def test_fdt_simple(self):
mso = PyMSO()
neigh, dist = calculate_distances_array((1, 1, 1), NeighType.vertex)
components = np.zeros((3, 3, 3), dtype=np.uint8)
components[1, 1, 1] = 1
mso.set_neighbourhood(neigh, dist)
mso.set_components(components)
mso.set_mu_array(np.ones(components.shape))
arr = np.zeros(components.shape)
arr[(0, 0, 0, 0, 2, 2, 2, 2), (0, 0, 2, 2, 0, 0, 2, 2),
(0, 2, 0, 2, 0, 2, 0, 2)] = np.sqrt(3)
arr[(0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2),
(0, 1, 1, 2, 0, 0, 2, 2, 0, 1, 1, 2),
(1, 0, 2, 1, 0, 2, 0, 2, 1, 0, 2, 1), ] = np.sqrt(2)
arr[(0, 1, 1, 1, 1, 2), (1, 0, 1, 1, 2, 1), (1, 1, 0, 2, 1, 1)] = 1
res = mso.calculate_FDT()
assert np.all(res == arr)
def sprawl(
cls,
sprawl_area: np.ndarray,
core_objects: np.ndarray,
data: np.ndarray,
components_num: int,
spacing,
side_connection: bool,
operator: Callable[[Any, Any], bool],
arguments: dict,
lower_bound,
upper_bound,
):
if components_num > 250:
raise SegmentationLimitException("Current implementation of MSO do not support more than 250 components")
mso = PyMSO()
neigh, dist = calculate_distances_array(spacing, get_neigh(side_connection))
components_arr = np.copy(core_objects).astype(np.uint8)
components_arr[components_arr > 0] += 1
components_arr[sprawl_area == 0] = 1
mso.set_neighbourhood(neigh, dist)
mso.set_components(components_arr, components_num + 1)
mso.set_use_background(False)
try:
mu_array = calculate_mu(data.copy("C"), lower_bound, upper_bound, MuType.base_mu)
except OverflowError:
raise SegmentationLimitException("Wrong range for ")
if arguments["reflective"]:
mu_array[mu_array < 0.5] = 1 - mu_array[mu_array < 0.5]
mso.set_mu_array(mu_array)
try:
mso.run_MSO(arguments["step_limits"])
except RuntimeError as e:
if e.args[0] == "to many steps: constrained dilation":
raise SegmentationLimitException(*e.args)
raise
# print("Steps: ", mso.steps_done(), file=sys.stderr)
result = mso.get_result_catted()
result[result > 0] -= 1
return result
def _test_background_simple(self):
components = np.ones((20, 20, 20), dtype=np.uint8)
components[1:-1, 1:-1, 1:-1] = 0
components[9:11, 9:11, 9:11] = 2
mu_array = np.zeros(components.shape)
mu_array[1:-1, 1:-1, 1:-1] = 0.7
mu_array[3:-3, 3:-3, 3:-3] = 0.6
mu_array[5:-5, 5:-5, 5:-5] = 0.4
mu_array[6:-6, 6:-6, 6:-6] = 0.6
mu_array[8:-8, 8:-8, 8:-8] = 0.7
mu_array[components > 0] = 1.0
mso = PyMSO()
neigh, dist = calculate_distances_array((1, 1, 1), NeighType.vertex)
mso.set_neighbourhood(neigh, dist)
mso.set_use_background(True)
mso.set_components(components, 3)
mso.set_mu_array(mu_array)
mso.set_components_num(3)
mso.run_MSO(10)
mso.get_result_catted()
def test_chain_component(self):
for i in range(2, 10):
components = np.zeros((10, 10, i * 10), dtype=np.uint8)
for j in range(i):
components[4:6, 4:6, (10 * j + 4):(10 * j + 6)] = j + 1
fdt = np.ones(components.shape, dtype=np.float64) * i * 10
sprawl_area = np.zeros(components.shape, dtype=np.uint8)
sprawl_area[2:8, 2:8, 2:(10 * i) - 2] = True
sprawl_area[components > 0] = False
components2 = np.zeros(components.shape, dtype=np.uint8)
for j in range(i):
components2[2:8, 2:8, (j * 10):(j + 1) * 10] = j + 1
components2[:, :, :2] = 0
components2[:, :, -2:] = 0
mso = PyMSO()
neigh, dist = calculate_distances_array((1, 1, 1),
NeighType.vertex)
mso.set_neighbourhood(neigh, dist)
mso.set_components(components)
mso.set_mu_array(np.ones(components.shape))
res = mso.constrained_dilation(fdt, components, sprawl_area)
assert np.all(res == components2)
def test_two_components_base(self):
components = np.zeros((10, 10, 20), dtype=np.uint8)
components[4:6, 4:6, 4:6] = 1
components[4:6, 4:6, 14:16] = 2
sprawl_area = np.zeros(components.shape, dtype=np.uint8)
sprawl_area[2:8, 2:8, 2:18] = True
sprawl_area[components > 0] = False
fdt = np.zeros(components.shape, dtype=np.float64)
mso = PyMSO()
neigh, dist = calculate_distances_array((1, 1, 1), NeighType.vertex)
mso.set_neighbourhood(neigh, dist)
mso.set_components(components)
mso.set_mu_array(np.ones(components.shape))
res = mso.constrained_dilation(fdt, components, sprawl_area)
assert np.all(res == components)
fdt[:] = 20
# fdt[:, :, 10] = 1
components2 = np.zeros(components.shape, dtype=np.uint8)
components2[2:8, 2:8, 2:10] = 1
components2[2:8, 2:8, 10:18] = 2
res = mso.constrained_dilation(fdt, components, sprawl_area)
assert np.all(res == components2)
def test_high_fdt_two_components(self):
components = np.zeros((10, 10, 20), dtype=np.uint8)
components[4:6, 4:6, 4:6] = 1
components[4:6, 4:6, 14:16] = 2
sprawl_area = np.zeros(components.shape, dtype=np.uint8)
sprawl_area[2:8, 2:8, 2:18] = True
sprawl_area[components > 0] = False
fdt = np.ones(components.shape, dtype=np.float64) * 20
mso = PyMSO()
neigh, dist = calculate_distances_array((1, 1, 1), NeighType.vertex)
mso.set_neighbourhood(neigh, dist)
mso.set_components(components)
mso.set_mu_array(np.ones(components.shape))
components2 = np.zeros(components.shape, dtype=np.uint8)
components2[2:8, 2:8, 2:10] = 1
components2[2:8, 2:8, 10:18] = 2
fdt2 = np.copy(fdt)
fdt2[:, :, 7] = 25
components3 = np.copy(components2)
components3[2:8, 2:8, 8:18] = 2
components3[2:8, 2:8, 7] = 0
res = mso.constrained_dilation(fdt2, components, sprawl_area)
assert np.all(res == components3)
fdt2 = np.copy(fdt)
fdt2[:, :, 12] = 25
components3 = np.copy(components2)
components3[2:8, 2:8, 2:12] = 1
components3[2:8, 2:8, 12] = 0
res = mso.constrained_dilation(fdt2, components, sprawl_area)
assert np.all(res == components3)
fdt2 = np.copy(fdt)
fdt2[:, :, 12] = 25
fdt2[:, :, 7] = 25
components3 = np.copy(components2)
components3[2:8, 2:8, 7:13] = 0
res = mso.constrained_dilation(fdt2, components, sprawl_area)
assert np.all(res == components3)
def test_two_components_bridge(self):
components = np.zeros((10, 10, 20), dtype=np.uint8)
components[:] = 1
components[2:8, 2:8, 2:18] = 0
components[4:6, 4:6, 4:6] = 2
components[4:6, 4:6, 14:16] = 3
mu_arr = np.zeros(components.shape, dtype=np.float64)
mu_arr[components == 0] = 0.5
mu_arr[components > 1] = 1
mu_arr[2:8, 2:8, (9, 10)] = 0.1
mso = PyMSO()
neigh, dist = calculate_distances_array((1, 1, 1), NeighType.vertex)
mso.set_neighbourhood(neigh, dist)
mso.set_components(components, 3)
mso.set_mu_array(mu_arr)
mso.set_components_num(3)
mso.run_MSO(10)
res = mso.get_result_catted()
assert mso.steps_done() == 2
arr = np.copy(components)
arr[arr == 1] = 0
arr[2:8, 2:8, 2:9] = 2
arr[2:8, 2:8, 11:18] = 3
assert np.all(res == arr)
def test_fdt_base(self):
mso = PyMSO()
neigh, dist = calculate_distances_array((1, 1, 1), NeighType.vertex)
components = np.zeros((10, 10, 10), dtype=np.uint8)
components[3:7, 3:7, 3:7] = 1
mso.set_neighbourhood(neigh, dist)
mso.set_components(components)
mso.set_mu_array(np.ones(components.shape))
res = mso.calculate_FDT()
arr = calculate_euclidean((components == 0).astype(np.uint8),
components, neigh, dist)
assert np.all(res == arr)
mso.set_mu_array(np.ones(components.shape) * 0.5)
res = mso.calculate_FDT()
assert np.all(res == arr * 0.5)
mu = np.ones(components.shape) * 0.5
mu[components > 0] = 1
mso.set_mu_array(mu)
arr *= 0.5
arr[arr > 0] += 0.25
arr[3:7, (0, 1, 2, 0, 1, 2, 9, 8, 7, 9, 8, 7),
(0, 1, 2, 9, 8, 7, 0, 1, 2, 9, 8, 7)] += np.sqrt(2) / 4 - 0.25
for i in range(3):
lb = i
ub = 9 - i
arr[lb, lb + 1:ub, (lb, ub)] += np.sqrt(2) / 4 - 0.25
arr[lb, (lb, ub), lb + 1:ub] += np.sqrt(2) / 4 - 0.25
arr[ub, lb + 1:ub, (lb, ub)] += np.sqrt(2) / 4 - 0.25
arr[ub, (lb, ub), lb + 1:ub] += np.sqrt(2) / 4 - 0.25
for el in itertools.product([lb, ub], repeat=3):
arr[el] += np.sqrt(3) / 4 - 0.25
for z, (y, x) in itertools.product([2, 7],
itertools.product([0, 9],
repeat=2)):
arr[z, y, x] += np.sqrt(2) / 4 - 0.25
for z, (y, x) in itertools.product([2, 7],
itertools.product([1, 8],
repeat=2)):
arr[z, y, x] += np.sqrt(2) / 4 - 0.25
for z, (y, x) in itertools.product([1, 8],
itertools.product([0, 9],
repeat=2)):
arr[z, y, x] += np.sqrt(2) / 4 - 0.25
res2 = mso.calculate_FDT()
assert np.allclose(res2, arr)
class BaseMultiScaleOpening(TwoLevelThresholdBaseAlgorithm,
ABC): # pragma: no cover
@classmethod
def get_fields(cls):
return [
AlgorithmProperty(
"threshold",
"Threshold",
next(iter(double_threshold_dict.keys())),
possible_values=double_threshold_dict,
property_type=AlgorithmDescribeBase,
),
AlgorithmProperty(
"mu_mid",
"Mu mid value",
next(iter(mu_mid_dict.keys())),
possible_values=mu_mid_dict,
property_type=AlgorithmDescribeBase,
),
AlgorithmProperty("step_limits",
"Limits of Steps",
100,
options_range=(1, 1000),
property_type=int),
] + super().get_fields()
def get_info_text(self):
return ("Threshold: " + ", ".join(map(str, self.threshold_info)) +
"\nMid sizes: " + ", ".join(
map(str, self._sizes_array[1:self.components_num + 1])) +
"\nFinal sizes: " + ", ".join(map(str, self.final_sizes[1:])) +
f"\nsteps: {self.steps}")
def __init__(self):
super().__init__()
self.finally_segment = None
self.final_sizes = []
self.threshold_info = [None, None]
self.steps = 0
self.mso = PyMSO()
self.mso.set_use_background(True)
def clean(self):
self.sprawl_area = None
self.mso = PyMSO()
self.mso.set_use_background(True)
super().clean()
def set_image(self, image):
super().set_image(image)
self.threshold_info = [None, None]
def calculation_run(self, report_fun) -> SegmentationResult:
if self.new_parameters["side_connection"] != self.parameters[
"side_connection"]:
neigh, dist = calculate_distances_array(
self.image.spacing,
get_neigh(self.new_parameters["side_connection"]))
self.mso.set_neighbourhood(neigh, dist)
segment_data = super().calculation_run(report_fun)
if segment_data is not None and self.components_num == 0:
self.final_sizes = []
return segment_data
if segment_data is None:
restarted = False
finally_segment = np.copy(self.finally_segment)
else:
self.finally_segment = segment_data.roi
finally_segment = segment_data.roi
if np.max(finally_segment) > 250:
raise SegmentationLimitException(
"Current implementation of MSO do not support more than 250 components"
)
components = finally_segment.astype(np.uint8)
components[components > 0] += 1
components[self.sprawl_area == 0] = 1
self.mso.set_components(components, self.components_num)
restarted = True
if (restarted or self.old_threshold_info[1] != self.threshold_info[1]
or self.new_parameters["mu_mid"] != self.parameters["mu_mid"]):
if self.threshold_operator(self.threshold_info[1],
self.threshold_info[0]):
self.final_sizes = np.bincount(finally_segment.flat)
return self.prepare_result(self.finally_segment)
mu_calc: BaseMuMid = mu_mid_dict[self.new_parameters["mu_mid"]
["name"]]
self.parameters["mu_mid"] = self.new_parameters["mu_mid"]
sprawl_area = (self.sprawl_area > 0).astype(np.uint8)
sprawl_area[finally_segment > 0] = 0
mid_val = mu_calc.value(
sprawl_area,
self.channel,
self.threshold_info[0],
self.threshold_info[1],
self.new_parameters["mu_mid"]["values"],
)
mu_array = calculate_mu_mid(self.channel, self.threshold_info[0],
mid_val, self.threshold_info[1])
self.mso.set_mu_array(mu_array)
restarted = True
if restarted or self.new_parameters["step_limits"] != self.parameters[
"step_limits"]:
self.parameters["step_limits"] = self.new_parameters["step_limits"]
count_steps_factor = 20 if self.image.is_2d else 3
self.mso.run_MSO(self.new_parameters["step_limits"],
count_steps_factor)
self.steps = self.mso.steps_done()
new_segment = self.mso.get_result_catted()
new_segment[new_segment > 0] -= 1
self.final_sizes = np.bincount(new_segment.flat)
return self.prepare_result(new_segment)