def test_image_with_invalid_shape(self):
for invalid_shape in ([1], [2, 1], [2, 4, 4, 4, 1]):
image = tf.zeros(invalid_shape, tf.uint8)
# pylint: disable=bad-continuation
with self.assertRaisesRegex(
ValueError, "`images` should have rank between 3 and 4"):
_ = distance_tranform_ops.euclidean_dist_transform(image)
image = tf.zeros([2, 4, 3], tf.uint8)
with self.assertRaisesRegex(ValueError,
"`images` must have only one channel"):
_ = distance_tranform_ops.euclidean_dist_transform(image)
def test_all_zeros():
image = tf.zeros([10, 10], tf.uint8)
expected_output = np.zeros([10, 10])
for output_dtype in [tf.float16, tf.float32, tf.float64]:
output = dist_ops.euclidean_dist_transform(image, dtype=output_dtype)
np.testing.assert_allclose(output, expected_output)
def test_batch_binary_images(self):
batch_size = 3
# yapf: disable
image = [[[0], [0], [0], [0], [0]],
[[0], [1], [1], [1], [0]],
[[0], [1], [1], [1], [0]],
[[0], [1], [1], [1], [0]],
[[0], [0], [0], [0], [0]]]
expected_output = np.array([
0, 0, 0, 0, 0,
0, 1, 1, 1, 0,
0, 1, 2, 1, 0,
0, 1, 1, 1, 0,
0, 0, 0, 0, 0
] * batch_size)
# yapf: enable
images = tf.constant([image] * batch_size, dtype=tf.uint8)
for output_dtype in [tf.float16, tf.float32, tf.float64]:
output = distance_tranform_ops.euclidean_dist_transform(
images, dtype=output_dtype)
output_flat = tf.reshape(output, [-1])
with self.subTest(output_dtype=output_dtype):
self.assertEqual(output.dtype, output_dtype)
self.assertEqual(output.shape, [batch_size, 5, 5, 1])
self.assertAllCloseAccordingToType(output_flat,
expected_output)
def test_single_binary_image(self):
# yapf: disable
image = [[[1], [1], [1], [1], [1]],
[[1], [1], [1], [1], [1]],
[[0], [1], [0], [1], [0]],
[[1], [0], [1], [0], [1]],
[[0], [1], [0], [1], [0]]]
# pylint: disable=bad-whitespace
expected_output = np.array([
2, 2.23606801, 2, 2.23606801, 2,
1, 1.41421354, 1, 1.41421354, 1,
0, 1, 0, 1, 0,
1, 0, 1, 0, 1,
0, 1, 0, 1, 0])
# yapf: enable
image = tf.constant(image, dtype=tf.uint8)
for output_dtype in [tf.float16, tf.float32, tf.float64]:
output = distance_tranform_ops.euclidean_dist_transform(
image, dtype=output_dtype)
output_flat = tf.reshape(output, [-1])
with self.subTest(output_dtype=output_dtype):
self.assertEqual(output.dtype, output_dtype)
self.assertEqual(output.shape, [5, 5, 1])
self.assertAllCloseAccordingToType(output_flat,
expected_output)
def test_all_zeros(self):
image = tf.zeros([10, 10, 1], tf.uint8)
expected_output = np.zeros([10, 10, 1])
for output_dtype in [tf.float16, tf.float32, tf.float64]:
output = distance_tranform_ops.euclidean_dist_transform(
image, dtype=output_dtype)
self.assertAllClose(output, expected_output)
def test_image_with_invalid_dtype(self):
image = [[[1], [1], [1], [1], [1]], [[1], [1], [1], [1], [1]],
[[0], [1], [0], [1], [0]], [[1], [0], [1], [0], [1]],
[[0], [1], [0], [1], [0]]]
image = tf.constant(image, dtype=tf.uint8)
for output_dtype in [tf.uint8, tf.int32, tf.int64]:
with self.assertRaisesRegex(
TypeError, "`dtype` must be float16, float32 or float64"):
_ = dist_ops.euclidean_dist_transform(image,
dtype=output_dtype)
def test_image_with_invalid_dtype(dtype):
image = [
[[1], [1], [1], [1], [1]],
[[1], [1], [1], [1], [1]],
[[0], [1], [0], [1], [0]],
[[1], [0], [1], [0], [1]],
[[0], [1], [0], [1], [0]],
]
image = tf.constant(image, dtype=tf.uint8)
with pytest.raises(TypeError, match="`dtype` must be float16, float32 or float64"):
_ = dist_ops.euclidean_dist_transform(image, dtype=dtype)
def test_multi_channels():
channels = 3
image = [
[[0], [0], [0], [0], [0]],
[[0], [1], [1], [1], [0]],
[[0], [1], [1], [1], [0]],
[[0], [1], [1], [1], [0]],
[[0], [0], [0], [0], [0]],
]
expected_output = np.tile(
np.expand_dims(
np.array([
0,
0,
0,
0,
0,
0,
1,
1,
1,
0,
0,
1,
2,
1,
0,
0,
1,
1,
1,
0,
0,
0,
0,
0,
0,
]),
axis=-1,
),
[1, 3],
)
image = np.tile(image, [1, 1, channels])
images = tf.constant([image], dtype=tf.uint8)
output = dist_ops.euclidean_dist_transform(images, dtype=tf.float32)
output_flat = tf.reshape(output, [-1, 3])
assert output.shape == [1, 5, 5, channels]
test_utils.assert_allclose_according_to_type(output_flat, expected_output)
def test_image_with_invalid_dtype(self):
# yapf: disable
image = [[[1], [1], [1], [1], [1]],
[[1], [1], [1], [1], [1]],
[[0], [1], [0], [1], [0]],
[[1], [0], [1], [0], [1]],
[[0], [1], [0], [1], [0]]]
# yapf: enable
image = tf.constant(image, dtype=tf.uint8)
for output_dtype in [tf.uint8, tf.int32, tf.int64]:
# pylint: disable=bad-continuation
with self.assertRaisesRegex(
TypeError, "`dtype` must be float16, float32 or float64"):
_ = distance_tranform_ops.euclidean_dist_transform(
image, dtype=output_dtype)
def test_single_binary_image(dtype):
image = [
[[1], [1], [1], [1], [1]],
[[1], [1], [1], [1], [1]],
[[0], [1], [0], [1], [0]],
[[1], [0], [1], [0], [1]],
[[0], [1], [0], [1], [0]],
]
expected_output = np.array(
[
2,
2.23606801,
2,
2.23606801,
2,
1,
1.41421354,
1,
1.41421354,
1,
0,
1,
0,
1,
0,
1,
0,
1,
0,
1,
0,
1,
0,
1,
0,
]
)
image = tf.constant(image, dtype=tf.uint8)
output = dist_ops.euclidean_dist_transform(image, dtype=dtype)
output_flat = tf.reshape(output, [-1])
assert output.dtype == dtype
assert output.shape == [5, 5, 1]
test_utils.assert_allclose_according_to_type(output_flat, expected_output)
def test_batch_binary_images(dtype):
batch_size = 3
image = [
[[0], [0], [0], [0], [0]],
[[0], [1], [1], [1], [0]],
[[0], [1], [1], [1], [0]],
[[0], [1], [1], [1], [0]],
[[0], [0], [0], [0], [0]],
]
expected_output = np.array([
0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 2, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0,
0
] * batch_size)
images = tf.constant([image] * batch_size, dtype=tf.uint8)
output = dist_ops.euclidean_dist_transform(images, dtype=dtype)
output_flat = tf.reshape(output, [-1])
assert output.shape == [batch_size, 5, 5, 1]
test_utils.assert_allclose_according_to_type(output_flat, expected_output)
def test_all_ones():
image = tf.ones([10, 10, 1], tf.uint8)
output = dist_ops.euclidean_dist_transform(image)
expected_output = np.full([10, 10, 1], tf.float32.max)
np.testing.assert_allclose(output, expected_output)
def test_image_with_invalid_shape():
image = tf.zeros([2, 4, 3], tf.uint8)
with pytest.raises(ValueError,
match="`images` must have only one channel"):
_ = dist_ops.euclidean_dist_transform(image)
def test_image_with_invalid_shape(self):
image = tf.zeros([2, 4, 3], tf.uint8)
with self.assertRaisesRegex(ValueError,
"`images` must have only one channel"):
_ = dist_ops.euclidean_dist_transform(image)
def test_all_ones(self):
image = tf.ones([10, 10, 1], tf.uint8)
output = distance_tranform_ops.euclidean_dist_transform(image)
expected_output = np.full([10, 10, 1], tf.float32.max)
self.assertAllClose(output, expected_output)
