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