def test_uint16(): 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_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) testing.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_compare_with_grey_dilation(): # compare the result of maximum filter with dilate image = (np.random.rand(100, 100) * 256).astype(np.uint8) out = np.empty_like(image) mask = np.ones(image.shape, dtype=np.uint8) for r in range(3, 20, 2): elem = np.ones((r, r), dtype=np.uint8) rank.maximum(image=image, selem=elem, out=out, mask=mask) cm = grey.dilation(image=image, selem=elem) assert_equal(out, cm)
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_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_binary_dilation(): strel = selem.square(3) binary_res = binary.binary_dilation(bw_img, strel) grey_res = img_as_bool(grey.dilation(bw_img, strel)) testing.assert_array_equal(binary_res, grey_res)
def test_binary_dilation(): strel = selem.square(3) binary_res = binary.binary_dilation(bw_img, strel) with expected_warnings(['precision loss']): grey_res = img_as_bool(grey.dilation(bw_img, strel)) testing.assert_array_equal(binary_res, grey_res)
def test_binary_dilation(): strel = selem.square(3) binary_res = binary.binary_dilation(bw_lena, strel) grey_res = grey.dilation(bw_lena, strel) testing.assert_array_equal(binary_res, grey_res)