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Improved plot_windowed_histogram doctoring, removed unnecessary imports and removed an internal testing line.

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Geoffrey French
2014-09-01 22:44:45 +01:00
parent b9ac11da3f
commit 0936522064
1 changed files with 6 additions and 9 deletions
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doc/examples/plot_windowed_histogram.py
+6 -9
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@@ -4,13 +4,13 @@ from __future__ import division
Sliding window histogram
========================
This example extracts a single coin from the coins image and generates a
histogram of its greyscale values.
This example extracts a single coin from the `skimage.data.coins` image and
generates a histogram of its greyscale values.
It then computes a sliding window histogram of the complete image using
rank.windowed_histogram. The local histogram for the region surrounding
each pixel in the image is compared to that of the single coin, with
a similarity measure being computed and displayed.
`skimage.filter.rank.windowed_histogram`. The local histogram for the region
surrounding each pixel in the image is compared to that of the single coin,
with a similarity measure being computed and displayed.
To demonstrate the rotational invariance of the technique, the same
test is performed on a version of the coins image rotated by 45 degrees.
@@ -20,9 +20,7 @@ import matplotlib
import matplotlib.pyplot as plt
from skimage import data
from skimage.util.dtype import dtype_range
from skimage.util import img_as_ubyte
from skimage import exposure
from skimage.morphology import disk
from skimage.filter import rank
from skimage import transform
@@ -57,9 +55,8 @@ def windowed_histogram_similarity(image, selem, reference_hist, n_bins):
return similarity
# Load the coins image
# Load the `skimage.data.coins` image
img = img_as_ubyte(data.coins())
# img = img_as_ubyte(plt.imread('../../skimage/data/coins.png'))
# Quantize to 16 levels of grayscale; this way the output image will have a
# 16-dimensional feature vector per pixel