def reader_function_path_list(path):
"""Take a path or list of paths and return a list of LayerData tuples.
Readers are expected to return data as a list of tuples, where each tuple
is (data, [add_kwargs, [layer_type]]), "add_kwargs" and "layer_type" are
both optional.
Parameters
----------
path : str or list of str
Path to file, or list of paths.
Returns
-------
layer_data : list of tuples
A list of LayerData tuples where each tuple in the list contains
(data, metadata, layer_type), where data is a numpy array, metadata is
a dict of keyword arguments for the corresponding viewer.add_* method
in napari, and layer_type is a lower-case string naming the type of layer.
Both "meta", and "layer_type" are optional. napari will default to
layer_type=="image" if not provided
"""
if not isinstance(path, list):
path_obj = Path(path)
if path_obj.is_dir():
files = list(filter(lambda x: x.is_file(), path_obj.iterdir()))
image_io = itk.ImageIOFactory.CreateImageIO(
str(files[0]), itk.CommonEnums.IOFileMode_ReadMode)
if isinstance(image_io, itk.GDCMImageIO):
# DICOMs are handled specially -- we identify a single series from
# all the files in the directory and ensure that they are sorted
# spatially
# itk.imread assumes a DICOM series if a directory is passed
image = itk.imread(path)
else:
file_strings = [str(f) for f in files]
image = itk.imread(file_strings)
else:
# handle both a string and a list of strings
image = itk.imread(path)
else:
# handle both a string and a list of strings
image = itk.imread(path)
image_layer = image_layer_from_image(image)
components = image.GetNumberOfComponentsPerPixel()
if components == 1:
channel_axis = None
else:
channel_axis = image_layer.data.ndim - 1
# optional kwargs for the corresponding viewer.add_* method
add_kwargs = {
'channel_axis': channel_axis,
'rgb': image_layer.rgb,
'metadata': image_layer.metadata,
'scale': image_layer.scale,
'translate': image_layer.translate,
}
layer_type = "image" # optional, default is "image"
return [(image_layer.data, add_kwargs, layer_type)]
def test_image_layer_from_image_translate():
data = np.random.randint(256, size=(10, 10), dtype=np.uint8)
image = itk.image_view_from_array(data)
origin = [1.1, 2.2]
image.SetOrigin(origin)
image_layer = itk_napari_conversion.image_layer_from_image(image)
assert np.array_equal(data, image_layer.data)
assert np.allclose(np.array(origin)[::-1], image_layer.translate)
def test_image_layer_from_image_scale():
data = np.random.randint(256, size=(10, 10), dtype=np.uint8)
image = itk.image_view_from_array(data)
spacing = [1.1, 2.2]
image.SetSpacing(spacing)
image_layer = itk_napari_conversion.image_layer_from_image(image)
assert np.array_equal(data, image_layer.data)
assert np.allclose(np.array(spacing)[::-1], image_layer.scale)
def test_image_layer_from_image_metadata():
data = np.random.randint(256, size=(10, 10), dtype=np.uint8)
image = itk.image_view_from_array(data)
image["wookies"] = 7
image["units"] = "mm"
image_layer = itk_napari_conversion.image_layer_from_image(image)
assert np.array_equal(data, image_layer.data)
assert image_layer.metadata["wookies"] == 7
assert image_layer.metadata["units"] == "mm"
def test_image_layer_from_image():
data = np.random.randint(256, size=(10, 10), dtype=np.uint8)
image = itk.image_view_from_array(data)
image_layer = itk_napari_conversion.image_layer_from_image(image)
assert np.array_equal(data, image_layer.data)
def test_image_layer_from_image_rgb():
data = np.random.randint(256, size=(10, 10, 3), dtype=np.uint8)
image = itk.image_view_from_array(data, itk.RGBPixel[itk.UC])
image_layer = itk_napari_conversion.image_layer_from_image(image)
assert np.array_equal(data, image_layer.data)
assert image_layer.rgb is True