From 96c2b3fc9d2039e0f4aa2ae5b7c23a0a0aad9650 Mon Sep 17 00:00:00 2001
From: Nicolas Poilvert <nicolas.poilvert@gmail.com>
Date: Mon, 13 Feb 2012 22:30:17 -0500
Subject: [PATCH] DOC: add example for util.shape.view_as_{blocks,windows}

---
 doc/examples/plot_view_as_blocks.py | 63 +++++++++++++++++++++++++++++
 1 file changed, 63 insertions(+)
 create mode 100644 doc/examples/plot_view_as_blocks.py

diff --git a/doc/examples/plot_view_as_blocks.py b/doc/examples/plot_view_as_blocks.py
new file mode 100644
index 00000000..305192dc
--- /dev/null
+++ b/doc/examples/plot_view_as_blocks.py
@@ -0,0 +1,63 @@
+"""
+============================
+Block views on images/arrays
+============================
+
+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 `scipy.misc` 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 'classic'
+`bicubic` rescaling of the original `lena` image.
+"""
+
+import numpy as np
+from scipy.misc import lena, imresize
+from matplotlib import pyplot as plt
+import matplotlib.cm as cm
+from skimage.util.shape import view_as_blocks
+
+
+# -- get `lena` from scipy in grayscale
+l = 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[0], view.shape[1], -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
+plt.figure(figsize=(10, 10))
+
+plt.subplot(221)
+plt.title("Original rescaled\n in bicubic mode");
+l_resized = imresize(l, view.shape[:2], interp='bicubic')
+plt.imshow(l_resized, cmap=cm.Greys_r)
+
+plt.subplot(222)
+plt.title("Block view with\n local mean pooling")
+plt.imshow(mean_view, cmap=cm.Greys_r)
+
+plt.subplot(223)
+plt.title("Block view with\n local max pooling");
+plt.imshow(max_view, cmap=cm.Greys_r)
+
+plt.subplot(224)
+plt.title("Block view with\n local median pooling")
+plt.imshow(median_view, cmap=cm.Greys_r)
+
+plt.subplots_adjust(hspace=0.4, wspace=0.4)
+plt.show()