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_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 run(self):
try:
self.result = calculate_pipeline(
image=self.image, mask=self.mask, pipeline=self.pipeline, report_fun=self.info_function
)
except Exception as e: # pylint: disable=W0703
self.error_signal.emit(e)
def test_pipeline_simple(self):
image = self.get_image()
prop1 = MaskProperty(
dilate=RadiusType.NO,
dilate_radius=-0,
fill_holes=RadiusType.NO,
max_holes_size=0,
save_components=False,
clip_to_mask=False,
)
parameters1 = {
"channel": 0,
"minimum_size": 30,
"threshold": {
"name": "Manual",
"values": {
"threshold": 5
}
},
"noise_filtering": {
"name": "None",
"values": {}
},
"side_connection": False,
}
parameters2 = {
"channel": 1,
"minimum_size": 30,
"threshold": {
"name": "Manual",
"values": {
"threshold": 5
}
},
"noise_filtering": {
"name": "None",
"values": {}
},
"side_connection": False,
}
seg_profile1 = SegmentationProfile(name="Unknown",
algorithm="Lower threshold",
values=parameters1)
pipeline_element = SegmentationPipelineElement(
mask_property=prop1, segmentation=seg_profile1)
seg_profile2 = SegmentationProfile(name="Unknown",
algorithm="Lower threshold",
values=parameters2)
pipeline = SegmentationPipeline(name="test",
segmentation=seg_profile2,
mask_history=[pipeline_element])
result = calculate_pipeline(image=image,
mask=None,
pipeline=pipeline,
report_fun=empty)
result_segmentation = np.zeros((50, 100, 100), dtype=np.uint8)
result_segmentation[10:40, 20:80, 40:60] = 1
assert np.all(result.segmentation == result_segmentation)