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"""
==========================================
Datasets with 3 or more spatial dimensions
==========================================
Most scikit-image functions are compatible with 3D datasets, i.e., images with
3 spatial dimensions (to be distinguished from 2D multichannel images, which
are also arrays with
three axes). :func:`skimage.data.cells3d` returns a 3D fluorescence microscopy
image of cells. The returned dataset is a 3D multichannel image with dimensions
provided in ``(z, c, y, x)`` order. Channel 0 contains cell membranes, while channel
1 contains nuclei.
The example below shows how to explore this dataset. This 3D image can be used
to test the various functions of scikit-image.
"""
from skimage import data
import plotly
import plotly.express as px
import numpy as np
img = data.cells3d()[20:]
# omit some slices that are partially empty
img = img[5:26]
upper_limit = 1.5 * np.percentile(img, q=99)
img = np.clip(img, 0, upper_limit)
fig = px.imshow(
img,
facet_col=1,
animation_frame=0,
binary_string=True,
binary_format="jpg",
)
fig.layout.annotations[0]["text"] = "Cell membranes"
fig.layout.annotations[1]["text"] = "Nuclei"
plotly.io.show(fig)
|
