def calculate_pipeline(image: Image, mask: typing.Optional[np.ndarray],
pipeline: SegmentationPipeline, report_fun):
history = []
report_fun("max", 2 * len(pipeline.mask_history) + 1)
for i, el in enumerate(pipeline.mask_history):
result, _ = calculate_segmentation_step(el.segmentation, image, mask)
segmentation = image.fit_array_to_image(result.roi)
report_fun("step", 2 * i + 1)
new_mask = calculate_mask(
mask_description=el.mask_property,
segmentation=segmentation,
old_mask=mask,
spacing=image.spacing,
time_axis=image.time_pos,
)
segmentation_parameters = {
"algorithm_name": el.segmentation.name,
"values": el.segmentation.values
}
history.append(
HistoryElement.create(segmentation, mask, segmentation_parameters,
el.mask_property))
report_fun("step", 2 * i + 2)
mask = image.fit_array_to_image(new_mask)
result, text = calculate_segmentation_step(pipeline.segmentation, image,
mask)
report_fun("step", 2 * len(pipeline.mask_history) + 1)
return PipelineResult(result.roi, result.additional_layers, mask, history,
text)
def save_components(
image: Image,
components: list,
segmentation: np.ndarray,
dir_path: str,
segmentation_info: typing.Optional[SegmentationInfo] = None,
range_changed=None,
step_changed=None,
):
if range_changed is None:
range_changed = empty_fun
if step_changed is None:
step_changed = empty_fun
segmentation = image.fit_array_to_image(segmentation)
if segmentation_info is None:
segmentation_info = SegmentationInfo(segmentation)
os.makedirs(dir_path, exist_ok=True)
file_name = os.path.splitext(os.path.basename(image.file_path))[0]
range_changed(0, 2 * len(components))
for i in components:
slices = segmentation_info.bound_info[i].get_slices()
cut_segmentation = segmentation[tuple(slices)]
cut_image = image.cut_image(slices)
im = cut_image.cut_image(cut_segmentation == i, replace_mask=True)
# print(f"[run] {im}")
ImageWriter.save(
im, os.path.join(dir_path, f"{file_name}_component{i}.tif"))
step_changed(2 * i + 1)
ImageWriter.save_mask(
im, os.path.join(dir_path, f"{file_name}_component{i}_mask.tif"))
step_changed(2 * i + 2)
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
segmentation = np.zeros(data.shape, dtype=np.uint8)
segmentation[data == 70] = 1
segmentation[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,
},
}
segmentation = image.fit_array_to_image(segmentation.squeeze())
return ProjectTuple(
file_path="test_data.tiff",
image=image,
roi=segmentation,
additional_layers={"denoised image": AdditionalLayerDescription(data=segmentation, layer_type="layer")},
mask=mask,
algorithm_parameters=algorithm_parameters,
)
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
segmentation = np.zeros(data.shape, dtype=np.uint8)
segmentation[data == 70] = 1
segmentation[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]), ""
)
segmentation = image.fit_array_to_image(segmentation.squeeze())
return ProjectTuple("test_data.tiff", image, segmentation, mask=mask)
