def test_One_Hot_Encoding(self):
with self.session():
src = np.array([[[[4], [3], [1], [0], [2]]]]).astype(np.float32)
deform = np.array([[[[0, 0, -.5], [0, 0, 0], [0, 0,
0.3], [0, 0, 1],
[0, 0, 1.5], [0, 0, 2.5], [0, 0, 4],
[0, 0, 5]]]]).astype(np.float32)
trg_graph = augmentation_ops.apply_deformation3d(
src,
deform, [],
extrapolation="zero_padding",
conversion="indexed_to_one_hot",
output_num_channels=5)
trg = trg_graph.eval()
self.assertAllEqual([1, 1, 8, 5], trg_graph.shape)
self.assertAllEqual([1, 1, 8, 5], trg.shape)
expected = np.array([[[[0.5, 0, 0, 0, 0.5], [0, 0, 0, 0, 1],
[0, 0, 0, 0.3, 0.7], [0, 0, 0, 1, 0],
[0, 0.5, 0, 0.5, 0], [0.5, 0.5, 0, 0, 0],
[0, 0, 1, 0, 0], [1, 0, 0, 0,
0]]]]).astype(float)
for x2 in range(8):
for ch in range(5):
self.assertAlmostEqual(
expected[0, 0, x2, ch],
trg[0, 0, x2, ch],
msg="expected {}, but got {} at x2={}, ch={}".format(
expected[0, 0, x2, ch], trg[0, 0, x2, ch], x2, ch))
def test_InterpolationNearest(self):
with self.session():
src = np.array([[[[0], [10], [20], [30]]]]).astype(np.float32)
deform = np.array([[[[0, 0, 0.5], [0, 0,
2.7]]]]).astype(np.float32)
trg = augmentation_ops.apply_deformation3d(
src, deform, [], interpolation="nearest").eval()
self.assertAllEqual(np.array([[[[10], [30]]]]), trg)
def test_InterpolationMixedNearestLinear(self):
with self.session():
src = np.array([[[[0], [10], [20], [30]]],
[[[5], [15], [25], [35]]]]).astype(np.float32)
deform = np.array([[[[0, 0, 0.5], [0, 0, 2.7]],
[[0, 1, 1.5], [1, 0, 2.1]]]]).astype(np.float32)
trg = augmentation_ops.apply_deformation3d(
src,
deform, [],
interpolation="mixed_nearest_linear",
extrapolation="zero_padding").eval()
self.assertAllClose(np.array([[[[5], [27]], [[0], [26]]]]), trg)
def test_ExtrapolationMirror(self):
with self.session():
src = np.array([[[[0], [1], [2], [3], [4]]]]).astype(np.float32)
deform = np.array([[[[0, 0, -10], [0, 0, -9], [0, 0, -8], [0, 0, -7],
[0, 0, -6], [0, 0, -5], [0, 0, -4], [0, 0, -3],
[0, 0, -2], [0, 0, -1], [0, 0, 0], [0, 0, 1],
[0, 0, 2], [0, 0, 3], [0, 0, 4], [0, 0, 5],
[0, 0, 6], [0, 0, 7], [0, 0, 8], [0, 0, 9],
[0, 0, 10]]]]).astype(np.float32)
trg = augmentation_ops.apply_deformation3d(
src, deform, []).eval()
self.assertAllEqual(
np.array([[[[2], [1], [0], [1], [2], [3], [4], [3], [2], [1], [0],
[1], [2], [3], [4], [3], [2], [1], [0], [1], [2]]]]), trg)
def test_ExtrapolationZero(self):
with self.session():
src = np.array([[[[10], [11], [12], [13], [14]]]]).astype(np.float32)
deform = np.array([[[[0, 0, -10], [0, 0, -9], [0, 0, -8], [0, 0, -7],
[0, 0, -6], [0, 0, -5], [0, 0, -4], [0, 0, -3],
[0, 0, -2], [0, 0, -1], [0, 0, 0], [0, 0, 1],
[0, 0, 2], [0, 0, 3], [0, 0, 4], [0, 0, 5],
[0, 0, 6], [0, 0, 7], [0, 0, 8], [0, 0, 9],
[0, 0, 10]]]]).astype(np.float32)
trg = augmentation_ops.apply_deformation3d(
src, deform, [], extrapolation="zero_padding").eval()
self.assertAllEqual(
np.array([[[[0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [10],
[11], [12], [13], [14], [0], [0], [0], [0], [0], [0]]]]),
trg)
def test_IdentityTransform(self):
with self.session():
src = np.random.random([4, 10, 7, 3]).astype(np.float32)
deformation = np.ndarray([4, 10, 7, 3], dtype=np.float32)
for x0 in range(deformation.shape[0]):
for x1 in range(deformation.shape[1]):
for x2 in range(deformation.shape[2]):
deformation[x0, x1, x2, 0] = x0
deformation[x0, x1, x2, 1] = x1
deformation[x0, x1, x2, 2] = x2
result = augmentation_ops.apply_deformation3d(src, deformation, [])
self.assertEqual(result.get_shape(), src.shape)
trg = result.eval()
self.assertAllEqual(trg, src)
def test_outputSpatialShape(self):
with self.session():
src = np.random.random([4, 10, 7, 3]).astype(np.float32)
deformation = np.ndarray([4, 10, 7, 3], dtype=np.float32)
for x0 in range(deformation.shape[0]):
for x1 in range(deformation.shape[1]):
for x2 in range(deformation.shape[2]):
deformation[x0, x1, x2, 0] = x0
deformation[x0, x1, x2, 1] = x1
deformation[x0, x1, x2, 2] = x2
result = augmentation_ops.apply_deformation3d(
src, deformation, [], output_spatial_shape=[-1, 6, 5])
self.assertEqual(result.get_shape(), [4, 6, 5, 3])
trg = result.eval()
self.assertAllEqual(trg, src[:, 2:-2, 1:-1, :])