def image_filter_wrapper(*args, **kwargs):
have_array_input = False
have_xarray_input = False
args_list = list(args)
for index, arg in enumerate(args):
if _HAVE_XARRAY and isinstance(arg, xr.DataArray):
have_xarray_input = True
image = itk.image_from_xarray(arg)
args_list[index] = image
elif is_arraylike(arg):
have_array_input = True
array = np.asarray(arg)
image = itk.image_view_from_array(array)
args_list[index] = image
potential_image_input_kwargs = ('input', 'input1', 'input2', 'input3')
for key, value in kwargs.items():
if (key.lower() in potential_image_input_kwargs
or "image" in key.lower()):
if _HAVE_XARRAY and isinstance(value, xr.DataArray):
have_xarray_input = True
image = itk.image_from_xarray(value)
kwargs[key] = image
elif is_arraylike(value):
have_array_input = True
array = np.asarray(value)
image = itk.image_view_from_array(array)
kwargs[key] = image
if have_xarray_input or have_array_input:
# Convert output itk.Image's to numpy.ndarray's
output = image_filter(*tuple(args_list), **kwargs)
if isinstance(output, tuple):
output_list = list(output)
for index, value in output_list:
if isinstance(value, itk.Image):
if have_xarray_input:
data_array = itk.xarray_from_image(value)
output_list[index] = data_array
else:
array = itk.array_from_image(value)
output_list[index] = array
return tuple(output_list)
else:
if isinstance(output, itk.Image):
if have_xarray_input:
output = itk.xarray_from_image(output)
else:
output = itk.array_from_image(output)
return output
else:
return image_filter(*args, **kwargs)
def nifti_to_zarr_ngff(nifti_file: str) -> str:
"""Convert the nifti file on disk to a Zarr NGFF store.
The Zarr store will have the same path with '.zarr' appended.
If the store already exists, it will not be re-created.
"""
import itk
import spatial_image_multiscale
import spatial_image_ngff
import zarr
store_path = convert_to_store_path(nifti_file)
if store_path.exists():
return store_path
image = itk.imread(str(nifti_file))
da = itk.xarray_from_image(image)
da.name = 'image'
scale_factors = [2, 2, 2, 2]
multiscale = spatial_image_multiscale.to_multiscale(da, scale_factors)
store_path = Path(str(nifti_file) + '.zarr')
store = zarr.NestedDirectoryStore(str(nifti_file) + '.zarr')
spatial_image_ngff.imwrite(multiscale, store)
# celery tasks must return a serializable type; using string here
return str(store_path)
def nifti_to_zarr_ngff(nifti_file: Union[str, Path]) -> Path:
"""Convert the nifti file on disk to a Zarr NGFF store.
The Zarr store will have the same path with '.zarr' appended.
If the store already exists, it will not be re-created.
"""
store_path = Path(str(nifti_file) + '.zarr')
if store_path.exists():
return store_path
image = itk.imread(str(nifti_file))
da = itk.xarray_from_image(image)
da.name = 'image'
scale_factors = [2, 2, 2, 2]
multiscale = spatial_image_multiscale.to_multiscale(da, scale_factors)
store_path = Path(str(nifti_file) + '.zarr')
store = zarr.NestedDirectoryStore(str(nifti_file) + '.zarr')
spatial_image_ngff.imwrite(multiscale, store)
return store_path
# xarray conversion
try:
import xarray as xr
print("Testing xarray conversion")
image = itk.imread(filename)
image.SetSpacing((0.1, 0.2))
image.SetOrigin((30.0, 44.0))
theta = np.radians(30)
cosine = np.cos(theta)
sine = np.sin(theta)
rotation = np.array(((cosine, -sine), (sine, cosine)))
image.SetDirection(rotation)
data_array = itk.xarray_from_image(image)
assert data_array.dims[0] == "y"
assert data_array.dims[1] == "x"
assert data_array.dims[2] == "c"
assert np.array_equal(data_array.values, itk.array_from_image(image))
assert len(data_array.coords["x"]) == 256
assert len(data_array.coords["y"]) == 256
assert len(data_array.coords["c"]) == 3
assert data_array.coords["x"][0] == 30.0
assert data_array.coords["x"][1] == 30.1
assert data_array.coords["y"][0] == 44.0
assert data_array.coords["y"][1] == 44.2
assert data_array.coords["c"][0] == 0
assert data_array.coords["c"][1] == 1
assert data_array.attrs["direction"][0, 0] == cosine
assert data_array.attrs["direction"][0, 1] == sine
# xarray conversion
try:
import xarray as xr
import numpy as np
print('Testing xarray conversion')
image = itk.imread(filename)
image.SetSpacing((0.1, 0.2))
image.SetOrigin((30., 44.))
theta = np.radians(30)
cosine = np.cos(theta)
sine = np.sin(theta)
rotation = np.array(((cosine, -sine), (sine, cosine)))
image.SetDirection(rotation)
data_array = itk.xarray_from_image(image)
assert data_array.dims[0] == 'y'
assert data_array.dims[1] == 'x'
assert data_array.dims[2] == 'c'
assert np.array_equal(data_array.values, itk.array_from_image(image))
assert len(data_array.coords['x']) == 256
assert len(data_array.coords['y']) == 256
assert len(data_array.coords['c']) == 3
assert data_array.coords['x'][0] == 30.0
assert data_array.coords['x'][1] == 30.1
assert data_array.coords['y'][0] == 44.0
assert data_array.coords['y'][1] == 44.2
assert data_array.coords['c'][0] == 0
assert data_array.coords['c'][1] == 1
assert data_array.attrs['direction'][0, 0] == cosine
assert data_array.attrs['direction'][0, 1] == sine
added = itk.add_image_filter(array1, array2)
assert isinstance(added, np.ndarray)
assert np.all(added == 3)
added = itk.add_image_filter(Input1=array1, Input2=array2)
assert isinstance(added, np.ndarray)
assert np.all(added == 3)
# support kwargs with "image" in the name
masked = itk.mask_image_filter(array1, mask_image=array2)
try:
import xarray as xr
image1 = itk.image_from_array(array1)
data_array1 = itk.xarray_from_image(image1)
image2 = itk.image_from_array(array2)
data_array2 = itk.xarray_from_image(image2)
added = itk.add_image_filter(data_array1, data_array2)
assert isinstance(added, xr.DataArray)
assert np.all(added == 3)
added = itk.add_image_filter(Input1=data_array1, Input2=data_array2)
assert isinstance(added, xr.DataArray)
assert np.all(added == 3)
# support kwargs with "image" in the name
masked = itk.mask_image_filter(data_array1, mask_image=data_array2)
except ImportError:
# Could not import xarray
def image_filter_wrapper(*args, **kwargs):
have_array_input = False
have_xarray_input = False
have_torch_input = False
args_list = list(args)
for index, arg in enumerate(args):
if _HAVE_XARRAY and isinstance(arg, xr.DataArray):
have_xarray_input = True
image = itk.image_from_xarray(arg)
args_list[index] = image
elif _HAVE_TORCH and isinstance(arg, torch.Tensor):
have_torch_input = True
image = itk.image_view_from_array(np.asarray(arg))
args_list[index] = image
elif not isinstance(arg, itk.Object) and is_arraylike(arg):
have_array_input = True
array = np.asarray(arg)
image = itk.image_view_from_array(array)
args_list[index] = image
potential_image_input_kwargs = ("input", "input1", "input2", "input3")
for key, value in kwargs.items():
if key.lower(
) in potential_image_input_kwargs or "image" in key.lower():
if _HAVE_XARRAY and isinstance(value, xr.DataArray):
have_xarray_input = True
image = itk.image_from_xarray(value)
kwargs[key] = image
elif _HAVE_TORCH and isinstance(value, torch.Tensor):
have_torch_input = True
image = itk.image_view_from_array(np.asarray(value))
kwargs[key] = image
elif not isinstance(value, itk.Object) and is_arraylike(value):
have_array_input = True
array = np.asarray(value)
image = itk.image_view_from_array(array)
kwargs[key] = image
if have_xarray_input or have_torch_input or have_array_input:
# Convert output itk.Image's to numpy.ndarray's
output = image_filter(*tuple(args_list), **kwargs)
if isinstance(output, tuple):
output_list = list(output)
for index, value in enumerate(output_list):
if isinstance(value, itk.Image):
if have_xarray_input:
data_array = itk.xarray_from_image(value)
output_list[index] = data_array
elif have_torch_input:
data_array = itk.array_view_from_image(value)
torch_tensor = torch.from_numpy(data_array)
output_list[index] = torch_tensor
else:
array = itk.array_view_from_image(value)
output_list[index] = array
return tuple(output_list)
else:
if isinstance(output, itk.Image):
if have_xarray_input:
output = itk.xarray_from_image(output)
elif have_torch_input:
output = itk.array_view_from_image(output)
output = torch.from_numpy(output)
else:
output = itk.array_view_from_image(output)
return output
else:
return image_filter(*args, **kwargs)
def image_filter_wrapper(*args, **kwargs):
have_array_input = False
have_xarray_input = False
have_torch_input = False
args_list = list(args)
for index, arg in enumerate(args):
if _HAVE_XARRAY and isinstance(arg, xr.DataArray):
have_xarray_input = True
image = itk.image_from_xarray(arg)
args_list[index] = image
elif _HAVE_TORCH and isinstance(arg, torch.Tensor):
have_torch_input = True
channels = arg.shape[0] # assume first dimension is channels
arr = np.asarray(arg)
if channels > 1: # change from contiguous to interleaved channel order
arr = move_last_dimension_to_first(arr)
image = itk.image_view_from_array(arr, is_vector=channels > 1)
args_list[index] = image
elif not isinstance(arg, itk.Object) and is_arraylike(arg):
have_array_input = True
array = np.asarray(arg)
image = itk.image_view_from_array(array)
args_list[index] = image
potential_image_input_kwargs = ("input", "input1", "input2", "input3")
for key, value in kwargs.items():
if key.lower(
) in potential_image_input_kwargs or "image" in key.lower():
if _HAVE_XARRAY and isinstance(value, xr.DataArray):
have_xarray_input = True
image = itk.image_from_xarray(value)
kwargs[key] = image
elif _HAVE_TORCH and isinstance(value, torch.Tensor):
have_torch_input = True
channels = value.shape[
0] # assume first dimension is channels
arr = np.asarray(value)
if (
channels > 1
): # change from contiguous to interleaved channel order
arr = move_last_dimension_to_first(arr)
image = itk.image_view_from_array(arr,
is_vector=channels > 1)
kwargs[key] = image
elif not isinstance(value, itk.Object) and is_arraylike(value):
have_array_input = True
array = np.asarray(value)
image = itk.image_view_from_array(array)
kwargs[key] = image
if have_xarray_input or have_torch_input or have_array_input:
# Convert output itk.Image's to numpy.ndarray's
output = image_filter(*tuple(args_list), **kwargs)
if isinstance(output, tuple):
output_list = list(output)
for index, value in enumerate(output_list):
if isinstance(value, itk.Image):
if have_xarray_input:
data_array = itk.xarray_from_image(value)
output_list[index] = data_array
elif have_torch_input:
channels = value.GetNumberOfComponentsPerPixel()
data_array = itk.array_view_from_image(value)
if (
channels > 1
): # change from interleaved to contiguous channel order
data_array = move_first_dimension_to_last(
data_array)
torch_tensor = torch.from_numpy(data_array)
output_list[index] = torch_tensor
else:
array = itk.array_view_from_image(value)
output_list[index] = array
return tuple(output_list)
else:
if isinstance(output, itk.Image):
if have_xarray_input:
output = itk.xarray_from_image(output)
elif have_torch_input:
channels = output.GetNumberOfComponentsPerPixel()
output = itk.array_view_from_image(output)
if (
channels > 1
): # change from interleaved to contiguous channel order
output = move_first_dimension_to_last(output)
output = torch.from_numpy(output)
else:
output = itk.array_view_from_image(output)
return output
else:
return image_filter(*args, **kwargs)
