def test_store_posteriors_nifti_invalid_entries(test_output_dirs: TestOutputDirectories) -> None:
image = np.array([0, 1, 2.71, np.nan])
header = ImageHeader(origin=(1, 1, 1), direction=(1, 0, 0, 1, 0, 0, 1, 0, 0), spacing=(1, 1, 1))
with pytest.raises(ValueError) as ex:
io_util.store_posteriors_as_nifti(image, header,
test_output_dirs.create_file_or_folder_path(default_image_name))
assert "invalid values" in ex.value.args[0]
assert "2.71" in ex.value.args[0]
assert "nan" in ex.value.args[0]
def test_store_posteriors_nifti_fail(test_output_dirs: TestOutputDirectories,
image: Any) -> None:
image = np.array(image)
header = ImageHeader(origin=(1, 1, 1),
direction=(1, 0, 0, 1, 0, 0, 1, 0, 0),
spacing=(1, 1, 1))
with pytest.raises(Exception):
io_util.store_posteriors_as_nifti(
image, header,
test_output_dirs.create_file_or_folder_path(default_image_name))
def test_store_posteriors_nifti(test_output_dirs: TestOutputDirectories,
image: Any, expected: Any) -> None:
image = np.array(image)
header = ImageHeader(origin=(1, 1, 1),
direction=(1, 0, 0, 0, 1, 0, 0, 0, 1),
spacing=(1, 1, 1))
io_util.store_posteriors_as_nifti(
image, header,
test_output_dirs.create_file_or_folder_path(default_image_name))
assert_nifti_content(
test_output_dirs.create_file_or_folder_path(default_image_name),
image.shape, header, list(expected), np.ubyte)
def store_inference_results(inference_result: InferencePipeline.Result,
config: SegmentationModelBase,
results_folder: Path,
image_header: ImageHeader) -> List[str]:
"""
Store the segmentation, posteriors, and binary predictions into Nifti files.
:param inference_result: The inference result for a given patient_id and epoch. Posteriors must be in
(Classes x Z x Y x X) shape, segmentation in (Z, Y, X)
:param config: The test configurations.
:param results_folder: The folder where the prediction should be stored.
:param image_header: The image header that was used in the input image.
"""
def create_file_path(_results_folder: Path, _file_name: str) -> Path:
"""
Create filename with Nifti extension
:param _results_folder: The results folder
:param _file_name: The name of the file
:return: A full path to the results folder for the file
"""
file_path = _file_name + MedicalImageFileType.NIFTI_COMPRESSED_GZ.value
return _results_folder / Path(file_path)
# create the directory for the given patient inside the results dir
patient_results_folder = get_patient_results_folder(
results_folder, inference_result.patient_id)
patient_results_folder.mkdir(exist_ok=True, parents=True)
# write the segmentations to disk
image_paths = [
io_util.store_as_ubyte_nifti(image=inference_result.segmentation,
header=image_header,
file_name=str(
create_file_path(
patient_results_folder,
"segmentation")))
]
class_names_and_indices = config.class_and_index_with_background().items()
binaries = binaries_from_multi_label_array(inference_result.segmentation,
config.number_of_classes)
# rescale posteriors if required and save them
for (class_name, index), binary in zip(class_names_and_indices, binaries):
posterior = inference_result.posteriors[index, ...]
# save the posterior map
file_name = "posterior_{}".format(class_name)
image_path = io_util.store_posteriors_as_nifti(
image=posterior,
header=image_header,
file_name=str(create_file_path(patient_results_folder, file_name)))
image_paths.append(image_path)
# save the binary mask
image_path = io_util.store_binary_mask_as_nifti(
image=binary,
header=image_header,
file_name=str(create_file_path(patient_results_folder,
class_name)))
image_paths.append(image_path)
# rescale and store uncertainty map as nifti
image_path = io_util.store_posteriors_as_nifti(
image=inference_result.uncertainty,
header=image_header,
file_name=str(create_file_path(patient_results_folder, "uncertainty")))
image_paths.append(image_path)
return image_paths
