import os import numpy as np from scipy import ndimage as ndi from skimage import color, data, transform from skimage import img_as_uint, img_as_ubyte, data_dir from skimage.morphology import grey, selem from skimage._shared._warnings import expected_warnings from skimage._shared import testing from skimage._shared.testing import (assert_array_equal, assert_equal, TestCase, parametrize) class TestMorphology(TestCase): # These expected outputs were generated with skimage v0.12.1 # using: # # from skimage.morphology.tests.test_grey import TestMorphology # import numpy as np # output = TestMorphology()._build_expected_output() # np.savez_compressed('gray_morph_output.npz', **output) def _build_expected_output(self): funcs = (grey.erosion, grey.dilation, grey.opening, grey.closing, grey.white_tophat, grey.black_tophat) selems_2D = (selem.square, selem.diamond, selem.disk, selem.star) with expected_warnings(['Possible precision loss']): image = img_as_ubyte(transform.downscale_local_mean( color.rgb2gray(data.coffee()), (20, 20))) output = {} for n in range(1, 4): for strel in selems_2D: for func in funcs: key = '{0}_{1}_{2}'.format( strel.__name__, n, func.__name__) output[key] = func(image, strel(n)) return output def test_gray_morphology(self): expected = dict(np.load( os.path.join(data_dir, 'gray_morph_output.npz'))) calculated = self._build_expected_output() assert_equal(expected, calculated) class TestEccentricStructuringElements(TestCase): def setUp(self): self.black_pixel = 255 * np.ones((4, 4), dtype=np.uint8) self.black_pixel[1, 1] = 0 self.white_pixel = 255 - self.black_pixel self.selems = [selem.square(2), selem.rectangle(2, 2), selem.rectangle(2, 1), selem.rectangle(1, 2)] def test_dilate_erode_symmetry(self): for s in self.selems: c = grey.erosion(self.black_pixel, s) d = grey.dilation(self.white_pixel, s) assert np.all(c == (255 - d)) def test_open_black_pixel(self): for s in self.selems: grey_open = grey.opening(self.black_pixel, s) assert np.all(grey_open == self.black_pixel) def test_close_white_pixel(self): for s in self.selems: grey_close = grey.closing(self.white_pixel, s) assert np.all(grey_close == self.white_pixel) def test_open_white_pixel(self): for s in self.selems: assert np.all(grey.opening(self.white_pixel, s) == 0) def test_close_black_pixel(self): for s in self.selems: assert np.all(grey.closing(self.black_pixel, s) == 255) def test_white_tophat_white_pixel(self): for s in self.selems: tophat = grey.white_tophat(self.white_pixel, s) assert np.all(tophat == self.white_pixel) def test_black_tophat_black_pixel(self): for s in self.selems: tophat = grey.black_tophat(self.black_pixel, s) assert np.all(tophat == (255 - self.black_pixel)) def test_white_tophat_black_pixel(self): for s in self.selems: tophat = grey.white_tophat(self.black_pixel, s) assert np.all(tophat == 0) def test_black_tophat_white_pixel(self): for s in self.selems: tophat = grey.black_tophat(self.white_pixel, s) assert np.all(tophat == 0) grey_functions = [grey.erosion, grey.dilation, grey.opening, grey.closing, grey.white_tophat, grey.black_tophat] @parametrize("function", grey_functions) def test_default_selem(function): strel = selem.diamond(radius=1) image = np.array([[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, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], np.uint8) im_expected = function(image, strel) im_test = function(image) testing.assert_array_equal(im_expected, im_test) def test_3d_fallback_default_selem(): # 3x3x3 cube inside a 7x7x7 image: image = np.zeros((7, 7, 7), np.bool) image[2:-2, 2:-2, 2:-2] = 1 opened = grey.opening(image) # expect a "hyper-cross" centered in the 5x5x5: image_expected = np.zeros((7, 7, 7), dtype=bool) image_expected[2:5, 2:5, 2:5] = ndi.generate_binary_structure(3, 1) assert_array_equal(opened, image_expected) grey_3d_fallback_functions = [grey.closing, grey.opening] @parametrize("function", grey_3d_fallback_functions) def test_3d_fallback_cube_selem(function): # 3x3x3 cube inside a 7x7x7 image: image = np.zeros((7, 7, 7), np.bool) image[2:-2, 2:-2, 2:-2] = 1 cube = np.ones((3, 3, 3), dtype=np.uint8) new_image = function(image, cube) testing.assert_array_equal(new_image, image) def test_3d_fallback_white_tophat(): image = np.zeros((7, 7, 7), dtype=bool) image[2, 2:4, 2:4] = 1 image[3, 2:5, 2:5] = 1 image[4, 3:5, 3:5] = 1 with expected_warnings(['operator.*deprecated|\A\Z']): new_image = grey.white_tophat(image) footprint = ndi.generate_binary_structure(3, 1) with expected_warnings(['operator.*deprecated|\A\Z']): image_expected = ndi.white_tophat( image.view(dtype=np.uint8), footprint=footprint) assert_array_equal(new_image, image_expected) def test_3d_fallback_black_tophat(): image = np.ones((7, 7, 7), dtype=bool) image[2, 2:4, 2:4] = 0 image[3, 2:5, 2:5] = 0 image[4, 3:5, 3:5] = 0 with expected_warnings(['operator.*deprecated|\A\Z']): new_image = grey.black_tophat(image) footprint = ndi.generate_binary_structure(3, 1) with expected_warnings(['operator.*deprecated|\A\Z']): image_expected = ndi.black_tophat( image.view(dtype=np.uint8), footprint=footprint) assert_array_equal(new_image, image_expected) def test_2d_ndimage_equivalence(): image = np.zeros((9, 9), np.uint8) image[2:-2, 2:-2] = 128 image[3:-3, 3:-3] = 196 image[4, 4] = 255 opened = grey.opening(image) closed = grey.closing(image) selem = ndi.generate_binary_structure(2, 1) ndimage_opened = ndi.grey_opening(image, footprint=selem) ndimage_closed = ndi.grey_closing(image, footprint=selem) assert_array_equal(opened, ndimage_opened) assert_array_equal(closed, ndimage_closed) # float test images im = np.array([[ 0.55, 0.72, 0.6 , 0.54, 0.42], [ 0.65, 0.44, 0.89, 0.96, 0.38], [ 0.79, 0.53, 0.57, 0.93, 0.07], [ 0.09, 0.02, 0.83, 0.78, 0.87], [ 0.98, 0.8 , 0.46, 0.78, 0.12]]) eroded = np.array([[ 0.55, 0.44, 0.54, 0.42, 0.38], [ 0.44, 0.44, 0.44, 0.38, 0.07], [ 0.09, 0.02, 0.53, 0.07, 0.07], [ 0.02, 0.02, 0.02, 0.78, 0.07], [ 0.09, 0.02, 0.46, 0.12, 0.12]]) dilated = np.array([[ 0.72, 0.72, 0.89, 0.96, 0.54], [ 0.79, 0.89, 0.96, 0.96, 0.96], [ 0.79, 0.79, 0.93, 0.96, 0.93], [ 0.98, 0.83, 0.83, 0.93, 0.87], [ 0.98, 0.98, 0.83, 0.78, 0.87]]) opened = np.array([[ 0.55, 0.55, 0.54, 0.54, 0.42], [ 0.55, 0.44, 0.54, 0.44, 0.38], [ 0.44, 0.53, 0.53, 0.78, 0.07], [ 0.09, 0.02, 0.78, 0.78, 0.78], [ 0.09, 0.46, 0.46, 0.78, 0.12]]) closed = np.array([[ 0.72, 0.72, 0.72, 0.54, 0.54], [ 0.72, 0.72, 0.89, 0.96, 0.54], [ 0.79, 0.79, 0.79, 0.93, 0.87], [ 0.79, 0.79, 0.83, 0.78, 0.87], [ 0.98, 0.83, 0.78, 0.78, 0.78]]) def test_float(): np.testing.assert_allclose(grey.erosion(im), eroded) np.testing.assert_allclose(grey.dilation(im), dilated) np.testing.assert_allclose(grey.opening(im), opened) np.testing.assert_allclose(grey.closing(im), closed) def test_uint16(): with expected_warnings(['Possible precision loss']): im16, eroded16, dilated16, opened16, closed16 = ( map(img_as_uint, [im, eroded, dilated, opened, closed])) np.testing.assert_allclose(grey.erosion(im16), eroded16) np.testing.assert_allclose(grey.dilation(im16), dilated16) np.testing.assert_allclose(grey.opening(im16), opened16) np.testing.assert_allclose(grey.closing(im16), closed16) def test_discontiguous_out_array(): image = np.array([[5, 6, 2], [7, 2, 2], [3, 5, 1]], np.uint8) out_array_big = np.zeros((5, 5), np.uint8) out_array = out_array_big[::2, ::2] expected_dilation = np.array([[7, 0, 6, 0, 6], [0, 0, 0, 0, 0], [7, 0, 7, 0, 2], [0, 0, 0, 0, 0], [7, 0, 5, 0, 5]], np.uint8) expected_erosion = np.array([[5, 0, 2, 0, 2], [0, 0, 0, 0, 0], [2, 0, 2, 0, 1], [0, 0, 0, 0, 0], [3, 0, 1, 0, 1]], np.uint8) grey.dilation(image, out=out_array) assert_array_equal(out_array_big, expected_dilation) grey.erosion(image, out=out_array) testing.assert_array_equal(out_array_big, expected_erosion) def test_1d_erosion(): image = np.array([1, 2, 3, 2, 1]) expected = np.array([1, 1, 2, 1, 1]) eroded = grey.erosion(image) testing.assert_array_equal(eroded, expected)