def test_normalize_no_scale_per_channel(): pixels = np.arange(27, dtype=np.float).reshape([3, 3, 3]) image = Image(pixels, copy=False) new_image = normalize(image, scale_func=None, mode='per_channel') assert_allclose(new_image.pixels[0], pixels[0] - 4.) assert_allclose(new_image.pixels[1], pixels[1] - 13.) assert_allclose(new_image.pixels[2], pixels[2] - 22.)
def test_normalize_scale_per_channel(): pixels = np.arange(27, dtype=np.float).reshape([3, 3, 3]) image = Image(pixels, copy=False) dummy_scale = lambda *a, **kwargs: np.array(2.0) new_image = normalize(image, scale_func=dummy_scale, mode='per_channel') assert_allclose(new_image.pixels[0], (pixels[0] - 4.) / 2.0) assert_allclose(new_image.pixels[1], (pixels[1] - 13.) / 2.0) assert_allclose(new_image.pixels[2], (pixels[2] - 22.) / 2.0)
def test_normalize_0_variance_warning(): pixels = np.arange(8, dtype=np.float).reshape([2, 2, 2]) image = Image(pixels, copy=False) dummy_scale = lambda *a, **kwargs: np.array([2.0, 0.0]) with warnings.catch_warnings(): warnings.simplefilter("ignore") new_image = normalize(image, scale_func=dummy_scale, error_on_divide_by_zero=False, mode='per_channel') assert_allclose(new_image.pixels[0], [[-0.75, -0.25], [0.25, 0.75]]) assert_allclose(new_image.pixels[1], [[-1.5, -0.5], [0.5, 1.5]])
def test_normalize_0_variance_raises(): image = Image.init_blank((2, 2)) dummy_scale = lambda *a, **kwargs: np.array(0.0) with raises(ValueError): normalize(image, scale_func=dummy_scale)
def test_normalize_unknown_mode_raises(): image = Image.init_blank((2, 2)) with raises(ValueError): normalize(image, mode='fake')
def test_normalize_scale_all(): pixels = np.arange(27, dtype=np.float).reshape([3, 3, 3]) dummy_scale = lambda *a, **kwargs: np.array(2.0) image = Image(pixels, copy=False) new_image = normalize(image, scale_func=dummy_scale, mode='all') assert_allclose(new_image.pixels, (pixels - 13.0) / 2.0)
def test_normalize_no_scale_all(): pixels = np.arange(27, dtype=np.float).reshape([3, 3, 3]) image = Image(pixels, copy=False) new_image = normalize(image, scale_func=None, mode='all') assert_allclose(new_image.pixels, pixels - 13.)
def test_normalize_0_variance_raises(): image = Image.init_blank((2, 2)) dummy_scale = lambda *a, **kwargs: np.array(0.0) normalize(image, scale_func=dummy_scale)
def test_normalize_unknown_mode_raises(): image = Image.init_blank((2, 2)) normalize(image, mode='fake')
def test_normalize_unknown_mode_raises(): image = Image.init_blank((2, 2)) with raises(ValueError): normalize(image, mode="fake")