This example illustrates the use of view_as_blocks from skimage.util.shape. Block views can be incredibly useful when one wants to perform local operations on non-overlapping image patches.
We use lena from skimage.data and virtually ‘slice’ it into square blocks. Then, on each block, we either pool the mean, the max or the median value of that block. The results are displayed altogether, along with a spline interpolation of order 3 rescaling of the original lena image.
import numpy as np from scipy import ndimage as ndi from matplotlib import pyplot as plt import matplotlib.cm as cm from skimage import data from skimage import color from skimage.util.shape import view_as_blocks # -- get `lena` from skimage.data in grayscale l = color.rgb2gray(data.lena()) # -- size of blocks block_shape = (4, 4) # -- see `lena` as a matrix of blocks (of shape # `block_shape`) view = view_as_blocks(l, block_shape) # -- collapse the last two dimensions in one flatten_view = view.reshape(view.shape, view.shape, -1) # -- resampling `lena` by taking either the `mean`, # the `max` or the `median` value of each blocks. mean_view = np.mean(flatten_view, axis=2) max_view = np.max(flatten_view, axis=2) median_view = np.median(flatten_view, axis=2) # -- display resampled images fig, axes = plt.subplots(2, 2, figsize=(8, 8)) ax0, ax1, ax2, ax3 = axes.ravel() ax0.set_title("Original rescaled with\n spline interpolation (order=3)") l_resized = ndi.zoom(l, 2, order=3) ax0.imshow(l_resized, cmap=cm.Greys_r) ax1.set_title("Block view with\n local mean pooling") ax1.imshow(mean_view, cmap=cm.Greys_r) ax2.set_title("Block view with\n local max pooling") ax2.imshow(max_view, cmap=cm.Greys_r) ax3.set_title("Block view with\n local median pooling") ax3.imshow(median_view, cmap=cm.Greys_r) plt.subplots_adjust(hspace=0.4, wspace=0.4) plt.show()
Python source code: download (generated using skimage 0.10dev)