import numpy as np from numpy.testing import assert_array_equal, run_module_suite from skimage.morphology import label as _label import skimage.measure._ccomp as ccomp from skimage._shared.utils import all_warnings np.random.seed(0) # The background label value # is supposed to be changed to 0 soon BG = -1 def label(*args, **kwargs): """Wrap the label function to avoid deprecation warning""" with all_warnings(): return _label(*args, **kwargs) class TestConnectedComponents: def setup(self): self.x = np.array([[0, 0, 3, 2, 1, 9], [0, 1, 1, 9, 2, 9], [0, 0, 1, 9, 9, 9], [3, 1, 1, 5, 3, 0]]) self.labels = np.array([[0, 0, 1, 2, 3, 4], [0, 5, 5, 4, 2, 4], [0, 0, 5, 4, 4, 4], [6, 5, 5, 7, 8, 9]]) def test_basic(self): assert_array_equal(label(self.x), self.labels) # Make sure data wasn't modified assert self.x[0, 2] == 3 def test_random(self): x = (np.random.rand(20, 30) * 5).astype(np.int) with all_warnings(): labels = label(x) n = labels.max() for i in range(n): values = x[labels == i] assert np.all(values == values[0]) def test_diag(self): x = np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]]) with all_warnings(): assert_array_equal(label(x), x) def test_4_vs_8(self): x = np.array([[0, 1], [1, 0]], dtype=int) with all_warnings(): assert_array_equal(label(x, 4), [[0, 1], [2, 3]]) assert_array_equal(label(x, 8), [[0, 1], [1, 0]]) def test_background(self): x = np.array([[1, 0, 0], [1, 1, 5], [0, 0, 0]]) with all_warnings(): assert_array_equal(label(x), [[0, 1, 1], [0, 0, 2], [3, 3, 3]]) assert_array_equal(label(x, background=0), [[0, -1, -1], [0, 0, 1], [-1, -1, -1]]) def test_background_two_regions(self): x = np.array([[0, 0, 6], [0, 0, 6], [5, 5, 5]]) res = label(x, background=0) assert_array_equal(res, [[-1, -1, 0], [-1, -1, 0], [+1, 1, 1]]) def test_background_one_region_center(self): x = np.array([[0, 0, 0], [0, 1, 0], [0, 0, 0]]) assert_array_equal(label(x, neighbors=4, background=0), [[-1, -1, -1], [-1, 0, -1], [-1, -1, -1]]) def test_return_num(self): x = np.array([[1, 0, 6], [0, 0, 6], [5, 5, 5]]) with all_warnings(): assert_array_equal(label(x, return_num=True)[1], 4) assert_array_equal(label(x, background=0, return_num=True)[1], 3) class TestConnectedComponents3d: def setup(self): self.x = np.zeros((3, 4, 5), int) self.x[0] = np.array([[0, 3, 2, 1, 9], [0, 1, 9, 2, 9], [0, 1, 9, 9, 9], [3, 1, 5, 3, 0]]) self.x[1] = np.array([[3, 3, 2, 1, 9], [0, 3, 9, 2, 1], [0, 3, 3, 1, 1], [3, 1, 3, 3, 0]]) self.x[2] = np.array([[3, 3, 8, 8, 0], [2, 3, 9, 8, 8], [2, 3, 0, 8, 0], [2, 1, 0, 0, 0]]) self.labels = np.zeros((3, 4, 5), int) self.labels[0] = np.array([[0, 1, 2, 3, 4], [0, 5, 4, 2, 4], [0, 5, 4, 4, 4], [1, 5, 6, 1, 7]]) self.labels[1] = np.array([[1, 1, 2, 3, 4], [0, 1, 4, 2, 3], [0, 1, 1, 3, 3], [1, 5, 1, 1, 7]]) self.labels[2] = np.array([[1, 1, 8, 8, 9], [10, 1, 4, 8, 8], [10, 1, 7, 8, 7], [10, 5, 7, 7, 7]]) def test_basic(self): labels = label(self.x) assert_array_equal(labels, self.labels) assert self.x[0, 0, 2] == 2, \ "Data was modified!" def test_random(self): x = (np.random.rand(20, 30) * 5).astype(np.int) with all_warnings(): labels = label(x) n = labels.max() for i in range(n): values = x[labels == i] assert np.all(values == values[0]) def test_diag(self): x = np.zeros((3, 3, 3), int) x[0, 2, 2] = 1 x[1, 1, 1] = 1 x[2, 0, 0] = 1 with all_warnings(): assert_array_equal(label(x), x) def test_4_vs_8(self): x = np.zeros((2, 2, 2), int) x[0, 1, 1] = 1 x[1, 0, 0] = 1 label4 = x.copy() label4[1, 0, 0] = 2 with all_warnings(): assert_array_equal(label(x, 4), label4) assert_array_equal(label(x, 8), x) def test_background(self): x = np.zeros((2, 3, 3), int) x[0] = np.array([[1, 0, 0], [1, 0, 0], [0, 0, 0]]) x[1] = np.array([[0, 0, 0], [0, 1, 5], [0, 0, 0]]) lnb = x.copy() lnb[0] = np.array([[0, 1, 1], [0, 1, 1], [1, 1, 1]]) lnb[1] = np.array([[1, 1, 1], [1, 0, 2], [1, 1, 1]]) lb = x.copy() lb[0] = np.array([[0, BG, BG], [0, BG, BG], [BG, BG, BG]]) lb[1] = np.array([[BG, BG, BG], [BG, 0, 1], [BG, BG, BG]]) with all_warnings(): assert_array_equal(label(x), lnb) assert_array_equal(label(x, background=0), lb) def test_background_two_regions(self): x = np.zeros((2, 3, 3), int) x[0] = np.array([[0, 0, 6], [0, 0, 6], [5, 5, 5]]) x[1] = np.array([[6, 6, 0], [5, 0, 0], [0, 0, 0]]) lb = x.copy() lb[0] = np.array([[BG, BG, 0], [BG, BG, 0], [1, 1, 1]]) lb[1] = np.array([[0, 0, BG], [1, BG, BG], [BG, BG, BG]]) res = label(x, background=0) assert_array_equal(res, lb) def test_background_one_region_center(self): x = np.zeros((3, 3, 3), int) x[1, 1, 1] = 1 lb = np.ones_like(x) * BG lb[1, 1, 1] = 0 assert_array_equal(label(x, neighbors=4, background=0), lb) def test_return_num(self): x = np.array([[1, 0, 6], [0, 0, 6], [5, 5, 5]]) with all_warnings(): assert_array_equal(label(x, return_num=True)[1], 4) assert_array_equal(label(x, background=0, return_num=True)[1], 3) def test_1D(self): x = np.array((0, 1, 2, 2, 1, 1, 0, 0)) xlen = len(x) y = np.array((0, 1, 2, 2, 3, 3, 4, 4)) reshapes = ((xlen,), (1, xlen), (xlen, 1), (1, xlen, 1), (xlen, 1, 1), (1, 1, xlen)) for reshape in reshapes: x2 = x.reshape(reshape) labelled = label(x2) assert_array_equal(y, labelled.flatten()) def test_nd(self): x = np.ones((1, 2, 3, 4)) np.testing.assert_raises(NotImplementedError, label, x) class TestSupport: def test_reshape(self): shapes_in = ((3, 1, 2), (1, 4, 5), (3, 1, 1), (2, 1), (1,)) for shape in shapes_in: shape = np.array(shape) numones = sum(shape == 1) inp = np.random.random(shape) fixed, swaps = ccomp.reshape_array(inp) shape2 = fixed.shape # now check that all ones are at the beginning for i in range(numones): assert shape2[i] == 1 back = ccomp.undo_reshape_array(fixed, swaps) # check that the undo works as expected assert_array_equal(inp, back) if __name__ == "__main__": run_module_suite()