0, 1, 0, 1, 1], [0, 0, 1, 1, 1], [0, 1, 1, 1, 0], [0, 1, 1, 1, 1], [0, 1, 1, 1, 1], [0, 0, 0, 0, 0]], dtype=dtype) out = xp.asarray(np.zeros(data.shape, dtype=bool)) ndimage.binary_hit_or_miss(data, struct, output=out) assert_array_almost_equal(expected, out) @pytest.mark.parametrize('dtype', types) def test_hit_or_miss02(self, dtype, xp): dtype = getattr(xp, dtype) struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] expected = [[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] struct = xp.asarray(struct) expected = xp.asarray(expected) data = xp.asarray([[0, 1, 0, 0, 1, 1, 1, 0], [1, 1, 1, 0, 0, 1, 0, 0], [0, 1, 0, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0]], dtype=dtype) out = ndimage.binary_hit_or_miss(data, struct) assert_array_almost_equal(expected, out) @pytest.mark.parametrize('dtype', types) def test_hit_or_miss03(self, dtype, xp): dtype = getattr(xp, dtype) struct1 = [[0, 0, 0], [1, 1, 1], [0, 0, 0]] struct2 = [[1, 1, 1], [0, 0, 0], [1, 1, 1]] expected = [[0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] struct1 = xp.asarray(struct1) struct2 = xp.asarray(struct2) expected = xp.asarray(expected) data = xp.asarray([[0, 1, 0, 0, 1, 1, 1, 0], [1, 1, 1, 0, 0, 0, 0, 0], [0, 1, 0, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 0, 1, 1, 0], [0, 0, 0, 0, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0]], dtype=dtype) out = ndimage.binary_hit_or_miss(data, struct1, struct2) assert_array_almost_equal(expected, out) class TestDilateFix: # pytest's setup_method seems to clash with the autouse `xp` fixture # so call _setup manually from all methods def _setup(self, xp): # dilation related setup self.array = xp.asarray([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 1, 0], [0, 0, 1, 1, 0], [0, 0, 0, 0, 0]], dtype=xp.uint8) self.sq3x3 = xp.ones((3, 3)) dilated3x3 = ndimage.binary_dilation(self.array, structure=self.sq3x3) if is_numpy(xp): self.dilated3x3 = dilated3x3.view(xp.uint8) else: astype = array_namespace(dilated3x3).astype self.dilated3x3 = astype(dilated3x3, xp.uint8) def test_dilation_square_structure(self, xp): self._setup(xp) result = ndimage.grey_dilation(self.array, structure=self.sq3x3) # +1 accounts for difference between grey and binary dilation assert_array_almost_equal(result, self.dilated3x3 + 1) def test_dilation_scalar_size(self, xp): self._setup(xp) result = ndimage.grey_dilation(self.array, size=3) assert_array_almost_equal(result, self.dilated3x3) class TestBinaryOpeningClosing: def _setup(self, xp): a = np.zeros((5, 5), dtype=bool) a[1:4, 1:4] = True a[4, 4] = True self.array = xp.asarray(a) self.sq3x3 = xp.ones((3, 3)) self.opened_old = ndimage.binary_opening(self.array, self.sq3x3, 1, None, 0) self.closed_old = ndimage.binary_closing(self.array, self.sq3x3, 1, None, 0) def test_opening_new_arguments(self, xp): self._setup(xp) opened_new = ndimage.binary_opening(self.array, self.sq3x3, 1, None, 0, None, 0, False) xp_assert_equal(opened_new, self.opened_old) def test_closing_new_arguments(self, xp): self._setup(xp) closed_new = ndimage.binary_closing(self.array, self.sq3x3, 1, None, 0, None, 0, False) xp_assert_equal(closed_new, self.closed_old) def test_binary_erosion_noninteger_iterations(xp): # regression test for gh-9905, gh-9909: ValueError for # non integer iterations data = xp.ones([1]) assert_raises(TypeError, ndimage.binary_erosion, data, iterations=0.5) assert_raises(TypeError, ndimage.binary_erosion, data, iterations=1.5) def test_binary_dilation_noninteger_iterations(xp): # regression test for gh-9905, gh-9909: ValueError for # non integer iterations data = xp.ones([1]) assert_raises(TypeError, ndimage.binary_dilation, data, iterations=0.5) assert_raises(TypeError, ndimage.binary_dilation, data, iterations=1.5) def test_binary_opening_noninteger_iterations(xp): # regression test for gh-9905, gh-9909: ValueError for # non integer iterations data = xp.ones([1]) assert_raises(TypeError, ndimage.binary_opening, data, iterations=0.5) assert_raises(TypeError, ndimage.binary_opening, data, iterations=1.5) def test_binary_closing_noninteger_iterations(xp): # regression test for gh-9905, gh-9909: ValueError for # non integer iterations data = xp.ones([1]) assert_raises(TypeError, ndimage.binary_closing, data, iterations=0.5) assert_raises(TypeError, ndimage.binary_closing, data, iterations=1.5) def test_binary_closing_noninteger_brute_force_passes_when_true(xp): # regression test for gh-9905, gh-9909: ValueError for # non integer iterations if is_cupy(xp): pytest.xfail("CuPy: NotImplementedError: only brute_force iteration") data = xp.ones([1]) xp_assert_equal(ndimage.binary_erosion(data, iterations=2, brute_force=1.5), ndimage.binary_erosion(data, iterations=2, brute_force=bool(1.5)) ) xp_assert_equal(ndimage.binary_erosion(data, iterations=2, brute_force=0.0), ndimage.binary_erosion(data, iterations=2, brute_force=bool(0.0)) ) @pytest.mark.parametrize( 'function', ['binary_erosion', 'binary_dilation', 'binary_opening', 'binary_closing'], ) @pytest.mark.parametrize('iterations', [1, 5]) @pytest.mark.parametrize('brute_force', [False, True]) def test_binary_input_as_output(function, iterations, brute_force, xp): rstate = np.random.RandomState(123) data = rstate.randint(low=0, high=2, size=100).astype(bool) ndi_func = getattr(ndimage, function) # input data is not modified data_orig = data.copy() expected = ndi_func(data, brute_force=brute_force, iterations=iterations) xp_assert_equal(data, data_orig) # data should now contain the expected result ndi_func(data, brute_force=brute_force, iterations=iterations, output=data) xp_assert_equal(expected, data) def test_binary_hit_or_miss_input_as_output(xp): if not (is_numpy(xp) or is_cupy(xp)): pytest.xfail("inplace output= is numpy-specific") rstate = np.random.RandomState(123) data = rstate.randint(low=0, high=2, size=100).astype(bool) # input data is not modified data_orig = data.copy() expected = ndimage.binary_hit_or_miss(data) xp_assert_equal(data, data_orig) # data should now contain the expected result ndimage.binary_hit_or_miss(data, output=data) xp_assert_equal(expected, data) def test_distance_transform_cdt_invalid_metric(xp): if is_cupy(xp): pytest.xfail("CuPy does not have distance_transform_cdt") msg = 'invalid metric provided' with pytest.raises(ValueError, match=msg): ndimage.distance_transform_cdt(xp.ones((5, 5)), metric="garbage")