foa3d.input

class foa3d.input.CustomFormatter(prog, indent_increment=2, max_help_position=24, width=None)

foa3d.input.get_cli_parser()

Parse command line arguments.

Returns

cli_args – populated namespace of command line arguments

Return type

see ArgumentParser.parse_args

foa3d.input.get_frangi_config(cli_args, in_img)

Get Frangi filter configuration.

Parameters

• cli_args (see ArgumentParser.parse_args) – populated namespace of command line arguments

• in_img (dict) –

  input image dictionary (extended)

  data: numpy.ndarray or NumPy memory-map object (axis order=(Z,Y,X) or (Z,Y,X,C) or (Z,C,Y,X))

  3D microscopy image

  ts_msk: numpy.ndarray (dtype=bool)

  tissue reconstruction binary mask

  ch_ax: int

  RGB image channel axis (either 1, 3, or None for grayscale images)

  fb_ch: int

  neuronal fibers channel

  bc_ch: int

  brain cell soma channel

  msk_bc: bool

  if True, mask neuronal bodies within the optionally provided channel

  psf_fwhm: numpy.ndarray (shape=(3,), dtype=float)

  3D FWHM of the PSF [μm]

  px_sz: numpy.ndarray (shape=(3,), dtype=float)

  pixel size [μm]

  name: str

  name of the 3D microscopy image

  is_vec: bool

  vector field flag

  shape: numpy.ndarray (shape=(3,), dtype=int)

  total image shape

  shape_um: numpy.ndarray (shape=(3,), dtype=float)

  total image shape [μm]

  item_sz: int

  image item size [B]

Returns

frangi_cfg –

Frangi filter configuration

alpha: float

plate-like score sensitivity

beta: float

blob-like score sensitivity

gamma: float

background score sensitivity

scales_px: numpy.ndarray (dtype=float)

Frangi filter scales [px]

scales_um: numpy.ndarray (dtype=float)

Frangi filter scales [μm]

smooth_sd: numpy.ndarray (shape=(3,), dtype=int)

3D standard deviation of the smoothing Gaussian filter [px]

px_sz: numpy.ndarray (shape=(3,), dtype=float)

pixel size [μm]

fb_thr: float or skimage.filters thresholding method

Frangi filter probability response threshold

bc_ch: int

neuronal bodies channel

fb_ch: int

myelinated fibers channel

msk_bc: bool

if True, mask neuronal bodies within the optionally provided channel

hsv_cmap: bool

generate HSV colormap of 3D fiber orientations

exp_all: bool

export all images

z_out: NumPy slice object

output z-range

Return type

dict

foa3d.input.get_image_info(cli_args)

Get microscopy image file path and format.

Parameters

cli_args (see ArgumentParser.parse_args) – populated namespace of command line arguments

Returns

• in_img (dict) –

  input image dictionary

  fb_ch: int

  neuronal fibers channel

  bc_ch: int

  brain cell soma channel

  msk_bc: bool

  if True, mask neuronal bodies within the optionally provided channel

  psf_fwhm: numpy.ndarray (shape=(3,), dtype=float)

  3D FWHM of the PSF [μm]

  px_sz: numpy.ndarray (shape=(3,), dtype=float)

  pixel size [μm]

  path: str

  path to the 3D microscopy image

  name: str

  name of the 3D microscopy image

  fmt: str

  format of the 3D microscopy image

  is_tiled: bool

  True for tiled reconstructions aligned using ZetaStitcher

• msk_mip (bool) – apply tissue reconstruction mask (binarized MIP)

foa3d.input.get_image_size(in_img)

Get information on the size of the input 3D microscopy image.

Parameters

in_img (dict) –

input image dictionary

data: numpy.ndarray or NumPy memory-map object (axis order=(Z,Y,X) or (Z,Y,X,C) or (Z,C,Y,X))

3D microscopy image

ch_ax: int

RGB image channel axis (either 1, 3, or None for grayscale images)

ts_msk: numpy.ndarray (dtype=bool)

tissue reconstruction binary mask

fb_ch: int

neuronal fibers channel

bc_ch: int

brain cell soma channel

msk_bc: bool

if True, mask neuronal bodies within the optionally provided channel

psf_fwhm: numpy.ndarray (shape=(3,), dtype=float)

3D FWHM of the PSF [μm]

px_sz: numpy.ndarray (shape=(3,), dtype=float)

pixel size [μm]

path: str

path to the 3D microscopy image

name: str

name of the 3D microscopy image

fmt: str

format of the 3D microscopy image

is_tiled: bool

True for tiled reconstructions aligned using ZetaStitcher

is_vec: bool

vector field flag

Return type

None

foa3d.input.get_resolution(cli_args)

Retrieve microscopy resolution information from command line arguments.

Parameters

cli_args (see ArgumentParser.parse_args) – populated namespace of command line arguments

Returns

• px_sz (tuple (shape=(3,), dtype=float)) – pixel size [μm]

• psf_fwhm (tuple (shape=(3,), dtype=float)) – 3D PSF FWHM [μm]

foa3d.input.get_resource_config(cli_args, frangi_cfg)

Retrieve resource usage configuration of the Foa3D tool.

Parameters

• cli_args (see ArgumentParser.parse_args) – populated namespace of command line arguments

• frangi_cfg (dict) –

  Frangi filter configuration

  alpha: float

  plate-like score sensitivity

  beta: float

  blob-like score sensitivity

  gamma: float

  background score sensitivity

  scales_px: numpy.ndarray (dtype=float)

  Frangi filter scales [px]

  scales_um: numpy.ndarray (dtype=float)

  Frangi filter scales [μm]

  smooth_sd: numpy.ndarray (shape=(3,), dtype=int)

  3D standard deviation of the smoothing Gaussian filter [px]

  px_sz: numpy.ndarray (shape=(3,), dtype=float)

  pixel size [μm]

  bc_ch: int

  neuronal bodies channel

  fb_ch: int

  myelinated fibers channel

  msk_bc: bool

  if True, mask neuronal bodies within the optionally provided channel

  hsv_cmap: bool

  generate HSV colormap of 3D fiber orientations

  exp_all: bool

  export all images

  rsz: numpy.ndarray (shape=(3,), dtype=float)

  3D image resize ratio

Return type

None

foa3d.input.load_data(in_img, tmp_dir, msk_mip=False)

Load 3D microscopy data.

Parameters

• in_img (dict) –

  input image dictionary

  fb_ch: int

  neuronal fibers channel

  bc_ch: int

  brain cell soma channel

  msk_bc: bool

  if True, mask neuronal bodies within the optionally provided channel

  psf_fwhm: numpy.ndarray (shape=(3,), dtype=float)

  3D FWHM of the PSF [μm]

  px_sz: numpy.ndarray (shape=(3,), dtype=float)

  pixel size [μm]

  path: str

  path to the 3D microscopy image

  name: str

  name of the 3D microscopy image

  fmt: str

  format of the 3D microscopy image

  is_tiled: bool

  True for tiled reconstructions aligned using ZetaStitcher

• tmp_dir (str) – path to temporary folder

• msk_mip (bool) – apply tissue reconstruction mask (binarized MIP)

Return type

None

foa3d.input.load_microscopy_image(cli_args)

Load 3D microscopy image from TIFF, or ZetaStitcher .yml file. Alternatively, the processing pipeline accepts as input TIFF or NumPy files of fiber orientation vector data: in this case, the Frangi filter stage will be skipped.

Parameters

cli_args (see ArgumentParser.parse_args) – populated namespace of command line arguments

Returns

• in_img (dict) –

  input image dictionary

  data: numpy.ndarray (axis order=(Z,Y,X) or (Z,Y,X,C) or (Z,C,Y,X))

  3D microscopy image

  ch_ax: int

  RGB image channel axis (either 1, 3, or None for grayscale images)

  ts_msk: numpy.ndarray (dtype=bool)

  tissue reconstruction binary mask

  fb_ch: int

  neuronal fibers channel

  bc_ch: int

  brain cell soma channel

  msk_bc: bool

  if True, mask neuronal bodies within the optionally provided channel

  psf_fwhm: numpy.ndarray (shape=(3,), dtype=float)

  3D FWHM of the PSF [μm]

  px_sz: numpy.ndarray (shape=(3,), dtype=float)

  pixel size [μm]

  path: str

  path to the 3D microscopy image

  name: str

  name of the 3D microscopy image

  fmt: str

  format of the 3D microscopy image

  is_tiled: bool

  True for tiled reconstructions aligned using ZetaStitcher

  is_vec: bool

  vector field flag

  shape: numpy.ndarray (shape=(3,), dtype=int)

  total image shape

  shape_um: numpy.ndarray (shape=(3,), dtype=float)

  total image shape [μm]

  item_sz: int

  image item size [B]

• save_dirs (dict) – saving directories (‘frangi’: Frangi filter, ‘odf’: ODF analysis, ‘tmp’: temporary files)