def test_resample_warped_live02(self):
# corresponds to resampling_test02 for C++
u_vectors = np.array(
[[0.0, 0.0, 0.0], [-.5, 0.0, 0.0], [1.5, 0.5, 0.0]],
dtype=np.float32)
v_vectors = np.array(
[[-1.0, 0.0, 0.0], [0.0, 0.0, 0.5], [-1.5, 0.5, -0.5]],
dtype=np.float32)
warp_field_template = np.stack((u_vectors, v_vectors), axis=2)
warp_field = warp_field_template.copy()
gradient_field_template = warp_field_template * 10
gradient_field = gradient_field_template.copy()
warped_live_template = np.array(
[[1.0, 1.0, 1.0], [0.5, 1.0, 1.0], [0.5, 0.5, -1.0]],
dtype=np.float32)
warped_live_field = warped_live_template.copy()
canonical_field = np.array(
[[-1.0, 0., 0.0], [0.0, 0., 1.0], [0.0, 0., 1.0]],
dtype=np.float32)
expected_new_warped_live_field = np.array(
[[1.0, 1.0, 1.0], [0.5, 1.0, 1.0], [1.0, 0.125, -1.0]],
dtype=np.float32)
expected_u_vectors = np.array(
[[0.0, 0.0, 0.0], [-0.5, 0.0, 0.0], [0.0, 0.5, 0.0]],
dtype=np.float32)
expected_v_vectors = np.array(
[[-1.0, 0.0, 0.0], [0.0, 0.0, 0.5], [0.0, 0.5, -0.5]],
dtype=np.float32)
warped_live_field = ipt.resample_warped_live(canonical_field,
warped_live_field,
warp_field,
gradient_field,
band_union_only=True,
known_values_only=False,
substitute_original=True)
out_u_vectors = warp_field[:, :, 0]
out_v_vectors = warp_field[:, :, 1]
self.assertTrue(
np.allclose(warped_live_field, expected_new_warped_live_field))
self.assertTrue(np.allclose(out_u_vectors, expected_u_vectors))
self.assertTrue(np.allclose(out_v_vectors, expected_v_vectors))
# re-prep data
warped_live_field = warped_live_template.copy()
warped_live_field, (out_u_vectors, out_v_vectors) = \
cpp_extension.resample(warped_live_field, canonical_field, u_vectors, v_vectors,
band_union_only=True, known_values_only=False, substitute_original=True)
self.assertTrue(
np.allclose(warped_live_field, expected_new_warped_live_field))
self.assertTrue(np.allclose(out_u_vectors, expected_u_vectors))
self.assertTrue(np.allclose(out_v_vectors, expected_v_vectors))
def test_resample_warped_live04(self):
u_vectors = np.array([[-0., -0., 0.03732542, 0.01575381],
[-0., 0.04549519, 0.01572882, 0.00634488],
[-0., 0.07203466, 0.01575179, 0.00622413],
[-0., 0.05771814, 0.01468342, 0.01397111]],
dtype=np.float32)
v_vectors = np.array([[-0., -0., 0.02127664, 0.01985903],
[-0., 0.04313552, 0.02502393, 0.02139519],
[-0., 0.02682102, 0.0205336, 0.02577237],
[-0., 0.02112256, 0.01908935, 0.02855439]],
dtype=np.float32)
warp_field_template = np.stack((u_vectors, v_vectors), axis=2)
warp_field = warp_field_template.copy()
gradient_field_template = warp_field_template * 10
gradient_field = gradient_field_template.copy()
warped_live_template = np.array(
[[1., 1., 0.49999955, 0.42499956],
[1., 0.44999936, 0.34999937, 0.32499936],
[1., 0.35000065, 0.25000066, 0.22500065],
[1., 0.20000044, 0.15000044, 0.07500044]],
dtype=np.float32)
warped_live_field = warped_live_template.copy()
canonical_field = np.array(
[[1.0000000e+00, 1.0000000e+00, 3.7499955e-01, 2.4999955e-01],
[1.0000000e+00, 3.2499936e-01, 1.9999936e-01, 1.4999935e-01],
[1.0000000e+00, 1.7500064e-01, 1.0000064e-01, 5.0000645e-02],
[1.0000000e+00, 7.5000443e-02, 4.4107438e-07, -9.9999562e-02]],
dtype=np.float32)
expected_new_warped_live_field = np.array(
[[1., 1., 0.49404836, 0.4321034],
[1., 0.44113636, 0.34710377, 0.32715625],
[1., 0.3388706, 0.24753733, 0.22598255],
[1., 0.21407352, 0.16514614, 0.11396749]],
dtype=np.float32)
warped_live_field = ipt.resample_warped_live(canonical_field,
warped_live_field,
warp_field,
gradient_field,
band_union_only=False,
known_values_only=False,
substitute_original=False)
out_u_vectors = warp_field[:, :, 0]
out_v_vectors = warp_field[:, :, 1]
self.assertTrue(
np.allclose(warped_live_field, expected_new_warped_live_field))
# re-prep data
warped_live_field = warped_live_template.copy()
warped_live_field, (out_u_vectors,
out_v_vectors) = cpp_extension.resample(
warped_live_field, canonical_field, u_vectors,
v_vectors)
self.assertTrue(
np.allclose(warped_live_field, expected_new_warped_live_field))
def test_resample_warped_live05(self):
u_vectors = np.array([[-0., -0., 0.0334751, 0.01388371],
[-0., 0.04041886, 0.0149368, 0.00573045],
[-0., 0.06464156, 0.01506416, 0.00579486],
[-0., 0.06037777, 0.0144603, 0.01164452]],
dtype=np.float32)
v_vectors = np.array([[-0., -0., 0.019718, 0.02146172],
[-0., 0.03823357, 0.02406227, 0.02212186],
[-0., 0.02261183, 0.01864575, 0.02234527],
[-0., 0.01906347, 0.01756042, 0.02574961]],
dtype=np.float32)
warp_field_template = np.stack((u_vectors, v_vectors), axis=2)
warp_field = warp_field_template.copy()
gradient_field_template = warp_field_template * 10
gradient_field = gradient_field_template.copy()
warped_live_template = np.array(
[[1., 1., 0.49404836, 0.4321034],
[1., 0.44113636, 0.34710377, 0.32715625],
[1., 0.3388706, 0.24753733, 0.22598255],
[1., 0.21407352, 0.16514614, 0.11396749]],
dtype=np.float32)
warped_live_field = warped_live_template.copy()
canonical_field = np.array(
[[1.0000000e+00, 1.0000000e+00, 3.7499955e-01, 2.4999955e-01],
[1.0000000e+00, 3.2499936e-01, 1.9999936e-01, 1.4999935e-01],
[1.0000000e+00, 1.7500064e-01, 1.0000064e-01, 5.0000645e-02],
[1.0000000e+00, 7.5000443e-02, 4.4107438e-07, -9.9999562e-02]],
dtype=np.float32)
expected_new_warped_live_field = np.array(
[[1., 1., 0.48910502, 0.43776682],
[1., 0.43342987, 0.34440944, 0.3287866],
[1., 0.33020678, 0.24566805, 0.22797936],
[1., 0.2261582, 0.17907946, 0.14683424]],
dtype=np.float32)
warped_live_field = ipt.resample_warped_live(canonical_field,
warped_live_field,
warp_field,
gradient_field,
band_union_only=False,
known_values_only=False,
substitute_original=False)
out_u_vectors = warp_field[:, :, 0]
out_v_vectors = warp_field[:, :, 1]
self.assertTrue(
np.allclose(warped_live_field, expected_new_warped_live_field))
# re-prep data
warped_live_field = warped_live_template.copy()
warped_live_field, (out_u_vectors, out_v_vectors) = \
cpp_extension.resample(warped_live_field, canonical_field, u_vectors, v_vectors)
self.assertTrue(
np.allclose(warped_live_field, expected_new_warped_live_field))
def __optimization_iteration_direct(self,
warped_live_field,
canonical_field,
warp_field,
data_component_field=None,
smoothing_component_field=None,
level_set_component_field=None,
band_union_only=True):
self.total_data_energy = 0.
self.total_smoothing_energy = 0.
self.total_level_set_energy = 0.
field_size = warp_field.shape[0]
live_gradient_y, live_gradient_x = np.gradient(warped_live_field)
for y in range(0, field_size):
for x in range(0, field_size):
if focus_coordinates_match(x, y):
print("Point: ", x, ",", y, sep='', end='')
gradient = 0.0
live_sdf = warped_live_field[y, x]
live_is_truncated = value_outside_narrow_band(live_sdf)
if band_union_only and voxel_is_outside_narrow_band_union(
warped_live_field, canonical_field, x, y):
continue
data_gradient, local_data_energy = \
dt.compute_local_data_term(warped_live_field, canonical_field, x, y, live_gradient_x,
live_gradient_y, method=self.data_term_method)
scaled_data_gradient = self.data_term_weight * data_gradient
self.total_data_energy += self.data_term_weight * local_data_energy
gradient += scaled_data_gradient
if focus_coordinates_match(x, y):
print(" Data grad: ",
BOLD_GREEN,
-data_gradient,
RESET,
sep='',
end='')
if data_component_field is not None:
data_component_field[y, x] = data_gradient
if self.level_set_term_enabled and not live_is_truncated:
level_set_gradient, local_level_set_energy = \
level_set_term_at_location(warped_live_field, x, y)
scaled_level_set_gradient = self.level_set_term_weight * level_set_gradient
self.total_level_set_energy += self.level_set_term_weight * local_level_set_energy
gradient += scaled_level_set_gradient
if level_set_component_field is not None:
level_set_component_field[y, x] = level_set_gradient
if focus_coordinates_match(x, y):
print(" Level-set grad (scaled): ",
BOLD_GREEN,
-scaled_level_set_gradient,
RESET,
sep='',
end='')
smoothing_gradient, local_smoothing_energy = \
st.compute_local_smoothing_term_gradient(warp_field, x, y, method=self.smoothing_term_method,
copy_if_zero=False,
isomorphic_enforcement_factor=
self.isomorphic_enforcement_factor)
scaled_smoothing_gradient = self.smoothing_term_weight * smoothing_gradient
self.total_smoothing_energy += self.smoothing_term_weight * local_smoothing_energy
gradient += scaled_smoothing_gradient
if smoothing_component_field is not None:
smoothing_component_field[y, x] = smoothing_gradient
if focus_coordinates_match(x, y):
print(" Smoothing grad (scaled): ",
BOLD_GREEN,
-scaled_smoothing_gradient,
RESET,
sep='',
end='')
self.gradient_field[y, x] = gradient
if self.sobolev_smoothing_enabled:
convolve_with_kernel_preserve_zeros(self.gradient_field,
self.sobolev_kernel, True)
max_warp = 0.0
max_warp_location = -1
# update the warp field based on the gradient
for y in range(0, field_size):
for x in range(0, field_size):
warp_field[y, x] = -self.gradient_field[
y, x] * self.gradient_descent_rate
if focus_coordinates_match(x, y):
print(" Warp: ",
BOLD_GREEN,
warp_field[y, x],
RESET,
" Warp length: ",
BOLD_GREEN,
np.linalg.norm(warp_field[y, x]),
RESET,
sep='')
warp_length = np.linalg.norm(warp_field[y, x])
if warp_length > max_warp:
max_warp = warp_length
max_warp_location = Point2d(x, y)
if (x, y) in self.focus_neighborhood_log:
log = self.focus_neighborhood_log[(x, y)]
log.warp_magnitudes.append(warp_length)
log.sdf_values.append(warped_live_field[y, x])
new_warped_live_field = resample_warped_live(canonical_field,
warped_live_field,
warp_field,
self.gradient_field,
band_union_only=False,
known_values_only=False,
substitute_original=False)
np.copyto(warped_live_field, new_warped_live_field)
return max_warp, max_warp_location
def test_resample_warped_live03(self):
u_vectors = np.array(
[[0., 0., 0., 0.],
[-1.95794283, 1.59443461, -0.80321548, -0.41660499],
[0.99072356, 0.33884474, -1.16845247, 1.46578561],
[0., 0., 0., 0.]],
dtype=np.float32)
v_vectors = np.array(
[[0., 0., 0., 0.],
[-0.89691237, -0.45941665, 1.36006788, -1.05888156],
[-0.47305308, -1.27971876, -0.38927596, -1.83300769],
[0., 0., 0., 0.]],
dtype=np.float32)
warp_field_template = np.stack((u_vectors, v_vectors), axis=2)
warp_field = warp_field_template.copy()
gradient_field_template = warp_field_template * 10
gradient_field = gradient_field_template.copy()
warped_live_template = np.array(
[[0., 0., 0., 0.], [
0.22921804, 0.04988099, -0.45673641, 0.05888156
], [-0.46242488, 0., -0.44900494, 0.92592539], [0., 0., 0., 0.]],
dtype=np.float32)
warped_live_field = warped_live_template.copy()
canonical_field = np.array(
[[0., 0., 0., 0.], [0.18455172, 0., 0., 0.45854051],
[0.4508187, 0.8420034, 0.41780566, 0.34275345],
[0.90931962, 0.20844429, 0.27169698, 0.57087366]],
dtype=np.float32)
expected_new_warped_live_field = np.array(
[[0., 0., 0., 0.], [1., -0.08121467, -0.05654262, 0.05888156],
[0.02212291, -0.08771848, -0.01639593, 1.], [0., 0., 0., 0.]],
dtype=np.float32)
expected_u_vectors = np.array(
[[0., 0., 0., 0.], [0., 1.59443461, -0.80321548, -0.41660499],
[0.99072356, 0.33884474, -1.16845247, 0.], [0., 0., 0., 0.]],
dtype=np.float32)
expected_v_vectors = np.array(
[[0., 0., 0., 0.], [0., -0.45941665, 1.36006788, -1.05888156],
[-0.47305308, -1.27971876, -0.38927596, 0.], [0., 0., 0., 0.]],
dtype=np.float32)
warped_live_field = ipt.resample_warped_live(canonical_field,
warped_live_field,
warp_field,
gradient_field,
band_union_only=False,
known_values_only=False,
substitute_original=False)
out_u_vectors = warp_field[:, :, 0]
out_v_vectors = warp_field[:, :, 1]
self.assertTrue(np.allclose(out_u_vectors, expected_u_vectors))
self.assertTrue(np.allclose(out_v_vectors, expected_v_vectors))
self.assertTrue(
np.allclose(warped_live_field, expected_new_warped_live_field))
# re-prep data
warped_live_field = warped_live_template.copy()
warped_live_field, (out_u_vectors,
out_v_vectors) = cpp_extension.resample(
warped_live_field, canonical_field, u_vectors,
v_vectors)
self.assertTrue(
np.allclose(warped_live_field, expected_new_warped_live_field))
self.assertTrue(np.allclose(out_u_vectors, expected_u_vectors))
self.assertTrue(np.allclose(out_v_vectors, expected_v_vectors))