wassname/scikit-image
1
0
Fork 0
mirror of https://github.com/wassname/scikit-image.git synced 2026-09-09 11:33:41 +08:00
Code Issues Packages Projects Releases Wiki Activity

Various fixes

Browse Source
This commit is contained in:
François Boulogne
2016-06-19 19:43:43 +02:00
parent 25f5729d57
commit 9fef985df8
4 changed files with 22 additions and 19 deletions
Download Patch File Download Diff File Expand all files Collapse all files
doc/examples/segmentation/plot_thresholding.py
+5 -3
View File
@@ -21,6 +21,7 @@ Thresholding is used to create a binary image from a grayscale image [1]_.
# the intra-class variance.
#
# .. [2] http://en.wikipedia.org/wiki/Otsu's_method
#
import matplotlib.pyplot as plt
from skimage import data
@@ -59,13 +60,14 @@ plt.show()
# thresholding algorithms provided by the library. At a glance, you can select
# the best algorithm for you data without a deep understanding of their
# mechanisms.
#
from skimage.filters import thresholding
from skimage.filters import try_all_threshold
img = data.page()
# Here, we specify a radius for local thresholding algorithms.
# If it is not specified, only global algorithms are called.
fig, ax = thresholding.try_all_threshold(img, radius=20,
figsize=(10, 8), verbose=False)
fig, ax = try_all_threshold(img, radius=20,
figsize=(10, 8), verbose=False)
plt.show()
doc/examples/xx_applications/plot_thresholding.py
+13 -13
View File
@@ -32,14 +32,14 @@ import matplotlib
import matplotlib.pyplot as plt
from skimage import data
from skimage.filters import thresholding
from skimage.filters import try_all_threshold
img = data.page()
# Here, we specify a radius for local thresholding algorithms.
# If it is not specified, only global algorithms are called.
fig, ax = thresholding.try_all_threshold(img, radius=20,
figsize=(10, 8), verbose=False)
fig, ax = try_all_threshold(img, radius=20,
figsize=(10, 8), verbose=False)
plt.show()
######################################################################
@@ -49,8 +49,9 @@ plt.show()
# Now, we illustrate how to apply one of these thresholding algorithms.
# This example uses the mean value of pixel intensities. It is a simple
# and naive threshold value, which is sometimes used as a guess value.
#
from skimage.filters.thresholding import threshold_mean
from skimage.filters import threshold_mean
image = data.camera()
@@ -77,12 +78,13 @@ plt.show()
#
# For pictures with a bimodal histogram, more specific algorithms can be used.
# For instance, the minimum algorithm takes a histogram of the image and smooths it
# repeatedly until there are only two peaks in the histogram. Then it
# finds the minimum value between the two peaks. After smoothing the
# repeatedly until there are only two peaks in the histogram. Then it
# finds the minimum value between the two peaks. After smoothing the
# histogram, there can be multiple pixel values with the minimum histogram
# count, so you can pick the 'min', 'mid', or 'max' of these values.
#
from skimage.filters.thresholding import threshold_minimum
from skimage.filters import threshold_minimum
image = data.camera()
@@ -131,11 +133,8 @@ plt.show()
# the intra-class variance.
#
# .. [2] http://en.wikipedia.org/wiki/Otsu's_method
#
import matplotlib
import matplotlib.pyplot as plt
from skimage import data
from skimage.filters import threshold_otsu
@@ -175,8 +174,9 @@ plt.show()
#
# Here, we binarize an image using the `threshold_adaptive` function, which
# calculates thresholds in regions with a characteristic 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.
# each pixel (i.e. local neighborhoods). Each threshold value is the weighted mean
# of the local neighborhood minus an offset value.
#
from skimage.filters import threshold_otsu, threshold_adaptive