#!/usr/bin/env python
# -*- coding: utf-8 -*-
# ------------------------------------------------------------------------------
# Copyright (c) 2022 Univ. Lyon, ENS de Lyon, UCB Lyon 1, CNRS, INRAe, Inria
# All rights reserved.
# This file is part of the TimageTK library, and is released under the "GPLv3"
# license. Please see the LICENSE.md file that should have been included as
# part of this package.
# ------------------------------------------------------------------------------
from timagetk.components.tissue_image import TissueImage3D
from timagetk.synthetic_data.labelled_image import example_layered_sphere_labelled_image
from timagetk.synthetic_data.util import pseudo_gradient_norm
[docs]
def wall_image_from_labelled_image(labelled_image, wall_sigma=None, wall_intensity=None, **kwargs):
"""Generate a 3D image representing the walls of a labelled image.
Parameters
----------
labelled_image : timagetk.LabelledImage
The cell image on which to compute the wall signal.
wall_sigma : float, optional
Sigma value determining the wall width, in real units.
Defaults to the image voxelsize ``seg_img.voxelsize``.
intensity : float, optional
Signal intensity of the wall interfaces.
Defaults to 2/3 of the maximum value based on `dtype`.
Other Parameters
----------------
dtype : {"uint8", "uint16"}
The bit-depth of the intensity image.
Defaults to ``'uint8'``.
count : bool, optional
Whether to have higher signal at wall junctions.
Defaults to ``True``.
Returns
-------
timagetk.SpatialImage
The generated cell wall intensity image.
See Also
--------
timagetk.synthetic_data.util.pseudo_gradient_norm
Examples
--------
>>> from timagetk.synthetic_data.labelled_image import example_layered_sphere_labelled_image
>>> from timagetk.synthetic_data.wall_image import wall_image_from_labelled_image
>>> from timagetk.visu.mplt import image_plot
>>> seg_img = example_layered_sphere_labelled_image()
>>> wall_img = wall_image_from_labelled_image(seg_img)
>>> z_sl = seg_img.get_shape("z") // 2
>>> from timagetk.visu.mplt import grayscale_imshow
>>> v = grayscale_imshow([seg_img, wall_img], slice_id=z_sl, cmap=['glasbey', 'gray'], val_range='auto')
"""
seg_img = TissueImage3D(labelled_image, not_a_label=0, background=1)
return pseudo_gradient_norm(seg_img, dtype=kwargs.get('dtype', "uint8"), wall_sigma=wall_sigma,
intensity=wall_intensity, count=kwargs.get('count', True))
[docs]
def example_layered_sphere_wall_image(extent=60., voxelsize=(0.6, 0.6, 0.6), n_points=12, n_layers=1,
wall_sigma=None, wall_intensity=None, return_points=False, **kwargs):
"""Generate a synthetic 3D image representing the walls of a spherical tissue.
The function creates a 3D image over a cubic volume of a given extent.
It consists of successive spherical cell layers of equal height around a central spherical cell.
The number of cells in each layer is set so that cells in every layer have similar volumes.
Parameters
----------
extent : float, optional
The extent of the output image (in µm).
Defaults to ``60.``.
voxelsize : list(float), optional
Length-3 list of image voxelsize (in µm), ZYX sorted.
Defaults to ``(0.6, 0.6, 0.6)``.
n_points : int, optional
The number of cells on innermost spherical layer.
Defaults to ``12``.
n_layers : int, optional
The number of layer around the central cell.
Defaults to ``1``.
wall_sigma : float, optional
Sigma value determining the wall width, in real units.
Defaults to the image voxelsize ``seg_img.voxelsize``.
intensity : float, optional
Signal intensity of the wall interfaces.
Defaults to 2/3 of the maximum value based on `dtype`.
return_points : bool, optional
Whether to return the points representing the cell centers.
Other Parameters
----------------
dtype : {"uint8", "uint16"}
The bit-depth of the intensity image.
Defaults to ``'uint8'``.
count : bool
Whether to have higher signal at wall junctions.
Defaults to ``True``.
Returns
-------
timagetk.SpatialImage
The generated cell wall reference image.
dict, optional
The 3D point positions, XYZ sorted, used to generate the cells.
See Also
--------
timagetk.synthetic_data.labelled_image.example_layered_sphere_labelled_image
timagetk.synthetic_data.util.pseudo_gradient_norm
Examples
--------
>>> from timagetk.synthetic_data.wall_image import example_layered_sphere_wall_image
>>> from timagetk.visu.stack import orthogonal_view
>>> wall_img = example_layered_sphere_wall_image()
>>> v = orthogonal_view(wall_img, suptitle="Synthetic 3D wall intensity image", cmap='gray', val_range='auto')
"""
seg_img, points = example_layered_sphere_labelled_image(extent, voxelsize, n_points, n_layers, return_points=True)
seg_img = TissueImage3D(seg_img, not_a_label=0, background=1)
img = pseudo_gradient_norm(seg_img, dtype=kwargs.get('dtype', "uint8"), wall_sigma=wall_sigma,
intensity=wall_intensity, count=kwargs.get('count', True))
if return_points:
return img, points
else:
return img
