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
