def test_pipeline(image, algorithm_parameters, mask_property, tmp_path): elem = SegmentationPipelineElement( segmentation=SegmentationProfile( name="", algorithm=algorithm_parameters["algorithm_name"], values=algorithm_parameters["values"]), mask_property=mask_property, ) algorithm_parameters = deepcopy(algorithm_parameters) algorithm_parameters["values"]["channel"] = 1 pipeline = SegmentationPipeline( name="", segmentation=SegmentationProfile( name="", algorithm=algorithm_parameters["algorithm_name"], values=algorithm_parameters["values"]), mask_history=[elem], ) result = calculate_pipeline(image, None, pipeline, lambda x, y: None) assert np.max(result.segmentation) == 2 pt = ProjectTuple( file_path=image.file_path, image=image, segmentation=result.segmentation, mask=result.mask, history=result.history, algorithm_parameters=algorithm_parameters, ) SaveProject.save(tmp_path / "project.tgz", pt) assert os.path.exists(tmp_path / "project.tgz") loaded = LoadProject.load([tmp_path / "project.tgz"]) assert np.all(loaded.segmentation == result.segmentation)
def test_pipeline_saving(self, qtbot, tmp_path, image, algorithm_parameters, mask_property): settings = PartSettings(tmp_path) settings.image = image settings.last_executed_algorithm = algorithm_parameters["algorithm_name"] algorithm = analysis_algorithm_dict[algorithm_parameters["algorithm_name"]]() algorithm.set_image(settings.image) algorithm.set_parameters(**algorithm_parameters["values"]) result = algorithm.calculation_run(lambda x, y: None) settings.set_segmentation_result(result) project_info = settings.get_project_info() mask = calculate_mask_from_project(mask_property, settings.get_project_info()) settings.add_history_element( create_history_element_from_project( project_info, mask_property, ) ) settings.mask = mask calculate_mask_from_project(mask_property, settings.get_project_info()) algorithm_parameters["values"]["channel"] = 1 algorithm.set_parameters(**algorithm_parameters["values"]) algorithm.set_mask(settings.mask) result2 = algorithm.calculation_run(lambda x, y: None) assert np.max(result2.roi) == 2 settings.set_segmentation_result(result2) project_info = settings.get_project_info() SaveProject.save(tmp_path / "project.tgz", project_info) assert os.path.exists(tmp_path / "project.tgz") loaded = LoadProject.load([tmp_path / "project.tgz"]) assert np.all(loaded.roi == result2.roi) assert len(loaded.history) == 1
def test_pipeline(image, algorithm_parameters, mask_property, tmp_path, use_mask): elem = SegmentationPipelineElement( segmentation=ROIExtractionProfile( name="", algorithm=algorithm_parameters["algorithm_name"], values=algorithm_parameters["values"]), mask_property=mask_property, ) algorithm_parameters = deepcopy(algorithm_parameters) algorithm_parameters["values"]["channel"] = 1 pipeline = SegmentationPipeline( name="", segmentation=ROIExtractionProfile( name="", algorithm=algorithm_parameters["algorithm_name"], values=algorithm_parameters["values"]), mask_history=[elem], ) mask = np.ones(image.get_channel(0).shape, dtype=np.uint8) if use_mask else None result = calculate_pipeline(image, mask, pipeline, lambda x, y: None) assert np.max(result.roi) == 2 pt = ProjectTuple( file_path=image.file_path, image=image, roi=result.roi, mask=result.mask, history=result.history, algorithm_parameters=algorithm_parameters, ) SaveProject.save(tmp_path / "project.tgz", pt) assert os.path.exists(tmp_path / "project.tgz") loaded = LoadProject.load([tmp_path / "project.tgz"]) assert np.all(loaded.roi == result.roi)
def test_load_segmentation(self, part_settings, data_test_dir): path = os.path.join(data_test_dir, "stack1_component1_1.tgz") pi = LoadProject.load([path]) part_settings.set_project_info(pi)
def napari_get_reader(path: str): for extension in LoadProject.get_extensions(): if path.endswith(extension) and os.path.exists( LoadProject.get_next_file([path])): return functools.partial(partseg_loader, LoadProject)
def test_save_project(self, tmpdir, analysis_project): SaveProject.save(os.path.join(tmpdir, "test1.tgz"), analysis_project) assert os.path.exists(os.path.join(tmpdir, "test1.tgz")) LoadProject.load([os.path.join(tmpdir, "test1.tgz")])
def test_load_project(self, data_test_dir): load_data = LoadProject.load( [os.path.join(data_test_dir, "stack1_component1_1.tgz")]) assert np.max(load_data.segmentation) == 2
def do_calculation(self, calculation: FileCalculation) -> CalculationResultList: """ Main function for calculation process :param calculation: calculation to do. :return: """ self.calculation = calculation self.reused_mask = calculation.calculation_plan.get_reused_mask() self.mask_dict = {} self.measurement = [] self.results = [] operation = calculation.calculation_plan.execution_tree.operation ext = path.splitext(calculation.file_path)[1] metadata = {"default_spacing": calculation.voxel_size} if operation == RootType.Image: for load_class in load_dict.values(): if load_class.partial() or load_class.number_of_files() != 1: continue if ext in load_class.get_extensions(): projects = load_class.load([calculation.file_path], metadata=metadata) break else: raise ValueError("File type not supported") elif operation == RootType.Project: projects = LoadProject.load([calculation.file_path], metadata=metadata) else: # operation == RootType.Mask_project try: projects = LoadProject.load([calculation.file_path], metadata=metadata) except (KeyError, WrongFileTypeException): # TODO identify exceptions projects = LoadMaskSegmentation.load([calculation.file_path], metadata=metadata) if isinstance(projects, ProjectTuple): projects = [projects] for project in projects: project: ProjectTuple self.image = project.image if operation == RootType.Mask_project: self.mask = project.mask[0] if operation == RootType.Project: self.mask = project.mask[0] self.segmentation = project.segmentation self.additional_layers = project.additional_layers self.history = project.history self.algorithm_parameters = project.algorithm_parameters self.iterate_over(calculation.calculation_plan.execution_tree) for el in self.measurement: el.set_filename( path.relpath(project.image.file_path, calculation.base_prefix)) self.results.append( ResponseData( path.relpath(project.image.file_path, calculation.base_prefix), self.measurement)) self.measurement = [] return self.results
def test_load_old_project(self, data_test_dir): load_data = LoadProject.load([os.path.join(data_test_dir, "stack1_component1.tgz")]) assert np.max(load_data.roi_info.roi) == 2