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replacing lena in examples and most tests

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Rebecca
2014-11-07 16:01:23 +02:00
committed by Stefan van der Walt
parent e8967abe78
commit 1b217f971e
36 changed files with 216 additions and 199 deletions
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doc/examples/plot_view_as_blocks.py
+6 -6
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@@ -7,10 +7,10 @@ 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
We use `astronaut` 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`
with a spline interpolation of order 3 rescaling of the original `astronaut`
image.
"""
@@ -24,20 +24,20 @@ from skimage import color
from skimage.util.shape import view_as_blocks
# -- get `lena` from skimage.data in grayscale
l = color.rgb2gray(data.lena())
# -- get `astronaut` from skimage.data in grayscale
l = color.rgb2gray(data.astronaut())
# -- size of blocks
block_shape = (4, 4)
# -- see `lena` as a matrix of blocks (of shape
# -- see `astronaut` 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`,
# -- resampling `astronaut` 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)