def test_save_segmentation(self, tmpdir, data_test_dir):
seg = LoadROIImage.load(
[os.path.join(data_test_dir, "test_nucleus_1_1.seg")], metadata={"default_spacing": (1, 1, 1)}
)
SaveROI.save(os.path.join(tmpdir, "segmentation.seg"), seg, {"relative_path": False})
assert os.path.exists(os.path.join(tmpdir, "segmentation.seg"))
os.makedirs(os.path.join(tmpdir, "seg_save"))
save_components(
seg.image,
seg.selected_components,
os.path.join(tmpdir, "seg_save"),
seg.roi_info,
SaveComponents.get_default_values(),
)
assert os.path.isdir(os.path.join(tmpdir, "seg_save"))
assert len(glob(os.path.join(tmpdir, "seg_save", "*"))) == 4
seg2 = LoadROI.load([os.path.join(tmpdir, "segmentation.seg")])
assert seg2 is not None
save_components(
seg.image,
[],
os.path.join(tmpdir, "seg_save2"),
seg.roi_info,
SaveComponents.get_default_values(),
)
assert os.path.isdir(os.path.join(tmpdir, "seg_save2"))
assert len(glob(os.path.join(tmpdir, "seg_save2", "*"))) == 8
def napari_write_labels(path: str, data: Any, meta: dict) -> Optional[str]:
if not isinstance(data, numpy.ndarray):
return
ext = os.path.splitext(path)[1]
if ext in SaveROI.get_extensions():
project = MaskProjectTuple(file_path="", image=None, roi=data)
SaveROI.save(path, project, parameters={})
return path
def test_save_segmentation_without_image(self, tmpdir, data_test_dir):
seg = LoadROIImage.load(
[os.path.join(data_test_dir, "test_nucleus_1_1.seg")], metadata={"default_spacing": (1, 1, 1)}
)
seg_clean = dataclasses.replace(seg, image=None, roi=reduce_array(seg.roi))
SaveROI.save(os.path.join(tmpdir, "segmentation.seg"), seg_clean, {"relative_path": False})
SaveROI.save(
os.path.join(tmpdir, "segmentation1.seg"),
seg_clean,
{"relative_path": False, "spacing": (210 * 10 ** -6, 70 * 10 ** -6, 70 * 10 ** -6)},
)
def test_save_project_with_history(self, tmp_path, stack_segmentation1, mask_property):
SaveROI.save(tmp_path / "test1.seg", stack_segmentation1, {"relative_path": False})
seg2 = dataclasses.replace(
stack_segmentation1,
history=[create_history_element_from_segmentation_tuple(stack_segmentation1, mask_property)],
selected_components=[1],
mask=stack_segmentation1.roi,
)
SaveROI.save(tmp_path / "test1.seg", seg2, {"relative_path": False})
with tarfile.open(tmp_path / "test1.seg", "r") as tf:
tf.getmember("mask.tif")
tf.getmember("segmentation.tif")
tf.getmember("history/history.json")
tf.getmember("history/arrays_0.npz")
def run_calculation(self):
while not self.queue.empty():
task: BatchTask = self.queue.get()
if isinstance(task.data, str):
file_path = task.data
if path.splitext(task.data)[1] == ".seg":
project_tuple = LoadROIImage.load([task.data])
else:
project_tuple = LoadStackImage.load([task.data])
elif isinstance(task.data, MaskProjectTuple):
project_tuple: MaskProjectTuple = task.data
file_path = project_tuple.image.file_path
else:
continue
try:
name = path.basename(file_path)
blank = get_mask(project_tuple.roi, project_tuple.mask, project_tuple.selected_components)
algorithm: StackAlgorithm = mask_algorithm_dict[task.parameters.algorithm]()
algorithm.set_image(project_tuple.image)
algorithm.set_mask(blank)
algorithm.set_parameters(**task.parameters.values)
if isinstance(task.save_prefix, tuple):
self.range_signal.emit(0, algorithm.get_steps_num() + 1)
else:
self.range_signal.emit(0, algorithm.get_steps_num())
# noinspection PyTypeChecker
segmentation = algorithm.calculation_run(partial(self.progress_info, name))
state2 = StackSettings.transform_state(
project_tuple, segmentation.roi_info, defaultdict(lambda: segmentation.parameters), []
)
if isinstance(task.save_prefix, tuple):
self.progress_info(name, "saving", algorithm.get_steps_num())
name = path.splitext(path.basename(file_path))[0] + ".seg"
re_end = re.compile(r"(.*_version)(\d+)\.seg$")
while path.exists(path.join(task.save_prefix[0], name)):
match = re_end.match(name)
if match:
num = int(match.group(2)) + 1
name = match.group(1) + str(num) + ".seg"
else:
name = path.splitext(path.basename(file_path))[0] + "_version1.seg"
SaveROI.save(path.join(task.save_prefix[0], name), state2, parameters=task.save_prefix[1])
else:
self.multiple_result.emit(state2)
except Exception as e: # pylint: disable=W0703
self.error_signal.emit(f"Exception occurred during proceed {file_path}. Exception info {e}")
self.index += 1
self.index = 0
self.execution_done.emit()
def napari_write_labels(path: str, data: Any, meta: dict) -> Optional[str]:
if not isinstance(data, numpy.ndarray):
return
ext = os.path.splitext(path)[1]
if ext in SaveROI.get_extensions():
project = MaskProjectTuple(file_path="",
image=None,
roi_info=ROIInfo(data))
SaveROI.save(path,
project,
parameters={
"spacing":
numpy.divide(meta["scale"], DEFAULT_SCALE_FACTOR)[-3:]
})
return path
def test_loading_new_segmentation(self, tmpdir, data_test_dir):
image_data = LoadStackImage.load([os.path.join(data_test_dir, "test_nucleus.tif")])
algorithm = ThresholdAlgorithm()
algorithm.set_image(image_data.image)
param = algorithm.get_default_values()
param["channel"] = 0
algorithm.set_parameters(**param)
res = algorithm.calculation_run(lambda x, y: None)
num = np.max(res.roi) + 1
data_dict = {str(i): deepcopy(res.parameters) for i in range(1, num)}
to_save = MaskProjectTuple(
image_data.image.file_path, image_data.image, None, res.roi, list(range(1, num)), data_dict
)
SaveROI.save(os.path.join(tmpdir, "segmentation2.seg"), to_save, {"relative_path": False})
seg2 = LoadSegmentation.load([os.path.join(tmpdir, "segmentation2.seg")])
assert seg2 is not None
def test_load_project_with_history(self, tmp_path, stack_segmentation1, mask_property):
image_location = tmp_path / "test1.tif"
SaveAsTiff.save(image_location, stack_segmentation1)
seg2 = dataclasses.replace(
stack_segmentation1,
history=[create_history_element_from_segmentation_tuple(stack_segmentation1, mask_property)],
selected_components=[1],
mask=stack_segmentation1.roi,
image=stack_segmentation1.image.substitute(file_path=image_location),
file_path=image_location,
)
SaveROI.save(tmp_path / "test1.seg", seg2, {"relative_path": False})
res = LoadSegmentation.load([tmp_path / "test1.seg"])
assert res.image == str(image_location)
assert res.mask is not None
assert len(res.history) == 1
assert res.history[0].mask_property == mask_property
cmp_dict = {str(k): v for k, v in stack_segmentation1.roi_extraction_parameters.items()}
assert str(res.history[0].segmentation_parameters["parameters"]) == str(cmp_dict)