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)