def test_matrix_is_list_of_arrays(self):
image = np.array([
[0, 10, 20],
[30, 40, 50]
], dtype=np.uint8)
image = np.tile(image[:, :, np.newaxis], (1, 1, 2))
matrices = [
np.float32([
[0.0, 0.0, 0.0],
[0.0, 1.0, 0.0],
[0.0, 0.0, 0.0]
]),
np.float32([
[0.0, 0.0, 0.0],
[0.0, 2.0, 0.0],
[0.0, 0.0, 0.0]
])
]
image_aug = iaa.convolve_(np.copy(image), matrices)
assert image_aug.dtype.name == "uint8"
assert image_aug.shape == (2, 3, 2)
assert np.array_equal(image_aug[:, :, 0], image[:, :, 0])
assert np.array_equal(image_aug[:, :, 1], 2*image[:, :, 1])
def test_2x2_identity_matrix_2d_image(self):
image = np.array([[0, 10, 20, 30], [40, 50, 60, 70]], dtype=np.uint8)
matrix = np.float32([[0.0, 0.0], [0.0, 1.0]])
image_aug = iaa.convolve_(np.copy(image), matrix)
assert image_aug.dtype.name == "uint8"
assert image_aug.shape == (2, 4)
assert np.array_equal(image_aug, image)
def test_1x1_identity_matrix_2d_image(self):
image = np.array([[0, 10, 20], [30, 40, 50]], dtype=np.uint8)
matrix = np.float32([[1.0]])
image_aug = iaa.convolve_(np.copy(image), matrix)
assert image_aug.dtype.name == "uint8"
assert image_aug.shape == (2, 3)
assert np.array_equal(image_aug, image)
def test_view_heightwise(self):
image = np.array([[0, 10, 20], [30, 40, 50]], dtype=np.uint8)
image_view = np.copy(image)[:2, :]
assert image_view.flags["OWNDATA"] is False
matrix = np.float32([[2.0]])
image_aug = iaa.convolve_(image_view, matrix)
assert image_aug.dtype.name == "uint8"
assert image_aug.shape == (2, 3)
assert np.array_equal(image_aug, 2 * image)
def test_noncontiguous(self):
image = np.array([[0, 10, 20], [30, 40, 50]], dtype=np.uint8)
image_nonc = np.array(image, dtype=np.uint8, order="F")
assert image_nonc.flags["C_CONTIGUOUS"] is False
matrix = np.float32([[2.0]])
image_aug = iaa.convolve_(image_nonc, matrix)
assert image_aug.dtype.name == "uint8"
assert image_aug.shape == (2, 3)
assert np.array_equal(image_aug, 2 * image)
def test_matrix_is_list_containing_only_none(self):
image = np.array([[0, 10, 20], [30, 40, 50]], dtype=np.uint8)
image = np.tile(image[:, :, np.newaxis], (1, 1, 2))
matrices = [None, None]
image_aug = iaa.convolve_(np.copy(image), matrices)
assert image_aug.dtype.name == "uint8"
assert image_aug.shape == (2, 3, 2)
assert np.array_equal(image_aug[:, :, 0], image[:, :, 0])
assert np.array_equal(image_aug[:, :, 1], image[:, :, 1])
def test_single_matrix_3d_image(self):
image = np.array([[0, 10, 20], [30, 40, 50]], dtype=np.uint8)
image = np.tile(image[:, :, np.newaxis], (1, 1, 2))
matrix = np.float32([[0.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0,
0.0]])
image_aug = iaa.convolve_(np.copy(image), matrix)
assert image_aug.dtype.name == "uint8"
assert image_aug.shape == (2, 3, 2)
assert np.array_equal(image_aug, 2 * image)
def test_zero_sized_axes(self):
shapes = [(0, 0), (0, 1), (1, 0), (0, 1, 0), (1, 0, 0), (0, 1, 1),
(1, 0, 1)]
for shape in shapes:
with self.subTest(shape=shape):
image = np.zeros(shape, dtype=np.uint8)
matrix = np.float32([[2.0]])
image_aug = iaa.convolve_(np.copy(image), matrix)
assert image_aug.shape == image.shape
def test_unusual_channel_numbers(self):
for nb_channels in [1, 2, 3, 4, 5, 10, 512, 513]:
with self.subTest(nb_channels=nb_channels):
image = np.array([[0, 10, 20], [30, 40, 50]], dtype=np.uint8)
image = image[:, :, np.newaxis]
image = np.tile(image, (1, 1, nb_channels))
matrix = np.float32([[2.0]])
image_aug = iaa.convolve_(np.copy(image), matrix)
assert image_aug.dtype.name == "uint8"
assert image_aug.shape == (2, 3, nb_channels)
assert np.array_equal(image_aug, 2 * image)
def test_single_matrix_2d_image(self):
image = np.array([[0, 10, 20], [30, 40, 50], [60, 70, 80]],
dtype=np.uint8)
matrix = np.float32([[0.0, 1.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0,
0.0]])
expected = np.array(
[[0 + 30, 10 + 40, 20 + 50], [30 + 0, 40 + 10, 50 + 20],
[60 + 30, 70 + 40, 80 + 50]],
dtype=np.float32)
image_aug = iaa.convolve_(np.copy(image), matrix)
assert image_aug.dtype.name == "uint8"
assert image_aug.shape == (3, 3)
assert np.array_equal(image_aug, expected)
def test_view_channelwise_1_channel(self):
image = np.array([[0, 10, 20], [30, 40, 50]], dtype=np.uint8)
image = np.tile(image[:, :, np.newaxis], (1, 1, 3))
image[:, :, 0] += 0
image[:, :, 1] += 1
image[:, :, 2] += 2
image_view = np.copy(image)[:, :, [False, True, False]]
assert image_view.flags["OWNDATA"] is False
assert image_view.base.shape == (1, 2, 3)
matrix = np.float32([[2.0]])
image_aug = iaa.convolve_(image_view, matrix)
assert image_aug.dtype.name == "uint8"
assert image_aug.shape == (2, 3, 1)
assert np.array_equal(image_aug, 2 * image[:, :, 1:2])
def test_1x1_identity_matrix_2d_image_small_image_sizes(self):
for height in np.arange(16):
for width in np.arange(16):
shapes = [(height, width), (height, width, 1),
(height, width, 3)]
for shape in shapes:
with self.subTest(shape=shape):
image = np.mod(
np.arange(int(np.prod(shape))).reshape(shape),
255).astype(np.uint8)
matrix = np.float32([[1.0]])
image_aug = iaa.convolve_(np.copy(image), matrix)
assert image_aug.dtype.name == "uint8"
assert image_aug.shape == shape
assert np.array_equal(image_aug, image)
