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):
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(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_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")])