mirror of
https://github.com/wassname/scikit-image.git
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emmanuelle
55f5103dd8
Modified travis_script.sh to account for the new structure of the gallery Added README.txt files in directories of gallery examples Fixed references to gallery images in user guide pages Fixed broken links
70 lines
2.2 KiB
Python
70 lines
2.2 KiB
Python
"""
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============================
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Block views on images/arrays
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============================
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This example illustrates the use of `view_as_blocks` from
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`skimage.util.shape`. Block views can be incredibly useful when one
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wants to perform local operations on non-overlapping image patches.
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We use `astronaut` from `skimage.data` and virtually 'slice' it into square
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blocks. Then, on each block, we either pool the mean, the max or the
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median value of that block. The results are displayed altogether, along
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with a spline interpolation of order 3 rescaling of the original `astronaut`
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image.
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"""
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import numpy as np
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from scipy import ndimage as ndi
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from matplotlib import pyplot as plt
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import matplotlib.cm as cm
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from skimage import data
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from skimage import color
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from skimage.util.shape import view_as_blocks
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# -- get `astronaut` from skimage.data in grayscale
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l = color.rgb2gray(data.astronaut())
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# -- size of blocks
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block_shape = (4, 4)
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# -- see `astronaut` as a matrix of blocks (of shape
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# `block_shape`)
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view = view_as_blocks(l, block_shape)
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# -- collapse the last two dimensions in one
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flatten_view = view.reshape(view.shape[0], view.shape[1], -1)
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# -- resampling `astronaut` by taking either the `mean`,
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# the `max` or the `median` value of each blocks.
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mean_view = np.mean(flatten_view, axis=2)
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max_view = np.max(flatten_view, axis=2)
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median_view = np.median(flatten_view, axis=2)
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# -- display resampled images
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fig, axes = plt.subplots(2, 2, figsize=(8, 8), sharex=True, sharey=True)
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ax0, ax1, ax2, ax3 = axes.ravel()
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ax0.set_title("Original rescaled with\n spline interpolation (order=3)")
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l_resized = ndi.zoom(l, 2, order=3)
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#ax0.imshow(l_resized, cmap=cm.Greys_r)
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ax0.imshow(l_resized, extent=(0, 128, 128, 0), interpolation='nearest', cmap=cm.Greys_r)
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ax0.set_axis_off()
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ax1.set_title("Block view with\n local mean pooling")
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ax1.imshow(mean_view, interpolation='nearest', cmap=cm.Greys_r)
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ax1.set_axis_off()
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ax2.set_title("Block view with\n local max pooling")
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ax2.imshow(max_view, interpolation='nearest', cmap=cm.Greys_r)
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ax2.set_axis_off()
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ax3.set_title("Block view with\n local median pooling")
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ax3.imshow(median_view, interpolation='nearest', cmap=cm.Greys_r)
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ax3.set_axis_off()
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fig.subplots_adjust(hspace=0.4, wspace=0.4)
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plt.show()
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