def store_image_as_short_nifti(image: np.ndarray, header: ImageHeader,
file_name: PathOrString,
args: Optional[SegmentationModelBase]) -> Path:
"""
Saves an image in nifti format as ubyte, and performs the following operations:
1) transpose the image back into X,Y,Z from Z,Y,X
2) perform linear scaling from config.output_range to window level range, or
byte range (0,255) if norm_method is not CT Window and scale is True
3) cast the image values to ubyte before saving
:param image: 3D image in shape: Z x Y x X.
:param header: ImageHeader
:param file_name: The name of the file for this image.
:param args: The model config.
:return: the path to the saved image
"""
if args is not None and args.norm_method == PhotometricNormalizationMethod.CtWindow:
output_range = get_range_for_window_level(args.level, args.window)
return store_as_nifti(image=image,
header=header,
file_name=file_name,
image_type=np.short,
scale=True,
input_range=args.output_range,
output_range=output_range)
# All normalization apart from CT Window can't be easily undone. The image should be somewhere in the
# range -1 to 1, scale up by a factor of 1000 so that we can use all of the np.short range.
return store_as_nifti(image=image * 1000,
header=header,
file_name=file_name,
image_type=np.short)
def test_store_image_as_short_nifti(test_output_dirs: TestOutputDirectories,
norm_method: PhotometricNormalizationMethod,
image_range: Any,
window_level: Any) -> None:
window, level = window_level if window_level else (400, 0)
image = np.random.random_sample((1, 2, 3))
image_shape = image.shape
args = SegmentationModelBase(norm_method=norm_method, window=window, level=level, should_validate=False)
# Get integer values that are in the image range
image1 = LinearTransform.transform(data=image, input_range=(0, 1), output_range=args.output_range)
image = image1.astype(np.short) # type: ignore
header = ImageHeader(origin=(1, 1, 1), direction=(1, 0, 0, 0, 1, 0, 0, 0, 1), spacing=(1, 1, 1))
nifti_name = test_output_dirs.create_file_or_folder_path(default_image_name)
io_util.store_image_as_short_nifti(image, header, nifti_name, args)
if norm_method == PhotometricNormalizationMethod.CtWindow:
output_range = get_range_for_window_level(args.level, args.window)
image = LinearTransform.transform(data=image, input_range=args.output_range, output_range=output_range)
image = image.astype(np.short)
else:
image = image * 1000
t = np.unique(image)
assert_nifti_content(nifti_name, image_shape, header, list(t), np.short)