def roi(self, val: Union[np.ndarray, ROIInfo]):
if val is None:
self._roi_info = ROIInfo(val)
self._additional_layers = {}
self.roi_clean.emit()
return
try:
if isinstance(val, np.ndarray):
self._roi_info = ROIInfo(self.image.fit_array_to_image(val))
else:
self._roi_info = val.fit_to_image(self.image)
except ValueError:
raise ValueError(ROI_NOT_FIT)
self._additional_layers = {}
self.roi_changed.emit(self._roi_info)
class ImageInfo:
image: Image
layers: List[NapariImage]
filter_info: List[Tuple[NoiseFilterType,
float]] = field(default_factory=list)
mask: Optional[Labels] = None
mask_array: Optional[np.ndarray] = None
roi: Optional[Labels] = None
roi_info: ROIInfo = field(default_factory=lambda: ROIInfo(None))
roi_count: int = 0
def coords_in(self, coords: Union[List[int], np.ndarray]) -> bool:
if not self.layers:
return False
fst_layer = self.layers[0]
moved_coords = self.translated_coords(coords)
return np.all(moved_coords >= 0) and np.all(
moved_coords < fst_layer.data.shape)
def translated_coords(self, coords: Union[List[int],
np.ndarray]) -> np.ndarray:
if not self.layers:
return np.array(coords)
fst_layer = self.layers[0]
return np.subtract(coords, fst_layer.translate_grid).astype(np.int)
def __init__(self):
super().__init__()
self._image: Optional[Image] = None
self._image_path = ""
self._roi_info = ROIInfo(None)
self._additional_layers = {}
self._parent: Optional[QWidget] = None
class ProjectInfoBase(Protocol):
"""
This is base protocol for Project Information.
:ivar str ~.file_path: path to current preceded file
:ivar Image ~.image: project image
:ivar numpy.ndarray ~.segmentation: numpy array representing current project ROI
:ivar SegmentationInfo ~.segmentation_info: segmentation metadata
:ivar Optional[numpy.ndarray] ~.mask: mask used in project
:ivar str errors: information about problems with current project
"""
file_path: str
image: Image
roi: np.ndarray
roi_info: ROIInfo = ROIInfo(None)
mask: Optional[np.ndarray]
errors: str = ""
def get_raw_copy(self):
"""
Create copy with only image
"""
raise NotImplementedError
def get_raw_mask_copy(self):
raise NotImplementedError
def is_raw(self):
raise NotImplementedError
def is_masked(self):
raise NotImplementedError
def _set_roi_info(self,
new_roi_info: ROIInfo,
save_chosen=True,
list_of_components=None,
segmentation_parameters=None):
if list_of_components is None:
list_of_components = []
if segmentation_parameters is None:
segmentation_parameters = defaultdict(lambda: None)
state = self.get_project_info()
try:
new_roi_info = new_roi_info.fit_to_image(self.image)
except ValueError:
raise ValueError("ROI do not fit to image")
if save_chosen:
state2 = self.transform_state(state, new_roi_info,
segmentation_parameters,
list_of_components, save_chosen)
self.chosen_components_widget.set_chose(
list(sorted(state2.roi_extraction_parameters.keys())),
state2.selected_components)
self.roi = state2.roi_info
self.components_parameters_dict = state2.roi_extraction_parameters
else:
selected_parameters = {
i: segmentation_parameters[i]
for i in new_roi_info.bound_info
}
self.chosen_components_widget.set_chose(
list(sorted(selected_parameters.keys())), list_of_components)
self.roi = new_roi_info
self.components_parameters_dict = segmentation_parameters
def analysis_project() -> ProjectTuple:
data = np.zeros((1, 50, 100, 100, 1), dtype=np.uint16)
data[0, 10:40, 10:40, 10:90] = 50
data[0, 10:40, 50:90, 10:90] = 50
data[0, 15:35, 15:35, 15:85] = 70
data[0, 15:35, 55:85, 15:85] = 60
data[0, 10:40, 40:50, 10:90] = 40
image = Image(
data, (10 / UNIT_SCALE[Units.nm.value], 5 / UNIT_SCALE[Units.nm.value], 5 / UNIT_SCALE[Units.nm.value]), ""
)
mask = data[0, ..., 0] > 0
roi = np.zeros(data.shape, dtype=np.uint8)
roi[data == 70] = 1
roi[data == 60] = 2
algorithm_parameters = {
"algorithm_name": "Lower Threshold",
"values": {
"threshold": 60,
"channel": 0,
"noise_filtering": {"name": "None", "values": {}},
"minimum_size": 10,
"side_connection": False,
},
}
roi_info = ROIInfo(roi.squeeze()).fit_to_image(image)
return ProjectTuple(
file_path="test_data.tiff",
image=image,
roi_info=roi_info,
additional_layers={"denoised image": AdditionalLayerDescription(data=roi, layer_type="layer")},
mask=mask,
algorithm_parameters=algorithm_parameters,
)
def compare_action(self):
if self.compare_btn.text() == "Compare":
self._settings.set_segmentation_to_compare(self._settings.roi_info)
self.compare_btn.setText("Remove")
else:
self._settings.set_segmentation_to_compare(ROIInfo(None))
self.compare_btn.setText("Compare")
def test_multiple_components(self, comp_num):
data = np.zeros((10 * comp_num, 10), dtype=np.uint8)
for i in range(comp_num):
data[i * 10 + 2:i * 10 + 8, 2:8] = i + 1
si = ROIInfo(data)
assert len(si.bound_info) == comp_num
assert set(si.bound_info.keys()) == set(range(1, comp_num + 1))
for i in range(comp_num):
assert np.all(si.bound_info[i + 1].lower == [i * 10 + 2, 2])
assert np.all(si.bound_info[i + 1].upper == [i * 10 + 7, 7])
assert len(si.sizes) == comp_num + 1
assert np.all(si.sizes[1:] == 36)
data[-1, 8] = 1
si = ROIInfo(data)
assert np.all(si.bound_info[1].lower == 2)
assert np.all(si.bound_info[1].upper == [10 * comp_num - 1, 8])
def get_roi_info_and_mask(self) -> Tuple[ROIInfo, Optional[np.ndarray]]:
self.arrays.seek(0)
seg = np.load(self.arrays)
self.arrays.seek(0)
alternative = {name: array for name, array in seg.items() if name not in {"roi", "mask"}}
roi_info = ROIInfo(seg["roi"], annotations=self.annotations, alternative=alternative)
mask = seg["mask"] if "mask" in seg else None
return roi_info, mask
def test_more_dims(self, dims):
si = ROIInfo(np.ones((10, ) * dims, dtype=np.uint8))
assert len(si.bound_info[1].lower) == dims
assert len(si.bound_info[1].upper) == dims
assert np.all(si.bound_info[1].lower == 0)
assert np.all(si.bound_info[1].upper == 9)
assert len(si.sizes) == 2
assert np.all(si.sizes == [0, 10**dims])
def stack_segmentation1(stack_image: MaskProjectTuple, mask_segmentation_parameters):
data = np.zeros([20, 40, 40], dtype=np.uint8)
for i, (x, y) in enumerate(itertools.product([0, 20], repeat=2), start=1):
data[1:-1, x + 2 : x + 18, y + 2 : y + 18] = i
data = ROIInfo(stack_image.image.fit_array_to_image(data))
parameters = {i: deepcopy(mask_segmentation_parameters) for i in range(1, 5)}
return dataclasses.replace(
stack_image, roi_info=data, roi_extraction_parameters=parameters, selected_components=[1, 3]
)
def test_mask(self, mask_prop):
mask = np.zeros((10, 10), dtype=np.uint8)
mask[1:-1, 1:-1] = 2
mask2 = np.copy(mask)
mask2[2:-2, 2:-2] = 4
roi_info = ROIInfo(mask2)
elem = HistoryElement.create(roi_info, mask, {}, mask_prop)
roi_info2, mask2 = elem.get_roi_info_and_mask()
assert np.all(mask == mask2)
assert np.all(roi_info.roi == roi_info2.roi)
def test_set_roi(self, qtbot, part_settings, image):
prop = ChannelProperty(part_settings, "test")
viewer = ResultImageView(part_settings, prop, "test")
qtbot.add_widget(prop)
qtbot.add_widget(viewer)
viewer.show()
part_settings.image = image
roi = ROIInfo((image.get_channel(0) > 0).astype(np.uint8))
roi = roi.fit_to_image(image)
viewer.set_roi(roi, image)
QCoreApplication.processEvents()
assert not viewer.roi_alternative_select.isVisible()
assert viewer.label1.isVisible()
assert viewer.label2.isVisible()
assert viewer.opacity.isVisible()
assert viewer.only_border.isVisible()
assert not viewer.roi_alternative_select.isVisible()
assert viewer.any_roi()
assert not viewer.available_alternatives()
viewer.hide()
def test_create(self, mask_prop):
roi_info = ROIInfo(np.zeros((10, 10), dtype=np.uint8))
elem = HistoryElement.create(roi_info, None, {}, mask_prop)
assert elem.mask_property == mask_prop
assert elem.roi_extraction_parameters == {}
param = {"a": 1, "b": 2}
elem2 = HistoryElement.create(roi_info, None, param, mask_prop)
assert elem2.roi_extraction_parameters == param
roi_info2, mask = elem2.get_roi_info_and_mask()
assert np.all(roi_info2.roi == 0)
assert mask is None
def image(self, value: Image):
if value is None:
return
self._image = value
if value.file_path is not None:
self.image_changed[str].emit(value.file_path)
self._image_changed()
self._roi_info = ROIInfo(None)
self.image_changed.emit(self._image)
self.image_changed[int].emit(self._image.channels)
def test_annotations(self, mask_prop):
data = np.zeros((10, 10), dtype=np.uint8)
data[1:5, 1:5] = 1
data[5:-1, 1:5] = 2
data[1:5, 5:-1] = 3
data[5:-1, 5:-1] = 4
annotations = {1: "a", 2: "b", 3: "c", 4: "d"}
roi_info = ROIInfo(data, annotations=annotations)
elem = HistoryElement.create(roi_info, None, {}, mask_prop)
roi_info2, mask2 = elem.get_roi_info_and_mask()
assert mask2 is None
assert np.all(roi_info2.roi == roi_info.roi)
def test_simple(self, num):
data = np.zeros((10, 10), dtype=np.uint8)
data[2:8, 2:8] = num
si = ROIInfo(data)
assert len(si.bound_info) == 1
assert num in si.bound_info
assert isinstance(si.bound_info[num], BoundInfo)
assert np.all(si.bound_info[num].lower == [2, 2])
assert np.all(si.bound_info[num].upper == [7, 7])
assert len(si.sizes) == num + 1
assert np.all(si.sizes[1:num] == 0)
assert si.sizes[num] == 36
def test_update_alternatives(self, base_settings, image_view, tmp_path):
roi = np.ones(base_settings.image.get_channel(0).shape, dtype=np.uint8)
roi[..., 1, 1] = 0
base_settings.roi = ROIInfo(
roi,
alternative={"test": np.ones(roi.shape, dtype=np.uint8) - roi})
image_view.update_roi_border()
assert np.all(image_view.image_info[str(tmp_path /
"test2.tiff")].roi.data == roi)
image_view.roi_alternative_selection = "test"
image_view.update_roi_border()
assert np.all(image_view.image_info[str(tmp_path /
"test2.tiff")].roi.data != roi)
def perform_roi_info_history_test(
self, project, save_path, mask_property, save_method: Type[SaveBase], load_method: Type[LoadBase]
):
alt1 = np.copy(project.roi_info.roi)
alt1[alt1 > 0] += 3
roi_info = ROIInfo(
roi=project.roi_info.roi, annotations={i: f"a{i}" for i in range(1, 5)}, alternative={"test": alt1}
)
history = []
for i in range(3):
alt2 = np.copy(alt1)
alt2[alt2 > 0] = i + 5
roi_info2 = ROIInfo(
roi=project.roi_info.roi,
annotations={i: f"a{i}_{j}" for j in range(1, 5)},
alternative={f"test{i}": alt2},
)
history.append(
HistoryElement.create(
roi_info2, alt1, {"algorithm_name": f"task_{i}", "values": {"a": 1}}, mask_property
)
)
proj = dataclasses.replace(project, roi_info=roi_info, history=history)
save_method.save(save_path / "data.tgz", proj, SaveROI.get_default_values())
proj2: ProjectInfoBase = load_method.load([save_path / "data.tgz"])
assert np.all(proj2.roi_info.roi == project.roi_info.roi)
assert set(proj2.roi_info.annotations) == {1, 2, 3, 4}
assert proj2.roi_info.annotations == {i: f"a{i}" for i in range(1, 5)}
assert "test" in proj2.roi_info.alternative
assert np.all(proj2.roi_info.alternative["test"] == alt1)
assert len(proj2.history) == 3
for i in range(3):
roi_info3, mask2 = proj2.history[i].get_roi_info_and_mask()
assert np.all(mask2 == alt1)
assert set(roi_info3.alternative) == {f"test{i}"}
assert np.all(roi_info3.alternative[f"test{i}"][alt1 > 0] == i + 5)
assert np.all(roi_info3.alternative[f"test{i}"][alt1 == 0] == 0)
assert roi_info3.annotations == {i: f"a{i}_{j}" for j in range(1, 5)}
assert proj2.history[i].roi_extraction_parameters == {"algorithm_name": f"task_{i}", "values": {"a": 1}}
def test_compare_view(part_settings, image2, qtbot):
prop = ChannelProperty(part_settings, "test")
view = CompareImageView(part_settings, prop, "test")
qtbot.add_widget(prop)
qtbot.add_widget(view)
part_settings.image = image2
roi = np.ones(image2.get_channel(0).shape, dtype=np.uint8)
assert view.image_info[str(image2.file_path)].roi is None
part_settings.roi = roi
assert view.image_info[str(image2.file_path)].roi is None
part_settings.set_segmentation_to_compare(ROIInfo(roi))
assert view.image_info[str(image2.file_path)].roi is not None
def perform_roi_info_test(self, project, save_path, save_method: Type[SaveBase], load_method: Type[LoadBase]):
alt1 = np.copy(project.roi_info.roi)
alt1[alt1 > 0] += 3
roi_info = ROIInfo(
roi=project.roi_info.roi, annotations={i: f"a{i}" for i in range(1, 5)}, alternative={"test": alt1}
)
proj = dataclasses.replace(project, roi_info=roi_info)
save_method.save(save_path / "data.tgz", proj, SaveROI.get_default_values())
proj2 = load_method.load([save_path / "data.tgz"])
assert np.all(proj2.roi_info.roi == project.roi_info.roi)
assert set(proj2.roi_info.annotations) == {1, 2, 3, 4}
assert proj2.roi_info.annotations == {i: f"a{i}" for i in range(1, 5)}
assert "test" in proj2.roi_info.alternative
assert np.all(proj2.roi_info.alternative["test"] == alt1)
def prev_mask(self):
history: HistoryElement = self.settings.history_pop()
history.arrays.seek(0)
seg = np.load(history.arrays)
history.arrays.seek(0)
# TODO Check me
# self.settings.roi = seg["segmentation"]
self.settings._set_roi_info( # pylint: disable=W0212
ROIInfo(seg["segmentation"]),
False,
history.roi_extraction_parameters["selected"],
history.roi_extraction_parameters["parameters"],
)
self.settings.mask = seg["mask"] if "mask" in seg else None
self.close()
def test_transform_state_simple(self, stack_image):
roi = np.zeros(stack_image.image.get_channel(0).shape, dtype=np.uint8)
roi[0, 1, 2:-2, 2:-2] = 1
roi[0, 2, 2:-2, 2:-2] = 2
roi[0, 3, 2:-2, 2:-2] = 3
roi_info = ROIInfo(roi).fit_to_image(stack_image.image)
roi_extraction_parameters = defaultdict(lambda: ROIExtractionProfile("aa", "aa", {1: "aa"}))
new_state = StackSettings.transform_state(
state=stack_image,
new_roi_info=roi_info,
new_roi_extraction_parameters=roi_extraction_parameters,
list_of_components=[2],
)
assert len(new_state.roi_extraction_parameters) == 3
assert new_state.selected_components == [2]
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 set_segmentation_result(self, result: SegmentationResult):
if result.info_text and self._parent is not None:
QMessageBox().information(self._parent, "Algorithm info", result.info_text)
self._additional_layers = result.additional_layers
self.last_executed_algorithm = result.parameters.algorithm
self.set(f"algorithms.{result.parameters.algorithm}", result.parameters.values)
try:
roi = self.image.fit_array_to_image(result.roi)
alternative_list = {
k: self.image.fit_array_to_image(v) for k, v in result.alternative_representation.items()
}
except ValueError:
raise ValueError("roi do not fit to image")
self._roi_info = ROIInfo(roi, result.roi_annotation, alternative_list)
self.roi_changed.emit(self._roi_info)
def save(
cls,
save_location: typing.Union[str, BytesIO, Path],
project_info: ProjectTuple,
parameters: dict,
range_changed=None,
step_changed=None,
):
if not os.path.exists(save_location):
os.makedirs(save_location)
if not os.path.isdir(save_location):
raise OSError("save location exist and is not a directory")
parameters = deepcopy(parameters)
if parameters["clip"]:
points = np.nonzero(project_info.roi_info.roi)
lower_bound = np.min(points, axis=1)
lower_bound = np.max([lower_bound - 3, [0, 0, 0]], axis=0)
upper_bound = np.max(points, axis=1)
upper_bound = np.max(
[upper_bound + 3,
np.array(project_info.roi_info.roi) - 1],
axis=0)
cut_area = tuple(
slice(x, y) for x, y in zip(lower_bound, upper_bound))
# WARNING time
image = project_info.image.cut_image((slice(None), ) + cut_area)
roi_info = ROIInfo(project_info.roi_info.roi[cut_area])
mask = project_info.mask[cut_area] if project_info.mask else None
project_info = dataclasses.replace(project_info,
image=image,
roi_info=roi_info,
mask=mask)
parameters["clip"] = False
parameters.update({"separated_objects": False})
SaveCmap.save(os.path.join(save_location, "density.cmap"),
project_info, parameters, range_changed, step_changed)
parameters.update({"separated_objects": True})
SaveCmap.save(os.path.join(save_location, "density.cmap"),
project_info, parameters, range_changed, step_changed)
SaveROIAsTIFF.save(os.path.join(save_location, "segmentation.tiff"),
project_info, {}, range_changed, step_changed)
SaveROIAsNumpy.save(os.path.join(save_location, "segmentation.npy"),
project_info, {}, range_changed, step_changed)
def test_additional(self, mask_prop):
data = np.zeros((10, 10), dtype=np.uint8)
data[1:-1, 1:-1] = 2
alternative = {}
for i in range(4):
alternative_array = np.copy(data)
alternative_array[alternative_array > 0] = i + 2
alternative[f"add{i}"] = alternative_array
roi_info = ROIInfo(data, alternative=alternative)
elem = HistoryElement.create(roi_info, None, {}, mask_prop)
roi_info2, mask2 = elem.get_roi_info_and_mask()
assert np.all(roi_info2.roi == roi_info.roi)
assert mask2 is None
assert len(roi_info.alternative) == 4
assert set(roi_info.alternative) == {f"add{i}" for i in range(4)}
for i in range(4):
arr = roi_info.alternative[f"add{i}"]
assert np.all(arr[arr > 0] == i + 2)
def analysis_project_reversed() -> ProjectTuple:
data = np.zeros((1, 50, 100, 100, 1), dtype=np.uint16)
data[0, 10:40, 10:40, 10:90] = 50
data[0, 10:40, 50:90, 10:90] = 50
data[0, 15:35, 15:35, 15:85] = 70
data[0, 15:35, 55:85, 15:85] = 60
data[0, 10:40, 40:50, 10:90] = 40
mask = data[0] > 0
roi = np.zeros(data.shape, dtype=np.uint8)
roi[data == 70] = 1
roi[data == 60] = 2
data = 100 - data
image = Image(
data, (10 / UNIT_SCALE[Units.nm.value], 5 / UNIT_SCALE[Units.nm.value], 5 / UNIT_SCALE[Units.nm.value]), ""
)
roi_info = ROIInfo(roi.squeeze()).fit_to_image(image)
return ProjectTuple("test_data.tiff", image, roi_info=roi_info, mask=mask)
def test_image_settings(self, tmp_path, image, roi, qtbot):
widget = base_settings.QWidget()
qtbot.addWidget(widget)
settings = base_settings.ImageSettings()
settings.set_parent(widget)
assert settings.image_spacing == ()
assert settings.image_shape == ()
assert settings.image_path == ""
assert settings.channels == 0
settings.image = image
assert settings.image_spacing == image.spacing
assert settings.image_path == str(tmp_path / "test.tiff")
assert settings.image_shape == (1, 1, 10, 10, 2)
with qtbot.waitSignal(settings.image_spacing_changed):
settings.image_spacing = (7, 7)
assert image.spacing == (7, 7)
with qtbot.waitSignal(settings.image_spacing_changed):
settings.image_spacing = (1, 8, 8)
assert image.spacing == (8, 8)
with pytest.raises(ValueError):
settings.image_spacing = (6, )
assert settings.is_image_2d()
assert settings.has_channels
with qtbot.waitSignal(settings.image_changed[str]):
settings.image_path = str(tmp_path / "test2.tiff")
assert image.file_path == str(tmp_path / "test2.tiff")
with pytest.raises(ValueError, match="roi do not fit to image"):
settings.roi = roi[:-2]
with qtbot.waitSignal(settings.roi_changed):
settings.roi = roi
assert all(settings.sizes == [64, 18, 18])
assert all(settings.components_mask() == [0, 1, 1])
assert tuple(settings.roi_info.bound_info.keys()) == (1, 2)
with qtbot.waitSignal(settings.roi_clean):
settings.roi = None
settings.roi = ROIInfo(roi)
settings.image = None
assert settings.image is not None
def test_with_roi_alternatives(self, part_settings, image2, qtbot):
prop = ChannelProperty(part_settings, "test")
view = ResultImageView(part_settings, prop, "test")
qtbot.add_widget(prop)
qtbot.add_widget(view)
part_settings.image = image2
view.show()
roi = np.ones(image2.get_channel(0).shape, dtype=np.uint8)
part_settings.roi = roi
assert not view.roi_alternative_select.isVisible()
part_settings.roi = ROIInfo(roi, alternative={"t1": roi, "t2": roi})
assert view.roi_alternative_select.isVisible()
assert view.roi_alternative_select.count() == 3
assert view.roi_alternative_select.currentText() == "ROI"
view.roi_alternative_select.setCurrentText("t1")
view.hide()
