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clean up

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This commit is contained in:
Olivier Debeir
2012-10-16 15:05:12 +02:00
parent 64c37c4e0c
commit 6e396f8598
6 changed files with 82 additions and 106 deletions
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doc/examples/plot_local_threshold.py
+6 -3
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@@ -14,9 +14,12 @@ calculates thresholds in regions of size `block_size` surrounding each pixel
(i.e. local neighborhoods). Each threshold value is the weighted mean of the
local neighborhood minus an offset value.
Added local threshold using rank filter
An other approach is to binarize locally the image using local histogram distribution.
to be adjusted ...
rank.threshold function set pixels higher than the local mean to 1, to 0 otherwize
rank.morph_contr_enh replaces each pixel by the local minimum (or local maximum) if the
pixel gray level is more close to the local minimum (resp. by the local maximum
if the pixel gray level is more close to the local maximum).
"""
import matplotlib.pyplot as plt
@@ -36,7 +39,7 @@ binary_global = image > global_thresh
block_size = 40
binary_adaptive = threshold_adaptive(image, block_size, offset=10)
selem = disk(10)
selem = disk(20)
loc_thresh = threshold(image,selem=selem)
loc_morph_contr_enh = morph_contr_enh(image,selem=selem)