mirror of
https://github.com/wassname/scikit-image.git
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Nicolas Poilvert
64 lines
1.8 KiB
Python
64 lines
1.8 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 `skimage.util.shape`.
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Block views can be incredibly useful when one wants to perform local operations
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on non-overlapping image patches.
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We use `lena` from `scipy.misc` and virtually 'slice' it into square blocks.
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Then, on each block, we either pool the mean, the max or the median value of
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that block. The results are displayed altogether, along with a 'classic'
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`bicubic` rescaling of the original `lena` image.
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"""
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import numpy as np
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from scipy.misc import lena, imresize
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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.util.shape import view_as_blocks
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# -- get `lena` from scipy in grayscale
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l = lena()
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# -- size of blocks
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block_shape = (4, 4)
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# -- see `lena` 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 `lena` 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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plt.figure(figsize=(10, 10))
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plt.subplot(221)
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plt.title("Original rescaled\n in bicubic mode");
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l_resized = imresize(l, view.shape[:2], interp='bicubic')
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plt.imshow(l_resized, cmap=cm.Greys_r)
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plt.subplot(222)
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plt.title("Block view with\n local mean pooling")
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plt.imshow(mean_view, cmap=cm.Greys_r)
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plt.subplot(223)
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plt.title("Block view with\n local max pooling");
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plt.imshow(max_view, cmap=cm.Greys_r)
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plt.subplot(224)
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plt.title("Block view with\n local median pooling")
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plt.imshow(median_view, cmap=cm.Greys_r)
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plt.subplots_adjust(hspace=0.4, wspace=0.4)
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plt.show()
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