def test_resample_numpy_copy_subvolume(self):
"""
Simply copy a sub-volume
"""
t = torch.arange(10 * 9 * 8).reshape((10, 9, 8)).numpy()
t_r = resample_3d(t, (1, 1, 1), (0, 0, 0), (2, 3, 4), (4, 5, 6),
(1, 1, 1))
t_expected = t[2:4, 3:5, 4:6]
assert (t_r == t_expected).all()
def test_resample_torch_id(self):
"""
identity
"""
t = torch.arange(10 * 9 * 8).reshape((10, 9, 8))
t_r = resample_3d(t, (1, 1, 1), (0, 0, 0), (0, 0, 0), (10, 9, 8),
(1, 1, 1),
interpolation_mode='nearest')
assert np.abs(t.float() - t_r).max() < 1e-5
def test_resample_torch_id(self):
"""
identity
"""
t = torch.arange(10 * 9 * 8).reshape((10, 9, 8))
t_r = resample_3d(t, (1, 1, 1), (0, 0, 0), (0, 0, 0), (10, 9, 8),
(1, 1, 1),
constant_value=42,
order=0)
assert (t == t_r).all()
def test_resample_numpy_copy_subvolume(self):
"""
Simply copy a sub-volume
"""
t = torch.arange(10 * 9 * 8).reshape((10, 9, 8)).numpy()
t_r = resample_3d(t, (1, 1, 1), (0, 0, 0), (2, 3, 4), (4, 5, 6),
(1, 1, 1),
align_corners=False)
t_expected = t[2:4, 3:5, 4:6]
assert np.abs(t_r - t_expected).max() < 1e-5
def test_resample_numpy_with_background(self):
"""
Simply copy a sub-volume with background voxels
"""
t = torch.arange(10 * 9 * 8).reshape((10, 9, 8)).numpy()
t_r = resample_3d(t, (1, 1, 1), (0, 0, 0), (2, 3, 4), (4, 5, 20),
(1, 1, 1),
constant_value=42)
t_r_valid = t_r[:, :, :2]
t_expected = t[2:4, 3:5, 4:6]
assert (t_r_valid == t_expected).all()
# outside voxel should be background value
t_r_background = t_r[:, :, 4:]
assert (t_r_background == 42).all()
def test_resample_numpy_with_background(self):
"""
Simply copy a sub-volume with background voxels
"""
t = torch.arange(10 * 9 * 8).reshape((10, 9, 8)).numpy()
t_r = resample_3d(t, (1, 1, 1), (0, 0, 0), (2, 3, 4), (4, 5, 20),
(1, 1, 1),
align_corners=False)
t_r_valid = t_r[:, :, :2]
t_expected = t[2:4, 3:5, 4:6]
assert np.abs((t_r_valid - t_expected)).max() < 1e-5
# outside voxel should be background value
t_r_background = t_r[:, :, 4:]
assert (t_r_background == 0).all()
def test_resample_numpy_interpolate_x(self):
"""
Interpolate in x axis
"""
t = torch.arange(10 * 9 * 8).reshape((10, 9, 8)).numpy()
t_r = resample_3d(t, (1, 1, 1), (0, 0, 0), (0, 0, 0), (10, 9, 8),
(1, 1, 0.49999),
interpolation_mode='nearest')
assert t_r.shape == (10, 9, 8 * 2)
for z in range(t_r.shape[0]):
for y in range(t_r.shape[1]):
for x in range(t_r.shape[2] -
1): # avoid the background value at the end
v = t_r[z, y, x]
v_expected = t[z, y, x // 2]
assert abs(v - v_expected) < 1e-4
def test_resample_numpy_interpolate_x(self):
"""
Interpolate in x axis
"""
t = torch.arange(10 * 9 * 8).reshape((10, 9, 8)).numpy()
t_r = resample_3d(t, (1, 1, 1), (0, 0, 0), (0, 0, 0), (10, 9, 8),
(1, 1, 0.49999),
constant_value=42,
order=0)
assert t_r.shape == (10, 9, 8 * 2)
for z in range(t_r.shape[0]):
for y in range(t_r.shape[1]):
for x in range(t_r.shape[2] -
1): # avoid the background value at the end
v = t_r[z, y, x]
v_expected = t[z, y, x // 2]
assert v == v_expected
